A COVID Global Economy: Policy Responses, Inflationary Concerns and Consumer Confidence

By Aaron Tom, Neil Song, Ben Zhao, Luke Melcher, and Tanvi Kigga

Abstract

COVID-19 has drastically changed our lives, and there is no doubt that it has affected the economy as well on a global scale. The global economy in 2020 was characterized by severe decline in early stages of the pandemic and a trend of recovery in later and current stages. The goal of our study is to examine the effect that COVID-19 has had on the global economy, and to to predict trends in economic indicators for each of the countries in our data set through COVID policy. The three economic variables we chose to examine the economy from were consumer confidence index, inflation rate index (CPI), and unemployment rate. We used our data to create a double prediction layer model. We then used 4 different types of models to determine the best model for our data, we also separated data into training and testing data for the model. We then used the model to predict future economic data. Overall there is a positive correlation for consumer confidence, negative correlation for unemployment, and mostly positive correlation for inflation, over the period of July 2021-December 2021. Methods Used We first coded a heatmap based off of the World Bank GDP data for January 2019 of the GDP of most of the countries in the world, in order to gain perspective on the global economy and what countries we could use in our analysis. But to analyze trends in the economy, yearly data wouldn’t work in the timeframe of 2-3 years, so we choose to use the OECD (Organisation for Economic Co-operation and Development) database, which is limited to about 50 countries compared to the 200+ countries in the world. It took quite a while to find the OECD database which contained many economic indicators but also had monthly data. We realized that most main economic indicators like GDP and trade data were done either quarterly or annually. In the end, we found several monthly economic indicators from roughly the past 30 months: unemployment rate, consumer confidence index, and inflation rate. The problem with analyzing all countries through the world bank is that some countries don’t have monthly data, other countries have no data at all, so instead we have normal distribution from 28 countries of the top 100 GDP countries in the world from the OECD. When looking closer at the data, it is actually fairly normally distributed. Our OECD dataset includes countries like US #1, Finland #42 , Latvia #95 (GDP) so we have around a normal distribution of the top 100 GDP countries in the world Our model involves 3 layers: a base predictor layer, a 1st layer, and a 2nd layer which is the prediction layer. The base layer contains: Month, Year, Country, Covid Rate. The 1st layer contains Covid policy data: Public Campaigns, Vaccination Policies, School Closures, Stay at Home Orders. The prediction layer contains economic data: Unemployment Rates, Consumer Confidence, and Inflation Rates

Graphical EDA from Slides

In our EDA (exploratory data analysis), there was a surge in unemployment rates in the beginning of the pandemic over March 2020 to April 2020. Countries that experienced the largest unemployment rates spikes were Costa Rica, Columbia, Greece, Spain, Chile, and the United States. We saw a dip in confidence in the beginning of the pandemic, but rising confidence in recovery months in late 2020 and 2021. Consumer confidence index is a survey that measures how optimistic or pessimistic someone is regarding their financial situation. A high confidence index indicates optimism about the market which means people will spend more money. A low confidence index indicates pessimism about the market which means that people will spend less money, to be conservative, or save money. Inflation grew regardless of the pandemic. We may speculate it is due to the Fed’s role with low interest rates and government stimulus throughout the pandemic. Low interest rates may affect inflation as a larger availability of credit and loans leads to more money in circulation, which leads to rising inflation. To determine the best model for our data we used: naive multiple linear regression, naive random forest, naive XGBoost Random Forest, and naive support vector machine models. After an analysis of the Least Squares Mean Error for each of these methods, we determined that SVM (Support Vector Machine) and XGBoost Random Forest were the best models for our variables. XGBoost is a machine learning algorithm that contains packages which our team used for statistical and prediction analysis. This package is based on boosting, which compiles multiple weak models together to form a stronger comprehensive model. Support vector machine is a machine learning algorithm regression which uses the help of many vectors to improve regression classification results. In the Residual VS fitted graph, we can say that this model is homoscedastic and linear as there are equal amounts of points above and below the line. In the second normal Q-Q plot as most of the points fall on the curve. Therefore, our plot is normal and we can conclude that all assumptions of the linear model are met.

Findings

Analyzing each of the 28 countries would be too much- we will examine one country from 3 different tiers (high GDP, middle GDP, and low GDP) from our specified countries to define and analyze a trend through multiple perspectives. Findings for all 28 countries can be explored through the Rmd file. For high tier- US, middle tier- Finland , low tier- Slovenia. The predictions will be for over the time period July 2021 to December 2021. The projected increase/decrease for unemployment for the US is -11.65% , for Finland -15.5% , and for Slovenia -11.93%. The projected increase/decrease for consumer confidence for the US is 1.42%, for Finland 2.31%, and for Slovenia 0.71%. The projected increase/decrease for inflation rate for the US is 0.22%, for Finland -0.22%, and for Slovenia 0.93%. Overall there is a positive correlation for consumer confidence, negative correlation for unemployment, and mostly positive correlation for inflation. Overall there is a positive correlation for consumer confidence, negative correlation for unemployment, and mostly positive correlation for inflation, over the period of July 2021 - December 2021. Our predictions mostly match expectations of recovery for the global economy in 2021.

Bibliography

“A Gentle Introduction to XGBoost for Applied Machine Learning.” Machine Learning Mastery. Last modified February 17, 2021. Accessed August 4, 2021. https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/. “Global Economic Effects of COVID-19.” Congressional Research Service. Last modified July 9, 2021. Accessed July 25, 2021. https://fas.org/sgp/crs/row/R46270.pdf. Organisation for Economic Co-operation and Development. Accessed August 2, 2021. https://stats.oecd.org/. “Policy Responses to the Coronavirus Pandemic.” Our World in Data. Accessed August 2, 2021. https://ourworldindata.org/policy-responses-covid. Song, Neil, Aaron Tom, Benjamin Zhao, Tanvi Kigga, and Luke Melcher. “Final Project.” GitHub. Accessed August 4, 2021. https://github.com/neilsong/upenn-data-science-academy/tree/master/Final%20Project.

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## [1000]   train-rmse:0.105701

## [22:38:12] WARNING: amalgamation/../src/learner.cc:573: 
## Parameters: { "nthreads" } might not be used.
## 
##   This may not be accurate due to some parameters are only used in language bindings but
##   passed down to XGBoost core.  Or some parameters are not used but slip through this
##   verification. Please open an issue if you find above cases.
## 
## 
## [1]  train-rmse:82.769875 
## Will train until train_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.572792 
## [3]  train-rmse:58.482998 
## [4]  train-rmse:49.164600 
## [5]  train-rmse:41.335327 
## [6]  train-rmse:34.758064 
## [7]  train-rmse:29.233557 
## [8]  train-rmse:24.594490 
## [9]  train-rmse:20.700333 
## [10] train-rmse:17.433109 
## [11] train-rmse:14.692600 
## [12] train-rmse:12.394763 
## [13] train-rmse:10.467232 
## [14] train-rmse:8.850953 
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## [16] train-rmse:6.352958 
## [17] train-rmse:5.401362 
## [18] train-rmse:4.599060 
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## [613]    train-rmse:0.002266 
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## [615]    train-rmse:0.002266 
## Stopping. Best iteration:
## [609]    train-rmse:0.002266
## [22:38:12] WARNING: amalgamation/../src/learner.cc:573: 
## Parameters: { "nthreads" } might not be used.
## 
##   This may not be accurate due to some parameters are only used in language bindings but
##   passed down to XGBoost core.  Or some parameters are not used but slip through this
##   verification. Please open an issue if you find above cases.
## 
## 
## [1]  train-rmse:96.896080 
## Will train until train_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:87.269157 
## [3]  train-rmse:78.606117 
## [4]  train-rmse:70.811279 
## [5]  train-rmse:63.798573 
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## [7]  train-rmse:51.817341 
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## [10] train-rmse:38.010288 
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## [999]    train-rmse:0.249770 
## [1000]   train-rmse:0.249677
## [22:38:13] WARNING: amalgamation/../src/learner.cc:573: 
## Parameters: { "nthreads" } might not be used.
## 
##   This may not be accurate due to some parameters are only used in language bindings but
##   passed down to XGBoost core.  Or some parameters are not used but slip through this
##   verification. Please open an issue if you find above cases.
## 
## 
## [1]  train-rmse:7.548731 
## Will train until train_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.983161 
## [3]  train-rmse:6.485629 
## [4]  train-rmse:6.037310 
## [5]  train-rmse:5.646307 
## [6]  train-rmse:5.301643 
## [7]  train-rmse:4.989468 
## [8]  train-rmse:4.718463 
## [9]  train-rmse:4.477948 
## [10] train-rmse:4.259922 
## [11] train-rmse:4.064321 
## [12] train-rmse:3.893762 
## [13] train-rmse:3.740545 
## [14] train-rmse:3.600930 
## [15] train-rmse:3.482635 
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## [18] train-rmse:3.191494 
## [19] train-rmse:3.113580 
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## [21] train-rmse:2.959785 
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## [999]    train-rmse:0.399671 
## [1000]   train-rmse:0.399172
## [22:38:14] WARNING: amalgamation/../src/learner.cc:573: 
## Parameters: { "nthreads" } might not be used.
## 
##   This may not be accurate due to some parameters are only used in language bindings but
##   passed down to XGBoost core.  Or some parameters are not used but slip through this
##   verification. Please open an issue if you find above cases.
## 
## 
## [1]  train-rmse:0.932100 
## Will train until train_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.614025 
## [3]  train-rmse:0.417054 
## [4]  train-rmse:0.299367 
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## [634]    train-rmse:0.002935 
## [635]    train-rmse:0.002934 
## [636]    train-rmse:0.002932 
## [637]    train-rmse:0.002909 
## [638]    train-rmse:0.002896 
## [639]    train-rmse:0.002892 
## [640]    train-rmse:0.002872 
## [641]    train-rmse:0.002820 
## [642]    train-rmse:0.002815 
## [643]    train-rmse:0.002806 
## [644]    train-rmse:0.002789 
## [645]    train-rmse:0.002787 
## [646]    train-rmse:0.002768 
## [647]    train-rmse:0.002759 
## [648]    train-rmse:0.002752 
## [649]    train-rmse:0.002747 
## [650]    train-rmse:0.002741 
## [651]    train-rmse:0.002730 
## [652]    train-rmse:0.002722 
## [653]    train-rmse:0.002713 
## [654]    train-rmse:0.002706 
## [655]    train-rmse:0.002693 
## [656]    train-rmse:0.002692 
## [657]    train-rmse:0.002691 
## [658]    train-rmse:0.002691 
## [659]    train-rmse:0.002691 
## [660]    train-rmse:0.002691 
## [661]    train-rmse:0.002691 
## [662]    train-rmse:0.002691 
## [663]    train-rmse:0.002691 
## Stopping. Best iteration:
## [657]    train-rmse:0.002691
## [22:38:14] WARNING: amalgamation/../src/learner.cc:573: 
## Parameters: { "nthreads" } might not be used.
## 
##   This may not be accurate due to some parameters are only used in language bindings but
##   passed down to XGBoost core.  Or some parameters are not used but slip through this
##   verification. Please open an issue if you find above cases.
## 
## 
## [1]  train-rmse:0.870897 
## Will train until train_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.762032 
## [3]  train-rmse:0.675984 
## [4]  train-rmse:0.605551 
## [5]  train-rmse:0.549712 
## [6]  train-rmse:0.501545 
## [7]  train-rmse:0.466478 
## [8]  train-rmse:0.434467 
## [9]  train-rmse:0.408927 
## [10] train-rmse:0.386002 
## [11] train-rmse:0.364975 
## [12] train-rmse:0.344814 
## [13] train-rmse:0.331946 
## [14] train-rmse:0.320573 
## [15] train-rmse:0.313138 
## [16] train-rmse:0.302860 
## [17] train-rmse:0.296268 
## [18] train-rmse:0.291417 
## [19] train-rmse:0.284563 
## [20] train-rmse:0.280222 
## [21] train-rmse:0.268778 
## [22] train-rmse:0.265794 
## [23] train-rmse:0.260859 
## [24] train-rmse:0.256389 
## [25] train-rmse:0.249119 
## [26] train-rmse:0.244145 
## [27] train-rmse:0.242332 
## [28] train-rmse:0.236559 
## [29] train-rmse:0.233053 
## [30] train-rmse:0.230645 
## [31] train-rmse:0.228325 
## [32] train-rmse:0.221820 
## [33] train-rmse:0.217462 
## [34] train-rmse:0.213717 
## [35] train-rmse:0.211203 
## [36] train-rmse:0.205866 
## [37] train-rmse:0.200434 
## [38] train-rmse:0.194344 
## [39] train-rmse:0.188902 
## [40] train-rmse:0.184601 
## [41] train-rmse:0.179928 
## [42] train-rmse:0.174743 
## [43] train-rmse:0.172398 
## [44] train-rmse:0.168426 
## [45] train-rmse:0.165215 
## [46] train-rmse:0.161847 
## [47] train-rmse:0.159821 
## [48] train-rmse:0.156752 
## [49] train-rmse:0.152887 
## [50] train-rmse:0.148657 
## [51] train-rmse:0.144047 
## [52] train-rmse:0.143253 
## [53] train-rmse:0.141105 
## [54] train-rmse:0.138062 
## [55] train-rmse:0.135915 
## [56] train-rmse:0.133710 
## [57] train-rmse:0.130738 
## [58] train-rmse:0.128748 
## [59] train-rmse:0.127103 
## [60] train-rmse:0.125397 
## [61] train-rmse:0.120975 
## [62] train-rmse:0.120118 
## [63] train-rmse:0.118172 
## [64] train-rmse:0.115038 
## [65] train-rmse:0.114484 
## [66] train-rmse:0.111334 
## [67] train-rmse:0.109942 
## [68] train-rmse:0.108311 
## [69] train-rmse:0.106304 
## [70] train-rmse:0.103623 
## [71] train-rmse:0.100040 
## [72] train-rmse:0.098381 
## [73] train-rmse:0.096446 
## [74] train-rmse:0.094436 
## [75] train-rmse:0.093088 
## [76] train-rmse:0.091248 
## [77] train-rmse:0.088574 
## [78] train-rmse:0.086877 
## [79] train-rmse:0.085757 
## [80] train-rmse:0.084132 
## [81] train-rmse:0.081309 
## [82] train-rmse:0.080507 
## [83] train-rmse:0.078830 
## [84] train-rmse:0.078203 
## [85] train-rmse:0.075197 
## [86] train-rmse:0.074282 
## [87] train-rmse:0.073061 
## [88] train-rmse:0.072343 
## [89] train-rmse:0.071082 
## [90] train-rmse:0.069922 
## [91] train-rmse:0.068476 
## [92] train-rmse:0.068079 
## [93] train-rmse:0.067462 
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## [96] train-rmse:0.062976 
## [97] train-rmse:0.062113 
## [98] train-rmse:0.061754 
## [99] train-rmse:0.061041 
## [100]    train-rmse:0.059552 
## [101]    train-rmse:0.059255 
## [102]    train-rmse:0.058354 
## [103]    train-rmse:0.057121 
## [104]    train-rmse:0.056782 
## [105]    train-rmse:0.055984 
## [106]    train-rmse:0.054698 
## [107]    train-rmse:0.053922 
## [108]    train-rmse:0.052959 
## [109]    train-rmse:0.052828 
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## [111]    train-rmse:0.051378 
## [112]    train-rmse:0.050223 
## [113]    train-rmse:0.049723 
## [114]    train-rmse:0.048591 
## [115]    train-rmse:0.047840 
## [116]    train-rmse:0.046989 
## [117]    train-rmse:0.045940 
## [118]    train-rmse:0.045191 
## [119]    train-rmse:0.044673 
## [120]    train-rmse:0.043656 
## [121]    train-rmse:0.043026 
## [122]    train-rmse:0.041752 
## [123]    train-rmse:0.040288 
## [124]    train-rmse:0.039491 
## [125]    train-rmse:0.038838 
## [126]    train-rmse:0.038151 
## [127]    train-rmse:0.037350 
## [128]    train-rmse:0.036830 
## [129]    train-rmse:0.035925 
## [130]    train-rmse:0.035067 
## [131]    train-rmse:0.034050 
## [132]    train-rmse:0.033441 
## [133]    train-rmse:0.032943 
## [134]    train-rmse:0.032156 
## [135]    train-rmse:0.031846 
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## [137]    train-rmse:0.030879 
## [138]    train-rmse:0.030614 
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## [140]    train-rmse:0.030028 
## [141]    train-rmse:0.029473 
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## [143]    train-rmse:0.028605 
## [144]    train-rmse:0.028364 
## [145]    train-rmse:0.028077 
## [146]    train-rmse:0.027900 
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## [148]    train-rmse:0.026543 
## [149]    train-rmse:0.026346 
## [150]    train-rmse:0.025991 
## [151]    train-rmse:0.025742 
## [152]    train-rmse:0.025504 
## [153]    train-rmse:0.025254 
## [154]    train-rmse:0.024398 
## [155]    train-rmse:0.023896 
## [156]    train-rmse:0.023335 
## [157]    train-rmse:0.022982 
## [158]    train-rmse:0.022625 
## [159]    train-rmse:0.021970 
## [160]    train-rmse:0.021804 
## [161]    train-rmse:0.021319 
## [162]    train-rmse:0.020874 
## [163]    train-rmse:0.020723 
## [164]    train-rmse:0.020482 
## [165]    train-rmse:0.020368 
## [166]    train-rmse:0.019869 
## [167]    train-rmse:0.019577 
## [168]    train-rmse:0.019439 
## [169]    train-rmse:0.019342 
## [170]    train-rmse:0.018949 
## [171]    train-rmse:0.018706 
## [172]    train-rmse:0.018288 
## [173]    train-rmse:0.017880 
## [174]    train-rmse:0.017312 
## [175]    train-rmse:0.017214 
## [176]    train-rmse:0.017176 
## [177]    train-rmse:0.016754 
## [178]    train-rmse:0.016485 
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## [180]    train-rmse:0.016135 
## [181]    train-rmse:0.016020 
## [182]    train-rmse:0.015553 
## [183]    train-rmse:0.015446 
## [184]    train-rmse:0.015288 
## [185]    train-rmse:0.014834 
## [186]    train-rmse:0.014458 
## [187]    train-rmse:0.014086 
## [188]    train-rmse:0.013596 
## [189]    train-rmse:0.013331 
## [190]    train-rmse:0.013225 
## [191]    train-rmse:0.012966 
## [192]    train-rmse:0.012868 
## [193]    train-rmse:0.012666 
## [194]    train-rmse:0.012509 
## [195]    train-rmse:0.012461 
## [196]    train-rmse:0.012304 
## [197]    train-rmse:0.011977 
## [198]    train-rmse:0.011820 
## [199]    train-rmse:0.011640 
## [200]    train-rmse:0.011605 
## [201]    train-rmse:0.011413 
## [202]    train-rmse:0.011224 
## [203]    train-rmse:0.011125 
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## [205]    train-rmse:0.010882 
## [206]    train-rmse:0.010661 
## [207]    train-rmse:0.010484 
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## [209]    train-rmse:0.010259 
## [210]    train-rmse:0.010068 
## [211]    train-rmse:0.010019 
## [212]    train-rmse:0.009860 
## [213]    train-rmse:0.009665 
## [214]    train-rmse:0.009476 
## [215]    train-rmse:0.009453 
## [216]    train-rmse:0.009347 
## [217]    train-rmse:0.009289 
## [218]    train-rmse:0.009146 
## [219]    train-rmse:0.009068 
## [220]    train-rmse:0.008955 
## [221]    train-rmse:0.008785 
## [222]    train-rmse:0.008518 
## [223]    train-rmse:0.008435 
## [224]    train-rmse:0.008340 
## [225]    train-rmse:0.008267 
## [226]    train-rmse:0.008186 
## [227]    train-rmse:0.008036 
## [228]    train-rmse:0.007878 
## [229]    train-rmse:0.007745 
## [230]    train-rmse:0.007632 
## [231]    train-rmse:0.007474 
## [232]    train-rmse:0.007327 
## [233]    train-rmse:0.007224 
## [234]    train-rmse:0.007089 
## [235]    train-rmse:0.007036 
## [236]    train-rmse:0.006952 
## [237]    train-rmse:0.006859 
## [238]    train-rmse:0.006774 
## [239]    train-rmse:0.006652 
## [240]    train-rmse:0.006603 
## [241]    train-rmse:0.006466 
## [242]    train-rmse:0.006330 
## [243]    train-rmse:0.006255 
## [244]    train-rmse:0.006128 
## [245]    train-rmse:0.006035 
## [246]    train-rmse:0.005943 
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## [248]    train-rmse:0.005823 
## [249]    train-rmse:0.005716 
## [250]    train-rmse:0.005662 
## [251]    train-rmse:0.005593 
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## [254]    train-rmse:0.005353 
## [255]    train-rmse:0.005345 
## [256]    train-rmse:0.005228 
## [257]    train-rmse:0.005152 
## [258]    train-rmse:0.005131 
## [259]    train-rmse:0.005095 
## [260]    train-rmse:0.004993 
## [261]    train-rmse:0.004929 
## [262]    train-rmse:0.004800 
## [263]    train-rmse:0.004784 
## [264]    train-rmse:0.004757 
## [265]    train-rmse:0.004727 
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## [267]    train-rmse:0.004560 
## [268]    train-rmse:0.004501 
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## [270]    train-rmse:0.004291 
## [271]    train-rmse:0.004223 
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## [274]    train-rmse:0.004015 
## [275]    train-rmse:0.003923 
## [276]    train-rmse:0.003838 
## [277]    train-rmse:0.003753 
## [278]    train-rmse:0.003747 
## [279]    train-rmse:0.003662 
## [280]    train-rmse:0.003603 
## [281]    train-rmse:0.003590 
## [282]    train-rmse:0.003545 
## [283]    train-rmse:0.003488 
## [284]    train-rmse:0.003456 
## [285]    train-rmse:0.003426 
## [286]    train-rmse:0.003375 
## [287]    train-rmse:0.003314 
## [288]    train-rmse:0.003291 
## [289]    train-rmse:0.003251 
## [290]    train-rmse:0.003231 
## [291]    train-rmse:0.003221 
## [292]    train-rmse:0.003180 
## [293]    train-rmse:0.003125 
## [294]    train-rmse:0.003101 
## [295]    train-rmse:0.003089 
## [296]    train-rmse:0.003049 
## [297]    train-rmse:0.003010 
## [298]    train-rmse:0.002932 
## [299]    train-rmse:0.002872 
## [300]    train-rmse:0.002853 
## [301]    train-rmse:0.002827 
## [302]    train-rmse:0.002827 
## [303]    train-rmse:0.002827 
## [304]    train-rmse:0.002827 
## [305]    train-rmse:0.002827 
## [306]    train-rmse:0.002827 
## [307]    train-rmse:0.002827 
## Stopping. Best iteration:
## [301]    train-rmse:0.002827
## [22:38:15] WARNING: amalgamation/../src/learner.cc:573: 
## Parameters: { "nthreads" } might not be used.
## 
##   This may not be accurate due to some parameters are only used in language bindings but
##   passed down to XGBoost core.  Or some parameters are not used but slip through this
##   verification. Please open an issue if you find above cases.
## 
## 
## [1]  train-rmse:1.110532 
## Will train until train_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.818772 
## [3]  train-rmse:0.649562 
## [4]  train-rmse:0.558389 
## [5]  train-rmse:0.481635 
## [6]  train-rmse:0.446110 
## [7]  train-rmse:0.421907 
## [8]  train-rmse:0.388818 
## [9]  train-rmse:0.374595 
## [10] train-rmse:0.333989 
## [11] train-rmse:0.324004 
## [12] train-rmse:0.316999 
## [13] train-rmse:0.305775 
## [14] train-rmse:0.289728 
## [15] train-rmse:0.261559 
## [16] train-rmse:0.246361 
## [17] train-rmse:0.239396 
## [18] train-rmse:0.230868 
## [19] train-rmse:0.215626 
## [20] train-rmse:0.206779 
## [21] train-rmse:0.192529 
## [22] train-rmse:0.178172 
## [23] train-rmse:0.170494 
## [24] train-rmse:0.166442 
## [25] train-rmse:0.162236 
## [26] train-rmse:0.149438 
## [27] train-rmse:0.143495 
## [28] train-rmse:0.135469 
## [29] train-rmse:0.128945 
## [30] train-rmse:0.118477 
## [31] train-rmse:0.114449 
## [32] train-rmse:0.107183 
## [33] train-rmse:0.105200 
## [34] train-rmse:0.099853 
## [35] train-rmse:0.097176 
## [36] train-rmse:0.088808 
## [37] train-rmse:0.084376 
## [38] train-rmse:0.080578 
## [39] train-rmse:0.074592 
## [40] train-rmse:0.072061 
## [41] train-rmse:0.071384 
## [42] train-rmse:0.068995 
## [43] train-rmse:0.064975 
## [44] train-rmse:0.064214 
## [45] train-rmse:0.060893 
## [46] train-rmse:0.059352 
## [47] train-rmse:0.055974 
## [48] train-rmse:0.055670 
## [49] train-rmse:0.054460 
## [50] train-rmse:0.052593 
## [51] train-rmse:0.051195 
## [52] train-rmse:0.048313 
## [53] train-rmse:0.044676 
## [54] train-rmse:0.042820 
## [55] train-rmse:0.040135 
## [56] train-rmse:0.038881 
## [57] train-rmse:0.037422 
## [58] train-rmse:0.035942 
## [59] train-rmse:0.034021 
## [60] train-rmse:0.032170 
## [61] train-rmse:0.030737 
## [62] train-rmse:0.029481 
## [63] train-rmse:0.028539 
## [64] train-rmse:0.027523 
## [65] train-rmse:0.026461 
## [66] train-rmse:0.025446 
## [67] train-rmse:0.025252 
## [68] train-rmse:0.024065 
## [69] train-rmse:0.022420 
## [70] train-rmse:0.021287 
## [71] train-rmse:0.020681 
## [72] train-rmse:0.019684 
## [73] train-rmse:0.018883 
## [74] train-rmse:0.017997 
## [75] train-rmse:0.017397 
## [76] train-rmse:0.016892 
## [77] train-rmse:0.015738 
## [78] train-rmse:0.015263 
## [79] train-rmse:0.014733 
## [80] train-rmse:0.014317 
## [81] train-rmse:0.013943 
## [82] train-rmse:0.013258 
## [83] train-rmse:0.012890 
## [84] train-rmse:0.012513 
## [85] train-rmse:0.011871 
## [86] train-rmse:0.011532 
## [87] train-rmse:0.011024 
## [88] train-rmse:0.010695 
## [89] train-rmse:0.010208 
## [90] train-rmse:0.009865 
## [91] train-rmse:0.009329 
## [92] train-rmse:0.009075 
## [93] train-rmse:0.008893 
## [94] train-rmse:0.008363 
## [95] train-rmse:0.007965 
## [96] train-rmse:0.007817 
## [97] train-rmse:0.007577 
## [98] train-rmse:0.007243 
## [99] train-rmse:0.006981 
## [100]    train-rmse:0.006744 
## [101]    train-rmse:0.006566 
## [102]    train-rmse:0.006340 
## [103]    train-rmse:0.006143 
## [104]    train-rmse:0.005707 
## [105]    train-rmse:0.005621 
## [106]    train-rmse:0.005400 
## [107]    train-rmse:0.005347 
## [108]    train-rmse:0.005150 
## [109]    train-rmse:0.005065 
## [110]    train-rmse:0.004958 
## [111]    train-rmse:0.004814 
## [112]    train-rmse:0.004652 
## [113]    train-rmse:0.004437 
## [114]    train-rmse:0.004198 
## [115]    train-rmse:0.004063 
## [116]    train-rmse:0.003866 
## [117]    train-rmse:0.003756 
## [118]    train-rmse:0.003731 
## [119]    train-rmse:0.003530 
## [120]    train-rmse:0.003408 
## [121]    train-rmse:0.003308 
## [122]    train-rmse:0.003136 
## [123]    train-rmse:0.003028 
## [124]    train-rmse:0.002953 
## [125]    train-rmse:0.002844 
## [126]    train-rmse:0.002713 
## [127]    train-rmse:0.002600 
## [128]    train-rmse:0.002503 
## [129]    train-rmse:0.002438 
## [130]    train-rmse:0.002372 
## [131]    train-rmse:0.002241 
## [132]    train-rmse:0.002179 
## [133]    train-rmse:0.002179 
## [134]    train-rmse:0.002179 
## [135]    train-rmse:0.002179 
## [136]    train-rmse:0.002179 
## [137]    train-rmse:0.002179 
## [138]    train-rmse:0.002179 
## Stopping. Best iteration:
## [132]    train-rmse:0.002179
## [22:38:15] WARNING: amalgamation/../src/learner.cc:573: 
## Parameters: { "nthreads" } might not be used.
## 
##   This may not be accurate due to some parameters are only used in language bindings but
##   passed down to XGBoost core.  Or some parameters are not used but slip through this
##   verification. Please open an issue if you find above cases.
## 
## 
## [1]  train-rmse:1.179358 
## Will train until train_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.880255 
## [3]  train-rmse:0.680660 
## [4]  train-rmse:0.553496 
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## [504]    train-rmse:0.010245 
## [505]    train-rmse:0.010130 
## [506]    train-rmse:0.010075 
## [507]    train-rmse:0.010000 
## [508]    train-rmse:0.009942 
## [509]    train-rmse:0.009870 
## [510]    train-rmse:0.009813 
## [511]    train-rmse:0.009722 
## [512]    train-rmse:0.009714 
## [513]    train-rmse:0.009650 
## [514]    train-rmse:0.009594 
## [515]    train-rmse:0.009529 
## [516]    train-rmse:0.009476 
## [517]    train-rmse:0.009458 
## [518]    train-rmse:0.009394 
## [519]    train-rmse:0.009346 
## [520]    train-rmse:0.009328 
## [521]    train-rmse:0.009288 
## [522]    train-rmse:0.009235 
## [523]    train-rmse:0.009160 
## [524]    train-rmse:0.009100 
## [525]    train-rmse:0.009055 
## [526]    train-rmse:0.008997 
## [527]    train-rmse:0.008991 
## [528]    train-rmse:0.008942 
## [529]    train-rmse:0.008909 
## [530]    train-rmse:0.008861 
## [531]    train-rmse:0.008801 
## [532]    train-rmse:0.008752 
## [533]    train-rmse:0.008730 
## [534]    train-rmse:0.008674 
## [535]    train-rmse:0.008651 
## [536]    train-rmse:0.008550 
## [537]    train-rmse:0.008546 
## [538]    train-rmse:0.008512 
## [539]    train-rmse:0.008467 
## [540]    train-rmse:0.008431 
## [541]    train-rmse:0.008399 
## [542]    train-rmse:0.008351 
## [543]    train-rmse:0.008317 
## [544]    train-rmse:0.008247 
## [545]    train-rmse:0.008227 
## [546]    train-rmse:0.008203 
## [547]    train-rmse:0.008183 
## [548]    train-rmse:0.008133 
## [549]    train-rmse:0.008127 
## [550]    train-rmse:0.008071 
## [551]    train-rmse:0.008018 
## [552]    train-rmse:0.008000 
## [553]    train-rmse:0.007993 
## [554]    train-rmse:0.007961 
## [555]    train-rmse:0.007943 
## [556]    train-rmse:0.007897 
## [557]    train-rmse:0.007878 
## [558]    train-rmse:0.007851 
## [559]    train-rmse:0.007816 
## [560]    train-rmse:0.007784 
## [561]    train-rmse:0.007732 
## [562]    train-rmse:0.007728 
## [563]    train-rmse:0.007670 
## [564]    train-rmse:0.007653 
## [565]    train-rmse:0.007646 
## [566]    train-rmse:0.007592 
## [567]    train-rmse:0.007553 
## [568]    train-rmse:0.007542 
## [569]    train-rmse:0.007492 
## [570]    train-rmse:0.007437 
## [571]    train-rmse:0.007407 
## [572]    train-rmse:0.007377 
## [573]    train-rmse:0.007374 
## [574]    train-rmse:0.007355 
## [575]    train-rmse:0.007324 
## [576]    train-rmse:0.007314 
## [577]    train-rmse:0.007288 
## [578]    train-rmse:0.007241 
## [579]    train-rmse:0.007200 
## [580]    train-rmse:0.007163 
## [581]    train-rmse:0.007148 
## [582]    train-rmse:0.007117 
## [583]    train-rmse:0.007070 
## [584]    train-rmse:0.007014 
## [585]    train-rmse:0.007008 
## [586]    train-rmse:0.006976 
## [587]    train-rmse:0.006938 
## [588]    train-rmse:0.006904 
## [589]    train-rmse:0.006864 
## [590]    train-rmse:0.006838 
## [591]    train-rmse:0.006815 
## [592]    train-rmse:0.006786 
## [593]    train-rmse:0.006735 
## [594]    train-rmse:0.006698 
## [595]    train-rmse:0.006645 
## [596]    train-rmse:0.006576 
## [597]    train-rmse:0.006535 
## [598]    train-rmse:0.006514 
## [599]    train-rmse:0.006504 
## [600]    train-rmse:0.006474 
## [601]    train-rmse:0.006462 
## [602]    train-rmse:0.006436 
## [603]    train-rmse:0.006404 
## [604]    train-rmse:0.006368 
## [605]    train-rmse:0.006342 
## [606]    train-rmse:0.006311 
## [607]    train-rmse:0.006264 
## [608]    train-rmse:0.006246 
## [609]    train-rmse:0.006221 
## [610]    train-rmse:0.006200 
## [611]    train-rmse:0.006165 
## [612]    train-rmse:0.006133 
## [613]    train-rmse:0.006120 
## [614]    train-rmse:0.006090 
## [615]    train-rmse:0.006082 
## [616]    train-rmse:0.006049 
## [617]    train-rmse:0.005990 
## [618]    train-rmse:0.005951 
## [619]    train-rmse:0.005938 
## [620]    train-rmse:0.005886 
## [621]    train-rmse:0.005869 
## [622]    train-rmse:0.005849 
## [623]    train-rmse:0.005835 
## [624]    train-rmse:0.005810 
## [625]    train-rmse:0.005773 
## [626]    train-rmse:0.005740 
## [627]    train-rmse:0.005699 
## [628]    train-rmse:0.005684 
## [629]    train-rmse:0.005647 
## [630]    train-rmse:0.005601 
## [631]    train-rmse:0.005573 
## [632]    train-rmse:0.005555 
## [633]    train-rmse:0.005528 
## [634]    train-rmse:0.005516 
## [635]    train-rmse:0.005487 
## [636]    train-rmse:0.005462 
## [637]    train-rmse:0.005442 
## [638]    train-rmse:0.005423 
## [639]    train-rmse:0.005403 
## [640]    train-rmse:0.005379 
## [641]    train-rmse:0.005366 
## [642]    train-rmse:0.005355 
## [643]    train-rmse:0.005349 
## [644]    train-rmse:0.005323 
## [645]    train-rmse:0.005298 
## [646]    train-rmse:0.005294 
## [647]    train-rmse:0.005280 
## [648]    train-rmse:0.005255 
## [649]    train-rmse:0.005231 
## [650]    train-rmse:0.005224 
## [651]    train-rmse:0.005189 
## [652]    train-rmse:0.005135 
## [653]    train-rmse:0.005100 
## [654]    train-rmse:0.005083 
## [655]    train-rmse:0.005052 
## [656]    train-rmse:0.005024 
## [657]    train-rmse:0.004991 
## [658]    train-rmse:0.004973 
## [659]    train-rmse:0.004951 
## [660]    train-rmse:0.004942 
## [661]    train-rmse:0.004926 
## [662]    train-rmse:0.004898 
## [663]    train-rmse:0.004868 
## [664]    train-rmse:0.004830 
## [665]    train-rmse:0.004809 
## [666]    train-rmse:0.004796 
## [667]    train-rmse:0.004769 
## [668]    train-rmse:0.004757 
## [669]    train-rmse:0.004734 
## [670]    train-rmse:0.004731 
## [671]    train-rmse:0.004722 
## [672]    train-rmse:0.004686 
## [673]    train-rmse:0.004666 
## [674]    train-rmse:0.004638 
## [675]    train-rmse:0.004599 
## [676]    train-rmse:0.004574 
## [677]    train-rmse:0.004557 
## [678]    train-rmse:0.004527 
## [679]    train-rmse:0.004487 
## [680]    train-rmse:0.004476 
## [681]    train-rmse:0.004445 
## [682]    train-rmse:0.004434 
## [683]    train-rmse:0.004426 
## [684]    train-rmse:0.004413 
## [685]    train-rmse:0.004399 
## [686]    train-rmse:0.004374 
## [687]    train-rmse:0.004354 
## [688]    train-rmse:0.004344 
## [689]    train-rmse:0.004326 
## [690]    train-rmse:0.004323 
## [691]    train-rmse:0.004302 
## [692]    train-rmse:0.004294 
## [693]    train-rmse:0.004281 
## [694]    train-rmse:0.004264 
## [695]    train-rmse:0.004244 
## [696]    train-rmse:0.004201 
## [697]    train-rmse:0.004182 
## [698]    train-rmse:0.004169 
## [699]    train-rmse:0.004162 
## [700]    train-rmse:0.004140 
## [701]    train-rmse:0.004125 
## [702]    train-rmse:0.004092 
## [703]    train-rmse:0.004076 
## [704]    train-rmse:0.004053 
## [705]    train-rmse:0.004038 
## [706]    train-rmse:0.004007 
## [707]    train-rmse:0.003993 
## [708]    train-rmse:0.003965 
## [709]    train-rmse:0.003946 
## [710]    train-rmse:0.003925 
## [711]    train-rmse:0.003905 
## [712]    train-rmse:0.003878 
## [713]    train-rmse:0.003866 
## [714]    train-rmse:0.003843 
## [715]    train-rmse:0.003827 
## [716]    train-rmse:0.003814 
## [717]    train-rmse:0.003794 
## [718]    train-rmse:0.003775 
## [719]    train-rmse:0.003760 
## [720]    train-rmse:0.003746 
## [721]    train-rmse:0.003733 
## [722]    train-rmse:0.003723 
## [723]    train-rmse:0.003721 
## [724]    train-rmse:0.003704 
## [725]    train-rmse:0.003682 
## [726]    train-rmse:0.003682 
## [727]    train-rmse:0.003682 
## [728]    train-rmse:0.003682 
## [729]    train-rmse:0.003682 
## [730]    train-rmse:0.003682 
## [731]    train-rmse:0.003682 
## Stopping. Best iteration:
## [725]    train-rmse:0.003682
## `summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

## [1] 0.753
## [1] 0.0312
## [1] 32
## [1] 1.19
## [1] 0.0312
## [1] 32
## [1] 2.13
## [1] 0.125
## [1] 2
## [1] 0.137
## [1] 0.0625
## [1] 32
## [1] 0.434
## [1] 0.25
## [1] 8
## [1] 0.52
## [1] 0.25
## [1] 4
## [1] 0.345
## [1] 0.0312
## [1] 32
## [1]  train-rmse:88.665695+0.088976   test-rmse:88.666461+0.404980 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.835214+0.080050   test-rmse:79.835893+0.413675 
## [3]  train-rmse:71.885242+0.071998   test-rmse:71.885820+0.421463 
## [4]  train-rmse:64.728087+0.064733   test-rmse:64.728551+0.428434 
## [5]  train-rmse:58.284824+0.058174   test-rmse:58.285159+0.434654 
## [6]  train-rmse:52.484374+0.052257   test-rmse:52.484567+0.440197 
## [7]  train-rmse:47.262778+0.046908   test-rmse:47.262816+0.445120 
## [8]  train-rmse:42.562434+0.042074   test-rmse:42.562303+0.449468 
## [9]  train-rmse:38.331508+0.037710   test-rmse:38.331192+0.453293 
## [10] train-rmse:34.523343+0.033759   test-rmse:34.522811+0.456630 
## [11] train-rmse:31.095926+0.030181   test-rmse:31.095166+0.459509 
## [12] train-rmse:28.011453+0.026945   test-rmse:28.010445+0.461960 
## [13] train-rmse:25.235913+0.024012   test-rmse:25.234629+0.463999 
## [14] train-rmse:22.738687+0.021363   test-rmse:22.737103+0.465637 
## [15] train-rmse:20.492223+0.018971   test-rmse:20.490315+0.466887 
## [16] train-rmse:18.471749+0.016823   test-rmse:18.469484+0.467747 
## [17] train-rmse:16.654969+0.014903   test-rmse:16.652318+0.468213 
## [18] train-rmse:15.021334+0.013222   test-rmse:15.020864+0.456548 
## [19] train-rmse:13.552676+0.011643   test-rmse:13.544601+0.458028 
## [20] train-rmse:12.232597+0.010248   test-rmse:12.227122+0.446788 
## [21] train-rmse:11.046271+0.009054   test-rmse:11.038224+0.443529 
## [22] train-rmse:9.980255+0.008025    test-rmse:9.968272+0.443284 
## [23] train-rmse:9.023094+0.007243    test-rmse:9.008939+0.438743 
## [24] train-rmse:8.164006+0.006919    test-rmse:8.150402+0.432761 
## [25] train-rmse:7.393469+0.006909    test-rmse:7.379096+0.426788 
## [26] train-rmse:6.703072+0.007057    test-rmse:6.686790+0.424458 
## [27] train-rmse:6.085288+0.007552    test-rmse:6.068708+0.418426 
## [28] train-rmse:5.532980+0.008304    test-rmse:5.519245+0.408053 
## [29] train-rmse:5.039910+0.009183    test-rmse:5.030345+0.400254 
## [30] train-rmse:4.600489+0.010213    test-rmse:4.594970+0.392018 
## [31] train-rmse:4.209734+0.011361    test-rmse:4.207355+0.381357 
## [32] train-rmse:3.862690+0.012582    test-rmse:3.860397+0.369796 
## [33] train-rmse:3.555588+0.013855    test-rmse:3.554603+0.357976 
## [34] train-rmse:3.284309+0.015268    test-rmse:3.287877+0.346969 
## [35] train-rmse:3.045633+0.016470    test-rmse:3.050797+0.332103 
## [36] train-rmse:2.836047+0.017489    test-rmse:2.845978+0.317524 
## [37] train-rmse:2.652766+0.018795    test-rmse:2.669513+0.300805 
## [38] train-rmse:2.493238+0.019878    test-rmse:2.514378+0.280145 
## [39] train-rmse:2.354711+0.021049    test-rmse:2.379089+0.265032 
## [40] train-rmse:2.234943+0.022094    test-rmse:2.265612+0.246200 
## [41] train-rmse:2.131615+0.023174    test-rmse:2.167513+0.224969 
## [42] train-rmse:2.042864+0.024109    test-rmse:2.084259+0.208171 
## [43] train-rmse:1.966769+0.024842    test-rmse:2.014315+0.193288 
## [44] train-rmse:1.901578+0.025421    test-rmse:1.955068+0.179009 
## [45] train-rmse:1.845874+0.025797    test-rmse:1.905672+0.164749 
## [46] train-rmse:1.798192+0.026300    test-rmse:1.864764+0.154294 
## [47] train-rmse:1.757437+0.026768    test-rmse:1.828740+0.143916 
## [48] train-rmse:1.722471+0.026893    test-rmse:1.792479+0.133593 
## [49] train-rmse:1.692650+0.027017    test-rmse:1.770749+0.127906 
## [50] train-rmse:1.666941+0.027115    test-rmse:1.749577+0.122198 
## [51] train-rmse:1.644496+0.027142    test-rmse:1.732634+0.118091 
## [52] train-rmse:1.625123+0.027103    test-rmse:1.714835+0.115536 
## [53] train-rmse:1.608072+0.026895    test-rmse:1.697876+0.115190 
## [54] train-rmse:1.593210+0.026858    test-rmse:1.684058+0.113254 
## [55] train-rmse:1.579970+0.026814    test-rmse:1.674333+0.112071 
## [56] train-rmse:1.568059+0.026671    test-rmse:1.663143+0.112117 
## [57] train-rmse:1.557282+0.026491    test-rmse:1.655770+0.114626 
## [58] train-rmse:1.547515+0.026363    test-rmse:1.647450+0.112845 
## [59] train-rmse:1.538555+0.026172    test-rmse:1.641133+0.112956 
## [60] train-rmse:1.530376+0.025954    test-rmse:1.632894+0.111383 
## [61] train-rmse:1.522861+0.025869    test-rmse:1.628526+0.111458 
## [62] train-rmse:1.515741+0.025723    test-rmse:1.622405+0.112412 
## [63] train-rmse:1.509057+0.025562    test-rmse:1.616240+0.112238 
## [64] train-rmse:1.502773+0.025388    test-rmse:1.612215+0.114254 
## [65] train-rmse:1.496724+0.025221    test-rmse:1.608070+0.114475 
## [66] train-rmse:1.490971+0.025089    test-rmse:1.603275+0.113735 
## [67] train-rmse:1.485414+0.024976    test-rmse:1.603660+0.114531 
## [68] train-rmse:1.480038+0.024820    test-rmse:1.595055+0.110439 
## [69] train-rmse:1.474867+0.024588    test-rmse:1.591213+0.111514 
## [70] train-rmse:1.469882+0.024491    test-rmse:1.587984+0.111379 
## [71] train-rmse:1.465024+0.024388    test-rmse:1.583794+0.110146 
## [72] train-rmse:1.460356+0.024308    test-rmse:1.580313+0.109840 
## [73] train-rmse:1.455718+0.024245    test-rmse:1.578482+0.111471 
## [74] train-rmse:1.451166+0.024171    test-rmse:1.577888+0.111679 
## [75] train-rmse:1.446683+0.024138    test-rmse:1.573363+0.111612 
## [76] train-rmse:1.442292+0.024023    test-rmse:1.572665+0.113238 
## [77] train-rmse:1.438008+0.023898    test-rmse:1.570687+0.113234 
## [78] train-rmse:1.433829+0.023839    test-rmse:1.561983+0.109377 
## [79] train-rmse:1.429693+0.023772    test-rmse:1.559732+0.108423 
## [80] train-rmse:1.425634+0.023685    test-rmse:1.558881+0.107172 
## [81] train-rmse:1.421640+0.023650    test-rmse:1.555921+0.106504 
## [82] train-rmse:1.417701+0.023619    test-rmse:1.552462+0.106590 
## [83] train-rmse:1.413817+0.023568    test-rmse:1.547982+0.104038 
## [84] train-rmse:1.409962+0.023528    test-rmse:1.545438+0.104367 
## [85] train-rmse:1.406151+0.023476    test-rmse:1.543635+0.108102 
## [86] train-rmse:1.402398+0.023462    test-rmse:1.543045+0.107295 
## [87] train-rmse:1.398724+0.023464    test-rmse:1.536945+0.104819 
## [88] train-rmse:1.395044+0.023457    test-rmse:1.536074+0.106939 
## [89] train-rmse:1.391433+0.023403    test-rmse:1.533099+0.106640 
## [90] train-rmse:1.387864+0.023353    test-rmse:1.532243+0.106567 
## [91] train-rmse:1.384348+0.023272    test-rmse:1.528383+0.106788 
## [92] train-rmse:1.380878+0.023232    test-rmse:1.524480+0.106910 
## [93] train-rmse:1.377462+0.023200    test-rmse:1.519961+0.106798 
## [94] train-rmse:1.374098+0.023147    test-rmse:1.517636+0.107068 
## [95] train-rmse:1.370741+0.023106    test-rmse:1.519765+0.105243 
## [96] train-rmse:1.367426+0.023079    test-rmse:1.519108+0.105685 
## [97] train-rmse:1.364149+0.023046    test-rmse:1.515813+0.105680 
## [98] train-rmse:1.360892+0.022997    test-rmse:1.515031+0.105377 
## [99] train-rmse:1.357666+0.022917    test-rmse:1.513143+0.104079 
## [100]    train-rmse:1.354468+0.022858    test-rmse:1.511719+0.101865 
## [101]    train-rmse:1.351313+0.022751    test-rmse:1.507047+0.100594 
## [102]    train-rmse:1.348158+0.022647    test-rmse:1.505214+0.099678 
## [103]    train-rmse:1.345074+0.022566    test-rmse:1.500629+0.101207 
## [104]    train-rmse:1.342012+0.022467    test-rmse:1.499437+0.102430 
## [105]    train-rmse:1.338983+0.022408    test-rmse:1.497183+0.103030 
## [106]    train-rmse:1.336001+0.022355    test-rmse:1.494013+0.101579 
## [107]    train-rmse:1.333009+0.022288    test-rmse:1.492739+0.099012 
## [108]    train-rmse:1.330071+0.022235    test-rmse:1.492788+0.101066 
## [109]    train-rmse:1.327160+0.022169    test-rmse:1.491697+0.100869 
## [110]    train-rmse:1.324319+0.022089    test-rmse:1.491584+0.100560 
## [111]    train-rmse:1.321475+0.022017    test-rmse:1.489363+0.100732 
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## [322]    train-rmse:1.031979+0.017866    test-rmse:1.281250+0.088828 
## [323]    train-rmse:1.031334+0.017853    test-rmse:1.280719+0.088851 
## [324]    train-rmse:1.030691+0.017843    test-rmse:1.280021+0.088774 
## [325]    train-rmse:1.030058+0.017825    test-rmse:1.279141+0.088647 
## [326]    train-rmse:1.029427+0.017813    test-rmse:1.278857+0.089064 
## [327]    train-rmse:1.028804+0.017799    test-rmse:1.278932+0.088698 
## [328]    train-rmse:1.028182+0.017782    test-rmse:1.277846+0.089336 
## [329]    train-rmse:1.027563+0.017766    test-rmse:1.277746+0.089174 
## [330]    train-rmse:1.026948+0.017750    test-rmse:1.277420+0.089113 
## [331]    train-rmse:1.026337+0.017739    test-rmse:1.277259+0.088921 
## [332]    train-rmse:1.025728+0.017726    test-rmse:1.277050+0.089405 
## [333]    train-rmse:1.025119+0.017716    test-rmse:1.276739+0.089830 
## [334]    train-rmse:1.024512+0.017706    test-rmse:1.276319+0.090754 
## [335]    train-rmse:1.023905+0.017694    test-rmse:1.276350+0.091173 
## [336]    train-rmse:1.023302+0.017685    test-rmse:1.275764+0.090604 
## [337]    train-rmse:1.022700+0.017674    test-rmse:1.274448+0.089283 
## [338]    train-rmse:1.022097+0.017666    test-rmse:1.273988+0.089077 
## [339]    train-rmse:1.021500+0.017655    test-rmse:1.273054+0.089167 
## [340]    train-rmse:1.020904+0.017645    test-rmse:1.272054+0.088808 
## [341]    train-rmse:1.020306+0.017637    test-rmse:1.271964+0.089178 
## [342]    train-rmse:1.019716+0.017631    test-rmse:1.272008+0.088929 
## [343]    train-rmse:1.019130+0.017622    test-rmse:1.271568+0.089042 
## [344]    train-rmse:1.018535+0.017603    test-rmse:1.271734+0.087800 
## [345]    train-rmse:1.017951+0.017589    test-rmse:1.271734+0.087277 
## [346]    train-rmse:1.017370+0.017582    test-rmse:1.270845+0.086973 
## [347]    train-rmse:1.016785+0.017573    test-rmse:1.271038+0.086610 
## [348]    train-rmse:1.016206+0.017566    test-rmse:1.271083+0.087530 
## [349]    train-rmse:1.015636+0.017556    test-rmse:1.269787+0.087470 
## [350]    train-rmse:1.015068+0.017545    test-rmse:1.269100+0.087878 
## [351]    train-rmse:1.014503+0.017540    test-rmse:1.268229+0.087522 
## [352]    train-rmse:1.013941+0.017535    test-rmse:1.268468+0.086915 
## [353]    train-rmse:1.013383+0.017529    test-rmse:1.267956+0.086883 
## [354]    train-rmse:1.012828+0.017520    test-rmse:1.268028+0.086785 
## [355]    train-rmse:1.012272+0.017509    test-rmse:1.267060+0.087002 
## [356]    train-rmse:1.011715+0.017496    test-rmse:1.266885+0.087636 
## [357]    train-rmse:1.011159+0.017495    test-rmse:1.266981+0.087798 
## [358]    train-rmse:1.010614+0.017491    test-rmse:1.266295+0.088506 
## [359]    train-rmse:1.010065+0.017493    test-rmse:1.266218+0.088791 
## [360]    train-rmse:1.009522+0.017487    test-rmse:1.265817+0.088763 
## [361]    train-rmse:1.008982+0.017479    test-rmse:1.265500+0.088884 
## [362]    train-rmse:1.008445+0.017472    test-rmse:1.264243+0.089167 
## [363]    train-rmse:1.007909+0.017462    test-rmse:1.263003+0.088070 
## [364]    train-rmse:1.007375+0.017455    test-rmse:1.262796+0.087866 
## [365]    train-rmse:1.006844+0.017447    test-rmse:1.263109+0.088095 
## [366]    train-rmse:1.006307+0.017444    test-rmse:1.262874+0.087931 
## [367]    train-rmse:1.005778+0.017438    test-rmse:1.261700+0.087417 
## [368]    train-rmse:1.005255+0.017436    test-rmse:1.262178+0.087027 
## [369]    train-rmse:1.004721+0.017432    test-rmse:1.261569+0.087753 
## [370]    train-rmse:1.004201+0.017435    test-rmse:1.261008+0.087467 
## [371]    train-rmse:1.003684+0.017434    test-rmse:1.260483+0.087338 
## [372]    train-rmse:1.003169+0.017432    test-rmse:1.259766+0.087502 
## [373]    train-rmse:1.002655+0.017432    test-rmse:1.259264+0.087217 
## [374]    train-rmse:1.002141+0.017431    test-rmse:1.260005+0.086411 
## [375]    train-rmse:1.001628+0.017427    test-rmse:1.259321+0.086198 
## [376]    train-rmse:1.001121+0.017426    test-rmse:1.259857+0.085756 
## [377]    train-rmse:1.000615+0.017426    test-rmse:1.258955+0.085915 
## [378]    train-rmse:1.000111+0.017426    test-rmse:1.258895+0.086179 
## [379]    train-rmse:0.999611+0.017420    test-rmse:1.258972+0.086162 
## [380]    train-rmse:0.999111+0.017419    test-rmse:1.258909+0.086164 
## [381]    train-rmse:0.998614+0.017417    test-rmse:1.259070+0.085779 
## [382]    train-rmse:0.998110+0.017404    test-rmse:1.259024+0.085606 
## [383]    train-rmse:0.997614+0.017405    test-rmse:1.258775+0.085924 
## [384]    train-rmse:0.997120+0.017404    test-rmse:1.258439+0.085839 
## [385]    train-rmse:0.996622+0.017409    test-rmse:1.258602+0.086792 
## [386]    train-rmse:0.996132+0.017405    test-rmse:1.258506+0.087116 
## [387]    train-rmse:0.995645+0.017402    test-rmse:1.258573+0.086755 
## [388]    train-rmse:0.995158+0.017402    test-rmse:1.258384+0.086830 
## [389]    train-rmse:0.994676+0.017400    test-rmse:1.258074+0.087097 
## [390]    train-rmse:0.994197+0.017400    test-rmse:1.257692+0.087473 
## [391]    train-rmse:0.993715+0.017398    test-rmse:1.256637+0.088079 
## [392]    train-rmse:0.993238+0.017399    test-rmse:1.256024+0.086459 
## [393]    train-rmse:0.992765+0.017404    test-rmse:1.255751+0.086517 
## [394]    train-rmse:0.992295+0.017407    test-rmse:1.255902+0.086750 
## [395]    train-rmse:0.991829+0.017408    test-rmse:1.255605+0.086570 
## [396]    train-rmse:0.991352+0.017406    test-rmse:1.255634+0.087067 
## [397]    train-rmse:0.990876+0.017399    test-rmse:1.255284+0.086894 
## [398]    train-rmse:0.990411+0.017400    test-rmse:1.254429+0.087107 
## [399]    train-rmse:0.989945+0.017403    test-rmse:1.254326+0.087034 
## [400]    train-rmse:0.989479+0.017405    test-rmse:1.253834+0.086791 
## [401]    train-rmse:0.989019+0.017407    test-rmse:1.253981+0.086912 
## [402]    train-rmse:0.988559+0.017413    test-rmse:1.253666+0.087037 
## [403]    train-rmse:0.988102+0.017414    test-rmse:1.253842+0.086726 
## [404]    train-rmse:0.987644+0.017415    test-rmse:1.253476+0.086932 
## [405]    train-rmse:0.987191+0.017415    test-rmse:1.253531+0.086414 
## [406]    train-rmse:0.986736+0.017419    test-rmse:1.253379+0.086152 
## [407]    train-rmse:0.986281+0.017417    test-rmse:1.253738+0.086065 
## [408]    train-rmse:0.985836+0.017418    test-rmse:1.253294+0.086159 
## [409]    train-rmse:0.985391+0.017419    test-rmse:1.252460+0.086600 
## [410]    train-rmse:0.984945+0.017420    test-rmse:1.251797+0.086523 
## [411]    train-rmse:0.984504+0.017422    test-rmse:1.251303+0.086747 
## [412]    train-rmse:0.984063+0.017421    test-rmse:1.250527+0.086571 
## [413]    train-rmse:0.983620+0.017422    test-rmse:1.250893+0.087263 
## [414]    train-rmse:0.983180+0.017421    test-rmse:1.251232+0.086943 
## [415]    train-rmse:0.982737+0.017428    test-rmse:1.251093+0.087485 
## [416]    train-rmse:0.982302+0.017429    test-rmse:1.250940+0.087854 
## [417]    train-rmse:0.981867+0.017429    test-rmse:1.250667+0.087745 
## [418]    train-rmse:0.981431+0.017427    test-rmse:1.249627+0.087350 
## [419]    train-rmse:0.980997+0.017428    test-rmse:1.249105+0.087145 
## [420]    train-rmse:0.980565+0.017433    test-rmse:1.249291+0.086513 
## [421]    train-rmse:0.980139+0.017432    test-rmse:1.248971+0.086396 
## [422]    train-rmse:0.979712+0.017426    test-rmse:1.248142+0.086439 
## [423]    train-rmse:0.979289+0.017427    test-rmse:1.247982+0.086505 
## [424]    train-rmse:0.978862+0.017432    test-rmse:1.248078+0.086029 
## [425]    train-rmse:0.978440+0.017432    test-rmse:1.247473+0.086144 
## [426]    train-rmse:0.978019+0.017434    test-rmse:1.246942+0.085897 
## [427]    train-rmse:0.977602+0.017441    test-rmse:1.246536+0.085840 
## [428]    train-rmse:0.977188+0.017446    test-rmse:1.245907+0.085510 
## [429]    train-rmse:0.976772+0.017455    test-rmse:1.244950+0.084219 
## [430]    train-rmse:0.976360+0.017456    test-rmse:1.244934+0.084569 
## [431]    train-rmse:0.975950+0.017453    test-rmse:1.245172+0.084722 
## [432]    train-rmse:0.975537+0.017453    test-rmse:1.245123+0.084471 
## [433]    train-rmse:0.975127+0.017455    test-rmse:1.244998+0.084752 
## [434]    train-rmse:0.974720+0.017455    test-rmse:1.244931+0.084750 
## [435]    train-rmse:0.974315+0.017454    test-rmse:1.244805+0.085156 
## [436]    train-rmse:0.973915+0.017454    test-rmse:1.244418+0.085042 
## [437]    train-rmse:0.973517+0.017453    test-rmse:1.244171+0.085405 
## [438]    train-rmse:0.973114+0.017455    test-rmse:1.244371+0.085739 
## [439]    train-rmse:0.972710+0.017448    test-rmse:1.245254+0.085268 
## [440]    train-rmse:0.972309+0.017449    test-rmse:1.244911+0.085679 
## [441]    train-rmse:0.971912+0.017450    test-rmse:1.244331+0.085947 
## [442]    train-rmse:0.971515+0.017448    test-rmse:1.244282+0.085715 
## [443]    train-rmse:0.971121+0.017450    test-rmse:1.244005+0.085661 
## [444]    train-rmse:0.970724+0.017451    test-rmse:1.244154+0.086285 
## [445]    train-rmse:0.970335+0.017452    test-rmse:1.243512+0.086098 
## [446]    train-rmse:0.969946+0.017449    test-rmse:1.243589+0.085761 
## [447]    train-rmse:0.969556+0.017453    test-rmse:1.243189+0.085590 
## [448]    train-rmse:0.969169+0.017455    test-rmse:1.243471+0.085478 
## [449]    train-rmse:0.968783+0.017457    test-rmse:1.243269+0.085415 
## [450]    train-rmse:0.968400+0.017459    test-rmse:1.242770+0.085234 
## [451]    train-rmse:0.968014+0.017458    test-rmse:1.243066+0.085979 
## [452]    train-rmse:0.967631+0.017462    test-rmse:1.242627+0.086407 
## [453]    train-rmse:0.967250+0.017466    test-rmse:1.242301+0.085900 
## [454]    train-rmse:0.966869+0.017471    test-rmse:1.241770+0.085898 
## [455]    train-rmse:0.966492+0.017472    test-rmse:1.241953+0.085671 
## [456]    train-rmse:0.966113+0.017468    test-rmse:1.241456+0.085639 
## [457]    train-rmse:0.965735+0.017471    test-rmse:1.241216+0.085769 
## [458]    train-rmse:0.965360+0.017476    test-rmse:1.240774+0.085959 
## [459]    train-rmse:0.964982+0.017486    test-rmse:1.240545+0.086960 
## [460]    train-rmse:0.964610+0.017493    test-rmse:1.241131+0.087266 
## [461]    train-rmse:0.964236+0.017501    test-rmse:1.241182+0.086920 
## [462]    train-rmse:0.963868+0.017508    test-rmse:1.240957+0.086874 
## [463]    train-rmse:0.963502+0.017513    test-rmse:1.240652+0.087149 
## [464]    train-rmse:0.963133+0.017521    test-rmse:1.240235+0.086735 
## [465]    train-rmse:0.962762+0.017521    test-rmse:1.240044+0.087009 
## [466]    train-rmse:0.962394+0.017524    test-rmse:1.238419+0.085653 
## [467]    train-rmse:0.962028+0.017524    test-rmse:1.238203+0.085476 
## [468]    train-rmse:0.961657+0.017527    test-rmse:1.237883+0.085434 
## [469]    train-rmse:0.961293+0.017533    test-rmse:1.238847+0.084460 
## [470]    train-rmse:0.960932+0.017538    test-rmse:1.238780+0.084634 
## [471]    train-rmse:0.960573+0.017542    test-rmse:1.238750+0.084164 
## [472]    train-rmse:0.960219+0.017547    test-rmse:1.238521+0.084090 
## [473]    train-rmse:0.959864+0.017552    test-rmse:1.238692+0.084218 
## [474]    train-rmse:0.959506+0.017558    test-rmse:1.238768+0.084302 
## Stopping. Best iteration:
## [468]    train-rmse:0.961657+0.017527    test-rmse:1.237883+0.085434
## 
## 1 
## [1]  train-rmse:88.665695+0.088976   test-rmse:88.666461+0.404980 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.835214+0.080050   test-rmse:79.835893+0.413675 
## [3]  train-rmse:71.885242+0.071998   test-rmse:71.885820+0.421463 
## [4]  train-rmse:64.728087+0.064733   test-rmse:64.728551+0.428434 
## [5]  train-rmse:58.284824+0.058174   test-rmse:58.285159+0.434654 
## [6]  train-rmse:52.484374+0.052257   test-rmse:52.484567+0.440197 
## [7]  train-rmse:47.262778+0.046908   test-rmse:47.262816+0.445120 
## [8]  train-rmse:42.562434+0.042074   test-rmse:42.562303+0.449468 
## [9]  train-rmse:38.331508+0.037710   test-rmse:38.331192+0.453293 
## [10] train-rmse:34.523343+0.033759   test-rmse:34.522811+0.456630 
## [11] train-rmse:31.095926+0.030181   test-rmse:31.095166+0.459509 
## [12] train-rmse:28.011453+0.026945   test-rmse:28.010445+0.461960 
## [13] train-rmse:25.235913+0.024012   test-rmse:25.234629+0.463999 
## [14] train-rmse:22.738687+0.021363   test-rmse:22.737103+0.465637 
## [15] train-rmse:20.492223+0.018971   test-rmse:20.490315+0.466887 
## [16] train-rmse:18.471749+0.016823   test-rmse:18.469484+0.467747 
## [17] train-rmse:16.654969+0.014903   test-rmse:16.652318+0.468213 
## [18] train-rmse:15.021334+0.013222   test-rmse:15.020864+0.456548 
## [19] train-rmse:13.552638+0.011590   test-rmse:13.550876+0.449119 
## [20] train-rmse:12.232204+0.010135   test-rmse:12.222750+0.446257 
## [21] train-rmse:11.045206+0.008913   test-rmse:11.035400+0.435400 
## [22] train-rmse:9.978030+0.008019    test-rmse:9.966148+0.429625 
## [23] train-rmse:9.018742+0.007174    test-rmse:9.000809+0.427222 
## [24] train-rmse:8.156825+0.006737    test-rmse:8.145320+0.416046 
## [25] train-rmse:7.382623+0.006335    test-rmse:7.368813+0.407825 
## [26] train-rmse:6.687697+0.006199    test-rmse:6.680066+0.394512 
## [27] train-rmse:6.063817+0.006278    test-rmse:6.054954+0.387910 
## [28] train-rmse:5.504728+0.006089    test-rmse:5.498833+0.379448 
## [29] train-rmse:5.003695+0.006597    test-rmse:4.997531+0.369393 
## [30] train-rmse:4.555438+0.006971    test-rmse:4.552144+0.361533 
## [31] train-rmse:4.155410+0.007013    test-rmse:4.152031+0.356011 
## [32] train-rmse:3.798181+0.007965    test-rmse:3.798917+0.348658 
## [33] train-rmse:3.480425+0.007882    test-rmse:3.490479+0.336623 
## [34] train-rmse:3.197386+0.008741    test-rmse:3.211733+0.323150 
## [35] train-rmse:2.947292+0.009883    test-rmse:2.970537+0.310529 
## [36] train-rmse:2.726225+0.011099    test-rmse:2.754650+0.297279 
## [37] train-rmse:2.531626+0.011054    test-rmse:2.563110+0.281378 
## [38] train-rmse:2.360084+0.010689    test-rmse:2.402166+0.269516 
## [39] train-rmse:2.209549+0.010642    test-rmse:2.259924+0.257042 
## [40] train-rmse:2.078889+0.010934    test-rmse:2.139972+0.247216 
## [41] train-rmse:1.964759+0.011584    test-rmse:2.034912+0.230664 
## [42] train-rmse:1.865176+0.011735    test-rmse:1.946129+0.215627 
## [43] train-rmse:1.779480+0.011557    test-rmse:1.867812+0.202941 
## [44] train-rmse:1.705572+0.011697    test-rmse:1.800987+0.192181 
## [45] train-rmse:1.642020+0.011842    test-rmse:1.748217+0.181679 
## [46] train-rmse:1.586074+0.012588    test-rmse:1.702903+0.170285 
## [47] train-rmse:1.538074+0.012773    test-rmse:1.661126+0.159043 
## [48] train-rmse:1.496895+0.012003    test-rmse:1.629049+0.148082 
## [49] train-rmse:1.462072+0.012124    test-rmse:1.601961+0.139067 
## [50] train-rmse:1.430791+0.011120    test-rmse:1.578572+0.131733 
## [51] train-rmse:1.404476+0.011064    test-rmse:1.556426+0.128339 
## [52] train-rmse:1.380232+0.012116    test-rmse:1.533386+0.122259 
## [53] train-rmse:1.359284+0.010761    test-rmse:1.518423+0.116701 
## [54] train-rmse:1.341063+0.010845    test-rmse:1.504538+0.114705 
## [55] train-rmse:1.323624+0.011517    test-rmse:1.492702+0.111689 
## [56] train-rmse:1.308540+0.011741    test-rmse:1.480470+0.109325 
## [57] train-rmse:1.295463+0.012396    test-rmse:1.471399+0.106024 
## [58] train-rmse:1.284149+0.012520    test-rmse:1.461757+0.105146 
## [59] train-rmse:1.273449+0.012731    test-rmse:1.453527+0.102499 
## [60] train-rmse:1.262597+0.011425    test-rmse:1.445628+0.101168 
## [61] train-rmse:1.253081+0.012169    test-rmse:1.439298+0.099704 
## [62] train-rmse:1.243640+0.013661    test-rmse:1.433569+0.096373 
## [63] train-rmse:1.235062+0.012555    test-rmse:1.427081+0.094231 
## [64] train-rmse:1.226692+0.012706    test-rmse:1.423584+0.093783 
## [65] train-rmse:1.219217+0.012925    test-rmse:1.418181+0.093899 
## [66] train-rmse:1.211317+0.011646    test-rmse:1.411221+0.093313 
## [67] train-rmse:1.204206+0.011502    test-rmse:1.406730+0.091086 
## [68] train-rmse:1.196591+0.012307    test-rmse:1.405032+0.091753 
## [69] train-rmse:1.189641+0.011621    test-rmse:1.402977+0.091157 
## [70] train-rmse:1.183895+0.011476    test-rmse:1.398239+0.090076 
## [71] train-rmse:1.178000+0.011735    test-rmse:1.392505+0.088055 
## [72] train-rmse:1.172300+0.011623    test-rmse:1.388604+0.089307 
## [73] train-rmse:1.166186+0.012771    test-rmse:1.380869+0.092128 
## [74] train-rmse:1.159405+0.013053    test-rmse:1.376677+0.091777 
## [75] train-rmse:1.154030+0.013257    test-rmse:1.374105+0.090524 
## [76] train-rmse:1.148085+0.012656    test-rmse:1.372643+0.089152 
## [77] train-rmse:1.143253+0.012682    test-rmse:1.368923+0.089953 
## [78] train-rmse:1.137850+0.012578    test-rmse:1.364153+0.089503 
## [79] train-rmse:1.132466+0.013410    test-rmse:1.355761+0.088702 
## [80] train-rmse:1.127134+0.013737    test-rmse:1.350543+0.090423 
## [81] train-rmse:1.121132+0.014047    test-rmse:1.346440+0.089797 
## [82] train-rmse:1.115874+0.013565    test-rmse:1.345134+0.089827 
## [83] train-rmse:1.111531+0.013528    test-rmse:1.341546+0.088989 
## [84] train-rmse:1.106950+0.013518    test-rmse:1.338749+0.088996 
## [85] train-rmse:1.102447+0.013610    test-rmse:1.334498+0.086219 
## [86] train-rmse:1.098208+0.013963    test-rmse:1.332509+0.086029 
## [87] train-rmse:1.093218+0.013447    test-rmse:1.326281+0.086091 
## [88] train-rmse:1.088461+0.013235    test-rmse:1.323566+0.086681 
## [89] train-rmse:1.083923+0.013037    test-rmse:1.320358+0.087568 
## [90] train-rmse:1.079225+0.012779    test-rmse:1.319126+0.087058 
## [91] train-rmse:1.075174+0.013011    test-rmse:1.316077+0.086893 
## [92] train-rmse:1.068309+0.013638    test-rmse:1.311998+0.085770 
## [93] train-rmse:1.064089+0.013372    test-rmse:1.306351+0.088362 
## [94] train-rmse:1.058663+0.013566    test-rmse:1.304936+0.087426 
## [95] train-rmse:1.054416+0.013717    test-rmse:1.302029+0.086665 
## [96] train-rmse:1.050777+0.014002    test-rmse:1.298862+0.087543 
## [97] train-rmse:1.046937+0.013658    test-rmse:1.296463+0.085787 
## [98] train-rmse:1.043175+0.014282    test-rmse:1.293138+0.087564 
## [99] train-rmse:1.038724+0.014551    test-rmse:1.290724+0.089079 
## [100]    train-rmse:1.035445+0.014653    test-rmse:1.289874+0.088592 
## [101]    train-rmse:1.030954+0.015939    test-rmse:1.285597+0.085810 
## [102]    train-rmse:1.025732+0.016237    test-rmse:1.283683+0.084370 
## [103]    train-rmse:1.021421+0.015783    test-rmse:1.280366+0.085559 
## [104]    train-rmse:1.016901+0.015977    test-rmse:1.278026+0.086194 
## [105]    train-rmse:1.013377+0.016336    test-rmse:1.277985+0.087824 
## [106]    train-rmse:1.009098+0.017086    test-rmse:1.274711+0.090343 
## [107]    train-rmse:1.005852+0.017294    test-rmse:1.272327+0.090554 
## [108]    train-rmse:1.002170+0.016543    test-rmse:1.270422+0.091051 
## [109]    train-rmse:0.997930+0.016761    test-rmse:1.266616+0.089724 
## [110]    train-rmse:0.994731+0.016490    test-rmse:1.265151+0.089321 
## [111]    train-rmse:0.991844+0.016399    test-rmse:1.262599+0.088635 
## [112]    train-rmse:0.988465+0.015993    test-rmse:1.260360+0.087739 
## [113]    train-rmse:0.984698+0.016436    test-rmse:1.258656+0.088870 
## [114]    train-rmse:0.980682+0.016695    test-rmse:1.256055+0.088245 
## [115]    train-rmse:0.976049+0.016720    test-rmse:1.252382+0.088092 
## [116]    train-rmse:0.972855+0.016873    test-rmse:1.250688+0.087196 
## [117]    train-rmse:0.968636+0.017420    test-rmse:1.246464+0.089151 
## [118]    train-rmse:0.965596+0.017643    test-rmse:1.246986+0.090805 
## [119]    train-rmse:0.962855+0.017873    test-rmse:1.245405+0.089520 
## [120]    train-rmse:0.957798+0.016865    test-rmse:1.239259+0.091196 
## [121]    train-rmse:0.954822+0.016975    test-rmse:1.237996+0.091231 
## [122]    train-rmse:0.951660+0.017462    test-rmse:1.235149+0.091469 
## [123]    train-rmse:0.948004+0.017455    test-rmse:1.234452+0.090571 
## [124]    train-rmse:0.945176+0.017616    test-rmse:1.233264+0.091067 
## [125]    train-rmse:0.942667+0.017501    test-rmse:1.231901+0.089812 
## [126]    train-rmse:0.939824+0.017835    test-rmse:1.227814+0.090533 
## [127]    train-rmse:0.936831+0.017292    test-rmse:1.225078+0.092216 
## [128]    train-rmse:0.933398+0.016201    test-rmse:1.225528+0.091571 
## [129]    train-rmse:0.928609+0.015985    test-rmse:1.224390+0.090350 
## [130]    train-rmse:0.925890+0.016501    test-rmse:1.223607+0.090782 
## [131]    train-rmse:0.923561+0.016737    test-rmse:1.222882+0.090409 
## [132]    train-rmse:0.920145+0.016964    test-rmse:1.219732+0.091566 
## [133]    train-rmse:0.917154+0.016686    test-rmse:1.217214+0.091095 
## [134]    train-rmse:0.914625+0.016286    test-rmse:1.215269+0.090883 
## [135]    train-rmse:0.911127+0.017067    test-rmse:1.213179+0.089520 
## [136]    train-rmse:0.908763+0.017076    test-rmse:1.212176+0.089577 
## [137]    train-rmse:0.906586+0.017198    test-rmse:1.210854+0.090728 
## [138]    train-rmse:0.903495+0.015997    test-rmse:1.208769+0.091480 
## [139]    train-rmse:0.900669+0.015594    test-rmse:1.206137+0.092723 
## [140]    train-rmse:0.897330+0.014516    test-rmse:1.205242+0.091257 
## [141]    train-rmse:0.894416+0.015288    test-rmse:1.203724+0.092401 
## [142]    train-rmse:0.891834+0.015651    test-rmse:1.202389+0.091953 
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## [145]    train-rmse:0.884251+0.016171    test-rmse:1.197126+0.091924 
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## [270]    train-rmse:0.660720+0.008307    test-rmse:1.078445+0.107779 
## [271]    train-rmse:0.659864+0.008236    test-rmse:1.078658+0.108306 
## [272]    train-rmse:0.658466+0.008850    test-rmse:1.078232+0.109035 
## [273]    train-rmse:0.657711+0.008817    test-rmse:1.077960+0.109190 
## [274]    train-rmse:0.656545+0.009237    test-rmse:1.077192+0.108841 
## [275]    train-rmse:0.655323+0.009156    test-rmse:1.076466+0.108377 
## [276]    train-rmse:0.653955+0.008892    test-rmse:1.075637+0.108875 
## [277]    train-rmse:0.652343+0.009767    test-rmse:1.074950+0.108662 
## [278]    train-rmse:0.651199+0.009547    test-rmse:1.074683+0.108562 
## [279]    train-rmse:0.649729+0.009381    test-rmse:1.074403+0.108195 
## [280]    train-rmse:0.648340+0.009425    test-rmse:1.073917+0.108767 
## [281]    train-rmse:0.647492+0.009387    test-rmse:1.073962+0.108828 
## [282]    train-rmse:0.646016+0.009354    test-rmse:1.072957+0.108368 
## [283]    train-rmse:0.644822+0.009215    test-rmse:1.072558+0.108444 
## [284]    train-rmse:0.643740+0.008792    test-rmse:1.071967+0.108052 
## [285]    train-rmse:0.643065+0.008825    test-rmse:1.072878+0.107666 
## [286]    train-rmse:0.641805+0.009169    test-rmse:1.072205+0.107169 
## [287]    train-rmse:0.640594+0.009151    test-rmse:1.071024+0.106988 
## [288]    train-rmse:0.639696+0.009550    test-rmse:1.070931+0.106642 
## [289]    train-rmse:0.638282+0.009927    test-rmse:1.070384+0.106496 
## [290]    train-rmse:0.637252+0.010084    test-rmse:1.069420+0.108098 
## [291]    train-rmse:0.635827+0.009628    test-rmse:1.068636+0.108328 
## [292]    train-rmse:0.635089+0.009681    test-rmse:1.067912+0.108893 
## [293]    train-rmse:0.633703+0.009887    test-rmse:1.065625+0.108487 
## [294]    train-rmse:0.632870+0.009805    test-rmse:1.064293+0.107643 
## [295]    train-rmse:0.631096+0.009317    test-rmse:1.062771+0.106721 
## [296]    train-rmse:0.630263+0.009332    test-rmse:1.062187+0.106912 
## [297]    train-rmse:0.629340+0.009760    test-rmse:1.061853+0.106428 
## [298]    train-rmse:0.628060+0.009600    test-rmse:1.061297+0.106279 
## [299]    train-rmse:0.627083+0.009426    test-rmse:1.060514+0.106124 
## [300]    train-rmse:0.625314+0.009424    test-rmse:1.060277+0.105395 
## [301]    train-rmse:0.624370+0.009352    test-rmse:1.060573+0.105247 
## [302]    train-rmse:0.623593+0.009430    test-rmse:1.060394+0.105330 
## [303]    train-rmse:0.622572+0.009875    test-rmse:1.060429+0.105161 
## [304]    train-rmse:0.621438+0.009547    test-rmse:1.061280+0.105228 
## [305]    train-rmse:0.619849+0.009678    test-rmse:1.059797+0.106435 
## [306]    train-rmse:0.618618+0.010530    test-rmse:1.059273+0.106390 
## [307]    train-rmse:0.617694+0.010360    test-rmse:1.058979+0.106325 
## [308]    train-rmse:0.616861+0.010298    test-rmse:1.058677+0.105918 
## [309]    train-rmse:0.616010+0.010412    test-rmse:1.057571+0.105191 
## [310]    train-rmse:0.615231+0.010385    test-rmse:1.057219+0.105315 
## [311]    train-rmse:0.614590+0.010256    test-rmse:1.057111+0.105689 
## [312]    train-rmse:0.613507+0.010353    test-rmse:1.057037+0.105455 
## [313]    train-rmse:0.612741+0.010462    test-rmse:1.056149+0.105011 
## [314]    train-rmse:0.611853+0.010494    test-rmse:1.056267+0.105102 
## [315]    train-rmse:0.610880+0.010372    test-rmse:1.056515+0.105180 
## [316]    train-rmse:0.609928+0.010822    test-rmse:1.055718+0.105122 
## [317]    train-rmse:0.608707+0.010415    test-rmse:1.055431+0.104923 
## [318]    train-rmse:0.607529+0.011218    test-rmse:1.054890+0.105455 
## [319]    train-rmse:0.606723+0.011234    test-rmse:1.054030+0.104995 
## [320]    train-rmse:0.605669+0.011742    test-rmse:1.054128+0.105176 
## [321]    train-rmse:0.604620+0.012066    test-rmse:1.053081+0.106231 
## [322]    train-rmse:0.603598+0.011851    test-rmse:1.052682+0.106281 
## [323]    train-rmse:0.602387+0.012261    test-rmse:1.052352+0.106099 
## [324]    train-rmse:0.601139+0.012609    test-rmse:1.050941+0.105426 
## [325]    train-rmse:0.599761+0.012607    test-rmse:1.051009+0.105505 
## [326]    train-rmse:0.599194+0.012667    test-rmse:1.050454+0.105258 
## [327]    train-rmse:0.597184+0.011803    test-rmse:1.050537+0.105692 
## [328]    train-rmse:0.596341+0.011673    test-rmse:1.051062+0.105655 
## [329]    train-rmse:0.595436+0.011377    test-rmse:1.051264+0.106029 
## [330]    train-rmse:0.594833+0.011335    test-rmse:1.050342+0.105444 
## [331]    train-rmse:0.593887+0.011099    test-rmse:1.049620+0.105597 
## [332]    train-rmse:0.592963+0.010888    test-rmse:1.049783+0.105934 
## [333]    train-rmse:0.592023+0.010589    test-rmse:1.049295+0.105954 
## [334]    train-rmse:0.591227+0.010925    test-rmse:1.048589+0.105599 
## [335]    train-rmse:0.589978+0.010766    test-rmse:1.048075+0.105281 
## [336]    train-rmse:0.589153+0.010576    test-rmse:1.047511+0.105798 
## [337]    train-rmse:0.588285+0.010514    test-rmse:1.047473+0.106466 
## [338]    train-rmse:0.587417+0.010950    test-rmse:1.047305+0.106662 
## [339]    train-rmse:0.586166+0.010712    test-rmse:1.046269+0.106364 
## [340]    train-rmse:0.585179+0.010870    test-rmse:1.045657+0.106422 
## [341]    train-rmse:0.584280+0.010815    test-rmse:1.045571+0.107680 
## [342]    train-rmse:0.582980+0.010520    test-rmse:1.045191+0.107307 
## [343]    train-rmse:0.581693+0.010157    test-rmse:1.044355+0.107229 
## [344]    train-rmse:0.580767+0.010211    test-rmse:1.043861+0.107203 
## [345]    train-rmse:0.580194+0.010427    test-rmse:1.043431+0.107122 
## [346]    train-rmse:0.579458+0.010484    test-rmse:1.043851+0.107380 
## [347]    train-rmse:0.578890+0.010457    test-rmse:1.043741+0.107308 
## [348]    train-rmse:0.577876+0.010313    test-rmse:1.042904+0.108715 
## [349]    train-rmse:0.577174+0.010461    test-rmse:1.042502+0.108580 
## [350]    train-rmse:0.576544+0.010645    test-rmse:1.042151+0.108501 
## [351]    train-rmse:0.575645+0.010969    test-rmse:1.041609+0.108380 
## [352]    train-rmse:0.574706+0.010717    test-rmse:1.041622+0.108276 
## [353]    train-rmse:0.573934+0.010788    test-rmse:1.041524+0.107662 
## [354]    train-rmse:0.572812+0.010848    test-rmse:1.041176+0.106673 
## [355]    train-rmse:0.571270+0.010289    test-rmse:1.040831+0.106629 
## [356]    train-rmse:0.570087+0.009826    test-rmse:1.041875+0.106636 
## [357]    train-rmse:0.569159+0.010660    test-rmse:1.040610+0.105535 
## [358]    train-rmse:0.568246+0.010481    test-rmse:1.040472+0.105584 
## [359]    train-rmse:0.567707+0.010522    test-rmse:1.040115+0.105607 
## [360]    train-rmse:0.567029+0.010388    test-rmse:1.039471+0.105347 
## [361]    train-rmse:0.566480+0.010276    test-rmse:1.039060+0.105429 
## [362]    train-rmse:0.565685+0.010379    test-rmse:1.038310+0.105490 
## [363]    train-rmse:0.564532+0.010381    test-rmse:1.038805+0.105494 
## [364]    train-rmse:0.563870+0.010646    test-rmse:1.038508+0.106092 
## [365]    train-rmse:0.563072+0.010509    test-rmse:1.037579+0.105865 
## [366]    train-rmse:0.562644+0.010501    test-rmse:1.037140+0.106037 
## [367]    train-rmse:0.560917+0.009697    test-rmse:1.035912+0.105399 
## [368]    train-rmse:0.559737+0.010176    test-rmse:1.035294+0.105549 
## [369]    train-rmse:0.559116+0.010338    test-rmse:1.035127+0.105631 
## [370]    train-rmse:0.558431+0.010829    test-rmse:1.035101+0.105347 
## [371]    train-rmse:0.557620+0.011241    test-rmse:1.035510+0.105176 
## [372]    train-rmse:0.556791+0.011667    test-rmse:1.034932+0.105921 
## [373]    train-rmse:0.556119+0.011481    test-rmse:1.035423+0.105747 
## [374]    train-rmse:0.555432+0.011560    test-rmse:1.035087+0.105557 
## [375]    train-rmse:0.554370+0.011170    test-rmse:1.035250+0.105747 
## [376]    train-rmse:0.553751+0.011148    test-rmse:1.034944+0.105736 
## [377]    train-rmse:0.553304+0.011203    test-rmse:1.034895+0.105150 
## [378]    train-rmse:0.552801+0.011176    test-rmse:1.034919+0.105112 
## [379]    train-rmse:0.551735+0.010834    test-rmse:1.034789+0.104699 
## [380]    train-rmse:0.551180+0.010999    test-rmse:1.034545+0.104956 
## [381]    train-rmse:0.550344+0.011176    test-rmse:1.035146+0.105229 
## [382]    train-rmse:0.549817+0.011336    test-rmse:1.034961+0.105195 
## [383]    train-rmse:0.549144+0.011190    test-rmse:1.034828+0.105570 
## [384]    train-rmse:0.548346+0.010951    test-rmse:1.034318+0.104862 
## [385]    train-rmse:0.547641+0.010866    test-rmse:1.033869+0.105322 
## [386]    train-rmse:0.546947+0.010684    test-rmse:1.033830+0.105208 
## [387]    train-rmse:0.546164+0.010437    test-rmse:1.033990+0.105107 
## [388]    train-rmse:0.545674+0.010471    test-rmse:1.033732+0.104642 
## [389]    train-rmse:0.544898+0.010691    test-rmse:1.032852+0.104367 
## [390]    train-rmse:0.544030+0.010623    test-rmse:1.032473+0.104269 
## [391]    train-rmse:0.542957+0.011248    test-rmse:1.030818+0.103966 
## [392]    train-rmse:0.541724+0.010961    test-rmse:1.030110+0.104092 
## [393]    train-rmse:0.540240+0.010683    test-rmse:1.029701+0.103667 
## [394]    train-rmse:0.539651+0.010606    test-rmse:1.029498+0.103835 
## [395]    train-rmse:0.538844+0.010790    test-rmse:1.029171+0.103623 
## [396]    train-rmse:0.537885+0.010843    test-rmse:1.029151+0.103605 
## [397]    train-rmse:0.537044+0.010753    test-rmse:1.029046+0.103699 
## [398]    train-rmse:0.535947+0.010589    test-rmse:1.029142+0.103822 
## [399]    train-rmse:0.535165+0.010365    test-rmse:1.029700+0.103865 
## [400]    train-rmse:0.534717+0.010355    test-rmse:1.029439+0.103754 
## [401]    train-rmse:0.534302+0.010349    test-rmse:1.029405+0.104053 
## [402]    train-rmse:0.533470+0.010499    test-rmse:1.029198+0.103186 
## [403]    train-rmse:0.532969+0.010451    test-rmse:1.029440+0.103100 
## Stopping. Best iteration:
## [397]    train-rmse:0.537044+0.010753    test-rmse:1.029046+0.103699
## 
## 2 
## [1]  train-rmse:88.665695+0.088976   test-rmse:88.666461+0.404980 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.835214+0.080050   test-rmse:79.835893+0.413675 
## [3]  train-rmse:71.885242+0.071998   test-rmse:71.885820+0.421463 
## [4]  train-rmse:64.728087+0.064733   test-rmse:64.728551+0.428434 
## [5]  train-rmse:58.284824+0.058174   test-rmse:58.285159+0.434654 
## [6]  train-rmse:52.484374+0.052257   test-rmse:52.484567+0.440197 
## [7]  train-rmse:47.262778+0.046908   test-rmse:47.262816+0.445120 
## [8]  train-rmse:42.562434+0.042074   test-rmse:42.562303+0.449468 
## [9]  train-rmse:38.331508+0.037710   test-rmse:38.331192+0.453293 
## [10] train-rmse:34.523343+0.033759   test-rmse:34.522811+0.456630 
## [11] train-rmse:31.095926+0.030181   test-rmse:31.095166+0.459509 
## [12] train-rmse:28.011453+0.026945   test-rmse:28.010445+0.461960 
## [13] train-rmse:25.235913+0.024012   test-rmse:25.234629+0.463999 
## [14] train-rmse:22.738687+0.021363   test-rmse:22.737103+0.465637 
## [15] train-rmse:20.492223+0.018971   test-rmse:20.490315+0.466887 
## [16] train-rmse:18.471749+0.016823   test-rmse:18.469484+0.467747 
## [17] train-rmse:16.654969+0.014903   test-rmse:16.652318+0.468213 
## [18] train-rmse:15.021334+0.013222   test-rmse:15.020864+0.456548 
## [19] train-rmse:13.552638+0.011590   test-rmse:13.550876+0.449119 
## [20] train-rmse:12.232204+0.010135   test-rmse:12.222750+0.446257 
## [21] train-rmse:11.045206+0.008913   test-rmse:11.035400+0.435400 
## [22] train-rmse:9.977548+0.008148    test-rmse:9.964506+0.428153 
## [23] train-rmse:9.017570+0.007287    test-rmse:9.001551+0.419615 
## [24] train-rmse:8.154575+0.006721    test-rmse:8.138352+0.406949 
## [25] train-rmse:7.378679+0.005902    test-rmse:7.363665+0.398571 
## [26] train-rmse:6.681479+0.005510    test-rmse:6.671474+0.389514 
## [27] train-rmse:6.055015+0.005640    test-rmse:6.045592+0.375872 
## [28] train-rmse:5.493136+0.005017    test-rmse:5.485153+0.365690 
## [29] train-rmse:4.988874+0.004903    test-rmse:4.982656+0.353738 
## [30] train-rmse:4.536124+0.004712    test-rmse:4.531850+0.339350 
## [31] train-rmse:4.130855+0.005113    test-rmse:4.132149+0.331838 
## [32] train-rmse:3.768240+0.006205    test-rmse:3.776049+0.326634 
## [33] train-rmse:3.442939+0.006166    test-rmse:3.456337+0.317813 
## [34] train-rmse:3.153799+0.006191    test-rmse:3.172286+0.306366 
## [35] train-rmse:2.895482+0.006930    test-rmse:2.919982+0.295833 
## [36] train-rmse:2.665737+0.007985    test-rmse:2.694852+0.285155 
## [37] train-rmse:2.460389+0.008358    test-rmse:2.502118+0.274936 
## [38] train-rmse:2.279883+0.007756    test-rmse:2.330656+0.268217 
## [39] train-rmse:2.120797+0.008383    test-rmse:2.182400+0.257294 
## [40] train-rmse:1.980208+0.008629    test-rmse:2.054770+0.243360 
## [41] train-rmse:1.856871+0.010496    test-rmse:1.943643+0.230783 
## [42] train-rmse:1.748659+0.011934    test-rmse:1.847181+0.217790 
## [43] train-rmse:1.654673+0.013301    test-rmse:1.762182+0.206912 
## [44] train-rmse:1.571111+0.012864    test-rmse:1.696539+0.196976 
## [45] train-rmse:1.498321+0.011995    test-rmse:1.637953+0.184650 
## [46] train-rmse:1.435402+0.012782    test-rmse:1.588724+0.171392 
## [47] train-rmse:1.380004+0.014126    test-rmse:1.541337+0.165529 
## [48] train-rmse:1.333345+0.014442    test-rmse:1.506098+0.155127 
## [49] train-rmse:1.292651+0.015210    test-rmse:1.473547+0.150618 
## [50] train-rmse:1.256625+0.014209    test-rmse:1.448805+0.143761 
## [51] train-rmse:1.223418+0.015356    test-rmse:1.423117+0.136740 
## [52] train-rmse:1.194328+0.015010    test-rmse:1.404151+0.131119 
## [53] train-rmse:1.170010+0.015750    test-rmse:1.388500+0.123547 
## [54] train-rmse:1.144717+0.016747    test-rmse:1.370911+0.118077 
## [55] train-rmse:1.124603+0.017796    test-rmse:1.357998+0.114670 
## [56] train-rmse:1.107166+0.018521    test-rmse:1.348497+0.109686 
## [57] train-rmse:1.091386+0.017607    test-rmse:1.337939+0.105838 
## [58] train-rmse:1.076254+0.018625    test-rmse:1.327581+0.102061 
## [59] train-rmse:1.062688+0.020053    test-rmse:1.320418+0.099094 
## [60] train-rmse:1.049903+0.020533    test-rmse:1.312104+0.095687 
## [61] train-rmse:1.038132+0.021367    test-rmse:1.305712+0.093929 
## [62] train-rmse:1.027356+0.019750    test-rmse:1.298222+0.092657 
## [63] train-rmse:1.017358+0.020232    test-rmse:1.292510+0.093446 
## [64] train-rmse:1.006364+0.021248    test-rmse:1.288475+0.090325 
## [65] train-rmse:0.996962+0.020292    test-rmse:1.283337+0.088478 
## [66] train-rmse:0.987870+0.020142    test-rmse:1.276131+0.088375 
## [67] train-rmse:0.980317+0.019631    test-rmse:1.271724+0.087538 
## [68] train-rmse:0.971699+0.020564    test-rmse:1.267674+0.087062 
## [69] train-rmse:0.964094+0.019554    test-rmse:1.261455+0.088168 
## [70] train-rmse:0.955287+0.017404    test-rmse:1.257709+0.088586 
## [71] train-rmse:0.947669+0.018120    test-rmse:1.251829+0.086238 
## [72] train-rmse:0.940090+0.018175    test-rmse:1.248575+0.084870 
## [73] train-rmse:0.933315+0.018397    test-rmse:1.244102+0.085701 
## [74] train-rmse:0.925868+0.018149    test-rmse:1.240008+0.087336 
## [75] train-rmse:0.919297+0.018529    test-rmse:1.235820+0.087580 
## [76] train-rmse:0.913667+0.018661    test-rmse:1.231392+0.086838 
## [77] train-rmse:0.905876+0.018878    test-rmse:1.228498+0.086617 
## [78] train-rmse:0.900579+0.018831    test-rmse:1.225208+0.083579 
## [79] train-rmse:0.893608+0.018026    test-rmse:1.218340+0.086228 
## [80] train-rmse:0.887791+0.017446    test-rmse:1.214930+0.086127 
## [81] train-rmse:0.881509+0.016063    test-rmse:1.212181+0.088649 
## [82] train-rmse:0.875760+0.016975    test-rmse:1.209930+0.088723 
## [83] train-rmse:0.870458+0.016853    test-rmse:1.205538+0.088892 
## [84] train-rmse:0.865957+0.016853    test-rmse:1.201200+0.088939 
## [85] train-rmse:0.860701+0.017140    test-rmse:1.198210+0.090612 
## [86] train-rmse:0.854599+0.017272    test-rmse:1.194822+0.090156 
## [87] train-rmse:0.849975+0.017574    test-rmse:1.192399+0.088009 
## [88] train-rmse:0.845095+0.016380    test-rmse:1.189661+0.089369 
## [89] train-rmse:0.839039+0.015112    test-rmse:1.184564+0.085082 
## [90] train-rmse:0.833039+0.016477    test-rmse:1.178333+0.085330 
## [91] train-rmse:0.827829+0.016441    test-rmse:1.173741+0.088091 
## [92] train-rmse:0.822773+0.016174    test-rmse:1.170924+0.088644 
## [93] train-rmse:0.818349+0.015873    test-rmse:1.166658+0.088818 
## [94] train-rmse:0.813666+0.016500    test-rmse:1.161777+0.084924 
## [95] train-rmse:0.809390+0.016320    test-rmse:1.158777+0.084419 
## [96] train-rmse:0.805332+0.015920    test-rmse:1.157354+0.084782 
## [97] train-rmse:0.800502+0.015751    test-rmse:1.154518+0.084399 
## [98] train-rmse:0.794120+0.016413    test-rmse:1.149541+0.083147 
## [99] train-rmse:0.789732+0.016586    test-rmse:1.147596+0.083298 
## [100]    train-rmse:0.784831+0.017405    test-rmse:1.144604+0.083519 
## [101]    train-rmse:0.780043+0.017576    test-rmse:1.142778+0.082115 
## [102]    train-rmse:0.776364+0.016610    test-rmse:1.139357+0.083494 
## [103]    train-rmse:0.772877+0.017087    test-rmse:1.136000+0.082766 
## [104]    train-rmse:0.768098+0.016310    test-rmse:1.134052+0.083425 
## [105]    train-rmse:0.764204+0.015383    test-rmse:1.130289+0.083553 
## [106]    train-rmse:0.760041+0.015236    test-rmse:1.129091+0.082871 
## [107]    train-rmse:0.756818+0.015339    test-rmse:1.127834+0.082737 
## [108]    train-rmse:0.752826+0.015692    test-rmse:1.126147+0.082442 
## [109]    train-rmse:0.749706+0.016027    test-rmse:1.124198+0.081318 
## [110]    train-rmse:0.746260+0.015163    test-rmse:1.122070+0.080954 
## [111]    train-rmse:0.742634+0.015214    test-rmse:1.120327+0.080940 
## [112]    train-rmse:0.738241+0.015717    test-rmse:1.119226+0.080366 
## [113]    train-rmse:0.733977+0.015059    test-rmse:1.116972+0.080500 
## [114]    train-rmse:0.731265+0.014836    test-rmse:1.115008+0.080250 
## [115]    train-rmse:0.726859+0.015534    test-rmse:1.111552+0.080568 
## [116]    train-rmse:0.724300+0.015360    test-rmse:1.110596+0.080721 
## [117]    train-rmse:0.721235+0.015102    test-rmse:1.109932+0.080559 
## [118]    train-rmse:0.717511+0.015273    test-rmse:1.109236+0.079664 
## [119]    train-rmse:0.713486+0.015448    test-rmse:1.107268+0.081984 
## [120]    train-rmse:0.710371+0.015921    test-rmse:1.105551+0.081291 
## [121]    train-rmse:0.706941+0.016038    test-rmse:1.103778+0.080668 
## [122]    train-rmse:0.703735+0.015930    test-rmse:1.101802+0.079306 
## [123]    train-rmse:0.700748+0.015452    test-rmse:1.100242+0.079720 
## [124]    train-rmse:0.696815+0.015906    test-rmse:1.096581+0.081265 
## [125]    train-rmse:0.693786+0.016449    test-rmse:1.094419+0.081053 
## [126]    train-rmse:0.690413+0.015611    test-rmse:1.092992+0.081249 
## [127]    train-rmse:0.686824+0.015168    test-rmse:1.091627+0.081136 
## [128]    train-rmse:0.684030+0.014669    test-rmse:1.090267+0.081732 
## [129]    train-rmse:0.680420+0.015063    test-rmse:1.089511+0.082631 
## [130]    train-rmse:0.677747+0.014999    test-rmse:1.088494+0.082629 
## [131]    train-rmse:0.674437+0.015078    test-rmse:1.086982+0.082648 
## [132]    train-rmse:0.671917+0.014548    test-rmse:1.085842+0.082809 
## [133]    train-rmse:0.668623+0.014833    test-rmse:1.084439+0.082771 
## [134]    train-rmse:0.665730+0.014816    test-rmse:1.083503+0.081620 
## [135]    train-rmse:0.662685+0.013769    test-rmse:1.082075+0.081476 
## [136]    train-rmse:0.659551+0.013364    test-rmse:1.080631+0.082605 
## [137]    train-rmse:0.656998+0.013167    test-rmse:1.078798+0.082402 
## [138]    train-rmse:0.653700+0.013051    test-rmse:1.078440+0.081399 
## [139]    train-rmse:0.651644+0.012854    test-rmse:1.077547+0.081245 
## [140]    train-rmse:0.649237+0.013287    test-rmse:1.077026+0.081263 
## [141]    train-rmse:0.646707+0.013070    test-rmse:1.075970+0.081767 
## [142]    train-rmse:0.644224+0.012278    test-rmse:1.075250+0.081989 
## [143]    train-rmse:0.641411+0.012368    test-rmse:1.074779+0.081447 
## [144]    train-rmse:0.638202+0.012834    test-rmse:1.074449+0.081728 
## [145]    train-rmse:0.634867+0.013091    test-rmse:1.073043+0.081830 
## [146]    train-rmse:0.632293+0.013165    test-rmse:1.071426+0.082655 
## [147]    train-rmse:0.628510+0.013798    test-rmse:1.069025+0.084248 
## [148]    train-rmse:0.626131+0.013357    test-rmse:1.068621+0.084455 
## [149]    train-rmse:0.624016+0.013355    test-rmse:1.068124+0.084508 
## [150]    train-rmse:0.621236+0.012365    test-rmse:1.065823+0.084133 
## [151]    train-rmse:0.619201+0.012258    test-rmse:1.065353+0.083571 
## [152]    train-rmse:0.615829+0.012240    test-rmse:1.064484+0.082737 
## [153]    train-rmse:0.613048+0.011327    test-rmse:1.062299+0.083562 
## [154]    train-rmse:0.610394+0.010884    test-rmse:1.062209+0.083949 
## [155]    train-rmse:0.607488+0.011531    test-rmse:1.061650+0.083990 
## [156]    train-rmse:0.604544+0.012042    test-rmse:1.059483+0.082272 
## [157]    train-rmse:0.601986+0.011331    test-rmse:1.059658+0.082558 
## [158]    train-rmse:0.600382+0.011227    test-rmse:1.058286+0.083190 
## [159]    train-rmse:0.598148+0.011594    test-rmse:1.058124+0.081950 
## [160]    train-rmse:0.596462+0.011642    test-rmse:1.056004+0.081443 
## [161]    train-rmse:0.592830+0.011510    test-rmse:1.052775+0.081372 
## [162]    train-rmse:0.590679+0.011490    test-rmse:1.051857+0.081256 
## [163]    train-rmse:0.587984+0.011938    test-rmse:1.048842+0.081392 
## [164]    train-rmse:0.585042+0.011048    test-rmse:1.047269+0.081132 
## [165]    train-rmse:0.582770+0.010505    test-rmse:1.045466+0.079451 
## [166]    train-rmse:0.580675+0.011142    test-rmse:1.045260+0.079783 
## [167]    train-rmse:0.578824+0.011368    test-rmse:1.044504+0.079998 
## [168]    train-rmse:0.576716+0.011395    test-rmse:1.043440+0.081030 
## [169]    train-rmse:0.573855+0.011470    test-rmse:1.044926+0.082710 
## [170]    train-rmse:0.571753+0.011213    test-rmse:1.045726+0.084308 
## [171]    train-rmse:0.570348+0.011465    test-rmse:1.045519+0.083980 
## [172]    train-rmse:0.567588+0.012660    test-rmse:1.045263+0.083426 
## [173]    train-rmse:0.565018+0.013243    test-rmse:1.046998+0.084279 
## [174]    train-rmse:0.562474+0.012860    test-rmse:1.045943+0.083223 
## Stopping. Best iteration:
## [168]    train-rmse:0.576716+0.011395    test-rmse:1.043440+0.081030
## 
## 3 
## [1]  train-rmse:88.665695+0.088976   test-rmse:88.666461+0.404980 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.835214+0.080050   test-rmse:79.835893+0.413675 
## [3]  train-rmse:71.885242+0.071998   test-rmse:71.885820+0.421463 
## [4]  train-rmse:64.728087+0.064733   test-rmse:64.728551+0.428434 
## [5]  train-rmse:58.284824+0.058174   test-rmse:58.285159+0.434654 
## [6]  train-rmse:52.484374+0.052257   test-rmse:52.484567+0.440197 
## [7]  train-rmse:47.262778+0.046908   test-rmse:47.262816+0.445120 
## [8]  train-rmse:42.562434+0.042074   test-rmse:42.562303+0.449468 
## [9]  train-rmse:38.331508+0.037710   test-rmse:38.331192+0.453293 
## [10] train-rmse:34.523343+0.033759   test-rmse:34.522811+0.456630 
## [11] train-rmse:31.095926+0.030181   test-rmse:31.095166+0.459509 
## [12] train-rmse:28.011453+0.026945   test-rmse:28.010445+0.461960 
## [13] train-rmse:25.235913+0.024012   test-rmse:25.234629+0.463999 
## [14] train-rmse:22.738687+0.021363   test-rmse:22.737103+0.465637 
## [15] train-rmse:20.492223+0.018971   test-rmse:20.490315+0.466887 
## [16] train-rmse:18.471749+0.016823   test-rmse:18.469484+0.467747 
## [17] train-rmse:16.654969+0.014903   test-rmse:16.652318+0.468213 
## [18] train-rmse:15.021334+0.013222   test-rmse:15.020864+0.456548 
## [19] train-rmse:13.552638+0.011590   test-rmse:13.550876+0.449119 
## [20] train-rmse:12.232204+0.010135   test-rmse:12.222750+0.446257 
## [21] train-rmse:11.045206+0.008913   test-rmse:11.035400+0.435400 
## [22] train-rmse:9.977467+0.008102    test-rmse:9.962921+0.427508 
## [23] train-rmse:9.017349+0.007255    test-rmse:8.999121+0.417020 
## [24] train-rmse:8.153999+0.006940    test-rmse:8.138505+0.407923 
## [25] train-rmse:7.377035+0.006059    test-rmse:7.362266+0.393684 
## [26] train-rmse:6.678746+0.005816    test-rmse:6.667051+0.379536 
## [27] train-rmse:6.051251+0.005637    test-rmse:6.044940+0.368063 
## [28] train-rmse:5.487670+0.004816    test-rmse:5.484530+0.351195 
## [29] train-rmse:4.980828+0.004387    test-rmse:4.978787+0.338219 
## [30] train-rmse:4.525984+0.004128    test-rmse:4.522226+0.329975 
## [31] train-rmse:4.116593+0.003738    test-rmse:4.120610+0.323908 
## [32] train-rmse:3.750095+0.004081    test-rmse:3.756790+0.316317 
## [33] train-rmse:3.420906+0.004089    test-rmse:3.432447+0.300881 
## [34] train-rmse:3.125754+0.003966    test-rmse:3.148254+0.287010 
## [35] train-rmse:2.862901+0.004892    test-rmse:2.894543+0.275162 
## [36] train-rmse:2.627469+0.005990    test-rmse:2.666050+0.267851 
## [37] train-rmse:2.416913+0.004719    test-rmse:2.469600+0.256763 
## [38] train-rmse:2.230513+0.005435    test-rmse:2.295225+0.245118 
## [39] train-rmse:2.063914+0.004794    test-rmse:2.144146+0.231756 
## [40] train-rmse:1.915314+0.004914    test-rmse:2.014027+0.224384 
## [41] train-rmse:1.783515+0.007448    test-rmse:1.898127+0.213247 
## [42] train-rmse:1.666476+0.007154    test-rmse:1.798208+0.204763 
## [43] train-rmse:1.566179+0.007122    test-rmse:1.709773+0.195997 
## [44] train-rmse:1.474782+0.006850    test-rmse:1.637473+0.185446 
## [45] train-rmse:1.396656+0.007268    test-rmse:1.574889+0.176179 
## [46] train-rmse:1.325393+0.007537    test-rmse:1.522324+0.166996 
## [47] train-rmse:1.263276+0.007981    test-rmse:1.474429+0.162400 
## [48] train-rmse:1.209777+0.008545    test-rmse:1.434887+0.153769 
## [49] train-rmse:1.163757+0.008516    test-rmse:1.401538+0.145268 
## [50] train-rmse:1.122001+0.008643    test-rmse:1.370902+0.140806 
## [51] train-rmse:1.083162+0.009492    test-rmse:1.344888+0.136070 
## [52] train-rmse:1.051570+0.008279    test-rmse:1.325004+0.129685 
## [53] train-rmse:1.023373+0.008078    test-rmse:1.304584+0.126998 
## [54] train-rmse:0.996264+0.007328    test-rmse:1.288933+0.122382 
## [55] train-rmse:0.971812+0.007820    test-rmse:1.273236+0.118902 
## [56] train-rmse:0.950984+0.009638    test-rmse:1.259607+0.115334 
## [57] train-rmse:0.933336+0.010063    test-rmse:1.246997+0.113006 
## [58] train-rmse:0.915426+0.008436    test-rmse:1.236597+0.111676 
## [59] train-rmse:0.899424+0.007435    test-rmse:1.227600+0.112069 
## [60] train-rmse:0.885081+0.007935    test-rmse:1.220666+0.108329 
## [61] train-rmse:0.869870+0.006846    test-rmse:1.213656+0.107245 
## [62] train-rmse:0.857049+0.006701    test-rmse:1.204262+0.105703 
## [63] train-rmse:0.845640+0.007496    test-rmse:1.196899+0.104043 
## [64] train-rmse:0.835258+0.006953    test-rmse:1.190794+0.102423 
## [65] train-rmse:0.823935+0.007981    test-rmse:1.181665+0.097754 
## [66] train-rmse:0.814050+0.006851    test-rmse:1.176907+0.095450 
## [67] train-rmse:0.804261+0.008057    test-rmse:1.172477+0.095892 
## [68] train-rmse:0.794833+0.008028    test-rmse:1.166048+0.092469 
## [69] train-rmse:0.785550+0.009316    test-rmse:1.159844+0.091380 
## [70] train-rmse:0.777416+0.010099    test-rmse:1.155742+0.090806 
## [71] train-rmse:0.769986+0.008397    test-rmse:1.150964+0.091427 
## [72] train-rmse:0.762171+0.007275    test-rmse:1.148112+0.090377 
## [73] train-rmse:0.755939+0.006821    test-rmse:1.143936+0.089813 
## [74] train-rmse:0.748358+0.006740    test-rmse:1.138809+0.089449 
## [75] train-rmse:0.740116+0.008462    test-rmse:1.134605+0.090303 
## [76] train-rmse:0.733436+0.007304    test-rmse:1.131039+0.090321 
## [77] train-rmse:0.726156+0.008537    test-rmse:1.125984+0.091210 
## [78] train-rmse:0.719312+0.009214    test-rmse:1.123724+0.089014 
## [79] train-rmse:0.712474+0.009731    test-rmse:1.116534+0.085333 
## [80] train-rmse:0.707225+0.009345    test-rmse:1.112954+0.085277 
## [81] train-rmse:0.700773+0.009794    test-rmse:1.106508+0.082580 
## [82] train-rmse:0.695012+0.009054    test-rmse:1.103486+0.083058 
## [83] train-rmse:0.689547+0.009084    test-rmse:1.102952+0.081087 
## [84] train-rmse:0.684065+0.009426    test-rmse:1.100401+0.081837 
## [85] train-rmse:0.677595+0.010166    test-rmse:1.095890+0.082789 
## [86] train-rmse:0.672497+0.010962    test-rmse:1.093800+0.082546 
## [87] train-rmse:0.667252+0.011136    test-rmse:1.089431+0.081779 
## [88] train-rmse:0.661915+0.009627    test-rmse:1.086426+0.082280 
## [89] train-rmse:0.656702+0.010694    test-rmse:1.086066+0.082372 
## [90] train-rmse:0.651744+0.011011    test-rmse:1.082703+0.082358 
## [91] train-rmse:0.646389+0.011843    test-rmse:1.079840+0.081757 
## [92] train-rmse:0.641645+0.012541    test-rmse:1.078152+0.082537 
## [93] train-rmse:0.636686+0.011754    test-rmse:1.075636+0.081474 
## [94] train-rmse:0.631811+0.011963    test-rmse:1.070393+0.080086 
## [95] train-rmse:0.627415+0.012657    test-rmse:1.068904+0.078415 
## [96] train-rmse:0.622964+0.012583    test-rmse:1.066035+0.078357 
## [97] train-rmse:0.618307+0.012482    test-rmse:1.063965+0.078617 
## [98] train-rmse:0.612389+0.011156    test-rmse:1.062098+0.078275 
## [99] train-rmse:0.607852+0.011753    test-rmse:1.059783+0.080731 
## [100]    train-rmse:0.604228+0.011820    test-rmse:1.058327+0.082016 
## [101]    train-rmse:0.600616+0.010809    test-rmse:1.056414+0.082284 
## [102]    train-rmse:0.595223+0.011516    test-rmse:1.054039+0.081541 
## [103]    train-rmse:0.591952+0.011644    test-rmse:1.053083+0.081463 
## [104]    train-rmse:0.587201+0.012139    test-rmse:1.051211+0.080751 
## [105]    train-rmse:0.583096+0.011748    test-rmse:1.050347+0.078909 
## [106]    train-rmse:0.578142+0.010978    test-rmse:1.046604+0.078130 
## [107]    train-rmse:0.574952+0.011171    test-rmse:1.045308+0.077868 
## [108]    train-rmse:0.571029+0.011893    test-rmse:1.040615+0.076993 
## [109]    train-rmse:0.565894+0.011485    test-rmse:1.038819+0.076675 
## [110]    train-rmse:0.562654+0.011426    test-rmse:1.039423+0.077842 
## [111]    train-rmse:0.558597+0.011558    test-rmse:1.037242+0.078146 
## [112]    train-rmse:0.554085+0.010771    test-rmse:1.036149+0.078226 
## [113]    train-rmse:0.550664+0.010113    test-rmse:1.035171+0.077479 
## [114]    train-rmse:0.546345+0.010882    test-rmse:1.033434+0.077151 
## [115]    train-rmse:0.542634+0.010860    test-rmse:1.034511+0.078727 
## [116]    train-rmse:0.538918+0.010592    test-rmse:1.031814+0.078213 
## [117]    train-rmse:0.535387+0.010797    test-rmse:1.030817+0.078261 
## [118]    train-rmse:0.532391+0.010707    test-rmse:1.028899+0.078857 
## [119]    train-rmse:0.529290+0.011108    test-rmse:1.027899+0.079220 
## [120]    train-rmse:0.525254+0.011479    test-rmse:1.029023+0.080310 
## [121]    train-rmse:0.522136+0.011355    test-rmse:1.027240+0.080080 
## [122]    train-rmse:0.519514+0.011953    test-rmse:1.024165+0.078807 
## [123]    train-rmse:0.515332+0.011968    test-rmse:1.022593+0.078761 
## [124]    train-rmse:0.512998+0.011825    test-rmse:1.020590+0.079875 
## [125]    train-rmse:0.509799+0.010883    test-rmse:1.019904+0.079774 
## [126]    train-rmse:0.506024+0.009539    test-rmse:1.018257+0.079963 
## [127]    train-rmse:0.502057+0.010304    test-rmse:1.013743+0.077253 
## [128]    train-rmse:0.498320+0.009405    test-rmse:1.012046+0.076304 
## [129]    train-rmse:0.496314+0.009747    test-rmse:1.010886+0.076745 
## [130]    train-rmse:0.492512+0.010171    test-rmse:1.011226+0.076825 
## [131]    train-rmse:0.489664+0.009443    test-rmse:1.012064+0.077169 
## [132]    train-rmse:0.486428+0.008225    test-rmse:1.008067+0.076164 
## [133]    train-rmse:0.482436+0.008112    test-rmse:1.007307+0.076684 
## [134]    train-rmse:0.478845+0.007552    test-rmse:1.006387+0.076152 
## [135]    train-rmse:0.476491+0.008108    test-rmse:1.007411+0.077611 
## [136]    train-rmse:0.473447+0.008202    test-rmse:1.004853+0.076372 
## [137]    train-rmse:0.470522+0.008488    test-rmse:1.003101+0.076537 
## [138]    train-rmse:0.467021+0.008199    test-rmse:1.004236+0.076877 
## [139]    train-rmse:0.463790+0.007967    test-rmse:1.003523+0.076775 
## [140]    train-rmse:0.461316+0.008252    test-rmse:1.002238+0.076350 
## [141]    train-rmse:0.457923+0.007610    test-rmse:1.001148+0.075614 
## [142]    train-rmse:0.455394+0.007414    test-rmse:1.000972+0.075632 
## [143]    train-rmse:0.452155+0.007455    test-rmse:1.001972+0.076834 
## [144]    train-rmse:0.449353+0.007460    test-rmse:1.000293+0.076383 
## [145]    train-rmse:0.446459+0.007878    test-rmse:0.999544+0.075978 
## [146]    train-rmse:0.444391+0.007658    test-rmse:0.999754+0.075489 
## [147]    train-rmse:0.441318+0.008198    test-rmse:0.999402+0.075496 
## [148]    train-rmse:0.438554+0.008407    test-rmse:0.997851+0.075146 
## [149]    train-rmse:0.436125+0.008319    test-rmse:0.997017+0.074905 
## [150]    train-rmse:0.434020+0.008595    test-rmse:0.996171+0.075206 
## [151]    train-rmse:0.431706+0.007512    test-rmse:0.994897+0.075416 
## [152]    train-rmse:0.429422+0.007245    test-rmse:0.994513+0.076968 
## [153]    train-rmse:0.427126+0.007693    test-rmse:0.993648+0.076600 
## [154]    train-rmse:0.424855+0.007412    test-rmse:0.992891+0.076559 
## [155]    train-rmse:0.422491+0.007499    test-rmse:0.992333+0.076292 
## [156]    train-rmse:0.420035+0.007709    test-rmse:0.992249+0.075846 
## [157]    train-rmse:0.418083+0.007332    test-rmse:0.991147+0.074845 
## [158]    train-rmse:0.416442+0.007308    test-rmse:0.991444+0.075282 
## [159]    train-rmse:0.413791+0.007326    test-rmse:0.992362+0.075725 
## [160]    train-rmse:0.412022+0.007436    test-rmse:0.991923+0.076632 
## [161]    train-rmse:0.409131+0.007506    test-rmse:0.990687+0.076148 
## [162]    train-rmse:0.406663+0.008118    test-rmse:0.988939+0.075743 
## [163]    train-rmse:0.403830+0.008170    test-rmse:0.988433+0.075544 
## [164]    train-rmse:0.401349+0.008931    test-rmse:0.987040+0.074030 
## [165]    train-rmse:0.399295+0.008297    test-rmse:0.987286+0.073813 
## [166]    train-rmse:0.396718+0.008521    test-rmse:0.985692+0.072743 
## [167]    train-rmse:0.394060+0.009075    test-rmse:0.985436+0.071979 
## [168]    train-rmse:0.392487+0.009254    test-rmse:0.985390+0.072915 
## [169]    train-rmse:0.390126+0.008911    test-rmse:0.985439+0.072915 
## [170]    train-rmse:0.387818+0.009249    test-rmse:0.984942+0.072708 
## [171]    train-rmse:0.386285+0.009326    test-rmse:0.983883+0.072267 
## [172]    train-rmse:0.384449+0.008804    test-rmse:0.982852+0.073063 
## [173]    train-rmse:0.382918+0.009097    test-rmse:0.982063+0.073448 
## [174]    train-rmse:0.381030+0.008784    test-rmse:0.981753+0.074643 
## [175]    train-rmse:0.378399+0.008929    test-rmse:0.981950+0.075815 
## [176]    train-rmse:0.376042+0.008357    test-rmse:0.982005+0.076585 
## [177]    train-rmse:0.373236+0.008043    test-rmse:0.980400+0.075705 
## [178]    train-rmse:0.371321+0.008131    test-rmse:0.979992+0.075263 
## [179]    train-rmse:0.369722+0.007484    test-rmse:0.979546+0.075278 
## [180]    train-rmse:0.367559+0.007413    test-rmse:0.978774+0.075148 
## [181]    train-rmse:0.365678+0.007395    test-rmse:0.978876+0.075603 
## [182]    train-rmse:0.363360+0.007727    test-rmse:0.978522+0.074877 
## [183]    train-rmse:0.361641+0.008168    test-rmse:0.978121+0.074793 
## [184]    train-rmse:0.359404+0.008480    test-rmse:0.977922+0.074794 
## [185]    train-rmse:0.357263+0.008299    test-rmse:0.977658+0.074948 
## [186]    train-rmse:0.355567+0.008306    test-rmse:0.977317+0.075210 
## [187]    train-rmse:0.353281+0.007772    test-rmse:0.976139+0.075326 
## [188]    train-rmse:0.350578+0.007935    test-rmse:0.975308+0.074788 
## [189]    train-rmse:0.349091+0.008844    test-rmse:0.973535+0.073570 
## [190]    train-rmse:0.347526+0.008521    test-rmse:0.974586+0.074204 
## [191]    train-rmse:0.345632+0.008366    test-rmse:0.973826+0.073175 
## [192]    train-rmse:0.343531+0.008698    test-rmse:0.973224+0.072633 
## [193]    train-rmse:0.341343+0.008646    test-rmse:0.973788+0.071871 
## [194]    train-rmse:0.339575+0.008169    test-rmse:0.973712+0.072067 
## [195]    train-rmse:0.337734+0.008410    test-rmse:0.973845+0.072313 
## [196]    train-rmse:0.336184+0.008465    test-rmse:0.973460+0.071718 
## [197]    train-rmse:0.334695+0.008654    test-rmse:0.973875+0.071724 
## [198]    train-rmse:0.332562+0.008477    test-rmse:0.974310+0.071253 
## Stopping. Best iteration:
## [192]    train-rmse:0.343531+0.008698    test-rmse:0.973224+0.072633
## 
## 4 
## [1]  train-rmse:88.665695+0.088976   test-rmse:88.666461+0.404980 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.835214+0.080050   test-rmse:79.835893+0.413675 
## [3]  train-rmse:71.885242+0.071998   test-rmse:71.885820+0.421463 
## [4]  train-rmse:64.728087+0.064733   test-rmse:64.728551+0.428434 
## [5]  train-rmse:58.284824+0.058174   test-rmse:58.285159+0.434654 
## [6]  train-rmse:52.484374+0.052257   test-rmse:52.484567+0.440197 
## [7]  train-rmse:47.262778+0.046908   test-rmse:47.262816+0.445120 
## [8]  train-rmse:42.562434+0.042074   test-rmse:42.562303+0.449468 
## [9]  train-rmse:38.331508+0.037710   test-rmse:38.331192+0.453293 
## [10] train-rmse:34.523343+0.033759   test-rmse:34.522811+0.456630 
## [11] train-rmse:31.095926+0.030181   test-rmse:31.095166+0.459509 
## [12] train-rmse:28.011453+0.026945   test-rmse:28.010445+0.461960 
## [13] train-rmse:25.235913+0.024012   test-rmse:25.234629+0.463999 
## [14] train-rmse:22.738687+0.021363   test-rmse:22.737103+0.465637 
## [15] train-rmse:20.492223+0.018971   test-rmse:20.490315+0.466887 
## [16] train-rmse:18.471749+0.016823   test-rmse:18.469484+0.467747 
## [17] train-rmse:16.654969+0.014903   test-rmse:16.652318+0.468213 
## [18] train-rmse:15.021334+0.013222   test-rmse:15.020864+0.456548 
## [19] train-rmse:13.552638+0.011590   test-rmse:13.550876+0.449119 
## [20] train-rmse:12.232204+0.010135   test-rmse:12.222750+0.446257 
## [21] train-rmse:11.045206+0.008913   test-rmse:11.035400+0.435400 
## [22] train-rmse:9.977437+0.008087    test-rmse:9.961850+0.427085 
## [23] train-rmse:9.017246+0.007304    test-rmse:9.003600+0.418966 
## [24] train-rmse:8.153400+0.006879    test-rmse:8.135442+0.405985 
## [25] train-rmse:7.376223+0.006002    test-rmse:7.358687+0.387746 
## [26] train-rmse:6.677960+0.005386    test-rmse:6.667460+0.375209 
## [27] train-rmse:6.049415+0.005624    test-rmse:6.040969+0.363101 
## [28] train-rmse:5.485107+0.004686    test-rmse:5.478143+0.348993 
## [29] train-rmse:4.977172+0.003795    test-rmse:4.970297+0.336528 
## [30] train-rmse:4.520442+0.003663    test-rmse:4.520325+0.322914 
## [31] train-rmse:4.110031+0.002698    test-rmse:4.112325+0.319790 
## [32] train-rmse:3.740829+0.002273    test-rmse:3.750294+0.306242 
## [33] train-rmse:3.409035+0.002741    test-rmse:3.428388+0.293507 
## [34] train-rmse:3.111739+0.003632    test-rmse:3.131453+0.282949 
## [35] train-rmse:2.844751+0.003186    test-rmse:2.874774+0.274099 
## [36] train-rmse:2.604950+0.003180    test-rmse:2.646873+0.261132 
## [37] train-rmse:2.388845+0.004153    test-rmse:2.443782+0.246163 
## [38] train-rmse:2.196900+0.005067    test-rmse:2.268277+0.237109 
## [39] train-rmse:2.026011+0.005865    test-rmse:2.113173+0.231621 
## [40] train-rmse:1.872878+0.004855    test-rmse:1.974863+0.224322 
## [41] train-rmse:1.736563+0.004693    test-rmse:1.858761+0.212726 
## [42] train-rmse:1.616836+0.006406    test-rmse:1.751996+0.204367 
## [43] train-rmse:1.509329+0.006383    test-rmse:1.662416+0.199235 
## [44] train-rmse:1.414040+0.006360    test-rmse:1.589799+0.186074 
## [45] train-rmse:1.330165+0.006533    test-rmse:1.525533+0.176845 
## [46] train-rmse:1.252364+0.008355    test-rmse:1.468310+0.170656 
## [47] train-rmse:1.185098+0.009960    test-rmse:1.417420+0.162849 
## [48] train-rmse:1.126240+0.009712    test-rmse:1.377790+0.157704 
## [49] train-rmse:1.073937+0.009453    test-rmse:1.340349+0.154146 
## [50] train-rmse:1.027644+0.009226    test-rmse:1.309016+0.151434 
## [51] train-rmse:0.986823+0.007796    test-rmse:1.282305+0.149147 
## [52] train-rmse:0.951952+0.008191    test-rmse:1.261119+0.146077 
## [53] train-rmse:0.919483+0.009281    test-rmse:1.242091+0.141481 
## [54] train-rmse:0.889168+0.008777    test-rmse:1.225548+0.137938 
## [55] train-rmse:0.863369+0.008882    test-rmse:1.213209+0.134968 
## [56] train-rmse:0.838097+0.008762    test-rmse:1.197570+0.132732 
## [57] train-rmse:0.816679+0.010318    test-rmse:1.185394+0.129618 
## [58] train-rmse:0.794550+0.009367    test-rmse:1.173909+0.127526 
## [59] train-rmse:0.777364+0.010250    test-rmse:1.163413+0.127713 
## [60] train-rmse:0.761223+0.011365    test-rmse:1.150376+0.123481 
## [61] train-rmse:0.747424+0.011453    test-rmse:1.142943+0.123276 
## [62] train-rmse:0.731691+0.012023    test-rmse:1.135478+0.123300 
## [63] train-rmse:0.720120+0.011858    test-rmse:1.127064+0.119879 
## [64] train-rmse:0.708589+0.011989    test-rmse:1.120140+0.116379 
## [65] train-rmse:0.696824+0.012848    test-rmse:1.116257+0.115129 
## [66] train-rmse:0.684685+0.012120    test-rmse:1.111660+0.113651 
## [67] train-rmse:0.673950+0.012854    test-rmse:1.105491+0.111563 
## [68] train-rmse:0.662472+0.013118    test-rmse:1.101079+0.111413 
## [69] train-rmse:0.653616+0.012848    test-rmse:1.096072+0.109885 
## [70] train-rmse:0.645084+0.014045    test-rmse:1.091717+0.111480 
## [71] train-rmse:0.633867+0.015120    test-rmse:1.085945+0.110298 
## [72] train-rmse:0.626069+0.015884    test-rmse:1.083419+0.109978 
## [73] train-rmse:0.617154+0.016282    test-rmse:1.077436+0.108133 
## [74] train-rmse:0.609814+0.017560    test-rmse:1.074067+0.106709 
## [75] train-rmse:0.601902+0.017789    test-rmse:1.072358+0.107540 
## [76] train-rmse:0.594758+0.018102    test-rmse:1.066670+0.107965 
## [77] train-rmse:0.587299+0.017513    test-rmse:1.061355+0.109578 
## [78] train-rmse:0.581064+0.017677    test-rmse:1.056383+0.107682 
## [79] train-rmse:0.575310+0.018133    test-rmse:1.056277+0.106754 
## [80] train-rmse:0.570111+0.017656    test-rmse:1.053211+0.109506 
## [81] train-rmse:0.564120+0.016981    test-rmse:1.050326+0.108137 
## [82] train-rmse:0.558749+0.016815    test-rmse:1.046125+0.105106 
## [83] train-rmse:0.552964+0.016490    test-rmse:1.043632+0.106490 
## [84] train-rmse:0.546760+0.016472    test-rmse:1.043329+0.107188 
## [85] train-rmse:0.540968+0.016988    test-rmse:1.039653+0.107683 
## [86] train-rmse:0.534046+0.017509    test-rmse:1.038048+0.107231 
## [87] train-rmse:0.528802+0.018068    test-rmse:1.036229+0.106579 
## [88] train-rmse:0.523306+0.018331    test-rmse:1.033765+0.105947 
## [89] train-rmse:0.516808+0.016806    test-rmse:1.030756+0.107135 
## [90] train-rmse:0.511963+0.017505    test-rmse:1.027529+0.109097 
## [91] train-rmse:0.506360+0.016870    test-rmse:1.024701+0.107499 
## [92] train-rmse:0.501931+0.017116    test-rmse:1.023179+0.107937 
## [93] train-rmse:0.497306+0.016941    test-rmse:1.022346+0.108073 
## [94] train-rmse:0.493167+0.016959    test-rmse:1.020243+0.108102 
## [95] train-rmse:0.486409+0.016926    test-rmse:1.016703+0.107259 
## [96] train-rmse:0.481599+0.016913    test-rmse:1.014006+0.104602 
## [97] train-rmse:0.477677+0.017227    test-rmse:1.010779+0.104333 
## [98] train-rmse:0.473876+0.017866    test-rmse:1.009573+0.104287 
## [99] train-rmse:0.469750+0.018055    test-rmse:1.008912+0.103987 
## [100]    train-rmse:0.465823+0.017670    test-rmse:1.007161+0.103335 
## [101]    train-rmse:0.462384+0.018365    test-rmse:1.006435+0.102403 
## [102]    train-rmse:0.457508+0.017708    test-rmse:1.005705+0.103019 
## [103]    train-rmse:0.453358+0.016431    test-rmse:1.003769+0.102519 
## [104]    train-rmse:0.449238+0.016333    test-rmse:1.002316+0.100978 
## [105]    train-rmse:0.444373+0.015528    test-rmse:1.000715+0.101084 
## [106]    train-rmse:0.440495+0.015478    test-rmse:1.000015+0.100705 
## [107]    train-rmse:0.436105+0.016244    test-rmse:1.000348+0.100788 
## [108]    train-rmse:0.431669+0.016760    test-rmse:0.998661+0.100881 
## [109]    train-rmse:0.428218+0.016023    test-rmse:0.996221+0.100432 
## [110]    train-rmse:0.424695+0.016155    test-rmse:0.995013+0.099317 
## [111]    train-rmse:0.420991+0.015972    test-rmse:0.992774+0.097688 
## [112]    train-rmse:0.416993+0.016650    test-rmse:0.991399+0.096892 
## [113]    train-rmse:0.414298+0.017103    test-rmse:0.989711+0.097103 
## [114]    train-rmse:0.411648+0.017253    test-rmse:0.988765+0.097023 
## [115]    train-rmse:0.407965+0.017813    test-rmse:0.988300+0.096996 
## [116]    train-rmse:0.404502+0.018259    test-rmse:0.987645+0.096921 
## [117]    train-rmse:0.401105+0.018222    test-rmse:0.987088+0.096622 
## [118]    train-rmse:0.398765+0.018376    test-rmse:0.986063+0.095735 
## [119]    train-rmse:0.395283+0.017775    test-rmse:0.985319+0.095800 
## [120]    train-rmse:0.392035+0.018029    test-rmse:0.983800+0.096171 
## [121]    train-rmse:0.388952+0.017823    test-rmse:0.983750+0.096340 
## [122]    train-rmse:0.386022+0.018342    test-rmse:0.983030+0.096062 
## [123]    train-rmse:0.382702+0.018439    test-rmse:0.982875+0.095498 
## [124]    train-rmse:0.380254+0.017974    test-rmse:0.982686+0.094924 
## [125]    train-rmse:0.377953+0.017702    test-rmse:0.980637+0.095449 
## [126]    train-rmse:0.374597+0.017454    test-rmse:0.981729+0.095595 
## [127]    train-rmse:0.371152+0.016977    test-rmse:0.980046+0.095072 
## [128]    train-rmse:0.368087+0.017225    test-rmse:0.979440+0.095823 
## [129]    train-rmse:0.365487+0.016565    test-rmse:0.978025+0.095454 
## [130]    train-rmse:0.362518+0.016607    test-rmse:0.977587+0.094794 
## [131]    train-rmse:0.360117+0.016470    test-rmse:0.976574+0.094012 
## [132]    train-rmse:0.356509+0.016248    test-rmse:0.975902+0.094827 
## [133]    train-rmse:0.354197+0.016061    test-rmse:0.975381+0.094806 
## [134]    train-rmse:0.351023+0.015907    test-rmse:0.975437+0.093919 
## [135]    train-rmse:0.345886+0.015837    test-rmse:0.975821+0.093408 
## [136]    train-rmse:0.343594+0.015711    test-rmse:0.974983+0.093059 
## [137]    train-rmse:0.340980+0.015393    test-rmse:0.974994+0.092284 
## [138]    train-rmse:0.337542+0.016141    test-rmse:0.973164+0.090926 
## [139]    train-rmse:0.335675+0.016092    test-rmse:0.973105+0.090934 
## [140]    train-rmse:0.333200+0.016425    test-rmse:0.972010+0.090836 
## [141]    train-rmse:0.330637+0.016039    test-rmse:0.970461+0.089947 
## [142]    train-rmse:0.328133+0.016314    test-rmse:0.968521+0.088979 
## [143]    train-rmse:0.325583+0.016254    test-rmse:0.968075+0.089425 
## [144]    train-rmse:0.321535+0.015186    test-rmse:0.968219+0.090175 
## [145]    train-rmse:0.319029+0.014988    test-rmse:0.968299+0.090121 
## [146]    train-rmse:0.316356+0.014182    test-rmse:0.968071+0.089429 
## [147]    train-rmse:0.313971+0.013885    test-rmse:0.966968+0.088544 
## [148]    train-rmse:0.311317+0.014090    test-rmse:0.964739+0.088993 
## [149]    train-rmse:0.308259+0.013419    test-rmse:0.964862+0.089023 
## [150]    train-rmse:0.305981+0.013077    test-rmse:0.964466+0.089831 
## [151]    train-rmse:0.303349+0.012536    test-rmse:0.963560+0.089872 
## [152]    train-rmse:0.301467+0.012811    test-rmse:0.963404+0.090212 
## [153]    train-rmse:0.299195+0.012886    test-rmse:0.963368+0.089934 
## [154]    train-rmse:0.296718+0.012182    test-rmse:0.963943+0.090808 
## [155]    train-rmse:0.294200+0.012085    test-rmse:0.963407+0.090404 
## [156]    train-rmse:0.291524+0.012196    test-rmse:0.962529+0.090383 
## [157]    train-rmse:0.289128+0.012121    test-rmse:0.960817+0.089130 
## [158]    train-rmse:0.286726+0.011945    test-rmse:0.960896+0.088705 
## [159]    train-rmse:0.284105+0.011019    test-rmse:0.960293+0.088614 
## [160]    train-rmse:0.281037+0.010005    test-rmse:0.959373+0.087862 
## [161]    train-rmse:0.278726+0.009658    test-rmse:0.958627+0.088111 
## [162]    train-rmse:0.276381+0.008938    test-rmse:0.958461+0.087971 
## [163]    train-rmse:0.274119+0.009590    test-rmse:0.957884+0.088181 
## [164]    train-rmse:0.271787+0.009328    test-rmse:0.957457+0.089349 
## [165]    train-rmse:0.270293+0.009114    test-rmse:0.957349+0.089564 
## [166]    train-rmse:0.267799+0.008843    test-rmse:0.957001+0.088770 
## [167]    train-rmse:0.266045+0.008685    test-rmse:0.956068+0.088567 
## [168]    train-rmse:0.264530+0.008475    test-rmse:0.956729+0.088959 
## [169]    train-rmse:0.263167+0.008238    test-rmse:0.955682+0.089317 
## [170]    train-rmse:0.261688+0.008454    test-rmse:0.955674+0.089541 
## [171]    train-rmse:0.259601+0.008081    test-rmse:0.956337+0.089985 
## [172]    train-rmse:0.257372+0.008849    test-rmse:0.955416+0.089130 
## [173]    train-rmse:0.255728+0.008618    test-rmse:0.955531+0.089180 
## [174]    train-rmse:0.253585+0.007793    test-rmse:0.955243+0.088396 
## [175]    train-rmse:0.251461+0.007539    test-rmse:0.955304+0.088130 
## [176]    train-rmse:0.249505+0.007583    test-rmse:0.953792+0.087589 
## [177]    train-rmse:0.247394+0.006714    test-rmse:0.953988+0.088444 
## [178]    train-rmse:0.245739+0.007573    test-rmse:0.953434+0.088444 
## [179]    train-rmse:0.243832+0.007148    test-rmse:0.953281+0.088231 
## [180]    train-rmse:0.242033+0.007255    test-rmse:0.952213+0.088581 
## [181]    train-rmse:0.240162+0.007722    test-rmse:0.952119+0.088868 
## [182]    train-rmse:0.238539+0.007320    test-rmse:0.951705+0.088273 
## [183]    train-rmse:0.236968+0.007550    test-rmse:0.951939+0.088063 
## [184]    train-rmse:0.234810+0.007272    test-rmse:0.952400+0.088384 
## [185]    train-rmse:0.233014+0.007579    test-rmse:0.952305+0.089109 
## [186]    train-rmse:0.231589+0.007718    test-rmse:0.951828+0.088384 
## [187]    train-rmse:0.230176+0.007795    test-rmse:0.951481+0.088638 
## [188]    train-rmse:0.228433+0.007622    test-rmse:0.951265+0.088898 
## [189]    train-rmse:0.225942+0.006665    test-rmse:0.950333+0.088691 
## [190]    train-rmse:0.224316+0.006375    test-rmse:0.949796+0.089324 
## [191]    train-rmse:0.222351+0.006931    test-rmse:0.949441+0.088988 
## [192]    train-rmse:0.220936+0.006803    test-rmse:0.949762+0.088753 
## [193]    train-rmse:0.218845+0.006350    test-rmse:0.949408+0.088644 
## [194]    train-rmse:0.216895+0.006447    test-rmse:0.948900+0.088127 
## [195]    train-rmse:0.215042+0.006762    test-rmse:0.949665+0.088458 
## [196]    train-rmse:0.214263+0.006841    test-rmse:0.949649+0.088424 
## [197]    train-rmse:0.212793+0.007285    test-rmse:0.949085+0.088742 
## [198]    train-rmse:0.211462+0.007087    test-rmse:0.948834+0.088194 
## [199]    train-rmse:0.209319+0.006927    test-rmse:0.949243+0.087873 
## [200]    train-rmse:0.208035+0.007177    test-rmse:0.949101+0.087541 
## [201]    train-rmse:0.206647+0.007279    test-rmse:0.949118+0.087722 
## [202]    train-rmse:0.205637+0.007247    test-rmse:0.949062+0.087378 
## [203]    train-rmse:0.203487+0.006836    test-rmse:0.948959+0.087690 
## [204]    train-rmse:0.201993+0.007118    test-rmse:0.948312+0.087602 
## [205]    train-rmse:0.200249+0.007260    test-rmse:0.947757+0.088089 
## [206]    train-rmse:0.198349+0.007437    test-rmse:0.947817+0.088058 
## [207]    train-rmse:0.196747+0.007504    test-rmse:0.947195+0.087512 
## [208]    train-rmse:0.195820+0.007487    test-rmse:0.946855+0.087390 
## [209]    train-rmse:0.194501+0.006951    test-rmse:0.946760+0.088110 
## [210]    train-rmse:0.193561+0.006849    test-rmse:0.946585+0.088112 
## [211]    train-rmse:0.191931+0.006232    test-rmse:0.946376+0.087644 
## [212]    train-rmse:0.190316+0.005845    test-rmse:0.946790+0.087320 
## [213]    train-rmse:0.189350+0.005817    test-rmse:0.946218+0.087040 
## [214]    train-rmse:0.188092+0.005730    test-rmse:0.946445+0.086906 
## [215]    train-rmse:0.186655+0.005829    test-rmse:0.945869+0.086031 
## [216]    train-rmse:0.185331+0.005263    test-rmse:0.945603+0.086166 
## [217]    train-rmse:0.183945+0.005665    test-rmse:0.945620+0.085966 
## [218]    train-rmse:0.183180+0.005787    test-rmse:0.945842+0.086081 
## [219]    train-rmse:0.181937+0.005767    test-rmse:0.945899+0.085898 
## [220]    train-rmse:0.180802+0.005315    test-rmse:0.945936+0.085879 
## [221]    train-rmse:0.179208+0.005251    test-rmse:0.945788+0.085686 
## [222]    train-rmse:0.178251+0.004941    test-rmse:0.945489+0.085834 
## [223]    train-rmse:0.177008+0.004887    test-rmse:0.945251+0.085658 
## [224]    train-rmse:0.176065+0.004969    test-rmse:0.945437+0.085549 
## [225]    train-rmse:0.175036+0.005166    test-rmse:0.945077+0.085292 
## [226]    train-rmse:0.173748+0.005781    test-rmse:0.944783+0.085455 
## [227]    train-rmse:0.172708+0.005481    test-rmse:0.945131+0.085712 
## [228]    train-rmse:0.171558+0.005219    test-rmse:0.945248+0.085779 
## [229]    train-rmse:0.170632+0.005044    test-rmse:0.945365+0.085804 
## [230]    train-rmse:0.169464+0.005236    test-rmse:0.945035+0.085804 
## [231]    train-rmse:0.168632+0.005392    test-rmse:0.944722+0.085677 
## [232]    train-rmse:0.167246+0.005853    test-rmse:0.944411+0.085432 
## [233]    train-rmse:0.165983+0.005632    test-rmse:0.944514+0.085188 
## [234]    train-rmse:0.164573+0.005668    test-rmse:0.944309+0.084798 
## [235]    train-rmse:0.163368+0.005731    test-rmse:0.944162+0.084616 
## [236]    train-rmse:0.162436+0.006091    test-rmse:0.944050+0.084552 
## [237]    train-rmse:0.161075+0.006362    test-rmse:0.943763+0.084417 
## [238]    train-rmse:0.159528+0.006042    test-rmse:0.943863+0.084538 
## [239]    train-rmse:0.158534+0.006116    test-rmse:0.943293+0.083933 
## [240]    train-rmse:0.157277+0.006272    test-rmse:0.943014+0.083863 
## [241]    train-rmse:0.156157+0.006386    test-rmse:0.942987+0.083755 
## [242]    train-rmse:0.155196+0.006105    test-rmse:0.942877+0.083557 
## [243]    train-rmse:0.154435+0.005848    test-rmse:0.942698+0.083665 
## [244]    train-rmse:0.153484+0.005939    test-rmse:0.942869+0.083981 
## [245]    train-rmse:0.152519+0.006089    test-rmse:0.942559+0.083690 
## [246]    train-rmse:0.151010+0.005517    test-rmse:0.942451+0.083508 
## [247]    train-rmse:0.150203+0.005407    test-rmse:0.942447+0.083774 
## [248]    train-rmse:0.149427+0.005585    test-rmse:0.942416+0.083672 
## [249]    train-rmse:0.148005+0.006222    test-rmse:0.942469+0.083676 
## [250]    train-rmse:0.147300+0.006144    test-rmse:0.942297+0.083643 
## [251]    train-rmse:0.146145+0.006396    test-rmse:0.942303+0.083546 
## [252]    train-rmse:0.145333+0.006229    test-rmse:0.942071+0.083750 
## [253]    train-rmse:0.144072+0.006492    test-rmse:0.941757+0.083835 
## [254]    train-rmse:0.143411+0.006507    test-rmse:0.941760+0.084008 
## [255]    train-rmse:0.142656+0.006934    test-rmse:0.941286+0.083783 
## [256]    train-rmse:0.141897+0.007028    test-rmse:0.941351+0.083607 
## [257]    train-rmse:0.140946+0.006985    test-rmse:0.941164+0.083859 
## [258]    train-rmse:0.140246+0.006851    test-rmse:0.940784+0.083696 
## [259]    train-rmse:0.139240+0.006866    test-rmse:0.940536+0.083801 
## [260]    train-rmse:0.138416+0.006960    test-rmse:0.940604+0.083812 
## [261]    train-rmse:0.137724+0.006967    test-rmse:0.940309+0.083879 
## [262]    train-rmse:0.136840+0.006754    test-rmse:0.940016+0.083659 
## [263]    train-rmse:0.135754+0.006664    test-rmse:0.940204+0.084264 
## [264]    train-rmse:0.134586+0.006401    test-rmse:0.940184+0.083966 
## [265]    train-rmse:0.133842+0.006595    test-rmse:0.939972+0.083797 
## [266]    train-rmse:0.132827+0.006851    test-rmse:0.940063+0.083880 
## [267]    train-rmse:0.131848+0.006982    test-rmse:0.939652+0.083881 
## [268]    train-rmse:0.131122+0.006852    test-rmse:0.939469+0.084052 
## [269]    train-rmse:0.130682+0.006940    test-rmse:0.939452+0.084127 
## [270]    train-rmse:0.129879+0.006827    test-rmse:0.939337+0.084004 
## [271]    train-rmse:0.128814+0.006655    test-rmse:0.939196+0.083793 
## [272]    train-rmse:0.127816+0.006636    test-rmse:0.939295+0.083513 
## [273]    train-rmse:0.127281+0.006851    test-rmse:0.939052+0.083403 
## [274]    train-rmse:0.126739+0.006853    test-rmse:0.939285+0.083676 
## [275]    train-rmse:0.125724+0.006764    test-rmse:0.939177+0.083429 
## [276]    train-rmse:0.124714+0.006793    test-rmse:0.939278+0.083462 
## [277]    train-rmse:0.123990+0.006860    test-rmse:0.939105+0.083435 
## [278]    train-rmse:0.123137+0.007081    test-rmse:0.939327+0.083274 
## [279]    train-rmse:0.122394+0.006897    test-rmse:0.939405+0.083342 
## Stopping. Best iteration:
## [273]    train-rmse:0.127281+0.006851    test-rmse:0.939052+0.083403
## 
## 5 
## [1]  train-rmse:88.665695+0.088976   test-rmse:88.666461+0.404980 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.835214+0.080050   test-rmse:79.835893+0.413675 
## [3]  train-rmse:71.885242+0.071998   test-rmse:71.885820+0.421463 
## [4]  train-rmse:64.728087+0.064733   test-rmse:64.728551+0.428434 
## [5]  train-rmse:58.284824+0.058174   test-rmse:58.285159+0.434654 
## [6]  train-rmse:52.484374+0.052257   test-rmse:52.484567+0.440197 
## [7]  train-rmse:47.262778+0.046908   test-rmse:47.262816+0.445120 
## [8]  train-rmse:42.562434+0.042074   test-rmse:42.562303+0.449468 
## [9]  train-rmse:38.331508+0.037710   test-rmse:38.331192+0.453293 
## [10] train-rmse:34.523343+0.033759   test-rmse:34.522811+0.456630 
## [11] train-rmse:31.095926+0.030181   test-rmse:31.095166+0.459509 
## [12] train-rmse:28.011453+0.026945   test-rmse:28.010445+0.461960 
## [13] train-rmse:25.235913+0.024012   test-rmse:25.234629+0.463999 
## [14] train-rmse:22.738687+0.021363   test-rmse:22.737103+0.465637 
## [15] train-rmse:20.492223+0.018971   test-rmse:20.490315+0.466887 
## [16] train-rmse:18.471749+0.016823   test-rmse:18.469484+0.467747 
## [17] train-rmse:16.654969+0.014903   test-rmse:16.652318+0.468213 
## [18] train-rmse:15.021334+0.013222   test-rmse:15.020864+0.456548 
## [19] train-rmse:13.552638+0.011590   test-rmse:13.550876+0.449119 
## [20] train-rmse:12.232204+0.010135   test-rmse:12.222750+0.446257 
## [21] train-rmse:11.045206+0.008913   test-rmse:11.035400+0.435400 
## [22] train-rmse:9.977437+0.008087    test-rmse:9.961850+0.427085 
## [23] train-rmse:9.017222+0.007327    test-rmse:9.002905+0.418550 
## [24] train-rmse:8.153142+0.007026    test-rmse:8.132628+0.403760 
## [25] train-rmse:7.375641+0.006523    test-rmse:7.358086+0.386144 
## [26] train-rmse:6.676874+0.006110    test-rmse:6.664854+0.372684 
## [27] train-rmse:6.048462+0.006179    test-rmse:6.036334+0.357361 
## [28] train-rmse:5.483426+0.005291    test-rmse:5.473213+0.339879 
## [29] train-rmse:4.974809+0.004622    test-rmse:4.962442+0.330863 
## [30] train-rmse:4.517822+0.004087    test-rmse:4.507892+0.324959 
## [31] train-rmse:4.105579+0.003650    test-rmse:4.106107+0.311515 
## [32] train-rmse:3.734930+0.002627    test-rmse:3.740299+0.301411 
## [33] train-rmse:3.401261+0.003039    test-rmse:3.410442+0.290344 
## [34] train-rmse:3.100665+0.004138    test-rmse:3.123887+0.270813 
## [35] train-rmse:2.831528+0.004083    test-rmse:2.868586+0.259876 
## [36] train-rmse:2.590556+0.004682    test-rmse:2.636841+0.247273 
## [37] train-rmse:2.372917+0.004714    test-rmse:2.432736+0.236897 
## [38] train-rmse:2.176590+0.005018    test-rmse:2.250900+0.228325 
## [39] train-rmse:2.000876+0.003932    test-rmse:2.093256+0.211542 
## [40] train-rmse:1.843353+0.003266    test-rmse:1.955518+0.196284 
## [41] train-rmse:1.704839+0.004721    test-rmse:1.831358+0.188988 
## [42] train-rmse:1.579760+0.006076    test-rmse:1.724393+0.180594 
## [43] train-rmse:1.466427+0.006131    test-rmse:1.636521+0.175116 
## [44] train-rmse:1.366982+0.006463    test-rmse:1.554056+0.167114 
## [45] train-rmse:1.278060+0.006746    test-rmse:1.483212+0.161318 
## [46] train-rmse:1.199380+0.007099    test-rmse:1.422570+0.151168 
## [47] train-rmse:1.126885+0.008301    test-rmse:1.366786+0.139767 
## [48] train-rmse:1.064318+0.009374    test-rmse:1.325082+0.135471 
## [49] train-rmse:1.008371+0.009196    test-rmse:1.288832+0.131199 
## [50] train-rmse:0.957748+0.009581    test-rmse:1.254673+0.125174 
## [51] train-rmse:0.911165+0.008953    test-rmse:1.228957+0.118002 
## [52] train-rmse:0.870335+0.009117    test-rmse:1.206568+0.116043 
## [53] train-rmse:0.832790+0.008251    test-rmse:1.187527+0.112111 
## [54] train-rmse:0.801401+0.009430    test-rmse:1.168955+0.109193 
## [55] train-rmse:0.770831+0.008759    test-rmse:1.151561+0.107158 
## [56] train-rmse:0.743744+0.008556    test-rmse:1.137683+0.102352 
## [57] train-rmse:0.720342+0.007931    test-rmse:1.129534+0.101969 
## [58] train-rmse:0.698761+0.007673    test-rmse:1.121406+0.101182 
## [59] train-rmse:0.678973+0.007344    test-rmse:1.108604+0.096879 
## [60] train-rmse:0.662322+0.006460    test-rmse:1.100298+0.095629 
## [61] train-rmse:0.645161+0.008161    test-rmse:1.093907+0.092609 
## [62] train-rmse:0.628248+0.008578    test-rmse:1.088278+0.092725 
## [63] train-rmse:0.613743+0.008204    test-rmse:1.081271+0.087542 
## [64] train-rmse:0.601220+0.009230    test-rmse:1.075273+0.084935 
## [65] train-rmse:0.588259+0.009329    test-rmse:1.070195+0.085046 
## [66] train-rmse:0.577493+0.009347    test-rmse:1.066949+0.083369 
## [67] train-rmse:0.564171+0.008538    test-rmse:1.060836+0.080995 
## [68] train-rmse:0.555147+0.008947    test-rmse:1.055406+0.080092 
## [69] train-rmse:0.542969+0.009257    test-rmse:1.052370+0.079455 
## [70] train-rmse:0.533887+0.008038    test-rmse:1.050760+0.079385 
## [71] train-rmse:0.525071+0.010090    test-rmse:1.046829+0.079743 
## [72] train-rmse:0.515294+0.008485    test-rmse:1.043426+0.079064 
## [73] train-rmse:0.508051+0.009301    test-rmse:1.040300+0.079455 
## [74] train-rmse:0.500557+0.009285    test-rmse:1.037318+0.077897 
## [75] train-rmse:0.492421+0.008530    test-rmse:1.035971+0.076690 
## [76] train-rmse:0.485436+0.009123    test-rmse:1.031434+0.075966 
## [77] train-rmse:0.478826+0.008167    test-rmse:1.028707+0.076121 
## [78] train-rmse:0.472638+0.008537    test-rmse:1.026620+0.076145 
## [79] train-rmse:0.464811+0.007245    test-rmse:1.021973+0.075559 
## [80] train-rmse:0.459577+0.007471    test-rmse:1.020232+0.075487 
## [81] train-rmse:0.452975+0.008184    test-rmse:1.018717+0.075687 
## [82] train-rmse:0.447193+0.006934    test-rmse:1.016286+0.076588 
## [83] train-rmse:0.441474+0.007161    test-rmse:1.013827+0.077181 
## [84] train-rmse:0.436286+0.007330    test-rmse:1.013268+0.077905 
## [85] train-rmse:0.430761+0.006259    test-rmse:1.009028+0.076979 
## [86] train-rmse:0.425062+0.006747    test-rmse:1.007343+0.076207 
## [87] train-rmse:0.420266+0.007237    test-rmse:1.005201+0.076023 
## [88] train-rmse:0.415358+0.007529    test-rmse:1.003976+0.076733 
## [89] train-rmse:0.410399+0.006982    test-rmse:1.002576+0.077264 
## [90] train-rmse:0.406306+0.006895    test-rmse:0.998659+0.076206 
## [91] train-rmse:0.401106+0.007743    test-rmse:0.997701+0.076369 
## [92] train-rmse:0.395562+0.006256    test-rmse:0.994453+0.076340 
## [93] train-rmse:0.391513+0.007033    test-rmse:0.993220+0.076860 
## [94] train-rmse:0.387657+0.006729    test-rmse:0.991834+0.077936 
## [95] train-rmse:0.383827+0.006395    test-rmse:0.990486+0.078494 
## [96] train-rmse:0.378130+0.006353    test-rmse:0.989015+0.079146 
## [97] train-rmse:0.373530+0.006877    test-rmse:0.987105+0.079439 
## [98] train-rmse:0.369718+0.006035    test-rmse:0.985841+0.079226 
## [99] train-rmse:0.364625+0.005247    test-rmse:0.984581+0.078430 
## [100]    train-rmse:0.359787+0.004183    test-rmse:0.984597+0.076913 
## [101]    train-rmse:0.356303+0.004898    test-rmse:0.983773+0.076499 
## [102]    train-rmse:0.351679+0.004478    test-rmse:0.983153+0.076204 
## [103]    train-rmse:0.346974+0.004826    test-rmse:0.982334+0.076611 
## [104]    train-rmse:0.343238+0.004971    test-rmse:0.981702+0.076163 
## [105]    train-rmse:0.339238+0.005135    test-rmse:0.980144+0.075599 
## [106]    train-rmse:0.333999+0.006748    test-rmse:0.980077+0.075651 
## [107]    train-rmse:0.329701+0.007264    test-rmse:0.978608+0.075766 
## [108]    train-rmse:0.326609+0.008243    test-rmse:0.977807+0.076324 
## [109]    train-rmse:0.323707+0.007990    test-rmse:0.977153+0.076308 
## [110]    train-rmse:0.319691+0.009786    test-rmse:0.977026+0.076481 
## [111]    train-rmse:0.316644+0.009753    test-rmse:0.975971+0.076904 
## [112]    train-rmse:0.313344+0.009231    test-rmse:0.975724+0.076650 
## [113]    train-rmse:0.309821+0.009616    test-rmse:0.974438+0.076668 
## [114]    train-rmse:0.305530+0.009082    test-rmse:0.973607+0.076952 
## [115]    train-rmse:0.301794+0.008139    test-rmse:0.972441+0.076627 
## [116]    train-rmse:0.297887+0.008061    test-rmse:0.971773+0.076408 
## [117]    train-rmse:0.293398+0.008611    test-rmse:0.971509+0.076583 
## [118]    train-rmse:0.289876+0.009041    test-rmse:0.970998+0.076178 
## [119]    train-rmse:0.287056+0.008475    test-rmse:0.970547+0.076150 
## [120]    train-rmse:0.284739+0.008357    test-rmse:0.969587+0.075212 
## [121]    train-rmse:0.282024+0.008789    test-rmse:0.969355+0.075162 
## [122]    train-rmse:0.279360+0.008277    test-rmse:0.968385+0.074389 
## [123]    train-rmse:0.276189+0.008120    test-rmse:0.967295+0.075111 
## [124]    train-rmse:0.273707+0.008477    test-rmse:0.967657+0.076185 
## [125]    train-rmse:0.271444+0.008897    test-rmse:0.967219+0.076478 
## [126]    train-rmse:0.268563+0.009226    test-rmse:0.965995+0.075508 
## [127]    train-rmse:0.265833+0.009936    test-rmse:0.965593+0.074901 
## [128]    train-rmse:0.263049+0.009949    test-rmse:0.965505+0.074709 
## [129]    train-rmse:0.260577+0.009717    test-rmse:0.963729+0.075902 
## [130]    train-rmse:0.258116+0.009333    test-rmse:0.964041+0.075583 
## [131]    train-rmse:0.255338+0.009611    test-rmse:0.963771+0.075799 
## [132]    train-rmse:0.252448+0.010100    test-rmse:0.963393+0.075138 
## [133]    train-rmse:0.248994+0.010186    test-rmse:0.963363+0.075061 
## [134]    train-rmse:0.245579+0.010216    test-rmse:0.962294+0.074731 
## [135]    train-rmse:0.242912+0.010261    test-rmse:0.961521+0.074294 
## [136]    train-rmse:0.240504+0.009658    test-rmse:0.961115+0.073933 
## [137]    train-rmse:0.238440+0.009754    test-rmse:0.960669+0.074050 
## [138]    train-rmse:0.234812+0.009936    test-rmse:0.959933+0.073538 
## [139]    train-rmse:0.232080+0.009587    test-rmse:0.959022+0.073418 
## [140]    train-rmse:0.229746+0.009200    test-rmse:0.958856+0.073584 
## [141]    train-rmse:0.227716+0.009292    test-rmse:0.958313+0.073328 
## [142]    train-rmse:0.225824+0.009132    test-rmse:0.958744+0.072927 
## [143]    train-rmse:0.223699+0.008664    test-rmse:0.958706+0.072771 
## [144]    train-rmse:0.221512+0.009387    test-rmse:0.957796+0.073223 
## [145]    train-rmse:0.220005+0.009538    test-rmse:0.957786+0.073529 
## [146]    train-rmse:0.217596+0.008986    test-rmse:0.958068+0.073565 
## [147]    train-rmse:0.214456+0.008802    test-rmse:0.957315+0.074123 
## [148]    train-rmse:0.211621+0.009848    test-rmse:0.957272+0.074344 
## [149]    train-rmse:0.208689+0.010151    test-rmse:0.956951+0.074489 
## [150]    train-rmse:0.207252+0.010531    test-rmse:0.956360+0.074415 
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## [152]    train-rmse:0.203511+0.010471    test-rmse:0.955504+0.073795 
## [153]    train-rmse:0.202140+0.010856    test-rmse:0.955477+0.073501 
## [154]    train-rmse:0.199718+0.010725    test-rmse:0.954505+0.073064 
## [155]    train-rmse:0.198257+0.010932    test-rmse:0.954344+0.073169 
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## [161]    train-rmse:0.186789+0.009251    test-rmse:0.951754+0.072618 
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## [271]    train-rmse:0.063585+0.005582    test-rmse:0.937930+0.076080 
## [272]    train-rmse:0.062975+0.005524    test-rmse:0.937959+0.076150 
## [273]    train-rmse:0.062233+0.005216    test-rmse:0.937783+0.076304 
## [274]    train-rmse:0.061607+0.005319    test-rmse:0.937712+0.076377 
## [275]    train-rmse:0.060823+0.004966    test-rmse:0.937709+0.076317 
## [276]    train-rmse:0.060390+0.005069    test-rmse:0.937552+0.076201 
## [277]    train-rmse:0.059887+0.004895    test-rmse:0.937447+0.076278 
## [278]    train-rmse:0.059433+0.004687    test-rmse:0.937473+0.076423 
## [279]    train-rmse:0.059032+0.004717    test-rmse:0.937249+0.076443 
## [280]    train-rmse:0.058553+0.004622    test-rmse:0.937128+0.076476 
## [281]    train-rmse:0.057879+0.004634    test-rmse:0.937148+0.076443 
## [282]    train-rmse:0.057316+0.004731    test-rmse:0.937156+0.076398 
## [283]    train-rmse:0.056721+0.004815    test-rmse:0.937067+0.076466 
## [284]    train-rmse:0.056000+0.004647    test-rmse:0.937050+0.076409 
## [285]    train-rmse:0.055394+0.004578    test-rmse:0.937056+0.076382 
## [286]    train-rmse:0.054614+0.004366    test-rmse:0.936928+0.076493 
## [287]    train-rmse:0.054029+0.004162    test-rmse:0.936826+0.076513 
## [288]    train-rmse:0.053491+0.004072    test-rmse:0.936877+0.076527 
## [289]    train-rmse:0.053111+0.004086    test-rmse:0.936721+0.076516 
## [290]    train-rmse:0.052633+0.003872    test-rmse:0.936661+0.076517 
## [291]    train-rmse:0.052131+0.003985    test-rmse:0.936610+0.076573 
## [292]    train-rmse:0.051618+0.003814    test-rmse:0.936477+0.076712 
## [293]    train-rmse:0.051204+0.003874    test-rmse:0.936397+0.076619 
## [294]    train-rmse:0.050647+0.003713    test-rmse:0.936410+0.076522 
## [295]    train-rmse:0.050089+0.003544    test-rmse:0.936393+0.076565 
## [296]    train-rmse:0.049610+0.003555    test-rmse:0.936402+0.076434 
## [297]    train-rmse:0.049304+0.003445    test-rmse:0.936348+0.076379 
## [298]    train-rmse:0.048732+0.003572    test-rmse:0.936310+0.076314 
## [299]    train-rmse:0.048263+0.003626    test-rmse:0.936267+0.076207 
## [300]    train-rmse:0.048025+0.003703    test-rmse:0.936254+0.076210 
## [301]    train-rmse:0.047690+0.003835    test-rmse:0.936222+0.076274 
## [302]    train-rmse:0.047179+0.003862    test-rmse:0.936166+0.076353 
## [303]    train-rmse:0.046874+0.003832    test-rmse:0.936107+0.076342 
## [304]    train-rmse:0.046406+0.003843    test-rmse:0.936051+0.076428 
## [305]    train-rmse:0.045963+0.003824    test-rmse:0.935969+0.076391 
## [306]    train-rmse:0.045590+0.003719    test-rmse:0.935939+0.076319 
## [307]    train-rmse:0.045110+0.003611    test-rmse:0.935806+0.076286 
## [308]    train-rmse:0.044619+0.003407    test-rmse:0.935799+0.076380 
## [309]    train-rmse:0.044148+0.003368    test-rmse:0.935921+0.076354 
## [310]    train-rmse:0.043799+0.003383    test-rmse:0.935863+0.076272 
## [311]    train-rmse:0.043422+0.003419    test-rmse:0.935855+0.076155 
## [312]    train-rmse:0.042887+0.003364    test-rmse:0.935855+0.076178 
## [313]    train-rmse:0.042519+0.003299    test-rmse:0.935850+0.076263 
## [314]    train-rmse:0.042204+0.003447    test-rmse:0.935854+0.076272 
## Stopping. Best iteration:
## [308]    train-rmse:0.044619+0.003407    test-rmse:0.935799+0.076380
## 
## 6 
## [1]  train-rmse:88.665695+0.088976   test-rmse:88.666461+0.404980 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.835214+0.080050   test-rmse:79.835893+0.413675 
## [3]  train-rmse:71.885242+0.071998   test-rmse:71.885820+0.421463 
## [4]  train-rmse:64.728087+0.064733   test-rmse:64.728551+0.428434 
## [5]  train-rmse:58.284824+0.058174   test-rmse:58.285159+0.434654 
## [6]  train-rmse:52.484374+0.052257   test-rmse:52.484567+0.440197 
## [7]  train-rmse:47.262778+0.046908   test-rmse:47.262816+0.445120 
## [8]  train-rmse:42.562434+0.042074   test-rmse:42.562303+0.449468 
## [9]  train-rmse:38.331508+0.037710   test-rmse:38.331192+0.453293 
## [10] train-rmse:34.523343+0.033759   test-rmse:34.522811+0.456630 
## [11] train-rmse:31.095926+0.030181   test-rmse:31.095166+0.459509 
## [12] train-rmse:28.011453+0.026945   test-rmse:28.010445+0.461960 
## [13] train-rmse:25.235913+0.024012   test-rmse:25.234629+0.463999 
## [14] train-rmse:22.738687+0.021363   test-rmse:22.737103+0.465637 
## [15] train-rmse:20.492223+0.018971   test-rmse:20.490315+0.466887 
## [16] train-rmse:18.471749+0.016823   test-rmse:18.469484+0.467747 
## [17] train-rmse:16.654969+0.014903   test-rmse:16.652318+0.468213 
## [18] train-rmse:15.021334+0.013222   test-rmse:15.020864+0.456548 
## [19] train-rmse:13.552638+0.011590   test-rmse:13.550876+0.449119 
## [20] train-rmse:12.232204+0.010135   test-rmse:12.222750+0.446257 
## [21] train-rmse:11.045206+0.008913   test-rmse:11.035400+0.435400 
## [22] train-rmse:9.977437+0.008087    test-rmse:9.961850+0.427085 
## [23] train-rmse:9.017222+0.007327    test-rmse:9.002905+0.418550 
## [24] train-rmse:8.153142+0.007026    test-rmse:8.132628+0.403760 
## [25] train-rmse:7.375596+0.006519    test-rmse:7.357645+0.385685 
## [26] train-rmse:6.676762+0.006198    test-rmse:6.663002+0.372359 
## [27] train-rmse:6.048609+0.005331    test-rmse:6.042072+0.356753 
## [28] train-rmse:5.483776+0.004307    test-rmse:5.474873+0.345259 
## [29] train-rmse:4.974539+0.003700    test-rmse:4.968185+0.332206 
## [30] train-rmse:4.516635+0.003296    test-rmse:4.515265+0.320132 
## [31] train-rmse:4.103717+0.003215    test-rmse:4.104181+0.307391 
## [32] train-rmse:3.732577+0.002477    test-rmse:3.744347+0.292441 
## [33] train-rmse:3.398377+0.002382    test-rmse:3.413745+0.285053 
## [34] train-rmse:3.097576+0.002912    test-rmse:3.118874+0.273649 
## [35] train-rmse:2.826310+0.002548    test-rmse:2.859871+0.259310 
## [36] train-rmse:2.583939+0.003976    test-rmse:2.624490+0.248432 
## [37] train-rmse:2.364182+0.003782    test-rmse:2.425086+0.233356 
## [38] train-rmse:2.164690+0.003606    test-rmse:2.240503+0.221094 
## [39] train-rmse:1.987538+0.005321    test-rmse:2.080321+0.214329 
## [40] train-rmse:1.828325+0.005303    test-rmse:1.943159+0.210974 
## [41] train-rmse:1.686256+0.005427    test-rmse:1.824285+0.205721 
## [42] train-rmse:1.556712+0.003061    test-rmse:1.714302+0.198470 
## [43] train-rmse:1.441230+0.002871    test-rmse:1.624937+0.189388 
## [44] train-rmse:1.334996+0.002384    test-rmse:1.542465+0.177284 
## [45] train-rmse:1.242547+0.003217    test-rmse:1.472385+0.168639 
## [46] train-rmse:1.158269+0.002591    test-rmse:1.407218+0.162373 
## [47] train-rmse:1.085129+0.004125    test-rmse:1.357174+0.156476 
## [48] train-rmse:1.016017+0.002760    test-rmse:1.310506+0.147836 
## [49] train-rmse:0.955680+0.005483    test-rmse:1.274477+0.140267 
## [50] train-rmse:0.900111+0.002976    test-rmse:1.238908+0.136970 
## [51] train-rmse:0.850654+0.004819    test-rmse:1.212583+0.132724 
## [52] train-rmse:0.806738+0.006845    test-rmse:1.188585+0.131320 
## [53] train-rmse:0.767984+0.005790    test-rmse:1.166787+0.129568 
## [54] train-rmse:0.732449+0.006261    test-rmse:1.146414+0.129451 
## [55] train-rmse:0.699768+0.007300    test-rmse:1.132010+0.128309 
## [56] train-rmse:0.671418+0.007511    test-rmse:1.117846+0.124012 
## [57] train-rmse:0.645090+0.009353    test-rmse:1.105650+0.120555 
## [58] train-rmse:0.621914+0.010649    test-rmse:1.095657+0.119593 
## [59] train-rmse:0.602064+0.011571    test-rmse:1.086870+0.119520 
## [60] train-rmse:0.581659+0.012937    test-rmse:1.078155+0.115051 
## [61] train-rmse:0.562888+0.012051    test-rmse:1.070885+0.114236 
## [62] train-rmse:0.544133+0.013156    test-rmse:1.064467+0.112085 
## [63] train-rmse:0.530103+0.014889    test-rmse:1.059795+0.110504 
## [64] train-rmse:0.515066+0.017636    test-rmse:1.056489+0.108336 
## [65] train-rmse:0.502533+0.016319    test-rmse:1.053387+0.108831 
## [66] train-rmse:0.490740+0.017282    test-rmse:1.049251+0.108974 
## [67] train-rmse:0.476377+0.016601    test-rmse:1.042342+0.105889 
## [68] train-rmse:0.465640+0.017482    test-rmse:1.039174+0.106383 
## [69] train-rmse:0.456891+0.018060    test-rmse:1.036479+0.105509 
## [70] train-rmse:0.446135+0.019648    test-rmse:1.033131+0.104423 
## [71] train-rmse:0.438680+0.019050    test-rmse:1.028107+0.103520 
## [72] train-rmse:0.428964+0.019527    test-rmse:1.025165+0.101774 
## [73] train-rmse:0.419245+0.018723    test-rmse:1.023830+0.102512 
## [74] train-rmse:0.410242+0.018323    test-rmse:1.022196+0.102595 
## [75] train-rmse:0.402930+0.018990    test-rmse:1.020306+0.102532 
## [76] train-rmse:0.395581+0.019579    test-rmse:1.020263+0.100888 
## [77] train-rmse:0.389071+0.017827    test-rmse:1.018802+0.100648 
## [78] train-rmse:0.382405+0.017261    test-rmse:1.015072+0.101278 
## [79] train-rmse:0.375151+0.017582    test-rmse:1.014603+0.101110 
## [80] train-rmse:0.369599+0.017946    test-rmse:1.013410+0.101636 
## [81] train-rmse:0.364870+0.017425    test-rmse:1.012359+0.102241 
## [82] train-rmse:0.358580+0.017322    test-rmse:1.010283+0.102174 
## [83] train-rmse:0.352804+0.018725    test-rmse:1.008240+0.101851 
## [84] train-rmse:0.348027+0.018517    test-rmse:1.007207+0.103200 
## [85] train-rmse:0.342808+0.018123    test-rmse:1.006526+0.103322 
## [86] train-rmse:0.335899+0.017128    test-rmse:1.005922+0.103185 
## [87] train-rmse:0.330548+0.017067    test-rmse:1.004300+0.103787 
## [88] train-rmse:0.325665+0.018992    test-rmse:1.002239+0.103807 
## [89] train-rmse:0.320658+0.018801    test-rmse:1.000390+0.102843 
## [90] train-rmse:0.315900+0.018231    test-rmse:0.999288+0.102790 
## [91] train-rmse:0.310643+0.017501    test-rmse:0.998092+0.103124 
## [92] train-rmse:0.306403+0.017586    test-rmse:0.998485+0.102391 
## [93] train-rmse:0.301187+0.017327    test-rmse:0.997620+0.102578 
## [94] train-rmse:0.295687+0.017155    test-rmse:0.996499+0.102415 
## [95] train-rmse:0.292768+0.016766    test-rmse:0.996332+0.103430 
## [96] train-rmse:0.287784+0.016091    test-rmse:0.995624+0.103055 
## [97] train-rmse:0.283393+0.016388    test-rmse:0.994439+0.103052 
## [98] train-rmse:0.279852+0.016071    test-rmse:0.994942+0.105099 
## [99] train-rmse:0.276721+0.016368    test-rmse:0.993347+0.105249 
## [100]    train-rmse:0.272027+0.015557    test-rmse:0.992815+0.105162 
## [101]    train-rmse:0.268536+0.014798    test-rmse:0.991938+0.104333 
## [102]    train-rmse:0.264032+0.014646    test-rmse:0.990682+0.102212 
## [103]    train-rmse:0.260734+0.014881    test-rmse:0.990382+0.102009 
## [104]    train-rmse:0.257581+0.014322    test-rmse:0.989895+0.101576 
## [105]    train-rmse:0.253454+0.013594    test-rmse:0.988885+0.101299 
## [106]    train-rmse:0.249771+0.013663    test-rmse:0.988708+0.101432 
## [107]    train-rmse:0.246819+0.013592    test-rmse:0.987728+0.101513 
## [108]    train-rmse:0.243539+0.013495    test-rmse:0.987872+0.101809 
## [109]    train-rmse:0.238467+0.013658    test-rmse:0.986943+0.101045 
## [110]    train-rmse:0.235722+0.013043    test-rmse:0.986837+0.101425 
## [111]    train-rmse:0.233236+0.013893    test-rmse:0.986828+0.100671 
## [112]    train-rmse:0.229258+0.014611    test-rmse:0.986099+0.100096 
## [113]    train-rmse:0.225983+0.014846    test-rmse:0.985773+0.100012 
## [114]    train-rmse:0.223105+0.013933    test-rmse:0.985682+0.099840 
## [115]    train-rmse:0.219610+0.013555    test-rmse:0.985874+0.099828 
## [116]    train-rmse:0.215811+0.014228    test-rmse:0.984780+0.099653 
## [117]    train-rmse:0.212816+0.013814    test-rmse:0.984228+0.099612 
## [118]    train-rmse:0.209747+0.013056    test-rmse:0.983579+0.100215 
## [119]    train-rmse:0.207034+0.012354    test-rmse:0.982801+0.100443 
## [120]    train-rmse:0.204720+0.012142    test-rmse:0.982298+0.100257 
## [121]    train-rmse:0.201584+0.011689    test-rmse:0.982102+0.100579 
## [122]    train-rmse:0.198772+0.011616    test-rmse:0.981552+0.100245 
## [123]    train-rmse:0.196849+0.011576    test-rmse:0.981281+0.099704 
## [124]    train-rmse:0.193608+0.011384    test-rmse:0.980778+0.099159 
## [125]    train-rmse:0.191264+0.011462    test-rmse:0.980931+0.099337 
## [126]    train-rmse:0.188419+0.010724    test-rmse:0.980853+0.099770 
## [127]    train-rmse:0.186056+0.010696    test-rmse:0.980911+0.099900 
## [128]    train-rmse:0.183396+0.011690    test-rmse:0.980942+0.099654 
## [129]    train-rmse:0.180835+0.010965    test-rmse:0.980770+0.100065 
## [130]    train-rmse:0.178684+0.011288    test-rmse:0.980433+0.099505 
## [131]    train-rmse:0.176520+0.010909    test-rmse:0.980743+0.099689 
## [132]    train-rmse:0.173762+0.010686    test-rmse:0.980701+0.099536 
## [133]    train-rmse:0.171738+0.010748    test-rmse:0.980673+0.099588 
## [134]    train-rmse:0.170005+0.010399    test-rmse:0.980246+0.099054 
## [135]    train-rmse:0.167502+0.009976    test-rmse:0.979865+0.098926 
## [136]    train-rmse:0.165527+0.009853    test-rmse:0.978962+0.098683 
## [137]    train-rmse:0.163691+0.009846    test-rmse:0.979018+0.098532 
## [138]    train-rmse:0.161724+0.009205    test-rmse:0.978827+0.098696 
## [139]    train-rmse:0.159110+0.008934    test-rmse:0.978337+0.098083 
## [140]    train-rmse:0.156967+0.008776    test-rmse:0.978321+0.098354 
## [141]    train-rmse:0.155375+0.008960    test-rmse:0.978104+0.098333 
## [142]    train-rmse:0.153017+0.008595    test-rmse:0.977055+0.097973 
## [143]    train-rmse:0.150857+0.007657    test-rmse:0.976384+0.098258 
## [144]    train-rmse:0.148587+0.007272    test-rmse:0.975263+0.097586 
## [145]    train-rmse:0.146904+0.006789    test-rmse:0.974228+0.097701 
## [146]    train-rmse:0.145522+0.007037    test-rmse:0.974228+0.098034 
## [147]    train-rmse:0.143492+0.006991    test-rmse:0.974211+0.098047 
## [148]    train-rmse:0.141618+0.007132    test-rmse:0.973505+0.097855 
## [149]    train-rmse:0.140507+0.007159    test-rmse:0.972834+0.097727 
## [150]    train-rmse:0.138466+0.007342    test-rmse:0.972742+0.097673 
## [151]    train-rmse:0.136909+0.006935    test-rmse:0.972154+0.097429 
## [152]    train-rmse:0.135475+0.007030    test-rmse:0.971773+0.097188 
## [153]    train-rmse:0.133642+0.007141    test-rmse:0.971696+0.097468 
## [154]    train-rmse:0.131290+0.007135    test-rmse:0.971612+0.097239 
## [155]    train-rmse:0.129244+0.006961    test-rmse:0.971004+0.097216 
## [156]    train-rmse:0.127380+0.006981    test-rmse:0.970408+0.096859 
## [157]    train-rmse:0.125104+0.006296    test-rmse:0.970258+0.096460 
## [158]    train-rmse:0.123192+0.006611    test-rmse:0.970035+0.096391 
## [159]    train-rmse:0.121859+0.006734    test-rmse:0.969863+0.096138 
## [160]    train-rmse:0.120404+0.007340    test-rmse:0.969664+0.095872 
## [161]    train-rmse:0.118915+0.007486    test-rmse:0.969617+0.095967 
## [162]    train-rmse:0.117084+0.006987    test-rmse:0.969396+0.096122 
## [163]    train-rmse:0.116094+0.006992    test-rmse:0.968972+0.095952 
## [164]    train-rmse:0.114400+0.006627    test-rmse:0.968793+0.095987 
## [165]    train-rmse:0.112942+0.006576    test-rmse:0.968576+0.096074 
## [166]    train-rmse:0.111229+0.006296    test-rmse:0.968310+0.096350 
## [167]    train-rmse:0.110189+0.005930    test-rmse:0.968074+0.096467 
## [168]    train-rmse:0.109016+0.005987    test-rmse:0.967700+0.096128 
## [169]    train-rmse:0.107410+0.006165    test-rmse:0.967255+0.095683 
## [170]    train-rmse:0.106171+0.006004    test-rmse:0.967236+0.095836 
## [171]    train-rmse:0.104450+0.005762    test-rmse:0.966846+0.096044 
## [172]    train-rmse:0.103002+0.006135    test-rmse:0.966649+0.096180 
## [173]    train-rmse:0.101122+0.005662    test-rmse:0.966419+0.096123 
## [174]    train-rmse:0.099910+0.005503    test-rmse:0.966271+0.096062 
## [175]    train-rmse:0.098819+0.005175    test-rmse:0.966302+0.095934 
## [176]    train-rmse:0.097664+0.005245    test-rmse:0.966198+0.095904 
## [177]    train-rmse:0.096331+0.004993    test-rmse:0.965954+0.095955 
## [178]    train-rmse:0.094976+0.005106    test-rmse:0.965907+0.095684 
## [179]    train-rmse:0.093744+0.004965    test-rmse:0.965743+0.095665 
## [180]    train-rmse:0.092683+0.004825    test-rmse:0.965676+0.095720 
## [181]    train-rmse:0.092109+0.004843    test-rmse:0.965660+0.095640 
## [182]    train-rmse:0.090981+0.004361    test-rmse:0.965731+0.095684 
## [183]    train-rmse:0.089671+0.004246    test-rmse:0.965397+0.095634 
## [184]    train-rmse:0.088417+0.004017    test-rmse:0.964983+0.095512 
## [185]    train-rmse:0.086863+0.003430    test-rmse:0.964927+0.095828 
## [186]    train-rmse:0.085690+0.003274    test-rmse:0.964772+0.095916 
## [187]    train-rmse:0.084658+0.003202    test-rmse:0.964640+0.095908 
## [188]    train-rmse:0.083558+0.003195    test-rmse:0.964514+0.095872 
## [189]    train-rmse:0.082172+0.003099    test-rmse:0.964491+0.095849 
## [190]    train-rmse:0.081126+0.003258    test-rmse:0.964340+0.095987 
## [191]    train-rmse:0.080128+0.003360    test-rmse:0.964381+0.096065 
## [192]    train-rmse:0.079221+0.003030    test-rmse:0.964452+0.096030 
## [193]    train-rmse:0.078270+0.002697    test-rmse:0.964380+0.095789 
## [194]    train-rmse:0.077544+0.002589    test-rmse:0.964312+0.095747 
## [195]    train-rmse:0.076213+0.002426    test-rmse:0.964285+0.095675 
## [196]    train-rmse:0.074989+0.002016    test-rmse:0.964225+0.095841 
## [197]    train-rmse:0.074274+0.001919    test-rmse:0.964272+0.095880 
## [198]    train-rmse:0.073309+0.001903    test-rmse:0.963989+0.095887 
## [199]    train-rmse:0.072023+0.001871    test-rmse:0.963993+0.096047 
## [200]    train-rmse:0.071049+0.001668    test-rmse:0.963893+0.096032 
## [201]    train-rmse:0.069856+0.001828    test-rmse:0.963735+0.095997 
## [202]    train-rmse:0.069076+0.002217    test-rmse:0.963733+0.096034 
## [203]    train-rmse:0.068396+0.002282    test-rmse:0.963855+0.096040 
## [204]    train-rmse:0.067622+0.002531    test-rmse:0.963769+0.096278 
## [205]    train-rmse:0.066728+0.002639    test-rmse:0.963726+0.096289 
## [206]    train-rmse:0.066032+0.002663    test-rmse:0.963845+0.096272 
## [207]    train-rmse:0.065479+0.002506    test-rmse:0.963983+0.096259 
## [208]    train-rmse:0.064638+0.002358    test-rmse:0.963884+0.096507 
## [209]    train-rmse:0.064001+0.002247    test-rmse:0.963926+0.096575 
## [210]    train-rmse:0.063293+0.002568    test-rmse:0.963909+0.096437 
## [211]    train-rmse:0.062594+0.002620    test-rmse:0.963838+0.096424 
## Stopping. Best iteration:
## [205]    train-rmse:0.066728+0.002639    test-rmse:0.963726+0.096289
## 
## 7 
## [1]  train-rmse:86.704080+0.086998   test-rmse:86.704831+0.406915 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.342288+0.076506   test-rmse:76.342926+0.417100 
## [3]  train-rmse:67.220384+0.067270   test-rmse:67.220891+0.426011 
## [4]  train-rmse:59.190229+0.059099   test-rmse:59.190588+0.433783 
## [5]  train-rmse:52.121395+0.051883   test-rmse:52.121578+0.440539 
## [6]  train-rmse:45.899076+0.045513   test-rmse:45.899065+0.446386 
## [7]  train-rmse:40.422199+0.039869   test-rmse:40.421979+0.451412 
## [8]  train-rmse:35.601835+0.034876   test-rmse:35.601369+0.455694 
## [9]  train-rmse:31.359680+0.030457   test-rmse:31.358941+0.459291 
## [10] train-rmse:27.626835+0.026543   test-rmse:27.625793+0.462249 
## [11] train-rmse:24.342670+0.023071   test-rmse:24.341286+0.464606 
## [12] train-rmse:21.453838+0.019997   test-rmse:21.452077+0.466382 
## [13] train-rmse:18.913416+0.017291   test-rmse:18.911232+0.467585 
## [14] train-rmse:16.680124+0.014931   test-rmse:16.677478+0.468208 
## [15] train-rmse:14.717049+0.012912   test-rmse:14.717037+0.454197 
## [16] train-rmse:12.991951+0.011029   test-rmse:12.983670+0.455294 
## [17] train-rmse:11.476346+0.009517   test-rmse:11.471345+0.443079 
## [18] train-rmse:10.145233+0.008151   test-rmse:10.138345+0.437914 
## [19] train-rmse:8.976784+0.007294    test-rmse:8.962900+0.437113 
## [20] train-rmse:7.951891+0.006687    test-rmse:7.938465+0.433674 
## [21] train-rmse:7.054369+0.006937    test-rmse:7.042167+0.423506 
## [22] train-rmse:6.269129+0.007379    test-rmse:6.256289+0.415896 
## [23] train-rmse:5.583360+0.008180    test-rmse:5.570893+0.409264 
## [24] train-rmse:4.985856+0.009218    test-rmse:4.977497+0.397643 
## [25] train-rmse:4.466506+0.010678    test-rmse:4.458933+0.389700 
## [26] train-rmse:4.016339+0.011866    test-rmse:4.013655+0.376027 
## [27] train-rmse:3.627186+0.013558    test-rmse:3.622800+0.359596 
## [28] train-rmse:3.292384+0.015168    test-rmse:3.294218+0.344284 
## [29] train-rmse:3.005499+0.016734    test-rmse:3.010627+0.322856 
## [30] train-rmse:2.760947+0.018266    test-rmse:2.772131+0.304294 
## [31] train-rmse:2.553549+0.019749    test-rmse:2.574415+0.282468 
## [32] train-rmse:2.378588+0.021157    test-rmse:2.407303+0.262411 
## [33] train-rmse:2.231700+0.022431    test-rmse:2.268397+0.240867 
## [34] train-rmse:2.108995+0.023572    test-rmse:2.145102+0.217420 
## [35] train-rmse:2.007121+0.024544    test-rmse:2.049820+0.198811 
## [36] train-rmse:1.922507+0.025521    test-rmse:1.972695+0.179145 
## [37] train-rmse:1.852647+0.026191    test-rmse:1.910984+0.163123 
## [38] train-rmse:1.794808+0.026727    test-rmse:1.857173+0.149043 
## [39] train-rmse:1.746817+0.026956    test-rmse:1.816441+0.137269 
## [40] train-rmse:1.707142+0.027379    test-rmse:1.781570+0.127688 
## [41] train-rmse:1.674071+0.027337    test-rmse:1.756122+0.123290 
## [42] train-rmse:1.646455+0.027452    test-rmse:1.724942+0.119143 
## [43] train-rmse:1.623093+0.027297    test-rmse:1.707829+0.120631 
## [44] train-rmse:1.603251+0.027125    test-rmse:1.692142+0.116654 
## [45] train-rmse:1.586231+0.026979    test-rmse:1.677148+0.113688 
## [46] train-rmse:1.571346+0.026762    test-rmse:1.666656+0.112107 
## [47] train-rmse:1.558221+0.026685    test-rmse:1.656941+0.113871 
## [48] train-rmse:1.546512+0.026524    test-rmse:1.647803+0.113556 
## [49] train-rmse:1.536030+0.026306    test-rmse:1.640681+0.114801 
## [50] train-rmse:1.526445+0.026090    test-rmse:1.633454+0.114529 
## [51] train-rmse:1.517701+0.025840    test-rmse:1.621985+0.113353 
## [52] train-rmse:1.509631+0.025689    test-rmse:1.617349+0.113383 
## [53] train-rmse:1.502053+0.025562    test-rmse:1.610319+0.114488 
## [54] train-rmse:1.494911+0.025415    test-rmse:1.602971+0.111574 
## [55] train-rmse:1.488129+0.025257    test-rmse:1.600621+0.112637 
## [56] train-rmse:1.481572+0.025086    test-rmse:1.596861+0.112030 
## [57] train-rmse:1.475211+0.024861    test-rmse:1.593811+0.113216 
## [58] train-rmse:1.469131+0.024813    test-rmse:1.590926+0.112989 
## [59] train-rmse:1.463309+0.024728    test-rmse:1.585002+0.113719 
## [60] train-rmse:1.457653+0.024575    test-rmse:1.582780+0.114276 
## [61] train-rmse:1.452173+0.024471    test-rmse:1.577552+0.112129 
## [62] train-rmse:1.446804+0.024368    test-rmse:1.569349+0.110418 
## [63] train-rmse:1.441539+0.024283    test-rmse:1.564154+0.108196 
## [64] train-rmse:1.436415+0.024162    test-rmse:1.561734+0.108474 
## [65] train-rmse:1.431380+0.024016    test-rmse:1.559514+0.108280 
## [66] train-rmse:1.426447+0.023945    test-rmse:1.554492+0.108998 
## [67] train-rmse:1.421622+0.023847    test-rmse:1.552394+0.107991 
## [68] train-rmse:1.416784+0.023756    test-rmse:1.552013+0.106979 
## [69] train-rmse:1.412078+0.023744    test-rmse:1.548105+0.110357 
## [70] train-rmse:1.407396+0.023749    test-rmse:1.544548+0.108771 
## [71] train-rmse:1.402794+0.023734    test-rmse:1.544237+0.110174 
## [72] train-rmse:1.398245+0.023636    test-rmse:1.538999+0.109321 
## [73] train-rmse:1.393912+0.023628    test-rmse:1.531204+0.105876 
## [74] train-rmse:1.389673+0.023567    test-rmse:1.527024+0.103315 
## [75] train-rmse:1.385444+0.023525    test-rmse:1.525331+0.103423 
## [76] train-rmse:1.381227+0.023481    test-rmse:1.520560+0.104564 
## [77] train-rmse:1.377064+0.023406    test-rmse:1.519592+0.103859 
## [78] train-rmse:1.372969+0.023304    test-rmse:1.518621+0.104653 
## [79] train-rmse:1.368855+0.023282    test-rmse:1.519486+0.105644 
## [80] train-rmse:1.364890+0.023187    test-rmse:1.516644+0.106943 
## [81] train-rmse:1.360960+0.023079    test-rmse:1.515752+0.104127 
## [82] train-rmse:1.357059+0.022935    test-rmse:1.516739+0.101886 
## [83] train-rmse:1.353272+0.022803    test-rmse:1.516377+0.103903 
## [84] train-rmse:1.349480+0.022709    test-rmse:1.510567+0.102675 
## [85] train-rmse:1.345742+0.022616    test-rmse:1.507813+0.101531 
## [86] train-rmse:1.342033+0.022538    test-rmse:1.501298+0.102092 
## [87] train-rmse:1.338384+0.022472    test-rmse:1.497375+0.101975 
## [88] train-rmse:1.334717+0.022412    test-rmse:1.491662+0.099283 
## [89] train-rmse:1.331138+0.022300    test-rmse:1.491300+0.095995 
## [90] train-rmse:1.327663+0.022240    test-rmse:1.490217+0.095654 
## [91] train-rmse:1.324201+0.022189    test-rmse:1.488388+0.095270 
## [92] train-rmse:1.320718+0.022138    test-rmse:1.486284+0.097165 
## [93] train-rmse:1.317301+0.022116    test-rmse:1.485750+0.098134 
## [94] train-rmse:1.313948+0.022058    test-rmse:1.484243+0.098120 
## [95] train-rmse:1.310628+0.021989    test-rmse:1.482577+0.097243 
## [96] train-rmse:1.307346+0.021910    test-rmse:1.480527+0.097825 
## [97] train-rmse:1.304086+0.021779    test-rmse:1.474613+0.096583 
## [98] train-rmse:1.300894+0.021693    test-rmse:1.471108+0.095032 
## [99] train-rmse:1.297782+0.021616    test-rmse:1.467844+0.095079 
## [100]    train-rmse:1.294695+0.021550    test-rmse:1.467399+0.092767 
## [101]    train-rmse:1.291591+0.021490    test-rmse:1.466474+0.088793 
## [102]    train-rmse:1.288544+0.021432    test-rmse:1.466216+0.090894 
## [103]    train-rmse:1.285473+0.021437    test-rmse:1.463553+0.091874 
## [104]    train-rmse:1.282448+0.021364    test-rmse:1.459837+0.090280 
## [105]    train-rmse:1.279489+0.021285    test-rmse:1.458935+0.091216 
## [106]    train-rmse:1.276542+0.021188    test-rmse:1.457912+0.092207 
## [107]    train-rmse:1.273636+0.021134    test-rmse:1.456680+0.091883 
## [108]    train-rmse:1.270768+0.021100    test-rmse:1.454833+0.093890 
## [109]    train-rmse:1.267939+0.021086    test-rmse:1.454667+0.090962 
## [110]    train-rmse:1.265145+0.021067    test-rmse:1.453125+0.091262 
## [111]    train-rmse:1.262352+0.021058    test-rmse:1.453282+0.089433 
## [112]    train-rmse:1.259625+0.021048    test-rmse:1.447618+0.089020 
## [113]    train-rmse:1.256940+0.021037    test-rmse:1.444823+0.088422 
## [114]    train-rmse:1.254275+0.021003    test-rmse:1.443473+0.087460 
## [115]    train-rmse:1.251630+0.020970    test-rmse:1.442133+0.086687 
## [116]    train-rmse:1.249011+0.020909    test-rmse:1.440153+0.087890 
## [117]    train-rmse:1.246431+0.020864    test-rmse:1.438311+0.086330 
## [118]    train-rmse:1.243871+0.020839    test-rmse:1.437917+0.085661 
## [119]    train-rmse:1.241318+0.020803    test-rmse:1.435949+0.085732 
## [120]    train-rmse:1.238779+0.020750    test-rmse:1.432894+0.084554 
## [121]    train-rmse:1.236242+0.020721    test-rmse:1.429983+0.085918 
## [122]    train-rmse:1.233736+0.020695    test-rmse:1.425807+0.084982 
## [123]    train-rmse:1.231258+0.020664    test-rmse:1.425577+0.085855 
## [124]    train-rmse:1.228795+0.020656    test-rmse:1.424343+0.087511 
## [125]    train-rmse:1.226350+0.020633    test-rmse:1.422387+0.088127 
## [126]    train-rmse:1.223974+0.020619    test-rmse:1.423445+0.086133 
## [127]    train-rmse:1.221629+0.020610    test-rmse:1.421683+0.085329 
## [128]    train-rmse:1.219278+0.020590    test-rmse:1.420349+0.087110 
## [129]    train-rmse:1.217002+0.020581    test-rmse:1.418689+0.086321 
## [130]    train-rmse:1.214726+0.020584    test-rmse:1.418104+0.084024 
## [131]    train-rmse:1.212492+0.020577    test-rmse:1.416355+0.085135 
## [132]    train-rmse:1.210227+0.020614    test-rmse:1.412367+0.083628 
## [133]    train-rmse:1.208011+0.020601    test-rmse:1.407752+0.083296 
## [134]    train-rmse:1.205836+0.020586    test-rmse:1.405387+0.082702 
## [135]    train-rmse:1.203664+0.020540    test-rmse:1.404981+0.083773 
## [136]    train-rmse:1.201532+0.020514    test-rmse:1.404250+0.084421 
## [137]    train-rmse:1.199412+0.020485    test-rmse:1.402360+0.087495 
## [138]    train-rmse:1.197311+0.020473    test-rmse:1.400381+0.086455 
## [139]    train-rmse:1.195228+0.020452    test-rmse:1.398328+0.087219 
## [140]    train-rmse:1.193159+0.020424    test-rmse:1.397585+0.087581 
## [141]    train-rmse:1.191070+0.020426    test-rmse:1.395739+0.086793 
## [142]    train-rmse:1.189026+0.020413    test-rmse:1.395995+0.086482 
## [143]    train-rmse:1.186998+0.020404    test-rmse:1.394710+0.087910 
## [144]    train-rmse:1.184969+0.020398    test-rmse:1.394946+0.085728 
## [145]    train-rmse:1.182958+0.020372    test-rmse:1.392724+0.084502 
## [146]    train-rmse:1.180974+0.020366    test-rmse:1.391078+0.083571 
## [147]    train-rmse:1.179046+0.020347    test-rmse:1.391208+0.084112 
## [148]    train-rmse:1.177127+0.020341    test-rmse:1.388858+0.084694 
## [149]    train-rmse:1.175185+0.020328    test-rmse:1.386422+0.083666 
## [150]    train-rmse:1.173312+0.020323    test-rmse:1.384355+0.082985 
## [151]    train-rmse:1.171438+0.020317    test-rmse:1.382511+0.082565 
## [152]    train-rmse:1.169597+0.020295    test-rmse:1.379553+0.083631 
## [153]    train-rmse:1.167773+0.020272    test-rmse:1.377579+0.084769 
## [154]    train-rmse:1.165969+0.020252    test-rmse:1.376358+0.084233 
## [155]    train-rmse:1.164168+0.020215    test-rmse:1.376439+0.084952 
## [156]    train-rmse:1.162378+0.020181    test-rmse:1.374844+0.084330 
## [157]    train-rmse:1.160610+0.020167    test-rmse:1.374273+0.085139 
## [158]    train-rmse:1.158854+0.020143    test-rmse:1.373262+0.085945 
## [159]    train-rmse:1.157127+0.020107    test-rmse:1.373519+0.083483 
## [160]    train-rmse:1.155423+0.020059    test-rmse:1.372733+0.083418 
## [161]    train-rmse:1.153730+0.020011    test-rmse:1.371005+0.083198 
## [162]    train-rmse:1.152054+0.019986    test-rmse:1.369485+0.083699 
## [163]    train-rmse:1.150386+0.019961    test-rmse:1.368293+0.083077 
## [164]    train-rmse:1.148699+0.019949    test-rmse:1.365441+0.085040 
## [165]    train-rmse:1.147061+0.019924    test-rmse:1.364625+0.084742 
## [166]    train-rmse:1.145429+0.019907    test-rmse:1.362832+0.084800 
## [167]    train-rmse:1.143812+0.019888    test-rmse:1.360899+0.084790 
## [168]    train-rmse:1.142211+0.019876    test-rmse:1.358491+0.083701 
## [169]    train-rmse:1.140616+0.019857    test-rmse:1.359658+0.085150 
## [170]    train-rmse:1.139023+0.019855    test-rmse:1.358171+0.085026 
## [171]    train-rmse:1.137447+0.019839    test-rmse:1.355200+0.083848 
## [172]    train-rmse:1.135878+0.019828    test-rmse:1.353468+0.084678 
## [173]    train-rmse:1.134344+0.019794    test-rmse:1.353125+0.082877 
## [174]    train-rmse:1.132825+0.019774    test-rmse:1.352181+0.083657 
## [175]    train-rmse:1.131315+0.019749    test-rmse:1.351149+0.083196 
## [176]    train-rmse:1.129804+0.019749    test-rmse:1.350103+0.084134 
## [177]    train-rmse:1.128305+0.019739    test-rmse:1.348034+0.084932 
## [178]    train-rmse:1.126814+0.019716    test-rmse:1.347512+0.086000 
## [179]    train-rmse:1.125330+0.019684    test-rmse:1.349272+0.084476 
## [180]    train-rmse:1.123844+0.019661    test-rmse:1.347945+0.084544 
## [181]    train-rmse:1.122360+0.019622    test-rmse:1.346836+0.085217 
## [182]    train-rmse:1.120939+0.019579    test-rmse:1.345626+0.085146 
## [183]    train-rmse:1.119528+0.019533    test-rmse:1.343673+0.084560 
## [184]    train-rmse:1.118139+0.019512    test-rmse:1.342751+0.085136 
## [185]    train-rmse:1.116754+0.019500    test-rmse:1.342728+0.082737 
## [186]    train-rmse:1.115372+0.019490    test-rmse:1.341139+0.081605 
## [187]    train-rmse:1.114011+0.019488    test-rmse:1.340173+0.080048 
## [188]    train-rmse:1.112660+0.019471    test-rmse:1.338165+0.081547 
## [189]    train-rmse:1.111310+0.019463    test-rmse:1.336048+0.082537 
## [190]    train-rmse:1.109976+0.019441    test-rmse:1.335922+0.082898 
## [191]    train-rmse:1.108656+0.019412    test-rmse:1.335509+0.081818 
## [192]    train-rmse:1.107349+0.019386    test-rmse:1.333978+0.081016 
## [193]    train-rmse:1.106049+0.019364    test-rmse:1.332585+0.080449 
## [194]    train-rmse:1.104755+0.019346    test-rmse:1.331990+0.080949 
## [195]    train-rmse:1.103471+0.019309    test-rmse:1.331097+0.080834 
## [196]    train-rmse:1.102182+0.019270    test-rmse:1.329851+0.081230 
## [197]    train-rmse:1.100902+0.019224    test-rmse:1.329042+0.080844 
## [198]    train-rmse:1.099628+0.019190    test-rmse:1.327954+0.081756 
## [199]    train-rmse:1.098373+0.019146    test-rmse:1.326843+0.082302 
## [200]    train-rmse:1.097113+0.019105    test-rmse:1.325495+0.081727 
## [201]    train-rmse:1.095872+0.019082    test-rmse:1.326079+0.082471 
## [202]    train-rmse:1.094649+0.019062    test-rmse:1.325477+0.083336 
## [203]    train-rmse:1.093434+0.019042    test-rmse:1.324546+0.083648 
## [204]    train-rmse:1.092227+0.019019    test-rmse:1.321908+0.083522 
## [205]    train-rmse:1.091030+0.018990    test-rmse:1.320063+0.084305 
## [206]    train-rmse:1.089841+0.018956    test-rmse:1.319762+0.085450 
## [207]    train-rmse:1.088637+0.018928    test-rmse:1.319259+0.086011 
## [208]    train-rmse:1.087463+0.018902    test-rmse:1.317803+0.086402 
## [209]    train-rmse:1.086310+0.018897    test-rmse:1.317040+0.086404 
## [210]    train-rmse:1.085162+0.018890    test-rmse:1.317742+0.084666 
## [211]    train-rmse:1.083999+0.018883    test-rmse:1.317644+0.084823 
## [212]    train-rmse:1.082858+0.018857    test-rmse:1.316063+0.084739 
## [213]    train-rmse:1.081728+0.018836    test-rmse:1.314530+0.085939 
## [214]    train-rmse:1.080610+0.018826    test-rmse:1.314000+0.085736 
## [215]    train-rmse:1.079497+0.018807    test-rmse:1.312456+0.087195 
## [216]    train-rmse:1.078400+0.018789    test-rmse:1.311745+0.086687 
## [217]    train-rmse:1.077302+0.018767    test-rmse:1.310785+0.086716 
## [218]    train-rmse:1.076221+0.018749    test-rmse:1.310905+0.087450 
## [219]    train-rmse:1.075132+0.018728    test-rmse:1.309182+0.087610 
## [220]    train-rmse:1.074068+0.018713    test-rmse:1.308129+0.087111 
## [221]    train-rmse:1.073014+0.018685    test-rmse:1.307135+0.087569 
## [222]    train-rmse:1.071965+0.018654    test-rmse:1.307521+0.088124 
## [223]    train-rmse:1.070937+0.018637    test-rmse:1.306936+0.087363 
## [224]    train-rmse:1.069908+0.018620    test-rmse:1.306337+0.087176 
## [225]    train-rmse:1.068885+0.018598    test-rmse:1.305944+0.086739 
## [226]    train-rmse:1.067862+0.018578    test-rmse:1.304535+0.086987 
## [227]    train-rmse:1.066855+0.018549    test-rmse:1.302716+0.088044 
## [228]    train-rmse:1.065848+0.018519    test-rmse:1.302649+0.087904 
## [229]    train-rmse:1.064836+0.018482    test-rmse:1.302590+0.085552 
## [230]    train-rmse:1.063843+0.018456    test-rmse:1.300906+0.085384 
## [231]    train-rmse:1.062867+0.018437    test-rmse:1.300407+0.085297 
## [232]    train-rmse:1.061888+0.018418    test-rmse:1.300010+0.085449 
## [233]    train-rmse:1.060914+0.018405    test-rmse:1.299469+0.085556 
## [234]    train-rmse:1.059943+0.018394    test-rmse:1.298584+0.086255 
## [235]    train-rmse:1.058991+0.018363    test-rmse:1.298118+0.086383 
## [236]    train-rmse:1.058051+0.018340    test-rmse:1.297589+0.086410 
## [237]    train-rmse:1.057116+0.018319    test-rmse:1.296390+0.087180 
## [238]    train-rmse:1.056169+0.018289    test-rmse:1.295695+0.087815 
## [239]    train-rmse:1.055244+0.018265    test-rmse:1.295779+0.087279 
## [240]    train-rmse:1.054323+0.018239    test-rmse:1.293970+0.088872 
## [241]    train-rmse:1.053414+0.018217    test-rmse:1.293590+0.088718 
## [242]    train-rmse:1.052497+0.018185    test-rmse:1.293743+0.087690 
## [243]    train-rmse:1.051589+0.018152    test-rmse:1.293003+0.088525 
## [244]    train-rmse:1.050683+0.018125    test-rmse:1.292278+0.087810 
## [245]    train-rmse:1.049789+0.018103    test-rmse:1.292515+0.087801 
## [246]    train-rmse:1.048897+0.018077    test-rmse:1.291780+0.088181 
## [247]    train-rmse:1.048004+0.018057    test-rmse:1.290790+0.087730 
## [248]    train-rmse:1.047105+0.018030    test-rmse:1.290270+0.087616 
## [249]    train-rmse:1.046221+0.017993    test-rmse:1.289270+0.088154 
## [250]    train-rmse:1.045344+0.017966    test-rmse:1.287599+0.088387 
## [251]    train-rmse:1.044473+0.017945    test-rmse:1.286850+0.088490 
## [252]    train-rmse:1.043613+0.017913    test-rmse:1.287200+0.087366 
## [253]    train-rmse:1.042769+0.017892    test-rmse:1.285969+0.087207 
## [254]    train-rmse:1.041934+0.017868    test-rmse:1.286245+0.087157 
## [255]    train-rmse:1.041099+0.017849    test-rmse:1.285158+0.088180 
## [256]    train-rmse:1.040270+0.017839    test-rmse:1.284365+0.087507 
## [257]    train-rmse:1.039440+0.017825    test-rmse:1.285531+0.086447 
## [258]    train-rmse:1.038622+0.017800    test-rmse:1.284664+0.086974 
## [259]    train-rmse:1.037808+0.017788    test-rmse:1.283800+0.087044 
## [260]    train-rmse:1.037012+0.017763    test-rmse:1.283629+0.087063 
## [261]    train-rmse:1.036212+0.017744    test-rmse:1.283681+0.086667 
## [262]    train-rmse:1.035415+0.017729    test-rmse:1.283322+0.087051 
## [263]    train-rmse:1.034619+0.017708    test-rmse:1.283479+0.087045 
## [264]    train-rmse:1.033825+0.017688    test-rmse:1.280938+0.085916 
## [265]    train-rmse:1.033041+0.017667    test-rmse:1.278579+0.086546 
## [266]    train-rmse:1.032262+0.017634    test-rmse:1.278746+0.086496 
## [267]    train-rmse:1.031485+0.017602    test-rmse:1.278189+0.087102 
## [268]    train-rmse:1.030712+0.017575    test-rmse:1.277718+0.087913 
## [269]    train-rmse:1.029942+0.017545    test-rmse:1.277798+0.087788 
## [270]    train-rmse:1.029183+0.017520    test-rmse:1.276743+0.086983 
## [271]    train-rmse:1.028421+0.017502    test-rmse:1.276798+0.088029 
## [272]    train-rmse:1.027663+0.017470    test-rmse:1.275992+0.087713 
## [273]    train-rmse:1.026922+0.017440    test-rmse:1.275728+0.087900 
## [274]    train-rmse:1.026174+0.017417    test-rmse:1.275351+0.087108 
## [275]    train-rmse:1.025435+0.017397    test-rmse:1.274784+0.086796 
## [276]    train-rmse:1.024704+0.017382    test-rmse:1.274054+0.086726 
## [277]    train-rmse:1.023974+0.017368    test-rmse:1.274073+0.087193 
## [278]    train-rmse:1.023245+0.017353    test-rmse:1.274482+0.086001 
## [279]    train-rmse:1.022518+0.017343    test-rmse:1.273914+0.086043 
## [280]    train-rmse:1.021798+0.017334    test-rmse:1.273474+0.085783 
## [281]    train-rmse:1.021075+0.017333    test-rmse:1.273395+0.085919 
## [282]    train-rmse:1.020364+0.017328    test-rmse:1.273061+0.086413 
## [283]    train-rmse:1.019656+0.017322    test-rmse:1.272409+0.086794 
## [284]    train-rmse:1.018952+0.017320    test-rmse:1.271669+0.086676 
## [285]    train-rmse:1.018250+0.017317    test-rmse:1.270589+0.086703 
## [286]    train-rmse:1.017552+0.017306    test-rmse:1.268603+0.084853 
## [287]    train-rmse:1.016860+0.017291    test-rmse:1.268189+0.084279 
## [288]    train-rmse:1.016166+0.017279    test-rmse:1.267595+0.084484 
## [289]    train-rmse:1.015478+0.017270    test-rmse:1.266644+0.085627 
## [290]    train-rmse:1.014796+0.017260    test-rmse:1.266147+0.086327 
## [291]    train-rmse:1.014113+0.017246    test-rmse:1.265162+0.086347 
## [292]    train-rmse:1.013443+0.017236    test-rmse:1.264786+0.085493 
## [293]    train-rmse:1.012768+0.017223    test-rmse:1.264552+0.085989 
## [294]    train-rmse:1.012095+0.017215    test-rmse:1.264529+0.086026 
## [295]    train-rmse:1.011429+0.017206    test-rmse:1.264520+0.085597 
## [296]    train-rmse:1.010754+0.017201    test-rmse:1.264158+0.085794 
## [297]    train-rmse:1.010089+0.017187    test-rmse:1.263909+0.085756 
## [298]    train-rmse:1.009435+0.017183    test-rmse:1.264149+0.085718 
## [299]    train-rmse:1.008785+0.017179    test-rmse:1.264202+0.087147 
## [300]    train-rmse:1.008143+0.017177    test-rmse:1.263500+0.087277 
## [301]    train-rmse:1.007486+0.017189    test-rmse:1.262536+0.087991 
## [302]    train-rmse:1.006849+0.017187    test-rmse:1.263226+0.086967 
## [303]    train-rmse:1.006217+0.017180    test-rmse:1.262990+0.086601 
## [304]    train-rmse:1.005581+0.017172    test-rmse:1.262086+0.086689 
## [305]    train-rmse:1.004950+0.017170    test-rmse:1.262034+0.086464 
## [306]    train-rmse:1.004316+0.017154    test-rmse:1.260778+0.087091 
## [307]    train-rmse:1.003689+0.017154    test-rmse:1.260596+0.087555 
## [308]    train-rmse:1.003060+0.017154    test-rmse:1.260277+0.086839 
## [309]    train-rmse:1.002441+0.017151    test-rmse:1.260718+0.086903 
## [310]    train-rmse:1.001822+0.017146    test-rmse:1.260062+0.086734 
## [311]    train-rmse:1.001201+0.017143    test-rmse:1.259913+0.086151 
## [312]    train-rmse:1.000583+0.017141    test-rmse:1.259615+0.086088 
## [313]    train-rmse:0.999975+0.017136    test-rmse:1.260016+0.085594 
## [314]    train-rmse:0.999368+0.017128    test-rmse:1.259450+0.085029 
## [315]    train-rmse:0.998763+0.017129    test-rmse:1.257933+0.083366 
## [316]    train-rmse:0.998170+0.017125    test-rmse:1.257925+0.083719 
## [317]    train-rmse:0.997571+0.017127    test-rmse:1.257337+0.083783 
## [318]    train-rmse:0.996970+0.017123    test-rmse:1.257070+0.083984 
## [319]    train-rmse:0.996386+0.017122    test-rmse:1.256448+0.084483 
## [320]    train-rmse:0.995792+0.017129    test-rmse:1.255756+0.084181 
## [321]    train-rmse:0.995208+0.017131    test-rmse:1.255965+0.083396 
## [322]    train-rmse:0.994629+0.017131    test-rmse:1.255801+0.084662 
## [323]    train-rmse:0.994051+0.017131    test-rmse:1.255255+0.085245 
## [324]    train-rmse:0.993476+0.017129    test-rmse:1.254558+0.086150 
## [325]    train-rmse:0.992904+0.017126    test-rmse:1.254318+0.085605 
## [326]    train-rmse:0.992324+0.017123    test-rmse:1.254988+0.085253 
## [327]    train-rmse:0.991742+0.017121    test-rmse:1.254801+0.085258 
## [328]    train-rmse:0.991159+0.017115    test-rmse:1.254325+0.085511 
## [329]    train-rmse:0.990602+0.017114    test-rmse:1.253902+0.085459 
## [330]    train-rmse:0.990043+0.017106    test-rmse:1.253597+0.085601 
## [331]    train-rmse:0.989490+0.017105    test-rmse:1.252218+0.085641 
## [332]    train-rmse:0.988943+0.017100    test-rmse:1.251926+0.085848 
## [333]    train-rmse:0.988391+0.017099    test-rmse:1.251676+0.086034 
## [334]    train-rmse:0.987839+0.017100    test-rmse:1.252289+0.085127 
## [335]    train-rmse:0.987301+0.017098    test-rmse:1.251290+0.086010 
## [336]    train-rmse:0.986765+0.017101    test-rmse:1.251012+0.085428 
## [337]    train-rmse:0.986225+0.017107    test-rmse:1.251080+0.085792 
## [338]    train-rmse:0.985691+0.017106    test-rmse:1.249854+0.085521 
## [339]    train-rmse:0.985161+0.017111    test-rmse:1.249125+0.085454 
## [340]    train-rmse:0.984629+0.017112    test-rmse:1.249071+0.085413 
## [341]    train-rmse:0.984100+0.017114    test-rmse:1.248819+0.085087 
## [342]    train-rmse:0.983563+0.017119    test-rmse:1.248388+0.085359 
## [343]    train-rmse:0.983039+0.017124    test-rmse:1.248614+0.084951 
## [344]    train-rmse:0.982515+0.017127    test-rmse:1.246898+0.083512 
## [345]    train-rmse:0.981996+0.017128    test-rmse:1.245948+0.083252 
## [346]    train-rmse:0.981477+0.017130    test-rmse:1.245683+0.083552 
## [347]    train-rmse:0.980954+0.017131    test-rmse:1.245398+0.083284 
## [348]    train-rmse:0.980429+0.017137    test-rmse:1.245013+0.083352 
## [349]    train-rmse:0.979912+0.017137    test-rmse:1.244278+0.083768 
## [350]    train-rmse:0.979391+0.017138    test-rmse:1.244305+0.083739 
## [351]    train-rmse:0.978880+0.017136    test-rmse:1.243320+0.084057 
## [352]    train-rmse:0.978367+0.017133    test-rmse:1.243478+0.083618 
## [353]    train-rmse:0.977850+0.017135    test-rmse:1.243468+0.083432 
## [354]    train-rmse:0.977340+0.017138    test-rmse:1.243434+0.083886 
## [355]    train-rmse:0.976841+0.017135    test-rmse:1.243592+0.083520 
## [356]    train-rmse:0.976344+0.017131    test-rmse:1.243556+0.083003 
## [357]    train-rmse:0.975846+0.017120    test-rmse:1.243360+0.083189 
## Stopping. Best iteration:
## [351]    train-rmse:0.978880+0.017136    test-rmse:1.243320+0.084057
## 
## 8 
## [1]  train-rmse:86.704080+0.086998   test-rmse:86.704831+0.406915 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.342288+0.076506   test-rmse:76.342926+0.417100 
## [3]  train-rmse:67.220384+0.067270   test-rmse:67.220891+0.426011 
## [4]  train-rmse:59.190229+0.059099   test-rmse:59.190588+0.433783 
## [5]  train-rmse:52.121395+0.051883   test-rmse:52.121578+0.440539 
## [6]  train-rmse:45.899076+0.045513   test-rmse:45.899065+0.446386 
## [7]  train-rmse:40.422199+0.039869   test-rmse:40.421979+0.451412 
## [8]  train-rmse:35.601835+0.034876   test-rmse:35.601369+0.455694 
## [9]  train-rmse:31.359680+0.030457   test-rmse:31.358941+0.459291 
## [10] train-rmse:27.626835+0.026543   test-rmse:27.625793+0.462249 
## [11] train-rmse:24.342670+0.023071   test-rmse:24.341286+0.464606 
## [12] train-rmse:21.453838+0.019997   test-rmse:21.452077+0.466382 
## [13] train-rmse:18.913416+0.017291   test-rmse:18.911232+0.467585 
## [14] train-rmse:16.680124+0.014931   test-rmse:16.677478+0.468208 
## [15] train-rmse:14.717049+0.012912   test-rmse:14.717037+0.454197 
## [16] train-rmse:12.991833+0.011108   test-rmse:12.980617+0.452674 
## [17] train-rmse:11.475719+0.009385   test-rmse:11.467150+0.438504 
## [18] train-rmse:10.143333+0.007698   test-rmse:10.125890+0.433347 
## [19] train-rmse:8.972312+0.006735    test-rmse:8.954921+0.424580 
## [20] train-rmse:7.944482+0.006174    test-rmse:7.927380+0.414847 
## [21] train-rmse:7.042126+0.005636    test-rmse:7.019776+0.406748 
## [22] train-rmse:6.250131+0.005900    test-rmse:6.228907+0.389574 
## [23] train-rmse:5.556402+0.006070    test-rmse:5.541040+0.381518 
## [24] train-rmse:4.950009+0.006754    test-rmse:4.936426+0.372615 
## [25] train-rmse:4.419668+0.007711    test-rmse:4.407692+0.362674 
## [26] train-rmse:3.958137+0.008119    test-rmse:3.955575+0.347945 
## [27] train-rmse:3.557235+0.008759    test-rmse:3.558009+0.339234 
## [28] train-rmse:3.208547+0.009296    test-rmse:3.217133+0.327508 
## [29] train-rmse:2.908189+0.010593    test-rmse:2.927775+0.316527 
## [30] train-rmse:2.649275+0.010514    test-rmse:2.677204+0.294808 
## [31] train-rmse:2.426731+0.011341    test-rmse:2.467638+0.276174 
## [32] train-rmse:2.238539+0.012203    test-rmse:2.285279+0.261334 
## [33] train-rmse:2.077648+0.013439    test-rmse:2.139274+0.241341 
## [34] train-rmse:1.941758+0.014501    test-rmse:2.017363+0.224198 
## [35] train-rmse:1.827234+0.015257    test-rmse:1.909494+0.208537 
## [36] train-rmse:1.730997+0.014873    test-rmse:1.827242+0.194657 
## [37] train-rmse:1.651358+0.015010    test-rmse:1.760242+0.178324 
## [38] train-rmse:1.584960+0.014805    test-rmse:1.703788+0.163212 
## [39] train-rmse:1.529850+0.015009    test-rmse:1.656900+0.150969 
## [40] train-rmse:1.483057+0.015091    test-rmse:1.620779+0.137863 
## [41] train-rmse:1.442070+0.017669    test-rmse:1.580902+0.132763 
## [42] train-rmse:1.408371+0.018003    test-rmse:1.558355+0.126694 
## [43] train-rmse:1.379870+0.018066    test-rmse:1.534090+0.119148 
## [44] train-rmse:1.356299+0.018593    test-rmse:1.516273+0.116176 
## [45] train-rmse:1.334776+0.018806    test-rmse:1.505502+0.110856 
## [46] train-rmse:1.314421+0.016125    test-rmse:1.495477+0.106201 
## [47] train-rmse:1.297625+0.017236    test-rmse:1.485312+0.104275 
## [48] train-rmse:1.283466+0.017004    test-rmse:1.470524+0.104345 
## [49] train-rmse:1.270566+0.017113    test-rmse:1.463942+0.104598 
## [50] train-rmse:1.257745+0.018041    test-rmse:1.455908+0.102158 
## [51] train-rmse:1.247099+0.018010    test-rmse:1.449907+0.100738 
## [52] train-rmse:1.235947+0.017147    test-rmse:1.442733+0.096760 
## [53] train-rmse:1.226125+0.017153    test-rmse:1.436204+0.095404 
## [54] train-rmse:1.216352+0.017205    test-rmse:1.425924+0.096439 
## [55] train-rmse:1.207848+0.017798    test-rmse:1.420473+0.093278 
## [56] train-rmse:1.199382+0.016127    test-rmse:1.416798+0.091333 
## [57] train-rmse:1.191848+0.016147    test-rmse:1.410022+0.094283 
## [58] train-rmse:1.184387+0.016092    test-rmse:1.403757+0.095936 
## [59] train-rmse:1.175934+0.017313    test-rmse:1.398237+0.094785 
## [60] train-rmse:1.167248+0.018747    test-rmse:1.393603+0.096214 
## [61] train-rmse:1.160500+0.018866    test-rmse:1.386059+0.093470 
## [62] train-rmse:1.153882+0.019129    test-rmse:1.382567+0.091987 
## [63] train-rmse:1.146630+0.018381    test-rmse:1.378043+0.092114 
## [64] train-rmse:1.140563+0.018303    test-rmse:1.374522+0.092312 
## [65] train-rmse:1.133561+0.019459    test-rmse:1.369061+0.091641 
## [66] train-rmse:1.127507+0.019461    test-rmse:1.366535+0.089086 
## [67] train-rmse:1.121461+0.019391    test-rmse:1.362154+0.089054 
## [68] train-rmse:1.115067+0.018443    test-rmse:1.355512+0.086979 
## [69] train-rmse:1.109546+0.018204    test-rmse:1.352563+0.086436 
## [70] train-rmse:1.103501+0.018481    test-rmse:1.346495+0.088135 
## [71] train-rmse:1.098022+0.018304    test-rmse:1.342106+0.087054 
## [72] train-rmse:1.090660+0.019713    test-rmse:1.337355+0.086733 
## [73] train-rmse:1.084746+0.019291    test-rmse:1.333456+0.089143 
## [74] train-rmse:1.079472+0.019028    test-rmse:1.329113+0.093105 
## [75] train-rmse:1.073005+0.020771    test-rmse:1.324806+0.091746 
## [76] train-rmse:1.068025+0.020786    test-rmse:1.320053+0.093253 
## [77] train-rmse:1.062771+0.021458    test-rmse:1.317270+0.093240 
## [78] train-rmse:1.056449+0.021326    test-rmse:1.314243+0.092541 
## [79] train-rmse:1.051751+0.021474    test-rmse:1.310583+0.089716 
## [80] train-rmse:1.046398+0.021167    test-rmse:1.307462+0.089638 
## [81] train-rmse:1.040793+0.022290    test-rmse:1.300854+0.085999 
## [82] train-rmse:1.036466+0.022378    test-rmse:1.295487+0.087583 
## [83] train-rmse:1.030589+0.022569    test-rmse:1.293702+0.087182 
## [84] train-rmse:1.026083+0.023062    test-rmse:1.292501+0.090529 
## [85] train-rmse:1.021978+0.022997    test-rmse:1.288645+0.093978 
## [86] train-rmse:1.018150+0.022968    test-rmse:1.287711+0.093773 
## [87] train-rmse:1.014206+0.023381    test-rmse:1.284714+0.093880 
## [88] train-rmse:1.010434+0.023294    test-rmse:1.281166+0.093304 
## [89] train-rmse:1.006329+0.023132    test-rmse:1.280051+0.093208 
## [90] train-rmse:1.002155+0.023410    test-rmse:1.277320+0.093663 
## [91] train-rmse:0.997598+0.023218    test-rmse:1.274572+0.092528 
## [92] train-rmse:0.992393+0.021385    test-rmse:1.271016+0.095315 
## [93] train-rmse:0.988796+0.021466    test-rmse:1.267248+0.097230 
## [94] train-rmse:0.983960+0.022439    test-rmse:1.262394+0.093390 
## [95] train-rmse:0.979563+0.021663    test-rmse:1.261549+0.094216 
## [96] train-rmse:0.976193+0.021813    test-rmse:1.257404+0.094491 
## [97] train-rmse:0.972745+0.021783    test-rmse:1.255488+0.094774 
## [98] train-rmse:0.969097+0.021159    test-rmse:1.252183+0.095636 
## [99] train-rmse:0.964567+0.020944    test-rmse:1.250952+0.096857 
## [100]    train-rmse:0.960708+0.020951    test-rmse:1.247306+0.098880 
## [101]    train-rmse:0.956646+0.021234    test-rmse:1.243452+0.098653 
## [102]    train-rmse:0.953142+0.021944    test-rmse:1.240477+0.098877 
## [103]    train-rmse:0.949151+0.022815    test-rmse:1.238995+0.097691 
## [104]    train-rmse:0.944890+0.021670    test-rmse:1.234893+0.097867 
## [105]    train-rmse:0.941513+0.021208    test-rmse:1.231238+0.097917 
## [106]    train-rmse:0.937592+0.021165    test-rmse:1.229942+0.098001 
## [107]    train-rmse:0.933003+0.021575    test-rmse:1.228079+0.097982 
## [108]    train-rmse:0.929710+0.021819    test-rmse:1.224763+0.097850 
## [109]    train-rmse:0.926447+0.022029    test-rmse:1.223316+0.097697 
## [110]    train-rmse:0.923296+0.022146    test-rmse:1.222034+0.098589 
## [111]    train-rmse:0.918341+0.020087    test-rmse:1.221313+0.100912 
## [112]    train-rmse:0.914816+0.020448    test-rmse:1.219110+0.101006 
## [113]    train-rmse:0.912002+0.020488    test-rmse:1.215268+0.101899 
## [114]    train-rmse:0.907923+0.021544    test-rmse:1.213292+0.102548 
## [115]    train-rmse:0.905170+0.021749    test-rmse:1.211540+0.103370 
## [116]    train-rmse:0.901684+0.020294    test-rmse:1.208801+0.106897 
## [117]    train-rmse:0.898478+0.019781    test-rmse:1.207178+0.105697 
## [118]    train-rmse:0.895634+0.019194    test-rmse:1.205816+0.105438 
## [119]    train-rmse:0.892041+0.018336    test-rmse:1.204700+0.105639 
## [120]    train-rmse:0.888709+0.018509    test-rmse:1.202915+0.105184 
## [121]    train-rmse:0.885054+0.019040    test-rmse:1.201704+0.104028 
## [122]    train-rmse:0.880900+0.017547    test-rmse:1.201000+0.102855 
## [123]    train-rmse:0.878299+0.017783    test-rmse:1.199596+0.103644 
## [124]    train-rmse:0.875846+0.017869    test-rmse:1.199549+0.103484 
## [125]    train-rmse:0.872882+0.018397    test-rmse:1.197899+0.103476 
## [126]    train-rmse:0.870501+0.018395    test-rmse:1.195202+0.102867 
## [127]    train-rmse:0.867019+0.018553    test-rmse:1.194515+0.104157 
## [128]    train-rmse:0.863976+0.017473    test-rmse:1.192078+0.104649 
## [129]    train-rmse:0.861618+0.017740    test-rmse:1.189310+0.104698 
## [130]    train-rmse:0.858612+0.017467    test-rmse:1.188877+0.105209 
## [131]    train-rmse:0.855712+0.017916    test-rmse:1.187445+0.107480 
## [132]    train-rmse:0.853466+0.017702    test-rmse:1.187341+0.106870 
## [133]    train-rmse:0.850315+0.016306    test-rmse:1.186146+0.107534 
## [134]    train-rmse:0.848167+0.016139    test-rmse:1.183904+0.108872 
## [135]    train-rmse:0.845739+0.016288    test-rmse:1.182625+0.108176 
## [136]    train-rmse:0.843397+0.016587    test-rmse:1.179973+0.107348 
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## [139]    train-rmse:0.836660+0.016581    test-rmse:1.177626+0.109275 
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## [236]    train-rmse:0.652797+0.006384    test-rmse:1.074741+0.107092 
## [237]    train-rmse:0.651615+0.006080    test-rmse:1.073725+0.107518 
## [238]    train-rmse:0.650266+0.006117    test-rmse:1.073078+0.109254 
## [239]    train-rmse:0.648240+0.007275    test-rmse:1.070654+0.106921 
## [240]    train-rmse:0.646402+0.007488    test-rmse:1.070948+0.106443 
## [241]    train-rmse:0.644684+0.008106    test-rmse:1.069855+0.106257 
## [242]    train-rmse:0.643455+0.007838    test-rmse:1.069911+0.106224 
## [243]    train-rmse:0.642442+0.007859    test-rmse:1.069595+0.105999 
## [244]    train-rmse:0.641560+0.007944    test-rmse:1.069309+0.106500 
## [245]    train-rmse:0.640739+0.007835    test-rmse:1.069143+0.106264 
## [246]    train-rmse:0.639709+0.007790    test-rmse:1.068697+0.106461 
## [247]    train-rmse:0.638885+0.007778    test-rmse:1.068349+0.106681 
## [248]    train-rmse:0.637831+0.007804    test-rmse:1.067667+0.107196 
## [249]    train-rmse:0.636950+0.007521    test-rmse:1.067379+0.107352 
## [250]    train-rmse:0.635324+0.007549    test-rmse:1.066534+0.107342 
## [251]    train-rmse:0.634125+0.007432    test-rmse:1.065665+0.107252 
## [252]    train-rmse:0.633336+0.007536    test-rmse:1.065591+0.106772 
## [253]    train-rmse:0.632489+0.007645    test-rmse:1.064127+0.106392 
## [254]    train-rmse:0.631477+0.007690    test-rmse:1.064227+0.106453 
## [255]    train-rmse:0.630503+0.007558    test-rmse:1.063776+0.106810 
## [256]    train-rmse:0.629049+0.006686    test-rmse:1.062671+0.107451 
## [257]    train-rmse:0.628152+0.006577    test-rmse:1.062570+0.108256 
## [258]    train-rmse:0.626636+0.005955    test-rmse:1.063194+0.108807 
## [259]    train-rmse:0.624310+0.006129    test-rmse:1.062676+0.107866 
## [260]    train-rmse:0.623235+0.006006    test-rmse:1.061229+0.108477 
## [261]    train-rmse:0.621471+0.007116    test-rmse:1.060449+0.108101 
## [262]    train-rmse:0.619968+0.007651    test-rmse:1.059079+0.106309 
## [263]    train-rmse:0.618925+0.007406    test-rmse:1.058052+0.107226 
## [264]    train-rmse:0.617844+0.007420    test-rmse:1.057452+0.107197 
## [265]    train-rmse:0.616133+0.007487    test-rmse:1.057213+0.106433 
## [266]    train-rmse:0.615106+0.007499    test-rmse:1.056822+0.106367 
## [267]    train-rmse:0.613965+0.007641    test-rmse:1.056411+0.106542 
## [268]    train-rmse:0.612616+0.007549    test-rmse:1.056299+0.106153 
## [269]    train-rmse:0.611590+0.007471    test-rmse:1.056778+0.105855 
## [270]    train-rmse:0.610034+0.008312    test-rmse:1.054095+0.103677 
## [271]    train-rmse:0.608807+0.008227    test-rmse:1.054495+0.103083 
## [272]    train-rmse:0.608087+0.008273    test-rmse:1.054620+0.102554 
## [273]    train-rmse:0.606739+0.008174    test-rmse:1.054721+0.102891 
## [274]    train-rmse:0.605347+0.007818    test-rmse:1.053749+0.104185 
## [275]    train-rmse:0.604374+0.007504    test-rmse:1.053673+0.104147 
## [276]    train-rmse:0.603199+0.007593    test-rmse:1.053555+0.104181 
## [277]    train-rmse:0.601718+0.008173    test-rmse:1.053464+0.103329 
## [278]    train-rmse:0.600917+0.008141    test-rmse:1.052925+0.103213 
## [279]    train-rmse:0.599121+0.008674    test-rmse:1.052632+0.103468 
## [280]    train-rmse:0.597708+0.008709    test-rmse:1.052965+0.103626 
## [281]    train-rmse:0.596650+0.008630    test-rmse:1.052156+0.103512 
## [282]    train-rmse:0.594967+0.008915    test-rmse:1.051876+0.102739 
## [283]    train-rmse:0.593069+0.009853    test-rmse:1.049829+0.102310 
## [284]    train-rmse:0.591860+0.010071    test-rmse:1.047836+0.101108 
## [285]    train-rmse:0.590753+0.010142    test-rmse:1.047835+0.101278 
## [286]    train-rmse:0.589167+0.009826    test-rmse:1.047281+0.102313 
## [287]    train-rmse:0.588590+0.009802    test-rmse:1.047444+0.102364 
## [288]    train-rmse:0.587740+0.009972    test-rmse:1.046702+0.101986 
## [289]    train-rmse:0.586891+0.010048    test-rmse:1.046824+0.101502 
## [290]    train-rmse:0.585624+0.009764    test-rmse:1.046040+0.102363 
## [291]    train-rmse:0.584914+0.009831    test-rmse:1.046042+0.101905 
## [292]    train-rmse:0.582865+0.008232    test-rmse:1.045048+0.102443 
## [293]    train-rmse:0.581939+0.007941    test-rmse:1.044935+0.102373 
## [294]    train-rmse:0.581191+0.008133    test-rmse:1.044375+0.101800 
## [295]    train-rmse:0.580557+0.008208    test-rmse:1.043990+0.101924 
## [296]    train-rmse:0.579390+0.008503    test-rmse:1.042995+0.101861 
## [297]    train-rmse:0.578200+0.008277    test-rmse:1.042409+0.102589 
## [298]    train-rmse:0.576802+0.009197    test-rmse:1.042327+0.102152 
## [299]    train-rmse:0.575824+0.009226    test-rmse:1.040828+0.102099 
## [300]    train-rmse:0.575007+0.009423    test-rmse:1.040681+0.101469 
## [301]    train-rmse:0.573457+0.009189    test-rmse:1.040156+0.101839 
## [302]    train-rmse:0.572727+0.009059    test-rmse:1.039407+0.101371 
## [303]    train-rmse:0.571990+0.008966    test-rmse:1.039359+0.101476 
## [304]    train-rmse:0.571484+0.009007    test-rmse:1.039216+0.101970 
## [305]    train-rmse:0.570761+0.009226    test-rmse:1.038964+0.101899 
## [306]    train-rmse:0.569475+0.009295    test-rmse:1.038712+0.102776 
## [307]    train-rmse:0.568717+0.009259    test-rmse:1.038500+0.102873 
## [308]    train-rmse:0.566988+0.009061    test-rmse:1.038450+0.103219 
## [309]    train-rmse:0.566328+0.008973    test-rmse:1.037752+0.102681 
## [310]    train-rmse:0.565252+0.009128    test-rmse:1.037242+0.102152 
## [311]    train-rmse:0.564255+0.009226    test-rmse:1.037667+0.102467 
## [312]    train-rmse:0.563214+0.009532    test-rmse:1.037074+0.102658 
## [313]    train-rmse:0.561244+0.008473    test-rmse:1.035463+0.101968 
## [314]    train-rmse:0.560579+0.008391    test-rmse:1.035337+0.101255 
## [315]    train-rmse:0.559641+0.008581    test-rmse:1.035057+0.102124 
## [316]    train-rmse:0.558950+0.008837    test-rmse:1.034346+0.101610 
## [317]    train-rmse:0.557907+0.009075    test-rmse:1.033491+0.102267 
## [318]    train-rmse:0.556657+0.008288    test-rmse:1.033602+0.101489 
## [319]    train-rmse:0.555338+0.008554    test-rmse:1.032831+0.100937 
## [320]    train-rmse:0.554142+0.008307    test-rmse:1.032174+0.101112 
## [321]    train-rmse:0.553011+0.007940    test-rmse:1.032073+0.100869 
## [322]    train-rmse:0.552166+0.007944    test-rmse:1.032995+0.101267 
## [323]    train-rmse:0.550908+0.008313    test-rmse:1.032212+0.101400 
## [324]    train-rmse:0.548420+0.008596    test-rmse:1.031900+0.101083 
## [325]    train-rmse:0.547583+0.008866    test-rmse:1.030930+0.101184 
## [326]    train-rmse:0.545710+0.007753    test-rmse:1.029994+0.101122 
## [327]    train-rmse:0.544329+0.008314    test-rmse:1.028280+0.100723 
## [328]    train-rmse:0.543486+0.008393    test-rmse:1.028525+0.101104 
## [329]    train-rmse:0.542284+0.008192    test-rmse:1.029077+0.101294 
## [330]    train-rmse:0.540998+0.008440    test-rmse:1.027107+0.100775 
## [331]    train-rmse:0.540476+0.008484    test-rmse:1.027079+0.100627 
## [332]    train-rmse:0.539446+0.008670    test-rmse:1.025164+0.100455 
## [333]    train-rmse:0.538651+0.008632    test-rmse:1.025377+0.100524 
## [334]    train-rmse:0.538175+0.008607    test-rmse:1.025645+0.100351 
## [335]    train-rmse:0.537441+0.008239    test-rmse:1.026610+0.099843 
## [336]    train-rmse:0.536249+0.007544    test-rmse:1.026532+0.099622 
## [337]    train-rmse:0.534923+0.007846    test-rmse:1.026873+0.099749 
## [338]    train-rmse:0.534073+0.008061    test-rmse:1.026619+0.100050 
## Stopping. Best iteration:
## [332]    train-rmse:0.539446+0.008670    test-rmse:1.025164+0.100455
## 
## 9 
## [1]  train-rmse:86.704080+0.086998   test-rmse:86.704831+0.406915 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.342288+0.076506   test-rmse:76.342926+0.417100 
## [3]  train-rmse:67.220384+0.067270   test-rmse:67.220891+0.426011 
## [4]  train-rmse:59.190229+0.059099   test-rmse:59.190588+0.433783 
## [5]  train-rmse:52.121395+0.051883   test-rmse:52.121578+0.440539 
## [6]  train-rmse:45.899076+0.045513   test-rmse:45.899065+0.446386 
## [7]  train-rmse:40.422199+0.039869   test-rmse:40.421979+0.451412 
## [8]  train-rmse:35.601835+0.034876   test-rmse:35.601369+0.455694 
## [9]  train-rmse:31.359680+0.030457   test-rmse:31.358941+0.459291 
## [10] train-rmse:27.626835+0.026543   test-rmse:27.625793+0.462249 
## [11] train-rmse:24.342670+0.023071   test-rmse:24.341286+0.464606 
## [12] train-rmse:21.453838+0.019997   test-rmse:21.452077+0.466382 
## [13] train-rmse:18.913416+0.017291   test-rmse:18.911232+0.467585 
## [14] train-rmse:16.680124+0.014931   test-rmse:16.677478+0.468208 
## [15] train-rmse:14.717049+0.012912   test-rmse:14.717037+0.454197 
## [16] train-rmse:12.991833+0.011108   test-rmse:12.980617+0.452674 
## [17] train-rmse:11.475719+0.009385   test-rmse:11.467150+0.438504 
## [18] train-rmse:10.143096+0.007693   test-rmse:10.131042+0.427921 
## [19] train-rmse:8.971152+0.006739    test-rmse:8.953272+0.422391 
## [20] train-rmse:7.941756+0.005865    test-rmse:7.925213+0.406111 
## [21] train-rmse:7.037091+0.005237    test-rmse:7.024215+0.395413 
## [22] train-rmse:6.242095+0.004933    test-rmse:6.232475+0.377173 
## [23] train-rmse:5.545368+0.004885    test-rmse:5.540685+0.366689 
## [24] train-rmse:4.934418+0.004738    test-rmse:4.929785+0.356840 
## [25] train-rmse:4.398644+0.004681    test-rmse:4.402532+0.339622 
## [26] train-rmse:3.930207+0.005302    test-rmse:3.938639+0.326907 
## [27] train-rmse:3.520380+0.004492    test-rmse:3.539965+0.309889 
## [28] train-rmse:3.163704+0.005069    test-rmse:3.188579+0.303191 
## [29] train-rmse:2.854341+0.005134    test-rmse:2.885191+0.292800 
## [30] train-rmse:2.584439+0.004857    test-rmse:2.624352+0.278255 
## [31] train-rmse:2.351231+0.006173    test-rmse:2.401692+0.261377 
## [32] train-rmse:2.151224+0.007667    test-rmse:2.214129+0.246748 
## [33] train-rmse:1.979010+0.007049    test-rmse:2.054866+0.232996 
## [34] train-rmse:1.833406+0.007399    test-rmse:1.925542+0.220043 
## [35] train-rmse:1.707353+0.009514    test-rmse:1.819360+0.204657 
## [36] train-rmse:1.601453+0.009317    test-rmse:1.725736+0.191957 
## [37] train-rmse:1.509911+0.013170    test-rmse:1.650120+0.177409 
## [38] train-rmse:1.433103+0.012547    test-rmse:1.589632+0.170777 
## [39] train-rmse:1.367858+0.010851    test-rmse:1.535444+0.158861 
## [40] train-rmse:1.312752+0.010527    test-rmse:1.494860+0.150065 
## [41] train-rmse:1.264542+0.009861    test-rmse:1.464016+0.139268 
## [42] train-rmse:1.225347+0.010740    test-rmse:1.432286+0.131465 
## [43] train-rmse:1.189490+0.011111    test-rmse:1.408030+0.125432 
## [44] train-rmse:1.161012+0.011462    test-rmse:1.388977+0.119893 
## [45] train-rmse:1.135430+0.011651    test-rmse:1.377121+0.113096 
## [46] train-rmse:1.112565+0.011691    test-rmse:1.365862+0.109924 
## [47] train-rmse:1.093293+0.010833    test-rmse:1.352510+0.108685 
## [48] train-rmse:1.076020+0.010788    test-rmse:1.342420+0.104002 
## [49] train-rmse:1.060163+0.012195    test-rmse:1.331269+0.102579 
## [50] train-rmse:1.044690+0.013171    test-rmse:1.319242+0.099994 
## [51] train-rmse:1.032244+0.013356    test-rmse:1.311483+0.097621 
## [52] train-rmse:1.019802+0.010299    test-rmse:1.302033+0.097176 
## [53] train-rmse:1.008191+0.011318    test-rmse:1.293036+0.096687 
## [54] train-rmse:0.998519+0.011743    test-rmse:1.287207+0.094869 
## [55] train-rmse:0.988116+0.012673    test-rmse:1.281922+0.092568 
## [56] train-rmse:0.978391+0.013318    test-rmse:1.276127+0.091420 
## [57] train-rmse:0.967640+0.014863    test-rmse:1.269706+0.086700 
## [58] train-rmse:0.958774+0.015656    test-rmse:1.263671+0.085165 
## [59] train-rmse:0.949566+0.015162    test-rmse:1.259254+0.087307 
## [60] train-rmse:0.942191+0.015785    test-rmse:1.252444+0.088168 
## [61] train-rmse:0.933919+0.016084    test-rmse:1.247983+0.086091 
## [62] train-rmse:0.927061+0.015748    test-rmse:1.242065+0.088068 
## [63] train-rmse:0.919211+0.016627    test-rmse:1.238329+0.088186 
## [64] train-rmse:0.911484+0.017291    test-rmse:1.231376+0.086956 
## [65] train-rmse:0.902673+0.013036    test-rmse:1.226428+0.086835 
## [66] train-rmse:0.893249+0.014624    test-rmse:1.222151+0.086008 
## [67] train-rmse:0.886203+0.015188    test-rmse:1.216711+0.088169 
## [68] train-rmse:0.879075+0.015360    test-rmse:1.215581+0.085840 
## [69] train-rmse:0.872236+0.016273    test-rmse:1.208029+0.087210 
## [70] train-rmse:0.863754+0.017829    test-rmse:1.203063+0.087230 
## [71] train-rmse:0.857883+0.017991    test-rmse:1.199501+0.088125 
## [72] train-rmse:0.850680+0.019284    test-rmse:1.197472+0.088904 
## [73] train-rmse:0.842898+0.017809    test-rmse:1.190361+0.088421 
## [74] train-rmse:0.837519+0.018492    test-rmse:1.187497+0.090668 
## [75] train-rmse:0.831463+0.019721    test-rmse:1.184899+0.089588 
## [76] train-rmse:0.825660+0.018277    test-rmse:1.181747+0.088802 
## [77] train-rmse:0.820376+0.018368    test-rmse:1.179340+0.087902 
## [78] train-rmse:0.815145+0.017897    test-rmse:1.172832+0.087845 
## [79] train-rmse:0.809104+0.018970    test-rmse:1.169097+0.086976 
## [80] train-rmse:0.803937+0.019852    test-rmse:1.166056+0.086220 
## [81] train-rmse:0.799415+0.019933    test-rmse:1.165443+0.086134 
## [82] train-rmse:0.792853+0.018666    test-rmse:1.160875+0.087851 
## [83] train-rmse:0.786054+0.019437    test-rmse:1.157600+0.087853 
## [84] train-rmse:0.780079+0.018815    test-rmse:1.153280+0.089705 
## [85] train-rmse:0.775205+0.019687    test-rmse:1.149385+0.089990 
## [86] train-rmse:0.770328+0.018608    test-rmse:1.145929+0.090262 
## [87] train-rmse:0.765286+0.018490    test-rmse:1.141520+0.089837 
## [88] train-rmse:0.760588+0.018789    test-rmse:1.139242+0.089024 
## [89] train-rmse:0.755584+0.018051    test-rmse:1.137717+0.088393 
## [90] train-rmse:0.750993+0.017639    test-rmse:1.135166+0.089356 
## [91] train-rmse:0.746272+0.017925    test-rmse:1.132517+0.090073 
## [92] train-rmse:0.742645+0.018644    test-rmse:1.131125+0.090185 
## [93] train-rmse:0.737007+0.018761    test-rmse:1.127076+0.089280 
## [94] train-rmse:0.732356+0.019157    test-rmse:1.125480+0.089202 
## [95] train-rmse:0.728396+0.018337    test-rmse:1.123453+0.088768 
## [96] train-rmse:0.724007+0.018406    test-rmse:1.121218+0.087772 
## [97] train-rmse:0.720293+0.018826    test-rmse:1.120594+0.087042 
## [98] train-rmse:0.716211+0.018609    test-rmse:1.118031+0.088949 
## [99] train-rmse:0.712767+0.018886    test-rmse:1.116263+0.089398 
## [100]    train-rmse:0.708601+0.018255    test-rmse:1.111807+0.088920 
## [101]    train-rmse:0.704216+0.018291    test-rmse:1.110415+0.088782 
## [102]    train-rmse:0.699559+0.017639    test-rmse:1.107177+0.088212 
## [103]    train-rmse:0.694240+0.018806    test-rmse:1.103917+0.087677 
## [104]    train-rmse:0.691153+0.019034    test-rmse:1.103287+0.087090 
## [105]    train-rmse:0.687632+0.018835    test-rmse:1.100500+0.088444 
## [106]    train-rmse:0.682102+0.020387    test-rmse:1.097528+0.088948 
## [107]    train-rmse:0.678154+0.020752    test-rmse:1.095083+0.091265 
## [108]    train-rmse:0.674018+0.020542    test-rmse:1.094317+0.090498 
## [109]    train-rmse:0.671693+0.020603    test-rmse:1.094033+0.088979 
## [110]    train-rmse:0.668016+0.021056    test-rmse:1.091630+0.087863 
## [111]    train-rmse:0.664726+0.021220    test-rmse:1.090755+0.090679 
## [112]    train-rmse:0.660485+0.020169    test-rmse:1.089873+0.094050 
## [113]    train-rmse:0.657405+0.020533    test-rmse:1.087799+0.094576 
## [114]    train-rmse:0.653853+0.019999    test-rmse:1.086478+0.095506 
## [115]    train-rmse:0.651058+0.020073    test-rmse:1.085043+0.094925 
## [116]    train-rmse:0.647083+0.018290    test-rmse:1.082767+0.095269 
## [117]    train-rmse:0.643738+0.018163    test-rmse:1.081347+0.096402 
## [118]    train-rmse:0.640519+0.018289    test-rmse:1.080454+0.095459 
## [119]    train-rmse:0.636962+0.019094    test-rmse:1.079492+0.095320 
## [120]    train-rmse:0.634611+0.019109    test-rmse:1.078185+0.095807 
## [121]    train-rmse:0.631673+0.019486    test-rmse:1.079813+0.097553 
## [122]    train-rmse:0.629634+0.019666    test-rmse:1.079992+0.097281 
## [123]    train-rmse:0.625256+0.019049    test-rmse:1.077889+0.097695 
## [124]    train-rmse:0.621630+0.016810    test-rmse:1.077511+0.100071 
## [125]    train-rmse:0.617197+0.016512    test-rmse:1.076111+0.101728 
## [126]    train-rmse:0.614210+0.017267    test-rmse:1.073864+0.100287 
## [127]    train-rmse:0.611750+0.017429    test-rmse:1.072997+0.100617 
## [128]    train-rmse:0.608234+0.017423    test-rmse:1.071460+0.099905 
## [129]    train-rmse:0.604212+0.017217    test-rmse:1.068377+0.100510 
## [130]    train-rmse:0.601718+0.017561    test-rmse:1.067617+0.100540 
## [131]    train-rmse:0.599745+0.017756    test-rmse:1.066908+0.100981 
## [132]    train-rmse:0.596439+0.016905    test-rmse:1.065049+0.101787 
## [133]    train-rmse:0.593663+0.015741    test-rmse:1.063673+0.102919 
## [134]    train-rmse:0.590906+0.015524    test-rmse:1.060944+0.100891 
## [135]    train-rmse:0.588785+0.015513    test-rmse:1.059787+0.101060 
## [136]    train-rmse:0.586436+0.015958    test-rmse:1.058761+0.101652 
## [137]    train-rmse:0.583195+0.016280    test-rmse:1.058885+0.101089 
## [138]    train-rmse:0.580981+0.015807    test-rmse:1.057687+0.101455 
## [139]    train-rmse:0.577723+0.014847    test-rmse:1.057139+0.104184 
## [140]    train-rmse:0.574049+0.014792    test-rmse:1.057326+0.102973 
## [141]    train-rmse:0.570971+0.014481    test-rmse:1.054309+0.102953 
## [142]    train-rmse:0.567641+0.013448    test-rmse:1.053081+0.102132 
## [143]    train-rmse:0.565610+0.014200    test-rmse:1.049777+0.100452 
## [144]    train-rmse:0.563906+0.014544    test-rmse:1.049293+0.099898 
## [145]    train-rmse:0.561923+0.014599    test-rmse:1.049203+0.099996 
## [146]    train-rmse:0.559682+0.014614    test-rmse:1.048085+0.100419 
## [147]    train-rmse:0.556616+0.015013    test-rmse:1.046815+0.099735 
## [148]    train-rmse:0.553113+0.014861    test-rmse:1.044745+0.101782 
## [149]    train-rmse:0.550525+0.014502    test-rmse:1.043875+0.101629 
## [150]    train-rmse:0.548296+0.014437    test-rmse:1.039069+0.096277 
## [151]    train-rmse:0.545903+0.014796    test-rmse:1.038077+0.095039 
## [152]    train-rmse:0.544026+0.015053    test-rmse:1.038777+0.094520 
## [153]    train-rmse:0.541745+0.014280    test-rmse:1.039583+0.093504 
## [154]    train-rmse:0.539156+0.014493    test-rmse:1.038365+0.093291 
## [155]    train-rmse:0.536077+0.014473    test-rmse:1.035877+0.092481 
## [156]    train-rmse:0.533493+0.014656    test-rmse:1.034607+0.092870 
## [157]    train-rmse:0.530683+0.015303    test-rmse:1.034178+0.092686 
## [158]    train-rmse:0.528134+0.013784    test-rmse:1.033667+0.092904 
## [159]    train-rmse:0.526349+0.013831    test-rmse:1.033119+0.092737 
## [160]    train-rmse:0.522321+0.012963    test-rmse:1.032215+0.093890 
## [161]    train-rmse:0.520378+0.012328    test-rmse:1.031760+0.092564 
## [162]    train-rmse:0.518496+0.012655    test-rmse:1.031560+0.093022 
## [163]    train-rmse:0.516841+0.012585    test-rmse:1.031357+0.093345 
## [164]    train-rmse:0.514116+0.011766    test-rmse:1.029666+0.094147 
## [165]    train-rmse:0.511672+0.011859    test-rmse:1.028618+0.093340 
## [166]    train-rmse:0.509969+0.011720    test-rmse:1.028033+0.093400 
## [167]    train-rmse:0.507353+0.012526    test-rmse:1.025734+0.092253 
## [168]    train-rmse:0.505086+0.012084    test-rmse:1.025556+0.091839 
## [169]    train-rmse:0.502995+0.012411    test-rmse:1.025234+0.091100 
## [170]    train-rmse:0.500473+0.011702    test-rmse:1.025458+0.093285 
## [171]    train-rmse:0.498420+0.011234    test-rmse:1.023507+0.090606 
## [172]    train-rmse:0.496127+0.011508    test-rmse:1.024965+0.092385 
## [173]    train-rmse:0.494431+0.011390    test-rmse:1.024524+0.092589 
## [174]    train-rmse:0.493141+0.011375    test-rmse:1.023583+0.092351 
## [175]    train-rmse:0.490919+0.010944    test-rmse:1.023152+0.092249 
## [176]    train-rmse:0.489650+0.010788    test-rmse:1.023142+0.092544 
## [177]    train-rmse:0.487730+0.010945    test-rmse:1.023373+0.094370 
## [178]    train-rmse:0.484924+0.011801    test-rmse:1.023579+0.093439 
## [179]    train-rmse:0.481377+0.012325    test-rmse:1.023501+0.094444 
## [180]    train-rmse:0.479025+0.012219    test-rmse:1.023064+0.094654 
## [181]    train-rmse:0.476936+0.012700    test-rmse:1.021587+0.095457 
## [182]    train-rmse:0.474365+0.013141    test-rmse:1.019987+0.092599 
## [183]    train-rmse:0.472425+0.013374    test-rmse:1.019326+0.093915 
## [184]    train-rmse:0.470109+0.013638    test-rmse:1.019381+0.094026 
## [185]    train-rmse:0.468789+0.013632    test-rmse:1.018383+0.094255 
## [186]    train-rmse:0.466276+0.012235    test-rmse:1.017481+0.095080 
## [187]    train-rmse:0.464232+0.012117    test-rmse:1.016962+0.094585 
## [188]    train-rmse:0.461107+0.011610    test-rmse:1.018991+0.096366 
## [189]    train-rmse:0.459261+0.011953    test-rmse:1.018051+0.095160 
## [190]    train-rmse:0.456555+0.012452    test-rmse:1.017671+0.095505 
## [191]    train-rmse:0.455357+0.012603    test-rmse:1.016760+0.094755 
## [192]    train-rmse:0.453367+0.011696    test-rmse:1.015143+0.094561 
## [193]    train-rmse:0.451423+0.011409    test-rmse:1.014708+0.094870 
## [194]    train-rmse:0.448766+0.011982    test-rmse:1.013908+0.093925 
## [195]    train-rmse:0.446901+0.012468    test-rmse:1.013951+0.095871 
## [196]    train-rmse:0.444973+0.012437    test-rmse:1.012945+0.095751 
## [197]    train-rmse:0.443235+0.012645    test-rmse:1.011833+0.094737 
## [198]    train-rmse:0.441703+0.012159    test-rmse:1.010936+0.095263 
## [199]    train-rmse:0.440097+0.011881    test-rmse:1.009444+0.093014 
## [200]    train-rmse:0.437686+0.012208    test-rmse:1.008475+0.092891 
## [201]    train-rmse:0.436340+0.012517    test-rmse:1.008668+0.092572 
## [202]    train-rmse:0.434320+0.012436    test-rmse:1.008864+0.092151 
## [203]    train-rmse:0.431816+0.012624    test-rmse:1.005680+0.088613 
## [204]    train-rmse:0.430343+0.012321    test-rmse:1.005270+0.088501 
## [205]    train-rmse:0.428601+0.012458    test-rmse:1.004756+0.088131 
## [206]    train-rmse:0.425877+0.011596    test-rmse:1.003557+0.087451 
## [207]    train-rmse:0.424627+0.011944    test-rmse:1.003363+0.086774 
## [208]    train-rmse:0.422641+0.011979    test-rmse:1.003712+0.086310 
## [209]    train-rmse:0.420897+0.012346    test-rmse:1.003238+0.086246 
## [210]    train-rmse:0.419360+0.012420    test-rmse:1.002877+0.086432 
## [211]    train-rmse:0.417834+0.012192    test-rmse:1.003465+0.087983 
## [212]    train-rmse:0.416384+0.012601    test-rmse:1.003961+0.087585 
## [213]    train-rmse:0.415545+0.012470    test-rmse:1.003123+0.087959 
## [214]    train-rmse:0.414074+0.011833    test-rmse:1.003766+0.087819 
## [215]    train-rmse:0.412659+0.011480    test-rmse:1.003330+0.087804 
## [216]    train-rmse:0.410573+0.011324    test-rmse:1.001878+0.087807 
## [217]    train-rmse:0.408743+0.011620    test-rmse:1.001070+0.087992 
## [218]    train-rmse:0.406878+0.011583    test-rmse:1.000861+0.087450 
## [219]    train-rmse:0.405001+0.012138    test-rmse:1.001019+0.087529 
## [220]    train-rmse:0.403826+0.012244    test-rmse:1.000120+0.087419 
## [221]    train-rmse:0.401993+0.012107    test-rmse:0.999254+0.088618 
## [222]    train-rmse:0.400077+0.012502    test-rmse:0.999329+0.087992 
## [223]    train-rmse:0.398991+0.012159    test-rmse:0.998720+0.088151 
## [224]    train-rmse:0.397054+0.012832    test-rmse:0.998773+0.088613 
## [225]    train-rmse:0.396145+0.012864    test-rmse:0.998152+0.088204 
## [226]    train-rmse:0.394690+0.012584    test-rmse:0.998050+0.088177 
## [227]    train-rmse:0.393521+0.012585    test-rmse:0.997852+0.088014 
## [228]    train-rmse:0.392544+0.012434    test-rmse:0.997749+0.088545 
## [229]    train-rmse:0.390934+0.012504    test-rmse:0.997465+0.087719 
## [230]    train-rmse:0.389550+0.012876    test-rmse:0.996821+0.086877 
## [231]    train-rmse:0.387099+0.014134    test-rmse:0.996248+0.086783 
## [232]    train-rmse:0.385896+0.014274    test-rmse:0.995571+0.087013 
## [233]    train-rmse:0.384892+0.014162    test-rmse:0.994561+0.087187 
## [234]    train-rmse:0.382849+0.014108    test-rmse:0.993563+0.086385 
## [235]    train-rmse:0.381361+0.014151    test-rmse:0.993162+0.086081 
## [236]    train-rmse:0.378996+0.013447    test-rmse:0.993582+0.086192 
## [237]    train-rmse:0.377725+0.013264    test-rmse:0.992693+0.086495 
## [238]    train-rmse:0.376084+0.013428    test-rmse:0.989124+0.082835 
## [239]    train-rmse:0.374999+0.013395    test-rmse:0.988659+0.081785 
## [240]    train-rmse:0.373607+0.013769    test-rmse:0.988549+0.082042 
## [241]    train-rmse:0.372129+0.013578    test-rmse:0.988512+0.082211 
## [242]    train-rmse:0.371046+0.013829    test-rmse:0.988281+0.082671 
## [243]    train-rmse:0.369564+0.014122    test-rmse:0.987993+0.082370 
## [244]    train-rmse:0.367068+0.014535    test-rmse:0.988112+0.081972 
## [245]    train-rmse:0.366040+0.014845    test-rmse:0.988310+0.082217 
## [246]    train-rmse:0.364780+0.015296    test-rmse:0.987977+0.082505 
## [247]    train-rmse:0.363084+0.015217    test-rmse:0.988563+0.082366 
## [248]    train-rmse:0.361598+0.015404    test-rmse:0.987838+0.081612 
## [249]    train-rmse:0.359951+0.014913    test-rmse:0.987137+0.082670 
## [250]    train-rmse:0.358882+0.015145    test-rmse:0.986809+0.082083 
## [251]    train-rmse:0.358096+0.014810    test-rmse:0.986562+0.082680 
## [252]    train-rmse:0.356340+0.015048    test-rmse:0.986652+0.082805 
## [253]    train-rmse:0.354282+0.015568    test-rmse:0.985808+0.083576 
## [254]    train-rmse:0.352994+0.015438    test-rmse:0.985718+0.082668 
## [255]    train-rmse:0.352060+0.015303    test-rmse:0.986041+0.082549 
## [256]    train-rmse:0.351113+0.015459    test-rmse:0.985983+0.082868 
## [257]    train-rmse:0.349807+0.015478    test-rmse:0.986549+0.082956 
## [258]    train-rmse:0.348717+0.015603    test-rmse:0.987827+0.084759 
## [259]    train-rmse:0.347684+0.015505    test-rmse:0.989395+0.085875 
## [260]    train-rmse:0.345802+0.015384    test-rmse:0.985830+0.082896 
## Stopping. Best iteration:
## [254]    train-rmse:0.352994+0.015438    test-rmse:0.985718+0.082668
## 
## 10 
## [1]  train-rmse:86.704080+0.086998   test-rmse:86.704831+0.406915 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.342288+0.076506   test-rmse:76.342926+0.417100 
## [3]  train-rmse:67.220384+0.067270   test-rmse:67.220891+0.426011 
## [4]  train-rmse:59.190229+0.059099   test-rmse:59.190588+0.433783 
## [5]  train-rmse:52.121395+0.051883   test-rmse:52.121578+0.440539 
## [6]  train-rmse:45.899076+0.045513   test-rmse:45.899065+0.446386 
## [7]  train-rmse:40.422199+0.039869   test-rmse:40.421979+0.451412 
## [8]  train-rmse:35.601835+0.034876   test-rmse:35.601369+0.455694 
## [9]  train-rmse:31.359680+0.030457   test-rmse:31.358941+0.459291 
## [10] train-rmse:27.626835+0.026543   test-rmse:27.625793+0.462249 
## [11] train-rmse:24.342670+0.023071   test-rmse:24.341286+0.464606 
## [12] train-rmse:21.453838+0.019997   test-rmse:21.452077+0.466382 
## [13] train-rmse:18.913416+0.017291   test-rmse:18.911232+0.467585 
## [14] train-rmse:16.680124+0.014931   test-rmse:16.677478+0.468208 
## [15] train-rmse:14.717049+0.012912   test-rmse:14.717037+0.454197 
## [16] train-rmse:12.991833+0.011108   test-rmse:12.980617+0.452674 
## [17] train-rmse:11.475719+0.009385   test-rmse:11.467150+0.438504 
## [18] train-rmse:10.143055+0.007675   test-rmse:10.129140+0.427151 
## [19] train-rmse:8.970805+0.006670    test-rmse:8.950908+0.420274 
## [20] train-rmse:7.940881+0.005761    test-rmse:7.923774+0.398042 
## [21] train-rmse:7.035267+0.004886    test-rmse:7.022973+0.382856 
## [22] train-rmse:6.239656+0.004130    test-rmse:6.228714+0.366489 
## [23] train-rmse:5.540599+0.004375    test-rmse:5.535328+0.349520 
## [24] train-rmse:4.926272+0.003805    test-rmse:4.922588+0.333672 
## [25] train-rmse:4.387596+0.003544    test-rmse:4.386799+0.321908 
## [26] train-rmse:3.915342+0.004811    test-rmse:3.923805+0.305630 
## [27] train-rmse:3.500066+0.004203    test-rmse:3.513140+0.295862 
## [28] train-rmse:3.138259+0.004168    test-rmse:3.154216+0.284798 
## [29] train-rmse:2.820254+0.004811    test-rmse:2.850427+0.267774 
## [30] train-rmse:2.543126+0.003110    test-rmse:2.583970+0.250353 
## [31] train-rmse:2.303836+0.004732    test-rmse:2.356156+0.239119 
## [32] train-rmse:2.096198+0.007209    test-rmse:2.166274+0.231501 
## [33] train-rmse:1.915114+0.006546    test-rmse:2.004724+0.215618 
## [34] train-rmse:1.760644+0.005823    test-rmse:1.868427+0.201297 
## [35] train-rmse:1.627484+0.007873    test-rmse:1.749707+0.188073 
## [36] train-rmse:1.510602+0.007302    test-rmse:1.653987+0.177685 
## [37] train-rmse:1.413919+0.008253    test-rmse:1.577743+0.165616 
## [38] train-rmse:1.328622+0.009954    test-rmse:1.513877+0.158158 
## [39] train-rmse:1.257624+0.009159    test-rmse:1.457135+0.150346 
## [40] train-rmse:1.196861+0.009355    test-rmse:1.412351+0.146479 
## [41] train-rmse:1.144686+0.011003    test-rmse:1.375775+0.138806 
## [42] train-rmse:1.099348+0.013528    test-rmse:1.344080+0.132186 
## [43] train-rmse:1.057451+0.016316    test-rmse:1.316189+0.128702 
## [44] train-rmse:1.022315+0.014672    test-rmse:1.294110+0.123208 
## [45] train-rmse:0.992570+0.013109    test-rmse:1.275631+0.119348 
## [46] train-rmse:0.966737+0.015244    test-rmse:1.262441+0.117650 
## [47] train-rmse:0.945293+0.016696    test-rmse:1.250188+0.114430 
## [48] train-rmse:0.926674+0.016245    test-rmse:1.238905+0.111647 
## [49] train-rmse:0.906332+0.015774    test-rmse:1.227595+0.111438 
## [50] train-rmse:0.889481+0.017089    test-rmse:1.219218+0.112881 
## [51] train-rmse:0.875433+0.018488    test-rmse:1.212891+0.110784 
## [52] train-rmse:0.861223+0.018713    test-rmse:1.202975+0.109123 
## [53] train-rmse:0.845192+0.018351    test-rmse:1.190458+0.103078 
## [54] train-rmse:0.830685+0.015213    test-rmse:1.182269+0.101987 
## [55] train-rmse:0.817301+0.014237    test-rmse:1.176063+0.097456 
## [56] train-rmse:0.805686+0.014815    test-rmse:1.171601+0.097256 
## [57] train-rmse:0.795274+0.017795    test-rmse:1.163265+0.097654 
## [58] train-rmse:0.785598+0.018420    test-rmse:1.156876+0.096016 
## [59] train-rmse:0.775891+0.018161    test-rmse:1.150525+0.094741 
## [60] train-rmse:0.767197+0.016806    test-rmse:1.147721+0.094400 
## [61] train-rmse:0.755606+0.015678    test-rmse:1.135810+0.089882 
## [62] train-rmse:0.746906+0.016359    test-rmse:1.132956+0.086809 
## [63] train-rmse:0.738506+0.017505    test-rmse:1.127141+0.086592 
## [64] train-rmse:0.729574+0.018246    test-rmse:1.123242+0.086255 
## [65] train-rmse:0.722468+0.020011    test-rmse:1.118068+0.083647 
## [66] train-rmse:0.715525+0.020213    test-rmse:1.112940+0.084087 
## [67] train-rmse:0.708479+0.020902    test-rmse:1.110054+0.084970 
## [68] train-rmse:0.701364+0.020463    test-rmse:1.102819+0.083106 
## [69] train-rmse:0.695061+0.019249    test-rmse:1.100267+0.080257 
## [70] train-rmse:0.685890+0.018908    test-rmse:1.092542+0.075384 
## [71] train-rmse:0.679859+0.018837    test-rmse:1.091357+0.075995 
## [72] train-rmse:0.672262+0.018442    test-rmse:1.085841+0.076101 
## [73] train-rmse:0.665548+0.018754    test-rmse:1.082520+0.075098 
## [74] train-rmse:0.657398+0.016967    test-rmse:1.076988+0.073952 
## [75] train-rmse:0.651762+0.017337    test-rmse:1.073481+0.074404 
## [76] train-rmse:0.644664+0.017392    test-rmse:1.070522+0.073607 
## [77] train-rmse:0.639027+0.018471    test-rmse:1.067089+0.072980 
## [78] train-rmse:0.632017+0.017608    test-rmse:1.060201+0.073169 
## [79] train-rmse:0.626935+0.017975    test-rmse:1.058939+0.071835 
## [80] train-rmse:0.620813+0.017962    test-rmse:1.054754+0.069868 
## [81] train-rmse:0.615692+0.019265    test-rmse:1.051806+0.070055 
## [82] train-rmse:0.611315+0.019508    test-rmse:1.049421+0.070939 
## [83] train-rmse:0.604835+0.018119    test-rmse:1.046421+0.071833 
## [84] train-rmse:0.600583+0.018051    test-rmse:1.044093+0.071342 
## [85] train-rmse:0.593806+0.016972    test-rmse:1.040526+0.070884 
## [86] train-rmse:0.587431+0.016434    test-rmse:1.038235+0.070507 
## [87] train-rmse:0.582348+0.015777    test-rmse:1.037421+0.073064 
## [88] train-rmse:0.576704+0.015286    test-rmse:1.036407+0.074354 
## [89] train-rmse:0.572879+0.015089    test-rmse:1.032616+0.074372 
## [90] train-rmse:0.568536+0.015229    test-rmse:1.029301+0.073157 
## [91] train-rmse:0.563624+0.015413    test-rmse:1.026316+0.072293 
## [92] train-rmse:0.557578+0.016920    test-rmse:1.023178+0.071706 
## [93] train-rmse:0.552687+0.017164    test-rmse:1.022557+0.070791 
## [94] train-rmse:0.548394+0.017056    test-rmse:1.018953+0.071025 
## [95] train-rmse:0.544978+0.016946    test-rmse:1.018571+0.071466 
## [96] train-rmse:0.541094+0.016928    test-rmse:1.016435+0.071221 
## [97] train-rmse:0.536273+0.016828    test-rmse:1.014458+0.071315 
## [98] train-rmse:0.531923+0.017140    test-rmse:1.012040+0.072359 
## [99] train-rmse:0.527964+0.018455    test-rmse:1.011088+0.069770 
## [100]    train-rmse:0.524932+0.018284    test-rmse:1.009985+0.069577 
## [101]    train-rmse:0.519799+0.017201    test-rmse:1.009130+0.069475 
## [102]    train-rmse:0.515424+0.016625    test-rmse:1.008363+0.069330 
## [103]    train-rmse:0.511615+0.015350    test-rmse:1.007395+0.069854 
## [104]    train-rmse:0.508001+0.015131    test-rmse:1.007097+0.068081 
## [105]    train-rmse:0.504367+0.015786    test-rmse:1.006597+0.067958 
## [106]    train-rmse:0.501459+0.016575    test-rmse:1.006180+0.066887 
## [107]    train-rmse:0.497477+0.016108    test-rmse:1.003957+0.067010 
## [108]    train-rmse:0.492344+0.014391    test-rmse:1.002141+0.066905 
## [109]    train-rmse:0.486942+0.014262    test-rmse:1.001140+0.068968 
## [110]    train-rmse:0.482266+0.014101    test-rmse:0.999099+0.067430 
## [111]    train-rmse:0.477538+0.012716    test-rmse:0.998589+0.065857 
## [112]    train-rmse:0.474314+0.013322    test-rmse:0.999203+0.064855 
## [113]    train-rmse:0.470249+0.013446    test-rmse:0.998476+0.065238 
## [114]    train-rmse:0.467588+0.013226    test-rmse:0.997313+0.065366 
## [115]    train-rmse:0.464584+0.013390    test-rmse:0.996732+0.065910 
## [116]    train-rmse:0.462028+0.013656    test-rmse:0.995573+0.065880 
## [117]    train-rmse:0.458859+0.014440    test-rmse:0.993471+0.066873 
## [118]    train-rmse:0.455741+0.013999    test-rmse:0.995611+0.065955 
## [119]    train-rmse:0.453312+0.013522    test-rmse:0.994880+0.065973 
## [120]    train-rmse:0.450147+0.013491    test-rmse:0.993140+0.066652 
## [121]    train-rmse:0.446793+0.013492    test-rmse:0.991093+0.066964 
## [122]    train-rmse:0.443637+0.013350    test-rmse:0.989543+0.065225 
## [123]    train-rmse:0.440938+0.013250    test-rmse:0.988985+0.066286 
## [124]    train-rmse:0.436289+0.013708    test-rmse:0.989593+0.065455 
## [125]    train-rmse:0.433094+0.013424    test-rmse:0.987760+0.065525 
## [126]    train-rmse:0.430216+0.014521    test-rmse:0.987592+0.066216 
## [127]    train-rmse:0.426819+0.013101    test-rmse:0.986923+0.065306 
## [128]    train-rmse:0.423292+0.012707    test-rmse:0.987521+0.064953 
## [129]    train-rmse:0.419641+0.013700    test-rmse:0.988126+0.064394 
## [130]    train-rmse:0.417443+0.013673    test-rmse:0.987547+0.063579 
## [131]    train-rmse:0.413440+0.012503    test-rmse:0.987408+0.063499 
## [132]    train-rmse:0.411290+0.012523    test-rmse:0.987138+0.062345 
## [133]    train-rmse:0.409273+0.012659    test-rmse:0.986470+0.062571 
## [134]    train-rmse:0.406316+0.013745    test-rmse:0.985087+0.062994 
## [135]    train-rmse:0.403297+0.013339    test-rmse:0.983628+0.062827 
## [136]    train-rmse:0.400215+0.013482    test-rmse:0.983478+0.061360 
## [137]    train-rmse:0.398100+0.013184    test-rmse:0.983476+0.060910 
## [138]    train-rmse:0.395844+0.013314    test-rmse:0.982135+0.060824 
## [139]    train-rmse:0.393289+0.012214    test-rmse:0.983126+0.060843 
## [140]    train-rmse:0.389266+0.011774    test-rmse:0.983129+0.061432 
## [141]    train-rmse:0.386793+0.012316    test-rmse:0.983735+0.059798 
## [142]    train-rmse:0.384615+0.012748    test-rmse:0.983194+0.059993 
## [143]    train-rmse:0.382615+0.012845    test-rmse:0.983991+0.060034 
## [144]    train-rmse:0.379641+0.013958    test-rmse:0.983534+0.058992 
## Stopping. Best iteration:
## [138]    train-rmse:0.395844+0.013314    test-rmse:0.982135+0.060824
## 
## 11 
## [1]  train-rmse:86.704080+0.086998   test-rmse:86.704831+0.406915 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.342288+0.076506   test-rmse:76.342926+0.417100 
## [3]  train-rmse:67.220384+0.067270   test-rmse:67.220891+0.426011 
## [4]  train-rmse:59.190229+0.059099   test-rmse:59.190588+0.433783 
## [5]  train-rmse:52.121395+0.051883   test-rmse:52.121578+0.440539 
## [6]  train-rmse:45.899076+0.045513   test-rmse:45.899065+0.446386 
## [7]  train-rmse:40.422199+0.039869   test-rmse:40.421979+0.451412 
## [8]  train-rmse:35.601835+0.034876   test-rmse:35.601369+0.455694 
## [9]  train-rmse:31.359680+0.030457   test-rmse:31.358941+0.459291 
## [10] train-rmse:27.626835+0.026543   test-rmse:27.625793+0.462249 
## [11] train-rmse:24.342670+0.023071   test-rmse:24.341286+0.464606 
## [12] train-rmse:21.453838+0.019997   test-rmse:21.452077+0.466382 
## [13] train-rmse:18.913416+0.017291   test-rmse:18.911232+0.467585 
## [14] train-rmse:16.680124+0.014931   test-rmse:16.677478+0.468208 
## [15] train-rmse:14.717049+0.012912   test-rmse:14.717037+0.454197 
## [16] train-rmse:12.991833+0.011108   test-rmse:12.980617+0.452674 
## [17] train-rmse:11.475719+0.009385   test-rmse:11.467150+0.438504 
## [18] train-rmse:10.143055+0.007675   test-rmse:10.129140+0.427151 
## [19] train-rmse:8.970698+0.006836    test-rmse:8.949548+0.418495 
## [20] train-rmse:7.940652+0.006047    test-rmse:7.923759+0.397811 
## [21] train-rmse:7.034612+0.005403    test-rmse:7.022922+0.382875 
## [22] train-rmse:6.237830+0.005355    test-rmse:6.226893+0.366578 
## [23] train-rmse:5.538006+0.004430    test-rmse:5.534187+0.345654 
## [24] train-rmse:4.922755+0.004016    test-rmse:4.920044+0.335732 
## [25] train-rmse:4.382811+0.004370    test-rmse:4.380032+0.321752 
## [26] train-rmse:3.907297+0.004005    test-rmse:3.914115+0.304359 
## [27] train-rmse:3.490110+0.002727    test-rmse:3.498806+0.298394 
## [28] train-rmse:3.122818+0.003839    test-rmse:3.146467+0.280012 
## [29] train-rmse:2.800955+0.004778    test-rmse:2.839688+0.262370 
## [30] train-rmse:2.520187+0.006565    test-rmse:2.571602+0.249825 
## [31] train-rmse:2.273294+0.005187    test-rmse:2.342704+0.238273 
## [32] train-rmse:2.057841+0.004450    test-rmse:2.144558+0.225066 
## [33] train-rmse:1.869773+0.003843    test-rmse:1.978923+0.214768 
## [34] train-rmse:1.709572+0.005938    test-rmse:1.836980+0.202975 
## [35] train-rmse:1.567774+0.006286    test-rmse:1.718737+0.196012 
## [36] train-rmse:1.447609+0.005907    test-rmse:1.614722+0.187842 
## [37] train-rmse:1.342543+0.004133    test-rmse:1.538247+0.177950 
## [38] train-rmse:1.250474+0.006412    test-rmse:1.466772+0.168391 
## [39] train-rmse:1.171992+0.005452    test-rmse:1.409623+0.161478 
## [40] train-rmse:1.102244+0.005861    test-rmse:1.365817+0.152776 
## [41] train-rmse:1.044782+0.005891    test-rmse:1.325839+0.145636 
## [42] train-rmse:0.990570+0.005212    test-rmse:1.292785+0.138173 
## [43] train-rmse:0.945659+0.003795    test-rmse:1.265614+0.133535 
## [44] train-rmse:0.908008+0.002999    test-rmse:1.243138+0.129270 
## [45] train-rmse:0.876103+0.003192    test-rmse:1.222983+0.126277 
## [46] train-rmse:0.848122+0.003886    test-rmse:1.208242+0.121067 
## [47] train-rmse:0.820667+0.003947    test-rmse:1.191799+0.117534 
## [48] train-rmse:0.796000+0.004021    test-rmse:1.179611+0.116793 
## [49] train-rmse:0.775833+0.003918    test-rmse:1.170476+0.114801 
## [50] train-rmse:0.755251+0.005424    test-rmse:1.161713+0.114072 
## [51] train-rmse:0.736938+0.006503    test-rmse:1.151879+0.113350 
## [52] train-rmse:0.722330+0.006359    test-rmse:1.141870+0.109422 
## [53] train-rmse:0.709004+0.006758    test-rmse:1.135985+0.106566 
## [54] train-rmse:0.695826+0.007725    test-rmse:1.129703+0.105409 
## [55] train-rmse:0.683472+0.008072    test-rmse:1.122939+0.105432 
## [56] train-rmse:0.670557+0.008993    test-rmse:1.113193+0.099639 
## [57] train-rmse:0.656986+0.009235    test-rmse:1.109603+0.099098 
## [58] train-rmse:0.645663+0.010709    test-rmse:1.101313+0.098561 
## [59] train-rmse:0.635653+0.012894    test-rmse:1.094812+0.098578 
## [60] train-rmse:0.624810+0.013866    test-rmse:1.092592+0.097425 
## [61] train-rmse:0.614151+0.011939    test-rmse:1.088421+0.099236 
## [62] train-rmse:0.605695+0.013252    test-rmse:1.085125+0.098051 
## [63] train-rmse:0.597494+0.011828    test-rmse:1.078611+0.094997 
## [64] train-rmse:0.589684+0.011791    test-rmse:1.075431+0.096207 
## [65] train-rmse:0.582496+0.011318    test-rmse:1.071633+0.096118 
## [66] train-rmse:0.573014+0.010305    test-rmse:1.068064+0.097667 
## [67] train-rmse:0.564248+0.011964    test-rmse:1.063347+0.097790 
## [68] train-rmse:0.557060+0.012473    test-rmse:1.059587+0.096981 
## [69] train-rmse:0.549469+0.012809    test-rmse:1.055966+0.096416 
## [70] train-rmse:0.542705+0.012059    test-rmse:1.049561+0.094755 
## [71] train-rmse:0.536287+0.012233    test-rmse:1.047785+0.093595 
## [72] train-rmse:0.529723+0.011253    test-rmse:1.043248+0.093285 
## [73] train-rmse:0.523866+0.012614    test-rmse:1.041289+0.095959 
## [74] train-rmse:0.516870+0.011812    test-rmse:1.036654+0.094659 
## [75] train-rmse:0.510010+0.011468    test-rmse:1.035313+0.094514 
## [76] train-rmse:0.502852+0.012892    test-rmse:1.033989+0.093068 
## [77] train-rmse:0.497872+0.011818    test-rmse:1.031220+0.093357 
## [78] train-rmse:0.493121+0.011819    test-rmse:1.029959+0.092367 
## [79] train-rmse:0.487301+0.010948    test-rmse:1.025962+0.090784 
## [80] train-rmse:0.482592+0.010999    test-rmse:1.024755+0.089915 
## [81] train-rmse:0.476678+0.009451    test-rmse:1.023257+0.091488 
## [82] train-rmse:0.472276+0.009469    test-rmse:1.022573+0.092620 
## [83] train-rmse:0.466728+0.008771    test-rmse:1.020164+0.092311 
## [84] train-rmse:0.459694+0.009301    test-rmse:1.017572+0.092041 
## [85] train-rmse:0.455854+0.009020    test-rmse:1.016612+0.093310 
## [86] train-rmse:0.450331+0.008764    test-rmse:1.013472+0.091722 
## [87] train-rmse:0.445247+0.008231    test-rmse:1.011551+0.089774 
## [88] train-rmse:0.440791+0.008135    test-rmse:1.010251+0.089780 
## [89] train-rmse:0.436490+0.008457    test-rmse:1.008157+0.090605 
## [90] train-rmse:0.431229+0.008756    test-rmse:1.007592+0.091451 
## [91] train-rmse:0.426669+0.009203    test-rmse:1.006179+0.091590 
## [92] train-rmse:0.423082+0.008967    test-rmse:1.006823+0.091820 
## [93] train-rmse:0.419047+0.008963    test-rmse:1.006008+0.091307 
## [94] train-rmse:0.415332+0.009451    test-rmse:1.003966+0.092459 
## [95] train-rmse:0.410474+0.008402    test-rmse:1.003079+0.092818 
## [96] train-rmse:0.403901+0.007559    test-rmse:1.000850+0.092550 
## [97] train-rmse:0.400574+0.008156    test-rmse:0.999753+0.092617 
## [98] train-rmse:0.397147+0.008900    test-rmse:0.999185+0.093183 
## [99] train-rmse:0.393315+0.008629    test-rmse:0.997653+0.093216 
## [100]    train-rmse:0.389139+0.008984    test-rmse:0.996035+0.092155 
## [101]    train-rmse:0.384450+0.007995    test-rmse:0.995639+0.092957 
## [102]    train-rmse:0.380502+0.007773    test-rmse:0.993857+0.092404 
## [103]    train-rmse:0.375697+0.008593    test-rmse:0.993042+0.091276 
## [104]    train-rmse:0.372598+0.009337    test-rmse:0.990983+0.091516 
## [105]    train-rmse:0.369038+0.010107    test-rmse:0.990204+0.090931 
## [106]    train-rmse:0.365780+0.009752    test-rmse:0.989605+0.091338 
## [107]    train-rmse:0.362613+0.010170    test-rmse:0.988056+0.092163 
## [108]    train-rmse:0.360204+0.009855    test-rmse:0.987375+0.091187 
## [109]    train-rmse:0.356010+0.010238    test-rmse:0.986178+0.091130 
## [110]    train-rmse:0.351969+0.010952    test-rmse:0.984351+0.091430 
## [111]    train-rmse:0.348077+0.010513    test-rmse:0.982920+0.092329 
## [112]    train-rmse:0.343875+0.009796    test-rmse:0.981807+0.092560 
## [113]    train-rmse:0.340158+0.009856    test-rmse:0.980503+0.091960 
## [114]    train-rmse:0.336397+0.010441    test-rmse:0.979629+0.091294 
## [115]    train-rmse:0.333010+0.009960    test-rmse:0.978937+0.091545 
## [116]    train-rmse:0.328919+0.010820    test-rmse:0.979617+0.092715 
## [117]    train-rmse:0.325776+0.010671    test-rmse:0.978554+0.092096 
## [118]    train-rmse:0.322750+0.010954    test-rmse:0.978497+0.092409 
## [119]    train-rmse:0.319513+0.010339    test-rmse:0.977228+0.092482 
## [120]    train-rmse:0.316591+0.010307    test-rmse:0.977538+0.092859 
## [121]    train-rmse:0.314482+0.010062    test-rmse:0.978161+0.092949 
## [122]    train-rmse:0.310930+0.010028    test-rmse:0.979847+0.092597 
## [123]    train-rmse:0.308195+0.009416    test-rmse:0.978291+0.093068 
## [124]    train-rmse:0.305544+0.009831    test-rmse:0.976825+0.092835 
## [125]    train-rmse:0.302772+0.010576    test-rmse:0.976872+0.092888 
## [126]    train-rmse:0.300429+0.010672    test-rmse:0.976181+0.093293 
## [127]    train-rmse:0.295719+0.010528    test-rmse:0.974909+0.092726 
## [128]    train-rmse:0.293971+0.010104    test-rmse:0.974336+0.091679 
## [129]    train-rmse:0.291610+0.010711    test-rmse:0.973687+0.091961 
## [130]    train-rmse:0.289153+0.011250    test-rmse:0.972299+0.090939 
## [131]    train-rmse:0.286939+0.011909    test-rmse:0.971131+0.090696 
## [132]    train-rmse:0.285042+0.011429    test-rmse:0.971272+0.090722 
## [133]    train-rmse:0.283001+0.011591    test-rmse:0.970692+0.090227 
## [134]    train-rmse:0.280663+0.011604    test-rmse:0.970422+0.090157 
## [135]    train-rmse:0.276908+0.009248    test-rmse:0.968656+0.090206 
## [136]    train-rmse:0.274504+0.009477    test-rmse:0.967715+0.089483 
## [137]    train-rmse:0.271881+0.010005    test-rmse:0.967028+0.089517 
## [138]    train-rmse:0.269593+0.009096    test-rmse:0.966073+0.089065 
## [139]    train-rmse:0.267256+0.009716    test-rmse:0.966111+0.088710 
## [140]    train-rmse:0.264502+0.010253    test-rmse:0.964906+0.088739 
## [141]    train-rmse:0.262076+0.010666    test-rmse:0.965368+0.088703 
## [142]    train-rmse:0.259379+0.010903    test-rmse:0.964597+0.088295 
## [143]    train-rmse:0.257527+0.011362    test-rmse:0.963849+0.087619 
## [144]    train-rmse:0.254496+0.010297    test-rmse:0.962781+0.088017 
## [145]    train-rmse:0.251945+0.009660    test-rmse:0.962058+0.088607 
## [146]    train-rmse:0.250115+0.009326    test-rmse:0.961996+0.088635 
## [147]    train-rmse:0.247882+0.009269    test-rmse:0.962128+0.088902 
## [148]    train-rmse:0.245034+0.008289    test-rmse:0.960626+0.088663 
## [149]    train-rmse:0.242308+0.008810    test-rmse:0.960278+0.087528 
## [150]    train-rmse:0.240163+0.008911    test-rmse:0.959890+0.087203 
## [151]    train-rmse:0.238187+0.008626    test-rmse:0.959053+0.087214 
## [152]    train-rmse:0.235444+0.008374    test-rmse:0.958438+0.087265 
## [153]    train-rmse:0.232554+0.007333    test-rmse:0.957084+0.088544 
## [154]    train-rmse:0.229955+0.008208    test-rmse:0.957168+0.088900 
## [155]    train-rmse:0.228480+0.007940    test-rmse:0.957288+0.088684 
## [156]    train-rmse:0.226222+0.008884    test-rmse:0.956847+0.088543 
## [157]    train-rmse:0.224975+0.009272    test-rmse:0.955927+0.089090 
## [158]    train-rmse:0.223276+0.008944    test-rmse:0.955616+0.090000 
## [159]    train-rmse:0.221707+0.008845    test-rmse:0.954958+0.089556 
## [160]    train-rmse:0.219804+0.008868    test-rmse:0.954233+0.089944 
## [161]    train-rmse:0.217736+0.009911    test-rmse:0.953298+0.089587 
## [162]    train-rmse:0.215349+0.010175    test-rmse:0.952787+0.089405 
## [163]    train-rmse:0.212566+0.009243    test-rmse:0.952486+0.089590 
## [164]    train-rmse:0.211334+0.008938    test-rmse:0.952159+0.089488 
## [165]    train-rmse:0.209407+0.009043    test-rmse:0.952072+0.089197 
## [166]    train-rmse:0.206721+0.008141    test-rmse:0.951288+0.088803 
## [167]    train-rmse:0.205140+0.008430    test-rmse:0.951619+0.089033 
## [168]    train-rmse:0.202482+0.007883    test-rmse:0.951127+0.088775 
## [169]    train-rmse:0.201149+0.007770    test-rmse:0.951416+0.088446 
## [170]    train-rmse:0.199973+0.007928    test-rmse:0.951701+0.088594 
## [171]    train-rmse:0.197751+0.007497    test-rmse:0.950816+0.088876 
## [172]    train-rmse:0.196291+0.007848    test-rmse:0.950349+0.088560 
## [173]    train-rmse:0.194376+0.007538    test-rmse:0.950209+0.087851 
## [174]    train-rmse:0.191952+0.007828    test-rmse:0.950059+0.087631 
## [175]    train-rmse:0.190266+0.007962    test-rmse:0.949814+0.087750 
## [176]    train-rmse:0.188511+0.007740    test-rmse:0.949328+0.087585 
## [177]    train-rmse:0.186772+0.007805    test-rmse:0.949411+0.087679 
## [178]    train-rmse:0.185193+0.007448    test-rmse:0.949396+0.087515 
## [179]    train-rmse:0.183841+0.007071    test-rmse:0.949404+0.087240 
## [180]    train-rmse:0.181938+0.007378    test-rmse:0.948380+0.087796 
## [181]    train-rmse:0.180786+0.007781    test-rmse:0.948402+0.088075 
## [182]    train-rmse:0.178886+0.007677    test-rmse:0.948627+0.088836 
## [183]    train-rmse:0.177593+0.007491    test-rmse:0.949143+0.089502 
## [184]    train-rmse:0.176158+0.007011    test-rmse:0.949627+0.089953 
## [185]    train-rmse:0.175028+0.007098    test-rmse:0.949039+0.089602 
## [186]    train-rmse:0.173088+0.006481    test-rmse:0.948812+0.089877 
## Stopping. Best iteration:
## [180]    train-rmse:0.181938+0.007378    test-rmse:0.948380+0.087796
## 
## 12 
## [1]  train-rmse:86.704080+0.086998   test-rmse:86.704831+0.406915 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.342288+0.076506   test-rmse:76.342926+0.417100 
## [3]  train-rmse:67.220384+0.067270   test-rmse:67.220891+0.426011 
## [4]  train-rmse:59.190229+0.059099   test-rmse:59.190588+0.433783 
## [5]  train-rmse:52.121395+0.051883   test-rmse:52.121578+0.440539 
## [6]  train-rmse:45.899076+0.045513   test-rmse:45.899065+0.446386 
## [7]  train-rmse:40.422199+0.039869   test-rmse:40.421979+0.451412 
## [8]  train-rmse:35.601835+0.034876   test-rmse:35.601369+0.455694 
## [9]  train-rmse:31.359680+0.030457   test-rmse:31.358941+0.459291 
## [10] train-rmse:27.626835+0.026543   test-rmse:27.625793+0.462249 
## [11] train-rmse:24.342670+0.023071   test-rmse:24.341286+0.464606 
## [12] train-rmse:21.453838+0.019997   test-rmse:21.452077+0.466382 
## [13] train-rmse:18.913416+0.017291   test-rmse:18.911232+0.467585 
## [14] train-rmse:16.680124+0.014931   test-rmse:16.677478+0.468208 
## [15] train-rmse:14.717049+0.012912   test-rmse:14.717037+0.454197 
## [16] train-rmse:12.991833+0.011108   test-rmse:12.980617+0.452674 
## [17] train-rmse:11.475719+0.009385   test-rmse:11.467150+0.438504 
## [18] train-rmse:10.143055+0.007675   test-rmse:10.129140+0.427151 
## [19] train-rmse:8.970594+0.006998    test-rmse:8.951129+0.420565 
## [20] train-rmse:7.940393+0.006392    test-rmse:7.924440+0.399271 
## [21] train-rmse:7.034083+0.005786    test-rmse:7.021996+0.378463 
## [22] train-rmse:6.237705+0.004877    test-rmse:6.224243+0.361078 
## [23] train-rmse:5.537108+0.004233    test-rmse:5.531768+0.340030 
## [24] train-rmse:4.921336+0.003293    test-rmse:4.911486+0.329909 
## [25] train-rmse:4.379399+0.003633    test-rmse:4.380186+0.315199 
## [26] train-rmse:3.901963+0.002947    test-rmse:3.907497+0.298529 
## [27] train-rmse:3.481934+0.002010    test-rmse:3.495515+0.283696 
## [28] train-rmse:3.112788+0.003388    test-rmse:3.130073+0.276865 
## [29] train-rmse:2.789583+0.003233    test-rmse:2.822822+0.260453 
## [30] train-rmse:2.503696+0.004980    test-rmse:2.553019+0.245390 
## [31] train-rmse:2.253766+0.004046    test-rmse:2.319729+0.238977 
## [32] train-rmse:2.033991+0.005451    test-rmse:2.118112+0.224690 
## [33] train-rmse:1.843204+0.006898    test-rmse:1.946255+0.213855 
## [34] train-rmse:1.674573+0.005389    test-rmse:1.806288+0.204142 
## [35] train-rmse:1.529390+0.006603    test-rmse:1.684071+0.198368 
## [36] train-rmse:1.401023+0.006681    test-rmse:1.581284+0.188235 
## [37] train-rmse:1.287080+0.005796    test-rmse:1.495351+0.182270 
## [38] train-rmse:1.190826+0.006350    test-rmse:1.424354+0.170469 
## [39] train-rmse:1.107082+0.005405    test-rmse:1.369207+0.160281 
## [40] train-rmse:1.035265+0.007627    test-rmse:1.317627+0.154360 
## [41] train-rmse:0.969493+0.006477    test-rmse:1.273239+0.148488 
## [42] train-rmse:0.912657+0.005357    test-rmse:1.239892+0.147021 
## [43] train-rmse:0.862928+0.004669    test-rmse:1.211322+0.145540 
## [44] train-rmse:0.820173+0.006104    test-rmse:1.186792+0.135439 
## [45] train-rmse:0.780902+0.005532    test-rmse:1.166339+0.129327 
## [46] train-rmse:0.747629+0.006976    test-rmse:1.153265+0.125459 
## [47] train-rmse:0.717309+0.009174    test-rmse:1.138345+0.122156 
## [48] train-rmse:0.691392+0.012725    test-rmse:1.124384+0.121553 
## [49] train-rmse:0.666819+0.012554    test-rmse:1.111356+0.114680 
## [50] train-rmse:0.646586+0.013169    test-rmse:1.103947+0.111727 
## [51] train-rmse:0.628650+0.011818    test-rmse:1.094232+0.107254 
## [52] train-rmse:0.610056+0.012979    test-rmse:1.087557+0.105382 
## [53] train-rmse:0.595081+0.014384    test-rmse:1.082235+0.103300 
## [54] train-rmse:0.579720+0.014154    test-rmse:1.075647+0.104424 
## [55] train-rmse:0.566743+0.013355    test-rmse:1.069725+0.104013 
## [56] train-rmse:0.554959+0.012901    test-rmse:1.066252+0.102118 
## [57] train-rmse:0.543265+0.014528    test-rmse:1.058743+0.102460 
## [58] train-rmse:0.531229+0.015271    test-rmse:1.054958+0.101044 
## [59] train-rmse:0.521945+0.016358    test-rmse:1.051242+0.100384 
## [60] train-rmse:0.511653+0.016170    test-rmse:1.047100+0.100830 
## [61] train-rmse:0.502842+0.013877    test-rmse:1.041152+0.097941 
## [62] train-rmse:0.493996+0.012357    test-rmse:1.037616+0.093982 
## [63] train-rmse:0.486152+0.012523    test-rmse:1.033604+0.093045 
## [64] train-rmse:0.474495+0.015397    test-rmse:1.028234+0.089977 
## [65] train-rmse:0.466683+0.015281    test-rmse:1.025770+0.088533 
## [66] train-rmse:0.459672+0.014440    test-rmse:1.024745+0.087544 
## [67] train-rmse:0.450737+0.015910    test-rmse:1.020846+0.084856 
## [68] train-rmse:0.443054+0.014022    test-rmse:1.016893+0.083783 
## [69] train-rmse:0.436098+0.015341    test-rmse:1.016059+0.083629 
## [70] train-rmse:0.429066+0.014588    test-rmse:1.012266+0.083862 
## [71] train-rmse:0.423191+0.013634    test-rmse:1.011072+0.083701 
## [72] train-rmse:0.415965+0.014451    test-rmse:1.009290+0.083948 
## [73] train-rmse:0.410243+0.016143    test-rmse:1.007727+0.083831 
## [74] train-rmse:0.404703+0.016071    test-rmse:1.006270+0.084088 
## [75] train-rmse:0.396776+0.016402    test-rmse:1.003585+0.082654 
## [76] train-rmse:0.392398+0.017172    test-rmse:1.001796+0.082475 
## [77] train-rmse:0.386225+0.016705    test-rmse:1.001106+0.080131 
## [78] train-rmse:0.380024+0.015480    test-rmse:0.999413+0.079723 
## [79] train-rmse:0.373376+0.017032    test-rmse:0.998366+0.079696 
## [80] train-rmse:0.368662+0.016237    test-rmse:0.996987+0.079637 
## [81] train-rmse:0.363862+0.016526    test-rmse:0.996594+0.080906 
## [82] train-rmse:0.359124+0.017378    test-rmse:0.995616+0.080054 
## [83] train-rmse:0.353209+0.017156    test-rmse:0.995519+0.080627 
## [84] train-rmse:0.348701+0.016154    test-rmse:0.995190+0.080765 
## [85] train-rmse:0.343866+0.016542    test-rmse:0.993344+0.079997 
## [86] train-rmse:0.339182+0.016174    test-rmse:0.993452+0.080102 
## [87] train-rmse:0.334709+0.017052    test-rmse:0.991608+0.079175 
## [88] train-rmse:0.329453+0.016946    test-rmse:0.990119+0.079898 
## [89] train-rmse:0.324356+0.016633    test-rmse:0.990753+0.079879 
## [90] train-rmse:0.321064+0.017289    test-rmse:0.990928+0.080042 
## [91] train-rmse:0.317640+0.017735    test-rmse:0.989931+0.079477 
## [92] train-rmse:0.312953+0.019646    test-rmse:0.990360+0.079555 
## [93] train-rmse:0.308637+0.019944    test-rmse:0.989954+0.079186 
## [94] train-rmse:0.305132+0.020269    test-rmse:0.988310+0.079675 
## [95] train-rmse:0.299457+0.020407    test-rmse:0.988176+0.080113 
## [96] train-rmse:0.296111+0.020352    test-rmse:0.986643+0.080253 
## [97] train-rmse:0.292542+0.019942    test-rmse:0.984697+0.081032 
## [98] train-rmse:0.288410+0.019980    test-rmse:0.983286+0.081232 
## [99] train-rmse:0.283618+0.020741    test-rmse:0.982539+0.080923 
## [100]    train-rmse:0.279727+0.019413    test-rmse:0.982607+0.081111 
## [101]    train-rmse:0.275532+0.018949    test-rmse:0.981460+0.080765 
## [102]    train-rmse:0.272854+0.018533    test-rmse:0.981326+0.080972 
## [103]    train-rmse:0.269511+0.018480    test-rmse:0.981130+0.081178 
## [104]    train-rmse:0.266493+0.018202    test-rmse:0.980468+0.080155 
## [105]    train-rmse:0.262324+0.017807    test-rmse:0.979678+0.080400 
## [106]    train-rmse:0.259527+0.017796    test-rmse:0.980552+0.080944 
## [107]    train-rmse:0.255637+0.018143    test-rmse:0.979445+0.080919 
## [108]    train-rmse:0.251620+0.017754    test-rmse:0.979267+0.081236 
## [109]    train-rmse:0.249297+0.016687    test-rmse:0.978937+0.080930 
## [110]    train-rmse:0.245640+0.015968    test-rmse:0.978686+0.079374 
## [111]    train-rmse:0.243069+0.015494    test-rmse:0.978949+0.079743 
## [112]    train-rmse:0.240518+0.015058    test-rmse:0.979272+0.078281 
## [113]    train-rmse:0.237936+0.014793    test-rmse:0.979446+0.077994 
## [114]    train-rmse:0.234642+0.014315    test-rmse:0.979572+0.078034 
## [115]    train-rmse:0.230584+0.014411    test-rmse:0.978818+0.078090 
## [116]    train-rmse:0.227795+0.013757    test-rmse:0.977417+0.078408 
## [117]    train-rmse:0.225011+0.013721    test-rmse:0.975843+0.078000 
## [118]    train-rmse:0.222484+0.014481    test-rmse:0.974727+0.077253 
## [119]    train-rmse:0.219080+0.014768    test-rmse:0.974033+0.077420 
## [120]    train-rmse:0.215991+0.013367    test-rmse:0.972592+0.077347 
## [121]    train-rmse:0.213631+0.012767    test-rmse:0.972230+0.077579 
## [122]    train-rmse:0.211712+0.012234    test-rmse:0.972054+0.077551 
## [123]    train-rmse:0.209605+0.011899    test-rmse:0.971556+0.077731 
## [124]    train-rmse:0.206377+0.011263    test-rmse:0.970340+0.076718 
## [125]    train-rmse:0.203984+0.010954    test-rmse:0.969085+0.076909 
## [126]    train-rmse:0.201251+0.011308    test-rmse:0.968311+0.076302 
## [127]    train-rmse:0.198626+0.011233    test-rmse:0.969196+0.075636 
## [128]    train-rmse:0.196550+0.011136    test-rmse:0.969494+0.075220 
## [129]    train-rmse:0.194111+0.010626    test-rmse:0.969772+0.075288 
## [130]    train-rmse:0.191597+0.010940    test-rmse:0.968740+0.075029 
## [131]    train-rmse:0.188987+0.010717    test-rmse:0.968409+0.074922 
## [132]    train-rmse:0.187230+0.010845    test-rmse:0.968050+0.074307 
## [133]    train-rmse:0.184733+0.010609    test-rmse:0.967540+0.073524 
## [134]    train-rmse:0.181759+0.010058    test-rmse:0.967502+0.073002 
## [135]    train-rmse:0.179861+0.009258    test-rmse:0.966869+0.072374 
## [136]    train-rmse:0.177692+0.009354    test-rmse:0.967183+0.072421 
## [137]    train-rmse:0.175782+0.009178    test-rmse:0.967453+0.072400 
## [138]    train-rmse:0.174494+0.009240    test-rmse:0.966643+0.072573 
## [139]    train-rmse:0.171864+0.009659    test-rmse:0.966524+0.072418 
## [140]    train-rmse:0.169469+0.009321    test-rmse:0.965971+0.072168 
## [141]    train-rmse:0.167554+0.009483    test-rmse:0.965433+0.071793 
## [142]    train-rmse:0.164971+0.009884    test-rmse:0.965702+0.070653 
## [143]    train-rmse:0.163362+0.010011    test-rmse:0.965257+0.070817 
## [144]    train-rmse:0.160874+0.010621    test-rmse:0.964881+0.071479 
## [145]    train-rmse:0.158723+0.011298    test-rmse:0.964511+0.072003 
## [146]    train-rmse:0.156627+0.011410    test-rmse:0.964111+0.072254 
## [147]    train-rmse:0.154645+0.011401    test-rmse:0.963332+0.072491 
## [148]    train-rmse:0.153028+0.011703    test-rmse:0.963068+0.072166 
## [149]    train-rmse:0.151855+0.011299    test-rmse:0.963135+0.072486 
## [150]    train-rmse:0.149921+0.011302    test-rmse:0.962786+0.072562 
## [151]    train-rmse:0.148265+0.011415    test-rmse:0.962280+0.072566 
## [152]    train-rmse:0.146487+0.010546    test-rmse:0.962433+0.072687 
## [153]    train-rmse:0.145251+0.010263    test-rmse:0.961838+0.073071 
## [154]    train-rmse:0.143683+0.010436    test-rmse:0.961743+0.073719 
## [155]    train-rmse:0.142435+0.010710    test-rmse:0.961432+0.073543 
## [156]    train-rmse:0.140123+0.010869    test-rmse:0.961497+0.073146 
## [157]    train-rmse:0.138368+0.011081    test-rmse:0.961632+0.073427 
## [158]    train-rmse:0.136783+0.010624    test-rmse:0.961998+0.073318 
## [159]    train-rmse:0.135260+0.010981    test-rmse:0.961439+0.073285 
## [160]    train-rmse:0.133459+0.010666    test-rmse:0.961306+0.072865 
## [161]    train-rmse:0.132177+0.010655    test-rmse:0.961511+0.072993 
## [162]    train-rmse:0.130696+0.010601    test-rmse:0.961677+0.072844 
## [163]    train-rmse:0.128970+0.010580    test-rmse:0.961434+0.072952 
## [164]    train-rmse:0.128078+0.010379    test-rmse:0.961267+0.073240 
## [165]    train-rmse:0.126609+0.009731    test-rmse:0.960665+0.073518 
## [166]    train-rmse:0.124510+0.010001    test-rmse:0.960569+0.073606 
## [167]    train-rmse:0.122613+0.010234    test-rmse:0.960915+0.073475 
## [168]    train-rmse:0.120984+0.009765    test-rmse:0.961185+0.073556 
## [169]    train-rmse:0.119224+0.009985    test-rmse:0.961120+0.073509 
## [170]    train-rmse:0.117225+0.009653    test-rmse:0.961248+0.073834 
## [171]    train-rmse:0.115772+0.009388    test-rmse:0.961384+0.073919 
## [172]    train-rmse:0.115151+0.009200    test-rmse:0.961208+0.073969 
## Stopping. Best iteration:
## [166]    train-rmse:0.124510+0.010001    test-rmse:0.960569+0.073606
## 
## 13 
## [1]  train-rmse:86.704080+0.086998   test-rmse:86.704831+0.406915 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.342288+0.076506   test-rmse:76.342926+0.417100 
## [3]  train-rmse:67.220384+0.067270   test-rmse:67.220891+0.426011 
## [4]  train-rmse:59.190229+0.059099   test-rmse:59.190588+0.433783 
## [5]  train-rmse:52.121395+0.051883   test-rmse:52.121578+0.440539 
## [6]  train-rmse:45.899076+0.045513   test-rmse:45.899065+0.446386 
## [7]  train-rmse:40.422199+0.039869   test-rmse:40.421979+0.451412 
## [8]  train-rmse:35.601835+0.034876   test-rmse:35.601369+0.455694 
## [9]  train-rmse:31.359680+0.030457   test-rmse:31.358941+0.459291 
## [10] train-rmse:27.626835+0.026543   test-rmse:27.625793+0.462249 
## [11] train-rmse:24.342670+0.023071   test-rmse:24.341286+0.464606 
## [12] train-rmse:21.453838+0.019997   test-rmse:21.452077+0.466382 
## [13] train-rmse:18.913416+0.017291   test-rmse:18.911232+0.467585 
## [14] train-rmse:16.680124+0.014931   test-rmse:16.677478+0.468208 
## [15] train-rmse:14.717049+0.012912   test-rmse:14.717037+0.454197 
## [16] train-rmse:12.991833+0.011108   test-rmse:12.980617+0.452674 
## [17] train-rmse:11.475719+0.009385   test-rmse:11.467150+0.438504 
## [18] train-rmse:10.143055+0.007675   test-rmse:10.129140+0.427151 
## [19] train-rmse:8.970594+0.006998    test-rmse:8.951129+0.420565 
## [20] train-rmse:7.940375+0.006409    test-rmse:7.923416+0.398633 
## [21] train-rmse:7.034011+0.005779    test-rmse:7.023178+0.380351 
## [22] train-rmse:6.237414+0.004653    test-rmse:6.228478+0.359306 
## [23] train-rmse:5.536330+0.004104    test-rmse:5.530238+0.341399 
## [24] train-rmse:4.919820+0.003306    test-rmse:4.914515+0.328485 
## [25] train-rmse:4.377724+0.003892    test-rmse:4.380428+0.312684 
## [26] train-rmse:3.899713+0.003604    test-rmse:3.907662+0.298251 
## [27] train-rmse:3.479117+0.002884    test-rmse:3.492796+0.285562 
## [28] train-rmse:3.108809+0.003021    test-rmse:3.132075+0.279514 
## [29] train-rmse:2.781641+0.002698    test-rmse:2.821122+0.260769 
## [30] train-rmse:2.496594+0.003034    test-rmse:2.548273+0.248669 
## [31] train-rmse:2.244193+0.003037    test-rmse:2.309772+0.234387 
## [32] train-rmse:2.020284+0.003501    test-rmse:2.116104+0.222245 
## [33] train-rmse:1.823621+0.002867    test-rmse:1.942473+0.214699 
## [34] train-rmse:1.653623+0.005050    test-rmse:1.799246+0.205481 
## [35] train-rmse:1.503829+0.004791    test-rmse:1.672562+0.193150 
## [36] train-rmse:1.373782+0.004945    test-rmse:1.569713+0.179833 
## [37] train-rmse:1.257132+0.004329    test-rmse:1.480403+0.169095 
## [38] train-rmse:1.154573+0.003422    test-rmse:1.402954+0.161533 
## [39] train-rmse:1.062082+0.003246    test-rmse:1.341325+0.156607 
## [40] train-rmse:0.985719+0.003322    test-rmse:1.290190+0.151753 
## [41] train-rmse:0.917472+0.000817    test-rmse:1.244278+0.145407 
## [42] train-rmse:0.855749+0.003709    test-rmse:1.203907+0.136880 
## [43] train-rmse:0.805928+0.004932    test-rmse:1.175399+0.133187 
## [44] train-rmse:0.759376+0.005332    test-rmse:1.149161+0.131152 
## [45] train-rmse:0.717417+0.007528    test-rmse:1.128992+0.129708 
## [46] train-rmse:0.679326+0.005420    test-rmse:1.105479+0.123670 
## [47] train-rmse:0.648151+0.007719    test-rmse:1.092549+0.121995 
## [48] train-rmse:0.618894+0.008254    test-rmse:1.076936+0.123509 
## [49] train-rmse:0.592846+0.009580    test-rmse:1.066839+0.121975 
## [50] train-rmse:0.568739+0.012721    test-rmse:1.056441+0.119750 
## [51] train-rmse:0.546602+0.012019    test-rmse:1.047933+0.120839 
## [52] train-rmse:0.526840+0.014457    test-rmse:1.043075+0.117101 
## [53] train-rmse:0.508341+0.016571    test-rmse:1.038945+0.114481 
## [54] train-rmse:0.492957+0.018171    test-rmse:1.034513+0.117603 
## [55] train-rmse:0.478702+0.018696    test-rmse:1.024936+0.114627 
## [56] train-rmse:0.464650+0.018884    test-rmse:1.022861+0.114766 
## [57] train-rmse:0.452890+0.018667    test-rmse:1.019400+0.114726 
## [58] train-rmse:0.440795+0.020037    test-rmse:1.016093+0.113576 
## [59] train-rmse:0.430291+0.018080    test-rmse:1.009624+0.111682 
## [60] train-rmse:0.419267+0.017195    test-rmse:1.004283+0.108923 
## [61] train-rmse:0.408731+0.018651    test-rmse:1.001990+0.106668 
## [62] train-rmse:0.399839+0.019575    test-rmse:0.999856+0.105555 
## [63] train-rmse:0.391102+0.019151    test-rmse:0.998066+0.105074 
## [64] train-rmse:0.384496+0.018857    test-rmse:0.996715+0.104883 
## [65] train-rmse:0.375635+0.017424    test-rmse:0.995586+0.105328 
## [66] train-rmse:0.368370+0.017294    test-rmse:0.993661+0.107574 
## [67] train-rmse:0.361937+0.017619    test-rmse:0.991450+0.107918 
## [68] train-rmse:0.354255+0.017714    test-rmse:0.987652+0.106210 
## [69] train-rmse:0.347752+0.016793    test-rmse:0.986312+0.106537 
## [70] train-rmse:0.341589+0.015376    test-rmse:0.982111+0.105467 
## [71] train-rmse:0.333715+0.014768    test-rmse:0.980812+0.103742 
## [72] train-rmse:0.328254+0.015713    test-rmse:0.979760+0.104409 
## [73] train-rmse:0.321754+0.015536    test-rmse:0.977830+0.106025 
## [74] train-rmse:0.315199+0.014233    test-rmse:0.975856+0.107278 
## [75] train-rmse:0.309178+0.013520    test-rmse:0.974887+0.107620 
## [76] train-rmse:0.302856+0.012379    test-rmse:0.973609+0.108180 
## [77] train-rmse:0.297427+0.012688    test-rmse:0.972522+0.107583 
## [78] train-rmse:0.292847+0.011767    test-rmse:0.971877+0.107876 
## [79] train-rmse:0.287798+0.012103    test-rmse:0.970427+0.108748 
## [80] train-rmse:0.282863+0.010631    test-rmse:0.969246+0.110001 
## [81] train-rmse:0.278126+0.010245    test-rmse:0.968275+0.108716 
## [82] train-rmse:0.273924+0.010870    test-rmse:0.967543+0.108585 
## [83] train-rmse:0.268909+0.010609    test-rmse:0.966862+0.107911 
## [84] train-rmse:0.264444+0.011483    test-rmse:0.965723+0.107203 
## [85] train-rmse:0.258376+0.010565    test-rmse:0.964212+0.105593 
## [86] train-rmse:0.253929+0.010301    test-rmse:0.963548+0.105875 
## [87] train-rmse:0.250392+0.010116    test-rmse:0.962601+0.106239 
## [88] train-rmse:0.244577+0.008998    test-rmse:0.962930+0.105973 
## [89] train-rmse:0.240612+0.009395    test-rmse:0.962452+0.104974 
## [90] train-rmse:0.237640+0.009313    test-rmse:0.962053+0.104154 
## [91] train-rmse:0.232887+0.009215    test-rmse:0.960360+0.104440 
## [92] train-rmse:0.228992+0.009806    test-rmse:0.959507+0.104766 
## [93] train-rmse:0.225312+0.010232    test-rmse:0.959742+0.105994 
## [94] train-rmse:0.221634+0.009777    test-rmse:0.958814+0.105481 
## [95] train-rmse:0.218166+0.008250    test-rmse:0.958089+0.105145 
## [96] train-rmse:0.214240+0.007682    test-rmse:0.957973+0.104511 
## [97] train-rmse:0.210695+0.007476    test-rmse:0.957378+0.104799 
## [98] train-rmse:0.207575+0.007190    test-rmse:0.956218+0.104884 
## [99] train-rmse:0.204110+0.007736    test-rmse:0.956448+0.104775 
## [100]    train-rmse:0.201613+0.007559    test-rmse:0.956108+0.105232 
## [101]    train-rmse:0.198704+0.008644    test-rmse:0.955913+0.104998 
## [102]    train-rmse:0.195514+0.008594    test-rmse:0.955442+0.105401 
## [103]    train-rmse:0.192769+0.008419    test-rmse:0.955982+0.104881 
## [104]    train-rmse:0.189434+0.008018    test-rmse:0.955283+0.104513 
## [105]    train-rmse:0.186630+0.007464    test-rmse:0.955034+0.104828 
## [106]    train-rmse:0.183766+0.007257    test-rmse:0.955017+0.104704 
## [107]    train-rmse:0.179547+0.008085    test-rmse:0.954023+0.104288 
## [108]    train-rmse:0.176993+0.008467    test-rmse:0.953725+0.103658 
## [109]    train-rmse:0.174659+0.008199    test-rmse:0.952917+0.103163 
## [110]    train-rmse:0.171136+0.008523    test-rmse:0.952290+0.103071 
## [111]    train-rmse:0.168474+0.007546    test-rmse:0.951245+0.102053 
## [112]    train-rmse:0.166346+0.007097    test-rmse:0.950729+0.102047 
## [113]    train-rmse:0.162767+0.007436    test-rmse:0.950314+0.102216 
## [114]    train-rmse:0.159839+0.007546    test-rmse:0.950428+0.101694 
## [115]    train-rmse:0.156769+0.007473    test-rmse:0.950002+0.101532 
## [116]    train-rmse:0.154417+0.007455    test-rmse:0.949937+0.101889 
## [117]    train-rmse:0.151658+0.006877    test-rmse:0.949956+0.101376 
## [118]    train-rmse:0.149499+0.006722    test-rmse:0.949069+0.100669 
## [119]    train-rmse:0.146994+0.007327    test-rmse:0.948952+0.100340 
## [120]    train-rmse:0.144233+0.007415    test-rmse:0.948663+0.100976 
## [121]    train-rmse:0.141891+0.007181    test-rmse:0.948268+0.100653 
## [122]    train-rmse:0.140141+0.007260    test-rmse:0.947950+0.100546 
## [123]    train-rmse:0.137449+0.007933    test-rmse:0.948111+0.100318 
## [124]    train-rmse:0.135010+0.008061    test-rmse:0.948984+0.100829 
## [125]    train-rmse:0.132867+0.008034    test-rmse:0.948479+0.100402 
## [126]    train-rmse:0.130730+0.007870    test-rmse:0.948456+0.100129 
## [127]    train-rmse:0.128727+0.008086    test-rmse:0.947892+0.099996 
## [128]    train-rmse:0.127197+0.008116    test-rmse:0.947793+0.099916 
## [129]    train-rmse:0.124525+0.007832    test-rmse:0.947500+0.100172 
## [130]    train-rmse:0.122199+0.007527    test-rmse:0.948117+0.100813 
## [131]    train-rmse:0.119796+0.006998    test-rmse:0.948010+0.100614 
## [132]    train-rmse:0.118107+0.007066    test-rmse:0.947630+0.100407 
## [133]    train-rmse:0.116217+0.007303    test-rmse:0.947464+0.099959 
## [134]    train-rmse:0.114155+0.007189    test-rmse:0.947394+0.100056 
## [135]    train-rmse:0.112785+0.007192    test-rmse:0.947060+0.100170 
## [136]    train-rmse:0.111388+0.007185    test-rmse:0.946980+0.100497 
## [137]    train-rmse:0.109216+0.007071    test-rmse:0.946682+0.100753 
## [138]    train-rmse:0.107597+0.007173    test-rmse:0.946436+0.100751 
## [139]    train-rmse:0.106243+0.007126    test-rmse:0.946122+0.100565 
## [140]    train-rmse:0.105028+0.007242    test-rmse:0.945984+0.100353 
## [141]    train-rmse:0.103691+0.007317    test-rmse:0.945986+0.100309 
## [142]    train-rmse:0.102199+0.007337    test-rmse:0.945883+0.100174 
## [143]    train-rmse:0.100827+0.007431    test-rmse:0.946083+0.100170 
## [144]    train-rmse:0.099577+0.007606    test-rmse:0.946078+0.100060 
## [145]    train-rmse:0.097644+0.007824    test-rmse:0.945969+0.099852 
## [146]    train-rmse:0.096177+0.007828    test-rmse:0.945504+0.100002 
## [147]    train-rmse:0.094894+0.007637    test-rmse:0.945516+0.100105 
## [148]    train-rmse:0.093491+0.007535    test-rmse:0.944926+0.099652 
## [149]    train-rmse:0.092128+0.007747    test-rmse:0.944844+0.099710 
## [150]    train-rmse:0.090495+0.007297    test-rmse:0.944462+0.099570 
## [151]    train-rmse:0.089230+0.007140    test-rmse:0.944649+0.099074 
## [152]    train-rmse:0.088007+0.007511    test-rmse:0.944587+0.099242 
## [153]    train-rmse:0.086617+0.007620    test-rmse:0.944292+0.099249 
## [154]    train-rmse:0.085676+0.007609    test-rmse:0.944465+0.098921 
## [155]    train-rmse:0.084516+0.007321    test-rmse:0.944321+0.098698 
## [156]    train-rmse:0.082687+0.007301    test-rmse:0.944337+0.098733 
## [157]    train-rmse:0.081478+0.007556    test-rmse:0.944024+0.098831 
## [158]    train-rmse:0.080344+0.007296    test-rmse:0.943372+0.098787 
## [159]    train-rmse:0.079523+0.007163    test-rmse:0.943324+0.098889 
## [160]    train-rmse:0.078156+0.007199    test-rmse:0.943416+0.098927 
## [161]    train-rmse:0.076976+0.006942    test-rmse:0.943478+0.099075 
## [162]    train-rmse:0.075496+0.006805    test-rmse:0.943231+0.099068 
## [163]    train-rmse:0.074364+0.006610    test-rmse:0.943124+0.099369 
## [164]    train-rmse:0.072681+0.006459    test-rmse:0.942947+0.099174 
## [165]    train-rmse:0.071782+0.006288    test-rmse:0.942939+0.099405 
## [166]    train-rmse:0.070678+0.006425    test-rmse:0.942889+0.099319 
## [167]    train-rmse:0.069470+0.006221    test-rmse:0.942808+0.099396 
## [168]    train-rmse:0.068749+0.006212    test-rmse:0.942755+0.099428 
## [169]    train-rmse:0.067644+0.006043    test-rmse:0.942619+0.099526 
## [170]    train-rmse:0.066411+0.005581    test-rmse:0.942379+0.099319 
## [171]    train-rmse:0.065309+0.005606    test-rmse:0.942357+0.099181 
## [172]    train-rmse:0.064047+0.005402    test-rmse:0.942122+0.098982 
## [173]    train-rmse:0.063025+0.005209    test-rmse:0.942207+0.099251 
## [174]    train-rmse:0.062284+0.005110    test-rmse:0.942020+0.099114 
## [175]    train-rmse:0.061436+0.005140    test-rmse:0.941943+0.099225 
## [176]    train-rmse:0.060590+0.005270    test-rmse:0.941991+0.099388 
## [177]    train-rmse:0.059578+0.005081    test-rmse:0.941820+0.099252 
## [178]    train-rmse:0.058795+0.005030    test-rmse:0.941632+0.099216 
## [179]    train-rmse:0.057799+0.004998    test-rmse:0.941491+0.099343 
## [180]    train-rmse:0.056804+0.004834    test-rmse:0.941410+0.099329 
## [181]    train-rmse:0.055800+0.004855    test-rmse:0.941392+0.099337 
## [182]    train-rmse:0.054993+0.004779    test-rmse:0.941454+0.099291 
## [183]    train-rmse:0.054300+0.004656    test-rmse:0.941353+0.099171 
## [184]    train-rmse:0.053452+0.004671    test-rmse:0.941442+0.099279 
## [185]    train-rmse:0.052806+0.004832    test-rmse:0.941465+0.099337 
## [186]    train-rmse:0.052034+0.004860    test-rmse:0.941459+0.099256 
## [187]    train-rmse:0.051037+0.004704    test-rmse:0.940921+0.099293 
## [188]    train-rmse:0.050434+0.004707    test-rmse:0.941056+0.099281 
## [189]    train-rmse:0.049862+0.004628    test-rmse:0.940845+0.099460 
## [190]    train-rmse:0.049083+0.004725    test-rmse:0.940646+0.099608 
## [191]    train-rmse:0.048318+0.004628    test-rmse:0.940628+0.099492 
## [192]    train-rmse:0.047461+0.004470    test-rmse:0.940472+0.099639 
## [193]    train-rmse:0.046862+0.004457    test-rmse:0.940378+0.099842 
## [194]    train-rmse:0.046076+0.004345    test-rmse:0.940423+0.099855 
## [195]    train-rmse:0.045346+0.004413    test-rmse:0.940438+0.099976 
## [196]    train-rmse:0.044734+0.004387    test-rmse:0.940486+0.100043 
## [197]    train-rmse:0.044027+0.004205    test-rmse:0.940570+0.100079 
## [198]    train-rmse:0.043208+0.004058    test-rmse:0.940525+0.100008 
## [199]    train-rmse:0.042667+0.004178    test-rmse:0.940518+0.100124 
## Stopping. Best iteration:
## [193]    train-rmse:0.046862+0.004457    test-rmse:0.940378+0.099842
## 
## 14 
## [1]  train-rmse:84.742499+0.085026   test-rmse:84.743227+0.408848 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.927655+0.073048   test-rmse:72.928244+0.420446 
## [3]  train-rmse:62.762302+0.062739   test-rmse:62.762725+0.430333 
## [4]  train-rmse:54.016514+0.053817   test-rmse:54.016744+0.438737 
## [5]  train-rmse:46.492472+0.046117   test-rmse:46.492479+0.445831 
## [6]  train-rmse:40.019990+0.039459   test-rmse:40.019755+0.451772 
## [7]  train-rmse:34.452692+0.033687   test-rmse:34.452158+0.456684 
## [8]  train-rmse:29.664635+0.028683   test-rmse:29.663766+0.460663 
## [9]  train-rmse:25.547519+0.024346   test-rmse:25.546270+0.463776 
## [10] train-rmse:22.008217+0.020588   test-rmse:22.006533+0.466070 
## [11] train-rmse:18.966660+0.017350   test-rmse:18.964487+0.467562 
## [12] train-rmse:16.354035+0.014591   test-rmse:16.351313+0.468250 
## [13] train-rmse:14.110245+0.012261   test-rmse:14.110562+0.451996 
## [14] train-rmse:12.184010+0.010093   test-rmse:12.173134+0.452602 
## [15] train-rmse:10.530898+0.008449   test-rmse:10.519598+0.447698 
## [16] train-rmse:9.113155+0.007247    test-rmse:9.105301+0.433855 
## [17] train-rmse:7.898617+0.006814    test-rmse:7.883248+0.431642 
## [18] train-rmse:6.859430+0.006913    test-rmse:6.845067+0.425213 
## [19] train-rmse:5.973051+0.007616    test-rmse:5.962075+0.414155 
## [20] train-rmse:5.218115+0.008760    test-rmse:5.210028+0.403066 
## [21] train-rmse:4.577487+0.010502    test-rmse:4.571826+0.389309 
## [22] train-rmse:4.035859+0.012181    test-rmse:4.031004+0.374521 
## [23] train-rmse:3.579486+0.013938    test-rmse:3.579301+0.364553 
## [24] train-rmse:3.197461+0.015976    test-rmse:3.199516+0.340930 
## [25] train-rmse:2.879811+0.017882    test-rmse:2.888447+0.322277 
## [26] train-rmse:2.617508+0.019709    test-rmse:2.628649+0.296257 
## [27] train-rmse:2.402199+0.021250    test-rmse:2.420778+0.272234 
## [28] train-rmse:2.227119+0.022672    test-rmse:2.253942+0.247927 
## [29] train-rmse:2.085533+0.024306    test-rmse:2.120294+0.219875 
## [30] train-rmse:1.971825+0.025607    test-rmse:2.015106+0.195273 
## [31] train-rmse:1.880680+0.026342    test-rmse:1.930531+0.173614 
## [32] train-rmse:1.807643+0.026881    test-rmse:1.864625+0.158112 
## [33] train-rmse:1.749528+0.027355    test-rmse:1.818068+0.144137 
## [34] train-rmse:1.702832+0.027433    test-rmse:1.777041+0.131970 
## [35] train-rmse:1.665310+0.027447    test-rmse:1.743669+0.123417 
## [36] train-rmse:1.634672+0.027219    test-rmse:1.720439+0.116937 
## [37] train-rmse:1.609485+0.027159    test-rmse:1.696919+0.112701 
## [38] train-rmse:1.588520+0.026953    test-rmse:1.685880+0.114312 
## [39] train-rmse:1.570805+0.026751    test-rmse:1.667205+0.112686 
## [40] train-rmse:1.555486+0.026422    test-rmse:1.653238+0.114802 
## [41] train-rmse:1.542205+0.026227    test-rmse:1.642403+0.113323 
## [42] train-rmse:1.530322+0.026066    test-rmse:1.635853+0.112786 
## [43] train-rmse:1.519565+0.025789    test-rmse:1.621275+0.109310 
## [44] train-rmse:1.509839+0.025426    test-rmse:1.614943+0.109258 
## [45] train-rmse:1.500902+0.025202    test-rmse:1.614325+0.112044 
## [46] train-rmse:1.492574+0.024884    test-rmse:1.610099+0.114982 
## [47] train-rmse:1.484668+0.024543    test-rmse:1.602810+0.115702 
## [48] train-rmse:1.477315+0.024364    test-rmse:1.598561+0.114682 
## [49] train-rmse:1.470218+0.024213    test-rmse:1.593933+0.114351 
## [50] train-rmse:1.463332+0.024032    test-rmse:1.589450+0.113864 
## [51] train-rmse:1.456678+0.023961    test-rmse:1.578855+0.112700 
## [52] train-rmse:1.450130+0.023849    test-rmse:1.576253+0.111375 
## [53] train-rmse:1.443891+0.023759    test-rmse:1.569805+0.111388 
## [54] train-rmse:1.437757+0.023635    test-rmse:1.567564+0.109677 
## [55] train-rmse:1.431768+0.023655    test-rmse:1.565946+0.109212 
## [56] train-rmse:1.426097+0.023489    test-rmse:1.560800+0.106283 
## [57] train-rmse:1.420460+0.023342    test-rmse:1.558206+0.106322 
## [58] train-rmse:1.414846+0.023218    test-rmse:1.554934+0.107369 
## [59] train-rmse:1.409320+0.023087    test-rmse:1.550126+0.108250 
## [60] train-rmse:1.403928+0.023009    test-rmse:1.544589+0.112510 
## [61] train-rmse:1.398578+0.022984    test-rmse:1.541425+0.112918 
## [62] train-rmse:1.393302+0.022986    test-rmse:1.533888+0.109650 
## [63] train-rmse:1.388239+0.022860    test-rmse:1.529321+0.109615 
## [64] train-rmse:1.383210+0.022807    test-rmse:1.527069+0.107632 
## [65] train-rmse:1.378272+0.022750    test-rmse:1.523428+0.110008 
## [66] train-rmse:1.373393+0.022689    test-rmse:1.519254+0.108652 
## [67] train-rmse:1.368617+0.022677    test-rmse:1.515987+0.109069 
## [68] train-rmse:1.364072+0.022524    test-rmse:1.512571+0.111627 
## [69] train-rmse:1.359466+0.022443    test-rmse:1.512036+0.112983 
## [70] train-rmse:1.354968+0.022316    test-rmse:1.511478+0.109268 
## [71] train-rmse:1.350489+0.022241    test-rmse:1.507670+0.107294 
## [72] train-rmse:1.346079+0.022082    test-rmse:1.503250+0.101790 
## [73] train-rmse:1.341759+0.021997    test-rmse:1.500888+0.100328 
## [74] train-rmse:1.337472+0.021912    test-rmse:1.498902+0.101475 
## [75] train-rmse:1.333184+0.021821    test-rmse:1.494531+0.099808 
## [76] train-rmse:1.328979+0.021752    test-rmse:1.494313+0.102370 
## [77] train-rmse:1.324881+0.021713    test-rmse:1.491226+0.102053 
## [78] train-rmse:1.320840+0.021631    test-rmse:1.487990+0.102041 
## [79] train-rmse:1.316833+0.021568    test-rmse:1.485206+0.099994 
## [80] train-rmse:1.312794+0.021423    test-rmse:1.484212+0.096645 
## [81] train-rmse:1.308895+0.021274    test-rmse:1.478935+0.098615 
## [82] train-rmse:1.305122+0.021199    test-rmse:1.475322+0.098768 
## [83] train-rmse:1.301398+0.021122    test-rmse:1.473464+0.098023 
## [84] train-rmse:1.297698+0.021039    test-rmse:1.469955+0.099021 
## [85] train-rmse:1.294050+0.020953    test-rmse:1.468750+0.098587 
## [86] train-rmse:1.290434+0.020873    test-rmse:1.466176+0.098471 
## [87] train-rmse:1.286891+0.020824    test-rmse:1.462206+0.093198 
## [88] train-rmse:1.283376+0.020749    test-rmse:1.454604+0.093002 
## [89] train-rmse:1.279894+0.020725    test-rmse:1.455462+0.093631 
## [90] train-rmse:1.276346+0.020649    test-rmse:1.456480+0.091085 
## [91] train-rmse:1.272866+0.020646    test-rmse:1.455133+0.091518 
## [92] train-rmse:1.269471+0.020639    test-rmse:1.453102+0.092322 
## [93] train-rmse:1.266166+0.020624    test-rmse:1.452664+0.092615 
## [94] train-rmse:1.262838+0.020605    test-rmse:1.452097+0.092453 
## [95] train-rmse:1.259607+0.020564    test-rmse:1.450279+0.094332 
## [96] train-rmse:1.256417+0.020553    test-rmse:1.447688+0.095005 
## [97] train-rmse:1.253272+0.020500    test-rmse:1.445333+0.094525 
## [98] train-rmse:1.250164+0.020480    test-rmse:1.443841+0.094429 
## [99] train-rmse:1.247138+0.020452    test-rmse:1.441503+0.093427 
## [100]    train-rmse:1.244145+0.020437    test-rmse:1.436200+0.092952 
## [101]    train-rmse:1.241157+0.020418    test-rmse:1.436242+0.090376 
## [102]    train-rmse:1.238204+0.020409    test-rmse:1.433761+0.092880 
## [103]    train-rmse:1.235275+0.020404    test-rmse:1.433269+0.090065 
## [104]    train-rmse:1.232387+0.020336    test-rmse:1.430735+0.090133 
## [105]    train-rmse:1.229480+0.020357    test-rmse:1.428813+0.090191 
## [106]    train-rmse:1.226661+0.020349    test-rmse:1.424107+0.090643 
## [107]    train-rmse:1.223889+0.020321    test-rmse:1.422719+0.089205 
## [108]    train-rmse:1.221137+0.020284    test-rmse:1.419579+0.087595 
## [109]    train-rmse:1.218383+0.020259    test-rmse:1.417030+0.088368 
## [110]    train-rmse:1.215673+0.020257    test-rmse:1.414860+0.087855 
## [111]    train-rmse:1.213021+0.020243    test-rmse:1.414187+0.085714 
## [112]    train-rmse:1.210355+0.020208    test-rmse:1.412948+0.084817 
## [113]    train-rmse:1.207727+0.020212    test-rmse:1.410856+0.089416 
## [114]    train-rmse:1.205110+0.020211    test-rmse:1.408900+0.089662 
## [115]    train-rmse:1.202570+0.020234    test-rmse:1.405436+0.090440 
## [116]    train-rmse:1.200053+0.020278    test-rmse:1.404901+0.092161 
## [117]    train-rmse:1.197570+0.020288    test-rmse:1.403068+0.089751 
## [118]    train-rmse:1.195115+0.020267    test-rmse:1.400472+0.089998 
## [119]    train-rmse:1.192703+0.020250    test-rmse:1.398272+0.090802 
## [120]    train-rmse:1.190291+0.020211    test-rmse:1.395939+0.091317 
## [121]    train-rmse:1.187928+0.020188    test-rmse:1.393197+0.090510 
## [122]    train-rmse:1.185523+0.020156    test-rmse:1.392823+0.090474 
## [123]    train-rmse:1.183181+0.020137    test-rmse:1.389955+0.091380 
## [124]    train-rmse:1.180883+0.020083    test-rmse:1.390101+0.089339 
## [125]    train-rmse:1.178637+0.020049    test-rmse:1.388832+0.088766 
## [126]    train-rmse:1.176393+0.020006    test-rmse:1.388004+0.084955 
## [127]    train-rmse:1.174118+0.019985    test-rmse:1.385624+0.085283 
## [128]    train-rmse:1.171935+0.019949    test-rmse:1.383619+0.086436 
## [129]    train-rmse:1.169765+0.019908    test-rmse:1.382693+0.085542 
## [130]    train-rmse:1.167636+0.019912    test-rmse:1.380658+0.085801 
## [131]    train-rmse:1.165508+0.019915    test-rmse:1.380234+0.087212 
## [132]    train-rmse:1.163366+0.019933    test-rmse:1.378540+0.087584 
## [133]    train-rmse:1.161272+0.019939    test-rmse:1.376775+0.087992 
## [134]    train-rmse:1.159210+0.019960    test-rmse:1.375215+0.087923 
## [135]    train-rmse:1.157176+0.019979    test-rmse:1.372799+0.089754 
## [136]    train-rmse:1.155157+0.019983    test-rmse:1.371479+0.088388 
## [137]    train-rmse:1.153184+0.019982    test-rmse:1.371815+0.085759 
## [138]    train-rmse:1.151190+0.019924    test-rmse:1.370676+0.086256 
## [139]    train-rmse:1.149273+0.019890    test-rmse:1.369081+0.085947 
## [140]    train-rmse:1.147351+0.019855    test-rmse:1.367862+0.084769 
## [141]    train-rmse:1.145423+0.019808    test-rmse:1.366303+0.085729 
## [142]    train-rmse:1.143516+0.019766    test-rmse:1.363859+0.086190 
## [143]    train-rmse:1.141629+0.019727    test-rmse:1.361357+0.086541 
## [144]    train-rmse:1.139728+0.019712    test-rmse:1.360741+0.085241 
## [145]    train-rmse:1.137884+0.019676    test-rmse:1.360798+0.087139 
## [146]    train-rmse:1.136030+0.019658    test-rmse:1.359401+0.087388 
## [147]    train-rmse:1.134196+0.019587    test-rmse:1.358508+0.087082 
## [148]    train-rmse:1.132406+0.019558    test-rmse:1.356480+0.086617 
## [149]    train-rmse:1.130635+0.019524    test-rmse:1.353741+0.086813 
## [150]    train-rmse:1.128825+0.019485    test-rmse:1.351843+0.085974 
## [151]    train-rmse:1.127075+0.019453    test-rmse:1.348363+0.087071 
## [152]    train-rmse:1.125363+0.019445    test-rmse:1.345551+0.087548 
## [153]    train-rmse:1.123653+0.019434    test-rmse:1.346567+0.088013 
## [154]    train-rmse:1.121971+0.019430    test-rmse:1.345328+0.089183 
## [155]    train-rmse:1.120312+0.019429    test-rmse:1.345729+0.086512 
## [156]    train-rmse:1.118656+0.019416    test-rmse:1.344192+0.085052 
## [157]    train-rmse:1.117024+0.019400    test-rmse:1.343546+0.084917 
## [158]    train-rmse:1.115413+0.019375    test-rmse:1.342344+0.084301 
## [159]    train-rmse:1.113814+0.019349    test-rmse:1.341543+0.083989 
## [160]    train-rmse:1.112219+0.019332    test-rmse:1.339992+0.085848 
## [161]    train-rmse:1.110616+0.019330    test-rmse:1.338746+0.086668 
## [162]    train-rmse:1.109046+0.019312    test-rmse:1.337204+0.086807 
## [163]    train-rmse:1.107497+0.019292    test-rmse:1.336805+0.084500 
## [164]    train-rmse:1.105962+0.019272    test-rmse:1.335253+0.085596 
## [165]    train-rmse:1.104445+0.019255    test-rmse:1.333942+0.086537 
## [166]    train-rmse:1.102933+0.019242    test-rmse:1.333979+0.086938 
## [167]    train-rmse:1.101441+0.019220    test-rmse:1.332712+0.085861 
## [168]    train-rmse:1.099957+0.019191    test-rmse:1.331614+0.085721 
## [169]    train-rmse:1.098487+0.019160    test-rmse:1.330521+0.085872 
## [170]    train-rmse:1.097038+0.019133    test-rmse:1.328711+0.086492 
## [171]    train-rmse:1.095594+0.019109    test-rmse:1.326643+0.086999 
## [172]    train-rmse:1.094161+0.019083    test-rmse:1.325479+0.088088 
## [173]    train-rmse:1.092760+0.019072    test-rmse:1.323997+0.087901 
## [174]    train-rmse:1.091374+0.019052    test-rmse:1.324484+0.087968 
## [175]    train-rmse:1.089968+0.019018    test-rmse:1.323258+0.087682 
## [176]    train-rmse:1.088591+0.019007    test-rmse:1.321704+0.087989 
## [177]    train-rmse:1.087170+0.018955    test-rmse:1.319785+0.088035 
## [178]    train-rmse:1.085819+0.018937    test-rmse:1.318758+0.088753 
## [179]    train-rmse:1.084476+0.018917    test-rmse:1.317839+0.088949 
## [180]    train-rmse:1.083144+0.018897    test-rmse:1.317963+0.089225 
## [181]    train-rmse:1.081837+0.018882    test-rmse:1.318529+0.088990 
## [182]    train-rmse:1.080541+0.018867    test-rmse:1.316667+0.088528 
## [183]    train-rmse:1.079255+0.018844    test-rmse:1.315571+0.088449 
## [184]    train-rmse:1.077959+0.018828    test-rmse:1.314920+0.089607 
## [185]    train-rmse:1.076674+0.018811    test-rmse:1.313633+0.090431 
## [186]    train-rmse:1.075422+0.018785    test-rmse:1.312147+0.090476 
## [187]    train-rmse:1.074178+0.018753    test-rmse:1.310828+0.090712 
## [188]    train-rmse:1.072942+0.018727    test-rmse:1.310055+0.088560 
## [189]    train-rmse:1.071715+0.018706    test-rmse:1.309789+0.087485 
## [190]    train-rmse:1.070501+0.018686    test-rmse:1.310188+0.085793 
## [191]    train-rmse:1.069274+0.018654    test-rmse:1.309456+0.085924 
## [192]    train-rmse:1.068051+0.018607    test-rmse:1.308216+0.085229 
## [193]    train-rmse:1.066840+0.018551    test-rmse:1.307511+0.084650 
## [194]    train-rmse:1.065648+0.018533    test-rmse:1.306636+0.084909 
## [195]    train-rmse:1.064470+0.018537    test-rmse:1.306416+0.085732 
## [196]    train-rmse:1.063331+0.018522    test-rmse:1.305090+0.085887 
## [197]    train-rmse:1.062190+0.018502    test-rmse:1.304271+0.087041 
## [198]    train-rmse:1.061064+0.018487    test-rmse:1.302680+0.087430 
## [199]    train-rmse:1.059951+0.018467    test-rmse:1.302685+0.087204 
## [200]    train-rmse:1.058842+0.018438    test-rmse:1.301591+0.088907 
## [201]    train-rmse:1.057727+0.018426    test-rmse:1.300673+0.089150 
## [202]    train-rmse:1.056643+0.018399    test-rmse:1.299826+0.089125 
## [203]    train-rmse:1.055555+0.018370    test-rmse:1.299325+0.089240 
## [204]    train-rmse:1.054481+0.018350    test-rmse:1.298998+0.089221 
## [205]    train-rmse:1.053407+0.018332    test-rmse:1.298395+0.089015 
## [206]    train-rmse:1.052338+0.018301    test-rmse:1.297538+0.088813 
## [207]    train-rmse:1.051286+0.018272    test-rmse:1.296691+0.088513 
## [208]    train-rmse:1.050229+0.018241    test-rmse:1.294342+0.089443 
## [209]    train-rmse:1.049163+0.018220    test-rmse:1.293635+0.089794 
## [210]    train-rmse:1.048110+0.018185    test-rmse:1.290359+0.091082 
## [211]    train-rmse:1.047076+0.018149    test-rmse:1.289089+0.089729 
## [212]    train-rmse:1.046048+0.018113    test-rmse:1.288792+0.091212 
## [213]    train-rmse:1.045024+0.018098    test-rmse:1.288899+0.091139 
## [214]    train-rmse:1.044005+0.018051    test-rmse:1.288087+0.091928 
## [215]    train-rmse:1.042991+0.018014    test-rmse:1.287956+0.090535 
## [216]    train-rmse:1.042001+0.017994    test-rmse:1.287929+0.090763 
## [217]    train-rmse:1.040996+0.017968    test-rmse:1.288762+0.089972 
## [218]    train-rmse:1.040025+0.017953    test-rmse:1.288841+0.089804 
## [219]    train-rmse:1.039069+0.017933    test-rmse:1.287865+0.090252 
## [220]    train-rmse:1.038118+0.017917    test-rmse:1.286760+0.090512 
## [221]    train-rmse:1.037176+0.017897    test-rmse:1.286599+0.091211 
## [222]    train-rmse:1.036236+0.017877    test-rmse:1.285145+0.091641 
## [223]    train-rmse:1.035307+0.017860    test-rmse:1.284895+0.090810 
## [224]    train-rmse:1.034389+0.017838    test-rmse:1.282750+0.088605 
## [225]    train-rmse:1.033474+0.017814    test-rmse:1.282169+0.088468 
## [226]    train-rmse:1.032570+0.017790    test-rmse:1.281051+0.088403 
## [227]    train-rmse:1.031663+0.017765    test-rmse:1.280298+0.088118 
## [228]    train-rmse:1.030761+0.017737    test-rmse:1.279943+0.088850 
## [229]    train-rmse:1.029862+0.017725    test-rmse:1.278342+0.089420 
## [230]    train-rmse:1.028974+0.017710    test-rmse:1.277578+0.089875 
## [231]    train-rmse:1.028082+0.017697    test-rmse:1.277390+0.089675 
## [232]    train-rmse:1.027192+0.017691    test-rmse:1.277342+0.088659 
## [233]    train-rmse:1.026320+0.017689    test-rmse:1.277272+0.089597 
## [234]    train-rmse:1.025455+0.017681    test-rmse:1.277893+0.089377 
## [235]    train-rmse:1.024588+0.017676    test-rmse:1.278279+0.089208 
## [236]    train-rmse:1.023725+0.017658    test-rmse:1.277064+0.088683 
## [237]    train-rmse:1.022876+0.017645    test-rmse:1.276625+0.088222 
## [238]    train-rmse:1.022033+0.017633    test-rmse:1.275645+0.088431 
## [239]    train-rmse:1.021206+0.017619    test-rmse:1.276872+0.089805 
## [240]    train-rmse:1.020373+0.017612    test-rmse:1.275506+0.090267 
## [241]    train-rmse:1.019534+0.017599    test-rmse:1.275104+0.089434 
## [242]    train-rmse:1.018708+0.017583    test-rmse:1.272860+0.089204 
## [243]    train-rmse:1.017878+0.017569    test-rmse:1.272268+0.089654 
## [244]    train-rmse:1.017048+0.017541    test-rmse:1.271275+0.089137 
## [245]    train-rmse:1.016233+0.017523    test-rmse:1.271853+0.087762 
## [246]    train-rmse:1.015423+0.017513    test-rmse:1.269756+0.088431 
## [247]    train-rmse:1.014617+0.017505    test-rmse:1.268532+0.089154 
## [248]    train-rmse:1.013822+0.017503    test-rmse:1.268074+0.088982 
## [249]    train-rmse:1.013039+0.017500    test-rmse:1.266803+0.088572 
## [250]    train-rmse:1.012262+0.017494    test-rmse:1.266498+0.088784 
## [251]    train-rmse:1.011494+0.017490    test-rmse:1.266433+0.088340 
## [252]    train-rmse:1.010725+0.017486    test-rmse:1.266397+0.088515 
## [253]    train-rmse:1.009953+0.017483    test-rmse:1.264920+0.088325 
## [254]    train-rmse:1.009175+0.017479    test-rmse:1.263130+0.086313 
## [255]    train-rmse:1.008418+0.017471    test-rmse:1.263113+0.086474 
## [256]    train-rmse:1.007660+0.017475    test-rmse:1.263086+0.086750 
## [257]    train-rmse:1.006910+0.017476    test-rmse:1.262938+0.087282 
## [258]    train-rmse:1.006157+0.017478    test-rmse:1.262127+0.087740 
## [259]    train-rmse:1.005416+0.017476    test-rmse:1.261422+0.087595 
## [260]    train-rmse:1.004665+0.017467    test-rmse:1.260590+0.088212 
## [261]    train-rmse:1.003913+0.017457    test-rmse:1.260554+0.087547 
## [262]    train-rmse:1.003167+0.017448    test-rmse:1.259690+0.087593 
## [263]    train-rmse:1.002440+0.017450    test-rmse:1.260052+0.086907 
## [264]    train-rmse:1.001721+0.017439    test-rmse:1.260013+0.087480 
## [265]    train-rmse:1.000995+0.017428    test-rmse:1.258971+0.086580 
## [266]    train-rmse:1.000278+0.017430    test-rmse:1.259211+0.087173 
## [267]    train-rmse:0.999561+0.017437    test-rmse:1.259338+0.087045 
## [268]    train-rmse:0.998850+0.017441    test-rmse:1.260053+0.086140 
## [269]    train-rmse:0.998151+0.017446    test-rmse:1.259612+0.085780 
## [270]    train-rmse:0.997459+0.017452    test-rmse:1.259053+0.085281 
## [271]    train-rmse:0.996763+0.017441    test-rmse:1.257874+0.085525 
## [272]    train-rmse:0.996073+0.017450    test-rmse:1.257367+0.085691 
## [273]    train-rmse:0.995388+0.017451    test-rmse:1.258473+0.086047 
## [274]    train-rmse:0.994697+0.017456    test-rmse:1.258676+0.086618 
## [275]    train-rmse:0.994021+0.017458    test-rmse:1.258584+0.086231 
## [276]    train-rmse:0.993352+0.017461    test-rmse:1.256478+0.084914 
## [277]    train-rmse:0.992682+0.017471    test-rmse:1.256487+0.084967 
## [278]    train-rmse:0.991999+0.017450    test-rmse:1.256491+0.085059 
## [279]    train-rmse:0.991330+0.017456    test-rmse:1.256672+0.085843 
## [280]    train-rmse:0.990669+0.017466    test-rmse:1.255885+0.086310 
## [281]    train-rmse:0.990010+0.017463    test-rmse:1.255773+0.085590 
## [282]    train-rmse:0.989355+0.017456    test-rmse:1.254946+0.086087 
## [283]    train-rmse:0.988706+0.017468    test-rmse:1.254173+0.086689 
## [284]    train-rmse:0.988059+0.017472    test-rmse:1.253488+0.086678 
## [285]    train-rmse:0.987426+0.017472    test-rmse:1.253069+0.085882 
## [286]    train-rmse:0.986792+0.017467    test-rmse:1.252961+0.086329 
## [287]    train-rmse:0.986157+0.017461    test-rmse:1.252498+0.086251 
## [288]    train-rmse:0.985517+0.017471    test-rmse:1.251159+0.085863 
## [289]    train-rmse:0.984891+0.017467    test-rmse:1.251190+0.084798 
## [290]    train-rmse:0.984262+0.017450    test-rmse:1.250660+0.084340 
## [291]    train-rmse:0.983649+0.017449    test-rmse:1.250237+0.084917 
## [292]    train-rmse:0.983034+0.017446    test-rmse:1.249430+0.086351 
## [293]    train-rmse:0.982423+0.017441    test-rmse:1.248965+0.086287 
## [294]    train-rmse:0.981810+0.017438    test-rmse:1.247725+0.086598 
## [295]    train-rmse:0.981203+0.017443    test-rmse:1.248005+0.086729 
## [296]    train-rmse:0.980596+0.017453    test-rmse:1.247365+0.086364 
## [297]    train-rmse:0.979995+0.017457    test-rmse:1.247779+0.085820 
## [298]    train-rmse:0.979389+0.017465    test-rmse:1.249410+0.084768 
## [299]    train-rmse:0.978786+0.017473    test-rmse:1.249008+0.084931 
## [300]    train-rmse:0.978192+0.017478    test-rmse:1.249396+0.084425 
## [301]    train-rmse:0.977599+0.017465    test-rmse:1.249580+0.083852 
## [302]    train-rmse:0.977008+0.017465    test-rmse:1.249139+0.084336 
## Stopping. Best iteration:
## [296]    train-rmse:0.980596+0.017453    test-rmse:1.247365+0.086364
## 
## 15 
## [1]  train-rmse:84.742499+0.085026   test-rmse:84.743227+0.408848 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.927655+0.073048   test-rmse:72.928244+0.420446 
## [3]  train-rmse:62.762302+0.062739   test-rmse:62.762725+0.430333 
## [4]  train-rmse:54.016514+0.053817   test-rmse:54.016744+0.438737 
## [5]  train-rmse:46.492472+0.046117   test-rmse:46.492479+0.445831 
## [6]  train-rmse:40.019990+0.039459   test-rmse:40.019755+0.451772 
## [7]  train-rmse:34.452692+0.033687   test-rmse:34.452158+0.456684 
## [8]  train-rmse:29.664635+0.028683   test-rmse:29.663766+0.460663 
## [9]  train-rmse:25.547519+0.024346   test-rmse:25.546270+0.463776 
## [10] train-rmse:22.008217+0.020588   test-rmse:22.006533+0.466070 
## [11] train-rmse:18.966660+0.017350   test-rmse:18.964487+0.467562 
## [12] train-rmse:16.354035+0.014591   test-rmse:16.351313+0.468250 
## [13] train-rmse:14.110245+0.012261   test-rmse:14.110562+0.451996 
## [14] train-rmse:12.183608+0.010200   test-rmse:12.178845+0.439003 
## [15] train-rmse:10.529751+0.008399   test-rmse:10.522686+0.433285 
## [16] train-rmse:9.109590+0.006667    test-rmse:9.091692+0.429292 
## [17] train-rmse:7.891496+0.006230    test-rmse:7.884601+0.409841 
## [18] train-rmse:6.847586+0.006614    test-rmse:6.834415+0.405290 
## [19] train-rmse:5.953083+0.006086    test-rmse:5.946275+0.384672 
## [20] train-rmse:5.187721+0.007833    test-rmse:5.181589+0.375376 
## [21] train-rmse:4.536197+0.007469    test-rmse:4.528135+0.370759 
## [22] train-rmse:3.981189+0.009029    test-rmse:3.976836+0.352790 
## [23] train-rmse:3.510541+0.009615    test-rmse:3.512352+0.330383 
## [24] train-rmse:3.111550+0.010470    test-rmse:3.125699+0.317216 
## [25] train-rmse:2.778005+0.011393    test-rmse:2.800493+0.298096 
## [26] train-rmse:2.498968+0.011814    test-rmse:2.537104+0.277763 
## [27] train-rmse:2.266053+0.012710    test-rmse:2.318601+0.256296 
## [28] train-rmse:2.075625+0.012995    test-rmse:2.139332+0.235257 
## [29] train-rmse:1.919071+0.013721    test-rmse:1.992278+0.214006 
## [30] train-rmse:1.790805+0.014085    test-rmse:1.879562+0.193275 
## [31] train-rmse:1.686474+0.015496    test-rmse:1.790345+0.175664 
## [32] train-rmse:1.602386+0.014340    test-rmse:1.721444+0.165667 
## [33] train-rmse:1.534133+0.014345    test-rmse:1.663030+0.156485 
## [34] train-rmse:1.478708+0.014751    test-rmse:1.615864+0.147485 
## [35] train-rmse:1.432309+0.017241    test-rmse:1.582915+0.136776 
## [36] train-rmse:1.394082+0.016608    test-rmse:1.557991+0.125688 
## [37] train-rmse:1.363524+0.015615    test-rmse:1.539460+0.120451 
## [38] train-rmse:1.337619+0.016373    test-rmse:1.518778+0.114736 
## [39] train-rmse:1.316414+0.016527    test-rmse:1.504307+0.108003 
## [40] train-rmse:1.298126+0.017179    test-rmse:1.493207+0.104260 
## [41] train-rmse:1.280951+0.017908    test-rmse:1.478162+0.105657 
## [42] train-rmse:1.266853+0.018032    test-rmse:1.469129+0.100092 
## [43] train-rmse:1.252794+0.019478    test-rmse:1.459971+0.098195 
## [44] train-rmse:1.241049+0.019193    test-rmse:1.452511+0.095836 
## [45] train-rmse:1.226717+0.018937    test-rmse:1.448980+0.093148 
## [46] train-rmse:1.215128+0.019455    test-rmse:1.441758+0.091602 
## [47] train-rmse:1.205153+0.018760    test-rmse:1.436087+0.091906 
## [48] train-rmse:1.195743+0.019209    test-rmse:1.423480+0.089894 
## [49] train-rmse:1.186498+0.019173    test-rmse:1.413333+0.087136 
## [50] train-rmse:1.177404+0.019865    test-rmse:1.408529+0.083807 
## [51] train-rmse:1.168797+0.018911    test-rmse:1.404350+0.080955 
## [52] train-rmse:1.160232+0.019634    test-rmse:1.398319+0.082145 
## [53] train-rmse:1.152347+0.019824    test-rmse:1.394886+0.082490 
## [54] train-rmse:1.144309+0.021169    test-rmse:1.389960+0.080195 
## [55] train-rmse:1.135569+0.021053    test-rmse:1.385027+0.079838 
## [56] train-rmse:1.128343+0.021850    test-rmse:1.379707+0.078623 
## [57] train-rmse:1.121549+0.022302    test-rmse:1.372619+0.080389 
## [58] train-rmse:1.114392+0.022471    test-rmse:1.369979+0.081308 
## [59] train-rmse:1.106431+0.022922    test-rmse:1.359601+0.085827 
## [60] train-rmse:1.099826+0.023339    test-rmse:1.355413+0.086357 
## [61] train-rmse:1.092283+0.022353    test-rmse:1.352054+0.085863 
## [62] train-rmse:1.085795+0.021818    test-rmse:1.348585+0.083706 
## [63] train-rmse:1.080100+0.022329    test-rmse:1.341265+0.083553 
## [64] train-rmse:1.073772+0.021309    test-rmse:1.334865+0.082710 
## [65] train-rmse:1.068184+0.021436    test-rmse:1.332886+0.083379 
## [66] train-rmse:1.060043+0.021501    test-rmse:1.323005+0.081996 
## [67] train-rmse:1.054207+0.021366    test-rmse:1.317823+0.086852 
## [68] train-rmse:1.047984+0.022415    test-rmse:1.315624+0.084222 
## [69] train-rmse:1.042742+0.022543    test-rmse:1.312307+0.082303 
## [70] train-rmse:1.036961+0.021771    test-rmse:1.308460+0.081804 
## [71] train-rmse:1.031824+0.022103    test-rmse:1.303321+0.083554 
## [72] train-rmse:1.024872+0.020920    test-rmse:1.302584+0.084781 
## [73] train-rmse:1.019982+0.021270    test-rmse:1.298967+0.085715 
## [74] train-rmse:1.014805+0.021687    test-rmse:1.294944+0.085059 
## [75] train-rmse:1.008319+0.022349    test-rmse:1.292510+0.085034 
## [76] train-rmse:1.003607+0.023178    test-rmse:1.285918+0.089405 
## [77] train-rmse:0.998521+0.023130    test-rmse:1.281429+0.090554 
## [78] train-rmse:0.993511+0.023049    test-rmse:1.280538+0.087094 
## [79] train-rmse:0.989029+0.023281    test-rmse:1.276460+0.086912 
## [80] train-rmse:0.984602+0.023354    test-rmse:1.273245+0.086913 
## [81] train-rmse:0.977529+0.023200    test-rmse:1.266222+0.085252 
## [82] train-rmse:0.972723+0.021886    test-rmse:1.264423+0.087143 
## [83] train-rmse:0.968324+0.021997    test-rmse:1.261821+0.086222 
## [84] train-rmse:0.962969+0.022743    test-rmse:1.257321+0.088019 
## [85] train-rmse:0.957317+0.023564    test-rmse:1.253990+0.088772 
## [86] train-rmse:0.952828+0.023300    test-rmse:1.248907+0.089440 
## [87] train-rmse:0.948796+0.023507    test-rmse:1.246904+0.091211 
## [88] train-rmse:0.945319+0.023517    test-rmse:1.244152+0.090266 
## [89] train-rmse:0.941023+0.024010    test-rmse:1.242455+0.089081 
## [90] train-rmse:0.935723+0.022106    test-rmse:1.241593+0.087786 
## [91] train-rmse:0.930672+0.022542    test-rmse:1.238409+0.087475 
## [92] train-rmse:0.926963+0.022949    test-rmse:1.234227+0.088488 
## [93] train-rmse:0.923479+0.022765    test-rmse:1.230091+0.091206 
## [94] train-rmse:0.919568+0.023244    test-rmse:1.227641+0.090212 
## [95] train-rmse:0.915758+0.023637    test-rmse:1.222773+0.089220 
## [96] train-rmse:0.911790+0.024389    test-rmse:1.219792+0.090052 
## [97] train-rmse:0.907683+0.025129    test-rmse:1.216137+0.088366 
## [98] train-rmse:0.903829+0.024241    test-rmse:1.214820+0.088068 
## [99] train-rmse:0.900575+0.024091    test-rmse:1.213117+0.089358 
## [100]    train-rmse:0.896369+0.023162    test-rmse:1.210547+0.089658 
## [101]    train-rmse:0.893371+0.022972    test-rmse:1.210319+0.090943 
## [102]    train-rmse:0.889189+0.021998    test-rmse:1.205004+0.091811 
## [103]    train-rmse:0.884372+0.020842    test-rmse:1.202092+0.091275 
## [104]    train-rmse:0.881400+0.020693    test-rmse:1.201260+0.091112 
## [105]    train-rmse:0.877298+0.021073    test-rmse:1.200002+0.091438 
## [106]    train-rmse:0.872856+0.020055    test-rmse:1.199264+0.091439 
## [107]    train-rmse:0.868706+0.019396    test-rmse:1.198721+0.091921 
## [108]    train-rmse:0.864891+0.019458    test-rmse:1.195938+0.089666 
## [109]    train-rmse:0.862335+0.019572    test-rmse:1.193951+0.091099 
## [110]    train-rmse:0.858559+0.019642    test-rmse:1.192038+0.091188 
## [111]    train-rmse:0.854181+0.019043    test-rmse:1.187124+0.097440 
## [112]    train-rmse:0.851013+0.017975    test-rmse:1.183803+0.099008 
## [113]    train-rmse:0.847868+0.017843    test-rmse:1.183075+0.099551 
## [114]    train-rmse:0.844501+0.017941    test-rmse:1.180887+0.099428 
## [115]    train-rmse:0.840683+0.016537    test-rmse:1.179006+0.099887 
## [116]    train-rmse:0.837829+0.016589    test-rmse:1.178713+0.098549 
## [117]    train-rmse:0.835443+0.016765    test-rmse:1.176665+0.099472 
## [118]    train-rmse:0.832398+0.018000    test-rmse:1.172677+0.097098 
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## [122]    train-rmse:0.818920+0.018931    test-rmse:1.165382+0.100314 
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## [125]    train-rmse:0.811409+0.018754    test-rmse:1.160714+0.099557 
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## [150]    train-rmse:0.746501+0.018906    test-rmse:1.129708+0.103697 
## [151]    train-rmse:0.743621+0.018168    test-rmse:1.127601+0.105215 
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## [153]    train-rmse:0.738874+0.018228    test-rmse:1.125391+0.103641 
## [154]    train-rmse:0.737103+0.018151    test-rmse:1.124130+0.103853 
## [155]    train-rmse:0.734541+0.018282    test-rmse:1.123319+0.104981 
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## [158]    train-rmse:0.727923+0.019394    test-rmse:1.120532+0.105049 
## [159]    train-rmse:0.726162+0.019516    test-rmse:1.119505+0.105463 
## [160]    train-rmse:0.723399+0.017734    test-rmse:1.118893+0.104613 
## [161]    train-rmse:0.721101+0.016589    test-rmse:1.117272+0.106229 
## [162]    train-rmse:0.719104+0.017190    test-rmse:1.115475+0.105065 
## [163]    train-rmse:0.716344+0.015928    test-rmse:1.114494+0.103132 
## [164]    train-rmse:0.713907+0.015301    test-rmse:1.112890+0.104399 
## [165]    train-rmse:0.711654+0.015109    test-rmse:1.112017+0.104426 
## [166]    train-rmse:0.709113+0.015106    test-rmse:1.110193+0.102113 
## [167]    train-rmse:0.706740+0.015215    test-rmse:1.108241+0.104002 
## [168]    train-rmse:0.703599+0.016552    test-rmse:1.105845+0.102528 
## [169]    train-rmse:0.700621+0.018675    test-rmse:1.102680+0.100872 
## [170]    train-rmse:0.698940+0.019252    test-rmse:1.103441+0.100857 
## [171]    train-rmse:0.697160+0.019579    test-rmse:1.101267+0.101917 
## [172]    train-rmse:0.695694+0.019453    test-rmse:1.100676+0.102271 
## [173]    train-rmse:0.694054+0.019515    test-rmse:1.100239+0.102243 
## [174]    train-rmse:0.692532+0.019516    test-rmse:1.098986+0.102516 
## [175]    train-rmse:0.690797+0.019390    test-rmse:1.099345+0.102561 
## [176]    train-rmse:0.688045+0.018119    test-rmse:1.098684+0.102995 
## [177]    train-rmse:0.685583+0.017038    test-rmse:1.097389+0.101524 
## [178]    train-rmse:0.683432+0.017311    test-rmse:1.095730+0.100489 
## [179]    train-rmse:0.681241+0.017476    test-rmse:1.094753+0.100813 
## [180]    train-rmse:0.679208+0.016462    test-rmse:1.094541+0.100933 
## [181]    train-rmse:0.677887+0.016369    test-rmse:1.094520+0.101999 
## [182]    train-rmse:0.675548+0.016201    test-rmse:1.094381+0.101625 
## [183]    train-rmse:0.673677+0.016301    test-rmse:1.093305+0.100796 
## [184]    train-rmse:0.671871+0.016566    test-rmse:1.090774+0.099030 
## [185]    train-rmse:0.670558+0.016651    test-rmse:1.090035+0.098657 
## [186]    train-rmse:0.668686+0.017267    test-rmse:1.089235+0.098678 
## [187]    train-rmse:0.667401+0.017025    test-rmse:1.089546+0.099074 
## [188]    train-rmse:0.666124+0.017066    test-rmse:1.089838+0.098319 
## [189]    train-rmse:0.664173+0.017787    test-rmse:1.088851+0.096870 
## [190]    train-rmse:0.661582+0.016985    test-rmse:1.089389+0.095972 
## [191]    train-rmse:0.659443+0.016804    test-rmse:1.087834+0.097178 
## [192]    train-rmse:0.657271+0.017604    test-rmse:1.086487+0.096880 
## [193]    train-rmse:0.655812+0.018222    test-rmse:1.086244+0.097656 
## [194]    train-rmse:0.654577+0.018242    test-rmse:1.084246+0.098427 
## [195]    train-rmse:0.653122+0.018061    test-rmse:1.083393+0.098679 
## [196]    train-rmse:0.650873+0.018143    test-rmse:1.081331+0.098566 
## [197]    train-rmse:0.649103+0.018194    test-rmse:1.081252+0.099001 
## [198]    train-rmse:0.648099+0.018280    test-rmse:1.080877+0.098566 
## [199]    train-rmse:0.646364+0.018268    test-rmse:1.081298+0.099096 
## [200]    train-rmse:0.644817+0.018377    test-rmse:1.080305+0.099974 
## [201]    train-rmse:0.643309+0.019050    test-rmse:1.077981+0.098854 
## [202]    train-rmse:0.642063+0.019233    test-rmse:1.077584+0.099197 
## [203]    train-rmse:0.640459+0.019698    test-rmse:1.076391+0.099152 
## [204]    train-rmse:0.637974+0.018197    test-rmse:1.076506+0.098815 
## [205]    train-rmse:0.636490+0.017748    test-rmse:1.075304+0.100168 
## [206]    train-rmse:0.633853+0.017008    test-rmse:1.075067+0.099418 
## [207]    train-rmse:0.632012+0.016814    test-rmse:1.074319+0.099728 
## [208]    train-rmse:0.630348+0.017302    test-rmse:1.072889+0.099209 
## [209]    train-rmse:0.629172+0.017159    test-rmse:1.072049+0.099506 
## [210]    train-rmse:0.626974+0.017165    test-rmse:1.071883+0.100788 
## [211]    train-rmse:0.625435+0.017601    test-rmse:1.070919+0.100841 
## [212]    train-rmse:0.624046+0.017559    test-rmse:1.070241+0.100500 
## [213]    train-rmse:0.623137+0.017529    test-rmse:1.069554+0.100312 
## [214]    train-rmse:0.620998+0.016500    test-rmse:1.068353+0.101729 
## [215]    train-rmse:0.619650+0.017122    test-rmse:1.068646+0.101289 
## [216]    train-rmse:0.618167+0.017370    test-rmse:1.068644+0.100393 
## [217]    train-rmse:0.617190+0.017559    test-rmse:1.068035+0.100612 
## [218]    train-rmse:0.615928+0.017875    test-rmse:1.066151+0.100590 
## [219]    train-rmse:0.614553+0.017579    test-rmse:1.065625+0.101119 
## [220]    train-rmse:0.612841+0.018182    test-rmse:1.063545+0.102113 
## [221]    train-rmse:0.611943+0.018202    test-rmse:1.064458+0.102803 
## [222]    train-rmse:0.611031+0.018340    test-rmse:1.064544+0.102874 
## [223]    train-rmse:0.609869+0.018409    test-rmse:1.063494+0.103358 
## [224]    train-rmse:0.608342+0.017817    test-rmse:1.062882+0.102511 
## [225]    train-rmse:0.606302+0.017452    test-rmse:1.061799+0.102874 
## [226]    train-rmse:0.604344+0.017062    test-rmse:1.061420+0.103471 
## [227]    train-rmse:0.603280+0.017135    test-rmse:1.061000+0.104202 
## [228]    train-rmse:0.601943+0.016852    test-rmse:1.060545+0.104461 
## [229]    train-rmse:0.600871+0.016972    test-rmse:1.059912+0.103748 
## [230]    train-rmse:0.599682+0.017613    test-rmse:1.060037+0.103909 
## [231]    train-rmse:0.598520+0.017991    test-rmse:1.059406+0.104287 
## [232]    train-rmse:0.596925+0.017192    test-rmse:1.058340+0.105422 
## [233]    train-rmse:0.594753+0.018374    test-rmse:1.058859+0.105187 
## [234]    train-rmse:0.593966+0.018480    test-rmse:1.058280+0.105510 
## [235]    train-rmse:0.592981+0.018649    test-rmse:1.057244+0.105420 
## [236]    train-rmse:0.591261+0.017986    test-rmse:1.056873+0.105652 
## [237]    train-rmse:0.589937+0.017469    test-rmse:1.056278+0.106187 
## [238]    train-rmse:0.588165+0.017362    test-rmse:1.055084+0.105978 
## [239]    train-rmse:0.586929+0.017331    test-rmse:1.054838+0.106086 
## [240]    train-rmse:0.585783+0.017291    test-rmse:1.054798+0.106485 
## [241]    train-rmse:0.584560+0.017474    test-rmse:1.054225+0.105224 
## [242]    train-rmse:0.583249+0.017690    test-rmse:1.054518+0.105028 
## [243]    train-rmse:0.582520+0.017828    test-rmse:1.054591+0.104705 
## [244]    train-rmse:0.581301+0.017386    test-rmse:1.053559+0.104800 
## [245]    train-rmse:0.579655+0.016685    test-rmse:1.052925+0.104702 
## [246]    train-rmse:0.578151+0.017189    test-rmse:1.052318+0.104308 
## [247]    train-rmse:0.577010+0.016918    test-rmse:1.051811+0.104902 
## [248]    train-rmse:0.575929+0.017093    test-rmse:1.052021+0.104564 
## [249]    train-rmse:0.574956+0.017487    test-rmse:1.051810+0.104990 
## [250]    train-rmse:0.573701+0.017469    test-rmse:1.051677+0.106163 
## [251]    train-rmse:0.572802+0.017495    test-rmse:1.051673+0.106712 
## [252]    train-rmse:0.571594+0.017648    test-rmse:1.051765+0.106679 
## [253]    train-rmse:0.570740+0.018084    test-rmse:1.051939+0.106513 
## [254]    train-rmse:0.569434+0.018276    test-rmse:1.050431+0.106458 
## [255]    train-rmse:0.568797+0.018299    test-rmse:1.049445+0.105832 
## [256]    train-rmse:0.567925+0.018340    test-rmse:1.048137+0.105666 
## [257]    train-rmse:0.566980+0.018149    test-rmse:1.048182+0.106110 
## [258]    train-rmse:0.565082+0.017611    test-rmse:1.047331+0.105997 
## [259]    train-rmse:0.564438+0.017573    test-rmse:1.046642+0.106183 
## [260]    train-rmse:0.562549+0.017492    test-rmse:1.046146+0.106670 
## [261]    train-rmse:0.561359+0.017258    test-rmse:1.045944+0.107593 
## [262]    train-rmse:0.559855+0.017479    test-rmse:1.044379+0.106825 
## [263]    train-rmse:0.558433+0.017101    test-rmse:1.044557+0.105738 
## [264]    train-rmse:0.557591+0.017021    test-rmse:1.044175+0.106251 
## [265]    train-rmse:0.556152+0.016868    test-rmse:1.043879+0.106952 
## [266]    train-rmse:0.554524+0.016262    test-rmse:1.043919+0.106782 
## [267]    train-rmse:0.553581+0.016722    test-rmse:1.043612+0.107606 
## [268]    train-rmse:0.551457+0.016824    test-rmse:1.042226+0.107603 
## [269]    train-rmse:0.550508+0.016523    test-rmse:1.042310+0.107405 
## [270]    train-rmse:0.549393+0.016995    test-rmse:1.041295+0.106527 
## [271]    train-rmse:0.547660+0.016697    test-rmse:1.040956+0.106559 
## [272]    train-rmse:0.546353+0.016351    test-rmse:1.040728+0.107408 
## [273]    train-rmse:0.544161+0.015872    test-rmse:1.039466+0.107872 
## [274]    train-rmse:0.542496+0.015568    test-rmse:1.039578+0.107995 
## [275]    train-rmse:0.541866+0.015604    test-rmse:1.039850+0.108548 
## [276]    train-rmse:0.540973+0.015580    test-rmse:1.039722+0.108051 
## [277]    train-rmse:0.540020+0.015513    test-rmse:1.040168+0.107509 
## [278]    train-rmse:0.538890+0.015485    test-rmse:1.039177+0.106026 
## [279]    train-rmse:0.537052+0.015307    test-rmse:1.037990+0.106125 
## [280]    train-rmse:0.535486+0.015562    test-rmse:1.036850+0.105872 
## [281]    train-rmse:0.534774+0.015601    test-rmse:1.036912+0.105634 
## [282]    train-rmse:0.534096+0.015528    test-rmse:1.035995+0.105286 
## [283]    train-rmse:0.533130+0.015310    test-rmse:1.035987+0.105852 
## [284]    train-rmse:0.532125+0.014965    test-rmse:1.035296+0.105533 
## [285]    train-rmse:0.530775+0.014986    test-rmse:1.033978+0.104618 
## [286]    train-rmse:0.530132+0.015013    test-rmse:1.033913+0.104878 
## [287]    train-rmse:0.529087+0.014870    test-rmse:1.033919+0.105253 
## [288]    train-rmse:0.528176+0.014744    test-rmse:1.034185+0.105660 
## [289]    train-rmse:0.526877+0.015299    test-rmse:1.033801+0.105632 
## [290]    train-rmse:0.526273+0.015251    test-rmse:1.033360+0.105354 
## [291]    train-rmse:0.525466+0.015244    test-rmse:1.032226+0.104059 
## [292]    train-rmse:0.523841+0.014601    test-rmse:1.031590+0.103880 
## [293]    train-rmse:0.522978+0.014645    test-rmse:1.032154+0.103941 
## [294]    train-rmse:0.521435+0.013997    test-rmse:1.031687+0.104550 
## [295]    train-rmse:0.520777+0.014248    test-rmse:1.031841+0.104649 
## [296]    train-rmse:0.519550+0.014577    test-rmse:1.031869+0.105325 
## [297]    train-rmse:0.518124+0.014301    test-rmse:1.031696+0.105397 
## [298]    train-rmse:0.517276+0.014196    test-rmse:1.031195+0.106636 
## [299]    train-rmse:0.516507+0.014104    test-rmse:1.031506+0.107292 
## [300]    train-rmse:0.515032+0.013930    test-rmse:1.031505+0.107686 
## [301]    train-rmse:0.514199+0.014425    test-rmse:1.030620+0.107692 
## [302]    train-rmse:0.513405+0.014482    test-rmse:1.029545+0.107583 
## [303]    train-rmse:0.511888+0.014408    test-rmse:1.029066+0.107212 
## [304]    train-rmse:0.510720+0.014085    test-rmse:1.029094+0.106967 
## [305]    train-rmse:0.509900+0.014001    test-rmse:1.028860+0.107148 
## [306]    train-rmse:0.509271+0.014181    test-rmse:1.028750+0.107488 
## [307]    train-rmse:0.508614+0.014106    test-rmse:1.029317+0.107331 
## [308]    train-rmse:0.507549+0.014531    test-rmse:1.028743+0.106972 
## [309]    train-rmse:0.506767+0.014251    test-rmse:1.028433+0.107036 
## [310]    train-rmse:0.505664+0.014031    test-rmse:1.028555+0.107328 
## [311]    train-rmse:0.504349+0.014269    test-rmse:1.028510+0.106613 
## [312]    train-rmse:0.503555+0.014523    test-rmse:1.027988+0.106592 
## [313]    train-rmse:0.502799+0.014422    test-rmse:1.027244+0.107449 
## [314]    train-rmse:0.501751+0.014265    test-rmse:1.027227+0.107156 
## [315]    train-rmse:0.500780+0.014402    test-rmse:1.026958+0.107305 
## [316]    train-rmse:0.499888+0.014608    test-rmse:1.026835+0.106925 
## [317]    train-rmse:0.499292+0.014501    test-rmse:1.026361+0.107040 
## [318]    train-rmse:0.497997+0.014627    test-rmse:1.025384+0.107238 
## [319]    train-rmse:0.494986+0.013631    test-rmse:1.025676+0.107781 
## [320]    train-rmse:0.493956+0.013339    test-rmse:1.024807+0.106949 
## [321]    train-rmse:0.492731+0.012903    test-rmse:1.022938+0.105212 
## [322]    train-rmse:0.491108+0.012514    test-rmse:1.022535+0.105502 
## [323]    train-rmse:0.490076+0.012316    test-rmse:1.022137+0.105757 
## [324]    train-rmse:0.489328+0.012670    test-rmse:1.021062+0.105455 
## [325]    train-rmse:0.488092+0.013805    test-rmse:1.021838+0.105692 
## [326]    train-rmse:0.486673+0.013665    test-rmse:1.021834+0.105967 
## [327]    train-rmse:0.485423+0.013797    test-rmse:1.021982+0.106242 
## [328]    train-rmse:0.484681+0.014065    test-rmse:1.021979+0.106466 
## [329]    train-rmse:0.483896+0.014324    test-rmse:1.021607+0.106438 
## [330]    train-rmse:0.481885+0.014860    test-rmse:1.021239+0.106354 
## Stopping. Best iteration:
## [324]    train-rmse:0.489328+0.012670    test-rmse:1.021062+0.105455
## 
## 16 
## [1]  train-rmse:84.742499+0.085026   test-rmse:84.743227+0.408848 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.927655+0.073048   test-rmse:72.928244+0.420446 
## [3]  train-rmse:62.762302+0.062739   test-rmse:62.762725+0.430333 
## [4]  train-rmse:54.016514+0.053817   test-rmse:54.016744+0.438737 
## [5]  train-rmse:46.492472+0.046117   test-rmse:46.492479+0.445831 
## [6]  train-rmse:40.019990+0.039459   test-rmse:40.019755+0.451772 
## [7]  train-rmse:34.452692+0.033687   test-rmse:34.452158+0.456684 
## [8]  train-rmse:29.664635+0.028683   test-rmse:29.663766+0.460663 
## [9]  train-rmse:25.547519+0.024346   test-rmse:25.546270+0.463776 
## [10] train-rmse:22.008217+0.020588   test-rmse:22.006533+0.466070 
## [11] train-rmse:18.966660+0.017350   test-rmse:18.964487+0.467562 
## [12] train-rmse:16.354035+0.014591   test-rmse:16.351313+0.468250 
## [13] train-rmse:14.110245+0.012261   test-rmse:14.110562+0.451996 
## [14] train-rmse:12.183608+0.010200   test-rmse:12.178845+0.439003 
## [15] train-rmse:10.529500+0.008613   test-rmse:10.520877+0.432219 
## [16] train-rmse:9.108732+0.006536    test-rmse:9.099234+0.420248 
## [17] train-rmse:7.888832+0.005903    test-rmse:7.872335+0.411980 
## [18] train-rmse:6.841837+0.004860    test-rmse:6.831770+0.391848 
## [19] train-rmse:5.943652+0.004959    test-rmse:5.940949+0.374660 
## [20] train-rmse:5.174116+0.004837    test-rmse:5.167166+0.357896 
## [21] train-rmse:4.515074+0.005982    test-rmse:4.507784+0.340483 
## [22] train-rmse:3.952779+0.006823    test-rmse:3.954761+0.321665 
## [23] train-rmse:3.471854+0.007874    test-rmse:3.479646+0.306723 
## [24] train-rmse:3.064675+0.010112    test-rmse:3.080165+0.297719 
## [25] train-rmse:2.719896+0.010735    test-rmse:2.740312+0.289242 
## [26] train-rmse:2.427605+0.010884    test-rmse:2.456641+0.276002 
## [27] train-rmse:2.180805+0.012943    test-rmse:2.228802+0.254658 
## [28] train-rmse:1.974533+0.014426    test-rmse:2.040599+0.236029 
## [29] train-rmse:1.805105+0.014818    test-rmse:1.888947+0.224124 
## [30] train-rmse:1.664772+0.016179    test-rmse:1.764780+0.210371 
## [31] train-rmse:1.546558+0.015255    test-rmse:1.663197+0.197674 
## [32] train-rmse:1.451736+0.017977    test-rmse:1.584781+0.182725 
## [33] train-rmse:1.371689+0.018426    test-rmse:1.519945+0.168108 
## [34] train-rmse:1.307162+0.018764    test-rmse:1.473080+0.156219 
## [35] train-rmse:1.256094+0.018852    test-rmse:1.434261+0.151055 
## [36] train-rmse:1.211007+0.021083    test-rmse:1.405616+0.136003 
## [37] train-rmse:1.175696+0.021527    test-rmse:1.374127+0.134638 
## [38] train-rmse:1.144391+0.023036    test-rmse:1.358640+0.128231 
## [39] train-rmse:1.116870+0.022685    test-rmse:1.341094+0.121863 
## [40] train-rmse:1.091544+0.021756    test-rmse:1.328125+0.114535 
## [41] train-rmse:1.073055+0.021532    test-rmse:1.315118+0.112638 
## [42] train-rmse:1.055353+0.021472    test-rmse:1.308723+0.112081 
## [43] train-rmse:1.039619+0.021168    test-rmse:1.297001+0.109073 
## [44] train-rmse:1.025172+0.022085    test-rmse:1.284590+0.105442 
## [45] train-rmse:1.011260+0.020760    test-rmse:1.276446+0.102147 
## [46] train-rmse:0.998476+0.021244    test-rmse:1.267383+0.099821 
## [47] train-rmse:0.986413+0.020967    test-rmse:1.260959+0.099009 
## [48] train-rmse:0.974996+0.019140    test-rmse:1.254188+0.099167 
## [49] train-rmse:0.964550+0.017238    test-rmse:1.243079+0.100397 
## [50] train-rmse:0.952061+0.019977    test-rmse:1.236344+0.100163 
## [51] train-rmse:0.942490+0.020365    test-rmse:1.231778+0.096068 
## [52] train-rmse:0.932237+0.019387    test-rmse:1.227072+0.096821 
## [53] train-rmse:0.920930+0.017642    test-rmse:1.222339+0.094844 
## [54] train-rmse:0.912613+0.017566    test-rmse:1.213994+0.098127 
## [55] train-rmse:0.903476+0.018612    test-rmse:1.204151+0.092648 
## [56] train-rmse:0.896096+0.018649    test-rmse:1.198629+0.091370 
## [57] train-rmse:0.888294+0.018822    test-rmse:1.191000+0.092877 
## [58] train-rmse:0.881140+0.019961    test-rmse:1.181705+0.091824 
## [59] train-rmse:0.873047+0.020024    test-rmse:1.177356+0.091516 
## [60] train-rmse:0.865549+0.019478    test-rmse:1.174646+0.091952 
## [61] train-rmse:0.858973+0.019025    test-rmse:1.172473+0.092426 
## [62] train-rmse:0.850799+0.020910    test-rmse:1.165133+0.088573 
## [63] train-rmse:0.841250+0.017443    test-rmse:1.157883+0.092123 
## [64] train-rmse:0.832286+0.018383    test-rmse:1.152567+0.096403 
## [65] train-rmse:0.823152+0.019070    test-rmse:1.146494+0.096973 
## [66] train-rmse:0.814411+0.019161    test-rmse:1.142951+0.094460 
## [67] train-rmse:0.809520+0.019505    test-rmse:1.138339+0.097752 
## [68] train-rmse:0.803853+0.019321    test-rmse:1.133760+0.095942 
## [69] train-rmse:0.797768+0.018020    test-rmse:1.130861+0.095890 
## [70] train-rmse:0.791605+0.017506    test-rmse:1.126756+0.097091 
## [71] train-rmse:0.785916+0.015943    test-rmse:1.120737+0.097745 
## [72] train-rmse:0.779041+0.016647    test-rmse:1.119511+0.095148 
## [73] train-rmse:0.773012+0.016450    test-rmse:1.115402+0.094820 
## [74] train-rmse:0.767296+0.015425    test-rmse:1.111303+0.096202 
## [75] train-rmse:0.762009+0.015981    test-rmse:1.108696+0.093444 
## [76] train-rmse:0.754397+0.017753    test-rmse:1.104557+0.091863 
## [77] train-rmse:0.749097+0.016857    test-rmse:1.098624+0.090300 
## [78] train-rmse:0.744217+0.017516    test-rmse:1.096252+0.091140 
## [79] train-rmse:0.739928+0.017218    test-rmse:1.095249+0.091984 
## [80] train-rmse:0.733797+0.016521    test-rmse:1.092996+0.092220 
## [81] train-rmse:0.727187+0.015676    test-rmse:1.089705+0.095234 
## [82] train-rmse:0.722561+0.016180    test-rmse:1.086039+0.095406 
## [83] train-rmse:0.717510+0.016480    test-rmse:1.081590+0.091376 
## [84] train-rmse:0.713123+0.017300    test-rmse:1.077385+0.091602 
## [85] train-rmse:0.708487+0.017232    test-rmse:1.074505+0.092252 
## [86] train-rmse:0.701433+0.018452    test-rmse:1.074495+0.091639 
## [87] train-rmse:0.696926+0.019393    test-rmse:1.072220+0.091646 
## [88] train-rmse:0.692830+0.020504    test-rmse:1.071003+0.091231 
## [89] train-rmse:0.687636+0.020277    test-rmse:1.068733+0.094752 
## [90] train-rmse:0.683077+0.020311    test-rmse:1.065795+0.094456 
## [91] train-rmse:0.676775+0.016960    test-rmse:1.061939+0.096793 
## [92] train-rmse:0.672384+0.017177    test-rmse:1.056908+0.097059 
## [93] train-rmse:0.669110+0.017490    test-rmse:1.056504+0.097423 
## [94] train-rmse:0.666094+0.017891    test-rmse:1.055040+0.096861 
## [95] train-rmse:0.662455+0.018597    test-rmse:1.052722+0.097959 
## [96] train-rmse:0.656613+0.019197    test-rmse:1.046651+0.093952 
## [97] train-rmse:0.651519+0.019569    test-rmse:1.044869+0.095763 
## [98] train-rmse:0.646446+0.015742    test-rmse:1.040795+0.097680 
## [99] train-rmse:0.642174+0.016306    test-rmse:1.041288+0.098184 
## [100]    train-rmse:0.637897+0.016118    test-rmse:1.037497+0.097019 
## [101]    train-rmse:0.634033+0.014809    test-rmse:1.035547+0.098304 
## [102]    train-rmse:0.630954+0.015047    test-rmse:1.033833+0.099374 
## [103]    train-rmse:0.627289+0.015869    test-rmse:1.031289+0.099956 
## [104]    train-rmse:0.624703+0.015979    test-rmse:1.030097+0.101264 
## [105]    train-rmse:0.621249+0.016070    test-rmse:1.030471+0.101254 
## [106]    train-rmse:0.617357+0.016710    test-rmse:1.029947+0.101830 
## [107]    train-rmse:0.613837+0.016314    test-rmse:1.028253+0.100337 
## [108]    train-rmse:0.609814+0.016953    test-rmse:1.024141+0.097321 
## [109]    train-rmse:0.605598+0.016006    test-rmse:1.022750+0.098342 
## [110]    train-rmse:0.602326+0.014727    test-rmse:1.019676+0.097920 
## [111]    train-rmse:0.598518+0.015370    test-rmse:1.018112+0.097465 
## [112]    train-rmse:0.594605+0.015955    test-rmse:1.016652+0.099449 
## [113]    train-rmse:0.590795+0.015762    test-rmse:1.015660+0.099395 
## [114]    train-rmse:0.588199+0.015153    test-rmse:1.014339+0.101161 
## [115]    train-rmse:0.585965+0.015327    test-rmse:1.013101+0.100981 
## [116]    train-rmse:0.582396+0.014916    test-rmse:1.011850+0.102582 
## [117]    train-rmse:0.579651+0.014420    test-rmse:1.010574+0.101719 
## [118]    train-rmse:0.577473+0.014276    test-rmse:1.009688+0.102400 
## [119]    train-rmse:0.574141+0.013842    test-rmse:1.007658+0.102719 
## [120]    train-rmse:0.570792+0.014510    test-rmse:1.004704+0.103069 
## [121]    train-rmse:0.568121+0.013603    test-rmse:1.004906+0.103592 
## [122]    train-rmse:0.565211+0.013808    test-rmse:1.004841+0.104461 
## [123]    train-rmse:0.561876+0.014510    test-rmse:1.003295+0.103105 
## [124]    train-rmse:0.558863+0.014452    test-rmse:0.999100+0.101070 
## [125]    train-rmse:0.555615+0.015281    test-rmse:0.998645+0.101178 
## [126]    train-rmse:0.552317+0.015718    test-rmse:0.995210+0.098855 
## [127]    train-rmse:0.547766+0.016064    test-rmse:0.992588+0.098394 
## [128]    train-rmse:0.543812+0.016492    test-rmse:0.991996+0.099611 
## [129]    train-rmse:0.540970+0.017044    test-rmse:0.989672+0.098729 
## [130]    train-rmse:0.537743+0.016011    test-rmse:0.988652+0.098751 
## [131]    train-rmse:0.534934+0.016983    test-rmse:0.986459+0.097739 
## [132]    train-rmse:0.532276+0.017085    test-rmse:0.984047+0.096449 
## [133]    train-rmse:0.529492+0.017120    test-rmse:0.984031+0.098134 
## [134]    train-rmse:0.526839+0.017845    test-rmse:0.984178+0.097877 
## [135]    train-rmse:0.524826+0.018489    test-rmse:0.984460+0.096672 
## [136]    train-rmse:0.521801+0.018930    test-rmse:0.986333+0.097890 
## [137]    train-rmse:0.518868+0.019079    test-rmse:0.986216+0.096825 
## [138]    train-rmse:0.516109+0.018467    test-rmse:0.986103+0.096529 
## [139]    train-rmse:0.513801+0.018122    test-rmse:0.985390+0.095914 
## Stopping. Best iteration:
## [133]    train-rmse:0.529492+0.017120    test-rmse:0.984031+0.098134
## 
## 17 
## [1]  train-rmse:84.742499+0.085026   test-rmse:84.743227+0.408848 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.927655+0.073048   test-rmse:72.928244+0.420446 
## [3]  train-rmse:62.762302+0.062739   test-rmse:62.762725+0.430333 
## [4]  train-rmse:54.016514+0.053817   test-rmse:54.016744+0.438737 
## [5]  train-rmse:46.492472+0.046117   test-rmse:46.492479+0.445831 
## [6]  train-rmse:40.019990+0.039459   test-rmse:40.019755+0.451772 
## [7]  train-rmse:34.452692+0.033687   test-rmse:34.452158+0.456684 
## [8]  train-rmse:29.664635+0.028683   test-rmse:29.663766+0.460663 
## [9]  train-rmse:25.547519+0.024346   test-rmse:25.546270+0.463776 
## [10] train-rmse:22.008217+0.020588   test-rmse:22.006533+0.466070 
## [11] train-rmse:18.966660+0.017350   test-rmse:18.964487+0.467562 
## [12] train-rmse:16.354035+0.014591   test-rmse:16.351313+0.468250 
## [13] train-rmse:14.110245+0.012261   test-rmse:14.110562+0.451996 
## [14] train-rmse:12.183608+0.010200   test-rmse:12.178845+0.439003 
## [15] train-rmse:10.529442+0.008666   test-rmse:10.519318+0.431322 
## [16] train-rmse:9.108445+0.006302    test-rmse:9.096704+0.419157 
## [17] train-rmse:7.887358+0.005490    test-rmse:7.870960+0.406401 
## [18] train-rmse:6.838835+0.004988    test-rmse:6.827381+0.387692 
## [19] train-rmse:5.939252+0.004959    test-rmse:5.930642+0.367666 
## [20] train-rmse:5.166632+0.003907    test-rmse:5.159530+0.348570 
## [21] train-rmse:4.504876+0.004369    test-rmse:4.507363+0.332362 
## [22] train-rmse:3.938223+0.004859    test-rmse:3.942135+0.317176 
## [23] train-rmse:3.451273+0.006832    test-rmse:3.466023+0.304600 
## [24] train-rmse:3.037006+0.007458    test-rmse:3.058632+0.294864 
## [25] train-rmse:2.685042+0.007897    test-rmse:2.714573+0.278351 
## [26] train-rmse:2.383725+0.009935    test-rmse:2.430817+0.261542 
## [27] train-rmse:2.130621+0.010371    test-rmse:2.190791+0.251144 
## [28] train-rmse:1.917250+0.013645    test-rmse:1.998533+0.231080 
## [29] train-rmse:1.733970+0.010164    test-rmse:1.837451+0.214249 
## [30] train-rmse:1.584181+0.011187    test-rmse:1.709089+0.202414 
## [31] train-rmse:1.458544+0.013966    test-rmse:1.609323+0.185834 
## [32] train-rmse:1.354215+0.013072    test-rmse:1.525740+0.168842 
## [33] train-rmse:1.266559+0.013719    test-rmse:1.456761+0.159797 
## [34] train-rmse:1.196098+0.012956    test-rmse:1.403260+0.151493 
## [35] train-rmse:1.136615+0.013676    test-rmse:1.360957+0.139734 
## [36] train-rmse:1.083677+0.013557    test-rmse:1.325985+0.132107 
## [37] train-rmse:1.037183+0.014181    test-rmse:1.297012+0.129672 
## [38] train-rmse:1.000467+0.013704    test-rmse:1.275664+0.121923 
## [39] train-rmse:0.967132+0.013974    test-rmse:1.255788+0.117925 
## [40] train-rmse:0.940800+0.011979    test-rmse:1.243341+0.115709 
## [41] train-rmse:0.917435+0.014406    test-rmse:1.227356+0.112278 
## [42] train-rmse:0.894079+0.012072    test-rmse:1.213755+0.109065 
## [43] train-rmse:0.874671+0.014169    test-rmse:1.199624+0.105693 
## [44] train-rmse:0.858780+0.014495    test-rmse:1.190709+0.103238 
## [45] train-rmse:0.842306+0.014614    test-rmse:1.181318+0.100319 
## [46] train-rmse:0.825071+0.014804    test-rmse:1.174946+0.097469 
## [47] train-rmse:0.811010+0.015839    test-rmse:1.163443+0.094401 
## [48] train-rmse:0.799041+0.015901    test-rmse:1.157855+0.094984 
## [49] train-rmse:0.788058+0.016941    test-rmse:1.149179+0.096578 
## [50] train-rmse:0.776608+0.015087    test-rmse:1.140283+0.096118 
## [51] train-rmse:0.766131+0.016348    test-rmse:1.133298+0.091786 
## [52] train-rmse:0.755658+0.017332    test-rmse:1.128637+0.089405 
## [53] train-rmse:0.746050+0.016407    test-rmse:1.122751+0.088741 
## [54] train-rmse:0.735420+0.017696    test-rmse:1.114312+0.089285 
## [55] train-rmse:0.725020+0.018234    test-rmse:1.109196+0.091090 
## [56] train-rmse:0.715494+0.020320    test-rmse:1.105368+0.090362 
## [57] train-rmse:0.706462+0.019172    test-rmse:1.100411+0.090865 
## [58] train-rmse:0.698835+0.019929    test-rmse:1.096810+0.091680 
## [59] train-rmse:0.689974+0.021019    test-rmse:1.094864+0.091579 
## [60] train-rmse:0.682973+0.020951    test-rmse:1.092718+0.090420 
## [61] train-rmse:0.674308+0.019126    test-rmse:1.087928+0.089293 
## [62] train-rmse:0.665497+0.019351    test-rmse:1.079495+0.084700 
## [63] train-rmse:0.658209+0.020532    test-rmse:1.074828+0.083479 
## [64] train-rmse:0.651915+0.020448    test-rmse:1.072834+0.087583 
## [65] train-rmse:0.644442+0.021097    test-rmse:1.070043+0.088212 
## [66] train-rmse:0.638834+0.021076    test-rmse:1.065021+0.089856 
## [67] train-rmse:0.629584+0.022122    test-rmse:1.060459+0.089028 
## [68] train-rmse:0.623211+0.021269    test-rmse:1.052600+0.085970 
## [69] train-rmse:0.616935+0.021520    test-rmse:1.048792+0.085562 
## [70] train-rmse:0.611251+0.019925    test-rmse:1.043238+0.082583 
## [71] train-rmse:0.603924+0.019113    test-rmse:1.039341+0.081007 
## [72] train-rmse:0.597320+0.018401    test-rmse:1.036438+0.080684 
## [73] train-rmse:0.591123+0.017978    test-rmse:1.034048+0.078723 
## [74] train-rmse:0.585373+0.017779    test-rmse:1.030438+0.076138 
## [75] train-rmse:0.581201+0.017483    test-rmse:1.028949+0.077338 
## [76] train-rmse:0.575232+0.018174    test-rmse:1.024719+0.076847 
## [77] train-rmse:0.570199+0.017610    test-rmse:1.022755+0.076556 
## [78] train-rmse:0.564836+0.017611    test-rmse:1.018007+0.072651 
## [79] train-rmse:0.558530+0.016537    test-rmse:1.018822+0.075272 
## [80] train-rmse:0.552307+0.018444    test-rmse:1.017794+0.074770 
## [81] train-rmse:0.546808+0.018592    test-rmse:1.015303+0.075619 
## [82] train-rmse:0.541249+0.019772    test-rmse:1.014169+0.075425 
## [83] train-rmse:0.535092+0.017339    test-rmse:1.011421+0.077406 
## [84] train-rmse:0.531177+0.016781    test-rmse:1.009339+0.077592 
## [85] train-rmse:0.526325+0.016846    test-rmse:1.007357+0.075144 
## [86] train-rmse:0.522063+0.016935    test-rmse:1.004769+0.074655 
## [87] train-rmse:0.517476+0.016217    test-rmse:1.002106+0.076650 
## [88] train-rmse:0.514137+0.016055    test-rmse:1.000067+0.076142 
## [89] train-rmse:0.509547+0.015333    test-rmse:0.999300+0.076341 
## [90] train-rmse:0.505311+0.015538    test-rmse:0.998167+0.076608 
## [91] train-rmse:0.500240+0.015338    test-rmse:0.999141+0.079811 
## [92] train-rmse:0.494178+0.014728    test-rmse:0.995675+0.079724 
## [93] train-rmse:0.490258+0.015758    test-rmse:0.994715+0.080561 
## [94] train-rmse:0.487062+0.016387    test-rmse:0.993842+0.080887 
## [95] train-rmse:0.482309+0.015387    test-rmse:0.993753+0.080927 
## [96] train-rmse:0.477155+0.016193    test-rmse:0.993038+0.082024 
## [97] train-rmse:0.473334+0.017128    test-rmse:0.990631+0.080413 
## [98] train-rmse:0.467727+0.017704    test-rmse:0.989837+0.081534 
## [99] train-rmse:0.463919+0.017494    test-rmse:0.988701+0.080367 
## [100]    train-rmse:0.459467+0.018368    test-rmse:0.987893+0.079652 
## [101]    train-rmse:0.455186+0.017155    test-rmse:0.986645+0.081307 
## [102]    train-rmse:0.450772+0.017454    test-rmse:0.983468+0.080002 
## [103]    train-rmse:0.446542+0.015522    test-rmse:0.983161+0.081755 
## [104]    train-rmse:0.443261+0.013741    test-rmse:0.980406+0.081484 
## [105]    train-rmse:0.439041+0.013244    test-rmse:0.979557+0.080236 
## [106]    train-rmse:0.434989+0.012731    test-rmse:0.979059+0.082002 
## [107]    train-rmse:0.430177+0.012499    test-rmse:0.977506+0.081419 
## [108]    train-rmse:0.425499+0.011688    test-rmse:0.975449+0.080046 
## [109]    train-rmse:0.421455+0.011394    test-rmse:0.974887+0.081374 
## [110]    train-rmse:0.417684+0.010864    test-rmse:0.974186+0.081252 
## [111]    train-rmse:0.413624+0.011087    test-rmse:0.972958+0.082279 
## [112]    train-rmse:0.410494+0.010897    test-rmse:0.972723+0.083312 
## [113]    train-rmse:0.407367+0.011520    test-rmse:0.971839+0.082379 
## [114]    train-rmse:0.405195+0.012011    test-rmse:0.971632+0.081142 
## [115]    train-rmse:0.401270+0.011257    test-rmse:0.970493+0.081419 
## [116]    train-rmse:0.397551+0.009821    test-rmse:0.969364+0.080907 
## [117]    train-rmse:0.395246+0.009708    test-rmse:0.968810+0.080379 
## [118]    train-rmse:0.393056+0.009284    test-rmse:0.967589+0.081453 
## [119]    train-rmse:0.390244+0.010664    test-rmse:0.967856+0.082826 
## [120]    train-rmse:0.387283+0.011295    test-rmse:0.965476+0.080115 
## [121]    train-rmse:0.383119+0.010900    test-rmse:0.966882+0.082487 
## [122]    train-rmse:0.380677+0.010558    test-rmse:0.966409+0.079643 
## [123]    train-rmse:0.377113+0.010690    test-rmse:0.965416+0.079552 
## [124]    train-rmse:0.373855+0.011564    test-rmse:0.965812+0.080398 
## [125]    train-rmse:0.370835+0.011762    test-rmse:0.965340+0.080168 
## [126]    train-rmse:0.367849+0.011467    test-rmse:0.963447+0.082318 
## [127]    train-rmse:0.364309+0.011652    test-rmse:0.962911+0.082361 
## [128]    train-rmse:0.362182+0.011466    test-rmse:0.963793+0.083202 
## [129]    train-rmse:0.359723+0.011368    test-rmse:0.963338+0.083149 
## [130]    train-rmse:0.357011+0.011930    test-rmse:0.964258+0.083841 
## [131]    train-rmse:0.353601+0.011000    test-rmse:0.961992+0.084555 
## [132]    train-rmse:0.351232+0.011047    test-rmse:0.962085+0.084338 
## [133]    train-rmse:0.348433+0.011513    test-rmse:0.961161+0.083424 
## [134]    train-rmse:0.345603+0.011248    test-rmse:0.960767+0.081951 
## [135]    train-rmse:0.343440+0.011493    test-rmse:0.960549+0.081846 
## [136]    train-rmse:0.342083+0.011502    test-rmse:0.960614+0.081913 
## [137]    train-rmse:0.340156+0.011240    test-rmse:0.959765+0.081493 
## [138]    train-rmse:0.338443+0.011532    test-rmse:0.958703+0.082010 
## [139]    train-rmse:0.335981+0.012025    test-rmse:0.958901+0.081096 
## [140]    train-rmse:0.333440+0.011380    test-rmse:0.958834+0.081195 
## [141]    train-rmse:0.331294+0.011860    test-rmse:0.958418+0.081403 
## [142]    train-rmse:0.328577+0.012675    test-rmse:0.956853+0.080939 
## [143]    train-rmse:0.326041+0.012786    test-rmse:0.956297+0.080215 
## [144]    train-rmse:0.322436+0.012160    test-rmse:0.955939+0.082444 
## [145]    train-rmse:0.319639+0.011904    test-rmse:0.955838+0.082827 
## [146]    train-rmse:0.315915+0.010983    test-rmse:0.954614+0.082456 
## [147]    train-rmse:0.312954+0.010632    test-rmse:0.954737+0.082781 
## [148]    train-rmse:0.310878+0.011060    test-rmse:0.955487+0.084615 
## [149]    train-rmse:0.308554+0.010744    test-rmse:0.956174+0.083491 
## [150]    train-rmse:0.306354+0.009798    test-rmse:0.955782+0.084750 
## [151]    train-rmse:0.304220+0.009926    test-rmse:0.955631+0.086214 
## [152]    train-rmse:0.301555+0.010597    test-rmse:0.954637+0.085441 
## Stopping. Best iteration:
## [146]    train-rmse:0.315915+0.010983    test-rmse:0.954614+0.082456
## 
## 18 
## [1]  train-rmse:84.742499+0.085026   test-rmse:84.743227+0.408848 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.927655+0.073048   test-rmse:72.928244+0.420446 
## [3]  train-rmse:62.762302+0.062739   test-rmse:62.762725+0.430333 
## [4]  train-rmse:54.016514+0.053817   test-rmse:54.016744+0.438737 
## [5]  train-rmse:46.492472+0.046117   test-rmse:46.492479+0.445831 
## [6]  train-rmse:40.019990+0.039459   test-rmse:40.019755+0.451772 
## [7]  train-rmse:34.452692+0.033687   test-rmse:34.452158+0.456684 
## [8]  train-rmse:29.664635+0.028683   test-rmse:29.663766+0.460663 
## [9]  train-rmse:25.547519+0.024346   test-rmse:25.546270+0.463776 
## [10] train-rmse:22.008217+0.020588   test-rmse:22.006533+0.466070 
## [11] train-rmse:18.966660+0.017350   test-rmse:18.964487+0.467562 
## [12] train-rmse:16.354035+0.014591   test-rmse:16.351313+0.468250 
## [13] train-rmse:14.110245+0.012261   test-rmse:14.110562+0.451996 
## [14] train-rmse:12.183608+0.010200   test-rmse:12.178845+0.439003 
## [15] train-rmse:10.529368+0.008735   test-rmse:10.521908+0.432823 
## [16] train-rmse:9.107897+0.006711    test-rmse:9.094078+0.417845 
## [17] train-rmse:7.887041+0.006472    test-rmse:7.868187+0.403657 
## [18] train-rmse:6.837426+0.005736    test-rmse:6.822989+0.379714 
## [19] train-rmse:5.936259+0.005709    test-rmse:5.925205+0.358837 
## [20] train-rmse:5.162189+0.004734    test-rmse:5.152269+0.336979 
## [21] train-rmse:4.498226+0.004320    test-rmse:4.494159+0.317391 
## [22] train-rmse:3.927566+0.005445    test-rmse:3.929249+0.308873 
## [23] train-rmse:3.439081+0.005593    test-rmse:3.444382+0.301607 
## [24] train-rmse:3.018464+0.006935    test-rmse:3.042511+0.279953 
## [25] train-rmse:2.657247+0.005705    test-rmse:2.701632+0.261497 
## [26] train-rmse:2.351098+0.008408    test-rmse:2.408890+0.249851 
## [27] train-rmse:2.087152+0.007696    test-rmse:2.170635+0.235374 
## [28] train-rmse:1.865797+0.010464    test-rmse:1.973869+0.221434 
## [29] train-rmse:1.676695+0.007365    test-rmse:1.810089+0.217425 
## [30] train-rmse:1.517725+0.007649    test-rmse:1.677287+0.205923 
## [31] train-rmse:1.382621+0.008076    test-rmse:1.566855+0.189237 
## [32] train-rmse:1.269454+0.012462    test-rmse:1.476536+0.173441 
## [33] train-rmse:1.172357+0.012679    test-rmse:1.408435+0.163126 
## [34] train-rmse:1.094294+0.014234    test-rmse:1.353170+0.158812 
## [35] train-rmse:1.028713+0.014171    test-rmse:1.303206+0.148840 
## [36] train-rmse:0.972514+0.013534    test-rmse:1.270894+0.140050 
## [37] train-rmse:0.924734+0.012094    test-rmse:1.241331+0.134170 
## [38] train-rmse:0.884228+0.013593    test-rmse:1.220713+0.125220 
## [39] train-rmse:0.848976+0.011668    test-rmse:1.201746+0.121165 
## [40] train-rmse:0.816050+0.010540    test-rmse:1.180745+0.112430 
## [41] train-rmse:0.789184+0.011259    test-rmse:1.165819+0.107546 
## [42] train-rmse:0.764367+0.009681    test-rmse:1.151919+0.105453 
## [43] train-rmse:0.742275+0.006017    test-rmse:1.140148+0.104427 
## [44] train-rmse:0.722584+0.009467    test-rmse:1.126387+0.099265 
## [45] train-rmse:0.703395+0.011328    test-rmse:1.116276+0.094115 
## [46] train-rmse:0.687798+0.013699    test-rmse:1.110772+0.095265 
## [47] train-rmse:0.674663+0.012884    test-rmse:1.103732+0.092957 
## [48] train-rmse:0.659393+0.012981    test-rmse:1.095017+0.090288 
## [49] train-rmse:0.649071+0.013811    test-rmse:1.087246+0.090196 
## [50] train-rmse:0.635788+0.014516    test-rmse:1.081302+0.089930 
## [51] train-rmse:0.623811+0.013130    test-rmse:1.078098+0.090137 
## [52] train-rmse:0.613099+0.015208    test-rmse:1.072382+0.091629 
## [53] train-rmse:0.603616+0.014368    test-rmse:1.065029+0.091229 
## [54] train-rmse:0.595118+0.014861    test-rmse:1.061888+0.092432 
## [55] train-rmse:0.584349+0.015055    test-rmse:1.056616+0.092953 
## [56] train-rmse:0.575058+0.016023    test-rmse:1.053404+0.092133 
## [57] train-rmse:0.565646+0.016829    test-rmse:1.051762+0.091077 
## [58] train-rmse:0.557443+0.017556    test-rmse:1.045018+0.085840 
## [59] train-rmse:0.549406+0.016870    test-rmse:1.038775+0.086615 
## [60] train-rmse:0.541405+0.016920    test-rmse:1.036194+0.087221 
## [61] train-rmse:0.533247+0.018695    test-rmse:1.031834+0.084859 
## [62] train-rmse:0.523282+0.018695    test-rmse:1.028043+0.084951 
## [63] train-rmse:0.514658+0.017089    test-rmse:1.023450+0.087223 
## [64] train-rmse:0.507699+0.015434    test-rmse:1.018856+0.085641 
## [65] train-rmse:0.501668+0.016575    test-rmse:1.015202+0.087971 
## [66] train-rmse:0.495018+0.016419    test-rmse:1.012358+0.086729 
## [67] train-rmse:0.487433+0.015916    test-rmse:1.012377+0.086206 
## [68] train-rmse:0.482061+0.016415    test-rmse:1.009002+0.088904 
## [69] train-rmse:0.476906+0.015726    test-rmse:1.004900+0.088835 
## [70] train-rmse:0.469211+0.015213    test-rmse:1.003885+0.089782 
## [71] train-rmse:0.462099+0.015664    test-rmse:1.001619+0.089841 
## [72] train-rmse:0.456328+0.015470    test-rmse:0.999267+0.089371 
## [73] train-rmse:0.452321+0.015711    test-rmse:0.997599+0.088403 
## [74] train-rmse:0.444806+0.014441    test-rmse:0.994691+0.089286 
## [75] train-rmse:0.438402+0.014545    test-rmse:0.991905+0.088333 
## [76] train-rmse:0.433189+0.015669    test-rmse:0.990779+0.086620 
## [77] train-rmse:0.427911+0.015225    test-rmse:0.987652+0.086815 
## [78] train-rmse:0.422310+0.015024    test-rmse:0.988007+0.086781 
## [79] train-rmse:0.416153+0.015256    test-rmse:0.984600+0.084992 
## [80] train-rmse:0.412261+0.015084    test-rmse:0.982566+0.085901 
## [81] train-rmse:0.408417+0.015379    test-rmse:0.980986+0.085287 
## [82] train-rmse:0.401952+0.015554    test-rmse:0.979041+0.084863 
## [83] train-rmse:0.397255+0.015119    test-rmse:0.979317+0.086275 
## [84] train-rmse:0.392814+0.015249    test-rmse:0.976665+0.086705 
## [85] train-rmse:0.387620+0.014368    test-rmse:0.975912+0.085769 
## [86] train-rmse:0.384050+0.014380    test-rmse:0.975047+0.085497 
## [87] train-rmse:0.380844+0.013798    test-rmse:0.973600+0.084685 
## [88] train-rmse:0.376325+0.014650    test-rmse:0.972527+0.084053 
## [89] train-rmse:0.371295+0.014999    test-rmse:0.971814+0.084975 
## [90] train-rmse:0.366995+0.014646    test-rmse:0.970177+0.085525 
## [91] train-rmse:0.361924+0.013201    test-rmse:0.966812+0.084506 
## [92] train-rmse:0.357616+0.013518    test-rmse:0.965848+0.083597 
## [93] train-rmse:0.353825+0.013143    test-rmse:0.964150+0.083826 
## [94] train-rmse:0.349842+0.013342    test-rmse:0.964725+0.083831 
## [95] train-rmse:0.346289+0.012028    test-rmse:0.964030+0.084278 
## [96] train-rmse:0.342316+0.011350    test-rmse:0.963815+0.083547 
## [97] train-rmse:0.338684+0.010008    test-rmse:0.963359+0.084377 
## [98] train-rmse:0.333736+0.010929    test-rmse:0.963776+0.084461 
## [99] train-rmse:0.330403+0.009956    test-rmse:0.962881+0.084291 
## [100]    train-rmse:0.327483+0.009984    test-rmse:0.963064+0.083936 
## [101]    train-rmse:0.324695+0.010353    test-rmse:0.961554+0.083390 
## [102]    train-rmse:0.319607+0.011655    test-rmse:0.962351+0.082681 
## [103]    train-rmse:0.315396+0.009590    test-rmse:0.963428+0.084170 
## [104]    train-rmse:0.310887+0.007879    test-rmse:0.962441+0.085735 
## [105]    train-rmse:0.308144+0.008946    test-rmse:0.960690+0.085896 
## [106]    train-rmse:0.304083+0.009987    test-rmse:0.959661+0.085194 
## [107]    train-rmse:0.299646+0.008153    test-rmse:0.959895+0.084777 
## [108]    train-rmse:0.296614+0.007180    test-rmse:0.960406+0.085027 
## [109]    train-rmse:0.293229+0.006332    test-rmse:0.959799+0.084727 
## [110]    train-rmse:0.288789+0.005000    test-rmse:0.957490+0.085116 
## [111]    train-rmse:0.286713+0.005023    test-rmse:0.957427+0.085856 
## [112]    train-rmse:0.283054+0.005406    test-rmse:0.957037+0.085020 
## [113]    train-rmse:0.279919+0.005709    test-rmse:0.956981+0.085026 
## [114]    train-rmse:0.277417+0.004637    test-rmse:0.958186+0.085138 
## [115]    train-rmse:0.274520+0.005179    test-rmse:0.958838+0.085061 
## [116]    train-rmse:0.271268+0.006008    test-rmse:0.957520+0.083980 
## [117]    train-rmse:0.269363+0.006270    test-rmse:0.957111+0.084174 
## [118]    train-rmse:0.266810+0.005797    test-rmse:0.956660+0.084115 
## [119]    train-rmse:0.262731+0.006427    test-rmse:0.956505+0.084336 
## [120]    train-rmse:0.260116+0.005479    test-rmse:0.955443+0.084110 
## [121]    train-rmse:0.257513+0.005002    test-rmse:0.955343+0.084221 
## [122]    train-rmse:0.253932+0.005391    test-rmse:0.953705+0.084567 
## [123]    train-rmse:0.251808+0.005345    test-rmse:0.953162+0.085398 
## [124]    train-rmse:0.248549+0.005953    test-rmse:0.953901+0.085179 
## [125]    train-rmse:0.246106+0.005481    test-rmse:0.953717+0.085913 
## [126]    train-rmse:0.243384+0.005812    test-rmse:0.952168+0.085176 
## [127]    train-rmse:0.241150+0.005466    test-rmse:0.951995+0.085584 
## [128]    train-rmse:0.239060+0.005305    test-rmse:0.952238+0.085027 
## [129]    train-rmse:0.236491+0.005168    test-rmse:0.952416+0.084958 
## [130]    train-rmse:0.233634+0.004753    test-rmse:0.952566+0.085011 
## [131]    train-rmse:0.230632+0.005148    test-rmse:0.951818+0.085271 
## [132]    train-rmse:0.228204+0.005590    test-rmse:0.951875+0.085418 
## [133]    train-rmse:0.225451+0.006739    test-rmse:0.951088+0.084634 
## [134]    train-rmse:0.223146+0.007438    test-rmse:0.951266+0.084415 
## [135]    train-rmse:0.220476+0.007307    test-rmse:0.950481+0.085015 
## [136]    train-rmse:0.218586+0.007597    test-rmse:0.949701+0.085480 
## [137]    train-rmse:0.215349+0.007455    test-rmse:0.949367+0.085941 
## [138]    train-rmse:0.212980+0.007083    test-rmse:0.949373+0.086091 
## [139]    train-rmse:0.210215+0.007534    test-rmse:0.949672+0.085954 
## [140]    train-rmse:0.207827+0.007782    test-rmse:0.949557+0.085995 
## [141]    train-rmse:0.206281+0.007939    test-rmse:0.948882+0.086463 
## [142]    train-rmse:0.204644+0.007426    test-rmse:0.949145+0.086813 
## [143]    train-rmse:0.203236+0.007206    test-rmse:0.948910+0.086520 
## [144]    train-rmse:0.201250+0.007350    test-rmse:0.948692+0.086414 
## [145]    train-rmse:0.199173+0.007464    test-rmse:0.948788+0.086818 
## [146]    train-rmse:0.196546+0.006533    test-rmse:0.948880+0.086628 
## [147]    train-rmse:0.195115+0.006493    test-rmse:0.948600+0.086480 
## [148]    train-rmse:0.193175+0.006309    test-rmse:0.948080+0.086293 
## [149]    train-rmse:0.191273+0.006209    test-rmse:0.947784+0.085896 
## [150]    train-rmse:0.189210+0.006896    test-rmse:0.947595+0.085801 
## [151]    train-rmse:0.187042+0.006835    test-rmse:0.946839+0.085892 
## [152]    train-rmse:0.185704+0.007179    test-rmse:0.946556+0.085460 
## [153]    train-rmse:0.183777+0.007239    test-rmse:0.946292+0.085515 
## [154]    train-rmse:0.182458+0.007397    test-rmse:0.945582+0.085923 
## [155]    train-rmse:0.180457+0.008084    test-rmse:0.945626+0.086058 
## [156]    train-rmse:0.178881+0.008424    test-rmse:0.945392+0.086016 
## [157]    train-rmse:0.176001+0.008196    test-rmse:0.945512+0.086073 
## [158]    train-rmse:0.174260+0.006995    test-rmse:0.945322+0.085959 
## [159]    train-rmse:0.172210+0.006359    test-rmse:0.945083+0.085749 
## [160]    train-rmse:0.170749+0.006161    test-rmse:0.944547+0.085321 
## [161]    train-rmse:0.169148+0.006047    test-rmse:0.944798+0.085381 
## [162]    train-rmse:0.167680+0.006333    test-rmse:0.944935+0.085759 
## [163]    train-rmse:0.166583+0.006658    test-rmse:0.944976+0.085793 
## [164]    train-rmse:0.164454+0.005900    test-rmse:0.944870+0.085773 
## [165]    train-rmse:0.162772+0.005449    test-rmse:0.944624+0.084959 
## [166]    train-rmse:0.161183+0.005310    test-rmse:0.944007+0.084796 
## [167]    train-rmse:0.159912+0.005252    test-rmse:0.944271+0.085042 
## [168]    train-rmse:0.158593+0.005829    test-rmse:0.944262+0.084934 
## [169]    train-rmse:0.156439+0.005375    test-rmse:0.944359+0.085480 
## [170]    train-rmse:0.154471+0.005834    test-rmse:0.944059+0.085390 
## [171]    train-rmse:0.152277+0.006303    test-rmse:0.943921+0.085488 
## [172]    train-rmse:0.150446+0.006101    test-rmse:0.943209+0.085441 
## [173]    train-rmse:0.148672+0.005774    test-rmse:0.942830+0.085348 
## [174]    train-rmse:0.146868+0.005279    test-rmse:0.942734+0.084939 
## [175]    train-rmse:0.145058+0.004912    test-rmse:0.942776+0.085550 
## [176]    train-rmse:0.143988+0.005132    test-rmse:0.942731+0.085662 
## [177]    train-rmse:0.142212+0.005640    test-rmse:0.942405+0.085983 
## [178]    train-rmse:0.140434+0.005634    test-rmse:0.942636+0.086088 
## [179]    train-rmse:0.139071+0.005454    test-rmse:0.942426+0.085890 
## [180]    train-rmse:0.137592+0.005883    test-rmse:0.942245+0.086556 
## [181]    train-rmse:0.136158+0.005814    test-rmse:0.942388+0.086741 
## [182]    train-rmse:0.134663+0.006378    test-rmse:0.942195+0.087224 
## [183]    train-rmse:0.133788+0.006579    test-rmse:0.942379+0.087593 
## [184]    train-rmse:0.132233+0.006389    test-rmse:0.942066+0.087755 
## [185]    train-rmse:0.130370+0.006100    test-rmse:0.941489+0.087452 
## [186]    train-rmse:0.128801+0.006317    test-rmse:0.941612+0.087692 
## [187]    train-rmse:0.127547+0.006182    test-rmse:0.941304+0.087487 
## [188]    train-rmse:0.126071+0.006116    test-rmse:0.941375+0.087819 
## [189]    train-rmse:0.124718+0.005856    test-rmse:0.941535+0.087852 
## [190]    train-rmse:0.123626+0.006103    test-rmse:0.941553+0.088157 
## [191]    train-rmse:0.122100+0.005694    test-rmse:0.942109+0.088894 
## [192]    train-rmse:0.120836+0.005872    test-rmse:0.941837+0.088738 
## [193]    train-rmse:0.119789+0.005979    test-rmse:0.941520+0.088268 
## Stopping. Best iteration:
## [187]    train-rmse:0.127547+0.006182    test-rmse:0.941304+0.087487
## 
## 19 
## [1]  train-rmse:84.742499+0.085026   test-rmse:84.743227+0.408848 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.927655+0.073048   test-rmse:72.928244+0.420446 
## [3]  train-rmse:62.762302+0.062739   test-rmse:62.762725+0.430333 
## [4]  train-rmse:54.016514+0.053817   test-rmse:54.016744+0.438737 
## [5]  train-rmse:46.492472+0.046117   test-rmse:46.492479+0.445831 
## [6]  train-rmse:40.019990+0.039459   test-rmse:40.019755+0.451772 
## [7]  train-rmse:34.452692+0.033687   test-rmse:34.452158+0.456684 
## [8]  train-rmse:29.664635+0.028683   test-rmse:29.663766+0.460663 
## [9]  train-rmse:25.547519+0.024346   test-rmse:25.546270+0.463776 
## [10] train-rmse:22.008217+0.020588   test-rmse:22.006533+0.466070 
## [11] train-rmse:18.966660+0.017350   test-rmse:18.964487+0.467562 
## [12] train-rmse:16.354035+0.014591   test-rmse:16.351313+0.468250 
## [13] train-rmse:14.110245+0.012261   test-rmse:14.110562+0.451996 
## [14] train-rmse:12.183608+0.010200   test-rmse:12.178845+0.439003 
## [15] train-rmse:10.529368+0.008735   test-rmse:10.521908+0.432823 
## [16] train-rmse:9.107897+0.006711    test-rmse:9.094078+0.417845 
## [17] train-rmse:7.886734+0.006698    test-rmse:7.868809+0.405459 
## [18] train-rmse:6.837121+0.006171    test-rmse:6.818811+0.376242 
## [19] train-rmse:5.935532+0.006036    test-rmse:5.918674+0.356092 
## [20] train-rmse:5.160097+0.004683    test-rmse:5.148081+0.338896 
## [21] train-rmse:4.494517+0.004738    test-rmse:4.489799+0.318715 
## [22] train-rmse:3.921800+0.003250    test-rmse:3.927604+0.310032 
## [23] train-rmse:3.429640+0.004183    test-rmse:3.449076+0.284133 
## [24] train-rmse:3.007066+0.002810    test-rmse:3.037118+0.272157 
## [25] train-rmse:2.644465+0.005156    test-rmse:2.693464+0.251524 
## [26] train-rmse:2.334633+0.006324    test-rmse:2.397529+0.235960 
## [27] train-rmse:2.065875+0.006102    test-rmse:2.155835+0.221377 
## [28] train-rmse:1.839178+0.003785    test-rmse:1.954665+0.216159 
## [29] train-rmse:1.641644+0.002590    test-rmse:1.784317+0.197189 
## [30] train-rmse:1.478259+0.002059    test-rmse:1.644288+0.183122 
## [31] train-rmse:1.336618+0.005224    test-rmse:1.533203+0.168049 
## [32] train-rmse:1.212153+0.005938    test-rmse:1.439769+0.157384 
## [33] train-rmse:1.110277+0.006040    test-rmse:1.369639+0.148010 
## [34] train-rmse:1.025883+0.006715    test-rmse:1.312197+0.137109 
## [35] train-rmse:0.953991+0.006805    test-rmse:1.263586+0.131643 
## [36] train-rmse:0.890717+0.006975    test-rmse:1.227849+0.120509 
## [37] train-rmse:0.837245+0.007149    test-rmse:1.197090+0.116561 
## [38] train-rmse:0.793249+0.010820    test-rmse:1.171232+0.119285 
## [39] train-rmse:0.754220+0.014411    test-rmse:1.148950+0.118164 
## [40] train-rmse:0.721033+0.015834    test-rmse:1.129580+0.113938 
## [41] train-rmse:0.693793+0.017120    test-rmse:1.114594+0.115444 
## [42] train-rmse:0.665271+0.017532    test-rmse:1.104649+0.112787 
## [43] train-rmse:0.642266+0.016752    test-rmse:1.096668+0.112014 
## [44] train-rmse:0.620277+0.014744    test-rmse:1.087312+0.109790 
## [45] train-rmse:0.601380+0.015852    test-rmse:1.079940+0.108188 
## [46] train-rmse:0.582180+0.017163    test-rmse:1.070107+0.104721 
## [47] train-rmse:0.568328+0.018394    test-rmse:1.065246+0.102709 
## [48] train-rmse:0.553844+0.017226    test-rmse:1.061494+0.099765 
## [49] train-rmse:0.539470+0.017651    test-rmse:1.055354+0.099111 
## [50] train-rmse:0.528166+0.018416    test-rmse:1.051412+0.098083 
## [51] train-rmse:0.517432+0.021036    test-rmse:1.045468+0.097580 
## [52] train-rmse:0.507569+0.019918    test-rmse:1.038733+0.095064 
## [53] train-rmse:0.497338+0.019795    test-rmse:1.035576+0.096039 
## [54] train-rmse:0.488884+0.019145    test-rmse:1.032391+0.095930 
## [55] train-rmse:0.476811+0.017254    test-rmse:1.025160+0.091376 
## [56] train-rmse:0.466917+0.018772    test-rmse:1.020591+0.090459 
## [57] train-rmse:0.458519+0.017509    test-rmse:1.016930+0.090700 
## [58] train-rmse:0.450780+0.017110    test-rmse:1.013115+0.091663 
## [59] train-rmse:0.442917+0.016404    test-rmse:1.011072+0.089406 
## [60] train-rmse:0.436273+0.017093    test-rmse:1.008180+0.089500 
## [61] train-rmse:0.426542+0.015472    test-rmse:1.006332+0.090336 
## [62] train-rmse:0.418016+0.015345    test-rmse:1.005159+0.089876 
## [63] train-rmse:0.410608+0.015145    test-rmse:1.001520+0.089409 
## [64] train-rmse:0.405240+0.013800    test-rmse:1.000953+0.092216 
## [65] train-rmse:0.399241+0.014038    test-rmse:0.999054+0.091744 
## [66] train-rmse:0.391658+0.015401    test-rmse:0.996590+0.091261 
## [67] train-rmse:0.383507+0.016247    test-rmse:0.995241+0.092786 
## [68] train-rmse:0.377530+0.015473    test-rmse:0.990087+0.091361 
## [69] train-rmse:0.372808+0.016462    test-rmse:0.987712+0.091687 
## [70] train-rmse:0.367508+0.015436    test-rmse:0.984390+0.090267 
## [71] train-rmse:0.359568+0.015370    test-rmse:0.982330+0.088416 
## [72] train-rmse:0.353453+0.017045    test-rmse:0.980615+0.089495 
## [73] train-rmse:0.348726+0.017496    test-rmse:0.981365+0.089240 
## [74] train-rmse:0.343052+0.017852    test-rmse:0.979451+0.088025 
## [75] train-rmse:0.338804+0.017246    test-rmse:0.978195+0.088015 
## [76] train-rmse:0.333102+0.016800    test-rmse:0.977567+0.087241 
## [77] train-rmse:0.327132+0.017247    test-rmse:0.978089+0.085197 
## [78] train-rmse:0.321309+0.016057    test-rmse:0.974985+0.085339 
## [79] train-rmse:0.317252+0.015591    test-rmse:0.974806+0.085627 
## [80] train-rmse:0.311427+0.015139    test-rmse:0.971412+0.085283 
## [81] train-rmse:0.306374+0.014673    test-rmse:0.969845+0.085107 
## [82] train-rmse:0.302205+0.014968    test-rmse:0.970400+0.085865 
## [83] train-rmse:0.297368+0.013785    test-rmse:0.967878+0.086221 
## [84] train-rmse:0.292358+0.013601    test-rmse:0.967182+0.085555 
## [85] train-rmse:0.288639+0.012818    test-rmse:0.967362+0.086290 
## [86] train-rmse:0.284570+0.013738    test-rmse:0.966608+0.088049 
## [87] train-rmse:0.279602+0.012544    test-rmse:0.964688+0.087566 
## [88] train-rmse:0.276188+0.012364    test-rmse:0.964527+0.087991 
## [89] train-rmse:0.273072+0.012198    test-rmse:0.963221+0.088623 
## [90] train-rmse:0.267928+0.013016    test-rmse:0.963012+0.086924 
## [91] train-rmse:0.265086+0.012631    test-rmse:0.963234+0.086528 
## [92] train-rmse:0.259824+0.011724    test-rmse:0.962424+0.086269 
## [93] train-rmse:0.255849+0.011679    test-rmse:0.961371+0.086088 
## [94] train-rmse:0.252798+0.011602    test-rmse:0.960302+0.085946 
## [95] train-rmse:0.249165+0.011926    test-rmse:0.959405+0.085464 
## [96] train-rmse:0.244891+0.012201    test-rmse:0.959818+0.084916 
## [97] train-rmse:0.241685+0.012763    test-rmse:0.959034+0.084920 
## [98] train-rmse:0.237101+0.012501    test-rmse:0.959082+0.086229 
## [99] train-rmse:0.233586+0.012941    test-rmse:0.959069+0.084631 
## [100]    train-rmse:0.229367+0.013367    test-rmse:0.958113+0.084446 
## [101]    train-rmse:0.225291+0.013199    test-rmse:0.956331+0.084332 
## [102]    train-rmse:0.221929+0.012751    test-rmse:0.956647+0.084441 
## [103]    train-rmse:0.219553+0.013244    test-rmse:0.956157+0.084564 
## [104]    train-rmse:0.216658+0.012213    test-rmse:0.955769+0.085206 
## [105]    train-rmse:0.213283+0.012136    test-rmse:0.955816+0.085274 
## [106]    train-rmse:0.210926+0.011644    test-rmse:0.954950+0.085270 
## [107]    train-rmse:0.207773+0.012678    test-rmse:0.954597+0.085534 
## [108]    train-rmse:0.204570+0.012884    test-rmse:0.952970+0.084715 
## [109]    train-rmse:0.200452+0.011688    test-rmse:0.952417+0.085592 
## [110]    train-rmse:0.198094+0.012079    test-rmse:0.952522+0.086196 
## [111]    train-rmse:0.195793+0.012069    test-rmse:0.952779+0.086120 
## [112]    train-rmse:0.192509+0.010964    test-rmse:0.952669+0.085706 
## [113]    train-rmse:0.190100+0.010784    test-rmse:0.952221+0.085586 
## [114]    train-rmse:0.186849+0.010354    test-rmse:0.951126+0.084283 
## [115]    train-rmse:0.184576+0.010711    test-rmse:0.951439+0.084714 
## [116]    train-rmse:0.182503+0.010316    test-rmse:0.951319+0.084493 
## [117]    train-rmse:0.179964+0.009810    test-rmse:0.951315+0.084810 
## [118]    train-rmse:0.178358+0.010433    test-rmse:0.951248+0.084702 
## [119]    train-rmse:0.176508+0.010525    test-rmse:0.950740+0.085200 
## [120]    train-rmse:0.173463+0.010968    test-rmse:0.950609+0.084932 
## [121]    train-rmse:0.170043+0.010387    test-rmse:0.950727+0.084682 
## [122]    train-rmse:0.166772+0.010091    test-rmse:0.951214+0.085206 
## [123]    train-rmse:0.164294+0.009851    test-rmse:0.951223+0.085120 
## [124]    train-rmse:0.162186+0.009141    test-rmse:0.950344+0.084854 
## [125]    train-rmse:0.159747+0.009048    test-rmse:0.951122+0.084720 
## [126]    train-rmse:0.157444+0.009316    test-rmse:0.951746+0.084393 
## [127]    train-rmse:0.155335+0.008593    test-rmse:0.951005+0.084770 
## [128]    train-rmse:0.152726+0.008556    test-rmse:0.950850+0.085031 
## [129]    train-rmse:0.150234+0.009156    test-rmse:0.950615+0.085010 
## [130]    train-rmse:0.148433+0.009292    test-rmse:0.951233+0.085124 
## Stopping. Best iteration:
## [124]    train-rmse:0.162186+0.009141    test-rmse:0.950344+0.084854
## 
## 20 
## [1]  train-rmse:84.742499+0.085026   test-rmse:84.743227+0.408848 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.927655+0.073048   test-rmse:72.928244+0.420446 
## [3]  train-rmse:62.762302+0.062739   test-rmse:62.762725+0.430333 
## [4]  train-rmse:54.016514+0.053817   test-rmse:54.016744+0.438737 
## [5]  train-rmse:46.492472+0.046117   test-rmse:46.492479+0.445831 
## [6]  train-rmse:40.019990+0.039459   test-rmse:40.019755+0.451772 
## [7]  train-rmse:34.452692+0.033687   test-rmse:34.452158+0.456684 
## [8]  train-rmse:29.664635+0.028683   test-rmse:29.663766+0.460663 
## [9]  train-rmse:25.547519+0.024346   test-rmse:25.546270+0.463776 
## [10] train-rmse:22.008217+0.020588   test-rmse:22.006533+0.466070 
## [11] train-rmse:18.966660+0.017350   test-rmse:18.964487+0.467562 
## [12] train-rmse:16.354035+0.014591   test-rmse:16.351313+0.468250 
## [13] train-rmse:14.110245+0.012261   test-rmse:14.110562+0.451996 
## [14] train-rmse:12.183608+0.010200   test-rmse:12.178845+0.439003 
## [15] train-rmse:10.529368+0.008735   test-rmse:10.521908+0.432823 
## [16] train-rmse:9.107897+0.006711    test-rmse:9.094078+0.417845 
## [17] train-rmse:7.886734+0.006698    test-rmse:7.868809+0.405459 
## [18] train-rmse:6.836921+0.006146    test-rmse:6.822795+0.376246 
## [19] train-rmse:5.935075+0.005890    test-rmse:5.924490+0.351750 
## [20] train-rmse:5.159242+0.004461    test-rmse:5.149406+0.336462 
## [21] train-rmse:4.492881+0.005183    test-rmse:4.482794+0.313941 
## [22] train-rmse:3.919514+0.004773    test-rmse:3.915231+0.296337 
## [23] train-rmse:3.426008+0.004326    test-rmse:3.438009+0.280984 
## [24] train-rmse:3.001721+0.004814    test-rmse:3.023674+0.264227 
## [25] train-rmse:2.636305+0.006881    test-rmse:2.677080+0.244242 
## [26] train-rmse:2.321738+0.006237    test-rmse:2.376434+0.234384 
## [27] train-rmse:2.048668+0.006523    test-rmse:2.122488+0.215488 
## [28] train-rmse:1.818568+0.005555    test-rmse:1.910601+0.202885 
## [29] train-rmse:1.618649+0.004959    test-rmse:1.743101+0.186453 
## [30] train-rmse:1.448233+0.004785    test-rmse:1.609576+0.171016 
## [31] train-rmse:1.303468+0.005015    test-rmse:1.494944+0.161892 
## [32] train-rmse:1.178555+0.005748    test-rmse:1.404649+0.155009 
## [33] train-rmse:1.071652+0.005033    test-rmse:1.325493+0.146896 
## [34] train-rmse:0.981546+0.008577    test-rmse:1.265439+0.138573 
## [35] train-rmse:0.901539+0.009556    test-rmse:1.215936+0.132898 
## [36] train-rmse:0.829996+0.007177    test-rmse:1.177237+0.128106 
## [37] train-rmse:0.768829+0.010801    test-rmse:1.145176+0.122155 
## [38] train-rmse:0.719834+0.011635    test-rmse:1.121900+0.120353 
## [39] train-rmse:0.677115+0.013752    test-rmse:1.101752+0.115800 
## [40] train-rmse:0.639756+0.015664    test-rmse:1.085095+0.112605 
## [41] train-rmse:0.608021+0.016976    test-rmse:1.067660+0.106640 
## [42] train-rmse:0.580286+0.018194    test-rmse:1.058029+0.103055 
## [43] train-rmse:0.553392+0.019773    test-rmse:1.050400+0.101450 
## [44] train-rmse:0.528929+0.017941    test-rmse:1.040949+0.101417 
## [45] train-rmse:0.504786+0.016247    test-rmse:1.032962+0.092489 
## [46] train-rmse:0.485500+0.014028    test-rmse:1.028677+0.091069 
## [47] train-rmse:0.469264+0.015995    test-rmse:1.021354+0.089071 
## [48] train-rmse:0.453688+0.015689    test-rmse:1.013222+0.084155 
## [49] train-rmse:0.438323+0.018302    test-rmse:1.009640+0.082538 
## [50] train-rmse:0.427268+0.019058    test-rmse:1.007262+0.083638 
## [51] train-rmse:0.415109+0.016957    test-rmse:1.004390+0.083248 
## [52] train-rmse:0.400287+0.015970    test-rmse:0.999314+0.083477 
## [53] train-rmse:0.389240+0.015187    test-rmse:0.993970+0.086437 
## [54] train-rmse:0.378711+0.013757    test-rmse:0.991418+0.085156 
## [55] train-rmse:0.367191+0.015603    test-rmse:0.989741+0.084824 
## [56] train-rmse:0.358650+0.017769    test-rmse:0.987702+0.083538 
## [57] train-rmse:0.350763+0.017484    test-rmse:0.985846+0.083749 
## [58] train-rmse:0.343929+0.015767    test-rmse:0.982579+0.080854 
## [59] train-rmse:0.335462+0.015339    test-rmse:0.981505+0.080138 
## [60] train-rmse:0.328575+0.016009    test-rmse:0.981122+0.082032 
## [61] train-rmse:0.322678+0.015612    test-rmse:0.978515+0.080326 
## [62] train-rmse:0.313641+0.013822    test-rmse:0.977643+0.079909 
## [63] train-rmse:0.307120+0.014160    test-rmse:0.976378+0.080431 
## [64] train-rmse:0.298379+0.014860    test-rmse:0.974172+0.077271 
## [65] train-rmse:0.291249+0.014393    test-rmse:0.971502+0.076754 
## [66] train-rmse:0.285376+0.013458    test-rmse:0.970753+0.077140 
## [67] train-rmse:0.278528+0.012876    test-rmse:0.967472+0.075307 
## [68] train-rmse:0.272962+0.014263    test-rmse:0.966646+0.074464 
## [69] train-rmse:0.268465+0.014915    test-rmse:0.964794+0.073264 
## [70] train-rmse:0.262693+0.014688    test-rmse:0.964453+0.072852 
## [71] train-rmse:0.257492+0.013445    test-rmse:0.962350+0.071804 
## [72] train-rmse:0.251716+0.013451    test-rmse:0.960590+0.071013 
## [73] train-rmse:0.246648+0.013399    test-rmse:0.959683+0.072171 
## [74] train-rmse:0.242524+0.013298    test-rmse:0.958282+0.071635 
## [75] train-rmse:0.236917+0.013315    test-rmse:0.958299+0.071467 
## [76] train-rmse:0.232257+0.014027    test-rmse:0.957538+0.071194 
## [77] train-rmse:0.227196+0.013971    test-rmse:0.956576+0.071364 
## [78] train-rmse:0.223625+0.013709    test-rmse:0.955747+0.070390 
## [79] train-rmse:0.218320+0.013071    test-rmse:0.956312+0.070236 
## [80] train-rmse:0.215081+0.013068    test-rmse:0.956295+0.071015 
## [81] train-rmse:0.211757+0.013199    test-rmse:0.954986+0.071364 
## [82] train-rmse:0.208393+0.013167    test-rmse:0.954225+0.070411 
## [83] train-rmse:0.205490+0.011940    test-rmse:0.953123+0.069921 
## [84] train-rmse:0.202461+0.012471    test-rmse:0.952335+0.070082 
## [85] train-rmse:0.197692+0.012506    test-rmse:0.951580+0.070123 
## [86] train-rmse:0.194607+0.011880    test-rmse:0.950608+0.069740 
## [87] train-rmse:0.191390+0.011385    test-rmse:0.950143+0.070115 
## [88] train-rmse:0.187213+0.011527    test-rmse:0.949427+0.069957 
## [89] train-rmse:0.183551+0.011047    test-rmse:0.949125+0.069765 
## [90] train-rmse:0.181118+0.011634    test-rmse:0.949092+0.070434 
## [91] train-rmse:0.176731+0.011040    test-rmse:0.947141+0.070183 
## [92] train-rmse:0.172126+0.010076    test-rmse:0.947183+0.071104 
## [93] train-rmse:0.169433+0.010646    test-rmse:0.946336+0.070799 
## [94] train-rmse:0.165690+0.009328    test-rmse:0.945934+0.069819 
## [95] train-rmse:0.162511+0.009224    test-rmse:0.944553+0.069430 
## [96] train-rmse:0.159945+0.008701    test-rmse:0.944438+0.068901 
## [97] train-rmse:0.157685+0.009307    test-rmse:0.943744+0.068534 
## [98] train-rmse:0.154587+0.009294    test-rmse:0.943524+0.068204 
## [99] train-rmse:0.151382+0.008451    test-rmse:0.942394+0.068183 
## [100]    train-rmse:0.148210+0.008071    test-rmse:0.942043+0.068107 
## [101]    train-rmse:0.145588+0.009079    test-rmse:0.941315+0.068001 
## [102]    train-rmse:0.142664+0.009093    test-rmse:0.940939+0.068000 
## [103]    train-rmse:0.139005+0.008269    test-rmse:0.939715+0.068174 
## [104]    train-rmse:0.136560+0.007994    test-rmse:0.939014+0.067872 
## [105]    train-rmse:0.133958+0.008096    test-rmse:0.938964+0.068452 
## [106]    train-rmse:0.131501+0.007573    test-rmse:0.939002+0.069158 
## [107]    train-rmse:0.128693+0.006780    test-rmse:0.937861+0.068920 
## [108]    train-rmse:0.127182+0.006617    test-rmse:0.937342+0.069053 
## [109]    train-rmse:0.125000+0.006591    test-rmse:0.936855+0.068666 
## [110]    train-rmse:0.123433+0.006879    test-rmse:0.936798+0.068134 
## [111]    train-rmse:0.121448+0.006648    test-rmse:0.936344+0.067555 
## [112]    train-rmse:0.118950+0.007415    test-rmse:0.936013+0.067347 
## [113]    train-rmse:0.116179+0.007071    test-rmse:0.934573+0.067085 
## [114]    train-rmse:0.113693+0.006682    test-rmse:0.934653+0.066624 
## [115]    train-rmse:0.111897+0.006958    test-rmse:0.934394+0.066409 
## [116]    train-rmse:0.109511+0.006556    test-rmse:0.933814+0.065885 
## [117]    train-rmse:0.107952+0.006592    test-rmse:0.934030+0.066034 
## [118]    train-rmse:0.106485+0.006851    test-rmse:0.933955+0.066115 
## [119]    train-rmse:0.104257+0.006636    test-rmse:0.933558+0.065956 
## [120]    train-rmse:0.102558+0.006807    test-rmse:0.932983+0.065972 
## [121]    train-rmse:0.100509+0.006775    test-rmse:0.932799+0.066473 
## [122]    train-rmse:0.098498+0.006759    test-rmse:0.932421+0.066099 
## [123]    train-rmse:0.096890+0.006839    test-rmse:0.931919+0.066181 
## [124]    train-rmse:0.095048+0.007210    test-rmse:0.931290+0.066044 
## [125]    train-rmse:0.093458+0.006590    test-rmse:0.931197+0.066331 
## [126]    train-rmse:0.091808+0.006293    test-rmse:0.931209+0.066817 
## [127]    train-rmse:0.090440+0.005964    test-rmse:0.930916+0.066836 
## [128]    train-rmse:0.088857+0.006052    test-rmse:0.930896+0.066866 
## [129]    train-rmse:0.087137+0.005942    test-rmse:0.930661+0.066955 
## [130]    train-rmse:0.085578+0.005501    test-rmse:0.930338+0.066816 
## [131]    train-rmse:0.084087+0.005877    test-rmse:0.929856+0.066801 
## [132]    train-rmse:0.082573+0.006444    test-rmse:0.929941+0.066707 
## [133]    train-rmse:0.080752+0.006486    test-rmse:0.929389+0.066996 
## [134]    train-rmse:0.079685+0.006359    test-rmse:0.929394+0.067252 
## [135]    train-rmse:0.078123+0.006481    test-rmse:0.928768+0.067803 
## [136]    train-rmse:0.076988+0.006495    test-rmse:0.928717+0.067455 
## [137]    train-rmse:0.075333+0.006094    test-rmse:0.928764+0.067595 
## [138]    train-rmse:0.073613+0.006132    test-rmse:0.928714+0.067761 
## [139]    train-rmse:0.071750+0.005862    test-rmse:0.928691+0.067780 
## [140]    train-rmse:0.070553+0.005662    test-rmse:0.928826+0.067910 
## [141]    train-rmse:0.069649+0.005729    test-rmse:0.928848+0.067845 
## [142]    train-rmse:0.068473+0.005516    test-rmse:0.928469+0.067798 
## [143]    train-rmse:0.067170+0.005224    test-rmse:0.928177+0.068160 
## [144]    train-rmse:0.065862+0.005299    test-rmse:0.928185+0.068231 
## [145]    train-rmse:0.065049+0.005207    test-rmse:0.928040+0.068500 
## [146]    train-rmse:0.063759+0.005012    test-rmse:0.927976+0.068656 
## [147]    train-rmse:0.062583+0.004598    test-rmse:0.927759+0.068859 
## [148]    train-rmse:0.061491+0.004343    test-rmse:0.927421+0.069052 
## [149]    train-rmse:0.060204+0.004242    test-rmse:0.927113+0.069428 
## [150]    train-rmse:0.058952+0.004210    test-rmse:0.926661+0.069222 
## [151]    train-rmse:0.057704+0.004093    test-rmse:0.926466+0.069190 
## [152]    train-rmse:0.056794+0.003971    test-rmse:0.926517+0.069211 
## [153]    train-rmse:0.055472+0.003759    test-rmse:0.926600+0.069283 
## [154]    train-rmse:0.054345+0.003532    test-rmse:0.926334+0.069044 
## [155]    train-rmse:0.053596+0.003278    test-rmse:0.926194+0.069140 
## [156]    train-rmse:0.052500+0.003186    test-rmse:0.925882+0.069039 
## [157]    train-rmse:0.051621+0.002703    test-rmse:0.925959+0.069350 
## [158]    train-rmse:0.050609+0.002504    test-rmse:0.925754+0.069494 
## [159]    train-rmse:0.049704+0.002556    test-rmse:0.925945+0.069615 
## [160]    train-rmse:0.048926+0.002530    test-rmse:0.925936+0.069552 
## [161]    train-rmse:0.048051+0.002571    test-rmse:0.925800+0.069734 
## [162]    train-rmse:0.047265+0.002346    test-rmse:0.925890+0.069799 
## [163]    train-rmse:0.046515+0.002382    test-rmse:0.925762+0.069907 
## [164]    train-rmse:0.045651+0.002212    test-rmse:0.925628+0.070008 
## [165]    train-rmse:0.044879+0.002277    test-rmse:0.925676+0.070049 
## [166]    train-rmse:0.044054+0.002279    test-rmse:0.925514+0.069995 
## [167]    train-rmse:0.043299+0.002364    test-rmse:0.925228+0.069818 
## [168]    train-rmse:0.042783+0.002450    test-rmse:0.925186+0.069875 
## [169]    train-rmse:0.041966+0.002364    test-rmse:0.925086+0.069964 
## [170]    train-rmse:0.041195+0.002410    test-rmse:0.925279+0.070023 
## [171]    train-rmse:0.040407+0.002105    test-rmse:0.925029+0.069966 
## [172]    train-rmse:0.039656+0.002012    test-rmse:0.924758+0.070215 
## [173]    train-rmse:0.039108+0.001936    test-rmse:0.924577+0.070102 
## [174]    train-rmse:0.038460+0.001920    test-rmse:0.924608+0.070222 
## [175]    train-rmse:0.037913+0.001993    test-rmse:0.924468+0.070195 
## [176]    train-rmse:0.037149+0.002170    test-rmse:0.924316+0.070169 
## [177]    train-rmse:0.036361+0.002036    test-rmse:0.924370+0.070209 
## [178]    train-rmse:0.035833+0.002107    test-rmse:0.924088+0.070200 
## [179]    train-rmse:0.035279+0.002032    test-rmse:0.923978+0.070213 
## [180]    train-rmse:0.034752+0.002101    test-rmse:0.924022+0.070222 
## [181]    train-rmse:0.034159+0.001984    test-rmse:0.924071+0.070281 
## [182]    train-rmse:0.033531+0.001993    test-rmse:0.924065+0.070359 
## [183]    train-rmse:0.033041+0.001832    test-rmse:0.924001+0.070422 
## [184]    train-rmse:0.032254+0.001810    test-rmse:0.924012+0.070542 
## [185]    train-rmse:0.031732+0.001756    test-rmse:0.923989+0.070659 
## Stopping. Best iteration:
## [179]    train-rmse:0.035279+0.002032    test-rmse:0.923978+0.070213
## 
## 21 
## [1]  train-rmse:82.780940+0.083029   test-rmse:82.781650+0.410772 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.591332+0.069678   test-rmse:69.591866+0.423700 
## [3]  train-rmse:58.506326+0.058401   test-rmse:58.506664+0.434438 
## [4]  train-rmse:49.190703+0.048890   test-rmse:49.190803+0.443303 
## [5]  train-rmse:41.362720+0.040844   test-rmse:41.362536+0.450558 
## [6]  train-rmse:34.785638+0.034034   test-rmse:34.785128+0.456398 
## [7]  train-rmse:29.260552+0.028262   test-rmse:29.259653+0.460980 
## [8]  train-rmse:24.620358+0.023369   test-rmse:24.619006+0.464415 
## [9]  train-rmse:20.724704+0.019222   test-rmse:20.722832+0.466766 
## [10] train-rmse:17.455756+0.015748   test-rmse:17.453284+0.468046 
## [11] train-rmse:14.714250+0.013047   test-rmse:14.705223+0.464215 
## [12] train-rmse:12.415611+0.010599   test-rmse:12.409716+0.446841 
## [13] train-rmse:10.489682+0.008431   test-rmse:10.475633+0.448375 
## [14] train-rmse:8.876798+0.006610    test-rmse:8.862031+0.441172 
## [15] train-rmse:7.528642+0.005909    test-rmse:7.514037+0.428347 
## [16] train-rmse:6.404087+0.005637    test-rmse:6.387192+0.418038 
## [17] train-rmse:5.469022+0.006274    test-rmse:5.451500+0.412101 
## [18] train-rmse:4.694578+0.007116    test-rmse:4.682648+0.396899 
## [19] train-rmse:4.056292+0.008787    test-rmse:4.052763+0.381816 
## [20] train-rmse:3.533026+0.009848    test-rmse:3.536434+0.360898 
## [21] train-rmse:3.107438+0.011942    test-rmse:3.112390+0.334544 
## [22] train-rmse:2.764123+0.013582    test-rmse:2.779130+0.307737 
## [23] train-rmse:2.489793+0.015024    test-rmse:2.514287+0.279045 
## [24] train-rmse:2.272413+0.016626    test-rmse:2.306339+0.247036 
## [25] train-rmse:2.102116+0.018124    test-rmse:2.145368+0.216356 
## [26] train-rmse:1.970162+0.019452    test-rmse:2.014307+0.195740 
## [27] train-rmse:1.868051+0.020384    test-rmse:1.921799+0.171585 
## [28] train-rmse:1.789008+0.021182    test-rmse:1.852984+0.152326 
## [29] train-rmse:1.727974+0.021890    test-rmse:1.795820+0.136733 
## [30] train-rmse:1.680749+0.022357    test-rmse:1.754544+0.125633 
## [31] train-rmse:1.643333+0.022539    test-rmse:1.724513+0.118809 
## [32] train-rmse:1.613511+0.022210    test-rmse:1.711525+0.118848 
## [33] train-rmse:1.589502+0.022014    test-rmse:1.692927+0.119020 
## [34] train-rmse:1.569324+0.021577    test-rmse:1.671661+0.121152 
## [35] train-rmse:1.552707+0.021582    test-rmse:1.649427+0.115711 
## [36] train-rmse:1.538179+0.021709    test-rmse:1.638440+0.114447 
## [37] train-rmse:1.525444+0.021726    test-rmse:1.625827+0.113176 
## [38] train-rmse:1.513835+0.021650    test-rmse:1.619423+0.112932 
## [39] train-rmse:1.503207+0.021593    test-rmse:1.614285+0.111111 
## [40] train-rmse:1.493476+0.021209    test-rmse:1.607992+0.113519 
## [41] train-rmse:1.484561+0.021242    test-rmse:1.602532+0.115949 
## [42] train-rmse:1.476175+0.021098    test-rmse:1.596492+0.119514 
## [43] train-rmse:1.468111+0.020947    test-rmse:1.591992+0.118571 
## [44] train-rmse:1.460268+0.020831    test-rmse:1.583863+0.115491 
## [45] train-rmse:1.452718+0.020682    test-rmse:1.570573+0.108583 
## [46] train-rmse:1.445486+0.020590    test-rmse:1.565881+0.109571 
## [47] train-rmse:1.438412+0.020532    test-rmse:1.560456+0.108750 
## [48] train-rmse:1.431588+0.020690    test-rmse:1.557692+0.106717 
## [49] train-rmse:1.424819+0.020759    test-rmse:1.559508+0.106905 
## [50] train-rmse:1.418338+0.020734    test-rmse:1.554626+0.108968 
## [51] train-rmse:1.412069+0.020725    test-rmse:1.551227+0.108023 
## [52] train-rmse:1.405839+0.020759    test-rmse:1.548964+0.107672 
## [53] train-rmse:1.399838+0.020684    test-rmse:1.542884+0.110363 
## [54] train-rmse:1.393957+0.020668    test-rmse:1.538535+0.109759 
## [55] train-rmse:1.388047+0.020651    test-rmse:1.533206+0.109777 
## [56] train-rmse:1.382253+0.020701    test-rmse:1.521542+0.103821 
## [57] train-rmse:1.376523+0.020710    test-rmse:1.518067+0.105083 
## [58] train-rmse:1.370879+0.020684    test-rmse:1.513213+0.108029 
## [59] train-rmse:1.365425+0.020645    test-rmse:1.510918+0.106949 
## [60] train-rmse:1.359995+0.020565    test-rmse:1.510248+0.106433 
## [61] train-rmse:1.354740+0.020532    test-rmse:1.507278+0.101706 
## [62] train-rmse:1.349625+0.020389    test-rmse:1.505257+0.104295 
## [63] train-rmse:1.344469+0.020321    test-rmse:1.503435+0.105864 
## [64] train-rmse:1.339434+0.020236    test-rmse:1.501626+0.104658 
## [65] train-rmse:1.334409+0.019997    test-rmse:1.497892+0.100214 
## [66] train-rmse:1.329581+0.019781    test-rmse:1.492883+0.099100 
## [67] train-rmse:1.324873+0.019703    test-rmse:1.488122+0.098634 
## [68] train-rmse:1.320259+0.019676    test-rmse:1.485749+0.097345 
## [69] train-rmse:1.315681+0.019614    test-rmse:1.481501+0.096206 
## [70] train-rmse:1.311110+0.019559    test-rmse:1.479614+0.095363 
## [71] train-rmse:1.306659+0.019562    test-rmse:1.478878+0.092116 
## [72] train-rmse:1.302233+0.019538    test-rmse:1.474405+0.095110 
## [73] train-rmse:1.297933+0.019541    test-rmse:1.475206+0.093689 
## [74] train-rmse:1.293701+0.019555    test-rmse:1.473793+0.093517 
## [75] train-rmse:1.289506+0.019554    test-rmse:1.470365+0.093905 
## [76] train-rmse:1.285347+0.019512    test-rmse:1.465395+0.095213 
## [77] train-rmse:1.281248+0.019483    test-rmse:1.461452+0.095438 
## [78] train-rmse:1.277240+0.019405    test-rmse:1.457553+0.095798 
## [79] train-rmse:1.273271+0.019294    test-rmse:1.455765+0.091788 
## [80] train-rmse:1.269324+0.019178    test-rmse:1.451550+0.092160 
## [81] train-rmse:1.265459+0.019168    test-rmse:1.449947+0.091601 
## [82] train-rmse:1.261674+0.019143    test-rmse:1.449388+0.090787 
## [83] train-rmse:1.258003+0.019057    test-rmse:1.448859+0.085584 
## [84] train-rmse:1.254387+0.019011    test-rmse:1.440704+0.084656 
## [85] train-rmse:1.250811+0.018974    test-rmse:1.438978+0.085250 
## [86] train-rmse:1.247256+0.018981    test-rmse:1.436556+0.086994 
## [87] train-rmse:1.243700+0.018982    test-rmse:1.435556+0.088502 
## [88] train-rmse:1.240266+0.019027    test-rmse:1.433075+0.088556 
## [89] train-rmse:1.236918+0.019017    test-rmse:1.431684+0.089070 
## [90] train-rmse:1.233528+0.019058    test-rmse:1.431067+0.085958 
## [91] train-rmse:1.230212+0.019086    test-rmse:1.429253+0.087315 
## [92] train-rmse:1.226905+0.019101    test-rmse:1.425528+0.088709 
## [93] train-rmse:1.223688+0.019121    test-rmse:1.423092+0.089162 
## [94] train-rmse:1.220561+0.019138    test-rmse:1.420966+0.090051 
## [95] train-rmse:1.217417+0.019135    test-rmse:1.418267+0.089453 
## [96] train-rmse:1.214287+0.019144    test-rmse:1.415055+0.087459 
## [97] train-rmse:1.211149+0.019162    test-rmse:1.412254+0.086763 
## [98] train-rmse:1.208093+0.019175    test-rmse:1.411289+0.086575 
## [99] train-rmse:1.205131+0.019168    test-rmse:1.408608+0.081239 
## [100]    train-rmse:1.202192+0.019190    test-rmse:1.406853+0.080121 
## [101]    train-rmse:1.199223+0.019179    test-rmse:1.401811+0.081660 
## [102]    train-rmse:1.196393+0.019181    test-rmse:1.398137+0.084814 
## [103]    train-rmse:1.193579+0.019156    test-rmse:1.397498+0.084349 
## [104]    train-rmse:1.190798+0.019144    test-rmse:1.394805+0.082705 
## [105]    train-rmse:1.188018+0.019154    test-rmse:1.394253+0.082494 
## [106]    train-rmse:1.185305+0.019143    test-rmse:1.391665+0.083158 
## [107]    train-rmse:1.182591+0.019170    test-rmse:1.388495+0.084289 
## [108]    train-rmse:1.179929+0.019167    test-rmse:1.388400+0.086123 
## [109]    train-rmse:1.177208+0.019200    test-rmse:1.385283+0.086592 
## [110]    train-rmse:1.174539+0.019195    test-rmse:1.382414+0.086832 
## [111]    train-rmse:1.171981+0.019145    test-rmse:1.380591+0.086661 
## [112]    train-rmse:1.169467+0.019105    test-rmse:1.379412+0.086516 
## [113]    train-rmse:1.167013+0.019058    test-rmse:1.377666+0.086681 
## [114]    train-rmse:1.164563+0.019026    test-rmse:1.375829+0.082686 
## [115]    train-rmse:1.162115+0.018983    test-rmse:1.374701+0.083565 
## [116]    train-rmse:1.159709+0.018948    test-rmse:1.374472+0.085311 
## [117]    train-rmse:1.157307+0.018936    test-rmse:1.372049+0.080767 
## [118]    train-rmse:1.154972+0.018935    test-rmse:1.371632+0.080356 
## [119]    train-rmse:1.152631+0.018931    test-rmse:1.370171+0.081181 
## [120]    train-rmse:1.150339+0.018948    test-rmse:1.370296+0.082296 
## [121]    train-rmse:1.148117+0.018950    test-rmse:1.368705+0.082597 
## [122]    train-rmse:1.145902+0.018942    test-rmse:1.365859+0.084107 
## [123]    train-rmse:1.143714+0.018954    test-rmse:1.363920+0.082534 
## [124]    train-rmse:1.141534+0.018976    test-rmse:1.362060+0.082081 
## [125]    train-rmse:1.139369+0.019017    test-rmse:1.362863+0.080164 
## [126]    train-rmse:1.137259+0.019043    test-rmse:1.360887+0.079078 
## [127]    train-rmse:1.135182+0.019053    test-rmse:1.357176+0.079385 
## [128]    train-rmse:1.133091+0.019032    test-rmse:1.355260+0.079080 
## [129]    train-rmse:1.131049+0.019022    test-rmse:1.353851+0.078529 
## [130]    train-rmse:1.129047+0.018993    test-rmse:1.351029+0.077299 
## [131]    train-rmse:1.127053+0.018955    test-rmse:1.348217+0.078248 
## [132]    train-rmse:1.125075+0.018924    test-rmse:1.346442+0.081195 
## [133]    train-rmse:1.123122+0.018879    test-rmse:1.344938+0.080144 
## [134]    train-rmse:1.121179+0.018825    test-rmse:1.343742+0.080163 
## [135]    train-rmse:1.119244+0.018772    test-rmse:1.342808+0.081416 
## [136]    train-rmse:1.117309+0.018765    test-rmse:1.340932+0.081762 
## [137]    train-rmse:1.115422+0.018747    test-rmse:1.336540+0.081459 
## [138]    train-rmse:1.113538+0.018728    test-rmse:1.335309+0.082298 
## [139]    train-rmse:1.111682+0.018718    test-rmse:1.333408+0.082420 
## [140]    train-rmse:1.109840+0.018687    test-rmse:1.332682+0.080673 
## [141]    train-rmse:1.108011+0.018646    test-rmse:1.332729+0.081860 
## [142]    train-rmse:1.106207+0.018603    test-rmse:1.330436+0.082335 
## [143]    train-rmse:1.104405+0.018588    test-rmse:1.329034+0.082563 
## [144]    train-rmse:1.102603+0.018565    test-rmse:1.328391+0.082900 
## [145]    train-rmse:1.100831+0.018558    test-rmse:1.326180+0.082810 
## [146]    train-rmse:1.099102+0.018519    test-rmse:1.325825+0.082428 
## [147]    train-rmse:1.097388+0.018510    test-rmse:1.324535+0.081790 
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## [149]    train-rmse:1.094040+0.018483    test-rmse:1.323608+0.082247 
## [150]    train-rmse:1.092420+0.018462    test-rmse:1.321957+0.083592 
## [151]    train-rmse:1.090819+0.018437    test-rmse:1.320917+0.081737 
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## [160]    train-rmse:1.076955+0.018271    test-rmse:1.311156+0.085104 
## [161]    train-rmse:1.075480+0.018267    test-rmse:1.309629+0.086141 
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## [257]    train-rmse:0.979665+0.017176    test-rmse:1.245651+0.081091 
## [258]    train-rmse:0.978969+0.017172    test-rmse:1.246092+0.080547 
## [259]    train-rmse:0.978268+0.017157    test-rmse:1.246627+0.080194 
## [260]    train-rmse:0.977579+0.017155    test-rmse:1.246793+0.080109 
## [261]    train-rmse:0.976892+0.017167    test-rmse:1.245926+0.080055 
## [262]    train-rmse:0.976220+0.017163    test-rmse:1.244810+0.080850 
## [263]    train-rmse:0.975554+0.017167    test-rmse:1.244741+0.080893 
## [264]    train-rmse:0.974877+0.017176    test-rmse:1.244221+0.081372 
## [265]    train-rmse:0.974215+0.017176    test-rmse:1.242690+0.080412 
## [266]    train-rmse:0.973555+0.017173    test-rmse:1.242766+0.080485 
## [267]    train-rmse:0.972911+0.017174    test-rmse:1.243238+0.080753 
## [268]    train-rmse:0.972269+0.017174    test-rmse:1.243096+0.080946 
## [269]    train-rmse:0.971624+0.017183    test-rmse:1.241884+0.081532 
## [270]    train-rmse:0.970967+0.017197    test-rmse:1.241706+0.081365 
## [271]    train-rmse:0.970333+0.017207    test-rmse:1.241114+0.080769 
## [272]    train-rmse:0.969688+0.017195    test-rmse:1.240704+0.080569 
## [273]    train-rmse:0.969057+0.017192    test-rmse:1.240051+0.079823 
## [274]    train-rmse:0.968431+0.017192    test-rmse:1.240617+0.079605 
## [275]    train-rmse:0.967799+0.017200    test-rmse:1.239362+0.079377 
## [276]    train-rmse:0.967176+0.017212    test-rmse:1.240172+0.078848 
## [277]    train-rmse:0.966556+0.017220    test-rmse:1.239506+0.079353 
## [278]    train-rmse:0.965937+0.017226    test-rmse:1.239681+0.079050 
## [279]    train-rmse:0.965298+0.017243    test-rmse:1.238654+0.080744 
## [280]    train-rmse:0.964683+0.017253    test-rmse:1.238114+0.081201 
## [281]    train-rmse:0.964074+0.017255    test-rmse:1.237541+0.081526 
## [282]    train-rmse:0.963468+0.017263    test-rmse:1.238330+0.081922 
## [283]    train-rmse:0.962873+0.017263    test-rmse:1.237581+0.082592 
## [284]    train-rmse:0.962266+0.017270    test-rmse:1.237284+0.082732 
## [285]    train-rmse:0.961658+0.017279    test-rmse:1.236398+0.082566 
## [286]    train-rmse:0.961080+0.017292    test-rmse:1.236561+0.082329 
## [287]    train-rmse:0.960503+0.017308    test-rmse:1.236322+0.081345 
## [288]    train-rmse:0.959899+0.017313    test-rmse:1.236671+0.080906 
## [289]    train-rmse:0.959305+0.017314    test-rmse:1.235740+0.081977 
## [290]    train-rmse:0.958725+0.017329    test-rmse:1.235412+0.082059 
## [291]    train-rmse:0.958153+0.017338    test-rmse:1.234985+0.082572 
## [292]    train-rmse:0.957584+0.017348    test-rmse:1.234943+0.082132 
## [293]    train-rmse:0.957007+0.017360    test-rmse:1.235650+0.081967 
## [294]    train-rmse:0.956435+0.017365    test-rmse:1.233742+0.080496 
## [295]    train-rmse:0.955858+0.017380    test-rmse:1.233132+0.080278 
## [296]    train-rmse:0.955290+0.017374    test-rmse:1.232641+0.079597 
## [297]    train-rmse:0.954713+0.017393    test-rmse:1.232615+0.079945 
## [298]    train-rmse:0.954154+0.017406    test-rmse:1.232625+0.080056 
## [299]    train-rmse:0.953603+0.017415    test-rmse:1.232879+0.079499 
## [300]    train-rmse:0.953052+0.017424    test-rmse:1.232857+0.079122 
## [301]    train-rmse:0.952495+0.017408    test-rmse:1.233037+0.079659 
## [302]    train-rmse:0.951948+0.017413    test-rmse:1.233145+0.079468 
## [303]    train-rmse:0.951407+0.017407    test-rmse:1.232769+0.079335 
## Stopping. Best iteration:
## [297]    train-rmse:0.954713+0.017393    test-rmse:1.232615+0.079945
## 
## 22 
## [1]  train-rmse:82.780940+0.083029   test-rmse:82.781650+0.410772 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.591332+0.069678   test-rmse:69.591866+0.423700 
## [3]  train-rmse:58.506326+0.058401   test-rmse:58.506664+0.434438 
## [4]  train-rmse:49.190703+0.048890   test-rmse:49.190803+0.443303 
## [5]  train-rmse:41.362720+0.040844   test-rmse:41.362536+0.450558 
## [6]  train-rmse:34.785638+0.034034   test-rmse:34.785128+0.456398 
## [7]  train-rmse:29.260552+0.028262   test-rmse:29.259653+0.460980 
## [8]  train-rmse:24.620358+0.023369   test-rmse:24.619006+0.464415 
## [9]  train-rmse:20.724704+0.019222   test-rmse:20.722832+0.466766 
## [10] train-rmse:17.455756+0.015748   test-rmse:17.453284+0.468046 
## [11] train-rmse:14.714250+0.013047   test-rmse:14.705223+0.464215 
## [12] train-rmse:12.415248+0.010202   test-rmse:12.403659+0.451383 
## [13] train-rmse:10.488003+0.008204   test-rmse:10.477141+0.435665 
## [14] train-rmse:8.872284+0.006744    test-rmse:8.856665+0.424004 
## [15] train-rmse:7.518793+0.005725    test-rmse:7.508668+0.409615 
## [16] train-rmse:6.386627+0.005217    test-rmse:6.375390+0.399833 
## [17] train-rmse:5.441035+0.005189    test-rmse:5.430448+0.379198 
## [18] train-rmse:4.652171+0.005393    test-rmse:4.644028+0.369327 
## [19] train-rmse:3.998137+0.005127    test-rmse:4.004705+0.351566 
## [20] train-rmse:3.457549+0.006013    test-rmse:3.463198+0.329429 
## [21] train-rmse:3.011018+0.006237    test-rmse:3.031272+0.312075 
## [22] train-rmse:2.647909+0.007554    test-rmse:2.685518+0.289283 
## [23] train-rmse:2.353257+0.007212    test-rmse:2.407068+0.268967 
## [24] train-rmse:2.116270+0.008782    test-rmse:2.187212+0.243296 
## [25] train-rmse:1.928027+0.008274    test-rmse:2.006154+0.222251 
## [26] train-rmse:1.779012+0.008611    test-rmse:1.873540+0.203308 
## [27] train-rmse:1.660105+0.010267    test-rmse:1.772369+0.183466 
## [28] train-rmse:1.568783+0.011328    test-rmse:1.693896+0.163638 
## [29] train-rmse:1.496386+0.011982    test-rmse:1.635492+0.142906 
## [30] train-rmse:1.439630+0.013132    test-rmse:1.589352+0.132532 
## [31] train-rmse:1.393388+0.010744    test-rmse:1.556478+0.117981 
## [32] train-rmse:1.356886+0.011090    test-rmse:1.533213+0.107674 
## [33] train-rmse:1.328427+0.011156    test-rmse:1.503316+0.108785 
## [34] train-rmse:1.303575+0.010797    test-rmse:1.488530+0.105219 
## [35] train-rmse:1.283688+0.011153    test-rmse:1.473674+0.106099 
## [36] train-rmse:1.263558+0.012779    test-rmse:1.462104+0.105405 
## [37] train-rmse:1.247143+0.012667    test-rmse:1.451316+0.101445 
## [38] train-rmse:1.233178+0.012627    test-rmse:1.440135+0.098596 
## [39] train-rmse:1.219742+0.010813    test-rmse:1.434842+0.096369 
## [40] train-rmse:1.208449+0.010828    test-rmse:1.429004+0.093150 
## [41] train-rmse:1.197662+0.010983    test-rmse:1.422462+0.092018 
## [42] train-rmse:1.187520+0.011138    test-rmse:1.412897+0.094169 
## [43] train-rmse:1.175919+0.010429    test-rmse:1.405048+0.091884 
## [44] train-rmse:1.163913+0.013608    test-rmse:1.399103+0.090532 
## [45] train-rmse:1.154393+0.013779    test-rmse:1.394177+0.089702 
## [46] train-rmse:1.145290+0.012658    test-rmse:1.388726+0.088413 
## [47] train-rmse:1.136276+0.012084    test-rmse:1.372170+0.095016 
## [48] train-rmse:1.128248+0.012351    test-rmse:1.369135+0.094336 
## [49] train-rmse:1.119426+0.013079    test-rmse:1.366269+0.096087 
## [50] train-rmse:1.110112+0.014724    test-rmse:1.354513+0.093965 
## [51] train-rmse:1.100996+0.014456    test-rmse:1.352720+0.095284 
## [52] train-rmse:1.094175+0.014716    test-rmse:1.350813+0.092796 
## [53] train-rmse:1.087208+0.014801    test-rmse:1.344265+0.092468 
## [54] train-rmse:1.078622+0.013609    test-rmse:1.340181+0.089325 
## [55] train-rmse:1.071382+0.013917    test-rmse:1.331416+0.088716 
## [56] train-rmse:1.064388+0.015298    test-rmse:1.326457+0.091423 
## [57] train-rmse:1.057513+0.015636    test-rmse:1.324247+0.092075 
## [58] train-rmse:1.049437+0.015138    test-rmse:1.320981+0.092427 
## [59] train-rmse:1.041515+0.015452    test-rmse:1.313514+0.097925 
## [60] train-rmse:1.035286+0.016561    test-rmse:1.311188+0.100393 
## [61] train-rmse:1.028578+0.016965    test-rmse:1.307459+0.100032 
## [62] train-rmse:1.021710+0.017329    test-rmse:1.300428+0.098081 
## [63] train-rmse:1.016125+0.018258    test-rmse:1.292472+0.100181 
## [64] train-rmse:1.007181+0.018888    test-rmse:1.289086+0.098079 
## [65] train-rmse:1.001169+0.018624    test-rmse:1.286823+0.099133 
## [66] train-rmse:0.996294+0.018644    test-rmse:1.282488+0.099456 
## [67] train-rmse:0.991173+0.019100    test-rmse:1.282442+0.100589 
## [68] train-rmse:0.985967+0.019185    test-rmse:1.278008+0.100213 
## [69] train-rmse:0.979583+0.019671    test-rmse:1.272953+0.097629 
## [70] train-rmse:0.974404+0.019989    test-rmse:1.270732+0.097649 
## [71] train-rmse:0.967891+0.020404    test-rmse:1.268866+0.101161 
## [72] train-rmse:0.961105+0.020992    test-rmse:1.266648+0.101947 
## [73] train-rmse:0.956464+0.021434    test-rmse:1.261373+0.103170 
## [74] train-rmse:0.951798+0.021642    test-rmse:1.256712+0.103782 
## [75] train-rmse:0.944503+0.019687    test-rmse:1.251180+0.106617 
## [76] train-rmse:0.939829+0.020002    test-rmse:1.249468+0.107107 
## [77] train-rmse:0.935410+0.020616    test-rmse:1.245117+0.108187 
## [78] train-rmse:0.931412+0.020688    test-rmse:1.241996+0.109850 
## [79] train-rmse:0.926045+0.021821    test-rmse:1.239105+0.105365 
## [80] train-rmse:0.920594+0.020503    test-rmse:1.233937+0.107664 
## [81] train-rmse:0.914743+0.021027    test-rmse:1.232071+0.105664 
## [82] train-rmse:0.910267+0.021278    test-rmse:1.229112+0.103003 
## [83] train-rmse:0.905981+0.021468    test-rmse:1.226781+0.106349 
## [84] train-rmse:0.901020+0.019894    test-rmse:1.222779+0.107670 
## [85] train-rmse:0.897424+0.019669    test-rmse:1.222725+0.109274 
## [86] train-rmse:0.891004+0.017976    test-rmse:1.222215+0.109321 
## [87] train-rmse:0.886129+0.017950    test-rmse:1.217804+0.109629 
## [88] train-rmse:0.881837+0.018626    test-rmse:1.215410+0.108628 
## [89] train-rmse:0.878200+0.018878    test-rmse:1.212348+0.109266 
## [90] train-rmse:0.873770+0.020144    test-rmse:1.210380+0.107979 
## [91] train-rmse:0.870741+0.020476    test-rmse:1.207770+0.108535 
## [92] train-rmse:0.865960+0.019487    test-rmse:1.201852+0.110110 
## [93] train-rmse:0.861960+0.020172    test-rmse:1.202018+0.108811 
## [94] train-rmse:0.858633+0.019472    test-rmse:1.201135+0.109796 
## [95] train-rmse:0.855274+0.019551    test-rmse:1.198779+0.109649 
## [96] train-rmse:0.851144+0.018112    test-rmse:1.197244+0.109691 
## [97] train-rmse:0.846930+0.018573    test-rmse:1.193921+0.109431 
## [98] train-rmse:0.841903+0.016167    test-rmse:1.192091+0.109375 
## [99] train-rmse:0.839063+0.016279    test-rmse:1.191423+0.109445 
## [100]    train-rmse:0.835821+0.016204    test-rmse:1.188712+0.110522 
## [101]    train-rmse:0.831964+0.014392    test-rmse:1.186292+0.113258 
## [102]    train-rmse:0.828419+0.014177    test-rmse:1.185253+0.113568 
## [103]    train-rmse:0.825278+0.014875    test-rmse:1.180704+0.116389 
## [104]    train-rmse:0.820991+0.014771    test-rmse:1.177869+0.117780 
## [105]    train-rmse:0.818307+0.014990    test-rmse:1.177310+0.118452 
## [106]    train-rmse:0.815034+0.015701    test-rmse:1.172894+0.116442 
## [107]    train-rmse:0.810943+0.014444    test-rmse:1.172200+0.114923 
## [108]    train-rmse:0.807960+0.013974    test-rmse:1.170681+0.116337 
## [109]    train-rmse:0.804569+0.013470    test-rmse:1.166953+0.117356 
## [110]    train-rmse:0.801316+0.014028    test-rmse:1.166131+0.117172 
## [111]    train-rmse:0.796471+0.014293    test-rmse:1.164749+0.115331 
## [112]    train-rmse:0.792812+0.014564    test-rmse:1.161866+0.113620 
## [113]    train-rmse:0.788829+0.012461    test-rmse:1.160019+0.113414 
## [114]    train-rmse:0.786171+0.012061    test-rmse:1.160234+0.112709 
## [115]    train-rmse:0.782625+0.013673    test-rmse:1.157886+0.112151 
## [116]    train-rmse:0.779511+0.012487    test-rmse:1.155091+0.113073 
## [117]    train-rmse:0.775880+0.013031    test-rmse:1.153101+0.113245 
## [118]    train-rmse:0.773067+0.013578    test-rmse:1.152389+0.113283 
## [119]    train-rmse:0.770756+0.013646    test-rmse:1.151282+0.115207 
## [120]    train-rmse:0.768250+0.013507    test-rmse:1.150591+0.116740 
## [121]    train-rmse:0.764367+0.014219    test-rmse:1.145447+0.111981 
## [122]    train-rmse:0.761537+0.015156    test-rmse:1.144289+0.111984 
## [123]    train-rmse:0.757783+0.014228    test-rmse:1.140936+0.113928 
## [124]    train-rmse:0.754308+0.015752    test-rmse:1.139821+0.113985 
## [125]    train-rmse:0.752002+0.015401    test-rmse:1.136586+0.116275 
## [126]    train-rmse:0.750091+0.015344    test-rmse:1.134492+0.115064 
## [127]    train-rmse:0.747628+0.015616    test-rmse:1.134149+0.115948 
## [128]    train-rmse:0.744881+0.014745    test-rmse:1.132740+0.117621 
## [129]    train-rmse:0.741330+0.014799    test-rmse:1.129737+0.116036 
## [130]    train-rmse:0.737902+0.013984    test-rmse:1.129149+0.115853 
## [131]    train-rmse:0.733656+0.014771    test-rmse:1.123991+0.110788 
## [132]    train-rmse:0.731241+0.014864    test-rmse:1.122816+0.110059 
## [133]    train-rmse:0.729013+0.014866    test-rmse:1.121824+0.110633 
## [134]    train-rmse:0.725823+0.015805    test-rmse:1.120724+0.110367 
## [135]    train-rmse:0.722719+0.015885    test-rmse:1.117974+0.111026 
## [136]    train-rmse:0.720119+0.016217    test-rmse:1.115199+0.111717 
## [137]    train-rmse:0.718392+0.016386    test-rmse:1.113953+0.110339 
## [138]    train-rmse:0.716118+0.016004    test-rmse:1.113368+0.110004 
## [139]    train-rmse:0.713343+0.017153    test-rmse:1.111927+0.109373 
## [140]    train-rmse:0.711489+0.017080    test-rmse:1.111038+0.108383 
## [141]    train-rmse:0.709585+0.016921    test-rmse:1.108518+0.108232 
## [142]    train-rmse:0.707140+0.016888    test-rmse:1.107038+0.106716 
## [143]    train-rmse:0.705270+0.017441    test-rmse:1.106669+0.107017 
## [144]    train-rmse:0.703300+0.017398    test-rmse:1.106633+0.108387 
## [145]    train-rmse:0.700578+0.017319    test-rmse:1.107117+0.106950 
## [146]    train-rmse:0.698770+0.017006    test-rmse:1.106544+0.107989 
## [147]    train-rmse:0.696259+0.016652    test-rmse:1.106781+0.106988 
## [148]    train-rmse:0.693965+0.016703    test-rmse:1.105673+0.107687 
## [149]    train-rmse:0.691438+0.016043    test-rmse:1.105180+0.107577 
## [150]    train-rmse:0.688380+0.015503    test-rmse:1.104373+0.107149 
## [151]    train-rmse:0.686867+0.015774    test-rmse:1.104052+0.107329 
## [152]    train-rmse:0.685375+0.015726    test-rmse:1.102789+0.107539 
## [153]    train-rmse:0.683602+0.015470    test-rmse:1.101855+0.107805 
## [154]    train-rmse:0.680567+0.014456    test-rmse:1.101018+0.109885 
## [155]    train-rmse:0.679240+0.014503    test-rmse:1.100378+0.110087 
## [156]    train-rmse:0.677341+0.014348    test-rmse:1.097575+0.107989 
## [157]    train-rmse:0.675125+0.013703    test-rmse:1.097708+0.106265 
## [158]    train-rmse:0.672813+0.013282    test-rmse:1.097335+0.105844 
## [159]    train-rmse:0.671375+0.013629    test-rmse:1.095822+0.104772 
## [160]    train-rmse:0.668259+0.013590    test-rmse:1.095002+0.106127 
## [161]    train-rmse:0.666226+0.013799    test-rmse:1.096062+0.105264 
## [162]    train-rmse:0.664810+0.013928    test-rmse:1.095369+0.104798 
## [163]    train-rmse:0.662710+0.014243    test-rmse:1.095266+0.104303 
## [164]    train-rmse:0.661203+0.014472    test-rmse:1.094467+0.104170 
## [165]    train-rmse:0.659604+0.014893    test-rmse:1.093620+0.105274 
## [166]    train-rmse:0.657111+0.014549    test-rmse:1.093275+0.103973 
## [167]    train-rmse:0.655248+0.014585    test-rmse:1.092432+0.103794 
## [168]    train-rmse:0.653734+0.014357    test-rmse:1.090981+0.103618 
## [169]    train-rmse:0.651628+0.014881    test-rmse:1.089655+0.103439 
## [170]    train-rmse:0.649004+0.013952    test-rmse:1.089524+0.103227 
## [171]    train-rmse:0.647505+0.013876    test-rmse:1.089274+0.102776 
## [172]    train-rmse:0.645552+0.014349    test-rmse:1.087100+0.100348 
## [173]    train-rmse:0.643804+0.013701    test-rmse:1.085573+0.099207 
## [174]    train-rmse:0.642181+0.013605    test-rmse:1.083425+0.098423 
## [175]    train-rmse:0.638649+0.013729    test-rmse:1.081033+0.098892 
## [176]    train-rmse:0.637062+0.013599    test-rmse:1.080699+0.098896 
## [177]    train-rmse:0.635626+0.013585    test-rmse:1.080462+0.098431 
## [178]    train-rmse:0.633830+0.013317    test-rmse:1.078664+0.097883 
## [179]    train-rmse:0.632504+0.013147    test-rmse:1.076485+0.097246 
## [180]    train-rmse:0.631018+0.013321    test-rmse:1.076315+0.097500 
## [181]    train-rmse:0.629676+0.013329    test-rmse:1.076417+0.098132 
## [182]    train-rmse:0.628445+0.013096    test-rmse:1.076239+0.098042 
## [183]    train-rmse:0.626914+0.013058    test-rmse:1.076470+0.097947 
## [184]    train-rmse:0.625536+0.013204    test-rmse:1.077029+0.097389 
## [185]    train-rmse:0.623502+0.012293    test-rmse:1.076705+0.097380 
## [186]    train-rmse:0.621864+0.012702    test-rmse:1.076999+0.096374 
## [187]    train-rmse:0.620303+0.012199    test-rmse:1.076498+0.096569 
## [188]    train-rmse:0.618623+0.012680    test-rmse:1.076673+0.097629 
## Stopping. Best iteration:
## [182]    train-rmse:0.628445+0.013096    test-rmse:1.076239+0.098042
## 
## 23 
## [1]  train-rmse:82.780940+0.083029   test-rmse:82.781650+0.410772 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.591332+0.069678   test-rmse:69.591866+0.423700 
## [3]  train-rmse:58.506326+0.058401   test-rmse:58.506664+0.434438 
## [4]  train-rmse:49.190703+0.048890   test-rmse:49.190803+0.443303 
## [5]  train-rmse:41.362720+0.040844   test-rmse:41.362536+0.450558 
## [6]  train-rmse:34.785638+0.034034   test-rmse:34.785128+0.456398 
## [7]  train-rmse:29.260552+0.028262   test-rmse:29.259653+0.460980 
## [8]  train-rmse:24.620358+0.023369   test-rmse:24.619006+0.464415 
## [9]  train-rmse:20.724704+0.019222   test-rmse:20.722832+0.466766 
## [10] train-rmse:17.455756+0.015748   test-rmse:17.453284+0.468046 
## [11] train-rmse:14.714250+0.013047   test-rmse:14.705223+0.464215 
## [12] train-rmse:12.415248+0.010202   test-rmse:12.403659+0.451383 
## [13] train-rmse:10.487754+0.008421   test-rmse:10.475114+0.434470 
## [14] train-rmse:8.871626+0.006995    test-rmse:8.854390+0.425077 
## [15] train-rmse:7.515582+0.005130    test-rmse:7.501034+0.404315 
## [16] train-rmse:6.380096+0.005329    test-rmse:6.367459+0.392690 
## [17] train-rmse:5.429276+0.005767    test-rmse:5.423361+0.363959 
## [18] train-rmse:4.633891+0.005466    test-rmse:4.630494+0.341878 
## [19] train-rmse:3.970904+0.006778    test-rmse:3.975269+0.323101 
## [20] train-rmse:3.419328+0.006168    test-rmse:3.430206+0.307103 
## [21] train-rmse:2.962253+0.005113    test-rmse:2.985760+0.286154 
## [22] train-rmse:2.584684+0.007475    test-rmse:2.620999+0.272101 
## [23] train-rmse:2.275609+0.007202    test-rmse:2.331899+0.255124 
## [24] train-rmse:2.024278+0.009534    test-rmse:2.095104+0.237285 
## [25] train-rmse:1.819430+0.012130    test-rmse:1.911041+0.221581 
## [26] train-rmse:1.656484+0.013330    test-rmse:1.769291+0.200873 
## [27] train-rmse:1.524889+0.015961    test-rmse:1.660966+0.181339 
## [28] train-rmse:1.421829+0.017684    test-rmse:1.578777+0.163657 
## [29] train-rmse:1.335870+0.012745    test-rmse:1.517872+0.149215 
## [30] train-rmse:1.270453+0.014080    test-rmse:1.464671+0.140681 
## [31] train-rmse:1.218165+0.015353    test-rmse:1.425980+0.136570 
## [32] train-rmse:1.172082+0.011938    test-rmse:1.391281+0.129434 
## [33] train-rmse:1.132556+0.011024    test-rmse:1.366760+0.123933 
## [34] train-rmse:1.100882+0.009929    test-rmse:1.347917+0.114153 
## [35] train-rmse:1.075918+0.011103    test-rmse:1.335013+0.111123 
## [36] train-rmse:1.053859+0.010667    test-rmse:1.325799+0.110887 
## [37] train-rmse:1.034670+0.009365    test-rmse:1.318307+0.108249 
## [38] train-rmse:1.017376+0.010989    test-rmse:1.303374+0.108471 
## [39] train-rmse:1.001803+0.009869    test-rmse:1.294981+0.101454 
## [40] train-rmse:0.989088+0.009351    test-rmse:1.283627+0.099558 
## [41] train-rmse:0.974139+0.011900    test-rmse:1.275442+0.096480 
## [42] train-rmse:0.960657+0.008691    test-rmse:1.263112+0.101810 
## [43] train-rmse:0.948254+0.007386    test-rmse:1.257721+0.100643 
## [44] train-rmse:0.938103+0.008048    test-rmse:1.248372+0.098863 
## [45] train-rmse:0.926126+0.007914    test-rmse:1.242229+0.098518 
## [46] train-rmse:0.915333+0.009092    test-rmse:1.234643+0.099841 
## [47] train-rmse:0.906294+0.008801    test-rmse:1.227887+0.097871 
## [48] train-rmse:0.896197+0.008344    test-rmse:1.225428+0.100955 
## [49] train-rmse:0.882155+0.008939    test-rmse:1.217495+0.102186 
## [50] train-rmse:0.873918+0.009791    test-rmse:1.209979+0.102042 
## [51] train-rmse:0.865099+0.009117    test-rmse:1.204982+0.102331 
## [52] train-rmse:0.857153+0.009757    test-rmse:1.201755+0.100636 
## [53] train-rmse:0.848373+0.011654    test-rmse:1.194284+0.099925 
## [54] train-rmse:0.840901+0.012343    test-rmse:1.187499+0.105130 
## [55] train-rmse:0.832164+0.010895    test-rmse:1.183279+0.104318 
## [56] train-rmse:0.823969+0.010778    test-rmse:1.180723+0.103150 
## [57] train-rmse:0.817667+0.010516    test-rmse:1.176264+0.101100 
## [58] train-rmse:0.810314+0.012527    test-rmse:1.169930+0.102696 
## [59] train-rmse:0.800249+0.014883    test-rmse:1.162631+0.105215 
## [60] train-rmse:0.794305+0.015137    test-rmse:1.158230+0.107279 
## [61] train-rmse:0.788025+0.015171    test-rmse:1.156994+0.105345 
## [62] train-rmse:0.781002+0.013740    test-rmse:1.151106+0.104494 
## [63] train-rmse:0.775076+0.013492    test-rmse:1.146973+0.105662 
## [64] train-rmse:0.765146+0.014245    test-rmse:1.137855+0.104590 
## [65] train-rmse:0.758881+0.014935    test-rmse:1.134884+0.103934 
## [66] train-rmse:0.751009+0.015339    test-rmse:1.125643+0.096358 
## [67] train-rmse:0.744769+0.016811    test-rmse:1.123762+0.096702 
## [68] train-rmse:0.739051+0.016299    test-rmse:1.118893+0.096165 
## [69] train-rmse:0.731484+0.016047    test-rmse:1.114813+0.097739 
## [70] train-rmse:0.723881+0.014993    test-rmse:1.111140+0.097955 
## [71] train-rmse:0.719172+0.016004    test-rmse:1.109408+0.099627 
## [72] train-rmse:0.710671+0.017693    test-rmse:1.104641+0.097501 
## [73] train-rmse:0.705522+0.017657    test-rmse:1.100378+0.094556 
## [74] train-rmse:0.700815+0.017448    test-rmse:1.098055+0.094893 
## [75] train-rmse:0.695948+0.018045    test-rmse:1.095690+0.095000 
## [76] train-rmse:0.688010+0.015699    test-rmse:1.094599+0.097094 
## [77] train-rmse:0.683797+0.015864    test-rmse:1.091750+0.099041 
## [78] train-rmse:0.676030+0.012742    test-rmse:1.090032+0.098533 
## [79] train-rmse:0.671515+0.012436    test-rmse:1.087557+0.100280 
## [80] train-rmse:0.665271+0.011147    test-rmse:1.082443+0.102992 
## [81] train-rmse:0.660840+0.011187    test-rmse:1.079930+0.102606 
## [82] train-rmse:0.655921+0.011247    test-rmse:1.076892+0.100883 
## [83] train-rmse:0.650987+0.010924    test-rmse:1.074584+0.101702 
## [84] train-rmse:0.646735+0.010780    test-rmse:1.072523+0.102101 
## [85] train-rmse:0.640662+0.010154    test-rmse:1.071386+0.101960 
## [86] train-rmse:0.635924+0.010321    test-rmse:1.070427+0.102650 
## [87] train-rmse:0.632006+0.010490    test-rmse:1.068110+0.102484 
## [88] train-rmse:0.628604+0.011101    test-rmse:1.068218+0.102966 
## [89] train-rmse:0.624755+0.010140    test-rmse:1.064786+0.103520 
## [90] train-rmse:0.620778+0.010875    test-rmse:1.061049+0.103054 
## [91] train-rmse:0.615345+0.011735    test-rmse:1.058401+0.099574 
## [92] train-rmse:0.611618+0.012150    test-rmse:1.058168+0.098615 
## [93] train-rmse:0.608153+0.012885    test-rmse:1.055767+0.098005 
## [94] train-rmse:0.604380+0.013307    test-rmse:1.056075+0.098272 
## [95] train-rmse:0.600144+0.013463    test-rmse:1.055971+0.098411 
## [96] train-rmse:0.596179+0.013925    test-rmse:1.053258+0.100076 
## [97] train-rmse:0.593518+0.013727    test-rmse:1.051573+0.100227 
## [98] train-rmse:0.588744+0.012265    test-rmse:1.048189+0.099765 
## [99] train-rmse:0.584478+0.013068    test-rmse:1.046345+0.099634 
## [100]    train-rmse:0.581702+0.012897    test-rmse:1.044013+0.099732 
## [101]    train-rmse:0.577276+0.014663    test-rmse:1.041522+0.098870 
## [102]    train-rmse:0.573221+0.013842    test-rmse:1.039565+0.097451 
## [103]    train-rmse:0.570460+0.013752    test-rmse:1.039312+0.096752 
## [104]    train-rmse:0.567231+0.013873    test-rmse:1.037018+0.095398 
## [105]    train-rmse:0.562002+0.011657    test-rmse:1.033529+0.094629 
## [106]    train-rmse:0.558123+0.011571    test-rmse:1.028876+0.089704 
## [107]    train-rmse:0.554633+0.012740    test-rmse:1.026714+0.089618 
## [108]    train-rmse:0.551222+0.012611    test-rmse:1.024926+0.090254 
## [109]    train-rmse:0.547528+0.011121    test-rmse:1.024511+0.090524 
## [110]    train-rmse:0.544955+0.011255    test-rmse:1.023488+0.090539 
## [111]    train-rmse:0.541903+0.011498    test-rmse:1.022952+0.088694 
## [112]    train-rmse:0.536897+0.012037    test-rmse:1.023395+0.087497 
## [113]    train-rmse:0.534353+0.011831    test-rmse:1.022057+0.088908 
## [114]    train-rmse:0.529614+0.009109    test-rmse:1.019714+0.090207 
## [115]    train-rmse:0.527318+0.009004    test-rmse:1.018495+0.089661 
## [116]    train-rmse:0.524952+0.008844    test-rmse:1.018832+0.090207 
## [117]    train-rmse:0.522542+0.008199    test-rmse:1.018157+0.090445 
## [118]    train-rmse:0.519472+0.009449    test-rmse:1.017270+0.089130 
## [119]    train-rmse:0.516841+0.009000    test-rmse:1.013510+0.089118 
## [120]    train-rmse:0.513598+0.009708    test-rmse:1.011734+0.090301 
## [121]    train-rmse:0.510596+0.008430    test-rmse:1.010702+0.090974 
## [122]    train-rmse:0.507278+0.007930    test-rmse:1.007919+0.088425 
## [123]    train-rmse:0.504210+0.008502    test-rmse:1.007099+0.088607 
## [124]    train-rmse:0.500785+0.010306    test-rmse:1.006511+0.089073 
## [125]    train-rmse:0.498979+0.010530    test-rmse:1.006538+0.088105 
## [126]    train-rmse:0.495318+0.011187    test-rmse:1.004898+0.087924 
## [127]    train-rmse:0.492199+0.010266    test-rmse:1.002657+0.088671 
## [128]    train-rmse:0.488322+0.010859    test-rmse:1.001284+0.086468 
## [129]    train-rmse:0.484060+0.011996    test-rmse:1.001017+0.086376 
## [130]    train-rmse:0.481413+0.012096    test-rmse:1.000115+0.085818 
## [131]    train-rmse:0.478238+0.012703    test-rmse:0.997099+0.082008 
## [132]    train-rmse:0.476010+0.012924    test-rmse:0.995801+0.081814 
## [133]    train-rmse:0.472901+0.012595    test-rmse:0.996448+0.081422 
## [134]    train-rmse:0.470188+0.013133    test-rmse:0.994562+0.080416 
## [135]    train-rmse:0.467035+0.012699    test-rmse:0.994172+0.080247 
## [136]    train-rmse:0.464332+0.013005    test-rmse:0.992785+0.080765 
## [137]    train-rmse:0.461219+0.013557    test-rmse:0.993005+0.079753 
## [138]    train-rmse:0.457989+0.013280    test-rmse:0.991787+0.078622 
## [139]    train-rmse:0.456376+0.013222    test-rmse:0.990314+0.080173 
## [140]    train-rmse:0.453535+0.012168    test-rmse:0.987912+0.081087 
## [141]    train-rmse:0.451278+0.012037    test-rmse:0.988536+0.080157 
## [142]    train-rmse:0.448053+0.011681    test-rmse:0.988419+0.081328 
## [143]    train-rmse:0.445390+0.012081    test-rmse:0.988063+0.081073 
## [144]    train-rmse:0.443623+0.012214    test-rmse:0.987901+0.080642 
## [145]    train-rmse:0.441558+0.012622    test-rmse:0.987295+0.079610 
## [146]    train-rmse:0.439808+0.012105    test-rmse:0.985061+0.078824 
## [147]    train-rmse:0.436794+0.011550    test-rmse:0.983676+0.079165 
## [148]    train-rmse:0.434343+0.011398    test-rmse:0.983769+0.080710 
## [149]    train-rmse:0.431978+0.011736    test-rmse:0.984135+0.081580 
## [150]    train-rmse:0.429820+0.011532    test-rmse:0.984366+0.081441 
## [151]    train-rmse:0.425900+0.010164    test-rmse:0.983272+0.082567 
## [152]    train-rmse:0.423721+0.010721    test-rmse:0.983359+0.082610 
## [153]    train-rmse:0.420637+0.011863    test-rmse:0.983069+0.083088 
## [154]    train-rmse:0.418394+0.012155    test-rmse:0.983125+0.083410 
## [155]    train-rmse:0.416556+0.012366    test-rmse:0.982525+0.083027 
## [156]    train-rmse:0.414437+0.012994    test-rmse:0.981733+0.082212 
## [157]    train-rmse:0.411390+0.012086    test-rmse:0.981024+0.082336 
## [158]    train-rmse:0.409206+0.012643    test-rmse:0.980269+0.082493 
## [159]    train-rmse:0.407124+0.012560    test-rmse:0.980041+0.082606 
## [160]    train-rmse:0.405209+0.012823    test-rmse:0.979490+0.082543 
## [161]    train-rmse:0.403160+0.011839    test-rmse:0.978712+0.083782 
## [162]    train-rmse:0.400752+0.012439    test-rmse:0.979964+0.085466 
## [163]    train-rmse:0.397966+0.012814    test-rmse:0.978140+0.083601 
## [164]    train-rmse:0.394711+0.012392    test-rmse:0.978602+0.083748 
## [165]    train-rmse:0.393100+0.012617    test-rmse:0.978289+0.083924 
## [166]    train-rmse:0.391007+0.012479    test-rmse:0.978235+0.084309 
## [167]    train-rmse:0.389410+0.012410    test-rmse:0.978072+0.082815 
## [168]    train-rmse:0.385948+0.012963    test-rmse:0.977592+0.082917 
## [169]    train-rmse:0.384320+0.012366    test-rmse:0.977804+0.082997 
## [170]    train-rmse:0.381626+0.010281    test-rmse:0.976431+0.085159 
## [171]    train-rmse:0.380193+0.009906    test-rmse:0.975234+0.084926 
## [172]    train-rmse:0.377454+0.009898    test-rmse:0.974307+0.085570 
## [173]    train-rmse:0.375519+0.009755    test-rmse:0.972962+0.083801 
## [174]    train-rmse:0.374315+0.010083    test-rmse:0.972429+0.083553 
## [175]    train-rmse:0.372209+0.009288    test-rmse:0.970359+0.084281 
## [176]    train-rmse:0.370268+0.009517    test-rmse:0.970525+0.082799 
## [177]    train-rmse:0.369152+0.009598    test-rmse:0.971057+0.082235 
## [178]    train-rmse:0.368130+0.009799    test-rmse:0.970949+0.081843 
## [179]    train-rmse:0.366439+0.010072    test-rmse:0.969409+0.081206 
## [180]    train-rmse:0.363855+0.009891    test-rmse:0.969531+0.081299 
## [181]    train-rmse:0.361190+0.010136    test-rmse:0.969424+0.081392 
## [182]    train-rmse:0.357889+0.009926    test-rmse:0.966203+0.080556 
## [183]    train-rmse:0.356144+0.009682    test-rmse:0.964510+0.080393 
## [184]    train-rmse:0.354455+0.010030    test-rmse:0.964773+0.080542 
## [185]    train-rmse:0.352241+0.009951    test-rmse:0.963076+0.079727 
## [186]    train-rmse:0.350087+0.010162    test-rmse:0.962517+0.080064 
## [187]    train-rmse:0.348338+0.010326    test-rmse:0.962215+0.079768 
## [188]    train-rmse:0.345846+0.011287    test-rmse:0.961325+0.078961 
## [189]    train-rmse:0.343762+0.011598    test-rmse:0.962733+0.079976 
## [190]    train-rmse:0.342366+0.011440    test-rmse:0.962333+0.080235 
## [191]    train-rmse:0.339755+0.011636    test-rmse:0.963162+0.079466 
## [192]    train-rmse:0.338502+0.011052    test-rmse:0.963276+0.079871 
## [193]    train-rmse:0.336566+0.011650    test-rmse:0.963524+0.079459 
## [194]    train-rmse:0.334448+0.011903    test-rmse:0.961991+0.079963 
## Stopping. Best iteration:
## [188]    train-rmse:0.345846+0.011287    test-rmse:0.961325+0.078961
## 
## 24 
## [1]  train-rmse:82.780940+0.083029   test-rmse:82.781650+0.410772 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.591332+0.069678   test-rmse:69.591866+0.423700 
## [3]  train-rmse:58.506326+0.058401   test-rmse:58.506664+0.434438 
## [4]  train-rmse:49.190703+0.048890   test-rmse:49.190803+0.443303 
## [5]  train-rmse:41.362720+0.040844   test-rmse:41.362536+0.450558 
## [6]  train-rmse:34.785638+0.034034   test-rmse:34.785128+0.456398 
## [7]  train-rmse:29.260552+0.028262   test-rmse:29.259653+0.460980 
## [8]  train-rmse:24.620358+0.023369   test-rmse:24.619006+0.464415 
## [9]  train-rmse:20.724704+0.019222   test-rmse:20.722832+0.466766 
## [10] train-rmse:17.455756+0.015748   test-rmse:17.453284+0.468046 
## [11] train-rmse:14.714250+0.013047   test-rmse:14.705223+0.464215 
## [12] train-rmse:12.415248+0.010202   test-rmse:12.403659+0.451383 
## [13] train-rmse:10.487754+0.008421   test-rmse:10.475114+0.434470 
## [14] train-rmse:8.871507+0.006889    test-rmse:8.851975+0.424152 
## [15] train-rmse:7.514893+0.005074    test-rmse:7.500034+0.397897 
## [16] train-rmse:6.377349+0.005123    test-rmse:6.364151+0.382597 
## [17] train-rmse:5.424938+0.004873    test-rmse:5.416162+0.359040 
## [18] train-rmse:4.625447+0.003727    test-rmse:4.624434+0.329523 
## [19] train-rmse:3.957230+0.003817    test-rmse:3.968668+0.309600 
## [20] train-rmse:3.399020+0.004954    test-rmse:3.415935+0.289789 
## [21] train-rmse:2.933592+0.005362    test-rmse:2.953484+0.274130 
## [22] train-rmse:2.545290+0.004864    test-rmse:2.581756+0.260607 
## [23] train-rmse:2.227797+0.005490    test-rmse:2.283701+0.246382 
## [24] train-rmse:1.963770+0.007263    test-rmse:2.041434+0.232958 
## [25] train-rmse:1.747861+0.008370    test-rmse:1.848228+0.215749 
## [26] train-rmse:1.570019+0.009462    test-rmse:1.693706+0.200552 
## [27] train-rmse:1.430925+0.009254    test-rmse:1.577966+0.186859 
## [28] train-rmse:1.316409+0.008121    test-rmse:1.485420+0.176644 
## [29] train-rmse:1.221807+0.011644    test-rmse:1.416319+0.160057 
## [30] train-rmse:1.146208+0.012850    test-rmse:1.368133+0.145661 
## [31] train-rmse:1.085474+0.016641    test-rmse:1.327805+0.140517 
## [32] train-rmse:1.035397+0.019575    test-rmse:1.299244+0.129969 
## [33] train-rmse:0.992874+0.020925    test-rmse:1.271994+0.121181 
## [34] train-rmse:0.959404+0.022609    test-rmse:1.250548+0.113957 
## [35] train-rmse:0.927927+0.020571    test-rmse:1.231331+0.109051 
## [36] train-rmse:0.904483+0.022682    test-rmse:1.219875+0.105914 
## [37] train-rmse:0.880331+0.020087    test-rmse:1.211319+0.101165 
## [38] train-rmse:0.858370+0.019055    test-rmse:1.195509+0.099380 
## [39] train-rmse:0.840774+0.015607    test-rmse:1.185837+0.101718 
## [40] train-rmse:0.825512+0.014991    test-rmse:1.177551+0.098718 
## [41] train-rmse:0.809817+0.015067    test-rmse:1.170384+0.096804 
## [42] train-rmse:0.792406+0.014611    test-rmse:1.159083+0.093603 
## [43] train-rmse:0.779269+0.014942    test-rmse:1.149926+0.094530 
## [44] train-rmse:0.766449+0.014590    test-rmse:1.141777+0.093371 
## [45] train-rmse:0.755570+0.012631    test-rmse:1.136969+0.086997 
## [46] train-rmse:0.743581+0.015199    test-rmse:1.130145+0.086496 
## [47] train-rmse:0.731120+0.016627    test-rmse:1.122497+0.081822 
## [48] train-rmse:0.722285+0.018185    test-rmse:1.117547+0.082293 
## [49] train-rmse:0.709187+0.017344    test-rmse:1.113414+0.082551 
## [50] train-rmse:0.699768+0.016448    test-rmse:1.105331+0.082870 
## [51] train-rmse:0.689251+0.013360    test-rmse:1.100887+0.084463 
## [52] train-rmse:0.680927+0.014102    test-rmse:1.096506+0.080214 
## [53] train-rmse:0.672358+0.016132    test-rmse:1.091098+0.082793 
## [54] train-rmse:0.661500+0.017212    test-rmse:1.078269+0.077200 
## [55] train-rmse:0.652425+0.017557    test-rmse:1.073365+0.079362 
## [56] train-rmse:0.645321+0.016801    test-rmse:1.069328+0.079882 
## [57] train-rmse:0.634924+0.014740    test-rmse:1.064048+0.079006 
## [58] train-rmse:0.627951+0.013590    test-rmse:1.061372+0.081713 
## [59] train-rmse:0.617923+0.013994    test-rmse:1.056347+0.079067 
## [60] train-rmse:0.611422+0.014366    test-rmse:1.049532+0.077106 
## [61] train-rmse:0.604792+0.013332    test-rmse:1.047786+0.083629 
## [62] train-rmse:0.598539+0.011770    test-rmse:1.046125+0.085994 
## [63] train-rmse:0.592774+0.011443    test-rmse:1.044118+0.086318 
## [64] train-rmse:0.584974+0.013161    test-rmse:1.036573+0.084794 
## [65] train-rmse:0.578946+0.013438    test-rmse:1.029233+0.081566 
## [66] train-rmse:0.572671+0.014850    test-rmse:1.027554+0.080955 
## [67] train-rmse:0.567526+0.013909    test-rmse:1.025426+0.079801 
## [68] train-rmse:0.560021+0.015676    test-rmse:1.026141+0.082398 
## [69] train-rmse:0.554910+0.015198    test-rmse:1.024406+0.081196 
## [70] train-rmse:0.549161+0.015297    test-rmse:1.022001+0.080534 
## [71] train-rmse:0.543185+0.013542    test-rmse:1.021042+0.079969 
## [72] train-rmse:0.534721+0.011152    test-rmse:1.017825+0.076423 
## [73] train-rmse:0.528655+0.012236    test-rmse:1.013873+0.072930 
## [74] train-rmse:0.523609+0.011713    test-rmse:1.012985+0.076540 
## [75] train-rmse:0.518344+0.011174    test-rmse:1.010386+0.076937 
## [76] train-rmse:0.512842+0.012597    test-rmse:1.009447+0.077146 
## [77] train-rmse:0.508919+0.011908    test-rmse:1.008302+0.077844 
## [78] train-rmse:0.501867+0.011090    test-rmse:1.007988+0.078522 
## [79] train-rmse:0.497656+0.010519    test-rmse:1.008693+0.082166 
## [80] train-rmse:0.492486+0.009855    test-rmse:1.005702+0.082220 
## [81] train-rmse:0.487464+0.011464    test-rmse:1.004301+0.083225 
## [82] train-rmse:0.482924+0.011967    test-rmse:1.006533+0.083833 
## [83] train-rmse:0.477399+0.010070    test-rmse:1.002564+0.084595 
## [84] train-rmse:0.471769+0.008828    test-rmse:0.998595+0.085112 
## [85] train-rmse:0.465401+0.010200    test-rmse:0.994651+0.083066 
## [86] train-rmse:0.460939+0.011114    test-rmse:0.996669+0.086324 
## [87] train-rmse:0.456348+0.009504    test-rmse:0.994173+0.087590 
## [88] train-rmse:0.452751+0.010052    test-rmse:0.993927+0.087810 
## [89] train-rmse:0.449703+0.009853    test-rmse:0.992418+0.088132 
## [90] train-rmse:0.443436+0.008269    test-rmse:0.989449+0.085243 
## [91] train-rmse:0.438977+0.009638    test-rmse:0.989065+0.084808 
## [92] train-rmse:0.435452+0.010488    test-rmse:0.988013+0.085673 
## [93] train-rmse:0.431678+0.011925    test-rmse:0.987357+0.086470 
## [94] train-rmse:0.427149+0.010184    test-rmse:0.986241+0.086369 
## [95] train-rmse:0.423737+0.010117    test-rmse:0.986119+0.085720 
## [96] train-rmse:0.418991+0.009242    test-rmse:0.985011+0.085043 
## [97] train-rmse:0.414805+0.010927    test-rmse:0.983910+0.083988 
## [98] train-rmse:0.409976+0.012132    test-rmse:0.980337+0.083920 
## [99] train-rmse:0.406359+0.012454    test-rmse:0.979937+0.085156 
## [100]    train-rmse:0.401584+0.011444    test-rmse:0.979892+0.085118 
## [101]    train-rmse:0.397640+0.011156    test-rmse:0.980116+0.085358 
## [102]    train-rmse:0.394147+0.010704    test-rmse:0.979539+0.086206 
## [103]    train-rmse:0.391010+0.011465    test-rmse:0.978441+0.086588 
## [104]    train-rmse:0.388086+0.011209    test-rmse:0.977280+0.086636 
## [105]    train-rmse:0.384844+0.010845    test-rmse:0.977260+0.086904 
## [106]    train-rmse:0.382169+0.010906    test-rmse:0.974857+0.086766 
## [107]    train-rmse:0.378795+0.011830    test-rmse:0.976441+0.087823 
## [108]    train-rmse:0.374444+0.011256    test-rmse:0.975290+0.089086 
## [109]    train-rmse:0.369069+0.010179    test-rmse:0.972684+0.087478 
## [110]    train-rmse:0.365212+0.011593    test-rmse:0.969034+0.085126 
## [111]    train-rmse:0.360839+0.009865    test-rmse:0.969221+0.085250 
## [112]    train-rmse:0.357304+0.009713    test-rmse:0.968357+0.086100 
## [113]    train-rmse:0.354104+0.009261    test-rmse:0.967638+0.085774 
## [114]    train-rmse:0.351206+0.009023    test-rmse:0.966709+0.086471 
## [115]    train-rmse:0.347811+0.009275    test-rmse:0.965234+0.085523 
## [116]    train-rmse:0.344277+0.009692    test-rmse:0.965024+0.085700 
## [117]    train-rmse:0.341587+0.009541    test-rmse:0.964441+0.085769 
## [118]    train-rmse:0.338080+0.010120    test-rmse:0.964100+0.086767 
## [119]    train-rmse:0.333500+0.010449    test-rmse:0.964096+0.086995 
## [120]    train-rmse:0.331319+0.010551    test-rmse:0.964108+0.087318 
## [121]    train-rmse:0.328158+0.010666    test-rmse:0.961823+0.086714 
## [122]    train-rmse:0.325489+0.011035    test-rmse:0.960824+0.086309 
## [123]    train-rmse:0.322090+0.012236    test-rmse:0.961246+0.087200 
## [124]    train-rmse:0.318380+0.012748    test-rmse:0.960644+0.087087 
## [125]    train-rmse:0.315153+0.013210    test-rmse:0.961144+0.088125 
## [126]    train-rmse:0.312964+0.012727    test-rmse:0.960221+0.089641 
## [127]    train-rmse:0.310262+0.012315    test-rmse:0.959747+0.088926 
## [128]    train-rmse:0.306611+0.011706    test-rmse:0.959649+0.089731 
## [129]    train-rmse:0.304860+0.012164    test-rmse:0.957698+0.088290 
## [130]    train-rmse:0.302176+0.011828    test-rmse:0.956427+0.087312 
## [131]    train-rmse:0.299593+0.010848    test-rmse:0.955054+0.087200 
## [132]    train-rmse:0.297156+0.010524    test-rmse:0.955740+0.088899 
## [133]    train-rmse:0.295063+0.010220    test-rmse:0.955597+0.088611 
## [134]    train-rmse:0.292598+0.010233    test-rmse:0.955250+0.088930 
## [135]    train-rmse:0.289405+0.009447    test-rmse:0.953750+0.088782 
## [136]    train-rmse:0.286026+0.008409    test-rmse:0.953252+0.088971 
## [137]    train-rmse:0.284113+0.008284    test-rmse:0.953423+0.089246 
## [138]    train-rmse:0.281973+0.008666    test-rmse:0.952969+0.088444 
## [139]    train-rmse:0.279455+0.008282    test-rmse:0.952095+0.086991 
## [140]    train-rmse:0.276956+0.008434    test-rmse:0.951585+0.085798 
## [141]    train-rmse:0.275460+0.008436    test-rmse:0.951239+0.085002 
## [142]    train-rmse:0.273745+0.008646    test-rmse:0.950810+0.085742 
## [143]    train-rmse:0.270956+0.009330    test-rmse:0.950814+0.086065 
## [144]    train-rmse:0.268192+0.008915    test-rmse:0.950271+0.086422 
## [145]    train-rmse:0.267083+0.009292    test-rmse:0.949578+0.086183 
## [146]    train-rmse:0.264141+0.009118    test-rmse:0.949349+0.086073 
## [147]    train-rmse:0.261188+0.008820    test-rmse:0.948978+0.086033 
## [148]    train-rmse:0.259345+0.009733    test-rmse:0.948056+0.085726 
## [149]    train-rmse:0.257092+0.009423    test-rmse:0.947557+0.085503 
## [150]    train-rmse:0.254505+0.009855    test-rmse:0.947485+0.085506 
## [151]    train-rmse:0.252708+0.009152    test-rmse:0.946972+0.085977 
## [152]    train-rmse:0.250417+0.008162    test-rmse:0.946711+0.086003 
## [153]    train-rmse:0.248666+0.008409    test-rmse:0.946271+0.085909 
## [154]    train-rmse:0.245957+0.008684    test-rmse:0.945682+0.084914 
## [155]    train-rmse:0.243892+0.008462    test-rmse:0.945130+0.085538 
## [156]    train-rmse:0.241105+0.009238    test-rmse:0.944802+0.084569 
## [157]    train-rmse:0.239394+0.009310    test-rmse:0.944750+0.084797 
## [158]    train-rmse:0.237723+0.009422    test-rmse:0.944729+0.084374 
## [159]    train-rmse:0.235364+0.009129    test-rmse:0.944341+0.083716 
## [160]    train-rmse:0.234047+0.008761    test-rmse:0.944238+0.084346 
## [161]    train-rmse:0.232230+0.008352    test-rmse:0.943986+0.083514 
## [162]    train-rmse:0.230610+0.008930    test-rmse:0.943499+0.083281 
## [163]    train-rmse:0.228919+0.009571    test-rmse:0.942834+0.082918 
## [164]    train-rmse:0.227379+0.009097    test-rmse:0.942477+0.083124 
## [165]    train-rmse:0.224622+0.008508    test-rmse:0.942902+0.083526 
## [166]    train-rmse:0.222978+0.008552    test-rmse:0.942442+0.084554 
## [167]    train-rmse:0.221358+0.008853    test-rmse:0.943266+0.084764 
## [168]    train-rmse:0.219469+0.008653    test-rmse:0.943150+0.085253 
## [169]    train-rmse:0.217777+0.008942    test-rmse:0.943682+0.085767 
## [170]    train-rmse:0.216387+0.009285    test-rmse:0.943519+0.085339 
## [171]    train-rmse:0.214832+0.009122    test-rmse:0.942894+0.085572 
## [172]    train-rmse:0.212275+0.010499    test-rmse:0.942721+0.086046 
## Stopping. Best iteration:
## [166]    train-rmse:0.222978+0.008552    test-rmse:0.942442+0.084554
## 
## 25 
## [1]  train-rmse:82.780940+0.083029   test-rmse:82.781650+0.410772 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.591332+0.069678   test-rmse:69.591866+0.423700 
## [3]  train-rmse:58.506326+0.058401   test-rmse:58.506664+0.434438 
## [4]  train-rmse:49.190703+0.048890   test-rmse:49.190803+0.443303 
## [5]  train-rmse:41.362720+0.040844   test-rmse:41.362536+0.450558 
## [6]  train-rmse:34.785638+0.034034   test-rmse:34.785128+0.456398 
## [7]  train-rmse:29.260552+0.028262   test-rmse:29.259653+0.460980 
## [8]  train-rmse:24.620358+0.023369   test-rmse:24.619006+0.464415 
## [9]  train-rmse:20.724704+0.019222   test-rmse:20.722832+0.466766 
## [10] train-rmse:17.455756+0.015748   test-rmse:17.453284+0.468046 
## [11] train-rmse:14.714250+0.013047   test-rmse:14.705223+0.464215 
## [12] train-rmse:12.415248+0.010202   test-rmse:12.403659+0.451383 
## [13] train-rmse:10.487754+0.008421   test-rmse:10.475114+0.434470 
## [14] train-rmse:8.871425+0.006820    test-rmse:8.850692+0.423682 
## [15] train-rmse:7.514660+0.004858    test-rmse:7.499711+0.397806 
## [16] train-rmse:6.376062+0.004903    test-rmse:6.361624+0.377460 
## [17] train-rmse:5.421941+0.004202    test-rmse:5.417193+0.347324 
## [18] train-rmse:4.620578+0.002343    test-rmse:4.612585+0.328409 
## [19] train-rmse:3.951043+0.002998    test-rmse:3.952878+0.310064 
## [20] train-rmse:3.388101+0.004365    test-rmse:3.400548+0.283840 
## [21] train-rmse:2.917234+0.002847    test-rmse:2.941066+0.269057 
## [22] train-rmse:2.522736+0.006667    test-rmse:2.567780+0.251415 
## [23] train-rmse:2.195011+0.005133    test-rmse:2.267853+0.239615 
## [24] train-rmse:1.923144+0.007552    test-rmse:2.012838+0.221401 
## [25] train-rmse:1.699241+0.008523    test-rmse:1.814498+0.207688 
## [26] train-rmse:1.513280+0.007395    test-rmse:1.656916+0.201281 
## [27] train-rmse:1.362997+0.007847    test-rmse:1.535616+0.191927 
## [28] train-rmse:1.237832+0.006415    test-rmse:1.440174+0.177816 
## [29] train-rmse:1.131952+0.009355    test-rmse:1.360910+0.167461 
## [30] train-rmse:1.050610+0.010663    test-rmse:1.308484+0.159199 
## [31] train-rmse:0.977367+0.013316    test-rmse:1.262453+0.150574 
## [32] train-rmse:0.920648+0.011554    test-rmse:1.231008+0.143220 
## [33] train-rmse:0.874845+0.011943    test-rmse:1.202314+0.144547 
## [34] train-rmse:0.833073+0.009841    test-rmse:1.173518+0.133217 
## [35] train-rmse:0.796042+0.012008    test-rmse:1.155738+0.126545 
## [36] train-rmse:0.765642+0.012030    test-rmse:1.143509+0.124311 
## [37] train-rmse:0.740770+0.013348    test-rmse:1.130954+0.121682 
## [38] train-rmse:0.720431+0.015166    test-rmse:1.121465+0.117424 
## [39] train-rmse:0.701529+0.014201    test-rmse:1.108513+0.107317 
## [40] train-rmse:0.682130+0.015655    test-rmse:1.099721+0.101449 
## [41] train-rmse:0.666128+0.016653    test-rmse:1.085811+0.099913 
## [42] train-rmse:0.652331+0.018099    test-rmse:1.079234+0.095551 
## [43] train-rmse:0.637723+0.016582    test-rmse:1.069353+0.091240 
## [44] train-rmse:0.623883+0.018380    test-rmse:1.063484+0.091878 
## [45] train-rmse:0.609655+0.016332    test-rmse:1.053871+0.087192 
## [46] train-rmse:0.597372+0.017570    test-rmse:1.050426+0.084961 
## [47] train-rmse:0.583827+0.019158    test-rmse:1.038729+0.083397 
## [48] train-rmse:0.573610+0.018111    test-rmse:1.034696+0.083996 
## [49] train-rmse:0.563919+0.018097    test-rmse:1.029534+0.082895 
## [50] train-rmse:0.552900+0.016769    test-rmse:1.020371+0.079818 
## [51] train-rmse:0.543755+0.017266    test-rmse:1.017351+0.079146 
## [52] train-rmse:0.536994+0.016134    test-rmse:1.012913+0.079370 
## [53] train-rmse:0.526718+0.017078    test-rmse:1.010112+0.079933 
## [54] train-rmse:0.518486+0.017247    test-rmse:1.010050+0.078591 
## [55] train-rmse:0.508040+0.016454    test-rmse:1.003565+0.074177 
## [56] train-rmse:0.499565+0.016958    test-rmse:1.001879+0.074602 
## [57] train-rmse:0.491697+0.015713    test-rmse:1.000346+0.074829 
## [58] train-rmse:0.485008+0.016201    test-rmse:0.998860+0.074882 
## [59] train-rmse:0.475024+0.014998    test-rmse:0.991933+0.075034 
## [60] train-rmse:0.466916+0.014706    test-rmse:0.990295+0.076217 
## [61] train-rmse:0.460427+0.014455    test-rmse:0.988167+0.077640 
## [62] train-rmse:0.454348+0.014228    test-rmse:0.987295+0.077666 
## [63] train-rmse:0.446763+0.014395    test-rmse:0.982087+0.076234 
## [64] train-rmse:0.439529+0.016912    test-rmse:0.980214+0.076025 
## [65] train-rmse:0.433040+0.016072    test-rmse:0.978039+0.076250 
## [66] train-rmse:0.427478+0.014612    test-rmse:0.976250+0.076047 
## [67] train-rmse:0.421347+0.015126    test-rmse:0.974602+0.075286 
## [68] train-rmse:0.415038+0.015439    test-rmse:0.973524+0.074117 
## [69] train-rmse:0.408504+0.015354    test-rmse:0.971508+0.075205 
## [70] train-rmse:0.404359+0.014894    test-rmse:0.969682+0.075780 
## [71] train-rmse:0.396068+0.013783    test-rmse:0.968940+0.075841 
## [72] train-rmse:0.389306+0.013412    test-rmse:0.967375+0.075624 
## [73] train-rmse:0.384889+0.012568    test-rmse:0.967100+0.075179 
## [74] train-rmse:0.379510+0.014261    test-rmse:0.963945+0.072079 
## [75] train-rmse:0.375805+0.013847    test-rmse:0.962803+0.072702 
## [76] train-rmse:0.368848+0.013457    test-rmse:0.962112+0.074400 
## [77] train-rmse:0.362519+0.014469    test-rmse:0.960644+0.075447 
## [78] train-rmse:0.358427+0.014079    test-rmse:0.959840+0.075864 
## [79] train-rmse:0.353202+0.014443    test-rmse:0.959858+0.076758 
## [80] train-rmse:0.347378+0.014000    test-rmse:0.958184+0.075920 
## [81] train-rmse:0.343989+0.013760    test-rmse:0.957132+0.076273 
## [82] train-rmse:0.337533+0.013337    test-rmse:0.954659+0.077854 
## [83] train-rmse:0.333111+0.013113    test-rmse:0.954236+0.078170 
## [84] train-rmse:0.328763+0.013402    test-rmse:0.952013+0.077188 
## [85] train-rmse:0.324136+0.013168    test-rmse:0.950933+0.076807 
## [86] train-rmse:0.319561+0.013414    test-rmse:0.951501+0.077416 
## [87] train-rmse:0.314459+0.012795    test-rmse:0.950358+0.078110 
## [88] train-rmse:0.310767+0.012798    test-rmse:0.948408+0.078906 
## [89] train-rmse:0.307177+0.013174    test-rmse:0.947751+0.078218 
## [90] train-rmse:0.303147+0.012205    test-rmse:0.946725+0.078508 
## [91] train-rmse:0.300139+0.012689    test-rmse:0.946378+0.078497 
## [92] train-rmse:0.295813+0.012578    test-rmse:0.945971+0.079921 
## [93] train-rmse:0.291502+0.012814    test-rmse:0.944826+0.079338 
## [94] train-rmse:0.288294+0.012380    test-rmse:0.943485+0.080591 
## [95] train-rmse:0.284688+0.011519    test-rmse:0.941406+0.080707 
## [96] train-rmse:0.280424+0.012222    test-rmse:0.941260+0.080984 
## [97] train-rmse:0.277196+0.012830    test-rmse:0.940927+0.081204 
## [98] train-rmse:0.273783+0.012705    test-rmse:0.940975+0.080906 
## [99] train-rmse:0.270160+0.013570    test-rmse:0.939455+0.080529 
## [100]    train-rmse:0.266154+0.014763    test-rmse:0.939667+0.081102 
## [101]    train-rmse:0.263260+0.014806    test-rmse:0.939256+0.081851 
## [102]    train-rmse:0.259993+0.013549    test-rmse:0.937382+0.082620 
## [103]    train-rmse:0.256371+0.013819    test-rmse:0.938098+0.082997 
## [104]    train-rmse:0.252228+0.014081    test-rmse:0.937579+0.083159 
## [105]    train-rmse:0.249885+0.013735    test-rmse:0.936981+0.084029 
## [106]    train-rmse:0.247029+0.013401    test-rmse:0.936970+0.084875 
## [107]    train-rmse:0.243611+0.013042    test-rmse:0.935924+0.085226 
## [108]    train-rmse:0.239397+0.014076    test-rmse:0.934982+0.085558 
## [109]    train-rmse:0.236694+0.014131    test-rmse:0.934975+0.085887 
## [110]    train-rmse:0.233490+0.013434    test-rmse:0.935175+0.085644 
## [111]    train-rmse:0.231647+0.013339    test-rmse:0.934624+0.085271 
## [112]    train-rmse:0.228993+0.013587    test-rmse:0.933457+0.085454 
## [113]    train-rmse:0.225878+0.012424    test-rmse:0.932641+0.085631 
## [114]    train-rmse:0.223601+0.012266    test-rmse:0.932364+0.085755 
## [115]    train-rmse:0.221184+0.012194    test-rmse:0.931788+0.086415 
## [116]    train-rmse:0.218041+0.011959    test-rmse:0.931331+0.085645 
## [117]    train-rmse:0.213993+0.011414    test-rmse:0.931511+0.085325 
## [118]    train-rmse:0.211775+0.010614    test-rmse:0.931228+0.085271 
## [119]    train-rmse:0.209095+0.011940    test-rmse:0.930376+0.085765 
## [120]    train-rmse:0.206527+0.011621    test-rmse:0.930163+0.085322 
## [121]    train-rmse:0.203790+0.011741    test-rmse:0.930021+0.085299 
## [122]    train-rmse:0.201506+0.011198    test-rmse:0.929943+0.085413 
## [123]    train-rmse:0.198889+0.011421    test-rmse:0.929300+0.086386 
## [124]    train-rmse:0.196936+0.011239    test-rmse:0.928957+0.085874 
## [125]    train-rmse:0.195142+0.011014    test-rmse:0.929229+0.085778 
## [126]    train-rmse:0.193034+0.010236    test-rmse:0.928908+0.085546 
## [127]    train-rmse:0.190022+0.010382    test-rmse:0.927925+0.086594 
## [128]    train-rmse:0.187794+0.010060    test-rmse:0.927636+0.086328 
## [129]    train-rmse:0.186006+0.009379    test-rmse:0.927751+0.086256 
## [130]    train-rmse:0.183553+0.009764    test-rmse:0.927283+0.086664 
## [131]    train-rmse:0.181434+0.009481    test-rmse:0.926712+0.087239 
## [132]    train-rmse:0.179329+0.009798    test-rmse:0.926224+0.087006 
## [133]    train-rmse:0.177110+0.009445    test-rmse:0.925847+0.086510 
## [134]    train-rmse:0.174993+0.009535    test-rmse:0.925598+0.086173 
## [135]    train-rmse:0.173057+0.009152    test-rmse:0.925448+0.085659 
## [136]    train-rmse:0.170246+0.009479    test-rmse:0.924941+0.085750 
## [137]    train-rmse:0.167973+0.009445    test-rmse:0.924782+0.085623 
## [138]    train-rmse:0.165763+0.009827    test-rmse:0.925292+0.085891 
## [139]    train-rmse:0.163472+0.009228    test-rmse:0.924311+0.084897 
## [140]    train-rmse:0.160954+0.009448    test-rmse:0.924220+0.085163 
## [141]    train-rmse:0.159192+0.009520    test-rmse:0.923452+0.085479 
## [142]    train-rmse:0.157127+0.009521    test-rmse:0.923168+0.085332 
## [143]    train-rmse:0.155687+0.009744    test-rmse:0.922934+0.086019 
## [144]    train-rmse:0.154080+0.009661    test-rmse:0.922427+0.086163 
## [145]    train-rmse:0.151437+0.009174    test-rmse:0.922208+0.086321 
## [146]    train-rmse:0.150030+0.008968    test-rmse:0.921893+0.086589 
## [147]    train-rmse:0.148131+0.009795    test-rmse:0.921447+0.086722 
## [148]    train-rmse:0.146005+0.009830    test-rmse:0.920948+0.086812 
## [149]    train-rmse:0.144879+0.009553    test-rmse:0.920756+0.086883 
## [150]    train-rmse:0.142897+0.009516    test-rmse:0.920600+0.086741 
## [151]    train-rmse:0.141252+0.009757    test-rmse:0.921069+0.086833 
## [152]    train-rmse:0.139733+0.009901    test-rmse:0.920945+0.086732 
## [153]    train-rmse:0.136747+0.009729    test-rmse:0.920360+0.086476 
## [154]    train-rmse:0.135716+0.009427    test-rmse:0.920315+0.086596 
## [155]    train-rmse:0.134663+0.009318    test-rmse:0.920091+0.086526 
## [156]    train-rmse:0.133176+0.009235    test-rmse:0.919493+0.086597 
## [157]    train-rmse:0.131788+0.009423    test-rmse:0.919821+0.086488 
## [158]    train-rmse:0.130375+0.009147    test-rmse:0.919479+0.086395 
## [159]    train-rmse:0.128176+0.009095    test-rmse:0.919301+0.086337 
## [160]    train-rmse:0.127417+0.009065    test-rmse:0.918882+0.086343 
## [161]    train-rmse:0.125281+0.008292    test-rmse:0.918115+0.086177 
## [162]    train-rmse:0.122954+0.007895    test-rmse:0.917861+0.086706 
## [163]    train-rmse:0.121936+0.007957    test-rmse:0.918097+0.086316 
## [164]    train-rmse:0.120568+0.007529    test-rmse:0.918260+0.086252 
## [165]    train-rmse:0.118795+0.007250    test-rmse:0.918172+0.086222 
## [166]    train-rmse:0.117190+0.007465    test-rmse:0.918244+0.085799 
## [167]    train-rmse:0.115487+0.007234    test-rmse:0.918343+0.085657 
## [168]    train-rmse:0.114482+0.007070    test-rmse:0.918170+0.085647 
## Stopping. Best iteration:
## [162]    train-rmse:0.122954+0.007895    test-rmse:0.917861+0.086706
## 
## 26 
## [1]  train-rmse:82.780940+0.083029   test-rmse:82.781650+0.410772 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.591332+0.069678   test-rmse:69.591866+0.423700 
## [3]  train-rmse:58.506326+0.058401   test-rmse:58.506664+0.434438 
## [4]  train-rmse:49.190703+0.048890   test-rmse:49.190803+0.443303 
## [5]  train-rmse:41.362720+0.040844   test-rmse:41.362536+0.450558 
## [6]  train-rmse:34.785638+0.034034   test-rmse:34.785128+0.456398 
## [7]  train-rmse:29.260552+0.028262   test-rmse:29.259653+0.460980 
## [8]  train-rmse:24.620358+0.023369   test-rmse:24.619006+0.464415 
## [9]  train-rmse:20.724704+0.019222   test-rmse:20.722832+0.466766 
## [10] train-rmse:17.455756+0.015748   test-rmse:17.453284+0.468046 
## [11] train-rmse:14.714250+0.013047   test-rmse:14.705223+0.464215 
## [12] train-rmse:12.415248+0.010202   test-rmse:12.403659+0.451383 
## [13] train-rmse:10.487754+0.008421   test-rmse:10.475114+0.434470 
## [14] train-rmse:8.871425+0.006820    test-rmse:8.850692+0.423682 
## [15] train-rmse:7.514562+0.004778    test-rmse:7.497542+0.397228 
## [16] train-rmse:6.375451+0.004812    test-rmse:6.363512+0.372509 
## [17] train-rmse:5.421326+0.004005    test-rmse:5.414337+0.340739 
## [18] train-rmse:4.618878+0.002482    test-rmse:4.609710+0.319742 
## [19] train-rmse:3.946483+0.002030    test-rmse:3.946499+0.296266 
## [20] train-rmse:3.380894+0.003843    test-rmse:3.395102+0.277658 
## [21] train-rmse:2.906696+0.002064    test-rmse:2.923854+0.266557 
## [22] train-rmse:2.510315+0.003258    test-rmse:2.549455+0.249310 
## [23] train-rmse:2.177122+0.005756    test-rmse:2.245776+0.228780 
## [24] train-rmse:1.900323+0.004076    test-rmse:1.990432+0.212736 
## [25] train-rmse:1.666189+0.004931    test-rmse:1.787286+0.195793 
## [26] train-rmse:1.472894+0.007233    test-rmse:1.635482+0.179546 
## [27] train-rmse:1.311724+0.007487    test-rmse:1.501022+0.165022 
## [28] train-rmse:1.178065+0.007997    test-rmse:1.409110+0.149272 
## [29] train-rmse:1.068670+0.009911    test-rmse:1.329159+0.140532 
## [30] train-rmse:0.976106+0.008521    test-rmse:1.266034+0.131248 
## [31] train-rmse:0.900204+0.010700    test-rmse:1.220736+0.122532 
## [32] train-rmse:0.836750+0.006920    test-rmse:1.184938+0.120341 
## [33] train-rmse:0.777009+0.007669    test-rmse:1.154582+0.110448 
## [34] train-rmse:0.732504+0.007253    test-rmse:1.134899+0.108141 
## [35] train-rmse:0.696188+0.009051    test-rmse:1.115361+0.106341 
## [36] train-rmse:0.666294+0.010510    test-rmse:1.101450+0.106178 
## [37] train-rmse:0.635729+0.012715    test-rmse:1.088074+0.101241 
## [38] train-rmse:0.610762+0.013652    test-rmse:1.080398+0.097450 
## [39] train-rmse:0.590070+0.014083    test-rmse:1.070815+0.097941 
## [40] train-rmse:0.572044+0.012897    test-rmse:1.063427+0.095591 
## [41] train-rmse:0.554913+0.013483    test-rmse:1.058048+0.093610 
## [42] train-rmse:0.539911+0.012547    test-rmse:1.048586+0.093561 
## [43] train-rmse:0.523811+0.016297    test-rmse:1.044740+0.093484 
## [44] train-rmse:0.512555+0.014667    test-rmse:1.039516+0.093997 
## [45] train-rmse:0.497826+0.015878    test-rmse:1.031736+0.088139 
## [46] train-rmse:0.488124+0.016432    test-rmse:1.027867+0.085739 
## [47] train-rmse:0.476411+0.017601    test-rmse:1.022286+0.080990 
## [48] train-rmse:0.464771+0.018149    test-rmse:1.020996+0.081899 
## [49] train-rmse:0.450399+0.018474    test-rmse:1.014853+0.084046 
## [50] train-rmse:0.441233+0.015797    test-rmse:1.012340+0.083185 
## [51] train-rmse:0.430152+0.015368    test-rmse:1.007296+0.082357 
## [52] train-rmse:0.420899+0.014685    test-rmse:1.005045+0.083343 
## [53] train-rmse:0.412204+0.014374    test-rmse:1.001864+0.081514 
## [54] train-rmse:0.405220+0.015711    test-rmse:0.996533+0.080615 
## [55] train-rmse:0.397115+0.014266    test-rmse:0.993778+0.079053 
## [56] train-rmse:0.387006+0.013518    test-rmse:0.988441+0.074691 
## [57] train-rmse:0.380354+0.011490    test-rmse:0.985677+0.075512 
## [58] train-rmse:0.371945+0.012047    test-rmse:0.982645+0.073688 
## [59] train-rmse:0.363430+0.011353    test-rmse:0.980515+0.073577 
## [60] train-rmse:0.355722+0.010566    test-rmse:0.979555+0.072236 
## [61] train-rmse:0.348471+0.011751    test-rmse:0.980223+0.072227 
## [62] train-rmse:0.340029+0.012575    test-rmse:0.976331+0.069269 
## [63] train-rmse:0.333961+0.014498    test-rmse:0.974428+0.069767 
## [64] train-rmse:0.327732+0.014123    test-rmse:0.971513+0.068732 
## [65] train-rmse:0.320733+0.014596    test-rmse:0.970405+0.068479 
## [66] train-rmse:0.315040+0.014195    test-rmse:0.970408+0.068948 
## [67] train-rmse:0.310093+0.013874    test-rmse:0.967601+0.069268 
## [68] train-rmse:0.304564+0.012663    test-rmse:0.966227+0.068123 
## [69] train-rmse:0.298252+0.012700    test-rmse:0.966162+0.067424 
## [70] train-rmse:0.292978+0.012700    test-rmse:0.963660+0.067085 
## [71] train-rmse:0.287959+0.013699    test-rmse:0.963505+0.066062 
## [72] train-rmse:0.283131+0.014379    test-rmse:0.964455+0.066757 
## [73] train-rmse:0.278635+0.013931    test-rmse:0.964292+0.066660 
## [74] train-rmse:0.273841+0.013209    test-rmse:0.964082+0.066917 
## [75] train-rmse:0.268725+0.013593    test-rmse:0.962587+0.067052 
## [76] train-rmse:0.264484+0.012921    test-rmse:0.962830+0.065122 
## [77] train-rmse:0.260270+0.012893    test-rmse:0.961172+0.066095 
## [78] train-rmse:0.256811+0.012504    test-rmse:0.959038+0.067315 
## [79] train-rmse:0.251673+0.011449    test-rmse:0.958358+0.068355 
## [80] train-rmse:0.246498+0.010158    test-rmse:0.957521+0.069467 
## [81] train-rmse:0.240343+0.010693    test-rmse:0.955238+0.069010 
## [82] train-rmse:0.234747+0.010157    test-rmse:0.954953+0.068881 
## [83] train-rmse:0.231159+0.009723    test-rmse:0.954392+0.068755 
## [84] train-rmse:0.227536+0.008889    test-rmse:0.953452+0.068666 
## [85] train-rmse:0.223306+0.009423    test-rmse:0.953364+0.068379 
## [86] train-rmse:0.219535+0.008562    test-rmse:0.952926+0.068495 
## [87] train-rmse:0.215248+0.009758    test-rmse:0.952720+0.067800 
## [88] train-rmse:0.212300+0.009240    test-rmse:0.951064+0.067588 
## [89] train-rmse:0.209599+0.009988    test-rmse:0.950840+0.067561 
## [90] train-rmse:0.206589+0.009641    test-rmse:0.949321+0.067932 
## [91] train-rmse:0.204024+0.010031    test-rmse:0.948013+0.067387 
## [92] train-rmse:0.200927+0.010162    test-rmse:0.947949+0.067834 
## [93] train-rmse:0.196393+0.010427    test-rmse:0.948707+0.068442 
## [94] train-rmse:0.191632+0.009428    test-rmse:0.948986+0.067256 
## [95] train-rmse:0.189515+0.009809    test-rmse:0.948784+0.066690 
## [96] train-rmse:0.186031+0.009732    test-rmse:0.947824+0.066333 
## [97] train-rmse:0.182569+0.010976    test-rmse:0.947141+0.066421 
## [98] train-rmse:0.179271+0.010768    test-rmse:0.947122+0.066708 
## [99] train-rmse:0.176170+0.010564    test-rmse:0.945979+0.067010 
## [100]    train-rmse:0.173336+0.009943    test-rmse:0.945627+0.066941 
## [101]    train-rmse:0.171031+0.009884    test-rmse:0.945033+0.065803 
## [102]    train-rmse:0.167821+0.009127    test-rmse:0.944770+0.065974 
## [103]    train-rmse:0.166279+0.009325    test-rmse:0.944055+0.065899 
## [104]    train-rmse:0.163829+0.008794    test-rmse:0.943933+0.066073 
## [105]    train-rmse:0.160594+0.009035    test-rmse:0.943215+0.065708 
## [106]    train-rmse:0.157083+0.008873    test-rmse:0.942046+0.066392 
## [107]    train-rmse:0.155350+0.008697    test-rmse:0.942107+0.066315 
## [108]    train-rmse:0.152987+0.008148    test-rmse:0.942049+0.067118 
## [109]    train-rmse:0.151248+0.008072    test-rmse:0.941479+0.067272 
## [110]    train-rmse:0.148503+0.008096    test-rmse:0.941465+0.067153 
## [111]    train-rmse:0.146393+0.007710    test-rmse:0.942001+0.067312 
## [112]    train-rmse:0.144478+0.007137    test-rmse:0.941729+0.067674 
## [113]    train-rmse:0.141384+0.006736    test-rmse:0.940708+0.068074 
## [114]    train-rmse:0.139701+0.007278    test-rmse:0.940304+0.067702 
## [115]    train-rmse:0.137308+0.007917    test-rmse:0.939945+0.067168 
## [116]    train-rmse:0.135491+0.007975    test-rmse:0.939498+0.067183 
## [117]    train-rmse:0.132961+0.008385    test-rmse:0.939971+0.067221 
## [118]    train-rmse:0.131238+0.008770    test-rmse:0.939619+0.066739 
## [119]    train-rmse:0.128662+0.008256    test-rmse:0.938821+0.066616 
## [120]    train-rmse:0.126688+0.007713    test-rmse:0.938715+0.066028 
## [121]    train-rmse:0.124732+0.008149    test-rmse:0.938413+0.066090 
## [122]    train-rmse:0.122794+0.007514    test-rmse:0.938289+0.066502 
## [123]    train-rmse:0.120868+0.007771    test-rmse:0.938068+0.066519 
## [124]    train-rmse:0.118954+0.007331    test-rmse:0.938283+0.066809 
## [125]    train-rmse:0.116902+0.007243    test-rmse:0.938688+0.067606 
## [126]    train-rmse:0.114549+0.006967    test-rmse:0.938296+0.067893 
## [127]    train-rmse:0.112964+0.006967    test-rmse:0.937515+0.068344 
## [128]    train-rmse:0.110326+0.007561    test-rmse:0.937231+0.069054 
## [129]    train-rmse:0.107927+0.007790    test-rmse:0.937262+0.069311 
## [130]    train-rmse:0.105615+0.007133    test-rmse:0.936791+0.069195 
## [131]    train-rmse:0.103837+0.007046    test-rmse:0.936136+0.069934 
## [132]    train-rmse:0.102509+0.006966    test-rmse:0.935823+0.070208 
## [133]    train-rmse:0.100624+0.007213    test-rmse:0.936048+0.070917 
## [134]    train-rmse:0.099285+0.007472    test-rmse:0.935974+0.070482 
## [135]    train-rmse:0.097266+0.007331    test-rmse:0.936151+0.070887 
## [136]    train-rmse:0.095917+0.007413    test-rmse:0.935688+0.070627 
## [137]    train-rmse:0.094385+0.006835    test-rmse:0.936035+0.071373 
## [138]    train-rmse:0.092854+0.006903    test-rmse:0.936022+0.071799 
## [139]    train-rmse:0.090687+0.006540    test-rmse:0.935780+0.071252 
## [140]    train-rmse:0.089187+0.006342    test-rmse:0.935122+0.071058 
## [141]    train-rmse:0.087242+0.006058    test-rmse:0.934905+0.070960 
## [142]    train-rmse:0.086148+0.006245    test-rmse:0.935138+0.071225 
## [143]    train-rmse:0.085209+0.006076    test-rmse:0.934985+0.071102 
## [144]    train-rmse:0.083669+0.006291    test-rmse:0.935065+0.070983 
## [145]    train-rmse:0.082319+0.006690    test-rmse:0.934246+0.070863 
## [146]    train-rmse:0.081055+0.006956    test-rmse:0.934039+0.071296 
## [147]    train-rmse:0.079546+0.006859    test-rmse:0.933397+0.071084 
## [148]    train-rmse:0.078486+0.006871    test-rmse:0.933283+0.071256 
## [149]    train-rmse:0.077336+0.006660    test-rmse:0.932994+0.071304 
## [150]    train-rmse:0.075828+0.006214    test-rmse:0.933303+0.070829 
## [151]    train-rmse:0.074887+0.006422    test-rmse:0.933009+0.070460 
## [152]    train-rmse:0.073920+0.006304    test-rmse:0.932563+0.070759 
## [153]    train-rmse:0.072745+0.005838    test-rmse:0.932781+0.070982 
## [154]    train-rmse:0.072000+0.005618    test-rmse:0.932754+0.071195 
## [155]    train-rmse:0.070959+0.005703    test-rmse:0.932320+0.071734 
## [156]    train-rmse:0.070181+0.005921    test-rmse:0.932130+0.072027 
## [157]    train-rmse:0.068992+0.005962    test-rmse:0.931783+0.071905 
## [158]    train-rmse:0.067984+0.005837    test-rmse:0.931749+0.072101 
## [159]    train-rmse:0.066605+0.005590    test-rmse:0.931989+0.072257 
## [160]    train-rmse:0.065784+0.005616    test-rmse:0.931803+0.072449 
## [161]    train-rmse:0.064979+0.005903    test-rmse:0.931884+0.072663 
## [162]    train-rmse:0.063828+0.005873    test-rmse:0.931680+0.072820 
## [163]    train-rmse:0.062986+0.005890    test-rmse:0.931531+0.072784 
## [164]    train-rmse:0.061695+0.005322    test-rmse:0.931428+0.072697 
## [165]    train-rmse:0.060666+0.004625    test-rmse:0.931611+0.072857 
## [166]    train-rmse:0.059876+0.004659    test-rmse:0.931358+0.072924 
## [167]    train-rmse:0.059156+0.004442    test-rmse:0.931152+0.072842 
## [168]    train-rmse:0.058318+0.004027    test-rmse:0.931293+0.072884 
## [169]    train-rmse:0.057531+0.004162    test-rmse:0.931196+0.072948 
## [170]    train-rmse:0.056268+0.003924    test-rmse:0.931120+0.073218 
## [171]    train-rmse:0.055613+0.003946    test-rmse:0.931107+0.073243 
## [172]    train-rmse:0.054912+0.003969    test-rmse:0.931047+0.073249 
## [173]    train-rmse:0.054211+0.003899    test-rmse:0.931278+0.073377 
## [174]    train-rmse:0.052865+0.003917    test-rmse:0.931578+0.073516 
## [175]    train-rmse:0.052032+0.003701    test-rmse:0.931574+0.073793 
## [176]    train-rmse:0.051454+0.003654    test-rmse:0.931495+0.073863 
## [177]    train-rmse:0.050791+0.003603    test-rmse:0.931251+0.074018 
## [178]    train-rmse:0.050061+0.003892    test-rmse:0.931060+0.074214 
## Stopping. Best iteration:
## [172]    train-rmse:0.054912+0.003969    test-rmse:0.931047+0.073249
## 
## 27 
## [1]  train-rmse:82.780940+0.083029   test-rmse:82.781650+0.410772 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.591332+0.069678   test-rmse:69.591866+0.423700 
## [3]  train-rmse:58.506326+0.058401   test-rmse:58.506664+0.434438 
## [4]  train-rmse:49.190703+0.048890   test-rmse:49.190803+0.443303 
## [5]  train-rmse:41.362720+0.040844   test-rmse:41.362536+0.450558 
## [6]  train-rmse:34.785638+0.034034   test-rmse:34.785128+0.456398 
## [7]  train-rmse:29.260552+0.028262   test-rmse:29.259653+0.460980 
## [8]  train-rmse:24.620358+0.023369   test-rmse:24.619006+0.464415 
## [9]  train-rmse:20.724704+0.019222   test-rmse:20.722832+0.466766 
## [10] train-rmse:17.455756+0.015748   test-rmse:17.453284+0.468046 
## [11] train-rmse:14.714250+0.013047   test-rmse:14.705223+0.464215 
## [12] train-rmse:12.415248+0.010202   test-rmse:12.403659+0.451383 
## [13] train-rmse:10.487754+0.008421   test-rmse:10.475114+0.434470 
## [14] train-rmse:8.871425+0.006820    test-rmse:8.850692+0.423682 
## [15] train-rmse:7.514562+0.004778    test-rmse:7.497542+0.397228 
## [16] train-rmse:6.375011+0.004761    test-rmse:6.363278+0.369040 
## [17] train-rmse:5.420127+0.003752    test-rmse:5.412896+0.337337 
## [18] train-rmse:4.617428+0.002545    test-rmse:4.609104+0.314990 
## [19] train-rmse:3.943628+0.003812    test-rmse:3.946012+0.290654 
## [20] train-rmse:3.375995+0.003474    test-rmse:3.385593+0.276752 
## [21] train-rmse:2.899824+0.001982    test-rmse:2.918255+0.262397 
## [22] train-rmse:2.499791+0.002879    test-rmse:2.540666+0.247461 
## [23] train-rmse:2.162015+0.004656    test-rmse:2.226820+0.230076 
## [24] train-rmse:1.880966+0.004871    test-rmse:1.972173+0.221078 
## [25] train-rmse:1.642736+0.003633    test-rmse:1.763213+0.207939 
## [26] train-rmse:1.445809+0.007201    test-rmse:1.600646+0.196332 
## [27] train-rmse:1.282820+0.009772    test-rmse:1.472293+0.181594 
## [28] train-rmse:1.143423+0.007551    test-rmse:1.371600+0.174375 
## [29] train-rmse:1.025242+0.007844    test-rmse:1.292708+0.159081 
## [30] train-rmse:0.931788+0.009468    test-rmse:1.231903+0.151610 
## [31] train-rmse:0.849289+0.008054    test-rmse:1.184502+0.141219 
## [32] train-rmse:0.779330+0.008863    test-rmse:1.147847+0.137148 
## [33] train-rmse:0.722442+0.007968    test-rmse:1.117241+0.131729 
## [34] train-rmse:0.676253+0.008289    test-rmse:1.096750+0.125778 
## [35] train-rmse:0.634501+0.007375    test-rmse:1.077847+0.113372 
## [36] train-rmse:0.601432+0.006732    test-rmse:1.060590+0.114471 
## [37] train-rmse:0.572735+0.005558    test-rmse:1.048096+0.105075 
## [38] train-rmse:0.545528+0.007041    test-rmse:1.037497+0.105901 
## [39] train-rmse:0.522201+0.007927    test-rmse:1.029370+0.102739 
## [40] train-rmse:0.500440+0.005856    test-rmse:1.020384+0.096330 
## [41] train-rmse:0.478978+0.008473    test-rmse:1.012377+0.092011 
## [42] train-rmse:0.459904+0.010329    test-rmse:1.005350+0.087585 
## [43] train-rmse:0.445771+0.010072    test-rmse:1.003254+0.086854 
## [44] train-rmse:0.434238+0.011647    test-rmse:0.998182+0.087835 
## [45] train-rmse:0.421605+0.015395    test-rmse:0.993052+0.085472 
## [46] train-rmse:0.409913+0.014785    test-rmse:0.990535+0.088418 
## [47] train-rmse:0.398512+0.014597    test-rmse:0.989664+0.087835 
## [48] train-rmse:0.385187+0.016707    test-rmse:0.986277+0.086111 
## [49] train-rmse:0.374177+0.017072    test-rmse:0.984871+0.083713 
## [50] train-rmse:0.365129+0.017207    test-rmse:0.984019+0.084937 
## [51] train-rmse:0.354567+0.017789    test-rmse:0.981638+0.086777 
## [52] train-rmse:0.346384+0.017092    test-rmse:0.979126+0.087189 
## [53] train-rmse:0.335302+0.015465    test-rmse:0.976285+0.086887 
## [54] train-rmse:0.327976+0.015767    test-rmse:0.975134+0.084959 
## [55] train-rmse:0.317668+0.014441    test-rmse:0.972588+0.084506 
## [56] train-rmse:0.309488+0.016097    test-rmse:0.969751+0.085162 
## [57] train-rmse:0.302068+0.015021    test-rmse:0.968193+0.083888 
## [58] train-rmse:0.293167+0.015544    test-rmse:0.965925+0.084357 
## [59] train-rmse:0.287837+0.015726    test-rmse:0.962070+0.084383 
## [60] train-rmse:0.281141+0.013815    test-rmse:0.959818+0.081820 
## [61] train-rmse:0.274744+0.012999    test-rmse:0.956075+0.082053 
## [62] train-rmse:0.267693+0.013764    test-rmse:0.954490+0.083563 
## [63] train-rmse:0.261733+0.013222    test-rmse:0.952891+0.082745 
## [64] train-rmse:0.253448+0.013455    test-rmse:0.950760+0.080389 
## [65] train-rmse:0.247657+0.015282    test-rmse:0.950598+0.080448 
## [66] train-rmse:0.242280+0.016109    test-rmse:0.950746+0.080059 
## [67] train-rmse:0.235533+0.015150    test-rmse:0.949315+0.079382 
## [68] train-rmse:0.231155+0.012735    test-rmse:0.948814+0.078156 
## [69] train-rmse:0.227227+0.011633    test-rmse:0.947314+0.079114 
## [70] train-rmse:0.221396+0.012245    test-rmse:0.947680+0.082136 
## [71] train-rmse:0.216239+0.013171    test-rmse:0.947169+0.081621 
## [72] train-rmse:0.212763+0.013588    test-rmse:0.946509+0.080144 
## [73] train-rmse:0.207642+0.014270    test-rmse:0.945932+0.080361 
## [74] train-rmse:0.201947+0.013597    test-rmse:0.943346+0.080631 
## [75] train-rmse:0.198719+0.013706    test-rmse:0.943283+0.081101 
## [76] train-rmse:0.193781+0.012839    test-rmse:0.943308+0.081385 
## [77] train-rmse:0.189261+0.012050    test-rmse:0.942539+0.081050 
## [78] train-rmse:0.184781+0.011314    test-rmse:0.941594+0.081159 
## [79] train-rmse:0.180778+0.011581    test-rmse:0.941622+0.080966 
## [80] train-rmse:0.177902+0.012398    test-rmse:0.941017+0.081242 
## [81] train-rmse:0.174377+0.012118    test-rmse:0.940262+0.081427 
## [82] train-rmse:0.169915+0.010277    test-rmse:0.939246+0.082434 
## [83] train-rmse:0.166159+0.011079    test-rmse:0.939229+0.083064 
## [84] train-rmse:0.162580+0.011171    test-rmse:0.938381+0.082996 
## [85] train-rmse:0.159440+0.011343    test-rmse:0.937126+0.083138 
## [86] train-rmse:0.155738+0.011340    test-rmse:0.937084+0.082951 
## [87] train-rmse:0.151194+0.010895    test-rmse:0.936594+0.082808 
## [88] train-rmse:0.149077+0.010922    test-rmse:0.935934+0.082721 
## [89] train-rmse:0.146477+0.010716    test-rmse:0.936075+0.082772 
## [90] train-rmse:0.143722+0.009853    test-rmse:0.935549+0.082043 
## [91] train-rmse:0.141019+0.010741    test-rmse:0.935326+0.081990 
## [92] train-rmse:0.136625+0.010118    test-rmse:0.934045+0.081318 
## [93] train-rmse:0.133863+0.010226    test-rmse:0.933749+0.081440 
## [94] train-rmse:0.130860+0.010956    test-rmse:0.933155+0.081178 
## [95] train-rmse:0.127592+0.010681    test-rmse:0.933190+0.080942 
## [96] train-rmse:0.125295+0.010884    test-rmse:0.932880+0.080700 
## [97] train-rmse:0.122890+0.010178    test-rmse:0.932247+0.079956 
## [98] train-rmse:0.121049+0.009917    test-rmse:0.932446+0.079743 
## [99] train-rmse:0.118734+0.009608    test-rmse:0.932337+0.079270 
## [100]    train-rmse:0.116291+0.009837    test-rmse:0.932212+0.078906 
## [101]    train-rmse:0.113426+0.008973    test-rmse:0.931727+0.077937 
## [102]    train-rmse:0.110537+0.008500    test-rmse:0.931582+0.077263 
## [103]    train-rmse:0.108279+0.008546    test-rmse:0.930535+0.076951 
## [104]    train-rmse:0.105892+0.008342    test-rmse:0.930887+0.077378 
## [105]    train-rmse:0.103895+0.008210    test-rmse:0.930271+0.076924 
## [106]    train-rmse:0.101550+0.008413    test-rmse:0.929345+0.076651 
## [107]    train-rmse:0.099834+0.008355    test-rmse:0.929031+0.076756 
## [108]    train-rmse:0.097798+0.008816    test-rmse:0.929122+0.076624 
## [109]    train-rmse:0.095737+0.008414    test-rmse:0.929005+0.076395 
## [110]    train-rmse:0.093869+0.008567    test-rmse:0.928719+0.075868 
## [111]    train-rmse:0.091785+0.009012    test-rmse:0.928701+0.076037 
## [112]    train-rmse:0.090173+0.008577    test-rmse:0.928401+0.075961 
## [113]    train-rmse:0.087927+0.008121    test-rmse:0.928280+0.076229 
## [114]    train-rmse:0.086345+0.007730    test-rmse:0.928267+0.076107 
## [115]    train-rmse:0.085212+0.007884    test-rmse:0.927990+0.076112 
## [116]    train-rmse:0.083652+0.007653    test-rmse:0.927548+0.076053 
## [117]    train-rmse:0.081418+0.007615    test-rmse:0.928155+0.075748 
## [118]    train-rmse:0.079792+0.007116    test-rmse:0.927812+0.075986 
## [119]    train-rmse:0.077583+0.006756    test-rmse:0.927685+0.076142 
## [120]    train-rmse:0.075829+0.007424    test-rmse:0.927629+0.076337 
## [121]    train-rmse:0.074158+0.007288    test-rmse:0.927687+0.076279 
## [122]    train-rmse:0.072730+0.007393    test-rmse:0.927405+0.076173 
## [123]    train-rmse:0.071794+0.007714    test-rmse:0.927495+0.076383 
## [124]    train-rmse:0.069673+0.007052    test-rmse:0.927270+0.076436 
## [125]    train-rmse:0.068397+0.006706    test-rmse:0.927086+0.076179 
## [126]    train-rmse:0.067145+0.006683    test-rmse:0.926934+0.076062 
## [127]    train-rmse:0.065250+0.006260    test-rmse:0.926608+0.076594 
## [128]    train-rmse:0.063962+0.006398    test-rmse:0.926577+0.076585 
## [129]    train-rmse:0.062365+0.005953    test-rmse:0.926142+0.076812 
## [130]    train-rmse:0.060895+0.005965    test-rmse:0.925688+0.077129 
## [131]    train-rmse:0.059125+0.005621    test-rmse:0.925580+0.077253 
## [132]    train-rmse:0.057941+0.005937    test-rmse:0.925618+0.077255 
## [133]    train-rmse:0.056970+0.006132    test-rmse:0.925824+0.077290 
## [134]    train-rmse:0.055815+0.006174    test-rmse:0.925648+0.077181 
## [135]    train-rmse:0.054604+0.005878    test-rmse:0.925632+0.077531 
## [136]    train-rmse:0.053678+0.005866    test-rmse:0.925712+0.077811 
## [137]    train-rmse:0.052638+0.005920    test-rmse:0.925692+0.077734 
## Stopping. Best iteration:
## [131]    train-rmse:0.059125+0.005621    test-rmse:0.925580+0.077253
## 
## 28 
## [1]  train-rmse:80.819420+0.081048   test-rmse:80.820097+0.412709 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:66.333314+0.066366   test-rmse:66.333795+0.426872 
## [3]  train-rmse:54.447817+0.054272   test-rmse:54.448058+0.438320 
## [4]  train-rmse:44.696953+0.044278   test-rmse:44.696904+0.447496 
## [5]  train-rmse:36.698441+0.036026   test-rmse:36.698036+0.454731 
## [6]  train-rmse:30.138679+0.029183   test-rmse:30.137853+0.460281 
## [7]  train-rmse:24.760486+0.023514   test-rmse:24.759153+0.464320 
## [8]  train-rmse:20.352989+0.018825   test-rmse:20.351057+0.466952 
## [9]  train-rmse:16.743348+0.014997   test-rmse:16.740718+0.468195 
## [10] train-rmse:13.788904+0.011956   test-rmse:13.790291+0.447132 
## [11] train-rmse:11.372036+0.009360   test-rmse:11.362891+0.448084 
## [12] train-rmse:9.396648+0.007374    test-rmse:9.384920+0.440194 
## [13] train-rmse:7.783930+0.006672    test-rmse:7.776361+0.421633 
## [14] train-rmse:6.471550+0.006810    test-rmse:6.463241+0.413743 
## [15] train-rmse:5.407175+0.008658    test-rmse:5.393536+0.405405 
## [16] train-rmse:4.548814+0.010388    test-rmse:4.538528+0.392710 
## [17] train-rmse:3.860529+0.012604    test-rmse:3.862296+0.374399 
## [18] train-rmse:3.313258+0.015092    test-rmse:3.316063+0.347203 
## [19] train-rmse:2.882221+0.017704    test-rmse:2.893749+0.318056 
## [20] train-rmse:2.547503+0.020207    test-rmse:2.568695+0.290207 
## [21] train-rmse:2.290470+0.022492    test-rmse:2.322391+0.259012 
## [22] train-rmse:2.094814+0.024025    test-rmse:2.137274+0.220411 
## [23] train-rmse:1.947854+0.025494    test-rmse:2.004964+0.190195 
## [24] train-rmse:1.838455+0.027248    test-rmse:1.905651+0.166674 
## [25] train-rmse:1.757096+0.027760    test-rmse:1.821294+0.143429 
## [26] train-rmse:1.696584+0.028113    test-rmse:1.770602+0.128485 
## [27] train-rmse:1.650527+0.028364    test-rmse:1.729482+0.120977 
## [28] train-rmse:1.615340+0.028383    test-rmse:1.700039+0.112378 
## [29] train-rmse:1.587896+0.028199    test-rmse:1.688420+0.116047 
## [30] train-rmse:1.565568+0.027807    test-rmse:1.666054+0.117322 
## [31] train-rmse:1.547122+0.027484    test-rmse:1.652016+0.116269 
## [32] train-rmse:1.531135+0.027058    test-rmse:1.638994+0.118884 
## [33] train-rmse:1.517224+0.026859    test-rmse:1.619370+0.112322 
## [34] train-rmse:1.505253+0.026678    test-rmse:1.607825+0.109804 
## [35] train-rmse:1.494327+0.026364    test-rmse:1.601272+0.111411 
## [36] train-rmse:1.484097+0.026003    test-rmse:1.596605+0.112956 
## [37] train-rmse:1.474353+0.025642    test-rmse:1.593324+0.111636 
## [38] train-rmse:1.465102+0.025344    test-rmse:1.590608+0.114552 
## [39] train-rmse:1.456425+0.025064    test-rmse:1.581138+0.108603 
## [40] train-rmse:1.448301+0.024959    test-rmse:1.575268+0.109411 
## [41] train-rmse:1.440277+0.024825    test-rmse:1.574786+0.110778 
## [42] train-rmse:1.432654+0.024782    test-rmse:1.571290+0.111167 
## [43] train-rmse:1.425017+0.024854    test-rmse:1.558402+0.103580 
## [44] train-rmse:1.417709+0.024624    test-rmse:1.551703+0.101153 
## [45] train-rmse:1.410434+0.024450    test-rmse:1.549689+0.101221 
## [46] train-rmse:1.403325+0.024333    test-rmse:1.544850+0.101637 
## [47] train-rmse:1.396309+0.023989    test-rmse:1.537829+0.101683 
## [48] train-rmse:1.389529+0.023790    test-rmse:1.534673+0.102257 
## [49] train-rmse:1.382943+0.023553    test-rmse:1.529425+0.107686 
## [50] train-rmse:1.376626+0.023556    test-rmse:1.525265+0.108177 
## [51] train-rmse:1.370334+0.023489    test-rmse:1.518658+0.109323 
## [52] train-rmse:1.364058+0.023509    test-rmse:1.515164+0.109217 
## [53] train-rmse:1.357975+0.023461    test-rmse:1.513241+0.105471 
## [54] train-rmse:1.351936+0.023345    test-rmse:1.502852+0.100343 
## [55] train-rmse:1.346294+0.023163    test-rmse:1.498318+0.098644 
## [56] train-rmse:1.340606+0.022975    test-rmse:1.496994+0.094653 
## [57] train-rmse:1.334935+0.022955    test-rmse:1.493410+0.097001 
## [58] train-rmse:1.329431+0.022874    test-rmse:1.491708+0.097565 
## [59] train-rmse:1.324044+0.022758    test-rmse:1.486477+0.096588 
## [60] train-rmse:1.318822+0.022540    test-rmse:1.486299+0.099572 
## [61] train-rmse:1.313706+0.022323    test-rmse:1.484239+0.098826 
## [62] train-rmse:1.308594+0.022196    test-rmse:1.479689+0.097189 
## [63] train-rmse:1.303591+0.021964    test-rmse:1.476913+0.095993 
## [64] train-rmse:1.298696+0.021833    test-rmse:1.475082+0.095288 
## [65] train-rmse:1.293922+0.021698    test-rmse:1.473592+0.090083 
## [66] train-rmse:1.289161+0.021480    test-rmse:1.469720+0.087414 
## [67] train-rmse:1.284495+0.021435    test-rmse:1.461746+0.086543 
## [68] train-rmse:1.279874+0.021408    test-rmse:1.457666+0.087292 
## [69] train-rmse:1.275315+0.021430    test-rmse:1.452515+0.090614 
## [70] train-rmse:1.270804+0.021486    test-rmse:1.447812+0.092119 
## [71] train-rmse:1.266509+0.021418    test-rmse:1.447670+0.092979 
## [72] train-rmse:1.262210+0.021474    test-rmse:1.447569+0.090176 
## [73] train-rmse:1.257994+0.021509    test-rmse:1.443646+0.089285 
## [74] train-rmse:1.253821+0.021499    test-rmse:1.439496+0.090275 
## [75] train-rmse:1.249751+0.021487    test-rmse:1.437564+0.088107 
## [76] train-rmse:1.245761+0.021489    test-rmse:1.433998+0.088584 
## [77] train-rmse:1.241781+0.021432    test-rmse:1.436426+0.089326 
## [78] train-rmse:1.237887+0.021304    test-rmse:1.431564+0.087903 
## [79] train-rmse:1.234083+0.021254    test-rmse:1.427325+0.089741 
## [80] train-rmse:1.230379+0.021168    test-rmse:1.425535+0.088818 
## [81] train-rmse:1.226634+0.020968    test-rmse:1.423276+0.082490 
## [82] train-rmse:1.222988+0.020914    test-rmse:1.421292+0.083446 
## [83] train-rmse:1.219367+0.020894    test-rmse:1.418282+0.083699 
## [84] train-rmse:1.215814+0.020905    test-rmse:1.415884+0.085850 
## [85] train-rmse:1.212285+0.020969    test-rmse:1.413257+0.084255 
## [86] train-rmse:1.208889+0.021005    test-rmse:1.410699+0.084025 
## [87] train-rmse:1.205487+0.020979    test-rmse:1.406085+0.081296 
## [88] train-rmse:1.202146+0.020968    test-rmse:1.403067+0.080440 
## [89] train-rmse:1.198855+0.020994    test-rmse:1.402469+0.080116 
## [90] train-rmse:1.195610+0.021061    test-rmse:1.399947+0.079028 
## [91] train-rmse:1.192429+0.021097    test-rmse:1.392881+0.082701 
## [92] train-rmse:1.189293+0.021082    test-rmse:1.390753+0.084094 
## [93] train-rmse:1.186235+0.021020    test-rmse:1.389693+0.083500 
## [94] train-rmse:1.183125+0.020995    test-rmse:1.389563+0.086014 
## [95] train-rmse:1.180063+0.020965    test-rmse:1.388023+0.082041 
## [96] train-rmse:1.177113+0.020851    test-rmse:1.386721+0.081080 
## [97] train-rmse:1.174154+0.020807    test-rmse:1.385802+0.080859 
## [98] train-rmse:1.171263+0.020715    test-rmse:1.384249+0.081049 
## [99] train-rmse:1.168406+0.020672    test-rmse:1.380382+0.082252 
## [100]    train-rmse:1.165637+0.020620    test-rmse:1.377891+0.082482 
## [101]    train-rmse:1.162877+0.020591    test-rmse:1.376374+0.082818 
## [102]    train-rmse:1.160148+0.020559    test-rmse:1.374301+0.081598 
## [103]    train-rmse:1.157454+0.020504    test-rmse:1.373070+0.083220 
## [104]    train-rmse:1.154791+0.020477    test-rmse:1.371658+0.082860 
## [105]    train-rmse:1.152174+0.020430    test-rmse:1.369242+0.082320 
## [106]    train-rmse:1.149598+0.020385    test-rmse:1.367959+0.083552 
## [107]    train-rmse:1.147047+0.020294    test-rmse:1.366990+0.081329 
## [108]    train-rmse:1.144479+0.020260    test-rmse:1.363043+0.081556 
## [109]    train-rmse:1.141963+0.020186    test-rmse:1.362069+0.076554 
## [110]    train-rmse:1.139551+0.020136    test-rmse:1.360738+0.075627 
## [111]    train-rmse:1.137110+0.020075    test-rmse:1.357833+0.077776 
## [112]    train-rmse:1.134815+0.020083    test-rmse:1.356667+0.078242 
## [113]    train-rmse:1.132486+0.020072    test-rmse:1.354589+0.077403 
## [114]    train-rmse:1.130174+0.020084    test-rmse:1.352598+0.078310 
## [115]    train-rmse:1.127893+0.020124    test-rmse:1.349676+0.078140 
## [116]    train-rmse:1.125612+0.020173    test-rmse:1.350440+0.080093 
## [117]    train-rmse:1.123381+0.020209    test-rmse:1.347989+0.079742 
## [118]    train-rmse:1.121205+0.020218    test-rmse:1.345390+0.080194 
## [119]    train-rmse:1.119062+0.020218    test-rmse:1.343127+0.082292 
## [120]    train-rmse:1.116940+0.020163    test-rmse:1.343923+0.082253 
## [121]    train-rmse:1.114815+0.020138    test-rmse:1.342579+0.081773 
## [122]    train-rmse:1.112729+0.020121    test-rmse:1.340777+0.082339 
## [123]    train-rmse:1.110654+0.020070    test-rmse:1.339508+0.083831 
## [124]    train-rmse:1.108630+0.019993    test-rmse:1.337538+0.078965 
## [125]    train-rmse:1.106639+0.019917    test-rmse:1.335357+0.080146 
## [126]    train-rmse:1.104651+0.019856    test-rmse:1.334731+0.081058 
## [127]    train-rmse:1.102665+0.019817    test-rmse:1.333143+0.081879 
## [128]    train-rmse:1.100704+0.019741    test-rmse:1.333888+0.080940 
## [129]    train-rmse:1.098747+0.019711    test-rmse:1.332047+0.081876 
## [130]    train-rmse:1.096828+0.019682    test-rmse:1.329197+0.080280 
## [131]    train-rmse:1.094936+0.019631    test-rmse:1.327071+0.079966 
## [132]    train-rmse:1.093077+0.019575    test-rmse:1.326674+0.080080 
## [133]    train-rmse:1.091216+0.019553    test-rmse:1.325367+0.079978 
## [134]    train-rmse:1.089365+0.019517    test-rmse:1.324856+0.080245 
## [135]    train-rmse:1.087566+0.019500    test-rmse:1.322706+0.081814 
## [136]    train-rmse:1.085769+0.019478    test-rmse:1.319284+0.082654 
## [137]    train-rmse:1.083988+0.019459    test-rmse:1.317945+0.082717 
## [138]    train-rmse:1.082240+0.019434    test-rmse:1.316147+0.082685 
## [139]    train-rmse:1.080549+0.019404    test-rmse:1.315063+0.082522 
## [140]    train-rmse:1.078858+0.019352    test-rmse:1.314497+0.079964 
## [141]    train-rmse:1.077207+0.019330    test-rmse:1.312785+0.080264 
## [142]    train-rmse:1.075552+0.019302    test-rmse:1.310996+0.080974 
## [143]    train-rmse:1.073890+0.019270    test-rmse:1.309302+0.080684 
## [144]    train-rmse:1.072259+0.019215    test-rmse:1.305571+0.081810 
## [145]    train-rmse:1.070649+0.019185    test-rmse:1.305113+0.081740 
## [146]    train-rmse:1.069075+0.019147    test-rmse:1.303013+0.082797 
## [147]    train-rmse:1.067487+0.019127    test-rmse:1.303697+0.082874 
## [148]    train-rmse:1.065950+0.019071    test-rmse:1.301934+0.083967 
## [149]    train-rmse:1.064420+0.019008    test-rmse:1.300599+0.085423 
## [150]    train-rmse:1.062912+0.018959    test-rmse:1.299501+0.084569 
## [151]    train-rmse:1.061395+0.018906    test-rmse:1.300883+0.080827 
## [152]    train-rmse:1.059915+0.018835    test-rmse:1.299422+0.080522 
## [153]    train-rmse:1.058456+0.018771    test-rmse:1.298853+0.081536 
## [154]    train-rmse:1.057025+0.018711    test-rmse:1.297204+0.082409 
## [155]    train-rmse:1.055617+0.018642    test-rmse:1.296054+0.083090 
## [156]    train-rmse:1.054222+0.018606    test-rmse:1.295893+0.083611 
## [157]    train-rmse:1.052843+0.018587    test-rmse:1.294966+0.082703 
## [158]    train-rmse:1.051467+0.018548    test-rmse:1.294451+0.082133 
## [159]    train-rmse:1.050104+0.018525    test-rmse:1.293964+0.082069 
## [160]    train-rmse:1.048743+0.018516    test-rmse:1.294028+0.081992 
## [161]    train-rmse:1.047409+0.018511    test-rmse:1.293081+0.082071 
## [162]    train-rmse:1.046078+0.018504    test-rmse:1.295603+0.080377 
## [163]    train-rmse:1.044753+0.018502    test-rmse:1.294148+0.082153 
## [164]    train-rmse:1.043438+0.018502    test-rmse:1.293383+0.084645 
## [165]    train-rmse:1.042136+0.018494    test-rmse:1.291165+0.084866 
## [166]    train-rmse:1.040867+0.018461    test-rmse:1.289719+0.083051 
## [167]    train-rmse:1.039606+0.018428    test-rmse:1.287453+0.083818 
## [168]    train-rmse:1.038358+0.018373    test-rmse:1.285665+0.083761 
## [169]    train-rmse:1.037115+0.018306    test-rmse:1.283221+0.085355 
## [170]    train-rmse:1.035876+0.018240    test-rmse:1.282403+0.085304 
## [171]    train-rmse:1.034665+0.018201    test-rmse:1.281760+0.084448 
## [172]    train-rmse:1.033430+0.018150    test-rmse:1.279929+0.084414 
## [173]    train-rmse:1.032239+0.018087    test-rmse:1.279847+0.085195 
## [174]    train-rmse:1.031071+0.018045    test-rmse:1.277517+0.082968 
## [175]    train-rmse:1.029902+0.017994    test-rmse:1.276141+0.082403 
## [176]    train-rmse:1.028742+0.017958    test-rmse:1.274746+0.083419 
## [177]    train-rmse:1.027568+0.017915    test-rmse:1.274216+0.082999 
## [178]    train-rmse:1.026407+0.017896    test-rmse:1.274369+0.082801 
## [179]    train-rmse:1.025236+0.017880    test-rmse:1.275512+0.082103 
## [180]    train-rmse:1.024092+0.017846    test-rmse:1.274126+0.082668 
## [181]    train-rmse:1.022948+0.017815    test-rmse:1.273634+0.081942 
## [182]    train-rmse:1.021845+0.017797    test-rmse:1.272409+0.082266 
## [183]    train-rmse:1.020757+0.017772    test-rmse:1.271707+0.082146 
## [184]    train-rmse:1.019690+0.017759    test-rmse:1.270126+0.081811 
## [185]    train-rmse:1.018621+0.017744    test-rmse:1.270056+0.081218 
## [186]    train-rmse:1.017552+0.017724    test-rmse:1.269368+0.081122 
## [187]    train-rmse:1.016502+0.017731    test-rmse:1.268198+0.081756 
## [188]    train-rmse:1.015454+0.017734    test-rmse:1.268387+0.081165 
## [189]    train-rmse:1.014388+0.017723    test-rmse:1.267323+0.081021 
## [190]    train-rmse:1.013364+0.017721    test-rmse:1.266783+0.080635 
## [191]    train-rmse:1.012320+0.017751    test-rmse:1.266563+0.083029 
## [192]    train-rmse:1.011282+0.017748    test-rmse:1.267162+0.082334 
## [193]    train-rmse:1.010277+0.017734    test-rmse:1.266666+0.082582 
## [194]    train-rmse:1.009243+0.017697    test-rmse:1.268241+0.082278 
## [195]    train-rmse:1.008231+0.017682    test-rmse:1.267365+0.083784 
## [196]    train-rmse:1.007250+0.017662    test-rmse:1.266486+0.083282 
## [197]    train-rmse:1.006282+0.017645    test-rmse:1.265381+0.083435 
## [198]    train-rmse:1.005322+0.017637    test-rmse:1.263407+0.085016 
## [199]    train-rmse:1.004375+0.017634    test-rmse:1.263821+0.084823 
## [200]    train-rmse:1.003440+0.017621    test-rmse:1.259848+0.082105 
## [201]    train-rmse:1.002488+0.017622    test-rmse:1.258698+0.081647 
## [202]    train-rmse:1.001555+0.017604    test-rmse:1.257934+0.081736 
## [203]    train-rmse:1.000642+0.017612    test-rmse:1.257111+0.082403 
## [204]    train-rmse:0.999677+0.017577    test-rmse:1.258180+0.081197 
## [205]    train-rmse:0.998743+0.017602    test-rmse:1.256911+0.082518 
## [206]    train-rmse:0.997826+0.017612    test-rmse:1.256664+0.082811 
## [207]    train-rmse:0.996906+0.017589    test-rmse:1.256399+0.082390 
## [208]    train-rmse:0.996016+0.017584    test-rmse:1.254893+0.082808 
## [209]    train-rmse:0.995114+0.017583    test-rmse:1.255344+0.082595 
## [210]    train-rmse:0.994212+0.017561    test-rmse:1.253266+0.083121 
## [211]    train-rmse:0.993302+0.017572    test-rmse:1.252878+0.082605 
## [212]    train-rmse:0.992402+0.017579    test-rmse:1.252803+0.082395 
## [213]    train-rmse:0.991529+0.017558    test-rmse:1.252429+0.082276 
## [214]    train-rmse:0.990666+0.017550    test-rmse:1.250863+0.082240 
## [215]    train-rmse:0.989812+0.017551    test-rmse:1.250513+0.082341 
## [216]    train-rmse:0.988965+0.017546    test-rmse:1.251774+0.080690 
## [217]    train-rmse:0.988107+0.017534    test-rmse:1.251808+0.081051 
## [218]    train-rmse:0.987276+0.017525    test-rmse:1.251166+0.080039 
## [219]    train-rmse:0.986434+0.017518    test-rmse:1.250084+0.080141 
## [220]    train-rmse:0.985606+0.017517    test-rmse:1.250809+0.079814 
## [221]    train-rmse:0.984800+0.017516    test-rmse:1.250201+0.080339 
## [222]    train-rmse:0.983976+0.017525    test-rmse:1.251141+0.080560 
## [223]    train-rmse:0.983174+0.017527    test-rmse:1.250247+0.080762 
## [224]    train-rmse:0.982371+0.017539    test-rmse:1.250148+0.080451 
## [225]    train-rmse:0.981573+0.017558    test-rmse:1.249939+0.081184 
## [226]    train-rmse:0.980772+0.017554    test-rmse:1.248006+0.081133 
## [227]    train-rmse:0.979989+0.017558    test-rmse:1.247310+0.081844 
## [228]    train-rmse:0.979186+0.017577    test-rmse:1.246591+0.081956 
## [229]    train-rmse:0.978408+0.017582    test-rmse:1.247351+0.082497 
## [230]    train-rmse:0.977622+0.017593    test-rmse:1.247179+0.082478 
## [231]    train-rmse:0.976855+0.017601    test-rmse:1.246493+0.081152 
## [232]    train-rmse:0.976085+0.017598    test-rmse:1.245641+0.082578 
## [233]    train-rmse:0.975328+0.017610    test-rmse:1.244535+0.082397 
## [234]    train-rmse:0.974579+0.017621    test-rmse:1.244845+0.082014 
## [235]    train-rmse:0.973829+0.017603    test-rmse:1.244406+0.078504 
## [236]    train-rmse:0.973089+0.017590    test-rmse:1.245193+0.078691 
## [237]    train-rmse:0.972358+0.017591    test-rmse:1.244556+0.078293 
## [238]    train-rmse:0.971628+0.017583    test-rmse:1.244632+0.078886 
## [239]    train-rmse:0.970893+0.017582    test-rmse:1.244278+0.078796 
## [240]    train-rmse:0.970164+0.017586    test-rmse:1.243881+0.078785 
## [241]    train-rmse:0.969436+0.017598    test-rmse:1.243677+0.079296 
## [242]    train-rmse:0.968728+0.017599    test-rmse:1.242138+0.079071 
## [243]    train-rmse:0.968007+0.017614    test-rmse:1.243673+0.078304 
## [244]    train-rmse:0.967301+0.017613    test-rmse:1.242960+0.078450 
## [245]    train-rmse:0.966614+0.017612    test-rmse:1.242452+0.078300 
## [246]    train-rmse:0.965930+0.017606    test-rmse:1.241310+0.078359 
## [247]    train-rmse:0.965245+0.017604    test-rmse:1.240663+0.078560 
## [248]    train-rmse:0.964533+0.017624    test-rmse:1.240344+0.079076 
## [249]    train-rmse:0.963844+0.017631    test-rmse:1.239338+0.078757 
## [250]    train-rmse:0.963158+0.017630    test-rmse:1.238943+0.078394 
## [251]    train-rmse:0.962476+0.017629    test-rmse:1.237548+0.078865 
## [252]    train-rmse:0.961818+0.017637    test-rmse:1.236887+0.078704 
## [253]    train-rmse:0.961136+0.017661    test-rmse:1.237217+0.079676 
## [254]    train-rmse:0.960476+0.017668    test-rmse:1.237212+0.079425 
## [255]    train-rmse:0.959803+0.017666    test-rmse:1.237063+0.078770 
## [256]    train-rmse:0.959135+0.017675    test-rmse:1.236906+0.079639 
## [257]    train-rmse:0.958475+0.017681    test-rmse:1.236630+0.079390 
## [258]    train-rmse:0.957819+0.017690    test-rmse:1.235978+0.079718 
## [259]    train-rmse:0.957176+0.017706    test-rmse:1.235338+0.079808 
## [260]    train-rmse:0.956536+0.017716    test-rmse:1.234997+0.079208 
## [261]    train-rmse:0.955888+0.017735    test-rmse:1.234410+0.079456 
## [262]    train-rmse:0.955246+0.017741    test-rmse:1.232956+0.079922 
## [263]    train-rmse:0.954618+0.017752    test-rmse:1.233347+0.080154 
## [264]    train-rmse:0.953997+0.017761    test-rmse:1.232478+0.081622 
## [265]    train-rmse:0.953366+0.017764    test-rmse:1.232535+0.080699 
## [266]    train-rmse:0.952748+0.017767    test-rmse:1.233169+0.080869 
## [267]    train-rmse:0.952134+0.017756    test-rmse:1.230876+0.079836 
## [268]    train-rmse:0.951534+0.017757    test-rmse:1.230997+0.080224 
## [269]    train-rmse:0.950911+0.017773    test-rmse:1.230208+0.079314 
## [270]    train-rmse:0.950312+0.017776    test-rmse:1.231504+0.078680 
## [271]    train-rmse:0.949710+0.017781    test-rmse:1.231447+0.078396 
## [272]    train-rmse:0.949098+0.017786    test-rmse:1.231239+0.078441 
## [273]    train-rmse:0.948491+0.017804    test-rmse:1.230694+0.078088 
## [274]    train-rmse:0.947902+0.017807    test-rmse:1.230967+0.077710 
## [275]    train-rmse:0.947320+0.017807    test-rmse:1.229335+0.077655 
## [276]    train-rmse:0.946741+0.017812    test-rmse:1.229978+0.078358 
## [277]    train-rmse:0.946165+0.017823    test-rmse:1.230931+0.078109 
## [278]    train-rmse:0.945590+0.017835    test-rmse:1.230237+0.077705 
## [279]    train-rmse:0.945015+0.017853    test-rmse:1.229954+0.077780 
## [280]    train-rmse:0.944435+0.017862    test-rmse:1.229792+0.078129 
## [281]    train-rmse:0.943865+0.017873    test-rmse:1.229797+0.077130 
## Stopping. Best iteration:
## [275]    train-rmse:0.947320+0.017807    test-rmse:1.229335+0.077655
## 
## 29 
## [1]  train-rmse:80.819420+0.081048   test-rmse:80.820097+0.412709 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:66.333314+0.066366   test-rmse:66.333795+0.426872 
## [3]  train-rmse:54.447817+0.054272   test-rmse:54.448058+0.438320 
## [4]  train-rmse:44.696953+0.044278   test-rmse:44.696904+0.447496 
## [5]  train-rmse:36.698441+0.036026   test-rmse:36.698036+0.454731 
## [6]  train-rmse:30.138679+0.029183   test-rmse:30.137853+0.460281 
## [7]  train-rmse:24.760486+0.023514   test-rmse:24.759153+0.464320 
## [8]  train-rmse:20.352989+0.018825   test-rmse:20.351057+0.466952 
## [9]  train-rmse:16.743348+0.014997   test-rmse:16.740718+0.468195 
## [10] train-rmse:13.788904+0.011956   test-rmse:13.790291+0.447132 
## [11] train-rmse:11.371515+0.009503   test-rmse:11.356805+0.444507 
## [12] train-rmse:9.393765+0.008274    test-rmse:9.376567+0.422516 
## [13] train-rmse:7.776440+0.007230    test-rmse:7.756271+0.416519 
## [14] train-rmse:6.456077+0.007160    test-rmse:6.440817+0.398866 
## [15] train-rmse:5.380383+0.006929    test-rmse:5.365981+0.377202 
## [16] train-rmse:4.505246+0.007005    test-rmse:4.496731+0.363044 
## [17] train-rmse:3.799239+0.008998    test-rmse:3.791541+0.339049 
## [18] train-rmse:3.231170+0.008353    test-rmse:3.236381+0.318229 
## [19] train-rmse:2.778242+0.010260    test-rmse:2.790408+0.293808 
## [20] train-rmse:2.422526+0.011151    test-rmse:2.448310+0.270822 
## [21] train-rmse:2.143502+0.013600    test-rmse:2.190067+0.241831 
## [22] train-rmse:1.926873+0.013498    test-rmse:2.000454+0.218455 
## [23] train-rmse:1.759794+0.015133    test-rmse:1.855684+0.191838 
## [24] train-rmse:1.633537+0.016386    test-rmse:1.742014+0.170413 
## [25] train-rmse:1.537277+0.015807    test-rmse:1.658970+0.148181 
## [26] train-rmse:1.465359+0.016877    test-rmse:1.600960+0.131365 
## [27] train-rmse:1.410498+0.019096    test-rmse:1.561388+0.124521 
## [28] train-rmse:1.368350+0.018887    test-rmse:1.530568+0.118342 
## [29] train-rmse:1.335620+0.018787    test-rmse:1.507970+0.110262 
## [30] train-rmse:1.307077+0.018452    test-rmse:1.489664+0.114363 
## [31] train-rmse:1.282380+0.020958    test-rmse:1.474761+0.113226 
## [32] train-rmse:1.262075+0.019412    test-rmse:1.456040+0.109626 
## [33] train-rmse:1.245870+0.019934    test-rmse:1.444064+0.106747 
## [34] train-rmse:1.229732+0.020493    test-rmse:1.428477+0.099170 
## [35] train-rmse:1.216175+0.020522    test-rmse:1.423147+0.102855 
## [36] train-rmse:1.202666+0.019298    test-rmse:1.414580+0.100917 
## [37] train-rmse:1.190243+0.019712    test-rmse:1.409708+0.104103 
## [38] train-rmse:1.177915+0.020404    test-rmse:1.401532+0.101467 
## [39] train-rmse:1.164573+0.017203    test-rmse:1.388263+0.095712 
## [40] train-rmse:1.152427+0.019976    test-rmse:1.386518+0.094379 
## [41] train-rmse:1.142055+0.019108    test-rmse:1.372165+0.104250 
## [42] train-rmse:1.132576+0.019976    test-rmse:1.364603+0.102373 
## [43] train-rmse:1.122488+0.020915    test-rmse:1.349007+0.098757 
## [44] train-rmse:1.114000+0.020847    test-rmse:1.343416+0.095915 
## [45] train-rmse:1.104597+0.020957    test-rmse:1.337735+0.093669 
## [46] train-rmse:1.096633+0.021672    test-rmse:1.333783+0.095490 
## [47] train-rmse:1.087612+0.023972    test-rmse:1.329608+0.091441 
## [48] train-rmse:1.078801+0.022523    test-rmse:1.321653+0.093408 
## [49] train-rmse:1.067915+0.021175    test-rmse:1.317700+0.092854 
## [50] train-rmse:1.058315+0.020217    test-rmse:1.312273+0.094140 
## [51] train-rmse:1.051811+0.020075    test-rmse:1.307016+0.091510 
## [52] train-rmse:1.043784+0.019735    test-rmse:1.304154+0.096561 
## [53] train-rmse:1.034812+0.018142    test-rmse:1.297694+0.092919 
## [54] train-rmse:1.028616+0.017927    test-rmse:1.293363+0.094769 
## [55] train-rmse:1.021434+0.017451    test-rmse:1.284945+0.095343 
## [56] train-rmse:1.013891+0.020085    test-rmse:1.277878+0.096196 
## [57] train-rmse:1.006343+0.020226    test-rmse:1.270991+0.096500 
## [58] train-rmse:0.997429+0.019720    test-rmse:1.265085+0.096900 
## [59] train-rmse:0.989553+0.020730    test-rmse:1.261231+0.098497 
## [60] train-rmse:0.983783+0.020585    test-rmse:1.257636+0.099209 
## [61] train-rmse:0.977142+0.020592    test-rmse:1.251446+0.097974 
## [62] train-rmse:0.969507+0.020488    test-rmse:1.251065+0.100026 
## [63] train-rmse:0.962998+0.019845    test-rmse:1.248213+0.098162 
## [64] train-rmse:0.957250+0.020095    test-rmse:1.244875+0.098422 
## [65] train-rmse:0.951907+0.020190    test-rmse:1.241049+0.096492 
## [66] train-rmse:0.946235+0.019051    test-rmse:1.238053+0.096485 
## [67] train-rmse:0.940895+0.019390    test-rmse:1.236706+0.097695 
## [68] train-rmse:0.935259+0.018631    test-rmse:1.231060+0.098891 
## [69] train-rmse:0.930946+0.018281    test-rmse:1.228141+0.098862 
## [70] train-rmse:0.925936+0.018389    test-rmse:1.224865+0.100128 
## [71] train-rmse:0.919019+0.018865    test-rmse:1.222702+0.101989 
## [72] train-rmse:0.912793+0.018007    test-rmse:1.219284+0.101052 
## [73] train-rmse:0.907935+0.018980    test-rmse:1.214205+0.102497 
## [74] train-rmse:0.903312+0.018629    test-rmse:1.211959+0.101647 
## [75] train-rmse:0.898485+0.017526    test-rmse:1.208319+0.102717 
## [76] train-rmse:0.893325+0.018725    test-rmse:1.207450+0.100859 
## [77] train-rmse:0.889268+0.019147    test-rmse:1.205654+0.102516 
## [78] train-rmse:0.885015+0.019911    test-rmse:1.199452+0.105483 
## [79] train-rmse:0.879941+0.019549    test-rmse:1.198607+0.105596 
## [80] train-rmse:0.875694+0.019293    test-rmse:1.196195+0.104259 
## [81] train-rmse:0.871758+0.018850    test-rmse:1.193674+0.104422 
## [82] train-rmse:0.868500+0.018641    test-rmse:1.191318+0.104401 
## [83] train-rmse:0.863275+0.018070    test-rmse:1.186999+0.107497 
## [84] train-rmse:0.858085+0.016671    test-rmse:1.180532+0.107703 
## [85] train-rmse:0.854650+0.016938    test-rmse:1.176461+0.108589 
## [86] train-rmse:0.849637+0.018383    test-rmse:1.175313+0.108811 
## [87] train-rmse:0.844344+0.016981    test-rmse:1.175281+0.109465 
## [88] train-rmse:0.841280+0.016874    test-rmse:1.171637+0.111336 
## [89] train-rmse:0.833947+0.014875    test-rmse:1.170757+0.112345 
## [90] train-rmse:0.830013+0.014528    test-rmse:1.171346+0.113507 
## [91] train-rmse:0.825718+0.012920    test-rmse:1.167931+0.113266 
## [92] train-rmse:0.821700+0.012906    test-rmse:1.167046+0.114409 
## [93] train-rmse:0.818618+0.012997    test-rmse:1.163473+0.113346 
## [94] train-rmse:0.815348+0.013719    test-rmse:1.161275+0.115729 
## [95] train-rmse:0.811808+0.013892    test-rmse:1.158452+0.115980 
## [96] train-rmse:0.807207+0.013233    test-rmse:1.156617+0.117690 
## [97] train-rmse:0.803497+0.012748    test-rmse:1.156831+0.118186 
## [98] train-rmse:0.800543+0.012809    test-rmse:1.156809+0.118081 
## [99] train-rmse:0.796178+0.012350    test-rmse:1.156749+0.118071 
## [100]    train-rmse:0.792385+0.011249    test-rmse:1.156443+0.117051 
## [101]    train-rmse:0.788180+0.013309    test-rmse:1.150682+0.110765 
## [102]    train-rmse:0.784225+0.012418    test-rmse:1.151102+0.111017 
## [103]    train-rmse:0.780134+0.012315    test-rmse:1.147814+0.112509 
## [104]    train-rmse:0.777641+0.012419    test-rmse:1.144133+0.115843 
## [105]    train-rmse:0.774959+0.012570    test-rmse:1.141661+0.115632 
## [106]    train-rmse:0.771955+0.012592    test-rmse:1.141093+0.115042 
## [107]    train-rmse:0.768911+0.011902    test-rmse:1.140223+0.116152 
## [108]    train-rmse:0.765877+0.011538    test-rmse:1.138990+0.116022 
## [109]    train-rmse:0.763242+0.010945    test-rmse:1.137904+0.116690 
## [110]    train-rmse:0.760232+0.010139    test-rmse:1.135302+0.116625 
## [111]    train-rmse:0.757679+0.010076    test-rmse:1.134874+0.117519 
## [112]    train-rmse:0.753971+0.008387    test-rmse:1.130385+0.116618 
## [113]    train-rmse:0.751308+0.008545    test-rmse:1.129973+0.116028 
## [114]    train-rmse:0.747899+0.008111    test-rmse:1.128092+0.115586 
## [115]    train-rmse:0.744269+0.007507    test-rmse:1.126969+0.115510 
## [116]    train-rmse:0.741176+0.007864    test-rmse:1.124745+0.114399 
## [117]    train-rmse:0.738620+0.008445    test-rmse:1.124747+0.113959 
## [118]    train-rmse:0.735817+0.008234    test-rmse:1.123515+0.116137 
## [119]    train-rmse:0.732489+0.008953    test-rmse:1.122639+0.114865 
## [120]    train-rmse:0.730193+0.009115    test-rmse:1.120645+0.117092 
## [121]    train-rmse:0.727667+0.009356    test-rmse:1.120377+0.116596 
## [122]    train-rmse:0.724871+0.009334    test-rmse:1.119690+0.116974 
## [123]    train-rmse:0.723046+0.009417    test-rmse:1.118784+0.118144 
## [124]    train-rmse:0.721130+0.009570    test-rmse:1.117819+0.116360 
## [125]    train-rmse:0.717597+0.010313    test-rmse:1.116696+0.116494 
## [126]    train-rmse:0.715495+0.010403    test-rmse:1.115615+0.116362 
## [127]    train-rmse:0.712977+0.010805    test-rmse:1.115436+0.116053 
## [128]    train-rmse:0.711157+0.010807    test-rmse:1.115753+0.114979 
## [129]    train-rmse:0.707876+0.012151    test-rmse:1.113857+0.114189 
## [130]    train-rmse:0.705744+0.011972    test-rmse:1.113107+0.114851 
## [131]    train-rmse:0.702976+0.010941    test-rmse:1.110749+0.115656 
## [132]    train-rmse:0.698974+0.008108    test-rmse:1.107858+0.115486 
## [133]    train-rmse:0.695377+0.007160    test-rmse:1.107482+0.115563 
## [134]    train-rmse:0.691048+0.007828    test-rmse:1.107360+0.117740 
## [135]    train-rmse:0.687916+0.007138    test-rmse:1.104802+0.114256 
## [136]    train-rmse:0.685799+0.006788    test-rmse:1.103198+0.114710 
## [137]    train-rmse:0.683681+0.007210    test-rmse:1.101242+0.115569 
## [138]    train-rmse:0.680261+0.006022    test-rmse:1.099385+0.114029 
## [139]    train-rmse:0.677618+0.005675    test-rmse:1.097988+0.112928 
## [140]    train-rmse:0.675130+0.006431    test-rmse:1.097453+0.113740 
## [141]    train-rmse:0.673639+0.006603    test-rmse:1.096903+0.114280 
## [142]    train-rmse:0.671677+0.006118    test-rmse:1.095587+0.115541 
## [143]    train-rmse:0.668594+0.006465    test-rmse:1.094012+0.115720 
## [144]    train-rmse:0.666307+0.007311    test-rmse:1.092388+0.116155 
## [145]    train-rmse:0.663933+0.007810    test-rmse:1.091320+0.116133 
## [146]    train-rmse:0.661215+0.008695    test-rmse:1.091509+0.115564 
## [147]    train-rmse:0.658465+0.008363    test-rmse:1.091761+0.114161 
## [148]    train-rmse:0.654842+0.009006    test-rmse:1.086505+0.115693 
## [149]    train-rmse:0.651726+0.010292    test-rmse:1.084320+0.115991 
## [150]    train-rmse:0.649736+0.009979    test-rmse:1.083852+0.116955 
## [151]    train-rmse:0.647561+0.009787    test-rmse:1.083313+0.117182 
## [152]    train-rmse:0.646418+0.009811    test-rmse:1.084019+0.116608 
## [153]    train-rmse:0.644946+0.010021    test-rmse:1.083894+0.116113 
## [154]    train-rmse:0.643289+0.010058    test-rmse:1.082954+0.117661 
## [155]    train-rmse:0.639157+0.006734    test-rmse:1.081165+0.120236 
## [156]    train-rmse:0.637128+0.007601    test-rmse:1.080107+0.119879 
## [157]    train-rmse:0.634707+0.007828    test-rmse:1.077235+0.121006 
## [158]    train-rmse:0.631620+0.006854    test-rmse:1.074600+0.119882 
## [159]    train-rmse:0.629262+0.006860    test-rmse:1.072800+0.119714 
## [160]    train-rmse:0.627257+0.006185    test-rmse:1.070639+0.121301 
## [161]    train-rmse:0.625828+0.005963    test-rmse:1.070526+0.120879 
## [162]    train-rmse:0.623569+0.005666    test-rmse:1.069593+0.121295 
## [163]    train-rmse:0.621556+0.006428    test-rmse:1.069709+0.120312 
## [164]    train-rmse:0.619987+0.006036    test-rmse:1.069426+0.120902 
## [165]    train-rmse:0.618468+0.006390    test-rmse:1.068707+0.121277 
## [166]    train-rmse:0.615222+0.006284    test-rmse:1.068571+0.119911 
## [167]    train-rmse:0.612604+0.006846    test-rmse:1.068948+0.118952 
## [168]    train-rmse:0.610457+0.006539    test-rmse:1.068279+0.118345 
## [169]    train-rmse:0.607239+0.006367    test-rmse:1.068837+0.121227 
## [170]    train-rmse:0.605896+0.006435    test-rmse:1.068065+0.121977 
## [171]    train-rmse:0.602776+0.005501    test-rmse:1.066937+0.119449 
## [172]    train-rmse:0.600779+0.004098    test-rmse:1.065732+0.118691 
## [173]    train-rmse:0.599541+0.004306    test-rmse:1.065580+0.119303 
## [174]    train-rmse:0.598082+0.004103    test-rmse:1.065993+0.117689 
## [175]    train-rmse:0.597047+0.004124    test-rmse:1.065958+0.116863 
## [176]    train-rmse:0.594968+0.004601    test-rmse:1.064611+0.115717 
## [177]    train-rmse:0.592683+0.005655    test-rmse:1.062032+0.116961 
## [178]    train-rmse:0.590248+0.005695    test-rmse:1.062418+0.114783 
## [179]    train-rmse:0.588839+0.005193    test-rmse:1.061416+0.114643 
## [180]    train-rmse:0.587338+0.005596    test-rmse:1.061650+0.114767 
## [181]    train-rmse:0.583578+0.004269    test-rmse:1.058426+0.112814 
## [182]    train-rmse:0.581129+0.004614    test-rmse:1.058847+0.111673 
## [183]    train-rmse:0.579463+0.004815    test-rmse:1.057752+0.111357 
## [184]    train-rmse:0.577799+0.005336    test-rmse:1.056646+0.111324 
## [185]    train-rmse:0.576290+0.005819    test-rmse:1.056545+0.111096 
## [186]    train-rmse:0.574374+0.004287    test-rmse:1.056000+0.110559 
## [187]    train-rmse:0.572551+0.004257    test-rmse:1.056379+0.109954 
## [188]    train-rmse:0.571191+0.004526    test-rmse:1.056008+0.110923 
## [189]    train-rmse:0.568653+0.003280    test-rmse:1.055247+0.110110 
## [190]    train-rmse:0.566710+0.002968    test-rmse:1.054477+0.110172 
## [191]    train-rmse:0.564912+0.003558    test-rmse:1.050996+0.112407 
## [192]    train-rmse:0.563567+0.003751    test-rmse:1.051289+0.111824 
## [193]    train-rmse:0.562259+0.003798    test-rmse:1.050671+0.111754 
## [194]    train-rmse:0.560756+0.003996    test-rmse:1.049932+0.111144 
## [195]    train-rmse:0.559140+0.004368    test-rmse:1.050156+0.110969 
## [196]    train-rmse:0.558103+0.004521    test-rmse:1.049855+0.111798 
## [197]    train-rmse:0.556629+0.003733    test-rmse:1.048797+0.111438 
## [198]    train-rmse:0.554630+0.004551    test-rmse:1.048348+0.111889 
## [199]    train-rmse:0.553224+0.004561    test-rmse:1.048557+0.111321 
## [200]    train-rmse:0.551367+0.004828    test-rmse:1.047598+0.111552 
## [201]    train-rmse:0.549911+0.004993    test-rmse:1.047415+0.112215 
## [202]    train-rmse:0.547711+0.004690    test-rmse:1.046014+0.112562 
## [203]    train-rmse:0.546898+0.004740    test-rmse:1.045144+0.113257 
## [204]    train-rmse:0.545850+0.004774    test-rmse:1.044445+0.113328 
## [205]    train-rmse:0.544668+0.004432    test-rmse:1.044088+0.112851 
## [206]    train-rmse:0.543637+0.004891    test-rmse:1.044697+0.112858 
## [207]    train-rmse:0.542256+0.004635    test-rmse:1.045091+0.112541 
## [208]    train-rmse:0.541427+0.004831    test-rmse:1.044442+0.112601 
## [209]    train-rmse:0.539883+0.004043    test-rmse:1.044454+0.111886 
## [210]    train-rmse:0.537862+0.003808    test-rmse:1.044093+0.111855 
## [211]    train-rmse:0.536470+0.004251    test-rmse:1.044343+0.112645 
## Stopping. Best iteration:
## [205]    train-rmse:0.544668+0.004432    test-rmse:1.044088+0.112851
## 
## 30 
## [1]  train-rmse:80.819420+0.081048   test-rmse:80.820097+0.412709 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:66.333314+0.066366   test-rmse:66.333795+0.426872 
## [3]  train-rmse:54.447817+0.054272   test-rmse:54.448058+0.438320 
## [4]  train-rmse:44.696953+0.044278   test-rmse:44.696904+0.447496 
## [5]  train-rmse:36.698441+0.036026   test-rmse:36.698036+0.454731 
## [6]  train-rmse:30.138679+0.029183   test-rmse:30.137853+0.460281 
## [7]  train-rmse:24.760486+0.023514   test-rmse:24.759153+0.464320 
## [8]  train-rmse:20.352989+0.018825   test-rmse:20.351057+0.466952 
## [9]  train-rmse:16.743348+0.014997   test-rmse:16.740718+0.468195 
## [10] train-rmse:13.788904+0.011956   test-rmse:13.790291+0.447132 
## [11] train-rmse:11.371515+0.009503   test-rmse:11.356805+0.444507 
## [12] train-rmse:9.393542+0.008409    test-rmse:9.381904+0.422309 
## [13] train-rmse:7.774136+0.007840    test-rmse:7.754848+0.407089 
## [14] train-rmse:6.448810+0.007115    test-rmse:6.437024+0.391278 
## [15] train-rmse:5.367030+0.007351    test-rmse:5.358054+0.366154 
## [16] train-rmse:4.484466+0.007099    test-rmse:4.481248+0.342888 
## [17] train-rmse:3.767561+0.007657    test-rmse:3.774301+0.323370 
## [18] train-rmse:3.186880+0.007726    test-rmse:3.198243+0.302540 
## [19] train-rmse:2.719341+0.009974    test-rmse:2.743260+0.287408 
## [20] train-rmse:2.347259+0.011960    test-rmse:2.391610+0.262809 
## [21] train-rmse:2.050320+0.011809    test-rmse:2.115811+0.249164 
## [22] train-rmse:1.815791+0.014440    test-rmse:1.911394+0.223008 
## [23] train-rmse:1.631817+0.010672    test-rmse:1.753333+0.199748 
## [24] train-rmse:1.492226+0.012053    test-rmse:1.636823+0.175206 
## [25] train-rmse:1.386101+0.012831    test-rmse:1.549425+0.156111 
## [26] train-rmse:1.301992+0.016778    test-rmse:1.483831+0.138412 
## [27] train-rmse:1.238505+0.017048    test-rmse:1.437241+0.128334 
## [28] train-rmse:1.188113+0.016401    test-rmse:1.404866+0.119542 
## [29] train-rmse:1.144197+0.012397    test-rmse:1.374383+0.110867 
## [30] train-rmse:1.107346+0.013158    test-rmse:1.350940+0.102683 
## [31] train-rmse:1.079381+0.014076    test-rmse:1.334519+0.095235 
## [32] train-rmse:1.055793+0.014075    test-rmse:1.324199+0.095185 
## [33] train-rmse:1.032947+0.016227    test-rmse:1.306130+0.093103 
## [34] train-rmse:1.011987+0.017515    test-rmse:1.294782+0.089816 
## [35] train-rmse:0.996994+0.017640    test-rmse:1.279958+0.090009 
## [36] train-rmse:0.980671+0.016208    test-rmse:1.270208+0.089004 
## [37] train-rmse:0.966545+0.014685    test-rmse:1.265282+0.086375 
## [38] train-rmse:0.953876+0.014561    test-rmse:1.256064+0.080829 
## [39] train-rmse:0.939666+0.013637    test-rmse:1.244335+0.087703 
## [40] train-rmse:0.927459+0.014629    test-rmse:1.233588+0.089465 
## [41] train-rmse:0.915681+0.014304    test-rmse:1.223565+0.091131 
## [42] train-rmse:0.899832+0.015430    test-rmse:1.211683+0.088697 
## [43] train-rmse:0.887519+0.015718    test-rmse:1.206956+0.086900 
## [44] train-rmse:0.877798+0.015202    test-rmse:1.200334+0.087366 
## [45] train-rmse:0.868638+0.016144    test-rmse:1.197526+0.084919 
## [46] train-rmse:0.861120+0.016375    test-rmse:1.190770+0.085223 
## [47] train-rmse:0.851653+0.016304    test-rmse:1.188952+0.083872 
## [48] train-rmse:0.840720+0.018651    test-rmse:1.182164+0.079005 
## [49] train-rmse:0.832016+0.018725    test-rmse:1.176420+0.080028 
## [50] train-rmse:0.820416+0.019274    test-rmse:1.160586+0.076866 
## [51] train-rmse:0.811085+0.019925    test-rmse:1.158317+0.075410 
## [52] train-rmse:0.803373+0.018826    test-rmse:1.155635+0.076747 
## [53] train-rmse:0.796033+0.019180    test-rmse:1.150632+0.078199 
## [54] train-rmse:0.789014+0.019357    test-rmse:1.147224+0.077472 
## [55] train-rmse:0.780246+0.021704    test-rmse:1.141093+0.079646 
## [56] train-rmse:0.767854+0.021461    test-rmse:1.134028+0.077191 
## [57] train-rmse:0.761535+0.022010    test-rmse:1.132623+0.076081 
## [58] train-rmse:0.755271+0.021610    test-rmse:1.128469+0.074254 
## [59] train-rmse:0.749825+0.021822    test-rmse:1.127107+0.073971 
## [60] train-rmse:0.742708+0.022669    test-rmse:1.123475+0.076306 
## [61] train-rmse:0.733806+0.021121    test-rmse:1.117443+0.074397 
## [62] train-rmse:0.724821+0.020337    test-rmse:1.114643+0.074808 
## [63] train-rmse:0.716636+0.020444    test-rmse:1.109868+0.074328 
## [64] train-rmse:0.710570+0.020960    test-rmse:1.106600+0.074731 
## [65] train-rmse:0.704910+0.021780    test-rmse:1.102600+0.074579 
## [66] train-rmse:0.698029+0.021114    test-rmse:1.096043+0.074996 
## [67] train-rmse:0.691923+0.019500    test-rmse:1.092597+0.072676 
## [68] train-rmse:0.686826+0.017984    test-rmse:1.088286+0.074002 
## [69] train-rmse:0.677756+0.018900    test-rmse:1.086347+0.072441 
## [70] train-rmse:0.671695+0.019002    test-rmse:1.081570+0.074302 
## [71] train-rmse:0.666529+0.019348    test-rmse:1.078479+0.073976 
## [72] train-rmse:0.660946+0.019755    test-rmse:1.075438+0.073687 
## [73] train-rmse:0.655616+0.020640    test-rmse:1.074822+0.073268 
## [74] train-rmse:0.651405+0.021010    test-rmse:1.074245+0.073040 
## [75] train-rmse:0.646682+0.020431    test-rmse:1.068973+0.074769 
## [76] train-rmse:0.640068+0.019041    test-rmse:1.067590+0.074489 
## [77] train-rmse:0.632474+0.020209    test-rmse:1.067506+0.074284 
## [78] train-rmse:0.628656+0.019630    test-rmse:1.063668+0.075995 
## [79] train-rmse:0.624935+0.019655    test-rmse:1.064703+0.074871 
## [80] train-rmse:0.619654+0.019273    test-rmse:1.061819+0.076288 
## [81] train-rmse:0.614319+0.019808    test-rmse:1.059853+0.075973 
## [82] train-rmse:0.609041+0.018623    test-rmse:1.058884+0.073808 
## [83] train-rmse:0.604436+0.017296    test-rmse:1.056298+0.075162 
## [84] train-rmse:0.600444+0.018595    test-rmse:1.053111+0.073351 
## [85] train-rmse:0.594036+0.017758    test-rmse:1.048157+0.070124 
## [86] train-rmse:0.589946+0.018871    test-rmse:1.047318+0.071022 
## [87] train-rmse:0.586492+0.018996    test-rmse:1.047899+0.070193 
## [88] train-rmse:0.582768+0.018426    test-rmse:1.045211+0.069014 
## [89] train-rmse:0.578962+0.017953    test-rmse:1.043718+0.070337 
## [90] train-rmse:0.574808+0.017614    test-rmse:1.042981+0.068064 
## [91] train-rmse:0.570511+0.018527    test-rmse:1.042060+0.068375 
## [92] train-rmse:0.566411+0.018251    test-rmse:1.043105+0.071457 
## [93] train-rmse:0.562329+0.015842    test-rmse:1.041095+0.074297 
## [94] train-rmse:0.557682+0.016614    test-rmse:1.038577+0.074271 
## [95] train-rmse:0.553876+0.016744    test-rmse:1.033714+0.073743 
## [96] train-rmse:0.550316+0.016657    test-rmse:1.031256+0.072371 
## [97] train-rmse:0.544119+0.014703    test-rmse:1.027744+0.070491 
## [98] train-rmse:0.539740+0.014845    test-rmse:1.025594+0.070068 
## [99] train-rmse:0.535436+0.015863    test-rmse:1.024571+0.069542 
## [100]    train-rmse:0.531604+0.016303    test-rmse:1.023528+0.067337 
## [101]    train-rmse:0.526194+0.015607    test-rmse:1.023153+0.063991 
## [102]    train-rmse:0.520114+0.014882    test-rmse:1.023170+0.064803 
## [103]    train-rmse:0.516161+0.016588    test-rmse:1.019835+0.062595 
## [104]    train-rmse:0.511187+0.017390    test-rmse:1.016104+0.063449 
## [105]    train-rmse:0.507552+0.016283    test-rmse:1.015088+0.064233 
## [106]    train-rmse:0.505442+0.016529    test-rmse:1.013184+0.065446 
## [107]    train-rmse:0.502201+0.017129    test-rmse:1.012413+0.065027 
## [108]    train-rmse:0.499381+0.016687    test-rmse:1.013822+0.067684 
## [109]    train-rmse:0.495757+0.016612    test-rmse:1.012760+0.066863 
## [110]    train-rmse:0.492345+0.016479    test-rmse:1.012145+0.068010 
## [111]    train-rmse:0.489902+0.016463    test-rmse:1.010925+0.068334 
## [112]    train-rmse:0.485118+0.014498    test-rmse:1.012061+0.071278 
## [113]    train-rmse:0.480386+0.016232    test-rmse:1.010162+0.069652 
## [114]    train-rmse:0.477827+0.016620    test-rmse:1.008748+0.070597 
## [115]    train-rmse:0.475139+0.016317    test-rmse:1.009314+0.071968 
## [116]    train-rmse:0.470864+0.015780    test-rmse:1.010595+0.073873 
## [117]    train-rmse:0.468289+0.015546    test-rmse:1.009140+0.074677 
## [118]    train-rmse:0.464994+0.017150    test-rmse:1.008187+0.073751 
## [119]    train-rmse:0.461649+0.017127    test-rmse:1.007492+0.072232 
## [120]    train-rmse:0.459160+0.017528    test-rmse:1.007350+0.072818 
## [121]    train-rmse:0.456517+0.015823    test-rmse:1.006481+0.073410 
## [122]    train-rmse:0.453318+0.015728    test-rmse:1.004489+0.074142 
## [123]    train-rmse:0.450352+0.016354    test-rmse:1.002917+0.072859 
## [124]    train-rmse:0.447275+0.016442    test-rmse:1.004445+0.073974 
## [125]    train-rmse:0.444516+0.016073    test-rmse:1.001715+0.071792 
## [126]    train-rmse:0.442427+0.015065    test-rmse:1.000403+0.072721 
## [127]    train-rmse:0.439231+0.015274    test-rmse:1.000069+0.072640 
## [128]    train-rmse:0.436706+0.015028    test-rmse:0.999831+0.072061 
## [129]    train-rmse:0.434106+0.015587    test-rmse:0.998310+0.071845 
## [130]    train-rmse:0.430536+0.016845    test-rmse:0.997747+0.071499 
## [131]    train-rmse:0.428302+0.017041    test-rmse:0.997723+0.072634 
## [132]    train-rmse:0.426000+0.016989    test-rmse:0.996447+0.072685 
## [133]    train-rmse:0.421921+0.016965    test-rmse:0.993571+0.071335 
## [134]    train-rmse:0.419501+0.016885    test-rmse:0.993121+0.069559 
## [135]    train-rmse:0.416680+0.016791    test-rmse:0.992149+0.069976 
## [136]    train-rmse:0.414158+0.017418    test-rmse:0.991345+0.071159 
## [137]    train-rmse:0.412637+0.017388    test-rmse:0.990293+0.071664 
## [138]    train-rmse:0.410666+0.017761    test-rmse:0.988646+0.070956 
## [139]    train-rmse:0.409113+0.017682    test-rmse:0.987086+0.070263 
## [140]    train-rmse:0.405594+0.018204    test-rmse:0.988773+0.071397 
## [141]    train-rmse:0.402999+0.017393    test-rmse:0.987616+0.072087 
## [142]    train-rmse:0.399300+0.017951    test-rmse:0.987005+0.071036 
## [143]    train-rmse:0.396536+0.018013    test-rmse:0.986206+0.069702 
## [144]    train-rmse:0.393347+0.016969    test-rmse:0.986376+0.069786 
## [145]    train-rmse:0.390781+0.017563    test-rmse:0.985586+0.070232 
## [146]    train-rmse:0.388442+0.016527    test-rmse:0.985367+0.071210 
## [147]    train-rmse:0.386497+0.016928    test-rmse:0.985953+0.070239 
## [148]    train-rmse:0.383956+0.017440    test-rmse:0.985753+0.070416 
## [149]    train-rmse:0.382102+0.017457    test-rmse:0.986208+0.070757 
## [150]    train-rmse:0.378994+0.017080    test-rmse:0.984949+0.069436 
## [151]    train-rmse:0.376861+0.017303    test-rmse:0.985219+0.071606 
## [152]    train-rmse:0.373725+0.017192    test-rmse:0.985406+0.072192 
## [153]    train-rmse:0.371129+0.016663    test-rmse:0.984211+0.072903 
## [154]    train-rmse:0.369165+0.017211    test-rmse:0.984527+0.073211 
## [155]    train-rmse:0.366817+0.017593    test-rmse:0.984451+0.073013 
## [156]    train-rmse:0.365072+0.017125    test-rmse:0.983496+0.072297 
## [157]    train-rmse:0.363569+0.017004    test-rmse:0.984256+0.071605 
## [158]    train-rmse:0.361842+0.016905    test-rmse:0.983841+0.071617 
## [159]    train-rmse:0.359014+0.017669    test-rmse:0.982128+0.070996 
## [160]    train-rmse:0.357232+0.017567    test-rmse:0.980797+0.070753 
## [161]    train-rmse:0.354781+0.016943    test-rmse:0.979392+0.070415 
## [162]    train-rmse:0.352207+0.017367    test-rmse:0.979461+0.070634 
## [163]    train-rmse:0.348965+0.016822    test-rmse:0.976901+0.067855 
## [164]    train-rmse:0.347014+0.016426    test-rmse:0.977557+0.066691 
## [165]    train-rmse:0.345467+0.016729    test-rmse:0.978142+0.067503 
## [166]    train-rmse:0.343448+0.015451    test-rmse:0.977375+0.067328 
## [167]    train-rmse:0.340660+0.014957    test-rmse:0.977382+0.067262 
## [168]    train-rmse:0.338934+0.015275    test-rmse:0.977535+0.066952 
## [169]    train-rmse:0.336889+0.015245    test-rmse:0.977835+0.068893 
## Stopping. Best iteration:
## [163]    train-rmse:0.348965+0.016822    test-rmse:0.976901+0.067855
## 
## 31 
## [1]  train-rmse:80.819420+0.081048   test-rmse:80.820097+0.412709 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:66.333314+0.066366   test-rmse:66.333795+0.426872 
## [3]  train-rmse:54.447817+0.054272   test-rmse:54.448058+0.438320 
## [4]  train-rmse:44.696953+0.044278   test-rmse:44.696904+0.447496 
## [5]  train-rmse:36.698441+0.036026   test-rmse:36.698036+0.454731 
## [6]  train-rmse:30.138679+0.029183   test-rmse:30.137853+0.460281 
## [7]  train-rmse:24.760486+0.023514   test-rmse:24.759153+0.464320 
## [8]  train-rmse:20.352989+0.018825   test-rmse:20.351057+0.466952 
## [9]  train-rmse:16.743348+0.014997   test-rmse:16.740718+0.468195 
## [10] train-rmse:13.788904+0.011956   test-rmse:13.790291+0.447132 
## [11] train-rmse:11.371515+0.009503   test-rmse:11.356805+0.444507 
## [12] train-rmse:9.393542+0.008409    test-rmse:9.381904+0.422309 
## [13] train-rmse:7.773823+0.007827    test-rmse:7.757796+0.396597 
## [14] train-rmse:6.446837+0.007422    test-rmse:6.435029+0.387149 
## [15] train-rmse:5.363486+0.004965    test-rmse:5.360439+0.362808 
## [16] train-rmse:4.477504+0.005641    test-rmse:4.478461+0.334759 
## [17] train-rmse:3.754570+0.006462    test-rmse:3.763430+0.309555 
## [18] train-rmse:3.164846+0.006729    test-rmse:3.189729+0.280894 
## [19] train-rmse:2.687296+0.008417    test-rmse:2.719001+0.271051 
## [20] train-rmse:2.303158+0.007993    test-rmse:2.348730+0.251600 
## [21] train-rmse:1.994930+0.007649    test-rmse:2.072259+0.232271 
## [22] train-rmse:1.745704+0.011605    test-rmse:1.844879+0.208659 
## [23] train-rmse:1.548974+0.011111    test-rmse:1.680931+0.193823 
## [24] train-rmse:1.394682+0.009385    test-rmse:1.560479+0.177264 
## [25] train-rmse:1.273841+0.011648    test-rmse:1.466850+0.157179 
## [26] train-rmse:1.180597+0.013476    test-rmse:1.395910+0.139417 
## [27] train-rmse:1.105822+0.016287    test-rmse:1.343702+0.135826 
## [28] train-rmse:1.041784+0.012265    test-rmse:1.305198+0.130898 
## [29] train-rmse:0.988975+0.012476    test-rmse:1.271933+0.120315 
## [30] train-rmse:0.950339+0.013837    test-rmse:1.251354+0.114337 
## [31] train-rmse:0.921127+0.012983    test-rmse:1.225992+0.114610 
## [32] train-rmse:0.892691+0.008973    test-rmse:1.213364+0.112364 
## [33] train-rmse:0.869407+0.010299    test-rmse:1.197881+0.108860 
## [34] train-rmse:0.847251+0.011737    test-rmse:1.185006+0.109741 
## [35] train-rmse:0.825359+0.012348    test-rmse:1.169502+0.105633 
## [36] train-rmse:0.806408+0.010568    test-rmse:1.159816+0.102014 
## [37] train-rmse:0.792171+0.011603    test-rmse:1.151228+0.103510 
## [38] train-rmse:0.778103+0.013725    test-rmse:1.141889+0.098526 
## [39] train-rmse:0.763701+0.012088    test-rmse:1.136403+0.096330 
## [40] train-rmse:0.749319+0.012805    test-rmse:1.127953+0.095664 
## [41] train-rmse:0.738145+0.012141    test-rmse:1.120149+0.096767 
## [42] train-rmse:0.722831+0.014215    test-rmse:1.114422+0.094776 
## [43] train-rmse:0.712837+0.015491    test-rmse:1.107534+0.093152 
## [44] train-rmse:0.702999+0.016642    test-rmse:1.100097+0.089671 
## [45] train-rmse:0.686120+0.015045    test-rmse:1.089840+0.086119 
## [46] train-rmse:0.675066+0.016080    test-rmse:1.085558+0.086057 
## [47] train-rmse:0.664936+0.015499    test-rmse:1.077003+0.082518 
## [48] train-rmse:0.652816+0.017394    test-rmse:1.069411+0.083066 
## [49] train-rmse:0.642633+0.016443    test-rmse:1.060838+0.078411 
## [50] train-rmse:0.632370+0.017122    test-rmse:1.056071+0.079016 
## [51] train-rmse:0.624665+0.017738    test-rmse:1.051077+0.077664 
## [52] train-rmse:0.616183+0.017361    test-rmse:1.048758+0.079697 
## [53] train-rmse:0.609433+0.016532    test-rmse:1.042744+0.078599 
## [54] train-rmse:0.599103+0.016966    test-rmse:1.035975+0.077608 
## [55] train-rmse:0.590569+0.015193    test-rmse:1.032669+0.079929 
## [56] train-rmse:0.582647+0.016117    test-rmse:1.030946+0.082290 
## [57] train-rmse:0.575082+0.016208    test-rmse:1.032445+0.085772 
## [58] train-rmse:0.568436+0.017200    test-rmse:1.029552+0.082664 
## [59] train-rmse:0.561989+0.017202    test-rmse:1.023905+0.077876 
## [60] train-rmse:0.555485+0.015750    test-rmse:1.022451+0.076117 
## [61] train-rmse:0.548729+0.013860    test-rmse:1.022311+0.076254 
## [62] train-rmse:0.541761+0.015301    test-rmse:1.022181+0.076071 
## [63] train-rmse:0.536742+0.015852    test-rmse:1.018957+0.076771 
## [64] train-rmse:0.528852+0.015517    test-rmse:1.016692+0.076175 
## [65] train-rmse:0.523079+0.014999    test-rmse:1.015286+0.077262 
## [66] train-rmse:0.517444+0.014894    test-rmse:1.013351+0.075355 
## [67] train-rmse:0.510309+0.015210    test-rmse:1.012087+0.076337 
## [68] train-rmse:0.501222+0.015979    test-rmse:1.010380+0.077203 
## [69] train-rmse:0.495931+0.016671    test-rmse:1.008078+0.076816 
## [70] train-rmse:0.490235+0.017854    test-rmse:1.007123+0.075794 
## [71] train-rmse:0.485026+0.017816    test-rmse:1.005544+0.075587 
## [72] train-rmse:0.479710+0.018041    test-rmse:1.006659+0.075604 
## [73] train-rmse:0.475315+0.017855    test-rmse:1.004410+0.076868 
## [74] train-rmse:0.470731+0.019395    test-rmse:1.003300+0.076974 
## [75] train-rmse:0.463590+0.020784    test-rmse:0.998799+0.077503 
## [76] train-rmse:0.457741+0.019881    test-rmse:0.997462+0.077110 
## [77] train-rmse:0.452403+0.017854    test-rmse:0.992198+0.074167 
## [78] train-rmse:0.447784+0.016362    test-rmse:0.989730+0.073008 
## [79] train-rmse:0.443247+0.016859    test-rmse:0.989149+0.074384 
## [80] train-rmse:0.439536+0.016739    test-rmse:0.988533+0.074239 
## [81] train-rmse:0.436247+0.016327    test-rmse:0.988317+0.073362 
## [82] train-rmse:0.429171+0.016715    test-rmse:0.986384+0.075412 
## [83] train-rmse:0.424611+0.014981    test-rmse:0.983103+0.076028 
## [84] train-rmse:0.420211+0.014584    test-rmse:0.981474+0.075582 
## [85] train-rmse:0.415772+0.015360    test-rmse:0.980587+0.075565 
## [86] train-rmse:0.412361+0.015134    test-rmse:0.979740+0.075547 
## [87] train-rmse:0.407209+0.014538    test-rmse:0.977452+0.075978 
## [88] train-rmse:0.402688+0.014006    test-rmse:0.979678+0.075598 
## [89] train-rmse:0.397964+0.014335    test-rmse:0.980021+0.075843 
## [90] train-rmse:0.392912+0.013345    test-rmse:0.979747+0.075742 
## [91] train-rmse:0.389395+0.014718    test-rmse:0.977221+0.074738 
## [92] train-rmse:0.385357+0.014473    test-rmse:0.976774+0.074077 
## [93] train-rmse:0.380419+0.013757    test-rmse:0.974191+0.074110 
## [94] train-rmse:0.377601+0.013310    test-rmse:0.973376+0.075174 
## [95] train-rmse:0.374119+0.013560    test-rmse:0.973045+0.075286 
## [96] train-rmse:0.371300+0.014053    test-rmse:0.972824+0.075198 
## [97] train-rmse:0.367068+0.013187    test-rmse:0.971194+0.075003 
## [98] train-rmse:0.361868+0.011776    test-rmse:0.969852+0.075411 
## [99] train-rmse:0.358290+0.011353    test-rmse:0.971318+0.077178 
## [100]    train-rmse:0.353616+0.011549    test-rmse:0.969360+0.076757 
## [101]    train-rmse:0.350435+0.011415    test-rmse:0.968436+0.074853 
## [102]    train-rmse:0.345481+0.013594    test-rmse:0.968312+0.075910 
## [103]    train-rmse:0.341975+0.014176    test-rmse:0.968623+0.073904 
## [104]    train-rmse:0.338914+0.014703    test-rmse:0.967426+0.073543 
## [105]    train-rmse:0.335289+0.014607    test-rmse:0.968297+0.072313 
## [106]    train-rmse:0.331827+0.015046    test-rmse:0.968098+0.072904 
## [107]    train-rmse:0.328113+0.013947    test-rmse:0.967834+0.074005 
## [108]    train-rmse:0.325005+0.013922    test-rmse:0.968214+0.073271 
## [109]    train-rmse:0.321896+0.014704    test-rmse:0.968587+0.073180 
## [110]    train-rmse:0.319035+0.015307    test-rmse:0.966789+0.071733 
## [111]    train-rmse:0.315908+0.016063    test-rmse:0.967595+0.070561 
## [112]    train-rmse:0.312895+0.016862    test-rmse:0.966794+0.070576 
## [113]    train-rmse:0.309628+0.015454    test-rmse:0.966644+0.073083 
## [114]    train-rmse:0.307460+0.015531    test-rmse:0.965846+0.072821 
## [115]    train-rmse:0.303845+0.014273    test-rmse:0.964865+0.072829 
## [116]    train-rmse:0.299324+0.012626    test-rmse:0.964237+0.072142 
## [117]    train-rmse:0.295375+0.010987    test-rmse:0.962991+0.072894 
## [118]    train-rmse:0.290911+0.010497    test-rmse:0.962627+0.072500 
## [119]    train-rmse:0.287661+0.009598    test-rmse:0.961276+0.072658 
## [120]    train-rmse:0.285707+0.009478    test-rmse:0.960917+0.072447 
## [121]    train-rmse:0.283695+0.010102    test-rmse:0.960384+0.071863 
## [122]    train-rmse:0.280617+0.010339    test-rmse:0.960174+0.072231 
## [123]    train-rmse:0.277617+0.010771    test-rmse:0.959509+0.071885 
## [124]    train-rmse:0.275790+0.010796    test-rmse:0.959402+0.072126 
## [125]    train-rmse:0.273856+0.010601    test-rmse:0.959617+0.073112 
## [126]    train-rmse:0.271232+0.010070    test-rmse:0.958938+0.073768 
## [127]    train-rmse:0.268355+0.009319    test-rmse:0.958811+0.073900 
## [128]    train-rmse:0.265899+0.008878    test-rmse:0.957287+0.073584 
## [129]    train-rmse:0.263924+0.008123    test-rmse:0.958188+0.074511 
## [130]    train-rmse:0.262197+0.007557    test-rmse:0.957733+0.074329 
## [131]    train-rmse:0.260798+0.007483    test-rmse:0.957038+0.074176 
## [132]    train-rmse:0.259085+0.007449    test-rmse:0.956692+0.074761 
## [133]    train-rmse:0.256794+0.007351    test-rmse:0.954459+0.072549 
## [134]    train-rmse:0.253898+0.007432    test-rmse:0.954355+0.072096 
## [135]    train-rmse:0.252048+0.007356    test-rmse:0.954530+0.071660 
## [136]    train-rmse:0.249673+0.007879    test-rmse:0.953857+0.071475 
## [137]    train-rmse:0.248043+0.008023    test-rmse:0.953676+0.072379 
## [138]    train-rmse:0.246205+0.008087    test-rmse:0.954153+0.073093 
## [139]    train-rmse:0.243856+0.007326    test-rmse:0.954100+0.072772 
## [140]    train-rmse:0.241698+0.007660    test-rmse:0.953606+0.073412 
## [141]    train-rmse:0.239222+0.007642    test-rmse:0.954291+0.073415 
## [142]    train-rmse:0.236901+0.008193    test-rmse:0.954006+0.073737 
## [143]    train-rmse:0.235332+0.008048    test-rmse:0.953705+0.073633 
## [144]    train-rmse:0.232302+0.007268    test-rmse:0.952542+0.073129 
## [145]    train-rmse:0.230135+0.006439    test-rmse:0.952441+0.072772 
## [146]    train-rmse:0.228450+0.006395    test-rmse:0.951473+0.070922 
## [147]    train-rmse:0.226768+0.006788    test-rmse:0.951795+0.072197 
## [148]    train-rmse:0.225618+0.006618    test-rmse:0.951427+0.072361 
## [149]    train-rmse:0.224207+0.006935    test-rmse:0.951604+0.072078 
## [150]    train-rmse:0.221801+0.007811    test-rmse:0.951641+0.072755 
## [151]    train-rmse:0.218745+0.007975    test-rmse:0.951819+0.072135 
## [152]    train-rmse:0.217115+0.007314    test-rmse:0.951356+0.072437 
## [153]    train-rmse:0.216028+0.007307    test-rmse:0.950963+0.073001 
## [154]    train-rmse:0.213679+0.007505    test-rmse:0.951027+0.073321 
## [155]    train-rmse:0.211297+0.007120    test-rmse:0.950704+0.073605 
## [156]    train-rmse:0.208704+0.006819    test-rmse:0.950475+0.074591 
## [157]    train-rmse:0.207083+0.006812    test-rmse:0.950226+0.074694 
## [158]    train-rmse:0.204638+0.007435    test-rmse:0.949752+0.074291 
## [159]    train-rmse:0.202018+0.005934    test-rmse:0.950013+0.073850 
## [160]    train-rmse:0.200623+0.005998    test-rmse:0.950104+0.073543 
## [161]    train-rmse:0.198693+0.006304    test-rmse:0.949401+0.073063 
## [162]    train-rmse:0.196851+0.006116    test-rmse:0.949066+0.073573 
## [163]    train-rmse:0.194773+0.006552    test-rmse:0.947923+0.072946 
## [164]    train-rmse:0.192944+0.006611    test-rmse:0.948097+0.073356 
## [165]    train-rmse:0.191532+0.007292    test-rmse:0.948208+0.073859 
## [166]    train-rmse:0.189623+0.007215    test-rmse:0.947940+0.072870 
## [167]    train-rmse:0.188161+0.006759    test-rmse:0.947776+0.073240 
## [168]    train-rmse:0.186572+0.007215    test-rmse:0.947728+0.073464 
## [169]    train-rmse:0.184826+0.007208    test-rmse:0.948264+0.073384 
## [170]    train-rmse:0.183646+0.007741    test-rmse:0.948215+0.073149 
## [171]    train-rmse:0.181242+0.008039    test-rmse:0.948099+0.073173 
## [172]    train-rmse:0.179638+0.008601    test-rmse:0.948625+0.073005 
## [173]    train-rmse:0.178358+0.008917    test-rmse:0.948409+0.072298 
## [174]    train-rmse:0.177295+0.009220    test-rmse:0.948222+0.071920 
## Stopping. Best iteration:
## [168]    train-rmse:0.186572+0.007215    test-rmse:0.947728+0.073464
## 
## 32 
## [1]  train-rmse:80.819420+0.081048   test-rmse:80.820097+0.412709 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:66.333314+0.066366   test-rmse:66.333795+0.426872 
## [3]  train-rmse:54.447817+0.054272   test-rmse:54.448058+0.438320 
## [4]  train-rmse:44.696953+0.044278   test-rmse:44.696904+0.447496 
## [5]  train-rmse:36.698441+0.036026   test-rmse:36.698036+0.454731 
## [6]  train-rmse:30.138679+0.029183   test-rmse:30.137853+0.460281 
## [7]  train-rmse:24.760486+0.023514   test-rmse:24.759153+0.464320 
## [8]  train-rmse:20.352989+0.018825   test-rmse:20.351057+0.466952 
## [9]  train-rmse:16.743348+0.014997   test-rmse:16.740718+0.468195 
## [10] train-rmse:13.788904+0.011956   test-rmse:13.790291+0.447132 
## [11] train-rmse:11.371515+0.009503   test-rmse:11.356805+0.444507 
## [12] train-rmse:9.393542+0.008409    test-rmse:9.381904+0.422309 
## [13] train-rmse:7.773669+0.007693    test-rmse:7.756012+0.396089 
## [14] train-rmse:6.445780+0.007520    test-rmse:6.426400+0.378108 
## [15] train-rmse:5.360090+0.006689    test-rmse:5.349834+0.346391 
## [16] train-rmse:4.470425+0.005588    test-rmse:4.472068+0.319541 
## [17] train-rmse:3.745166+0.005033    test-rmse:3.751271+0.302699 
## [18] train-rmse:3.150287+0.002838    test-rmse:3.164235+0.282641 
## [19] train-rmse:2.667812+0.003652    test-rmse:2.686908+0.265602 
## [20] train-rmse:2.273107+0.002879    test-rmse:2.322022+0.238910 
## [21] train-rmse:1.956172+0.006963    test-rmse:2.039410+0.219757 
## [22] train-rmse:1.697738+0.004602    test-rmse:1.807777+0.198578 
## [23] train-rmse:1.488913+0.006462    test-rmse:1.632750+0.181726 
## [24] train-rmse:1.327057+0.009986    test-rmse:1.500673+0.169537 
## [25] train-rmse:1.193992+0.009662    test-rmse:1.407268+0.145727 
## [26] train-rmse:1.092179+0.008992    test-rmse:1.334706+0.130173 
## [27] train-rmse:1.008116+0.009928    test-rmse:1.282571+0.123284 
## [28] train-rmse:0.937455+0.009162    test-rmse:1.244492+0.115596 
## [29] train-rmse:0.883188+0.010385    test-rmse:1.214061+0.106346 
## [30] train-rmse:0.839849+0.008718    test-rmse:1.189425+0.101802 
## [31] train-rmse:0.799488+0.010497    test-rmse:1.167847+0.097386 
## [32] train-rmse:0.765916+0.012686    test-rmse:1.150637+0.096231 
## [33] train-rmse:0.737449+0.014545    test-rmse:1.138562+0.092856 
## [34] train-rmse:0.713997+0.014708    test-rmse:1.123761+0.086504 
## [35] train-rmse:0.692246+0.013755    test-rmse:1.113322+0.084509 
## [36] train-rmse:0.673225+0.013833    test-rmse:1.100233+0.082652 
## [37] train-rmse:0.652670+0.011768    test-rmse:1.091449+0.082318 
## [38] train-rmse:0.635979+0.014143    test-rmse:1.083911+0.081187 
## [39] train-rmse:0.622289+0.013882    test-rmse:1.078142+0.081406 
## [40] train-rmse:0.605206+0.013750    test-rmse:1.073324+0.077567 
## [41] train-rmse:0.593753+0.014143    test-rmse:1.067923+0.078180 
## [42] train-rmse:0.579143+0.014235    test-rmse:1.060188+0.073396 
## [43] train-rmse:0.567767+0.014393    test-rmse:1.056166+0.071562 
## [44] train-rmse:0.556856+0.014763    test-rmse:1.049009+0.073455 
## [45] train-rmse:0.545778+0.014654    test-rmse:1.044560+0.070404 
## [46] train-rmse:0.534792+0.011880    test-rmse:1.042861+0.073159 
## [47] train-rmse:0.524140+0.012325    test-rmse:1.040327+0.072826 
## [48] train-rmse:0.514502+0.012410    test-rmse:1.033843+0.073478 
## [49] train-rmse:0.504174+0.014610    test-rmse:1.030586+0.073545 
## [50] train-rmse:0.497437+0.015398    test-rmse:1.028740+0.072957 
## [51] train-rmse:0.489882+0.015753    test-rmse:1.025313+0.073856 
## [52] train-rmse:0.482291+0.015680    test-rmse:1.022258+0.076853 
## [53] train-rmse:0.473704+0.014892    test-rmse:1.016478+0.075350 
## [54] train-rmse:0.464789+0.012844    test-rmse:1.014333+0.076117 
## [55] train-rmse:0.456411+0.011277    test-rmse:1.011871+0.075033 
## [56] train-rmse:0.450950+0.011097    test-rmse:1.010551+0.073879 
## [57] train-rmse:0.444583+0.011785    test-rmse:1.006401+0.073284 
## [58] train-rmse:0.437559+0.012178    test-rmse:1.005416+0.073884 
## [59] train-rmse:0.430236+0.015076    test-rmse:1.001390+0.072457 
## [60] train-rmse:0.422289+0.014972    test-rmse:1.000009+0.068856 
## [61] train-rmse:0.415228+0.015421    test-rmse:0.998106+0.069464 
## [62] train-rmse:0.408082+0.015486    test-rmse:0.992464+0.065899 
## [63] train-rmse:0.398682+0.014165    test-rmse:0.989383+0.067004 
## [64] train-rmse:0.393983+0.013340    test-rmse:0.990469+0.067389 
## [65] train-rmse:0.387585+0.012976    test-rmse:0.988318+0.068528 
## [66] train-rmse:0.382951+0.013438    test-rmse:0.986119+0.069927 
## [67] train-rmse:0.376417+0.013953    test-rmse:0.987025+0.071328 
## [68] train-rmse:0.372165+0.013832    test-rmse:0.986636+0.072744 
## [69] train-rmse:0.367868+0.013358    test-rmse:0.986449+0.071425 
## [70] train-rmse:0.360010+0.013611    test-rmse:0.985793+0.072548 
## [71] train-rmse:0.355900+0.012764    test-rmse:0.984032+0.071658 
## [72] train-rmse:0.348023+0.013490    test-rmse:0.978078+0.071508 
## [73] train-rmse:0.341930+0.012800    test-rmse:0.977422+0.073146 
## [74] train-rmse:0.335362+0.014225    test-rmse:0.975356+0.073710 
## [75] train-rmse:0.330377+0.015279    test-rmse:0.974149+0.075204 
## [76] train-rmse:0.324460+0.013544    test-rmse:0.972201+0.076625 
## [77] train-rmse:0.320232+0.012779    test-rmse:0.970977+0.075778 
## [78] train-rmse:0.316864+0.012793    test-rmse:0.970672+0.075545 
## [79] train-rmse:0.312165+0.012423    test-rmse:0.970620+0.075616 
## [80] train-rmse:0.307841+0.011789    test-rmse:0.970887+0.075514 
## [81] train-rmse:0.303942+0.011532    test-rmse:0.969708+0.077447 
## [82] train-rmse:0.299892+0.012356    test-rmse:0.968528+0.077671 
## [83] train-rmse:0.296382+0.011756    test-rmse:0.965620+0.076782 
## [84] train-rmse:0.290993+0.012684    test-rmse:0.964614+0.076692 
## [85] train-rmse:0.287632+0.011923    test-rmse:0.963254+0.076136 
## [86] train-rmse:0.283997+0.011737    test-rmse:0.963072+0.076856 
## [87] train-rmse:0.280458+0.011843    test-rmse:0.963312+0.077616 
## [88] train-rmse:0.274668+0.010445    test-rmse:0.961765+0.077722 
## [89] train-rmse:0.270930+0.009762    test-rmse:0.961845+0.077975 
## [90] train-rmse:0.266981+0.009018    test-rmse:0.960558+0.077295 
## [91] train-rmse:0.264830+0.009885    test-rmse:0.958876+0.077404 
## [92] train-rmse:0.260391+0.010531    test-rmse:0.958852+0.078028 
## [93] train-rmse:0.256933+0.011647    test-rmse:0.958091+0.078951 
## [94] train-rmse:0.253517+0.011501    test-rmse:0.957709+0.078480 
## [95] train-rmse:0.248114+0.011582    test-rmse:0.957735+0.079527 
## [96] train-rmse:0.245618+0.011511    test-rmse:0.958081+0.080540 
## [97] train-rmse:0.242688+0.010466    test-rmse:0.957218+0.080256 
## [98] train-rmse:0.238995+0.011882    test-rmse:0.955990+0.079000 
## [99] train-rmse:0.235121+0.014042    test-rmse:0.956379+0.081081 
## [100]    train-rmse:0.231652+0.014700    test-rmse:0.954061+0.082241 
## [101]    train-rmse:0.228739+0.013038    test-rmse:0.952405+0.082070 
## [102]    train-rmse:0.225466+0.012310    test-rmse:0.952519+0.082683 
## [103]    train-rmse:0.220876+0.010407    test-rmse:0.951516+0.083440 
## [104]    train-rmse:0.217393+0.010598    test-rmse:0.949461+0.082133 
## [105]    train-rmse:0.214765+0.010761    test-rmse:0.949073+0.082048 
## [106]    train-rmse:0.210464+0.011796    test-rmse:0.950092+0.083377 
## [107]    train-rmse:0.207709+0.010511    test-rmse:0.950042+0.083656 
## [108]    train-rmse:0.205231+0.009890    test-rmse:0.949369+0.084135 
## [109]    train-rmse:0.203134+0.009735    test-rmse:0.948640+0.083836 
## [110]    train-rmse:0.200271+0.010641    test-rmse:0.948554+0.083627 
## [111]    train-rmse:0.197199+0.011082    test-rmse:0.948528+0.084326 
## [112]    train-rmse:0.194159+0.012224    test-rmse:0.947259+0.083707 
## [113]    train-rmse:0.191727+0.011628    test-rmse:0.946875+0.084216 
## [114]    train-rmse:0.189430+0.011613    test-rmse:0.946748+0.084584 
## [115]    train-rmse:0.187102+0.011457    test-rmse:0.946925+0.084146 
## [116]    train-rmse:0.184461+0.010997    test-rmse:0.946499+0.083401 
## [117]    train-rmse:0.181829+0.010918    test-rmse:0.946074+0.083475 
## [118]    train-rmse:0.178754+0.011708    test-rmse:0.946216+0.083757 
## [119]    train-rmse:0.177015+0.011633    test-rmse:0.946181+0.083858 
## [120]    train-rmse:0.173885+0.010332    test-rmse:0.946398+0.084231 
## [121]    train-rmse:0.171355+0.010830    test-rmse:0.946008+0.084372 
## [122]    train-rmse:0.168853+0.010176    test-rmse:0.945558+0.084651 
## [123]    train-rmse:0.166235+0.010681    test-rmse:0.945920+0.084875 
## [124]    train-rmse:0.164107+0.010447    test-rmse:0.945905+0.085125 
## [125]    train-rmse:0.162123+0.010301    test-rmse:0.945298+0.084709 
## [126]    train-rmse:0.159471+0.010000    test-rmse:0.944167+0.084049 
## [127]    train-rmse:0.156995+0.009685    test-rmse:0.944131+0.083929 
## [128]    train-rmse:0.155154+0.009481    test-rmse:0.944152+0.083885 
## [129]    train-rmse:0.152923+0.009390    test-rmse:0.943819+0.083302 
## [130]    train-rmse:0.150864+0.009154    test-rmse:0.942885+0.083557 
## [131]    train-rmse:0.148271+0.009081    test-rmse:0.942597+0.083743 
## [132]    train-rmse:0.145983+0.009273    test-rmse:0.942603+0.083376 
## [133]    train-rmse:0.143567+0.008557    test-rmse:0.942289+0.082851 
## [134]    train-rmse:0.141948+0.008251    test-rmse:0.941639+0.083262 
## [135]    train-rmse:0.139921+0.008195    test-rmse:0.941361+0.083454 
## [136]    train-rmse:0.137312+0.007574    test-rmse:0.941576+0.083177 
## [137]    train-rmse:0.135708+0.007403    test-rmse:0.941652+0.083405 
## [138]    train-rmse:0.134289+0.006954    test-rmse:0.941503+0.083503 
## [139]    train-rmse:0.132222+0.007451    test-rmse:0.941059+0.083624 
## [140]    train-rmse:0.130162+0.008156    test-rmse:0.940245+0.083731 
## [141]    train-rmse:0.128197+0.008225    test-rmse:0.939988+0.083584 
## [142]    train-rmse:0.126309+0.008265    test-rmse:0.940034+0.083193 
## [143]    train-rmse:0.125315+0.008172    test-rmse:0.940207+0.083146 
## [144]    train-rmse:0.123712+0.008422    test-rmse:0.939812+0.083580 
## [145]    train-rmse:0.122628+0.008540    test-rmse:0.939851+0.083606 
## [146]    train-rmse:0.121246+0.008492    test-rmse:0.940107+0.083937 
## [147]    train-rmse:0.119332+0.008782    test-rmse:0.939678+0.083753 
## [148]    train-rmse:0.117769+0.008652    test-rmse:0.939431+0.083498 
## [149]    train-rmse:0.116410+0.008672    test-rmse:0.939351+0.083406 
## [150]    train-rmse:0.114777+0.008603    test-rmse:0.938938+0.083237 
## [151]    train-rmse:0.112720+0.008183    test-rmse:0.939076+0.082910 
## [152]    train-rmse:0.111180+0.008441    test-rmse:0.938718+0.082600 
## [153]    train-rmse:0.109707+0.008647    test-rmse:0.938748+0.082887 
## [154]    train-rmse:0.108313+0.008486    test-rmse:0.938519+0.083026 
## [155]    train-rmse:0.107190+0.008759    test-rmse:0.938533+0.083339 
## [156]    train-rmse:0.106109+0.008949    test-rmse:0.938534+0.083118 
## [157]    train-rmse:0.104529+0.008144    test-rmse:0.938912+0.083226 
## [158]    train-rmse:0.103278+0.008060    test-rmse:0.938846+0.082998 
## [159]    train-rmse:0.102119+0.008178    test-rmse:0.938986+0.082921 
## [160]    train-rmse:0.100506+0.007689    test-rmse:0.938901+0.082794 
## Stopping. Best iteration:
## [154]    train-rmse:0.108313+0.008486    test-rmse:0.938519+0.083026
## 
## 33 
## [1]  train-rmse:80.819420+0.081048   test-rmse:80.820097+0.412709 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:66.333314+0.066366   test-rmse:66.333795+0.426872 
## [3]  train-rmse:54.447817+0.054272   test-rmse:54.448058+0.438320 
## [4]  train-rmse:44.696953+0.044278   test-rmse:44.696904+0.447496 
## [5]  train-rmse:36.698441+0.036026   test-rmse:36.698036+0.454731 
## [6]  train-rmse:30.138679+0.029183   test-rmse:30.137853+0.460281 
## [7]  train-rmse:24.760486+0.023514   test-rmse:24.759153+0.464320 
## [8]  train-rmse:20.352989+0.018825   test-rmse:20.351057+0.466952 
## [9]  train-rmse:16.743348+0.014997   test-rmse:16.740718+0.468195 
## [10] train-rmse:13.788904+0.011956   test-rmse:13.790291+0.447132 
## [11] train-rmse:11.371515+0.009503   test-rmse:11.356805+0.444507 
## [12] train-rmse:9.393542+0.008409    test-rmse:9.381904+0.422309 
## [13] train-rmse:7.773535+0.007585    test-rmse:7.755639+0.395987 
## [14] train-rmse:6.445268+0.007575    test-rmse:6.428719+0.370497 
## [15] train-rmse:5.359345+0.006554    test-rmse:5.347835+0.338583 
## [16] train-rmse:4.467974+0.005467    test-rmse:4.466988+0.305928 
## [17] train-rmse:3.739305+0.003977    test-rmse:3.748250+0.296789 
## [18] train-rmse:3.140834+0.004315    test-rmse:3.171692+0.271045 
## [19] train-rmse:2.653176+0.004880    test-rmse:2.705858+0.246839 
## [20] train-rmse:2.254940+0.007708    test-rmse:2.320668+0.229771 
## [21] train-rmse:1.928765+0.007854    test-rmse:2.030222+0.208594 
## [22] train-rmse:1.666461+0.010621    test-rmse:1.801403+0.187537 
## [23] train-rmse:1.451935+0.008300    test-rmse:1.619086+0.180809 
## [24] train-rmse:1.276395+0.007753    test-rmse:1.490536+0.165259 
## [25] train-rmse:1.140052+0.009629    test-rmse:1.390468+0.159227 
## [26] train-rmse:1.029220+0.009171    test-rmse:1.318902+0.148053 
## [27] train-rmse:0.934817+0.008710    test-rmse:1.251324+0.144817 
## [28] train-rmse:0.858545+0.010843    test-rmse:1.207491+0.134884 
## [29] train-rmse:0.799191+0.013350    test-rmse:1.176845+0.129329 
## [30] train-rmse:0.752631+0.013990    test-rmse:1.150284+0.121774 
## [31] train-rmse:0.712843+0.011727    test-rmse:1.130602+0.122466 
## [32] train-rmse:0.671918+0.016003    test-rmse:1.112494+0.111020 
## [33] train-rmse:0.638628+0.016305    test-rmse:1.096713+0.106731 
## [34] train-rmse:0.616317+0.018508    test-rmse:1.087265+0.102570 
## [35] train-rmse:0.589914+0.017040    test-rmse:1.076822+0.095331 
## [36] train-rmse:0.570089+0.017263    test-rmse:1.068650+0.097314 
## [37] train-rmse:0.549030+0.018102    test-rmse:1.060712+0.094376 
## [38] train-rmse:0.532026+0.021208    test-rmse:1.057269+0.094151 
## [39] train-rmse:0.515905+0.020746    test-rmse:1.050011+0.095465 
## [40] train-rmse:0.502283+0.021191    test-rmse:1.044343+0.092841 
## [41] train-rmse:0.485029+0.018655    test-rmse:1.038837+0.092682 
## [42] train-rmse:0.472996+0.020129    test-rmse:1.032057+0.090157 
## [43] train-rmse:0.462481+0.021055    test-rmse:1.029289+0.089117 
## [44] train-rmse:0.447982+0.021069    test-rmse:1.027935+0.088943 
## [45] train-rmse:0.437472+0.018474    test-rmse:1.025263+0.089205 
## [46] train-rmse:0.428767+0.017812    test-rmse:1.016618+0.086405 
## [47] train-rmse:0.420707+0.017178    test-rmse:1.009852+0.083725 
## [48] train-rmse:0.407085+0.016045    test-rmse:1.009326+0.083508 
## [49] train-rmse:0.397562+0.018202    test-rmse:1.008506+0.084639 
## [50] train-rmse:0.386453+0.016757    test-rmse:1.007813+0.086171 
## [51] train-rmse:0.378929+0.016922    test-rmse:1.006931+0.085195 
## [52] train-rmse:0.371578+0.019646    test-rmse:1.003286+0.084899 
## [53] train-rmse:0.361735+0.017520    test-rmse:0.999982+0.081632 
## [54] train-rmse:0.354173+0.017196    test-rmse:0.997029+0.083340 
## [55] train-rmse:0.346961+0.015591    test-rmse:0.996972+0.082327 
## [56] train-rmse:0.341174+0.017825    test-rmse:0.995201+0.082659 
## [57] train-rmse:0.332665+0.014414    test-rmse:0.997084+0.084358 
## [58] train-rmse:0.324176+0.014570    test-rmse:0.996939+0.083142 
## [59] train-rmse:0.318277+0.015174    test-rmse:0.995944+0.083192 
## [60] train-rmse:0.310778+0.012026    test-rmse:0.994765+0.085180 
## [61] train-rmse:0.305899+0.011551    test-rmse:0.993150+0.085876 
## [62] train-rmse:0.301081+0.010739    test-rmse:0.991951+0.085896 
## [63] train-rmse:0.294229+0.010933    test-rmse:0.990866+0.087484 
## [64] train-rmse:0.287719+0.011313    test-rmse:0.989077+0.087107 
## [65] train-rmse:0.282239+0.012048    test-rmse:0.987646+0.086777 
## [66] train-rmse:0.275074+0.013133    test-rmse:0.987802+0.089053 
## [67] train-rmse:0.268366+0.013514    test-rmse:0.986646+0.089422 
## [68] train-rmse:0.262883+0.013102    test-rmse:0.987139+0.087995 
## [69] train-rmse:0.258160+0.014030    test-rmse:0.987181+0.087804 
## [70] train-rmse:0.253875+0.013268    test-rmse:0.984902+0.087792 
## [71] train-rmse:0.247591+0.013822    test-rmse:0.985882+0.087580 
## [72] train-rmse:0.243424+0.012270    test-rmse:0.985521+0.086292 
## [73] train-rmse:0.239348+0.013094    test-rmse:0.985155+0.087139 
## [74] train-rmse:0.233011+0.014247    test-rmse:0.983273+0.085813 
## [75] train-rmse:0.229987+0.014111    test-rmse:0.983169+0.086397 
## [76] train-rmse:0.226294+0.012988    test-rmse:0.982464+0.086798 
## [77] train-rmse:0.220911+0.012254    test-rmse:0.982030+0.086038 
## [78] train-rmse:0.217344+0.013603    test-rmse:0.982051+0.086339 
## [79] train-rmse:0.214051+0.012528    test-rmse:0.980997+0.085981 
## [80] train-rmse:0.209766+0.013927    test-rmse:0.980847+0.086357 
## [81] train-rmse:0.206163+0.014329    test-rmse:0.982496+0.086659 
## [82] train-rmse:0.201255+0.013003    test-rmse:0.980785+0.086491 
## [83] train-rmse:0.198117+0.013470    test-rmse:0.981233+0.086651 
## [84] train-rmse:0.195118+0.014056    test-rmse:0.981293+0.087131 
## [85] train-rmse:0.190516+0.013191    test-rmse:0.980023+0.085706 
## [86] train-rmse:0.186892+0.014180    test-rmse:0.980638+0.085424 
## [87] train-rmse:0.182814+0.014126    test-rmse:0.980122+0.085044 
## [88] train-rmse:0.179230+0.013613    test-rmse:0.978767+0.084474 
## [89] train-rmse:0.175679+0.012940    test-rmse:0.977990+0.083629 
## [90] train-rmse:0.172445+0.013278    test-rmse:0.977408+0.083854 
## [91] train-rmse:0.169301+0.012673    test-rmse:0.977037+0.083034 
## [92] train-rmse:0.164155+0.013486    test-rmse:0.976629+0.083784 
## [93] train-rmse:0.160358+0.011498    test-rmse:0.976336+0.083781 
## [94] train-rmse:0.155409+0.010103    test-rmse:0.974879+0.084551 
## [95] train-rmse:0.152609+0.009766    test-rmse:0.975290+0.084460 
## [96] train-rmse:0.150026+0.009975    test-rmse:0.974474+0.084464 
## [97] train-rmse:0.147564+0.010353    test-rmse:0.974447+0.084876 
## [98] train-rmse:0.145572+0.010367    test-rmse:0.974174+0.084990 
## [99] train-rmse:0.143211+0.010347    test-rmse:0.974176+0.085173 
## [100]    train-rmse:0.140425+0.010584    test-rmse:0.973849+0.084855 
## [101]    train-rmse:0.138731+0.010192    test-rmse:0.973361+0.085146 
## [102]    train-rmse:0.135966+0.010075    test-rmse:0.973652+0.085620 
## [103]    train-rmse:0.132527+0.008091    test-rmse:0.972822+0.085956 
## [104]    train-rmse:0.130380+0.008391    test-rmse:0.971980+0.085863 
## [105]    train-rmse:0.126108+0.008393    test-rmse:0.971951+0.086504 
## [106]    train-rmse:0.123494+0.009007    test-rmse:0.971991+0.086730 
## [107]    train-rmse:0.120835+0.008787    test-rmse:0.971458+0.086915 
## [108]    train-rmse:0.118684+0.009409    test-rmse:0.971198+0.086946 
## [109]    train-rmse:0.115621+0.008444    test-rmse:0.969992+0.086191 
## [110]    train-rmse:0.112442+0.006661    test-rmse:0.969065+0.086439 
## [111]    train-rmse:0.110767+0.006149    test-rmse:0.968312+0.086426 
## [112]    train-rmse:0.107483+0.006847    test-rmse:0.967830+0.086699 
## [113]    train-rmse:0.105585+0.006547    test-rmse:0.966961+0.086309 
## [114]    train-rmse:0.103442+0.006519    test-rmse:0.967024+0.086176 
## [115]    train-rmse:0.101389+0.006221    test-rmse:0.967020+0.085835 
## [116]    train-rmse:0.099685+0.006040    test-rmse:0.966952+0.085689 
## [117]    train-rmse:0.098206+0.006345    test-rmse:0.967341+0.086078 
## [118]    train-rmse:0.096494+0.005932    test-rmse:0.966978+0.085509 
## [119]    train-rmse:0.094943+0.005859    test-rmse:0.966961+0.085635 
## [120]    train-rmse:0.093329+0.006010    test-rmse:0.967099+0.085482 
## [121]    train-rmse:0.091625+0.005932    test-rmse:0.967192+0.085313 
## [122]    train-rmse:0.089981+0.005982    test-rmse:0.966805+0.085191 
## [123]    train-rmse:0.088708+0.005686    test-rmse:0.966684+0.085336 
## [124]    train-rmse:0.087160+0.005889    test-rmse:0.966733+0.085121 
## [125]    train-rmse:0.085683+0.006440    test-rmse:0.966417+0.085022 
## [126]    train-rmse:0.084259+0.006469    test-rmse:0.965705+0.085244 
## [127]    train-rmse:0.082300+0.006707    test-rmse:0.965728+0.085551 
## [128]    train-rmse:0.080594+0.006826    test-rmse:0.965317+0.085392 
## [129]    train-rmse:0.079158+0.006627    test-rmse:0.965421+0.085666 
## [130]    train-rmse:0.077572+0.006344    test-rmse:0.965432+0.085970 
## [131]    train-rmse:0.075994+0.006351    test-rmse:0.965163+0.085802 
## [132]    train-rmse:0.074490+0.006218    test-rmse:0.965070+0.085636 
## [133]    train-rmse:0.073135+0.006033    test-rmse:0.964840+0.086345 
## [134]    train-rmse:0.072028+0.006243    test-rmse:0.964851+0.086456 
## [135]    train-rmse:0.070891+0.005838    test-rmse:0.964919+0.086700 
## [136]    train-rmse:0.069712+0.005591    test-rmse:0.964447+0.086062 
## [137]    train-rmse:0.068263+0.005791    test-rmse:0.964357+0.086215 
## [138]    train-rmse:0.067225+0.005574    test-rmse:0.964293+0.086282 
## [139]    train-rmse:0.065658+0.005602    test-rmse:0.964550+0.086527 
## [140]    train-rmse:0.064611+0.005739    test-rmse:0.964618+0.086673 
## [141]    train-rmse:0.063474+0.005817    test-rmse:0.964904+0.087066 
## [142]    train-rmse:0.062158+0.006040    test-rmse:0.965019+0.087240 
## [143]    train-rmse:0.060880+0.005852    test-rmse:0.964915+0.087664 
## [144]    train-rmse:0.060082+0.005643    test-rmse:0.964519+0.087792 
## Stopping. Best iteration:
## [138]    train-rmse:0.067225+0.005574    test-rmse:0.964293+0.086282
## 
## 34 
## [1]  train-rmse:80.819420+0.081048   test-rmse:80.820097+0.412709 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:66.333314+0.066366   test-rmse:66.333795+0.426872 
## [3]  train-rmse:54.447817+0.054272   test-rmse:54.448058+0.438320 
## [4]  train-rmse:44.696953+0.044278   test-rmse:44.696904+0.447496 
## [5]  train-rmse:36.698441+0.036026   test-rmse:36.698036+0.454731 
## [6]  train-rmse:30.138679+0.029183   test-rmse:30.137853+0.460281 
## [7]  train-rmse:24.760486+0.023514   test-rmse:24.759153+0.464320 
## [8]  train-rmse:20.352989+0.018825   test-rmse:20.351057+0.466952 
## [9]  train-rmse:16.743348+0.014997   test-rmse:16.740718+0.468195 
## [10] train-rmse:13.788904+0.011956   test-rmse:13.790291+0.447132 
## [11] train-rmse:11.371515+0.009503   test-rmse:11.356805+0.444507 
## [12] train-rmse:9.393542+0.008409    test-rmse:9.381904+0.422309 
## [13] train-rmse:7.773535+0.007585    test-rmse:7.755639+0.395987 
## [14] train-rmse:6.445014+0.007635    test-rmse:6.432822+0.370739 
## [15] train-rmse:5.359024+0.006186    test-rmse:5.353262+0.340785 
## [16] train-rmse:4.467431+0.004738    test-rmse:4.470474+0.307119 
## [17] train-rmse:3.736851+0.004093    test-rmse:3.748738+0.295580 
## [18] train-rmse:3.135057+0.003928    test-rmse:3.172904+0.269596 
## [19] train-rmse:2.643281+0.002209    test-rmse:2.699248+0.252877 
## [20] train-rmse:2.242118+0.004098    test-rmse:2.316569+0.242920 
## [21] train-rmse:1.915653+0.007624    test-rmse:2.020117+0.226067 
## [22] train-rmse:1.643430+0.008715    test-rmse:1.784824+0.202783 
## [23] train-rmse:1.422283+0.008704    test-rmse:1.601837+0.188423 
## [24] train-rmse:1.242880+0.009529    test-rmse:1.461869+0.172190 
## [25] train-rmse:1.099078+0.008365    test-rmse:1.352903+0.158954 
## [26] train-rmse:0.975534+0.010952    test-rmse:1.271310+0.141467 
## [27] train-rmse:0.876338+0.011461    test-rmse:1.215834+0.133791 
## [28] train-rmse:0.797167+0.013144    test-rmse:1.174435+0.121516 
## [29] train-rmse:0.732709+0.012019    test-rmse:1.138948+0.115968 
## [30] train-rmse:0.679938+0.007179    test-rmse:1.120062+0.108524 
## [31] train-rmse:0.635040+0.009004    test-rmse:1.103248+0.101276 
## [32] train-rmse:0.594089+0.011796    test-rmse:1.091791+0.099154 
## [33] train-rmse:0.562493+0.011293    test-rmse:1.078215+0.098921 
## [34] train-rmse:0.534605+0.014406    test-rmse:1.072696+0.097775 
## [35] train-rmse:0.511712+0.015817    test-rmse:1.061724+0.088869 
## [36] train-rmse:0.493251+0.021040    test-rmse:1.056920+0.088441 
## [37] train-rmse:0.473853+0.020340    test-rmse:1.051905+0.085423 
## [38] train-rmse:0.457637+0.017869    test-rmse:1.046455+0.086130 
## [39] train-rmse:0.442693+0.018019    test-rmse:1.043356+0.085574 
## [40] train-rmse:0.424653+0.017720    test-rmse:1.039530+0.084252 
## [41] train-rmse:0.410108+0.021278    test-rmse:1.035783+0.085916 
## [42] train-rmse:0.393004+0.020310    test-rmse:1.033155+0.087196 
## [43] train-rmse:0.379723+0.019006    test-rmse:1.031714+0.090894 
## [44] train-rmse:0.368132+0.019099    test-rmse:1.032093+0.091237 
## [45] train-rmse:0.356188+0.017625    test-rmse:1.028424+0.092747 
## [46] train-rmse:0.344747+0.017548    test-rmse:1.024932+0.093379 
## [47] train-rmse:0.335513+0.013977    test-rmse:1.022803+0.095222 
## [48] train-rmse:0.325796+0.017098    test-rmse:1.022589+0.093844 
## [49] train-rmse:0.318141+0.018088    test-rmse:1.019855+0.094658 
## [50] train-rmse:0.308925+0.017412    test-rmse:1.016129+0.091360 
## [51] train-rmse:0.299095+0.019786    test-rmse:1.015726+0.091252 
## [52] train-rmse:0.288034+0.017912    test-rmse:1.013814+0.090938 
## [53] train-rmse:0.278204+0.017578    test-rmse:1.009948+0.086163 
## [54] train-rmse:0.273187+0.016303    test-rmse:1.008605+0.086592 
## [55] train-rmse:0.266803+0.016062    test-rmse:1.008120+0.086737 
## [56] train-rmse:0.258796+0.014037    test-rmse:1.006264+0.087406 
## [57] train-rmse:0.251861+0.013964    test-rmse:1.006041+0.086658 
## [58] train-rmse:0.245687+0.014914    test-rmse:1.005825+0.086308 
## [59] train-rmse:0.240009+0.015018    test-rmse:1.005038+0.088506 
## [60] train-rmse:0.231478+0.015980    test-rmse:1.004580+0.088062 
## [61] train-rmse:0.222556+0.013583    test-rmse:1.005519+0.088535 
## [62] train-rmse:0.217745+0.014058    test-rmse:1.003840+0.089202 
## [63] train-rmse:0.211493+0.014192    test-rmse:1.001571+0.088074 
## [64] train-rmse:0.207824+0.013478    test-rmse:1.000684+0.088347 
## [65] train-rmse:0.201122+0.013887    test-rmse:1.000841+0.088433 
## [66] train-rmse:0.193464+0.013519    test-rmse:0.998984+0.088312 
## [67] train-rmse:0.189188+0.013481    test-rmse:0.999916+0.088810 
## [68] train-rmse:0.184970+0.013407    test-rmse:0.999365+0.087544 
## [69] train-rmse:0.180790+0.013667    test-rmse:0.998478+0.088209 
## [70] train-rmse:0.176319+0.013453    test-rmse:0.997590+0.088542 
## [71] train-rmse:0.172514+0.013340    test-rmse:0.998061+0.088757 
## [72] train-rmse:0.169031+0.013013    test-rmse:0.997631+0.088829 
## [73] train-rmse:0.164212+0.012771    test-rmse:0.998001+0.087318 
## [74] train-rmse:0.159197+0.012950    test-rmse:0.998036+0.087210 
## [75] train-rmse:0.155992+0.011903    test-rmse:0.997615+0.086825 
## [76] train-rmse:0.152264+0.011364    test-rmse:0.997036+0.086447 
## [77] train-rmse:0.148919+0.010910    test-rmse:0.996005+0.087218 
## [78] train-rmse:0.145356+0.010966    test-rmse:0.994933+0.087867 
## [79] train-rmse:0.141634+0.010377    test-rmse:0.994047+0.087831 
## [80] train-rmse:0.137900+0.010235    test-rmse:0.992937+0.087508 
## [81] train-rmse:0.134795+0.010341    test-rmse:0.991983+0.088522 
## [82] train-rmse:0.131403+0.009955    test-rmse:0.991912+0.087703 
## [83] train-rmse:0.128776+0.010001    test-rmse:0.991412+0.087451 
## [84] train-rmse:0.124833+0.010165    test-rmse:0.989687+0.086270 
## [85] train-rmse:0.122327+0.010247    test-rmse:0.988826+0.086921 
## [86] train-rmse:0.119437+0.010202    test-rmse:0.987495+0.086203 
## [87] train-rmse:0.115871+0.010585    test-rmse:0.986829+0.086078 
## [88] train-rmse:0.113322+0.010848    test-rmse:0.986569+0.086385 
## [89] train-rmse:0.110479+0.009732    test-rmse:0.986333+0.087235 
## [90] train-rmse:0.107978+0.009517    test-rmse:0.985336+0.086678 
## [91] train-rmse:0.105547+0.009700    test-rmse:0.984614+0.086596 
## [92] train-rmse:0.102162+0.008855    test-rmse:0.984585+0.086468 
## [93] train-rmse:0.099626+0.007917    test-rmse:0.984193+0.086782 
## [94] train-rmse:0.097405+0.007276    test-rmse:0.984278+0.087375 
## [95] train-rmse:0.095467+0.007408    test-rmse:0.983836+0.087534 
## [96] train-rmse:0.093460+0.007525    test-rmse:0.983126+0.087386 
## [97] train-rmse:0.091164+0.007672    test-rmse:0.982775+0.087058 
## [98] train-rmse:0.089548+0.007633    test-rmse:0.982568+0.087086 
## [99] train-rmse:0.086911+0.006722    test-rmse:0.982127+0.087443 
## [100]    train-rmse:0.085053+0.005980    test-rmse:0.981976+0.087354 
## [101]    train-rmse:0.082709+0.006286    test-rmse:0.981226+0.087687 
## [102]    train-rmse:0.081284+0.006660    test-rmse:0.980800+0.087706 
## [103]    train-rmse:0.079442+0.006878    test-rmse:0.980859+0.087586 
## [104]    train-rmse:0.077734+0.006471    test-rmse:0.980335+0.087364 
## [105]    train-rmse:0.075929+0.006488    test-rmse:0.980350+0.087752 
## [106]    train-rmse:0.074316+0.006375    test-rmse:0.980209+0.087806 
## [107]    train-rmse:0.072253+0.005853    test-rmse:0.980025+0.087955 
## [108]    train-rmse:0.069714+0.005644    test-rmse:0.979996+0.088718 
## [109]    train-rmse:0.068503+0.005928    test-rmse:0.979996+0.088602 
## [110]    train-rmse:0.066804+0.006037    test-rmse:0.979990+0.088868 
## [111]    train-rmse:0.065169+0.006147    test-rmse:0.979875+0.089188 
## [112]    train-rmse:0.063082+0.005864    test-rmse:0.979540+0.089245 
## [113]    train-rmse:0.061846+0.005759    test-rmse:0.979269+0.089076 
## [114]    train-rmse:0.060915+0.005724    test-rmse:0.979425+0.089397 
## [115]    train-rmse:0.059302+0.005182    test-rmse:0.979308+0.089123 
## [116]    train-rmse:0.057948+0.005265    test-rmse:0.979098+0.089329 
## [117]    train-rmse:0.056529+0.005265    test-rmse:0.979375+0.089524 
## [118]    train-rmse:0.055301+0.005075    test-rmse:0.979095+0.089328 
## [119]    train-rmse:0.054190+0.004730    test-rmse:0.979253+0.089094 
## [120]    train-rmse:0.053136+0.004809    test-rmse:0.979030+0.088801 
## [121]    train-rmse:0.051722+0.004642    test-rmse:0.978944+0.088794 
## [122]    train-rmse:0.050276+0.004386    test-rmse:0.978906+0.088770 
## [123]    train-rmse:0.049053+0.004250    test-rmse:0.978602+0.088869 
## [124]    train-rmse:0.048130+0.004248    test-rmse:0.978680+0.088607 
## [125]    train-rmse:0.047023+0.003811    test-rmse:0.978582+0.088733 
## [126]    train-rmse:0.046108+0.003951    test-rmse:0.978704+0.089001 
## [127]    train-rmse:0.045285+0.003880    test-rmse:0.978506+0.089057 
## [128]    train-rmse:0.043978+0.003819    test-rmse:0.978194+0.088907 
## [129]    train-rmse:0.042937+0.003858    test-rmse:0.978114+0.089170 
## [130]    train-rmse:0.042107+0.003633    test-rmse:0.978082+0.089319 
## [131]    train-rmse:0.041243+0.003762    test-rmse:0.977866+0.089024 
## [132]    train-rmse:0.040265+0.003697    test-rmse:0.977929+0.088918 
## [133]    train-rmse:0.039247+0.003691    test-rmse:0.977954+0.089042 
## [134]    train-rmse:0.038139+0.003327    test-rmse:0.977849+0.089461 
## [135]    train-rmse:0.037184+0.003071    test-rmse:0.977596+0.089073 
## [136]    train-rmse:0.036465+0.003250    test-rmse:0.977681+0.089240 
## [137]    train-rmse:0.035800+0.003094    test-rmse:0.977554+0.089255 
## [138]    train-rmse:0.034905+0.002970    test-rmse:0.977539+0.089277 
## [139]    train-rmse:0.033803+0.003029    test-rmse:0.977506+0.089054 
## [140]    train-rmse:0.033081+0.003079    test-rmse:0.977473+0.089089 
## [141]    train-rmse:0.032463+0.003104    test-rmse:0.977454+0.089068 
## [142]    train-rmse:0.031481+0.002683    test-rmse:0.977443+0.089145 
## [143]    train-rmse:0.030846+0.002631    test-rmse:0.977436+0.089223 
## [144]    train-rmse:0.030082+0.002397    test-rmse:0.977452+0.089320 
## [145]    train-rmse:0.029382+0.002196    test-rmse:0.977368+0.089388 
## [146]    train-rmse:0.028869+0.002341    test-rmse:0.977350+0.089573 
## [147]    train-rmse:0.028291+0.002336    test-rmse:0.977309+0.089570 
## [148]    train-rmse:0.027506+0.002134    test-rmse:0.977109+0.089607 
## [149]    train-rmse:0.026876+0.001953    test-rmse:0.977025+0.089604 
## [150]    train-rmse:0.026236+0.002077    test-rmse:0.977042+0.089581 
## [151]    train-rmse:0.025800+0.001973    test-rmse:0.977010+0.089621 
## [152]    train-rmse:0.025298+0.001945    test-rmse:0.976951+0.089799 
## [153]    train-rmse:0.024546+0.002074    test-rmse:0.976975+0.089862 
## [154]    train-rmse:0.024094+0.001951    test-rmse:0.976721+0.090002 
## [155]    train-rmse:0.023479+0.001943    test-rmse:0.976734+0.090062 
## [156]    train-rmse:0.023131+0.001914    test-rmse:0.976796+0.090262 
## [157]    train-rmse:0.022529+0.001879    test-rmse:0.976840+0.090321 
## [158]    train-rmse:0.022146+0.001828    test-rmse:0.976834+0.090380 
## [159]    train-rmse:0.021702+0.001773    test-rmse:0.976780+0.090308 
## [160]    train-rmse:0.021148+0.001656    test-rmse:0.976789+0.090402 
## Stopping. Best iteration:
## [154]    train-rmse:0.024094+0.001951    test-rmse:0.976721+0.090002
## 
## 35 
## [1]  train-rmse:78.857910+0.079069   test-rmse:78.858575+0.414633 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:63.153612+0.063143   test-rmse:63.154042+0.429953 
## [3]  train-rmse:50.582123+0.050320   test-rmse:50.582268+0.441991 
## [4]  train-rmse:40.519815+0.039977   test-rmse:40.519597+0.451317 
## [5]  train-rmse:32.467526+0.031623   test-rmse:32.466867+0.458362 
## [6]  train-rmse:26.025803+0.024854   test-rmse:26.024606+0.463429 
## [7]  train-rmse:20.875072+0.019383   test-rmse:20.873221+0.466687 
## [8]  train-rmse:16.759770+0.015015   test-rmse:16.757143+0.468190 
## [9]  train-rmse:13.474406+0.011635   test-rmse:13.476270+0.444762 
## [10] train-rmse:10.853386+0.008865   test-rmse:10.843712+0.445509 
## [11] train-rmse:8.765037+0.007065    test-rmse:8.752229+0.435732 
## [12] train-rmse:7.104254+0.006597    test-rmse:7.093096+0.420411 
## [13] train-rmse:5.789055+0.007417    test-rmse:5.778875+0.409606 
## [14] train-rmse:4.754012+0.010013    test-rmse:4.740514+0.395594 
## [15] train-rmse:3.945171+0.012308    test-rmse:3.941774+0.373329 
## [16] train-rmse:3.318506+0.014985    test-rmse:3.318884+0.343306 
## [17] train-rmse:2.840826+0.018138    test-rmse:2.854816+0.312217 
## [18] train-rmse:2.480765+0.021118    test-rmse:2.507821+0.275452 
## [19] train-rmse:2.213968+0.023683    test-rmse:2.254453+0.233909 
## [20] train-rmse:2.019322+0.025758    test-rmse:2.076611+0.199651 
## [21] train-rmse:1.878894+0.026507    test-rmse:1.945525+0.173039 
## [22] train-rmse:1.777949+0.027535    test-rmse:1.854114+0.153811 
## [23] train-rmse:1.704840+0.029147    test-rmse:1.779650+0.136732 
## [24] train-rmse:1.652708+0.029209    test-rmse:1.733624+0.121661 
## [25] train-rmse:1.613709+0.028814    test-rmse:1.699818+0.116516 
## [26] train-rmse:1.583657+0.028271    test-rmse:1.682481+0.116167 
## [27] train-rmse:1.560006+0.027839    test-rmse:1.660964+0.114434 
## [28] train-rmse:1.540504+0.027517    test-rmse:1.651773+0.116838 
## [29] train-rmse:1.524056+0.027372    test-rmse:1.638167+0.120415 
## [30] train-rmse:1.509581+0.027131    test-rmse:1.625305+0.124114 
## [31] train-rmse:1.496618+0.026863    test-rmse:1.602925+0.114487 
## [32] train-rmse:1.485111+0.026457    test-rmse:1.593056+0.114352 
## [33] train-rmse:1.474473+0.026308    test-rmse:1.589836+0.114537 
## [34] train-rmse:1.464384+0.025995    test-rmse:1.587951+0.111852 
## [35] train-rmse:1.454971+0.025530    test-rmse:1.578757+0.109685 
## [36] train-rmse:1.445767+0.025243    test-rmse:1.566633+0.107842 
## [37] train-rmse:1.436789+0.025018    test-rmse:1.563568+0.110852 
## [38] train-rmse:1.428164+0.024803    test-rmse:1.558981+0.109244 
## [39] train-rmse:1.419869+0.024710    test-rmse:1.556212+0.109430 
## [40] train-rmse:1.411603+0.024622    test-rmse:1.551075+0.107280 
## [41] train-rmse:1.403698+0.024494    test-rmse:1.544982+0.107677 
## [42] train-rmse:1.396103+0.024381    test-rmse:1.536105+0.112232 
## [43] train-rmse:1.388596+0.024306    test-rmse:1.524757+0.107919 
## [44] train-rmse:1.381193+0.024145    test-rmse:1.521287+0.106296 
## [45] train-rmse:1.374201+0.024037    test-rmse:1.519783+0.109538 
## [46] train-rmse:1.367340+0.023899    test-rmse:1.515285+0.109465 
## [47] train-rmse:1.360548+0.023819    test-rmse:1.513337+0.102997 
## [48] train-rmse:1.353837+0.023797    test-rmse:1.509301+0.096041 
## [49] train-rmse:1.347179+0.023615    test-rmse:1.506412+0.094383 
## [50] train-rmse:1.340702+0.023460    test-rmse:1.502285+0.094716 
## [51] train-rmse:1.334351+0.023314    test-rmse:1.499114+0.094216 
## [52] train-rmse:1.328296+0.023038    test-rmse:1.495438+0.094311 
## [53] train-rmse:1.322298+0.022833    test-rmse:1.492477+0.094615 
## [54] train-rmse:1.316389+0.022581    test-rmse:1.487709+0.094640 
## [55] train-rmse:1.310606+0.022282    test-rmse:1.488744+0.098319 
## [56] train-rmse:1.304937+0.022085    test-rmse:1.478738+0.097785 
## [57] train-rmse:1.299313+0.021831    test-rmse:1.473300+0.100140 
## [58] train-rmse:1.293789+0.021749    test-rmse:1.472908+0.099890 
## [59] train-rmse:1.288557+0.021598    test-rmse:1.469126+0.101545 
## [60] train-rmse:1.283530+0.021586    test-rmse:1.465207+0.095793 
## [61] train-rmse:1.278515+0.021639    test-rmse:1.461904+0.096105 
## [62] train-rmse:1.273616+0.021547    test-rmse:1.458796+0.094365 
## [63] train-rmse:1.268710+0.021459    test-rmse:1.459466+0.090707 
## [64] train-rmse:1.263762+0.021228    test-rmse:1.456206+0.089462 
## [65] train-rmse:1.258954+0.021196    test-rmse:1.448790+0.089722 
## [66] train-rmse:1.254187+0.021248    test-rmse:1.444976+0.090907 
## [67] train-rmse:1.249551+0.021285    test-rmse:1.443552+0.092897 
## [68] train-rmse:1.245049+0.021276    test-rmse:1.440593+0.092333 
## [69] train-rmse:1.240665+0.021202    test-rmse:1.436995+0.091778 
## [70] train-rmse:1.236326+0.021181    test-rmse:1.432632+0.091768 
## [71] train-rmse:1.232055+0.021135    test-rmse:1.425860+0.092760 
## [72] train-rmse:1.227891+0.021059    test-rmse:1.424436+0.091433 
## [73] train-rmse:1.223745+0.020986    test-rmse:1.420209+0.091372 
## [74] train-rmse:1.219667+0.020822    test-rmse:1.418307+0.095107 
## [75] train-rmse:1.215599+0.020629    test-rmse:1.410859+0.094437 
## [76] train-rmse:1.211615+0.020661    test-rmse:1.408748+0.093504 
## [77] train-rmse:1.207701+0.020679    test-rmse:1.408080+0.087754 
## [78] train-rmse:1.203889+0.020503    test-rmse:1.405832+0.086759 
## [79] train-rmse:1.200178+0.020483    test-rmse:1.403656+0.086215 
## [80] train-rmse:1.196548+0.020430    test-rmse:1.400120+0.090547 
## [81] train-rmse:1.192994+0.020465    test-rmse:1.394558+0.089774 
## [82] train-rmse:1.189466+0.020546    test-rmse:1.396325+0.088550 
## [83] train-rmse:1.185883+0.020598    test-rmse:1.394230+0.089672 
## [84] train-rmse:1.182465+0.020545    test-rmse:1.391738+0.091224 
## [85] train-rmse:1.179096+0.020515    test-rmse:1.389413+0.091936 
## [86] train-rmse:1.175727+0.020463    test-rmse:1.389518+0.086942 
## [87] train-rmse:1.172476+0.020395    test-rmse:1.387551+0.086641 
## [88] train-rmse:1.169282+0.020324    test-rmse:1.383800+0.087940 
## [89] train-rmse:1.166122+0.020263    test-rmse:1.379868+0.087080 
## [90] train-rmse:1.163045+0.020268    test-rmse:1.378449+0.087090 
## [91] train-rmse:1.159989+0.020273    test-rmse:1.374857+0.088142 
## [92] train-rmse:1.157024+0.020254    test-rmse:1.372173+0.087315 
## [93] train-rmse:1.154086+0.020201    test-rmse:1.367960+0.087447 
## [94] train-rmse:1.151238+0.020073    test-rmse:1.365916+0.085461 
## [95] train-rmse:1.148427+0.019962    test-rmse:1.365131+0.086328 
## [96] train-rmse:1.145638+0.019866    test-rmse:1.364209+0.087090 
## [97] train-rmse:1.142847+0.019761    test-rmse:1.359874+0.086824 
## [98] train-rmse:1.140107+0.019681    test-rmse:1.357831+0.087569 
## [99] train-rmse:1.137409+0.019616    test-rmse:1.356735+0.090039 
## [100]    train-rmse:1.134715+0.019581    test-rmse:1.357023+0.088245 
## [101]    train-rmse:1.132085+0.019573    test-rmse:1.355021+0.089186 
## [102]    train-rmse:1.129469+0.019524    test-rmse:1.353501+0.087680 
## [103]    train-rmse:1.126919+0.019496    test-rmse:1.352443+0.086244 
## [104]    train-rmse:1.124348+0.019440    test-rmse:1.353265+0.082207 
## [105]    train-rmse:1.121784+0.019377    test-rmse:1.351823+0.081171 
## [106]    train-rmse:1.119363+0.019372    test-rmse:1.349077+0.081908 
## [107]    train-rmse:1.116964+0.019306    test-rmse:1.344529+0.082817 
## [108]    train-rmse:1.114524+0.019227    test-rmse:1.342813+0.084257 
## [109]    train-rmse:1.112206+0.019260    test-rmse:1.340808+0.085410 
## [110]    train-rmse:1.109921+0.019237    test-rmse:1.339251+0.084459 
## [111]    train-rmse:1.107599+0.019172    test-rmse:1.338722+0.085357 
## [112]    train-rmse:1.105379+0.019166    test-rmse:1.336174+0.084948 
## [113]    train-rmse:1.103172+0.019151    test-rmse:1.330225+0.088533 
## [114]    train-rmse:1.100995+0.019131    test-rmse:1.329034+0.090433 
## [115]    train-rmse:1.098848+0.019081    test-rmse:1.328149+0.091842 
## [116]    train-rmse:1.096691+0.019053    test-rmse:1.325335+0.091974 
## [117]    train-rmse:1.094588+0.019021    test-rmse:1.322748+0.090786 
## [118]    train-rmse:1.092491+0.019012    test-rmse:1.321281+0.090182 
## [119]    train-rmse:1.090435+0.018989    test-rmse:1.318522+0.089056 
## [120]    train-rmse:1.088418+0.018983    test-rmse:1.317627+0.090163 
## [121]    train-rmse:1.086462+0.018939    test-rmse:1.316622+0.089742 
## [122]    train-rmse:1.084458+0.018870    test-rmse:1.317165+0.086721 
## [123]    train-rmse:1.082520+0.018845    test-rmse:1.316029+0.087195 
## [124]    train-rmse:1.080609+0.018791    test-rmse:1.316322+0.088422 
## [125]    train-rmse:1.078736+0.018731    test-rmse:1.314691+0.088842 
## [126]    train-rmse:1.076875+0.018667    test-rmse:1.312880+0.089090 
## [127]    train-rmse:1.075022+0.018616    test-rmse:1.311541+0.090202 
## [128]    train-rmse:1.073199+0.018550    test-rmse:1.310357+0.087662 
## [129]    train-rmse:1.071384+0.018490    test-rmse:1.309509+0.088076 
## [130]    train-rmse:1.069607+0.018428    test-rmse:1.307846+0.088003 
## [131]    train-rmse:1.067833+0.018367    test-rmse:1.307060+0.087532 
## [132]    train-rmse:1.066089+0.018308    test-rmse:1.306343+0.089877 
## [133]    train-rmse:1.064320+0.018322    test-rmse:1.307475+0.089680 
## [134]    train-rmse:1.062623+0.018300    test-rmse:1.307123+0.090266 
## [135]    train-rmse:1.060940+0.018274    test-rmse:1.305771+0.091949 
## [136]    train-rmse:1.059317+0.018236    test-rmse:1.304332+0.091215 
## [137]    train-rmse:1.057728+0.018185    test-rmse:1.299562+0.092946 
## [138]    train-rmse:1.056142+0.018133    test-rmse:1.297683+0.092370 
## [139]    train-rmse:1.054558+0.018082    test-rmse:1.296185+0.092334 
## [140]    train-rmse:1.052997+0.018039    test-rmse:1.296122+0.093025 
## [141]    train-rmse:1.051467+0.017975    test-rmse:1.294265+0.094365 
## [142]    train-rmse:1.049934+0.017946    test-rmse:1.292512+0.095332 
## [143]    train-rmse:1.048397+0.017904    test-rmse:1.291619+0.093963 
## [144]    train-rmse:1.046899+0.017886    test-rmse:1.290585+0.094623 
## [145]    train-rmse:1.045410+0.017824    test-rmse:1.292104+0.091256 
## [146]    train-rmse:1.043949+0.017771    test-rmse:1.291087+0.090756 
## [147]    train-rmse:1.042501+0.017721    test-rmse:1.289300+0.090302 
## [148]    train-rmse:1.041061+0.017645    test-rmse:1.290050+0.090346 
## [149]    train-rmse:1.039642+0.017613    test-rmse:1.288920+0.090545 
## [150]    train-rmse:1.038249+0.017559    test-rmse:1.287060+0.090779 
## [151]    train-rmse:1.036860+0.017516    test-rmse:1.286235+0.090445 
## [152]    train-rmse:1.035464+0.017487    test-rmse:1.284237+0.091164 
## [153]    train-rmse:1.034093+0.017469    test-rmse:1.282329+0.092811 
## [154]    train-rmse:1.032759+0.017447    test-rmse:1.281839+0.091741 
## [155]    train-rmse:1.031430+0.017416    test-rmse:1.279707+0.091387 
## [156]    train-rmse:1.030104+0.017381    test-rmse:1.279612+0.092673 
## [157]    train-rmse:1.028756+0.017334    test-rmse:1.280115+0.092620 
## [158]    train-rmse:1.027452+0.017297    test-rmse:1.279717+0.092028 
## [159]    train-rmse:1.026167+0.017264    test-rmse:1.279537+0.091498 
## [160]    train-rmse:1.024880+0.017253    test-rmse:1.275555+0.088979 
## [161]    train-rmse:1.023606+0.017229    test-rmse:1.274472+0.090025 
## [162]    train-rmse:1.022386+0.017198    test-rmse:1.273328+0.088927 
## [163]    train-rmse:1.021144+0.017145    test-rmse:1.273637+0.089763 
## [164]    train-rmse:1.019924+0.017113    test-rmse:1.272542+0.089972 
## [165]    train-rmse:1.018679+0.017120    test-rmse:1.271811+0.090075 
## [166]    train-rmse:1.017482+0.017107    test-rmse:1.271031+0.089368 
## [167]    train-rmse:1.016297+0.017091    test-rmse:1.270718+0.090128 
## [168]    train-rmse:1.015130+0.017075    test-rmse:1.271977+0.087996 
## [169]    train-rmse:1.013983+0.017064    test-rmse:1.270701+0.088260 
## [170]    train-rmse:1.012831+0.017071    test-rmse:1.269554+0.088447 
## [171]    train-rmse:1.011703+0.017074    test-rmse:1.267913+0.088060 
## [172]    train-rmse:1.010580+0.017073    test-rmse:1.267580+0.090043 
## [173]    train-rmse:1.009441+0.017080    test-rmse:1.267179+0.089764 
## [174]    train-rmse:1.008334+0.017078    test-rmse:1.267006+0.088880 
## [175]    train-rmse:1.007241+0.017049    test-rmse:1.267221+0.088621 
## [176]    train-rmse:1.006127+0.017044    test-rmse:1.265363+0.089352 
## [177]    train-rmse:1.005072+0.017028    test-rmse:1.265085+0.088800 
## [178]    train-rmse:1.003967+0.017008    test-rmse:1.266202+0.089401 
## [179]    train-rmse:1.002897+0.016981    test-rmse:1.264934+0.089696 
## [180]    train-rmse:1.001850+0.016961    test-rmse:1.264350+0.089863 
## [181]    train-rmse:1.000780+0.016977    test-rmse:1.263263+0.089753 
## [182]    train-rmse:0.999727+0.016971    test-rmse:1.261813+0.090329 
## [183]    train-rmse:0.998670+0.016955    test-rmse:1.262054+0.089752 
## [184]    train-rmse:0.997622+0.016945    test-rmse:1.260338+0.090641 
## [185]    train-rmse:0.996596+0.016942    test-rmse:1.257710+0.091117 
## [186]    train-rmse:0.995565+0.016925    test-rmse:1.257238+0.090489 
## [187]    train-rmse:0.994555+0.016901    test-rmse:1.257199+0.090219 
## [188]    train-rmse:0.993552+0.016912    test-rmse:1.257159+0.090056 
## [189]    train-rmse:0.992537+0.016926    test-rmse:1.255645+0.086078 
## [190]    train-rmse:0.991560+0.016941    test-rmse:1.254704+0.086426 
## [191]    train-rmse:0.990597+0.016942    test-rmse:1.254024+0.086520 
## [192]    train-rmse:0.989629+0.016965    test-rmse:1.254425+0.088307 
## [193]    train-rmse:0.988676+0.016975    test-rmse:1.252924+0.089989 
## [194]    train-rmse:0.987726+0.016982    test-rmse:1.251759+0.090992 
## [195]    train-rmse:0.986795+0.016978    test-rmse:1.251618+0.090854 
## [196]    train-rmse:0.985863+0.016961    test-rmse:1.250855+0.090769 
## [197]    train-rmse:0.984957+0.016965    test-rmse:1.251553+0.090274 
## [198]    train-rmse:0.984028+0.016972    test-rmse:1.252462+0.089975 
## [199]    train-rmse:0.983106+0.016980    test-rmse:1.251505+0.090653 
## [200]    train-rmse:0.982217+0.016993    test-rmse:1.251202+0.090916 
## [201]    train-rmse:0.981309+0.016964    test-rmse:1.250288+0.089929 
## [202]    train-rmse:0.980429+0.016965    test-rmse:1.249451+0.089982 
## [203]    train-rmse:0.979530+0.016988    test-rmse:1.249531+0.087646 
## [204]    train-rmse:0.978643+0.016993    test-rmse:1.248638+0.088701 
## [205]    train-rmse:0.977784+0.017009    test-rmse:1.247558+0.089207 
## [206]    train-rmse:0.976917+0.017029    test-rmse:1.246998+0.089093 
## [207]    train-rmse:0.976080+0.017039    test-rmse:1.246121+0.089706 
## [208]    train-rmse:0.975248+0.017048    test-rmse:1.246693+0.089010 
## [209]    train-rmse:0.974419+0.017048    test-rmse:1.246010+0.089019 
## [210]    train-rmse:0.973558+0.017071    test-rmse:1.245481+0.088985 
## [211]    train-rmse:0.972729+0.017075    test-rmse:1.245360+0.088253 
## [212]    train-rmse:0.971875+0.017069    test-rmse:1.244487+0.087665 
## [213]    train-rmse:0.971054+0.017073    test-rmse:1.244751+0.088158 
## [214]    train-rmse:0.970241+0.017082    test-rmse:1.243614+0.088008 
## [215]    train-rmse:0.969438+0.017088    test-rmse:1.244843+0.087132 
## [216]    train-rmse:0.968648+0.017081    test-rmse:1.244034+0.086594 
## [217]    train-rmse:0.967825+0.017107    test-rmse:1.241810+0.085008 
## [218]    train-rmse:0.967055+0.017117    test-rmse:1.240940+0.086593 
## [219]    train-rmse:0.966270+0.017143    test-rmse:1.239857+0.086032 
## [220]    train-rmse:0.965496+0.017157    test-rmse:1.240526+0.084949 
## [221]    train-rmse:0.964735+0.017163    test-rmse:1.240228+0.084843 
## [222]    train-rmse:0.963960+0.017180    test-rmse:1.239931+0.085018 
## [223]    train-rmse:0.963200+0.017187    test-rmse:1.240133+0.085801 
## [224]    train-rmse:0.962446+0.017195    test-rmse:1.239088+0.086656 
## [225]    train-rmse:0.961708+0.017212    test-rmse:1.237667+0.087218 
## [226]    train-rmse:0.960973+0.017220    test-rmse:1.238059+0.086687 
## [227]    train-rmse:0.960233+0.017234    test-rmse:1.237809+0.087726 
## [228]    train-rmse:0.959499+0.017252    test-rmse:1.238453+0.088085 
## [229]    train-rmse:0.958732+0.017283    test-rmse:1.237478+0.088094 
## [230]    train-rmse:0.958016+0.017295    test-rmse:1.236069+0.088213 
## [231]    train-rmse:0.957298+0.017303    test-rmse:1.235179+0.087679 
## [232]    train-rmse:0.956584+0.017329    test-rmse:1.236106+0.087788 
## [233]    train-rmse:0.955858+0.017334    test-rmse:1.236025+0.088645 
## [234]    train-rmse:0.955144+0.017334    test-rmse:1.234923+0.088890 
## [235]    train-rmse:0.954450+0.017336    test-rmse:1.234350+0.088426 
## [236]    train-rmse:0.953737+0.017318    test-rmse:1.233108+0.087144 
## [237]    train-rmse:0.953048+0.017321    test-rmse:1.232361+0.088877 
## [238]    train-rmse:0.952367+0.017320    test-rmse:1.232118+0.088746 
## [239]    train-rmse:0.951661+0.017343    test-rmse:1.231743+0.088363 
## [240]    train-rmse:0.950971+0.017355    test-rmse:1.231396+0.087816 
## [241]    train-rmse:0.950277+0.017349    test-rmse:1.230243+0.088491 
## [242]    train-rmse:0.949599+0.017347    test-rmse:1.229885+0.088129 
## [243]    train-rmse:0.948902+0.017346    test-rmse:1.229398+0.088770 
## [244]    train-rmse:0.948234+0.017365    test-rmse:1.230903+0.087353 
## [245]    train-rmse:0.947572+0.017375    test-rmse:1.230583+0.087374 
## [246]    train-rmse:0.946922+0.017385    test-rmse:1.230162+0.086113 
## [247]    train-rmse:0.946278+0.017385    test-rmse:1.229234+0.086852 
## [248]    train-rmse:0.945628+0.017393    test-rmse:1.229915+0.087231 
## [249]    train-rmse:0.944976+0.017413    test-rmse:1.230398+0.086521 
## [250]    train-rmse:0.944321+0.017427    test-rmse:1.230234+0.086740 
## [251]    train-rmse:0.943675+0.017440    test-rmse:1.229576+0.087542 
## [252]    train-rmse:0.943057+0.017456    test-rmse:1.229149+0.087770 
## [253]    train-rmse:0.942452+0.017469    test-rmse:1.229724+0.087382 
## [254]    train-rmse:0.941838+0.017475    test-rmse:1.229854+0.086232 
## [255]    train-rmse:0.941219+0.017496    test-rmse:1.229590+0.085988 
## [256]    train-rmse:0.940542+0.017487    test-rmse:1.229236+0.085529 
## [257]    train-rmse:0.939921+0.017503    test-rmse:1.228894+0.085992 
## [258]    train-rmse:0.939304+0.017520    test-rmse:1.227731+0.085925 
## [259]    train-rmse:0.938693+0.017532    test-rmse:1.227929+0.085507 
## [260]    train-rmse:0.938088+0.017542    test-rmse:1.227302+0.086027 
## [261]    train-rmse:0.937466+0.017546    test-rmse:1.228016+0.086251 
## [262]    train-rmse:0.936856+0.017558    test-rmse:1.227718+0.087413 
## [263]    train-rmse:0.936240+0.017577    test-rmse:1.227523+0.086711 
## [264]    train-rmse:0.935628+0.017587    test-rmse:1.227159+0.087264 
## [265]    train-rmse:0.935031+0.017605    test-rmse:1.226053+0.087267 
## [266]    train-rmse:0.934435+0.017624    test-rmse:1.224299+0.085722 
## [267]    train-rmse:0.933849+0.017643    test-rmse:1.222889+0.085140 
## [268]    train-rmse:0.933245+0.017619    test-rmse:1.223249+0.084529 
## [269]    train-rmse:0.932646+0.017620    test-rmse:1.223280+0.085194 
## [270]    train-rmse:0.932076+0.017638    test-rmse:1.222906+0.085660 
## [271]    train-rmse:0.931506+0.017662    test-rmse:1.222760+0.085743 
## [272]    train-rmse:0.930947+0.017670    test-rmse:1.222985+0.085746 
## [273]    train-rmse:0.930391+0.017691    test-rmse:1.222951+0.086273 
## [274]    train-rmse:0.929838+0.017686    test-rmse:1.222864+0.085292 
## [275]    train-rmse:0.929252+0.017693    test-rmse:1.223596+0.085240 
## [276]    train-rmse:0.928693+0.017673    test-rmse:1.223223+0.085532 
## [277]    train-rmse:0.928152+0.017679    test-rmse:1.222223+0.084849 
## [278]    train-rmse:0.927607+0.017678    test-rmse:1.223337+0.085559 
## [279]    train-rmse:0.927080+0.017680    test-rmse:1.222959+0.085808 
## [280]    train-rmse:0.926555+0.017684    test-rmse:1.222065+0.085602 
## [281]    train-rmse:0.926016+0.017688    test-rmse:1.222234+0.085034 
## [282]    train-rmse:0.925469+0.017710    test-rmse:1.222135+0.084824 
## [283]    train-rmse:0.924943+0.017716    test-rmse:1.222062+0.085085 
## [284]    train-rmse:0.924422+0.017732    test-rmse:1.220637+0.084555 
## [285]    train-rmse:0.923896+0.017746    test-rmse:1.220719+0.085252 
## [286]    train-rmse:0.923378+0.017755    test-rmse:1.220914+0.086274 
## [287]    train-rmse:0.922864+0.017768    test-rmse:1.221086+0.085406 
## [288]    train-rmse:0.922340+0.017761    test-rmse:1.220457+0.086183 
## [289]    train-rmse:0.921830+0.017773    test-rmse:1.219695+0.086200 
## [290]    train-rmse:0.921325+0.017786    test-rmse:1.219212+0.086508 
## [291]    train-rmse:0.920809+0.017800    test-rmse:1.218861+0.086653 
## [292]    train-rmse:0.920292+0.017823    test-rmse:1.219467+0.086897 
## [293]    train-rmse:0.919801+0.017832    test-rmse:1.219455+0.087039 
## [294]    train-rmse:0.919301+0.017834    test-rmse:1.218015+0.085405 
## [295]    train-rmse:0.918796+0.017840    test-rmse:1.218103+0.084617 
## [296]    train-rmse:0.918295+0.017844    test-rmse:1.217608+0.086240 
## [297]    train-rmse:0.917793+0.017851    test-rmse:1.216708+0.086770 
## [298]    train-rmse:0.917302+0.017854    test-rmse:1.217151+0.085772 
## [299]    train-rmse:0.916816+0.017854    test-rmse:1.217528+0.085280 
## [300]    train-rmse:0.916326+0.017854    test-rmse:1.216764+0.084649 
## [301]    train-rmse:0.915833+0.017857    test-rmse:1.216521+0.085980 
## [302]    train-rmse:0.915355+0.017863    test-rmse:1.216735+0.085697 
## [303]    train-rmse:0.914883+0.017874    test-rmse:1.216156+0.086165 
## [304]    train-rmse:0.914410+0.017887    test-rmse:1.216518+0.085878 
## [305]    train-rmse:0.913910+0.017884    test-rmse:1.216612+0.085639 
## [306]    train-rmse:0.913436+0.017898    test-rmse:1.217503+0.085492 
## [307]    train-rmse:0.912966+0.017912    test-rmse:1.216968+0.085430 
## [308]    train-rmse:0.912493+0.017904    test-rmse:1.217213+0.085167 
## [309]    train-rmse:0.912009+0.017928    test-rmse:1.216637+0.085533 
## Stopping. Best iteration:
## [303]    train-rmse:0.914883+0.017874    test-rmse:1.216156+0.086165
## 
## 36 
## [1]  train-rmse:78.857910+0.079069   test-rmse:78.858575+0.414633 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:63.153612+0.063143   test-rmse:63.154042+0.429953 
## [3]  train-rmse:50.582123+0.050320   test-rmse:50.582268+0.441991 
## [4]  train-rmse:40.519815+0.039977   test-rmse:40.519597+0.451317 
## [5]  train-rmse:32.467526+0.031623   test-rmse:32.466867+0.458362 
## [6]  train-rmse:26.025803+0.024854   test-rmse:26.024606+0.463429 
## [7]  train-rmse:20.875072+0.019383   test-rmse:20.873221+0.466687 
## [8]  train-rmse:16.759770+0.015015   test-rmse:16.757143+0.468190 
## [9]  train-rmse:13.474406+0.011635   test-rmse:13.476270+0.444762 
## [10] train-rmse:10.852426+0.008666   test-rmse:10.838746+0.439334 
## [11] train-rmse:8.760172+0.007617    test-rmse:8.745579+0.415025 
## [12] train-rmse:7.092363+0.006688    test-rmse:7.074333+0.407666 
## [13] train-rmse:5.766406+0.007399    test-rmse:5.751751+0.398661 
## [14] train-rmse:4.713473+0.007583    test-rmse:4.707145+0.367522 
## [15] train-rmse:3.885132+0.011202    test-rmse:3.888046+0.346253 
## [16] train-rmse:3.235925+0.012777    test-rmse:3.246648+0.332867 
## [17] train-rmse:2.733982+0.015483    test-rmse:2.754968+0.317623 
## [18] train-rmse:2.349385+0.017672    test-rmse:2.386950+0.281270 
## [19] train-rmse:2.056709+0.019943    test-rmse:2.113266+0.244227 
## [20] train-rmse:1.838839+0.020825    test-rmse:1.918523+0.213870 
## [21] train-rmse:1.680965+0.022861    test-rmse:1.778909+0.194384 
## [22] train-rmse:1.562397+0.024143    test-rmse:1.676191+0.168318 
## [23] train-rmse:1.477719+0.024673    test-rmse:1.606477+0.151562 
## [24] train-rmse:1.412742+0.025595    test-rmse:1.554448+0.132031 
## [25] train-rmse:1.365451+0.027220    test-rmse:1.522529+0.125914 
## [26] train-rmse:1.325749+0.024964    test-rmse:1.495197+0.116072 
## [27] train-rmse:1.295675+0.024716    test-rmse:1.474354+0.108786 
## [28] train-rmse:1.272020+0.025710    test-rmse:1.463045+0.099642 
## [29] train-rmse:1.253275+0.025995    test-rmse:1.447622+0.099377 
## [30] train-rmse:1.236278+0.025596    test-rmse:1.435190+0.100108 
## [31] train-rmse:1.217552+0.026381    test-rmse:1.428096+0.095632 
## [32] train-rmse:1.202741+0.027612    test-rmse:1.419748+0.094230 
## [33] train-rmse:1.188663+0.025906    test-rmse:1.409838+0.098252 
## [34] train-rmse:1.176712+0.025999    test-rmse:1.403694+0.100467 
## [35] train-rmse:1.164134+0.027639    test-rmse:1.393243+0.093624 
## [36] train-rmse:1.153417+0.028158    test-rmse:1.383543+0.089509 
## [37] train-rmse:1.142160+0.027523    test-rmse:1.374212+0.088990 
## [38] train-rmse:1.129911+0.028283    test-rmse:1.370898+0.085196 
## [39] train-rmse:1.118827+0.029403    test-rmse:1.357102+0.093548 
## [40] train-rmse:1.108382+0.028502    test-rmse:1.348600+0.094982 
## [41] train-rmse:1.099521+0.028176    test-rmse:1.345062+0.093798 
## [42] train-rmse:1.090137+0.028342    test-rmse:1.343727+0.090590 
## [43] train-rmse:1.079438+0.027457    test-rmse:1.339040+0.089758 
## [44] train-rmse:1.070243+0.027724    test-rmse:1.331831+0.090854 
## [45] train-rmse:1.060713+0.028998    test-rmse:1.323062+0.091661 
## [46] train-rmse:1.053166+0.029637    test-rmse:1.313649+0.087112 
## [47] train-rmse:1.045567+0.029511    test-rmse:1.305405+0.090179 
## [48] train-rmse:1.035056+0.031208    test-rmse:1.298961+0.088359 
## [49] train-rmse:1.027003+0.030979    test-rmse:1.290900+0.095296 
## [50] train-rmse:1.019762+0.031366    test-rmse:1.286535+0.092632 
## [51] train-rmse:1.012983+0.031503    test-rmse:1.283750+0.088886 
## [52] train-rmse:1.003575+0.028772    test-rmse:1.277492+0.085198 
## [53] train-rmse:0.997589+0.028867    test-rmse:1.272514+0.086420 
## [54] train-rmse:0.990260+0.027639    test-rmse:1.268136+0.086127 
## [55] train-rmse:0.984012+0.027761    test-rmse:1.266085+0.084227 
## [56] train-rmse:0.974322+0.027332    test-rmse:1.258498+0.082211 
## [57] train-rmse:0.966999+0.027084    test-rmse:1.252365+0.085253 
## [58] train-rmse:0.961407+0.027265    test-rmse:1.249727+0.087800 
## [59] train-rmse:0.956131+0.027303    test-rmse:1.245025+0.086313 
## [60] train-rmse:0.947875+0.026645    test-rmse:1.236823+0.093022 
## [61] train-rmse:0.939675+0.027262    test-rmse:1.232393+0.092777 
## [62] train-rmse:0.933225+0.025048    test-rmse:1.226946+0.095339 
## [63] train-rmse:0.927022+0.026153    test-rmse:1.223311+0.096760 
## [64] train-rmse:0.918913+0.024604    test-rmse:1.219713+0.098978 
## [65] train-rmse:0.912058+0.024905    test-rmse:1.217304+0.096292 
## [66] train-rmse:0.907322+0.024658    test-rmse:1.213900+0.095353 
## [67] train-rmse:0.902364+0.025046    test-rmse:1.210923+0.095606 
## [68] train-rmse:0.895354+0.026419    test-rmse:1.208210+0.094637 
## [69] train-rmse:0.890163+0.027577    test-rmse:1.206775+0.096373 
## [70] train-rmse:0.883346+0.023967    test-rmse:1.202909+0.099201 
## [71] train-rmse:0.878873+0.023754    test-rmse:1.196712+0.098920 
## [72] train-rmse:0.872217+0.023453    test-rmse:1.195995+0.098776 
## [73] train-rmse:0.866550+0.025572    test-rmse:1.190948+0.097842 
## [74] train-rmse:0.859684+0.023099    test-rmse:1.188863+0.099564 
## [75] train-rmse:0.855627+0.022968    test-rmse:1.186011+0.100472 
## [76] train-rmse:0.851652+0.022916    test-rmse:1.184896+0.096668 
## [77] train-rmse:0.845360+0.022253    test-rmse:1.181745+0.093486 
## [78] train-rmse:0.841394+0.022266    test-rmse:1.177780+0.092968 
## [79] train-rmse:0.835815+0.022864    test-rmse:1.174031+0.092544 
## [80] train-rmse:0.831372+0.023245    test-rmse:1.172061+0.092576 
## [81] train-rmse:0.826387+0.023449    test-rmse:1.170792+0.092505 
## [82] train-rmse:0.822556+0.023622    test-rmse:1.169378+0.092841 
## [83] train-rmse:0.818633+0.022275    test-rmse:1.166298+0.095858 
## [84] train-rmse:0.812846+0.023967    test-rmse:1.160148+0.092444 
## [85] train-rmse:0.808887+0.023920    test-rmse:1.156404+0.091057 
## [86] train-rmse:0.804064+0.024755    test-rmse:1.153873+0.092019 
## [87] train-rmse:0.798319+0.023924    test-rmse:1.150407+0.095174 
## [88] train-rmse:0.794452+0.023952    test-rmse:1.149823+0.094382 
## [89] train-rmse:0.790182+0.024064    test-rmse:1.146881+0.092440 
## [90] train-rmse:0.786727+0.024795    test-rmse:1.143779+0.092679 
## [91] train-rmse:0.782677+0.025182    test-rmse:1.142337+0.091075 
## [92] train-rmse:0.779046+0.025529    test-rmse:1.141979+0.093406 
## [93] train-rmse:0.775251+0.024512    test-rmse:1.136795+0.094304 
## [94] train-rmse:0.771017+0.025210    test-rmse:1.135418+0.095206 
## [95] train-rmse:0.766837+0.026019    test-rmse:1.130469+0.094478 
## [96] train-rmse:0.762556+0.027352    test-rmse:1.128320+0.093087 
## [97] train-rmse:0.758461+0.025728    test-rmse:1.126166+0.094408 
## [98] train-rmse:0.755240+0.026447    test-rmse:1.124178+0.093094 
## [99] train-rmse:0.750577+0.027448    test-rmse:1.122774+0.091232 
## [100]    train-rmse:0.747319+0.027592    test-rmse:1.123112+0.090157 
## [101]    train-rmse:0.744807+0.027699    test-rmse:1.122359+0.091252 
## [102]    train-rmse:0.740779+0.027587    test-rmse:1.119831+0.092169 
## [103]    train-rmse:0.738205+0.027694    test-rmse:1.118345+0.092748 
## [104]    train-rmse:0.735466+0.027999    test-rmse:1.116356+0.093591 
## [105]    train-rmse:0.732992+0.028154    test-rmse:1.115723+0.094678 
## [106]    train-rmse:0.727852+0.025553    test-rmse:1.112718+0.095801 
## [107]    train-rmse:0.724578+0.024190    test-rmse:1.108730+0.097521 
## [108]    train-rmse:0.721122+0.023306    test-rmse:1.105458+0.100143 
## [109]    train-rmse:0.716649+0.022798    test-rmse:1.104510+0.099047 
## [110]    train-rmse:0.713402+0.022555    test-rmse:1.101726+0.097441 
## [111]    train-rmse:0.709746+0.023488    test-rmse:1.099922+0.095865 
## [112]    train-rmse:0.707401+0.023279    test-rmse:1.098669+0.097312 
## [113]    train-rmse:0.703992+0.024823    test-rmse:1.096287+0.096759 
## [114]    train-rmse:0.699909+0.023606    test-rmse:1.095364+0.098128 
## [115]    train-rmse:0.697949+0.023812    test-rmse:1.093862+0.098835 
## [116]    train-rmse:0.694577+0.023166    test-rmse:1.093248+0.096204 
## [117]    train-rmse:0.691989+0.022456    test-rmse:1.090134+0.098097 
## [118]    train-rmse:0.689762+0.022224    test-rmse:1.090382+0.100138 
## [119]    train-rmse:0.686312+0.021418    test-rmse:1.089447+0.101414 
## [120]    train-rmse:0.682888+0.020469    test-rmse:1.087507+0.102057 
## [121]    train-rmse:0.679645+0.021110    test-rmse:1.083885+0.101084 
## [122]    train-rmse:0.677020+0.021394    test-rmse:1.083291+0.101860 
## [123]    train-rmse:0.674135+0.021902    test-rmse:1.082528+0.100241 
## [124]    train-rmse:0.671157+0.022711    test-rmse:1.081373+0.100269 
## [125]    train-rmse:0.669526+0.022879    test-rmse:1.081710+0.100929 
## [126]    train-rmse:0.666782+0.021006    test-rmse:1.080819+0.101064 
## [127]    train-rmse:0.664978+0.021399    test-rmse:1.080172+0.100480 
## [128]    train-rmse:0.663243+0.021314    test-rmse:1.079240+0.100245 
## [129]    train-rmse:0.660138+0.020090    test-rmse:1.078861+0.100049 
## [130]    train-rmse:0.658188+0.019919    test-rmse:1.077372+0.099610 
## [131]    train-rmse:0.655107+0.021091    test-rmse:1.076769+0.099122 
## [132]    train-rmse:0.653378+0.021394    test-rmse:1.076789+0.098195 
## [133]    train-rmse:0.649837+0.019400    test-rmse:1.078112+0.097398 
## [134]    train-rmse:0.647838+0.019774    test-rmse:1.078116+0.096664 
## [135]    train-rmse:0.645626+0.020451    test-rmse:1.077071+0.096445 
## [136]    train-rmse:0.643808+0.020736    test-rmse:1.077040+0.097078 
## [137]    train-rmse:0.641730+0.020788    test-rmse:1.075775+0.097766 
## [138]    train-rmse:0.638946+0.020726    test-rmse:1.073637+0.097282 
## [139]    train-rmse:0.637444+0.020547    test-rmse:1.071171+0.096241 
## [140]    train-rmse:0.633896+0.019621    test-rmse:1.069498+0.096252 
## [141]    train-rmse:0.632083+0.019894    test-rmse:1.068126+0.096190 
## [142]    train-rmse:0.630036+0.020533    test-rmse:1.067463+0.096006 
## [143]    train-rmse:0.625490+0.021068    test-rmse:1.064049+0.096011 
## [144]    train-rmse:0.623981+0.021091    test-rmse:1.063560+0.095918 
## [145]    train-rmse:0.622384+0.020879    test-rmse:1.061765+0.096360 
## [146]    train-rmse:0.620468+0.020676    test-rmse:1.061738+0.096979 
## [147]    train-rmse:0.618512+0.020015    test-rmse:1.060182+0.097512 
## [148]    train-rmse:0.616650+0.019821    test-rmse:1.059807+0.097246 
## [149]    train-rmse:0.613195+0.017539    test-rmse:1.057429+0.099482 
## [150]    train-rmse:0.610854+0.017314    test-rmse:1.056893+0.097828 
## [151]    train-rmse:0.608356+0.017925    test-rmse:1.056540+0.097050 
## [152]    train-rmse:0.605525+0.016777    test-rmse:1.053186+0.097469 
## [153]    train-rmse:0.603900+0.016428    test-rmse:1.053139+0.098292 
## [154]    train-rmse:0.601129+0.016915    test-rmse:1.053494+0.098009 
## [155]    train-rmse:0.599258+0.016594    test-rmse:1.054473+0.097160 
## [156]    train-rmse:0.596055+0.016963    test-rmse:1.052120+0.094909 
## [157]    train-rmse:0.594592+0.017045    test-rmse:1.050943+0.095826 
## [158]    train-rmse:0.592739+0.018086    test-rmse:1.050471+0.096528 
## [159]    train-rmse:0.591337+0.017567    test-rmse:1.050133+0.098302 
## [160]    train-rmse:0.589109+0.017588    test-rmse:1.049290+0.099262 
## [161]    train-rmse:0.586789+0.017323    test-rmse:1.046516+0.099546 
## [162]    train-rmse:0.585211+0.017353    test-rmse:1.045684+0.100662 
## [163]    train-rmse:0.581245+0.015901    test-rmse:1.045493+0.100067 
## [164]    train-rmse:0.579575+0.015607    test-rmse:1.044059+0.099611 
## [165]    train-rmse:0.578228+0.015815    test-rmse:1.041714+0.099383 
## [166]    train-rmse:0.575594+0.016074    test-rmse:1.041625+0.100352 
## [167]    train-rmse:0.573811+0.014914    test-rmse:1.041488+0.099958 
## [168]    train-rmse:0.572662+0.014878    test-rmse:1.041834+0.100631 
## [169]    train-rmse:0.571302+0.015042    test-rmse:1.041054+0.100595 
## [170]    train-rmse:0.568622+0.016546    test-rmse:1.042452+0.101632 
## [171]    train-rmse:0.565232+0.015210    test-rmse:1.042767+0.103675 
## [172]    train-rmse:0.561336+0.015565    test-rmse:1.042967+0.104527 
## [173]    train-rmse:0.559072+0.016162    test-rmse:1.040569+0.105219 
## [174]    train-rmse:0.558008+0.016248    test-rmse:1.039456+0.105028 
## [175]    train-rmse:0.556974+0.016208    test-rmse:1.039212+0.104334 
## [176]    train-rmse:0.555650+0.016137    test-rmse:1.039774+0.104219 
## [177]    train-rmse:0.554088+0.016612    test-rmse:1.039492+0.103732 
## [178]    train-rmse:0.550795+0.015156    test-rmse:1.038642+0.104000 
## [179]    train-rmse:0.549859+0.015049    test-rmse:1.039047+0.103182 
## [180]    train-rmse:0.547309+0.015286    test-rmse:1.036765+0.102230 
## [181]    train-rmse:0.546262+0.015607    test-rmse:1.035978+0.102959 
## [182]    train-rmse:0.542705+0.016817    test-rmse:1.030712+0.100251 
## [183]    train-rmse:0.541538+0.016535    test-rmse:1.031423+0.099329 
## [184]    train-rmse:0.540257+0.016426    test-rmse:1.031296+0.099532 
## [185]    train-rmse:0.539188+0.016522    test-rmse:1.031276+0.099939 
## [186]    train-rmse:0.537468+0.015412    test-rmse:1.031945+0.099633 
## [187]    train-rmse:0.535899+0.015010    test-rmse:1.030909+0.099748 
## [188]    train-rmse:0.534851+0.015065    test-rmse:1.029611+0.099773 
## [189]    train-rmse:0.532651+0.015021    test-rmse:1.030022+0.099866 
## [190]    train-rmse:0.531448+0.015426    test-rmse:1.029799+0.099357 
## [191]    train-rmse:0.528916+0.014717    test-rmse:1.028402+0.099874 
## [192]    train-rmse:0.526591+0.014637    test-rmse:1.027353+0.100830 
## [193]    train-rmse:0.525156+0.014773    test-rmse:1.026694+0.100182 
## [194]    train-rmse:0.523643+0.013949    test-rmse:1.026660+0.100676 
## [195]    train-rmse:0.521934+0.013478    test-rmse:1.024611+0.101326 
## [196]    train-rmse:0.519597+0.014248    test-rmse:1.023134+0.100550 
## [197]    train-rmse:0.517717+0.013737    test-rmse:1.023704+0.100370 
## [198]    train-rmse:0.516156+0.014067    test-rmse:1.022906+0.100585 
## [199]    train-rmse:0.514237+0.013800    test-rmse:1.022683+0.101672 
## [200]    train-rmse:0.513387+0.013783    test-rmse:1.023109+0.100747 
## [201]    train-rmse:0.512158+0.013869    test-rmse:1.022123+0.100014 
## [202]    train-rmse:0.509406+0.013292    test-rmse:1.021459+0.100361 
## [203]    train-rmse:0.506346+0.012135    test-rmse:1.021085+0.102044 
## [204]    train-rmse:0.504909+0.011981    test-rmse:1.020590+0.101157 
## [205]    train-rmse:0.502582+0.012944    test-rmse:1.020679+0.101142 
## [206]    train-rmse:0.501195+0.012216    test-rmse:1.020198+0.101558 
## [207]    train-rmse:0.499664+0.012711    test-rmse:1.020316+0.102297 
## [208]    train-rmse:0.498667+0.012934    test-rmse:1.020075+0.102217 
## [209]    train-rmse:0.497315+0.013466    test-rmse:1.019281+0.101931 
## [210]    train-rmse:0.495369+0.013696    test-rmse:1.018929+0.102980 
## [211]    train-rmse:0.493988+0.013978    test-rmse:1.019098+0.102163 
## [212]    train-rmse:0.492585+0.014042    test-rmse:1.018498+0.102486 
## [213]    train-rmse:0.491277+0.013624    test-rmse:1.017619+0.102005 
## [214]    train-rmse:0.488905+0.012941    test-rmse:1.017902+0.101875 
## [215]    train-rmse:0.487911+0.012885    test-rmse:1.017375+0.101645 
## [216]    train-rmse:0.486762+0.013464    test-rmse:1.017081+0.101474 
## [217]    train-rmse:0.484885+0.013737    test-rmse:1.015462+0.101100 
## [218]    train-rmse:0.483660+0.013456    test-rmse:1.015561+0.101184 
## [219]    train-rmse:0.482241+0.013486    test-rmse:1.016333+0.100089 
## [220]    train-rmse:0.481193+0.013543    test-rmse:1.016955+0.100071 
## [221]    train-rmse:0.480244+0.013348    test-rmse:1.017603+0.099113 
## [222]    train-rmse:0.479172+0.012870    test-rmse:1.017498+0.099710 
## [223]    train-rmse:0.477660+0.012281    test-rmse:1.016640+0.099796 
## Stopping. Best iteration:
## [217]    train-rmse:0.484885+0.013737    test-rmse:1.015462+0.101100
## 
## 37 
## [1]  train-rmse:78.857910+0.079069   test-rmse:78.858575+0.414633 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:63.153612+0.063143   test-rmse:63.154042+0.429953 
## [3]  train-rmse:50.582123+0.050320   test-rmse:50.582268+0.441991 
## [4]  train-rmse:40.519815+0.039977   test-rmse:40.519597+0.451317 
## [5]  train-rmse:32.467526+0.031623   test-rmse:32.466867+0.458362 
## [6]  train-rmse:26.025803+0.024854   test-rmse:26.024606+0.463429 
## [7]  train-rmse:20.875072+0.019383   test-rmse:20.873221+0.466687 
## [8]  train-rmse:16.759770+0.015015   test-rmse:16.757143+0.468190 
## [9]  train-rmse:13.474406+0.011635   test-rmse:13.476270+0.444762 
## [10] train-rmse:10.852426+0.008666   test-rmse:10.838746+0.439334 
## [11] train-rmse:8.759242+0.007449    test-rmse:8.745693+0.414592 
## [12] train-rmse:7.088593+0.006045    test-rmse:7.080451+0.399357 
## [13] train-rmse:5.756763+0.006210    test-rmse:5.760522+0.372936 
## [14] train-rmse:4.697592+0.006788    test-rmse:4.704601+0.345775 
## [15] train-rmse:3.857221+0.009025    test-rmse:3.873337+0.322704 
## [16] train-rmse:3.194791+0.010580    test-rmse:3.211338+0.307144 
## [17] train-rmse:2.676304+0.012846    test-rmse:2.709172+0.283114 
## [18] train-rmse:2.271337+0.015507    test-rmse:2.322867+0.257837 
## [19] train-rmse:1.961739+0.014953    test-rmse:2.041723+0.237554 
## [20] train-rmse:1.725658+0.016507    test-rmse:1.820227+0.212935 
## [21] train-rmse:1.548601+0.017094    test-rmse:1.669126+0.188374 
## [22] train-rmse:1.415140+0.017843    test-rmse:1.563355+0.165173 
## [23] train-rmse:1.319247+0.017349    test-rmse:1.491538+0.148319 
## [24] train-rmse:1.244937+0.020385    test-rmse:1.430465+0.139231 
## [25] train-rmse:1.189363+0.020445    test-rmse:1.393451+0.127977 
## [26] train-rmse:1.145466+0.022232    test-rmse:1.368714+0.118917 
## [27] train-rmse:1.107728+0.021084    test-rmse:1.343357+0.110976 
## [28] train-rmse:1.078415+0.021026    test-rmse:1.323957+0.103595 
## [29] train-rmse:1.052614+0.018400    test-rmse:1.309853+0.105316 
## [30] train-rmse:1.026906+0.017818    test-rmse:1.293232+0.105362 
## [31] train-rmse:1.008753+0.019260    test-rmse:1.284770+0.103958 
## [32] train-rmse:0.989132+0.015657    test-rmse:1.269847+0.099130 
## [33] train-rmse:0.969486+0.016291    test-rmse:1.255264+0.103737 
## [34] train-rmse:0.954508+0.015892    test-rmse:1.244952+0.104540 
## [35] train-rmse:0.938714+0.017113    test-rmse:1.235829+0.101926 
## [36] train-rmse:0.920494+0.014781    test-rmse:1.228411+0.103007 
## [37] train-rmse:0.908385+0.014755    test-rmse:1.222384+0.102559 
## [38] train-rmse:0.895220+0.017573    test-rmse:1.212128+0.102865 
## [39] train-rmse:0.882244+0.015908    test-rmse:1.206803+0.098762 
## [40] train-rmse:0.872037+0.016918    test-rmse:1.202307+0.099379 
## [41] train-rmse:0.861223+0.015052    test-rmse:1.195409+0.100548 
## [42] train-rmse:0.847465+0.014286    test-rmse:1.184438+0.110748 
## [43] train-rmse:0.838063+0.014087    test-rmse:1.179897+0.110477 
## [44] train-rmse:0.827410+0.014947    test-rmse:1.171977+0.111134 
## [45] train-rmse:0.819009+0.015260    test-rmse:1.167254+0.112775 
## [46] train-rmse:0.808400+0.014681    test-rmse:1.162705+0.112414 
## [47] train-rmse:0.798853+0.015159    test-rmse:1.153449+0.106412 
## [48] train-rmse:0.787990+0.013201    test-rmse:1.149849+0.107877 
## [49] train-rmse:0.778315+0.013364    test-rmse:1.147370+0.107220 
## [50] train-rmse:0.770113+0.013017    test-rmse:1.135315+0.100175 
## [51] train-rmse:0.762206+0.014888    test-rmse:1.128961+0.102357 
## [52] train-rmse:0.755300+0.015898    test-rmse:1.123240+0.102164 
## [53] train-rmse:0.745369+0.016184    test-rmse:1.119161+0.101512 
## [54] train-rmse:0.737736+0.017730    test-rmse:1.112223+0.099521 
## [55] train-rmse:0.730821+0.017119    test-rmse:1.109690+0.099342 
## [56] train-rmse:0.722992+0.018412    test-rmse:1.107390+0.099504 
## [57] train-rmse:0.716445+0.017159    test-rmse:1.103256+0.101235 
## [58] train-rmse:0.709016+0.015476    test-rmse:1.100502+0.101588 
## [59] train-rmse:0.701251+0.016350    test-rmse:1.092794+0.099945 
## [60] train-rmse:0.694865+0.017361    test-rmse:1.091900+0.099811 
## [61] train-rmse:0.687242+0.019524    test-rmse:1.088508+0.098823 
## [62] train-rmse:0.678829+0.019528    test-rmse:1.081948+0.099161 
## [63] train-rmse:0.673479+0.019657    test-rmse:1.079968+0.097346 
## [64] train-rmse:0.665371+0.020159    test-rmse:1.076970+0.093997 
## [65] train-rmse:0.659251+0.019848    test-rmse:1.072466+0.096266 
## [66] train-rmse:0.652251+0.018612    test-rmse:1.070068+0.101047 
## [67] train-rmse:0.647179+0.018824    test-rmse:1.067930+0.102004 
## [68] train-rmse:0.642146+0.019530    test-rmse:1.065502+0.102322 
## [69] train-rmse:0.636865+0.021167    test-rmse:1.062818+0.102097 
## [70] train-rmse:0.632675+0.021120    test-rmse:1.062146+0.103176 
## [71] train-rmse:0.624912+0.022172    test-rmse:1.058648+0.102279 
## [72] train-rmse:0.619100+0.022366    test-rmse:1.054328+0.103777 
## [73] train-rmse:0.615039+0.021680    test-rmse:1.049280+0.105898 
## [74] train-rmse:0.609387+0.019794    test-rmse:1.047042+0.106730 
## [75] train-rmse:0.603407+0.021278    test-rmse:1.047355+0.106622 
## [76] train-rmse:0.599016+0.020542    test-rmse:1.046862+0.108116 
## [77] train-rmse:0.594252+0.019680    test-rmse:1.043768+0.107714 
## [78] train-rmse:0.588657+0.018992    test-rmse:1.042939+0.105199 
## [79] train-rmse:0.581219+0.019705    test-rmse:1.039777+0.109688 
## [80] train-rmse:0.575261+0.019334    test-rmse:1.041155+0.106820 
## [81] train-rmse:0.569752+0.018879    test-rmse:1.043472+0.106549 
## [82] train-rmse:0.565279+0.018563    test-rmse:1.042437+0.105924 
## [83] train-rmse:0.561337+0.017876    test-rmse:1.039048+0.105022 
## [84] train-rmse:0.556608+0.017706    test-rmse:1.039363+0.104116 
## [85] train-rmse:0.552767+0.016530    test-rmse:1.037094+0.104401 
## [86] train-rmse:0.549316+0.016433    test-rmse:1.036256+0.105804 
## [87] train-rmse:0.544709+0.015852    test-rmse:1.035748+0.105317 
## [88] train-rmse:0.540082+0.016207    test-rmse:1.033585+0.105429 
## [89] train-rmse:0.537117+0.016851    test-rmse:1.030789+0.105568 
## [90] train-rmse:0.534482+0.016821    test-rmse:1.030583+0.105333 
## [91] train-rmse:0.531012+0.017552    test-rmse:1.030483+0.105040 
## [92] train-rmse:0.527880+0.018385    test-rmse:1.029049+0.105521 
## [93] train-rmse:0.523352+0.018254    test-rmse:1.025939+0.102577 
## [94] train-rmse:0.519171+0.017111    test-rmse:1.023580+0.103639 
## [95] train-rmse:0.516281+0.016292    test-rmse:1.022277+0.104055 
## [96] train-rmse:0.512977+0.016822    test-rmse:1.021054+0.104220 
## [97] train-rmse:0.509637+0.015977    test-rmse:1.020442+0.104461 
## [98] train-rmse:0.505821+0.016953    test-rmse:1.017959+0.102101 
## [99] train-rmse:0.502894+0.017176    test-rmse:1.015626+0.103713 
## [100]    train-rmse:0.497667+0.018168    test-rmse:1.013229+0.099082 
## [101]    train-rmse:0.493520+0.017918    test-rmse:1.012449+0.098701 
## [102]    train-rmse:0.489121+0.016645    test-rmse:1.010555+0.099250 
## [103]    train-rmse:0.483981+0.018731    test-rmse:1.008374+0.096744 
## [104]    train-rmse:0.480862+0.018240    test-rmse:1.007916+0.095808 
## [105]    train-rmse:0.477546+0.017118    test-rmse:1.006142+0.097570 
## [106]    train-rmse:0.473830+0.016799    test-rmse:1.005064+0.097508 
## [107]    train-rmse:0.470137+0.016509    test-rmse:1.003379+0.097563 
## [108]    train-rmse:0.467460+0.016618    test-rmse:1.002534+0.098025 
## [109]    train-rmse:0.464456+0.016991    test-rmse:1.000007+0.097290 
## [110]    train-rmse:0.460445+0.017562    test-rmse:0.999988+0.097674 
## [111]    train-rmse:0.456788+0.017581    test-rmse:0.997802+0.099212 
## [112]    train-rmse:0.452578+0.018579    test-rmse:0.994273+0.097249 
## [113]    train-rmse:0.449633+0.017455    test-rmse:0.992494+0.098004 
## [114]    train-rmse:0.446067+0.015589    test-rmse:0.991647+0.097413 
## [115]    train-rmse:0.443220+0.016822    test-rmse:0.991110+0.095370 
## [116]    train-rmse:0.440429+0.016323    test-rmse:0.990583+0.094544 
## [117]    train-rmse:0.437484+0.016188    test-rmse:0.989242+0.093796 
## [118]    train-rmse:0.434017+0.016737    test-rmse:0.990432+0.094196 
## [119]    train-rmse:0.431399+0.017336    test-rmse:0.989126+0.093998 
## [120]    train-rmse:0.428128+0.017137    test-rmse:0.988131+0.094179 
## [121]    train-rmse:0.424050+0.017428    test-rmse:0.991085+0.097345 
## [122]    train-rmse:0.420381+0.018227    test-rmse:0.989704+0.097487 
## [123]    train-rmse:0.417928+0.018419    test-rmse:0.987942+0.098122 
## [124]    train-rmse:0.415580+0.018901    test-rmse:0.986537+0.096907 
## [125]    train-rmse:0.413855+0.018588    test-rmse:0.985520+0.096245 
## [126]    train-rmse:0.411641+0.018449    test-rmse:0.986461+0.095750 
## [127]    train-rmse:0.409638+0.017726    test-rmse:0.985377+0.096124 
## [128]    train-rmse:0.407202+0.017940    test-rmse:0.984313+0.095616 
## [129]    train-rmse:0.405328+0.018309    test-rmse:0.983993+0.095575 
## [130]    train-rmse:0.401378+0.016313    test-rmse:0.984392+0.098252 
## [131]    train-rmse:0.398265+0.016900    test-rmse:0.986005+0.098738 
## [132]    train-rmse:0.395536+0.018464    test-rmse:0.986516+0.099265 
## [133]    train-rmse:0.392936+0.019521    test-rmse:0.984268+0.099452 
## [134]    train-rmse:0.390318+0.019635    test-rmse:0.983431+0.098445 
## [135]    train-rmse:0.386899+0.020507    test-rmse:0.984804+0.099276 
## [136]    train-rmse:0.381874+0.021246    test-rmse:0.984143+0.099307 
## [137]    train-rmse:0.378938+0.019691    test-rmse:0.983118+0.098274 
## [138]    train-rmse:0.375886+0.020618    test-rmse:0.981958+0.097350 
## [139]    train-rmse:0.372631+0.020745    test-rmse:0.981970+0.096607 
## [140]    train-rmse:0.370038+0.020100    test-rmse:0.980595+0.097176 
## [141]    train-rmse:0.367171+0.021117    test-rmse:0.981836+0.097693 
## [142]    train-rmse:0.364333+0.020088    test-rmse:0.982530+0.097344 
## [143]    train-rmse:0.362086+0.020433    test-rmse:0.982420+0.097929 
## [144]    train-rmse:0.359202+0.021383    test-rmse:0.982224+0.098547 
## [145]    train-rmse:0.356871+0.021522    test-rmse:0.980504+0.099594 
## [146]    train-rmse:0.354864+0.020537    test-rmse:0.982430+0.100683 
## [147]    train-rmse:0.352594+0.020919    test-rmse:0.984794+0.101469 
## [148]    train-rmse:0.349855+0.021000    test-rmse:0.980449+0.099430 
## [149]    train-rmse:0.347679+0.020717    test-rmse:0.979716+0.099143 
## [150]    train-rmse:0.345960+0.020256    test-rmse:0.979542+0.098037 
## [151]    train-rmse:0.344130+0.020848    test-rmse:0.979157+0.097842 
## [152]    train-rmse:0.341068+0.021966    test-rmse:0.978816+0.096492 
## [153]    train-rmse:0.338279+0.022777    test-rmse:0.978147+0.096369 
## [154]    train-rmse:0.335075+0.023418    test-rmse:0.974999+0.093651 
## [155]    train-rmse:0.333757+0.023808    test-rmse:0.975481+0.093755 
## [156]    train-rmse:0.331084+0.022224    test-rmse:0.971863+0.091546 
## [157]    train-rmse:0.329328+0.021845    test-rmse:0.971895+0.091877 
## [158]    train-rmse:0.327642+0.022368    test-rmse:0.971537+0.091304 
## [159]    train-rmse:0.326317+0.022414    test-rmse:0.971678+0.091324 
## [160]    train-rmse:0.323635+0.020839    test-rmse:0.972660+0.092860 
## [161]    train-rmse:0.321359+0.020870    test-rmse:0.973585+0.093334 
## [162]    train-rmse:0.318719+0.021238    test-rmse:0.972710+0.092582 
## [163]    train-rmse:0.317388+0.021753    test-rmse:0.972435+0.092147 
## [164]    train-rmse:0.315143+0.021602    test-rmse:0.969236+0.091163 
## [165]    train-rmse:0.311735+0.021086    test-rmse:0.969445+0.090735 
## [166]    train-rmse:0.309642+0.021506    test-rmse:0.969535+0.089803 
## [167]    train-rmse:0.308085+0.021350    test-rmse:0.969315+0.089194 
## [168]    train-rmse:0.306410+0.021485    test-rmse:0.969708+0.089532 
## [169]    train-rmse:0.304932+0.021294    test-rmse:0.969063+0.089621 
## [170]    train-rmse:0.303375+0.020815    test-rmse:0.968213+0.090505 
## [171]    train-rmse:0.301539+0.021347    test-rmse:0.969009+0.090656 
## [172]    train-rmse:0.299291+0.021488    test-rmse:0.968464+0.088977 
## [173]    train-rmse:0.297604+0.021606    test-rmse:0.967906+0.088996 
## [174]    train-rmse:0.295649+0.021715    test-rmse:0.965179+0.088436 
## [175]    train-rmse:0.294433+0.021653    test-rmse:0.965381+0.088998 
## [176]    train-rmse:0.293392+0.021762    test-rmse:0.965807+0.089787 
## [177]    train-rmse:0.290723+0.021847    test-rmse:0.966328+0.089288 
## [178]    train-rmse:0.289093+0.022085    test-rmse:0.966432+0.089248 
## [179]    train-rmse:0.286501+0.022543    test-rmse:0.964524+0.088161 
## [180]    train-rmse:0.285647+0.022774    test-rmse:0.964702+0.087405 
## [181]    train-rmse:0.283377+0.023092    test-rmse:0.964047+0.087142 
## [182]    train-rmse:0.282038+0.022753    test-rmse:0.965034+0.087677 
## [183]    train-rmse:0.280862+0.022734    test-rmse:0.965352+0.087825 
## [184]    train-rmse:0.278779+0.022579    test-rmse:0.965072+0.088388 
## [185]    train-rmse:0.277310+0.022430    test-rmse:0.964563+0.088209 
## [186]    train-rmse:0.275864+0.022226    test-rmse:0.963964+0.089696 
## [187]    train-rmse:0.274508+0.022570    test-rmse:0.964278+0.089629 
## [188]    train-rmse:0.272877+0.022447    test-rmse:0.963696+0.089505 
## [189]    train-rmse:0.271030+0.022285    test-rmse:0.962822+0.089383 
## [190]    train-rmse:0.269513+0.021142    test-rmse:0.962859+0.089270 
## [191]    train-rmse:0.267783+0.019963    test-rmse:0.962665+0.089141 
## [192]    train-rmse:0.266019+0.020552    test-rmse:0.962032+0.088938 
## [193]    train-rmse:0.264754+0.020722    test-rmse:0.962055+0.088368 
## [194]    train-rmse:0.262866+0.018927    test-rmse:0.960423+0.089192 
## [195]    train-rmse:0.261815+0.019032    test-rmse:0.960824+0.088663 
## [196]    train-rmse:0.260784+0.018719    test-rmse:0.959579+0.089721 
## [197]    train-rmse:0.259509+0.018938    test-rmse:0.959818+0.089661 
## [198]    train-rmse:0.258427+0.019268    test-rmse:0.959933+0.089929 
## [199]    train-rmse:0.256808+0.018984    test-rmse:0.961101+0.091040 
## [200]    train-rmse:0.255528+0.019170    test-rmse:0.960974+0.090937 
## [201]    train-rmse:0.253569+0.020159    test-rmse:0.960867+0.091177 
## [202]    train-rmse:0.252453+0.020565    test-rmse:0.960124+0.090519 
## Stopping. Best iteration:
## [196]    train-rmse:0.260784+0.018719    test-rmse:0.959579+0.089721
## 
## 38 
## [1]  train-rmse:78.857910+0.079069   test-rmse:78.858575+0.414633 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:63.153612+0.063143   test-rmse:63.154042+0.429953 
## [3]  train-rmse:50.582123+0.050320   test-rmse:50.582268+0.441991 
## [4]  train-rmse:40.519815+0.039977   test-rmse:40.519597+0.451317 
## [5]  train-rmse:32.467526+0.031623   test-rmse:32.466867+0.458362 
## [6]  train-rmse:26.025803+0.024854   test-rmse:26.024606+0.463429 
## [7]  train-rmse:20.875072+0.019383   test-rmse:20.873221+0.466687 
## [8]  train-rmse:16.759770+0.015015   test-rmse:16.757143+0.468190 
## [9]  train-rmse:13.474406+0.011635   test-rmse:13.476270+0.444762 
## [10] train-rmse:10.852426+0.008666   test-rmse:10.838746+0.439334 
## [11] train-rmse:8.759135+0.007336    test-rmse:8.743939+0.416209 
## [12] train-rmse:7.088188+0.006208    test-rmse:7.075411+0.397595 
## [13] train-rmse:5.754667+0.008005    test-rmse:5.756630+0.371827 
## [14] train-rmse:4.691234+0.006763    test-rmse:4.695342+0.340636 
## [15] train-rmse:3.846405+0.007943    test-rmse:3.851380+0.316140 
## [16] train-rmse:3.174847+0.010724    test-rmse:3.188920+0.284109 
## [17] train-rmse:2.644962+0.012145    test-rmse:2.667269+0.264712 
## [18] train-rmse:2.225875+0.010746    test-rmse:2.279051+0.235077 
## [19] train-rmse:1.901342+0.010404    test-rmse:1.988449+0.221348 
## [20] train-rmse:1.648107+0.008932    test-rmse:1.763134+0.198580 
## [21] train-rmse:1.458843+0.009537    test-rmse:1.604870+0.178974 
## [22] train-rmse:1.310042+0.010949    test-rmse:1.485528+0.158244 
## [23] train-rmse:1.200180+0.008647    test-rmse:1.405071+0.142676 
## [24] train-rmse:1.116047+0.006273    test-rmse:1.343319+0.134687 
## [25] train-rmse:1.048030+0.008233    test-rmse:1.301351+0.125086 
## [26] train-rmse:0.994486+0.012543    test-rmse:1.274755+0.116649 
## [27] train-rmse:0.950414+0.012186    test-rmse:1.251115+0.111983 
## [28] train-rmse:0.913315+0.009481    test-rmse:1.224838+0.108050 
## [29] train-rmse:0.883467+0.013909    test-rmse:1.210012+0.108184 
## [30] train-rmse:0.858994+0.015547    test-rmse:1.194299+0.097852 
## [31] train-rmse:0.836185+0.015187    test-rmse:1.179476+0.098491 
## [32] train-rmse:0.818231+0.013814    test-rmse:1.170292+0.097820 
## [33] train-rmse:0.802173+0.016109    test-rmse:1.161961+0.094020 
## [34] train-rmse:0.785951+0.020469    test-rmse:1.153989+0.097513 
## [35] train-rmse:0.769144+0.019442    test-rmse:1.147453+0.098380 
## [36] train-rmse:0.754276+0.017597    test-rmse:1.140717+0.093189 
## [37] train-rmse:0.739086+0.016931    test-rmse:1.130044+0.091483 
## [38] train-rmse:0.723494+0.014384    test-rmse:1.118396+0.090510 
## [39] train-rmse:0.710397+0.017084    test-rmse:1.110088+0.093818 
## [40] train-rmse:0.693659+0.018838    test-rmse:1.100236+0.095914 
## [41] train-rmse:0.682775+0.018769    test-rmse:1.094581+0.094149 
## [42] train-rmse:0.673394+0.018680    test-rmse:1.083776+0.090512 
## [43] train-rmse:0.663549+0.018179    test-rmse:1.077072+0.090707 
## [44] train-rmse:0.648498+0.015664    test-rmse:1.066304+0.086876 
## [45] train-rmse:0.639039+0.016426    test-rmse:1.063815+0.087232 
## [46] train-rmse:0.628388+0.017706    test-rmse:1.057757+0.084772 
## [47] train-rmse:0.619912+0.017356    test-rmse:1.052184+0.087248 
## [48] train-rmse:0.612665+0.016576    test-rmse:1.049191+0.087510 
## [49] train-rmse:0.601051+0.014732    test-rmse:1.043668+0.088273 
## [50] train-rmse:0.590403+0.017137    test-rmse:1.038913+0.087405 
## [51] train-rmse:0.582229+0.016103    test-rmse:1.032996+0.086140 
## [52] train-rmse:0.575133+0.018064    test-rmse:1.028372+0.088202 
## [53] train-rmse:0.567166+0.017394    test-rmse:1.025985+0.088044 
## [54] train-rmse:0.560557+0.018755    test-rmse:1.020697+0.084267 
## [55] train-rmse:0.554620+0.018422    test-rmse:1.017236+0.084048 
## [56] train-rmse:0.543921+0.015316    test-rmse:1.016306+0.085288 
## [57] train-rmse:0.537917+0.014551    test-rmse:1.013471+0.086986 
## [58] train-rmse:0.530797+0.016233    test-rmse:1.011995+0.084898 
## [59] train-rmse:0.522922+0.015084    test-rmse:1.010561+0.085363 
## [60] train-rmse:0.517250+0.014664    test-rmse:1.007892+0.084717 
## [61] train-rmse:0.511326+0.013798    test-rmse:1.008629+0.082970 
## [62] train-rmse:0.503670+0.014605    test-rmse:1.005189+0.083867 
## [63] train-rmse:0.495872+0.011591    test-rmse:1.002202+0.082282 
## [64] train-rmse:0.488232+0.009512    test-rmse:0.998214+0.082213 
## [65] train-rmse:0.482396+0.008513    test-rmse:0.999208+0.083063 
## [66] train-rmse:0.477238+0.009540    test-rmse:0.997965+0.083131 
## [67] train-rmse:0.473142+0.009599    test-rmse:0.996658+0.083023 
## [68] train-rmse:0.468054+0.008722    test-rmse:0.997164+0.081179 
## [69] train-rmse:0.463332+0.008686    test-rmse:0.994139+0.083488 
## [70] train-rmse:0.458127+0.008916    test-rmse:0.995859+0.082617 
## [71] train-rmse:0.452882+0.009724    test-rmse:0.993472+0.083032 
## [72] train-rmse:0.448576+0.010873    test-rmse:0.992094+0.083667 
## [73] train-rmse:0.442598+0.014121    test-rmse:0.989392+0.081478 
## [74] train-rmse:0.437472+0.013401    test-rmse:0.990247+0.081857 
## [75] train-rmse:0.433049+0.013492    test-rmse:0.987462+0.082165 
## [76] train-rmse:0.428657+0.013461    test-rmse:0.986597+0.083161 
## [77] train-rmse:0.422023+0.011832    test-rmse:0.989145+0.084088 
## [78] train-rmse:0.416334+0.010725    test-rmse:0.988515+0.086672 
## [79] train-rmse:0.412128+0.009778    test-rmse:0.986894+0.088130 
## [80] train-rmse:0.406730+0.009257    test-rmse:0.984976+0.088091 
## [81] train-rmse:0.403385+0.008778    test-rmse:0.985634+0.087870 
## [82] train-rmse:0.398326+0.009388    test-rmse:0.986004+0.088677 
## [83] train-rmse:0.394374+0.009305    test-rmse:0.987622+0.090123 
## [84] train-rmse:0.388958+0.009332    test-rmse:0.985630+0.091536 
## [85] train-rmse:0.385012+0.009895    test-rmse:0.988352+0.092418 
## [86] train-rmse:0.380834+0.009004    test-rmse:0.988012+0.093027 
## Stopping. Best iteration:
## [80] train-rmse:0.406730+0.009257    test-rmse:0.984976+0.088091
## 
## 39 
## [1]  train-rmse:78.857910+0.079069   test-rmse:78.858575+0.414633 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:63.153612+0.063143   test-rmse:63.154042+0.429953 
## [3]  train-rmse:50.582123+0.050320   test-rmse:50.582268+0.441991 
## [4]  train-rmse:40.519815+0.039977   test-rmse:40.519597+0.451317 
## [5]  train-rmse:32.467526+0.031623   test-rmse:32.466867+0.458362 
## [6]  train-rmse:26.025803+0.024854   test-rmse:26.024606+0.463429 
## [7]  train-rmse:20.875072+0.019383   test-rmse:20.873221+0.466687 
## [8]  train-rmse:16.759770+0.015015   test-rmse:16.757143+0.468190 
## [9]  train-rmse:13.474406+0.011635   test-rmse:13.476270+0.444762 
## [10] train-rmse:10.852426+0.008666   test-rmse:10.838746+0.439334 
## [11] train-rmse:8.759135+0.007336    test-rmse:8.743939+0.416209 
## [12] train-rmse:7.088128+0.006268    test-rmse:7.074247+0.396839 
## [13] train-rmse:5.752654+0.008641    test-rmse:5.748869+0.358116 
## [14] train-rmse:4.687146+0.007543    test-rmse:4.690633+0.327726 
## [15] train-rmse:3.838498+0.009425    test-rmse:3.839707+0.307485 
## [16] train-rmse:3.160484+0.009091    test-rmse:3.165089+0.286072 
## [17] train-rmse:2.624630+0.012915    test-rmse:2.643750+0.252214 
## [18] train-rmse:2.196287+0.010853    test-rmse:2.242631+0.224153 
## [19] train-rmse:1.856291+0.011662    test-rmse:1.940439+0.207572 
## [20] train-rmse:1.596164+0.012112    test-rmse:1.710245+0.192884 
## [21] train-rmse:1.393509+0.008430    test-rmse:1.549738+0.177933 
## [22] train-rmse:1.233851+0.008873    test-rmse:1.428295+0.161981 
## [23] train-rmse:1.112598+0.006305    test-rmse:1.348428+0.146972 
## [24] train-rmse:1.016959+0.009331    test-rmse:1.285660+0.140241 
## [25] train-rmse:0.932699+0.011284    test-rmse:1.242975+0.128963 
## [26] train-rmse:0.876148+0.011305    test-rmse:1.211713+0.125818 
## [27] train-rmse:0.823744+0.011078    test-rmse:1.178630+0.110488 
## [28] train-rmse:0.786522+0.009850    test-rmse:1.154305+0.111688 
## [29] train-rmse:0.753734+0.009553    test-rmse:1.138748+0.103734 
## [30] train-rmse:0.724689+0.013701    test-rmse:1.127850+0.103552 
## [31] train-rmse:0.701096+0.015486    test-rmse:1.109592+0.101369 
## [32] train-rmse:0.682026+0.015794    test-rmse:1.098884+0.102093 
## [33] train-rmse:0.661543+0.013192    test-rmse:1.092875+0.100733 
## [34] train-rmse:0.643208+0.013442    test-rmse:1.083665+0.100410 
## [35] train-rmse:0.622298+0.016946    test-rmse:1.078159+0.094832 
## [36] train-rmse:0.609020+0.015675    test-rmse:1.072923+0.092068 
## [37] train-rmse:0.595831+0.016274    test-rmse:1.066094+0.095681 
## [38] train-rmse:0.582903+0.014364    test-rmse:1.061376+0.093634 
## [39] train-rmse:0.568390+0.012051    test-rmse:1.050592+0.088986 
## [40] train-rmse:0.555058+0.015470    test-rmse:1.045350+0.089034 
## [41] train-rmse:0.542878+0.015431    test-rmse:1.038232+0.088791 
## [42] train-rmse:0.529929+0.016938    test-rmse:1.036016+0.089394 
## [43] train-rmse:0.519479+0.017610    test-rmse:1.034393+0.090707 
## [44] train-rmse:0.507735+0.018555    test-rmse:1.032348+0.089608 
## [45] train-rmse:0.498851+0.017128    test-rmse:1.029136+0.092492 
## [46] train-rmse:0.486755+0.019613    test-rmse:1.026517+0.094882 
## [47] train-rmse:0.476638+0.016743    test-rmse:1.021914+0.095519 
## [48] train-rmse:0.467546+0.018746    test-rmse:1.019482+0.096710 
## [49] train-rmse:0.458744+0.018104    test-rmse:1.013582+0.096376 
## [50] train-rmse:0.448744+0.014578    test-rmse:1.007350+0.093868 
## [51] train-rmse:0.442622+0.013134    test-rmse:1.004380+0.093624 
## [52] train-rmse:0.435057+0.013891    test-rmse:1.002677+0.090431 
## [53] train-rmse:0.424405+0.014846    test-rmse:0.999483+0.092054 
## [54] train-rmse:0.417455+0.014646    test-rmse:0.996141+0.091466 
## [55] train-rmse:0.410655+0.016942    test-rmse:0.992723+0.092593 
## [56] train-rmse:0.405278+0.016269    test-rmse:0.990453+0.093774 
## [57] train-rmse:0.399904+0.016524    test-rmse:0.991776+0.093381 
## [58] train-rmse:0.395246+0.016120    test-rmse:0.989600+0.094189 
## [59] train-rmse:0.386022+0.014167    test-rmse:0.988758+0.096195 
## [60] train-rmse:0.378094+0.012975    test-rmse:0.986270+0.096651 
## [61] train-rmse:0.371079+0.014672    test-rmse:0.983626+0.098018 
## [62] train-rmse:0.362668+0.014944    test-rmse:0.982200+0.099607 
## [63] train-rmse:0.355399+0.012947    test-rmse:0.980353+0.099084 
## [64] train-rmse:0.350101+0.011930    test-rmse:0.981822+0.099466 
## [65] train-rmse:0.342872+0.013021    test-rmse:0.980316+0.098028 
## [66] train-rmse:0.335310+0.011976    test-rmse:0.979954+0.095474 
## [67] train-rmse:0.331229+0.010929    test-rmse:0.977856+0.097875 
## [68] train-rmse:0.326018+0.010522    test-rmse:0.977872+0.095283 
## [69] train-rmse:0.319782+0.010024    test-rmse:0.975974+0.095127 
## [70] train-rmse:0.314911+0.009638    test-rmse:0.975068+0.095907 
## [71] train-rmse:0.309653+0.010959    test-rmse:0.972505+0.094413 
## [72] train-rmse:0.305136+0.010173    test-rmse:0.971425+0.093700 
## [73] train-rmse:0.300146+0.011164    test-rmse:0.971418+0.094385 
## [74] train-rmse:0.294874+0.011324    test-rmse:0.972708+0.095366 
## [75] train-rmse:0.289198+0.010695    test-rmse:0.972739+0.095531 
## [76] train-rmse:0.284783+0.012365    test-rmse:0.971585+0.094244 
## [77] train-rmse:0.280805+0.013351    test-rmse:0.969262+0.095192 
## [78] train-rmse:0.276274+0.012303    test-rmse:0.967980+0.096338 
## [79] train-rmse:0.270950+0.011268    test-rmse:0.967391+0.096335 
## [80] train-rmse:0.266125+0.009798    test-rmse:0.966201+0.096679 
## [81] train-rmse:0.262226+0.009070    test-rmse:0.965917+0.096192 
## [82] train-rmse:0.258167+0.010773    test-rmse:0.964005+0.096097 
## [83] train-rmse:0.253119+0.008885    test-rmse:0.963093+0.096175 
## [84] train-rmse:0.249231+0.008028    test-rmse:0.962565+0.096348 
## [85] train-rmse:0.245057+0.007719    test-rmse:0.961218+0.095959 
## [86] train-rmse:0.241973+0.008431    test-rmse:0.960672+0.096853 
## [87] train-rmse:0.238247+0.009220    test-rmse:0.959936+0.096486 
## [88] train-rmse:0.232967+0.007692    test-rmse:0.957680+0.095949 
## [89] train-rmse:0.228484+0.007564    test-rmse:0.957784+0.096538 
## [90] train-rmse:0.223671+0.007842    test-rmse:0.956665+0.097554 
## [91] train-rmse:0.219993+0.008499    test-rmse:0.956124+0.099082 
## [92] train-rmse:0.216290+0.009483    test-rmse:0.954982+0.099761 
## [93] train-rmse:0.212325+0.009075    test-rmse:0.954984+0.100300 
## [94] train-rmse:0.207664+0.008554    test-rmse:0.954083+0.099803 
## [95] train-rmse:0.204782+0.008094    test-rmse:0.953626+0.100991 
## [96] train-rmse:0.201440+0.009076    test-rmse:0.952858+0.101971 
## [97] train-rmse:0.198478+0.009252    test-rmse:0.953296+0.101499 
## [98] train-rmse:0.194800+0.009043    test-rmse:0.952004+0.102125 
## [99] train-rmse:0.191975+0.008511    test-rmse:0.951525+0.101760 
## [100]    train-rmse:0.188270+0.007545    test-rmse:0.950583+0.101351 
## [101]    train-rmse:0.184843+0.008512    test-rmse:0.950061+0.100386 
## [102]    train-rmse:0.181355+0.007707    test-rmse:0.949102+0.099555 
## [103]    train-rmse:0.178169+0.008598    test-rmse:0.948785+0.100018 
## [104]    train-rmse:0.175911+0.008424    test-rmse:0.948859+0.100507 
## [105]    train-rmse:0.173857+0.008065    test-rmse:0.947818+0.100527 
## [106]    train-rmse:0.171135+0.008163    test-rmse:0.947000+0.100774 
## [107]    train-rmse:0.169268+0.008028    test-rmse:0.948402+0.102031 
## [108]    train-rmse:0.167408+0.008024    test-rmse:0.948130+0.101903 
## [109]    train-rmse:0.165291+0.008133    test-rmse:0.947441+0.102239 
## [110]    train-rmse:0.163003+0.007889    test-rmse:0.946681+0.101374 
## [111]    train-rmse:0.159807+0.007017    test-rmse:0.946637+0.102302 
## [112]    train-rmse:0.157444+0.007487    test-rmse:0.945984+0.101651 
## [113]    train-rmse:0.155895+0.007662    test-rmse:0.945881+0.102219 
## [114]    train-rmse:0.153726+0.007605    test-rmse:0.945756+0.102386 
## [115]    train-rmse:0.151474+0.007887    test-rmse:0.946439+0.102559 
## [116]    train-rmse:0.148486+0.008228    test-rmse:0.946016+0.102300 
## [117]    train-rmse:0.145294+0.007467    test-rmse:0.946198+0.101862 
## [118]    train-rmse:0.143477+0.007219    test-rmse:0.945669+0.101577 
## [119]    train-rmse:0.141310+0.007707    test-rmse:0.946334+0.101658 
## [120]    train-rmse:0.138109+0.007124    test-rmse:0.946656+0.101139 
## [121]    train-rmse:0.135954+0.007323    test-rmse:0.947147+0.101675 
## [122]    train-rmse:0.134249+0.007277    test-rmse:0.946523+0.102096 
## [123]    train-rmse:0.132589+0.006936    test-rmse:0.946751+0.102408 
## [124]    train-rmse:0.130955+0.006679    test-rmse:0.946007+0.103017 
## Stopping. Best iteration:
## [118]    train-rmse:0.143477+0.007219    test-rmse:0.945669+0.101577
## 
## 40 
## [1]  train-rmse:78.857910+0.079069   test-rmse:78.858575+0.414633 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:63.153612+0.063143   test-rmse:63.154042+0.429953 
## [3]  train-rmse:50.582123+0.050320   test-rmse:50.582268+0.441991 
## [4]  train-rmse:40.519815+0.039977   test-rmse:40.519597+0.451317 
## [5]  train-rmse:32.467526+0.031623   test-rmse:32.466867+0.458362 
## [6]  train-rmse:26.025803+0.024854   test-rmse:26.024606+0.463429 
## [7]  train-rmse:20.875072+0.019383   test-rmse:20.873221+0.466687 
## [8]  train-rmse:16.759770+0.015015   test-rmse:16.757143+0.468190 
## [9]  train-rmse:13.474406+0.011635   test-rmse:13.476270+0.444762 
## [10] train-rmse:10.852426+0.008666   test-rmse:10.838746+0.439334 
## [11] train-rmse:8.759135+0.007336    test-rmse:8.743939+0.416209 
## [12] train-rmse:7.088128+0.006268    test-rmse:7.074247+0.396839 
## [13] train-rmse:5.751411+0.008302    test-rmse:5.741578+0.357091 
## [14] train-rmse:4.684732+0.006363    test-rmse:4.679960+0.325245 
## [15] train-rmse:3.833123+0.007578    test-rmse:3.829197+0.292860 
## [16] train-rmse:3.150947+0.005766    test-rmse:3.156574+0.279847 
## [17] train-rmse:2.607791+0.006664    test-rmse:2.637946+0.254843 
## [18] train-rmse:2.172666+0.007642    test-rmse:2.234521+0.228476 
## [19] train-rmse:1.828573+0.010415    test-rmse:1.939658+0.211039 
## [20] train-rmse:1.555231+0.014794    test-rmse:1.709795+0.193682 
## [21] train-rmse:1.343123+0.014555    test-rmse:1.542797+0.171717 
## [22] train-rmse:1.177717+0.011444    test-rmse:1.431141+0.158918 
## [23] train-rmse:1.041746+0.013599    test-rmse:1.336225+0.147629 
## [24] train-rmse:0.935808+0.013913    test-rmse:1.263592+0.142382 
## [25] train-rmse:0.854287+0.016791    test-rmse:1.214794+0.135788 
## [26] train-rmse:0.790626+0.014036    test-rmse:1.186393+0.135561 
## [27] train-rmse:0.735342+0.013399    test-rmse:1.163713+0.127475 
## [28] train-rmse:0.691350+0.011434    test-rmse:1.137136+0.119936 
## [29] train-rmse:0.654125+0.013430    test-rmse:1.125680+0.120408 
## [30] train-rmse:0.626602+0.014198    test-rmse:1.109689+0.118218 
## [31] train-rmse:0.599810+0.016539    test-rmse:1.097224+0.118862 
## [32] train-rmse:0.578053+0.016357    test-rmse:1.078242+0.112353 
## [33] train-rmse:0.557172+0.014068    test-rmse:1.073121+0.114379 
## [34] train-rmse:0.539670+0.015200    test-rmse:1.061608+0.106545 
## [35] train-rmse:0.522693+0.009881    test-rmse:1.055335+0.106592 
## [36] train-rmse:0.505465+0.008828    test-rmse:1.052320+0.109544 
## [37] train-rmse:0.489998+0.009192    test-rmse:1.044505+0.106948 
## [38] train-rmse:0.473277+0.014022    test-rmse:1.040501+0.106749 
## [39] train-rmse:0.459716+0.015516    test-rmse:1.035040+0.104072 
## [40] train-rmse:0.448458+0.015515    test-rmse:1.031074+0.102458 
## [41] train-rmse:0.438073+0.015066    test-rmse:1.025360+0.103946 
## [42] train-rmse:0.423887+0.012940    test-rmse:1.020171+0.103827 
## [43] train-rmse:0.408405+0.010340    test-rmse:1.013618+0.102221 
## [44] train-rmse:0.397216+0.010223    test-rmse:1.010500+0.101545 
## [45] train-rmse:0.388541+0.009224    test-rmse:1.007536+0.101876 
## [46] train-rmse:0.377652+0.010911    test-rmse:1.005196+0.102947 
## [47] train-rmse:0.368635+0.011698    test-rmse:1.002262+0.104180 
## [48] train-rmse:0.362499+0.011302    test-rmse:1.000990+0.104806 
## [49] train-rmse:0.353992+0.010698    test-rmse:0.999509+0.103590 
## [50] train-rmse:0.345039+0.009104    test-rmse:0.998049+0.104708 
## [51] train-rmse:0.336439+0.010140    test-rmse:0.994523+0.104343 
## [52] train-rmse:0.329096+0.011186    test-rmse:0.991832+0.105979 
## [53] train-rmse:0.323809+0.011155    test-rmse:0.990057+0.104345 
## [54] train-rmse:0.317606+0.010986    test-rmse:0.988162+0.103258 
## [55] train-rmse:0.309401+0.009794    test-rmse:0.987043+0.101786 
## [56] train-rmse:0.302110+0.009491    test-rmse:0.983525+0.103051 
## [57] train-rmse:0.295215+0.009289    test-rmse:0.982574+0.102953 
## [58] train-rmse:0.287274+0.009208    test-rmse:0.980267+0.101433 
## [59] train-rmse:0.280023+0.011501    test-rmse:0.979967+0.101593 
## [60] train-rmse:0.272917+0.012135    test-rmse:0.979076+0.101742 
## [61] train-rmse:0.266955+0.011022    test-rmse:0.980194+0.100567 
## [62] train-rmse:0.262680+0.011131    test-rmse:0.978025+0.098980 
## [63] train-rmse:0.257134+0.011475    test-rmse:0.977595+0.098136 
## [64] train-rmse:0.249397+0.009823    test-rmse:0.976604+0.096652 
## [65] train-rmse:0.242615+0.009327    test-rmse:0.974859+0.094303 
## [66] train-rmse:0.238588+0.010250    test-rmse:0.974622+0.095033 
## [67] train-rmse:0.232684+0.010642    test-rmse:0.974069+0.095238 
## [68] train-rmse:0.227472+0.009245    test-rmse:0.972638+0.094535 
## [69] train-rmse:0.222164+0.009064    test-rmse:0.971671+0.094173 
## [70] train-rmse:0.216830+0.009422    test-rmse:0.971612+0.095469 
## [71] train-rmse:0.212507+0.009656    test-rmse:0.970913+0.094990 
## [72] train-rmse:0.208139+0.010151    test-rmse:0.970495+0.095637 
## [73] train-rmse:0.202761+0.011687    test-rmse:0.970657+0.096136 
## [74] train-rmse:0.197694+0.010627    test-rmse:0.969591+0.095888 
## [75] train-rmse:0.193883+0.010517    test-rmse:0.969301+0.095259 
## [76] train-rmse:0.189434+0.011496    test-rmse:0.970582+0.095383 
## [77] train-rmse:0.184438+0.009831    test-rmse:0.970160+0.095511 
## [78] train-rmse:0.180414+0.010153    test-rmse:0.969747+0.095417 
## [79] train-rmse:0.175465+0.009490    test-rmse:0.969639+0.096219 
## [80] train-rmse:0.172295+0.009085    test-rmse:0.968610+0.095599 
## [81] train-rmse:0.169668+0.009239    test-rmse:0.967960+0.096000 
## [82] train-rmse:0.165668+0.008449    test-rmse:0.967116+0.095101 
## [83] train-rmse:0.162068+0.007831    test-rmse:0.967903+0.095268 
## [84] train-rmse:0.158978+0.007874    test-rmse:0.967281+0.095368 
## [85] train-rmse:0.155153+0.008519    test-rmse:0.966554+0.095458 
## [86] train-rmse:0.151791+0.008313    test-rmse:0.966550+0.095926 
## [87] train-rmse:0.149399+0.007815    test-rmse:0.964699+0.096839 
## [88] train-rmse:0.145606+0.008049    test-rmse:0.963707+0.097497 
## [89] train-rmse:0.142986+0.008631    test-rmse:0.963615+0.096861 
## [90] train-rmse:0.139451+0.008608    test-rmse:0.963770+0.097302 
## [91] train-rmse:0.135619+0.008731    test-rmse:0.963224+0.097126 
## [92] train-rmse:0.133127+0.009042    test-rmse:0.962523+0.098198 
## [93] train-rmse:0.130185+0.008984    test-rmse:0.963005+0.098153 
## [94] train-rmse:0.127394+0.007830    test-rmse:0.963538+0.098031 
## [95] train-rmse:0.124426+0.008079    test-rmse:0.963855+0.098336 
## [96] train-rmse:0.121369+0.008087    test-rmse:0.963462+0.099437 
## [97] train-rmse:0.119275+0.008106    test-rmse:0.963247+0.099566 
## [98] train-rmse:0.116888+0.007902    test-rmse:0.963089+0.100252 
## Stopping. Best iteration:
## [92] train-rmse:0.133127+0.009042    test-rmse:0.962523+0.098198
## 
## 41 
## [1]  train-rmse:78.857910+0.079069   test-rmse:78.858575+0.414633 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:63.153612+0.063143   test-rmse:63.154042+0.429953 
## [3]  train-rmse:50.582123+0.050320   test-rmse:50.582268+0.441991 
## [4]  train-rmse:40.519815+0.039977   test-rmse:40.519597+0.451317 
## [5]  train-rmse:32.467526+0.031623   test-rmse:32.466867+0.458362 
## [6]  train-rmse:26.025803+0.024854   test-rmse:26.024606+0.463429 
## [7]  train-rmse:20.875072+0.019383   test-rmse:20.873221+0.466687 
## [8]  train-rmse:16.759770+0.015015   test-rmse:16.757143+0.468190 
## [9]  train-rmse:13.474406+0.011635   test-rmse:13.476270+0.444762 
## [10] train-rmse:10.852426+0.008666   test-rmse:10.838746+0.439334 
## [11] train-rmse:8.759135+0.007336    test-rmse:8.743939+0.416209 
## [12] train-rmse:7.088128+0.006268    test-rmse:7.074247+0.396839 
## [13] train-rmse:5.750586+0.007736    test-rmse:5.739268+0.353021 
## [14] train-rmse:4.683024+0.006483    test-rmse:4.684312+0.320164 
## [15] train-rmse:3.828848+0.006601    test-rmse:3.832846+0.293336 
## [16] train-rmse:3.144562+0.005502    test-rmse:3.157162+0.275440 
## [17] train-rmse:2.598045+0.005033    test-rmse:2.635319+0.255638 
## [18] train-rmse:2.157342+0.003564    test-rmse:2.230373+0.232386 
## [19] train-rmse:1.812300+0.005812    test-rmse:1.924762+0.209328 
## [20] train-rmse:1.530198+0.003553    test-rmse:1.686053+0.186685 
## [21] train-rmse:1.310598+0.004464    test-rmse:1.510553+0.167365 
## [22] train-rmse:1.135846+0.010400    test-rmse:1.385806+0.153874 
## [23] train-rmse:0.990994+0.006306    test-rmse:1.290105+0.147833 
## [24] train-rmse:0.879758+0.007254    test-rmse:1.221196+0.137522 
## [25] train-rmse:0.789806+0.002945    test-rmse:1.167073+0.134782 
## [26] train-rmse:0.719412+0.006114    test-rmse:1.130746+0.136801 
## [27] train-rmse:0.662046+0.011476    test-rmse:1.101146+0.128637 
## [28] train-rmse:0.614421+0.015704    test-rmse:1.076293+0.119783 
## [29] train-rmse:0.574653+0.015335    test-rmse:1.056972+0.109112 
## [30] train-rmse:0.543114+0.018825    test-rmse:1.043055+0.108732 
## [31] train-rmse:0.514943+0.020272    test-rmse:1.035800+0.106138 
## [32] train-rmse:0.493972+0.019509    test-rmse:1.027119+0.107835 
## [33] train-rmse:0.469206+0.022059    test-rmse:1.019310+0.111685 
## [34] train-rmse:0.447174+0.017736    test-rmse:1.010226+0.103399 
## [35] train-rmse:0.429909+0.017576    test-rmse:1.001382+0.099593 
## [36] train-rmse:0.412067+0.015685    test-rmse:0.992462+0.099594 
## [37] train-rmse:0.397346+0.015973    test-rmse:0.989338+0.100488 
## [38] train-rmse:0.380808+0.012532    test-rmse:0.983482+0.099805 
## [39] train-rmse:0.370250+0.013670    test-rmse:0.978417+0.102265 
## [40] train-rmse:0.359480+0.012755    test-rmse:0.973799+0.103031 
## [41] train-rmse:0.346573+0.013179    test-rmse:0.970567+0.101332 
## [42] train-rmse:0.333772+0.012920    test-rmse:0.971717+0.100454 
## [43] train-rmse:0.321059+0.013992    test-rmse:0.967538+0.098947 
## [44] train-rmse:0.309351+0.016983    test-rmse:0.963293+0.097865 
## [45] train-rmse:0.301214+0.014843    test-rmse:0.960254+0.097654 
## [46] train-rmse:0.292636+0.015888    test-rmse:0.958798+0.098305 
## [47] train-rmse:0.284675+0.013963    test-rmse:0.958068+0.097696 
## [48] train-rmse:0.277378+0.013766    test-rmse:0.957815+0.097042 
## [49] train-rmse:0.268993+0.013639    test-rmse:0.957722+0.099466 
## [50] train-rmse:0.256852+0.011086    test-rmse:0.956213+0.099138 
## [51] train-rmse:0.250717+0.011880    test-rmse:0.954983+0.100218 
## [52] train-rmse:0.243782+0.009536    test-rmse:0.951626+0.100250 
## [53] train-rmse:0.234025+0.007062    test-rmse:0.948600+0.097911 
## [54] train-rmse:0.228391+0.007346    test-rmse:0.946768+0.097105 
## [55] train-rmse:0.223501+0.008770    test-rmse:0.947201+0.096873 
## [56] train-rmse:0.216817+0.007889    test-rmse:0.943728+0.095549 
## [57] train-rmse:0.210915+0.007132    test-rmse:0.942199+0.097210 
## [58] train-rmse:0.204394+0.007860    test-rmse:0.942448+0.097064 
## [59] train-rmse:0.198175+0.009438    test-rmse:0.941538+0.097346 
## [60] train-rmse:0.194119+0.008808    test-rmse:0.940567+0.097758 
## [61] train-rmse:0.187940+0.007234    test-rmse:0.938852+0.098203 
## [62] train-rmse:0.181416+0.006400    test-rmse:0.937859+0.097467 
## [63] train-rmse:0.177012+0.005270    test-rmse:0.936883+0.096926 
## [64] train-rmse:0.173411+0.005261    test-rmse:0.935956+0.096873 
## [65] train-rmse:0.167746+0.004906    test-rmse:0.935939+0.097220 
## [66] train-rmse:0.163397+0.005430    test-rmse:0.935191+0.097323 
## [67] train-rmse:0.157724+0.004754    test-rmse:0.933754+0.097154 
## [68] train-rmse:0.152440+0.004662    test-rmse:0.934610+0.097325 
## [69] train-rmse:0.148094+0.003456    test-rmse:0.934567+0.095864 
## [70] train-rmse:0.144077+0.005139    test-rmse:0.933165+0.094051 
## [71] train-rmse:0.140238+0.005936    test-rmse:0.932622+0.094006 
## [72] train-rmse:0.136367+0.004451    test-rmse:0.931605+0.095268 
## [73] train-rmse:0.133859+0.005935    test-rmse:0.931995+0.096429 
## [74] train-rmse:0.129924+0.005590    test-rmse:0.931040+0.095130 
## [75] train-rmse:0.126033+0.004360    test-rmse:0.930593+0.094412 
## [76] train-rmse:0.122633+0.004131    test-rmse:0.929899+0.093998 
## [77] train-rmse:0.118808+0.004566    test-rmse:0.929934+0.094111 
## [78] train-rmse:0.116337+0.004469    test-rmse:0.929677+0.094028 
## [79] train-rmse:0.113275+0.005026    test-rmse:0.929065+0.094364 
## [80] train-rmse:0.111082+0.005505    test-rmse:0.928648+0.094349 
## [81] train-rmse:0.108586+0.005013    test-rmse:0.927502+0.094772 
## [82] train-rmse:0.104670+0.005529    test-rmse:0.927410+0.095108 
## [83] train-rmse:0.101312+0.005526    test-rmse:0.927328+0.095032 
## [84] train-rmse:0.099019+0.005886    test-rmse:0.927350+0.095196 
## [85] train-rmse:0.095984+0.006118    test-rmse:0.927186+0.094345 
## [86] train-rmse:0.093052+0.006029    test-rmse:0.926670+0.094813 
## [87] train-rmse:0.090258+0.005555    test-rmse:0.926279+0.095523 
## [88] train-rmse:0.087928+0.005595    test-rmse:0.926022+0.095658 
## [89] train-rmse:0.085711+0.005139    test-rmse:0.926100+0.095291 
## [90] train-rmse:0.083669+0.004782    test-rmse:0.926167+0.095420 
## [91] train-rmse:0.081317+0.003535    test-rmse:0.925842+0.094990 
## [92] train-rmse:0.078236+0.003336    test-rmse:0.925533+0.095089 
## [93] train-rmse:0.076485+0.003201    test-rmse:0.925178+0.094584 
## [94] train-rmse:0.075281+0.003288    test-rmse:0.924931+0.094579 
## [95] train-rmse:0.073045+0.003512    test-rmse:0.924773+0.094578 
## [96] train-rmse:0.070718+0.003332    test-rmse:0.924447+0.094368 
## [97] train-rmse:0.068936+0.003489    test-rmse:0.924279+0.094107 
## [98] train-rmse:0.067164+0.003242    test-rmse:0.924102+0.094027 
## [99] train-rmse:0.064939+0.002869    test-rmse:0.923844+0.093990 
## [100]    train-rmse:0.063397+0.002446    test-rmse:0.923988+0.093924 
## [101]    train-rmse:0.061784+0.002631    test-rmse:0.923991+0.093660 
## [102]    train-rmse:0.059640+0.002764    test-rmse:0.923643+0.094154 
## [103]    train-rmse:0.057163+0.002874    test-rmse:0.923700+0.094194 
## [104]    train-rmse:0.055862+0.002888    test-rmse:0.923426+0.094278 
## [105]    train-rmse:0.054860+0.002501    test-rmse:0.923145+0.094289 
## [106]    train-rmse:0.053184+0.002285    test-rmse:0.923182+0.094452 
## [107]    train-rmse:0.052186+0.002189    test-rmse:0.923250+0.094525 
## [108]    train-rmse:0.050776+0.002264    test-rmse:0.922982+0.094405 
## [109]    train-rmse:0.049170+0.001764    test-rmse:0.922885+0.094635 
## [110]    train-rmse:0.047735+0.001360    test-rmse:0.922435+0.095273 
## [111]    train-rmse:0.046798+0.001452    test-rmse:0.922313+0.095230 
## [112]    train-rmse:0.045522+0.001469    test-rmse:0.921858+0.095577 
## [113]    train-rmse:0.044213+0.001360    test-rmse:0.922101+0.095736 
## [114]    train-rmse:0.043093+0.001459    test-rmse:0.922121+0.095819 
## [115]    train-rmse:0.041874+0.001367    test-rmse:0.922313+0.095810 
## [116]    train-rmse:0.040902+0.001233    test-rmse:0.922194+0.095664 
## [117]    train-rmse:0.040153+0.001075    test-rmse:0.922266+0.095668 
## [118]    train-rmse:0.039082+0.001226    test-rmse:0.922449+0.095562 
## Stopping. Best iteration:
## [112]    train-rmse:0.045522+0.001469    test-rmse:0.921858+0.095577
## 
## 42 
## [1]  train-rmse:76.896455+0.077079   test-rmse:76.897090+0.416550 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:60.052252+0.059983   test-rmse:60.052622+0.432945 
## [3]  train-rmse:46.904609+0.046553   test-rmse:46.904639+0.445440 
## [4]  train-rmse:36.644199+0.035970   test-rmse:36.643796+0.454772 
## [5]  train-rmse:28.639430+0.027617   test-rmse:28.638477+0.461462 
## [6]  train-rmse:22.397527+0.021009   test-rmse:22.395899+0.465834 
## [7]  train-rmse:17.534218+0.015835   test-rmse:17.531762+0.468024 
## [8]  train-rmse:13.749436+0.012173   test-rmse:13.738001+0.462524 
## [9]  train-rmse:10.805906+0.009591   test-rmse:10.799528+0.436844 
## [10] train-rmse:8.520625+0.007969    test-rmse:8.506156+0.435495 
## [11] train-rmse:6.750453+0.007086    test-rmse:6.731134+0.421727 
## [12] train-rmse:5.387451+0.007276    test-rmse:5.370603+0.408660 
## [13] train-rmse:4.346145+0.009686    test-rmse:4.342020+0.382658 
## [14] train-rmse:3.558936+0.010907    test-rmse:3.565225+0.358475 
## [15] train-rmse:2.971066+0.013697    test-rmse:2.978190+0.319563 
## [16] train-rmse:2.541632+0.016073    test-rmse:2.568087+0.281587 
## [17] train-rmse:2.233041+0.017586    test-rmse:2.272055+0.241215 
## [18] train-rmse:2.015123+0.019303    test-rmse:2.065885+0.195050 
## [19] train-rmse:1.863276+0.021045    test-rmse:1.931342+0.161071 
## [20] train-rmse:1.758274+0.022065    test-rmse:1.838130+0.139910 
## [21] train-rmse:1.685860+0.022744    test-rmse:1.764093+0.130603 
## [22] train-rmse:1.634244+0.023415    test-rmse:1.715123+0.122936 
## [23] train-rmse:1.596191+0.023872    test-rmse:1.687203+0.120962 
## [24] train-rmse:1.567964+0.023911    test-rmse:1.664607+0.117727 
## [25] train-rmse:1.545505+0.023061    test-rmse:1.651629+0.119992 
## [26] train-rmse:1.527019+0.022405    test-rmse:1.637546+0.119110 
## [27] train-rmse:1.510939+0.022311    test-rmse:1.625066+0.118863 
## [28] train-rmse:1.497074+0.022108    test-rmse:1.612915+0.122684 
## [29] train-rmse:1.484309+0.022042    test-rmse:1.602839+0.125130 
## [30] train-rmse:1.472477+0.022100    test-rmse:1.587914+0.118223 
## [31] train-rmse:1.461251+0.022193    test-rmse:1.577046+0.115896 
## [32] train-rmse:1.450801+0.022046    test-rmse:1.573253+0.115434 
## [33] train-rmse:1.441085+0.021714    test-rmse:1.565504+0.115475 
## [34] train-rmse:1.431597+0.021309    test-rmse:1.563440+0.114222 
## [35] train-rmse:1.422385+0.021227    test-rmse:1.564279+0.113422 
## [36] train-rmse:1.413407+0.021122    test-rmse:1.555124+0.108096 
## [37] train-rmse:1.404520+0.021054    test-rmse:1.549663+0.110972 
## [38] train-rmse:1.395881+0.021165    test-rmse:1.541237+0.115521 
## [39] train-rmse:1.387643+0.021066    test-rmse:1.532674+0.115491 
## [40] train-rmse:1.379480+0.021344    test-rmse:1.525606+0.112728 
## [41] train-rmse:1.371396+0.021240    test-rmse:1.522447+0.112768 
## [42] train-rmse:1.363679+0.021230    test-rmse:1.514196+0.100152 
## [43] train-rmse:1.356291+0.021056    test-rmse:1.509152+0.099261 
## [44] train-rmse:1.349128+0.021028    test-rmse:1.506621+0.104261 
## [45] train-rmse:1.341944+0.021123    test-rmse:1.503523+0.104458 
## [46] train-rmse:1.334882+0.021014    test-rmse:1.499045+0.103101 
## [47] train-rmse:1.328073+0.020947    test-rmse:1.491173+0.103963 
## [48] train-rmse:1.321458+0.020806    test-rmse:1.480278+0.100511 
## [49] train-rmse:1.314967+0.020624    test-rmse:1.476384+0.100985 
## [50] train-rmse:1.308700+0.020500    test-rmse:1.473398+0.094959 
## [51] train-rmse:1.302290+0.020127    test-rmse:1.474934+0.095334 
## [52] train-rmse:1.296236+0.020051    test-rmse:1.471957+0.095102 
## [53] train-rmse:1.290415+0.019876    test-rmse:1.466993+0.093341 
## [54] train-rmse:1.284700+0.019757    test-rmse:1.464126+0.095966 
## [55] train-rmse:1.279089+0.019588    test-rmse:1.453689+0.095701 
## [56] train-rmse:1.273597+0.019608    test-rmse:1.451878+0.091031 
## [57] train-rmse:1.268125+0.019581    test-rmse:1.452674+0.089601 
## [58] train-rmse:1.262791+0.019625    test-rmse:1.455202+0.086267 
## [59] train-rmse:1.257525+0.019574    test-rmse:1.450827+0.090088 
## [60] train-rmse:1.252582+0.019596    test-rmse:1.448251+0.090973 
## [61] train-rmse:1.247573+0.019730    test-rmse:1.444175+0.094475 
## [62] train-rmse:1.242695+0.019860    test-rmse:1.440157+0.093992 
## [63] train-rmse:1.237793+0.020039    test-rmse:1.434572+0.093620 
## [64] train-rmse:1.232882+0.019999    test-rmse:1.430743+0.094156 
## [65] train-rmse:1.228239+0.019972    test-rmse:1.424936+0.089781 
## [66] train-rmse:1.223751+0.019868    test-rmse:1.420951+0.087781 
## [67] train-rmse:1.219268+0.019738    test-rmse:1.417404+0.091563 
## [68] train-rmse:1.214795+0.019714    test-rmse:1.415552+0.091388 
## [69] train-rmse:1.210376+0.019706    test-rmse:1.415575+0.091058 
## [70] train-rmse:1.206094+0.019701    test-rmse:1.407581+0.089535 
## [71] train-rmse:1.202006+0.019724    test-rmse:1.399802+0.092693 
## [72] train-rmse:1.197989+0.019687    test-rmse:1.396941+0.088526 
## [73] train-rmse:1.193868+0.019557    test-rmse:1.395345+0.089557 
## [74] train-rmse:1.189988+0.019511    test-rmse:1.393261+0.088686 
## [75] train-rmse:1.186228+0.019479    test-rmse:1.391630+0.091243 
## [76] train-rmse:1.182466+0.019437    test-rmse:1.389009+0.093073 
## [77] train-rmse:1.178809+0.019442    test-rmse:1.389618+0.090132 
## [78] train-rmse:1.175190+0.019424    test-rmse:1.385951+0.089783 
## [79] train-rmse:1.171604+0.019361    test-rmse:1.382724+0.089785 
## [80] train-rmse:1.168017+0.019283    test-rmse:1.378652+0.091125 
## [81] train-rmse:1.164625+0.019334    test-rmse:1.377794+0.091476 
## [82] train-rmse:1.161207+0.019404    test-rmse:1.377138+0.091988 
## [83] train-rmse:1.157853+0.019440    test-rmse:1.374745+0.090458 
## [84] train-rmse:1.154535+0.019424    test-rmse:1.372919+0.091517 
## [85] train-rmse:1.151424+0.019382    test-rmse:1.373663+0.088560 
## [86] train-rmse:1.148320+0.019266    test-rmse:1.371300+0.087640 
## [87] train-rmse:1.145269+0.019215    test-rmse:1.366598+0.085485 
## [88] train-rmse:1.142242+0.019140    test-rmse:1.362172+0.085629 
## [89] train-rmse:1.139284+0.019067    test-rmse:1.360004+0.087623 
## [90] train-rmse:1.136390+0.019068    test-rmse:1.357064+0.088229 
## [91] train-rmse:1.133489+0.019053    test-rmse:1.353080+0.087235 
## [92] train-rmse:1.130617+0.019039    test-rmse:1.351632+0.086877 
## [93] train-rmse:1.127773+0.019073    test-rmse:1.348601+0.085683 
## [94] train-rmse:1.124988+0.019056    test-rmse:1.346842+0.085296 
## [95] train-rmse:1.122278+0.019000    test-rmse:1.345019+0.084893 
## [96] train-rmse:1.119594+0.018951    test-rmse:1.342984+0.085150 
## [97] train-rmse:1.116923+0.018858    test-rmse:1.340475+0.086434 
## [98] train-rmse:1.114277+0.018790    test-rmse:1.337632+0.088178 
## [99] train-rmse:1.111718+0.018747    test-rmse:1.338513+0.086246 
## [100]    train-rmse:1.109157+0.018710    test-rmse:1.340281+0.082710 
## [101]    train-rmse:1.106690+0.018696    test-rmse:1.339078+0.082255 
## [102]    train-rmse:1.104218+0.018687    test-rmse:1.338902+0.085601 
## [103]    train-rmse:1.101776+0.018699    test-rmse:1.336605+0.088682 
## [104]    train-rmse:1.099374+0.018686    test-rmse:1.333790+0.085771 
## [105]    train-rmse:1.097076+0.018700    test-rmse:1.331137+0.086642 
## [106]    train-rmse:1.094720+0.018601    test-rmse:1.328976+0.085780 
## [107]    train-rmse:1.092423+0.018598    test-rmse:1.325074+0.085324 
## [108]    train-rmse:1.090100+0.018541    test-rmse:1.323093+0.085432 
## [109]    train-rmse:1.087812+0.018501    test-rmse:1.319354+0.085660 
## [110]    train-rmse:1.085632+0.018475    test-rmse:1.318210+0.085208 
## [111]    train-rmse:1.083485+0.018453    test-rmse:1.317449+0.088305 
## [112]    train-rmse:1.081379+0.018414    test-rmse:1.315689+0.089217 
## [113]    train-rmse:1.079323+0.018338    test-rmse:1.314559+0.090947 
## [114]    train-rmse:1.077301+0.018285    test-rmse:1.312050+0.089770 
## [115]    train-rmse:1.075298+0.018255    test-rmse:1.310374+0.089548 
## [116]    train-rmse:1.073308+0.018245    test-rmse:1.310372+0.089554 
## [117]    train-rmse:1.071369+0.018216    test-rmse:1.309732+0.090067 
## [118]    train-rmse:1.069449+0.018174    test-rmse:1.308533+0.090628 
## [119]    train-rmse:1.067520+0.018112    test-rmse:1.307072+0.091880 
## [120]    train-rmse:1.065620+0.018095    test-rmse:1.305881+0.093431 
## [121]    train-rmse:1.063746+0.018037    test-rmse:1.306041+0.092346 
## [122]    train-rmse:1.061886+0.017993    test-rmse:1.305069+0.092001 
## [123]    train-rmse:1.060038+0.017995    test-rmse:1.299077+0.091420 
## [124]    train-rmse:1.058172+0.017947    test-rmse:1.300259+0.088456 
## [125]    train-rmse:1.056348+0.017926    test-rmse:1.298800+0.087812 
## [126]    train-rmse:1.054577+0.017883    test-rmse:1.296158+0.088168 
## [127]    train-rmse:1.052841+0.017824    test-rmse:1.295106+0.087882 
## [128]    train-rmse:1.051123+0.017792    test-rmse:1.293674+0.089073 
## [129]    train-rmse:1.049469+0.017757    test-rmse:1.292342+0.091518 
## [130]    train-rmse:1.047856+0.017724    test-rmse:1.291116+0.092048 
## [131]    train-rmse:1.046246+0.017685    test-rmse:1.289945+0.092032 
## [132]    train-rmse:1.044652+0.017642    test-rmse:1.288354+0.091019 
## [133]    train-rmse:1.043009+0.017562    test-rmse:1.289203+0.091173 
## [134]    train-rmse:1.041423+0.017539    test-rmse:1.287281+0.092660 
## [135]    train-rmse:1.039835+0.017488    test-rmse:1.286691+0.092771 
## [136]    train-rmse:1.038324+0.017465    test-rmse:1.286666+0.091097 
## [137]    train-rmse:1.036815+0.017416    test-rmse:1.285565+0.090887 
## [138]    train-rmse:1.035312+0.017388    test-rmse:1.284278+0.091467 
## [139]    train-rmse:1.033857+0.017398    test-rmse:1.283381+0.089925 
## [140]    train-rmse:1.032423+0.017397    test-rmse:1.281791+0.090128 
## [141]    train-rmse:1.030983+0.017426    test-rmse:1.280748+0.090451 
## [142]    train-rmse:1.029586+0.017414    test-rmse:1.279984+0.089012 
## [143]    train-rmse:1.028181+0.017396    test-rmse:1.276805+0.085330 
## [144]    train-rmse:1.026789+0.017373    test-rmse:1.276616+0.084898 
## [145]    train-rmse:1.025406+0.017346    test-rmse:1.276966+0.085442 
## [146]    train-rmse:1.024029+0.017314    test-rmse:1.276658+0.086136 
## [147]    train-rmse:1.022682+0.017279    test-rmse:1.275267+0.086489 
## [148]    train-rmse:1.021333+0.017243    test-rmse:1.274303+0.087402 
## [149]    train-rmse:1.019990+0.017203    test-rmse:1.274419+0.089193 
## [150]    train-rmse:1.018644+0.017181    test-rmse:1.272992+0.087802 
## [151]    train-rmse:1.017279+0.017120    test-rmse:1.273807+0.085549 
## [152]    train-rmse:1.015981+0.017131    test-rmse:1.272625+0.086689 
## [153]    train-rmse:1.014699+0.017147    test-rmse:1.272087+0.087142 
## [154]    train-rmse:1.013416+0.017156    test-rmse:1.272001+0.087932 
## [155]    train-rmse:1.012177+0.017148    test-rmse:1.269974+0.088709 
## [156]    train-rmse:1.010947+0.017139    test-rmse:1.270040+0.088520 
## [157]    train-rmse:1.009711+0.017105    test-rmse:1.269940+0.087628 
## [158]    train-rmse:1.008505+0.017088    test-rmse:1.269773+0.087863 
## [159]    train-rmse:1.007292+0.017060    test-rmse:1.266839+0.089256 
## [160]    train-rmse:1.006054+0.017026    test-rmse:1.266104+0.089973 
## [161]    train-rmse:1.004854+0.017012    test-rmse:1.265782+0.089703 
## [162]    train-rmse:1.003647+0.017019    test-rmse:1.264252+0.089160 
## [163]    train-rmse:1.002456+0.017005    test-rmse:1.264391+0.088708 
## [164]    train-rmse:1.001277+0.016989    test-rmse:1.264425+0.090625 
## [165]    train-rmse:1.000144+0.017000    test-rmse:1.262932+0.087466 
## [166]    train-rmse:0.998999+0.016997    test-rmse:1.261882+0.086170 
## [167]    train-rmse:0.997868+0.017010    test-rmse:1.261219+0.087466 
## [168]    train-rmse:0.996758+0.017017    test-rmse:1.259778+0.087471 
## [169]    train-rmse:0.995639+0.016997    test-rmse:1.258192+0.086186 
## [170]    train-rmse:0.994539+0.017016    test-rmse:1.258923+0.083760 
## [171]    train-rmse:0.993469+0.017026    test-rmse:1.258372+0.084174 
## [172]    train-rmse:0.992403+0.017038    test-rmse:1.260619+0.083788 
## [173]    train-rmse:0.991318+0.016997    test-rmse:1.258051+0.084183 
## [174]    train-rmse:0.990256+0.017004    test-rmse:1.256242+0.084367 
## [175]    train-rmse:0.989207+0.017028    test-rmse:1.255164+0.085067 
## [176]    train-rmse:0.988175+0.017032    test-rmse:1.253928+0.086247 
## [177]    train-rmse:0.987117+0.017074    test-rmse:1.251989+0.087014 
## [178]    train-rmse:0.986107+0.017080    test-rmse:1.252659+0.086114 
## [179]    train-rmse:0.985081+0.017089    test-rmse:1.251571+0.085719 
## [180]    train-rmse:0.984080+0.017107    test-rmse:1.251037+0.085375 
## [181]    train-rmse:0.983103+0.017116    test-rmse:1.251404+0.085881 
## [182]    train-rmse:0.982079+0.017127    test-rmse:1.251020+0.086099 
## [183]    train-rmse:0.981109+0.017138    test-rmse:1.251959+0.085751 
## [184]    train-rmse:0.980151+0.017154    test-rmse:1.251405+0.085075 
## [185]    train-rmse:0.979163+0.017187    test-rmse:1.251091+0.085717 
## [186]    train-rmse:0.978188+0.017194    test-rmse:1.250243+0.085984 
## [187]    train-rmse:0.977224+0.017175    test-rmse:1.249147+0.084842 
## [188]    train-rmse:0.976281+0.017205    test-rmse:1.248428+0.085174 
## [189]    train-rmse:0.975320+0.017238    test-rmse:1.247733+0.084711 
## [190]    train-rmse:0.974381+0.017245    test-rmse:1.247560+0.084465 
## [191]    train-rmse:0.973453+0.017218    test-rmse:1.246577+0.084115 
## [192]    train-rmse:0.972534+0.017226    test-rmse:1.246256+0.084229 
## [193]    train-rmse:0.971590+0.017250    test-rmse:1.244361+0.085009 
## [194]    train-rmse:0.970675+0.017255    test-rmse:1.244383+0.084710 
## [195]    train-rmse:0.969755+0.017266    test-rmse:1.243726+0.085235 
## [196]    train-rmse:0.968868+0.017277    test-rmse:1.244107+0.087480 
## [197]    train-rmse:0.968005+0.017294    test-rmse:1.244492+0.086143 
## [198]    train-rmse:0.967142+0.017312    test-rmse:1.243241+0.085238 
## [199]    train-rmse:0.966265+0.017307    test-rmse:1.243073+0.085701 
## [200]    train-rmse:0.965409+0.017318    test-rmse:1.240188+0.085140 
## [201]    train-rmse:0.964552+0.017333    test-rmse:1.240566+0.085664 
## [202]    train-rmse:0.963693+0.017348    test-rmse:1.240578+0.086104 
## [203]    train-rmse:0.962875+0.017343    test-rmse:1.240454+0.086439 
## [204]    train-rmse:0.962063+0.017345    test-rmse:1.239781+0.086343 
## [205]    train-rmse:0.961220+0.017369    test-rmse:1.240027+0.086036 
## [206]    train-rmse:0.960405+0.017389    test-rmse:1.237031+0.083822 
## [207]    train-rmse:0.959617+0.017408    test-rmse:1.236286+0.084021 
## [208]    train-rmse:0.958788+0.017425    test-rmse:1.236025+0.083605 
## [209]    train-rmse:0.958011+0.017448    test-rmse:1.236285+0.083926 
## [210]    train-rmse:0.957207+0.017426    test-rmse:1.237540+0.084863 
## [211]    train-rmse:0.956421+0.017450    test-rmse:1.236301+0.085136 
## [212]    train-rmse:0.955654+0.017469    test-rmse:1.237318+0.085328 
## [213]    train-rmse:0.954873+0.017494    test-rmse:1.236998+0.084395 
## [214]    train-rmse:0.954078+0.017504    test-rmse:1.237970+0.085086 
## Stopping. Best iteration:
## [208]    train-rmse:0.958788+0.017425    test-rmse:1.236025+0.083605
## 
## 43 
## [1]  train-rmse:76.896455+0.077079   test-rmse:76.897090+0.416550 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:60.052252+0.059983   test-rmse:60.052622+0.432945 
## [3]  train-rmse:46.904609+0.046553   test-rmse:46.904639+0.445440 
## [4]  train-rmse:36.644199+0.035970   test-rmse:36.643796+0.454772 
## [5]  train-rmse:28.639430+0.027617   test-rmse:28.638477+0.461462 
## [6]  train-rmse:22.397527+0.021009   test-rmse:22.395899+0.465834 
## [7]  train-rmse:17.534218+0.015835   test-rmse:17.531762+0.468024 
## [8]  train-rmse:13.749436+0.012173   test-rmse:13.738001+0.462524 
## [9]  train-rmse:10.804913+0.008776   test-rmse:10.788056+0.441575 
## [10] train-rmse:8.515530+0.006917    test-rmse:8.500487+0.411150 
## [11] train-rmse:6.736689+0.006281    test-rmse:6.717966+0.403835 
## [12] train-rmse:5.360953+0.007973    test-rmse:5.350553+0.381451 
## [13] train-rmse:4.298171+0.008735    test-rmse:4.300034+0.362988 
## [14] train-rmse:3.486509+0.012234    test-rmse:3.489929+0.332223 
## [15] train-rmse:2.872144+0.012687    test-rmse:2.877938+0.303592 
## [16] train-rmse:2.414804+0.015389    test-rmse:2.447518+0.266372 
## [17] train-rmse:2.075482+0.014838    test-rmse:2.139145+0.229220 
## [18] train-rmse:1.831749+0.013976    test-rmse:1.916506+0.195245 
## [19] train-rmse:1.656901+0.016862    test-rmse:1.753624+0.169555 
## [20] train-rmse:1.533837+0.015165    test-rmse:1.653067+0.150688 
## [21] train-rmse:1.448478+0.014794    test-rmse:1.584506+0.127604 
## [22] train-rmse:1.385631+0.016975    test-rmse:1.533386+0.118473 
## [23] train-rmse:1.340671+0.017713    test-rmse:1.504677+0.105621 
## [24] train-rmse:1.303057+0.014600    test-rmse:1.483400+0.097439 
## [25] train-rmse:1.276015+0.015236    test-rmse:1.459993+0.098653 
## [26] train-rmse:1.253126+0.014756    test-rmse:1.440055+0.100620 
## [27] train-rmse:1.231256+0.015646    test-rmse:1.430348+0.100359 
## [28] train-rmse:1.213981+0.016144    test-rmse:1.417800+0.097591 
## [29] train-rmse:1.196649+0.014329    test-rmse:1.409616+0.093038 
## [30] train-rmse:1.182331+0.013510    test-rmse:1.399810+0.092772 
## [31] train-rmse:1.168732+0.013596    test-rmse:1.390749+0.091128 
## [32] train-rmse:1.154670+0.014732    test-rmse:1.383779+0.088786 
## [33] train-rmse:1.137778+0.013248    test-rmse:1.369275+0.098931 
## [34] train-rmse:1.126383+0.013827    test-rmse:1.353383+0.102507 
## [35] train-rmse:1.112774+0.015724    test-rmse:1.346151+0.095970 
## [36] train-rmse:1.102322+0.015815    test-rmse:1.345649+0.094224 
## [37] train-rmse:1.091775+0.015193    test-rmse:1.340439+0.087788 
## [38] train-rmse:1.080964+0.016909    test-rmse:1.333046+0.089982 
## [39] train-rmse:1.066740+0.015602    test-rmse:1.327736+0.090350 
## [40] train-rmse:1.057373+0.015654    test-rmse:1.321690+0.089695 
## [41] train-rmse:1.048601+0.015936    test-rmse:1.314425+0.092473 
## [42] train-rmse:1.040404+0.016263    test-rmse:1.307431+0.089821 
## [43] train-rmse:1.032229+0.016604    test-rmse:1.301352+0.095919 
## [44] train-rmse:1.020977+0.017192    test-rmse:1.287187+0.091455 
## [45] train-rmse:1.012302+0.017774    test-rmse:1.278845+0.091592 
## [46] train-rmse:1.004203+0.018363    test-rmse:1.267839+0.096683 
## [47] train-rmse:0.996937+0.018598    test-rmse:1.266249+0.092504 
## [48] train-rmse:0.989194+0.017572    test-rmse:1.263433+0.090876 
## [49] train-rmse:0.982152+0.017390    test-rmse:1.259843+0.090624 
## [50] train-rmse:0.972621+0.017961    test-rmse:1.256629+0.090206 
## [51] train-rmse:0.966216+0.018068    test-rmse:1.256220+0.090147 
## [52] train-rmse:0.957188+0.018157    test-rmse:1.246141+0.089801 
## [53] train-rmse:0.949513+0.015841    test-rmse:1.234537+0.096070 
## [54] train-rmse:0.942143+0.016598    test-rmse:1.227220+0.093489 
## [55] train-rmse:0.935907+0.017283    test-rmse:1.223858+0.094287 
## [56] train-rmse:0.928446+0.019061    test-rmse:1.223123+0.096749 
## [57] train-rmse:0.922509+0.019237    test-rmse:1.220498+0.095185 
## [58] train-rmse:0.915015+0.019410    test-rmse:1.215392+0.095153 
## [59] train-rmse:0.908827+0.019759    test-rmse:1.210263+0.095297 
## [60] train-rmse:0.902342+0.020710    test-rmse:1.205757+0.098360 
## [61] train-rmse:0.896036+0.021692    test-rmse:1.203157+0.097884 
## [62] train-rmse:0.889180+0.021994    test-rmse:1.197058+0.096313 
## [63] train-rmse:0.884166+0.021260    test-rmse:1.193614+0.096038 
## [64] train-rmse:0.877260+0.020542    test-rmse:1.189816+0.094457 
## [65] train-rmse:0.872307+0.021097    test-rmse:1.187519+0.094683 
## [66] train-rmse:0.864070+0.018682    test-rmse:1.179374+0.095936 
## [67] train-rmse:0.859261+0.019129    test-rmse:1.176551+0.094210 
## [68] train-rmse:0.853584+0.020492    test-rmse:1.176108+0.093833 
## [69] train-rmse:0.849057+0.020688    test-rmse:1.173560+0.093832 
## [70] train-rmse:0.843174+0.022590    test-rmse:1.170489+0.092917 
## [71] train-rmse:0.839214+0.022552    test-rmse:1.167563+0.094292 
## [72] train-rmse:0.834597+0.022034    test-rmse:1.166843+0.094047 
## [73] train-rmse:0.828845+0.020250    test-rmse:1.164318+0.093714 
## [74] train-rmse:0.823668+0.020986    test-rmse:1.159696+0.092331 
## [75] train-rmse:0.818527+0.020742    test-rmse:1.157636+0.092531 
## [76] train-rmse:0.813715+0.021268    test-rmse:1.153134+0.096548 
## [77] train-rmse:0.808957+0.021816    test-rmse:1.151882+0.096589 
## [78] train-rmse:0.805192+0.022100    test-rmse:1.148687+0.097722 
## [79] train-rmse:0.801212+0.021903    test-rmse:1.146091+0.096716 
## [80] train-rmse:0.794078+0.022364    test-rmse:1.144308+0.096455 
## [81] train-rmse:0.787042+0.018118    test-rmse:1.135093+0.101277 
## [82] train-rmse:0.782876+0.016293    test-rmse:1.132883+0.101131 
## [83] train-rmse:0.779106+0.016208    test-rmse:1.131344+0.100088 
## [84] train-rmse:0.775017+0.017418    test-rmse:1.129574+0.100854 
## [85] train-rmse:0.771951+0.017745    test-rmse:1.128280+0.101523 
## [86] train-rmse:0.767290+0.015134    test-rmse:1.126649+0.103225 
## [87] train-rmse:0.762799+0.015150    test-rmse:1.124387+0.103723 
## [88] train-rmse:0.757049+0.015132    test-rmse:1.118692+0.102369 
## [89] train-rmse:0.752002+0.014698    test-rmse:1.116787+0.104137 
## [90] train-rmse:0.748524+0.013813    test-rmse:1.117193+0.104162 
## [91] train-rmse:0.745379+0.013725    test-rmse:1.116502+0.103009 
## [92] train-rmse:0.742159+0.014425    test-rmse:1.113599+0.102204 
## [93] train-rmse:0.737806+0.013075    test-rmse:1.108573+0.102024 
## [94] train-rmse:0.733652+0.013738    test-rmse:1.104179+0.101479 
## [95] train-rmse:0.730455+0.013350    test-rmse:1.104047+0.102693 
## [96] train-rmse:0.727587+0.013552    test-rmse:1.104315+0.102198 
## [97] train-rmse:0.723227+0.015302    test-rmse:1.103167+0.101314 
## [98] train-rmse:0.719937+0.015402    test-rmse:1.100155+0.099417 
## [99] train-rmse:0.713063+0.016708    test-rmse:1.091626+0.089074 
## [100]    train-rmse:0.709530+0.016101    test-rmse:1.089437+0.090419 
## [101]    train-rmse:0.706871+0.015932    test-rmse:1.087330+0.090883 
## [102]    train-rmse:0.703599+0.016800    test-rmse:1.085358+0.088092 
## [103]    train-rmse:0.698929+0.016660    test-rmse:1.084593+0.089574 
## [104]    train-rmse:0.696682+0.016910    test-rmse:1.083222+0.089153 
## [105]    train-rmse:0.693504+0.015047    test-rmse:1.077591+0.090566 
## [106]    train-rmse:0.690006+0.016596    test-rmse:1.072631+0.087881 
## [107]    train-rmse:0.687816+0.016641    test-rmse:1.072990+0.086302 
## [108]    train-rmse:0.685640+0.016699    test-rmse:1.072210+0.087185 
## [109]    train-rmse:0.682460+0.015115    test-rmse:1.072933+0.085466 
## [110]    train-rmse:0.679296+0.014413    test-rmse:1.072693+0.085933 
## [111]    train-rmse:0.675408+0.013611    test-rmse:1.071585+0.088803 
## [112]    train-rmse:0.672365+0.013646    test-rmse:1.070091+0.087568 
## [113]    train-rmse:0.667613+0.014280    test-rmse:1.069036+0.087426 
## [114]    train-rmse:0.664465+0.014250    test-rmse:1.067411+0.087780 
## [115]    train-rmse:0.661848+0.014439    test-rmse:1.064312+0.086796 
## [116]    train-rmse:0.657709+0.013539    test-rmse:1.060548+0.088035 
## [117]    train-rmse:0.655543+0.013282    test-rmse:1.057744+0.088715 
## [118]    train-rmse:0.653771+0.013329    test-rmse:1.058602+0.089682 
## [119]    train-rmse:0.650193+0.014267    test-rmse:1.062999+0.091758 
## [120]    train-rmse:0.648226+0.014725    test-rmse:1.061926+0.091221 
## [121]    train-rmse:0.642698+0.017108    test-rmse:1.059256+0.089560 
## [122]    train-rmse:0.640161+0.017777    test-rmse:1.056778+0.089168 
## [123]    train-rmse:0.638213+0.017531    test-rmse:1.056093+0.089084 
## [124]    train-rmse:0.635337+0.016807    test-rmse:1.054863+0.089961 
## [125]    train-rmse:0.632852+0.016136    test-rmse:1.054831+0.089910 
## [126]    train-rmse:0.631123+0.016340    test-rmse:1.053751+0.089076 
## [127]    train-rmse:0.627985+0.016394    test-rmse:1.051657+0.088836 
## [128]    train-rmse:0.625340+0.015642    test-rmse:1.050482+0.089529 
## [129]    train-rmse:0.623835+0.015649    test-rmse:1.049490+0.089500 
## [130]    train-rmse:0.621711+0.014860    test-rmse:1.047933+0.091170 
## [131]    train-rmse:0.619775+0.015067    test-rmse:1.046553+0.092697 
## [132]    train-rmse:0.615901+0.014073    test-rmse:1.047324+0.092829 
## [133]    train-rmse:0.613880+0.014287    test-rmse:1.045835+0.092345 
## [134]    train-rmse:0.611034+0.013653    test-rmse:1.044585+0.091834 
## [135]    train-rmse:0.609733+0.013725    test-rmse:1.043729+0.092818 
## [136]    train-rmse:0.606223+0.011170    test-rmse:1.040947+0.093724 
## [137]    train-rmse:0.603870+0.011657    test-rmse:1.042750+0.092742 
## [138]    train-rmse:0.601869+0.011818    test-rmse:1.043076+0.092837 
## [139]    train-rmse:0.598776+0.012939    test-rmse:1.041587+0.093104 
## [140]    train-rmse:0.596919+0.012782    test-rmse:1.040523+0.091330 
## [141]    train-rmse:0.594052+0.010792    test-rmse:1.038798+0.092336 
## [142]    train-rmse:0.592734+0.010923    test-rmse:1.038845+0.091786 
## [143]    train-rmse:0.590290+0.010252    test-rmse:1.039046+0.090711 
## [144]    train-rmse:0.587609+0.010154    test-rmse:1.037316+0.089203 
## [145]    train-rmse:0.585484+0.009449    test-rmse:1.037423+0.088909 
## [146]    train-rmse:0.583074+0.009568    test-rmse:1.037229+0.089182 
## [147]    train-rmse:0.580326+0.009704    test-rmse:1.036380+0.089613 
## [148]    train-rmse:0.578721+0.009562    test-rmse:1.035430+0.090481 
## [149]    train-rmse:0.576229+0.010408    test-rmse:1.034338+0.089537 
## [150]    train-rmse:0.574195+0.011202    test-rmse:1.034731+0.088751 
## [151]    train-rmse:0.572810+0.011059    test-rmse:1.034228+0.088703 
## [152]    train-rmse:0.571063+0.010220    test-rmse:1.032443+0.087950 
## [153]    train-rmse:0.568610+0.010645    test-rmse:1.033172+0.089072 
## [154]    train-rmse:0.566834+0.011404    test-rmse:1.032452+0.090796 
## [155]    train-rmse:0.563896+0.009996    test-rmse:1.029946+0.091229 
## [156]    train-rmse:0.561340+0.009279    test-rmse:1.028866+0.091801 
## [157]    train-rmse:0.559251+0.009060    test-rmse:1.028490+0.091813 
## [158]    train-rmse:0.557661+0.009060    test-rmse:1.028792+0.091168 
## [159]    train-rmse:0.556100+0.009419    test-rmse:1.029621+0.090733 
## [160]    train-rmse:0.554368+0.009950    test-rmse:1.032364+0.090026 
## [161]    train-rmse:0.551471+0.008411    test-rmse:1.030291+0.090852 
## [162]    train-rmse:0.548909+0.007078    test-rmse:1.029249+0.090646 
## [163]    train-rmse:0.547088+0.006991    test-rmse:1.028320+0.090155 
## [164]    train-rmse:0.543124+0.007231    test-rmse:1.029504+0.090505 
## [165]    train-rmse:0.540725+0.007683    test-rmse:1.032572+0.092704 
## [166]    train-rmse:0.539001+0.008547    test-rmse:1.030379+0.091425 
## [167]    train-rmse:0.537372+0.007706    test-rmse:1.030065+0.091930 
## [168]    train-rmse:0.535764+0.008010    test-rmse:1.029772+0.093572 
## [169]    train-rmse:0.533904+0.008226    test-rmse:1.029161+0.093272 
## Stopping. Best iteration:
## [163]    train-rmse:0.547088+0.006991    test-rmse:1.028320+0.090155
## 
## 44 
## [1]  train-rmse:76.896455+0.077079   test-rmse:76.897090+0.416550 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:60.052252+0.059983   test-rmse:60.052622+0.432945 
## [3]  train-rmse:46.904609+0.046553   test-rmse:46.904639+0.445440 
## [4]  train-rmse:36.644199+0.035970   test-rmse:36.643796+0.454772 
## [5]  train-rmse:28.639430+0.027617   test-rmse:28.638477+0.461462 
## [6]  train-rmse:22.397527+0.021009   test-rmse:22.395899+0.465834 
## [7]  train-rmse:17.534218+0.015835   test-rmse:17.531762+0.468024 
## [8]  train-rmse:13.749436+0.012173   test-rmse:13.738001+0.462524 
## [9]  train-rmse:10.804913+0.008776   test-rmse:10.788056+0.441575 
## [10] train-rmse:8.514516+0.007377    test-rmse:8.495779+0.409644 
## [11] train-rmse:6.732652+0.006112    test-rmse:6.720037+0.393282 
## [12] train-rmse:5.350131+0.006080    test-rmse:5.353168+0.361359 
## [13] train-rmse:4.278890+0.007523    test-rmse:4.281658+0.332328 
## [14] train-rmse:3.451165+0.008825    test-rmse:3.463964+0.310787 
## [15] train-rmse:2.817305+0.008576    test-rmse:2.858522+0.286480 
## [16] train-rmse:2.340357+0.011068    test-rmse:2.398580+0.265362 
## [17] train-rmse:1.984555+0.012848    test-rmse:2.068355+0.231438 
## [18] train-rmse:1.717021+0.011798    test-rmse:1.822433+0.204692 
## [19] train-rmse:1.520393+0.009900    test-rmse:1.650863+0.179030 
## [20] train-rmse:1.384575+0.009172    test-rmse:1.539478+0.157507 
## [21] train-rmse:1.285569+0.010894    test-rmse:1.463729+0.147479 
## [22] train-rmse:1.214097+0.011009    test-rmse:1.408845+0.131961 
## [23] train-rmse:1.160926+0.011252    test-rmse:1.370460+0.119535 
## [24] train-rmse:1.115528+0.010843    test-rmse:1.345494+0.108228 
## [25] train-rmse:1.080657+0.010818    test-rmse:1.322740+0.104876 
## [26] train-rmse:1.048981+0.012306    test-rmse:1.313233+0.098370 
## [27] train-rmse:1.024552+0.010007    test-rmse:1.302197+0.096595 
## [28] train-rmse:1.004104+0.008001    test-rmse:1.283109+0.097688 
## [29] train-rmse:0.985585+0.007460    test-rmse:1.269511+0.095349 
## [30] train-rmse:0.961861+0.014339    test-rmse:1.258746+0.087929 
## [31] train-rmse:0.945407+0.012567    test-rmse:1.251799+0.090061 
## [32] train-rmse:0.929225+0.013912    test-rmse:1.246444+0.085335 
## [33] train-rmse:0.913197+0.015773    test-rmse:1.235742+0.090946 
## [34] train-rmse:0.899525+0.015994    test-rmse:1.229403+0.092043 
## [35] train-rmse:0.883919+0.014369    test-rmse:1.211965+0.088235 
## [36] train-rmse:0.872830+0.013551    test-rmse:1.205876+0.087553 
## [37] train-rmse:0.859941+0.015584    test-rmse:1.199719+0.084791 
## [38] train-rmse:0.845719+0.017527    test-rmse:1.186335+0.087472 
## [39] train-rmse:0.834169+0.016203    test-rmse:1.178189+0.085238 
## [40] train-rmse:0.823019+0.014560    test-rmse:1.173485+0.092269 
## [41] train-rmse:0.814117+0.015156    test-rmse:1.171622+0.093019 
## [42] train-rmse:0.802839+0.018008    test-rmse:1.167327+0.092011 
## [43] train-rmse:0.791521+0.016419    test-rmse:1.155335+0.093074 
## [44] train-rmse:0.780238+0.016843    test-rmse:1.148096+0.090400 
## [45] train-rmse:0.770725+0.016979    test-rmse:1.144067+0.090471 
## [46] train-rmse:0.759857+0.017639    test-rmse:1.136808+0.090478 
## [47] train-rmse:0.750288+0.017930    test-rmse:1.133707+0.090612 
## [48] train-rmse:0.741910+0.017844    test-rmse:1.129774+0.094729 
## [49] train-rmse:0.736055+0.018204    test-rmse:1.124353+0.091460 
## [50] train-rmse:0.726695+0.018814    test-rmse:1.118105+0.088369 
## [51] train-rmse:0.717365+0.018570    test-rmse:1.117124+0.088737 
## [52] train-rmse:0.709800+0.017994    test-rmse:1.114133+0.090819 
## [53] train-rmse:0.704279+0.018813    test-rmse:1.110681+0.092681 
## [54] train-rmse:0.695890+0.019598    test-rmse:1.102515+0.092337 
## [55] train-rmse:0.685553+0.021500    test-rmse:1.100258+0.090886 
## [56] train-rmse:0.680274+0.021695    test-rmse:1.097814+0.092457 
## [57] train-rmse:0.673355+0.021282    test-rmse:1.097774+0.094626 
## [58] train-rmse:0.664443+0.018933    test-rmse:1.094078+0.096556 
## [59] train-rmse:0.657648+0.018154    test-rmse:1.086220+0.089989 
## [60] train-rmse:0.651063+0.017770    test-rmse:1.083454+0.090565 
## [61] train-rmse:0.642273+0.019044    test-rmse:1.080145+0.094421 
## [62] train-rmse:0.636635+0.017978    test-rmse:1.078573+0.094583 
## [63] train-rmse:0.627988+0.018160    test-rmse:1.079778+0.095388 
## [64] train-rmse:0.622104+0.017651    test-rmse:1.079297+0.099473 
## [65] train-rmse:0.614479+0.017314    test-rmse:1.076441+0.099587 
## [66] train-rmse:0.609085+0.017919    test-rmse:1.071497+0.102454 
## [67] train-rmse:0.602700+0.020186    test-rmse:1.067667+0.102761 
## [68] train-rmse:0.597315+0.017743    test-rmse:1.066488+0.104377 
## [69] train-rmse:0.591682+0.016421    test-rmse:1.065306+0.104373 
## [70] train-rmse:0.587739+0.016249    test-rmse:1.064965+0.103779 
## [71] train-rmse:0.583047+0.017136    test-rmse:1.064249+0.103512 
## [72] train-rmse:0.577487+0.016545    test-rmse:1.056367+0.101067 
## [73] train-rmse:0.571512+0.015167    test-rmse:1.055310+0.102186 
## [74] train-rmse:0.563575+0.012955    test-rmse:1.056568+0.104298 
## [75] train-rmse:0.559035+0.012377    test-rmse:1.053285+0.102426 
## [76] train-rmse:0.554174+0.011469    test-rmse:1.053005+0.104055 
## [77] train-rmse:0.545658+0.012498    test-rmse:1.051295+0.103000 
## [78] train-rmse:0.539877+0.011441    test-rmse:1.052898+0.104838 
## [79] train-rmse:0.534587+0.011791    test-rmse:1.051485+0.106612 
## [80] train-rmse:0.529919+0.013198    test-rmse:1.050027+0.105577 
## [81] train-rmse:0.526060+0.013247    test-rmse:1.050025+0.106252 
## [82] train-rmse:0.519860+0.013324    test-rmse:1.045635+0.105193 
## [83] train-rmse:0.515184+0.014287    test-rmse:1.045348+0.106084 
## [84] train-rmse:0.512031+0.013271    test-rmse:1.045132+0.103501 
## [85] train-rmse:0.508000+0.012520    test-rmse:1.044497+0.102141 
## [86] train-rmse:0.503848+0.012574    test-rmse:1.042985+0.103050 
## [87] train-rmse:0.499630+0.013462    test-rmse:1.041206+0.102799 
## [88] train-rmse:0.495822+0.014648    test-rmse:1.039335+0.102135 
## [89] train-rmse:0.491741+0.014684    test-rmse:1.036502+0.099069 
## [90] train-rmse:0.488318+0.014320    test-rmse:1.036790+0.099291 
## [91] train-rmse:0.483515+0.015379    test-rmse:1.034501+0.096813 
## [92] train-rmse:0.480051+0.015118    test-rmse:1.031793+0.098066 
## [93] train-rmse:0.476184+0.015397    test-rmse:1.031041+0.098920 
## [94] train-rmse:0.472716+0.014440    test-rmse:1.029433+0.098918 
## [95] train-rmse:0.468632+0.014048    test-rmse:1.028923+0.098868 
## [96] train-rmse:0.464909+0.014470    test-rmse:1.025526+0.095981 
## [97] train-rmse:0.460892+0.015563    test-rmse:1.023737+0.095976 
## [98] train-rmse:0.457871+0.015330    test-rmse:1.023260+0.096876 
## [99] train-rmse:0.454651+0.016276    test-rmse:1.022760+0.098054 
## [100]    train-rmse:0.451508+0.016588    test-rmse:1.022282+0.098841 
## [101]    train-rmse:0.448484+0.015601    test-rmse:1.018862+0.098478 
## [102]    train-rmse:0.444585+0.016906    test-rmse:1.015842+0.095251 
## [103]    train-rmse:0.440460+0.017257    test-rmse:1.017131+0.096330 
## [104]    train-rmse:0.437277+0.017136    test-rmse:1.016974+0.096172 
## [105]    train-rmse:0.434606+0.016696    test-rmse:1.015970+0.095422 
## [106]    train-rmse:0.429794+0.016872    test-rmse:1.012591+0.093320 
## [107]    train-rmse:0.425370+0.015742    test-rmse:1.010576+0.091340 
## [108]    train-rmse:0.421343+0.015341    test-rmse:1.010780+0.091248 
## [109]    train-rmse:0.416032+0.012165    test-rmse:1.009591+0.093372 
## [110]    train-rmse:0.413010+0.012566    test-rmse:1.008605+0.093290 
## [111]    train-rmse:0.410550+0.013272    test-rmse:1.006949+0.093100 
## [112]    train-rmse:0.408196+0.012927    test-rmse:1.006313+0.092908 
## [113]    train-rmse:0.406221+0.012848    test-rmse:1.004275+0.092456 
## [114]    train-rmse:0.402689+0.011066    test-rmse:1.004839+0.091282 
## [115]    train-rmse:0.399618+0.012073    test-rmse:1.006084+0.092507 
## [116]    train-rmse:0.395948+0.012454    test-rmse:1.004998+0.091763 
## [117]    train-rmse:0.390914+0.014410    test-rmse:1.003970+0.090616 
## [118]    train-rmse:0.388319+0.014925    test-rmse:1.003628+0.091972 
## [119]    train-rmse:0.385366+0.015021    test-rmse:1.002865+0.092486 
## [120]    train-rmse:0.381859+0.014830    test-rmse:1.003035+0.092209 
## [121]    train-rmse:0.378298+0.015446    test-rmse:1.001452+0.091377 
## [122]    train-rmse:0.375497+0.015136    test-rmse:1.002883+0.092934 
## [123]    train-rmse:0.371726+0.016492    test-rmse:1.002086+0.093302 
## [124]    train-rmse:0.369498+0.017401    test-rmse:1.002761+0.092283 
## [125]    train-rmse:0.366043+0.015904    test-rmse:0.999224+0.091978 
## [126]    train-rmse:0.362373+0.016543    test-rmse:0.998010+0.090897 
## [127]    train-rmse:0.359646+0.017242    test-rmse:0.996402+0.090413 
## [128]    train-rmse:0.357287+0.017537    test-rmse:0.996525+0.090402 
## [129]    train-rmse:0.355452+0.017295    test-rmse:0.995629+0.090203 
## [130]    train-rmse:0.352366+0.017506    test-rmse:0.991565+0.090834 
## [131]    train-rmse:0.349709+0.017209    test-rmse:0.991739+0.090199 
## [132]    train-rmse:0.346578+0.016114    test-rmse:0.990620+0.091296 
## [133]    train-rmse:0.344203+0.016052    test-rmse:0.990552+0.091451 
## [134]    train-rmse:0.340040+0.013450    test-rmse:0.990195+0.091351 
## [135]    train-rmse:0.337647+0.013299    test-rmse:0.988719+0.091894 
## [136]    train-rmse:0.334008+0.013630    test-rmse:0.988165+0.091113 
## [137]    train-rmse:0.332445+0.014094    test-rmse:0.988122+0.091270 
## [138]    train-rmse:0.331046+0.014334    test-rmse:0.986904+0.091175 
## [139]    train-rmse:0.329454+0.014542    test-rmse:0.987497+0.091168 
## [140]    train-rmse:0.327337+0.015164    test-rmse:0.986269+0.090797 
## [141]    train-rmse:0.324819+0.014269    test-rmse:0.985753+0.091661 
## [142]    train-rmse:0.321889+0.013862    test-rmse:0.986023+0.091540 
## [143]    train-rmse:0.319885+0.014529    test-rmse:0.987203+0.092824 
## [144]    train-rmse:0.317740+0.013773    test-rmse:0.986606+0.093402 
## [145]    train-rmse:0.314718+0.012936    test-rmse:0.984602+0.093296 
## [146]    train-rmse:0.312057+0.013076    test-rmse:0.984883+0.093186 
## [147]    train-rmse:0.310686+0.012587    test-rmse:0.985228+0.092306 
## [148]    train-rmse:0.308727+0.012446    test-rmse:0.986279+0.094142 
## [149]    train-rmse:0.305869+0.011276    test-rmse:0.985556+0.093159 
## [150]    train-rmse:0.302646+0.011353    test-rmse:0.985383+0.093360 
## [151]    train-rmse:0.300529+0.011774    test-rmse:0.984960+0.094652 
## Stopping. Best iteration:
## [145]    train-rmse:0.314718+0.012936    test-rmse:0.984602+0.093296
## 
## 45 
## [1]  train-rmse:76.896455+0.077079   test-rmse:76.897090+0.416550 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:60.052252+0.059983   test-rmse:60.052622+0.432945 
## [3]  train-rmse:46.904609+0.046553   test-rmse:46.904639+0.445440 
## [4]  train-rmse:36.644199+0.035970   test-rmse:36.643796+0.454772 
## [5]  train-rmse:28.639430+0.027617   test-rmse:28.638477+0.461462 
## [6]  train-rmse:22.397527+0.021009   test-rmse:22.395899+0.465834 
## [7]  train-rmse:17.534218+0.015835   test-rmse:17.531762+0.468024 
## [8]  train-rmse:13.749436+0.012173   test-rmse:13.738001+0.462524 
## [9]  train-rmse:10.804913+0.008776   test-rmse:10.788056+0.441575 
## [10] train-rmse:8.514278+0.007378    test-rmse:8.492002+0.409061 
## [11] train-rmse:6.731914+0.006375    test-rmse:6.720350+0.384020 
## [12] train-rmse:5.345361+0.007477    test-rmse:5.341201+0.365062 
## [13] train-rmse:4.270096+0.008152    test-rmse:4.270712+0.338442 
## [14] train-rmse:3.435314+0.009720    test-rmse:3.440555+0.312358 
## [15] train-rmse:2.789173+0.009758    test-rmse:2.818763+0.271837 
## [16] train-rmse:2.297521+0.010113    test-rmse:2.350539+0.238722 
## [17] train-rmse:1.924136+0.014636    test-rmse:2.005824+0.220531 
## [18] train-rmse:1.643423+0.014540    test-rmse:1.763966+0.196018 
## [19] train-rmse:1.437299+0.012528    test-rmse:1.595382+0.170247 
## [20] train-rmse:1.284594+0.013694    test-rmse:1.481403+0.147742 
## [21] train-rmse:1.170286+0.016003    test-rmse:1.402556+0.140265 
## [22] train-rmse:1.080363+0.016527    test-rmse:1.338482+0.124748 
## [23] train-rmse:1.021300+0.017998    test-rmse:1.303308+0.114008 
## [24] train-rmse:0.973583+0.015964    test-rmse:1.268169+0.104348 
## [25] train-rmse:0.930666+0.014741    test-rmse:1.240754+0.104498 
## [26] train-rmse:0.899980+0.013149    test-rmse:1.217586+0.106313 
## [27] train-rmse:0.866860+0.011676    test-rmse:1.203414+0.101186 
## [28] train-rmse:0.841494+0.015935    test-rmse:1.186392+0.105321 
## [29] train-rmse:0.819782+0.016713    test-rmse:1.177443+0.101627 
## [30] train-rmse:0.801431+0.020139    test-rmse:1.166370+0.091598 
## [31] train-rmse:0.782234+0.020006    test-rmse:1.151170+0.081672 
## [32] train-rmse:0.762138+0.019307    test-rmse:1.137594+0.079998 
## [33] train-rmse:0.746536+0.015702    test-rmse:1.124938+0.084391 
## [34] train-rmse:0.731106+0.016834    test-rmse:1.118932+0.086046 
## [35] train-rmse:0.717340+0.016103    test-rmse:1.103787+0.086588 
## [36] train-rmse:0.700374+0.016797    test-rmse:1.096787+0.085156 
## [37] train-rmse:0.688341+0.017903    test-rmse:1.088022+0.085525 
## [38] train-rmse:0.675938+0.020645    test-rmse:1.082677+0.085734 
## [39] train-rmse:0.662479+0.019195    test-rmse:1.076324+0.079780 
## [40] train-rmse:0.647993+0.022094    test-rmse:1.067738+0.076889 
## [41] train-rmse:0.636731+0.021809    test-rmse:1.064396+0.076669 
## [42] train-rmse:0.624063+0.020839    test-rmse:1.057954+0.076453 
## [43] train-rmse:0.613446+0.019250    test-rmse:1.049621+0.074441 
## [44] train-rmse:0.605716+0.018654    test-rmse:1.044381+0.076068 
## [45] train-rmse:0.597946+0.017411    test-rmse:1.038981+0.077130 
## [46] train-rmse:0.585755+0.016739    test-rmse:1.038243+0.076573 
## [47] train-rmse:0.572304+0.016458    test-rmse:1.033957+0.076419 
## [48] train-rmse:0.561702+0.018218    test-rmse:1.028592+0.076318 
## [49] train-rmse:0.553124+0.016742    test-rmse:1.024943+0.074508 
## [50] train-rmse:0.546002+0.015719    test-rmse:1.021003+0.074288 
## [51] train-rmse:0.536868+0.015813    test-rmse:1.012464+0.070353 
## [52] train-rmse:0.528657+0.018558    test-rmse:1.008884+0.069663 
## [53] train-rmse:0.522474+0.018166    test-rmse:1.005174+0.070170 
## [54] train-rmse:0.514151+0.015973    test-rmse:1.004076+0.070942 
## [55] train-rmse:0.504746+0.014955    test-rmse:1.002369+0.071813 
## [56] train-rmse:0.496530+0.015052    test-rmse:0.999825+0.070486 
## [57] train-rmse:0.489550+0.015568    test-rmse:0.997062+0.070925 
## [58] train-rmse:0.483942+0.017809    test-rmse:0.994392+0.071482 
## [59] train-rmse:0.477666+0.016473    test-rmse:0.991795+0.070158 
## [60] train-rmse:0.472004+0.015794    test-rmse:0.993499+0.068736 
## [61] train-rmse:0.466873+0.016174    test-rmse:0.991179+0.068308 
## [62] train-rmse:0.461102+0.013906    test-rmse:0.991136+0.066860 
## [63] train-rmse:0.455466+0.013820    test-rmse:0.988842+0.065591 
## [64] train-rmse:0.448760+0.014135    test-rmse:0.986096+0.069685 
## [65] train-rmse:0.443918+0.014137    test-rmse:0.986692+0.070962 
## [66] train-rmse:0.438637+0.015109    test-rmse:0.986219+0.070290 
## [67] train-rmse:0.430859+0.015290    test-rmse:0.979229+0.068750 
## [68] train-rmse:0.423923+0.015653    test-rmse:0.979762+0.068806 
## [69] train-rmse:0.420296+0.016261    test-rmse:0.978848+0.068849 
## [70] train-rmse:0.413799+0.014298    test-rmse:0.977120+0.068681 
## [71] train-rmse:0.409051+0.013438    test-rmse:0.978973+0.067883 
## [72] train-rmse:0.402774+0.013482    test-rmse:0.981598+0.069911 
## [73] train-rmse:0.396791+0.014957    test-rmse:0.980034+0.070229 
## [74] train-rmse:0.389706+0.011695    test-rmse:0.976389+0.070573 
## [75] train-rmse:0.384215+0.012451    test-rmse:0.972010+0.069560 
## [76] train-rmse:0.379144+0.012163    test-rmse:0.971506+0.069121 
## [77] train-rmse:0.371723+0.011537    test-rmse:0.970947+0.067950 
## [78] train-rmse:0.366163+0.011890    test-rmse:0.973150+0.069395 
## [79] train-rmse:0.362009+0.012401    test-rmse:0.970970+0.068665 
## [80] train-rmse:0.354856+0.012637    test-rmse:0.970979+0.070165 
## [81] train-rmse:0.350655+0.010419    test-rmse:0.970932+0.068524 
## [82] train-rmse:0.346362+0.012186    test-rmse:0.970355+0.067827 
## [83] train-rmse:0.341391+0.012523    test-rmse:0.969781+0.067230 
## [84] train-rmse:0.336248+0.011188    test-rmse:0.969116+0.067319 
## [85] train-rmse:0.332853+0.010152    test-rmse:0.967021+0.066733 
## [86] train-rmse:0.328323+0.010607    test-rmse:0.965546+0.064757 
## [87] train-rmse:0.323994+0.011918    test-rmse:0.965026+0.063635 
## [88] train-rmse:0.318588+0.012172    test-rmse:0.963986+0.064184 
## [89] train-rmse:0.312546+0.009993    test-rmse:0.963605+0.064222 
## [90] train-rmse:0.309202+0.010244    test-rmse:0.962101+0.064876 
## [91] train-rmse:0.305930+0.010065    test-rmse:0.960913+0.065985 
## [92] train-rmse:0.300645+0.011572    test-rmse:0.960536+0.066135 
## [93] train-rmse:0.297723+0.011326    test-rmse:0.961526+0.065818 
## [94] train-rmse:0.293980+0.013252    test-rmse:0.962741+0.066644 
## [95] train-rmse:0.290109+0.013057    test-rmse:0.963735+0.066398 
## [96] train-rmse:0.286020+0.013693    test-rmse:0.961482+0.065266 
## [97] train-rmse:0.283428+0.013536    test-rmse:0.959933+0.067711 
## [98] train-rmse:0.279723+0.013083    test-rmse:0.959001+0.068237 
## [99] train-rmse:0.276569+0.013769    test-rmse:0.958432+0.068417 
## [100]    train-rmse:0.273926+0.014233    test-rmse:0.958636+0.068465 
## [101]    train-rmse:0.268185+0.014310    test-rmse:0.956616+0.068110 
## [102]    train-rmse:0.265732+0.014538    test-rmse:0.954134+0.066605 
## [103]    train-rmse:0.262536+0.014215    test-rmse:0.953752+0.066330 
## [104]    train-rmse:0.259713+0.013857    test-rmse:0.952544+0.065333 
## [105]    train-rmse:0.256888+0.012764    test-rmse:0.952384+0.065362 
## [106]    train-rmse:0.254026+0.012361    test-rmse:0.950627+0.064891 
## [107]    train-rmse:0.251622+0.013089    test-rmse:0.949601+0.065538 
## [108]    train-rmse:0.249033+0.012959    test-rmse:0.950387+0.066516 
## [109]    train-rmse:0.246831+0.013329    test-rmse:0.949529+0.065797 
## [110]    train-rmse:0.243737+0.012523    test-rmse:0.949893+0.065115 
## [111]    train-rmse:0.240240+0.012810    test-rmse:0.948097+0.066665 
## [112]    train-rmse:0.238206+0.013331    test-rmse:0.946612+0.066393 
## [113]    train-rmse:0.235768+0.013350    test-rmse:0.945656+0.065832 
## [114]    train-rmse:0.234062+0.013619    test-rmse:0.945523+0.066522 
## [115]    train-rmse:0.231930+0.013614    test-rmse:0.945569+0.065579 
## [116]    train-rmse:0.228971+0.014837    test-rmse:0.945598+0.065704 
## [117]    train-rmse:0.226087+0.014535    test-rmse:0.945476+0.065568 
## [118]    train-rmse:0.222643+0.014809    test-rmse:0.945132+0.066184 
## [119]    train-rmse:0.220248+0.014906    test-rmse:0.944603+0.065636 
## [120]    train-rmse:0.218682+0.015046    test-rmse:0.944121+0.065991 
## [121]    train-rmse:0.216444+0.014282    test-rmse:0.944315+0.065497 
## [122]    train-rmse:0.213932+0.015759    test-rmse:0.942989+0.064483 
## [123]    train-rmse:0.209878+0.014921    test-rmse:0.943317+0.063528 
## [124]    train-rmse:0.206145+0.013678    test-rmse:0.943102+0.063884 
## [125]    train-rmse:0.204831+0.013940    test-rmse:0.943161+0.063442 
## [126]    train-rmse:0.202250+0.013483    test-rmse:0.942587+0.063057 
## [127]    train-rmse:0.199302+0.014123    test-rmse:0.942184+0.063609 
## [128]    train-rmse:0.196941+0.014554    test-rmse:0.942469+0.064308 
## [129]    train-rmse:0.194685+0.013781    test-rmse:0.942019+0.064927 
## [130]    train-rmse:0.192711+0.014518    test-rmse:0.941799+0.064093 
## [131]    train-rmse:0.190860+0.014438    test-rmse:0.942261+0.064200 
## [132]    train-rmse:0.189381+0.014899    test-rmse:0.942452+0.064292 
## [133]    train-rmse:0.187526+0.014701    test-rmse:0.942915+0.064732 
## [134]    train-rmse:0.184806+0.014484    test-rmse:0.941802+0.064534 
## [135]    train-rmse:0.183119+0.015103    test-rmse:0.941607+0.064851 
## [136]    train-rmse:0.181509+0.015158    test-rmse:0.940549+0.064673 
## [137]    train-rmse:0.179893+0.014762    test-rmse:0.940337+0.064671 
## [138]    train-rmse:0.177906+0.015126    test-rmse:0.940682+0.064717 
## [139]    train-rmse:0.176074+0.014844    test-rmse:0.940465+0.064006 
## [140]    train-rmse:0.173902+0.013630    test-rmse:0.939629+0.063794 
## [141]    train-rmse:0.171047+0.013046    test-rmse:0.939242+0.063680 
## [142]    train-rmse:0.168340+0.012686    test-rmse:0.939397+0.063792 
## [143]    train-rmse:0.167052+0.012514    test-rmse:0.940107+0.064788 
## [144]    train-rmse:0.164140+0.010973    test-rmse:0.939279+0.065749 
## [145]    train-rmse:0.161455+0.011058    test-rmse:0.938675+0.065488 
## [146]    train-rmse:0.159707+0.011381    test-rmse:0.937763+0.065326 
## [147]    train-rmse:0.158000+0.012105    test-rmse:0.937670+0.065446 
## [148]    train-rmse:0.156596+0.011574    test-rmse:0.937852+0.065211 
## [149]    train-rmse:0.155016+0.011471    test-rmse:0.938445+0.065144 
## [150]    train-rmse:0.153158+0.011517    test-rmse:0.938166+0.065025 
## [151]    train-rmse:0.151505+0.012034    test-rmse:0.938555+0.065697 
## [152]    train-rmse:0.149281+0.011552    test-rmse:0.938911+0.065927 
## [153]    train-rmse:0.147504+0.011047    test-rmse:0.938128+0.065256 
## Stopping. Best iteration:
## [147]    train-rmse:0.158000+0.012105    test-rmse:0.937670+0.065446
## 
## 46 
## [1]  train-rmse:76.896455+0.077079   test-rmse:76.897090+0.416550 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:60.052252+0.059983   test-rmse:60.052622+0.432945 
## [3]  train-rmse:46.904609+0.046553   test-rmse:46.904639+0.445440 
## [4]  train-rmse:36.644199+0.035970   test-rmse:36.643796+0.454772 
## [5]  train-rmse:28.639430+0.027617   test-rmse:28.638477+0.461462 
## [6]  train-rmse:22.397527+0.021009   test-rmse:22.395899+0.465834 
## [7]  train-rmse:17.534218+0.015835   test-rmse:17.531762+0.468024 
## [8]  train-rmse:13.749436+0.012173   test-rmse:13.738001+0.462524 
## [9]  train-rmse:10.804913+0.008776   test-rmse:10.788056+0.441575 
## [10] train-rmse:8.514278+0.007378    test-rmse:8.492002+0.409061 
## [11] train-rmse:6.731825+0.006492    test-rmse:6.718663+0.382900 
## [12] train-rmse:5.342437+0.008647    test-rmse:5.330328+0.353701 
## [13] train-rmse:4.263768+0.006998    test-rmse:4.267382+0.326484 
## [14] train-rmse:3.422952+0.008331    test-rmse:3.425732+0.310146 
## [15] train-rmse:2.772603+0.012154    test-rmse:2.793116+0.273267 
## [16] train-rmse:2.269429+0.010037    test-rmse:2.315988+0.254329 
## [17] train-rmse:1.887198+0.015606    test-rmse:1.973568+0.218795 
## [18] train-rmse:1.589601+0.013774    test-rmse:1.718819+0.192429 
## [19] train-rmse:1.370235+0.014299    test-rmse:1.539014+0.171252 
## [20] train-rmse:1.208974+0.016674    test-rmse:1.407789+0.152988 
## [21] train-rmse:1.080774+0.011602    test-rmse:1.329509+0.144786 
## [22] train-rmse:0.982299+0.015400    test-rmse:1.266628+0.136276 
## [23] train-rmse:0.911736+0.019415    test-rmse:1.217775+0.133514 
## [24] train-rmse:0.851812+0.021977    test-rmse:1.184027+0.123478 
## [25] train-rmse:0.806365+0.020390    test-rmse:1.160304+0.122775 
## [26] train-rmse:0.768690+0.024131    test-rmse:1.138447+0.116995 
## [27] train-rmse:0.738330+0.023494    test-rmse:1.127567+0.117346 
## [28] train-rmse:0.709931+0.020396    test-rmse:1.114765+0.118634 
## [29] train-rmse:0.684629+0.024752    test-rmse:1.102121+0.114679 
## [30] train-rmse:0.663191+0.025503    test-rmse:1.087151+0.115782 
## [31] train-rmse:0.644227+0.026702    test-rmse:1.077250+0.116081 
## [32] train-rmse:0.627956+0.026658    test-rmse:1.071187+0.118599 
## [33] train-rmse:0.610023+0.028754    test-rmse:1.065057+0.116394 
## [34] train-rmse:0.592878+0.028943    test-rmse:1.056763+0.116711 
## [35] train-rmse:0.572465+0.022805    test-rmse:1.051548+0.111339 
## [36] train-rmse:0.558030+0.021360    test-rmse:1.048526+0.114096 
## [37] train-rmse:0.545338+0.021704    test-rmse:1.040584+0.107410 
## [38] train-rmse:0.535392+0.021580    test-rmse:1.034892+0.112183 
## [39] train-rmse:0.525626+0.022481    test-rmse:1.031908+0.111925 
## [40] train-rmse:0.513639+0.019829    test-rmse:1.026384+0.111384 
## [41] train-rmse:0.500233+0.016401    test-rmse:1.015962+0.105620 
## [42] train-rmse:0.490277+0.017077    test-rmse:1.017993+0.104756 
## [43] train-rmse:0.479145+0.019539    test-rmse:1.011353+0.104496 
## [44] train-rmse:0.467592+0.019549    test-rmse:1.008933+0.109173 
## [45] train-rmse:0.459178+0.017729    test-rmse:1.005957+0.109534 
## [46] train-rmse:0.449985+0.014927    test-rmse:1.004217+0.113436 
## [47] train-rmse:0.440332+0.012403    test-rmse:1.001005+0.113143 
## [48] train-rmse:0.429117+0.010701    test-rmse:1.000334+0.114983 
## [49] train-rmse:0.421790+0.010888    test-rmse:0.997538+0.112601 
## [50] train-rmse:0.413478+0.010987    test-rmse:0.994951+0.112724 
## [51] train-rmse:0.405232+0.011203    test-rmse:0.995839+0.111445 
## [52] train-rmse:0.396702+0.008579    test-rmse:0.994128+0.112189 
## [53] train-rmse:0.389082+0.009573    test-rmse:0.996502+0.113666 
## [54] train-rmse:0.381799+0.010590    test-rmse:0.991831+0.112318 
## [55] train-rmse:0.376520+0.011577    test-rmse:0.989461+0.112385 
## [56] train-rmse:0.368826+0.013646    test-rmse:0.985138+0.110704 
## [57] train-rmse:0.361875+0.013769    test-rmse:0.984325+0.109038 
## [58] train-rmse:0.353536+0.012259    test-rmse:0.982581+0.107371 
## [59] train-rmse:0.345166+0.011678    test-rmse:0.980330+0.110060 
## [60] train-rmse:0.338215+0.009889    test-rmse:0.977952+0.109251 
## [61] train-rmse:0.331283+0.009599    test-rmse:0.978264+0.108029 
## [62] train-rmse:0.324821+0.009124    test-rmse:0.977620+0.109561 
## [63] train-rmse:0.318340+0.010606    test-rmse:0.975708+0.109479 
## [64] train-rmse:0.313464+0.011644    test-rmse:0.974588+0.109809 
## [65] train-rmse:0.307852+0.011513    test-rmse:0.973166+0.108695 
## [66] train-rmse:0.302282+0.009818    test-rmse:0.972363+0.109770 
## [67] train-rmse:0.297285+0.009925    test-rmse:0.970819+0.110449 
## [68] train-rmse:0.291619+0.009124    test-rmse:0.968165+0.110448 
## [69] train-rmse:0.285454+0.007375    test-rmse:0.965104+0.106776 
## [70] train-rmse:0.280360+0.006827    test-rmse:0.962406+0.108870 
## [71] train-rmse:0.274962+0.007114    test-rmse:0.961860+0.108169 
## [72] train-rmse:0.269216+0.008072    test-rmse:0.960261+0.105113 
## [73] train-rmse:0.265677+0.007311    test-rmse:0.960168+0.106930 
## [74] train-rmse:0.261130+0.006975    test-rmse:0.961473+0.107850 
## [75] train-rmse:0.257102+0.007413    test-rmse:0.961589+0.108738 
## [76] train-rmse:0.251556+0.005623    test-rmse:0.961053+0.109216 
## [77] train-rmse:0.247628+0.006425    test-rmse:0.959816+0.109795 
## [78] train-rmse:0.243899+0.006752    test-rmse:0.961454+0.111423 
## [79] train-rmse:0.239423+0.008506    test-rmse:0.960375+0.111846 
## [80] train-rmse:0.235622+0.009766    test-rmse:0.959713+0.111029 
## [81] train-rmse:0.231505+0.008767    test-rmse:0.957350+0.109541 
## [82] train-rmse:0.225414+0.009482    test-rmse:0.956885+0.109450 
## [83] train-rmse:0.221646+0.008590    test-rmse:0.956435+0.109038 
## [84] train-rmse:0.218046+0.007204    test-rmse:0.956377+0.109721 
## [85] train-rmse:0.212916+0.008069    test-rmse:0.957906+0.110733 
## [86] train-rmse:0.209502+0.008284    test-rmse:0.957996+0.111178 
## [87] train-rmse:0.206834+0.007790    test-rmse:0.957944+0.111257 
## [88] train-rmse:0.201828+0.009000    test-rmse:0.958549+0.111119 
## [89] train-rmse:0.199213+0.009209    test-rmse:0.958341+0.110759 
## [90] train-rmse:0.194913+0.009662    test-rmse:0.956409+0.109030 
## Stopping. Best iteration:
## [84] train-rmse:0.218046+0.007204    test-rmse:0.956377+0.109721
## 
## 47 
## [1]  train-rmse:76.896455+0.077079   test-rmse:76.897090+0.416550 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:60.052252+0.059983   test-rmse:60.052622+0.432945 
## [3]  train-rmse:46.904609+0.046553   test-rmse:46.904639+0.445440 
## [4]  train-rmse:36.644199+0.035970   test-rmse:36.643796+0.454772 
## [5]  train-rmse:28.639430+0.027617   test-rmse:28.638477+0.461462 
## [6]  train-rmse:22.397527+0.021009   test-rmse:22.395899+0.465834 
## [7]  train-rmse:17.534218+0.015835   test-rmse:17.531762+0.468024 
## [8]  train-rmse:13.749436+0.012173   test-rmse:13.738001+0.462524 
## [9]  train-rmse:10.804913+0.008776   test-rmse:10.788056+0.441575 
## [10] train-rmse:8.514278+0.007378    test-rmse:8.492002+0.409061 
## [11] train-rmse:6.731743+0.006603    test-rmse:6.716073+0.381233 
## [12] train-rmse:5.340679+0.008389    test-rmse:5.321861+0.351220 
## [13] train-rmse:4.260448+0.009109    test-rmse:4.251978+0.316453 
## [14] train-rmse:3.413410+0.008295    test-rmse:3.418892+0.304478 
## [15] train-rmse:2.755259+0.008858    test-rmse:2.790929+0.268782 
## [16] train-rmse:2.246507+0.009694    test-rmse:2.317167+0.249859 
## [17] train-rmse:1.853000+0.008344    test-rmse:1.952723+0.217413 
## [18] train-rmse:1.547897+0.011987    test-rmse:1.700737+0.192923 
## [19] train-rmse:1.317425+0.012376    test-rmse:1.513192+0.186246 
## [20] train-rmse:1.137973+0.014568    test-rmse:1.379605+0.167934 
## [21] train-rmse:1.003701+0.013908    test-rmse:1.297206+0.150412 
## [22] train-rmse:0.904643+0.020242    test-rmse:1.229107+0.132901 
## [23] train-rmse:0.823781+0.023489    test-rmse:1.185738+0.124618 
## [24] train-rmse:0.760005+0.021356    test-rmse:1.150482+0.114071 
## [25] train-rmse:0.712398+0.022440    test-rmse:1.126148+0.111416 
## [26] train-rmse:0.670427+0.017503    test-rmse:1.101765+0.106193 
## [27] train-rmse:0.637509+0.015839    test-rmse:1.083744+0.105135 
## [28] train-rmse:0.604530+0.018737    test-rmse:1.074194+0.102970 
## [29] train-rmse:0.578565+0.021037    test-rmse:1.065657+0.101977 
## [30] train-rmse:0.558184+0.019883    test-rmse:1.049548+0.096530 
## [31] train-rmse:0.533959+0.023842    test-rmse:1.042449+0.094846 
## [32] train-rmse:0.513861+0.021520    test-rmse:1.037104+0.089961 
## [33] train-rmse:0.491910+0.019414    test-rmse:1.032744+0.091968 
## [34] train-rmse:0.478511+0.019688    test-rmse:1.024424+0.090275 
## [35] train-rmse:0.458901+0.021097    test-rmse:1.017935+0.087071 
## [36] train-rmse:0.445768+0.021117    test-rmse:1.015206+0.088419 
## [37] train-rmse:0.431800+0.022547    test-rmse:1.005403+0.085772 
## [38] train-rmse:0.420184+0.020920    test-rmse:1.002687+0.089094 
## [39] train-rmse:0.407434+0.016779    test-rmse:0.995362+0.088582 
## [40] train-rmse:0.395226+0.020082    test-rmse:0.993631+0.088677 
## [41] train-rmse:0.384347+0.019172    test-rmse:0.989706+0.088093 
## [42] train-rmse:0.374824+0.020148    test-rmse:0.987263+0.089054 
## [43] train-rmse:0.364358+0.018931    test-rmse:0.981532+0.086939 
## [44] train-rmse:0.353606+0.019962    test-rmse:0.979991+0.086891 
## [45] train-rmse:0.346068+0.018766    test-rmse:0.980789+0.084875 
## [46] train-rmse:0.336389+0.017500    test-rmse:0.980440+0.085234 
## [47] train-rmse:0.327612+0.018739    test-rmse:0.979050+0.084938 
## [48] train-rmse:0.319609+0.016161    test-rmse:0.977536+0.085444 
## [49] train-rmse:0.311929+0.014293    test-rmse:0.973582+0.084443 
## [50] train-rmse:0.304127+0.013036    test-rmse:0.972708+0.084173 
## [51] train-rmse:0.296458+0.012006    test-rmse:0.969050+0.085039 
## [52] train-rmse:0.288113+0.012593    test-rmse:0.965259+0.085343 
## [53] train-rmse:0.281399+0.014710    test-rmse:0.965421+0.087605 
## [54] train-rmse:0.275815+0.016137    test-rmse:0.965604+0.087130 
## [55] train-rmse:0.270323+0.015152    test-rmse:0.965087+0.085108 
## [56] train-rmse:0.261869+0.012150    test-rmse:0.963766+0.085622 
## [57] train-rmse:0.255763+0.011413    test-rmse:0.963245+0.086735 
## [58] train-rmse:0.249574+0.010768    test-rmse:0.962888+0.085440 
## [59] train-rmse:0.243846+0.010921    test-rmse:0.961769+0.085622 
## [60] train-rmse:0.238403+0.011756    test-rmse:0.961304+0.085122 
## [61] train-rmse:0.231983+0.011942    test-rmse:0.959910+0.085878 
## [62] train-rmse:0.225797+0.011532    test-rmse:0.957904+0.085329 
## [63] train-rmse:0.220381+0.009582    test-rmse:0.957519+0.085157 
## [64] train-rmse:0.216031+0.009590    test-rmse:0.956395+0.084411 
## [65] train-rmse:0.211534+0.010122    test-rmse:0.955559+0.085062 
## [66] train-rmse:0.207053+0.009385    test-rmse:0.956176+0.084064 
## [67] train-rmse:0.202867+0.009660    test-rmse:0.956062+0.083508 
## [68] train-rmse:0.198025+0.008943    test-rmse:0.954550+0.083197 
## [69] train-rmse:0.194267+0.009020    test-rmse:0.954243+0.083275 
## [70] train-rmse:0.188934+0.008016    test-rmse:0.953583+0.081531 
## [71] train-rmse:0.184849+0.006797    test-rmse:0.953858+0.081031 
## [72] train-rmse:0.180559+0.006297    test-rmse:0.952059+0.080352 
## [73] train-rmse:0.174996+0.005649    test-rmse:0.952461+0.078785 
## [74] train-rmse:0.168017+0.005670    test-rmse:0.953513+0.078079 
## [75] train-rmse:0.164592+0.006744    test-rmse:0.952503+0.078038 
## [76] train-rmse:0.161131+0.007155    test-rmse:0.951432+0.078451 
## [77] train-rmse:0.158514+0.007335    test-rmse:0.950112+0.078247 
## [78] train-rmse:0.153281+0.007425    test-rmse:0.949866+0.077529 
## [79] train-rmse:0.150820+0.007099    test-rmse:0.949190+0.077452 
## [80] train-rmse:0.147321+0.007343    test-rmse:0.949316+0.077517 
## [81] train-rmse:0.144070+0.006255    test-rmse:0.948812+0.078009 
## [82] train-rmse:0.140938+0.007026    test-rmse:0.947659+0.077955 
## [83] train-rmse:0.138810+0.007170    test-rmse:0.947082+0.076911 
## [84] train-rmse:0.136437+0.006888    test-rmse:0.946649+0.076396 
## [85] train-rmse:0.133849+0.006240    test-rmse:0.946691+0.076117 
## [86] train-rmse:0.130824+0.006964    test-rmse:0.946423+0.076420 
## [87] train-rmse:0.128370+0.006694    test-rmse:0.946064+0.076262 
## [88] train-rmse:0.126176+0.006798    test-rmse:0.945711+0.076035 
## [89] train-rmse:0.122301+0.006174    test-rmse:0.945333+0.076152 
## [90] train-rmse:0.118958+0.006684    test-rmse:0.945077+0.076162 
## [91] train-rmse:0.115724+0.005709    test-rmse:0.943704+0.075703 
## [92] train-rmse:0.112262+0.005949    test-rmse:0.942937+0.075414 
## [93] train-rmse:0.109467+0.005711    test-rmse:0.942192+0.074452 
## [94] train-rmse:0.107452+0.005954    test-rmse:0.942328+0.074740 
## [95] train-rmse:0.105262+0.005921    test-rmse:0.942387+0.074491 
## [96] train-rmse:0.103666+0.006187    test-rmse:0.942355+0.074748 
## [97] train-rmse:0.101737+0.006669    test-rmse:0.941990+0.075091 
## [98] train-rmse:0.098593+0.005895    test-rmse:0.942018+0.075150 
## [99] train-rmse:0.095835+0.006702    test-rmse:0.941790+0.075819 
## [100]    train-rmse:0.092901+0.006272    test-rmse:0.942685+0.075616 
## [101]    train-rmse:0.090815+0.005254    test-rmse:0.942654+0.075631 
## [102]    train-rmse:0.089254+0.005618    test-rmse:0.942471+0.075919 
## [103]    train-rmse:0.087281+0.005488    test-rmse:0.942219+0.076395 
## [104]    train-rmse:0.085377+0.005594    test-rmse:0.942062+0.076316 
## [105]    train-rmse:0.082674+0.005462    test-rmse:0.942363+0.076843 
## Stopping. Best iteration:
## [99] train-rmse:0.095835+0.006702    test-rmse:0.941790+0.075819
## 
## 48 
## [1]  train-rmse:76.896455+0.077079   test-rmse:76.897090+0.416550 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:60.052252+0.059983   test-rmse:60.052622+0.432945 
## [3]  train-rmse:46.904609+0.046553   test-rmse:46.904639+0.445440 
## [4]  train-rmse:36.644199+0.035970   test-rmse:36.643796+0.454772 
## [5]  train-rmse:28.639430+0.027617   test-rmse:28.638477+0.461462 
## [6]  train-rmse:22.397527+0.021009   test-rmse:22.395899+0.465834 
## [7]  train-rmse:17.534218+0.015835   test-rmse:17.531762+0.468024 
## [8]  train-rmse:13.749436+0.012173   test-rmse:13.738001+0.462524 
## [9]  train-rmse:10.804913+0.008776   test-rmse:10.788056+0.441575 
## [10] train-rmse:8.514278+0.007378    test-rmse:8.492002+0.409061 
## [11] train-rmse:6.731659+0.006717    test-rmse:6.716589+0.381560 
## [12] train-rmse:5.339480+0.007778    test-rmse:5.317118+0.347855 
## [13] train-rmse:4.258057+0.007489    test-rmse:4.245498+0.318131 
## [14] train-rmse:3.409498+0.006037    test-rmse:3.419650+0.291134 
## [15] train-rmse:2.748231+0.008470    test-rmse:2.779001+0.255160 
## [16] train-rmse:2.232388+0.007418    test-rmse:2.299972+0.226047 
## [17] train-rmse:1.832645+0.008668    test-rmse:1.934604+0.196661 
## [18] train-rmse:1.515130+0.007885    test-rmse:1.674570+0.165560 
## [19] train-rmse:1.273429+0.006509    test-rmse:1.482773+0.144616 
## [20] train-rmse:1.085668+0.002917    test-rmse:1.348618+0.132379 
## [21] train-rmse:0.935338+0.006628    test-rmse:1.253050+0.109949 
## [22] train-rmse:0.826954+0.004923    test-rmse:1.186072+0.102095 
## [23] train-rmse:0.738197+0.004949    test-rmse:1.130504+0.101030 
## [24] train-rmse:0.666418+0.008133    test-rmse:1.094800+0.101765 
## [25] train-rmse:0.612941+0.012465    test-rmse:1.071036+0.096712 
## [26] train-rmse:0.571206+0.013215    test-rmse:1.057725+0.093005 
## [27] train-rmse:0.532006+0.011181    test-rmse:1.032859+0.090675 
## [28] train-rmse:0.493967+0.016124    test-rmse:1.025345+0.080755 
## [29] train-rmse:0.467998+0.017233    test-rmse:1.016510+0.083467 
## [30] train-rmse:0.445291+0.013267    test-rmse:1.004785+0.083098 
## [31] train-rmse:0.428354+0.013416    test-rmse:0.999999+0.080752 
## [32] train-rmse:0.403108+0.012133    test-rmse:0.994156+0.079428 
## [33] train-rmse:0.388992+0.014322    test-rmse:0.993058+0.083018 
## [34] train-rmse:0.370334+0.010803    test-rmse:0.983726+0.080539 
## [35] train-rmse:0.354851+0.008208    test-rmse:0.981751+0.083623 
## [36] train-rmse:0.341870+0.007795    test-rmse:0.981128+0.083482 
## [37] train-rmse:0.329151+0.009055    test-rmse:0.978348+0.081105 
## [38] train-rmse:0.318080+0.007234    test-rmse:0.974758+0.081900 
## [39] train-rmse:0.306142+0.007378    test-rmse:0.972070+0.081336 
## [40] train-rmse:0.292470+0.009483    test-rmse:0.968888+0.080757 
## [41] train-rmse:0.280107+0.010517    test-rmse:0.967708+0.080398 
## [42] train-rmse:0.272271+0.012182    test-rmse:0.965499+0.080218 
## [43] train-rmse:0.265539+0.013126    test-rmse:0.962591+0.080348 
## [44] train-rmse:0.255281+0.012358    test-rmse:0.959612+0.078969 
## [45] train-rmse:0.247361+0.010521    test-rmse:0.959106+0.078327 
## [46] train-rmse:0.238909+0.012028    test-rmse:0.960106+0.079403 
## [47] train-rmse:0.233557+0.011961    test-rmse:0.957552+0.080233 
## [48] train-rmse:0.225601+0.010213    test-rmse:0.956190+0.081882 
## [49] train-rmse:0.219393+0.010512    test-rmse:0.956271+0.081192 
## [50] train-rmse:0.211328+0.010677    test-rmse:0.956903+0.083117 
## [51] train-rmse:0.204882+0.011400    test-rmse:0.956186+0.084224 
## [52] train-rmse:0.197201+0.013692    test-rmse:0.954867+0.083873 
## [53] train-rmse:0.188903+0.013160    test-rmse:0.953559+0.082571 
## [54] train-rmse:0.182969+0.013273    test-rmse:0.953194+0.082168 
## [55] train-rmse:0.178153+0.014300    test-rmse:0.952094+0.081415 
## [56] train-rmse:0.170710+0.012670    test-rmse:0.952239+0.081412 
## [57] train-rmse:0.165860+0.014196    test-rmse:0.951710+0.081868 
## [58] train-rmse:0.162084+0.014093    test-rmse:0.952443+0.081479 
## [59] train-rmse:0.156050+0.012528    test-rmse:0.951417+0.081357 
## [60] train-rmse:0.150197+0.010801    test-rmse:0.952473+0.080814 
## [61] train-rmse:0.145823+0.011158    test-rmse:0.951401+0.080809 
## [62] train-rmse:0.143087+0.010997    test-rmse:0.949987+0.080904 
## [63] train-rmse:0.138952+0.009850    test-rmse:0.949820+0.080727 
## [64] train-rmse:0.134436+0.009977    test-rmse:0.950975+0.080724 
## [65] train-rmse:0.130184+0.010840    test-rmse:0.951349+0.080816 
## [66] train-rmse:0.126506+0.011038    test-rmse:0.951416+0.082045 
## [67] train-rmse:0.123170+0.010966    test-rmse:0.951633+0.081673 
## [68] train-rmse:0.119422+0.011606    test-rmse:0.951448+0.081549 
## [69] train-rmse:0.115806+0.011076    test-rmse:0.950413+0.081593 
## Stopping. Best iteration:
## [63] train-rmse:0.138952+0.009850    test-rmse:0.949820+0.080727
## 
## 49 
## [1]  train-rmse:74.935020+0.075103   test-rmse:74.935632+0.418470 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:57.029236+0.056907   test-rmse:57.029542+0.435851 
## [3]  train-rmse:43.410643+0.042960   test-rmse:43.410540+0.448676 
## [4]  train-rmse:33.055426+0.032244   test-rmse:33.054807+0.457865 
## [5]  train-rmse:25.185130+0.023974   test-rmse:25.183843+0.464019 
## [6]  train-rmse:19.208041+0.017613   test-rmse:19.205912+0.467458 
## [7]  train-rmse:14.674725+0.012875   test-rmse:14.671561+0.468210 
## [8]  train-rmse:11.239684+0.009540   test-rmse:11.239101+0.439296 
## [9]  train-rmse:8.642351+0.007677    test-rmse:8.631867+0.438326 
## [10] train-rmse:6.682843+0.007924    test-rmse:6.669499+0.424188 
## [11] train-rmse:5.215550+0.009488    test-rmse:5.199744+0.406549 
## [12] train-rmse:4.126593+0.011388    test-rmse:4.122066+0.375559 
## [13] train-rmse:3.330459+0.014980    test-rmse:3.332867+0.349905 
## [14] train-rmse:2.757762+0.018063    test-rmse:2.767284+0.302645 
## [15] train-rmse:2.356120+0.022009    test-rmse:2.383882+0.257325 
## [16] train-rmse:2.081772+0.024216    test-rmse:2.124629+0.208428 
## [17] train-rmse:1.896119+0.026045    test-rmse:1.954976+0.173216 
## [18] train-rmse:1.772374+0.026797    test-rmse:1.845467+0.140368 
## [19] train-rmse:1.689648+0.027088    test-rmse:1.776784+0.122007 
## [20] train-rmse:1.632914+0.027034    test-rmse:1.712518+0.112980 
## [21] train-rmse:1.593556+0.026761    test-rmse:1.674075+0.111435 
## [22] train-rmse:1.563809+0.026171    test-rmse:1.657001+0.115151 
## [23] train-rmse:1.540315+0.025459    test-rmse:1.640314+0.114869 
## [24] train-rmse:1.520678+0.025163    test-rmse:1.629391+0.113721 
## [25] train-rmse:1.504467+0.024422    test-rmse:1.613442+0.110285 
## [26] train-rmse:1.489585+0.023901    test-rmse:1.601941+0.115872 
## [27] train-rmse:1.476215+0.023518    test-rmse:1.578980+0.106867 
## [28] train-rmse:1.464156+0.023374    test-rmse:1.577524+0.110048 
## [29] train-rmse:1.452589+0.023228    test-rmse:1.571269+0.111697 
## [30] train-rmse:1.441092+0.022882    test-rmse:1.570171+0.115633 
## [31] train-rmse:1.430417+0.022819    test-rmse:1.565089+0.114195 
## [32] train-rmse:1.420445+0.022722    test-rmse:1.559852+0.112981 
## [33] train-rmse:1.410634+0.022479    test-rmse:1.551819+0.113239 
## [34] train-rmse:1.401260+0.022377    test-rmse:1.550884+0.113607 
## [35] train-rmse:1.392024+0.022360    test-rmse:1.540397+0.107310 
## [36] train-rmse:1.383117+0.022482    test-rmse:1.531274+0.106471 
## [37] train-rmse:1.374416+0.022335    test-rmse:1.523039+0.113682 
## [38] train-rmse:1.366121+0.022225    test-rmse:1.519469+0.109271 
## [39] train-rmse:1.357822+0.022327    test-rmse:1.508454+0.106146 
## [40] train-rmse:1.350014+0.022061    test-rmse:1.503197+0.105378 
## [41] train-rmse:1.342318+0.021965    test-rmse:1.503788+0.099313 
## [42] train-rmse:1.334700+0.021576    test-rmse:1.500424+0.100188 
## [43] train-rmse:1.327288+0.021372    test-rmse:1.492691+0.101672 
## [44] train-rmse:1.319955+0.021168    test-rmse:1.484968+0.100479 
## [45] train-rmse:1.312612+0.021064    test-rmse:1.476745+0.102291 
## [46] train-rmse:1.305603+0.020927    test-rmse:1.474376+0.102836 
## [47] train-rmse:1.298827+0.020769    test-rmse:1.470577+0.101140 
## [48] train-rmse:1.292180+0.020663    test-rmse:1.465951+0.099364 
## [49] train-rmse:1.285624+0.020517    test-rmse:1.461403+0.099841 
## [50] train-rmse:1.279320+0.020303    test-rmse:1.456461+0.095557 
## [51] train-rmse:1.273199+0.020108    test-rmse:1.454277+0.095393 
## [52] train-rmse:1.267239+0.020037    test-rmse:1.449857+0.097276 
## [53] train-rmse:1.261388+0.019907    test-rmse:1.450388+0.092261 
## [54] train-rmse:1.255705+0.019789    test-rmse:1.441092+0.090194 
## [55] train-rmse:1.250034+0.019847    test-rmse:1.436643+0.090189 
## [56] train-rmse:1.244616+0.019863    test-rmse:1.437245+0.093168 
## [57] train-rmse:1.239302+0.019947    test-rmse:1.431495+0.094531 
## [58] train-rmse:1.234132+0.019995    test-rmse:1.427033+0.097982 
## [59] train-rmse:1.229126+0.019921    test-rmse:1.429018+0.096794 
## [60] train-rmse:1.224059+0.019895    test-rmse:1.425888+0.092393 
## [61] train-rmse:1.219048+0.019862    test-rmse:1.421340+0.090113 
## [62] train-rmse:1.214127+0.019888    test-rmse:1.415824+0.090151 
## [63] train-rmse:1.209393+0.019888    test-rmse:1.412322+0.089915 
## [64] train-rmse:1.204834+0.019859    test-rmse:1.408401+0.090058 
## [65] train-rmse:1.200376+0.019813    test-rmse:1.405277+0.091430 
## [66] train-rmse:1.195935+0.019840    test-rmse:1.398207+0.088725 
## [67] train-rmse:1.191577+0.019792    test-rmse:1.394143+0.086034 
## [68] train-rmse:1.187278+0.019732    test-rmse:1.389674+0.085404 
## [69] train-rmse:1.183103+0.019697    test-rmse:1.383808+0.090767 
## [70] train-rmse:1.178993+0.019632    test-rmse:1.382426+0.091227 
## [71] train-rmse:1.174902+0.019575    test-rmse:1.377884+0.089020 
## [72] train-rmse:1.171006+0.019491    test-rmse:1.374176+0.087958 
## [73] train-rmse:1.167094+0.019281    test-rmse:1.376894+0.083899 
## [74] train-rmse:1.163300+0.019242    test-rmse:1.372532+0.084217 
## [75] train-rmse:1.159567+0.019258    test-rmse:1.370970+0.084056 
## [76] train-rmse:1.155804+0.019158    test-rmse:1.368969+0.085040 
## [77] train-rmse:1.152219+0.019160    test-rmse:1.362887+0.088781 
## [78] train-rmse:1.148690+0.019155    test-rmse:1.360628+0.093388 
## [79] train-rmse:1.145177+0.019205    test-rmse:1.359198+0.092481 
## [80] train-rmse:1.141808+0.019138    test-rmse:1.359363+0.089838 
## [81] train-rmse:1.138467+0.019062    test-rmse:1.355728+0.090336 
## [82] train-rmse:1.135170+0.019103    test-rmse:1.355506+0.088538 
## [83] train-rmse:1.132001+0.019064    test-rmse:1.353938+0.089548 
## [84] train-rmse:1.128895+0.019067    test-rmse:1.350688+0.088248 
## [85] train-rmse:1.125872+0.019118    test-rmse:1.347364+0.081442 
## [86] train-rmse:1.122766+0.019051    test-rmse:1.345054+0.079263 
## [87] train-rmse:1.119822+0.019002    test-rmse:1.343080+0.077802 
## [88] train-rmse:1.116923+0.018941    test-rmse:1.337054+0.077620 
## [89] train-rmse:1.113989+0.018863    test-rmse:1.334389+0.078780 
## [90] train-rmse:1.111158+0.018828    test-rmse:1.334883+0.081185 
## [91] train-rmse:1.108318+0.018797    test-rmse:1.331690+0.081433 
## [92] train-rmse:1.105587+0.018698    test-rmse:1.331253+0.082589 
## [93] train-rmse:1.102883+0.018553    test-rmse:1.330565+0.084394 
## [94] train-rmse:1.100285+0.018419    test-rmse:1.327535+0.088306 
## [95] train-rmse:1.097697+0.018322    test-rmse:1.325767+0.088282 
## [96] train-rmse:1.095097+0.018226    test-rmse:1.324300+0.087874 
## [97] train-rmse:1.092603+0.018216    test-rmse:1.321332+0.087429 
## [98] train-rmse:1.090073+0.018172    test-rmse:1.317092+0.088039 
## [99] train-rmse:1.087665+0.018179    test-rmse:1.315312+0.088470 
## [100]    train-rmse:1.085311+0.018170    test-rmse:1.312714+0.087069 
## [101]    train-rmse:1.082973+0.018158    test-rmse:1.313428+0.088162 
## [102]    train-rmse:1.080597+0.018138    test-rmse:1.314409+0.083616 
## [103]    train-rmse:1.078269+0.018174    test-rmse:1.311490+0.085575 
## [104]    train-rmse:1.076032+0.018109    test-rmse:1.309613+0.085687 
## [105]    train-rmse:1.073840+0.018025    test-rmse:1.305775+0.086259 
## [106]    train-rmse:1.071620+0.017951    test-rmse:1.304225+0.087964 
## [107]    train-rmse:1.069483+0.017896    test-rmse:1.306695+0.086718 
## [108]    train-rmse:1.067353+0.017842    test-rmse:1.304284+0.087421 
## [109]    train-rmse:1.065240+0.017782    test-rmse:1.302301+0.086944 
## [110]    train-rmse:1.063156+0.017731    test-rmse:1.300328+0.085778 
## [111]    train-rmse:1.061108+0.017667    test-rmse:1.300305+0.086659 
## [112]    train-rmse:1.059159+0.017671    test-rmse:1.299592+0.089275 
## [113]    train-rmse:1.057232+0.017670    test-rmse:1.298662+0.088553 
## [114]    train-rmse:1.055291+0.017614    test-rmse:1.295527+0.089121 
## [115]    train-rmse:1.053394+0.017592    test-rmse:1.294448+0.090743 
## [116]    train-rmse:1.051415+0.017475    test-rmse:1.292391+0.090212 
## [117]    train-rmse:1.049572+0.017397    test-rmse:1.290760+0.090896 
## [118]    train-rmse:1.047662+0.017368    test-rmse:1.289111+0.090218 
## [119]    train-rmse:1.045813+0.017262    test-rmse:1.287394+0.089920 
## [120]    train-rmse:1.044011+0.017230    test-rmse:1.286287+0.090512 
## [121]    train-rmse:1.042220+0.017198    test-rmse:1.286676+0.090569 
## [122]    train-rmse:1.040430+0.017177    test-rmse:1.290108+0.088203 
## [123]    train-rmse:1.038712+0.017121    test-rmse:1.287840+0.089935 
## [124]    train-rmse:1.037006+0.017049    test-rmse:1.286121+0.091782 
## [125]    train-rmse:1.035341+0.017012    test-rmse:1.283155+0.092169 
## [126]    train-rmse:1.033721+0.016979    test-rmse:1.283404+0.089417 
## [127]    train-rmse:1.032072+0.016898    test-rmse:1.281247+0.090998 
## [128]    train-rmse:1.030441+0.016850    test-rmse:1.277434+0.086801 
## [129]    train-rmse:1.028887+0.016802    test-rmse:1.277170+0.085418 
## [130]    train-rmse:1.027317+0.016763    test-rmse:1.274846+0.084146 
## [131]    train-rmse:1.025781+0.016680    test-rmse:1.274554+0.083605 
## [132]    train-rmse:1.024217+0.016645    test-rmse:1.271229+0.083598 
## [133]    train-rmse:1.022699+0.016620    test-rmse:1.270014+0.085741 
## [134]    train-rmse:1.021196+0.016584    test-rmse:1.269039+0.085650 
## [135]    train-rmse:1.019717+0.016575    test-rmse:1.269023+0.084878 
## [136]    train-rmse:1.018242+0.016541    test-rmse:1.267060+0.084965 
## [137]    train-rmse:1.016815+0.016545    test-rmse:1.267298+0.086321 
## [138]    train-rmse:1.015385+0.016540    test-rmse:1.266651+0.084792 
## [139]    train-rmse:1.013927+0.016523    test-rmse:1.264585+0.085164 
## [140]    train-rmse:1.012493+0.016550    test-rmse:1.264438+0.086486 
## [141]    train-rmse:1.011115+0.016547    test-rmse:1.262737+0.086972 
## [142]    train-rmse:1.009754+0.016552    test-rmse:1.263032+0.086859 
## [143]    train-rmse:1.008433+0.016547    test-rmse:1.264237+0.086031 
## [144]    train-rmse:1.007072+0.016471    test-rmse:1.262815+0.085793 
## [145]    train-rmse:1.005752+0.016476    test-rmse:1.261909+0.086304 
## [146]    train-rmse:1.004433+0.016438    test-rmse:1.260824+0.086596 
## [147]    train-rmse:1.003142+0.016425    test-rmse:1.260624+0.086873 
## [148]    train-rmse:1.001862+0.016414    test-rmse:1.260159+0.086281 
## [149]    train-rmse:1.000589+0.016400    test-rmse:1.257107+0.086200 
## [150]    train-rmse:0.999353+0.016380    test-rmse:1.255396+0.086730 
## [151]    train-rmse:0.998104+0.016361    test-rmse:1.257835+0.084822 
## [152]    train-rmse:0.996837+0.016365    test-rmse:1.256809+0.084371 
## [153]    train-rmse:0.995591+0.016378    test-rmse:1.256048+0.083834 
## [154]    train-rmse:0.994354+0.016408    test-rmse:1.255116+0.083576 
## [155]    train-rmse:0.993154+0.016411    test-rmse:1.254197+0.082834 
## [156]    train-rmse:0.991956+0.016411    test-rmse:1.251920+0.080087 
## [157]    train-rmse:0.990804+0.016427    test-rmse:1.251545+0.079575 
## [158]    train-rmse:0.989638+0.016426    test-rmse:1.250453+0.077888 
## [159]    train-rmse:0.988482+0.016430    test-rmse:1.252389+0.078985 
## [160]    train-rmse:0.987300+0.016443    test-rmse:1.251502+0.078949 
## [161]    train-rmse:0.986143+0.016451    test-rmse:1.251107+0.079377 
## [162]    train-rmse:0.985005+0.016438    test-rmse:1.251258+0.078952 
## [163]    train-rmse:0.983874+0.016433    test-rmse:1.250350+0.079105 
## [164]    train-rmse:0.982752+0.016429    test-rmse:1.249968+0.078201 
## [165]    train-rmse:0.981615+0.016447    test-rmse:1.250266+0.079238 
## [166]    train-rmse:0.980517+0.016424    test-rmse:1.248901+0.079960 
## [167]    train-rmse:0.979447+0.016429    test-rmse:1.247355+0.079852 
## [168]    train-rmse:0.978396+0.016445    test-rmse:1.245545+0.078802 
## [169]    train-rmse:0.977356+0.016440    test-rmse:1.243998+0.080584 
## [170]    train-rmse:0.976337+0.016448    test-rmse:1.242893+0.080731 
## [171]    train-rmse:0.975297+0.016395    test-rmse:1.241249+0.080282 
## [172]    train-rmse:0.974286+0.016367    test-rmse:1.239508+0.081138 
## [173]    train-rmse:0.973278+0.016357    test-rmse:1.239021+0.081892 
## [174]    train-rmse:0.972296+0.016363    test-rmse:1.239498+0.081579 
## [175]    train-rmse:0.971274+0.016381    test-rmse:1.238127+0.082074 
## [176]    train-rmse:0.970286+0.016402    test-rmse:1.237848+0.083085 
## [177]    train-rmse:0.969328+0.016409    test-rmse:1.237413+0.083782 
## [178]    train-rmse:0.968366+0.016419    test-rmse:1.237250+0.084832 
## [179]    train-rmse:0.967398+0.016416    test-rmse:1.236130+0.083732 
## [180]    train-rmse:0.966416+0.016363    test-rmse:1.236514+0.083331 
## [181]    train-rmse:0.965499+0.016361    test-rmse:1.235908+0.085800 
## [182]    train-rmse:0.964559+0.016388    test-rmse:1.236593+0.085697 
## [183]    train-rmse:0.963650+0.016397    test-rmse:1.236524+0.086034 
## [184]    train-rmse:0.962676+0.016436    test-rmse:1.235197+0.084574 
## [185]    train-rmse:0.961762+0.016451    test-rmse:1.235192+0.084199 
## [186]    train-rmse:0.960856+0.016468    test-rmse:1.234064+0.084662 
## [187]    train-rmse:0.959948+0.016506    test-rmse:1.232306+0.084300 
## [188]    train-rmse:0.959054+0.016545    test-rmse:1.231685+0.085234 
## [189]    train-rmse:0.958150+0.016542    test-rmse:1.230921+0.084706 
## [190]    train-rmse:0.957250+0.016577    test-rmse:1.231317+0.083042 
## [191]    train-rmse:0.956372+0.016585    test-rmse:1.230968+0.082570 
## [192]    train-rmse:0.955518+0.016616    test-rmse:1.231253+0.082512 
## [193]    train-rmse:0.954639+0.016610    test-rmse:1.230093+0.082993 
## [194]    train-rmse:0.953797+0.016647    test-rmse:1.229971+0.082431 
## [195]    train-rmse:0.952956+0.016673    test-rmse:1.230282+0.082415 
## [196]    train-rmse:0.952123+0.016681    test-rmse:1.231063+0.081630 
## [197]    train-rmse:0.951275+0.016688    test-rmse:1.230911+0.082268 
## [198]    train-rmse:0.950437+0.016683    test-rmse:1.229667+0.083389 
## [199]    train-rmse:0.949602+0.016701    test-rmse:1.231226+0.082316 
## [200]    train-rmse:0.948793+0.016712    test-rmse:1.229880+0.082583 
## [201]    train-rmse:0.947972+0.016729    test-rmse:1.229174+0.083023 
## [202]    train-rmse:0.947181+0.016722    test-rmse:1.229642+0.081833 
## [203]    train-rmse:0.946359+0.016708    test-rmse:1.228277+0.083123 
## [204]    train-rmse:0.945575+0.016707    test-rmse:1.225472+0.081726 
## [205]    train-rmse:0.944778+0.016725    test-rmse:1.225672+0.081168 
## [206]    train-rmse:0.944004+0.016727    test-rmse:1.227262+0.079816 
## [207]    train-rmse:0.943249+0.016736    test-rmse:1.226217+0.079177 
## [208]    train-rmse:0.942488+0.016765    test-rmse:1.226009+0.079249 
## [209]    train-rmse:0.941750+0.016785    test-rmse:1.225359+0.079726 
## [210]    train-rmse:0.940976+0.016788    test-rmse:1.226230+0.080866 
## [211]    train-rmse:0.940233+0.016791    test-rmse:1.226487+0.080226 
## [212]    train-rmse:0.939443+0.016830    test-rmse:1.227248+0.080971 
## [213]    train-rmse:0.938690+0.016822    test-rmse:1.227230+0.081146 
## [214]    train-rmse:0.937927+0.016831    test-rmse:1.226621+0.081019 
## [215]    train-rmse:0.937199+0.016836    test-rmse:1.225799+0.081695 
## Stopping. Best iteration:
## [209]    train-rmse:0.941750+0.016785    test-rmse:1.225359+0.079726
## 
## 50 
## [1]  train-rmse:74.935020+0.075103   test-rmse:74.935632+0.418470 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:57.029236+0.056907   test-rmse:57.029542+0.435851 
## [3]  train-rmse:43.410643+0.042960   test-rmse:43.410540+0.448676 
## [4]  train-rmse:33.055426+0.032244   test-rmse:33.054807+0.457865 
## [5]  train-rmse:25.185130+0.023974   test-rmse:25.183843+0.464019 
## [6]  train-rmse:19.208041+0.017613   test-rmse:19.205912+0.467458 
## [7]  train-rmse:14.674725+0.012875   test-rmse:14.671561+0.468210 
## [8]  train-rmse:11.238713+0.009134   test-rmse:11.227100+0.442238 
## [9]  train-rmse:8.637307+0.007415    test-rmse:8.617466+0.425233 
## [10] train-rmse:6.668908+0.006979    test-rmse:6.649441+0.403020 
## [11] train-rmse:5.189688+0.009079    test-rmse:5.180838+0.375972 
## [12] train-rmse:4.079013+0.010891    test-rmse:4.079147+0.355457 
## [13] train-rmse:3.256189+0.016107    test-rmse:3.264118+0.326310 
## [14] train-rmse:2.656253+0.017865    test-rmse:2.676228+0.285568 
## [15] train-rmse:2.224352+0.020547    test-rmse:2.271785+0.243380 
## [16] train-rmse:1.916230+0.022320    test-rmse:1.995965+0.209779 
## [17] train-rmse:1.703852+0.022787    test-rmse:1.813962+0.173971 
## [18] train-rmse:1.560290+0.025170    test-rmse:1.689839+0.145537 
## [19] train-rmse:1.463990+0.025241    test-rmse:1.608006+0.115946 
## [20] train-rmse:1.394078+0.022260    test-rmse:1.550141+0.097680 
## [21] train-rmse:1.344312+0.022392    test-rmse:1.515296+0.092964 
## [22] train-rmse:1.306479+0.024941    test-rmse:1.489482+0.085542 
## [23] train-rmse:1.275759+0.021584    test-rmse:1.476465+0.080392 
## [24] train-rmse:1.252866+0.021138    test-rmse:1.454103+0.080621 
## [25] train-rmse:1.232191+0.021053    test-rmse:1.444030+0.076643 
## [26] train-rmse:1.213757+0.022210    test-rmse:1.439232+0.077279 
## [27] train-rmse:1.192823+0.023064    test-rmse:1.431902+0.074877 
## [28] train-rmse:1.178392+0.022596    test-rmse:1.412090+0.074403 
## [29] train-rmse:1.164047+0.022555    test-rmse:1.400925+0.077809 
## [30] train-rmse:1.149467+0.024167    test-rmse:1.384290+0.073845 
## [31] train-rmse:1.135691+0.024976    test-rmse:1.379129+0.067837 
## [32] train-rmse:1.123327+0.024229    test-rmse:1.368127+0.071006 
## [33] train-rmse:1.111179+0.024920    test-rmse:1.359739+0.072053 
## [34] train-rmse:1.096417+0.023181    test-rmse:1.341576+0.081635 
## [35] train-rmse:1.086184+0.023714    test-rmse:1.328716+0.079851 
## [36] train-rmse:1.075141+0.024763    test-rmse:1.324817+0.076057 
## [37] train-rmse:1.062043+0.021166    test-rmse:1.318224+0.072820 
## [38] train-rmse:1.050829+0.021919    test-rmse:1.313072+0.071447 
## [39] train-rmse:1.040726+0.021639    test-rmse:1.310335+0.068794 
## [40] train-rmse:1.030090+0.023498    test-rmse:1.301451+0.068553 
## [41] train-rmse:1.020981+0.023043    test-rmse:1.295620+0.069519 
## [42] train-rmse:1.012160+0.023176    test-rmse:1.288605+0.073320 
## [43] train-rmse:1.003179+0.023742    test-rmse:1.277872+0.075805 
## [44] train-rmse:0.993485+0.022792    test-rmse:1.268779+0.074045 
## [45] train-rmse:0.985397+0.023537    test-rmse:1.261165+0.083608 
## [46] train-rmse:0.977073+0.024553    test-rmse:1.253772+0.080360 
## [47] train-rmse:0.966087+0.022270    test-rmse:1.254608+0.077577 
## [48] train-rmse:0.959498+0.021908    test-rmse:1.251433+0.076943 
## [49] train-rmse:0.948924+0.022919    test-rmse:1.249026+0.075543 
## [50] train-rmse:0.939145+0.024563    test-rmse:1.243509+0.075539 
## [51] train-rmse:0.932552+0.024919    test-rmse:1.241127+0.074510 
## [52] train-rmse:0.924951+0.022145    test-rmse:1.233312+0.078345 
## [53] train-rmse:0.916931+0.022386    test-rmse:1.223628+0.078219 
## [54] train-rmse:0.911043+0.022579    test-rmse:1.222060+0.078692 
## [55] train-rmse:0.905027+0.022851    test-rmse:1.218820+0.078962 
## [56] train-rmse:0.897042+0.023063    test-rmse:1.212774+0.081474 
## [57] train-rmse:0.891145+0.023645    test-rmse:1.208657+0.081452 
## [58] train-rmse:0.883180+0.022792    test-rmse:1.205073+0.086327 
## [59] train-rmse:0.877091+0.023289    test-rmse:1.201736+0.089132 
## [60] train-rmse:0.871684+0.023149    test-rmse:1.198521+0.087683 
## [61] train-rmse:0.863432+0.020799    test-rmse:1.194484+0.084783 
## [62] train-rmse:0.854338+0.022888    test-rmse:1.187960+0.083027 
## [63] train-rmse:0.848069+0.023449    test-rmse:1.186408+0.083454 
## [64] train-rmse:0.843759+0.023785    test-rmse:1.179385+0.079807 
## [65] train-rmse:0.837638+0.019275    test-rmse:1.173295+0.084158 
## [66] train-rmse:0.832733+0.019539    test-rmse:1.171360+0.086710 
## [67] train-rmse:0.826754+0.021121    test-rmse:1.165697+0.088032 
## [68] train-rmse:0.819695+0.022469    test-rmse:1.164161+0.083898 
## [69] train-rmse:0.815455+0.022406    test-rmse:1.162988+0.083434 
## [70] train-rmse:0.810863+0.022947    test-rmse:1.159298+0.083821 
## [71] train-rmse:0.804866+0.024345    test-rmse:1.156025+0.082191 
## [72] train-rmse:0.800265+0.024609    test-rmse:1.152573+0.081998 
## [73] train-rmse:0.792869+0.024596    test-rmse:1.153344+0.082198 
## [74] train-rmse:0.788497+0.023827    test-rmse:1.148367+0.087607 
## [75] train-rmse:0.782764+0.021952    test-rmse:1.144282+0.092491 
## [76] train-rmse:0.778300+0.022082    test-rmse:1.142391+0.090704 
## [77] train-rmse:0.772143+0.021560    test-rmse:1.142913+0.089659 
## [78] train-rmse:0.765476+0.021463    test-rmse:1.140171+0.089994 
## [79] train-rmse:0.762087+0.021084    test-rmse:1.136182+0.088410 
## [80] train-rmse:0.759037+0.020995    test-rmse:1.135396+0.088052 
## [81] train-rmse:0.754522+0.021740    test-rmse:1.130956+0.090068 
## [82] train-rmse:0.750017+0.020969    test-rmse:1.129090+0.091300 
## [83] train-rmse:0.746141+0.021659    test-rmse:1.127565+0.091430 
## [84] train-rmse:0.740922+0.021555    test-rmse:1.124604+0.093473 
## [85] train-rmse:0.737710+0.021920    test-rmse:1.121736+0.094093 
## [86] train-rmse:0.734740+0.022057    test-rmse:1.119826+0.093061 
## [87] train-rmse:0.730888+0.022306    test-rmse:1.116837+0.094670 
## [88] train-rmse:0.728186+0.022408    test-rmse:1.116084+0.094089 
## [89] train-rmse:0.723504+0.023923    test-rmse:1.114346+0.091822 
## [90] train-rmse:0.720911+0.024063    test-rmse:1.112000+0.092935 
## [91] train-rmse:0.718010+0.023931    test-rmse:1.111321+0.094298 
## [92] train-rmse:0.714984+0.023885    test-rmse:1.109641+0.093214 
## [93] train-rmse:0.711587+0.025165    test-rmse:1.109464+0.091271 
## [94] train-rmse:0.705574+0.022247    test-rmse:1.105546+0.091135 
## [95] train-rmse:0.701933+0.020198    test-rmse:1.105745+0.092682 
## [96] train-rmse:0.698928+0.019647    test-rmse:1.104676+0.092486 
## [97] train-rmse:0.694577+0.019377    test-rmse:1.101678+0.091298 
## [98] train-rmse:0.689625+0.021224    test-rmse:1.096392+0.088164 
## [99] train-rmse:0.685774+0.020734    test-rmse:1.091989+0.090846 
## [100]    train-rmse:0.681858+0.021725    test-rmse:1.093096+0.089168 
## [101]    train-rmse:0.675505+0.021617    test-rmse:1.090454+0.091193 
## [102]    train-rmse:0.672830+0.021554    test-rmse:1.089253+0.091981 
## [103]    train-rmse:0.668991+0.021519    test-rmse:1.087504+0.092759 
## [104]    train-rmse:0.666779+0.021626    test-rmse:1.086752+0.091321 
## [105]    train-rmse:0.664354+0.021500    test-rmse:1.085974+0.092415 
## [106]    train-rmse:0.660920+0.020738    test-rmse:1.084986+0.092830 
## [107]    train-rmse:0.657776+0.019850    test-rmse:1.083514+0.093189 
## [108]    train-rmse:0.655035+0.019902    test-rmse:1.083567+0.092814 
## [109]    train-rmse:0.651297+0.022503    test-rmse:1.082792+0.089845 
## [110]    train-rmse:0.647279+0.021848    test-rmse:1.083172+0.089902 
## [111]    train-rmse:0.644060+0.021614    test-rmse:1.080915+0.091353 
## [112]    train-rmse:0.641009+0.022099    test-rmse:1.077135+0.094557 
## [113]    train-rmse:0.637208+0.022871    test-rmse:1.074869+0.093491 
## [114]    train-rmse:0.635386+0.023037    test-rmse:1.073353+0.093468 
## [115]    train-rmse:0.632368+0.022709    test-rmse:1.071774+0.093549 
## [116]    train-rmse:0.629523+0.023658    test-rmse:1.071733+0.095252 
## [117]    train-rmse:0.626568+0.024291    test-rmse:1.071067+0.094348 
## [118]    train-rmse:0.623936+0.023237    test-rmse:1.071247+0.093775 
## [119]    train-rmse:0.621314+0.022732    test-rmse:1.072651+0.093079 
## [120]    train-rmse:0.616978+0.020638    test-rmse:1.069853+0.094334 
## [121]    train-rmse:0.614674+0.021544    test-rmse:1.069850+0.093472 
## [122]    train-rmse:0.610423+0.023553    test-rmse:1.066468+0.094750 
## [123]    train-rmse:0.606653+0.022463    test-rmse:1.065970+0.094300 
## [124]    train-rmse:0.604147+0.022596    test-rmse:1.063159+0.094761 
## [125]    train-rmse:0.599603+0.022857    test-rmse:1.057865+0.090155 
## [126]    train-rmse:0.596414+0.021324    test-rmse:1.057077+0.093882 
## [127]    train-rmse:0.594412+0.022048    test-rmse:1.057104+0.094205 
## [128]    train-rmse:0.591158+0.021836    test-rmse:1.055765+0.093455 
## [129]    train-rmse:0.588099+0.021536    test-rmse:1.054383+0.094585 
## [130]    train-rmse:0.586293+0.021793    test-rmse:1.054201+0.095214 
## [131]    train-rmse:0.583448+0.021099    test-rmse:1.053208+0.097316 
## [132]    train-rmse:0.579772+0.020350    test-rmse:1.053237+0.094875 
## [133]    train-rmse:0.577267+0.021087    test-rmse:1.053919+0.096213 
## [134]    train-rmse:0.575295+0.021170    test-rmse:1.053011+0.096368 
## [135]    train-rmse:0.571985+0.020162    test-rmse:1.051390+0.096290 
## [136]    train-rmse:0.568666+0.017904    test-rmse:1.050494+0.097849 
## [137]    train-rmse:0.565859+0.016339    test-rmse:1.048617+0.097197 
## [138]    train-rmse:0.563106+0.016139    test-rmse:1.048995+0.098617 
## [139]    train-rmse:0.560083+0.016302    test-rmse:1.048665+0.098440 
## [140]    train-rmse:0.558539+0.016609    test-rmse:1.048736+0.097531 
## [141]    train-rmse:0.556709+0.017156    test-rmse:1.047948+0.097659 
## [142]    train-rmse:0.555295+0.017233    test-rmse:1.048148+0.096718 
## [143]    train-rmse:0.553382+0.017849    test-rmse:1.046258+0.097100 
## [144]    train-rmse:0.551941+0.017663    test-rmse:1.046892+0.096164 
## [145]    train-rmse:0.549129+0.016020    test-rmse:1.047408+0.095312 
## [146]    train-rmse:0.547544+0.015944    test-rmse:1.047636+0.096355 
## [147]    train-rmse:0.545607+0.015341    test-rmse:1.045421+0.096766 
## [148]    train-rmse:0.543874+0.014787    test-rmse:1.044194+0.097835 
## [149]    train-rmse:0.541646+0.014150    test-rmse:1.044268+0.097306 
## [150]    train-rmse:0.539596+0.013768    test-rmse:1.042997+0.098112 
## [151]    train-rmse:0.537473+0.012911    test-rmse:1.043332+0.096836 
## [152]    train-rmse:0.535629+0.012852    test-rmse:1.043215+0.098754 
## [153]    train-rmse:0.533907+0.012778    test-rmse:1.042486+0.098875 
## [154]    train-rmse:0.531973+0.013780    test-rmse:1.041516+0.096747 
## [155]    train-rmse:0.530157+0.014130    test-rmse:1.040928+0.096093 
## [156]    train-rmse:0.527907+0.014184    test-rmse:1.038284+0.095336 
## [157]    train-rmse:0.525384+0.014183    test-rmse:1.037653+0.095731 
## [158]    train-rmse:0.523666+0.014271    test-rmse:1.036638+0.095481 
## [159]    train-rmse:0.522736+0.014069    test-rmse:1.036244+0.096070 
## [160]    train-rmse:0.520210+0.013803    test-rmse:1.035438+0.094896 
## [161]    train-rmse:0.517944+0.014568    test-rmse:1.032302+0.092855 
## [162]    train-rmse:0.516585+0.014721    test-rmse:1.032343+0.091838 
## [163]    train-rmse:0.512815+0.013337    test-rmse:1.028263+0.095389 
## [164]    train-rmse:0.511276+0.013339    test-rmse:1.025973+0.096125 
## [165]    train-rmse:0.509287+0.012877    test-rmse:1.024610+0.094994 
## [166]    train-rmse:0.507667+0.013665    test-rmse:1.024562+0.095420 
## [167]    train-rmse:0.505776+0.014305    test-rmse:1.022932+0.097068 
## [168]    train-rmse:0.504391+0.014259    test-rmse:1.022619+0.097474 
## [169]    train-rmse:0.502646+0.013515    test-rmse:1.023682+0.097734 
## [170]    train-rmse:0.501187+0.013927    test-rmse:1.023515+0.099161 
## [171]    train-rmse:0.498790+0.013819    test-rmse:1.026233+0.098488 
## [172]    train-rmse:0.497749+0.014040    test-rmse:1.024996+0.098704 
## [173]    train-rmse:0.496720+0.013883    test-rmse:1.025218+0.098495 
## [174]    train-rmse:0.494277+0.012714    test-rmse:1.023563+0.098745 
## Stopping. Best iteration:
## [168]    train-rmse:0.504391+0.014259    test-rmse:1.022619+0.097474
## 
## 51 
## [1]  train-rmse:74.935020+0.075103   test-rmse:74.935632+0.418470 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:57.029236+0.056907   test-rmse:57.029542+0.435851 
## [3]  train-rmse:43.410643+0.042960   test-rmse:43.410540+0.448676 
## [4]  train-rmse:33.055426+0.032244   test-rmse:33.054807+0.457865 
## [5]  train-rmse:25.185130+0.023974   test-rmse:25.183843+0.464019 
## [6]  train-rmse:19.208041+0.017613   test-rmse:19.205912+0.467458 
## [7]  train-rmse:14.674725+0.012875   test-rmse:14.671561+0.468210 
## [8]  train-rmse:11.238713+0.009134   test-rmse:11.227100+0.442238 
## [9]  train-rmse:8.636649+0.007690    test-rmse:8.608432+0.418295 
## [10] train-rmse:6.663610+0.006629    test-rmse:6.639250+0.395611 
## [11] train-rmse:5.174001+0.005237    test-rmse:5.158836+0.369689 
## [12] train-rmse:4.050766+0.008634    test-rmse:4.050448+0.322760 
## [13] train-rmse:3.208322+0.010715    test-rmse:3.225224+0.296392 
## [14] train-rmse:2.587312+0.014571    test-rmse:2.615970+0.269692 
## [15] train-rmse:2.133344+0.016521    test-rmse:2.193103+0.236814 
## [16] train-rmse:1.807538+0.020095    test-rmse:1.897527+0.197924 
## [17] train-rmse:1.572903+0.017177    test-rmse:1.687371+0.170391 
## [18] train-rmse:1.410968+0.019551    test-rmse:1.558236+0.136622 
## [19] train-rmse:1.295715+0.025199    test-rmse:1.474469+0.111808 
## [20] train-rmse:1.216686+0.025422    test-rmse:1.423358+0.106217 
## [21] train-rmse:1.157880+0.024658    test-rmse:1.388283+0.099073 
## [22] train-rmse:1.109782+0.020951    test-rmse:1.358232+0.093351 
## [23] train-rmse:1.074192+0.022328    test-rmse:1.345084+0.086293 
## [24] train-rmse:1.047274+0.022411    test-rmse:1.320673+0.084308 
## [25] train-rmse:1.022595+0.025489    test-rmse:1.299892+0.082185 
## [26] train-rmse:0.999313+0.025056    test-rmse:1.287032+0.084025 
## [27] train-rmse:0.976347+0.025808    test-rmse:1.273389+0.076935 
## [28] train-rmse:0.959697+0.025921    test-rmse:1.262542+0.075769 
## [29] train-rmse:0.938351+0.023359    test-rmse:1.247648+0.087636 
## [30] train-rmse:0.921158+0.025669    test-rmse:1.230332+0.089045 
## [31] train-rmse:0.901824+0.025726    test-rmse:1.224148+0.088278 
## [32] train-rmse:0.882126+0.029522    test-rmse:1.217398+0.092797 
## [33] train-rmse:0.869287+0.028308    test-rmse:1.210741+0.094738 
## [34] train-rmse:0.854341+0.024579    test-rmse:1.202162+0.091718 
## [35] train-rmse:0.843250+0.025076    test-rmse:1.193923+0.088293 
## [36] train-rmse:0.830147+0.022708    test-rmse:1.183838+0.088980 
## [37] train-rmse:0.815554+0.019221    test-rmse:1.178396+0.092408 
## [38] train-rmse:0.802844+0.020606    test-rmse:1.171203+0.088713 
## [39] train-rmse:0.789666+0.021283    test-rmse:1.165683+0.087903 
## [40] train-rmse:0.780969+0.022415    test-rmse:1.158051+0.088810 
## [41] train-rmse:0.769130+0.022839    test-rmse:1.152356+0.086882 
## [42] train-rmse:0.759722+0.022757    test-rmse:1.148866+0.087820 
## [43] train-rmse:0.749531+0.020791    test-rmse:1.138584+0.083950 
## [44] train-rmse:0.737566+0.021796    test-rmse:1.131360+0.084764 
## [45] train-rmse:0.729671+0.021799    test-rmse:1.128220+0.087527 
## [46] train-rmse:0.721810+0.023149    test-rmse:1.124310+0.087477 
## [47] train-rmse:0.713780+0.022412    test-rmse:1.117738+0.087556 
## [48] train-rmse:0.707082+0.021233    test-rmse:1.116312+0.087613 
## [49] train-rmse:0.699182+0.020659    test-rmse:1.111302+0.091215 
## [50] train-rmse:0.691945+0.022288    test-rmse:1.109458+0.091885 
## [51] train-rmse:0.684419+0.022844    test-rmse:1.106147+0.091093 
## [52] train-rmse:0.677477+0.022228    test-rmse:1.104718+0.092139 
## [53] train-rmse:0.668667+0.023121    test-rmse:1.099788+0.092747 
## [54] train-rmse:0.662273+0.022917    test-rmse:1.094006+0.093696 
## [55] train-rmse:0.654267+0.025718    test-rmse:1.092823+0.092116 
## [56] train-rmse:0.645442+0.027595    test-rmse:1.084762+0.082799 
## [57] train-rmse:0.640119+0.027766    test-rmse:1.083624+0.083532 
## [58] train-rmse:0.634024+0.027512    test-rmse:1.079382+0.082676 
## [59] train-rmse:0.626376+0.027039    test-rmse:1.076564+0.082182 
## [60] train-rmse:0.618690+0.025717    test-rmse:1.074280+0.082567 
## [61] train-rmse:0.612936+0.023727    test-rmse:1.072346+0.083838 
## [62] train-rmse:0.604973+0.027323    test-rmse:1.062310+0.080060 
## [63] train-rmse:0.599904+0.027599    test-rmse:1.058928+0.080113 
## [64] train-rmse:0.593197+0.028369    test-rmse:1.058914+0.080407 
## [65] train-rmse:0.588994+0.027658    test-rmse:1.057483+0.078632 
## [66] train-rmse:0.580198+0.027899    test-rmse:1.054035+0.073981 
## [67] train-rmse:0.573339+0.028362    test-rmse:1.048837+0.077276 
## [68] train-rmse:0.567052+0.026611    test-rmse:1.047743+0.076531 
## [69] train-rmse:0.562449+0.026752    test-rmse:1.047750+0.078505 
## [70] train-rmse:0.557604+0.025942    test-rmse:1.047042+0.077574 
## [71] train-rmse:0.553730+0.025810    test-rmse:1.047022+0.076721 
## [72] train-rmse:0.547516+0.027348    test-rmse:1.047603+0.080238 
## [73] train-rmse:0.543775+0.026342    test-rmse:1.046576+0.079822 
## [74] train-rmse:0.539685+0.026490    test-rmse:1.045327+0.079241 
## [75] train-rmse:0.532762+0.023966    test-rmse:1.042894+0.080919 
## [76] train-rmse:0.527856+0.024835    test-rmse:1.042227+0.079816 
## [77] train-rmse:0.521586+0.025673    test-rmse:1.040262+0.079390 
## [78] train-rmse:0.513737+0.027019    test-rmse:1.033380+0.072667 
## [79] train-rmse:0.508486+0.024004    test-rmse:1.030443+0.073329 
## [80] train-rmse:0.504801+0.024806    test-rmse:1.029608+0.073800 
## [81] train-rmse:0.500674+0.026302    test-rmse:1.033324+0.074355 
## [82] train-rmse:0.496426+0.026815    test-rmse:1.033788+0.074781 
## [83] train-rmse:0.492133+0.027029    test-rmse:1.034715+0.076317 
## [84] train-rmse:0.484970+0.025001    test-rmse:1.030392+0.074591 
## [85] train-rmse:0.481135+0.025607    test-rmse:1.028944+0.072869 
## [86] train-rmse:0.478155+0.025911    test-rmse:1.029264+0.072339 
## [87] train-rmse:0.473360+0.024237    test-rmse:1.026348+0.073901 
## [88] train-rmse:0.470107+0.024573    test-rmse:1.023392+0.073491 
## [89] train-rmse:0.465459+0.024332    test-rmse:1.025401+0.074166 
## [90] train-rmse:0.461537+0.023076    test-rmse:1.025711+0.076502 
## [91] train-rmse:0.457034+0.021855    test-rmse:1.023334+0.075045 
## [92] train-rmse:0.453284+0.021348    test-rmse:1.020513+0.074040 
## [93] train-rmse:0.448774+0.021625    test-rmse:1.020397+0.073657 
## [94] train-rmse:0.444366+0.021408    test-rmse:1.020338+0.071564 
## [95] train-rmse:0.440968+0.020458    test-rmse:1.019873+0.072908 
## [96] train-rmse:0.437526+0.020843    test-rmse:1.019519+0.073626 
## [97] train-rmse:0.433581+0.021520    test-rmse:1.018818+0.072706 
## [98] train-rmse:0.430127+0.021298    test-rmse:1.018280+0.074467 
## [99] train-rmse:0.426659+0.022616    test-rmse:1.016776+0.074665 
## [100]    train-rmse:0.423260+0.023195    test-rmse:1.015829+0.074957 
## [101]    train-rmse:0.420230+0.023634    test-rmse:1.016685+0.073840 
## [102]    train-rmse:0.415707+0.022073    test-rmse:1.014907+0.072968 
## [103]    train-rmse:0.412562+0.021848    test-rmse:1.014803+0.072083 
## [104]    train-rmse:0.408739+0.020713    test-rmse:1.014750+0.072201 
## [105]    train-rmse:0.404463+0.021686    test-rmse:1.015612+0.076429 
## [106]    train-rmse:0.400985+0.021411    test-rmse:1.015770+0.076591 
## [107]    train-rmse:0.397967+0.021332    test-rmse:1.014001+0.076570 
## [108]    train-rmse:0.394798+0.021646    test-rmse:1.013238+0.075463 
## [109]    train-rmse:0.392011+0.022052    test-rmse:1.012346+0.075538 
## [110]    train-rmse:0.388606+0.021786    test-rmse:1.011387+0.076590 
## [111]    train-rmse:0.385347+0.021842    test-rmse:1.010227+0.074638 
## [112]    train-rmse:0.381429+0.019069    test-rmse:1.010030+0.075713 
## [113]    train-rmse:0.378803+0.019082    test-rmse:1.010514+0.075766 
## [114]    train-rmse:0.376474+0.018101    test-rmse:1.010380+0.077305 
## [115]    train-rmse:0.372942+0.018245    test-rmse:1.010208+0.076735 
## [116]    train-rmse:0.371035+0.017637    test-rmse:1.010693+0.075906 
## [117]    train-rmse:0.366694+0.015128    test-rmse:1.009349+0.075477 
## [118]    train-rmse:0.362871+0.015899    test-rmse:1.004267+0.072867 
## [119]    train-rmse:0.359155+0.016183    test-rmse:1.003616+0.073250 
## [120]    train-rmse:0.356365+0.016489    test-rmse:1.003241+0.073303 
## [121]    train-rmse:0.354065+0.016996    test-rmse:1.003924+0.072558 
## [122]    train-rmse:0.350494+0.017799    test-rmse:1.002997+0.072202 
## [123]    train-rmse:0.348012+0.018688    test-rmse:1.002934+0.073318 
## [124]    train-rmse:0.345623+0.018960    test-rmse:1.002819+0.073174 
## [125]    train-rmse:0.342727+0.019643    test-rmse:1.003435+0.074459 
## [126]    train-rmse:0.340960+0.019559    test-rmse:1.003213+0.073426 
## [127]    train-rmse:0.337712+0.020344    test-rmse:1.002591+0.073275 
## [128]    train-rmse:0.335119+0.020089    test-rmse:1.002950+0.074688 
## [129]    train-rmse:0.332676+0.019351    test-rmse:1.002201+0.075008 
## [130]    train-rmse:0.330432+0.019377    test-rmse:1.002404+0.074832 
## [131]    train-rmse:0.327772+0.019732    test-rmse:1.002595+0.073245 
## [132]    train-rmse:0.325179+0.019272    test-rmse:1.002036+0.073363 
## [133]    train-rmse:0.322297+0.019742    test-rmse:1.000581+0.073318 
## [134]    train-rmse:0.319285+0.020277    test-rmse:0.998118+0.071178 
## [135]    train-rmse:0.317141+0.020716    test-rmse:0.998378+0.070507 
## [136]    train-rmse:0.315824+0.020429    test-rmse:0.999092+0.070698 
## [137]    train-rmse:0.313340+0.019226    test-rmse:0.998717+0.070586 
## [138]    train-rmse:0.310775+0.019770    test-rmse:0.999197+0.070199 
## [139]    train-rmse:0.308534+0.019698    test-rmse:0.996819+0.069550 
## [140]    train-rmse:0.305745+0.021116    test-rmse:0.996704+0.069731 
## [141]    train-rmse:0.304263+0.021463    test-rmse:0.996653+0.070124 
## [142]    train-rmse:0.302030+0.021954    test-rmse:0.996126+0.069944 
## [143]    train-rmse:0.298716+0.021615    test-rmse:0.995843+0.069033 
## [144]    train-rmse:0.295502+0.020455    test-rmse:0.995318+0.069094 
## [145]    train-rmse:0.293162+0.020801    test-rmse:0.995183+0.069061 
## [146]    train-rmse:0.291529+0.019980    test-rmse:0.994388+0.069793 
## [147]    train-rmse:0.288881+0.020019    test-rmse:0.993684+0.068760 
## [148]    train-rmse:0.285803+0.020205    test-rmse:0.994871+0.069715 
## [149]    train-rmse:0.284339+0.020156    test-rmse:0.995568+0.069900 
## [150]    train-rmse:0.282639+0.020096    test-rmse:0.995048+0.070211 
## [151]    train-rmse:0.280612+0.019788    test-rmse:0.994677+0.070530 
## [152]    train-rmse:0.278658+0.020081    test-rmse:0.995123+0.070688 
## [153]    train-rmse:0.276853+0.020492    test-rmse:0.995283+0.070402 
## Stopping. Best iteration:
## [147]    train-rmse:0.288881+0.020019    test-rmse:0.993684+0.068760
## 
## 52 
## [1]  train-rmse:74.935020+0.075103   test-rmse:74.935632+0.418470 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:57.029236+0.056907   test-rmse:57.029542+0.435851 
## [3]  train-rmse:43.410643+0.042960   test-rmse:43.410540+0.448676 
## [4]  train-rmse:33.055426+0.032244   test-rmse:33.054807+0.457865 
## [5]  train-rmse:25.185130+0.023974   test-rmse:25.183843+0.464019 
## [6]  train-rmse:19.208041+0.017613   test-rmse:19.205912+0.467458 
## [7]  train-rmse:14.674725+0.012875   test-rmse:14.671561+0.468210 
## [8]  train-rmse:11.238713+0.009134   test-rmse:11.227100+0.442238 
## [9]  train-rmse:8.636015+0.007803    test-rmse:8.608589+0.419396 
## [10] train-rmse:6.661101+0.007070    test-rmse:6.642772+0.386531 
## [11] train-rmse:5.166514+0.006650    test-rmse:5.166150+0.357397 
## [12] train-rmse:4.038740+0.007711    test-rmse:4.036264+0.330414 
## [13] train-rmse:3.188585+0.008339    test-rmse:3.190353+0.304602 
## [14] train-rmse:2.547782+0.007514    test-rmse:2.571887+0.271421 
## [15] train-rmse:2.077108+0.013777    test-rmse:2.136527+0.230384 
## [16] train-rmse:1.730713+0.015900    test-rmse:1.834734+0.207958 
## [17] train-rmse:1.478562+0.014143    test-rmse:1.623053+0.175780 
## [18] train-rmse:1.297641+0.015222    test-rmse:1.487384+0.145946 
## [19] train-rmse:1.167802+0.012056    test-rmse:1.400614+0.130524 
## [20] train-rmse:1.081958+0.012990    test-rmse:1.334916+0.114997 
## [21] train-rmse:1.013480+0.013336    test-rmse:1.294695+0.106721 
## [22] train-rmse:0.959440+0.015303    test-rmse:1.266572+0.095564 
## [23] train-rmse:0.918078+0.015909    test-rmse:1.239053+0.094356 
## [24] train-rmse:0.884193+0.011837    test-rmse:1.220639+0.093586 
## [25] train-rmse:0.851228+0.012522    test-rmse:1.207051+0.086000 
## [26] train-rmse:0.825303+0.014577    test-rmse:1.189782+0.085207 
## [27] train-rmse:0.806625+0.016066    test-rmse:1.181086+0.085882 
## [28] train-rmse:0.784928+0.016357    test-rmse:1.163685+0.091221 
## [29] train-rmse:0.765009+0.019901    test-rmse:1.157306+0.093566 
## [30] train-rmse:0.745316+0.019180    test-rmse:1.142150+0.085501 
## [31] train-rmse:0.728653+0.018560    test-rmse:1.129643+0.086090 
## [32] train-rmse:0.712512+0.022892    test-rmse:1.123438+0.079547 
## [33] train-rmse:0.694314+0.025482    test-rmse:1.114666+0.083949 
## [34] train-rmse:0.682266+0.023974    test-rmse:1.104518+0.086559 
## [35] train-rmse:0.664948+0.021517    test-rmse:1.094724+0.092855 
## [36] train-rmse:0.649406+0.020694    test-rmse:1.091678+0.094731 
## [37] train-rmse:0.636184+0.017576    test-rmse:1.082051+0.100124 
## [38] train-rmse:0.622993+0.018145    test-rmse:1.073132+0.104980 
## [39] train-rmse:0.611551+0.014157    test-rmse:1.065172+0.107868 
## [40] train-rmse:0.600226+0.014523    test-rmse:1.057655+0.105099 
## [41] train-rmse:0.588709+0.016761    test-rmse:1.055268+0.105672 
## [42] train-rmse:0.578819+0.016213    test-rmse:1.051356+0.106443 
## [43] train-rmse:0.565858+0.016064    test-rmse:1.053175+0.103862 
## [44] train-rmse:0.557532+0.016702    test-rmse:1.047612+0.104303 
## [45] train-rmse:0.545621+0.017554    test-rmse:1.038985+0.099137 
## [46] train-rmse:0.538301+0.019198    test-rmse:1.034830+0.101197 
## [47] train-rmse:0.527470+0.016474    test-rmse:1.029408+0.102716 
## [48] train-rmse:0.520349+0.016658    test-rmse:1.027851+0.103426 
## [49] train-rmse:0.513548+0.016665    test-rmse:1.021956+0.100954 
## [50] train-rmse:0.502507+0.015717    test-rmse:1.018465+0.105216 
## [51] train-rmse:0.494602+0.017279    test-rmse:1.015199+0.106173 
## [52] train-rmse:0.486069+0.017698    test-rmse:1.011490+0.107404 
## [53] train-rmse:0.475988+0.015540    test-rmse:1.008609+0.106688 
## [54] train-rmse:0.469098+0.013666    test-rmse:1.006585+0.106654 
## [55] train-rmse:0.462860+0.013813    test-rmse:1.008070+0.105246 
## [56] train-rmse:0.457526+0.013928    test-rmse:1.008364+0.103300 
## [57] train-rmse:0.451238+0.014537    test-rmse:1.003654+0.106226 
## [58] train-rmse:0.445447+0.015420    test-rmse:0.999280+0.104829 
## [59] train-rmse:0.439351+0.014980    test-rmse:0.998780+0.106624 
## [60] train-rmse:0.431624+0.017396    test-rmse:0.999140+0.107689 
## [61] train-rmse:0.424462+0.015852    test-rmse:0.993928+0.107033 
## [62] train-rmse:0.415961+0.016558    test-rmse:0.993713+0.108141 
## [63] train-rmse:0.410653+0.016569    test-rmse:0.992201+0.108280 
## [64] train-rmse:0.405039+0.016863    test-rmse:0.991387+0.107661 
## [65] train-rmse:0.398964+0.018494    test-rmse:0.990202+0.108175 
## [66] train-rmse:0.394825+0.016895    test-rmse:0.989858+0.109290 
## [67] train-rmse:0.388673+0.018615    test-rmse:0.986325+0.110181 
## [68] train-rmse:0.383570+0.019675    test-rmse:0.985550+0.110240 
## [69] train-rmse:0.377306+0.020782    test-rmse:0.984589+0.109796 
## [70] train-rmse:0.373355+0.021556    test-rmse:0.983829+0.109199 
## [71] train-rmse:0.369145+0.022358    test-rmse:0.985226+0.109282 
## [72] train-rmse:0.363256+0.021641    test-rmse:0.983668+0.109429 
## [73] train-rmse:0.357923+0.020577    test-rmse:0.982547+0.110867 
## [74] train-rmse:0.354381+0.020929    test-rmse:0.979378+0.109737 
## [75] train-rmse:0.348315+0.021878    test-rmse:0.978193+0.108978 
## [76] train-rmse:0.342154+0.020430    test-rmse:0.976839+0.111447 
## [77] train-rmse:0.336562+0.019319    test-rmse:0.977306+0.110164 
## [78] train-rmse:0.329966+0.020372    test-rmse:0.974743+0.108568 
## [79] train-rmse:0.325308+0.019881    test-rmse:0.973421+0.107899 
## [80] train-rmse:0.320260+0.018784    test-rmse:0.973757+0.107482 
## [81] train-rmse:0.316668+0.019060    test-rmse:0.973779+0.107102 
## [82] train-rmse:0.310906+0.018253    test-rmse:0.972159+0.106231 
## [83] train-rmse:0.306728+0.017779    test-rmse:0.972145+0.105377 
## [84] train-rmse:0.298350+0.015805    test-rmse:0.970718+0.106385 
## [85] train-rmse:0.294220+0.016021    test-rmse:0.968465+0.108060 
## [86] train-rmse:0.291194+0.015792    test-rmse:0.967681+0.108187 
## [87] train-rmse:0.287058+0.015137    test-rmse:0.966800+0.108975 
## [88] train-rmse:0.283051+0.015170    test-rmse:0.966107+0.109369 
## [89] train-rmse:0.279712+0.015728    test-rmse:0.968467+0.110331 
## [90] train-rmse:0.275543+0.015696    test-rmse:0.968531+0.110024 
## [91] train-rmse:0.271292+0.015682    test-rmse:0.967190+0.108504 
## [92] train-rmse:0.268669+0.015603    test-rmse:0.967138+0.108755 
## [93] train-rmse:0.265320+0.016188    test-rmse:0.965673+0.107968 
## [94] train-rmse:0.261933+0.016412    test-rmse:0.964000+0.107707 
## [95] train-rmse:0.257799+0.016087    test-rmse:0.962964+0.107786 
## [96] train-rmse:0.254532+0.016527    test-rmse:0.963139+0.106516 
## [97] train-rmse:0.251154+0.016091    test-rmse:0.962671+0.106044 
## [98] train-rmse:0.248196+0.016626    test-rmse:0.961414+0.106295 
## [99] train-rmse:0.243388+0.016009    test-rmse:0.960722+0.107109 
## [100]    train-rmse:0.241640+0.016377    test-rmse:0.959719+0.107783 
## [101]    train-rmse:0.238014+0.016861    test-rmse:0.960198+0.109389 
## [102]    train-rmse:0.234891+0.017105    test-rmse:0.958902+0.109959 
## [103]    train-rmse:0.230226+0.016612    test-rmse:0.956081+0.109050 
## [104]    train-rmse:0.226316+0.016668    test-rmse:0.954975+0.107619 
## [105]    train-rmse:0.223304+0.016627    test-rmse:0.955205+0.106394 
## [106]    train-rmse:0.220841+0.016684    test-rmse:0.954665+0.106353 
## [107]    train-rmse:0.217025+0.015429    test-rmse:0.955133+0.107557 
## [108]    train-rmse:0.214179+0.015643    test-rmse:0.954707+0.106745 
## [109]    train-rmse:0.211307+0.015957    test-rmse:0.953680+0.107261 
## [110]    train-rmse:0.207986+0.015649    test-rmse:0.953296+0.106688 
## [111]    train-rmse:0.206104+0.015814    test-rmse:0.952742+0.106926 
## [112]    train-rmse:0.203795+0.015562    test-rmse:0.951239+0.107077 
## [113]    train-rmse:0.202142+0.015634    test-rmse:0.951279+0.107174 
## [114]    train-rmse:0.200145+0.015424    test-rmse:0.952003+0.107041 
## [115]    train-rmse:0.196988+0.015486    test-rmse:0.951118+0.106512 
## [116]    train-rmse:0.195302+0.015428    test-rmse:0.952554+0.106734 
## [117]    train-rmse:0.192756+0.016215    test-rmse:0.952038+0.107207 
## [118]    train-rmse:0.190486+0.014438    test-rmse:0.952065+0.106132 
## [119]    train-rmse:0.188166+0.014598    test-rmse:0.951589+0.105602 
## [120]    train-rmse:0.185924+0.013521    test-rmse:0.952351+0.106237 
## [121]    train-rmse:0.182613+0.012867    test-rmse:0.950960+0.105602 
## [122]    train-rmse:0.181205+0.012537    test-rmse:0.950826+0.106608 
## [123]    train-rmse:0.178137+0.011142    test-rmse:0.950002+0.107455 
## [124]    train-rmse:0.175228+0.010442    test-rmse:0.950090+0.108254 
## [125]    train-rmse:0.173595+0.010683    test-rmse:0.948334+0.108528 
## [126]    train-rmse:0.170364+0.011606    test-rmse:0.947475+0.108345 
## [127]    train-rmse:0.167669+0.011770    test-rmse:0.948173+0.108934 
## [128]    train-rmse:0.164747+0.011654    test-rmse:0.947315+0.108066 
## [129]    train-rmse:0.161904+0.011023    test-rmse:0.946984+0.108051 
## [130]    train-rmse:0.160189+0.011439    test-rmse:0.946366+0.108390 
## [131]    train-rmse:0.157089+0.012158    test-rmse:0.946394+0.109335 
## [132]    train-rmse:0.154851+0.012015    test-rmse:0.946164+0.109529 
## [133]    train-rmse:0.153050+0.012048    test-rmse:0.946323+0.109237 
## [134]    train-rmse:0.151348+0.012433    test-rmse:0.946431+0.109410 
## [135]    train-rmse:0.149250+0.012536    test-rmse:0.946549+0.109598 
## [136]    train-rmse:0.147594+0.012376    test-rmse:0.946545+0.109886 
## [137]    train-rmse:0.146257+0.011679    test-rmse:0.946671+0.110282 
## [138]    train-rmse:0.144880+0.011802    test-rmse:0.945922+0.110401 
## [139]    train-rmse:0.143282+0.011943    test-rmse:0.945654+0.110817 
## [140]    train-rmse:0.141886+0.012288    test-rmse:0.946217+0.110577 
## [141]    train-rmse:0.140778+0.011693    test-rmse:0.947199+0.110266 
## [142]    train-rmse:0.138820+0.010582    test-rmse:0.946872+0.110723 
## [143]    train-rmse:0.137196+0.010134    test-rmse:0.946182+0.110500 
## [144]    train-rmse:0.135858+0.010146    test-rmse:0.946127+0.110915 
## [145]    train-rmse:0.133962+0.010435    test-rmse:0.946176+0.111114 
## Stopping. Best iteration:
## [139]    train-rmse:0.143282+0.011943    test-rmse:0.945654+0.110817
## 
## 53 
## [1]  train-rmse:74.935020+0.075103   test-rmse:74.935632+0.418470 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:57.029236+0.056907   test-rmse:57.029542+0.435851 
## [3]  train-rmse:43.410643+0.042960   test-rmse:43.410540+0.448676 
## [4]  train-rmse:33.055426+0.032244   test-rmse:33.054807+0.457865 
## [5]  train-rmse:25.185130+0.023974   test-rmse:25.183843+0.464019 
## [6]  train-rmse:19.208041+0.017613   test-rmse:19.205912+0.467458 
## [7]  train-rmse:14.674725+0.012875   test-rmse:14.671561+0.468210 
## [8]  train-rmse:11.238713+0.009134   test-rmse:11.227100+0.442238 
## [9]  train-rmse:8.636015+0.007803    test-rmse:8.608589+0.419396 
## [10] train-rmse:6.660518+0.006648    test-rmse:6.642849+0.386460 
## [11] train-rmse:5.162739+0.006744    test-rmse:5.149284+0.350638 
## [12] train-rmse:4.031857+0.006012    test-rmse:4.021761+0.326130 
## [13] train-rmse:3.170431+0.003914    test-rmse:3.174890+0.285096 
## [14] train-rmse:2.524245+0.008130    test-rmse:2.539529+0.253394 
## [15] train-rmse:2.040536+0.008570    test-rmse:2.102637+0.227543 
## [16] train-rmse:1.679775+0.008056    test-rmse:1.786546+0.189456 
## [17] train-rmse:1.415806+0.015572    test-rmse:1.566267+0.166746 
## [18] train-rmse:1.222588+0.015373    test-rmse:1.415305+0.146753 
## [19] train-rmse:1.085599+0.013906    test-rmse:1.316235+0.125936 
## [20] train-rmse:0.978497+0.015714    test-rmse:1.250910+0.115497 
## [21] train-rmse:0.901399+0.015430    test-rmse:1.210679+0.100409 
## [22] train-rmse:0.840758+0.016294    test-rmse:1.171675+0.089423 
## [23] train-rmse:0.796773+0.017100    test-rmse:1.146971+0.082961 
## [24] train-rmse:0.762086+0.015401    test-rmse:1.127306+0.084764 
## [25] train-rmse:0.723250+0.015201    test-rmse:1.109577+0.089575 
## [26] train-rmse:0.696481+0.015745    test-rmse:1.092804+0.091907 
## [27] train-rmse:0.664863+0.019510    test-rmse:1.074074+0.085905 
## [28] train-rmse:0.644913+0.019082    test-rmse:1.065361+0.084426 
## [29] train-rmse:0.624588+0.021563    test-rmse:1.059645+0.081240 
## [30] train-rmse:0.608574+0.023025    test-rmse:1.054271+0.084928 
## [31] train-rmse:0.586221+0.019619    test-rmse:1.046918+0.083781 
## [32] train-rmse:0.570304+0.017449    test-rmse:1.040940+0.080418 
## [33] train-rmse:0.556767+0.018267    test-rmse:1.037591+0.077106 
## [34] train-rmse:0.545259+0.019902    test-rmse:1.033473+0.080664 
## [35] train-rmse:0.530597+0.016543    test-rmse:1.031330+0.075308 
## [36] train-rmse:0.520009+0.017851    test-rmse:1.027003+0.072559 
## [37] train-rmse:0.499789+0.018266    test-rmse:1.022652+0.073746 
## [38] train-rmse:0.489954+0.020638    test-rmse:1.016131+0.078366 
## [39] train-rmse:0.478797+0.018260    test-rmse:1.008529+0.074618 
## [40] train-rmse:0.468087+0.017321    test-rmse:1.004998+0.076739 
## [41] train-rmse:0.456416+0.018681    test-rmse:1.001889+0.074498 
## [42] train-rmse:0.447217+0.018620    test-rmse:1.000625+0.072229 
## [43] train-rmse:0.436666+0.016845    test-rmse:0.998193+0.076557 
## [44] train-rmse:0.426736+0.017028    test-rmse:0.995130+0.074863 
## [45] train-rmse:0.416958+0.018442    test-rmse:0.991681+0.074709 
## [46] train-rmse:0.407579+0.018140    test-rmse:0.987173+0.071774 
## [47] train-rmse:0.398552+0.013193    test-rmse:0.986875+0.072425 
## [48] train-rmse:0.388813+0.015103    test-rmse:0.988279+0.073720 
## [49] train-rmse:0.377330+0.015053    test-rmse:0.985938+0.073279 
## [50] train-rmse:0.370046+0.017595    test-rmse:0.985151+0.073857 
## [51] train-rmse:0.361811+0.015038    test-rmse:0.980636+0.076278 
## [52] train-rmse:0.355334+0.012919    test-rmse:0.979871+0.076721 
## [53] train-rmse:0.345285+0.013987    test-rmse:0.976941+0.078403 
## [54] train-rmse:0.336173+0.012134    test-rmse:0.975406+0.077320 
## [55] train-rmse:0.327075+0.015172    test-rmse:0.975883+0.075241 
## [56] train-rmse:0.321228+0.016385    test-rmse:0.976325+0.076194 
## [57] train-rmse:0.314467+0.017816    test-rmse:0.974626+0.077311 
## [58] train-rmse:0.308379+0.016092    test-rmse:0.973919+0.077272 
## [59] train-rmse:0.303429+0.015698    test-rmse:0.973200+0.077534 
## [60] train-rmse:0.295816+0.014867    test-rmse:0.972466+0.078176 
## [61] train-rmse:0.291153+0.014412    test-rmse:0.969604+0.077773 
## [62] train-rmse:0.285602+0.014807    test-rmse:0.968890+0.078866 
## [63] train-rmse:0.277910+0.014999    test-rmse:0.966014+0.078928 
## [64] train-rmse:0.272893+0.015350    test-rmse:0.962133+0.079714 
## [65] train-rmse:0.267919+0.014198    test-rmse:0.960352+0.080287 
## [66] train-rmse:0.263257+0.013170    test-rmse:0.961626+0.081157 
## [67] train-rmse:0.259432+0.012903    test-rmse:0.959015+0.082643 
## [68] train-rmse:0.255249+0.012545    test-rmse:0.958361+0.081032 
## [69] train-rmse:0.249414+0.010983    test-rmse:0.955615+0.079998 
## [70] train-rmse:0.244683+0.011947    test-rmse:0.954317+0.081839 
## [71] train-rmse:0.240744+0.011909    test-rmse:0.953480+0.080323 
## [72] train-rmse:0.235938+0.012954    test-rmse:0.953428+0.082026 
## [73] train-rmse:0.229280+0.011876    test-rmse:0.951312+0.081829 
## [74] train-rmse:0.226156+0.011180    test-rmse:0.950218+0.083014 
## [75] train-rmse:0.221067+0.011865    test-rmse:0.949071+0.083870 
## [76] train-rmse:0.216186+0.013169    test-rmse:0.948551+0.084103 
## [77] train-rmse:0.211688+0.012465    test-rmse:0.948002+0.083207 
## [78] train-rmse:0.205929+0.013109    test-rmse:0.946791+0.083800 
## [79] train-rmse:0.202565+0.013303    test-rmse:0.946889+0.085349 
## [80] train-rmse:0.198046+0.011930    test-rmse:0.946088+0.086053 
## [81] train-rmse:0.193705+0.010401    test-rmse:0.945241+0.085206 
## [82] train-rmse:0.188766+0.008764    test-rmse:0.943319+0.085831 
## [83] train-rmse:0.185760+0.009203    test-rmse:0.943091+0.085763 
## [84] train-rmse:0.181601+0.007759    test-rmse:0.941256+0.085585 
## [85] train-rmse:0.176296+0.007541    test-rmse:0.941112+0.086108 
## [86] train-rmse:0.173382+0.008071    test-rmse:0.940306+0.086476 
## [87] train-rmse:0.170376+0.007088    test-rmse:0.941155+0.086218 
## [88] train-rmse:0.166658+0.007636    test-rmse:0.939465+0.086097 
## [89] train-rmse:0.163630+0.007972    test-rmse:0.939474+0.086228 
## [90] train-rmse:0.161397+0.008201    test-rmse:0.939892+0.086256 
## [91] train-rmse:0.158469+0.008984    test-rmse:0.939530+0.086313 
## [92] train-rmse:0.156116+0.008512    test-rmse:0.938907+0.086806 
## [93] train-rmse:0.153925+0.007454    test-rmse:0.937502+0.085916 
## [94] train-rmse:0.151227+0.008118    test-rmse:0.936664+0.085084 
## [95] train-rmse:0.148221+0.007231    test-rmse:0.935342+0.085040 
## [96] train-rmse:0.145252+0.007904    test-rmse:0.934603+0.085774 
## [97] train-rmse:0.142828+0.008403    test-rmse:0.934015+0.086129 
## [98] train-rmse:0.140609+0.008499    test-rmse:0.933978+0.086758 
## [99] train-rmse:0.137772+0.008219    test-rmse:0.933185+0.085790 
## [100]    train-rmse:0.135288+0.008199    test-rmse:0.932878+0.085472 
## [101]    train-rmse:0.133825+0.007786    test-rmse:0.932643+0.085568 
## [102]    train-rmse:0.131710+0.007754    test-rmse:0.932231+0.085848 
## [103]    train-rmse:0.129174+0.008103    test-rmse:0.932234+0.086327 
## [104]    train-rmse:0.127416+0.008576    test-rmse:0.932113+0.086344 
## [105]    train-rmse:0.125186+0.008266    test-rmse:0.932261+0.086502 
## [106]    train-rmse:0.122721+0.007519    test-rmse:0.931979+0.087422 
## [107]    train-rmse:0.121098+0.006233    test-rmse:0.931919+0.087486 
## [108]    train-rmse:0.119067+0.006922    test-rmse:0.931629+0.087705 
## [109]    train-rmse:0.115737+0.007158    test-rmse:0.930498+0.087620 
## [110]    train-rmse:0.113571+0.007438    test-rmse:0.930212+0.087659 
## [111]    train-rmse:0.111264+0.006829    test-rmse:0.929676+0.088710 
## [112]    train-rmse:0.109233+0.006023    test-rmse:0.929345+0.088749 
## [113]    train-rmse:0.107413+0.006316    test-rmse:0.928931+0.089094 
## [114]    train-rmse:0.105932+0.006228    test-rmse:0.928860+0.088770 
## [115]    train-rmse:0.103174+0.005271    test-rmse:0.928772+0.089430 
## [116]    train-rmse:0.101117+0.005336    test-rmse:0.928439+0.089741 
## [117]    train-rmse:0.099340+0.004820    test-rmse:0.928376+0.089232 
## [118]    train-rmse:0.097634+0.005221    test-rmse:0.928878+0.089666 
## [119]    train-rmse:0.095522+0.004654    test-rmse:0.928782+0.089963 
## [120]    train-rmse:0.093565+0.004695    test-rmse:0.928787+0.089634 
## [121]    train-rmse:0.092347+0.004999    test-rmse:0.929040+0.090260 
## [122]    train-rmse:0.090802+0.005341    test-rmse:0.929049+0.090322 
## [123]    train-rmse:0.089331+0.004829    test-rmse:0.928858+0.090152 
## Stopping. Best iteration:
## [117]    train-rmse:0.099340+0.004820    test-rmse:0.928376+0.089232
## 
## 54 
## [1]  train-rmse:74.935020+0.075103   test-rmse:74.935632+0.418470 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:57.029236+0.056907   test-rmse:57.029542+0.435851 
## [3]  train-rmse:43.410643+0.042960   test-rmse:43.410540+0.448676 
## [4]  train-rmse:33.055426+0.032244   test-rmse:33.054807+0.457865 
## [5]  train-rmse:25.185130+0.023974   test-rmse:25.183843+0.464019 
## [6]  train-rmse:19.208041+0.017613   test-rmse:19.205912+0.467458 
## [7]  train-rmse:14.674725+0.012875   test-rmse:14.671561+0.468210 
## [8]  train-rmse:11.238713+0.009134   test-rmse:11.227100+0.442238 
## [9]  train-rmse:8.636015+0.007803    test-rmse:8.608589+0.419396 
## [10] train-rmse:6.660398+0.006583    test-rmse:6.644245+0.385203 
## [11] train-rmse:5.160363+0.006611    test-rmse:5.142799+0.344526 
## [12] train-rmse:4.025490+0.005701    test-rmse:4.015275+0.314266 
## [13] train-rmse:3.161160+0.003525    test-rmse:3.170353+0.270909 
## [14] train-rmse:2.503890+0.006299    test-rmse:2.541269+0.236272 
## [15] train-rmse:2.012984+0.011058    test-rmse:2.069990+0.206994 
## [16] train-rmse:1.640012+0.008932    test-rmse:1.749368+0.179027 
## [17] train-rmse:1.363394+0.008662    test-rmse:1.537284+0.159505 
## [18] train-rmse:1.155899+0.007150    test-rmse:1.384478+0.143114 
## [19] train-rmse:1.001021+0.008769    test-rmse:1.281201+0.125973 
## [20] train-rmse:0.885007+0.010870    test-rmse:1.205282+0.124716 
## [21] train-rmse:0.802414+0.015040    test-rmse:1.157217+0.110275 
## [22] train-rmse:0.739346+0.016014    test-rmse:1.124501+0.107889 
## [23] train-rmse:0.688970+0.017213    test-rmse:1.098641+0.103888 
## [24] train-rmse:0.650074+0.018255    test-rmse:1.082187+0.104890 
## [25] train-rmse:0.620865+0.017360    test-rmse:1.072592+0.104082 
## [26] train-rmse:0.588640+0.016119    test-rmse:1.047952+0.099693 
## [27] train-rmse:0.559927+0.018775    test-rmse:1.040366+0.093349 
## [28] train-rmse:0.538700+0.018499    test-rmse:1.034207+0.096583 
## [29] train-rmse:0.519160+0.020154    test-rmse:1.026941+0.098036 
## [30] train-rmse:0.503057+0.022174    test-rmse:1.017276+0.096979 
## [31] train-rmse:0.488283+0.019972    test-rmse:1.018090+0.100552 
## [32] train-rmse:0.467448+0.019453    test-rmse:1.013002+0.105366 
## [33] train-rmse:0.450951+0.016293    test-rmse:1.007143+0.102638 
## [34] train-rmse:0.436814+0.014756    test-rmse:0.997902+0.102857 
## [35] train-rmse:0.423661+0.015284    test-rmse:0.994177+0.100852 
## [36] train-rmse:0.411648+0.012179    test-rmse:0.992274+0.107637 
## [37] train-rmse:0.398914+0.015340    test-rmse:0.988695+0.107272 
## [38] train-rmse:0.386675+0.013366    test-rmse:0.987378+0.103938 
## [39] train-rmse:0.372051+0.013101    test-rmse:0.985293+0.098216 
## [40] train-rmse:0.361013+0.011275    test-rmse:0.983077+0.096345 
## [41] train-rmse:0.349165+0.015250    test-rmse:0.979292+0.095586 
## [42] train-rmse:0.339010+0.014082    test-rmse:0.979450+0.101517 
## [43] train-rmse:0.329648+0.012855    test-rmse:0.977999+0.100698 
## [44] train-rmse:0.321014+0.012869    test-rmse:0.976417+0.102799 
## [45] train-rmse:0.311451+0.012749    test-rmse:0.978216+0.106309 
## [46] train-rmse:0.304436+0.012477    test-rmse:0.976406+0.105563 
## [47] train-rmse:0.295845+0.012325    test-rmse:0.973395+0.103019 
## [48] train-rmse:0.288325+0.010807    test-rmse:0.971194+0.103228 
## [49] train-rmse:0.280505+0.013420    test-rmse:0.971379+0.101827 
## [50] train-rmse:0.273602+0.012242    test-rmse:0.970834+0.102927 
## [51] train-rmse:0.264446+0.011282    test-rmse:0.968092+0.100891 
## [52] train-rmse:0.256371+0.009885    test-rmse:0.965602+0.100973 
## [53] train-rmse:0.248191+0.010608    test-rmse:0.967592+0.103822 
## [54] train-rmse:0.243567+0.012172    test-rmse:0.965630+0.104436 
## [55] train-rmse:0.235819+0.010870    test-rmse:0.964569+0.103988 
## [56] train-rmse:0.231270+0.010688    test-rmse:0.964102+0.104706 
## [57] train-rmse:0.224927+0.009901    test-rmse:0.965166+0.105499 
## [58] train-rmse:0.217383+0.012184    test-rmse:0.964231+0.106035 
## [59] train-rmse:0.210368+0.011667    test-rmse:0.963049+0.105178 
## [60] train-rmse:0.206629+0.012189    test-rmse:0.962002+0.104110 
## [61] train-rmse:0.200566+0.010217    test-rmse:0.959712+0.104002 
## [62] train-rmse:0.193993+0.011268    test-rmse:0.961909+0.104571 
## [63] train-rmse:0.188364+0.010981    test-rmse:0.961580+0.105485 
## [64] train-rmse:0.183152+0.008854    test-rmse:0.961390+0.106957 
## [65] train-rmse:0.177378+0.010971    test-rmse:0.959674+0.108356 
## [66] train-rmse:0.172512+0.012166    test-rmse:0.960066+0.106121 
## [67] train-rmse:0.168456+0.013176    test-rmse:0.959979+0.105330 
## [68] train-rmse:0.161134+0.011144    test-rmse:0.959030+0.104763 
## [69] train-rmse:0.157304+0.012026    test-rmse:0.958891+0.104981 
## [70] train-rmse:0.154579+0.011346    test-rmse:0.958419+0.105822 
## [71] train-rmse:0.150687+0.010452    test-rmse:0.958112+0.107049 
## [72] train-rmse:0.146261+0.009631    test-rmse:0.957284+0.106575 
## [73] train-rmse:0.142692+0.010206    test-rmse:0.957577+0.106455 
## [74] train-rmse:0.139207+0.010912    test-rmse:0.957172+0.106204 
## [75] train-rmse:0.135585+0.011215    test-rmse:0.957028+0.106945 
## [76] train-rmse:0.132083+0.011591    test-rmse:0.956199+0.107424 
## [77] train-rmse:0.128221+0.011509    test-rmse:0.955249+0.106860 
## [78] train-rmse:0.125335+0.011858    test-rmse:0.954720+0.106734 
## [79] train-rmse:0.122763+0.011767    test-rmse:0.954491+0.106746 
## [80] train-rmse:0.119742+0.011595    test-rmse:0.953879+0.106206 
## [81] train-rmse:0.117183+0.010780    test-rmse:0.954111+0.106628 
## [82] train-rmse:0.112971+0.009601    test-rmse:0.953832+0.106219 
## [83] train-rmse:0.110171+0.008533    test-rmse:0.954599+0.106251 
## [84] train-rmse:0.107325+0.008151    test-rmse:0.954421+0.105949 
## [85] train-rmse:0.104300+0.008163    test-rmse:0.953423+0.106815 
## [86] train-rmse:0.102285+0.007996    test-rmse:0.952927+0.107104 
## [87] train-rmse:0.099953+0.007561    test-rmse:0.951443+0.106542 
## [88] train-rmse:0.097625+0.007029    test-rmse:0.951438+0.106366 
## [89] train-rmse:0.094712+0.006893    test-rmse:0.951586+0.106264 
## [90] train-rmse:0.091809+0.007245    test-rmse:0.951602+0.106174 
## [91] train-rmse:0.089439+0.006569    test-rmse:0.951468+0.106352 
## [92] train-rmse:0.087347+0.006608    test-rmse:0.950734+0.106493 
## [93] train-rmse:0.085627+0.006178    test-rmse:0.950601+0.106469 
## [94] train-rmse:0.083711+0.006471    test-rmse:0.950517+0.106043 
## [95] train-rmse:0.081677+0.006193    test-rmse:0.950133+0.106142 
## [96] train-rmse:0.079471+0.006099    test-rmse:0.950657+0.106458 
## [97] train-rmse:0.077771+0.006247    test-rmse:0.950951+0.106422 
## [98] train-rmse:0.075551+0.006548    test-rmse:0.950886+0.106530 
## [99] train-rmse:0.073691+0.006245    test-rmse:0.950506+0.106364 
## [100]    train-rmse:0.071686+0.007361    test-rmse:0.949939+0.106777 
## [101]    train-rmse:0.069959+0.007230    test-rmse:0.949596+0.106927 
## [102]    train-rmse:0.068348+0.007055    test-rmse:0.949095+0.106868 
## [103]    train-rmse:0.066652+0.006558    test-rmse:0.949163+0.107030 
## [104]    train-rmse:0.065348+0.006221    test-rmse:0.949269+0.107352 
## [105]    train-rmse:0.063820+0.006735    test-rmse:0.948880+0.107119 
## [106]    train-rmse:0.062699+0.006817    test-rmse:0.948585+0.106978 
## [107]    train-rmse:0.061227+0.007066    test-rmse:0.948280+0.107218 
## [108]    train-rmse:0.059982+0.006942    test-rmse:0.948492+0.107609 
## [109]    train-rmse:0.058363+0.007075    test-rmse:0.948147+0.107734 
## [110]    train-rmse:0.056741+0.006546    test-rmse:0.948047+0.107594 
## [111]    train-rmse:0.054732+0.005939    test-rmse:0.948234+0.107650 
## [112]    train-rmse:0.053465+0.006216    test-rmse:0.948556+0.107477 
## [113]    train-rmse:0.052314+0.006269    test-rmse:0.949041+0.107475 
## [114]    train-rmse:0.050864+0.005835    test-rmse:0.948813+0.107743 
## [115]    train-rmse:0.049690+0.005755    test-rmse:0.948592+0.108106 
## [116]    train-rmse:0.048234+0.005549    test-rmse:0.948634+0.107855 
## Stopping. Best iteration:
## [110]    train-rmse:0.056741+0.006546    test-rmse:0.948047+0.107594
## 
## 55 
## [1]  train-rmse:74.935020+0.075103   test-rmse:74.935632+0.418470 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:57.029236+0.056907   test-rmse:57.029542+0.435851 
## [3]  train-rmse:43.410643+0.042960   test-rmse:43.410540+0.448676 
## [4]  train-rmse:33.055426+0.032244   test-rmse:33.054807+0.457865 
## [5]  train-rmse:25.185130+0.023974   test-rmse:25.183843+0.464019 
## [6]  train-rmse:19.208041+0.017613   test-rmse:19.205912+0.467458 
## [7]  train-rmse:14.674725+0.012875   test-rmse:14.671561+0.468210 
## [8]  train-rmse:11.238713+0.009134   test-rmse:11.227100+0.442238 
## [9]  train-rmse:8.636015+0.007803    test-rmse:8.608589+0.419396 
## [10] train-rmse:6.660398+0.006583    test-rmse:6.644245+0.385203 
## [11] train-rmse:5.159152+0.006123    test-rmse:5.144778+0.346228 
## [12] train-rmse:4.024053+0.004692    test-rmse:4.019185+0.314474 
## [13] train-rmse:3.157202+0.003696    test-rmse:3.168115+0.273580 
## [14] train-rmse:2.500809+0.005587    test-rmse:2.531928+0.252475 
## [15] train-rmse:2.001572+0.005372    test-rmse:2.083514+0.214700 
## [16] train-rmse:1.617225+0.005919    test-rmse:1.753884+0.177185 
## [17] train-rmse:1.333152+0.008202    test-rmse:1.508060+0.168761 
## [18] train-rmse:1.117422+0.013866    test-rmse:1.353566+0.159002 
## [19] train-rmse:0.949758+0.014047    test-rmse:1.246327+0.156165 
## [20] train-rmse:0.829371+0.018327    test-rmse:1.180750+0.149868 
## [21] train-rmse:0.737908+0.017727    test-rmse:1.132681+0.147446 
## [22] train-rmse:0.666289+0.024301    test-rmse:1.101511+0.142637 
## [23] train-rmse:0.605825+0.022685    test-rmse:1.081187+0.136142 
## [24] train-rmse:0.555896+0.027568    test-rmse:1.065627+0.125809 
## [25] train-rmse:0.521711+0.032205    test-rmse:1.051083+0.125148 
## [26] train-rmse:0.491863+0.029039    test-rmse:1.044756+0.124402 
## [27] train-rmse:0.466115+0.032050    test-rmse:1.034374+0.123631 
## [28] train-rmse:0.443330+0.030303    test-rmse:1.029024+0.126743 
## [29] train-rmse:0.419147+0.026529    test-rmse:1.018507+0.115545 
## [30] train-rmse:0.401129+0.029628    test-rmse:1.018732+0.110465 
## [31] train-rmse:0.384971+0.029741    test-rmse:1.018258+0.115010 
## [32] train-rmse:0.368436+0.031589    test-rmse:1.013635+0.114983 
## [33] train-rmse:0.353437+0.030070    test-rmse:1.008166+0.111807 
## [34] train-rmse:0.339691+0.031208    test-rmse:1.005819+0.115956 
## [35] train-rmse:0.325674+0.025338    test-rmse:1.002565+0.115419 
## [36] train-rmse:0.314481+0.025651    test-rmse:0.999704+0.113922 
## [37] train-rmse:0.303328+0.025063    test-rmse:0.996950+0.116790 
## [38] train-rmse:0.293681+0.023380    test-rmse:0.998182+0.119016 
## [39] train-rmse:0.279569+0.025703    test-rmse:0.989916+0.112077 
## [40] train-rmse:0.268903+0.022890    test-rmse:0.991273+0.114364 
## [41] train-rmse:0.257791+0.023430    test-rmse:0.990868+0.114484 
## [42] train-rmse:0.250981+0.022108    test-rmse:0.989694+0.116010 
## [43] train-rmse:0.244997+0.021960    test-rmse:0.988912+0.114517 
## [44] train-rmse:0.234514+0.021504    test-rmse:0.984064+0.112197 
## [45] train-rmse:0.225443+0.020613    test-rmse:0.983865+0.112140 
## [46] train-rmse:0.218577+0.019308    test-rmse:0.984023+0.111462 
## [47] train-rmse:0.212729+0.019434    test-rmse:0.982189+0.109886 
## [48] train-rmse:0.204226+0.017377    test-rmse:0.982375+0.112060 
## [49] train-rmse:0.197077+0.017525    test-rmse:0.980772+0.112713 
## [50] train-rmse:0.191373+0.016966    test-rmse:0.982099+0.112571 
## [51] train-rmse:0.182531+0.018458    test-rmse:0.981107+0.113124 
## [52] train-rmse:0.176892+0.019347    test-rmse:0.979862+0.113612 
## [53] train-rmse:0.169851+0.020221    test-rmse:0.979359+0.113587 
## [54] train-rmse:0.165584+0.019336    test-rmse:0.979842+0.113711 
## [55] train-rmse:0.159210+0.018921    test-rmse:0.978688+0.114539 
## [56] train-rmse:0.154760+0.018349    test-rmse:0.978907+0.113700 
## [57] train-rmse:0.150953+0.019783    test-rmse:0.978560+0.114488 
## [58] train-rmse:0.145895+0.018556    test-rmse:0.978554+0.114809 
## [59] train-rmse:0.139689+0.015706    test-rmse:0.978040+0.115928 
## [60] train-rmse:0.134947+0.014862    test-rmse:0.978131+0.116647 
## [61] train-rmse:0.130295+0.013322    test-rmse:0.977184+0.116432 
## [62] train-rmse:0.127067+0.013005    test-rmse:0.976707+0.116499 
## [63] train-rmse:0.124427+0.013312    test-rmse:0.976389+0.116217 
## [64] train-rmse:0.120559+0.014135    test-rmse:0.976607+0.116948 
## [65] train-rmse:0.116931+0.012893    test-rmse:0.975833+0.116479 
## [66] train-rmse:0.113864+0.012481    test-rmse:0.976231+0.117270 
## [67] train-rmse:0.109099+0.010514    test-rmse:0.975982+0.117023 
## [68] train-rmse:0.105457+0.010069    test-rmse:0.976150+0.116617 
## [69] train-rmse:0.101886+0.008838    test-rmse:0.974807+0.115915 
## [70] train-rmse:0.099023+0.008882    test-rmse:0.975199+0.116238 
## [71] train-rmse:0.094936+0.009304    test-rmse:0.975095+0.116167 
## [72] train-rmse:0.092027+0.008978    test-rmse:0.974888+0.116695 
## [73] train-rmse:0.089777+0.008543    test-rmse:0.974268+0.116100 
## [74] train-rmse:0.086881+0.008824    test-rmse:0.974049+0.116326 
## [75] train-rmse:0.084473+0.008875    test-rmse:0.974021+0.115595 
## [76] train-rmse:0.081166+0.009161    test-rmse:0.973391+0.115303 
## [77] train-rmse:0.078618+0.008948    test-rmse:0.973143+0.115298 
## [78] train-rmse:0.076184+0.008867    test-rmse:0.973111+0.115548 
## [79] train-rmse:0.073569+0.008490    test-rmse:0.972208+0.115219 
## [80] train-rmse:0.071407+0.007695    test-rmse:0.971572+0.114136 
## [81] train-rmse:0.068937+0.007181    test-rmse:0.971776+0.114316 
## [82] train-rmse:0.065634+0.006665    test-rmse:0.971835+0.114044 
## [83] train-rmse:0.064095+0.006434    test-rmse:0.971948+0.114020 
## [84] train-rmse:0.062341+0.006352    test-rmse:0.972176+0.114301 
## [85] train-rmse:0.060892+0.006300    test-rmse:0.972318+0.114412 
## [86] train-rmse:0.058806+0.006120    test-rmse:0.972105+0.114833 
## Stopping. Best iteration:
## [80] train-rmse:0.071407+0.007695    test-rmse:0.971572+0.114136
## 
## 56 
## [1]  train-rmse:72.973630+0.073108   test-rmse:72.974214+0.420392 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:54.084589+0.053899   test-rmse:54.084826+0.438661 
## [3]  train-rmse:40.095586+0.039547   test-rmse:40.095350+0.451697 
## [4]  train-rmse:29.739208+0.028775   test-rmse:29.738348+0.460589 
## [5]  train-rmse:22.077126+0.020671   test-rmse:22.075455+0.466019 
## [6]  train-rmse:16.415070+0.014660   test-rmse:16.412361+0.468234 
## [7]  train-rmse:12.237465+0.010317   test-rmse:12.240607+0.437841 
## [8]  train-rmse:9.159632+0.007283    test-rmse:9.149301+0.436970 
## [9]  train-rmse:6.898798+0.006999    test-rmse:6.883439+0.422561 
## [10] train-rmse:5.250659+0.009739    test-rmse:5.233542+0.405578 
## [11] train-rmse:4.061358+0.012394    test-rmse:4.056931+0.377645 
## [12] train-rmse:3.219060+0.016570    test-rmse:3.219210+0.347440 
## [13] train-rmse:2.634353+0.019967    test-rmse:2.645930+0.297148 
## [14] train-rmse:2.239980+0.024141    test-rmse:2.272309+0.242974 
## [15] train-rmse:1.983030+0.026624    test-rmse:2.031633+0.192651 
## [16] train-rmse:1.817444+0.029112    test-rmse:1.870761+0.165300 
## [17] train-rmse:1.711025+0.029461    test-rmse:1.775197+0.137429 
## [18] train-rmse:1.642144+0.029382    test-rmse:1.715303+0.118613 
## [19] train-rmse:1.595444+0.029381    test-rmse:1.679700+0.114742 
## [20] train-rmse:1.562368+0.028358    test-rmse:1.643086+0.110186 
## [21] train-rmse:1.537445+0.027714    test-rmse:1.621119+0.114362 
## [22] train-rmse:1.516575+0.026849    test-rmse:1.615248+0.122625 
## [23] train-rmse:1.498212+0.026748    test-rmse:1.600867+0.119241 
## [24] train-rmse:1.482716+0.025897    test-rmse:1.596272+0.120051 
## [25] train-rmse:1.468465+0.025056    test-rmse:1.577072+0.109651 
## [26] train-rmse:1.455567+0.025006    test-rmse:1.569000+0.109049 
## [27] train-rmse:1.443815+0.024726    test-rmse:1.565964+0.110352 
## [28] train-rmse:1.432093+0.024456    test-rmse:1.553771+0.110213 
## [29] train-rmse:1.420810+0.024044    test-rmse:1.544614+0.112083 
## [30] train-rmse:1.409927+0.023808    test-rmse:1.545894+0.112737 
## [31] train-rmse:1.399462+0.023402    test-rmse:1.545415+0.113169 
## [32] train-rmse:1.389362+0.022990    test-rmse:1.541511+0.112687 
## [33] train-rmse:1.379532+0.022837    test-rmse:1.533389+0.101031 
## [34] train-rmse:1.369996+0.022849    test-rmse:1.526946+0.102916 
## [35] train-rmse:1.360800+0.022920    test-rmse:1.516653+0.106752 
## [36] train-rmse:1.351811+0.023166    test-rmse:1.503930+0.095811 
## [37] train-rmse:1.343433+0.022997    test-rmse:1.501093+0.098949 
## [38] train-rmse:1.335236+0.022775    test-rmse:1.495320+0.102245 
## [39] train-rmse:1.327195+0.022560    test-rmse:1.488570+0.099328 
## [40] train-rmse:1.319221+0.022476    test-rmse:1.482956+0.101179 
## [41] train-rmse:1.311501+0.022128    test-rmse:1.473541+0.100167 
## [42] train-rmse:1.303985+0.021797    test-rmse:1.472904+0.098832 
## [43] train-rmse:1.296621+0.021512    test-rmse:1.466409+0.099781 
## [44] train-rmse:1.289365+0.021052    test-rmse:1.464541+0.101540 
## [45] train-rmse:1.282254+0.020744    test-rmse:1.461203+0.104556 
## [46] train-rmse:1.275369+0.020491    test-rmse:1.456788+0.102548 
## [47] train-rmse:1.268720+0.020365    test-rmse:1.451585+0.097517 
## [48] train-rmse:1.262216+0.020181    test-rmse:1.449846+0.092843 
## [49] train-rmse:1.255884+0.020221    test-rmse:1.445492+0.091823 
## [50] train-rmse:1.249678+0.020327    test-rmse:1.438944+0.092024 
## [51] train-rmse:1.243613+0.020391    test-rmse:1.436694+0.095503 
## [52] train-rmse:1.237886+0.020473    test-rmse:1.438902+0.091675 
## [53] train-rmse:1.232114+0.020622    test-rmse:1.433530+0.086937 
## [54] train-rmse:1.226452+0.020492    test-rmse:1.431834+0.087507 
## [55] train-rmse:1.221083+0.020549    test-rmse:1.426004+0.089475 
## [56] train-rmse:1.215789+0.020599    test-rmse:1.421500+0.092147 
## [57] train-rmse:1.210539+0.020696    test-rmse:1.420218+0.090010 
## [58] train-rmse:1.205452+0.020820    test-rmse:1.418927+0.093445 
## [59] train-rmse:1.200413+0.020991    test-rmse:1.413584+0.091132 
## [60] train-rmse:1.195423+0.021025    test-rmse:1.407494+0.091755 
## [61] train-rmse:1.190707+0.020964    test-rmse:1.396272+0.095616 
## [62] train-rmse:1.186098+0.020945    test-rmse:1.393758+0.097336 
## [63] train-rmse:1.181347+0.021053    test-rmse:1.387711+0.096665 
## [64] train-rmse:1.176974+0.020976    test-rmse:1.384878+0.093889 
## [65] train-rmse:1.172646+0.020775    test-rmse:1.382143+0.091788 
## [66] train-rmse:1.168382+0.020585    test-rmse:1.376954+0.093257 
## [67] train-rmse:1.164158+0.020431    test-rmse:1.373545+0.093064 
## [68] train-rmse:1.160087+0.020349    test-rmse:1.373817+0.094530 
## [69] train-rmse:1.156012+0.020247    test-rmse:1.371235+0.094511 
## [70] train-rmse:1.152028+0.020186    test-rmse:1.369722+0.094849 
## [71] train-rmse:1.148009+0.020000    test-rmse:1.372605+0.089938 
## [72] train-rmse:1.144251+0.019902    test-rmse:1.369078+0.090785 
## [73] train-rmse:1.140612+0.019888    test-rmse:1.365381+0.091383 
## [74] train-rmse:1.137056+0.019820    test-rmse:1.365473+0.089692 
## [75] train-rmse:1.133573+0.019790    test-rmse:1.364924+0.090284 
## [76] train-rmse:1.130063+0.019729    test-rmse:1.359597+0.092051 
## [77] train-rmse:1.126653+0.019627    test-rmse:1.352722+0.092446 
## [78] train-rmse:1.123359+0.019546    test-rmse:1.349677+0.091726 
## [79] train-rmse:1.120085+0.019535    test-rmse:1.345873+0.093210 
## [80] train-rmse:1.116905+0.019536    test-rmse:1.340881+0.089980 
## [81] train-rmse:1.113747+0.019525    test-rmse:1.338909+0.093137 
## [82] train-rmse:1.110689+0.019588    test-rmse:1.332140+0.090758 
## [83] train-rmse:1.107695+0.019677    test-rmse:1.332294+0.091118 
## [84] train-rmse:1.104739+0.019754    test-rmse:1.330217+0.090460 
## [85] train-rmse:1.101808+0.019790    test-rmse:1.325510+0.089770 
## [86] train-rmse:1.098901+0.019785    test-rmse:1.326375+0.085318 
## [87] train-rmse:1.096015+0.019681    test-rmse:1.325546+0.083802 
## [88] train-rmse:1.093293+0.019636    test-rmse:1.325066+0.081602 
## [89] train-rmse:1.090615+0.019577    test-rmse:1.322293+0.083835 
## [90] train-rmse:1.087963+0.019510    test-rmse:1.322616+0.086067 
## [91] train-rmse:1.085339+0.019496    test-rmse:1.320807+0.084929 
## [92] train-rmse:1.082720+0.019489    test-rmse:1.318767+0.086066 
## [93] train-rmse:1.080153+0.019414    test-rmse:1.317159+0.086133 
## [94] train-rmse:1.077593+0.019382    test-rmse:1.316707+0.087167 
## [95] train-rmse:1.075051+0.019362    test-rmse:1.315563+0.090092 
## [96] train-rmse:1.072664+0.019367    test-rmse:1.313961+0.090892 
## [97] train-rmse:1.070336+0.019357    test-rmse:1.311389+0.090234 
## [98] train-rmse:1.068069+0.019335    test-rmse:1.307997+0.092748 
## [99] train-rmse:1.065784+0.019312    test-rmse:1.305830+0.093699 
## [100]    train-rmse:1.063523+0.019228    test-rmse:1.305746+0.095480 
## [101]    train-rmse:1.061257+0.019156    test-rmse:1.304157+0.095663 
## [102]    train-rmse:1.059004+0.019103    test-rmse:1.300927+0.093249 
## [103]    train-rmse:1.056782+0.018987    test-rmse:1.298494+0.092991 
## [104]    train-rmse:1.054647+0.018917    test-rmse:1.297555+0.089185 
## [105]    train-rmse:1.052494+0.018784    test-rmse:1.297135+0.089595 
## [106]    train-rmse:1.050429+0.018700    test-rmse:1.296424+0.085810 
## [107]    train-rmse:1.048342+0.018592    test-rmse:1.296612+0.089258 
## [108]    train-rmse:1.046327+0.018497    test-rmse:1.295699+0.089074 
## [109]    train-rmse:1.044361+0.018369    test-rmse:1.296072+0.088435 
## [110]    train-rmse:1.042423+0.018305    test-rmse:1.292307+0.090058 
## [111]    train-rmse:1.040537+0.018232    test-rmse:1.290887+0.088929 
## [112]    train-rmse:1.038703+0.018197    test-rmse:1.290551+0.088989 
## [113]    train-rmse:1.036906+0.018155    test-rmse:1.290267+0.090538 
## [114]    train-rmse:1.035119+0.018108    test-rmse:1.289214+0.090629 
## [115]    train-rmse:1.033343+0.018047    test-rmse:1.287613+0.092843 
## [116]    train-rmse:1.031556+0.018008    test-rmse:1.285527+0.094105 
## [117]    train-rmse:1.029824+0.017984    test-rmse:1.283989+0.093109 
## [118]    train-rmse:1.028081+0.017956    test-rmse:1.282722+0.095344 
## [119]    train-rmse:1.026402+0.017951    test-rmse:1.282596+0.094424 
## [120]    train-rmse:1.024704+0.017894    test-rmse:1.281655+0.094046 
## [121]    train-rmse:1.022966+0.017889    test-rmse:1.281288+0.092928 
## [122]    train-rmse:1.021285+0.017850    test-rmse:1.276261+0.088993 
## [123]    train-rmse:1.019653+0.017801    test-rmse:1.274919+0.090830 
## [124]    train-rmse:1.018037+0.017751    test-rmse:1.275910+0.089687 
## [125]    train-rmse:1.016482+0.017718    test-rmse:1.273418+0.091760 
## [126]    train-rmse:1.014943+0.017694    test-rmse:1.273753+0.091828 
## [127]    train-rmse:1.013389+0.017655    test-rmse:1.271814+0.091660 
## [128]    train-rmse:1.011863+0.017665    test-rmse:1.270020+0.092295 
## [129]    train-rmse:1.010356+0.017677    test-rmse:1.269342+0.092384 
## [130]    train-rmse:1.008849+0.017648    test-rmse:1.270756+0.088239 
## [131]    train-rmse:1.007400+0.017624    test-rmse:1.269356+0.089606 
## [132]    train-rmse:1.005945+0.017597    test-rmse:1.266166+0.089238 
## [133]    train-rmse:1.004486+0.017561    test-rmse:1.266673+0.088398 
## [134]    train-rmse:1.003042+0.017551    test-rmse:1.267771+0.088177 
## [135]    train-rmse:1.001646+0.017553    test-rmse:1.266596+0.088810 
## [136]    train-rmse:1.000277+0.017566    test-rmse:1.265353+0.088761 
## [137]    train-rmse:0.998934+0.017568    test-rmse:1.263601+0.088411 
## [138]    train-rmse:0.997620+0.017566    test-rmse:1.262629+0.089272 
## [139]    train-rmse:0.996274+0.017597    test-rmse:1.260933+0.089754 
## [140]    train-rmse:0.994907+0.017649    test-rmse:1.260474+0.089258 
## [141]    train-rmse:0.993560+0.017627    test-rmse:1.260791+0.089786 
## [142]    train-rmse:0.992256+0.017611    test-rmse:1.260988+0.088912 
## [143]    train-rmse:0.990987+0.017598    test-rmse:1.260172+0.089766 
## [144]    train-rmse:0.989690+0.017588    test-rmse:1.255735+0.087450 
## [145]    train-rmse:0.988430+0.017582    test-rmse:1.253875+0.086279 
## [146]    train-rmse:0.987212+0.017588    test-rmse:1.253572+0.085561 
## [147]    train-rmse:0.985991+0.017603    test-rmse:1.254821+0.083133 
## [148]    train-rmse:0.984799+0.017570    test-rmse:1.255466+0.082385 
## [149]    train-rmse:0.983599+0.017572    test-rmse:1.254773+0.082462 
## [150]    train-rmse:0.982385+0.017593    test-rmse:1.253464+0.081456 
## [151]    train-rmse:0.981197+0.017587    test-rmse:1.253609+0.082198 
## [152]    train-rmse:0.980007+0.017603    test-rmse:1.252331+0.081710 
## [153]    train-rmse:0.978844+0.017618    test-rmse:1.251457+0.082392 
## [154]    train-rmse:0.977684+0.017632    test-rmse:1.252159+0.081753 
## [155]    train-rmse:0.976498+0.017643    test-rmse:1.251490+0.082678 
## [156]    train-rmse:0.975354+0.017652    test-rmse:1.249336+0.084208 
## [157]    train-rmse:0.974206+0.017650    test-rmse:1.249483+0.083568 
## [158]    train-rmse:0.973028+0.017690    test-rmse:1.248260+0.084506 
## [159]    train-rmse:0.971956+0.017696    test-rmse:1.247708+0.085043 
## [160]    train-rmse:0.970890+0.017688    test-rmse:1.247548+0.084669 
## [161]    train-rmse:0.969821+0.017680    test-rmse:1.249832+0.086221 
## [162]    train-rmse:0.968763+0.017675    test-rmse:1.247873+0.086733 
## [163]    train-rmse:0.967723+0.017656    test-rmse:1.246645+0.086495 
## [164]    train-rmse:0.966688+0.017661    test-rmse:1.246624+0.085794 
## [165]    train-rmse:0.965597+0.017665    test-rmse:1.243460+0.087108 
## [166]    train-rmse:0.964580+0.017681    test-rmse:1.244090+0.084329 
## [167]    train-rmse:0.963513+0.017734    test-rmse:1.242831+0.085814 
## [168]    train-rmse:0.962491+0.017702    test-rmse:1.242393+0.085403 
## [169]    train-rmse:0.961512+0.017709    test-rmse:1.241549+0.085171 
## [170]    train-rmse:0.960479+0.017742    test-rmse:1.239215+0.084687 
## [171]    train-rmse:0.959489+0.017741    test-rmse:1.238846+0.083102 
## [172]    train-rmse:0.958509+0.017750    test-rmse:1.238517+0.082865 
## [173]    train-rmse:0.957524+0.017801    test-rmse:1.239652+0.082014 
## [174]    train-rmse:0.956534+0.017786    test-rmse:1.238351+0.083063 
## [175]    train-rmse:0.955568+0.017782    test-rmse:1.236940+0.084677 
## [176]    train-rmse:0.954608+0.017819    test-rmse:1.236711+0.086683 
## [177]    train-rmse:0.953626+0.017861    test-rmse:1.235013+0.086500 
## [178]    train-rmse:0.952687+0.017886    test-rmse:1.234226+0.087117 
## [179]    train-rmse:0.951783+0.017913    test-rmse:1.234059+0.087484 
## [180]    train-rmse:0.950845+0.017948    test-rmse:1.233350+0.087957 
## [181]    train-rmse:0.949906+0.017984    test-rmse:1.232426+0.086668 
## [182]    train-rmse:0.949024+0.017996    test-rmse:1.234170+0.087436 
## [183]    train-rmse:0.948134+0.017987    test-rmse:1.234070+0.087215 
## [184]    train-rmse:0.947259+0.018012    test-rmse:1.232964+0.087112 
## [185]    train-rmse:0.946326+0.018036    test-rmse:1.234186+0.086748 
## [186]    train-rmse:0.945454+0.018065    test-rmse:1.233266+0.087016 
## [187]    train-rmse:0.944594+0.018077    test-rmse:1.231131+0.086859 
## [188]    train-rmse:0.943739+0.018085    test-rmse:1.232694+0.087572 
## [189]    train-rmse:0.942889+0.018100    test-rmse:1.231906+0.087248 
## [190]    train-rmse:0.942035+0.018120    test-rmse:1.233614+0.086077 
## [191]    train-rmse:0.941214+0.018127    test-rmse:1.230877+0.084473 
## [192]    train-rmse:0.940401+0.018135    test-rmse:1.230294+0.086147 
## [193]    train-rmse:0.939532+0.018149    test-rmse:1.230413+0.086310 
## [194]    train-rmse:0.938751+0.018174    test-rmse:1.229920+0.085587 
## [195]    train-rmse:0.937943+0.018183    test-rmse:1.230094+0.085050 
## [196]    train-rmse:0.937122+0.018220    test-rmse:1.229314+0.085812 
## [197]    train-rmse:0.936331+0.018244    test-rmse:1.229183+0.084468 
## [198]    train-rmse:0.935544+0.018230    test-rmse:1.229043+0.086638 
## [199]    train-rmse:0.934729+0.018225    test-rmse:1.228431+0.085192 
## [200]    train-rmse:0.933924+0.018242    test-rmse:1.228353+0.084820 
## [201]    train-rmse:0.933161+0.018250    test-rmse:1.227678+0.084490 
## [202]    train-rmse:0.932406+0.018249    test-rmse:1.227228+0.083698 
## [203]    train-rmse:0.931590+0.018257    test-rmse:1.227731+0.083559 
## [204]    train-rmse:0.930809+0.018275    test-rmse:1.227584+0.083811 
## [205]    train-rmse:0.930043+0.018298    test-rmse:1.227330+0.084362 
## [206]    train-rmse:0.929280+0.018334    test-rmse:1.227434+0.083070 
## [207]    train-rmse:0.928503+0.018307    test-rmse:1.228073+0.083014 
## [208]    train-rmse:0.927774+0.018342    test-rmse:1.228391+0.084046 
## Stopping. Best iteration:
## [202]    train-rmse:0.932406+0.018249    test-rmse:1.227228+0.083698
## 
## 57 
## [1]  train-rmse:72.973630+0.073108   test-rmse:72.974214+0.420392 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:54.084589+0.053899   test-rmse:54.084826+0.438661 
## [3]  train-rmse:40.095586+0.039547   test-rmse:40.095350+0.451697 
## [4]  train-rmse:29.739208+0.028775   test-rmse:29.738348+0.460589 
## [5]  train-rmse:22.077126+0.020671   test-rmse:22.075455+0.466019 
## [6]  train-rmse:16.415070+0.014660   test-rmse:16.412361+0.468234 
## [7]  train-rmse:12.237465+0.010317   test-rmse:12.240607+0.437841 
## [8]  train-rmse:9.156411+0.006799    test-rmse:9.143914+0.430245 
## [9]  train-rmse:6.887993+0.006220    test-rmse:6.871933+0.407301 
## [10] train-rmse:5.224180+0.010063    test-rmse:5.213276+0.384102 
## [11] train-rmse:4.011316+0.012293    test-rmse:4.007571+0.356028 
## [12] train-rmse:3.141297+0.017120    test-rmse:3.149305+0.325292 
## [13] train-rmse:2.523336+0.019784    test-rmse:2.542827+0.283536 
## [14] train-rmse:2.097613+0.022876    test-rmse:2.143921+0.239107 
## [15] train-rmse:1.805832+0.021361    test-rmse:1.882381+0.208533 
## [16] train-rmse:1.617358+0.023826    test-rmse:1.713227+0.184478 
## [17] train-rmse:1.490755+0.025618    test-rmse:1.616309+0.147115 
## [18] train-rmse:1.408316+0.029656    test-rmse:1.564903+0.121343 
## [19] train-rmse:1.353707+0.030193    test-rmse:1.512231+0.112636 
## [20] train-rmse:1.312934+0.029157    test-rmse:1.483818+0.108866 
## [21] train-rmse:1.277292+0.028771    test-rmse:1.462809+0.103921 
## [22] train-rmse:1.253455+0.028757    test-rmse:1.442158+0.089322 
## [23] train-rmse:1.232087+0.026953    test-rmse:1.425547+0.087289 
## [24] train-rmse:1.213040+0.027987    test-rmse:1.417745+0.080473 
## [25] train-rmse:1.194523+0.030221    test-rmse:1.410213+0.079661 
## [26] train-rmse:1.179137+0.028998    test-rmse:1.399001+0.083387 
## [27] train-rmse:1.164583+0.029399    test-rmse:1.388615+0.079316 
## [28] train-rmse:1.148668+0.026762    test-rmse:1.390766+0.074902 
## [29] train-rmse:1.132970+0.027713    test-rmse:1.373202+0.074496 
## [30] train-rmse:1.120745+0.026956    test-rmse:1.365111+0.068586 
## [31] train-rmse:1.106522+0.025002    test-rmse:1.356266+0.069287 
## [32] train-rmse:1.091251+0.025632    test-rmse:1.347727+0.069113 
## [33] train-rmse:1.074429+0.027555    test-rmse:1.335337+0.080332 
## [34] train-rmse:1.064554+0.027239    test-rmse:1.321011+0.085132 
## [35] train-rmse:1.053507+0.027837    test-rmse:1.318938+0.081271 
## [36] train-rmse:1.044299+0.027663    test-rmse:1.315815+0.077985 
## [37] train-rmse:1.034043+0.027935    test-rmse:1.308831+0.082203 
## [38] train-rmse:1.022946+0.025131    test-rmse:1.306299+0.082758 
## [39] train-rmse:1.010986+0.026564    test-rmse:1.300606+0.078144 
## [40] train-rmse:1.002747+0.026599    test-rmse:1.289854+0.081817 
## [41] train-rmse:0.993007+0.026085    test-rmse:1.287458+0.083669 
## [42] train-rmse:0.982165+0.023877    test-rmse:1.283487+0.078253 
## [43] train-rmse:0.972138+0.024978    test-rmse:1.272894+0.079815 
## [44] train-rmse:0.964182+0.024224    test-rmse:1.264627+0.076932 
## [45] train-rmse:0.953469+0.021074    test-rmse:1.263081+0.076687 
## [46] train-rmse:0.944074+0.020524    test-rmse:1.252712+0.081183 
## [47] train-rmse:0.936355+0.018753    test-rmse:1.249364+0.084409 
## [48] train-rmse:0.926785+0.019617    test-rmse:1.246183+0.084419 
## [49] train-rmse:0.918633+0.016723    test-rmse:1.241487+0.086222 
## [50] train-rmse:0.911529+0.015978    test-rmse:1.237596+0.086754 
## [51] train-rmse:0.905247+0.015884    test-rmse:1.228755+0.085912 
## [52] train-rmse:0.899113+0.014766    test-rmse:1.224717+0.086288 
## [53] train-rmse:0.891919+0.014444    test-rmse:1.223118+0.082485 
## [54] train-rmse:0.885542+0.015217    test-rmse:1.217364+0.082056 
## [55] train-rmse:0.877563+0.014337    test-rmse:1.215005+0.086209 
## [56] train-rmse:0.868236+0.012461    test-rmse:1.211310+0.087413 
## [57] train-rmse:0.858556+0.017949    test-rmse:1.199555+0.081428 
## [58] train-rmse:0.850423+0.017679    test-rmse:1.194759+0.084807 
## [59] train-rmse:0.844885+0.017183    test-rmse:1.195593+0.086986 
## [60] train-rmse:0.837416+0.017800    test-rmse:1.195634+0.085735 
## [61] train-rmse:0.832381+0.017254    test-rmse:1.195489+0.087221 
## [62] train-rmse:0.827356+0.015957    test-rmse:1.187125+0.091143 
## [63] train-rmse:0.820048+0.014493    test-rmse:1.180830+0.088876 
## [64] train-rmse:0.815619+0.014015    test-rmse:1.177139+0.089400 
## [65] train-rmse:0.811009+0.013677    test-rmse:1.173046+0.090995 
## [66] train-rmse:0.804056+0.011678    test-rmse:1.169250+0.093541 
## [67] train-rmse:0.799612+0.011809    test-rmse:1.165258+0.092420 
## [68] train-rmse:0.794866+0.010666    test-rmse:1.163068+0.092542 
## [69] train-rmse:0.790050+0.011194    test-rmse:1.156674+0.092800 
## [70] train-rmse:0.781916+0.011098    test-rmse:1.155194+0.097737 
## [71] train-rmse:0.775826+0.011746    test-rmse:1.154591+0.097291 
## [72] train-rmse:0.771935+0.011322    test-rmse:1.155103+0.093189 
## [73] train-rmse:0.767006+0.012049    test-rmse:1.151435+0.094397 
## [74] train-rmse:0.761765+0.010650    test-rmse:1.144133+0.092659 
## [75] train-rmse:0.758620+0.010630    test-rmse:1.142616+0.092805 
## [76] train-rmse:0.754549+0.010593    test-rmse:1.140062+0.092545 
## [77] train-rmse:0.748963+0.011967    test-rmse:1.134733+0.096573 
## [78] train-rmse:0.744780+0.012569    test-rmse:1.134482+0.095882 
## [79] train-rmse:0.739900+0.010296    test-rmse:1.135018+0.101680 
## [80] train-rmse:0.735814+0.010410    test-rmse:1.132114+0.100246 
## [81] train-rmse:0.731440+0.009807    test-rmse:1.126785+0.105588 
## [82] train-rmse:0.727094+0.009370    test-rmse:1.128847+0.103562 
## [83] train-rmse:0.721463+0.009861    test-rmse:1.123489+0.100347 
## [84] train-rmse:0.717594+0.010274    test-rmse:1.120284+0.101822 
## [85] train-rmse:0.713063+0.012155    test-rmse:1.119077+0.100065 
## [86] train-rmse:0.709646+0.012471    test-rmse:1.116437+0.099548 
## [87] train-rmse:0.702281+0.011678    test-rmse:1.109869+0.100753 
## [88] train-rmse:0.698777+0.011612    test-rmse:1.109345+0.099930 
## [89] train-rmse:0.693450+0.010982    test-rmse:1.108738+0.103563 
## [90] train-rmse:0.689310+0.011149    test-rmse:1.106444+0.107805 
## [91] train-rmse:0.684880+0.011522    test-rmse:1.105143+0.108044 
## [92] train-rmse:0.681795+0.011079    test-rmse:1.102976+0.107504 
## [93] train-rmse:0.678572+0.009768    test-rmse:1.105308+0.111663 
## [94] train-rmse:0.674631+0.009341    test-rmse:1.103170+0.111998 
## [95] train-rmse:0.671561+0.009562    test-rmse:1.099243+0.115552 
## [96] train-rmse:0.666023+0.008346    test-rmse:1.097582+0.115138 
## [97] train-rmse:0.662361+0.007204    test-rmse:1.097782+0.114237 
## [98] train-rmse:0.658380+0.008086    test-rmse:1.097711+0.112586 
## [99] train-rmse:0.654865+0.008863    test-rmse:1.096816+0.113625 
## [100]    train-rmse:0.651965+0.009578    test-rmse:1.094303+0.116105 
## [101]    train-rmse:0.646782+0.008730    test-rmse:1.096074+0.117530 
## [102]    train-rmse:0.643289+0.010170    test-rmse:1.096440+0.118661 
## [103]    train-rmse:0.639345+0.008383    test-rmse:1.094270+0.116221 
## [104]    train-rmse:0.635261+0.007947    test-rmse:1.091730+0.116748 
## [105]    train-rmse:0.633191+0.008005    test-rmse:1.091761+0.118703 
## [106]    train-rmse:0.630357+0.007741    test-rmse:1.087991+0.117858 
## [107]    train-rmse:0.627658+0.008704    test-rmse:1.085760+0.115485 
## [108]    train-rmse:0.624724+0.008722    test-rmse:1.084588+0.115303 
## [109]    train-rmse:0.621997+0.008170    test-rmse:1.084988+0.116494 
## [110]    train-rmse:0.618602+0.005739    test-rmse:1.082628+0.117548 
## [111]    train-rmse:0.615668+0.006115    test-rmse:1.081023+0.118864 
## [112]    train-rmse:0.612541+0.005973    test-rmse:1.077279+0.120742 
## [113]    train-rmse:0.610079+0.005884    test-rmse:1.076986+0.120070 
## [114]    train-rmse:0.607023+0.005930    test-rmse:1.076006+0.119603 
## [115]    train-rmse:0.603524+0.006194    test-rmse:1.074731+0.120786 
## [116]    train-rmse:0.601584+0.006313    test-rmse:1.073923+0.121094 
## [117]    train-rmse:0.600005+0.006354    test-rmse:1.073749+0.123833 
## [118]    train-rmse:0.597135+0.005636    test-rmse:1.073174+0.125388 
## [119]    train-rmse:0.593991+0.005511    test-rmse:1.071950+0.123522 
## [120]    train-rmse:0.590576+0.005827    test-rmse:1.070704+0.122988 
## [121]    train-rmse:0.585888+0.009571    test-rmse:1.070927+0.123759 
## [122]    train-rmse:0.583712+0.010505    test-rmse:1.070844+0.123607 
## [123]    train-rmse:0.580272+0.010400    test-rmse:1.067814+0.121368 
## [124]    train-rmse:0.578576+0.009929    test-rmse:1.066019+0.119018 
## [125]    train-rmse:0.574276+0.009572    test-rmse:1.064125+0.121154 
## [126]    train-rmse:0.570809+0.008499    test-rmse:1.063509+0.120454 
## [127]    train-rmse:0.567326+0.008436    test-rmse:1.065097+0.120316 
## [128]    train-rmse:0.563252+0.006667    test-rmse:1.061794+0.121484 
## [129]    train-rmse:0.561618+0.006796    test-rmse:1.062549+0.122477 
## [130]    train-rmse:0.558549+0.007043    test-rmse:1.060133+0.123014 
## [131]    train-rmse:0.553732+0.006238    test-rmse:1.056961+0.120717 
## [132]    train-rmse:0.551379+0.007041    test-rmse:1.055834+0.120096 
## [133]    train-rmse:0.549890+0.007153    test-rmse:1.055356+0.120297 
## [134]    train-rmse:0.547856+0.007171    test-rmse:1.054917+0.121169 
## [135]    train-rmse:0.546259+0.006869    test-rmse:1.053066+0.121610 
## [136]    train-rmse:0.544299+0.007605    test-rmse:1.052929+0.121955 
## [137]    train-rmse:0.541028+0.008598    test-rmse:1.051204+0.123731 
## [138]    train-rmse:0.538431+0.008506    test-rmse:1.051203+0.124014 
## [139]    train-rmse:0.535480+0.009752    test-rmse:1.050426+0.124906 
## [140]    train-rmse:0.532739+0.009542    test-rmse:1.050099+0.126243 
## [141]    train-rmse:0.530718+0.009407    test-rmse:1.048868+0.125713 
## [142]    train-rmse:0.529561+0.009507    test-rmse:1.048395+0.125576 
## [143]    train-rmse:0.525838+0.008390    test-rmse:1.047718+0.125228 
## [144]    train-rmse:0.522078+0.009811    test-rmse:1.044507+0.125144 
## [145]    train-rmse:0.519433+0.010108    test-rmse:1.042551+0.123983 
## [146]    train-rmse:0.517438+0.009836    test-rmse:1.042371+0.122907 
## [147]    train-rmse:0.514391+0.009800    test-rmse:1.041725+0.123927 
## [148]    train-rmse:0.512221+0.010197    test-rmse:1.041576+0.122567 
## [149]    train-rmse:0.510155+0.009104    test-rmse:1.042162+0.123598 
## [150]    train-rmse:0.508385+0.009383    test-rmse:1.040886+0.123447 
## [151]    train-rmse:0.507013+0.009710    test-rmse:1.041073+0.124121 
## [152]    train-rmse:0.504844+0.010409    test-rmse:1.040773+0.123632 
## [153]    train-rmse:0.501317+0.009440    test-rmse:1.039382+0.126004 
## [154]    train-rmse:0.499477+0.009355    test-rmse:1.039063+0.125343 
## [155]    train-rmse:0.498103+0.009478    test-rmse:1.037705+0.125729 
## [156]    train-rmse:0.496196+0.009024    test-rmse:1.037765+0.126374 
## [157]    train-rmse:0.494843+0.008753    test-rmse:1.037076+0.126087 
## [158]    train-rmse:0.493269+0.008711    test-rmse:1.037631+0.126137 
## [159]    train-rmse:0.491703+0.008382    test-rmse:1.036961+0.125954 
## [160]    train-rmse:0.489951+0.008539    test-rmse:1.037828+0.125759 
## [161]    train-rmse:0.487127+0.008193    test-rmse:1.037363+0.126628 
## [162]    train-rmse:0.485326+0.007860    test-rmse:1.037456+0.128027 
## [163]    train-rmse:0.484114+0.007661    test-rmse:1.036308+0.127975 
## [164]    train-rmse:0.482853+0.007761    test-rmse:1.036142+0.128766 
## [165]    train-rmse:0.479604+0.007624    test-rmse:1.034626+0.128091 
## [166]    train-rmse:0.476693+0.008910    test-rmse:1.034301+0.128265 
## [167]    train-rmse:0.474250+0.009090    test-rmse:1.034450+0.129236 
## [168]    train-rmse:0.471533+0.008739    test-rmse:1.032864+0.128636 
## [169]    train-rmse:0.470407+0.008954    test-rmse:1.031946+0.128834 
## [170]    train-rmse:0.468697+0.009049    test-rmse:1.032096+0.128328 
## [171]    train-rmse:0.466080+0.009718    test-rmse:1.029616+0.127969 
## [172]    train-rmse:0.464237+0.008990    test-rmse:1.030362+0.128485 
## [173]    train-rmse:0.461868+0.008055    test-rmse:1.031599+0.127704 
## [174]    train-rmse:0.459969+0.008016    test-rmse:1.030702+0.126770 
## [175]    train-rmse:0.458372+0.008021    test-rmse:1.029362+0.128396 
## [176]    train-rmse:0.456869+0.007525    test-rmse:1.029824+0.128631 
## [177]    train-rmse:0.454343+0.007517    test-rmse:1.027208+0.127322 
## [178]    train-rmse:0.452644+0.007743    test-rmse:1.028301+0.128057 
## [179]    train-rmse:0.451130+0.008283    test-rmse:1.027255+0.127149 
## [180]    train-rmse:0.450101+0.008428    test-rmse:1.027322+0.127076 
## [181]    train-rmse:0.446885+0.006812    test-rmse:1.025776+0.128461 
## [182]    train-rmse:0.444554+0.005398    test-rmse:1.023957+0.129762 
## [183]    train-rmse:0.443143+0.005516    test-rmse:1.023608+0.129504 
## [184]    train-rmse:0.441032+0.005227    test-rmse:1.024018+0.129798 
## [185]    train-rmse:0.439298+0.005158    test-rmse:1.020449+0.128335 
## [186]    train-rmse:0.438024+0.004915    test-rmse:1.020560+0.128090 
## [187]    train-rmse:0.436925+0.004987    test-rmse:1.021347+0.127528 
## [188]    train-rmse:0.435319+0.005149    test-rmse:1.021374+0.127057 
## [189]    train-rmse:0.433467+0.004833    test-rmse:1.021712+0.127377 
## [190]    train-rmse:0.431800+0.004420    test-rmse:1.021371+0.127662 
## [191]    train-rmse:0.430205+0.004628    test-rmse:1.021511+0.127193 
## Stopping. Best iteration:
## [185]    train-rmse:0.439298+0.005158    test-rmse:1.020449+0.128335
## 
## 58 
## [1]  train-rmse:72.973630+0.073108   test-rmse:72.974214+0.420392 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:54.084589+0.053899   test-rmse:54.084826+0.438661 
## [3]  train-rmse:40.095586+0.039547   test-rmse:40.095350+0.451697 
## [4]  train-rmse:29.739208+0.028775   test-rmse:29.738348+0.460589 
## [5]  train-rmse:22.077126+0.020671   test-rmse:22.075455+0.466019 
## [6]  train-rmse:16.415070+0.014660   test-rmse:16.412361+0.468234 
## [7]  train-rmse:12.237465+0.010317   test-rmse:12.240607+0.437841 
## [8]  train-rmse:9.155728+0.007342    test-rmse:9.140358+0.428084 
## [9]  train-rmse:6.882654+0.006442    test-rmse:6.863594+0.396710 
## [10] train-rmse:5.212489+0.006415    test-rmse:5.210272+0.366685 
## [11] train-rmse:3.987073+0.008676    test-rmse:3.987200+0.330467 
## [12] train-rmse:3.097813+0.010924    test-rmse:3.114515+0.303933 
## [13] train-rmse:2.461255+0.013317    test-rmse:2.495023+0.267409 
## [14] train-rmse:2.011895+0.015347    test-rmse:2.073607+0.236143 
## [15] train-rmse:1.700970+0.016456    test-rmse:1.794139+0.201720 
## [16] train-rmse:1.488530+0.014076    test-rmse:1.606072+0.179130 
## [17] train-rmse:1.345403+0.016698    test-rmse:1.500503+0.145191 
## [18] train-rmse:1.248004+0.016783    test-rmse:1.438207+0.128927 
## [19] train-rmse:1.176389+0.018901    test-rmse:1.385250+0.117952 
## [20] train-rmse:1.125674+0.021611    test-rmse:1.351014+0.102923 
## [21] train-rmse:1.086659+0.022195    test-rmse:1.325577+0.097626 
## [22] train-rmse:1.051614+0.025405    test-rmse:1.309471+0.097724 
## [23] train-rmse:1.022938+0.023545    test-rmse:1.292705+0.090293 
## [24] train-rmse:1.001944+0.022116    test-rmse:1.279923+0.089563 
## [25] train-rmse:0.980343+0.022457    test-rmse:1.261601+0.088158 
## [26] train-rmse:0.960252+0.021736    test-rmse:1.248521+0.098394 
## [27] train-rmse:0.938635+0.017369    test-rmse:1.230653+0.099541 
## [28] train-rmse:0.917890+0.014929    test-rmse:1.212411+0.105084 
## [29] train-rmse:0.902406+0.013677    test-rmse:1.201370+0.108026 
## [30] train-rmse:0.886618+0.014359    test-rmse:1.194768+0.108371 
## [31] train-rmse:0.872413+0.013989    test-rmse:1.179951+0.104845 
## [32] train-rmse:0.855522+0.014612    test-rmse:1.173968+0.103207 
## [33] train-rmse:0.839460+0.011914    test-rmse:1.167652+0.101507 
## [34] train-rmse:0.825673+0.012132    test-rmse:1.157850+0.095400 
## [35] train-rmse:0.812796+0.008490    test-rmse:1.148926+0.096306 
## [36] train-rmse:0.797448+0.013501    test-rmse:1.141912+0.093429 
## [37] train-rmse:0.786560+0.014625    test-rmse:1.137657+0.092621 
## [38] train-rmse:0.774251+0.014089    test-rmse:1.129089+0.100102 
## [39] train-rmse:0.762353+0.013396    test-rmse:1.124718+0.100627 
## [40] train-rmse:0.754156+0.013909    test-rmse:1.121436+0.098899 
## [41] train-rmse:0.745092+0.013153    test-rmse:1.112663+0.100240 
## [42] train-rmse:0.735490+0.012263    test-rmse:1.099555+0.090952 
## [43] train-rmse:0.721773+0.006930    test-rmse:1.092263+0.092413 
## [44] train-rmse:0.711528+0.005559    test-rmse:1.088117+0.094988 
## [45] train-rmse:0.700069+0.007355    test-rmse:1.080354+0.095455 
## [46] train-rmse:0.687461+0.011057    test-rmse:1.075216+0.092905 
## [47] train-rmse:0.678345+0.013126    test-rmse:1.062520+0.085601 
## [48] train-rmse:0.671134+0.011657    test-rmse:1.057250+0.091565 
## [49] train-rmse:0.661850+0.010910    test-rmse:1.052651+0.087304 
## [50] train-rmse:0.654720+0.011474    test-rmse:1.049388+0.088127 
## [51] train-rmse:0.648136+0.011097    test-rmse:1.045344+0.088218 
## [52] train-rmse:0.641885+0.012232    test-rmse:1.045688+0.092041 
## [53] train-rmse:0.633143+0.011124    test-rmse:1.044310+0.093705 
## [54] train-rmse:0.623260+0.012722    test-rmse:1.040455+0.093199 
## [55] train-rmse:0.614228+0.015908    test-rmse:1.041055+0.098777 
## [56] train-rmse:0.605682+0.018307    test-rmse:1.038453+0.094703 
## [57] train-rmse:0.596221+0.017625    test-rmse:1.030287+0.092146 
## [58] train-rmse:0.588874+0.016618    test-rmse:1.026446+0.090795 
## [59] train-rmse:0.583327+0.016075    test-rmse:1.021325+0.090867 
## [60] train-rmse:0.576733+0.014728    test-rmse:1.022734+0.091349 
## [61] train-rmse:0.572639+0.013924    test-rmse:1.020223+0.090705 
## [62] train-rmse:0.566538+0.014953    test-rmse:1.024330+0.095151 
## [63] train-rmse:0.560241+0.015509    test-rmse:1.024228+0.094680 
## [64] train-rmse:0.554699+0.016992    test-rmse:1.022870+0.094276 
## [65] train-rmse:0.550678+0.015818    test-rmse:1.021730+0.093037 
## [66] train-rmse:0.544860+0.016352    test-rmse:1.018609+0.090783 
## [67] train-rmse:0.540325+0.015217    test-rmse:1.016561+0.090976 
## [68] train-rmse:0.533813+0.015191    test-rmse:1.016578+0.092201 
## [69] train-rmse:0.525041+0.017412    test-rmse:1.015018+0.087683 
## [70] train-rmse:0.519745+0.017988    test-rmse:1.014663+0.086698 
## [71] train-rmse:0.513353+0.015872    test-rmse:1.012887+0.087694 
## [72] train-rmse:0.509088+0.016201    test-rmse:1.011528+0.087538 
## [73] train-rmse:0.506387+0.016171    test-rmse:1.009543+0.086814 
## [74] train-rmse:0.499471+0.019701    test-rmse:1.008725+0.085536 
## [75] train-rmse:0.491664+0.016805    test-rmse:1.007696+0.086627 
## [76] train-rmse:0.486192+0.017299    test-rmse:1.007750+0.087200 
## [77] train-rmse:0.481039+0.018151    test-rmse:1.005480+0.085017 
## [78] train-rmse:0.476811+0.017711    test-rmse:1.005375+0.083377 
## [79] train-rmse:0.472603+0.019001    test-rmse:1.003008+0.079731 
## [80] train-rmse:0.466958+0.017921    test-rmse:1.002009+0.080593 
## [81] train-rmse:0.463069+0.018576    test-rmse:1.000718+0.080283 
## [82] train-rmse:0.459500+0.018884    test-rmse:1.000679+0.079397 
## [83] train-rmse:0.456517+0.018930    test-rmse:1.000400+0.079540 
## [84] train-rmse:0.452315+0.018040    test-rmse:0.998802+0.078222 
## [85] train-rmse:0.447141+0.017473    test-rmse:0.998758+0.080051 
## [86] train-rmse:0.444042+0.016905    test-rmse:0.998869+0.079293 
## [87] train-rmse:0.439257+0.017150    test-rmse:0.998051+0.077150 
## [88] train-rmse:0.433972+0.015671    test-rmse:0.995494+0.076211 
## [89] train-rmse:0.429028+0.017010    test-rmse:0.993836+0.078118 
## [90] train-rmse:0.420693+0.018509    test-rmse:0.990869+0.079825 
## [91] train-rmse:0.416360+0.016925    test-rmse:0.990298+0.080427 
## [92] train-rmse:0.412690+0.018249    test-rmse:0.990030+0.081920 
## [93] train-rmse:0.408225+0.018501    test-rmse:0.986455+0.085437 
## [94] train-rmse:0.403652+0.016812    test-rmse:0.986454+0.086301 
## [95] train-rmse:0.400063+0.016293    test-rmse:0.985555+0.085291 
## [96] train-rmse:0.395785+0.015799    test-rmse:0.985574+0.085615 
## [97] train-rmse:0.393191+0.015848    test-rmse:0.983931+0.086210 
## [98] train-rmse:0.390577+0.016228    test-rmse:0.982572+0.086794 
## [99] train-rmse:0.386333+0.015561    test-rmse:0.982137+0.086724 
## [100]    train-rmse:0.383860+0.015810    test-rmse:0.981069+0.086557 
## [101]    train-rmse:0.380332+0.016132    test-rmse:0.981493+0.085806 
## [102]    train-rmse:0.377079+0.015951    test-rmse:0.981728+0.085520 
## [103]    train-rmse:0.372240+0.015881    test-rmse:0.983510+0.087446 
## [104]    train-rmse:0.367046+0.012667    test-rmse:0.981614+0.087799 
## [105]    train-rmse:0.363296+0.011910    test-rmse:0.982322+0.087509 
## [106]    train-rmse:0.360432+0.010610    test-rmse:0.981737+0.087978 
## Stopping. Best iteration:
## [100]    train-rmse:0.383860+0.015810    test-rmse:0.981069+0.086557
## 
## 59 
## [1]  train-rmse:72.973630+0.073108   test-rmse:72.974214+0.420392 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:54.084589+0.053899   test-rmse:54.084826+0.438661 
## [3]  train-rmse:40.095586+0.039547   test-rmse:40.095350+0.451697 
## [4]  train-rmse:29.739208+0.028775   test-rmse:29.738348+0.460589 
## [5]  train-rmse:22.077126+0.020671   test-rmse:22.075455+0.466019 
## [6]  train-rmse:16.415070+0.014660   test-rmse:16.412361+0.468234 
## [7]  train-rmse:12.237465+0.010317   test-rmse:12.240607+0.437841 
## [8]  train-rmse:9.155564+0.007504    test-rmse:9.137473+0.426410 
## [9]  train-rmse:6.879157+0.007055    test-rmse:6.867291+0.385217 
## [10] train-rmse:5.205387+0.007009    test-rmse:5.208089+0.358468 
## [11] train-rmse:3.973393+0.006980    test-rmse:3.976760+0.325585 
## [12] train-rmse:3.072351+0.008815    test-rmse:3.090943+0.299116 
## [13] train-rmse:2.414133+0.011016    test-rmse:2.445845+0.257389 
## [14] train-rmse:1.943794+0.010864    test-rmse:2.004950+0.233428 
## [15] train-rmse:1.613237+0.011681    test-rmse:1.711723+0.196162 
## [16] train-rmse:1.378179+0.006328    test-rmse:1.533324+0.168753 
## [17] train-rmse:1.215671+0.013205    test-rmse:1.408210+0.149481 
## [18] train-rmse:1.105901+0.014251    test-rmse:1.335548+0.137089 
## [19] train-rmse:1.028808+0.012265    test-rmse:1.278465+0.134593 
## [20] train-rmse:0.968699+0.014232    test-rmse:1.237509+0.126268 
## [21] train-rmse:0.923862+0.011493    test-rmse:1.208611+0.128935 
## [22] train-rmse:0.889061+0.010631    test-rmse:1.191663+0.125267 
## [23] train-rmse:0.854393+0.016148    test-rmse:1.174381+0.122850 
## [24] train-rmse:0.828953+0.018445    test-rmse:1.160471+0.119887 
## [25] train-rmse:0.807440+0.018036    test-rmse:1.139431+0.114725 
## [26] train-rmse:0.784102+0.019350    test-rmse:1.124941+0.104662 
## [27] train-rmse:0.764906+0.018082    test-rmse:1.115838+0.104131 
## [28] train-rmse:0.744183+0.017348    test-rmse:1.108275+0.103483 
## [29] train-rmse:0.726802+0.019991    test-rmse:1.099507+0.103857 
## [30] train-rmse:0.710870+0.022574    test-rmse:1.092487+0.103647 
## [31] train-rmse:0.696080+0.022766    test-rmse:1.084234+0.105879 
## [32] train-rmse:0.680561+0.017813    test-rmse:1.073838+0.104190 
## [33] train-rmse:0.665686+0.019121    test-rmse:1.065138+0.101041 
## [34] train-rmse:0.651104+0.014534    test-rmse:1.054086+0.098888 
## [35] train-rmse:0.637818+0.017989    test-rmse:1.050463+0.099448 
## [36] train-rmse:0.619447+0.017513    test-rmse:1.042467+0.102211 
## [37] train-rmse:0.607359+0.019874    test-rmse:1.031268+0.104394 
## [38] train-rmse:0.594443+0.018340    test-rmse:1.025614+0.104568 
## [39] train-rmse:0.582455+0.016287    test-rmse:1.020819+0.107104 
## [40] train-rmse:0.572130+0.014370    test-rmse:1.020053+0.112534 
## [41] train-rmse:0.562370+0.011560    test-rmse:1.015781+0.108956 
## [42] train-rmse:0.551205+0.011800    test-rmse:1.017215+0.108965 
## [43] train-rmse:0.539786+0.012829    test-rmse:1.016565+0.109349 
## [44] train-rmse:0.531084+0.012383    test-rmse:1.013486+0.111194 
## [45] train-rmse:0.522938+0.011392    test-rmse:1.008485+0.109546 
## [46] train-rmse:0.512974+0.011398    test-rmse:1.008270+0.110840 
## [47] train-rmse:0.506875+0.010700    test-rmse:1.006999+0.110949 
## [48] train-rmse:0.500811+0.013112    test-rmse:1.004037+0.109966 
## [49] train-rmse:0.491687+0.013618    test-rmse:0.999949+0.108638 
## [50] train-rmse:0.481815+0.012469    test-rmse:0.999340+0.108284 
## [51] train-rmse:0.475163+0.011129    test-rmse:0.997877+0.107042 
## [52] train-rmse:0.467890+0.010722    test-rmse:1.000277+0.111841 
## [53] train-rmse:0.459042+0.007367    test-rmse:0.999903+0.109985 
## [54] train-rmse:0.450502+0.008938    test-rmse:0.996505+0.109623 
## [55] train-rmse:0.444861+0.008947    test-rmse:0.990102+0.104112 
## [56] train-rmse:0.436301+0.010516    test-rmse:0.984568+0.099334 
## [57] train-rmse:0.425298+0.011181    test-rmse:0.987119+0.099696 
## [58] train-rmse:0.419779+0.011267    test-rmse:0.988044+0.100398 
## [59] train-rmse:0.411473+0.011253    test-rmse:0.986297+0.100294 
## [60] train-rmse:0.406634+0.010835    test-rmse:0.984680+0.100762 
## [61] train-rmse:0.401932+0.009955    test-rmse:0.984244+0.099833 
## [62] train-rmse:0.395470+0.010520    test-rmse:0.982265+0.100455 
## [63] train-rmse:0.388708+0.011464    test-rmse:0.977268+0.099236 
## [64] train-rmse:0.381457+0.012854    test-rmse:0.977437+0.099516 
## [65] train-rmse:0.374374+0.012780    test-rmse:0.974447+0.099206 
## [66] train-rmse:0.367352+0.013532    test-rmse:0.972302+0.095308 
## [67] train-rmse:0.360906+0.014683    test-rmse:0.969780+0.092511 
## [68] train-rmse:0.356972+0.014357    test-rmse:0.967014+0.091057 
## [69] train-rmse:0.351764+0.015223    test-rmse:0.965358+0.089829 
## [70] train-rmse:0.347511+0.016592    test-rmse:0.966664+0.090488 
## [71] train-rmse:0.339715+0.015063    test-rmse:0.961200+0.085955 
## [72] train-rmse:0.333414+0.015244    test-rmse:0.958061+0.085648 
## [73] train-rmse:0.324584+0.012456    test-rmse:0.953754+0.083040 
## [74] train-rmse:0.318243+0.011491    test-rmse:0.952301+0.082127 
## [75] train-rmse:0.313397+0.012352    test-rmse:0.950485+0.081595 
## [76] train-rmse:0.307697+0.011189    test-rmse:0.949348+0.082447 
## [77] train-rmse:0.304072+0.011743    test-rmse:0.948223+0.082521 
## [78] train-rmse:0.300026+0.011382    test-rmse:0.947237+0.082752 
## [79] train-rmse:0.296562+0.011590    test-rmse:0.946450+0.082284 
## [80] train-rmse:0.291763+0.011648    test-rmse:0.942505+0.082668 
## [81] train-rmse:0.285861+0.011065    test-rmse:0.942286+0.081605 
## [82] train-rmse:0.280957+0.012614    test-rmse:0.942128+0.082336 
## [83] train-rmse:0.276977+0.014002    test-rmse:0.941773+0.083014 
## [84] train-rmse:0.270622+0.015157    test-rmse:0.939687+0.082871 
## [85] train-rmse:0.267358+0.016400    test-rmse:0.938822+0.083026 
## [86] train-rmse:0.262655+0.015732    test-rmse:0.937104+0.083784 
## [87] train-rmse:0.259462+0.016652    test-rmse:0.935747+0.082588 
## [88] train-rmse:0.256171+0.017464    test-rmse:0.935646+0.082326 
## [89] train-rmse:0.251724+0.018170    test-rmse:0.935071+0.081486 
## [90] train-rmse:0.245455+0.017274    test-rmse:0.932456+0.080530 
## [91] train-rmse:0.241679+0.017324    test-rmse:0.931528+0.080354 
## [92] train-rmse:0.238397+0.018528    test-rmse:0.932083+0.079377 
## [93] train-rmse:0.234995+0.018600    test-rmse:0.930671+0.079430 
## [94] train-rmse:0.231243+0.018414    test-rmse:0.930879+0.080597 
## [95] train-rmse:0.227887+0.018642    test-rmse:0.929290+0.079634 
## [96] train-rmse:0.225315+0.018712    test-rmse:0.928694+0.080178 
## [97] train-rmse:0.222222+0.018044    test-rmse:0.928376+0.079993 
## [98] train-rmse:0.220353+0.018327    test-rmse:0.928298+0.080097 
## [99] train-rmse:0.216909+0.019044    test-rmse:0.928269+0.079600 
## [100]    train-rmse:0.214353+0.019544    test-rmse:0.927403+0.080458 
## [101]    train-rmse:0.211838+0.019251    test-rmse:0.926364+0.081109 
## [102]    train-rmse:0.208952+0.019197    test-rmse:0.925474+0.079808 
## [103]    train-rmse:0.206665+0.019848    test-rmse:0.923619+0.078547 
## [104]    train-rmse:0.203250+0.018964    test-rmse:0.924814+0.078197 
## [105]    train-rmse:0.199885+0.017813    test-rmse:0.924714+0.077026 
## [106]    train-rmse:0.197200+0.017776    test-rmse:0.924518+0.076676 
## [107]    train-rmse:0.193794+0.016767    test-rmse:0.924800+0.077209 
## [108]    train-rmse:0.191344+0.016705    test-rmse:0.924705+0.077724 
## [109]    train-rmse:0.189084+0.015915    test-rmse:0.923902+0.078370 
## Stopping. Best iteration:
## [103]    train-rmse:0.206665+0.019848    test-rmse:0.923619+0.078547
## 
## 60 
## [1]  train-rmse:72.973630+0.073108   test-rmse:72.974214+0.420392 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:54.084589+0.053899   test-rmse:54.084826+0.438661 
## [3]  train-rmse:40.095586+0.039547   test-rmse:40.095350+0.451697 
## [4]  train-rmse:29.739208+0.028775   test-rmse:29.738348+0.460589 
## [5]  train-rmse:22.077126+0.020671   test-rmse:22.075455+0.466019 
## [6]  train-rmse:16.415070+0.014660   test-rmse:16.412361+0.468234 
## [7]  train-rmse:12.237465+0.010317   test-rmse:12.240607+0.437841 
## [8]  train-rmse:9.155361+0.007719    test-rmse:9.142366+0.429291 
## [9]  train-rmse:6.877293+0.007322    test-rmse:6.875144+0.382179 
## [10] train-rmse:5.198431+0.006320    test-rmse:5.199193+0.337700 
## [11] train-rmse:3.962500+0.007430    test-rmse:3.973835+0.306252 
## [12] train-rmse:3.053235+0.010408    test-rmse:3.072072+0.276186 
## [13] train-rmse:2.381977+0.006934    test-rmse:2.436991+0.251740 
## [14] train-rmse:1.897714+0.013897    test-rmse:1.995047+0.214167 
## [15] train-rmse:1.543975+0.009682    test-rmse:1.694245+0.176336 
## [16] train-rmse:1.294722+0.011563    test-rmse:1.491682+0.166057 
## [17] train-rmse:1.118951+0.015624    test-rmse:1.365647+0.155846 
## [18] train-rmse:0.988465+0.014531    test-rmse:1.281125+0.146541 
## [19] train-rmse:0.903860+0.013958    test-rmse:1.231157+0.137614 
## [20] train-rmse:0.834542+0.014177    test-rmse:1.192333+0.122163 
## [21] train-rmse:0.785759+0.017877    test-rmse:1.169892+0.110418 
## [22] train-rmse:0.746146+0.012668    test-rmse:1.146286+0.109238 
## [23] train-rmse:0.713834+0.008315    test-rmse:1.123142+0.102294 
## [24] train-rmse:0.686540+0.010710    test-rmse:1.113448+0.096743 
## [25] train-rmse:0.659379+0.006767    test-rmse:1.104722+0.093107 
## [26] train-rmse:0.638288+0.007910    test-rmse:1.099062+0.092561 
## [27] train-rmse:0.613399+0.006372    test-rmse:1.079381+0.080481 
## [28] train-rmse:0.595079+0.008767    test-rmse:1.067370+0.083204 
## [29] train-rmse:0.576086+0.009097    test-rmse:1.058600+0.077608 
## [30] train-rmse:0.557572+0.008745    test-rmse:1.047449+0.065919 
## [31] train-rmse:0.542042+0.008321    test-rmse:1.042526+0.064809 
## [32] train-rmse:0.524208+0.007719    test-rmse:1.031395+0.060220 
## [33] train-rmse:0.507940+0.006175    test-rmse:1.026598+0.057190 
## [34] train-rmse:0.496060+0.006723    test-rmse:1.025077+0.056013 
## [35] train-rmse:0.484544+0.006274    test-rmse:1.023633+0.056277 
## [36] train-rmse:0.472981+0.004103    test-rmse:1.017902+0.056039 
## [37] train-rmse:0.461765+0.004014    test-rmse:1.012833+0.053257 
## [38] train-rmse:0.450563+0.003215    test-rmse:1.011880+0.055347 
## [39] train-rmse:0.441452+0.005069    test-rmse:1.008287+0.056370 
## [40] train-rmse:0.428872+0.008023    test-rmse:1.004780+0.053170 
## [41] train-rmse:0.419812+0.007677    test-rmse:1.000843+0.055070 
## [42] train-rmse:0.410402+0.009487    test-rmse:0.998429+0.052752 
## [43] train-rmse:0.398953+0.010534    test-rmse:0.999105+0.048901 
## [44] train-rmse:0.387825+0.008922    test-rmse:0.996355+0.049198 
## [45] train-rmse:0.377270+0.009726    test-rmse:0.992854+0.052519 
## [46] train-rmse:0.368458+0.008559    test-rmse:0.992287+0.052597 
## [47] train-rmse:0.361683+0.007200    test-rmse:0.991581+0.052022 
## [48] train-rmse:0.353102+0.008904    test-rmse:0.990499+0.051491 
## [49] train-rmse:0.342884+0.010090    test-rmse:0.990050+0.053777 
## [50] train-rmse:0.333863+0.011985    test-rmse:0.989323+0.052575 
## [51] train-rmse:0.326952+0.011592    test-rmse:0.988100+0.051503 
## [52] train-rmse:0.318657+0.009710    test-rmse:0.986053+0.051149 
## [53] train-rmse:0.311416+0.008375    test-rmse:0.987077+0.049834 
## [54] train-rmse:0.303157+0.009570    test-rmse:0.984781+0.048959 
## [55] train-rmse:0.297958+0.008390    test-rmse:0.983087+0.048773 
## [56] train-rmse:0.290547+0.009510    test-rmse:0.981393+0.048466 
## [57] train-rmse:0.284118+0.009297    test-rmse:0.981990+0.049728 
## [58] train-rmse:0.279089+0.008667    test-rmse:0.981022+0.049596 
## [59] train-rmse:0.272438+0.007534    test-rmse:0.979511+0.047326 
## [60] train-rmse:0.268143+0.007577    test-rmse:0.981334+0.047949 
## [61] train-rmse:0.262192+0.006868    test-rmse:0.982245+0.047561 
## [62] train-rmse:0.255746+0.006634    test-rmse:0.980415+0.048804 
## [63] train-rmse:0.250304+0.006763    test-rmse:0.980162+0.050729 
## [64] train-rmse:0.245536+0.008906    test-rmse:0.979879+0.050258 
## [65] train-rmse:0.240516+0.007717    test-rmse:0.979095+0.050439 
## [66] train-rmse:0.234967+0.007294    test-rmse:0.977313+0.052415 
## [67] train-rmse:0.229892+0.008578    test-rmse:0.976040+0.052597 
## [68] train-rmse:0.225883+0.007421    test-rmse:0.975804+0.053479 
## [69] train-rmse:0.221288+0.008992    test-rmse:0.976025+0.055507 
## [70] train-rmse:0.217897+0.009810    test-rmse:0.975277+0.054050 
## [71] train-rmse:0.212289+0.008934    test-rmse:0.974956+0.055491 
## [72] train-rmse:0.207624+0.008267    test-rmse:0.974302+0.054081 
## [73] train-rmse:0.204414+0.008038    test-rmse:0.973942+0.054643 
## [74] train-rmse:0.199037+0.008452    test-rmse:0.973183+0.054769 
## [75] train-rmse:0.194607+0.008019    test-rmse:0.973427+0.054497 
## [76] train-rmse:0.189660+0.007497    test-rmse:0.972666+0.053396 
## [77] train-rmse:0.185808+0.007337    test-rmse:0.970124+0.052292 
## [78] train-rmse:0.182329+0.009018    test-rmse:0.969710+0.052886 
## [79] train-rmse:0.178782+0.009048    test-rmse:0.968686+0.053593 
## [80] train-rmse:0.174581+0.009014    test-rmse:0.968684+0.052954 
## [81] train-rmse:0.171215+0.008270    test-rmse:0.969837+0.053603 
## [82] train-rmse:0.167666+0.007455    test-rmse:0.968124+0.051606 
## [83] train-rmse:0.163438+0.006329    test-rmse:0.967950+0.050166 
## [84] train-rmse:0.160840+0.006091    test-rmse:0.968372+0.049764 
## [85] train-rmse:0.157258+0.007865    test-rmse:0.968543+0.049235 
## [86] train-rmse:0.154543+0.008031    test-rmse:0.968252+0.049128 
## [87] train-rmse:0.151760+0.007732    test-rmse:0.967984+0.049459 
## [88] train-rmse:0.147924+0.008440    test-rmse:0.967053+0.050314 
## [89] train-rmse:0.144550+0.007612    test-rmse:0.966415+0.050032 
## [90] train-rmse:0.141204+0.006511    test-rmse:0.966456+0.049389 
## [91] train-rmse:0.138127+0.007854    test-rmse:0.965891+0.049517 
## [92] train-rmse:0.134902+0.006168    test-rmse:0.964621+0.048364 
## [93] train-rmse:0.132089+0.006235    test-rmse:0.964972+0.048473 
## [94] train-rmse:0.129859+0.005572    test-rmse:0.965733+0.049180 
## [95] train-rmse:0.127773+0.005371    test-rmse:0.965768+0.049480 
## [96] train-rmse:0.126062+0.006254    test-rmse:0.964972+0.049479 
## [97] train-rmse:0.122911+0.005834    test-rmse:0.964152+0.049244 
## [98] train-rmse:0.120693+0.005356    test-rmse:0.963414+0.048638 
## [99] train-rmse:0.117923+0.006357    test-rmse:0.963488+0.049055 
## [100]    train-rmse:0.116083+0.006283    test-rmse:0.963642+0.049194 
## [101]    train-rmse:0.114876+0.006659    test-rmse:0.963160+0.048890 
## [102]    train-rmse:0.113620+0.006313    test-rmse:0.963240+0.048965 
## [103]    train-rmse:0.112036+0.006846    test-rmse:0.963312+0.049798 
## [104]    train-rmse:0.109414+0.006474    test-rmse:0.963022+0.049380 
## [105]    train-rmse:0.106673+0.006660    test-rmse:0.962803+0.049325 
## [106]    train-rmse:0.104987+0.007220    test-rmse:0.962582+0.049277 
## [107]    train-rmse:0.103202+0.006648    test-rmse:0.963587+0.049437 
## [108]    train-rmse:0.101147+0.006477    test-rmse:0.964021+0.049799 
## [109]    train-rmse:0.099301+0.005897    test-rmse:0.962637+0.049300 
## [110]    train-rmse:0.096737+0.005419    test-rmse:0.962209+0.048929 
## [111]    train-rmse:0.094933+0.005445    test-rmse:0.961905+0.049066 
## [112]    train-rmse:0.091845+0.004699    test-rmse:0.961731+0.047952 
## [113]    train-rmse:0.090148+0.004633    test-rmse:0.960926+0.047842 
## [114]    train-rmse:0.088074+0.004194    test-rmse:0.961099+0.047565 
## [115]    train-rmse:0.086457+0.003823    test-rmse:0.961541+0.047924 
## [116]    train-rmse:0.084859+0.004454    test-rmse:0.961271+0.047803 
## [117]    train-rmse:0.083807+0.004538    test-rmse:0.961075+0.046797 
## [118]    train-rmse:0.082674+0.004732    test-rmse:0.961006+0.046883 
## [119]    train-rmse:0.081177+0.004192    test-rmse:0.961096+0.046823 
## Stopping. Best iteration:
## [113]    train-rmse:0.090148+0.004633    test-rmse:0.960926+0.047842
## 
## 61 
## [1]  train-rmse:72.973630+0.073108   test-rmse:72.974214+0.420392 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:54.084589+0.053899   test-rmse:54.084826+0.438661 
## [3]  train-rmse:40.095586+0.039547   test-rmse:40.095350+0.451697 
## [4]  train-rmse:29.739208+0.028775   test-rmse:29.738348+0.460589 
## [5]  train-rmse:22.077126+0.020671   test-rmse:22.075455+0.466019 
## [6]  train-rmse:16.415070+0.014660   test-rmse:16.412361+0.468234 
## [7]  train-rmse:12.237465+0.010317   test-rmse:12.240607+0.437841 
## [8]  train-rmse:9.155361+0.007719    test-rmse:9.142366+0.429291 
## [9]  train-rmse:6.876760+0.007375    test-rmse:6.871432+0.379394 
## [10] train-rmse:5.196428+0.006609    test-rmse:5.194213+0.339542 
## [11] train-rmse:3.957937+0.005722    test-rmse:3.980494+0.302889 
## [12] train-rmse:3.041883+0.008263    test-rmse:3.074372+0.263195 
## [13] train-rmse:2.366863+0.008128    test-rmse:2.438697+0.232456 
## [14] train-rmse:1.874855+0.012074    test-rmse:1.968381+0.210792 
## [15] train-rmse:1.505423+0.014224    test-rmse:1.646157+0.178191 
## [16] train-rmse:1.241383+0.008842    test-rmse:1.452040+0.151515 
## [17] train-rmse:1.054920+0.012045    test-rmse:1.322563+0.123226 
## [18] train-rmse:0.915674+0.015636    test-rmse:1.244093+0.103876 
## [19] train-rmse:0.818278+0.020616    test-rmse:1.191036+0.099374 
## [20] train-rmse:0.744068+0.025735    test-rmse:1.148260+0.087330 
## [21] train-rmse:0.684276+0.019076    test-rmse:1.114193+0.085892 
## [22] train-rmse:0.636383+0.022706    test-rmse:1.086956+0.077759 
## [23] train-rmse:0.602993+0.018825    test-rmse:1.069505+0.072608 
## [24] train-rmse:0.573211+0.022877    test-rmse:1.058274+0.069654 
## [25] train-rmse:0.543839+0.017983    test-rmse:1.045138+0.072025 
## [26] train-rmse:0.520002+0.021367    test-rmse:1.037126+0.073419 
## [27] train-rmse:0.496109+0.016967    test-rmse:1.030223+0.071798 
## [28] train-rmse:0.473767+0.024244    test-rmse:1.022047+0.069197 
## [29] train-rmse:0.452690+0.022091    test-rmse:1.015176+0.066381 
## [30] train-rmse:0.435810+0.023262    test-rmse:1.015923+0.073817 
## [31] train-rmse:0.418434+0.024443    test-rmse:1.008036+0.077347 
## [32] train-rmse:0.404274+0.022242    test-rmse:1.001293+0.080078 
## [33] train-rmse:0.387664+0.020951    test-rmse:0.999151+0.075570 
## [34] train-rmse:0.375638+0.018073    test-rmse:1.000815+0.078488 
## [35] train-rmse:0.364253+0.015876    test-rmse:0.996042+0.078826 
## [36] train-rmse:0.351926+0.012866    test-rmse:0.992466+0.076265 
## [37] train-rmse:0.341078+0.014618    test-rmse:0.989027+0.078243 
## [38] train-rmse:0.331844+0.017044    test-rmse:0.985463+0.080725 
## [39] train-rmse:0.320734+0.014505    test-rmse:0.983561+0.080813 
## [40] train-rmse:0.308427+0.014360    test-rmse:0.980038+0.079890 
## [41] train-rmse:0.301112+0.015137    test-rmse:0.980295+0.079994 
## [42] train-rmse:0.292978+0.014395    test-rmse:0.978485+0.080205 
## [43] train-rmse:0.280856+0.014309    test-rmse:0.977279+0.078820 
## [44] train-rmse:0.272890+0.014555    test-rmse:0.976201+0.080045 
## [45] train-rmse:0.265188+0.012403    test-rmse:0.976059+0.083716 
## [46] train-rmse:0.259536+0.012260    test-rmse:0.975367+0.083705 
## [47] train-rmse:0.251579+0.012250    test-rmse:0.974735+0.081155 
## [48] train-rmse:0.244698+0.012056    test-rmse:0.974862+0.080805 
## [49] train-rmse:0.235547+0.013205    test-rmse:0.972258+0.080572 
## [50] train-rmse:0.227559+0.013066    test-rmse:0.971796+0.080280 
## [51] train-rmse:0.220593+0.011601    test-rmse:0.971539+0.081010 
## [52] train-rmse:0.215801+0.011938    test-rmse:0.970680+0.080601 
## [53] train-rmse:0.210679+0.011401    test-rmse:0.966881+0.078790 
## [54] train-rmse:0.201867+0.010805    test-rmse:0.966473+0.079578 
## [55] train-rmse:0.196787+0.010848    test-rmse:0.965525+0.079884 
## [56] train-rmse:0.190520+0.010831    test-rmse:0.966050+0.080669 
## [57] train-rmse:0.184933+0.010346    test-rmse:0.965656+0.081420 
## [58] train-rmse:0.179832+0.010120    test-rmse:0.965321+0.080411 
## [59] train-rmse:0.174394+0.009437    test-rmse:0.965216+0.081082 
## [60] train-rmse:0.170401+0.008469    test-rmse:0.965296+0.082175 
## [61] train-rmse:0.165938+0.008865    test-rmse:0.964495+0.082184 
## [62] train-rmse:0.160258+0.007588    test-rmse:0.963836+0.081775 
## [63] train-rmse:0.156074+0.007268    test-rmse:0.963298+0.082118 
## [64] train-rmse:0.151530+0.007079    test-rmse:0.963278+0.081941 
## [65] train-rmse:0.147261+0.006067    test-rmse:0.961901+0.083020 
## [66] train-rmse:0.142068+0.007510    test-rmse:0.961452+0.084104 
## [67] train-rmse:0.138140+0.007659    test-rmse:0.960880+0.084094 
## [68] train-rmse:0.134696+0.007636    test-rmse:0.960836+0.085481 
## [69] train-rmse:0.131013+0.007453    test-rmse:0.960844+0.085918 
## [70] train-rmse:0.127716+0.007908    test-rmse:0.961102+0.085245 
## [71] train-rmse:0.124056+0.008618    test-rmse:0.960814+0.085726 
## [72] train-rmse:0.120570+0.007526    test-rmse:0.959885+0.085444 
## [73] train-rmse:0.116958+0.007301    test-rmse:0.958612+0.082664 
## [74] train-rmse:0.112998+0.006239    test-rmse:0.958531+0.082875 
## [75] train-rmse:0.109632+0.006292    test-rmse:0.958307+0.082896 
## [76] train-rmse:0.106902+0.006736    test-rmse:0.958856+0.083335 
## [77] train-rmse:0.103552+0.006238    test-rmse:0.959133+0.083341 
## [78] train-rmse:0.101204+0.006828    test-rmse:0.958336+0.083608 
## [79] train-rmse:0.098365+0.006526    test-rmse:0.958102+0.083371 
## [80] train-rmse:0.094595+0.005161    test-rmse:0.957593+0.083349 
## [81] train-rmse:0.091938+0.005755    test-rmse:0.957639+0.083502 
## [82] train-rmse:0.089400+0.005332    test-rmse:0.957100+0.082994 
## [83] train-rmse:0.087365+0.005922    test-rmse:0.956010+0.083647 
## [84] train-rmse:0.085144+0.005807    test-rmse:0.955545+0.083550 
## [85] train-rmse:0.083294+0.005341    test-rmse:0.955765+0.083763 
## [86] train-rmse:0.080436+0.005301    test-rmse:0.955137+0.084309 
## [87] train-rmse:0.077917+0.004622    test-rmse:0.955254+0.084233 
## [88] train-rmse:0.076281+0.004936    test-rmse:0.954860+0.084392 
## [89] train-rmse:0.074107+0.004963    test-rmse:0.955047+0.083678 
## [90] train-rmse:0.072603+0.004849    test-rmse:0.954901+0.083731 
## [91] train-rmse:0.070856+0.004453    test-rmse:0.954889+0.083572 
## [92] train-rmse:0.068930+0.004304    test-rmse:0.954675+0.083438 
## [93] train-rmse:0.067265+0.004143    test-rmse:0.954153+0.083346 
## [94] train-rmse:0.065664+0.003682    test-rmse:0.953795+0.083301 
## [95] train-rmse:0.063539+0.003116    test-rmse:0.953767+0.083493 
## [96] train-rmse:0.061740+0.002728    test-rmse:0.953497+0.083158 
## [97] train-rmse:0.059738+0.002266    test-rmse:0.952906+0.083449 
## [98] train-rmse:0.057874+0.002105    test-rmse:0.952443+0.083137 
## [99] train-rmse:0.056467+0.002049    test-rmse:0.952919+0.082893 
## [100]    train-rmse:0.055404+0.002257    test-rmse:0.952480+0.082851 
## [101]    train-rmse:0.053536+0.001895    test-rmse:0.952755+0.083254 
## [102]    train-rmse:0.052246+0.001772    test-rmse:0.953050+0.083305 
## [103]    train-rmse:0.050429+0.001903    test-rmse:0.952642+0.082877 
## [104]    train-rmse:0.049569+0.001808    test-rmse:0.952443+0.082846 
## Stopping. Best iteration:
## [98] train-rmse:0.057874+0.002105    test-rmse:0.952443+0.083137
## 
## 62 
## [1]  train-rmse:72.973630+0.073108   test-rmse:72.974214+0.420392 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:54.084589+0.053899   test-rmse:54.084826+0.438661 
## [3]  train-rmse:40.095586+0.039547   test-rmse:40.095350+0.451697 
## [4]  train-rmse:29.739208+0.028775   test-rmse:29.738348+0.460589 
## [5]  train-rmse:22.077126+0.020671   test-rmse:22.075455+0.466019 
## [6]  train-rmse:16.415070+0.014660   test-rmse:16.412361+0.468234 
## [7]  train-rmse:12.237465+0.010317   test-rmse:12.240607+0.437841 
## [8]  train-rmse:9.155361+0.007719    test-rmse:9.142366+0.429291 
## [9]  train-rmse:6.876760+0.007375    test-rmse:6.871432+0.379394 
## [10] train-rmse:5.196267+0.006432    test-rmse:5.196129+0.337892 
## [11] train-rmse:3.956837+0.005706    test-rmse:3.984291+0.296929 
## [12] train-rmse:3.035722+0.003814    test-rmse:3.071126+0.261048 
## [13] train-rmse:2.356468+0.006097    test-rmse:2.428076+0.224582 
## [14] train-rmse:1.854293+0.009393    test-rmse:1.959793+0.192101 
## [15] train-rmse:1.485353+0.009129    test-rmse:1.623821+0.176258 
## [16] train-rmse:1.212614+0.005812    test-rmse:1.410961+0.153199 
## [17] train-rmse:1.012880+0.009153    test-rmse:1.287023+0.143409 
## [18] train-rmse:0.869788+0.016097    test-rmse:1.203840+0.134159 
## [19] train-rmse:0.762472+0.013805    test-rmse:1.130893+0.130654 
## [20] train-rmse:0.681796+0.014903    test-rmse:1.091936+0.127890 
## [21] train-rmse:0.624782+0.012924    test-rmse:1.062411+0.129353 
## [22] train-rmse:0.572218+0.021034    test-rmse:1.046368+0.125450 
## [23] train-rmse:0.529003+0.020629    test-rmse:1.026881+0.108340 
## [24] train-rmse:0.492073+0.024258    test-rmse:1.006276+0.092300 
## [25] train-rmse:0.461088+0.024474    test-rmse:0.999428+0.093010 
## [26] train-rmse:0.430711+0.021890    test-rmse:0.989586+0.094189 
## [27] train-rmse:0.407381+0.025238    test-rmse:0.984587+0.092845 
## [28] train-rmse:0.384880+0.020439    test-rmse:0.974364+0.086327 
## [29] train-rmse:0.365295+0.019167    test-rmse:0.969375+0.084685 
## [30] train-rmse:0.348438+0.016468    test-rmse:0.962917+0.084412 
## [31] train-rmse:0.332892+0.017203    test-rmse:0.962661+0.082821 
## [32] train-rmse:0.321296+0.016746    test-rmse:0.958820+0.084132 
## [33] train-rmse:0.307360+0.019646    test-rmse:0.955162+0.084249 
## [34] train-rmse:0.294788+0.018805    test-rmse:0.949384+0.081915 
## [35] train-rmse:0.279448+0.016046    test-rmse:0.946405+0.078014 
## [36] train-rmse:0.267340+0.016010    test-rmse:0.942548+0.077271 
## [37] train-rmse:0.257937+0.016855    test-rmse:0.941639+0.077647 
## [38] train-rmse:0.246434+0.016694    test-rmse:0.938682+0.078546 
## [39] train-rmse:0.236761+0.020385    test-rmse:0.936399+0.080826 
## [40] train-rmse:0.227884+0.018745    test-rmse:0.936936+0.079912 
## [41] train-rmse:0.218300+0.019016    test-rmse:0.934741+0.080188 
## [42] train-rmse:0.208394+0.017743    test-rmse:0.932778+0.083083 
## [43] train-rmse:0.201435+0.015168    test-rmse:0.931106+0.079955 
## [44] train-rmse:0.193939+0.014488    test-rmse:0.928982+0.080215 
## [45] train-rmse:0.188670+0.013098    test-rmse:0.928229+0.081458 
## [46] train-rmse:0.183584+0.014351    test-rmse:0.924768+0.082031 
## [47] train-rmse:0.176794+0.013872    test-rmse:0.923820+0.081095 
## [48] train-rmse:0.169551+0.015369    test-rmse:0.924586+0.081165 
## [49] train-rmse:0.164010+0.016424    test-rmse:0.924201+0.080737 
## [50] train-rmse:0.158563+0.016441    test-rmse:0.924013+0.080541 
## [51] train-rmse:0.154546+0.015489    test-rmse:0.923481+0.080087 
## [52] train-rmse:0.148177+0.016274    test-rmse:0.921365+0.079353 
## [53] train-rmse:0.141293+0.017219    test-rmse:0.920686+0.079345 
## [54] train-rmse:0.136253+0.014578    test-rmse:0.920657+0.079260 
## [55] train-rmse:0.131411+0.015137    test-rmse:0.920091+0.078591 
## [56] train-rmse:0.126331+0.014152    test-rmse:0.919759+0.078082 
## [57] train-rmse:0.121725+0.014420    test-rmse:0.920526+0.078300 
## [58] train-rmse:0.118527+0.014315    test-rmse:0.919478+0.078311 
## [59] train-rmse:0.113639+0.012877    test-rmse:0.918356+0.078250 
## [60] train-rmse:0.108688+0.012304    test-rmse:0.917742+0.078009 
## [61] train-rmse:0.105163+0.011895    test-rmse:0.916763+0.079173 
## [62] train-rmse:0.100760+0.010743    test-rmse:0.916751+0.079253 
## [63] train-rmse:0.096458+0.009759    test-rmse:0.915900+0.079485 
## [64] train-rmse:0.093566+0.009873    test-rmse:0.916098+0.079602 
## [65] train-rmse:0.090962+0.009346    test-rmse:0.916463+0.079370 
## [66] train-rmse:0.087760+0.009024    test-rmse:0.915845+0.079728 
## [67] train-rmse:0.084655+0.009542    test-rmse:0.915881+0.079407 
## [68] train-rmse:0.081301+0.008501    test-rmse:0.916221+0.079595 
## [69] train-rmse:0.078758+0.008873    test-rmse:0.916617+0.079674 
## [70] train-rmse:0.075959+0.008594    test-rmse:0.916304+0.079777 
## [71] train-rmse:0.074042+0.008720    test-rmse:0.916599+0.080205 
## [72] train-rmse:0.071463+0.008571    test-rmse:0.915284+0.080274 
## [73] train-rmse:0.068007+0.007779    test-rmse:0.915866+0.079856 
## [74] train-rmse:0.065062+0.008085    test-rmse:0.914738+0.079724 
## [75] train-rmse:0.061975+0.007887    test-rmse:0.914280+0.079630 
## [76] train-rmse:0.059965+0.008182    test-rmse:0.913730+0.079764 
## [77] train-rmse:0.056818+0.008435    test-rmse:0.914048+0.079816 
## [78] train-rmse:0.054410+0.008153    test-rmse:0.913967+0.079635 
## [79] train-rmse:0.052096+0.008570    test-rmse:0.913686+0.079373 
## [80] train-rmse:0.050368+0.008824    test-rmse:0.913833+0.079476 
## [81] train-rmse:0.048666+0.007676    test-rmse:0.913560+0.079411 
## [82] train-rmse:0.046970+0.007272    test-rmse:0.913279+0.079404 
## [83] train-rmse:0.045431+0.007161    test-rmse:0.913269+0.079372 
## [84] train-rmse:0.043921+0.007533    test-rmse:0.912798+0.079479 
## [85] train-rmse:0.042736+0.007271    test-rmse:0.912715+0.079499 
## [86] train-rmse:0.041634+0.007089    test-rmse:0.912617+0.079677 
## [87] train-rmse:0.040108+0.006494    test-rmse:0.912776+0.079889 
## [88] train-rmse:0.038829+0.006067    test-rmse:0.913066+0.080202 
## [89] train-rmse:0.037510+0.005545    test-rmse:0.912815+0.080267 
## [90] train-rmse:0.036227+0.005581    test-rmse:0.912845+0.080228 
## [91] train-rmse:0.034794+0.005436    test-rmse:0.913144+0.080628 
## [92] train-rmse:0.033633+0.005356    test-rmse:0.913205+0.080613 
## Stopping. Best iteration:
## [86] train-rmse:0.041634+0.007089    test-rmse:0.912617+0.079677
## 
## 63 
## [1]  train-rmse:71.012277+0.071119   test-rmse:71.012837+0.422304 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:51.218313+0.050969   test-rmse:51.218473+0.441381 
## [3]  train-rmse:36.954831+0.036295   test-rmse:36.954435+0.454495 
## [4]  train-rmse:26.681634+0.025555   test-rmse:26.680503+0.462943 
## [5]  train-rmse:19.289380+0.017699   test-rmse:19.287267+0.467424 
## [6]  train-rmse:13.979685+0.012191   test-rmse:13.976312+0.468039 
## [7]  train-rmse:10.171997+0.008739   test-rmse:10.171457+0.434201 
## [8]  train-rmse:7.450598+0.007508    test-rmse:7.438130+0.429725 
## [9]  train-rmse:5.516366+0.009325    test-rmse:5.503641+0.410081 
## [10] train-rmse:4.160364+0.013054    test-rmse:4.148558+0.384124 
## [11] train-rmse:3.225769+0.016056    test-rmse:3.232590+0.344427 
## [12] train-rmse:2.599935+0.019835    test-rmse:2.609445+0.291587 
## [13] train-rmse:2.193887+0.022586    test-rmse:2.224136+0.234764 
## [14] train-rmse:1.939006+0.025861    test-rmse:1.993414+0.181519 
## [15] train-rmse:1.782560+0.027651    test-rmse:1.850735+0.141718 
## [16] train-rmse:1.685900+0.029675    test-rmse:1.746834+0.122956 
## [17] train-rmse:1.624023+0.029173    test-rmse:1.692415+0.114841 
## [18] train-rmse:1.582816+0.028032    test-rmse:1.660570+0.113489 
## [19] train-rmse:1.551868+0.027169    test-rmse:1.647506+0.117608 
## [20] train-rmse:1.527974+0.026034    test-rmse:1.626408+0.117422 
## [21] train-rmse:1.508268+0.025417    test-rmse:1.617338+0.120765 
## [22] train-rmse:1.490398+0.025023    test-rmse:1.598495+0.121851 
## [23] train-rmse:1.474053+0.024861    test-rmse:1.581589+0.116209 
## [24] train-rmse:1.459820+0.024520    test-rmse:1.578852+0.117128 
## [25] train-rmse:1.446610+0.023802    test-rmse:1.571705+0.115985 
## [26] train-rmse:1.433911+0.023386    test-rmse:1.557670+0.104555 
## [27] train-rmse:1.421908+0.023534    test-rmse:1.559270+0.109388 
## [28] train-rmse:1.410379+0.023262    test-rmse:1.554256+0.109671 
## [29] train-rmse:1.399238+0.022959    test-rmse:1.545607+0.105937 
## [30] train-rmse:1.388590+0.022822    test-rmse:1.538915+0.104171 
## [31] train-rmse:1.378081+0.022598    test-rmse:1.530276+0.107627 
## [32] train-rmse:1.367967+0.022568    test-rmse:1.517293+0.109704 
## [33] train-rmse:1.358192+0.022573    test-rmse:1.507859+0.110514 
## [34] train-rmse:1.348614+0.022616    test-rmse:1.505888+0.101618 
## [35] train-rmse:1.339259+0.022465    test-rmse:1.503240+0.094094 
## [36] train-rmse:1.330152+0.022395    test-rmse:1.502841+0.095743 
## [37] train-rmse:1.321698+0.022201    test-rmse:1.497795+0.098943 
## [38] train-rmse:1.313216+0.022139    test-rmse:1.480001+0.099134 
## [39] train-rmse:1.304674+0.022213    test-rmse:1.474650+0.099554 
## [40] train-rmse:1.296580+0.021963    test-rmse:1.469158+0.097109 
## [41] train-rmse:1.288807+0.021667    test-rmse:1.465120+0.094922 
## [42] train-rmse:1.281025+0.021389    test-rmse:1.464639+0.094454 
## [43] train-rmse:1.273681+0.020926    test-rmse:1.456245+0.095000 
## [44] train-rmse:1.266581+0.020596    test-rmse:1.449875+0.095006 
## [45] train-rmse:1.259761+0.020474    test-rmse:1.443933+0.096502 
## [46] train-rmse:1.253267+0.020384    test-rmse:1.439474+0.099853 
## [47] train-rmse:1.246882+0.020225    test-rmse:1.433990+0.096376 
## [48] train-rmse:1.240577+0.020061    test-rmse:1.430215+0.093747 
## [49] train-rmse:1.234329+0.019936    test-rmse:1.428976+0.086495 
## [50] train-rmse:1.228238+0.019917    test-rmse:1.426755+0.085896 
## [51] train-rmse:1.222291+0.020026    test-rmse:1.419925+0.087421 
## [52] train-rmse:1.216507+0.020115    test-rmse:1.411139+0.086627 
## [53] train-rmse:1.210871+0.020320    test-rmse:1.411976+0.082226 
## [54] train-rmse:1.205335+0.020446    test-rmse:1.405876+0.084012 
## [55] train-rmse:1.200089+0.020579    test-rmse:1.406432+0.086461 
## [56] train-rmse:1.194949+0.020540    test-rmse:1.399265+0.087354 
## [57] train-rmse:1.189758+0.020751    test-rmse:1.398477+0.093681 
## [58] train-rmse:1.184707+0.020839    test-rmse:1.390664+0.089747 
## [59] train-rmse:1.179821+0.020931    test-rmse:1.387525+0.093000 
## [60] train-rmse:1.174904+0.020988    test-rmse:1.380083+0.094433 
## [61] train-rmse:1.170190+0.021062    test-rmse:1.378302+0.094744 
## [62] train-rmse:1.165655+0.021051    test-rmse:1.374265+0.095804 
## [63] train-rmse:1.161233+0.020975    test-rmse:1.370231+0.096937 
## [64] train-rmse:1.156904+0.020867    test-rmse:1.371524+0.088799 
## [65] train-rmse:1.152614+0.020747    test-rmse:1.366172+0.088456 
## [66] train-rmse:1.148320+0.020547    test-rmse:1.362858+0.088368 
## [67] train-rmse:1.144183+0.020461    test-rmse:1.361421+0.090062 
## [68] train-rmse:1.140172+0.020344    test-rmse:1.361860+0.088386 
## [69] train-rmse:1.136296+0.020208    test-rmse:1.358760+0.086520 
## [70] train-rmse:1.132494+0.020100    test-rmse:1.356796+0.087138 
## [71] train-rmse:1.128825+0.020020    test-rmse:1.354814+0.085342 
## [72] train-rmse:1.125214+0.019924    test-rmse:1.355251+0.086053 
## [73] train-rmse:1.121703+0.019817    test-rmse:1.351695+0.084895 
## [74] train-rmse:1.118279+0.019772    test-rmse:1.348776+0.083845 
## [75] train-rmse:1.114889+0.019725    test-rmse:1.349295+0.078044 
## [76] train-rmse:1.111507+0.019697    test-rmse:1.348556+0.082326 
## [77] train-rmse:1.108142+0.019734    test-rmse:1.344542+0.082818 
## [78] train-rmse:1.104899+0.019633    test-rmse:1.340938+0.083053 
## [79] train-rmse:1.101756+0.019555    test-rmse:1.335043+0.082554 
## [80] train-rmse:1.098601+0.019503    test-rmse:1.327415+0.083383 
## [81] train-rmse:1.095498+0.019428    test-rmse:1.322677+0.083795 
## [82] train-rmse:1.092459+0.019409    test-rmse:1.320069+0.082994 
## [83] train-rmse:1.089523+0.019447    test-rmse:1.317506+0.083292 
## [84] train-rmse:1.086683+0.019458    test-rmse:1.312863+0.086671 
## [85] train-rmse:1.083856+0.019440    test-rmse:1.314445+0.086344 
## [86] train-rmse:1.081087+0.019370    test-rmse:1.313630+0.085939 
## [87] train-rmse:1.078358+0.019350    test-rmse:1.314057+0.087224 
## [88] train-rmse:1.075677+0.019266    test-rmse:1.312263+0.087169 
## [89] train-rmse:1.073122+0.019217    test-rmse:1.313591+0.087774 
## [90] train-rmse:1.070482+0.019144    test-rmse:1.311224+0.087293 
## [91] train-rmse:1.067973+0.019135    test-rmse:1.309299+0.088749 
## [92] train-rmse:1.065442+0.019147    test-rmse:1.306731+0.087342 
## [93] train-rmse:1.063018+0.019160    test-rmse:1.306587+0.088638 
## [94] train-rmse:1.060681+0.019120    test-rmse:1.303203+0.090248 
## [95] train-rmse:1.058322+0.019166    test-rmse:1.303252+0.091195 
## [96] train-rmse:1.056039+0.019045    test-rmse:1.299934+0.091876 
## [97] train-rmse:1.053733+0.018925    test-rmse:1.298887+0.091874 
## [98] train-rmse:1.051493+0.018827    test-rmse:1.292900+0.091775 
## [99] train-rmse:1.049298+0.018743    test-rmse:1.292223+0.089426 
## [100]    train-rmse:1.047073+0.018629    test-rmse:1.290392+0.089206 
## [101]    train-rmse:1.044964+0.018584    test-rmse:1.289704+0.089583 
## [102]    train-rmse:1.042865+0.018551    test-rmse:1.292203+0.088596 
## [103]    train-rmse:1.040785+0.018497    test-rmse:1.290132+0.088802 
## [104]    train-rmse:1.038745+0.018500    test-rmse:1.290384+0.088215 
## [105]    train-rmse:1.036759+0.018461    test-rmse:1.291530+0.088006 
## [106]    train-rmse:1.034796+0.018383    test-rmse:1.289073+0.083620 
## [107]    train-rmse:1.032866+0.018254    test-rmse:1.287962+0.083369 
## [108]    train-rmse:1.031005+0.018194    test-rmse:1.285326+0.084268 
## [109]    train-rmse:1.029175+0.018138    test-rmse:1.278888+0.080317 
## [110]    train-rmse:1.027383+0.018107    test-rmse:1.278312+0.080123 
## [111]    train-rmse:1.025599+0.018066    test-rmse:1.277816+0.080916 
## [112]    train-rmse:1.023789+0.017976    test-rmse:1.277253+0.081167 
## [113]    train-rmse:1.022048+0.017914    test-rmse:1.276564+0.084553 
## [114]    train-rmse:1.020278+0.017870    test-rmse:1.274018+0.085856 
## [115]    train-rmse:1.018582+0.017841    test-rmse:1.270975+0.086604 
## [116]    train-rmse:1.016903+0.017827    test-rmse:1.270593+0.087049 
## [117]    train-rmse:1.015241+0.017824    test-rmse:1.269062+0.087613 
## [118]    train-rmse:1.013581+0.017816    test-rmse:1.268315+0.087721 
## [119]    train-rmse:1.011956+0.017771    test-rmse:1.267794+0.087654 
## [120]    train-rmse:1.010311+0.017745    test-rmse:1.265567+0.089584 
## [121]    train-rmse:1.008654+0.017735    test-rmse:1.266353+0.090201 
## [122]    train-rmse:1.007069+0.017734    test-rmse:1.264324+0.090059 
## [123]    train-rmse:1.005460+0.017663    test-rmse:1.264646+0.088879 
## [124]    train-rmse:1.003933+0.017658    test-rmse:1.262699+0.089279 
## [125]    train-rmse:1.002364+0.017595    test-rmse:1.265567+0.089404 
## [126]    train-rmse:1.000782+0.017540    test-rmse:1.262399+0.089878 
## [127]    train-rmse:0.999282+0.017489    test-rmse:1.262561+0.089320 
## [128]    train-rmse:0.997811+0.017475    test-rmse:1.262785+0.087658 
## [129]    train-rmse:0.996399+0.017432    test-rmse:1.260441+0.088470 
## [130]    train-rmse:0.994948+0.017420    test-rmse:1.257903+0.087949 
## [131]    train-rmse:0.993588+0.017407    test-rmse:1.258283+0.088055 
## [132]    train-rmse:0.992224+0.017402    test-rmse:1.257655+0.089046 
## [133]    train-rmse:0.990846+0.017399    test-rmse:1.256958+0.089972 
## [134]    train-rmse:0.989506+0.017365    test-rmse:1.257025+0.088632 
## [135]    train-rmse:0.988121+0.017328    test-rmse:1.255365+0.088223 
## [136]    train-rmse:0.986769+0.017336    test-rmse:1.254530+0.088104 
## [137]    train-rmse:0.985446+0.017345    test-rmse:1.253261+0.089259 
## [138]    train-rmse:0.984138+0.017386    test-rmse:1.252822+0.089422 
## [139]    train-rmse:0.982776+0.017401    test-rmse:1.253864+0.087846 
## [140]    train-rmse:0.981466+0.017412    test-rmse:1.249574+0.087574 
## [141]    train-rmse:0.980177+0.017433    test-rmse:1.248501+0.087360 
## [142]    train-rmse:0.978945+0.017450    test-rmse:1.247761+0.086876 
## [143]    train-rmse:0.977633+0.017481    test-rmse:1.250913+0.085487 
## [144]    train-rmse:0.976415+0.017495    test-rmse:1.249997+0.083709 
## [145]    train-rmse:0.975204+0.017488    test-rmse:1.250217+0.081461 
## [146]    train-rmse:0.973952+0.017456    test-rmse:1.249788+0.081265 
## [147]    train-rmse:0.972782+0.017444    test-rmse:1.248348+0.077167 
## [148]    train-rmse:0.971626+0.017420    test-rmse:1.247269+0.077970 
## [149]    train-rmse:0.970413+0.017444    test-rmse:1.247222+0.080132 
## [150]    train-rmse:0.969257+0.017433    test-rmse:1.245324+0.079912 
## [151]    train-rmse:0.968127+0.017412    test-rmse:1.245919+0.081866 
## [152]    train-rmse:0.966990+0.017432    test-rmse:1.243795+0.083350 
## [153]    train-rmse:0.965848+0.017468    test-rmse:1.242612+0.084641 
## [154]    train-rmse:0.964764+0.017464    test-rmse:1.243851+0.084117 
## [155]    train-rmse:0.963682+0.017455    test-rmse:1.243073+0.083537 
## [156]    train-rmse:0.962594+0.017429    test-rmse:1.242455+0.084723 
## [157]    train-rmse:0.961460+0.017419    test-rmse:1.242137+0.084282 
## [158]    train-rmse:0.960399+0.017447    test-rmse:1.239710+0.085434 
## [159]    train-rmse:0.959322+0.017420    test-rmse:1.240929+0.084888 
## [160]    train-rmse:0.958251+0.017422    test-rmse:1.239531+0.083848 
## [161]    train-rmse:0.957149+0.017344    test-rmse:1.239385+0.083063 
## [162]    train-rmse:0.956083+0.017373    test-rmse:1.239322+0.082829 
## [163]    train-rmse:0.955042+0.017405    test-rmse:1.238627+0.083167 
## [164]    train-rmse:0.954035+0.017442    test-rmse:1.236909+0.083382 
## [165]    train-rmse:0.953025+0.017484    test-rmse:1.236647+0.082712 
## [166]    train-rmse:0.952051+0.017524    test-rmse:1.236955+0.083200 
## [167]    train-rmse:0.951042+0.017571    test-rmse:1.239271+0.083750 
## [168]    train-rmse:0.950074+0.017602    test-rmse:1.236162+0.079841 
## [169]    train-rmse:0.949114+0.017618    test-rmse:1.237401+0.079709 
## [170]    train-rmse:0.948187+0.017631    test-rmse:1.235672+0.080261 
## [171]    train-rmse:0.947253+0.017641    test-rmse:1.236058+0.079751 
## [172]    train-rmse:0.946267+0.017639    test-rmse:1.235507+0.079419 
## [173]    train-rmse:0.945345+0.017653    test-rmse:1.235369+0.078140 
## [174]    train-rmse:0.944441+0.017661    test-rmse:1.235966+0.079205 
## [175]    train-rmse:0.943500+0.017683    test-rmse:1.236357+0.079203 
## [176]    train-rmse:0.942615+0.017697    test-rmse:1.235828+0.079402 
## [177]    train-rmse:0.941693+0.017718    test-rmse:1.235963+0.081386 
## [178]    train-rmse:0.940780+0.017721    test-rmse:1.233946+0.081750 
## [179]    train-rmse:0.939896+0.017730    test-rmse:1.232272+0.081543 
## [180]    train-rmse:0.939019+0.017741    test-rmse:1.232197+0.081153 
## [181]    train-rmse:0.938163+0.017735    test-rmse:1.230738+0.082779 
## [182]    train-rmse:0.937290+0.017717    test-rmse:1.231286+0.082697 
## [183]    train-rmse:0.936445+0.017711    test-rmse:1.230802+0.082726 
## [184]    train-rmse:0.935541+0.017749    test-rmse:1.229958+0.082434 
## [185]    train-rmse:0.934675+0.017769    test-rmse:1.231055+0.082279 
## [186]    train-rmse:0.933840+0.017770    test-rmse:1.229862+0.082185 
## [187]    train-rmse:0.933013+0.017794    test-rmse:1.230302+0.081724 
## [188]    train-rmse:0.932143+0.017804    test-rmse:1.230012+0.081522 
## [189]    train-rmse:0.931308+0.017844    test-rmse:1.229895+0.082091 
## [190]    train-rmse:0.930516+0.017856    test-rmse:1.227673+0.080544 
## [191]    train-rmse:0.929715+0.017883    test-rmse:1.230245+0.081584 
## [192]    train-rmse:0.928945+0.017897    test-rmse:1.230363+0.081936 
## [193]    train-rmse:0.928154+0.017927    test-rmse:1.230121+0.080352 
## [194]    train-rmse:0.927392+0.017933    test-rmse:1.228089+0.080942 
## [195]    train-rmse:0.926635+0.017957    test-rmse:1.227103+0.081402 
## [196]    train-rmse:0.925843+0.017971    test-rmse:1.226765+0.082123 
## [197]    train-rmse:0.925088+0.018002    test-rmse:1.225558+0.082135 
## [198]    train-rmse:0.924350+0.018014    test-rmse:1.225660+0.081265 
## [199]    train-rmse:0.923628+0.017996    test-rmse:1.226696+0.083146 
## [200]    train-rmse:0.922862+0.018007    test-rmse:1.225980+0.083374 
## [201]    train-rmse:0.922111+0.018008    test-rmse:1.225161+0.083521 
## [202]    train-rmse:0.921368+0.018022    test-rmse:1.224277+0.084119 
## [203]    train-rmse:0.920653+0.018035    test-rmse:1.224241+0.084032 
## [204]    train-rmse:0.919937+0.018046    test-rmse:1.224789+0.083849 
## [205]    train-rmse:0.919208+0.018063    test-rmse:1.223929+0.084547 
## [206]    train-rmse:0.918504+0.018087    test-rmse:1.223230+0.085405 
## [207]    train-rmse:0.917813+0.018117    test-rmse:1.223719+0.085137 
## [208]    train-rmse:0.917110+0.018139    test-rmse:1.223166+0.085559 
## [209]    train-rmse:0.916417+0.018154    test-rmse:1.220769+0.083282 
## [210]    train-rmse:0.915731+0.018175    test-rmse:1.221186+0.083343 
## [211]    train-rmse:0.915027+0.018169    test-rmse:1.221608+0.083278 
## [212]    train-rmse:0.914336+0.018130    test-rmse:1.221293+0.082339 
## [213]    train-rmse:0.913653+0.018127    test-rmse:1.222614+0.083033 
## [214]    train-rmse:0.912995+0.018148    test-rmse:1.221587+0.082099 
## [215]    train-rmse:0.912296+0.018141    test-rmse:1.221915+0.080638 
## Stopping. Best iteration:
## [209]    train-rmse:0.916417+0.018154    test-rmse:1.220769+0.083282
## 
## 64 
## [1]  train-rmse:71.012277+0.071119   test-rmse:71.012837+0.422304 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:51.218313+0.050969   test-rmse:51.218473+0.441381 
## [3]  train-rmse:36.954831+0.036295   test-rmse:36.954435+0.454495 
## [4]  train-rmse:26.681634+0.025555   test-rmse:26.680503+0.462943 
## [5]  train-rmse:19.289380+0.017699   test-rmse:19.287267+0.467424 
## [6]  train-rmse:13.979685+0.012191   test-rmse:13.976312+0.468039 
## [7]  train-rmse:10.170048+0.008156   test-rmse:10.157296+0.437450 
## [8]  train-rmse:7.441018+0.007129    test-rmse:7.434018+0.393737 
## [9]  train-rmse:5.490967+0.007118    test-rmse:5.483415+0.384501 
## [10] train-rmse:4.109676+0.009871    test-rmse:4.116099+0.356537 
## [11] train-rmse:3.141007+0.013569    test-rmse:3.155103+0.318665 
## [12] train-rmse:2.480066+0.013818    test-rmse:2.509807+0.281035 
## [13] train-rmse:2.038811+0.015797    test-rmse:2.099317+0.245454 
## [14] train-rmse:1.751689+0.018755    test-rmse:1.834901+0.200935 
## [15] train-rmse:1.569318+0.021082    test-rmse:1.684946+0.174611 
## [16] train-rmse:1.452350+0.019007    test-rmse:1.595816+0.146714 
## [17] train-rmse:1.378174+0.019404    test-rmse:1.529655+0.135719 
## [18] train-rmse:1.330199+0.019410    test-rmse:1.498342+0.126552 
## [19] train-rmse:1.293076+0.021989    test-rmse:1.474579+0.121377 
## [20] train-rmse:1.265574+0.022770    test-rmse:1.450416+0.123687 
## [21] train-rmse:1.239626+0.022258    test-rmse:1.437372+0.113850 
## [22] train-rmse:1.209369+0.020905    test-rmse:1.425513+0.114273 
## [23] train-rmse:1.189206+0.019330    test-rmse:1.418510+0.102349 
## [24] train-rmse:1.172627+0.018479    test-rmse:1.400557+0.103686 
## [25] train-rmse:1.156401+0.018161    test-rmse:1.390734+0.103233 
## [26] train-rmse:1.140375+0.019932    test-rmse:1.381126+0.100739 
## [27] train-rmse:1.123386+0.018016    test-rmse:1.367073+0.104132 
## [28] train-rmse:1.107566+0.016271    test-rmse:1.349525+0.097475 
## [29] train-rmse:1.094461+0.016504    test-rmse:1.340884+0.102255 
## [30] train-rmse:1.082426+0.016799    test-rmse:1.332705+0.101536 
## [31] train-rmse:1.070433+0.017016    test-rmse:1.320026+0.101731 
## [32] train-rmse:1.058184+0.018447    test-rmse:1.320258+0.104342 
## [33] train-rmse:1.042951+0.018883    test-rmse:1.313011+0.108713 
## [34] train-rmse:1.030969+0.018941    test-rmse:1.304973+0.108978 
## [35] train-rmse:1.016467+0.019943    test-rmse:1.300682+0.111074 
## [36] train-rmse:1.006541+0.019523    test-rmse:1.293832+0.108725 
## [37] train-rmse:0.997868+0.019055    test-rmse:1.288439+0.107258 
## [38] train-rmse:0.988296+0.017387    test-rmse:1.285378+0.105403 
## [39] train-rmse:0.976416+0.012974    test-rmse:1.272945+0.112417 
## [40] train-rmse:0.965758+0.013855    test-rmse:1.265561+0.111994 
## [41] train-rmse:0.956825+0.014567    test-rmse:1.261009+0.112670 
## [42] train-rmse:0.945461+0.017588    test-rmse:1.258246+0.112558 
## [43] train-rmse:0.935909+0.017375    test-rmse:1.252013+0.112424 
## [44] train-rmse:0.928541+0.017683    test-rmse:1.244889+0.114648 
## [45] train-rmse:0.918112+0.019444    test-rmse:1.243625+0.118281 
## [46] train-rmse:0.911468+0.019168    test-rmse:1.239992+0.117835 
## [47] train-rmse:0.903837+0.018450    test-rmse:1.235226+0.117092 
## [48] train-rmse:0.895079+0.021190    test-rmse:1.231027+0.118043 
## [49] train-rmse:0.887489+0.022497    test-rmse:1.224120+0.117237 
## [50] train-rmse:0.878858+0.021823    test-rmse:1.218059+0.119181 
## [51] train-rmse:0.873179+0.022871    test-rmse:1.211265+0.117724 
## [52] train-rmse:0.868113+0.022545    test-rmse:1.206184+0.116518 
## [53] train-rmse:0.862833+0.022946    test-rmse:1.201797+0.118178 
## [54] train-rmse:0.854955+0.023541    test-rmse:1.197478+0.119499 
## [55] train-rmse:0.849649+0.024158    test-rmse:1.195248+0.118778 
## [56] train-rmse:0.842917+0.021567    test-rmse:1.192535+0.122087 
## [57] train-rmse:0.832836+0.024891    test-rmse:1.187692+0.123623 
## [58] train-rmse:0.826253+0.025443    test-rmse:1.191000+0.121280 
## [59] train-rmse:0.821638+0.025615    test-rmse:1.184724+0.118585 
## [60] train-rmse:0.815209+0.026953    test-rmse:1.179678+0.121481 
## [61] train-rmse:0.808303+0.024591    test-rmse:1.180643+0.120886 
## [62] train-rmse:0.801264+0.020916    test-rmse:1.178034+0.121405 
## [63] train-rmse:0.794425+0.020586    test-rmse:1.177507+0.120991 
## [64] train-rmse:0.787884+0.018319    test-rmse:1.172473+0.121925 
## [65] train-rmse:0.783616+0.019014    test-rmse:1.171027+0.123175 
## [66] train-rmse:0.778467+0.016566    test-rmse:1.167258+0.130663 
## [67] train-rmse:0.770282+0.018902    test-rmse:1.156679+0.126911 
## [68] train-rmse:0.766206+0.019768    test-rmse:1.154470+0.126437 
## [69] train-rmse:0.761034+0.019701    test-rmse:1.148913+0.124700 
## [70] train-rmse:0.757599+0.019843    test-rmse:1.145855+0.126587 
## [71] train-rmse:0.753228+0.021184    test-rmse:1.144395+0.125029 
## [72] train-rmse:0.747473+0.019347    test-rmse:1.143868+0.125310 
## [73] train-rmse:0.741358+0.019192    test-rmse:1.145490+0.124497 
## [74] train-rmse:0.735892+0.020210    test-rmse:1.136045+0.124420 
## [75] train-rmse:0.731404+0.019996    test-rmse:1.130973+0.126119 
## [76] train-rmse:0.725323+0.019331    test-rmse:1.127843+0.125897 
## [77] train-rmse:0.720277+0.021178    test-rmse:1.126766+0.125905 
## [78] train-rmse:0.715002+0.022716    test-rmse:1.126968+0.122558 
## [79] train-rmse:0.709486+0.022163    test-rmse:1.123375+0.123745 
## [80] train-rmse:0.704950+0.023921    test-rmse:1.121715+0.122683 
## [81] train-rmse:0.701297+0.025271    test-rmse:1.121054+0.119266 
## [82] train-rmse:0.696914+0.023831    test-rmse:1.119183+0.118658 
## [83] train-rmse:0.692722+0.022503    test-rmse:1.119075+0.117562 
## [84] train-rmse:0.687963+0.021428    test-rmse:1.116888+0.119387 
## [85] train-rmse:0.683850+0.021840    test-rmse:1.113883+0.118922 
## [86] train-rmse:0.679259+0.022379    test-rmse:1.107669+0.118194 
## [87] train-rmse:0.672343+0.022423    test-rmse:1.103082+0.114990 
## [88] train-rmse:0.667426+0.021204    test-rmse:1.101440+0.116814 
## [89] train-rmse:0.664412+0.020575    test-rmse:1.099183+0.118194 
## [90] train-rmse:0.661678+0.020577    test-rmse:1.098834+0.119082 
## [91] train-rmse:0.656538+0.018508    test-rmse:1.094927+0.116028 
## [92] train-rmse:0.652095+0.016877    test-rmse:1.095572+0.114392 
## [93] train-rmse:0.647986+0.016600    test-rmse:1.090694+0.113559 
## [94] train-rmse:0.645594+0.016599    test-rmse:1.087552+0.111786 
## [95] train-rmse:0.642464+0.017012    test-rmse:1.084484+0.113001 
## [96] train-rmse:0.637571+0.018967    test-rmse:1.081285+0.112794 
## [97] train-rmse:0.634860+0.018351    test-rmse:1.079070+0.110728 
## [98] train-rmse:0.629294+0.015860    test-rmse:1.076641+0.109768 
## [99] train-rmse:0.625320+0.014296    test-rmse:1.077188+0.109803 
## [100]    train-rmse:0.620690+0.013423    test-rmse:1.076046+0.109250 
## [101]    train-rmse:0.618612+0.013484    test-rmse:1.076313+0.109042 
## [102]    train-rmse:0.615491+0.014586    test-rmse:1.075181+0.108834 
## [103]    train-rmse:0.612345+0.015020    test-rmse:1.072350+0.110001 
## [104]    train-rmse:0.608357+0.013694    test-rmse:1.071381+0.108848 
## [105]    train-rmse:0.606331+0.014307    test-rmse:1.071571+0.108329 
## [106]    train-rmse:0.602012+0.013404    test-rmse:1.069062+0.106766 
## [107]    train-rmse:0.599466+0.013523    test-rmse:1.070428+0.105303 
## [108]    train-rmse:0.596464+0.013110    test-rmse:1.071339+0.105435 
## [109]    train-rmse:0.594464+0.012783    test-rmse:1.070608+0.104762 
## [110]    train-rmse:0.591627+0.014581    test-rmse:1.070210+0.104925 
## [111]    train-rmse:0.587429+0.013626    test-rmse:1.069308+0.105214 
## [112]    train-rmse:0.584638+0.013190    test-rmse:1.067608+0.103095 
## [113]    train-rmse:0.582595+0.012999    test-rmse:1.066710+0.104146 
## [114]    train-rmse:0.580716+0.013226    test-rmse:1.065863+0.104128 
## [115]    train-rmse:0.578204+0.013723    test-rmse:1.064287+0.102567 
## [116]    train-rmse:0.574919+0.013790    test-rmse:1.063335+0.100924 
## [117]    train-rmse:0.570305+0.013398    test-rmse:1.060216+0.101418 
## [118]    train-rmse:0.566922+0.012004    test-rmse:1.059260+0.102767 
## [119]    train-rmse:0.564251+0.011565    test-rmse:1.058857+0.102834 
## [120]    train-rmse:0.561603+0.011957    test-rmse:1.058705+0.101806 
## [121]    train-rmse:0.558946+0.012417    test-rmse:1.058827+0.100834 
## [122]    train-rmse:0.554624+0.013507    test-rmse:1.056851+0.101667 
## [123]    train-rmse:0.552820+0.013357    test-rmse:1.057977+0.101143 
## [124]    train-rmse:0.550824+0.013033    test-rmse:1.056627+0.100513 
## [125]    train-rmse:0.547579+0.011468    test-rmse:1.055507+0.100384 
## [126]    train-rmse:0.545598+0.011946    test-rmse:1.054477+0.101069 
## [127]    train-rmse:0.542834+0.012231    test-rmse:1.053598+0.101481 
## [128]    train-rmse:0.538577+0.009693    test-rmse:1.053398+0.101633 
## [129]    train-rmse:0.535415+0.009929    test-rmse:1.054320+0.103154 
## [130]    train-rmse:0.532996+0.010566    test-rmse:1.053620+0.102307 
## [131]    train-rmse:0.529629+0.008223    test-rmse:1.053479+0.104198 
## [132]    train-rmse:0.527619+0.008610    test-rmse:1.055416+0.103696 
## [133]    train-rmse:0.525148+0.009107    test-rmse:1.056026+0.104656 
## [134]    train-rmse:0.523334+0.009188    test-rmse:1.055282+0.105028 
## Stopping. Best iteration:
## [128]    train-rmse:0.538577+0.009693    test-rmse:1.053398+0.101633
## 
## 65 
## [1]  train-rmse:71.012277+0.071119   test-rmse:71.012837+0.422304 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:51.218313+0.050969   test-rmse:51.218473+0.441381 
## [3]  train-rmse:36.954831+0.036295   test-rmse:36.954435+0.454495 
## [4]  train-rmse:26.681634+0.025555   test-rmse:26.680503+0.462943 
## [5]  train-rmse:19.289380+0.017699   test-rmse:19.287267+0.467424 
## [6]  train-rmse:13.979685+0.012191   test-rmse:13.976312+0.468039 
## [7]  train-rmse:10.170048+0.008156   test-rmse:10.157296+0.437450 
## [8]  train-rmse:7.439169+0.007163    test-rmse:7.428380+0.392670 
## [9]  train-rmse:5.478573+0.004028    test-rmse:5.478081+0.366618 
## [10] train-rmse:4.084144+0.004614    test-rmse:4.084038+0.336601 
## [11] train-rmse:3.098640+0.004108    test-rmse:3.106681+0.305281 
## [12] train-rmse:2.406223+0.010361    test-rmse:2.431456+0.271317 
## [13] train-rmse:1.935586+0.006031    test-rmse:1.989484+0.241208 
## [14] train-rmse:1.623749+0.009544    test-rmse:1.716385+0.206856 
## [15] train-rmse:1.410843+0.015110    test-rmse:1.554967+0.167700 
## [16] train-rmse:1.279634+0.017087    test-rmse:1.467748+0.146637 
## [17] train-rmse:1.193682+0.016322    test-rmse:1.412275+0.135016 
## [18] train-rmse:1.124449+0.015878    test-rmse:1.368784+0.116071 
## [19] train-rmse:1.082941+0.015349    test-rmse:1.341063+0.108611 
## [20] train-rmse:1.050507+0.015294    test-rmse:1.322574+0.108965 
## [21] train-rmse:1.021377+0.014776    test-rmse:1.307045+0.101832 
## [22] train-rmse:0.991845+0.012481    test-rmse:1.281254+0.098836 
## [23] train-rmse:0.971931+0.012867    test-rmse:1.264992+0.088154 
## [24] train-rmse:0.951927+0.012295    test-rmse:1.259859+0.085703 
## [25] train-rmse:0.925773+0.017893    test-rmse:1.243937+0.078364 
## [26] train-rmse:0.909923+0.018733    test-rmse:1.236638+0.078129 
## [27] train-rmse:0.889084+0.016125    test-rmse:1.222819+0.077879 
## [28] train-rmse:0.869800+0.019537    test-rmse:1.216375+0.076390 
## [29] train-rmse:0.850126+0.017928    test-rmse:1.208246+0.077099 
## [30] train-rmse:0.837482+0.018082    test-rmse:1.199845+0.077612 
## [31] train-rmse:0.824786+0.017694    test-rmse:1.193485+0.076818 
## [32] train-rmse:0.811864+0.020385    test-rmse:1.182192+0.078601 
## [33] train-rmse:0.799370+0.022574    test-rmse:1.169322+0.079901 
## [34] train-rmse:0.784744+0.023296    test-rmse:1.154895+0.077381 
## [35] train-rmse:0.772776+0.026017    test-rmse:1.143520+0.076102 
## [36] train-rmse:0.760745+0.024184    test-rmse:1.136900+0.081360 
## [37] train-rmse:0.747157+0.022412    test-rmse:1.129801+0.079370 
## [38] train-rmse:0.731307+0.021504    test-rmse:1.125015+0.081035 
## [39] train-rmse:0.720192+0.023700    test-rmse:1.117198+0.080296 
## [40] train-rmse:0.708832+0.021557    test-rmse:1.099040+0.066971 
## [41] train-rmse:0.700748+0.021120    test-rmse:1.093293+0.063307 
## [42] train-rmse:0.685227+0.022251    test-rmse:1.086416+0.069884 
## [43] train-rmse:0.676482+0.022942    test-rmse:1.083065+0.066742 
## [44] train-rmse:0.666825+0.025369    test-rmse:1.075794+0.066276 
## [45] train-rmse:0.658490+0.022966    test-rmse:1.070875+0.067102 
## [46] train-rmse:0.651079+0.021825    test-rmse:1.069724+0.066671 
## [47] train-rmse:0.642689+0.020668    test-rmse:1.065983+0.069252 
## [48] train-rmse:0.636948+0.020558    test-rmse:1.061232+0.069960 
## [49] train-rmse:0.626845+0.019410    test-rmse:1.061749+0.072918 
## [50] train-rmse:0.616837+0.018175    test-rmse:1.059654+0.071259 
## [51] train-rmse:0.607460+0.018801    test-rmse:1.051269+0.074272 
## [52] train-rmse:0.601501+0.019348    test-rmse:1.049525+0.073808 
## [53] train-rmse:0.594407+0.017047    test-rmse:1.047850+0.073260 
## [54] train-rmse:0.589515+0.017827    test-rmse:1.045018+0.072286 
## [55] train-rmse:0.583328+0.018498    test-rmse:1.044731+0.070211 
## [56] train-rmse:0.576006+0.020141    test-rmse:1.037358+0.073280 
## [57] train-rmse:0.567316+0.016102    test-rmse:1.032043+0.073051 
## [58] train-rmse:0.561267+0.013896    test-rmse:1.033057+0.071482 
## [59] train-rmse:0.550609+0.013492    test-rmse:1.029750+0.068522 
## [60] train-rmse:0.543822+0.013658    test-rmse:1.028186+0.070357 
## [61] train-rmse:0.536344+0.014602    test-rmse:1.020557+0.065652 
## [62] train-rmse:0.532603+0.013706    test-rmse:1.018354+0.064940 
## [63] train-rmse:0.524799+0.018125    test-rmse:1.018325+0.062357 
## [64] train-rmse:0.518057+0.020104    test-rmse:1.015942+0.062838 
## [65] train-rmse:0.512396+0.019825    test-rmse:1.015500+0.061136 
## [66] train-rmse:0.507463+0.018960    test-rmse:1.012864+0.062942 
## [67] train-rmse:0.498592+0.021042    test-rmse:1.015558+0.064709 
## [68] train-rmse:0.494108+0.021576    test-rmse:1.014687+0.063024 
## [69] train-rmse:0.488940+0.022609    test-rmse:1.011176+0.061429 
## [70] train-rmse:0.485796+0.022539    test-rmse:1.008696+0.063838 
## [71] train-rmse:0.478494+0.019493    test-rmse:1.008825+0.062794 
## [72] train-rmse:0.472971+0.017961    test-rmse:1.004158+0.061032 
## [73] train-rmse:0.469278+0.017847    test-rmse:1.003684+0.059957 
## [74] train-rmse:0.463127+0.015345    test-rmse:0.999343+0.059902 
## [75] train-rmse:0.459065+0.014781    test-rmse:0.998869+0.059580 
## [76] train-rmse:0.455133+0.015622    test-rmse:0.999052+0.060738 
## [77] train-rmse:0.449916+0.016052    test-rmse:0.996709+0.062213 
## [78] train-rmse:0.444395+0.015276    test-rmse:0.995900+0.061828 
## [79] train-rmse:0.438237+0.014716    test-rmse:0.994280+0.061429 
## [80] train-rmse:0.435691+0.014716    test-rmse:0.994173+0.060803 
## [81] train-rmse:0.429932+0.014472    test-rmse:0.988593+0.056735 
## [82] train-rmse:0.425294+0.012690    test-rmse:0.988041+0.056751 
## [83] train-rmse:0.418106+0.013459    test-rmse:0.989624+0.059181 
## [84] train-rmse:0.414695+0.013142    test-rmse:0.990660+0.059657 
## [85] train-rmse:0.410839+0.014715    test-rmse:0.990109+0.058202 
## [86] train-rmse:0.406020+0.014134    test-rmse:0.987966+0.058438 
## [87] train-rmse:0.401514+0.013580    test-rmse:0.987426+0.058289 
## [88] train-rmse:0.396885+0.013161    test-rmse:0.987040+0.058223 
## [89] train-rmse:0.393882+0.012829    test-rmse:0.986591+0.059270 
## [90] train-rmse:0.388860+0.014852    test-rmse:0.985498+0.060077 
## [91] train-rmse:0.384975+0.015931    test-rmse:0.983959+0.058098 
## [92] train-rmse:0.382228+0.016806    test-rmse:0.984007+0.057447 
## [93] train-rmse:0.377531+0.014663    test-rmse:0.979043+0.055020 
## [94] train-rmse:0.373625+0.014581    test-rmse:0.979142+0.054475 
## [95] train-rmse:0.369847+0.014511    test-rmse:0.980512+0.055633 
## [96] train-rmse:0.365434+0.014112    test-rmse:0.978882+0.057321 
## [97] train-rmse:0.361541+0.015165    test-rmse:0.980243+0.057104 
## [98] train-rmse:0.357378+0.014763    test-rmse:0.980170+0.056486 
## [99] train-rmse:0.353337+0.015552    test-rmse:0.980777+0.058687 
## [100]    train-rmse:0.349286+0.017013    test-rmse:0.978837+0.057492 
## [101]    train-rmse:0.346994+0.017331    test-rmse:0.976548+0.056261 
## [102]    train-rmse:0.343888+0.017366    test-rmse:0.975207+0.056729 
## [103]    train-rmse:0.341462+0.017013    test-rmse:0.975734+0.055804 
## [104]    train-rmse:0.337249+0.015164    test-rmse:0.974478+0.057501 
## [105]    train-rmse:0.334478+0.015863    test-rmse:0.977280+0.059914 
## [106]    train-rmse:0.331000+0.015635    test-rmse:0.975718+0.058096 
## [107]    train-rmse:0.327259+0.016301    test-rmse:0.973230+0.058710 
## [108]    train-rmse:0.323372+0.016207    test-rmse:0.973764+0.058348 
## [109]    train-rmse:0.320606+0.016464    test-rmse:0.973260+0.058794 
## [110]    train-rmse:0.317174+0.015612    test-rmse:0.973884+0.057322 
## [111]    train-rmse:0.312201+0.012833    test-rmse:0.974306+0.061671 
## [112]    train-rmse:0.308932+0.011924    test-rmse:0.977201+0.060096 
## [113]    train-rmse:0.307215+0.012727    test-rmse:0.976833+0.060391 
## Stopping. Best iteration:
## [107]    train-rmse:0.327259+0.016301    test-rmse:0.973230+0.058710
## 
## 66 
## [1]  train-rmse:71.012277+0.071119   test-rmse:71.012837+0.422304 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:51.218313+0.050969   test-rmse:51.218473+0.441381 
## [3]  train-rmse:36.954831+0.036295   test-rmse:36.954435+0.454495 
## [4]  train-rmse:26.681634+0.025555   test-rmse:26.680503+0.462943 
## [5]  train-rmse:19.289380+0.017699   test-rmse:19.287267+0.467424 
## [6]  train-rmse:13.979685+0.012191   test-rmse:13.976312+0.468039 
## [7]  train-rmse:10.170048+0.008156   test-rmse:10.157296+0.437450 
## [8]  train-rmse:7.437512+0.007594    test-rmse:7.418994+0.389481 
## [9]  train-rmse:5.475252+0.006010    test-rmse:5.458251+0.353602 
## [10] train-rmse:4.072230+0.006118    test-rmse:4.070478+0.333730 
## [11] train-rmse:3.070557+0.006010    test-rmse:3.088122+0.308058 
## [12] train-rmse:2.363172+0.003590    test-rmse:2.404569+0.263449 
## [13] train-rmse:1.876769+0.006319    test-rmse:1.953727+0.226589 
## [14] train-rmse:1.535387+0.008401    test-rmse:1.678883+0.202472 
## [15] train-rmse:1.313288+0.008403    test-rmse:1.505460+0.188607 
## [16] train-rmse:1.163416+0.009332    test-rmse:1.383978+0.166802 
## [17] train-rmse:1.061005+0.019153    test-rmse:1.331822+0.160501 
## [18] train-rmse:0.987337+0.016146    test-rmse:1.292507+0.143125 
## [19] train-rmse:0.926327+0.022761    test-rmse:1.268512+0.131940 
## [20] train-rmse:0.886497+0.025142    test-rmse:1.244996+0.127046 
## [21] train-rmse:0.846856+0.024729    test-rmse:1.213822+0.131587 
## [22] train-rmse:0.819303+0.025133    test-rmse:1.191210+0.127346 
## [23] train-rmse:0.794449+0.024575    test-rmse:1.177676+0.125326 
## [24] train-rmse:0.769836+0.023957    test-rmse:1.156248+0.123672 
## [25] train-rmse:0.746989+0.021969    test-rmse:1.146747+0.122236 
## [26] train-rmse:0.728224+0.021907    test-rmse:1.131430+0.120729 
## [27] train-rmse:0.709590+0.025003    test-rmse:1.117078+0.114193 
## [28] train-rmse:0.691067+0.022417    test-rmse:1.109480+0.110726 
## [29] train-rmse:0.676429+0.022646    test-rmse:1.098926+0.107969 
## [30] train-rmse:0.658691+0.025581    test-rmse:1.087712+0.112509 
## [31] train-rmse:0.641549+0.024010    test-rmse:1.081305+0.111983 
## [32] train-rmse:0.630386+0.022417    test-rmse:1.074232+0.109741 
## [33] train-rmse:0.618712+0.021810    test-rmse:1.063689+0.103204 
## [34] train-rmse:0.604864+0.022778    test-rmse:1.062214+0.103251 
## [35] train-rmse:0.591487+0.022084    test-rmse:1.057243+0.103348 
## [36] train-rmse:0.579988+0.020660    test-rmse:1.053265+0.098463 
## [37] train-rmse:0.561168+0.018208    test-rmse:1.045887+0.094130 
## [38] train-rmse:0.552765+0.017169    test-rmse:1.040312+0.095702 
## [39] train-rmse:0.544838+0.018269    test-rmse:1.040877+0.099349 
## [40] train-rmse:0.531413+0.018048    test-rmse:1.033666+0.096368 
## [41] train-rmse:0.517624+0.018362    test-rmse:1.028414+0.094165 
## [42] train-rmse:0.505341+0.019231    test-rmse:1.025430+0.094104 
## [43] train-rmse:0.495674+0.019635    test-rmse:1.022388+0.092027 
## [44] train-rmse:0.486319+0.018378    test-rmse:1.022336+0.090800 
## [45] train-rmse:0.478584+0.016725    test-rmse:1.018111+0.091134 
## [46] train-rmse:0.472512+0.016111    test-rmse:1.014664+0.092685 
## [47] train-rmse:0.464727+0.015195    test-rmse:1.013955+0.090367 
## [48] train-rmse:0.456819+0.015877    test-rmse:1.014068+0.091626 
## [49] train-rmse:0.449363+0.015685    test-rmse:1.009045+0.091172 
## [50] train-rmse:0.440161+0.017058    test-rmse:1.003892+0.092593 
## [51] train-rmse:0.433560+0.015230    test-rmse:1.002514+0.090798 
## [52] train-rmse:0.421281+0.015093    test-rmse:0.999271+0.091492 
## [53] train-rmse:0.414040+0.016127    test-rmse:0.994846+0.091056 
## [54] train-rmse:0.408756+0.016584    test-rmse:0.991619+0.091491 
## [55] train-rmse:0.403407+0.016354    test-rmse:0.990226+0.090505 
## [56] train-rmse:0.396750+0.014210    test-rmse:0.992873+0.088893 
## [57] train-rmse:0.387909+0.013703    test-rmse:0.988177+0.085044 
## [58] train-rmse:0.382638+0.013768    test-rmse:0.987626+0.082828 
## [59] train-rmse:0.376880+0.011941    test-rmse:0.983948+0.082409 
## [60] train-rmse:0.371068+0.013876    test-rmse:0.982336+0.082865 
## [61] train-rmse:0.364507+0.014294    test-rmse:0.981009+0.079872 
## [62] train-rmse:0.357128+0.014480    test-rmse:0.976427+0.075508 
## [63] train-rmse:0.348927+0.015408    test-rmse:0.974691+0.075714 
## [64] train-rmse:0.344071+0.015026    test-rmse:0.971934+0.077071 
## [65] train-rmse:0.339757+0.014469    test-rmse:0.968962+0.076442 
## [66] train-rmse:0.333965+0.014098    test-rmse:0.967957+0.077707 
## [67] train-rmse:0.329384+0.014128    test-rmse:0.966998+0.078039 
## [68] train-rmse:0.325820+0.012910    test-rmse:0.965483+0.076239 
## [69] train-rmse:0.320650+0.012147    test-rmse:0.964542+0.077316 
## [70] train-rmse:0.314459+0.012938    test-rmse:0.964828+0.074865 
## [71] train-rmse:0.309522+0.013554    test-rmse:0.964260+0.074854 
## [72] train-rmse:0.305322+0.014481    test-rmse:0.964024+0.074744 
## [73] train-rmse:0.301185+0.013713    test-rmse:0.963630+0.073747 
## [74] train-rmse:0.295470+0.013014    test-rmse:0.964881+0.076886 
## [75] train-rmse:0.289218+0.015266    test-rmse:0.962659+0.076769 
## [76] train-rmse:0.285034+0.014904    test-rmse:0.961882+0.076276 
## [77] train-rmse:0.281014+0.015974    test-rmse:0.961774+0.075764 
## [78] train-rmse:0.275737+0.016466    test-rmse:0.961343+0.075884 
## [79] train-rmse:0.271743+0.016971    test-rmse:0.960409+0.076274 
## [80] train-rmse:0.265729+0.014402    test-rmse:0.959791+0.075991 
## [81] train-rmse:0.261263+0.014186    test-rmse:0.959098+0.076446 
## [82] train-rmse:0.258835+0.014318    test-rmse:0.958608+0.075135 
## [83] train-rmse:0.254422+0.012329    test-rmse:0.957747+0.075151 
## [84] train-rmse:0.248491+0.012204    test-rmse:0.958419+0.077562 
## [85] train-rmse:0.245749+0.013007    test-rmse:0.956774+0.077833 
## [86] train-rmse:0.242026+0.013436    test-rmse:0.955657+0.076943 
## [87] train-rmse:0.238979+0.013878    test-rmse:0.955451+0.076654 
## [88] train-rmse:0.235559+0.012628    test-rmse:0.953850+0.076145 
## [89] train-rmse:0.231566+0.013347    test-rmse:0.952183+0.076570 
## [90] train-rmse:0.227895+0.013195    test-rmse:0.951364+0.076070 
## [91] train-rmse:0.222183+0.013445    test-rmse:0.951749+0.076989 
## [92] train-rmse:0.219217+0.013096    test-rmse:0.950685+0.078160 
## [93] train-rmse:0.216615+0.013802    test-rmse:0.949935+0.077439 
## [94] train-rmse:0.214587+0.014178    test-rmse:0.949719+0.076639 
## [95] train-rmse:0.212158+0.014294    test-rmse:0.950620+0.076814 
## [96] train-rmse:0.210345+0.014116    test-rmse:0.950326+0.076869 
## [97] train-rmse:0.206881+0.013617    test-rmse:0.949309+0.075232 
## [98] train-rmse:0.203777+0.013348    test-rmse:0.949917+0.076880 
## [99] train-rmse:0.199759+0.011246    test-rmse:0.948771+0.077851 
## [100]    train-rmse:0.196998+0.012104    test-rmse:0.948338+0.077424 
## [101]    train-rmse:0.193779+0.011446    test-rmse:0.948474+0.077303 
## [102]    train-rmse:0.190434+0.011104    test-rmse:0.947532+0.077470 
## [103]    train-rmse:0.187430+0.011297    test-rmse:0.947859+0.077917 
## [104]    train-rmse:0.185195+0.011485    test-rmse:0.948272+0.078083 
## [105]    train-rmse:0.181906+0.012854    test-rmse:0.947935+0.078804 
## [106]    train-rmse:0.179750+0.012798    test-rmse:0.947503+0.079433 
## [107]    train-rmse:0.176873+0.012309    test-rmse:0.948938+0.079568 
## [108]    train-rmse:0.174823+0.012333    test-rmse:0.947944+0.079048 
## [109]    train-rmse:0.171854+0.012373    test-rmse:0.947892+0.078794 
## [110]    train-rmse:0.169287+0.012871    test-rmse:0.949162+0.080064 
## [111]    train-rmse:0.166240+0.012326    test-rmse:0.949318+0.079590 
## [112]    train-rmse:0.163408+0.012738    test-rmse:0.948407+0.078780 
## Stopping. Best iteration:
## [106]    train-rmse:0.179750+0.012798    test-rmse:0.947503+0.079433
## 
## 67 
## [1]  train-rmse:71.012277+0.071119   test-rmse:71.012837+0.422304 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:51.218313+0.050969   test-rmse:51.218473+0.441381 
## [3]  train-rmse:36.954831+0.036295   test-rmse:36.954435+0.454495 
## [4]  train-rmse:26.681634+0.025555   test-rmse:26.680503+0.462943 
## [5]  train-rmse:19.289380+0.017699   test-rmse:19.287267+0.467424 
## [6]  train-rmse:13.979685+0.012191   test-rmse:13.976312+0.468039 
## [7]  train-rmse:10.170048+0.008156   test-rmse:10.157296+0.437450 
## [8]  train-rmse:7.437276+0.007927    test-rmse:7.420348+0.391249 
## [9]  train-rmse:5.473658+0.006767    test-rmse:5.458193+0.352705 
## [10] train-rmse:4.066124+0.006158    test-rmse:4.058031+0.320367 
## [11] train-rmse:3.057552+0.006737    test-rmse:3.064833+0.289573 
## [12] train-rmse:2.335579+0.006335    test-rmse:2.373450+0.259513 
## [13] train-rmse:1.827084+0.007926    test-rmse:1.917101+0.219591 
## [14] train-rmse:1.471440+0.007532    test-rmse:1.630888+0.180684 
## [15] train-rmse:1.238705+0.010871    test-rmse:1.439712+0.168553 
## [16] train-rmse:1.072491+0.004429    test-rmse:1.322173+0.145441 
## [17] train-rmse:0.956113+0.009082    test-rmse:1.258598+0.134480 
## [18] train-rmse:0.863132+0.008409    test-rmse:1.202700+0.119975 
## [19] train-rmse:0.802485+0.013400    test-rmse:1.169790+0.110761 
## [20] train-rmse:0.753704+0.015021    test-rmse:1.152251+0.110310 
## [21] train-rmse:0.710454+0.009726    test-rmse:1.124978+0.112750 
## [22] train-rmse:0.682172+0.011022    test-rmse:1.111564+0.111085 
## [23] train-rmse:0.654280+0.012410    test-rmse:1.098979+0.105147 
## [24] train-rmse:0.630403+0.014147    test-rmse:1.084464+0.102850 
## [25] train-rmse:0.610843+0.018266    test-rmse:1.076083+0.104927 
## [26] train-rmse:0.592957+0.017253    test-rmse:1.061792+0.098756 
## [27] train-rmse:0.571170+0.023132    test-rmse:1.056365+0.095413 
## [28] train-rmse:0.553513+0.021072    test-rmse:1.044397+0.088302 
## [29] train-rmse:0.536134+0.020131    test-rmse:1.038005+0.089748 
## [30] train-rmse:0.523019+0.023305    test-rmse:1.032788+0.088437 
## [31] train-rmse:0.505760+0.023524    test-rmse:1.030690+0.087965 
## [32] train-rmse:0.492420+0.017911    test-rmse:1.020100+0.085564 
## [33] train-rmse:0.476457+0.022450    test-rmse:1.018988+0.082070 
## [34] train-rmse:0.461680+0.019861    test-rmse:1.021236+0.076441 
## [35] train-rmse:0.452824+0.021759    test-rmse:1.020599+0.076809 
## [36] train-rmse:0.444285+0.020793    test-rmse:1.014830+0.074098 
## [37] train-rmse:0.430459+0.020049    test-rmse:1.006316+0.074370 
## [38] train-rmse:0.418761+0.017898    test-rmse:1.006972+0.074189 
## [39] train-rmse:0.407295+0.019284    test-rmse:1.006201+0.074084 
## [40] train-rmse:0.396020+0.020031    test-rmse:1.001683+0.073922 
## [41] train-rmse:0.387222+0.016766    test-rmse:0.999775+0.073579 
## [42] train-rmse:0.377003+0.017363    test-rmse:0.997048+0.076022 
## [43] train-rmse:0.365315+0.017950    test-rmse:0.994198+0.076241 
## [44] train-rmse:0.358466+0.016605    test-rmse:0.993362+0.076763 
## [45] train-rmse:0.350573+0.017885    test-rmse:0.988857+0.073614 
## [46] train-rmse:0.341570+0.018619    test-rmse:0.986734+0.075472 
## [47] train-rmse:0.333115+0.016649    test-rmse:0.988211+0.075507 
## [48] train-rmse:0.326041+0.016396    test-rmse:0.986738+0.073108 
## [49] train-rmse:0.319590+0.017061    test-rmse:0.985267+0.070379 
## [50] train-rmse:0.310745+0.016152    test-rmse:0.983624+0.067322 
## [51] train-rmse:0.304989+0.014448    test-rmse:0.982101+0.067894 
## [52] train-rmse:0.297103+0.013727    test-rmse:0.982797+0.068313 
## [53] train-rmse:0.289960+0.014501    test-rmse:0.982248+0.069821 
## [54] train-rmse:0.281976+0.012913    test-rmse:0.983740+0.069510 
## [55] train-rmse:0.275206+0.013413    test-rmse:0.982651+0.071332 
## [56] train-rmse:0.269609+0.012564    test-rmse:0.981153+0.070564 
## [57] train-rmse:0.264462+0.012672    test-rmse:0.979268+0.071019 
## [58] train-rmse:0.258675+0.012299    test-rmse:0.980934+0.069867 
## [59] train-rmse:0.252426+0.010947    test-rmse:0.977356+0.069551 
## [60] train-rmse:0.245705+0.010690    test-rmse:0.975598+0.070342 
## [61] train-rmse:0.241192+0.008990    test-rmse:0.978354+0.070622 
## [62] train-rmse:0.235909+0.010160    test-rmse:0.976280+0.069355 
## [63] train-rmse:0.229207+0.010923    test-rmse:0.974469+0.069875 
## [64] train-rmse:0.223050+0.012964    test-rmse:0.974864+0.070450 
## [65] train-rmse:0.217833+0.010903    test-rmse:0.975573+0.071725 
## [66] train-rmse:0.210532+0.012164    test-rmse:0.976549+0.070678 
## [67] train-rmse:0.206259+0.011368    test-rmse:0.976342+0.072317 
## [68] train-rmse:0.201046+0.011979    test-rmse:0.975390+0.072825 
## [69] train-rmse:0.198569+0.012057    test-rmse:0.974856+0.075194 
## Stopping. Best iteration:
## [63] train-rmse:0.229207+0.010923    test-rmse:0.974469+0.069875
## 
## 68 
## [1]  train-rmse:71.012277+0.071119   test-rmse:71.012837+0.422304 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:51.218313+0.050969   test-rmse:51.218473+0.441381 
## [3]  train-rmse:36.954831+0.036295   test-rmse:36.954435+0.454495 
## [4]  train-rmse:26.681634+0.025555   test-rmse:26.680503+0.462943 
## [5]  train-rmse:19.289380+0.017699   test-rmse:19.287267+0.467424 
## [6]  train-rmse:13.979685+0.012191   test-rmse:13.976312+0.468039 
## [7]  train-rmse:10.170048+0.008156   test-rmse:10.157296+0.437450 
## [8]  train-rmse:7.437276+0.007927    test-rmse:7.420348+0.391249 
## [9]  train-rmse:5.472792+0.006635    test-rmse:5.453734+0.352232 
## [10] train-rmse:4.062338+0.006368    test-rmse:4.058126+0.306594 
## [11] train-rmse:3.048901+0.005638    test-rmse:3.056859+0.288898 
## [12] train-rmse:2.320676+0.007329    test-rmse:2.362782+0.279943 
## [13] train-rmse:1.802639+0.006990    test-rmse:1.905803+0.238018 
## [14] train-rmse:1.433201+0.016683    test-rmse:1.602175+0.206894 
## [15] train-rmse:1.171445+0.017737    test-rmse:1.405243+0.185818 
## [16] train-rmse:0.994569+0.021781    test-rmse:1.289781+0.167055 
## [17] train-rmse:0.866938+0.025597    test-rmse:1.223424+0.159744 
## [18] train-rmse:0.772165+0.020176    test-rmse:1.168381+0.140452 
## [19] train-rmse:0.708045+0.021047    test-rmse:1.143182+0.142856 
## [20] train-rmse:0.658497+0.022858    test-rmse:1.121540+0.138393 
## [21] train-rmse:0.611157+0.026914    test-rmse:1.110419+0.132136 
## [22] train-rmse:0.578705+0.027182    test-rmse:1.098812+0.136750 
## [23] train-rmse:0.550929+0.032048    test-rmse:1.085531+0.129805 
## [24] train-rmse:0.524624+0.030018    test-rmse:1.068957+0.113684 
## [25] train-rmse:0.500487+0.028997    test-rmse:1.064548+0.113852 
## [26] train-rmse:0.479717+0.030299    test-rmse:1.057480+0.115231 
## [27] train-rmse:0.456218+0.033784    test-rmse:1.056594+0.118608 
## [28] train-rmse:0.439946+0.033058    test-rmse:1.048553+0.119766 
## [29] train-rmse:0.424989+0.030414    test-rmse:1.044369+0.120875 
## [30] train-rmse:0.408401+0.028136    test-rmse:1.041696+0.125722 
## [31] train-rmse:0.393884+0.030185    test-rmse:1.035044+0.121534 
## [32] train-rmse:0.378184+0.027579    test-rmse:1.033896+0.121562 
## [33] train-rmse:0.362711+0.030699    test-rmse:1.034320+0.119773 
## [34] train-rmse:0.347900+0.033224    test-rmse:1.030189+0.118911 
## [35] train-rmse:0.338648+0.032704    test-rmse:1.029963+0.123492 
## [36] train-rmse:0.328238+0.029325    test-rmse:1.025201+0.121078 
## [37] train-rmse:0.319174+0.028263    test-rmse:1.020672+0.118130 
## [38] train-rmse:0.308738+0.032145    test-rmse:1.018426+0.116539 
## [39] train-rmse:0.296445+0.026493    test-rmse:1.014156+0.114323 
## [40] train-rmse:0.287064+0.026126    test-rmse:1.009196+0.112683 
## [41] train-rmse:0.273712+0.027541    test-rmse:1.007655+0.117470 
## [42] train-rmse:0.265727+0.027094    test-rmse:1.006011+0.119572 
## [43] train-rmse:0.256238+0.027094    test-rmse:1.003362+0.118284 
## [44] train-rmse:0.249694+0.025260    test-rmse:1.001881+0.120076 
## [45] train-rmse:0.243443+0.024973    test-rmse:1.001898+0.121346 
## [46] train-rmse:0.236280+0.023736    test-rmse:1.001541+0.121198 
## [47] train-rmse:0.230426+0.024186    test-rmse:1.001095+0.121667 
## [48] train-rmse:0.224696+0.024630    test-rmse:0.998730+0.122281 
## [49] train-rmse:0.218089+0.023772    test-rmse:0.997840+0.123028 
## [50] train-rmse:0.209163+0.021069    test-rmse:0.996396+0.121951 
## [51] train-rmse:0.203362+0.020860    test-rmse:0.995253+0.123162 
## [52] train-rmse:0.196792+0.022660    test-rmse:0.993863+0.123515 
## [53] train-rmse:0.188558+0.020878    test-rmse:0.993403+0.124167 
## [54] train-rmse:0.180948+0.020013    test-rmse:0.989990+0.123795 
## [55] train-rmse:0.174725+0.018559    test-rmse:0.991242+0.124774 
## [56] train-rmse:0.170315+0.017652    test-rmse:0.989072+0.124692 
## [57] train-rmse:0.165159+0.017556    test-rmse:0.989234+0.124630 
## [58] train-rmse:0.157727+0.015039    test-rmse:0.990244+0.126666 
## [59] train-rmse:0.152954+0.015498    test-rmse:0.988110+0.127637 
## [60] train-rmse:0.148267+0.016206    test-rmse:0.987575+0.127527 
## [61] train-rmse:0.144361+0.015000    test-rmse:0.987064+0.128022 
## [62] train-rmse:0.141219+0.015718    test-rmse:0.986862+0.128416 
## [63] train-rmse:0.136215+0.017680    test-rmse:0.985685+0.126856 
## [64] train-rmse:0.132265+0.017258    test-rmse:0.986217+0.126195 
## [65] train-rmse:0.127899+0.016201    test-rmse:0.986183+0.126377 
## [66] train-rmse:0.124477+0.015749    test-rmse:0.985632+0.128443 
## [67] train-rmse:0.120287+0.016017    test-rmse:0.985290+0.129377 
## [68] train-rmse:0.116350+0.015825    test-rmse:0.984593+0.128451 
## [69] train-rmse:0.113031+0.015231    test-rmse:0.984578+0.128889 
## [70] train-rmse:0.109740+0.015114    test-rmse:0.984586+0.128833 
## [71] train-rmse:0.106496+0.014406    test-rmse:0.983709+0.129072 
## [72] train-rmse:0.103518+0.014172    test-rmse:0.983482+0.129218 
## [73] train-rmse:0.099926+0.014009    test-rmse:0.984284+0.129948 
## [74] train-rmse:0.097647+0.014479    test-rmse:0.984066+0.129602 
## [75] train-rmse:0.095390+0.014422    test-rmse:0.984097+0.128978 
## [76] train-rmse:0.092057+0.013119    test-rmse:0.983664+0.129444 
## [77] train-rmse:0.089244+0.013945    test-rmse:0.983783+0.129604 
## [78] train-rmse:0.086733+0.013010    test-rmse:0.983444+0.128974 
## [79] train-rmse:0.083993+0.012181    test-rmse:0.983742+0.128958 
## [80] train-rmse:0.081790+0.012142    test-rmse:0.983676+0.129268 
## [81] train-rmse:0.079392+0.011538    test-rmse:0.984035+0.129078 
## [82] train-rmse:0.077905+0.011049    test-rmse:0.983766+0.128986 
## [83] train-rmse:0.075798+0.010915    test-rmse:0.983735+0.129373 
## [84] train-rmse:0.073453+0.010876    test-rmse:0.983467+0.129584 
## Stopping. Best iteration:
## [78] train-rmse:0.086733+0.013010    test-rmse:0.983444+0.128974
## 
## 69 
## [1]  train-rmse:71.012277+0.071119   test-rmse:71.012837+0.422304 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:51.218313+0.050969   test-rmse:51.218473+0.441381 
## [3]  train-rmse:36.954831+0.036295   test-rmse:36.954435+0.454495 
## [4]  train-rmse:26.681634+0.025555   test-rmse:26.680503+0.462943 
## [5]  train-rmse:19.289380+0.017699   test-rmse:19.287267+0.467424 
## [6]  train-rmse:13.979685+0.012191   test-rmse:13.976312+0.468039 
## [7]  train-rmse:10.170048+0.008156   test-rmse:10.157296+0.437450 
## [8]  train-rmse:7.437276+0.007927    test-rmse:7.420348+0.391249 
## [9]  train-rmse:5.471940+0.006566    test-rmse:5.449733+0.350528 
## [10] train-rmse:4.059608+0.006214    test-rmse:4.054649+0.297683 
## [11] train-rmse:3.041809+0.008453    test-rmse:3.046802+0.279105 
## [12] train-rmse:2.312454+0.006919    test-rmse:2.348750+0.267493 
## [13] train-rmse:1.784525+0.003180    test-rmse:1.879647+0.226991 
## [14] train-rmse:1.399430+0.006235    test-rmse:1.567198+0.191346 
## [15] train-rmse:1.128842+0.007618    test-rmse:1.385443+0.172339 
## [16] train-rmse:0.932953+0.010335    test-rmse:1.262540+0.160460 
## [17] train-rmse:0.797706+0.009780    test-rmse:1.194373+0.148086 
## [18] train-rmse:0.697093+0.012270    test-rmse:1.145983+0.134817 
## [19] train-rmse:0.625067+0.009644    test-rmse:1.116499+0.135385 
## [20] train-rmse:0.568582+0.013486    test-rmse:1.099894+0.132093 
## [21] train-rmse:0.527745+0.012283    test-rmse:1.092086+0.130900 
## [22] train-rmse:0.489926+0.010164    test-rmse:1.075318+0.118939 
## [23] train-rmse:0.462278+0.012607    test-rmse:1.070882+0.115211 
## [24] train-rmse:0.435182+0.010352    test-rmse:1.068405+0.110094 
## [25] train-rmse:0.409446+0.003615    test-rmse:1.057059+0.106242 
## [26] train-rmse:0.387354+0.009125    test-rmse:1.051025+0.110332 
## [27] train-rmse:0.369917+0.010025    test-rmse:1.045130+0.104813 
## [28] train-rmse:0.349184+0.012856    test-rmse:1.043385+0.101203 
## [29] train-rmse:0.330369+0.014317    test-rmse:1.041795+0.105484 
## [30] train-rmse:0.313901+0.010390    test-rmse:1.035035+0.101819 
## [31] train-rmse:0.296310+0.009260    test-rmse:1.031529+0.099917 
## [32] train-rmse:0.283242+0.006746    test-rmse:1.026871+0.100314 
## [33] train-rmse:0.268818+0.006808    test-rmse:1.020720+0.094965 
## [34] train-rmse:0.258594+0.008673    test-rmse:1.020987+0.099349 
## [35] train-rmse:0.244651+0.008101    test-rmse:1.020965+0.099872 
## [36] train-rmse:0.233147+0.009463    test-rmse:1.018643+0.099201 
## [37] train-rmse:0.223544+0.011393    test-rmse:1.015997+0.098723 
## [38] train-rmse:0.215343+0.010774    test-rmse:1.016138+0.099194 
## [39] train-rmse:0.209298+0.010306    test-rmse:1.012782+0.097549 
## [40] train-rmse:0.197113+0.008628    test-rmse:1.011067+0.096383 
## [41] train-rmse:0.189220+0.006651    test-rmse:1.009615+0.097949 
## [42] train-rmse:0.182927+0.007703    test-rmse:1.006226+0.097118 
## [43] train-rmse:0.174814+0.008818    test-rmse:1.003253+0.097696 
## [44] train-rmse:0.166539+0.007517    test-rmse:1.003147+0.097957 
## [45] train-rmse:0.161238+0.008949    test-rmse:1.001885+0.096463 
## [46] train-rmse:0.153009+0.010553    test-rmse:1.000420+0.095228 
## [47] train-rmse:0.147659+0.011125    test-rmse:0.999741+0.096272 
## [48] train-rmse:0.142322+0.011945    test-rmse:0.999608+0.098037 
## [49] train-rmse:0.137953+0.011135    test-rmse:0.998249+0.097814 
## [50] train-rmse:0.130798+0.009671    test-rmse:0.997443+0.098232 
## [51] train-rmse:0.126355+0.009009    test-rmse:0.996702+0.098353 
## [52] train-rmse:0.121124+0.008218    test-rmse:0.996572+0.098880 
## [53] train-rmse:0.116503+0.007878    test-rmse:0.995773+0.098784 
## [54] train-rmse:0.110911+0.006234    test-rmse:0.994881+0.098347 
## [55] train-rmse:0.105968+0.006243    test-rmse:0.993239+0.098956 
## [56] train-rmse:0.102557+0.006274    test-rmse:0.993927+0.100053 
## [57] train-rmse:0.099821+0.005628    test-rmse:0.994273+0.100141 
## [58] train-rmse:0.094885+0.004782    test-rmse:0.993263+0.099935 
## [59] train-rmse:0.089948+0.004523    test-rmse:0.993984+0.099498 
## [60] train-rmse:0.085874+0.003816    test-rmse:0.993720+0.099115 
## [61] train-rmse:0.082850+0.003599    test-rmse:0.993171+0.099652 
## [62] train-rmse:0.080374+0.004466    test-rmse:0.992758+0.100525 
## [63] train-rmse:0.078009+0.004736    test-rmse:0.992645+0.101713 
## [64] train-rmse:0.074440+0.003664    test-rmse:0.991612+0.101350 
## [65] train-rmse:0.071548+0.003809    test-rmse:0.992042+0.100801 
## [66] train-rmse:0.068200+0.004446    test-rmse:0.991811+0.100994 
## [67] train-rmse:0.065301+0.004352    test-rmse:0.992404+0.100921 
## [68] train-rmse:0.063670+0.004145    test-rmse:0.991719+0.100466 
## [69] train-rmse:0.060892+0.004320    test-rmse:0.991287+0.100343 
## [70] train-rmse:0.058061+0.003643    test-rmse:0.990548+0.101421 
## [71] train-rmse:0.055190+0.004259    test-rmse:0.990467+0.101489 
## [72] train-rmse:0.053503+0.004055    test-rmse:0.990947+0.101293 
## [73] train-rmse:0.051898+0.003914    test-rmse:0.990618+0.101395 
## [74] train-rmse:0.050281+0.004489    test-rmse:0.989928+0.101376 
## [75] train-rmse:0.048206+0.004099    test-rmse:0.990026+0.101311 
## [76] train-rmse:0.046467+0.003723    test-rmse:0.989273+0.101158 
## [77] train-rmse:0.044580+0.003904    test-rmse:0.989285+0.101008 
## [78] train-rmse:0.042844+0.004230    test-rmse:0.989448+0.100985 
## [79] train-rmse:0.040985+0.004248    test-rmse:0.989410+0.101643 
## [80] train-rmse:0.039416+0.004036    test-rmse:0.989418+0.101907 
## [81] train-rmse:0.038508+0.004058    test-rmse:0.989377+0.102083 
## [82] train-rmse:0.037011+0.003767    test-rmse:0.989417+0.102063 
## Stopping. Best iteration:
## [76] train-rmse:0.046467+0.003723    test-rmse:0.989273+0.101158
## 
## 70 
## [1]  train-rmse:69.050973+0.069138   test-rmse:69.051505+0.424216 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:48.430438+0.048124   test-rmse:48.430518+0.444003 
## [3]  train-rmse:33.983752+0.033210   test-rmse:33.983192+0.457073 
## [4]  train-rmse:23.869216+0.022582   test-rmse:23.867778+0.464907 
## [5]  train-rmse:16.797328+0.015059   test-rmse:16.794708+0.468178 
## [6]  train-rmse:11.863720+0.009987   test-rmse:11.868057+0.432780 
## [7]  train-rmse:8.429082+0.006884    test-rmse:8.418294+0.431156 
## [8]  train-rmse:6.049948+0.008000    test-rmse:6.038449+0.413768 
## [9]  train-rmse:4.423552+0.012513    test-rmse:4.412873+0.390475 
## [10] train-rmse:3.330880+0.017256    test-rmse:3.331439+0.354611 
## [11] train-rmse:2.619719+0.021351    test-rmse:2.631955+0.298284 
## [12] train-rmse:2.174924+0.024751    test-rmse:2.208733+0.237482 
## [13] train-rmse:1.906422+0.027786    test-rmse:1.964113+0.179014 
## [14] train-rmse:1.748558+0.029713    test-rmse:1.822861+0.139936 
## [15] train-rmse:1.653591+0.031006    test-rmse:1.720749+0.122533 
## [16] train-rmse:1.596365+0.030950    test-rmse:1.676778+0.112201 
## [17] train-rmse:1.557923+0.029832    test-rmse:1.634716+0.102656 
## [18] train-rmse:1.529840+0.028431    test-rmse:1.620144+0.107093 
## [19] train-rmse:1.507683+0.027925    test-rmse:1.615213+0.119034 
## [20] train-rmse:1.488697+0.027203    test-rmse:1.603535+0.116307 
## [21] train-rmse:1.470624+0.027280    test-rmse:1.588309+0.108348 
## [22] train-rmse:1.454887+0.026406    test-rmse:1.580705+0.113256 
## [23] train-rmse:1.440829+0.026137    test-rmse:1.570358+0.110121 
## [24] train-rmse:1.427884+0.025697    test-rmse:1.559791+0.106714 
## [25] train-rmse:1.415073+0.025104    test-rmse:1.542006+0.102294 
## [26] train-rmse:1.402536+0.024645    test-rmse:1.539613+0.101606 
## [27] train-rmse:1.390895+0.023940    test-rmse:1.541009+0.104144 
## [28] train-rmse:1.379494+0.023438    test-rmse:1.533480+0.102018 
## [29] train-rmse:1.368789+0.023182    test-rmse:1.523345+0.098328 
## [30] train-rmse:1.358188+0.023066    test-rmse:1.516733+0.097012 
## [31] train-rmse:1.347889+0.022919    test-rmse:1.501620+0.099727 
## [32] train-rmse:1.337903+0.022831    test-rmse:1.497793+0.106296 
## [33] train-rmse:1.328392+0.022878    test-rmse:1.484280+0.106470 
## [34] train-rmse:1.319148+0.022740    test-rmse:1.479832+0.097744 
## [35] train-rmse:1.310114+0.022322    test-rmse:1.478094+0.089042 
## [36] train-rmse:1.301034+0.022007    test-rmse:1.476850+0.091653 
## [37] train-rmse:1.292135+0.022052    test-rmse:1.473935+0.095153 
## [38] train-rmse:1.283724+0.021703    test-rmse:1.466334+0.091974 
## [39] train-rmse:1.275421+0.021386    test-rmse:1.450584+0.093996 
## [40] train-rmse:1.267500+0.021098    test-rmse:1.450908+0.095262 
## [41] train-rmse:1.260048+0.021046    test-rmse:1.442651+0.094943 
## [42] train-rmse:1.252716+0.020964    test-rmse:1.436728+0.096391 
## [43] train-rmse:1.245562+0.020976    test-rmse:1.434272+0.095733 
## [44] train-rmse:1.238887+0.020956    test-rmse:1.430362+0.093393 
## [45] train-rmse:1.232265+0.020864    test-rmse:1.425192+0.088950 
## [46] train-rmse:1.225704+0.020759    test-rmse:1.422505+0.087808 
## [47] train-rmse:1.219518+0.020776    test-rmse:1.414806+0.089543 
## [48] train-rmse:1.213461+0.020831    test-rmse:1.412296+0.094830 
## [49] train-rmse:1.207470+0.020845    test-rmse:1.413194+0.088347 
## [50] train-rmse:1.201510+0.020811    test-rmse:1.405852+0.084052 
## [51] train-rmse:1.195906+0.020742    test-rmse:1.407263+0.078006 
## [52] train-rmse:1.190418+0.020718    test-rmse:1.397641+0.077820 
## [53] train-rmse:1.184999+0.020744    test-rmse:1.393665+0.078079 
## [54] train-rmse:1.179541+0.020774    test-rmse:1.382247+0.083931 
## [55] train-rmse:1.174137+0.020837    test-rmse:1.379506+0.085567 
## [56] train-rmse:1.169056+0.020765    test-rmse:1.372307+0.086175 
## [57] train-rmse:1.164059+0.020682    test-rmse:1.370321+0.087737 
## [58] train-rmse:1.159086+0.020617    test-rmse:1.366753+0.082510 
## [59] train-rmse:1.154378+0.020531    test-rmse:1.361413+0.081537 
## [60] train-rmse:1.149716+0.020361    test-rmse:1.356275+0.084092 
## [61] train-rmse:1.145379+0.020267    test-rmse:1.355076+0.085727 
## [62] train-rmse:1.141093+0.020128    test-rmse:1.356524+0.086495 
## [63] train-rmse:1.136960+0.020138    test-rmse:1.350671+0.082844 
## [64] train-rmse:1.132831+0.020029    test-rmse:1.351274+0.084792 
## [65] train-rmse:1.128847+0.019765    test-rmse:1.351000+0.084819 
## [66] train-rmse:1.124983+0.019533    test-rmse:1.347062+0.082272 
## [67] train-rmse:1.121022+0.019378    test-rmse:1.347039+0.084992 
## [68] train-rmse:1.117138+0.019370    test-rmse:1.344735+0.087675 
## [69] train-rmse:1.113473+0.019288    test-rmse:1.341173+0.088027 
## [70] train-rmse:1.109910+0.019287    test-rmse:1.338750+0.085437 
## [71] train-rmse:1.106483+0.019267    test-rmse:1.338580+0.082577 
## [72] train-rmse:1.103142+0.019224    test-rmse:1.333180+0.084575 
## [73] train-rmse:1.099876+0.019203    test-rmse:1.328796+0.084999 
## [74] train-rmse:1.096565+0.019069    test-rmse:1.324921+0.086721 
## [75] train-rmse:1.093313+0.019038    test-rmse:1.322644+0.087468 
## [76] train-rmse:1.089999+0.019027    test-rmse:1.316965+0.085329 
## [77] train-rmse:1.086691+0.018957    test-rmse:1.313481+0.086423 
## [78] train-rmse:1.083557+0.018959    test-rmse:1.310443+0.086208 
## [79] train-rmse:1.080498+0.019031    test-rmse:1.307124+0.086531 
## [80] train-rmse:1.077600+0.019063    test-rmse:1.310319+0.081386 
## [81] train-rmse:1.074731+0.019038    test-rmse:1.308306+0.080552 
## [82] train-rmse:1.071945+0.018996    test-rmse:1.305687+0.081820 
## [83] train-rmse:1.069165+0.018950    test-rmse:1.304395+0.083330 
## [84] train-rmse:1.066479+0.018950    test-rmse:1.300217+0.086030 
## [85] train-rmse:1.063811+0.018974    test-rmse:1.300384+0.086971 
## [86] train-rmse:1.061243+0.018921    test-rmse:1.297661+0.087070 
## [87] train-rmse:1.058742+0.018869    test-rmse:1.295357+0.084537 
## [88] train-rmse:1.056182+0.018722    test-rmse:1.294793+0.083691 
## [89] train-rmse:1.053706+0.018597    test-rmse:1.289916+0.086128 
## [90] train-rmse:1.051248+0.018458    test-rmse:1.290449+0.085638 
## [91] train-rmse:1.048856+0.018309    test-rmse:1.288417+0.086522 
## [92] train-rmse:1.046549+0.018221    test-rmse:1.287908+0.087369 
## [93] train-rmse:1.044281+0.018174    test-rmse:1.286883+0.089608 
## [94] train-rmse:1.042010+0.018150    test-rmse:1.287105+0.092002 
## [95] train-rmse:1.039746+0.018138    test-rmse:1.283666+0.089797 
## [96] train-rmse:1.037575+0.018093    test-rmse:1.285353+0.087920 
## [97] train-rmse:1.035491+0.018006    test-rmse:1.284038+0.089719 
## [98] train-rmse:1.033427+0.017977    test-rmse:1.283837+0.090905 
## [99] train-rmse:1.031407+0.017914    test-rmse:1.280880+0.092046 
## [100]    train-rmse:1.029353+0.017863    test-rmse:1.279109+0.091004 
## [101]    train-rmse:1.027237+0.017865    test-rmse:1.274168+0.090503 
## [102]    train-rmse:1.025260+0.017884    test-rmse:1.272338+0.088609 
## [103]    train-rmse:1.023343+0.017916    test-rmse:1.270613+0.089556 
## [104]    train-rmse:1.021470+0.017906    test-rmse:1.270835+0.089172 
## [105]    train-rmse:1.019614+0.017922    test-rmse:1.269422+0.090339 
## [106]    train-rmse:1.017776+0.017977    test-rmse:1.264882+0.085184 
## [107]    train-rmse:1.015924+0.017931    test-rmse:1.266600+0.081608 
## [108]    train-rmse:1.014151+0.017896    test-rmse:1.265398+0.080834 
## [109]    train-rmse:1.012319+0.017773    test-rmse:1.264024+0.080549 
## [110]    train-rmse:1.010533+0.017732    test-rmse:1.264514+0.079161 
## [111]    train-rmse:1.008768+0.017656    test-rmse:1.262949+0.080797 
## [112]    train-rmse:1.006994+0.017620    test-rmse:1.259266+0.080213 
## [113]    train-rmse:1.005336+0.017581    test-rmse:1.257776+0.081005 
## [114]    train-rmse:1.003694+0.017571    test-rmse:1.255677+0.084402 
## [115]    train-rmse:1.002087+0.017542    test-rmse:1.254739+0.083404 
## [116]    train-rmse:1.000416+0.017452    test-rmse:1.254937+0.081936 
## [117]    train-rmse:0.998771+0.017432    test-rmse:1.252912+0.081232 
## [118]    train-rmse:0.997188+0.017395    test-rmse:1.248542+0.083212 
## [119]    train-rmse:0.995579+0.017380    test-rmse:1.249336+0.082592 
## [120]    train-rmse:0.994026+0.017321    test-rmse:1.248509+0.081208 
## [121]    train-rmse:0.992499+0.017259    test-rmse:1.249642+0.083705 
## [122]    train-rmse:0.990987+0.017247    test-rmse:1.247994+0.083005 
## [123]    train-rmse:0.989514+0.017231    test-rmse:1.247811+0.082440 
## [124]    train-rmse:0.988061+0.017225    test-rmse:1.246392+0.083249 
## [125]    train-rmse:0.986634+0.017251    test-rmse:1.248510+0.083536 
## [126]    train-rmse:0.985142+0.017285    test-rmse:1.247455+0.083716 
## [127]    train-rmse:0.983750+0.017310    test-rmse:1.246616+0.083893 
## [128]    train-rmse:0.982379+0.017343    test-rmse:1.245770+0.085955 
## [129]    train-rmse:0.980998+0.017354    test-rmse:1.244030+0.086359 
## [130]    train-rmse:0.979650+0.017388    test-rmse:1.242848+0.087730 
## [131]    train-rmse:0.978288+0.017444    test-rmse:1.244343+0.087058 
## [132]    train-rmse:0.976892+0.017484    test-rmse:1.244266+0.085643 
## [133]    train-rmse:0.975544+0.017478    test-rmse:1.243038+0.085093 
## [134]    train-rmse:0.974232+0.017474    test-rmse:1.242582+0.084859 
## [135]    train-rmse:0.972947+0.017472    test-rmse:1.240634+0.085881 
## [136]    train-rmse:0.971663+0.017506    test-rmse:1.242390+0.085125 
## [137]    train-rmse:0.970348+0.017457    test-rmse:1.241633+0.085554 
## [138]    train-rmse:0.969051+0.017419    test-rmse:1.242139+0.085846 
## [139]    train-rmse:0.967785+0.017405    test-rmse:1.240339+0.085341 
## [140]    train-rmse:0.966555+0.017424    test-rmse:1.239503+0.088546 
## [141]    train-rmse:0.965354+0.017437    test-rmse:1.238574+0.089412 
## [142]    train-rmse:0.964171+0.017459    test-rmse:1.242387+0.089095 
## [143]    train-rmse:0.962988+0.017483    test-rmse:1.239633+0.090382 
## [144]    train-rmse:0.961816+0.017518    test-rmse:1.240532+0.083951 
## [145]    train-rmse:0.960621+0.017524    test-rmse:1.240176+0.083879 
## [146]    train-rmse:0.959486+0.017520    test-rmse:1.240321+0.083476 
## [147]    train-rmse:0.958389+0.017528    test-rmse:1.238893+0.082616 
## Stopping. Best iteration:
## [141]    train-rmse:0.965354+0.017437    test-rmse:1.238574+0.089412
## 
## 71 
## [1]  train-rmse:69.050973+0.069138   test-rmse:69.051505+0.424216 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:48.430438+0.048124   test-rmse:48.430518+0.444003 
## [3]  train-rmse:33.983752+0.033210   test-rmse:33.983192+0.457073 
## [4]  train-rmse:23.869216+0.022582   test-rmse:23.867778+0.464907 
## [5]  train-rmse:16.797328+0.015059   test-rmse:16.794708+0.468178 
## [6]  train-rmse:11.863720+0.009987   test-rmse:11.868057+0.432780 
## [7]  train-rmse:8.424224+0.006312    test-rmse:8.411042+0.423267 
## [8]  train-rmse:6.034749+0.006969    test-rmse:6.008414+0.398843 
## [9]  train-rmse:4.384879+0.011842    test-rmse:4.373720+0.361940 
## [10] train-rmse:3.260753+0.015530    test-rmse:3.264289+0.317248 
## [11] train-rmse:2.514409+0.022465    test-rmse:2.545968+0.270126 
## [12] train-rmse:2.033088+0.024959    test-rmse:2.087428+0.219100 
## [13] train-rmse:1.731607+0.030585    test-rmse:1.814687+0.178127 
## [14] train-rmse:1.548073+0.032210    test-rmse:1.672455+0.137808 
## [15] train-rmse:1.429408+0.025615    test-rmse:1.581070+0.118265 
## [16] train-rmse:1.358756+0.023436    test-rmse:1.521415+0.103970 
## [17] train-rmse:1.306793+0.024727    test-rmse:1.481702+0.089397 
## [18] train-rmse:1.272175+0.024300    test-rmse:1.463159+0.086131 
## [19] train-rmse:1.245761+0.025104    test-rmse:1.442652+0.095407 
## [20] train-rmse:1.221929+0.024948    test-rmse:1.431227+0.091318 
## [21] train-rmse:1.198351+0.026134    test-rmse:1.421183+0.086780 
## [22] train-rmse:1.181019+0.025472    test-rmse:1.404348+0.086129 
## [23] train-rmse:1.161925+0.026366    test-rmse:1.394392+0.083270 
## [24] train-rmse:1.143733+0.026167    test-rmse:1.387934+0.081963 
## [25] train-rmse:1.128700+0.024963    test-rmse:1.377959+0.083827 
## [26] train-rmse:1.113603+0.025357    test-rmse:1.351075+0.097028 
## [27] train-rmse:1.097676+0.026502    test-rmse:1.339931+0.098740 
## [28] train-rmse:1.085779+0.026655    test-rmse:1.332696+0.099339 
## [29] train-rmse:1.071238+0.027647    test-rmse:1.326344+0.091828 
## [30] train-rmse:1.055310+0.029395    test-rmse:1.319155+0.089887 
## [31] train-rmse:1.043131+0.029827    test-rmse:1.313891+0.085824 
## [32] train-rmse:1.030178+0.032037    test-rmse:1.305996+0.085807 
## [33] train-rmse:1.017864+0.033243    test-rmse:1.300477+0.089099 
## [34] train-rmse:1.006227+0.029734    test-rmse:1.291600+0.090673 
## [35] train-rmse:0.994233+0.031720    test-rmse:1.277376+0.095142 
## [36] train-rmse:0.984141+0.031331    test-rmse:1.270762+0.095926 
## [37] train-rmse:0.974102+0.032002    test-rmse:1.263872+0.098941 
## [38] train-rmse:0.957815+0.029909    test-rmse:1.259518+0.099829 
## [39] train-rmse:0.946440+0.032436    test-rmse:1.252270+0.103337 
## [40] train-rmse:0.936733+0.029399    test-rmse:1.246200+0.103894 
## [41] train-rmse:0.924863+0.029387    test-rmse:1.237242+0.103652 
## [42] train-rmse:0.917417+0.029488    test-rmse:1.231737+0.104812 
## [43] train-rmse:0.908357+0.030655    test-rmse:1.223979+0.102820 
## [44] train-rmse:0.900807+0.031535    test-rmse:1.215874+0.098352 
## [45] train-rmse:0.891187+0.027279    test-rmse:1.212022+0.095971 
## [46] train-rmse:0.884874+0.028123    test-rmse:1.206836+0.098777 
## [47] train-rmse:0.875276+0.025072    test-rmse:1.202038+0.103422 
## [48] train-rmse:0.869576+0.024660    test-rmse:1.197613+0.105463 
## [49] train-rmse:0.861938+0.021566    test-rmse:1.193858+0.108627 
## [50] train-rmse:0.851454+0.019533    test-rmse:1.191041+0.109099 
## [51] train-rmse:0.840607+0.023852    test-rmse:1.184871+0.100714 
## [52] train-rmse:0.835540+0.023883    test-rmse:1.184285+0.097097 
## [53] train-rmse:0.826659+0.020407    test-rmse:1.179988+0.100987 
## [54] train-rmse:0.820504+0.018846    test-rmse:1.175365+0.104758 
## [55] train-rmse:0.814722+0.020040    test-rmse:1.169682+0.104369 
## [56] train-rmse:0.807780+0.019746    test-rmse:1.167427+0.104587 
## [57] train-rmse:0.802953+0.019801    test-rmse:1.165221+0.102676 
## [58] train-rmse:0.796620+0.019488    test-rmse:1.159619+0.099923 
## [59] train-rmse:0.789982+0.019364    test-rmse:1.154235+0.101054 
## [60] train-rmse:0.784163+0.020009    test-rmse:1.149334+0.101228 
## [61] train-rmse:0.778047+0.018064    test-rmse:1.149313+0.101059 
## [62] train-rmse:0.771371+0.017942    test-rmse:1.144605+0.103106 
## [63] train-rmse:0.764695+0.021464    test-rmse:1.139057+0.095373 
## [64] train-rmse:0.758878+0.020240    test-rmse:1.136565+0.097465 
## [65] train-rmse:0.754025+0.020388    test-rmse:1.132279+0.093243 
## [66] train-rmse:0.747706+0.019569    test-rmse:1.135909+0.099432 
## [67] train-rmse:0.744102+0.019770    test-rmse:1.137016+0.096822 
## [68] train-rmse:0.739315+0.020659    test-rmse:1.137923+0.095733 
## [69] train-rmse:0.735073+0.020756    test-rmse:1.133177+0.096542 
## [70] train-rmse:0.728995+0.018605    test-rmse:1.129581+0.101041 
## [71] train-rmse:0.725380+0.017688    test-rmse:1.123591+0.101965 
## [72] train-rmse:0.721400+0.018340    test-rmse:1.118195+0.105604 
## [73] train-rmse:0.715457+0.019308    test-rmse:1.111242+0.107351 
## [74] train-rmse:0.711126+0.019259    test-rmse:1.110792+0.106745 
## [75] train-rmse:0.706422+0.017289    test-rmse:1.110942+0.104222 
## [76] train-rmse:0.700687+0.017166    test-rmse:1.116458+0.103655 
## [77] train-rmse:0.697770+0.016865    test-rmse:1.115059+0.103756 
## [78] train-rmse:0.692518+0.014800    test-rmse:1.112247+0.106641 
## [79] train-rmse:0.689249+0.015894    test-rmse:1.112441+0.107278 
## [80] train-rmse:0.681633+0.016365    test-rmse:1.108908+0.106607 
## [81] train-rmse:0.678325+0.016632    test-rmse:1.110455+0.107929 
## [82] train-rmse:0.674760+0.015742    test-rmse:1.105736+0.110812 
## [83] train-rmse:0.671353+0.015292    test-rmse:1.106236+0.110348 
## [84] train-rmse:0.667580+0.014280    test-rmse:1.103087+0.110395 
## [85] train-rmse:0.661858+0.015501    test-rmse:1.102241+0.111436 
## [86] train-rmse:0.657537+0.014235    test-rmse:1.100682+0.112753 
## [87] train-rmse:0.654286+0.014748    test-rmse:1.098381+0.113537 
## [88] train-rmse:0.649062+0.015794    test-rmse:1.098008+0.113264 
## [89] train-rmse:0.644695+0.015882    test-rmse:1.099647+0.109755 
## [90] train-rmse:0.642033+0.016029    test-rmse:1.098168+0.108218 
## [91] train-rmse:0.637764+0.017515    test-rmse:1.097534+0.108735 
## [92] train-rmse:0.633893+0.017891    test-rmse:1.095922+0.107995 
## [93] train-rmse:0.628728+0.017218    test-rmse:1.090360+0.106384 
## [94] train-rmse:0.626369+0.016959    test-rmse:1.089892+0.107868 
## [95] train-rmse:0.623171+0.016634    test-rmse:1.089949+0.105768 
## [96] train-rmse:0.620151+0.015825    test-rmse:1.089135+0.105886 
## [97] train-rmse:0.617611+0.016290    test-rmse:1.088267+0.106856 
## [98] train-rmse:0.613143+0.014795    test-rmse:1.088279+0.105300 
## [99] train-rmse:0.610225+0.014475    test-rmse:1.085939+0.106837 
## [100]    train-rmse:0.604891+0.015014    test-rmse:1.084418+0.105413 
## [101]    train-rmse:0.602237+0.015759    test-rmse:1.082836+0.106504 
## [102]    train-rmse:0.598970+0.017070    test-rmse:1.081574+0.108292 
## [103]    train-rmse:0.596614+0.017694    test-rmse:1.082129+0.109505 
## [104]    train-rmse:0.594168+0.017788    test-rmse:1.082658+0.109618 
## [105]    train-rmse:0.591854+0.017249    test-rmse:1.081102+0.107616 
## [106]    train-rmse:0.588947+0.016954    test-rmse:1.080960+0.105369 
## [107]    train-rmse:0.584825+0.015381    test-rmse:1.080293+0.105257 
## [108]    train-rmse:0.580694+0.015593    test-rmse:1.081350+0.103880 
## [109]    train-rmse:0.577157+0.015893    test-rmse:1.075357+0.101681 
## [110]    train-rmse:0.573710+0.015981    test-rmse:1.075198+0.102137 
## [111]    train-rmse:0.571147+0.016130    test-rmse:1.074440+0.102747 
## [112]    train-rmse:0.568073+0.016830    test-rmse:1.073335+0.102766 
## [113]    train-rmse:0.565430+0.017177    test-rmse:1.073427+0.102969 
## [114]    train-rmse:0.562654+0.017678    test-rmse:1.072059+0.101849 
## [115]    train-rmse:0.560018+0.017051    test-rmse:1.074480+0.102913 
## [116]    train-rmse:0.554271+0.015963    test-rmse:1.071629+0.103118 
## [117]    train-rmse:0.551573+0.016242    test-rmse:1.068762+0.102198 
## [118]    train-rmse:0.548898+0.017090    test-rmse:1.068311+0.100691 
## [119]    train-rmse:0.546463+0.017476    test-rmse:1.066319+0.101257 
## [120]    train-rmse:0.542073+0.016487    test-rmse:1.065233+0.102563 
## [121]    train-rmse:0.539896+0.016288    test-rmse:1.064865+0.103087 
## [122]    train-rmse:0.537112+0.017541    test-rmse:1.062981+0.100177 
## [123]    train-rmse:0.534116+0.018993    test-rmse:1.063664+0.100547 
## [124]    train-rmse:0.531816+0.018702    test-rmse:1.063428+0.100241 
## [125]    train-rmse:0.529723+0.018793    test-rmse:1.060607+0.100104 
## [126]    train-rmse:0.527041+0.019331    test-rmse:1.061102+0.099869 
## [127]    train-rmse:0.524710+0.019128    test-rmse:1.060943+0.099013 
## [128]    train-rmse:0.521671+0.018841    test-rmse:1.059875+0.098242 
## [129]    train-rmse:0.518475+0.018361    test-rmse:1.058886+0.099151 
## [130]    train-rmse:0.514450+0.017653    test-rmse:1.058527+0.100696 
## [131]    train-rmse:0.511069+0.016692    test-rmse:1.054926+0.101157 
## [132]    train-rmse:0.508879+0.016657    test-rmse:1.054008+0.100700 
## [133]    train-rmse:0.506286+0.015501    test-rmse:1.053491+0.100849 
## [134]    train-rmse:0.503766+0.015308    test-rmse:1.054132+0.102600 
## [135]    train-rmse:0.500024+0.013706    test-rmse:1.051186+0.102951 
## [136]    train-rmse:0.497535+0.014094    test-rmse:1.049921+0.103676 
## [137]    train-rmse:0.496079+0.014050    test-rmse:1.048260+0.105017 
## [138]    train-rmse:0.493438+0.012776    test-rmse:1.046810+0.102174 
## [139]    train-rmse:0.491552+0.012686    test-rmse:1.045525+0.101695 
## [140]    train-rmse:0.489636+0.012257    test-rmse:1.044378+0.101701 
## [141]    train-rmse:0.486246+0.011298    test-rmse:1.043234+0.103376 
## [142]    train-rmse:0.483285+0.011455    test-rmse:1.044264+0.106361 
## [143]    train-rmse:0.481575+0.010772    test-rmse:1.044578+0.105958 
## [144]    train-rmse:0.479713+0.010921    test-rmse:1.043382+0.105848 
## [145]    train-rmse:0.478136+0.010541    test-rmse:1.043923+0.105460 
## [146]    train-rmse:0.476017+0.011261    test-rmse:1.044310+0.105124 
## [147]    train-rmse:0.472810+0.011529    test-rmse:1.041865+0.102806 
## [148]    train-rmse:0.468870+0.010080    test-rmse:1.038627+0.104613 
## [149]    train-rmse:0.466287+0.009573    test-rmse:1.037185+0.105244 
## [150]    train-rmse:0.464998+0.009326    test-rmse:1.036472+0.106019 
## [151]    train-rmse:0.463704+0.009318    test-rmse:1.037226+0.106043 
## [152]    train-rmse:0.461992+0.009420    test-rmse:1.036798+0.105577 
## [153]    train-rmse:0.459089+0.008961    test-rmse:1.036039+0.106419 
## [154]    train-rmse:0.457820+0.009084    test-rmse:1.036345+0.106454 
## [155]    train-rmse:0.456152+0.009424    test-rmse:1.033633+0.104698 
## [156]    train-rmse:0.454182+0.009603    test-rmse:1.031927+0.104812 
## [157]    train-rmse:0.451813+0.008967    test-rmse:1.031305+0.104029 
## [158]    train-rmse:0.449996+0.009347    test-rmse:1.031549+0.103635 
## [159]    train-rmse:0.448952+0.009289    test-rmse:1.030959+0.103720 
## [160]    train-rmse:0.446475+0.010960    test-rmse:1.029734+0.103780 
## [161]    train-rmse:0.444404+0.011940    test-rmse:1.029621+0.105843 
## [162]    train-rmse:0.442207+0.011843    test-rmse:1.029725+0.105459 
## [163]    train-rmse:0.441046+0.012237    test-rmse:1.029972+0.105313 
## [164]    train-rmse:0.438672+0.012349    test-rmse:1.031439+0.104609 
## [165]    train-rmse:0.437625+0.012210    test-rmse:1.032366+0.104817 
## [166]    train-rmse:0.435994+0.011538    test-rmse:1.032556+0.105741 
## [167]    train-rmse:0.433805+0.011319    test-rmse:1.030957+0.105628 
## Stopping. Best iteration:
## [161]    train-rmse:0.444404+0.011940    test-rmse:1.029621+0.105843
## 
## 72 
## [1]  train-rmse:69.050973+0.069138   test-rmse:69.051505+0.424216 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:48.430438+0.048124   test-rmse:48.430518+0.444003 
## [3]  train-rmse:33.983752+0.033210   test-rmse:33.983192+0.457073 
## [4]  train-rmse:23.869216+0.022582   test-rmse:23.867778+0.464907 
## [5]  train-rmse:16.797328+0.015059   test-rmse:16.794708+0.468178 
## [6]  train-rmse:11.863720+0.009987   test-rmse:11.868057+0.432780 
## [7]  train-rmse:8.422620+0.007025    test-rmse:8.400161+0.415016 
## [8]  train-rmse:6.027573+0.003569    test-rmse:6.005910+0.381006 
## [9]  train-rmse:4.365732+0.008304    test-rmse:4.357714+0.328537 
## [10] train-rmse:3.219781+0.008011    test-rmse:3.229160+0.288109 
## [11] train-rmse:2.449275+0.007237    test-rmse:2.475848+0.262684 
## [12] train-rmse:1.932423+0.009021    test-rmse:1.994884+0.219672 
## [13] train-rmse:1.600686+0.011609    test-rmse:1.713805+0.174458 
## [14] train-rmse:1.394251+0.013776    test-rmse:1.546422+0.143141 
## [15] train-rmse:1.257608+0.011355    test-rmse:1.441379+0.117864 
## [16] train-rmse:1.174274+0.012408    test-rmse:1.391762+0.100167 
## [17] train-rmse:1.118882+0.015058    test-rmse:1.345837+0.094506 
## [18] train-rmse:1.066737+0.010941    test-rmse:1.318723+0.090887 
## [19] train-rmse:1.032784+0.014794    test-rmse:1.290251+0.086413 
## [20] train-rmse:1.000832+0.017023    test-rmse:1.276924+0.089611 
## [21] train-rmse:0.976652+0.016109    test-rmse:1.259255+0.083849 
## [22] train-rmse:0.949799+0.016821    test-rmse:1.241272+0.089016 
## [23] train-rmse:0.930175+0.017396    test-rmse:1.228320+0.093754 
## [24] train-rmse:0.903795+0.013443    test-rmse:1.206525+0.093376 
## [25] train-rmse:0.887491+0.012644    test-rmse:1.196739+0.096168 
## [26] train-rmse:0.871725+0.010560    test-rmse:1.187049+0.100018 
## [27] train-rmse:0.857637+0.010806    test-rmse:1.179045+0.098188 
## [28] train-rmse:0.843169+0.010168    test-rmse:1.162898+0.097811 
## [29] train-rmse:0.825873+0.011206    test-rmse:1.155826+0.100102 
## [30] train-rmse:0.809039+0.010161    test-rmse:1.149839+0.101119 
## [31] train-rmse:0.797534+0.009615    test-rmse:1.148354+0.101516 
## [32] train-rmse:0.783912+0.010688    test-rmse:1.139760+0.103623 
## [33] train-rmse:0.766916+0.007733    test-rmse:1.134763+0.108496 
## [34] train-rmse:0.756116+0.006527    test-rmse:1.128934+0.108805 
## [35] train-rmse:0.739207+0.009933    test-rmse:1.117201+0.111320 
## [36] train-rmse:0.727698+0.012001    test-rmse:1.113535+0.115297 
## [37] train-rmse:0.714979+0.009736    test-rmse:1.111120+0.111996 
## [38] train-rmse:0.701995+0.009691    test-rmse:1.114871+0.112239 
## [39] train-rmse:0.690978+0.012740    test-rmse:1.104460+0.102188 
## [40] train-rmse:0.679917+0.011807    test-rmse:1.099002+0.102892 
## [41] train-rmse:0.669941+0.009380    test-rmse:1.094832+0.109236 
## [42] train-rmse:0.661354+0.009990    test-rmse:1.092471+0.115615 
## [43] train-rmse:0.651354+0.008193    test-rmse:1.091049+0.115813 
## [44] train-rmse:0.643127+0.007271    test-rmse:1.088320+0.117136 
## [45] train-rmse:0.633657+0.010767    test-rmse:1.083812+0.120233 
## [46] train-rmse:0.621223+0.008183    test-rmse:1.075041+0.121616 
## [47] train-rmse:0.613601+0.007597    test-rmse:1.071266+0.121239 
## [48] train-rmse:0.605797+0.011379    test-rmse:1.064078+0.116220 
## [49] train-rmse:0.597284+0.012378    test-rmse:1.063040+0.113915 
## [50] train-rmse:0.588816+0.011860    test-rmse:1.059592+0.114248 
## [51] train-rmse:0.582416+0.010004    test-rmse:1.055725+0.113706 
## [52] train-rmse:0.575619+0.011460    test-rmse:1.054359+0.115534 
## [53] train-rmse:0.566209+0.010964    test-rmse:1.049208+0.113435 
## [54] train-rmse:0.559229+0.011502    test-rmse:1.048946+0.113269 
## [55] train-rmse:0.553487+0.012207    test-rmse:1.045969+0.113951 
## [56] train-rmse:0.548035+0.012561    test-rmse:1.045428+0.116527 
## [57] train-rmse:0.543638+0.012663    test-rmse:1.045328+0.116821 
## [58] train-rmse:0.532027+0.015011    test-rmse:1.046857+0.116347 
## [59] train-rmse:0.522622+0.014261    test-rmse:1.042624+0.120776 
## [60] train-rmse:0.512158+0.015734    test-rmse:1.045864+0.121844 
## [61] train-rmse:0.506744+0.015382    test-rmse:1.044491+0.121835 
## [62] train-rmse:0.499784+0.013234    test-rmse:1.039339+0.121783 
## [63] train-rmse:0.495199+0.012653    test-rmse:1.037266+0.118493 
## [64] train-rmse:0.489537+0.008697    test-rmse:1.035460+0.121227 
## [65] train-rmse:0.486383+0.009058    test-rmse:1.036356+0.120371 
## [66] train-rmse:0.481091+0.007777    test-rmse:1.036092+0.120790 
## [67] train-rmse:0.474535+0.008747    test-rmse:1.033896+0.118772 
## [68] train-rmse:0.466826+0.007495    test-rmse:1.029402+0.123189 
## [69] train-rmse:0.461082+0.006949    test-rmse:1.028842+0.121131 
## [70] train-rmse:0.456333+0.006287    test-rmse:1.027154+0.122731 
## [71] train-rmse:0.450822+0.005639    test-rmse:1.026109+0.124728 
## [72] train-rmse:0.446134+0.005792    test-rmse:1.023662+0.123959 
## [73] train-rmse:0.440549+0.007140    test-rmse:1.027842+0.124492 
## [74] train-rmse:0.434875+0.007809    test-rmse:1.025662+0.125210 
## [75] train-rmse:0.428952+0.009132    test-rmse:1.020354+0.121619 
## [76] train-rmse:0.424309+0.008559    test-rmse:1.019195+0.121201 
## [77] train-rmse:0.420320+0.008108    test-rmse:1.017772+0.119275 
## [78] train-rmse:0.415457+0.008351    test-rmse:1.009866+0.118131 
## [79] train-rmse:0.410880+0.008136    test-rmse:1.008654+0.118758 
## [80] train-rmse:0.406574+0.009947    test-rmse:1.008967+0.116992 
## [81] train-rmse:0.402085+0.010608    test-rmse:1.007647+0.116568 
## [82] train-rmse:0.396695+0.013616    test-rmse:1.006788+0.116183 
## [83] train-rmse:0.392734+0.013965    test-rmse:1.004850+0.116637 
## [84] train-rmse:0.388206+0.015581    test-rmse:1.003986+0.118488 
## [85] train-rmse:0.384724+0.016618    test-rmse:1.004060+0.119689 
## [86] train-rmse:0.380949+0.016061    test-rmse:1.000693+0.120798 
## [87] train-rmse:0.377210+0.017315    test-rmse:0.996755+0.121427 
## [88] train-rmse:0.371683+0.014588    test-rmse:0.995418+0.121645 
## [89] train-rmse:0.369132+0.013807    test-rmse:0.994654+0.121847 
## [90] train-rmse:0.365291+0.014146    test-rmse:0.994937+0.120357 
## [91] train-rmse:0.361493+0.014205    test-rmse:0.993828+0.120712 
## [92] train-rmse:0.357830+0.015045    test-rmse:0.994142+0.121133 
## [93] train-rmse:0.353663+0.013548    test-rmse:0.992878+0.122383 
## [94] train-rmse:0.349498+0.013288    test-rmse:0.993138+0.121416 
## [95] train-rmse:0.345443+0.012629    test-rmse:0.991046+0.122297 
## [96] train-rmse:0.342422+0.012670    test-rmse:0.989055+0.123966 
## [97] train-rmse:0.339821+0.012594    test-rmse:0.988962+0.125175 
## [98] train-rmse:0.333735+0.013322    test-rmse:0.989510+0.126824 
## [99] train-rmse:0.331532+0.013323    test-rmse:0.987667+0.127416 
## [100]    train-rmse:0.328108+0.012855    test-rmse:0.988073+0.126659 
## [101]    train-rmse:0.323450+0.013827    test-rmse:0.987386+0.125193 
## [102]    train-rmse:0.319321+0.012884    test-rmse:0.987308+0.124882 
## [103]    train-rmse:0.315388+0.011583    test-rmse:0.986382+0.126490 
## [104]    train-rmse:0.311869+0.013062    test-rmse:0.987087+0.125516 
## [105]    train-rmse:0.309621+0.012966    test-rmse:0.986184+0.125843 
## [106]    train-rmse:0.305559+0.014535    test-rmse:0.985983+0.125963 
## [107]    train-rmse:0.302722+0.014547    test-rmse:0.986193+0.125069 
## [108]    train-rmse:0.298261+0.013754    test-rmse:0.984082+0.124366 
## [109]    train-rmse:0.292413+0.015278    test-rmse:0.982539+0.124225 
## [110]    train-rmse:0.288870+0.016863    test-rmse:0.982756+0.122657 
## [111]    train-rmse:0.285723+0.016707    test-rmse:0.982702+0.121801 
## [112]    train-rmse:0.283658+0.016279    test-rmse:0.981990+0.123143 
## [113]    train-rmse:0.280341+0.016517    test-rmse:0.980886+0.123605 
## [114]    train-rmse:0.277103+0.017288    test-rmse:0.981469+0.123457 
## [115]    train-rmse:0.274059+0.017017    test-rmse:0.979835+0.123428 
## [116]    train-rmse:0.271448+0.017476    test-rmse:0.980044+0.125431 
## [117]    train-rmse:0.269270+0.018106    test-rmse:0.981706+0.124352 
## [118]    train-rmse:0.266671+0.018502    test-rmse:0.981564+0.124253 
## [119]    train-rmse:0.264142+0.017923    test-rmse:0.982192+0.125312 
## [120]    train-rmse:0.263003+0.017669    test-rmse:0.981853+0.125495 
## [121]    train-rmse:0.260008+0.017410    test-rmse:0.979941+0.126067 
## Stopping. Best iteration:
## [115]    train-rmse:0.274059+0.017017    test-rmse:0.979835+0.123428
## 
## 73 
## [1]  train-rmse:69.050973+0.069138   test-rmse:69.051505+0.424216 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:48.430438+0.048124   test-rmse:48.430518+0.444003 
## [3]  train-rmse:33.983752+0.033210   test-rmse:33.983192+0.457073 
## [4]  train-rmse:23.869216+0.022582   test-rmse:23.867778+0.464907 
## [5]  train-rmse:16.797328+0.015059   test-rmse:16.794708+0.468178 
## [6]  train-rmse:11.863720+0.009987   test-rmse:11.868057+0.432780 
## [7]  train-rmse:8.422296+0.007322    test-rmse:8.396843+0.412998 
## [8]  train-rmse:6.023561+0.003611    test-rmse:6.008353+0.370402 
## [9]  train-rmse:4.353975+0.007001    test-rmse:4.345759+0.324619 
## [10] train-rmse:3.198622+0.009282    test-rmse:3.204965+0.274877 
## [11] train-rmse:2.407831+0.014468    test-rmse:2.434740+0.251771 
## [12] train-rmse:1.867000+0.020369    test-rmse:1.933744+0.207380 
## [13] train-rmse:1.518229+0.021792    test-rmse:1.640208+0.178152 
## [14] train-rmse:1.287069+0.022655    test-rmse:1.465636+0.148404 
## [15] train-rmse:1.134755+0.023096    test-rmse:1.354259+0.116162 
## [16] train-rmse:1.035463+0.027581    test-rmse:1.296111+0.103375 
## [17] train-rmse:0.967455+0.030304    test-rmse:1.257116+0.097028 
## [18] train-rmse:0.915606+0.029587    test-rmse:1.231326+0.090709 
## [19] train-rmse:0.870559+0.028324    test-rmse:1.212730+0.083503 
## [20] train-rmse:0.836999+0.027404    test-rmse:1.191819+0.081235 
## [21] train-rmse:0.804985+0.032563    test-rmse:1.180850+0.079826 
## [22] train-rmse:0.783142+0.032086    test-rmse:1.167352+0.078251 
## [23] train-rmse:0.757191+0.030764    test-rmse:1.156199+0.079030 
## [24] train-rmse:0.734877+0.032405    test-rmse:1.142926+0.075687 
## [25] train-rmse:0.716653+0.033029    test-rmse:1.136343+0.073938 
## [26] train-rmse:0.697729+0.028084    test-rmse:1.124016+0.077628 
## [27] train-rmse:0.673485+0.031614    test-rmse:1.113135+0.083275 
## [28] train-rmse:0.654788+0.030778    test-rmse:1.107826+0.083866 
## [29] train-rmse:0.642162+0.029425    test-rmse:1.096469+0.079677 
## [30] train-rmse:0.621788+0.025598    test-rmse:1.084129+0.080684 
## [31] train-rmse:0.606321+0.023256    test-rmse:1.073520+0.080705 
## [32] train-rmse:0.591664+0.027171    test-rmse:1.064766+0.077075 
## [33] train-rmse:0.578267+0.023917    test-rmse:1.057456+0.076382 
## [34] train-rmse:0.561966+0.025658    test-rmse:1.056224+0.081263 
## [35] train-rmse:0.551848+0.025091    test-rmse:1.053457+0.083965 
## [36] train-rmse:0.542202+0.025018    test-rmse:1.047449+0.085009 
## [37] train-rmse:0.529639+0.026471    test-rmse:1.046464+0.086358 
## [38] train-rmse:0.517078+0.024298    test-rmse:1.042962+0.081503 
## [39] train-rmse:0.506886+0.028088    test-rmse:1.043490+0.078789 
## [40] train-rmse:0.496461+0.023979    test-rmse:1.040012+0.080891 
## [41] train-rmse:0.489012+0.023445    test-rmse:1.037533+0.083023 
## [42] train-rmse:0.482607+0.022722    test-rmse:1.037219+0.081900 
## [43] train-rmse:0.473715+0.024284    test-rmse:1.031415+0.081604 
## [44] train-rmse:0.460946+0.021365    test-rmse:1.025033+0.080319 
## [45] train-rmse:0.450796+0.023067    test-rmse:1.022648+0.079973 
## [46] train-rmse:0.441986+0.019263    test-rmse:1.017813+0.078799 
## [47] train-rmse:0.435737+0.019920    test-rmse:1.015344+0.078537 
## [48] train-rmse:0.428139+0.019474    test-rmse:1.019362+0.080974 
## [49] train-rmse:0.421872+0.020940    test-rmse:1.018116+0.081165 
## [50] train-rmse:0.416361+0.020251    test-rmse:1.018874+0.080798 
## [51] train-rmse:0.404824+0.019979    test-rmse:1.015401+0.083387 
## [52] train-rmse:0.395116+0.019393    test-rmse:1.014203+0.083704 
## [53] train-rmse:0.389106+0.018735    test-rmse:1.012656+0.083840 
## [54] train-rmse:0.381517+0.018718    test-rmse:1.012718+0.084294 
## [55] train-rmse:0.373398+0.019750    test-rmse:1.015604+0.089825 
## [56] train-rmse:0.364895+0.017746    test-rmse:1.013905+0.088075 
## [57] train-rmse:0.358071+0.015375    test-rmse:1.010172+0.087734 
## [58] train-rmse:0.352445+0.016017    test-rmse:1.008130+0.087764 
## [59] train-rmse:0.346111+0.014392    test-rmse:1.005191+0.085624 
## [60] train-rmse:0.340810+0.012500    test-rmse:1.005002+0.086380 
## [61] train-rmse:0.333881+0.010271    test-rmse:1.003767+0.087613 
## [62] train-rmse:0.330037+0.009870    test-rmse:1.001992+0.087694 
## [63] train-rmse:0.320915+0.014385    test-rmse:1.000283+0.089536 
## [64] train-rmse:0.314252+0.014294    test-rmse:1.001457+0.091182 
## [65] train-rmse:0.310383+0.014022    test-rmse:1.001270+0.090147 
## [66] train-rmse:0.306451+0.013693    test-rmse:0.999817+0.089013 
## [67] train-rmse:0.301335+0.014760    test-rmse:1.001058+0.092219 
## [68] train-rmse:0.293539+0.014296    test-rmse:0.995598+0.088325 
## [69] train-rmse:0.290629+0.014396    test-rmse:0.996344+0.088021 
## [70] train-rmse:0.286104+0.014656    test-rmse:0.994247+0.088551 
## [71] train-rmse:0.280046+0.014996    test-rmse:0.994176+0.085062 
## [72] train-rmse:0.273416+0.014853    test-rmse:0.989925+0.084623 
## [73] train-rmse:0.268938+0.012240    test-rmse:0.990228+0.085039 
## [74] train-rmse:0.262503+0.013410    test-rmse:0.990139+0.084878 
## [75] train-rmse:0.257216+0.013377    test-rmse:0.989031+0.083918 
## [76] train-rmse:0.253823+0.015008    test-rmse:0.987452+0.084298 
## [77] train-rmse:0.251020+0.014700    test-rmse:0.986785+0.085242 
## [78] train-rmse:0.245883+0.014460    test-rmse:0.986772+0.083938 
## [79] train-rmse:0.241839+0.016163    test-rmse:0.987289+0.083284 
## [80] train-rmse:0.236603+0.015701    test-rmse:0.985246+0.081199 
## [81] train-rmse:0.232151+0.014631    test-rmse:0.984915+0.080452 
## [82] train-rmse:0.226801+0.014337    test-rmse:0.984643+0.080401 
## [83] train-rmse:0.223544+0.013917    test-rmse:0.985057+0.079984 
## [84] train-rmse:0.219341+0.014656    test-rmse:0.984484+0.079914 
## [85] train-rmse:0.214702+0.015740    test-rmse:0.985799+0.080263 
## [86] train-rmse:0.211338+0.015737    test-rmse:0.983495+0.079813 
## [87] train-rmse:0.206570+0.014578    test-rmse:0.983598+0.080116 
## [88] train-rmse:0.202950+0.015219    test-rmse:0.982931+0.080708 
## [89] train-rmse:0.199547+0.013571    test-rmse:0.982366+0.081907 
## [90] train-rmse:0.196507+0.013480    test-rmse:0.982791+0.082336 
## [91] train-rmse:0.192424+0.014219    test-rmse:0.982643+0.080998 
## [92] train-rmse:0.187929+0.014103    test-rmse:0.983236+0.081949 
## [93] train-rmse:0.185017+0.013728    test-rmse:0.982824+0.082530 
## [94] train-rmse:0.181997+0.014401    test-rmse:0.982609+0.083134 
## [95] train-rmse:0.178102+0.014814    test-rmse:0.981088+0.083218 
## [96] train-rmse:0.175593+0.014497    test-rmse:0.980334+0.083288 
## [97] train-rmse:0.172292+0.014177    test-rmse:0.981600+0.084609 
## [98] train-rmse:0.169294+0.014390    test-rmse:0.981417+0.085026 
## [99] train-rmse:0.165918+0.014753    test-rmse:0.981142+0.085699 
## [100]    train-rmse:0.163628+0.014422    test-rmse:0.980781+0.085014 
## [101]    train-rmse:0.161134+0.014928    test-rmse:0.979831+0.084758 
## [102]    train-rmse:0.158717+0.015299    test-rmse:0.980235+0.086231 
## [103]    train-rmse:0.156468+0.016027    test-rmse:0.980398+0.086092 
## [104]    train-rmse:0.154175+0.016801    test-rmse:0.979522+0.085788 
## [105]    train-rmse:0.151043+0.015409    test-rmse:0.978968+0.086118 
## [106]    train-rmse:0.148378+0.015017    test-rmse:0.980080+0.086366 
## [107]    train-rmse:0.146180+0.015040    test-rmse:0.979677+0.086336 
## [108]    train-rmse:0.143999+0.014831    test-rmse:0.979326+0.085662 
## [109]    train-rmse:0.141282+0.015043    test-rmse:0.980104+0.086257 
## [110]    train-rmse:0.138415+0.015078    test-rmse:0.979901+0.087004 
## [111]    train-rmse:0.137131+0.014910    test-rmse:0.980074+0.086933 
## Stopping. Best iteration:
## [105]    train-rmse:0.151043+0.015409    test-rmse:0.978968+0.086118
## 
## 74 
## [1]  train-rmse:69.050973+0.069138   test-rmse:69.051505+0.424216 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:48.430438+0.048124   test-rmse:48.430518+0.444003 
## [3]  train-rmse:33.983752+0.033210   test-rmse:33.983192+0.457073 
## [4]  train-rmse:23.869216+0.022582   test-rmse:23.867778+0.464907 
## [5]  train-rmse:16.797328+0.015059   test-rmse:16.794708+0.468178 
## [6]  train-rmse:11.863720+0.009987   test-rmse:11.868057+0.432780 
## [7]  train-rmse:8.421914+0.007726    test-rmse:8.402332+0.416388 
## [8]  train-rmse:6.020259+0.006925    test-rmse:6.006833+0.371065 
## [9]  train-rmse:4.348825+0.006797    test-rmse:4.338621+0.318428 
## [10] train-rmse:3.181702+0.008314    test-rmse:3.188730+0.275117 
## [11] train-rmse:2.373846+0.014241    test-rmse:2.409102+0.245717 
## [12] train-rmse:1.827958+0.013096    test-rmse:1.903134+0.218724 
## [13] train-rmse:1.456549+0.027733    test-rmse:1.596820+0.170109 
## [14] train-rmse:1.205318+0.026193    test-rmse:1.419493+0.127960 
## [15] train-rmse:1.046383+0.025483    test-rmse:1.303151+0.112401 
## [16] train-rmse:0.939962+0.027885    test-rmse:1.242938+0.101731 
## [17] train-rmse:0.857582+0.031275    test-rmse:1.191404+0.094580 
## [18] train-rmse:0.799014+0.027784    test-rmse:1.154115+0.086355 
## [19] train-rmse:0.751809+0.028314    test-rmse:1.129885+0.090800 
## [20] train-rmse:0.711343+0.027646    test-rmse:1.107622+0.090636 
## [21] train-rmse:0.680460+0.031644    test-rmse:1.097867+0.090598 
## [22] train-rmse:0.650425+0.029611    test-rmse:1.088321+0.086544 
## [23] train-rmse:0.622748+0.028854    test-rmse:1.083559+0.096287 
## [24] train-rmse:0.602676+0.029053    test-rmse:1.064186+0.097288 
## [25] train-rmse:0.580161+0.035033    test-rmse:1.057280+0.102309 
## [26] train-rmse:0.555178+0.030859    test-rmse:1.042244+0.099783 
## [27] train-rmse:0.534297+0.032847    test-rmse:1.029557+0.101715 
## [28] train-rmse:0.517516+0.032496    test-rmse:1.022878+0.098846 
## [29] train-rmse:0.501231+0.027389    test-rmse:1.020082+0.101771 
## [30] train-rmse:0.488672+0.028380    test-rmse:1.014008+0.103082 
## [31] train-rmse:0.473958+0.026674    test-rmse:1.006803+0.103979 
## [32] train-rmse:0.460839+0.029587    test-rmse:1.002405+0.100801 
## [33] train-rmse:0.444416+0.026214    test-rmse:0.998224+0.103433 
## [34] train-rmse:0.430470+0.021884    test-rmse:0.999466+0.102002 
## [35] train-rmse:0.419339+0.018836    test-rmse:0.995730+0.103909 
## [36] train-rmse:0.403875+0.024224    test-rmse:0.992807+0.104361 
## [37] train-rmse:0.391903+0.023681    test-rmse:0.986549+0.104913 
## [38] train-rmse:0.381122+0.023298    test-rmse:0.986195+0.102194 
## [39] train-rmse:0.369875+0.021129    test-rmse:0.984919+0.100248 
## [40] train-rmse:0.362633+0.019513    test-rmse:0.986075+0.099109 
## [41] train-rmse:0.350688+0.017496    test-rmse:0.983852+0.100944 
## [42] train-rmse:0.341994+0.018321    test-rmse:0.982439+0.101891 
## [43] train-rmse:0.333645+0.017909    test-rmse:0.981283+0.101443 
## [44] train-rmse:0.323973+0.019433    test-rmse:0.976011+0.103241 
## [45] train-rmse:0.312580+0.016078    test-rmse:0.974071+0.100173 
## [46] train-rmse:0.306970+0.015227    test-rmse:0.972218+0.100726 
## [47] train-rmse:0.299537+0.014915    test-rmse:0.971931+0.103412 
## [48] train-rmse:0.290801+0.014415    test-rmse:0.971419+0.104587 
## [49] train-rmse:0.283896+0.014950    test-rmse:0.972036+0.104319 
## [50] train-rmse:0.274970+0.015305    test-rmse:0.971304+0.103405 
## [51] train-rmse:0.269602+0.014228    test-rmse:0.970437+0.102572 
## [52] train-rmse:0.264043+0.016767    test-rmse:0.968669+0.101217 
## [53] train-rmse:0.257836+0.017266    test-rmse:0.969505+0.102281 
## [54] train-rmse:0.250359+0.019323    test-rmse:0.968167+0.102099 
## [55] train-rmse:0.243094+0.019109    test-rmse:0.969513+0.101597 
## [56] train-rmse:0.236385+0.017866    test-rmse:0.968822+0.103925 
## [57] train-rmse:0.229041+0.017571    test-rmse:0.969672+0.101824 
## [58] train-rmse:0.222433+0.017026    test-rmse:0.969193+0.101564 
## [59] train-rmse:0.214963+0.016935    test-rmse:0.967457+0.101677 
## [60] train-rmse:0.208329+0.014097    test-rmse:0.967080+0.101594 
## [61] train-rmse:0.203328+0.013801    test-rmse:0.966916+0.102026 
## [62] train-rmse:0.199697+0.013996    test-rmse:0.966968+0.101672 
## [63] train-rmse:0.194814+0.012854    test-rmse:0.967858+0.101280 
## [64] train-rmse:0.188431+0.013816    test-rmse:0.966986+0.100607 
## [65] train-rmse:0.183082+0.014003    test-rmse:0.967263+0.101584 
## [66] train-rmse:0.178884+0.012665    test-rmse:0.966007+0.102147 
## [67] train-rmse:0.175078+0.013251    test-rmse:0.964584+0.101817 
## [68] train-rmse:0.170638+0.013483    test-rmse:0.963810+0.101154 
## [69] train-rmse:0.165995+0.013855    test-rmse:0.963112+0.101792 
## [70] train-rmse:0.162784+0.013252    test-rmse:0.963815+0.101663 
## [71] train-rmse:0.159114+0.013358    test-rmse:0.963525+0.103320 
## [72] train-rmse:0.155234+0.013024    test-rmse:0.962840+0.103550 
## [73] train-rmse:0.151316+0.012880    test-rmse:0.962394+0.103747 
## [74] train-rmse:0.148411+0.013294    test-rmse:0.962768+0.104977 
## [75] train-rmse:0.145553+0.012953    test-rmse:0.963218+0.104431 
## [76] train-rmse:0.141411+0.012366    test-rmse:0.963121+0.102177 
## [77] train-rmse:0.138407+0.012779    test-rmse:0.962038+0.101456 
## [78] train-rmse:0.136161+0.012024    test-rmse:0.960412+0.101913 
## [79] train-rmse:0.132560+0.012150    test-rmse:0.959760+0.103033 
## [80] train-rmse:0.129832+0.011695    test-rmse:0.960531+0.103442 
## [81] train-rmse:0.126022+0.011507    test-rmse:0.960381+0.103024 
## [82] train-rmse:0.123358+0.011384    test-rmse:0.960859+0.102317 
## [83] train-rmse:0.121024+0.011351    test-rmse:0.960409+0.102400 
## [84] train-rmse:0.119347+0.011234    test-rmse:0.960561+0.102409 
## [85] train-rmse:0.115960+0.010619    test-rmse:0.961963+0.102799 
## Stopping. Best iteration:
## [79] train-rmse:0.132560+0.012150    test-rmse:0.959760+0.103033
## 
## 75 
## [1]  train-rmse:69.050973+0.069138   test-rmse:69.051505+0.424216 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:48.430438+0.048124   test-rmse:48.430518+0.444003 
## [3]  train-rmse:33.983752+0.033210   test-rmse:33.983192+0.457073 
## [4]  train-rmse:23.869216+0.022582   test-rmse:23.867778+0.464907 
## [5]  train-rmse:16.797328+0.015059   test-rmse:16.794708+0.468178 
## [6]  train-rmse:11.863720+0.009987   test-rmse:11.868057+0.432780 
## [7]  train-rmse:8.421914+0.007726    test-rmse:8.402332+0.416388 
## [8]  train-rmse:6.020005+0.006779    test-rmse:6.011603+0.366924 
## [9]  train-rmse:4.344490+0.005714    test-rmse:4.343985+0.310398 
## [10] train-rmse:3.176032+0.008185    test-rmse:3.184665+0.268561 
## [11] train-rmse:2.360933+0.011537    test-rmse:2.406966+0.240298 
## [12] train-rmse:1.790304+0.009778    test-rmse:1.887393+0.183872 
## [13] train-rmse:1.406366+0.012901    test-rmse:1.559486+0.159783 
## [14] train-rmse:1.145912+0.008057    test-rmse:1.353288+0.151562 
## [15] train-rmse:0.963783+0.023931    test-rmse:1.242465+0.138017 
## [16] train-rmse:0.845963+0.020179    test-rmse:1.175803+0.119155 
## [17] train-rmse:0.756645+0.027632    test-rmse:1.134532+0.115003 
## [18] train-rmse:0.685541+0.025135    test-rmse:1.106541+0.103737 
## [19] train-rmse:0.640833+0.032456    test-rmse:1.076971+0.091481 
## [20] train-rmse:0.597820+0.034171    test-rmse:1.065658+0.088250 
## [21] train-rmse:0.560555+0.028165    test-rmse:1.054880+0.081973 
## [22] train-rmse:0.531712+0.027879    test-rmse:1.051068+0.083874 
## [23] train-rmse:0.498524+0.031506    test-rmse:1.041435+0.082613 
## [24] train-rmse:0.475041+0.031874    test-rmse:1.032153+0.087213 
## [25] train-rmse:0.453521+0.026129    test-rmse:1.025471+0.089163 
## [26] train-rmse:0.434093+0.024030    test-rmse:1.021070+0.085682 
## [27] train-rmse:0.417407+0.029181    test-rmse:1.019555+0.091234 
## [28] train-rmse:0.402731+0.027170    test-rmse:1.007078+0.083422 
## [29] train-rmse:0.385556+0.021885    test-rmse:1.001447+0.088357 
## [30] train-rmse:0.372312+0.023193    test-rmse:0.997021+0.091982 
## [31] train-rmse:0.358919+0.021919    test-rmse:0.992728+0.094321 
## [32] train-rmse:0.348062+0.022187    test-rmse:0.993776+0.094992 
## [33] train-rmse:0.335172+0.022610    test-rmse:0.990825+0.093780 
## [34] train-rmse:0.324074+0.022171    test-rmse:0.984498+0.097515 
## [35] train-rmse:0.313646+0.024759    test-rmse:0.984330+0.097653 
## [36] train-rmse:0.300467+0.023184    test-rmse:0.979984+0.098288 
## [37] train-rmse:0.290832+0.023129    test-rmse:0.979628+0.093904 
## [38] train-rmse:0.281245+0.023623    test-rmse:0.978700+0.097299 
## [39] train-rmse:0.269718+0.018140    test-rmse:0.976325+0.098820 
## [40] train-rmse:0.262221+0.016970    test-rmse:0.973487+0.101448 
## [41] train-rmse:0.252926+0.016162    test-rmse:0.972788+0.102762 
## [42] train-rmse:0.243280+0.016068    test-rmse:0.970177+0.102589 
## [43] train-rmse:0.234726+0.014761    test-rmse:0.969789+0.100607 
## [44] train-rmse:0.228112+0.014120    test-rmse:0.967610+0.099691 
## [45] train-rmse:0.222226+0.013748    test-rmse:0.966478+0.100031 
## [46] train-rmse:0.212428+0.010226    test-rmse:0.967449+0.103864 
## [47] train-rmse:0.202447+0.010652    test-rmse:0.965098+0.104476 
## [48] train-rmse:0.196874+0.010758    test-rmse:0.965163+0.104921 
## [49] train-rmse:0.190362+0.009634    test-rmse:0.964105+0.104417 
## [50] train-rmse:0.182647+0.007417    test-rmse:0.964539+0.107258 
## [51] train-rmse:0.176543+0.007465    test-rmse:0.964315+0.106598 
## [52] train-rmse:0.169709+0.007856    test-rmse:0.963943+0.106475 
## [53] train-rmse:0.164099+0.009018    test-rmse:0.964781+0.106837 
## [54] train-rmse:0.158377+0.009647    test-rmse:0.963449+0.105277 
## [55] train-rmse:0.153653+0.011255    test-rmse:0.963472+0.105000 
## [56] train-rmse:0.147938+0.009573    test-rmse:0.962553+0.103559 
## [57] train-rmse:0.141924+0.010291    test-rmse:0.962075+0.103901 
## [58] train-rmse:0.137825+0.009164    test-rmse:0.961393+0.102668 
## [59] train-rmse:0.131186+0.008268    test-rmse:0.958968+0.101089 
## [60] train-rmse:0.126965+0.007356    test-rmse:0.958162+0.101437 
## [61] train-rmse:0.121297+0.008300    test-rmse:0.956926+0.101711 
## [62] train-rmse:0.117682+0.008940    test-rmse:0.955935+0.101210 
## [63] train-rmse:0.112626+0.008687    test-rmse:0.954983+0.102785 
## [64] train-rmse:0.108685+0.007932    test-rmse:0.954159+0.102916 
## [65] train-rmse:0.105736+0.007936    test-rmse:0.953094+0.103052 
## [66] train-rmse:0.102259+0.008120    test-rmse:0.952880+0.103391 
## [67] train-rmse:0.099836+0.007990    test-rmse:0.952590+0.103555 
## [68] train-rmse:0.097170+0.008240    test-rmse:0.952272+0.102958 
## [69] train-rmse:0.094132+0.007990    test-rmse:0.952554+0.103495 
## [70] train-rmse:0.090500+0.008013    test-rmse:0.952552+0.104030 
## [71] train-rmse:0.087314+0.008361    test-rmse:0.952851+0.104014 
## [72] train-rmse:0.084632+0.007416    test-rmse:0.952933+0.103961 
## [73] train-rmse:0.082633+0.007584    test-rmse:0.952888+0.103759 
## [74] train-rmse:0.079968+0.007863    test-rmse:0.951677+0.103996 
## [75] train-rmse:0.077909+0.008011    test-rmse:0.950568+0.103933 
## [76] train-rmse:0.074631+0.007426    test-rmse:0.951625+0.104176 
## [77] train-rmse:0.072006+0.007933    test-rmse:0.951605+0.104087 
## [78] train-rmse:0.069417+0.008339    test-rmse:0.951257+0.104234 
## [79] train-rmse:0.067562+0.007717    test-rmse:0.950640+0.103942 
## [80] train-rmse:0.065987+0.007960    test-rmse:0.950028+0.103857 
## [81] train-rmse:0.064166+0.008253    test-rmse:0.949807+0.103899 
## [82] train-rmse:0.062178+0.007832    test-rmse:0.949328+0.104560 
## [83] train-rmse:0.060560+0.007384    test-rmse:0.949059+0.104495 
## [84] train-rmse:0.058654+0.006421    test-rmse:0.948085+0.104547 
## [85] train-rmse:0.056145+0.007266    test-rmse:0.948389+0.104586 
## [86] train-rmse:0.054266+0.006341    test-rmse:0.948876+0.104536 
## [87] train-rmse:0.053022+0.006304    test-rmse:0.948617+0.105006 
## [88] train-rmse:0.051243+0.006618    test-rmse:0.948738+0.105319 
## [89] train-rmse:0.049324+0.005763    test-rmse:0.948974+0.105490 
## [90] train-rmse:0.047935+0.005921    test-rmse:0.949210+0.105315 
## Stopping. Best iteration:
## [84] train-rmse:0.058654+0.006421    test-rmse:0.948085+0.104547
## 
## 76 
## [1]  train-rmse:69.050973+0.069138   test-rmse:69.051505+0.424216 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:48.430438+0.048124   test-rmse:48.430518+0.444003 
## [3]  train-rmse:33.983752+0.033210   test-rmse:33.983192+0.457073 
## [4]  train-rmse:23.869216+0.022582   test-rmse:23.867778+0.464907 
## [5]  train-rmse:16.797328+0.015059   test-rmse:16.794708+0.468178 
## [6]  train-rmse:11.863720+0.009987   test-rmse:11.868057+0.432780 
## [7]  train-rmse:8.421914+0.007726    test-rmse:8.402332+0.416388 
## [8]  train-rmse:6.019871+0.006715    test-rmse:6.018442+0.361345 
## [9]  train-rmse:4.343651+0.006682    test-rmse:4.348947+0.297078 
## [10] train-rmse:3.168806+0.005095    test-rmse:3.182927+0.257728 
## [11] train-rmse:2.342072+0.013464    test-rmse:2.389965+0.218968 
## [12] train-rmse:1.764748+0.015907    test-rmse:1.869661+0.166401 
## [13] train-rmse:1.363945+0.014187    test-rmse:1.534824+0.140309 
## [14] train-rmse:1.083733+0.017025    test-rmse:1.339141+0.125047 
## [15] train-rmse:0.890555+0.020488    test-rmse:1.216112+0.115246 
## [16] train-rmse:0.752790+0.027973    test-rmse:1.138593+0.102159 
## [17] train-rmse:0.656654+0.032079    test-rmse:1.099628+0.096102 
## [18] train-rmse:0.593114+0.034028    test-rmse:1.070868+0.098283 
## [19] train-rmse:0.537789+0.041202    test-rmse:1.044629+0.090198 
## [20] train-rmse:0.495312+0.046188    test-rmse:1.035363+0.087211 
## [21] train-rmse:0.453614+0.036122    test-rmse:1.027970+0.082828 
## [22] train-rmse:0.426603+0.033863    test-rmse:1.026023+0.083760 
## [23] train-rmse:0.402506+0.033430    test-rmse:1.018778+0.082394 
## [24] train-rmse:0.381683+0.037933    test-rmse:1.015053+0.082535 
## [25] train-rmse:0.364039+0.035709    test-rmse:1.011818+0.082831 
## [26] train-rmse:0.345981+0.037648    test-rmse:1.004353+0.084153 
## [27] train-rmse:0.329892+0.037734    test-rmse:1.001890+0.087075 
## [28] train-rmse:0.314319+0.037931    test-rmse:0.996699+0.088172 
## [29] train-rmse:0.295617+0.031053    test-rmse:0.994624+0.088979 
## [30] train-rmse:0.281915+0.025065    test-rmse:0.985990+0.086715 
## [31] train-rmse:0.270566+0.026798    test-rmse:0.982848+0.085638 
## [32] train-rmse:0.255533+0.022273    test-rmse:0.980079+0.084975 
## [33] train-rmse:0.242118+0.023646    test-rmse:0.973345+0.086484 
## [34] train-rmse:0.229361+0.019902    test-rmse:0.970485+0.088052 
## [35] train-rmse:0.220864+0.020494    test-rmse:0.968403+0.089923 
## [36] train-rmse:0.209974+0.019595    test-rmse:0.965711+0.089716 
## [37] train-rmse:0.201088+0.019728    test-rmse:0.966111+0.087717 
## [38] train-rmse:0.192069+0.018290    test-rmse:0.966746+0.087894 
## [39] train-rmse:0.183375+0.018070    test-rmse:0.962069+0.090780 
## [40] train-rmse:0.174511+0.016480    test-rmse:0.961040+0.090924 
## [41] train-rmse:0.166015+0.016148    test-rmse:0.958010+0.092116 
## [42] train-rmse:0.158049+0.016906    test-rmse:0.956993+0.092482 
## [43] train-rmse:0.151338+0.018298    test-rmse:0.956120+0.093659 
## [44] train-rmse:0.144647+0.017085    test-rmse:0.956113+0.093340 
## [45] train-rmse:0.137993+0.016318    test-rmse:0.955992+0.093729 
## [46] train-rmse:0.132047+0.015395    test-rmse:0.955162+0.093053 
## [47] train-rmse:0.126943+0.015470    test-rmse:0.954493+0.093813 
## [48] train-rmse:0.121188+0.014135    test-rmse:0.952647+0.094531 
## [49] train-rmse:0.115731+0.015347    test-rmse:0.951321+0.095289 
## [50] train-rmse:0.112201+0.014892    test-rmse:0.950530+0.095603 
## [51] train-rmse:0.107060+0.014066    test-rmse:0.950768+0.095473 
## [52] train-rmse:0.102058+0.013055    test-rmse:0.949292+0.095656 
## [53] train-rmse:0.096963+0.012858    test-rmse:0.948612+0.095259 
## [54] train-rmse:0.094166+0.013278    test-rmse:0.947930+0.095355 
## [55] train-rmse:0.089740+0.012415    test-rmse:0.946755+0.096670 
## [56] train-rmse:0.086998+0.011537    test-rmse:0.946373+0.095938 
## [57] train-rmse:0.084327+0.011199    test-rmse:0.946635+0.095837 
## [58] train-rmse:0.079590+0.010513    test-rmse:0.947326+0.095770 
## [59] train-rmse:0.077086+0.009656    test-rmse:0.946692+0.095546 
## [60] train-rmse:0.074038+0.009193    test-rmse:0.946156+0.095308 
## [61] train-rmse:0.070391+0.008773    test-rmse:0.946369+0.095098 
## [62] train-rmse:0.067440+0.008448    test-rmse:0.946375+0.094536 
## [63] train-rmse:0.064099+0.008388    test-rmse:0.945952+0.095107 
## [64] train-rmse:0.060949+0.008100    test-rmse:0.945960+0.095254 
## [65] train-rmse:0.059049+0.007525    test-rmse:0.945255+0.095561 
## [66] train-rmse:0.056051+0.006699    test-rmse:0.944903+0.095565 
## [67] train-rmse:0.053705+0.006362    test-rmse:0.945048+0.095441 
## [68] train-rmse:0.050738+0.005748    test-rmse:0.944732+0.095551 
## [69] train-rmse:0.049056+0.005361    test-rmse:0.944945+0.095782 
## [70] train-rmse:0.046997+0.005237    test-rmse:0.944514+0.095780 
## [71] train-rmse:0.045518+0.005190    test-rmse:0.944579+0.096119 
## [72] train-rmse:0.044193+0.005169    test-rmse:0.944038+0.096155 
## [73] train-rmse:0.041894+0.005065    test-rmse:0.943185+0.096597 
## [74] train-rmse:0.040346+0.004900    test-rmse:0.943067+0.097181 
## [75] train-rmse:0.038742+0.004985    test-rmse:0.942983+0.097453 
## [76] train-rmse:0.037162+0.004283    test-rmse:0.942623+0.097461 
## [77] train-rmse:0.036132+0.003940    test-rmse:0.942620+0.097260 
## [78] train-rmse:0.034381+0.003691    test-rmse:0.942545+0.097102 
## [79] train-rmse:0.032860+0.003857    test-rmse:0.942803+0.097285 
## [80] train-rmse:0.031461+0.003714    test-rmse:0.942787+0.097088 
## [81] train-rmse:0.030253+0.003642    test-rmse:0.942737+0.097100 
## [82] train-rmse:0.028979+0.003166    test-rmse:0.942736+0.096715 
## [83] train-rmse:0.027841+0.003052    test-rmse:0.942752+0.096403 
## [84] train-rmse:0.026618+0.003179    test-rmse:0.942736+0.096619 
## Stopping. Best iteration:
## [78] train-rmse:0.034381+0.003691    test-rmse:0.942545+0.097102
## 
## 77 
## [1]  train-rmse:67.089722+0.067155   test-rmse:67.090222+0.426122 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:45.720995+0.045347   test-rmse:45.720976+0.446533 
## [3]  train-rmse:31.177771+0.030287   test-rmse:31.177018+0.459422 
## [4]  train-rmse:21.288879+0.019834   test-rmse:21.287091+0.466458 
## [5]  train-rmse:14.577831+0.012778   test-rmse:14.574641+0.468189 
## [6]  train-rmse:10.035147+0.008594   test-rmse:10.035415+0.429019 
## [7]  train-rmse:6.972645+0.007444    test-rmse:6.959648+0.423008 
## [8]  train-rmse:4.925980+0.010311    test-rmse:4.914400+0.397346 
## [9]  train-rmse:3.587957+0.015052    test-rmse:3.586788+0.357965 
## [10] train-rmse:2.737162+0.020655    test-rmse:2.750996+0.315122 
## [11] train-rmse:2.220138+0.023745    test-rmse:2.244753+0.241066 
## [12] train-rmse:1.920317+0.025530    test-rmse:1.968043+0.180942 
## [13] train-rmse:1.749796+0.027180    test-rmse:1.822636+0.135432 
## [14] train-rmse:1.652111+0.028203    test-rmse:1.737535+0.117227 
## [15] train-rmse:1.592832+0.029314    test-rmse:1.668086+0.107415 
## [16] train-rmse:1.555096+0.028349    test-rmse:1.644565+0.109735 
## [17] train-rmse:1.526478+0.026995    test-rmse:1.625208+0.109081 
## [18] train-rmse:1.503427+0.025305    test-rmse:1.609643+0.111229 
## [19] train-rmse:1.484162+0.025497    test-rmse:1.594021+0.117232 
## [20] train-rmse:1.466949+0.024989    test-rmse:1.583322+0.113872 
## [21] train-rmse:1.450607+0.025352    test-rmse:1.574973+0.109928 
## [22] train-rmse:1.434865+0.024904    test-rmse:1.566116+0.112959 
## [23] train-rmse:1.420755+0.024906    test-rmse:1.559472+0.116094 
## [24] train-rmse:1.406981+0.024941    test-rmse:1.538411+0.104964 
## [25] train-rmse:1.394080+0.024909    test-rmse:1.542677+0.104912 
## [26] train-rmse:1.382167+0.024290    test-rmse:1.535312+0.105024 
## [27] train-rmse:1.370496+0.023658    test-rmse:1.532179+0.104209 
## [28] train-rmse:1.359375+0.023499    test-rmse:1.526732+0.100928 
## [29] train-rmse:1.348797+0.023148    test-rmse:1.516656+0.106640 
## [30] train-rmse:1.338269+0.023097    test-rmse:1.504216+0.111599 
## [31] train-rmse:1.327827+0.023032    test-rmse:1.491791+0.108117 
## [32] train-rmse:1.317850+0.022720    test-rmse:1.488276+0.107420 
## [33] train-rmse:1.308176+0.022861    test-rmse:1.484443+0.096106 
## [34] train-rmse:1.298921+0.022583    test-rmse:1.474930+0.093804 
## [35] train-rmse:1.289973+0.022158    test-rmse:1.464839+0.095738 
## [36] train-rmse:1.280880+0.021755    test-rmse:1.463830+0.088254 
## [37] train-rmse:1.271818+0.021449    test-rmse:1.460171+0.088737 
## [38] train-rmse:1.262743+0.021445    test-rmse:1.457282+0.088674 
## [39] train-rmse:1.254495+0.021074    test-rmse:1.440884+0.090750 
## [40] train-rmse:1.246639+0.021030    test-rmse:1.433945+0.092499 
## [41] train-rmse:1.239034+0.020970    test-rmse:1.433043+0.094757 
## [42] train-rmse:1.231783+0.021095    test-rmse:1.428980+0.095082 
## [43] train-rmse:1.224861+0.021132    test-rmse:1.424003+0.092781 
## [44] train-rmse:1.218257+0.021119    test-rmse:1.420865+0.090678 
## [45] train-rmse:1.211799+0.021393    test-rmse:1.415397+0.093550 
## [46] train-rmse:1.205463+0.021502    test-rmse:1.409726+0.093702 
## [47] train-rmse:1.199270+0.021553    test-rmse:1.402113+0.093718 
## [48] train-rmse:1.193370+0.021688    test-rmse:1.401221+0.085385 
## [49] train-rmse:1.187655+0.021586    test-rmse:1.394846+0.082662 
## [50] train-rmse:1.181968+0.021503    test-rmse:1.388511+0.077996 
## [51] train-rmse:1.176425+0.021436    test-rmse:1.392271+0.076562 
## [52] train-rmse:1.170938+0.021546    test-rmse:1.390822+0.075554 
## [53] train-rmse:1.165544+0.021537    test-rmse:1.382378+0.073654 
## [54] train-rmse:1.160149+0.021455    test-rmse:1.375647+0.081736 
## [55] train-rmse:1.155075+0.021452    test-rmse:1.375415+0.086114 
## [56] train-rmse:1.150067+0.021367    test-rmse:1.369256+0.087023 
## [57] train-rmse:1.145307+0.021223    test-rmse:1.364862+0.084894 
## [58] train-rmse:1.140692+0.021009    test-rmse:1.362713+0.087384 
## [59] train-rmse:1.136134+0.020782    test-rmse:1.356179+0.086630 
## [60] train-rmse:1.131603+0.020624    test-rmse:1.353246+0.082524 
## [61] train-rmse:1.127344+0.020561    test-rmse:1.347776+0.081805 
## [62] train-rmse:1.123025+0.020486    test-rmse:1.345907+0.081751 
## [63] train-rmse:1.118739+0.020470    test-rmse:1.345105+0.086272 
## [64] train-rmse:1.114626+0.020533    test-rmse:1.342092+0.085052 
## [65] train-rmse:1.110768+0.020456    test-rmse:1.340583+0.083995 
## [66] train-rmse:1.106927+0.020312    test-rmse:1.338597+0.084220 
## [67] train-rmse:1.103242+0.020123    test-rmse:1.335889+0.082527 
## [68] train-rmse:1.099672+0.019979    test-rmse:1.332145+0.086352 
## [69] train-rmse:1.096011+0.019743    test-rmse:1.328456+0.087105 
## [70] train-rmse:1.092545+0.019611    test-rmse:1.322684+0.086721 
## [71] train-rmse:1.089055+0.019516    test-rmse:1.317140+0.087982 
## [72] train-rmse:1.085671+0.019468    test-rmse:1.319599+0.086716 
## [73] train-rmse:1.082312+0.019537    test-rmse:1.318744+0.084619 
## [74] train-rmse:1.079109+0.019557    test-rmse:1.318711+0.085145 
## [75] train-rmse:1.075986+0.019589    test-rmse:1.316596+0.084768 
## [76] train-rmse:1.073023+0.019585    test-rmse:1.314321+0.085667 
## [77] train-rmse:1.070058+0.019660    test-rmse:1.309851+0.089972 
## [78] train-rmse:1.067103+0.019716    test-rmse:1.311776+0.087339 
## [79] train-rmse:1.064203+0.019703    test-rmse:1.308515+0.086147 
## [80] train-rmse:1.061283+0.019642    test-rmse:1.306457+0.088079 
## [81] train-rmse:1.058512+0.019682    test-rmse:1.303963+0.087905 
## [82] train-rmse:1.055770+0.019670    test-rmse:1.301085+0.089456 
## [83] train-rmse:1.053160+0.019584    test-rmse:1.302633+0.086913 
## [84] train-rmse:1.050602+0.019471    test-rmse:1.299921+0.085651 
## [85] train-rmse:1.048077+0.019370    test-rmse:1.297766+0.085934 
## [86] train-rmse:1.045625+0.019322    test-rmse:1.297101+0.084835 
## [87] train-rmse:1.043199+0.019291    test-rmse:1.292026+0.086897 
## [88] train-rmse:1.040816+0.019255    test-rmse:1.287916+0.086997 
## [89] train-rmse:1.038507+0.019154    test-rmse:1.287485+0.085387 
## [90] train-rmse:1.036238+0.019049    test-rmse:1.285498+0.086638 
## [91] train-rmse:1.033946+0.018935    test-rmse:1.282688+0.086842 
## [92] train-rmse:1.031665+0.018924    test-rmse:1.277761+0.087121 
## [93] train-rmse:1.029378+0.018877    test-rmse:1.277284+0.088260 
## [94] train-rmse:1.027189+0.018864    test-rmse:1.277524+0.090447 
## [95] train-rmse:1.025060+0.018837    test-rmse:1.276164+0.087643 
## [96] train-rmse:1.022959+0.018800    test-rmse:1.274460+0.091426 
## [97] train-rmse:1.020838+0.018742    test-rmse:1.274954+0.090422 
## [98] train-rmse:1.018753+0.018733    test-rmse:1.273044+0.090609 
## [99] train-rmse:1.016759+0.018709    test-rmse:1.268972+0.083962 
## [100]    train-rmse:1.014793+0.018666    test-rmse:1.270372+0.081160 
## [101]    train-rmse:1.012826+0.018694    test-rmse:1.268747+0.081968 
## [102]    train-rmse:1.010944+0.018683    test-rmse:1.267969+0.081579 
## [103]    train-rmse:1.009091+0.018582    test-rmse:1.268468+0.080083 
## [104]    train-rmse:1.007196+0.018507    test-rmse:1.270440+0.080507 
## [105]    train-rmse:1.005393+0.018451    test-rmse:1.269411+0.082776 
## [106]    train-rmse:1.003548+0.018539    test-rmse:1.266711+0.083119 
## [107]    train-rmse:1.001817+0.018536    test-rmse:1.265250+0.082228 
## [108]    train-rmse:1.000157+0.018566    test-rmse:1.263119+0.081195 
## [109]    train-rmse:0.998515+0.018580    test-rmse:1.263378+0.079358 
## [110]    train-rmse:0.996839+0.018574    test-rmse:1.260337+0.083414 
## [111]    train-rmse:0.995159+0.018539    test-rmse:1.260376+0.081713 
## [112]    train-rmse:0.993495+0.018489    test-rmse:1.258048+0.084029 
## [113]    train-rmse:0.991827+0.018461    test-rmse:1.256855+0.084849 
## [114]    train-rmse:0.990131+0.018326    test-rmse:1.257476+0.084209 
## [115]    train-rmse:0.988546+0.018293    test-rmse:1.256526+0.082737 
## [116]    train-rmse:0.986999+0.018254    test-rmse:1.256946+0.084074 
## [117]    train-rmse:0.985463+0.018200    test-rmse:1.256707+0.084386 
## [118]    train-rmse:0.983876+0.018140    test-rmse:1.256928+0.085356 
## [119]    train-rmse:0.982356+0.018140    test-rmse:1.256890+0.085003 
## [120]    train-rmse:0.980804+0.018099    test-rmse:1.256867+0.085155 
## [121]    train-rmse:0.979361+0.018117    test-rmse:1.256438+0.085783 
## [122]    train-rmse:0.977950+0.018140    test-rmse:1.255511+0.085198 
## [123]    train-rmse:0.976504+0.018168    test-rmse:1.254216+0.083758 
## [124]    train-rmse:0.975140+0.018165    test-rmse:1.254174+0.084714 
## [125]    train-rmse:0.973687+0.018181    test-rmse:1.252732+0.084022 
## [126]    train-rmse:0.972288+0.018187    test-rmse:1.251163+0.083257 
## [127]    train-rmse:0.970909+0.018200    test-rmse:1.249653+0.083139 
## [128]    train-rmse:0.969585+0.018223    test-rmse:1.244851+0.081901 
## [129]    train-rmse:0.968281+0.018254    test-rmse:1.239935+0.079054 
## [130]    train-rmse:0.966955+0.018293    test-rmse:1.241376+0.078397 
## [131]    train-rmse:0.965656+0.018299    test-rmse:1.241401+0.079077 
## [132]    train-rmse:0.964388+0.018298    test-rmse:1.242247+0.078424 
## [133]    train-rmse:0.963149+0.018262    test-rmse:1.242149+0.078571 
## [134]    train-rmse:0.961868+0.018292    test-rmse:1.240370+0.079239 
## [135]    train-rmse:0.960579+0.018339    test-rmse:1.240869+0.081542 
## Stopping. Best iteration:
## [129]    train-rmse:0.968281+0.018254    test-rmse:1.239935+0.079054
## 
## 78 
## [1]  train-rmse:67.089722+0.067155   test-rmse:67.090222+0.426122 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:45.720995+0.045347   test-rmse:45.720976+0.446533 
## [3]  train-rmse:31.177771+0.030287   test-rmse:31.177018+0.459422 
## [4]  train-rmse:21.288879+0.019834   test-rmse:21.287091+0.466458 
## [5]  train-rmse:14.577831+0.012778   test-rmse:14.574641+0.468189 
## [6]  train-rmse:10.033350+0.007985   test-rmse:10.019403+0.432742 
## [7]  train-rmse:6.960861+0.007028    test-rmse:6.943355+0.397670 
## [8]  train-rmse:4.894359+0.006876    test-rmse:4.881230+0.373566 
## [9]  train-rmse:3.518489+0.012354    test-rmse:3.520147+0.320042 
## [10] train-rmse:2.627537+0.012463    test-rmse:2.661704+0.276642 
## [11] train-rmse:2.068578+0.014812    test-rmse:2.132296+0.222587 
## [12] train-rmse:1.730319+0.015588    test-rmse:1.833462+0.168407 
## [13] train-rmse:1.532963+0.013786    test-rmse:1.664094+0.127202 
## [14] train-rmse:1.415704+0.015160    test-rmse:1.580893+0.102509 
## [15] train-rmse:1.343711+0.016516    test-rmse:1.519277+0.111611 
## [16] train-rmse:1.293835+0.013492    test-rmse:1.484210+0.112397 
## [17] train-rmse:1.258596+0.012955    test-rmse:1.456795+0.116635 
## [18] train-rmse:1.232493+0.013178    test-rmse:1.435607+0.118706 
## [19] train-rmse:1.206117+0.018663    test-rmse:1.424911+0.116737 
## [20] train-rmse:1.186224+0.017856    test-rmse:1.413820+0.111777 
## [21] train-rmse:1.163204+0.012263    test-rmse:1.384037+0.102288 
## [22] train-rmse:1.143312+0.014946    test-rmse:1.363997+0.110169 
## [23] train-rmse:1.126491+0.014411    test-rmse:1.353524+0.106650 
## [24] train-rmse:1.109524+0.014975    test-rmse:1.338468+0.109669 
## [25] train-rmse:1.091166+0.010868    test-rmse:1.330766+0.111162 
## [26] train-rmse:1.069556+0.011341    test-rmse:1.324362+0.114198 
## [27] train-rmse:1.054306+0.015313    test-rmse:1.314666+0.111129 
## [28] train-rmse:1.039464+0.016217    test-rmse:1.306954+0.108593 
## [29] train-rmse:1.026651+0.017334    test-rmse:1.299843+0.105854 
## [30] train-rmse:1.012946+0.016938    test-rmse:1.287827+0.098558 
## [31] train-rmse:0.997953+0.016038    test-rmse:1.282833+0.098334 
## [32] train-rmse:0.987661+0.015865    test-rmse:1.278806+0.102459 
## [33] train-rmse:0.977263+0.014307    test-rmse:1.270549+0.105846 
## [34] train-rmse:0.964969+0.012507    test-rmse:1.261183+0.105827 
## [35] train-rmse:0.953988+0.010127    test-rmse:1.255920+0.103875 
## [36] train-rmse:0.944109+0.008598    test-rmse:1.246447+0.101856 
## [37] train-rmse:0.936139+0.007969    test-rmse:1.245863+0.099637 
## [38] train-rmse:0.926914+0.007143    test-rmse:1.232682+0.106512 
## [39] train-rmse:0.917488+0.005120    test-rmse:1.223310+0.103988 
## [40] train-rmse:0.907333+0.004568    test-rmse:1.218337+0.101488 
## [41] train-rmse:0.898454+0.007469    test-rmse:1.218606+0.101560 
## [42] train-rmse:0.888497+0.005355    test-rmse:1.207852+0.104002 
## [43] train-rmse:0.877463+0.004949    test-rmse:1.200880+0.103229 
## [44] train-rmse:0.870598+0.005859    test-rmse:1.198687+0.100703 
## [45] train-rmse:0.861049+0.004959    test-rmse:1.199513+0.103911 
## [46] train-rmse:0.852395+0.006137    test-rmse:1.193725+0.107184 
## [47] train-rmse:0.842334+0.005225    test-rmse:1.189827+0.107047 
## [48] train-rmse:0.836952+0.005465    test-rmse:1.188210+0.109899 
## [49] train-rmse:0.828792+0.005526    test-rmse:1.182177+0.113868 
## [50] train-rmse:0.822616+0.005557    test-rmse:1.181603+0.117574 
## [51] train-rmse:0.815382+0.005552    test-rmse:1.173815+0.118658 
## [52] train-rmse:0.806953+0.006054    test-rmse:1.173307+0.118065 
## [53] train-rmse:0.800129+0.004992    test-rmse:1.164103+0.107288 
## [54] train-rmse:0.794052+0.005646    test-rmse:1.158231+0.104444 
## [55] train-rmse:0.784943+0.008535    test-rmse:1.158576+0.106331 
## [56] train-rmse:0.780891+0.008633    test-rmse:1.160132+0.102041 
## [57] train-rmse:0.774470+0.010586    test-rmse:1.160854+0.104166 
## [58] train-rmse:0.765210+0.010894    test-rmse:1.155606+0.105102 
## [59] train-rmse:0.759226+0.010543    test-rmse:1.152995+0.108823 
## [60] train-rmse:0.751816+0.008719    test-rmse:1.147183+0.108407 
## [61] train-rmse:0.745067+0.010813    test-rmse:1.143995+0.105632 
## [62] train-rmse:0.738072+0.008746    test-rmse:1.142485+0.106067 
## [63] train-rmse:0.731533+0.007549    test-rmse:1.133123+0.099934 
## [64] train-rmse:0.727663+0.007231    test-rmse:1.132975+0.099952 
## [65] train-rmse:0.721074+0.007777    test-rmse:1.128217+0.095942 
## [66] train-rmse:0.716009+0.008708    test-rmse:1.124771+0.094034 
## [67] train-rmse:0.708488+0.006125    test-rmse:1.125453+0.091995 
## [68] train-rmse:0.703128+0.006430    test-rmse:1.124437+0.093020 
## [69] train-rmse:0.699343+0.007275    test-rmse:1.123163+0.090656 
## [70] train-rmse:0.692877+0.011489    test-rmse:1.122285+0.092986 
## [71] train-rmse:0.685643+0.011073    test-rmse:1.123154+0.093671 
## [72] train-rmse:0.681829+0.010515    test-rmse:1.123738+0.094538 
## [73] train-rmse:0.675239+0.013011    test-rmse:1.119279+0.095024 
## [74] train-rmse:0.669884+0.014312    test-rmse:1.119611+0.091837 
## [75] train-rmse:0.664557+0.014737    test-rmse:1.110242+0.089834 
## [76] train-rmse:0.657124+0.012618    test-rmse:1.106655+0.091170 
## [77] train-rmse:0.653596+0.011799    test-rmse:1.106083+0.089823 
## [78] train-rmse:0.648497+0.013236    test-rmse:1.104557+0.089833 
## [79] train-rmse:0.644644+0.013874    test-rmse:1.102149+0.091242 
## [80] train-rmse:0.639394+0.014503    test-rmse:1.099765+0.093683 
## [81] train-rmse:0.636618+0.014703    test-rmse:1.100051+0.094781 
## [82] train-rmse:0.633974+0.015131    test-rmse:1.100023+0.095213 
## [83] train-rmse:0.630039+0.014977    test-rmse:1.097221+0.096064 
## [84] train-rmse:0.625225+0.015880    test-rmse:1.092101+0.089163 
## [85] train-rmse:0.622306+0.016324    test-rmse:1.090475+0.087131 
## [86] train-rmse:0.619378+0.015849    test-rmse:1.089792+0.087882 
## [87] train-rmse:0.615283+0.015520    test-rmse:1.088426+0.088721 
## [88] train-rmse:0.610116+0.014203    test-rmse:1.085193+0.090321 
## [89] train-rmse:0.606470+0.013495    test-rmse:1.083396+0.089041 
## [90] train-rmse:0.603465+0.014722    test-rmse:1.081525+0.087903 
## [91] train-rmse:0.598855+0.014720    test-rmse:1.079340+0.089618 
## [92] train-rmse:0.595070+0.013519    test-rmse:1.078882+0.089423 
## [93] train-rmse:0.591043+0.011589    test-rmse:1.075463+0.089522 
## [94] train-rmse:0.588236+0.011169    test-rmse:1.074087+0.090148 
## [95] train-rmse:0.584973+0.010096    test-rmse:1.074700+0.087764 
## [96] train-rmse:0.580252+0.008251    test-rmse:1.071044+0.087650 
## [97] train-rmse:0.577306+0.008030    test-rmse:1.068821+0.088070 
## [98] train-rmse:0.573545+0.007600    test-rmse:1.065043+0.089798 
## [99] train-rmse:0.570377+0.008028    test-rmse:1.064138+0.090316 
## [100]    train-rmse:0.566304+0.009134    test-rmse:1.063642+0.090791 
## [101]    train-rmse:0.563226+0.009983    test-rmse:1.061445+0.089031 
## [102]    train-rmse:0.560372+0.011583    test-rmse:1.060815+0.088721 
## [103]    train-rmse:0.558915+0.011538    test-rmse:1.059712+0.088923 
## [104]    train-rmse:0.554473+0.010420    test-rmse:1.057993+0.089142 
## [105]    train-rmse:0.551988+0.010027    test-rmse:1.057301+0.089743 
## [106]    train-rmse:0.548088+0.009352    test-rmse:1.056751+0.089928 
## [107]    train-rmse:0.545378+0.009568    test-rmse:1.056323+0.089038 
## [108]    train-rmse:0.542422+0.011100    test-rmse:1.055792+0.089581 
## [109]    train-rmse:0.538814+0.011666    test-rmse:1.055305+0.089214 
## [110]    train-rmse:0.536019+0.012095    test-rmse:1.053970+0.090149 
## [111]    train-rmse:0.531131+0.010420    test-rmse:1.054976+0.087285 
## [112]    train-rmse:0.528407+0.010950    test-rmse:1.052720+0.087522 
## [113]    train-rmse:0.526220+0.010462    test-rmse:1.050248+0.088839 
## [114]    train-rmse:0.522477+0.010580    test-rmse:1.047945+0.090070 
## [115]    train-rmse:0.520161+0.010755    test-rmse:1.049079+0.091305 
## [116]    train-rmse:0.516031+0.012154    test-rmse:1.046531+0.091836 
## [117]    train-rmse:0.513508+0.012522    test-rmse:1.047855+0.091593 
## [118]    train-rmse:0.510832+0.013380    test-rmse:1.046645+0.090252 
## [119]    train-rmse:0.507953+0.013242    test-rmse:1.047975+0.090643 
## [120]    train-rmse:0.505244+0.013772    test-rmse:1.047685+0.090962 
## [121]    train-rmse:0.503003+0.012642    test-rmse:1.046099+0.091132 
## [122]    train-rmse:0.498219+0.009097    test-rmse:1.046139+0.091385 
## [123]    train-rmse:0.495625+0.007931    test-rmse:1.044903+0.091156 
## [124]    train-rmse:0.492802+0.006135    test-rmse:1.047728+0.090760 
## [125]    train-rmse:0.490357+0.005843    test-rmse:1.048268+0.091402 
## [126]    train-rmse:0.488035+0.006516    test-rmse:1.047249+0.090171 
## [127]    train-rmse:0.486909+0.006634    test-rmse:1.047139+0.090273 
## [128]    train-rmse:0.483447+0.005408    test-rmse:1.042244+0.091284 
## [129]    train-rmse:0.480526+0.004922    test-rmse:1.042217+0.091002 
## [130]    train-rmse:0.478050+0.004648    test-rmse:1.041434+0.090415 
## [131]    train-rmse:0.476158+0.004142    test-rmse:1.042275+0.090903 
## [132]    train-rmse:0.474216+0.004169    test-rmse:1.041110+0.090886 
## [133]    train-rmse:0.472292+0.004306    test-rmse:1.039173+0.090916 
## [134]    train-rmse:0.470841+0.004738    test-rmse:1.037889+0.092103 
## [135]    train-rmse:0.467492+0.005533    test-rmse:1.035814+0.090102 
## [136]    train-rmse:0.464650+0.005324    test-rmse:1.036369+0.090973 
## [137]    train-rmse:0.462893+0.005879    test-rmse:1.034580+0.089774 
## [138]    train-rmse:0.460144+0.004968    test-rmse:1.034392+0.089892 
## [139]    train-rmse:0.457893+0.004622    test-rmse:1.032983+0.089591 
## [140]    train-rmse:0.455592+0.004791    test-rmse:1.032411+0.090474 
## [141]    train-rmse:0.453575+0.005325    test-rmse:1.033382+0.090217 
## [142]    train-rmse:0.450667+0.006050    test-rmse:1.030813+0.086168 
## [143]    train-rmse:0.447757+0.005113    test-rmse:1.030111+0.085442 
## [144]    train-rmse:0.444649+0.005158    test-rmse:1.028962+0.086054 
## [145]    train-rmse:0.442295+0.005030    test-rmse:1.027547+0.085366 
## [146]    train-rmse:0.439881+0.005046    test-rmse:1.024776+0.083942 
## [147]    train-rmse:0.437484+0.005057    test-rmse:1.023248+0.084244 
## [148]    train-rmse:0.435281+0.004609    test-rmse:1.024259+0.084530 
## [149]    train-rmse:0.434071+0.004598    test-rmse:1.023962+0.084810 
## [150]    train-rmse:0.430792+0.005171    test-rmse:1.023714+0.082900 
## [151]    train-rmse:0.428251+0.005473    test-rmse:1.018158+0.082875 
## [152]    train-rmse:0.425916+0.005619    test-rmse:1.017358+0.083237 
## [153]    train-rmse:0.424639+0.005875    test-rmse:1.016172+0.083064 
## [154]    train-rmse:0.422999+0.006214    test-rmse:1.015005+0.083002 
## [155]    train-rmse:0.421804+0.006742    test-rmse:1.015044+0.082933 
## [156]    train-rmse:0.419179+0.005308    test-rmse:1.014951+0.082559 
## [157]    train-rmse:0.416542+0.005006    test-rmse:1.014580+0.083748 
## [158]    train-rmse:0.414766+0.004716    test-rmse:1.013724+0.085715 
## [159]    train-rmse:0.412468+0.004701    test-rmse:1.012200+0.085779 
## [160]    train-rmse:0.410976+0.004426    test-rmse:1.012216+0.085420 
## [161]    train-rmse:0.409357+0.003655    test-rmse:1.011959+0.086254 
## [162]    train-rmse:0.407300+0.003956    test-rmse:1.011604+0.086477 
## [163]    train-rmse:0.405280+0.004976    test-rmse:1.011877+0.086201 
## [164]    train-rmse:0.403320+0.005280    test-rmse:1.011225+0.086920 
## [165]    train-rmse:0.401262+0.005130    test-rmse:1.010520+0.086047 
## [166]    train-rmse:0.399847+0.005128    test-rmse:1.010520+0.086352 
## [167]    train-rmse:0.397421+0.005411    test-rmse:1.012315+0.086435 
## [168]    train-rmse:0.395841+0.005043    test-rmse:1.013934+0.086230 
## [169]    train-rmse:0.393999+0.005901    test-rmse:1.013673+0.086017 
## [170]    train-rmse:0.392433+0.005646    test-rmse:1.011953+0.086519 
## [171]    train-rmse:0.391001+0.005512    test-rmse:1.012757+0.086056 
## [172]    train-rmse:0.389565+0.005706    test-rmse:1.012395+0.085268 
## Stopping. Best iteration:
## [166]    train-rmse:0.399847+0.005128    test-rmse:1.010520+0.086352
## 
## 79 
## [1]  train-rmse:67.089722+0.067155   test-rmse:67.090222+0.426122 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:45.720995+0.045347   test-rmse:45.720976+0.446533 
## [3]  train-rmse:31.177771+0.030287   test-rmse:31.177018+0.459422 
## [4]  train-rmse:21.288879+0.019834   test-rmse:21.287091+0.466458 
## [5]  train-rmse:14.577831+0.012778   test-rmse:14.574641+0.468189 
## [6]  train-rmse:10.033350+0.007985   test-rmse:10.019403+0.432742 
## [7]  train-rmse:6.958338+0.006971    test-rmse:6.938001+0.398116 
## [8]  train-rmse:4.877566+0.006298    test-rmse:4.864121+0.355916 
## [9]  train-rmse:3.490456+0.008463    test-rmse:3.493121+0.302810 
## [10] train-rmse:2.570018+0.009349    test-rmse:2.601317+0.263719 
## [11] train-rmse:1.983004+0.014944    test-rmse:2.039573+0.217836 
## [12] train-rmse:1.605373+0.019850    test-rmse:1.717158+0.184751 
## [13] train-rmse:1.378243+0.012586    test-rmse:1.543584+0.141911 
## [14] train-rmse:1.243240+0.014777    test-rmse:1.442030+0.129568 
## [15] train-rmse:1.156878+0.011653    test-rmse:1.385019+0.121799 
## [16] train-rmse:1.096515+0.014297    test-rmse:1.356316+0.114353 
## [17] train-rmse:1.048629+0.009632    test-rmse:1.329313+0.106042 
## [18] train-rmse:1.013323+0.007005    test-rmse:1.314982+0.107757 
## [19] train-rmse:0.987291+0.005681    test-rmse:1.302156+0.109527 
## [20] train-rmse:0.961461+0.006620    test-rmse:1.280936+0.112325 
## [21] train-rmse:0.938157+0.005347    test-rmse:1.268361+0.099527 
## [22] train-rmse:0.915478+0.006057    test-rmse:1.254266+0.096260 
## [23] train-rmse:0.892866+0.006967    test-rmse:1.235327+0.088898 
## [24] train-rmse:0.873414+0.008587    test-rmse:1.230346+0.089670 
## [25] train-rmse:0.847432+0.007787    test-rmse:1.212680+0.086580 
## [26] train-rmse:0.829949+0.005155    test-rmse:1.200854+0.088529 
## [27] train-rmse:0.812621+0.005254    test-rmse:1.187869+0.084004 
## [28] train-rmse:0.793851+0.007396    test-rmse:1.184283+0.078247 
## [29] train-rmse:0.778049+0.007604    test-rmse:1.171812+0.076398 
## [30] train-rmse:0.762417+0.006708    test-rmse:1.162485+0.079924 
## [31] train-rmse:0.747558+0.005884    test-rmse:1.150395+0.073638 
## [32] train-rmse:0.728644+0.007688    test-rmse:1.140578+0.077568 
## [33] train-rmse:0.718190+0.007064    test-rmse:1.136365+0.079276 
## [34] train-rmse:0.708911+0.008147    test-rmse:1.132377+0.075984 
## [35] train-rmse:0.697488+0.011424    test-rmse:1.120901+0.075705 
## [36] train-rmse:0.687205+0.010680    test-rmse:1.117676+0.071153 
## [37] train-rmse:0.676052+0.011412    test-rmse:1.119964+0.066995 
## [38] train-rmse:0.665553+0.011264    test-rmse:1.110253+0.066127 
## [39] train-rmse:0.655460+0.010218    test-rmse:1.104836+0.065845 
## [40] train-rmse:0.643975+0.010712    test-rmse:1.096191+0.067086 
## [41] train-rmse:0.634813+0.010890    test-rmse:1.091468+0.070804 
## [42] train-rmse:0.623594+0.011306    test-rmse:1.082792+0.059628 
## [43] train-rmse:0.615242+0.010503    test-rmse:1.083395+0.058907 
## [44] train-rmse:0.607082+0.010515    test-rmse:1.081752+0.062839 
## [45] train-rmse:0.599239+0.013639    test-rmse:1.075658+0.063968 
## [46] train-rmse:0.591033+0.009843    test-rmse:1.072939+0.058745 
## [47] train-rmse:0.582987+0.008845    test-rmse:1.071689+0.057676 
## [48] train-rmse:0.575150+0.009110    test-rmse:1.070819+0.058989 
## [49] train-rmse:0.570804+0.009141    test-rmse:1.067875+0.056947 
## [50] train-rmse:0.561607+0.011220    test-rmse:1.070190+0.055859 
## [51] train-rmse:0.550354+0.010054    test-rmse:1.073555+0.061348 
## [52] train-rmse:0.542371+0.009506    test-rmse:1.071443+0.060377 
## [53] train-rmse:0.534986+0.009593    test-rmse:1.067056+0.058248 
## [54] train-rmse:0.527658+0.010489    test-rmse:1.066448+0.052703 
## [55] train-rmse:0.520015+0.008472    test-rmse:1.066050+0.052033 
## [56] train-rmse:0.512345+0.006834    test-rmse:1.057877+0.045071 
## [57] train-rmse:0.503860+0.007244    test-rmse:1.053517+0.046755 
## [58] train-rmse:0.496350+0.007513    test-rmse:1.051809+0.045817 
## [59] train-rmse:0.490005+0.007402    test-rmse:1.048732+0.044130 
## [60] train-rmse:0.483549+0.010170    test-rmse:1.051268+0.049120 
## [61] train-rmse:0.477833+0.008955    test-rmse:1.049583+0.048505 
## [62] train-rmse:0.472420+0.009854    test-rmse:1.049248+0.047036 
## [63] train-rmse:0.467185+0.010625    test-rmse:1.049530+0.047627 
## [64] train-rmse:0.463522+0.010143    test-rmse:1.048648+0.047274 
## [65] train-rmse:0.458097+0.011121    test-rmse:1.045774+0.046355 
## [66] train-rmse:0.453061+0.010086    test-rmse:1.043331+0.044206 
## [67] train-rmse:0.446750+0.007077    test-rmse:1.042582+0.043573 
## [68] train-rmse:0.440814+0.007699    test-rmse:1.040355+0.044781 
## [69] train-rmse:0.433314+0.008466    test-rmse:1.040283+0.043035 
## [70] train-rmse:0.424903+0.009937    test-rmse:1.040819+0.046852 
## [71] train-rmse:0.419872+0.008894    test-rmse:1.039487+0.044516 
## [72] train-rmse:0.414878+0.010479    test-rmse:1.038972+0.043282 
## [73] train-rmse:0.409205+0.007960    test-rmse:1.040400+0.043087 
## [74] train-rmse:0.404770+0.010444    test-rmse:1.040498+0.043367 
## [75] train-rmse:0.398828+0.010901    test-rmse:1.040846+0.040784 
## [76] train-rmse:0.393285+0.011220    test-rmse:1.041349+0.043285 
## [77] train-rmse:0.387509+0.013459    test-rmse:1.041531+0.044517 
## [78] train-rmse:0.381919+0.012771    test-rmse:1.038287+0.045380 
## [79] train-rmse:0.378489+0.012778    test-rmse:1.037101+0.044753 
## [80] train-rmse:0.375285+0.012287    test-rmse:1.036898+0.043731 
## [81] train-rmse:0.369743+0.012470    test-rmse:1.033973+0.041294 
## [82] train-rmse:0.365684+0.012543    test-rmse:1.030732+0.039911 
## [83] train-rmse:0.362040+0.011570    test-rmse:1.030173+0.040157 
## [84] train-rmse:0.358501+0.010798    test-rmse:1.030518+0.040588 
## [85] train-rmse:0.354259+0.011305    test-rmse:1.026861+0.041297 
## [86] train-rmse:0.349861+0.011458    test-rmse:1.027640+0.039925 
## [87] train-rmse:0.347543+0.010943    test-rmse:1.027021+0.040395 
## [88] train-rmse:0.343805+0.012107    test-rmse:1.023347+0.037625 
## [89] train-rmse:0.340473+0.012152    test-rmse:1.022638+0.038025 
## [90] train-rmse:0.335395+0.011463    test-rmse:1.022528+0.036481 
## [91] train-rmse:0.330161+0.010115    test-rmse:1.022579+0.035044 
## [92] train-rmse:0.326060+0.007922    test-rmse:1.023146+0.034720 
## [93] train-rmse:0.323116+0.008492    test-rmse:1.022458+0.033632 
## [94] train-rmse:0.320255+0.008947    test-rmse:1.021556+0.034690 
## [95] train-rmse:0.316761+0.007761    test-rmse:1.022527+0.035287 
## [96] train-rmse:0.314122+0.007517    test-rmse:1.021714+0.033664 
## [97] train-rmse:0.311361+0.008063    test-rmse:1.021430+0.032784 
## [98] train-rmse:0.305525+0.009790    test-rmse:1.020884+0.033017 
## [99] train-rmse:0.302845+0.009701    test-rmse:1.021763+0.034259 
## [100]    train-rmse:0.299774+0.010728    test-rmse:1.021411+0.032726 
## [101]    train-rmse:0.297342+0.011244    test-rmse:1.019748+0.033893 
## [102]    train-rmse:0.294561+0.010832    test-rmse:1.021899+0.035113 
## [103]    train-rmse:0.290426+0.011654    test-rmse:1.020375+0.035899 
## [104]    train-rmse:0.288807+0.011676    test-rmse:1.021152+0.036242 
## [105]    train-rmse:0.286299+0.010960    test-rmse:1.019068+0.037980 
## [106]    train-rmse:0.283225+0.010605    test-rmse:1.019792+0.036509 
## [107]    train-rmse:0.280606+0.011439    test-rmse:1.019614+0.036872 
## [108]    train-rmse:0.276950+0.013238    test-rmse:1.019821+0.036873 
## [109]    train-rmse:0.272663+0.012709    test-rmse:1.019210+0.036423 
## [110]    train-rmse:0.270315+0.012345    test-rmse:1.018986+0.035290 
## [111]    train-rmse:0.267776+0.012646    test-rmse:1.019998+0.036739 
## [112]    train-rmse:0.265206+0.012859    test-rmse:1.018748+0.035561 
## [113]    train-rmse:0.262922+0.012442    test-rmse:1.018486+0.036028 
## [114]    train-rmse:0.258486+0.013099    test-rmse:1.016109+0.036321 
## [115]    train-rmse:0.255313+0.013215    test-rmse:1.016655+0.037733 
## [116]    train-rmse:0.253445+0.012805    test-rmse:1.016207+0.037185 
## [117]    train-rmse:0.250038+0.011590    test-rmse:1.015969+0.038141 
## [118]    train-rmse:0.247560+0.011278    test-rmse:1.015887+0.038486 
## [119]    train-rmse:0.245002+0.010387    test-rmse:1.014430+0.039771 
## [120]    train-rmse:0.242515+0.009914    test-rmse:1.013139+0.039937 
## [121]    train-rmse:0.240322+0.008917    test-rmse:1.012867+0.039912 
## [122]    train-rmse:0.238060+0.009160    test-rmse:1.010202+0.043261 
## [123]    train-rmse:0.235970+0.009191    test-rmse:1.009213+0.044334 
## [124]    train-rmse:0.233422+0.009313    test-rmse:1.007799+0.044537 
## [125]    train-rmse:0.230901+0.008935    test-rmse:1.007340+0.043624 
## [126]    train-rmse:0.227700+0.009703    test-rmse:1.005869+0.045534 
## [127]    train-rmse:0.226051+0.009816    test-rmse:1.006058+0.045981 
## [128]    train-rmse:0.224316+0.009632    test-rmse:1.005579+0.046493 
## [129]    train-rmse:0.222636+0.010000    test-rmse:1.004700+0.046430 
## [130]    train-rmse:0.219738+0.010332    test-rmse:1.003977+0.047121 
## [131]    train-rmse:0.217457+0.010449    test-rmse:1.003630+0.044884 
## [132]    train-rmse:0.215235+0.010954    test-rmse:1.003873+0.046200 
## [133]    train-rmse:0.212324+0.011867    test-rmse:1.003565+0.046127 
## [134]    train-rmse:0.210504+0.011584    test-rmse:1.004178+0.047192 
## [135]    train-rmse:0.207833+0.012532    test-rmse:1.002681+0.046130 
## [136]    train-rmse:0.205332+0.013630    test-rmse:1.002328+0.044888 
## [137]    train-rmse:0.203175+0.013867    test-rmse:1.002070+0.044738 
## [138]    train-rmse:0.201722+0.013761    test-rmse:1.002076+0.045134 
## [139]    train-rmse:0.198556+0.012386    test-rmse:1.002441+0.046254 
## [140]    train-rmse:0.196918+0.011789    test-rmse:1.002156+0.046390 
## [141]    train-rmse:0.194627+0.011625    test-rmse:1.002440+0.046779 
## [142]    train-rmse:0.192388+0.011870    test-rmse:1.001857+0.046967 
## [143]    train-rmse:0.190415+0.011542    test-rmse:1.000497+0.046484 
## [144]    train-rmse:0.188774+0.011400    test-rmse:1.000185+0.046308 
## [145]    train-rmse:0.186427+0.011416    test-rmse:0.999967+0.046827 
## [146]    train-rmse:0.184977+0.011377    test-rmse:1.001287+0.047367 
## [147]    train-rmse:0.183364+0.011568    test-rmse:1.000360+0.048623 
## [148]    train-rmse:0.181268+0.011322    test-rmse:0.999693+0.047906 
## [149]    train-rmse:0.179814+0.011600    test-rmse:1.000200+0.048710 
## [150]    train-rmse:0.177711+0.010514    test-rmse:0.999379+0.049410 
## [151]    train-rmse:0.175822+0.010326    test-rmse:0.998310+0.048860 
## [152]    train-rmse:0.174220+0.010669    test-rmse:0.998687+0.048343 
## [153]    train-rmse:0.172620+0.010393    test-rmse:0.999129+0.048508 
## [154]    train-rmse:0.171493+0.010286    test-rmse:0.999546+0.048483 
## [155]    train-rmse:0.170249+0.010799    test-rmse:0.999633+0.048878 
## [156]    train-rmse:0.168969+0.010567    test-rmse:0.999503+0.049439 
## [157]    train-rmse:0.167268+0.010224    test-rmse:0.999624+0.050190 
## Stopping. Best iteration:
## [151]    train-rmse:0.175822+0.010326    test-rmse:0.998310+0.048860
## 
## 80 
## [1]  train-rmse:67.089722+0.067155   test-rmse:67.090222+0.426122 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:45.720995+0.045347   test-rmse:45.720976+0.446533 
## [3]  train-rmse:31.177771+0.030287   test-rmse:31.177018+0.459422 
## [4]  train-rmse:21.288879+0.019834   test-rmse:21.287091+0.466458 
## [5]  train-rmse:14.577831+0.012778   test-rmse:14.574641+0.468189 
## [6]  train-rmse:10.033350+0.007985   test-rmse:10.019403+0.432742 
## [7]  train-rmse:6.956906+0.007680    test-rmse:6.934376+0.398368 
## [8]  train-rmse:4.872694+0.007449    test-rmse:4.858835+0.348119 
## [9]  train-rmse:3.470061+0.006883    test-rmse:3.473220+0.299592 
## [10] train-rmse:2.536244+0.006255    test-rmse:2.576310+0.250312 
## [11] train-rmse:1.915540+0.007966    test-rmse:2.004242+0.230139 
## [12] train-rmse:1.523008+0.011436    test-rmse:1.684663+0.202591 
## [13] train-rmse:1.277627+0.018611    test-rmse:1.478431+0.185683 
## [14] train-rmse:1.116616+0.009198    test-rmse:1.372157+0.158453 
## [15] train-rmse:1.020066+0.005357    test-rmse:1.318178+0.151066 
## [16] train-rmse:0.952997+0.007605    test-rmse:1.276803+0.143651 
## [17] train-rmse:0.906487+0.007915    test-rmse:1.241829+0.139844 
## [18] train-rmse:0.866357+0.007435    test-rmse:1.216814+0.138375 
## [19] train-rmse:0.823058+0.009540    test-rmse:1.192293+0.126413 
## [20] train-rmse:0.794079+0.011791    test-rmse:1.171485+0.128949 
## [21] train-rmse:0.767563+0.013932    test-rmse:1.162257+0.127317 
## [22] train-rmse:0.745397+0.012557    test-rmse:1.147047+0.122275 
## [23] train-rmse:0.716064+0.009671    test-rmse:1.138226+0.125883 
## [24] train-rmse:0.689699+0.014119    test-rmse:1.128800+0.125474 
## [25] train-rmse:0.671358+0.013642    test-rmse:1.123582+0.123098 
## [26] train-rmse:0.650340+0.013934    test-rmse:1.108509+0.118763 
## [27] train-rmse:0.633929+0.012353    test-rmse:1.103678+0.121629 
## [28] train-rmse:0.613539+0.010762    test-rmse:1.087659+0.113910 
## [29] train-rmse:0.599436+0.015197    test-rmse:1.084613+0.113490 
## [30] train-rmse:0.580552+0.011276    test-rmse:1.081559+0.111294 
## [31] train-rmse:0.563293+0.016187    test-rmse:1.073960+0.114482 
## [32] train-rmse:0.548889+0.015232    test-rmse:1.066204+0.112507 
## [33] train-rmse:0.538207+0.016614    test-rmse:1.064426+0.111093 
## [34] train-rmse:0.523241+0.017142    test-rmse:1.061011+0.113211 
## [35] train-rmse:0.509455+0.013928    test-rmse:1.052798+0.110273 
## [36] train-rmse:0.496333+0.017387    test-rmse:1.049917+0.106823 
## [37] train-rmse:0.481142+0.017796    test-rmse:1.051090+0.107123 
## [38] train-rmse:0.467526+0.017820    test-rmse:1.049128+0.109862 
## [39] train-rmse:0.460105+0.018802    test-rmse:1.043863+0.107420 
## [40] train-rmse:0.450015+0.017715    test-rmse:1.042174+0.112168 
## [41] train-rmse:0.437499+0.014596    test-rmse:1.041963+0.109711 
## [42] train-rmse:0.428084+0.012049    test-rmse:1.039659+0.106040 
## [43] train-rmse:0.421240+0.012268    test-rmse:1.035800+0.103347 
## [44] train-rmse:0.413745+0.012974    test-rmse:1.032662+0.105035 
## [45] train-rmse:0.406211+0.012264    test-rmse:1.029006+0.104973 
## [46] train-rmse:0.397959+0.012351    test-rmse:1.030096+0.108316 
## [47] train-rmse:0.387438+0.010594    test-rmse:1.029119+0.109283 
## [48] train-rmse:0.379576+0.009851    test-rmse:1.029674+0.111040 
## [49] train-rmse:0.372512+0.011261    test-rmse:1.026187+0.111051 
## [50] train-rmse:0.362431+0.013090    test-rmse:1.022809+0.107627 
## [51] train-rmse:0.353614+0.016554    test-rmse:1.023423+0.107849 
## [52] train-rmse:0.347168+0.015210    test-rmse:1.019415+0.104823 
## [53] train-rmse:0.340412+0.013127    test-rmse:1.016040+0.103817 
## [54] train-rmse:0.332437+0.011228    test-rmse:1.012835+0.101892 
## [55] train-rmse:0.326642+0.010561    test-rmse:1.008421+0.101820 
## [56] train-rmse:0.321813+0.010319    test-rmse:1.005549+0.102796 
## [57] train-rmse:0.315600+0.010078    test-rmse:1.007709+0.103728 
## [58] train-rmse:0.310795+0.012421    test-rmse:1.007622+0.103939 
## [59] train-rmse:0.304346+0.011681    test-rmse:1.005196+0.102405 
## [60] train-rmse:0.297620+0.012022    test-rmse:1.003783+0.101053 
## [61] train-rmse:0.292933+0.011351    test-rmse:1.004030+0.098981 
## [62] train-rmse:0.287287+0.009464    test-rmse:1.001175+0.098498 
## [63] train-rmse:0.282982+0.009734    test-rmse:1.002573+0.099139 
## [64] train-rmse:0.275170+0.009098    test-rmse:1.001157+0.099718 
## [65] train-rmse:0.269767+0.010164    test-rmse:1.000761+0.099760 
## [66] train-rmse:0.266844+0.009838    test-rmse:0.998719+0.100444 
## [67] train-rmse:0.263086+0.009851    test-rmse:0.997860+0.101307 
## [68] train-rmse:0.258617+0.011796    test-rmse:0.997753+0.099765 
## [69] train-rmse:0.253306+0.011185    test-rmse:0.998878+0.098948 
## [70] train-rmse:0.250082+0.012212    test-rmse:0.998238+0.099704 
## [71] train-rmse:0.245170+0.012182    test-rmse:0.997912+0.099890 
## [72] train-rmse:0.239390+0.011235    test-rmse:0.997557+0.100097 
## [73] train-rmse:0.232674+0.010275    test-rmse:0.997575+0.100935 
## [74] train-rmse:0.228959+0.010916    test-rmse:0.996451+0.099538 
## [75] train-rmse:0.225378+0.011774    test-rmse:0.996794+0.100509 
## [76] train-rmse:0.223161+0.011250    test-rmse:0.995358+0.101477 
## [77] train-rmse:0.219252+0.010478    test-rmse:0.993581+0.101900 
## [78] train-rmse:0.214842+0.011953    test-rmse:0.991804+0.102585 
## [79] train-rmse:0.211119+0.013828    test-rmse:0.991408+0.102134 
## [80] train-rmse:0.206116+0.014963    test-rmse:0.992010+0.103446 
## [81] train-rmse:0.201936+0.015371    test-rmse:0.990555+0.103897 
## [82] train-rmse:0.197373+0.015740    test-rmse:0.992538+0.104442 
## [83] train-rmse:0.193040+0.015941    test-rmse:0.992060+0.104332 
## [84] train-rmse:0.190183+0.015459    test-rmse:0.990611+0.105028 
## [85] train-rmse:0.186215+0.015256    test-rmse:0.991446+0.105204 
## [86] train-rmse:0.182848+0.016572    test-rmse:0.991072+0.104160 
## [87] train-rmse:0.180259+0.015561    test-rmse:0.990036+0.104072 
## [88] train-rmse:0.176741+0.016061    test-rmse:0.989997+0.104096 
## [89] train-rmse:0.173491+0.014900    test-rmse:0.989473+0.103842 
## [90] train-rmse:0.170741+0.013336    test-rmse:0.989815+0.103167 
## [91] train-rmse:0.167623+0.013757    test-rmse:0.990749+0.103063 
## [92] train-rmse:0.165567+0.013787    test-rmse:0.990327+0.103426 
## [93] train-rmse:0.163603+0.013916    test-rmse:0.989393+0.102838 
## [94] train-rmse:0.160820+0.014381    test-rmse:0.989583+0.102723 
## [95] train-rmse:0.158273+0.013511    test-rmse:0.989214+0.103594 
## [96] train-rmse:0.155806+0.013274    test-rmse:0.989096+0.103789 
## [97] train-rmse:0.152438+0.012603    test-rmse:0.988312+0.104589 
## [98] train-rmse:0.149402+0.012266    test-rmse:0.987211+0.105118 
## [99] train-rmse:0.146172+0.012245    test-rmse:0.987487+0.105425 
## [100]    train-rmse:0.143426+0.012775    test-rmse:0.987538+0.105934 
## [101]    train-rmse:0.139545+0.012514    test-rmse:0.986602+0.105910 
## [102]    train-rmse:0.137441+0.012083    test-rmse:0.987620+0.105310 
## [103]    train-rmse:0.136018+0.012057    test-rmse:0.987601+0.105175 
## [104]    train-rmse:0.133491+0.011383    test-rmse:0.987779+0.105221 
## [105]    train-rmse:0.131804+0.011523    test-rmse:0.986916+0.104670 
## [106]    train-rmse:0.129218+0.011037    test-rmse:0.985951+0.105238 
## [107]    train-rmse:0.127478+0.010853    test-rmse:0.986051+0.105517 
## [108]    train-rmse:0.125741+0.010656    test-rmse:0.985447+0.105557 
## [109]    train-rmse:0.122389+0.010240    test-rmse:0.984915+0.104893 
## [110]    train-rmse:0.120697+0.009718    test-rmse:0.984833+0.105023 
## [111]    train-rmse:0.118600+0.009544    test-rmse:0.983963+0.105664 
## [112]    train-rmse:0.116699+0.009009    test-rmse:0.983164+0.105158 
## [113]    train-rmse:0.114976+0.009021    test-rmse:0.982418+0.104456 
## [114]    train-rmse:0.113000+0.009078    test-rmse:0.982274+0.103610 
## [115]    train-rmse:0.110841+0.008901    test-rmse:0.982522+0.103167 
## [116]    train-rmse:0.108397+0.007685    test-rmse:0.983378+0.103569 
## [117]    train-rmse:0.106646+0.007849    test-rmse:0.983455+0.104021 
## [118]    train-rmse:0.104197+0.008665    test-rmse:0.983395+0.103882 
## [119]    train-rmse:0.102206+0.008606    test-rmse:0.983466+0.104231 
## [120]    train-rmse:0.100153+0.008380    test-rmse:0.983040+0.104789 
## Stopping. Best iteration:
## [114]    train-rmse:0.113000+0.009078    test-rmse:0.982274+0.103610
## 
## 81 
## [1]  train-rmse:67.089722+0.067155   test-rmse:67.090222+0.426122 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:45.720995+0.045347   test-rmse:45.720976+0.446533 
## [3]  train-rmse:31.177771+0.030287   test-rmse:31.177018+0.459422 
## [4]  train-rmse:21.288879+0.019834   test-rmse:21.287091+0.466458 
## [5]  train-rmse:14.577831+0.012778   test-rmse:14.574641+0.468189 
## [6]  train-rmse:10.033350+0.007985   test-rmse:10.019403+0.432742 
## [7]  train-rmse:6.956287+0.007933    test-rmse:6.928342+0.392669 
## [8]  train-rmse:4.869886+0.007605    test-rmse:4.849505+0.342202 
## [9]  train-rmse:3.462509+0.006692    test-rmse:3.458477+0.295323 
## [10] train-rmse:2.515476+0.007873    test-rmse:2.548650+0.250993 
## [11] train-rmse:1.875970+0.013144    test-rmse:1.954929+0.217260 
## [12] train-rmse:1.459934+0.013508    test-rmse:1.618167+0.186677 
## [13] train-rmse:1.200365+0.015045    test-rmse:1.423315+0.175581 
## [14] train-rmse:1.033313+0.024410    test-rmse:1.305405+0.164056 
## [15] train-rmse:0.915805+0.021380    test-rmse:1.246094+0.149270 
## [16] train-rmse:0.842514+0.025823    test-rmse:1.204662+0.144297 
## [17] train-rmse:0.784503+0.027133    test-rmse:1.179407+0.138032 
## [18] train-rmse:0.738357+0.017792    test-rmse:1.141910+0.127976 
## [19] train-rmse:0.699127+0.025338    test-rmse:1.118456+0.124614 
## [20] train-rmse:0.665714+0.024472    test-rmse:1.106991+0.116689 
## [21] train-rmse:0.639457+0.026286    test-rmse:1.100314+0.118245 
## [22] train-rmse:0.613614+0.027377    test-rmse:1.090065+0.114892 
## [23] train-rmse:0.589479+0.024453    test-rmse:1.081126+0.116300 
## [24] train-rmse:0.564804+0.019093    test-rmse:1.071142+0.118434 
## [25] train-rmse:0.549740+0.018065    test-rmse:1.063216+0.117917 
## [26] train-rmse:0.527894+0.019154    test-rmse:1.051483+0.114410 
## [27] train-rmse:0.514392+0.020640    test-rmse:1.046118+0.114377 
## [28] train-rmse:0.493985+0.023146    test-rmse:1.035025+0.103106 
## [29] train-rmse:0.478914+0.021962    test-rmse:1.031011+0.102450 
## [30] train-rmse:0.465420+0.021302    test-rmse:1.030964+0.110415 
## [31] train-rmse:0.453726+0.022778    test-rmse:1.028855+0.108367 
## [32] train-rmse:0.436057+0.019812    test-rmse:1.016645+0.104890 
## [33] train-rmse:0.423081+0.021640    test-rmse:1.015287+0.100308 
## [34] train-rmse:0.411886+0.025068    test-rmse:1.010778+0.098583 
## [35] train-rmse:0.394388+0.022186    test-rmse:1.002986+0.094754 
## [36] train-rmse:0.379713+0.021448    test-rmse:1.001716+0.094934 
## [37] train-rmse:0.368488+0.020075    test-rmse:1.002049+0.093945 
## [38] train-rmse:0.360448+0.019309    test-rmse:1.000359+0.093266 
## [39] train-rmse:0.348100+0.017689    test-rmse:1.000034+0.095049 
## [40] train-rmse:0.336232+0.019795    test-rmse:0.998471+0.094074 
## [41] train-rmse:0.324132+0.016384    test-rmse:0.994577+0.093983 
## [42] train-rmse:0.312723+0.012564    test-rmse:0.995130+0.090275 
## [43] train-rmse:0.302672+0.015240    test-rmse:0.995199+0.092293 
## [44] train-rmse:0.293621+0.016198    test-rmse:0.990665+0.093443 
## [45] train-rmse:0.285876+0.017651    test-rmse:0.987182+0.091207 
## [46] train-rmse:0.279311+0.016977    test-rmse:0.982211+0.091787 
## [47] train-rmse:0.272470+0.016869    test-rmse:0.983680+0.092725 
## [48] train-rmse:0.260527+0.017400    test-rmse:0.983156+0.091477 
## [49] train-rmse:0.254399+0.016140    test-rmse:0.982817+0.091475 
## [50] train-rmse:0.246937+0.014072    test-rmse:0.980851+0.092321 
## [51] train-rmse:0.241149+0.012287    test-rmse:0.981009+0.091892 
## [52] train-rmse:0.231355+0.012657    test-rmse:0.978459+0.092378 
## [53] train-rmse:0.224220+0.015434    test-rmse:0.977695+0.092143 
## [54] train-rmse:0.217320+0.013000    test-rmse:0.973743+0.093266 
## [55] train-rmse:0.212106+0.012908    test-rmse:0.972848+0.092897 
## [56] train-rmse:0.207660+0.013040    test-rmse:0.972600+0.092268 
## [57] train-rmse:0.200845+0.013372    test-rmse:0.972285+0.094202 
## [58] train-rmse:0.196084+0.013073    test-rmse:0.972436+0.094881 
## [59] train-rmse:0.187903+0.015106    test-rmse:0.972234+0.093727 
## [60] train-rmse:0.182731+0.015776    test-rmse:0.971376+0.093096 
## [61] train-rmse:0.178853+0.016017    test-rmse:0.971463+0.093416 
## [62] train-rmse:0.172521+0.014070    test-rmse:0.972798+0.094657 
## [63] train-rmse:0.167674+0.014099    test-rmse:0.969996+0.094277 
## [64] train-rmse:0.164553+0.013714    test-rmse:0.970190+0.094745 
## [65] train-rmse:0.160690+0.014260    test-rmse:0.968263+0.096309 
## [66] train-rmse:0.156842+0.014719    test-rmse:0.967872+0.097124 
## [67] train-rmse:0.153822+0.015277    test-rmse:0.967724+0.097592 
## [68] train-rmse:0.151073+0.015498    test-rmse:0.965345+0.098959 
## [69] train-rmse:0.145202+0.014280    test-rmse:0.964776+0.099020 
## [70] train-rmse:0.140617+0.012677    test-rmse:0.964346+0.099886 
## [71] train-rmse:0.138330+0.012741    test-rmse:0.963747+0.099403 
## [72] train-rmse:0.133610+0.011255    test-rmse:0.964483+0.100219 
## [73] train-rmse:0.130181+0.011536    test-rmse:0.964024+0.099744 
## [74] train-rmse:0.128549+0.011968    test-rmse:0.964636+0.100130 
## [75] train-rmse:0.123525+0.013178    test-rmse:0.964205+0.100890 
## [76] train-rmse:0.119933+0.012340    test-rmse:0.963413+0.100006 
## [77] train-rmse:0.116451+0.010609    test-rmse:0.963531+0.100550 
## [78] train-rmse:0.113575+0.011097    test-rmse:0.963244+0.100523 
## [79] train-rmse:0.111594+0.010410    test-rmse:0.963220+0.100090 
## [80] train-rmse:0.108063+0.010964    test-rmse:0.963110+0.100017 
## [81] train-rmse:0.104696+0.010423    test-rmse:0.963006+0.100318 
## [82] train-rmse:0.101196+0.009148    test-rmse:0.963251+0.100337 
## [83] train-rmse:0.098515+0.008977    test-rmse:0.962491+0.100597 
## [84] train-rmse:0.095263+0.008656    test-rmse:0.962582+0.100796 
## [85] train-rmse:0.092855+0.009262    test-rmse:0.962612+0.100727 
## [86] train-rmse:0.090992+0.008989    test-rmse:0.962813+0.100907 
## [87] train-rmse:0.089385+0.008878    test-rmse:0.962680+0.101223 
## [88] train-rmse:0.086615+0.008094    test-rmse:0.962631+0.100868 
## [89] train-rmse:0.084549+0.008536    test-rmse:0.962501+0.101142 
## Stopping. Best iteration:
## [83] train-rmse:0.098515+0.008977    test-rmse:0.962491+0.100597
## 
## 82 
## [1]  train-rmse:67.089722+0.067155   test-rmse:67.090222+0.426122 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:45.720995+0.045347   test-rmse:45.720976+0.446533 
## [3]  train-rmse:31.177771+0.030287   test-rmse:31.177018+0.459422 
## [4]  train-rmse:21.288879+0.019834   test-rmse:21.287091+0.466458 
## [5]  train-rmse:14.577831+0.012778   test-rmse:14.574641+0.468189 
## [6]  train-rmse:10.033350+0.007985   test-rmse:10.019403+0.432742 
## [7]  train-rmse:6.956287+0.007933    test-rmse:6.928342+0.392669 
## [8]  train-rmse:4.868580+0.007211    test-rmse:4.845417+0.341855 
## [9]  train-rmse:3.456576+0.007332    test-rmse:3.450492+0.280140 
## [10] train-rmse:2.497755+0.008618    test-rmse:2.533760+0.254205 
## [11] train-rmse:1.850610+0.008693    test-rmse:1.949656+0.231075 
## [12] train-rmse:1.413716+0.005851    test-rmse:1.598175+0.201705 
## [13] train-rmse:1.132529+0.013211    test-rmse:1.398451+0.166158 
## [14] train-rmse:0.945690+0.013688    test-rmse:1.269640+0.152285 
## [15] train-rmse:0.818157+0.016349    test-rmse:1.189824+0.144827 
## [16] train-rmse:0.731902+0.020714    test-rmse:1.152963+0.132263 
## [17] train-rmse:0.667798+0.020090    test-rmse:1.126590+0.133157 
## [18] train-rmse:0.621693+0.020676    test-rmse:1.106770+0.129230 
## [19] train-rmse:0.586626+0.026359    test-rmse:1.090716+0.134020 
## [20] train-rmse:0.552378+0.025679    test-rmse:1.077178+0.134767 
## [21] train-rmse:0.520590+0.020066    test-rmse:1.059872+0.125874 
## [22] train-rmse:0.495942+0.019082    test-rmse:1.051975+0.128329 
## [23] train-rmse:0.469992+0.019538    test-rmse:1.041908+0.133380 
## [24] train-rmse:0.452201+0.020366    test-rmse:1.029210+0.127647 
## [25] train-rmse:0.436874+0.019212    test-rmse:1.019212+0.121968 
## [26] train-rmse:0.419404+0.013421    test-rmse:1.011079+0.118360 
## [27] train-rmse:0.401320+0.016166    test-rmse:1.006988+0.119895 
## [28] train-rmse:0.383231+0.013822    test-rmse:1.001242+0.119779 
## [29] train-rmse:0.365749+0.013692    test-rmse:0.999472+0.116311 
## [30] train-rmse:0.349158+0.013758    test-rmse:0.998948+0.116781 
## [31] train-rmse:0.337948+0.014351    test-rmse:0.997234+0.118858 
## [32] train-rmse:0.322119+0.014740    test-rmse:0.987515+0.118133 
## [33] train-rmse:0.309504+0.019932    test-rmse:0.985648+0.118667 
## [34] train-rmse:0.300242+0.017502    test-rmse:0.984679+0.119432 
## [35] train-rmse:0.289527+0.017272    test-rmse:0.982615+0.119984 
## [36] train-rmse:0.277690+0.016121    test-rmse:0.981911+0.119982 
## [37] train-rmse:0.265858+0.015814    test-rmse:0.979149+0.121243 
## [38] train-rmse:0.252781+0.013739    test-rmse:0.973372+0.117986 
## [39] train-rmse:0.244644+0.010520    test-rmse:0.973961+0.120213 
## [40] train-rmse:0.234698+0.010214    test-rmse:0.974919+0.123117 
## [41] train-rmse:0.227114+0.012821    test-rmse:0.972680+0.119220 
## [42] train-rmse:0.220655+0.012546    test-rmse:0.970708+0.121181 
## [43] train-rmse:0.214774+0.012173    test-rmse:0.972655+0.121757 
## [44] train-rmse:0.205000+0.011713    test-rmse:0.969938+0.124761 
## [45] train-rmse:0.196736+0.013520    test-rmse:0.966750+0.125753 
## [46] train-rmse:0.190302+0.011504    test-rmse:0.964893+0.127115 
## [47] train-rmse:0.184833+0.011123    test-rmse:0.964585+0.126579 
## [48] train-rmse:0.175915+0.012143    test-rmse:0.963254+0.127250 
## [49] train-rmse:0.170024+0.014291    test-rmse:0.962588+0.127713 
## [50] train-rmse:0.161440+0.014198    test-rmse:0.962476+0.128023 
## [51] train-rmse:0.157331+0.013682    test-rmse:0.961754+0.128068 
## [52] train-rmse:0.153396+0.012364    test-rmse:0.960640+0.128203 
## [53] train-rmse:0.147368+0.014537    test-rmse:0.959936+0.125502 
## [54] train-rmse:0.141831+0.015118    test-rmse:0.959843+0.125969 
## [55] train-rmse:0.136721+0.013477    test-rmse:0.957831+0.127700 
## [56] train-rmse:0.130331+0.014048    test-rmse:0.955823+0.129182 
## [57] train-rmse:0.124948+0.013962    test-rmse:0.955032+0.129304 
## [58] train-rmse:0.120835+0.013602    test-rmse:0.955127+0.130845 
## [59] train-rmse:0.117040+0.011839    test-rmse:0.953341+0.131629 
## [60] train-rmse:0.113607+0.012044    test-rmse:0.953550+0.132040 
## [61] train-rmse:0.108584+0.012008    test-rmse:0.953124+0.131240 
## [62] train-rmse:0.105726+0.012132    test-rmse:0.953400+0.130512 
## [63] train-rmse:0.099611+0.010550    test-rmse:0.952382+0.130313 
## [64] train-rmse:0.095994+0.010703    test-rmse:0.952107+0.130542 
## [65] train-rmse:0.092989+0.011659    test-rmse:0.951703+0.130036 
## [66] train-rmse:0.089284+0.010742    test-rmse:0.951825+0.130521 
## [67] train-rmse:0.085392+0.010324    test-rmse:0.951764+0.129942 
## [68] train-rmse:0.083276+0.010281    test-rmse:0.952017+0.129650 
## [69] train-rmse:0.081678+0.010343    test-rmse:0.951680+0.129890 
## [70] train-rmse:0.079139+0.009962    test-rmse:0.951601+0.129883 
## [71] train-rmse:0.076058+0.009364    test-rmse:0.951851+0.130056 
## [72] train-rmse:0.073380+0.009086    test-rmse:0.952281+0.130363 
## [73] train-rmse:0.070717+0.007952    test-rmse:0.952218+0.129978 
## [74] train-rmse:0.068611+0.008069    test-rmse:0.951719+0.130181 
## [75] train-rmse:0.065854+0.007744    test-rmse:0.951470+0.130360 
## [76] train-rmse:0.064033+0.007620    test-rmse:0.951567+0.130199 
## [77] train-rmse:0.061637+0.007728    test-rmse:0.951592+0.130011 
## [78] train-rmse:0.058643+0.007119    test-rmse:0.951454+0.129783 
## [79] train-rmse:0.056493+0.007175    test-rmse:0.951058+0.130093 
## [80] train-rmse:0.054696+0.006624    test-rmse:0.950935+0.130190 
## [81] train-rmse:0.052298+0.006978    test-rmse:0.951362+0.130595 
## [82] train-rmse:0.050780+0.006870    test-rmse:0.951030+0.130705 
## [83] train-rmse:0.049415+0.006959    test-rmse:0.951088+0.130517 
## [84] train-rmse:0.047574+0.006701    test-rmse:0.950773+0.130456 
## [85] train-rmse:0.045595+0.006084    test-rmse:0.951026+0.130728 
## [86] train-rmse:0.043447+0.006004    test-rmse:0.950941+0.130908 
## [87] train-rmse:0.041917+0.005868    test-rmse:0.950643+0.130974 
## [88] train-rmse:0.040708+0.005957    test-rmse:0.950786+0.130723 
## [89] train-rmse:0.038939+0.005647    test-rmse:0.950416+0.130645 
## [90] train-rmse:0.037714+0.005915    test-rmse:0.950424+0.130691 
## [91] train-rmse:0.036124+0.005988    test-rmse:0.950629+0.130559 
## [92] train-rmse:0.034998+0.005862    test-rmse:0.950718+0.130806 
## [93] train-rmse:0.033765+0.005713    test-rmse:0.950535+0.131140 
## [94] train-rmse:0.032983+0.005857    test-rmse:0.950398+0.131235 
## [95] train-rmse:0.032205+0.005677    test-rmse:0.950289+0.131341 
## [96] train-rmse:0.031254+0.005789    test-rmse:0.950303+0.131321 
## [97] train-rmse:0.029769+0.005949    test-rmse:0.950255+0.131200 
## [98] train-rmse:0.028715+0.005545    test-rmse:0.950336+0.131353 
## [99] train-rmse:0.027992+0.005419    test-rmse:0.950094+0.131453 
## [100]    train-rmse:0.027273+0.005403    test-rmse:0.950412+0.131308 
## [101]    train-rmse:0.026681+0.005614    test-rmse:0.950355+0.131293 
## [102]    train-rmse:0.025738+0.005663    test-rmse:0.950345+0.131220 
## [103]    train-rmse:0.024835+0.005451    test-rmse:0.950424+0.131086 
## [104]    train-rmse:0.023828+0.005053    test-rmse:0.950657+0.131064 
## [105]    train-rmse:0.022934+0.005134    test-rmse:0.950646+0.131018 
## Stopping. Best iteration:
## [99] train-rmse:0.027992+0.005419    test-rmse:0.950094+0.131453
## 
## 83 
## [1]  train-rmse:67.089722+0.067155   test-rmse:67.090222+0.426122 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:45.720995+0.045347   test-rmse:45.720976+0.446533 
## [3]  train-rmse:31.177771+0.030287   test-rmse:31.177018+0.459422 
## [4]  train-rmse:21.288879+0.019834   test-rmse:21.287091+0.466458 
## [5]  train-rmse:14.577831+0.012778   test-rmse:14.574641+0.468189 
## [6]  train-rmse:10.033350+0.007985   test-rmse:10.019403+0.432742 
## [7]  train-rmse:6.956287+0.007933    test-rmse:6.928342+0.392669 
## [8]  train-rmse:4.867821+0.007179    test-rmse:4.840103+0.344260 
## [9]  train-rmse:3.452738+0.008047    test-rmse:3.444994+0.277931 
## [10] train-rmse:2.488319+0.009252    test-rmse:2.526181+0.252209 
## [11] train-rmse:1.836971+0.014895    test-rmse:1.938387+0.222542 
## [12] train-rmse:1.387315+0.010646    test-rmse:1.574310+0.195619 
## [13] train-rmse:1.092997+0.017196    test-rmse:1.374158+0.174826 
## [14] train-rmse:0.888691+0.016086    test-rmse:1.247996+0.155203 
## [15] train-rmse:0.746396+0.018556    test-rmse:1.166628+0.123272 
## [16] train-rmse:0.650449+0.015990    test-rmse:1.116639+0.118748 
## [17] train-rmse:0.583005+0.018383    test-rmse:1.086660+0.104937 
## [18] train-rmse:0.532540+0.025126    test-rmse:1.071279+0.111787 
## [19] train-rmse:0.487477+0.034467    test-rmse:1.063139+0.114961 
## [20] train-rmse:0.457492+0.032546    test-rmse:1.050534+0.111757 
## [21] train-rmse:0.417279+0.023760    test-rmse:1.037835+0.108237 
## [22] train-rmse:0.393038+0.022467    test-rmse:1.029022+0.108360 
## [23] train-rmse:0.365392+0.021834    test-rmse:1.021121+0.109435 
## [24] train-rmse:0.346151+0.021397    test-rmse:1.018517+0.111291 
## [25] train-rmse:0.326435+0.022573    test-rmse:1.012978+0.116293 
## [26] train-rmse:0.310040+0.021828    test-rmse:1.010569+0.118110 
## [27] train-rmse:0.292313+0.020338    test-rmse:1.007744+0.118600 
## [28] train-rmse:0.275945+0.021799    test-rmse:1.001396+0.120437 
## [29] train-rmse:0.263505+0.022512    test-rmse:0.995314+0.120283 
## [30] train-rmse:0.249335+0.023577    test-rmse:0.992613+0.119644 
## [31] train-rmse:0.238702+0.022662    test-rmse:0.989592+0.118595 
## [32] train-rmse:0.225614+0.020034    test-rmse:0.987463+0.120719 
## [33] train-rmse:0.218123+0.019387    test-rmse:0.984709+0.122533 
## [34] train-rmse:0.207470+0.020446    test-rmse:0.985475+0.122770 
## [35] train-rmse:0.197229+0.020864    test-rmse:0.984610+0.122683 
## [36] train-rmse:0.189198+0.017501    test-rmse:0.983307+0.123988 
## [37] train-rmse:0.179543+0.018753    test-rmse:0.981133+0.124503 
## [38] train-rmse:0.171671+0.019359    test-rmse:0.980061+0.124913 
## [39] train-rmse:0.166234+0.019556    test-rmse:0.976861+0.124929 
## [40] train-rmse:0.157958+0.019946    test-rmse:0.975696+0.123368 
## [41] train-rmse:0.149835+0.017068    test-rmse:0.975003+0.125449 
## [42] train-rmse:0.142294+0.013992    test-rmse:0.973754+0.124970 
## [43] train-rmse:0.133784+0.013182    test-rmse:0.973170+0.125125 
## [44] train-rmse:0.128105+0.013303    test-rmse:0.972571+0.125260 
## [45] train-rmse:0.122302+0.011970    test-rmse:0.972574+0.123808 
## [46] train-rmse:0.117873+0.011184    test-rmse:0.971893+0.123623 
## [47] train-rmse:0.111348+0.009955    test-rmse:0.973099+0.122377 
## [48] train-rmse:0.107038+0.010762    test-rmse:0.970540+0.123424 
## [49] train-rmse:0.101366+0.011409    test-rmse:0.969891+0.124874 
## [50] train-rmse:0.097624+0.011415    test-rmse:0.968927+0.125543 
## [51] train-rmse:0.093178+0.012039    test-rmse:0.968521+0.124885 
## [52] train-rmse:0.088851+0.011049    test-rmse:0.968707+0.124914 
## [53] train-rmse:0.084979+0.009914    test-rmse:0.968110+0.124885 
## [54] train-rmse:0.082122+0.009328    test-rmse:0.967014+0.124645 
## [55] train-rmse:0.079430+0.008921    test-rmse:0.966761+0.124507 
## [56] train-rmse:0.074952+0.008717    test-rmse:0.966839+0.125575 
## [57] train-rmse:0.070989+0.007048    test-rmse:0.967105+0.124832 
## [58] train-rmse:0.068446+0.007537    test-rmse:0.966635+0.124947 
## [59] train-rmse:0.065160+0.006939    test-rmse:0.967213+0.126039 
## [60] train-rmse:0.063064+0.006237    test-rmse:0.966833+0.125570 
## [61] train-rmse:0.058857+0.005502    test-rmse:0.967858+0.125441 
## [62] train-rmse:0.056732+0.005992    test-rmse:0.968105+0.125737 
## [63] train-rmse:0.054330+0.005292    test-rmse:0.967559+0.125580 
## [64] train-rmse:0.051801+0.004104    test-rmse:0.967717+0.125612 
## Stopping. Best iteration:
## [58] train-rmse:0.068446+0.007537    test-rmse:0.966635+0.124947
## 
## 84 
## [1]  train-rmse:65.128522+0.065151   test-rmse:65.128984+0.428029 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:43.089980+0.042641   test-rmse:43.089869+0.448962 
## [3]  train-rmse:28.532279+0.027510   test-rmse:28.531320+0.461535 
## [4]  train-rmse:18.927944+0.017318   test-rmse:18.925764+0.467564 
## [5]  train-rmse:12.609187+0.010926   test-rmse:12.605351+0.467328 
## [6]  train-rmse:8.465691+0.007995    test-rmse:8.464860+0.425645 
## [7]  train-rmse:5.768139+0.008530    test-rmse:5.765397+0.404039 
## [8]  train-rmse:4.039267+0.012868    test-rmse:4.032792+0.378724 
## [9]  train-rmse:2.967184+0.018449    test-rmse:2.974938+0.326507 
## [10] train-rmse:2.328054+0.023721    test-rmse:2.355448+0.270681 
## [11] train-rmse:1.968015+0.026199    test-rmse:2.008427+0.192188 
## [12] train-rmse:1.773680+0.026876    test-rmse:1.834205+0.146352 
## [13] train-rmse:1.666251+0.026661    test-rmse:1.749280+0.123601 
## [14] train-rmse:1.602783+0.026666    test-rmse:1.698691+0.121517 
## [15] train-rmse:1.560992+0.028581    test-rmse:1.644681+0.109782 
## [16] train-rmse:1.531632+0.027736    test-rmse:1.630104+0.114997 
## [17] train-rmse:1.507780+0.026228    test-rmse:1.612489+0.111091 
## [18] train-rmse:1.486238+0.024173    test-rmse:1.598828+0.112324 
## [19] train-rmse:1.467742+0.024640    test-rmse:1.582284+0.117589 
## [20] train-rmse:1.450937+0.023898    test-rmse:1.571867+0.113546 
## [21] train-rmse:1.435051+0.023938    test-rmse:1.569471+0.117913 
## [22] train-rmse:1.419363+0.024041    test-rmse:1.566913+0.113601 
## [23] train-rmse:1.404865+0.024066    test-rmse:1.555006+0.114824 
## [24] train-rmse:1.391161+0.023928    test-rmse:1.530228+0.103088 
## [25] train-rmse:1.378189+0.023949    test-rmse:1.534257+0.103182 
## [26] train-rmse:1.365964+0.023803    test-rmse:1.526954+0.103043 
## [27] train-rmse:1.354394+0.023297    test-rmse:1.514704+0.097936 
## [28] train-rmse:1.342697+0.022928    test-rmse:1.500785+0.098217 
## [29] train-rmse:1.331495+0.022954    test-rmse:1.493688+0.108424 
## [30] train-rmse:1.320692+0.022901    test-rmse:1.491054+0.098335 
## [31] train-rmse:1.310168+0.022856    test-rmse:1.485991+0.096375 
## [32] train-rmse:1.300268+0.022841    test-rmse:1.483012+0.096813 
## [33] train-rmse:1.290638+0.022525    test-rmse:1.475828+0.086476 
## [34] train-rmse:1.281418+0.022223    test-rmse:1.472043+0.088529 
## [35] train-rmse:1.272152+0.022051    test-rmse:1.463163+0.091139 
## [36] train-rmse:1.263070+0.021895    test-rmse:1.455360+0.085765 
## [37] train-rmse:1.254197+0.021675    test-rmse:1.453839+0.087269 
## [38] train-rmse:1.245507+0.021316    test-rmse:1.438188+0.091075 
## [39] train-rmse:1.237112+0.020884    test-rmse:1.428659+0.093226 
## [40] train-rmse:1.229168+0.020705    test-rmse:1.421684+0.094785 
## [41] train-rmse:1.221320+0.020715    test-rmse:1.417245+0.096834 
## [42] train-rmse:1.214231+0.020980    test-rmse:1.411018+0.094911 
## [43] train-rmse:1.207431+0.020879    test-rmse:1.407154+0.098625 
## [44] train-rmse:1.200623+0.020913    test-rmse:1.410117+0.089416 
## [45] train-rmse:1.194322+0.020972    test-rmse:1.408983+0.083445 
## [46] train-rmse:1.188107+0.021263    test-rmse:1.403239+0.089194 
## [47] train-rmse:1.182108+0.021378    test-rmse:1.397397+0.084583 
## [48] train-rmse:1.176189+0.021460    test-rmse:1.387906+0.076843 
## [49] train-rmse:1.170253+0.021418    test-rmse:1.383165+0.078129 
## [50] train-rmse:1.164591+0.021530    test-rmse:1.379683+0.076285 
## [51] train-rmse:1.159129+0.021368    test-rmse:1.374172+0.081452 
## [52] train-rmse:1.153887+0.021191    test-rmse:1.367962+0.083346 
## [53] train-rmse:1.148693+0.021151    test-rmse:1.370467+0.085547 
## [54] train-rmse:1.143550+0.021268    test-rmse:1.366183+0.086644 
## [55] train-rmse:1.138591+0.021234    test-rmse:1.366199+0.087966 
## [56] train-rmse:1.133826+0.021156    test-rmse:1.360390+0.089010 
## [57] train-rmse:1.129260+0.020952    test-rmse:1.355135+0.089782 
## [58] train-rmse:1.124752+0.020778    test-rmse:1.349934+0.088618 
## [59] train-rmse:1.120090+0.020595    test-rmse:1.345750+0.087406 
## [60] train-rmse:1.115642+0.020351    test-rmse:1.343127+0.089294 
## [61] train-rmse:1.111203+0.020271    test-rmse:1.338977+0.091939 
## [62] train-rmse:1.107039+0.020123    test-rmse:1.335366+0.087440 
## [63] train-rmse:1.103030+0.020091    test-rmse:1.330095+0.087779 
## [64] train-rmse:1.099181+0.020076    test-rmse:1.328796+0.088927 
## [65] train-rmse:1.095172+0.019881    test-rmse:1.325013+0.085108 
## [66] train-rmse:1.091471+0.019732    test-rmse:1.325174+0.086217 
## [67] train-rmse:1.087871+0.019568    test-rmse:1.323701+0.085865 
## [68] train-rmse:1.084366+0.019316    test-rmse:1.318236+0.084686 
## [69] train-rmse:1.080877+0.019172    test-rmse:1.312551+0.083967 
## [70] train-rmse:1.077423+0.019091    test-rmse:1.313319+0.089697 
## [71] train-rmse:1.074060+0.018928    test-rmse:1.311286+0.089989 
## [72] train-rmse:1.070961+0.018945    test-rmse:1.311485+0.087420 
## [73] train-rmse:1.067891+0.018959    test-rmse:1.310523+0.087916 
## [74] train-rmse:1.064889+0.018966    test-rmse:1.308462+0.091342 
## [75] train-rmse:1.061819+0.019059    test-rmse:1.308008+0.086325 
## [76] train-rmse:1.058852+0.019096    test-rmse:1.304786+0.086391 
## [77] train-rmse:1.055901+0.019048    test-rmse:1.306028+0.085790 
## [78] train-rmse:1.052944+0.019035    test-rmse:1.305732+0.085620 
## [79] train-rmse:1.050193+0.018960    test-rmse:1.302854+0.085115 
## [80] train-rmse:1.047548+0.018913    test-rmse:1.299787+0.089795 
## [81] train-rmse:1.044830+0.018712    test-rmse:1.298795+0.090359 
## [82] train-rmse:1.042343+0.018608    test-rmse:1.297048+0.090217 
## [83] train-rmse:1.039819+0.018562    test-rmse:1.294209+0.089197 
## [84] train-rmse:1.037360+0.018463    test-rmse:1.292088+0.088418 
## [85] train-rmse:1.034946+0.018412    test-rmse:1.292074+0.088119 
## [86] train-rmse:1.032537+0.018305    test-rmse:1.287380+0.088516 
## [87] train-rmse:1.030228+0.018232    test-rmse:1.284064+0.087803 
## [88] train-rmse:1.027946+0.018176    test-rmse:1.283356+0.086452 
## [89] train-rmse:1.025656+0.018181    test-rmse:1.281584+0.088844 
## [90] train-rmse:1.023407+0.018208    test-rmse:1.279532+0.087755 
## [91] train-rmse:1.021230+0.018137    test-rmse:1.275806+0.085888 
## [92] train-rmse:1.019085+0.018084    test-rmse:1.272125+0.085941 
## [93] train-rmse:1.016976+0.018061    test-rmse:1.271929+0.087991 
## [94] train-rmse:1.014917+0.018061    test-rmse:1.273394+0.087255 
## [95] train-rmse:1.012846+0.018080    test-rmse:1.267529+0.082836 
## [96] train-rmse:1.010838+0.018054    test-rmse:1.266103+0.085849 
## [97] train-rmse:1.008802+0.018091    test-rmse:1.265922+0.087391 
## [98] train-rmse:1.006827+0.018026    test-rmse:1.266178+0.088390 
## [99] train-rmse:1.004849+0.018005    test-rmse:1.264789+0.087551 
## [100]    train-rmse:1.002921+0.017953    test-rmse:1.263989+0.088534 
## [101]    train-rmse:1.000970+0.018057    test-rmse:1.262453+0.086870 
## [102]    train-rmse:0.998963+0.018094    test-rmse:1.262919+0.085632 
## [103]    train-rmse:0.997063+0.018064    test-rmse:1.260790+0.086832 
## [104]    train-rmse:0.995271+0.018102    test-rmse:1.259645+0.087250 
## [105]    train-rmse:0.993489+0.018112    test-rmse:1.256704+0.087644 
## [106]    train-rmse:0.991775+0.018080    test-rmse:1.254388+0.087715 
## [107]    train-rmse:0.990019+0.018050    test-rmse:1.253950+0.087281 
## [108]    train-rmse:0.988306+0.018068    test-rmse:1.252236+0.085806 
## [109]    train-rmse:0.986608+0.018099    test-rmse:1.253403+0.086489 
## [110]    train-rmse:0.985004+0.018093    test-rmse:1.254525+0.087247 
## [111]    train-rmse:0.983395+0.018067    test-rmse:1.254069+0.088686 
## [112]    train-rmse:0.981733+0.018047    test-rmse:1.253877+0.087905 
## [113]    train-rmse:0.980178+0.018015    test-rmse:1.251165+0.088711 
## [114]    train-rmse:0.978646+0.017980    test-rmse:1.247095+0.090155 
## [115]    train-rmse:0.977172+0.017950    test-rmse:1.245128+0.090149 
## [116]    train-rmse:0.975691+0.017938    test-rmse:1.246651+0.087617 
## [117]    train-rmse:0.974174+0.017931    test-rmse:1.249386+0.088641 
## [118]    train-rmse:0.972682+0.017913    test-rmse:1.247787+0.089957 
## [119]    train-rmse:0.971203+0.017909    test-rmse:1.248535+0.087516 
## [120]    train-rmse:0.969714+0.017836    test-rmse:1.246636+0.086870 
## [121]    train-rmse:0.968306+0.017815    test-rmse:1.246033+0.085073 
## Stopping. Best iteration:
## [115]    train-rmse:0.977172+0.017950    test-rmse:1.245128+0.090149
## 
## 85 
## [1]  train-rmse:65.128522+0.065151   test-rmse:65.128984+0.428029 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:43.089980+0.042641   test-rmse:43.089869+0.448962 
## [3]  train-rmse:28.532279+0.027510   test-rmse:28.531320+0.461535 
## [4]  train-rmse:18.927944+0.017318   test-rmse:18.925764+0.467564 
## [5]  train-rmse:12.609187+0.010926   test-rmse:12.605351+0.467328 
## [6]  train-rmse:8.460439+0.007583    test-rmse:8.445984+0.428384 
## [7]  train-rmse:5.744850+0.008552    test-rmse:5.714723+0.395340 
## [8]  train-rmse:3.979996+0.010019    test-rmse:3.971292+0.364271 
## [9]  train-rmse:2.859986+0.014353    test-rmse:2.874296+0.300106 
## [10] train-rmse:2.173353+0.019284    test-rmse:2.229326+0.246594 
## [11] train-rmse:1.770674+0.021479    test-rmse:1.871746+0.193060 
## [12] train-rmse:1.544911+0.018980    test-rmse:1.690000+0.140128 
## [13] train-rmse:1.415208+0.025213    test-rmse:1.580073+0.109067 
## [14] train-rmse:1.342219+0.025725    test-rmse:1.525668+0.098071 
## [15] train-rmse:1.294784+0.025276    test-rmse:1.490578+0.089998 
## [16] train-rmse:1.261005+0.024718    test-rmse:1.478200+0.088940 
## [17] train-rmse:1.234049+0.025961    test-rmse:1.455296+0.095371 
## [18] train-rmse:1.206680+0.022876    test-rmse:1.435248+0.091469 
## [19] train-rmse:1.183617+0.020678    test-rmse:1.420515+0.095012 
## [20] train-rmse:1.161179+0.016900    test-rmse:1.402755+0.097517 
## [21] train-rmse:1.141060+0.018726    test-rmse:1.383427+0.111694 
## [22] train-rmse:1.122417+0.017094    test-rmse:1.371142+0.108032 
## [23] train-rmse:1.106618+0.017851    test-rmse:1.351100+0.108066 
## [24] train-rmse:1.087129+0.013421    test-rmse:1.333235+0.109685 
## [25] train-rmse:1.067154+0.017776    test-rmse:1.335767+0.106496 
## [26] train-rmse:1.047368+0.017184    test-rmse:1.316433+0.095380 
## [27] train-rmse:1.030545+0.020206    test-rmse:1.305039+0.087849 
## [28] train-rmse:1.015904+0.018096    test-rmse:1.297201+0.086225 
## [29] train-rmse:1.002214+0.017059    test-rmse:1.292300+0.085366 
## [30] train-rmse:0.989427+0.015558    test-rmse:1.287143+0.085556 
## [31] train-rmse:0.976963+0.016146    test-rmse:1.280757+0.087267 
## [32] train-rmse:0.964628+0.016948    test-rmse:1.267686+0.096550 
## [33] train-rmse:0.952071+0.018997    test-rmse:1.260276+0.096982 
## [34] train-rmse:0.941045+0.020960    test-rmse:1.255108+0.093666 
## [35] train-rmse:0.929648+0.020368    test-rmse:1.251324+0.094110 
## [36] train-rmse:0.921150+0.020725    test-rmse:1.243674+0.095272 
## [37] train-rmse:0.912471+0.021637    test-rmse:1.237736+0.095891 
## [38] train-rmse:0.898801+0.019918    test-rmse:1.235739+0.099550 
## [39] train-rmse:0.889041+0.019975    test-rmse:1.226946+0.106113 
## [40] train-rmse:0.881781+0.019790    test-rmse:1.223212+0.105617 
## [41] train-rmse:0.870389+0.020298    test-rmse:1.212521+0.106473 
## [42] train-rmse:0.858882+0.017511    test-rmse:1.201731+0.100397 
## [43] train-rmse:0.851900+0.016354    test-rmse:1.203855+0.099643 
## [44] train-rmse:0.843474+0.017258    test-rmse:1.200345+0.100457 
## [45] train-rmse:0.835331+0.019594    test-rmse:1.202061+0.099753 
## [46] train-rmse:0.829464+0.019560    test-rmse:1.197547+0.102962 
## [47] train-rmse:0.822970+0.019514    test-rmse:1.192263+0.106756 
## [48] train-rmse:0.815629+0.017157    test-rmse:1.188570+0.109221 
## [49] train-rmse:0.808374+0.017433    test-rmse:1.183242+0.113892 
## [50] train-rmse:0.801518+0.015442    test-rmse:1.177177+0.115783 
## [51] train-rmse:0.796273+0.014736    test-rmse:1.174805+0.113157 
## [52] train-rmse:0.789478+0.016138    test-rmse:1.168784+0.116618 
## [53] train-rmse:0.781826+0.017723    test-rmse:1.160890+0.111886 
## [54] train-rmse:0.774990+0.015538    test-rmse:1.158941+0.108456 
## [55] train-rmse:0.767917+0.017870    test-rmse:1.156816+0.111228 
## [56] train-rmse:0.760454+0.019856    test-rmse:1.154355+0.112691 
## [57] train-rmse:0.753098+0.016935    test-rmse:1.149542+0.119510 
## [58] train-rmse:0.743637+0.017663    test-rmse:1.148058+0.120164 
## [59] train-rmse:0.737797+0.020336    test-rmse:1.142931+0.111768 
## [60] train-rmse:0.730482+0.018289    test-rmse:1.138639+0.110434 
## [61] train-rmse:0.724320+0.016696    test-rmse:1.134605+0.114005 
## [62] train-rmse:0.719762+0.016511    test-rmse:1.132774+0.110315 
## [63] train-rmse:0.715137+0.016467    test-rmse:1.133328+0.111071 
## [64] train-rmse:0.710842+0.015764    test-rmse:1.127866+0.113190 
## [65] train-rmse:0.706800+0.015751    test-rmse:1.125907+0.114582 
## [66] train-rmse:0.699706+0.015736    test-rmse:1.123741+0.109440 
## [67] train-rmse:0.692769+0.014191    test-rmse:1.117530+0.110042 
## [68] train-rmse:0.684575+0.014877    test-rmse:1.117901+0.110382 
## [69] train-rmse:0.677820+0.015799    test-rmse:1.116740+0.109271 
## [70] train-rmse:0.672927+0.015809    test-rmse:1.117887+0.113109 
## [71] train-rmse:0.667032+0.017054    test-rmse:1.118182+0.111000 
## [72] train-rmse:0.663071+0.017856    test-rmse:1.116969+0.113906 
## [73] train-rmse:0.657485+0.019243    test-rmse:1.117337+0.110263 
## [74] train-rmse:0.653125+0.018420    test-rmse:1.115433+0.112105 
## [75] train-rmse:0.649372+0.018838    test-rmse:1.111473+0.108546 
## [76] train-rmse:0.644418+0.018206    test-rmse:1.110713+0.108280 
## [77] train-rmse:0.638851+0.019344    test-rmse:1.109253+0.110279 
## [78] train-rmse:0.634149+0.021118    test-rmse:1.104571+0.106510 
## [79] train-rmse:0.629793+0.020923    test-rmse:1.102476+0.109026 
## [80] train-rmse:0.627010+0.020647    test-rmse:1.100830+0.107494 
## [81] train-rmse:0.622696+0.021392    test-rmse:1.099249+0.105704 
## [82] train-rmse:0.618937+0.020974    test-rmse:1.096940+0.106350 
## [83] train-rmse:0.614393+0.021484    test-rmse:1.098752+0.107014 
## [84] train-rmse:0.611324+0.020829    test-rmse:1.098543+0.106413 
## [85] train-rmse:0.606672+0.020873    test-rmse:1.098818+0.106016 
## [86] train-rmse:0.601992+0.020794    test-rmse:1.098823+0.104599 
## [87] train-rmse:0.597446+0.021985    test-rmse:1.094927+0.101153 
## [88] train-rmse:0.592849+0.020263    test-rmse:1.095773+0.099884 
## [89] train-rmse:0.589523+0.019485    test-rmse:1.096360+0.101536 
## [90] train-rmse:0.586253+0.019567    test-rmse:1.096325+0.103444 
## [91] train-rmse:0.581371+0.019843    test-rmse:1.095480+0.103398 
## [92] train-rmse:0.575359+0.016420    test-rmse:1.093253+0.105271 
## [93] train-rmse:0.570625+0.015617    test-rmse:1.093020+0.107663 
## [94] train-rmse:0.568352+0.015004    test-rmse:1.092258+0.109143 
## [95] train-rmse:0.565511+0.014919    test-rmse:1.092213+0.106499 
## [96] train-rmse:0.561765+0.014617    test-rmse:1.093040+0.104396 
## [97] train-rmse:0.559067+0.014470    test-rmse:1.091311+0.105181 
## [98] train-rmse:0.556366+0.014495    test-rmse:1.090417+0.104984 
## [99] train-rmse:0.554220+0.014424    test-rmse:1.089745+0.103747 
## [100]    train-rmse:0.551160+0.015555    test-rmse:1.086692+0.101453 
## [101]    train-rmse:0.548321+0.015362    test-rmse:1.087295+0.102343 
## [102]    train-rmse:0.543449+0.017176    test-rmse:1.087572+0.100528 
## [103]    train-rmse:0.539729+0.015507    test-rmse:1.085498+0.101870 
## [104]    train-rmse:0.536920+0.016224    test-rmse:1.085351+0.102690 
## [105]    train-rmse:0.530830+0.015377    test-rmse:1.075902+0.099079 
## [106]    train-rmse:0.528429+0.016319    test-rmse:1.075420+0.099820 
## [107]    train-rmse:0.526119+0.016071    test-rmse:1.074131+0.100195 
## [108]    train-rmse:0.522466+0.015105    test-rmse:1.069109+0.101306 
## [109]    train-rmse:0.518517+0.013629    test-rmse:1.069734+0.101706 
## [110]    train-rmse:0.514311+0.012623    test-rmse:1.069062+0.101551 
## [111]    train-rmse:0.511543+0.012400    test-rmse:1.066563+0.098274 
## [112]    train-rmse:0.508821+0.012701    test-rmse:1.065726+0.098889 
## [113]    train-rmse:0.505815+0.012382    test-rmse:1.063610+0.098226 
## [114]    train-rmse:0.503390+0.011684    test-rmse:1.062156+0.098732 
## [115]    train-rmse:0.501861+0.011736    test-rmse:1.060929+0.099093 
## [116]    train-rmse:0.498868+0.012575    test-rmse:1.061792+0.098452 
## [117]    train-rmse:0.496835+0.012924    test-rmse:1.059464+0.098500 
## [118]    train-rmse:0.494474+0.012384    test-rmse:1.059296+0.098768 
## [119]    train-rmse:0.492212+0.013386    test-rmse:1.059431+0.100490 
## [120]    train-rmse:0.489029+0.013783    test-rmse:1.059431+0.100023 
## [121]    train-rmse:0.486867+0.013751    test-rmse:1.059181+0.100079 
## [122]    train-rmse:0.483743+0.014031    test-rmse:1.055780+0.102199 
## [123]    train-rmse:0.481693+0.014612    test-rmse:1.054647+0.102732 
## [124]    train-rmse:0.479621+0.014572    test-rmse:1.054650+0.103051 
## [125]    train-rmse:0.476264+0.013808    test-rmse:1.051530+0.103105 
## [126]    train-rmse:0.474378+0.013705    test-rmse:1.048955+0.103324 
## [127]    train-rmse:0.471687+0.013164    test-rmse:1.048135+0.102753 
## [128]    train-rmse:0.468604+0.014097    test-rmse:1.048739+0.103152 
## [129]    train-rmse:0.465759+0.015901    test-rmse:1.051949+0.104795 
## [130]    train-rmse:0.464278+0.015987    test-rmse:1.052349+0.104400 
## [131]    train-rmse:0.462170+0.016018    test-rmse:1.051228+0.104961 
## [132]    train-rmse:0.460474+0.016811    test-rmse:1.050806+0.105279 
## [133]    train-rmse:0.457668+0.016826    test-rmse:1.048218+0.106109 
## Stopping. Best iteration:
## [127]    train-rmse:0.471687+0.013164    test-rmse:1.048135+0.102753
## 
## 86 
## [1]  train-rmse:65.128522+0.065151   test-rmse:65.128984+0.428029 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:43.089980+0.042641   test-rmse:43.089869+0.448962 
## [3]  train-rmse:28.532279+0.027510   test-rmse:28.531320+0.461535 
## [4]  train-rmse:18.927944+0.017318   test-rmse:18.925764+0.467564 
## [5]  train-rmse:12.609187+0.010926   test-rmse:12.605351+0.467328 
## [6]  train-rmse:8.456805+0.006678    test-rmse:8.441080+0.420318 
## [7]  train-rmse:5.736973+0.007885    test-rmse:5.719805+0.374520 
## [8]  train-rmse:3.954219+0.008727    test-rmse:3.954740+0.319372 
## [9]  train-rmse:2.813389+0.016074    test-rmse:2.830636+0.269047 
## [10] train-rmse:2.096177+0.011308    test-rmse:2.153867+0.230326 
## [11] train-rmse:1.654753+0.006092    test-rmse:1.773606+0.182035 
## [12] train-rmse:1.401861+0.008930    test-rmse:1.576046+0.146730 
## [13] train-rmse:1.257049+0.008939    test-rmse:1.480697+0.123631 
## [14] train-rmse:1.164658+0.011656    test-rmse:1.420311+0.119311 
## [15] train-rmse:1.103522+0.009256    test-rmse:1.382678+0.110912 
## [16] train-rmse:1.058265+0.006769    test-rmse:1.353882+0.108574 
## [17] train-rmse:1.023860+0.005625    test-rmse:1.334327+0.106751 
## [18] train-rmse:0.994223+0.007645    test-rmse:1.311624+0.106606 
## [19] train-rmse:0.968054+0.010682    test-rmse:1.299929+0.112611 
## [20] train-rmse:0.943521+0.012396    test-rmse:1.289886+0.105795 
## [21] train-rmse:0.915054+0.012573    test-rmse:1.271625+0.107094 
## [22] train-rmse:0.888634+0.011765    test-rmse:1.257902+0.105941 
## [23] train-rmse:0.866651+0.019277    test-rmse:1.247333+0.101381 
## [24] train-rmse:0.847588+0.021501    test-rmse:1.236136+0.105267 
## [25] train-rmse:0.830151+0.018554    test-rmse:1.232070+0.100285 
## [26] train-rmse:0.813659+0.018256    test-rmse:1.220104+0.096658 
## [27] train-rmse:0.799044+0.020165    test-rmse:1.214860+0.093914 
## [28] train-rmse:0.782006+0.023323    test-rmse:1.210170+0.089609 
## [29] train-rmse:0.763952+0.019635    test-rmse:1.205173+0.091919 
## [30] train-rmse:0.748087+0.020570    test-rmse:1.191837+0.091142 
## [31] train-rmse:0.734693+0.020588    test-rmse:1.195672+0.096946 
## [32] train-rmse:0.717789+0.012194    test-rmse:1.183833+0.097229 
## [33] train-rmse:0.702172+0.015718    test-rmse:1.171365+0.097617 
## [34] train-rmse:0.693480+0.014816    test-rmse:1.163003+0.098866 
## [35] train-rmse:0.682386+0.016534    test-rmse:1.158014+0.097083 
## [36] train-rmse:0.670756+0.020321    test-rmse:1.157314+0.099737 
## [37] train-rmse:0.660088+0.019962    test-rmse:1.151243+0.098267 
## [38] train-rmse:0.649537+0.020162    test-rmse:1.148104+0.100350 
## [39] train-rmse:0.639141+0.021943    test-rmse:1.147401+0.107093 
## [40] train-rmse:0.624124+0.018751    test-rmse:1.143439+0.110460 
## [41] train-rmse:0.615408+0.021519    test-rmse:1.144342+0.107241 
## [42] train-rmse:0.605505+0.025392    test-rmse:1.142884+0.104590 
## [43] train-rmse:0.598350+0.024525    test-rmse:1.136519+0.106188 
## [44] train-rmse:0.585989+0.017366    test-rmse:1.138123+0.111045 
## [45] train-rmse:0.577745+0.018744    test-rmse:1.132203+0.110188 
## [46] train-rmse:0.569240+0.016679    test-rmse:1.131111+0.108725 
## [47] train-rmse:0.560173+0.015492    test-rmse:1.128757+0.107133 
## [48] train-rmse:0.551898+0.012652    test-rmse:1.124938+0.104356 
## [49] train-rmse:0.541510+0.013083    test-rmse:1.123304+0.101329 
## [50] train-rmse:0.533761+0.014872    test-rmse:1.120688+0.100169 
## [51] train-rmse:0.525436+0.016363    test-rmse:1.122406+0.107828 
## [52] train-rmse:0.516278+0.017568    test-rmse:1.124006+0.109359 
## [53] train-rmse:0.506744+0.018112    test-rmse:1.113190+0.106059 
## [54] train-rmse:0.501177+0.018410    test-rmse:1.112218+0.103999 
## [55] train-rmse:0.494664+0.018549    test-rmse:1.111391+0.105148 
## [56] train-rmse:0.488298+0.018834    test-rmse:1.114332+0.109094 
## [57] train-rmse:0.484516+0.018196    test-rmse:1.110965+0.109336 
## [58] train-rmse:0.476226+0.017716    test-rmse:1.107444+0.110610 
## [59] train-rmse:0.471426+0.018252    test-rmse:1.109308+0.108806 
## [60] train-rmse:0.464818+0.019230    test-rmse:1.108685+0.109040 
## [61] train-rmse:0.458148+0.018689    test-rmse:1.107945+0.109787 
## [62] train-rmse:0.452405+0.020694    test-rmse:1.105858+0.109669 
## [63] train-rmse:0.447038+0.020549    test-rmse:1.105420+0.110764 
## [64] train-rmse:0.440330+0.019570    test-rmse:1.101685+0.113093 
## [65] train-rmse:0.434125+0.020829    test-rmse:1.098815+0.112174 
## [66] train-rmse:0.426683+0.022445    test-rmse:1.096566+0.112350 
## [67] train-rmse:0.420346+0.021032    test-rmse:1.093628+0.113865 
## [68] train-rmse:0.414357+0.019399    test-rmse:1.095022+0.117468 
## [69] train-rmse:0.407959+0.019804    test-rmse:1.090877+0.115218 
## [70] train-rmse:0.401373+0.020168    test-rmse:1.090914+0.113746 
## [71] train-rmse:0.396959+0.020880    test-rmse:1.089441+0.110185 
## [72] train-rmse:0.391124+0.016979    test-rmse:1.087869+0.108147 
## [73] train-rmse:0.385767+0.017325    test-rmse:1.083124+0.107220 
## [74] train-rmse:0.383058+0.017462    test-rmse:1.082169+0.107071 
## [75] train-rmse:0.378813+0.017579    test-rmse:1.082045+0.107813 
## [76] train-rmse:0.375223+0.018072    test-rmse:1.079798+0.110179 
## [77] train-rmse:0.371268+0.018403    test-rmse:1.081355+0.110414 
## [78] train-rmse:0.366174+0.019254    test-rmse:1.079428+0.109489 
## [79] train-rmse:0.362158+0.019472    test-rmse:1.078168+0.108026 
## [80] train-rmse:0.357438+0.016242    test-rmse:1.077019+0.108140 
## [81] train-rmse:0.352781+0.016937    test-rmse:1.075878+0.107988 
## [82] train-rmse:0.348474+0.015511    test-rmse:1.076585+0.110232 
## [83] train-rmse:0.343748+0.016162    test-rmse:1.075319+0.109957 
## [84] train-rmse:0.339095+0.017430    test-rmse:1.074936+0.109563 
## [85] train-rmse:0.334183+0.015668    test-rmse:1.076600+0.111515 
## [86] train-rmse:0.329776+0.016676    test-rmse:1.075999+0.113329 
## [87] train-rmse:0.326439+0.016475    test-rmse:1.076066+0.114343 
## [88] train-rmse:0.321270+0.014153    test-rmse:1.075541+0.114128 
## [89] train-rmse:0.317732+0.015147    test-rmse:1.075685+0.113742 
## [90] train-rmse:0.313522+0.013201    test-rmse:1.074797+0.114335 
## [91] train-rmse:0.310473+0.011656    test-rmse:1.074361+0.115202 
## [92] train-rmse:0.307500+0.011561    test-rmse:1.072468+0.116598 
## [93] train-rmse:0.302368+0.013090    test-rmse:1.067012+0.109186 
## [94] train-rmse:0.298288+0.012585    test-rmse:1.065440+0.110055 
## [95] train-rmse:0.294483+0.013540    test-rmse:1.065141+0.109611 
## [96] train-rmse:0.291749+0.013435    test-rmse:1.064917+0.110198 
## [97] train-rmse:0.288155+0.013522    test-rmse:1.064880+0.110507 
## [98] train-rmse:0.283407+0.013519    test-rmse:1.063353+0.111630 
## [99] train-rmse:0.280233+0.013520    test-rmse:1.063878+0.111919 
## [100]    train-rmse:0.277921+0.014011    test-rmse:1.064380+0.111637 
## [101]    train-rmse:0.275444+0.013335    test-rmse:1.064138+0.113741 
## [102]    train-rmse:0.272131+0.014050    test-rmse:1.063683+0.115344 
## [103]    train-rmse:0.268968+0.013814    test-rmse:1.062520+0.116220 
## [104]    train-rmse:0.265378+0.013778    test-rmse:1.059016+0.113918 
## [105]    train-rmse:0.263152+0.014649    test-rmse:1.059419+0.116091 
## [106]    train-rmse:0.258929+0.014195    test-rmse:1.058637+0.115971 
## [107]    train-rmse:0.255440+0.013797    test-rmse:1.058031+0.115063 
## [108]    train-rmse:0.252827+0.013489    test-rmse:1.058198+0.115121 
## [109]    train-rmse:0.250109+0.013346    test-rmse:1.058875+0.115921 
## [110]    train-rmse:0.247390+0.014530    test-rmse:1.058519+0.115983 
## [111]    train-rmse:0.244397+0.015314    test-rmse:1.058654+0.115764 
## [112]    train-rmse:0.241722+0.014825    test-rmse:1.059067+0.116321 
## [113]    train-rmse:0.238005+0.012865    test-rmse:1.057961+0.116248 
## [114]    train-rmse:0.235007+0.011382    test-rmse:1.057771+0.116251 
## [115]    train-rmse:0.233111+0.010480    test-rmse:1.056383+0.117455 
## [116]    train-rmse:0.230473+0.010972    test-rmse:1.053653+0.115092 
## [117]    train-rmse:0.227397+0.010207    test-rmse:1.052644+0.115398 
## [118]    train-rmse:0.225099+0.010329    test-rmse:1.052454+0.115525 
## [119]    train-rmse:0.222882+0.010997    test-rmse:1.051864+0.115314 
## [120]    train-rmse:0.220756+0.010876    test-rmse:1.050947+0.116022 
## [121]    train-rmse:0.218288+0.011612    test-rmse:1.049737+0.115743 
## [122]    train-rmse:0.215394+0.012054    test-rmse:1.049286+0.115582 
## [123]    train-rmse:0.213401+0.012829    test-rmse:1.049079+0.116069 
## [124]    train-rmse:0.210204+0.012772    test-rmse:1.048409+0.116672 
## [125]    train-rmse:0.207678+0.013375    test-rmse:1.046854+0.115722 
## [126]    train-rmse:0.206191+0.013934    test-rmse:1.047759+0.117380 
## [127]    train-rmse:0.204150+0.014193    test-rmse:1.046119+0.117501 
## [128]    train-rmse:0.201715+0.013658    test-rmse:1.046752+0.118029 
## [129]    train-rmse:0.198133+0.014227    test-rmse:1.046389+0.118400 
## [130]    train-rmse:0.195708+0.014803    test-rmse:1.046967+0.118705 
## [131]    train-rmse:0.194135+0.015207    test-rmse:1.047087+0.118057 
## [132]    train-rmse:0.192838+0.015549    test-rmse:1.047553+0.117973 
## [133]    train-rmse:0.190631+0.014855    test-rmse:1.046669+0.118126 
## Stopping. Best iteration:
## [127]    train-rmse:0.204150+0.014193    test-rmse:1.046119+0.117501
## 
## 87 
## [1]  train-rmse:65.128522+0.065151   test-rmse:65.128984+0.428029 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:43.089980+0.042641   test-rmse:43.089869+0.448962 
## [3]  train-rmse:28.532279+0.027510   test-rmse:28.531320+0.461535 
## [4]  train-rmse:18.927944+0.017318   test-rmse:18.925764+0.467564 
## [5]  train-rmse:12.609187+0.010926   test-rmse:12.605351+0.467328 
## [6]  train-rmse:8.456477+0.006423    test-rmse:8.436758+0.418648 
## [7]  train-rmse:5.731794+0.006942    test-rmse:5.721747+0.374621 
## [8]  train-rmse:3.941269+0.007399    test-rmse:3.948083+0.327056 
## [9]  train-rmse:2.781966+0.010492    test-rmse:2.802684+0.275992 
## [10] train-rmse:2.031557+0.005323    test-rmse:2.096557+0.217198 
## [11] train-rmse:1.565414+0.017722    test-rmse:1.695085+0.168777 
## [12] train-rmse:1.285343+0.021237    test-rmse:1.498880+0.135082 
## [13] train-rmse:1.118656+0.023424    test-rmse:1.393472+0.116944 
## [14] train-rmse:1.013624+0.024029    test-rmse:1.326587+0.112592 
## [15] train-rmse:0.944530+0.020381    test-rmse:1.279855+0.104336 
## [16] train-rmse:0.885623+0.020654    test-rmse:1.247476+0.093867 
## [17] train-rmse:0.841032+0.021189    test-rmse:1.224027+0.090078 
## [18] train-rmse:0.808243+0.019929    test-rmse:1.195770+0.086041 
## [19] train-rmse:0.779467+0.022765    test-rmse:1.180681+0.084573 
## [20] train-rmse:0.753867+0.022744    test-rmse:1.168504+0.079976 
## [21] train-rmse:0.730130+0.021382    test-rmse:1.152878+0.085025 
## [22] train-rmse:0.705698+0.020101    test-rmse:1.139178+0.081963 
## [23] train-rmse:0.682106+0.012892    test-rmse:1.126461+0.081881 
## [24] train-rmse:0.666910+0.013313    test-rmse:1.124710+0.086730 
## [25] train-rmse:0.643875+0.005883    test-rmse:1.111357+0.083081 
## [26] train-rmse:0.626373+0.006382    test-rmse:1.096856+0.081906 
## [27] train-rmse:0.606762+0.013116    test-rmse:1.089537+0.080201 
## [28] train-rmse:0.593972+0.014973    test-rmse:1.084795+0.083543 
## [29] train-rmse:0.579135+0.013897    test-rmse:1.083497+0.089663 
## [30] train-rmse:0.567197+0.016850    test-rmse:1.074595+0.090479 
## [31] train-rmse:0.549067+0.016246    test-rmse:1.071021+0.086944 
## [32] train-rmse:0.539221+0.018853    test-rmse:1.069531+0.086886 
## [33] train-rmse:0.525797+0.023266    test-rmse:1.059197+0.075109 
## [34] train-rmse:0.513818+0.019780    test-rmse:1.055491+0.076614 
## [35] train-rmse:0.498580+0.016464    test-rmse:1.044190+0.076422 
## [36] train-rmse:0.488600+0.014448    test-rmse:1.043274+0.078992 
## [37] train-rmse:0.472431+0.013345    test-rmse:1.037466+0.082567 
## [38] train-rmse:0.461381+0.012823    test-rmse:1.029293+0.082141 
## [39] train-rmse:0.448364+0.013954    test-rmse:1.025989+0.087163 
## [40] train-rmse:0.438385+0.015951    test-rmse:1.021693+0.093893 
## [41] train-rmse:0.428412+0.018523    test-rmse:1.018686+0.093785 
## [42] train-rmse:0.416139+0.015246    test-rmse:1.017371+0.096313 
## [43] train-rmse:0.409181+0.015625    test-rmse:1.011386+0.100708 
## [44] train-rmse:0.399943+0.014559    test-rmse:1.006540+0.097520 
## [45] train-rmse:0.388592+0.014147    test-rmse:1.007066+0.097109 
## [46] train-rmse:0.379752+0.013242    test-rmse:1.006496+0.096237 
## [47] train-rmse:0.370593+0.011377    test-rmse:1.004974+0.095256 
## [48] train-rmse:0.364541+0.011270    test-rmse:1.003569+0.096162 
## [49] train-rmse:0.356039+0.011676    test-rmse:1.003318+0.095311 
## [50] train-rmse:0.346360+0.009019    test-rmse:1.002202+0.099142 
## [51] train-rmse:0.339249+0.010425    test-rmse:1.000632+0.097185 
## [52] train-rmse:0.331665+0.010523    test-rmse:1.000527+0.097854 
## [53] train-rmse:0.325280+0.011183    test-rmse:0.999603+0.098228 
## [54] train-rmse:0.315855+0.012467    test-rmse:1.002244+0.098712 
## [55] train-rmse:0.308015+0.010392    test-rmse:0.999486+0.100205 
## [56] train-rmse:0.301855+0.009355    test-rmse:0.997327+0.098373 
## [57] train-rmse:0.294850+0.010475    test-rmse:0.994954+0.097801 
## [58] train-rmse:0.287286+0.010663    test-rmse:0.993162+0.096902 
## [59] train-rmse:0.281370+0.008473    test-rmse:0.991901+0.097178 
## [60] train-rmse:0.274485+0.006344    test-rmse:0.989871+0.096447 
## [61] train-rmse:0.268612+0.007114    test-rmse:0.989452+0.095471 
## [62] train-rmse:0.264585+0.007545    test-rmse:0.990622+0.095403 
## [63] train-rmse:0.257866+0.008424    test-rmse:0.989971+0.092762 
## [64] train-rmse:0.255130+0.008737    test-rmse:0.988923+0.094307 
## [65] train-rmse:0.249999+0.010092    test-rmse:0.987597+0.092734 
## [66] train-rmse:0.243511+0.008648    test-rmse:0.986971+0.093350 
## [67] train-rmse:0.238208+0.007368    test-rmse:0.985238+0.092383 
## [68] train-rmse:0.233919+0.007252    test-rmse:0.984320+0.092001 
## [69] train-rmse:0.230065+0.006195    test-rmse:0.984316+0.092042 
## [70] train-rmse:0.225577+0.005805    test-rmse:0.983321+0.091856 
## [71] train-rmse:0.220826+0.005227    test-rmse:0.980615+0.092117 
## [72] train-rmse:0.216121+0.006805    test-rmse:0.980788+0.093070 
## [73] train-rmse:0.210186+0.007654    test-rmse:0.980972+0.093197 
## [74] train-rmse:0.206651+0.006877    test-rmse:0.981385+0.092631 
## [75] train-rmse:0.202815+0.007251    test-rmse:0.979281+0.092243 
## [76] train-rmse:0.198095+0.006047    test-rmse:0.977968+0.092573 
## [77] train-rmse:0.194376+0.005648    test-rmse:0.976668+0.093480 
## [78] train-rmse:0.190297+0.005265    test-rmse:0.976557+0.093060 
## [79] train-rmse:0.187204+0.004985    test-rmse:0.974836+0.093756 
## [80] train-rmse:0.183513+0.005814    test-rmse:0.974341+0.093949 
## [81] train-rmse:0.180927+0.006061    test-rmse:0.973835+0.094045 
## [82] train-rmse:0.177661+0.005436    test-rmse:0.973270+0.093593 
## [83] train-rmse:0.173773+0.005451    test-rmse:0.972975+0.094101 
## [84] train-rmse:0.169841+0.005556    test-rmse:0.973472+0.094414 
## [85] train-rmse:0.167090+0.005180    test-rmse:0.972887+0.094027 
## [86] train-rmse:0.163499+0.004980    test-rmse:0.971955+0.094381 
## [87] train-rmse:0.160244+0.004721    test-rmse:0.971521+0.094501 
## [88] train-rmse:0.156268+0.004989    test-rmse:0.971239+0.096039 
## [89] train-rmse:0.154042+0.004602    test-rmse:0.972799+0.096391 
## [90] train-rmse:0.151864+0.005156    test-rmse:0.972601+0.096052 
## [91] train-rmse:0.149224+0.005716    test-rmse:0.972755+0.095280 
## [92] train-rmse:0.147171+0.005778    test-rmse:0.972187+0.095732 
## [93] train-rmse:0.144059+0.005338    test-rmse:0.971687+0.095793 
## [94] train-rmse:0.141498+0.004851    test-rmse:0.971316+0.095333 
## Stopping. Best iteration:
## [88] train-rmse:0.156268+0.004989    test-rmse:0.971239+0.096039
## 
## 88 
## [1]  train-rmse:65.128522+0.065151   test-rmse:65.128984+0.428029 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:43.089980+0.042641   test-rmse:43.089869+0.448962 
## [3]  train-rmse:28.532279+0.027510   test-rmse:28.531320+0.461535 
## [4]  train-rmse:18.927944+0.017318   test-rmse:18.925764+0.467564 
## [5]  train-rmse:12.609187+0.010926   test-rmse:12.605351+0.467328 
## [6]  train-rmse:8.456477+0.006423    test-rmse:8.436758+0.418648 
## [7]  train-rmse:5.729794+0.006249    test-rmse:5.716134+0.369583 
## [8]  train-rmse:3.935685+0.007207    test-rmse:3.948879+0.309717 
## [9]  train-rmse:2.762046+0.010375    test-rmse:2.797214+0.254629 
## [10] train-rmse:1.996407+0.006365    test-rmse:2.083439+0.227091 
## [11] train-rmse:1.513624+0.011994    test-rmse:1.666029+0.175350 
## [12] train-rmse:1.215888+0.011368    test-rmse:1.434886+0.142019 
## [13] train-rmse:1.018687+0.019588    test-rmse:1.311294+0.120045 
## [14] train-rmse:0.899905+0.016744    test-rmse:1.248309+0.110211 
## [15] train-rmse:0.824034+0.018719    test-rmse:1.200279+0.101228 
## [16] train-rmse:0.761427+0.020642    test-rmse:1.167397+0.096943 
## [17] train-rmse:0.721866+0.019939    test-rmse:1.146198+0.092884 
## [18] train-rmse:0.681485+0.023085    test-rmse:1.129741+0.085434 
## [19] train-rmse:0.647806+0.023855    test-rmse:1.111845+0.086879 
## [20] train-rmse:0.618133+0.019569    test-rmse:1.103531+0.089420 
## [21] train-rmse:0.589335+0.016135    test-rmse:1.092935+0.096912 
## [22] train-rmse:0.564785+0.019980    test-rmse:1.081005+0.103045 
## [23] train-rmse:0.539887+0.022514    test-rmse:1.073387+0.102453 
## [24] train-rmse:0.520340+0.021600    test-rmse:1.063112+0.106047 
## [25] train-rmse:0.495563+0.018118    test-rmse:1.056638+0.104264 
## [26] train-rmse:0.477120+0.014207    test-rmse:1.049546+0.101675 
## [27] train-rmse:0.464791+0.013480    test-rmse:1.042585+0.106752 
## [28] train-rmse:0.451125+0.011378    test-rmse:1.038550+0.105639 
## [29] train-rmse:0.439106+0.011173    test-rmse:1.032267+0.102460 
## [30] train-rmse:0.422910+0.014941    test-rmse:1.027885+0.106010 
## [31] train-rmse:0.405657+0.013160    test-rmse:1.028468+0.109164 
## [32] train-rmse:0.394089+0.015951    test-rmse:1.026148+0.114297 
## [33] train-rmse:0.381659+0.014933    test-rmse:1.021551+0.110140 
## [34] train-rmse:0.370520+0.014974    test-rmse:1.021165+0.110426 
## [35] train-rmse:0.360634+0.012010    test-rmse:1.022687+0.109250 
## [36] train-rmse:0.348740+0.014426    test-rmse:1.025438+0.113281 
## [37] train-rmse:0.337545+0.013364    test-rmse:1.023552+0.113385 
## [38] train-rmse:0.325506+0.009890    test-rmse:1.021728+0.112904 
## [39] train-rmse:0.314001+0.011330    test-rmse:1.023229+0.114464 
## [40] train-rmse:0.303476+0.012056    test-rmse:1.021751+0.114099 
## Stopping. Best iteration:
## [34] train-rmse:0.370520+0.014974    test-rmse:1.021165+0.110426
## 
## 89 
## [1]  train-rmse:65.128522+0.065151   test-rmse:65.128984+0.428029 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:43.089980+0.042641   test-rmse:43.089869+0.448962 
## [3]  train-rmse:28.532279+0.027510   test-rmse:28.531320+0.461535 
## [4]  train-rmse:18.927944+0.017318   test-rmse:18.925764+0.467564 
## [5]  train-rmse:12.609187+0.010926   test-rmse:12.605351+0.467328 
## [6]  train-rmse:8.456477+0.006423    test-rmse:8.436758+0.418648 
## [7]  train-rmse:5.729794+0.006249    test-rmse:5.716134+0.369583 
## [8]  train-rmse:3.933380+0.007138    test-rmse:3.940611+0.312459 
## [9]  train-rmse:2.755998+0.011000    test-rmse:2.788011+0.258246 
## [10] train-rmse:1.974774+0.007483    test-rmse:2.074872+0.225849 
## [11] train-rmse:1.475134+0.017680    test-rmse:1.660618+0.193689 
## [12] train-rmse:1.157109+0.010332    test-rmse:1.421439+0.156018 
## [13] train-rmse:0.946331+0.017679    test-rmse:1.285673+0.142913 
## [14] train-rmse:0.808836+0.023608    test-rmse:1.211103+0.134573 
## [15] train-rmse:0.721768+0.025865    test-rmse:1.161835+0.134315 
## [16] train-rmse:0.652842+0.022927    test-rmse:1.136157+0.124310 
## [17] train-rmse:0.612110+0.024798    test-rmse:1.119858+0.121396 
## [18] train-rmse:0.574349+0.030156    test-rmse:1.098470+0.121149 
## [19] train-rmse:0.541366+0.024671    test-rmse:1.086095+0.122691 
## [20] train-rmse:0.518444+0.026806    test-rmse:1.076554+0.119706 
## [21] train-rmse:0.482400+0.021993    test-rmse:1.059558+0.105363 
## [22] train-rmse:0.461784+0.024375    test-rmse:1.052687+0.104087 
## [23] train-rmse:0.440039+0.027350    test-rmse:1.039748+0.104707 
## [24] train-rmse:0.416751+0.030357    test-rmse:1.043093+0.103681 
## [25] train-rmse:0.396978+0.028602    test-rmse:1.036609+0.102453 
## [26] train-rmse:0.378307+0.023023    test-rmse:1.029436+0.102521 
## [27] train-rmse:0.364016+0.023911    test-rmse:1.029187+0.101911 
## [28] train-rmse:0.344842+0.019082    test-rmse:1.017718+0.094467 
## [29] train-rmse:0.330635+0.023393    test-rmse:1.013348+0.095115 
## [30] train-rmse:0.316048+0.023214    test-rmse:1.009928+0.095750 
## [31] train-rmse:0.305659+0.024416    test-rmse:1.008285+0.100016 
## [32] train-rmse:0.290112+0.024755    test-rmse:1.006563+0.099474 
## [33] train-rmse:0.275987+0.023421    test-rmse:1.002566+0.095194 
## [34] train-rmse:0.260375+0.022908    test-rmse:1.004300+0.095015 
## [35] train-rmse:0.252851+0.022779    test-rmse:1.002760+0.097270 
## [36] train-rmse:0.246707+0.022016    test-rmse:1.001907+0.098947 
## [37] train-rmse:0.236542+0.019628    test-rmse:0.999845+0.100852 
## [38] train-rmse:0.227724+0.021844    test-rmse:1.001520+0.097968 
## [39] train-rmse:0.220581+0.021525    test-rmse:1.002784+0.096342 
## [40] train-rmse:0.211745+0.018494    test-rmse:1.001012+0.098198 
## [41] train-rmse:0.204953+0.017083    test-rmse:0.999003+0.099350 
## [42] train-rmse:0.196098+0.018572    test-rmse:0.999316+0.098196 
## [43] train-rmse:0.189304+0.019344    test-rmse:0.998219+0.098856 
## [44] train-rmse:0.183825+0.017811    test-rmse:0.996390+0.098283 
## [45] train-rmse:0.177798+0.018420    test-rmse:0.995702+0.098760 
## [46] train-rmse:0.172901+0.019923    test-rmse:0.992601+0.099074 
## [47] train-rmse:0.164291+0.016554    test-rmse:0.990216+0.099968 
## [48] train-rmse:0.159528+0.017686    test-rmse:0.990531+0.100710 
## [49] train-rmse:0.152966+0.016499    test-rmse:0.989354+0.098013 
## [50] train-rmse:0.147255+0.016674    test-rmse:0.989182+0.098276 
## [51] train-rmse:0.142763+0.015700    test-rmse:0.988604+0.097927 
## [52] train-rmse:0.137633+0.016083    test-rmse:0.989831+0.097106 
## [53] train-rmse:0.131996+0.015180    test-rmse:0.989328+0.095726 
## [54] train-rmse:0.128448+0.014805    test-rmse:0.987757+0.094494 
## [55] train-rmse:0.124317+0.015286    test-rmse:0.987864+0.094588 
## [56] train-rmse:0.118839+0.014445    test-rmse:0.988040+0.094621 
## [57] train-rmse:0.112971+0.011994    test-rmse:0.987158+0.094767 
## [58] train-rmse:0.108073+0.012177    test-rmse:0.985734+0.094936 
## [59] train-rmse:0.102831+0.011333    test-rmse:0.985821+0.094265 
## [60] train-rmse:0.099385+0.010940    test-rmse:0.984731+0.096524 
## [61] train-rmse:0.096066+0.011628    test-rmse:0.983306+0.095206 
## [62] train-rmse:0.092346+0.010919    test-rmse:0.983223+0.095084 
## [63] train-rmse:0.088704+0.010372    test-rmse:0.982140+0.094430 
## [64] train-rmse:0.084940+0.009646    test-rmse:0.982068+0.094799 
## [65] train-rmse:0.081371+0.008650    test-rmse:0.980755+0.093917 
## [66] train-rmse:0.078531+0.009178    test-rmse:0.979955+0.094762 
## [67] train-rmse:0.074612+0.008319    test-rmse:0.979136+0.094533 
## [68] train-rmse:0.072082+0.008325    test-rmse:0.979011+0.094629 
## [69] train-rmse:0.069586+0.008229    test-rmse:0.978332+0.094524 
## [70] train-rmse:0.067844+0.008622    test-rmse:0.977756+0.094389 
## [71] train-rmse:0.065909+0.008591    test-rmse:0.977763+0.094445 
## [72] train-rmse:0.063586+0.008235    test-rmse:0.977423+0.094502 
## [73] train-rmse:0.060755+0.007755    test-rmse:0.976871+0.095536 
## [74] train-rmse:0.058835+0.007479    test-rmse:0.977050+0.095294 
## [75] train-rmse:0.056410+0.006938    test-rmse:0.977350+0.095200 
## [76] train-rmse:0.054587+0.006564    test-rmse:0.977435+0.095478 
## [77] train-rmse:0.051965+0.006219    test-rmse:0.977348+0.095898 
## [78] train-rmse:0.050019+0.006225    test-rmse:0.977071+0.095563 
## [79] train-rmse:0.047831+0.005596    test-rmse:0.976684+0.095998 
## [80] train-rmse:0.046364+0.005522    test-rmse:0.976513+0.095944 
## [81] train-rmse:0.044829+0.005339    test-rmse:0.976248+0.096112 
## [82] train-rmse:0.043718+0.005676    test-rmse:0.975615+0.096360 
## [83] train-rmse:0.042122+0.005876    test-rmse:0.975598+0.096225 
## [84] train-rmse:0.040737+0.005413    test-rmse:0.975491+0.095798 
## [85] train-rmse:0.038898+0.004905    test-rmse:0.975484+0.095714 
## [86] train-rmse:0.037363+0.004370    test-rmse:0.975377+0.095524 
## [87] train-rmse:0.036078+0.004188    test-rmse:0.975208+0.095666 
## [88] train-rmse:0.034903+0.003837    test-rmse:0.975056+0.095981 
## [89] train-rmse:0.033636+0.003316    test-rmse:0.974785+0.095509 
## [90] train-rmse:0.032629+0.003314    test-rmse:0.974531+0.095567 
## [91] train-rmse:0.031202+0.003243    test-rmse:0.974552+0.095438 
## [92] train-rmse:0.029556+0.002918    test-rmse:0.974644+0.095310 
## [93] train-rmse:0.028696+0.002685    test-rmse:0.974842+0.095268 
## [94] train-rmse:0.027812+0.002493    test-rmse:0.974584+0.095403 
## [95] train-rmse:0.026790+0.002098    test-rmse:0.974387+0.095400 
## [96] train-rmse:0.025690+0.001820    test-rmse:0.974128+0.095451 
## [97] train-rmse:0.024662+0.001531    test-rmse:0.973939+0.095851 
## [98] train-rmse:0.023836+0.001287    test-rmse:0.973774+0.095939 
## [99] train-rmse:0.023125+0.001324    test-rmse:0.973826+0.095891 
## [100]    train-rmse:0.022158+0.001483    test-rmse:0.973835+0.096016 
## [101]    train-rmse:0.021369+0.001816    test-rmse:0.973705+0.096095 
## [102]    train-rmse:0.020571+0.001768    test-rmse:0.973553+0.096222 
## [103]    train-rmse:0.020033+0.001963    test-rmse:0.973421+0.096129 
## [104]    train-rmse:0.019227+0.001764    test-rmse:0.973263+0.096221 
## [105]    train-rmse:0.018481+0.001660    test-rmse:0.973223+0.096232 
## [106]    train-rmse:0.018090+0.001627    test-rmse:0.973176+0.096249 
## [107]    train-rmse:0.017444+0.001643    test-rmse:0.973138+0.096082 
## [108]    train-rmse:0.016831+0.001444    test-rmse:0.973149+0.096001 
## [109]    train-rmse:0.016205+0.001584    test-rmse:0.973339+0.096150 
## [110]    train-rmse:0.015836+0.001476    test-rmse:0.973212+0.096184 
## [111]    train-rmse:0.015323+0.001366    test-rmse:0.973127+0.096276 
## [112]    train-rmse:0.014793+0.001376    test-rmse:0.973100+0.096323 
## [113]    train-rmse:0.014199+0.001310    test-rmse:0.972995+0.096287 
## [114]    train-rmse:0.013770+0.001365    test-rmse:0.972849+0.096322 
## [115]    train-rmse:0.013216+0.001320    test-rmse:0.972866+0.096302 
## [116]    train-rmse:0.012647+0.001253    test-rmse:0.972876+0.096316 
## [117]    train-rmse:0.012210+0.001233    test-rmse:0.972780+0.096225 
## [118]    train-rmse:0.011826+0.001114    test-rmse:0.972709+0.096239 
## [119]    train-rmse:0.011317+0.000924    test-rmse:0.972773+0.096341 
## [120]    train-rmse:0.011009+0.000912    test-rmse:0.972799+0.096392 
## [121]    train-rmse:0.010680+0.000935    test-rmse:0.972713+0.096441 
## [122]    train-rmse:0.010287+0.000903    test-rmse:0.972624+0.096449 
## [123]    train-rmse:0.009864+0.000899    test-rmse:0.972640+0.096470 
## [124]    train-rmse:0.009437+0.000906    test-rmse:0.972621+0.096494 
## [125]    train-rmse:0.009064+0.000814    test-rmse:0.972544+0.096555 
## [126]    train-rmse:0.008738+0.000694    test-rmse:0.972544+0.096658 
## [127]    train-rmse:0.008527+0.000716    test-rmse:0.972518+0.096640 
## [128]    train-rmse:0.008222+0.000732    test-rmse:0.972521+0.096623 
## [129]    train-rmse:0.007954+0.000697    test-rmse:0.972548+0.096626 
## [130]    train-rmse:0.007579+0.000654    test-rmse:0.972473+0.096729 
## [131]    train-rmse:0.007288+0.000690    test-rmse:0.972500+0.096785 
## [132]    train-rmse:0.007138+0.000670    test-rmse:0.972485+0.096837 
## [133]    train-rmse:0.006900+0.000616    test-rmse:0.972516+0.096882 
## [134]    train-rmse:0.006681+0.000581    test-rmse:0.972553+0.096920 
## [135]    train-rmse:0.006531+0.000587    test-rmse:0.972561+0.096941 
## [136]    train-rmse:0.006340+0.000571    test-rmse:0.972605+0.096984 
## Stopping. Best iteration:
## [130]    train-rmse:0.007579+0.000654    test-rmse:0.972473+0.096729
## 
## 90 
## [1]  train-rmse:65.128522+0.065151   test-rmse:65.128984+0.428029 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:43.089980+0.042641   test-rmse:43.089869+0.448962 
## [3]  train-rmse:28.532279+0.027510   test-rmse:28.531320+0.461535 
## [4]  train-rmse:18.927944+0.017318   test-rmse:18.925764+0.467564 
## [5]  train-rmse:12.609187+0.010926   test-rmse:12.605351+0.467328 
## [6]  train-rmse:8.456477+0.006423    test-rmse:8.436758+0.418648 
## [7]  train-rmse:5.729794+0.006249    test-rmse:5.716134+0.369583 
## [8]  train-rmse:3.931654+0.006881    test-rmse:3.937356+0.307082 
## [9]  train-rmse:2.749839+0.008635    test-rmse:2.787607+0.245075 
## [10] train-rmse:1.962017+0.008346    test-rmse:2.053653+0.217761 
## [11] train-rmse:1.449139+0.007638    test-rmse:1.617486+0.179987 
## [12] train-rmse:1.116789+0.013939    test-rmse:1.376983+0.149298 
## [13] train-rmse:0.900163+0.013266    test-rmse:1.248227+0.123912 
## [14] train-rmse:0.751772+0.009670    test-rmse:1.158289+0.102223 
## [15] train-rmse:0.653226+0.019083    test-rmse:1.115684+0.090110 
## [16] train-rmse:0.583291+0.018341    test-rmse:1.084450+0.087553 
## [17] train-rmse:0.526927+0.012263    test-rmse:1.067269+0.071192 
## [18] train-rmse:0.487917+0.012830    test-rmse:1.045325+0.067576 
## [19] train-rmse:0.446217+0.019407    test-rmse:1.038956+0.073396 
## [20] train-rmse:0.416409+0.015185    test-rmse:1.031565+0.073506 
## [21] train-rmse:0.391069+0.019229    test-rmse:1.027450+0.075515 
## [22] train-rmse:0.365039+0.018247    test-rmse:1.016286+0.066982 
## [23] train-rmse:0.344793+0.019976    test-rmse:1.011241+0.064104 
## [24] train-rmse:0.328298+0.018500    test-rmse:1.009442+0.066903 
## [25] train-rmse:0.312724+0.018908    test-rmse:1.006122+0.068464 
## [26] train-rmse:0.295205+0.019237    test-rmse:1.001634+0.063608 
## [27] train-rmse:0.274537+0.015824    test-rmse:0.995719+0.062584 
## [28] train-rmse:0.261430+0.014886    test-rmse:0.998070+0.065439 
## [29] train-rmse:0.241963+0.015494    test-rmse:0.993217+0.066125 
## [30] train-rmse:0.230928+0.011838    test-rmse:0.989127+0.067564 
## [31] train-rmse:0.219212+0.014316    test-rmse:0.989453+0.068719 
## [32] train-rmse:0.211551+0.016920    test-rmse:0.986823+0.067891 
## [33] train-rmse:0.201144+0.017037    test-rmse:0.985573+0.067552 
## [34] train-rmse:0.189157+0.014130    test-rmse:0.984756+0.067646 
## [35] train-rmse:0.179512+0.013011    test-rmse:0.984136+0.067108 
## [36] train-rmse:0.172138+0.012100    test-rmse:0.980900+0.065801 
## [37] train-rmse:0.164891+0.010314    test-rmse:0.980337+0.066645 
## [38] train-rmse:0.154820+0.009741    test-rmse:0.979125+0.065394 
## [39] train-rmse:0.147071+0.009011    test-rmse:0.977689+0.063915 
## [40] train-rmse:0.138717+0.008305    test-rmse:0.974209+0.065176 
## [41] train-rmse:0.133364+0.007704    test-rmse:0.973894+0.066201 
## [42] train-rmse:0.127510+0.007052    test-rmse:0.974329+0.066410 
## [43] train-rmse:0.117762+0.005737    test-rmse:0.974098+0.066729 
## [44] train-rmse:0.112575+0.005832    test-rmse:0.975157+0.067158 
## [45] train-rmse:0.108418+0.006015    test-rmse:0.974847+0.068853 
## [46] train-rmse:0.102752+0.005153    test-rmse:0.974630+0.069315 
## [47] train-rmse:0.097523+0.004816    test-rmse:0.973256+0.068308 
## [48] train-rmse:0.091882+0.004919    test-rmse:0.973502+0.068177 
## [49] train-rmse:0.086759+0.003642    test-rmse:0.973134+0.068022 
## [50] train-rmse:0.083487+0.003295    test-rmse:0.972749+0.068205 
## [51] train-rmse:0.080231+0.004885    test-rmse:0.972923+0.068660 
## [52] train-rmse:0.075510+0.004879    test-rmse:0.972100+0.067841 
## [53] train-rmse:0.072077+0.003893    test-rmse:0.971740+0.067947 
## [54] train-rmse:0.068003+0.003518    test-rmse:0.971914+0.068157 
## [55] train-rmse:0.065061+0.003252    test-rmse:0.971851+0.068263 
## [56] train-rmse:0.061561+0.003382    test-rmse:0.971507+0.069103 
## [57] train-rmse:0.058912+0.003481    test-rmse:0.971310+0.068561 
## [58] train-rmse:0.055952+0.003970    test-rmse:0.970835+0.068805 
## [59] train-rmse:0.053624+0.003330    test-rmse:0.971343+0.068459 
## [60] train-rmse:0.050157+0.004342    test-rmse:0.971024+0.068410 
## [61] train-rmse:0.047844+0.004492    test-rmse:0.970508+0.067882 
## [62] train-rmse:0.045851+0.004311    test-rmse:0.969945+0.067560 
## [63] train-rmse:0.043566+0.003262    test-rmse:0.969837+0.068137 
## [64] train-rmse:0.041712+0.002663    test-rmse:0.969637+0.068238 
## [65] train-rmse:0.039497+0.002328    test-rmse:0.970197+0.068437 
## [66] train-rmse:0.037755+0.002784    test-rmse:0.970502+0.068779 
## [67] train-rmse:0.036094+0.002537    test-rmse:0.970436+0.068599 
## [68] train-rmse:0.034383+0.002590    test-rmse:0.970556+0.068861 
## [69] train-rmse:0.031932+0.002076    test-rmse:0.970379+0.069068 
## [70] train-rmse:0.030522+0.002001    test-rmse:0.970608+0.069197 
## Stopping. Best iteration:
## [64] train-rmse:0.041712+0.002663    test-rmse:0.969637+0.068238
## 
## 91 
## [1]  train-rmse:63.167377+0.063167   test-rmse:63.167806+0.429924 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:40.537449+0.040014   test-rmse:40.537229+0.451282 
## [3]  train-rmse:26.042731+0.024892   test-rmse:26.041535+0.463398 
## [4]  train-rmse:16.774179+0.015039   test-rmse:16.771557+0.468173 
## [5]  train-rmse:10.867463+0.008994   test-rmse:10.873367+0.425402 
## [6]  train-rmse:7.118900+0.007397    test-rmse:7.108045+0.421111 
## [7]  train-rmse:4.764347+0.011628    test-rmse:4.756193+0.392741 
## [8]  train-rmse:3.328396+0.018694    test-rmse:3.338913+0.354373 
## [9]  train-rmse:2.487714+0.026405    test-rmse:2.512488+0.299437 
## [10] train-rmse:2.022803+0.030115    test-rmse:2.067346+0.217651 
## [11] train-rmse:1.782618+0.032111    test-rmse:1.846605+0.160528 
## [12] train-rmse:1.657092+0.032434    test-rmse:1.745003+0.131462 
## [13] train-rmse:1.587646+0.032171    test-rmse:1.689104+0.125140 
## [14] train-rmse:1.543321+0.031505    test-rmse:1.632660+0.108990 
## [15] train-rmse:1.513898+0.030900    test-rmse:1.605572+0.111804 
## [16] train-rmse:1.490483+0.029751    test-rmse:1.592937+0.124634 
## [17] train-rmse:1.469684+0.028659    test-rmse:1.579261+0.120899 
## [18] train-rmse:1.450165+0.028455    test-rmse:1.569438+0.116273 
## [19] train-rmse:1.432927+0.027744    test-rmse:1.559773+0.113072 
## [20] train-rmse:1.417346+0.027243    test-rmse:1.553118+0.115824 
## [21] train-rmse:1.402598+0.026699    test-rmse:1.544881+0.116623 
## [22] train-rmse:1.388181+0.026192    test-rmse:1.537092+0.113370 
## [23] train-rmse:1.373845+0.025341    test-rmse:1.511353+0.102574 
## [24] train-rmse:1.360449+0.024764    test-rmse:1.513409+0.110346 
## [25] train-rmse:1.347828+0.024531    test-rmse:1.502897+0.119814 
## [26] train-rmse:1.336114+0.024267    test-rmse:1.498907+0.108056 
## [27] train-rmse:1.324571+0.023585    test-rmse:1.491644+0.094134 
## [28] train-rmse:1.313094+0.023084    test-rmse:1.482651+0.095098 
## [29] train-rmse:1.302246+0.022845    test-rmse:1.472871+0.091665 
## [30] train-rmse:1.291439+0.022460    test-rmse:1.465865+0.098868 
## [31] train-rmse:1.280435+0.022956    test-rmse:1.457908+0.098749 
## [32] train-rmse:1.270304+0.022787    test-rmse:1.449608+0.100625 
## [33] train-rmse:1.260982+0.022502    test-rmse:1.445648+0.099889 
## [34] train-rmse:1.252040+0.022297    test-rmse:1.441879+0.097733 
## [35] train-rmse:1.243195+0.022265    test-rmse:1.435705+0.095738 
## [36] train-rmse:1.234425+0.022087    test-rmse:1.431075+0.092112 
## [37] train-rmse:1.226190+0.022108    test-rmse:1.426219+0.085078 
## [38] train-rmse:1.218182+0.022004    test-rmse:1.422267+0.088515 
## [39] train-rmse:1.210518+0.021719    test-rmse:1.411903+0.094321 
## [40] train-rmse:1.203173+0.021406    test-rmse:1.402180+0.096691 
## [41] train-rmse:1.196089+0.021085    test-rmse:1.395394+0.094562 
## [42] train-rmse:1.189422+0.020977    test-rmse:1.389780+0.100058 
## [43] train-rmse:1.182864+0.020835    test-rmse:1.396138+0.092189 
## [44] train-rmse:1.176437+0.020781    test-rmse:1.390836+0.086275 
## [45] train-rmse:1.170282+0.020872    test-rmse:1.379107+0.094560 
## [46] train-rmse:1.164272+0.021000    test-rmse:1.373708+0.083982 
## [47] train-rmse:1.158460+0.021090    test-rmse:1.370067+0.081921 
## [48] train-rmse:1.152552+0.021182    test-rmse:1.366534+0.083623 
## [49] train-rmse:1.146837+0.021077    test-rmse:1.363879+0.082831 
## [50] train-rmse:1.141489+0.020943    test-rmse:1.358710+0.081032 
## [51] train-rmse:1.136382+0.020845    test-rmse:1.362748+0.088672 
## [52] train-rmse:1.131367+0.020810    test-rmse:1.357592+0.089566 
## [53] train-rmse:1.126488+0.020567    test-rmse:1.351649+0.086591 
## [54] train-rmse:1.121633+0.020349    test-rmse:1.346830+0.087881 
## [55] train-rmse:1.117067+0.020134    test-rmse:1.343243+0.089387 
## [56] train-rmse:1.112462+0.019905    test-rmse:1.341426+0.089776 
## [57] train-rmse:1.108006+0.019709    test-rmse:1.337100+0.092701 
## [58] train-rmse:1.103665+0.019751    test-rmse:1.333402+0.094067 
## [59] train-rmse:1.099139+0.019666    test-rmse:1.327803+0.097872 
## [60] train-rmse:1.094813+0.019638    test-rmse:1.325917+0.096212 
## [61] train-rmse:1.090873+0.019480    test-rmse:1.327377+0.088829 
## [62] train-rmse:1.087090+0.019335    test-rmse:1.322321+0.095937 
## [63] train-rmse:1.083323+0.019314    test-rmse:1.316849+0.092526 
## [64] train-rmse:1.079624+0.019243    test-rmse:1.312460+0.095261 
## [65] train-rmse:1.076097+0.019087    test-rmse:1.306194+0.095037 
## [66] train-rmse:1.072624+0.018956    test-rmse:1.305558+0.093932 
## [67] train-rmse:1.069286+0.018886    test-rmse:1.302934+0.094019 
## [68] train-rmse:1.065910+0.018846    test-rmse:1.306312+0.093832 
## [69] train-rmse:1.062707+0.018862    test-rmse:1.302433+0.093247 
## [70] train-rmse:1.059493+0.018827    test-rmse:1.301884+0.096033 
## [71] train-rmse:1.056356+0.018820    test-rmse:1.302894+0.095549 
## [72] train-rmse:1.053265+0.018804    test-rmse:1.301380+0.094599 
## [73] train-rmse:1.050344+0.018800    test-rmse:1.297606+0.097074 
## [74] train-rmse:1.047529+0.018762    test-rmse:1.298170+0.093880 
## [75] train-rmse:1.044758+0.018769    test-rmse:1.296090+0.092696 
## [76] train-rmse:1.042043+0.018706    test-rmse:1.294729+0.093343 
## [77] train-rmse:1.039415+0.018660    test-rmse:1.294290+0.093760 
## [78] train-rmse:1.036834+0.018563    test-rmse:1.292220+0.097245 
## [79] train-rmse:1.034180+0.018481    test-rmse:1.289580+0.095457 
## [80] train-rmse:1.031690+0.018458    test-rmse:1.286535+0.096215 
## [81] train-rmse:1.029294+0.018327    test-rmse:1.288442+0.092636 
## [82] train-rmse:1.026874+0.018176    test-rmse:1.285998+0.092006 
## [83] train-rmse:1.024526+0.018107    test-rmse:1.281776+0.089937 
## [84] train-rmse:1.022190+0.018049    test-rmse:1.281103+0.091598 
## [85] train-rmse:1.019820+0.017967    test-rmse:1.277218+0.092782 
## [86] train-rmse:1.017468+0.017911    test-rmse:1.276021+0.095146 
## [87] train-rmse:1.015192+0.017797    test-rmse:1.270412+0.089972 
## [88] train-rmse:1.012960+0.017667    test-rmse:1.265684+0.091410 
## [89] train-rmse:1.010760+0.017561    test-rmse:1.262255+0.089228 
## [90] train-rmse:1.008506+0.017512    test-rmse:1.260580+0.088399 
## [91] train-rmse:1.006292+0.017490    test-rmse:1.262824+0.086071 
## [92] train-rmse:1.004144+0.017473    test-rmse:1.263925+0.086292 
## [93] train-rmse:1.002071+0.017512    test-rmse:1.262654+0.087729 
## [94] train-rmse:1.000062+0.017588    test-rmse:1.260378+0.089254 
## [95] train-rmse:0.998116+0.017638    test-rmse:1.257990+0.088063 
## [96] train-rmse:0.996187+0.017645    test-rmse:1.257731+0.088711 
## [97] train-rmse:0.994273+0.017670    test-rmse:1.256342+0.090508 
## [98] train-rmse:0.992397+0.017645    test-rmse:1.256676+0.089633 
## [99] train-rmse:0.990359+0.017611    test-rmse:1.254221+0.093454 
## [100]    train-rmse:0.988516+0.017596    test-rmse:1.253489+0.092830 
## [101]    train-rmse:0.986685+0.017542    test-rmse:1.251943+0.091937 
## [102]    train-rmse:0.984861+0.017456    test-rmse:1.252437+0.088654 
## [103]    train-rmse:0.983071+0.017441    test-rmse:1.251378+0.088592 
## [104]    train-rmse:0.981350+0.017420    test-rmse:1.250581+0.086525 
## [105]    train-rmse:0.979577+0.017450    test-rmse:1.248701+0.086270 
## [106]    train-rmse:0.977828+0.017448    test-rmse:1.250662+0.084747 
## [107]    train-rmse:0.976149+0.017455    test-rmse:1.250366+0.086375 
## [108]    train-rmse:0.974552+0.017469    test-rmse:1.249588+0.086669 
## [109]    train-rmse:0.972961+0.017506    test-rmse:1.247808+0.085292 
## [110]    train-rmse:0.971426+0.017576    test-rmse:1.248719+0.086380 
## [111]    train-rmse:0.969878+0.017666    test-rmse:1.246183+0.085009 
## [112]    train-rmse:0.968318+0.017645    test-rmse:1.245321+0.089794 
## [113]    train-rmse:0.966790+0.017725    test-rmse:1.245235+0.087927 
## [114]    train-rmse:0.965277+0.017690    test-rmse:1.245152+0.088412 
## [115]    train-rmse:0.963832+0.017692    test-rmse:1.241154+0.084408 
## [116]    train-rmse:0.962421+0.017709    test-rmse:1.240247+0.084686 
## [117]    train-rmse:0.961007+0.017730    test-rmse:1.240209+0.083227 
## [118]    train-rmse:0.959512+0.017725    test-rmse:1.240677+0.082660 
## [119]    train-rmse:0.958089+0.017794    test-rmse:1.240791+0.081938 
## [120]    train-rmse:0.956701+0.017858    test-rmse:1.238970+0.083839 
## [121]    train-rmse:0.955282+0.017858    test-rmse:1.237122+0.083820 
## [122]    train-rmse:0.953852+0.017780    test-rmse:1.236597+0.084720 
## [123]    train-rmse:0.952528+0.017790    test-rmse:1.238527+0.084290 
## [124]    train-rmse:0.951179+0.017773    test-rmse:1.237690+0.085654 
## [125]    train-rmse:0.949861+0.017759    test-rmse:1.238109+0.087537 
## [126]    train-rmse:0.948536+0.017747    test-rmse:1.237075+0.084313 
## [127]    train-rmse:0.947274+0.017731    test-rmse:1.236566+0.084948 
## [128]    train-rmse:0.945994+0.017775    test-rmse:1.235212+0.087155 
## [129]    train-rmse:0.944812+0.017807    test-rmse:1.233984+0.085491 
## [130]    train-rmse:0.943604+0.017885    test-rmse:1.234285+0.085873 
## [131]    train-rmse:0.942340+0.017944    test-rmse:1.233408+0.085350 
## [132]    train-rmse:0.941120+0.017937    test-rmse:1.234237+0.084653 
## [133]    train-rmse:0.939923+0.017917    test-rmse:1.232784+0.085348 
## [134]    train-rmse:0.938645+0.018004    test-rmse:1.233612+0.084413 
## [135]    train-rmse:0.937413+0.018052    test-rmse:1.233321+0.083810 
## [136]    train-rmse:0.936226+0.018087    test-rmse:1.233160+0.083308 
## [137]    train-rmse:0.935098+0.018107    test-rmse:1.231631+0.085283 
## [138]    train-rmse:0.933923+0.018159    test-rmse:1.230444+0.083131 
## [139]    train-rmse:0.932772+0.018106    test-rmse:1.228305+0.083640 
## [140]    train-rmse:0.931649+0.018124    test-rmse:1.228719+0.085283 
## [141]    train-rmse:0.930549+0.018163    test-rmse:1.226896+0.085739 
## [142]    train-rmse:0.929483+0.018200    test-rmse:1.226810+0.085504 
## [143]    train-rmse:0.928425+0.018198    test-rmse:1.224676+0.084915 
## [144]    train-rmse:0.927358+0.018246    test-rmse:1.226828+0.087243 
## [145]    train-rmse:0.926310+0.018268    test-rmse:1.224430+0.088353 
## [146]    train-rmse:0.925320+0.018281    test-rmse:1.224171+0.088290 
## [147]    train-rmse:0.924327+0.018307    test-rmse:1.222388+0.087831 
## [148]    train-rmse:0.923365+0.018302    test-rmse:1.220601+0.086161 
## [149]    train-rmse:0.922377+0.018323    test-rmse:1.219249+0.086984 
## [150]    train-rmse:0.921299+0.018298    test-rmse:1.220390+0.084981 
## [151]    train-rmse:0.920321+0.018326    test-rmse:1.219103+0.086782 
## [152]    train-rmse:0.919346+0.018413    test-rmse:1.218526+0.086476 
## [153]    train-rmse:0.918411+0.018471    test-rmse:1.217552+0.087564 
## [154]    train-rmse:0.917467+0.018501    test-rmse:1.215865+0.086270 
## [155]    train-rmse:0.916556+0.018541    test-rmse:1.215706+0.087165 
## [156]    train-rmse:0.915659+0.018574    test-rmse:1.215963+0.087054 
## [157]    train-rmse:0.914712+0.018648    test-rmse:1.216393+0.085848 
## [158]    train-rmse:0.913728+0.018682    test-rmse:1.215183+0.085787 
## [159]    train-rmse:0.912837+0.018673    test-rmse:1.217085+0.083291 
## [160]    train-rmse:0.911915+0.018741    test-rmse:1.215417+0.085499 
## [161]    train-rmse:0.911008+0.018775    test-rmse:1.215753+0.084537 
## [162]    train-rmse:0.910132+0.018796    test-rmse:1.215105+0.085186 
## [163]    train-rmse:0.909273+0.018834    test-rmse:1.218792+0.086373 
## [164]    train-rmse:0.908417+0.018828    test-rmse:1.219193+0.084061 
## [165]    train-rmse:0.907527+0.018844    test-rmse:1.219017+0.084083 
## [166]    train-rmse:0.906692+0.018866    test-rmse:1.219889+0.087156 
## [167]    train-rmse:0.905844+0.018868    test-rmse:1.219964+0.087609 
## [168]    train-rmse:0.905040+0.018856    test-rmse:1.221664+0.086923 
## Stopping. Best iteration:
## [162]    train-rmse:0.910132+0.018796    test-rmse:1.215105+0.085186
## 
## 92 
## [1]  train-rmse:63.167377+0.063167   test-rmse:63.167806+0.429924 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:40.537449+0.040014   test-rmse:40.537229+0.451282 
## [3]  train-rmse:26.042731+0.024892   test-rmse:26.041535+0.463398 
## [4]  train-rmse:16.774179+0.015039   test-rmse:16.771557+0.468173 
## [5]  train-rmse:10.867463+0.008994   test-rmse:10.873367+0.425402 
## [6]  train-rmse:7.108491+0.006411    test-rmse:7.096666+0.405457 
## [7]  train-rmse:4.734394+0.009254    test-rmse:4.730032+0.377466 
## [8]  train-rmse:3.259567+0.016598    test-rmse:3.269057+0.314256 
## [9]  train-rmse:2.375473+0.025802    test-rmse:2.408750+0.271933 
## [10] train-rmse:1.869481+0.027837    test-rmse:1.942971+0.221006 
## [11] train-rmse:1.587394+0.022911    test-rmse:1.699738+0.168216 
## [12] train-rmse:1.436747+0.023297    test-rmse:1.584953+0.141997 
## [13] train-rmse:1.351293+0.022012    test-rmse:1.523033+0.118289 
## [14] train-rmse:1.296467+0.019080    test-rmse:1.491602+0.105978 
## [15] train-rmse:1.260937+0.019268    test-rmse:1.468681+0.112418 
## [16] train-rmse:1.226273+0.018484    test-rmse:1.450827+0.118968 
## [17] train-rmse:1.199066+0.016913    test-rmse:1.442599+0.107962 
## [18] train-rmse:1.172837+0.015378    test-rmse:1.421935+0.105547 
## [19] train-rmse:1.148475+0.017910    test-rmse:1.415694+0.109305 
## [20] train-rmse:1.129101+0.017903    test-rmse:1.386319+0.109335 
## [21] train-rmse:1.109104+0.016528    test-rmse:1.369576+0.114167 
## [22] train-rmse:1.088868+0.015314    test-rmse:1.359001+0.107122 
## [23] train-rmse:1.065609+0.005994    test-rmse:1.351858+0.109414 
## [24] train-rmse:1.046845+0.009112    test-rmse:1.337379+0.115991 
## [25] train-rmse:1.033670+0.008675    test-rmse:1.329351+0.113096 
## [26] train-rmse:1.015194+0.011530    test-rmse:1.306127+0.096187 
## [27] train-rmse:1.000221+0.013956    test-rmse:1.290095+0.093881 
## [28] train-rmse:0.983869+0.014311    test-rmse:1.277630+0.099181 
## [29] train-rmse:0.971826+0.014934    test-rmse:1.269127+0.105064 
## [30] train-rmse:0.958296+0.012367    test-rmse:1.262438+0.102925 
## [31] train-rmse:0.947986+0.012530    test-rmse:1.256515+0.104884 
## [32] train-rmse:0.937061+0.014134    test-rmse:1.248041+0.105025 
## [33] train-rmse:0.926285+0.013665    test-rmse:1.246844+0.100878 
## [34] train-rmse:0.914933+0.014346    test-rmse:1.241364+0.099507 
## [35] train-rmse:0.903923+0.012466    test-rmse:1.231602+0.091060 
## [36] train-rmse:0.890521+0.010135    test-rmse:1.224561+0.091376 
## [37] train-rmse:0.881080+0.011932    test-rmse:1.218883+0.094190 
## [38] train-rmse:0.869193+0.011913    test-rmse:1.204825+0.102207 
## [39] train-rmse:0.854327+0.016471    test-rmse:1.202052+0.104635 
## [40] train-rmse:0.842363+0.013973    test-rmse:1.198893+0.106215 
## [41] train-rmse:0.833319+0.011196    test-rmse:1.194088+0.105722 
## [42] train-rmse:0.818638+0.007780    test-rmse:1.188931+0.102749 
## [43] train-rmse:0.811820+0.007192    test-rmse:1.187441+0.104964 
## [44] train-rmse:0.805030+0.006598    test-rmse:1.187242+0.109776 
## [45] train-rmse:0.796334+0.007684    test-rmse:1.178715+0.103936 
## [46] train-rmse:0.788874+0.007577    test-rmse:1.179007+0.105907 
## [47] train-rmse:0.782326+0.008608    test-rmse:1.177070+0.107149 
## [48] train-rmse:0.773677+0.009340    test-rmse:1.168441+0.105724 
## [49] train-rmse:0.762827+0.009887    test-rmse:1.163475+0.104394 
## [50] train-rmse:0.754346+0.010095    test-rmse:1.162550+0.095470 
## [51] train-rmse:0.747824+0.009079    test-rmse:1.157630+0.095312 
## [52] train-rmse:0.740446+0.005793    test-rmse:1.154770+0.098143 
## [53] train-rmse:0.733420+0.008052    test-rmse:1.149705+0.090616 
## [54] train-rmse:0.727422+0.010578    test-rmse:1.146811+0.088466 
## [55] train-rmse:0.720318+0.009321    test-rmse:1.142689+0.094316 
## [56] train-rmse:0.715138+0.008920    test-rmse:1.138633+0.097852 
## [57] train-rmse:0.710660+0.009880    test-rmse:1.134126+0.100046 
## [58] train-rmse:0.702496+0.010617    test-rmse:1.127523+0.097366 
## [59] train-rmse:0.696647+0.010937    test-rmse:1.121610+0.096636 
## [60] train-rmse:0.690161+0.009342    test-rmse:1.122812+0.096005 
## [61] train-rmse:0.685873+0.009342    test-rmse:1.121678+0.097906 
## [62] train-rmse:0.679213+0.010796    test-rmse:1.118851+0.093436 
## [63] train-rmse:0.674618+0.011646    test-rmse:1.120750+0.093828 
## [64] train-rmse:0.670117+0.010447    test-rmse:1.118936+0.097086 
## [65] train-rmse:0.664020+0.009177    test-rmse:1.122641+0.100493 
## [66] train-rmse:0.658062+0.010053    test-rmse:1.117826+0.099120 
## [67] train-rmse:0.653774+0.011537    test-rmse:1.117174+0.098510 
## [68] train-rmse:0.648860+0.012603    test-rmse:1.116330+0.097989 
## [69] train-rmse:0.643245+0.012994    test-rmse:1.115340+0.097528 
## [70] train-rmse:0.635804+0.013136    test-rmse:1.108637+0.103699 
## [71] train-rmse:0.629743+0.013477    test-rmse:1.107247+0.104417 
## [72] train-rmse:0.624686+0.013236    test-rmse:1.107500+0.106158 
## [73] train-rmse:0.620692+0.013869    test-rmse:1.106724+0.106219 
## [74] train-rmse:0.613624+0.014666    test-rmse:1.096584+0.097349 
## [75] train-rmse:0.610488+0.014964    test-rmse:1.096361+0.098359 
## [76] train-rmse:0.607192+0.015228    test-rmse:1.095644+0.097894 
## [77] train-rmse:0.601642+0.014903    test-rmse:1.091939+0.097968 
## [78] train-rmse:0.597154+0.013990    test-rmse:1.090526+0.098309 
## [79] train-rmse:0.593340+0.014804    test-rmse:1.087015+0.096733 
## [80] train-rmse:0.588846+0.014290    test-rmse:1.086265+0.097835 
## [81] train-rmse:0.583305+0.012279    test-rmse:1.082901+0.099291 
## [82] train-rmse:0.578981+0.011313    test-rmse:1.080008+0.103845 
## [83] train-rmse:0.574969+0.012357    test-rmse:1.079236+0.100565 
## [84] train-rmse:0.571612+0.012504    test-rmse:1.080505+0.100815 
## [85] train-rmse:0.567838+0.011676    test-rmse:1.080114+0.099564 
## [86] train-rmse:0.565348+0.011214    test-rmse:1.076975+0.098769 
## [87] train-rmse:0.562581+0.012212    test-rmse:1.074094+0.098042 
## [88] train-rmse:0.558559+0.010276    test-rmse:1.072595+0.098522 
## [89] train-rmse:0.551765+0.010697    test-rmse:1.069659+0.099723 
## [90] train-rmse:0.548713+0.010953    test-rmse:1.069298+0.100218 
## [91] train-rmse:0.545886+0.011383    test-rmse:1.069528+0.099531 
## [92] train-rmse:0.541982+0.012281    test-rmse:1.069118+0.099003 
## [93] train-rmse:0.538557+0.013187    test-rmse:1.067774+0.103874 
## [94] train-rmse:0.534325+0.010338    test-rmse:1.066242+0.103889 
## [95] train-rmse:0.530149+0.008159    test-rmse:1.063464+0.105316 
## [96] train-rmse:0.526437+0.008610    test-rmse:1.064441+0.105329 
## [97] train-rmse:0.524052+0.008666    test-rmse:1.063898+0.105355 
## [98] train-rmse:0.520573+0.008672    test-rmse:1.063632+0.107083 
## [99] train-rmse:0.517488+0.008812    test-rmse:1.061758+0.106044 
## [100]    train-rmse:0.515436+0.009396    test-rmse:1.059427+0.104433 
## [101]    train-rmse:0.511041+0.009465    test-rmse:1.058884+0.105604 
## [102]    train-rmse:0.507592+0.010069    test-rmse:1.058445+0.104326 
## [103]    train-rmse:0.503987+0.010407    test-rmse:1.058616+0.104382 
## [104]    train-rmse:0.500991+0.011022    test-rmse:1.059593+0.103267 
## [105]    train-rmse:0.498289+0.011226    test-rmse:1.057912+0.102740 
## [106]    train-rmse:0.495143+0.011714    test-rmse:1.057375+0.102131 
## [107]    train-rmse:0.492338+0.013230    test-rmse:1.055302+0.100570 
## [108]    train-rmse:0.489458+0.013394    test-rmse:1.053935+0.100378 
## [109]    train-rmse:0.487377+0.013839    test-rmse:1.053428+0.098543 
## [110]    train-rmse:0.484818+0.013708    test-rmse:1.053044+0.098955 
## [111]    train-rmse:0.482491+0.014204    test-rmse:1.053921+0.099470 
## [112]    train-rmse:0.479342+0.014837    test-rmse:1.046998+0.097609 
## [113]    train-rmse:0.476178+0.015242    test-rmse:1.045718+0.098770 
## [114]    train-rmse:0.474606+0.015770    test-rmse:1.045069+0.097371 
## [115]    train-rmse:0.471407+0.014067    test-rmse:1.045188+0.097237 
## [116]    train-rmse:0.469161+0.014195    test-rmse:1.046179+0.099842 
## [117]    train-rmse:0.466212+0.015042    test-rmse:1.045191+0.101289 
## [118]    train-rmse:0.464430+0.015254    test-rmse:1.045081+0.101265 
## [119]    train-rmse:0.463036+0.015497    test-rmse:1.043885+0.101769 
## [120]    train-rmse:0.461055+0.014682    test-rmse:1.043349+0.101228 
## [121]    train-rmse:0.457237+0.014828    test-rmse:1.044385+0.101490 
## [122]    train-rmse:0.455056+0.014807    test-rmse:1.044680+0.098662 
## [123]    train-rmse:0.452490+0.013946    test-rmse:1.043923+0.098430 
## [124]    train-rmse:0.449593+0.013287    test-rmse:1.044197+0.097668 
## [125]    train-rmse:0.446979+0.014048    test-rmse:1.042465+0.096486 
## [126]    train-rmse:0.445003+0.014081    test-rmse:1.043049+0.096323 
## [127]    train-rmse:0.442263+0.013789    test-rmse:1.042272+0.095753 
## [128]    train-rmse:0.440933+0.013516    test-rmse:1.042149+0.096899 
## [129]    train-rmse:0.437748+0.013547    test-rmse:1.043044+0.098693 
## [130]    train-rmse:0.435140+0.013766    test-rmse:1.037414+0.096905 
## [131]    train-rmse:0.433452+0.014725    test-rmse:1.036450+0.096620 
## [132]    train-rmse:0.430454+0.015284    test-rmse:1.035866+0.096711 
## [133]    train-rmse:0.428424+0.014811    test-rmse:1.034271+0.097263 
## [134]    train-rmse:0.426500+0.014996    test-rmse:1.032416+0.098587 
## [135]    train-rmse:0.424737+0.014561    test-rmse:1.030423+0.098737 
## [136]    train-rmse:0.422827+0.015380    test-rmse:1.029381+0.099183 
## [137]    train-rmse:0.421142+0.014953    test-rmse:1.029024+0.098492 
## [138]    train-rmse:0.417193+0.013649    test-rmse:1.025849+0.098907 
## [139]    train-rmse:0.414939+0.013807    test-rmse:1.024735+0.099717 
## [140]    train-rmse:0.412000+0.013546    test-rmse:1.023393+0.097449 
## [141]    train-rmse:0.410029+0.013160    test-rmse:1.024035+0.097749 
## [142]    train-rmse:0.406872+0.011320    test-rmse:1.022447+0.099900 
## [143]    train-rmse:0.404123+0.010685    test-rmse:1.022882+0.100510 
## [144]    train-rmse:0.401953+0.011813    test-rmse:1.021880+0.100782 
## [145]    train-rmse:0.400951+0.011656    test-rmse:1.021174+0.101643 
## [146]    train-rmse:0.399263+0.012122    test-rmse:1.020198+0.100354 
## [147]    train-rmse:0.396768+0.012635    test-rmse:1.021256+0.100320 
## [148]    train-rmse:0.394811+0.013093    test-rmse:1.022011+0.100777 
## [149]    train-rmse:0.393560+0.013135    test-rmse:1.021621+0.101614 
## [150]    train-rmse:0.390256+0.013529    test-rmse:1.017144+0.098261 
## [151]    train-rmse:0.386938+0.013970    test-rmse:1.014204+0.099818 
## [152]    train-rmse:0.384793+0.013167    test-rmse:1.014056+0.100081 
## [153]    train-rmse:0.382851+0.013933    test-rmse:1.014427+0.099948 
## [154]    train-rmse:0.380759+0.013737    test-rmse:1.015286+0.099317 
## [155]    train-rmse:0.379005+0.013587    test-rmse:1.015983+0.100537 
## [156]    train-rmse:0.377035+0.013111    test-rmse:1.015920+0.100862 
## [157]    train-rmse:0.375717+0.012829    test-rmse:1.014934+0.101144 
## [158]    train-rmse:0.373660+0.013303    test-rmse:1.015428+0.100156 
## Stopping. Best iteration:
## [152]    train-rmse:0.384793+0.013167    test-rmse:1.014056+0.100081
## 
## 93 
## [1]  train-rmse:63.167377+0.063167   test-rmse:63.167806+0.429924 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:40.537449+0.040014   test-rmse:40.537229+0.451282 
## [3]  train-rmse:26.042731+0.024892   test-rmse:26.041535+0.463398 
## [4]  train-rmse:16.774179+0.015039   test-rmse:16.771557+0.468173 
## [5]  train-rmse:10.867463+0.008994   test-rmse:10.873367+0.425402 
## [6]  train-rmse:7.106011+0.006748    test-rmse:7.085501+0.396855 
## [7]  train-rmse:4.718623+0.003599    test-rmse:4.721924+0.364739 
## [8]  train-rmse:3.219054+0.011312    test-rmse:3.242144+0.299721 
## [9]  train-rmse:2.296407+0.010827    test-rmse:2.337673+0.257433 
## [10] train-rmse:1.749960+0.014075    test-rmse:1.837462+0.213316 
## [11] train-rmse:1.436272+0.021982    test-rmse:1.589656+0.174545 
## [12] train-rmse:1.261480+0.032824    test-rmse:1.470713+0.151604 
## [13] train-rmse:1.166567+0.035756    test-rmse:1.394704+0.146870 
## [14] train-rmse:1.104997+0.034148    test-rmse:1.360821+0.132846 
## [15] train-rmse:1.057456+0.031930    test-rmse:1.330234+0.120436 
## [16] train-rmse:1.015283+0.037196    test-rmse:1.310682+0.107988 
## [17] train-rmse:0.974894+0.031290    test-rmse:1.286566+0.113084 
## [18] train-rmse:0.944280+0.030166    test-rmse:1.275443+0.105746 
## [19] train-rmse:0.919084+0.027171    test-rmse:1.261440+0.109036 
## [20] train-rmse:0.892745+0.024284    test-rmse:1.235746+0.115190 
## [21] train-rmse:0.870071+0.025195    test-rmse:1.223269+0.114105 
## [22] train-rmse:0.848050+0.021418    test-rmse:1.215429+0.117143 
## [23] train-rmse:0.824919+0.019795    test-rmse:1.200408+0.111956 
## [24] train-rmse:0.806454+0.018662    test-rmse:1.181236+0.114052 
## [25] train-rmse:0.790335+0.017952    test-rmse:1.179714+0.113176 
## [26] train-rmse:0.775530+0.019562    test-rmse:1.163392+0.117558 
## [27] train-rmse:0.758444+0.018290    test-rmse:1.154461+0.117703 
## [28] train-rmse:0.742228+0.017957    test-rmse:1.146174+0.119689 
## [29] train-rmse:0.727151+0.021996    test-rmse:1.139388+0.122588 
## [30] train-rmse:0.711517+0.023698    test-rmse:1.140212+0.128371 
## [31] train-rmse:0.699411+0.023290    test-rmse:1.132836+0.128583 
## [32] train-rmse:0.681491+0.021290    test-rmse:1.126097+0.130657 
## [33] train-rmse:0.665383+0.023959    test-rmse:1.118019+0.128580 
## [34] train-rmse:0.650251+0.021809    test-rmse:1.103953+0.113816 
## [35] train-rmse:0.639287+0.021395    test-rmse:1.095532+0.115536 
## [36] train-rmse:0.628640+0.021887    test-rmse:1.092120+0.118089 
## [37] train-rmse:0.613337+0.016641    test-rmse:1.095119+0.121958 
## [38] train-rmse:0.603367+0.015927    test-rmse:1.090623+0.118936 
## [39] train-rmse:0.592920+0.015406    test-rmse:1.086236+0.122642 
## [40] train-rmse:0.585662+0.015617    test-rmse:1.086520+0.120722 
## [41] train-rmse:0.574181+0.013149    test-rmse:1.081874+0.118673 
## [42] train-rmse:0.560533+0.015406    test-rmse:1.076753+0.121080 
## [43] train-rmse:0.550968+0.011611    test-rmse:1.069214+0.109372 
## [44] train-rmse:0.543551+0.009899    test-rmse:1.067598+0.110782 
## [45] train-rmse:0.536559+0.009341    test-rmse:1.065334+0.112095 
## [46] train-rmse:0.527828+0.008157    test-rmse:1.064278+0.111738 
## [47] train-rmse:0.519689+0.011156    test-rmse:1.068185+0.112643 
## [48] train-rmse:0.510899+0.013405    test-rmse:1.063437+0.113773 
## [49] train-rmse:0.503978+0.012892    test-rmse:1.062615+0.115106 
## [50] train-rmse:0.497211+0.013848    test-rmse:1.059741+0.116462 
## [51] train-rmse:0.483750+0.013251    test-rmse:1.054210+0.118278 
## [52] train-rmse:0.474420+0.013190    test-rmse:1.045363+0.113534 
## [53] train-rmse:0.466811+0.010790    test-rmse:1.037868+0.118869 
## [54] train-rmse:0.460032+0.009607    test-rmse:1.036064+0.118088 
## [55] train-rmse:0.454981+0.010820    test-rmse:1.035240+0.118398 
## [56] train-rmse:0.449109+0.009259    test-rmse:1.033481+0.116219 
## [57] train-rmse:0.441593+0.011888    test-rmse:1.032282+0.116721 
## [58] train-rmse:0.435539+0.011486    test-rmse:1.034613+0.117152 
## [59] train-rmse:0.429501+0.013844    test-rmse:1.032562+0.117570 
## [60] train-rmse:0.425123+0.012751    test-rmse:1.035119+0.115319 
## [61] train-rmse:0.417453+0.013974    test-rmse:1.033429+0.115662 
## [62] train-rmse:0.413456+0.014287    test-rmse:1.035224+0.115730 
## [63] train-rmse:0.406575+0.015874    test-rmse:1.035770+0.114234 
## Stopping. Best iteration:
## [57] train-rmse:0.441593+0.011888    test-rmse:1.032282+0.116721
## 
## 94 
## [1]  train-rmse:63.167377+0.063167   test-rmse:63.167806+0.429924 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:40.537449+0.040014   test-rmse:40.537229+0.451282 
## [3]  train-rmse:26.042731+0.024892   test-rmse:26.041535+0.463398 
## [4]  train-rmse:16.774179+0.015039   test-rmse:16.771557+0.468173 
## [5]  train-rmse:10.867463+0.008994   test-rmse:10.873367+0.425402 
## [6]  train-rmse:7.104368+0.006686    test-rmse:7.075519+0.392453 
## [7]  train-rmse:4.711631+0.004317    test-rmse:4.721595+0.349681 
## [8]  train-rmse:3.202509+0.007727    test-rmse:3.222562+0.290372 
## [9]  train-rmse:2.260776+0.005711    test-rmse:2.310395+0.244498 
## [10] train-rmse:1.691476+0.013508    test-rmse:1.798696+0.202549 
## [11] train-rmse:1.352608+0.012953    test-rmse:1.529242+0.166038 
## [12] train-rmse:1.161231+0.011602    test-rmse:1.401128+0.155664 
## [13] train-rmse:1.052712+0.009545    test-rmse:1.340649+0.149266 
## [14] train-rmse:0.983347+0.013649    test-rmse:1.287543+0.149372 
## [15] train-rmse:0.927558+0.015405    test-rmse:1.246587+0.144054 
## [16] train-rmse:0.879551+0.017920    test-rmse:1.223701+0.139919 
## [17] train-rmse:0.843123+0.019838    test-rmse:1.202138+0.141553 
## [18] train-rmse:0.805561+0.018850    test-rmse:1.189004+0.142281 
## [19] train-rmse:0.765485+0.017744    test-rmse:1.165430+0.129040 
## [20] train-rmse:0.740331+0.018758    test-rmse:1.150370+0.126282 
## [21] train-rmse:0.719131+0.019083    test-rmse:1.148420+0.117290 
## [22] train-rmse:0.691068+0.019484    test-rmse:1.135770+0.129060 
## [23] train-rmse:0.670045+0.022854    test-rmse:1.125401+0.133947 
## [24] train-rmse:0.649326+0.027531    test-rmse:1.117921+0.132073 
## [25] train-rmse:0.634481+0.023557    test-rmse:1.101460+0.133631 
## [26] train-rmse:0.612969+0.024514    test-rmse:1.099151+0.135326 
## [27] train-rmse:0.595334+0.026441    test-rmse:1.086868+0.139072 
## [28] train-rmse:0.573313+0.027573    test-rmse:1.079789+0.131139 
## [29] train-rmse:0.560162+0.023787    test-rmse:1.074383+0.130288 
## [30] train-rmse:0.545629+0.022631    test-rmse:1.071372+0.123386 
## [31] train-rmse:0.534888+0.022394    test-rmse:1.063125+0.126968 
## [32] train-rmse:0.519840+0.022888    test-rmse:1.059356+0.128131 
## [33] train-rmse:0.504689+0.026964    test-rmse:1.060561+0.131202 
## [34] train-rmse:0.492964+0.026679    test-rmse:1.058889+0.133170 
## [35] train-rmse:0.479230+0.026822    test-rmse:1.050792+0.134050 
## [36] train-rmse:0.469620+0.026905    test-rmse:1.051531+0.136016 
## [37] train-rmse:0.455629+0.027779    test-rmse:1.050387+0.136863 
## [38] train-rmse:0.445602+0.027037    test-rmse:1.048136+0.135975 
## [39] train-rmse:0.436244+0.027759    test-rmse:1.040033+0.134453 
## [40] train-rmse:0.428696+0.025857    test-rmse:1.037051+0.137689 
## [41] train-rmse:0.418282+0.025328    test-rmse:1.038410+0.138315 
## [42] train-rmse:0.407506+0.025890    test-rmse:1.042075+0.140107 
## [43] train-rmse:0.400490+0.027928    test-rmse:1.042227+0.139007 
## [44] train-rmse:0.391405+0.026825    test-rmse:1.040440+0.136969 
## [45] train-rmse:0.382303+0.026450    test-rmse:1.039313+0.135493 
## [46] train-rmse:0.371946+0.029761    test-rmse:1.033474+0.135163 
## [47] train-rmse:0.362761+0.030970    test-rmse:1.029115+0.137925 
## [48] train-rmse:0.354389+0.032660    test-rmse:1.025747+0.139503 
## [49] train-rmse:0.345161+0.029501    test-rmse:1.021144+0.139441 
## [50] train-rmse:0.340246+0.029230    test-rmse:1.015670+0.137106 
## [51] train-rmse:0.334376+0.030398    test-rmse:1.013279+0.137636 
## [52] train-rmse:0.326351+0.029074    test-rmse:1.010488+0.138153 
## [53] train-rmse:0.319265+0.029901    test-rmse:1.005825+0.140542 
## [54] train-rmse:0.312014+0.031563    test-rmse:1.007133+0.139813 
## [55] train-rmse:0.303131+0.030380    test-rmse:1.009516+0.137698 
## [56] train-rmse:0.297303+0.031913    test-rmse:1.011900+0.137334 
## [57] train-rmse:0.287660+0.027921    test-rmse:1.009514+0.135773 
## [58] train-rmse:0.281193+0.029713    test-rmse:1.009517+0.135923 
## [59] train-rmse:0.274125+0.029357    test-rmse:1.006674+0.136438 
## Stopping. Best iteration:
## [53] train-rmse:0.319265+0.029901    test-rmse:1.005825+0.140542
## 
## 95 
## [1]  train-rmse:63.167377+0.063167   test-rmse:63.167806+0.429924 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:40.537449+0.040014   test-rmse:40.537229+0.451282 
## [3]  train-rmse:26.042731+0.024892   test-rmse:26.041535+0.463398 
## [4]  train-rmse:16.774179+0.015039   test-rmse:16.771557+0.468173 
## [5]  train-rmse:10.867463+0.008994   test-rmse:10.873367+0.425402 
## [6]  train-rmse:7.103617+0.007464    test-rmse:7.079943+0.395225 
## [7]  train-rmse:4.707817+0.005542    test-rmse:4.712634+0.330122 
## [8]  train-rmse:3.190933+0.006557    test-rmse:3.196629+0.269146 
## [9]  train-rmse:2.231872+0.012791    test-rmse:2.268306+0.227356 
## [10] train-rmse:1.642334+0.009334    test-rmse:1.746908+0.199394 
## [11] train-rmse:1.279783+0.011757    test-rmse:1.477860+0.155072 
## [12] train-rmse:1.058515+0.016501    test-rmse:1.334176+0.140169 
## [13] train-rmse:0.923932+0.015905    test-rmse:1.260219+0.119980 
## [14] train-rmse:0.841174+0.018054    test-rmse:1.220252+0.116870 
## [15] train-rmse:0.784275+0.020576    test-rmse:1.186415+0.119831 
## [16] train-rmse:0.731049+0.022182    test-rmse:1.157089+0.129610 
## [17] train-rmse:0.684391+0.035777    test-rmse:1.135481+0.132520 
## [18] train-rmse:0.650668+0.032225    test-rmse:1.106988+0.115423 
## [19] train-rmse:0.623194+0.030062    test-rmse:1.096983+0.118982 
## [20] train-rmse:0.593706+0.028840    test-rmse:1.090429+0.117429 
## [21] train-rmse:0.570963+0.030261    test-rmse:1.083840+0.116519 
## [22] train-rmse:0.548708+0.024460    test-rmse:1.081699+0.113771 
## [23] train-rmse:0.523959+0.026129    test-rmse:1.073680+0.119082 
## [24] train-rmse:0.507403+0.026602    test-rmse:1.064521+0.119898 
## [25] train-rmse:0.487800+0.032631    test-rmse:1.063767+0.117058 
## [26] train-rmse:0.469130+0.035500    test-rmse:1.062055+0.114155 
## [27] train-rmse:0.448915+0.033477    test-rmse:1.055657+0.112107 
## [28] train-rmse:0.432938+0.034647    test-rmse:1.050785+0.109930 
## [29] train-rmse:0.419940+0.036026    test-rmse:1.038215+0.099862 
## [30] train-rmse:0.401269+0.033809    test-rmse:1.043181+0.105417 
## [31] train-rmse:0.389848+0.030074    test-rmse:1.044885+0.106011 
## [32] train-rmse:0.374060+0.030151    test-rmse:1.040248+0.102540 
## [33] train-rmse:0.359063+0.028021    test-rmse:1.038191+0.102182 
## [34] train-rmse:0.345060+0.031746    test-rmse:1.037238+0.101189 
## [35] train-rmse:0.330694+0.032002    test-rmse:1.033166+0.102733 
## [36] train-rmse:0.322138+0.030049    test-rmse:1.033820+0.100422 
## [37] train-rmse:0.312443+0.029869    test-rmse:1.031550+0.095776 
## [38] train-rmse:0.303733+0.029298    test-rmse:1.028773+0.097788 
## [39] train-rmse:0.293391+0.027639    test-rmse:1.025482+0.098238 
## [40] train-rmse:0.280543+0.024040    test-rmse:1.023781+0.100590 
## [41] train-rmse:0.274291+0.025239    test-rmse:1.022693+0.099115 
## [42] train-rmse:0.265468+0.023636    test-rmse:1.019636+0.098592 
## [43] train-rmse:0.257754+0.020594    test-rmse:1.018615+0.098798 
## [44] train-rmse:0.247482+0.021421    test-rmse:1.015467+0.096588 
## [45] train-rmse:0.238765+0.016980    test-rmse:1.011646+0.098524 
## [46] train-rmse:0.229141+0.013482    test-rmse:1.010080+0.097802 
## [47] train-rmse:0.222693+0.014094    test-rmse:1.010287+0.098587 
## [48] train-rmse:0.214648+0.015391    test-rmse:1.006601+0.099447 
## [49] train-rmse:0.208413+0.014955    test-rmse:1.005256+0.097762 
## [50] train-rmse:0.200885+0.017749    test-rmse:1.003339+0.096914 
## [51] train-rmse:0.195915+0.017496    test-rmse:1.001936+0.095574 
## [52] train-rmse:0.190775+0.017943    test-rmse:0.999576+0.095419 
## [53] train-rmse:0.184595+0.018541    test-rmse:0.998191+0.094886 
## [54] train-rmse:0.178124+0.016750    test-rmse:0.994747+0.092551 
## [55] train-rmse:0.171961+0.014889    test-rmse:0.994883+0.090883 
## [56] train-rmse:0.167779+0.015605    test-rmse:0.993977+0.091188 
## [57] train-rmse:0.162093+0.014089    test-rmse:0.993162+0.091059 
## [58] train-rmse:0.157675+0.014345    test-rmse:0.993902+0.091536 
## [59] train-rmse:0.151863+0.012597    test-rmse:0.993183+0.090569 
## [60] train-rmse:0.147824+0.011289    test-rmse:0.993452+0.090776 
## [61] train-rmse:0.144177+0.011854    test-rmse:0.992321+0.089915 
## [62] train-rmse:0.139479+0.011334    test-rmse:0.992430+0.089436 
## [63] train-rmse:0.135292+0.009990    test-rmse:0.992294+0.089857 
## [64] train-rmse:0.130611+0.009643    test-rmse:0.992138+0.089811 
## [65] train-rmse:0.127335+0.009664    test-rmse:0.993492+0.089642 
## [66] train-rmse:0.122935+0.007796    test-rmse:0.994273+0.090952 
## [67] train-rmse:0.119372+0.006792    test-rmse:0.996417+0.091463 
## [68] train-rmse:0.116260+0.007408    test-rmse:0.995924+0.089652 
## [69] train-rmse:0.112651+0.006638    test-rmse:0.995291+0.089448 
## [70] train-rmse:0.108541+0.006697    test-rmse:0.994897+0.089223 
## Stopping. Best iteration:
## [64] train-rmse:0.130611+0.009643    test-rmse:0.992138+0.089811
## 
## 96 
## [1]  train-rmse:63.167377+0.063167   test-rmse:63.167806+0.429924 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:40.537449+0.040014   test-rmse:40.537229+0.451282 
## [3]  train-rmse:26.042731+0.024892   test-rmse:26.041535+0.463398 
## [4]  train-rmse:16.774179+0.015039   test-rmse:16.771557+0.468173 
## [5]  train-rmse:10.867463+0.008994   test-rmse:10.873367+0.425402 
## [6]  train-rmse:7.103617+0.007464    test-rmse:7.079943+0.395225 
## [7]  train-rmse:4.706805+0.004449    test-rmse:4.709556+0.333135 
## [8]  train-rmse:3.186273+0.007021    test-rmse:3.206045+0.261238 
## [9]  train-rmse:2.215385+0.005774    test-rmse:2.272518+0.222085 
## [10] train-rmse:1.603987+0.017996    test-rmse:1.717170+0.200457 
## [11] train-rmse:1.220525+0.014496    test-rmse:1.435018+0.156794 
## [12] train-rmse:0.983356+0.013033    test-rmse:1.268876+0.150402 
## [13] train-rmse:0.840171+0.010302    test-rmse:1.189044+0.128932 
## [14] train-rmse:0.747081+0.009192    test-rmse:1.135267+0.119651 
## [15] train-rmse:0.677027+0.017346    test-rmse:1.105563+0.107775 
## [16] train-rmse:0.630166+0.016125    test-rmse:1.085191+0.099111 
## [17] train-rmse:0.590651+0.010665    test-rmse:1.072080+0.087596 
## [18] train-rmse:0.542438+0.011533    test-rmse:1.046286+0.084501 
## [19] train-rmse:0.509522+0.017448    test-rmse:1.042674+0.093513 
## [20] train-rmse:0.481831+0.024569    test-rmse:1.030894+0.086647 
## [21] train-rmse:0.458981+0.022273    test-rmse:1.025916+0.083189 
## [22] train-rmse:0.432797+0.023657    test-rmse:1.018443+0.082212 
## [23] train-rmse:0.411347+0.023047    test-rmse:1.012634+0.078892 
## [24] train-rmse:0.390131+0.023044    test-rmse:0.999023+0.081197 
## [25] train-rmse:0.374573+0.027378    test-rmse:0.996433+0.078265 
## [26] train-rmse:0.357718+0.025222    test-rmse:0.991919+0.087065 
## [27] train-rmse:0.342376+0.034247    test-rmse:0.987835+0.086638 
## [28] train-rmse:0.323997+0.029897    test-rmse:0.988475+0.087137 
## [29] train-rmse:0.310439+0.033172    test-rmse:0.986466+0.090525 
## [30] train-rmse:0.295836+0.029914    test-rmse:0.985868+0.090835 
## [31] train-rmse:0.282594+0.026935    test-rmse:0.980555+0.091203 
## [32] train-rmse:0.266223+0.026211    test-rmse:0.982685+0.092307 
## [33] train-rmse:0.253296+0.025899    test-rmse:0.984085+0.090503 
## [34] train-rmse:0.241184+0.026216    test-rmse:0.982026+0.088775 
## [35] train-rmse:0.228830+0.024185    test-rmse:0.979167+0.089638 
## [36] train-rmse:0.219904+0.023915    test-rmse:0.974837+0.090548 
## [37] train-rmse:0.211429+0.024501    test-rmse:0.974415+0.089828 
## [38] train-rmse:0.201848+0.023163    test-rmse:0.973161+0.090125 
## [39] train-rmse:0.192410+0.020887    test-rmse:0.971652+0.090559 
## [40] train-rmse:0.183217+0.021134    test-rmse:0.969818+0.090103 
## [41] train-rmse:0.175607+0.021756    test-rmse:0.967811+0.089940 
## [42] train-rmse:0.168928+0.020645    test-rmse:0.964137+0.091693 
## [43] train-rmse:0.162134+0.018535    test-rmse:0.965365+0.093483 
## [44] train-rmse:0.156771+0.019570    test-rmse:0.964405+0.093307 
## [45] train-rmse:0.149164+0.019888    test-rmse:0.963198+0.093524 
## [46] train-rmse:0.143407+0.018784    test-rmse:0.961079+0.095305 
## [47] train-rmse:0.139265+0.018549    test-rmse:0.960373+0.095247 
## [48] train-rmse:0.132549+0.019144    test-rmse:0.960741+0.095826 
## [49] train-rmse:0.126678+0.019636    test-rmse:0.960359+0.095603 
## [50] train-rmse:0.119062+0.015132    test-rmse:0.959994+0.095859 
## [51] train-rmse:0.115155+0.015389    test-rmse:0.959302+0.094926 
## [52] train-rmse:0.109359+0.013784    test-rmse:0.958126+0.095061 
## [53] train-rmse:0.104917+0.012570    test-rmse:0.958384+0.096116 
## [54] train-rmse:0.101697+0.012346    test-rmse:0.957728+0.096987 
## [55] train-rmse:0.097539+0.012404    test-rmse:0.958332+0.098909 
## [56] train-rmse:0.094085+0.012203    test-rmse:0.958565+0.099749 
## [57] train-rmse:0.090312+0.011981    test-rmse:0.958506+0.099960 
## [58] train-rmse:0.085950+0.010456    test-rmse:0.959064+0.099419 
## [59] train-rmse:0.083216+0.010410    test-rmse:0.958174+0.099894 
## [60] train-rmse:0.080609+0.010414    test-rmse:0.958056+0.100043 
## Stopping. Best iteration:
## [54] train-rmse:0.101697+0.012346    test-rmse:0.957728+0.096987
## 
## 97 
## [1]  train-rmse:63.167377+0.063167   test-rmse:63.167806+0.429924 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:40.537449+0.040014   test-rmse:40.537229+0.451282 
## [3]  train-rmse:26.042731+0.024892   test-rmse:26.041535+0.463398 
## [4]  train-rmse:16.774179+0.015039   test-rmse:16.771557+0.468173 
## [5]  train-rmse:10.867463+0.008994   test-rmse:10.873367+0.425402 
## [6]  train-rmse:7.103617+0.007464    test-rmse:7.079943+0.395225 
## [7]  train-rmse:4.705812+0.003392    test-rmse:4.706798+0.336177 
## [8]  train-rmse:3.178809+0.002953    test-rmse:3.190824+0.265549 
## [9]  train-rmse:2.200409+0.011126    test-rmse:2.274945+0.217438 
## [10] train-rmse:1.581109+0.012675    test-rmse:1.725791+0.185168 
## [11] train-rmse:1.180676+0.014045    test-rmse:1.425697+0.156041 
## [12] train-rmse:0.935345+0.017815    test-rmse:1.259583+0.151866 
## [13] train-rmse:0.782418+0.019617    test-rmse:1.188382+0.141068 
## [14] train-rmse:0.674153+0.030224    test-rmse:1.145269+0.132585 
## [15] train-rmse:0.600860+0.040058    test-rmse:1.117354+0.114493 
## [16] train-rmse:0.543412+0.033446    test-rmse:1.091657+0.106647 
## [17] train-rmse:0.495893+0.033821    test-rmse:1.076450+0.108362 
## [18] train-rmse:0.448394+0.043272    test-rmse:1.066799+0.105082 
## [19] train-rmse:0.414982+0.040699    test-rmse:1.052229+0.103850 
## [20] train-rmse:0.387445+0.036660    test-rmse:1.049510+0.108687 
## [21] train-rmse:0.363852+0.037943    test-rmse:1.039916+0.105038 
## [22] train-rmse:0.340627+0.036171    test-rmse:1.035780+0.102330 
## [23] train-rmse:0.320414+0.036438    test-rmse:1.033726+0.105221 
## [24] train-rmse:0.304537+0.034133    test-rmse:1.031532+0.105162 
## [25] train-rmse:0.286266+0.035894    test-rmse:1.027430+0.104335 
## [26] train-rmse:0.264446+0.031542    test-rmse:1.027487+0.103224 
## [27] train-rmse:0.249825+0.027453    test-rmse:1.023954+0.103966 
## [28] train-rmse:0.236958+0.025776    test-rmse:1.022770+0.107848 
## [29] train-rmse:0.223543+0.028715    test-rmse:1.020345+0.109746 
## [30] train-rmse:0.209439+0.021964    test-rmse:1.022090+0.108915 
## [31] train-rmse:0.194936+0.021254    test-rmse:1.023307+0.111975 
## [32] train-rmse:0.184636+0.021860    test-rmse:1.021858+0.111252 
## [33] train-rmse:0.175376+0.020706    test-rmse:1.019251+0.110610 
## [34] train-rmse:0.168291+0.020368    test-rmse:1.020044+0.108614 
## [35] train-rmse:0.161753+0.021832    test-rmse:1.019632+0.107779 
## [36] train-rmse:0.151544+0.020609    test-rmse:1.018068+0.106707 
## [37] train-rmse:0.143846+0.019801    test-rmse:1.013974+0.105769 
## [38] train-rmse:0.133919+0.017211    test-rmse:1.013835+0.107899 
## [39] train-rmse:0.126633+0.014332    test-rmse:1.011506+0.106783 
## [40] train-rmse:0.119541+0.013687    test-rmse:1.009367+0.106522 
## [41] train-rmse:0.114548+0.013288    test-rmse:1.008039+0.107858 
## [42] train-rmse:0.107831+0.012403    test-rmse:1.007230+0.108960 
## [43] train-rmse:0.102671+0.011493    test-rmse:1.007503+0.108056 
## [44] train-rmse:0.097764+0.011080    test-rmse:1.007312+0.107203 
## [45] train-rmse:0.092515+0.009174    test-rmse:1.005474+0.106967 
## [46] train-rmse:0.087564+0.011823    test-rmse:1.005254+0.106471 
## [47] train-rmse:0.083761+0.011387    test-rmse:1.004473+0.106184 
## [48] train-rmse:0.078146+0.010118    test-rmse:1.003654+0.107121 
## [49] train-rmse:0.074159+0.010936    test-rmse:1.004838+0.106565 
## [50] train-rmse:0.069244+0.010497    test-rmse:1.004652+0.107387 
## [51] train-rmse:0.065053+0.009835    test-rmse:1.004205+0.108034 
## [52] train-rmse:0.062231+0.009191    test-rmse:1.003092+0.108708 
## [53] train-rmse:0.059557+0.008718    test-rmse:1.002363+0.109054 
## [54] train-rmse:0.056845+0.008741    test-rmse:1.001113+0.108774 
## [55] train-rmse:0.054721+0.008155    test-rmse:1.001251+0.109316 
## [56] train-rmse:0.051986+0.007773    test-rmse:1.001719+0.109371 
## [57] train-rmse:0.050236+0.007394    test-rmse:1.002199+0.109553 
## [58] train-rmse:0.047743+0.007091    test-rmse:1.001603+0.109039 
## [59] train-rmse:0.044984+0.006015    test-rmse:1.001253+0.108493 
## [60] train-rmse:0.042590+0.006110    test-rmse:1.001294+0.109165 
## Stopping. Best iteration:
## [54] train-rmse:0.056845+0.008741    test-rmse:1.001113+0.108774
## 
## 98 
## [1]  train-rmse:61.206300+0.061172   test-rmse:61.206691+0.431814 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:38.063428+0.037460   test-rmse:38.063095+0.453499 
## [3]  train-rmse:23.704581+0.022414   test-rmse:23.703120+0.465001 
## [4]  train-rmse:14.815793+0.013021   test-rmse:14.812671+0.468221 
## [5]  train-rmse:9.336035+0.008127    test-rmse:9.337172+0.420874 
## [6]  train-rmse:5.980202+0.007938    test-rmse:5.969196+0.411210 
## [7]  train-rmse:3.961538+0.012777    test-rmse:3.955978+0.371040 
## [8]  train-rmse:2.798525+0.019652    test-rmse:2.815248+0.316911 
## [9]  train-rmse:2.162510+0.025445    test-rmse:2.202568+0.252381 
## [10] train-rmse:1.840861+0.030190    test-rmse:1.903047+0.170753 
## [11] train-rmse:1.683423+0.030311    test-rmse:1.764420+0.132421 
## [12] train-rmse:1.600581+0.028836    test-rmse:1.701198+0.120824 
## [13] train-rmse:1.551937+0.027363    test-rmse:1.659438+0.124240 
## [14] train-rmse:1.517298+0.026956    test-rmse:1.611966+0.107623 
## [15] train-rmse:1.491412+0.026330    test-rmse:1.600330+0.109984 
## [16] train-rmse:1.469360+0.024899    test-rmse:1.582148+0.113442 
## [17] train-rmse:1.449292+0.024252    test-rmse:1.563663+0.105406 
## [18] train-rmse:1.431214+0.024985    test-rmse:1.558559+0.106518 
## [19] train-rmse:1.414177+0.025236    test-rmse:1.551344+0.110605 
## [20] train-rmse:1.398572+0.025069    test-rmse:1.545082+0.107640 
## [21] train-rmse:1.383665+0.024523    test-rmse:1.533115+0.109514 
## [22] train-rmse:1.369892+0.024145    test-rmse:1.526258+0.109787 
## [23] train-rmse:1.356057+0.023537    test-rmse:1.519825+0.107015 
## [24] train-rmse:1.342142+0.022311    test-rmse:1.511347+0.090781 
## [25] train-rmse:1.329335+0.022039    test-rmse:1.501247+0.097749 
## [26] train-rmse:1.317170+0.021966    test-rmse:1.492495+0.093802 
## [27] train-rmse:1.305540+0.021767    test-rmse:1.476604+0.096205 
## [28] train-rmse:1.294402+0.021737    test-rmse:1.468270+0.097720 
## [29] train-rmse:1.283473+0.021521    test-rmse:1.459113+0.099244 
## [30] train-rmse:1.272932+0.021161    test-rmse:1.460128+0.105812 
## [31] train-rmse:1.261682+0.021356    test-rmse:1.450207+0.092900 
## [32] train-rmse:1.251482+0.021640    test-rmse:1.439736+0.092946 
## [33] train-rmse:1.241682+0.020950    test-rmse:1.434428+0.093599 
## [34] train-rmse:1.232904+0.020725    test-rmse:1.429840+0.094708 
## [35] train-rmse:1.224371+0.020625    test-rmse:1.423278+0.092858 
## [36] train-rmse:1.216085+0.020568    test-rmse:1.420536+0.091154 
## [37] train-rmse:1.207836+0.020733    test-rmse:1.417695+0.085380 
## [38] train-rmse:1.199994+0.020575    test-rmse:1.415534+0.085463 
## [39] train-rmse:1.192631+0.020553    test-rmse:1.404687+0.087978 
## [40] train-rmse:1.185512+0.020414    test-rmse:1.395402+0.089704 
## [41] train-rmse:1.178499+0.020261    test-rmse:1.389689+0.087640 
## [42] train-rmse:1.172024+0.020058    test-rmse:1.386194+0.088052 
## [43] train-rmse:1.165633+0.020086    test-rmse:1.384960+0.079004 
## [44] train-rmse:1.159139+0.019885    test-rmse:1.380598+0.073242 
## [45] train-rmse:1.153081+0.019813    test-rmse:1.371966+0.072341 
## [46] train-rmse:1.147226+0.019670    test-rmse:1.365375+0.077134 
## [47] train-rmse:1.141591+0.019488    test-rmse:1.363694+0.079994 
## [48] train-rmse:1.135958+0.019330    test-rmse:1.357134+0.082982 
## [49] train-rmse:1.130429+0.019215    test-rmse:1.355031+0.075777 
## [50] train-rmse:1.125151+0.019028    test-rmse:1.350017+0.073549 
## [51] train-rmse:1.119942+0.018895    test-rmse:1.350446+0.078449 
## [52] train-rmse:1.114746+0.019025    test-rmse:1.347145+0.079049 
## [53] train-rmse:1.109802+0.018956    test-rmse:1.342443+0.079123 
## [54] train-rmse:1.105154+0.018871    test-rmse:1.339224+0.078949 
## [55] train-rmse:1.100713+0.018718    test-rmse:1.334377+0.080060 
## [56] train-rmse:1.096332+0.018662    test-rmse:1.328965+0.085450 
## [57] train-rmse:1.091977+0.018713    test-rmse:1.327976+0.089802 
## [58] train-rmse:1.087827+0.018615    test-rmse:1.328594+0.083467 
## [59] train-rmse:1.083714+0.018595    test-rmse:1.325541+0.088396 
## [60] train-rmse:1.079840+0.018569    test-rmse:1.321972+0.088979 
## [61] train-rmse:1.075892+0.018376    test-rmse:1.316977+0.089169 
## [62] train-rmse:1.072132+0.018147    test-rmse:1.314730+0.091053 
## [63] train-rmse:1.068462+0.017999    test-rmse:1.311649+0.090509 
## [64] train-rmse:1.064862+0.017851    test-rmse:1.302575+0.089995 
## [65] train-rmse:1.061447+0.017715    test-rmse:1.300546+0.089649 
## [66] train-rmse:1.058090+0.017694    test-rmse:1.295591+0.089541 
## [67] train-rmse:1.054831+0.017647    test-rmse:1.294973+0.089082 
## [68] train-rmse:1.051699+0.017573    test-rmse:1.293381+0.089244 
## [69] train-rmse:1.048402+0.017480    test-rmse:1.289356+0.090133 
## [70] train-rmse:1.045282+0.017501    test-rmse:1.287803+0.091392 
## [71] train-rmse:1.042362+0.017589    test-rmse:1.290780+0.090916 
## [72] train-rmse:1.039445+0.017595    test-rmse:1.291017+0.091319 
## [73] train-rmse:1.036574+0.017607    test-rmse:1.291299+0.091924 
## [74] train-rmse:1.033847+0.017447    test-rmse:1.289400+0.089612 
## [75] train-rmse:1.031137+0.017311    test-rmse:1.284529+0.087928 
## [76] train-rmse:1.028496+0.017190    test-rmse:1.284912+0.085871 
## [77] train-rmse:1.025915+0.017101    test-rmse:1.282329+0.086713 
## [78] train-rmse:1.023343+0.017166    test-rmse:1.279688+0.085944 
## [79] train-rmse:1.020840+0.017194    test-rmse:1.274882+0.086883 
## [80] train-rmse:1.018454+0.017148    test-rmse:1.272123+0.087876 
## [81] train-rmse:1.016065+0.017115    test-rmse:1.273654+0.090668 
## [82] train-rmse:1.013667+0.017016    test-rmse:1.273124+0.089594 
## [83] train-rmse:1.011363+0.016964    test-rmse:1.274055+0.088585 
## [84] train-rmse:1.009107+0.017001    test-rmse:1.275403+0.086017 
## [85] train-rmse:1.006741+0.017019    test-rmse:1.269510+0.080815 
## [86] train-rmse:1.004524+0.017014    test-rmse:1.270934+0.081176 
## [87] train-rmse:1.002332+0.016985    test-rmse:1.268227+0.084087 
## [88] train-rmse:1.000223+0.016935    test-rmse:1.265089+0.086376 
## [89] train-rmse:0.998078+0.016896    test-rmse:1.265150+0.088439 
## [90] train-rmse:0.996062+0.016836    test-rmse:1.265658+0.087188 
## [91] train-rmse:0.993972+0.016839    test-rmse:1.265885+0.088443 
## [92] train-rmse:0.991883+0.016763    test-rmse:1.259139+0.088218 
## [93] train-rmse:0.989892+0.016825    test-rmse:1.258214+0.087892 
## [94] train-rmse:0.987966+0.016874    test-rmse:1.261103+0.086137 
## [95] train-rmse:0.986074+0.016919    test-rmse:1.262200+0.084951 
## [96] train-rmse:0.984180+0.016910    test-rmse:1.258539+0.086109 
## [97] train-rmse:0.982359+0.016928    test-rmse:1.257397+0.087808 
## [98] train-rmse:0.980403+0.016786    test-rmse:1.255355+0.084605 
## [99] train-rmse:0.978624+0.016826    test-rmse:1.250066+0.084735 
## [100]    train-rmse:0.976774+0.016896    test-rmse:1.248974+0.084851 
## [101]    train-rmse:0.974862+0.017025    test-rmse:1.248827+0.082334 
## [102]    train-rmse:0.973095+0.017113    test-rmse:1.247150+0.081318 
## [103]    train-rmse:0.971334+0.017071    test-rmse:1.248839+0.081075 
## [104]    train-rmse:0.969625+0.017149    test-rmse:1.249846+0.083660 
## [105]    train-rmse:0.967945+0.017201    test-rmse:1.248456+0.082952 
## [106]    train-rmse:0.966374+0.017246    test-rmse:1.246976+0.083296 
## [107]    train-rmse:0.964739+0.017289    test-rmse:1.245511+0.084412 
## [108]    train-rmse:0.963110+0.017366    test-rmse:1.243821+0.084596 
## [109]    train-rmse:0.961566+0.017471    test-rmse:1.243737+0.086062 
## [110]    train-rmse:0.960086+0.017506    test-rmse:1.242749+0.083178 
## [111]    train-rmse:0.958456+0.017605    test-rmse:1.242900+0.086453 
## [112]    train-rmse:0.956866+0.017661    test-rmse:1.242070+0.084703 
## [113]    train-rmse:0.955420+0.017655    test-rmse:1.242123+0.082057 
## [114]    train-rmse:0.953980+0.017572    test-rmse:1.240963+0.082005 
## [115]    train-rmse:0.952564+0.017590    test-rmse:1.240392+0.079738 
## [116]    train-rmse:0.951136+0.017609    test-rmse:1.238597+0.079228 
## [117]    train-rmse:0.949776+0.017639    test-rmse:1.236196+0.082103 
## [118]    train-rmse:0.948377+0.017702    test-rmse:1.234832+0.082106 
## [119]    train-rmse:0.947068+0.017736    test-rmse:1.234394+0.082115 
## [120]    train-rmse:0.945678+0.017740    test-rmse:1.232235+0.078538 
## [121]    train-rmse:0.944354+0.017753    test-rmse:1.234582+0.076826 
## [122]    train-rmse:0.943024+0.017697    test-rmse:1.236370+0.077714 
## [123]    train-rmse:0.941696+0.017695    test-rmse:1.236973+0.079391 
## [124]    train-rmse:0.940346+0.017713    test-rmse:1.235054+0.079903 
## [125]    train-rmse:0.939056+0.017720    test-rmse:1.235698+0.079931 
## [126]    train-rmse:0.937769+0.017748    test-rmse:1.233342+0.080856 
## Stopping. Best iteration:
## [120]    train-rmse:0.945678+0.017740    test-rmse:1.232235+0.078538
## 
## 99 
## [1]  train-rmse:61.206300+0.061172   test-rmse:61.206691+0.431814 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:38.063428+0.037460   test-rmse:38.063095+0.453499 
## [3]  train-rmse:23.704581+0.022414   test-rmse:23.703120+0.465001 
## [4]  train-rmse:14.815793+0.013021   test-rmse:14.812671+0.468221 
## [5]  train-rmse:9.334342+0.007963    test-rmse:9.323402+0.427411 
## [6]  train-rmse:5.964672+0.010702    test-rmse:5.963430+0.378448 
## [7]  train-rmse:3.911708+0.013264    test-rmse:3.928905+0.365325 
## [8]  train-rmse:2.698953+0.018620    test-rmse:2.729413+0.301356 
## [9]  train-rmse:2.013266+0.026951    test-rmse:2.093623+0.253492 
## [10] train-rmse:1.649253+0.030973    test-rmse:1.758732+0.197313 
## [11] train-rmse:1.464292+0.034330    test-rmse:1.612720+0.160453 
## [12] train-rmse:1.366563+0.036411    test-rmse:1.547933+0.136549 
## [13] train-rmse:1.303465+0.032348    test-rmse:1.521655+0.122212 
## [14] train-rmse:1.258863+0.020856    test-rmse:1.478727+0.114049 
## [15] train-rmse:1.222949+0.020409    test-rmse:1.440246+0.112913 
## [16] train-rmse:1.193012+0.024532    test-rmse:1.431345+0.112592 
## [17] train-rmse:1.165409+0.027481    test-rmse:1.410106+0.096261 
## [18] train-rmse:1.139458+0.025365    test-rmse:1.390989+0.097194 
## [19] train-rmse:1.119852+0.026324    test-rmse:1.375419+0.097881 
## [20] train-rmse:1.098685+0.025248    test-rmse:1.369428+0.099171 
## [21] train-rmse:1.076780+0.023154    test-rmse:1.353042+0.098568 
## [22] train-rmse:1.059098+0.022019    test-rmse:1.340419+0.102346 
## [23] train-rmse:1.043423+0.022754    test-rmse:1.328036+0.101680 
## [24] train-rmse:1.027498+0.023481    test-rmse:1.317190+0.096933 
## [25] train-rmse:1.010446+0.027677    test-rmse:1.306704+0.087867 
## [26] train-rmse:0.993733+0.028119    test-rmse:1.296445+0.082198 
## [27] train-rmse:0.979146+0.029818    test-rmse:1.290919+0.085611 
## [28] train-rmse:0.966542+0.030955    test-rmse:1.284183+0.081986 
## [29] train-rmse:0.954346+0.032650    test-rmse:1.282332+0.083568 
## [30] train-rmse:0.941642+0.033926    test-rmse:1.273982+0.080183 
## [31] train-rmse:0.929511+0.031731    test-rmse:1.261332+0.089009 
## [32] train-rmse:0.918655+0.031363    test-rmse:1.252632+0.091345 
## [33] train-rmse:0.905206+0.030042    test-rmse:1.245952+0.092779 
## [34] train-rmse:0.894502+0.029187    test-rmse:1.245215+0.091459 
## [35] train-rmse:0.885474+0.028020    test-rmse:1.238303+0.090067 
## [36] train-rmse:0.876049+0.030294    test-rmse:1.233947+0.093407 
## [37] train-rmse:0.863362+0.032808    test-rmse:1.223194+0.093201 
## [38] train-rmse:0.854751+0.031683    test-rmse:1.221084+0.093813 
## [39] train-rmse:0.846841+0.031029    test-rmse:1.221806+0.095141 
## [40] train-rmse:0.835574+0.029173    test-rmse:1.215674+0.096757 
## [41] train-rmse:0.825394+0.031597    test-rmse:1.211786+0.093141 
## [42] train-rmse:0.815776+0.028920    test-rmse:1.203188+0.091357 
## [43] train-rmse:0.808830+0.028468    test-rmse:1.202864+0.096016 
## [44] train-rmse:0.801276+0.028130    test-rmse:1.197791+0.091435 
## [45] train-rmse:0.790110+0.027906    test-rmse:1.190224+0.091090 
## [46] train-rmse:0.784984+0.027534    test-rmse:1.181671+0.088582 
## [47] train-rmse:0.779257+0.028406    test-rmse:1.181621+0.090757 
## [48] train-rmse:0.773865+0.028251    test-rmse:1.176860+0.087096 
## [49] train-rmse:0.764500+0.022889    test-rmse:1.174953+0.083205 
## [50] train-rmse:0.756852+0.020254    test-rmse:1.172395+0.082940 
## [51] train-rmse:0.747504+0.019570    test-rmse:1.173248+0.080972 
## [52] train-rmse:0.741761+0.017462    test-rmse:1.165649+0.087944 
## [53] train-rmse:0.733069+0.015869    test-rmse:1.159903+0.086910 
## [54] train-rmse:0.724655+0.014864    test-rmse:1.157789+0.088092 
## [55] train-rmse:0.720265+0.015807    test-rmse:1.158506+0.088257 
## [56] train-rmse:0.712082+0.017719    test-rmse:1.156543+0.086716 
## [57] train-rmse:0.704480+0.017556    test-rmse:1.152026+0.089041 
## [58] train-rmse:0.696870+0.020765    test-rmse:1.145605+0.091815 
## [59] train-rmse:0.687681+0.020035    test-rmse:1.143628+0.089465 
## [60] train-rmse:0.681634+0.019719    test-rmse:1.139496+0.087705 
## [61] train-rmse:0.675987+0.019932    test-rmse:1.133024+0.084993 
## [62] train-rmse:0.669686+0.020175    test-rmse:1.133339+0.086995 
## [63] train-rmse:0.663155+0.018753    test-rmse:1.132797+0.084184 
## [64] train-rmse:0.656930+0.018637    test-rmse:1.126184+0.087571 
## [65] train-rmse:0.652892+0.017943    test-rmse:1.126027+0.087426 
## [66] train-rmse:0.649472+0.017562    test-rmse:1.126563+0.085319 
## [67] train-rmse:0.644924+0.017444    test-rmse:1.125477+0.086193 
## [68] train-rmse:0.638834+0.015265    test-rmse:1.119882+0.086528 
## [69] train-rmse:0.634092+0.016170    test-rmse:1.117278+0.084507 
## [70] train-rmse:0.629990+0.017656    test-rmse:1.113135+0.082203 
## [71] train-rmse:0.626669+0.018960    test-rmse:1.108904+0.082856 
## [72] train-rmse:0.622598+0.019577    test-rmse:1.110095+0.080915 
## [73] train-rmse:0.616712+0.018755    test-rmse:1.110688+0.082482 
## [74] train-rmse:0.611751+0.018660    test-rmse:1.109290+0.082342 
## [75] train-rmse:0.605280+0.018267    test-rmse:1.106920+0.079815 
## [76] train-rmse:0.596512+0.017858    test-rmse:1.105344+0.083256 
## [77] train-rmse:0.593823+0.017922    test-rmse:1.102519+0.080453 
## [78] train-rmse:0.588390+0.017421    test-rmse:1.100698+0.080840 
## [79] train-rmse:0.583682+0.019288    test-rmse:1.094794+0.076698 
## [80] train-rmse:0.580198+0.019560    test-rmse:1.096702+0.074891 
## [81] train-rmse:0.574497+0.018324    test-rmse:1.095227+0.079300 
## [82] train-rmse:0.571526+0.018253    test-rmse:1.095554+0.077636 
## [83] train-rmse:0.567427+0.016461    test-rmse:1.095333+0.074885 
## [84] train-rmse:0.563904+0.017147    test-rmse:1.093309+0.074774 
## [85] train-rmse:0.561128+0.017559    test-rmse:1.093406+0.075921 
## [86] train-rmse:0.557348+0.016169    test-rmse:1.090028+0.076526 
## [87] train-rmse:0.553728+0.015362    test-rmse:1.089182+0.075317 
## [88] train-rmse:0.551209+0.014736    test-rmse:1.088793+0.075095 
## [89] train-rmse:0.547232+0.014771    test-rmse:1.085828+0.073058 
## [90] train-rmse:0.542895+0.013281    test-rmse:1.082649+0.075115 
## [91] train-rmse:0.538309+0.015348    test-rmse:1.078216+0.075418 
## [92] train-rmse:0.534311+0.015781    test-rmse:1.077753+0.078860 
## [93] train-rmse:0.530850+0.016962    test-rmse:1.074954+0.079903 
## [94] train-rmse:0.526008+0.018468    test-rmse:1.073812+0.076248 
## [95] train-rmse:0.522430+0.018904    test-rmse:1.072596+0.075439 
## [96] train-rmse:0.520417+0.019042    test-rmse:1.071897+0.076318 
## [97] train-rmse:0.517368+0.020012    test-rmse:1.071947+0.076527 
## [98] train-rmse:0.512720+0.021528    test-rmse:1.069145+0.076664 
## [99] train-rmse:0.510256+0.021560    test-rmse:1.069282+0.076439 
## [100]    train-rmse:0.508245+0.021210    test-rmse:1.068689+0.076792 
## [101]    train-rmse:0.505053+0.020719    test-rmse:1.065957+0.077333 
## [102]    train-rmse:0.501814+0.019683    test-rmse:1.067525+0.080122 
## [103]    train-rmse:0.498741+0.018936    test-rmse:1.065525+0.079115 
## [104]    train-rmse:0.495783+0.019508    test-rmse:1.066356+0.081780 
## [105]    train-rmse:0.492195+0.018847    test-rmse:1.065391+0.080386 
## [106]    train-rmse:0.488826+0.019013    test-rmse:1.064816+0.078919 
## [107]    train-rmse:0.483173+0.017562    test-rmse:1.060634+0.077297 
## [108]    train-rmse:0.480467+0.017279    test-rmse:1.058793+0.076277 
## [109]    train-rmse:0.476736+0.015142    test-rmse:1.057151+0.077824 
## [110]    train-rmse:0.473505+0.016520    test-rmse:1.058039+0.076234 
## [111]    train-rmse:0.469844+0.017542    test-rmse:1.057874+0.076976 
## [112]    train-rmse:0.466034+0.018303    test-rmse:1.057010+0.076091 
## [113]    train-rmse:0.464501+0.018263    test-rmse:1.056553+0.077184 
## [114]    train-rmse:0.461772+0.018610    test-rmse:1.056782+0.077721 
## [115]    train-rmse:0.458295+0.018664    test-rmse:1.054569+0.076203 
## [116]    train-rmse:0.456457+0.018636    test-rmse:1.055212+0.076705 
## [117]    train-rmse:0.454517+0.018567    test-rmse:1.054625+0.076528 
## [118]    train-rmse:0.451930+0.018495    test-rmse:1.056620+0.076320 
## [119]    train-rmse:0.449998+0.018112    test-rmse:1.057327+0.076008 
## [120]    train-rmse:0.447690+0.017380    test-rmse:1.055023+0.076355 
## [121]    train-rmse:0.444377+0.018896    test-rmse:1.053288+0.074830 
## [122]    train-rmse:0.440503+0.017858    test-rmse:1.050944+0.078872 
## [123]    train-rmse:0.436571+0.017766    test-rmse:1.050343+0.078967 
## [124]    train-rmse:0.433677+0.018452    test-rmse:1.050260+0.080884 
## [125]    train-rmse:0.430266+0.016708    test-rmse:1.049625+0.079143 
## [126]    train-rmse:0.427137+0.016926    test-rmse:1.050588+0.079809 
## [127]    train-rmse:0.423862+0.015390    test-rmse:1.049695+0.081517 
## [128]    train-rmse:0.420676+0.016247    test-rmse:1.051637+0.083406 
## [129]    train-rmse:0.418771+0.016213    test-rmse:1.049759+0.081840 
## [130]    train-rmse:0.416735+0.016520    test-rmse:1.052013+0.081811 
## [131]    train-rmse:0.413216+0.015680    test-rmse:1.049768+0.082395 
## Stopping. Best iteration:
## [125]    train-rmse:0.430266+0.016708    test-rmse:1.049625+0.079143
## 
## 100 
## [1]  train-rmse:61.206300+0.061172   test-rmse:61.206691+0.431814 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:38.063428+0.037460   test-rmse:38.063095+0.453499 
## [3]  train-rmse:23.704581+0.022414   test-rmse:23.703120+0.465001 
## [4]  train-rmse:14.815793+0.013021   test-rmse:14.812671+0.468221 
## [5]  train-rmse:9.334342+0.007963    test-rmse:9.323402+0.427411 
## [6]  train-rmse:5.959580+0.009265    test-rmse:5.947716+0.372978 
## [7]  train-rmse:3.887371+0.010622    test-rmse:3.889910+0.336216 
## [8]  train-rmse:2.647127+0.015181    test-rmse:2.669477+0.284725 
## [9]  train-rmse:1.922413+0.017145    test-rmse:2.003553+0.250023 
## [10] train-rmse:1.529429+0.017036    test-rmse:1.650112+0.186618 
## [11] train-rmse:1.312724+0.012719    test-rmse:1.480860+0.158129 
## [12] train-rmse:1.192451+0.014783    test-rmse:1.407844+0.135609 
## [13] train-rmse:1.117744+0.020673    test-rmse:1.367564+0.111202 
## [14] train-rmse:1.056168+0.020456    test-rmse:1.336417+0.098564 
## [15] train-rmse:1.014632+0.015373    test-rmse:1.313155+0.093901 
## [16] train-rmse:0.980207+0.019747    test-rmse:1.288570+0.090379 
## [17] train-rmse:0.952346+0.018425    test-rmse:1.272707+0.094985 
## [18] train-rmse:0.919852+0.022856    test-rmse:1.257086+0.093940 
## [19] train-rmse:0.894709+0.024654    test-rmse:1.245359+0.097554 
## [20] train-rmse:0.871331+0.027620    test-rmse:1.227915+0.101618 
## [21] train-rmse:0.844766+0.023743    test-rmse:1.217436+0.099851 
## [22] train-rmse:0.822870+0.024367    test-rmse:1.201368+0.085708 
## [23] train-rmse:0.802510+0.026910    test-rmse:1.183122+0.076703 
## [24] train-rmse:0.785654+0.027860    test-rmse:1.169261+0.085263 
## [25] train-rmse:0.771822+0.028755    test-rmse:1.164189+0.083455 
## [26] train-rmse:0.754571+0.026089    test-rmse:1.152781+0.078961 
## [27] train-rmse:0.735599+0.028363    test-rmse:1.148465+0.081003 
## [28] train-rmse:0.719974+0.026499    test-rmse:1.145792+0.081799 
## [29] train-rmse:0.706573+0.028151    test-rmse:1.137968+0.077330 
## [30] train-rmse:0.694275+0.026041    test-rmse:1.129430+0.083094 
## [31] train-rmse:0.684637+0.027065    test-rmse:1.116724+0.079053 
## [32] train-rmse:0.669482+0.030349    test-rmse:1.111502+0.081623 
## [33] train-rmse:0.655290+0.026898    test-rmse:1.103304+0.083145 
## [34] train-rmse:0.642277+0.026204    test-rmse:1.092884+0.076799 
## [35] train-rmse:0.630545+0.028942    test-rmse:1.088336+0.077733 
## [36] train-rmse:0.620939+0.030372    test-rmse:1.073426+0.064361 
## [37] train-rmse:0.610749+0.029787    test-rmse:1.072825+0.063262 
## [38] train-rmse:0.601182+0.033218    test-rmse:1.064988+0.057650 
## [39] train-rmse:0.593137+0.034935    test-rmse:1.059589+0.058699 
## [40] train-rmse:0.582988+0.035027    test-rmse:1.053449+0.057747 
## [41] train-rmse:0.572598+0.032909    test-rmse:1.052265+0.057925 
## [42] train-rmse:0.561277+0.029792    test-rmse:1.041810+0.055606 
## [43] train-rmse:0.552195+0.026961    test-rmse:1.038877+0.058190 
## [44] train-rmse:0.544291+0.026015    test-rmse:1.033256+0.061590 
## [45] train-rmse:0.536932+0.027323    test-rmse:1.022584+0.062471 
## [46] train-rmse:0.528980+0.025383    test-rmse:1.019349+0.065027 
## [47] train-rmse:0.518711+0.024144    test-rmse:1.015731+0.062468 
## [48] train-rmse:0.512383+0.026391    test-rmse:1.010942+0.063859 
## [49] train-rmse:0.503375+0.028477    test-rmse:1.009235+0.065284 
## [50] train-rmse:0.496976+0.026269    test-rmse:1.005536+0.064312 
## [51] train-rmse:0.491767+0.026633    test-rmse:1.004247+0.064114 
## [52] train-rmse:0.483037+0.027483    test-rmse:1.006096+0.063189 
## [53] train-rmse:0.472446+0.027953    test-rmse:1.002720+0.059216 
## [54] train-rmse:0.463104+0.025692    test-rmse:1.001247+0.058640 
## [55] train-rmse:0.454614+0.025365    test-rmse:1.005686+0.061611 
## [56] train-rmse:0.448104+0.024450    test-rmse:1.002979+0.060037 
## [57] train-rmse:0.443059+0.024027    test-rmse:1.001003+0.061402 
## [58] train-rmse:0.436662+0.022619    test-rmse:0.996464+0.058660 
## [59] train-rmse:0.430432+0.022097    test-rmse:0.996127+0.060150 
## [60] train-rmse:0.424381+0.023772    test-rmse:0.999711+0.063297 
## [61] train-rmse:0.419901+0.026435    test-rmse:0.998438+0.062146 
## [62] train-rmse:0.412113+0.026353    test-rmse:0.998353+0.066466 
## [63] train-rmse:0.403951+0.025838    test-rmse:0.999008+0.063860 
## [64] train-rmse:0.398654+0.025187    test-rmse:1.000616+0.066246 
## [65] train-rmse:0.393666+0.022596    test-rmse:0.999619+0.064474 
## Stopping. Best iteration:
## [59] train-rmse:0.430432+0.022097    test-rmse:0.996127+0.060150
## 
## 101 
## [1]  train-rmse:61.206300+0.061172   test-rmse:61.206691+0.431814 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:38.063428+0.037460   test-rmse:38.063095+0.453499 
## [3]  train-rmse:23.704581+0.022414   test-rmse:23.703120+0.465001 
## [4]  train-rmse:14.815793+0.013021   test-rmse:14.812671+0.468221 
## [5]  train-rmse:9.334342+0.007963    test-rmse:9.323402+0.427411 
## [6]  train-rmse:5.956052+0.008980    test-rmse:5.946320+0.375514 
## [7]  train-rmse:3.876816+0.008401    test-rmse:3.871795+0.332488 
## [8]  train-rmse:2.615169+0.013751    test-rmse:2.633736+0.263273 
## [9]  train-rmse:1.859091+0.023967    test-rmse:1.937477+0.205648 
## [10] train-rmse:1.426907+0.023415    test-rmse:1.559973+0.159525 
## [11] train-rmse:1.193325+0.028425    test-rmse:1.390498+0.135620 
## [12] train-rmse:1.052858+0.041994    test-rmse:1.318621+0.110268 
## [13] train-rmse:0.967708+0.042092    test-rmse:1.277211+0.095002 
## [14] train-rmse:0.901471+0.038562    test-rmse:1.248531+0.085173 
## [15] train-rmse:0.857331+0.036262    test-rmse:1.213814+0.078263 
## [16] train-rmse:0.816093+0.040678    test-rmse:1.188488+0.073972 
## [17] train-rmse:0.782813+0.035771    test-rmse:1.175635+0.071430 
## [18] train-rmse:0.752870+0.036396    test-rmse:1.155688+0.069682 
## [19] train-rmse:0.721247+0.030488    test-rmse:1.129783+0.069258 
## [20] train-rmse:0.692574+0.032079    test-rmse:1.105371+0.052823 
## [21] train-rmse:0.668434+0.032055    test-rmse:1.096803+0.052387 
## [22] train-rmse:0.651403+0.031080    test-rmse:1.090184+0.050271 
## [23] train-rmse:0.631539+0.033403    test-rmse:1.087087+0.055281 
## [24] train-rmse:0.609102+0.033162    test-rmse:1.075835+0.048238 
## [25] train-rmse:0.589047+0.032740    test-rmse:1.069960+0.043674 
## [26] train-rmse:0.565483+0.035445    test-rmse:1.064098+0.048215 
## [27] train-rmse:0.550540+0.031964    test-rmse:1.059101+0.051997 
## [28] train-rmse:0.532543+0.032164    test-rmse:1.050733+0.050195 
## [29] train-rmse:0.516935+0.036814    test-rmse:1.043970+0.045193 
## [30] train-rmse:0.502472+0.034714    test-rmse:1.044540+0.049700 
## [31] train-rmse:0.491952+0.033561    test-rmse:1.041597+0.052390 
## [32] train-rmse:0.478301+0.029062    test-rmse:1.043385+0.049546 
## [33] train-rmse:0.466166+0.028356    test-rmse:1.036575+0.049119 
## [34] train-rmse:0.455704+0.029802    test-rmse:1.034304+0.048566 
## [35] train-rmse:0.447537+0.031437    test-rmse:1.034424+0.051085 
## [36] train-rmse:0.437341+0.030033    test-rmse:1.033735+0.046523 
## [37] train-rmse:0.423895+0.029379    test-rmse:1.026532+0.049810 
## [38] train-rmse:0.413709+0.029958    test-rmse:1.015294+0.047573 
## [39] train-rmse:0.403938+0.032378    test-rmse:1.017696+0.047416 
## [40] train-rmse:0.393554+0.031155    test-rmse:1.019313+0.050074 
## [41] train-rmse:0.384452+0.030317    test-rmse:1.017084+0.047986 
## [42] train-rmse:0.378859+0.031292    test-rmse:1.013506+0.047907 
## [43] train-rmse:0.370147+0.033967    test-rmse:1.013418+0.050751 
## [44] train-rmse:0.360376+0.030534    test-rmse:1.010550+0.053398 
## [45] train-rmse:0.351531+0.027787    test-rmse:1.005223+0.052209 
## [46] train-rmse:0.342972+0.028669    test-rmse:1.004350+0.051209 
## [47] train-rmse:0.335841+0.028453    test-rmse:1.002860+0.052535 
## [48] train-rmse:0.324429+0.025866    test-rmse:0.996107+0.053354 
## [49] train-rmse:0.313311+0.022154    test-rmse:0.996512+0.054201 
## [50] train-rmse:0.306586+0.021506    test-rmse:0.995630+0.054430 
## [51] train-rmse:0.299520+0.023480    test-rmse:0.992994+0.053675 
## [52] train-rmse:0.293287+0.024176    test-rmse:0.991318+0.053943 
## [53] train-rmse:0.288510+0.023497    test-rmse:0.992197+0.054791 
## [54] train-rmse:0.282673+0.024521    test-rmse:0.992257+0.051932 
## [55] train-rmse:0.275155+0.026179    test-rmse:0.990374+0.050235 
## [56] train-rmse:0.266189+0.025361    test-rmse:0.989159+0.053939 
## [57] train-rmse:0.257615+0.025159    test-rmse:0.988024+0.053586 
## [58] train-rmse:0.252302+0.023090    test-rmse:0.988909+0.051223 
## [59] train-rmse:0.246521+0.022094    test-rmse:0.988942+0.052008 
## [60] train-rmse:0.239882+0.020393    test-rmse:0.985029+0.049705 
## [61] train-rmse:0.233045+0.019835    test-rmse:0.985690+0.049739 
## [62] train-rmse:0.228907+0.017874    test-rmse:0.984438+0.050134 
## [63] train-rmse:0.223925+0.018006    test-rmse:0.983665+0.050672 
## [64] train-rmse:0.218446+0.017682    test-rmse:0.984220+0.053465 
## [65] train-rmse:0.212931+0.018125    test-rmse:0.984785+0.053973 
## [66] train-rmse:0.207961+0.018226    test-rmse:0.985896+0.055155 
## [67] train-rmse:0.204162+0.018408    test-rmse:0.985898+0.055501 
## [68] train-rmse:0.198589+0.017424    test-rmse:0.986322+0.056162 
## [69] train-rmse:0.194298+0.016612    test-rmse:0.985853+0.055750 
## Stopping. Best iteration:
## [63] train-rmse:0.223925+0.018006    test-rmse:0.983665+0.050672
## 
## 102 
## [1]  train-rmse:61.206300+0.061172   test-rmse:61.206691+0.431814 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:38.063428+0.037460   test-rmse:38.063095+0.453499 
## [3]  train-rmse:23.704581+0.022414   test-rmse:23.703120+0.465001 
## [4]  train-rmse:14.815793+0.013021   test-rmse:14.812671+0.468221 
## [5]  train-rmse:9.334342+0.007963    test-rmse:9.323402+0.427411 
## [6]  train-rmse:5.954602+0.008652    test-rmse:5.940598+0.372931 
## [7]  train-rmse:3.871042+0.006881    test-rmse:3.874928+0.326396 
## [8]  train-rmse:2.597212+0.008252    test-rmse:2.625575+0.264568 
## [9]  train-rmse:1.816630+0.013650    test-rmse:1.905601+0.215494 
## [10] train-rmse:1.357399+0.024623    test-rmse:1.550566+0.155210 
## [11] train-rmse:1.093482+0.022295    test-rmse:1.365776+0.129504 
## [12] train-rmse:0.935583+0.028714    test-rmse:1.261058+0.103611 
## [13] train-rmse:0.848413+0.033954    test-rmse:1.213906+0.091588 
## [14] train-rmse:0.772816+0.026976    test-rmse:1.175449+0.080873 
## [15] train-rmse:0.720443+0.031332    test-rmse:1.153618+0.091297 
## [16] train-rmse:0.670526+0.034161    test-rmse:1.137670+0.089578 
## [17] train-rmse:0.638438+0.033252    test-rmse:1.122583+0.085342 
## [18] train-rmse:0.609356+0.034696    test-rmse:1.117775+0.093041 
## [19] train-rmse:0.588540+0.035855    test-rmse:1.097949+0.093141 
## [20] train-rmse:0.569082+0.035208    test-rmse:1.088615+0.088113 
## [21] train-rmse:0.540009+0.039343    test-rmse:1.068154+0.080536 
## [22] train-rmse:0.519388+0.036871    test-rmse:1.067038+0.078395 
## [23] train-rmse:0.496491+0.038691    test-rmse:1.063822+0.080704 
## [24] train-rmse:0.480660+0.038866    test-rmse:1.057783+0.078406 
## [25] train-rmse:0.458094+0.032866    test-rmse:1.048519+0.076879 
## [26] train-rmse:0.443261+0.031124    test-rmse:1.041520+0.080290 
## [27] train-rmse:0.426313+0.030179    test-rmse:1.039356+0.078130 
## [28] train-rmse:0.409380+0.025721    test-rmse:1.032058+0.078070 
## [29] train-rmse:0.395068+0.026080    test-rmse:1.034913+0.085419 
## [30] train-rmse:0.379836+0.023167    test-rmse:1.029322+0.087342 
## [31] train-rmse:0.366693+0.023772    test-rmse:1.025707+0.086022 
## [32] train-rmse:0.353563+0.024152    test-rmse:1.027050+0.086419 
## [33] train-rmse:0.341055+0.023539    test-rmse:1.026268+0.085745 
## [34] train-rmse:0.330301+0.027911    test-rmse:1.020799+0.090718 
## [35] train-rmse:0.318609+0.030669    test-rmse:1.020290+0.091172 
## [36] train-rmse:0.311437+0.030116    test-rmse:1.020087+0.088812 
## [37] train-rmse:0.302289+0.027984    test-rmse:1.019790+0.091417 
## [38] train-rmse:0.291737+0.029528    test-rmse:1.013245+0.087973 
## [39] train-rmse:0.283956+0.028477    test-rmse:1.013895+0.088675 
## [40] train-rmse:0.275150+0.028387    test-rmse:1.015105+0.089383 
## [41] train-rmse:0.268032+0.025029    test-rmse:1.012693+0.092255 
## [42] train-rmse:0.260058+0.023564    test-rmse:1.009310+0.089307 
## [43] train-rmse:0.251355+0.021619    test-rmse:1.009284+0.093396 
## [44] train-rmse:0.244132+0.023759    test-rmse:1.009285+0.092496 
## [45] train-rmse:0.234851+0.022539    test-rmse:1.008857+0.091813 
## [46] train-rmse:0.224435+0.018442    test-rmse:1.006112+0.093206 
## [47] train-rmse:0.214799+0.018784    test-rmse:1.001019+0.090465 
## [48] train-rmse:0.207143+0.019120    test-rmse:0.997478+0.096216 
## [49] train-rmse:0.199233+0.016796    test-rmse:0.997196+0.093814 
## [50] train-rmse:0.194333+0.017819    test-rmse:0.995663+0.095120 
## [51] train-rmse:0.186042+0.017710    test-rmse:0.997601+0.097215 
## [52] train-rmse:0.181457+0.017506    test-rmse:0.997509+0.099210 
## [53] train-rmse:0.175237+0.017281    test-rmse:0.995207+0.099153 
## [54] train-rmse:0.170043+0.016992    test-rmse:0.995727+0.097884 
## [55] train-rmse:0.162098+0.015381    test-rmse:0.996350+0.100242 
## [56] train-rmse:0.157647+0.014200    test-rmse:0.996496+0.098460 
## [57] train-rmse:0.152984+0.012263    test-rmse:0.995633+0.098633 
## [58] train-rmse:0.147891+0.011542    test-rmse:0.995279+0.098912 
## [59] train-rmse:0.144802+0.011669    test-rmse:0.995253+0.099868 
## Stopping. Best iteration:
## [53] train-rmse:0.175237+0.017281    test-rmse:0.995207+0.099153
## 
## 103 
## [1]  train-rmse:61.206300+0.061172   test-rmse:61.206691+0.431814 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:38.063428+0.037460   test-rmse:38.063095+0.453499 
## [3]  train-rmse:23.704581+0.022414   test-rmse:23.703120+0.465001 
## [4]  train-rmse:14.815793+0.013021   test-rmse:14.812671+0.468221 
## [5]  train-rmse:9.334342+0.007963    test-rmse:9.323402+0.427411 
## [6]  train-rmse:5.953824+0.007985    test-rmse:5.934971+0.371769 
## [7]  train-rmse:3.866855+0.006520    test-rmse:3.866282+0.327001 
## [8]  train-rmse:2.585452+0.008821    test-rmse:2.622243+0.256336 
## [9]  train-rmse:1.788230+0.007985    test-rmse:1.899666+0.208489 
## [10] train-rmse:1.313849+0.014863    test-rmse:1.520799+0.176661 
## [11] train-rmse:1.036072+0.030977    test-rmse:1.331825+0.157503 
## [12] train-rmse:0.868894+0.030416    test-rmse:1.228308+0.137842 
## [13] train-rmse:0.766524+0.035703    test-rmse:1.189324+0.129373 
## [14] train-rmse:0.687238+0.036331    test-rmse:1.155337+0.119975 
## [15] train-rmse:0.631808+0.036890    test-rmse:1.131803+0.115000 
## [16] train-rmse:0.585091+0.031720    test-rmse:1.113451+0.114785 
## [17] train-rmse:0.538242+0.029412    test-rmse:1.095162+0.107216 
## [18] train-rmse:0.512665+0.028148    test-rmse:1.085863+0.106001 
## [19] train-rmse:0.487769+0.029550    test-rmse:1.075304+0.099075 
## [20] train-rmse:0.457842+0.030557    test-rmse:1.072002+0.106771 
## [21] train-rmse:0.437605+0.032188    test-rmse:1.069125+0.108587 
## [22] train-rmse:0.411253+0.022711    test-rmse:1.061725+0.104071 
## [23] train-rmse:0.394121+0.023355    test-rmse:1.060973+0.107604 
## [24] train-rmse:0.372562+0.019953    test-rmse:1.055414+0.111842 
## [25] train-rmse:0.355266+0.018348    test-rmse:1.052932+0.107247 
## [26] train-rmse:0.337838+0.017398    test-rmse:1.053007+0.105094 
## [27] train-rmse:0.320543+0.018581    test-rmse:1.046381+0.107699 
## [28] train-rmse:0.304611+0.017256    test-rmse:1.040036+0.108603 
## [29] train-rmse:0.288729+0.013751    test-rmse:1.034578+0.108081 
## [30] train-rmse:0.279890+0.013522    test-rmse:1.033108+0.107599 
## [31] train-rmse:0.266317+0.016762    test-rmse:1.031944+0.108426 
## [32] train-rmse:0.256497+0.015275    test-rmse:1.024986+0.107423 
## [33] train-rmse:0.243057+0.015896    test-rmse:1.022914+0.111055 
## [34] train-rmse:0.230832+0.015712    test-rmse:1.020091+0.112978 
## [35] train-rmse:0.219017+0.013039    test-rmse:1.019382+0.111989 
## [36] train-rmse:0.209170+0.013580    test-rmse:1.016708+0.111425 
## [37] train-rmse:0.199875+0.017004    test-rmse:1.016271+0.112603 
## [38] train-rmse:0.192837+0.015615    test-rmse:1.017011+0.113535 
## [39] train-rmse:0.183804+0.018593    test-rmse:1.015163+0.112832 
## [40] train-rmse:0.176023+0.017314    test-rmse:1.011827+0.113505 
## [41] train-rmse:0.170065+0.018140    test-rmse:1.010099+0.115508 
## [42] train-rmse:0.162370+0.016583    test-rmse:1.011121+0.116512 
## [43] train-rmse:0.158019+0.016776    test-rmse:1.009699+0.116594 
## [44] train-rmse:0.149731+0.018190    test-rmse:1.009149+0.116437 
## [45] train-rmse:0.143798+0.018808    test-rmse:1.007334+0.116687 
## [46] train-rmse:0.137081+0.018022    test-rmse:1.007812+0.114395 
## [47] train-rmse:0.130243+0.016949    test-rmse:1.007574+0.115375 
## [48] train-rmse:0.124785+0.016392    test-rmse:1.008310+0.116072 
## [49] train-rmse:0.118529+0.014480    test-rmse:1.007769+0.116770 
## [50] train-rmse:0.112738+0.012800    test-rmse:1.007966+0.116779 
## [51] train-rmse:0.108671+0.014334    test-rmse:1.006768+0.116982 
## [52] train-rmse:0.103140+0.013437    test-rmse:1.007970+0.117790 
## [53] train-rmse:0.098117+0.012652    test-rmse:1.009689+0.117277 
## [54] train-rmse:0.093792+0.012445    test-rmse:1.009941+0.117620 
## [55] train-rmse:0.091018+0.012147    test-rmse:1.010331+0.118477 
## [56] train-rmse:0.086867+0.011380    test-rmse:1.009150+0.119032 
## [57] train-rmse:0.082954+0.011044    test-rmse:1.008641+0.118982 
## Stopping. Best iteration:
## [51] train-rmse:0.108671+0.014334    test-rmse:1.006768+0.116982
## 
## 104 
## [1]  train-rmse:61.206300+0.061172   test-rmse:61.206691+0.431814 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:38.063428+0.037460   test-rmse:38.063095+0.453499 
## [3]  train-rmse:23.704581+0.022414   test-rmse:23.703120+0.465001 
## [4]  train-rmse:14.815793+0.013021   test-rmse:14.812671+0.468221 
## [5]  train-rmse:9.334342+0.007963    test-rmse:9.323402+0.427411 
## [6]  train-rmse:5.953824+0.007985    test-rmse:5.934971+0.371769 
## [7]  train-rmse:3.864373+0.005977    test-rmse:3.863765+0.321895 
## [8]  train-rmse:2.578048+0.008099    test-rmse:2.629716+0.250392 
## [9]  train-rmse:1.767350+0.008561    test-rmse:1.895938+0.198284 
## [10] train-rmse:1.286898+0.016758    test-rmse:1.500295+0.168409 
## [11] train-rmse:0.975282+0.022017    test-rmse:1.305683+0.127989 
## [12] train-rmse:0.797926+0.034461    test-rmse:1.195842+0.114084 
## [13] train-rmse:0.677562+0.039606    test-rmse:1.145878+0.093093 
## [14] train-rmse:0.597819+0.041050    test-rmse:1.113286+0.081332 
## [15] train-rmse:0.538172+0.046386    test-rmse:1.097950+0.078027 
## [16] train-rmse:0.499039+0.043019    test-rmse:1.082584+0.079449 
## [17] train-rmse:0.462622+0.055602    test-rmse:1.075678+0.077304 
## [18] train-rmse:0.419633+0.041898    test-rmse:1.063736+0.088866 
## [19] train-rmse:0.397018+0.038547    test-rmse:1.054048+0.096899 
## [20] train-rmse:0.376696+0.040447    test-rmse:1.041786+0.096601 
## [21] train-rmse:0.347612+0.038709    test-rmse:1.036885+0.103155 
## [22] train-rmse:0.323915+0.033255    test-rmse:1.035033+0.104025 
## [23] train-rmse:0.307088+0.032832    test-rmse:1.031728+0.106769 
## [24] train-rmse:0.284231+0.027239    test-rmse:1.031695+0.110947 
## [25] train-rmse:0.265712+0.026552    test-rmse:1.032078+0.110373 
## [26] train-rmse:0.248385+0.030753    test-rmse:1.030510+0.111007 
## [27] train-rmse:0.235763+0.028729    test-rmse:1.026588+0.113871 
## [28] train-rmse:0.221521+0.030030    test-rmse:1.025215+0.116632 
## [29] train-rmse:0.205813+0.030605    test-rmse:1.022037+0.113232 
## [30] train-rmse:0.195094+0.030210    test-rmse:1.021904+0.112551 
## [31] train-rmse:0.180665+0.024981    test-rmse:1.018480+0.112022 
## [32] train-rmse:0.169819+0.023446    test-rmse:1.018050+0.111419 
## [33] train-rmse:0.161108+0.024257    test-rmse:1.017440+0.110848 
## [34] train-rmse:0.153745+0.022032    test-rmse:1.015949+0.111296 
## [35] train-rmse:0.147013+0.022294    test-rmse:1.014709+0.112222 
## [36] train-rmse:0.137607+0.022847    test-rmse:1.014057+0.115007 
## [37] train-rmse:0.129083+0.020966    test-rmse:1.014157+0.111978 
## [38] train-rmse:0.121718+0.018641    test-rmse:1.010925+0.113255 
## [39] train-rmse:0.116694+0.018229    test-rmse:1.009527+0.113825 
## [40] train-rmse:0.111895+0.016748    test-rmse:1.009198+0.112540 
## [41] train-rmse:0.105071+0.016630    test-rmse:1.009891+0.114032 
## [42] train-rmse:0.097475+0.015051    test-rmse:1.010812+0.114516 
## [43] train-rmse:0.092353+0.015191    test-rmse:1.010304+0.113934 
## [44] train-rmse:0.088516+0.015572    test-rmse:1.009982+0.113762 
## [45] train-rmse:0.081408+0.014324    test-rmse:1.010359+0.112567 
## [46] train-rmse:0.077189+0.014783    test-rmse:1.010639+0.112833 
## Stopping. Best iteration:
## [40] train-rmse:0.111895+0.016748    test-rmse:1.009198+0.112540
## 
## 105 
## [1]  train-rmse:59.245290+0.059177   test-rmse:59.245643+0.433703 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:35.667956+0.034978   test-rmse:35.667497+0.455608 
## [3]  train-rmse:21.513287+0.020080   test-rmse:21.511532+0.466322 
## [4]  train-rmse:13.041444+0.011310   test-rmse:13.037761+0.467604 
## [5]  train-rmse:7.998222+0.007843    test-rmse:7.998328+0.417416 
## [6]  train-rmse:5.027708+0.009628    test-rmse:5.030977+0.393844 
## [7]  train-rmse:3.329908+0.015255    test-rmse:3.334289+0.351356 
## [8]  train-rmse:2.415650+0.022642    test-rmse:2.447139+0.283287 
## [9]  train-rmse:1.952082+0.026684    test-rmse:2.006050+0.212866 
## [10] train-rmse:1.733217+0.030236    test-rmse:1.809564+0.145537 
## [11] train-rmse:1.629524+0.029410    test-rmse:1.717783+0.124389 
## [12] train-rmse:1.571849+0.026819    test-rmse:1.677169+0.121622 
## [13] train-rmse:1.533831+0.024745    test-rmse:1.621557+0.109307 
## [14] train-rmse:1.503744+0.024040    test-rmse:1.599529+0.109819 
## [15] train-rmse:1.479170+0.023349    test-rmse:1.590049+0.110163 
## [16] train-rmse:1.457765+0.022485    test-rmse:1.576332+0.113421 
## [17] train-rmse:1.437602+0.022251    test-rmse:1.571652+0.113718 
## [18] train-rmse:1.419087+0.023416    test-rmse:1.553950+0.105347 
## [19] train-rmse:1.402029+0.023702    test-rmse:1.545092+0.107669 
## [20] train-rmse:1.385517+0.023961    test-rmse:1.535283+0.104598 
## [21] train-rmse:1.370357+0.023605    test-rmse:1.529963+0.111903 
## [22] train-rmse:1.356322+0.022772    test-rmse:1.520344+0.107026 
## [23] train-rmse:1.342071+0.021692    test-rmse:1.513982+0.089696 
## [24] train-rmse:1.328626+0.021020    test-rmse:1.502288+0.087149 
## [25] train-rmse:1.315764+0.020436    test-rmse:1.493301+0.093189 
## [26] train-rmse:1.303801+0.020315    test-rmse:1.481693+0.102339 
## [27] train-rmse:1.292196+0.019954    test-rmse:1.477807+0.101942 
## [28] train-rmse:1.280920+0.019479    test-rmse:1.463875+0.103457 
## [29] train-rmse:1.269716+0.019395    test-rmse:1.454616+0.088206 
## [30] train-rmse:1.258843+0.019003    test-rmse:1.453382+0.094591 
## [31] train-rmse:1.248241+0.019022    test-rmse:1.441289+0.090614 
## [32] train-rmse:1.237887+0.019151    test-rmse:1.427158+0.089168 
## [33] train-rmse:1.228431+0.018817    test-rmse:1.420009+0.092632 
## [34] train-rmse:1.219396+0.018757    test-rmse:1.413411+0.092293 
## [35] train-rmse:1.210702+0.018749    test-rmse:1.415884+0.092483 
## [36] train-rmse:1.202352+0.018953    test-rmse:1.408081+0.094976 
## [37] train-rmse:1.194430+0.019018    test-rmse:1.408870+0.087314 
## [38] train-rmse:1.186866+0.019045    test-rmse:1.406238+0.085767 
## [39] train-rmse:1.179413+0.018971    test-rmse:1.397468+0.081849 
## [40] train-rmse:1.172245+0.018839    test-rmse:1.383015+0.079966 
## [41] train-rmse:1.165646+0.018848    test-rmse:1.383649+0.078085 
## [42] train-rmse:1.159148+0.018992    test-rmse:1.383742+0.078489 
## [43] train-rmse:1.152934+0.018850    test-rmse:1.372594+0.088444 
## [44] train-rmse:1.146871+0.018775    test-rmse:1.368485+0.092235 
## [45] train-rmse:1.140832+0.018561    test-rmse:1.362434+0.087400 
## [46] train-rmse:1.135018+0.018349    test-rmse:1.354009+0.089978 
## [47] train-rmse:1.129395+0.018247    test-rmse:1.350404+0.091755 
## [48] train-rmse:1.123841+0.018139    test-rmse:1.346289+0.091656 
## [49] train-rmse:1.118499+0.018038    test-rmse:1.343690+0.084858 
## [50] train-rmse:1.113147+0.018046    test-rmse:1.338143+0.083469 
## [51] train-rmse:1.108027+0.017828    test-rmse:1.341726+0.083544 
## [52] train-rmse:1.103043+0.017831    test-rmse:1.336081+0.082109 
## [53] train-rmse:1.098155+0.017718    test-rmse:1.330457+0.081228 
## [54] train-rmse:1.093539+0.017602    test-rmse:1.327323+0.082892 
## [55] train-rmse:1.089034+0.017534    test-rmse:1.326605+0.084475 
## [56] train-rmse:1.084874+0.017383    test-rmse:1.320929+0.086973 
## [57] train-rmse:1.080672+0.017260    test-rmse:1.318088+0.089111 
## [58] train-rmse:1.076689+0.017219    test-rmse:1.313173+0.089172 
## [59] train-rmse:1.072521+0.017204    test-rmse:1.311130+0.092718 
## [60] train-rmse:1.068699+0.017013    test-rmse:1.308090+0.094626 
## [61] train-rmse:1.064987+0.016707    test-rmse:1.301415+0.092845 
## [62] train-rmse:1.061460+0.016662    test-rmse:1.299352+0.093066 
## [63] train-rmse:1.057979+0.016595    test-rmse:1.294934+0.095058 
## [64] train-rmse:1.054613+0.016587    test-rmse:1.291807+0.094091 
## [65] train-rmse:1.051364+0.016585    test-rmse:1.292513+0.091345 
## [66] train-rmse:1.048102+0.016529    test-rmse:1.289971+0.086587 
## [67] train-rmse:1.044850+0.016460    test-rmse:1.291453+0.087871 
## [68] train-rmse:1.041675+0.016307    test-rmse:1.288277+0.088462 
## [69] train-rmse:1.038638+0.016274    test-rmse:1.281949+0.088205 
## [70] train-rmse:1.035627+0.016210    test-rmse:1.282005+0.092086 
## [71] train-rmse:1.032716+0.016255    test-rmse:1.281422+0.090338 
## [72] train-rmse:1.029867+0.016258    test-rmse:1.277972+0.087728 
## [73] train-rmse:1.027156+0.016208    test-rmse:1.277152+0.087601 
## [74] train-rmse:1.024541+0.016146    test-rmse:1.274104+0.088192 
## [75] train-rmse:1.021891+0.016172    test-rmse:1.269962+0.088059 
## [76] train-rmse:1.019293+0.016263    test-rmse:1.270135+0.090474 
## [77] train-rmse:1.016645+0.016007    test-rmse:1.269116+0.089488 
## [78] train-rmse:1.014071+0.015889    test-rmse:1.268275+0.089119 
## [79] train-rmse:1.011582+0.015823    test-rmse:1.260438+0.083553 
## [80] train-rmse:1.009208+0.015838    test-rmse:1.263525+0.079648 
## [81] train-rmse:1.006838+0.015809    test-rmse:1.265497+0.079776 
## [82] train-rmse:1.004489+0.015695    test-rmse:1.263393+0.079977 
## [83] train-rmse:1.002273+0.015687    test-rmse:1.261157+0.080729 
## [84] train-rmse:1.000079+0.015680    test-rmse:1.264524+0.077112 
## [85] train-rmse:0.997818+0.015714    test-rmse:1.261374+0.079745 
## Stopping. Best iteration:
## [79] train-rmse:1.011582+0.015823    test-rmse:1.260438+0.083553
## 
## 106 
## [1]  train-rmse:59.245290+0.059177   test-rmse:59.245643+0.433703 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:35.667956+0.034978   test-rmse:35.667497+0.455608 
## [3]  train-rmse:21.513287+0.020080   test-rmse:21.511532+0.466322 
## [4]  train-rmse:13.041444+0.011310   test-rmse:13.037761+0.467604 
## [5]  train-rmse:7.992215+0.007328    test-rmse:7.976335+0.420551 
## [6]  train-rmse:4.996688+0.009735    test-rmse:4.977064+0.381281 
## [7]  train-rmse:3.247068+0.009899    test-rmse:3.268571+0.345926 
## [8]  train-rmse:2.272548+0.015085    test-rmse:2.333002+0.264931 
## [9]  train-rmse:1.762441+0.021221    test-rmse:1.878030+0.184807 
## [10] train-rmse:1.502267+0.017421    test-rmse:1.663084+0.131381 
## [11] train-rmse:1.379054+0.020156    test-rmse:1.574759+0.104553 
## [12] train-rmse:1.304261+0.015342    test-rmse:1.515768+0.098075 
## [13] train-rmse:1.262375+0.014490    test-rmse:1.499029+0.095753 
## [14] train-rmse:1.224506+0.015886    test-rmse:1.458244+0.108503 
## [15] train-rmse:1.191232+0.016049    test-rmse:1.441120+0.107187 
## [16] train-rmse:1.164090+0.016208    test-rmse:1.428724+0.108628 
## [17] train-rmse:1.137076+0.019811    test-rmse:1.401846+0.098725 
## [18] train-rmse:1.115024+0.020039    test-rmse:1.388015+0.096325 
## [19] train-rmse:1.094147+0.020896    test-rmse:1.361024+0.106296 
## [20] train-rmse:1.074463+0.021155    test-rmse:1.348134+0.099898 
## [21] train-rmse:1.056761+0.022198    test-rmse:1.341788+0.104189 
## [22] train-rmse:1.037780+0.022463    test-rmse:1.331502+0.101849 
## [23] train-rmse:1.015602+0.026318    test-rmse:1.321570+0.106558 
## [24] train-rmse:1.001847+0.025832    test-rmse:1.313030+0.112496 
## [25] train-rmse:0.981979+0.022244    test-rmse:1.304360+0.116176 
## [26] train-rmse:0.961579+0.022796    test-rmse:1.290487+0.117040 
## [27] train-rmse:0.947117+0.023590    test-rmse:1.279909+0.109113 
## [28] train-rmse:0.929456+0.025918    test-rmse:1.270911+0.106917 
## [29] train-rmse:0.918701+0.026558    test-rmse:1.262204+0.110976 
## [30] train-rmse:0.907244+0.028031    test-rmse:1.262453+0.112326 
## [31] train-rmse:0.895317+0.029015    test-rmse:1.255173+0.114246 
## [32] train-rmse:0.883632+0.030534    test-rmse:1.250440+0.119150 
## [33] train-rmse:0.873772+0.030123    test-rmse:1.246801+0.118481 
## [34] train-rmse:0.863347+0.031706    test-rmse:1.238737+0.122021 
## [35] train-rmse:0.852542+0.032636    test-rmse:1.222228+0.122949 
## [36] train-rmse:0.844768+0.032491    test-rmse:1.220834+0.123228 
## [37] train-rmse:0.834364+0.034081    test-rmse:1.216475+0.126179 
## [38] train-rmse:0.821469+0.035914    test-rmse:1.201034+0.121090 
## [39] train-rmse:0.809534+0.031373    test-rmse:1.198866+0.115330 
## [40] train-rmse:0.802600+0.031775    test-rmse:1.200596+0.116028 
## [41] train-rmse:0.795546+0.030805    test-rmse:1.199619+0.114706 
## [42] train-rmse:0.781065+0.028987    test-rmse:1.191932+0.120797 
## [43] train-rmse:0.770553+0.032044    test-rmse:1.181616+0.112170 
## [44] train-rmse:0.763263+0.033595    test-rmse:1.179541+0.115279 
## [45] train-rmse:0.757287+0.034347    test-rmse:1.175011+0.120978 
## [46] train-rmse:0.750101+0.034022    test-rmse:1.169887+0.121396 
## [47] train-rmse:0.742234+0.030199    test-rmse:1.168533+0.120379 
## [48] train-rmse:0.735287+0.028275    test-rmse:1.165307+0.120586 
## [49] train-rmse:0.729020+0.028111    test-rmse:1.158029+0.122208 
## [50] train-rmse:0.721673+0.027093    test-rmse:1.161059+0.114587 
## [51] train-rmse:0.713554+0.028736    test-rmse:1.157983+0.111042 
## [52] train-rmse:0.703220+0.024154    test-rmse:1.148183+0.111240 
## [53] train-rmse:0.696727+0.025241    test-rmse:1.154746+0.116974 
## [54] train-rmse:0.687348+0.024731    test-rmse:1.145983+0.105014 
## [55] train-rmse:0.677662+0.025922    test-rmse:1.143643+0.111782 
## [56] train-rmse:0.670116+0.026962    test-rmse:1.139812+0.113251 
## [57] train-rmse:0.665011+0.028194    test-rmse:1.136592+0.111643 
## [58] train-rmse:0.659082+0.030053    test-rmse:1.136604+0.110601 
## [59] train-rmse:0.652654+0.032166    test-rmse:1.128800+0.100375 
## [60] train-rmse:0.645428+0.029971    test-rmse:1.126653+0.104843 
## [61] train-rmse:0.641110+0.030908    test-rmse:1.127091+0.108836 
## [62] train-rmse:0.636079+0.028487    test-rmse:1.126372+0.109472 
## [63] train-rmse:0.631046+0.028318    test-rmse:1.123100+0.109597 
## [64] train-rmse:0.627605+0.028231    test-rmse:1.120386+0.110618 
## [65] train-rmse:0.622441+0.028944    test-rmse:1.118975+0.108909 
## [66] train-rmse:0.615437+0.024526    test-rmse:1.119130+0.112912 
## [67] train-rmse:0.610162+0.023670    test-rmse:1.111278+0.115037 
## [68] train-rmse:0.604722+0.024231    test-rmse:1.108087+0.112438 
## [69] train-rmse:0.599401+0.026001    test-rmse:1.104543+0.110338 
## [70] train-rmse:0.595658+0.026414    test-rmse:1.103906+0.110352 
## [71] train-rmse:0.591177+0.027937    test-rmse:1.100873+0.112146 
## [72] train-rmse:0.587283+0.027409    test-rmse:1.100925+0.112919 
## [73] train-rmse:0.584898+0.027926    test-rmse:1.098821+0.113275 
## [74] train-rmse:0.579342+0.026276    test-rmse:1.096459+0.112457 
## [75] train-rmse:0.577008+0.026958    test-rmse:1.096367+0.111446 
## [76] train-rmse:0.574727+0.027307    test-rmse:1.093532+0.112640 
## [77] train-rmse:0.571451+0.026949    test-rmse:1.091840+0.113477 
## [78] train-rmse:0.569127+0.026941    test-rmse:1.090606+0.111321 
## [79] train-rmse:0.564954+0.025908    test-rmse:1.090603+0.112634 
## [80] train-rmse:0.559634+0.024927    test-rmse:1.088702+0.110916 
## [81] train-rmse:0.555318+0.025050    test-rmse:1.086313+0.108540 
## [82] train-rmse:0.551328+0.025537    test-rmse:1.087320+0.109433 
## [83] train-rmse:0.546615+0.026448    test-rmse:1.080195+0.104318 
## [84] train-rmse:0.542280+0.026630    test-rmse:1.077492+0.104163 
## [85] train-rmse:0.537021+0.028633    test-rmse:1.079473+0.106926 
## [86] train-rmse:0.533084+0.029545    test-rmse:1.077862+0.105405 
## [87] train-rmse:0.530218+0.030548    test-rmse:1.076930+0.104530 
## [88] train-rmse:0.524354+0.031225    test-rmse:1.075332+0.105223 
## [89] train-rmse:0.519707+0.032594    test-rmse:1.070299+0.102160 
## [90] train-rmse:0.514675+0.033791    test-rmse:1.071858+0.102412 
## [91] train-rmse:0.510682+0.030018    test-rmse:1.069616+0.102529 
## [92] train-rmse:0.504053+0.026905    test-rmse:1.072025+0.111580 
## [93] train-rmse:0.501222+0.027164    test-rmse:1.071013+0.111178 
## [94] train-rmse:0.498989+0.027362    test-rmse:1.070947+0.113297 
## [95] train-rmse:0.497031+0.027049    test-rmse:1.068806+0.115198 
## [96] train-rmse:0.494636+0.027093    test-rmse:1.072388+0.113118 
## [97] train-rmse:0.491068+0.028595    test-rmse:1.068565+0.110038 
## [98] train-rmse:0.487383+0.029065    test-rmse:1.069090+0.110717 
## [99] train-rmse:0.484841+0.029744    test-rmse:1.068463+0.111412 
## [100]    train-rmse:0.481462+0.029508    test-rmse:1.067944+0.110019 
## [101]    train-rmse:0.477002+0.028085    test-rmse:1.065918+0.106289 
## [102]    train-rmse:0.474410+0.027733    test-rmse:1.064578+0.106853 
## [103]    train-rmse:0.472297+0.028189    test-rmse:1.064215+0.105806 
## [104]    train-rmse:0.468311+0.027428    test-rmse:1.061320+0.107639 
## [105]    train-rmse:0.465969+0.027633    test-rmse:1.059430+0.109309 
## [106]    train-rmse:0.463341+0.028294    test-rmse:1.059449+0.109953 
## [107]    train-rmse:0.461386+0.028184    test-rmse:1.057088+0.109956 
## [108]    train-rmse:0.456819+0.024499    test-rmse:1.053991+0.110547 
## [109]    train-rmse:0.453871+0.023618    test-rmse:1.052515+0.108090 
## [110]    train-rmse:0.450939+0.024376    test-rmse:1.051714+0.107435 
## [111]    train-rmse:0.448369+0.024065    test-rmse:1.053127+0.106008 
## [112]    train-rmse:0.445419+0.023728    test-rmse:1.053104+0.106461 
## [113]    train-rmse:0.442365+0.023348    test-rmse:1.050824+0.106495 
## [114]    train-rmse:0.440453+0.023851    test-rmse:1.051552+0.107760 
## [115]    train-rmse:0.437713+0.023480    test-rmse:1.050134+0.104974 
## [116]    train-rmse:0.435315+0.022717    test-rmse:1.049474+0.106149 
## [117]    train-rmse:0.431256+0.023712    test-rmse:1.040010+0.094570 
## [118]    train-rmse:0.429498+0.023122    test-rmse:1.038685+0.094329 
## [119]    train-rmse:0.426565+0.024359    test-rmse:1.031637+0.088877 
## [120]    train-rmse:0.423005+0.023840    test-rmse:1.031045+0.088473 
## [121]    train-rmse:0.419510+0.023352    test-rmse:1.030391+0.087843 
## [122]    train-rmse:0.417452+0.023884    test-rmse:1.030306+0.087800 
## [123]    train-rmse:0.414942+0.024587    test-rmse:1.028719+0.089216 
## [124]    train-rmse:0.412810+0.025402    test-rmse:1.027430+0.088523 
## [125]    train-rmse:0.410455+0.024479    test-rmse:1.025366+0.088155 
## [126]    train-rmse:0.408574+0.023991    test-rmse:1.026540+0.087735 
## [127]    train-rmse:0.406455+0.024133    test-rmse:1.027147+0.089849 
## [128]    train-rmse:0.403929+0.025007    test-rmse:1.019995+0.087121 
## [129]    train-rmse:0.402123+0.025494    test-rmse:1.020003+0.087822 
## [130]    train-rmse:0.398556+0.026224    test-rmse:1.017153+0.087116 
## [131]    train-rmse:0.395573+0.025046    test-rmse:1.016555+0.088247 
## [132]    train-rmse:0.391302+0.022524    test-rmse:1.015372+0.090354 
## [133]    train-rmse:0.388957+0.022392    test-rmse:1.015094+0.089947 
## [134]    train-rmse:0.386451+0.022942    test-rmse:1.013700+0.088102 
## [135]    train-rmse:0.384829+0.023019    test-rmse:1.013327+0.089169 
## [136]    train-rmse:0.383031+0.023019    test-rmse:1.012568+0.090324 
## [137]    train-rmse:0.381064+0.022117    test-rmse:1.011616+0.091412 
## [138]    train-rmse:0.379138+0.021758    test-rmse:1.011762+0.092133 
## [139]    train-rmse:0.377553+0.021778    test-rmse:1.010116+0.093585 
## [140]    train-rmse:0.374318+0.021575    test-rmse:1.005994+0.093720 
## [141]    train-rmse:0.372325+0.021427    test-rmse:1.005963+0.095577 
## [142]    train-rmse:0.369728+0.019466    test-rmse:1.004693+0.099107 
## [143]    train-rmse:0.366410+0.019497    test-rmse:1.003397+0.098262 
## [144]    train-rmse:0.363808+0.018744    test-rmse:1.002025+0.097463 
## [145]    train-rmse:0.362186+0.018151    test-rmse:1.001284+0.098346 
## [146]    train-rmse:0.360881+0.018506    test-rmse:1.000107+0.098318 
## [147]    train-rmse:0.358765+0.017920    test-rmse:0.999529+0.097846 
## [148]    train-rmse:0.357042+0.018544    test-rmse:0.999172+0.098053 
## [149]    train-rmse:0.355181+0.018016    test-rmse:0.998683+0.099720 
## [150]    train-rmse:0.353828+0.018293    test-rmse:0.997602+0.099392 
## [151]    train-rmse:0.352262+0.018449    test-rmse:0.997374+0.099003 
## [152]    train-rmse:0.350631+0.018099    test-rmse:0.994357+0.100038 
## [153]    train-rmse:0.349355+0.017964    test-rmse:0.994277+0.101871 
## [154]    train-rmse:0.347421+0.017711    test-rmse:0.993849+0.102569 
## [155]    train-rmse:0.346123+0.017799    test-rmse:0.993842+0.103732 
## [156]    train-rmse:0.344631+0.017970    test-rmse:0.992821+0.102807 
## [157]    train-rmse:0.342386+0.018101    test-rmse:0.992662+0.102264 
## [158]    train-rmse:0.339750+0.018158    test-rmse:0.993334+0.102877 
## [159]    train-rmse:0.337616+0.017555    test-rmse:0.992723+0.102956 
## [160]    train-rmse:0.335298+0.017679    test-rmse:0.991226+0.102330 
## [161]    train-rmse:0.333787+0.018128    test-rmse:0.991564+0.102682 
## [162]    train-rmse:0.331360+0.018893    test-rmse:0.991059+0.102864 
## [163]    train-rmse:0.329373+0.018929    test-rmse:0.991001+0.101355 
## [164]    train-rmse:0.328002+0.019031    test-rmse:0.991031+0.102176 
## [165]    train-rmse:0.325839+0.019352    test-rmse:0.990915+0.101896 
## [166]    train-rmse:0.323075+0.020866    test-rmse:0.990728+0.102025 
## [167]    train-rmse:0.322291+0.020996    test-rmse:0.989382+0.102331 
## [168]    train-rmse:0.320433+0.020124    test-rmse:0.988710+0.101366 
## [169]    train-rmse:0.318376+0.020049    test-rmse:0.988742+0.099952 
## [170]    train-rmse:0.316782+0.020058    test-rmse:0.988534+0.100681 
## [171]    train-rmse:0.314963+0.019920    test-rmse:0.988891+0.100892 
## [172]    train-rmse:0.313211+0.018690    test-rmse:0.987400+0.100758 
## [173]    train-rmse:0.310648+0.017270    test-rmse:0.985467+0.103298 
## [174]    train-rmse:0.309419+0.017455    test-rmse:0.985046+0.103737 
## [175]    train-rmse:0.307840+0.017202    test-rmse:0.984085+0.103571 
## [176]    train-rmse:0.306228+0.017218    test-rmse:0.985534+0.103794 
## [177]    train-rmse:0.305056+0.017311    test-rmse:0.985572+0.104021 
## [178]    train-rmse:0.303417+0.016600    test-rmse:0.985173+0.104274 
## [179]    train-rmse:0.301962+0.017130    test-rmse:0.984631+0.104358 
## [180]    train-rmse:0.300776+0.017002    test-rmse:0.985568+0.104410 
## [181]    train-rmse:0.299520+0.017246    test-rmse:0.986184+0.104247 
## Stopping. Best iteration:
## [175]    train-rmse:0.307840+0.017202    test-rmse:0.984085+0.103571
## 
## 107 
## [1]  train-rmse:59.245290+0.059177   test-rmse:59.245643+0.433703 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:35.667956+0.034978   test-rmse:35.667497+0.455608 
## [3]  train-rmse:21.513287+0.020080   test-rmse:21.511532+0.466322 
## [4]  train-rmse:13.041444+0.011310   test-rmse:13.037761+0.467604 
## [5]  train-rmse:7.989149+0.005702    test-rmse:7.970775+0.412728 
## [6]  train-rmse:4.985493+0.010897    test-rmse:4.965905+0.362435 
## [7]  train-rmse:3.207324+0.012162    test-rmse:3.234830+0.290773 
## [8]  train-rmse:2.204240+0.017924    test-rmse:2.246474+0.248913 
## [9]  train-rmse:1.649812+0.016210    test-rmse:1.755868+0.197723 
## [10] train-rmse:1.356822+0.018566    test-rmse:1.526433+0.141656 
## [11] train-rmse:1.207466+0.022972    test-rmse:1.413869+0.118898 
## [12] train-rmse:1.125866+0.024381    test-rmse:1.354361+0.117052 
## [13] train-rmse:1.064997+0.025053    test-rmse:1.323968+0.125704 
## [14] train-rmse:1.021806+0.022902    test-rmse:1.291193+0.122466 
## [15] train-rmse:0.982917+0.023178    test-rmse:1.267402+0.113798 
## [16] train-rmse:0.957048+0.025561    test-rmse:1.251984+0.115374 
## [17] train-rmse:0.921109+0.033240    test-rmse:1.242249+0.114997 
## [18] train-rmse:0.898583+0.033736    test-rmse:1.223506+0.117732 
## [19] train-rmse:0.866529+0.023217    test-rmse:1.213486+0.108584 
## [20] train-rmse:0.844560+0.020559    test-rmse:1.196884+0.113014 
## [21] train-rmse:0.824005+0.022383    test-rmse:1.180055+0.106663 
## [22] train-rmse:0.808461+0.023972    test-rmse:1.169038+0.109082 
## [23] train-rmse:0.788102+0.024221    test-rmse:1.141977+0.104298 
## [24] train-rmse:0.765152+0.028331    test-rmse:1.130313+0.102395 
## [25] train-rmse:0.750583+0.026463    test-rmse:1.119731+0.100460 
## [26] train-rmse:0.737247+0.025375    test-rmse:1.113780+0.095649 
## [27] train-rmse:0.721287+0.027158    test-rmse:1.109287+0.098180 
## [28] train-rmse:0.701149+0.028856    test-rmse:1.109030+0.112256 
## [29] train-rmse:0.686720+0.028725    test-rmse:1.097636+0.117331 
## [30] train-rmse:0.677741+0.029498    test-rmse:1.093538+0.116717 
## [31] train-rmse:0.661762+0.023644    test-rmse:1.087420+0.114159 
## [32] train-rmse:0.642753+0.023400    test-rmse:1.080921+0.114950 
## [33] train-rmse:0.633767+0.024188    test-rmse:1.077242+0.118569 
## [34] train-rmse:0.622424+0.023871    test-rmse:1.073181+0.117218 
## [35] train-rmse:0.611884+0.026298    test-rmse:1.072548+0.111915 
## [36] train-rmse:0.601600+0.023597    test-rmse:1.068622+0.113688 
## [37] train-rmse:0.590711+0.023617    test-rmse:1.063811+0.118622 
## [38] train-rmse:0.580640+0.024053    test-rmse:1.064538+0.116467 
## [39] train-rmse:0.572486+0.023243    test-rmse:1.056600+0.120178 
## [40] train-rmse:0.561766+0.019726    test-rmse:1.051133+0.120823 
## [41] train-rmse:0.546344+0.021300    test-rmse:1.045951+0.118331 
## [42] train-rmse:0.537146+0.022442    test-rmse:1.042443+0.116569 
## [43] train-rmse:0.521900+0.018233    test-rmse:1.037901+0.121797 
## [44] train-rmse:0.514822+0.018006    test-rmse:1.035724+0.120116 
## [45] train-rmse:0.508927+0.016670    test-rmse:1.033825+0.119874 
## [46] train-rmse:0.499728+0.018807    test-rmse:1.032598+0.119944 
## [47] train-rmse:0.487214+0.022837    test-rmse:1.027182+0.116853 
## [48] train-rmse:0.478883+0.021235    test-rmse:1.024645+0.116467 
## [49] train-rmse:0.469125+0.022624    test-rmse:1.027829+0.116101 
## [50] train-rmse:0.463328+0.022373    test-rmse:1.025419+0.115997 
## [51] train-rmse:0.456568+0.023959    test-rmse:1.023499+0.113771 
## [52] train-rmse:0.449506+0.023265    test-rmse:1.021550+0.115123 
## [53] train-rmse:0.442981+0.022846    test-rmse:1.018936+0.116238 
## [54] train-rmse:0.436600+0.022288    test-rmse:1.017470+0.116762 
## [55] train-rmse:0.427790+0.018786    test-rmse:1.012563+0.119567 
## [56] train-rmse:0.419140+0.021080    test-rmse:1.009981+0.118356 
## [57] train-rmse:0.410020+0.022480    test-rmse:1.006174+0.120824 
## [58] train-rmse:0.403102+0.020003    test-rmse:1.006337+0.123727 
## [59] train-rmse:0.395351+0.020893    test-rmse:1.001937+0.126599 
## [60] train-rmse:0.389978+0.020457    test-rmse:1.001098+0.125346 
## [61] train-rmse:0.382654+0.021102    test-rmse:0.998296+0.126462 
## [62] train-rmse:0.377974+0.019871    test-rmse:0.995556+0.126598 
## [63] train-rmse:0.372585+0.021928    test-rmse:0.994821+0.126599 
## [64] train-rmse:0.367131+0.020666    test-rmse:0.992754+0.128726 
## [65] train-rmse:0.361442+0.019156    test-rmse:0.990425+0.128227 
## [66] train-rmse:0.357242+0.019261    test-rmse:0.990858+0.127126 
## [67] train-rmse:0.353230+0.021418    test-rmse:0.990361+0.128222 
## [68] train-rmse:0.347288+0.022505    test-rmse:0.989358+0.127430 
## [69] train-rmse:0.339995+0.020296    test-rmse:0.989383+0.126805 
## [70] train-rmse:0.336427+0.020885    test-rmse:0.988493+0.125364 
## [71] train-rmse:0.330580+0.019663    test-rmse:0.985804+0.126182 
## [72] train-rmse:0.326548+0.020127    test-rmse:0.985427+0.128220 
## [73] train-rmse:0.323015+0.018600    test-rmse:0.983559+0.129279 
## [74] train-rmse:0.317908+0.018220    test-rmse:0.983623+0.129348 
## [75] train-rmse:0.313241+0.019908    test-rmse:0.981323+0.129151 
## [76] train-rmse:0.306774+0.017877    test-rmse:0.980679+0.129175 
## [77] train-rmse:0.303592+0.018490    test-rmse:0.979335+0.128568 
## [78] train-rmse:0.298456+0.018050    test-rmse:0.976112+0.130882 
## [79] train-rmse:0.292319+0.015176    test-rmse:0.977395+0.131100 
## [80] train-rmse:0.287838+0.013301    test-rmse:0.976877+0.130592 
## [81] train-rmse:0.283354+0.013946    test-rmse:0.976343+0.129502 
## [82] train-rmse:0.279489+0.012592    test-rmse:0.975377+0.130021 
## [83] train-rmse:0.274939+0.012695    test-rmse:0.973832+0.130236 
## [84] train-rmse:0.269991+0.011694    test-rmse:0.973141+0.130111 
## [85] train-rmse:0.265295+0.011880    test-rmse:0.974440+0.130977 
## [86] train-rmse:0.261937+0.013168    test-rmse:0.973348+0.131424 
## [87] train-rmse:0.258572+0.013203    test-rmse:0.971440+0.131455 
## [88] train-rmse:0.254129+0.012756    test-rmse:0.970675+0.131938 
## [89] train-rmse:0.251838+0.013554    test-rmse:0.970772+0.130718 
## [90] train-rmse:0.249487+0.013555    test-rmse:0.970667+0.131586 
## [91] train-rmse:0.246305+0.012994    test-rmse:0.971349+0.132368 
## [92] train-rmse:0.242462+0.012089    test-rmse:0.972618+0.132131 
## [93] train-rmse:0.240155+0.011539    test-rmse:0.971803+0.132569 
## [94] train-rmse:0.236113+0.011258    test-rmse:0.972123+0.132358 
## [95] train-rmse:0.234138+0.011584    test-rmse:0.970884+0.133380 
## [96] train-rmse:0.230883+0.011112    test-rmse:0.970998+0.134054 
## Stopping. Best iteration:
## [90] train-rmse:0.249487+0.013555    test-rmse:0.970667+0.131586
## 
## 108 
## [1]  train-rmse:59.245290+0.059177   test-rmse:59.245643+0.433703 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:35.667956+0.034978   test-rmse:35.667497+0.455608 
## [3]  train-rmse:21.513287+0.020080   test-rmse:21.511532+0.466322 
## [4]  train-rmse:13.041444+0.011310   test-rmse:13.037761+0.467604 
## [5]  train-rmse:7.989149+0.005702    test-rmse:7.970775+0.412728 
## [6]  train-rmse:4.979993+0.009478    test-rmse:4.968226+0.353445 
## [7]  train-rmse:3.193258+0.013836    test-rmse:3.207039+0.288137 
## [8]  train-rmse:2.159060+0.020770    test-rmse:2.201037+0.232272 
## [9]  train-rmse:1.580682+0.026144    test-rmse:1.692577+0.167235 
## [10] train-rmse:1.265316+0.034873    test-rmse:1.469643+0.123929 
## [11] train-rmse:1.103308+0.041038    test-rmse:1.359073+0.126128 
## [12] train-rmse:0.993168+0.025125    test-rmse:1.300962+0.114321 
## [13] train-rmse:0.935717+0.024434    test-rmse:1.279592+0.095795 
## [14] train-rmse:0.884898+0.029714    test-rmse:1.258860+0.086387 
## [15] train-rmse:0.835896+0.031718    test-rmse:1.240327+0.081697 
## [16] train-rmse:0.799074+0.033108    test-rmse:1.218187+0.081836 
## [17] train-rmse:0.771207+0.040466    test-rmse:1.201679+0.077359 
## [18] train-rmse:0.744875+0.045267    test-rmse:1.187385+0.075032 
## [19] train-rmse:0.713686+0.030329    test-rmse:1.171900+0.076014 
## [20] train-rmse:0.693296+0.027926    test-rmse:1.164927+0.077681 
## [21] train-rmse:0.663536+0.027960    test-rmse:1.153443+0.086766 
## [22] train-rmse:0.642765+0.031231    test-rmse:1.129702+0.065178 
## [23] train-rmse:0.618320+0.033026    test-rmse:1.128982+0.058487 
## [24] train-rmse:0.596288+0.030987    test-rmse:1.119405+0.054856 
## [25] train-rmse:0.572091+0.031338    test-rmse:1.115052+0.049598 
## [26] train-rmse:0.552191+0.032044    test-rmse:1.106913+0.046587 
## [27] train-rmse:0.540821+0.031323    test-rmse:1.100196+0.051414 
## [28] train-rmse:0.525818+0.035152    test-rmse:1.088368+0.039573 
## [29] train-rmse:0.507770+0.025906    test-rmse:1.081191+0.041767 
## [30] train-rmse:0.495623+0.024225    test-rmse:1.078951+0.040460 
## [31] train-rmse:0.480408+0.026088    test-rmse:1.074036+0.046243 
## [32] train-rmse:0.465476+0.030138    test-rmse:1.070655+0.047353 
## [33] train-rmse:0.455755+0.029913    test-rmse:1.064355+0.043703 
## [34] train-rmse:0.437394+0.029101    test-rmse:1.057722+0.043749 
## [35] train-rmse:0.420420+0.024928    test-rmse:1.049106+0.045137 
## [36] train-rmse:0.406601+0.023398    test-rmse:1.053078+0.048018 
## [37] train-rmse:0.395923+0.020885    test-rmse:1.050455+0.047732 
## [38] train-rmse:0.383989+0.019726    test-rmse:1.053792+0.046165 
## [39] train-rmse:0.372694+0.023538    test-rmse:1.047984+0.048235 
## [40] train-rmse:0.364940+0.025065    test-rmse:1.047236+0.049333 
## [41] train-rmse:0.355609+0.025702    test-rmse:1.042139+0.050232 
## [42] train-rmse:0.344360+0.027560    test-rmse:1.040643+0.050323 
## [43] train-rmse:0.335129+0.026919    test-rmse:1.040162+0.050828 
## [44] train-rmse:0.326247+0.024736    test-rmse:1.041012+0.053146 
## [45] train-rmse:0.317523+0.026765    test-rmse:1.040715+0.053101 
## [46] train-rmse:0.309620+0.027536    test-rmse:1.037839+0.052828 
## [47] train-rmse:0.301871+0.026003    test-rmse:1.036169+0.051291 
## [48] train-rmse:0.295466+0.026599    test-rmse:1.034248+0.051360 
## [49] train-rmse:0.288179+0.023965    test-rmse:1.030779+0.054800 
## [50] train-rmse:0.282296+0.022650    test-rmse:1.030304+0.056626 
## [51] train-rmse:0.275712+0.021672    test-rmse:1.028789+0.055999 
## [52] train-rmse:0.269101+0.022151    test-rmse:1.026669+0.056246 
## [53] train-rmse:0.261132+0.019352    test-rmse:1.026137+0.056028 
## [54] train-rmse:0.255708+0.017627    test-rmse:1.024499+0.057178 
## [55] train-rmse:0.249765+0.016626    test-rmse:1.025480+0.059170 
## [56] train-rmse:0.243874+0.017336    test-rmse:1.024501+0.060687 
## [57] train-rmse:0.238394+0.017444    test-rmse:1.022393+0.060806 
## [58] train-rmse:0.231489+0.018427    test-rmse:1.021399+0.060796 
## [59] train-rmse:0.226481+0.018155    test-rmse:1.020631+0.060671 
## [60] train-rmse:0.218036+0.016010    test-rmse:1.020516+0.060316 
## [61] train-rmse:0.212694+0.014701    test-rmse:1.019152+0.061353 
## [62] train-rmse:0.208017+0.016410    test-rmse:1.018908+0.062262 
## [63] train-rmse:0.204232+0.015543    test-rmse:1.019611+0.061183 
## [64] train-rmse:0.200253+0.015195    test-rmse:1.019320+0.061437 
## [65] train-rmse:0.194885+0.014955    test-rmse:1.020368+0.060946 
## [66] train-rmse:0.188110+0.013109    test-rmse:1.018580+0.060321 
## [67] train-rmse:0.183154+0.013288    test-rmse:1.017534+0.060051 
## [68] train-rmse:0.178559+0.012750    test-rmse:1.016373+0.059136 
## [69] train-rmse:0.173131+0.012546    test-rmse:1.014869+0.059104 
## [70] train-rmse:0.168828+0.013248    test-rmse:1.015998+0.058355 
## [71] train-rmse:0.164754+0.013265    test-rmse:1.014898+0.057707 
## [72] train-rmse:0.161621+0.013486    test-rmse:1.015439+0.057479 
## [73] train-rmse:0.158102+0.013714    test-rmse:1.014554+0.058286 
## [74] train-rmse:0.154714+0.013852    test-rmse:1.014137+0.059659 
## [75] train-rmse:0.150873+0.012063    test-rmse:1.013941+0.057567 
## [76] train-rmse:0.148049+0.011903    test-rmse:1.013128+0.058357 
## [77] train-rmse:0.144795+0.011170    test-rmse:1.012091+0.058538 
## [78] train-rmse:0.141696+0.010361    test-rmse:1.010469+0.058030 
## [79] train-rmse:0.138923+0.009548    test-rmse:1.010041+0.058008 
## [80] train-rmse:0.136320+0.008038    test-rmse:1.010056+0.058412 
## [81] train-rmse:0.132713+0.008447    test-rmse:1.009868+0.058543 
## [82] train-rmse:0.129596+0.008899    test-rmse:1.008421+0.059487 
## [83] train-rmse:0.127634+0.009182    test-rmse:1.008574+0.059860 
## [84] train-rmse:0.125435+0.008677    test-rmse:1.008310+0.060302 
## [85] train-rmse:0.123148+0.008117    test-rmse:1.007797+0.060622 
## [86] train-rmse:0.121395+0.008220    test-rmse:1.007924+0.060456 
## [87] train-rmse:0.118397+0.007479    test-rmse:1.008225+0.061658 
## [88] train-rmse:0.115346+0.006270    test-rmse:1.008103+0.061733 
## [89] train-rmse:0.112396+0.006491    test-rmse:1.008023+0.060761 
## [90] train-rmse:0.109765+0.007525    test-rmse:1.007210+0.061452 
## [91] train-rmse:0.107645+0.006503    test-rmse:1.006947+0.061360 
## [92] train-rmse:0.105685+0.006449    test-rmse:1.006772+0.060763 
## [93] train-rmse:0.102586+0.006755    test-rmse:1.006206+0.061172 
## [94] train-rmse:0.100402+0.006717    test-rmse:1.005856+0.061556 
## [95] train-rmse:0.098326+0.006697    test-rmse:1.004515+0.062507 
## [96] train-rmse:0.096455+0.006565    test-rmse:1.004242+0.061156 
## [97] train-rmse:0.094075+0.006332    test-rmse:1.004463+0.061127 
## [98] train-rmse:0.092155+0.006503    test-rmse:1.003997+0.061438 
## [99] train-rmse:0.090277+0.006295    test-rmse:1.004472+0.060600 
## [100]    train-rmse:0.087587+0.006148    test-rmse:1.004318+0.060334 
## [101]    train-rmse:0.086395+0.006402    test-rmse:1.003934+0.059971 
## [102]    train-rmse:0.084196+0.006359    test-rmse:1.003695+0.060518 
## [103]    train-rmse:0.082036+0.006702    test-rmse:1.004171+0.059757 
## [104]    train-rmse:0.079706+0.005483    test-rmse:1.003109+0.059292 
## [105]    train-rmse:0.078233+0.004938    test-rmse:1.002651+0.059664 
## [106]    train-rmse:0.076403+0.004580    test-rmse:1.003297+0.059703 
## [107]    train-rmse:0.074777+0.004541    test-rmse:1.003332+0.058913 
## [108]    train-rmse:0.072916+0.004406    test-rmse:1.003368+0.058696 
## [109]    train-rmse:0.071076+0.004096    test-rmse:1.003354+0.058133 
## [110]    train-rmse:0.069816+0.003956    test-rmse:1.003476+0.058351 
## [111]    train-rmse:0.068837+0.004006    test-rmse:1.003086+0.058336 
## Stopping. Best iteration:
## [105]    train-rmse:0.078233+0.004938    test-rmse:1.002651+0.059664
## 
## 109 
## [1]  train-rmse:59.245290+0.059177   test-rmse:59.245643+0.433703 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:35.667956+0.034978   test-rmse:35.667497+0.455608 
## [3]  train-rmse:21.513287+0.020080   test-rmse:21.511532+0.466322 
## [4]  train-rmse:13.041444+0.011310   test-rmse:13.037761+0.467604 
## [5]  train-rmse:7.989149+0.005702    test-rmse:7.970775+0.412728 
## [6]  train-rmse:4.976391+0.007664    test-rmse:4.964721+0.342478 
## [7]  train-rmse:3.184545+0.010114    test-rmse:3.188088+0.279668 
## [8]  train-rmse:2.131958+0.012685    test-rmse:2.180726+0.221207 
## [9]  train-rmse:1.516723+0.009640    test-rmse:1.649733+0.170722 
## [10] train-rmse:1.172230+0.016259    test-rmse:1.391573+0.128836 
## [11] train-rmse:0.990639+0.020340    test-rmse:1.270521+0.115012 
## [12] train-rmse:0.882073+0.016787    test-rmse:1.215870+0.095836 
## [13] train-rmse:0.813372+0.015261    test-rmse:1.187841+0.098459 
## [14] train-rmse:0.759028+0.014644    test-rmse:1.165200+0.098117 
## [15] train-rmse:0.710631+0.015465    test-rmse:1.145620+0.102919 
## [16] train-rmse:0.666382+0.014330    test-rmse:1.127009+0.095319 
## [17] train-rmse:0.637245+0.015774    test-rmse:1.108588+0.097978 
## [18] train-rmse:0.604486+0.022511    test-rmse:1.094599+0.098929 
## [19] train-rmse:0.570025+0.019133    test-rmse:1.087188+0.087569 
## [20] train-rmse:0.543033+0.016928    test-rmse:1.078696+0.087052 
## [21] train-rmse:0.523031+0.014271    test-rmse:1.062897+0.087247 
## [22] train-rmse:0.503589+0.014715    test-rmse:1.065971+0.088226 
## [23] train-rmse:0.481741+0.015828    test-rmse:1.059954+0.090231 
## [24] train-rmse:0.460057+0.019803    test-rmse:1.049542+0.090531 
## [25] train-rmse:0.441225+0.018587    test-rmse:1.043223+0.088273 
## [26] train-rmse:0.419388+0.013946    test-rmse:1.043303+0.087851 
## [27] train-rmse:0.403921+0.019034    test-rmse:1.036031+0.088286 
## [28] train-rmse:0.387898+0.013287    test-rmse:1.030135+0.088953 
## [29] train-rmse:0.376645+0.011036    test-rmse:1.021603+0.084646 
## [30] train-rmse:0.361981+0.009527    test-rmse:1.017138+0.085497 
## [31] train-rmse:0.344149+0.015347    test-rmse:1.013087+0.088455 
## [32] train-rmse:0.333822+0.014346    test-rmse:1.014165+0.085845 
## [33] train-rmse:0.321260+0.016061    test-rmse:1.012293+0.084070 
## [34] train-rmse:0.305386+0.013432    test-rmse:1.012854+0.085997 
## [35] train-rmse:0.293085+0.016330    test-rmse:1.011640+0.084882 
## [36] train-rmse:0.283248+0.014669    test-rmse:1.008288+0.083485 
## [37] train-rmse:0.274783+0.014253    test-rmse:1.005805+0.082253 
## [38] train-rmse:0.263088+0.014111    test-rmse:1.000718+0.085501 
## [39] train-rmse:0.251439+0.014831    test-rmse:0.999493+0.081957 
## [40] train-rmse:0.241963+0.012765    test-rmse:0.994602+0.082382 
## [41] train-rmse:0.231211+0.014750    test-rmse:0.994751+0.084689 
## [42] train-rmse:0.222667+0.012528    test-rmse:0.990879+0.083867 
## [43] train-rmse:0.216303+0.014366    test-rmse:0.987587+0.083423 
## [44] train-rmse:0.208978+0.011462    test-rmse:0.984625+0.084454 
## [45] train-rmse:0.203814+0.011586    test-rmse:0.984815+0.084651 
## [46] train-rmse:0.196857+0.011642    test-rmse:0.982541+0.082595 
## [47] train-rmse:0.190358+0.013002    test-rmse:0.984109+0.081766 
## [48] train-rmse:0.185731+0.013066    test-rmse:0.983326+0.081867 
## [49] train-rmse:0.176994+0.015039    test-rmse:0.984780+0.081685 
## [50] train-rmse:0.170720+0.011552    test-rmse:0.984932+0.082689 
## [51] train-rmse:0.165220+0.012400    test-rmse:0.983581+0.082607 
## [52] train-rmse:0.161072+0.012124    test-rmse:0.981423+0.082281 
## [53] train-rmse:0.155014+0.009698    test-rmse:0.983176+0.082352 
## [54] train-rmse:0.151600+0.009840    test-rmse:0.983495+0.081396 
## [55] train-rmse:0.147567+0.009282    test-rmse:0.981696+0.082027 
## [56] train-rmse:0.141702+0.010993    test-rmse:0.983447+0.083929 
## [57] train-rmse:0.136264+0.010806    test-rmse:0.981576+0.084312 
## [58] train-rmse:0.130950+0.012063    test-rmse:0.981079+0.085105 
## [59] train-rmse:0.127094+0.010952    test-rmse:0.981099+0.085711 
## [60] train-rmse:0.120796+0.010887    test-rmse:0.980127+0.085983 
## [61] train-rmse:0.117597+0.010271    test-rmse:0.979651+0.086961 
## [62] train-rmse:0.115100+0.010310    test-rmse:0.980350+0.087767 
## [63] train-rmse:0.110830+0.011253    test-rmse:0.978691+0.088096 
## [64] train-rmse:0.106078+0.011295    test-rmse:0.978222+0.088133 
## [65] train-rmse:0.101327+0.010094    test-rmse:0.977219+0.088086 
## [66] train-rmse:0.097990+0.010489    test-rmse:0.977949+0.088261 
## [67] train-rmse:0.095198+0.010486    test-rmse:0.977036+0.088071 
## [68] train-rmse:0.092170+0.010632    test-rmse:0.976712+0.088013 
## [69] train-rmse:0.088696+0.009948    test-rmse:0.975710+0.088110 
## [70] train-rmse:0.086840+0.009984    test-rmse:0.975546+0.088992 
## [71] train-rmse:0.084388+0.009399    test-rmse:0.974516+0.088664 
## [72] train-rmse:0.081799+0.009600    test-rmse:0.974553+0.088027 
## [73] train-rmse:0.078499+0.009240    test-rmse:0.974588+0.088646 
## [74] train-rmse:0.075454+0.009227    test-rmse:0.974486+0.088859 
## [75] train-rmse:0.073551+0.008383    test-rmse:0.974307+0.089228 
## [76] train-rmse:0.071212+0.007796    test-rmse:0.973612+0.090540 
## [77] train-rmse:0.068700+0.007591    test-rmse:0.973694+0.090248 
## [78] train-rmse:0.066457+0.007534    test-rmse:0.973453+0.090459 
## [79] train-rmse:0.064799+0.007002    test-rmse:0.973347+0.090184 
## [80] train-rmse:0.062188+0.006574    test-rmse:0.973405+0.090667 
## [81] train-rmse:0.059694+0.006122    test-rmse:0.972748+0.090428 
## [82] train-rmse:0.057572+0.005773    test-rmse:0.972793+0.090467 
## [83] train-rmse:0.054940+0.005461    test-rmse:0.972412+0.090394 
## [84] train-rmse:0.052953+0.005937    test-rmse:0.971996+0.090594 
## [85] train-rmse:0.051083+0.006162    test-rmse:0.971527+0.091322 
## [86] train-rmse:0.050013+0.006406    test-rmse:0.971249+0.091243 
## [87] train-rmse:0.048262+0.005618    test-rmse:0.970514+0.091008 
## [88] train-rmse:0.047308+0.005323    test-rmse:0.970261+0.090774 
## [89] train-rmse:0.046576+0.005313    test-rmse:0.970174+0.091057 
## [90] train-rmse:0.044800+0.005549    test-rmse:0.970031+0.090858 
## [91] train-rmse:0.042348+0.005324    test-rmse:0.970227+0.090545 
## [92] train-rmse:0.040613+0.005147    test-rmse:0.969892+0.091010 
## [93] train-rmse:0.039469+0.004840    test-rmse:0.969823+0.090957 
## [94] train-rmse:0.038258+0.004673    test-rmse:0.970002+0.091121 
## [95] train-rmse:0.037311+0.004642    test-rmse:0.969652+0.091040 
## [96] train-rmse:0.036508+0.004577    test-rmse:0.970031+0.091002 
## [97] train-rmse:0.035568+0.004244    test-rmse:0.970256+0.091071 
## [98] train-rmse:0.034471+0.003867    test-rmse:0.969999+0.091200 
## [99] train-rmse:0.033538+0.003508    test-rmse:0.970179+0.091298 
## [100]    train-rmse:0.032346+0.003572    test-rmse:0.969903+0.091014 
## [101]    train-rmse:0.031570+0.003330    test-rmse:0.970014+0.091107 
## Stopping. Best iteration:
## [95] train-rmse:0.037311+0.004642    test-rmse:0.969652+0.091040
## 
## 110 
## [1]  train-rmse:59.245290+0.059177   test-rmse:59.245643+0.433703 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:35.667956+0.034978   test-rmse:35.667497+0.455608 
## [3]  train-rmse:21.513287+0.020080   test-rmse:21.511532+0.466322 
## [4]  train-rmse:13.041444+0.011310   test-rmse:13.037761+0.467604 
## [5]  train-rmse:7.989149+0.005702    test-rmse:7.970775+0.412728 
## [6]  train-rmse:4.974738+0.007389    test-rmse:4.968379+0.340208 
## [7]  train-rmse:3.177615+0.006767    test-rmse:3.182698+0.286292 
## [8]  train-rmse:2.115333+0.010135    test-rmse:2.168740+0.251418 
## [9]  train-rmse:1.486553+0.022376    test-rmse:1.615388+0.185004 
## [10] train-rmse:1.117040+0.012471    test-rmse:1.358315+0.169594 
## [11] train-rmse:0.916025+0.023526    test-rmse:1.239783+0.143830 
## [12] train-rmse:0.776697+0.018818    test-rmse:1.168682+0.144612 
## [13] train-rmse:0.697067+0.011972    test-rmse:1.136428+0.145083 
## [14] train-rmse:0.637729+0.019503    test-rmse:1.109534+0.130994 
## [15] train-rmse:0.583299+0.016578    test-rmse:1.081892+0.104458 
## [16] train-rmse:0.540363+0.021833    test-rmse:1.071440+0.093826 
## [17] train-rmse:0.514185+0.024136    test-rmse:1.052918+0.096640 
## [18] train-rmse:0.475351+0.007393    test-rmse:1.044139+0.098963 
## [19] train-rmse:0.445932+0.010484    test-rmse:1.034844+0.098770 
## [20] train-rmse:0.422721+0.014290    test-rmse:1.027603+0.098330 
## [21] train-rmse:0.397086+0.014736    test-rmse:1.027807+0.098680 
## [22] train-rmse:0.380357+0.012038    test-rmse:1.022759+0.100749 
## [23] train-rmse:0.355935+0.013175    test-rmse:1.019591+0.101852 
## [24] train-rmse:0.334903+0.016607    test-rmse:1.020415+0.100400 
## [25] train-rmse:0.322806+0.018004    test-rmse:1.015379+0.097303 
## [26] train-rmse:0.302318+0.015191    test-rmse:1.012514+0.095638 
## [27] train-rmse:0.288269+0.013637    test-rmse:1.009261+0.095771 
## [28] train-rmse:0.274184+0.013448    test-rmse:1.008239+0.099869 
## [29] train-rmse:0.263122+0.010476    test-rmse:1.006514+0.101739 
## [30] train-rmse:0.251556+0.010847    test-rmse:1.006238+0.100341 
## [31] train-rmse:0.238650+0.010437    test-rmse:1.002681+0.097116 
## [32] train-rmse:0.227517+0.011688    test-rmse:1.001470+0.097943 
## [33] train-rmse:0.216156+0.009893    test-rmse:0.997693+0.101039 
## [34] train-rmse:0.205002+0.009224    test-rmse:0.997069+0.101493 
## [35] train-rmse:0.197229+0.008597    test-rmse:0.993752+0.102920 
## [36] train-rmse:0.187324+0.006907    test-rmse:0.990993+0.099776 
## [37] train-rmse:0.177061+0.007018    test-rmse:0.986507+0.102056 
## [38] train-rmse:0.168725+0.007774    test-rmse:0.986542+0.103554 
## [39] train-rmse:0.161422+0.006639    test-rmse:0.986551+0.104901 
## [40] train-rmse:0.151891+0.005538    test-rmse:0.985341+0.106791 
## [41] train-rmse:0.144403+0.007542    test-rmse:0.982658+0.104002 
## [42] train-rmse:0.138478+0.008171    test-rmse:0.981136+0.103275 
## [43] train-rmse:0.132182+0.006670    test-rmse:0.981971+0.104837 
## [44] train-rmse:0.126274+0.005349    test-rmse:0.980797+0.105162 
## [45] train-rmse:0.121281+0.005053    test-rmse:0.979686+0.107447 
## [46] train-rmse:0.115401+0.004762    test-rmse:0.980267+0.106738 
## [47] train-rmse:0.109273+0.002669    test-rmse:0.979534+0.106563 
## [48] train-rmse:0.104639+0.003396    test-rmse:0.979451+0.106592 
## [49] train-rmse:0.101286+0.002742    test-rmse:0.978098+0.106296 
## [50] train-rmse:0.096312+0.002215    test-rmse:0.977168+0.105332 
## [51] train-rmse:0.091829+0.002716    test-rmse:0.977002+0.105762 
## [52] train-rmse:0.087833+0.002427    test-rmse:0.976914+0.105224 
## [53] train-rmse:0.082889+0.003227    test-rmse:0.976913+0.104587 
## [54] train-rmse:0.079323+0.002642    test-rmse:0.975835+0.106080 
## [55] train-rmse:0.075573+0.002095    test-rmse:0.975210+0.106199 
## [56] train-rmse:0.071297+0.002407    test-rmse:0.975930+0.107424 
## [57] train-rmse:0.067984+0.001928    test-rmse:0.974895+0.107409 
## [58] train-rmse:0.064698+0.002554    test-rmse:0.974187+0.107346 
## [59] train-rmse:0.062765+0.003069    test-rmse:0.974306+0.107544 
## [60] train-rmse:0.060296+0.002370    test-rmse:0.973758+0.107937 
## [61] train-rmse:0.057139+0.002740    test-rmse:0.973445+0.107429 
## [62] train-rmse:0.053999+0.002613    test-rmse:0.974009+0.107736 
## [63] train-rmse:0.051525+0.002767    test-rmse:0.972611+0.108493 
## [64] train-rmse:0.049119+0.002393    test-rmse:0.972046+0.107920 
## [65] train-rmse:0.046341+0.002415    test-rmse:0.972283+0.107888 
## [66] train-rmse:0.044923+0.002773    test-rmse:0.972453+0.107833 
## [67] train-rmse:0.042885+0.002034    test-rmse:0.972235+0.107764 
## [68] train-rmse:0.041000+0.002159    test-rmse:0.972618+0.107604 
## [69] train-rmse:0.039401+0.002160    test-rmse:0.972311+0.107861 
## [70] train-rmse:0.037749+0.001900    test-rmse:0.972309+0.108086 
## Stopping. Best iteration:
## [64] train-rmse:0.049119+0.002393    test-rmse:0.972046+0.107920
## 
## 111 
## [1]  train-rmse:59.245290+0.059177   test-rmse:59.245643+0.433703 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:35.667956+0.034978   test-rmse:35.667497+0.455608 
## [3]  train-rmse:21.513287+0.020080   test-rmse:21.511532+0.466322 
## [4]  train-rmse:13.041444+0.011310   test-rmse:13.037761+0.467604 
## [5]  train-rmse:7.989149+0.005702    test-rmse:7.970775+0.412728 
## [6]  train-rmse:4.974033+0.007000    test-rmse:4.964287+0.340916 
## [7]  train-rmse:3.171186+0.004632    test-rmse:3.176656+0.280722 
## [8]  train-rmse:2.101106+0.009282    test-rmse:2.140290+0.231008 
## [9]  train-rmse:1.454380+0.026098    test-rmse:1.591939+0.182822 
## [10] train-rmse:1.070309+0.016361    test-rmse:1.347266+0.142547 
## [11] train-rmse:0.849865+0.025751    test-rmse:1.220017+0.102769 
## [12] train-rmse:0.708743+0.029210    test-rmse:1.164335+0.092221 
## [13] train-rmse:0.615929+0.028800    test-rmse:1.129369+0.071930 
## [14] train-rmse:0.553579+0.032115    test-rmse:1.102359+0.070547 
## [15] train-rmse:0.500903+0.028548    test-rmse:1.093246+0.072871 
## [16] train-rmse:0.470936+0.028501    test-rmse:1.074189+0.076167 
## [17] train-rmse:0.439394+0.028249    test-rmse:1.066455+0.069947 
## [18] train-rmse:0.410293+0.030757    test-rmse:1.055017+0.065837 
## [19] train-rmse:0.382777+0.026387    test-rmse:1.051513+0.066292 
## [20] train-rmse:0.352991+0.026051    test-rmse:1.045673+0.066964 
## [21] train-rmse:0.326541+0.024720    test-rmse:1.041511+0.070161 
## [22] train-rmse:0.304190+0.024799    test-rmse:1.037956+0.067912 
## [23] train-rmse:0.286573+0.025108    test-rmse:1.035378+0.069291 
## [24] train-rmse:0.269483+0.024144    test-rmse:1.034004+0.064451 
## [25] train-rmse:0.251426+0.020443    test-rmse:1.028465+0.068566 
## [26] train-rmse:0.230873+0.020595    test-rmse:1.027642+0.063020 
## [27] train-rmse:0.217123+0.019806    test-rmse:1.026490+0.066485 
## [28] train-rmse:0.206651+0.018142    test-rmse:1.020268+0.065688 
## [29] train-rmse:0.195682+0.018390    test-rmse:1.018249+0.063253 
## [30] train-rmse:0.183001+0.018345    test-rmse:1.016344+0.062573 
## [31] train-rmse:0.171982+0.015299    test-rmse:1.015498+0.064276 
## [32] train-rmse:0.157113+0.015358    test-rmse:1.014430+0.063293 
## [33] train-rmse:0.148741+0.012606    test-rmse:1.012426+0.063467 
## [34] train-rmse:0.140448+0.013093    test-rmse:1.013896+0.064062 
## [35] train-rmse:0.133458+0.012038    test-rmse:1.013061+0.061906 
## [36] train-rmse:0.124907+0.012900    test-rmse:1.013635+0.062292 
## [37] train-rmse:0.117205+0.012691    test-rmse:1.013062+0.063246 
## [38] train-rmse:0.110685+0.011959    test-rmse:1.012320+0.063719 
## [39] train-rmse:0.105131+0.010604    test-rmse:1.011198+0.063309 
## [40] train-rmse:0.097541+0.011550    test-rmse:1.010232+0.063289 
## [41] train-rmse:0.091927+0.008769    test-rmse:1.010349+0.062693 
## [42] train-rmse:0.086329+0.007594    test-rmse:1.008501+0.062882 
## [43] train-rmse:0.081542+0.007212    test-rmse:1.007040+0.061559 
## [44] train-rmse:0.076030+0.007896    test-rmse:1.006475+0.061283 
## [45] train-rmse:0.072817+0.008649    test-rmse:1.006449+0.060446 
## [46] train-rmse:0.068749+0.008516    test-rmse:1.006926+0.060989 
## [47] train-rmse:0.065803+0.007862    test-rmse:1.006732+0.061274 
## [48] train-rmse:0.062938+0.007796    test-rmse:1.006044+0.061513 
## [49] train-rmse:0.059286+0.007388    test-rmse:1.005574+0.061430 
## [50] train-rmse:0.054532+0.007015    test-rmse:1.006467+0.059243 
## [51] train-rmse:0.051310+0.006475    test-rmse:1.006166+0.058933 
## [52] train-rmse:0.048218+0.004857    test-rmse:1.005682+0.059603 
## [53] train-rmse:0.045585+0.004122    test-rmse:1.005878+0.059530 
## [54] train-rmse:0.042939+0.004327    test-rmse:1.005947+0.059445 
## [55] train-rmse:0.039929+0.004049    test-rmse:1.005973+0.059628 
## Stopping. Best iteration:
## [49] train-rmse:0.059286+0.007388    test-rmse:1.005574+0.061430
## 
## 112
##    max_depth  eta
## 63         7 0.26
## [1]  train-rmse:96.903592+0.106998   test-rmse:96.903929+0.480417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:87.282645+0.098508   test-rmse:87.282849+0.494309 
## [3]  train-rmse:78.624342+0.091095   test-rmse:78.624364+0.507694 
## [4]  train-rmse:70.833145+0.084703   test-rmse:70.832979+0.520704 
## [5]  train-rmse:63.823124+0.079254   test-rmse:63.822745+0.533458 
## [6]  train-rmse:57.516975+0.074698   test-rmse:57.516330+0.546089 
## [7]  train-rmse:51.845108+0.070990   test-rmse:51.844165+0.558710 
## [8]  train-rmse:46.744962+0.068087   test-rmse:46.743658+0.571428 
## [9]  train-rmse:42.160251+0.065957   test-rmse:42.158527+0.584358 
## [10] train-rmse:38.040358+0.064573   test-rmse:38.038154+0.597586 
## [11] train-rmse:34.337589+0.063141   test-rmse:34.344373+0.598399 
## [12] train-rmse:31.009436+0.061320   test-rmse:31.036346+0.600886 
## [13] train-rmse:28.018613+0.059096   test-rmse:28.051969+0.611933 
## [14] train-rmse:25.331300+0.055997   test-rmse:25.385387+0.619286 
## [15] train-rmse:22.917261+0.053241   test-rmse:22.964550+0.616728 
## [16] train-rmse:20.750379+0.050484   test-rmse:20.810078+0.610616 
## [17] train-rmse:18.806157+0.047987   test-rmse:18.857919+0.606590 
## [18] train-rmse:17.063534+0.045831   test-rmse:17.127583+0.600467 
## [19] train-rmse:15.502573+0.043972   test-rmse:15.558737+0.596481 
## [20] train-rmse:14.106478+0.042550   test-rmse:14.191969+0.616710 
## [21] train-rmse:12.858692+0.041691   test-rmse:12.938224+0.614787 
## [22] train-rmse:11.745963+0.041384   test-rmse:11.835577+0.604483 
## [23] train-rmse:10.754624+0.041085   test-rmse:10.836637+0.603875 
## [24] train-rmse:9.873867+0.042059    test-rmse:9.972510+0.616216 
## [25] train-rmse:9.091924+0.042606    test-rmse:9.193679+0.612675 
## [26] train-rmse:8.400282+0.044121    test-rmse:8.494716+0.607543 
## [27] train-rmse:7.790210+0.045483    test-rmse:7.890323+0.615830 
## [28] train-rmse:7.252296+0.046687    test-rmse:7.385747+0.634971 
## [29] train-rmse:6.779517+0.047894    test-rmse:6.897802+0.622580 
## [30] train-rmse:6.366342+0.049495    test-rmse:6.487876+0.604393 
## [31] train-rmse:6.005059+0.051310    test-rmse:6.118924+0.586602 
## [32] train-rmse:5.689480+0.052852    test-rmse:5.818287+0.606057 
## [33] train-rmse:5.415944+0.054295    test-rmse:5.549357+0.581365 
## [34] train-rmse:5.178796+0.055855    test-rmse:5.297457+0.558788 
## [35] train-rmse:4.972802+0.056846    test-rmse:5.104747+0.573445 
## [36] train-rmse:4.794287+0.057711    test-rmse:4.921263+0.552506 
## [37] train-rmse:4.640263+0.058945    test-rmse:4.772928+0.523762 
## [38] train-rmse:4.506117+0.060137    test-rmse:4.636273+0.517995 
## [39] train-rmse:4.389116+0.060343    test-rmse:4.530475+0.530665 
## [40] train-rmse:4.287646+0.060738    test-rmse:4.444312+0.520585 
## [41] train-rmse:4.198071+0.061035    test-rmse:4.346438+0.495953 
## [42] train-rmse:4.120055+0.061210    test-rmse:4.274099+0.489672 
## [43] train-rmse:4.050620+0.061200    test-rmse:4.201074+0.473330 
## [44] train-rmse:3.988714+0.060388    test-rmse:4.142445+0.475686 
## [45] train-rmse:3.933689+0.059885    test-rmse:4.103595+0.464808 
## [46] train-rmse:3.883860+0.059826    test-rmse:4.067777+0.474075 
## [47] train-rmse:3.838262+0.060116    test-rmse:4.025269+0.458694 
## [48] train-rmse:3.796637+0.059751    test-rmse:3.976245+0.442089 
## [49] train-rmse:3.758468+0.059036    test-rmse:3.928249+0.420669 
## [50] train-rmse:3.723391+0.058599    test-rmse:3.902388+0.425327 
## [51] train-rmse:3.690660+0.058522    test-rmse:3.875715+0.422292 
## [52] train-rmse:3.659829+0.058215    test-rmse:3.850083+0.415499 
## [53] train-rmse:3.630793+0.057996    test-rmse:3.834967+0.411094 
## [54] train-rmse:3.602926+0.057660    test-rmse:3.802037+0.393660 
## [55] train-rmse:3.576359+0.056880    test-rmse:3.766074+0.376168 
## [56] train-rmse:3.550854+0.056185    test-rmse:3.746730+0.379353 
## [57] train-rmse:3.526225+0.055748    test-rmse:3.735875+0.374414 
## [58] train-rmse:3.502404+0.055071    test-rmse:3.708421+0.377895 
## [59] train-rmse:3.479471+0.054280    test-rmse:3.690267+0.376342 
## [60] train-rmse:3.457237+0.053640    test-rmse:3.662622+0.376605 
## [61] train-rmse:3.435344+0.053104    test-rmse:3.652858+0.380850 
## [62] train-rmse:3.414048+0.052767    test-rmse:3.625762+0.360948 
## [63] train-rmse:3.393196+0.052273    test-rmse:3.594258+0.348530 
## [64] train-rmse:3.372873+0.051912    test-rmse:3.578087+0.347720 
## [65] train-rmse:3.353038+0.051359    test-rmse:3.565719+0.348965 
## [66] train-rmse:3.333316+0.050907    test-rmse:3.553415+0.350842 
## [67] train-rmse:3.314217+0.050445    test-rmse:3.544933+0.350829 
## [68] train-rmse:3.295234+0.049893    test-rmse:3.527507+0.352104 
## [69] train-rmse:3.276604+0.049408    test-rmse:3.505140+0.354170 
## [70] train-rmse:3.258104+0.048952    test-rmse:3.483844+0.355248 
## [71] train-rmse:3.240065+0.048399    test-rmse:3.459347+0.344933 
## [72] train-rmse:3.222293+0.047937    test-rmse:3.446914+0.345836 
## [73] train-rmse:3.204828+0.047415    test-rmse:3.420611+0.332509 
## [74] train-rmse:3.187461+0.046894    test-rmse:3.401838+0.325588 
## [75] train-rmse:3.170562+0.046449    test-rmse:3.391586+0.326547 
## [76] train-rmse:3.153782+0.046062    test-rmse:3.377451+0.327475 
## [77] train-rmse:3.137351+0.045781    test-rmse:3.371954+0.333790 
## [78] train-rmse:3.121101+0.045359    test-rmse:3.362648+0.331523 
## [79] train-rmse:3.104908+0.044813    test-rmse:3.341221+0.331401 
## [80] train-rmse:3.088973+0.044420    test-rmse:3.326879+0.333415 
## [81] train-rmse:3.073178+0.044027    test-rmse:3.298440+0.323687 
## [82] train-rmse:3.057675+0.043542    test-rmse:3.287951+0.329483 
## [83] train-rmse:3.042435+0.043109    test-rmse:3.272361+0.316386 
## [84] train-rmse:3.027334+0.042770    test-rmse:3.257774+0.319493 
## [85] train-rmse:3.012406+0.042398    test-rmse:3.243692+0.313072 
## [86] train-rmse:2.997534+0.042074    test-rmse:3.233806+0.309283 
## [87] train-rmse:2.982731+0.041600    test-rmse:3.220859+0.314009 
## [88] train-rmse:2.968294+0.041468    test-rmse:3.207263+0.316890 
## [89] train-rmse:2.953923+0.041275    test-rmse:3.187017+0.322676 
## [90] train-rmse:2.939558+0.041102    test-rmse:3.184920+0.325062 
## [91] train-rmse:2.925387+0.041004    test-rmse:3.176291+0.324241 
## [92] train-rmse:2.911553+0.040836    test-rmse:3.161354+0.324413 
## [93] train-rmse:2.897717+0.040595    test-rmse:3.145950+0.325037 
## [94] train-rmse:2.884026+0.040437    test-rmse:3.131773+0.329414 
## [95] train-rmse:2.870369+0.040273    test-rmse:3.110937+0.315655 
## [96] train-rmse:2.857152+0.040070    test-rmse:3.094489+0.304656 
## [97] train-rmse:2.844020+0.039919    test-rmse:3.081269+0.301896 
## [98] train-rmse:2.830901+0.039750    test-rmse:3.068549+0.300823 
## [99] train-rmse:2.817922+0.039476    test-rmse:3.057225+0.303779 
## [100]    train-rmse:2.805105+0.039307    test-rmse:3.046798+0.305313 
## [101]    train-rmse:2.792347+0.039121    test-rmse:3.034076+0.309921 
## [102]    train-rmse:2.779664+0.039015    test-rmse:3.022683+0.314246 
## [103]    train-rmse:2.767098+0.038924    test-rmse:3.011178+0.316329 
## [104]    train-rmse:2.754755+0.038883    test-rmse:3.006051+0.317657 
## [105]    train-rmse:2.742474+0.038950    test-rmse:2.999476+0.318410 
## [106]    train-rmse:2.730348+0.038862    test-rmse:2.986871+0.322651 
## [107]    train-rmse:2.718297+0.038783    test-rmse:2.969309+0.325080 
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## [737]    train-rmse:0.783406+0.016745    test-rmse:1.102184+0.151119 
## [738]    train-rmse:0.783033+0.016756    test-rmse:1.101605+0.151591 
## [739]    train-rmse:0.782666+0.016771    test-rmse:1.100831+0.151681 
## [740]    train-rmse:0.782299+0.016784    test-rmse:1.100577+0.150918 
## [741]    train-rmse:0.781935+0.016799    test-rmse:1.100383+0.151217 
## [742]    train-rmse:0.781570+0.016815    test-rmse:1.100060+0.151225 
## [743]    train-rmse:0.781202+0.016834    test-rmse:1.099669+0.151014 
## [744]    train-rmse:0.780839+0.016848    test-rmse:1.099970+0.151186 
## [745]    train-rmse:0.780480+0.016863    test-rmse:1.099798+0.151350 
## [746]    train-rmse:0.780121+0.016872    test-rmse:1.099928+0.151303 
## [747]    train-rmse:0.779766+0.016882    test-rmse:1.100024+0.151204 
## [748]    train-rmse:0.779408+0.016894    test-rmse:1.099749+0.151436 
## [749]    train-rmse:0.779055+0.016906    test-rmse:1.099233+0.151471 
## [750]    train-rmse:0.778703+0.016918    test-rmse:1.099097+0.151218 
## [751]    train-rmse:0.778353+0.016931    test-rmse:1.098502+0.151002 
## [752]    train-rmse:0.778000+0.016941    test-rmse:1.098439+0.151027 
## [753]    train-rmse:0.777652+0.016952    test-rmse:1.097894+0.150925 
## [754]    train-rmse:0.777305+0.016963    test-rmse:1.097103+0.151151 
## [755]    train-rmse:0.776961+0.016979    test-rmse:1.096959+0.150912 
## [756]    train-rmse:0.776616+0.016996    test-rmse:1.096852+0.151018 
## [757]    train-rmse:0.776274+0.017010    test-rmse:1.096602+0.151140 
## [758]    train-rmse:0.775931+0.017026    test-rmse:1.095849+0.150760 
## [759]    train-rmse:0.775589+0.017038    test-rmse:1.095490+0.150394 
## [760]    train-rmse:0.775253+0.017052    test-rmse:1.095667+0.150524 
## [761]    train-rmse:0.774918+0.017062    test-rmse:1.094988+0.151229 
## [762]    train-rmse:0.774585+0.017075    test-rmse:1.094562+0.151390 
## [763]    train-rmse:0.774253+0.017090    test-rmse:1.094352+0.151586 
## [764]    train-rmse:0.773919+0.017102    test-rmse:1.094192+0.151478 
## [765]    train-rmse:0.773588+0.017112    test-rmse:1.094031+0.151309 
## [766]    train-rmse:0.773256+0.017127    test-rmse:1.093969+0.150978 
## [767]    train-rmse:0.772927+0.017140    test-rmse:1.093316+0.150876 
## [768]    train-rmse:0.772602+0.017152    test-rmse:1.093059+0.150821 
## [769]    train-rmse:0.772277+0.017165    test-rmse:1.092814+0.150987 
## [770]    train-rmse:0.771955+0.017178    test-rmse:1.092731+0.151450 
## [771]    train-rmse:0.771636+0.017191    test-rmse:1.092366+0.151545 
## [772]    train-rmse:0.771314+0.017201    test-rmse:1.091984+0.151155 
## [773]    train-rmse:0.770995+0.017215    test-rmse:1.091602+0.151243 
## [774]    train-rmse:0.770677+0.017231    test-rmse:1.091917+0.151085 
## [775]    train-rmse:0.770362+0.017246    test-rmse:1.090975+0.151770 
## [776]    train-rmse:0.770048+0.017258    test-rmse:1.090965+0.151478 
## [777]    train-rmse:0.769736+0.017272    test-rmse:1.090559+0.151756 
## [778]    train-rmse:0.769424+0.017285    test-rmse:1.090764+0.151674 
## [779]    train-rmse:0.769108+0.017294    test-rmse:1.090267+0.151997 
## [780]    train-rmse:0.768798+0.017307    test-rmse:1.089621+0.151693 
## [781]    train-rmse:0.768487+0.017320    test-rmse:1.089068+0.151790 
## [782]    train-rmse:0.768177+0.017334    test-rmse:1.087365+0.150612 
## [783]    train-rmse:0.767868+0.017345    test-rmse:1.087235+0.150599 
## [784]    train-rmse:0.767559+0.017359    test-rmse:1.087573+0.150178 
## [785]    train-rmse:0.767249+0.017369    test-rmse:1.086903+0.150719 
## [786]    train-rmse:0.766945+0.017381    test-rmse:1.086516+0.150059 
## [787]    train-rmse:0.766642+0.017392    test-rmse:1.086466+0.150385 
## [788]    train-rmse:0.766341+0.017405    test-rmse:1.086083+0.150401 
## [789]    train-rmse:0.766035+0.017413    test-rmse:1.085833+0.150014 
## [790]    train-rmse:0.765731+0.017426    test-rmse:1.085446+0.150199 
## [791]    train-rmse:0.765432+0.017435    test-rmse:1.085384+0.150054 
## [792]    train-rmse:0.765131+0.017441    test-rmse:1.085055+0.149481 
## [793]    train-rmse:0.764832+0.017453    test-rmse:1.084672+0.149612 
## [794]    train-rmse:0.764534+0.017468    test-rmse:1.084115+0.149650 
## [795]    train-rmse:0.764241+0.017479    test-rmse:1.083807+0.149507 
## [796]    train-rmse:0.763946+0.017493    test-rmse:1.083911+0.149555 
## [797]    train-rmse:0.763656+0.017503    test-rmse:1.084081+0.149427 
## [798]    train-rmse:0.763369+0.017516    test-rmse:1.084164+0.149442 
## [799]    train-rmse:0.763081+0.017528    test-rmse:1.083896+0.149611 
## [800]    train-rmse:0.762793+0.017541    test-rmse:1.083920+0.149563 
## [801]    train-rmse:0.762507+0.017553    test-rmse:1.083792+0.149705 
## [802]    train-rmse:0.762220+0.017559    test-rmse:1.083429+0.149429 
## [803]    train-rmse:0.761936+0.017569    test-rmse:1.082810+0.149623 
## [804]    train-rmse:0.761653+0.017574    test-rmse:1.082625+0.149943 
## [805]    train-rmse:0.761366+0.017579    test-rmse:1.082434+0.150083 
## [806]    train-rmse:0.761084+0.017590    test-rmse:1.082825+0.149914 
## [807]    train-rmse:0.760799+0.017593    test-rmse:1.082326+0.150278 
## [808]    train-rmse:0.760519+0.017600    test-rmse:1.082151+0.150312 
## [809]    train-rmse:0.760242+0.017609    test-rmse:1.082224+0.149972 
## [810]    train-rmse:0.759964+0.017612    test-rmse:1.082050+0.149798 
## [811]    train-rmse:0.759684+0.017625    test-rmse:1.081975+0.150131 
## [812]    train-rmse:0.759407+0.017630    test-rmse:1.081779+0.150233 
## [813]    train-rmse:0.759135+0.017640    test-rmse:1.081031+0.149860 
## [814]    train-rmse:0.758860+0.017649    test-rmse:1.080049+0.149747 
## [815]    train-rmse:0.758586+0.017656    test-rmse:1.080276+0.149715 
## [816]    train-rmse:0.758311+0.017660    test-rmse:1.079974+0.150510 
## [817]    train-rmse:0.758040+0.017669    test-rmse:1.079414+0.150699 
## [818]    train-rmse:0.757771+0.017675    test-rmse:1.079244+0.150767 
## [819]    train-rmse:0.757503+0.017682    test-rmse:1.079234+0.150773 
## [820]    train-rmse:0.757236+0.017689    test-rmse:1.078434+0.151380 
## [821]    train-rmse:0.756970+0.017695    test-rmse:1.077973+0.151181 
## [822]    train-rmse:0.756703+0.017702    test-rmse:1.078074+0.151070 
## [823]    train-rmse:0.756432+0.017707    test-rmse:1.077155+0.151322 
## [824]    train-rmse:0.756166+0.017713    test-rmse:1.076501+0.150706 
## [825]    train-rmse:0.755897+0.017720    test-rmse:1.075911+0.150362 
## [826]    train-rmse:0.755632+0.017727    test-rmse:1.075417+0.148899 
## [827]    train-rmse:0.755370+0.017732    test-rmse:1.075281+0.148946 
## [828]    train-rmse:0.755110+0.017740    test-rmse:1.075581+0.148710 
## [829]    train-rmse:0.754847+0.017749    test-rmse:1.075066+0.148580 
## [830]    train-rmse:0.754585+0.017751    test-rmse:1.075011+0.148879 
## [831]    train-rmse:0.754326+0.017760    test-rmse:1.075138+0.148524 
## [832]    train-rmse:0.754067+0.017770    test-rmse:1.074997+0.148725 
## [833]    train-rmse:0.753804+0.017779    test-rmse:1.074880+0.148920 
## [834]    train-rmse:0.753545+0.017790    test-rmse:1.074592+0.149073 
## [835]    train-rmse:0.753290+0.017796    test-rmse:1.074230+0.148658 
## [836]    train-rmse:0.753034+0.017803    test-rmse:1.074170+0.148762 
## [837]    train-rmse:0.752781+0.017806    test-rmse:1.074466+0.148780 
## [838]    train-rmse:0.752527+0.017813    test-rmse:1.074817+0.148317 
## [839]    train-rmse:0.752270+0.017816    test-rmse:1.074333+0.148915 
## [840]    train-rmse:0.752016+0.017819    test-rmse:1.074366+0.148707 
## [841]    train-rmse:0.751767+0.017825    test-rmse:1.073755+0.148573 
## [842]    train-rmse:0.751518+0.017831    test-rmse:1.073457+0.148710 
## [843]    train-rmse:0.751266+0.017833    test-rmse:1.073318+0.148485 
## [844]    train-rmse:0.751018+0.017839    test-rmse:1.073100+0.147887 
## [845]    train-rmse:0.750770+0.017841    test-rmse:1.072952+0.147781 
## [846]    train-rmse:0.750525+0.017845    test-rmse:1.072755+0.147581 
## [847]    train-rmse:0.750281+0.017849    test-rmse:1.072535+0.147833 
## [848]    train-rmse:0.750036+0.017853    test-rmse:1.072183+0.148008 
## [849]    train-rmse:0.749791+0.017857    test-rmse:1.072261+0.148353 
## [850]    train-rmse:0.749548+0.017862    test-rmse:1.071916+0.148601 
## [851]    train-rmse:0.749307+0.017867    test-rmse:1.071674+0.148654 
## [852]    train-rmse:0.749067+0.017871    test-rmse:1.071653+0.148468 
## [853]    train-rmse:0.748823+0.017878    test-rmse:1.071021+0.148196 
## [854]    train-rmse:0.748583+0.017882    test-rmse:1.070751+0.148419 
## [855]    train-rmse:0.748345+0.017883    test-rmse:1.070813+0.148354 
## [856]    train-rmse:0.748104+0.017882    test-rmse:1.070987+0.148156 
## [857]    train-rmse:0.747867+0.017885    test-rmse:1.070888+0.148162 
## [858]    train-rmse:0.747626+0.017893    test-rmse:1.070862+0.148435 
## [859]    train-rmse:0.747391+0.017894    test-rmse:1.070740+0.148339 
## [860]    train-rmse:0.747154+0.017895    test-rmse:1.069146+0.148677 
## [861]    train-rmse:0.746920+0.017898    test-rmse:1.069287+0.148927 
## [862]    train-rmse:0.746689+0.017901    test-rmse:1.069005+0.148499 
## [863]    train-rmse:0.746455+0.017905    test-rmse:1.069529+0.148339 
## [864]    train-rmse:0.746219+0.017906    test-rmse:1.069390+0.148497 
## [865]    train-rmse:0.745989+0.017909    test-rmse:1.069186+0.148631 
## [866]    train-rmse:0.745760+0.017913    test-rmse:1.069030+0.148437 
## [867]    train-rmse:0.745532+0.017917    test-rmse:1.068813+0.148437 
## [868]    train-rmse:0.745304+0.017920    test-rmse:1.068600+0.148319 
## [869]    train-rmse:0.745075+0.017923    test-rmse:1.068685+0.148409 
## [870]    train-rmse:0.744845+0.017924    test-rmse:1.068917+0.148235 
## [871]    train-rmse:0.744609+0.017924    test-rmse:1.068227+0.148248 
## [872]    train-rmse:0.744381+0.017926    test-rmse:1.068161+0.148408 
## [873]    train-rmse:0.744155+0.017932    test-rmse:1.068407+0.148355 
## [874]    train-rmse:0.743931+0.017933    test-rmse:1.068331+0.148195 
## [875]    train-rmse:0.743705+0.017932    test-rmse:1.067589+0.148101 
## [876]    train-rmse:0.743478+0.017935    test-rmse:1.067882+0.148189 
## [877]    train-rmse:0.743257+0.017937    test-rmse:1.067537+0.147678 
## [878]    train-rmse:0.743031+0.017938    test-rmse:1.067311+0.148035 
## [879]    train-rmse:0.742810+0.017940    test-rmse:1.066739+0.147646 
## [880]    train-rmse:0.742587+0.017938    test-rmse:1.066209+0.147699 
## [881]    train-rmse:0.742365+0.017939    test-rmse:1.066472+0.147811 
## [882]    train-rmse:0.742146+0.017939    test-rmse:1.066279+0.147890 
## [883]    train-rmse:0.741928+0.017939    test-rmse:1.066029+0.148054 
## [884]    train-rmse:0.741710+0.017937    test-rmse:1.066295+0.147681 
## [885]    train-rmse:0.741487+0.017933    test-rmse:1.066041+0.147763 
## [886]    train-rmse:0.741272+0.017934    test-rmse:1.065272+0.146621 
## [887]    train-rmse:0.741052+0.017934    test-rmse:1.065239+0.146741 
## [888]    train-rmse:0.740838+0.017934    test-rmse:1.065092+0.146628 
## [889]    train-rmse:0.740625+0.017934    test-rmse:1.064752+0.146320 
## [890]    train-rmse:0.740407+0.017936    test-rmse:1.064753+0.146624 
## [891]    train-rmse:0.740194+0.017935    test-rmse:1.064655+0.146815 
## [892]    train-rmse:0.739980+0.017936    test-rmse:1.064738+0.146291 
## [893]    train-rmse:0.739764+0.017934    test-rmse:1.064696+0.146380 
## [894]    train-rmse:0.739551+0.017934    test-rmse:1.064588+0.146260 
## [895]    train-rmse:0.739339+0.017933    test-rmse:1.064056+0.146447 
## [896]    train-rmse:0.739129+0.017932    test-rmse:1.063958+0.146411 
## [897]    train-rmse:0.738920+0.017932    test-rmse:1.064182+0.146537 
## [898]    train-rmse:0.738707+0.017926    test-rmse:1.063925+0.146915 
## [899]    train-rmse:0.738497+0.017929    test-rmse:1.063887+0.147000 
## [900]    train-rmse:0.738289+0.017927    test-rmse:1.063900+0.147008 
## [901]    train-rmse:0.738083+0.017925    test-rmse:1.063911+0.146602 
## [902]    train-rmse:0.737875+0.017924    test-rmse:1.063755+0.146669 
## [903]    train-rmse:0.737670+0.017920    test-rmse:1.063263+0.146434 
## [904]    train-rmse:0.737464+0.017918    test-rmse:1.063367+0.146555 
## [905]    train-rmse:0.737258+0.017916    test-rmse:1.062932+0.146053 
## [906]    train-rmse:0.737050+0.017916    test-rmse:1.063307+0.145573 
## [907]    train-rmse:0.736844+0.017914    test-rmse:1.063131+0.146052 
## [908]    train-rmse:0.736641+0.017911    test-rmse:1.062858+0.146034 
## [909]    train-rmse:0.736437+0.017910    test-rmse:1.063036+0.146387 
## [910]    train-rmse:0.736234+0.017908    test-rmse:1.063546+0.146186 
## [911]    train-rmse:0.736030+0.017903    test-rmse:1.063157+0.146542 
## [912]    train-rmse:0.735827+0.017900    test-rmse:1.062527+0.146775 
## [913]    train-rmse:0.735627+0.017900    test-rmse:1.062439+0.146587 
## [914]    train-rmse:0.735427+0.017898    test-rmse:1.062170+0.146898 
## [915]    train-rmse:0.735229+0.017898    test-rmse:1.062208+0.146912 
## [916]    train-rmse:0.735031+0.017897    test-rmse:1.062001+0.146809 
## [917]    train-rmse:0.734833+0.017895    test-rmse:1.061860+0.147259 
## [918]    train-rmse:0.734631+0.017893    test-rmse:1.062049+0.146843 
## [919]    train-rmse:0.734433+0.017889    test-rmse:1.061841+0.147219 
## [920]    train-rmse:0.734238+0.017888    test-rmse:1.061259+0.146890 
## [921]    train-rmse:0.734041+0.017890    test-rmse:1.061374+0.146006 
## [922]    train-rmse:0.733845+0.017885    test-rmse:1.061080+0.145893 
## [923]    train-rmse:0.733650+0.017881    test-rmse:1.060941+0.145829 
## [924]    train-rmse:0.733455+0.017879    test-rmse:1.060721+0.145976 
## [925]    train-rmse:0.733262+0.017876    test-rmse:1.060577+0.146166 
## [926]    train-rmse:0.733070+0.017873    test-rmse:1.060225+0.146092 
## [927]    train-rmse:0.732876+0.017868    test-rmse:1.059538+0.146654 
## [928]    train-rmse:0.732684+0.017864    test-rmse:1.058707+0.145490 
## [929]    train-rmse:0.732492+0.017859    test-rmse:1.059009+0.145525 
## [930]    train-rmse:0.732302+0.017855    test-rmse:1.059044+0.145674 
## [931]    train-rmse:0.732113+0.017850    test-rmse:1.059063+0.145554 
## [932]    train-rmse:0.731924+0.017847    test-rmse:1.059376+0.145418 
## [933]    train-rmse:0.731733+0.017845    test-rmse:1.059018+0.145856 
## [934]    train-rmse:0.731545+0.017839    test-rmse:1.058967+0.145881 
## Stopping. Best iteration:
## [928]    train-rmse:0.732684+0.017864    test-rmse:1.058707+0.145490
## 
## 1 
## [1]  train-rmse:96.903592+0.106998   test-rmse:96.903929+0.480417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:87.282645+0.098508   test-rmse:87.282849+0.494309 
## [3]  train-rmse:78.624342+0.091095   test-rmse:78.624364+0.507694 
## [4]  train-rmse:70.833145+0.084703   test-rmse:70.832979+0.520704 
## [5]  train-rmse:63.823124+0.079254   test-rmse:63.822745+0.533458 
## [6]  train-rmse:57.516975+0.074698   test-rmse:57.516330+0.546089 
## [7]  train-rmse:51.845108+0.070990   test-rmse:51.844165+0.558710 
## [8]  train-rmse:46.744962+0.068087   test-rmse:46.743658+0.571428 
## [9]  train-rmse:42.160251+0.065957   test-rmse:42.158527+0.584358 
## [10] train-rmse:38.040358+0.064573   test-rmse:38.038154+0.597586 
## [11] train-rmse:34.337210+0.063071   test-rmse:34.350626+0.598556 
## [12] train-rmse:31.005955+0.060200   test-rmse:31.042059+0.599867 
## [13] train-rmse:28.009255+0.055404   test-rmse:28.072289+0.607604 
## [14] train-rmse:25.314196+0.049445   test-rmse:25.390021+0.600070 
## [15] train-rmse:22.890372+0.044958   test-rmse:22.965470+0.576291 
## [16] train-rmse:20.711772+0.039023   test-rmse:20.796414+0.575351 
## [17] train-rmse:18.753540+0.035875   test-rmse:18.835082+0.556648 
## [18] train-rmse:16.994682+0.033591   test-rmse:17.089226+0.560594 
## [19] train-rmse:15.416491+0.032215   test-rmse:15.503699+0.550490 
## [20] train-rmse:14.001346+0.031400   test-rmse:14.086849+0.552402 
## [21] train-rmse:12.732217+0.031486   test-rmse:12.836036+0.558618 
## [22] train-rmse:11.596165+0.031514   test-rmse:11.708262+0.574927 
## [23] train-rmse:10.580631+0.032645   test-rmse:10.674199+0.554746 
## [24] train-rmse:9.673749+0.033271    test-rmse:9.782996+0.560681 
## [25] train-rmse:8.864457+0.034644    test-rmse:8.985166+0.566239 
## [26] train-rmse:8.145084+0.036482    test-rmse:8.256056+0.552985 
## [27] train-rmse:7.505425+0.038456    test-rmse:7.622562+0.554320 
## [28] train-rmse:6.938516+0.039990    test-rmse:7.071684+0.551833 
## [29] train-rmse:6.436337+0.041666    test-rmse:6.584945+0.564107 
## [30] train-rmse:5.992657+0.043146    test-rmse:6.132801+0.543434 
## [31] train-rmse:5.599359+0.041883    test-rmse:5.746200+0.544379 
## [32] train-rmse:5.255394+0.043138    test-rmse:5.434934+0.545927 
## [33] train-rmse:4.952521+0.044036    test-rmse:5.128967+0.525312 
## [34] train-rmse:4.687117+0.044381    test-rmse:4.875904+0.526018 
## [35] train-rmse:4.454935+0.045175    test-rmse:4.646489+0.508077 
## [36] train-rmse:4.251331+0.045322    test-rmse:4.449914+0.497761 
## [37] train-rmse:4.073334+0.046035    test-rmse:4.280028+0.487697 
## [38] train-rmse:3.917132+0.045833    test-rmse:4.131685+0.471516 
## [39] train-rmse:3.780072+0.045524    test-rmse:4.004770+0.472105 
## [40] train-rmse:3.657792+0.044667    test-rmse:3.896157+0.448417 
## [41] train-rmse:3.550741+0.044948    test-rmse:3.800533+0.439780 
## [42] train-rmse:3.456389+0.044801    test-rmse:3.699295+0.424059 
## [43] train-rmse:3.372716+0.044682    test-rmse:3.631084+0.420264 
## [44] train-rmse:3.297363+0.043517    test-rmse:3.547196+0.389610 
## [45] train-rmse:3.230409+0.042676    test-rmse:3.493195+0.386518 
## [46] train-rmse:3.170498+0.042351    test-rmse:3.440386+0.380181 
## [47] train-rmse:3.114505+0.041358    test-rmse:3.385934+0.389243 
## [48] train-rmse:3.063113+0.042380    test-rmse:3.349741+0.392263 
## [49] train-rmse:3.013688+0.039554    test-rmse:3.297581+0.374530 
## [50] train-rmse:2.970185+0.038830    test-rmse:3.260338+0.363578 
## [51] train-rmse:2.928829+0.038117    test-rmse:3.218999+0.349595 
## [52] train-rmse:2.891077+0.038308    test-rmse:3.189837+0.348906 
## [53] train-rmse:2.855265+0.037460    test-rmse:3.159027+0.346956 
## [54] train-rmse:2.821757+0.036800    test-rmse:3.126200+0.340613 
## [55] train-rmse:2.790148+0.036493    test-rmse:3.095666+0.347363 
## [56] train-rmse:2.758153+0.036958    test-rmse:3.075680+0.346777 
## [57] train-rmse:2.728014+0.035848    test-rmse:3.053263+0.342571 
## [58] train-rmse:2.698781+0.034103    test-rmse:3.023863+0.327888 
## [59] train-rmse:2.671520+0.033896    test-rmse:3.001965+0.323873 
## [60] train-rmse:2.644417+0.033326    test-rmse:2.978071+0.330616 
## [61] train-rmse:2.617995+0.032143    test-rmse:2.953800+0.321751 
## [62] train-rmse:2.592088+0.030681    test-rmse:2.936543+0.321686 
## [63] train-rmse:2.567522+0.030192    test-rmse:2.917478+0.323878 
## [64] train-rmse:2.543110+0.029797    test-rmse:2.899063+0.322537 
## [65] train-rmse:2.518494+0.028454    test-rmse:2.871226+0.316812 
## [66] train-rmse:2.495353+0.027529    test-rmse:2.851102+0.309918 
## [67] train-rmse:2.473253+0.027109    test-rmse:2.828504+0.300830 
## [68] train-rmse:2.450428+0.026959    test-rmse:2.812867+0.306010 
## [69] train-rmse:2.428450+0.025834    test-rmse:2.796345+0.311279 
## [70] train-rmse:2.405915+0.025244    test-rmse:2.777141+0.311361 
## [71] train-rmse:2.384275+0.024448    test-rmse:2.759391+0.307141 
## [72] train-rmse:2.363316+0.023832    test-rmse:2.738882+0.311051 
## [73] train-rmse:2.343199+0.023461    test-rmse:2.724545+0.312446 
## [74] train-rmse:2.323428+0.022953    test-rmse:2.704173+0.301245 
## [75] train-rmse:2.303517+0.022460    test-rmse:2.691909+0.297497 
## [76] train-rmse:2.284273+0.022127    test-rmse:2.666253+0.293056 
## [77] train-rmse:2.264605+0.021559    test-rmse:2.646537+0.296157 
## [78] train-rmse:2.244519+0.021168    test-rmse:2.634618+0.294553 
## [79] train-rmse:2.225779+0.020927    test-rmse:2.617140+0.296813 
## [80] train-rmse:2.207266+0.020979    test-rmse:2.602281+0.289520 
## [81] train-rmse:2.188771+0.020991    test-rmse:2.583908+0.287384 
## [82] train-rmse:2.170850+0.020502    test-rmse:2.560650+0.293416 
## [83] train-rmse:2.153246+0.020045    test-rmse:2.543946+0.284298 
## [84] train-rmse:2.136200+0.019745    test-rmse:2.524607+0.282740 
## [85] train-rmse:2.118832+0.019326    test-rmse:2.511680+0.282851 
## [86] train-rmse:2.102110+0.019008    test-rmse:2.498890+0.282130 
## [87] train-rmse:2.085137+0.018581    test-rmse:2.487189+0.284486 
## [88] train-rmse:2.067816+0.019155    test-rmse:2.474048+0.285322 
## [89] train-rmse:2.051373+0.018733    test-rmse:2.462911+0.283801 
## [90] train-rmse:2.034893+0.018621    test-rmse:2.443560+0.281345 
## [91] train-rmse:2.018908+0.018593    test-rmse:2.427170+0.279420 
## [92] train-rmse:2.002918+0.018043    test-rmse:2.411539+0.274876 
## [93] train-rmse:1.987355+0.017915    test-rmse:2.397809+0.279991 
## [94] train-rmse:1.971693+0.017131    test-rmse:2.386819+0.278944 
## [95] train-rmse:1.956479+0.016861    test-rmse:2.370896+0.283631 
## [96] train-rmse:1.941229+0.016119    test-rmse:2.354609+0.276198 
## [97] train-rmse:1.925771+0.016264    test-rmse:2.341434+0.273060 
## [98] train-rmse:1.911131+0.016125    test-rmse:2.326584+0.272796 
## [99] train-rmse:1.896270+0.015789    test-rmse:2.322678+0.271345 
## [100]    train-rmse:1.882030+0.015644    test-rmse:2.303847+0.271833 
## [101]    train-rmse:1.866661+0.015573    test-rmse:2.293366+0.273826 
## [102]    train-rmse:1.852421+0.015209    test-rmse:2.283741+0.272162 
## [103]    train-rmse:1.838406+0.014976    test-rmse:2.273086+0.273254 
## [104]    train-rmse:1.824779+0.014728    test-rmse:2.261181+0.276057 
## [105]    train-rmse:1.811187+0.014586    test-rmse:2.245065+0.275264 
## [106]    train-rmse:1.797672+0.014384    test-rmse:2.229132+0.272661 
## [107]    train-rmse:1.784318+0.014305    test-rmse:2.215784+0.275457 
## [108]    train-rmse:1.771001+0.014037    test-rmse:2.201090+0.268053 
## [109]    train-rmse:1.757999+0.013770    test-rmse:2.190097+0.268286 
## [110]    train-rmse:1.745126+0.013473    test-rmse:2.173695+0.262031 
## [111]    train-rmse:1.732274+0.012858    test-rmse:2.165490+0.262344 
## [112]    train-rmse:1.719885+0.012775    test-rmse:2.152237+0.264736 
## [113]    train-rmse:1.707483+0.012655    test-rmse:2.143244+0.264831 
## [114]    train-rmse:1.694282+0.011628    test-rmse:2.134399+0.270257 
## [115]    train-rmse:1.681553+0.011673    test-rmse:2.117653+0.268367 
## [116]    train-rmse:1.669361+0.011377    test-rmse:2.106793+0.266011 
## [117]    train-rmse:1.657426+0.011349    test-rmse:2.100528+0.264376 
## [118]    train-rmse:1.645802+0.011176    test-rmse:2.097308+0.264801 
## [119]    train-rmse:1.633558+0.011105    test-rmse:2.086915+0.258842 
## [120]    train-rmse:1.621967+0.010572    test-rmse:2.074351+0.260120 
## [121]    train-rmse:1.610235+0.010282    test-rmse:2.062573+0.264257 
## [122]    train-rmse:1.599007+0.010117    test-rmse:2.051115+0.263751 
## [123]    train-rmse:1.587746+0.009740    test-rmse:2.041220+0.267092 
## [124]    train-rmse:1.576520+0.009836    test-rmse:2.029643+0.261723 
## [125]    train-rmse:1.564994+0.010080    test-rmse:2.017223+0.262869 
## [126]    train-rmse:1.553977+0.009799    test-rmse:2.010119+0.264941 
## [127]    train-rmse:1.543246+0.009873    test-rmse:1.997727+0.255952 
## [128]    train-rmse:1.532340+0.009181    test-rmse:1.992044+0.259457 
## [129]    train-rmse:1.521974+0.009184    test-rmse:1.986086+0.261415 
## [130]    train-rmse:1.511460+0.009134    test-rmse:1.974616+0.258318 
## [131]    train-rmse:1.499958+0.010429    test-rmse:1.965112+0.258271 
## [132]    train-rmse:1.489294+0.009940    test-rmse:1.950557+0.257224 
## [133]    train-rmse:1.478877+0.010248    test-rmse:1.944028+0.257350 
## [134]    train-rmse:1.469115+0.010254    test-rmse:1.936921+0.258736 
## [135]    train-rmse:1.459153+0.010669    test-rmse:1.928157+0.253796 
## [136]    train-rmse:1.449372+0.010812    test-rmse:1.922667+0.256647 
## [137]    train-rmse:1.439380+0.010697    test-rmse:1.913348+0.257972 
## [138]    train-rmse:1.429580+0.010438    test-rmse:1.905724+0.262494 
## [139]    train-rmse:1.420136+0.010577    test-rmse:1.898883+0.260725 
## [140]    train-rmse:1.410754+0.010626    test-rmse:1.892410+0.259282 
## [141]    train-rmse:1.401603+0.010637    test-rmse:1.886125+0.258770 
## [142]    train-rmse:1.392042+0.010637    test-rmse:1.875085+0.258349 
## [143]    train-rmse:1.382722+0.010169    test-rmse:1.866551+0.257013 
## [144]    train-rmse:1.373546+0.009995    test-rmse:1.855902+0.256088 
## [145]    train-rmse:1.364765+0.010062    test-rmse:1.847603+0.253063 
## [146]    train-rmse:1.356021+0.010124    test-rmse:1.839949+0.253654 
## [147]    train-rmse:1.347286+0.010141    test-rmse:1.830954+0.256962 
## [148]    train-rmse:1.338449+0.010448    test-rmse:1.825051+0.257063 
## [149]    train-rmse:1.329887+0.010565    test-rmse:1.817862+0.254655 
## [150]    train-rmse:1.321360+0.010504    test-rmse:1.811424+0.255010 
## [151]    train-rmse:1.313152+0.010431    test-rmse:1.804433+0.257253 
## [152]    train-rmse:1.304677+0.010396    test-rmse:1.798590+0.257170 
## [153]    train-rmse:1.296459+0.010547    test-rmse:1.789004+0.255523 
## [154]    train-rmse:1.287998+0.010938    test-rmse:1.778922+0.249423 
## [155]    train-rmse:1.279989+0.010929    test-rmse:1.770021+0.251782 
## [156]    train-rmse:1.272011+0.011308    test-rmse:1.766465+0.250348 
## [157]    train-rmse:1.264098+0.011379    test-rmse:1.758351+0.249977 
## [158]    train-rmse:1.256025+0.011442    test-rmse:1.752294+0.251360 
## [159]    train-rmse:1.248079+0.011745    test-rmse:1.744829+0.253363 
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## [164]    train-rmse:1.209765+0.012763    test-rmse:1.713070+0.245930 
## [165]    train-rmse:1.202536+0.012887    test-rmse:1.707224+0.248306 
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## [167]    train-rmse:1.187972+0.013306    test-rmse:1.692567+0.249034 
## [168]    train-rmse:1.181007+0.013258    test-rmse:1.685654+0.249949 
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## [174]    train-rmse:1.138456+0.013921    test-rmse:1.654854+0.245190 
## [175]    train-rmse:1.131560+0.013556    test-rmse:1.650953+0.247440 
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## [177]    train-rmse:1.118289+0.013974    test-rmse:1.637793+0.248945 
## [178]    train-rmse:1.111607+0.014085    test-rmse:1.631629+0.248747 
## [179]    train-rmse:1.105058+0.014161    test-rmse:1.628482+0.248615 
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## [182]    train-rmse:1.086267+0.014294    test-rmse:1.614131+0.248479 
## [183]    train-rmse:1.080026+0.014233    test-rmse:1.609011+0.248844 
## [184]    train-rmse:1.073910+0.014375    test-rmse:1.604997+0.249606 
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## [186]    train-rmse:1.061329+0.014074    test-rmse:1.590486+0.248044 
## [187]    train-rmse:1.054967+0.014343    test-rmse:1.586990+0.248522 
## [188]    train-rmse:1.048953+0.014664    test-rmse:1.580947+0.248550 
## [189]    train-rmse:1.043242+0.014774    test-rmse:1.574277+0.249775 
## [190]    train-rmse:1.037193+0.014803    test-rmse:1.570348+0.249188 
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## [192]    train-rmse:1.025318+0.015143    test-rmse:1.558356+0.246565 
## [193]    train-rmse:1.019769+0.015438    test-rmse:1.554245+0.247946 
## [194]    train-rmse:1.014122+0.015223    test-rmse:1.549808+0.245872 
## [195]    train-rmse:1.008896+0.015300    test-rmse:1.545958+0.245863 
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## [197]    train-rmse:0.998241+0.015450    test-rmse:1.535582+0.247362 
## [198]    train-rmse:0.993023+0.015452    test-rmse:1.530976+0.247063 
## [199]    train-rmse:0.987666+0.015351    test-rmse:1.525121+0.248376 
## [200]    train-rmse:0.982421+0.015442    test-rmse:1.521286+0.248996 
## [201]    train-rmse:0.977086+0.015398    test-rmse:1.518621+0.250807 
## [202]    train-rmse:0.971992+0.015370    test-rmse:1.515171+0.249578 
## [203]    train-rmse:0.966777+0.015301    test-rmse:1.512957+0.249738 
## [204]    train-rmse:0.961841+0.015391    test-rmse:1.510956+0.249441 
## [205]    train-rmse:0.956748+0.015279    test-rmse:1.506553+0.249425 
## [206]    train-rmse:0.951763+0.015427    test-rmse:1.502455+0.248339 
## [207]    train-rmse:0.946692+0.015585    test-rmse:1.496976+0.249949 
## [208]    train-rmse:0.941464+0.015850    test-rmse:1.492329+0.250450 
## [209]    train-rmse:0.936179+0.015772    test-rmse:1.489017+0.249720 
## [210]    train-rmse:0.931476+0.015921    test-rmse:1.483649+0.252417 
## [211]    train-rmse:0.926850+0.016001    test-rmse:1.478952+0.252127 
## [212]    train-rmse:0.922269+0.016128    test-rmse:1.477074+0.253534 
## [213]    train-rmse:0.917737+0.015946    test-rmse:1.474035+0.254733 
## [214]    train-rmse:0.913057+0.015917    test-rmse:1.470684+0.254138 
## [215]    train-rmse:0.908522+0.016060    test-rmse:1.466791+0.254726 
## [216]    train-rmse:0.903878+0.016455    test-rmse:1.460176+0.255378 
## [217]    train-rmse:0.899412+0.016584    test-rmse:1.456761+0.252774 
## [218]    train-rmse:0.895215+0.016548    test-rmse:1.452752+0.249776 
## [219]    train-rmse:0.890947+0.016412    test-rmse:1.451746+0.250495 
## [220]    train-rmse:0.886517+0.016562    test-rmse:1.449682+0.250473 
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## [643]    train-rmse:0.328002+0.017485    test-rmse:1.077753+0.268139 
## [644]    train-rmse:0.327547+0.017488    test-rmse:1.077969+0.267826 
## [645]    train-rmse:0.327289+0.017481    test-rmse:1.078035+0.267593 
## [646]    train-rmse:0.326650+0.017204    test-rmse:1.077873+0.267375 
## [647]    train-rmse:0.326020+0.016954    test-rmse:1.077919+0.267336 
## [648]    train-rmse:0.325546+0.016721    test-rmse:1.077507+0.267324 
## [649]    train-rmse:0.324778+0.017014    test-rmse:1.077295+0.266913 
## [650]    train-rmse:0.324275+0.016955    test-rmse:1.077411+0.266617 
## [651]    train-rmse:0.323691+0.016915    test-rmse:1.077273+0.266987 
## [652]    train-rmse:0.323154+0.017039    test-rmse:1.077130+0.266705 
## [653]    train-rmse:0.322952+0.017031    test-rmse:1.076783+0.266562 
## [654]    train-rmse:0.322660+0.017068    test-rmse:1.076765+0.266461 
## [655]    train-rmse:0.322116+0.017052    test-rmse:1.076904+0.266285 
## [656]    train-rmse:0.321722+0.017321    test-rmse:1.076750+0.266311 
## [657]    train-rmse:0.321298+0.017515    test-rmse:1.076321+0.266148 
## [658]    train-rmse:0.321026+0.017514    test-rmse:1.075979+0.265757 
## [659]    train-rmse:0.320593+0.017405    test-rmse:1.075891+0.265798 
## [660]    train-rmse:0.320282+0.017389    test-rmse:1.075844+0.265781 
## [661]    train-rmse:0.319980+0.017404    test-rmse:1.075851+0.265723 
## [662]    train-rmse:0.319469+0.017526    test-rmse:1.075624+0.265572 
## [663]    train-rmse:0.319142+0.017488    test-rmse:1.075622+0.265610 
## [664]    train-rmse:0.318708+0.017540    test-rmse:1.075658+0.265558 
## [665]    train-rmse:0.318335+0.017525    test-rmse:1.075623+0.265620 
## [666]    train-rmse:0.317887+0.017696    test-rmse:1.075797+0.265792 
## [667]    train-rmse:0.317352+0.017503    test-rmse:1.075588+0.265818 
## [668]    train-rmse:0.317124+0.017503    test-rmse:1.075174+0.265345 
## [669]    train-rmse:0.316648+0.017369    test-rmse:1.074586+0.265301 
## [670]    train-rmse:0.316190+0.017725    test-rmse:1.074530+0.265274 
## [671]    train-rmse:0.315724+0.018084    test-rmse:1.074866+0.265199 
## [672]    train-rmse:0.315354+0.018092    test-rmse:1.074840+0.265402 
## [673]    train-rmse:0.315009+0.018248    test-rmse:1.074743+0.265557 
## [674]    train-rmse:0.314421+0.018304    test-rmse:1.074420+0.265624 
## [675]    train-rmse:0.314097+0.018330    test-rmse:1.074088+0.265574 
## [676]    train-rmse:0.313690+0.018252    test-rmse:1.074021+0.265546 
## [677]    train-rmse:0.313251+0.018310    test-rmse:1.074079+0.265991 
## [678]    train-rmse:0.312822+0.018305    test-rmse:1.074129+0.265960 
## [679]    train-rmse:0.312329+0.018092    test-rmse:1.074074+0.266029 
## [680]    train-rmse:0.312067+0.018111    test-rmse:1.073782+0.265876 
## [681]    train-rmse:0.311700+0.018028    test-rmse:1.073589+0.265678 
## [682]    train-rmse:0.311167+0.017955    test-rmse:1.073932+0.266052 
## [683]    train-rmse:0.310924+0.017913    test-rmse:1.073894+0.265841 
## [684]    train-rmse:0.310462+0.017818    test-rmse:1.073656+0.265944 
## [685]    train-rmse:0.310208+0.017815    test-rmse:1.073500+0.265717 
## [686]    train-rmse:0.309861+0.018006    test-rmse:1.073639+0.265661 
## [687]    train-rmse:0.309509+0.018167    test-rmse:1.073401+0.265451 
## [688]    train-rmse:0.309308+0.018185    test-rmse:1.072958+0.265633 
## [689]    train-rmse:0.308843+0.018121    test-rmse:1.073046+0.265626 
## [690]    train-rmse:0.308415+0.018135    test-rmse:1.073127+0.265585 
## [691]    train-rmse:0.308069+0.018302    test-rmse:1.072559+0.266115 
## [692]    train-rmse:0.307676+0.018239    test-rmse:1.072363+0.266122 
## [693]    train-rmse:0.307383+0.018293    test-rmse:1.072479+0.266343 
## [694]    train-rmse:0.306795+0.018175    test-rmse:1.072501+0.266089 
## [695]    train-rmse:0.306522+0.018211    test-rmse:1.072430+0.265855 
## [696]    train-rmse:0.306152+0.018512    test-rmse:1.072227+0.265854 
## [697]    train-rmse:0.305800+0.018521    test-rmse:1.072215+0.265947 
## [698]    train-rmse:0.305441+0.018467    test-rmse:1.072328+0.265907 
## [699]    train-rmse:0.305084+0.018405    test-rmse:1.072009+0.266148 
## [700]    train-rmse:0.304769+0.018319    test-rmse:1.072021+0.266331 
## [701]    train-rmse:0.304539+0.018352    test-rmse:1.071799+0.266048 
## [702]    train-rmse:0.304246+0.018362    test-rmse:1.071001+0.266471 
## [703]    train-rmse:0.303809+0.018277    test-rmse:1.071281+0.266263 
## [704]    train-rmse:0.303327+0.018072    test-rmse:1.071301+0.266155 
## [705]    train-rmse:0.302652+0.017937    test-rmse:1.071296+0.266141 
## [706]    train-rmse:0.302326+0.018116    test-rmse:1.071194+0.266042 
## [707]    train-rmse:0.301862+0.018128    test-rmse:1.071414+0.266470 
## [708]    train-rmse:0.301362+0.018118    test-rmse:1.071251+0.266249 
## Stopping. Best iteration:
## [702]    train-rmse:0.304246+0.018362    test-rmse:1.071001+0.266471
## 
## 2 
## [1]  train-rmse:96.903592+0.106998   test-rmse:96.903929+0.480417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:87.282645+0.098508   test-rmse:87.282849+0.494309 
## [3]  train-rmse:78.624342+0.091095   test-rmse:78.624364+0.507694 
## [4]  train-rmse:70.833145+0.084703   test-rmse:70.832979+0.520704 
## [5]  train-rmse:63.823124+0.079254   test-rmse:63.822745+0.533458 
## [6]  train-rmse:57.516975+0.074698   test-rmse:57.516330+0.546089 
## [7]  train-rmse:51.845108+0.070990   test-rmse:51.844165+0.558710 
## [8]  train-rmse:46.744962+0.068087   test-rmse:46.743658+0.571428 
## [9]  train-rmse:42.160251+0.065957   test-rmse:42.158527+0.584358 
## [10] train-rmse:38.040358+0.064573   test-rmse:38.038154+0.597586 
## [11] train-rmse:34.337210+0.063071   test-rmse:34.350626+0.598556 
## [12] train-rmse:31.005821+0.060048   test-rmse:31.044042+0.597332 
## [13] train-rmse:28.008132+0.054748   test-rmse:28.071744+0.610449 
## [14] train-rmse:25.311016+0.048086   test-rmse:25.390260+0.601052 
## [15] train-rmse:22.884224+0.042327   test-rmse:22.971323+0.589327 
## [16] train-rmse:20.701371+0.037871   test-rmse:20.785068+0.568733 
## [17] train-rmse:18.738375+0.033563   test-rmse:18.835100+0.571964 
## [18] train-rmse:16.971872+0.031783   test-rmse:17.066980+0.561134 
## [19] train-rmse:15.384260+0.030548   test-rmse:15.478114+0.557996 
## [20] train-rmse:13.957898+0.029638   test-rmse:14.042649+0.557835 
## [21] train-rmse:12.677213+0.028364   test-rmse:12.778338+0.562113 
## [22] train-rmse:11.527256+0.028784   test-rmse:11.633744+0.553770 
## [23] train-rmse:10.495689+0.029370   test-rmse:10.612807+0.562064 
## [24] train-rmse:9.572374+0.029971    test-rmse:9.695868+0.550398 
## [25] train-rmse:8.743929+0.028937    test-rmse:8.849699+0.540415 
## [26] train-rmse:8.004857+0.029804    test-rmse:8.130794+0.543777 
## [27] train-rmse:7.345813+0.030585    test-rmse:7.482567+0.533334 
## [28] train-rmse:6.755814+0.032033    test-rmse:6.893641+0.518299 
## [29] train-rmse:6.229023+0.029540    test-rmse:6.384279+0.507222 
## [30] train-rmse:5.759412+0.031546    test-rmse:5.922803+0.509892 
## [31] train-rmse:5.344315+0.032235    test-rmse:5.514337+0.505877 
## [32] train-rmse:4.975162+0.031747    test-rmse:5.165829+0.510527 
## [33] train-rmse:4.650118+0.032892    test-rmse:4.838814+0.492203 
## [34] train-rmse:4.363351+0.034567    test-rmse:4.561770+0.484460 
## [35] train-rmse:4.108070+0.033875    test-rmse:4.315639+0.473393 
## [36] train-rmse:3.882311+0.033429    test-rmse:4.101073+0.452401 
## [37] train-rmse:3.685141+0.033598    test-rmse:3.911038+0.448415 
## [38] train-rmse:3.508515+0.033490    test-rmse:3.747826+0.433565 
## [39] train-rmse:3.350417+0.030687    test-rmse:3.608910+0.423742 
## [40] train-rmse:3.213553+0.031283    test-rmse:3.494155+0.407783 
## [41] train-rmse:3.091098+0.030877    test-rmse:3.389190+0.401497 
## [42] train-rmse:2.979849+0.026216    test-rmse:3.275588+0.381273 
## [43] train-rmse:2.884033+0.026533    test-rmse:3.194915+0.383231 
## [44] train-rmse:2.798796+0.026348    test-rmse:3.116867+0.377812 
## [45] train-rmse:2.719700+0.026421    test-rmse:3.043164+0.351006 
## [46] train-rmse:2.649731+0.024950    test-rmse:2.991760+0.342303 
## [47] train-rmse:2.586577+0.025698    test-rmse:2.934555+0.339942 
## [48] train-rmse:2.526526+0.025498    test-rmse:2.884629+0.343305 
## [49] train-rmse:2.472533+0.024096    test-rmse:2.829507+0.319778 
## [50] train-rmse:2.424282+0.024094    test-rmse:2.793675+0.312961 
## [51] train-rmse:2.375536+0.025040    test-rmse:2.758217+0.303034 
## [52] train-rmse:2.333454+0.025205    test-rmse:2.725695+0.295124 
## [53] train-rmse:2.292683+0.024996    test-rmse:2.693239+0.307182 
## [54] train-rmse:2.255788+0.024462    test-rmse:2.666601+0.308425 
## [55] train-rmse:2.219617+0.024176    test-rmse:2.641296+0.306462 
## [56] train-rmse:2.182849+0.025536    test-rmse:2.612987+0.298060 
## [57] train-rmse:2.150143+0.024507    test-rmse:2.588918+0.289460 
## [58] train-rmse:2.116657+0.025669    test-rmse:2.561536+0.281016 
## [59] train-rmse:2.087312+0.025129    test-rmse:2.535829+0.277106 
## [60] train-rmse:2.058541+0.024382    test-rmse:2.507661+0.273682 
## [61] train-rmse:2.029610+0.024237    test-rmse:2.479990+0.268914 
## [62] train-rmse:2.001742+0.024147    test-rmse:2.460402+0.266293 
## [63] train-rmse:1.975201+0.023934    test-rmse:2.440250+0.258861 
## [64] train-rmse:1.949534+0.023198    test-rmse:2.417282+0.260614 
## [65] train-rmse:1.924036+0.022079    test-rmse:2.387024+0.255067 
## [66] train-rmse:1.899453+0.021873    test-rmse:2.361898+0.256676 
## [67] train-rmse:1.875271+0.021109    test-rmse:2.345415+0.257878 
## [68] train-rmse:1.851507+0.020796    test-rmse:2.327594+0.250987 
## [69] train-rmse:1.829417+0.020409    test-rmse:2.309421+0.245577 
## [70] train-rmse:1.806714+0.019564    test-rmse:2.290039+0.246706 
## [71] train-rmse:1.784406+0.019613    test-rmse:2.270980+0.254781 
## [72] train-rmse:1.763078+0.019050    test-rmse:2.257684+0.247921 
## [73] train-rmse:1.742121+0.018421    test-rmse:2.236868+0.246796 
## [74] train-rmse:1.721717+0.017802    test-rmse:2.217871+0.243398 
## [75] train-rmse:1.700607+0.017264    test-rmse:2.204715+0.249051 
## [76] train-rmse:1.680621+0.018057    test-rmse:2.184071+0.246844 
## [77] train-rmse:1.661626+0.017733    test-rmse:2.170901+0.250446 
## [78] train-rmse:1.642222+0.017159    test-rmse:2.150408+0.242492 
## [79] train-rmse:1.623694+0.016990    test-rmse:2.137242+0.245361 
## [80] train-rmse:1.604965+0.016349    test-rmse:2.124931+0.245435 
## [81] train-rmse:1.587128+0.016321    test-rmse:2.104764+0.244285 
## [82] train-rmse:1.569691+0.015807    test-rmse:2.085683+0.244183 
## [83] train-rmse:1.551302+0.014431    test-rmse:2.070711+0.245473 
## [84] train-rmse:1.533813+0.014606    test-rmse:2.058394+0.249921 
## [85] train-rmse:1.516013+0.014103    test-rmse:2.043800+0.247432 
## [86] train-rmse:1.496976+0.016374    test-rmse:2.033882+0.244091 
## [87] train-rmse:1.479359+0.014947    test-rmse:2.018201+0.244769 
## [88] train-rmse:1.463447+0.014570    test-rmse:2.007119+0.245113 
## [89] train-rmse:1.447509+0.014517    test-rmse:1.989501+0.246643 
## [90] train-rmse:1.432259+0.014339    test-rmse:1.977473+0.248920 
## [91] train-rmse:1.415645+0.014313    test-rmse:1.963090+0.246674 
## [92] train-rmse:1.399829+0.015237    test-rmse:1.953849+0.245409 
## [93] train-rmse:1.385246+0.015082    test-rmse:1.941619+0.242928 
## [94] train-rmse:1.369812+0.013210    test-rmse:1.927496+0.243206 
## [95] train-rmse:1.355254+0.012970    test-rmse:1.918435+0.242440 
## [96] train-rmse:1.340821+0.012438    test-rmse:1.906441+0.246024 
## [97] train-rmse:1.326442+0.011994    test-rmse:1.896638+0.251337 
## [98] train-rmse:1.313196+0.011759    test-rmse:1.881718+0.250331 
## [99] train-rmse:1.299696+0.011019    test-rmse:1.869127+0.246965 
## [100]    train-rmse:1.286556+0.010647    test-rmse:1.855851+0.240834 
## [101]    train-rmse:1.273213+0.010424    test-rmse:1.848254+0.243112 
## [102]    train-rmse:1.257594+0.012365    test-rmse:1.835447+0.242621 
## [103]    train-rmse:1.244479+0.012499    test-rmse:1.826766+0.242272 
## [104]    train-rmse:1.231748+0.011902    test-rmse:1.819227+0.247286 
## [105]    train-rmse:1.219535+0.011800    test-rmse:1.810168+0.244075 
## [106]    train-rmse:1.206731+0.012440    test-rmse:1.798558+0.246603 
## [107]    train-rmse:1.194615+0.012212    test-rmse:1.784182+0.248236 
## [108]    train-rmse:1.183190+0.012268    test-rmse:1.771883+0.249021 
## [109]    train-rmse:1.171204+0.011913    test-rmse:1.764920+0.247194 
## [110]    train-rmse:1.158337+0.012072    test-rmse:1.756620+0.247600 
## [111]    train-rmse:1.146330+0.011554    test-rmse:1.745850+0.248103 
## [112]    train-rmse:1.135189+0.011512    test-rmse:1.739679+0.249819 
## [113]    train-rmse:1.123415+0.010648    test-rmse:1.730883+0.249553 
## [114]    train-rmse:1.111460+0.010976    test-rmse:1.724260+0.250055 
## [115]    train-rmse:1.100974+0.010887    test-rmse:1.711868+0.249483 
## [116]    train-rmse:1.090631+0.010725    test-rmse:1.702592+0.251122 
## [117]    train-rmse:1.080183+0.010524    test-rmse:1.692196+0.250705 
## [118]    train-rmse:1.069826+0.010185    test-rmse:1.686030+0.253367 
## [119]    train-rmse:1.059226+0.009815    test-rmse:1.681119+0.255662 
## [120]    train-rmse:1.049402+0.009678    test-rmse:1.673920+0.256199 
## [121]    train-rmse:1.039158+0.009515    test-rmse:1.667199+0.256811 
## [122]    train-rmse:1.029656+0.009296    test-rmse:1.658630+0.257627 
## [123]    train-rmse:1.019512+0.009914    test-rmse:1.652224+0.258241 
## [124]    train-rmse:1.010347+0.009657    test-rmse:1.645830+0.258448 
## [125]    train-rmse:1.001202+0.009809    test-rmse:1.636329+0.261243 
## [126]    train-rmse:0.991644+0.009447    test-rmse:1.629005+0.259847 
## [127]    train-rmse:0.982690+0.009474    test-rmse:1.619063+0.261220 
## [128]    train-rmse:0.973060+0.009289    test-rmse:1.610814+0.259828 
## [129]    train-rmse:0.964136+0.008670    test-rmse:1.602560+0.262765 
## [130]    train-rmse:0.954598+0.007785    test-rmse:1.594702+0.265521 
## [131]    train-rmse:0.945696+0.008386    test-rmse:1.589669+0.266315 
## [132]    train-rmse:0.937389+0.008377    test-rmse:1.583448+0.267336 
## [133]    train-rmse:0.929256+0.008366    test-rmse:1.575776+0.268741 
## [134]    train-rmse:0.919870+0.009865    test-rmse:1.572914+0.268754 
## [135]    train-rmse:0.912052+0.009729    test-rmse:1.567937+0.271249 
## [136]    train-rmse:0.903756+0.010363    test-rmse:1.560852+0.272053 
## [137]    train-rmse:0.895723+0.010372    test-rmse:1.556918+0.270041 
## [138]    train-rmse:0.887716+0.010295    test-rmse:1.550990+0.270602 
## [139]    train-rmse:0.879189+0.011057    test-rmse:1.544469+0.270900 
## [140]    train-rmse:0.871843+0.010864    test-rmse:1.538197+0.271677 
## [141]    train-rmse:0.864804+0.010782    test-rmse:1.531964+0.273067 
## [142]    train-rmse:0.856788+0.010941    test-rmse:1.523477+0.273453 
## [143]    train-rmse:0.849180+0.011793    test-rmse:1.517657+0.273658 
## [144]    train-rmse:0.841112+0.011202    test-rmse:1.513010+0.273499 
## [145]    train-rmse:0.833697+0.010775    test-rmse:1.510299+0.275377 
## [146]    train-rmse:0.826107+0.010552    test-rmse:1.502720+0.277743 
## [147]    train-rmse:0.819113+0.010852    test-rmse:1.496734+0.269304 
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## [357]    train-rmse:0.277770+0.009669    test-rmse:1.155041+0.292921 
## [358]    train-rmse:0.276788+0.009861    test-rmse:1.154554+0.293378 
## [359]    train-rmse:0.275829+0.010027    test-rmse:1.154460+0.293336 
## [360]    train-rmse:0.274597+0.010645    test-rmse:1.154070+0.292678 
## [361]    train-rmse:0.273906+0.010641    test-rmse:1.153731+0.293102 
## [362]    train-rmse:0.273201+0.010491    test-rmse:1.153419+0.293110 
## [363]    train-rmse:0.272533+0.010530    test-rmse:1.153394+0.293306 
## [364]    train-rmse:0.271544+0.010600    test-rmse:1.153445+0.293268 
## [365]    train-rmse:0.270717+0.010534    test-rmse:1.153438+0.293702 
## [366]    train-rmse:0.269553+0.011065    test-rmse:1.152254+0.293713 
## [367]    train-rmse:0.268542+0.010936    test-rmse:1.152008+0.293989 
## [368]    train-rmse:0.267851+0.010856    test-rmse:1.151884+0.294034 
## [369]    train-rmse:0.266835+0.011050    test-rmse:1.151385+0.294257 
## [370]    train-rmse:0.265964+0.011108    test-rmse:1.150957+0.294420 
## [371]    train-rmse:0.265009+0.010995    test-rmse:1.150275+0.294780 
## [372]    train-rmse:0.264065+0.010747    test-rmse:1.149885+0.294365 
## [373]    train-rmse:0.263053+0.011023    test-rmse:1.149352+0.294454 
## [374]    train-rmse:0.262310+0.010962    test-rmse:1.148877+0.294641 
## [375]    train-rmse:0.261620+0.010920    test-rmse:1.149062+0.294804 
## [376]    train-rmse:0.260696+0.011187    test-rmse:1.148958+0.294814 
## [377]    train-rmse:0.260137+0.011209    test-rmse:1.148307+0.294694 
## [378]    train-rmse:0.259324+0.011209    test-rmse:1.148067+0.294927 
## [379]    train-rmse:0.258506+0.010850    test-rmse:1.147856+0.295404 
## [380]    train-rmse:0.257778+0.010790    test-rmse:1.147619+0.295571 
## [381]    train-rmse:0.256692+0.010721    test-rmse:1.147846+0.295366 
## [382]    train-rmse:0.255905+0.010566    test-rmse:1.147329+0.295724 
## [383]    train-rmse:0.255215+0.010410    test-rmse:1.147185+0.295704 
## [384]    train-rmse:0.254559+0.010393    test-rmse:1.146828+0.295363 
## [385]    train-rmse:0.253811+0.010632    test-rmse:1.146802+0.295332 
## [386]    train-rmse:0.252863+0.010453    test-rmse:1.146625+0.295911 
## [387]    train-rmse:0.252005+0.010589    test-rmse:1.146245+0.296295 
## [388]    train-rmse:0.251163+0.010578    test-rmse:1.145963+0.295770 
## [389]    train-rmse:0.250541+0.010609    test-rmse:1.145650+0.295844 
## [390]    train-rmse:0.249998+0.010685    test-rmse:1.145395+0.295860 
## [391]    train-rmse:0.249377+0.010608    test-rmse:1.145027+0.296033 
## [392]    train-rmse:0.248381+0.010754    test-rmse:1.144018+0.297094 
## [393]    train-rmse:0.247833+0.010696    test-rmse:1.143843+0.297220 
## [394]    train-rmse:0.247108+0.010760    test-rmse:1.143680+0.297171 
## [395]    train-rmse:0.246192+0.010984    test-rmse:1.143781+0.297490 
## [396]    train-rmse:0.245360+0.011198    test-rmse:1.143852+0.297096 
## [397]    train-rmse:0.244713+0.011168    test-rmse:1.143740+0.297105 
## [398]    train-rmse:0.243723+0.011141    test-rmse:1.143754+0.297334 
## [399]    train-rmse:0.243110+0.011089    test-rmse:1.142802+0.298180 
## [400]    train-rmse:0.242200+0.011009    test-rmse:1.142558+0.297886 
## [401]    train-rmse:0.241756+0.011134    test-rmse:1.142356+0.297795 
## [402]    train-rmse:0.241117+0.011042    test-rmse:1.142104+0.297348 
## [403]    train-rmse:0.240481+0.010993    test-rmse:1.141914+0.297230 
## [404]    train-rmse:0.239829+0.011075    test-rmse:1.141742+0.297359 
## [405]    train-rmse:0.238697+0.011613    test-rmse:1.141614+0.297723 
## [406]    train-rmse:0.237728+0.011704    test-rmse:1.141743+0.297763 
## [407]    train-rmse:0.236909+0.011466    test-rmse:1.141480+0.297379 
## [408]    train-rmse:0.236199+0.011375    test-rmse:1.141307+0.297586 
## [409]    train-rmse:0.235387+0.011217    test-rmse:1.140922+0.297446 
## [410]    train-rmse:0.234769+0.011401    test-rmse:1.140406+0.297775 
## [411]    train-rmse:0.234167+0.011276    test-rmse:1.140370+0.297612 
## [412]    train-rmse:0.233285+0.011452    test-rmse:1.140524+0.297532 
## [413]    train-rmse:0.232469+0.011643    test-rmse:1.140389+0.297387 
## [414]    train-rmse:0.231781+0.011510    test-rmse:1.139594+0.297823 
## [415]    train-rmse:0.231149+0.011578    test-rmse:1.139357+0.297699 
## [416]    train-rmse:0.230538+0.011808    test-rmse:1.139089+0.297744 
## [417]    train-rmse:0.229663+0.011625    test-rmse:1.138767+0.298032 
## [418]    train-rmse:0.228622+0.011524    test-rmse:1.138792+0.297770 
## [419]    train-rmse:0.227940+0.011371    test-rmse:1.138936+0.297658 
## [420]    train-rmse:0.227303+0.011333    test-rmse:1.138965+0.298023 
## [421]    train-rmse:0.226701+0.011408    test-rmse:1.138929+0.297965 
## [422]    train-rmse:0.226189+0.011324    test-rmse:1.138940+0.298280 
## [423]    train-rmse:0.225682+0.011325    test-rmse:1.138543+0.298340 
## [424]    train-rmse:0.225024+0.011542    test-rmse:1.138560+0.298033 
## [425]    train-rmse:0.224136+0.011628    test-rmse:1.138556+0.298427 
## [426]    train-rmse:0.223678+0.011546    test-rmse:1.138316+0.298496 
## [427]    train-rmse:0.222835+0.011492    test-rmse:1.138144+0.298653 
## [428]    train-rmse:0.222156+0.011287    test-rmse:1.137954+0.298791 
## [429]    train-rmse:0.221632+0.011213    test-rmse:1.137773+0.299071 
## [430]    train-rmse:0.221233+0.011178    test-rmse:1.137335+0.298728 
## [431]    train-rmse:0.220738+0.011166    test-rmse:1.137077+0.298897 
## [432]    train-rmse:0.220097+0.011130    test-rmse:1.136990+0.299001 
## [433]    train-rmse:0.219596+0.011121    test-rmse:1.136760+0.298981 
## [434]    train-rmse:0.219025+0.011351    test-rmse:1.136713+0.298893 
## [435]    train-rmse:0.218323+0.011239    test-rmse:1.136403+0.298770 
## [436]    train-rmse:0.217374+0.011029    test-rmse:1.136552+0.298979 
## [437]    train-rmse:0.216825+0.010839    test-rmse:1.136202+0.299273 
## [438]    train-rmse:0.216145+0.011065    test-rmse:1.135736+0.299492 
## [439]    train-rmse:0.215575+0.010837    test-rmse:1.135470+0.300334 
## [440]    train-rmse:0.214856+0.011335    test-rmse:1.135470+0.300170 
## [441]    train-rmse:0.214416+0.011361    test-rmse:1.135279+0.300101 
## [442]    train-rmse:0.213757+0.011161    test-rmse:1.135478+0.300109 
## [443]    train-rmse:0.213112+0.011136    test-rmse:1.135287+0.299884 
## [444]    train-rmse:0.212480+0.011240    test-rmse:1.134806+0.300326 
## [445]    train-rmse:0.211817+0.011341    test-rmse:1.134527+0.300613 
## [446]    train-rmse:0.211257+0.011435    test-rmse:1.134557+0.300419 
## [447]    train-rmse:0.210674+0.011417    test-rmse:1.134596+0.300525 
## [448]    train-rmse:0.210086+0.011233    test-rmse:1.134486+0.300934 
## [449]    train-rmse:0.209508+0.011244    test-rmse:1.134598+0.301105 
## [450]    train-rmse:0.208825+0.011160    test-rmse:1.134544+0.301205 
## [451]    train-rmse:0.208177+0.011044    test-rmse:1.134197+0.301171 
## [452]    train-rmse:0.207900+0.011031    test-rmse:1.133741+0.301625 
## [453]    train-rmse:0.207534+0.011024    test-rmse:1.133573+0.301559 
## [454]    train-rmse:0.206664+0.010956    test-rmse:1.133537+0.301469 
## [455]    train-rmse:0.206134+0.011127    test-rmse:1.133365+0.301568 
## [456]    train-rmse:0.205404+0.011017    test-rmse:1.133740+0.301300 
## [457]    train-rmse:0.204990+0.010925    test-rmse:1.133497+0.301382 
## [458]    train-rmse:0.204426+0.010912    test-rmse:1.133635+0.301615 
## [459]    train-rmse:0.203910+0.010833    test-rmse:1.133660+0.301456 
## [460]    train-rmse:0.203483+0.010930    test-rmse:1.133748+0.301593 
## [461]    train-rmse:0.203037+0.010884    test-rmse:1.133338+0.301693 
## [462]    train-rmse:0.202663+0.010924    test-rmse:1.133074+0.301836 
## [463]    train-rmse:0.202104+0.011031    test-rmse:1.132940+0.301723 
## [464]    train-rmse:0.201568+0.011072    test-rmse:1.132662+0.301930 
## [465]    train-rmse:0.200849+0.011024    test-rmse:1.132565+0.302061 
## [466]    train-rmse:0.200221+0.011018    test-rmse:1.132613+0.302095 
## [467]    train-rmse:0.199749+0.011013    test-rmse:1.132781+0.302163 
## [468]    train-rmse:0.199080+0.011050    test-rmse:1.132886+0.302202 
## [469]    train-rmse:0.198565+0.011107    test-rmse:1.132676+0.301990 
## [470]    train-rmse:0.198237+0.011014    test-rmse:1.132604+0.301999 
## [471]    train-rmse:0.197799+0.010926    test-rmse:1.132438+0.302102 
## [472]    train-rmse:0.197259+0.011012    test-rmse:1.132364+0.302268 
## [473]    train-rmse:0.196906+0.011115    test-rmse:1.132442+0.302286 
## [474]    train-rmse:0.196416+0.010985    test-rmse:1.131869+0.302146 
## [475]    train-rmse:0.195916+0.010845    test-rmse:1.131708+0.302258 
## [476]    train-rmse:0.195391+0.010784    test-rmse:1.131467+0.302209 
## [477]    train-rmse:0.194901+0.010838    test-rmse:1.131460+0.302255 
## [478]    train-rmse:0.194351+0.010962    test-rmse:1.131383+0.302391 
## [479]    train-rmse:0.193833+0.011124    test-rmse:1.131312+0.302304 
## [480]    train-rmse:0.193229+0.011332    test-rmse:1.131453+0.302377 
## [481]    train-rmse:0.192672+0.011353    test-rmse:1.131092+0.302482 
## [482]    train-rmse:0.192107+0.011475    test-rmse:1.131194+0.302600 
## [483]    train-rmse:0.191715+0.011393    test-rmse:1.131236+0.302901 
## [484]    train-rmse:0.191360+0.011289    test-rmse:1.130957+0.303019 
## [485]    train-rmse:0.190852+0.011258    test-rmse:1.130870+0.303038 
## [486]    train-rmse:0.190365+0.011292    test-rmse:1.130639+0.303254 
## [487]    train-rmse:0.189745+0.011334    test-rmse:1.130612+0.303008 
## [488]    train-rmse:0.189106+0.011249    test-rmse:1.130522+0.303146 
## [489]    train-rmse:0.188355+0.011308    test-rmse:1.130731+0.303128 
## [490]    train-rmse:0.187868+0.011207    test-rmse:1.130687+0.303186 
## [491]    train-rmse:0.187330+0.011178    test-rmse:1.130573+0.303410 
## [492]    train-rmse:0.186882+0.011214    test-rmse:1.130649+0.303444 
## [493]    train-rmse:0.186452+0.011167    test-rmse:1.130772+0.303442 
## [494]    train-rmse:0.185982+0.011242    test-rmse:1.130669+0.303458 
## Stopping. Best iteration:
## [488]    train-rmse:0.189106+0.011249    test-rmse:1.130522+0.303146
## 
## 3 
## [1]  train-rmse:96.903592+0.106998   test-rmse:96.903929+0.480417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:87.282645+0.098508   test-rmse:87.282849+0.494309 
## [3]  train-rmse:78.624342+0.091095   test-rmse:78.624364+0.507694 
## [4]  train-rmse:70.833145+0.084703   test-rmse:70.832979+0.520704 
## [5]  train-rmse:63.823124+0.079254   test-rmse:63.822745+0.533458 
## [6]  train-rmse:57.516975+0.074698   test-rmse:57.516330+0.546089 
## [7]  train-rmse:51.845108+0.070990   test-rmse:51.844165+0.558710 
## [8]  train-rmse:46.744962+0.068087   test-rmse:46.743658+0.571428 
## [9]  train-rmse:42.160251+0.065957   test-rmse:42.158527+0.584358 
## [10] train-rmse:38.040358+0.064573   test-rmse:38.038154+0.597586 
## [11] train-rmse:34.337210+0.063071   test-rmse:34.350626+0.598556 
## [12] train-rmse:31.005821+0.060048   test-rmse:31.044042+0.597332 
## [13] train-rmse:28.008132+0.054748   test-rmse:28.071744+0.610449 
## [14] train-rmse:25.311016+0.048086   test-rmse:25.390260+0.601052 
## [15] train-rmse:22.884224+0.042327   test-rmse:22.971323+0.589327 
## [16] train-rmse:20.700483+0.038078   test-rmse:20.782687+0.564264 
## [17] train-rmse:18.736104+0.033770   test-rmse:18.827295+0.567654 
## [18] train-rmse:16.966929+0.032027   test-rmse:17.060569+0.556279 
## [19] train-rmse:15.374780+0.030624   test-rmse:15.465001+0.549492 
## [20] train-rmse:13.942235+0.028910   test-rmse:14.035342+0.540714 
## [21] train-rmse:12.654367+0.027510   test-rmse:12.748046+0.529373 
## [22] train-rmse:11.496546+0.027583   test-rmse:11.606951+0.539584 
## [23] train-rmse:10.453759+0.025810   test-rmse:10.552590+0.541009 
## [24] train-rmse:9.519689+0.026060    test-rmse:9.622914+0.533528 
## [25] train-rmse:8.679701+0.024064    test-rmse:8.794529+0.560827 
## [26] train-rmse:7.926602+0.025737    test-rmse:8.038001+0.540362 
## [27] train-rmse:7.252198+0.026198    test-rmse:7.353726+0.520842 
## [28] train-rmse:6.648564+0.026714    test-rmse:6.762973+0.530259 
## [29] train-rmse:6.105827+0.025679    test-rmse:6.235793+0.508695 
## [30] train-rmse:5.623034+0.026529    test-rmse:5.746186+0.497288 
## [31] train-rmse:5.190838+0.027496    test-rmse:5.341873+0.493737 
## [32] train-rmse:4.801772+0.024996    test-rmse:4.965796+0.489566 
## [33] train-rmse:4.458838+0.025350    test-rmse:4.630845+0.472616 
## [34] train-rmse:4.152818+0.025026    test-rmse:4.338804+0.466934 
## [35] train-rmse:3.877183+0.026667    test-rmse:4.085898+0.469876 
## [36] train-rmse:3.636001+0.028157    test-rmse:3.860339+0.468396 
## [37] train-rmse:3.419039+0.027924    test-rmse:3.658858+0.455913 
## [38] train-rmse:3.228188+0.028970    test-rmse:3.481032+0.446157 
## [39] train-rmse:3.058397+0.030489    test-rmse:3.337229+0.431669 
## [40] train-rmse:2.903593+0.027416    test-rmse:3.190352+0.406235 
## [41] train-rmse:2.769421+0.027654    test-rmse:3.073312+0.402438 
## [42] train-rmse:2.649114+0.027862    test-rmse:2.973549+0.386669 
## [43] train-rmse:2.541394+0.027832    test-rmse:2.883240+0.369306 
## [44] train-rmse:2.445686+0.027970    test-rmse:2.802418+0.368262 
## [45] train-rmse:2.358421+0.028853    test-rmse:2.729399+0.369333 
## [46] train-rmse:2.280267+0.028303    test-rmse:2.661520+0.343710 
## [47] train-rmse:2.209564+0.028417    test-rmse:2.605615+0.332642 
## [48] train-rmse:2.145625+0.029241    test-rmse:2.553902+0.328658 
## [49] train-rmse:2.086887+0.029653    test-rmse:2.504333+0.319988 
## [50] train-rmse:2.032135+0.027744    test-rmse:2.453161+0.304228 
## [51] train-rmse:1.981325+0.027743    test-rmse:2.423234+0.303755 
## [52] train-rmse:1.935006+0.028006    test-rmse:2.383931+0.300244 
## [53] train-rmse:1.890053+0.026021    test-rmse:2.345159+0.284860 
## [54] train-rmse:1.846184+0.023441    test-rmse:2.307485+0.278394 
## [55] train-rmse:1.808109+0.023603    test-rmse:2.280474+0.273104 
## [56] train-rmse:1.769440+0.024854    test-rmse:2.254789+0.273173 
## [57] train-rmse:1.734500+0.024363    test-rmse:2.223066+0.271658 
## [58] train-rmse:1.697390+0.027237    test-rmse:2.199058+0.265533 
## [59] train-rmse:1.666238+0.026607    test-rmse:2.173754+0.262998 
## [60] train-rmse:1.636324+0.026570    test-rmse:2.149478+0.263205 
## [61] train-rmse:1.607752+0.026730    test-rmse:2.126721+0.255685 
## [62] train-rmse:1.578643+0.025259    test-rmse:2.099942+0.251567 
## [63] train-rmse:1.549086+0.028969    test-rmse:2.080003+0.250084 
## [64] train-rmse:1.523846+0.028459    test-rmse:2.056242+0.246061 
## [65] train-rmse:1.497083+0.027808    test-rmse:2.042523+0.251329 
## [66] train-rmse:1.472907+0.027471    test-rmse:2.026683+0.249550 
## [67] train-rmse:1.449172+0.027718    test-rmse:2.002898+0.247503 
## [68] train-rmse:1.420988+0.025970    test-rmse:1.986222+0.238729 
## [69] train-rmse:1.398490+0.025683    test-rmse:1.967102+0.245180 
## [70] train-rmse:1.376615+0.025444    test-rmse:1.953882+0.244249 
## [71] train-rmse:1.356103+0.025414    test-rmse:1.934058+0.242862 
## [72] train-rmse:1.333878+0.025379    test-rmse:1.915151+0.245720 
## [73] train-rmse:1.313837+0.024937    test-rmse:1.902176+0.241435 
## [74] train-rmse:1.291606+0.025316    test-rmse:1.881730+0.237436 
## [75] train-rmse:1.272377+0.025283    test-rmse:1.863983+0.238270 
## [76] train-rmse:1.252038+0.023408    test-rmse:1.850452+0.238345 
## [77] train-rmse:1.234160+0.023093    test-rmse:1.838326+0.238799 
## [78] train-rmse:1.214411+0.022648    test-rmse:1.821860+0.237007 
## [79] train-rmse:1.196087+0.023493    test-rmse:1.803190+0.238962 
## [80] train-rmse:1.178382+0.022418    test-rmse:1.792489+0.243001 
## [81] train-rmse:1.160766+0.022456    test-rmse:1.780261+0.243947 
## [82] train-rmse:1.140807+0.023191    test-rmse:1.767845+0.241006 
## [83] train-rmse:1.124011+0.023812    test-rmse:1.756335+0.244113 
## [84] train-rmse:1.106786+0.022235    test-rmse:1.743578+0.245425 
## [85] train-rmse:1.090249+0.022079    test-rmse:1.732577+0.247463 
## [86] train-rmse:1.075206+0.021732    test-rmse:1.721578+0.247818 
## [87] train-rmse:1.057975+0.022961    test-rmse:1.709214+0.249843 
## [88] train-rmse:1.043267+0.023138    test-rmse:1.696931+0.250241 
## [89] train-rmse:1.029316+0.022803    test-rmse:1.688577+0.252262 
## [90] train-rmse:1.013801+0.022371    test-rmse:1.675930+0.253234 
## [91] train-rmse:1.000715+0.022109    test-rmse:1.662351+0.253152 
## [92] train-rmse:0.987033+0.022017    test-rmse:1.652577+0.254956 
## [93] train-rmse:0.973606+0.022222    test-rmse:1.643001+0.256112 
## [94] train-rmse:0.960244+0.022339    test-rmse:1.632743+0.259543 
## [95] train-rmse:0.946181+0.023838    test-rmse:1.617831+0.253668 
## [96] train-rmse:0.933436+0.023830    test-rmse:1.610599+0.253750 
## [97] train-rmse:0.921712+0.023693    test-rmse:1.600531+0.253001 
## [98] train-rmse:0.909326+0.023013    test-rmse:1.594244+0.252592 
## [99] train-rmse:0.897928+0.022947    test-rmse:1.578558+0.246719 
## [100]    train-rmse:0.885456+0.023257    test-rmse:1.570441+0.246262 
## [101]    train-rmse:0.873453+0.022776    test-rmse:1.562890+0.245832 
## [102]    train-rmse:0.861987+0.022056    test-rmse:1.550823+0.249653 
## [103]    train-rmse:0.851366+0.022097    test-rmse:1.541686+0.249869 
## [104]    train-rmse:0.839440+0.023526    test-rmse:1.536269+0.251635 
## [105]    train-rmse:0.827946+0.022872    test-rmse:1.521524+0.251637 
## [106]    train-rmse:0.815705+0.024339    test-rmse:1.516384+0.249437 
## [107]    train-rmse:0.805447+0.024231    test-rmse:1.509278+0.252074 
## [108]    train-rmse:0.795549+0.024193    test-rmse:1.497993+0.243701 
## [109]    train-rmse:0.785988+0.024019    test-rmse:1.490171+0.245959 
## [110]    train-rmse:0.776664+0.023677    test-rmse:1.480548+0.245029 
## [111]    train-rmse:0.767067+0.024041    test-rmse:1.475295+0.243924 
## [112]    train-rmse:0.757595+0.023334    test-rmse:1.468347+0.245507 
## [113]    train-rmse:0.748397+0.023709    test-rmse:1.462601+0.244658 
## [114]    train-rmse:0.737169+0.025252    test-rmse:1.455137+0.248055 
## [115]    train-rmse:0.728488+0.024794    test-rmse:1.446354+0.243194 
## [116]    train-rmse:0.720038+0.025038    test-rmse:1.441708+0.241292 
## [117]    train-rmse:0.711637+0.025183    test-rmse:1.434921+0.242374 
## [118]    train-rmse:0.702818+0.026446    test-rmse:1.427572+0.242346 
## [119]    train-rmse:0.694453+0.025576    test-rmse:1.416390+0.237563 
## [120]    train-rmse:0.686343+0.025526    test-rmse:1.411368+0.238639 
## [121]    train-rmse:0.678469+0.025848    test-rmse:1.405135+0.239622 
## [122]    train-rmse:0.670859+0.025624    test-rmse:1.400493+0.241050 
## [123]    train-rmse:0.663863+0.025301    test-rmse:1.395125+0.241349 
## [124]    train-rmse:0.656291+0.024596    test-rmse:1.391483+0.240409 
## [125]    train-rmse:0.648607+0.024871    test-rmse:1.384986+0.233709 
## [126]    train-rmse:0.641169+0.024940    test-rmse:1.379436+0.234988 
## [127]    train-rmse:0.634697+0.024546    test-rmse:1.372742+0.238586 
## [128]    train-rmse:0.628035+0.024267    test-rmse:1.371045+0.237266 
## [129]    train-rmse:0.620886+0.023612    test-rmse:1.365329+0.240247 
## [130]    train-rmse:0.614350+0.023380    test-rmse:1.358305+0.234609 
## [131]    train-rmse:0.607197+0.024132    test-rmse:1.353549+0.235087 
## [132]    train-rmse:0.600604+0.024159    test-rmse:1.348844+0.236758 
## [133]    train-rmse:0.594488+0.024040    test-rmse:1.344875+0.237468 
## [134]    train-rmse:0.588699+0.023977    test-rmse:1.340064+0.238077 
## [135]    train-rmse:0.582437+0.023314    test-rmse:1.332230+0.234272 
## [136]    train-rmse:0.575837+0.023980    test-rmse:1.329198+0.232271 
## [137]    train-rmse:0.570257+0.024084    test-rmse:1.325982+0.232015 
## [138]    train-rmse:0.564194+0.023665    test-rmse:1.322096+0.232958 
## [139]    train-rmse:0.558310+0.022684    test-rmse:1.319865+0.235373 
## [140]    train-rmse:0.552761+0.022785    test-rmse:1.315714+0.236588 
## [141]    train-rmse:0.547049+0.022552    test-rmse:1.311820+0.236878 
## [142]    train-rmse:0.542318+0.022430    test-rmse:1.308677+0.236236 
## [143]    train-rmse:0.537545+0.022349    test-rmse:1.305601+0.237128 
## [144]    train-rmse:0.532658+0.022228    test-rmse:1.301588+0.235933 
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## [147]    train-rmse:0.515350+0.021704    test-rmse:1.292881+0.238980 
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## [286]    train-rmse:0.201963+0.012069    test-rmse:1.155984+0.279490 
## [287]    train-rmse:0.200989+0.012011    test-rmse:1.155509+0.279822 
## [288]    train-rmse:0.200056+0.011712    test-rmse:1.155401+0.279872 
## [289]    train-rmse:0.198757+0.011576    test-rmse:1.155147+0.279971 
## [290]    train-rmse:0.197601+0.011499    test-rmse:1.154943+0.280443 
## [291]    train-rmse:0.196484+0.011515    test-rmse:1.154874+0.280475 
## [292]    train-rmse:0.195544+0.011481    test-rmse:1.154761+0.280425 
## [293]    train-rmse:0.194422+0.011219    test-rmse:1.154605+0.280513 
## [294]    train-rmse:0.193333+0.011424    test-rmse:1.154588+0.280565 
## [295]    train-rmse:0.192168+0.011508    test-rmse:1.154430+0.280661 
## [296]    train-rmse:0.191404+0.011413    test-rmse:1.154077+0.280901 
## [297]    train-rmse:0.190235+0.011743    test-rmse:1.153884+0.280892 
## [298]    train-rmse:0.189417+0.011655    test-rmse:1.153663+0.280872 
## [299]    train-rmse:0.188327+0.011431    test-rmse:1.153353+0.281313 
## [300]    train-rmse:0.187439+0.011274    test-rmse:1.152903+0.281562 
## [301]    train-rmse:0.186613+0.011517    test-rmse:1.152828+0.281618 
## [302]    train-rmse:0.185411+0.011083    test-rmse:1.152641+0.281528 
## [303]    train-rmse:0.184765+0.011111    test-rmse:1.152104+0.281511 
## [304]    train-rmse:0.184014+0.011191    test-rmse:1.151463+0.281900 
## [305]    train-rmse:0.183132+0.010948    test-rmse:1.151474+0.282382 
## [306]    train-rmse:0.182374+0.010822    test-rmse:1.151537+0.282668 
## [307]    train-rmse:0.181554+0.010698    test-rmse:1.151433+0.282531 
## [308]    train-rmse:0.180549+0.010873    test-rmse:1.151262+0.282755 
## [309]    train-rmse:0.179787+0.010803    test-rmse:1.151196+0.282677 
## [310]    train-rmse:0.178953+0.010844    test-rmse:1.151141+0.282751 
## [311]    train-rmse:0.178306+0.010910    test-rmse:1.151057+0.282693 
## [312]    train-rmse:0.177310+0.010872    test-rmse:1.150446+0.283125 
## [313]    train-rmse:0.176344+0.010232    test-rmse:1.150511+0.283474 
## [314]    train-rmse:0.175557+0.009972    test-rmse:1.150604+0.283636 
## [315]    train-rmse:0.174792+0.010069    test-rmse:1.150536+0.283742 
## [316]    train-rmse:0.174188+0.009813    test-rmse:1.150281+0.283817 
## [317]    train-rmse:0.173072+0.009691    test-rmse:1.150349+0.284017 
## [318]    train-rmse:0.172198+0.009670    test-rmse:1.150385+0.284086 
## [319]    train-rmse:0.171090+0.009510    test-rmse:1.150267+0.284015 
## [320]    train-rmse:0.170400+0.009478    test-rmse:1.150017+0.284135 
## [321]    train-rmse:0.169641+0.009782    test-rmse:1.150476+0.283871 
## [322]    train-rmse:0.168577+0.009761    test-rmse:1.150738+0.283724 
## [323]    train-rmse:0.167405+0.009498    test-rmse:1.150619+0.283899 
## [324]    train-rmse:0.166294+0.009536    test-rmse:1.150123+0.284024 
## [325]    train-rmse:0.165559+0.009665    test-rmse:1.150124+0.284140 
## [326]    train-rmse:0.164672+0.009599    test-rmse:1.149867+0.284175 
## [327]    train-rmse:0.164021+0.009560    test-rmse:1.149702+0.284291 
## [328]    train-rmse:0.162847+0.009751    test-rmse:1.149910+0.284265 
## [329]    train-rmse:0.162210+0.009605    test-rmse:1.149690+0.284266 
## [330]    train-rmse:0.161404+0.009721    test-rmse:1.149100+0.284602 
## [331]    train-rmse:0.160499+0.009766    test-rmse:1.149041+0.284653 
## [332]    train-rmse:0.159831+0.009798    test-rmse:1.148610+0.284766 
## [333]    train-rmse:0.159105+0.009731    test-rmse:1.148790+0.284881 
## [334]    train-rmse:0.158428+0.009644    test-rmse:1.148610+0.284854 
## [335]    train-rmse:0.157782+0.009722    test-rmse:1.148727+0.285038 
## [336]    train-rmse:0.156794+0.009451    test-rmse:1.149049+0.284899 
## [337]    train-rmse:0.155957+0.009519    test-rmse:1.148837+0.285038 
## [338]    train-rmse:0.155093+0.009376    test-rmse:1.148834+0.285087 
## [339]    train-rmse:0.154322+0.009337    test-rmse:1.148594+0.285443 
## [340]    train-rmse:0.153512+0.009315    test-rmse:1.148638+0.285319 
## [341]    train-rmse:0.152883+0.009460    test-rmse:1.148464+0.285529 
## [342]    train-rmse:0.152102+0.009543    test-rmse:1.148295+0.285833 
## [343]    train-rmse:0.151425+0.009685    test-rmse:1.148218+0.285777 
## [344]    train-rmse:0.150874+0.009681    test-rmse:1.147820+0.285971 
## [345]    train-rmse:0.150127+0.009776    test-rmse:1.147770+0.285913 
## [346]    train-rmse:0.149333+0.009640    test-rmse:1.147529+0.285779 
## [347]    train-rmse:0.148477+0.009487    test-rmse:1.147714+0.285833 
## [348]    train-rmse:0.147729+0.009647    test-rmse:1.147639+0.285786 
## [349]    train-rmse:0.147062+0.009758    test-rmse:1.147155+0.286046 
## [350]    train-rmse:0.146205+0.010091    test-rmse:1.147313+0.286022 
## [351]    train-rmse:0.145596+0.010078    test-rmse:1.147171+0.285915 
## [352]    train-rmse:0.145152+0.009958    test-rmse:1.147172+0.286133 
## [353]    train-rmse:0.144555+0.009653    test-rmse:1.147086+0.286340 
## [354]    train-rmse:0.143866+0.009824    test-rmse:1.146810+0.286557 
## [355]    train-rmse:0.143112+0.009704    test-rmse:1.146960+0.286395 
## [356]    train-rmse:0.142207+0.009568    test-rmse:1.146911+0.286644 
## [357]    train-rmse:0.141450+0.009632    test-rmse:1.146976+0.286708 
## [358]    train-rmse:0.140935+0.009550    test-rmse:1.146943+0.286875 
## [359]    train-rmse:0.140454+0.009400    test-rmse:1.146738+0.286895 
## [360]    train-rmse:0.139626+0.009076    test-rmse:1.146451+0.286973 
## [361]    train-rmse:0.138945+0.008882    test-rmse:1.146428+0.286937 
## [362]    train-rmse:0.138209+0.009069    test-rmse:1.146473+0.286956 
## [363]    train-rmse:0.137626+0.009116    test-rmse:1.146220+0.287094 
## [364]    train-rmse:0.137184+0.009115    test-rmse:1.146069+0.286929 
## [365]    train-rmse:0.136451+0.008655    test-rmse:1.145965+0.286955 
## [366]    train-rmse:0.135570+0.008029    test-rmse:1.146107+0.286728 
## [367]    train-rmse:0.134805+0.007759    test-rmse:1.145532+0.286958 
## [368]    train-rmse:0.133962+0.007746    test-rmse:1.145411+0.287122 
## [369]    train-rmse:0.133302+0.007385    test-rmse:1.145407+0.287219 
## [370]    train-rmse:0.132828+0.007173    test-rmse:1.145245+0.287269 
## [371]    train-rmse:0.132135+0.007093    test-rmse:1.145248+0.287215 
## [372]    train-rmse:0.131567+0.007080    test-rmse:1.145071+0.287199 
## [373]    train-rmse:0.131029+0.007125    test-rmse:1.144974+0.287187 
## [374]    train-rmse:0.130659+0.007109    test-rmse:1.144921+0.287023 
## [375]    train-rmse:0.130178+0.007225    test-rmse:1.144880+0.287045 
## [376]    train-rmse:0.129672+0.007352    test-rmse:1.144580+0.287024 
## [377]    train-rmse:0.129096+0.007429    test-rmse:1.144654+0.287050 
## [378]    train-rmse:0.128401+0.007480    test-rmse:1.144592+0.287005 
## [379]    train-rmse:0.127758+0.007225    test-rmse:1.144499+0.287089 
## [380]    train-rmse:0.127200+0.007386    test-rmse:1.144305+0.287143 
## [381]    train-rmse:0.126571+0.007335    test-rmse:1.143980+0.287369 
## [382]    train-rmse:0.126105+0.007311    test-rmse:1.143988+0.287479 
## [383]    train-rmse:0.125550+0.007236    test-rmse:1.143839+0.287613 
## [384]    train-rmse:0.124970+0.007325    test-rmse:1.143649+0.287700 
## [385]    train-rmse:0.124566+0.007259    test-rmse:1.143806+0.287781 
## [386]    train-rmse:0.123969+0.007096    test-rmse:1.143636+0.287702 
## [387]    train-rmse:0.123398+0.007189    test-rmse:1.143713+0.287706 
## [388]    train-rmse:0.122776+0.007193    test-rmse:1.143910+0.287615 
## [389]    train-rmse:0.122354+0.007244    test-rmse:1.143827+0.287501 
## [390]    train-rmse:0.121952+0.007238    test-rmse:1.143756+0.287432 
## [391]    train-rmse:0.121549+0.007345    test-rmse:1.143825+0.287591 
## [392]    train-rmse:0.120985+0.007120    test-rmse:1.143855+0.287656 
## Stopping. Best iteration:
## [386]    train-rmse:0.123969+0.007096    test-rmse:1.143636+0.287702
## 
## 4 
## [1]  train-rmse:96.903592+0.106998   test-rmse:96.903929+0.480417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:87.282645+0.098508   test-rmse:87.282849+0.494309 
## [3]  train-rmse:78.624342+0.091095   test-rmse:78.624364+0.507694 
## [4]  train-rmse:70.833145+0.084703   test-rmse:70.832979+0.520704 
## [5]  train-rmse:63.823124+0.079254   test-rmse:63.822745+0.533458 
## [6]  train-rmse:57.516975+0.074698   test-rmse:57.516330+0.546089 
## [7]  train-rmse:51.845108+0.070990   test-rmse:51.844165+0.558710 
## [8]  train-rmse:46.744962+0.068087   test-rmse:46.743658+0.571428 
## [9]  train-rmse:42.160251+0.065957   test-rmse:42.158527+0.584358 
## [10] train-rmse:38.040358+0.064573   test-rmse:38.038154+0.597586 
## [11] train-rmse:34.337210+0.063071   test-rmse:34.350626+0.598556 
## [12] train-rmse:31.005821+0.060048   test-rmse:31.044042+0.597332 
## [13] train-rmse:28.008132+0.054748   test-rmse:28.071744+0.610449 
## [14] train-rmse:25.311016+0.048086   test-rmse:25.390260+0.601052 
## [15] train-rmse:22.884224+0.042327   test-rmse:22.971323+0.589327 
## [16] train-rmse:20.699700+0.038362   test-rmse:20.782865+0.565840 
## [17] train-rmse:18.734460+0.034159   test-rmse:18.824948+0.561699 
## [18] train-rmse:16.963329+0.032186   test-rmse:17.055491+0.547623 
## [19] train-rmse:15.368465+0.030162   test-rmse:15.461173+0.540547 
## [20] train-rmse:13.932268+0.028128   test-rmse:14.028353+0.533924 
## [21] train-rmse:12.639703+0.026371   test-rmse:12.736051+0.520671 
## [22] train-rmse:11.476635+0.024693   test-rmse:11.580206+0.516285 
## [23] train-rmse:10.426596+0.023228   test-rmse:10.539773+0.536950 
## [24] train-rmse:9.482156+0.021908    test-rmse:9.591154+0.523666 
## [25] train-rmse:8.633551+0.019821    test-rmse:8.733526+0.523319 
## [26] train-rmse:7.869128+0.020533    test-rmse:7.986303+0.526708 
## [27] train-rmse:7.182900+0.019147    test-rmse:7.295854+0.530794 
## [28] train-rmse:6.566392+0.018017    test-rmse:6.680100+0.513088 
## [29] train-rmse:6.013836+0.018919    test-rmse:6.143164+0.508353 
## [30] train-rmse:5.513999+0.017053    test-rmse:5.652347+0.489139 
## [31] train-rmse:5.067886+0.017827    test-rmse:5.216839+0.478874 
## [32] train-rmse:4.667098+0.018197    test-rmse:4.844472+0.478200 
## [33] train-rmse:4.309964+0.018821    test-rmse:4.499325+0.464482 
## [34] train-rmse:3.986878+0.017744    test-rmse:4.193132+0.446282 
## [35] train-rmse:3.698471+0.017958    test-rmse:3.925855+0.437626 
## [36] train-rmse:3.443495+0.019932    test-rmse:3.687417+0.422269 
## [37] train-rmse:3.213454+0.023743    test-rmse:3.477290+0.419771 
## [38] train-rmse:3.007678+0.024786    test-rmse:3.280186+0.394904 
## [39] train-rmse:2.816959+0.022828    test-rmse:3.110194+0.380425 
## [40] train-rmse:2.652534+0.025694    test-rmse:2.964076+0.376494 
## [41] train-rmse:2.505734+0.026277    test-rmse:2.837358+0.369247 
## [42] train-rmse:2.375023+0.028401    test-rmse:2.716156+0.354777 
## [43] train-rmse:2.256770+0.030219    test-rmse:2.615709+0.353980 
## [44] train-rmse:2.150164+0.031895    test-rmse:2.532646+0.341226 
## [45] train-rmse:2.055365+0.033083    test-rmse:2.450775+0.322188 
## [46] train-rmse:1.966370+0.031956    test-rmse:2.382960+0.314859 
## [47] train-rmse:1.885901+0.037561    test-rmse:2.325225+0.312266 
## [48] train-rmse:1.815731+0.037979    test-rmse:2.266220+0.296277 
## [49] train-rmse:1.749418+0.038024    test-rmse:2.217639+0.293201 
## [50] train-rmse:1.690438+0.036317    test-rmse:2.172166+0.282692 
## [51] train-rmse:1.634682+0.036548    test-rmse:2.132887+0.280583 
## [52] train-rmse:1.581546+0.038209    test-rmse:2.091524+0.277827 
## [53] train-rmse:1.531213+0.041435    test-rmse:2.063877+0.270672 
## [54] train-rmse:1.489683+0.042067    test-rmse:2.025278+0.271871 
## [55] train-rmse:1.449629+0.041845    test-rmse:1.999578+0.269677 
## [56] train-rmse:1.412571+0.040857    test-rmse:1.978213+0.258881 
## [57] train-rmse:1.378179+0.040258    test-rmse:1.948633+0.262679 
## [58] train-rmse:1.341751+0.042375    test-rmse:1.924473+0.258425 
## [59] train-rmse:1.304019+0.040700    test-rmse:1.901107+0.248581 
## [60] train-rmse:1.270714+0.042636    test-rmse:1.880424+0.244226 
## [61] train-rmse:1.242459+0.042154    test-rmse:1.862686+0.244706 
## [62] train-rmse:1.215016+0.041125    test-rmse:1.837689+0.241960 
## [63] train-rmse:1.189895+0.040188    test-rmse:1.817514+0.244969 
## [64] train-rmse:1.165141+0.040186    test-rmse:1.799251+0.241337 
## [65] train-rmse:1.138550+0.040730    test-rmse:1.785826+0.238063 
## [66] train-rmse:1.113167+0.037774    test-rmse:1.763967+0.237183 
## [67] train-rmse:1.089425+0.038728    test-rmse:1.750505+0.242244 
## [68] train-rmse:1.068337+0.038603    test-rmse:1.734823+0.236377 
## [69] train-rmse:1.047564+0.037250    test-rmse:1.719139+0.236399 
## [70] train-rmse:1.027774+0.036975    test-rmse:1.706148+0.236273 
## [71] train-rmse:1.006206+0.037450    test-rmse:1.694827+0.235989 
## [72] train-rmse:0.986006+0.035453    test-rmse:1.679829+0.237180 
## [73] train-rmse:0.965625+0.033541    test-rmse:1.664528+0.240042 
## [74] train-rmse:0.947695+0.032413    test-rmse:1.651046+0.239534 
## [75] train-rmse:0.928196+0.034650    test-rmse:1.637819+0.242917 
## [76] train-rmse:0.910062+0.034710    test-rmse:1.625333+0.241638 
## [77] train-rmse:0.893707+0.034002    test-rmse:1.615088+0.244080 
## [78] train-rmse:0.876332+0.032898    test-rmse:1.599936+0.242384 
## [79] train-rmse:0.860506+0.032912    test-rmse:1.591479+0.244443 
## [80] train-rmse:0.844505+0.032211    test-rmse:1.576663+0.245172 
## [81] train-rmse:0.830048+0.031549    test-rmse:1.565051+0.246045 
## [82] train-rmse:0.816064+0.031133    test-rmse:1.556077+0.245606 
## [83] train-rmse:0.802184+0.029360    test-rmse:1.545776+0.245797 
## [84] train-rmse:0.789188+0.028659    test-rmse:1.536408+0.250504 
## [85] train-rmse:0.775707+0.028845    test-rmse:1.527237+0.249593 
## [86] train-rmse:0.763128+0.028591    test-rmse:1.514399+0.244251 
## [87] train-rmse:0.749896+0.029373    test-rmse:1.508597+0.243172 
## [88] train-rmse:0.738054+0.029034    test-rmse:1.499889+0.243358 
## [89] train-rmse:0.724829+0.028642    test-rmse:1.490592+0.245267 
## [90] train-rmse:0.713235+0.028545    test-rmse:1.482510+0.244148 
## [91] train-rmse:0.702033+0.028238    test-rmse:1.476963+0.244871 
## [92] train-rmse:0.689936+0.027567    test-rmse:1.466961+0.235655 
## [93] train-rmse:0.678178+0.029223    test-rmse:1.456916+0.239021 
## [94] train-rmse:0.667957+0.028701    test-rmse:1.448678+0.237508 
## [95] train-rmse:0.657851+0.028470    test-rmse:1.440952+0.233469 
## [96] train-rmse:0.648274+0.027974    test-rmse:1.431722+0.235054 
## [97] train-rmse:0.638337+0.027664    test-rmse:1.425181+0.234471 
## [98] train-rmse:0.628699+0.027480    test-rmse:1.418942+0.234073 
## [99] train-rmse:0.619284+0.026544    test-rmse:1.412553+0.229921 
## [100]    train-rmse:0.609246+0.027170    test-rmse:1.406538+0.232317 
## [101]    train-rmse:0.600010+0.027635    test-rmse:1.399666+0.232838 
## [102]    train-rmse:0.591817+0.027391    test-rmse:1.393330+0.234088 
## [103]    train-rmse:0.582704+0.026035    test-rmse:1.387857+0.233525 
## [104]    train-rmse:0.574103+0.026157    test-rmse:1.384167+0.236263 
## [105]    train-rmse:0.564956+0.026136    test-rmse:1.377012+0.231371 
## [106]    train-rmse:0.557295+0.025186    test-rmse:1.368392+0.232062 
## [107]    train-rmse:0.549484+0.024643    test-rmse:1.366547+0.234150 
## [108]    train-rmse:0.542338+0.024222    test-rmse:1.358964+0.229328 
## [109]    train-rmse:0.534527+0.023894    test-rmse:1.355342+0.229647 
## [110]    train-rmse:0.527639+0.023931    test-rmse:1.350651+0.229294 
## [111]    train-rmse:0.520576+0.024073    test-rmse:1.347438+0.230613 
## [112]    train-rmse:0.513601+0.023241    test-rmse:1.342108+0.229435 
## [113]    train-rmse:0.506604+0.023430    test-rmse:1.339851+0.228779 
## [114]    train-rmse:0.499119+0.023813    test-rmse:1.337596+0.229553 
## [115]    train-rmse:0.492914+0.023466    test-rmse:1.332166+0.230606 
## [116]    train-rmse:0.485751+0.024168    test-rmse:1.327691+0.232446 
## [117]    train-rmse:0.479410+0.023448    test-rmse:1.323268+0.233067 
## [118]    train-rmse:0.473759+0.023469    test-rmse:1.321473+0.233425 
## [119]    train-rmse:0.467314+0.022758    test-rmse:1.319077+0.233014 
## [120]    train-rmse:0.462146+0.022179    test-rmse:1.315979+0.232939 
## [121]    train-rmse:0.456676+0.022147    test-rmse:1.311925+0.235538 
## [122]    train-rmse:0.450854+0.022462    test-rmse:1.307702+0.236628 
## [123]    train-rmse:0.445741+0.021727    test-rmse:1.304230+0.238117 
## [124]    train-rmse:0.440027+0.021391    test-rmse:1.300545+0.236846 
## [125]    train-rmse:0.434951+0.021546    test-rmse:1.298191+0.237070 
## [126]    train-rmse:0.429769+0.021340    test-rmse:1.296335+0.237057 
## [127]    train-rmse:0.424489+0.021453    test-rmse:1.293868+0.236813 
## [128]    train-rmse:0.419433+0.020688    test-rmse:1.292201+0.237056 
## [129]    train-rmse:0.414693+0.020191    test-rmse:1.288396+0.236522 
## [130]    train-rmse:0.409712+0.019672    test-rmse:1.286895+0.237040 
## [131]    train-rmse:0.404203+0.020522    test-rmse:1.282972+0.237536 
## [132]    train-rmse:0.400020+0.020081    test-rmse:1.281754+0.238619 
## [133]    train-rmse:0.395452+0.020467    test-rmse:1.279638+0.239818 
## [134]    train-rmse:0.390781+0.019841    test-rmse:1.277213+0.239498 
## [135]    train-rmse:0.386398+0.019603    test-rmse:1.275360+0.240995 
## [136]    train-rmse:0.382545+0.019788    test-rmse:1.273108+0.241788 
## [137]    train-rmse:0.378436+0.019884    test-rmse:1.271663+0.241701 
## [138]    train-rmse:0.374470+0.019293    test-rmse:1.269453+0.240990 
## [139]    train-rmse:0.370751+0.018991    test-rmse:1.267147+0.241837 
## [140]    train-rmse:0.367393+0.018818    test-rmse:1.264719+0.242067 
## [141]    train-rmse:0.362998+0.018251    test-rmse:1.264986+0.242724 
## [142]    train-rmse:0.358640+0.018698    test-rmse:1.262934+0.242421 
## [143]    train-rmse:0.355058+0.018517    test-rmse:1.260579+0.243230 
## [144]    train-rmse:0.351902+0.018200    test-rmse:1.258440+0.243243 
## [145]    train-rmse:0.348192+0.018013    test-rmse:1.256975+0.242980 
## [146]    train-rmse:0.344595+0.018197    test-rmse:1.254425+0.242351 
## [147]    train-rmse:0.341272+0.018185    test-rmse:1.251858+0.243261 
## [148]    train-rmse:0.338307+0.018001    test-rmse:1.249469+0.244667 
## [149]    train-rmse:0.335005+0.018258    test-rmse:1.248110+0.244234 
## [150]    train-rmse:0.331720+0.017443    test-rmse:1.246899+0.244559 
## [151]    train-rmse:0.328858+0.017614    test-rmse:1.244885+0.245595 
## [152]    train-rmse:0.326025+0.017513    test-rmse:1.243079+0.245837 
## [153]    train-rmse:0.322409+0.016491    test-rmse:1.241515+0.245918 
## [154]    train-rmse:0.319111+0.016428    test-rmse:1.240173+0.246066 
## [155]    train-rmse:0.316188+0.016699    test-rmse:1.238426+0.246967 
## [156]    train-rmse:0.312105+0.015752    test-rmse:1.238013+0.247302 
## [157]    train-rmse:0.308797+0.015664    test-rmse:1.237356+0.248229 
## [158]    train-rmse:0.306255+0.015648    test-rmse:1.236296+0.248277 
## [159]    train-rmse:0.303268+0.015325    test-rmse:1.233570+0.249148 
## [160]    train-rmse:0.300701+0.015199    test-rmse:1.232436+0.250057 
## [161]    train-rmse:0.297388+0.014863    test-rmse:1.231059+0.250206 
## [162]    train-rmse:0.294767+0.014385    test-rmse:1.230273+0.250428 
## [163]    train-rmse:0.292195+0.014194    test-rmse:1.229293+0.250527 
## [164]    train-rmse:0.288537+0.013211    test-rmse:1.229038+0.250988 
## [165]    train-rmse:0.285974+0.013237    test-rmse:1.227310+0.252029 
## [166]    train-rmse:0.283360+0.012870    test-rmse:1.225292+0.252009 
## [167]    train-rmse:0.280947+0.012605    test-rmse:1.224778+0.251567 
## [168]    train-rmse:0.278265+0.012519    test-rmse:1.224542+0.251662 
## [169]    train-rmse:0.275821+0.012664    test-rmse:1.222911+0.252510 
## [170]    train-rmse:0.272918+0.012088    test-rmse:1.222112+0.253119 
## [171]    train-rmse:0.270705+0.011970    test-rmse:1.221067+0.253021 
## [172]    train-rmse:0.268556+0.011509    test-rmse:1.220128+0.253105 
## [173]    train-rmse:0.265925+0.011446    test-rmse:1.220049+0.253295 
## [174]    train-rmse:0.262882+0.010542    test-rmse:1.218784+0.253804 
## [175]    train-rmse:0.260686+0.009821    test-rmse:1.217477+0.253957 
## [176]    train-rmse:0.257550+0.008985    test-rmse:1.217005+0.253348 
## [177]    train-rmse:0.255820+0.008875    test-rmse:1.215755+0.253882 
## [178]    train-rmse:0.253762+0.008932    test-rmse:1.215328+0.254492 
## [179]    train-rmse:0.251311+0.009645    test-rmse:1.214553+0.254412 
## [180]    train-rmse:0.248974+0.009162    test-rmse:1.214038+0.254596 
## [181]    train-rmse:0.246218+0.009297    test-rmse:1.212486+0.254363 
## [182]    train-rmse:0.243764+0.008358    test-rmse:1.211355+0.253883 
## [183]    train-rmse:0.242235+0.008309    test-rmse:1.211152+0.253929 
## [184]    train-rmse:0.240505+0.008316    test-rmse:1.210015+0.254491 
## [185]    train-rmse:0.237607+0.007541    test-rmse:1.210215+0.254534 
## [186]    train-rmse:0.235775+0.007490    test-rmse:1.209427+0.254310 
## [187]    train-rmse:0.233676+0.007910    test-rmse:1.209263+0.254189 
## [188]    train-rmse:0.231992+0.007693    test-rmse:1.208296+0.254355 
## [189]    train-rmse:0.229685+0.007153    test-rmse:1.207559+0.254391 
## [190]    train-rmse:0.227831+0.006818    test-rmse:1.207219+0.254279 
## [191]    train-rmse:0.225697+0.006295    test-rmse:1.206489+0.254834 
## [192]    train-rmse:0.224081+0.006305    test-rmse:1.206462+0.254431 
## [193]    train-rmse:0.221895+0.006192    test-rmse:1.205203+0.253211 
## [194]    train-rmse:0.220163+0.006247    test-rmse:1.205154+0.252841 
## [195]    train-rmse:0.218650+0.006204    test-rmse:1.205172+0.252963 
## [196]    train-rmse:0.216999+0.005909    test-rmse:1.204710+0.252745 
## [197]    train-rmse:0.214853+0.005376    test-rmse:1.204009+0.252949 
## [198]    train-rmse:0.213059+0.004871    test-rmse:1.202667+0.253538 
## [199]    train-rmse:0.210987+0.004406    test-rmse:1.202358+0.252825 
## [200]    train-rmse:0.209240+0.004807    test-rmse:1.201602+0.253548 
## [201]    train-rmse:0.207073+0.004996    test-rmse:1.201386+0.253656 
## [202]    train-rmse:0.205109+0.005065    test-rmse:1.200831+0.253381 
## [203]    train-rmse:0.203802+0.004843    test-rmse:1.199517+0.252185 
## [204]    train-rmse:0.202331+0.004946    test-rmse:1.199470+0.252393 
## [205]    train-rmse:0.200377+0.005083    test-rmse:1.198145+0.252300 
## [206]    train-rmse:0.198945+0.004988    test-rmse:1.197779+0.252383 
## [207]    train-rmse:0.196619+0.004025    test-rmse:1.197207+0.252134 
## [208]    train-rmse:0.195095+0.003996    test-rmse:1.196570+0.251726 
## [209]    train-rmse:0.193525+0.004076    test-rmse:1.196420+0.252232 
## [210]    train-rmse:0.191893+0.004300    test-rmse:1.196139+0.251910 
## [211]    train-rmse:0.190177+0.004439    test-rmse:1.196175+0.251960 
## [212]    train-rmse:0.188559+0.003956    test-rmse:1.196002+0.252319 
## [213]    train-rmse:0.187289+0.003731    test-rmse:1.195189+0.252786 
## [214]    train-rmse:0.185955+0.003558    test-rmse:1.194947+0.253015 
## [215]    train-rmse:0.184589+0.003093    test-rmse:1.194793+0.253140 
## [216]    train-rmse:0.183050+0.003238    test-rmse:1.194746+0.252741 
## [217]    train-rmse:0.181514+0.003561    test-rmse:1.194573+0.252635 
## [218]    train-rmse:0.180448+0.003563    test-rmse:1.194190+0.252378 
## [219]    train-rmse:0.178753+0.003740    test-rmse:1.193749+0.251604 
## [220]    train-rmse:0.177414+0.003414    test-rmse:1.193763+0.251415 
## [221]    train-rmse:0.175938+0.003413    test-rmse:1.193212+0.251497 
## [222]    train-rmse:0.174381+0.003255    test-rmse:1.192331+0.251547 
## [223]    train-rmse:0.172863+0.003736    test-rmse:1.192082+0.251642 
## [224]    train-rmse:0.171252+0.003931    test-rmse:1.191320+0.250911 
## [225]    train-rmse:0.170133+0.003748    test-rmse:1.190685+0.251043 
## [226]    train-rmse:0.169001+0.003742    test-rmse:1.190344+0.250929 
## [227]    train-rmse:0.167628+0.003850    test-rmse:1.190057+0.250634 
## [228]    train-rmse:0.165910+0.003993    test-rmse:1.190209+0.250755 
## [229]    train-rmse:0.164657+0.004068    test-rmse:1.190119+0.250863 
## [230]    train-rmse:0.162875+0.004070    test-rmse:1.190296+0.250778 
## [231]    train-rmse:0.161705+0.004298    test-rmse:1.189815+0.250808 
## [232]    train-rmse:0.160266+0.004528    test-rmse:1.189124+0.250798 
## [233]    train-rmse:0.159378+0.004477    test-rmse:1.188938+0.250952 
## [234]    train-rmse:0.158099+0.004549    test-rmse:1.189073+0.251458 
## [235]    train-rmse:0.157053+0.004561    test-rmse:1.188792+0.251573 
## [236]    train-rmse:0.156005+0.004605    test-rmse:1.188526+0.251678 
## [237]    train-rmse:0.154981+0.004682    test-rmse:1.188213+0.252013 
## [238]    train-rmse:0.153904+0.004178    test-rmse:1.188047+0.251976 
## [239]    train-rmse:0.152780+0.004129    test-rmse:1.187501+0.251968 
## [240]    train-rmse:0.151948+0.004046    test-rmse:1.187562+0.252090 
## [241]    train-rmse:0.151011+0.003864    test-rmse:1.187383+0.252382 
## [242]    train-rmse:0.149862+0.003579    test-rmse:1.186942+0.252050 
## [243]    train-rmse:0.148444+0.003699    test-rmse:1.186667+0.252134 
## [244]    train-rmse:0.147327+0.003444    test-rmse:1.186784+0.252110 
## [245]    train-rmse:0.145610+0.003179    test-rmse:1.186453+0.252017 
## [246]    train-rmse:0.144712+0.003195    test-rmse:1.186195+0.251823 
## [247]    train-rmse:0.143938+0.003316    test-rmse:1.185722+0.252114 
## [248]    train-rmse:0.142968+0.003120    test-rmse:1.185421+0.252382 
## [249]    train-rmse:0.141815+0.003517    test-rmse:1.185328+0.252313 
## [250]    train-rmse:0.140855+0.003301    test-rmse:1.185321+0.252511 
## [251]    train-rmse:0.139423+0.003357    test-rmse:1.185240+0.252473 
## [252]    train-rmse:0.138152+0.003729    test-rmse:1.184882+0.251856 
## [253]    train-rmse:0.137558+0.003719    test-rmse:1.184642+0.252142 
## [254]    train-rmse:0.136542+0.003707    test-rmse:1.184465+0.252238 
## [255]    train-rmse:0.135568+0.003500    test-rmse:1.184077+0.252533 
## [256]    train-rmse:0.134253+0.003192    test-rmse:1.183841+0.252431 
## [257]    train-rmse:0.133301+0.003201    test-rmse:1.183221+0.252994 
## [258]    train-rmse:0.132338+0.003554    test-rmse:1.182987+0.252916 
## [259]    train-rmse:0.131402+0.003390    test-rmse:1.182923+0.252907 
## [260]    train-rmse:0.130219+0.003633    test-rmse:1.183082+0.252798 
## [261]    train-rmse:0.129175+0.003873    test-rmse:1.182822+0.252912 
## [262]    train-rmse:0.128222+0.003785    test-rmse:1.182488+0.252612 
## [263]    train-rmse:0.127622+0.003665    test-rmse:1.182318+0.252574 
## [264]    train-rmse:0.126735+0.003736    test-rmse:1.182487+0.252449 
## [265]    train-rmse:0.125406+0.003485    test-rmse:1.183212+0.252245 
## [266]    train-rmse:0.124531+0.003728    test-rmse:1.182801+0.252405 
## [267]    train-rmse:0.123859+0.003690    test-rmse:1.182817+0.252380 
## [268]    train-rmse:0.122753+0.003567    test-rmse:1.182735+0.252385 
## [269]    train-rmse:0.121618+0.003624    test-rmse:1.182635+0.252406 
## Stopping. Best iteration:
## [263]    train-rmse:0.127622+0.003665    test-rmse:1.182318+0.252574
## 
## 5 
## [1]  train-rmse:96.903592+0.106998   test-rmse:96.903929+0.480417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:87.282645+0.098508   test-rmse:87.282849+0.494309 
## [3]  train-rmse:78.624342+0.091095   test-rmse:78.624364+0.507694 
## [4]  train-rmse:70.833145+0.084703   test-rmse:70.832979+0.520704 
## [5]  train-rmse:63.823124+0.079254   test-rmse:63.822745+0.533458 
## [6]  train-rmse:57.516975+0.074698   test-rmse:57.516330+0.546089 
## [7]  train-rmse:51.845108+0.070990   test-rmse:51.844165+0.558710 
## [8]  train-rmse:46.744962+0.068087   test-rmse:46.743658+0.571428 
## [9]  train-rmse:42.160251+0.065957   test-rmse:42.158527+0.584358 
## [10] train-rmse:38.040358+0.064573   test-rmse:38.038154+0.597586 
## [11] train-rmse:34.337210+0.063071   test-rmse:34.350626+0.598556 
## [12] train-rmse:31.005821+0.060048   test-rmse:31.044042+0.597332 
## [13] train-rmse:28.008132+0.054748   test-rmse:28.071744+0.610449 
## [14] train-rmse:25.311016+0.048086   test-rmse:25.390260+0.601052 
## [15] train-rmse:22.884224+0.042327   test-rmse:22.971323+0.589327 
## [16] train-rmse:20.699214+0.038558   test-rmse:20.779786+0.566136 
## [17] train-rmse:18.732640+0.035501   test-rmse:18.809895+0.548494 
## [18] train-rmse:16.961778+0.032157   test-rmse:17.050837+0.549521 
## [19] train-rmse:15.365906+0.030230   test-rmse:15.456689+0.542893 
## [20] train-rmse:13.928139+0.028072   test-rmse:14.024890+0.538734 
## [21] train-rmse:12.632778+0.026272   test-rmse:12.727751+0.521288 
## [22] train-rmse:11.466344+0.024935   test-rmse:11.565012+0.514069 
## [23] train-rmse:10.413596+0.020772   test-rmse:10.517577+0.535645 
## [24] train-rmse:9.463138+0.022247    test-rmse:9.572681+0.524821 
## [25] train-rmse:8.610215+0.021615    test-rmse:8.719829+0.515855 
## [26] train-rmse:7.840771+0.019867    test-rmse:7.962845+0.532539 
## [27] train-rmse:7.147232+0.021635    test-rmse:7.276132+0.514949 
## [28] train-rmse:6.524273+0.019741    test-rmse:6.652128+0.504456 
## [29] train-rmse:5.963593+0.018418    test-rmse:6.099874+0.494550 
## [30] train-rmse:5.457976+0.020633    test-rmse:5.604529+0.502882 
## [31] train-rmse:5.002970+0.017012    test-rmse:5.162404+0.487746 
## [32] train-rmse:4.594770+0.017220    test-rmse:4.769087+0.468035 
## [33] train-rmse:4.227106+0.017223    test-rmse:4.425534+0.473939 
## [34] train-rmse:3.898614+0.016758    test-rmse:4.111958+0.457947 
## [35] train-rmse:3.602056+0.019763    test-rmse:3.820944+0.442582 
## [36] train-rmse:3.335971+0.021901    test-rmse:3.565324+0.429736 
## [37] train-rmse:3.094387+0.024820    test-rmse:3.346400+0.414779 
## [38] train-rmse:2.878213+0.024383    test-rmse:3.154377+0.407571 
## [39] train-rmse:2.681442+0.022533    test-rmse:2.985375+0.396473 
## [40] train-rmse:2.506811+0.029625    test-rmse:2.826628+0.380868 
## [41] train-rmse:2.349030+0.031148    test-rmse:2.697128+0.378217 
## [42] train-rmse:2.208151+0.031636    test-rmse:2.576620+0.376687 
## [43] train-rmse:2.078746+0.039001    test-rmse:2.476051+0.360403 
## [44] train-rmse:1.963920+0.041806    test-rmse:2.380741+0.352011 
## [45] train-rmse:1.854647+0.044382    test-rmse:2.295589+0.338518 
## [46] train-rmse:1.763569+0.045591    test-rmse:2.227750+0.329889 
## [47] train-rmse:1.677277+0.049558    test-rmse:2.167187+0.324836 
## [48] train-rmse:1.599060+0.049080    test-rmse:2.110247+0.315758 
## [49] train-rmse:1.529008+0.050307    test-rmse:2.056606+0.306483 
## [50] train-rmse:1.461772+0.053289    test-rmse:2.015686+0.301743 
## [51] train-rmse:1.402007+0.053433    test-rmse:1.981012+0.296968 
## [52] train-rmse:1.346771+0.052253    test-rmse:1.943169+0.284261 
## [53] train-rmse:1.297314+0.053890    test-rmse:1.914223+0.283852 
## [54] train-rmse:1.252922+0.053918    test-rmse:1.886825+0.272234 
## [55] train-rmse:1.208042+0.051536    test-rmse:1.857006+0.265848 
## [56] train-rmse:1.169023+0.051105    test-rmse:1.827363+0.265396 
## [57] train-rmse:1.131584+0.050248    test-rmse:1.806749+0.259060 
## [58] train-rmse:1.098067+0.050103    test-rmse:1.783080+0.259119 
## [59] train-rmse:1.062950+0.048713    test-rmse:1.763860+0.253046 
## [60] train-rmse:1.033644+0.048141    test-rmse:1.741747+0.249647 
## [61] train-rmse:1.005274+0.047371    test-rmse:1.717432+0.252832 
## [62] train-rmse:0.979046+0.047440    test-rmse:1.697538+0.251771 
## [63] train-rmse:0.950940+0.049120    test-rmse:1.684476+0.248590 
## [64] train-rmse:0.925769+0.047494    test-rmse:1.670457+0.250401 
## [65] train-rmse:0.902198+0.046417    test-rmse:1.653356+0.247043 
## [66] train-rmse:0.881092+0.045308    test-rmse:1.634769+0.247647 
## [67] train-rmse:0.858658+0.043357    test-rmse:1.616204+0.246047 
## [68] train-rmse:0.835035+0.043560    test-rmse:1.601609+0.251664 
## [69] train-rmse:0.815638+0.042759    test-rmse:1.592387+0.250551 
## [70] train-rmse:0.794662+0.043181    test-rmse:1.572212+0.253767 
## [71] train-rmse:0.776025+0.041558    test-rmse:1.557254+0.248159 
## [72] train-rmse:0.757651+0.042529    test-rmse:1.544349+0.248063 
## [73] train-rmse:0.738613+0.042237    test-rmse:1.529150+0.250937 
## [74] train-rmse:0.722363+0.041104    test-rmse:1.519494+0.250127 
## [75] train-rmse:0.706800+0.041001    test-rmse:1.512424+0.253673 
## [76] train-rmse:0.691681+0.041641    test-rmse:1.503133+0.254362 
## [77] train-rmse:0.677671+0.040774    test-rmse:1.492978+0.254472 
## [78] train-rmse:0.662581+0.038655    test-rmse:1.481230+0.254087 
## [79] train-rmse:0.648072+0.037138    test-rmse:1.473033+0.254505 
## [80] train-rmse:0.634800+0.036798    test-rmse:1.469482+0.256567 
## [81] train-rmse:0.622153+0.036703    test-rmse:1.457238+0.252266 
## [82] train-rmse:0.607037+0.037154    test-rmse:1.448234+0.251116 
## [83] train-rmse:0.595336+0.036594    test-rmse:1.441387+0.251581 
## [84] train-rmse:0.584005+0.037128    test-rmse:1.435770+0.251858 
## [85] train-rmse:0.572932+0.036265    test-rmse:1.425418+0.248404 
## [86] train-rmse:0.561885+0.036553    test-rmse:1.418768+0.248361 
## [87] train-rmse:0.551605+0.035794    test-rmse:1.411102+0.250616 
## [88] train-rmse:0.541118+0.035757    test-rmse:1.401813+0.252825 
## [89] train-rmse:0.531442+0.035944    test-rmse:1.397073+0.252382 
## [90] train-rmse:0.521641+0.034499    test-rmse:1.387563+0.249721 
## [91] train-rmse:0.511062+0.035028    test-rmse:1.383686+0.251624 
## [92] train-rmse:0.502460+0.034534    test-rmse:1.376429+0.248456 
## [93] train-rmse:0.493087+0.033768    test-rmse:1.370455+0.247343 
## [94] train-rmse:0.484444+0.033436    test-rmse:1.364160+0.248611 
## [95] train-rmse:0.476820+0.033303    test-rmse:1.361433+0.248315 
## [96] train-rmse:0.469244+0.032812    test-rmse:1.351878+0.246671 
## [97] train-rmse:0.461757+0.031807    test-rmse:1.348243+0.245659 
## [98] train-rmse:0.453301+0.032380    test-rmse:1.344142+0.246312 
## [99] train-rmse:0.444972+0.030469    test-rmse:1.338619+0.243416 
## [100]    train-rmse:0.437927+0.029251    test-rmse:1.334821+0.245566 
## [101]    train-rmse:0.431187+0.029208    test-rmse:1.330351+0.245793 
## [102]    train-rmse:0.423375+0.029863    test-rmse:1.326989+0.247850 
## [103]    train-rmse:0.416181+0.029917    test-rmse:1.324188+0.246415 
## [104]    train-rmse:0.409635+0.029824    test-rmse:1.320241+0.246018 
## [105]    train-rmse:0.403538+0.029767    test-rmse:1.316547+0.247813 
## [106]    train-rmse:0.397074+0.028787    test-rmse:1.313011+0.248842 
## [107]    train-rmse:0.391120+0.029096    test-rmse:1.309233+0.249122 
## [108]    train-rmse:0.384363+0.030368    test-rmse:1.307633+0.248683 
## [109]    train-rmse:0.378301+0.029082    test-rmse:1.303133+0.245997 
## [110]    train-rmse:0.372191+0.029558    test-rmse:1.300700+0.246379 
## [111]    train-rmse:0.366620+0.028336    test-rmse:1.296042+0.243207 
## [112]    train-rmse:0.361630+0.027929    test-rmse:1.292400+0.241062 
## [113]    train-rmse:0.356477+0.028016    test-rmse:1.290964+0.241134 
## [114]    train-rmse:0.351178+0.028376    test-rmse:1.288292+0.242376 
## [115]    train-rmse:0.346085+0.028029    test-rmse:1.284555+0.241241 
## [116]    train-rmse:0.340727+0.027961    test-rmse:1.283024+0.242667 
## [117]    train-rmse:0.335780+0.027166    test-rmse:1.280618+0.240869 
## [118]    train-rmse:0.331494+0.026828    test-rmse:1.279179+0.242046 
## [119]    train-rmse:0.326711+0.026628    test-rmse:1.277205+0.243573 
## [120]    train-rmse:0.321615+0.025205    test-rmse:1.273660+0.240513 
## [121]    train-rmse:0.317994+0.025221    test-rmse:1.271764+0.241039 
## [122]    train-rmse:0.312508+0.023304    test-rmse:1.270580+0.240728 
## [123]    train-rmse:0.308431+0.022845    test-rmse:1.269762+0.241339 
## [124]    train-rmse:0.303980+0.021850    test-rmse:1.268961+0.242416 
## [125]    train-rmse:0.300433+0.021705    test-rmse:1.267265+0.242871 
## [126]    train-rmse:0.296554+0.020407    test-rmse:1.265817+0.242774 
## [127]    train-rmse:0.292300+0.020908    test-rmse:1.263650+0.243359 
## [128]    train-rmse:0.288726+0.020209    test-rmse:1.260817+0.241495 
## [129]    train-rmse:0.284627+0.020792    test-rmse:1.260294+0.242122 
## [130]    train-rmse:0.280781+0.020478    test-rmse:1.257875+0.240386 
## [131]    train-rmse:0.277573+0.020199    test-rmse:1.256148+0.241622 
## [132]    train-rmse:0.274602+0.020436    test-rmse:1.255212+0.241683 
## [133]    train-rmse:0.270878+0.019892    test-rmse:1.253310+0.240837 
## [134]    train-rmse:0.267904+0.019324    test-rmse:1.251982+0.240880 
## [135]    train-rmse:0.264169+0.019584    test-rmse:1.251634+0.240555 
## [136]    train-rmse:0.261170+0.020116    test-rmse:1.250272+0.241026 
## [137]    train-rmse:0.257551+0.019114    test-rmse:1.249342+0.241745 
## [138]    train-rmse:0.253078+0.018510    test-rmse:1.248620+0.242290 
## [139]    train-rmse:0.250326+0.018615    test-rmse:1.247628+0.242466 
## [140]    train-rmse:0.247423+0.018372    test-rmse:1.246732+0.242170 
## [141]    train-rmse:0.244275+0.018036    test-rmse:1.245261+0.243681 
## [142]    train-rmse:0.241549+0.017722    test-rmse:1.244745+0.244085 
## [143]    train-rmse:0.238305+0.017903    test-rmse:1.244053+0.243994 
## [144]    train-rmse:0.235429+0.018042    test-rmse:1.243404+0.244993 
## [145]    train-rmse:0.232189+0.017150    test-rmse:1.243493+0.245016 
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## [261]    train-rmse:0.067361+0.004845    test-rmse:1.212833+0.248570 
## [262]    train-rmse:0.066791+0.004716    test-rmse:1.212693+0.248594 
## [263]    train-rmse:0.066147+0.004941    test-rmse:1.212611+0.248587 
## [264]    train-rmse:0.065311+0.005049    test-rmse:1.212626+0.248506 
## [265]    train-rmse:0.064751+0.005103    test-rmse:1.212696+0.248631 
## [266]    train-rmse:0.064151+0.005077    test-rmse:1.212614+0.248625 
## [267]    train-rmse:0.063498+0.005040    test-rmse:1.212426+0.248447 
## [268]    train-rmse:0.062709+0.004901    test-rmse:1.212267+0.248468 
## [269]    train-rmse:0.062293+0.004901    test-rmse:1.212241+0.248379 
## [270]    train-rmse:0.061671+0.004988    test-rmse:1.212245+0.248155 
## [271]    train-rmse:0.061002+0.005106    test-rmse:1.212249+0.248099 
## [272]    train-rmse:0.060459+0.005219    test-rmse:1.212239+0.248044 
## [273]    train-rmse:0.059724+0.004808    test-rmse:1.212245+0.247992 
## [274]    train-rmse:0.059164+0.004741    test-rmse:1.212087+0.247848 
## [275]    train-rmse:0.058608+0.004716    test-rmse:1.211961+0.247756 
## [276]    train-rmse:0.058156+0.004792    test-rmse:1.212006+0.247822 
## [277]    train-rmse:0.057752+0.004727    test-rmse:1.211988+0.247764 
## [278]    train-rmse:0.057318+0.004622    test-rmse:1.211907+0.247709 
## [279]    train-rmse:0.056952+0.004534    test-rmse:1.211936+0.247788 
## [280]    train-rmse:0.056397+0.004523    test-rmse:1.211974+0.247692 
## [281]    train-rmse:0.055680+0.004509    test-rmse:1.211761+0.247767 
## [282]    train-rmse:0.055181+0.004473    test-rmse:1.211628+0.247909 
## [283]    train-rmse:0.054639+0.004293    test-rmse:1.211506+0.247919 
## [284]    train-rmse:0.054158+0.004368    test-rmse:1.211497+0.247825 
## [285]    train-rmse:0.053630+0.004305    test-rmse:1.211407+0.247720 
## [286]    train-rmse:0.053171+0.004423    test-rmse:1.211211+0.247796 
## [287]    train-rmse:0.052620+0.004245    test-rmse:1.211242+0.247878 
## [288]    train-rmse:0.051954+0.004214    test-rmse:1.211051+0.247714 
## [289]    train-rmse:0.051444+0.004116    test-rmse:1.210976+0.247765 
## [290]    train-rmse:0.050968+0.004185    test-rmse:1.210967+0.247605 
## [291]    train-rmse:0.050536+0.004031    test-rmse:1.211056+0.247645 
## [292]    train-rmse:0.050122+0.004159    test-rmse:1.211075+0.247612 
## [293]    train-rmse:0.049566+0.004047    test-rmse:1.211000+0.247427 
## [294]    train-rmse:0.049127+0.004111    test-rmse:1.210903+0.247367 
## [295]    train-rmse:0.048754+0.004104    test-rmse:1.210747+0.247326 
## [296]    train-rmse:0.048311+0.004007    test-rmse:1.210566+0.247312 
## [297]    train-rmse:0.047687+0.003919    test-rmse:1.210565+0.247311 
## [298]    train-rmse:0.047164+0.003678    test-rmse:1.210546+0.247386 
## [299]    train-rmse:0.046731+0.003580    test-rmse:1.210460+0.247416 
## [300]    train-rmse:0.046177+0.003582    test-rmse:1.210410+0.247475 
## [301]    train-rmse:0.045851+0.003684    test-rmse:1.210358+0.247429 
## [302]    train-rmse:0.045210+0.003621    test-rmse:1.210401+0.247506 
## [303]    train-rmse:0.044885+0.003584    test-rmse:1.210426+0.247423 
## [304]    train-rmse:0.044384+0.003440    test-rmse:1.210390+0.247387 
## [305]    train-rmse:0.043975+0.003322    test-rmse:1.210407+0.247363 
## [306]    train-rmse:0.043519+0.003149    test-rmse:1.210330+0.247369 
## [307]    train-rmse:0.043228+0.003150    test-rmse:1.210290+0.247420 
## [308]    train-rmse:0.042930+0.003098    test-rmse:1.210273+0.247362 
## [309]    train-rmse:0.042585+0.002923    test-rmse:1.210229+0.247435 
## [310]    train-rmse:0.042179+0.003010    test-rmse:1.210210+0.247549 
## [311]    train-rmse:0.041737+0.002994    test-rmse:1.210082+0.247519 
## [312]    train-rmse:0.041409+0.003088    test-rmse:1.210122+0.247490 
## [313]    train-rmse:0.040927+0.002947    test-rmse:1.210026+0.247417 
## [314]    train-rmse:0.040493+0.002952    test-rmse:1.209934+0.247436 
## [315]    train-rmse:0.040042+0.003008    test-rmse:1.209863+0.247461 
## [316]    train-rmse:0.039764+0.002929    test-rmse:1.209846+0.247474 
## [317]    train-rmse:0.039466+0.002958    test-rmse:1.209807+0.247476 
## [318]    train-rmse:0.039054+0.002924    test-rmse:1.209866+0.247423 
## [319]    train-rmse:0.038730+0.002874    test-rmse:1.209821+0.247384 
## [320]    train-rmse:0.038336+0.002900    test-rmse:1.209625+0.247151 
## [321]    train-rmse:0.038075+0.002812    test-rmse:1.209714+0.247181 
## [322]    train-rmse:0.037710+0.002812    test-rmse:1.209678+0.247205 
## [323]    train-rmse:0.037351+0.002709    test-rmse:1.209631+0.247140 
## [324]    train-rmse:0.037069+0.002698    test-rmse:1.209652+0.247124 
## [325]    train-rmse:0.036692+0.002708    test-rmse:1.209492+0.247147 
## [326]    train-rmse:0.036362+0.002778    test-rmse:1.209541+0.247118 
## [327]    train-rmse:0.036069+0.002784    test-rmse:1.209532+0.247140 
## [328]    train-rmse:0.035804+0.002710    test-rmse:1.209395+0.247175 
## [329]    train-rmse:0.035279+0.002646    test-rmse:1.209380+0.247147 
## [330]    train-rmse:0.035013+0.002630    test-rmse:1.209311+0.247113 
## [331]    train-rmse:0.034783+0.002685    test-rmse:1.209318+0.247094 
## [332]    train-rmse:0.034378+0.002593    test-rmse:1.209375+0.247085 
## [333]    train-rmse:0.034139+0.002569    test-rmse:1.209396+0.247073 
## [334]    train-rmse:0.033754+0.002605    test-rmse:1.209464+0.247110 
## [335]    train-rmse:0.033438+0.002525    test-rmse:1.209453+0.247097 
## [336]    train-rmse:0.033075+0.002415    test-rmse:1.209494+0.247032 
## Stopping. Best iteration:
## [330]    train-rmse:0.035013+0.002630    test-rmse:1.209311+0.247113
## 
## 6 
## [1]  train-rmse:96.903592+0.106998   test-rmse:96.903929+0.480417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:87.282645+0.098508   test-rmse:87.282849+0.494309 
## [3]  train-rmse:78.624342+0.091095   test-rmse:78.624364+0.507694 
## [4]  train-rmse:70.833145+0.084703   test-rmse:70.832979+0.520704 
## [5]  train-rmse:63.823124+0.079254   test-rmse:63.822745+0.533458 
## [6]  train-rmse:57.516975+0.074698   test-rmse:57.516330+0.546089 
## [7]  train-rmse:51.845108+0.070990   test-rmse:51.844165+0.558710 
## [8]  train-rmse:46.744962+0.068087   test-rmse:46.743658+0.571428 
## [9]  train-rmse:42.160251+0.065957   test-rmse:42.158527+0.584358 
## [10] train-rmse:38.040358+0.064573   test-rmse:38.038154+0.597586 
## [11] train-rmse:34.337210+0.063071   test-rmse:34.350626+0.598556 
## [12] train-rmse:31.005821+0.060048   test-rmse:31.044042+0.597332 
## [13] train-rmse:28.008132+0.054748   test-rmse:28.071744+0.610449 
## [14] train-rmse:25.311016+0.048086   test-rmse:25.390260+0.601052 
## [15] train-rmse:22.884224+0.042327   test-rmse:22.971323+0.589327 
## [16] train-rmse:20.699120+0.038556   test-rmse:20.776404+0.565117 
## [17] train-rmse:18.732227+0.035347   test-rmse:18.806225+0.551060 
## [18] train-rmse:16.960527+0.032078   test-rmse:17.051561+0.548661 
## [19] train-rmse:15.363785+0.030173   test-rmse:15.453523+0.547278 
## [20] train-rmse:13.925187+0.027859   test-rmse:14.016791+0.538118 
## [21] train-rmse:12.628441+0.026397   test-rmse:12.719716+0.523291 
## [22] train-rmse:11.460509+0.024725   test-rmse:11.555876+0.517683 
## [23] train-rmse:10.405973+0.023184   test-rmse:10.513737+0.530469 
## [24] train-rmse:9.450404+0.021873    test-rmse:9.562061+0.527873 
## [25] train-rmse:8.593586+0.020524    test-rmse:8.699985+0.515564 
## [26] train-rmse:7.819321+0.018185    test-rmse:7.942457+0.526541 
## [27] train-rmse:7.119594+0.019717    test-rmse:7.244492+0.513656 
## [28] train-rmse:6.491688+0.018154    test-rmse:6.616384+0.500985 
## [29] train-rmse:5.922559+0.013914    test-rmse:6.067761+0.505958 
## [30] train-rmse:5.409595+0.014938    test-rmse:5.563262+0.486892 
## [31] train-rmse:4.945688+0.017245    test-rmse:5.107922+0.474043 
## [32] train-rmse:4.529206+0.016881    test-rmse:4.707647+0.459525 
## [33] train-rmse:4.154580+0.015890    test-rmse:4.353662+0.457948 
## [34] train-rmse:3.817614+0.016413    test-rmse:4.020540+0.453289 
## [35] train-rmse:3.511798+0.015001    test-rmse:3.735819+0.436369 
## [36] train-rmse:3.237723+0.014854    test-rmse:3.479325+0.426283 
## [37] train-rmse:2.990964+0.016874    test-rmse:3.251292+0.410163 
## [38] train-rmse:2.768075+0.016523    test-rmse:3.059122+0.401471 
## [39] train-rmse:2.568963+0.016977    test-rmse:2.881828+0.395944 
## [40] train-rmse:2.387283+0.020207    test-rmse:2.716601+0.385618 
## [41] train-rmse:2.228047+0.020761    test-rmse:2.577999+0.375698 
## [42] train-rmse:2.080347+0.020191    test-rmse:2.461563+0.369516 
## [43] train-rmse:1.947399+0.018072    test-rmse:2.351671+0.352871 
## [44] train-rmse:1.824270+0.020653    test-rmse:2.252942+0.347317 
## [45] train-rmse:1.711438+0.022935    test-rmse:2.173382+0.335905 
## [46] train-rmse:1.610031+0.023591    test-rmse:2.099643+0.319942 
## [47] train-rmse:1.520385+0.025886    test-rmse:2.034813+0.318073 
## [48] train-rmse:1.436894+0.023609    test-rmse:1.972896+0.301116 
## [49] train-rmse:1.359636+0.029695    test-rmse:1.925192+0.291040 
## [50] train-rmse:1.289203+0.030152    test-rmse:1.875411+0.284089 
## [51] train-rmse:1.223412+0.029692    test-rmse:1.830740+0.280501 
## [52] train-rmse:1.164917+0.029138    test-rmse:1.791876+0.272738 
## [53] train-rmse:1.111661+0.032644    test-rmse:1.759923+0.275050 
## [54] train-rmse:1.066081+0.032944    test-rmse:1.730979+0.272665 
## [55] train-rmse:1.021644+0.033897    test-rmse:1.704071+0.265895 
## [56] train-rmse:0.981832+0.035657    test-rmse:1.682905+0.261600 
## [57] train-rmse:0.944074+0.035785    test-rmse:1.661272+0.259075 
## [58] train-rmse:0.907958+0.034833    test-rmse:1.637381+0.255850 
## [59] train-rmse:0.873163+0.035855    test-rmse:1.615099+0.256005 
## [60] train-rmse:0.842191+0.037772    test-rmse:1.597590+0.255652 
## [61] train-rmse:0.814739+0.036335    test-rmse:1.578486+0.258930 
## [62] train-rmse:0.786970+0.036819    test-rmse:1.554846+0.262054 
## [63] train-rmse:0.762222+0.037636    test-rmse:1.541225+0.259383 
## [64] train-rmse:0.739707+0.037105    test-rmse:1.525889+0.260196 
## [65] train-rmse:0.717641+0.036817    test-rmse:1.516474+0.263383 
## [66] train-rmse:0.697421+0.037477    test-rmse:1.507365+0.260252 
## [67] train-rmse:0.676396+0.035998    test-rmse:1.494669+0.261080 
## [68] train-rmse:0.659020+0.035811    test-rmse:1.482273+0.254677 
## [69] train-rmse:0.640118+0.035985    test-rmse:1.471215+0.254907 
## [70] train-rmse:0.622171+0.037659    test-rmse:1.460511+0.250348 
## [71] train-rmse:0.605557+0.037423    test-rmse:1.450496+0.251393 
## [72] train-rmse:0.590287+0.036887    test-rmse:1.439117+0.254208 
## [73] train-rmse:0.574305+0.036979    test-rmse:1.429032+0.253361 
## [74] train-rmse:0.559085+0.033178    test-rmse:1.418036+0.248544 
## [75] train-rmse:0.545246+0.033137    test-rmse:1.414635+0.250243 
## [76] train-rmse:0.532492+0.033695    test-rmse:1.404688+0.251886 
## [77] train-rmse:0.518860+0.031999    test-rmse:1.395494+0.247605 
## [78] train-rmse:0.507176+0.032067    test-rmse:1.389294+0.245387 
## [79] train-rmse:0.494961+0.032088    test-rmse:1.381248+0.243380 
## [80] train-rmse:0.484869+0.030783    test-rmse:1.373514+0.247845 
## [81] train-rmse:0.473937+0.031166    test-rmse:1.369038+0.248159 
## [82] train-rmse:0.463482+0.031138    test-rmse:1.362944+0.246230 
## [83] train-rmse:0.454085+0.031284    test-rmse:1.358079+0.245779 
## [84] train-rmse:0.445567+0.031167    test-rmse:1.352343+0.247117 
## [85] train-rmse:0.435850+0.030425    test-rmse:1.348883+0.247468 
## [86] train-rmse:0.426999+0.030024    test-rmse:1.342698+0.250709 
## [87] train-rmse:0.418416+0.029268    test-rmse:1.339383+0.252203 
## [88] train-rmse:0.410361+0.029576    test-rmse:1.336131+0.252390 
## [89] train-rmse:0.402820+0.029604    test-rmse:1.331422+0.251962 
## [90] train-rmse:0.394924+0.029334    test-rmse:1.328206+0.252958 
## [91] train-rmse:0.385952+0.026935    test-rmse:1.324968+0.251452 
## [92] train-rmse:0.377670+0.026195    test-rmse:1.321500+0.253228 
## [93] train-rmse:0.370458+0.025594    test-rmse:1.319633+0.254545 
## [94] train-rmse:0.363654+0.025131    test-rmse:1.316978+0.254882 
## [95] train-rmse:0.356270+0.025488    test-rmse:1.315782+0.255790 
## [96] train-rmse:0.350045+0.025504    test-rmse:1.312682+0.256837 
## [97] train-rmse:0.344147+0.023895    test-rmse:1.308600+0.257574 
## [98] train-rmse:0.338003+0.023900    test-rmse:1.305068+0.257706 
## [99] train-rmse:0.332077+0.023660    test-rmse:1.304204+0.257508 
## [100]    train-rmse:0.325646+0.023448    test-rmse:1.300819+0.257649 
## [101]    train-rmse:0.320053+0.023614    test-rmse:1.297990+0.259335 
## [102]    train-rmse:0.314433+0.023697    test-rmse:1.295668+0.259495 
## [103]    train-rmse:0.309610+0.022906    test-rmse:1.293477+0.261109 
## [104]    train-rmse:0.305229+0.022362    test-rmse:1.292247+0.262367 
## [105]    train-rmse:0.299732+0.022068    test-rmse:1.290408+0.263506 
## [106]    train-rmse:0.295239+0.021602    test-rmse:1.288195+0.262610 
## [107]    train-rmse:0.289549+0.020993    test-rmse:1.286291+0.263683 
## [108]    train-rmse:0.283817+0.020548    test-rmse:1.285945+0.264243 
## [109]    train-rmse:0.279563+0.020644    test-rmse:1.284485+0.264237 
## [110]    train-rmse:0.275401+0.020884    test-rmse:1.282713+0.264777 
## [111]    train-rmse:0.271322+0.020918    test-rmse:1.281359+0.265803 
## [112]    train-rmse:0.267363+0.020155    test-rmse:1.280789+0.265254 
## [113]    train-rmse:0.264035+0.020135    test-rmse:1.278386+0.265683 
## [114]    train-rmse:0.259217+0.020447    test-rmse:1.276975+0.266538 
## [115]    train-rmse:0.255305+0.020377    test-rmse:1.274727+0.267612 
## [116]    train-rmse:0.250683+0.019609    test-rmse:1.273777+0.267870 
## [117]    train-rmse:0.246829+0.019358    test-rmse:1.272315+0.268348 
## [118]    train-rmse:0.243722+0.019306    test-rmse:1.271884+0.267721 
## [119]    train-rmse:0.240175+0.019574    test-rmse:1.270856+0.268471 
## [120]    train-rmse:0.236642+0.018894    test-rmse:1.268569+0.269243 
## [121]    train-rmse:0.232612+0.019223    test-rmse:1.267131+0.269383 
## [122]    train-rmse:0.229103+0.019080    test-rmse:1.266821+0.269820 
## [123]    train-rmse:0.225729+0.019573    test-rmse:1.266207+0.270188 
## [124]    train-rmse:0.221587+0.018123    test-rmse:1.263636+0.268981 
## [125]    train-rmse:0.217668+0.017071    test-rmse:1.262615+0.268706 
## [126]    train-rmse:0.215044+0.017258    test-rmse:1.261031+0.269493 
## [127]    train-rmse:0.212033+0.016935    test-rmse:1.260701+0.269829 
## [128]    train-rmse:0.208629+0.016447    test-rmse:1.260269+0.270555 
## [129]    train-rmse:0.205026+0.016128    test-rmse:1.260273+0.271052 
## [130]    train-rmse:0.201569+0.015338    test-rmse:1.259222+0.270686 
## [131]    train-rmse:0.198709+0.014486    test-rmse:1.258547+0.270815 
## [132]    train-rmse:0.195584+0.014589    test-rmse:1.258870+0.270469 
## [133]    train-rmse:0.193077+0.014565    test-rmse:1.258614+0.270217 
## [134]    train-rmse:0.189869+0.014081    test-rmse:1.257470+0.269862 
## [135]    train-rmse:0.186646+0.014451    test-rmse:1.257296+0.269885 
## [136]    train-rmse:0.184339+0.014631    test-rmse:1.256736+0.269987 
## [137]    train-rmse:0.181060+0.014898    test-rmse:1.256457+0.270077 
## [138]    train-rmse:0.178481+0.014738    test-rmse:1.255773+0.270964 
## [139]    train-rmse:0.176332+0.014329    test-rmse:1.255462+0.271413 
## [140]    train-rmse:0.173984+0.013023    test-rmse:1.254506+0.271904 
## [141]    train-rmse:0.171895+0.013281    test-rmse:1.254193+0.272017 
## [142]    train-rmse:0.168671+0.012658    test-rmse:1.253811+0.271674 
## [143]    train-rmse:0.166364+0.012856    test-rmse:1.253297+0.271981 
## [144]    train-rmse:0.164105+0.013233    test-rmse:1.253041+0.272457 
## [145]    train-rmse:0.161466+0.012815    test-rmse:1.252535+0.272568 
## [146]    train-rmse:0.159265+0.012840    test-rmse:1.252224+0.272869 
## [147]    train-rmse:0.156132+0.012287    test-rmse:1.251834+0.272951 
## [148]    train-rmse:0.154179+0.012193    test-rmse:1.251606+0.273127 
## [149]    train-rmse:0.151629+0.011666    test-rmse:1.251398+0.273425 
## [150]    train-rmse:0.149652+0.011087    test-rmse:1.250804+0.273697 
## [151]    train-rmse:0.146940+0.010298    test-rmse:1.250390+0.273755 
## [152]    train-rmse:0.144715+0.010188    test-rmse:1.249973+0.273503 
## [153]    train-rmse:0.143247+0.009923    test-rmse:1.249699+0.273559 
## [154]    train-rmse:0.141020+0.009755    test-rmse:1.249469+0.273307 
## [155]    train-rmse:0.138761+0.009652    test-rmse:1.249125+0.273856 
## [156]    train-rmse:0.137336+0.009435    test-rmse:1.248307+0.273636 
## [157]    train-rmse:0.135687+0.009207    test-rmse:1.248024+0.273358 
## [158]    train-rmse:0.133575+0.009057    test-rmse:1.247615+0.273366 
## [159]    train-rmse:0.131577+0.009335    test-rmse:1.247449+0.273437 
## [160]    train-rmse:0.129809+0.009293    test-rmse:1.247399+0.273588 
## [161]    train-rmse:0.127798+0.008592    test-rmse:1.247264+0.273514 
## [162]    train-rmse:0.125989+0.008988    test-rmse:1.247080+0.273416 
## [163]    train-rmse:0.124197+0.009200    test-rmse:1.246953+0.273375 
## [164]    train-rmse:0.122647+0.008835    test-rmse:1.246946+0.273285 
## [165]    train-rmse:0.120915+0.008907    test-rmse:1.246557+0.273037 
## [166]    train-rmse:0.119128+0.008439    test-rmse:1.245743+0.273612 
## [167]    train-rmse:0.117459+0.008705    test-rmse:1.245127+0.273810 
## [168]    train-rmse:0.115793+0.008248    test-rmse:1.244669+0.273935 
## [169]    train-rmse:0.114211+0.008399    test-rmse:1.244893+0.273796 
## [170]    train-rmse:0.112675+0.008213    test-rmse:1.244501+0.273573 
## [171]    train-rmse:0.111072+0.008496    test-rmse:1.244730+0.273507 
## [172]    train-rmse:0.109566+0.008720    test-rmse:1.244919+0.273703 
## [173]    train-rmse:0.108810+0.008828    test-rmse:1.244556+0.273695 
## [174]    train-rmse:0.107443+0.008650    test-rmse:1.244393+0.273709 
## [175]    train-rmse:0.106387+0.008513    test-rmse:1.244302+0.273453 
## [176]    train-rmse:0.104890+0.008503    test-rmse:1.244220+0.273105 
## [177]    train-rmse:0.103862+0.008564    test-rmse:1.244019+0.273375 
## [178]    train-rmse:0.102919+0.008868    test-rmse:1.243595+0.273471 
## [179]    train-rmse:0.101228+0.008481    test-rmse:1.243170+0.273764 
## [180]    train-rmse:0.099942+0.008477    test-rmse:1.242513+0.273494 
## [181]    train-rmse:0.098768+0.008601    test-rmse:1.242269+0.273518 
## [182]    train-rmse:0.097345+0.008250    test-rmse:1.242053+0.273682 
## [183]    train-rmse:0.096283+0.008486    test-rmse:1.241993+0.273797 
## [184]    train-rmse:0.095226+0.008528    test-rmse:1.242088+0.273478 
## [185]    train-rmse:0.093916+0.008634    test-rmse:1.241361+0.273454 
## [186]    train-rmse:0.092864+0.008663    test-rmse:1.240936+0.273366 
## [187]    train-rmse:0.091351+0.008591    test-rmse:1.240764+0.273515 
## [188]    train-rmse:0.090548+0.008842    test-rmse:1.240674+0.273754 
## [189]    train-rmse:0.089372+0.008863    test-rmse:1.240534+0.273671 
## [190]    train-rmse:0.087980+0.008715    test-rmse:1.240472+0.273669 
## [191]    train-rmse:0.086418+0.008465    test-rmse:1.240286+0.273732 
## [192]    train-rmse:0.084876+0.008305    test-rmse:1.239730+0.273670 
## [193]    train-rmse:0.083833+0.008582    test-rmse:1.239712+0.273815 
## [194]    train-rmse:0.083025+0.008478    test-rmse:1.239605+0.273932 
## [195]    train-rmse:0.082003+0.008840    test-rmse:1.239267+0.273980 
## [196]    train-rmse:0.080692+0.008508    test-rmse:1.239305+0.273919 
## [197]    train-rmse:0.079500+0.008342    test-rmse:1.239275+0.273880 
## [198]    train-rmse:0.078578+0.008355    test-rmse:1.238881+0.273293 
## [199]    train-rmse:0.077473+0.008778    test-rmse:1.238544+0.273116 
## [200]    train-rmse:0.076577+0.008451    test-rmse:1.238623+0.273319 
## [201]    train-rmse:0.075450+0.008518    test-rmse:1.238115+0.272737 
## [202]    train-rmse:0.074568+0.008351    test-rmse:1.238116+0.272791 
## [203]    train-rmse:0.073706+0.008046    test-rmse:1.238038+0.272763 
## [204]    train-rmse:0.072835+0.008268    test-rmse:1.237921+0.272720 
## [205]    train-rmse:0.072170+0.008181    test-rmse:1.237903+0.272743 
## [206]    train-rmse:0.071296+0.007885    test-rmse:1.237710+0.272356 
## [207]    train-rmse:0.070366+0.007507    test-rmse:1.237461+0.272412 
## [208]    train-rmse:0.069678+0.007669    test-rmse:1.237228+0.272565 
## [209]    train-rmse:0.068508+0.007709    test-rmse:1.237243+0.272626 
## [210]    train-rmse:0.067705+0.007903    test-rmse:1.237095+0.272585 
## [211]    train-rmse:0.066560+0.007699    test-rmse:1.237203+0.272439 
## [212]    train-rmse:0.065770+0.007517    test-rmse:1.237046+0.272527 
## [213]    train-rmse:0.064901+0.007493    test-rmse:1.236986+0.272350 
## [214]    train-rmse:0.063906+0.007078    test-rmse:1.236889+0.272335 
## [215]    train-rmse:0.063116+0.006887    test-rmse:1.236672+0.272136 
## [216]    train-rmse:0.062369+0.006870    test-rmse:1.236877+0.272133 
## [217]    train-rmse:0.061790+0.006808    test-rmse:1.236754+0.272164 
## [218]    train-rmse:0.060969+0.006814    test-rmse:1.236743+0.272292 
## [219]    train-rmse:0.060091+0.006609    test-rmse:1.236765+0.272197 
## [220]    train-rmse:0.059248+0.006403    test-rmse:1.236577+0.272223 
## [221]    train-rmse:0.058604+0.006411    test-rmse:1.236470+0.272306 
## [222]    train-rmse:0.057669+0.006204    test-rmse:1.236467+0.272395 
## [223]    train-rmse:0.057038+0.006061    test-rmse:1.236415+0.272462 
## [224]    train-rmse:0.056312+0.005958    test-rmse:1.236309+0.272541 
## [225]    train-rmse:0.055785+0.005909    test-rmse:1.236252+0.272552 
## [226]    train-rmse:0.055240+0.005782    test-rmse:1.236114+0.272682 
## [227]    train-rmse:0.054642+0.005744    test-rmse:1.235956+0.272606 
## [228]    train-rmse:0.053926+0.005770    test-rmse:1.235905+0.272533 
## [229]    train-rmse:0.052989+0.005818    test-rmse:1.236235+0.272454 
## [230]    train-rmse:0.052256+0.005723    test-rmse:1.236124+0.272499 
## [231]    train-rmse:0.051587+0.005620    test-rmse:1.236071+0.272501 
## [232]    train-rmse:0.050959+0.005545    test-rmse:1.235927+0.272445 
## [233]    train-rmse:0.050444+0.005460    test-rmse:1.235855+0.272306 
## [234]    train-rmse:0.049873+0.005658    test-rmse:1.235781+0.272327 
## [235]    train-rmse:0.049271+0.005406    test-rmse:1.235700+0.272366 
## [236]    train-rmse:0.048696+0.005477    test-rmse:1.235708+0.272464 
## [237]    train-rmse:0.048046+0.005125    test-rmse:1.235647+0.272486 
## [238]    train-rmse:0.047534+0.005082    test-rmse:1.235611+0.272586 
## [239]    train-rmse:0.046932+0.004966    test-rmse:1.235534+0.272607 
## [240]    train-rmse:0.046319+0.005072    test-rmse:1.235445+0.272566 
## [241]    train-rmse:0.045749+0.004760    test-rmse:1.235492+0.272742 
## [242]    train-rmse:0.045277+0.004759    test-rmse:1.235382+0.272706 
## [243]    train-rmse:0.044571+0.004765    test-rmse:1.235379+0.272709 
## [244]    train-rmse:0.044104+0.004769    test-rmse:1.235262+0.272761 
## [245]    train-rmse:0.043597+0.004847    test-rmse:1.235160+0.272867 
## [246]    train-rmse:0.043102+0.004871    test-rmse:1.234977+0.272786 
## [247]    train-rmse:0.042667+0.004861    test-rmse:1.234883+0.272753 
## [248]    train-rmse:0.042048+0.004999    test-rmse:1.234813+0.272746 
## [249]    train-rmse:0.041629+0.004749    test-rmse:1.234759+0.272767 
## [250]    train-rmse:0.041148+0.004821    test-rmse:1.234848+0.272814 
## [251]    train-rmse:0.040462+0.004915    test-rmse:1.234655+0.272536 
## [252]    train-rmse:0.039962+0.004881    test-rmse:1.234735+0.272683 
## [253]    train-rmse:0.039553+0.004884    test-rmse:1.234671+0.272680 
## [254]    train-rmse:0.039082+0.004891    test-rmse:1.234648+0.272679 
## [255]    train-rmse:0.038437+0.004761    test-rmse:1.234755+0.272725 
## [256]    train-rmse:0.038073+0.004809    test-rmse:1.234718+0.272803 
## [257]    train-rmse:0.037652+0.004858    test-rmse:1.234692+0.272727 
## [258]    train-rmse:0.037117+0.004780    test-rmse:1.234709+0.272783 
## [259]    train-rmse:0.036536+0.004590    test-rmse:1.234638+0.272882 
## [260]    train-rmse:0.036065+0.004442    test-rmse:1.234594+0.272966 
## [261]    train-rmse:0.035560+0.004489    test-rmse:1.234519+0.272998 
## [262]    train-rmse:0.035233+0.004304    test-rmse:1.234502+0.273018 
## [263]    train-rmse:0.034891+0.004393    test-rmse:1.234499+0.273050 
## [264]    train-rmse:0.034492+0.004251    test-rmse:1.234510+0.273031 
## [265]    train-rmse:0.033993+0.003960    test-rmse:1.234525+0.272944 
## [266]    train-rmse:0.033602+0.003878    test-rmse:1.234551+0.272976 
## [267]    train-rmse:0.033233+0.003992    test-rmse:1.234462+0.273045 
## [268]    train-rmse:0.032800+0.003820    test-rmse:1.234505+0.273154 
## [269]    train-rmse:0.032488+0.003831    test-rmse:1.234456+0.273169 
## [270]    train-rmse:0.032011+0.003922    test-rmse:1.234533+0.273176 
## [271]    train-rmse:0.031644+0.003729    test-rmse:1.234497+0.273254 
## [272]    train-rmse:0.031137+0.003691    test-rmse:1.234522+0.273179 
## [273]    train-rmse:0.030792+0.003706    test-rmse:1.234582+0.273160 
## [274]    train-rmse:0.030446+0.003704    test-rmse:1.234569+0.273210 
## [275]    train-rmse:0.030136+0.003655    test-rmse:1.234618+0.273181 
## Stopping. Best iteration:
## [269]    train-rmse:0.032488+0.003831    test-rmse:1.234456+0.273169
## 
## 7 
## [1]  train-rmse:94.766116+0.105094   test-rmse:94.766423+0.483432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:83.478061+0.095217   test-rmse:83.478186+0.500072 
## [3]  train-rmse:73.545764+0.086897   test-rmse:73.545670+0.516055 
## [4]  train-rmse:64.807895+0.080011   test-rmse:64.807548+0.531590 
## [5]  train-rmse:57.122527+0.074434   test-rmse:57.121858+0.546922 
## [6]  train-rmse:50.364803+0.070103   test-rmse:50.363757+0.562250 
## [7]  train-rmse:44.424928+0.066945   test-rmse:44.423422+0.577769 
## [8]  train-rmse:39.206363+0.064904   test-rmse:39.204310+0.593657 
## [9]  train-rmse:34.622293+0.063135   test-rmse:34.630677+0.594785 
## [10] train-rmse:30.594723+0.060722   test-rmse:30.630489+0.604196 
## [11] train-rmse:27.056456+0.057873   test-rmse:27.095997+0.613561 
## [12] train-rmse:23.949549+0.054320   test-rmse:23.995303+0.615775 
## [13] train-rmse:21.222715+0.050457   test-rmse:21.269670+0.604507 
## [14] train-rmse:18.832115+0.047483   test-rmse:18.883423+0.604515 
## [15] train-rmse:16.738141+0.044457   test-rmse:16.790695+0.593163 
## [16] train-rmse:14.907185+0.042292   test-rmse:14.963802+0.593355 
## [17] train-rmse:13.307986+0.040939   test-rmse:13.386424+0.612319 
## [18] train-rmse:11.915518+0.039770   test-rmse:11.992147+0.611774 
## [19] train-rmse:10.704840+0.039595   test-rmse:10.781294+0.594755 
## [20] train-rmse:9.655175+0.040497    test-rmse:9.750042+0.623974 
## [21] train-rmse:8.748829+0.041598    test-rmse:8.856167+0.612053 
## [22] train-rmse:7.967544+0.043582    test-rmse:8.063941+0.603644 
## [23] train-rmse:7.298479+0.045003    test-rmse:7.402483+0.613350 
## [24] train-rmse:6.725725+0.046385    test-rmse:6.869280+0.634522 
## [25] train-rmse:6.238510+0.048091    test-rmse:6.361015+0.615094 
## [26] train-rmse:5.825917+0.050446    test-rmse:5.933825+0.593497 
## [27] train-rmse:5.477669+0.052186    test-rmse:5.605874+0.605462 
## [28] train-rmse:5.183190+0.053479    test-rmse:5.315651+0.573886 
## [29] train-rmse:4.936568+0.055848    test-rmse:5.075822+0.581816 
## [30] train-rmse:4.728897+0.057870    test-rmse:4.860011+0.562708 
## [31] train-rmse:4.554202+0.058722    test-rmse:4.685291+0.533175 
## [32] train-rmse:4.406881+0.058998    test-rmse:4.561367+0.533926 
## [33] train-rmse:4.281675+0.059823    test-rmse:4.422080+0.504101 
## [34] train-rmse:4.175976+0.060859    test-rmse:4.321895+0.502744 
## [35] train-rmse:4.085136+0.060910    test-rmse:4.236879+0.506586 
## [36] train-rmse:4.006612+0.060149    test-rmse:4.150903+0.467718 
## [37] train-rmse:3.938293+0.059631    test-rmse:4.102550+0.473081 
## [38] train-rmse:3.877959+0.059648    test-rmse:4.039883+0.436625 
## [39] train-rmse:3.824328+0.059555    test-rmse:3.994279+0.435802 
## [40] train-rmse:3.776243+0.059393    test-rmse:3.951819+0.437385 
## [41] train-rmse:3.732029+0.058969    test-rmse:3.910642+0.436886 
## [42] train-rmse:3.691358+0.058459    test-rmse:3.887112+0.427359 
## [43] train-rmse:3.653409+0.057759    test-rmse:3.849881+0.427098 
## [44] train-rmse:3.618822+0.057420    test-rmse:3.821198+0.401793 
## [45] train-rmse:3.586348+0.057267    test-rmse:3.779050+0.381496 
## [46] train-rmse:3.555542+0.056969    test-rmse:3.754748+0.391545 
## [47] train-rmse:3.525970+0.056124    test-rmse:3.728019+0.387419 
## [48] train-rmse:3.497606+0.055057    test-rmse:3.695316+0.392074 
## [49] train-rmse:3.469982+0.054110    test-rmse:3.675901+0.386560 
## [50] train-rmse:3.443124+0.053374    test-rmse:3.662275+0.373258 
## [51] train-rmse:3.416811+0.052758    test-rmse:3.634631+0.379196 
## [52] train-rmse:3.391136+0.052163    test-rmse:3.617841+0.377881 
## [53] train-rmse:3.366028+0.051610    test-rmse:3.587519+0.363524 
## [54] train-rmse:3.342311+0.051285    test-rmse:3.564214+0.350329 
## [55] train-rmse:3.319122+0.050930    test-rmse:3.542951+0.356820 
## [56] train-rmse:3.296104+0.050593    test-rmse:3.521122+0.355594 
## [57] train-rmse:3.273719+0.050104    test-rmse:3.501817+0.357790 
## [58] train-rmse:3.251579+0.049395    test-rmse:3.464170+0.343679 
## [59] train-rmse:3.229884+0.048764    test-rmse:3.440221+0.343694 
## [60] train-rmse:3.208474+0.047991    test-rmse:3.425516+0.343185 
## [61] train-rmse:3.187311+0.047335    test-rmse:3.408892+0.340170 
## [62] train-rmse:3.166658+0.046698    test-rmse:3.397014+0.344714 
## [63] train-rmse:3.146309+0.046207    test-rmse:3.379760+0.334851 
## [64] train-rmse:3.126258+0.045461    test-rmse:3.356166+0.318556 
## [65] train-rmse:3.106896+0.045121    test-rmse:3.341214+0.328238 
## [66] train-rmse:3.087715+0.044846    test-rmse:3.318751+0.328035 
## [67] train-rmse:3.068877+0.044232    test-rmse:3.308646+0.331519 
## [68] train-rmse:3.050239+0.043716    test-rmse:3.279506+0.318850 
## [69] train-rmse:3.031692+0.043239    test-rmse:3.262915+0.321418 
## [70] train-rmse:3.013485+0.042738    test-rmse:3.235063+0.310994 
## [71] train-rmse:2.995650+0.042399    test-rmse:3.217766+0.311133 
## [72] train-rmse:2.977853+0.041925    test-rmse:3.196720+0.312941 
## [73] train-rmse:2.960309+0.041729    test-rmse:3.183120+0.316217 
## [74] train-rmse:2.942912+0.041604    test-rmse:3.167007+0.316777 
## [75] train-rmse:2.925803+0.041484    test-rmse:3.157465+0.316244 
## [76] train-rmse:2.908966+0.041365    test-rmse:3.139078+0.309315 
## [77] train-rmse:2.892168+0.041301    test-rmse:3.126406+0.310663 
## [78] train-rmse:2.875678+0.041174    test-rmse:3.118964+0.313998 
## [79] train-rmse:2.859456+0.040967    test-rmse:3.100724+0.313807 
## [80] train-rmse:2.843318+0.040664    test-rmse:3.088933+0.318687 
## [81] train-rmse:2.827551+0.040381    test-rmse:3.076002+0.321455 
## [82] train-rmse:2.811992+0.040038    test-rmse:3.065432+0.326999 
## [83] train-rmse:2.796430+0.039724    test-rmse:3.050329+0.331446 
## [84] train-rmse:2.781111+0.039452    test-rmse:3.034502+0.327882 
## [85] train-rmse:2.766115+0.039369    test-rmse:3.021771+0.328506 
## [86] train-rmse:2.751163+0.039486    test-rmse:3.003059+0.323376 
## [87] train-rmse:2.736360+0.039508    test-rmse:2.985837+0.323292 
## [88] train-rmse:2.721704+0.039462    test-rmse:2.974367+0.330859 
## [89] train-rmse:2.707014+0.039507    test-rmse:2.947522+0.318986 
## [90] train-rmse:2.692566+0.039330    test-rmse:2.937948+0.317518 
## [91] train-rmse:2.678204+0.039231    test-rmse:2.921951+0.305261 
## [92] train-rmse:2.664053+0.039200    test-rmse:2.914937+0.306954 
## [93] train-rmse:2.650177+0.039170    test-rmse:2.905672+0.308340 
## [94] train-rmse:2.636434+0.039057    test-rmse:2.891467+0.309542 
## [95] train-rmse:2.622955+0.038936    test-rmse:2.878142+0.314874 
## [96] train-rmse:2.609680+0.038702    test-rmse:2.867878+0.310649 
## [97] train-rmse:2.596369+0.038489    test-rmse:2.859884+0.311408 
## [98] train-rmse:2.583111+0.038206    test-rmse:2.849674+0.311116 
## [99] train-rmse:2.570096+0.037967    test-rmse:2.834406+0.313350 
## [100]    train-rmse:2.557125+0.037620    test-rmse:2.821091+0.311535 
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## [103]    train-rmse:2.518893+0.037088    test-rmse:2.771507+0.311464 
## [104]    train-rmse:2.506304+0.036877    test-rmse:2.756091+0.302488 
## [105]    train-rmse:2.493777+0.036649    test-rmse:2.748220+0.304900 
## [106]    train-rmse:2.481343+0.036324    test-rmse:2.739901+0.305028 
## [107]    train-rmse:2.468914+0.036141    test-rmse:2.725454+0.302887 
## [108]    train-rmse:2.456625+0.035980    test-rmse:2.716745+0.305003 
## [109]    train-rmse:2.444775+0.035944    test-rmse:2.706300+0.309505 
## [110]    train-rmse:2.433212+0.035855    test-rmse:2.693004+0.310564 
## [111]    train-rmse:2.421697+0.035815    test-rmse:2.682687+0.314703 
## [112]    train-rmse:2.410250+0.035722    test-rmse:2.670822+0.310986 
## [113]    train-rmse:2.398803+0.035544    test-rmse:2.659878+0.312571 
## [114]    train-rmse:2.387401+0.035252    test-rmse:2.653937+0.314120 
## [115]    train-rmse:2.376103+0.034869    test-rmse:2.645432+0.314620 
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## [117]    train-rmse:2.353672+0.034195    test-rmse:2.621417+0.323520 
## [118]    train-rmse:2.342577+0.033958    test-rmse:2.611687+0.314784 
## [119]    train-rmse:2.331587+0.033792    test-rmse:2.601762+0.318058 
## [120]    train-rmse:2.320629+0.033621    test-rmse:2.593339+0.315844 
## [121]    train-rmse:2.309663+0.033414    test-rmse:2.574618+0.307881 
## [122]    train-rmse:2.298895+0.033134    test-rmse:2.564875+0.303102 
## [123]    train-rmse:2.288124+0.032957    test-rmse:2.553236+0.293841 
## [124]    train-rmse:2.277461+0.032735    test-rmse:2.545357+0.295104 
## [125]    train-rmse:2.267026+0.032496    test-rmse:2.530596+0.298129 
## [126]    train-rmse:2.256724+0.032324    test-rmse:2.521716+0.293176 
## [127]    train-rmse:2.246367+0.032121    test-rmse:2.512166+0.290174 
## [128]    train-rmse:2.236206+0.032074    test-rmse:2.498886+0.292459 
## [129]    train-rmse:2.226155+0.031963    test-rmse:2.487673+0.294983 
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## [133]    train-rmse:2.186213+0.031168    test-rmse:2.451652+0.294499 
## [134]    train-rmse:2.176331+0.031007    test-rmse:2.440614+0.295177 
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## [137]    train-rmse:2.147226+0.030444    test-rmse:2.421414+0.296394 
## [138]    train-rmse:2.137764+0.030245    test-rmse:2.409338+0.284685 
## [139]    train-rmse:2.128405+0.030062    test-rmse:2.398506+0.275466 
## [140]    train-rmse:2.119097+0.029902    test-rmse:2.385816+0.273302 
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## [707]    train-rmse:0.747070+0.015898    test-rmse:1.073377+0.156124 
## [708]    train-rmse:0.746784+0.015910    test-rmse:1.072739+0.155641 
## [709]    train-rmse:0.746505+0.015920    test-rmse:1.072577+0.155715 
## [710]    train-rmse:0.746225+0.015928    test-rmse:1.072093+0.156096 
## [711]    train-rmse:0.745933+0.015935    test-rmse:1.071737+0.156336 
## [712]    train-rmse:0.745651+0.015938    test-rmse:1.071063+0.156566 
## [713]    train-rmse:0.745374+0.015946    test-rmse:1.071469+0.156304 
## [714]    train-rmse:0.745095+0.015950    test-rmse:1.070944+0.155948 
## [715]    train-rmse:0.744817+0.015955    test-rmse:1.071065+0.155737 
## [716]    train-rmse:0.744539+0.015959    test-rmse:1.070477+0.156229 
## [717]    train-rmse:0.744266+0.015965    test-rmse:1.070280+0.156106 
## [718]    train-rmse:0.743984+0.015968    test-rmse:1.070452+0.155798 
## [719]    train-rmse:0.743710+0.015977    test-rmse:1.069963+0.155930 
## [720]    train-rmse:0.743431+0.015981    test-rmse:1.069579+0.156296 
## [721]    train-rmse:0.743157+0.015985    test-rmse:1.069680+0.156308 
## [722]    train-rmse:0.742886+0.015990    test-rmse:1.069520+0.156917 
## [723]    train-rmse:0.742616+0.015998    test-rmse:1.069460+0.156708 
## [724]    train-rmse:0.742346+0.016006    test-rmse:1.068877+0.156975 
## [725]    train-rmse:0.742079+0.016015    test-rmse:1.068548+0.156363 
## [726]    train-rmse:0.741815+0.016022    test-rmse:1.068655+0.156101 
## [727]    train-rmse:0.741549+0.016030    test-rmse:1.067833+0.156113 
## [728]    train-rmse:0.741288+0.016035    test-rmse:1.067537+0.156184 
## [729]    train-rmse:0.741029+0.016041    test-rmse:1.067686+0.156422 
## [730]    train-rmse:0.740770+0.016046    test-rmse:1.067812+0.155686 
## [731]    train-rmse:0.740506+0.016050    test-rmse:1.067654+0.156340 
## [732]    train-rmse:0.740245+0.016053    test-rmse:1.067179+0.156355 
## [733]    train-rmse:0.739988+0.016054    test-rmse:1.067534+0.155977 
## [734]    train-rmse:0.739731+0.016056    test-rmse:1.067411+0.156270 
## [735]    train-rmse:0.739473+0.016056    test-rmse:1.067442+0.156072 
## [736]    train-rmse:0.739218+0.016060    test-rmse:1.066962+0.155650 
## [737]    train-rmse:0.738965+0.016062    test-rmse:1.066818+0.154999 
## [738]    train-rmse:0.738711+0.016066    test-rmse:1.066163+0.155331 
## [739]    train-rmse:0.738460+0.016071    test-rmse:1.065087+0.154006 
## [740]    train-rmse:0.738209+0.016076    test-rmse:1.065134+0.154088 
## [741]    train-rmse:0.737959+0.016080    test-rmse:1.064583+0.153992 
## [742]    train-rmse:0.737706+0.016083    test-rmse:1.064445+0.153718 
## [743]    train-rmse:0.737452+0.016086    test-rmse:1.064759+0.153674 
## [744]    train-rmse:0.737205+0.016088    test-rmse:1.065018+0.153629 
## [745]    train-rmse:0.736957+0.016089    test-rmse:1.064496+0.154014 
## [746]    train-rmse:0.736710+0.016090    test-rmse:1.064593+0.153978 
## [747]    train-rmse:0.736461+0.016091    test-rmse:1.064378+0.154083 
## [748]    train-rmse:0.736210+0.016091    test-rmse:1.064523+0.153854 
## [749]    train-rmse:0.735965+0.016096    test-rmse:1.064609+0.154161 
## [750]    train-rmse:0.735719+0.016094    test-rmse:1.064259+0.153926 
## [751]    train-rmse:0.735479+0.016095    test-rmse:1.064137+0.153930 
## [752]    train-rmse:0.735234+0.016094    test-rmse:1.063894+0.154185 
## [753]    train-rmse:0.734990+0.016092    test-rmse:1.063875+0.154042 
## [754]    train-rmse:0.734753+0.016090    test-rmse:1.064010+0.153912 
## [755]    train-rmse:0.734515+0.016091    test-rmse:1.063491+0.154128 
## [756]    train-rmse:0.734275+0.016090    test-rmse:1.063419+0.153752 
## [757]    train-rmse:0.734041+0.016091    test-rmse:1.063334+0.153527 
## [758]    train-rmse:0.733807+0.016089    test-rmse:1.062778+0.153668 
## [759]    train-rmse:0.733574+0.016086    test-rmse:1.062387+0.153339 
## [760]    train-rmse:0.733336+0.016082    test-rmse:1.062688+0.152911 
## [761]    train-rmse:0.733105+0.016083    test-rmse:1.063183+0.152952 
## [762]    train-rmse:0.732871+0.016083    test-rmse:1.062612+0.153245 
## [763]    train-rmse:0.732641+0.016082    test-rmse:1.062775+0.152953 
## [764]    train-rmse:0.732405+0.016082    test-rmse:1.062707+0.153099 
## [765]    train-rmse:0.732173+0.016083    test-rmse:1.062338+0.153331 
## [766]    train-rmse:0.731943+0.016081    test-rmse:1.062306+0.153339 
## [767]    train-rmse:0.731715+0.016079    test-rmse:1.062155+0.153566 
## [768]    train-rmse:0.731490+0.016079    test-rmse:1.061808+0.153861 
## [769]    train-rmse:0.731266+0.016077    test-rmse:1.061365+0.153758 
## [770]    train-rmse:0.731042+0.016077    test-rmse:1.061480+0.153783 
## [771]    train-rmse:0.730818+0.016075    test-rmse:1.060960+0.154266 
## [772]    train-rmse:0.730594+0.016073    test-rmse:1.060489+0.154090 
## [773]    train-rmse:0.730375+0.016070    test-rmse:1.059902+0.154318 
## [774]    train-rmse:0.730155+0.016067    test-rmse:1.059960+0.154693 
## [775]    train-rmse:0.729932+0.016067    test-rmse:1.059736+0.154014 
## [776]    train-rmse:0.729712+0.016064    test-rmse:1.059064+0.153938 
## [777]    train-rmse:0.729486+0.016050    test-rmse:1.058769+0.153824 
## [778]    train-rmse:0.729264+0.016046    test-rmse:1.058803+0.153339 
## [779]    train-rmse:0.729042+0.016042    test-rmse:1.058373+0.153326 
## [780]    train-rmse:0.728820+0.016031    test-rmse:1.057997+0.153218 
## [781]    train-rmse:0.728605+0.016027    test-rmse:1.057208+0.152756 
## [782]    train-rmse:0.728383+0.016020    test-rmse:1.056926+0.152935 
## [783]    train-rmse:0.728169+0.016018    test-rmse:1.055725+0.151417 
## [784]    train-rmse:0.727951+0.016010    test-rmse:1.055265+0.151412 
## [785]    train-rmse:0.727732+0.016006    test-rmse:1.054916+0.151418 
## [786]    train-rmse:0.727517+0.016001    test-rmse:1.054991+0.151143 
## [787]    train-rmse:0.727298+0.015993    test-rmse:1.055231+0.151024 
## [788]    train-rmse:0.727086+0.015986    test-rmse:1.054430+0.151000 
## [789]    train-rmse:0.726876+0.015981    test-rmse:1.053941+0.151002 
## [790]    train-rmse:0.726666+0.015977    test-rmse:1.053647+0.151054 
## [791]    train-rmse:0.726455+0.015972    test-rmse:1.053450+0.150594 
## [792]    train-rmse:0.726245+0.015967    test-rmse:1.053171+0.150450 
## [793]    train-rmse:0.726034+0.015962    test-rmse:1.053328+0.150138 
## [794]    train-rmse:0.725823+0.015956    test-rmse:1.053330+0.149881 
## [795]    train-rmse:0.725610+0.015949    test-rmse:1.053001+0.150221 
## [796]    train-rmse:0.725400+0.015940    test-rmse:1.052814+0.149818 
## [797]    train-rmse:0.725188+0.015935    test-rmse:1.052750+0.150072 
## [798]    train-rmse:0.724983+0.015924    test-rmse:1.052524+0.149994 
## [799]    train-rmse:0.724778+0.015916    test-rmse:1.051822+0.149917 
## [800]    train-rmse:0.724572+0.015906    test-rmse:1.052313+0.150018 
## [801]    train-rmse:0.724370+0.015897    test-rmse:1.052277+0.149691 
## [802]    train-rmse:0.724166+0.015888    test-rmse:1.051972+0.149579 
## [803]    train-rmse:0.723961+0.015881    test-rmse:1.051864+0.149583 
## [804]    train-rmse:0.723759+0.015873    test-rmse:1.051271+0.149664 
## [805]    train-rmse:0.723559+0.015864    test-rmse:1.050937+0.149233 
## [806]    train-rmse:0.723359+0.015856    test-rmse:1.050573+0.149403 
## [807]    train-rmse:0.723161+0.015848    test-rmse:1.051010+0.149207 
## [808]    train-rmse:0.722961+0.015839    test-rmse:1.050593+0.149554 
## [809]    train-rmse:0.722761+0.015830    test-rmse:1.050004+0.149532 
## [810]    train-rmse:0.722563+0.015821    test-rmse:1.049531+0.149136 
## [811]    train-rmse:0.722360+0.015810    test-rmse:1.049681+0.149002 
## [812]    train-rmse:0.722161+0.015800    test-rmse:1.049449+0.149339 
## [813]    train-rmse:0.721960+0.015789    test-rmse:1.049789+0.149079 
## [814]    train-rmse:0.721765+0.015781    test-rmse:1.049411+0.149155 
## [815]    train-rmse:0.721570+0.015768    test-rmse:1.048661+0.148678 
## [816]    train-rmse:0.721376+0.015757    test-rmse:1.048919+0.148644 
## [817]    train-rmse:0.721179+0.015750    test-rmse:1.048406+0.148530 
## [818]    train-rmse:0.720987+0.015739    test-rmse:1.048386+0.148376 
## [819]    train-rmse:0.720793+0.015728    test-rmse:1.048283+0.147946 
## [820]    train-rmse:0.720603+0.015717    test-rmse:1.048082+0.148064 
## [821]    train-rmse:0.720404+0.015706    test-rmse:1.048345+0.148142 
## [822]    train-rmse:0.720215+0.015695    test-rmse:1.048091+0.147443 
## [823]    train-rmse:0.720018+0.015683    test-rmse:1.047599+0.147703 
## [824]    train-rmse:0.719830+0.015674    test-rmse:1.046525+0.146537 
## [825]    train-rmse:0.719642+0.015664    test-rmse:1.046117+0.146587 
## [826]    train-rmse:0.719453+0.015652    test-rmse:1.046160+0.146465 
## [827]    train-rmse:0.719266+0.015640    test-rmse:1.045639+0.146968 
## [828]    train-rmse:0.719080+0.015632    test-rmse:1.045536+0.147068 
## [829]    train-rmse:0.718894+0.015622    test-rmse:1.045511+0.147030 
## [830]    train-rmse:0.718706+0.015609    test-rmse:1.045775+0.146930 
## [831]    train-rmse:0.718520+0.015595    test-rmse:1.046004+0.146759 
## [832]    train-rmse:0.718334+0.015582    test-rmse:1.046043+0.146981 
## [833]    train-rmse:0.718146+0.015568    test-rmse:1.045814+0.146623 
## [834]    train-rmse:0.717960+0.015557    test-rmse:1.045794+0.145971 
## [835]    train-rmse:0.717772+0.015543    test-rmse:1.045633+0.145502 
## Stopping. Best iteration:
## [829]    train-rmse:0.718894+0.015622    test-rmse:1.045511+0.147030
## 
## 8 
## [1]  train-rmse:94.766116+0.105094   test-rmse:94.766423+0.483432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:83.478061+0.095217   test-rmse:83.478186+0.500072 
## [3]  train-rmse:73.545764+0.086897   test-rmse:73.545670+0.516055 
## [4]  train-rmse:64.807895+0.080011   test-rmse:64.807548+0.531590 
## [5]  train-rmse:57.122527+0.074434   test-rmse:57.121858+0.546922 
## [6]  train-rmse:50.364803+0.070103   test-rmse:50.363757+0.562250 
## [7]  train-rmse:44.424928+0.066945   test-rmse:44.423422+0.577769 
## [8]  train-rmse:39.206363+0.064904   test-rmse:39.204310+0.593657 
## [9]  train-rmse:34.622293+0.063135   test-rmse:34.630677+0.594785 
## [10] train-rmse:30.591222+0.059482   test-rmse:30.630729+0.602525 
## [11] train-rmse:27.046633+0.053406   test-rmse:27.109487+0.606996 
## [12] train-rmse:23.929207+0.045920   test-rmse:24.000442+0.580116 
## [13] train-rmse:21.189291+0.040281   test-rmse:21.265156+0.581461 
## [14] train-rmse:18.782400+0.033623   test-rmse:18.877167+0.584945 
## [15] train-rmse:16.669828+0.030754   test-rmse:16.754118+0.563482 
## [16] train-rmse:14.816258+0.029109   test-rmse:14.915749+0.566890 
## [17] train-rmse:13.192993+0.028337   test-rmse:13.299956+0.582644 
## [18] train-rmse:11.772369+0.028212   test-rmse:11.866388+0.551522 
## [19] train-rmse:10.532035+0.028831   test-rmse:10.641003+0.557631 
## [20] train-rmse:9.451512+0.029733    test-rmse:9.582640+0.569439 
## [21] train-rmse:8.511694+0.031360    test-rmse:8.645439+0.583578 
## [22] train-rmse:7.695953+0.033340    test-rmse:7.825109+0.558207 
## [23] train-rmse:6.990903+0.035285    test-rmse:7.141761+0.549602 
## [24] train-rmse:6.381549+0.037432    test-rmse:6.558248+0.557563 
## [25] train-rmse:5.855398+0.036452    test-rmse:6.019081+0.540192 
## [26] train-rmse:5.406448+0.038580    test-rmse:5.585065+0.542725 
## [27] train-rmse:5.022503+0.039907    test-rmse:5.204402+0.518089 
## [28] train-rmse:4.694001+0.040314    test-rmse:4.883385+0.518065 
## [29] train-rmse:4.412902+0.039738    test-rmse:4.638226+0.519981 
## [30] train-rmse:4.173967+0.040546    test-rmse:4.378171+0.491890 
## [31] train-rmse:3.971348+0.039758    test-rmse:4.201956+0.490766 
## [32] train-rmse:3.798811+0.039949    test-rmse:4.049231+0.475419 
## [33] train-rmse:3.650476+0.039560    test-rmse:3.906412+0.447988 
## [34] train-rmse:3.523801+0.039527    test-rmse:3.783196+0.438369 
## [35] train-rmse:3.414176+0.040930    test-rmse:3.682786+0.436392 
## [36] train-rmse:3.317690+0.039459    test-rmse:3.596762+0.424499 
## [37] train-rmse:3.234079+0.039003    test-rmse:3.501574+0.391774 
## [38] train-rmse:3.160492+0.038832    test-rmse:3.439888+0.380744 
## [39] train-rmse:3.090999+0.034231    test-rmse:3.397616+0.381123 
## [40] train-rmse:3.032294+0.034604    test-rmse:3.339730+0.373057 
## [41] train-rmse:2.978680+0.034096    test-rmse:3.291501+0.372030 
## [42] train-rmse:2.929123+0.034896    test-rmse:3.238516+0.362613 
## [43] train-rmse:2.883536+0.034495    test-rmse:3.185558+0.340520 
## [44] train-rmse:2.839349+0.033727    test-rmse:3.153715+0.345419 
## [45] train-rmse:2.796578+0.030197    test-rmse:3.121506+0.338315 
## [46] train-rmse:2.758005+0.031026    test-rmse:3.099283+0.344197 
## [47] train-rmse:2.722105+0.030384    test-rmse:3.054430+0.319845 
## [48] train-rmse:2.687866+0.029540    test-rmse:3.021460+0.319865 
## [49] train-rmse:2.655206+0.029340    test-rmse:2.996221+0.307268 
## [50] train-rmse:2.621266+0.029133    test-rmse:2.973288+0.313774 
## [51] train-rmse:2.590715+0.028645    test-rmse:2.943425+0.317500 
## [52] train-rmse:2.560634+0.027598    test-rmse:2.918515+0.317844 
## [53] train-rmse:2.530313+0.027601    test-rmse:2.897631+0.307560 
## [54] train-rmse:2.501892+0.026490    test-rmse:2.869777+0.295579 
## [55] train-rmse:2.474875+0.025955    test-rmse:2.842185+0.301409 
## [56] train-rmse:2.447003+0.025092    test-rmse:2.817418+0.295264 
## [57] train-rmse:2.419785+0.023897    test-rmse:2.795675+0.287105 
## [58] train-rmse:2.393858+0.024172    test-rmse:2.777074+0.292829 
## [59] train-rmse:2.367000+0.024643    test-rmse:2.763482+0.294832 
## [60] train-rmse:2.342787+0.024269    test-rmse:2.729130+0.275141 
## [61] train-rmse:2.318562+0.023762    test-rmse:2.700914+0.276099 
## [62] train-rmse:2.294430+0.022425    test-rmse:2.683863+0.272957 
## [63] train-rmse:2.269907+0.022619    test-rmse:2.652857+0.275957 
## [64] train-rmse:2.246380+0.021834    test-rmse:2.643622+0.276739 
## [65] train-rmse:2.221955+0.019875    test-rmse:2.623680+0.265328 
## [66] train-rmse:2.200239+0.019549    test-rmse:2.606271+0.266657 
## [67] train-rmse:2.177671+0.020026    test-rmse:2.592163+0.264254 
## [68] train-rmse:2.156747+0.019597    test-rmse:2.571740+0.269749 
## [69] train-rmse:2.134652+0.019360    test-rmse:2.552505+0.266601 
## [70] train-rmse:2.113583+0.019045    test-rmse:2.534218+0.265147 
## [71] train-rmse:2.093020+0.018967    test-rmse:2.511239+0.268927 
## [72] train-rmse:2.071789+0.018299    test-rmse:2.494173+0.258889 
## [73] train-rmse:2.051619+0.017872    test-rmse:2.480035+0.262967 
## [74] train-rmse:2.031588+0.016894    test-rmse:2.469166+0.263922 
## [75] train-rmse:2.012615+0.016494    test-rmse:2.448805+0.266812 
## [76] train-rmse:1.993592+0.015368    test-rmse:2.432633+0.265514 
## [77] train-rmse:1.975055+0.015179    test-rmse:2.421154+0.263308 
## [78] train-rmse:1.956678+0.014741    test-rmse:2.390761+0.259549 
## [79] train-rmse:1.937212+0.014664    test-rmse:2.371700+0.257875 
## [80] train-rmse:1.918824+0.013980    test-rmse:2.360433+0.254648 
## [81] train-rmse:1.901237+0.013596    test-rmse:2.347834+0.259273 
## [82] train-rmse:1.884075+0.013274    test-rmse:2.326786+0.255350 
## [83] train-rmse:1.866069+0.012714    test-rmse:2.319575+0.257489 
## [84] train-rmse:1.848641+0.012368    test-rmse:2.304911+0.263187 
## [85] train-rmse:1.831688+0.011925    test-rmse:2.289220+0.257483 
## [86] train-rmse:1.815077+0.011303    test-rmse:2.274496+0.255499 
## [87] train-rmse:1.798341+0.010551    test-rmse:2.259965+0.258934 
## [88] train-rmse:1.781853+0.010041    test-rmse:2.242714+0.256117 
## [89] train-rmse:1.764975+0.009435    test-rmse:2.230112+0.259138 
## [90] train-rmse:1.749402+0.008964    test-rmse:2.218319+0.262777 
## [91] train-rmse:1.733880+0.009090    test-rmse:2.203511+0.255311 
## [92] train-rmse:1.718898+0.008890    test-rmse:2.187171+0.249950 
## [93] train-rmse:1.703895+0.008650    test-rmse:2.170961+0.249084 
## [94] train-rmse:1.688317+0.007952    test-rmse:2.155731+0.246938 
## [95] train-rmse:1.673549+0.007949    test-rmse:2.145410+0.247018 
## [96] train-rmse:1.659022+0.007654    test-rmse:2.133947+0.250005 
## [97] train-rmse:1.644848+0.007478    test-rmse:2.116389+0.252146 
## [98] train-rmse:1.630218+0.007273    test-rmse:2.111290+0.251380 
## [99] train-rmse:1.616281+0.007237    test-rmse:2.107750+0.250957 
## [100]    train-rmse:1.601351+0.006645    test-rmse:2.096617+0.248019 
## [101]    train-rmse:1.587503+0.006125    test-rmse:2.081166+0.249065 
## [102]    train-rmse:1.574036+0.005957    test-rmse:2.062210+0.249173 
## [103]    train-rmse:1.560912+0.006288    test-rmse:2.048003+0.244194 
## [104]    train-rmse:1.547906+0.006134    test-rmse:2.037545+0.242729 
## [105]    train-rmse:1.534564+0.006131    test-rmse:2.030440+0.244951 
## [106]    train-rmse:1.521680+0.006524    test-rmse:2.019237+0.249017 
## [107]    train-rmse:1.509188+0.006307    test-rmse:2.003407+0.246679 
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## [110]    train-rmse:1.471864+0.006339    test-rmse:1.974325+0.245439 
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## [126]    train-rmse:1.293262+0.006916    test-rmse:1.824312+0.242528 
## [127]    train-rmse:1.283319+0.007099    test-rmse:1.814900+0.239099 
## [128]    train-rmse:1.273695+0.007439    test-rmse:1.805359+0.239259 
## [129]    train-rmse:1.263670+0.007135    test-rmse:1.797467+0.238043 
## [130]    train-rmse:1.254125+0.007318    test-rmse:1.789310+0.237630 
## [131]    train-rmse:1.244739+0.007485    test-rmse:1.780180+0.238561 
## [132]    train-rmse:1.235369+0.007783    test-rmse:1.773997+0.239593 
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## [135]    train-rmse:1.207562+0.007461    test-rmse:1.750186+0.239192 
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## [501]    train-rmse:0.341658+0.014280    test-rmse:1.127039+0.284039 
## [502]    train-rmse:0.341017+0.014273    test-rmse:1.126969+0.284098 
## [503]    train-rmse:0.340614+0.014280    test-rmse:1.126586+0.284156 
## [504]    train-rmse:0.340242+0.014266    test-rmse:1.126814+0.284107 
## [505]    train-rmse:0.339404+0.014484    test-rmse:1.126095+0.283945 
## [506]    train-rmse:0.338521+0.014235    test-rmse:1.125637+0.283495 
## [507]    train-rmse:0.337829+0.014117    test-rmse:1.125518+0.283454 
## [508]    train-rmse:0.337242+0.014008    test-rmse:1.125361+0.282843 
## [509]    train-rmse:0.336755+0.014002    test-rmse:1.123791+0.283197 
## [510]    train-rmse:0.336250+0.014019    test-rmse:1.123696+0.282987 
## [511]    train-rmse:0.335782+0.014342    test-rmse:1.123081+0.282499 
## [512]    train-rmse:0.335308+0.014235    test-rmse:1.122913+0.282317 
## [513]    train-rmse:0.334954+0.014211    test-rmse:1.122116+0.282099 
## [514]    train-rmse:0.334461+0.014475    test-rmse:1.122255+0.282315 
## [515]    train-rmse:0.333936+0.014687    test-rmse:1.121744+0.281965 
## [516]    train-rmse:0.333505+0.014888    test-rmse:1.121472+0.281971 
## [517]    train-rmse:0.333025+0.014957    test-rmse:1.121736+0.281980 
## [518]    train-rmse:0.332432+0.015010    test-rmse:1.121728+0.282042 
## [519]    train-rmse:0.331919+0.015077    test-rmse:1.121332+0.282411 
## [520]    train-rmse:0.331395+0.014921    test-rmse:1.122028+0.281844 
## [521]    train-rmse:0.330964+0.014933    test-rmse:1.121624+0.282099 
## [522]    train-rmse:0.330414+0.014809    test-rmse:1.121985+0.282379 
## [523]    train-rmse:0.329709+0.014983    test-rmse:1.121799+0.281841 
## [524]    train-rmse:0.328860+0.015360    test-rmse:1.121189+0.281499 
## [525]    train-rmse:0.328117+0.015554    test-rmse:1.120672+0.281732 
## [526]    train-rmse:0.327474+0.015944    test-rmse:1.120649+0.281698 
## [527]    train-rmse:0.327005+0.016167    test-rmse:1.120541+0.281879 
## [528]    train-rmse:0.326282+0.015986    test-rmse:1.120238+0.281960 
## [529]    train-rmse:0.325946+0.016028    test-rmse:1.120256+0.281796 
## [530]    train-rmse:0.325580+0.015978    test-rmse:1.120170+0.281813 
## [531]    train-rmse:0.325107+0.016061    test-rmse:1.120066+0.281719 
## [532]    train-rmse:0.324592+0.015914    test-rmse:1.119396+0.281775 
## [533]    train-rmse:0.324218+0.015867    test-rmse:1.119120+0.281499 
## [534]    train-rmse:0.323810+0.015843    test-rmse:1.119101+0.281650 
## [535]    train-rmse:0.323270+0.015730    test-rmse:1.118464+0.282071 
## [536]    train-rmse:0.322685+0.015508    test-rmse:1.118601+0.282305 
## [537]    train-rmse:0.321901+0.015551    test-rmse:1.118687+0.282381 
## [538]    train-rmse:0.321394+0.015434    test-rmse:1.118889+0.282158 
## [539]    train-rmse:0.320848+0.015500    test-rmse:1.118099+0.282469 
## [540]    train-rmse:0.320196+0.015280    test-rmse:1.118276+0.282870 
## [541]    train-rmse:0.319816+0.015289    test-rmse:1.118252+0.282944 
## [542]    train-rmse:0.319491+0.015233    test-rmse:1.118141+0.282578 
## [543]    train-rmse:0.319193+0.015202    test-rmse:1.117899+0.282380 
## [544]    train-rmse:0.318639+0.014951    test-rmse:1.117537+0.282233 
## [545]    train-rmse:0.318357+0.014921    test-rmse:1.117646+0.281882 
## [546]    train-rmse:0.318011+0.015053    test-rmse:1.117559+0.281770 
## [547]    train-rmse:0.317162+0.015047    test-rmse:1.116249+0.282836 
## [548]    train-rmse:0.316631+0.014876    test-rmse:1.116200+0.282827 
## [549]    train-rmse:0.315965+0.015052    test-rmse:1.116365+0.282616 
## [550]    train-rmse:0.315570+0.015222    test-rmse:1.116035+0.283017 
## [551]    train-rmse:0.314893+0.015191    test-rmse:1.116148+0.282866 
## [552]    train-rmse:0.314468+0.015113    test-rmse:1.116071+0.283419 
## [553]    train-rmse:0.313903+0.014983    test-rmse:1.116375+0.283803 
## [554]    train-rmse:0.313397+0.014928    test-rmse:1.116016+0.283490 
## [555]    train-rmse:0.312508+0.015128    test-rmse:1.115402+0.282677 
## [556]    train-rmse:0.311527+0.015289    test-rmse:1.115134+0.282740 
## [557]    train-rmse:0.311182+0.015265    test-rmse:1.114819+0.282951 
## [558]    train-rmse:0.310744+0.015611    test-rmse:1.114648+0.282524 
## [559]    train-rmse:0.310264+0.015762    test-rmse:1.114690+0.282193 
## [560]    train-rmse:0.309612+0.015680    test-rmse:1.114906+0.282508 
## [561]    train-rmse:0.308993+0.015549    test-rmse:1.114481+0.282218 
## [562]    train-rmse:0.308478+0.015527    test-rmse:1.113697+0.282737 
## [563]    train-rmse:0.308078+0.015463    test-rmse:1.113619+0.282688 
## [564]    train-rmse:0.307721+0.015418    test-rmse:1.113537+0.282509 
## [565]    train-rmse:0.307311+0.015460    test-rmse:1.113491+0.282644 
## [566]    train-rmse:0.306632+0.015672    test-rmse:1.113391+0.282975 
## [567]    train-rmse:0.306157+0.015677    test-rmse:1.113437+0.283176 
## [568]    train-rmse:0.305579+0.016076    test-rmse:1.113645+0.282897 
## [569]    train-rmse:0.304973+0.015976    test-rmse:1.113413+0.282965 
## [570]    train-rmse:0.304620+0.015947    test-rmse:1.113077+0.283624 
## [571]    train-rmse:0.304065+0.015896    test-rmse:1.113147+0.284101 
## [572]    train-rmse:0.303661+0.015886    test-rmse:1.113025+0.283918 
## [573]    train-rmse:0.303324+0.015833    test-rmse:1.113224+0.283989 
## [574]    train-rmse:0.302763+0.015731    test-rmse:1.113542+0.284202 
## [575]    train-rmse:0.302043+0.015435    test-rmse:1.113805+0.284063 
## [576]    train-rmse:0.301562+0.015331    test-rmse:1.113594+0.284053 
## [577]    train-rmse:0.301191+0.015307    test-rmse:1.113634+0.283570 
## [578]    train-rmse:0.300933+0.015346    test-rmse:1.113307+0.283338 
## Stopping. Best iteration:
## [572]    train-rmse:0.303661+0.015886    test-rmse:1.113025+0.283918
## 
## 9 
## [1]  train-rmse:94.766116+0.105094   test-rmse:94.766423+0.483432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:83.478061+0.095217   test-rmse:83.478186+0.500072 
## [3]  train-rmse:73.545764+0.086897   test-rmse:73.545670+0.516055 
## [4]  train-rmse:64.807895+0.080011   test-rmse:64.807548+0.531590 
## [5]  train-rmse:57.122527+0.074434   test-rmse:57.121858+0.546922 
## [6]  train-rmse:50.364803+0.070103   test-rmse:50.363757+0.562250 
## [7]  train-rmse:44.424928+0.066945   test-rmse:44.423422+0.577769 
## [8]  train-rmse:39.206363+0.064904   test-rmse:39.204310+0.593657 
## [9]  train-rmse:34.622293+0.063135   test-rmse:34.630677+0.594785 
## [10] train-rmse:30.591081+0.059323   test-rmse:30.633139+0.599495 
## [11] train-rmse:27.044432+0.052606   test-rmse:27.116927+0.608458 
## [12] train-rmse:23.924558+0.044243   test-rmse:24.000367+0.581464 
## [13] train-rmse:21.180680+0.037427   test-rmse:21.262963+0.569043 
## [14] train-rmse:18.769037+0.030462   test-rmse:18.868901+0.580277 
## [15] train-rmse:16.646216+0.028802   test-rmse:16.744065+0.566385 
## [16] train-rmse:14.781228+0.027676   test-rmse:14.877556+0.562284 
## [17] train-rmse:13.144516+0.025905   test-rmse:13.240838+0.558682 
## [18] train-rmse:11.708013+0.025421   test-rmse:11.817619+0.566114 
## [19] train-rmse:10.449898+0.027004   test-rmse:10.565119+0.556238 
## [20] train-rmse:9.349082+0.027036    test-rmse:9.467515+0.540108 
## [21] train-rmse:8.384510+0.026125    test-rmse:8.518904+0.556707 
## [22] train-rmse:7.544330+0.026955    test-rmse:7.676664+0.547996 
## [23] train-rmse:6.811611+0.026988    test-rmse:6.965998+0.559498 
## [24] train-rmse:6.169861+0.025357    test-rmse:6.325715+0.531711 
## [25] train-rmse:5.618868+0.025744    test-rmse:5.783679+0.534846 
## [26] train-rmse:5.142511+0.026903    test-rmse:5.304263+0.511496 
## [27] train-rmse:4.729720+0.027540    test-rmse:4.923034+0.501924 
## [28] train-rmse:4.372540+0.029575    test-rmse:4.576643+0.480154 
## [29] train-rmse:4.064365+0.028080    test-rmse:4.297324+0.485290 
## [30] train-rmse:3.798282+0.025136    test-rmse:4.037570+0.458516 
## [31] train-rmse:3.572058+0.025330    test-rmse:3.819568+0.445247 
## [32] train-rmse:3.375524+0.022065    test-rmse:3.627331+0.436148 
## [33] train-rmse:3.207947+0.021133    test-rmse:3.471546+0.419714 
## [34] train-rmse:3.063050+0.021231    test-rmse:3.349843+0.418248 
## [35] train-rmse:2.935828+0.025476    test-rmse:3.250824+0.410694 
## [36] train-rmse:2.823274+0.022691    test-rmse:3.158177+0.399002 
## [37] train-rmse:2.724622+0.021346    test-rmse:3.077488+0.376727 
## [38] train-rmse:2.640115+0.021912    test-rmse:3.010983+0.366407 
## [39] train-rmse:2.564328+0.020107    test-rmse:2.942058+0.357326 
## [40] train-rmse:2.496710+0.020753    test-rmse:2.878821+0.352020 
## [41] train-rmse:2.434770+0.020278    test-rmse:2.819786+0.342647 
## [42] train-rmse:2.378122+0.019649    test-rmse:2.770066+0.324367 
## [43] train-rmse:2.326586+0.019253    test-rmse:2.737963+0.325026 
## [44] train-rmse:2.275161+0.021400    test-rmse:2.688385+0.321581 
## [45] train-rmse:2.229104+0.018526    test-rmse:2.634666+0.294136 
## [46] train-rmse:2.187026+0.017785    test-rmse:2.605209+0.286797 
## [47] train-rmse:2.147196+0.017552    test-rmse:2.573551+0.288386 
## [48] train-rmse:2.109804+0.017762    test-rmse:2.542914+0.284184 
## [49] train-rmse:2.072802+0.018742    test-rmse:2.513946+0.278775 
## [50] train-rmse:2.038266+0.017073    test-rmse:2.483258+0.271675 
## [51] train-rmse:2.005229+0.016514    test-rmse:2.451280+0.276788 
## [52] train-rmse:1.973634+0.016449    test-rmse:2.427045+0.274476 
## [53] train-rmse:1.943410+0.015952    test-rmse:2.406586+0.274349 
## [54] train-rmse:1.911532+0.014085    test-rmse:2.384350+0.262042 
## [55] train-rmse:1.882938+0.013588    test-rmse:2.344396+0.259806 
## [56] train-rmse:1.851637+0.015979    test-rmse:2.330886+0.260342 
## [57] train-rmse:1.822498+0.018350    test-rmse:2.303040+0.266713 
## [58] train-rmse:1.795389+0.017645    test-rmse:2.284219+0.268178 
## [59] train-rmse:1.768698+0.016904    test-rmse:2.261195+0.262715 
## [60] train-rmse:1.742761+0.015577    test-rmse:2.242259+0.262833 
## [61] train-rmse:1.718078+0.015367    test-rmse:2.218057+0.256612 
## [62] train-rmse:1.693009+0.016129    test-rmse:2.198022+0.263977 
## [63] train-rmse:1.669871+0.015802    test-rmse:2.172909+0.257297 
## [64] train-rmse:1.645181+0.015300    test-rmse:2.156074+0.250034 
## [65] train-rmse:1.622905+0.015303    test-rmse:2.138266+0.249819 
## [66] train-rmse:1.600465+0.015518    test-rmse:2.121834+0.249472 
## [67] train-rmse:1.578593+0.015107    test-rmse:2.102598+0.253874 
## [68] train-rmse:1.555993+0.016993    test-rmse:2.087659+0.249219 
## [69] train-rmse:1.534641+0.016049    test-rmse:2.060076+0.247424 
## [70] train-rmse:1.513063+0.016213    test-rmse:2.045443+0.247732 
## [71] train-rmse:1.493281+0.015999    test-rmse:2.028319+0.243842 
## [72] train-rmse:1.473850+0.015459    test-rmse:2.012606+0.248843 
## [73] train-rmse:1.454694+0.015549    test-rmse:1.997327+0.249966 
## [74] train-rmse:1.434820+0.015276    test-rmse:1.986840+0.252473 
## [75] train-rmse:1.415986+0.014636    test-rmse:1.963347+0.249517 
## [76] train-rmse:1.398104+0.014131    test-rmse:1.943463+0.250342 
## [77] train-rmse:1.380152+0.015334    test-rmse:1.928311+0.247990 
## [78] train-rmse:1.363011+0.015424    test-rmse:1.910620+0.242822 
## [79] train-rmse:1.344939+0.015339    test-rmse:1.896330+0.245007 
## [80] train-rmse:1.328385+0.015162    test-rmse:1.885932+0.244295 
## [81] train-rmse:1.311351+0.014438    test-rmse:1.867754+0.241059 
## [82] train-rmse:1.294669+0.015434    test-rmse:1.855176+0.240640 
## [83] train-rmse:1.277381+0.015880    test-rmse:1.843488+0.240787 
## [84] train-rmse:1.257646+0.014799    test-rmse:1.828344+0.241494 
## [85] train-rmse:1.241806+0.014878    test-rmse:1.817898+0.242599 
## [86] train-rmse:1.227087+0.015461    test-rmse:1.806913+0.242104 
## [87] train-rmse:1.212126+0.015180    test-rmse:1.795720+0.237298 
## [88] train-rmse:1.196816+0.014259    test-rmse:1.775276+0.237974 
## [89] train-rmse:1.181982+0.013908    test-rmse:1.764759+0.238278 
## [90] train-rmse:1.168062+0.013638    test-rmse:1.750770+0.237084 
## [91] train-rmse:1.153298+0.013281    test-rmse:1.737937+0.238183 
## [92] train-rmse:1.140081+0.013141    test-rmse:1.727680+0.239890 
## [93] train-rmse:1.123371+0.017139    test-rmse:1.720286+0.242884 
## [94] train-rmse:1.109494+0.016864    test-rmse:1.712341+0.243919 
## [95] train-rmse:1.096101+0.016859    test-rmse:1.700593+0.246386 
## [96] train-rmse:1.083070+0.017138    test-rmse:1.689979+0.245444 
## [97] train-rmse:1.070193+0.016559    test-rmse:1.677337+0.248055 
## [98] train-rmse:1.058087+0.015972    test-rmse:1.663513+0.250414 
## [99] train-rmse:1.046417+0.015926    test-rmse:1.654357+0.250385 
## [100]    train-rmse:1.034286+0.016305    test-rmse:1.645292+0.252174 
## [101]    train-rmse:1.022014+0.015477    test-rmse:1.634126+0.255159 
## [102]    train-rmse:1.009695+0.015413    test-rmse:1.628862+0.253767 
## [103]    train-rmse:0.998190+0.015858    test-rmse:1.621879+0.252990 
## [104]    train-rmse:0.987363+0.015547    test-rmse:1.611921+0.251288 
## [105]    train-rmse:0.976707+0.015875    test-rmse:1.603269+0.252503 
## [106]    train-rmse:0.965882+0.015679    test-rmse:1.594983+0.252962 
## [107]    train-rmse:0.955177+0.015046    test-rmse:1.588796+0.252218 
## [108]    train-rmse:0.944615+0.014414    test-rmse:1.578585+0.256763 
## [109]    train-rmse:0.934645+0.014225    test-rmse:1.569258+0.257098 
## [110]    train-rmse:0.924071+0.014606    test-rmse:1.561995+0.258762 
## [111]    train-rmse:0.914174+0.014865    test-rmse:1.554668+0.260907 
## [112]    train-rmse:0.904169+0.015681    test-rmse:1.547426+0.260530 
## [113]    train-rmse:0.893907+0.016239    test-rmse:1.540747+0.260571 
## [114]    train-rmse:0.884603+0.016379    test-rmse:1.534812+0.263640 
## [115]    train-rmse:0.875715+0.016346    test-rmse:1.528542+0.264341 
## [116]    train-rmse:0.867115+0.016028    test-rmse:1.521286+0.265748 
## [117]    train-rmse:0.858546+0.015981    test-rmse:1.512480+0.266824 
## [118]    train-rmse:0.848847+0.015142    test-rmse:1.504480+0.264747 
## [119]    train-rmse:0.839394+0.015382    test-rmse:1.493549+0.256098 
## [120]    train-rmse:0.830784+0.014784    test-rmse:1.490966+0.257123 
## [121]    train-rmse:0.822715+0.015014    test-rmse:1.483745+0.258046 
## [122]    train-rmse:0.814130+0.014702    test-rmse:1.481035+0.261702 
## [123]    train-rmse:0.806050+0.014467    test-rmse:1.472662+0.254111 
## [124]    train-rmse:0.798407+0.014014    test-rmse:1.464134+0.255338 
## [125]    train-rmse:0.790664+0.013617    test-rmse:1.456870+0.256050 
## [126]    train-rmse:0.783151+0.013960    test-rmse:1.451938+0.257322 
## [127]    train-rmse:0.775378+0.013600    test-rmse:1.443917+0.257532 
## [128]    train-rmse:0.767717+0.014359    test-rmse:1.439745+0.258793 
## [129]    train-rmse:0.759949+0.014137    test-rmse:1.432421+0.256453 
## [130]    train-rmse:0.752254+0.013536    test-rmse:1.427507+0.257529 
## [131]    train-rmse:0.743945+0.014109    test-rmse:1.420703+0.258812 
## [132]    train-rmse:0.737331+0.013843    test-rmse:1.415357+0.258202 
## [133]    train-rmse:0.730593+0.014087    test-rmse:1.411532+0.258954 
## [134]    train-rmse:0.723203+0.014968    test-rmse:1.406519+0.259486 
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## [344]    train-rmse:0.229199+0.008650    test-rmse:1.093395+0.285597 
## [345]    train-rmse:0.228414+0.008502    test-rmse:1.093020+0.285177 
## [346]    train-rmse:0.227813+0.008404    test-rmse:1.092593+0.285088 
## [347]    train-rmse:0.226586+0.008517    test-rmse:1.092447+0.285210 
## [348]    train-rmse:0.225683+0.008591    test-rmse:1.092746+0.285350 
## [349]    train-rmse:0.224797+0.008646    test-rmse:1.092485+0.285417 
## [350]    train-rmse:0.224099+0.008750    test-rmse:1.092000+0.285713 
## [351]    train-rmse:0.223384+0.008685    test-rmse:1.091617+0.285499 
## [352]    train-rmse:0.222592+0.009004    test-rmse:1.092057+0.285331 
## [353]    train-rmse:0.221996+0.008968    test-rmse:1.091721+0.285496 
## [354]    train-rmse:0.221108+0.008936    test-rmse:1.091526+0.285474 
## [355]    train-rmse:0.220441+0.008992    test-rmse:1.091088+0.285579 
## [356]    train-rmse:0.219512+0.008912    test-rmse:1.090760+0.286131 
## [357]    train-rmse:0.218865+0.008770    test-rmse:1.090750+0.286351 
## [358]    train-rmse:0.217876+0.008719    test-rmse:1.090605+0.286723 
## [359]    train-rmse:0.217286+0.008689    test-rmse:1.090237+0.286690 
## [360]    train-rmse:0.216584+0.008695    test-rmse:1.090208+0.286509 
## [361]    train-rmse:0.215885+0.008553    test-rmse:1.090243+0.286872 
## [362]    train-rmse:0.215198+0.008566    test-rmse:1.090117+0.286604 
## [363]    train-rmse:0.214503+0.008564    test-rmse:1.090472+0.286631 
## [364]    train-rmse:0.214000+0.008566    test-rmse:1.089875+0.287175 
## [365]    train-rmse:0.213215+0.008371    test-rmse:1.089768+0.287185 
## [366]    train-rmse:0.212684+0.008308    test-rmse:1.089592+0.287084 
## [367]    train-rmse:0.211571+0.008647    test-rmse:1.089563+0.287142 
## [368]    train-rmse:0.210651+0.008597    test-rmse:1.089557+0.286791 
## [369]    train-rmse:0.209980+0.008545    test-rmse:1.089367+0.286734 
## [370]    train-rmse:0.209424+0.008813    test-rmse:1.089180+0.286853 
## [371]    train-rmse:0.208732+0.008635    test-rmse:1.089258+0.286993 
## [372]    train-rmse:0.207689+0.008859    test-rmse:1.089846+0.288394 
## [373]    train-rmse:0.206770+0.008829    test-rmse:1.089640+0.289028 
## [374]    train-rmse:0.205956+0.008717    test-rmse:1.089321+0.289699 
## [375]    train-rmse:0.205420+0.008761    test-rmse:1.089313+0.289619 
## [376]    train-rmse:0.204713+0.008627    test-rmse:1.089209+0.289898 
## Stopping. Best iteration:
## [370]    train-rmse:0.209424+0.008813    test-rmse:1.089180+0.286853
## 
## 10 
## [1]  train-rmse:94.766116+0.105094   test-rmse:94.766423+0.483432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:83.478061+0.095217   test-rmse:83.478186+0.500072 
## [3]  train-rmse:73.545764+0.086897   test-rmse:73.545670+0.516055 
## [4]  train-rmse:64.807895+0.080011   test-rmse:64.807548+0.531590 
## [5]  train-rmse:57.122527+0.074434   test-rmse:57.121858+0.546922 
## [6]  train-rmse:50.364803+0.070103   test-rmse:50.363757+0.562250 
## [7]  train-rmse:44.424928+0.066945   test-rmse:44.423422+0.577769 
## [8]  train-rmse:39.206363+0.064904   test-rmse:39.204310+0.593657 
## [9]  train-rmse:34.622293+0.063135   test-rmse:34.630677+0.594785 
## [10] train-rmse:30.591081+0.059323   test-rmse:30.633139+0.599495 
## [11] train-rmse:27.044432+0.052606   test-rmse:27.116927+0.608458 
## [12] train-rmse:23.924558+0.044243   test-rmse:24.000367+0.581464 
## [13] train-rmse:21.179843+0.037983   test-rmse:21.260459+0.568265 
## [14] train-rmse:18.765142+0.033113   test-rmse:18.845933+0.566596 
## [15] train-rmse:16.641043+0.027979   test-rmse:16.735798+0.565393 
## [16] train-rmse:14.769916+0.026410   test-rmse:14.858769+0.559388 
## [17] train-rmse:13.124335+0.024421   test-rmse:13.212948+0.543444 
## [18] train-rmse:11.678605+0.023895   test-rmse:11.768421+0.533339 
## [19] train-rmse:10.406373+0.021848   test-rmse:10.490011+0.527254 
## [20] train-rmse:9.291683+0.020976    test-rmse:9.396597+0.543450 
## [21] train-rmse:8.312294+0.020685    test-rmse:8.426048+0.526347 
## [22] train-rmse:7.455733+0.020218    test-rmse:7.568301+0.524141 
## [23] train-rmse:6.706077+0.019020    test-rmse:6.840491+0.526798 
## [24] train-rmse:6.049048+0.019995    test-rmse:6.181267+0.513519 
## [25] train-rmse:5.478027+0.020484    test-rmse:5.615741+0.495991 
## [26] train-rmse:4.977582+0.022875    test-rmse:5.142073+0.486243 
## [27] train-rmse:4.544526+0.022719    test-rmse:4.725749+0.490618 
## [28] train-rmse:4.166672+0.027342    test-rmse:4.353921+0.471481 
## [29] train-rmse:3.836347+0.023318    test-rmse:4.050294+0.477598 
## [30] train-rmse:3.546311+0.030549    test-rmse:3.786223+0.439024 
## [31] train-rmse:3.295700+0.030374    test-rmse:3.557617+0.434266 
## [32] train-rmse:3.082782+0.031078    test-rmse:3.360198+0.420410 
## [33] train-rmse:2.894734+0.035328    test-rmse:3.211101+0.401690 
## [34] train-rmse:2.732664+0.037979    test-rmse:3.061364+0.380391 
## [35] train-rmse:2.593546+0.039142    test-rmse:2.916688+0.367966 
## [36] train-rmse:2.472514+0.040309    test-rmse:2.824840+0.348979 
## [37] train-rmse:2.364889+0.041997    test-rmse:2.738479+0.331113 
## [38] train-rmse:2.263053+0.040206    test-rmse:2.668750+0.326780 
## [39] train-rmse:2.179393+0.040679    test-rmse:2.584484+0.315298 
## [40] train-rmse:2.103974+0.041084    test-rmse:2.524366+0.311480 
## [41] train-rmse:2.032187+0.041071    test-rmse:2.471273+0.293841 
## [42] train-rmse:1.968513+0.037210    test-rmse:2.411283+0.278940 
## [43] train-rmse:1.909954+0.035559    test-rmse:2.366178+0.271433 
## [44] train-rmse:1.858124+0.036614    test-rmse:2.325584+0.267207 
## [45] train-rmse:1.809513+0.036295    test-rmse:2.294975+0.260998 
## [46] train-rmse:1.764954+0.035661    test-rmse:2.249833+0.251488 
## [47] train-rmse:1.721570+0.032697    test-rmse:2.212988+0.247703 
## [48] train-rmse:1.681073+0.032419    test-rmse:2.183125+0.249856 
## [49] train-rmse:1.638695+0.038736    test-rmse:2.158639+0.252946 
## [50] train-rmse:1.603962+0.037550    test-rmse:2.128837+0.249920 
## [51] train-rmse:1.567104+0.037887    test-rmse:2.099652+0.247464 
## [52] train-rmse:1.534981+0.037297    test-rmse:2.077305+0.239922 
## [53] train-rmse:1.500359+0.036570    test-rmse:2.046379+0.241335 
## [54] train-rmse:1.470272+0.036232    test-rmse:2.024745+0.242127 
## [55] train-rmse:1.441158+0.034954    test-rmse:2.000875+0.240998 
## [56] train-rmse:1.412802+0.034438    test-rmse:1.975775+0.234938 
## [57] train-rmse:1.385888+0.032572    test-rmse:1.951375+0.230038 
## [58] train-rmse:1.359340+0.030738    test-rmse:1.936601+0.229596 
## [59] train-rmse:1.333503+0.030611    test-rmse:1.918165+0.232401 
## [60] train-rmse:1.308760+0.030558    test-rmse:1.897306+0.234402 
## [61] train-rmse:1.281424+0.033040    test-rmse:1.878919+0.231419 
## [62] train-rmse:1.257777+0.031951    test-rmse:1.858079+0.235654 
## [63] train-rmse:1.234251+0.032075    test-rmse:1.842517+0.232445 
## [64] train-rmse:1.210899+0.032789    test-rmse:1.828265+0.229888 
## [65] train-rmse:1.187698+0.030176    test-rmse:1.804302+0.221624 
## [66] train-rmse:1.166668+0.029884    test-rmse:1.784033+0.224742 
## [67] train-rmse:1.143717+0.032161    test-rmse:1.769163+0.219564 
## [68] train-rmse:1.123249+0.031931    test-rmse:1.754949+0.224881 
## [69] train-rmse:1.103281+0.031467    test-rmse:1.739764+0.224306 
## [70] train-rmse:1.083888+0.031054    test-rmse:1.729112+0.220502 
## [71] train-rmse:1.064651+0.031925    test-rmse:1.714494+0.222607 
## [72] train-rmse:1.046649+0.031299    test-rmse:1.696541+0.224134 
## [73] train-rmse:1.028506+0.030658    test-rmse:1.678625+0.228301 
## [74] train-rmse:1.010997+0.030468    test-rmse:1.665150+0.228946 
## [75] train-rmse:0.994701+0.030296    test-rmse:1.651672+0.228930 
## [76] train-rmse:0.978261+0.029610    test-rmse:1.639404+0.231983 
## [77] train-rmse:0.962003+0.029442    test-rmse:1.627078+0.234561 
## [78] train-rmse:0.946678+0.028757    test-rmse:1.616591+0.234014 
## [79] train-rmse:0.929988+0.029530    test-rmse:1.605796+0.233613 
## [80] train-rmse:0.914986+0.028403    test-rmse:1.596868+0.236982 
## [81] train-rmse:0.900009+0.028014    test-rmse:1.584142+0.233934 
## [82] train-rmse:0.885682+0.028284    test-rmse:1.572630+0.234728 
## [83] train-rmse:0.872228+0.027880    test-rmse:1.562483+0.234829 
## [84] train-rmse:0.857438+0.026564    test-rmse:1.551501+0.231617 
## [85] train-rmse:0.842751+0.026613    test-rmse:1.541719+0.235737 
## [86] train-rmse:0.829518+0.026764    test-rmse:1.533355+0.237421 
## [87] train-rmse:0.816259+0.026615    test-rmse:1.526072+0.241927 
## [88] train-rmse:0.803339+0.025724    test-rmse:1.511196+0.242760 
## [89] train-rmse:0.791312+0.025626    test-rmse:1.500480+0.243408 
## [90] train-rmse:0.778798+0.025913    test-rmse:1.494884+0.239767 
## [91] train-rmse:0.766700+0.026051    test-rmse:1.487791+0.242083 
## [92] train-rmse:0.754470+0.026243    test-rmse:1.481476+0.241415 
## [93] train-rmse:0.743284+0.025181    test-rmse:1.472540+0.241333 
## [94] train-rmse:0.731830+0.026091    test-rmse:1.463317+0.242040 
## [95] train-rmse:0.721468+0.026041    test-rmse:1.458543+0.240542 
## [96] train-rmse:0.710793+0.026129    test-rmse:1.449789+0.242157 
## [97] train-rmse:0.700806+0.026218    test-rmse:1.443784+0.245655 
## [98] train-rmse:0.690855+0.025704    test-rmse:1.438389+0.247380 
## [99] train-rmse:0.680749+0.025542    test-rmse:1.432242+0.245537 
## [100]    train-rmse:0.671303+0.026084    test-rmse:1.423416+0.247261 
## [101]    train-rmse:0.660813+0.025386    test-rmse:1.417634+0.248168 
## [102]    train-rmse:0.652048+0.025037    test-rmse:1.409377+0.243820 
## [103]    train-rmse:0.643088+0.024298    test-rmse:1.402431+0.244299 
## [104]    train-rmse:0.634312+0.023822    test-rmse:1.397956+0.242880 
## [105]    train-rmse:0.626185+0.023575    test-rmse:1.393174+0.244954 
## [106]    train-rmse:0.617696+0.021958    test-rmse:1.386206+0.242152 
## [107]    train-rmse:0.610042+0.021609    test-rmse:1.377721+0.245738 
## [108]    train-rmse:0.602689+0.021245    test-rmse:1.372807+0.246072 
## [109]    train-rmse:0.594303+0.020714    test-rmse:1.368646+0.245893 
## [110]    train-rmse:0.585766+0.020828    test-rmse:1.364244+0.244574 
## [111]    train-rmse:0.578689+0.020530    test-rmse:1.359447+0.245557 
## [112]    train-rmse:0.572038+0.020489    test-rmse:1.351848+0.245910 
## [113]    train-rmse:0.564883+0.021172    test-rmse:1.346981+0.247555 
## [114]    train-rmse:0.558319+0.020846    test-rmse:1.341973+0.248669 
## [115]    train-rmse:0.551691+0.020560    test-rmse:1.336433+0.246672 
## [116]    train-rmse:0.543868+0.021137    test-rmse:1.333395+0.247893 
## [117]    train-rmse:0.536810+0.020918    test-rmse:1.326587+0.249043 
## [118]    train-rmse:0.530043+0.020247    test-rmse:1.323987+0.250299 
## [119]    train-rmse:0.524095+0.019676    test-rmse:1.320443+0.252146 
## [120]    train-rmse:0.518256+0.019715    test-rmse:1.316431+0.254225 
## [121]    train-rmse:0.512600+0.019787    test-rmse:1.313782+0.256860 
## [122]    train-rmse:0.506096+0.019533    test-rmse:1.307671+0.252278 
## [123]    train-rmse:0.499977+0.020174    test-rmse:1.305435+0.253734 
## [124]    train-rmse:0.494060+0.019444    test-rmse:1.302316+0.255187 
## [125]    train-rmse:0.488474+0.019541    test-rmse:1.298283+0.255272 
## [126]    train-rmse:0.483423+0.019520    test-rmse:1.294397+0.255986 
## [127]    train-rmse:0.478563+0.019469    test-rmse:1.292368+0.257445 
## [128]    train-rmse:0.473416+0.019546    test-rmse:1.287871+0.252910 
## [129]    train-rmse:0.468299+0.019070    test-rmse:1.284734+0.253719 
## [130]    train-rmse:0.462920+0.019105    test-rmse:1.281211+0.254339 
## [131]    train-rmse:0.457469+0.019253    test-rmse:1.279915+0.254585 
## [132]    train-rmse:0.452738+0.019505    test-rmse:1.274985+0.256421 
## [133]    train-rmse:0.448308+0.019608    test-rmse:1.272415+0.257485 
## [134]    train-rmse:0.442522+0.018553    test-rmse:1.268803+0.254451 
## [135]    train-rmse:0.437535+0.018379    test-rmse:1.264038+0.256731 
## [136]    train-rmse:0.433286+0.017995    test-rmse:1.262069+0.257309 
## [137]    train-rmse:0.428784+0.017635    test-rmse:1.260413+0.257603 
## [138]    train-rmse:0.424690+0.017596    test-rmse:1.257106+0.258311 
## [139]    train-rmse:0.419953+0.018209    test-rmse:1.256043+0.258329 
## [140]    train-rmse:0.414768+0.018328    test-rmse:1.253679+0.259712 
## [141]    train-rmse:0.410605+0.018092    test-rmse:1.252530+0.260743 
## [142]    train-rmse:0.406420+0.017682    test-rmse:1.250753+0.261636 
## [143]    train-rmse:0.402465+0.017647    test-rmse:1.249199+0.260879 
## [144]    train-rmse:0.398664+0.017319    test-rmse:1.245497+0.262709 
## [145]    train-rmse:0.394940+0.017486    test-rmse:1.244379+0.263030 
## [146]    train-rmse:0.390838+0.017227    test-rmse:1.243028+0.264100 
## [147]    train-rmse:0.387548+0.017050    test-rmse:1.241234+0.263402 
## [148]    train-rmse:0.383949+0.017072    test-rmse:1.238777+0.264064 
## [149]    train-rmse:0.380624+0.017262    test-rmse:1.237615+0.264619 
## [150]    train-rmse:0.376645+0.017393    test-rmse:1.235685+0.264128 
## [151]    train-rmse:0.373005+0.017774    test-rmse:1.232076+0.265742 
## [152]    train-rmse:0.369644+0.017597    test-rmse:1.229922+0.267004 
## [153]    train-rmse:0.366495+0.017731    test-rmse:1.226607+0.267263 
## [154]    train-rmse:0.363131+0.017785    test-rmse:1.223451+0.266790 
## [155]    train-rmse:0.359436+0.017619    test-rmse:1.221045+0.268174 
## [156]    train-rmse:0.356162+0.017525    test-rmse:1.220509+0.269042 
## [157]    train-rmse:0.353221+0.017745    test-rmse:1.218835+0.269505 
## [158]    train-rmse:0.350146+0.018034    test-rmse:1.218253+0.270243 
## [159]    train-rmse:0.347030+0.018215    test-rmse:1.215986+0.271598 
## [160]    train-rmse:0.343964+0.017573    test-rmse:1.214186+0.272394 
## [161]    train-rmse:0.341270+0.017497    test-rmse:1.212329+0.272478 
## [162]    train-rmse:0.338346+0.016813    test-rmse:1.211465+0.273018 
## [163]    train-rmse:0.334920+0.015919    test-rmse:1.210240+0.271428 
## [164]    train-rmse:0.331803+0.015699    test-rmse:1.208653+0.272477 
## [165]    train-rmse:0.328518+0.015887    test-rmse:1.207330+0.273449 
## [166]    train-rmse:0.326105+0.015607    test-rmse:1.206582+0.273305 
## [167]    train-rmse:0.323343+0.015856    test-rmse:1.205491+0.273514 
## [168]    train-rmse:0.320198+0.015235    test-rmse:1.204847+0.273502 
## [169]    train-rmse:0.317740+0.015238    test-rmse:1.203966+0.274369 
## [170]    train-rmse:0.315200+0.015190    test-rmse:1.202952+0.274928 
## [171]    train-rmse:0.312738+0.015034    test-rmse:1.202498+0.275881 
## [172]    train-rmse:0.309805+0.015716    test-rmse:1.201323+0.276077 
## [173]    train-rmse:0.306546+0.014830    test-rmse:1.199677+0.275911 
## [174]    train-rmse:0.304052+0.014327    test-rmse:1.199403+0.276611 
## [175]    train-rmse:0.301017+0.014823    test-rmse:1.198565+0.276714 
## [176]    train-rmse:0.298503+0.014839    test-rmse:1.197421+0.277425 
## [177]    train-rmse:0.296580+0.014856    test-rmse:1.197089+0.277053 
## [178]    train-rmse:0.294592+0.014388    test-rmse:1.195872+0.277490 
## [179]    train-rmse:0.292428+0.014095    test-rmse:1.195499+0.276905 
## [180]    train-rmse:0.290191+0.013949    test-rmse:1.194411+0.276577 
## [181]    train-rmse:0.288186+0.013896    test-rmse:1.192736+0.278454 
## [182]    train-rmse:0.285803+0.013763    test-rmse:1.192579+0.278793 
## [183]    train-rmse:0.283833+0.013593    test-rmse:1.192087+0.278737 
## [184]    train-rmse:0.281747+0.013594    test-rmse:1.190233+0.276786 
## [185]    train-rmse:0.279623+0.013496    test-rmse:1.189233+0.277232 
## [186]    train-rmse:0.277356+0.013139    test-rmse:1.189154+0.277533 
## [187]    train-rmse:0.274977+0.012831    test-rmse:1.188851+0.277621 
## [188]    train-rmse:0.272894+0.012387    test-rmse:1.188277+0.277260 
## [189]    train-rmse:0.271159+0.012275    test-rmse:1.187576+0.277000 
## [190]    train-rmse:0.268870+0.013206    test-rmse:1.186257+0.277325 
## [191]    train-rmse:0.266890+0.013304    test-rmse:1.185969+0.276954 
## [192]    train-rmse:0.264488+0.013430    test-rmse:1.184939+0.277319 
## [193]    train-rmse:0.262808+0.012893    test-rmse:1.183871+0.277508 
## [194]    train-rmse:0.260703+0.013015    test-rmse:1.183825+0.278195 
## [195]    train-rmse:0.258918+0.013088    test-rmse:1.183066+0.278522 
## [196]    train-rmse:0.257298+0.013173    test-rmse:1.182625+0.278245 
## [197]    train-rmse:0.255663+0.013107    test-rmse:1.182073+0.277063 
## [198]    train-rmse:0.254222+0.013090    test-rmse:1.181278+0.277495 
## [199]    train-rmse:0.252570+0.013219    test-rmse:1.180005+0.277460 
## [200]    train-rmse:0.250361+0.012455    test-rmse:1.179330+0.277533 
## [201]    train-rmse:0.248536+0.012431    test-rmse:1.178745+0.277703 
## [202]    train-rmse:0.246476+0.012068    test-rmse:1.178133+0.277575 
## [203]    train-rmse:0.245055+0.011902    test-rmse:1.178541+0.277638 
## [204]    train-rmse:0.243685+0.012036    test-rmse:1.177946+0.278182 
## [205]    train-rmse:0.242361+0.012465    test-rmse:1.177513+0.278113 
## [206]    train-rmse:0.240335+0.012742    test-rmse:1.177314+0.278395 
## [207]    train-rmse:0.239071+0.012806    test-rmse:1.176318+0.279216 
## [208]    train-rmse:0.237669+0.013044    test-rmse:1.175705+0.279270 
## [209]    train-rmse:0.235832+0.012536    test-rmse:1.174497+0.279515 
## [210]    train-rmse:0.233600+0.012263    test-rmse:1.173089+0.280835 
## [211]    train-rmse:0.232224+0.012232    test-rmse:1.172719+0.280851 
## [212]    train-rmse:0.230412+0.011858    test-rmse:1.172141+0.281184 
## [213]    train-rmse:0.228458+0.011644    test-rmse:1.171681+0.281160 
## [214]    train-rmse:0.226666+0.011129    test-rmse:1.171530+0.281365 
## [215]    train-rmse:0.225128+0.010863    test-rmse:1.171512+0.281282 
## [216]    train-rmse:0.223294+0.010673    test-rmse:1.171635+0.280791 
## [217]    train-rmse:0.221406+0.010536    test-rmse:1.171199+0.281030 
## [218]    train-rmse:0.219757+0.011034    test-rmse:1.171309+0.280777 
## [219]    train-rmse:0.218282+0.011302    test-rmse:1.171087+0.281256 
## [220]    train-rmse:0.217073+0.011151    test-rmse:1.170266+0.281703 
## [221]    train-rmse:0.215707+0.011259    test-rmse:1.169342+0.281938 
## [222]    train-rmse:0.214321+0.011360    test-rmse:1.169026+0.282138 
## [223]    train-rmse:0.212771+0.011729    test-rmse:1.168412+0.282125 
## [224]    train-rmse:0.211520+0.011446    test-rmse:1.167891+0.282368 
## [225]    train-rmse:0.210304+0.011376    test-rmse:1.168125+0.282203 
## [226]    train-rmse:0.208674+0.011151    test-rmse:1.167131+0.280807 
## [227]    train-rmse:0.207255+0.010787    test-rmse:1.166312+0.280910 
## [228]    train-rmse:0.206286+0.010919    test-rmse:1.165993+0.280803 
## [229]    train-rmse:0.205066+0.010947    test-rmse:1.166040+0.280922 
## [230]    train-rmse:0.203788+0.011318    test-rmse:1.165773+0.280826 
## [231]    train-rmse:0.202452+0.011251    test-rmse:1.165764+0.280733 
## [232]    train-rmse:0.201084+0.011102    test-rmse:1.165489+0.281118 
## [233]    train-rmse:0.199690+0.010597    test-rmse:1.164785+0.281311 
## [234]    train-rmse:0.198550+0.010677    test-rmse:1.164277+0.281690 
## [235]    train-rmse:0.197279+0.010584    test-rmse:1.164062+0.281895 
## [236]    train-rmse:0.195617+0.010285    test-rmse:1.163665+0.282179 
## [237]    train-rmse:0.194726+0.010304    test-rmse:1.163277+0.282241 
## [238]    train-rmse:0.193527+0.010361    test-rmse:1.163088+0.282442 
## [239]    train-rmse:0.192576+0.010214    test-rmse:1.162796+0.282495 
## [240]    train-rmse:0.191291+0.010085    test-rmse:1.162532+0.282372 
## [241]    train-rmse:0.190127+0.010438    test-rmse:1.161834+0.282803 
## [242]    train-rmse:0.189001+0.010362    test-rmse:1.161159+0.282590 
## [243]    train-rmse:0.187462+0.010048    test-rmse:1.160866+0.282881 
## [244]    train-rmse:0.186232+0.009787    test-rmse:1.160275+0.282725 
## [245]    train-rmse:0.185010+0.009265    test-rmse:1.160197+0.282983 
## [246]    train-rmse:0.184116+0.009256    test-rmse:1.159396+0.283458 
## [247]    train-rmse:0.183184+0.009346    test-rmse:1.159234+0.283487 
## [248]    train-rmse:0.182105+0.009045    test-rmse:1.159143+0.283708 
## [249]    train-rmse:0.181143+0.008946    test-rmse:1.159247+0.283979 
## [250]    train-rmse:0.179818+0.008574    test-rmse:1.158954+0.284278 
## [251]    train-rmse:0.178827+0.008672    test-rmse:1.158881+0.284137 
## [252]    train-rmse:0.177834+0.008323    test-rmse:1.158612+0.284131 
## [253]    train-rmse:0.176656+0.008344    test-rmse:1.157668+0.284109 
## [254]    train-rmse:0.175582+0.008762    test-rmse:1.157461+0.284359 
## [255]    train-rmse:0.174304+0.009093    test-rmse:1.156498+0.284583 
## [256]    train-rmse:0.172913+0.009252    test-rmse:1.156452+0.284176 
## [257]    train-rmse:0.171874+0.009312    test-rmse:1.156125+0.284375 
## [258]    train-rmse:0.170658+0.009349    test-rmse:1.155466+0.284948 
## [259]    train-rmse:0.169292+0.009220    test-rmse:1.155397+0.284972 
## [260]    train-rmse:0.168206+0.009270    test-rmse:1.155381+0.284977 
## [261]    train-rmse:0.167429+0.009099    test-rmse:1.154782+0.285229 
## [262]    train-rmse:0.166662+0.009107    test-rmse:1.154742+0.284915 
## [263]    train-rmse:0.165593+0.009284    test-rmse:1.154552+0.285024 
## [264]    train-rmse:0.164656+0.009092    test-rmse:1.154230+0.285008 
## [265]    train-rmse:0.163822+0.008957    test-rmse:1.153473+0.284818 
## [266]    train-rmse:0.162506+0.008687    test-rmse:1.153569+0.285433 
## [267]    train-rmse:0.161517+0.008673    test-rmse:1.153542+0.285314 
## [268]    train-rmse:0.160470+0.008625    test-rmse:1.153545+0.285495 
## [269]    train-rmse:0.159406+0.008499    test-rmse:1.153568+0.285856 
## [270]    train-rmse:0.158257+0.008828    test-rmse:1.153720+0.286221 
## [271]    train-rmse:0.157533+0.008915    test-rmse:1.153630+0.286091 
## Stopping. Best iteration:
## [265]    train-rmse:0.163822+0.008957    test-rmse:1.153473+0.284818
## 
## 11 
## [1]  train-rmse:94.766116+0.105094   test-rmse:94.766423+0.483432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:83.478061+0.095217   test-rmse:83.478186+0.500072 
## [3]  train-rmse:73.545764+0.086897   test-rmse:73.545670+0.516055 
## [4]  train-rmse:64.807895+0.080011   test-rmse:64.807548+0.531590 
## [5]  train-rmse:57.122527+0.074434   test-rmse:57.121858+0.546922 
## [6]  train-rmse:50.364803+0.070103   test-rmse:50.363757+0.562250 
## [7]  train-rmse:44.424928+0.066945   test-rmse:44.423422+0.577769 
## [8]  train-rmse:39.206363+0.064904   test-rmse:39.204310+0.593657 
## [9]  train-rmse:34.622293+0.063135   test-rmse:34.630677+0.594785 
## [10] train-rmse:30.591081+0.059323   test-rmse:30.633139+0.599495 
## [11] train-rmse:27.044432+0.052606   test-rmse:27.116927+0.608458 
## [12] train-rmse:23.924558+0.044243   test-rmse:24.000367+0.581464 
## [13] train-rmse:21.179292+0.038293   test-rmse:21.256309+0.567112 
## [14] train-rmse:18.763729+0.033512   test-rmse:18.846606+0.553622 
## [15] train-rmse:16.635810+0.030452   test-rmse:16.714029+0.545435 
## [16] train-rmse:14.763425+0.026037   test-rmse:14.859881+0.563238 
## [17] train-rmse:13.112334+0.024482   test-rmse:13.218849+0.547795 
## [18] train-rmse:11.659372+0.022727   test-rmse:11.769373+0.535353 
## [19] train-rmse:10.377710+0.021390   test-rmse:10.481675+0.538021 
## [20] train-rmse:9.252015+0.019227    test-rmse:9.371911+0.544917 
## [21] train-rmse:8.260731+0.017732    test-rmse:8.370771+0.535472 
## [22] train-rmse:7.387540+0.018301    test-rmse:7.501691+0.523754 
## [23] train-rmse:6.624639+0.017662    test-rmse:6.754430+0.531739 
## [24] train-rmse:5.952468+0.017521    test-rmse:6.089171+0.513031 
## [25] train-rmse:5.363936+0.018444    test-rmse:5.518246+0.499198 
## [26] train-rmse:4.847252+0.023244    test-rmse:5.010321+0.471890 
## [27] train-rmse:4.390979+0.018833    test-rmse:4.574961+0.478059 
## [28] train-rmse:3.996956+0.019195    test-rmse:4.192845+0.460193 
## [29] train-rmse:3.647203+0.016162    test-rmse:3.873672+0.445782 
## [30] train-rmse:3.343811+0.019817    test-rmse:3.592677+0.424085 
## [31] train-rmse:3.080003+0.020058    test-rmse:3.354422+0.417743 
## [32] train-rmse:2.853310+0.020742    test-rmse:3.150157+0.405749 
## [33] train-rmse:2.652145+0.021954    test-rmse:2.967490+0.380995 
## [34] train-rmse:2.478271+0.021275    test-rmse:2.810214+0.369633 
## [35] train-rmse:2.322253+0.028088    test-rmse:2.682745+0.353102 
## [36] train-rmse:2.181247+0.026653    test-rmse:2.564531+0.333046 
## [37] train-rmse:2.063408+0.029940    test-rmse:2.463990+0.321731 
## [38] train-rmse:1.956376+0.030114    test-rmse:2.389258+0.305024 
## [39] train-rmse:1.864952+0.030690    test-rmse:2.319180+0.308247 
## [40] train-rmse:1.785654+0.031229    test-rmse:2.252531+0.300180 
## [41] train-rmse:1.712445+0.032381    test-rmse:2.205995+0.296163 
## [42] train-rmse:1.644534+0.040502    test-rmse:2.150766+0.277110 
## [43] train-rmse:1.583027+0.039398    test-rmse:2.103278+0.273955 
## [44] train-rmse:1.527952+0.038686    test-rmse:2.074138+0.276846 
## [45] train-rmse:1.472383+0.037236    test-rmse:2.031433+0.271434 
## [46] train-rmse:1.423954+0.034157    test-rmse:1.995403+0.262118 
## [47] train-rmse:1.374815+0.040741    test-rmse:1.966648+0.254629 
## [48] train-rmse:1.332587+0.038715    test-rmse:1.938385+0.252669 
## [49] train-rmse:1.295548+0.038282    test-rmse:1.907571+0.250618 
## [50] train-rmse:1.259944+0.039310    test-rmse:1.886847+0.251343 
## [51] train-rmse:1.226550+0.038010    test-rmse:1.866558+0.246322 
## [52] train-rmse:1.195045+0.037261    test-rmse:1.842712+0.254234 
## [53] train-rmse:1.164036+0.036304    test-rmse:1.816936+0.253298 
## [54] train-rmse:1.135287+0.033967    test-rmse:1.792764+0.247378 
## [55] train-rmse:1.108092+0.033525    test-rmse:1.774698+0.241359 
## [56] train-rmse:1.081958+0.033121    test-rmse:1.748049+0.243934 
## [57] train-rmse:1.055138+0.033966    test-rmse:1.734406+0.243092 
## [58] train-rmse:1.030870+0.033774    test-rmse:1.718989+0.241204 
## [59] train-rmse:1.005124+0.038119    test-rmse:1.697670+0.244139 
## [60] train-rmse:0.981761+0.040725    test-rmse:1.688197+0.247412 
## [61] train-rmse:0.960542+0.040066    test-rmse:1.670020+0.245238 
## [62] train-rmse:0.937997+0.038919    test-rmse:1.653821+0.245963 
## [63] train-rmse:0.915999+0.038733    test-rmse:1.640444+0.244509 
## [64] train-rmse:0.896091+0.037996    test-rmse:1.624731+0.252383 
## [65] train-rmse:0.877535+0.037249    test-rmse:1.609208+0.242521 
## [66] train-rmse:0.859337+0.036698    test-rmse:1.591475+0.249789 
## [67] train-rmse:0.841671+0.035798    test-rmse:1.574217+0.251430 
## [68] train-rmse:0.824260+0.034427    test-rmse:1.562767+0.252540 
## [69] train-rmse:0.807836+0.034229    test-rmse:1.548778+0.246673 
## [70] train-rmse:0.791855+0.033454    test-rmse:1.540067+0.247316 
## [71] train-rmse:0.775953+0.031870    test-rmse:1.526339+0.249459 
## [72] train-rmse:0.761250+0.032169    test-rmse:1.512251+0.251105 
## [73] train-rmse:0.746440+0.032244    test-rmse:1.503270+0.251628 
## [74] train-rmse:0.731638+0.031198    test-rmse:1.487983+0.247564 
## [75] train-rmse:0.715911+0.032686    test-rmse:1.478697+0.247421 
## [76] train-rmse:0.701430+0.032668    test-rmse:1.469743+0.246276 
## [77] train-rmse:0.688690+0.032142    test-rmse:1.462200+0.250247 
## [78] train-rmse:0.676931+0.032131    test-rmse:1.449443+0.245096 
## [79] train-rmse:0.664581+0.032720    test-rmse:1.436439+0.248150 
## [80] train-rmse:0.651010+0.032893    test-rmse:1.432002+0.248506 
## [81] train-rmse:0.640085+0.032300    test-rmse:1.423289+0.247754 
## [82] train-rmse:0.628600+0.032011    test-rmse:1.415018+0.250097 
## [83] train-rmse:0.618256+0.031376    test-rmse:1.409087+0.250724 
## [84] train-rmse:0.606879+0.029756    test-rmse:1.402365+0.253694 
## [85] train-rmse:0.595871+0.028900    test-rmse:1.390010+0.250661 
## [86] train-rmse:0.584452+0.030145    test-rmse:1.387186+0.252931 
## [87] train-rmse:0.575532+0.029253    test-rmse:1.377004+0.247628 
## [88] train-rmse:0.565472+0.028443    test-rmse:1.373514+0.248215 
## [89] train-rmse:0.555541+0.027652    test-rmse:1.368732+0.247688 
## [90] train-rmse:0.546029+0.027137    test-rmse:1.363214+0.249193 
## [91] train-rmse:0.537784+0.026937    test-rmse:1.355635+0.246818 
## [92] train-rmse:0.528745+0.026176    test-rmse:1.351565+0.245869 
## [93] train-rmse:0.519497+0.026678    test-rmse:1.346487+0.247139 
## [94] train-rmse:0.512283+0.026691    test-rmse:1.342220+0.250350 
## [95] train-rmse:0.504061+0.026389    test-rmse:1.339077+0.250011 
## [96] train-rmse:0.495300+0.026740    test-rmse:1.333184+0.251200 
## [97] train-rmse:0.488135+0.026153    test-rmse:1.328971+0.250880 
## [98] train-rmse:0.481121+0.025983    test-rmse:1.325380+0.252071 
## [99] train-rmse:0.472725+0.026028    test-rmse:1.324071+0.254150 
## [100]    train-rmse:0.464815+0.024948    test-rmse:1.321146+0.254694 
## [101]    train-rmse:0.458254+0.025403    test-rmse:1.314372+0.256212 
## [102]    train-rmse:0.451588+0.025663    test-rmse:1.312609+0.257525 
## [103]    train-rmse:0.444413+0.024104    test-rmse:1.309875+0.257476 
## [104]    train-rmse:0.438464+0.023663    test-rmse:1.305880+0.256882 
## [105]    train-rmse:0.431665+0.024278    test-rmse:1.300370+0.260916 
## [106]    train-rmse:0.424816+0.022897    test-rmse:1.296996+0.260082 
## [107]    train-rmse:0.418023+0.022378    test-rmse:1.293509+0.263441 
## [108]    train-rmse:0.412011+0.022284    test-rmse:1.290475+0.264262 
## [109]    train-rmse:0.406737+0.022217    test-rmse:1.288960+0.265262 
## [110]    train-rmse:0.401658+0.021711    test-rmse:1.286671+0.264970 
## [111]    train-rmse:0.396309+0.021511    test-rmse:1.284209+0.265303 
## [112]    train-rmse:0.391358+0.021754    test-rmse:1.281756+0.266462 
## [113]    train-rmse:0.386034+0.020889    test-rmse:1.280520+0.267177 
## [114]    train-rmse:0.380767+0.020474    test-rmse:1.276983+0.269824 
## [115]    train-rmse:0.375853+0.020861    test-rmse:1.275907+0.270951 
## [116]    train-rmse:0.371039+0.020411    test-rmse:1.275133+0.271430 
## [117]    train-rmse:0.367163+0.020340    test-rmse:1.273406+0.271063 
## [118]    train-rmse:0.363232+0.020823    test-rmse:1.270886+0.271508 
## [119]    train-rmse:0.358196+0.020988    test-rmse:1.270194+0.271345 
## [120]    train-rmse:0.354774+0.020938    test-rmse:1.268372+0.272262 
## [121]    train-rmse:0.349822+0.019978    test-rmse:1.265931+0.273755 
## [122]    train-rmse:0.346000+0.019811    test-rmse:1.265371+0.274543 
## [123]    train-rmse:0.340710+0.020894    test-rmse:1.262766+0.273227 
## [124]    train-rmse:0.336827+0.020588    test-rmse:1.262212+0.274467 
## [125]    train-rmse:0.332375+0.019802    test-rmse:1.259991+0.273890 
## [126]    train-rmse:0.328641+0.019376    test-rmse:1.259687+0.274237 
## [127]    train-rmse:0.323879+0.019419    test-rmse:1.255862+0.270321 
## [128]    train-rmse:0.319900+0.018186    test-rmse:1.254687+0.271011 
## [129]    train-rmse:0.316110+0.018593    test-rmse:1.253621+0.270970 
## [130]    train-rmse:0.312847+0.018586    test-rmse:1.252510+0.271558 
## [131]    train-rmse:0.310032+0.018963    test-rmse:1.250021+0.272352 
## [132]    train-rmse:0.306061+0.018987    test-rmse:1.249687+0.273711 
## [133]    train-rmse:0.302335+0.018477    test-rmse:1.248791+0.274195 
## [134]    train-rmse:0.299408+0.018964    test-rmse:1.246630+0.274694 
## [135]    train-rmse:0.296296+0.019043    test-rmse:1.246959+0.275391 
## [136]    train-rmse:0.293364+0.018461    test-rmse:1.245127+0.276742 
## [137]    train-rmse:0.289649+0.017366    test-rmse:1.243772+0.277416 
## [138]    train-rmse:0.286593+0.016766    test-rmse:1.242903+0.277022 
## [139]    train-rmse:0.283240+0.017101    test-rmse:1.242820+0.277329 
## [140]    train-rmse:0.280172+0.016099    test-rmse:1.242353+0.277955 
## [141]    train-rmse:0.277216+0.015812    test-rmse:1.240850+0.277179 
## [142]    train-rmse:0.273628+0.016706    test-rmse:1.241121+0.276694 
## [143]    train-rmse:0.270856+0.016105    test-rmse:1.239698+0.276073 
## [144]    train-rmse:0.267869+0.016400    test-rmse:1.239528+0.276451 
## [145]    train-rmse:0.265466+0.016139    test-rmse:1.239294+0.277657 
## [146]    train-rmse:0.262048+0.015834    test-rmse:1.239180+0.276481 
## [147]    train-rmse:0.259192+0.015628    test-rmse:1.239324+0.276625 
## [148]    train-rmse:0.256024+0.015335    test-rmse:1.239002+0.276429 
## [149]    train-rmse:0.253395+0.015895    test-rmse:1.238422+0.276323 
## [150]    train-rmse:0.251067+0.015808    test-rmse:1.237204+0.277494 
## [151]    train-rmse:0.248131+0.015135    test-rmse:1.236637+0.276968 
## [152]    train-rmse:0.245640+0.014755    test-rmse:1.235130+0.275219 
## [153]    train-rmse:0.243330+0.014542    test-rmse:1.234574+0.274987 
## [154]    train-rmse:0.240603+0.014242    test-rmse:1.233443+0.276139 
## [155]    train-rmse:0.238742+0.014196    test-rmse:1.232664+0.276672 
## [156]    train-rmse:0.236476+0.014278    test-rmse:1.232208+0.276393 
## [157]    train-rmse:0.234297+0.014180    test-rmse:1.231699+0.275721 
## [158]    train-rmse:0.232130+0.014388    test-rmse:1.231323+0.275489 
## [159]    train-rmse:0.229874+0.014348    test-rmse:1.230992+0.275452 
## [160]    train-rmse:0.227469+0.014137    test-rmse:1.229173+0.276478 
## [161]    train-rmse:0.225251+0.013978    test-rmse:1.229381+0.277477 
## [162]    train-rmse:0.222566+0.013673    test-rmse:1.229551+0.277770 
## [163]    train-rmse:0.220654+0.013634    test-rmse:1.228253+0.278155 
## [164]    train-rmse:0.218715+0.014069    test-rmse:1.228125+0.278357 
## [165]    train-rmse:0.216471+0.014124    test-rmse:1.227874+0.278002 
## [166]    train-rmse:0.213591+0.012888    test-rmse:1.226425+0.276906 
## [167]    train-rmse:0.211419+0.013246    test-rmse:1.225468+0.276782 
## [168]    train-rmse:0.209287+0.013219    test-rmse:1.225350+0.277721 
## [169]    train-rmse:0.207237+0.013558    test-rmse:1.224706+0.277275 
## [170]    train-rmse:0.205339+0.013573    test-rmse:1.224222+0.277124 
## [171]    train-rmse:0.202771+0.012978    test-rmse:1.224226+0.276336 
## [172]    train-rmse:0.201103+0.013028    test-rmse:1.223996+0.276765 
## [173]    train-rmse:0.198761+0.013187    test-rmse:1.223876+0.277757 
## [174]    train-rmse:0.197493+0.013402    test-rmse:1.223769+0.277917 
## [175]    train-rmse:0.195594+0.013134    test-rmse:1.222449+0.278253 
## [176]    train-rmse:0.193295+0.013027    test-rmse:1.222620+0.277065 
## [177]    train-rmse:0.191902+0.012869    test-rmse:1.222494+0.276990 
## [178]    train-rmse:0.190375+0.012930    test-rmse:1.222389+0.276872 
## [179]    train-rmse:0.188325+0.012761    test-rmse:1.222219+0.277273 
## [180]    train-rmse:0.186694+0.012592    test-rmse:1.222438+0.277943 
## [181]    train-rmse:0.184353+0.012255    test-rmse:1.221881+0.277619 
## [182]    train-rmse:0.182958+0.012356    test-rmse:1.221187+0.277852 
## [183]    train-rmse:0.181627+0.012318    test-rmse:1.220737+0.276734 
## [184]    train-rmse:0.179593+0.011588    test-rmse:1.220614+0.276846 
## [185]    train-rmse:0.177848+0.011201    test-rmse:1.221025+0.277139 
## [186]    train-rmse:0.176062+0.011401    test-rmse:1.220983+0.277039 
## [187]    train-rmse:0.174914+0.011375    test-rmse:1.220215+0.276832 
## [188]    train-rmse:0.173738+0.011390    test-rmse:1.220032+0.276528 
## [189]    train-rmse:0.172197+0.011113    test-rmse:1.220093+0.276765 
## [190]    train-rmse:0.170644+0.010849    test-rmse:1.220173+0.277115 
## [191]    train-rmse:0.168962+0.010651    test-rmse:1.220443+0.277490 
## [192]    train-rmse:0.167038+0.010456    test-rmse:1.219938+0.277096 
## [193]    train-rmse:0.165231+0.009559    test-rmse:1.219674+0.277075 
## [194]    train-rmse:0.163611+0.010080    test-rmse:1.219604+0.276942 
## [195]    train-rmse:0.161515+0.010046    test-rmse:1.218864+0.277014 
## [196]    train-rmse:0.160390+0.009886    test-rmse:1.218784+0.276934 
## [197]    train-rmse:0.158691+0.010112    test-rmse:1.218925+0.276820 
## [198]    train-rmse:0.157329+0.010106    test-rmse:1.218783+0.276810 
## [199]    train-rmse:0.156122+0.010106    test-rmse:1.218841+0.277110 
## [200]    train-rmse:0.154778+0.009902    test-rmse:1.218795+0.277194 
## [201]    train-rmse:0.153684+0.009862    test-rmse:1.218507+0.276995 
## [202]    train-rmse:0.152200+0.010300    test-rmse:1.218267+0.277133 
## [203]    train-rmse:0.150818+0.010294    test-rmse:1.217726+0.277263 
## [204]    train-rmse:0.149391+0.010257    test-rmse:1.217739+0.277481 
## [205]    train-rmse:0.148108+0.009874    test-rmse:1.217676+0.277513 
## [206]    train-rmse:0.146698+0.010030    test-rmse:1.217153+0.277924 
## [207]    train-rmse:0.144895+0.009774    test-rmse:1.217371+0.278087 
## [208]    train-rmse:0.143400+0.009591    test-rmse:1.217204+0.278492 
## [209]    train-rmse:0.142199+0.009816    test-rmse:1.217059+0.278133 
## [210]    train-rmse:0.140980+0.009735    test-rmse:1.216899+0.278640 
## [211]    train-rmse:0.139754+0.010001    test-rmse:1.216848+0.279008 
## [212]    train-rmse:0.138782+0.009872    test-rmse:1.216606+0.278797 
## [213]    train-rmse:0.137826+0.009833    test-rmse:1.216188+0.278667 
## [214]    train-rmse:0.136780+0.009659    test-rmse:1.216014+0.278907 
## [215]    train-rmse:0.135185+0.009458    test-rmse:1.215636+0.278755 
## [216]    train-rmse:0.133991+0.009117    test-rmse:1.215199+0.278824 
## [217]    train-rmse:0.132833+0.009041    test-rmse:1.215017+0.278778 
## [218]    train-rmse:0.131494+0.008751    test-rmse:1.215113+0.278770 
## [219]    train-rmse:0.129771+0.008852    test-rmse:1.215003+0.278585 
## [220]    train-rmse:0.128580+0.008816    test-rmse:1.215196+0.278387 
## [221]    train-rmse:0.127465+0.008599    test-rmse:1.215047+0.278299 
## [222]    train-rmse:0.126439+0.008600    test-rmse:1.215163+0.278501 
## [223]    train-rmse:0.125310+0.008157    test-rmse:1.215189+0.278808 
## [224]    train-rmse:0.124539+0.008103    test-rmse:1.214931+0.279003 
## [225]    train-rmse:0.123306+0.008062    test-rmse:1.214882+0.279033 
## [226]    train-rmse:0.122683+0.008012    test-rmse:1.214769+0.278881 
## [227]    train-rmse:0.121923+0.007794    test-rmse:1.214792+0.279135 
## [228]    train-rmse:0.120612+0.007055    test-rmse:1.214566+0.279024 
## [229]    train-rmse:0.119543+0.007330    test-rmse:1.214852+0.279021 
## [230]    train-rmse:0.118171+0.007488    test-rmse:1.214719+0.278998 
## [231]    train-rmse:0.117226+0.007198    test-rmse:1.214727+0.278928 
## [232]    train-rmse:0.116554+0.007224    test-rmse:1.214766+0.278984 
## [233]    train-rmse:0.115311+0.007071    test-rmse:1.214624+0.278879 
## [234]    train-rmse:0.114412+0.007196    test-rmse:1.214506+0.279190 
## [235]    train-rmse:0.113465+0.006645    test-rmse:1.214290+0.279019 
## [236]    train-rmse:0.112206+0.006574    test-rmse:1.214201+0.279081 
## [237]    train-rmse:0.111198+0.006239    test-rmse:1.213839+0.279282 
## [238]    train-rmse:0.110240+0.006363    test-rmse:1.213945+0.279194 
## [239]    train-rmse:0.109069+0.006168    test-rmse:1.213739+0.279363 
## [240]    train-rmse:0.108174+0.006286    test-rmse:1.213800+0.279514 
## [241]    train-rmse:0.106966+0.006223    test-rmse:1.213570+0.279577 
## [242]    train-rmse:0.105940+0.006090    test-rmse:1.213694+0.279552 
## [243]    train-rmse:0.104890+0.006102    test-rmse:1.213682+0.279646 
## [244]    train-rmse:0.103625+0.005907    test-rmse:1.213631+0.279673 
## [245]    train-rmse:0.102761+0.006368    test-rmse:1.213473+0.279860 
## [246]    train-rmse:0.102200+0.006239    test-rmse:1.213371+0.279941 
## [247]    train-rmse:0.101274+0.006164    test-rmse:1.213351+0.280233 
## [248]    train-rmse:0.100213+0.005842    test-rmse:1.213089+0.279973 
## [249]    train-rmse:0.099342+0.005880    test-rmse:1.212908+0.279669 
## [250]    train-rmse:0.098466+0.005736    test-rmse:1.212807+0.279597 
## [251]    train-rmse:0.097577+0.005873    test-rmse:1.212667+0.279597 
## [252]    train-rmse:0.096681+0.005683    test-rmse:1.212831+0.279528 
## [253]    train-rmse:0.095834+0.005808    test-rmse:1.212947+0.279368 
## [254]    train-rmse:0.095096+0.005732    test-rmse:1.212851+0.279385 
## [255]    train-rmse:0.093909+0.005740    test-rmse:1.213342+0.279327 
## [256]    train-rmse:0.093039+0.005697    test-rmse:1.212960+0.279214 
## [257]    train-rmse:0.092080+0.005558    test-rmse:1.213027+0.279256 
## Stopping. Best iteration:
## [251]    train-rmse:0.097577+0.005873    test-rmse:1.212667+0.279597
## 
## 12 
## [1]  train-rmse:94.766116+0.105094   test-rmse:94.766423+0.483432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:83.478061+0.095217   test-rmse:83.478186+0.500072 
## [3]  train-rmse:73.545764+0.086897   test-rmse:73.545670+0.516055 
## [4]  train-rmse:64.807895+0.080011   test-rmse:64.807548+0.531590 
## [5]  train-rmse:57.122527+0.074434   test-rmse:57.121858+0.546922 
## [6]  train-rmse:50.364803+0.070103   test-rmse:50.363757+0.562250 
## [7]  train-rmse:44.424928+0.066945   test-rmse:44.423422+0.577769 
## [8]  train-rmse:39.206363+0.064904   test-rmse:39.204310+0.593657 
## [9]  train-rmse:34.622293+0.063135   test-rmse:34.630677+0.594785 
## [10] train-rmse:30.591081+0.059323   test-rmse:30.633139+0.599495 
## [11] train-rmse:27.044432+0.052606   test-rmse:27.116927+0.608458 
## [12] train-rmse:23.924558+0.044243   test-rmse:24.000367+0.581464 
## [13] train-rmse:21.179186+0.038168   test-rmse:21.254095+0.569931 
## [14] train-rmse:18.763117+0.033214   test-rmse:18.846948+0.554102 
## [15] train-rmse:16.633769+0.029552   test-rmse:16.718301+0.537407 
## [16] train-rmse:14.759743+0.024871   test-rmse:14.865453+0.554510 
## [17] train-rmse:13.106433+0.023083   test-rmse:13.213154+0.550035 
## [18] train-rmse:11.649263+0.022642   test-rmse:11.754813+0.531973 
## [19] train-rmse:10.363184+0.020328   test-rmse:10.455913+0.532696 
## [20] train-rmse:9.231174+0.018751    test-rmse:9.346748+0.545403 
## [21] train-rmse:8.234509+0.018663    test-rmse:8.358545+0.530804 
## [22] train-rmse:7.355970+0.017605    test-rmse:7.470321+0.527576 
## [23] train-rmse:6.583057+0.017371    test-rmse:6.703227+0.506742 
## [24] train-rmse:5.902712+0.017313    test-rmse:6.038969+0.522256 
## [25] train-rmse:5.303167+0.016856    test-rmse:5.444801+0.502666 
## [26] train-rmse:4.774540+0.011522    test-rmse:4.938081+0.514155 
## [27] train-rmse:4.310325+0.012062    test-rmse:4.501785+0.490404 
## [28] train-rmse:3.903009+0.012535    test-rmse:4.117182+0.464566 
## [29] train-rmse:3.541850+0.013060    test-rmse:3.780119+0.439052 
## [30] train-rmse:3.228953+0.015855    test-rmse:3.490742+0.415358 
## [31] train-rmse:2.944791+0.021266    test-rmse:3.239411+0.415041 
## [32] train-rmse:2.698317+0.020821    test-rmse:3.026283+0.395378 
## [33] train-rmse:2.487551+0.023420    test-rmse:2.846586+0.386577 
## [34] train-rmse:2.301964+0.024751    test-rmse:2.673977+0.362545 
## [35] train-rmse:2.134016+0.025926    test-rmse:2.534117+0.343824 
## [36] train-rmse:1.989028+0.024635    test-rmse:2.421753+0.338408 
## [37] train-rmse:1.863416+0.028760    test-rmse:2.312954+0.316013 
## [38] train-rmse:1.748818+0.029301    test-rmse:2.223962+0.310499 
## [39] train-rmse:1.650628+0.028585    test-rmse:2.150311+0.308472 
## [40] train-rmse:1.555914+0.034733    test-rmse:2.089170+0.298411 
## [41] train-rmse:1.477830+0.033070    test-rmse:2.030426+0.285116 
## [42] train-rmse:1.402782+0.032547    test-rmse:1.973685+0.271119 
## [43] train-rmse:1.338031+0.034974    test-rmse:1.932324+0.268160 
## [44] train-rmse:1.278539+0.034069    test-rmse:1.888461+0.262692 
## [45] train-rmse:1.221516+0.034048    test-rmse:1.852421+0.254257 
## [46] train-rmse:1.174382+0.033113    test-rmse:1.824720+0.257167 
## [47] train-rmse:1.131473+0.031951    test-rmse:1.798117+0.251463 
## [48] train-rmse:1.086617+0.033529    test-rmse:1.767787+0.245196 
## [49] train-rmse:1.046298+0.035641    test-rmse:1.740198+0.244140 
## [50] train-rmse:1.012750+0.035114    test-rmse:1.711977+0.244244 
## [51] train-rmse:0.979244+0.037334    test-rmse:1.694344+0.241326 
## [52] train-rmse:0.946322+0.036137    test-rmse:1.661793+0.242149 
## [53] train-rmse:0.917903+0.035254    test-rmse:1.644892+0.243838 
## [54] train-rmse:0.891422+0.033801    test-rmse:1.626912+0.246044 
## [55] train-rmse:0.861855+0.035214    test-rmse:1.609171+0.247209 
## [56] train-rmse:0.836060+0.034811    test-rmse:1.595088+0.246181 
## [57] train-rmse:0.813230+0.035037    test-rmse:1.578776+0.247924 
## [58] train-rmse:0.791398+0.035115    test-rmse:1.568263+0.254398 
## [59] train-rmse:0.770203+0.034056    test-rmse:1.542789+0.246693 
## [60] train-rmse:0.749630+0.032875    test-rmse:1.529651+0.246386 
## [61] train-rmse:0.730123+0.032711    test-rmse:1.514970+0.249522 
## [62] train-rmse:0.711720+0.032274    test-rmse:1.497211+0.245601 
## [63] train-rmse:0.691270+0.031532    test-rmse:1.485434+0.247018 
## [64] train-rmse:0.674734+0.030472    test-rmse:1.472180+0.242000 
## [65] train-rmse:0.658638+0.030240    test-rmse:1.464777+0.244849 
## [66] train-rmse:0.640444+0.029343    test-rmse:1.448869+0.247804 
## [67] train-rmse:0.623643+0.028859    test-rmse:1.445952+0.247136 
## [68] train-rmse:0.608406+0.030654    test-rmse:1.438148+0.247603 
## [69] train-rmse:0.594244+0.030252    test-rmse:1.430188+0.247989 
## [70] train-rmse:0.580658+0.030171    test-rmse:1.422839+0.247060 
## [71] train-rmse:0.567929+0.030617    test-rmse:1.414334+0.248791 
## [72] train-rmse:0.555924+0.029667    test-rmse:1.405194+0.254303 
## [73] train-rmse:0.539409+0.029793    test-rmse:1.396233+0.250334 
## [74] train-rmse:0.528390+0.029821    test-rmse:1.388087+0.246315 
## [75] train-rmse:0.515470+0.029133    test-rmse:1.382877+0.248330 
## [76] train-rmse:0.505067+0.029281    test-rmse:1.376754+0.247854 
## [77] train-rmse:0.495028+0.029530    test-rmse:1.368047+0.245531 
## [78] train-rmse:0.484631+0.028223    test-rmse:1.363108+0.247683 
## [79] train-rmse:0.475530+0.028284    test-rmse:1.357823+0.247800 
## [80] train-rmse:0.465648+0.027625    test-rmse:1.352665+0.248470 
## [81] train-rmse:0.456583+0.027378    test-rmse:1.347032+0.249173 
## [82] train-rmse:0.446224+0.025867    test-rmse:1.344835+0.249528 
## [83] train-rmse:0.436529+0.025593    test-rmse:1.340284+0.249449 
## [84] train-rmse:0.428596+0.025594    test-rmse:1.335953+0.250026 
## [85] train-rmse:0.419666+0.024503    test-rmse:1.328778+0.247088 
## [86] train-rmse:0.410948+0.025295    test-rmse:1.324393+0.248304 
## [87] train-rmse:0.403306+0.023824    test-rmse:1.319131+0.245648 
## [88] train-rmse:0.396069+0.023103    test-rmse:1.317244+0.246247 
## [89] train-rmse:0.389468+0.022983    test-rmse:1.313944+0.247488 
## [90] train-rmse:0.382502+0.022632    test-rmse:1.309655+0.247954 
## [91] train-rmse:0.375891+0.021574    test-rmse:1.305041+0.245916 
## [92] train-rmse:0.369044+0.021673    test-rmse:1.303296+0.247629 
## [93] train-rmse:0.363009+0.021390    test-rmse:1.300025+0.247341 
## [94] train-rmse:0.356756+0.021048    test-rmse:1.293177+0.243888 
## [95] train-rmse:0.350312+0.019920    test-rmse:1.290950+0.243738 
## [96] train-rmse:0.344265+0.019711    test-rmse:1.289109+0.245806 
## [97] train-rmse:0.338233+0.020466    test-rmse:1.285937+0.247264 
## [98] train-rmse:0.332809+0.020644    test-rmse:1.283669+0.249816 
## [99] train-rmse:0.326957+0.019383    test-rmse:1.279224+0.249795 
## [100]    train-rmse:0.321785+0.019224    test-rmse:1.276684+0.249542 
## [101]    train-rmse:0.316847+0.018109    test-rmse:1.273464+0.249083 
## [102]    train-rmse:0.311230+0.017777    test-rmse:1.272679+0.249619 
## [103]    train-rmse:0.307002+0.017481    test-rmse:1.269490+0.250171 
## [104]    train-rmse:0.301912+0.016117    test-rmse:1.267448+0.251076 
## [105]    train-rmse:0.297042+0.014951    test-rmse:1.266783+0.252707 
## [106]    train-rmse:0.290827+0.012914    test-rmse:1.264588+0.253841 
## [107]    train-rmse:0.286443+0.012110    test-rmse:1.264108+0.254733 
## [108]    train-rmse:0.281733+0.012246    test-rmse:1.263107+0.254605 
## [109]    train-rmse:0.277446+0.011825    test-rmse:1.260973+0.255394 
## [110]    train-rmse:0.273333+0.010940    test-rmse:1.260108+0.256858 
## [111]    train-rmse:0.269778+0.010882    test-rmse:1.258627+0.257284 
## [112]    train-rmse:0.266234+0.010437    test-rmse:1.257074+0.256735 
## [113]    train-rmse:0.262495+0.010872    test-rmse:1.256520+0.256237 
## [114]    train-rmse:0.258166+0.011188    test-rmse:1.254873+0.257519 
## [115]    train-rmse:0.252917+0.010536    test-rmse:1.253049+0.258247 
## [116]    train-rmse:0.249196+0.010405    test-rmse:1.253019+0.259306 
## [117]    train-rmse:0.244802+0.009996    test-rmse:1.251496+0.259304 
## [118]    train-rmse:0.241158+0.008793    test-rmse:1.250186+0.259690 
## [119]    train-rmse:0.237973+0.008389    test-rmse:1.249598+0.260201 
## [120]    train-rmse:0.234063+0.008127    test-rmse:1.247899+0.260762 
## [121]    train-rmse:0.231213+0.007371    test-rmse:1.247759+0.261109 
## [122]    train-rmse:0.227918+0.006758    test-rmse:1.246617+0.261440 
## [123]    train-rmse:0.224974+0.007165    test-rmse:1.244725+0.261166 
## [124]    train-rmse:0.221285+0.007286    test-rmse:1.243736+0.261884 
## [125]    train-rmse:0.218692+0.007325    test-rmse:1.243097+0.261971 
## [126]    train-rmse:0.215503+0.007554    test-rmse:1.242325+0.262368 
## [127]    train-rmse:0.211730+0.007362    test-rmse:1.241037+0.261864 
## [128]    train-rmse:0.209050+0.007011    test-rmse:1.239977+0.262532 
## [129]    train-rmse:0.206685+0.006958    test-rmse:1.239924+0.263240 
## [130]    train-rmse:0.204189+0.006790    test-rmse:1.239183+0.263567 
## [131]    train-rmse:0.202143+0.006720    test-rmse:1.237585+0.264277 
## [132]    train-rmse:0.198873+0.006693    test-rmse:1.236859+0.264947 
## [133]    train-rmse:0.195282+0.006871    test-rmse:1.236780+0.266183 
## [134]    train-rmse:0.192047+0.005463    test-rmse:1.235806+0.266199 
## [135]    train-rmse:0.188932+0.006070    test-rmse:1.235104+0.266463 
## [136]    train-rmse:0.186626+0.006188    test-rmse:1.234378+0.266902 
## [137]    train-rmse:0.184211+0.005693    test-rmse:1.233033+0.267553 
## [138]    train-rmse:0.181551+0.006295    test-rmse:1.231986+0.267628 
## [139]    train-rmse:0.178963+0.005476    test-rmse:1.231586+0.267814 
## [140]    train-rmse:0.176205+0.006056    test-rmse:1.231357+0.267802 
## [141]    train-rmse:0.173772+0.006039    test-rmse:1.231153+0.268540 
## [142]    train-rmse:0.171388+0.005797    test-rmse:1.231132+0.268234 
## [143]    train-rmse:0.169107+0.006200    test-rmse:1.230886+0.267881 
## [144]    train-rmse:0.166174+0.006195    test-rmse:1.230582+0.268769 
## [145]    train-rmse:0.164544+0.006078    test-rmse:1.230508+0.269125 
## [146]    train-rmse:0.162333+0.005991    test-rmse:1.229972+0.268695 
## [147]    train-rmse:0.160379+0.006525    test-rmse:1.229544+0.268498 
## [148]    train-rmse:0.157663+0.006613    test-rmse:1.228964+0.268775 
## [149]    train-rmse:0.155448+0.006635    test-rmse:1.228689+0.268204 
## [150]    train-rmse:0.153848+0.006605    test-rmse:1.228105+0.268445 
## [151]    train-rmse:0.152304+0.006224    test-rmse:1.227279+0.268597 
## [152]    train-rmse:0.150510+0.006083    test-rmse:1.226938+0.268221 
## [153]    train-rmse:0.148184+0.005675    test-rmse:1.226875+0.268880 
## [154]    train-rmse:0.145767+0.005865    test-rmse:1.226480+0.269557 
## [155]    train-rmse:0.143348+0.006086    test-rmse:1.225906+0.269944 
## [156]    train-rmse:0.141487+0.005950    test-rmse:1.225196+0.270339 
## [157]    train-rmse:0.139494+0.006197    test-rmse:1.224901+0.270513 
## [158]    train-rmse:0.137478+0.005752    test-rmse:1.224534+0.270281 
## [159]    train-rmse:0.136050+0.005970    test-rmse:1.223701+0.270544 
## [160]    train-rmse:0.134410+0.006217    test-rmse:1.223295+0.270110 
## [161]    train-rmse:0.132886+0.006341    test-rmse:1.223325+0.270368 
## [162]    train-rmse:0.130932+0.006376    test-rmse:1.223345+0.271092 
## [163]    train-rmse:0.129156+0.006388    test-rmse:1.222934+0.271715 
## [164]    train-rmse:0.127044+0.005998    test-rmse:1.222750+0.271955 
## [165]    train-rmse:0.125538+0.005474    test-rmse:1.222548+0.272270 
## [166]    train-rmse:0.123555+0.005489    test-rmse:1.222401+0.272648 
## [167]    train-rmse:0.122260+0.005778    test-rmse:1.221747+0.273120 
## [168]    train-rmse:0.120409+0.005241    test-rmse:1.221882+0.272887 
## [169]    train-rmse:0.119335+0.005362    test-rmse:1.221195+0.272783 
## [170]    train-rmse:0.117817+0.005007    test-rmse:1.220587+0.272433 
## [171]    train-rmse:0.116546+0.004551    test-rmse:1.220246+0.272559 
## [172]    train-rmse:0.115059+0.004729    test-rmse:1.220127+0.272721 
## [173]    train-rmse:0.112915+0.004457    test-rmse:1.220195+0.273126 
## [174]    train-rmse:0.111193+0.004417    test-rmse:1.220361+0.272920 
## [175]    train-rmse:0.109606+0.004363    test-rmse:1.219872+0.272744 
## [176]    train-rmse:0.108445+0.004659    test-rmse:1.219375+0.272933 
## [177]    train-rmse:0.107081+0.004968    test-rmse:1.219123+0.273484 
## [178]    train-rmse:0.105437+0.005080    test-rmse:1.218933+0.273281 
## [179]    train-rmse:0.104332+0.004778    test-rmse:1.218852+0.273624 
## [180]    train-rmse:0.103353+0.004834    test-rmse:1.218566+0.273857 
## [181]    train-rmse:0.102265+0.004550    test-rmse:1.218684+0.274135 
## [182]    train-rmse:0.101252+0.004739    test-rmse:1.218031+0.274309 
## [183]    train-rmse:0.099836+0.004786    test-rmse:1.218003+0.274472 
## [184]    train-rmse:0.098428+0.004692    test-rmse:1.217396+0.274648 
## [185]    train-rmse:0.097019+0.004749    test-rmse:1.217529+0.274769 
## [186]    train-rmse:0.095962+0.004668    test-rmse:1.217249+0.274613 
## [187]    train-rmse:0.094786+0.004620    test-rmse:1.217085+0.274923 
## [188]    train-rmse:0.093181+0.004599    test-rmse:1.217329+0.274948 
## [189]    train-rmse:0.092011+0.004655    test-rmse:1.217294+0.275059 
## [190]    train-rmse:0.090657+0.004542    test-rmse:1.217352+0.274912 
## [191]    train-rmse:0.089387+0.004667    test-rmse:1.217242+0.274966 
## [192]    train-rmse:0.088489+0.004471    test-rmse:1.216931+0.275366 
## [193]    train-rmse:0.087759+0.004344    test-rmse:1.216851+0.275363 
## [194]    train-rmse:0.086318+0.003904    test-rmse:1.216951+0.275142 
## [195]    train-rmse:0.085310+0.003841    test-rmse:1.216744+0.275236 
## [196]    train-rmse:0.084536+0.003941    test-rmse:1.216414+0.275514 
## [197]    train-rmse:0.083457+0.004057    test-rmse:1.216337+0.275262 
## [198]    train-rmse:0.082385+0.004121    test-rmse:1.216166+0.275499 
## [199]    train-rmse:0.081633+0.003950    test-rmse:1.216145+0.275551 
## [200]    train-rmse:0.080788+0.003813    test-rmse:1.215658+0.275884 
## [201]    train-rmse:0.079851+0.003765    test-rmse:1.215596+0.276116 
## [202]    train-rmse:0.078484+0.003322    test-rmse:1.215289+0.276254 
## [203]    train-rmse:0.077414+0.003132    test-rmse:1.215268+0.276459 
## [204]    train-rmse:0.076356+0.002985    test-rmse:1.215243+0.276692 
## [205]    train-rmse:0.075417+0.003057    test-rmse:1.215437+0.276770 
## [206]    train-rmse:0.074824+0.003248    test-rmse:1.215185+0.276734 
## [207]    train-rmse:0.074023+0.003122    test-rmse:1.215122+0.276898 
## [208]    train-rmse:0.073330+0.003215    test-rmse:1.215023+0.276846 
## [209]    train-rmse:0.072312+0.003168    test-rmse:1.215229+0.276737 
## [210]    train-rmse:0.071284+0.003486    test-rmse:1.214983+0.277000 
## [211]    train-rmse:0.070410+0.003398    test-rmse:1.215044+0.277011 
## [212]    train-rmse:0.069761+0.003496    test-rmse:1.214950+0.277049 
## [213]    train-rmse:0.068972+0.003534    test-rmse:1.215118+0.276993 
## [214]    train-rmse:0.067975+0.003486    test-rmse:1.214924+0.276660 
## [215]    train-rmse:0.066892+0.003482    test-rmse:1.214819+0.276936 
## [216]    train-rmse:0.066263+0.003428    test-rmse:1.214633+0.277076 
## [217]    train-rmse:0.065632+0.003608    test-rmse:1.214512+0.277037 
## [218]    train-rmse:0.064708+0.003441    test-rmse:1.214266+0.277231 
## [219]    train-rmse:0.064026+0.003462    test-rmse:1.214236+0.277341 
## [220]    train-rmse:0.063207+0.003415    test-rmse:1.214051+0.277351 
## [221]    train-rmse:0.062534+0.003436    test-rmse:1.213720+0.277529 
## [222]    train-rmse:0.061803+0.003352    test-rmse:1.213631+0.277665 
## [223]    train-rmse:0.061094+0.003324    test-rmse:1.213604+0.277684 
## [224]    train-rmse:0.060025+0.003438    test-rmse:1.213541+0.277603 
## [225]    train-rmse:0.059205+0.003540    test-rmse:1.213646+0.277921 
## [226]    train-rmse:0.058689+0.003482    test-rmse:1.213350+0.277936 
## [227]    train-rmse:0.058001+0.003586    test-rmse:1.213287+0.277879 
## [228]    train-rmse:0.057278+0.003721    test-rmse:1.213161+0.277981 
## [229]    train-rmse:0.056722+0.003719    test-rmse:1.213265+0.277721 
## [230]    train-rmse:0.056208+0.003676    test-rmse:1.213184+0.277958 
## [231]    train-rmse:0.055618+0.003599    test-rmse:1.213181+0.277708 
## [232]    train-rmse:0.054750+0.003507    test-rmse:1.212867+0.277641 
## [233]    train-rmse:0.054144+0.003572    test-rmse:1.212829+0.277746 
## [234]    train-rmse:0.053542+0.003468    test-rmse:1.212705+0.277710 
## [235]    train-rmse:0.052797+0.003605    test-rmse:1.212617+0.277620 
## [236]    train-rmse:0.052386+0.003588    test-rmse:1.212534+0.277744 
## [237]    train-rmse:0.051645+0.003517    test-rmse:1.212588+0.277690 
## [238]    train-rmse:0.050993+0.003601    test-rmse:1.212640+0.277760 
## [239]    train-rmse:0.050384+0.003500    test-rmse:1.212600+0.277837 
## [240]    train-rmse:0.049925+0.003483    test-rmse:1.212603+0.277754 
## [241]    train-rmse:0.049294+0.003598    test-rmse:1.212520+0.277885 
## [242]    train-rmse:0.048804+0.003469    test-rmse:1.212583+0.278009 
## [243]    train-rmse:0.048112+0.003322    test-rmse:1.212612+0.278086 
## [244]    train-rmse:0.047742+0.003371    test-rmse:1.212654+0.278202 
## [245]    train-rmse:0.047243+0.003246    test-rmse:1.212595+0.278326 
## [246]    train-rmse:0.046681+0.003192    test-rmse:1.212535+0.278357 
## [247]    train-rmse:0.046201+0.003307    test-rmse:1.212554+0.278465 
## Stopping. Best iteration:
## [241]    train-rmse:0.049294+0.003598    test-rmse:1.212520+0.277885
## 
## 13 
## [1]  train-rmse:94.766116+0.105094   test-rmse:94.766423+0.483432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:83.478061+0.095217   test-rmse:83.478186+0.500072 
## [3]  train-rmse:73.545764+0.086897   test-rmse:73.545670+0.516055 
## [4]  train-rmse:64.807895+0.080011   test-rmse:64.807548+0.531590 
## [5]  train-rmse:57.122527+0.074434   test-rmse:57.121858+0.546922 
## [6]  train-rmse:50.364803+0.070103   test-rmse:50.363757+0.562250 
## [7]  train-rmse:44.424928+0.066945   test-rmse:44.423422+0.577769 
## [8]  train-rmse:39.206363+0.064904   test-rmse:39.204310+0.593657 
## [9]  train-rmse:34.622293+0.063135   test-rmse:34.630677+0.594785 
## [10] train-rmse:30.591081+0.059323   test-rmse:30.633139+0.599495 
## [11] train-rmse:27.044432+0.052606   test-rmse:27.116927+0.608458 
## [12] train-rmse:23.924558+0.044243   test-rmse:24.000367+0.581464 
## [13] train-rmse:21.179186+0.038168   test-rmse:21.254095+0.569931 
## [14] train-rmse:18.762979+0.033086   test-rmse:18.843525+0.557408 
## [15] train-rmse:16.632680+0.028932   test-rmse:16.718552+0.544604 
## [16] train-rmse:14.757554+0.024911   test-rmse:14.865136+0.560231 
## [17] train-rmse:13.102790+0.023009   test-rmse:13.202217+0.552515 
## [18] train-rmse:11.643877+0.021336   test-rmse:11.750754+0.538480 
## [19] train-rmse:10.355913+0.020557   test-rmse:10.464216+0.543100 
## [20] train-rmse:9.218523+0.018455    test-rmse:9.346267+0.549347 
## [21] train-rmse:8.217220+0.018132    test-rmse:8.347294+0.530971 
## [22] train-rmse:7.333927+0.016836    test-rmse:7.463266+0.532799 
## [23] train-rmse:6.552563+0.018974    test-rmse:6.682205+0.509463 
## [24] train-rmse:5.864949+0.019722    test-rmse:6.012543+0.508536 
## [25] train-rmse:5.258397+0.021594    test-rmse:5.411270+0.493331 
## [26] train-rmse:4.722021+0.018879    test-rmse:4.885467+0.478523 
## [27] train-rmse:4.250854+0.020659    test-rmse:4.431355+0.489662 
## [28] train-rmse:3.837302+0.022213    test-rmse:4.033625+0.471267 
## [29] train-rmse:3.469695+0.020687    test-rmse:3.686606+0.449447 
## [30] train-rmse:3.145822+0.020978    test-rmse:3.397280+0.450598 
## [31] train-rmse:2.862378+0.021459    test-rmse:3.138633+0.431523 
## [32] train-rmse:2.612560+0.022417    test-rmse:2.919195+0.411351 
## [33] train-rmse:2.390129+0.022528    test-rmse:2.720612+0.403131 
## [34] train-rmse:2.191950+0.025833    test-rmse:2.562663+0.394256 
## [35] train-rmse:2.020805+0.024631    test-rmse:2.415637+0.380515 
## [36] train-rmse:1.868489+0.027085    test-rmse:2.287722+0.360976 
## [37] train-rmse:1.731521+0.027047    test-rmse:2.186001+0.342348 
## [38] train-rmse:1.606885+0.029319    test-rmse:2.096015+0.323283 
## [39] train-rmse:1.503576+0.028755    test-rmse:2.013728+0.306714 
## [40] train-rmse:1.405592+0.028462    test-rmse:1.952164+0.293355 
## [41] train-rmse:1.322797+0.026667    test-rmse:1.889183+0.283278 
## [42] train-rmse:1.247702+0.027196    test-rmse:1.839083+0.279111 
## [43] train-rmse:1.178622+0.030939    test-rmse:1.792909+0.262313 
## [44] train-rmse:1.113680+0.033517    test-rmse:1.752779+0.257383 
## [45] train-rmse:1.056234+0.033284    test-rmse:1.714562+0.247096 
## [46] train-rmse:1.007645+0.031703    test-rmse:1.674651+0.244106 
## [47] train-rmse:0.959439+0.034449    test-rmse:1.649825+0.242261 
## [48] train-rmse:0.917371+0.035758    test-rmse:1.620708+0.239042 
## [49] train-rmse:0.875850+0.034963    test-rmse:1.596263+0.243363 
## [50] train-rmse:0.840890+0.035597    test-rmse:1.577667+0.241462 
## [51] train-rmse:0.808761+0.035124    test-rmse:1.559133+0.241214 
## [52] train-rmse:0.780028+0.035103    test-rmse:1.540922+0.241719 
## [53] train-rmse:0.748018+0.033336    test-rmse:1.530475+0.237721 
## [54] train-rmse:0.719514+0.034224    test-rmse:1.513829+0.243915 
## [55] train-rmse:0.693554+0.034531    test-rmse:1.497194+0.243097 
## [56] train-rmse:0.670193+0.034754    test-rmse:1.484927+0.240240 
## [57] train-rmse:0.648733+0.035009    test-rmse:1.472748+0.240937 
## [58] train-rmse:0.627698+0.031873    test-rmse:1.453543+0.234412 
## [59] train-rmse:0.606004+0.032087    test-rmse:1.437280+0.230069 
## [60] train-rmse:0.587359+0.032613    test-rmse:1.428356+0.228952 
## [61] train-rmse:0.569310+0.033034    test-rmse:1.421497+0.233517 
## [62] train-rmse:0.552546+0.031331    test-rmse:1.409101+0.228061 
## [63] train-rmse:0.536202+0.031625    test-rmse:1.401383+0.229478 
## [64] train-rmse:0.520277+0.032536    test-rmse:1.390616+0.225699 
## [65] train-rmse:0.505365+0.031543    test-rmse:1.382635+0.225121 
## [66] train-rmse:0.489942+0.032409    test-rmse:1.376451+0.227388 
## [67] train-rmse:0.478311+0.031543    test-rmse:1.370357+0.230933 
## [68] train-rmse:0.466757+0.030564    test-rmse:1.364303+0.225084 
## [69] train-rmse:0.455400+0.030793    test-rmse:1.358774+0.225043 
## [70] train-rmse:0.444080+0.029612    test-rmse:1.353659+0.224933 
## [71] train-rmse:0.433692+0.029624    test-rmse:1.348458+0.224968 
## [72] train-rmse:0.422932+0.029305    test-rmse:1.343679+0.227171 
## [73] train-rmse:0.413376+0.029390    test-rmse:1.336890+0.227720 
## [74] train-rmse:0.402857+0.029134    test-rmse:1.336719+0.227804 
## [75] train-rmse:0.393698+0.028401    test-rmse:1.332070+0.226744 
## [76] train-rmse:0.384855+0.026223    test-rmse:1.325308+0.223922 
## [77] train-rmse:0.375395+0.027111    test-rmse:1.320748+0.221336 
## [78] train-rmse:0.366922+0.027251    test-rmse:1.316730+0.222160 
## [79] train-rmse:0.358023+0.025669    test-rmse:1.313345+0.221381 
## [80] train-rmse:0.351107+0.025674    test-rmse:1.308541+0.222988 
## [81] train-rmse:0.343708+0.025718    test-rmse:1.306119+0.225719 
## [82] train-rmse:0.336218+0.024662    test-rmse:1.300935+0.223847 
## [83] train-rmse:0.328380+0.025244    test-rmse:1.298408+0.223763 
## [84] train-rmse:0.320870+0.023829    test-rmse:1.295740+0.225820 
## [85] train-rmse:0.313020+0.024223    test-rmse:1.292435+0.227372 
## [86] train-rmse:0.306983+0.023717    test-rmse:1.289607+0.227920 
## [87] train-rmse:0.300711+0.024033    test-rmse:1.288137+0.228517 
## [88] train-rmse:0.294143+0.022703    test-rmse:1.284098+0.227203 
## [89] train-rmse:0.288392+0.023031    test-rmse:1.281763+0.230038 
## [90] train-rmse:0.281938+0.022000    test-rmse:1.280250+0.230745 
## [91] train-rmse:0.276233+0.022299    test-rmse:1.277438+0.230647 
## [92] train-rmse:0.270434+0.020662    test-rmse:1.272981+0.230311 
## [93] train-rmse:0.264093+0.021803    test-rmse:1.271576+0.230275 
## [94] train-rmse:0.259324+0.022255    test-rmse:1.269741+0.231091 
## [95] train-rmse:0.254548+0.021721    test-rmse:1.267902+0.231831 
## [96] train-rmse:0.250547+0.022050    test-rmse:1.264945+0.232322 
## [97] train-rmse:0.245454+0.022109    test-rmse:1.263707+0.233908 
## [98] train-rmse:0.240055+0.018987    test-rmse:1.263156+0.234902 
## [99] train-rmse:0.235418+0.018542    test-rmse:1.262011+0.235928 
## [100]    train-rmse:0.231290+0.018807    test-rmse:1.260219+0.235873 
## [101]    train-rmse:0.226685+0.018885    test-rmse:1.259724+0.237335 
## [102]    train-rmse:0.222065+0.019336    test-rmse:1.259184+0.237376 
## [103]    train-rmse:0.217910+0.019367    test-rmse:1.259251+0.237448 
## [104]    train-rmse:0.213785+0.020103    test-rmse:1.258012+0.237665 
## [105]    train-rmse:0.210401+0.020545    test-rmse:1.256111+0.237462 
## [106]    train-rmse:0.207112+0.020404    test-rmse:1.255716+0.238166 
## [107]    train-rmse:0.203450+0.020338    test-rmse:1.254164+0.239872 
## [108]    train-rmse:0.199139+0.018909    test-rmse:1.253157+0.240757 
## [109]    train-rmse:0.195451+0.017716    test-rmse:1.253332+0.240987 
## [110]    train-rmse:0.191983+0.016646    test-rmse:1.252961+0.241208 
## [111]    train-rmse:0.188731+0.016336    test-rmse:1.252688+0.241406 
## [112]    train-rmse:0.185807+0.016460    test-rmse:1.252303+0.242155 
## [113]    train-rmse:0.182434+0.015854    test-rmse:1.252133+0.242846 
## [114]    train-rmse:0.179681+0.015200    test-rmse:1.250712+0.243944 
## [115]    train-rmse:0.176334+0.015665    test-rmse:1.250267+0.244321 
## [116]    train-rmse:0.172500+0.014684    test-rmse:1.250269+0.245044 
## [117]    train-rmse:0.170240+0.014683    test-rmse:1.249910+0.245391 
## [118]    train-rmse:0.167358+0.014921    test-rmse:1.249178+0.245207 
## [119]    train-rmse:0.164394+0.014285    test-rmse:1.248344+0.246212 
## [120]    train-rmse:0.161169+0.013423    test-rmse:1.247893+0.245622 
## [121]    train-rmse:0.158762+0.013608    test-rmse:1.247603+0.245433 
## [122]    train-rmse:0.155987+0.013866    test-rmse:1.247401+0.244912 
## [123]    train-rmse:0.152987+0.013738    test-rmse:1.246773+0.245320 
## [124]    train-rmse:0.150041+0.013555    test-rmse:1.246845+0.246548 
## [125]    train-rmse:0.147349+0.012578    test-rmse:1.246468+0.246713 
## [126]    train-rmse:0.145371+0.012370    test-rmse:1.246549+0.247536 
## [127]    train-rmse:0.142514+0.011379    test-rmse:1.245672+0.248369 
## [128]    train-rmse:0.139800+0.010819    test-rmse:1.244979+0.248694 
## [129]    train-rmse:0.136335+0.009767    test-rmse:1.245502+0.249296 
## [130]    train-rmse:0.134316+0.010119    test-rmse:1.245385+0.249095 
## [131]    train-rmse:0.132378+0.009940    test-rmse:1.244981+0.249659 
## [132]    train-rmse:0.130273+0.009723    test-rmse:1.244798+0.249837 
## [133]    train-rmse:0.127764+0.009773    test-rmse:1.244397+0.249864 
## [134]    train-rmse:0.126053+0.009655    test-rmse:1.243445+0.250554 
## [135]    train-rmse:0.124070+0.009801    test-rmse:1.243260+0.250644 
## [136]    train-rmse:0.122293+0.010229    test-rmse:1.242803+0.250769 
## [137]    train-rmse:0.120384+0.010061    test-rmse:1.242707+0.250634 
## [138]    train-rmse:0.118250+0.010177    test-rmse:1.242780+0.250765 
## [139]    train-rmse:0.116312+0.010197    test-rmse:1.242572+0.251469 
## [140]    train-rmse:0.114439+0.010250    test-rmse:1.242557+0.251124 
## [141]    train-rmse:0.112576+0.009783    test-rmse:1.242584+0.251685 
## [142]    train-rmse:0.111160+0.009525    test-rmse:1.242237+0.251883 
## [143]    train-rmse:0.109414+0.009252    test-rmse:1.241767+0.252071 
## [144]    train-rmse:0.107594+0.009846    test-rmse:1.241623+0.251943 
## [145]    train-rmse:0.105886+0.009810    test-rmse:1.242112+0.251870 
## [146]    train-rmse:0.103848+0.009210    test-rmse:1.242249+0.252087 
## [147]    train-rmse:0.102124+0.009492    test-rmse:1.242187+0.252440 
## [148]    train-rmse:0.100658+0.009263    test-rmse:1.241830+0.252568 
## [149]    train-rmse:0.099023+0.008854    test-rmse:1.241307+0.253035 
## [150]    train-rmse:0.097362+0.008958    test-rmse:1.240925+0.252954 
## [151]    train-rmse:0.096011+0.008780    test-rmse:1.240917+0.253026 
## [152]    train-rmse:0.094528+0.008419    test-rmse:1.240884+0.253353 
## [153]    train-rmse:0.092968+0.008090    test-rmse:1.240634+0.253335 
## [154]    train-rmse:0.091428+0.007646    test-rmse:1.240752+0.253483 
## [155]    train-rmse:0.090314+0.007330    test-rmse:1.240361+0.253883 
## [156]    train-rmse:0.088662+0.006949    test-rmse:1.240063+0.254375 
## [157]    train-rmse:0.087280+0.006726    test-rmse:1.240044+0.254437 
## [158]    train-rmse:0.085644+0.006669    test-rmse:1.239969+0.254690 
## [159]    train-rmse:0.084400+0.006540    test-rmse:1.239845+0.254532 
## [160]    train-rmse:0.083132+0.006316    test-rmse:1.239865+0.254400 
## [161]    train-rmse:0.082287+0.006317    test-rmse:1.239689+0.254355 
## [162]    train-rmse:0.080762+0.005806    test-rmse:1.239306+0.254207 
## [163]    train-rmse:0.079824+0.005897    test-rmse:1.238887+0.254451 
## [164]    train-rmse:0.078417+0.005855    test-rmse:1.238673+0.254656 
## [165]    train-rmse:0.077193+0.005747    test-rmse:1.238600+0.254787 
## [166]    train-rmse:0.075847+0.005610    test-rmse:1.238569+0.254640 
## [167]    train-rmse:0.074945+0.005430    test-rmse:1.238334+0.254702 
## [168]    train-rmse:0.073794+0.005312    test-rmse:1.238294+0.254715 
## [169]    train-rmse:0.072578+0.005242    test-rmse:1.238293+0.254981 
## [170]    train-rmse:0.071662+0.005242    test-rmse:1.238345+0.255067 
## [171]    train-rmse:0.070440+0.005369    test-rmse:1.238237+0.254886 
## [172]    train-rmse:0.069449+0.005580    test-rmse:1.238216+0.254719 
## [173]    train-rmse:0.068638+0.005453    test-rmse:1.238347+0.254930 
## [174]    train-rmse:0.067525+0.005005    test-rmse:1.238452+0.254931 
## [175]    train-rmse:0.066727+0.005164    test-rmse:1.238406+0.255033 
## [176]    train-rmse:0.065975+0.005127    test-rmse:1.238210+0.255042 
## [177]    train-rmse:0.064857+0.005447    test-rmse:1.238382+0.254930 
## [178]    train-rmse:0.063831+0.005474    test-rmse:1.238271+0.255225 
## [179]    train-rmse:0.063085+0.005301    test-rmse:1.238292+0.255140 
## [180]    train-rmse:0.062069+0.005398    test-rmse:1.238245+0.255243 
## [181]    train-rmse:0.061198+0.005284    test-rmse:1.238367+0.255493 
## [182]    train-rmse:0.060243+0.005003    test-rmse:1.238091+0.255238 
## [183]    train-rmse:0.059368+0.005102    test-rmse:1.238130+0.255401 
## [184]    train-rmse:0.058423+0.004925    test-rmse:1.237960+0.255461 
## [185]    train-rmse:0.057685+0.004900    test-rmse:1.237815+0.255489 
## [186]    train-rmse:0.057057+0.004811    test-rmse:1.237799+0.255458 
## [187]    train-rmse:0.056179+0.004762    test-rmse:1.237659+0.255335 
## [188]    train-rmse:0.055398+0.004668    test-rmse:1.237505+0.255089 
## [189]    train-rmse:0.054549+0.004491    test-rmse:1.237519+0.255093 
## [190]    train-rmse:0.053624+0.004262    test-rmse:1.237560+0.255128 
## [191]    train-rmse:0.052818+0.004317    test-rmse:1.237577+0.255236 
## [192]    train-rmse:0.051966+0.003874    test-rmse:1.237606+0.255446 
## [193]    train-rmse:0.050866+0.003917    test-rmse:1.237737+0.255442 
## [194]    train-rmse:0.050303+0.003890    test-rmse:1.237562+0.255524 
## Stopping. Best iteration:
## [188]    train-rmse:0.055398+0.004668    test-rmse:1.237505+0.255089
## 
## 14 
## [1]  train-rmse:92.628808+0.103203   test-rmse:92.629086+0.486485 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.759407+0.092047   test-rmse:79.759461+0.505884 
## [3]  train-rmse:68.693979+0.083013   test-rmse:68.693753+0.524472 
## [4]  train-rmse:59.182194+0.075874   test-rmse:59.181623+0.542625 
## [5]  train-rmse:51.008879+0.070487   test-rmse:51.007880+0.560691 
## [6]  train-rmse:43.989113+0.066746   test-rmse:43.987572+0.578998 
## [7]  train-rmse:37.964006+0.064561   test-rmse:37.961789+0.597840 
## [8]  train-rmse:32.793479+0.062609   test-rmse:32.804148+0.599778 
## [9]  train-rmse:28.355840+0.059727   test-rmse:28.394154+0.605770 
## [10] train-rmse:24.547662+0.056449   test-rmse:24.577396+0.602940 
## [11] train-rmse:21.283196+0.052833   test-rmse:21.331104+0.594859 
## [12] train-rmse:18.487003+0.050689   test-rmse:18.524194+0.588696 
## [13] train-rmse:16.097431+0.048114   test-rmse:16.159480+0.591071 
## [14] train-rmse:14.058384+0.046750   test-rmse:14.115977+0.588073 
## [15] train-rmse:12.322882+0.046188   test-rmse:12.384434+0.587699 
## [16] train-rmse:10.849941+0.047199   test-rmse:10.940862+0.609699 
## [17] train-rmse:9.606294+0.048004    test-rmse:9.685658+0.603839 
## [18] train-rmse:8.559377+0.050329    test-rmse:8.650224+0.586773 
## [19] train-rmse:7.681997+0.052965    test-rmse:7.792186+0.616601 
## [20] train-rmse:6.951996+0.055043    test-rmse:7.055595+0.606184 
## [21] train-rmse:6.346683+0.057353    test-rmse:6.466274+0.582588 
## [22] train-rmse:5.847920+0.059772    test-rmse:5.974706+0.600619 
## [23] train-rmse:5.440075+0.061542    test-rmse:5.551264+0.575458 
## [24] train-rmse:5.106299+0.063485    test-rmse:5.218925+0.556433 
## [25] train-rmse:4.833225+0.064562    test-rmse:4.978670+0.561681 
## [26] train-rmse:4.612543+0.066525    test-rmse:4.733785+0.528780 
## [27] train-rmse:4.432452+0.067487    test-rmse:4.552582+0.499697 
## [28] train-rmse:4.283432+0.067850    test-rmse:4.429581+0.493118 
## [29] train-rmse:4.159952+0.067827    test-rmse:4.298094+0.481846 
## [30] train-rmse:4.057023+0.066921    test-rmse:4.214193+0.485508 
## [31] train-rmse:3.971433+0.066729    test-rmse:4.124089+0.461181 
## [32] train-rmse:3.897616+0.066581    test-rmse:4.051681+0.438929 
## [33] train-rmse:3.832328+0.066193    test-rmse:3.991574+0.420961 
## [34] train-rmse:3.774209+0.065372    test-rmse:3.948151+0.410040 
## [35] train-rmse:3.722405+0.064571    test-rmse:3.905919+0.407543 
## [36] train-rmse:3.677078+0.063322    test-rmse:3.865971+0.412688 
## [37] train-rmse:3.635250+0.062598    test-rmse:3.837397+0.411279 
## [38] train-rmse:3.596220+0.062428    test-rmse:3.789863+0.395193 
## [39] train-rmse:3.559458+0.061975    test-rmse:3.740172+0.373843 
## [40] train-rmse:3.524423+0.061438    test-rmse:3.710604+0.371944 
## [41] train-rmse:3.490956+0.060637    test-rmse:3.678137+0.366203 
## [42] train-rmse:3.458851+0.059681    test-rmse:3.651878+0.359232 
## [43] train-rmse:3.427960+0.058475    test-rmse:3.633640+0.347220 
## [44] train-rmse:3.398064+0.057351    test-rmse:3.606522+0.357253 
## [45] train-rmse:3.369306+0.056548    test-rmse:3.594849+0.356982 
## [46] train-rmse:3.340722+0.056098    test-rmse:3.563774+0.359184 
## [47] train-rmse:3.312832+0.055479    test-rmse:3.549550+0.369767 
## [48] train-rmse:3.285970+0.054688    test-rmse:3.505894+0.351938 
## [49] train-rmse:3.259703+0.053941    test-rmse:3.470456+0.339144 
## [50] train-rmse:3.234303+0.053394    test-rmse:3.445721+0.336129 
## [51] train-rmse:3.209070+0.052788    test-rmse:3.424100+0.333702 
## [52] train-rmse:3.184415+0.052139    test-rmse:3.400719+0.323716 
## [53] train-rmse:3.160211+0.051257    test-rmse:3.387402+0.322376 
## [54] train-rmse:3.136323+0.050288    test-rmse:3.364107+0.320974 
## [55] train-rmse:3.113122+0.049243    test-rmse:3.351343+0.330419 
## [56] train-rmse:3.090305+0.048856    test-rmse:3.326410+0.331083 
## [57] train-rmse:3.068134+0.048317    test-rmse:3.315565+0.337560 
## [58] train-rmse:3.046122+0.047532    test-rmse:3.279419+0.321239 
## [59] train-rmse:3.024574+0.046938    test-rmse:3.244388+0.310056 
## [60] train-rmse:3.003517+0.046475    test-rmse:3.229552+0.317407 
## [61] train-rmse:2.982543+0.045840    test-rmse:3.213800+0.316487 
## [62] train-rmse:2.961963+0.045511    test-rmse:3.206205+0.309078 
## [63] train-rmse:2.941740+0.045145    test-rmse:3.178193+0.316613 
## [64] train-rmse:2.921411+0.044768    test-rmse:3.164965+0.315406 
## [65] train-rmse:2.901655+0.044316    test-rmse:3.148111+0.318147 
## [66] train-rmse:2.882110+0.044190    test-rmse:3.123792+0.311605 
## [67] train-rmse:2.862795+0.044151    test-rmse:3.106331+0.313575 
## [68] train-rmse:2.843499+0.043989    test-rmse:3.090525+0.316713 
## [69] train-rmse:2.824581+0.044086    test-rmse:3.067362+0.320214 
## [70] train-rmse:2.806202+0.044126    test-rmse:3.053240+0.321660 
## [71] train-rmse:2.788175+0.043861    test-rmse:3.031178+0.306954 
## [72] train-rmse:2.770396+0.043519    test-rmse:3.007328+0.296552 
## [73] train-rmse:2.752929+0.043123    test-rmse:2.992202+0.303984 
## [74] train-rmse:2.735537+0.042831    test-rmse:2.970887+0.306345 
## [75] train-rmse:2.718538+0.042643    test-rmse:2.958802+0.308604 
## [76] train-rmse:2.701692+0.042311    test-rmse:2.938917+0.301725 
## [77] train-rmse:2.684957+0.042038    test-rmse:2.924760+0.307081 
## [78] train-rmse:2.668281+0.041937    test-rmse:2.910433+0.307987 
## [79] train-rmse:2.651886+0.041934    test-rmse:2.894194+0.314270 
## [80] train-rmse:2.635629+0.042086    test-rmse:2.879393+0.315079 
## [81] train-rmse:2.619442+0.042006    test-rmse:2.870276+0.311490 
## [82] train-rmse:2.603442+0.041816    test-rmse:2.866100+0.311223 
## [83] train-rmse:2.587641+0.041742    test-rmse:2.846589+0.313760 
## [84] train-rmse:2.572088+0.041586    test-rmse:2.828795+0.314879 
## [85] train-rmse:2.556806+0.041200    test-rmse:2.814295+0.297644 
## [86] train-rmse:2.541713+0.040790    test-rmse:2.795155+0.286192 
## [87] train-rmse:2.526834+0.040198    test-rmse:2.784060+0.285448 
## [88] train-rmse:2.512256+0.039880    test-rmse:2.766904+0.286940 
## [89] train-rmse:2.497766+0.039517    test-rmse:2.749730+0.293433 
## [90] train-rmse:2.483282+0.039219    test-rmse:2.740643+0.303397 
## [91] train-rmse:2.468920+0.038855    test-rmse:2.724283+0.298793 
## [92] train-rmse:2.454653+0.038732    test-rmse:2.702117+0.303900 
## [93] train-rmse:2.440610+0.038598    test-rmse:2.694355+0.304043 
## [94] train-rmse:2.426637+0.038481    test-rmse:2.686241+0.302456 
## [95] train-rmse:2.412800+0.038342    test-rmse:2.670662+0.305408 
## [96] train-rmse:2.399195+0.038020    test-rmse:2.653778+0.307315 
## [97] train-rmse:2.385849+0.037483    test-rmse:2.649277+0.306538 
## [98] train-rmse:2.372713+0.037049    test-rmse:2.641622+0.308983 
## [99] train-rmse:2.359591+0.036658    test-rmse:2.631745+0.309656 
## [100]    train-rmse:2.346625+0.036368    test-rmse:2.623197+0.312123 
## [101]    train-rmse:2.333784+0.036128    test-rmse:2.607379+0.302241 
## [102]    train-rmse:2.321104+0.035727    test-rmse:2.592702+0.287815 
## [103]    train-rmse:2.308437+0.035375    test-rmse:2.568550+0.284910 
## [104]    train-rmse:2.295750+0.035006    test-rmse:2.554230+0.290126 
## [105]    train-rmse:2.283309+0.034800    test-rmse:2.536733+0.293937 
## [106]    train-rmse:2.271080+0.034650    test-rmse:2.525985+0.291929 
## [107]    train-rmse:2.258934+0.034472    test-rmse:2.513541+0.286318 
## [108]    train-rmse:2.246730+0.034371    test-rmse:2.499880+0.281792 
## [109]    train-rmse:2.234453+0.034292    test-rmse:2.486931+0.284895 
## [110]    train-rmse:2.222295+0.034144    test-rmse:2.474979+0.286494 
## [111]    train-rmse:2.210382+0.034040    test-rmse:2.467429+0.284902 
## [112]    train-rmse:2.198426+0.033874    test-rmse:2.457711+0.286915 
## [113]    train-rmse:2.186891+0.033597    test-rmse:2.452376+0.286187 
## [114]    train-rmse:2.175481+0.033212    test-rmse:2.441162+0.292792 
## [115]    train-rmse:2.164190+0.032841    test-rmse:2.431088+0.285279 
## [116]    train-rmse:2.153067+0.032600    test-rmse:2.421220+0.286316 
## [117]    train-rmse:2.142019+0.032412    test-rmse:2.413888+0.285605 
## [118]    train-rmse:2.130981+0.032122    test-rmse:2.402396+0.280877 
## [119]    train-rmse:2.119963+0.031820    test-rmse:2.386901+0.285259 
## [120]    train-rmse:2.109132+0.031487    test-rmse:2.383386+0.282937 
## [121]    train-rmse:2.098239+0.031107    test-rmse:2.372398+0.280711 
## [122]    train-rmse:2.087369+0.030749    test-rmse:2.357995+0.285521 
## [123]    train-rmse:2.076549+0.030436    test-rmse:2.346043+0.292062 
## [124]    train-rmse:2.065847+0.030052    test-rmse:2.335380+0.292781 
## [125]    train-rmse:2.055357+0.029854    test-rmse:2.320648+0.290001 
## [126]    train-rmse:2.045064+0.029612    test-rmse:2.311919+0.284980 
## [127]    train-rmse:2.034735+0.029332    test-rmse:2.296235+0.277128 
## [128]    train-rmse:2.024562+0.029143    test-rmse:2.282363+0.261991 
## [129]    train-rmse:2.014542+0.028801    test-rmse:2.276015+0.258459 
## [130]    train-rmse:2.004516+0.028451    test-rmse:2.272508+0.258888 
## [131]    train-rmse:1.994616+0.028138    test-rmse:2.259840+0.264178 
## [132]    train-rmse:1.984798+0.027883    test-rmse:2.250814+0.266906 
## [133]    train-rmse:1.975018+0.027666    test-rmse:2.242771+0.271236 
## [134]    train-rmse:1.965277+0.027469    test-rmse:2.236990+0.272776 
## [135]    train-rmse:1.955563+0.027306    test-rmse:2.225821+0.268046 
## [136]    train-rmse:1.945870+0.027147    test-rmse:2.215068+0.266671 
## [137]    train-rmse:1.936229+0.027022    test-rmse:2.204113+0.269991 
## [138]    train-rmse:1.926787+0.026923    test-rmse:2.195652+0.264236 
## [139]    train-rmse:1.917350+0.026759    test-rmse:2.187280+0.263687 
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## [141]    train-rmse:1.898905+0.026283    test-rmse:2.164161+0.273246 
## [142]    train-rmse:1.889787+0.026097    test-rmse:2.156935+0.275362 
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## [480]    train-rmse:0.807312+0.013574    test-rmse:1.121643+0.163451 
## [481]    train-rmse:0.806632+0.013613    test-rmse:1.120781+0.162979 
## [482]    train-rmse:0.805959+0.013645    test-rmse:1.120118+0.163169 
## [483]    train-rmse:0.805293+0.013683    test-rmse:1.120152+0.163219 
## [484]    train-rmse:0.804637+0.013712    test-rmse:1.119091+0.162667 
## [485]    train-rmse:0.803980+0.013741    test-rmse:1.117938+0.163286 
## [486]    train-rmse:0.803334+0.013774    test-rmse:1.117217+0.163402 
## [487]    train-rmse:0.802696+0.013799    test-rmse:1.116071+0.162571 
## [488]    train-rmse:0.802052+0.013832    test-rmse:1.115464+0.162705 
## [489]    train-rmse:0.801414+0.013858    test-rmse:1.115146+0.162640 
## [490]    train-rmse:0.800788+0.013880    test-rmse:1.113688+0.163000 
## [491]    train-rmse:0.800165+0.013907    test-rmse:1.113662+0.163026 
## [492]    train-rmse:0.799540+0.013932    test-rmse:1.113411+0.163151 
## [493]    train-rmse:0.798928+0.013969    test-rmse:1.112706+0.162670 
## [494]    train-rmse:0.798318+0.014009    test-rmse:1.111910+0.162952 
## [495]    train-rmse:0.797718+0.014039    test-rmse:1.111394+0.162952 
## [496]    train-rmse:0.797116+0.014063    test-rmse:1.111387+0.163448 
## [497]    train-rmse:0.796526+0.014084    test-rmse:1.110760+0.163131 
## [498]    train-rmse:0.795940+0.014108    test-rmse:1.109391+0.163693 
## [499]    train-rmse:0.795353+0.014133    test-rmse:1.109953+0.163418 
## [500]    train-rmse:0.794764+0.014158    test-rmse:1.109908+0.162826 
## [501]    train-rmse:0.794172+0.014182    test-rmse:1.109420+0.162898 
## [502]    train-rmse:0.793588+0.014204    test-rmse:1.109021+0.163450 
## [503]    train-rmse:0.793000+0.014229    test-rmse:1.108169+0.162696 
## [504]    train-rmse:0.792419+0.014253    test-rmse:1.107197+0.162270 
## [505]    train-rmse:0.791840+0.014278    test-rmse:1.107364+0.162080 
## [506]    train-rmse:0.791267+0.014301    test-rmse:1.105410+0.159656 
## [507]    train-rmse:0.790697+0.014312    test-rmse:1.105307+0.158260 
## [508]    train-rmse:0.790119+0.014327    test-rmse:1.104835+0.158936 
## [509]    train-rmse:0.789556+0.014348    test-rmse:1.104203+0.157572 
## [510]    train-rmse:0.788992+0.014365    test-rmse:1.103820+0.158029 
## [511]    train-rmse:0.788439+0.014388    test-rmse:1.103707+0.158381 
## [512]    train-rmse:0.787890+0.014417    test-rmse:1.103792+0.157912 
## [513]    train-rmse:0.787327+0.014453    test-rmse:1.102693+0.158368 
## [514]    train-rmse:0.786787+0.014474    test-rmse:1.102551+0.157936 
## [515]    train-rmse:0.786255+0.014489    test-rmse:1.101608+0.158307 
## [516]    train-rmse:0.785727+0.014504    test-rmse:1.100836+0.158484 
## [517]    train-rmse:0.785190+0.014524    test-rmse:1.099681+0.158866 
## [518]    train-rmse:0.784666+0.014543    test-rmse:1.099244+0.159342 
## [519]    train-rmse:0.784145+0.014563    test-rmse:1.099043+0.159512 
## [520]    train-rmse:0.783629+0.014586    test-rmse:1.098768+0.159476 
## [521]    train-rmse:0.783113+0.014611    test-rmse:1.098980+0.159197 
## [522]    train-rmse:0.782593+0.014633    test-rmse:1.097872+0.159188 
## [523]    train-rmse:0.782075+0.014648    test-rmse:1.097366+0.159629 
## [524]    train-rmse:0.781570+0.014665    test-rmse:1.096550+0.159501 
## [525]    train-rmse:0.781071+0.014683    test-rmse:1.096027+0.159644 
## [526]    train-rmse:0.780563+0.014690    test-rmse:1.095542+0.159439 
## [527]    train-rmse:0.780057+0.014710    test-rmse:1.095713+0.159202 
## [528]    train-rmse:0.779552+0.014733    test-rmse:1.095857+0.159395 
## [529]    train-rmse:0.779054+0.014753    test-rmse:1.095601+0.160274 
## [530]    train-rmse:0.778563+0.014774    test-rmse:1.094668+0.160078 
## [531]    train-rmse:0.778071+0.014786    test-rmse:1.094382+0.160007 
## [532]    train-rmse:0.777586+0.014813    test-rmse:1.093279+0.157265 
## [533]    train-rmse:0.777102+0.014832    test-rmse:1.092672+0.155928 
## [534]    train-rmse:0.776623+0.014846    test-rmse:1.092044+0.156601 
## [535]    train-rmse:0.776141+0.014854    test-rmse:1.091857+0.156634 
## [536]    train-rmse:0.775669+0.014870    test-rmse:1.090956+0.156578 
## [537]    train-rmse:0.775195+0.014887    test-rmse:1.090229+0.156992 
## [538]    train-rmse:0.774723+0.014902    test-rmse:1.089988+0.156987 
## [539]    train-rmse:0.774257+0.014916    test-rmse:1.089836+0.157040 
## [540]    train-rmse:0.773790+0.014933    test-rmse:1.089974+0.157303 
## [541]    train-rmse:0.773326+0.014951    test-rmse:1.089817+0.157771 
## [542]    train-rmse:0.772874+0.014970    test-rmse:1.089678+0.157709 
## [543]    train-rmse:0.772412+0.015000    test-rmse:1.089571+0.157864 
## [544]    train-rmse:0.771964+0.015013    test-rmse:1.088932+0.158654 
## [545]    train-rmse:0.771513+0.015031    test-rmse:1.088658+0.158733 
## [546]    train-rmse:0.771061+0.015041    test-rmse:1.088889+0.158961 
## [547]    train-rmse:0.770603+0.015050    test-rmse:1.088197+0.159220 
## [548]    train-rmse:0.770162+0.015071    test-rmse:1.088171+0.159790 
## [549]    train-rmse:0.769719+0.015091    test-rmse:1.088164+0.159670 
## [550]    train-rmse:0.769283+0.015108    test-rmse:1.087796+0.159637 
## [551]    train-rmse:0.768848+0.015125    test-rmse:1.087436+0.159676 
## [552]    train-rmse:0.768414+0.015136    test-rmse:1.086855+0.159618 
## [553]    train-rmse:0.767978+0.015151    test-rmse:1.086123+0.159944 
## [554]    train-rmse:0.767541+0.015162    test-rmse:1.085734+0.159593 
## [555]    train-rmse:0.767112+0.015178    test-rmse:1.084581+0.159915 
## [556]    train-rmse:0.766672+0.015189    test-rmse:1.084738+0.159958 
## [557]    train-rmse:0.766233+0.015196    test-rmse:1.082805+0.157441 
## [558]    train-rmse:0.765812+0.015205    test-rmse:1.081707+0.156355 
## [559]    train-rmse:0.765386+0.015212    test-rmse:1.081003+0.155821 
## [560]    train-rmse:0.764955+0.015221    test-rmse:1.081273+0.155754 
## [561]    train-rmse:0.764540+0.015231    test-rmse:1.081113+0.156002 
## [562]    train-rmse:0.764136+0.015241    test-rmse:1.081306+0.155843 
## [563]    train-rmse:0.763728+0.015251    test-rmse:1.080951+0.155813 
## [564]    train-rmse:0.763311+0.015269    test-rmse:1.081079+0.155714 
## [565]    train-rmse:0.762904+0.015276    test-rmse:1.081012+0.155593 
## [566]    train-rmse:0.762501+0.015292    test-rmse:1.080681+0.155346 
## [567]    train-rmse:0.762097+0.015311    test-rmse:1.080052+0.155496 
## [568]    train-rmse:0.761699+0.015317    test-rmse:1.079870+0.155821 
## [569]    train-rmse:0.761299+0.015329    test-rmse:1.079806+0.156028 
## [570]    train-rmse:0.760893+0.015336    test-rmse:1.079978+0.155359 
## [571]    train-rmse:0.760498+0.015350    test-rmse:1.079851+0.155204 
## [572]    train-rmse:0.760113+0.015362    test-rmse:1.079700+0.154843 
## [573]    train-rmse:0.759712+0.015362    test-rmse:1.079034+0.155100 
## [574]    train-rmse:0.759322+0.015374    test-rmse:1.078067+0.155135 
## [575]    train-rmse:0.758931+0.015381    test-rmse:1.078329+0.154839 
## [576]    train-rmse:0.758545+0.015389    test-rmse:1.077577+0.155059 
## [577]    train-rmse:0.758159+0.015399    test-rmse:1.077362+0.154976 
## [578]    train-rmse:0.757777+0.015412    test-rmse:1.076587+0.155104 
## [579]    train-rmse:0.757398+0.015423    test-rmse:1.075524+0.155256 
## [580]    train-rmse:0.757016+0.015437    test-rmse:1.075539+0.154156 
## [581]    train-rmse:0.756637+0.015443    test-rmse:1.074527+0.154783 
## [582]    train-rmse:0.756257+0.015444    test-rmse:1.075326+0.154725 
## [583]    train-rmse:0.755885+0.015452    test-rmse:1.075364+0.154612 
## [584]    train-rmse:0.755507+0.015456    test-rmse:1.075410+0.154884 
## [585]    train-rmse:0.755137+0.015462    test-rmse:1.075106+0.155690 
## [586]    train-rmse:0.754768+0.015468    test-rmse:1.075011+0.155860 
## [587]    train-rmse:0.754402+0.015477    test-rmse:1.075149+0.155567 
## Stopping. Best iteration:
## [581]    train-rmse:0.756637+0.015443    test-rmse:1.074527+0.154783
## 
## 15 
## [1]  train-rmse:92.628808+0.103203   test-rmse:92.629086+0.486485 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.759407+0.092047   test-rmse:79.759461+0.505884 
## [3]  train-rmse:68.693979+0.083013   test-rmse:68.693753+0.524472 
## [4]  train-rmse:59.182194+0.075874   test-rmse:59.181623+0.542625 
## [5]  train-rmse:51.008879+0.070487   test-rmse:51.007880+0.560691 
## [6]  train-rmse:43.989113+0.066746   test-rmse:43.987572+0.578998 
## [7]  train-rmse:37.964006+0.064561   test-rmse:37.961789+0.597840 
## [8]  train-rmse:32.792771+0.062486   test-rmse:32.812824+0.600191 
## [9]  train-rmse:28.348289+0.057070   test-rmse:28.400813+0.605839 
## [10] train-rmse:24.529821+0.049480   test-rmse:24.598526+0.594332 
## [11] train-rmse:21.249653+0.042361   test-rmse:21.327018+0.565084 
## [12] train-rmse:18.435195+0.036069   test-rmse:18.530101+0.571643 
## [13] train-rmse:16.022484+0.033409   test-rmse:16.127978+0.582419 
## [14] train-rmse:13.956643+0.033214   test-rmse:14.051034+0.557772 
## [15] train-rmse:12.189947+0.033130   test-rmse:12.290844+0.560556 
## [16] train-rmse:10.684749+0.033781   test-rmse:10.793786+0.574657 
## [17] train-rmse:9.403027+0.036683    test-rmse:9.506849+0.547340 
## [18] train-rmse:8.316173+0.038990    test-rmse:8.451922+0.547666 
## [19] train-rmse:7.397939+0.041192    test-rmse:7.544376+0.561493 
## [20] train-rmse:6.625793+0.043480    test-rmse:6.760919+0.542466 
## [21] train-rmse:5.979619+0.045728    test-rmse:6.129418+0.542723 
## [22] train-rmse:5.437990+0.047738    test-rmse:5.627482+0.541603 
## [23] train-rmse:4.985261+0.046209    test-rmse:5.160653+0.512791 
## [24] train-rmse:4.613228+0.047104    test-rmse:4.807307+0.522268 
## [25] train-rmse:4.297025+0.042824    test-rmse:4.522742+0.516395 
## [26] train-rmse:4.041135+0.042023    test-rmse:4.253515+0.474533 
## [27] train-rmse:3.830546+0.040913    test-rmse:4.062612+0.463171 
## [28] train-rmse:3.654722+0.040499    test-rmse:3.890591+0.463423 
## [29] train-rmse:3.503339+0.038792    test-rmse:3.763626+0.436524 
## [30] train-rmse:3.379332+0.038489    test-rmse:3.643699+0.407765 
## [31] train-rmse:3.272816+0.035998    test-rmse:3.559215+0.393888 
## [32] train-rmse:3.182411+0.035907    test-rmse:3.475124+0.404545 
## [33] train-rmse:3.104259+0.034747    test-rmse:3.410206+0.411955 
## [34] train-rmse:3.034785+0.034487    test-rmse:3.328467+0.370048 
## [35] train-rmse:2.969529+0.033639    test-rmse:3.278747+0.365274 
## [36] train-rmse:2.912711+0.033209    test-rmse:3.228880+0.351440 
## [37] train-rmse:2.861708+0.033835    test-rmse:3.188498+0.351523 
## [38] train-rmse:2.811957+0.030985    test-rmse:3.143469+0.346617 
## [39] train-rmse:2.767679+0.030597    test-rmse:3.100885+0.335508 
## [40] train-rmse:2.725281+0.030508    test-rmse:3.049835+0.318568 
## [41] train-rmse:2.681515+0.029203    test-rmse:3.016280+0.325163 
## [42] train-rmse:2.643170+0.028911    test-rmse:2.986726+0.327882 
## [43] train-rmse:2.606529+0.028123    test-rmse:2.960445+0.322327 
## [44] train-rmse:2.570585+0.027538    test-rmse:2.921680+0.320492 
## [45] train-rmse:2.534560+0.027293    test-rmse:2.886485+0.312341 
## [46] train-rmse:2.501862+0.026678    test-rmse:2.861588+0.294186 
## [47] train-rmse:2.470374+0.026340    test-rmse:2.829932+0.302536 
## [48] train-rmse:2.438204+0.027110    test-rmse:2.804389+0.311366 
## [49] train-rmse:2.406773+0.025717    test-rmse:2.779125+0.308187 
## [50] train-rmse:2.375930+0.023030    test-rmse:2.753207+0.305190 
## [51] train-rmse:2.346908+0.022281    test-rmse:2.725464+0.301712 
## [52] train-rmse:2.315946+0.022105    test-rmse:2.702956+0.289706 
## [53] train-rmse:2.287425+0.021150    test-rmse:2.682599+0.285256 
## [54] train-rmse:2.259278+0.020233    test-rmse:2.662835+0.294835 
## [55] train-rmse:2.232614+0.019692    test-rmse:2.642617+0.292446 
## [56] train-rmse:2.206153+0.019484    test-rmse:2.613635+0.287029 
## [57] train-rmse:2.180431+0.019492    test-rmse:2.591387+0.295765 
## [58] train-rmse:2.155299+0.019190    test-rmse:2.567255+0.292223 
## [59] train-rmse:2.130395+0.019084    test-rmse:2.546360+0.286557 
## [60] train-rmse:2.105023+0.018212    test-rmse:2.527044+0.277301 
## [61] train-rmse:2.079956+0.018045    test-rmse:2.513123+0.282160 
## [62] train-rmse:2.057103+0.017684    test-rmse:2.494133+0.286899 
## [63] train-rmse:2.033851+0.015754    test-rmse:2.474370+0.278546 
## [64] train-rmse:2.011358+0.015404    test-rmse:2.449775+0.276404 
## [65] train-rmse:1.988585+0.015116    test-rmse:2.435292+0.277430 
## [66] train-rmse:1.966704+0.014495    test-rmse:2.415898+0.277086 
## [67] train-rmse:1.944805+0.014132    test-rmse:2.394639+0.272694 
## [68] train-rmse:1.923648+0.013525    test-rmse:2.371795+0.272127 
## [69] train-rmse:1.902518+0.012820    test-rmse:2.354337+0.276708 
## [70] train-rmse:1.881863+0.012428    test-rmse:2.336547+0.277703 
## [71] train-rmse:1.861356+0.012259    test-rmse:2.322202+0.277616 
## [72] train-rmse:1.841925+0.011709    test-rmse:2.300112+0.277995 
## [73] train-rmse:1.822686+0.011483    test-rmse:2.285231+0.277378 
## [74] train-rmse:1.802655+0.010882    test-rmse:2.265583+0.278170 
## [75] train-rmse:1.781877+0.009408    test-rmse:2.251731+0.268996 
## [76] train-rmse:1.763311+0.009082    test-rmse:2.229091+0.267263 
## [77] train-rmse:1.744832+0.009021    test-rmse:2.215070+0.265310 
## [78] train-rmse:1.726816+0.008794    test-rmse:2.201691+0.266013 
## [79] train-rmse:1.708747+0.008084    test-rmse:2.190412+0.261963 
## [80] train-rmse:1.690620+0.008655    test-rmse:2.176095+0.268568 
## [81] train-rmse:1.673361+0.008095    test-rmse:2.163307+0.273893 
## [82] train-rmse:1.656307+0.007825    test-rmse:2.144432+0.272714 
## [83] train-rmse:1.639235+0.007613    test-rmse:2.125949+0.263921 
## [84] train-rmse:1.622455+0.007216    test-rmse:2.105747+0.258822 
## [85] train-rmse:1.606497+0.006961    test-rmse:2.092747+0.255976 
## [86] train-rmse:1.590523+0.007106    test-rmse:2.076224+0.252221 
## [87] train-rmse:1.574023+0.006765    test-rmse:2.061382+0.246637 
## [88] train-rmse:1.558793+0.006488    test-rmse:2.047991+0.246542 
## [89] train-rmse:1.543882+0.006304    test-rmse:2.032054+0.249244 
## [90] train-rmse:1.529146+0.005903    test-rmse:2.013974+0.248879 
## [91] train-rmse:1.514421+0.005910    test-rmse:2.003584+0.256165 
## [92] train-rmse:1.499362+0.006804    test-rmse:1.992011+0.255747 
## [93] train-rmse:1.485169+0.006923    test-rmse:1.981602+0.253770 
## [94] train-rmse:1.469863+0.007034    test-rmse:1.975081+0.257125 
## [95] train-rmse:1.454097+0.009714    test-rmse:1.966680+0.260261 
## [96] train-rmse:1.439529+0.009750    test-rmse:1.955309+0.258577 
## [97] train-rmse:1.426123+0.009951    test-rmse:1.944925+0.252231 
## [98] train-rmse:1.412814+0.010115    test-rmse:1.931860+0.250854 
## [99] train-rmse:1.399701+0.010341    test-rmse:1.918292+0.249027 
## [100]    train-rmse:1.387184+0.010306    test-rmse:1.901454+0.252540 
## [101]    train-rmse:1.374765+0.009925    test-rmse:1.894115+0.252890 
## [102]    train-rmse:1.362302+0.010019    test-rmse:1.884876+0.251871 
## [103]    train-rmse:1.349948+0.009924    test-rmse:1.876625+0.253689 
## [104]    train-rmse:1.337463+0.010073    test-rmse:1.871214+0.253132 
## [105]    train-rmse:1.325150+0.010336    test-rmse:1.855049+0.250042 
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## [315]    train-rmse:0.444041+0.008063    test-rmse:1.197249+0.282601 
## [316]    train-rmse:0.443111+0.008078    test-rmse:1.196486+0.282520 
## [317]    train-rmse:0.441988+0.008003    test-rmse:1.196718+0.283041 
## [318]    train-rmse:0.440974+0.008085    test-rmse:1.196309+0.282708 
## [319]    train-rmse:0.439944+0.007997    test-rmse:1.195289+0.282104 
## [320]    train-rmse:0.438904+0.007968    test-rmse:1.195587+0.282953 
## [321]    train-rmse:0.437880+0.008017    test-rmse:1.195443+0.282717 
## [322]    train-rmse:0.436126+0.008563    test-rmse:1.193795+0.283466 
## [323]    train-rmse:0.435267+0.008510    test-rmse:1.193056+0.283744 
## [324]    train-rmse:0.434304+0.008518    test-rmse:1.192193+0.284037 
## [325]    train-rmse:0.433412+0.008578    test-rmse:1.191378+0.283919 
## [326]    train-rmse:0.432559+0.008411    test-rmse:1.190316+0.285063 
## [327]    train-rmse:0.431449+0.008756    test-rmse:1.187507+0.286753 
## [328]    train-rmse:0.430673+0.008621    test-rmse:1.186459+0.286726 
## [329]    train-rmse:0.429597+0.008458    test-rmse:1.185038+0.286636 
## [330]    train-rmse:0.428118+0.008734    test-rmse:1.184752+0.287369 
## [331]    train-rmse:0.427284+0.008733    test-rmse:1.184255+0.287276 
## [332]    train-rmse:0.426254+0.008790    test-rmse:1.183171+0.286429 
## [333]    train-rmse:0.425467+0.008784    test-rmse:1.182730+0.286323 
## [334]    train-rmse:0.424597+0.008868    test-rmse:1.182162+0.286433 
## [335]    train-rmse:0.423765+0.008731    test-rmse:1.181976+0.286985 
## [336]    train-rmse:0.422893+0.008615    test-rmse:1.181873+0.286191 
## [337]    train-rmse:0.421960+0.008465    test-rmse:1.181478+0.287333 
## [338]    train-rmse:0.420869+0.008908    test-rmse:1.180449+0.287118 
## [339]    train-rmse:0.420101+0.008930    test-rmse:1.180471+0.287178 
## [340]    train-rmse:0.419219+0.008871    test-rmse:1.179569+0.287021 
## [341]    train-rmse:0.418287+0.008980    test-rmse:1.179534+0.286922 
## [342]    train-rmse:0.417478+0.009003    test-rmse:1.178096+0.286780 
## [343]    train-rmse:0.416518+0.008921    test-rmse:1.178286+0.286422 
## [344]    train-rmse:0.415106+0.009460    test-rmse:1.177276+0.286955 
## [345]    train-rmse:0.413967+0.009143    test-rmse:1.176366+0.287250 
## [346]    train-rmse:0.413147+0.009103    test-rmse:1.176548+0.288040 
## [347]    train-rmse:0.412408+0.009138    test-rmse:1.175996+0.288080 
## [348]    train-rmse:0.411652+0.009093    test-rmse:1.175170+0.288798 
## [349]    train-rmse:0.410599+0.009443    test-rmse:1.174723+0.288738 
## [350]    train-rmse:0.409747+0.009588    test-rmse:1.174163+0.288073 
## [351]    train-rmse:0.408573+0.009249    test-rmse:1.174019+0.288943 
## [352]    train-rmse:0.407859+0.009165    test-rmse:1.172318+0.289874 
## [353]    train-rmse:0.407206+0.009035    test-rmse:1.171409+0.289530 
## [354]    train-rmse:0.406200+0.009520    test-rmse:1.171424+0.289672 
## [355]    train-rmse:0.405268+0.009660    test-rmse:1.170523+0.290397 
## [356]    train-rmse:0.404493+0.009664    test-rmse:1.169818+0.290683 
## [357]    train-rmse:0.403577+0.009648    test-rmse:1.169218+0.291699 
## [358]    train-rmse:0.402961+0.009670    test-rmse:1.168516+0.291282 
## [359]    train-rmse:0.402220+0.009664    test-rmse:1.168232+0.291405 
## [360]    train-rmse:0.401433+0.009661    test-rmse:1.167880+0.291535 
## [361]    train-rmse:0.400129+0.010128    test-rmse:1.167197+0.291668 
## [362]    train-rmse:0.399197+0.010501    test-rmse:1.166417+0.291778 
## [363]    train-rmse:0.397711+0.010944    test-rmse:1.165669+0.292206 
## [364]    train-rmse:0.397053+0.010951    test-rmse:1.165355+0.291707 
## [365]    train-rmse:0.395809+0.011373    test-rmse:1.163629+0.292455 
## [366]    train-rmse:0.394470+0.011524    test-rmse:1.163799+0.292827 
## [367]    train-rmse:0.393457+0.011189    test-rmse:1.163733+0.292672 
## [368]    train-rmse:0.392797+0.011102    test-rmse:1.163470+0.292873 
## [369]    train-rmse:0.391843+0.010824    test-rmse:1.162732+0.293911 
## [370]    train-rmse:0.391026+0.011013    test-rmse:1.162658+0.293301 
## [371]    train-rmse:0.389857+0.011198    test-rmse:1.162304+0.293525 
## [372]    train-rmse:0.388762+0.011321    test-rmse:1.162785+0.293463 
## [373]    train-rmse:0.387456+0.010807    test-rmse:1.162591+0.293527 
## [374]    train-rmse:0.386237+0.010424    test-rmse:1.161260+0.293329 
## [375]    train-rmse:0.385070+0.010472    test-rmse:1.161038+0.293740 
## [376]    train-rmse:0.384449+0.010428    test-rmse:1.161040+0.293959 
## [377]    train-rmse:0.383603+0.010169    test-rmse:1.160482+0.294630 
## [378]    train-rmse:0.383009+0.010166    test-rmse:1.160193+0.294535 
## [379]    train-rmse:0.382430+0.010044    test-rmse:1.159612+0.294906 
## [380]    train-rmse:0.381715+0.009889    test-rmse:1.158282+0.295895 
## [381]    train-rmse:0.381089+0.009853    test-rmse:1.157532+0.295136 
## [382]    train-rmse:0.379685+0.009980    test-rmse:1.157296+0.294921 
## [383]    train-rmse:0.378792+0.010261    test-rmse:1.157033+0.294803 
## [384]    train-rmse:0.378174+0.010290    test-rmse:1.156571+0.295014 
## [385]    train-rmse:0.377596+0.010270    test-rmse:1.156182+0.295136 
## [386]    train-rmse:0.376341+0.010638    test-rmse:1.156679+0.295031 
## [387]    train-rmse:0.375568+0.010397    test-rmse:1.156376+0.295405 
## [388]    train-rmse:0.374724+0.010196    test-rmse:1.156487+0.294964 
## [389]    train-rmse:0.373520+0.010611    test-rmse:1.156138+0.294944 
## [390]    train-rmse:0.372949+0.010553    test-rmse:1.155866+0.295204 
## [391]    train-rmse:0.372183+0.010772    test-rmse:1.155322+0.294872 
## [392]    train-rmse:0.371184+0.010921    test-rmse:1.155235+0.295268 
## [393]    train-rmse:0.370572+0.010827    test-rmse:1.155279+0.295383 
## [394]    train-rmse:0.369790+0.010758    test-rmse:1.154750+0.295316 
## [395]    train-rmse:0.369126+0.010697    test-rmse:1.154557+0.295702 
## [396]    train-rmse:0.368407+0.010776    test-rmse:1.153396+0.295768 
## [397]    train-rmse:0.367941+0.010796    test-rmse:1.152929+0.295040 
## [398]    train-rmse:0.366974+0.010788    test-rmse:1.153801+0.294741 
## [399]    train-rmse:0.366273+0.010979    test-rmse:1.153754+0.294890 
## [400]    train-rmse:0.365744+0.010957    test-rmse:1.153322+0.294953 
## [401]    train-rmse:0.365195+0.010947    test-rmse:1.153014+0.295012 
## [402]    train-rmse:0.364533+0.010981    test-rmse:1.153002+0.295287 
## [403]    train-rmse:0.363446+0.010566    test-rmse:1.152697+0.295428 
## [404]    train-rmse:0.362705+0.010539    test-rmse:1.153000+0.295887 
## [405]    train-rmse:0.362298+0.010562    test-rmse:1.151765+0.295326 
## [406]    train-rmse:0.361198+0.010876    test-rmse:1.151634+0.295214 
## [407]    train-rmse:0.360067+0.011148    test-rmse:1.152231+0.294850 
## [408]    train-rmse:0.359476+0.010974    test-rmse:1.151600+0.295001 
## [409]    train-rmse:0.358684+0.010693    test-rmse:1.151281+0.294437 
## [410]    train-rmse:0.357881+0.010599    test-rmse:1.151238+0.294270 
## [411]    train-rmse:0.357285+0.010769    test-rmse:1.150961+0.293700 
## [412]    train-rmse:0.356474+0.010551    test-rmse:1.150835+0.294001 
## [413]    train-rmse:0.355680+0.010439    test-rmse:1.149939+0.294596 
## [414]    train-rmse:0.354613+0.010673    test-rmse:1.149545+0.294883 
## [415]    train-rmse:0.354085+0.010555    test-rmse:1.149659+0.294718 
## [416]    train-rmse:0.353348+0.010356    test-rmse:1.149608+0.294985 
## [417]    train-rmse:0.352717+0.010523    test-rmse:1.149274+0.295574 
## [418]    train-rmse:0.352035+0.010377    test-rmse:1.149109+0.295754 
## [419]    train-rmse:0.351273+0.010681    test-rmse:1.148892+0.295597 
## [420]    train-rmse:0.350803+0.010665    test-rmse:1.148266+0.295675 
## [421]    train-rmse:0.350145+0.010562    test-rmse:1.147056+0.296030 
## [422]    train-rmse:0.349427+0.010280    test-rmse:1.146950+0.295823 
## [423]    train-rmse:0.348321+0.010540    test-rmse:1.146870+0.296256 
## [424]    train-rmse:0.347400+0.010225    test-rmse:1.146184+0.296449 
## [425]    train-rmse:0.346755+0.010481    test-rmse:1.145861+0.296561 
## [426]    train-rmse:0.345630+0.011158    test-rmse:1.145673+0.296222 
## [427]    train-rmse:0.344932+0.011605    test-rmse:1.145142+0.296328 
## [428]    train-rmse:0.344276+0.011724    test-rmse:1.144901+0.295898 
## [429]    train-rmse:0.343615+0.011925    test-rmse:1.144665+0.296372 
## [430]    train-rmse:0.342771+0.012271    test-rmse:1.144623+0.295678 
## [431]    train-rmse:0.342090+0.012046    test-rmse:1.144651+0.296495 
## [432]    train-rmse:0.341219+0.011728    test-rmse:1.144587+0.296309 
## [433]    train-rmse:0.340790+0.011741    test-rmse:1.144559+0.295941 
## [434]    train-rmse:0.340100+0.011610    test-rmse:1.144560+0.296003 
## [435]    train-rmse:0.339534+0.011786    test-rmse:1.144511+0.295719 
## [436]    train-rmse:0.338696+0.011982    test-rmse:1.144174+0.295805 
## [437]    train-rmse:0.338145+0.012202    test-rmse:1.144273+0.295519 
## [438]    train-rmse:0.337527+0.012324    test-rmse:1.144118+0.295626 
## [439]    train-rmse:0.336883+0.012208    test-rmse:1.143403+0.295428 
## [440]    train-rmse:0.336309+0.012248    test-rmse:1.143335+0.295157 
## [441]    train-rmse:0.335765+0.012163    test-rmse:1.143310+0.295041 
## [442]    train-rmse:0.335148+0.012278    test-rmse:1.142932+0.295434 
## [443]    train-rmse:0.334363+0.012453    test-rmse:1.143592+0.296020 
## [444]    train-rmse:0.333934+0.012471    test-rmse:1.143507+0.296127 
## [445]    train-rmse:0.333589+0.012486    test-rmse:1.142821+0.295945 
## [446]    train-rmse:0.332959+0.012407    test-rmse:1.142598+0.295654 
## [447]    train-rmse:0.332156+0.012388    test-rmse:1.142336+0.295499 
## [448]    train-rmse:0.331476+0.012324    test-rmse:1.141079+0.296120 
## [449]    train-rmse:0.330791+0.012451    test-rmse:1.140522+0.296173 
## [450]    train-rmse:0.330332+0.012473    test-rmse:1.140558+0.296124 
## [451]    train-rmse:0.329922+0.012531    test-rmse:1.140658+0.295855 
## [452]    train-rmse:0.329302+0.012775    test-rmse:1.140482+0.295729 
## [453]    train-rmse:0.328558+0.012657    test-rmse:1.139975+0.295529 
## [454]    train-rmse:0.328143+0.012663    test-rmse:1.139458+0.295989 
## [455]    train-rmse:0.327389+0.012399    test-rmse:1.139227+0.295703 
## [456]    train-rmse:0.326997+0.012347    test-rmse:1.139045+0.295859 
## [457]    train-rmse:0.326487+0.012302    test-rmse:1.139050+0.295907 
## [458]    train-rmse:0.325478+0.012716    test-rmse:1.138709+0.296314 
## [459]    train-rmse:0.324618+0.013009    test-rmse:1.138764+0.296920 
## [460]    train-rmse:0.323924+0.012710    test-rmse:1.137734+0.296693 
## [461]    train-rmse:0.322977+0.012294    test-rmse:1.137095+0.297179 
## [462]    train-rmse:0.322602+0.012280    test-rmse:1.137046+0.297076 
## [463]    train-rmse:0.321989+0.012426    test-rmse:1.136548+0.296964 
## [464]    train-rmse:0.321558+0.012324    test-rmse:1.136151+0.298340 
## [465]    train-rmse:0.321069+0.012508    test-rmse:1.136341+0.298062 
## [466]    train-rmse:0.320277+0.012570    test-rmse:1.135894+0.297528 
## [467]    train-rmse:0.319398+0.012640    test-rmse:1.135809+0.297514 
## [468]    train-rmse:0.318802+0.012520    test-rmse:1.134889+0.298100 
## [469]    train-rmse:0.318478+0.012474    test-rmse:1.134569+0.298393 
## [470]    train-rmse:0.317986+0.012653    test-rmse:1.134873+0.298001 
## [471]    train-rmse:0.317125+0.013161    test-rmse:1.134710+0.298074 
## [472]    train-rmse:0.316386+0.012890    test-rmse:1.134475+0.298566 
## [473]    train-rmse:0.315739+0.012919    test-rmse:1.134246+0.298852 
## [474]    train-rmse:0.315387+0.013019    test-rmse:1.134138+0.298771 
## [475]    train-rmse:0.314847+0.013035    test-rmse:1.133968+0.298707 
## [476]    train-rmse:0.314266+0.013033    test-rmse:1.133903+0.299489 
## [477]    train-rmse:0.313701+0.012870    test-rmse:1.134085+0.299904 
## [478]    train-rmse:0.312836+0.013072    test-rmse:1.133881+0.299254 
## [479]    train-rmse:0.312343+0.013036    test-rmse:1.133777+0.299223 
## [480]    train-rmse:0.311793+0.012840    test-rmse:1.133537+0.299325 
## [481]    train-rmse:0.311394+0.012927    test-rmse:1.133367+0.299176 
## [482]    train-rmse:0.310924+0.013125    test-rmse:1.132633+0.299104 
## [483]    train-rmse:0.310531+0.013168    test-rmse:1.132688+0.299099 
## [484]    train-rmse:0.309957+0.013028    test-rmse:1.133041+0.299806 
## [485]    train-rmse:0.309151+0.012969    test-rmse:1.133078+0.299527 
## [486]    train-rmse:0.308584+0.012820    test-rmse:1.133127+0.299555 
## [487]    train-rmse:0.307827+0.012970    test-rmse:1.133273+0.299419 
## [488]    train-rmse:0.307133+0.012917    test-rmse:1.133338+0.299316 
## Stopping. Best iteration:
## [482]    train-rmse:0.310924+0.013125    test-rmse:1.132633+0.299104
## 
## 16 
## [1]  train-rmse:92.628808+0.103203   test-rmse:92.629086+0.486485 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.759407+0.092047   test-rmse:79.759461+0.505884 
## [3]  train-rmse:68.693979+0.083013   test-rmse:68.693753+0.524472 
## [4]  train-rmse:59.182194+0.075874   test-rmse:59.181623+0.542625 
## [5]  train-rmse:51.008879+0.070487   test-rmse:51.007880+0.560691 
## [6]  train-rmse:43.989113+0.066746   test-rmse:43.987572+0.578998 
## [7]  train-rmse:37.964006+0.064561   test-rmse:37.961789+0.597840 
## [8]  train-rmse:32.792771+0.062486   test-rmse:32.812824+0.600191 
## [9]  train-rmse:28.347628+0.056546   test-rmse:28.405012+0.603047 
## [10] train-rmse:24.526212+0.047562   test-rmse:24.604208+0.597355 
## [11] train-rmse:21.241542+0.040416   test-rmse:21.330120+0.584586 
## [12] train-rmse:18.420442+0.033843   test-rmse:18.524625+0.584364 
## [13] train-rmse:15.995581+0.031368   test-rmse:16.096637+0.568124 
## [14] train-rmse:13.915861+0.030179   test-rmse:14.005689+0.571934 
## [15] train-rmse:12.132837+0.027478   test-rmse:12.239895+0.575865 
## [16] train-rmse:10.605318+0.027634   test-rmse:10.716857+0.553933 
## [17] train-rmse:9.302092+0.027405    test-rmse:9.412489+0.532183 
## [18] train-rmse:8.188368+0.030193    test-rmse:8.303805+0.526104 
## [19] train-rmse:7.238961+0.030166    test-rmse:7.370027+0.546766 
## [20] train-rmse:6.433768+0.032662    test-rmse:6.591290+0.541301 
## [21] train-rmse:5.751019+0.034573    test-rmse:5.912105+0.540102 
## [22] train-rmse:5.172746+0.031415    test-rmse:5.336810+0.513995 
## [23] train-rmse:4.688737+0.031903    test-rmse:4.879754+0.517789 
## [24] train-rmse:4.282114+0.033487    test-rmse:4.502809+0.511387 
## [25] train-rmse:3.942267+0.035933    test-rmse:4.172514+0.465181 
## [26] train-rmse:3.655424+0.040266    test-rmse:3.909865+0.459659 
## [27] train-rmse:3.413934+0.038489    test-rmse:3.698667+0.450476 
## [28] train-rmse:3.214332+0.038927    test-rmse:3.492458+0.433132 
## [29] train-rmse:3.045497+0.039632    test-rmse:3.339020+0.410841 
## [30] train-rmse:2.903797+0.038975    test-rmse:3.221544+0.397068 
## [31] train-rmse:2.782187+0.039236    test-rmse:3.118725+0.402439 
## [32] train-rmse:2.671534+0.036999    test-rmse:3.034277+0.374924 
## [33] train-rmse:2.577829+0.037071    test-rmse:2.954087+0.357534 
## [34] train-rmse:2.499145+0.037057    test-rmse:2.882853+0.353547 
## [35] train-rmse:2.428784+0.036801    test-rmse:2.823288+0.350158 
## [36] train-rmse:2.366095+0.036078    test-rmse:2.765769+0.334934 
## [37] train-rmse:2.305613+0.036094    test-rmse:2.727075+0.331560 
## [38] train-rmse:2.252655+0.036320    test-rmse:2.673830+0.338230 
## [39] train-rmse:2.199143+0.036343    test-rmse:2.636575+0.329572 
## [40] train-rmse:2.150645+0.032506    test-rmse:2.587223+0.312432 
## [41] train-rmse:2.106068+0.030921    test-rmse:2.549829+0.296527 
## [42] train-rmse:2.064198+0.028942    test-rmse:2.519928+0.293085 
## [43] train-rmse:2.024535+0.028484    test-rmse:2.480527+0.293350 
## [44] train-rmse:1.986333+0.027449    test-rmse:2.459694+0.286656 
## [45] train-rmse:1.947686+0.027721    test-rmse:2.438860+0.290949 
## [46] train-rmse:1.912729+0.026469    test-rmse:2.409987+0.302643 
## [47] train-rmse:1.878424+0.024960    test-rmse:2.377100+0.296227 
## [48] train-rmse:1.845839+0.024371    test-rmse:2.347507+0.292668 
## [49] train-rmse:1.812299+0.025089    test-rmse:2.320634+0.284265 
## [50] train-rmse:1.779229+0.028222    test-rmse:2.285463+0.284274 
## [51] train-rmse:1.749561+0.027727    test-rmse:2.263956+0.288556 
## [52] train-rmse:1.720344+0.027383    test-rmse:2.238049+0.288538 
## [53] train-rmse:1.691353+0.025907    test-rmse:2.216105+0.273568 
## [54] train-rmse:1.663460+0.025890    test-rmse:2.193439+0.280663 
## [55] train-rmse:1.636001+0.025302    test-rmse:2.173336+0.282040 
## [56] train-rmse:1.608830+0.024465    test-rmse:2.154349+0.275687 
## [57] train-rmse:1.581359+0.027106    test-rmse:2.138143+0.275296 
## [58] train-rmse:1.554050+0.026750    test-rmse:2.112117+0.272556 
## [59] train-rmse:1.529599+0.026475    test-rmse:2.090383+0.276229 
## [60] train-rmse:1.505450+0.025829    test-rmse:2.071432+0.262245 
## [61] train-rmse:1.482357+0.024824    test-rmse:2.047227+0.267703 
## [62] train-rmse:1.459393+0.024398    test-rmse:2.034258+0.262260 
## [63] train-rmse:1.437467+0.024017    test-rmse:2.014616+0.270319 
## [64] train-rmse:1.415256+0.024607    test-rmse:1.990780+0.259347 
## [65] train-rmse:1.391793+0.023978    test-rmse:1.970963+0.256790 
## [66] train-rmse:1.370431+0.023283    test-rmse:1.953395+0.256259 
## [67] train-rmse:1.347862+0.020861    test-rmse:1.935076+0.254926 
## [68] train-rmse:1.328147+0.020923    test-rmse:1.914576+0.253732 
## [69] train-rmse:1.307662+0.019679    test-rmse:1.893852+0.260944 
## [70] train-rmse:1.288998+0.019416    test-rmse:1.876384+0.261539 
## [71] train-rmse:1.269592+0.018498    test-rmse:1.862373+0.262711 
## [72] train-rmse:1.250279+0.018222    test-rmse:1.846284+0.259624 
## [73] train-rmse:1.232810+0.017880    test-rmse:1.834468+0.256254 
## [74] train-rmse:1.214774+0.017559    test-rmse:1.818080+0.256584 
## [75] train-rmse:1.196938+0.018851    test-rmse:1.805853+0.258726 
## [76] train-rmse:1.179790+0.018913    test-rmse:1.792201+0.265422 
## [77] train-rmse:1.163653+0.018936    test-rmse:1.777992+0.265211 
## [78] train-rmse:1.146184+0.016765    test-rmse:1.763878+0.261840 
## [79] train-rmse:1.130176+0.016303    test-rmse:1.750570+0.261315 
## [80] train-rmse:1.115217+0.015816    test-rmse:1.738939+0.264841 
## [81] train-rmse:1.100148+0.015600    test-rmse:1.725906+0.268309 
## [82] train-rmse:1.085251+0.015317    test-rmse:1.714902+0.267995 
## [83] train-rmse:1.068581+0.014679    test-rmse:1.706341+0.273003 
## [84] train-rmse:1.054380+0.013997    test-rmse:1.683373+0.266174 
## [85] train-rmse:1.039009+0.013893    test-rmse:1.668364+0.262364 
## [86] train-rmse:1.024742+0.014746    test-rmse:1.661008+0.261985 
## [87] train-rmse:1.011574+0.014834    test-rmse:1.651693+0.264016 
## [88] train-rmse:0.998252+0.015316    test-rmse:1.644026+0.264740 
## [89] train-rmse:0.984896+0.015613    test-rmse:1.630992+0.261197 
## [90] train-rmse:0.972653+0.015677    test-rmse:1.622384+0.263343 
## [91] train-rmse:0.958772+0.016424    test-rmse:1.611097+0.268501 
## [92] train-rmse:0.946584+0.016540    test-rmse:1.599233+0.269609 
## [93] train-rmse:0.932689+0.015761    test-rmse:1.589686+0.266245 
## [94] train-rmse:0.921092+0.015772    test-rmse:1.583766+0.265506 
## [95] train-rmse:0.909216+0.015937    test-rmse:1.574744+0.267680 
## [96] train-rmse:0.897861+0.016255    test-rmse:1.567768+0.270301 
## [97] train-rmse:0.886795+0.015491    test-rmse:1.556839+0.273253 
## [98] train-rmse:0.875948+0.015041    test-rmse:1.549013+0.276591 
## [99] train-rmse:0.864568+0.015652    test-rmse:1.539146+0.269037 
## [100]    train-rmse:0.853355+0.015506    test-rmse:1.528483+0.269776 
## [101]    train-rmse:0.842992+0.014876    test-rmse:1.522524+0.269029 
## [102]    train-rmse:0.830809+0.016498    test-rmse:1.519140+0.270010 
## [103]    train-rmse:0.821155+0.016505    test-rmse:1.513130+0.266597 
## [104]    train-rmse:0.811773+0.016262    test-rmse:1.501469+0.267506 
## [105]    train-rmse:0.801953+0.016654    test-rmse:1.495998+0.268101 
## [106]    train-rmse:0.792624+0.017077    test-rmse:1.491286+0.269688 
## [107]    train-rmse:0.783718+0.017014    test-rmse:1.482112+0.270432 
## [108]    train-rmse:0.774914+0.017297    test-rmse:1.473903+0.270721 
## [109]    train-rmse:0.764901+0.016986    test-rmse:1.464596+0.267943 
## [110]    train-rmse:0.755818+0.015777    test-rmse:1.460493+0.269259 
## [111]    train-rmse:0.747890+0.015717    test-rmse:1.454504+0.266991 
## [112]    train-rmse:0.739975+0.015640    test-rmse:1.449879+0.267377 
## [113]    train-rmse:0.731911+0.015889    test-rmse:1.442768+0.269984 
## [114]    train-rmse:0.724299+0.016008    test-rmse:1.438345+0.268932 
## [115]    train-rmse:0.716586+0.016088    test-rmse:1.429546+0.271883 
## [116]    train-rmse:0.708839+0.015644    test-rmse:1.424905+0.270161 
## [117]    train-rmse:0.701476+0.015589    test-rmse:1.419480+0.269921 
## [118]    train-rmse:0.694288+0.015629    test-rmse:1.412456+0.270972 
## [119]    train-rmse:0.687622+0.015595    test-rmse:1.407645+0.273177 
## [120]    train-rmse:0.679858+0.016026    test-rmse:1.401894+0.275284 
## [121]    train-rmse:0.672606+0.015763    test-rmse:1.394797+0.275272 
## [122]    train-rmse:0.664146+0.016778    test-rmse:1.391843+0.274910 
## [123]    train-rmse:0.657619+0.016149    test-rmse:1.387747+0.276524 
## [124]    train-rmse:0.651457+0.016002    test-rmse:1.384292+0.276351 
## [125]    train-rmse:0.644357+0.016466    test-rmse:1.378274+0.278213 
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## [128]    train-rmse:0.623815+0.016808    test-rmse:1.364628+0.278949 
## [129]    train-rmse:0.618220+0.016805    test-rmse:1.360882+0.280154 
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## [249]    train-rmse:0.280352+0.010577    test-rmse:1.180647+0.294890 
## [250]    train-rmse:0.278674+0.010387    test-rmse:1.180953+0.294725 
## [251]    train-rmse:0.277383+0.010478    test-rmse:1.180371+0.294674 
## [252]    train-rmse:0.275982+0.010796    test-rmse:1.180413+0.294533 
## [253]    train-rmse:0.274199+0.010821    test-rmse:1.179484+0.295073 
## [254]    train-rmse:0.272857+0.010974    test-rmse:1.178528+0.294288 
## [255]    train-rmse:0.271570+0.011095    test-rmse:1.178127+0.294414 
## [256]    train-rmse:0.270403+0.010943    test-rmse:1.178128+0.294523 
## [257]    train-rmse:0.268492+0.011302    test-rmse:1.177900+0.294488 
## [258]    train-rmse:0.267245+0.011263    test-rmse:1.177985+0.294634 
## [259]    train-rmse:0.266220+0.011081    test-rmse:1.177013+0.294746 
## [260]    train-rmse:0.265057+0.011442    test-rmse:1.176769+0.294743 
## [261]    train-rmse:0.263692+0.011389    test-rmse:1.175435+0.295030 
## [262]    train-rmse:0.262436+0.011504    test-rmse:1.174605+0.294637 
## [263]    train-rmse:0.261513+0.011479    test-rmse:1.174153+0.295256 
## [264]    train-rmse:0.260316+0.011457    test-rmse:1.173990+0.295505 
## [265]    train-rmse:0.259142+0.011072    test-rmse:1.172877+0.294941 
## [266]    train-rmse:0.257646+0.011083    test-rmse:1.172639+0.294485 
## [267]    train-rmse:0.256673+0.011331    test-rmse:1.172518+0.294467 
## [268]    train-rmse:0.255461+0.011246    test-rmse:1.172718+0.294937 
## [269]    train-rmse:0.254427+0.011480    test-rmse:1.172852+0.294825 
## [270]    train-rmse:0.253373+0.011629    test-rmse:1.171960+0.295490 
## [271]    train-rmse:0.252385+0.011649    test-rmse:1.171604+0.295691 
## [272]    train-rmse:0.251233+0.011888    test-rmse:1.171232+0.296002 
## [273]    train-rmse:0.249936+0.011909    test-rmse:1.170967+0.295324 
## [274]    train-rmse:0.248588+0.011711    test-rmse:1.170316+0.295908 
## [275]    train-rmse:0.247609+0.011592    test-rmse:1.169633+0.295727 
## [276]    train-rmse:0.246723+0.011739    test-rmse:1.169591+0.295686 
## [277]    train-rmse:0.245612+0.011601    test-rmse:1.169248+0.296079 
## [278]    train-rmse:0.244734+0.011392    test-rmse:1.169340+0.295814 
## [279]    train-rmse:0.243210+0.011437    test-rmse:1.169177+0.295561 
## [280]    train-rmse:0.242068+0.011430    test-rmse:1.169063+0.296201 
## [281]    train-rmse:0.241090+0.011514    test-rmse:1.169050+0.295801 
## [282]    train-rmse:0.240308+0.011697    test-rmse:1.168927+0.295875 
## [283]    train-rmse:0.239155+0.011779    test-rmse:1.168927+0.296571 
## [284]    train-rmse:0.238017+0.011448    test-rmse:1.167814+0.296025 
## [285]    train-rmse:0.236866+0.011440    test-rmse:1.167585+0.296227 
## [286]    train-rmse:0.235919+0.011191    test-rmse:1.167567+0.295789 
## [287]    train-rmse:0.235026+0.011247    test-rmse:1.166772+0.296495 
## [288]    train-rmse:0.233846+0.011248    test-rmse:1.167069+0.296285 
## [289]    train-rmse:0.233163+0.011190    test-rmse:1.166811+0.296229 
## [290]    train-rmse:0.232200+0.011299    test-rmse:1.166388+0.296104 
## [291]    train-rmse:0.231418+0.011190    test-rmse:1.165972+0.296268 
## [292]    train-rmse:0.230305+0.011114    test-rmse:1.166019+0.296399 
## [293]    train-rmse:0.228987+0.011372    test-rmse:1.165367+0.296918 
## [294]    train-rmse:0.227933+0.011226    test-rmse:1.164877+0.296783 
## [295]    train-rmse:0.226991+0.011211    test-rmse:1.164467+0.296943 
## [296]    train-rmse:0.225764+0.011803    test-rmse:1.164949+0.296697 
## [297]    train-rmse:0.225060+0.011788    test-rmse:1.164650+0.296519 
## [298]    train-rmse:0.224334+0.011847    test-rmse:1.164336+0.296730 
## [299]    train-rmse:0.223749+0.011744    test-rmse:1.163957+0.296737 
## [300]    train-rmse:0.222976+0.011804    test-rmse:1.163793+0.296560 
## [301]    train-rmse:0.222432+0.011794    test-rmse:1.163696+0.296047 
## [302]    train-rmse:0.221380+0.011694    test-rmse:1.163404+0.296185 
## [303]    train-rmse:0.220340+0.011512    test-rmse:1.163264+0.296413 
## [304]    train-rmse:0.219627+0.011584    test-rmse:1.163181+0.296242 
## [305]    train-rmse:0.218804+0.011556    test-rmse:1.162546+0.296545 
## [306]    train-rmse:0.217884+0.011552    test-rmse:1.162224+0.296925 
## [307]    train-rmse:0.216996+0.011563    test-rmse:1.161790+0.297239 
## [308]    train-rmse:0.216384+0.011501    test-rmse:1.161546+0.297335 
## [309]    train-rmse:0.215530+0.011551    test-rmse:1.161737+0.297541 
## [310]    train-rmse:0.214648+0.011474    test-rmse:1.161406+0.297701 
## [311]    train-rmse:0.213739+0.011208    test-rmse:1.161610+0.297734 
## [312]    train-rmse:0.212923+0.011082    test-rmse:1.160631+0.297524 
## [313]    train-rmse:0.212202+0.011088    test-rmse:1.160660+0.297370 
## [314]    train-rmse:0.211284+0.011209    test-rmse:1.159198+0.298133 
## [315]    train-rmse:0.210427+0.011490    test-rmse:1.159029+0.298306 
## [316]    train-rmse:0.209754+0.011557    test-rmse:1.158946+0.298157 
## [317]    train-rmse:0.208994+0.011421    test-rmse:1.158505+0.298202 
## [318]    train-rmse:0.208415+0.011418    test-rmse:1.158132+0.298197 
## [319]    train-rmse:0.207640+0.011334    test-rmse:1.158101+0.298355 
## [320]    train-rmse:0.206824+0.010995    test-rmse:1.157808+0.298490 
## [321]    train-rmse:0.206277+0.010911    test-rmse:1.157618+0.298741 
## [322]    train-rmse:0.205369+0.011173    test-rmse:1.157359+0.298678 
## [323]    train-rmse:0.204655+0.011254    test-rmse:1.156790+0.298026 
## [324]    train-rmse:0.203955+0.011287    test-rmse:1.156838+0.298136 
## [325]    train-rmse:0.203171+0.011175    test-rmse:1.156902+0.298173 
## [326]    train-rmse:0.202264+0.011289    test-rmse:1.156346+0.298036 
## [327]    train-rmse:0.201637+0.011580    test-rmse:1.156224+0.298300 
## [328]    train-rmse:0.201086+0.011672    test-rmse:1.156071+0.298298 
## [329]    train-rmse:0.200299+0.011444    test-rmse:1.156172+0.298418 
## [330]    train-rmse:0.199599+0.011520    test-rmse:1.155868+0.298548 
## [331]    train-rmse:0.198922+0.011367    test-rmse:1.155472+0.298367 
## [332]    train-rmse:0.198194+0.011396    test-rmse:1.155332+0.298312 
## [333]    train-rmse:0.197330+0.011170    test-rmse:1.154741+0.298482 
## [334]    train-rmse:0.196698+0.011174    test-rmse:1.154768+0.298618 
## [335]    train-rmse:0.195982+0.011029    test-rmse:1.154750+0.298855 
## [336]    train-rmse:0.195129+0.010927    test-rmse:1.154003+0.298884 
## [337]    train-rmse:0.194563+0.010971    test-rmse:1.153705+0.298973 
## [338]    train-rmse:0.193990+0.010932    test-rmse:1.153450+0.298817 
## [339]    train-rmse:0.193239+0.010728    test-rmse:1.153469+0.298861 
## [340]    train-rmse:0.192651+0.010651    test-rmse:1.153414+0.299119 
## [341]    train-rmse:0.191591+0.010354    test-rmse:1.153344+0.299085 
## [342]    train-rmse:0.190772+0.010176    test-rmse:1.152360+0.297235 
## [343]    train-rmse:0.190075+0.010155    test-rmse:1.152063+0.297500 
## [344]    train-rmse:0.188946+0.010152    test-rmse:1.152297+0.297893 
## [345]    train-rmse:0.187717+0.009491    test-rmse:1.152108+0.297993 
## [346]    train-rmse:0.187087+0.009367    test-rmse:1.151909+0.298155 
## [347]    train-rmse:0.186405+0.009292    test-rmse:1.151340+0.298591 
## [348]    train-rmse:0.185636+0.009298    test-rmse:1.151506+0.298388 
## [349]    train-rmse:0.184930+0.009173    test-rmse:1.151722+0.298477 
## [350]    train-rmse:0.184178+0.009303    test-rmse:1.151665+0.298431 
## [351]    train-rmse:0.183233+0.009774    test-rmse:1.151525+0.299496 
## [352]    train-rmse:0.182611+0.009807    test-rmse:1.151652+0.299522 
## [353]    train-rmse:0.181664+0.010266    test-rmse:1.151436+0.298758 
## Stopping. Best iteration:
## [347]    train-rmse:0.186405+0.009292    test-rmse:1.151340+0.298591
## 
## 17 
## [1]  train-rmse:92.628808+0.103203   test-rmse:92.629086+0.486485 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.759407+0.092047   test-rmse:79.759461+0.505884 
## [3]  train-rmse:68.693979+0.083013   test-rmse:68.693753+0.524472 
## [4]  train-rmse:59.182194+0.075874   test-rmse:59.181623+0.542625 
## [5]  train-rmse:51.008879+0.070487   test-rmse:51.007880+0.560691 
## [6]  train-rmse:43.989113+0.066746   test-rmse:43.987572+0.578998 
## [7]  train-rmse:37.964006+0.064561   test-rmse:37.961789+0.597840 
## [8]  train-rmse:32.792771+0.062486   test-rmse:32.812824+0.600191 
## [9]  train-rmse:28.347628+0.056546   test-rmse:28.405012+0.603047 
## [10] train-rmse:24.526212+0.047562   test-rmse:24.604208+0.597355 
## [11] train-rmse:21.241343+0.040405   test-rmse:21.326121+0.583443 
## [12] train-rmse:18.417935+0.034250   test-rmse:18.525899+0.581104 
## [13] train-rmse:15.987990+0.031400   test-rmse:16.097872+0.562048 
## [14] train-rmse:13.899948+0.029311   test-rmse:14.004335+0.548106 
## [15] train-rmse:12.106548+0.026770   test-rmse:12.200467+0.539913 
## [16] train-rmse:10.566577+0.025728   test-rmse:10.654600+0.525526 
## [17] train-rmse:9.245970+0.024571    test-rmse:9.318455+0.530479 
## [18] train-rmse:8.113030+0.022106    test-rmse:8.220829+0.549446 
## [19] train-rmse:7.145043+0.020269    test-rmse:7.251480+0.552039 
## [20] train-rmse:6.318365+0.021331    test-rmse:6.439033+0.545423 
## [21] train-rmse:5.615859+0.021277    test-rmse:5.741820+0.516183 
## [22] train-rmse:5.014909+0.025425    test-rmse:5.164890+0.528896 
## [23] train-rmse:4.499328+0.026528    test-rmse:4.663292+0.498491 
## [24] train-rmse:4.067427+0.031250    test-rmse:4.260924+0.480041 
## [25] train-rmse:3.700867+0.033557    test-rmse:3.929454+0.448814 
## [26] train-rmse:3.391949+0.037551    test-rmse:3.651677+0.436393 
## [27] train-rmse:3.130254+0.038607    test-rmse:3.416798+0.420749 
## [28] train-rmse:2.910513+0.042151    test-rmse:3.216516+0.394886 
## [29] train-rmse:2.722758+0.040492    test-rmse:3.057391+0.385395 
## [30] train-rmse:2.562062+0.040565    test-rmse:2.906973+0.364539 
## [31] train-rmse:2.425791+0.039594    test-rmse:2.793578+0.352945 
## [32] train-rmse:2.309401+0.040177    test-rmse:2.697833+0.343470 
## [33] train-rmse:2.209442+0.040588    test-rmse:2.607066+0.336313 
## [34] train-rmse:2.118800+0.036569    test-rmse:2.543287+0.315626 
## [35] train-rmse:2.036311+0.035313    test-rmse:2.478330+0.306889 
## [36] train-rmse:1.966546+0.035187    test-rmse:2.425188+0.302620 
## [37] train-rmse:1.901708+0.034231    test-rmse:2.379828+0.299966 
## [38] train-rmse:1.841937+0.038279    test-rmse:2.329353+0.297985 
## [39] train-rmse:1.788312+0.040255    test-rmse:2.278325+0.298999 
## [40] train-rmse:1.734776+0.038901    test-rmse:2.237533+0.288436 
## [41] train-rmse:1.688591+0.038298    test-rmse:2.203624+0.277849 
## [42] train-rmse:1.642625+0.041805    test-rmse:2.170823+0.281256 
## [43] train-rmse:1.602968+0.041083    test-rmse:2.135278+0.279941 
## [44] train-rmse:1.559047+0.037155    test-rmse:2.101594+0.273562 
## [45] train-rmse:1.521753+0.035633    test-rmse:2.075482+0.262629 
## [46] train-rmse:1.487321+0.034085    test-rmse:2.044639+0.267221 
## [47] train-rmse:1.448860+0.033730    test-rmse:2.010192+0.270319 
## [48] train-rmse:1.414449+0.030986    test-rmse:1.983332+0.262608 
## [49] train-rmse:1.383427+0.030459    test-rmse:1.959689+0.258600 
## [50] train-rmse:1.353096+0.030606    test-rmse:1.937165+0.252270 
## [51] train-rmse:1.317339+0.038883    test-rmse:1.916070+0.255689 
## [52] train-rmse:1.289024+0.038041    test-rmse:1.893005+0.264215 
## [53] train-rmse:1.260365+0.036573    test-rmse:1.866290+0.262819 
## [54] train-rmse:1.233526+0.035413    test-rmse:1.849035+0.257198 
## [55] train-rmse:1.207988+0.034933    test-rmse:1.829824+0.253823 
## [56] train-rmse:1.178216+0.035792    test-rmse:1.814126+0.256777 
## [57] train-rmse:1.154237+0.036667    test-rmse:1.791999+0.259621 
## [58] train-rmse:1.130658+0.035165    test-rmse:1.770380+0.267604 
## [59] train-rmse:1.108143+0.034504    test-rmse:1.751134+0.266851 
## [60] train-rmse:1.084954+0.034082    test-rmse:1.737515+0.267527 
## [61] train-rmse:1.062460+0.031703    test-rmse:1.719709+0.266688 
## [62] train-rmse:1.037890+0.031664    test-rmse:1.694823+0.270496 
## [63] train-rmse:1.017582+0.031477    test-rmse:1.682751+0.271250 
## [64] train-rmse:0.996420+0.030182    test-rmse:1.666283+0.272670 
## [65] train-rmse:0.978059+0.030362    test-rmse:1.648989+0.271968 
## [66] train-rmse:0.958396+0.029011    test-rmse:1.630865+0.269798 
## [67] train-rmse:0.940411+0.028233    test-rmse:1.620585+0.270448 
## [68] train-rmse:0.923131+0.027486    test-rmse:1.606645+0.276087 
## [69] train-rmse:0.903489+0.029087    test-rmse:1.590664+0.281437 
## [70] train-rmse:0.887215+0.028122    test-rmse:1.575050+0.280705 
## [71] train-rmse:0.868708+0.026310    test-rmse:1.562451+0.278872 
## [72] train-rmse:0.852425+0.025542    test-rmse:1.550088+0.283032 
## [73] train-rmse:0.837043+0.024506    test-rmse:1.540096+0.282773 
## [74] train-rmse:0.821659+0.025669    test-rmse:1.523682+0.276027 
## [75] train-rmse:0.807449+0.025266    test-rmse:1.513999+0.275437 
## [76] train-rmse:0.791779+0.025180    test-rmse:1.500579+0.279358 
## [77] train-rmse:0.778611+0.024653    test-rmse:1.489202+0.278272 
## [78] train-rmse:0.761959+0.024294    test-rmse:1.482267+0.279012 
## [79] train-rmse:0.748409+0.023253    test-rmse:1.467942+0.273725 
## [80] train-rmse:0.734879+0.020591    test-rmse:1.457676+0.271737 
## [81] train-rmse:0.722100+0.021584    test-rmse:1.447751+0.276969 
## [82] train-rmse:0.710063+0.021153    test-rmse:1.438219+0.276593 
## [83] train-rmse:0.697233+0.020777    test-rmse:1.427705+0.265874 
## [84] train-rmse:0.684813+0.020926    test-rmse:1.416711+0.267658 
## [85] train-rmse:0.673918+0.020404    test-rmse:1.407837+0.268698 
## [86] train-rmse:0.663161+0.019269    test-rmse:1.402704+0.271555 
## [87] train-rmse:0.653186+0.019044    test-rmse:1.393690+0.269983 
## [88] train-rmse:0.643568+0.018903    test-rmse:1.381817+0.263402 
## [89] train-rmse:0.633112+0.018083    test-rmse:1.372095+0.265016 
## [90] train-rmse:0.623452+0.017934    test-rmse:1.367043+0.267144 
## [91] train-rmse:0.613031+0.017213    test-rmse:1.362792+0.266841 
## [92] train-rmse:0.602994+0.016844    test-rmse:1.353080+0.267927 
## [93] train-rmse:0.592508+0.016678    test-rmse:1.348744+0.270875 
## [94] train-rmse:0.582081+0.018307    test-rmse:1.338098+0.266353 
## [95] train-rmse:0.574257+0.017773    test-rmse:1.333723+0.267411 
## [96] train-rmse:0.566133+0.017163    test-rmse:1.330152+0.268328 
## [97] train-rmse:0.558327+0.016882    test-rmse:1.320019+0.269692 
## [98] train-rmse:0.550934+0.016762    test-rmse:1.313082+0.269742 
## [99] train-rmse:0.541672+0.017810    test-rmse:1.304445+0.264394 
## [100]    train-rmse:0.533891+0.018127    test-rmse:1.300872+0.266476 
## [101]    train-rmse:0.526544+0.017716    test-rmse:1.299776+0.267357 
## [102]    train-rmse:0.520348+0.017325    test-rmse:1.293355+0.267296 
## [103]    train-rmse:0.513372+0.016483    test-rmse:1.289422+0.267958 
## [104]    train-rmse:0.506756+0.015662    test-rmse:1.284730+0.269254 
## [105]    train-rmse:0.499646+0.015752    test-rmse:1.277779+0.263825 
## [106]    train-rmse:0.492435+0.016399    test-rmse:1.276007+0.265669 
## [107]    train-rmse:0.486179+0.016484    test-rmse:1.271881+0.266568 
## [108]    train-rmse:0.480394+0.016553    test-rmse:1.267856+0.266785 
## [109]    train-rmse:0.474442+0.015983    test-rmse:1.263800+0.265953 
## [110]    train-rmse:0.468563+0.016005    test-rmse:1.260984+0.267044 
## [111]    train-rmse:0.462390+0.015424    test-rmse:1.254544+0.261586 
## [112]    train-rmse:0.456913+0.015709    test-rmse:1.251015+0.261707 
## [113]    train-rmse:0.451272+0.015149    test-rmse:1.248885+0.261415 
## [114]    train-rmse:0.444765+0.014334    test-rmse:1.247636+0.263477 
## [115]    train-rmse:0.438974+0.014845    test-rmse:1.244646+0.264358 
## [116]    train-rmse:0.433821+0.014776    test-rmse:1.241339+0.264221 
## [117]    train-rmse:0.428737+0.014374    test-rmse:1.239087+0.266253 
## [118]    train-rmse:0.423516+0.015218    test-rmse:1.235305+0.266852 
## [119]    train-rmse:0.416671+0.013718    test-rmse:1.233069+0.265648 
## [120]    train-rmse:0.412105+0.013902    test-rmse:1.229125+0.267048 
## [121]    train-rmse:0.406561+0.013955    test-rmse:1.225133+0.268221 
## [122]    train-rmse:0.401692+0.013686    test-rmse:1.224086+0.269395 
## [123]    train-rmse:0.397694+0.013361    test-rmse:1.221452+0.270831 
## [124]    train-rmse:0.393265+0.012959    test-rmse:1.217379+0.270408 
## [125]    train-rmse:0.387838+0.012116    test-rmse:1.215333+0.271567 
## [126]    train-rmse:0.383695+0.011853    test-rmse:1.214627+0.272559 
## [127]    train-rmse:0.379248+0.012431    test-rmse:1.212463+0.272603 
## [128]    train-rmse:0.375110+0.012086    test-rmse:1.211008+0.274101 
## [129]    train-rmse:0.371120+0.011981    test-rmse:1.207551+0.274475 
## [130]    train-rmse:0.367182+0.011693    test-rmse:1.204633+0.273175 
## [131]    train-rmse:0.363239+0.011301    test-rmse:1.202594+0.273927 
## [132]    train-rmse:0.359121+0.011271    test-rmse:1.202374+0.273736 
## [133]    train-rmse:0.355527+0.010437    test-rmse:1.198164+0.274189 
## [134]    train-rmse:0.352289+0.010947    test-rmse:1.196325+0.275625 
## [135]    train-rmse:0.347912+0.010789    test-rmse:1.194410+0.276773 
## [136]    train-rmse:0.344764+0.010595    test-rmse:1.192574+0.276384 
## [137]    train-rmse:0.341362+0.010138    test-rmse:1.189847+0.276145 
## [138]    train-rmse:0.336883+0.010873    test-rmse:1.187482+0.277884 
## [139]    train-rmse:0.333329+0.011366    test-rmse:1.185471+0.277114 
## [140]    train-rmse:0.330036+0.010942    test-rmse:1.184508+0.277342 
## [141]    train-rmse:0.325984+0.010887    test-rmse:1.183494+0.277767 
## [142]    train-rmse:0.322710+0.011337    test-rmse:1.182082+0.278997 
## [143]    train-rmse:0.319608+0.011110    test-rmse:1.179817+0.281253 
## [144]    train-rmse:0.316888+0.010792    test-rmse:1.178281+0.279467 
## [145]    train-rmse:0.313192+0.010093    test-rmse:1.176682+0.280878 
## [146]    train-rmse:0.309278+0.009775    test-rmse:1.176469+0.281026 
## [147]    train-rmse:0.306551+0.010045    test-rmse:1.175410+0.281207 
## [148]    train-rmse:0.304339+0.009814    test-rmse:1.173479+0.281555 
## [149]    train-rmse:0.301371+0.008864    test-rmse:1.171551+0.281646 
## [150]    train-rmse:0.298574+0.008842    test-rmse:1.171273+0.282423 
## [151]    train-rmse:0.296068+0.008444    test-rmse:1.170372+0.282522 
## [152]    train-rmse:0.292720+0.008118    test-rmse:1.168951+0.281997 
## [153]    train-rmse:0.290101+0.008902    test-rmse:1.168230+0.282510 
## [154]    train-rmse:0.287426+0.009500    test-rmse:1.167416+0.282661 
## [155]    train-rmse:0.284388+0.009571    test-rmse:1.167718+0.282536 
## [156]    train-rmse:0.281553+0.009233    test-rmse:1.167290+0.282205 
## [157]    train-rmse:0.279282+0.009364    test-rmse:1.166861+0.282745 
## [158]    train-rmse:0.276083+0.008533    test-rmse:1.165079+0.283513 
## [159]    train-rmse:0.273576+0.008843    test-rmse:1.164047+0.284379 
## [160]    train-rmse:0.271377+0.009376    test-rmse:1.162106+0.286014 
## [161]    train-rmse:0.269043+0.009184    test-rmse:1.161680+0.285752 
## [162]    train-rmse:0.266886+0.009176    test-rmse:1.160610+0.285984 
## [163]    train-rmse:0.264300+0.008857    test-rmse:1.159834+0.286528 
## [164]    train-rmse:0.261921+0.008578    test-rmse:1.158590+0.285888 
## [165]    train-rmse:0.259764+0.008667    test-rmse:1.157670+0.286137 
## [166]    train-rmse:0.257828+0.008463    test-rmse:1.157267+0.287530 
## [167]    train-rmse:0.255811+0.008582    test-rmse:1.156533+0.287545 
## [168]    train-rmse:0.253875+0.008237    test-rmse:1.154967+0.287974 
## [169]    train-rmse:0.251436+0.007770    test-rmse:1.155254+0.288272 
## [170]    train-rmse:0.249821+0.007375    test-rmse:1.154128+0.288894 
## [171]    train-rmse:0.247829+0.007580    test-rmse:1.153206+0.290239 
## [172]    train-rmse:0.245988+0.007590    test-rmse:1.153483+0.290291 
## [173]    train-rmse:0.243595+0.007858    test-rmse:1.153138+0.291060 
## [174]    train-rmse:0.241963+0.007763    test-rmse:1.151828+0.288962 
## [175]    train-rmse:0.239438+0.007752    test-rmse:1.150950+0.288436 
## [176]    train-rmse:0.237645+0.007416    test-rmse:1.150244+0.288628 
## [177]    train-rmse:0.235460+0.006955    test-rmse:1.147786+0.290257 
## [178]    train-rmse:0.233348+0.007079    test-rmse:1.147561+0.290245 
## [179]    train-rmse:0.231460+0.007073    test-rmse:1.147153+0.291246 
## [180]    train-rmse:0.229905+0.007072    test-rmse:1.145390+0.290734 
## [181]    train-rmse:0.227486+0.006988    test-rmse:1.145769+0.290644 
## [182]    train-rmse:0.226011+0.006644    test-rmse:1.145194+0.290933 
## [183]    train-rmse:0.224316+0.006527    test-rmse:1.144818+0.290888 
## [184]    train-rmse:0.222755+0.006288    test-rmse:1.144312+0.291156 
## [185]    train-rmse:0.220503+0.006432    test-rmse:1.144231+0.291325 
## [186]    train-rmse:0.218761+0.006898    test-rmse:1.143020+0.292139 
## [187]    train-rmse:0.217371+0.007171    test-rmse:1.141643+0.293075 
## [188]    train-rmse:0.215149+0.006733    test-rmse:1.142130+0.293040 
## [189]    train-rmse:0.213284+0.006609    test-rmse:1.141833+0.292512 
## [190]    train-rmse:0.211499+0.006263    test-rmse:1.141158+0.293351 
## [191]    train-rmse:0.209932+0.006314    test-rmse:1.140993+0.293121 
## [192]    train-rmse:0.208570+0.006411    test-rmse:1.141059+0.293489 
## [193]    train-rmse:0.207415+0.006456    test-rmse:1.140372+0.293403 
## [194]    train-rmse:0.205865+0.006326    test-rmse:1.140277+0.293026 
## [195]    train-rmse:0.204400+0.006386    test-rmse:1.140264+0.293272 
## [196]    train-rmse:0.202724+0.006246    test-rmse:1.139909+0.293140 
## [197]    train-rmse:0.201105+0.005944    test-rmse:1.139442+0.293321 
## [198]    train-rmse:0.199142+0.006140    test-rmse:1.139028+0.293859 
## [199]    train-rmse:0.197528+0.006110    test-rmse:1.138527+0.294090 
## [200]    train-rmse:0.195812+0.006382    test-rmse:1.138187+0.294130 
## [201]    train-rmse:0.194048+0.006274    test-rmse:1.137896+0.294085 
## [202]    train-rmse:0.192419+0.006635    test-rmse:1.137136+0.294753 
## [203]    train-rmse:0.190809+0.006475    test-rmse:1.137112+0.294529 
## [204]    train-rmse:0.189549+0.006431    test-rmse:1.136739+0.294526 
## [205]    train-rmse:0.187568+0.006424    test-rmse:1.137066+0.294895 
## [206]    train-rmse:0.185722+0.005824    test-rmse:1.135545+0.295575 
## [207]    train-rmse:0.184701+0.005867    test-rmse:1.135207+0.295777 
## [208]    train-rmse:0.183136+0.005600    test-rmse:1.135072+0.295839 
## [209]    train-rmse:0.181326+0.005723    test-rmse:1.134190+0.295005 
## [210]    train-rmse:0.180067+0.005610    test-rmse:1.133905+0.295130 
## [211]    train-rmse:0.178682+0.005521    test-rmse:1.133809+0.295267 
## [212]    train-rmse:0.177009+0.005595    test-rmse:1.133517+0.295400 
## [213]    train-rmse:0.175695+0.005235    test-rmse:1.132965+0.296086 
## [214]    train-rmse:0.174790+0.005113    test-rmse:1.132335+0.296210 
## [215]    train-rmse:0.173140+0.005431    test-rmse:1.132237+0.295985 
## [216]    train-rmse:0.171794+0.005745    test-rmse:1.132344+0.296016 
## [217]    train-rmse:0.170351+0.006489    test-rmse:1.132257+0.295639 
## [218]    train-rmse:0.169126+0.006235    test-rmse:1.131955+0.295955 
## [219]    train-rmse:0.167518+0.006331    test-rmse:1.131444+0.295877 
## [220]    train-rmse:0.166347+0.006339    test-rmse:1.130354+0.296423 
## [221]    train-rmse:0.165258+0.006059    test-rmse:1.129892+0.296633 
## [222]    train-rmse:0.164309+0.006119    test-rmse:1.129478+0.296801 
## [223]    train-rmse:0.163155+0.006213    test-rmse:1.128846+0.296728 
## [224]    train-rmse:0.162163+0.006194    test-rmse:1.128320+0.297291 
## [225]    train-rmse:0.161143+0.006332    test-rmse:1.128012+0.297579 
## [226]    train-rmse:0.160247+0.006509    test-rmse:1.127888+0.297372 
## [227]    train-rmse:0.159271+0.006513    test-rmse:1.127276+0.297121 
## [228]    train-rmse:0.158310+0.006487    test-rmse:1.126894+0.297099 
## [229]    train-rmse:0.157291+0.006610    test-rmse:1.126507+0.297348 
## [230]    train-rmse:0.155486+0.006923    test-rmse:1.126668+0.297470 
## [231]    train-rmse:0.154235+0.006489    test-rmse:1.126544+0.297592 
## [232]    train-rmse:0.153277+0.006230    test-rmse:1.126338+0.297744 
## [233]    train-rmse:0.152542+0.006131    test-rmse:1.126133+0.297921 
## [234]    train-rmse:0.151388+0.006043    test-rmse:1.126118+0.297800 
## [235]    train-rmse:0.150333+0.006435    test-rmse:1.125554+0.298042 
## [236]    train-rmse:0.149266+0.006347    test-rmse:1.125252+0.297003 
## [237]    train-rmse:0.148043+0.006695    test-rmse:1.125537+0.296715 
## [238]    train-rmse:0.147169+0.006456    test-rmse:1.125381+0.297169 
## [239]    train-rmse:0.146158+0.006737    test-rmse:1.125389+0.297198 
## [240]    train-rmse:0.145471+0.006651    test-rmse:1.125307+0.297398 
## [241]    train-rmse:0.144772+0.006669    test-rmse:1.125112+0.297731 
## [242]    train-rmse:0.143702+0.006788    test-rmse:1.124612+0.297868 
## [243]    train-rmse:0.142360+0.006853    test-rmse:1.124406+0.298015 
## [244]    train-rmse:0.141308+0.006617    test-rmse:1.124246+0.298336 
## [245]    train-rmse:0.140603+0.006499    test-rmse:1.123794+0.298473 
## [246]    train-rmse:0.139604+0.006223    test-rmse:1.123622+0.298748 
## [247]    train-rmse:0.138934+0.006476    test-rmse:1.123598+0.298723 
## [248]    train-rmse:0.137895+0.006401    test-rmse:1.123548+0.298446 
## [249]    train-rmse:0.137048+0.006557    test-rmse:1.123601+0.298449 
## [250]    train-rmse:0.136034+0.007059    test-rmse:1.123513+0.298369 
## [251]    train-rmse:0.135355+0.006978    test-rmse:1.123695+0.298536 
## [252]    train-rmse:0.134356+0.006599    test-rmse:1.123684+0.298509 
## [253]    train-rmse:0.133391+0.006863    test-rmse:1.123688+0.298605 
## [254]    train-rmse:0.132591+0.006893    test-rmse:1.123417+0.298861 
## [255]    train-rmse:0.131524+0.007071    test-rmse:1.123670+0.298719 
## [256]    train-rmse:0.130825+0.007058    test-rmse:1.123335+0.298854 
## [257]    train-rmse:0.130035+0.007144    test-rmse:1.123382+0.299093 
## [258]    train-rmse:0.129322+0.006982    test-rmse:1.123243+0.299075 
## [259]    train-rmse:0.128314+0.006476    test-rmse:1.122734+0.299212 
## [260]    train-rmse:0.127658+0.006594    test-rmse:1.122672+0.299339 
## [261]    train-rmse:0.126581+0.006382    test-rmse:1.122449+0.299222 
## [262]    train-rmse:0.125626+0.006197    test-rmse:1.122136+0.299165 
## [263]    train-rmse:0.124950+0.006160    test-rmse:1.121815+0.298990 
## [264]    train-rmse:0.124087+0.006560    test-rmse:1.121780+0.298963 
## [265]    train-rmse:0.122891+0.006130    test-rmse:1.121925+0.299077 
## [266]    train-rmse:0.122090+0.005997    test-rmse:1.121696+0.299361 
## [267]    train-rmse:0.121232+0.005645    test-rmse:1.121668+0.299434 
## [268]    train-rmse:0.120407+0.006041    test-rmse:1.121793+0.299135 
## [269]    train-rmse:0.119636+0.005990    test-rmse:1.121765+0.299024 
## [270]    train-rmse:0.118833+0.005836    test-rmse:1.121845+0.299006 
## [271]    train-rmse:0.118157+0.005825    test-rmse:1.121675+0.299132 
## [272]    train-rmse:0.117313+0.005807    test-rmse:1.121378+0.299425 
## [273]    train-rmse:0.116261+0.005807    test-rmse:1.121351+0.299456 
## [274]    train-rmse:0.115508+0.005791    test-rmse:1.121079+0.299462 
## [275]    train-rmse:0.114724+0.005833    test-rmse:1.120739+0.299239 
## [276]    train-rmse:0.113926+0.005996    test-rmse:1.120305+0.298994 
## [277]    train-rmse:0.113132+0.006067    test-rmse:1.120351+0.298755 
## [278]    train-rmse:0.112335+0.005951    test-rmse:1.120075+0.298834 
## [279]    train-rmse:0.111764+0.005987    test-rmse:1.120046+0.298797 
## [280]    train-rmse:0.110797+0.006097    test-rmse:1.119738+0.298438 
## [281]    train-rmse:0.110131+0.006344    test-rmse:1.119601+0.298493 
## [282]    train-rmse:0.109311+0.006323    test-rmse:1.119690+0.298555 
## [283]    train-rmse:0.108377+0.006611    test-rmse:1.119558+0.298478 
## [284]    train-rmse:0.107718+0.006438    test-rmse:1.119499+0.298620 
## [285]    train-rmse:0.107079+0.006181    test-rmse:1.119246+0.298711 
## [286]    train-rmse:0.106348+0.006259    test-rmse:1.119354+0.298579 
## [287]    train-rmse:0.105516+0.006324    test-rmse:1.119378+0.298315 
## [288]    train-rmse:0.104858+0.006187    test-rmse:1.119510+0.298219 
## [289]    train-rmse:0.104302+0.006225    test-rmse:1.119383+0.298011 
## [290]    train-rmse:0.103801+0.006265    test-rmse:1.119395+0.298130 
## [291]    train-rmse:0.103274+0.006231    test-rmse:1.119465+0.298176 
## Stopping. Best iteration:
## [285]    train-rmse:0.107079+0.006181    test-rmse:1.119246+0.298711
## 
## 18 
## [1]  train-rmse:92.628808+0.103203   test-rmse:92.629086+0.486485 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.759407+0.092047   test-rmse:79.759461+0.505884 
## [3]  train-rmse:68.693979+0.083013   test-rmse:68.693753+0.524472 
## [4]  train-rmse:59.182194+0.075874   test-rmse:59.181623+0.542625 
## [5]  train-rmse:51.008879+0.070487   test-rmse:51.007880+0.560691 
## [6]  train-rmse:43.989113+0.066746   test-rmse:43.987572+0.578998 
## [7]  train-rmse:37.964006+0.064561   test-rmse:37.961789+0.597840 
## [8]  train-rmse:32.792771+0.062486   test-rmse:32.812824+0.600191 
## [9]  train-rmse:28.347628+0.056546   test-rmse:28.405012+0.603047 
## [10] train-rmse:24.526212+0.047562   test-rmse:24.604208+0.597355 
## [11] train-rmse:21.241343+0.040405   test-rmse:21.326121+0.583443 
## [12] train-rmse:18.415967+0.034544   test-rmse:18.527125+0.579424 
## [13] train-rmse:15.983780+0.031428   test-rmse:16.089447+0.551215 
## [14] train-rmse:13.890473+0.028814   test-rmse:14.001859+0.546493 
## [15] train-rmse:12.089009+0.026069   test-rmse:12.199322+0.540545 
## [16] train-rmse:10.538138+0.023684   test-rmse:10.626556+0.548689 
## [17] train-rmse:9.207120+0.024305    test-rmse:9.300124+0.524380 
## [18] train-rmse:8.059525+0.022565    test-rmse:8.171307+0.545520 
## [19] train-rmse:7.076084+0.019516    test-rmse:7.201030+0.537516 
## [20] train-rmse:6.233626+0.019041    test-rmse:6.360407+0.513603 
## [21] train-rmse:5.507929+0.018915    test-rmse:5.648438+0.508370 
## [22] train-rmse:4.890658+0.019760    test-rmse:5.039857+0.491070 
## [23] train-rmse:4.357581+0.019375    test-rmse:4.520526+0.471837 
## [24] train-rmse:3.901884+0.025466    test-rmse:4.090338+0.469460 
## [25] train-rmse:3.515432+0.028141    test-rmse:3.728666+0.450209 
## [26] train-rmse:3.189928+0.033211    test-rmse:3.440098+0.439700 
## [27] train-rmse:2.904858+0.036309    test-rmse:3.186586+0.403608 
## [28] train-rmse:2.666581+0.042399    test-rmse:2.966299+0.393865 
## [29] train-rmse:2.458811+0.040437    test-rmse:2.792751+0.367025 
## [30] train-rmse:2.282203+0.042723    test-rmse:2.645800+0.350057 
## [31] train-rmse:2.134697+0.045338    test-rmse:2.529281+0.331596 
## [32] train-rmse:2.003862+0.044479    test-rmse:2.430332+0.303059 
## [33] train-rmse:1.890703+0.044821    test-rmse:2.354167+0.297641 
## [34] train-rmse:1.794785+0.045176    test-rmse:2.272879+0.287327 
## [35] train-rmse:1.707384+0.044817    test-rmse:2.208685+0.262659 
## [36] train-rmse:1.630441+0.046289    test-rmse:2.156159+0.243206 
## [37] train-rmse:1.562038+0.047108    test-rmse:2.100206+0.245081 
## [38] train-rmse:1.501329+0.044239    test-rmse:2.052522+0.234414 
## [39] train-rmse:1.447227+0.043572    test-rmse:2.013938+0.225810 
## [40] train-rmse:1.397460+0.041573    test-rmse:1.972698+0.209255 
## [41] train-rmse:1.347161+0.045499    test-rmse:1.946615+0.209568 
## [42] train-rmse:1.300459+0.044677    test-rmse:1.915868+0.200629 
## [43] train-rmse:1.257741+0.043102    test-rmse:1.884895+0.204649 
## [44] train-rmse:1.219892+0.041763    test-rmse:1.849334+0.201563 
## [45] train-rmse:1.184778+0.040873    test-rmse:1.818692+0.202044 
## [46] train-rmse:1.142539+0.043893    test-rmse:1.795602+0.194271 
## [47] train-rmse:1.110219+0.043203    test-rmse:1.769443+0.195867 
## [48] train-rmse:1.079772+0.042282    test-rmse:1.747855+0.195905 
## [49] train-rmse:1.048071+0.045047    test-rmse:1.723307+0.195096 
## [50] train-rmse:1.014865+0.042467    test-rmse:1.703307+0.192071 
## [51] train-rmse:0.980308+0.041566    test-rmse:1.686309+0.196053 
## [52] train-rmse:0.955157+0.040383    test-rmse:1.667789+0.201479 
## [53] train-rmse:0.930742+0.038035    test-rmse:1.646599+0.198185 
## [54] train-rmse:0.907047+0.037440    test-rmse:1.626072+0.196141 
## [55] train-rmse:0.881750+0.037133    test-rmse:1.612698+0.203141 
## [56] train-rmse:0.858792+0.035767    test-rmse:1.593074+0.202943 
## [57] train-rmse:0.835931+0.034872    test-rmse:1.572181+0.210214 
## [58] train-rmse:0.815943+0.033622    test-rmse:1.565838+0.207437 
## [59] train-rmse:0.794679+0.031713    test-rmse:1.551327+0.204282 
## [60] train-rmse:0.774195+0.033001    test-rmse:1.537966+0.203626 
## [61] train-rmse:0.756367+0.031329    test-rmse:1.518938+0.207373 
## [62] train-rmse:0.739558+0.031040    test-rmse:1.505554+0.206576 
## [63] train-rmse:0.722979+0.030801    test-rmse:1.492616+0.209552 
## [64] train-rmse:0.706306+0.030779    test-rmse:1.483812+0.206374 
## [65] train-rmse:0.691290+0.029528    test-rmse:1.474007+0.209145 
## [66] train-rmse:0.673252+0.030606    test-rmse:1.462405+0.213164 
## [67] train-rmse:0.655321+0.030212    test-rmse:1.453033+0.215823 
## [68] train-rmse:0.641961+0.029103    test-rmse:1.444718+0.216271 
## [69] train-rmse:0.628899+0.028193    test-rmse:1.436064+0.218266 
## [70] train-rmse:0.616068+0.027956    test-rmse:1.430055+0.220309 
## [71] train-rmse:0.603594+0.026870    test-rmse:1.421996+0.220909 
## [72] train-rmse:0.592228+0.026660    test-rmse:1.412847+0.221706 
## [73] train-rmse:0.578655+0.023792    test-rmse:1.400295+0.213344 
## [74] train-rmse:0.564817+0.023934    test-rmse:1.393845+0.213099 
## [75] train-rmse:0.553835+0.022996    test-rmse:1.388325+0.210717 
## [76] train-rmse:0.542980+0.021425    test-rmse:1.379603+0.216466 
## [77] train-rmse:0.532767+0.021738    test-rmse:1.371610+0.211132 
## [78] train-rmse:0.523461+0.021428    test-rmse:1.365299+0.210224 
## [79] train-rmse:0.514207+0.021035    test-rmse:1.362637+0.210422 
## [80] train-rmse:0.504366+0.020991    test-rmse:1.357366+0.210041 
## [81] train-rmse:0.495838+0.020515    test-rmse:1.349390+0.211397 
## [82] train-rmse:0.484853+0.018309    test-rmse:1.345246+0.213274 
## [83] train-rmse:0.476123+0.016626    test-rmse:1.340418+0.214050 
## [84] train-rmse:0.467788+0.016652    test-rmse:1.336089+0.214444 
## [85] train-rmse:0.460534+0.016199    test-rmse:1.331874+0.214548 
## [86] train-rmse:0.452076+0.015892    test-rmse:1.329027+0.216906 
## [87] train-rmse:0.442455+0.016433    test-rmse:1.327552+0.219520 
## [88] train-rmse:0.435300+0.016016    test-rmse:1.322627+0.219630 
## [89] train-rmse:0.427422+0.014350    test-rmse:1.319034+0.219796 
## [90] train-rmse:0.420829+0.013940    test-rmse:1.316207+0.219103 
## [91] train-rmse:0.414380+0.013038    test-rmse:1.313995+0.220411 
## [92] train-rmse:0.407319+0.013223    test-rmse:1.312287+0.223108 
## [93] train-rmse:0.401477+0.012777    test-rmse:1.307538+0.224879 
## [94] train-rmse:0.395187+0.012277    test-rmse:1.304306+0.225895 
## [95] train-rmse:0.389344+0.011242    test-rmse:1.300582+0.225759 
## [96] train-rmse:0.382391+0.010420    test-rmse:1.298948+0.227430 
## [97] train-rmse:0.375975+0.010586    test-rmse:1.297582+0.228140 
## [98] train-rmse:0.369653+0.010549    test-rmse:1.294964+0.228307 
## [99] train-rmse:0.364645+0.010199    test-rmse:1.292091+0.228678 
## [100]    train-rmse:0.358792+0.010118    test-rmse:1.289126+0.229076 
## [101]    train-rmse:0.354525+0.010728    test-rmse:1.286305+0.230272 
## [102]    train-rmse:0.348868+0.009866    test-rmse:1.285858+0.228296 
## [103]    train-rmse:0.343477+0.007844    test-rmse:1.284087+0.228215 
## [104]    train-rmse:0.337998+0.007127    test-rmse:1.279914+0.228112 
## [105]    train-rmse:0.333727+0.007256    test-rmse:1.277861+0.229145 
## [106]    train-rmse:0.328150+0.007803    test-rmse:1.275470+0.230719 
## [107]    train-rmse:0.323263+0.007484    test-rmse:1.275066+0.229928 
## [108]    train-rmse:0.319046+0.007318    test-rmse:1.271901+0.231614 
## [109]    train-rmse:0.314526+0.007541    test-rmse:1.272429+0.232987 
## [110]    train-rmse:0.310703+0.007032    test-rmse:1.271295+0.234107 
## [111]    train-rmse:0.307467+0.006830    test-rmse:1.270621+0.233042 
## [112]    train-rmse:0.303021+0.006271    test-rmse:1.268157+0.232792 
## [113]    train-rmse:0.298822+0.006213    test-rmse:1.267963+0.233898 
## [114]    train-rmse:0.295077+0.006745    test-rmse:1.265729+0.234029 
## [115]    train-rmse:0.291712+0.006677    test-rmse:1.264050+0.233860 
## [116]    train-rmse:0.287341+0.006850    test-rmse:1.262432+0.234022 
## [117]    train-rmse:0.283363+0.006261    test-rmse:1.262508+0.234070 
## [118]    train-rmse:0.280054+0.005961    test-rmse:1.261944+0.233716 
## [119]    train-rmse:0.277221+0.005847    test-rmse:1.259624+0.235365 
## [120]    train-rmse:0.274070+0.005872    test-rmse:1.258927+0.234720 
## [121]    train-rmse:0.269453+0.006271    test-rmse:1.257347+0.235863 
## [122]    train-rmse:0.266241+0.006004    test-rmse:1.256688+0.236576 
## [123]    train-rmse:0.263180+0.006199    test-rmse:1.256018+0.236416 
## [124]    train-rmse:0.260029+0.005826    test-rmse:1.254940+0.236486 
## [125]    train-rmse:0.256541+0.005057    test-rmse:1.254626+0.236873 
## [126]    train-rmse:0.252672+0.004392    test-rmse:1.253520+0.236232 
## [127]    train-rmse:0.249118+0.004685    test-rmse:1.252535+0.235810 
## [128]    train-rmse:0.245390+0.004943    test-rmse:1.251923+0.236392 
## [129]    train-rmse:0.242219+0.003996    test-rmse:1.251654+0.236294 
## [130]    train-rmse:0.240166+0.003985    test-rmse:1.250475+0.237224 
## [131]    train-rmse:0.237035+0.003764    test-rmse:1.249436+0.236395 
## [132]    train-rmse:0.234386+0.003649    test-rmse:1.249622+0.234288 
## [133]    train-rmse:0.231407+0.003555    test-rmse:1.248603+0.234572 
## [134]    train-rmse:0.229135+0.003469    test-rmse:1.248213+0.234999 
## [135]    train-rmse:0.226566+0.003276    test-rmse:1.247959+0.235594 
## [136]    train-rmse:0.224266+0.003438    test-rmse:1.247350+0.235320 
## [137]    train-rmse:0.221272+0.003152    test-rmse:1.247440+0.235183 
## [138]    train-rmse:0.218743+0.002689    test-rmse:1.246557+0.235745 
## [139]    train-rmse:0.215136+0.002884    test-rmse:1.245825+0.236066 
## [140]    train-rmse:0.212277+0.002237    test-rmse:1.244412+0.236562 
## [141]    train-rmse:0.210243+0.002177    test-rmse:1.242870+0.238147 
## [142]    train-rmse:0.207654+0.002741    test-rmse:1.242494+0.238070 
## [143]    train-rmse:0.205716+0.002971    test-rmse:1.242286+0.238542 
## [144]    train-rmse:0.203583+0.003177    test-rmse:1.241392+0.239433 
## [145]    train-rmse:0.200894+0.003910    test-rmse:1.240336+0.239863 
## [146]    train-rmse:0.199102+0.003999    test-rmse:1.239676+0.239131 
## [147]    train-rmse:0.195725+0.003293    test-rmse:1.239557+0.238745 
## [148]    train-rmse:0.194142+0.003183    test-rmse:1.239453+0.239129 
## [149]    train-rmse:0.191921+0.003882    test-rmse:1.239418+0.239325 
## [150]    train-rmse:0.189720+0.003915    test-rmse:1.239542+0.239639 
## [151]    train-rmse:0.187519+0.003360    test-rmse:1.239169+0.239712 
## [152]    train-rmse:0.185857+0.003279    test-rmse:1.238792+0.240167 
## [153]    train-rmse:0.183475+0.002795    test-rmse:1.238237+0.239538 
## [154]    train-rmse:0.181507+0.002687    test-rmse:1.238085+0.238984 
## [155]    train-rmse:0.178834+0.002889    test-rmse:1.237744+0.238807 
## [156]    train-rmse:0.176333+0.002790    test-rmse:1.236996+0.238203 
## [157]    train-rmse:0.174609+0.002195    test-rmse:1.236812+0.238045 
## [158]    train-rmse:0.173014+0.002307    test-rmse:1.236413+0.238447 
## [159]    train-rmse:0.171316+0.002356    test-rmse:1.235433+0.239068 
## [160]    train-rmse:0.168982+0.001979    test-rmse:1.236321+0.238508 
## [161]    train-rmse:0.166135+0.002215    test-rmse:1.235735+0.238731 
## [162]    train-rmse:0.164642+0.002149    test-rmse:1.235243+0.238594 
## [163]    train-rmse:0.163089+0.001986    test-rmse:1.235528+0.238611 
## [164]    train-rmse:0.161153+0.001978    test-rmse:1.235055+0.239019 
## [165]    train-rmse:0.159363+0.002295    test-rmse:1.234835+0.239171 
## [166]    train-rmse:0.157852+0.002422    test-rmse:1.234483+0.239284 
## [167]    train-rmse:0.156409+0.002122    test-rmse:1.233077+0.239251 
## [168]    train-rmse:0.154670+0.002257    test-rmse:1.233466+0.239182 
## [169]    train-rmse:0.153290+0.002065    test-rmse:1.233385+0.239618 
## [170]    train-rmse:0.151357+0.002790    test-rmse:1.233111+0.239474 
## [171]    train-rmse:0.150154+0.002718    test-rmse:1.232798+0.239672 
## [172]    train-rmse:0.148816+0.002476    test-rmse:1.232806+0.239599 
## [173]    train-rmse:0.147396+0.002554    test-rmse:1.232611+0.239719 
## [174]    train-rmse:0.146301+0.002525    test-rmse:1.231751+0.240094 
## [175]    train-rmse:0.144943+0.002208    test-rmse:1.230972+0.239414 
## [176]    train-rmse:0.143120+0.002605    test-rmse:1.231018+0.239550 
## [177]    train-rmse:0.141388+0.002610    test-rmse:1.230668+0.239756 
## [178]    train-rmse:0.139882+0.002584    test-rmse:1.230188+0.240148 
## [179]    train-rmse:0.138702+0.002497    test-rmse:1.229980+0.240302 
## [180]    train-rmse:0.137710+0.002443    test-rmse:1.229621+0.240131 
## [181]    train-rmse:0.136522+0.002850    test-rmse:1.229372+0.240111 
## [182]    train-rmse:0.134382+0.002587    test-rmse:1.228965+0.240095 
## [183]    train-rmse:0.132883+0.002548    test-rmse:1.228732+0.240244 
## [184]    train-rmse:0.131656+0.003046    test-rmse:1.228780+0.240318 
## [185]    train-rmse:0.130571+0.002884    test-rmse:1.228669+0.240370 
## [186]    train-rmse:0.129025+0.003253    test-rmse:1.228214+0.240129 
## [187]    train-rmse:0.127844+0.003301    test-rmse:1.228248+0.240070 
## [188]    train-rmse:0.126735+0.003359    test-rmse:1.228356+0.240277 
## [189]    train-rmse:0.125666+0.003342    test-rmse:1.227393+0.240454 
## [190]    train-rmse:0.124824+0.003686    test-rmse:1.227133+0.240331 
## [191]    train-rmse:0.123809+0.003715    test-rmse:1.227173+0.240335 
## [192]    train-rmse:0.122254+0.003575    test-rmse:1.227250+0.240390 
## [193]    train-rmse:0.121098+0.003611    test-rmse:1.227344+0.240437 
## [194]    train-rmse:0.119467+0.003625    test-rmse:1.227562+0.240640 
## [195]    train-rmse:0.117741+0.003677    test-rmse:1.227462+0.240468 
## [196]    train-rmse:0.116371+0.003407    test-rmse:1.227541+0.240288 
## Stopping. Best iteration:
## [190]    train-rmse:0.124824+0.003686    test-rmse:1.227133+0.240331
## 
## 19 
## [1]  train-rmse:92.628808+0.103203   test-rmse:92.629086+0.486485 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.759407+0.092047   test-rmse:79.759461+0.505884 
## [3]  train-rmse:68.693979+0.083013   test-rmse:68.693753+0.524472 
## [4]  train-rmse:59.182194+0.075874   test-rmse:59.181623+0.542625 
## [5]  train-rmse:51.008879+0.070487   test-rmse:51.007880+0.560691 
## [6]  train-rmse:43.989113+0.066746   test-rmse:43.987572+0.578998 
## [7]  train-rmse:37.964006+0.064561   test-rmse:37.961789+0.597840 
## [8]  train-rmse:32.792771+0.062486   test-rmse:32.812824+0.600191 
## [9]  train-rmse:28.347628+0.056546   test-rmse:28.405012+0.603047 
## [10] train-rmse:24.526212+0.047562   test-rmse:24.604208+0.597355 
## [11] train-rmse:21.241343+0.040405   test-rmse:21.326121+0.583443 
## [12] train-rmse:18.414927+0.034101   test-rmse:18.520150+0.581977 
## [13] train-rmse:15.981665+0.030714   test-rmse:16.083777+0.552404 
## [14] train-rmse:13.885936+0.027627   test-rmse:13.991632+0.554503 
## [15] train-rmse:12.080579+0.023917   test-rmse:12.179302+0.547038 
## [16] train-rmse:10.522182+0.020020   test-rmse:10.612564+0.547292 
## [17] train-rmse:9.183809+0.019721    test-rmse:9.275141+0.533896 
## [18] train-rmse:8.027399+0.017655    test-rmse:8.140117+0.538313 
## [19] train-rmse:7.034784+0.016064    test-rmse:7.150319+0.535187 
## [20] train-rmse:6.179970+0.017560    test-rmse:6.303632+0.509384 
## [21] train-rmse:5.441886+0.014972    test-rmse:5.579309+0.519260 
## [22] train-rmse:4.806141+0.012229    test-rmse:4.966443+0.483660 
## [23] train-rmse:4.263855+0.015642    test-rmse:4.439594+0.460046 
## [24] train-rmse:3.794540+0.019936    test-rmse:3.995575+0.447081 
## [25] train-rmse:3.392017+0.025451    test-rmse:3.620750+0.425271 
## [26] train-rmse:3.045492+0.024152    test-rmse:3.322166+0.418018 
## [27] train-rmse:2.749623+0.026686    test-rmse:3.055042+0.415737 
## [28] train-rmse:2.494835+0.031156    test-rmse:2.829554+0.376248 
## [29] train-rmse:2.280093+0.032507    test-rmse:2.632071+0.363469 
## [30] train-rmse:2.094578+0.032295    test-rmse:2.477372+0.347401 
## [31] train-rmse:1.934402+0.037078    test-rmse:2.352338+0.328524 
## [32] train-rmse:1.794763+0.037208    test-rmse:2.243624+0.324666 
## [33] train-rmse:1.671803+0.043997    test-rmse:2.151537+0.303918 
## [34] train-rmse:1.564354+0.047482    test-rmse:2.074438+0.282509 
## [35] train-rmse:1.473247+0.046765    test-rmse:2.005517+0.284614 
## [36] train-rmse:1.393900+0.047181    test-rmse:1.940063+0.278011 
## [37] train-rmse:1.324410+0.047831    test-rmse:1.887873+0.276064 
## [38] train-rmse:1.253463+0.047729    test-rmse:1.855050+0.273390 
## [39] train-rmse:1.194889+0.047663    test-rmse:1.816118+0.266012 
## [40] train-rmse:1.140423+0.045617    test-rmse:1.780549+0.264626 
## [41] train-rmse:1.093394+0.044648    test-rmse:1.745305+0.260010 
## [42] train-rmse:1.047818+0.044893    test-rmse:1.722373+0.261210 
## [43] train-rmse:1.006418+0.043427    test-rmse:1.690966+0.259115 
## [44] train-rmse:0.970213+0.042565    test-rmse:1.666465+0.262518 
## [45] train-rmse:0.935481+0.040894    test-rmse:1.642895+0.268958 
## [46] train-rmse:0.902928+0.040542    test-rmse:1.622011+0.263715 
## [47] train-rmse:0.870148+0.036583    test-rmse:1.597407+0.265530 
## [48] train-rmse:0.837452+0.038504    test-rmse:1.580838+0.272487 
## [49] train-rmse:0.810654+0.037732    test-rmse:1.561056+0.274160 
## [50] train-rmse:0.784257+0.034747    test-rmse:1.539165+0.263360 
## [51] train-rmse:0.756611+0.033818    test-rmse:1.513612+0.264885 
## [52] train-rmse:0.735290+0.033132    test-rmse:1.492252+0.260415 
## [53] train-rmse:0.707012+0.034582    test-rmse:1.474192+0.253949 
## [54] train-rmse:0.685732+0.034170    test-rmse:1.463029+0.258299 
## [55] train-rmse:0.666632+0.033473    test-rmse:1.453149+0.260034 
## [56] train-rmse:0.647383+0.032149    test-rmse:1.437604+0.260364 
## [57] train-rmse:0.629596+0.031768    test-rmse:1.427358+0.259276 
## [58] train-rmse:0.610685+0.030872    test-rmse:1.411980+0.252763 
## [59] train-rmse:0.594765+0.030892    test-rmse:1.395769+0.254300 
## [60] train-rmse:0.577509+0.032127    test-rmse:1.387702+0.255940 
## [61] train-rmse:0.561821+0.033284    test-rmse:1.378830+0.253983 
## [62] train-rmse:0.547392+0.032840    test-rmse:1.371321+0.254294 
## [63] train-rmse:0.532022+0.031058    test-rmse:1.363819+0.253917 
## [64] train-rmse:0.519890+0.030643    test-rmse:1.357392+0.253865 
## [65] train-rmse:0.506890+0.030673    test-rmse:1.346990+0.248414 
## [66] train-rmse:0.494471+0.030678    test-rmse:1.339486+0.249064 
## [67] train-rmse:0.483706+0.030238    test-rmse:1.333717+0.248655 
## [68] train-rmse:0.471601+0.030194    test-rmse:1.322778+0.251787 
## [69] train-rmse:0.460878+0.031018    test-rmse:1.320343+0.251374 
## [70] train-rmse:0.449916+0.030757    test-rmse:1.315858+0.254470 
## [71] train-rmse:0.438590+0.030906    test-rmse:1.310088+0.257377 
## [72] train-rmse:0.428285+0.028802    test-rmse:1.304447+0.258094 
## [73] train-rmse:0.419538+0.028761    test-rmse:1.297357+0.261047 
## [74] train-rmse:0.411161+0.028804    test-rmse:1.293728+0.260898 
## [75] train-rmse:0.402697+0.027840    test-rmse:1.288562+0.262416 
## [76] train-rmse:0.394545+0.027006    test-rmse:1.286629+0.263049 
## [77] train-rmse:0.383708+0.022846    test-rmse:1.282305+0.263153 
## [78] train-rmse:0.376896+0.022456    test-rmse:1.279006+0.263578 
## [79] train-rmse:0.368656+0.021818    test-rmse:1.275591+0.263945 
## [80] train-rmse:0.361759+0.021974    test-rmse:1.272736+0.263907 
## [81] train-rmse:0.354413+0.022177    test-rmse:1.266621+0.267144 
## [82] train-rmse:0.348225+0.020688    test-rmse:1.264798+0.267956 
## [83] train-rmse:0.341662+0.021315    test-rmse:1.263192+0.267680 
## [84] train-rmse:0.335718+0.021187    test-rmse:1.260332+0.267306 
## [85] train-rmse:0.328855+0.020389    test-rmse:1.255881+0.268934 
## [86] train-rmse:0.321112+0.016842    test-rmse:1.253431+0.267250 
## [87] train-rmse:0.315102+0.017524    test-rmse:1.252255+0.265214 
## [88] train-rmse:0.308814+0.017218    test-rmse:1.247829+0.266506 
## [89] train-rmse:0.302050+0.016745    test-rmse:1.244899+0.266053 
## [90] train-rmse:0.297371+0.017267    test-rmse:1.240727+0.267898 
## [91] train-rmse:0.291944+0.017144    test-rmse:1.241168+0.268712 
## [92] train-rmse:0.285490+0.017526    test-rmse:1.239772+0.269455 
## [93] train-rmse:0.280585+0.017175    test-rmse:1.237098+0.270063 
## [94] train-rmse:0.276567+0.016708    test-rmse:1.234456+0.267674 
## [95] train-rmse:0.271446+0.016258    test-rmse:1.231941+0.269009 
## [96] train-rmse:0.267977+0.016295    test-rmse:1.230002+0.268876 
## [97] train-rmse:0.261854+0.017498    test-rmse:1.229032+0.270069 
## [98] train-rmse:0.257182+0.017186    test-rmse:1.227934+0.272055 
## [99] train-rmse:0.251701+0.016751    test-rmse:1.225698+0.271794 
## [100]    train-rmse:0.247148+0.017400    test-rmse:1.225543+0.271375 
## [101]    train-rmse:0.242976+0.017053    test-rmse:1.223510+0.272194 
## [102]    train-rmse:0.238424+0.015805    test-rmse:1.221236+0.272237 
## [103]    train-rmse:0.234411+0.015574    test-rmse:1.220371+0.272403 
## [104]    train-rmse:0.231390+0.015270    test-rmse:1.219855+0.272887 
## [105]    train-rmse:0.226377+0.014482    test-rmse:1.217148+0.273436 
## [106]    train-rmse:0.222267+0.014388    test-rmse:1.217221+0.273664 
## [107]    train-rmse:0.219624+0.014213    test-rmse:1.215625+0.273936 
## [108]    train-rmse:0.215609+0.013619    test-rmse:1.215091+0.272977 
## [109]    train-rmse:0.211767+0.013879    test-rmse:1.215385+0.272981 
## [110]    train-rmse:0.208398+0.012838    test-rmse:1.213772+0.273605 
## [111]    train-rmse:0.205324+0.012629    test-rmse:1.213198+0.274148 
## [112]    train-rmse:0.201943+0.012281    test-rmse:1.212108+0.273472 
## [113]    train-rmse:0.198080+0.012103    test-rmse:1.211352+0.273721 
## [114]    train-rmse:0.194386+0.012860    test-rmse:1.210700+0.274114 
## [115]    train-rmse:0.191115+0.013056    test-rmse:1.209936+0.275040 
## [116]    train-rmse:0.188367+0.012977    test-rmse:1.208875+0.274395 
## [117]    train-rmse:0.184864+0.012899    test-rmse:1.207990+0.273678 
## [118]    train-rmse:0.181809+0.013070    test-rmse:1.207502+0.273617 
## [119]    train-rmse:0.178800+0.013078    test-rmse:1.207087+0.273932 
## [120]    train-rmse:0.176092+0.012950    test-rmse:1.207171+0.273963 
## [121]    train-rmse:0.173507+0.013114    test-rmse:1.205890+0.274151 
## [122]    train-rmse:0.170769+0.013263    test-rmse:1.205571+0.273740 
## [123]    train-rmse:0.167950+0.013448    test-rmse:1.205502+0.273592 
## [124]    train-rmse:0.165430+0.013438    test-rmse:1.204771+0.274053 
## [125]    train-rmse:0.162229+0.013686    test-rmse:1.204835+0.274252 
## [126]    train-rmse:0.159688+0.013302    test-rmse:1.204256+0.274924 
## [127]    train-rmse:0.157059+0.013839    test-rmse:1.203762+0.275171 
## [128]    train-rmse:0.154770+0.013866    test-rmse:1.202718+0.275299 
## [129]    train-rmse:0.152987+0.013535    test-rmse:1.201453+0.276210 
## [130]    train-rmse:0.150621+0.013500    test-rmse:1.201766+0.275989 
## [131]    train-rmse:0.147488+0.012277    test-rmse:1.200648+0.274986 
## [132]    train-rmse:0.145815+0.012410    test-rmse:1.200820+0.275012 
## [133]    train-rmse:0.143360+0.012739    test-rmse:1.201039+0.274544 
## [134]    train-rmse:0.141472+0.012163    test-rmse:1.199706+0.273335 
## [135]    train-rmse:0.139085+0.011414    test-rmse:1.198806+0.274072 
## [136]    train-rmse:0.137350+0.011508    test-rmse:1.198226+0.273941 
## [137]    train-rmse:0.135165+0.011295    test-rmse:1.198116+0.274025 
## [138]    train-rmse:0.132893+0.011035    test-rmse:1.197589+0.274376 
## [139]    train-rmse:0.131383+0.010804    test-rmse:1.197137+0.274621 
## [140]    train-rmse:0.128987+0.010627    test-rmse:1.196303+0.273553 
## [141]    train-rmse:0.127225+0.010021    test-rmse:1.195957+0.273817 
## [142]    train-rmse:0.125590+0.010077    test-rmse:1.196001+0.273304 
## [143]    train-rmse:0.123970+0.009714    test-rmse:1.196079+0.273630 
## [144]    train-rmse:0.121709+0.009540    test-rmse:1.195456+0.274128 
## [145]    train-rmse:0.119289+0.009152    test-rmse:1.194780+0.274500 
## [146]    train-rmse:0.117837+0.008999    test-rmse:1.194534+0.274198 
## [147]    train-rmse:0.116153+0.009148    test-rmse:1.194642+0.274388 
## [148]    train-rmse:0.114355+0.008874    test-rmse:1.194013+0.273601 
## [149]    train-rmse:0.113199+0.009070    test-rmse:1.193641+0.274133 
## [150]    train-rmse:0.111823+0.009034    test-rmse:1.193091+0.274029 
## [151]    train-rmse:0.110338+0.009394    test-rmse:1.192691+0.274040 
## [152]    train-rmse:0.108635+0.009262    test-rmse:1.192803+0.274223 
## [153]    train-rmse:0.107032+0.008782    test-rmse:1.192885+0.274251 
## [154]    train-rmse:0.105105+0.008838    test-rmse:1.192850+0.274491 
## [155]    train-rmse:0.102946+0.008461    test-rmse:1.193017+0.274642 
## [156]    train-rmse:0.101076+0.009056    test-rmse:1.192795+0.274764 
## [157]    train-rmse:0.099447+0.008602    test-rmse:1.192503+0.274746 
## [158]    train-rmse:0.098302+0.008312    test-rmse:1.192336+0.275177 
## [159]    train-rmse:0.096366+0.007856    test-rmse:1.192138+0.275328 
## [160]    train-rmse:0.095418+0.008170    test-rmse:1.192183+0.275387 
## [161]    train-rmse:0.094213+0.007810    test-rmse:1.191912+0.275408 
## [162]    train-rmse:0.092594+0.007448    test-rmse:1.191782+0.275523 
## [163]    train-rmse:0.091201+0.007523    test-rmse:1.191391+0.275740 
## [164]    train-rmse:0.089600+0.007186    test-rmse:1.191168+0.276024 
## [165]    train-rmse:0.088010+0.006795    test-rmse:1.191095+0.276107 
## [166]    train-rmse:0.086599+0.006498    test-rmse:1.191134+0.276169 
## [167]    train-rmse:0.085749+0.006437    test-rmse:1.191334+0.276612 
## [168]    train-rmse:0.084229+0.006369    test-rmse:1.191354+0.276756 
## [169]    train-rmse:0.082891+0.005707    test-rmse:1.190453+0.276087 
## [170]    train-rmse:0.081711+0.005766    test-rmse:1.190004+0.275807 
## [171]    train-rmse:0.080712+0.005772    test-rmse:1.189870+0.275814 
## [172]    train-rmse:0.079598+0.005974    test-rmse:1.189590+0.275699 
## [173]    train-rmse:0.078589+0.005875    test-rmse:1.189739+0.275931 
## [174]    train-rmse:0.077412+0.005802    test-rmse:1.189591+0.275752 
## [175]    train-rmse:0.075973+0.005663    test-rmse:1.189594+0.275916 
## [176]    train-rmse:0.075005+0.005585    test-rmse:1.189088+0.275784 
## [177]    train-rmse:0.073721+0.005379    test-rmse:1.189208+0.275830 
## [178]    train-rmse:0.072778+0.005222    test-rmse:1.189240+0.275696 
## [179]    train-rmse:0.072085+0.005246    test-rmse:1.189102+0.275749 
## [180]    train-rmse:0.070982+0.005568    test-rmse:1.189061+0.275597 
## [181]    train-rmse:0.070230+0.005464    test-rmse:1.189099+0.275640 
## [182]    train-rmse:0.069073+0.005300    test-rmse:1.189205+0.275580 
## [183]    train-rmse:0.068008+0.005355    test-rmse:1.188799+0.275601 
## [184]    train-rmse:0.067007+0.005450    test-rmse:1.188704+0.275775 
## [185]    train-rmse:0.066117+0.005391    test-rmse:1.188565+0.275865 
## [186]    train-rmse:0.064924+0.005335    test-rmse:1.188700+0.275918 
## [187]    train-rmse:0.064232+0.005135    test-rmse:1.188551+0.276002 
## [188]    train-rmse:0.063408+0.005245    test-rmse:1.188633+0.276044 
## [189]    train-rmse:0.062461+0.005062    test-rmse:1.188458+0.276029 
## [190]    train-rmse:0.061612+0.005082    test-rmse:1.188704+0.276457 
## [191]    train-rmse:0.060764+0.005027    test-rmse:1.188593+0.276477 
## [192]    train-rmse:0.059881+0.005095    test-rmse:1.188763+0.276270 
## [193]    train-rmse:0.059026+0.005087    test-rmse:1.188526+0.276455 
## [194]    train-rmse:0.058219+0.004970    test-rmse:1.188416+0.276494 
## [195]    train-rmse:0.057220+0.004891    test-rmse:1.188405+0.276620 
## [196]    train-rmse:0.056557+0.004887    test-rmse:1.188181+0.276431 
## [197]    train-rmse:0.055664+0.004563    test-rmse:1.187723+0.276435 
## [198]    train-rmse:0.054941+0.004716    test-rmse:1.187639+0.276408 
## [199]    train-rmse:0.054016+0.004660    test-rmse:1.187578+0.276342 
## [200]    train-rmse:0.053223+0.004669    test-rmse:1.187516+0.276492 
## [201]    train-rmse:0.052130+0.004445    test-rmse:1.187592+0.276302 
## [202]    train-rmse:0.051239+0.004106    test-rmse:1.187898+0.276751 
## [203]    train-rmse:0.050444+0.004067    test-rmse:1.187893+0.276685 
## [204]    train-rmse:0.049631+0.003950    test-rmse:1.188063+0.276529 
## [205]    train-rmse:0.048821+0.004037    test-rmse:1.188371+0.276308 
## [206]    train-rmse:0.048193+0.004079    test-rmse:1.188259+0.276391 
## Stopping. Best iteration:
## [200]    train-rmse:0.053223+0.004669    test-rmse:1.187516+0.276492
## 
## 20 
## [1]  train-rmse:92.628808+0.103203   test-rmse:92.629086+0.486485 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:79.759407+0.092047   test-rmse:79.759461+0.505884 
## [3]  train-rmse:68.693979+0.083013   test-rmse:68.693753+0.524472 
## [4]  train-rmse:59.182194+0.075874   test-rmse:59.181623+0.542625 
## [5]  train-rmse:51.008879+0.070487   test-rmse:51.007880+0.560691 
## [6]  train-rmse:43.989113+0.066746   test-rmse:43.987572+0.578998 
## [7]  train-rmse:37.964006+0.064561   test-rmse:37.961789+0.597840 
## [8]  train-rmse:32.792771+0.062486   test-rmse:32.812824+0.600191 
## [9]  train-rmse:28.347628+0.056546   test-rmse:28.405012+0.603047 
## [10] train-rmse:24.526212+0.047562   test-rmse:24.604208+0.597355 
## [11] train-rmse:21.241343+0.040405   test-rmse:21.326121+0.583443 
## [12] train-rmse:18.414287+0.033773   test-rmse:18.518230+0.578389 
## [13] train-rmse:15.979761+0.029789   test-rmse:16.078246+0.563472 
## [14] train-rmse:13.882815+0.027581   test-rmse:13.987163+0.543768 
## [15] train-rmse:12.074928+0.023745   test-rmse:12.176397+0.532731 
## [16] train-rmse:10.518055+0.021118   test-rmse:10.621855+0.524769 
## [17] train-rmse:9.171425+0.019747    test-rmse:9.275629+0.527784 
## [18] train-rmse:8.011062+0.019951    test-rmse:8.128771+0.537786 
## [19] train-rmse:7.010764+0.015221    test-rmse:7.129887+0.515363 
## [20] train-rmse:6.148265+0.013362    test-rmse:6.279884+0.499722 
## [21] train-rmse:5.400888+0.009871    test-rmse:5.543436+0.478121 
## [22] train-rmse:4.759112+0.011125    test-rmse:4.912639+0.465521 
## [23] train-rmse:4.202014+0.011775    test-rmse:4.369683+0.465114 
## [24] train-rmse:3.725125+0.009861    test-rmse:3.916361+0.458159 
## [25] train-rmse:3.314427+0.012040    test-rmse:3.534912+0.439006 
## [26] train-rmse:2.959787+0.015255    test-rmse:3.215922+0.411766 
## [27] train-rmse:2.657719+0.019670    test-rmse:2.958186+0.387959 
## [28] train-rmse:2.392989+0.020023    test-rmse:2.736462+0.383405 
## [29] train-rmse:2.166363+0.022185    test-rmse:2.542173+0.377744 
## [30] train-rmse:1.965084+0.023520    test-rmse:2.375177+0.366483 
## [31] train-rmse:1.794347+0.031018    test-rmse:2.248976+0.354729 
## [32] train-rmse:1.645176+0.034919    test-rmse:2.145884+0.339242 
## [33] train-rmse:1.514419+0.035790    test-rmse:2.054989+0.326594 
## [34] train-rmse:1.400753+0.037878    test-rmse:1.969232+0.316107 
## [35] train-rmse:1.300646+0.040879    test-rmse:1.902877+0.311757 
## [36] train-rmse:1.213459+0.040080    test-rmse:1.846693+0.303502 
## [37] train-rmse:1.135276+0.037200    test-rmse:1.791972+0.293508 
## [38] train-rmse:1.067429+0.037880    test-rmse:1.743405+0.291123 
## [39] train-rmse:1.006715+0.037609    test-rmse:1.701767+0.287720 
## [40] train-rmse:0.954485+0.037809    test-rmse:1.666566+0.284140 
## [41] train-rmse:0.903766+0.036001    test-rmse:1.630396+0.278569 
## [42] train-rmse:0.859188+0.036467    test-rmse:1.603991+0.278506 
## [43] train-rmse:0.816912+0.035001    test-rmse:1.584111+0.277567 
## [44] train-rmse:0.779089+0.036418    test-rmse:1.559079+0.278939 
## [45] train-rmse:0.746254+0.035606    test-rmse:1.535959+0.279086 
## [46] train-rmse:0.712989+0.036023    test-rmse:1.517036+0.278879 
## [47] train-rmse:0.685821+0.035614    test-rmse:1.498857+0.280517 
## [48] train-rmse:0.656579+0.030632    test-rmse:1.482794+0.278931 
## [49] train-rmse:0.629080+0.030965    test-rmse:1.465499+0.287585 
## [50] train-rmse:0.605157+0.032968    test-rmse:1.453162+0.287294 
## [51] train-rmse:0.581778+0.030853    test-rmse:1.435254+0.280789 
## [52] train-rmse:0.559398+0.031674    test-rmse:1.422646+0.275605 
## [53] train-rmse:0.539659+0.032297    test-rmse:1.410523+0.274421 
## [54] train-rmse:0.521001+0.033384    test-rmse:1.406704+0.271601 
## [55] train-rmse:0.502590+0.032754    test-rmse:1.396503+0.276299 
## [56] train-rmse:0.487253+0.032398    test-rmse:1.389032+0.274313 
## [57] train-rmse:0.472966+0.032594    test-rmse:1.380783+0.275677 
## [58] train-rmse:0.456110+0.030003    test-rmse:1.367127+0.272252 
## [59] train-rmse:0.441301+0.030768    test-rmse:1.358893+0.269119 
## [60] train-rmse:0.429268+0.030170    test-rmse:1.351423+0.271240 
## [61] train-rmse:0.417836+0.030033    test-rmse:1.345700+0.270074 
## [62] train-rmse:0.403600+0.030083    test-rmse:1.340638+0.270857 
## [63] train-rmse:0.393467+0.030674    test-rmse:1.336619+0.270680 
## [64] train-rmse:0.380555+0.028689    test-rmse:1.328341+0.268275 
## [65] train-rmse:0.370706+0.027776    test-rmse:1.326895+0.271120 
## [66] train-rmse:0.360972+0.026823    test-rmse:1.323261+0.271202 
## [67] train-rmse:0.351977+0.026555    test-rmse:1.320478+0.271939 
## [68] train-rmse:0.343710+0.026147    test-rmse:1.316646+0.272034 
## [69] train-rmse:0.334249+0.024864    test-rmse:1.315070+0.272704 
## [70] train-rmse:0.326016+0.024150    test-rmse:1.309217+0.274817 
## [71] train-rmse:0.318050+0.023972    test-rmse:1.304483+0.274629 
## [72] train-rmse:0.308366+0.023817    test-rmse:1.303186+0.276963 
## [73] train-rmse:0.300585+0.021790    test-rmse:1.298678+0.273564 
## [74] train-rmse:0.293728+0.020600    test-rmse:1.294505+0.270968 
## [75] train-rmse:0.285849+0.019175    test-rmse:1.290344+0.272913 
## [76] train-rmse:0.280004+0.018924    test-rmse:1.286455+0.272651 
## [77] train-rmse:0.273406+0.018395    test-rmse:1.284542+0.273338 
## [78] train-rmse:0.267037+0.016325    test-rmse:1.283796+0.274569 
## [79] train-rmse:0.260734+0.016387    test-rmse:1.283447+0.274029 
## [80] train-rmse:0.254737+0.016875    test-rmse:1.280340+0.275327 
## [81] train-rmse:0.248774+0.016417    test-rmse:1.277649+0.272586 
## [82] train-rmse:0.243998+0.016440    test-rmse:1.275950+0.273335 
## [83] train-rmse:0.238874+0.015631    test-rmse:1.274099+0.273551 
## [84] train-rmse:0.233429+0.016331    test-rmse:1.272724+0.274739 
## [85] train-rmse:0.228045+0.016936    test-rmse:1.269821+0.275915 
## [86] train-rmse:0.222497+0.015979    test-rmse:1.268936+0.276228 
## [87] train-rmse:0.217532+0.015145    test-rmse:1.266897+0.277012 
## [88] train-rmse:0.211986+0.015019    test-rmse:1.266681+0.276831 
## [89] train-rmse:0.206713+0.015431    test-rmse:1.266574+0.275488 
## [90] train-rmse:0.203306+0.015645    test-rmse:1.264488+0.275940 
## [91] train-rmse:0.199127+0.014420    test-rmse:1.263488+0.277232 
## [92] train-rmse:0.194735+0.014148    test-rmse:1.262345+0.278450 
## [93] train-rmse:0.190894+0.013668    test-rmse:1.260852+0.279214 
## [94] train-rmse:0.187178+0.013642    test-rmse:1.260101+0.279784 
## [95] train-rmse:0.182527+0.013973    test-rmse:1.260532+0.279368 
## [96] train-rmse:0.178737+0.013967    test-rmse:1.259497+0.278961 
## [97] train-rmse:0.175504+0.013626    test-rmse:1.258317+0.278804 
## [98] train-rmse:0.171643+0.013076    test-rmse:1.256676+0.277744 
## [99] train-rmse:0.168790+0.013323    test-rmse:1.255847+0.278666 
## [100]    train-rmse:0.164935+0.012905    test-rmse:1.255172+0.278472 
## [101]    train-rmse:0.161612+0.013851    test-rmse:1.255302+0.277319 
## [102]    train-rmse:0.158243+0.013684    test-rmse:1.254423+0.277190 
## [103]    train-rmse:0.153977+0.012911    test-rmse:1.253688+0.276483 
## [104]    train-rmse:0.150662+0.012334    test-rmse:1.252428+0.275424 
## [105]    train-rmse:0.147647+0.011510    test-rmse:1.251838+0.276727 
## [106]    train-rmse:0.143990+0.011003    test-rmse:1.251180+0.276273 
## [107]    train-rmse:0.140791+0.010533    test-rmse:1.250295+0.276419 
## [108]    train-rmse:0.138114+0.010485    test-rmse:1.248855+0.275404 
## [109]    train-rmse:0.135692+0.010971    test-rmse:1.248566+0.275276 
## [110]    train-rmse:0.133578+0.010866    test-rmse:1.247146+0.275264 
## [111]    train-rmse:0.131213+0.011298    test-rmse:1.246786+0.275295 
## [112]    train-rmse:0.128677+0.011162    test-rmse:1.246457+0.275553 
## [113]    train-rmse:0.126002+0.010663    test-rmse:1.245729+0.275745 
## [114]    train-rmse:0.123710+0.010714    test-rmse:1.245316+0.275745 
## [115]    train-rmse:0.121729+0.010674    test-rmse:1.244913+0.276357 
## [116]    train-rmse:0.119043+0.011507    test-rmse:1.244420+0.276052 
## [117]    train-rmse:0.116842+0.011446    test-rmse:1.244507+0.275688 
## [118]    train-rmse:0.114993+0.011170    test-rmse:1.244812+0.276208 
## [119]    train-rmse:0.112714+0.010616    test-rmse:1.244135+0.275695 
## [120]    train-rmse:0.110525+0.009827    test-rmse:1.244183+0.276101 
## [121]    train-rmse:0.107525+0.009164    test-rmse:1.244209+0.275776 
## [122]    train-rmse:0.105335+0.009108    test-rmse:1.243932+0.275501 
## [123]    train-rmse:0.103116+0.009164    test-rmse:1.243976+0.275798 
## [124]    train-rmse:0.100840+0.008883    test-rmse:1.243760+0.275591 
## [125]    train-rmse:0.099461+0.008957    test-rmse:1.243125+0.275517 
## [126]    train-rmse:0.097555+0.008854    test-rmse:1.242881+0.275510 
## [127]    train-rmse:0.095443+0.008472    test-rmse:1.242645+0.275411 
## [128]    train-rmse:0.093446+0.008596    test-rmse:1.243136+0.275172 
## [129]    train-rmse:0.091608+0.008324    test-rmse:1.242517+0.274978 
## [130]    train-rmse:0.089808+0.008327    test-rmse:1.242282+0.274988 
## [131]    train-rmse:0.088257+0.008405    test-rmse:1.242519+0.274832 
## [132]    train-rmse:0.086590+0.008107    test-rmse:1.241929+0.274957 
## [133]    train-rmse:0.084513+0.007703    test-rmse:1.241958+0.273873 
## [134]    train-rmse:0.082686+0.007061    test-rmse:1.241853+0.274033 
## [135]    train-rmse:0.081251+0.007037    test-rmse:1.241537+0.274197 
## [136]    train-rmse:0.080277+0.007028    test-rmse:1.241456+0.274286 
## [137]    train-rmse:0.077906+0.006655    test-rmse:1.241675+0.273708 
## [138]    train-rmse:0.076527+0.006635    test-rmse:1.241359+0.273600 
## [139]    train-rmse:0.075346+0.006587    test-rmse:1.241532+0.273968 
## [140]    train-rmse:0.074104+0.006564    test-rmse:1.241191+0.273883 
## [141]    train-rmse:0.072515+0.006267    test-rmse:1.241033+0.273831 
## [142]    train-rmse:0.071261+0.005948    test-rmse:1.240880+0.273750 
## [143]    train-rmse:0.069974+0.006023    test-rmse:1.241244+0.274039 
## [144]    train-rmse:0.068447+0.005623    test-rmse:1.240800+0.273015 
## [145]    train-rmse:0.067657+0.005352    test-rmse:1.240696+0.273057 
## [146]    train-rmse:0.066482+0.005209    test-rmse:1.240579+0.272895 
## [147]    train-rmse:0.065352+0.005587    test-rmse:1.240356+0.273047 
## [148]    train-rmse:0.064086+0.005489    test-rmse:1.240477+0.273322 
## [149]    train-rmse:0.063185+0.005455    test-rmse:1.240705+0.273127 
## [150]    train-rmse:0.062008+0.005477    test-rmse:1.240920+0.272813 
## [151]    train-rmse:0.060788+0.005568    test-rmse:1.240832+0.272781 
## [152]    train-rmse:0.059798+0.005779    test-rmse:1.240836+0.272705 
## [153]    train-rmse:0.058765+0.005625    test-rmse:1.240925+0.272459 
## Stopping. Best iteration:
## [147]    train-rmse:0.065352+0.005587    test-rmse:1.240356+0.273047
## 
## 21 
## [1]  train-rmse:90.491687+0.101310   test-rmse:90.491937+0.489579 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.126776+0.089017   test-rmse:76.126749+0.511751 
## [3]  train-rmse:64.064040+0.079442   test-rmse:64.063658+0.532994 
## [4]  train-rmse:53.938632+0.072315   test-rmse:53.937809+0.553884 
## [5]  train-rmse:45.444263+0.067432   test-rmse:45.442850+0.574932 
## [6]  train-rmse:38.323881+0.064650   test-rmse:38.321713+0.596609 
## [7]  train-rmse:32.357844+0.062426   test-rmse:32.370365+0.599210 
## [8]  train-rmse:27.358503+0.058615   test-rmse:27.407408+0.614574 
## [9]  train-rmse:23.170743+0.052849   test-rmse:23.224391+0.637225 
## [10] train-rmse:19.667929+0.048399   test-rmse:19.740667+0.625096 
## [11] train-rmse:16.742503+0.043954   test-rmse:16.803793+0.620919 
## [12] train-rmse:14.306709+0.042070   test-rmse:14.377406+0.619376 
## [13] train-rmse:12.283999+0.040262   test-rmse:12.387327+0.648636 
## [14] train-rmse:10.611733+0.039515   test-rmse:10.719909+0.646098 
## [15] train-rmse:9.235868+0.041705    test-rmse:9.346363+0.623531 
## [16] train-rmse:8.111741+0.043445    test-rmse:8.204748+0.608984 
## [17] train-rmse:7.199134+0.046579    test-rmse:7.294492+0.601291 
## [18] train-rmse:6.462629+0.050242    test-rmse:6.598453+0.624610 
## [19] train-rmse:5.872721+0.051575    test-rmse:6.002141+0.624286 
## [20] train-rmse:5.405293+0.054498    test-rmse:5.538347+0.600798 
## [21] train-rmse:5.033612+0.057069    test-rmse:5.168147+0.561662 
## [22] train-rmse:4.740029+0.059030    test-rmse:4.891245+0.574892 
## [23] train-rmse:4.509976+0.061202    test-rmse:4.635351+0.534952 
## [24] train-rmse:4.327411+0.063208    test-rmse:4.459016+0.485014 
## [25] train-rmse:4.179536+0.063854    test-rmse:4.341757+0.502667 
## [26] train-rmse:4.058545+0.062590    test-rmse:4.217640+0.490722 
## [27] train-rmse:3.960876+0.061372    test-rmse:4.111519+0.463799 
## [28] train-rmse:3.879916+0.061372    test-rmse:4.018334+0.421420 
## [29] train-rmse:3.809761+0.061427    test-rmse:3.973112+0.412768 
## [30] train-rmse:3.747749+0.061541    test-rmse:3.923940+0.400307 
## [31] train-rmse:3.691604+0.060279    test-rmse:3.885414+0.409655 
## [32] train-rmse:3.641962+0.059215    test-rmse:3.831084+0.412890 
## [33] train-rmse:3.596686+0.057934    test-rmse:3.794003+0.401146 
## [34] train-rmse:3.554404+0.057106    test-rmse:3.749558+0.398107 
## [35] train-rmse:3.514885+0.056873    test-rmse:3.708760+0.372835 
## [36] train-rmse:3.477558+0.056297    test-rmse:3.665133+0.353343 
## [37] train-rmse:3.441546+0.055316    test-rmse:3.635282+0.350642 
## [38] train-rmse:3.406470+0.054404    test-rmse:3.604503+0.355388 
## [39] train-rmse:3.373364+0.053650    test-rmse:3.569442+0.354315 
## [40] train-rmse:3.341013+0.052711    test-rmse:3.554999+0.345100 
## [41] train-rmse:3.308954+0.051941    test-rmse:3.532639+0.348564 
## [42] train-rmse:3.278226+0.050777    test-rmse:3.510768+0.350749 
## [43] train-rmse:3.247957+0.049892    test-rmse:3.479737+0.353929 
## [44] train-rmse:3.218564+0.049667    test-rmse:3.446994+0.362562 
## [45] train-rmse:3.189886+0.049271    test-rmse:3.410206+0.348295 
## [46] train-rmse:3.161797+0.048589    test-rmse:3.378059+0.338935 
## [47] train-rmse:3.134295+0.048027    test-rmse:3.344989+0.319797 
## [48] train-rmse:3.107594+0.047107    test-rmse:3.324157+0.316392 
## [49] train-rmse:3.081909+0.046546    test-rmse:3.312504+0.305530 
## [50] train-rmse:3.056283+0.045644    test-rmse:3.284962+0.314984 
## [51] train-rmse:3.031076+0.044819    test-rmse:3.258885+0.320411 
## [52] train-rmse:3.006901+0.044031    test-rmse:3.246054+0.326569 
## [53] train-rmse:2.983017+0.043254    test-rmse:3.223984+0.326907 
## [54] train-rmse:2.959649+0.042934    test-rmse:3.200305+0.331821 
## [55] train-rmse:2.936290+0.042438    test-rmse:3.179442+0.337457 
## [56] train-rmse:2.913165+0.042207    test-rmse:3.152873+0.341150 
## [57] train-rmse:2.890428+0.042016    test-rmse:3.126373+0.324213 
## [58] train-rmse:2.868086+0.041972    test-rmse:3.104460+0.331539 
## [59] train-rmse:2.845725+0.041722    test-rmse:3.092960+0.332985 
## [60] train-rmse:2.823853+0.041709    test-rmse:3.067622+0.323395 
## [61] train-rmse:2.802682+0.041935    test-rmse:3.047628+0.301163 
## [62] train-rmse:2.781847+0.041784    test-rmse:3.027281+0.298471 
## [63] train-rmse:2.761243+0.041839    test-rmse:3.010870+0.305895 
## [64] train-rmse:2.740967+0.041360    test-rmse:2.989614+0.305876 
## [65] train-rmse:2.720963+0.040889    test-rmse:2.970443+0.308017 
## [66] train-rmse:2.701600+0.040459    test-rmse:2.956668+0.307800 
## [67] train-rmse:2.682395+0.040089    test-rmse:2.931545+0.313120 
## [68] train-rmse:2.663745+0.040105    test-rmse:2.915645+0.316904 
## [69] train-rmse:2.645068+0.039952    test-rmse:2.895035+0.326200 
## [70] train-rmse:2.626554+0.039807    test-rmse:2.884789+0.321686 
## [71] train-rmse:2.608080+0.039730    test-rmse:2.859263+0.309785 
## [72] train-rmse:2.589723+0.039677    test-rmse:2.832817+0.304272 
## [73] train-rmse:2.571909+0.039451    test-rmse:2.814269+0.312480 
## [74] train-rmse:2.554201+0.039276    test-rmse:2.793250+0.292762 
## [75] train-rmse:2.536653+0.039119    test-rmse:2.779994+0.297790 
## [76] train-rmse:2.519423+0.038888    test-rmse:2.767445+0.301979 
## [77] train-rmse:2.502365+0.038647    test-rmse:2.753523+0.310582 
## [78] train-rmse:2.485731+0.038295    test-rmse:2.732561+0.308361 
## [79] train-rmse:2.469307+0.037696    test-rmse:2.724065+0.305890 
## [80] train-rmse:2.453169+0.037263    test-rmse:2.706810+0.309102 
## [81] train-rmse:2.437138+0.036919    test-rmse:2.700820+0.304160 
## [82] train-rmse:2.421257+0.036410    test-rmse:2.693543+0.304759 
## [83] train-rmse:2.405453+0.036081    test-rmse:2.678342+0.307899 
## [84] train-rmse:2.389744+0.035632    test-rmse:2.652496+0.317798 
## [85] train-rmse:2.374333+0.035400    test-rmse:2.643735+0.320143 
## [86] train-rmse:2.358955+0.035261    test-rmse:2.629127+0.321136 
## [87] train-rmse:2.343958+0.035092    test-rmse:2.606375+0.319166 
## [88] train-rmse:2.329076+0.034955    test-rmse:2.585518+0.311582 
## [89] train-rmse:2.314337+0.034770    test-rmse:2.573013+0.298971 
## [90] train-rmse:2.299768+0.034550    test-rmse:2.553195+0.290187 
## [91] train-rmse:2.285417+0.034093    test-rmse:2.534181+0.274445 
## [92] train-rmse:2.271204+0.033565    test-rmse:2.527269+0.281779 
## [93] train-rmse:2.257108+0.033107    test-rmse:2.512535+0.286058 
## [94] train-rmse:2.243157+0.032840    test-rmse:2.502441+0.280698 
## [95] train-rmse:2.229248+0.032432    test-rmse:2.489407+0.285454 
## [96] train-rmse:2.215480+0.032072    test-rmse:2.476863+0.282218 
## [97] train-rmse:2.202090+0.031943    test-rmse:2.460758+0.282624 
## [98] train-rmse:2.188820+0.031774    test-rmse:2.448302+0.289855 
## [99] train-rmse:2.175584+0.031579    test-rmse:2.437542+0.289797 
## [100]    train-rmse:2.162226+0.031361    test-rmse:2.428798+0.291739 
## [101]    train-rmse:2.149028+0.031163    test-rmse:2.416360+0.286655 
## [102]    train-rmse:2.136053+0.030830    test-rmse:2.404649+0.291440 
## [103]    train-rmse:2.123280+0.030462    test-rmse:2.383873+0.289755 
## [104]    train-rmse:2.110775+0.030115    test-rmse:2.370163+0.279724 
## [105]    train-rmse:2.098465+0.029858    test-rmse:2.359545+0.283193 
## [106]    train-rmse:2.086273+0.029614    test-rmse:2.352037+0.284660 
## [107]    train-rmse:2.073977+0.029321    test-rmse:2.338790+0.290199 
## [108]    train-rmse:2.061892+0.028988    test-rmse:2.327364+0.287788 
## [109]    train-rmse:2.049968+0.028633    test-rmse:2.318353+0.285764 
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## [529]    train-rmse:0.746366+0.014749    test-rmse:1.059315+0.149440 
## [530]    train-rmse:0.745978+0.014743    test-rmse:1.059443+0.149133 
## [531]    train-rmse:0.745601+0.014735    test-rmse:1.058752+0.149260 
## [532]    train-rmse:0.745225+0.014734    test-rmse:1.058567+0.149313 
## [533]    train-rmse:0.744854+0.014729    test-rmse:1.057517+0.148848 
## [534]    train-rmse:0.744485+0.014725    test-rmse:1.057077+0.148983 
## [535]    train-rmse:0.744110+0.014723    test-rmse:1.057389+0.148711 
## [536]    train-rmse:0.743744+0.014717    test-rmse:1.056652+0.149131 
## [537]    train-rmse:0.743380+0.014710    test-rmse:1.055868+0.148839 
## [538]    train-rmse:0.743018+0.014704    test-rmse:1.055764+0.148886 
## [539]    train-rmse:0.742642+0.014701    test-rmse:1.055599+0.148790 
## [540]    train-rmse:0.742277+0.014697    test-rmse:1.056218+0.148154 
## [541]    train-rmse:0.741917+0.014686    test-rmse:1.055691+0.147484 
## [542]    train-rmse:0.741552+0.014666    test-rmse:1.056337+0.147527 
## [543]    train-rmse:0.741190+0.014662    test-rmse:1.056267+0.147434 
## [544]    train-rmse:0.740841+0.014659    test-rmse:1.055860+0.147581 
## [545]    train-rmse:0.740487+0.014649    test-rmse:1.054491+0.147167 
## [546]    train-rmse:0.740130+0.014633    test-rmse:1.054744+0.147298 
## [547]    train-rmse:0.739776+0.014618    test-rmse:1.054235+0.146892 
## [548]    train-rmse:0.739430+0.014606    test-rmse:1.054707+0.147042 
## [549]    train-rmse:0.739070+0.014581    test-rmse:1.053797+0.147715 
## [550]    train-rmse:0.738726+0.014571    test-rmse:1.053216+0.148088 
## [551]    train-rmse:0.738378+0.014556    test-rmse:1.053074+0.148537 
## [552]    train-rmse:0.738042+0.014549    test-rmse:1.052697+0.148655 
## [553]    train-rmse:0.737708+0.014542    test-rmse:1.052859+0.148342 
## [554]    train-rmse:0.737372+0.014534    test-rmse:1.052849+0.148399 
## [555]    train-rmse:0.737030+0.014522    test-rmse:1.052172+0.148810 
## [556]    train-rmse:0.736696+0.014515    test-rmse:1.052599+0.148970 
## [557]    train-rmse:0.736363+0.014507    test-rmse:1.052847+0.148273 
## [558]    train-rmse:0.736024+0.014496    test-rmse:1.052304+0.149158 
## [559]    train-rmse:0.735702+0.014491    test-rmse:1.051821+0.149431 
## [560]    train-rmse:0.735376+0.014483    test-rmse:1.051991+0.149228 
## [561]    train-rmse:0.735042+0.014469    test-rmse:1.051149+0.148744 
## [562]    train-rmse:0.734713+0.014455    test-rmse:1.050553+0.147939 
## [563]    train-rmse:0.734391+0.014445    test-rmse:1.050932+0.148045 
## [564]    train-rmse:0.734065+0.014426    test-rmse:1.050224+0.148104 
## [565]    train-rmse:0.733748+0.014416    test-rmse:1.050279+0.148470 
## [566]    train-rmse:0.733421+0.014404    test-rmse:1.050001+0.148840 
## [567]    train-rmse:0.733095+0.014398    test-rmse:1.050222+0.148565 
## [568]    train-rmse:0.732771+0.014381    test-rmse:1.049975+0.148704 
## [569]    train-rmse:0.732457+0.014366    test-rmse:1.049951+0.148767 
## [570]    train-rmse:0.732142+0.014359    test-rmse:1.049551+0.149239 
## [571]    train-rmse:0.731832+0.014347    test-rmse:1.048534+0.147265 
## [572]    train-rmse:0.731519+0.014331    test-rmse:1.048020+0.147446 
## [573]    train-rmse:0.731203+0.014315    test-rmse:1.047801+0.147900 
## [574]    train-rmse:0.730899+0.014300    test-rmse:1.047250+0.148053 
## [575]    train-rmse:0.730588+0.014283    test-rmse:1.047207+0.147909 
## [576]    train-rmse:0.730284+0.014272    test-rmse:1.047022+0.147849 
## [577]    train-rmse:0.729990+0.014257    test-rmse:1.046094+0.147924 
## [578]    train-rmse:0.729695+0.014245    test-rmse:1.046505+0.148081 
## [579]    train-rmse:0.729395+0.014233    test-rmse:1.046326+0.147257 
## [580]    train-rmse:0.729095+0.014214    test-rmse:1.046683+0.147018 
## [581]    train-rmse:0.728793+0.014196    test-rmse:1.046554+0.146373 
## [582]    train-rmse:0.728497+0.014175    test-rmse:1.045430+0.146673 
## [583]    train-rmse:0.728204+0.014156    test-rmse:1.044928+0.146774 
## [584]    train-rmse:0.727915+0.014140    test-rmse:1.044574+0.146929 
## [585]    train-rmse:0.727629+0.014125    test-rmse:1.043912+0.147108 
## [586]    train-rmse:0.727345+0.014110    test-rmse:1.043940+0.147045 
## [587]    train-rmse:0.727055+0.014086    test-rmse:1.043363+0.147402 
## [588]    train-rmse:0.726765+0.014069    test-rmse:1.043390+0.147460 
## [589]    train-rmse:0.726469+0.014048    test-rmse:1.043099+0.147756 
## [590]    train-rmse:0.726184+0.014033    test-rmse:1.042845+0.147975 
## [591]    train-rmse:0.725890+0.014011    test-rmse:1.043200+0.147559 
## [592]    train-rmse:0.725604+0.013992    test-rmse:1.043766+0.147297 
## [593]    train-rmse:0.725324+0.013976    test-rmse:1.043625+0.147241 
## [594]    train-rmse:0.725044+0.013959    test-rmse:1.043712+0.147275 
## [595]    train-rmse:0.724762+0.013943    test-rmse:1.043745+0.147444 
## [596]    train-rmse:0.724487+0.013925    test-rmse:1.044016+0.146746 
## Stopping. Best iteration:
## [590]    train-rmse:0.726184+0.014033    test-rmse:1.042845+0.147975
## 
## 22 
## [1]  train-rmse:90.491687+0.101310   test-rmse:90.491937+0.489579 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.126776+0.089017   test-rmse:76.126749+0.511751 
## [3]  train-rmse:64.064040+0.079442   test-rmse:64.063658+0.532994 
## [4]  train-rmse:53.938632+0.072315   test-rmse:53.937809+0.553884 
## [5]  train-rmse:45.444263+0.067432   test-rmse:45.442850+0.574932 
## [6]  train-rmse:38.323881+0.064650   test-rmse:38.321713+0.596609 
## [7]  train-rmse:32.357049+0.062292   test-rmse:32.380286+0.599805 
## [8]  train-rmse:27.349028+0.055196   test-rmse:27.413797+0.615298 
## [9]  train-rmse:23.147529+0.046101   test-rmse:23.226967+0.598006 
## [10] train-rmse:19.623934+0.038763   test-rmse:19.711613+0.564451 
## [11] train-rmse:16.674452+0.033225   test-rmse:16.782382+0.573776 
## [12] train-rmse:14.208644+0.032145   test-rmse:14.317282+0.580789 
## [13] train-rmse:12.149940+0.034348   test-rmse:12.251966+0.547746 
## [14] train-rmse:10.436074+0.036068   test-rmse:10.552537+0.560775 
## [15] train-rmse:9.018139+0.038012    test-rmse:9.155961+0.589833 
## [16] train-rmse:7.847373+0.041476    test-rmse:7.961882+0.548147 
## [17] train-rmse:6.884117+0.043745    test-rmse:7.027852+0.558354 
## [18] train-rmse:6.100016+0.046284    test-rmse:6.249827+0.543626 
## [19] train-rmse:5.461676+0.049190    test-rmse:5.621882+0.532285 
## [20] train-rmse:4.945171+0.051543    test-rmse:5.109951+0.503055 
## [21] train-rmse:4.531039+0.051524    test-rmse:4.728603+0.489762 
## [22] train-rmse:4.190742+0.047651    test-rmse:4.425835+0.499028 
## [23] train-rmse:3.921890+0.047731    test-rmse:4.129078+0.440438 
## [24] train-rmse:3.705702+0.047054    test-rmse:3.927053+0.437467 
## [25] train-rmse:3.530664+0.047114    test-rmse:3.772518+0.433361 
## [26] train-rmse:3.366821+0.038626    test-rmse:3.636475+0.435691 
## [27] train-rmse:3.245789+0.038681    test-rmse:3.506495+0.379611 
## [28] train-rmse:3.146206+0.038254    test-rmse:3.413056+0.371867 
## [29] train-rmse:3.060468+0.037558    test-rmse:3.331509+0.359848 
## [30] train-rmse:2.983176+0.037077    test-rmse:3.282703+0.367776 
## [31] train-rmse:2.916563+0.037083    test-rmse:3.190757+0.326154 
## [32] train-rmse:2.854361+0.035998    test-rmse:3.138829+0.335776 
## [33] train-rmse:2.798994+0.034674    test-rmse:3.091708+0.331384 
## [34] train-rmse:2.745161+0.034132    test-rmse:3.063597+0.313991 
## [35] train-rmse:2.698107+0.034119    test-rmse:3.026549+0.315817 
## [36] train-rmse:2.652673+0.033570    test-rmse:2.986135+0.318285 
## [37] train-rmse:2.609624+0.031913    test-rmse:2.946655+0.330503 
## [38] train-rmse:2.568761+0.031454    test-rmse:2.896550+0.290021 
## [39] train-rmse:2.528192+0.031224    test-rmse:2.871639+0.294539 
## [40] train-rmse:2.489654+0.030631    test-rmse:2.850185+0.293170 
## [41] train-rmse:2.452128+0.030418    test-rmse:2.804601+0.287896 
## [42] train-rmse:2.416684+0.030170    test-rmse:2.770143+0.295215 
## [43] train-rmse:2.381557+0.028094    test-rmse:2.755613+0.296611 
## [44] train-rmse:2.345848+0.027293    test-rmse:2.720604+0.281854 
## [45] train-rmse:2.312782+0.026394    test-rmse:2.691562+0.290720 
## [46] train-rmse:2.280385+0.025215    test-rmse:2.656658+0.284181 
## [47] train-rmse:2.249087+0.024685    test-rmse:2.629030+0.286986 
## [48] train-rmse:2.217004+0.023649    test-rmse:2.608859+0.284595 
## [49] train-rmse:2.186020+0.022976    test-rmse:2.576362+0.270042 
## [50] train-rmse:2.156163+0.022593    test-rmse:2.553350+0.266320 
## [51] train-rmse:2.127536+0.021821    test-rmse:2.529997+0.262376 
## [52] train-rmse:2.098348+0.022860    test-rmse:2.511495+0.270710 
## [53] train-rmse:2.071036+0.022635    test-rmse:2.485520+0.274132 
## [54] train-rmse:2.044524+0.022027    test-rmse:2.466961+0.266137 
## [55] train-rmse:2.018373+0.021750    test-rmse:2.441084+0.263081 
## [56] train-rmse:1.992172+0.021094    test-rmse:2.416462+0.254274 
## [57] train-rmse:1.966410+0.020613    test-rmse:2.382053+0.251877 
## [58] train-rmse:1.941208+0.021042    test-rmse:2.365548+0.250166 
## [59] train-rmse:1.916944+0.020540    test-rmse:2.336865+0.254745 
## [60] train-rmse:1.893086+0.020268    test-rmse:2.311304+0.250755 
## [61] train-rmse:1.870091+0.019812    test-rmse:2.299047+0.250823 
## [62] train-rmse:1.847209+0.019511    test-rmse:2.279076+0.240341 
## [63] train-rmse:1.823188+0.019030    test-rmse:2.254221+0.237320 
## [64] train-rmse:1.800516+0.018470    test-rmse:2.237898+0.239067 
## [65] train-rmse:1.777229+0.018090    test-rmse:2.216892+0.237358 
## [66] train-rmse:1.755539+0.017455    test-rmse:2.201178+0.243042 
## [67] train-rmse:1.733742+0.017496    test-rmse:2.186801+0.245008 
## [68] train-rmse:1.712988+0.017152    test-rmse:2.174515+0.245994 
## [69] train-rmse:1.692628+0.017067    test-rmse:2.156325+0.256774 
## [70] train-rmse:1.672919+0.016660    test-rmse:2.137013+0.258319 
## [71] train-rmse:1.653914+0.016334    test-rmse:2.117702+0.256823 
## [72] train-rmse:1.634480+0.016105    test-rmse:2.106852+0.255795 
## [73] train-rmse:1.615390+0.015602    test-rmse:2.091371+0.254940 
## [74] train-rmse:1.595791+0.014204    test-rmse:2.076327+0.252693 
## [75] train-rmse:1.577648+0.014209    test-rmse:2.062652+0.253600 
## [76] train-rmse:1.559295+0.014786    test-rmse:2.038278+0.247149 
## [77] train-rmse:1.541874+0.014673    test-rmse:2.012678+0.233838 
## [78] train-rmse:1.524605+0.014269    test-rmse:2.002513+0.236702 
## [79] train-rmse:1.507158+0.013956    test-rmse:1.990640+0.239903 
## [80] train-rmse:1.490240+0.013572    test-rmse:1.978906+0.237575 
## [81] train-rmse:1.473897+0.013392    test-rmse:1.961161+0.236238 
## [82] train-rmse:1.458043+0.013489    test-rmse:1.950181+0.237465 
## [83] train-rmse:1.442592+0.013569    test-rmse:1.938483+0.234360 
## [84] train-rmse:1.427294+0.013308    test-rmse:1.924628+0.231687 
## [85] train-rmse:1.411099+0.013492    test-rmse:1.915609+0.234485 
## [86] train-rmse:1.395963+0.013269    test-rmse:1.902893+0.235441 
## [87] train-rmse:1.380893+0.013122    test-rmse:1.892611+0.231996 
## [88] train-rmse:1.366193+0.012809    test-rmse:1.877033+0.232695 
## [89] train-rmse:1.352148+0.012810    test-rmse:1.863051+0.229992 
## [90] train-rmse:1.337752+0.013476    test-rmse:1.852606+0.232079 
## [91] train-rmse:1.323988+0.013451    test-rmse:1.846747+0.233225 
## [92] train-rmse:1.309803+0.013514    test-rmse:1.835211+0.235058 
## [93] train-rmse:1.296577+0.013751    test-rmse:1.819771+0.236777 
## [94] train-rmse:1.282601+0.014240    test-rmse:1.809028+0.235359 
## [95] train-rmse:1.269219+0.014715    test-rmse:1.796415+0.232288 
## [96] train-rmse:1.256222+0.014588    test-rmse:1.786627+0.238247 
## [97] train-rmse:1.243503+0.014985    test-rmse:1.776545+0.235872 
## [98] train-rmse:1.231028+0.014894    test-rmse:1.771388+0.233717 
## [99] train-rmse:1.218727+0.015149    test-rmse:1.759366+0.235627 
## [100]    train-rmse:1.206469+0.014935    test-rmse:1.750498+0.238072 
## [101]    train-rmse:1.194814+0.014689    test-rmse:1.736089+0.235652 
## [102]    train-rmse:1.183365+0.014845    test-rmse:1.721164+0.237297 
## [103]    train-rmse:1.170609+0.013934    test-rmse:1.712575+0.235138 
## [104]    train-rmse:1.158723+0.013389    test-rmse:1.706151+0.237250 
## [105]    train-rmse:1.147080+0.013003    test-rmse:1.695769+0.239811 
## [106]    train-rmse:1.136335+0.012922    test-rmse:1.674916+0.234643 
## [107]    train-rmse:1.125471+0.013206    test-rmse:1.665307+0.237121 
## [108]    train-rmse:1.114824+0.013072    test-rmse:1.659196+0.237296 
## [109]    train-rmse:1.104505+0.013147    test-rmse:1.651820+0.235282 
## [110]    train-rmse:1.094200+0.013614    test-rmse:1.645336+0.237078 
## [111]    train-rmse:1.083843+0.013583    test-rmse:1.636055+0.240756 
## [112]    train-rmse:1.073873+0.013731    test-rmse:1.629218+0.238991 
## [113]    train-rmse:1.064146+0.013421    test-rmse:1.623039+0.237435 
## [114]    train-rmse:1.054909+0.013498    test-rmse:1.614348+0.238438 
## [115]    train-rmse:1.044788+0.012837    test-rmse:1.602917+0.240048 
## [116]    train-rmse:1.035136+0.012949    test-rmse:1.597565+0.244204 
## [117]    train-rmse:1.025533+0.013419    test-rmse:1.587982+0.240056 
## [118]    train-rmse:1.016350+0.013620    test-rmse:1.582294+0.240699 
## [119]    train-rmse:1.007091+0.014229    test-rmse:1.575546+0.237898 
## [120]    train-rmse:0.998489+0.014219    test-rmse:1.569714+0.237701 
## [121]    train-rmse:0.989706+0.014313    test-rmse:1.562559+0.238419 
## [122]    train-rmse:0.980982+0.014477    test-rmse:1.556062+0.239411 
## [123]    train-rmse:0.972534+0.014484    test-rmse:1.550440+0.240427 
## [124]    train-rmse:0.964632+0.014569    test-rmse:1.541534+0.243883 
## [125]    train-rmse:0.956977+0.014708    test-rmse:1.537334+0.244520 
## [126]    train-rmse:0.949339+0.015068    test-rmse:1.532571+0.241739 
## [127]    train-rmse:0.940711+0.015582    test-rmse:1.525242+0.240113 
## [128]    train-rmse:0.932533+0.016335    test-rmse:1.520492+0.241244 
## [129]    train-rmse:0.925142+0.016275    test-rmse:1.513201+0.243769 
## [130]    train-rmse:0.916717+0.017308    test-rmse:1.505411+0.240801 
## [131]    train-rmse:0.909814+0.017413    test-rmse:1.501193+0.239973 
## [132]    train-rmse:0.902622+0.017289    test-rmse:1.496197+0.238919 
## [133]    train-rmse:0.895771+0.017412    test-rmse:1.488712+0.238680 
## [134]    train-rmse:0.888968+0.017346    test-rmse:1.484817+0.238723 
## [135]    train-rmse:0.882166+0.017623    test-rmse:1.479253+0.241466 
## [136]    train-rmse:0.875431+0.017670    test-rmse:1.473610+0.243508 
## [137]    train-rmse:0.868731+0.017487    test-rmse:1.470695+0.245149 
## [138]    train-rmse:0.861961+0.017711    test-rmse:1.467662+0.247996 
## [139]    train-rmse:0.855697+0.017859    test-rmse:1.462422+0.244424 
## [140]    train-rmse:0.849380+0.017651    test-rmse:1.457663+0.245115 
## [141]    train-rmse:0.842441+0.017822    test-rmse:1.452026+0.244141 
## [142]    train-rmse:0.836216+0.018176    test-rmse:1.446916+0.245452 
## [143]    train-rmse:0.830361+0.018215    test-rmse:1.442063+0.243725 
## [144]    train-rmse:0.824336+0.018023    test-rmse:1.437949+0.245185 
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## [353]    train-rmse:0.358416+0.013337    test-rmse:1.134783+0.280164 
## [354]    train-rmse:0.357575+0.013213    test-rmse:1.134642+0.280190 
## [355]    train-rmse:0.356722+0.013320    test-rmse:1.134733+0.280453 
## [356]    train-rmse:0.355588+0.013063    test-rmse:1.134922+0.279981 
## [357]    train-rmse:0.354790+0.012878    test-rmse:1.134455+0.279941 
## [358]    train-rmse:0.353935+0.012801    test-rmse:1.134687+0.280158 
## [359]    train-rmse:0.353151+0.012663    test-rmse:1.134172+0.280305 
## [360]    train-rmse:0.352127+0.013004    test-rmse:1.134025+0.280408 
## [361]    train-rmse:0.351587+0.013008    test-rmse:1.133841+0.280541 
## [362]    train-rmse:0.350850+0.013252    test-rmse:1.133472+0.280283 
## [363]    train-rmse:0.350352+0.013216    test-rmse:1.132862+0.280471 
## [364]    train-rmse:0.349121+0.012744    test-rmse:1.131726+0.280656 
## [365]    train-rmse:0.348533+0.012833    test-rmse:1.131821+0.281103 
## [366]    train-rmse:0.347891+0.012801    test-rmse:1.131674+0.281987 
## [367]    train-rmse:0.347148+0.012603    test-rmse:1.132495+0.281565 
## [368]    train-rmse:0.346633+0.012555    test-rmse:1.132914+0.281609 
## [369]    train-rmse:0.345084+0.012804    test-rmse:1.131994+0.281078 
## [370]    train-rmse:0.344512+0.012668    test-rmse:1.132085+0.280834 
## [371]    train-rmse:0.343797+0.012678    test-rmse:1.131370+0.280870 
## [372]    train-rmse:0.342829+0.012713    test-rmse:1.130354+0.280416 
## [373]    train-rmse:0.341848+0.012480    test-rmse:1.130225+0.279819 
## [374]    train-rmse:0.340687+0.012718    test-rmse:1.129623+0.279674 
## [375]    train-rmse:0.339953+0.012654    test-rmse:1.129853+0.280176 
## [376]    train-rmse:0.339084+0.012919    test-rmse:1.129598+0.280362 
## [377]    train-rmse:0.338181+0.012940    test-rmse:1.128965+0.280066 
## [378]    train-rmse:0.337296+0.013323    test-rmse:1.128512+0.280692 
## [379]    train-rmse:0.336795+0.013313    test-rmse:1.127949+0.280543 
## [380]    train-rmse:0.336277+0.013238    test-rmse:1.127391+0.280640 
## [381]    train-rmse:0.335747+0.013188    test-rmse:1.127510+0.280574 
## [382]    train-rmse:0.335228+0.013152    test-rmse:1.127403+0.280583 
## [383]    train-rmse:0.334113+0.013203    test-rmse:1.125519+0.281603 
## [384]    train-rmse:0.333384+0.013323    test-rmse:1.126329+0.281906 
## [385]    train-rmse:0.332646+0.013710    test-rmse:1.126248+0.281554 
## [386]    train-rmse:0.332028+0.013833    test-rmse:1.125907+0.281711 
## [387]    train-rmse:0.331364+0.013798    test-rmse:1.126425+0.281985 
## [388]    train-rmse:0.330276+0.014190    test-rmse:1.125890+0.281495 
## [389]    train-rmse:0.329677+0.014083    test-rmse:1.125686+0.281651 
## Stopping. Best iteration:
## [383]    train-rmse:0.334113+0.013203    test-rmse:1.125519+0.281603
## 
## 23 
## [1]  train-rmse:90.491687+0.101310   test-rmse:90.491937+0.489579 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.126776+0.089017   test-rmse:76.126749+0.511751 
## [3]  train-rmse:64.064040+0.079442   test-rmse:64.063658+0.532994 
## [4]  train-rmse:53.938632+0.072315   test-rmse:53.937809+0.553884 
## [5]  train-rmse:45.444263+0.067432   test-rmse:45.442850+0.574932 
## [6]  train-rmse:38.323881+0.064650   test-rmse:38.321713+0.596609 
## [7]  train-rmse:32.357049+0.062292   test-rmse:32.380286+0.599805 
## [8]  train-rmse:27.347881+0.054482   test-rmse:27.420350+0.612889 
## [9]  train-rmse:23.142614+0.043845   test-rmse:23.227096+0.593161 
## [10] train-rmse:19.611997+0.037776   test-rmse:19.706151+0.580679 
## [11] train-rmse:16.651396+0.030630   test-rmse:16.767246+0.591673 
## [12] train-rmse:14.167588+0.030039   test-rmse:14.270114+0.572736 
## [13] train-rmse:12.089057+0.030663   test-rmse:12.179702+0.567446 
## [14] train-rmse:10.353299+0.030622   test-rmse:10.432156+0.533161 
## [15] train-rmse:8.907558+0.030730    test-rmse:9.004630+0.509209 
## [16] train-rmse:7.701161+0.032188    test-rmse:7.821867+0.524842 
## [17] train-rmse:6.703909+0.035334    test-rmse:6.830093+0.494633 
## [18] train-rmse:5.881015+0.038111    test-rmse:6.038751+0.503424 
## [19] train-rmse:5.195771+0.033648    test-rmse:5.400219+0.517045 
## [20] train-rmse:4.638617+0.035798    test-rmse:4.839467+0.496035 
## [21] train-rmse:4.188560+0.036161    test-rmse:4.376245+0.462619 
## [22] train-rmse:3.819552+0.036793    test-rmse:4.042189+0.461901 
## [23] train-rmse:3.521521+0.038637    test-rmse:3.755845+0.433760 
## [24] train-rmse:3.277710+0.038342    test-rmse:3.543826+0.426294 
## [25] train-rmse:3.075690+0.036744    test-rmse:3.360617+0.418248 
## [26] train-rmse:2.908592+0.038050    test-rmse:3.206487+0.390463 
## [27] train-rmse:2.772706+0.036619    test-rmse:3.098846+0.377681 
## [28] train-rmse:2.653330+0.032184    test-rmse:2.998277+0.357334 
## [29] train-rmse:2.556178+0.032559    test-rmse:2.898095+0.346243 
## [30] train-rmse:2.468814+0.034103    test-rmse:2.827607+0.333614 
## [31] train-rmse:2.388296+0.031391    test-rmse:2.773312+0.316409 
## [32] train-rmse:2.319519+0.029312    test-rmse:2.727025+0.318330 
## [33] train-rmse:2.258380+0.029014    test-rmse:2.677041+0.320729 
## [34] train-rmse:2.202527+0.027795    test-rmse:2.626609+0.308875 
## [35] train-rmse:2.149187+0.028457    test-rmse:2.582302+0.308667 
## [36] train-rmse:2.099757+0.029865    test-rmse:2.528266+0.296102 
## [37] train-rmse:2.051885+0.028917    test-rmse:2.488359+0.300452 
## [38] train-rmse:2.007129+0.026379    test-rmse:2.459535+0.292517 
## [39] train-rmse:1.963686+0.026033    test-rmse:2.421023+0.279966 
## [40] train-rmse:1.923075+0.024958    test-rmse:2.390459+0.278678 
## [41] train-rmse:1.884113+0.024798    test-rmse:2.354354+0.281032 
## [42] train-rmse:1.844200+0.027480    test-rmse:2.313241+0.293863 
## [43] train-rmse:1.805113+0.025525    test-rmse:2.295412+0.289056 
## [44] train-rmse:1.768535+0.022358    test-rmse:2.267334+0.280770 
## [45] train-rmse:1.731184+0.016102    test-rmse:2.237600+0.276495 
## [46] train-rmse:1.698893+0.015917    test-rmse:2.204862+0.274134 
## [47] train-rmse:1.666808+0.016772    test-rmse:2.167818+0.278385 
## [48] train-rmse:1.635210+0.016218    test-rmse:2.135258+0.275417 
## [49] train-rmse:1.602724+0.014945    test-rmse:2.123468+0.276002 
## [50] train-rmse:1.572864+0.013783    test-rmse:2.099934+0.273013 
## [51] train-rmse:1.541996+0.017965    test-rmse:2.077904+0.271803 
## [52] train-rmse:1.514303+0.017925    test-rmse:2.044962+0.266649 
## [53] train-rmse:1.487196+0.017197    test-rmse:2.026108+0.274262 
## [54] train-rmse:1.460451+0.017552    test-rmse:2.004882+0.273477 
## [55] train-rmse:1.434970+0.016875    test-rmse:1.983723+0.277711 
## [56] train-rmse:1.407565+0.018829    test-rmse:1.960044+0.269998 
## [57] train-rmse:1.383120+0.017327    test-rmse:1.938318+0.272809 
## [58] train-rmse:1.359607+0.016932    test-rmse:1.915643+0.268924 
## [59] train-rmse:1.336597+0.016701    test-rmse:1.900095+0.269774 
## [60] train-rmse:1.312930+0.016587    test-rmse:1.886782+0.266562 
## [61] train-rmse:1.290999+0.015998    test-rmse:1.864494+0.271941 
## [62] train-rmse:1.268540+0.013696    test-rmse:1.839264+0.273589 
## [63] train-rmse:1.246824+0.014374    test-rmse:1.825169+0.271813 
## [64] train-rmse:1.225127+0.016212    test-rmse:1.811246+0.270851 
## [65] train-rmse:1.205670+0.016360    test-rmse:1.793584+0.266798 
## [66] train-rmse:1.186120+0.015815    test-rmse:1.775227+0.269888 
## [67] train-rmse:1.167350+0.015335    test-rmse:1.758692+0.269707 
## [68] train-rmse:1.148202+0.015884    test-rmse:1.747024+0.271103 
## [69] train-rmse:1.128973+0.014660    test-rmse:1.725006+0.264522 
## [70] train-rmse:1.111376+0.014147    test-rmse:1.711865+0.264594 
## [71] train-rmse:1.094548+0.013933    test-rmse:1.699217+0.268610 
## [72] train-rmse:1.076954+0.013304    test-rmse:1.686362+0.272469 
## [73] train-rmse:1.059026+0.010730    test-rmse:1.672575+0.268978 
## [74] train-rmse:1.041352+0.012704    test-rmse:1.659071+0.269885 
## [75] train-rmse:1.026224+0.012631    test-rmse:1.646367+0.274682 
## [76] train-rmse:1.010248+0.012746    test-rmse:1.633550+0.274047 
## [77] train-rmse:0.994408+0.014762    test-rmse:1.622897+0.272112 
## [78] train-rmse:0.979677+0.014980    test-rmse:1.610654+0.277823 
## [79] train-rmse:0.964258+0.013819    test-rmse:1.604203+0.279123 
## [80] train-rmse:0.949938+0.013723    test-rmse:1.588260+0.279027 
## [81] train-rmse:0.936057+0.013282    test-rmse:1.578893+0.279477 
## [82] train-rmse:0.921151+0.012542    test-rmse:1.572337+0.278386 
## [83] train-rmse:0.908552+0.012123    test-rmse:1.563287+0.281624 
## [84] train-rmse:0.894941+0.011199    test-rmse:1.552766+0.281924 
## [85] train-rmse:0.881620+0.012891    test-rmse:1.545873+0.281508 
## [86] train-rmse:0.869982+0.013130    test-rmse:1.537571+0.282409 
## [87] train-rmse:0.858121+0.012929    test-rmse:1.521997+0.272608 
## [88] train-rmse:0.846772+0.013558    test-rmse:1.507965+0.271336 
## [89] train-rmse:0.835264+0.012799    test-rmse:1.504839+0.272544 
## [90] train-rmse:0.824147+0.012408    test-rmse:1.497095+0.271390 
## [91] train-rmse:0.814143+0.012430    test-rmse:1.491010+0.271988 
## [92] train-rmse:0.802554+0.012289    test-rmse:1.480748+0.273028 
## [93] train-rmse:0.792263+0.012496    test-rmse:1.468547+0.271557 
## [94] train-rmse:0.781581+0.012683    test-rmse:1.463981+0.275816 
## [95] train-rmse:0.771024+0.013900    test-rmse:1.457859+0.278185 
## [96] train-rmse:0.761076+0.012967    test-rmse:1.452995+0.281839 
## [97] train-rmse:0.751050+0.012845    test-rmse:1.447148+0.280297 
## [98] train-rmse:0.740736+0.013030    test-rmse:1.442254+0.278706 
## [99] train-rmse:0.730740+0.014077    test-rmse:1.427296+0.268003 
## [100]    train-rmse:0.721634+0.014777    test-rmse:1.421526+0.267448 
## [101]    train-rmse:0.711879+0.017076    test-rmse:1.416579+0.269507 
## [102]    train-rmse:0.701540+0.016070    test-rmse:1.415001+0.271976 
## [103]    train-rmse:0.692925+0.015689    test-rmse:1.411555+0.272113 
## [104]    train-rmse:0.684198+0.015595    test-rmse:1.404627+0.273800 
## [105]    train-rmse:0.676060+0.014262    test-rmse:1.392956+0.264673 
## [106]    train-rmse:0.667767+0.014931    test-rmse:1.388199+0.265401 
## [107]    train-rmse:0.659738+0.015057    test-rmse:1.385413+0.265789 
## [108]    train-rmse:0.652518+0.015420    test-rmse:1.381146+0.268105 
## [109]    train-rmse:0.645476+0.015441    test-rmse:1.376334+0.270224 
## [110]    train-rmse:0.638566+0.015691    test-rmse:1.369038+0.272177 
## [111]    train-rmse:0.631826+0.015770    test-rmse:1.365288+0.271545 
## [112]    train-rmse:0.625045+0.014776    test-rmse:1.361725+0.273747 
## [113]    train-rmse:0.618417+0.014904    test-rmse:1.354388+0.268837 
## [114]    train-rmse:0.611836+0.015091    test-rmse:1.348523+0.270758 
## [115]    train-rmse:0.605779+0.014904    test-rmse:1.345390+0.271070 
## [116]    train-rmse:0.598230+0.014925    test-rmse:1.341055+0.272576 
## [117]    train-rmse:0.591608+0.014520    test-rmse:1.339144+0.273374 
## [118]    train-rmse:0.584911+0.015399    test-rmse:1.335637+0.270849 
## [119]    train-rmse:0.577788+0.016888    test-rmse:1.327910+0.270882 
## [120]    train-rmse:0.572176+0.016084    test-rmse:1.323992+0.271111 
## [121]    train-rmse:0.566615+0.015960    test-rmse:1.320931+0.271651 
## [122]    train-rmse:0.560007+0.016821    test-rmse:1.318169+0.271677 
## [123]    train-rmse:0.554499+0.016473    test-rmse:1.312023+0.272259 
## [124]    train-rmse:0.548671+0.015518    test-rmse:1.308795+0.271213 
## [125]    train-rmse:0.543780+0.015592    test-rmse:1.306374+0.273083 
## [126]    train-rmse:0.538789+0.015428    test-rmse:1.303946+0.274587 
## [127]    train-rmse:0.533110+0.016012    test-rmse:1.303694+0.275998 
## [128]    train-rmse:0.528364+0.015720    test-rmse:1.296826+0.279332 
## [129]    train-rmse:0.522963+0.015116    test-rmse:1.295569+0.280704 
## [130]    train-rmse:0.517460+0.015699    test-rmse:1.288189+0.273367 
## [131]    train-rmse:0.512780+0.015588    test-rmse:1.284770+0.274362 
## [132]    train-rmse:0.508243+0.015885    test-rmse:1.283082+0.276035 
## [133]    train-rmse:0.502600+0.016272    test-rmse:1.278774+0.277126 
## [134]    train-rmse:0.498144+0.015580    test-rmse:1.278260+0.276506 
## [135]    train-rmse:0.493480+0.016095    test-rmse:1.271515+0.270598 
## [136]    train-rmse:0.488990+0.016945    test-rmse:1.267084+0.271716 
## [137]    train-rmse:0.484701+0.016791    test-rmse:1.263642+0.272809 
## [138]    train-rmse:0.480148+0.016258    test-rmse:1.261332+0.272888 
## [139]    train-rmse:0.475673+0.016398    test-rmse:1.259071+0.273642 
## [140]    train-rmse:0.471758+0.016987    test-rmse:1.255148+0.274316 
## [141]    train-rmse:0.466784+0.016254    test-rmse:1.253343+0.275677 
## [142]    train-rmse:0.462757+0.016698    test-rmse:1.251620+0.273914 
## [143]    train-rmse:0.458600+0.016522    test-rmse:1.250023+0.275088 
## [144]    train-rmse:0.454309+0.016223    test-rmse:1.248941+0.275452 
## [145]    train-rmse:0.450454+0.016173    test-rmse:1.242799+0.269999 
## [146]    train-rmse:0.446745+0.016432    test-rmse:1.241181+0.271446 
## [147]    train-rmse:0.443447+0.016598    test-rmse:1.240587+0.271921 
## [148]    train-rmse:0.439714+0.016916    test-rmse:1.239803+0.272454 
## [149]    train-rmse:0.436181+0.016746    test-rmse:1.238171+0.272697 
## [150]    train-rmse:0.432408+0.016897    test-rmse:1.237135+0.271897 
## [151]    train-rmse:0.427647+0.015414    test-rmse:1.236062+0.272341 
## [152]    train-rmse:0.424445+0.015504    test-rmse:1.234654+0.271908 
## [153]    train-rmse:0.420736+0.015131    test-rmse:1.232947+0.272598 
## [154]    train-rmse:0.417757+0.015302    test-rmse:1.231598+0.272454 
## [155]    train-rmse:0.414849+0.015570    test-rmse:1.229324+0.274129 
## [156]    train-rmse:0.411800+0.015704    test-rmse:1.229386+0.273477 
## [157]    train-rmse:0.408649+0.015896    test-rmse:1.227817+0.274783 
## [158]    train-rmse:0.405814+0.015841    test-rmse:1.226308+0.274425 
## [159]    train-rmse:0.402979+0.015541    test-rmse:1.223589+0.274684 
## [160]    train-rmse:0.400062+0.015858    test-rmse:1.223172+0.274600 
## [161]    train-rmse:0.396484+0.015044    test-rmse:1.222207+0.275227 
## [162]    train-rmse:0.393820+0.015191    test-rmse:1.222793+0.275787 
## [163]    train-rmse:0.390897+0.014582    test-rmse:1.222059+0.276315 
## [164]    train-rmse:0.388204+0.014620    test-rmse:1.217957+0.278203 
## [165]    train-rmse:0.385662+0.014432    test-rmse:1.216522+0.277891 
## [166]    train-rmse:0.382702+0.015028    test-rmse:1.215132+0.279045 
## [167]    train-rmse:0.378802+0.015390    test-rmse:1.214288+0.278937 
## [168]    train-rmse:0.376623+0.015349    test-rmse:1.213897+0.279523 
## [169]    train-rmse:0.373909+0.015461    test-rmse:1.209766+0.278443 
## [170]    train-rmse:0.371252+0.015512    test-rmse:1.209150+0.279833 
## [171]    train-rmse:0.368387+0.015831    test-rmse:1.208191+0.280262 
## [172]    train-rmse:0.366184+0.015985    test-rmse:1.207643+0.281121 
## [173]    train-rmse:0.363447+0.016458    test-rmse:1.205298+0.281781 
## [174]    train-rmse:0.361216+0.015806    test-rmse:1.202869+0.281992 
## [175]    train-rmse:0.358818+0.015321    test-rmse:1.202128+0.282830 
## [176]    train-rmse:0.356441+0.015115    test-rmse:1.201757+0.283292 
## [177]    train-rmse:0.354498+0.015083    test-rmse:1.201275+0.284222 
## [178]    train-rmse:0.352574+0.015180    test-rmse:1.201132+0.284771 
## [179]    train-rmse:0.349537+0.014985    test-rmse:1.199589+0.284633 
## [180]    train-rmse:0.347091+0.015064    test-rmse:1.199796+0.284852 
## [181]    train-rmse:0.344518+0.015642    test-rmse:1.199385+0.285344 
## [182]    train-rmse:0.342239+0.015705    test-rmse:1.196917+0.286912 
## [183]    train-rmse:0.340003+0.015713    test-rmse:1.196728+0.287140 
## [184]    train-rmse:0.338142+0.015527    test-rmse:1.193808+0.287099 
## [185]    train-rmse:0.335662+0.015294    test-rmse:1.193111+0.286995 
## [186]    train-rmse:0.333244+0.015742    test-rmse:1.191259+0.285829 
## [187]    train-rmse:0.331272+0.016001    test-rmse:1.189893+0.287081 
## [188]    train-rmse:0.329487+0.015781    test-rmse:1.189219+0.287270 
## [189]    train-rmse:0.328038+0.015571    test-rmse:1.188363+0.287384 
## [190]    train-rmse:0.326441+0.015768    test-rmse:1.185741+0.287107 
## [191]    train-rmse:0.324854+0.015711    test-rmse:1.185263+0.287997 
## [192]    train-rmse:0.322865+0.015875    test-rmse:1.183292+0.288864 
## [193]    train-rmse:0.320942+0.016008    test-rmse:1.183168+0.288899 
## [194]    train-rmse:0.319009+0.016373    test-rmse:1.182657+0.289039 
## [195]    train-rmse:0.316372+0.015959    test-rmse:1.182483+0.289202 
## [196]    train-rmse:0.314683+0.015676    test-rmse:1.181827+0.289146 
## [197]    train-rmse:0.312367+0.015722    test-rmse:1.180619+0.288617 
## [198]    train-rmse:0.310918+0.015703    test-rmse:1.179971+0.288623 
## [199]    train-rmse:0.308429+0.014657    test-rmse:1.179714+0.287106 
## [200]    train-rmse:0.306791+0.014502    test-rmse:1.178971+0.287160 
## [201]    train-rmse:0.305161+0.014846    test-rmse:1.178796+0.288059 
## [202]    train-rmse:0.303437+0.014295    test-rmse:1.178208+0.288234 
## [203]    train-rmse:0.302040+0.014226    test-rmse:1.177390+0.288685 
## [204]    train-rmse:0.300417+0.014124    test-rmse:1.176415+0.288326 
## [205]    train-rmse:0.298243+0.013893    test-rmse:1.175612+0.287423 
## [206]    train-rmse:0.296630+0.013465    test-rmse:1.173645+0.287827 
## [207]    train-rmse:0.295173+0.013529    test-rmse:1.172630+0.287729 
## [208]    train-rmse:0.293635+0.013057    test-rmse:1.172127+0.288290 
## [209]    train-rmse:0.292085+0.013372    test-rmse:1.171789+0.289670 
## [210]    train-rmse:0.290482+0.013322    test-rmse:1.171107+0.289480 
## [211]    train-rmse:0.288635+0.013278    test-rmse:1.170837+0.289878 
## [212]    train-rmse:0.286893+0.013539    test-rmse:1.169781+0.291284 
## [213]    train-rmse:0.285469+0.013599    test-rmse:1.170338+0.291177 
## [214]    train-rmse:0.283758+0.013937    test-rmse:1.169435+0.291009 
## [215]    train-rmse:0.281982+0.014550    test-rmse:1.168744+0.291326 
## [216]    train-rmse:0.280566+0.014612    test-rmse:1.166896+0.291875 
## [217]    train-rmse:0.279157+0.014608    test-rmse:1.165928+0.290966 
## [218]    train-rmse:0.277948+0.014338    test-rmse:1.165830+0.291113 
## [219]    train-rmse:0.276536+0.013988    test-rmse:1.164845+0.291855 
## [220]    train-rmse:0.275023+0.014244    test-rmse:1.164947+0.291809 
## [221]    train-rmse:0.273140+0.014135    test-rmse:1.164228+0.292413 
## [222]    train-rmse:0.271366+0.014098    test-rmse:1.163285+0.293113 
## [223]    train-rmse:0.269857+0.013924    test-rmse:1.162560+0.293077 
## [224]    train-rmse:0.268123+0.014453    test-rmse:1.162065+0.293507 
## [225]    train-rmse:0.266379+0.013581    test-rmse:1.162328+0.293592 
## [226]    train-rmse:0.265204+0.013507    test-rmse:1.162146+0.293670 
## [227]    train-rmse:0.263618+0.012981    test-rmse:1.161120+0.293702 
## [228]    train-rmse:0.262290+0.013117    test-rmse:1.160586+0.293700 
## [229]    train-rmse:0.261357+0.013131    test-rmse:1.159531+0.293056 
## [230]    train-rmse:0.259434+0.013202    test-rmse:1.159009+0.293199 
## [231]    train-rmse:0.258518+0.013194    test-rmse:1.157451+0.293372 
## [232]    train-rmse:0.257364+0.012992    test-rmse:1.157179+0.293300 
## [233]    train-rmse:0.256555+0.013006    test-rmse:1.156746+0.292889 
## [234]    train-rmse:0.255007+0.013251    test-rmse:1.156023+0.293205 
## [235]    train-rmse:0.253098+0.012896    test-rmse:1.154439+0.293038 
## [236]    train-rmse:0.251640+0.013027    test-rmse:1.155189+0.293678 
## [237]    train-rmse:0.250420+0.013296    test-rmse:1.154699+0.293414 
## [238]    train-rmse:0.248638+0.012449    test-rmse:1.154584+0.293822 
## [239]    train-rmse:0.247459+0.012829    test-rmse:1.154652+0.294263 
## [240]    train-rmse:0.246215+0.013083    test-rmse:1.154313+0.294445 
## [241]    train-rmse:0.244603+0.012936    test-rmse:1.154176+0.294574 
## [242]    train-rmse:0.243700+0.012952    test-rmse:1.153613+0.294387 
## [243]    train-rmse:0.242471+0.012993    test-rmse:1.153449+0.294657 
## [244]    train-rmse:0.241601+0.012934    test-rmse:1.152955+0.294436 
## [245]    train-rmse:0.240723+0.013026    test-rmse:1.151506+0.294580 
## [246]    train-rmse:0.239113+0.012999    test-rmse:1.151078+0.294709 
## [247]    train-rmse:0.237968+0.013258    test-rmse:1.151155+0.294771 
## [248]    train-rmse:0.236850+0.012782    test-rmse:1.151108+0.294885 
## [249]    train-rmse:0.235767+0.012447    test-rmse:1.151378+0.294972 
## [250]    train-rmse:0.234661+0.012472    test-rmse:1.150661+0.294760 
## [251]    train-rmse:0.233229+0.012498    test-rmse:1.149901+0.294885 
## [252]    train-rmse:0.232049+0.012520    test-rmse:1.149802+0.295142 
## [253]    train-rmse:0.231235+0.012353    test-rmse:1.149570+0.295396 
## [254]    train-rmse:0.229979+0.012273    test-rmse:1.149647+0.295194 
## [255]    train-rmse:0.228993+0.012380    test-rmse:1.149907+0.295692 
## [256]    train-rmse:0.227903+0.012382    test-rmse:1.149572+0.295849 
## [257]    train-rmse:0.226732+0.012160    test-rmse:1.149220+0.296030 
## [258]    train-rmse:0.225702+0.012132    test-rmse:1.149525+0.296108 
## [259]    train-rmse:0.224454+0.012089    test-rmse:1.149598+0.296704 
## [260]    train-rmse:0.223480+0.012343    test-rmse:1.149797+0.296626 
## [261]    train-rmse:0.222395+0.012447    test-rmse:1.149410+0.296724 
## [262]    train-rmse:0.221282+0.012537    test-rmse:1.149621+0.296435 
## [263]    train-rmse:0.220206+0.012499    test-rmse:1.149172+0.295405 
## [264]    train-rmse:0.219378+0.012666    test-rmse:1.148038+0.295704 
## [265]    train-rmse:0.218407+0.012760    test-rmse:1.148133+0.296052 
## [266]    train-rmse:0.217465+0.012970    test-rmse:1.147877+0.295965 
## [267]    train-rmse:0.216519+0.013068    test-rmse:1.147970+0.295609 
## [268]    train-rmse:0.215641+0.013072    test-rmse:1.148191+0.295453 
## [269]    train-rmse:0.214789+0.013057    test-rmse:1.147711+0.295944 
## [270]    train-rmse:0.213854+0.013097    test-rmse:1.147253+0.296280 
## [271]    train-rmse:0.212897+0.013296    test-rmse:1.144962+0.293996 
## [272]    train-rmse:0.211640+0.012786    test-rmse:1.144587+0.293986 
## [273]    train-rmse:0.210617+0.012578    test-rmse:1.145575+0.293409 
## [274]    train-rmse:0.209369+0.012677    test-rmse:1.145929+0.293613 
## [275]    train-rmse:0.208374+0.012608    test-rmse:1.146023+0.294308 
## [276]    train-rmse:0.207338+0.012519    test-rmse:1.145867+0.293926 
## [277]    train-rmse:0.205948+0.012500    test-rmse:1.145512+0.294153 
## [278]    train-rmse:0.204643+0.013124    test-rmse:1.145506+0.294385 
## Stopping. Best iteration:
## [272]    train-rmse:0.211640+0.012786    test-rmse:1.144587+0.293986
## 
## 24 
## [1]  train-rmse:90.491687+0.101310   test-rmse:90.491937+0.489579 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.126776+0.089017   test-rmse:76.126749+0.511751 
## [3]  train-rmse:64.064040+0.079442   test-rmse:64.063658+0.532994 
## [4]  train-rmse:53.938632+0.072315   test-rmse:53.937809+0.553884 
## [5]  train-rmse:45.444263+0.067432   test-rmse:45.442850+0.574932 
## [6]  train-rmse:38.323881+0.064650   test-rmse:38.321713+0.596609 
## [7]  train-rmse:32.357049+0.062292   test-rmse:32.380286+0.599805 
## [8]  train-rmse:27.347881+0.054482   test-rmse:27.420350+0.612889 
## [9]  train-rmse:23.142614+0.043845   test-rmse:23.227096+0.593161 
## [10] train-rmse:19.611012+0.037429   test-rmse:19.700200+0.582908 
## [11] train-rmse:16.646035+0.030843   test-rmse:16.760352+0.587068 
## [12] train-rmse:14.154684+0.028559   test-rmse:14.259100+0.558952 
## [13] train-rmse:12.062907+0.026808   test-rmse:12.162189+0.547296 
## [14] train-rmse:10.309098+0.024499   test-rmse:10.397156+0.551498 
## [15] train-rmse:8.842632+0.026958    test-rmse:8.921511+0.528925 
## [16] train-rmse:7.617306+0.028478    test-rmse:7.703463+0.524612 
## [17] train-rmse:6.592495+0.026597    test-rmse:6.705683+0.533850 
## [18] train-rmse:5.743550+0.028846    test-rmse:5.885709+0.529533 
## [19] train-rmse:5.034208+0.026301    test-rmse:5.177221+0.497896 
## [20] train-rmse:4.447442+0.028633    test-rmse:4.606941+0.477093 
## [21] train-rmse:3.966193+0.032774    test-rmse:4.174609+0.456850 
## [22] train-rmse:3.567169+0.031533    test-rmse:3.805248+0.441233 
## [23] train-rmse:3.240059+0.037816    test-rmse:3.493340+0.425490 
## [24] train-rmse:2.963663+0.044280    test-rmse:3.268147+0.401403 
## [25] train-rmse:2.742812+0.044574    test-rmse:3.046287+0.369113 
## [26] train-rmse:2.557539+0.043530    test-rmse:2.900777+0.338487 
## [27] train-rmse:2.403897+0.045031    test-rmse:2.750604+0.308441 
## [28] train-rmse:2.271459+0.044232    test-rmse:2.663076+0.297432 
## [29] train-rmse:2.160621+0.046944    test-rmse:2.572861+0.275154 
## [30] train-rmse:2.064611+0.048068    test-rmse:2.487781+0.257613 
## [31] train-rmse:1.978280+0.046708    test-rmse:2.407052+0.249760 
## [32] train-rmse:1.905271+0.044628    test-rmse:2.335887+0.249578 
## [33] train-rmse:1.831861+0.040033    test-rmse:2.291313+0.237120 
## [34] train-rmse:1.771489+0.037845    test-rmse:2.243072+0.232075 
## [35] train-rmse:1.715998+0.037131    test-rmse:2.197152+0.239506 
## [36] train-rmse:1.665384+0.036649    test-rmse:2.155272+0.240312 
## [37] train-rmse:1.613263+0.031269    test-rmse:2.109532+0.226551 
## [38] train-rmse:1.560703+0.028705    test-rmse:2.072122+0.208360 
## [39] train-rmse:1.510612+0.034265    test-rmse:2.046922+0.210808 
## [40] train-rmse:1.469225+0.033822    test-rmse:2.022417+0.216269 
## [41] train-rmse:1.430866+0.031706    test-rmse:1.990089+0.202115 
## [42] train-rmse:1.392162+0.032079    test-rmse:1.953599+0.198640 
## [43] train-rmse:1.357030+0.032230    test-rmse:1.920543+0.193606 
## [44] train-rmse:1.323115+0.031459    test-rmse:1.894428+0.192768 
## [45] train-rmse:1.290132+0.031034    test-rmse:1.865998+0.201641 
## [46] train-rmse:1.258778+0.031736    test-rmse:1.840430+0.195281 
## [47] train-rmse:1.223949+0.029256    test-rmse:1.822329+0.198324 
## [48] train-rmse:1.193977+0.029175    test-rmse:1.795200+0.195671 
## [49] train-rmse:1.165747+0.028708    test-rmse:1.772113+0.198332 
## [50] train-rmse:1.135219+0.031615    test-rmse:1.746779+0.205005 
## [51] train-rmse:1.109349+0.030619    test-rmse:1.718146+0.198326 
## [52] train-rmse:1.083256+0.029795    test-rmse:1.697928+0.198791 
## [53] train-rmse:1.057198+0.029633    test-rmse:1.674025+0.198017 
## [54] train-rmse:1.033781+0.029218    test-rmse:1.656989+0.201153 
## [55] train-rmse:1.009793+0.029950    test-rmse:1.641736+0.201896 
## [56] train-rmse:0.987464+0.030512    test-rmse:1.627214+0.201597 
## [57] train-rmse:0.963804+0.029383    test-rmse:1.611437+0.197171 
## [58] train-rmse:0.942968+0.029347    test-rmse:1.596480+0.198378 
## [59] train-rmse:0.921847+0.028170    test-rmse:1.578499+0.203363 
## [60] train-rmse:0.897770+0.024554    test-rmse:1.567357+0.197379 
## [61] train-rmse:0.879090+0.024440    test-rmse:1.554066+0.195967 
## [62] train-rmse:0.860938+0.024636    test-rmse:1.540723+0.197382 
## [63] train-rmse:0.843970+0.024035    test-rmse:1.525811+0.204563 
## [64] train-rmse:0.826230+0.026068    test-rmse:1.511890+0.203602 
## [65] train-rmse:0.809702+0.025212    test-rmse:1.499835+0.204912 
## [66] train-rmse:0.791931+0.024320    test-rmse:1.483625+0.205079 
## [67] train-rmse:0.775772+0.022275    test-rmse:1.469672+0.202661 
## [68] train-rmse:0.758976+0.022660    test-rmse:1.465026+0.204666 
## [69] train-rmse:0.742470+0.022874    test-rmse:1.457951+0.203550 
## [70] train-rmse:0.727677+0.022662    test-rmse:1.445451+0.202791 
## [71] train-rmse:0.713950+0.022157    test-rmse:1.439182+0.205517 
## [72] train-rmse:0.700695+0.021837    test-rmse:1.425112+0.203472 
## [73] train-rmse:0.688245+0.021809    test-rmse:1.417600+0.202968 
## [74] train-rmse:0.675123+0.021176    test-rmse:1.404309+0.203999 
## [75] train-rmse:0.663160+0.020744    test-rmse:1.396936+0.205381 
## [76] train-rmse:0.649567+0.019931    test-rmse:1.386632+0.203908 
## [77] train-rmse:0.637952+0.019730    test-rmse:1.380493+0.206653 
## [78] train-rmse:0.626662+0.019126    test-rmse:1.375299+0.210071 
## [79] train-rmse:0.615142+0.019151    test-rmse:1.364951+0.212573 
## [80] train-rmse:0.604852+0.019318    test-rmse:1.359448+0.211935 
## [81] train-rmse:0.594004+0.018744    test-rmse:1.350500+0.213324 
## [82] train-rmse:0.582630+0.017842    test-rmse:1.344759+0.215650 
## [83] train-rmse:0.573492+0.017648    test-rmse:1.337378+0.219431 
## [84] train-rmse:0.563670+0.018370    test-rmse:1.329688+0.219004 
## [85] train-rmse:0.554951+0.017949    test-rmse:1.323867+0.219373 
## [86] train-rmse:0.546838+0.018025    test-rmse:1.313627+0.221886 
## [87] train-rmse:0.536787+0.020257    test-rmse:1.305364+0.215432 
## [88] train-rmse:0.528044+0.020367    test-rmse:1.299729+0.217429 
## [89] train-rmse:0.518774+0.019834    test-rmse:1.295952+0.215694 
## [90] train-rmse:0.510517+0.019232    test-rmse:1.289097+0.219262 
## [91] train-rmse:0.502695+0.019535    test-rmse:1.287002+0.220079 
## [92] train-rmse:0.494443+0.019013    test-rmse:1.279295+0.213104 
## [93] train-rmse:0.487351+0.018999    test-rmse:1.276960+0.215283 
## [94] train-rmse:0.479389+0.019781    test-rmse:1.272341+0.217821 
## [95] train-rmse:0.472059+0.019768    test-rmse:1.267593+0.218217 
## [96] train-rmse:0.464840+0.021353    test-rmse:1.264907+0.218614 
## [97] train-rmse:0.458587+0.020640    test-rmse:1.258947+0.221347 
## [98] train-rmse:0.451733+0.019234    test-rmse:1.253262+0.216069 
## [99] train-rmse:0.445384+0.019396    test-rmse:1.248277+0.215926 
## [100]    train-rmse:0.439489+0.019412    test-rmse:1.245634+0.215932 
## [101]    train-rmse:0.433940+0.019153    test-rmse:1.242882+0.216051 
## [102]    train-rmse:0.428351+0.019952    test-rmse:1.239641+0.216505 
## [103]    train-rmse:0.421474+0.018786    test-rmse:1.236190+0.218072 
## [104]    train-rmse:0.415282+0.018892    test-rmse:1.233421+0.217688 
## [105]    train-rmse:0.409567+0.018745    test-rmse:1.231581+0.218815 
## [106]    train-rmse:0.404320+0.019094    test-rmse:1.230185+0.219218 
## [107]    train-rmse:0.398071+0.019532    test-rmse:1.228295+0.220757 
## [108]    train-rmse:0.392921+0.018979    test-rmse:1.222277+0.215489 
## [109]    train-rmse:0.388811+0.018973    test-rmse:1.220220+0.216241 
## [110]    train-rmse:0.382924+0.019126    test-rmse:1.216355+0.217894 
## [111]    train-rmse:0.377280+0.018668    test-rmse:1.214898+0.217941 
## [112]    train-rmse:0.371898+0.017365    test-rmse:1.213011+0.218093 
## [113]    train-rmse:0.367337+0.016706    test-rmse:1.211158+0.218699 
## [114]    train-rmse:0.363043+0.017158    test-rmse:1.209926+0.219261 
## [115]    train-rmse:0.358059+0.016827    test-rmse:1.208405+0.221347 
## [116]    train-rmse:0.353819+0.016790    test-rmse:1.206569+0.223571 
## [117]    train-rmse:0.349917+0.017137    test-rmse:1.205151+0.223886 
## [118]    train-rmse:0.346339+0.017188    test-rmse:1.201604+0.224988 
## [119]    train-rmse:0.341953+0.017151    test-rmse:1.199128+0.227075 
## [120]    train-rmse:0.337653+0.016981    test-rmse:1.197560+0.229457 
## [121]    train-rmse:0.334016+0.017052    test-rmse:1.196435+0.228710 
## [122]    train-rmse:0.330261+0.017985    test-rmse:1.195360+0.228798 
## [123]    train-rmse:0.325418+0.017347    test-rmse:1.194118+0.228404 
## [124]    train-rmse:0.321457+0.016653    test-rmse:1.193092+0.228420 
## [125]    train-rmse:0.318496+0.016411    test-rmse:1.191175+0.229726 
## [126]    train-rmse:0.314481+0.016076    test-rmse:1.189778+0.229636 
## [127]    train-rmse:0.311136+0.016341    test-rmse:1.189004+0.230006 
## [128]    train-rmse:0.306653+0.015129    test-rmse:1.187369+0.231421 
## [129]    train-rmse:0.302742+0.015412    test-rmse:1.186307+0.231261 
## [130]    train-rmse:0.298906+0.014538    test-rmse:1.184326+0.231455 
## [131]    train-rmse:0.296172+0.014295    test-rmse:1.184151+0.231271 
## [132]    train-rmse:0.293238+0.014231    test-rmse:1.183587+0.232087 
## [133]    train-rmse:0.290489+0.014467    test-rmse:1.182047+0.232524 
## [134]    train-rmse:0.287481+0.014389    test-rmse:1.180363+0.232674 
## [135]    train-rmse:0.284654+0.014170    test-rmse:1.179781+0.233098 
## [136]    train-rmse:0.282087+0.014587    test-rmse:1.178837+0.232824 
## [137]    train-rmse:0.278933+0.014889    test-rmse:1.177066+0.234386 
## [138]    train-rmse:0.276010+0.015117    test-rmse:1.175070+0.235084 
## [139]    train-rmse:0.272769+0.013723    test-rmse:1.174703+0.234942 
## [140]    train-rmse:0.268936+0.012811    test-rmse:1.174914+0.235714 
## [141]    train-rmse:0.266379+0.012776    test-rmse:1.174619+0.235556 
## [142]    train-rmse:0.263963+0.013289    test-rmse:1.173855+0.236764 
## [143]    train-rmse:0.261449+0.012685    test-rmse:1.173767+0.235756 
## [144]    train-rmse:0.258478+0.013068    test-rmse:1.173026+0.236429 
## [145]    train-rmse:0.255661+0.013187    test-rmse:1.173036+0.236971 
## [146]    train-rmse:0.252496+0.012496    test-rmse:1.171584+0.238171 
## [147]    train-rmse:0.250225+0.012061    test-rmse:1.170747+0.237502 
## [148]    train-rmse:0.247856+0.012561    test-rmse:1.169859+0.237797 
## [149]    train-rmse:0.245704+0.012730    test-rmse:1.169410+0.237703 
## [150]    train-rmse:0.243497+0.013525    test-rmse:1.168471+0.237556 
## [151]    train-rmse:0.240997+0.013337    test-rmse:1.168400+0.237914 
## [152]    train-rmse:0.238328+0.012495    test-rmse:1.166886+0.238768 
## [153]    train-rmse:0.236345+0.012669    test-rmse:1.165409+0.239517 
## [154]    train-rmse:0.234397+0.013007    test-rmse:1.163909+0.239805 
## [155]    train-rmse:0.232498+0.012788    test-rmse:1.163775+0.240628 
## [156]    train-rmse:0.230565+0.013179    test-rmse:1.163605+0.240536 
## [157]    train-rmse:0.228834+0.012724    test-rmse:1.163361+0.240412 
## [158]    train-rmse:0.227285+0.013004    test-rmse:1.162750+0.240922 
## [159]    train-rmse:0.225206+0.013182    test-rmse:1.162122+0.240976 
## [160]    train-rmse:0.222570+0.013362    test-rmse:1.161569+0.239976 
## [161]    train-rmse:0.220736+0.013677    test-rmse:1.160836+0.240541 
## [162]    train-rmse:0.219236+0.013580    test-rmse:1.159454+0.241299 
## [163]    train-rmse:0.216990+0.013823    test-rmse:1.159403+0.241534 
## [164]    train-rmse:0.214807+0.013350    test-rmse:1.158829+0.241637 
## [165]    train-rmse:0.212116+0.012275    test-rmse:1.159325+0.241359 
## [166]    train-rmse:0.210173+0.012091    test-rmse:1.157708+0.242412 
## [167]    train-rmse:0.208804+0.012064    test-rmse:1.157710+0.242919 
## [168]    train-rmse:0.206212+0.012087    test-rmse:1.157592+0.242972 
## [169]    train-rmse:0.204333+0.012473    test-rmse:1.157890+0.242982 
## [170]    train-rmse:0.202040+0.012135    test-rmse:1.156337+0.243662 
## [171]    train-rmse:0.200240+0.011619    test-rmse:1.155256+0.244212 
## [172]    train-rmse:0.198587+0.011210    test-rmse:1.154748+0.244179 
## [173]    train-rmse:0.197191+0.011310    test-rmse:1.154862+0.243919 
## [174]    train-rmse:0.195216+0.011499    test-rmse:1.154752+0.243898 
## [175]    train-rmse:0.193350+0.011213    test-rmse:1.154772+0.244103 
## [176]    train-rmse:0.191677+0.011462    test-rmse:1.154622+0.243906 
## [177]    train-rmse:0.189934+0.011074    test-rmse:1.153631+0.244427 
## [178]    train-rmse:0.188708+0.011110    test-rmse:1.154118+0.243944 
## [179]    train-rmse:0.186968+0.011012    test-rmse:1.153401+0.244492 
## [180]    train-rmse:0.185212+0.010657    test-rmse:1.153438+0.244490 
## [181]    train-rmse:0.183482+0.010838    test-rmse:1.153606+0.244422 
## [182]    train-rmse:0.180950+0.010407    test-rmse:1.153052+0.244542 
## [183]    train-rmse:0.179385+0.010418    test-rmse:1.152805+0.245295 
## [184]    train-rmse:0.178023+0.010046    test-rmse:1.152833+0.245611 
## [185]    train-rmse:0.176598+0.010356    test-rmse:1.152291+0.245775 
## [186]    train-rmse:0.175272+0.010788    test-rmse:1.151682+0.245668 
## [187]    train-rmse:0.174264+0.011146    test-rmse:1.150856+0.245654 
## [188]    train-rmse:0.172513+0.010797    test-rmse:1.150685+0.245736 
## [189]    train-rmse:0.171141+0.010696    test-rmse:1.150560+0.245756 
## [190]    train-rmse:0.169299+0.010021    test-rmse:1.150121+0.246059 
## [191]    train-rmse:0.167834+0.010202    test-rmse:1.149649+0.246281 
## [192]    train-rmse:0.166781+0.010220    test-rmse:1.149315+0.246467 
## [193]    train-rmse:0.165592+0.010342    test-rmse:1.148463+0.246575 
## [194]    train-rmse:0.164545+0.010361    test-rmse:1.148355+0.246780 
## [195]    train-rmse:0.163248+0.010399    test-rmse:1.147919+0.246763 
## [196]    train-rmse:0.161770+0.010337    test-rmse:1.147813+0.246980 
## [197]    train-rmse:0.160110+0.010382    test-rmse:1.147493+0.247237 
## [198]    train-rmse:0.158540+0.010406    test-rmse:1.147136+0.247266 
## [199]    train-rmse:0.157385+0.010478    test-rmse:1.146841+0.248050 
## [200]    train-rmse:0.156212+0.010578    test-rmse:1.146844+0.248103 
## [201]    train-rmse:0.154829+0.010309    test-rmse:1.147457+0.247738 
## [202]    train-rmse:0.153813+0.010207    test-rmse:1.147087+0.247707 
## [203]    train-rmse:0.152385+0.010241    test-rmse:1.147450+0.247728 
## [204]    train-rmse:0.150567+0.009515    test-rmse:1.147420+0.247810 
## [205]    train-rmse:0.149686+0.009628    test-rmse:1.147386+0.248093 
## Stopping. Best iteration:
## [199]    train-rmse:0.157385+0.010478    test-rmse:1.146841+0.248050
## 
## 25 
## [1]  train-rmse:90.491687+0.101310   test-rmse:90.491937+0.489579 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.126776+0.089017   test-rmse:76.126749+0.511751 
## [3]  train-rmse:64.064040+0.079442   test-rmse:64.063658+0.532994 
## [4]  train-rmse:53.938632+0.072315   test-rmse:53.937809+0.553884 
## [5]  train-rmse:45.444263+0.067432   test-rmse:45.442850+0.574932 
## [6]  train-rmse:38.323881+0.064650   test-rmse:38.321713+0.596609 
## [7]  train-rmse:32.357049+0.062292   test-rmse:32.380286+0.599805 
## [8]  train-rmse:27.347881+0.054482   test-rmse:27.420350+0.612889 
## [9]  train-rmse:23.142614+0.043845   test-rmse:23.227096+0.593161 
## [10] train-rmse:19.610397+0.037134   test-rmse:19.706818+0.582337 
## [11] train-rmse:16.642647+0.030876   test-rmse:16.754448+0.578970 
## [12] train-rmse:14.146475+0.029134   test-rmse:14.255316+0.558178 
## [13] train-rmse:12.047483+0.026878   test-rmse:12.152864+0.549636 
## [14] train-rmse:10.282251+0.022133   test-rmse:10.368125+0.560137 
## [15] train-rmse:8.800834+0.024085    test-rmse:8.882288+0.542207 
## [16] train-rmse:7.557792+0.022823    test-rmse:7.638580+0.530966 
## [17] train-rmse:6.516109+0.024624    test-rmse:6.609978+0.528065 
## [18] train-rmse:5.643814+0.025692    test-rmse:5.755417+0.506203 
## [19] train-rmse:4.915964+0.029467    test-rmse:5.050901+0.474536 
## [20] train-rmse:4.302048+0.026658    test-rmse:4.470345+0.476621 
## [21] train-rmse:3.795513+0.031403    test-rmse:4.000362+0.446758 
## [22] train-rmse:3.371460+0.035674    test-rmse:3.614480+0.435628 
## [23] train-rmse:3.018717+0.034558    test-rmse:3.301068+0.392178 
## [24] train-rmse:2.730403+0.038704    test-rmse:3.059644+0.361830 
## [25] train-rmse:2.488238+0.039523    test-rmse:2.837257+0.338981 
## [26] train-rmse:2.277696+0.036074    test-rmse:2.664395+0.310878 
## [27] train-rmse:2.108968+0.037094    test-rmse:2.514939+0.290200 
## [28] train-rmse:1.964397+0.038432    test-rmse:2.401671+0.276939 
## [29] train-rmse:1.834571+0.047451    test-rmse:2.309616+0.269668 
## [30] train-rmse:1.731628+0.048200    test-rmse:2.228800+0.254096 
## [31] train-rmse:1.643333+0.048063    test-rmse:2.156324+0.238189 
## [32] train-rmse:1.560722+0.045882    test-rmse:2.093652+0.219220 
## [33] train-rmse:1.490061+0.053305    test-rmse:2.051681+0.213471 
## [34] train-rmse:1.423838+0.052579    test-rmse:2.013682+0.219036 
## [35] train-rmse:1.365556+0.047382    test-rmse:1.965362+0.206069 
## [36] train-rmse:1.315041+0.045824    test-rmse:1.922681+0.205279 
## [37] train-rmse:1.264295+0.045972    test-rmse:1.893587+0.200103 
## [38] train-rmse:1.212711+0.044133    test-rmse:1.852171+0.196074 
## [39] train-rmse:1.171039+0.042442    test-rmse:1.816606+0.195290 
## [40] train-rmse:1.131067+0.039328    test-rmse:1.784764+0.190276 
## [41] train-rmse:1.094584+0.038563    test-rmse:1.757174+0.190440 
## [42] train-rmse:1.058378+0.038644    test-rmse:1.723592+0.197728 
## [43] train-rmse:1.020302+0.044719    test-rmse:1.690458+0.200854 
## [44] train-rmse:0.988736+0.042548    test-rmse:1.668748+0.201688 
## [45] train-rmse:0.957981+0.041155    test-rmse:1.644216+0.202019 
## [46] train-rmse:0.929760+0.040225    test-rmse:1.619538+0.200979 
## [47] train-rmse:0.903648+0.040050    test-rmse:1.600528+0.199100 
## [48] train-rmse:0.874915+0.043614    test-rmse:1.581455+0.202474 
## [49] train-rmse:0.849713+0.042029    test-rmse:1.562102+0.209246 
## [50] train-rmse:0.825876+0.040632    test-rmse:1.545796+0.213378 
## [51] train-rmse:0.800712+0.040248    test-rmse:1.525959+0.215919 
## [52] train-rmse:0.779012+0.038879    test-rmse:1.509945+0.217982 
## [53] train-rmse:0.757086+0.037818    test-rmse:1.496758+0.215806 
## [54] train-rmse:0.736202+0.035977    test-rmse:1.481466+0.223673 
## [55] train-rmse:0.716610+0.035079    test-rmse:1.466249+0.226816 
## [56] train-rmse:0.697161+0.033779    test-rmse:1.456510+0.226085 
## [57] train-rmse:0.678804+0.031924    test-rmse:1.440590+0.226167 
## [58] train-rmse:0.661327+0.031052    test-rmse:1.432730+0.225141 
## [59] train-rmse:0.644308+0.030599    test-rmse:1.424585+0.228976 
## [60] train-rmse:0.628675+0.029623    test-rmse:1.409533+0.232329 
## [61] train-rmse:0.613823+0.029262    test-rmse:1.400173+0.236232 
## [62] train-rmse:0.599399+0.029452    test-rmse:1.389559+0.236791 
## [63] train-rmse:0.585458+0.028601    test-rmse:1.377397+0.238427 
## [64] train-rmse:0.570399+0.028277    test-rmse:1.372848+0.237448 
## [65] train-rmse:0.557028+0.027272    test-rmse:1.366429+0.236558 
## [66] train-rmse:0.545453+0.026522    test-rmse:1.355786+0.230613 
## [67] train-rmse:0.533944+0.025686    test-rmse:1.345816+0.234643 
## [68] train-rmse:0.522398+0.024291    test-rmse:1.338804+0.234982 
## [69] train-rmse:0.511982+0.023765    test-rmse:1.332528+0.235857 
## [70] train-rmse:0.500175+0.023782    test-rmse:1.327565+0.239062 
## [71] train-rmse:0.488199+0.022330    test-rmse:1.320978+0.236387 
## [72] train-rmse:0.479137+0.021584    test-rmse:1.315979+0.237136 
## [73] train-rmse:0.469965+0.021378    test-rmse:1.308302+0.239138 
## [74] train-rmse:0.461156+0.020427    test-rmse:1.301834+0.238827 
## [75] train-rmse:0.451510+0.020202    test-rmse:1.295455+0.240891 
## [76] train-rmse:0.442504+0.019338    test-rmse:1.288606+0.235252 
## [77] train-rmse:0.433267+0.018653    test-rmse:1.285771+0.237631 
## [78] train-rmse:0.425283+0.018600    test-rmse:1.283249+0.238370 
## [79] train-rmse:0.417021+0.017706    test-rmse:1.276194+0.235512 
## [80] train-rmse:0.409741+0.016691    test-rmse:1.271712+0.234489 
## [81] train-rmse:0.402161+0.016809    test-rmse:1.268050+0.235084 
## [82] train-rmse:0.396014+0.016485    test-rmse:1.262377+0.236896 
## [83] train-rmse:0.387080+0.015647    test-rmse:1.259909+0.238302 
## [84] train-rmse:0.379749+0.015409    test-rmse:1.257724+0.240882 
## [85] train-rmse:0.373121+0.015402    test-rmse:1.255383+0.241168 
## [86] train-rmse:0.367005+0.014595    test-rmse:1.251973+0.241014 
## [87] train-rmse:0.359755+0.014806    test-rmse:1.249986+0.241092 
## [88] train-rmse:0.353370+0.015263    test-rmse:1.247637+0.241169 
## [89] train-rmse:0.346299+0.012990    test-rmse:1.243869+0.240232 
## [90] train-rmse:0.340773+0.012690    test-rmse:1.242174+0.239271 
## [91] train-rmse:0.335758+0.012312    test-rmse:1.239906+0.240463 
## [92] train-rmse:0.329868+0.011213    test-rmse:1.239085+0.242624 
## [93] train-rmse:0.322852+0.011848    test-rmse:1.238018+0.242938 
## [94] train-rmse:0.317707+0.011872    test-rmse:1.235151+0.245200 
## [95] train-rmse:0.311642+0.011156    test-rmse:1.233783+0.244913 
## [96] train-rmse:0.307035+0.011033    test-rmse:1.228967+0.246636 
## [97] train-rmse:0.302838+0.010529    test-rmse:1.228543+0.247438 
## [98] train-rmse:0.298660+0.010963    test-rmse:1.227049+0.248817 
## [99] train-rmse:0.292465+0.009416    test-rmse:1.224543+0.250109 
## [100]    train-rmse:0.287442+0.008941    test-rmse:1.222727+0.250241 
## [101]    train-rmse:0.282553+0.009839    test-rmse:1.223939+0.249418 
## [102]    train-rmse:0.278610+0.009680    test-rmse:1.223353+0.250842 
## [103]    train-rmse:0.274750+0.009643    test-rmse:1.222206+0.251641 
## [104]    train-rmse:0.271500+0.009724    test-rmse:1.219027+0.252057 
## [105]    train-rmse:0.267184+0.009977    test-rmse:1.217204+0.252303 
## [106]    train-rmse:0.263234+0.009477    test-rmse:1.216348+0.251986 
## [107]    train-rmse:0.259271+0.010142    test-rmse:1.215100+0.250909 
## [108]    train-rmse:0.255370+0.009939    test-rmse:1.214152+0.252169 
## [109]    train-rmse:0.251078+0.010263    test-rmse:1.213980+0.251858 
## [110]    train-rmse:0.246855+0.009855    test-rmse:1.213153+0.252362 
## [111]    train-rmse:0.244021+0.009915    test-rmse:1.212923+0.252186 
## [112]    train-rmse:0.240685+0.009982    test-rmse:1.211883+0.251487 
## [113]    train-rmse:0.236475+0.010904    test-rmse:1.211590+0.251273 
## [114]    train-rmse:0.232480+0.010836    test-rmse:1.210096+0.251307 
## [115]    train-rmse:0.229107+0.010359    test-rmse:1.209795+0.252152 
## [116]    train-rmse:0.226007+0.009321    test-rmse:1.209509+0.253127 
## [117]    train-rmse:0.222917+0.008928    test-rmse:1.208514+0.253371 
## [118]    train-rmse:0.219177+0.008351    test-rmse:1.207683+0.254045 
## [119]    train-rmse:0.214889+0.008362    test-rmse:1.206756+0.253056 
## [120]    train-rmse:0.211205+0.008131    test-rmse:1.205716+0.254538 
## [121]    train-rmse:0.208460+0.008217    test-rmse:1.205939+0.254157 
## [122]    train-rmse:0.204778+0.007418    test-rmse:1.204697+0.253269 
## [123]    train-rmse:0.201794+0.006730    test-rmse:1.203916+0.252813 
## [124]    train-rmse:0.199641+0.007350    test-rmse:1.203220+0.252835 
## [125]    train-rmse:0.196268+0.007156    test-rmse:1.202486+0.253543 
## [126]    train-rmse:0.193551+0.006629    test-rmse:1.202050+0.253303 
## [127]    train-rmse:0.191206+0.006771    test-rmse:1.201874+0.253114 
## [128]    train-rmse:0.188898+0.006746    test-rmse:1.201816+0.253207 
## [129]    train-rmse:0.186885+0.006961    test-rmse:1.201172+0.253256 
## [130]    train-rmse:0.184143+0.006705    test-rmse:1.201172+0.253422 
## [131]    train-rmse:0.181358+0.005798    test-rmse:1.199692+0.254142 
## [132]    train-rmse:0.178778+0.006863    test-rmse:1.199017+0.254641 
## [133]    train-rmse:0.176240+0.006288    test-rmse:1.198619+0.254534 
## [134]    train-rmse:0.173987+0.006005    test-rmse:1.198515+0.254943 
## [135]    train-rmse:0.172024+0.005581    test-rmse:1.198029+0.254638 
## [136]    train-rmse:0.169221+0.005215    test-rmse:1.197724+0.255344 
## [137]    train-rmse:0.166900+0.005167    test-rmse:1.197185+0.255110 
## [138]    train-rmse:0.164442+0.004788    test-rmse:1.195793+0.255124 
## [139]    train-rmse:0.162373+0.004839    test-rmse:1.195228+0.254813 
## [140]    train-rmse:0.159980+0.004592    test-rmse:1.194290+0.254688 
## [141]    train-rmse:0.158194+0.004595    test-rmse:1.193916+0.255023 
## [142]    train-rmse:0.156392+0.004798    test-rmse:1.193640+0.255186 
## [143]    train-rmse:0.154696+0.004967    test-rmse:1.193680+0.255061 
## [144]    train-rmse:0.153167+0.005362    test-rmse:1.192854+0.256065 
## [145]    train-rmse:0.151281+0.005327    test-rmse:1.191583+0.254814 
## [146]    train-rmse:0.149204+0.004973    test-rmse:1.191149+0.255119 
## [147]    train-rmse:0.147030+0.004610    test-rmse:1.191037+0.255027 
## [148]    train-rmse:0.145414+0.004354    test-rmse:1.190608+0.255245 
## [149]    train-rmse:0.143184+0.003505    test-rmse:1.190491+0.254840 
## [150]    train-rmse:0.141555+0.003561    test-rmse:1.189949+0.254744 
## [151]    train-rmse:0.139859+0.003430    test-rmse:1.189414+0.254533 
## [152]    train-rmse:0.138923+0.003512    test-rmse:1.189095+0.254212 
## [153]    train-rmse:0.136535+0.003302    test-rmse:1.188507+0.254391 
## [154]    train-rmse:0.135182+0.003217    test-rmse:1.187648+0.254849 
## [155]    train-rmse:0.132903+0.004043    test-rmse:1.187323+0.255030 
## [156]    train-rmse:0.131577+0.004266    test-rmse:1.186986+0.255238 
## [157]    train-rmse:0.130291+0.004221    test-rmse:1.186696+0.255264 
## [158]    train-rmse:0.128845+0.003939    test-rmse:1.186597+0.255032 
## [159]    train-rmse:0.127687+0.003874    test-rmse:1.186413+0.255047 
## [160]    train-rmse:0.125968+0.004116    test-rmse:1.185910+0.255117 
## [161]    train-rmse:0.124685+0.003937    test-rmse:1.185019+0.254315 
## [162]    train-rmse:0.122819+0.004003    test-rmse:1.184630+0.254042 
## [163]    train-rmse:0.121314+0.004067    test-rmse:1.185181+0.254086 
## [164]    train-rmse:0.119990+0.004048    test-rmse:1.184764+0.253933 
## [165]    train-rmse:0.118364+0.004294    test-rmse:1.184269+0.253369 
## [166]    train-rmse:0.117069+0.003906    test-rmse:1.183793+0.253618 
## [167]    train-rmse:0.115475+0.004368    test-rmse:1.183459+0.254078 
## [168]    train-rmse:0.114318+0.004079    test-rmse:1.183530+0.253631 
## [169]    train-rmse:0.112564+0.003699    test-rmse:1.182740+0.252060 
## [170]    train-rmse:0.111318+0.003836    test-rmse:1.182833+0.252134 
## [171]    train-rmse:0.110298+0.003999    test-rmse:1.182022+0.252089 
## [172]    train-rmse:0.109028+0.004050    test-rmse:1.181681+0.251867 
## [173]    train-rmse:0.107287+0.004207    test-rmse:1.181615+0.251804 
## [174]    train-rmse:0.106259+0.004140    test-rmse:1.181843+0.251670 
## [175]    train-rmse:0.104599+0.003467    test-rmse:1.181716+0.251529 
## [176]    train-rmse:0.103515+0.003548    test-rmse:1.181453+0.251544 
## [177]    train-rmse:0.102429+0.003443    test-rmse:1.181281+0.251414 
## [178]    train-rmse:0.100969+0.003108    test-rmse:1.181393+0.251314 
## [179]    train-rmse:0.099853+0.002817    test-rmse:1.181119+0.251539 
## [180]    train-rmse:0.098917+0.002776    test-rmse:1.180693+0.251747 
## [181]    train-rmse:0.097761+0.003120    test-rmse:1.180366+0.251694 
## [182]    train-rmse:0.096705+0.003028    test-rmse:1.180183+0.251567 
## [183]    train-rmse:0.095656+0.003018    test-rmse:1.180024+0.251145 
## [184]    train-rmse:0.094308+0.003481    test-rmse:1.180145+0.251080 
## [185]    train-rmse:0.093254+0.003902    test-rmse:1.180143+0.251434 
## [186]    train-rmse:0.092335+0.003552    test-rmse:1.179800+0.251389 
## [187]    train-rmse:0.091327+0.003589    test-rmse:1.179648+0.251139 
## [188]    train-rmse:0.090329+0.003789    test-rmse:1.179206+0.251032 
## [189]    train-rmse:0.089306+0.003526    test-rmse:1.179130+0.250915 
## [190]    train-rmse:0.088050+0.003041    test-rmse:1.178861+0.250929 
## [191]    train-rmse:0.086962+0.003034    test-rmse:1.179307+0.250766 
## [192]    train-rmse:0.085970+0.003063    test-rmse:1.179094+0.250832 
## [193]    train-rmse:0.084880+0.002989    test-rmse:1.178685+0.250820 
## [194]    train-rmse:0.083215+0.002699    test-rmse:1.178824+0.250935 
## [195]    train-rmse:0.082250+0.002524    test-rmse:1.178448+0.250726 
## [196]    train-rmse:0.081269+0.002985    test-rmse:1.178366+0.250681 
## [197]    train-rmse:0.080596+0.003010    test-rmse:1.178184+0.250825 
## [198]    train-rmse:0.079602+0.002717    test-rmse:1.178164+0.251128 
## [199]    train-rmse:0.078825+0.002795    test-rmse:1.178134+0.250847 
## [200]    train-rmse:0.078059+0.002735    test-rmse:1.178131+0.250681 
## [201]    train-rmse:0.077218+0.003077    test-rmse:1.178039+0.250882 
## [202]    train-rmse:0.076268+0.003255    test-rmse:1.177428+0.251077 
## [203]    train-rmse:0.075686+0.003276    test-rmse:1.177268+0.251340 
## [204]    train-rmse:0.075061+0.003197    test-rmse:1.177101+0.251502 
## [205]    train-rmse:0.074219+0.003134    test-rmse:1.176968+0.251381 
## [206]    train-rmse:0.073233+0.003216    test-rmse:1.176854+0.251665 
## [207]    train-rmse:0.072335+0.003248    test-rmse:1.176849+0.251621 
## [208]    train-rmse:0.071412+0.003230    test-rmse:1.176799+0.251486 
## [209]    train-rmse:0.070760+0.002981    test-rmse:1.176565+0.251202 
## [210]    train-rmse:0.069955+0.002861    test-rmse:1.176488+0.251050 
## [211]    train-rmse:0.069064+0.002944    test-rmse:1.176490+0.250892 
## [212]    train-rmse:0.068309+0.002976    test-rmse:1.176388+0.250950 
## [213]    train-rmse:0.067665+0.002965    test-rmse:1.176490+0.250848 
## [214]    train-rmse:0.066788+0.002712    test-rmse:1.176496+0.251010 
## [215]    train-rmse:0.065942+0.002714    test-rmse:1.176437+0.250924 
## [216]    train-rmse:0.065172+0.002955    test-rmse:1.176435+0.250877 
## [217]    train-rmse:0.064306+0.003026    test-rmse:1.176167+0.251204 
## [218]    train-rmse:0.063596+0.002820    test-rmse:1.175845+0.251272 
## [219]    train-rmse:0.063004+0.002698    test-rmse:1.175804+0.251315 
## [220]    train-rmse:0.062412+0.002517    test-rmse:1.175674+0.250840 
## [221]    train-rmse:0.061687+0.002488    test-rmse:1.175679+0.250723 
## [222]    train-rmse:0.061127+0.002432    test-rmse:1.175535+0.250816 
## [223]    train-rmse:0.060337+0.002371    test-rmse:1.175481+0.250708 
## [224]    train-rmse:0.059674+0.002196    test-rmse:1.175370+0.250647 
## [225]    train-rmse:0.059206+0.002021    test-rmse:1.175356+0.250623 
## [226]    train-rmse:0.058645+0.002146    test-rmse:1.175055+0.250607 
## [227]    train-rmse:0.057772+0.002089    test-rmse:1.174882+0.250663 
## [228]    train-rmse:0.057081+0.001944    test-rmse:1.174668+0.250248 
## [229]    train-rmse:0.056618+0.001856    test-rmse:1.174715+0.250268 
## [230]    train-rmse:0.056040+0.001704    test-rmse:1.174498+0.250300 
## [231]    train-rmse:0.055667+0.001712    test-rmse:1.174470+0.250276 
## [232]    train-rmse:0.054823+0.001933    test-rmse:1.174633+0.250046 
## [233]    train-rmse:0.054316+0.001877    test-rmse:1.174503+0.250017 
## [234]    train-rmse:0.053711+0.001704    test-rmse:1.174689+0.250204 
## [235]    train-rmse:0.053019+0.001473    test-rmse:1.174625+0.249939 
## [236]    train-rmse:0.052610+0.001446    test-rmse:1.174625+0.250009 
## [237]    train-rmse:0.051769+0.001881    test-rmse:1.174627+0.250092 
## Stopping. Best iteration:
## [231]    train-rmse:0.055667+0.001712    test-rmse:1.174470+0.250276
## 
## 26 
## [1]  train-rmse:90.491687+0.101310   test-rmse:90.491937+0.489579 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.126776+0.089017   test-rmse:76.126749+0.511751 
## [3]  train-rmse:64.064040+0.079442   test-rmse:64.063658+0.532994 
## [4]  train-rmse:53.938632+0.072315   test-rmse:53.937809+0.553884 
## [5]  train-rmse:45.444263+0.067432   test-rmse:45.442850+0.574932 
## [6]  train-rmse:38.323881+0.064650   test-rmse:38.321713+0.596609 
## [7]  train-rmse:32.357049+0.062292   test-rmse:32.380286+0.599805 
## [8]  train-rmse:27.347881+0.054482   test-rmse:27.420350+0.612889 
## [9]  train-rmse:23.142614+0.043845   test-rmse:23.227096+0.593161 
## [10] train-rmse:19.610094+0.036791   test-rmse:19.703792+0.586145 
## [11] train-rmse:16.641395+0.030207   test-rmse:16.745301+0.583392 
## [12] train-rmse:14.142440+0.028373   test-rmse:14.246529+0.559704 
## [13] train-rmse:12.039050+0.026215   test-rmse:12.150034+0.548388 
## [14] train-rmse:10.270221+0.023820   test-rmse:10.386441+0.534831 
## [15] train-rmse:8.778229+0.023154    test-rmse:8.883696+0.513424 
## [16] train-rmse:7.526449+0.022603    test-rmse:7.640949+0.493221 
## [17] train-rmse:6.474282+0.023214    test-rmse:6.601322+0.513513 
## [18] train-rmse:5.587813+0.023537    test-rmse:5.722885+0.497660 
## [19] train-rmse:4.844880+0.025248    test-rmse:4.993995+0.487410 
## [20] train-rmse:4.215217+0.023024    test-rmse:4.390858+0.455253 
## [21] train-rmse:3.694864+0.022909    test-rmse:3.918911+0.444046 
## [22] train-rmse:3.257291+0.024194    test-rmse:3.502098+0.430216 
## [23] train-rmse:2.891112+0.024719    test-rmse:3.176954+0.396478 
## [24] train-rmse:2.580362+0.027083    test-rmse:2.919586+0.362780 
## [25] train-rmse:2.321712+0.029340    test-rmse:2.698569+0.355269 
## [26] train-rmse:2.100181+0.032959    test-rmse:2.525763+0.340667 
## [27] train-rmse:1.914004+0.040719    test-rmse:2.378226+0.329643 
## [28] train-rmse:1.760109+0.040593    test-rmse:2.263155+0.323877 
## [29] train-rmse:1.628957+0.041529    test-rmse:2.165142+0.314448 
## [30] train-rmse:1.516870+0.044325    test-rmse:2.089349+0.307635 
## [31] train-rmse:1.413608+0.050334    test-rmse:2.025223+0.291372 
## [32] train-rmse:1.327010+0.046696    test-rmse:1.963090+0.284345 
## [33] train-rmse:1.249300+0.043234    test-rmse:1.916630+0.268297 
## [34] train-rmse:1.179596+0.040531    test-rmse:1.868423+0.258992 
## [35] train-rmse:1.116107+0.035827    test-rmse:1.823107+0.253290 
## [36] train-rmse:1.063200+0.038612    test-rmse:1.787580+0.247315 
## [37] train-rmse:1.016543+0.036843    test-rmse:1.755233+0.250447 
## [38] train-rmse:0.968619+0.034122    test-rmse:1.720290+0.255574 
## [39] train-rmse:0.930324+0.032594    test-rmse:1.695138+0.257174 
## [40] train-rmse:0.888469+0.036994    test-rmse:1.662235+0.254836 
## [41] train-rmse:0.854031+0.033894    test-rmse:1.636739+0.259840 
## [42] train-rmse:0.819305+0.033354    test-rmse:1.611147+0.263372 
## [43] train-rmse:0.785075+0.031273    test-rmse:1.587407+0.265978 
## [44] train-rmse:0.753135+0.031404    test-rmse:1.569267+0.274294 
## [45] train-rmse:0.727447+0.030617    test-rmse:1.542670+0.265261 
## [46] train-rmse:0.700230+0.026208    test-rmse:1.527223+0.269810 
## [47] train-rmse:0.677237+0.024888    test-rmse:1.509106+0.271303 
## [48] train-rmse:0.656402+0.024147    test-rmse:1.496595+0.277724 
## [49] train-rmse:0.631241+0.026411    test-rmse:1.484773+0.278756 
## [50] train-rmse:0.612375+0.025877    test-rmse:1.472779+0.278258 
## [51] train-rmse:0.593035+0.025871    test-rmse:1.461015+0.280790 
## [52] train-rmse:0.574761+0.024674    test-rmse:1.446289+0.277959 
## [53] train-rmse:0.555060+0.022907    test-rmse:1.437759+0.274096 
## [54] train-rmse:0.537888+0.022094    test-rmse:1.428895+0.278412 
## [55] train-rmse:0.519717+0.023160    test-rmse:1.424446+0.281930 
## [56] train-rmse:0.505463+0.022674    test-rmse:1.416187+0.282310 
## [57] train-rmse:0.491883+0.022687    test-rmse:1.409396+0.281768 
## [58] train-rmse:0.477460+0.021134    test-rmse:1.401914+0.280375 
## [59] train-rmse:0.464643+0.020687    test-rmse:1.395601+0.285395 
## [60] train-rmse:0.452338+0.020831    test-rmse:1.388228+0.286623 
## [61] train-rmse:0.441331+0.019642    test-rmse:1.382247+0.288753 
## [62] train-rmse:0.427861+0.020551    test-rmse:1.375533+0.290595 
## [63] train-rmse:0.415108+0.021528    test-rmse:1.372823+0.290263 
## [64] train-rmse:0.404081+0.019095    test-rmse:1.366684+0.291838 
## [65] train-rmse:0.394267+0.018092    test-rmse:1.360834+0.290330 
## [66] train-rmse:0.384778+0.018459    test-rmse:1.355286+0.291718 
## [67] train-rmse:0.374703+0.018851    test-rmse:1.351409+0.291821 
## [68] train-rmse:0.364222+0.019941    test-rmse:1.349716+0.294590 
## [69] train-rmse:0.356682+0.019642    test-rmse:1.345385+0.293954 
## [70] train-rmse:0.348636+0.019843    test-rmse:1.341040+0.295204 
## [71] train-rmse:0.338611+0.016220    test-rmse:1.336187+0.295928 
## [72] train-rmse:0.330747+0.014739    test-rmse:1.332917+0.292699 
## [73] train-rmse:0.322817+0.015672    test-rmse:1.331252+0.294897 
## [74] train-rmse:0.315253+0.014868    test-rmse:1.323653+0.290739 
## [75] train-rmse:0.307681+0.014018    test-rmse:1.320057+0.293774 
## [76] train-rmse:0.301390+0.013861    test-rmse:1.318435+0.295147 
## [77] train-rmse:0.295380+0.013808    test-rmse:1.316075+0.295796 
## [78] train-rmse:0.288474+0.013939    test-rmse:1.314124+0.296064 
## [79] train-rmse:0.281268+0.012971    test-rmse:1.312444+0.297820 
## [80] train-rmse:0.276196+0.012649    test-rmse:1.309283+0.297159 
## [81] train-rmse:0.271108+0.012138    test-rmse:1.308178+0.298285 
## [82] train-rmse:0.265712+0.012129    test-rmse:1.306312+0.298556 
## [83] train-rmse:0.260919+0.012552    test-rmse:1.304054+0.298630 
## [84] train-rmse:0.254736+0.011196    test-rmse:1.301844+0.300253 
## [85] train-rmse:0.249356+0.011906    test-rmse:1.300878+0.299576 
## [86] train-rmse:0.242530+0.011633    test-rmse:1.298560+0.300057 
## [87] train-rmse:0.237460+0.010557    test-rmse:1.295484+0.299041 
## [88] train-rmse:0.233384+0.011167    test-rmse:1.294721+0.300138 
## [89] train-rmse:0.229435+0.010238    test-rmse:1.293888+0.300884 
## [90] train-rmse:0.225618+0.010414    test-rmse:1.292768+0.300400 
## [91] train-rmse:0.221488+0.009836    test-rmse:1.290922+0.302100 
## [92] train-rmse:0.215662+0.011081    test-rmse:1.289751+0.302541 
## [93] train-rmse:0.211374+0.010945    test-rmse:1.289831+0.302637 
## [94] train-rmse:0.207797+0.011264    test-rmse:1.288842+0.301911 
## [95] train-rmse:0.203168+0.011108    test-rmse:1.288706+0.301804 
## [96] train-rmse:0.198774+0.010547    test-rmse:1.287539+0.302634 
## [97] train-rmse:0.195057+0.010058    test-rmse:1.287657+0.303045 
## [98] train-rmse:0.191191+0.009659    test-rmse:1.287539+0.302761 
## [99] train-rmse:0.188286+0.010170    test-rmse:1.286687+0.302365 
## [100]    train-rmse:0.184351+0.010132    test-rmse:1.286770+0.302372 
## [101]    train-rmse:0.181060+0.009167    test-rmse:1.287258+0.302922 
## [102]    train-rmse:0.177605+0.009838    test-rmse:1.286181+0.304151 
## [103]    train-rmse:0.174369+0.009838    test-rmse:1.285395+0.304226 
## [104]    train-rmse:0.170937+0.009392    test-rmse:1.284581+0.304306 
## [105]    train-rmse:0.168281+0.009399    test-rmse:1.283721+0.305156 
## [106]    train-rmse:0.164623+0.008742    test-rmse:1.282333+0.303880 
## [107]    train-rmse:0.160808+0.009164    test-rmse:1.281053+0.303157 
## [108]    train-rmse:0.158184+0.009240    test-rmse:1.279658+0.303375 
## [109]    train-rmse:0.154805+0.009768    test-rmse:1.279933+0.302492 
## [110]    train-rmse:0.151878+0.009920    test-rmse:1.278936+0.303329 
## [111]    train-rmse:0.148961+0.009421    test-rmse:1.278407+0.303996 
## [112]    train-rmse:0.146175+0.008983    test-rmse:1.277630+0.304021 
## [113]    train-rmse:0.144323+0.008683    test-rmse:1.277453+0.303959 
## [114]    train-rmse:0.141825+0.008772    test-rmse:1.276955+0.304133 
## [115]    train-rmse:0.139507+0.008991    test-rmse:1.276818+0.304722 
## [116]    train-rmse:0.137127+0.008872    test-rmse:1.277109+0.304301 
## [117]    train-rmse:0.134894+0.008370    test-rmse:1.277029+0.304812 
## [118]    train-rmse:0.132444+0.008474    test-rmse:1.275850+0.304791 
## [119]    train-rmse:0.129684+0.008676    test-rmse:1.276243+0.304578 
## [120]    train-rmse:0.127370+0.009072    test-rmse:1.276061+0.304393 
## [121]    train-rmse:0.125093+0.008925    test-rmse:1.276462+0.304005 
## [122]    train-rmse:0.123036+0.009242    test-rmse:1.275837+0.303972 
## [123]    train-rmse:0.120882+0.008891    test-rmse:1.276393+0.304133 
## [124]    train-rmse:0.119051+0.009060    test-rmse:1.276543+0.303768 
## [125]    train-rmse:0.117422+0.008961    test-rmse:1.276314+0.303936 
## [126]    train-rmse:0.115676+0.009049    test-rmse:1.276349+0.304222 
## [127]    train-rmse:0.113758+0.009493    test-rmse:1.276198+0.304077 
## [128]    train-rmse:0.111656+0.009345    test-rmse:1.276203+0.304251 
## Stopping. Best iteration:
## [122]    train-rmse:0.123036+0.009242    test-rmse:1.275837+0.303972
## 
## 27 
## [1]  train-rmse:90.491687+0.101310   test-rmse:90.491937+0.489579 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:76.126776+0.089017   test-rmse:76.126749+0.511751 
## [3]  train-rmse:64.064040+0.079442   test-rmse:64.063658+0.532994 
## [4]  train-rmse:53.938632+0.072315   test-rmse:53.937809+0.553884 
## [5]  train-rmse:45.444263+0.067432   test-rmse:45.442850+0.574932 
## [6]  train-rmse:38.323881+0.064650   test-rmse:38.321713+0.596609 
## [7]  train-rmse:32.357049+0.062292   test-rmse:32.380286+0.599805 
## [8]  train-rmse:27.347881+0.054482   test-rmse:27.420350+0.612889 
## [9]  train-rmse:23.142614+0.043845   test-rmse:23.227096+0.593161 
## [10] train-rmse:19.610094+0.036791   test-rmse:19.703792+0.586145 
## [11] train-rmse:16.640550+0.030101   test-rmse:16.744168+0.579503 
## [12] train-rmse:14.139348+0.028534   test-rmse:14.255473+0.564351 
## [13] train-rmse:12.033090+0.025776   test-rmse:12.134680+0.555104 
## [14] train-rmse:10.260358+0.022901   test-rmse:10.360152+0.540845 
## [15] train-rmse:8.760678+0.021462    test-rmse:8.847631+0.532347 
## [16] train-rmse:7.502907+0.022787    test-rmse:7.594884+0.517849 
## [17] train-rmse:6.438340+0.020465    test-rmse:6.559697+0.529041 
## [18] train-rmse:5.541810+0.018999    test-rmse:5.671659+0.495632 
## [19] train-rmse:4.786810+0.016905    test-rmse:4.940214+0.477249 
## [20] train-rmse:4.153128+0.017113    test-rmse:4.330265+0.447208 
## [21] train-rmse:3.617587+0.016057    test-rmse:3.839933+0.442076 
## [22] train-rmse:3.167512+0.017769    test-rmse:3.407156+0.412359 
## [23] train-rmse:2.791443+0.019810    test-rmse:3.067735+0.390249 
## [24] train-rmse:2.473084+0.023583    test-rmse:2.791110+0.365581 
## [25] train-rmse:2.192799+0.030016    test-rmse:2.551064+0.357304 
## [26] train-rmse:1.962941+0.030562    test-rmse:2.359100+0.334306 
## [27] train-rmse:1.767521+0.029188    test-rmse:2.205886+0.302148 
## [28] train-rmse:1.604362+0.032527    test-rmse:2.076234+0.276655 
## [29] train-rmse:1.459207+0.032204    test-rmse:1.971579+0.253365 
## [30] train-rmse:1.338357+0.032577    test-rmse:1.887390+0.245276 
## [31] train-rmse:1.234029+0.028029    test-rmse:1.812015+0.232954 
## [32] train-rmse:1.138560+0.034412    test-rmse:1.758618+0.222570 
## [33] train-rmse:1.060524+0.036307    test-rmse:1.708025+0.216917 
## [34] train-rmse:0.995652+0.036596    test-rmse:1.665162+0.215091 
## [35] train-rmse:0.931661+0.037640    test-rmse:1.632586+0.210219 
## [36] train-rmse:0.876204+0.041000    test-rmse:1.591881+0.209055 
## [37] train-rmse:0.828557+0.037420    test-rmse:1.563314+0.211605 
## [38] train-rmse:0.783131+0.037669    test-rmse:1.537547+0.216673 
## [39] train-rmse:0.745291+0.039333    test-rmse:1.512925+0.218829 
## [40] train-rmse:0.708433+0.038226    test-rmse:1.487464+0.221897 
## [41] train-rmse:0.675667+0.033008    test-rmse:1.472031+0.216708 
## [42] train-rmse:0.648182+0.032641    test-rmse:1.450918+0.220914 
## [43] train-rmse:0.617280+0.032791    test-rmse:1.436117+0.221226 
## [44] train-rmse:0.589578+0.034444    test-rmse:1.420265+0.226409 
## [45] train-rmse:0.567228+0.033946    test-rmse:1.405023+0.225934 
## [46] train-rmse:0.545373+0.033817    test-rmse:1.394005+0.225618 
## [47] train-rmse:0.521603+0.031443    test-rmse:1.383021+0.229259 
## [48] train-rmse:0.502668+0.032761    test-rmse:1.376235+0.231528 
## [49] train-rmse:0.485723+0.030408    test-rmse:1.364576+0.232633 
## [50] train-rmse:0.469481+0.029924    test-rmse:1.356213+0.231898 
## [51] train-rmse:0.452063+0.029685    test-rmse:1.345957+0.230028 
## [52] train-rmse:0.436371+0.026810    test-rmse:1.334940+0.229376 
## [53] train-rmse:0.421010+0.026944    test-rmse:1.328382+0.229398 
## [54] train-rmse:0.406807+0.026355    test-rmse:1.326130+0.232855 
## [55] train-rmse:0.394442+0.024679    test-rmse:1.320212+0.232179 
## [56] train-rmse:0.382399+0.024577    test-rmse:1.315816+0.232857 
## [57] train-rmse:0.371013+0.022938    test-rmse:1.308341+0.238439 
## [58] train-rmse:0.360400+0.022013    test-rmse:1.305012+0.238162 
## [59] train-rmse:0.351165+0.021103    test-rmse:1.299187+0.239119 
## [60] train-rmse:0.341420+0.021534    test-rmse:1.295823+0.239151 
## [61] train-rmse:0.332266+0.021703    test-rmse:1.290351+0.240183 
## [62] train-rmse:0.321151+0.018803    test-rmse:1.288060+0.241197 
## [63] train-rmse:0.312487+0.017379    test-rmse:1.284554+0.243375 
## [64] train-rmse:0.303341+0.017924    test-rmse:1.281034+0.243410 
## [65] train-rmse:0.295428+0.016513    test-rmse:1.277818+0.243255 
## [66] train-rmse:0.287922+0.015826    test-rmse:1.274941+0.245288 
## [67] train-rmse:0.279148+0.015863    test-rmse:1.269902+0.241850 
## [68] train-rmse:0.269432+0.012852    test-rmse:1.269208+0.241896 
## [69] train-rmse:0.261822+0.012989    test-rmse:1.267003+0.242660 
## [70] train-rmse:0.253846+0.012714    test-rmse:1.264220+0.241762 
## [71] train-rmse:0.247464+0.012544    test-rmse:1.263131+0.244124 
## [72] train-rmse:0.242088+0.012044    test-rmse:1.261176+0.243674 
## [73] train-rmse:0.236344+0.011214    test-rmse:1.258899+0.243549 
## [74] train-rmse:0.229193+0.011525    test-rmse:1.257325+0.243059 
## [75] train-rmse:0.222796+0.010296    test-rmse:1.254034+0.243768 
## [76] train-rmse:0.218100+0.009836    test-rmse:1.252194+0.244531 
## [77] train-rmse:0.213399+0.008975    test-rmse:1.250173+0.246168 
## [78] train-rmse:0.207653+0.008925    test-rmse:1.248293+0.246989 
## [79] train-rmse:0.201373+0.009461    test-rmse:1.248076+0.246690 
## [80] train-rmse:0.196628+0.008574    test-rmse:1.246957+0.245919 
## [81] train-rmse:0.192215+0.009429    test-rmse:1.245758+0.247181 
## [82] train-rmse:0.187920+0.008382    test-rmse:1.244677+0.246501 
## [83] train-rmse:0.183536+0.008886    test-rmse:1.244189+0.246853 
## [84] train-rmse:0.179114+0.008457    test-rmse:1.242792+0.247697 
## [85] train-rmse:0.173585+0.009102    test-rmse:1.242105+0.248025 
## [86] train-rmse:0.169555+0.009266    test-rmse:1.241486+0.248468 
## [87] train-rmse:0.165373+0.008617    test-rmse:1.241127+0.248170 
## [88] train-rmse:0.161822+0.007903    test-rmse:1.240298+0.247805 
## [89] train-rmse:0.158253+0.008169    test-rmse:1.240256+0.248093 
## [90] train-rmse:0.155232+0.007936    test-rmse:1.239361+0.247904 
## [91] train-rmse:0.151885+0.007370    test-rmse:1.237927+0.248755 
## [92] train-rmse:0.147625+0.006241    test-rmse:1.237379+0.248511 
## [93] train-rmse:0.144311+0.006286    test-rmse:1.236554+0.247908 
## [94] train-rmse:0.140910+0.006660    test-rmse:1.237133+0.247136 
## [95] train-rmse:0.138286+0.006545    test-rmse:1.236461+0.246909 
## [96] train-rmse:0.135193+0.006837    test-rmse:1.235718+0.246754 
## [97] train-rmse:0.132401+0.006823    test-rmse:1.234661+0.246967 
## [98] train-rmse:0.129163+0.006002    test-rmse:1.234465+0.247145 
## [99] train-rmse:0.127048+0.005673    test-rmse:1.233923+0.246996 
## [100]    train-rmse:0.124202+0.006013    test-rmse:1.232829+0.247009 
## [101]    train-rmse:0.121725+0.005791    test-rmse:1.232478+0.247048 
## [102]    train-rmse:0.117289+0.005327    test-rmse:1.231241+0.246198 
## [103]    train-rmse:0.114797+0.005662    test-rmse:1.230964+0.246195 
## [104]    train-rmse:0.112254+0.005200    test-rmse:1.231198+0.246144 
## [105]    train-rmse:0.109763+0.004647    test-rmse:1.230893+0.246381 
## [106]    train-rmse:0.106970+0.004455    test-rmse:1.230543+0.246825 
## [107]    train-rmse:0.104036+0.003410    test-rmse:1.230448+0.246688 
## [108]    train-rmse:0.101752+0.003522    test-rmse:1.230471+0.246389 
## [109]    train-rmse:0.098990+0.004009    test-rmse:1.230064+0.246371 
## [110]    train-rmse:0.096246+0.003974    test-rmse:1.229039+0.245972 
## [111]    train-rmse:0.094160+0.004332    test-rmse:1.228122+0.246691 
## [112]    train-rmse:0.092057+0.004026    test-rmse:1.228050+0.246557 
## [113]    train-rmse:0.089708+0.003892    test-rmse:1.227781+0.246988 
## [114]    train-rmse:0.087918+0.004350    test-rmse:1.227693+0.246418 
## [115]    train-rmse:0.085812+0.004327    test-rmse:1.227438+0.246347 
## [116]    train-rmse:0.084049+0.004621    test-rmse:1.227462+0.246436 
## [117]    train-rmse:0.082399+0.004414    test-rmse:1.226447+0.246511 
## [118]    train-rmse:0.080654+0.004791    test-rmse:1.225893+0.246613 
## [119]    train-rmse:0.078780+0.004990    test-rmse:1.225440+0.246487 
## [120]    train-rmse:0.076668+0.004811    test-rmse:1.225432+0.246500 
## [121]    train-rmse:0.075017+0.004609    test-rmse:1.225416+0.246510 
## [122]    train-rmse:0.073126+0.004376    test-rmse:1.225446+0.246654 
## [123]    train-rmse:0.071276+0.004157    test-rmse:1.225854+0.247118 
## [124]    train-rmse:0.069578+0.003505    test-rmse:1.225631+0.247233 
## [125]    train-rmse:0.068037+0.003218    test-rmse:1.225427+0.247381 
## [126]    train-rmse:0.066627+0.003140    test-rmse:1.225412+0.247198 
## [127]    train-rmse:0.065443+0.003093    test-rmse:1.225225+0.247287 
## [128]    train-rmse:0.063638+0.003287    test-rmse:1.225246+0.247797 
## [129]    train-rmse:0.062206+0.003464    test-rmse:1.225188+0.247971 
## [130]    train-rmse:0.061028+0.003570    test-rmse:1.225105+0.248032 
## [131]    train-rmse:0.059720+0.003371    test-rmse:1.225064+0.248029 
## [132]    train-rmse:0.058322+0.003243    test-rmse:1.224408+0.247963 
## [133]    train-rmse:0.057127+0.003584    test-rmse:1.223780+0.247611 
## [134]    train-rmse:0.055715+0.003230    test-rmse:1.223512+0.247812 
## [135]    train-rmse:0.054517+0.002960    test-rmse:1.223501+0.247774 
## [136]    train-rmse:0.053714+0.003067    test-rmse:1.223371+0.247847 
## [137]    train-rmse:0.052710+0.002894    test-rmse:1.223230+0.247785 
## [138]    train-rmse:0.051229+0.002569    test-rmse:1.222729+0.247740 
## [139]    train-rmse:0.050417+0.002764    test-rmse:1.222321+0.247871 
## [140]    train-rmse:0.049285+0.002355    test-rmse:1.221555+0.247546 
## [141]    train-rmse:0.048207+0.002470    test-rmse:1.221609+0.247707 
## [142]    train-rmse:0.047154+0.002346    test-rmse:1.221615+0.247714 
## [143]    train-rmse:0.046373+0.002194    test-rmse:1.221634+0.247774 
## [144]    train-rmse:0.045666+0.002286    test-rmse:1.221446+0.247903 
## [145]    train-rmse:0.044634+0.002443    test-rmse:1.221169+0.247820 
## [146]    train-rmse:0.043867+0.002262    test-rmse:1.221151+0.247666 
## [147]    train-rmse:0.043207+0.002287    test-rmse:1.221049+0.247869 
## [148]    train-rmse:0.042469+0.002174    test-rmse:1.221091+0.247710 
## [149]    train-rmse:0.041734+0.002182    test-rmse:1.220783+0.247575 
## [150]    train-rmse:0.040707+0.002046    test-rmse:1.220787+0.247551 
## [151]    train-rmse:0.039693+0.002177    test-rmse:1.220619+0.247561 
## [152]    train-rmse:0.038923+0.002372    test-rmse:1.220685+0.247359 
## [153]    train-rmse:0.038275+0.002457    test-rmse:1.220609+0.247552 
## [154]    train-rmse:0.037537+0.002311    test-rmse:1.220742+0.247377 
## [155]    train-rmse:0.036718+0.002021    test-rmse:1.220667+0.247403 
## [156]    train-rmse:0.035788+0.001915    test-rmse:1.220547+0.247401 
## [157]    train-rmse:0.035110+0.001720    test-rmse:1.220333+0.247415 
## [158]    train-rmse:0.034368+0.001686    test-rmse:1.220291+0.247358 
## [159]    train-rmse:0.033707+0.001973    test-rmse:1.220251+0.247435 
## [160]    train-rmse:0.032915+0.001906    test-rmse:1.219991+0.247336 
## [161]    train-rmse:0.032176+0.001994    test-rmse:1.220125+0.247499 
## [162]    train-rmse:0.031455+0.001812    test-rmse:1.220115+0.247525 
## [163]    train-rmse:0.030836+0.001725    test-rmse:1.220026+0.247528 
## [164]    train-rmse:0.030197+0.001719    test-rmse:1.219958+0.247478 
## [165]    train-rmse:0.029671+0.001558    test-rmse:1.219882+0.247516 
## [166]    train-rmse:0.029030+0.001831    test-rmse:1.219677+0.247408 
## [167]    train-rmse:0.028493+0.001738    test-rmse:1.219531+0.247365 
## [168]    train-rmse:0.027724+0.001879    test-rmse:1.219395+0.247599 
## [169]    train-rmse:0.027116+0.001829    test-rmse:1.219215+0.247670 
## [170]    train-rmse:0.026486+0.001996    test-rmse:1.219141+0.247569 
## [171]    train-rmse:0.025756+0.002202    test-rmse:1.219057+0.247667 
## [172]    train-rmse:0.025142+0.002206    test-rmse:1.218891+0.247480 
## [173]    train-rmse:0.024700+0.002147    test-rmse:1.218851+0.247438 
## [174]    train-rmse:0.023956+0.002113    test-rmse:1.218790+0.247442 
## [175]    train-rmse:0.023532+0.002047    test-rmse:1.218726+0.247353 
## [176]    train-rmse:0.023117+0.001910    test-rmse:1.218612+0.247314 
## [177]    train-rmse:0.022507+0.001930    test-rmse:1.218449+0.247287 
## [178]    train-rmse:0.022036+0.001981    test-rmse:1.218420+0.247256 
## [179]    train-rmse:0.021731+0.001994    test-rmse:1.218409+0.247296 
## [180]    train-rmse:0.021355+0.002032    test-rmse:1.218396+0.247297 
## [181]    train-rmse:0.020985+0.001860    test-rmse:1.218428+0.247340 
## [182]    train-rmse:0.020558+0.001868    test-rmse:1.218426+0.247354 
## [183]    train-rmse:0.020261+0.001839    test-rmse:1.218417+0.247372 
## [184]    train-rmse:0.019982+0.001724    test-rmse:1.218355+0.247400 
## [185]    train-rmse:0.019608+0.001844    test-rmse:1.218332+0.247376 
## [186]    train-rmse:0.019154+0.001733    test-rmse:1.218281+0.247380 
## [187]    train-rmse:0.018822+0.001776    test-rmse:1.218246+0.247409 
## [188]    train-rmse:0.018505+0.001720    test-rmse:1.218083+0.247320 
## [189]    train-rmse:0.018116+0.001727    test-rmse:1.218122+0.247341 
## [190]    train-rmse:0.017894+0.001660    test-rmse:1.218103+0.247327 
## [191]    train-rmse:0.017557+0.001741    test-rmse:1.218066+0.247319 
## [192]    train-rmse:0.017206+0.001597    test-rmse:1.218026+0.247415 
## [193]    train-rmse:0.016898+0.001537    test-rmse:1.217988+0.247441 
## [194]    train-rmse:0.016591+0.001511    test-rmse:1.217931+0.247376 
## [195]    train-rmse:0.016309+0.001546    test-rmse:1.217984+0.247349 
## [196]    train-rmse:0.015956+0.001393    test-rmse:1.217971+0.247354 
## [197]    train-rmse:0.015618+0.001433    test-rmse:1.217969+0.247298 
## [198]    train-rmse:0.015321+0.001381    test-rmse:1.217911+0.247269 
## [199]    train-rmse:0.015058+0.001349    test-rmse:1.217850+0.247265 
## [200]    train-rmse:0.014799+0.001431    test-rmse:1.217811+0.247248 
## [201]    train-rmse:0.014536+0.001401    test-rmse:1.217838+0.247264 
## [202]    train-rmse:0.014217+0.001430    test-rmse:1.217811+0.247255 
## [203]    train-rmse:0.013926+0.001324    test-rmse:1.217845+0.247179 
## [204]    train-rmse:0.013610+0.001379    test-rmse:1.217855+0.247197 
## [205]    train-rmse:0.013333+0.001311    test-rmse:1.217849+0.247156 
## [206]    train-rmse:0.013092+0.001311    test-rmse:1.217811+0.247181 
## [207]    train-rmse:0.012847+0.001238    test-rmse:1.217791+0.247179 
## [208]    train-rmse:0.012661+0.001152    test-rmse:1.217796+0.247223 
## [209]    train-rmse:0.012452+0.001233    test-rmse:1.217789+0.247214 
## [210]    train-rmse:0.012192+0.001220    test-rmse:1.217741+0.247194 
## [211]    train-rmse:0.012027+0.001211    test-rmse:1.217736+0.247205 
## [212]    train-rmse:0.011847+0.001180    test-rmse:1.217740+0.247226 
## [213]    train-rmse:0.011551+0.001147    test-rmse:1.217729+0.247217 
## [214]    train-rmse:0.011324+0.001023    test-rmse:1.217712+0.247219 
## [215]    train-rmse:0.011118+0.001091    test-rmse:1.217666+0.247260 
## [216]    train-rmse:0.010946+0.001036    test-rmse:1.217677+0.247296 
## [217]    train-rmse:0.010773+0.000973    test-rmse:1.217683+0.247328 
## [218]    train-rmse:0.010550+0.001013    test-rmse:1.217720+0.247325 
## [219]    train-rmse:0.010366+0.001057    test-rmse:1.217700+0.247325 
## [220]    train-rmse:0.010123+0.001063    test-rmse:1.217672+0.247361 
## [221]    train-rmse:0.009972+0.001056    test-rmse:1.217652+0.247366 
## [222]    train-rmse:0.009760+0.001097    test-rmse:1.217652+0.247345 
## [223]    train-rmse:0.009585+0.001092    test-rmse:1.217618+0.247379 
## [224]    train-rmse:0.009416+0.001088    test-rmse:1.217598+0.247343 
## [225]    train-rmse:0.009288+0.001120    test-rmse:1.217584+0.247344 
## [226]    train-rmse:0.009097+0.001069    test-rmse:1.217610+0.247364 
## [227]    train-rmse:0.008957+0.001031    test-rmse:1.217575+0.247352 
## [228]    train-rmse:0.008837+0.000999    test-rmse:1.217542+0.247362 
## [229]    train-rmse:0.008688+0.001041    test-rmse:1.217480+0.247353 
## [230]    train-rmse:0.008554+0.001023    test-rmse:1.217461+0.247360 
## [231]    train-rmse:0.008391+0.000978    test-rmse:1.217416+0.247357 
## [232]    train-rmse:0.008266+0.000951    test-rmse:1.217427+0.247393 
## [233]    train-rmse:0.008117+0.000989    test-rmse:1.217377+0.247401 
## [234]    train-rmse:0.007980+0.000976    test-rmse:1.217362+0.247423 
## [235]    train-rmse:0.007856+0.000978    test-rmse:1.217336+0.247402 
## [236]    train-rmse:0.007697+0.000970    test-rmse:1.217361+0.247378 
## [237]    train-rmse:0.007577+0.001000    test-rmse:1.217343+0.247403 
## [238]    train-rmse:0.007436+0.000942    test-rmse:1.217327+0.247400 
## [239]    train-rmse:0.007312+0.000926    test-rmse:1.217327+0.247418 
## [240]    train-rmse:0.007166+0.000881    test-rmse:1.217329+0.247418 
## [241]    train-rmse:0.007025+0.000829    test-rmse:1.217297+0.247444 
## [242]    train-rmse:0.006887+0.000835    test-rmse:1.217278+0.247438 
## [243]    train-rmse:0.006744+0.000770    test-rmse:1.217307+0.247452 
## [244]    train-rmse:0.006616+0.000770    test-rmse:1.217309+0.247442 
## [245]    train-rmse:0.006509+0.000728    test-rmse:1.217306+0.247458 
## [246]    train-rmse:0.006368+0.000758    test-rmse:1.217315+0.247441 
## [247]    train-rmse:0.006232+0.000784    test-rmse:1.217304+0.247443 
## [248]    train-rmse:0.006122+0.000755    test-rmse:1.217311+0.247462 
## Stopping. Best iteration:
## [242]    train-rmse:0.006887+0.000835    test-rmse:1.217278+0.247438
## 
## 28 
## [1]  train-rmse:88.354776+0.099451   test-rmse:88.354991+0.492721 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.580200+0.086121   test-rmse:72.580084+0.517683 
## [3]  train-rmse:59.651027+0.076207   test-rmse:59.650480+0.541664 
## [4]  train-rmse:49.060283+0.069346   test-rmse:49.059156+0.565463 
## [5]  train-rmse:40.392603+0.065298   test-rmse:40.390688+0.589811 
## [6]  train-rmse:33.305450+0.062294   test-rmse:33.325024+0.602958 
## [7]  train-rmse:27.508679+0.058860   test-rmse:27.554566+0.620972 
## [8]  train-rmse:22.770783+0.052111   test-rmse:22.835533+0.630949 
## [9]  train-rmse:18.903834+0.046090   test-rmse:18.976729+0.635782 
## [10] train-rmse:15.756250+0.041559   test-rmse:15.829300+0.614686 
## [11] train-rmse:13.202671+0.038209   test-rmse:13.283407+0.619139 
## [12] train-rmse:11.141500+0.037056   test-rmse:11.259582+0.653336 
## [13] train-rmse:9.487753+0.037294    test-rmse:9.599920+0.644895 
## [14] train-rmse:8.170487+0.041202    test-rmse:8.280559+0.618018 
## [15] train-rmse:7.130328+0.044029    test-rmse:7.233652+0.605272 
## [16] train-rmse:6.316209+0.046702    test-rmse:6.451578+0.650261 
## [17] train-rmse:5.686757+0.050673    test-rmse:5.825238+0.592969 
## [18] train-rmse:5.201585+0.054575    test-rmse:5.354011+0.596740 
## [19] train-rmse:4.833377+0.056455    test-rmse:4.964974+0.558675 
## [20] train-rmse:4.552054+0.058826    test-rmse:4.709401+0.580361 
## [21] train-rmse:4.332538+0.060598    test-rmse:4.485790+0.517057 
## [22] train-rmse:4.162644+0.062126    test-rmse:4.324848+0.478652 
## [23] train-rmse:4.030247+0.060889    test-rmse:4.192762+0.476440 
## [24] train-rmse:3.924270+0.060278    test-rmse:4.092720+0.483915 
## [25] train-rmse:3.836081+0.060291    test-rmse:3.994037+0.451272 
## [26] train-rmse:3.761173+0.060593    test-rmse:3.937055+0.423560 
## [27] train-rmse:3.696754+0.059158    test-rmse:3.909680+0.420039 
## [28] train-rmse:3.640555+0.057655    test-rmse:3.837839+0.390707 
## [29] train-rmse:3.590137+0.056377    test-rmse:3.802343+0.382483 
## [30] train-rmse:3.542737+0.055282    test-rmse:3.763977+0.382153 
## [31] train-rmse:3.498500+0.054505    test-rmse:3.719457+0.384227 
## [32] train-rmse:3.455911+0.053867    test-rmse:3.647723+0.351571 
## [33] train-rmse:3.414998+0.053394    test-rmse:3.613521+0.371913 
## [34] train-rmse:3.375688+0.052771    test-rmse:3.588664+0.358605 
## [35] train-rmse:3.338638+0.052095    test-rmse:3.538701+0.334134 
## [36] train-rmse:3.302863+0.050327    test-rmse:3.513398+0.332699 
## [37] train-rmse:3.268821+0.048858    test-rmse:3.491195+0.336209 
## [38] train-rmse:3.235354+0.047960    test-rmse:3.461427+0.335507 
## [39] train-rmse:3.202406+0.046915    test-rmse:3.441848+0.336438 
## [40] train-rmse:3.170376+0.045651    test-rmse:3.414826+0.347883 
## [41] train-rmse:3.139339+0.045092    test-rmse:3.378871+0.337009 
## [42] train-rmse:3.108593+0.044517    test-rmse:3.328404+0.314520 
## [43] train-rmse:3.078644+0.044210    test-rmse:3.297090+0.320145 
## [44] train-rmse:3.049492+0.043772    test-rmse:3.294551+0.324082 
## [45] train-rmse:3.020278+0.042906    test-rmse:3.261720+0.324378 
## [46] train-rmse:2.992573+0.042398    test-rmse:3.238803+0.318829 
## [47] train-rmse:2.965773+0.041581    test-rmse:3.218809+0.321168 
## [48] train-rmse:2.939748+0.040748    test-rmse:3.196153+0.330196 
## [49] train-rmse:2.914021+0.040104    test-rmse:3.163465+0.313626 
## [50] train-rmse:2.888573+0.039700    test-rmse:3.137511+0.314046 
## [51] train-rmse:2.863348+0.039372    test-rmse:3.116422+0.321134 
## [52] train-rmse:2.838581+0.039203    test-rmse:3.098934+0.326517 
## [53] train-rmse:2.814428+0.038891    test-rmse:3.074495+0.327502 
## [54] train-rmse:2.790275+0.038617    test-rmse:3.052695+0.328372 
## [55] train-rmse:2.766607+0.038667    test-rmse:3.018165+0.323456 
## [56] train-rmse:2.743227+0.038882    test-rmse:2.981754+0.304698 
## [57] train-rmse:2.720113+0.039096    test-rmse:2.957214+0.307508 
## [58] train-rmse:2.697934+0.038984    test-rmse:2.945052+0.309659 
## [59] train-rmse:2.676099+0.038844    test-rmse:2.925038+0.319406 
## [60] train-rmse:2.654651+0.038828    test-rmse:2.910090+0.321736 
## [61] train-rmse:2.633650+0.038529    test-rmse:2.905263+0.314411 
## [62] train-rmse:2.612728+0.038214    test-rmse:2.880835+0.298281 
## [63] train-rmse:2.592502+0.037772    test-rmse:2.868216+0.304699 
## [64] train-rmse:2.572053+0.037583    test-rmse:2.847792+0.309117 
## [65] train-rmse:2.552209+0.037554    test-rmse:2.833647+0.306230 
## [66] train-rmse:2.532728+0.037141    test-rmse:2.811809+0.309316 
## [67] train-rmse:2.513213+0.036895    test-rmse:2.793742+0.310122 
## [68] train-rmse:2.493889+0.036478    test-rmse:2.766936+0.304892 
## [69] train-rmse:2.474990+0.036105    test-rmse:2.751485+0.314692 
## [70] train-rmse:2.456238+0.035787    test-rmse:2.723656+0.293843 
## [71] train-rmse:2.437690+0.035064    test-rmse:2.701153+0.288752 
## [72] train-rmse:2.419593+0.034700    test-rmse:2.686823+0.295221 
## [73] train-rmse:2.401555+0.034379    test-rmse:2.671405+0.299691 
## [74] train-rmse:2.383796+0.033956    test-rmse:2.651234+0.308420 
## [75] train-rmse:2.366308+0.033603    test-rmse:2.640647+0.315073 
## [76] train-rmse:2.349043+0.033170    test-rmse:2.619194+0.316792 
## [77] train-rmse:2.332365+0.032803    test-rmse:2.607481+0.300476 
## [78] train-rmse:2.315730+0.032486    test-rmse:2.588751+0.289747 
## [79] train-rmse:2.299380+0.032258    test-rmse:2.575058+0.286864 
## [80] train-rmse:2.283098+0.032203    test-rmse:2.559220+0.291224 
## [81] train-rmse:2.267115+0.031999    test-rmse:2.539968+0.290121 
## [82] train-rmse:2.251110+0.031818    test-rmse:2.517953+0.281562 
## [83] train-rmse:2.235078+0.031426    test-rmse:2.507936+0.283210 
## [84] train-rmse:2.219481+0.031065    test-rmse:2.495868+0.290949 
## [85] train-rmse:2.203865+0.030674    test-rmse:2.481093+0.286313 
## [86] train-rmse:2.188219+0.030421    test-rmse:2.466408+0.282141 
## [87] train-rmse:2.173101+0.030324    test-rmse:2.447541+0.289644 
## [88] train-rmse:2.158077+0.029984    test-rmse:2.437271+0.292857 
## [89] train-rmse:2.143415+0.029577    test-rmse:2.427928+0.291936 
## [90] train-rmse:2.128721+0.029162    test-rmse:2.409289+0.272943 
## [91] train-rmse:2.114310+0.028668    test-rmse:2.393881+0.280248 
## [92] train-rmse:2.100185+0.028314    test-rmse:2.380846+0.285672 
## [93] train-rmse:2.086388+0.028082    test-rmse:2.365719+0.279541 
## [94] train-rmse:2.072586+0.027844    test-rmse:2.353176+0.277702 
## [95] train-rmse:2.059015+0.027534    test-rmse:2.339427+0.280666 
## [96] train-rmse:2.045459+0.027186    test-rmse:2.321109+0.275261 
## [97] train-rmse:2.032058+0.026843    test-rmse:2.307884+0.272459 
## [98] train-rmse:2.018798+0.026435    test-rmse:2.295791+0.262962 
## [99] train-rmse:2.005475+0.026132    test-rmse:2.278371+0.254167 
## [100]    train-rmse:1.992281+0.025850    test-rmse:2.252606+0.255487 
## [101]    train-rmse:1.979233+0.025632    test-rmse:2.244127+0.257798 
## [102]    train-rmse:1.966227+0.025300    test-rmse:2.226949+0.258788 
## [103]    train-rmse:1.953382+0.025049    test-rmse:2.213304+0.255341 
## [104]    train-rmse:1.940805+0.024846    test-rmse:2.203781+0.256239 
## [105]    train-rmse:1.928423+0.024772    test-rmse:2.198071+0.257491 
## [106]    train-rmse:1.916245+0.024430    test-rmse:2.183981+0.266002 
## [107]    train-rmse:1.904135+0.024188    test-rmse:2.173656+0.268022 
## [108]    train-rmse:1.892442+0.023919    test-rmse:2.166145+0.266329 
## [109]    train-rmse:1.880748+0.023736    test-rmse:2.155259+0.261218 
## [110]    train-rmse:1.869172+0.023405    test-rmse:2.146470+0.260180 
## [111]    train-rmse:1.857552+0.023108    test-rmse:2.131926+0.256475 
## [112]    train-rmse:1.846225+0.022834    test-rmse:2.126431+0.259220 
## [113]    train-rmse:1.834940+0.022585    test-rmse:2.113743+0.264203 
## [114]    train-rmse:1.823689+0.022298    test-rmse:2.110979+0.260243 
## [115]    train-rmse:1.812578+0.022100    test-rmse:2.102247+0.261836 
## [116]    train-rmse:1.801557+0.021848    test-rmse:2.088968+0.246127 
## [117]    train-rmse:1.790687+0.021692    test-rmse:2.081031+0.244223 
## [118]    train-rmse:1.779807+0.021495    test-rmse:2.066845+0.246847 
## [119]    train-rmse:1.768861+0.021377    test-rmse:2.054955+0.249362 
## [120]    train-rmse:1.758075+0.021189    test-rmse:2.047121+0.250146 
## [121]    train-rmse:1.747465+0.020804    test-rmse:2.034331+0.249155 
## [122]    train-rmse:1.737052+0.020603    test-rmse:2.022781+0.240493 
## [123]    train-rmse:1.726703+0.020522    test-rmse:2.014978+0.247153 
## [124]    train-rmse:1.716390+0.020357    test-rmse:2.005596+0.248161 
## [125]    train-rmse:1.706372+0.020232    test-rmse:1.992461+0.248546 
## [126]    train-rmse:1.696455+0.020005    test-rmse:1.984733+0.246470 
## [127]    train-rmse:1.686609+0.019825    test-rmse:1.974231+0.249632 
## [128]    train-rmse:1.677038+0.019616    test-rmse:1.968484+0.248128 
## [129]    train-rmse:1.667570+0.019431    test-rmse:1.966031+0.250378 
## [130]    train-rmse:1.658179+0.019322    test-rmse:1.955194+0.254780 
## [131]    train-rmse:1.648903+0.019248    test-rmse:1.947865+0.254285 
## [132]    train-rmse:1.639710+0.019017    test-rmse:1.938088+0.253876 
## [133]    train-rmse:1.630544+0.018727    test-rmse:1.930067+0.250185 
## [134]    train-rmse:1.621357+0.018415    test-rmse:1.919773+0.252206 
## [135]    train-rmse:1.612174+0.018230    test-rmse:1.908771+0.248698 
## [136]    train-rmse:1.603289+0.018044    test-rmse:1.894286+0.245765 
## [137]    train-rmse:1.594327+0.017856    test-rmse:1.879918+0.232715 
## [138]    train-rmse:1.585421+0.017663    test-rmse:1.873755+0.232980 
## [139]    train-rmse:1.576698+0.017590    test-rmse:1.866733+0.238113 
## [140]    train-rmse:1.568086+0.017448    test-rmse:1.861172+0.238388 
## [141]    train-rmse:1.559578+0.017419    test-rmse:1.854457+0.239197 
## [142]    train-rmse:1.551182+0.017365    test-rmse:1.845275+0.236738 
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## [563]    train-rmse:0.713562+0.013954    test-rmse:1.043508+0.135565 
## [564]    train-rmse:0.713306+0.013936    test-rmse:1.042475+0.134768 
## [565]    train-rmse:0.713039+0.013917    test-rmse:1.042452+0.134173 
## [566]    train-rmse:0.712766+0.013896    test-rmse:1.042796+0.134165 
## [567]    train-rmse:0.712507+0.013882    test-rmse:1.042632+0.133742 
## [568]    train-rmse:0.712239+0.013869    test-rmse:1.042345+0.134087 
## [569]    train-rmse:0.711981+0.013853    test-rmse:1.040918+0.133704 
## [570]    train-rmse:0.711724+0.013835    test-rmse:1.040277+0.133460 
## [571]    train-rmse:0.711475+0.013818    test-rmse:1.039804+0.133065 
## [572]    train-rmse:0.711214+0.013803    test-rmse:1.039339+0.132966 
## [573]    train-rmse:0.710960+0.013790    test-rmse:1.039358+0.133120 
## [574]    train-rmse:0.710704+0.013771    test-rmse:1.039331+0.132733 
## [575]    train-rmse:0.710457+0.013754    test-rmse:1.039197+0.132654 
## [576]    train-rmse:0.710205+0.013732    test-rmse:1.039827+0.132069 
## [577]    train-rmse:0.709956+0.013717    test-rmse:1.039942+0.132197 
## [578]    train-rmse:0.709712+0.013703    test-rmse:1.040158+0.131995 
## [579]    train-rmse:0.709460+0.013688    test-rmse:1.039870+0.131282 
## [580]    train-rmse:0.709212+0.013671    test-rmse:1.040000+0.130861 
## [581]    train-rmse:0.708959+0.013654    test-rmse:1.039806+0.129946 
## Stopping. Best iteration:
## [575]    train-rmse:0.710457+0.013754    test-rmse:1.039197+0.132654
## 
## 29 
## [1]  train-rmse:88.354776+0.099451   test-rmse:88.354991+0.492721 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.580200+0.086121   test-rmse:72.580084+0.517683 
## [3]  train-rmse:59.651027+0.076207   test-rmse:59.650480+0.541664 
## [4]  train-rmse:49.060283+0.069346   test-rmse:49.059156+0.565463 
## [5]  train-rmse:40.392603+0.065298   test-rmse:40.390688+0.589811 
## [6]  train-rmse:33.305450+0.062294   test-rmse:33.325024+0.602958 
## [7]  train-rmse:27.501240+0.056303   test-rmse:27.565675+0.605247 
## [8]  train-rmse:22.748923+0.043983   test-rmse:22.834604+0.595306 
## [9]  train-rmse:18.857207+0.034942   test-rmse:18.942480+0.554897 
## [10] train-rmse:15.680107+0.030189   test-rmse:15.792283+0.570148 
## [11] train-rmse:13.091815+0.028666   test-rmse:13.211942+0.589982 
## [12] train-rmse:10.988389+0.029503   test-rmse:11.109339+0.600467 
## [13] train-rmse:9.284139+0.030401    test-rmse:9.403491+0.561410 
## [14] train-rmse:7.915690+0.032548    test-rmse:8.052855+0.573083 
## [15] train-rmse:6.820068+0.036483    test-rmse:6.947404+0.549529 
## [16] train-rmse:5.951501+0.039115    test-rmse:6.089870+0.546498 
## [17] train-rmse:5.265249+0.041469    test-rmse:5.423926+0.508121 
## [18] train-rmse:4.729290+0.046590    test-rmse:4.896954+0.504263 
## [19] train-rmse:4.312518+0.046447    test-rmse:4.499525+0.509782 
## [20] train-rmse:3.984436+0.046445    test-rmse:4.193286+0.474173 
## [21] train-rmse:3.726576+0.048184    test-rmse:3.969123+0.471700 
## [22] train-rmse:3.523119+0.048990    test-rmse:3.776852+0.453719 
## [23] train-rmse:3.354244+0.043740    test-rmse:3.644185+0.449980 
## [24] train-rmse:3.223950+0.043715    test-rmse:3.513074+0.419787 
## [25] train-rmse:3.114803+0.040811    test-rmse:3.392081+0.386838 
## [26] train-rmse:3.023158+0.040075    test-rmse:3.325596+0.376176 
## [27] train-rmse:2.945873+0.039198    test-rmse:3.240113+0.360405 
## [28] train-rmse:2.874294+0.035007    test-rmse:3.178414+0.337242 
## [29] train-rmse:2.809829+0.035283    test-rmse:3.127522+0.353776 
## [30] train-rmse:2.752241+0.034650    test-rmse:3.066547+0.330498 
## [31] train-rmse:2.696467+0.035282    test-rmse:3.020176+0.330344 
## [32] train-rmse:2.646891+0.033870    test-rmse:2.977808+0.326633 
## [33] train-rmse:2.599772+0.033213    test-rmse:2.932623+0.314249 
## [34] train-rmse:2.552564+0.032144    test-rmse:2.894003+0.306721 
## [35] train-rmse:2.507859+0.029122    test-rmse:2.850493+0.311785 
## [36] train-rmse:2.465164+0.028412    test-rmse:2.811457+0.293933 
## [37] train-rmse:2.422934+0.028904    test-rmse:2.776210+0.307306 
## [38] train-rmse:2.383384+0.028312    test-rmse:2.739430+0.299264 
## [39] train-rmse:2.344935+0.027318    test-rmse:2.701950+0.298541 
## [40] train-rmse:2.308398+0.026565    test-rmse:2.670362+0.283024 
## [41] train-rmse:2.270663+0.024163    test-rmse:2.651344+0.284238 
## [42] train-rmse:2.234721+0.022722    test-rmse:2.604272+0.281601 
## [43] train-rmse:2.200480+0.022284    test-rmse:2.581229+0.284662 
## [44] train-rmse:2.167258+0.021871    test-rmse:2.549736+0.281936 
## [45] train-rmse:2.134353+0.021607    test-rmse:2.513220+0.277506 
## [46] train-rmse:2.101975+0.021406    test-rmse:2.482495+0.267558 
## [47] train-rmse:2.070065+0.021706    test-rmse:2.458103+0.265417 
## [48] train-rmse:2.039868+0.020980    test-rmse:2.430024+0.252010 
## [49] train-rmse:2.010596+0.020378    test-rmse:2.405600+0.258449 
## [50] train-rmse:1.980836+0.019309    test-rmse:2.383979+0.265010 
## [51] train-rmse:1.952731+0.018669    test-rmse:2.347554+0.258450 
## [52] train-rmse:1.923951+0.017156    test-rmse:2.315165+0.258385 
## [53] train-rmse:1.896824+0.016641    test-rmse:2.286675+0.256380 
## [54] train-rmse:1.870243+0.015960    test-rmse:2.267987+0.256060 
## [55] train-rmse:1.842488+0.014014    test-rmse:2.251363+0.254020 
## [56] train-rmse:1.815610+0.012721    test-rmse:2.222551+0.240465 
## [57] train-rmse:1.790428+0.011483    test-rmse:2.198938+0.247726 
## [58] train-rmse:1.766061+0.009976    test-rmse:2.189796+0.248188 
## [59] train-rmse:1.741615+0.009899    test-rmse:2.171895+0.250531 
## [60] train-rmse:1.718405+0.009515    test-rmse:2.147085+0.248611 
## [61] train-rmse:1.694615+0.009040    test-rmse:2.129032+0.237498 
## [62] train-rmse:1.669440+0.009687    test-rmse:2.105237+0.242892 
## [63] train-rmse:1.646894+0.009501    test-rmse:2.092453+0.243439 
## [64] train-rmse:1.625275+0.009041    test-rmse:2.076485+0.248900 
## [65] train-rmse:1.603930+0.008601    test-rmse:2.056785+0.251123 
## [66] train-rmse:1.582787+0.008632    test-rmse:2.038523+0.247078 
## [67] train-rmse:1.562254+0.007929    test-rmse:2.017499+0.243799 
## [68] train-rmse:1.542704+0.007057    test-rmse:2.000550+0.249751 
## [69] train-rmse:1.523218+0.006402    test-rmse:1.976692+0.248244 
## [70] train-rmse:1.504139+0.006067    test-rmse:1.965080+0.250880 
## [71] train-rmse:1.485071+0.005779    test-rmse:1.947909+0.237707 
## [72] train-rmse:1.466679+0.005003    test-rmse:1.935800+0.245203 
## [73] train-rmse:1.446616+0.007634    test-rmse:1.926907+0.244001 
## [74] train-rmse:1.428509+0.006945    test-rmse:1.914659+0.240568 
## [75] train-rmse:1.410975+0.007099    test-rmse:1.894520+0.243572 
## [76] train-rmse:1.393649+0.007042    test-rmse:1.881825+0.237420 
## [77] train-rmse:1.376794+0.006905    test-rmse:1.866773+0.235850 
## [78] train-rmse:1.358657+0.007039    test-rmse:1.843515+0.231741 
## [79] train-rmse:1.341998+0.006476    test-rmse:1.821258+0.229005 
## [80] train-rmse:1.326079+0.006693    test-rmse:1.808397+0.229920 
## [81] train-rmse:1.310639+0.006222    test-rmse:1.795737+0.231766 
## [82] train-rmse:1.295556+0.005959    test-rmse:1.779291+0.228098 
## [83] train-rmse:1.279371+0.005763    test-rmse:1.769112+0.230331 
## [84] train-rmse:1.264131+0.005821    test-rmse:1.758783+0.228932 
## [85] train-rmse:1.249129+0.006578    test-rmse:1.745655+0.232259 
## [86] train-rmse:1.235153+0.007079    test-rmse:1.736314+0.227592 
## [87] train-rmse:1.221157+0.007365    test-rmse:1.729048+0.231988 
## [88] train-rmse:1.207424+0.007976    test-rmse:1.719965+0.239097 
## [89] train-rmse:1.193762+0.008409    test-rmse:1.701142+0.230412 
## [90] train-rmse:1.178893+0.006540    test-rmse:1.689677+0.225960 
## [91] train-rmse:1.165895+0.006789    test-rmse:1.678592+0.229716 
## [92] train-rmse:1.152603+0.007169    test-rmse:1.667281+0.232987 
## [93] train-rmse:1.139760+0.007028    test-rmse:1.654003+0.232709 
## [94] train-rmse:1.127460+0.007192    test-rmse:1.645444+0.227533 
## [95] train-rmse:1.115472+0.007260    test-rmse:1.636014+0.227052 
## [96] train-rmse:1.103773+0.007174    test-rmse:1.624495+0.232335 
## [97] train-rmse:1.091213+0.006575    test-rmse:1.616107+0.231078 
## [98] train-rmse:1.079924+0.006658    test-rmse:1.605198+0.232806 
## [99] train-rmse:1.068134+0.006905    test-rmse:1.595931+0.238437 
## [100]    train-rmse:1.055596+0.007622    test-rmse:1.587633+0.236316 
## [101]    train-rmse:1.044238+0.007547    test-rmse:1.579739+0.234900 
## [102]    train-rmse:1.033547+0.008267    test-rmse:1.570386+0.238584 
## [103]    train-rmse:1.022473+0.008490    test-rmse:1.568024+0.241276 
## [104]    train-rmse:1.012438+0.008698    test-rmse:1.554768+0.239903 
## [105]    train-rmse:1.001504+0.008583    test-rmse:1.548684+0.241972 
## [106]    train-rmse:0.991775+0.008967    test-rmse:1.543464+0.244418 
## [107]    train-rmse:0.982288+0.009103    test-rmse:1.535521+0.244000 
## [108]    train-rmse:0.972827+0.009049    test-rmse:1.527179+0.241386 
## [109]    train-rmse:0.963824+0.009479    test-rmse:1.518975+0.241558 
## [110]    train-rmse:0.954801+0.009906    test-rmse:1.512743+0.236955 
## [111]    train-rmse:0.945101+0.011247    test-rmse:1.499604+0.235204 
## [112]    train-rmse:0.936208+0.011439    test-rmse:1.489522+0.233138 
## [113]    train-rmse:0.926727+0.011617    test-rmse:1.486163+0.236179 
## [114]    train-rmse:0.918118+0.011955    test-rmse:1.480734+0.240918 
## [115]    train-rmse:0.907929+0.011287    test-rmse:1.477175+0.242069 
## [116]    train-rmse:0.898864+0.011117    test-rmse:1.471630+0.242332 
## [117]    train-rmse:0.890878+0.011343    test-rmse:1.465929+0.242418 
## [118]    train-rmse:0.882839+0.011251    test-rmse:1.457834+0.240976 
## [119]    train-rmse:0.874887+0.011471    test-rmse:1.451032+0.241866 
## [120]    train-rmse:0.867292+0.011672    test-rmse:1.442560+0.241769 
## [121]    train-rmse:0.859571+0.011553    test-rmse:1.435464+0.241136 
## [122]    train-rmse:0.851902+0.011681    test-rmse:1.428856+0.243975 
## [123]    train-rmse:0.844502+0.011873    test-rmse:1.419518+0.239947 
## [124]    train-rmse:0.837530+0.011865    test-rmse:1.413588+0.240518 
## [125]    train-rmse:0.830352+0.011842    test-rmse:1.411413+0.241600 
## [126]    train-rmse:0.823319+0.011741    test-rmse:1.406877+0.239001 
## [127]    train-rmse:0.815809+0.011736    test-rmse:1.404574+0.240465 
## [128]    train-rmse:0.808657+0.012846    test-rmse:1.402404+0.242149 
## [129]    train-rmse:0.801803+0.013123    test-rmse:1.398882+0.241557 
## [130]    train-rmse:0.795773+0.013535    test-rmse:1.397682+0.241574 
## [131]    train-rmse:0.788918+0.014269    test-rmse:1.394585+0.241873 
## [132]    train-rmse:0.782127+0.013897    test-rmse:1.388189+0.242235 
## [133]    train-rmse:0.775449+0.014963    test-rmse:1.383632+0.240349 
## [134]    train-rmse:0.768515+0.014939    test-rmse:1.375608+0.242663 
## [135]    train-rmse:0.761742+0.014858    test-rmse:1.371461+0.242452 
## [136]    train-rmse:0.756209+0.014811    test-rmse:1.365652+0.239839 
## [137]    train-rmse:0.750277+0.015086    test-rmse:1.361300+0.241689 
## [138]    train-rmse:0.745098+0.015084    test-rmse:1.356512+0.244622 
## [139]    train-rmse:0.739083+0.015769    test-rmse:1.355540+0.245241 
## [140]    train-rmse:0.733764+0.016013    test-rmse:1.352551+0.246823 
## [141]    train-rmse:0.728219+0.015956    test-rmse:1.347313+0.244027 
## [142]    train-rmse:0.722830+0.016226    test-rmse:1.345190+0.242961 
## [143]    train-rmse:0.717413+0.016469    test-rmse:1.340901+0.241231 
## [144]    train-rmse:0.710729+0.014871    test-rmse:1.337554+0.241760 
## [145]    train-rmse:0.705319+0.015130    test-rmse:1.334210+0.244242 
## [146]    train-rmse:0.700082+0.014754    test-rmse:1.331829+0.244813 
## [147]    train-rmse:0.695070+0.014714    test-rmse:1.326351+0.240803 
## [148]    train-rmse:0.690272+0.014666    test-rmse:1.320493+0.240576 
## [149]    train-rmse:0.685625+0.014966    test-rmse:1.317064+0.241864 
## [150]    train-rmse:0.681344+0.014885    test-rmse:1.313800+0.243488 
## [151]    train-rmse:0.677136+0.015018    test-rmse:1.311002+0.244433 
## [152]    train-rmse:0.672316+0.015015    test-rmse:1.309907+0.243783 
## [153]    train-rmse:0.667637+0.014549    test-rmse:1.307554+0.240815 
## [154]    train-rmse:0.663528+0.014523    test-rmse:1.305904+0.242057 
## [155]    train-rmse:0.659048+0.014409    test-rmse:1.300036+0.243215 
## [156]    train-rmse:0.654778+0.014450    test-rmse:1.295985+0.243839 
## [157]    train-rmse:0.649574+0.013192    test-rmse:1.292917+0.244439 
## [158]    train-rmse:0.644200+0.014207    test-rmse:1.292176+0.245011 
## [159]    train-rmse:0.639106+0.014801    test-rmse:1.290862+0.245163 
## [160]    train-rmse:0.635114+0.015302    test-rmse:1.290830+0.244033 
## [161]    train-rmse:0.631278+0.015345    test-rmse:1.285170+0.242448 
## [162]    train-rmse:0.627548+0.015123    test-rmse:1.281468+0.243276 
## [163]    train-rmse:0.623593+0.014975    test-rmse:1.277931+0.246303 
## [164]    train-rmse:0.619642+0.014787    test-rmse:1.272952+0.248009 
## [165]    train-rmse:0.615844+0.014362    test-rmse:1.268710+0.248875 
## [166]    train-rmse:0.610952+0.014241    test-rmse:1.268214+0.249324 
## [167]    train-rmse:0.605974+0.015215    test-rmse:1.264699+0.250069 
## [168]    train-rmse:0.602572+0.015366    test-rmse:1.263839+0.250360 
## [169]    train-rmse:0.599071+0.015432    test-rmse:1.261737+0.253242 
## [170]    train-rmse:0.594391+0.015941    test-rmse:1.259706+0.252858 
## [171]    train-rmse:0.591028+0.016285    test-rmse:1.258640+0.252755 
## [172]    train-rmse:0.587853+0.016267    test-rmse:1.256813+0.253049 
## [173]    train-rmse:0.584768+0.016030    test-rmse:1.255589+0.249996 
## [174]    train-rmse:0.581561+0.016049    test-rmse:1.253472+0.248936 
## [175]    train-rmse:0.578037+0.015672    test-rmse:1.252224+0.249736 
## [176]    train-rmse:0.575217+0.016104    test-rmse:1.248788+0.250593 
## [177]    train-rmse:0.572463+0.016117    test-rmse:1.247912+0.251144 
## [178]    train-rmse:0.569607+0.016048    test-rmse:1.245566+0.252851 
## [179]    train-rmse:0.566220+0.015411    test-rmse:1.241525+0.252142 
## [180]    train-rmse:0.563303+0.015151    test-rmse:1.239541+0.252274 
## [181]    train-rmse:0.560292+0.015743    test-rmse:1.235824+0.250342 
## [182]    train-rmse:0.557219+0.016624    test-rmse:1.232112+0.252810 
## [183]    train-rmse:0.554660+0.016582    test-rmse:1.230761+0.252996 
## [184]    train-rmse:0.551661+0.016572    test-rmse:1.228863+0.253382 
## [185]    train-rmse:0.548558+0.016336    test-rmse:1.228189+0.254305 
## [186]    train-rmse:0.545694+0.016647    test-rmse:1.227018+0.253587 
## [187]    train-rmse:0.543211+0.016577    test-rmse:1.225635+0.253252 
## [188]    train-rmse:0.540883+0.016481    test-rmse:1.223193+0.254425 
## [189]    train-rmse:0.537262+0.016710    test-rmse:1.221930+0.256116 
## [190]    train-rmse:0.534394+0.017390    test-rmse:1.221627+0.257004 
## [191]    train-rmse:0.531888+0.017203    test-rmse:1.219954+0.257323 
## [192]    train-rmse:0.529581+0.017464    test-rmse:1.218913+0.258098 
## [193]    train-rmse:0.526957+0.017539    test-rmse:1.216458+0.258893 
## [194]    train-rmse:0.524721+0.017611    test-rmse:1.215083+0.258465 
## [195]    train-rmse:0.522134+0.017469    test-rmse:1.215673+0.258521 
## [196]    train-rmse:0.520018+0.017573    test-rmse:1.211301+0.258679 
## [197]    train-rmse:0.517625+0.017864    test-rmse:1.209112+0.259020 
## [198]    train-rmse:0.515382+0.017909    test-rmse:1.207531+0.259574 
## [199]    train-rmse:0.513187+0.017852    test-rmse:1.206402+0.260401 
## [200]    train-rmse:0.510284+0.017874    test-rmse:1.204919+0.261807 
## [201]    train-rmse:0.508191+0.017728    test-rmse:1.203060+0.261614 
## [202]    train-rmse:0.506094+0.017535    test-rmse:1.202246+0.261384 
## [203]    train-rmse:0.504012+0.017454    test-rmse:1.202782+0.262711 
## [204]    train-rmse:0.501621+0.017205    test-rmse:1.200948+0.261589 
## [205]    train-rmse:0.499445+0.017258    test-rmse:1.199084+0.262025 
## [206]    train-rmse:0.496533+0.016435    test-rmse:1.196466+0.262377 
## [207]    train-rmse:0.494380+0.016762    test-rmse:1.195771+0.262330 
## [208]    train-rmse:0.492276+0.016906    test-rmse:1.194794+0.262474 
## [209]    train-rmse:0.490019+0.017008    test-rmse:1.193101+0.262910 
## [210]    train-rmse:0.487945+0.017375    test-rmse:1.192022+0.263836 
## [211]    train-rmse:0.486007+0.017647    test-rmse:1.192455+0.264084 
## [212]    train-rmse:0.483560+0.017450    test-rmse:1.191550+0.264046 
## [213]    train-rmse:0.481677+0.017408    test-rmse:1.190126+0.264244 
## [214]    train-rmse:0.479802+0.017337    test-rmse:1.189377+0.264487 
## [215]    train-rmse:0.478050+0.017295    test-rmse:1.188563+0.264983 
## [216]    train-rmse:0.475692+0.017797    test-rmse:1.187757+0.264704 
## [217]    train-rmse:0.474072+0.017817    test-rmse:1.185984+0.265007 
## [218]    train-rmse:0.472377+0.017853    test-rmse:1.185228+0.264938 
## [219]    train-rmse:0.470150+0.017596    test-rmse:1.183379+0.265804 
## [220]    train-rmse:0.468436+0.017431    test-rmse:1.180743+0.266066 
## [221]    train-rmse:0.466750+0.017538    test-rmse:1.180904+0.266020 
## [222]    train-rmse:0.465087+0.017840    test-rmse:1.181260+0.265401 
## [223]    train-rmse:0.463562+0.018016    test-rmse:1.180647+0.267059 
## [224]    train-rmse:0.462015+0.018049    test-rmse:1.179569+0.267221 
## [225]    train-rmse:0.460341+0.018256    test-rmse:1.176695+0.268874 
## [226]    train-rmse:0.458653+0.018832    test-rmse:1.175750+0.268446 
## [227]    train-rmse:0.457310+0.018859    test-rmse:1.173337+0.269033 
## [228]    train-rmse:0.455966+0.018798    test-rmse:1.172733+0.268823 
## [229]    train-rmse:0.454239+0.018790    test-rmse:1.172584+0.269591 
## [230]    train-rmse:0.452493+0.019275    test-rmse:1.171203+0.270212 
## [231]    train-rmse:0.451175+0.019423    test-rmse:1.169791+0.270644 
## [232]    train-rmse:0.449376+0.019152    test-rmse:1.168303+0.270336 
## [233]    train-rmse:0.447816+0.019100    test-rmse:1.168289+0.271362 
## [234]    train-rmse:0.446374+0.018947    test-rmse:1.166012+0.272252 
## [235]    train-rmse:0.444897+0.018762    test-rmse:1.165773+0.271959 
## [236]    train-rmse:0.443548+0.018747    test-rmse:1.165084+0.270902 
## [237]    train-rmse:0.441718+0.018686    test-rmse:1.161791+0.265989 
## [238]    train-rmse:0.440141+0.018671    test-rmse:1.162150+0.265767 
## [239]    train-rmse:0.438550+0.018483    test-rmse:1.161948+0.266379 
## [240]    train-rmse:0.437222+0.018757    test-rmse:1.161646+0.265815 
## [241]    train-rmse:0.435799+0.018969    test-rmse:1.161159+0.266413 
## [242]    train-rmse:0.434605+0.019065    test-rmse:1.159237+0.265881 
## [243]    train-rmse:0.433440+0.018956    test-rmse:1.157034+0.266877 
## [244]    train-rmse:0.432067+0.019098    test-rmse:1.156223+0.267240 
## [245]    train-rmse:0.430628+0.018944    test-rmse:1.156579+0.267192 
## [246]    train-rmse:0.429547+0.019171    test-rmse:1.155089+0.268266 
## [247]    train-rmse:0.428228+0.019290    test-rmse:1.154950+0.269074 
## [248]    train-rmse:0.426800+0.019735    test-rmse:1.154404+0.268694 
## [249]    train-rmse:0.425364+0.019606    test-rmse:1.153598+0.268672 
## [250]    train-rmse:0.424150+0.019533    test-rmse:1.153368+0.268451 
## [251]    train-rmse:0.422917+0.019340    test-rmse:1.153340+0.269257 
## [252]    train-rmse:0.421479+0.019225    test-rmse:1.153964+0.269154 
## [253]    train-rmse:0.419640+0.019296    test-rmse:1.153294+0.269065 
## [254]    train-rmse:0.418533+0.019366    test-rmse:1.152149+0.269634 
## [255]    train-rmse:0.417502+0.019588    test-rmse:1.151007+0.268376 
## [256]    train-rmse:0.416354+0.019719    test-rmse:1.151303+0.268802 
## [257]    train-rmse:0.414605+0.019693    test-rmse:1.148960+0.268775 
## [258]    train-rmse:0.413222+0.019581    test-rmse:1.147954+0.269993 
## [259]    train-rmse:0.411966+0.019399    test-rmse:1.148854+0.268770 
## [260]    train-rmse:0.410523+0.019085    test-rmse:1.147507+0.269928 
## [261]    train-rmse:0.409443+0.018937    test-rmse:1.146298+0.269495 
## [262]    train-rmse:0.407713+0.019119    test-rmse:1.144716+0.270831 
## [263]    train-rmse:0.406142+0.019120    test-rmse:1.144966+0.271548 
## [264]    train-rmse:0.404662+0.018857    test-rmse:1.143642+0.270896 
## [265]    train-rmse:0.403595+0.018876    test-rmse:1.143400+0.271192 
## [266]    train-rmse:0.402347+0.019157    test-rmse:1.142790+0.270894 
## [267]    train-rmse:0.401491+0.019187    test-rmse:1.142120+0.271124 
## [268]    train-rmse:0.400506+0.019203    test-rmse:1.140121+0.270178 
## [269]    train-rmse:0.399694+0.019389    test-rmse:1.139224+0.270640 
## [270]    train-rmse:0.398768+0.019296    test-rmse:1.138616+0.270699 
## [271]    train-rmse:0.397496+0.018895    test-rmse:1.137675+0.270536 
## [272]    train-rmse:0.396383+0.018861    test-rmse:1.137222+0.270428 
## [273]    train-rmse:0.395287+0.018718    test-rmse:1.136004+0.271553 
## [274]    train-rmse:0.394061+0.018420    test-rmse:1.136186+0.272212 
## [275]    train-rmse:0.392737+0.018253    test-rmse:1.135286+0.272887 
## [276]    train-rmse:0.391746+0.018244    test-rmse:1.132446+0.269247 
## [277]    train-rmse:0.390328+0.018410    test-rmse:1.131978+0.268558 
## [278]    train-rmse:0.389502+0.018427    test-rmse:1.132208+0.269054 
## [279]    train-rmse:0.388428+0.018345    test-rmse:1.132688+0.269162 
## [280]    train-rmse:0.387534+0.018499    test-rmse:1.131207+0.269318 
## [281]    train-rmse:0.385863+0.018899    test-rmse:1.129397+0.270018 
## [282]    train-rmse:0.384836+0.019060    test-rmse:1.129736+0.269796 
## [283]    train-rmse:0.384034+0.019121    test-rmse:1.129186+0.270174 
## [284]    train-rmse:0.383006+0.019253    test-rmse:1.129069+0.270265 
## [285]    train-rmse:0.381867+0.019590    test-rmse:1.129468+0.269835 
## [286]    train-rmse:0.380948+0.019515    test-rmse:1.129068+0.269587 
## [287]    train-rmse:0.379459+0.019226    test-rmse:1.128726+0.270074 
## [288]    train-rmse:0.378560+0.019136    test-rmse:1.128170+0.269651 
## [289]    train-rmse:0.377712+0.019065    test-rmse:1.128419+0.270308 
## [290]    train-rmse:0.377044+0.018989    test-rmse:1.126739+0.270423 
## [291]    train-rmse:0.376069+0.018884    test-rmse:1.126407+0.270381 
## [292]    train-rmse:0.375260+0.019056    test-rmse:1.126217+0.270470 
## [293]    train-rmse:0.373421+0.018408    test-rmse:1.126240+0.271019 
## [294]    train-rmse:0.372663+0.018351    test-rmse:1.125429+0.271650 
## [295]    train-rmse:0.371806+0.018282    test-rmse:1.125485+0.272674 
## [296]    train-rmse:0.371010+0.018315    test-rmse:1.125127+0.272519 
## [297]    train-rmse:0.369821+0.018882    test-rmse:1.124474+0.271863 
## [298]    train-rmse:0.369071+0.018986    test-rmse:1.124752+0.272037 
## [299]    train-rmse:0.368180+0.018906    test-rmse:1.124435+0.272181 
## [300]    train-rmse:0.367464+0.018919    test-rmse:1.124359+0.272087 
## [301]    train-rmse:0.366153+0.018643    test-rmse:1.123097+0.271627 
## [302]    train-rmse:0.365094+0.018306    test-rmse:1.123386+0.272301 
## [303]    train-rmse:0.364361+0.018229    test-rmse:1.121478+0.272234 
## [304]    train-rmse:0.363457+0.018149    test-rmse:1.121413+0.271962 
## [305]    train-rmse:0.361681+0.018190    test-rmse:1.120871+0.271670 
## [306]    train-rmse:0.360355+0.018095    test-rmse:1.120589+0.271676 
## [307]    train-rmse:0.358465+0.018175    test-rmse:1.119868+0.271625 
## [308]    train-rmse:0.357722+0.018469    test-rmse:1.118612+0.271194 
## [309]    train-rmse:0.356742+0.018278    test-rmse:1.119048+0.271050 
## [310]    train-rmse:0.355914+0.018234    test-rmse:1.118842+0.271204 
## [311]    train-rmse:0.355356+0.018243    test-rmse:1.118794+0.271618 
## [312]    train-rmse:0.354299+0.018226    test-rmse:1.118425+0.271778 
## [313]    train-rmse:0.353147+0.018288    test-rmse:1.117416+0.271297 
## [314]    train-rmse:0.352431+0.018292    test-rmse:1.117124+0.272092 
## [315]    train-rmse:0.351509+0.018459    test-rmse:1.116747+0.271715 
## [316]    train-rmse:0.350358+0.018336    test-rmse:1.116191+0.271480 
## [317]    train-rmse:0.349501+0.018089    test-rmse:1.115979+0.271797 
## [318]    train-rmse:0.348505+0.017857    test-rmse:1.115993+0.272550 
## [319]    train-rmse:0.347575+0.017601    test-rmse:1.116116+0.273113 
## [320]    train-rmse:0.346740+0.017566    test-rmse:1.116203+0.273596 
## [321]    train-rmse:0.345954+0.017731    test-rmse:1.115204+0.272505 
## [322]    train-rmse:0.345351+0.017752    test-rmse:1.115058+0.272344 
## [323]    train-rmse:0.344522+0.017576    test-rmse:1.113693+0.272515 
## [324]    train-rmse:0.343479+0.017481    test-rmse:1.113354+0.272515 
## [325]    train-rmse:0.342814+0.017473    test-rmse:1.113486+0.272175 
## [326]    train-rmse:0.341445+0.017225    test-rmse:1.113702+0.272819 
## [327]    train-rmse:0.340640+0.017007    test-rmse:1.112229+0.273631 
## [328]    train-rmse:0.339998+0.016922    test-rmse:1.112022+0.273486 
## [329]    train-rmse:0.339312+0.016920    test-rmse:1.111903+0.273615 
## [330]    train-rmse:0.337966+0.016678    test-rmse:1.111898+0.272842 
## [331]    train-rmse:0.336972+0.016597    test-rmse:1.111039+0.272780 
## [332]    train-rmse:0.336129+0.017041    test-rmse:1.110857+0.272371 
## [333]    train-rmse:0.334967+0.016892    test-rmse:1.110637+0.273308 
## [334]    train-rmse:0.334380+0.016957    test-rmse:1.110074+0.273703 
## [335]    train-rmse:0.333305+0.016671    test-rmse:1.110043+0.273714 
## [336]    train-rmse:0.332799+0.016700    test-rmse:1.109101+0.273636 
## [337]    train-rmse:0.331906+0.016441    test-rmse:1.109092+0.273456 
## [338]    train-rmse:0.331100+0.016259    test-rmse:1.109425+0.273166 
## [339]    train-rmse:0.330483+0.016334    test-rmse:1.109096+0.273016 
## [340]    train-rmse:0.330012+0.016321    test-rmse:1.108831+0.272949 
## [341]    train-rmse:0.329057+0.016730    test-rmse:1.108927+0.272294 
## [342]    train-rmse:0.327858+0.016624    test-rmse:1.108261+0.272507 
## [343]    train-rmse:0.327029+0.016613    test-rmse:1.107395+0.273057 
## [344]    train-rmse:0.326432+0.016767    test-rmse:1.107047+0.272750 
## [345]    train-rmse:0.325807+0.016750    test-rmse:1.106953+0.272977 
## [346]    train-rmse:0.324892+0.017219    test-rmse:1.107092+0.273716 
## [347]    train-rmse:0.324194+0.017158    test-rmse:1.107565+0.273251 
## [348]    train-rmse:0.323457+0.016903    test-rmse:1.107450+0.274264 
## [349]    train-rmse:0.322191+0.016852    test-rmse:1.106605+0.273943 
## [350]    train-rmse:0.321345+0.017065    test-rmse:1.106126+0.274146 
## [351]    train-rmse:0.320700+0.016999    test-rmse:1.106696+0.274066 
## [352]    train-rmse:0.319843+0.016672    test-rmse:1.106311+0.274055 
## [353]    train-rmse:0.319241+0.016704    test-rmse:1.106137+0.273716 
## [354]    train-rmse:0.318109+0.016802    test-rmse:1.105304+0.273542 
## [355]    train-rmse:0.317390+0.016793    test-rmse:1.105189+0.273765 
## [356]    train-rmse:0.316841+0.016739    test-rmse:1.105136+0.273343 
## [357]    train-rmse:0.315606+0.016562    test-rmse:1.104314+0.273799 
## [358]    train-rmse:0.314860+0.016660    test-rmse:1.103437+0.274693 
## [359]    train-rmse:0.313887+0.016317    test-rmse:1.103559+0.274946 
## [360]    train-rmse:0.312789+0.016143    test-rmse:1.103040+0.274994 
## [361]    train-rmse:0.311868+0.016039    test-rmse:1.102902+0.274725 
## [362]    train-rmse:0.311387+0.016089    test-rmse:1.102949+0.274607 
## [363]    train-rmse:0.310938+0.016057    test-rmse:1.102935+0.273804 
## [364]    train-rmse:0.310206+0.015998    test-rmse:1.102951+0.273671 
## [365]    train-rmse:0.309247+0.016230    test-rmse:1.102855+0.273628 
## [366]    train-rmse:0.308412+0.016562    test-rmse:1.102780+0.273987 
## [367]    train-rmse:0.307966+0.016550    test-rmse:1.102736+0.274309 
## [368]    train-rmse:0.307288+0.016862    test-rmse:1.102633+0.273656 
## [369]    train-rmse:0.306325+0.016610    test-rmse:1.102467+0.273500 
## [370]    train-rmse:0.305388+0.016452    test-rmse:1.101242+0.274052 
## [371]    train-rmse:0.304745+0.016267    test-rmse:1.101551+0.273668 
## [372]    train-rmse:0.303801+0.016151    test-rmse:1.101207+0.273442 
## [373]    train-rmse:0.303155+0.016010    test-rmse:1.100830+0.272971 
## [374]    train-rmse:0.302744+0.015983    test-rmse:1.100630+0.273088 
## [375]    train-rmse:0.302024+0.015702    test-rmse:1.100186+0.272935 
## [376]    train-rmse:0.301104+0.015643    test-rmse:1.101004+0.272632 
## [377]    train-rmse:0.300403+0.015541    test-rmse:1.101583+0.272814 
## [378]    train-rmse:0.299539+0.015348    test-rmse:1.101545+0.273140 
## [379]    train-rmse:0.298824+0.015484    test-rmse:1.101345+0.273199 
## [380]    train-rmse:0.298363+0.015370    test-rmse:1.101260+0.273685 
## [381]    train-rmse:0.297711+0.015484    test-rmse:1.102198+0.274189 
## Stopping. Best iteration:
## [375]    train-rmse:0.302024+0.015702    test-rmse:1.100186+0.272935
## 
## 30 
## [1]  train-rmse:88.354776+0.099451   test-rmse:88.354991+0.492721 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.580200+0.086121   test-rmse:72.580084+0.517683 
## [3]  train-rmse:59.651027+0.076207   test-rmse:59.650480+0.541664 
## [4]  train-rmse:49.060283+0.069346   test-rmse:49.059156+0.565463 
## [5]  train-rmse:40.392603+0.065298   test-rmse:40.390688+0.589811 
## [6]  train-rmse:33.305450+0.062294   test-rmse:33.325024+0.602958 
## [7]  train-rmse:27.499631+0.054718   test-rmse:27.567201+0.606506 
## [8]  train-rmse:22.742521+0.042423   test-rmse:22.827392+0.586102 
## [9]  train-rmse:18.844127+0.035821   test-rmse:18.944245+0.567467 
## [10] train-rmse:15.655837+0.030143   test-rmse:15.772980+0.580372 
## [11] train-rmse:13.045770+0.030218   test-rmse:13.161424+0.577854 
## [12] train-rmse:10.916797+0.029511   test-rmse:11.042306+0.563497 
## [13] train-rmse:9.184949+0.031596    test-rmse:9.303873+0.537073 
## [14] train-rmse:7.782081+0.032437    test-rmse:7.893612+0.497883 
## [15] train-rmse:6.650337+0.033765    test-rmse:6.799557+0.519137 
## [16] train-rmse:5.742958+0.035483    test-rmse:5.927860+0.527346 
## [17] train-rmse:5.014527+0.035904    test-rmse:5.194970+0.497376 
## [18] train-rmse:4.428476+0.030274    test-rmse:4.651711+0.465674 
## [19] train-rmse:3.969247+0.031642    test-rmse:4.216757+0.453661 
## [20] train-rmse:3.604374+0.032167    test-rmse:3.895411+0.434965 
## [21] train-rmse:3.316318+0.031981    test-rmse:3.620239+0.398874 
## [22] train-rmse:3.089347+0.031727    test-rmse:3.420059+0.394112 
## [23] train-rmse:2.904711+0.032087    test-rmse:3.246001+0.374138 
## [24] train-rmse:2.753156+0.028050    test-rmse:3.120859+0.350856 
## [25] train-rmse:2.630628+0.027160    test-rmse:3.011106+0.330538 
## [26] train-rmse:2.522051+0.027301    test-rmse:2.917670+0.311054 
## [27] train-rmse:2.433128+0.025556    test-rmse:2.842542+0.309584 
## [28] train-rmse:2.354186+0.025549    test-rmse:2.773059+0.311495 
## [29] train-rmse:2.279863+0.020255    test-rmse:2.718762+0.290765 
## [30] train-rmse:2.205109+0.020918    test-rmse:2.663625+0.248074 
## [31] train-rmse:2.148069+0.020199    test-rmse:2.610134+0.247277 
## [32] train-rmse:2.090926+0.020720    test-rmse:2.566875+0.255003 
## [33] train-rmse:2.038438+0.019519    test-rmse:2.522859+0.263028 
## [34] train-rmse:1.986624+0.019610    test-rmse:2.469702+0.261483 
## [35] train-rmse:1.937226+0.020806    test-rmse:2.429064+0.251930 
## [36] train-rmse:1.892482+0.020009    test-rmse:2.379215+0.233664 
## [37] train-rmse:1.847614+0.018067    test-rmse:2.351953+0.235871 
## [38] train-rmse:1.806151+0.017741    test-rmse:2.322679+0.246247 
## [39] train-rmse:1.767271+0.016616    test-rmse:2.291414+0.247721 
## [40] train-rmse:1.726941+0.017586    test-rmse:2.263771+0.247713 
## [41] train-rmse:1.688846+0.015646    test-rmse:2.222185+0.235154 
## [42] train-rmse:1.651066+0.014257    test-rmse:2.181652+0.225617 
## [43] train-rmse:1.616168+0.013171    test-rmse:2.148092+0.222977 
## [44] train-rmse:1.582360+0.013233    test-rmse:2.119085+0.225099 
## [45] train-rmse:1.548959+0.010616    test-rmse:2.100504+0.224974 
## [46] train-rmse:1.515945+0.011616    test-rmse:2.081021+0.228670 
## [47] train-rmse:1.485299+0.011117    test-rmse:2.046717+0.228403 
## [48] train-rmse:1.455782+0.010955    test-rmse:2.022526+0.224492 
## [49] train-rmse:1.426020+0.010190    test-rmse:1.995267+0.214043 
## [50] train-rmse:1.397077+0.010633    test-rmse:1.969251+0.223302 
## [51] train-rmse:1.368177+0.011243    test-rmse:1.951902+0.219014 
## [52] train-rmse:1.341063+0.010512    test-rmse:1.936571+0.218106 
## [53] train-rmse:1.313508+0.011630    test-rmse:1.909412+0.224692 
## [54] train-rmse:1.289015+0.011596    test-rmse:1.894929+0.222136 
## [55] train-rmse:1.262988+0.009765    test-rmse:1.863196+0.211479 
## [56] train-rmse:1.239362+0.009976    test-rmse:1.843078+0.211775 
## [57] train-rmse:1.217114+0.009901    test-rmse:1.822482+0.212702 
## [58] train-rmse:1.194828+0.010224    test-rmse:1.806715+0.211595 
## [59] train-rmse:1.171755+0.010234    test-rmse:1.793199+0.210073 
## [60] train-rmse:1.151337+0.009164    test-rmse:1.778525+0.213718 
## [61] train-rmse:1.131519+0.009567    test-rmse:1.760612+0.217303 
## [62] train-rmse:1.110275+0.009082    test-rmse:1.746604+0.216878 
## [63] train-rmse:1.090703+0.008884    test-rmse:1.723569+0.215173 
## [64] train-rmse:1.071172+0.008531    test-rmse:1.711148+0.215685 
## [65] train-rmse:1.052987+0.008086    test-rmse:1.694789+0.219957 
## [66] train-rmse:1.033610+0.008632    test-rmse:1.683858+0.218856 
## [67] train-rmse:1.016552+0.009020    test-rmse:1.669742+0.221590 
## [68] train-rmse:0.999848+0.009482    test-rmse:1.654494+0.226350 
## [69] train-rmse:0.982154+0.010362    test-rmse:1.638473+0.223970 
## [70] train-rmse:0.962439+0.012092    test-rmse:1.624970+0.216840 
## [71] train-rmse:0.946522+0.012208    test-rmse:1.613912+0.220911 
## [72] train-rmse:0.931159+0.013389    test-rmse:1.601234+0.225690 
## [73] train-rmse:0.915652+0.012945    test-rmse:1.587479+0.229359 
## [74] train-rmse:0.901163+0.012603    test-rmse:1.575399+0.234000 
## [75] train-rmse:0.886501+0.013471    test-rmse:1.568485+0.236635 
## [76] train-rmse:0.872714+0.013175    test-rmse:1.556881+0.232355 
## [77] train-rmse:0.858539+0.012985    test-rmse:1.548682+0.237126 
## [78] train-rmse:0.844864+0.012671    test-rmse:1.541764+0.234422 
## [79] train-rmse:0.832138+0.012731    test-rmse:1.529167+0.232267 
## [80] train-rmse:0.818933+0.011435    test-rmse:1.518041+0.233852 
## [81] train-rmse:0.806231+0.011297    test-rmse:1.508636+0.239101 
## [82] train-rmse:0.793055+0.011031    test-rmse:1.503121+0.242079 
## [83] train-rmse:0.781003+0.010542    test-rmse:1.491953+0.243142 
## [84] train-rmse:0.768480+0.012052    test-rmse:1.478178+0.236129 
## [85] train-rmse:0.757239+0.011615    test-rmse:1.472636+0.236679 
## [86] train-rmse:0.746176+0.010903    test-rmse:1.466297+0.243214 
## [87] train-rmse:0.735203+0.011199    test-rmse:1.460757+0.246494 
## [88] train-rmse:0.725714+0.011161    test-rmse:1.456030+0.245707 
## [89] train-rmse:0.715779+0.011744    test-rmse:1.444717+0.248366 
## [90] train-rmse:0.706325+0.012356    test-rmse:1.439741+0.248098 
## [91] train-rmse:0.697152+0.012400    test-rmse:1.434806+0.248253 
## [92] train-rmse:0.688493+0.012514    test-rmse:1.428708+0.250444 
## [93] train-rmse:0.677514+0.012260    test-rmse:1.417355+0.246577 
## [94] train-rmse:0.667899+0.011218    test-rmse:1.410257+0.243243 
## [95] train-rmse:0.657543+0.012987    test-rmse:1.403007+0.243558 
## [96] train-rmse:0.649904+0.012910    test-rmse:1.398112+0.248718 
## [97] train-rmse:0.641021+0.012621    test-rmse:1.393271+0.242249 
## [98] train-rmse:0.633779+0.012455    test-rmse:1.388155+0.246888 
## [99] train-rmse:0.624715+0.012053    test-rmse:1.378575+0.246625 
## [100]    train-rmse:0.616829+0.011856    test-rmse:1.376178+0.245976 
## [101]    train-rmse:0.608668+0.013154    test-rmse:1.371703+0.248786 
## [102]    train-rmse:0.601082+0.012974    test-rmse:1.367286+0.250975 
## [103]    train-rmse:0.594044+0.012970    test-rmse:1.363419+0.250174 
## [104]    train-rmse:0.586686+0.013834    test-rmse:1.357298+0.251916 
## [105]    train-rmse:0.579487+0.012944    test-rmse:1.351690+0.254266 
## [106]    train-rmse:0.571874+0.014337    test-rmse:1.346144+0.256265 
## [107]    train-rmse:0.565136+0.014885    test-rmse:1.343188+0.256240 
## [108]    train-rmse:0.559367+0.014487    test-rmse:1.340480+0.256013 
## [109]    train-rmse:0.552282+0.013812    test-rmse:1.337621+0.256581 
## [110]    train-rmse:0.546317+0.013573    test-rmse:1.332339+0.258785 
## [111]    train-rmse:0.540177+0.013337    test-rmse:1.330356+0.260699 
## [112]    train-rmse:0.534653+0.013467    test-rmse:1.323110+0.262970 
## [113]    train-rmse:0.529535+0.013364    test-rmse:1.321291+0.262945 
## [114]    train-rmse:0.522813+0.012790    test-rmse:1.317023+0.261019 
## [115]    train-rmse:0.516621+0.011940    test-rmse:1.308042+0.254110 
## [116]    train-rmse:0.510249+0.011294    test-rmse:1.307928+0.253612 
## [117]    train-rmse:0.505322+0.011934    test-rmse:1.303769+0.255281 
## [118]    train-rmse:0.500874+0.011464    test-rmse:1.300091+0.256327 
## [119]    train-rmse:0.495441+0.011065    test-rmse:1.298035+0.257563 
## [120]    train-rmse:0.491149+0.011101    test-rmse:1.293339+0.260378 
## [121]    train-rmse:0.486216+0.011170    test-rmse:1.286784+0.255102 
## [122]    train-rmse:0.481031+0.010817    test-rmse:1.285499+0.255795 
## [123]    train-rmse:0.476254+0.010989    test-rmse:1.283009+0.256260 
## [124]    train-rmse:0.471777+0.010414    test-rmse:1.279709+0.257637 
## [125]    train-rmse:0.467733+0.010349    test-rmse:1.277806+0.257526 
## [126]    train-rmse:0.463179+0.010027    test-rmse:1.276737+0.258421 
## [127]    train-rmse:0.458078+0.009982    test-rmse:1.276153+0.258798 
## [128]    train-rmse:0.454408+0.010059    test-rmse:1.271961+0.260188 
## [129]    train-rmse:0.450354+0.009795    test-rmse:1.272247+0.259642 
## [130]    train-rmse:0.445638+0.009783    test-rmse:1.271561+0.261043 
## [131]    train-rmse:0.441870+0.009661    test-rmse:1.268547+0.261347 
## [132]    train-rmse:0.437335+0.009818    test-rmse:1.266905+0.263476 
## [133]    train-rmse:0.433515+0.010076    test-rmse:1.261638+0.268949 
## [134]    train-rmse:0.429613+0.010619    test-rmse:1.259674+0.270264 
## [135]    train-rmse:0.426299+0.010483    test-rmse:1.254655+0.268972 
## [136]    train-rmse:0.422830+0.010204    test-rmse:1.255061+0.268174 
## [137]    train-rmse:0.419813+0.010096    test-rmse:1.253726+0.268891 
## [138]    train-rmse:0.416133+0.009677    test-rmse:1.251346+0.270348 
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## [233]    train-rmse:0.219942+0.009903    test-rmse:1.159131+0.293720 
## [234]    train-rmse:0.218882+0.009964    test-rmse:1.158966+0.294155 
## [235]    train-rmse:0.217747+0.009906    test-rmse:1.158418+0.294508 
## [236]    train-rmse:0.217006+0.009945    test-rmse:1.158256+0.294959 
## [237]    train-rmse:0.215809+0.010002    test-rmse:1.158034+0.295094 
## [238]    train-rmse:0.213902+0.009565    test-rmse:1.158988+0.295119 
## [239]    train-rmse:0.212723+0.009107    test-rmse:1.158442+0.295517 
## [240]    train-rmse:0.211557+0.009000    test-rmse:1.158494+0.295264 
## [241]    train-rmse:0.210494+0.009207    test-rmse:1.158569+0.295954 
## [242]    train-rmse:0.209654+0.009155    test-rmse:1.158081+0.296495 
## [243]    train-rmse:0.208776+0.009153    test-rmse:1.158314+0.296465 
## Stopping. Best iteration:
## [237]    train-rmse:0.215809+0.010002    test-rmse:1.158034+0.295094
## 
## 31 
## [1]  train-rmse:88.354776+0.099451   test-rmse:88.354991+0.492721 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.580200+0.086121   test-rmse:72.580084+0.517683 
## [3]  train-rmse:59.651027+0.076207   test-rmse:59.650480+0.541664 
## [4]  train-rmse:49.060283+0.069346   test-rmse:49.059156+0.565463 
## [5]  train-rmse:40.392603+0.065298   test-rmse:40.390688+0.589811 
## [6]  train-rmse:33.305450+0.062294   test-rmse:33.325024+0.602958 
## [7]  train-rmse:27.499631+0.054718   test-rmse:27.567201+0.606506 
## [8]  train-rmse:22.742521+0.042423   test-rmse:22.827392+0.586102 
## [9]  train-rmse:18.842251+0.035443   test-rmse:18.939813+0.563510 
## [10] train-rmse:15.644787+0.031946   test-rmse:15.741622+0.546056 
## [11] train-rmse:13.025645+0.027952   test-rmse:13.147386+0.573694 
## [12] train-rmse:10.877414+0.025790   test-rmse:10.979798+0.557818 
## [13] train-rmse:9.121586+0.025211    test-rmse:9.232568+0.543558 
## [14] train-rmse:7.689630+0.023213    test-rmse:7.793666+0.537134 
## [15] train-rmse:6.522987+0.020950    test-rmse:6.669151+0.540671 
## [16] train-rmse:5.579319+0.021161    test-rmse:5.715168+0.522890 
## [17] train-rmse:4.814496+0.018837    test-rmse:4.995788+0.506893 
## [18] train-rmse:4.196805+0.017980    test-rmse:4.398920+0.481394 
## [19] train-rmse:3.705448+0.018163    test-rmse:3.940366+0.451674 
## [20] train-rmse:3.311872+0.016557    test-rmse:3.556118+0.414984 
## [21] train-rmse:2.995890+0.018572    test-rmse:3.285905+0.408715 
## [22] train-rmse:2.734809+0.023812    test-rmse:3.041479+0.368574 
## [23] train-rmse:2.526822+0.025525    test-rmse:2.865014+0.352558 
## [24] train-rmse:2.359924+0.024734    test-rmse:2.710076+0.329976 
## [25] train-rmse:2.216305+0.029866    test-rmse:2.586653+0.289685 
## [26] train-rmse:2.099520+0.032084    test-rmse:2.495176+0.272530 
## [27] train-rmse:1.999319+0.029836    test-rmse:2.410047+0.283143 
## [28] train-rmse:1.901764+0.032505    test-rmse:2.334843+0.268638 
## [29] train-rmse:1.825347+0.030712    test-rmse:2.271738+0.252600 
## [30] train-rmse:1.756897+0.026589    test-rmse:2.213991+0.241290 
## [31] train-rmse:1.695756+0.026399    test-rmse:2.163303+0.240296 
## [32] train-rmse:1.634951+0.023416    test-rmse:2.127759+0.242823 
## [33] train-rmse:1.582133+0.023692    test-rmse:2.088775+0.236255 
## [34] train-rmse:1.528979+0.017639    test-rmse:2.045184+0.232333 
## [35] train-rmse:1.476290+0.021901    test-rmse:2.007545+0.229958 
## [36] train-rmse:1.432885+0.021388    test-rmse:1.964505+0.228836 
## [37] train-rmse:1.391731+0.021318    test-rmse:1.932514+0.221394 
## [38] train-rmse:1.350602+0.017354    test-rmse:1.898130+0.221036 
## [39] train-rmse:1.312542+0.016719    test-rmse:1.873587+0.213894 
## [40] train-rmse:1.277370+0.016142    test-rmse:1.849197+0.221300 
## [41] train-rmse:1.240726+0.017654    test-rmse:1.825618+0.225314 
## [42] train-rmse:1.206309+0.016815    test-rmse:1.795923+0.225215 
## [43] train-rmse:1.172337+0.020781    test-rmse:1.765740+0.225197 
## [44] train-rmse:1.141344+0.019409    test-rmse:1.744854+0.217953 
## [45] train-rmse:1.111007+0.020095    test-rmse:1.723179+0.219984 
## [46] train-rmse:1.082077+0.020163    test-rmse:1.700156+0.220366 
## [47] train-rmse:1.050906+0.019252    test-rmse:1.677650+0.225569 
## [48] train-rmse:1.023700+0.017468    test-rmse:1.661920+0.234489 
## [49] train-rmse:0.996142+0.019023    test-rmse:1.635615+0.239715 
## [50] train-rmse:0.971186+0.019504    test-rmse:1.616564+0.247338 
## [51] train-rmse:0.947003+0.019488    test-rmse:1.598650+0.243939 
## [52] train-rmse:0.924058+0.019336    test-rmse:1.575426+0.252399 
## [53] train-rmse:0.901789+0.017873    test-rmse:1.561300+0.249246 
## [54] train-rmse:0.880235+0.016904    test-rmse:1.548622+0.247570 
## [55] train-rmse:0.859662+0.016895    test-rmse:1.530511+0.253372 
## [56] train-rmse:0.840226+0.017138    test-rmse:1.511096+0.240105 
## [57] train-rmse:0.817057+0.014789    test-rmse:1.495461+0.247076 
## [58] train-rmse:0.799175+0.014676    test-rmse:1.477824+0.250383 
## [59] train-rmse:0.777856+0.014161    test-rmse:1.462465+0.241864 
## [60] train-rmse:0.760344+0.013790    test-rmse:1.450669+0.245030 
## [61] train-rmse:0.744568+0.013203    test-rmse:1.440056+0.244100 
## [62] train-rmse:0.726642+0.013323    test-rmse:1.426069+0.245437 
## [63] train-rmse:0.711530+0.013913    test-rmse:1.416891+0.248437 
## [64] train-rmse:0.696350+0.015453    test-rmse:1.405077+0.249382 
## [65] train-rmse:0.682828+0.016008    test-rmse:1.396673+0.250696 
## [66] train-rmse:0.663449+0.018143    test-rmse:1.386196+0.253823 
## [67] train-rmse:0.649272+0.015497    test-rmse:1.380975+0.255349 
## [68] train-rmse:0.636266+0.015474    test-rmse:1.361752+0.254025 
## [69] train-rmse:0.623288+0.015453    test-rmse:1.355886+0.259809 
## [70] train-rmse:0.611623+0.015228    test-rmse:1.351917+0.260564 
## [71] train-rmse:0.599669+0.015189    test-rmse:1.340426+0.261134 
## [72] train-rmse:0.588254+0.015090    test-rmse:1.333211+0.263228 
## [73] train-rmse:0.576944+0.015512    test-rmse:1.327637+0.265830 
## [74] train-rmse:0.566022+0.015784    test-rmse:1.321149+0.266233 
## [75] train-rmse:0.553932+0.013670    test-rmse:1.308165+0.254905 
## [76] train-rmse:0.544243+0.013686    test-rmse:1.306771+0.256582 
## [77] train-rmse:0.535219+0.013649    test-rmse:1.297479+0.256987 
## [78] train-rmse:0.524469+0.015040    test-rmse:1.295315+0.255674 
## [79] train-rmse:0.514942+0.014988    test-rmse:1.287033+0.260533 
## [80] train-rmse:0.505852+0.015389    test-rmse:1.279788+0.262778 
## [81] train-rmse:0.496923+0.013993    test-rmse:1.269985+0.254359 
## [82] train-rmse:0.488033+0.014633    test-rmse:1.265024+0.257074 
## [83] train-rmse:0.480552+0.014306    test-rmse:1.263319+0.258981 
## [84] train-rmse:0.472838+0.014527    test-rmse:1.260026+0.260429 
## [85] train-rmse:0.464871+0.014833    test-rmse:1.253851+0.264198 
## [86] train-rmse:0.457739+0.015510    test-rmse:1.251205+0.264264 
## [87] train-rmse:0.449572+0.014401    test-rmse:1.242432+0.256584 
## [88] train-rmse:0.442268+0.014442    test-rmse:1.239428+0.258407 
## [89] train-rmse:0.436143+0.014427    test-rmse:1.236415+0.257801 
## [90] train-rmse:0.429397+0.014732    test-rmse:1.234471+0.260334 
## [91] train-rmse:0.423284+0.014006    test-rmse:1.231974+0.260766 
## [92] train-rmse:0.415980+0.014321    test-rmse:1.226005+0.253181 
## [93] train-rmse:0.410263+0.014344    test-rmse:1.222088+0.256463 
## [94] train-rmse:0.403576+0.014349    test-rmse:1.219103+0.258129 
## [95] train-rmse:0.397656+0.013624    test-rmse:1.218108+0.261523 
## [96] train-rmse:0.391106+0.014868    test-rmse:1.216217+0.265505 
## [97] train-rmse:0.386475+0.014748    test-rmse:1.214375+0.266537 
## [98] train-rmse:0.380678+0.013756    test-rmse:1.210361+0.268048 
## [99] train-rmse:0.375366+0.013823    test-rmse:1.209768+0.270873 
## [100]    train-rmse:0.369662+0.013185    test-rmse:1.208577+0.269609 
## [101]    train-rmse:0.365625+0.013072    test-rmse:1.206750+0.270016 
## [102]    train-rmse:0.360802+0.013496    test-rmse:1.203258+0.270715 
## [103]    train-rmse:0.355371+0.014466    test-rmse:1.201377+0.272261 
## [104]    train-rmse:0.350998+0.014063    test-rmse:1.197462+0.272956 
## [105]    train-rmse:0.346438+0.013496    test-rmse:1.193731+0.266807 
## [106]    train-rmse:0.342802+0.013515    test-rmse:1.192410+0.266871 
## [107]    train-rmse:0.338682+0.013683    test-rmse:1.190285+0.267708 
## [108]    train-rmse:0.332446+0.011633    test-rmse:1.190103+0.267238 
## [109]    train-rmse:0.328594+0.011662    test-rmse:1.189747+0.266706 
## [110]    train-rmse:0.324571+0.012115    test-rmse:1.186288+0.269285 
## [111]    train-rmse:0.320013+0.012271    test-rmse:1.186332+0.270314 
## [112]    train-rmse:0.316686+0.012298    test-rmse:1.185233+0.270867 
## [113]    train-rmse:0.312920+0.011378    test-rmse:1.184720+0.272376 
## [114]    train-rmse:0.308936+0.011263    test-rmse:1.183725+0.273697 
## [115]    train-rmse:0.304778+0.011285    test-rmse:1.181365+0.276769 
## [116]    train-rmse:0.300864+0.010487    test-rmse:1.180654+0.277113 
## [117]    train-rmse:0.296918+0.010393    test-rmse:1.179876+0.276909 
## [118]    train-rmse:0.292865+0.010841    test-rmse:1.177720+0.277746 
## [119]    train-rmse:0.289296+0.010944    test-rmse:1.176903+0.278618 
## [120]    train-rmse:0.286824+0.010401    test-rmse:1.176355+0.279516 
## [121]    train-rmse:0.282675+0.010619    test-rmse:1.174522+0.280279 
## [122]    train-rmse:0.279217+0.010393    test-rmse:1.174185+0.280670 
## [123]    train-rmse:0.276949+0.009923    test-rmse:1.171942+0.282314 
## [124]    train-rmse:0.272916+0.010144    test-rmse:1.171349+0.282324 
## [125]    train-rmse:0.269582+0.009837    test-rmse:1.169030+0.279041 
## [126]    train-rmse:0.266229+0.010295    test-rmse:1.168387+0.279403 
## [127]    train-rmse:0.263302+0.010999    test-rmse:1.166629+0.280897 
## [128]    train-rmse:0.259065+0.009530    test-rmse:1.166173+0.281674 
## [129]    train-rmse:0.256622+0.009760    test-rmse:1.165921+0.281411 
## [130]    train-rmse:0.253131+0.009945    test-rmse:1.163916+0.282844 
## [131]    train-rmse:0.250829+0.009730    test-rmse:1.163189+0.283671 
## [132]    train-rmse:0.248300+0.009645    test-rmse:1.162494+0.283454 
## [133]    train-rmse:0.245718+0.010297    test-rmse:1.161883+0.283342 
## [134]    train-rmse:0.242731+0.010533    test-rmse:1.161133+0.283452 
## [135]    train-rmse:0.240691+0.010427    test-rmse:1.160310+0.283095 
## [136]    train-rmse:0.238502+0.010284    test-rmse:1.160226+0.285029 
## [137]    train-rmse:0.236705+0.010555    test-rmse:1.158592+0.285320 
## [138]    train-rmse:0.234432+0.010179    test-rmse:1.158022+0.285446 
## [139]    train-rmse:0.231279+0.009332    test-rmse:1.156610+0.285420 
## [140]    train-rmse:0.228117+0.009265    test-rmse:1.155977+0.285177 
## [141]    train-rmse:0.226171+0.009171    test-rmse:1.155599+0.285515 
## [142]    train-rmse:0.223937+0.009829    test-rmse:1.155973+0.286009 
## [143]    train-rmse:0.221521+0.010634    test-rmse:1.155460+0.286956 
## [144]    train-rmse:0.219512+0.010295    test-rmse:1.154634+0.287186 
## [145]    train-rmse:0.216743+0.011016    test-rmse:1.153563+0.287909 
## [146]    train-rmse:0.214714+0.010869    test-rmse:1.153444+0.287966 
## [147]    train-rmse:0.212763+0.011015    test-rmse:1.153649+0.287784 
## [148]    train-rmse:0.210392+0.011013    test-rmse:1.153371+0.286535 
## [149]    train-rmse:0.208306+0.011295    test-rmse:1.152964+0.288628 
## [150]    train-rmse:0.205906+0.011288    test-rmse:1.150720+0.288392 
## [151]    train-rmse:0.203247+0.012037    test-rmse:1.149840+0.289125 
## [152]    train-rmse:0.201334+0.012360    test-rmse:1.149351+0.289354 
## [153]    train-rmse:0.199248+0.011713    test-rmse:1.149116+0.289368 
## [154]    train-rmse:0.197493+0.011823    test-rmse:1.148496+0.289393 
## [155]    train-rmse:0.195007+0.011196    test-rmse:1.147983+0.289457 
## [156]    train-rmse:0.193110+0.011089    test-rmse:1.147946+0.289805 
## [157]    train-rmse:0.191029+0.010987    test-rmse:1.147649+0.290077 
## [158]    train-rmse:0.189661+0.010615    test-rmse:1.146994+0.290102 
## [159]    train-rmse:0.188293+0.010706    test-rmse:1.146730+0.289991 
## [160]    train-rmse:0.186378+0.010996    test-rmse:1.146508+0.289825 
## [161]    train-rmse:0.184624+0.011437    test-rmse:1.145734+0.289148 
## [162]    train-rmse:0.182646+0.011760    test-rmse:1.145405+0.289393 
## [163]    train-rmse:0.180310+0.012462    test-rmse:1.145762+0.289713 
## [164]    train-rmse:0.178574+0.012027    test-rmse:1.145473+0.289727 
## [165]    train-rmse:0.176586+0.011738    test-rmse:1.146228+0.289175 
## [166]    train-rmse:0.174632+0.011858    test-rmse:1.145584+0.289687 
## [167]    train-rmse:0.173165+0.012167    test-rmse:1.145329+0.289821 
## [168]    train-rmse:0.171461+0.011863    test-rmse:1.145305+0.290070 
## [169]    train-rmse:0.169997+0.011833    test-rmse:1.144729+0.290329 
## [170]    train-rmse:0.168925+0.011950    test-rmse:1.144178+0.290282 
## [171]    train-rmse:0.166930+0.011661    test-rmse:1.143543+0.290146 
## [172]    train-rmse:0.165636+0.011554    test-rmse:1.143270+0.289979 
## [173]    train-rmse:0.164220+0.011263    test-rmse:1.143251+0.290706 
## [174]    train-rmse:0.162771+0.011012    test-rmse:1.143155+0.290959 
## [175]    train-rmse:0.161095+0.011455    test-rmse:1.143114+0.290917 
## [176]    train-rmse:0.159613+0.011414    test-rmse:1.143272+0.291470 
## [177]    train-rmse:0.157178+0.011029    test-rmse:1.142689+0.291480 
## [178]    train-rmse:0.155540+0.011442    test-rmse:1.142132+0.292647 
## [179]    train-rmse:0.154319+0.011263    test-rmse:1.141635+0.293122 
## [180]    train-rmse:0.152549+0.010725    test-rmse:1.141723+0.293121 
## [181]    train-rmse:0.151164+0.010500    test-rmse:1.141392+0.293189 
## [182]    train-rmse:0.149905+0.010162    test-rmse:1.141231+0.293422 
## [183]    train-rmse:0.148770+0.010185    test-rmse:1.141577+0.294054 
## [184]    train-rmse:0.147391+0.010167    test-rmse:1.141619+0.294312 
## [185]    train-rmse:0.145756+0.009691    test-rmse:1.141899+0.294104 
## [186]    train-rmse:0.144747+0.009596    test-rmse:1.140960+0.294183 
## [187]    train-rmse:0.143537+0.009577    test-rmse:1.141109+0.294542 
## [188]    train-rmse:0.142371+0.009843    test-rmse:1.140891+0.294517 
## [189]    train-rmse:0.141037+0.009909    test-rmse:1.141073+0.294741 
## [190]    train-rmse:0.139751+0.009742    test-rmse:1.140394+0.294083 
## [191]    train-rmse:0.138562+0.009777    test-rmse:1.139659+0.294585 
## [192]    train-rmse:0.137348+0.009398    test-rmse:1.139519+0.294660 
## [193]    train-rmse:0.136268+0.009390    test-rmse:1.139192+0.294720 
## [194]    train-rmse:0.134602+0.009420    test-rmse:1.139360+0.294777 
## [195]    train-rmse:0.133358+0.009629    test-rmse:1.139226+0.294994 
## [196]    train-rmse:0.132335+0.009947    test-rmse:1.139198+0.295314 
## [197]    train-rmse:0.130911+0.009696    test-rmse:1.138824+0.294796 
## [198]    train-rmse:0.129380+0.009645    test-rmse:1.137605+0.292792 
## [199]    train-rmse:0.128611+0.009700    test-rmse:1.136334+0.291085 
## [200]    train-rmse:0.127392+0.009419    test-rmse:1.136574+0.290924 
## [201]    train-rmse:0.126065+0.009089    test-rmse:1.136543+0.290758 
## [202]    train-rmse:0.125216+0.009471    test-rmse:1.136507+0.290625 
## [203]    train-rmse:0.123913+0.009109    test-rmse:1.135375+0.289322 
## [204]    train-rmse:0.123020+0.009255    test-rmse:1.135531+0.289267 
## [205]    train-rmse:0.121885+0.009405    test-rmse:1.135339+0.289288 
## [206]    train-rmse:0.120725+0.008928    test-rmse:1.135382+0.289751 
## [207]    train-rmse:0.119442+0.009571    test-rmse:1.134581+0.288256 
## [208]    train-rmse:0.118126+0.009374    test-rmse:1.134616+0.288110 
## [209]    train-rmse:0.117482+0.009370    test-rmse:1.134585+0.288102 
## [210]    train-rmse:0.116668+0.009601    test-rmse:1.134567+0.288330 
## [211]    train-rmse:0.115544+0.009638    test-rmse:1.134612+0.288354 
## [212]    train-rmse:0.114125+0.009423    test-rmse:1.134615+0.288313 
## [213]    train-rmse:0.112890+0.009678    test-rmse:1.134553+0.288428 
## [214]    train-rmse:0.111874+0.009796    test-rmse:1.134419+0.287913 
## [215]    train-rmse:0.110852+0.009826    test-rmse:1.134216+0.288093 
## [216]    train-rmse:0.110084+0.009728    test-rmse:1.132937+0.287209 
## [217]    train-rmse:0.109144+0.009594    test-rmse:1.133174+0.287669 
## [218]    train-rmse:0.107982+0.009181    test-rmse:1.133246+0.288181 
## [219]    train-rmse:0.107390+0.009120    test-rmse:1.133130+0.287929 
## [220]    train-rmse:0.106518+0.009177    test-rmse:1.133096+0.287793 
## [221]    train-rmse:0.105745+0.009061    test-rmse:1.132691+0.287979 
## [222]    train-rmse:0.104753+0.008918    test-rmse:1.132669+0.288245 
## [223]    train-rmse:0.103355+0.008631    test-rmse:1.132267+0.288020 
## [224]    train-rmse:0.102491+0.008822    test-rmse:1.132371+0.288511 
## [225]    train-rmse:0.101580+0.008676    test-rmse:1.132306+0.288368 
## [226]    train-rmse:0.100618+0.008790    test-rmse:1.132489+0.288055 
## [227]    train-rmse:0.099800+0.008727    test-rmse:1.132237+0.288293 
## [228]    train-rmse:0.098800+0.008833    test-rmse:1.132240+0.288433 
## [229]    train-rmse:0.097681+0.008739    test-rmse:1.132114+0.288459 
## [230]    train-rmse:0.096937+0.008667    test-rmse:1.132004+0.288442 
## [231]    train-rmse:0.096056+0.008852    test-rmse:1.132057+0.288725 
## [232]    train-rmse:0.095157+0.008695    test-rmse:1.131501+0.288486 
## [233]    train-rmse:0.094565+0.008628    test-rmse:1.131541+0.288603 
## [234]    train-rmse:0.093861+0.008540    test-rmse:1.131174+0.288598 
## [235]    train-rmse:0.093014+0.008652    test-rmse:1.131678+0.288221 
## [236]    train-rmse:0.092373+0.008368    test-rmse:1.131522+0.288061 
## [237]    train-rmse:0.091590+0.008351    test-rmse:1.131596+0.287991 
## [238]    train-rmse:0.090284+0.008034    test-rmse:1.131393+0.288238 
## [239]    train-rmse:0.089073+0.007978    test-rmse:1.131326+0.288450 
## [240]    train-rmse:0.088316+0.007978    test-rmse:1.131250+0.288211 
## Stopping. Best iteration:
## [234]    train-rmse:0.093861+0.008540    test-rmse:1.131174+0.288598
## 
## 32 
## [1]  train-rmse:88.354776+0.099451   test-rmse:88.354991+0.492721 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.580200+0.086121   test-rmse:72.580084+0.517683 
## [3]  train-rmse:59.651027+0.076207   test-rmse:59.650480+0.541664 
## [4]  train-rmse:49.060283+0.069346   test-rmse:49.059156+0.565463 
## [5]  train-rmse:40.392603+0.065298   test-rmse:40.390688+0.589811 
## [6]  train-rmse:33.305450+0.062294   test-rmse:33.325024+0.602958 
## [7]  train-rmse:27.499631+0.054718   test-rmse:27.567201+0.606506 
## [8]  train-rmse:22.742521+0.042423   test-rmse:22.827392+0.586102 
## [9]  train-rmse:18.841019+0.034764   test-rmse:18.946899+0.564596 
## [10] train-rmse:15.638930+0.030528   test-rmse:15.729740+0.548585 
## [11] train-rmse:13.012615+0.025019   test-rmse:13.141128+0.575844 
## [12] train-rmse:10.852866+0.021930   test-rmse:10.960652+0.588384 
## [13] train-rmse:9.079022+0.021885    test-rmse:9.178371+0.562475 
## [14] train-rmse:7.630168+0.022282    test-rmse:7.724710+0.549960 
## [15] train-rmse:6.441231+0.017252    test-rmse:6.582625+0.572170 
## [16] train-rmse:5.474906+0.018851    test-rmse:5.608682+0.531231 
## [17] train-rmse:4.684672+0.023730    test-rmse:4.858099+0.526226 
## [18] train-rmse:4.036770+0.020664    test-rmse:4.242577+0.479451 
## [19] train-rmse:3.510921+0.024447    test-rmse:3.746395+0.457844 
## [20] train-rmse:3.083893+0.026959    test-rmse:3.388646+0.446422 
## [21] train-rmse:2.745172+0.030389    test-rmse:3.058159+0.402310 
## [22] train-rmse:2.466371+0.029226    test-rmse:2.812963+0.387304 
## [23] train-rmse:2.243746+0.027398    test-rmse:2.649514+0.361389 
## [24] train-rmse:2.061665+0.027664    test-rmse:2.495970+0.336585 
## [25] train-rmse:1.907814+0.027212    test-rmse:2.360075+0.309364 
## [26] train-rmse:1.779583+0.031090    test-rmse:2.246652+0.272894 
## [27] train-rmse:1.671760+0.030203    test-rmse:2.168855+0.263900 
## [28] train-rmse:1.576253+0.039794    test-rmse:2.112822+0.272138 
## [29] train-rmse:1.496887+0.040200    test-rmse:2.042314+0.266297 
## [30] train-rmse:1.425729+0.037673    test-rmse:1.992286+0.260996 
## [31] train-rmse:1.362020+0.036215    test-rmse:1.947470+0.245883 
## [32] train-rmse:1.298543+0.035860    test-rmse:1.891730+0.229172 
## [33] train-rmse:1.244693+0.033178    test-rmse:1.850008+0.219147 
## [34] train-rmse:1.197855+0.030814    test-rmse:1.813071+0.212447 
## [35] train-rmse:1.150661+0.031944    test-rmse:1.772647+0.218479 
## [36] train-rmse:1.101674+0.032988    test-rmse:1.752219+0.216403 
## [37] train-rmse:1.063969+0.033049    test-rmse:1.725976+0.210195 
## [38] train-rmse:1.021807+0.039212    test-rmse:1.695766+0.206251 
## [39] train-rmse:0.987947+0.038270    test-rmse:1.666291+0.205713 
## [40] train-rmse:0.949259+0.036425    test-rmse:1.645575+0.209435 
## [41] train-rmse:0.916638+0.032300    test-rmse:1.623121+0.211000 
## [42] train-rmse:0.886934+0.031926    test-rmse:1.599842+0.209175 
## [43] train-rmse:0.859188+0.033037    test-rmse:1.586169+0.214335 
## [44] train-rmse:0.832262+0.032836    test-rmse:1.564403+0.207289 
## [45] train-rmse:0.806033+0.030063    test-rmse:1.546545+0.207792 
## [46] train-rmse:0.783420+0.028893    test-rmse:1.528278+0.211007 
## [47] train-rmse:0.753396+0.028316    test-rmse:1.513314+0.208594 
## [48] train-rmse:0.730143+0.028503    test-rmse:1.502009+0.208116 
## [49] train-rmse:0.709867+0.028441    test-rmse:1.486269+0.209528 
## [50] train-rmse:0.691309+0.027380    test-rmse:1.475012+0.214861 
## [51] train-rmse:0.667996+0.025217    test-rmse:1.458941+0.222982 
## [52] train-rmse:0.649558+0.022620    test-rmse:1.450325+0.224324 
## [53] train-rmse:0.629945+0.023585    test-rmse:1.438120+0.228026 
## [54] train-rmse:0.613896+0.024005    test-rmse:1.424717+0.231980 
## [55] train-rmse:0.594702+0.025410    test-rmse:1.419247+0.234251 
## [56] train-rmse:0.580258+0.024568    test-rmse:1.412132+0.237100 
## [57] train-rmse:0.565796+0.024688    test-rmse:1.397249+0.238616 
## [58] train-rmse:0.551883+0.024650    test-rmse:1.387038+0.240388 
## [59] train-rmse:0.536582+0.024746    test-rmse:1.380169+0.243639 
## [60] train-rmse:0.522926+0.023255    test-rmse:1.375988+0.247340 
## [61] train-rmse:0.509017+0.022657    test-rmse:1.366194+0.251826 
## [62] train-rmse:0.494679+0.021306    test-rmse:1.356826+0.244447 
## [63] train-rmse:0.482950+0.022328    test-rmse:1.350784+0.247212 
## [64] train-rmse:0.472022+0.022383    test-rmse:1.342841+0.248934 
## [65] train-rmse:0.461710+0.021551    test-rmse:1.336596+0.242953 
## [66] train-rmse:0.451600+0.021680    test-rmse:1.330695+0.246441 
## [67] train-rmse:0.441012+0.021882    test-rmse:1.321864+0.250437 
## [68] train-rmse:0.431238+0.022035    test-rmse:1.318591+0.252670 
## [69] train-rmse:0.422093+0.021737    test-rmse:1.312294+0.256620 
## [70] train-rmse:0.413970+0.021163    test-rmse:1.307116+0.255866 
## [71] train-rmse:0.404616+0.021394    test-rmse:1.302108+0.254496 
## [72] train-rmse:0.395218+0.021711    test-rmse:1.299678+0.254972 
## [73] train-rmse:0.386001+0.019862    test-rmse:1.295836+0.256042 
## [74] train-rmse:0.378154+0.019525    test-rmse:1.290624+0.258325 
## [75] train-rmse:0.370886+0.018828    test-rmse:1.286921+0.262180 
## [76] train-rmse:0.364539+0.018567    test-rmse:1.282244+0.264047 
## [77] train-rmse:0.357284+0.018978    test-rmse:1.280699+0.266246 
## [78] train-rmse:0.349922+0.018129    test-rmse:1.275413+0.261875 
## [79] train-rmse:0.344041+0.018014    test-rmse:1.272393+0.262333 
## [80] train-rmse:0.338987+0.017495    test-rmse:1.269157+0.261540 
## [81] train-rmse:0.331951+0.017892    test-rmse:1.264945+0.265639 
## [82] train-rmse:0.325435+0.018476    test-rmse:1.262601+0.266871 
## [83] train-rmse:0.319085+0.017625    test-rmse:1.263347+0.264718 
## [84] train-rmse:0.313907+0.017885    test-rmse:1.262176+0.266223 
## [85] train-rmse:0.308267+0.015864    test-rmse:1.260791+0.266599 
## [86] train-rmse:0.301611+0.013774    test-rmse:1.258242+0.266855 
## [87] train-rmse:0.295081+0.014409    test-rmse:1.256391+0.268018 
## [88] train-rmse:0.289365+0.015542    test-rmse:1.255483+0.268446 
## [89] train-rmse:0.284508+0.016003    test-rmse:1.251604+0.271865 
## [90] train-rmse:0.280110+0.015000    test-rmse:1.250520+0.271804 
## [91] train-rmse:0.274834+0.013902    test-rmse:1.249079+0.272408 
## [92] train-rmse:0.270080+0.013483    test-rmse:1.247491+0.271404 
## [93] train-rmse:0.264972+0.014840    test-rmse:1.246436+0.271132 
## [94] train-rmse:0.259306+0.014529    test-rmse:1.245717+0.269051 
## [95] train-rmse:0.255148+0.014887    test-rmse:1.242303+0.271209 
## [96] train-rmse:0.250478+0.014945    test-rmse:1.241474+0.271896 
## [97] train-rmse:0.246975+0.015214    test-rmse:1.240551+0.272947 
## [98] train-rmse:0.243093+0.014996    test-rmse:1.239261+0.273035 
## [99] train-rmse:0.237566+0.012525    test-rmse:1.237369+0.270928 
## [100]    train-rmse:0.234153+0.012599    test-rmse:1.236055+0.269807 
## [101]    train-rmse:0.230806+0.012984    test-rmse:1.232421+0.271582 
## [102]    train-rmse:0.226685+0.013056    test-rmse:1.231096+0.271312 
## [103]    train-rmse:0.223556+0.012826    test-rmse:1.230723+0.271004 
## [104]    train-rmse:0.218475+0.010912    test-rmse:1.229625+0.271157 
## [105]    train-rmse:0.214483+0.010732    test-rmse:1.229272+0.273164 
## [106]    train-rmse:0.211759+0.010652    test-rmse:1.229364+0.274999 
## [107]    train-rmse:0.208439+0.010638    test-rmse:1.228221+0.275459 
## [108]    train-rmse:0.204906+0.010333    test-rmse:1.227490+0.275640 
## [109]    train-rmse:0.201305+0.010152    test-rmse:1.226264+0.274360 
## [110]    train-rmse:0.198902+0.010354    test-rmse:1.226905+0.275279 
## [111]    train-rmse:0.196207+0.010225    test-rmse:1.226656+0.275808 
## [112]    train-rmse:0.193225+0.010164    test-rmse:1.225824+0.275427 
## [113]    train-rmse:0.190717+0.010416    test-rmse:1.224557+0.275605 
## [114]    train-rmse:0.187364+0.009134    test-rmse:1.224796+0.275102 
## [115]    train-rmse:0.184771+0.009571    test-rmse:1.223228+0.276174 
## [116]    train-rmse:0.180938+0.008523    test-rmse:1.222564+0.276437 
## [117]    train-rmse:0.178152+0.008113    test-rmse:1.221348+0.277280 
## [118]    train-rmse:0.175679+0.008233    test-rmse:1.221882+0.277339 
## [119]    train-rmse:0.173536+0.008521    test-rmse:1.221123+0.277805 
## [120]    train-rmse:0.170919+0.009063    test-rmse:1.220533+0.278792 
## [121]    train-rmse:0.168300+0.008727    test-rmse:1.219898+0.277696 
## [122]    train-rmse:0.165151+0.007442    test-rmse:1.219282+0.277156 
## [123]    train-rmse:0.163064+0.006959    test-rmse:1.218592+0.276839 
## [124]    train-rmse:0.161078+0.006611    test-rmse:1.217714+0.277049 
## [125]    train-rmse:0.159045+0.006967    test-rmse:1.218129+0.276251 
## [126]    train-rmse:0.155786+0.006475    test-rmse:1.218743+0.276016 
## [127]    train-rmse:0.153755+0.006803    test-rmse:1.217913+0.276643 
## [128]    train-rmse:0.151366+0.006156    test-rmse:1.217786+0.276582 
## [129]    train-rmse:0.149693+0.006335    test-rmse:1.217605+0.276725 
## [130]    train-rmse:0.147549+0.006367    test-rmse:1.217559+0.276404 
## [131]    train-rmse:0.145908+0.006626    test-rmse:1.216739+0.276455 
## [132]    train-rmse:0.143638+0.006579    test-rmse:1.216223+0.276715 
## [133]    train-rmse:0.141805+0.006425    test-rmse:1.215534+0.276922 
## [134]    train-rmse:0.139088+0.005784    test-rmse:1.214485+0.276992 
## [135]    train-rmse:0.136490+0.005376    test-rmse:1.214844+0.276824 
## [136]    train-rmse:0.135115+0.005136    test-rmse:1.214697+0.277194 
## [137]    train-rmse:0.132961+0.005299    test-rmse:1.214269+0.277349 
## [138]    train-rmse:0.130678+0.005352    test-rmse:1.213410+0.275376 
## [139]    train-rmse:0.129083+0.005364    test-rmse:1.212912+0.275638 
## [140]    train-rmse:0.126512+0.005423    test-rmse:1.212322+0.275666 
## [141]    train-rmse:0.125121+0.005650    test-rmse:1.212487+0.275788 
## [142]    train-rmse:0.122740+0.005944    test-rmse:1.211812+0.274428 
## [143]    train-rmse:0.120691+0.005848    test-rmse:1.211555+0.274780 
## [144]    train-rmse:0.119469+0.005642    test-rmse:1.211630+0.274592 
## [145]    train-rmse:0.117053+0.004679    test-rmse:1.211314+0.274935 
## [146]    train-rmse:0.115533+0.004460    test-rmse:1.210882+0.274879 
## [147]    train-rmse:0.113428+0.004058    test-rmse:1.211046+0.274926 
## [148]    train-rmse:0.111473+0.003438    test-rmse:1.211484+0.275137 
## [149]    train-rmse:0.110405+0.003570    test-rmse:1.211458+0.275094 
## [150]    train-rmse:0.108788+0.003899    test-rmse:1.212572+0.274603 
## [151]    train-rmse:0.107706+0.003975    test-rmse:1.212621+0.274771 
## [152]    train-rmse:0.105694+0.004575    test-rmse:1.212454+0.274687 
## Stopping. Best iteration:
## [146]    train-rmse:0.115533+0.004460    test-rmse:1.210882+0.274879
## 
## 33 
## [1]  train-rmse:88.354776+0.099451   test-rmse:88.354991+0.492721 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.580200+0.086121   test-rmse:72.580084+0.517683 
## [3]  train-rmse:59.651027+0.076207   test-rmse:59.650480+0.541664 
## [4]  train-rmse:49.060283+0.069346   test-rmse:49.059156+0.565463 
## [5]  train-rmse:40.392603+0.065298   test-rmse:40.390688+0.589811 
## [6]  train-rmse:33.305450+0.062294   test-rmse:33.325024+0.602958 
## [7]  train-rmse:27.499631+0.054718   test-rmse:27.567201+0.606506 
## [8]  train-rmse:22.742521+0.042423   test-rmse:22.827392+0.586102 
## [9]  train-rmse:18.840151+0.034039   test-rmse:18.938184+0.574569 
## [10] train-rmse:15.636043+0.029316   test-rmse:15.726484+0.547297 
## [11] train-rmse:13.004530+0.022807   test-rmse:13.141543+0.576410 
## [12] train-rmse:10.842777+0.024247   test-rmse:10.973904+0.566458 
## [13] train-rmse:9.058053+0.021846    test-rmse:9.186889+0.556585 
## [14] train-rmse:7.596620+0.021553    test-rmse:7.710081+0.532686 
## [15] train-rmse:6.394477+0.018866    test-rmse:6.532602+0.545129 
## [16] train-rmse:5.409853+0.014536    test-rmse:5.573584+0.524558 
## [17] train-rmse:4.601075+0.017307    test-rmse:4.774124+0.493996 
## [18] train-rmse:3.937223+0.013127    test-rmse:4.131430+0.466455 
## [19] train-rmse:3.399393+0.016135    test-rmse:3.627981+0.463389 
## [20] train-rmse:2.960402+0.014656    test-rmse:3.215964+0.438620 
## [21] train-rmse:2.593486+0.028489    test-rmse:2.891126+0.404564 
## [22] train-rmse:2.296057+0.029463    test-rmse:2.630954+0.376057 
## [23] train-rmse:2.047519+0.041259    test-rmse:2.426057+0.374204 
## [24] train-rmse:1.846067+0.043900    test-rmse:2.260523+0.344273 
## [25] train-rmse:1.687405+0.045395    test-rmse:2.133172+0.323670 
## [26] train-rmse:1.540249+0.042737    test-rmse:2.041258+0.287226 
## [27] train-rmse:1.424480+0.047196    test-rmse:1.963719+0.277695 
## [28] train-rmse:1.328239+0.046543    test-rmse:1.889818+0.262515 
## [29] train-rmse:1.243423+0.044734    test-rmse:1.826042+0.256813 
## [30] train-rmse:1.162013+0.045013    test-rmse:1.770095+0.254409 
## [31] train-rmse:1.096321+0.046087    test-rmse:1.726992+0.240772 
## [32] train-rmse:1.040388+0.044388    test-rmse:1.685653+0.239514 
## [33] train-rmse:0.987358+0.041876    test-rmse:1.655270+0.233092 
## [34] train-rmse:0.939718+0.038650    test-rmse:1.618175+0.243229 
## [35] train-rmse:0.897945+0.037084    test-rmse:1.592260+0.238500 
## [36] train-rmse:0.856894+0.034976    test-rmse:1.558087+0.240069 
## [37] train-rmse:0.822103+0.035353    test-rmse:1.533173+0.226849 
## [38] train-rmse:0.786105+0.036563    test-rmse:1.511146+0.226977 
## [39] train-rmse:0.756116+0.036283    test-rmse:1.497431+0.227292 
## [40] train-rmse:0.726692+0.038407    test-rmse:1.477980+0.227944 
## [41] train-rmse:0.697145+0.041446    test-rmse:1.467907+0.224800 
## [42] train-rmse:0.671428+0.037844    test-rmse:1.448856+0.230476 
## [43] train-rmse:0.647094+0.037824    test-rmse:1.428625+0.222422 
## [44] train-rmse:0.620734+0.036212    test-rmse:1.416983+0.219869 
## [45] train-rmse:0.599643+0.036112    test-rmse:1.402331+0.219442 
## [46] train-rmse:0.577185+0.037529    test-rmse:1.395626+0.221574 
## [47] train-rmse:0.558790+0.037622    test-rmse:1.381587+0.224954 
## [48] train-rmse:0.540878+0.035949    test-rmse:1.369848+0.215790 
## [49] train-rmse:0.521859+0.036567    test-rmse:1.363273+0.220141 
## [50] train-rmse:0.503676+0.032140    test-rmse:1.353733+0.224452 
## [51] train-rmse:0.488324+0.031144    test-rmse:1.346330+0.228582 
## [52] train-rmse:0.472706+0.031537    test-rmse:1.340072+0.228984 
## [53] train-rmse:0.459417+0.031475    test-rmse:1.334334+0.233489 
## [54] train-rmse:0.445570+0.028712    test-rmse:1.324439+0.234745 
## [55] train-rmse:0.432812+0.027730    test-rmse:1.319717+0.238435 
## [56] train-rmse:0.420772+0.027544    test-rmse:1.317467+0.241124 
## [57] train-rmse:0.408359+0.025563    test-rmse:1.308190+0.232864 
## [58] train-rmse:0.397235+0.025536    test-rmse:1.300782+0.232976 
## [59] train-rmse:0.387608+0.025389    test-rmse:1.299127+0.236286 
## [60] train-rmse:0.376227+0.025341    test-rmse:1.287850+0.232804 
## [61] train-rmse:0.366511+0.025290    test-rmse:1.283405+0.234487 
## [62] train-rmse:0.358604+0.024942    test-rmse:1.280020+0.233722 
## [63] train-rmse:0.349349+0.024465    test-rmse:1.275602+0.237729 
## [64] train-rmse:0.340124+0.024242    test-rmse:1.273712+0.240961 
## [65] train-rmse:0.328982+0.022881    test-rmse:1.274075+0.241645 
## [66] train-rmse:0.320511+0.022719    test-rmse:1.270456+0.242319 
## [67] train-rmse:0.313026+0.022615    test-rmse:1.269726+0.243468 
## [68] train-rmse:0.304862+0.022472    test-rmse:1.267873+0.246296 
## [69] train-rmse:0.297705+0.021002    test-rmse:1.266471+0.247182 
## [70] train-rmse:0.289518+0.017944    test-rmse:1.264024+0.249351 
## [71] train-rmse:0.283109+0.017858    test-rmse:1.261882+0.248496 
## [72] train-rmse:0.277287+0.017950    test-rmse:1.259019+0.251795 
## [73] train-rmse:0.270503+0.017167    test-rmse:1.256930+0.252010 
## [74] train-rmse:0.265244+0.016428    test-rmse:1.256352+0.253621 
## [75] train-rmse:0.258909+0.015595    test-rmse:1.255916+0.254116 
## [76] train-rmse:0.253943+0.015839    test-rmse:1.254239+0.253846 
## [77] train-rmse:0.248766+0.016352    test-rmse:1.253625+0.253611 
## [78] train-rmse:0.243464+0.016171    test-rmse:1.253333+0.255399 
## [79] train-rmse:0.238441+0.015406    test-rmse:1.251981+0.256698 
## [80] train-rmse:0.233460+0.014817    test-rmse:1.251037+0.258416 
## [81] train-rmse:0.228942+0.014574    test-rmse:1.249417+0.257968 
## [82] train-rmse:0.222533+0.014742    test-rmse:1.247765+0.259183 
## [83] train-rmse:0.216992+0.013585    test-rmse:1.246081+0.258489 
## [84] train-rmse:0.211293+0.012383    test-rmse:1.246232+0.259027 
## [85] train-rmse:0.205862+0.011760    test-rmse:1.246891+0.258930 
## [86] train-rmse:0.202476+0.012071    test-rmse:1.244504+0.260557 
## [87] train-rmse:0.198537+0.010994    test-rmse:1.244351+0.260890 
## [88] train-rmse:0.194376+0.011233    test-rmse:1.243791+0.260918 
## [89] train-rmse:0.190165+0.011194    test-rmse:1.242446+0.260739 
## [90] train-rmse:0.186059+0.011100    test-rmse:1.242417+0.261594 
## [91] train-rmse:0.182236+0.010050    test-rmse:1.241216+0.262442 
## [92] train-rmse:0.177891+0.009511    test-rmse:1.240342+0.262994 
## [93] train-rmse:0.173579+0.009850    test-rmse:1.240510+0.263915 
## [94] train-rmse:0.170756+0.009393    test-rmse:1.239595+0.263743 
## [95] train-rmse:0.167807+0.009491    test-rmse:1.238774+0.264231 
## [96] train-rmse:0.164787+0.010019    test-rmse:1.237201+0.264828 
## [97] train-rmse:0.161050+0.010326    test-rmse:1.236694+0.266282 
## [98] train-rmse:0.158713+0.010145    test-rmse:1.236483+0.267005 
## [99] train-rmse:0.156011+0.010181    test-rmse:1.235202+0.265118 
## [100]    train-rmse:0.152372+0.010079    test-rmse:1.233631+0.265542 
## [101]    train-rmse:0.149816+0.009603    test-rmse:1.231867+0.265904 
## [102]    train-rmse:0.146551+0.009858    test-rmse:1.231202+0.265884 
## [103]    train-rmse:0.143133+0.009084    test-rmse:1.231040+0.265853 
## [104]    train-rmse:0.140376+0.009140    test-rmse:1.229539+0.266225 
## [105]    train-rmse:0.137883+0.008923    test-rmse:1.228040+0.266366 
## [106]    train-rmse:0.135627+0.009018    test-rmse:1.227936+0.266137 
## [107]    train-rmse:0.132665+0.008553    test-rmse:1.227126+0.264905 
## [108]    train-rmse:0.129803+0.009305    test-rmse:1.227063+0.264742 
## [109]    train-rmse:0.127168+0.009557    test-rmse:1.226894+0.265050 
## [110]    train-rmse:0.124855+0.009868    test-rmse:1.227080+0.265172 
## [111]    train-rmse:0.122180+0.010229    test-rmse:1.226694+0.265045 
## [112]    train-rmse:0.119798+0.009757    test-rmse:1.226258+0.264815 
## [113]    train-rmse:0.117731+0.009865    test-rmse:1.226192+0.265446 
## [114]    train-rmse:0.115664+0.009409    test-rmse:1.226265+0.265730 
## [115]    train-rmse:0.112947+0.008459    test-rmse:1.225581+0.265380 
## [116]    train-rmse:0.111382+0.008248    test-rmse:1.224727+0.265100 
## [117]    train-rmse:0.109409+0.008036    test-rmse:1.224674+0.264764 
## [118]    train-rmse:0.108109+0.008163    test-rmse:1.224347+0.264988 
## [119]    train-rmse:0.105466+0.008412    test-rmse:1.224068+0.265106 
## [120]    train-rmse:0.103441+0.008837    test-rmse:1.223483+0.265089 
## [121]    train-rmse:0.101520+0.008983    test-rmse:1.223290+0.264950 
## [122]    train-rmse:0.099562+0.009321    test-rmse:1.223220+0.265047 
## [123]    train-rmse:0.097718+0.009098    test-rmse:1.223220+0.265066 
## [124]    train-rmse:0.095261+0.009064    test-rmse:1.222584+0.265328 
## [125]    train-rmse:0.093389+0.008939    test-rmse:1.222043+0.265584 
## [126]    train-rmse:0.091195+0.008042    test-rmse:1.222483+0.265758 
## [127]    train-rmse:0.089407+0.008150    test-rmse:1.222292+0.265710 
## [128]    train-rmse:0.088009+0.008119    test-rmse:1.221679+0.265507 
## [129]    train-rmse:0.086113+0.007489    test-rmse:1.221096+0.265419 
## [130]    train-rmse:0.084720+0.007535    test-rmse:1.221042+0.265308 
## [131]    train-rmse:0.083381+0.007666    test-rmse:1.221211+0.265118 
## [132]    train-rmse:0.081756+0.007786    test-rmse:1.221259+0.265223 
## [133]    train-rmse:0.080114+0.007382    test-rmse:1.221342+0.265155 
## [134]    train-rmse:0.078579+0.007169    test-rmse:1.220639+0.265310 
## [135]    train-rmse:0.076956+0.006739    test-rmse:1.220299+0.265324 
## [136]    train-rmse:0.075772+0.006653    test-rmse:1.220351+0.265863 
## [137]    train-rmse:0.074795+0.006771    test-rmse:1.220446+0.265781 
## [138]    train-rmse:0.073543+0.006732    test-rmse:1.220357+0.265885 
## [139]    train-rmse:0.072465+0.006603    test-rmse:1.219796+0.264875 
## [140]    train-rmse:0.071231+0.006440    test-rmse:1.219412+0.264868 
## [141]    train-rmse:0.069846+0.006151    test-rmse:1.219459+0.264468 
## [142]    train-rmse:0.068056+0.005752    test-rmse:1.219490+0.264558 
## [143]    train-rmse:0.067052+0.006054    test-rmse:1.219647+0.264578 
## [144]    train-rmse:0.065606+0.005932    test-rmse:1.219461+0.264308 
## [145]    train-rmse:0.064408+0.006077    test-rmse:1.219382+0.264141 
## [146]    train-rmse:0.063013+0.005919    test-rmse:1.219441+0.264392 
## [147]    train-rmse:0.061976+0.005810    test-rmse:1.219186+0.264418 
## [148]    train-rmse:0.061133+0.005756    test-rmse:1.219218+0.264297 
## [149]    train-rmse:0.059922+0.005360    test-rmse:1.219198+0.264526 
## [150]    train-rmse:0.058727+0.005725    test-rmse:1.218791+0.263568 
## [151]    train-rmse:0.057828+0.005655    test-rmse:1.218803+0.263746 
## [152]    train-rmse:0.056994+0.005492    test-rmse:1.218420+0.263705 
## [153]    train-rmse:0.055862+0.005856    test-rmse:1.218176+0.263975 
## [154]    train-rmse:0.055042+0.005416    test-rmse:1.218260+0.263740 
## [155]    train-rmse:0.053878+0.005147    test-rmse:1.217772+0.262936 
## [156]    train-rmse:0.052977+0.005128    test-rmse:1.217510+0.262911 
## [157]    train-rmse:0.051883+0.005249    test-rmse:1.217640+0.262788 
## [158]    train-rmse:0.051332+0.005356    test-rmse:1.217577+0.262791 
## [159]    train-rmse:0.050363+0.005452    test-rmse:1.217319+0.262641 
## [160]    train-rmse:0.049222+0.004961    test-rmse:1.217396+0.262687 
## [161]    train-rmse:0.048612+0.004728    test-rmse:1.217289+0.262734 
## [162]    train-rmse:0.047612+0.004590    test-rmse:1.217020+0.262131 
## [163]    train-rmse:0.046776+0.004490    test-rmse:1.217394+0.261977 
## [164]    train-rmse:0.045763+0.004520    test-rmse:1.217308+0.261962 
## [165]    train-rmse:0.044801+0.004526    test-rmse:1.217463+0.262113 
## [166]    train-rmse:0.044040+0.004523    test-rmse:1.217384+0.262047 
## [167]    train-rmse:0.043394+0.004349    test-rmse:1.217344+0.261988 
## [168]    train-rmse:0.042716+0.004107    test-rmse:1.217243+0.261988 
## Stopping. Best iteration:
## [162]    train-rmse:0.047612+0.004590    test-rmse:1.217020+0.262131
## 
## 34 
## [1]  train-rmse:88.354776+0.099451   test-rmse:88.354991+0.492721 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:72.580200+0.086121   test-rmse:72.580084+0.517683 
## [3]  train-rmse:59.651027+0.076207   test-rmse:59.650480+0.541664 
## [4]  train-rmse:49.060283+0.069346   test-rmse:49.059156+0.565463 
## [5]  train-rmse:40.392603+0.065298   test-rmse:40.390688+0.589811 
## [6]  train-rmse:33.305450+0.062294   test-rmse:33.325024+0.602958 
## [7]  train-rmse:27.499631+0.054718   test-rmse:27.567201+0.606506 
## [8]  train-rmse:22.742521+0.042423   test-rmse:22.827392+0.586102 
## [9]  train-rmse:18.839725+0.033893   test-rmse:18.936239+0.569217 
## [10] train-rmse:15.635067+0.029561   test-rmse:15.716343+0.546027 
## [11] train-rmse:13.001250+0.024985   test-rmse:13.099206+0.531626 
## [12] train-rmse:10.835387+0.022667   test-rmse:10.975627+0.543076 
## [13] train-rmse:9.047487+0.016770    test-rmse:9.167870+0.550670 
## [14] train-rmse:7.576279+0.019151    test-rmse:7.685661+0.547533 
## [15] train-rmse:6.364456+0.015178    test-rmse:6.497015+0.529417 
## [16] train-rmse:5.367526+0.016078    test-rmse:5.530261+0.532106 
## [17] train-rmse:4.549011+0.015244    test-rmse:4.721163+0.503008 
## [18] train-rmse:3.879230+0.017351    test-rmse:4.102155+0.499748 
## [19] train-rmse:3.322755+0.019451    test-rmse:3.559009+0.480683 
## [20] train-rmse:2.870203+0.016937    test-rmse:3.139189+0.448459 
## [21] train-rmse:2.490767+0.016106    test-rmse:2.792940+0.419527 
## [22] train-rmse:2.179956+0.016903    test-rmse:2.531513+0.391975 
## [23] train-rmse:1.921918+0.024814    test-rmse:2.331269+0.365084 
## [24] train-rmse:1.713961+0.026514    test-rmse:2.169927+0.344775 
## [25] train-rmse:1.531515+0.028100    test-rmse:2.036824+0.328138 
## [26] train-rmse:1.379396+0.034154    test-rmse:1.941791+0.303299 
## [27] train-rmse:1.261103+0.035519    test-rmse:1.856952+0.296315 
## [28] train-rmse:1.149221+0.040049    test-rmse:1.793448+0.275472 
## [29] train-rmse:1.061394+0.037572    test-rmse:1.734154+0.272244 
## [30] train-rmse:0.981153+0.043003    test-rmse:1.689719+0.265052 
## [31] train-rmse:0.913920+0.040335    test-rmse:1.648801+0.264519 
## [32] train-rmse:0.855662+0.041270    test-rmse:1.607724+0.259591 
## [33] train-rmse:0.802416+0.042299    test-rmse:1.579702+0.261996 
## [34] train-rmse:0.753126+0.037709    test-rmse:1.554829+0.263919 
## [35] train-rmse:0.712059+0.040441    test-rmse:1.533892+0.259476 
## [36] train-rmse:0.676013+0.036720    test-rmse:1.500838+0.249468 
## [37] train-rmse:0.640051+0.033827    test-rmse:1.470580+0.242865 
## [38] train-rmse:0.611964+0.034270    test-rmse:1.453140+0.245991 
## [39] train-rmse:0.584974+0.032568    test-rmse:1.435704+0.244907 
## [40] train-rmse:0.555364+0.031831    test-rmse:1.420069+0.245924 
## [41] train-rmse:0.529412+0.032200    test-rmse:1.402773+0.249811 
## [42] train-rmse:0.507193+0.029762    test-rmse:1.386927+0.245381 
## [43] train-rmse:0.485507+0.026424    test-rmse:1.376251+0.240975 
## [44] train-rmse:0.467125+0.025606    test-rmse:1.364535+0.244138 
## [45] train-rmse:0.446958+0.023202    test-rmse:1.352410+0.246812 
## [46] train-rmse:0.428871+0.022797    test-rmse:1.346833+0.251286 
## [47] train-rmse:0.413199+0.022799    test-rmse:1.340009+0.251778 
## [48] train-rmse:0.398157+0.023053    test-rmse:1.329952+0.251842 
## [49] train-rmse:0.383734+0.021381    test-rmse:1.323954+0.256829 
## [50] train-rmse:0.368606+0.018582    test-rmse:1.317938+0.248926 
## [51] train-rmse:0.355307+0.018182    test-rmse:1.312147+0.249484 
## [52] train-rmse:0.344064+0.017547    test-rmse:1.307682+0.253276 
## [53] train-rmse:0.331898+0.016506    test-rmse:1.301073+0.256847 
## [54] train-rmse:0.320633+0.016951    test-rmse:1.295466+0.256434 
## [55] train-rmse:0.309786+0.014695    test-rmse:1.293910+0.256830 
## [56] train-rmse:0.300012+0.015078    test-rmse:1.291293+0.257236 
## [57] train-rmse:0.290434+0.015671    test-rmse:1.289162+0.256975 
## [58] train-rmse:0.279873+0.014183    test-rmse:1.286124+0.260034 
## [59] train-rmse:0.272818+0.013786    test-rmse:1.281712+0.260137 
## [60] train-rmse:0.262630+0.014396    test-rmse:1.280757+0.262835 
## [61] train-rmse:0.254902+0.013491    test-rmse:1.278486+0.264915 
## [62] train-rmse:0.247725+0.013312    test-rmse:1.276052+0.265245 
## [63] train-rmse:0.239493+0.011531    test-rmse:1.273308+0.267029 
## [64] train-rmse:0.230768+0.008762    test-rmse:1.272672+0.267675 
## [65] train-rmse:0.225372+0.008715    test-rmse:1.270153+0.269530 
## [66] train-rmse:0.219089+0.009147    test-rmse:1.269997+0.269667 
## [67] train-rmse:0.212359+0.010133    test-rmse:1.267806+0.270655 
## [68] train-rmse:0.206016+0.009669    test-rmse:1.265106+0.268703 
## [69] train-rmse:0.201216+0.010041    test-rmse:1.263629+0.268686 
## [70] train-rmse:0.195681+0.008878    test-rmse:1.262197+0.268226 
## [71] train-rmse:0.189377+0.007158    test-rmse:1.263592+0.267540 
## [72] train-rmse:0.184983+0.006436    test-rmse:1.263110+0.266687 
## [73] train-rmse:0.179969+0.007358    test-rmse:1.261831+0.267100 
## [74] train-rmse:0.175377+0.008112    test-rmse:1.261139+0.267561 
## [75] train-rmse:0.171753+0.008226    test-rmse:1.260584+0.267742 
## [76] train-rmse:0.166618+0.006949    test-rmse:1.259425+0.268071 
## [77] train-rmse:0.162172+0.007717    test-rmse:1.259070+0.268023 
## [78] train-rmse:0.157468+0.007503    test-rmse:1.258668+0.268751 
## [79] train-rmse:0.154424+0.007725    test-rmse:1.257412+0.269693 
## [80] train-rmse:0.150119+0.007572    test-rmse:1.257492+0.270275 
## [81] train-rmse:0.145728+0.007652    test-rmse:1.257183+0.269874 
## [82] train-rmse:0.142028+0.007338    test-rmse:1.257971+0.271617 
## [83] train-rmse:0.139038+0.007739    test-rmse:1.257403+0.272181 
## [84] train-rmse:0.135806+0.008062    test-rmse:1.257251+0.271637 
## [85] train-rmse:0.131954+0.007555    test-rmse:1.256916+0.271542 
## [86] train-rmse:0.128438+0.007356    test-rmse:1.257224+0.272319 
## [87] train-rmse:0.125705+0.007450    test-rmse:1.256496+0.272702 
## [88] train-rmse:0.121726+0.007196    test-rmse:1.256039+0.273057 
## [89] train-rmse:0.118462+0.007345    test-rmse:1.255277+0.272561 
## [90] train-rmse:0.115486+0.007114    test-rmse:1.254312+0.273482 
## [91] train-rmse:0.112757+0.006301    test-rmse:1.254176+0.274223 
## [92] train-rmse:0.110054+0.005872    test-rmse:1.254273+0.274185 
## [93] train-rmse:0.107030+0.006085    test-rmse:1.254756+0.273522 
## [94] train-rmse:0.103318+0.005106    test-rmse:1.254606+0.274492 
## [95] train-rmse:0.101177+0.005203    test-rmse:1.253471+0.272371 
## [96] train-rmse:0.098711+0.004984    test-rmse:1.253558+0.272623 
## [97] train-rmse:0.096754+0.005387    test-rmse:1.253125+0.272595 
## [98] train-rmse:0.094312+0.005346    test-rmse:1.253055+0.272695 
## [99] train-rmse:0.092525+0.005235    test-rmse:1.251646+0.271345 
## [100]    train-rmse:0.090302+0.005719    test-rmse:1.251155+0.271376 
## [101]    train-rmse:0.088924+0.005738    test-rmse:1.251067+0.271452 
## [102]    train-rmse:0.086945+0.005239    test-rmse:1.251281+0.271437 
## [103]    train-rmse:0.084875+0.004357    test-rmse:1.251017+0.271143 
## [104]    train-rmse:0.082860+0.004650    test-rmse:1.250786+0.271673 
## [105]    train-rmse:0.079831+0.004379    test-rmse:1.250326+0.270255 
## [106]    train-rmse:0.077076+0.004022    test-rmse:1.250109+0.269397 
## [107]    train-rmse:0.074822+0.003727    test-rmse:1.250305+0.268823 
## [108]    train-rmse:0.073211+0.003701    test-rmse:1.250704+0.269139 
## [109]    train-rmse:0.071642+0.003493    test-rmse:1.250774+0.268942 
## [110]    train-rmse:0.069762+0.003993    test-rmse:1.250839+0.269121 
## [111]    train-rmse:0.068384+0.003657    test-rmse:1.250689+0.268777 
## [112]    train-rmse:0.067091+0.003573    test-rmse:1.250385+0.268562 
## Stopping. Best iteration:
## [106]    train-rmse:0.077076+0.004022    test-rmse:1.250109+0.269397
## 
## 35 
## [1]  train-rmse:86.218085+0.097587   test-rmse:86.218257+0.495906 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.119772+0.083348   test-rmse:69.119559+0.523711 
## [3]  train-rmse:55.450065+0.073313   test-rmse:55.449316+0.550523 
## [4]  train-rmse:44.530696+0.066998   test-rmse:44.529209+0.577462 
## [5]  train-rmse:35.819717+0.064113   test-rmse:35.817187+0.605511 
## [6]  train-rmse:28.873706+0.061822   test-rmse:28.904146+0.614944 
## [7]  train-rmse:23.336712+0.056588   test-rmse:23.387526+0.621094 
## [8]  train-rmse:18.928374+0.051429   test-rmse:18.990305+0.625622 
## [9]  train-rmse:15.431930+0.048481   test-rmse:15.493831+0.597266 
## [10] train-rmse:12.669229+0.045690   test-rmse:12.742270+0.601259 
## [11] train-rmse:10.501816+0.045914   test-rmse:10.604033+0.649982 
## [12] train-rmse:8.815721+0.048505    test-rmse:8.927163+0.620500 
## [13] train-rmse:7.514515+0.050123    test-rmse:7.618751+0.603155 
## [14] train-rmse:6.524028+0.052757    test-rmse:6.652014+0.641810 
## [15] train-rmse:5.777394+0.055370    test-rmse:5.910574+0.598407 
## [16] train-rmse:5.220425+0.058246    test-rmse:5.336339+0.553171 
## [17] train-rmse:4.810975+0.061909    test-rmse:4.934905+0.557642 
## [18] train-rmse:4.508372+0.064293    test-rmse:4.682256+0.565118 
## [19] train-rmse:4.282185+0.065402    test-rmse:4.442564+0.508581 
## [20] train-rmse:4.109592+0.064963    test-rmse:4.246249+0.459739 
## [21] train-rmse:3.976104+0.064884    test-rmse:4.132040+0.454648 
## [22] train-rmse:3.870823+0.063387    test-rmse:4.055262+0.462062 
## [23] train-rmse:3.784360+0.062307    test-rmse:3.946165+0.422955 
## [24] train-rmse:3.709631+0.061285    test-rmse:3.886796+0.403979 
## [25] train-rmse:3.645084+0.059759    test-rmse:3.850590+0.406598 
## [26] train-rmse:3.588719+0.059298    test-rmse:3.791692+0.369599 
## [27] train-rmse:3.535645+0.057971    test-rmse:3.739218+0.373724 
## [28] train-rmse:3.485913+0.057234    test-rmse:3.701119+0.360517 
## [29] train-rmse:3.440924+0.055801    test-rmse:3.659184+0.373136 
## [30] train-rmse:3.398031+0.055024    test-rmse:3.624568+0.377294 
## [31] train-rmse:3.355489+0.054011    test-rmse:3.565587+0.339375 
## [32] train-rmse:3.314723+0.053083    test-rmse:3.531149+0.346737 
## [33] train-rmse:3.274417+0.051574    test-rmse:3.466545+0.319726 
## [34] train-rmse:3.236015+0.049898    test-rmse:3.440429+0.318641 
## [35] train-rmse:3.199475+0.049112    test-rmse:3.417135+0.322469 
## [36] train-rmse:3.162980+0.048468    test-rmse:3.391542+0.323862 
## [37] train-rmse:3.128213+0.047941    test-rmse:3.364447+0.324509 
## [38] train-rmse:3.094414+0.046125    test-rmse:3.320302+0.314838 
## [39] train-rmse:3.061657+0.044855    test-rmse:3.298414+0.327793 
## [40] train-rmse:3.030097+0.043880    test-rmse:3.267268+0.332706 
## [41] train-rmse:2.999544+0.043105    test-rmse:3.247096+0.325556 
## [42] train-rmse:2.969364+0.042384    test-rmse:3.210209+0.299150 
## [43] train-rmse:2.939879+0.042052    test-rmse:3.196606+0.296713 
## [44] train-rmse:2.910241+0.041700    test-rmse:3.157764+0.312227 
## [45] train-rmse:2.881501+0.041255    test-rmse:3.127583+0.321564 
## [46] train-rmse:2.853371+0.041124    test-rmse:3.081012+0.299589 
## [47] train-rmse:2.825721+0.040675    test-rmse:3.059525+0.305155 
## [48] train-rmse:2.798717+0.040331    test-rmse:3.042005+0.312355 
## [49] train-rmse:2.771636+0.039916    test-rmse:3.019322+0.318041 
## [50] train-rmse:2.745194+0.039504    test-rmse:2.994363+0.324798 
## [51] train-rmse:2.719699+0.039090    test-rmse:2.960506+0.328566 
## [52] train-rmse:2.694419+0.038779    test-rmse:2.935939+0.326799 
## [53] train-rmse:2.669667+0.039132    test-rmse:2.920622+0.310212 
## [54] train-rmse:2.646250+0.038758    test-rmse:2.902270+0.293392 
## [55] train-rmse:2.622906+0.038279    test-rmse:2.876555+0.303775 
## [56] train-rmse:2.599714+0.037749    test-rmse:2.863495+0.298120 
## [57] train-rmse:2.577125+0.037520    test-rmse:2.850918+0.300991 
## [58] train-rmse:2.554708+0.037585    test-rmse:2.838630+0.301326 
## [59] train-rmse:2.532644+0.037277    test-rmse:2.814767+0.304514 
## [60] train-rmse:2.510681+0.037161    test-rmse:2.791268+0.304927 
## [61] train-rmse:2.489285+0.036558    test-rmse:2.772596+0.308003 
## [62] train-rmse:2.468228+0.036209    test-rmse:2.745587+0.305731 
## [63] train-rmse:2.447332+0.035938    test-rmse:2.719047+0.306870 
## [64] train-rmse:2.426846+0.035659    test-rmse:2.680348+0.293122 
## [65] train-rmse:2.406683+0.035292    test-rmse:2.670618+0.292618 
## [66] train-rmse:2.386877+0.034867    test-rmse:2.658774+0.295694 
## [67] train-rmse:2.367536+0.034449    test-rmse:2.640261+0.306142 
## [68] train-rmse:2.348219+0.033927    test-rmse:2.625994+0.310927 
## [69] train-rmse:2.329026+0.033406    test-rmse:2.610937+0.315361 
## [70] train-rmse:2.309952+0.033058    test-rmse:2.591817+0.314083 
## [71] train-rmse:2.291152+0.032664    test-rmse:2.565239+0.306264 
## [72] train-rmse:2.272911+0.031906    test-rmse:2.551557+0.312891 
## [73] train-rmse:2.254728+0.031141    test-rmse:2.529740+0.298557 
## [74] train-rmse:2.237077+0.030741    test-rmse:2.512182+0.300064 
## [75] train-rmse:2.219856+0.030537    test-rmse:2.505601+0.297933 
## [76] train-rmse:2.202671+0.030292    test-rmse:2.479265+0.301712 
## [77] train-rmse:2.185534+0.030171    test-rmse:2.458143+0.305023 
## [78] train-rmse:2.168636+0.029788    test-rmse:2.438746+0.298451 
## [79] train-rmse:2.151869+0.029529    test-rmse:2.423521+0.301170 
## [80] train-rmse:2.135128+0.029301    test-rmse:2.405507+0.303217 
## [81] train-rmse:2.118782+0.028711    test-rmse:2.391837+0.296819 
## [82] train-rmse:2.102441+0.028145    test-rmse:2.362709+0.277395 
## [83] train-rmse:2.086618+0.027757    test-rmse:2.348219+0.281991 
## [84] train-rmse:2.071097+0.027289    test-rmse:2.330444+0.271418 
## [85] train-rmse:2.055963+0.026972    test-rmse:2.324268+0.268049 
## [86] train-rmse:2.040710+0.026505    test-rmse:2.316762+0.260803 
## [87] train-rmse:2.025870+0.026184    test-rmse:2.306909+0.263506 
## [88] train-rmse:2.011064+0.026009    test-rmse:2.293797+0.270169 
## [89] train-rmse:1.996335+0.025888    test-rmse:2.278997+0.275270 
## [90] train-rmse:1.981941+0.025592    test-rmse:2.263124+0.271595 
## [91] train-rmse:1.967617+0.025192    test-rmse:2.245970+0.275666 
## [92] train-rmse:1.953482+0.024892    test-rmse:2.231597+0.271879 
## [93] train-rmse:1.939543+0.024742    test-rmse:2.211124+0.269073 
## [94] train-rmse:1.925611+0.024517    test-rmse:2.202589+0.267956 
## [95] train-rmse:1.911851+0.024051    test-rmse:2.180753+0.270574 
## [96] train-rmse:1.898345+0.023444    test-rmse:2.173583+0.274940 
## [97] train-rmse:1.884920+0.022782    test-rmse:2.165446+0.277579 
## [98] train-rmse:1.871866+0.022432    test-rmse:2.147985+0.267360 
## [99] train-rmse:1.858987+0.021918    test-rmse:2.139122+0.270197 
## [100]    train-rmse:1.846059+0.021454    test-rmse:2.127578+0.266446 
## [101]    train-rmse:1.833476+0.021011    test-rmse:2.103529+0.267701 
## [102]    train-rmse:1.820952+0.020649    test-rmse:2.097551+0.269572 
## [103]    train-rmse:1.808548+0.020433    test-rmse:2.089180+0.275390 
## [104]    train-rmse:1.796114+0.020018    test-rmse:2.080944+0.277437 
## [105]    train-rmse:1.783851+0.019737    test-rmse:2.077537+0.278678 
## [106]    train-rmse:1.771777+0.019303    test-rmse:2.057163+0.260424 
## [107]    train-rmse:1.760065+0.019258    test-rmse:2.039884+0.256922 
## [108]    train-rmse:1.748475+0.019135    test-rmse:2.033204+0.259720 
## [109]    train-rmse:1.736825+0.018957    test-rmse:2.027062+0.260967 
## [110]    train-rmse:1.725601+0.018969    test-rmse:2.019663+0.257285 
## [111]    train-rmse:1.714407+0.018874    test-rmse:2.000655+0.249253 
## [112]    train-rmse:1.703394+0.018589    test-rmse:1.991647+0.250879 
## [113]    train-rmse:1.692367+0.018192    test-rmse:1.977747+0.257935 
## [114]    train-rmse:1.681405+0.017771    test-rmse:1.967977+0.258552 
## [115]    train-rmse:1.670523+0.017301    test-rmse:1.955029+0.259019 
## [116]    train-rmse:1.659744+0.016892    test-rmse:1.945102+0.252866 
## [117]    train-rmse:1.649094+0.016391    test-rmse:1.939036+0.253024 
## [118]    train-rmse:1.638572+0.016060    test-rmse:1.935494+0.249459 
## [119]    train-rmse:1.628055+0.015878    test-rmse:1.924262+0.246179 
## [120]    train-rmse:1.617710+0.015735    test-rmse:1.916897+0.243894 
## [121]    train-rmse:1.607541+0.015508    test-rmse:1.903308+0.247850 
## [122]    train-rmse:1.597589+0.015327    test-rmse:1.895527+0.247335 
## [123]    train-rmse:1.587800+0.015163    test-rmse:1.890033+0.249206 
## [124]    train-rmse:1.578007+0.015076    test-rmse:1.880832+0.245583 
## [125]    train-rmse:1.568333+0.015095    test-rmse:1.859078+0.235960 
## [126]    train-rmse:1.558858+0.014954    test-rmse:1.852672+0.229487 
## [127]    train-rmse:1.549598+0.014709    test-rmse:1.843562+0.231127 
## [128]    train-rmse:1.540384+0.014415    test-rmse:1.830161+0.232207 
## [129]    train-rmse:1.531110+0.014184    test-rmse:1.821603+0.236882 
## [130]    train-rmse:1.521934+0.014000    test-rmse:1.815146+0.238510 
## [131]    train-rmse:1.512964+0.013713    test-rmse:1.804483+0.241447 
## [132]    train-rmse:1.504198+0.013633    test-rmse:1.799674+0.244028 
## [133]    train-rmse:1.495535+0.013492    test-rmse:1.784928+0.235597 
## [134]    train-rmse:1.486949+0.013502    test-rmse:1.775055+0.235300 
## [135]    train-rmse:1.478432+0.013552    test-rmse:1.767678+0.232809 
## [136]    train-rmse:1.470021+0.013478    test-rmse:1.763902+0.236623 
## [137]    train-rmse:1.461653+0.013306    test-rmse:1.752451+0.223720 
## [138]    train-rmse:1.453370+0.013100    test-rmse:1.740320+0.226411 
## [139]    train-rmse:1.445102+0.012856    test-rmse:1.735590+0.224477 
## [140]    train-rmse:1.436911+0.012590    test-rmse:1.728333+0.226049 
## [141]    train-rmse:1.428735+0.012248    test-rmse:1.717908+0.228028 
## [142]    train-rmse:1.420568+0.011946    test-rmse:1.713844+0.226327 
## [143]    train-rmse:1.412543+0.011767    test-rmse:1.705012+0.228469 
## [144]    train-rmse:1.404609+0.011661    test-rmse:1.697341+0.227845 
## [145]    train-rmse:1.396827+0.011601    test-rmse:1.690735+0.225898 
## [146]    train-rmse:1.389083+0.011541    test-rmse:1.680298+0.230640 
## [147]    train-rmse:1.381449+0.011456    test-rmse:1.675190+0.229186 
## [148]    train-rmse:1.373846+0.011358    test-rmse:1.667447+0.225523 
## [149]    train-rmse:1.366478+0.011307    test-rmse:1.663035+0.221560 
## [150]    train-rmse:1.359259+0.011305    test-rmse:1.660887+0.221104 
## [151]    train-rmse:1.352149+0.011156    test-rmse:1.659970+0.221798 
## [152]    train-rmse:1.345198+0.011082    test-rmse:1.650928+0.221115 
## [153]    train-rmse:1.338212+0.010961    test-rmse:1.639918+0.225660 
## [154]    train-rmse:1.331298+0.010783    test-rmse:1.633648+0.226666 
## [155]    train-rmse:1.324292+0.010573    test-rmse:1.621329+0.228522 
## [156]    train-rmse:1.317518+0.010448    test-rmse:1.617666+0.229092 
## [157]    train-rmse:1.310839+0.010378    test-rmse:1.611989+0.224644 
## [158]    train-rmse:1.304085+0.010349    test-rmse:1.601681+0.221015 
## [159]    train-rmse:1.297571+0.010316    test-rmse:1.595561+0.221157 
## [160]    train-rmse:1.291180+0.010264    test-rmse:1.594327+0.222507 
## [161]    train-rmse:1.284923+0.010182    test-rmse:1.584663+0.209730 
## [162]    train-rmse:1.278652+0.010079    test-rmse:1.578619+0.210867 
## [163]    train-rmse:1.272437+0.009987    test-rmse:1.574240+0.208036 
## [164]    train-rmse:1.266356+0.009905    test-rmse:1.569680+0.207973 
## [165]    train-rmse:1.260271+0.009834    test-rmse:1.560314+0.210552 
## [166]    train-rmse:1.254167+0.009771    test-rmse:1.559166+0.209678 
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## [377]    train-rmse:0.769400+0.012719    test-rmse:1.105886+0.185430 
## [378]    train-rmse:0.768745+0.012735    test-rmse:1.105174+0.185463 
## [379]    train-rmse:0.768118+0.012758    test-rmse:1.102749+0.182580 
## [380]    train-rmse:0.767487+0.012784    test-rmse:1.102539+0.182242 
## [381]    train-rmse:0.766865+0.012809    test-rmse:1.101217+0.182530 
## [382]    train-rmse:0.766252+0.012838    test-rmse:1.101227+0.183019 
## [383]    train-rmse:0.765656+0.012867    test-rmse:1.100075+0.184118 
## [384]    train-rmse:0.765065+0.012889    test-rmse:1.099896+0.184472 
## [385]    train-rmse:0.764490+0.012912    test-rmse:1.099779+0.184850 
## [386]    train-rmse:0.763922+0.012936    test-rmse:1.099817+0.184725 
## [387]    train-rmse:0.763347+0.012960    test-rmse:1.098823+0.183855 
## [388]    train-rmse:0.762767+0.012985    test-rmse:1.099050+0.184254 
## [389]    train-rmse:0.762188+0.013015    test-rmse:1.098068+0.184878 
## [390]    train-rmse:0.761610+0.013014    test-rmse:1.097598+0.183870 
## [391]    train-rmse:0.761038+0.013031    test-rmse:1.096332+0.184661 
## [392]    train-rmse:0.760495+0.013041    test-rmse:1.095805+0.185115 
## [393]    train-rmse:0.759931+0.013041    test-rmse:1.095453+0.185243 
## [394]    train-rmse:0.759390+0.013052    test-rmse:1.094900+0.184866 
## [395]    train-rmse:0.758846+0.013061    test-rmse:1.094335+0.185023 
## [396]    train-rmse:0.758303+0.013090    test-rmse:1.094397+0.185458 
## [397]    train-rmse:0.757759+0.013096    test-rmse:1.094832+0.184899 
## [398]    train-rmse:0.757219+0.013109    test-rmse:1.094260+0.184859 
## [399]    train-rmse:0.756693+0.013123    test-rmse:1.093868+0.184857 
## [400]    train-rmse:0.756153+0.013138    test-rmse:1.093111+0.184815 
## [401]    train-rmse:0.755627+0.013156    test-rmse:1.092517+0.185511 
## [402]    train-rmse:0.755105+0.013177    test-rmse:1.092352+0.185468 
## [403]    train-rmse:0.754564+0.013179    test-rmse:1.091968+0.185807 
## [404]    train-rmse:0.754055+0.013187    test-rmse:1.092377+0.185476 
## [405]    train-rmse:0.753549+0.013204    test-rmse:1.093143+0.185046 
## [406]    train-rmse:0.753039+0.013214    test-rmse:1.091527+0.185587 
## [407]    train-rmse:0.752542+0.013217    test-rmse:1.091771+0.185768 
## [408]    train-rmse:0.752040+0.013228    test-rmse:1.091338+0.185593 
## [409]    train-rmse:0.751543+0.013239    test-rmse:1.091704+0.185498 
## [410]    train-rmse:0.751032+0.013252    test-rmse:1.089957+0.186102 
## [411]    train-rmse:0.750542+0.013260    test-rmse:1.089779+0.186326 
## [412]    train-rmse:0.750051+0.013270    test-rmse:1.089315+0.186576 
## [413]    train-rmse:0.749549+0.013303    test-rmse:1.088788+0.185365 
## [414]    train-rmse:0.749070+0.013321    test-rmse:1.087591+0.184054 
## [415]    train-rmse:0.748578+0.013327    test-rmse:1.087547+0.183445 
## [416]    train-rmse:0.748096+0.013342    test-rmse:1.086640+0.183768 
## [417]    train-rmse:0.747628+0.013349    test-rmse:1.086897+0.184112 
## [418]    train-rmse:0.747160+0.013350    test-rmse:1.085018+0.181311 
## [419]    train-rmse:0.746692+0.013355    test-rmse:1.085295+0.181592 
## [420]    train-rmse:0.746229+0.013365    test-rmse:1.085828+0.181424 
## [421]    train-rmse:0.745759+0.013382    test-rmse:1.085159+0.182065 
## [422]    train-rmse:0.745294+0.013401    test-rmse:1.085400+0.182624 
## [423]    train-rmse:0.744839+0.013408    test-rmse:1.085809+0.182196 
## [424]    train-rmse:0.744383+0.013415    test-rmse:1.085453+0.182107 
## Stopping. Best iteration:
## [418]    train-rmse:0.747160+0.013350    test-rmse:1.085018+0.181311
## 
## 36 
## [1]  train-rmse:86.218085+0.097587   test-rmse:86.218257+0.495906 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.119772+0.083348   test-rmse:69.119559+0.523711 
## [3]  train-rmse:55.450065+0.073313   test-rmse:55.449316+0.550523 
## [4]  train-rmse:44.530696+0.066998   test-rmse:44.529209+0.577462 
## [5]  train-rmse:35.819717+0.064113   test-rmse:35.817187+0.605511 
## [6]  train-rmse:28.868193+0.060253   test-rmse:28.910774+0.605132 
## [7]  train-rmse:23.313352+0.045540   test-rmse:23.415351+0.630046 
## [8]  train-rmse:18.877434+0.034820   test-rmse:18.968087+0.577209 
## [9]  train-rmse:15.345303+0.030199   test-rmse:15.463877+0.589790 
## [10] train-rmse:12.541798+0.029245   test-rmse:12.630278+0.565191 
## [11] train-rmse:10.324019+0.030061   test-rmse:10.421563+0.553936 
## [12] train-rmse:8.576948+0.030183    test-rmse:8.715715+0.573531 
## [13] train-rmse:7.215962+0.030788    test-rmse:7.357945+0.588687 
## [14] train-rmse:6.163901+0.032520    test-rmse:6.315908+0.570193 
## [15] train-rmse:5.356311+0.035271    test-rmse:5.536542+0.517035 
## [16] train-rmse:4.741948+0.035781    test-rmse:4.926266+0.510589 
## [17] train-rmse:4.275764+0.033822    test-rmse:4.491846+0.501499 
## [18] train-rmse:3.923732+0.034852    test-rmse:4.163768+0.472551 
## [19] train-rmse:3.656502+0.033676    test-rmse:3.875276+0.436680 
## [20] train-rmse:3.450685+0.033489    test-rmse:3.682739+0.416965 
## [21] train-rmse:3.287942+0.034668    test-rmse:3.542844+0.395608 
## [22] train-rmse:3.159897+0.034985    test-rmse:3.452547+0.390182 
## [23] train-rmse:3.053346+0.034238    test-rmse:3.347280+0.349068 
## [24] train-rmse:2.964834+0.033394    test-rmse:3.269519+0.342367 
## [25] train-rmse:2.886680+0.032282    test-rmse:3.194679+0.336490 
## [26] train-rmse:2.814434+0.032696    test-rmse:3.121556+0.310406 
## [27] train-rmse:2.747466+0.026438    test-rmse:3.072872+0.297847 
## [28] train-rmse:2.687352+0.026735    test-rmse:3.031016+0.309229 
## [29] train-rmse:2.631787+0.027045    test-rmse:2.985978+0.319562 
## [30] train-rmse:2.579818+0.026598    test-rmse:2.932128+0.298502 
## [31] train-rmse:2.522730+0.024119    test-rmse:2.883731+0.278007 
## [32] train-rmse:2.475294+0.023876    test-rmse:2.843100+0.285840 
## [33] train-rmse:2.429654+0.022483    test-rmse:2.781082+0.289105 
## [34] train-rmse:2.385882+0.022161    test-rmse:2.747967+0.288312 
## [35] train-rmse:2.343070+0.022035    test-rmse:2.713314+0.296870 
## [36] train-rmse:2.301775+0.021245    test-rmse:2.673454+0.282416 
## [37] train-rmse:2.261933+0.020435    test-rmse:2.637899+0.282528 
## [38] train-rmse:2.221566+0.019816    test-rmse:2.606102+0.271064 
## [39] train-rmse:2.182538+0.020330    test-rmse:2.583256+0.282848 
## [40] train-rmse:2.142258+0.017242    test-rmse:2.552862+0.271146 
## [41] train-rmse:2.106065+0.016370    test-rmse:2.529139+0.275057 
## [42] train-rmse:2.071403+0.016111    test-rmse:2.486720+0.268249 
## [43] train-rmse:2.036724+0.015171    test-rmse:2.456735+0.273107 
## [44] train-rmse:2.002208+0.012455    test-rmse:2.428173+0.267585 
## [45] train-rmse:1.969739+0.011796    test-rmse:2.383105+0.264149 
## [46] train-rmse:1.938152+0.011525    test-rmse:2.349785+0.246655 
## [47] train-rmse:1.906713+0.010287    test-rmse:2.330678+0.251695 
## [48] train-rmse:1.875837+0.007503    test-rmse:2.299507+0.244335 
## [49] train-rmse:1.845247+0.006949    test-rmse:2.264379+0.244014 
## [50] train-rmse:1.816866+0.006540    test-rmse:2.247602+0.240841 
## [51] train-rmse:1.788380+0.006375    test-rmse:2.223226+0.245311 
## [52] train-rmse:1.760423+0.006797    test-rmse:2.190486+0.234343 
## [53] train-rmse:1.733529+0.006791    test-rmse:2.171672+0.226303 
## [54] train-rmse:1.706977+0.006430    test-rmse:2.156438+0.225727 
## [55] train-rmse:1.680952+0.006266    test-rmse:2.131714+0.237805 
## [56] train-rmse:1.655384+0.006223    test-rmse:2.110375+0.233397 
## [57] train-rmse:1.630186+0.004929    test-rmse:2.098119+0.237248 
## [58] train-rmse:1.605742+0.004130    test-rmse:2.072430+0.235692 
## [59] train-rmse:1.582021+0.004738    test-rmse:2.053160+0.240065 
## [60] train-rmse:1.559239+0.004932    test-rmse:2.029859+0.239073 
## [61] train-rmse:1.536972+0.004962    test-rmse:2.000208+0.240103 
## [62] train-rmse:1.515612+0.004887    test-rmse:1.991490+0.238148 
## [63] train-rmse:1.494189+0.004891    test-rmse:1.965137+0.233352 
## [64] train-rmse:1.473183+0.004689    test-rmse:1.940635+0.236181 
## [65] train-rmse:1.452741+0.005391    test-rmse:1.919362+0.241590 
## [66] train-rmse:1.431507+0.005862    test-rmse:1.912762+0.244916 
## [67] train-rmse:1.410734+0.007043    test-rmse:1.894594+0.245280 
## [68] train-rmse:1.391793+0.007461    test-rmse:1.872878+0.240364 
## [69] train-rmse:1.372597+0.008372    test-rmse:1.858550+0.241857 
## [70] train-rmse:1.352899+0.008639    test-rmse:1.838541+0.238757 
## [71] train-rmse:1.334979+0.009339    test-rmse:1.825949+0.234117 
## [72] train-rmse:1.315601+0.007417    test-rmse:1.807089+0.226962 
## [73] train-rmse:1.297649+0.008668    test-rmse:1.792324+0.229050 
## [74] train-rmse:1.280896+0.009147    test-rmse:1.778951+0.230001 
## [75] train-rmse:1.264127+0.009122    test-rmse:1.765308+0.220329 
## [76] train-rmse:1.247200+0.010981    test-rmse:1.753163+0.224138 
## [77] train-rmse:1.228896+0.010733    test-rmse:1.738843+0.221003 
## [78] train-rmse:1.213603+0.010873    test-rmse:1.724174+0.227250 
## [79] train-rmse:1.198436+0.011413    test-rmse:1.707784+0.225108 
## [80] train-rmse:1.182989+0.011433    test-rmse:1.697655+0.229482 
## [81] train-rmse:1.168184+0.012199    test-rmse:1.688532+0.233987 
## [82] train-rmse:1.152946+0.012994    test-rmse:1.676170+0.233409 
## [83] train-rmse:1.138916+0.013066    test-rmse:1.656395+0.235188 
## [84] train-rmse:1.123834+0.013258    test-rmse:1.646545+0.233053 
## [85] train-rmse:1.110248+0.013276    test-rmse:1.640380+0.232415 
## [86] train-rmse:1.096200+0.014121    test-rmse:1.629025+0.229983 
## [87] train-rmse:1.083146+0.013983    test-rmse:1.614771+0.230247 
## [88] train-rmse:1.070198+0.014059    test-rmse:1.604252+0.228414 
## [89] train-rmse:1.058093+0.014418    test-rmse:1.588689+0.230304 
## [90] train-rmse:1.045951+0.014729    test-rmse:1.576449+0.230848 
## [91] train-rmse:1.032558+0.015587    test-rmse:1.567920+0.233474 
## [92] train-rmse:1.020536+0.016052    test-rmse:1.560032+0.237895 
## [93] train-rmse:1.008990+0.015696    test-rmse:1.556087+0.240733 
## [94] train-rmse:0.997822+0.015718    test-rmse:1.547303+0.236983 
## [95] train-rmse:0.986745+0.016316    test-rmse:1.536304+0.231561 
## [96] train-rmse:0.975469+0.016477    test-rmse:1.527216+0.230482 
## [97] train-rmse:0.964193+0.016506    test-rmse:1.518627+0.234549 
## [98] train-rmse:0.952534+0.016845    test-rmse:1.514084+0.236460 
## [99] train-rmse:0.942020+0.017825    test-rmse:1.507193+0.237012 
## [100]    train-rmse:0.931826+0.018031    test-rmse:1.500077+0.237048 
## [101]    train-rmse:0.922172+0.018285    test-rmse:1.490848+0.232499 
## [102]    train-rmse:0.912882+0.018438    test-rmse:1.482531+0.233993 
## [103]    train-rmse:0.903174+0.018884    test-rmse:1.471248+0.234039 
## [104]    train-rmse:0.894241+0.019323    test-rmse:1.462392+0.234372 
## [105]    train-rmse:0.884969+0.019539    test-rmse:1.449857+0.230499 
## [106]    train-rmse:0.875903+0.019935    test-rmse:1.446902+0.233025 
## [107]    train-rmse:0.866303+0.020344    test-rmse:1.441915+0.230924 
## [108]    train-rmse:0.857951+0.019969    test-rmse:1.435209+0.230675 
## [109]    train-rmse:0.849942+0.020035    test-rmse:1.428362+0.230107 
## [110]    train-rmse:0.841434+0.020475    test-rmse:1.420818+0.231053 
## [111]    train-rmse:0.833189+0.020643    test-rmse:1.409766+0.232620 
## [112]    train-rmse:0.825610+0.020937    test-rmse:1.405198+0.234887 
## [113]    train-rmse:0.818249+0.021103    test-rmse:1.401596+0.234965 
## [114]    train-rmse:0.810588+0.021061    test-rmse:1.395747+0.231348 
## [115]    train-rmse:0.802665+0.021979    test-rmse:1.390771+0.233628 
## [116]    train-rmse:0.795348+0.022307    test-rmse:1.386245+0.235739 
## [117]    train-rmse:0.788267+0.022194    test-rmse:1.378408+0.234912 
## [118]    train-rmse:0.780602+0.023587    test-rmse:1.369389+0.236839 
## [119]    train-rmse:0.772452+0.022765    test-rmse:1.368180+0.237234 
## [120]    train-rmse:0.764083+0.021786    test-rmse:1.362865+0.234901 
## [121]    train-rmse:0.756882+0.021524    test-rmse:1.360154+0.234351 
## [122]    train-rmse:0.750647+0.021744    test-rmse:1.350868+0.226225 
## [123]    train-rmse:0.743963+0.021147    test-rmse:1.345932+0.229144 
## [124]    train-rmse:0.737431+0.021774    test-rmse:1.338812+0.227987 
## [125]    train-rmse:0.731480+0.021826    test-rmse:1.334523+0.229041 
## [126]    train-rmse:0.724942+0.021622    test-rmse:1.330007+0.232122 
## [127]    train-rmse:0.719403+0.021849    test-rmse:1.326715+0.231575 
## [128]    train-rmse:0.713462+0.021349    test-rmse:1.324772+0.233391 
## [129]    train-rmse:0.708116+0.021420    test-rmse:1.319296+0.230123 
## [130]    train-rmse:0.702257+0.021940    test-rmse:1.314804+0.229174 
## [131]    train-rmse:0.697042+0.021975    test-rmse:1.312120+0.231726 
## [132]    train-rmse:0.691898+0.021984    test-rmse:1.311201+0.230613 
## [133]    train-rmse:0.685571+0.021813    test-rmse:1.302427+0.229279 
## [134]    train-rmse:0.680443+0.022147    test-rmse:1.296747+0.233191 
## [135]    train-rmse:0.675576+0.021874    test-rmse:1.287746+0.228883 
## [136]    train-rmse:0.670304+0.021896    test-rmse:1.284164+0.227473 
## [137]    train-rmse:0.665427+0.022242    test-rmse:1.280652+0.226973 
## [138]    train-rmse:0.660314+0.022424    test-rmse:1.276845+0.225215 
## [139]    train-rmse:0.654705+0.022606    test-rmse:1.273833+0.223021 
## [140]    train-rmse:0.650039+0.022548    test-rmse:1.271407+0.223478 
## [141]    train-rmse:0.645619+0.022507    test-rmse:1.269823+0.225514 
## [142]    train-rmse:0.641437+0.022367    test-rmse:1.268972+0.225052 
## [143]    train-rmse:0.637015+0.022852    test-rmse:1.267453+0.224971 
## [144]    train-rmse:0.632774+0.022737    test-rmse:1.267088+0.225524 
## [145]    train-rmse:0.628417+0.022639    test-rmse:1.265294+0.224852 
## [146]    train-rmse:0.623917+0.023082    test-rmse:1.264057+0.224214 
## [147]    train-rmse:0.619943+0.022927    test-rmse:1.259650+0.226783 
## [148]    train-rmse:0.616202+0.022898    test-rmse:1.254455+0.229880 
## [149]    train-rmse:0.611773+0.022788    test-rmse:1.252898+0.229257 
## [150]    train-rmse:0.607965+0.023157    test-rmse:1.247025+0.231427 
## [151]    train-rmse:0.603884+0.023046    test-rmse:1.245647+0.232346 
## [152]    train-rmse:0.599828+0.023500    test-rmse:1.240651+0.235280 
## [153]    train-rmse:0.595040+0.023823    test-rmse:1.238349+0.236150 
## [154]    train-rmse:0.591810+0.023785    test-rmse:1.236371+0.236565 
## [155]    train-rmse:0.588166+0.023556    test-rmse:1.234072+0.236181 
## [156]    train-rmse:0.584692+0.023550    test-rmse:1.231368+0.234446 
## [157]    train-rmse:0.581066+0.022935    test-rmse:1.230478+0.235482 
## [158]    train-rmse:0.577782+0.023059    test-rmse:1.230267+0.235541 
## [159]    train-rmse:0.574020+0.023344    test-rmse:1.227626+0.235024 
## [160]    train-rmse:0.569931+0.022544    test-rmse:1.223175+0.236265 
## [161]    train-rmse:0.566091+0.022468    test-rmse:1.217914+0.233320 
## [162]    train-rmse:0.562189+0.022487    test-rmse:1.215864+0.234957 
## [163]    train-rmse:0.558686+0.022101    test-rmse:1.213483+0.236692 
## [164]    train-rmse:0.555883+0.022160    test-rmse:1.211424+0.236777 
## [165]    train-rmse:0.552721+0.021962    test-rmse:1.209265+0.234940 
## [166]    train-rmse:0.549800+0.021879    test-rmse:1.208146+0.235417 
## [167]    train-rmse:0.546885+0.022024    test-rmse:1.206312+0.236161 
## [168]    train-rmse:0.543708+0.021828    test-rmse:1.204840+0.236993 
## [169]    train-rmse:0.540971+0.021784    test-rmse:1.203586+0.236236 
## [170]    train-rmse:0.538084+0.021968    test-rmse:1.201943+0.236343 
## [171]    train-rmse:0.535503+0.022269    test-rmse:1.200097+0.237941 
## [172]    train-rmse:0.532627+0.022381    test-rmse:1.199447+0.239141 
## [173]    train-rmse:0.530042+0.022614    test-rmse:1.196278+0.241335 
## [174]    train-rmse:0.527527+0.022484    test-rmse:1.195862+0.242443 
## [175]    train-rmse:0.525307+0.022426    test-rmse:1.195057+0.242718 
## [176]    train-rmse:0.522320+0.022603    test-rmse:1.191443+0.244512 
## [177]    train-rmse:0.518026+0.020423    test-rmse:1.190557+0.243937 
## [178]    train-rmse:0.515450+0.020412    test-rmse:1.188210+0.243791 
## [179]    train-rmse:0.513011+0.020685    test-rmse:1.186067+0.244752 
## [180]    train-rmse:0.508699+0.018941    test-rmse:1.186946+0.246602 
## [181]    train-rmse:0.506057+0.019040    test-rmse:1.186682+0.247511 
## [182]    train-rmse:0.503765+0.018981    test-rmse:1.183422+0.248920 
## [183]    train-rmse:0.500960+0.019180    test-rmse:1.182686+0.248090 
## [184]    train-rmse:0.498479+0.018788    test-rmse:1.182157+0.249029 
## [185]    train-rmse:0.495056+0.019477    test-rmse:1.180904+0.250184 
## [186]    train-rmse:0.492765+0.019730    test-rmse:1.177589+0.247411 
## [187]    train-rmse:0.490443+0.020319    test-rmse:1.176377+0.247780 
## [188]    train-rmse:0.487584+0.019916    test-rmse:1.176346+0.247656 
## [189]    train-rmse:0.485608+0.019838    test-rmse:1.175053+0.247635 
## [190]    train-rmse:0.483149+0.019787    test-rmse:1.173514+0.247985 
## [191]    train-rmse:0.481105+0.019777    test-rmse:1.171608+0.248555 
## [192]    train-rmse:0.479100+0.019207    test-rmse:1.170238+0.248019 
## [193]    train-rmse:0.477056+0.019156    test-rmse:1.169453+0.249310 
## [194]    train-rmse:0.475050+0.018840    test-rmse:1.168169+0.249312 
## [195]    train-rmse:0.472896+0.019046    test-rmse:1.166008+0.249557 
## [196]    train-rmse:0.470954+0.019380    test-rmse:1.164853+0.250157 
## [197]    train-rmse:0.468651+0.019238    test-rmse:1.164610+0.250121 
## [198]    train-rmse:0.466110+0.019110    test-rmse:1.160380+0.250639 
## [199]    train-rmse:0.463927+0.019312    test-rmse:1.160648+0.252202 
## [200]    train-rmse:0.462161+0.019314    test-rmse:1.160785+0.253151 
## [201]    train-rmse:0.460372+0.019524    test-rmse:1.160499+0.253745 
## [202]    train-rmse:0.458176+0.019513    test-rmse:1.158124+0.255339 
## [203]    train-rmse:0.456152+0.019826    test-rmse:1.154185+0.255898 
## [204]    train-rmse:0.454566+0.019839    test-rmse:1.150765+0.257208 
## [205]    train-rmse:0.452939+0.019894    test-rmse:1.149683+0.256677 
## [206]    train-rmse:0.451262+0.020214    test-rmse:1.148661+0.257031 
## [207]    train-rmse:0.449050+0.020247    test-rmse:1.147858+0.257345 
## [208]    train-rmse:0.447614+0.020237    test-rmse:1.146687+0.257832 
## [209]    train-rmse:0.445435+0.020389    test-rmse:1.145930+0.258835 
## [210]    train-rmse:0.443770+0.020478    test-rmse:1.144818+0.258743 
## [211]    train-rmse:0.442366+0.020316    test-rmse:1.144060+0.258991 
## [212]    train-rmse:0.440414+0.020170    test-rmse:1.141962+0.260499 
## [213]    train-rmse:0.438180+0.020529    test-rmse:1.141487+0.261013 
## [214]    train-rmse:0.436640+0.020332    test-rmse:1.140104+0.260831 
## [215]    train-rmse:0.435013+0.020346    test-rmse:1.139702+0.261835 
## [216]    train-rmse:0.433264+0.020001    test-rmse:1.139122+0.263367 
## [217]    train-rmse:0.431807+0.019969    test-rmse:1.138135+0.263011 
## [218]    train-rmse:0.430441+0.019848    test-rmse:1.137518+0.263382 
## [219]    train-rmse:0.429063+0.020137    test-rmse:1.136289+0.263261 
## [220]    train-rmse:0.427031+0.020639    test-rmse:1.134953+0.263457 
## [221]    train-rmse:0.425620+0.020633    test-rmse:1.133965+0.263528 
## [222]    train-rmse:0.424006+0.020482    test-rmse:1.133276+0.264650 
## [223]    train-rmse:0.422555+0.020342    test-rmse:1.132710+0.264589 
## [224]    train-rmse:0.421139+0.020287    test-rmse:1.132604+0.265026 
## [225]    train-rmse:0.419604+0.020273    test-rmse:1.132856+0.265874 
## [226]    train-rmse:0.418312+0.020072    test-rmse:1.132687+0.265707 
## [227]    train-rmse:0.417003+0.019918    test-rmse:1.130660+0.267263 
## [228]    train-rmse:0.415645+0.020166    test-rmse:1.129033+0.267724 
## [229]    train-rmse:0.414428+0.020253    test-rmse:1.128414+0.267786 
## [230]    train-rmse:0.413033+0.020821    test-rmse:1.127801+0.267190 
## [231]    train-rmse:0.411542+0.020748    test-rmse:1.127720+0.267770 
## [232]    train-rmse:0.410329+0.020822    test-rmse:1.127073+0.267579 
## [233]    train-rmse:0.409113+0.020902    test-rmse:1.126447+0.267746 
## [234]    train-rmse:0.408057+0.021056    test-rmse:1.124306+0.268150 
## [235]    train-rmse:0.406663+0.021109    test-rmse:1.124346+0.268142 
## [236]    train-rmse:0.404108+0.019712    test-rmse:1.124276+0.267350 
## [237]    train-rmse:0.401898+0.019633    test-rmse:1.123969+0.267622 
## [238]    train-rmse:0.400299+0.019992    test-rmse:1.123363+0.267669 
## [239]    train-rmse:0.399153+0.020245    test-rmse:1.122908+0.268232 
## [240]    train-rmse:0.397868+0.019942    test-rmse:1.123015+0.267516 
## [241]    train-rmse:0.396715+0.019634    test-rmse:1.122068+0.268094 
## [242]    train-rmse:0.395626+0.019649    test-rmse:1.121383+0.267297 
## [243]    train-rmse:0.394355+0.019500    test-rmse:1.121135+0.267020 
## [244]    train-rmse:0.393004+0.019477    test-rmse:1.120127+0.267628 
## [245]    train-rmse:0.391694+0.019529    test-rmse:1.120017+0.267615 
## [246]    train-rmse:0.390691+0.019441    test-rmse:1.119604+0.268132 
## [247]    train-rmse:0.389385+0.019589    test-rmse:1.117962+0.268949 
## [248]    train-rmse:0.387969+0.019705    test-rmse:1.117113+0.269469 
## [249]    train-rmse:0.386747+0.019604    test-rmse:1.116950+0.270300 
## [250]    train-rmse:0.384978+0.019310    test-rmse:1.116266+0.270608 
## [251]    train-rmse:0.383720+0.019263    test-rmse:1.115941+0.271365 
## [252]    train-rmse:0.382267+0.019387    test-rmse:1.115120+0.271236 
## [253]    train-rmse:0.380523+0.019248    test-rmse:1.114719+0.269728 
## [254]    train-rmse:0.379018+0.019291    test-rmse:1.114356+0.269805 
## [255]    train-rmse:0.377910+0.019408    test-rmse:1.113778+0.270327 
## [256]    train-rmse:0.376648+0.019356    test-rmse:1.111708+0.272497 
## [257]    train-rmse:0.375833+0.019366    test-rmse:1.111482+0.272862 
## [258]    train-rmse:0.375057+0.019330    test-rmse:1.110964+0.273065 
## [259]    train-rmse:0.373353+0.020170    test-rmse:1.110526+0.272262 
## [260]    train-rmse:0.371562+0.019447    test-rmse:1.109749+0.272523 
## [261]    train-rmse:0.369722+0.020074    test-rmse:1.110543+0.273488 
## [262]    train-rmse:0.367869+0.019689    test-rmse:1.111340+0.274632 
## [263]    train-rmse:0.367140+0.019788    test-rmse:1.110666+0.274362 
## [264]    train-rmse:0.365712+0.018998    test-rmse:1.109851+0.274352 
## [265]    train-rmse:0.364434+0.019645    test-rmse:1.109817+0.274293 
## [266]    train-rmse:0.362999+0.019863    test-rmse:1.109461+0.274234 
## [267]    train-rmse:0.361613+0.019623    test-rmse:1.109695+0.274346 
## [268]    train-rmse:0.360773+0.019663    test-rmse:1.109301+0.275094 
## [269]    train-rmse:0.359749+0.019844    test-rmse:1.108037+0.274197 
## [270]    train-rmse:0.357994+0.019855    test-rmse:1.107237+0.274957 
## [271]    train-rmse:0.357080+0.019915    test-rmse:1.107210+0.275390 
## [272]    train-rmse:0.355803+0.019390    test-rmse:1.105431+0.276029 
## [273]    train-rmse:0.355003+0.019618    test-rmse:1.104902+0.276098 
## [274]    train-rmse:0.354170+0.019624    test-rmse:1.104375+0.275061 
## [275]    train-rmse:0.353005+0.019613    test-rmse:1.103686+0.275045 
## [276]    train-rmse:0.351767+0.019513    test-rmse:1.103244+0.275003 
## [277]    train-rmse:0.350810+0.019491    test-rmse:1.102841+0.274415 
## [278]    train-rmse:0.349130+0.019671    test-rmse:1.102435+0.274166 
## [279]    train-rmse:0.348414+0.019625    test-rmse:1.102070+0.273890 
## [280]    train-rmse:0.347455+0.019760    test-rmse:1.101632+0.274263 
## [281]    train-rmse:0.346641+0.019739    test-rmse:1.101181+0.274114 
## [282]    train-rmse:0.345031+0.019700    test-rmse:1.099548+0.274273 
## [283]    train-rmse:0.343754+0.019865    test-rmse:1.099412+0.274421 
## [284]    train-rmse:0.342764+0.019923    test-rmse:1.099115+0.274829 
## [285]    train-rmse:0.341659+0.019743    test-rmse:1.099049+0.274619 
## [286]    train-rmse:0.340529+0.019353    test-rmse:1.097974+0.274815 
## [287]    train-rmse:0.339817+0.019345    test-rmse:1.097136+0.274329 
## [288]    train-rmse:0.339224+0.019352    test-rmse:1.096687+0.274345 
## [289]    train-rmse:0.338386+0.019299    test-rmse:1.097016+0.274883 
## [290]    train-rmse:0.337370+0.019510    test-rmse:1.096212+0.274172 
## [291]    train-rmse:0.336244+0.019674    test-rmse:1.095778+0.273548 
## [292]    train-rmse:0.335221+0.019635    test-rmse:1.095417+0.273810 
## [293]    train-rmse:0.334274+0.019708    test-rmse:1.094863+0.274333 
## [294]    train-rmse:0.333395+0.019649    test-rmse:1.094468+0.274653 
## [295]    train-rmse:0.332681+0.019846    test-rmse:1.094375+0.274980 
## [296]    train-rmse:0.330931+0.019214    test-rmse:1.093997+0.274970 
## [297]    train-rmse:0.329714+0.018523    test-rmse:1.094135+0.274550 
## [298]    train-rmse:0.329111+0.018538    test-rmse:1.093408+0.274513 
## [299]    train-rmse:0.328368+0.018894    test-rmse:1.093832+0.274967 
## [300]    train-rmse:0.327608+0.019043    test-rmse:1.093752+0.275495 
## [301]    train-rmse:0.326842+0.019077    test-rmse:1.093239+0.275846 
## [302]    train-rmse:0.326056+0.019218    test-rmse:1.092844+0.275273 
## [303]    train-rmse:0.324802+0.018660    test-rmse:1.092415+0.275339 
## [304]    train-rmse:0.324228+0.018644    test-rmse:1.092099+0.275082 
## [305]    train-rmse:0.323701+0.018589    test-rmse:1.091442+0.275081 
## [306]    train-rmse:0.322410+0.018902    test-rmse:1.090704+0.275459 
## [307]    train-rmse:0.321207+0.018896    test-rmse:1.090608+0.275280 
## [308]    train-rmse:0.319869+0.017920    test-rmse:1.090895+0.275087 
## [309]    train-rmse:0.319287+0.017943    test-rmse:1.090531+0.274645 
## [310]    train-rmse:0.318737+0.017919    test-rmse:1.090504+0.275084 
## [311]    train-rmse:0.317534+0.017945    test-rmse:1.090965+0.275284 
## [312]    train-rmse:0.316554+0.017737    test-rmse:1.090829+0.274979 
## [313]    train-rmse:0.315951+0.017681    test-rmse:1.089260+0.275448 
## [314]    train-rmse:0.315290+0.017781    test-rmse:1.088562+0.275102 
## [315]    train-rmse:0.313979+0.017218    test-rmse:1.089375+0.274693 
## [316]    train-rmse:0.313067+0.017392    test-rmse:1.089399+0.274413 
## [317]    train-rmse:0.312448+0.017362    test-rmse:1.089199+0.274877 
## [318]    train-rmse:0.311892+0.017418    test-rmse:1.089011+0.275043 
## [319]    train-rmse:0.310857+0.017421    test-rmse:1.088938+0.274807 
## [320]    train-rmse:0.309936+0.017968    test-rmse:1.088899+0.274410 
## Stopping. Best iteration:
## [314]    train-rmse:0.315290+0.017781    test-rmse:1.088562+0.275102
## 
## 37 
## [1]  train-rmse:86.218085+0.097587   test-rmse:86.218257+0.495906 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.119772+0.083348   test-rmse:69.119559+0.523711 
## [3]  train-rmse:55.450065+0.073313   test-rmse:55.449316+0.550523 
## [4]  train-rmse:44.530696+0.066998   test-rmse:44.529209+0.577462 
## [5]  train-rmse:35.819717+0.064113   test-rmse:35.817187+0.605511 
## [6]  train-rmse:28.868193+0.060253   test-rmse:28.910774+0.605132 
## [7]  train-rmse:23.310282+0.045896   test-rmse:23.406557+0.627461 
## [8]  train-rmse:18.867857+0.037343   test-rmse:18.979796+0.599473 
## [9]  train-rmse:15.320496+0.031877   test-rmse:15.416832+0.552494 
## [10] train-rmse:12.495280+0.029742   test-rmse:12.622691+0.589008 
## [11] train-rmse:10.249727+0.030474   test-rmse:10.357590+0.580554 
## [12] train-rmse:8.469106+0.033116    test-rmse:8.581459+0.544196 
## [13] train-rmse:7.067838+0.037151    test-rmse:7.223211+0.548161 
## [14] train-rmse:5.965211+0.045675    test-rmse:6.097338+0.495274 
## [15] train-rmse:5.110312+0.049227    test-rmse:5.273886+0.502933 
## [16] train-rmse:4.446475+0.050509    test-rmse:4.635513+0.508720 
## [17] train-rmse:3.929727+0.051423    test-rmse:4.176275+0.490657 
## [18] train-rmse:3.530173+0.051804    test-rmse:3.808676+0.424984 
## [19] train-rmse:3.226414+0.047610    test-rmse:3.520594+0.369592 
## [20] train-rmse:2.979064+0.025503    test-rmse:3.313464+0.347687 
## [21] train-rmse:2.794493+0.025309    test-rmse:3.154703+0.356407 
## [22] train-rmse:2.645804+0.024712    test-rmse:3.032894+0.365605 
## [23] train-rmse:2.522955+0.023611    test-rmse:2.912602+0.332101 
## [24] train-rmse:2.416105+0.022665    test-rmse:2.838580+0.301444 
## [25] train-rmse:2.331027+0.020790    test-rmse:2.766785+0.292535 
## [26] train-rmse:2.247177+0.014072    test-rmse:2.718555+0.295144 
## [27] train-rmse:2.177042+0.014143    test-rmse:2.639537+0.287034 
## [28] train-rmse:2.111555+0.016866    test-rmse:2.577315+0.279900 
## [29] train-rmse:2.046492+0.011060    test-rmse:2.530067+0.272708 
## [30] train-rmse:1.991383+0.010206    test-rmse:2.490141+0.282484 
## [31] train-rmse:1.939821+0.010608    test-rmse:2.443497+0.281249 
## [32] train-rmse:1.890277+0.011569    test-rmse:2.396193+0.275094 
## [33] train-rmse:1.841614+0.013109    test-rmse:2.350421+0.266005 
## [34] train-rmse:1.795195+0.013309    test-rmse:2.306006+0.242627 
## [35] train-rmse:1.749466+0.012582    test-rmse:2.266859+0.242224 
## [36] train-rmse:1.707352+0.012688    test-rmse:2.240713+0.253119 
## [37] train-rmse:1.667585+0.013658    test-rmse:2.207986+0.254870 
## [38] train-rmse:1.626479+0.015783    test-rmse:2.165326+0.245010 
## [39] train-rmse:1.589034+0.015297    test-rmse:2.135029+0.235116 
## [40] train-rmse:1.552012+0.015419    test-rmse:2.098025+0.224196 
## [41] train-rmse:1.516658+0.016026    test-rmse:2.069857+0.230202 
## [42] train-rmse:1.480254+0.014729    test-rmse:2.047961+0.235476 
## [43] train-rmse:1.446928+0.014844    test-rmse:2.027200+0.237010 
## [44] train-rmse:1.411770+0.016230    test-rmse:1.994632+0.235586 
## [45] train-rmse:1.382116+0.016030    test-rmse:1.971452+0.243921 
## [46] train-rmse:1.352897+0.016774    test-rmse:1.945252+0.242433 
## [47] train-rmse:1.322823+0.014340    test-rmse:1.916883+0.233120 
## [48] train-rmse:1.294357+0.013421    test-rmse:1.898089+0.235149 
## [49] train-rmse:1.266166+0.012941    test-rmse:1.865289+0.227943 
## [50] train-rmse:1.238275+0.014042    test-rmse:1.848276+0.226408 
## [51] train-rmse:1.211182+0.015544    test-rmse:1.833215+0.230915 
## [52] train-rmse:1.186385+0.015749    test-rmse:1.816686+0.228186 
## [53] train-rmse:1.162392+0.015206    test-rmse:1.791249+0.225396 
## [54] train-rmse:1.138856+0.013574    test-rmse:1.765491+0.221103 
## [55] train-rmse:1.116635+0.013538    test-rmse:1.748398+0.219160 
## [56] train-rmse:1.094095+0.012878    test-rmse:1.735381+0.226489 
## [57] train-rmse:1.073062+0.012225    test-rmse:1.715582+0.229782 
## [58] train-rmse:1.051386+0.010883    test-rmse:1.705234+0.229679 
## [59] train-rmse:1.028823+0.011918    test-rmse:1.687666+0.229031 
## [60] train-rmse:1.008029+0.012413    test-rmse:1.667919+0.222529 
## [61] train-rmse:0.989948+0.012554    test-rmse:1.654684+0.226704 
## [62] train-rmse:0.972911+0.012225    test-rmse:1.642695+0.226391 
## [63] train-rmse:0.955790+0.011915    test-rmse:1.624150+0.225998 
## [64] train-rmse:0.937422+0.012273    test-rmse:1.613666+0.224736 
## [65] train-rmse:0.920370+0.012278    test-rmse:1.600685+0.231221 
## [66] train-rmse:0.902850+0.011287    test-rmse:1.589817+0.225198 
## [67] train-rmse:0.886272+0.012304    test-rmse:1.577939+0.225831 
## [68] train-rmse:0.871071+0.011740    test-rmse:1.568569+0.227074 
## [69] train-rmse:0.854409+0.015629    test-rmse:1.555822+0.226153 
## [70] train-rmse:0.840212+0.016067    test-rmse:1.542672+0.228121 
## [71] train-rmse:0.824723+0.017606    test-rmse:1.528310+0.231083 
## [72] train-rmse:0.810258+0.018028    test-rmse:1.520593+0.226342 
## [73] train-rmse:0.795796+0.016770    test-rmse:1.513377+0.230321 
## [74] train-rmse:0.783715+0.016879    test-rmse:1.503872+0.231239 
## [75] train-rmse:0.771058+0.016730    test-rmse:1.499243+0.230216 
## [76] train-rmse:0.757739+0.016611    test-rmse:1.491165+0.233399 
## [77] train-rmse:0.744815+0.015308    test-rmse:1.469709+0.223441 
## [78] train-rmse:0.732932+0.015140    test-rmse:1.457105+0.226109 
## [79] train-rmse:0.721079+0.015839    test-rmse:1.451261+0.228477 
## [80] train-rmse:0.707562+0.016161    test-rmse:1.448437+0.233584 
## [81] train-rmse:0.697124+0.016368    test-rmse:1.440173+0.232952 
## [82] train-rmse:0.685447+0.016118    test-rmse:1.433172+0.232485 
## [83] train-rmse:0.675434+0.016526    test-rmse:1.421965+0.234681 
## [84] train-rmse:0.666332+0.016534    test-rmse:1.416147+0.236127 
## [85] train-rmse:0.655429+0.016308    test-rmse:1.404917+0.225163 
## [86] train-rmse:0.646513+0.016102    test-rmse:1.397432+0.227815 
## [87] train-rmse:0.636745+0.015610    test-rmse:1.391181+0.228484 
## [88] train-rmse:0.627441+0.015780    test-rmse:1.387988+0.228267 
## [89] train-rmse:0.617163+0.017370    test-rmse:1.383233+0.226689 
## [90] train-rmse:0.607855+0.018100    test-rmse:1.379000+0.228835 
## [91] train-rmse:0.598474+0.017392    test-rmse:1.374718+0.231132 
## [92] train-rmse:0.590298+0.016903    test-rmse:1.367667+0.232239 
## [93] train-rmse:0.583256+0.016407    test-rmse:1.363660+0.233067 
## [94] train-rmse:0.575124+0.016839    test-rmse:1.355879+0.227596 
## [95] train-rmse:0.567983+0.016688    test-rmse:1.350765+0.230369 
## [96] train-rmse:0.559971+0.016807    test-rmse:1.346833+0.232306 
## [97] train-rmse:0.552719+0.016591    test-rmse:1.344681+0.233092 
## [98] train-rmse:0.546466+0.016402    test-rmse:1.332491+0.227854 
## [99] train-rmse:0.539259+0.016588    test-rmse:1.326287+0.231868 
## [100]    train-rmse:0.533067+0.016666    test-rmse:1.326975+0.232141 
## [101]    train-rmse:0.526825+0.017056    test-rmse:1.322727+0.233315 
## [102]    train-rmse:0.520826+0.017172    test-rmse:1.315297+0.231413 
## [103]    train-rmse:0.515526+0.017308    test-rmse:1.309851+0.234179 
## [104]    train-rmse:0.509678+0.017301    test-rmse:1.308103+0.233034 
## [105]    train-rmse:0.502919+0.017172    test-rmse:1.304113+0.232026 
## [106]    train-rmse:0.498074+0.017184    test-rmse:1.296587+0.235577 
## [107]    train-rmse:0.493076+0.016534    test-rmse:1.294050+0.237308 
## [108]    train-rmse:0.487719+0.016779    test-rmse:1.292180+0.237344 
## [109]    train-rmse:0.482544+0.017096    test-rmse:1.290785+0.237438 
## [110]    train-rmse:0.476465+0.014189    test-rmse:1.288659+0.239182 
## [111]    train-rmse:0.471687+0.013881    test-rmse:1.285122+0.240555 
## [112]    train-rmse:0.465905+0.012835    test-rmse:1.279609+0.235471 
## [113]    train-rmse:0.461207+0.012359    test-rmse:1.280028+0.236076 
## [114]    train-rmse:0.455781+0.011361    test-rmse:1.276339+0.236583 
## [115]    train-rmse:0.451068+0.012037    test-rmse:1.271331+0.236309 
## [116]    train-rmse:0.447238+0.011865    test-rmse:1.268678+0.236953 
## [117]    train-rmse:0.442458+0.011962    test-rmse:1.267052+0.238211 
## [118]    train-rmse:0.438343+0.012242    test-rmse:1.267258+0.238247 
## [119]    train-rmse:0.434012+0.012541    test-rmse:1.266207+0.240452 
## [120]    train-rmse:0.428833+0.010894    test-rmse:1.261236+0.233847 
## [121]    train-rmse:0.425014+0.010538    test-rmse:1.260180+0.233374 
## [122]    train-rmse:0.420770+0.011371    test-rmse:1.258420+0.234541 
## [123]    train-rmse:0.416822+0.010799    test-rmse:1.255988+0.234896 
## [124]    train-rmse:0.412934+0.011452    test-rmse:1.254284+0.235212 
## [125]    train-rmse:0.408913+0.011928    test-rmse:1.251880+0.236412 
## [126]    train-rmse:0.405013+0.011958    test-rmse:1.251345+0.236865 
## [127]    train-rmse:0.401955+0.011739    test-rmse:1.248964+0.238345 
## [128]    train-rmse:0.397943+0.011174    test-rmse:1.243539+0.232523 
## [129]    train-rmse:0.394468+0.010637    test-rmse:1.242035+0.233363 
## [130]    train-rmse:0.391350+0.010735    test-rmse:1.241013+0.233420 
## [131]    train-rmse:0.388152+0.010724    test-rmse:1.238078+0.234195 
## [132]    train-rmse:0.384403+0.010630    test-rmse:1.235927+0.235539 
## [133]    train-rmse:0.380629+0.011472    test-rmse:1.235839+0.237467 
## [134]    train-rmse:0.377121+0.011740    test-rmse:1.235184+0.238562 
## [135]    train-rmse:0.374411+0.011747    test-rmse:1.234635+0.237890 
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## [137]    train-rmse:0.366258+0.013477    test-rmse:1.233028+0.237872 
## [138]    train-rmse:0.363212+0.013389    test-rmse:1.228912+0.235220 
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## [266]    train-rmse:0.156666+0.014188    test-rmse:1.159616+0.263840 
## [267]    train-rmse:0.156012+0.014054    test-rmse:1.159211+0.263613 
## [268]    train-rmse:0.155353+0.014122    test-rmse:1.159268+0.263968 
## [269]    train-rmse:0.154427+0.014252    test-rmse:1.159014+0.264087 
## [270]    train-rmse:0.153663+0.014042    test-rmse:1.158773+0.263288 
## [271]    train-rmse:0.152733+0.013872    test-rmse:1.158870+0.263565 
## [272]    train-rmse:0.151853+0.013598    test-rmse:1.158901+0.263566 
## [273]    train-rmse:0.150848+0.013701    test-rmse:1.158943+0.264051 
## [274]    train-rmse:0.149816+0.013421    test-rmse:1.157604+0.262095 
## [275]    train-rmse:0.147975+0.012354    test-rmse:1.157312+0.261926 
## [276]    train-rmse:0.146959+0.012248    test-rmse:1.157023+0.262097 
## [277]    train-rmse:0.146377+0.012521    test-rmse:1.156640+0.262359 
## [278]    train-rmse:0.145722+0.012468    test-rmse:1.156648+0.262166 
## [279]    train-rmse:0.144846+0.012339    test-rmse:1.157014+0.262167 
## [280]    train-rmse:0.143812+0.011879    test-rmse:1.156762+0.262371 
## [281]    train-rmse:0.142940+0.012114    test-rmse:1.156546+0.262167 
## [282]    train-rmse:0.142180+0.012049    test-rmse:1.156446+0.262252 
## [283]    train-rmse:0.141492+0.011873    test-rmse:1.156148+0.262343 
## [284]    train-rmse:0.140896+0.011854    test-rmse:1.155857+0.262322 
## [285]    train-rmse:0.139922+0.011551    test-rmse:1.156200+0.262509 
## [286]    train-rmse:0.138858+0.011582    test-rmse:1.155865+0.263176 
## [287]    train-rmse:0.138096+0.011532    test-rmse:1.156208+0.263293 
## [288]    train-rmse:0.137110+0.011601    test-rmse:1.156368+0.263226 
## [289]    train-rmse:0.136198+0.011500    test-rmse:1.156030+0.263247 
## [290]    train-rmse:0.135312+0.011277    test-rmse:1.156082+0.263169 
## Stopping. Best iteration:
## [284]    train-rmse:0.140896+0.011854    test-rmse:1.155857+0.262322
## 
## 38 
## [1]  train-rmse:86.218085+0.097587   test-rmse:86.218257+0.495906 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.119772+0.083348   test-rmse:69.119559+0.523711 
## [3]  train-rmse:55.450065+0.073313   test-rmse:55.449316+0.550523 
## [4]  train-rmse:44.530696+0.066998   test-rmse:44.529209+0.577462 
## [5]  train-rmse:35.819717+0.064113   test-rmse:35.817187+0.605511 
## [6]  train-rmse:28.868193+0.060253   test-rmse:28.910774+0.605132 
## [7]  train-rmse:23.310282+0.045896   test-rmse:23.406557+0.627461 
## [8]  train-rmse:18.866006+0.037435   test-rmse:18.967372+0.593530 
## [9]  train-rmse:15.310408+0.033279   test-rmse:15.412543+0.568451 
## [10] train-rmse:12.469649+0.030121   test-rmse:12.553802+0.547333 
## [11] train-rmse:10.201716+0.025635   test-rmse:10.315598+0.567040 
## [12] train-rmse:8.392718+0.021801    test-rmse:8.486664+0.542367 
## [13] train-rmse:6.956828+0.027223    test-rmse:7.070615+0.515208 
## [14] train-rmse:5.823456+0.028633    test-rmse:5.965077+0.527750 
## [15] train-rmse:4.931354+0.031885    test-rmse:5.076442+0.490182 
## [16] train-rmse:4.232689+0.033822    test-rmse:4.415469+0.476963 
## [17] train-rmse:3.676451+0.028771    test-rmse:3.877354+0.440170 
## [18] train-rmse:3.245486+0.034802    test-rmse:3.501201+0.437606 
## [19] train-rmse:2.905614+0.032326    test-rmse:3.194676+0.406835 
## [20] train-rmse:2.638290+0.036327    test-rmse:2.960297+0.366572 
## [21] train-rmse:2.432202+0.037506    test-rmse:2.789654+0.337009 
## [22] train-rmse:2.265150+0.042802    test-rmse:2.651296+0.310619 
## [23] train-rmse:2.127086+0.040069    test-rmse:2.538440+0.289776 
## [24] train-rmse:2.014603+0.038954    test-rmse:2.448708+0.298286 
## [25] train-rmse:1.912718+0.041912    test-rmse:2.365478+0.294881 
## [26] train-rmse:1.824213+0.042572    test-rmse:2.295223+0.274256 
## [27] train-rmse:1.751265+0.042173    test-rmse:2.225413+0.271267 
## [28] train-rmse:1.682198+0.037851    test-rmse:2.164430+0.263222 
## [29] train-rmse:1.621622+0.037248    test-rmse:2.113382+0.265547 
## [30] train-rmse:1.553579+0.023052    test-rmse:2.068851+0.265307 
## [31] train-rmse:1.501768+0.021349    test-rmse:2.034422+0.266447 
## [32] train-rmse:1.452346+0.020311    test-rmse:1.993328+0.262706 
## [33] train-rmse:1.407518+0.019259    test-rmse:1.957455+0.272803 
## [34] train-rmse:1.357203+0.021014    test-rmse:1.908490+0.245073 
## [35] train-rmse:1.313674+0.020872    test-rmse:1.867169+0.246047 
## [36] train-rmse:1.271876+0.017279    test-rmse:1.845775+0.243175 
## [37] train-rmse:1.232905+0.017033    test-rmse:1.824238+0.243374 
## [38] train-rmse:1.192917+0.021756    test-rmse:1.802296+0.252524 
## [39] train-rmse:1.158170+0.022243    test-rmse:1.776657+0.255054 
## [40] train-rmse:1.117669+0.019533    test-rmse:1.752363+0.249964 
## [41] train-rmse:1.084004+0.019090    test-rmse:1.728570+0.253049 
## [42] train-rmse:1.052970+0.017986    test-rmse:1.693650+0.247926 
## [43] train-rmse:1.023588+0.017526    test-rmse:1.674904+0.252205 
## [44] train-rmse:0.994469+0.017166    test-rmse:1.644183+0.254878 
## [45] train-rmse:0.962334+0.017064    test-rmse:1.624755+0.255962 
## [46] train-rmse:0.936258+0.018449    test-rmse:1.601403+0.259215 
## [47] train-rmse:0.908328+0.020998    test-rmse:1.583935+0.260341 
## [48] train-rmse:0.883231+0.019437    test-rmse:1.567804+0.263714 
## [49] train-rmse:0.858719+0.017926    test-rmse:1.543494+0.267655 
## [50] train-rmse:0.836007+0.017725    test-rmse:1.533069+0.268552 
## [51] train-rmse:0.815321+0.016445    test-rmse:1.516840+0.267250 
## [52] train-rmse:0.794997+0.016553    test-rmse:1.502714+0.271180 
## [53] train-rmse:0.771043+0.018321    test-rmse:1.485964+0.274375 
## [54] train-rmse:0.752063+0.019184    test-rmse:1.467327+0.274971 
## [55] train-rmse:0.734268+0.019500    test-rmse:1.460307+0.280412 
## [56] train-rmse:0.714490+0.022633    test-rmse:1.439122+0.266890 
## [57] train-rmse:0.696843+0.022806    test-rmse:1.427421+0.263116 
## [58] train-rmse:0.680545+0.023755    test-rmse:1.411887+0.264519 
## [59] train-rmse:0.665243+0.024359    test-rmse:1.401071+0.265723 
## [60] train-rmse:0.649657+0.022689    test-rmse:1.395792+0.268858 
## [61] train-rmse:0.633657+0.021168    test-rmse:1.371533+0.257673 
## [62] train-rmse:0.619602+0.021792    test-rmse:1.364373+0.261781 
## [63] train-rmse:0.606231+0.021783    test-rmse:1.352254+0.254356 
## [64] train-rmse:0.593238+0.021401    test-rmse:1.342055+0.255958 
## [65] train-rmse:0.582192+0.021878    test-rmse:1.327091+0.257485 
## [66] train-rmse:0.569598+0.021126    test-rmse:1.321461+0.256216 
## [67] train-rmse:0.558521+0.021275    test-rmse:1.317633+0.256032 
## [68] train-rmse:0.547903+0.021383    test-rmse:1.314211+0.254827 
## [69] train-rmse:0.535864+0.020959    test-rmse:1.306722+0.254896 
## [70] train-rmse:0.525399+0.020499    test-rmse:1.298055+0.258392 
## [71] train-rmse:0.515048+0.021237    test-rmse:1.289706+0.263841 
## [72] train-rmse:0.504710+0.020652    test-rmse:1.279236+0.256278 
## [73] train-rmse:0.495167+0.020956    test-rmse:1.273390+0.257464 
## [74] train-rmse:0.485794+0.021569    test-rmse:1.271157+0.259830 
## [75] train-rmse:0.477251+0.021059    test-rmse:1.263048+0.261687 
## [76] train-rmse:0.467397+0.020728    test-rmse:1.258061+0.262616 
## [77] train-rmse:0.459397+0.020880    test-rmse:1.252509+0.261160 
## [78] train-rmse:0.450706+0.019602    test-rmse:1.245954+0.256389 
## [79] train-rmse:0.441137+0.018582    test-rmse:1.240534+0.260267 
## [80] train-rmse:0.434365+0.018369    test-rmse:1.238192+0.261236 
## [81] train-rmse:0.426317+0.018932    test-rmse:1.238218+0.261640 
## [82] train-rmse:0.418094+0.018371    test-rmse:1.234204+0.261426 
## [83] train-rmse:0.412077+0.018688    test-rmse:1.230976+0.261449 
## [84] train-rmse:0.405552+0.018867    test-rmse:1.230034+0.260764 
## [85] train-rmse:0.399782+0.018296    test-rmse:1.227038+0.262076 
## [86] train-rmse:0.392828+0.018349    test-rmse:1.221908+0.265095 
## [87] train-rmse:0.386548+0.019316    test-rmse:1.218878+0.264845 
## [88] train-rmse:0.381109+0.019394    test-rmse:1.215635+0.264792 
## [89] train-rmse:0.374826+0.018987    test-rmse:1.213137+0.263597 
## [90] train-rmse:0.369171+0.017902    test-rmse:1.208612+0.266292 
## [91] train-rmse:0.363164+0.017928    test-rmse:1.207151+0.266646 
## [92] train-rmse:0.358007+0.018105    test-rmse:1.205794+0.266767 
## [93] train-rmse:0.352053+0.018546    test-rmse:1.202060+0.269837 
## [94] train-rmse:0.346766+0.018384    test-rmse:1.197774+0.264956 
## [95] train-rmse:0.341240+0.017775    test-rmse:1.196777+0.264203 
## [96] train-rmse:0.336847+0.017811    test-rmse:1.193781+0.265162 
## [97] train-rmse:0.332298+0.017945    test-rmse:1.193499+0.265212 
## [98] train-rmse:0.328596+0.017888    test-rmse:1.191339+0.263737 
## [99] train-rmse:0.323141+0.017443    test-rmse:1.187614+0.265488 
## [100]    train-rmse:0.318225+0.016931    test-rmse:1.186502+0.265730 
## [101]    train-rmse:0.314101+0.016930    test-rmse:1.185738+0.264929 
## [102]    train-rmse:0.310288+0.016665    test-rmse:1.183797+0.264800 
## [103]    train-rmse:0.306081+0.016146    test-rmse:1.180457+0.265202 
## [104]    train-rmse:0.302266+0.015687    test-rmse:1.180026+0.265610 
## [105]    train-rmse:0.298805+0.015500    test-rmse:1.179156+0.266234 
## [106]    train-rmse:0.295571+0.015400    test-rmse:1.177372+0.266721 
## [107]    train-rmse:0.291818+0.015407    test-rmse:1.175493+0.267338 
## [108]    train-rmse:0.287148+0.015256    test-rmse:1.175073+0.267753 
## [109]    train-rmse:0.283099+0.014223    test-rmse:1.173359+0.267685 
## [110]    train-rmse:0.278997+0.014706    test-rmse:1.171382+0.266977 
## [111]    train-rmse:0.275274+0.015481    test-rmse:1.169575+0.268741 
## [112]    train-rmse:0.271595+0.014283    test-rmse:1.168437+0.269876 
## [113]    train-rmse:0.268668+0.014148    test-rmse:1.168391+0.270610 
## [114]    train-rmse:0.265513+0.014292    test-rmse:1.167463+0.271018 
## [115]    train-rmse:0.261972+0.014097    test-rmse:1.166003+0.271585 
## [116]    train-rmse:0.258982+0.013898    test-rmse:1.165721+0.271804 
## [117]    train-rmse:0.255558+0.013733    test-rmse:1.164801+0.272037 
## [118]    train-rmse:0.253172+0.014035    test-rmse:1.164060+0.271455 
## [119]    train-rmse:0.250165+0.013632    test-rmse:1.163334+0.272272 
## [120]    train-rmse:0.246703+0.014832    test-rmse:1.162886+0.271988 
## [121]    train-rmse:0.244399+0.014674    test-rmse:1.162824+0.272734 
## [122]    train-rmse:0.241795+0.014586    test-rmse:1.159541+0.268529 
## [123]    train-rmse:0.239354+0.014683    test-rmse:1.158966+0.268959 
## [124]    train-rmse:0.236383+0.014260    test-rmse:1.158671+0.269207 
## [125]    train-rmse:0.233121+0.015360    test-rmse:1.157771+0.268658 
## [126]    train-rmse:0.230262+0.014634    test-rmse:1.157497+0.268584 
## [127]    train-rmse:0.227906+0.014175    test-rmse:1.156060+0.268999 
## [128]    train-rmse:0.225372+0.013963    test-rmse:1.155408+0.270038 
## [129]    train-rmse:0.222201+0.014608    test-rmse:1.154972+0.270002 
## [130]    train-rmse:0.219661+0.015010    test-rmse:1.154423+0.270934 
## [131]    train-rmse:0.217516+0.014832    test-rmse:1.153837+0.271280 
## [132]    train-rmse:0.215372+0.015094    test-rmse:1.153503+0.271751 
## [133]    train-rmse:0.213185+0.014958    test-rmse:1.152916+0.272603 
## [134]    train-rmse:0.209979+0.014651    test-rmse:1.153098+0.272575 
## [135]    train-rmse:0.207623+0.014759    test-rmse:1.152362+0.272838 
## [136]    train-rmse:0.204954+0.014387    test-rmse:1.151090+0.272495 
## [137]    train-rmse:0.203159+0.014247    test-rmse:1.150085+0.272915 
## [138]    train-rmse:0.199703+0.013823    test-rmse:1.148813+0.272332 
## [139]    train-rmse:0.197941+0.014352    test-rmse:1.149016+0.273565 
## [140]    train-rmse:0.195635+0.014442    test-rmse:1.147938+0.273269 
## [141]    train-rmse:0.193148+0.013958    test-rmse:1.147702+0.271914 
## [142]    train-rmse:0.191164+0.014536    test-rmse:1.147156+0.272126 
## [143]    train-rmse:0.188766+0.015223    test-rmse:1.146686+0.273029 
## [144]    train-rmse:0.186486+0.014784    test-rmse:1.145735+0.273628 
## [145]    train-rmse:0.184350+0.014422    test-rmse:1.144631+0.273898 
## [146]    train-rmse:0.182477+0.014535    test-rmse:1.144906+0.274186 
## [147]    train-rmse:0.181021+0.014203    test-rmse:1.145092+0.273755 
## [148]    train-rmse:0.178941+0.014168    test-rmse:1.145020+0.273390 
## [149]    train-rmse:0.177282+0.013906    test-rmse:1.144208+0.273870 
## [150]    train-rmse:0.175463+0.013207    test-rmse:1.143821+0.273634 
## [151]    train-rmse:0.173692+0.013400    test-rmse:1.143388+0.272639 
## [152]    train-rmse:0.171866+0.014200    test-rmse:1.143264+0.272216 
## [153]    train-rmse:0.169873+0.014365    test-rmse:1.142963+0.272382 
## [154]    train-rmse:0.168213+0.014161    test-rmse:1.143261+0.273342 
## [155]    train-rmse:0.166336+0.014005    test-rmse:1.143465+0.272896 
## [156]    train-rmse:0.164117+0.013526    test-rmse:1.142655+0.273026 
## [157]    train-rmse:0.162202+0.013091    test-rmse:1.142558+0.272871 
## [158]    train-rmse:0.160219+0.013477    test-rmse:1.141379+0.273745 
## [159]    train-rmse:0.159023+0.013298    test-rmse:1.141252+0.273850 
## [160]    train-rmse:0.157100+0.012715    test-rmse:1.139635+0.272246 
## [161]    train-rmse:0.155692+0.012365    test-rmse:1.140036+0.272393 
## [162]    train-rmse:0.154102+0.012353    test-rmse:1.139415+0.272775 
## [163]    train-rmse:0.152463+0.012223    test-rmse:1.139199+0.273387 
## [164]    train-rmse:0.151064+0.012392    test-rmse:1.138758+0.273933 
## [165]    train-rmse:0.149451+0.012049    test-rmse:1.137924+0.272957 
## [166]    train-rmse:0.147717+0.011695    test-rmse:1.137280+0.273570 
## [167]    train-rmse:0.146537+0.011756    test-rmse:1.137107+0.273775 
## [168]    train-rmse:0.144542+0.011576    test-rmse:1.137403+0.273357 
## [169]    train-rmse:0.143318+0.011717    test-rmse:1.137049+0.273558 
## [170]    train-rmse:0.142030+0.011514    test-rmse:1.136867+0.273947 
## [171]    train-rmse:0.140677+0.011341    test-rmse:1.136788+0.273033 
## [172]    train-rmse:0.139681+0.011315    test-rmse:1.136180+0.273155 
## [173]    train-rmse:0.138048+0.010974    test-rmse:1.135643+0.273211 
## [174]    train-rmse:0.136891+0.011062    test-rmse:1.135481+0.273129 
## [175]    train-rmse:0.135127+0.011258    test-rmse:1.135183+0.273333 
## [176]    train-rmse:0.133148+0.010947    test-rmse:1.134393+0.273378 
## [177]    train-rmse:0.132115+0.010766    test-rmse:1.134425+0.273757 
## [178]    train-rmse:0.130733+0.010663    test-rmse:1.133936+0.273503 
## [179]    train-rmse:0.129637+0.010442    test-rmse:1.134109+0.273949 
## [180]    train-rmse:0.128642+0.010381    test-rmse:1.133792+0.274201 
## [181]    train-rmse:0.127014+0.010029    test-rmse:1.134082+0.273755 
## [182]    train-rmse:0.125793+0.010044    test-rmse:1.133040+0.271987 
## [183]    train-rmse:0.124380+0.009791    test-rmse:1.132649+0.272014 
## [184]    train-rmse:0.123206+0.009811    test-rmse:1.132423+0.272315 
## [185]    train-rmse:0.122047+0.009795    test-rmse:1.131944+0.272334 
## [186]    train-rmse:0.119851+0.009854    test-rmse:1.132153+0.272395 
## [187]    train-rmse:0.119002+0.009876    test-rmse:1.131114+0.270976 
## [188]    train-rmse:0.118001+0.009813    test-rmse:1.131244+0.271134 
## [189]    train-rmse:0.116848+0.009401    test-rmse:1.130965+0.271237 
## [190]    train-rmse:0.115821+0.009329    test-rmse:1.130696+0.271790 
## [191]    train-rmse:0.114868+0.009081    test-rmse:1.130494+0.272144 
## [192]    train-rmse:0.114267+0.009023    test-rmse:1.130223+0.272111 
## [193]    train-rmse:0.113518+0.008981    test-rmse:1.129829+0.272202 
## [194]    train-rmse:0.111790+0.008475    test-rmse:1.130002+0.272360 
## [195]    train-rmse:0.109808+0.007527    test-rmse:1.129640+0.272547 
## [196]    train-rmse:0.107637+0.007051    test-rmse:1.130782+0.273329 
## [197]    train-rmse:0.106574+0.007250    test-rmse:1.131312+0.272951 
## [198]    train-rmse:0.105865+0.007052    test-rmse:1.130498+0.272750 
## [199]    train-rmse:0.104967+0.006856    test-rmse:1.129351+0.271740 
## [200]    train-rmse:0.104017+0.006691    test-rmse:1.129409+0.271632 
## [201]    train-rmse:0.102999+0.007007    test-rmse:1.129225+0.271755 
## [202]    train-rmse:0.101898+0.006848    test-rmse:1.129123+0.271547 
## [203]    train-rmse:0.101128+0.006923    test-rmse:1.129361+0.271436 
## [204]    train-rmse:0.100148+0.006433    test-rmse:1.129232+0.271541 
## [205]    train-rmse:0.099140+0.006860    test-rmse:1.128710+0.271585 
## [206]    train-rmse:0.098234+0.007093    test-rmse:1.127933+0.271832 
## [207]    train-rmse:0.097334+0.006615    test-rmse:1.128476+0.271830 
## [208]    train-rmse:0.096583+0.006774    test-rmse:1.128181+0.272506 
## [209]    train-rmse:0.095806+0.007091    test-rmse:1.128139+0.272475 
## [210]    train-rmse:0.095137+0.007299    test-rmse:1.128167+0.272932 
## [211]    train-rmse:0.094268+0.007174    test-rmse:1.128252+0.273068 
## [212]    train-rmse:0.093110+0.006659    test-rmse:1.128094+0.273098 
## Stopping. Best iteration:
## [206]    train-rmse:0.098234+0.007093    test-rmse:1.127933+0.271832
## 
## 39 
## [1]  train-rmse:86.218085+0.097587   test-rmse:86.218257+0.495906 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.119772+0.083348   test-rmse:69.119559+0.523711 
## [3]  train-rmse:55.450065+0.073313   test-rmse:55.449316+0.550523 
## [4]  train-rmse:44.530696+0.066998   test-rmse:44.529209+0.577462 
## [5]  train-rmse:35.819717+0.064113   test-rmse:35.817187+0.605511 
## [6]  train-rmse:28.868193+0.060253   test-rmse:28.910774+0.605132 
## [7]  train-rmse:23.310282+0.045896   test-rmse:23.406557+0.627461 
## [8]  train-rmse:18.864911+0.037248   test-rmse:18.966760+0.596436 
## [9]  train-rmse:15.305449+0.033583   test-rmse:15.411134+0.559722 
## [10] train-rmse:12.456289+0.030877   test-rmse:12.561671+0.532906 
## [11] train-rmse:10.175636+0.026978   test-rmse:10.264841+0.529039 
## [12] train-rmse:8.346776+0.030590    test-rmse:8.467034+0.540278 
## [13] train-rmse:6.889319+0.029617    test-rmse:7.001238+0.521320 
## [14] train-rmse:5.724944+0.033015    test-rmse:5.873543+0.518644 
## [15] train-rmse:4.802166+0.036944    test-rmse:4.975841+0.487872 
## [16] train-rmse:4.069513+0.039333    test-rmse:4.276470+0.451544 
## [17] train-rmse:3.482504+0.040276    test-rmse:3.718835+0.457488 
## [18] train-rmse:3.023774+0.040293    test-rmse:3.274318+0.410695 
## [19] train-rmse:2.659198+0.032426    test-rmse:2.982196+0.405317 
## [20] train-rmse:2.362885+0.027158    test-rmse:2.706749+0.378938 
## [21] train-rmse:2.125495+0.028959    test-rmse:2.511262+0.343606 
## [22] train-rmse:1.943669+0.030828    test-rmse:2.379552+0.325694 
## [23] train-rmse:1.791426+0.030396    test-rmse:2.241104+0.297300 
## [24] train-rmse:1.670095+0.031209    test-rmse:2.155301+0.280638 
## [25] train-rmse:1.563173+0.030774    test-rmse:2.074725+0.261604 
## [26] train-rmse:1.478079+0.031576    test-rmse:2.005500+0.248021 
## [27] train-rmse:1.401038+0.029938    test-rmse:1.928714+0.238354 
## [28] train-rmse:1.332678+0.027751    test-rmse:1.889715+0.222907 
## [29] train-rmse:1.267665+0.024348    test-rmse:1.834323+0.237841 
## [30] train-rmse:1.210246+0.026990    test-rmse:1.792095+0.223701 
## [31] train-rmse:1.155816+0.028851    test-rmse:1.759501+0.231556 
## [32] train-rmse:1.109284+0.027045    test-rmse:1.730906+0.217315 
## [33] train-rmse:1.064781+0.026562    test-rmse:1.694901+0.215674 
## [34] train-rmse:1.023346+0.025178    test-rmse:1.654332+0.217055 
## [35] train-rmse:0.981231+0.023291    test-rmse:1.621570+0.206846 
## [36] train-rmse:0.943897+0.023915    test-rmse:1.595587+0.214168 
## [37] train-rmse:0.909462+0.022899    test-rmse:1.567166+0.214898 
## [38] train-rmse:0.871249+0.030864    test-rmse:1.543759+0.217626 
## [39] train-rmse:0.838870+0.027619    test-rmse:1.518971+0.216098 
## [40] train-rmse:0.810449+0.027653    test-rmse:1.496368+0.216232 
## [41] train-rmse:0.778922+0.026789    test-rmse:1.476487+0.224888 
## [42] train-rmse:0.753112+0.026109    test-rmse:1.458070+0.228862 
## [43] train-rmse:0.729826+0.025546    test-rmse:1.439518+0.233019 
## [44] train-rmse:0.706379+0.024891    test-rmse:1.423822+0.235898 
## [45] train-rmse:0.675334+0.026669    test-rmse:1.401679+0.238591 
## [46] train-rmse:0.654637+0.026639    test-rmse:1.389796+0.234554 
## [47] train-rmse:0.635481+0.024835    test-rmse:1.378549+0.239723 
## [48] train-rmse:0.611803+0.022531    test-rmse:1.368790+0.237075 
## [49] train-rmse:0.592649+0.023704    test-rmse:1.354911+0.236949 
## [50] train-rmse:0.575773+0.023658    test-rmse:1.344592+0.239648 
## [51] train-rmse:0.559440+0.024628    test-rmse:1.332076+0.231859 
## [52] train-rmse:0.543765+0.023860    test-rmse:1.325459+0.232316 
## [53] train-rmse:0.528272+0.024000    test-rmse:1.316846+0.231445 
## [54] train-rmse:0.511523+0.024730    test-rmse:1.305331+0.235173 
## [55] train-rmse:0.498103+0.023383    test-rmse:1.298559+0.236954 
## [56] train-rmse:0.485262+0.024106    test-rmse:1.291051+0.237910 
## [57] train-rmse:0.470381+0.022513    test-rmse:1.286274+0.243617 
## [58] train-rmse:0.458259+0.020469    test-rmse:1.275946+0.241374 
## [59] train-rmse:0.447381+0.020639    test-rmse:1.271916+0.246314 
## [60] train-rmse:0.436803+0.020336    test-rmse:1.264362+0.248667 
## [61] train-rmse:0.427361+0.019988    test-rmse:1.260081+0.250666 
## [62] train-rmse:0.416589+0.020077    test-rmse:1.254759+0.250755 
## [63] train-rmse:0.404133+0.020472    test-rmse:1.250282+0.251273 
## [64] train-rmse:0.394973+0.020653    test-rmse:1.247406+0.250369 
## [65] train-rmse:0.384650+0.019402    test-rmse:1.243004+0.253369 
## [66] train-rmse:0.374825+0.018058    test-rmse:1.236099+0.257135 
## [67] train-rmse:0.366799+0.016826    test-rmse:1.233031+0.257675 
## [68] train-rmse:0.359798+0.017331    test-rmse:1.231463+0.256895 
## [69] train-rmse:0.352531+0.017877    test-rmse:1.228652+0.257492 
## [70] train-rmse:0.343921+0.015679    test-rmse:1.228582+0.258157 
## [71] train-rmse:0.337126+0.015669    test-rmse:1.226873+0.260579 
## [72] train-rmse:0.329287+0.015760    test-rmse:1.225121+0.260836 
## [73] train-rmse:0.322310+0.014527    test-rmse:1.222394+0.261493 
## [74] train-rmse:0.315375+0.012644    test-rmse:1.221944+0.262162 
## [75] train-rmse:0.309773+0.011782    test-rmse:1.217781+0.266196 
## [76] train-rmse:0.303867+0.011356    test-rmse:1.216742+0.267069 
## [77] train-rmse:0.297681+0.010646    test-rmse:1.214846+0.267430 
## [78] train-rmse:0.290764+0.009598    test-rmse:1.210768+0.266588 
## [79] train-rmse:0.285750+0.009989    test-rmse:1.207630+0.268137 
## [80] train-rmse:0.279231+0.009308    test-rmse:1.205593+0.269207 
## [81] train-rmse:0.272974+0.009974    test-rmse:1.204091+0.268946 
## [82] train-rmse:0.268921+0.009689    test-rmse:1.202469+0.269971 
## [83] train-rmse:0.264443+0.009217    test-rmse:1.202405+0.269597 
## [84] train-rmse:0.259301+0.009430    test-rmse:1.201769+0.269665 
## [85] train-rmse:0.254192+0.010030    test-rmse:1.201314+0.270227 
## [86] train-rmse:0.249211+0.009613    test-rmse:1.201116+0.270680 
## [87] train-rmse:0.244293+0.009558    test-rmse:1.199273+0.270262 
## [88] train-rmse:0.238676+0.010562    test-rmse:1.195951+0.269117 
## [89] train-rmse:0.234700+0.010708    test-rmse:1.194821+0.269268 
## [90] train-rmse:0.230248+0.010226    test-rmse:1.193679+0.270245 
## [91] train-rmse:0.225238+0.011167    test-rmse:1.191410+0.270697 
## [92] train-rmse:0.221662+0.011103    test-rmse:1.190584+0.271203 
## [93] train-rmse:0.217547+0.011978    test-rmse:1.189398+0.271099 
## [94] train-rmse:0.213103+0.011374    test-rmse:1.189604+0.271204 
## [95] train-rmse:0.209789+0.011908    test-rmse:1.189717+0.270655 
## [96] train-rmse:0.204948+0.012029    test-rmse:1.188759+0.271537 
## [97] train-rmse:0.201775+0.011863    test-rmse:1.188639+0.272788 
## [98] train-rmse:0.198499+0.011225    test-rmse:1.187606+0.274064 
## [99] train-rmse:0.195072+0.011322    test-rmse:1.187302+0.273937 
## [100]    train-rmse:0.191379+0.011813    test-rmse:1.187251+0.272903 
## [101]    train-rmse:0.187068+0.010120    test-rmse:1.186933+0.272306 
## [102]    train-rmse:0.183569+0.010071    test-rmse:1.186331+0.271513 
## [103]    train-rmse:0.181202+0.010208    test-rmse:1.185616+0.271790 
## [104]    train-rmse:0.177796+0.010274    test-rmse:1.184195+0.272031 
## [105]    train-rmse:0.174438+0.010579    test-rmse:1.183330+0.272418 
## [106]    train-rmse:0.171338+0.010889    test-rmse:1.182376+0.274022 
## [107]    train-rmse:0.167587+0.011258    test-rmse:1.182436+0.274128 
## [108]    train-rmse:0.164459+0.009783    test-rmse:1.182420+0.274872 
## [109]    train-rmse:0.162696+0.009210    test-rmse:1.180159+0.273540 
## [110]    train-rmse:0.160612+0.009317    test-rmse:1.179967+0.273269 
## [111]    train-rmse:0.157815+0.008726    test-rmse:1.179311+0.273841 
## [112]    train-rmse:0.155639+0.008776    test-rmse:1.179426+0.273829 
## [113]    train-rmse:0.152322+0.009586    test-rmse:1.179116+0.273925 
## [114]    train-rmse:0.150328+0.009904    test-rmse:1.178870+0.274011 
## [115]    train-rmse:0.147367+0.009594    test-rmse:1.178157+0.273546 
## [116]    train-rmse:0.145109+0.010122    test-rmse:1.177613+0.273293 
## [117]    train-rmse:0.141108+0.009003    test-rmse:1.177806+0.273322 
## [118]    train-rmse:0.139192+0.009384    test-rmse:1.178268+0.273634 
## [119]    train-rmse:0.137226+0.008933    test-rmse:1.177537+0.273970 
## [120]    train-rmse:0.134134+0.007972    test-rmse:1.177732+0.274194 
## [121]    train-rmse:0.131996+0.007940    test-rmse:1.176150+0.272598 
## [122]    train-rmse:0.129811+0.007884    test-rmse:1.175787+0.272521 
## [123]    train-rmse:0.128072+0.006886    test-rmse:1.176021+0.272614 
## [124]    train-rmse:0.126482+0.006895    test-rmse:1.176297+0.272467 
## [125]    train-rmse:0.123660+0.005721    test-rmse:1.176874+0.272657 
## [126]    train-rmse:0.121696+0.005343    test-rmse:1.176596+0.272771 
## [127]    train-rmse:0.119107+0.006244    test-rmse:1.176141+0.273194 
## [128]    train-rmse:0.117152+0.006120    test-rmse:1.176086+0.273611 
## Stopping. Best iteration:
## [122]    train-rmse:0.129811+0.007884    test-rmse:1.175787+0.272521
## 
## 40 
## [1]  train-rmse:86.218085+0.097587   test-rmse:86.218257+0.495906 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.119772+0.083348   test-rmse:69.119559+0.523711 
## [3]  train-rmse:55.450065+0.073313   test-rmse:55.449316+0.550523 
## [4]  train-rmse:44.530696+0.066998   test-rmse:44.529209+0.577462 
## [5]  train-rmse:35.819717+0.064113   test-rmse:35.817187+0.605511 
## [6]  train-rmse:28.868193+0.060253   test-rmse:28.910774+0.605132 
## [7]  train-rmse:23.310282+0.045896   test-rmse:23.406557+0.627461 
## [8]  train-rmse:18.864508+0.036756   test-rmse:18.962976+0.601228 
## [9]  train-rmse:15.303019+0.032641   test-rmse:15.401728+0.555499 
## [10] train-rmse:12.448038+0.028808   test-rmse:12.556415+0.541169 
## [11] train-rmse:10.158663+0.026132   test-rmse:10.303151+0.563010 
## [12] train-rmse:8.323074+0.024827    test-rmse:8.475917+0.532949 
## [13] train-rmse:6.842557+0.026020    test-rmse:6.991238+0.522417 
## [14] train-rmse:5.665543+0.022189    test-rmse:5.836693+0.532774 
## [15] train-rmse:4.722709+0.026120    test-rmse:4.901020+0.506425 
## [16] train-rmse:3.961930+0.015887    test-rmse:4.165998+0.464783 
## [17] train-rmse:3.361242+0.014950    test-rmse:3.632963+0.467426 
## [18] train-rmse:2.879510+0.011112    test-rmse:3.194591+0.417171 
## [19] train-rmse:2.503801+0.012158    test-rmse:2.857520+0.381168 
## [20] train-rmse:2.199069+0.014432    test-rmse:2.598141+0.367128 
## [21] train-rmse:1.952066+0.016376    test-rmse:2.369736+0.337458 
## [22] train-rmse:1.750896+0.021453    test-rmse:2.212502+0.319904 
## [23] train-rmse:1.584359+0.021647    test-rmse:2.093126+0.271008 
## [24] train-rmse:1.457716+0.021829    test-rmse:1.981258+0.251610 
## [25] train-rmse:1.345736+0.026388    test-rmse:1.909642+0.232505 
## [26] train-rmse:1.251222+0.030929    test-rmse:1.850079+0.211057 
## [27] train-rmse:1.171879+0.032670    test-rmse:1.794595+0.200517 
## [28] train-rmse:1.096428+0.024878    test-rmse:1.749471+0.203395 
## [29] train-rmse:1.023028+0.025165    test-rmse:1.700074+0.207921 
## [30] train-rmse:0.967825+0.025789    test-rmse:1.656643+0.212888 
## [31] train-rmse:0.914003+0.030271    test-rmse:1.621180+0.205887 
## [32] train-rmse:0.866332+0.026267    test-rmse:1.595999+0.208228 
## [33] train-rmse:0.817117+0.024649    test-rmse:1.564510+0.212695 
## [34] train-rmse:0.780459+0.022982    test-rmse:1.541036+0.205651 
## [35] train-rmse:0.746534+0.022574    test-rmse:1.509517+0.208074 
## [36] train-rmse:0.711200+0.023871    test-rmse:1.489443+0.206509 
## [37] train-rmse:0.679492+0.025133    test-rmse:1.477832+0.213524 
## [38] train-rmse:0.652267+0.025000    test-rmse:1.453517+0.201660 
## [39] train-rmse:0.626816+0.023245    test-rmse:1.433149+0.214013 
## [40] train-rmse:0.601663+0.024054    test-rmse:1.418752+0.213614 
## [41] train-rmse:0.576732+0.026285    test-rmse:1.403282+0.215345 
## [42] train-rmse:0.554698+0.026305    test-rmse:1.388150+0.207509 
## [43] train-rmse:0.536162+0.025973    test-rmse:1.376110+0.208229 
## [44] train-rmse:0.516351+0.023952    test-rmse:1.365861+0.207206 
## [45] train-rmse:0.499137+0.023982    test-rmse:1.352243+0.211187 
## [46] train-rmse:0.477675+0.023743    test-rmse:1.345830+0.217322 
## [47] train-rmse:0.461042+0.023745    test-rmse:1.338681+0.221715 
## [48] train-rmse:0.446918+0.023215    test-rmse:1.329168+0.222965 
## [49] train-rmse:0.432483+0.022118    test-rmse:1.321775+0.223380 
## [50] train-rmse:0.416969+0.022025    test-rmse:1.314476+0.228809 
## [51] train-rmse:0.401320+0.024111    test-rmse:1.310357+0.227921 
## [52] train-rmse:0.389272+0.023922    test-rmse:1.303409+0.227307 
## [53] train-rmse:0.377605+0.023375    test-rmse:1.296969+0.229266 
## [54] train-rmse:0.364426+0.022718    test-rmse:1.293612+0.230678 
## [55] train-rmse:0.351741+0.021755    test-rmse:1.290719+0.232657 
## [56] train-rmse:0.340674+0.020684    test-rmse:1.286453+0.235214 
## [57] train-rmse:0.331946+0.021080    test-rmse:1.280183+0.235927 
## [58] train-rmse:0.320571+0.017363    test-rmse:1.278196+0.237766 
## [59] train-rmse:0.312539+0.016842    test-rmse:1.270595+0.236708 
## [60] train-rmse:0.303767+0.015445    test-rmse:1.268248+0.239362 
## [61] train-rmse:0.293297+0.014731    test-rmse:1.266668+0.241027 
## [62] train-rmse:0.285582+0.015183    test-rmse:1.263511+0.242387 
## [63] train-rmse:0.277296+0.013635    test-rmse:1.260484+0.241055 
## [64] train-rmse:0.270815+0.013866    test-rmse:1.260105+0.242271 
## [65] train-rmse:0.263958+0.012836    test-rmse:1.257536+0.243718 
## [66] train-rmse:0.256778+0.012166    test-rmse:1.254423+0.242858 
## [67] train-rmse:0.249180+0.012751    test-rmse:1.254545+0.241537 
## [68] train-rmse:0.243050+0.013052    test-rmse:1.253009+0.242546 
## [69] train-rmse:0.237702+0.013346    test-rmse:1.250882+0.243866 
## [70] train-rmse:0.230375+0.013659    test-rmse:1.248935+0.245311 
## [71] train-rmse:0.225515+0.013704    test-rmse:1.248669+0.246018 
## [72] train-rmse:0.218962+0.011310    test-rmse:1.245517+0.244805 
## [73] train-rmse:0.214458+0.011694    test-rmse:1.243848+0.244820 
## [74] train-rmse:0.210035+0.012053    test-rmse:1.242843+0.245725 
## [75] train-rmse:0.204262+0.012826    test-rmse:1.240934+0.245002 
## [76] train-rmse:0.199775+0.012411    test-rmse:1.240640+0.243694 
## [77] train-rmse:0.196021+0.012177    test-rmse:1.240167+0.243597 
## [78] train-rmse:0.191694+0.010993    test-rmse:1.238542+0.243048 
## [79] train-rmse:0.187002+0.011720    test-rmse:1.237398+0.244520 
## [80] train-rmse:0.181888+0.011827    test-rmse:1.236388+0.243874 
## [81] train-rmse:0.177222+0.011783    test-rmse:1.236155+0.243591 
## [82] train-rmse:0.172802+0.011726    test-rmse:1.235514+0.244018 
## [83] train-rmse:0.169157+0.011755    test-rmse:1.234728+0.243900 
## [84] train-rmse:0.166341+0.011968    test-rmse:1.233816+0.243807 
## [85] train-rmse:0.162573+0.012333    test-rmse:1.233974+0.244123 
## [86] train-rmse:0.159111+0.011318    test-rmse:1.234149+0.244368 
## [87] train-rmse:0.155584+0.010643    test-rmse:1.231906+0.244389 
## [88] train-rmse:0.152524+0.010588    test-rmse:1.230324+0.244285 
## [89] train-rmse:0.149537+0.010755    test-rmse:1.230437+0.244949 
## [90] train-rmse:0.147177+0.010520    test-rmse:1.231091+0.245220 
## [91] train-rmse:0.144827+0.010835    test-rmse:1.230267+0.244805 
## [92] train-rmse:0.141239+0.010931    test-rmse:1.229961+0.244898 
## [93] train-rmse:0.138340+0.011308    test-rmse:1.228392+0.245127 
## [94] train-rmse:0.136075+0.010947    test-rmse:1.227368+0.245264 
## [95] train-rmse:0.131775+0.009162    test-rmse:1.227649+0.245275 
## [96] train-rmse:0.128948+0.008326    test-rmse:1.226569+0.244992 
## [97] train-rmse:0.126018+0.007477    test-rmse:1.226402+0.244963 
## [98] train-rmse:0.123322+0.007745    test-rmse:1.225663+0.244246 
## [99] train-rmse:0.120181+0.007188    test-rmse:1.225249+0.244104 
## [100]    train-rmse:0.117271+0.007603    test-rmse:1.225073+0.244209 
## [101]    train-rmse:0.113635+0.007389    test-rmse:1.224210+0.242853 
## [102]    train-rmse:0.110344+0.006476    test-rmse:1.223817+0.243363 
## [103]    train-rmse:0.107583+0.006701    test-rmse:1.223861+0.243571 
## [104]    train-rmse:0.105173+0.006933    test-rmse:1.222830+0.243249 
## [105]    train-rmse:0.103263+0.007471    test-rmse:1.222476+0.242924 
## [106]    train-rmse:0.101000+0.007940    test-rmse:1.221542+0.243499 
## [107]    train-rmse:0.098472+0.007367    test-rmse:1.221439+0.242533 
## [108]    train-rmse:0.096399+0.007824    test-rmse:1.220760+0.242249 
## [109]    train-rmse:0.094148+0.006456    test-rmse:1.220403+0.242797 
## [110]    train-rmse:0.092735+0.006640    test-rmse:1.220235+0.242969 
## [111]    train-rmse:0.090607+0.006604    test-rmse:1.219856+0.243215 
## [112]    train-rmse:0.088842+0.006942    test-rmse:1.219761+0.243393 
## [113]    train-rmse:0.086840+0.006274    test-rmse:1.218890+0.242446 
## [114]    train-rmse:0.085670+0.006326    test-rmse:1.218301+0.242815 
## [115]    train-rmse:0.083715+0.005771    test-rmse:1.218094+0.243192 
## [116]    train-rmse:0.082025+0.005860    test-rmse:1.217923+0.243014 
## [117]    train-rmse:0.080396+0.006092    test-rmse:1.217683+0.243564 
## [118]    train-rmse:0.079074+0.006295    test-rmse:1.217594+0.243472 
## [119]    train-rmse:0.076999+0.006494    test-rmse:1.217878+0.243495 
## [120]    train-rmse:0.075297+0.006270    test-rmse:1.217269+0.243370 
## [121]    train-rmse:0.074006+0.006187    test-rmse:1.217055+0.243523 
## [122]    train-rmse:0.072840+0.006311    test-rmse:1.216867+0.243833 
## [123]    train-rmse:0.070987+0.006200    test-rmse:1.217156+0.244396 
## [124]    train-rmse:0.069320+0.005982    test-rmse:1.216774+0.244377 
## [125]    train-rmse:0.067918+0.005968    test-rmse:1.216727+0.244350 
## [126]    train-rmse:0.066766+0.005850    test-rmse:1.216173+0.244277 
## [127]    train-rmse:0.064674+0.004728    test-rmse:1.216050+0.244842 
## [128]    train-rmse:0.063501+0.004531    test-rmse:1.215952+0.245076 
## [129]    train-rmse:0.062390+0.004775    test-rmse:1.215756+0.245132 
## [130]    train-rmse:0.061231+0.003911    test-rmse:1.215820+0.245442 
## [131]    train-rmse:0.060207+0.004097    test-rmse:1.215471+0.245538 
## [132]    train-rmse:0.058899+0.003799    test-rmse:1.215365+0.245163 
## [133]    train-rmse:0.057988+0.003871    test-rmse:1.215363+0.244875 
## [134]    train-rmse:0.056288+0.003777    test-rmse:1.215217+0.244952 
## [135]    train-rmse:0.055549+0.003909    test-rmse:1.215057+0.244857 
## [136]    train-rmse:0.054442+0.004268    test-rmse:1.214773+0.245079 
## [137]    train-rmse:0.053603+0.004189    test-rmse:1.214609+0.245016 
## [138]    train-rmse:0.052781+0.004294    test-rmse:1.214724+0.245219 
## [139]    train-rmse:0.051319+0.004118    test-rmse:1.214067+0.245135 
## [140]    train-rmse:0.050214+0.004082    test-rmse:1.213892+0.245113 
## [141]    train-rmse:0.049190+0.004279    test-rmse:1.213939+0.245303 
## [142]    train-rmse:0.048502+0.004030    test-rmse:1.213568+0.245003 
## [143]    train-rmse:0.047669+0.004193    test-rmse:1.213584+0.244927 
## [144]    train-rmse:0.046979+0.003975    test-rmse:1.213273+0.244925 
## [145]    train-rmse:0.045904+0.003621    test-rmse:1.213284+0.244894 
## [146]    train-rmse:0.044701+0.003838    test-rmse:1.213448+0.244765 
## [147]    train-rmse:0.043923+0.003925    test-rmse:1.213288+0.244756 
## [148]    train-rmse:0.042766+0.004028    test-rmse:1.213095+0.244514 
## [149]    train-rmse:0.042083+0.004075    test-rmse:1.212591+0.244001 
## [150]    train-rmse:0.041190+0.004019    test-rmse:1.212624+0.244010 
## [151]    train-rmse:0.040299+0.003748    test-rmse:1.212446+0.244125 
## [152]    train-rmse:0.039584+0.003905    test-rmse:1.212393+0.244092 
## [153]    train-rmse:0.039074+0.003938    test-rmse:1.212300+0.244023 
## [154]    train-rmse:0.038353+0.003940    test-rmse:1.212223+0.243967 
## [155]    train-rmse:0.037704+0.003685    test-rmse:1.212304+0.243878 
## [156]    train-rmse:0.036908+0.003456    test-rmse:1.212180+0.243864 
## [157]    train-rmse:0.036300+0.003605    test-rmse:1.212022+0.243852 
## [158]    train-rmse:0.035670+0.003647    test-rmse:1.212117+0.243819 
## [159]    train-rmse:0.035057+0.003420    test-rmse:1.211965+0.243927 
## [160]    train-rmse:0.034413+0.003585    test-rmse:1.211766+0.243974 
## [161]    train-rmse:0.033790+0.003546    test-rmse:1.211725+0.243974 
## [162]    train-rmse:0.033074+0.003380    test-rmse:1.211733+0.243894 
## [163]    train-rmse:0.032505+0.003162    test-rmse:1.211631+0.243941 
## [164]    train-rmse:0.031722+0.002819    test-rmse:1.211618+0.243797 
## [165]    train-rmse:0.031334+0.002796    test-rmse:1.211581+0.243858 
## [166]    train-rmse:0.030807+0.002862    test-rmse:1.211667+0.243934 
## [167]    train-rmse:0.030243+0.003053    test-rmse:1.211740+0.244007 
## [168]    train-rmse:0.029440+0.002880    test-rmse:1.211453+0.243607 
## [169]    train-rmse:0.028931+0.002711    test-rmse:1.211468+0.243571 
## [170]    train-rmse:0.028444+0.002621    test-rmse:1.211455+0.243553 
## [171]    train-rmse:0.027807+0.002548    test-rmse:1.211453+0.243497 
## [172]    train-rmse:0.027165+0.002563    test-rmse:1.211520+0.243681 
## [173]    train-rmse:0.026602+0.002486    test-rmse:1.211439+0.243646 
## [174]    train-rmse:0.026145+0.002474    test-rmse:1.211467+0.243727 
## [175]    train-rmse:0.025658+0.002564    test-rmse:1.211345+0.243716 
## [176]    train-rmse:0.024979+0.002322    test-rmse:1.211386+0.243602 
## [177]    train-rmse:0.024450+0.002184    test-rmse:1.211339+0.243688 
## [178]    train-rmse:0.024000+0.002145    test-rmse:1.211281+0.243723 
## [179]    train-rmse:0.023560+0.002005    test-rmse:1.211244+0.243696 
## [180]    train-rmse:0.022992+0.001889    test-rmse:1.211280+0.243797 
## [181]    train-rmse:0.022459+0.001825    test-rmse:1.211134+0.243793 
## [182]    train-rmse:0.022055+0.001871    test-rmse:1.211130+0.243916 
## [183]    train-rmse:0.021697+0.001782    test-rmse:1.211174+0.243911 
## [184]    train-rmse:0.021212+0.001797    test-rmse:1.211224+0.243887 
## [185]    train-rmse:0.020916+0.001833    test-rmse:1.211114+0.243907 
## [186]    train-rmse:0.020433+0.001793    test-rmse:1.211204+0.244036 
## [187]    train-rmse:0.020086+0.001671    test-rmse:1.211215+0.244040 
## [188]    train-rmse:0.019802+0.001680    test-rmse:1.211228+0.244074 
## [189]    train-rmse:0.019408+0.001705    test-rmse:1.211199+0.244098 
## [190]    train-rmse:0.019051+0.001761    test-rmse:1.211150+0.244101 
## [191]    train-rmse:0.018694+0.001749    test-rmse:1.211084+0.244014 
## [192]    train-rmse:0.018419+0.001648    test-rmse:1.211085+0.243998 
## [193]    train-rmse:0.018082+0.001631    test-rmse:1.211088+0.243964 
## [194]    train-rmse:0.017696+0.001601    test-rmse:1.211183+0.244238 
## [195]    train-rmse:0.017263+0.001498    test-rmse:1.211200+0.244216 
## [196]    train-rmse:0.016860+0.001432    test-rmse:1.211268+0.244204 
## [197]    train-rmse:0.016517+0.001359    test-rmse:1.211264+0.244194 
## Stopping. Best iteration:
## [191]    train-rmse:0.018694+0.001749    test-rmse:1.211084+0.244014
## 
## 41 
## [1]  train-rmse:86.218085+0.097587   test-rmse:86.218257+0.495906 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:69.119772+0.083348   test-rmse:69.119559+0.523711 
## [3]  train-rmse:55.450065+0.073313   test-rmse:55.449316+0.550523 
## [4]  train-rmse:44.530696+0.066998   test-rmse:44.529209+0.577462 
## [5]  train-rmse:35.819717+0.064113   test-rmse:35.817187+0.605511 
## [6]  train-rmse:28.868193+0.060253   test-rmse:28.910774+0.605132 
## [7]  train-rmse:23.310282+0.045896   test-rmse:23.406557+0.627461 
## [8]  train-rmse:18.864508+0.036756   test-rmse:18.962976+0.601228 
## [9]  train-rmse:15.301535+0.032064   test-rmse:15.394958+0.558768 
## [10] train-rmse:12.443528+0.028864   test-rmse:12.545145+0.539702 
## [11] train-rmse:10.149762+0.025610   test-rmse:10.258270+0.525967 
## [12] train-rmse:8.306724+0.021494    test-rmse:8.439027+0.534927 
## [13] train-rmse:6.816993+0.021466    test-rmse:6.958118+0.528510 
## [14] train-rmse:5.627154+0.019552    test-rmse:5.771085+0.491517 
## [15] train-rmse:4.664539+0.019977    test-rmse:4.857436+0.492940 
## [16] train-rmse:3.893047+0.015855    test-rmse:4.114369+0.452195 
## [17] train-rmse:3.280281+0.017422    test-rmse:3.518115+0.429761 
## [18] train-rmse:2.787811+0.015588    test-rmse:3.046772+0.402898 
## [19] train-rmse:2.386155+0.017808    test-rmse:2.735240+0.395151 
## [20] train-rmse:2.064322+0.022050    test-rmse:2.455285+0.364409 
## [21] train-rmse:1.802798+0.024300    test-rmse:2.244791+0.350233 
## [22] train-rmse:1.595191+0.031092    test-rmse:2.077181+0.333235 
## [23] train-rmse:1.427243+0.032893    test-rmse:1.954787+0.329157 
## [24] train-rmse:1.285178+0.032119    test-rmse:1.851543+0.311873 
## [25] train-rmse:1.165193+0.038460    test-rmse:1.783970+0.295759 
## [26] train-rmse:1.064234+0.040630    test-rmse:1.713994+0.289953 
## [27] train-rmse:0.983545+0.041683    test-rmse:1.662286+0.286935 
## [28] train-rmse:0.912150+0.039011    test-rmse:1.618842+0.285246 
## [29] train-rmse:0.850997+0.038620    test-rmse:1.577708+0.293522 
## [30] train-rmse:0.799752+0.038160    test-rmse:1.546014+0.292070 
## [31] train-rmse:0.749592+0.040222    test-rmse:1.517193+0.288828 
## [32] train-rmse:0.709855+0.040589    test-rmse:1.489939+0.290350 
## [33] train-rmse:0.670596+0.039708    test-rmse:1.456668+0.279572 
## [34] train-rmse:0.635537+0.040179    test-rmse:1.433235+0.283797 
## [35] train-rmse:0.603121+0.042774    test-rmse:1.413442+0.282225 
## [36] train-rmse:0.572722+0.043169    test-rmse:1.394258+0.284372 
## [37] train-rmse:0.544210+0.039307    test-rmse:1.372240+0.277375 
## [38] train-rmse:0.514340+0.039182    test-rmse:1.357795+0.271019 
## [39] train-rmse:0.489737+0.038949    test-rmse:1.347472+0.275347 
## [40] train-rmse:0.470323+0.039364    test-rmse:1.336568+0.274228 
## [41] train-rmse:0.450380+0.038645    test-rmse:1.326425+0.268053 
## [42] train-rmse:0.432656+0.039892    test-rmse:1.316009+0.269917 
## [43] train-rmse:0.413489+0.037036    test-rmse:1.309669+0.269562 
## [44] train-rmse:0.397377+0.037107    test-rmse:1.306758+0.273514 
## [45] train-rmse:0.380983+0.033687    test-rmse:1.299125+0.274965 
## [46] train-rmse:0.365869+0.035549    test-rmse:1.289381+0.278758 
## [47] train-rmse:0.353871+0.034066    test-rmse:1.282488+0.278965 
## [48] train-rmse:0.342527+0.033923    test-rmse:1.278609+0.280621 
## [49] train-rmse:0.329063+0.031570    test-rmse:1.276604+0.284438 
## [50] train-rmse:0.315428+0.026978    test-rmse:1.270113+0.286260 
## [51] train-rmse:0.301866+0.025077    test-rmse:1.265626+0.285899 
## [52] train-rmse:0.292112+0.023777    test-rmse:1.263711+0.286333 
## [53] train-rmse:0.282882+0.023428    test-rmse:1.259411+0.288468 
## [54] train-rmse:0.273838+0.023682    test-rmse:1.256442+0.288856 
## [55] train-rmse:0.265206+0.022758    test-rmse:1.255907+0.290640 
## [56] train-rmse:0.257892+0.021875    test-rmse:1.251537+0.289061 
## [57] train-rmse:0.250912+0.021644    test-rmse:1.246954+0.291026 
## [58] train-rmse:0.243351+0.021554    test-rmse:1.243064+0.288168 
## [59] train-rmse:0.235900+0.020677    test-rmse:1.241441+0.288193 
## [60] train-rmse:0.228563+0.021511    test-rmse:1.238418+0.287866 
## [61] train-rmse:0.221715+0.022528    test-rmse:1.237348+0.289136 
## [62] train-rmse:0.214502+0.022214    test-rmse:1.236871+0.290612 
## [63] train-rmse:0.207456+0.019404    test-rmse:1.236015+0.290111 
## [64] train-rmse:0.200350+0.018266    test-rmse:1.233855+0.290832 
## [65] train-rmse:0.193766+0.016455    test-rmse:1.231872+0.290889 
## [66] train-rmse:0.187697+0.016567    test-rmse:1.232042+0.291928 
## [67] train-rmse:0.181398+0.017787    test-rmse:1.230479+0.291303 
## [68] train-rmse:0.175705+0.018289    test-rmse:1.230131+0.291009 
## [69] train-rmse:0.170179+0.018541    test-rmse:1.228409+0.291402 
## [70] train-rmse:0.164963+0.019504    test-rmse:1.227855+0.292190 
## [71] train-rmse:0.159011+0.015921    test-rmse:1.226549+0.290712 
## [72] train-rmse:0.154137+0.014749    test-rmse:1.225826+0.289659 
## [73] train-rmse:0.149912+0.015386    test-rmse:1.224129+0.291324 
## [74] train-rmse:0.143934+0.014932    test-rmse:1.223883+0.290824 
## [75] train-rmse:0.140041+0.014596    test-rmse:1.223648+0.291505 
## [76] train-rmse:0.137231+0.014877    test-rmse:1.223749+0.292179 
## [77] train-rmse:0.132512+0.014259    test-rmse:1.223494+0.291609 
## [78] train-rmse:0.128374+0.013169    test-rmse:1.222841+0.293247 
## [79] train-rmse:0.124050+0.012747    test-rmse:1.221911+0.293400 
## [80] train-rmse:0.120529+0.010512    test-rmse:1.221512+0.293709 
## [81] train-rmse:0.116538+0.011357    test-rmse:1.220864+0.293648 
## [82] train-rmse:0.113143+0.011595    test-rmse:1.220306+0.294149 
## [83] train-rmse:0.109779+0.010694    test-rmse:1.219251+0.293654 
## [84] train-rmse:0.106206+0.010029    test-rmse:1.218892+0.293613 
## [85] train-rmse:0.103737+0.009420    test-rmse:1.218599+0.293172 
## [86] train-rmse:0.100079+0.007995    test-rmse:1.218664+0.293481 
## [87] train-rmse:0.096818+0.007062    test-rmse:1.218220+0.293331 
## [88] train-rmse:0.094622+0.007082    test-rmse:1.218592+0.293650 
## [89] train-rmse:0.092112+0.006904    test-rmse:1.217755+0.293127 
## [90] train-rmse:0.090223+0.006645    test-rmse:1.217410+0.293566 
## [91] train-rmse:0.087287+0.006673    test-rmse:1.217451+0.293778 
## [92] train-rmse:0.085050+0.005776    test-rmse:1.217073+0.294567 
## [93] train-rmse:0.082545+0.005637    test-rmse:1.216919+0.294552 
## [94] train-rmse:0.080723+0.005532    test-rmse:1.216612+0.294295 
## [95] train-rmse:0.078271+0.005235    test-rmse:1.215949+0.294244 
## [96] train-rmse:0.076165+0.004702    test-rmse:1.216188+0.294657 
## [97] train-rmse:0.073916+0.004356    test-rmse:1.216508+0.295323 
## [98] train-rmse:0.071921+0.004716    test-rmse:1.216257+0.295482 
## [99] train-rmse:0.069478+0.004067    test-rmse:1.215700+0.295481 
## [100]    train-rmse:0.067675+0.004096    test-rmse:1.215797+0.295423 
## [101]    train-rmse:0.065487+0.003376    test-rmse:1.215825+0.295596 
## [102]    train-rmse:0.063844+0.003160    test-rmse:1.215652+0.295457 
## [103]    train-rmse:0.061632+0.003578    test-rmse:1.215449+0.295339 
## [104]    train-rmse:0.059407+0.003155    test-rmse:1.214891+0.295306 
## [105]    train-rmse:0.057827+0.003692    test-rmse:1.214514+0.295937 
## [106]    train-rmse:0.055974+0.003715    test-rmse:1.214505+0.295969 
## [107]    train-rmse:0.054496+0.003499    test-rmse:1.214508+0.295845 
## [108]    train-rmse:0.052960+0.003151    test-rmse:1.213999+0.295213 
## [109]    train-rmse:0.051542+0.003277    test-rmse:1.213904+0.295760 
## [110]    train-rmse:0.049863+0.003066    test-rmse:1.213624+0.295520 
## [111]    train-rmse:0.048453+0.003130    test-rmse:1.213315+0.295506 
## [112]    train-rmse:0.047426+0.003250    test-rmse:1.213129+0.295831 
## [113]    train-rmse:0.046375+0.003100    test-rmse:1.212874+0.295993 
## [114]    train-rmse:0.045193+0.003434    test-rmse:1.212702+0.295921 
## [115]    train-rmse:0.044350+0.003338    test-rmse:1.212741+0.296115 
## [116]    train-rmse:0.043302+0.003519    test-rmse:1.212858+0.296863 
## [117]    train-rmse:0.042259+0.003368    test-rmse:1.212860+0.297013 
## [118]    train-rmse:0.041134+0.003026    test-rmse:1.212472+0.297230 
## [119]    train-rmse:0.039901+0.002876    test-rmse:1.212401+0.297366 
## [120]    train-rmse:0.038772+0.002444    test-rmse:1.212357+0.297457 
## [121]    train-rmse:0.037954+0.002492    test-rmse:1.212443+0.297284 
## [122]    train-rmse:0.036900+0.002362    test-rmse:1.212323+0.297351 
## [123]    train-rmse:0.035922+0.002245    test-rmse:1.212082+0.297217 
## [124]    train-rmse:0.034966+0.002302    test-rmse:1.211940+0.297176 
## [125]    train-rmse:0.033933+0.002171    test-rmse:1.212089+0.297142 
## [126]    train-rmse:0.033218+0.002216    test-rmse:1.211979+0.297276 
## [127]    train-rmse:0.032210+0.002304    test-rmse:1.212124+0.297036 
## [128]    train-rmse:0.031317+0.002167    test-rmse:1.212020+0.297028 
## [129]    train-rmse:0.030717+0.001916    test-rmse:1.211986+0.297066 
## [130]    train-rmse:0.029678+0.001730    test-rmse:1.211833+0.296772 
## [131]    train-rmse:0.028906+0.001515    test-rmse:1.211875+0.296922 
## [132]    train-rmse:0.028239+0.001543    test-rmse:1.211764+0.296777 
## [133]    train-rmse:0.027628+0.001465    test-rmse:1.211690+0.296765 
## [134]    train-rmse:0.026746+0.001288    test-rmse:1.211770+0.296545 
## [135]    train-rmse:0.026220+0.001411    test-rmse:1.211761+0.296532 
## [136]    train-rmse:0.025352+0.001361    test-rmse:1.211814+0.296513 
## [137]    train-rmse:0.024650+0.001589    test-rmse:1.211765+0.296507 
## [138]    train-rmse:0.024076+0.001544    test-rmse:1.211750+0.296526 
## [139]    train-rmse:0.023413+0.001258    test-rmse:1.211755+0.296571 
## Stopping. Best iteration:
## [133]    train-rmse:0.027628+0.001465    test-rmse:1.211690+0.296765
## 
## 42 
## [1]  train-rmse:84.081620+0.095738   test-rmse:84.081755+0.499144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:65.745564+0.080728   test-rmse:65.745244+0.529833 
## [3]  train-rmse:51.456281+0.070760   test-rmse:51.455305+0.559619 
## [4]  train-rmse:40.333913+0.065280   test-rmse:40.331998+0.589993 
## [5]  train-rmse:31.689540+0.061479   test-rmse:31.714312+0.607511 
## [6]  train-rmse:24.970252+0.056849   test-rmse:25.029365+0.633135 
## [7]  train-rmse:19.754920+0.049853   test-rmse:19.798399+0.633426 
## [8]  train-rmse:15.720236+0.042300   test-rmse:15.790007+0.614441 
## [9]  train-rmse:12.615792+0.038547   test-rmse:12.670548+0.614834 
## [10] train-rmse:10.245422+0.037236   test-rmse:10.369963+0.659447 
## [11] train-rmse:8.455333+0.038142    test-rmse:8.546730+0.640634 
## [12] train-rmse:7.119962+0.043697    test-rmse:7.218161+0.606465 
## [13] train-rmse:6.138735+0.048589    test-rmse:6.250143+0.624065 
## [14] train-rmse:5.429717+0.050998    test-rmse:5.600702+0.674514 
## [15] train-rmse:4.915263+0.053617    test-rmse:5.058797+0.603845 
## [16] train-rmse:4.553632+0.056731    test-rmse:4.679948+0.561406 
## [17] train-rmse:4.294095+0.058540    test-rmse:4.429185+0.542903 
## [18] train-rmse:4.101971+0.059061    test-rmse:4.301763+0.545234 
## [19] train-rmse:3.956456+0.058702    test-rmse:4.138639+0.491650 
## [20] train-rmse:3.845893+0.057742    test-rmse:4.010007+0.476791 
## [21] train-rmse:3.755253+0.058087    test-rmse:3.914267+0.427814 
## [22] train-rmse:3.678922+0.057797    test-rmse:3.830782+0.426185 
## [23] train-rmse:3.611507+0.057684    test-rmse:3.802728+0.430768 
## [24] train-rmse:3.551422+0.055776    test-rmse:3.769495+0.418760 
## [25] train-rmse:3.495733+0.053244    test-rmse:3.719601+0.372572 
## [26] train-rmse:3.443229+0.049542    test-rmse:3.679601+0.388349 
## [27] train-rmse:3.394372+0.048256    test-rmse:3.627570+0.383475 
## [28] train-rmse:3.347520+0.046979    test-rmse:3.563454+0.338718 
## [29] train-rmse:3.303036+0.047434    test-rmse:3.521593+0.333630 
## [30] train-rmse:3.260062+0.047774    test-rmse:3.487151+0.349810 
## [31] train-rmse:3.219151+0.046681    test-rmse:3.441458+0.323274 
## [32] train-rmse:3.179069+0.046601    test-rmse:3.405821+0.329017 
## [33] train-rmse:3.140590+0.045814    test-rmse:3.395258+0.330508 
## [34] train-rmse:3.102832+0.045440    test-rmse:3.352875+0.340494 
## [35] train-rmse:3.065597+0.044798    test-rmse:3.322165+0.347883 
## [36] train-rmse:3.030082+0.042983    test-rmse:3.274847+0.338624 
## [37] train-rmse:2.994517+0.041213    test-rmse:3.229832+0.339269 
## [38] train-rmse:2.959191+0.039082    test-rmse:3.183756+0.298569 
## [39] train-rmse:2.926901+0.038687    test-rmse:3.166186+0.303982 
## [40] train-rmse:2.895282+0.038491    test-rmse:3.137110+0.306385 
## [41] train-rmse:2.864496+0.038919    test-rmse:3.108226+0.315855 
## [42] train-rmse:2.834139+0.039469    test-rmse:3.081711+0.318316 
## [43] train-rmse:2.804027+0.039668    test-rmse:3.062950+0.311535 
## [44] train-rmse:2.774069+0.039862    test-rmse:3.042898+0.317225 
## [45] train-rmse:2.744747+0.039763    test-rmse:3.008620+0.329865 
## [46] train-rmse:2.715994+0.039845    test-rmse:2.970539+0.311976 
## [47] train-rmse:2.688264+0.040006    test-rmse:2.935135+0.319441 
## [48] train-rmse:2.661036+0.039612    test-rmse:2.917966+0.324675 
## [49] train-rmse:2.634322+0.039107    test-rmse:2.891818+0.314544 
## [50] train-rmse:2.608247+0.038127    test-rmse:2.873470+0.321340 
## [51] train-rmse:2.582717+0.037201    test-rmse:2.822808+0.299498 
## [52] train-rmse:2.557826+0.036798    test-rmse:2.812292+0.302120 
## [53] train-rmse:2.533668+0.036372    test-rmse:2.802599+0.302309 
## [54] train-rmse:2.509583+0.035995    test-rmse:2.774355+0.297515 
## [55] train-rmse:2.485661+0.035175    test-rmse:2.758169+0.293739 
## [56] train-rmse:2.462341+0.035124    test-rmse:2.737093+0.300555 
## [57] train-rmse:2.439203+0.035201    test-rmse:2.711220+0.305600 
## [58] train-rmse:2.416407+0.035460    test-rmse:2.690032+0.310711 
## [59] train-rmse:2.393961+0.035538    test-rmse:2.666071+0.308235 
## [60] train-rmse:2.371907+0.035188    test-rmse:2.638015+0.292491 
## [61] train-rmse:2.350388+0.034735    test-rmse:2.610671+0.286891 
## [62] train-rmse:2.329274+0.034466    test-rmse:2.593779+0.295615 
## [63] train-rmse:2.308714+0.034231    test-rmse:2.581491+0.298565 
## [64] train-rmse:2.288301+0.033732    test-rmse:2.560947+0.299192 
## [65] train-rmse:2.268142+0.033192    test-rmse:2.548138+0.287748 
## [66] train-rmse:2.248505+0.032643    test-rmse:2.527383+0.274053 
## [67] train-rmse:2.229124+0.032176    test-rmse:2.501638+0.252447 
## [68] train-rmse:2.210012+0.031589    test-rmse:2.487342+0.249316 
## [69] train-rmse:2.191188+0.031083    test-rmse:2.470133+0.249308 
## [70] train-rmse:2.172343+0.030464    test-rmse:2.452849+0.257406 
## [71] train-rmse:2.153898+0.030199    test-rmse:2.439900+0.260858 
## [72] train-rmse:2.135342+0.030043    test-rmse:2.416554+0.267152 
## [73] train-rmse:2.117069+0.029931    test-rmse:2.392808+0.265287 
## [74] train-rmse:2.098786+0.029786    test-rmse:2.376875+0.274515 
## [75] train-rmse:2.080767+0.029623    test-rmse:2.360547+0.274422 
## [76] train-rmse:2.063090+0.029385    test-rmse:2.341924+0.276445 
## [77] train-rmse:2.045918+0.029412    test-rmse:2.317374+0.275039 
## [78] train-rmse:2.028891+0.029100    test-rmse:2.299823+0.273662 
## [79] train-rmse:2.012282+0.028519    test-rmse:2.277988+0.264853 
## [80] train-rmse:1.995656+0.027859    test-rmse:2.265764+0.269221 
## [81] train-rmse:1.979308+0.027108    test-rmse:2.254339+0.261727 
## [82] train-rmse:1.963354+0.026712    test-rmse:2.241040+0.272673 
## [83] train-rmse:1.948047+0.026512    test-rmse:2.224617+0.266577 
## [84] train-rmse:1.932729+0.026395    test-rmse:2.204782+0.262297 
## [85] train-rmse:1.917388+0.026136    test-rmse:2.194632+0.265175 
## [86] train-rmse:1.902557+0.026030    test-rmse:2.178578+0.264746 
## [87] train-rmse:1.887865+0.025931    test-rmse:2.168132+0.265890 
## [88] train-rmse:1.873212+0.025678    test-rmse:2.154724+0.265185 
## [89] train-rmse:1.858633+0.025576    test-rmse:2.143531+0.262081 
## [90] train-rmse:1.844259+0.025373    test-rmse:2.125979+0.261466 
## [91] train-rmse:1.830163+0.025296    test-rmse:2.112577+0.258759 
## [92] train-rmse:1.816343+0.024934    test-rmse:2.097299+0.250048 
## [93] train-rmse:1.802610+0.024573    test-rmse:2.081292+0.248678 
## [94] train-rmse:1.789020+0.024149    test-rmse:2.073025+0.245277 
## [95] train-rmse:1.775667+0.023575    test-rmse:2.058870+0.252075 
## [96] train-rmse:1.762506+0.023174    test-rmse:2.038594+0.236219 
## [97] train-rmse:1.749555+0.022905    test-rmse:2.028203+0.238061 
## [98] train-rmse:1.736760+0.022479    test-rmse:2.012899+0.241139 
## [99] train-rmse:1.724002+0.022104    test-rmse:2.001775+0.245559 
## [100]    train-rmse:1.711515+0.021635    test-rmse:1.989584+0.245968 
## [101]    train-rmse:1.699101+0.021126    test-rmse:1.975675+0.233235 
## [102]    train-rmse:1.686868+0.020697    test-rmse:1.966647+0.238804 
## [103]    train-rmse:1.674887+0.020400    test-rmse:1.955943+0.230967 
## [104]    train-rmse:1.663061+0.020205    test-rmse:1.944035+0.228353 
## [105]    train-rmse:1.651151+0.019993    test-rmse:1.936502+0.232080 
## [106]    train-rmse:1.639273+0.019905    test-rmse:1.927401+0.237521 
## [107]    train-rmse:1.627702+0.019759    test-rmse:1.917948+0.240477 
## [108]    train-rmse:1.616228+0.019686    test-rmse:1.899061+0.243529 
## [109]    train-rmse:1.604939+0.019649    test-rmse:1.892423+0.250332 
## [110]    train-rmse:1.593810+0.019591    test-rmse:1.883608+0.250743 
## [111]    train-rmse:1.582774+0.019454    test-rmse:1.867894+0.233655 
## [112]    train-rmse:1.572027+0.019012    test-rmse:1.856987+0.230844 
## [113]    train-rmse:1.561304+0.018685    test-rmse:1.850647+0.229702 
## [114]    train-rmse:1.550922+0.018486    test-rmse:1.834643+0.222873 
## [115]    train-rmse:1.540656+0.018183    test-rmse:1.827283+0.219454 
## [116]    train-rmse:1.530489+0.017910    test-rmse:1.820043+0.223218 
## [117]    train-rmse:1.520341+0.017680    test-rmse:1.809685+0.227065 
## [118]    train-rmse:1.510257+0.017363    test-rmse:1.805260+0.227420 
## [119]    train-rmse:1.500471+0.017269    test-rmse:1.792885+0.223992 
## [120]    train-rmse:1.490787+0.017066    test-rmse:1.786121+0.218892 
## [121]    train-rmse:1.480880+0.017045    test-rmse:1.777621+0.223356 
## [122]    train-rmse:1.471434+0.016784    test-rmse:1.765019+0.222846 
## [123]    train-rmse:1.462011+0.016502    test-rmse:1.757094+0.211491 
## [124]    train-rmse:1.452626+0.016081    test-rmse:1.751148+0.212239 
## [125]    train-rmse:1.443303+0.015728    test-rmse:1.745021+0.217890 
## [126]    train-rmse:1.434029+0.015502    test-rmse:1.737717+0.216980 
## [127]    train-rmse:1.424974+0.015397    test-rmse:1.728284+0.213350 
## [128]    train-rmse:1.416116+0.015216    test-rmse:1.718282+0.215227 
## [129]    train-rmse:1.407262+0.015114    test-rmse:1.712707+0.213210 
## [130]    train-rmse:1.398637+0.015143    test-rmse:1.702447+0.216504 
## [131]    train-rmse:1.390027+0.015176    test-rmse:1.694235+0.219262 
## [132]    train-rmse:1.381558+0.015077    test-rmse:1.684418+0.219539 
## [133]    train-rmse:1.373108+0.015023    test-rmse:1.673753+0.220348 
## [134]    train-rmse:1.364808+0.014933    test-rmse:1.666023+0.223684 
## [135]    train-rmse:1.356694+0.014882    test-rmse:1.657017+0.226677 
## [136]    train-rmse:1.348805+0.014843    test-rmse:1.652243+0.221793 
## [137]    train-rmse:1.341064+0.014751    test-rmse:1.648543+0.217178 
## [138]    train-rmse:1.333383+0.014633    test-rmse:1.641307+0.216807 
## [139]    train-rmse:1.325754+0.014526    test-rmse:1.630502+0.210792 
## [140]    train-rmse:1.318169+0.014412    test-rmse:1.613424+0.201291 
## [141]    train-rmse:1.310704+0.014401    test-rmse:1.610179+0.199313 
## [142]    train-rmse:1.303328+0.014373    test-rmse:1.600393+0.201444 
## [143]    train-rmse:1.295958+0.014272    test-rmse:1.593299+0.201663 
## [144]    train-rmse:1.288623+0.014142    test-rmse:1.587795+0.197751 
## [145]    train-rmse:1.281524+0.013955    test-rmse:1.586152+0.197203 
## [146]    train-rmse:1.274428+0.013742    test-rmse:1.574316+0.190198 
## [147]    train-rmse:1.267467+0.013605    test-rmse:1.571699+0.192306 
## [148]    train-rmse:1.260666+0.013479    test-rmse:1.566743+0.194720 
## [149]    train-rmse:1.253998+0.013395    test-rmse:1.559172+0.190797 
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## [358]    train-rmse:0.754769+0.016904    test-rmse:1.082504+0.157341 
## [359]    train-rmse:0.754187+0.016915    test-rmse:1.079717+0.155798 
## [360]    train-rmse:0.753601+0.016916    test-rmse:1.079587+0.157227 
## [361]    train-rmse:0.753019+0.016946    test-rmse:1.080620+0.156209 
## [362]    train-rmse:0.752437+0.016966    test-rmse:1.080037+0.156020 
## [363]    train-rmse:0.751858+0.016985    test-rmse:1.080164+0.156455 
## [364]    train-rmse:0.751286+0.017008    test-rmse:1.078417+0.156780 
## [365]    train-rmse:0.750720+0.017026    test-rmse:1.077974+0.157487 
## [366]    train-rmse:0.750156+0.017028    test-rmse:1.077429+0.156252 
## [367]    train-rmse:0.749593+0.017044    test-rmse:1.077386+0.156228 
## [368]    train-rmse:0.749036+0.017053    test-rmse:1.077266+0.155912 
## [369]    train-rmse:0.748501+0.017065    test-rmse:1.076927+0.154978 
## [370]    train-rmse:0.747963+0.017084    test-rmse:1.077287+0.154672 
## [371]    train-rmse:0.747431+0.017099    test-rmse:1.076789+0.154891 
## [372]    train-rmse:0.746899+0.017104    test-rmse:1.076643+0.155738 
## [373]    train-rmse:0.746351+0.017119    test-rmse:1.077120+0.155628 
## [374]    train-rmse:0.745817+0.017120    test-rmse:1.076434+0.155932 
## [375]    train-rmse:0.745293+0.017130    test-rmse:1.075798+0.156244 
## [376]    train-rmse:0.744779+0.017144    test-rmse:1.074762+0.155852 
## [377]    train-rmse:0.744266+0.017155    test-rmse:1.074532+0.154928 
## [378]    train-rmse:0.743755+0.017172    test-rmse:1.075050+0.154378 
## [379]    train-rmse:0.743223+0.017175    test-rmse:1.074523+0.154229 
## [380]    train-rmse:0.742706+0.017183    test-rmse:1.073869+0.153936 
## [381]    train-rmse:0.742204+0.017195    test-rmse:1.073311+0.153929 
## [382]    train-rmse:0.741704+0.017197    test-rmse:1.072285+0.154596 
## [383]    train-rmse:0.741185+0.017194    test-rmse:1.072710+0.154445 
## [384]    train-rmse:0.740691+0.017200    test-rmse:1.072522+0.154429 
## [385]    train-rmse:0.740197+0.017218    test-rmse:1.071176+0.155452 
## [386]    train-rmse:0.739682+0.017206    test-rmse:1.071243+0.155376 
## [387]    train-rmse:0.739207+0.017225    test-rmse:1.071520+0.155606 
## [388]    train-rmse:0.738732+0.017230    test-rmse:1.070390+0.155685 
## [389]    train-rmse:0.738253+0.017239    test-rmse:1.069623+0.154291 
## [390]    train-rmse:0.737763+0.017213    test-rmse:1.069078+0.153986 
## [391]    train-rmse:0.737299+0.017218    test-rmse:1.069070+0.153840 
## [392]    train-rmse:0.736826+0.017223    test-rmse:1.069247+0.153720 
## [393]    train-rmse:0.736368+0.017232    test-rmse:1.068763+0.153709 
## [394]    train-rmse:0.735923+0.017240    test-rmse:1.067005+0.153350 
## [395]    train-rmse:0.735447+0.017202    test-rmse:1.066518+0.152684 
## [396]    train-rmse:0.735000+0.017209    test-rmse:1.066673+0.152042 
## [397]    train-rmse:0.734552+0.017219    test-rmse:1.063974+0.149053 
## [398]    train-rmse:0.734102+0.017191    test-rmse:1.063423+0.148791 
## [399]    train-rmse:0.733646+0.017192    test-rmse:1.063220+0.146677 
## [400]    train-rmse:0.733197+0.017180    test-rmse:1.062885+0.146636 
## [401]    train-rmse:0.732765+0.017183    test-rmse:1.062781+0.146781 
## [402]    train-rmse:0.732319+0.017173    test-rmse:1.062159+0.145932 
## [403]    train-rmse:0.731885+0.017168    test-rmse:1.061081+0.145080 
## [404]    train-rmse:0.731457+0.017168    test-rmse:1.061268+0.145118 
## [405]    train-rmse:0.731025+0.017170    test-rmse:1.061239+0.145247 
## [406]    train-rmse:0.730584+0.017179    test-rmse:1.061402+0.145363 
## [407]    train-rmse:0.730149+0.017164    test-rmse:1.060432+0.145752 
## [408]    train-rmse:0.729727+0.017140    test-rmse:1.060909+0.146440 
## [409]    train-rmse:0.729305+0.017133    test-rmse:1.060385+0.146854 
## [410]    train-rmse:0.728882+0.017125    test-rmse:1.059323+0.146752 
## [411]    train-rmse:0.728455+0.017115    test-rmse:1.057470+0.146582 
## [412]    train-rmse:0.728038+0.017114    test-rmse:1.057041+0.145236 
## [413]    train-rmse:0.727624+0.017104    test-rmse:1.056789+0.145806 
## [414]    train-rmse:0.727213+0.017097    test-rmse:1.056052+0.145570 
## [415]    train-rmse:0.726804+0.017073    test-rmse:1.055346+0.146406 
## [416]    train-rmse:0.726409+0.017053    test-rmse:1.054942+0.146874 
## [417]    train-rmse:0.726017+0.017040    test-rmse:1.054370+0.147245 
## [418]    train-rmse:0.725630+0.017025    test-rmse:1.054739+0.146799 
## [419]    train-rmse:0.725253+0.017007    test-rmse:1.053716+0.146099 
## [420]    train-rmse:0.724851+0.016996    test-rmse:1.054051+0.145737 
## [421]    train-rmse:0.724475+0.016986    test-rmse:1.053947+0.146430 
## [422]    train-rmse:0.724106+0.016969    test-rmse:1.054219+0.145795 
## [423]    train-rmse:0.723687+0.016960    test-rmse:1.054200+0.147023 
## [424]    train-rmse:0.723314+0.016933    test-rmse:1.053315+0.148118 
## [425]    train-rmse:0.722937+0.016901    test-rmse:1.053237+0.148171 
## [426]    train-rmse:0.722563+0.016876    test-rmse:1.052346+0.148827 
## [427]    train-rmse:0.722197+0.016855    test-rmse:1.052739+0.148506 
## [428]    train-rmse:0.721817+0.016831    test-rmse:1.051742+0.148493 
## [429]    train-rmse:0.721449+0.016814    test-rmse:1.051481+0.147677 
## [430]    train-rmse:0.721063+0.016793    test-rmse:1.051611+0.147779 
## [431]    train-rmse:0.720689+0.016787    test-rmse:1.051230+0.147730 
## [432]    train-rmse:0.720300+0.016753    test-rmse:1.050403+0.146821 
## [433]    train-rmse:0.719943+0.016737    test-rmse:1.049541+0.147146 
## [434]    train-rmse:0.719564+0.016709    test-rmse:1.049280+0.145774 
## [435]    train-rmse:0.719206+0.016684    test-rmse:1.049154+0.145487 
## [436]    train-rmse:0.718839+0.016661    test-rmse:1.048852+0.145395 
## [437]    train-rmse:0.718481+0.016641    test-rmse:1.049218+0.145507 
## [438]    train-rmse:0.718116+0.016631    test-rmse:1.048916+0.145109 
## [439]    train-rmse:0.717764+0.016604    test-rmse:1.049880+0.144571 
## [440]    train-rmse:0.717408+0.016585    test-rmse:1.049272+0.145232 
## [441]    train-rmse:0.717065+0.016572    test-rmse:1.049503+0.145401 
## [442]    train-rmse:0.716704+0.016562    test-rmse:1.048763+0.145798 
## [443]    train-rmse:0.716349+0.016554    test-rmse:1.048938+0.145430 
## [444]    train-rmse:0.716001+0.016522    test-rmse:1.048272+0.144753 
## [445]    train-rmse:0.715664+0.016501    test-rmse:1.047693+0.144030 
## [446]    train-rmse:0.715322+0.016489    test-rmse:1.046065+0.144347 
## [447]    train-rmse:0.714983+0.016470    test-rmse:1.044656+0.143075 
## [448]    train-rmse:0.714654+0.016456    test-rmse:1.044049+0.143243 
## [449]    train-rmse:0.714313+0.016448    test-rmse:1.045204+0.143304 
## [450]    train-rmse:0.713959+0.016421    test-rmse:1.045788+0.142833 
## [451]    train-rmse:0.713627+0.016411    test-rmse:1.044986+0.142435 
## [452]    train-rmse:0.713287+0.016388    test-rmse:1.044639+0.142299 
## [453]    train-rmse:0.712947+0.016374    test-rmse:1.044478+0.142569 
## [454]    train-rmse:0.712601+0.016348    test-rmse:1.045127+0.141672 
## Stopping. Best iteration:
## [448]    train-rmse:0.714654+0.016456    test-rmse:1.044049+0.143243
## 
## 43 
## [1]  train-rmse:84.081620+0.095738   test-rmse:84.081755+0.499144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:65.745564+0.080728   test-rmse:65.745244+0.529833 
## [3]  train-rmse:51.456281+0.070760   test-rmse:51.455305+0.559619 
## [4]  train-rmse:40.333913+0.065280   test-rmse:40.331998+0.589993 
## [5]  train-rmse:31.689540+0.061479   test-rmse:31.714312+0.607511 
## [6]  train-rmse:24.957962+0.052538   test-rmse:25.039115+0.615759 
## [7]  train-rmse:19.717437+0.037545   test-rmse:19.787439+0.561703 
## [8]  train-rmse:15.648228+0.028053   test-rmse:15.753311+0.560288 
## [9]  train-rmse:12.500815+0.027719   test-rmse:12.628483+0.595321 
## [10] train-rmse:10.073285+0.028941   test-rmse:10.197140+0.587130 
## [11] train-rmse:8.221207+0.035252    test-rmse:8.369077+0.616239 
## [12] train-rmse:6.817221+0.041172    test-rmse:6.956753+0.550897 
## [13] train-rmse:5.767234+0.042342    test-rmse:5.926906+0.572645 
## [14] train-rmse:4.987617+0.050615    test-rmse:5.185270+0.554882 
## [15] train-rmse:4.412418+0.056959    test-rmse:4.617399+0.493994 
## [16] train-rmse:3.992286+0.059297    test-rmse:4.206299+0.478053 
## [17] train-rmse:3.681498+0.055484    test-rmse:3.930489+0.470373 
## [18] train-rmse:3.450023+0.056217    test-rmse:3.708428+0.419877 
## [19] train-rmse:3.275561+0.054657    test-rmse:3.555849+0.429959 
## [20] train-rmse:3.135297+0.054724    test-rmse:3.426770+0.397267 
## [21] train-rmse:3.022824+0.053705    test-rmse:3.321586+0.351889 
## [22] train-rmse:2.920914+0.047114    test-rmse:3.254305+0.335403 
## [23] train-rmse:2.839463+0.044230    test-rmse:3.177903+0.330682 
## [24] train-rmse:2.767029+0.045540    test-rmse:3.119421+0.325373 
## [25] train-rmse:2.700709+0.044671    test-rmse:3.077603+0.318244 
## [26] train-rmse:2.638893+0.044930    test-rmse:2.999127+0.303642 
## [27] train-rmse:2.578474+0.042047    test-rmse:2.948836+0.312639 
## [28] train-rmse:2.520063+0.038520    test-rmse:2.911349+0.326823 
## [29] train-rmse:2.466357+0.036295    test-rmse:2.855780+0.300759 
## [30] train-rmse:2.415192+0.035780    test-rmse:2.813618+0.299459 
## [31] train-rmse:2.364734+0.034989    test-rmse:2.770110+0.308051 
## [32] train-rmse:2.320013+0.033767    test-rmse:2.708792+0.297370 
## [33] train-rmse:2.276371+0.033571    test-rmse:2.665638+0.297880 
## [34] train-rmse:2.233466+0.032995    test-rmse:2.642848+0.300773 
## [35] train-rmse:2.191558+0.032262    test-rmse:2.605932+0.292073 
## [36] train-rmse:2.150933+0.030981    test-rmse:2.572459+0.275019 
## [37] train-rmse:2.109911+0.030052    test-rmse:2.531727+0.277200 
## [38] train-rmse:2.068172+0.028552    test-rmse:2.483761+0.264594 
## [39] train-rmse:2.029739+0.027739    test-rmse:2.442902+0.267616 
## [40] train-rmse:1.992890+0.025881    test-rmse:2.410205+0.257123 
## [41] train-rmse:1.956477+0.024104    test-rmse:2.380101+0.259705 
## [42] train-rmse:1.921971+0.022925    test-rmse:2.351815+0.273575 
## [43] train-rmse:1.886790+0.021403    test-rmse:2.322923+0.278539 
## [44] train-rmse:1.854075+0.020375    test-rmse:2.300308+0.279481 
## [45] train-rmse:1.822224+0.019305    test-rmse:2.276265+0.271720 
## [46] train-rmse:1.791266+0.018672    test-rmse:2.252007+0.266670 
## [47] train-rmse:1.760471+0.016079    test-rmse:2.214421+0.261899 
## [48] train-rmse:1.729730+0.013984    test-rmse:2.187814+0.260035 
## [49] train-rmse:1.699872+0.013912    test-rmse:2.158646+0.253941 
## [50] train-rmse:1.670972+0.012180    test-rmse:2.129412+0.253848 
## [51] train-rmse:1.643923+0.012412    test-rmse:2.107461+0.257450 
## [52] train-rmse:1.617655+0.012250    test-rmse:2.087613+0.268942 
## [53] train-rmse:1.591088+0.012023    test-rmse:2.063913+0.260149 
## [54] train-rmse:1.565868+0.011963    test-rmse:2.044531+0.268117 
## [55] train-rmse:1.541186+0.011368    test-rmse:2.022206+0.259445 
## [56] train-rmse:1.517019+0.010735    test-rmse:1.998157+0.265318 
## [57] train-rmse:1.493523+0.009726    test-rmse:1.974822+0.257993 
## [58] train-rmse:1.469832+0.009166    test-rmse:1.954202+0.261812 
## [59] train-rmse:1.447000+0.008857    test-rmse:1.938221+0.263246 
## [60] train-rmse:1.424888+0.009124    test-rmse:1.922559+0.262566 
## [61] train-rmse:1.402726+0.010070    test-rmse:1.900765+0.261557 
## [62] train-rmse:1.381970+0.009444    test-rmse:1.883381+0.259055 
## [63] train-rmse:1.361448+0.009618    test-rmse:1.863426+0.243496 
## [64] train-rmse:1.341776+0.009333    test-rmse:1.843773+0.229997 
## [65] train-rmse:1.322117+0.009610    test-rmse:1.834003+0.232485 
## [66] train-rmse:1.302406+0.010117    test-rmse:1.814119+0.238269 
## [67] train-rmse:1.283357+0.010906    test-rmse:1.796659+0.237635 
## [68] train-rmse:1.265791+0.010793    test-rmse:1.775130+0.239049 
## [69] train-rmse:1.247394+0.011170    test-rmse:1.756811+0.239718 
## [70] train-rmse:1.230071+0.010998    test-rmse:1.744085+0.245663 
## [71] train-rmse:1.212425+0.010532    test-rmse:1.728935+0.251287 
## [72] train-rmse:1.195325+0.011358    test-rmse:1.713163+0.244073 
## [73] train-rmse:1.178698+0.010460    test-rmse:1.702990+0.246740 
## [74] train-rmse:1.160792+0.012112    test-rmse:1.688249+0.246705 
## [75] train-rmse:1.145411+0.011576    test-rmse:1.675389+0.246069 
## [76] train-rmse:1.129702+0.012132    test-rmse:1.669104+0.250786 
## [77] train-rmse:1.113408+0.012271    test-rmse:1.657879+0.248620 
## [78] train-rmse:1.098734+0.012768    test-rmse:1.644680+0.249861 
## [79] train-rmse:1.084849+0.013059    test-rmse:1.622432+0.242172 
## [80] train-rmse:1.070274+0.012306    test-rmse:1.611646+0.244577 
## [81] train-rmse:1.056448+0.012288    test-rmse:1.603603+0.242845 
## [82] train-rmse:1.043090+0.012500    test-rmse:1.584807+0.245831 
## [83] train-rmse:1.030650+0.012609    test-rmse:1.570514+0.248898 
## [84] train-rmse:1.018206+0.013077    test-rmse:1.565935+0.248717 
## [85] train-rmse:1.006457+0.013052    test-rmse:1.557931+0.247440 
## [86] train-rmse:0.994563+0.012484    test-rmse:1.546294+0.249523 
## [87] train-rmse:0.981188+0.014338    test-rmse:1.536722+0.252506 
## [88] train-rmse:0.968195+0.015760    test-rmse:1.530918+0.252686 
## [89] train-rmse:0.956550+0.015289    test-rmse:1.521815+0.249220 
## [90] train-rmse:0.945311+0.014961    test-rmse:1.511069+0.248348 
## [91] train-rmse:0.933051+0.015446    test-rmse:1.505272+0.252743 
## [92] train-rmse:0.922521+0.015473    test-rmse:1.497569+0.252861 
## [93] train-rmse:0.911480+0.016673    test-rmse:1.488032+0.252094 
## [94] train-rmse:0.901502+0.016651    test-rmse:1.480638+0.252059 
## [95] train-rmse:0.891831+0.016305    test-rmse:1.472721+0.253462 
## [96] train-rmse:0.882512+0.016573    test-rmse:1.467586+0.254974 
## [97] train-rmse:0.872184+0.015619    test-rmse:1.456311+0.251164 
## [98] train-rmse:0.862466+0.016238    test-rmse:1.446335+0.250282 
## [99] train-rmse:0.854025+0.016094    test-rmse:1.433421+0.254872 
## [100]    train-rmse:0.843360+0.018355    test-rmse:1.425719+0.254041 
## [101]    train-rmse:0.834973+0.018639    test-rmse:1.421840+0.254348 
## [102]    train-rmse:0.826768+0.018769    test-rmse:1.416174+0.251997 
## [103]    train-rmse:0.818162+0.018980    test-rmse:1.412027+0.249984 
## [104]    train-rmse:0.808915+0.020265    test-rmse:1.408538+0.252282 
## [105]    train-rmse:0.799435+0.021030    test-rmse:1.406075+0.256754 
## [106]    train-rmse:0.789911+0.020324    test-rmse:1.401999+0.260358 
## [107]    train-rmse:0.782030+0.020568    test-rmse:1.396591+0.260774 
## [108]    train-rmse:0.774830+0.020702    test-rmse:1.392095+0.262924 
## [109]    train-rmse:0.767861+0.020584    test-rmse:1.385026+0.259418 
## [110]    train-rmse:0.759686+0.019895    test-rmse:1.377905+0.255438 
## [111]    train-rmse:0.753242+0.019991    test-rmse:1.371893+0.260602 
## [112]    train-rmse:0.745114+0.021336    test-rmse:1.367523+0.258293 
## [113]    train-rmse:0.738791+0.021383    test-rmse:1.363421+0.257704 
## [114]    train-rmse:0.731127+0.020829    test-rmse:1.360986+0.257411 
## [115]    train-rmse:0.724782+0.020916    test-rmse:1.358097+0.256661 
## [116]    train-rmse:0.717906+0.021290    test-rmse:1.353075+0.255662 
## [117]    train-rmse:0.711900+0.020835    test-rmse:1.347714+0.254676 
## [118]    train-rmse:0.705733+0.021136    test-rmse:1.344044+0.256261 
## [119]    train-rmse:0.700240+0.021113    test-rmse:1.340533+0.256969 
## [120]    train-rmse:0.694596+0.021394    test-rmse:1.338124+0.261048 
## [121]    train-rmse:0.689306+0.021399    test-rmse:1.330731+0.254073 
## [122]    train-rmse:0.683484+0.021506    test-rmse:1.327324+0.256647 
## [123]    train-rmse:0.677173+0.019959    test-rmse:1.322839+0.260391 
## [124]    train-rmse:0.671696+0.019553    test-rmse:1.320962+0.261079 
## [125]    train-rmse:0.666375+0.018868    test-rmse:1.316432+0.263752 
## [126]    train-rmse:0.661764+0.018701    test-rmse:1.312360+0.262988 
## [127]    train-rmse:0.655875+0.017178    test-rmse:1.308039+0.262524 
## [128]    train-rmse:0.650870+0.017473    test-rmse:1.306432+0.262803 
## [129]    train-rmse:0.646357+0.017732    test-rmse:1.303898+0.262760 
## [130]    train-rmse:0.641024+0.017493    test-rmse:1.297089+0.264803 
## [131]    train-rmse:0.636058+0.018867    test-rmse:1.291495+0.266991 
## [132]    train-rmse:0.631086+0.017996    test-rmse:1.288177+0.267794 
## [133]    train-rmse:0.625631+0.017268    test-rmse:1.282575+0.268871 
## [134]    train-rmse:0.619754+0.017941    test-rmse:1.280755+0.269828 
## [135]    train-rmse:0.615050+0.018178    test-rmse:1.281140+0.270790 
## [136]    train-rmse:0.610382+0.018626    test-rmse:1.280371+0.271512 
## [137]    train-rmse:0.605876+0.018676    test-rmse:1.277271+0.273159 
## [138]    train-rmse:0.602180+0.018463    test-rmse:1.274092+0.271736 
## [139]    train-rmse:0.598335+0.018389    test-rmse:1.271778+0.269510 
## [140]    train-rmse:0.594676+0.018144    test-rmse:1.269689+0.269677 
## [141]    train-rmse:0.590868+0.017935    test-rmse:1.269626+0.270629 
## [142]    train-rmse:0.586674+0.017276    test-rmse:1.267990+0.269287 
## [143]    train-rmse:0.582484+0.016900    test-rmse:1.266659+0.271008 
## [144]    train-rmse:0.578762+0.017336    test-rmse:1.264728+0.271112 
## [145]    train-rmse:0.574841+0.017080    test-rmse:1.262874+0.272437 
## [146]    train-rmse:0.570300+0.017222    test-rmse:1.261343+0.272664 
## [147]    train-rmse:0.566843+0.017632    test-rmse:1.259412+0.272520 
## [148]    train-rmse:0.563648+0.017682    test-rmse:1.255219+0.274156 
## [149]    train-rmse:0.560089+0.017486    test-rmse:1.252764+0.274994 
## [150]    train-rmse:0.556874+0.017758    test-rmse:1.251880+0.274149 
## [151]    train-rmse:0.552603+0.019225    test-rmse:1.251068+0.274332 
## [152]    train-rmse:0.549364+0.019677    test-rmse:1.248450+0.274198 
## [153]    train-rmse:0.545869+0.019324    test-rmse:1.244626+0.275210 
## [154]    train-rmse:0.543053+0.019704    test-rmse:1.243254+0.274513 
## [155]    train-rmse:0.538263+0.018436    test-rmse:1.242735+0.274186 
## [156]    train-rmse:0.535278+0.018666    test-rmse:1.240153+0.274724 
## [157]    train-rmse:0.532369+0.018310    test-rmse:1.237807+0.276358 
## [158]    train-rmse:0.529780+0.018234    test-rmse:1.237709+0.275493 
## [159]    train-rmse:0.527030+0.017980    test-rmse:1.236430+0.276604 
## [160]    train-rmse:0.523487+0.017709    test-rmse:1.232389+0.276928 
## [161]    train-rmse:0.520886+0.017752    test-rmse:1.226378+0.269130 
## [162]    train-rmse:0.517914+0.017747    test-rmse:1.223720+0.267812 
## [163]    train-rmse:0.515314+0.017646    test-rmse:1.221902+0.269396 
## [164]    train-rmse:0.512490+0.017805    test-rmse:1.220897+0.268838 
## [165]    train-rmse:0.509019+0.018402    test-rmse:1.220278+0.269020 
## [166]    train-rmse:0.506143+0.018216    test-rmse:1.219019+0.266924 
## [167]    train-rmse:0.503692+0.018148    test-rmse:1.218977+0.267957 
## [168]    train-rmse:0.500955+0.018032    test-rmse:1.218696+0.268996 
## [169]    train-rmse:0.498358+0.018146    test-rmse:1.217641+0.268387 
## [170]    train-rmse:0.495535+0.017588    test-rmse:1.213771+0.267875 
## [171]    train-rmse:0.493128+0.017228    test-rmse:1.209660+0.270016 
## [172]    train-rmse:0.490851+0.017293    test-rmse:1.208883+0.269858 
## [173]    train-rmse:0.488178+0.016736    test-rmse:1.208096+0.271487 
## [174]    train-rmse:0.485876+0.016176    test-rmse:1.207060+0.272135 
## [175]    train-rmse:0.483737+0.016165    test-rmse:1.205517+0.273830 
## [176]    train-rmse:0.481766+0.016064    test-rmse:1.204605+0.273993 
## [177]    train-rmse:0.479562+0.016416    test-rmse:1.202788+0.273205 
## [178]    train-rmse:0.477494+0.016310    test-rmse:1.202696+0.273935 
## [179]    train-rmse:0.475408+0.016131    test-rmse:1.200647+0.274229 
## [180]    train-rmse:0.471797+0.016573    test-rmse:1.199849+0.273349 
## [181]    train-rmse:0.469581+0.016630    test-rmse:1.199673+0.272955 
## [182]    train-rmse:0.467602+0.016569    test-rmse:1.195400+0.272224 
## [183]    train-rmse:0.465816+0.016614    test-rmse:1.194572+0.272792 
## [184]    train-rmse:0.463134+0.017198    test-rmse:1.191748+0.273853 
## [185]    train-rmse:0.461197+0.017211    test-rmse:1.190821+0.274233 
## [186]    train-rmse:0.459135+0.016766    test-rmse:1.190188+0.274806 
## [187]    train-rmse:0.457614+0.016779    test-rmse:1.189265+0.274673 
## [188]    train-rmse:0.455835+0.016796    test-rmse:1.187597+0.274741 
## [189]    train-rmse:0.454008+0.016846    test-rmse:1.182921+0.269772 
## [190]    train-rmse:0.452104+0.017307    test-rmse:1.183212+0.269460 
## [191]    train-rmse:0.450424+0.016908    test-rmse:1.181234+0.269503 
## [192]    train-rmse:0.448278+0.017241    test-rmse:1.180091+0.269151 
## [193]    train-rmse:0.446040+0.016674    test-rmse:1.177142+0.271904 
## [194]    train-rmse:0.444437+0.016687    test-rmse:1.177094+0.271200 
## [195]    train-rmse:0.442455+0.016608    test-rmse:1.175005+0.272073 
## [196]    train-rmse:0.440818+0.016543    test-rmse:1.175908+0.272780 
## [197]    train-rmse:0.439287+0.016732    test-rmse:1.175298+0.272891 
## [198]    train-rmse:0.437727+0.016835    test-rmse:1.173819+0.273285 
## [199]    train-rmse:0.436224+0.016632    test-rmse:1.170856+0.273345 
## [200]    train-rmse:0.434514+0.016563    test-rmse:1.169930+0.271658 
## [201]    train-rmse:0.432238+0.015912    test-rmse:1.168474+0.273058 
## [202]    train-rmse:0.430776+0.015582    test-rmse:1.166671+0.274536 
## [203]    train-rmse:0.429318+0.015694    test-rmse:1.166757+0.275358 
## [204]    train-rmse:0.427596+0.015509    test-rmse:1.166072+0.275029 
## [205]    train-rmse:0.425532+0.015344    test-rmse:1.166589+0.275314 
## [206]    train-rmse:0.423449+0.015857    test-rmse:1.165060+0.275818 
## [207]    train-rmse:0.422073+0.015873    test-rmse:1.163443+0.276922 
## [208]    train-rmse:0.420652+0.015795    test-rmse:1.163059+0.277546 
## [209]    train-rmse:0.418947+0.015238    test-rmse:1.163005+0.279573 
## [210]    train-rmse:0.417397+0.015202    test-rmse:1.160343+0.280272 
## [211]    train-rmse:0.415972+0.014944    test-rmse:1.159625+0.279390 
## [212]    train-rmse:0.414568+0.014822    test-rmse:1.159403+0.279140 
## [213]    train-rmse:0.413088+0.015236    test-rmse:1.159476+0.280058 
## [214]    train-rmse:0.411415+0.015089    test-rmse:1.158941+0.279737 
## [215]    train-rmse:0.409604+0.015436    test-rmse:1.158419+0.280944 
## [216]    train-rmse:0.407977+0.015791    test-rmse:1.157847+0.281331 
## [217]    train-rmse:0.406120+0.015947    test-rmse:1.157543+0.282278 
## [218]    train-rmse:0.404629+0.015790    test-rmse:1.155770+0.281225 
## [219]    train-rmse:0.402934+0.015660    test-rmse:1.155052+0.280878 
## [220]    train-rmse:0.401752+0.015453    test-rmse:1.155301+0.281500 
## [221]    train-rmse:0.399773+0.014774    test-rmse:1.152193+0.280585 
## [222]    train-rmse:0.398270+0.015061    test-rmse:1.151615+0.280065 
## [223]    train-rmse:0.396619+0.015689    test-rmse:1.151013+0.280137 
## [224]    train-rmse:0.395150+0.016376    test-rmse:1.150787+0.279805 
## [225]    train-rmse:0.393665+0.016048    test-rmse:1.150912+0.280050 
## [226]    train-rmse:0.392577+0.015862    test-rmse:1.150311+0.279635 
## [227]    train-rmse:0.391425+0.015958    test-rmse:1.150613+0.279374 
## [228]    train-rmse:0.390268+0.015713    test-rmse:1.149734+0.279288 
## [229]    train-rmse:0.388742+0.015855    test-rmse:1.147083+0.280593 
## [230]    train-rmse:0.387266+0.015672    test-rmse:1.146841+0.279639 
## [231]    train-rmse:0.386245+0.015614    test-rmse:1.146036+0.279515 
## [232]    train-rmse:0.385014+0.015664    test-rmse:1.144133+0.278565 
## [233]    train-rmse:0.383854+0.015528    test-rmse:1.142233+0.279199 
## [234]    train-rmse:0.382251+0.016102    test-rmse:1.142799+0.280179 
## [235]    train-rmse:0.380153+0.015964    test-rmse:1.142274+0.279958 
## [236]    train-rmse:0.378688+0.015824    test-rmse:1.142579+0.280934 
## [237]    train-rmse:0.376916+0.015637    test-rmse:1.143577+0.281324 
## [238]    train-rmse:0.375717+0.016052    test-rmse:1.143512+0.281138 
## [239]    train-rmse:0.374351+0.016634    test-rmse:1.143584+0.280622 
## Stopping. Best iteration:
## [233]    train-rmse:0.383854+0.015528    test-rmse:1.142233+0.279199
## 
## 44 
## [1]  train-rmse:84.081620+0.095738   test-rmse:84.081755+0.499144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:65.745564+0.080728   test-rmse:65.745244+0.529833 
## [3]  train-rmse:51.456281+0.070760   test-rmse:51.455305+0.559619 
## [4]  train-rmse:40.333913+0.065280   test-rmse:40.331998+0.589993 
## [5]  train-rmse:31.689540+0.061479   test-rmse:31.714312+0.607511 
## [6]  train-rmse:24.953949+0.049329   test-rmse:25.041610+0.619070 
## [7]  train-rmse:19.706671+0.036005   test-rmse:19.808996+0.591268 
## [8]  train-rmse:15.621615+0.027126   test-rmse:15.737683+0.580544 
## [9]  train-rmse:12.447722+0.025853   test-rmse:12.537473+0.582635 
## [10] train-rmse:9.988609+0.022876    test-rmse:10.101578+0.601499 
## [11] train-rmse:8.090604+0.024929    test-rmse:8.224543+0.569874 
## [12] train-rmse:6.640176+0.033135    test-rmse:6.758231+0.512123 
## [13] train-rmse:5.535445+0.040268    test-rmse:5.700192+0.521364 
## [14] train-rmse:4.702128+0.038646    test-rmse:4.917873+0.528464 
## [15] train-rmse:4.081141+0.041958    test-rmse:4.276463+0.466363 
## [16] train-rmse:3.606524+0.040842    test-rmse:3.843258+0.448298 
## [17] train-rmse:3.260567+0.039679    test-rmse:3.548522+0.427203 
## [18] train-rmse:3.000935+0.041847    test-rmse:3.302871+0.408704 
## [19] train-rmse:2.796549+0.029419    test-rmse:3.101693+0.372966 
## [20] train-rmse:2.633887+0.025568    test-rmse:2.986683+0.357589 
## [21] train-rmse:2.505731+0.023749    test-rmse:2.870765+0.345030 
## [22] train-rmse:2.399237+0.020470    test-rmse:2.773112+0.310459 
## [23] train-rmse:2.307078+0.022180    test-rmse:2.695857+0.289475 
## [24] train-rmse:2.218183+0.028319    test-rmse:2.633579+0.270892 
## [25] train-rmse:2.142810+0.023492    test-rmse:2.553390+0.259275 
## [26] train-rmse:2.075447+0.022302    test-rmse:2.488151+0.267092 
## [27] train-rmse:2.011602+0.023326    test-rmse:2.444460+0.261704 
## [28] train-rmse:1.953810+0.022936    test-rmse:2.389163+0.265753 
## [29] train-rmse:1.898001+0.022218    test-rmse:2.338042+0.255781 
## [30] train-rmse:1.834361+0.024906    test-rmse:2.295561+0.250633 
## [31] train-rmse:1.782446+0.025765    test-rmse:2.261249+0.240815 
## [32] train-rmse:1.732106+0.026542    test-rmse:2.213312+0.235002 
## [33] train-rmse:1.684941+0.025270    test-rmse:2.162518+0.232948 
## [34] train-rmse:1.641332+0.025493    test-rmse:2.119970+0.232808 
## [35] train-rmse:1.597351+0.025901    test-rmse:2.091938+0.239046 
## [36] train-rmse:1.554999+0.025841    test-rmse:2.067965+0.248978 
## [37] train-rmse:1.514155+0.025190    test-rmse:2.038253+0.235847 
## [38] train-rmse:1.474027+0.024374    test-rmse:2.008324+0.241028 
## [39] train-rmse:1.437040+0.026065    test-rmse:1.969392+0.247338 
## [40] train-rmse:1.401754+0.025917    test-rmse:1.940925+0.247292 
## [41] train-rmse:1.366013+0.026553    test-rmse:1.911801+0.248948 
## [42] train-rmse:1.333649+0.025731    test-rmse:1.876210+0.241584 
## [43] train-rmse:1.301425+0.025606    test-rmse:1.860946+0.244975 
## [44] train-rmse:1.269426+0.027056    test-rmse:1.834197+0.249550 
## [45] train-rmse:1.240371+0.026886    test-rmse:1.813098+0.245607 
## [46] train-rmse:1.209668+0.024895    test-rmse:1.793330+0.251897 
## [47] train-rmse:1.179548+0.022489    test-rmse:1.768360+0.250372 
## [48] train-rmse:1.152183+0.023236    test-rmse:1.743468+0.252092 
## [49] train-rmse:1.127320+0.023023    test-rmse:1.719947+0.255337 
## [50] train-rmse:1.100792+0.022942    test-rmse:1.700236+0.265776 
## [51] train-rmse:1.076475+0.022727    test-rmse:1.680000+0.273955 
## [52] train-rmse:1.052780+0.023763    test-rmse:1.662841+0.271493 
## [53] train-rmse:1.028723+0.022875    test-rmse:1.647026+0.270092 
## [54] train-rmse:1.006000+0.023694    test-rmse:1.629339+0.259062 
## [55] train-rmse:0.985169+0.022621    test-rmse:1.611425+0.257852 
## [56] train-rmse:0.964361+0.021751    test-rmse:1.591913+0.256349 
## [57] train-rmse:0.944006+0.022991    test-rmse:1.574311+0.259077 
## [58] train-rmse:0.924912+0.023427    test-rmse:1.560789+0.259032 
## [59] train-rmse:0.904102+0.023715    test-rmse:1.550044+0.260595 
## [60] train-rmse:0.886887+0.024010    test-rmse:1.537572+0.269564 
## [61] train-rmse:0.868807+0.022988    test-rmse:1.530373+0.271847 
## [62] train-rmse:0.852061+0.023089    test-rmse:1.499382+0.246275 
## [63] train-rmse:0.834558+0.021676    test-rmse:1.491311+0.247321 
## [64] train-rmse:0.819705+0.021671    test-rmse:1.475569+0.250332 
## [65] train-rmse:0.804502+0.021947    test-rmse:1.467607+0.254573 
## [66] train-rmse:0.788588+0.021146    test-rmse:1.455456+0.256339 
## [67] train-rmse:0.775075+0.020829    test-rmse:1.449405+0.255984 
## [68] train-rmse:0.759091+0.022881    test-rmse:1.438969+0.252002 
## [69] train-rmse:0.744734+0.021557    test-rmse:1.427323+0.253063 
## [70] train-rmse:0.731457+0.021256    test-rmse:1.410899+0.240787 
## [71] train-rmse:0.717486+0.022612    test-rmse:1.402288+0.242019 
## [72] train-rmse:0.704648+0.022968    test-rmse:1.394509+0.240716 
## [73] train-rmse:0.692745+0.023052    test-rmse:1.383640+0.245029 
## [74] train-rmse:0.681786+0.023469    test-rmse:1.377360+0.246201 
## [75] train-rmse:0.669396+0.020888    test-rmse:1.364190+0.247998 
## [76] train-rmse:0.657532+0.021043    test-rmse:1.360381+0.249063 
## [77] train-rmse:0.647288+0.020941    test-rmse:1.350902+0.251128 
## [78] train-rmse:0.633584+0.019132    test-rmse:1.341530+0.249384 
## [79] train-rmse:0.623735+0.018750    test-rmse:1.338573+0.253529 
## [80] train-rmse:0.614387+0.018640    test-rmse:1.328465+0.256691 
## [81] train-rmse:0.605729+0.017694    test-rmse:1.319509+0.257635 
## [82] train-rmse:0.596066+0.017180    test-rmse:1.313218+0.262558 
## [83] train-rmse:0.586011+0.017187    test-rmse:1.302817+0.249356 
## [84] train-rmse:0.577689+0.016655    test-rmse:1.296515+0.251701 
## [85] train-rmse:0.569519+0.016753    test-rmse:1.292213+0.252587 
## [86] train-rmse:0.560959+0.015817    test-rmse:1.287905+0.253029 
## [87] train-rmse:0.552031+0.017113    test-rmse:1.279211+0.255816 
## [88] train-rmse:0.544211+0.017066    test-rmse:1.276631+0.257108 
## [89] train-rmse:0.537594+0.016872    test-rmse:1.271608+0.261320 
## [90] train-rmse:0.530723+0.015957    test-rmse:1.265405+0.261602 
## [91] train-rmse:0.523633+0.016230    test-rmse:1.262325+0.261733 
## [92] train-rmse:0.516636+0.016817    test-rmse:1.256592+0.262662 
## [93] train-rmse:0.510401+0.016402    test-rmse:1.251784+0.261472 
## [94] train-rmse:0.503696+0.017247    test-rmse:1.247700+0.256405 
## [95] train-rmse:0.497819+0.016425    test-rmse:1.245135+0.257820 
## [96] train-rmse:0.491141+0.016405    test-rmse:1.242561+0.259048 
## [97] train-rmse:0.485006+0.016136    test-rmse:1.240685+0.261421 
## [98] train-rmse:0.479311+0.016085    test-rmse:1.236446+0.261575 
## [99] train-rmse:0.474082+0.016229    test-rmse:1.234196+0.262361 
## [100]    train-rmse:0.468491+0.016483    test-rmse:1.229649+0.262868 
## [101]    train-rmse:0.463971+0.016194    test-rmse:1.226167+0.263702 
## [102]    train-rmse:0.458697+0.015883    test-rmse:1.223639+0.264340 
## [103]    train-rmse:0.452770+0.015664    test-rmse:1.221485+0.263103 
## [104]    train-rmse:0.446903+0.015754    test-rmse:1.212711+0.253927 
## [105]    train-rmse:0.442269+0.015915    test-rmse:1.207213+0.258673 
## [106]    train-rmse:0.437884+0.015846    test-rmse:1.205374+0.258848 
## [107]    train-rmse:0.434097+0.015920    test-rmse:1.202283+0.261526 
## [108]    train-rmse:0.428877+0.016516    test-rmse:1.199770+0.262404 
## [109]    train-rmse:0.424710+0.016987    test-rmse:1.194862+0.264190 
## [110]    train-rmse:0.420323+0.016390    test-rmse:1.191973+0.264203 
## [111]    train-rmse:0.416888+0.016439    test-rmse:1.189853+0.263855 
## [112]    train-rmse:0.412381+0.016805    test-rmse:1.187544+0.263191 
## [113]    train-rmse:0.408397+0.016838    test-rmse:1.187906+0.264550 
## [114]    train-rmse:0.402515+0.016350    test-rmse:1.184502+0.261525 
## [115]    train-rmse:0.398875+0.016935    test-rmse:1.183742+0.263361 
## [116]    train-rmse:0.394733+0.017643    test-rmse:1.181448+0.263775 
## [117]    train-rmse:0.390801+0.017196    test-rmse:1.177032+0.264014 
## [118]    train-rmse:0.387099+0.017722    test-rmse:1.175178+0.264153 
## [119]    train-rmse:0.383601+0.017527    test-rmse:1.175146+0.264334 
## [120]    train-rmse:0.379159+0.018230    test-rmse:1.173739+0.264477 
## [121]    train-rmse:0.374988+0.016952    test-rmse:1.171899+0.265879 
## [122]    train-rmse:0.371886+0.016853    test-rmse:1.171453+0.266075 
## [123]    train-rmse:0.368450+0.016201    test-rmse:1.170519+0.263965 
## [124]    train-rmse:0.364897+0.015484    test-rmse:1.169970+0.265340 
## [125]    train-rmse:0.361787+0.015723    test-rmse:1.168939+0.265640 
## [126]    train-rmse:0.357616+0.014909    test-rmse:1.166418+0.266835 
## [127]    train-rmse:0.353589+0.016143    test-rmse:1.161625+0.271000 
## [128]    train-rmse:0.351239+0.016300    test-rmse:1.160609+0.272020 
## [129]    train-rmse:0.348132+0.015930    test-rmse:1.159317+0.272864 
## [130]    train-rmse:0.345500+0.016221    test-rmse:1.158598+0.272653 
## [131]    train-rmse:0.342077+0.015771    test-rmse:1.157601+0.272439 
## [132]    train-rmse:0.338573+0.015136    test-rmse:1.155668+0.278005 
## [133]    train-rmse:0.335115+0.013578    test-rmse:1.154312+0.277568 
## [134]    train-rmse:0.332385+0.014368    test-rmse:1.154679+0.277230 
## [135]    train-rmse:0.329901+0.014501    test-rmse:1.154584+0.277912 
## [136]    train-rmse:0.327001+0.014164    test-rmse:1.154931+0.278493 
## [137]    train-rmse:0.323229+0.013664    test-rmse:1.155433+0.277633 
## [138]    train-rmse:0.320323+0.014321    test-rmse:1.154285+0.278234 
## [139]    train-rmse:0.317488+0.014103    test-rmse:1.153567+0.278123 
## [140]    train-rmse:0.315162+0.014239    test-rmse:1.153004+0.278029 
## [141]    train-rmse:0.313132+0.014254    test-rmse:1.152608+0.277799 
## [142]    train-rmse:0.309598+0.014047    test-rmse:1.151534+0.277749 
## [143]    train-rmse:0.307113+0.014381    test-rmse:1.151093+0.278679 
## [144]    train-rmse:0.304470+0.014042    test-rmse:1.149416+0.279390 
## [145]    train-rmse:0.302510+0.014234    test-rmse:1.149049+0.280747 
## [146]    train-rmse:0.300058+0.014542    test-rmse:1.147877+0.280753 
## [147]    train-rmse:0.298520+0.014583    test-rmse:1.145941+0.281689 
## [148]    train-rmse:0.296390+0.015042    test-rmse:1.145145+0.281619 
## [149]    train-rmse:0.294214+0.015201    test-rmse:1.143779+0.281763 
## [150]    train-rmse:0.291717+0.015691    test-rmse:1.141518+0.282075 
## [151]    train-rmse:0.289349+0.015588    test-rmse:1.141147+0.282853 
## [152]    train-rmse:0.286933+0.015224    test-rmse:1.142086+0.285613 
## [153]    train-rmse:0.284184+0.015681    test-rmse:1.140683+0.285809 
## [154]    train-rmse:0.282051+0.015944    test-rmse:1.140070+0.285861 
## [155]    train-rmse:0.278759+0.015569    test-rmse:1.139561+0.285924 
## [156]    train-rmse:0.276827+0.015341    test-rmse:1.138517+0.286755 
## [157]    train-rmse:0.274321+0.015259    test-rmse:1.138629+0.286939 
## [158]    train-rmse:0.272648+0.015485    test-rmse:1.137850+0.287919 
## [159]    train-rmse:0.270820+0.015679    test-rmse:1.136726+0.288545 
## [160]    train-rmse:0.269200+0.015460    test-rmse:1.138084+0.291473 
## [161]    train-rmse:0.266810+0.015101    test-rmse:1.137943+0.291614 
## [162]    train-rmse:0.265051+0.014710    test-rmse:1.138364+0.291571 
## [163]    train-rmse:0.262818+0.014154    test-rmse:1.137049+0.292386 
## [164]    train-rmse:0.260527+0.014184    test-rmse:1.136558+0.292318 
## [165]    train-rmse:0.258409+0.014472    test-rmse:1.136153+0.291932 
## [166]    train-rmse:0.256351+0.014642    test-rmse:1.136286+0.292112 
## [167]    train-rmse:0.254083+0.014660    test-rmse:1.135520+0.296199 
## [168]    train-rmse:0.252837+0.014445    test-rmse:1.134718+0.296238 
## [169]    train-rmse:0.251009+0.014379    test-rmse:1.134270+0.296008 
## [170]    train-rmse:0.248628+0.013883    test-rmse:1.133495+0.295799 
## [171]    train-rmse:0.246010+0.014193    test-rmse:1.133331+0.296183 
## [172]    train-rmse:0.244487+0.014105    test-rmse:1.132495+0.295231 
## [173]    train-rmse:0.243078+0.013956    test-rmse:1.132232+0.295305 
## [174]    train-rmse:0.240907+0.013837    test-rmse:1.132073+0.295228 
## [175]    train-rmse:0.239596+0.014094    test-rmse:1.132116+0.295892 
## [176]    train-rmse:0.237757+0.013668    test-rmse:1.131100+0.297313 
## [177]    train-rmse:0.236441+0.013881    test-rmse:1.130386+0.297430 
## [178]    train-rmse:0.234507+0.013919    test-rmse:1.130116+0.297158 
## [179]    train-rmse:0.232836+0.014469    test-rmse:1.129927+0.296706 
## [180]    train-rmse:0.231396+0.014501    test-rmse:1.129317+0.296465 
## [181]    train-rmse:0.229374+0.014022    test-rmse:1.128562+0.297134 
## [182]    train-rmse:0.228116+0.013948    test-rmse:1.128503+0.297069 
## [183]    train-rmse:0.226428+0.014639    test-rmse:1.127581+0.298178 
## [184]    train-rmse:0.224846+0.014788    test-rmse:1.128626+0.299476 
## [185]    train-rmse:0.223188+0.015305    test-rmse:1.127959+0.299406 
## [186]    train-rmse:0.221817+0.015902    test-rmse:1.127517+0.298639 
## [187]    train-rmse:0.220062+0.015273    test-rmse:1.127552+0.298578 
## [188]    train-rmse:0.217767+0.014523    test-rmse:1.127856+0.298640 
## [189]    train-rmse:0.216069+0.014876    test-rmse:1.127312+0.298896 
## [190]    train-rmse:0.214674+0.015148    test-rmse:1.127082+0.298762 
## [191]    train-rmse:0.212878+0.015381    test-rmse:1.126792+0.298534 
## [192]    train-rmse:0.211510+0.015196    test-rmse:1.126313+0.298764 
## [193]    train-rmse:0.210628+0.015098    test-rmse:1.126477+0.299203 
## [194]    train-rmse:0.209244+0.015127    test-rmse:1.126882+0.300008 
## [195]    train-rmse:0.208283+0.015446    test-rmse:1.126557+0.299891 
## [196]    train-rmse:0.207103+0.015248    test-rmse:1.125192+0.300221 
## [197]    train-rmse:0.205687+0.015231    test-rmse:1.124270+0.299592 
## [198]    train-rmse:0.204467+0.015420    test-rmse:1.124796+0.301877 
## [199]    train-rmse:0.203082+0.015700    test-rmse:1.124114+0.301806 
## [200]    train-rmse:0.201908+0.015414    test-rmse:1.123188+0.301688 
## [201]    train-rmse:0.200707+0.015464    test-rmse:1.122993+0.302068 
## [202]    train-rmse:0.199800+0.015546    test-rmse:1.121791+0.301957 
## [203]    train-rmse:0.198928+0.015485    test-rmse:1.121468+0.302760 
## [204]    train-rmse:0.197341+0.015423    test-rmse:1.120695+0.302889 
## [205]    train-rmse:0.195928+0.015431    test-rmse:1.120149+0.302987 
## [206]    train-rmse:0.194465+0.015282    test-rmse:1.119294+0.302064 
## [207]    train-rmse:0.193390+0.015434    test-rmse:1.119496+0.302242 
## [208]    train-rmse:0.191926+0.015310    test-rmse:1.119517+0.302645 
## [209]    train-rmse:0.190602+0.015855    test-rmse:1.119340+0.302859 
## [210]    train-rmse:0.189633+0.016056    test-rmse:1.119008+0.302582 
## [211]    train-rmse:0.187688+0.015353    test-rmse:1.119253+0.302534 
## [212]    train-rmse:0.186321+0.014931    test-rmse:1.119183+0.302553 
## [213]    train-rmse:0.185597+0.014842    test-rmse:1.119169+0.303067 
## [214]    train-rmse:0.183623+0.014355    test-rmse:1.119163+0.302770 
## [215]    train-rmse:0.182412+0.014220    test-rmse:1.117978+0.303056 
## [216]    train-rmse:0.181437+0.014581    test-rmse:1.118116+0.302905 
## [217]    train-rmse:0.180370+0.014414    test-rmse:1.118123+0.302874 
## [218]    train-rmse:0.179356+0.014215    test-rmse:1.117811+0.303346 
## [219]    train-rmse:0.178480+0.014383    test-rmse:1.117775+0.303793 
## [220]    train-rmse:0.177592+0.014300    test-rmse:1.117446+0.303863 
## [221]    train-rmse:0.176596+0.014432    test-rmse:1.117032+0.303155 
## [222]    train-rmse:0.174945+0.014218    test-rmse:1.117477+0.302850 
## [223]    train-rmse:0.173484+0.013651    test-rmse:1.117193+0.303153 
## [224]    train-rmse:0.172667+0.013606    test-rmse:1.117693+0.303259 
## [225]    train-rmse:0.171947+0.013527    test-rmse:1.116767+0.304041 
## [226]    train-rmse:0.171078+0.013337    test-rmse:1.116828+0.304022 
## [227]    train-rmse:0.170132+0.013558    test-rmse:1.116777+0.304126 
## [228]    train-rmse:0.168656+0.013028    test-rmse:1.116190+0.304186 
## [229]    train-rmse:0.167247+0.012582    test-rmse:1.117146+0.304686 
## [230]    train-rmse:0.165618+0.012611    test-rmse:1.116857+0.304356 
## [231]    train-rmse:0.164320+0.012358    test-rmse:1.116856+0.304433 
## [232]    train-rmse:0.162891+0.012161    test-rmse:1.116533+0.304623 
## [233]    train-rmse:0.162265+0.012109    test-rmse:1.116326+0.305206 
## [234]    train-rmse:0.161726+0.012201    test-rmse:1.116334+0.305635 
## Stopping. Best iteration:
## [228]    train-rmse:0.168656+0.013028    test-rmse:1.116190+0.304186
## 
## 45 
## [1]  train-rmse:84.081620+0.095738   test-rmse:84.081755+0.499144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:65.745564+0.080728   test-rmse:65.745244+0.529833 
## [3]  train-rmse:51.456281+0.070760   test-rmse:51.455305+0.559619 
## [4]  train-rmse:40.333913+0.065280   test-rmse:40.331998+0.589993 
## [5]  train-rmse:31.689540+0.061479   test-rmse:31.714312+0.607511 
## [6]  train-rmse:24.953949+0.049329   test-rmse:25.041610+0.619070 
## [7]  train-rmse:19.706037+0.036258   test-rmse:19.802895+0.589006 
## [8]  train-rmse:15.613110+0.029067   test-rmse:15.705885+0.542770 
## [9]  train-rmse:12.425888+0.023358   test-rmse:12.554310+0.565264 
## [10] train-rmse:9.943915+0.021968    test-rmse:10.060056+0.557322 
## [11] train-rmse:8.018847+0.025852    test-rmse:8.131086+0.551784 
## [12] train-rmse:6.531100+0.028117    test-rmse:6.646201+0.522701 
## [13] train-rmse:5.388417+0.026464    test-rmse:5.551147+0.535090 
## [14] train-rmse:4.514148+0.029276    test-rmse:4.714462+0.477970 
## [15] train-rmse:3.842059+0.030385    test-rmse:4.052024+0.479891 
## [16] train-rmse:3.326161+0.030269    test-rmse:3.582356+0.464007 
## [17] train-rmse:2.943736+0.034928    test-rmse:3.218309+0.422861 
## [18] train-rmse:2.655560+0.034291    test-rmse:2.986689+0.382133 
## [19] train-rmse:2.431561+0.038573    test-rmse:2.777437+0.348375 
## [20] train-rmse:2.255226+0.035630    test-rmse:2.636314+0.328869 
## [21] train-rmse:2.099312+0.034171    test-rmse:2.508234+0.321136 
## [22] train-rmse:1.984961+0.032298    test-rmse:2.410090+0.303500 
## [23] train-rmse:1.880780+0.026758    test-rmse:2.323506+0.256767 
## [24] train-rmse:1.784014+0.031595    test-rmse:2.246882+0.235495 
## [25] train-rmse:1.702943+0.030734    test-rmse:2.172913+0.224115 
## [26] train-rmse:1.625317+0.039194    test-rmse:2.124681+0.231973 
## [27] train-rmse:1.560731+0.038036    test-rmse:2.080120+0.228086 
## [28] train-rmse:1.503749+0.037317    test-rmse:2.036507+0.227903 
## [29] train-rmse:1.446632+0.035998    test-rmse:1.996011+0.233262 
## [30] train-rmse:1.389764+0.033684    test-rmse:1.945578+0.228934 
## [31] train-rmse:1.339737+0.034201    test-rmse:1.896041+0.220762 
## [32] train-rmse:1.291781+0.033385    test-rmse:1.862710+0.211967 
## [33] train-rmse:1.249048+0.032930    test-rmse:1.834458+0.218933 
## [34] train-rmse:1.208528+0.032207    test-rmse:1.798671+0.234307 
## [35] train-rmse:1.169276+0.030574    test-rmse:1.778018+0.229849 
## [36] train-rmse:1.130887+0.030173    test-rmse:1.753240+0.220578 
## [37] train-rmse:1.090793+0.026254    test-rmse:1.715991+0.228873 
## [38] train-rmse:1.054171+0.031238    test-rmse:1.698281+0.232803 
## [39] train-rmse:1.016798+0.028485    test-rmse:1.663631+0.234380 
## [40] train-rmse:0.981546+0.031609    test-rmse:1.635321+0.235446 
## [41] train-rmse:0.951088+0.031605    test-rmse:1.610414+0.236715 
## [42] train-rmse:0.922545+0.029063    test-rmse:1.594554+0.235014 
## [43] train-rmse:0.896391+0.028713    test-rmse:1.578717+0.233639 
## [44] train-rmse:0.870577+0.028698    test-rmse:1.557042+0.241300 
## [45] train-rmse:0.843859+0.030926    test-rmse:1.536173+0.241643 
## [46] train-rmse:0.821795+0.030933    test-rmse:1.522723+0.236630 
## [47] train-rmse:0.799965+0.030241    test-rmse:1.507719+0.241434 
## [48] train-rmse:0.776406+0.031301    test-rmse:1.494160+0.233697 
## [49] train-rmse:0.755388+0.028767    test-rmse:1.479217+0.231591 
## [50] train-rmse:0.734033+0.028177    test-rmse:1.462775+0.234061 
## [51] train-rmse:0.715358+0.028867    test-rmse:1.451506+0.240748 
## [52] train-rmse:0.696194+0.027870    test-rmse:1.442879+0.244286 
## [53] train-rmse:0.679006+0.026750    test-rmse:1.421052+0.224012 
## [54] train-rmse:0.662752+0.026151    test-rmse:1.405760+0.229997 
## [55] train-rmse:0.647907+0.026023    test-rmse:1.391310+0.234455 
## [56] train-rmse:0.633177+0.025888    test-rmse:1.382882+0.234721 
## [57] train-rmse:0.615938+0.028251    test-rmse:1.377345+0.234908 
## [58] train-rmse:0.602284+0.028774    test-rmse:1.369295+0.238148 
## [59] train-rmse:0.589245+0.027678    test-rmse:1.340591+0.224176 
## [60] train-rmse:0.575983+0.025744    test-rmse:1.335135+0.222769 
## [61] train-rmse:0.563535+0.025845    test-rmse:1.324600+0.224609 
## [62] train-rmse:0.551147+0.024846    test-rmse:1.315449+0.228800 
## [63] train-rmse:0.540376+0.024318    test-rmse:1.311550+0.229481 
## [64] train-rmse:0.526838+0.024859    test-rmse:1.303699+0.232721 
## [65] train-rmse:0.517238+0.024775    test-rmse:1.285879+0.223608 
## [66] train-rmse:0.506750+0.024512    test-rmse:1.281601+0.222978 
## [67] train-rmse:0.496549+0.022557    test-rmse:1.272268+0.225889 
## [68] train-rmse:0.485391+0.021055    test-rmse:1.265933+0.228052 
## [69] train-rmse:0.476244+0.022128    test-rmse:1.262305+0.230241 
## [70] train-rmse:0.467512+0.022658    test-rmse:1.260644+0.231968 
## [71] train-rmse:0.459033+0.022334    test-rmse:1.255172+0.233950 
## [72] train-rmse:0.449669+0.024624    test-rmse:1.243172+0.237921 
## [73] train-rmse:0.440851+0.023369    test-rmse:1.232094+0.232416 
## [74] train-rmse:0.432651+0.023061    test-rmse:1.230805+0.230889 
## [75] train-rmse:0.424975+0.022694    test-rmse:1.229176+0.231234 
## [76] train-rmse:0.417869+0.022209    test-rmse:1.227835+0.231377 
## [77] train-rmse:0.411319+0.023139    test-rmse:1.225070+0.231843 
## [78] train-rmse:0.401404+0.022161    test-rmse:1.216113+0.232562 
## [79] train-rmse:0.395517+0.022041    test-rmse:1.210761+0.231322 
## [80] train-rmse:0.388956+0.021874    test-rmse:1.206177+0.233187 
## [81] train-rmse:0.382555+0.020213    test-rmse:1.200715+0.231660 
## [82] train-rmse:0.376982+0.020692    test-rmse:1.199381+0.232519 
## [83] train-rmse:0.369936+0.020837    test-rmse:1.196287+0.233022 
## [84] train-rmse:0.364530+0.020958    test-rmse:1.194063+0.233824 
## [85] train-rmse:0.359945+0.021129    test-rmse:1.191304+0.234342 
## [86] train-rmse:0.354811+0.021005    test-rmse:1.191311+0.234018 
## [87] train-rmse:0.348161+0.020555    test-rmse:1.189822+0.234508 
## [88] train-rmse:0.342388+0.021295    test-rmse:1.186446+0.235378 
## [89] train-rmse:0.337070+0.020847    test-rmse:1.180888+0.236532 
## [90] train-rmse:0.332093+0.020333    test-rmse:1.179746+0.237611 
## [91] train-rmse:0.326652+0.018685    test-rmse:1.178504+0.238819 
## [92] train-rmse:0.320881+0.017977    test-rmse:1.176322+0.240696 
## [93] train-rmse:0.315882+0.017668    test-rmse:1.173017+0.243276 
## [94] train-rmse:0.311597+0.016698    test-rmse:1.169801+0.242550 
## [95] train-rmse:0.307038+0.016336    test-rmse:1.168244+0.244479 
## [96] train-rmse:0.303671+0.015724    test-rmse:1.166863+0.244846 
## [97] train-rmse:0.299088+0.014499    test-rmse:1.166167+0.245126 
## [98] train-rmse:0.295470+0.014746    test-rmse:1.162991+0.245934 
## [99] train-rmse:0.290697+0.014510    test-rmse:1.161720+0.245555 
## [100]    train-rmse:0.286069+0.013284    test-rmse:1.160168+0.247652 
## [101]    train-rmse:0.282463+0.013295    test-rmse:1.157854+0.247330 
## [102]    train-rmse:0.279254+0.012509    test-rmse:1.154230+0.248837 
## [103]    train-rmse:0.275464+0.012186    test-rmse:1.152761+0.251335 
## [104]    train-rmse:0.271614+0.012489    test-rmse:1.151499+0.251539 
## [105]    train-rmse:0.268281+0.013196    test-rmse:1.150385+0.251346 
## [106]    train-rmse:0.263704+0.013051    test-rmse:1.148865+0.252866 
## [107]    train-rmse:0.260546+0.013643    test-rmse:1.146694+0.252207 
## [108]    train-rmse:0.256907+0.012852    test-rmse:1.145319+0.253495 
## [109]    train-rmse:0.253868+0.012586    test-rmse:1.143706+0.253326 
## [110]    train-rmse:0.250896+0.012188    test-rmse:1.143225+0.254406 
## [111]    train-rmse:0.248056+0.011990    test-rmse:1.141444+0.255061 
## [112]    train-rmse:0.245486+0.012308    test-rmse:1.140551+0.253784 
## [113]    train-rmse:0.241194+0.012997    test-rmse:1.137308+0.254193 
## [114]    train-rmse:0.237355+0.013709    test-rmse:1.137632+0.253532 
## [115]    train-rmse:0.234630+0.014469    test-rmse:1.136214+0.253387 
## [116]    train-rmse:0.232094+0.015190    test-rmse:1.135346+0.253804 
## [117]    train-rmse:0.229628+0.014889    test-rmse:1.133941+0.254514 
## [118]    train-rmse:0.225423+0.015006    test-rmse:1.133160+0.255223 
## [119]    train-rmse:0.222829+0.015507    test-rmse:1.132745+0.254851 
## [120]    train-rmse:0.220588+0.015327    test-rmse:1.131789+0.255210 
## [121]    train-rmse:0.217273+0.014863    test-rmse:1.131427+0.255489 
## [122]    train-rmse:0.213886+0.014648    test-rmse:1.130750+0.256402 
## [123]    train-rmse:0.211498+0.014634    test-rmse:1.130686+0.255048 
## [124]    train-rmse:0.209363+0.014700    test-rmse:1.129112+0.255793 
## [125]    train-rmse:0.207354+0.014613    test-rmse:1.128099+0.255674 
## [126]    train-rmse:0.204929+0.014667    test-rmse:1.125699+0.256750 
## [127]    train-rmse:0.202496+0.014676    test-rmse:1.125568+0.257122 
## [128]    train-rmse:0.199784+0.014331    test-rmse:1.125317+0.256410 
## [129]    train-rmse:0.197398+0.014440    test-rmse:1.124251+0.256857 
## [130]    train-rmse:0.194788+0.014362    test-rmse:1.124627+0.257697 
## [131]    train-rmse:0.192362+0.013873    test-rmse:1.123299+0.257862 
## [132]    train-rmse:0.190185+0.013812    test-rmse:1.122387+0.258194 
## [133]    train-rmse:0.187659+0.013789    test-rmse:1.121298+0.256378 
## [134]    train-rmse:0.185354+0.013779    test-rmse:1.120571+0.256729 
## [135]    train-rmse:0.183646+0.013846    test-rmse:1.120219+0.257298 
## [136]    train-rmse:0.181211+0.012738    test-rmse:1.119945+0.257363 
## [137]    train-rmse:0.178998+0.012476    test-rmse:1.119800+0.257911 
## [138]    train-rmse:0.176339+0.012308    test-rmse:1.118063+0.255769 
## [139]    train-rmse:0.174272+0.011783    test-rmse:1.117633+0.256558 
## [140]    train-rmse:0.172051+0.011778    test-rmse:1.117492+0.257615 
## [141]    train-rmse:0.170221+0.011467    test-rmse:1.116999+0.257638 
## [142]    train-rmse:0.168179+0.011799    test-rmse:1.116630+0.257877 
## [143]    train-rmse:0.166312+0.010947    test-rmse:1.116319+0.257716 
## [144]    train-rmse:0.163697+0.010714    test-rmse:1.115992+0.257563 
## [145]    train-rmse:0.162104+0.010828    test-rmse:1.115333+0.256932 
## [146]    train-rmse:0.160248+0.011071    test-rmse:1.114255+0.257938 
## [147]    train-rmse:0.158596+0.011442    test-rmse:1.114128+0.258039 
## [148]    train-rmse:0.156929+0.011264    test-rmse:1.114720+0.257797 
## [149]    train-rmse:0.155261+0.010597    test-rmse:1.114731+0.257787 
## [150]    train-rmse:0.153409+0.010324    test-rmse:1.114380+0.257839 
## [151]    train-rmse:0.152095+0.010220    test-rmse:1.114106+0.257633 
## [152]    train-rmse:0.150611+0.009896    test-rmse:1.113547+0.257333 
## [153]    train-rmse:0.148487+0.010047    test-rmse:1.113689+0.257234 
## [154]    train-rmse:0.147284+0.009988    test-rmse:1.113495+0.257833 
## [155]    train-rmse:0.145466+0.009633    test-rmse:1.112550+0.255133 
## [156]    train-rmse:0.144010+0.009721    test-rmse:1.112428+0.255498 
## [157]    train-rmse:0.141791+0.009465    test-rmse:1.111914+0.255545 
## [158]    train-rmse:0.140339+0.009527    test-rmse:1.111807+0.256135 
## [159]    train-rmse:0.138632+0.009363    test-rmse:1.111868+0.256022 
## [160]    train-rmse:0.136673+0.009432    test-rmse:1.111872+0.256203 
## [161]    train-rmse:0.134654+0.009529    test-rmse:1.111732+0.255970 
## [162]    train-rmse:0.133282+0.009255    test-rmse:1.111297+0.255556 
## [163]    train-rmse:0.131485+0.008069    test-rmse:1.110197+0.256745 
## [164]    train-rmse:0.130213+0.007737    test-rmse:1.110099+0.256353 
## [165]    train-rmse:0.128200+0.006466    test-rmse:1.109973+0.256585 
## [166]    train-rmse:0.127039+0.006312    test-rmse:1.109126+0.256106 
## [167]    train-rmse:0.125445+0.006865    test-rmse:1.109261+0.255943 
## [168]    train-rmse:0.124059+0.006583    test-rmse:1.109419+0.255694 
## [169]    train-rmse:0.122005+0.006275    test-rmse:1.109097+0.254926 
## [170]    train-rmse:0.120777+0.006030    test-rmse:1.108808+0.254949 
## [171]    train-rmse:0.119352+0.006177    test-rmse:1.108469+0.254587 
## [172]    train-rmse:0.118141+0.006336    test-rmse:1.108755+0.254399 
## [173]    train-rmse:0.116893+0.006140    test-rmse:1.108677+0.254650 
## [174]    train-rmse:0.115816+0.006124    test-rmse:1.108835+0.254738 
## [175]    train-rmse:0.114748+0.005964    test-rmse:1.108824+0.254514 
## [176]    train-rmse:0.113373+0.006250    test-rmse:1.108681+0.254428 
## [177]    train-rmse:0.111634+0.006527    test-rmse:1.108804+0.254268 
## Stopping. Best iteration:
## [171]    train-rmse:0.119352+0.006177    test-rmse:1.108469+0.254587
## 
## 46 
## [1]  train-rmse:84.081620+0.095738   test-rmse:84.081755+0.499144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:65.745564+0.080728   test-rmse:65.745244+0.529833 
## [3]  train-rmse:51.456281+0.070760   test-rmse:51.455305+0.559619 
## [4]  train-rmse:40.333913+0.065280   test-rmse:40.331998+0.589993 
## [5]  train-rmse:31.689540+0.061479   test-rmse:31.714312+0.607511 
## [6]  train-rmse:24.953949+0.049329   test-rmse:25.041610+0.619070 
## [7]  train-rmse:19.705462+0.036373   test-rmse:19.793211+0.583360 
## [8]  train-rmse:15.607711+0.030525   test-rmse:15.702199+0.539144 
## [9]  train-rmse:12.409847+0.025609   test-rmse:12.514664+0.528006 
## [10] train-rmse:9.914462+0.020587    test-rmse:10.051362+0.567605 
## [11] train-rmse:7.962759+0.025247    test-rmse:8.084005+0.548263 
## [12] train-rmse:6.444892+0.024938    test-rmse:6.579505+0.523065 
## [13] train-rmse:5.258193+0.012877    test-rmse:5.431518+0.534114 
## [14] train-rmse:4.346881+0.010188    test-rmse:4.565814+0.473969 
## [15] train-rmse:3.647234+0.018033    test-rmse:3.885820+0.454851 
## [16] train-rmse:3.101052+0.023369    test-rmse:3.378118+0.407172 
## [17] train-rmse:2.685935+0.031946    test-rmse:3.020060+0.387355 
## [18] train-rmse:2.368331+0.034207    test-rmse:2.730209+0.338711 
## [19] train-rmse:2.123408+0.040926    test-rmse:2.517248+0.309516 
## [20] train-rmse:1.929676+0.044834    test-rmse:2.347084+0.266359 
## [21] train-rmse:1.774736+0.046201    test-rmse:2.221561+0.252004 
## [22] train-rmse:1.647664+0.053469    test-rmse:2.125923+0.238121 
## [23] train-rmse:1.531658+0.041936    test-rmse:2.034912+0.222050 
## [24] train-rmse:1.436925+0.040347    test-rmse:1.953803+0.197764 
## [25] train-rmse:1.360411+0.039315    test-rmse:1.915058+0.206289 
## [26] train-rmse:1.292937+0.037912    test-rmse:1.857107+0.199417 
## [27] train-rmse:1.228067+0.036636    test-rmse:1.805131+0.204865 
## [28] train-rmse:1.171073+0.036642    test-rmse:1.768471+0.207724 
## [29] train-rmse:1.112525+0.041198    test-rmse:1.724898+0.208111 
## [30] train-rmse:1.063865+0.039070    test-rmse:1.692481+0.201429 
## [31] train-rmse:1.018581+0.036370    test-rmse:1.663654+0.205269 
## [32] train-rmse:0.972593+0.038807    test-rmse:1.630262+0.211932 
## [33] train-rmse:0.934587+0.037388    test-rmse:1.598548+0.212445 
## [34] train-rmse:0.897590+0.035904    test-rmse:1.574263+0.218151 
## [35] train-rmse:0.861534+0.034915    test-rmse:1.539745+0.224104 
## [36] train-rmse:0.827381+0.032606    test-rmse:1.525332+0.228572 
## [37] train-rmse:0.796052+0.030990    test-rmse:1.503630+0.234734 
## [38] train-rmse:0.768355+0.030525    test-rmse:1.478331+0.236287 
## [39] train-rmse:0.734593+0.026476    test-rmse:1.465520+0.234462 
## [40] train-rmse:0.707481+0.024952    test-rmse:1.452709+0.241952 
## [41] train-rmse:0.684357+0.024093    test-rmse:1.436771+0.243302 
## [42] train-rmse:0.662510+0.022613    test-rmse:1.421880+0.247061 
## [43] train-rmse:0.639019+0.022031    test-rmse:1.397080+0.258835 
## [44] train-rmse:0.615526+0.026085    test-rmse:1.390536+0.257533 
## [45] train-rmse:0.596331+0.024927    test-rmse:1.378485+0.259267 
## [46] train-rmse:0.577039+0.023403    test-rmse:1.367317+0.263248 
## [47] train-rmse:0.558689+0.023321    test-rmse:1.359882+0.265897 
## [48] train-rmse:0.542712+0.023178    test-rmse:1.349598+0.265182 
## [49] train-rmse:0.522056+0.025977    test-rmse:1.338382+0.268901 
## [50] train-rmse:0.504790+0.024655    test-rmse:1.335054+0.268728 
## [51] train-rmse:0.485852+0.022428    test-rmse:1.326144+0.267608 
## [52] train-rmse:0.473556+0.022404    test-rmse:1.319406+0.269918 
## [53] train-rmse:0.460837+0.023646    test-rmse:1.312628+0.267656 
## [54] train-rmse:0.449116+0.021847    test-rmse:1.308624+0.272514 
## [55] train-rmse:0.436617+0.020188    test-rmse:1.300698+0.278127 
## [56] train-rmse:0.425198+0.020375    test-rmse:1.293028+0.281380 
## [57] train-rmse:0.412953+0.019777    test-rmse:1.289112+0.285930 
## [58] train-rmse:0.402765+0.018118    test-rmse:1.280037+0.290789 
## [59] train-rmse:0.391163+0.016916    test-rmse:1.268698+0.281385 
## [60] train-rmse:0.380097+0.018183    test-rmse:1.265045+0.281511 
## [61] train-rmse:0.371557+0.018490    test-rmse:1.261376+0.284588 
## [62] train-rmse:0.362683+0.018884    test-rmse:1.256847+0.284478 
## [63] train-rmse:0.354314+0.017947    test-rmse:1.253722+0.286990 
## [64] train-rmse:0.345866+0.017082    test-rmse:1.247490+0.294126 
## [65] train-rmse:0.337868+0.016589    test-rmse:1.247436+0.296268 
## [66] train-rmse:0.330777+0.014932    test-rmse:1.243107+0.294218 
## [67] train-rmse:0.322270+0.014763    test-rmse:1.241072+0.295925 
## [68] train-rmse:0.314863+0.014865    test-rmse:1.238390+0.298324 
## [69] train-rmse:0.309059+0.014890    test-rmse:1.237082+0.299808 
## [70] train-rmse:0.302583+0.015407    test-rmse:1.233929+0.301833 
## [71] train-rmse:0.295829+0.015842    test-rmse:1.233404+0.302910 
## [72] train-rmse:0.289512+0.014716    test-rmse:1.229463+0.300393 
## [73] train-rmse:0.281733+0.014276    test-rmse:1.227663+0.303300 
## [74] train-rmse:0.276398+0.014390    test-rmse:1.227195+0.304427 
## [75] train-rmse:0.269441+0.013579    test-rmse:1.225761+0.305360 
## [76] train-rmse:0.263532+0.011888    test-rmse:1.221511+0.299874 
## [77] train-rmse:0.259056+0.012342    test-rmse:1.220885+0.300203 
## [78] train-rmse:0.253778+0.013075    test-rmse:1.219078+0.300363 
## [79] train-rmse:0.249146+0.013803    test-rmse:1.218327+0.300540 
## [80] train-rmse:0.243300+0.013621    test-rmse:1.217238+0.303519 
## [81] train-rmse:0.238561+0.014215    test-rmse:1.216372+0.304439 
## [82] train-rmse:0.233775+0.012941    test-rmse:1.212226+0.299692 
## [83] train-rmse:0.229035+0.014226    test-rmse:1.210785+0.300114 
## [84] train-rmse:0.224539+0.014473    test-rmse:1.210041+0.298338 
## [85] train-rmse:0.220875+0.014354    test-rmse:1.210120+0.298446 
## [86] train-rmse:0.215279+0.011935    test-rmse:1.209140+0.298992 
## [87] train-rmse:0.210354+0.011168    test-rmse:1.207854+0.298363 
## [88] train-rmse:0.205890+0.011587    test-rmse:1.207461+0.297726 
## [89] train-rmse:0.201996+0.010971    test-rmse:1.206851+0.296048 
## [90] train-rmse:0.197795+0.012152    test-rmse:1.206935+0.296451 
## [91] train-rmse:0.194403+0.011993    test-rmse:1.206368+0.296095 
## [92] train-rmse:0.190751+0.011324    test-rmse:1.205858+0.297254 
## [93] train-rmse:0.188058+0.011117    test-rmse:1.204972+0.297909 
## [94] train-rmse:0.184011+0.010973    test-rmse:1.206666+0.298628 
## [95] train-rmse:0.180642+0.011865    test-rmse:1.206351+0.297960 
## [96] train-rmse:0.177562+0.011937    test-rmse:1.203449+0.294216 
## [97] train-rmse:0.174564+0.011933    test-rmse:1.202109+0.295012 
## [98] train-rmse:0.171304+0.011462    test-rmse:1.201399+0.294760 
## [99] train-rmse:0.167640+0.010162    test-rmse:1.201237+0.296158 
## [100]    train-rmse:0.165001+0.010492    test-rmse:1.200512+0.296363 
## [101]    train-rmse:0.161476+0.011512    test-rmse:1.200995+0.295678 
## [102]    train-rmse:0.158750+0.012033    test-rmse:1.200075+0.296166 
## [103]    train-rmse:0.155363+0.011739    test-rmse:1.199126+0.297192 
## [104]    train-rmse:0.151977+0.010293    test-rmse:1.198525+0.296729 
## [105]    train-rmse:0.149878+0.009755    test-rmse:1.197771+0.297188 
## [106]    train-rmse:0.147702+0.010052    test-rmse:1.197733+0.297185 
## [107]    train-rmse:0.145383+0.010195    test-rmse:1.196601+0.297790 
## [108]    train-rmse:0.142200+0.008839    test-rmse:1.196816+0.298003 
## [109]    train-rmse:0.139511+0.008490    test-rmse:1.196348+0.297440 
## [110]    train-rmse:0.137464+0.008830    test-rmse:1.196095+0.297373 
## [111]    train-rmse:0.134711+0.007957    test-rmse:1.195597+0.297257 
## [112]    train-rmse:0.132334+0.008027    test-rmse:1.195649+0.296957 
## [113]    train-rmse:0.129227+0.008977    test-rmse:1.195258+0.296846 
## [114]    train-rmse:0.127569+0.008770    test-rmse:1.194429+0.296971 
## [115]    train-rmse:0.124985+0.008979    test-rmse:1.194423+0.297314 
## [116]    train-rmse:0.123294+0.009175    test-rmse:1.193896+0.296953 
## [117]    train-rmse:0.121094+0.009000    test-rmse:1.193249+0.297122 
## [118]    train-rmse:0.118757+0.008864    test-rmse:1.193049+0.297248 
## [119]    train-rmse:0.117300+0.008556    test-rmse:1.192278+0.296595 
## [120]    train-rmse:0.114932+0.008112    test-rmse:1.191508+0.296403 
## [121]    train-rmse:0.111998+0.008434    test-rmse:1.191442+0.296801 
## [122]    train-rmse:0.110322+0.008402    test-rmse:1.190794+0.296965 
## [123]    train-rmse:0.108319+0.008457    test-rmse:1.190256+0.296900 
## [124]    train-rmse:0.106217+0.008298    test-rmse:1.189745+0.297007 
## [125]    train-rmse:0.104354+0.008548    test-rmse:1.189515+0.297045 
## [126]    train-rmse:0.101416+0.007939    test-rmse:1.189613+0.297022 
## [127]    train-rmse:0.099942+0.007870    test-rmse:1.189330+0.297160 
## [128]    train-rmse:0.098507+0.007464    test-rmse:1.188669+0.297301 
## [129]    train-rmse:0.096532+0.007423    test-rmse:1.188170+0.297310 
## [130]    train-rmse:0.095341+0.007645    test-rmse:1.187613+0.297355 
## [131]    train-rmse:0.094047+0.007596    test-rmse:1.186922+0.297487 
## [132]    train-rmse:0.092688+0.007857    test-rmse:1.186972+0.297671 
## [133]    train-rmse:0.091620+0.007843    test-rmse:1.187045+0.297606 
## [134]    train-rmse:0.089516+0.007915    test-rmse:1.186994+0.297692 
## [135]    train-rmse:0.087864+0.007244    test-rmse:1.186868+0.297648 
## [136]    train-rmse:0.086402+0.007058    test-rmse:1.186600+0.298167 
## [137]    train-rmse:0.085484+0.006850    test-rmse:1.186740+0.298394 
## [138]    train-rmse:0.084283+0.006650    test-rmse:1.185975+0.298722 
## [139]    train-rmse:0.083257+0.006568    test-rmse:1.185947+0.298940 
## [140]    train-rmse:0.082208+0.006770    test-rmse:1.185919+0.299034 
## [141]    train-rmse:0.080017+0.006437    test-rmse:1.185793+0.299213 
## [142]    train-rmse:0.078883+0.006358    test-rmse:1.185653+0.299297 
## [143]    train-rmse:0.077962+0.006368    test-rmse:1.185195+0.299581 
## [144]    train-rmse:0.076787+0.006266    test-rmse:1.184988+0.299140 
## [145]    train-rmse:0.075478+0.005841    test-rmse:1.184789+0.299222 
## [146]    train-rmse:0.074095+0.006004    test-rmse:1.184766+0.299010 
## [147]    train-rmse:0.072913+0.006113    test-rmse:1.184863+0.298822 
## [148]    train-rmse:0.071887+0.006008    test-rmse:1.184652+0.298722 
## [149]    train-rmse:0.070755+0.005958    test-rmse:1.184949+0.299022 
## [150]    train-rmse:0.069912+0.005773    test-rmse:1.185486+0.300101 
## [151]    train-rmse:0.069130+0.005624    test-rmse:1.185353+0.300203 
## [152]    train-rmse:0.068281+0.005538    test-rmse:1.185217+0.300176 
## [153]    train-rmse:0.067072+0.005732    test-rmse:1.184964+0.300295 
## [154]    train-rmse:0.065774+0.005767    test-rmse:1.184789+0.300135 
## Stopping. Best iteration:
## [148]    train-rmse:0.071887+0.006008    test-rmse:1.184652+0.298722
## 
## 47 
## [1]  train-rmse:84.081620+0.095738   test-rmse:84.081755+0.499144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:65.745564+0.080728   test-rmse:65.745244+0.529833 
## [3]  train-rmse:51.456281+0.070760   test-rmse:51.455305+0.559619 
## [4]  train-rmse:40.333913+0.065280   test-rmse:40.331998+0.589993 
## [5]  train-rmse:31.689540+0.061479   test-rmse:31.714312+0.607511 
## [6]  train-rmse:24.953949+0.049329   test-rmse:25.041610+0.619070 
## [7]  train-rmse:19.705462+0.036373   test-rmse:19.793211+0.583360 
## [8]  train-rmse:15.605001+0.030877   test-rmse:15.690680+0.537880 
## [9]  train-rmse:12.401264+0.026182   test-rmse:12.500978+0.522424 
## [10] train-rmse:9.897780+0.022328    test-rmse:10.041477+0.564657 
## [11] train-rmse:7.934351+0.030769    test-rmse:8.083247+0.540704 
## [12] train-rmse:6.395160+0.031510    test-rmse:6.543273+0.514415 
## [13] train-rmse:5.198807+0.026439    test-rmse:5.383026+0.529193 
## [14] train-rmse:4.265459+0.027533    test-rmse:4.476462+0.484545 
## [15] train-rmse:3.537128+0.030327    test-rmse:3.779949+0.433896 
## [16] train-rmse:2.967039+0.027086    test-rmse:3.241263+0.396838 
## [17] train-rmse:2.523238+0.030952    test-rmse:2.832517+0.399873 
## [18] train-rmse:2.180661+0.024114    test-rmse:2.551530+0.380491 
## [19] train-rmse:1.913056+0.030437    test-rmse:2.323402+0.334402 
## [20] train-rmse:1.700472+0.029251    test-rmse:2.156486+0.304580 
## [21] train-rmse:1.531006+0.029290    test-rmse:2.020937+0.283738 
## [22] train-rmse:1.387833+0.038067    test-rmse:1.914834+0.269539 
## [23] train-rmse:1.271281+0.046710    test-rmse:1.837523+0.253537 
## [24] train-rmse:1.172628+0.049182    test-rmse:1.745523+0.247836 
## [25] train-rmse:1.086898+0.036883    test-rmse:1.687784+0.237411 
## [26] train-rmse:1.017564+0.038896    test-rmse:1.641104+0.234006 
## [27] train-rmse:0.957670+0.037240    test-rmse:1.596950+0.231481 
## [28] train-rmse:0.899905+0.037396    test-rmse:1.545903+0.223132 
## [29] train-rmse:0.852123+0.035580    test-rmse:1.513163+0.218280 
## [30] train-rmse:0.809393+0.036542    test-rmse:1.481555+0.222506 
## [31] train-rmse:0.766450+0.039089    test-rmse:1.464825+0.229471 
## [32] train-rmse:0.726623+0.033613    test-rmse:1.442272+0.236551 
## [33] train-rmse:0.688524+0.030508    test-rmse:1.421641+0.236589 
## [34] train-rmse:0.655709+0.031804    test-rmse:1.404670+0.243033 
## [35] train-rmse:0.625926+0.029885    test-rmse:1.373782+0.240120 
## [36] train-rmse:0.597530+0.029875    test-rmse:1.357575+0.241504 
## [37] train-rmse:0.571479+0.030362    test-rmse:1.347348+0.239355 
## [38] train-rmse:0.549033+0.028751    test-rmse:1.335754+0.247769 
## [39] train-rmse:0.522270+0.027736    test-rmse:1.320579+0.241484 
## [40] train-rmse:0.503012+0.026933    test-rmse:1.310670+0.246417 
## [41] train-rmse:0.484200+0.026788    test-rmse:1.297855+0.248862 
## [42] train-rmse:0.466589+0.025321    test-rmse:1.285306+0.241588 
## [43] train-rmse:0.451063+0.025132    test-rmse:1.281072+0.238872 
## [44] train-rmse:0.433807+0.023020    test-rmse:1.273113+0.239678 
## [45] train-rmse:0.418490+0.022218    test-rmse:1.265669+0.242320 
## [46] train-rmse:0.403256+0.024939    test-rmse:1.258972+0.242026 
## [47] train-rmse:0.391059+0.023959    test-rmse:1.250080+0.245067 
## [48] train-rmse:0.376676+0.020833    test-rmse:1.245804+0.245564 
## [49] train-rmse:0.363244+0.018791    test-rmse:1.237136+0.253047 
## [50] train-rmse:0.352947+0.017354    test-rmse:1.234737+0.254486 
## [51] train-rmse:0.339735+0.019533    test-rmse:1.230614+0.255692 
## [52] train-rmse:0.328175+0.018504    test-rmse:1.223961+0.260008 
## [53] train-rmse:0.318114+0.016855    test-rmse:1.219907+0.257257 
## [54] train-rmse:0.310179+0.016265    test-rmse:1.215397+0.256705 
## [55] train-rmse:0.301734+0.017317    test-rmse:1.212756+0.258205 
## [56] train-rmse:0.293793+0.017399    test-rmse:1.212057+0.259125 
## [57] train-rmse:0.285188+0.017873    test-rmse:1.210207+0.261063 
## [58] train-rmse:0.277603+0.016744    test-rmse:1.206818+0.260495 
## [59] train-rmse:0.266640+0.015834    test-rmse:1.201896+0.260012 
## [60] train-rmse:0.259390+0.016194    test-rmse:1.199626+0.260568 
## [61] train-rmse:0.252396+0.015683    test-rmse:1.198184+0.259377 
## [62] train-rmse:0.245178+0.017058    test-rmse:1.196252+0.260871 
## [63] train-rmse:0.235212+0.016900    test-rmse:1.194219+0.260229 
## [64] train-rmse:0.228810+0.015618    test-rmse:1.192849+0.259473 
## [65] train-rmse:0.222398+0.015781    test-rmse:1.192388+0.260567 
## [66] train-rmse:0.216312+0.015334    test-rmse:1.190691+0.260463 
## [67] train-rmse:0.210343+0.014529    test-rmse:1.189102+0.262504 
## [68] train-rmse:0.205233+0.014156    test-rmse:1.185843+0.264046 
## [69] train-rmse:0.199589+0.013560    test-rmse:1.184531+0.264236 
## [70] train-rmse:0.193983+0.013376    test-rmse:1.182757+0.264300 
## [71] train-rmse:0.188870+0.011751    test-rmse:1.183336+0.268203 
## [72] train-rmse:0.184552+0.012112    test-rmse:1.182744+0.267869 
## [73] train-rmse:0.179359+0.011440    test-rmse:1.182073+0.268032 
## [74] train-rmse:0.175414+0.011126    test-rmse:1.180158+0.266851 
## [75] train-rmse:0.170297+0.011291    test-rmse:1.179497+0.266689 
## [76] train-rmse:0.165288+0.011257    test-rmse:1.179597+0.266303 
## [77] train-rmse:0.162327+0.010871    test-rmse:1.178943+0.265779 
## [78] train-rmse:0.158611+0.010750    test-rmse:1.179533+0.267614 
## [79] train-rmse:0.154516+0.010520    test-rmse:1.180283+0.268127 
## [80] train-rmse:0.150850+0.010977    test-rmse:1.179617+0.267600 
## [81] train-rmse:0.147289+0.010426    test-rmse:1.178719+0.266869 
## [82] train-rmse:0.143489+0.009814    test-rmse:1.177891+0.267551 
## [83] train-rmse:0.139219+0.010193    test-rmse:1.178150+0.267709 
## [84] train-rmse:0.135074+0.010089    test-rmse:1.178327+0.267544 
## [85] train-rmse:0.131870+0.009972    test-rmse:1.177072+0.267922 
## [86] train-rmse:0.129302+0.009733    test-rmse:1.176600+0.267249 
## [87] train-rmse:0.126907+0.009972    test-rmse:1.176267+0.267106 
## [88] train-rmse:0.123498+0.009969    test-rmse:1.175828+0.267333 
## [89] train-rmse:0.120566+0.010170    test-rmse:1.175487+0.267546 
## [90] train-rmse:0.117793+0.009925    test-rmse:1.175473+0.267283 
## [91] train-rmse:0.114518+0.009978    test-rmse:1.175198+0.267630 
## [92] train-rmse:0.111251+0.010315    test-rmse:1.175445+0.267316 
## [93] train-rmse:0.108525+0.010180    test-rmse:1.175340+0.267299 
## [94] train-rmse:0.105481+0.010388    test-rmse:1.175752+0.268133 
## [95] train-rmse:0.102764+0.009265    test-rmse:1.175435+0.268391 
## [96] train-rmse:0.100024+0.007590    test-rmse:1.174705+0.268742 
## [97] train-rmse:0.096827+0.007431    test-rmse:1.174198+0.267206 
## [98] train-rmse:0.094677+0.006896    test-rmse:1.174012+0.267148 
## [99] train-rmse:0.091853+0.005868    test-rmse:1.172993+0.267050 
## [100]    train-rmse:0.089176+0.005150    test-rmse:1.172803+0.267782 
## [101]    train-rmse:0.086911+0.004837    test-rmse:1.173016+0.267481 
## [102]    train-rmse:0.085068+0.004823    test-rmse:1.172660+0.267859 
## [103]    train-rmse:0.083361+0.005147    test-rmse:1.172720+0.268388 
## [104]    train-rmse:0.081845+0.005013    test-rmse:1.172491+0.268716 
## [105]    train-rmse:0.079379+0.004841    test-rmse:1.172388+0.269094 
## [106]    train-rmse:0.077217+0.005272    test-rmse:1.172013+0.269492 
## [107]    train-rmse:0.075300+0.005085    test-rmse:1.172154+0.269779 
## [108]    train-rmse:0.073781+0.005132    test-rmse:1.171811+0.269717 
## [109]    train-rmse:0.072184+0.004789    test-rmse:1.171988+0.269478 
## [110]    train-rmse:0.069922+0.004405    test-rmse:1.171565+0.268452 
## [111]    train-rmse:0.068455+0.004525    test-rmse:1.171442+0.268763 
## [112]    train-rmse:0.066583+0.004265    test-rmse:1.171777+0.269182 
## [113]    train-rmse:0.065107+0.004249    test-rmse:1.171619+0.269387 
## [114]    train-rmse:0.063679+0.004534    test-rmse:1.171334+0.269802 
## [115]    train-rmse:0.062346+0.004513    test-rmse:1.171457+0.269885 
## [116]    train-rmse:0.061063+0.004631    test-rmse:1.171109+0.269782 
## [117]    train-rmse:0.060007+0.004301    test-rmse:1.170932+0.270082 
## [118]    train-rmse:0.058314+0.003958    test-rmse:1.171045+0.270180 
## [119]    train-rmse:0.057223+0.003747    test-rmse:1.171009+0.270116 
## [120]    train-rmse:0.055831+0.003335    test-rmse:1.170934+0.270251 
## [121]    train-rmse:0.054781+0.003316    test-rmse:1.170914+0.270073 
## [122]    train-rmse:0.053361+0.003146    test-rmse:1.170825+0.270214 
## [123]    train-rmse:0.052350+0.003248    test-rmse:1.170810+0.269908 
## [124]    train-rmse:0.051324+0.003239    test-rmse:1.170218+0.269807 
## [125]    train-rmse:0.050362+0.003463    test-rmse:1.170050+0.269789 
## [126]    train-rmse:0.049053+0.003289    test-rmse:1.169823+0.269423 
## [127]    train-rmse:0.048103+0.003288    test-rmse:1.169703+0.269534 
## [128]    train-rmse:0.047041+0.003464    test-rmse:1.169542+0.269385 
## [129]    train-rmse:0.046218+0.003199    test-rmse:1.169656+0.270266 
## [130]    train-rmse:0.044953+0.003299    test-rmse:1.169724+0.270420 
## [131]    train-rmse:0.043818+0.003014    test-rmse:1.169745+0.270482 
## [132]    train-rmse:0.042493+0.003024    test-rmse:1.169629+0.270599 
## [133]    train-rmse:0.041345+0.002558    test-rmse:1.169735+0.270389 
## [134]    train-rmse:0.040525+0.002581    test-rmse:1.169622+0.270338 
## Stopping. Best iteration:
## [128]    train-rmse:0.047041+0.003464    test-rmse:1.169542+0.269385
## 
## 48 
## [1]  train-rmse:84.081620+0.095738   test-rmse:84.081755+0.499144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:65.745564+0.080728   test-rmse:65.745244+0.529833 
## [3]  train-rmse:51.456281+0.070760   test-rmse:51.455305+0.559619 
## [4]  train-rmse:40.333913+0.065280   test-rmse:40.331998+0.589993 
## [5]  train-rmse:31.689540+0.061479   test-rmse:31.714312+0.607511 
## [6]  train-rmse:24.953949+0.049329   test-rmse:25.041610+0.619070 
## [7]  train-rmse:19.705462+0.036373   test-rmse:19.793211+0.583360 
## [8]  train-rmse:15.602282+0.031192   test-rmse:15.695041+0.540999 
## [9]  train-rmse:12.394326+0.027930   test-rmse:12.499482+0.522623 
## [10] train-rmse:9.884900+0.024095    test-rmse:10.020661+0.561586 
## [11] train-rmse:7.910858+0.024049    test-rmse:8.053069+0.542563 
## [12] train-rmse:6.357084+0.026270    test-rmse:6.513093+0.513524 
## [13] train-rmse:5.147407+0.025174    test-rmse:5.308363+0.496852 
## [14] train-rmse:4.198837+0.027177    test-rmse:4.411802+0.484336 
## [15] train-rmse:3.450412+0.027015    test-rmse:3.685586+0.447219 
## [16] train-rmse:2.868001+0.026804    test-rmse:3.144885+0.398283 
## [17] train-rmse:2.413372+0.030439    test-rmse:2.757687+0.419668 
## [18] train-rmse:2.054539+0.023356    test-rmse:2.433906+0.387548 
## [19] train-rmse:1.766474+0.019117    test-rmse:2.193244+0.359996 
## [20] train-rmse:1.541685+0.033665    test-rmse:2.029756+0.324657 
## [21] train-rmse:1.361239+0.034085    test-rmse:1.897945+0.290899 
## [22] train-rmse:1.211678+0.029340    test-rmse:1.800501+0.286125 
## [23] train-rmse:1.095683+0.034197    test-rmse:1.721328+0.260803 
## [24] train-rmse:1.002758+0.033813    test-rmse:1.649401+0.258370 
## [25] train-rmse:0.918172+0.033671    test-rmse:1.605994+0.260599 
## [26] train-rmse:0.851585+0.038448    test-rmse:1.556772+0.260314 
## [27] train-rmse:0.791131+0.035478    test-rmse:1.520260+0.271965 
## [28] train-rmse:0.736585+0.031317    test-rmse:1.488850+0.261995 
## [29] train-rmse:0.684642+0.034490    test-rmse:1.457188+0.265806 
## [30] train-rmse:0.645630+0.033635    test-rmse:1.434789+0.269474 
## [31] train-rmse:0.609224+0.029601    test-rmse:1.409652+0.266260 
## [32] train-rmse:0.574612+0.027660    test-rmse:1.391951+0.265100 
## [33] train-rmse:0.543210+0.029678    test-rmse:1.373672+0.265552 
## [34] train-rmse:0.517691+0.028066    test-rmse:1.353446+0.261853 
## [35] train-rmse:0.493716+0.026424    test-rmse:1.338345+0.269617 
## [36] train-rmse:0.471494+0.024631    test-rmse:1.326199+0.268374 
## [37] train-rmse:0.448964+0.024004    test-rmse:1.317744+0.270182 
## [38] train-rmse:0.429505+0.021606    test-rmse:1.305100+0.266219 
## [39] train-rmse:0.408275+0.019209    test-rmse:1.300023+0.272475 
## [40] train-rmse:0.391714+0.018535    test-rmse:1.290239+0.268219 
## [41] train-rmse:0.377442+0.017534    test-rmse:1.282955+0.268704 
## [42] train-rmse:0.359354+0.018426    test-rmse:1.276596+0.270995 
## [43] train-rmse:0.344069+0.020930    test-rmse:1.273180+0.270406 
## [44] train-rmse:0.332108+0.020193    test-rmse:1.266691+0.267606 
## [45] train-rmse:0.319348+0.018411    test-rmse:1.259545+0.269156 
## [46] train-rmse:0.306135+0.017248    test-rmse:1.255146+0.272254 
## [47] train-rmse:0.289059+0.018529    test-rmse:1.252679+0.275577 
## [48] train-rmse:0.277341+0.018617    test-rmse:1.245859+0.276847 
## [49] train-rmse:0.268571+0.017421    test-rmse:1.242455+0.278164 
## [50] train-rmse:0.255220+0.016534    test-rmse:1.242069+0.279171 
## [51] train-rmse:0.247915+0.016864    test-rmse:1.238881+0.280887 
## [52] train-rmse:0.239948+0.016983    test-rmse:1.233641+0.281787 
## [53] train-rmse:0.230500+0.016319    test-rmse:1.231432+0.284590 
## [54] train-rmse:0.222471+0.014386    test-rmse:1.228025+0.284231 
## [55] train-rmse:0.215602+0.015512    test-rmse:1.225135+0.284622 
## [56] train-rmse:0.206540+0.014740    test-rmse:1.222422+0.284072 
## [57] train-rmse:0.198280+0.011220    test-rmse:1.221448+0.284574 
## [58] train-rmse:0.191759+0.010826    test-rmse:1.220815+0.284591 
## [59] train-rmse:0.185028+0.011629    test-rmse:1.219770+0.285566 
## [60] train-rmse:0.177441+0.009455    test-rmse:1.218341+0.285376 
## [61] train-rmse:0.171875+0.009745    test-rmse:1.216492+0.286777 
## [62] train-rmse:0.166204+0.010242    test-rmse:1.214922+0.288023 
## [63] train-rmse:0.160898+0.010842    test-rmse:1.215226+0.289282 
## [64] train-rmse:0.154845+0.010832    test-rmse:1.212828+0.289945 
## [65] train-rmse:0.150135+0.010139    test-rmse:1.212217+0.290703 
## [66] train-rmse:0.145617+0.008023    test-rmse:1.210899+0.290280 
## [67] train-rmse:0.140814+0.006892    test-rmse:1.209994+0.288617 
## [68] train-rmse:0.135496+0.005796    test-rmse:1.208238+0.289616 
## [69] train-rmse:0.131208+0.007197    test-rmse:1.207392+0.290226 
## [70] train-rmse:0.126726+0.008155    test-rmse:1.205842+0.290758 
## [71] train-rmse:0.122957+0.008614    test-rmse:1.204905+0.291745 
## [72] train-rmse:0.118794+0.006947    test-rmse:1.204766+0.291865 
## [73] train-rmse:0.115297+0.007443    test-rmse:1.203757+0.292623 
## [74] train-rmse:0.111696+0.007528    test-rmse:1.201846+0.291962 
## [75] train-rmse:0.108645+0.007039    test-rmse:1.201139+0.292614 
## [76] train-rmse:0.104455+0.007762    test-rmse:1.200903+0.292657 
## [77] train-rmse:0.101278+0.007805    test-rmse:1.200449+0.293249 
## [78] train-rmse:0.098864+0.008375    test-rmse:1.200299+0.293374 
## [79] train-rmse:0.096409+0.008772    test-rmse:1.200025+0.293120 
## [80] train-rmse:0.093466+0.008735    test-rmse:1.199375+0.293600 
## [81] train-rmse:0.091127+0.008307    test-rmse:1.199280+0.293039 
## [82] train-rmse:0.088237+0.008728    test-rmse:1.198879+0.292943 
## [83] train-rmse:0.085953+0.008595    test-rmse:1.198651+0.293626 
## [84] train-rmse:0.083573+0.008114    test-rmse:1.198069+0.294162 
## [85] train-rmse:0.081758+0.008280    test-rmse:1.197455+0.294386 
## [86] train-rmse:0.079537+0.008396    test-rmse:1.197382+0.294726 
## [87] train-rmse:0.077058+0.008217    test-rmse:1.196768+0.295061 
## [88] train-rmse:0.074748+0.007779    test-rmse:1.196242+0.295336 
## [89] train-rmse:0.072527+0.007902    test-rmse:1.196236+0.295313 
## [90] train-rmse:0.070917+0.007734    test-rmse:1.196404+0.295511 
## [91] train-rmse:0.068748+0.007034    test-rmse:1.196321+0.295894 
## [92] train-rmse:0.066862+0.007154    test-rmse:1.196104+0.295878 
## [93] train-rmse:0.064883+0.007159    test-rmse:1.196056+0.295824 
## [94] train-rmse:0.063198+0.007232    test-rmse:1.195555+0.296408 
## [95] train-rmse:0.061054+0.006790    test-rmse:1.195190+0.295812 
## [96] train-rmse:0.059640+0.006784    test-rmse:1.195205+0.295498 
## [97] train-rmse:0.057051+0.006330    test-rmse:1.194921+0.296002 
## [98] train-rmse:0.055871+0.006443    test-rmse:1.194533+0.296179 
## [99] train-rmse:0.053515+0.006160    test-rmse:1.194234+0.296342 
## [100]    train-rmse:0.052259+0.005844    test-rmse:1.194025+0.296211 
## [101]    train-rmse:0.050680+0.006330    test-rmse:1.194055+0.296350 
## [102]    train-rmse:0.048952+0.005836    test-rmse:1.193971+0.296770 
## [103]    train-rmse:0.047330+0.005921    test-rmse:1.194072+0.296675 
## [104]    train-rmse:0.046417+0.006055    test-rmse:1.193684+0.296900 
## [105]    train-rmse:0.045159+0.005634    test-rmse:1.193666+0.297092 
## [106]    train-rmse:0.043886+0.005551    test-rmse:1.193723+0.297121 
## [107]    train-rmse:0.042617+0.005551    test-rmse:1.193607+0.296993 
## [108]    train-rmse:0.041109+0.005123    test-rmse:1.193365+0.297012 
## [109]    train-rmse:0.040119+0.005128    test-rmse:1.193334+0.296965 
## [110]    train-rmse:0.038988+0.004894    test-rmse:1.193347+0.296672 
## [111]    train-rmse:0.037489+0.004570    test-rmse:1.193110+0.296507 
## [112]    train-rmse:0.036513+0.004366    test-rmse:1.193234+0.296633 
## [113]    train-rmse:0.035339+0.004348    test-rmse:1.192975+0.296314 
## [114]    train-rmse:0.034146+0.004148    test-rmse:1.192876+0.296253 
## [115]    train-rmse:0.033445+0.004249    test-rmse:1.192839+0.296354 
## [116]    train-rmse:0.032582+0.004339    test-rmse:1.192826+0.296423 
## [117]    train-rmse:0.031529+0.004322    test-rmse:1.192524+0.296524 
## [118]    train-rmse:0.030389+0.004402    test-rmse:1.192304+0.296597 
## [119]    train-rmse:0.029600+0.004200    test-rmse:1.192111+0.296682 
## [120]    train-rmse:0.028623+0.003745    test-rmse:1.191994+0.296560 
## [121]    train-rmse:0.027703+0.003792    test-rmse:1.192133+0.296688 
## [122]    train-rmse:0.027020+0.003625    test-rmse:1.191826+0.296565 
## [123]    train-rmse:0.026297+0.003527    test-rmse:1.191825+0.296600 
## [124]    train-rmse:0.025544+0.003442    test-rmse:1.191682+0.296653 
## [125]    train-rmse:0.024855+0.003596    test-rmse:1.191705+0.296772 
## [126]    train-rmse:0.024334+0.003604    test-rmse:1.191626+0.296738 
## [127]    train-rmse:0.023574+0.003342    test-rmse:1.191356+0.296754 
## [128]    train-rmse:0.022783+0.003435    test-rmse:1.191457+0.296689 
## [129]    train-rmse:0.022279+0.003549    test-rmse:1.191468+0.296681 
## [130]    train-rmse:0.021760+0.003550    test-rmse:1.191220+0.296466 
## [131]    train-rmse:0.021292+0.003604    test-rmse:1.191267+0.296504 
## [132]    train-rmse:0.020806+0.003685    test-rmse:1.191250+0.296469 
## [133]    train-rmse:0.020100+0.003585    test-rmse:1.191135+0.296315 
## [134]    train-rmse:0.019437+0.003651    test-rmse:1.190974+0.296301 
## [135]    train-rmse:0.018921+0.003532    test-rmse:1.190822+0.296373 
## [136]    train-rmse:0.018381+0.003356    test-rmse:1.190604+0.296306 
## [137]    train-rmse:0.017829+0.003344    test-rmse:1.190617+0.296348 
## [138]    train-rmse:0.017434+0.003343    test-rmse:1.190580+0.296291 
## [139]    train-rmse:0.017027+0.003272    test-rmse:1.190503+0.296256 
## [140]    train-rmse:0.016536+0.003143    test-rmse:1.190466+0.296372 
## [141]    train-rmse:0.016177+0.003159    test-rmse:1.190398+0.296380 
## [142]    train-rmse:0.015683+0.003058    test-rmse:1.190350+0.296415 
## [143]    train-rmse:0.015262+0.002863    test-rmse:1.190333+0.296398 
## [144]    train-rmse:0.014655+0.002742    test-rmse:1.190426+0.296461 
## [145]    train-rmse:0.014192+0.002692    test-rmse:1.190441+0.296418 
## [146]    train-rmse:0.013794+0.002637    test-rmse:1.190435+0.296402 
## [147]    train-rmse:0.013329+0.002445    test-rmse:1.190376+0.296392 
## [148]    train-rmse:0.013011+0.002394    test-rmse:1.190296+0.296367 
## [149]    train-rmse:0.012679+0.002469    test-rmse:1.190314+0.296392 
## [150]    train-rmse:0.012380+0.002484    test-rmse:1.190282+0.296414 
## [151]    train-rmse:0.012017+0.002422    test-rmse:1.190330+0.296427 
## [152]    train-rmse:0.011746+0.002442    test-rmse:1.190332+0.296467 
## [153]    train-rmse:0.011422+0.002424    test-rmse:1.190322+0.296497 
## [154]    train-rmse:0.011066+0.002386    test-rmse:1.190358+0.296453 
## [155]    train-rmse:0.010777+0.002325    test-rmse:1.190345+0.296426 
## [156]    train-rmse:0.010547+0.002286    test-rmse:1.190265+0.296409 
## [157]    train-rmse:0.010205+0.002263    test-rmse:1.190224+0.296452 
## [158]    train-rmse:0.009862+0.002257    test-rmse:1.190211+0.296504 
## [159]    train-rmse:0.009579+0.002125    test-rmse:1.190089+0.296508 
## [160]    train-rmse:0.009347+0.002110    test-rmse:1.190094+0.296511 
## [161]    train-rmse:0.009079+0.002079    test-rmse:1.190103+0.296484 
## [162]    train-rmse:0.008765+0.002073    test-rmse:1.190122+0.296461 
## [163]    train-rmse:0.008486+0.001887    test-rmse:1.190084+0.296494 
## [164]    train-rmse:0.008254+0.001804    test-rmse:1.189998+0.296464 
## [165]    train-rmse:0.008050+0.001707    test-rmse:1.189933+0.296490 
## [166]    train-rmse:0.007873+0.001701    test-rmse:1.189932+0.296534 
## [167]    train-rmse:0.007689+0.001648    test-rmse:1.189926+0.296549 
## [168]    train-rmse:0.007476+0.001611    test-rmse:1.189932+0.296584 
## [169]    train-rmse:0.007260+0.001613    test-rmse:1.189927+0.296558 
## [170]    train-rmse:0.007003+0.001527    test-rmse:1.189921+0.296569 
## [171]    train-rmse:0.006811+0.001477    test-rmse:1.189866+0.296593 
## [172]    train-rmse:0.006604+0.001413    test-rmse:1.189818+0.296554 
## [173]    train-rmse:0.006345+0.001283    test-rmse:1.189812+0.296601 
## [174]    train-rmse:0.006212+0.001248    test-rmse:1.189781+0.296585 
## [175]    train-rmse:0.006033+0.001217    test-rmse:1.189776+0.296608 
## [176]    train-rmse:0.005862+0.001093    test-rmse:1.189775+0.296588 
## [177]    train-rmse:0.005710+0.001020    test-rmse:1.189735+0.296585 
## [178]    train-rmse:0.005583+0.001007    test-rmse:1.189765+0.296589 
## [179]    train-rmse:0.005435+0.000989    test-rmse:1.189802+0.296624 
## [180]    train-rmse:0.005301+0.000983    test-rmse:1.189765+0.296627 
## [181]    train-rmse:0.005106+0.000956    test-rmse:1.189780+0.296644 
## [182]    train-rmse:0.004946+0.000888    test-rmse:1.189755+0.296589 
## [183]    train-rmse:0.004770+0.000822    test-rmse:1.189748+0.296604 
## Stopping. Best iteration:
## [177]    train-rmse:0.005710+0.001020    test-rmse:1.189735+0.296585
## 
## 49 
## [1]  train-rmse:81.945409+0.093908   test-rmse:81.945506+0.502440 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:62.457670+0.078257   test-rmse:62.457233+0.536077 
## [3]  train-rmse:47.664860+0.068582   test-rmse:47.663622+0.569012 
## [4]  train-rmse:36.454471+0.064225   test-rmse:36.452043+0.603174 
## [5]  train-rmse:27.969937+0.061508   test-rmse:28.007095+0.615836 
## [6]  train-rmse:21.549896+0.056392   test-rmse:21.572466+0.610108 
## [7]  train-rmse:16.706704+0.050158   test-rmse:16.758855+0.589503 
## [8]  train-rmse:13.075257+0.047529   test-rmse:13.113197+0.583535 
## [9]  train-rmse:10.375291+0.046383   test-rmse:10.490280+0.629833 
## [10] train-rmse:8.393507+0.048055    test-rmse:8.494915+0.610455 
## [11] train-rmse:6.962017+0.050820    test-rmse:7.034570+0.584090 
## [12] train-rmse:5.944969+0.053048    test-rmse:6.075599+0.621844 
## [13] train-rmse:5.232127+0.053606    test-rmse:5.366875+0.587680 
## [14] train-rmse:4.741363+0.057719    test-rmse:4.873162+0.564543 
## [15] train-rmse:4.404529+0.061690    test-rmse:4.541155+0.500262 
## [16] train-rmse:4.169462+0.061076    test-rmse:4.322474+0.482919 
## [17] train-rmse:3.997986+0.059965    test-rmse:4.155512+0.484125 
## [18] train-rmse:3.865744+0.058721    test-rmse:4.051967+0.495428 
## [19] train-rmse:3.764081+0.059053    test-rmse:3.949823+0.451903 
## [20] train-rmse:3.679791+0.059208    test-rmse:3.853639+0.373148 
## [21] train-rmse:3.608367+0.058167    test-rmse:3.785030+0.362486 
## [22] train-rmse:3.543241+0.056712    test-rmse:3.752032+0.377415 
## [23] train-rmse:3.483056+0.055947    test-rmse:3.703800+0.371296 
## [24] train-rmse:3.427825+0.053496    test-rmse:3.656058+0.367822 
## [25] train-rmse:3.374272+0.051636    test-rmse:3.580830+0.332883 
## [26] train-rmse:3.322942+0.050535    test-rmse:3.554553+0.340958 
## [27] train-rmse:3.274668+0.049528    test-rmse:3.497638+0.348513 
## [28] train-rmse:3.229207+0.049135    test-rmse:3.452332+0.314688 
## [29] train-rmse:3.184866+0.047540    test-rmse:3.424331+0.317925 
## [30] train-rmse:3.142288+0.045773    test-rmse:3.388358+0.311670 
## [31] train-rmse:3.101166+0.044865    test-rmse:3.355507+0.324575 
## [32] train-rmse:3.060458+0.044819    test-rmse:3.304386+0.318068 
## [33] train-rmse:3.020681+0.044250    test-rmse:3.268771+0.317601 
## [34] train-rmse:2.984133+0.043057    test-rmse:3.241430+0.312206 
## [35] train-rmse:2.947466+0.041899    test-rmse:3.176598+0.296200 
## [36] train-rmse:2.910920+0.040659    test-rmse:3.149057+0.304690 
## [37] train-rmse:2.875730+0.039578    test-rmse:3.132214+0.295995 
## [38] train-rmse:2.842200+0.039085    test-rmse:3.104769+0.302863 
## [39] train-rmse:2.808772+0.038854    test-rmse:3.056280+0.307608 
## [40] train-rmse:2.776230+0.038797    test-rmse:3.029255+0.318348 
## [41] train-rmse:2.743966+0.039251    test-rmse:3.005164+0.323849 
## [42] train-rmse:2.712214+0.039076    test-rmse:2.971701+0.293729 
## [43] train-rmse:2.681738+0.038506    test-rmse:2.950165+0.288351 
## [44] train-rmse:2.652044+0.038927    test-rmse:2.925963+0.299430 
## [45] train-rmse:2.622748+0.039206    test-rmse:2.891648+0.296631 
## [46] train-rmse:2.594406+0.038816    test-rmse:2.847379+0.306375 
## [47] train-rmse:2.566962+0.038380    test-rmse:2.835777+0.307586 
## [48] train-rmse:2.540356+0.038240    test-rmse:2.803360+0.285363 
## [49] train-rmse:2.514231+0.037666    test-rmse:2.780167+0.279739 
## [50] train-rmse:2.488318+0.037041    test-rmse:2.756944+0.293708 
## [51] train-rmse:2.462842+0.036706    test-rmse:2.728132+0.294144 
## [52] train-rmse:2.438017+0.036305    test-rmse:2.710246+0.290261 
## [53] train-rmse:2.413126+0.036220    test-rmse:2.679698+0.301044 
## [54] train-rmse:2.388428+0.035738    test-rmse:2.646954+0.301968 
## [55] train-rmse:2.364119+0.034534    test-rmse:2.634812+0.306757 
## [56] train-rmse:2.340782+0.034288    test-rmse:2.622614+0.309451 
## [57] train-rmse:2.317526+0.033778    test-rmse:2.598629+0.305560 
## [58] train-rmse:2.294827+0.032966    test-rmse:2.556305+0.287536 
## [59] train-rmse:2.272658+0.032482    test-rmse:2.538690+0.290181 
## [60] train-rmse:2.250597+0.032158    test-rmse:2.511180+0.271882 
## [61] train-rmse:2.229020+0.031928    test-rmse:2.486303+0.282126 
## [62] train-rmse:2.208111+0.031555    test-rmse:2.469395+0.269859 
## [63] train-rmse:2.187372+0.031020    test-rmse:2.460361+0.272049 
## [64] train-rmse:2.167059+0.030390    test-rmse:2.445949+0.274720 
## [65] train-rmse:2.146952+0.030028    test-rmse:2.420849+0.265850 
## [66] train-rmse:2.127067+0.029226    test-rmse:2.400030+0.261310 
## [67] train-rmse:2.107400+0.028708    test-rmse:2.383994+0.261997 
## [68] train-rmse:2.087797+0.027927    test-rmse:2.362811+0.261151 
## [69] train-rmse:2.068367+0.027246    test-rmse:2.345765+0.257533 
## [70] train-rmse:2.049440+0.026897    test-rmse:2.332638+0.257407 
## [71] train-rmse:2.030792+0.026187    test-rmse:2.320916+0.261370 
## [72] train-rmse:2.012067+0.025608    test-rmse:2.300347+0.270953 
## [73] train-rmse:1.994209+0.024956    test-rmse:2.288683+0.269951 
## [74] train-rmse:1.976659+0.024435    test-rmse:2.269095+0.262860 
## [75] train-rmse:1.959237+0.023988    test-rmse:2.245170+0.263807 
## [76] train-rmse:1.942098+0.023907    test-rmse:2.226142+0.256321 
## [77] train-rmse:1.924990+0.023741    test-rmse:2.206574+0.265201 
## [78] train-rmse:1.908218+0.023472    test-rmse:2.191992+0.264162 
## [79] train-rmse:1.891903+0.023223    test-rmse:2.179009+0.260306 
## [80] train-rmse:1.875703+0.023143    test-rmse:2.167486+0.260781 
## [81] train-rmse:1.859570+0.022800    test-rmse:2.144140+0.240517 
## [82] train-rmse:1.843789+0.022280    test-rmse:2.129544+0.238416 
## [83] train-rmse:1.828166+0.022081    test-rmse:2.121564+0.243437 
## [84] train-rmse:1.812606+0.021797    test-rmse:2.108075+0.246757 
## [85] train-rmse:1.797752+0.021429    test-rmse:2.083368+0.244731 
## [86] train-rmse:1.782992+0.021143    test-rmse:2.065401+0.252658 
## [87] train-rmse:1.768324+0.020937    test-rmse:2.047364+0.240849 
## [88] train-rmse:1.754045+0.020548    test-rmse:2.030606+0.241643 
## [89] train-rmse:1.740099+0.020210    test-rmse:2.004631+0.232490 
## [90] train-rmse:1.726376+0.019811    test-rmse:1.991532+0.232769 
## [91] train-rmse:1.712771+0.019632    test-rmse:1.982681+0.239134 
## [92] train-rmse:1.699366+0.019252    test-rmse:1.975723+0.241486 
## [93] train-rmse:1.685800+0.018830    test-rmse:1.960120+0.246763 
## [94] train-rmse:1.672473+0.018739    test-rmse:1.953127+0.254117 
## [95] train-rmse:1.659532+0.018463    test-rmse:1.946382+0.254207 
## [96] train-rmse:1.646719+0.018081    test-rmse:1.935032+0.248180 
## [97] train-rmse:1.633973+0.017626    test-rmse:1.924042+0.250650 
## [98] train-rmse:1.621343+0.017436    test-rmse:1.916317+0.247528 
## [99] train-rmse:1.608847+0.017284    test-rmse:1.903749+0.240488 
## [100]    train-rmse:1.596503+0.017052    test-rmse:1.896841+0.242583 
## [101]    train-rmse:1.584435+0.016893    test-rmse:1.888228+0.242436 
## [102]    train-rmse:1.572481+0.016729    test-rmse:1.883939+0.239117 
## [103]    train-rmse:1.560668+0.016820    test-rmse:1.865528+0.221926 
## [104]    train-rmse:1.548824+0.016830    test-rmse:1.858571+0.218943 
## [105]    train-rmse:1.537470+0.016450    test-rmse:1.846252+0.224397 
## [106]    train-rmse:1.526023+0.016219    test-rmse:1.840478+0.226007 
## [107]    train-rmse:1.515199+0.016078    test-rmse:1.827218+0.226172 
## [108]    train-rmse:1.504310+0.015907    test-rmse:1.814465+0.221398 
## [109]    train-rmse:1.493565+0.015707    test-rmse:1.802693+0.222349 
## [110]    train-rmse:1.483006+0.015678    test-rmse:1.783900+0.226497 
## [111]    train-rmse:1.472521+0.015746    test-rmse:1.774098+0.228229 
## [112]    train-rmse:1.462230+0.015773    test-rmse:1.765291+0.233195 
## [113]    train-rmse:1.452146+0.015692    test-rmse:1.752506+0.224775 
## [114]    train-rmse:1.442247+0.015387    test-rmse:1.740476+0.218736 
## [115]    train-rmse:1.432380+0.015200    test-rmse:1.732010+0.214042 
## [116]    train-rmse:1.422831+0.015014    test-rmse:1.726341+0.217477 
## [117]    train-rmse:1.413330+0.014987    test-rmse:1.712400+0.219154 
## [118]    train-rmse:1.403802+0.014900    test-rmse:1.709124+0.220264 
## [119]    train-rmse:1.394403+0.014734    test-rmse:1.697442+0.216668 
## [120]    train-rmse:1.385148+0.014551    test-rmse:1.683509+0.214902 
## [121]    train-rmse:1.376089+0.014351    test-rmse:1.678295+0.212922 
## [122]    train-rmse:1.367112+0.014111    test-rmse:1.671177+0.213633 
## [123]    train-rmse:1.358207+0.013792    test-rmse:1.663423+0.213945 
## [124]    train-rmse:1.349450+0.013611    test-rmse:1.652027+0.215627 
## [125]    train-rmse:1.340866+0.013504    test-rmse:1.643392+0.218720 
## [126]    train-rmse:1.332434+0.013308    test-rmse:1.628543+0.204605 
## [127]    train-rmse:1.324155+0.013394    test-rmse:1.620601+0.202265 
## [128]    train-rmse:1.315821+0.013462    test-rmse:1.618153+0.203210 
## [129]    train-rmse:1.307359+0.013532    test-rmse:1.614262+0.203786 
## [130]    train-rmse:1.299240+0.013612    test-rmse:1.611547+0.203792 
## [131]    train-rmse:1.291153+0.013850    test-rmse:1.602031+0.201216 
## [132]    train-rmse:1.283319+0.013945    test-rmse:1.596727+0.204615 
## [133]    train-rmse:1.275549+0.014038    test-rmse:1.591157+0.194741 
## [134]    train-rmse:1.267925+0.013971    test-rmse:1.580022+0.193582 
## [135]    train-rmse:1.260317+0.013931    test-rmse:1.573576+0.195097 
## [136]    train-rmse:1.252814+0.013954    test-rmse:1.565144+0.197828 
## [137]    train-rmse:1.245424+0.014060    test-rmse:1.555071+0.198024 
## [138]    train-rmse:1.238198+0.014048    test-rmse:1.546879+0.201533 
## [139]    train-rmse:1.231150+0.014008    test-rmse:1.541618+0.204338 
## [140]    train-rmse:1.224299+0.013997    test-rmse:1.532690+0.201947 
## [141]    train-rmse:1.217494+0.013842    test-rmse:1.522328+0.191245 
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## [143]    train-rmse:1.204244+0.013737    test-rmse:1.511192+0.189191 
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## [266]    train-rmse:0.814996+0.018209    test-rmse:1.151646+0.168630 
## [267]    train-rmse:0.813766+0.018238    test-rmse:1.150176+0.169767 
## [268]    train-rmse:0.812592+0.018302    test-rmse:1.149958+0.170501 
## [269]    train-rmse:0.811425+0.018369    test-rmse:1.147422+0.167060 
## [270]    train-rmse:0.810282+0.018409    test-rmse:1.146895+0.166939 
## [271]    train-rmse:0.809145+0.018454    test-rmse:1.146739+0.166327 
## [272]    train-rmse:0.808022+0.018495    test-rmse:1.145526+0.166966 
## [273]    train-rmse:0.806908+0.018544    test-rmse:1.143872+0.167143 
## [274]    train-rmse:0.805811+0.018573    test-rmse:1.142270+0.167119 
## [275]    train-rmse:0.804694+0.018615    test-rmse:1.141769+0.168499 
## [276]    train-rmse:0.803618+0.018655    test-rmse:1.139268+0.167043 
## [277]    train-rmse:0.802548+0.018705    test-rmse:1.138558+0.166369 
## [278]    train-rmse:0.801480+0.018751    test-rmse:1.136337+0.165853 
## [279]    train-rmse:0.800461+0.018794    test-rmse:1.134296+0.165307 
## [280]    train-rmse:0.799438+0.018843    test-rmse:1.134697+0.163472 
## [281]    train-rmse:0.798447+0.018883    test-rmse:1.133772+0.163596 
## [282]    train-rmse:0.797464+0.018919    test-rmse:1.133704+0.163329 
## [283]    train-rmse:0.796487+0.018952    test-rmse:1.129861+0.165802 
## [284]    train-rmse:0.795489+0.018976    test-rmse:1.127994+0.164927 
## [285]    train-rmse:0.794515+0.019014    test-rmse:1.126486+0.163792 
## [286]    train-rmse:0.793558+0.019059    test-rmse:1.125614+0.164891 
## [287]    train-rmse:0.792611+0.019121    test-rmse:1.125291+0.164247 
## [288]    train-rmse:0.791693+0.019179    test-rmse:1.124761+0.164810 
## [289]    train-rmse:0.790755+0.019236    test-rmse:1.125031+0.165076 
## [290]    train-rmse:0.789860+0.019290    test-rmse:1.124808+0.166259 
## [291]    train-rmse:0.788976+0.019313    test-rmse:1.124272+0.165655 
## [292]    train-rmse:0.788092+0.019354    test-rmse:1.123738+0.165762 
## [293]    train-rmse:0.787221+0.019410    test-rmse:1.123107+0.165528 
## [294]    train-rmse:0.786348+0.019447    test-rmse:1.121795+0.165854 
## [295]    train-rmse:0.785470+0.019490    test-rmse:1.122018+0.165481 
## [296]    train-rmse:0.784588+0.019546    test-rmse:1.121454+0.165083 
## [297]    train-rmse:0.783725+0.019572    test-rmse:1.120936+0.165517 
## [298]    train-rmse:0.782871+0.019601    test-rmse:1.121139+0.164991 
## [299]    train-rmse:0.782068+0.019623    test-rmse:1.120874+0.163580 
## [300]    train-rmse:0.781251+0.019648    test-rmse:1.119856+0.164099 
## [301]    train-rmse:0.780447+0.019675    test-rmse:1.119180+0.164553 
## [302]    train-rmse:0.779654+0.019716    test-rmse:1.118504+0.164524 
## [303]    train-rmse:0.778858+0.019755    test-rmse:1.117091+0.163111 
## [304]    train-rmse:0.778074+0.019787    test-rmse:1.116892+0.163686 
## [305]    train-rmse:0.777301+0.019815    test-rmse:1.115255+0.163489 
## [306]    train-rmse:0.776483+0.019847    test-rmse:1.116309+0.164186 
## [307]    train-rmse:0.775729+0.019879    test-rmse:1.115123+0.163448 
## [308]    train-rmse:0.774956+0.019886    test-rmse:1.114959+0.163813 
## [309]    train-rmse:0.774182+0.019909    test-rmse:1.114668+0.164351 
## [310]    train-rmse:0.773430+0.019942    test-rmse:1.114965+0.163658 
## [311]    train-rmse:0.772709+0.019956    test-rmse:1.115139+0.164290 
## [312]    train-rmse:0.771975+0.019971    test-rmse:1.114038+0.163103 
## [313]    train-rmse:0.771255+0.019986    test-rmse:1.112530+0.163774 
## [314]    train-rmse:0.770537+0.020007    test-rmse:1.111867+0.164275 
## [315]    train-rmse:0.769839+0.020026    test-rmse:1.113316+0.163502 
## [316]    train-rmse:0.769120+0.020051    test-rmse:1.111191+0.162746 
## [317]    train-rmse:0.768414+0.020056    test-rmse:1.111380+0.163612 
## [318]    train-rmse:0.767727+0.020061    test-rmse:1.109039+0.160563 
## [319]    train-rmse:0.766997+0.020105    test-rmse:1.106375+0.161686 
## [320]    train-rmse:0.766329+0.020126    test-rmse:1.104649+0.161088 
## [321]    train-rmse:0.765659+0.020142    test-rmse:1.104916+0.160029 
## [322]    train-rmse:0.764977+0.020173    test-rmse:1.104381+0.160565 
## [323]    train-rmse:0.764294+0.020200    test-rmse:1.103904+0.160590 
## [324]    train-rmse:0.763633+0.020232    test-rmse:1.101987+0.160820 
## [325]    train-rmse:0.762964+0.020253    test-rmse:1.102225+0.161040 
## [326]    train-rmse:0.762300+0.020299    test-rmse:1.100874+0.162373 
## [327]    train-rmse:0.761648+0.020308    test-rmse:1.100492+0.162228 
## [328]    train-rmse:0.760992+0.020296    test-rmse:1.100983+0.162364 
## [329]    train-rmse:0.760346+0.020311    test-rmse:1.100253+0.161438 
## [330]    train-rmse:0.759714+0.020334    test-rmse:1.098213+0.161415 
## [331]    train-rmse:0.759096+0.020352    test-rmse:1.098053+0.160781 
## [332]    train-rmse:0.758464+0.020382    test-rmse:1.098000+0.160732 
## [333]    train-rmse:0.757852+0.020410    test-rmse:1.097950+0.160588 
## [334]    train-rmse:0.757234+0.020428    test-rmse:1.097084+0.160394 
## [335]    train-rmse:0.756626+0.020446    test-rmse:1.096735+0.160843 
## [336]    train-rmse:0.756026+0.020470    test-rmse:1.096390+0.160837 
## [337]    train-rmse:0.755440+0.020494    test-rmse:1.096209+0.161135 
## [338]    train-rmse:0.754843+0.020527    test-rmse:1.095687+0.160465 
## [339]    train-rmse:0.754249+0.020527    test-rmse:1.095600+0.160009 
## [340]    train-rmse:0.753692+0.020536    test-rmse:1.094840+0.159819 
## [341]    train-rmse:0.753113+0.020529    test-rmse:1.094265+0.160526 
## [342]    train-rmse:0.752529+0.020525    test-rmse:1.094347+0.160013 
## [343]    train-rmse:0.751959+0.020522    test-rmse:1.094208+0.159601 
## [344]    train-rmse:0.751381+0.020522    test-rmse:1.094190+0.160493 
## [345]    train-rmse:0.750818+0.020539    test-rmse:1.093432+0.160290 
## [346]    train-rmse:0.750251+0.020518    test-rmse:1.092598+0.160571 
## [347]    train-rmse:0.749680+0.020549    test-rmse:1.091506+0.161493 
## [348]    train-rmse:0.749142+0.020553    test-rmse:1.090831+0.161522 
## [349]    train-rmse:0.748572+0.020514    test-rmse:1.088725+0.160975 
## [350]    train-rmse:0.748039+0.020522    test-rmse:1.088443+0.159229 
## [351]    train-rmse:0.747478+0.020482    test-rmse:1.086795+0.157178 
## [352]    train-rmse:0.746945+0.020485    test-rmse:1.086010+0.156441 
## [353]    train-rmse:0.746412+0.020465    test-rmse:1.085401+0.154622 
## [354]    train-rmse:0.745883+0.020456    test-rmse:1.084853+0.155997 
## [355]    train-rmse:0.745360+0.020454    test-rmse:1.084448+0.155467 
## [356]    train-rmse:0.744832+0.020462    test-rmse:1.084421+0.155299 
## [357]    train-rmse:0.744309+0.020457    test-rmse:1.083666+0.155301 
## [358]    train-rmse:0.743794+0.020446    test-rmse:1.084833+0.155341 
## [359]    train-rmse:0.743283+0.020436    test-rmse:1.082347+0.153038 
## [360]    train-rmse:0.742787+0.020421    test-rmse:1.082625+0.153341 
## [361]    train-rmse:0.742284+0.020409    test-rmse:1.083474+0.153150 
## [362]    train-rmse:0.741793+0.020409    test-rmse:1.082324+0.152996 
## [363]    train-rmse:0.741306+0.020409    test-rmse:1.082432+0.152116 
## [364]    train-rmse:0.740807+0.020407    test-rmse:1.082059+0.153093 
## [365]    train-rmse:0.740310+0.020377    test-rmse:1.081360+0.152076 
## [366]    train-rmse:0.739812+0.020356    test-rmse:1.081188+0.151965 
## [367]    train-rmse:0.739331+0.020346    test-rmse:1.080411+0.152258 
## [368]    train-rmse:0.738829+0.020352    test-rmse:1.080375+0.152745 
## [369]    train-rmse:0.738362+0.020347    test-rmse:1.080129+0.153535 
## [370]    train-rmse:0.737902+0.020338    test-rmse:1.078082+0.153669 
## [371]    train-rmse:0.737420+0.020298    test-rmse:1.077276+0.152130 
## [372]    train-rmse:0.736950+0.020296    test-rmse:1.076167+0.151679 
## [373]    train-rmse:0.736475+0.020274    test-rmse:1.074643+0.151424 
## [374]    train-rmse:0.736030+0.020262    test-rmse:1.074500+0.151707 
## [375]    train-rmse:0.735584+0.020249    test-rmse:1.073639+0.151320 
## [376]    train-rmse:0.735116+0.020225    test-rmse:1.072737+0.151652 
## [377]    train-rmse:0.734655+0.020222    test-rmse:1.073074+0.151828 
## [378]    train-rmse:0.734201+0.020187    test-rmse:1.072684+0.150572 
## [379]    train-rmse:0.733738+0.020184    test-rmse:1.071858+0.149881 
## [380]    train-rmse:0.733306+0.020176    test-rmse:1.071270+0.150304 
## [381]    train-rmse:0.732867+0.020165    test-rmse:1.071411+0.150303 
## [382]    train-rmse:0.732434+0.020155    test-rmse:1.071709+0.150256 
## [383]    train-rmse:0.731955+0.020159    test-rmse:1.072341+0.150515 
## [384]    train-rmse:0.731537+0.020140    test-rmse:1.072586+0.150214 
## [385]    train-rmse:0.731118+0.020116    test-rmse:1.072158+0.150395 
## [386]    train-rmse:0.730696+0.020105    test-rmse:1.072709+0.150533 
## Stopping. Best iteration:
## [380]    train-rmse:0.733306+0.020176    test-rmse:1.071270+0.150304
## 
## 50 
## [1]  train-rmse:81.945409+0.093908   test-rmse:81.945506+0.502440 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:62.457670+0.078257   test-rmse:62.457233+0.536077 
## [3]  train-rmse:47.664860+0.068582   test-rmse:47.663622+0.569012 
## [4]  train-rmse:36.454471+0.064225   test-rmse:36.452043+0.603174 
## [5]  train-rmse:27.964685+0.060685   test-rmse:28.013021+0.606000 
## [6]  train-rmse:21.521181+0.043421   test-rmse:21.618757+0.603288 
## [7]  train-rmse:16.640378+0.030921   test-rmse:16.724844+0.547950 
## [8]  train-rmse:12.962080+0.027932   test-rmse:13.094154+0.593607 
## [9]  train-rmse:10.202474+0.027471   test-rmse:10.308182+0.536817 
## [10] train-rmse:8.152006+0.029800    test-rmse:8.272982+0.561366 
## [11] train-rmse:6.646593+0.033471    test-rmse:6.782075+0.567231 
## [12] train-rmse:5.550908+0.034022    test-rmse:5.702840+0.563062 
## [13] train-rmse:4.766235+0.039063    test-rmse:4.980341+0.553072 
## [14] train-rmse:4.210285+0.044273    test-rmse:4.403562+0.463663 
## [15] train-rmse:3.815366+0.045338    test-rmse:4.030817+0.471455 
## [16] train-rmse:3.528030+0.043597    test-rmse:3.792359+0.464442 
## [17] train-rmse:3.318546+0.039466    test-rmse:3.566883+0.367523 
## [18] train-rmse:3.163046+0.038860    test-rmse:3.426777+0.363554 
## [19] train-rmse:3.031580+0.030755    test-rmse:3.343476+0.374238 
## [20] train-rmse:2.928986+0.027567    test-rmse:3.246068+0.386229 
## [21] train-rmse:2.841169+0.027219    test-rmse:3.172113+0.386044 
## [22] train-rmse:2.760548+0.026613    test-rmse:3.118785+0.362876 
## [23] train-rmse:2.684486+0.022042    test-rmse:3.051784+0.331197 
## [24] train-rmse:2.616830+0.019598    test-rmse:2.972848+0.301820 
## [25] train-rmse:2.551247+0.017699    test-rmse:2.932272+0.303475 
## [26] train-rmse:2.487708+0.017133    test-rmse:2.865791+0.303270 
## [27] train-rmse:2.432913+0.016792    test-rmse:2.835060+0.306689 
## [28] train-rmse:2.378976+0.015781    test-rmse:2.774957+0.281159 
## [29] train-rmse:2.326016+0.014222    test-rmse:2.734814+0.294325 
## [30] train-rmse:2.275741+0.015172    test-rmse:2.682341+0.292616 
## [31] train-rmse:2.228122+0.014359    test-rmse:2.639018+0.306456 
## [32] train-rmse:2.181573+0.014471    test-rmse:2.596154+0.296203 
## [33] train-rmse:2.135444+0.014896    test-rmse:2.562372+0.278132 
## [34] train-rmse:2.092993+0.015781    test-rmse:2.524941+0.284559 
## [35] train-rmse:2.049420+0.012541    test-rmse:2.490628+0.294440 
## [36] train-rmse:2.007693+0.014245    test-rmse:2.454092+0.290164 
## [37] train-rmse:1.968607+0.014035    test-rmse:2.419093+0.293885 
## [38] train-rmse:1.930176+0.014186    test-rmse:2.369330+0.291749 
## [39] train-rmse:1.891843+0.013829    test-rmse:2.329920+0.264140 
## [40] train-rmse:1.855790+0.014190    test-rmse:2.289523+0.261308 
## [41] train-rmse:1.819381+0.013317    test-rmse:2.257176+0.253454 
## [42] train-rmse:1.785440+0.013426    test-rmse:2.226426+0.254182 
## [43] train-rmse:1.751936+0.013848    test-rmse:2.214230+0.259938 
## [44] train-rmse:1.720745+0.014034    test-rmse:2.180618+0.264613 
## [45] train-rmse:1.686377+0.011481    test-rmse:2.157705+0.266260 
## [46] train-rmse:1.655853+0.011236    test-rmse:2.131593+0.271966 
## [47] train-rmse:1.625350+0.012855    test-rmse:2.100362+0.252805 
## [48] train-rmse:1.596277+0.011994    test-rmse:2.082875+0.256763 
## [49] train-rmse:1.567902+0.011947    test-rmse:2.058213+0.254532 
## [50] train-rmse:1.540573+0.012437    test-rmse:2.030922+0.250720 
## [51] train-rmse:1.515262+0.012323    test-rmse:2.003981+0.234598 
## [52] train-rmse:1.489508+0.012065    test-rmse:1.983918+0.231551 
## [53] train-rmse:1.463758+0.011792    test-rmse:1.971901+0.231946 
## [54] train-rmse:1.439735+0.012178    test-rmse:1.944262+0.230427 
## [55] train-rmse:1.416337+0.012321    test-rmse:1.926945+0.229355 
## [56] train-rmse:1.393432+0.011849    test-rmse:1.905698+0.228330 
## [57] train-rmse:1.370505+0.012874    test-rmse:1.880026+0.229300 
## [58] train-rmse:1.346785+0.013406    test-rmse:1.851751+0.233446 
## [59] train-rmse:1.325367+0.014478    test-rmse:1.835123+0.237311 
## [60] train-rmse:1.302974+0.015745    test-rmse:1.821232+0.233544 
## [61] train-rmse:1.281807+0.015625    test-rmse:1.802358+0.234663 
## [62] train-rmse:1.260532+0.016231    test-rmse:1.790666+0.242916 
## [63] train-rmse:1.239172+0.018105    test-rmse:1.769638+0.231511 
## [64] train-rmse:1.220748+0.018699    test-rmse:1.751066+0.239999 
## [65] train-rmse:1.203132+0.018818    test-rmse:1.734206+0.238532 
## [66] train-rmse:1.185396+0.018351    test-rmse:1.716945+0.240057 
## [67] train-rmse:1.167452+0.017854    test-rmse:1.701490+0.230303 
## [68] train-rmse:1.150317+0.018061    test-rmse:1.689117+0.231004 
## [69] train-rmse:1.133743+0.018035    test-rmse:1.675272+0.235213 
## [70] train-rmse:1.117298+0.019034    test-rmse:1.656720+0.233677 
## [71] train-rmse:1.101295+0.019586    test-rmse:1.646676+0.234520 
## [72] train-rmse:1.083594+0.017743    test-rmse:1.636828+0.228477 
## [73] train-rmse:1.067329+0.017830    test-rmse:1.624883+0.229614 
## [74] train-rmse:1.053349+0.017876    test-rmse:1.604157+0.223064 
## [75] train-rmse:1.039288+0.017935    test-rmse:1.594813+0.230635 
## [76] train-rmse:1.024884+0.018181    test-rmse:1.576612+0.231022 
## [77] train-rmse:1.011652+0.018058    test-rmse:1.566112+0.231361 
## [78] train-rmse:0.997380+0.018930    test-rmse:1.556202+0.232069 
## [79] train-rmse:0.983588+0.018283    test-rmse:1.545081+0.237476 
## [80] train-rmse:0.970882+0.018525    test-rmse:1.533503+0.236622 
## [81] train-rmse:0.956811+0.019178    test-rmse:1.524823+0.235343 
## [82] train-rmse:0.943015+0.020827    test-rmse:1.519196+0.231215 
## [83] train-rmse:0.930622+0.021317    test-rmse:1.507311+0.236371 
## [84] train-rmse:0.919315+0.021645    test-rmse:1.499478+0.234103 
## [85] train-rmse:0.908009+0.022476    test-rmse:1.493367+0.233024 
## [86] train-rmse:0.896690+0.021339    test-rmse:1.484170+0.232573 
## [87] train-rmse:0.884456+0.023292    test-rmse:1.477973+0.237830 
## [88] train-rmse:0.874079+0.023484    test-rmse:1.466665+0.238109 
## [89] train-rmse:0.863123+0.024239    test-rmse:1.460227+0.243138 
## [90] train-rmse:0.853176+0.024209    test-rmse:1.454808+0.243300 
## [91] train-rmse:0.841707+0.023394    test-rmse:1.444502+0.242796 
## [92] train-rmse:0.832741+0.023379    test-rmse:1.431448+0.239942 
## [93] train-rmse:0.823105+0.022975    test-rmse:1.423626+0.244388 
## [94] train-rmse:0.814209+0.023111    test-rmse:1.414093+0.244874 
## [95] train-rmse:0.805049+0.023844    test-rmse:1.411041+0.244830 
## [96] train-rmse:0.796649+0.023992    test-rmse:1.405358+0.244874 
## [97] train-rmse:0.787737+0.024063    test-rmse:1.399830+0.242050 
## [98] train-rmse:0.779745+0.024587    test-rmse:1.396463+0.239140 
## [99] train-rmse:0.769590+0.020994    test-rmse:1.395315+0.241645 
## [100]    train-rmse:0.760955+0.022497    test-rmse:1.391031+0.243116 
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## [108]    train-rmse:0.704077+0.022913    test-rmse:1.353618+0.243968 
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## [302]    train-rmse:0.277556+0.018035    test-rmse:1.110370+0.278888 
## [303]    train-rmse:0.276258+0.017962    test-rmse:1.110456+0.278751 
## [304]    train-rmse:0.275469+0.018228    test-rmse:1.110440+0.278820 
## [305]    train-rmse:0.274784+0.018399    test-rmse:1.110317+0.279194 
## [306]    train-rmse:0.273413+0.018130    test-rmse:1.109711+0.278334 
## [307]    train-rmse:0.272889+0.018070    test-rmse:1.109547+0.278291 
## [308]    train-rmse:0.271955+0.018177    test-rmse:1.110063+0.278188 
## [309]    train-rmse:0.271172+0.018367    test-rmse:1.109607+0.278226 
## [310]    train-rmse:0.270454+0.018460    test-rmse:1.109116+0.277887 
## [311]    train-rmse:0.269936+0.018557    test-rmse:1.108793+0.277999 
## [312]    train-rmse:0.269187+0.018615    test-rmse:1.108398+0.277524 
## [313]    train-rmse:0.267975+0.018756    test-rmse:1.108528+0.277939 
## [314]    train-rmse:0.266631+0.019094    test-rmse:1.108493+0.278831 
## [315]    train-rmse:0.265639+0.019236    test-rmse:1.108392+0.279021 
## [316]    train-rmse:0.265130+0.019088    test-rmse:1.107548+0.280015 
## [317]    train-rmse:0.264251+0.018695    test-rmse:1.107669+0.279748 
## [318]    train-rmse:0.263565+0.018680    test-rmse:1.107541+0.279712 
## [319]    train-rmse:0.263036+0.018827    test-rmse:1.106699+0.279153 
## [320]    train-rmse:0.262362+0.018320    test-rmse:1.106688+0.278625 
## [321]    train-rmse:0.261319+0.017380    test-rmse:1.106400+0.278302 
## [322]    train-rmse:0.260065+0.017642    test-rmse:1.106182+0.278058 
## [323]    train-rmse:0.259180+0.017732    test-rmse:1.105799+0.278119 
## [324]    train-rmse:0.258326+0.017716    test-rmse:1.105956+0.278085 
## [325]    train-rmse:0.257563+0.017782    test-rmse:1.105761+0.278358 
## [326]    train-rmse:0.256686+0.018213    test-rmse:1.106400+0.277978 
## [327]    train-rmse:0.256083+0.018290    test-rmse:1.106757+0.278024 
## [328]    train-rmse:0.255227+0.018583    test-rmse:1.107040+0.278500 
## [329]    train-rmse:0.254477+0.018874    test-rmse:1.107397+0.277772 
## [330]    train-rmse:0.253462+0.018467    test-rmse:1.107566+0.278415 
## [331]    train-rmse:0.252609+0.018460    test-rmse:1.108062+0.278606 
## Stopping. Best iteration:
## [325]    train-rmse:0.257563+0.017782    test-rmse:1.105761+0.278358
## 
## 51 
## [1]  train-rmse:81.945409+0.093908   test-rmse:81.945506+0.502440 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:62.457670+0.078257   test-rmse:62.457233+0.536077 
## [3]  train-rmse:47.664860+0.068582   test-rmse:47.663622+0.569012 
## [4]  train-rmse:36.454471+0.064225   test-rmse:36.452043+0.603174 
## [5]  train-rmse:27.964685+0.060685   test-rmse:28.013021+0.606000 
## [6]  train-rmse:21.516921+0.044302   test-rmse:21.607782+0.599820 
## [7]  train-rmse:16.626704+0.036872   test-rmse:16.724599+0.578938 
## [8]  train-rmse:12.922753+0.032367   test-rmse:13.036829+0.588952 
## [9]  train-rmse:10.130322+0.034175   test-rmse:10.218783+0.571032 
## [10] train-rmse:8.031383+0.034770    test-rmse:8.159022+0.557093 
## [11] train-rmse:6.467814+0.037630    test-rmse:6.608135+0.533067 
## [12] train-rmse:5.313037+0.043228    test-rmse:5.467592+0.488888 
## [13] train-rmse:4.474999+0.047671    test-rmse:4.648073+0.497674 
## [14] train-rmse:3.862779+0.051137    test-rmse:4.047443+0.473609 
## [15] train-rmse:3.417908+0.049425    test-rmse:3.649137+0.411472 
## [16] train-rmse:3.085204+0.036717    test-rmse:3.349706+0.396220 
## [17] train-rmse:2.847310+0.036697    test-rmse:3.146167+0.347705 
## [18] train-rmse:2.665618+0.034013    test-rmse:2.983192+0.342085 
## [19] train-rmse:2.521713+0.031933    test-rmse:2.862768+0.325210 
## [20] train-rmse:2.403688+0.033878    test-rmse:2.740151+0.309262 
## [21] train-rmse:2.305880+0.033197    test-rmse:2.659825+0.298278 
## [22] train-rmse:2.217405+0.028199    test-rmse:2.596221+0.274876 
## [23] train-rmse:2.134482+0.030104    test-rmse:2.524109+0.305094 
## [24] train-rmse:2.061073+0.029717    test-rmse:2.449683+0.288070 
## [25] train-rmse:1.992637+0.029602    test-rmse:2.399160+0.275562 
## [26] train-rmse:1.929547+0.027445    test-rmse:2.347323+0.283139 
## [27] train-rmse:1.864657+0.023987    test-rmse:2.266543+0.283145 
## [28] train-rmse:1.802867+0.021855    test-rmse:2.204268+0.261565 
## [29] train-rmse:1.745465+0.021352    test-rmse:2.183488+0.243856 
## [30] train-rmse:1.690344+0.024070    test-rmse:2.151576+0.233771 
## [31] train-rmse:1.642063+0.024876    test-rmse:2.103540+0.232836 
## [32] train-rmse:1.592420+0.024370    test-rmse:2.061766+0.246265 
## [33] train-rmse:1.547023+0.020451    test-rmse:2.028832+0.249918 
## [34] train-rmse:1.503310+0.021699    test-rmse:1.986471+0.249296 
## [35] train-rmse:1.463866+0.020598    test-rmse:1.946623+0.243481 
## [36] train-rmse:1.417800+0.023751    test-rmse:1.915485+0.234317 
## [37] train-rmse:1.376906+0.023499    test-rmse:1.882398+0.234722 
## [38] train-rmse:1.340809+0.022639    test-rmse:1.846507+0.230298 
## [39] train-rmse:1.307066+0.021724    test-rmse:1.822508+0.230781 
## [40] train-rmse:1.274739+0.020657    test-rmse:1.795759+0.221555 
## [41] train-rmse:1.242033+0.020086    test-rmse:1.773729+0.232652 
## [42] train-rmse:1.207857+0.019963    test-rmse:1.743168+0.229688 
## [43] train-rmse:1.175961+0.019202    test-rmse:1.720872+0.235006 
## [44] train-rmse:1.146555+0.018797    test-rmse:1.689020+0.236572 
## [45] train-rmse:1.115702+0.017819    test-rmse:1.667302+0.232034 
## [46] train-rmse:1.089314+0.017591    test-rmse:1.645594+0.232831 
## [47] train-rmse:1.063264+0.018224    test-rmse:1.622519+0.234451 
## [48] train-rmse:1.036292+0.019243    test-rmse:1.601025+0.234733 
## [49] train-rmse:1.009552+0.021295    test-rmse:1.583182+0.236959 
## [50] train-rmse:0.985187+0.021226    test-rmse:1.565880+0.233585 
## [51] train-rmse:0.962186+0.019549    test-rmse:1.544019+0.235683 
## [52] train-rmse:0.940407+0.019192    test-rmse:1.528552+0.238177 
## [53] train-rmse:0.919385+0.019108    test-rmse:1.515391+0.243767 
## [54] train-rmse:0.896745+0.019449    test-rmse:1.503114+0.251395 
## [55] train-rmse:0.873046+0.023198    test-rmse:1.489057+0.248766 
## [56] train-rmse:0.854017+0.020883    test-rmse:1.470094+0.242901 
## [57] train-rmse:0.836662+0.020897    test-rmse:1.457291+0.241411 
## [58] train-rmse:0.816976+0.023534    test-rmse:1.443902+0.241144 
## [59] train-rmse:0.800456+0.023031    test-rmse:1.432293+0.246347 
## [60] train-rmse:0.782227+0.022695    test-rmse:1.422877+0.246644 
## [61] train-rmse:0.765379+0.022740    test-rmse:1.408447+0.246883 
## [62] train-rmse:0.751003+0.022975    test-rmse:1.396107+0.248208 
## [63] train-rmse:0.737076+0.022479    test-rmse:1.385844+0.248054 
## [64] train-rmse:0.721411+0.022305    test-rmse:1.373036+0.253743 
## [65] train-rmse:0.708285+0.022561    test-rmse:1.363592+0.254579 
## [66] train-rmse:0.694531+0.022314    test-rmse:1.352584+0.256127 
## [67] train-rmse:0.683177+0.021795    test-rmse:1.345288+0.254622 
## [68] train-rmse:0.671802+0.021423    test-rmse:1.340162+0.252106 
## [69] train-rmse:0.661291+0.021417    test-rmse:1.334052+0.252490 
## [70] train-rmse:0.647597+0.022065    test-rmse:1.326596+0.247009 
## [71] train-rmse:0.634195+0.022170    test-rmse:1.312021+0.251305 
## [72] train-rmse:0.623908+0.022451    test-rmse:1.301683+0.250433 
## [73] train-rmse:0.612037+0.021126    test-rmse:1.293530+0.253506 
## [74] train-rmse:0.601860+0.021647    test-rmse:1.288159+0.254962 
## [75] train-rmse:0.592366+0.022436    test-rmse:1.284255+0.256883 
## [76] train-rmse:0.583654+0.022375    test-rmse:1.278853+0.259240 
## [77] train-rmse:0.575071+0.021606    test-rmse:1.275241+0.262398 
## [78] train-rmse:0.566494+0.021420    test-rmse:1.271062+0.264079 
## [79] train-rmse:0.555376+0.020195    test-rmse:1.270628+0.266471 
## [80] train-rmse:0.544871+0.019304    test-rmse:1.263398+0.263894 
## [81] train-rmse:0.534386+0.018358    test-rmse:1.258333+0.264561 
## [82] train-rmse:0.527111+0.017721    test-rmse:1.252973+0.264815 
## [83] train-rmse:0.518491+0.017856    test-rmse:1.248545+0.266410 
## [84] train-rmse:0.511493+0.018379    test-rmse:1.244031+0.267025 
## [85] train-rmse:0.504262+0.018576    test-rmse:1.240385+0.268186 
## [86] train-rmse:0.497540+0.018379    test-rmse:1.237459+0.269507 
## [87] train-rmse:0.490491+0.018955    test-rmse:1.234370+0.270048 
## [88] train-rmse:0.484131+0.019538    test-rmse:1.232255+0.272389 
## [89] train-rmse:0.476925+0.020307    test-rmse:1.231027+0.272647 
## [90] train-rmse:0.470524+0.020068    test-rmse:1.227118+0.275107 
## [91] train-rmse:0.465349+0.019936    test-rmse:1.224771+0.274173 
## [92] train-rmse:0.460271+0.019375    test-rmse:1.217931+0.269475 
## [93] train-rmse:0.453461+0.020563    test-rmse:1.216901+0.268482 
## [94] train-rmse:0.447476+0.021113    test-rmse:1.214571+0.266064 
## [95] train-rmse:0.442814+0.021106    test-rmse:1.212178+0.266603 
## [96] train-rmse:0.436815+0.020985    test-rmse:1.212352+0.266031 
## [97] train-rmse:0.431538+0.021780    test-rmse:1.208185+0.265084 
## [98] train-rmse:0.426208+0.022657    test-rmse:1.205892+0.269051 
## [99] train-rmse:0.421328+0.022329    test-rmse:1.201205+0.271956 
## [100]    train-rmse:0.416174+0.021365    test-rmse:1.199148+0.273041 
## [101]    train-rmse:0.411793+0.021197    test-rmse:1.198472+0.275287 
## [102]    train-rmse:0.406026+0.022306    test-rmse:1.195547+0.276476 
## [103]    train-rmse:0.401305+0.022361    test-rmse:1.193780+0.276642 
## [104]    train-rmse:0.397123+0.022598    test-rmse:1.192897+0.277973 
## [105]    train-rmse:0.393293+0.022643    test-rmse:1.188803+0.276179 
## [106]    train-rmse:0.389033+0.022790    test-rmse:1.187412+0.277492 
## [107]    train-rmse:0.384949+0.022478    test-rmse:1.187009+0.277477 
## [108]    train-rmse:0.381099+0.022758    test-rmse:1.184639+0.275763 
## [109]    train-rmse:0.376850+0.022620    test-rmse:1.183798+0.277795 
## [110]    train-rmse:0.372840+0.022798    test-rmse:1.182624+0.278333 
## [111]    train-rmse:0.368188+0.021759    test-rmse:1.183800+0.279099 
## [112]    train-rmse:0.365323+0.021547    test-rmse:1.183072+0.280149 
## [113]    train-rmse:0.362074+0.022172    test-rmse:1.182792+0.279252 
## [114]    train-rmse:0.359309+0.022374    test-rmse:1.181612+0.278776 
## [115]    train-rmse:0.355580+0.021240    test-rmse:1.177003+0.279928 
## [116]    train-rmse:0.352644+0.021361    test-rmse:1.175318+0.281042 
## [117]    train-rmse:0.347713+0.020571    test-rmse:1.171827+0.274439 
## [118]    train-rmse:0.344536+0.021041    test-rmse:1.171307+0.275434 
## [119]    train-rmse:0.340562+0.020291    test-rmse:1.168524+0.276453 
## [120]    train-rmse:0.337632+0.020099    test-rmse:1.165890+0.277575 
## [121]    train-rmse:0.334358+0.019067    test-rmse:1.165029+0.276894 
## [122]    train-rmse:0.330909+0.019655    test-rmse:1.162450+0.275286 
## [123]    train-rmse:0.327817+0.019689    test-rmse:1.162249+0.275615 
## [124]    train-rmse:0.323951+0.019215    test-rmse:1.160272+0.276255 
## [125]    train-rmse:0.320891+0.020196    test-rmse:1.159810+0.276064 
## [126]    train-rmse:0.318173+0.020674    test-rmse:1.157993+0.277347 
## [127]    train-rmse:0.315624+0.020803    test-rmse:1.156428+0.278307 
## [128]    train-rmse:0.313380+0.020836    test-rmse:1.156611+0.279126 
## [129]    train-rmse:0.309795+0.019989    test-rmse:1.154903+0.279254 
## [130]    train-rmse:0.307396+0.020159    test-rmse:1.153457+0.278652 
## [131]    train-rmse:0.305035+0.019832    test-rmse:1.152281+0.278788 
## [132]    train-rmse:0.302413+0.019177    test-rmse:1.150433+0.279832 
## [133]    train-rmse:0.297763+0.017443    test-rmse:1.150094+0.278851 
## [134]    train-rmse:0.295094+0.017770    test-rmse:1.149372+0.279241 
## [135]    train-rmse:0.292332+0.017796    test-rmse:1.148372+0.279589 
## [136]    train-rmse:0.289543+0.017759    test-rmse:1.148706+0.279410 
## [137]    train-rmse:0.285448+0.015374    test-rmse:1.148869+0.279871 
## [138]    train-rmse:0.283314+0.015439    test-rmse:1.147139+0.279304 
## [139]    train-rmse:0.280981+0.014962    test-rmse:1.145290+0.279925 
## [140]    train-rmse:0.278624+0.014756    test-rmse:1.144504+0.280047 
## [141]    train-rmse:0.276421+0.014431    test-rmse:1.144105+0.280366 
## [142]    train-rmse:0.274148+0.015023    test-rmse:1.143101+0.279809 
## [143]    train-rmse:0.272083+0.014453    test-rmse:1.142280+0.279022 
## [144]    train-rmse:0.270043+0.014888    test-rmse:1.141700+0.278326 
## [145]    train-rmse:0.267432+0.015495    test-rmse:1.140317+0.278621 
## [146]    train-rmse:0.265121+0.015219    test-rmse:1.139558+0.279574 
## [147]    train-rmse:0.262608+0.015874    test-rmse:1.137508+0.281227 
## [148]    train-rmse:0.260720+0.016342    test-rmse:1.137380+0.281174 
## [149]    train-rmse:0.259104+0.016546    test-rmse:1.137059+0.282075 
## [150]    train-rmse:0.256701+0.015898    test-rmse:1.136862+0.281737 
## [151]    train-rmse:0.254237+0.015877    test-rmse:1.136374+0.282246 
## [152]    train-rmse:0.252136+0.015339    test-rmse:1.135871+0.282876 
## [153]    train-rmse:0.250333+0.014834    test-rmse:1.135963+0.284014 
## [154]    train-rmse:0.248978+0.014790    test-rmse:1.136070+0.284169 
## [155]    train-rmse:0.246692+0.014953    test-rmse:1.135518+0.284660 
## [156]    train-rmse:0.244797+0.015007    test-rmse:1.135551+0.284745 
## [157]    train-rmse:0.243436+0.014917    test-rmse:1.135258+0.284642 
## [158]    train-rmse:0.241740+0.015147    test-rmse:1.134025+0.283958 
## [159]    train-rmse:0.239708+0.015411    test-rmse:1.133530+0.283284 
## [160]    train-rmse:0.237843+0.016126    test-rmse:1.133433+0.283817 
## [161]    train-rmse:0.235649+0.016122    test-rmse:1.133512+0.284494 
## [162]    train-rmse:0.233912+0.016100    test-rmse:1.133449+0.284676 
## [163]    train-rmse:0.232074+0.015868    test-rmse:1.132717+0.284330 
## [164]    train-rmse:0.229531+0.016261    test-rmse:1.132819+0.284194 
## [165]    train-rmse:0.227745+0.016140    test-rmse:1.132971+0.284717 
## [166]    train-rmse:0.225833+0.015832    test-rmse:1.132986+0.285984 
## [167]    train-rmse:0.224347+0.015838    test-rmse:1.132005+0.286033 
## [168]    train-rmse:0.223124+0.015829    test-rmse:1.130941+0.286498 
## [169]    train-rmse:0.220529+0.014966    test-rmse:1.131060+0.286940 
## [170]    train-rmse:0.219251+0.014981    test-rmse:1.130049+0.287275 
## [171]    train-rmse:0.217530+0.015069    test-rmse:1.129325+0.286911 
## [172]    train-rmse:0.215438+0.014060    test-rmse:1.130366+0.286143 
## [173]    train-rmse:0.214198+0.013924    test-rmse:1.130292+0.286240 
## [174]    train-rmse:0.211924+0.013143    test-rmse:1.130427+0.286195 
## [175]    train-rmse:0.210999+0.013308    test-rmse:1.129551+0.286761 
## [176]    train-rmse:0.209602+0.013675    test-rmse:1.129071+0.286491 
## [177]    train-rmse:0.207981+0.013687    test-rmse:1.129506+0.286215 
## [178]    train-rmse:0.206113+0.012839    test-rmse:1.128650+0.286427 
## [179]    train-rmse:0.204816+0.013015    test-rmse:1.128458+0.286041 
## [180]    train-rmse:0.202390+0.012129    test-rmse:1.127046+0.284610 
## [181]    train-rmse:0.200895+0.012400    test-rmse:1.127380+0.285509 
## [182]    train-rmse:0.199405+0.011967    test-rmse:1.127226+0.286770 
## [183]    train-rmse:0.198013+0.012163    test-rmse:1.127513+0.287083 
## [184]    train-rmse:0.196365+0.012143    test-rmse:1.127209+0.287242 
## [185]    train-rmse:0.194808+0.011769    test-rmse:1.126731+0.287268 
## [186]    train-rmse:0.193221+0.012262    test-rmse:1.126225+0.287389 
## [187]    train-rmse:0.191994+0.011865    test-rmse:1.125682+0.288010 
## [188]    train-rmse:0.190547+0.011695    test-rmse:1.126076+0.288191 
## [189]    train-rmse:0.188115+0.011095    test-rmse:1.125519+0.287843 
## [190]    train-rmse:0.187191+0.011186    test-rmse:1.125074+0.287642 
## [191]    train-rmse:0.186286+0.011084    test-rmse:1.125087+0.288021 
## [192]    train-rmse:0.184768+0.011686    test-rmse:1.125193+0.287688 
## [193]    train-rmse:0.183020+0.011168    test-rmse:1.125483+0.287430 
## [194]    train-rmse:0.181276+0.011268    test-rmse:1.125094+0.286169 
## [195]    train-rmse:0.179818+0.011396    test-rmse:1.124214+0.285374 
## [196]    train-rmse:0.178261+0.011946    test-rmse:1.123885+0.284329 
## [197]    train-rmse:0.176725+0.012266    test-rmse:1.123297+0.284189 
## [198]    train-rmse:0.175610+0.012056    test-rmse:1.123212+0.284439 
## [199]    train-rmse:0.174757+0.011964    test-rmse:1.123056+0.284756 
## [200]    train-rmse:0.173564+0.011907    test-rmse:1.122536+0.284659 
## [201]    train-rmse:0.171960+0.011462    test-rmse:1.122329+0.284526 
## [202]    train-rmse:0.171465+0.011335    test-rmse:1.122074+0.284440 
## [203]    train-rmse:0.170271+0.011755    test-rmse:1.122041+0.284874 
## [204]    train-rmse:0.169043+0.011773    test-rmse:1.122248+0.284693 
## [205]    train-rmse:0.167595+0.011850    test-rmse:1.121903+0.285755 
## [206]    train-rmse:0.166624+0.012043    test-rmse:1.121330+0.286462 
## [207]    train-rmse:0.165549+0.012329    test-rmse:1.121640+0.286685 
## [208]    train-rmse:0.164677+0.012363    test-rmse:1.121157+0.286085 
## [209]    train-rmse:0.163708+0.012288    test-rmse:1.120379+0.286454 
## [210]    train-rmse:0.162435+0.012299    test-rmse:1.120632+0.286799 
## [211]    train-rmse:0.161300+0.012402    test-rmse:1.120791+0.286725 
## [212]    train-rmse:0.159867+0.012520    test-rmse:1.120431+0.286731 
## [213]    train-rmse:0.158938+0.012341    test-rmse:1.120230+0.286847 
## [214]    train-rmse:0.157386+0.011176    test-rmse:1.120852+0.286511 
## [215]    train-rmse:0.156539+0.011432    test-rmse:1.121034+0.286651 
## [216]    train-rmse:0.155588+0.011499    test-rmse:1.120299+0.287284 
## [217]    train-rmse:0.154581+0.011208    test-rmse:1.120356+0.287369 
## [218]    train-rmse:0.153361+0.011051    test-rmse:1.120206+0.287245 
## [219]    train-rmse:0.152601+0.011174    test-rmse:1.120363+0.287417 
## [220]    train-rmse:0.151427+0.010981    test-rmse:1.119760+0.286781 
## [221]    train-rmse:0.150390+0.010606    test-rmse:1.120331+0.286785 
## [222]    train-rmse:0.148994+0.010130    test-rmse:1.120542+0.286307 
## [223]    train-rmse:0.148213+0.010206    test-rmse:1.120820+0.286032 
## [224]    train-rmse:0.147187+0.010213    test-rmse:1.120837+0.286006 
## [225]    train-rmse:0.145964+0.009726    test-rmse:1.120447+0.285902 
## [226]    train-rmse:0.144977+0.009997    test-rmse:1.120335+0.285603 
## Stopping. Best iteration:
## [220]    train-rmse:0.151427+0.010981    test-rmse:1.119760+0.286781
## 
## 52 
## [1]  train-rmse:81.945409+0.093908   test-rmse:81.945506+0.502440 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:62.457670+0.078257   test-rmse:62.457233+0.536077 
## [3]  train-rmse:47.664860+0.068582   test-rmse:47.663622+0.569012 
## [4]  train-rmse:36.454471+0.064225   test-rmse:36.452043+0.603174 
## [5]  train-rmse:27.964685+0.060685   test-rmse:28.013021+0.606000 
## [6]  train-rmse:21.516921+0.044302   test-rmse:21.607782+0.599820 
## [7]  train-rmse:16.622252+0.036724   test-rmse:16.726139+0.563579 
## [8]  train-rmse:12.901630+0.031627   test-rmse:13.028168+0.549716 
## [9]  train-rmse:10.087566+0.030662   test-rmse:10.202823+0.535852 
## [10] train-rmse:7.951679+0.026526    test-rmse:8.094495+0.591939 
## [11] train-rmse:6.351447+0.032111    test-rmse:6.522367+0.520072 
## [12] train-rmse:5.148003+0.027712    test-rmse:5.337664+0.499825 
## [13] train-rmse:4.252136+0.030293    test-rmse:4.512160+0.484762 
## [14] train-rmse:3.592654+0.031720    test-rmse:3.890971+0.467608 
## [15] train-rmse:3.101608+0.032978    test-rmse:3.431092+0.412827 
## [16] train-rmse:2.744452+0.033244    test-rmse:3.107276+0.379444 
## [17] train-rmse:2.478171+0.032639    test-rmse:2.877711+0.351197 
## [18] train-rmse:2.271461+0.026734    test-rmse:2.710464+0.328276 
## [19] train-rmse:2.111261+0.026331    test-rmse:2.545805+0.285741 
## [20] train-rmse:1.984998+0.022932    test-rmse:2.457097+0.276445 
## [21] train-rmse:1.874009+0.020667    test-rmse:2.375788+0.277695 
## [22] train-rmse:1.779970+0.019070    test-rmse:2.303615+0.263862 
## [23] train-rmse:1.693343+0.024082    test-rmse:2.219293+0.254508 
## [24] train-rmse:1.619252+0.022267    test-rmse:2.147772+0.223864 
## [25] train-rmse:1.549367+0.018182    test-rmse:2.096146+0.207885 
## [26] train-rmse:1.485771+0.020650    test-rmse:2.036178+0.210860 
## [27] train-rmse:1.425800+0.018231    test-rmse:1.995072+0.212057 
## [28] train-rmse:1.372503+0.018174    test-rmse:1.954583+0.208373 
## [29] train-rmse:1.322064+0.016543    test-rmse:1.916385+0.217967 
## [30] train-rmse:1.271257+0.014310    test-rmse:1.868208+0.216360 
## [31] train-rmse:1.223497+0.015807    test-rmse:1.829958+0.221936 
## [32] train-rmse:1.176770+0.017845    test-rmse:1.802508+0.215611 
## [33] train-rmse:1.137066+0.018768    test-rmse:1.763517+0.210081 
## [34] train-rmse:1.093488+0.013191    test-rmse:1.727575+0.209704 
## [35] train-rmse:1.049927+0.019137    test-rmse:1.700152+0.222903 
## [36] train-rmse:1.011678+0.018985    test-rmse:1.659679+0.226012 
## [37] train-rmse:0.976642+0.018742    test-rmse:1.636169+0.230963 
## [38] train-rmse:0.944421+0.019895    test-rmse:1.615729+0.224483 
## [39] train-rmse:0.911176+0.020484    test-rmse:1.601976+0.227371 
## [40] train-rmse:0.883112+0.018764    test-rmse:1.572400+0.241386 
## [41] train-rmse:0.857542+0.018487    test-rmse:1.549327+0.243139 
## [42] train-rmse:0.828690+0.016751    test-rmse:1.516058+0.235666 
## [43] train-rmse:0.800130+0.014475    test-rmse:1.491692+0.225334 
## [44] train-rmse:0.776232+0.013600    test-rmse:1.468238+0.229530 
## [45] train-rmse:0.748223+0.012480    test-rmse:1.457519+0.235618 
## [46] train-rmse:0.727393+0.012353    test-rmse:1.444170+0.232812 
## [47] train-rmse:0.705065+0.011901    test-rmse:1.430317+0.237034 
## [48] train-rmse:0.683856+0.010850    test-rmse:1.420026+0.239797 
## [49] train-rmse:0.663387+0.011240    test-rmse:1.402598+0.243222 
## [50] train-rmse:0.644225+0.010441    test-rmse:1.395133+0.240492 
## [51] train-rmse:0.626269+0.012249    test-rmse:1.385353+0.246259 
## [52] train-rmse:0.609309+0.012507    test-rmse:1.370011+0.247091 
## [53] train-rmse:0.591036+0.013590    test-rmse:1.355726+0.233389 
## [54] train-rmse:0.575153+0.013574    test-rmse:1.348922+0.236238 
## [55] train-rmse:0.560955+0.013928    test-rmse:1.343433+0.237617 
## [56] train-rmse:0.547514+0.013766    test-rmse:1.332432+0.239598 
## [57] train-rmse:0.533323+0.013769    test-rmse:1.328427+0.239447 
## [58] train-rmse:0.521357+0.014037    test-rmse:1.324181+0.240633 
## [59] train-rmse:0.506372+0.012656    test-rmse:1.308028+0.231556 
## [60] train-rmse:0.495563+0.012557    test-rmse:1.303453+0.235370 
## [61] train-rmse:0.484372+0.011826    test-rmse:1.301326+0.232918 
## [62] train-rmse:0.472063+0.012305    test-rmse:1.298360+0.230294 
## [63] train-rmse:0.462410+0.011983    test-rmse:1.292859+0.231637 
## [64] train-rmse:0.453228+0.010983    test-rmse:1.291077+0.234777 
## [65] train-rmse:0.441716+0.010149    test-rmse:1.279498+0.228177 
## [66] train-rmse:0.432784+0.010061    test-rmse:1.271580+0.232876 
## [67] train-rmse:0.424348+0.010064    test-rmse:1.266508+0.234184 
## [68] train-rmse:0.416476+0.010216    test-rmse:1.262662+0.235200 
## [69] train-rmse:0.408574+0.010510    test-rmse:1.257896+0.237369 
## [70] train-rmse:0.401420+0.009988    test-rmse:1.251989+0.244325 
## [71] train-rmse:0.393753+0.010463    test-rmse:1.251049+0.242611 
## [72] train-rmse:0.387588+0.009885    test-rmse:1.246442+0.243946 
## [73] train-rmse:0.379080+0.009172    test-rmse:1.242086+0.246780 
## [74] train-rmse:0.371554+0.010373    test-rmse:1.238886+0.248948 
## [75] train-rmse:0.363699+0.009542    test-rmse:1.232587+0.246949 
## [76] train-rmse:0.358508+0.009314    test-rmse:1.224555+0.251419 
## [77] train-rmse:0.351283+0.009858    test-rmse:1.221125+0.252165 
## [78] train-rmse:0.345358+0.009312    test-rmse:1.217840+0.253033 
## [79] train-rmse:0.339588+0.009121    test-rmse:1.215909+0.252940 
## [80] train-rmse:0.334421+0.008654    test-rmse:1.211864+0.253258 
## [81] train-rmse:0.329578+0.007789    test-rmse:1.209120+0.253498 
## [82] train-rmse:0.324510+0.007657    test-rmse:1.208837+0.254901 
## [83] train-rmse:0.319334+0.007793    test-rmse:1.204922+0.257211 
## [84] train-rmse:0.313999+0.006968    test-rmse:1.202831+0.256774 
## [85] train-rmse:0.308445+0.007897    test-rmse:1.199616+0.258729 
## [86] train-rmse:0.304990+0.007143    test-rmse:1.196091+0.258932 
## [87] train-rmse:0.300419+0.007446    test-rmse:1.193999+0.259578 
## [88] train-rmse:0.295086+0.006353    test-rmse:1.190131+0.264509 
## [89] train-rmse:0.290009+0.007039    test-rmse:1.188225+0.263820 
## [90] train-rmse:0.285140+0.007415    test-rmse:1.187916+0.263668 
## [91] train-rmse:0.281547+0.007158    test-rmse:1.186670+0.264308 
## [92] train-rmse:0.278442+0.007144    test-rmse:1.185769+0.263850 
## [93] train-rmse:0.274897+0.007181    test-rmse:1.185165+0.264085 
## [94] train-rmse:0.270833+0.006427    test-rmse:1.184219+0.265726 
## [95] train-rmse:0.266239+0.006346    test-rmse:1.181179+0.263003 
## [96] train-rmse:0.262636+0.006100    test-rmse:1.178614+0.264591 
## [97] train-rmse:0.259045+0.006903    test-rmse:1.177629+0.264571 
## [98] train-rmse:0.256436+0.006822    test-rmse:1.175788+0.264605 
## [99] train-rmse:0.252589+0.006266    test-rmse:1.174493+0.265592 
## [100]    train-rmse:0.248871+0.005692    test-rmse:1.173207+0.266664 
## [101]    train-rmse:0.245793+0.005778    test-rmse:1.171547+0.268461 
## [102]    train-rmse:0.242732+0.006280    test-rmse:1.170281+0.269092 
## [103]    train-rmse:0.239432+0.005256    test-rmse:1.166263+0.270731 
## [104]    train-rmse:0.236548+0.005136    test-rmse:1.165350+0.271735 
## [105]    train-rmse:0.234099+0.005813    test-rmse:1.164107+0.271344 
## [106]    train-rmse:0.230311+0.006931    test-rmse:1.164769+0.271092 
## [107]    train-rmse:0.226795+0.006512    test-rmse:1.162773+0.270656 
## [108]    train-rmse:0.224237+0.006486    test-rmse:1.161492+0.271246 
## [109]    train-rmse:0.220108+0.006117    test-rmse:1.161474+0.271832 
## [110]    train-rmse:0.215670+0.006947    test-rmse:1.159026+0.271955 
## [111]    train-rmse:0.212740+0.006043    test-rmse:1.159306+0.271718 
## [112]    train-rmse:0.209354+0.004648    test-rmse:1.158380+0.272324 
## [113]    train-rmse:0.204466+0.004169    test-rmse:1.158342+0.272562 
## [114]    train-rmse:0.201790+0.004577    test-rmse:1.157828+0.272398 
## [115]    train-rmse:0.199133+0.003778    test-rmse:1.156623+0.273618 
## [116]    train-rmse:0.196353+0.004475    test-rmse:1.155882+0.272883 
## [117]    train-rmse:0.193569+0.005125    test-rmse:1.154680+0.273179 
## [118]    train-rmse:0.191486+0.004997    test-rmse:1.154208+0.273366 
## [119]    train-rmse:0.188840+0.005945    test-rmse:1.154082+0.273385 
## [120]    train-rmse:0.186245+0.006008    test-rmse:1.153217+0.273816 
## [121]    train-rmse:0.184077+0.005933    test-rmse:1.152723+0.274268 
## [122]    train-rmse:0.180323+0.005938    test-rmse:1.150949+0.274241 
## [123]    train-rmse:0.178543+0.006052    test-rmse:1.150302+0.274929 
## [124]    train-rmse:0.175738+0.006253    test-rmse:1.149926+0.274725 
## [125]    train-rmse:0.173122+0.005962    test-rmse:1.150131+0.275023 
## [126]    train-rmse:0.169986+0.006018    test-rmse:1.150068+0.275113 
## [127]    train-rmse:0.167838+0.006570    test-rmse:1.149816+0.275443 
## [128]    train-rmse:0.165702+0.007152    test-rmse:1.147768+0.275110 
## [129]    train-rmse:0.163660+0.007181    test-rmse:1.148864+0.274729 
## [130]    train-rmse:0.161110+0.006889    test-rmse:1.147716+0.274391 
## [131]    train-rmse:0.159394+0.007089    test-rmse:1.146516+0.275211 
## [132]    train-rmse:0.157230+0.007284    test-rmse:1.147139+0.276064 
## [133]    train-rmse:0.154959+0.006762    test-rmse:1.146183+0.276359 
## [134]    train-rmse:0.152337+0.006874    test-rmse:1.145458+0.276449 
## [135]    train-rmse:0.149922+0.006749    test-rmse:1.144930+0.276981 
## [136]    train-rmse:0.148253+0.006178    test-rmse:1.144649+0.277781 
## [137]    train-rmse:0.145538+0.005868    test-rmse:1.144302+0.278205 
## [138]    train-rmse:0.143444+0.005626    test-rmse:1.143081+0.278956 
## [139]    train-rmse:0.141924+0.006033    test-rmse:1.142890+0.278893 
## [140]    train-rmse:0.140356+0.006100    test-rmse:1.142673+0.279114 
## [141]    train-rmse:0.138293+0.006553    test-rmse:1.142312+0.279199 
## [142]    train-rmse:0.137079+0.006648    test-rmse:1.142338+0.278102 
## [143]    train-rmse:0.135265+0.005908    test-rmse:1.142580+0.278463 
## [144]    train-rmse:0.132679+0.005886    test-rmse:1.141782+0.278995 
## [145]    train-rmse:0.131174+0.005959    test-rmse:1.141027+0.279308 
## [146]    train-rmse:0.129185+0.005698    test-rmse:1.141012+0.279839 
## [147]    train-rmse:0.127788+0.005847    test-rmse:1.140765+0.279876 
## [148]    train-rmse:0.125762+0.005781    test-rmse:1.140223+0.280180 
## [149]    train-rmse:0.124561+0.005549    test-rmse:1.140103+0.279924 
## [150]    train-rmse:0.122680+0.005761    test-rmse:1.139943+0.280273 
## [151]    train-rmse:0.121013+0.005528    test-rmse:1.140187+0.280670 
## [152]    train-rmse:0.119601+0.005231    test-rmse:1.140020+0.281317 
## [153]    train-rmse:0.117744+0.004746    test-rmse:1.139810+0.281672 
## [154]    train-rmse:0.116695+0.004943    test-rmse:1.139456+0.281907 
## [155]    train-rmse:0.115274+0.004591    test-rmse:1.139594+0.282489 
## [156]    train-rmse:0.114064+0.005061    test-rmse:1.139569+0.282487 
## [157]    train-rmse:0.113066+0.004984    test-rmse:1.139607+0.282526 
## [158]    train-rmse:0.111942+0.005195    test-rmse:1.139238+0.282474 
## [159]    train-rmse:0.110945+0.005774    test-rmse:1.138774+0.282721 
## [160]    train-rmse:0.109784+0.005760    test-rmse:1.138629+0.282592 
## [161]    train-rmse:0.107984+0.005557    test-rmse:1.137834+0.282728 
## [162]    train-rmse:0.106307+0.005452    test-rmse:1.137298+0.282839 
## [163]    train-rmse:0.105395+0.005244    test-rmse:1.137019+0.283504 
## [164]    train-rmse:0.103415+0.004752    test-rmse:1.137084+0.283378 
## [165]    train-rmse:0.102080+0.004535    test-rmse:1.136765+0.283017 
## [166]    train-rmse:0.100965+0.004996    test-rmse:1.136561+0.283209 
## [167]    train-rmse:0.099606+0.005113    test-rmse:1.135899+0.283455 
## [168]    train-rmse:0.098430+0.004899    test-rmse:1.136163+0.284253 
## [169]    train-rmse:0.096963+0.005024    test-rmse:1.135982+0.284632 
## [170]    train-rmse:0.095904+0.005257    test-rmse:1.135496+0.284994 
## [171]    train-rmse:0.094392+0.005227    test-rmse:1.135356+0.285172 
## [172]    train-rmse:0.093330+0.005320    test-rmse:1.134406+0.285166 
## [173]    train-rmse:0.092015+0.004929    test-rmse:1.134287+0.285328 
## [174]    train-rmse:0.090873+0.004947    test-rmse:1.134451+0.285350 
## [175]    train-rmse:0.089934+0.005135    test-rmse:1.134074+0.285564 
## [176]    train-rmse:0.089047+0.004829    test-rmse:1.134075+0.285919 
## [177]    train-rmse:0.087794+0.005211    test-rmse:1.133816+0.285917 
## [178]    train-rmse:0.086778+0.005484    test-rmse:1.133872+0.285591 
## [179]    train-rmse:0.085555+0.005743    test-rmse:1.133364+0.285252 
## [180]    train-rmse:0.084478+0.005909    test-rmse:1.133267+0.285101 
## [181]    train-rmse:0.083432+0.005794    test-rmse:1.132970+0.285057 
## [182]    train-rmse:0.082540+0.005955    test-rmse:1.132956+0.285107 
## [183]    train-rmse:0.081710+0.006178    test-rmse:1.132937+0.285003 
## [184]    train-rmse:0.080634+0.006228    test-rmse:1.133209+0.285246 
## [185]    train-rmse:0.079419+0.005730    test-rmse:1.133005+0.285477 
## [186]    train-rmse:0.078261+0.005584    test-rmse:1.132527+0.285282 
## [187]    train-rmse:0.077250+0.005411    test-rmse:1.132411+0.285558 
## [188]    train-rmse:0.076508+0.005289    test-rmse:1.132153+0.285774 
## [189]    train-rmse:0.075780+0.005318    test-rmse:1.132117+0.285667 
## [190]    train-rmse:0.074647+0.005293    test-rmse:1.132023+0.285870 
## [191]    train-rmse:0.073725+0.005173    test-rmse:1.132465+0.285830 
## [192]    train-rmse:0.073080+0.005018    test-rmse:1.132389+0.286078 
## [193]    train-rmse:0.072240+0.004935    test-rmse:1.131935+0.285791 
## [194]    train-rmse:0.071260+0.004632    test-rmse:1.131839+0.285827 
## [195]    train-rmse:0.070423+0.004897    test-rmse:1.131890+0.285764 
## [196]    train-rmse:0.069866+0.004813    test-rmse:1.131698+0.285682 
## [197]    train-rmse:0.069034+0.005057    test-rmse:1.131563+0.285915 
## [198]    train-rmse:0.068325+0.005258    test-rmse:1.131140+0.286159 
## [199]    train-rmse:0.067394+0.005004    test-rmse:1.131167+0.286215 
## [200]    train-rmse:0.066115+0.005471    test-rmse:1.130948+0.286806 
## [201]    train-rmse:0.064980+0.005780    test-rmse:1.131064+0.286825 
## [202]    train-rmse:0.064553+0.005917    test-rmse:1.130973+0.286754 
## [203]    train-rmse:0.063860+0.005754    test-rmse:1.131123+0.286711 
## [204]    train-rmse:0.063260+0.005668    test-rmse:1.131002+0.286773 
## [205]    train-rmse:0.062429+0.005565    test-rmse:1.130791+0.286776 
## [206]    train-rmse:0.061614+0.005601    test-rmse:1.130387+0.286983 
## [207]    train-rmse:0.060694+0.005912    test-rmse:1.130413+0.286997 
## [208]    train-rmse:0.059955+0.005848    test-rmse:1.130100+0.287070 
## [209]    train-rmse:0.059377+0.005759    test-rmse:1.130074+0.287070 
## [210]    train-rmse:0.058783+0.005953    test-rmse:1.130072+0.287005 
## [211]    train-rmse:0.058074+0.006098    test-rmse:1.130095+0.286854 
## [212]    train-rmse:0.057213+0.006176    test-rmse:1.130000+0.286987 
## [213]    train-rmse:0.056474+0.006047    test-rmse:1.130002+0.287006 
## [214]    train-rmse:0.055948+0.006030    test-rmse:1.130053+0.287108 
## [215]    train-rmse:0.055534+0.005981    test-rmse:1.129811+0.287000 
## [216]    train-rmse:0.054932+0.005792    test-rmse:1.129634+0.287035 
## [217]    train-rmse:0.054155+0.005614    test-rmse:1.129342+0.287101 
## [218]    train-rmse:0.053726+0.005719    test-rmse:1.129316+0.287533 
## [219]    train-rmse:0.053078+0.005440    test-rmse:1.129059+0.287540 
## [220]    train-rmse:0.052370+0.005130    test-rmse:1.129148+0.287819 
## [221]    train-rmse:0.051645+0.005125    test-rmse:1.129261+0.287809 
## [222]    train-rmse:0.050958+0.005119    test-rmse:1.129104+0.287781 
## [223]    train-rmse:0.050310+0.005008    test-rmse:1.129062+0.287607 
## [224]    train-rmse:0.049615+0.005063    test-rmse:1.129324+0.287764 
## [225]    train-rmse:0.049098+0.005098    test-rmse:1.129216+0.287781 
## Stopping. Best iteration:
## [219]    train-rmse:0.053078+0.005440    test-rmse:1.129059+0.287540
## 
## 53 
## [1]  train-rmse:81.945409+0.093908   test-rmse:81.945506+0.502440 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:62.457670+0.078257   test-rmse:62.457233+0.536077 
## [3]  train-rmse:47.664860+0.068582   test-rmse:47.663622+0.569012 
## [4]  train-rmse:36.454471+0.064225   test-rmse:36.452043+0.603174 
## [5]  train-rmse:27.964685+0.060685   test-rmse:28.013021+0.606000 
## [6]  train-rmse:21.516921+0.044302   test-rmse:21.607782+0.599820 
## [7]  train-rmse:16.617937+0.036973   test-rmse:16.728899+0.559902 
## [8]  train-rmse:12.886534+0.032290   test-rmse:13.002651+0.537229 
## [9]  train-rmse:10.053380+0.030104   test-rmse:10.224078+0.575322 
## [10] train-rmse:7.892023+0.030433    test-rmse:8.043042+0.556857 
## [11] train-rmse:6.257841+0.027019    test-rmse:6.420309+0.527548 
## [12] train-rmse:5.017586+0.026935    test-rmse:5.214201+0.534011 
## [13] train-rmse:4.079962+0.016810    test-rmse:4.292396+0.489891 
## [14] train-rmse:3.386739+0.016562    test-rmse:3.649147+0.453423 
## [15] train-rmse:2.865897+0.020398    test-rmse:3.193246+0.437701 
## [16] train-rmse:2.475899+0.022872    test-rmse:2.853624+0.363278 
## [17] train-rmse:2.187099+0.020673    test-rmse:2.602668+0.335560 
## [18] train-rmse:1.955952+0.028263    test-rmse:2.434040+0.301447 
## [19] train-rmse:1.779072+0.037453    test-rmse:2.301157+0.298675 
## [20] train-rmse:1.642555+0.042356    test-rmse:2.181042+0.267714 
## [21] train-rmse:1.528542+0.045346    test-rmse:2.106417+0.248318 
## [22] train-rmse:1.433369+0.037728    test-rmse:2.032524+0.235640 
## [23] train-rmse:1.350830+0.034339    test-rmse:1.977086+0.222338 
## [24] train-rmse:1.262866+0.038290    test-rmse:1.925010+0.208963 
## [25] train-rmse:1.197746+0.036888    test-rmse:1.870319+0.195981 
## [26] train-rmse:1.139780+0.034967    test-rmse:1.820931+0.193895 
## [27] train-rmse:1.086892+0.033412    test-rmse:1.774885+0.199676 
## [28] train-rmse:1.031090+0.032044    test-rmse:1.742878+0.204397 
## [29] train-rmse:0.985016+0.031404    test-rmse:1.707585+0.200128 
## [30] train-rmse:0.935875+0.035718    test-rmse:1.668296+0.193784 
## [31] train-rmse:0.894294+0.034228    test-rmse:1.639061+0.199075 
## [32] train-rmse:0.857655+0.032561    test-rmse:1.618180+0.204934 
## [33] train-rmse:0.821792+0.030994    test-rmse:1.594882+0.208881 
## [34] train-rmse:0.789366+0.029156    test-rmse:1.550792+0.211146 
## [35] train-rmse:0.758799+0.027838    test-rmse:1.534925+0.216259 
## [36] train-rmse:0.727813+0.026693    test-rmse:1.514387+0.214685 
## [37] train-rmse:0.700567+0.026800    test-rmse:1.496685+0.216063 
## [38] train-rmse:0.676507+0.024438    test-rmse:1.475577+0.208148 
## [39] train-rmse:0.649828+0.025435    test-rmse:1.455127+0.213881 
## [40] train-rmse:0.626796+0.026365    test-rmse:1.440286+0.207554 
## [41] train-rmse:0.601506+0.028032    test-rmse:1.427741+0.207794 
## [42] train-rmse:0.580209+0.027967    test-rmse:1.413333+0.200875 
## [43] train-rmse:0.561028+0.021285    test-rmse:1.403065+0.200901 
## [44] train-rmse:0.541858+0.021422    test-rmse:1.397725+0.206370 
## [45] train-rmse:0.520753+0.023259    test-rmse:1.389216+0.204101 
## [46] train-rmse:0.502609+0.019970    test-rmse:1.372606+0.205543 
## [47] train-rmse:0.487406+0.020738    test-rmse:1.365351+0.206944 
## [48] train-rmse:0.474033+0.020435    test-rmse:1.355475+0.211156 
## [49] train-rmse:0.458421+0.021518    test-rmse:1.348639+0.212184 
## [50] train-rmse:0.445472+0.023126    test-rmse:1.340454+0.211919 
## [51] train-rmse:0.432396+0.022712    test-rmse:1.331901+0.209335 
## [52] train-rmse:0.419443+0.021123    test-rmse:1.330420+0.211201 
## [53] train-rmse:0.405379+0.019596    test-rmse:1.322879+0.214452 
## [54] train-rmse:0.394998+0.019231    test-rmse:1.316695+0.213314 
## [55] train-rmse:0.382367+0.019654    test-rmse:1.310397+0.211844 
## [56] train-rmse:0.371689+0.015353    test-rmse:1.300562+0.210980 
## [57] train-rmse:0.361787+0.015796    test-rmse:1.298880+0.213383 
## [58] train-rmse:0.352192+0.016108    test-rmse:1.293740+0.214570 
## [59] train-rmse:0.342837+0.016289    test-rmse:1.289051+0.210085 
## [60] train-rmse:0.332629+0.016171    test-rmse:1.285455+0.210295 
## [61] train-rmse:0.324742+0.015961    test-rmse:1.283588+0.213106 
## [62] train-rmse:0.317707+0.015348    test-rmse:1.279486+0.216677 
## [63] train-rmse:0.309975+0.015131    test-rmse:1.277135+0.214214 
## [64] train-rmse:0.302951+0.014658    test-rmse:1.275024+0.215382 
## [65] train-rmse:0.295667+0.014615    test-rmse:1.274590+0.212901 
## [66] train-rmse:0.290109+0.014794    test-rmse:1.271319+0.213627 
## [67] train-rmse:0.282979+0.013729    test-rmse:1.269077+0.214336 
## [68] train-rmse:0.277400+0.014528    test-rmse:1.265802+0.217374 
## [69] train-rmse:0.270736+0.013303    test-rmse:1.264884+0.216562 
## [70] train-rmse:0.264738+0.013339    test-rmse:1.263207+0.215938 
## [71] train-rmse:0.257945+0.014272    test-rmse:1.260744+0.212506 
## [72] train-rmse:0.252573+0.013560    test-rmse:1.261384+0.213104 
## [73] train-rmse:0.246412+0.014661    test-rmse:1.260852+0.213064 
## [74] train-rmse:0.241312+0.015873    test-rmse:1.257647+0.215029 
## [75] train-rmse:0.235902+0.015432    test-rmse:1.256128+0.214446 
## [76] train-rmse:0.232548+0.014704    test-rmse:1.254376+0.215696 
## [77] train-rmse:0.227342+0.016807    test-rmse:1.252764+0.218472 
## [78] train-rmse:0.222906+0.016529    test-rmse:1.251533+0.218685 
## [79] train-rmse:0.216417+0.015044    test-rmse:1.250099+0.218258 
## [80] train-rmse:0.212901+0.015573    test-rmse:1.250698+0.218613 
## [81] train-rmse:0.208258+0.015996    test-rmse:1.250443+0.219565 
## [82] train-rmse:0.204374+0.016103    test-rmse:1.250462+0.220549 
## [83] train-rmse:0.200688+0.015145    test-rmse:1.248485+0.219146 
## [84] train-rmse:0.197029+0.015880    test-rmse:1.246524+0.220982 
## [85] train-rmse:0.193841+0.015266    test-rmse:1.244857+0.220400 
## [86] train-rmse:0.189414+0.015434    test-rmse:1.244306+0.220641 
## [87] train-rmse:0.184092+0.014524    test-rmse:1.243932+0.221466 
## [88] train-rmse:0.181196+0.015108    test-rmse:1.243297+0.221700 
## [89] train-rmse:0.177726+0.014825    test-rmse:1.243701+0.221920 
## [90] train-rmse:0.172222+0.014128    test-rmse:1.244566+0.222536 
## [91] train-rmse:0.169049+0.013273    test-rmse:1.244322+0.224360 
## [92] train-rmse:0.165726+0.012740    test-rmse:1.244077+0.224859 
## [93] train-rmse:0.162556+0.012675    test-rmse:1.243038+0.226006 
## [94] train-rmse:0.159916+0.012144    test-rmse:1.243570+0.226373 
## [95] train-rmse:0.157646+0.011843    test-rmse:1.243049+0.226871 
## [96] train-rmse:0.154364+0.012330    test-rmse:1.242683+0.226614 
## [97] train-rmse:0.151208+0.011898    test-rmse:1.241509+0.224390 
## [98] train-rmse:0.147706+0.011336    test-rmse:1.242285+0.224575 
## [99] train-rmse:0.145532+0.010960    test-rmse:1.242000+0.224819 
## [100]    train-rmse:0.142997+0.011031    test-rmse:1.241547+0.224580 
## [101]    train-rmse:0.140733+0.010189    test-rmse:1.240438+0.224070 
## [102]    train-rmse:0.137783+0.011142    test-rmse:1.240297+0.224959 
## [103]    train-rmse:0.133725+0.011557    test-rmse:1.241080+0.225404 
## [104]    train-rmse:0.131740+0.011748    test-rmse:1.240013+0.226306 
## [105]    train-rmse:0.129431+0.012389    test-rmse:1.239693+0.226573 
## [106]    train-rmse:0.127280+0.012394    test-rmse:1.240223+0.227320 
## [107]    train-rmse:0.125422+0.013067    test-rmse:1.240051+0.227560 
## [108]    train-rmse:0.122835+0.012316    test-rmse:1.240223+0.227619 
## [109]    train-rmse:0.120620+0.011917    test-rmse:1.240018+0.228415 
## [110]    train-rmse:0.118762+0.012213    test-rmse:1.239522+0.229547 
## [111]    train-rmse:0.115560+0.011392    test-rmse:1.239336+0.229422 
## [112]    train-rmse:0.112867+0.010764    test-rmse:1.239125+0.229710 
## [113]    train-rmse:0.110524+0.011091    test-rmse:1.238902+0.230036 
## [114]    train-rmse:0.108342+0.010482    test-rmse:1.238555+0.230032 
## [115]    train-rmse:0.106486+0.010044    test-rmse:1.238048+0.229930 
## [116]    train-rmse:0.104258+0.010137    test-rmse:1.238028+0.229653 
## [117]    train-rmse:0.102649+0.010413    test-rmse:1.238289+0.229644 
## [118]    train-rmse:0.100078+0.011162    test-rmse:1.238294+0.230145 
## [119]    train-rmse:0.097652+0.011192    test-rmse:1.238285+0.230437 
## [120]    train-rmse:0.096263+0.011069    test-rmse:1.237443+0.230385 
## [121]    train-rmse:0.094784+0.010564    test-rmse:1.236978+0.230563 
## [122]    train-rmse:0.092916+0.010793    test-rmse:1.236598+0.230913 
## [123]    train-rmse:0.091274+0.010487    test-rmse:1.236448+0.231215 
## [124]    train-rmse:0.089508+0.010904    test-rmse:1.236233+0.231572 
## [125]    train-rmse:0.088066+0.010768    test-rmse:1.235799+0.231055 
## [126]    train-rmse:0.086319+0.011037    test-rmse:1.235915+0.231803 
## [127]    train-rmse:0.084690+0.011149    test-rmse:1.235949+0.231574 
## [128]    train-rmse:0.082596+0.010156    test-rmse:1.235479+0.231526 
## [129]    train-rmse:0.081259+0.009538    test-rmse:1.235161+0.231868 
## [130]    train-rmse:0.079830+0.009602    test-rmse:1.235184+0.231662 
## [131]    train-rmse:0.078567+0.009359    test-rmse:1.234928+0.232035 
## [132]    train-rmse:0.076678+0.009412    test-rmse:1.235091+0.232501 
## [133]    train-rmse:0.075524+0.009534    test-rmse:1.234732+0.232866 
## [134]    train-rmse:0.074143+0.009532    test-rmse:1.234328+0.233123 
## [135]    train-rmse:0.072199+0.009153    test-rmse:1.234225+0.232767 
## [136]    train-rmse:0.070866+0.009004    test-rmse:1.234445+0.232775 
## [137]    train-rmse:0.069265+0.009531    test-rmse:1.234377+0.232728 
## [138]    train-rmse:0.068505+0.009430    test-rmse:1.234277+0.232783 
## [139]    train-rmse:0.067304+0.009537    test-rmse:1.234229+0.232875 
## [140]    train-rmse:0.066235+0.009456    test-rmse:1.233897+0.233012 
## [141]    train-rmse:0.064641+0.008947    test-rmse:1.233618+0.232900 
## [142]    train-rmse:0.063560+0.008772    test-rmse:1.233300+0.232686 
## [143]    train-rmse:0.062450+0.008613    test-rmse:1.233292+0.232852 
## [144]    train-rmse:0.061373+0.008654    test-rmse:1.233172+0.233333 
## [145]    train-rmse:0.059802+0.008221    test-rmse:1.233134+0.233261 
## [146]    train-rmse:0.058461+0.008485    test-rmse:1.233114+0.233532 
## [147]    train-rmse:0.057614+0.008211    test-rmse:1.233122+0.233735 
## [148]    train-rmse:0.056647+0.007729    test-rmse:1.233319+0.233597 
## [149]    train-rmse:0.055318+0.007423    test-rmse:1.233299+0.233720 
## [150]    train-rmse:0.054370+0.007434    test-rmse:1.233159+0.233947 
## [151]    train-rmse:0.053637+0.007561    test-rmse:1.232949+0.234232 
## [152]    train-rmse:0.052478+0.007806    test-rmse:1.233009+0.234195 
## [153]    train-rmse:0.051507+0.007694    test-rmse:1.233067+0.234203 
## [154]    train-rmse:0.050670+0.007535    test-rmse:1.232927+0.234148 
## [155]    train-rmse:0.049840+0.007425    test-rmse:1.232869+0.234162 
## [156]    train-rmse:0.049225+0.007296    test-rmse:1.232777+0.234179 
## [157]    train-rmse:0.048439+0.007419    test-rmse:1.232816+0.234084 
## [158]    train-rmse:0.047424+0.007331    test-rmse:1.232661+0.234049 
## [159]    train-rmse:0.046468+0.007083    test-rmse:1.232329+0.233792 
## [160]    train-rmse:0.045743+0.006902    test-rmse:1.232234+0.233895 
## [161]    train-rmse:0.045091+0.006794    test-rmse:1.232050+0.233883 
## [162]    train-rmse:0.044608+0.006810    test-rmse:1.231920+0.233918 
## [163]    train-rmse:0.043641+0.006485    test-rmse:1.231867+0.234184 
## [164]    train-rmse:0.043192+0.006460    test-rmse:1.231759+0.234194 
## [165]    train-rmse:0.042373+0.006343    test-rmse:1.231753+0.234043 
## [166]    train-rmse:0.041600+0.006235    test-rmse:1.231923+0.234297 
## [167]    train-rmse:0.040784+0.005907    test-rmse:1.232008+0.234247 
## [168]    train-rmse:0.039874+0.005753    test-rmse:1.231927+0.233987 
## [169]    train-rmse:0.039147+0.005732    test-rmse:1.231869+0.233941 
## [170]    train-rmse:0.038469+0.005839    test-rmse:1.231706+0.233977 
## [171]    train-rmse:0.038000+0.006010    test-rmse:1.231746+0.234089 
## [172]    train-rmse:0.037154+0.005963    test-rmse:1.231765+0.234143 
## [173]    train-rmse:0.036410+0.005924    test-rmse:1.231328+0.233973 
## [174]    train-rmse:0.035632+0.005768    test-rmse:1.231308+0.233915 
## [175]    train-rmse:0.034761+0.005530    test-rmse:1.231250+0.233903 
## [176]    train-rmse:0.034264+0.005608    test-rmse:1.231096+0.233874 
## [177]    train-rmse:0.033521+0.005774    test-rmse:1.230935+0.233943 
## [178]    train-rmse:0.032789+0.005712    test-rmse:1.230964+0.233973 
## [179]    train-rmse:0.032292+0.005510    test-rmse:1.231102+0.234201 
## [180]    train-rmse:0.031598+0.005491    test-rmse:1.230948+0.234066 
## [181]    train-rmse:0.031045+0.005340    test-rmse:1.231002+0.234181 
## [182]    train-rmse:0.030518+0.005467    test-rmse:1.230954+0.234199 
## [183]    train-rmse:0.030085+0.005509    test-rmse:1.230646+0.233885 
## [184]    train-rmse:0.029480+0.005318    test-rmse:1.230489+0.233818 
## [185]    train-rmse:0.029061+0.005184    test-rmse:1.230422+0.233836 
## [186]    train-rmse:0.028415+0.004911    test-rmse:1.230346+0.233918 
## [187]    train-rmse:0.028024+0.004809    test-rmse:1.230428+0.233808 
## [188]    train-rmse:0.027544+0.004939    test-rmse:1.230464+0.233915 
## [189]    train-rmse:0.027044+0.004734    test-rmse:1.230493+0.233773 
## [190]    train-rmse:0.026770+0.004756    test-rmse:1.230561+0.233727 
## [191]    train-rmse:0.026383+0.004736    test-rmse:1.230466+0.233776 
## [192]    train-rmse:0.025883+0.004676    test-rmse:1.230320+0.233663 
## [193]    train-rmse:0.025382+0.004475    test-rmse:1.230264+0.233617 
## [194]    train-rmse:0.024881+0.004479    test-rmse:1.230395+0.233560 
## [195]    train-rmse:0.024456+0.004471    test-rmse:1.230513+0.233552 
## [196]    train-rmse:0.024048+0.004611    test-rmse:1.230397+0.233536 
## [197]    train-rmse:0.023681+0.004627    test-rmse:1.230555+0.233566 
## [198]    train-rmse:0.023362+0.004568    test-rmse:1.230509+0.233551 
## [199]    train-rmse:0.022822+0.004326    test-rmse:1.230596+0.233540 
## Stopping. Best iteration:
## [193]    train-rmse:0.025382+0.004475    test-rmse:1.230264+0.233617
## 
## 54 
## [1]  train-rmse:81.945409+0.093908   test-rmse:81.945506+0.502440 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:62.457670+0.078257   test-rmse:62.457233+0.536077 
## [3]  train-rmse:47.664860+0.068582   test-rmse:47.663622+0.569012 
## [4]  train-rmse:36.454471+0.064225   test-rmse:36.452043+0.603174 
## [5]  train-rmse:27.964685+0.060685   test-rmse:28.013021+0.606000 
## [6]  train-rmse:21.516921+0.044302   test-rmse:21.607782+0.599820 
## [7]  train-rmse:16.613988+0.036793   test-rmse:16.713533+0.569142 
## [8]  train-rmse:12.878342+0.030916   test-rmse:12.997253+0.543238 
## [9]  train-rmse:10.035884+0.030378   test-rmse:10.191464+0.577035 
## [10] train-rmse:7.864926+0.037195    test-rmse:8.020629+0.537540 
## [11] train-rmse:6.203277+0.031683    test-rmse:6.352221+0.523988 
## [12] train-rmse:4.943953+0.027639    test-rmse:5.129925+0.527131 
## [13] train-rmse:3.984136+0.024721    test-rmse:4.217616+0.474127 
## [14] train-rmse:3.254753+0.021480    test-rmse:3.505916+0.432766 
## [15] train-rmse:2.707729+0.021706    test-rmse:3.015811+0.385520 
## [16] train-rmse:2.300881+0.024272    test-rmse:2.644885+0.335157 
## [17] train-rmse:1.982190+0.022968    test-rmse:2.410987+0.312198 
## [18] train-rmse:1.731792+0.025799    test-rmse:2.220551+0.294522 
## [19] train-rmse:1.540609+0.031831    test-rmse:2.076838+0.257758 
## [20] train-rmse:1.385509+0.032026    test-rmse:1.954849+0.223139 
## [21] train-rmse:1.265290+0.025842    test-rmse:1.870147+0.219271 
## [22] train-rmse:1.161740+0.031733    test-rmse:1.818290+0.199714 
## [23] train-rmse:1.079210+0.032874    test-rmse:1.760727+0.205325 
## [24] train-rmse:1.009389+0.027364    test-rmse:1.716186+0.199216 
## [25] train-rmse:0.938681+0.021767    test-rmse:1.666950+0.192549 
## [26] train-rmse:0.881572+0.020902    test-rmse:1.622379+0.197672 
## [27] train-rmse:0.828717+0.020255    test-rmse:1.593805+0.212260 
## [28] train-rmse:0.784937+0.019124    test-rmse:1.562676+0.211103 
## [29] train-rmse:0.745189+0.017771    test-rmse:1.532110+0.200311 
## [30] train-rmse:0.704434+0.018417    test-rmse:1.505506+0.202307 
## [31] train-rmse:0.668138+0.021196    test-rmse:1.494668+0.208115 
## [32] train-rmse:0.635777+0.021850    test-rmse:1.471065+0.213435 
## [33] train-rmse:0.602679+0.018303    test-rmse:1.449093+0.211828 
## [34] train-rmse:0.578077+0.016691    test-rmse:1.435675+0.215496 
## [35] train-rmse:0.552017+0.018047    test-rmse:1.422005+0.215731 
## [36] train-rmse:0.527340+0.018034    test-rmse:1.408955+0.220066 
## [37] train-rmse:0.507459+0.017578    test-rmse:1.399700+0.222566 
## [38] train-rmse:0.486774+0.015390    test-rmse:1.378726+0.216578 
## [39] train-rmse:0.466501+0.016330    test-rmse:1.372419+0.223887 
## [40] train-rmse:0.449403+0.015678    test-rmse:1.365114+0.223724 
## [41] train-rmse:0.432163+0.016611    test-rmse:1.351208+0.215061 
## [42] train-rmse:0.417241+0.016451    test-rmse:1.344875+0.220101 
## [43] train-rmse:0.398828+0.015397    test-rmse:1.340435+0.222858 
## [44] train-rmse:0.384410+0.016065    test-rmse:1.340334+0.225464 
## [45] train-rmse:0.369786+0.015630    test-rmse:1.330095+0.221307 
## [46] train-rmse:0.356935+0.015031    test-rmse:1.324067+0.224042 
## [47] train-rmse:0.345384+0.015261    test-rmse:1.322983+0.224778 
## [48] train-rmse:0.334297+0.012289    test-rmse:1.319157+0.229891 
## [49] train-rmse:0.322280+0.014084    test-rmse:1.317081+0.229212 
## [50] train-rmse:0.310132+0.011325    test-rmse:1.308934+0.225941 
## [51] train-rmse:0.302500+0.011307    test-rmse:1.307274+0.226583 
## [52] train-rmse:0.294037+0.010738    test-rmse:1.305071+0.227876 
## [53] train-rmse:0.284784+0.010740    test-rmse:1.302651+0.227577 
## [54] train-rmse:0.275277+0.007712    test-rmse:1.302617+0.227379 
## [55] train-rmse:0.268576+0.007350    test-rmse:1.300712+0.227026 
## [56] train-rmse:0.258890+0.005800    test-rmse:1.300306+0.227082 
## [57] train-rmse:0.252466+0.005997    test-rmse:1.298864+0.228214 
## [58] train-rmse:0.245020+0.005710    test-rmse:1.297959+0.229618 
## [59] train-rmse:0.237418+0.006340    test-rmse:1.296402+0.229750 
## [60] train-rmse:0.229815+0.005375    test-rmse:1.296830+0.229876 
## [61] train-rmse:0.222808+0.006648    test-rmse:1.293901+0.232185 
## [62] train-rmse:0.215938+0.006809    test-rmse:1.292745+0.233276 
## [63] train-rmse:0.210411+0.006754    test-rmse:1.293068+0.233664 
## [64] train-rmse:0.205004+0.007031    test-rmse:1.290731+0.234062 
## [65] train-rmse:0.198406+0.007128    test-rmse:1.289870+0.233622 
## [66] train-rmse:0.192723+0.007596    test-rmse:1.290158+0.235221 
## [67] train-rmse:0.185049+0.009679    test-rmse:1.288926+0.236365 
## [68] train-rmse:0.180674+0.009937    test-rmse:1.287328+0.235275 
## [69] train-rmse:0.176283+0.010022    test-rmse:1.288662+0.235327 
## [70] train-rmse:0.171329+0.011162    test-rmse:1.288351+0.235323 
## [71] train-rmse:0.166093+0.012499    test-rmse:1.286545+0.235927 
## [72] train-rmse:0.161837+0.012508    test-rmse:1.287381+0.236116 
## [73] train-rmse:0.158574+0.012013    test-rmse:1.285824+0.237282 
## [74] train-rmse:0.154643+0.012300    test-rmse:1.285124+0.237929 
## [75] train-rmse:0.151289+0.012007    test-rmse:1.284186+0.238646 
## [76] train-rmse:0.147940+0.012085    test-rmse:1.283504+0.238836 
## [77] train-rmse:0.144399+0.012708    test-rmse:1.283186+0.238898 
## [78] train-rmse:0.141338+0.012816    test-rmse:1.282811+0.239307 
## [79] train-rmse:0.138309+0.011880    test-rmse:1.283380+0.239301 
## [80] train-rmse:0.133377+0.010567    test-rmse:1.283192+0.238867 
## [81] train-rmse:0.128937+0.009960    test-rmse:1.283417+0.240995 
## [82] train-rmse:0.124049+0.010725    test-rmse:1.283467+0.240629 
## [83] train-rmse:0.121818+0.010527    test-rmse:1.283002+0.241528 
## [84] train-rmse:0.118162+0.010547    test-rmse:1.282393+0.241789 
## [85] train-rmse:0.114552+0.010470    test-rmse:1.282204+0.242516 
## [86] train-rmse:0.111384+0.010761    test-rmse:1.280854+0.241665 
## [87] train-rmse:0.108215+0.009555    test-rmse:1.281100+0.241290 
## [88] train-rmse:0.105125+0.008823    test-rmse:1.281211+0.241222 
## [89] train-rmse:0.102144+0.008525    test-rmse:1.281277+0.240855 
## [90] train-rmse:0.099990+0.008829    test-rmse:1.281481+0.241520 
## [91] train-rmse:0.097387+0.008240    test-rmse:1.281241+0.241810 
## [92] train-rmse:0.094623+0.008787    test-rmse:1.281277+0.242090 
## Stopping. Best iteration:
## [86] train-rmse:0.111384+0.010761    test-rmse:1.280854+0.241665
## 
## 55 
## [1]  train-rmse:81.945409+0.093908   test-rmse:81.945506+0.502440 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:62.457670+0.078257   test-rmse:62.457233+0.536077 
## [3]  train-rmse:47.664860+0.068582   test-rmse:47.663622+0.569012 
## [4]  train-rmse:36.454471+0.064225   test-rmse:36.452043+0.603174 
## [5]  train-rmse:27.964685+0.060685   test-rmse:28.013021+0.606000 
## [6]  train-rmse:21.516921+0.044302   test-rmse:21.607782+0.599820 
## [7]  train-rmse:16.611249+0.036771   test-rmse:16.709704+0.552788 
## [8]  train-rmse:12.873152+0.031018   test-rmse:12.972887+0.531801 
## [9]  train-rmse:10.024280+0.029354   test-rmse:10.174735+0.575881 
## [10] train-rmse:7.847697+0.031522    test-rmse:8.005752+0.544642 
## [11] train-rmse:6.178972+0.036264    test-rmse:6.343513+0.503038 
## [12] train-rmse:4.904946+0.032873    test-rmse:5.067323+0.458663 
## [13] train-rmse:3.926009+0.024533    test-rmse:4.145320+0.465340 
## [14] train-rmse:3.178020+0.017111    test-rmse:3.455237+0.418345 
## [15] train-rmse:2.607629+0.011430    test-rmse:2.968947+0.441520 
## [16] train-rmse:2.166919+0.006395    test-rmse:2.581184+0.412348 
## [17] train-rmse:1.833274+0.004454    test-rmse:2.287488+0.388680 
## [18] train-rmse:1.562058+0.019022    test-rmse:2.073139+0.338250 
## [19] train-rmse:1.367637+0.020643    test-rmse:1.913477+0.309236 
## [20] train-rmse:1.203408+0.026309    test-rmse:1.801469+0.289940 
## [21] train-rmse:1.082884+0.027335    test-rmse:1.722078+0.270993 
## [22] train-rmse:0.978031+0.034034    test-rmse:1.669556+0.255578 
## [23] train-rmse:0.895748+0.032110    test-rmse:1.599569+0.255565 
## [24] train-rmse:0.823677+0.029588    test-rmse:1.558410+0.259476 
## [25] train-rmse:0.759777+0.029135    test-rmse:1.517646+0.250555 
## [26] train-rmse:0.706476+0.025171    test-rmse:1.473135+0.241412 
## [27] train-rmse:0.655306+0.023759    test-rmse:1.453494+0.248554 
## [28] train-rmse:0.616694+0.021803    test-rmse:1.424815+0.247998 
## [29] train-rmse:0.583292+0.021489    test-rmse:1.396845+0.238794 
## [30] train-rmse:0.547148+0.020321    test-rmse:1.382000+0.230618 
## [31] train-rmse:0.519330+0.019231    test-rmse:1.358210+0.237024 
## [32] train-rmse:0.488721+0.016424    test-rmse:1.344832+0.231663 
## [33] train-rmse:0.462032+0.018049    test-rmse:1.336076+0.239619 
## [34] train-rmse:0.440698+0.016371    test-rmse:1.323593+0.231279 
## [35] train-rmse:0.420156+0.017744    test-rmse:1.314293+0.227485 
## [36] train-rmse:0.400764+0.016658    test-rmse:1.306180+0.233446 
## [37] train-rmse:0.383260+0.016459    test-rmse:1.301393+0.230882 
## [38] train-rmse:0.366165+0.013877    test-rmse:1.289661+0.226428 
## [39] train-rmse:0.352945+0.013540    test-rmse:1.285003+0.225504 
## [40] train-rmse:0.337309+0.013562    test-rmse:1.281943+0.227225 
## [41] train-rmse:0.322840+0.014144    test-rmse:1.279579+0.228668 
## [42] train-rmse:0.309373+0.015500    test-rmse:1.273662+0.228868 
## [43] train-rmse:0.292646+0.017783    test-rmse:1.268317+0.229497 
## [44] train-rmse:0.280343+0.016832    test-rmse:1.265745+0.231413 
## [45] train-rmse:0.265971+0.014359    test-rmse:1.263123+0.229328 
## [46] train-rmse:0.255761+0.015811    test-rmse:1.262353+0.228554 
## [47] train-rmse:0.246514+0.014883    test-rmse:1.262737+0.229511 
## [48] train-rmse:0.235286+0.014960    test-rmse:1.258308+0.225214 
## [49] train-rmse:0.225621+0.014240    test-rmse:1.256343+0.224648 
## [50] train-rmse:0.218789+0.014386    test-rmse:1.254725+0.226475 
## [51] train-rmse:0.210227+0.013233    test-rmse:1.253538+0.226759 
## [52] train-rmse:0.200562+0.014190    test-rmse:1.253670+0.226825 
## [53] train-rmse:0.193579+0.013108    test-rmse:1.251897+0.228036 
## [54] train-rmse:0.185822+0.012435    test-rmse:1.250636+0.230215 
## [55] train-rmse:0.178894+0.012124    test-rmse:1.248596+0.230095 
## [56] train-rmse:0.172932+0.010613    test-rmse:1.248024+0.230269 
## [57] train-rmse:0.166789+0.010504    test-rmse:1.247404+0.230363 
## [58] train-rmse:0.161079+0.011735    test-rmse:1.247533+0.231269 
## [59] train-rmse:0.154992+0.009932    test-rmse:1.247417+0.231485 
## [60] train-rmse:0.149981+0.010655    test-rmse:1.246755+0.231803 
## [61] train-rmse:0.143679+0.009968    test-rmse:1.245323+0.232766 
## [62] train-rmse:0.138953+0.009526    test-rmse:1.244951+0.233691 
## [63] train-rmse:0.133542+0.008860    test-rmse:1.244077+0.234033 
## [64] train-rmse:0.128200+0.008204    test-rmse:1.245347+0.234781 
## [65] train-rmse:0.123493+0.007745    test-rmse:1.244816+0.234280 
## [66] train-rmse:0.119628+0.007497    test-rmse:1.245085+0.234383 
## [67] train-rmse:0.115093+0.007517    test-rmse:1.244525+0.234888 
## [68] train-rmse:0.110724+0.007692    test-rmse:1.243213+0.234906 
## [69] train-rmse:0.107110+0.007149    test-rmse:1.242201+0.235257 
## [70] train-rmse:0.103651+0.006883    test-rmse:1.242187+0.234818 
## [71] train-rmse:0.100878+0.006554    test-rmse:1.242206+0.235101 
## [72] train-rmse:0.097894+0.007134    test-rmse:1.242448+0.235124 
## [73] train-rmse:0.093607+0.007715    test-rmse:1.242185+0.235933 
## [74] train-rmse:0.090707+0.008061    test-rmse:1.242074+0.236372 
## [75] train-rmse:0.087651+0.007951    test-rmse:1.241650+0.235649 
## [76] train-rmse:0.085270+0.007562    test-rmse:1.241825+0.235777 
## [77] train-rmse:0.082628+0.008150    test-rmse:1.240999+0.236278 
## [78] train-rmse:0.079949+0.006971    test-rmse:1.240597+0.236097 
## [79] train-rmse:0.077235+0.007548    test-rmse:1.240986+0.236173 
## [80] train-rmse:0.074578+0.007089    test-rmse:1.240968+0.235865 
## [81] train-rmse:0.071770+0.007059    test-rmse:1.240785+0.235817 
## [82] train-rmse:0.069822+0.007592    test-rmse:1.240120+0.235625 
## [83] train-rmse:0.067238+0.007400    test-rmse:1.239605+0.236175 
## [84] train-rmse:0.064595+0.007626    test-rmse:1.239429+0.235918 
## [85] train-rmse:0.062118+0.007096    test-rmse:1.239705+0.236118 
## [86] train-rmse:0.059404+0.006291    test-rmse:1.239866+0.236706 
## [87] train-rmse:0.057909+0.006315    test-rmse:1.239834+0.236954 
## [88] train-rmse:0.056275+0.005760    test-rmse:1.239908+0.237102 
## [89] train-rmse:0.053624+0.005034    test-rmse:1.239834+0.237350 
## [90] train-rmse:0.051997+0.005144    test-rmse:1.239192+0.237622 
## [91] train-rmse:0.050597+0.005373    test-rmse:1.238785+0.237478 
## [92] train-rmse:0.049051+0.005382    test-rmse:1.238467+0.237791 
## [93] train-rmse:0.047086+0.005378    test-rmse:1.238936+0.237717 
## [94] train-rmse:0.045561+0.005417    test-rmse:1.238638+0.237976 
## [95] train-rmse:0.043914+0.005250    test-rmse:1.238464+0.238035 
## [96] train-rmse:0.042331+0.005086    test-rmse:1.238581+0.238008 
## [97] train-rmse:0.040968+0.004850    test-rmse:1.238564+0.238543 
## [98] train-rmse:0.039350+0.004636    test-rmse:1.238456+0.238291 
## [99] train-rmse:0.038088+0.004628    test-rmse:1.238350+0.238163 
## [100]    train-rmse:0.037003+0.004355    test-rmse:1.238369+0.238305 
## [101]    train-rmse:0.035840+0.003937    test-rmse:1.238261+0.237997 
## [102]    train-rmse:0.034747+0.004112    test-rmse:1.238016+0.238055 
## [103]    train-rmse:0.033617+0.003890    test-rmse:1.237546+0.238225 
## [104]    train-rmse:0.032422+0.003487    test-rmse:1.237583+0.238413 
## [105]    train-rmse:0.031109+0.003379    test-rmse:1.237166+0.237720 
## [106]    train-rmse:0.030192+0.003309    test-rmse:1.237159+0.237884 
## [107]    train-rmse:0.029419+0.003308    test-rmse:1.237014+0.237888 
## [108]    train-rmse:0.028556+0.003173    test-rmse:1.236870+0.238098 
## [109]    train-rmse:0.027427+0.003056    test-rmse:1.236771+0.238150 
## [110]    train-rmse:0.026667+0.002622    test-rmse:1.236863+0.237978 
## [111]    train-rmse:0.025912+0.002531    test-rmse:1.236751+0.238076 
## [112]    train-rmse:0.025353+0.002552    test-rmse:1.236855+0.238305 
## [113]    train-rmse:0.024828+0.002329    test-rmse:1.236746+0.238281 
## [114]    train-rmse:0.024098+0.002265    test-rmse:1.236662+0.238263 
## [115]    train-rmse:0.023594+0.002224    test-rmse:1.236621+0.238318 
## [116]    train-rmse:0.022963+0.002267    test-rmse:1.236489+0.238306 
## [117]    train-rmse:0.022172+0.002450    test-rmse:1.236203+0.238423 
## [118]    train-rmse:0.021435+0.002356    test-rmse:1.235987+0.238001 
## [119]    train-rmse:0.020796+0.002249    test-rmse:1.235861+0.237961 
## [120]    train-rmse:0.020210+0.002252    test-rmse:1.235833+0.237889 
## [121]    train-rmse:0.019278+0.002283    test-rmse:1.235661+0.237823 
## [122]    train-rmse:0.018720+0.002411    test-rmse:1.235695+0.237800 
## [123]    train-rmse:0.018102+0.002259    test-rmse:1.235642+0.237774 
## [124]    train-rmse:0.017434+0.002045    test-rmse:1.235509+0.237795 
## [125]    train-rmse:0.016905+0.002028    test-rmse:1.235409+0.237806 
## [126]    train-rmse:0.016265+0.002023    test-rmse:1.235363+0.237765 
## [127]    train-rmse:0.015626+0.001881    test-rmse:1.235460+0.237765 
## [128]    train-rmse:0.015055+0.001847    test-rmse:1.235377+0.237812 
## [129]    train-rmse:0.014470+0.001699    test-rmse:1.235406+0.237756 
## [130]    train-rmse:0.014142+0.001687    test-rmse:1.235384+0.237771 
## [131]    train-rmse:0.013608+0.001607    test-rmse:1.235324+0.237815 
## [132]    train-rmse:0.013248+0.001701    test-rmse:1.235334+0.237770 
## [133]    train-rmse:0.012972+0.001706    test-rmse:1.235257+0.237716 
## [134]    train-rmse:0.012588+0.001634    test-rmse:1.235166+0.237520 
## [135]    train-rmse:0.012292+0.001553    test-rmse:1.235119+0.237537 
## [136]    train-rmse:0.011967+0.001561    test-rmse:1.235116+0.237609 
## [137]    train-rmse:0.011572+0.001484    test-rmse:1.235084+0.237595 
## [138]    train-rmse:0.011177+0.001323    test-rmse:1.235050+0.237646 
## [139]    train-rmse:0.010789+0.001153    test-rmse:1.235070+0.237594 
## [140]    train-rmse:0.010433+0.001082    test-rmse:1.235060+0.237753 
## [141]    train-rmse:0.010187+0.001016    test-rmse:1.234944+0.237622 
## [142]    train-rmse:0.009867+0.000923    test-rmse:1.234997+0.237639 
## [143]    train-rmse:0.009560+0.000838    test-rmse:1.234945+0.237665 
## [144]    train-rmse:0.009279+0.000830    test-rmse:1.234954+0.237670 
## [145]    train-rmse:0.008955+0.000932    test-rmse:1.234994+0.237702 
## [146]    train-rmse:0.008659+0.000937    test-rmse:1.235026+0.237715 
## [147]    train-rmse:0.008355+0.000950    test-rmse:1.235048+0.237716 
## Stopping. Best iteration:
## [141]    train-rmse:0.010187+0.001016    test-rmse:1.234944+0.237622
## 
## 56 
## [1]  train-rmse:79.809479+0.092102   test-rmse:79.809535+0.505799 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:59.256192+0.075941   test-rmse:59.255628+0.542464 
## [3]  train-rmse:44.071015+0.066791   test-rmse:44.069481+0.578759 
## [4]  train-rmse:32.877449+0.063877   test-rmse:32.874400+0.617127 
## [5]  train-rmse:24.634475+0.060742   test-rmse:24.690776+0.625206 
## [6]  train-rmse:18.573545+0.055391   test-rmse:18.609407+0.621394 
## [7]  train-rmse:14.143569+0.051891   test-rmse:14.205308+0.597702 
## [8]  train-rmse:10.931946+0.050383   test-rmse:10.978015+0.588382 
## [9]  train-rmse:8.636010+0.051580    test-rmse:8.769294+0.635027 
## [10] train-rmse:7.025644+0.055247    test-rmse:7.142430+0.606061 
## [11] train-rmse:5.919090+0.058471    test-rmse:6.003713+0.562276 
## [12] train-rmse:5.171796+0.060088    test-rmse:5.299922+0.614464 
## [13] train-rmse:4.671005+0.060374    test-rmse:4.833563+0.576035 
## [14] train-rmse:4.338928+0.065031    test-rmse:4.479763+0.522340 
## [15] train-rmse:4.113815+0.064835    test-rmse:4.252527+0.469725 
## [16] train-rmse:3.948042+0.063418    test-rmse:4.098868+0.427799 
## [17] train-rmse:3.826532+0.061062    test-rmse:3.983302+0.425828 
## [18] train-rmse:3.727605+0.058472    test-rmse:3.924465+0.447582 
## [19] train-rmse:3.643206+0.059181    test-rmse:3.823848+0.384948 
## [20] train-rmse:3.570969+0.057627    test-rmse:3.779028+0.385576 
## [21] train-rmse:3.505118+0.057445    test-rmse:3.733223+0.391874 
## [22] train-rmse:3.443301+0.057131    test-rmse:3.692211+0.383069 
## [23] train-rmse:3.384345+0.054585    test-rmse:3.606337+0.335266 
## [24] train-rmse:3.329655+0.052102    test-rmse:3.557780+0.335683 
## [25] train-rmse:3.275569+0.050473    test-rmse:3.497089+0.342052 
## [26] train-rmse:3.224459+0.047754    test-rmse:3.453455+0.349163 
## [27] train-rmse:3.176153+0.046465    test-rmse:3.391236+0.313192 
## [28] train-rmse:3.130627+0.045176    test-rmse:3.367875+0.322023 
## [29] train-rmse:3.085913+0.043220    test-rmse:3.324723+0.309271 
## [30] train-rmse:3.042128+0.042642    test-rmse:3.284526+0.315184 
## [31] train-rmse:3.000215+0.042896    test-rmse:3.253518+0.323465 
## [32] train-rmse:2.959851+0.042357    test-rmse:3.220669+0.330662 
## [33] train-rmse:2.920841+0.042023    test-rmse:3.167170+0.294741 
## [34] train-rmse:2.882607+0.041136    test-rmse:3.141497+0.310211 
## [35] train-rmse:2.845701+0.041177    test-rmse:3.093701+0.309397 
## [36] train-rmse:2.809753+0.041172    test-rmse:3.061944+0.313596 
## [37] train-rmse:2.774004+0.041137    test-rmse:3.023282+0.309623 
## [38] train-rmse:2.738782+0.040702    test-rmse:2.984594+0.311667 
## [39] train-rmse:2.703897+0.041269    test-rmse:2.956680+0.317583 
## [40] train-rmse:2.669960+0.041054    test-rmse:2.899883+0.294403 
## [41] train-rmse:2.638000+0.040212    test-rmse:2.884652+0.297749 
## [42] train-rmse:2.606767+0.039753    test-rmse:2.853462+0.287784 
## [43] train-rmse:2.575377+0.039126    test-rmse:2.842830+0.290789 
## [44] train-rmse:2.545046+0.038159    test-rmse:2.812230+0.297312 
## [45] train-rmse:2.515315+0.037097    test-rmse:2.788086+0.308555 
## [46] train-rmse:2.487626+0.036629    test-rmse:2.768432+0.310905 
## [47] train-rmse:2.459873+0.036413    test-rmse:2.727722+0.281069 
## [48] train-rmse:2.432531+0.035417    test-rmse:2.701580+0.284734 
## [49] train-rmse:2.405908+0.035111    test-rmse:2.677022+0.284357 
## [50] train-rmse:2.379617+0.034789    test-rmse:2.640748+0.291497 
## [51] train-rmse:2.353399+0.033944    test-rmse:2.620551+0.292246 
## [52] train-rmse:2.328024+0.033408    test-rmse:2.606364+0.298628 
## [53] train-rmse:2.303504+0.033293    test-rmse:2.596157+0.305678 
## [54] train-rmse:2.279462+0.032974    test-rmse:2.570384+0.302735 
## [55] train-rmse:2.255495+0.032499    test-rmse:2.528953+0.295175 
## [56] train-rmse:2.231617+0.031896    test-rmse:2.490076+0.283343 
## [57] train-rmse:2.208006+0.030990    test-rmse:2.468546+0.265585 
## [58] train-rmse:2.185175+0.030879    test-rmse:2.445148+0.261373 
## [59] train-rmse:2.162560+0.030740    test-rmse:2.431730+0.263472 
## [60] train-rmse:2.140662+0.030238    test-rmse:2.411788+0.253667 
## [61] train-rmse:2.118710+0.029696    test-rmse:2.387990+0.263672 
## [62] train-rmse:2.096979+0.029127    test-rmse:2.380474+0.264291 
## [63] train-rmse:2.075571+0.028676    test-rmse:2.356812+0.258958 
## [64] train-rmse:2.054835+0.027800    test-rmse:2.338487+0.260357 
## [65] train-rmse:2.034195+0.026815    test-rmse:2.313322+0.268361 
## [66] train-rmse:2.013919+0.025923    test-rmse:2.288591+0.273806 
## [67] train-rmse:1.994346+0.025384    test-rmse:2.277435+0.275860 
## [68] train-rmse:1.974659+0.024591    test-rmse:2.252341+0.273510 
## [69] train-rmse:1.955723+0.023586    test-rmse:2.241631+0.262662 
## [70] train-rmse:1.936833+0.022406    test-rmse:2.212372+0.244564 
## [71] train-rmse:1.918480+0.021735    test-rmse:2.189974+0.254913 
## [72] train-rmse:1.900490+0.021123    test-rmse:2.172888+0.244981 
## [73] train-rmse:1.882604+0.020743    test-rmse:2.160842+0.246438 
## [74] train-rmse:1.865011+0.020740    test-rmse:2.132112+0.247987 
## [75] train-rmse:1.847601+0.020512    test-rmse:2.125387+0.250951 
## [76] train-rmse:1.830351+0.020401    test-rmse:2.112352+0.259194 
## [77] train-rmse:1.813647+0.019956    test-rmse:2.093888+0.244087 
## [78] train-rmse:1.797150+0.019354    test-rmse:2.079649+0.243054 
## [79] train-rmse:1.780980+0.018642    test-rmse:2.065201+0.242805 
## [80] train-rmse:1.764909+0.017885    test-rmse:2.055943+0.233342 
## [81] train-rmse:1.748846+0.017492    test-rmse:2.039019+0.235519 
## [82] train-rmse:1.733485+0.017170    test-rmse:2.025688+0.238217 
## [83] train-rmse:1.718182+0.016630    test-rmse:2.012199+0.247671 
## [84] train-rmse:1.703592+0.016118    test-rmse:1.998285+0.247093 
## [85] train-rmse:1.689488+0.016095    test-rmse:1.985400+0.237948 
## [86] train-rmse:1.675308+0.015984    test-rmse:1.967582+0.235698 
## [87] train-rmse:1.661161+0.015949    test-rmse:1.957580+0.236556 
## [88] train-rmse:1.646944+0.015819    test-rmse:1.944803+0.242410 
## [89] train-rmse:1.633021+0.015623    test-rmse:1.902446+0.232818 
## [90] train-rmse:1.619252+0.015436    test-rmse:1.897303+0.231609 
## [91] train-rmse:1.605417+0.015315    test-rmse:1.891689+0.235265 
## [92] train-rmse:1.592160+0.014930    test-rmse:1.879788+0.234758 
## [93] train-rmse:1.579240+0.014444    test-rmse:1.863553+0.234180 
## [94] train-rmse:1.566625+0.013977    test-rmse:1.855547+0.230631 
## [95] train-rmse:1.553996+0.013705    test-rmse:1.853264+0.228533 
## [96] train-rmse:1.541488+0.013436    test-rmse:1.828836+0.228316 
## [97] train-rmse:1.529199+0.013066    test-rmse:1.813957+0.227532 
## [98] train-rmse:1.516996+0.012714    test-rmse:1.797378+0.216332 
## [99] train-rmse:1.504814+0.012246    test-rmse:1.785123+0.221790 
## [100]    train-rmse:1.492712+0.012173    test-rmse:1.780279+0.224470 
## [101]    train-rmse:1.480625+0.011956    test-rmse:1.771165+0.220361 
## [102]    train-rmse:1.468968+0.011471    test-rmse:1.764931+0.223863 
## [103]    train-rmse:1.457872+0.011100    test-rmse:1.760592+0.225116 
## [104]    train-rmse:1.446883+0.010673    test-rmse:1.752113+0.225315 
## [105]    train-rmse:1.436063+0.010221    test-rmse:1.737617+0.229658 
## [106]    train-rmse:1.425280+0.010036    test-rmse:1.727315+0.231488 
## [107]    train-rmse:1.414656+0.009753    test-rmse:1.712684+0.229816 
## [108]    train-rmse:1.404377+0.009672    test-rmse:1.703646+0.224829 
## [109]    train-rmse:1.394292+0.009630    test-rmse:1.692654+0.218779 
## [110]    train-rmse:1.384245+0.009584    test-rmse:1.672464+0.200574 
## [111]    train-rmse:1.374255+0.009441    test-rmse:1.661372+0.200490 
## [112]    train-rmse:1.364349+0.009341    test-rmse:1.647809+0.204444 
## [113]    train-rmse:1.354574+0.009259    test-rmse:1.641566+0.202767 
## [114]    train-rmse:1.344991+0.009144    test-rmse:1.634869+0.207512 
## [115]    train-rmse:1.335466+0.008876    test-rmse:1.626292+0.208038 
## [116]    train-rmse:1.326029+0.008915    test-rmse:1.620434+0.208050 
## [117]    train-rmse:1.316810+0.008949    test-rmse:1.613346+0.209565 
## [118]    train-rmse:1.307901+0.008849    test-rmse:1.605677+0.206947 
## [119]    train-rmse:1.298980+0.008667    test-rmse:1.593422+0.198874 
## [120]    train-rmse:1.290266+0.008738    test-rmse:1.583876+0.204579 
## [121]    train-rmse:1.281615+0.008621    test-rmse:1.573359+0.204058 
## [122]    train-rmse:1.273300+0.008644    test-rmse:1.565469+0.201355 
## [123]    train-rmse:1.265137+0.008634    test-rmse:1.561021+0.201102 
## [124]    train-rmse:1.257066+0.008772    test-rmse:1.548482+0.200905 
## [125]    train-rmse:1.249093+0.008693    test-rmse:1.542710+0.203371 
## [126]    train-rmse:1.241156+0.008745    test-rmse:1.538180+0.203814 
## [127]    train-rmse:1.233335+0.008629    test-rmse:1.526687+0.207946 
## [128]    train-rmse:1.225800+0.008509    test-rmse:1.523401+0.207540 
## [129]    train-rmse:1.218352+0.008422    test-rmse:1.516421+0.209644 
## [130]    train-rmse:1.210998+0.008406    test-rmse:1.507512+0.208672 
## [131]    train-rmse:1.203528+0.008408    test-rmse:1.501995+0.206975 
## [132]    train-rmse:1.196183+0.008483    test-rmse:1.491135+0.201809 
## [133]    train-rmse:1.189015+0.008555    test-rmse:1.483138+0.186680 
## [134]    train-rmse:1.181924+0.008587    test-rmse:1.475697+0.189249 
## [135]    train-rmse:1.174920+0.008709    test-rmse:1.472460+0.181710 
## [136]    train-rmse:1.168067+0.008696    test-rmse:1.465941+0.183807 
## [137]    train-rmse:1.161380+0.008788    test-rmse:1.461813+0.181660 
## [138]    train-rmse:1.154774+0.008799    test-rmse:1.454882+0.185410 
## [139]    train-rmse:1.148355+0.008837    test-rmse:1.447546+0.182633 
## [140]    train-rmse:1.141982+0.008837    test-rmse:1.441857+0.186538 
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## [349]    train-rmse:0.728120+0.011637    test-rmse:1.057653+0.146310 
## [350]    train-rmse:0.727627+0.011616    test-rmse:1.056549+0.145902 
## [351]    train-rmse:0.727128+0.011628    test-rmse:1.056949+0.145370 
## [352]    train-rmse:0.726629+0.011628    test-rmse:1.057213+0.146001 
## [353]    train-rmse:0.726152+0.011598    test-rmse:1.056738+0.145877 
## [354]    train-rmse:0.725691+0.011577    test-rmse:1.057495+0.145905 
## [355]    train-rmse:0.725230+0.011569    test-rmse:1.058034+0.145435 
## [356]    train-rmse:0.724767+0.011559    test-rmse:1.056325+0.145039 
## [357]    train-rmse:0.724293+0.011568    test-rmse:1.055571+0.144412 
## [358]    train-rmse:0.723839+0.011560    test-rmse:1.056112+0.144705 
## [359]    train-rmse:0.723376+0.011580    test-rmse:1.056017+0.144873 
## [360]    train-rmse:0.722922+0.011573    test-rmse:1.055854+0.143609 
## [361]    train-rmse:0.722484+0.011568    test-rmse:1.054716+0.143463 
## [362]    train-rmse:0.722052+0.011551    test-rmse:1.054281+0.144666 
## [363]    train-rmse:0.721591+0.011526    test-rmse:1.053478+0.143198 
## [364]    train-rmse:0.721148+0.011532    test-rmse:1.053737+0.143579 
## [365]    train-rmse:0.720719+0.011545    test-rmse:1.052355+0.143997 
## [366]    train-rmse:0.720273+0.011536    test-rmse:1.052386+0.144311 
## [367]    train-rmse:0.719835+0.011537    test-rmse:1.053247+0.144019 
## [368]    train-rmse:0.719401+0.011524    test-rmse:1.052862+0.144766 
## [369]    train-rmse:0.718986+0.011507    test-rmse:1.052274+0.144167 
## [370]    train-rmse:0.718561+0.011499    test-rmse:1.051784+0.144191 
## [371]    train-rmse:0.718127+0.011487    test-rmse:1.052065+0.144439 
## [372]    train-rmse:0.717709+0.011477    test-rmse:1.050909+0.144639 
## [373]    train-rmse:0.717274+0.011428    test-rmse:1.051062+0.143704 
## [374]    train-rmse:0.716868+0.011412    test-rmse:1.049913+0.143328 
## [375]    train-rmse:0.716448+0.011376    test-rmse:1.050011+0.143540 
## [376]    train-rmse:0.716018+0.011366    test-rmse:1.050561+0.142086 
## [377]    train-rmse:0.715605+0.011353    test-rmse:1.050342+0.142605 
## [378]    train-rmse:0.715201+0.011342    test-rmse:1.050146+0.141226 
## [379]    train-rmse:0.714792+0.011331    test-rmse:1.048598+0.141271 
## [380]    train-rmse:0.714392+0.011325    test-rmse:1.047812+0.140994 
## [381]    train-rmse:0.713987+0.011291    test-rmse:1.047300+0.141411 
## [382]    train-rmse:0.713603+0.011284    test-rmse:1.047346+0.141082 
## [383]    train-rmse:0.713199+0.011265    test-rmse:1.046867+0.140905 
## [384]    train-rmse:0.712800+0.011257    test-rmse:1.046824+0.140854 
## [385]    train-rmse:0.712413+0.011244    test-rmse:1.047210+0.140260 
## [386]    train-rmse:0.712026+0.011231    test-rmse:1.045767+0.140168 
## [387]    train-rmse:0.711640+0.011219    test-rmse:1.045178+0.139160 
## [388]    train-rmse:0.711261+0.011205    test-rmse:1.043855+0.139211 
## [389]    train-rmse:0.710871+0.011200    test-rmse:1.044323+0.139809 
## [390]    train-rmse:0.710503+0.011187    test-rmse:1.043605+0.140758 
## [391]    train-rmse:0.710110+0.011163    test-rmse:1.043117+0.140094 
## [392]    train-rmse:0.709732+0.011151    test-rmse:1.042907+0.138858 
## [393]    train-rmse:0.709366+0.011138    test-rmse:1.042334+0.138284 
## [394]    train-rmse:0.708980+0.011130    test-rmse:1.043103+0.137706 
## [395]    train-rmse:0.708604+0.011118    test-rmse:1.043513+0.137910 
## [396]    train-rmse:0.708205+0.011139    test-rmse:1.043674+0.137860 
## [397]    train-rmse:0.707842+0.011135    test-rmse:1.042902+0.138134 
## [398]    train-rmse:0.707480+0.011141    test-rmse:1.043064+0.137721 
## [399]    train-rmse:0.707123+0.011138    test-rmse:1.044009+0.137111 
## Stopping. Best iteration:
## [393]    train-rmse:0.709366+0.011138    test-rmse:1.042334+0.138284
## 
## 57 
## [1]  train-rmse:79.809479+0.092102   test-rmse:79.809535+0.505799 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:59.256192+0.075941   test-rmse:59.255628+0.542464 
## [3]  train-rmse:44.071015+0.066791   test-rmse:44.069481+0.578759 
## [4]  train-rmse:32.877449+0.063877   test-rmse:32.874400+0.617127 
## [5]  train-rmse:24.618845+0.054846   test-rmse:24.675687+0.630838 
## [6]  train-rmse:18.523824+0.040246   test-rmse:18.603025+0.566420 
## [7]  train-rmse:14.038760+0.031048   test-rmse:14.150045+0.573013 
## [8]  train-rmse:10.763609+0.029543   test-rmse:10.854733+0.526361 
## [9]  train-rmse:8.395366+0.029747    test-rmse:8.485099+0.537584 
## [10] train-rmse:6.701759+0.037745    test-rmse:6.831168+0.560858 
## [11] train-rmse:5.510709+0.040196    test-rmse:5.658718+0.587676 
## [12] train-rmse:4.681621+0.036896    test-rmse:4.848968+0.523884 
## [13] train-rmse:4.115300+0.042750    test-rmse:4.315836+0.490857 
## [14] train-rmse:3.724777+0.037536    test-rmse:3.978559+0.477879 
## [15] train-rmse:3.449985+0.037318    test-rmse:3.668437+0.427493 
## [16] train-rmse:3.244994+0.046101    test-rmse:3.473936+0.384532 
## [17] train-rmse:3.096177+0.047244    test-rmse:3.352070+0.378223 
## [18] train-rmse:2.972492+0.043455    test-rmse:3.220401+0.350697 
## [19] train-rmse:2.865677+0.034914    test-rmse:3.145087+0.347987 
## [20] train-rmse:2.774897+0.033987    test-rmse:3.054364+0.331104 
## [21] train-rmse:2.694661+0.032215    test-rmse:3.001736+0.326110 
## [22] train-rmse:2.615632+0.030808    test-rmse:2.947552+0.309614 
## [23] train-rmse:2.543806+0.026636    test-rmse:2.895282+0.292367 
## [24] train-rmse:2.479995+0.025109    test-rmse:2.848669+0.305921 
## [25] train-rmse:2.415582+0.020045    test-rmse:2.771881+0.290644 
## [26] train-rmse:2.359060+0.020262    test-rmse:2.727940+0.287780 
## [27] train-rmse:2.303012+0.018763    test-rmse:2.688346+0.279984 
## [28] train-rmse:2.246595+0.017270    test-rmse:2.635403+0.280509 
## [29] train-rmse:2.192423+0.018323    test-rmse:2.601972+0.290267 
## [30] train-rmse:2.140963+0.015385    test-rmse:2.555196+0.279269 
## [31] train-rmse:2.092893+0.014887    test-rmse:2.501933+0.258591 
## [32] train-rmse:2.047490+0.013309    test-rmse:2.464498+0.267705 
## [33] train-rmse:2.000641+0.012118    test-rmse:2.420678+0.270201 
## [34] train-rmse:1.956874+0.009879    test-rmse:2.375642+0.267970 
## [35] train-rmse:1.916078+0.009774    test-rmse:2.335662+0.279608 
## [36] train-rmse:1.876192+0.008717    test-rmse:2.288222+0.285948 
## [37] train-rmse:1.837415+0.008460    test-rmse:2.248697+0.273956 
## [38] train-rmse:1.799231+0.007105    test-rmse:2.231597+0.270138 
## [39] train-rmse:1.761242+0.006353    test-rmse:2.185656+0.252172 
## [40] train-rmse:1.725182+0.003151    test-rmse:2.151969+0.254816 
## [41] train-rmse:1.688205+0.005344    test-rmse:2.118046+0.258138 
## [42] train-rmse:1.654100+0.004600    test-rmse:2.100135+0.261768 
## [43] train-rmse:1.622078+0.004437    test-rmse:2.072166+0.268418 
## [44] train-rmse:1.590829+0.005317    test-rmse:2.048466+0.250913 
## [45] train-rmse:1.560053+0.005936    test-rmse:2.027683+0.254391 
## [46] train-rmse:1.530726+0.006389    test-rmse:1.991671+0.254922 
## [47] train-rmse:1.500978+0.006515    test-rmse:1.971972+0.261644 
## [48] train-rmse:1.472486+0.006474    test-rmse:1.941537+0.259991 
## [49] train-rmse:1.445639+0.007254    test-rmse:1.921949+0.253564 
## [50] train-rmse:1.419524+0.007188    test-rmse:1.885935+0.245488 
## [51] train-rmse:1.392181+0.006011    test-rmse:1.866855+0.248570 
## [52] train-rmse:1.366065+0.006518    test-rmse:1.852972+0.250687 
## [53] train-rmse:1.340468+0.006648    test-rmse:1.838942+0.245942 
## [54] train-rmse:1.315693+0.006652    test-rmse:1.822805+0.254838 
## [55] train-rmse:1.293365+0.007505    test-rmse:1.799294+0.259006 
## [56] train-rmse:1.269975+0.009130    test-rmse:1.772896+0.262980 
## [57] train-rmse:1.248643+0.009744    test-rmse:1.752850+0.253844 
## [58] train-rmse:1.227687+0.010029    test-rmse:1.735310+0.252577 
## [59] train-rmse:1.206682+0.010034    test-rmse:1.715497+0.258926 
## [60] train-rmse:1.185912+0.010747    test-rmse:1.705567+0.253399 
## [61] train-rmse:1.164728+0.011856    test-rmse:1.692500+0.250599 
## [62] train-rmse:1.146157+0.011971    test-rmse:1.678937+0.245855 
## [63] train-rmse:1.126239+0.013797    test-rmse:1.663849+0.250736 
## [64] train-rmse:1.108678+0.013660    test-rmse:1.648684+0.254211 
## [65] train-rmse:1.091439+0.013835    test-rmse:1.634951+0.248434 
## [66] train-rmse:1.074522+0.014509    test-rmse:1.625371+0.252740 
## [67] train-rmse:1.058831+0.015050    test-rmse:1.612850+0.247897 
## [68] train-rmse:1.043086+0.014262    test-rmse:1.598380+0.250523 
## [69] train-rmse:1.028281+0.014497    test-rmse:1.585079+0.248292 
## [70] train-rmse:1.013238+0.014898    test-rmse:1.570629+0.255353 
## [71] train-rmse:0.998363+0.014618    test-rmse:1.556169+0.250724 
## [72] train-rmse:0.983866+0.016143    test-rmse:1.545816+0.257653 
## [73] train-rmse:0.967890+0.016915    test-rmse:1.532229+0.255111 
## [74] train-rmse:0.954796+0.017302    test-rmse:1.519934+0.257850 
## [75] train-rmse:0.940909+0.017935    test-rmse:1.505471+0.255223 
## [76] train-rmse:0.927772+0.017361    test-rmse:1.495486+0.257695 
## [77] train-rmse:0.915151+0.016739    test-rmse:1.489795+0.264216 
## [78] train-rmse:0.903260+0.017428    test-rmse:1.484092+0.261950 
## [79] train-rmse:0.891878+0.017383    test-rmse:1.478563+0.264849 
## [80] train-rmse:0.880124+0.018124    test-rmse:1.476140+0.266085 
## [81] train-rmse:0.869641+0.018062    test-rmse:1.460086+0.264290 
## [82] train-rmse:0.857144+0.019040    test-rmse:1.451690+0.262241 
## [83] train-rmse:0.845922+0.020217    test-rmse:1.440155+0.262233 
## [84] train-rmse:0.835317+0.020997    test-rmse:1.432471+0.262821 
## [85] train-rmse:0.824890+0.021479    test-rmse:1.425156+0.264028 
## [86] train-rmse:0.815277+0.021888    test-rmse:1.419965+0.264610 
## [87] train-rmse:0.804335+0.022791    test-rmse:1.413419+0.265322 
## [88] train-rmse:0.795245+0.023772    test-rmse:1.399059+0.257067 
## [89] train-rmse:0.786325+0.023463    test-rmse:1.392480+0.254250 
## [90] train-rmse:0.777414+0.023966    test-rmse:1.387723+0.254026 
## [91] train-rmse:0.767145+0.020678    test-rmse:1.386183+0.254281 
## [92] train-rmse:0.758676+0.020408    test-rmse:1.379914+0.251773 
## [93] train-rmse:0.750774+0.019942    test-rmse:1.369887+0.254088 
## [94] train-rmse:0.741433+0.020623    test-rmse:1.366462+0.253813 
## [95] train-rmse:0.733206+0.020253    test-rmse:1.359006+0.253304 
## [96] train-rmse:0.725304+0.020923    test-rmse:1.354280+0.256455 
## [97] train-rmse:0.717914+0.021545    test-rmse:1.346315+0.258400 
## [98] train-rmse:0.711570+0.021362    test-rmse:1.336604+0.259170 
## [99] train-rmse:0.704820+0.021246    test-rmse:1.334758+0.260143 
## [100]    train-rmse:0.697190+0.021670    test-rmse:1.328012+0.257064 
## [101]    train-rmse:0.689436+0.021531    test-rmse:1.326268+0.258230 
## [102]    train-rmse:0.683273+0.021433    test-rmse:1.321747+0.261845 
## [103]    train-rmse:0.677493+0.021501    test-rmse:1.314314+0.255212 
## [104]    train-rmse:0.671088+0.021965    test-rmse:1.310485+0.258629 
## [105]    train-rmse:0.663616+0.020167    test-rmse:1.308256+0.256950 
## [106]    train-rmse:0.658091+0.019967    test-rmse:1.304879+0.258673 
## [107]    train-rmse:0.651242+0.020799    test-rmse:1.299043+0.261472 
## [108]    train-rmse:0.645282+0.020477    test-rmse:1.297115+0.264346 
## [109]    train-rmse:0.639999+0.020641    test-rmse:1.291014+0.266860 
## [110]    train-rmse:0.633723+0.020124    test-rmse:1.283345+0.268709 
## [111]    train-rmse:0.628679+0.020321    test-rmse:1.280023+0.268865 
## [112]    train-rmse:0.623490+0.019719    test-rmse:1.275926+0.267940 
## [113]    train-rmse:0.618249+0.020233    test-rmse:1.272196+0.264928 
## [114]    train-rmse:0.612913+0.021305    test-rmse:1.269353+0.266654 
## [115]    train-rmse:0.608405+0.021339    test-rmse:1.264044+0.267976 
## [116]    train-rmse:0.603136+0.021617    test-rmse:1.262660+0.270397 
## [117]    train-rmse:0.597040+0.021111    test-rmse:1.255187+0.270267 
## [118]    train-rmse:0.592283+0.020789    test-rmse:1.254462+0.272732 
## [119]    train-rmse:0.587665+0.021075    test-rmse:1.253172+0.274947 
## [120]    train-rmse:0.582495+0.021073    test-rmse:1.253094+0.275675 
## [121]    train-rmse:0.578168+0.020984    test-rmse:1.249382+0.275855 
## [122]    train-rmse:0.574122+0.020976    test-rmse:1.250154+0.276827 
## [123]    train-rmse:0.569533+0.020877    test-rmse:1.247937+0.277947 
## [124]    train-rmse:0.564223+0.020933    test-rmse:1.247834+0.281349 
## [125]    train-rmse:0.561045+0.020732    test-rmse:1.242408+0.283413 
## [126]    train-rmse:0.557382+0.020893    test-rmse:1.239840+0.280709 
## [127]    train-rmse:0.552912+0.020604    test-rmse:1.236279+0.275885 
## [128]    train-rmse:0.549635+0.020624    test-rmse:1.235791+0.278105 
## [129]    train-rmse:0.545934+0.020947    test-rmse:1.235096+0.278633 
## [130]    train-rmse:0.541287+0.021201    test-rmse:1.231694+0.278760 
## [131]    train-rmse:0.536569+0.022838    test-rmse:1.229861+0.278606 
## [132]    train-rmse:0.533610+0.023168    test-rmse:1.223431+0.281248 
## [133]    train-rmse:0.529773+0.023229    test-rmse:1.219501+0.282294 
## [134]    train-rmse:0.525418+0.022239    test-rmse:1.215762+0.281582 
## [135]    train-rmse:0.522182+0.022005    test-rmse:1.214782+0.283467 
## [136]    train-rmse:0.519286+0.022146    test-rmse:1.213337+0.283009 
## [137]    train-rmse:0.516536+0.022119    test-rmse:1.213258+0.283681 
## [138]    train-rmse:0.513357+0.022485    test-rmse:1.211821+0.283259 
## [139]    train-rmse:0.510442+0.022678    test-rmse:1.209970+0.284651 
## [140]    train-rmse:0.507392+0.022245    test-rmse:1.209720+0.286919 
## [141]    train-rmse:0.504763+0.021940    test-rmse:1.205392+0.287069 
## [142]    train-rmse:0.501362+0.022332    test-rmse:1.205294+0.287177 
## [143]    train-rmse:0.498077+0.022319    test-rmse:1.203920+0.289837 
## [144]    train-rmse:0.495731+0.022140    test-rmse:1.200885+0.290929 
## [145]    train-rmse:0.493449+0.021980    test-rmse:1.198639+0.290623 
## [146]    train-rmse:0.488654+0.020874    test-rmse:1.197036+0.294167 
## [147]    train-rmse:0.485681+0.020299    test-rmse:1.196461+0.291946 
## [148]    train-rmse:0.482496+0.021415    test-rmse:1.196308+0.291366 
## [149]    train-rmse:0.480063+0.021139    test-rmse:1.194376+0.290752 
## [150]    train-rmse:0.477874+0.021277    test-rmse:1.192956+0.291400 
## [151]    train-rmse:0.475827+0.021126    test-rmse:1.190246+0.290915 
## [152]    train-rmse:0.473469+0.021077    test-rmse:1.189726+0.290065 
## [153]    train-rmse:0.470993+0.020980    test-rmse:1.188467+0.291335 
## [154]    train-rmse:0.468662+0.020937    test-rmse:1.186967+0.291740 
## [155]    train-rmse:0.465856+0.020575    test-rmse:1.185294+0.292078 
## [156]    train-rmse:0.463231+0.020494    test-rmse:1.185225+0.292588 
## [157]    train-rmse:0.460646+0.020735    test-rmse:1.184055+0.293696 
## [158]    train-rmse:0.458782+0.020445    test-rmse:1.183304+0.293333 
## [159]    train-rmse:0.456825+0.020720    test-rmse:1.182148+0.292270 
## [160]    train-rmse:0.454991+0.020778    test-rmse:1.180047+0.292748 
## [161]    train-rmse:0.452911+0.020960    test-rmse:1.174233+0.285402 
## [162]    train-rmse:0.451279+0.021021    test-rmse:1.174109+0.284890 
## [163]    train-rmse:0.449238+0.021202    test-rmse:1.171434+0.286995 
## [164]    train-rmse:0.446113+0.020766    test-rmse:1.169179+0.287128 
## [165]    train-rmse:0.444429+0.020569    test-rmse:1.166853+0.286704 
## [166]    train-rmse:0.441955+0.021493    test-rmse:1.164599+0.287479 
## [167]    train-rmse:0.439570+0.021909    test-rmse:1.164188+0.287435 
## [168]    train-rmse:0.436810+0.021924    test-rmse:1.163973+0.288689 
## [169]    train-rmse:0.435147+0.021793    test-rmse:1.162632+0.289031 
## [170]    train-rmse:0.433562+0.021853    test-rmse:1.162708+0.289666 
## [171]    train-rmse:0.431226+0.022232    test-rmse:1.163365+0.289439 
## [172]    train-rmse:0.429664+0.022329    test-rmse:1.162074+0.289424 
## [173]    train-rmse:0.427919+0.022303    test-rmse:1.163097+0.288795 
## [174]    train-rmse:0.426008+0.022623    test-rmse:1.163992+0.289354 
## [175]    train-rmse:0.422597+0.020754    test-rmse:1.163123+0.290913 
## [176]    train-rmse:0.420810+0.020726    test-rmse:1.162750+0.289952 
## [177]    train-rmse:0.418883+0.019926    test-rmse:1.160292+0.289636 
## [178]    train-rmse:0.417256+0.020031    test-rmse:1.159548+0.290604 
## [179]    train-rmse:0.416037+0.020049    test-rmse:1.158583+0.291132 
## [180]    train-rmse:0.413716+0.019376    test-rmse:1.157732+0.291379 
## [181]    train-rmse:0.412233+0.019350    test-rmse:1.154965+0.288060 
## [182]    train-rmse:0.410434+0.019270    test-rmse:1.154313+0.287832 
## [183]    train-rmse:0.408088+0.019507    test-rmse:1.152615+0.287251 
## [184]    train-rmse:0.406418+0.019271    test-rmse:1.153318+0.287631 
## [185]    train-rmse:0.405047+0.019440    test-rmse:1.153198+0.287996 
## [186]    train-rmse:0.403625+0.019288    test-rmse:1.152960+0.288108 
## [187]    train-rmse:0.400780+0.018416    test-rmse:1.150773+0.286942 
## [188]    train-rmse:0.399271+0.018273    test-rmse:1.150407+0.288019 
## [189]    train-rmse:0.396878+0.017741    test-rmse:1.150319+0.287738 
## [190]    train-rmse:0.394944+0.016933    test-rmse:1.148738+0.288775 
## [191]    train-rmse:0.393497+0.016947    test-rmse:1.148184+0.289107 
## [192]    train-rmse:0.391267+0.016455    test-rmse:1.147790+0.288801 
## [193]    train-rmse:0.388591+0.015189    test-rmse:1.147081+0.289556 
## [194]    train-rmse:0.386647+0.015328    test-rmse:1.148219+0.291647 
## [195]    train-rmse:0.385279+0.015740    test-rmse:1.148160+0.290799 
## [196]    train-rmse:0.384055+0.015734    test-rmse:1.146851+0.290937 
## [197]    train-rmse:0.380929+0.014958    test-rmse:1.146044+0.290660 
## [198]    train-rmse:0.379343+0.015263    test-rmse:1.145665+0.290356 
## [199]    train-rmse:0.378111+0.015314    test-rmse:1.144366+0.289938 
## [200]    train-rmse:0.375947+0.014075    test-rmse:1.143096+0.288736 
## [201]    train-rmse:0.374599+0.013809    test-rmse:1.143024+0.288399 
## [202]    train-rmse:0.373426+0.013495    test-rmse:1.142150+0.287985 
## [203]    train-rmse:0.371923+0.012656    test-rmse:1.140387+0.288359 
## [204]    train-rmse:0.370299+0.012595    test-rmse:1.140123+0.288766 
## [205]    train-rmse:0.368291+0.012997    test-rmse:1.141146+0.289045 
## [206]    train-rmse:0.366378+0.013032    test-rmse:1.140877+0.289597 
## [207]    train-rmse:0.363885+0.013895    test-rmse:1.140365+0.287405 
## [208]    train-rmse:0.361869+0.013837    test-rmse:1.139846+0.286753 
## [209]    train-rmse:0.359688+0.014083    test-rmse:1.139482+0.286890 
## [210]    train-rmse:0.358316+0.013825    test-rmse:1.138764+0.286917 
## [211]    train-rmse:0.357130+0.013333    test-rmse:1.138631+0.287243 
## [212]    train-rmse:0.355539+0.013261    test-rmse:1.136944+0.285259 
## [213]    train-rmse:0.353267+0.013129    test-rmse:1.136255+0.285871 
## [214]    train-rmse:0.352298+0.013102    test-rmse:1.136128+0.285707 
## [215]    train-rmse:0.351007+0.013235    test-rmse:1.134697+0.286435 
## [216]    train-rmse:0.350002+0.013055    test-rmse:1.134389+0.287356 
## [217]    train-rmse:0.348598+0.013366    test-rmse:1.133480+0.286926 
## [218]    train-rmse:0.347237+0.013664    test-rmse:1.133746+0.287193 
## [219]    train-rmse:0.346176+0.013758    test-rmse:1.132796+0.287813 
## [220]    train-rmse:0.345378+0.013549    test-rmse:1.132111+0.287955 
## [221]    train-rmse:0.343329+0.013506    test-rmse:1.130890+0.287993 
## [222]    train-rmse:0.341841+0.013452    test-rmse:1.130369+0.288747 
## [223]    train-rmse:0.340152+0.013130    test-rmse:1.130114+0.288530 
## [224]    train-rmse:0.339239+0.012660    test-rmse:1.129468+0.288298 
## [225]    train-rmse:0.338353+0.012561    test-rmse:1.128938+0.288067 
## [226]    train-rmse:0.337496+0.012409    test-rmse:1.129129+0.288722 
## [227]    train-rmse:0.335448+0.012421    test-rmse:1.128128+0.287348 
## [228]    train-rmse:0.333734+0.012960    test-rmse:1.127051+0.285633 
## [229]    train-rmse:0.332296+0.013108    test-rmse:1.125777+0.285159 
## [230]    train-rmse:0.330971+0.012995    test-rmse:1.125983+0.286509 
## [231]    train-rmse:0.330099+0.012737    test-rmse:1.126748+0.286658 
## [232]    train-rmse:0.328102+0.012495    test-rmse:1.125641+0.287272 
## [233]    train-rmse:0.326889+0.012302    test-rmse:1.125750+0.286948 
## [234]    train-rmse:0.325585+0.011924    test-rmse:1.125435+0.286821 
## [235]    train-rmse:0.324499+0.011643    test-rmse:1.124466+0.287245 
## [236]    train-rmse:0.323615+0.011599    test-rmse:1.123926+0.287573 
## [237]    train-rmse:0.322378+0.012220    test-rmse:1.123282+0.288616 
## [238]    train-rmse:0.321191+0.011527    test-rmse:1.123292+0.287047 
## [239]    train-rmse:0.319854+0.011795    test-rmse:1.122780+0.287457 
## [240]    train-rmse:0.319276+0.011641    test-rmse:1.122365+0.288112 
## [241]    train-rmse:0.317706+0.010957    test-rmse:1.122240+0.288628 
## [242]    train-rmse:0.316688+0.011050    test-rmse:1.121318+0.287927 
## [243]    train-rmse:0.315514+0.011132    test-rmse:1.121388+0.287642 
## [244]    train-rmse:0.314428+0.011374    test-rmse:1.121904+0.287694 
## [245]    train-rmse:0.313649+0.011454    test-rmse:1.121918+0.287532 
## [246]    train-rmse:0.312858+0.011138    test-rmse:1.120997+0.286896 
## [247]    train-rmse:0.311455+0.011220    test-rmse:1.120527+0.287321 
## [248]    train-rmse:0.310497+0.011226    test-rmse:1.120499+0.287243 
## [249]    train-rmse:0.309231+0.011220    test-rmse:1.120266+0.287506 
## [250]    train-rmse:0.307567+0.011719    test-rmse:1.119713+0.287100 
## [251]    train-rmse:0.306612+0.011722    test-rmse:1.119279+0.287348 
## [252]    train-rmse:0.305414+0.011918    test-rmse:1.118701+0.287710 
## [253]    train-rmse:0.304732+0.011885    test-rmse:1.118614+0.287762 
## [254]    train-rmse:0.303012+0.011801    test-rmse:1.118778+0.287855 
## [255]    train-rmse:0.301751+0.012117    test-rmse:1.118048+0.287992 
## [256]    train-rmse:0.300619+0.012364    test-rmse:1.117373+0.288178 
## [257]    train-rmse:0.299171+0.012553    test-rmse:1.117482+0.288204 
## [258]    train-rmse:0.298088+0.012692    test-rmse:1.116833+0.288214 
## [259]    train-rmse:0.296828+0.012226    test-rmse:1.116202+0.288759 
## [260]    train-rmse:0.295631+0.012578    test-rmse:1.114852+0.289586 
## [261]    train-rmse:0.294724+0.012665    test-rmse:1.114791+0.290283 
## [262]    train-rmse:0.293282+0.012271    test-rmse:1.116280+0.290112 
## [263]    train-rmse:0.292726+0.012154    test-rmse:1.115618+0.289823 
## [264]    train-rmse:0.291482+0.012658    test-rmse:1.116137+0.289725 
## [265]    train-rmse:0.290112+0.012833    test-rmse:1.116982+0.290136 
## [266]    train-rmse:0.289286+0.012961    test-rmse:1.117491+0.289647 
## [267]    train-rmse:0.288678+0.012974    test-rmse:1.116567+0.289394 
## Stopping. Best iteration:
## [261]    train-rmse:0.294724+0.012665    test-rmse:1.114791+0.290283
## 
## 58 
## [1]  train-rmse:79.809479+0.092102   test-rmse:79.809535+0.505799 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:59.256192+0.075941   test-rmse:59.255628+0.542464 
## [3]  train-rmse:44.071015+0.066791   test-rmse:44.069481+0.578759 
## [4]  train-rmse:32.877449+0.063877   test-rmse:32.874400+0.617127 
## [5]  train-rmse:24.614156+0.051485   test-rmse:24.681269+0.627135 
## [6]  train-rmse:18.506317+0.036734   test-rmse:18.594059+0.596130 
## [7]  train-rmse:14.002013+0.029239   test-rmse:14.074514+0.529601 
## [8]  train-rmse:10.693251+0.027448   test-rmse:10.798520+0.564686 
## [9]  train-rmse:8.273753+0.029730    test-rmse:8.358817+0.557105 
## [10] train-rmse:6.514360+0.035823    test-rmse:6.644401+0.548786 
## [11] train-rmse:5.258885+0.048743    test-rmse:5.404754+0.507402 
## [12] train-rmse:4.364118+0.058693    test-rmse:4.498511+0.436104 
## [13] train-rmse:3.744103+0.054990    test-rmse:3.924537+0.436294 
## [14] train-rmse:3.299108+0.050096    test-rmse:3.532879+0.422723 
## [15] train-rmse:2.988710+0.052487    test-rmse:3.258235+0.381828 
## [16] train-rmse:2.758348+0.047292    test-rmse:3.038487+0.326751 
## [17] train-rmse:2.584406+0.050466    test-rmse:2.887138+0.290905 
## [18] train-rmse:2.441488+0.049342    test-rmse:2.764575+0.281589 
## [19] train-rmse:2.327198+0.044394    test-rmse:2.655996+0.287138 
## [20] train-rmse:2.231152+0.042987    test-rmse:2.572827+0.293801 
## [21] train-rmse:2.139228+0.037538    test-rmse:2.514186+0.280575 
## [22] train-rmse:2.056273+0.035608    test-rmse:2.439781+0.266171 
## [23] train-rmse:1.981731+0.033937    test-rmse:2.372428+0.260549 
## [24] train-rmse:1.910377+0.035367    test-rmse:2.317149+0.291686 
## [25] train-rmse:1.844744+0.031660    test-rmse:2.266827+0.298508 
## [26] train-rmse:1.781235+0.030497    test-rmse:2.203532+0.300823 
## [27] train-rmse:1.721650+0.029095    test-rmse:2.164712+0.283426 
## [28] train-rmse:1.666336+0.024908    test-rmse:2.124022+0.273105 
## [29] train-rmse:1.612430+0.024291    test-rmse:2.071570+0.274195 
## [30] train-rmse:1.560648+0.026156    test-rmse:2.027130+0.281178 
## [31] train-rmse:1.513081+0.024782    test-rmse:1.974549+0.282878 
## [32] train-rmse:1.467303+0.023088    test-rmse:1.941073+0.290210 
## [33] train-rmse:1.423722+0.021889    test-rmse:1.897445+0.279042 
## [34] train-rmse:1.381878+0.020950    test-rmse:1.857728+0.285803 
## [35] train-rmse:1.339266+0.024483    test-rmse:1.825220+0.275765 
## [36] train-rmse:1.300991+0.023568    test-rmse:1.791275+0.283012 
## [37] train-rmse:1.260623+0.023050    test-rmse:1.761150+0.271335 
## [38] train-rmse:1.224650+0.020216    test-rmse:1.734687+0.275572 
## [39] train-rmse:1.190557+0.018513    test-rmse:1.712979+0.281662 
## [40] train-rmse:1.157900+0.016855    test-rmse:1.682496+0.279131 
## [41] train-rmse:1.123524+0.018059    test-rmse:1.649973+0.277178 
## [42] train-rmse:1.093392+0.017625    test-rmse:1.618723+0.275178 
## [43] train-rmse:1.064672+0.017158    test-rmse:1.591451+0.270729 
## [44] train-rmse:1.036351+0.017184    test-rmse:1.566810+0.279177 
## [45] train-rmse:1.010859+0.017563    test-rmse:1.549730+0.272851 
## [46] train-rmse:0.985147+0.017630    test-rmse:1.535277+0.278462 
## [47] train-rmse:0.962336+0.017330    test-rmse:1.514352+0.280747 
## [48] train-rmse:0.938477+0.016209    test-rmse:1.503368+0.283314 
## [49] train-rmse:0.914929+0.016132    test-rmse:1.486259+0.281551 
## [50] train-rmse:0.893219+0.016048    test-rmse:1.469454+0.279990 
## [51] train-rmse:0.871511+0.015371    test-rmse:1.442725+0.279442 
## [52] train-rmse:0.850285+0.015961    test-rmse:1.429372+0.289364 
## [53] train-rmse:0.828570+0.018237    test-rmse:1.414840+0.294961 
## [54] train-rmse:0.810030+0.018200    test-rmse:1.405934+0.290783 
## [55] train-rmse:0.792748+0.016753    test-rmse:1.392710+0.300281 
## [56] train-rmse:0.775993+0.016568    test-rmse:1.379885+0.301844 
## [57] train-rmse:0.757456+0.018519    test-rmse:1.371121+0.299794 
## [58] train-rmse:0.739394+0.020496    test-rmse:1.357869+0.298942 
## [59] train-rmse:0.723399+0.019742    test-rmse:1.345274+0.295427 
## [60] train-rmse:0.707587+0.019114    test-rmse:1.332716+0.302018 
## [61] train-rmse:0.692294+0.020260    test-rmse:1.326938+0.296491 
## [62] train-rmse:0.677077+0.020924    test-rmse:1.321333+0.294959 
## [63] train-rmse:0.664323+0.020690    test-rmse:1.312222+0.295761 
## [64] train-rmse:0.652202+0.020920    test-rmse:1.302210+0.297726 
## [65] train-rmse:0.640488+0.022572    test-rmse:1.296198+0.298559 
## [66] train-rmse:0.628936+0.023125    test-rmse:1.283255+0.300537 
## [67] train-rmse:0.618440+0.023297    test-rmse:1.278454+0.302627 
## [68] train-rmse:0.606413+0.020542    test-rmse:1.271731+0.302731 
## [69] train-rmse:0.596580+0.020603    test-rmse:1.266253+0.302137 
## [70] train-rmse:0.585684+0.020320    test-rmse:1.261441+0.302218 
## [71] train-rmse:0.573870+0.020397    test-rmse:1.256289+0.302620 
## [72] train-rmse:0.563668+0.018420    test-rmse:1.248077+0.304356 
## [73] train-rmse:0.555104+0.017683    test-rmse:1.237860+0.304615 
## [74] train-rmse:0.546148+0.017202    test-rmse:1.234458+0.305431 
## [75] train-rmse:0.536966+0.018157    test-rmse:1.227535+0.307093 
## [76] train-rmse:0.527921+0.016036    test-rmse:1.224672+0.309836 
## [77] train-rmse:0.519218+0.014432    test-rmse:1.224250+0.314249 
## [78] train-rmse:0.510949+0.014921    test-rmse:1.222839+0.314961 
## [79] train-rmse:0.502198+0.015250    test-rmse:1.218415+0.317486 
## [80] train-rmse:0.493168+0.016803    test-rmse:1.215569+0.318687 
## [81] train-rmse:0.486400+0.016031    test-rmse:1.212176+0.319078 
## [82] train-rmse:0.480049+0.016447    test-rmse:1.205914+0.320915 
## [83] train-rmse:0.473919+0.015831    test-rmse:1.200909+0.325934 
## [84] train-rmse:0.466192+0.015385    test-rmse:1.198371+0.327360 
## [85] train-rmse:0.458469+0.016286    test-rmse:1.196133+0.329575 
## [86] train-rmse:0.453265+0.015940    test-rmse:1.189451+0.326547 
## [87] train-rmse:0.446902+0.016519    test-rmse:1.186076+0.323738 
## [88] train-rmse:0.441207+0.017162    test-rmse:1.183668+0.325577 
## [89] train-rmse:0.436031+0.017169    test-rmse:1.175982+0.326289 
## [90] train-rmse:0.429591+0.015974    test-rmse:1.174695+0.327139 
## [91] train-rmse:0.424334+0.016706    test-rmse:1.173661+0.326774 
## [92] train-rmse:0.419174+0.016637    test-rmse:1.173020+0.326699 
## [93] train-rmse:0.414554+0.016773    test-rmse:1.167871+0.328084 
## [94] train-rmse:0.408835+0.018394    test-rmse:1.163513+0.329184 
## [95] train-rmse:0.403875+0.018806    test-rmse:1.162236+0.329947 
## [96] train-rmse:0.398417+0.019752    test-rmse:1.161824+0.331943 
## [97] train-rmse:0.393343+0.019213    test-rmse:1.160457+0.333195 
## [98] train-rmse:0.387919+0.017936    test-rmse:1.158857+0.335774 
## [99] train-rmse:0.383946+0.017656    test-rmse:1.156038+0.337922 
## [100]    train-rmse:0.378492+0.016723    test-rmse:1.152404+0.336480 
## [101]    train-rmse:0.373488+0.017040    test-rmse:1.150796+0.338346 
## [102]    train-rmse:0.368739+0.016699    test-rmse:1.150291+0.338208 
## [103]    train-rmse:0.364239+0.016182    test-rmse:1.149902+0.336082 
## [104]    train-rmse:0.359565+0.015642    test-rmse:1.150282+0.335753 
## [105]    train-rmse:0.355926+0.015131    test-rmse:1.149264+0.335069 
## [106]    train-rmse:0.351708+0.014588    test-rmse:1.148948+0.339906 
## [107]    train-rmse:0.348381+0.014458    test-rmse:1.147208+0.340111 
## [108]    train-rmse:0.345400+0.014490    test-rmse:1.145463+0.340661 
## [109]    train-rmse:0.342429+0.014582    test-rmse:1.143658+0.339463 
## [110]    train-rmse:0.339164+0.015028    test-rmse:1.141499+0.340530 
## [111]    train-rmse:0.335559+0.014035    test-rmse:1.139255+0.337756 
## [112]    train-rmse:0.332007+0.015322    test-rmse:1.136160+0.338157 
## [113]    train-rmse:0.327696+0.016901    test-rmse:1.134095+0.339689 
## [114]    train-rmse:0.324575+0.016476    test-rmse:1.131809+0.340083 
## [115]    train-rmse:0.320014+0.016493    test-rmse:1.131941+0.341327 
## [116]    train-rmse:0.316915+0.015369    test-rmse:1.130504+0.340055 
## [117]    train-rmse:0.314080+0.015733    test-rmse:1.130127+0.341089 
## [118]    train-rmse:0.310596+0.015443    test-rmse:1.129754+0.343009 
## [119]    train-rmse:0.305968+0.014348    test-rmse:1.127334+0.343728 
## [120]    train-rmse:0.302917+0.013814    test-rmse:1.125843+0.345529 
## [121]    train-rmse:0.299920+0.013441    test-rmse:1.123930+0.344228 
## [122]    train-rmse:0.296551+0.014881    test-rmse:1.122679+0.344678 
## [123]    train-rmse:0.293616+0.014541    test-rmse:1.122272+0.344947 
## [124]    train-rmse:0.291481+0.014889    test-rmse:1.119477+0.346645 
## [125]    train-rmse:0.289298+0.015102    test-rmse:1.118775+0.346677 
## [126]    train-rmse:0.286030+0.013823    test-rmse:1.118418+0.346896 
## [127]    train-rmse:0.282106+0.014555    test-rmse:1.117616+0.346370 
## [128]    train-rmse:0.279512+0.014298    test-rmse:1.116695+0.347329 
## [129]    train-rmse:0.277180+0.014254    test-rmse:1.115427+0.347992 
## [130]    train-rmse:0.274928+0.013855    test-rmse:1.114196+0.348202 
## [131]    train-rmse:0.272283+0.014231    test-rmse:1.113534+0.349038 
## [132]    train-rmse:0.270066+0.014490    test-rmse:1.113049+0.349768 
## [133]    train-rmse:0.266192+0.013596    test-rmse:1.112704+0.350765 
## [134]    train-rmse:0.263240+0.013518    test-rmse:1.112783+0.350925 
## [135]    train-rmse:0.260517+0.012518    test-rmse:1.112138+0.351644 
## [136]    train-rmse:0.258553+0.012305    test-rmse:1.110965+0.349903 
## [137]    train-rmse:0.255692+0.013226    test-rmse:1.112057+0.352657 
## [138]    train-rmse:0.253353+0.013213    test-rmse:1.112018+0.353437 
## [139]    train-rmse:0.251435+0.013076    test-rmse:1.111678+0.354490 
## [140]    train-rmse:0.248109+0.013720    test-rmse:1.111125+0.355363 
## [141]    train-rmse:0.246348+0.013805    test-rmse:1.111073+0.355038 
## [142]    train-rmse:0.244255+0.013691    test-rmse:1.110070+0.355494 
## [143]    train-rmse:0.241622+0.013861    test-rmse:1.109358+0.355537 
## [144]    train-rmse:0.240296+0.014095    test-rmse:1.106872+0.356870 
## [145]    train-rmse:0.237529+0.013829    test-rmse:1.106606+0.359075 
## [146]    train-rmse:0.235435+0.014036    test-rmse:1.105127+0.358816 
## [147]    train-rmse:0.232628+0.013886    test-rmse:1.105060+0.359803 
## [148]    train-rmse:0.230316+0.012924    test-rmse:1.105334+0.360152 
## [149]    train-rmse:0.228706+0.013208    test-rmse:1.104653+0.359265 
## [150]    train-rmse:0.226575+0.013170    test-rmse:1.105002+0.360367 
## [151]    train-rmse:0.223881+0.014657    test-rmse:1.104693+0.360525 
## [152]    train-rmse:0.222058+0.014335    test-rmse:1.104639+0.360609 
## [153]    train-rmse:0.220274+0.014225    test-rmse:1.103934+0.361619 
## [154]    train-rmse:0.217673+0.014880    test-rmse:1.103600+0.360201 
## [155]    train-rmse:0.216074+0.014780    test-rmse:1.103365+0.360512 
## [156]    train-rmse:0.214653+0.014713    test-rmse:1.102591+0.360430 
## [157]    train-rmse:0.212805+0.014255    test-rmse:1.101457+0.361374 
## [158]    train-rmse:0.211193+0.013882    test-rmse:1.101220+0.360837 
## [159]    train-rmse:0.209632+0.013742    test-rmse:1.101039+0.361013 
## [160]    train-rmse:0.207977+0.014027    test-rmse:1.100693+0.361217 
## [161]    train-rmse:0.206380+0.014053    test-rmse:1.100998+0.361936 
## [162]    train-rmse:0.204943+0.014386    test-rmse:1.100568+0.362537 
## [163]    train-rmse:0.202485+0.013211    test-rmse:1.099405+0.363667 
## [164]    train-rmse:0.200537+0.012721    test-rmse:1.099422+0.363346 
## [165]    train-rmse:0.198462+0.013072    test-rmse:1.099330+0.363229 
## [166]    train-rmse:0.197160+0.013101    test-rmse:1.099174+0.363299 
## [167]    train-rmse:0.195520+0.012882    test-rmse:1.099034+0.363385 
## [168]    train-rmse:0.193952+0.013115    test-rmse:1.098583+0.363504 
## [169]    train-rmse:0.192515+0.012533    test-rmse:1.097922+0.362159 
## [170]    train-rmse:0.191182+0.012257    test-rmse:1.097834+0.362029 
## [171]    train-rmse:0.189601+0.013110    test-rmse:1.097742+0.362211 
## [172]    train-rmse:0.188385+0.013162    test-rmse:1.096048+0.361453 
## [173]    train-rmse:0.186855+0.013730    test-rmse:1.096137+0.361372 
## [174]    train-rmse:0.185286+0.014399    test-rmse:1.096766+0.361171 
## [175]    train-rmse:0.183868+0.014570    test-rmse:1.096819+0.361219 
## [176]    train-rmse:0.182219+0.014534    test-rmse:1.096288+0.361560 
## [177]    train-rmse:0.180591+0.014858    test-rmse:1.097183+0.361223 
## [178]    train-rmse:0.178660+0.014257    test-rmse:1.097411+0.361383 
## Stopping. Best iteration:
## [172]    train-rmse:0.188385+0.013162    test-rmse:1.096048+0.361453
## 
## 59 
## [1]  train-rmse:79.809479+0.092102   test-rmse:79.809535+0.505799 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:59.256192+0.075941   test-rmse:59.255628+0.542464 
## [3]  train-rmse:44.071015+0.066791   test-rmse:44.069481+0.578759 
## [4]  train-rmse:32.877449+0.063877   test-rmse:32.874400+0.617127 
## [5]  train-rmse:24.614156+0.051485   test-rmse:24.681269+0.627135 
## [6]  train-rmse:18.505762+0.036764   test-rmse:18.586601+0.594150 
## [7]  train-rmse:13.991944+0.030477   test-rmse:14.063530+0.521900 
## [8]  train-rmse:10.659516+0.026283   test-rmse:10.771836+0.541636 
## [9]  train-rmse:8.206978+0.023201    test-rmse:8.366505+0.576332 
## [10] train-rmse:6.404964+0.024609    test-rmse:6.531586+0.529098 
## [11] train-rmse:5.099514+0.033020    test-rmse:5.246194+0.497309 
## [12] train-rmse:4.157289+0.038677    test-rmse:4.353924+0.493435 
## [13] train-rmse:3.471049+0.034651    test-rmse:3.719858+0.430112 
## [14] train-rmse:2.989294+0.029568    test-rmse:3.273025+0.377721 
## [15] train-rmse:2.637283+0.042304    test-rmse:2.964900+0.377535 
## [16] train-rmse:2.389280+0.041189    test-rmse:2.751946+0.347444 
## [17] train-rmse:2.189467+0.048458    test-rmse:2.613825+0.292384 
## [18] train-rmse:2.028198+0.049407    test-rmse:2.473158+0.250086 
## [19] train-rmse:1.900346+0.041810    test-rmse:2.353399+0.222164 
## [20] train-rmse:1.784591+0.035322    test-rmse:2.262904+0.205007 
## [21] train-rmse:1.690691+0.032379    test-rmse:2.207786+0.199742 
## [22] train-rmse:1.604263+0.040375    test-rmse:2.124023+0.196424 
## [23] train-rmse:1.528976+0.038758    test-rmse:2.085101+0.200434 
## [24] train-rmse:1.463242+0.039250    test-rmse:2.021041+0.186800 
## [25] train-rmse:1.395346+0.038429    test-rmse:1.973152+0.175164 
## [26] train-rmse:1.336650+0.039142    test-rmse:1.920606+0.180342 
## [27] train-rmse:1.282160+0.037360    test-rmse:1.861067+0.174916 
## [28] train-rmse:1.221782+0.040870    test-rmse:1.830181+0.167459 
## [29] train-rmse:1.175684+0.041099    test-rmse:1.785170+0.159381 
## [30] train-rmse:1.129000+0.038492    test-rmse:1.755504+0.165982 
## [31] train-rmse:1.084232+0.037987    test-rmse:1.729840+0.163075 
## [32] train-rmse:1.041403+0.030361    test-rmse:1.697426+0.165898 
## [33] train-rmse:0.999985+0.030522    test-rmse:1.678007+0.161987 
## [34] train-rmse:0.964101+0.031845    test-rmse:1.644123+0.164218 
## [35] train-rmse:0.928768+0.031884    test-rmse:1.607884+0.173005 
## [36] train-rmse:0.895105+0.030320    test-rmse:1.588723+0.170454 
## [37] train-rmse:0.862395+0.027938    test-rmse:1.563290+0.182722 
## [38] train-rmse:0.834567+0.028981    test-rmse:1.540058+0.183976 
## [39] train-rmse:0.807107+0.028437    test-rmse:1.522223+0.186388 
## [40] train-rmse:0.778493+0.027474    test-rmse:1.504352+0.188389 
## [41] train-rmse:0.753994+0.026124    test-rmse:1.484436+0.191433 
## [42] train-rmse:0.731394+0.025879    test-rmse:1.472876+0.198605 
## [43] train-rmse:0.708147+0.024076    test-rmse:1.449761+0.201028 
## [44] train-rmse:0.681893+0.027291    test-rmse:1.442959+0.206032 
## [45] train-rmse:0.661689+0.026394    test-rmse:1.437653+0.210855 
## [46] train-rmse:0.639457+0.026698    test-rmse:1.429321+0.210663 
## [47] train-rmse:0.621103+0.026231    test-rmse:1.417218+0.212969 
## [48] train-rmse:0.604187+0.027135    test-rmse:1.403644+0.213638 
## [49] train-rmse:0.586049+0.026745    test-rmse:1.394297+0.216116 
## [50] train-rmse:0.570069+0.024984    test-rmse:1.381271+0.220189 
## [51] train-rmse:0.554544+0.023710    test-rmse:1.373722+0.224331 
## [52] train-rmse:0.541207+0.024196    test-rmse:1.365996+0.225137 
## [53] train-rmse:0.526532+0.022800    test-rmse:1.362397+0.226275 
## [54] train-rmse:0.514104+0.022084    test-rmse:1.350083+0.226404 
## [55] train-rmse:0.500592+0.022938    test-rmse:1.344753+0.232372 
## [56] train-rmse:0.488805+0.023491    test-rmse:1.340654+0.232586 
## [57] train-rmse:0.475623+0.021467    test-rmse:1.336338+0.233270 
## [58] train-rmse:0.463456+0.023044    test-rmse:1.330088+0.235923 
## [59] train-rmse:0.454115+0.023448    test-rmse:1.325317+0.228957 
## [60] train-rmse:0.444582+0.022579    test-rmse:1.319533+0.230896 
## [61] train-rmse:0.433541+0.023457    test-rmse:1.313689+0.237191 
## [62] train-rmse:0.422250+0.023568    test-rmse:1.311031+0.240821 
## [63] train-rmse:0.412840+0.023005    test-rmse:1.303962+0.242931 
## [64] train-rmse:0.404253+0.023487    test-rmse:1.301127+0.245498 
## [65] train-rmse:0.394620+0.024516    test-rmse:1.294895+0.235915 
## [66] train-rmse:0.386251+0.023043    test-rmse:1.290084+0.241218 
## [67] train-rmse:0.376645+0.021294    test-rmse:1.285468+0.241008 
## [68] train-rmse:0.368825+0.021483    test-rmse:1.279824+0.240860 
## [69] train-rmse:0.360530+0.019571    test-rmse:1.276638+0.242522 
## [70] train-rmse:0.353712+0.018520    test-rmse:1.275368+0.242842 
## [71] train-rmse:0.346361+0.018978    test-rmse:1.265073+0.237530 
## [72] train-rmse:0.340233+0.018037    test-rmse:1.264546+0.236943 
## [73] train-rmse:0.332897+0.017516    test-rmse:1.262678+0.239180 
## [74] train-rmse:0.326306+0.016129    test-rmse:1.262435+0.237636 
## [75] train-rmse:0.319661+0.016438    test-rmse:1.256822+0.238570 
## [76] train-rmse:0.313437+0.016629    test-rmse:1.254371+0.238875 
## [77] train-rmse:0.307391+0.014801    test-rmse:1.252957+0.238849 
## [78] train-rmse:0.301620+0.014351    test-rmse:1.253846+0.240666 
## [79] train-rmse:0.296685+0.014877    test-rmse:1.252017+0.240921 
## [80] train-rmse:0.291592+0.015029    test-rmse:1.247013+0.245659 
## [81] train-rmse:0.286847+0.015007    test-rmse:1.246903+0.245589 
## [82] train-rmse:0.281580+0.016173    test-rmse:1.244723+0.246687 
## [83] train-rmse:0.276125+0.016802    test-rmse:1.242925+0.248038 
## [84] train-rmse:0.270717+0.016014    test-rmse:1.242300+0.248123 
## [85] train-rmse:0.265403+0.014484    test-rmse:1.240050+0.249870 
## [86] train-rmse:0.261274+0.013881    test-rmse:1.239323+0.250538 
## [87] train-rmse:0.257434+0.013765    test-rmse:1.238345+0.252310 
## [88] train-rmse:0.253692+0.013957    test-rmse:1.237295+0.252826 
## [89] train-rmse:0.249406+0.013557    test-rmse:1.235311+0.253646 
## [90] train-rmse:0.244182+0.013812    test-rmse:1.235085+0.253846 
## [91] train-rmse:0.240012+0.012710    test-rmse:1.233590+0.255019 
## [92] train-rmse:0.236414+0.013188    test-rmse:1.232791+0.255117 
## [93] train-rmse:0.232677+0.012259    test-rmse:1.231045+0.257243 
## [94] train-rmse:0.228711+0.013036    test-rmse:1.230598+0.259049 
## [95] train-rmse:0.224694+0.012805    test-rmse:1.230811+0.260409 
## [96] train-rmse:0.220931+0.011705    test-rmse:1.231152+0.261606 
## [97] train-rmse:0.216061+0.011819    test-rmse:1.230117+0.261180 
## [98] train-rmse:0.212366+0.010549    test-rmse:1.230082+0.261873 
## [99] train-rmse:0.209258+0.010421    test-rmse:1.229073+0.262033 
## [100]    train-rmse:0.205501+0.009435    test-rmse:1.229450+0.262228 
## [101]    train-rmse:0.202128+0.009987    test-rmse:1.228959+0.262343 
## [102]    train-rmse:0.199333+0.008958    test-rmse:1.228723+0.262947 
## [103]    train-rmse:0.196153+0.009302    test-rmse:1.228206+0.261897 
## [104]    train-rmse:0.192937+0.009257    test-rmse:1.227074+0.262687 
## [105]    train-rmse:0.189739+0.010009    test-rmse:1.227522+0.262975 
## [106]    train-rmse:0.186639+0.010075    test-rmse:1.225743+0.263534 
## [107]    train-rmse:0.184805+0.009797    test-rmse:1.225582+0.263894 
## [108]    train-rmse:0.182509+0.008966    test-rmse:1.224314+0.264771 
## [109]    train-rmse:0.179452+0.009758    test-rmse:1.223366+0.263676 
## [110]    train-rmse:0.175765+0.009368    test-rmse:1.222556+0.263616 
## [111]    train-rmse:0.173119+0.009993    test-rmse:1.221416+0.262793 
## [112]    train-rmse:0.170758+0.010270    test-rmse:1.221199+0.262997 
## [113]    train-rmse:0.167755+0.010697    test-rmse:1.219989+0.263401 
## [114]    train-rmse:0.165156+0.010584    test-rmse:1.219315+0.263902 
## [115]    train-rmse:0.162884+0.010952    test-rmse:1.219958+0.264313 
## [116]    train-rmse:0.160787+0.010585    test-rmse:1.219775+0.264702 
## [117]    train-rmse:0.158256+0.010279    test-rmse:1.218743+0.264896 
## [118]    train-rmse:0.156335+0.010234    test-rmse:1.218063+0.264791 
## [119]    train-rmse:0.154027+0.009914    test-rmse:1.217689+0.264825 
## [120]    train-rmse:0.151670+0.010153    test-rmse:1.217890+0.265001 
## [121]    train-rmse:0.150023+0.010196    test-rmse:1.217827+0.265298 
## [122]    train-rmse:0.148386+0.009977    test-rmse:1.217823+0.265180 
## [123]    train-rmse:0.145467+0.008862    test-rmse:1.218410+0.265444 
## [124]    train-rmse:0.143296+0.008917    test-rmse:1.217707+0.265996 
## [125]    train-rmse:0.141604+0.008995    test-rmse:1.216995+0.265611 
## [126]    train-rmse:0.139429+0.009745    test-rmse:1.216901+0.266084 
## [127]    train-rmse:0.137781+0.008967    test-rmse:1.216306+0.266278 
## [128]    train-rmse:0.135735+0.008891    test-rmse:1.216152+0.266417 
## [129]    train-rmse:0.133830+0.008806    test-rmse:1.214463+0.266210 
## [130]    train-rmse:0.132014+0.008450    test-rmse:1.214441+0.266542 
## [131]    train-rmse:0.130507+0.008306    test-rmse:1.214158+0.266312 
## [132]    train-rmse:0.128364+0.008078    test-rmse:1.214352+0.266147 
## [133]    train-rmse:0.126052+0.007839    test-rmse:1.212309+0.264514 
## [134]    train-rmse:0.124052+0.007407    test-rmse:1.212003+0.264265 
## [135]    train-rmse:0.122100+0.008449    test-rmse:1.211673+0.265130 
## [136]    train-rmse:0.120268+0.008051    test-rmse:1.211617+0.264884 
## [137]    train-rmse:0.119284+0.008008    test-rmse:1.211380+0.265113 
## [138]    train-rmse:0.117198+0.007994    test-rmse:1.211847+0.264823 
## [139]    train-rmse:0.115838+0.008121    test-rmse:1.211658+0.265257 
## [140]    train-rmse:0.113892+0.008399    test-rmse:1.211483+0.265123 
## [141]    train-rmse:0.112361+0.008278    test-rmse:1.211014+0.265342 
## [142]    train-rmse:0.110919+0.008029    test-rmse:1.210700+0.265593 
## [143]    train-rmse:0.109135+0.007258    test-rmse:1.210354+0.265263 
## [144]    train-rmse:0.107264+0.007151    test-rmse:1.210835+0.264986 
## [145]    train-rmse:0.105479+0.006975    test-rmse:1.211338+0.264993 
## [146]    train-rmse:0.103778+0.006297    test-rmse:1.210642+0.264630 
## [147]    train-rmse:0.102435+0.005821    test-rmse:1.210080+0.264649 
## [148]    train-rmse:0.101297+0.006010    test-rmse:1.209529+0.264144 
## [149]    train-rmse:0.100497+0.006342    test-rmse:1.209123+0.263992 
## [150]    train-rmse:0.099014+0.006561    test-rmse:1.209058+0.264204 
## [151]    train-rmse:0.097187+0.006496    test-rmse:1.209004+0.264312 
## [152]    train-rmse:0.095830+0.006663    test-rmse:1.209072+0.264298 
## [153]    train-rmse:0.094659+0.006675    test-rmse:1.208881+0.264205 
## [154]    train-rmse:0.093275+0.006715    test-rmse:1.208399+0.264416 
## [155]    train-rmse:0.091876+0.006797    test-rmse:1.208557+0.264487 
## [156]    train-rmse:0.090559+0.006809    test-rmse:1.208890+0.264317 
## [157]    train-rmse:0.089778+0.006903    test-rmse:1.208721+0.264286 
## [158]    train-rmse:0.088889+0.007160    test-rmse:1.208159+0.264931 
## [159]    train-rmse:0.087758+0.007241    test-rmse:1.207835+0.264884 
## [160]    train-rmse:0.086880+0.007108    test-rmse:1.207855+0.265202 
## [161]    train-rmse:0.086103+0.007087    test-rmse:1.207397+0.265146 
## [162]    train-rmse:0.084954+0.007406    test-rmse:1.207587+0.265170 
## [163]    train-rmse:0.084089+0.007505    test-rmse:1.207334+0.264955 
## [164]    train-rmse:0.082455+0.007278    test-rmse:1.207150+0.265273 
## [165]    train-rmse:0.081514+0.007501    test-rmse:1.206864+0.264711 
## [166]    train-rmse:0.080794+0.007673    test-rmse:1.206886+0.264674 
## [167]    train-rmse:0.079789+0.007788    test-rmse:1.207241+0.264707 
## [168]    train-rmse:0.078459+0.007543    test-rmse:1.206155+0.263544 
## [169]    train-rmse:0.077447+0.007532    test-rmse:1.205726+0.263904 
## [170]    train-rmse:0.076717+0.007452    test-rmse:1.205596+0.263846 
## [171]    train-rmse:0.075692+0.007214    test-rmse:1.206291+0.264956 
## [172]    train-rmse:0.074624+0.006945    test-rmse:1.206466+0.266311 
## [173]    train-rmse:0.074100+0.006952    test-rmse:1.206159+0.266512 
## [174]    train-rmse:0.073314+0.006659    test-rmse:1.205853+0.266562 
## [175]    train-rmse:0.072506+0.006908    test-rmse:1.205372+0.266658 
## [176]    train-rmse:0.071599+0.006540    test-rmse:1.205107+0.266941 
## [177]    train-rmse:0.071009+0.006476    test-rmse:1.204459+0.265889 
## [178]    train-rmse:0.069984+0.006446    test-rmse:1.204505+0.265920 
## [179]    train-rmse:0.069287+0.006545    test-rmse:1.204240+0.265635 
## [180]    train-rmse:0.068013+0.006584    test-rmse:1.203958+0.265379 
## [181]    train-rmse:0.066900+0.006455    test-rmse:1.204168+0.265772 
## [182]    train-rmse:0.066317+0.006616    test-rmse:1.204094+0.265864 
## [183]    train-rmse:0.065562+0.006459    test-rmse:1.203863+0.266018 
## [184]    train-rmse:0.064587+0.006414    test-rmse:1.203765+0.266167 
## [185]    train-rmse:0.063783+0.006348    test-rmse:1.203198+0.265304 
## [186]    train-rmse:0.062940+0.006578    test-rmse:1.202903+0.264848 
## [187]    train-rmse:0.062218+0.006751    test-rmse:1.202983+0.264615 
## [188]    train-rmse:0.061469+0.006703    test-rmse:1.202912+0.264795 
## [189]    train-rmse:0.060329+0.006542    test-rmse:1.202868+0.264810 
## [190]    train-rmse:0.059770+0.006532    test-rmse:1.202830+0.264990 
## [191]    train-rmse:0.059258+0.006739    test-rmse:1.202705+0.265031 
## [192]    train-rmse:0.058483+0.006725    test-rmse:1.202568+0.265181 
## [193]    train-rmse:0.057560+0.006452    test-rmse:1.201569+0.264551 
## [194]    train-rmse:0.057004+0.006772    test-rmse:1.201484+0.264642 
## [195]    train-rmse:0.056251+0.006467    test-rmse:1.201482+0.264679 
## [196]    train-rmse:0.055691+0.006511    test-rmse:1.201199+0.264730 
## [197]    train-rmse:0.055025+0.006712    test-rmse:1.201326+0.264534 
## [198]    train-rmse:0.054329+0.006779    test-rmse:1.201414+0.264783 
## [199]    train-rmse:0.053749+0.006645    test-rmse:1.201395+0.264757 
## [200]    train-rmse:0.053155+0.006525    test-rmse:1.201378+0.265046 
## [201]    train-rmse:0.052645+0.006629    test-rmse:1.201378+0.265052 
## [202]    train-rmse:0.051989+0.006587    test-rmse:1.201267+0.265136 
## Stopping. Best iteration:
## [196]    train-rmse:0.055691+0.006511    test-rmse:1.201199+0.264730
## 
## 60 
## [1]  train-rmse:79.809479+0.092102   test-rmse:79.809535+0.505799 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:59.256192+0.075941   test-rmse:59.255628+0.542464 
## [3]  train-rmse:44.071015+0.066791   test-rmse:44.069481+0.578759 
## [4]  train-rmse:32.877449+0.063877   test-rmse:32.874400+0.617127 
## [5]  train-rmse:24.614156+0.051485   test-rmse:24.681269+0.627135 
## [6]  train-rmse:18.505762+0.036764   test-rmse:18.586601+0.594150 
## [7]  train-rmse:13.983832+0.031561   test-rmse:14.052284+0.524473 
## [8]  train-rmse:10.637425+0.024293   test-rmse:10.766325+0.576050 
## [9]  train-rmse:8.158321+0.026011    test-rmse:8.287571+0.541326 
## [10] train-rmse:6.321855+0.020681    test-rmse:6.447181+0.528021 
## [11] train-rmse:4.975350+0.027693    test-rmse:5.132631+0.538519 
## [12] train-rmse:3.983543+0.030135    test-rmse:4.185880+0.481579 
## [13] train-rmse:3.272550+0.034439    test-rmse:3.515236+0.450243 
## [14] train-rmse:2.737396+0.023057    test-rmse:3.029197+0.395248 
## [15] train-rmse:2.359951+0.013946    test-rmse:2.701978+0.361824 
## [16] train-rmse:2.072142+0.021409    test-rmse:2.495889+0.329945 
## [17] train-rmse:1.867856+0.022464    test-rmse:2.314856+0.288424 
## [18] train-rmse:1.707267+0.024998    test-rmse:2.182618+0.280317 
## [19] train-rmse:1.569173+0.036006    test-rmse:2.081442+0.252271 
## [20] train-rmse:1.464921+0.035836    test-rmse:2.019723+0.232268 
## [21] train-rmse:1.375988+0.036855    test-rmse:1.944152+0.229297 
## [22] train-rmse:1.294613+0.037838    test-rmse:1.875842+0.216432 
## [23] train-rmse:1.211586+0.045513    test-rmse:1.831568+0.230929 
## [24] train-rmse:1.143012+0.041921    test-rmse:1.773352+0.211269 
## [25] train-rmse:1.082662+0.037386    test-rmse:1.722185+0.212685 
## [26] train-rmse:1.028085+0.040252    test-rmse:1.686823+0.212724 
## [27] train-rmse:0.978925+0.039744    test-rmse:1.643611+0.217596 
## [28] train-rmse:0.932247+0.040757    test-rmse:1.614215+0.217373 
## [29] train-rmse:0.885377+0.036656    test-rmse:1.578875+0.225640 
## [30] train-rmse:0.846488+0.035548    test-rmse:1.562452+0.232931 
## [31] train-rmse:0.806831+0.026896    test-rmse:1.521451+0.227064 
## [32] train-rmse:0.773149+0.026479    test-rmse:1.497278+0.226477 
## [33] train-rmse:0.739320+0.027182    test-rmse:1.479309+0.228972 
## [34] train-rmse:0.706224+0.027013    test-rmse:1.450559+0.228967 
## [35] train-rmse:0.674753+0.029676    test-rmse:1.432603+0.229272 
## [36] train-rmse:0.648940+0.027843    test-rmse:1.415042+0.229980 
## [37] train-rmse:0.624678+0.024044    test-rmse:1.387793+0.213316 
## [38] train-rmse:0.603232+0.023072    test-rmse:1.376980+0.220814 
## [39] train-rmse:0.580050+0.022822    test-rmse:1.363737+0.222395 
## [40] train-rmse:0.559103+0.022034    test-rmse:1.355566+0.228981 
## [41] train-rmse:0.537718+0.024105    test-rmse:1.344808+0.236536 
## [42] train-rmse:0.522988+0.023468    test-rmse:1.332314+0.241182 
## [43] train-rmse:0.506007+0.022775    test-rmse:1.323967+0.242304 
## [44] train-rmse:0.486072+0.021053    test-rmse:1.311849+0.246543 
## [45] train-rmse:0.469901+0.021013    test-rmse:1.307492+0.248927 
## [46] train-rmse:0.453909+0.020757    test-rmse:1.301660+0.253601 
## [47] train-rmse:0.440049+0.021810    test-rmse:1.289341+0.250208 
## [48] train-rmse:0.425395+0.020400    test-rmse:1.286713+0.252270 
## [49] train-rmse:0.412455+0.019560    test-rmse:1.276139+0.242189 
## [50] train-rmse:0.400712+0.019027    test-rmse:1.269870+0.244243 
## [51] train-rmse:0.388178+0.018991    test-rmse:1.262334+0.238411 
## [52] train-rmse:0.378167+0.018851    test-rmse:1.259853+0.243999 
## [53] train-rmse:0.366578+0.018343    test-rmse:1.256972+0.242890 
## [54] train-rmse:0.356067+0.015986    test-rmse:1.253625+0.241722 
## [55] train-rmse:0.348038+0.016947    test-rmse:1.251346+0.244207 
## [56] train-rmse:0.336440+0.012941    test-rmse:1.242667+0.240070 
## [57] train-rmse:0.328211+0.013377    test-rmse:1.241006+0.240072 
## [58] train-rmse:0.319387+0.013736    test-rmse:1.236325+0.238935 
## [59] train-rmse:0.309225+0.011681    test-rmse:1.235665+0.240869 
## [60] train-rmse:0.301795+0.013429    test-rmse:1.232625+0.241036 
## [61] train-rmse:0.293779+0.011957    test-rmse:1.227524+0.246209 
## [62] train-rmse:0.287641+0.011340    test-rmse:1.225928+0.247459 
## [63] train-rmse:0.281413+0.011225    test-rmse:1.220633+0.240942 
## [64] train-rmse:0.275007+0.010576    test-rmse:1.218746+0.241352 
## [65] train-rmse:0.269580+0.011117    test-rmse:1.217956+0.243002 
## [66] train-rmse:0.263859+0.011020    test-rmse:1.216783+0.241600 
## [67] train-rmse:0.257625+0.011661    test-rmse:1.214891+0.245493 
## [68] train-rmse:0.250314+0.009598    test-rmse:1.211758+0.245125 
## [69] train-rmse:0.244026+0.009199    test-rmse:1.210147+0.242841 
## [70] train-rmse:0.238043+0.007246    test-rmse:1.210000+0.244139 
## [71] train-rmse:0.233470+0.008069    test-rmse:1.208781+0.245189 
## [72] train-rmse:0.228706+0.007841    test-rmse:1.209421+0.245810 
## [73] train-rmse:0.222614+0.005762    test-rmse:1.208420+0.246840 
## [74] train-rmse:0.216029+0.006782    test-rmse:1.207503+0.248069 
## [75] train-rmse:0.210618+0.006227    test-rmse:1.206151+0.248692 
## [76] train-rmse:0.205893+0.005528    test-rmse:1.204766+0.247693 
## [77] train-rmse:0.202412+0.005477    test-rmse:1.204238+0.247727 
## [78] train-rmse:0.197188+0.005078    test-rmse:1.204391+0.247144 
## [79] train-rmse:0.191296+0.006033    test-rmse:1.205681+0.244495 
## [80] train-rmse:0.187137+0.006723    test-rmse:1.205411+0.245347 
## [81] train-rmse:0.182210+0.006731    test-rmse:1.204811+0.245402 
## [82] train-rmse:0.177085+0.007054    test-rmse:1.205157+0.244806 
## [83] train-rmse:0.172897+0.007286    test-rmse:1.204989+0.244708 
## Stopping. Best iteration:
## [77] train-rmse:0.202412+0.005477    test-rmse:1.204238+0.247727
## 
## 61 
## [1]  train-rmse:79.809479+0.092102   test-rmse:79.809535+0.505799 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:59.256192+0.075941   test-rmse:59.255628+0.542464 
## [3]  train-rmse:44.071015+0.066791   test-rmse:44.069481+0.578759 
## [4]  train-rmse:32.877449+0.063877   test-rmse:32.874400+0.617127 
## [5]  train-rmse:24.614156+0.051485   test-rmse:24.681269+0.627135 
## [6]  train-rmse:18.505762+0.036764   test-rmse:18.586601+0.594150 
## [7]  train-rmse:13.976202+0.032315   test-rmse:14.050951+0.523410 
## [8]  train-rmse:10.619082+0.022655   test-rmse:10.747934+0.574611 
## [9]  train-rmse:8.130783+0.024911    test-rmse:8.262527+0.537729 
## [10] train-rmse:6.264807+0.021943    test-rmse:6.394779+0.530510 
## [11] train-rmse:4.889850+0.013924    test-rmse:5.058619+0.500756 
## [12] train-rmse:3.881220+0.008913    test-rmse:4.095274+0.497993 
## [13] train-rmse:3.132597+0.018000    test-rmse:3.391050+0.443168 
## [14] train-rmse:2.581711+0.024653    test-rmse:2.876187+0.408493 
## [15] train-rmse:2.175788+0.032227    test-rmse:2.539935+0.371330 
## [16] train-rmse:1.872813+0.037115    test-rmse:2.301511+0.339305 
## [17] train-rmse:1.651579+0.036493    test-rmse:2.124641+0.324529 
## [18] train-rmse:1.482054+0.035398    test-rmse:1.985246+0.319313 
## [19] train-rmse:1.337175+0.029476    test-rmse:1.879206+0.306576 
## [20] train-rmse:1.229528+0.024495    test-rmse:1.793061+0.288175 
## [21] train-rmse:1.112706+0.034821    test-rmse:1.723280+0.270923 
## [22] train-rmse:1.028813+0.029732    test-rmse:1.666871+0.268385 
## [23] train-rmse:0.961495+0.030854    test-rmse:1.612169+0.264037 
## [24] train-rmse:0.891983+0.036976    test-rmse:1.576199+0.270183 
## [25] train-rmse:0.837400+0.033044    test-rmse:1.538207+0.261903 
## [26] train-rmse:0.789640+0.033200    test-rmse:1.506504+0.263813 
## [27] train-rmse:0.744108+0.028802    test-rmse:1.465335+0.272135 
## [28] train-rmse:0.702256+0.025298    test-rmse:1.446292+0.273129 
## [29] train-rmse:0.662246+0.028888    test-rmse:1.420446+0.286362 
## [30] train-rmse:0.628745+0.027432    test-rmse:1.398876+0.266047 
## [31] train-rmse:0.599152+0.026225    test-rmse:1.373657+0.264992 
## [32] train-rmse:0.573738+0.025601    test-rmse:1.359618+0.267609 
## [33] train-rmse:0.548658+0.025764    test-rmse:1.345666+0.268911 
## [34] train-rmse:0.520256+0.018894    test-rmse:1.329941+0.252711 
## [35] train-rmse:0.498569+0.018229    test-rmse:1.311214+0.246279 
## [36] train-rmse:0.478292+0.018830    test-rmse:1.302822+0.249019 
## [37] train-rmse:0.456557+0.017852    test-rmse:1.292277+0.249261 
## [38] train-rmse:0.439305+0.016011    test-rmse:1.278470+0.242930 
## [39] train-rmse:0.422756+0.016688    test-rmse:1.267350+0.245575 
## [40] train-rmse:0.403787+0.011623    test-rmse:1.260648+0.243771 
## [41] train-rmse:0.391411+0.012049    test-rmse:1.253055+0.243552 
## [42] train-rmse:0.377507+0.012793    test-rmse:1.245400+0.245198 
## [43] train-rmse:0.358103+0.010350    test-rmse:1.242190+0.248652 
## [44] train-rmse:0.341853+0.009818    test-rmse:1.238028+0.251133 
## [45] train-rmse:0.332074+0.010452    test-rmse:1.235771+0.251678 
## [46] train-rmse:0.318565+0.009169    test-rmse:1.231742+0.250705 
## [47] train-rmse:0.306835+0.010952    test-rmse:1.228605+0.250027 
## [48] train-rmse:0.296854+0.009544    test-rmse:1.226442+0.251805 
## [49] train-rmse:0.285646+0.007145    test-rmse:1.219592+0.244823 
## [50] train-rmse:0.276984+0.006284    test-rmse:1.219032+0.246753 
## [51] train-rmse:0.269009+0.006670    test-rmse:1.216587+0.241916 
## [52] train-rmse:0.258533+0.005889    test-rmse:1.211641+0.245431 
## [53] train-rmse:0.249442+0.004636    test-rmse:1.207502+0.239437 
## [54] train-rmse:0.242876+0.005715    test-rmse:1.208554+0.240719 
## [55] train-rmse:0.233157+0.005527    test-rmse:1.206532+0.242155 
## [56] train-rmse:0.227583+0.005966    test-rmse:1.205860+0.242910 
## [57] train-rmse:0.222099+0.005679    test-rmse:1.202600+0.241785 
## [58] train-rmse:0.213999+0.006148    test-rmse:1.197744+0.241524 
## [59] train-rmse:0.206618+0.007210    test-rmse:1.196440+0.241919 
## [60] train-rmse:0.200899+0.007911    test-rmse:1.194064+0.241401 
## [61] train-rmse:0.196233+0.007725    test-rmse:1.192437+0.241476 
## [62] train-rmse:0.188806+0.005823    test-rmse:1.190177+0.244093 
## [63] train-rmse:0.182690+0.007200    test-rmse:1.188980+0.243676 
## [64] train-rmse:0.175451+0.008191    test-rmse:1.185510+0.242147 
## [65] train-rmse:0.170139+0.007013    test-rmse:1.185610+0.242230 
## [66] train-rmse:0.164791+0.006814    test-rmse:1.184431+0.242924 
## [67] train-rmse:0.160119+0.006156    test-rmse:1.182241+0.242961 
## [68] train-rmse:0.155742+0.006510    test-rmse:1.181394+0.243357 
## [69] train-rmse:0.150682+0.007246    test-rmse:1.180035+0.242874 
## [70] train-rmse:0.146604+0.008795    test-rmse:1.179259+0.244947 
## [71] train-rmse:0.142069+0.008200    test-rmse:1.179030+0.245277 
## [72] train-rmse:0.137186+0.007876    test-rmse:1.176778+0.246049 
## [73] train-rmse:0.133793+0.008540    test-rmse:1.176603+0.246955 
## [74] train-rmse:0.128342+0.008349    test-rmse:1.175871+0.246757 
## [75] train-rmse:0.124613+0.007982    test-rmse:1.174111+0.246759 
## [76] train-rmse:0.121740+0.008195    test-rmse:1.173811+0.246638 
## [77] train-rmse:0.117279+0.007963    test-rmse:1.173372+0.246347 
## [78] train-rmse:0.113675+0.008105    test-rmse:1.172400+0.246246 
## [79] train-rmse:0.111220+0.008259    test-rmse:1.172374+0.246224 
## [80] train-rmse:0.108155+0.007608    test-rmse:1.172028+0.246267 
## [81] train-rmse:0.105122+0.007478    test-rmse:1.171341+0.246327 
## [82] train-rmse:0.102536+0.007578    test-rmse:1.169931+0.245099 
## [83] train-rmse:0.100274+0.007337    test-rmse:1.169699+0.245490 
## [84] train-rmse:0.096900+0.007873    test-rmse:1.169624+0.244953 
## [85] train-rmse:0.094072+0.008095    test-rmse:1.169324+0.245304 
## [86] train-rmse:0.091532+0.007058    test-rmse:1.169396+0.245139 
## [87] train-rmse:0.088602+0.007322    test-rmse:1.169315+0.245171 
## [88] train-rmse:0.086761+0.007047    test-rmse:1.169640+0.245155 
## [89] train-rmse:0.084807+0.007347    test-rmse:1.169169+0.244856 
## [90] train-rmse:0.083260+0.007424    test-rmse:1.168884+0.244773 
## [91] train-rmse:0.081036+0.007466    test-rmse:1.168778+0.244665 
## [92] train-rmse:0.078766+0.007857    test-rmse:1.168803+0.245175 
## [93] train-rmse:0.076683+0.008154    test-rmse:1.168386+0.245452 
## [94] train-rmse:0.074059+0.008507    test-rmse:1.168368+0.245580 
## [95] train-rmse:0.071038+0.008379    test-rmse:1.168741+0.245410 
## [96] train-rmse:0.068983+0.008408    test-rmse:1.168214+0.245762 
## [97] train-rmse:0.067263+0.009016    test-rmse:1.167713+0.246032 
## [98] train-rmse:0.065394+0.009265    test-rmse:1.167554+0.246120 
## [99] train-rmse:0.063889+0.008500    test-rmse:1.167447+0.246069 
## [100]    train-rmse:0.061868+0.007419    test-rmse:1.167361+0.246016 
## [101]    train-rmse:0.060329+0.007141    test-rmse:1.166642+0.246047 
## [102]    train-rmse:0.058124+0.007046    test-rmse:1.166712+0.245895 
## [103]    train-rmse:0.056974+0.007045    test-rmse:1.166038+0.246344 
## [104]    train-rmse:0.055658+0.007486    test-rmse:1.165885+0.245651 
## [105]    train-rmse:0.054005+0.007317    test-rmse:1.165936+0.245464 
## [106]    train-rmse:0.053134+0.007178    test-rmse:1.165716+0.245491 
## [107]    train-rmse:0.051610+0.007384    test-rmse:1.165807+0.245277 
## [108]    train-rmse:0.049551+0.005799    test-rmse:1.165635+0.244897 
## [109]    train-rmse:0.048117+0.005118    test-rmse:1.165830+0.244655 
## [110]    train-rmse:0.047081+0.005447    test-rmse:1.165792+0.244630 
## [111]    train-rmse:0.045987+0.005489    test-rmse:1.165379+0.244335 
## [112]    train-rmse:0.045064+0.005548    test-rmse:1.165294+0.244296 
## [113]    train-rmse:0.044279+0.005428    test-rmse:1.165507+0.244283 
## [114]    train-rmse:0.043067+0.005566    test-rmse:1.165359+0.244449 
## [115]    train-rmse:0.042066+0.005218    test-rmse:1.165406+0.244222 
## [116]    train-rmse:0.040989+0.004938    test-rmse:1.165231+0.244164 
## [117]    train-rmse:0.040039+0.004840    test-rmse:1.165133+0.244173 
## [118]    train-rmse:0.038928+0.004992    test-rmse:1.165078+0.244258 
## [119]    train-rmse:0.038059+0.004930    test-rmse:1.165185+0.244149 
## [120]    train-rmse:0.037321+0.005119    test-rmse:1.165145+0.244319 
## [121]    train-rmse:0.036305+0.005062    test-rmse:1.164939+0.244308 
## [122]    train-rmse:0.035491+0.005288    test-rmse:1.164938+0.244383 
## [123]    train-rmse:0.034370+0.005171    test-rmse:1.164855+0.244273 
## [124]    train-rmse:0.033589+0.004993    test-rmse:1.164700+0.244303 
## [125]    train-rmse:0.032407+0.004526    test-rmse:1.164686+0.244277 
## [126]    train-rmse:0.031477+0.004604    test-rmse:1.164541+0.244067 
## [127]    train-rmse:0.030571+0.004598    test-rmse:1.164481+0.243968 
## [128]    train-rmse:0.029912+0.004451    test-rmse:1.164383+0.243714 
## [129]    train-rmse:0.029204+0.004470    test-rmse:1.164333+0.243675 
## [130]    train-rmse:0.028280+0.004643    test-rmse:1.164164+0.243576 
## [131]    train-rmse:0.027607+0.004703    test-rmse:1.164141+0.243724 
## [132]    train-rmse:0.026919+0.004564    test-rmse:1.163921+0.243599 
## [133]    train-rmse:0.026096+0.004399    test-rmse:1.163746+0.243626 
## [134]    train-rmse:0.025341+0.004437    test-rmse:1.163673+0.243654 
## [135]    train-rmse:0.024462+0.004344    test-rmse:1.163580+0.243727 
## [136]    train-rmse:0.023859+0.004038    test-rmse:1.163591+0.243735 
## [137]    train-rmse:0.023398+0.003989    test-rmse:1.163499+0.243898 
## [138]    train-rmse:0.022788+0.003930    test-rmse:1.163438+0.243893 
## [139]    train-rmse:0.022074+0.003698    test-rmse:1.163428+0.244162 
## [140]    train-rmse:0.021606+0.003544    test-rmse:1.163329+0.243979 
## [141]    train-rmse:0.021158+0.003552    test-rmse:1.163231+0.243939 
## [142]    train-rmse:0.020471+0.003375    test-rmse:1.163282+0.243943 
## [143]    train-rmse:0.019715+0.003176    test-rmse:1.163397+0.243845 
## [144]    train-rmse:0.019028+0.003017    test-rmse:1.163260+0.243825 
## [145]    train-rmse:0.018611+0.003027    test-rmse:1.163229+0.243888 
## [146]    train-rmse:0.018200+0.003120    test-rmse:1.163112+0.243771 
## [147]    train-rmse:0.017544+0.003036    test-rmse:1.163027+0.243833 
## [148]    train-rmse:0.017096+0.003041    test-rmse:1.162903+0.243871 
## [149]    train-rmse:0.016437+0.002843    test-rmse:1.163079+0.243868 
## [150]    train-rmse:0.016053+0.002770    test-rmse:1.163116+0.243840 
## [151]    train-rmse:0.015713+0.002803    test-rmse:1.163043+0.243873 
## [152]    train-rmse:0.015320+0.002712    test-rmse:1.163006+0.243927 
## [153]    train-rmse:0.014918+0.002829    test-rmse:1.163080+0.243923 
## [154]    train-rmse:0.014591+0.002831    test-rmse:1.163057+0.243942 
## Stopping. Best iteration:
## [148]    train-rmse:0.017096+0.003041    test-rmse:1.162903+0.243871
## 
## 62 
## [1]  train-rmse:79.809479+0.092102   test-rmse:79.809535+0.505799 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:59.256192+0.075941   test-rmse:59.255628+0.542464 
## [3]  train-rmse:44.071015+0.066791   test-rmse:44.069481+0.578759 
## [4]  train-rmse:32.877449+0.063877   test-rmse:32.874400+0.617127 
## [5]  train-rmse:24.614156+0.051485   test-rmse:24.681269+0.627135 
## [6]  train-rmse:18.505762+0.036764   test-rmse:18.586601+0.594150 
## [7]  train-rmse:13.970517+0.031727   test-rmse:14.038209+0.542316 
## [8]  train-rmse:10.607168+0.022797   test-rmse:10.730211+0.577112 
## [9]  train-rmse:8.107371+0.026487    test-rmse:8.253823+0.545305 
## [10] train-rmse:6.231761+0.014967    test-rmse:6.365112+0.541642 
## [11] train-rmse:4.845479+0.016390    test-rmse:5.001965+0.501427 
## [12] train-rmse:3.808283+0.022923    test-rmse:4.032027+0.480626 
## [13] train-rmse:3.045267+0.031563    test-rmse:3.319165+0.427932 
## [14] train-rmse:2.466924+0.029217    test-rmse:2.795407+0.380491 
## [15] train-rmse:2.046680+0.033731    test-rmse:2.432418+0.357601 
## [16] train-rmse:1.720032+0.042996    test-rmse:2.168479+0.331452 
## [17] train-rmse:1.468407+0.038476    test-rmse:1.988995+0.304032 
## [18] train-rmse:1.283174+0.036979    test-rmse:1.844564+0.277773 
## [19] train-rmse:1.132673+0.042599    test-rmse:1.771451+0.255048 
## [20] train-rmse:1.012059+0.034718    test-rmse:1.679931+0.235921 
## [21] train-rmse:0.914349+0.037310    test-rmse:1.617931+0.225747 
## [22] train-rmse:0.837376+0.036893    test-rmse:1.581570+0.235197 
## [23] train-rmse:0.775957+0.035742    test-rmse:1.538457+0.237032 
## [24] train-rmse:0.714892+0.034431    test-rmse:1.508559+0.228325 
## [25] train-rmse:0.663080+0.035329    test-rmse:1.470706+0.232324 
## [26] train-rmse:0.615968+0.031475    test-rmse:1.441655+0.218866 
## [27] train-rmse:0.576787+0.028881    test-rmse:1.413596+0.219881 
## [28] train-rmse:0.537837+0.031067    test-rmse:1.410117+0.224335 
## [29] train-rmse:0.508184+0.031970    test-rmse:1.393931+0.226073 
## [30] train-rmse:0.478530+0.032311    test-rmse:1.373462+0.218612 
## [31] train-rmse:0.451287+0.031445    test-rmse:1.356365+0.228635 
## [32] train-rmse:0.427414+0.027673    test-rmse:1.347400+0.233936 
## [33] train-rmse:0.405837+0.027339    test-rmse:1.338150+0.234445 
## [34] train-rmse:0.385521+0.023217    test-rmse:1.330269+0.239062 
## [35] train-rmse:0.366765+0.021119    test-rmse:1.320423+0.242512 
## [36] train-rmse:0.349514+0.020552    test-rmse:1.314156+0.246368 
## [37] train-rmse:0.331146+0.018925    test-rmse:1.307739+0.250132 
## [38] train-rmse:0.316063+0.018494    test-rmse:1.304125+0.248745 
## [39] train-rmse:0.301846+0.017958    test-rmse:1.300137+0.250870 
## [40] train-rmse:0.291642+0.017759    test-rmse:1.295887+0.254136 
## [41] train-rmse:0.277238+0.018966    test-rmse:1.289871+0.256156 
## [42] train-rmse:0.263910+0.016155    test-rmse:1.286571+0.257849 
## [43] train-rmse:0.252199+0.014425    test-rmse:1.284804+0.258369 
## [44] train-rmse:0.243111+0.015908    test-rmse:1.282711+0.257470 
## [45] train-rmse:0.233831+0.014124    test-rmse:1.279875+0.257419 
## [46] train-rmse:0.224619+0.013896    test-rmse:1.279358+0.257243 
## [47] train-rmse:0.214004+0.013228    test-rmse:1.274983+0.256341 
## [48] train-rmse:0.204983+0.013388    test-rmse:1.272715+0.254320 
## [49] train-rmse:0.197481+0.013375    test-rmse:1.269349+0.256132 
## [50] train-rmse:0.188403+0.013154    test-rmse:1.268677+0.257831 
## [51] train-rmse:0.181104+0.013587    test-rmse:1.270462+0.259095 
## [52] train-rmse:0.174930+0.012034    test-rmse:1.269331+0.259548 
## [53] train-rmse:0.167999+0.012643    test-rmse:1.268074+0.260441 
## [54] train-rmse:0.159708+0.012314    test-rmse:1.266256+0.262183 
## [55] train-rmse:0.153989+0.013993    test-rmse:1.265820+0.262618 
## [56] train-rmse:0.147761+0.014734    test-rmse:1.264263+0.264584 
## [57] train-rmse:0.140500+0.014225    test-rmse:1.262812+0.265999 
## [58] train-rmse:0.134655+0.013742    test-rmse:1.261450+0.263002 
## [59] train-rmse:0.128779+0.011764    test-rmse:1.259389+0.263278 
## [60] train-rmse:0.124129+0.011053    test-rmse:1.259032+0.263313 
## [61] train-rmse:0.119271+0.011496    test-rmse:1.258313+0.262337 
## [62] train-rmse:0.116099+0.012237    test-rmse:1.255889+0.263522 
## [63] train-rmse:0.111325+0.010886    test-rmse:1.253121+0.260480 
## [64] train-rmse:0.107440+0.010324    test-rmse:1.253069+0.260792 
## [65] train-rmse:0.102562+0.010083    test-rmse:1.252914+0.260749 
## [66] train-rmse:0.098979+0.009156    test-rmse:1.251773+0.259768 
## [67] train-rmse:0.094724+0.009035    test-rmse:1.251696+0.259840 
## [68] train-rmse:0.092551+0.008496    test-rmse:1.251645+0.259527 
## [69] train-rmse:0.089110+0.008948    test-rmse:1.252382+0.258976 
## [70] train-rmse:0.085977+0.008923    test-rmse:1.252613+0.259370 
## [71] train-rmse:0.081665+0.008194    test-rmse:1.251856+0.259717 
## [72] train-rmse:0.078767+0.007848    test-rmse:1.251926+0.259636 
## [73] train-rmse:0.075648+0.006984    test-rmse:1.250712+0.259937 
## [74] train-rmse:0.072663+0.006225    test-rmse:1.250853+0.260283 
## [75] train-rmse:0.069737+0.006370    test-rmse:1.249978+0.260330 
## [76] train-rmse:0.067401+0.006046    test-rmse:1.249322+0.260169 
## [77] train-rmse:0.065136+0.006031    test-rmse:1.248687+0.260711 
## [78] train-rmse:0.063263+0.005787    test-rmse:1.248372+0.260224 
## [79] train-rmse:0.061083+0.005524    test-rmse:1.248553+0.260132 
## [80] train-rmse:0.059636+0.005731    test-rmse:1.248680+0.260184 
## [81] train-rmse:0.057390+0.005302    test-rmse:1.248484+0.259916 
## [82] train-rmse:0.055729+0.005184    test-rmse:1.247943+0.260424 
## [83] train-rmse:0.053018+0.004996    test-rmse:1.247308+0.259971 
## [84] train-rmse:0.050934+0.004935    test-rmse:1.247340+0.260108 
## [85] train-rmse:0.049690+0.004894    test-rmse:1.247011+0.260565 
## [86] train-rmse:0.047834+0.004624    test-rmse:1.246223+0.259599 
## [87] train-rmse:0.045911+0.004246    test-rmse:1.245831+0.259535 
## [88] train-rmse:0.044161+0.004420    test-rmse:1.245558+0.259547 
## [89] train-rmse:0.042296+0.004096    test-rmse:1.246005+0.260441 
## [90] train-rmse:0.041160+0.004235    test-rmse:1.245398+0.260210 
## [91] train-rmse:0.040129+0.004027    test-rmse:1.245511+0.260046 
## [92] train-rmse:0.038559+0.003830    test-rmse:1.244874+0.259772 
## [93] train-rmse:0.037075+0.003974    test-rmse:1.244613+0.259821 
## [94] train-rmse:0.035429+0.004090    test-rmse:1.244813+0.260230 
## [95] train-rmse:0.034410+0.004498    test-rmse:1.244758+0.260072 
## [96] train-rmse:0.033253+0.004131    test-rmse:1.244211+0.260227 
## [97] train-rmse:0.032270+0.004072    test-rmse:1.244198+0.260373 
## [98] train-rmse:0.031374+0.004086    test-rmse:1.244007+0.260168 
## [99] train-rmse:0.030493+0.004115    test-rmse:1.243901+0.260259 
## [100]    train-rmse:0.028926+0.003862    test-rmse:1.243869+0.260024 
## [101]    train-rmse:0.027811+0.003480    test-rmse:1.244069+0.260139 
## [102]    train-rmse:0.026627+0.003265    test-rmse:1.244043+0.260344 
## [103]    train-rmse:0.025877+0.003225    test-rmse:1.243967+0.260336 
## [104]    train-rmse:0.025001+0.003371    test-rmse:1.244135+0.260330 
## [105]    train-rmse:0.024422+0.003416    test-rmse:1.244171+0.260475 
## [106]    train-rmse:0.023533+0.003227    test-rmse:1.244271+0.260473 
## Stopping. Best iteration:
## [100]    train-rmse:0.028926+0.003862    test-rmse:1.243869+0.260024
## 
## 63 
## [1]  train-rmse:77.673825+0.090310   test-rmse:77.673837+0.509215 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:56.141246+0.073781   test-rmse:56.140539+0.549016 
## [3]  train-rmse:40.670034+0.065404   test-rmse:40.668152+0.588925 
## [4]  train-rmse:29.583382+0.061017   test-rmse:29.625472+0.605847 
## [5]  train-rmse:21.646907+0.057207   test-rmse:21.735936+0.643300 
## [6]  train-rmse:15.983337+0.051232   test-rmse:16.050024+0.645204 
## [7]  train-rmse:11.978003+0.051675   test-rmse:12.076230+0.612720 
## [8]  train-rmse:9.184355+0.053515    test-rmse:9.318300+0.698265 
## [9]  train-rmse:7.273962+0.061099    test-rmse:7.417010+0.646397 
## [10] train-rmse:5.999996+0.071971    test-rmse:6.119492+0.603607 
## [11] train-rmse:5.168102+0.072229    test-rmse:5.276947+0.567624 
## [12] train-rmse:4.627800+0.077435    test-rmse:4.767812+0.558824 
## [13] train-rmse:4.289076+0.079941    test-rmse:4.464644+0.556615 
## [14] train-rmse:4.060899+0.077814    test-rmse:4.190719+0.488553 
## [15] train-rmse:3.897036+0.074924    test-rmse:4.061487+0.478012 
## [16] train-rmse:3.773423+0.069573    test-rmse:3.919488+0.390057 
## [17] train-rmse:3.678410+0.067873    test-rmse:3.856132+0.391725 
## [18] train-rmse:3.597593+0.065723    test-rmse:3.780941+0.407116 
## [19] train-rmse:3.526715+0.063055    test-rmse:3.739173+0.393329 
## [20] train-rmse:3.459155+0.059498    test-rmse:3.685062+0.381678 
## [21] train-rmse:3.393745+0.056674    test-rmse:3.663849+0.399339 
## [22] train-rmse:3.331230+0.053912    test-rmse:3.590382+0.356464 
## [23] train-rmse:3.271891+0.050008    test-rmse:3.510515+0.369788 
## [24] train-rmse:3.214746+0.047283    test-rmse:3.457933+0.368552 
## [25] train-rmse:3.161659+0.046919    test-rmse:3.369284+0.320630 
## [26] train-rmse:3.111678+0.045292    test-rmse:3.344943+0.321722 
## [27] train-rmse:3.063851+0.043944    test-rmse:3.278756+0.315941 
## [28] train-rmse:3.018711+0.043270    test-rmse:3.254675+0.321072 
## [29] train-rmse:2.975718+0.041957    test-rmse:3.197086+0.291649 
## [30] train-rmse:2.933236+0.040829    test-rmse:3.155788+0.300846 
## [31] train-rmse:2.892198+0.040460    test-rmse:3.137363+0.309243 
## [32] train-rmse:2.852188+0.039822    test-rmse:3.111884+0.310575 
## [33] train-rmse:2.812072+0.039062    test-rmse:3.073699+0.306116 
## [34] train-rmse:2.773507+0.038860    test-rmse:3.036285+0.311318 
## [35] train-rmse:2.735861+0.039147    test-rmse:3.006152+0.315409 
## [36] train-rmse:2.699062+0.039240    test-rmse:2.985197+0.320633 
## [37] train-rmse:2.662474+0.039506    test-rmse:2.936649+0.331592 
## [38] train-rmse:2.625769+0.038911    test-rmse:2.904053+0.336173 
## [39] train-rmse:2.590994+0.038789    test-rmse:2.856039+0.293888 
## [40] train-rmse:2.558277+0.038145    test-rmse:2.821006+0.288044 
## [41] train-rmse:2.525595+0.037756    test-rmse:2.802160+0.289549 
## [42] train-rmse:2.494160+0.036993    test-rmse:2.774853+0.309062 
## [43] train-rmse:2.463797+0.035616    test-rmse:2.741448+0.306169 
## [44] train-rmse:2.434654+0.034945    test-rmse:2.686588+0.292901 
## [45] train-rmse:2.405650+0.034591    test-rmse:2.673165+0.282707 
## [46] train-rmse:2.377180+0.033935    test-rmse:2.646631+0.280408 
## [47] train-rmse:2.349579+0.033568    test-rmse:2.621803+0.282967 
## [48] train-rmse:2.322012+0.033247    test-rmse:2.593642+0.289729 
## [49] train-rmse:2.294778+0.032724    test-rmse:2.567674+0.298991 
## [50] train-rmse:2.268723+0.032704    test-rmse:2.546359+0.299396 
## [51] train-rmse:2.243220+0.031773    test-rmse:2.519124+0.283646 
## [52] train-rmse:2.218117+0.031199    test-rmse:2.505986+0.289979 
## [53] train-rmse:2.192959+0.030534    test-rmse:2.482088+0.306871 
## [54] train-rmse:2.167860+0.029901    test-rmse:2.459933+0.295504 
## [55] train-rmse:2.142882+0.028512    test-rmse:2.436658+0.292425 
## [56] train-rmse:2.119516+0.027465    test-rmse:2.425163+0.294335 
## [57] train-rmse:2.096064+0.026014    test-rmse:2.380408+0.270931 
## [58] train-rmse:2.073447+0.025527    test-rmse:2.360859+0.270050 
## [59] train-rmse:2.051420+0.025174    test-rmse:2.345926+0.281343 
## [60] train-rmse:2.030025+0.024834    test-rmse:2.327318+0.287025 
## [61] train-rmse:2.008544+0.024566    test-rmse:2.307361+0.293636 
## [62] train-rmse:1.987133+0.024408    test-rmse:2.284290+0.290833 
## [63] train-rmse:1.966248+0.023799    test-rmse:2.260699+0.278947 
## [64] train-rmse:1.945554+0.023190    test-rmse:2.228410+0.269057 
## [65] train-rmse:1.925277+0.022753    test-rmse:2.212894+0.269784 
## [66] train-rmse:1.905014+0.022307    test-rmse:2.181723+0.267611 
## [67] train-rmse:1.884852+0.021511    test-rmse:2.155400+0.271198 
## [68] train-rmse:1.864838+0.021195    test-rmse:2.142540+0.263591 
## [69] train-rmse:1.845521+0.020809    test-rmse:2.124884+0.264020 
## [70] train-rmse:1.827060+0.020680    test-rmse:2.102875+0.250750 
## [71] train-rmse:1.809195+0.020814    test-rmse:2.096424+0.250851 
## [72] train-rmse:1.791367+0.020813    test-rmse:2.073622+0.249554 
## [73] train-rmse:1.773533+0.020723    test-rmse:2.047958+0.260821 
## [74] train-rmse:1.756374+0.020748    test-rmse:2.039087+0.258902 
## [75] train-rmse:1.739751+0.020441    test-rmse:2.016701+0.263280 
## [76] train-rmse:1.723345+0.020269    test-rmse:2.006436+0.266406 
## [77] train-rmse:1.707022+0.020204    test-rmse:1.996618+0.255876 
## [78] train-rmse:1.691074+0.020139    test-rmse:1.969599+0.237642 
## [79] train-rmse:1.675561+0.019839    test-rmse:1.962112+0.237831 
## [80] train-rmse:1.659905+0.019402    test-rmse:1.950932+0.242473 
## [81] train-rmse:1.644019+0.018745    test-rmse:1.942062+0.241057 
## [82] train-rmse:1.629171+0.018026    test-rmse:1.915894+0.233567 
## [83] train-rmse:1.614757+0.017449    test-rmse:1.909395+0.237379 
## [84] train-rmse:1.600594+0.016930    test-rmse:1.895151+0.238599 
## [85] train-rmse:1.586504+0.016262    test-rmse:1.883410+0.239722 
## [86] train-rmse:1.572499+0.015756    test-rmse:1.863616+0.240604 
## [87] train-rmse:1.558671+0.015491    test-rmse:1.844559+0.229547 
## [88] train-rmse:1.544868+0.015130    test-rmse:1.835243+0.235039 
## [89] train-rmse:1.531512+0.014825    test-rmse:1.829038+0.232381 
## [90] train-rmse:1.518406+0.014561    test-rmse:1.813459+0.229974 
## [91] train-rmse:1.505220+0.014419    test-rmse:1.799344+0.229833 
## [92] train-rmse:1.492122+0.014428    test-rmse:1.784499+0.233924 
## [93] train-rmse:1.479310+0.014296    test-rmse:1.776463+0.226351 
## [94] train-rmse:1.466624+0.014161    test-rmse:1.768444+0.227208 
## [95] train-rmse:1.454179+0.014233    test-rmse:1.756106+0.230054 
## [96] train-rmse:1.442121+0.013900    test-rmse:1.742958+0.237025 
## [97] train-rmse:1.430290+0.013630    test-rmse:1.730617+0.232096 
## [98] train-rmse:1.418517+0.013367    test-rmse:1.724459+0.232424 
## [99] train-rmse:1.407401+0.013101    test-rmse:1.695119+0.217383 
## [100]    train-rmse:1.396210+0.012872    test-rmse:1.688497+0.218649 
## [101]    train-rmse:1.385231+0.012856    test-rmse:1.682865+0.220508 
## [102]    train-rmse:1.374729+0.012739    test-rmse:1.672359+0.219215 
## [103]    train-rmse:1.364302+0.012511    test-rmse:1.662973+0.216850 
## [104]    train-rmse:1.353841+0.012367    test-rmse:1.656245+0.213727 
## [105]    train-rmse:1.343474+0.012330    test-rmse:1.642272+0.217748 
## [106]    train-rmse:1.333577+0.012007    test-rmse:1.635918+0.217842 
## [107]    train-rmse:1.323731+0.011647    test-rmse:1.627389+0.215481 
## [108]    train-rmse:1.314011+0.011314    test-rmse:1.620386+0.216116 
## [109]    train-rmse:1.304261+0.010970    test-rmse:1.608670+0.209916 
## [110]    train-rmse:1.294604+0.010673    test-rmse:1.601963+0.208159 
## [111]    train-rmse:1.284987+0.010233    test-rmse:1.584698+0.202982 
## [112]    train-rmse:1.275790+0.010065    test-rmse:1.578181+0.208499 
## [113]    train-rmse:1.266772+0.009874    test-rmse:1.563872+0.209573 
## [114]    train-rmse:1.257931+0.009825    test-rmse:1.560750+0.209496 
## [115]    train-rmse:1.249111+0.009815    test-rmse:1.551265+0.208630 
## [116]    train-rmse:1.240313+0.009761    test-rmse:1.545277+0.206938 
## [117]    train-rmse:1.231738+0.009833    test-rmse:1.531310+0.207662 
## [118]    train-rmse:1.223438+0.009818    test-rmse:1.523625+0.210637 
## [119]    train-rmse:1.215166+0.009818    test-rmse:1.520329+0.212089 
## [120]    train-rmse:1.207089+0.009818    test-rmse:1.507176+0.196928 
## [121]    train-rmse:1.199263+0.009850    test-rmse:1.499774+0.199352 
## [122]    train-rmse:1.191277+0.009899    test-rmse:1.498201+0.199951 
## [123]    train-rmse:1.183684+0.009739    test-rmse:1.493141+0.202019 
## [124]    train-rmse:1.176460+0.009678    test-rmse:1.486956+0.206392 
## [125]    train-rmse:1.169221+0.009703    test-rmse:1.474933+0.198169 
## [126]    train-rmse:1.162097+0.009703    test-rmse:1.465288+0.197203 
## [127]    train-rmse:1.155132+0.009659    test-rmse:1.455939+0.198252 
## [128]    train-rmse:1.148207+0.009619    test-rmse:1.454266+0.197894 
## [129]    train-rmse:1.141268+0.009687    test-rmse:1.448085+0.200040 
## [130]    train-rmse:1.134554+0.009528    test-rmse:1.443222+0.201439 
## [131]    train-rmse:1.127882+0.009401    test-rmse:1.436324+0.198942 
## [132]    train-rmse:1.121346+0.009081    test-rmse:1.429811+0.202222 
## [133]    train-rmse:1.114837+0.008858    test-rmse:1.424314+0.200452 
## [134]    train-rmse:1.108431+0.008614    test-rmse:1.412244+0.197711 
## [135]    train-rmse:1.102211+0.008531    test-rmse:1.402284+0.198169 
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## [230]    train-rmse:0.806531+0.009457    test-rmse:1.132880+0.168846 
## [231]    train-rmse:0.805152+0.009509    test-rmse:1.130895+0.168674 
## [232]    train-rmse:0.803875+0.009606    test-rmse:1.129808+0.167742 
## [233]    train-rmse:0.802570+0.009696    test-rmse:1.126771+0.168202 
## [234]    train-rmse:0.801288+0.009798    test-rmse:1.127195+0.167534 
## [235]    train-rmse:0.800043+0.009879    test-rmse:1.125334+0.167978 
## [236]    train-rmse:0.798777+0.009943    test-rmse:1.127011+0.167698 
## [237]    train-rmse:0.797570+0.009984    test-rmse:1.126291+0.169182 
## [238]    train-rmse:0.796361+0.010039    test-rmse:1.123407+0.171658 
## [239]    train-rmse:0.795109+0.010082    test-rmse:1.124938+0.170408 
## [240]    train-rmse:0.793861+0.010190    test-rmse:1.122409+0.169528 
## [241]    train-rmse:0.792686+0.010261    test-rmse:1.120328+0.171793 
## [242]    train-rmse:0.791552+0.010323    test-rmse:1.119609+0.172391 
## [243]    train-rmse:0.790445+0.010394    test-rmse:1.120349+0.172084 
## [244]    train-rmse:0.789316+0.010426    test-rmse:1.119369+0.172389 
## [245]    train-rmse:0.788200+0.010501    test-rmse:1.118154+0.173218 
## [246]    train-rmse:0.787108+0.010535    test-rmse:1.117315+0.173155 
## [247]    train-rmse:0.786026+0.010576    test-rmse:1.116807+0.173270 
## [248]    train-rmse:0.784957+0.010610    test-rmse:1.115094+0.173354 
## [249]    train-rmse:0.783880+0.010641    test-rmse:1.115315+0.172239 
## [250]    train-rmse:0.782865+0.010699    test-rmse:1.114263+0.169897 
## [251]    train-rmse:0.781764+0.010743    test-rmse:1.112190+0.170996 
## [252]    train-rmse:0.780760+0.010822    test-rmse:1.112101+0.170573 
## [253]    train-rmse:0.779772+0.010895    test-rmse:1.112135+0.170773 
## [254]    train-rmse:0.778792+0.010969    test-rmse:1.110191+0.171294 
## [255]    train-rmse:0.777826+0.011038    test-rmse:1.110194+0.171144 
## [256]    train-rmse:0.776846+0.011098    test-rmse:1.107945+0.170379 
## [257]    train-rmse:0.775872+0.011177    test-rmse:1.108562+0.171126 
## [258]    train-rmse:0.774878+0.011234    test-rmse:1.107288+0.171454 
## [259]    train-rmse:0.773944+0.011300    test-rmse:1.106718+0.171790 
## [260]    train-rmse:0.772990+0.011339    test-rmse:1.107435+0.172428 
## [261]    train-rmse:0.772043+0.011390    test-rmse:1.102592+0.168859 
## [262]    train-rmse:0.771129+0.011449    test-rmse:1.101453+0.169342 
## [263]    train-rmse:0.770211+0.011528    test-rmse:1.100758+0.170034 
## [264]    train-rmse:0.769307+0.011596    test-rmse:1.099116+0.168341 
## [265]    train-rmse:0.768396+0.011669    test-rmse:1.099165+0.167857 
## [266]    train-rmse:0.767480+0.011705    test-rmse:1.098572+0.168251 
## [267]    train-rmse:0.766602+0.011718    test-rmse:1.098466+0.168754 
## [268]    train-rmse:0.765728+0.011743    test-rmse:1.096131+0.169299 
## [269]    train-rmse:0.764880+0.011803    test-rmse:1.095769+0.169025 
## [270]    train-rmse:0.763982+0.011867    test-rmse:1.093712+0.170121 
## [271]    train-rmse:0.763147+0.011920    test-rmse:1.093776+0.169671 
## [272]    train-rmse:0.762333+0.011974    test-rmse:1.092570+0.170170 
## [273]    train-rmse:0.761522+0.012038    test-rmse:1.092162+0.169832 
## [274]    train-rmse:0.760706+0.012049    test-rmse:1.090604+0.170679 
## [275]    train-rmse:0.759888+0.012061    test-rmse:1.089916+0.171514 
## [276]    train-rmse:0.759075+0.012103    test-rmse:1.091726+0.171063 
## [277]    train-rmse:0.758241+0.012136    test-rmse:1.091075+0.171137 
## [278]    train-rmse:0.757471+0.012180    test-rmse:1.088494+0.171613 
## [279]    train-rmse:0.756658+0.012217    test-rmse:1.088897+0.171286 
## [280]    train-rmse:0.755900+0.012262    test-rmse:1.088322+0.171483 
## [281]    train-rmse:0.755144+0.012286    test-rmse:1.088010+0.171975 
## [282]    train-rmse:0.754403+0.012308    test-rmse:1.088834+0.170840 
## [283]    train-rmse:0.753655+0.012329    test-rmse:1.088445+0.171296 
## [284]    train-rmse:0.752906+0.012350    test-rmse:1.087627+0.171148 
## [285]    train-rmse:0.752155+0.012369    test-rmse:1.087773+0.171924 
## [286]    train-rmse:0.751434+0.012363    test-rmse:1.087061+0.171367 
## [287]    train-rmse:0.750707+0.012342    test-rmse:1.088058+0.168786 
## [288]    train-rmse:0.749941+0.012358    test-rmse:1.085926+0.169338 
## [289]    train-rmse:0.749238+0.012380    test-rmse:1.085246+0.168212 
## [290]    train-rmse:0.748548+0.012402    test-rmse:1.084408+0.168627 
## [291]    train-rmse:0.747860+0.012423    test-rmse:1.084637+0.169166 
## [292]    train-rmse:0.747190+0.012441    test-rmse:1.084704+0.169766 
## [293]    train-rmse:0.746517+0.012434    test-rmse:1.082986+0.168120 
## [294]    train-rmse:0.745829+0.012427    test-rmse:1.084231+0.167131 
## [295]    train-rmse:0.745139+0.012452    test-rmse:1.084406+0.167702 
## [296]    train-rmse:0.744473+0.012495    test-rmse:1.085501+0.168489 
## [297]    train-rmse:0.743818+0.012534    test-rmse:1.084555+0.167741 
## [298]    train-rmse:0.743171+0.012573    test-rmse:1.084089+0.168084 
## [299]    train-rmse:0.742547+0.012606    test-rmse:1.083239+0.168122 
## Stopping. Best iteration:
## [293]    train-rmse:0.746517+0.012434    test-rmse:1.082986+0.168120
## 
## 64 
## [1]  train-rmse:77.673825+0.090310   test-rmse:77.673837+0.509215 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:56.141246+0.073781   test-rmse:56.140539+0.549016 
## [3]  train-rmse:40.670034+0.065404   test-rmse:40.668152+0.588925 
## [4]  train-rmse:29.583382+0.061017   test-rmse:29.625472+0.605847 
## [5]  train-rmse:21.623629+0.047512   test-rmse:21.720874+0.648633 
## [6]  train-rmse:15.918484+0.035098   test-rmse:16.005864+0.580751 
## [7]  train-rmse:11.848531+0.034455   test-rmse:11.984302+0.591692 
## [8]  train-rmse:8.978132+0.037325    test-rmse:9.063821+0.529134 
## [9]  train-rmse:6.988340+0.039910    test-rmse:7.096236+0.545274 
## [10] train-rmse:5.618695+0.043090    test-rmse:5.753589+0.541540 
## [11] train-rmse:4.701192+0.048269    test-rmse:4.891447+0.546311 
## [12] train-rmse:4.095224+0.049868    test-rmse:4.285748+0.476164 
## [13] train-rmse:3.686998+0.048687    test-rmse:3.954349+0.465924 
## [14] train-rmse:3.406569+0.046027    test-rmse:3.671667+0.427515 
## [15] train-rmse:3.208467+0.046093    test-rmse:3.458316+0.368388 
## [16] train-rmse:3.050930+0.036466    test-rmse:3.315211+0.331076 
## [17] train-rmse:2.932349+0.032639    test-rmse:3.227169+0.344653 
## [18] train-rmse:2.834480+0.032115    test-rmse:3.153049+0.337092 
## [19] train-rmse:2.744050+0.030052    test-rmse:3.059028+0.360969 
## [20] train-rmse:2.662361+0.032143    test-rmse:2.995022+0.363269 
## [21] train-rmse:2.586843+0.032949    test-rmse:2.917780+0.341633 
## [22] train-rmse:2.506110+0.025704    test-rmse:2.845381+0.283204 
## [23] train-rmse:2.438730+0.025007    test-rmse:2.773795+0.273568 
## [24] train-rmse:2.371549+0.021045    test-rmse:2.734770+0.290539 
## [25] train-rmse:2.310919+0.018185    test-rmse:2.694504+0.311607 
## [26] train-rmse:2.252767+0.019330    test-rmse:2.646528+0.308379 
## [27] train-rmse:2.194710+0.016328    test-rmse:2.605940+0.305629 
## [28] train-rmse:2.141731+0.016858    test-rmse:2.566008+0.287962 
## [29] train-rmse:2.091189+0.015527    test-rmse:2.517455+0.286037 
## [30] train-rmse:2.039084+0.015687    test-rmse:2.467181+0.269044 
## [31] train-rmse:1.991155+0.016466    test-rmse:2.418454+0.285548 
## [32] train-rmse:1.941487+0.016018    test-rmse:2.372288+0.275515 
## [33] train-rmse:1.893266+0.016053    test-rmse:2.336549+0.280049 
## [34] train-rmse:1.850306+0.015335    test-rmse:2.294557+0.289330 
## [35] train-rmse:1.806655+0.012062    test-rmse:2.259196+0.274588 
## [36] train-rmse:1.766190+0.010223    test-rmse:2.220315+0.259091 
## [37] train-rmse:1.727682+0.010129    test-rmse:2.191305+0.253671 
## [38] train-rmse:1.691083+0.009269    test-rmse:2.151929+0.258582 
## [39] train-rmse:1.655226+0.008405    test-rmse:2.133582+0.262065 
## [40] train-rmse:1.620291+0.008471    test-rmse:2.091142+0.250930 
## [41] train-rmse:1.586289+0.006358    test-rmse:2.071500+0.263276 
## [42] train-rmse:1.553373+0.006917    test-rmse:2.055855+0.265993 
## [43] train-rmse:1.516925+0.008104    test-rmse:2.030424+0.269530 
## [44] train-rmse:1.486705+0.008021    test-rmse:2.001574+0.243451 
## [45] train-rmse:1.457449+0.008919    test-rmse:1.977083+0.252215 
## [46] train-rmse:1.428617+0.009716    test-rmse:1.947191+0.235471 
## [47] train-rmse:1.400994+0.009431    test-rmse:1.916370+0.242472 
## [48] train-rmse:1.373245+0.009993    test-rmse:1.888481+0.232624 
## [49] train-rmse:1.345139+0.010469    test-rmse:1.858279+0.233219 
## [50] train-rmse:1.318948+0.008987    test-rmse:1.841841+0.232298 
## [51] train-rmse:1.292473+0.012789    test-rmse:1.825755+0.230381 
## [52] train-rmse:1.269009+0.012915    test-rmse:1.810148+0.236820 
## [53] train-rmse:1.245614+0.014664    test-rmse:1.801275+0.238835 
## [54] train-rmse:1.223181+0.014582    test-rmse:1.784266+0.243026 
## [55] train-rmse:1.200910+0.014863    test-rmse:1.769425+0.233505 
## [56] train-rmse:1.179410+0.014386    test-rmse:1.749997+0.231323 
## [57] train-rmse:1.156466+0.014821    test-rmse:1.726140+0.241366 
## [58] train-rmse:1.136403+0.016592    test-rmse:1.701336+0.246595 
## [59] train-rmse:1.114886+0.016362    test-rmse:1.688464+0.241360 
## [60] train-rmse:1.094864+0.015928    test-rmse:1.673888+0.238308 
## [61] train-rmse:1.076219+0.015015    test-rmse:1.654790+0.237310 
## [62] train-rmse:1.058306+0.014919    test-rmse:1.639030+0.243080 
## [63] train-rmse:1.041831+0.014234    test-rmse:1.625144+0.240617 
## [64] train-rmse:1.026348+0.014607    test-rmse:1.609374+0.245705 
## [65] train-rmse:1.010955+0.015079    test-rmse:1.593295+0.240502 
## [66] train-rmse:0.996108+0.015206    test-rmse:1.578414+0.239559 
## [67] train-rmse:0.979017+0.015218    test-rmse:1.563208+0.243040 
## [68] train-rmse:0.963117+0.015480    test-rmse:1.553286+0.245460 
## [69] train-rmse:0.946825+0.014863    test-rmse:1.543714+0.248542 
## [70] train-rmse:0.932255+0.014900    test-rmse:1.533466+0.250112 
## [71] train-rmse:0.917509+0.015928    test-rmse:1.523824+0.241835 
## [72] train-rmse:0.904392+0.016141    test-rmse:1.518432+0.244733 
## [73] train-rmse:0.892059+0.016356    test-rmse:1.512213+0.245021 
## [74] train-rmse:0.880079+0.016106    test-rmse:1.502151+0.249489 
## [75] train-rmse:0.866302+0.017824    test-rmse:1.490041+0.248994 
## [76] train-rmse:0.855237+0.017842    test-rmse:1.483594+0.253935 
## [77] train-rmse:0.843652+0.017242    test-rmse:1.477914+0.257547 
## [78] train-rmse:0.830191+0.018518    test-rmse:1.465364+0.253487 
## [79] train-rmse:0.819160+0.019138    test-rmse:1.453276+0.255059 
## [80] train-rmse:0.807503+0.019065    test-rmse:1.447414+0.256261 
## [81] train-rmse:0.796903+0.018961    test-rmse:1.437094+0.251901 
## [82] train-rmse:0.785972+0.019066    test-rmse:1.432038+0.253299 
## [83] train-rmse:0.776819+0.019144    test-rmse:1.421927+0.262395 
## [84] train-rmse:0.767827+0.018767    test-rmse:1.418367+0.259989 
## [85] train-rmse:0.759242+0.018295    test-rmse:1.413462+0.260115 
## [86] train-rmse:0.750819+0.018296    test-rmse:1.404192+0.262069 
## [87] train-rmse:0.739742+0.017189    test-rmse:1.400169+0.261291 
## [88] train-rmse:0.731084+0.016766    test-rmse:1.395927+0.263847 
## [89] train-rmse:0.722194+0.016531    test-rmse:1.389884+0.265645 
## [90] train-rmse:0.712102+0.014601    test-rmse:1.380885+0.265796 
## [91] train-rmse:0.702771+0.014683    test-rmse:1.379390+0.268328 
## [92] train-rmse:0.693861+0.014519    test-rmse:1.374431+0.271209 
## [93] train-rmse:0.686947+0.014151    test-rmse:1.370908+0.271085 
## [94] train-rmse:0.679234+0.014336    test-rmse:1.362450+0.268901 
## [95] train-rmse:0.672994+0.014269    test-rmse:1.356096+0.269996 
## [96] train-rmse:0.664904+0.014645    test-rmse:1.350175+0.273673 
## [97] train-rmse:0.658521+0.014729    test-rmse:1.344877+0.265835 
## [98] train-rmse:0.652596+0.014573    test-rmse:1.345109+0.265240 
## [99] train-rmse:0.644823+0.012981    test-rmse:1.343373+0.268374 
## [100]    train-rmse:0.638911+0.012189    test-rmse:1.333302+0.271480 
## [101]    train-rmse:0.631149+0.011615    test-rmse:1.328064+0.270802 
## [102]    train-rmse:0.624152+0.011747    test-rmse:1.323180+0.270738 
## [103]    train-rmse:0.617910+0.012449    test-rmse:1.318614+0.273912 
## [104]    train-rmse:0.612756+0.012260    test-rmse:1.315519+0.272933 
## [105]    train-rmse:0.606429+0.011229    test-rmse:1.315103+0.274922 
## [106]    train-rmse:0.601025+0.011748    test-rmse:1.308955+0.276278 
## [107]    train-rmse:0.594895+0.010453    test-rmse:1.307755+0.278766 
## [108]    train-rmse:0.590677+0.010310    test-rmse:1.305352+0.281431 
## [109]    train-rmse:0.585635+0.011049    test-rmse:1.304730+0.279330 
## [110]    train-rmse:0.579729+0.009977    test-rmse:1.303083+0.282376 
## [111]    train-rmse:0.574138+0.009909    test-rmse:1.298320+0.285275 
## [112]    train-rmse:0.570067+0.009333    test-rmse:1.293437+0.284192 
## [113]    train-rmse:0.565218+0.009150    test-rmse:1.288093+0.287659 
## [114]    train-rmse:0.560850+0.009272    test-rmse:1.284977+0.289368 
## [115]    train-rmse:0.556583+0.008886    test-rmse:1.285888+0.291854 
## [116]    train-rmse:0.552798+0.009469    test-rmse:1.281414+0.296461 
## [117]    train-rmse:0.548575+0.009323    test-rmse:1.277856+0.295466 
## [118]    train-rmse:0.544824+0.009159    test-rmse:1.271268+0.293193 
## [119]    train-rmse:0.540332+0.009040    test-rmse:1.269043+0.291301 
## [120]    train-rmse:0.536613+0.008496    test-rmse:1.269644+0.292401 
## [121]    train-rmse:0.532102+0.007578    test-rmse:1.268485+0.291194 
## [122]    train-rmse:0.528029+0.007614    test-rmse:1.266347+0.293318 
## [123]    train-rmse:0.524037+0.008638    test-rmse:1.265656+0.292426 
## [124]    train-rmse:0.520396+0.008523    test-rmse:1.262337+0.293318 
## [125]    train-rmse:0.517172+0.007844    test-rmse:1.259630+0.293546 
## [126]    train-rmse:0.513573+0.007210    test-rmse:1.257853+0.290801 
## [127]    train-rmse:0.509046+0.009414    test-rmse:1.256834+0.289933 
## [128]    train-rmse:0.505459+0.010289    test-rmse:1.255221+0.291009 
## [129]    train-rmse:0.502137+0.009958    test-rmse:1.254062+0.291922 
## [130]    train-rmse:0.499675+0.010278    test-rmse:1.254149+0.293110 
## [131]    train-rmse:0.495664+0.009463    test-rmse:1.248885+0.293944 
## [132]    train-rmse:0.490947+0.008336    test-rmse:1.246700+0.296111 
## [133]    train-rmse:0.488364+0.007947    test-rmse:1.245815+0.297031 
## [134]    train-rmse:0.485393+0.008274    test-rmse:1.242313+0.300997 
## [135]    train-rmse:0.481626+0.007635    test-rmse:1.241049+0.302144 
## [136]    train-rmse:0.477820+0.007154    test-rmse:1.238650+0.304242 
## [137]    train-rmse:0.475223+0.006854    test-rmse:1.236004+0.304998 
## [138]    train-rmse:0.471693+0.007832    test-rmse:1.232897+0.307768 
## [139]    train-rmse:0.469214+0.007594    test-rmse:1.228338+0.306044 
## [140]    train-rmse:0.465711+0.007751    test-rmse:1.227270+0.306020 
## [141]    train-rmse:0.463102+0.007414    test-rmse:1.224772+0.306934 
## [142]    train-rmse:0.460970+0.007241    test-rmse:1.221584+0.306245 
## [143]    train-rmse:0.458540+0.008132    test-rmse:1.221067+0.306549 
## [144]    train-rmse:0.455126+0.008411    test-rmse:1.221941+0.305941 
## [145]    train-rmse:0.452502+0.007985    test-rmse:1.219346+0.305893 
## [146]    train-rmse:0.450401+0.008452    test-rmse:1.219347+0.305333 
## [147]    train-rmse:0.447113+0.008792    test-rmse:1.220078+0.305770 
## [148]    train-rmse:0.444959+0.008758    test-rmse:1.220429+0.305346 
## [149]    train-rmse:0.442655+0.008513    test-rmse:1.219227+0.305236 
## [150]    train-rmse:0.439829+0.008343    test-rmse:1.213823+0.305733 
## [151]    train-rmse:0.436882+0.008004    test-rmse:1.212990+0.304752 
## [152]    train-rmse:0.434210+0.008318    test-rmse:1.211130+0.306317 
## [153]    train-rmse:0.432585+0.008511    test-rmse:1.209113+0.307340 
## [154]    train-rmse:0.430409+0.008729    test-rmse:1.204353+0.300177 
## [155]    train-rmse:0.428385+0.008709    test-rmse:1.203952+0.299966 
## [156]    train-rmse:0.425963+0.009018    test-rmse:1.203875+0.301469 
## [157]    train-rmse:0.423652+0.008964    test-rmse:1.203972+0.301852 
## [158]    train-rmse:0.421053+0.009184    test-rmse:1.201318+0.302139 
## [159]    train-rmse:0.418145+0.008580    test-rmse:1.199154+0.303742 
## [160]    train-rmse:0.415915+0.008012    test-rmse:1.197889+0.304243 
## [161]    train-rmse:0.413610+0.007868    test-rmse:1.197796+0.305409 
## [162]    train-rmse:0.411560+0.008957    test-rmse:1.196977+0.305460 
## [163]    train-rmse:0.409394+0.008614    test-rmse:1.197542+0.307547 
## [164]    train-rmse:0.407727+0.008591    test-rmse:1.196819+0.308654 
## [165]    train-rmse:0.405003+0.007921    test-rmse:1.196725+0.307019 
## [166]    train-rmse:0.402940+0.007305    test-rmse:1.196825+0.306470 
## [167]    train-rmse:0.400443+0.007941    test-rmse:1.197735+0.305192 
## [168]    train-rmse:0.398413+0.008479    test-rmse:1.197245+0.305942 
## [169]    train-rmse:0.397005+0.008639    test-rmse:1.195576+0.306491 
## [170]    train-rmse:0.395504+0.008747    test-rmse:1.194146+0.307024 
## [171]    train-rmse:0.393256+0.010133    test-rmse:1.193357+0.306829 
## [172]    train-rmse:0.391083+0.010978    test-rmse:1.194341+0.305194 
## [173]    train-rmse:0.388736+0.011188    test-rmse:1.192751+0.306122 
## [174]    train-rmse:0.385995+0.011417    test-rmse:1.192587+0.306360 
## [175]    train-rmse:0.383967+0.010846    test-rmse:1.193526+0.307062 
## [176]    train-rmse:0.382411+0.011434    test-rmse:1.193568+0.305103 
## [177]    train-rmse:0.379691+0.012601    test-rmse:1.193446+0.305722 
## [178]    train-rmse:0.377809+0.012589    test-rmse:1.188900+0.301525 
## [179]    train-rmse:0.375403+0.012130    test-rmse:1.188360+0.301500 
## [180]    train-rmse:0.373575+0.011971    test-rmse:1.186672+0.301616 
## [181]    train-rmse:0.372400+0.011872    test-rmse:1.186638+0.300820 
## [182]    train-rmse:0.371160+0.011956    test-rmse:1.186647+0.300764 
## [183]    train-rmse:0.369128+0.011573    test-rmse:1.186429+0.300432 
## [184]    train-rmse:0.366880+0.011637    test-rmse:1.185262+0.299826 
## [185]    train-rmse:0.365609+0.011777    test-rmse:1.184753+0.300620 
## [186]    train-rmse:0.362996+0.011586    test-rmse:1.184357+0.302055 
## [187]    train-rmse:0.361456+0.011420    test-rmse:1.182559+0.302164 
## [188]    train-rmse:0.359902+0.011263    test-rmse:1.181439+0.302838 
## [189]    train-rmse:0.358298+0.011121    test-rmse:1.180123+0.302500 
## [190]    train-rmse:0.355878+0.011974    test-rmse:1.181292+0.303230 
## [191]    train-rmse:0.353532+0.011341    test-rmse:1.181727+0.304130 
## [192]    train-rmse:0.351705+0.010968    test-rmse:1.181740+0.303307 
## [193]    train-rmse:0.350144+0.011696    test-rmse:1.180595+0.302186 
## [194]    train-rmse:0.348481+0.011501    test-rmse:1.179817+0.302244 
## [195]    train-rmse:0.346809+0.011002    test-rmse:1.178704+0.303525 
## [196]    train-rmse:0.345671+0.010817    test-rmse:1.177744+0.304199 
## [197]    train-rmse:0.344669+0.010671    test-rmse:1.176735+0.304548 
## [198]    train-rmse:0.342833+0.010429    test-rmse:1.173114+0.305389 
## [199]    train-rmse:0.341491+0.010350    test-rmse:1.172848+0.305565 
## [200]    train-rmse:0.339945+0.010494    test-rmse:1.172391+0.307416 
## [201]    train-rmse:0.338699+0.010328    test-rmse:1.170741+0.307887 
## [202]    train-rmse:0.336931+0.009772    test-rmse:1.170160+0.307585 
## [203]    train-rmse:0.334777+0.010039    test-rmse:1.170251+0.307123 
## [204]    train-rmse:0.333966+0.009994    test-rmse:1.169801+0.307688 
## [205]    train-rmse:0.332225+0.009768    test-rmse:1.168432+0.307774 
## [206]    train-rmse:0.330943+0.009567    test-rmse:1.167654+0.307420 
## [207]    train-rmse:0.329990+0.009853    test-rmse:1.166657+0.307654 
## [208]    train-rmse:0.328556+0.010769    test-rmse:1.165970+0.307840 
## [209]    train-rmse:0.326315+0.010554    test-rmse:1.166166+0.308022 
## [210]    train-rmse:0.325226+0.010587    test-rmse:1.164620+0.307376 
## [211]    train-rmse:0.324195+0.010634    test-rmse:1.164657+0.307463 
## [212]    train-rmse:0.322575+0.010445    test-rmse:1.163995+0.307212 
## [213]    train-rmse:0.321351+0.010123    test-rmse:1.162768+0.308590 
## [214]    train-rmse:0.318884+0.009859    test-rmse:1.161972+0.309108 
## [215]    train-rmse:0.318050+0.009871    test-rmse:1.162161+0.309222 
## [216]    train-rmse:0.316962+0.010083    test-rmse:1.161836+0.309350 
## [217]    train-rmse:0.316080+0.009906    test-rmse:1.160916+0.310127 
## [218]    train-rmse:0.314508+0.009632    test-rmse:1.159069+0.309315 
## [219]    train-rmse:0.313420+0.009512    test-rmse:1.157781+0.309021 
## [220]    train-rmse:0.311748+0.009497    test-rmse:1.158178+0.309879 
## [221]    train-rmse:0.310731+0.009712    test-rmse:1.156922+0.309504 
## [222]    train-rmse:0.309542+0.009304    test-rmse:1.157783+0.311329 
## [223]    train-rmse:0.307989+0.009352    test-rmse:1.157562+0.311179 
## [224]    train-rmse:0.307180+0.009324    test-rmse:1.157683+0.310827 
## [225]    train-rmse:0.306211+0.009061    test-rmse:1.156920+0.309223 
## [226]    train-rmse:0.305216+0.008780    test-rmse:1.156528+0.309553 
## [227]    train-rmse:0.303856+0.009526    test-rmse:1.156600+0.309617 
## [228]    train-rmse:0.302726+0.009634    test-rmse:1.156101+0.309420 
## [229]    train-rmse:0.301282+0.009630    test-rmse:1.155596+0.309589 
## [230]    train-rmse:0.299979+0.010230    test-rmse:1.155422+0.310467 
## [231]    train-rmse:0.298390+0.009536    test-rmse:1.155833+0.309878 
## [232]    train-rmse:0.297597+0.009574    test-rmse:1.155674+0.310474 
## [233]    train-rmse:0.296785+0.009377    test-rmse:1.156163+0.310488 
## [234]    train-rmse:0.296131+0.009218    test-rmse:1.155672+0.310903 
## [235]    train-rmse:0.295339+0.009430    test-rmse:1.155702+0.311299 
## [236]    train-rmse:0.294423+0.009351    test-rmse:1.155436+0.310951 
## Stopping. Best iteration:
## [230]    train-rmse:0.299979+0.010230    test-rmse:1.155422+0.310467
## 
## 65 
## [1]  train-rmse:77.673825+0.090310   test-rmse:77.673837+0.509215 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:56.141246+0.073781   test-rmse:56.140539+0.549016 
## [3]  train-rmse:40.670034+0.065404   test-rmse:40.668152+0.588925 
## [4]  train-rmse:29.583382+0.061017   test-rmse:29.625472+0.605847 
## [5]  train-rmse:21.613113+0.040697   test-rmse:21.729475+0.638688 
## [6]  train-rmse:15.887792+0.030287   test-rmse:16.007352+0.606523 
## [7]  train-rmse:11.786035+0.028937   test-rmse:11.880795+0.541095 
## [8]  train-rmse:8.870852+0.032306    test-rmse:8.990332+0.571737 
## [9]  train-rmse:6.804078+0.036482    test-rmse:6.948308+0.542521 
## [10] train-rmse:5.366765+0.046729    test-rmse:5.500766+0.523540 
## [11] train-rmse:4.375623+0.056921    test-rmse:4.576805+0.508942 
## [12] train-rmse:3.708178+0.057004    test-rmse:3.922250+0.459197 
## [13] train-rmse:3.241259+0.049604    test-rmse:3.511211+0.410721 
## [14] train-rmse:2.921435+0.047931    test-rmse:3.222807+0.402836 
## [15] train-rmse:2.689569+0.047259    test-rmse:3.023026+0.402726 
## [16] train-rmse:2.517431+0.039992    test-rmse:2.877716+0.354419 
## [17] train-rmse:2.369729+0.040327    test-rmse:2.746548+0.274496 
## [18] train-rmse:2.253569+0.042391    test-rmse:2.659640+0.284771 
## [19] train-rmse:2.156036+0.043821    test-rmse:2.584510+0.282499 
## [20] train-rmse:2.062524+0.035329    test-rmse:2.510377+0.294657 
## [21] train-rmse:1.983611+0.033787    test-rmse:2.438100+0.298399 
## [22] train-rmse:1.906531+0.029751    test-rmse:2.370640+0.272662 
## [23] train-rmse:1.838699+0.028773    test-rmse:2.301009+0.284086 
## [24] train-rmse:1.770419+0.031172    test-rmse:2.253767+0.290240 
## [25] train-rmse:1.704743+0.031702    test-rmse:2.222048+0.303380 
## [26] train-rmse:1.645842+0.033769    test-rmse:2.164273+0.287324 
## [27] train-rmse:1.590011+0.033515    test-rmse:2.111505+0.280267 
## [28] train-rmse:1.537366+0.031153    test-rmse:2.056874+0.267283 
## [29] train-rmse:1.487204+0.028447    test-rmse:2.017841+0.264458 
## [30] train-rmse:1.438912+0.031638    test-rmse:1.991436+0.257315 
## [31] train-rmse:1.393752+0.031593    test-rmse:1.959000+0.267410 
## [32] train-rmse:1.348822+0.030103    test-rmse:1.901364+0.265875 
## [33] train-rmse:1.305194+0.030864    test-rmse:1.867005+0.265342 
## [34] train-rmse:1.267397+0.030215    test-rmse:1.826239+0.263620 
## [35] train-rmse:1.228975+0.031783    test-rmse:1.796036+0.242586 
## [36] train-rmse:1.191061+0.027163    test-rmse:1.759678+0.238450 
## [37] train-rmse:1.157096+0.026535    test-rmse:1.733456+0.238966 
## [38] train-rmse:1.121495+0.027167    test-rmse:1.713592+0.230768 
## [39] train-rmse:1.088284+0.027934    test-rmse:1.684177+0.229130 
## [40] train-rmse:1.055561+0.027262    test-rmse:1.650964+0.237057 
## [41] train-rmse:1.022802+0.029218    test-rmse:1.633470+0.249955 
## [42] train-rmse:0.994104+0.026654    test-rmse:1.616071+0.255131 
## [43] train-rmse:0.968154+0.025709    test-rmse:1.578426+0.249194 
## [44] train-rmse:0.942024+0.026327    test-rmse:1.568041+0.250538 
## [45] train-rmse:0.917953+0.026123    test-rmse:1.545512+0.256688 
## [46] train-rmse:0.892362+0.023452    test-rmse:1.528740+0.263636 
## [47] train-rmse:0.869261+0.025084    test-rmse:1.513402+0.264478 
## [48] train-rmse:0.846246+0.023753    test-rmse:1.496662+0.254848 
## [49] train-rmse:0.819406+0.023357    test-rmse:1.467170+0.257197 
## [50] train-rmse:0.798826+0.022680    test-rmse:1.452944+0.262230 
## [51] train-rmse:0.778389+0.023307    test-rmse:1.434213+0.262609 
## [52] train-rmse:0.761701+0.023747    test-rmse:1.425636+0.260132 
## [53] train-rmse:0.744065+0.023291    test-rmse:1.406960+0.267709 
## [54] train-rmse:0.728134+0.024535    test-rmse:1.399669+0.267087 
## [55] train-rmse:0.712561+0.023953    test-rmse:1.390601+0.269630 
## [56] train-rmse:0.695703+0.024363    test-rmse:1.383400+0.267071 
## [57] train-rmse:0.680322+0.023935    test-rmse:1.374823+0.263917 
## [58] train-rmse:0.666066+0.023448    test-rmse:1.352251+0.243880 
## [59] train-rmse:0.651814+0.021915    test-rmse:1.344135+0.249086 
## [60] train-rmse:0.636168+0.022960    test-rmse:1.335044+0.254548 
## [61] train-rmse:0.624385+0.022473    test-rmse:1.325325+0.257373 
## [62] train-rmse:0.611742+0.022906    test-rmse:1.313538+0.253021 
## [63] train-rmse:0.600440+0.023626    test-rmse:1.312005+0.253270 
## [64] train-rmse:0.589755+0.023599    test-rmse:1.300471+0.254614 
## [65] train-rmse:0.578598+0.021955    test-rmse:1.293563+0.252773 
## [66] train-rmse:0.567481+0.022857    test-rmse:1.285718+0.253390 
## [67] train-rmse:0.557174+0.023519    test-rmse:1.272079+0.262687 
## [68] train-rmse:0.546854+0.021653    test-rmse:1.266751+0.259929 
## [69] train-rmse:0.537761+0.021099    test-rmse:1.258898+0.260896 
## [70] train-rmse:0.528787+0.021400    test-rmse:1.255482+0.264309 
## [71] train-rmse:0.518889+0.019181    test-rmse:1.247685+0.259857 
## [72] train-rmse:0.509252+0.017981    test-rmse:1.242829+0.261542 
## [73] train-rmse:0.498951+0.017743    test-rmse:1.237112+0.263170 
## [74] train-rmse:0.491143+0.018603    test-rmse:1.228863+0.266187 
## [75] train-rmse:0.483891+0.018017    test-rmse:1.225462+0.268668 
## [76] train-rmse:0.475401+0.016388    test-rmse:1.219279+0.268355 
## [77] train-rmse:0.468762+0.016514    test-rmse:1.212502+0.269368 
## [78] train-rmse:0.461147+0.016955    test-rmse:1.207953+0.269602 
## [79] train-rmse:0.454635+0.017153    test-rmse:1.205829+0.269106 
## [80] train-rmse:0.448678+0.017032    test-rmse:1.196743+0.260795 
## [81] train-rmse:0.442375+0.016783    test-rmse:1.191431+0.263618 
## [82] train-rmse:0.435725+0.016753    test-rmse:1.189496+0.261814 
## [83] train-rmse:0.430070+0.017458    test-rmse:1.188217+0.261187 
## [84] train-rmse:0.421207+0.017636    test-rmse:1.187372+0.264213 
## [85] train-rmse:0.414727+0.015848    test-rmse:1.182027+0.267135 
## [86] train-rmse:0.409799+0.016223    test-rmse:1.174251+0.272050 
## [87] train-rmse:0.404297+0.016383    test-rmse:1.171933+0.271940 
## [88] train-rmse:0.399554+0.016121    test-rmse:1.168985+0.268340 
## [89] train-rmse:0.395021+0.015986    test-rmse:1.167488+0.269324 
## [90] train-rmse:0.390192+0.015617    test-rmse:1.167793+0.270387 
## [91] train-rmse:0.385292+0.014983    test-rmse:1.165318+0.270702 
## [92] train-rmse:0.378250+0.014638    test-rmse:1.160807+0.268858 
## [93] train-rmse:0.374239+0.014950    test-rmse:1.159218+0.268956 
## [94] train-rmse:0.369270+0.015717    test-rmse:1.157023+0.268478 
## [95] train-rmse:0.363644+0.015903    test-rmse:1.157056+0.269372 
## [96] train-rmse:0.359611+0.015168    test-rmse:1.155000+0.269107 
## [97] train-rmse:0.355759+0.015318    test-rmse:1.155530+0.268704 
## [98] train-rmse:0.351247+0.014065    test-rmse:1.155415+0.271553 
## [99] train-rmse:0.347461+0.014427    test-rmse:1.155026+0.272309 
## [100]    train-rmse:0.343206+0.014514    test-rmse:1.154018+0.273265 
## [101]    train-rmse:0.338870+0.015874    test-rmse:1.150133+0.272877 
## [102]    train-rmse:0.335120+0.015135    test-rmse:1.148754+0.273633 
## [103]    train-rmse:0.332010+0.015651    test-rmse:1.146160+0.275431 
## [104]    train-rmse:0.328532+0.015442    test-rmse:1.145203+0.275322 
## [105]    train-rmse:0.324469+0.014966    test-rmse:1.145041+0.275886 
## [106]    train-rmse:0.319845+0.016000    test-rmse:1.142812+0.279344 
## [107]    train-rmse:0.315953+0.014973    test-rmse:1.141648+0.280592 
## [108]    train-rmse:0.312688+0.014323    test-rmse:1.141350+0.281317 
## [109]    train-rmse:0.308327+0.014366    test-rmse:1.139462+0.280408 
## [110]    train-rmse:0.305141+0.014366    test-rmse:1.135630+0.284693 
## [111]    train-rmse:0.301837+0.014288    test-rmse:1.136783+0.287820 
## [112]    train-rmse:0.297565+0.013506    test-rmse:1.135021+0.288278 
## [113]    train-rmse:0.294953+0.013028    test-rmse:1.132990+0.288762 
## [114]    train-rmse:0.292042+0.012969    test-rmse:1.128994+0.291027 
## [115]    train-rmse:0.289277+0.013981    test-rmse:1.128772+0.291661 
## [116]    train-rmse:0.285902+0.014760    test-rmse:1.128770+0.291446 
## [117]    train-rmse:0.282325+0.013850    test-rmse:1.128204+0.292356 
## [118]    train-rmse:0.279842+0.013633    test-rmse:1.130365+0.294735 
## [119]    train-rmse:0.276669+0.014286    test-rmse:1.129010+0.294440 
## [120]    train-rmse:0.274470+0.014015    test-rmse:1.127218+0.294298 
## [121]    train-rmse:0.272028+0.014259    test-rmse:1.124543+0.295209 
## [122]    train-rmse:0.268835+0.013990    test-rmse:1.123982+0.294052 
## [123]    train-rmse:0.266128+0.013483    test-rmse:1.122715+0.294791 
## [124]    train-rmse:0.263096+0.012519    test-rmse:1.121529+0.295032 
## [125]    train-rmse:0.260305+0.012173    test-rmse:1.119811+0.295608 
## [126]    train-rmse:0.257516+0.011904    test-rmse:1.117273+0.295940 
## [127]    train-rmse:0.255632+0.011787    test-rmse:1.114521+0.297415 
## [128]    train-rmse:0.253807+0.012496    test-rmse:1.113746+0.296370 
## [129]    train-rmse:0.250762+0.012511    test-rmse:1.114286+0.296286 
## [130]    train-rmse:0.248163+0.012558    test-rmse:1.113822+0.296526 
## [131]    train-rmse:0.246219+0.012467    test-rmse:1.110485+0.293643 
## [132]    train-rmse:0.243276+0.013064    test-rmse:1.108620+0.295265 
## [133]    train-rmse:0.240420+0.013115    test-rmse:1.107682+0.296755 
## [134]    train-rmse:0.237782+0.013547    test-rmse:1.108482+0.296365 
## [135]    train-rmse:0.235624+0.013802    test-rmse:1.108341+0.296661 
## [136]    train-rmse:0.234353+0.013683    test-rmse:1.107266+0.296751 
## [137]    train-rmse:0.231757+0.013860    test-rmse:1.107514+0.295971 
## [138]    train-rmse:0.229494+0.013430    test-rmse:1.106663+0.297503 
## [139]    train-rmse:0.227544+0.013192    test-rmse:1.106594+0.298142 
## [140]    train-rmse:0.225651+0.013173    test-rmse:1.106353+0.296655 
## [141]    train-rmse:0.223687+0.013186    test-rmse:1.105591+0.297127 
## [142]    train-rmse:0.221915+0.013212    test-rmse:1.105745+0.296930 
## [143]    train-rmse:0.220219+0.012974    test-rmse:1.105999+0.296925 
## [144]    train-rmse:0.218316+0.012838    test-rmse:1.104068+0.298176 
## [145]    train-rmse:0.216318+0.012031    test-rmse:1.103080+0.299094 
## [146]    train-rmse:0.214082+0.012177    test-rmse:1.103774+0.298995 
## [147]    train-rmse:0.212383+0.011754    test-rmse:1.104294+0.299017 
## [148]    train-rmse:0.210892+0.011721    test-rmse:1.103911+0.297796 
## [149]    train-rmse:0.208941+0.011800    test-rmse:1.103027+0.298251 
## [150]    train-rmse:0.207309+0.011729    test-rmse:1.103041+0.298590 
## [151]    train-rmse:0.205980+0.011974    test-rmse:1.102322+0.298933 
## [152]    train-rmse:0.203302+0.011652    test-rmse:1.101470+0.298575 
## [153]    train-rmse:0.201932+0.011673    test-rmse:1.100538+0.298960 
## [154]    train-rmse:0.200216+0.011593    test-rmse:1.100067+0.298618 
## [155]    train-rmse:0.197862+0.011696    test-rmse:1.099613+0.299497 
## [156]    train-rmse:0.196876+0.011710    test-rmse:1.099360+0.300462 
## [157]    train-rmse:0.195293+0.011535    test-rmse:1.099106+0.301039 
## [158]    train-rmse:0.193684+0.011339    test-rmse:1.099442+0.301376 
## [159]    train-rmse:0.191790+0.011257    test-rmse:1.100051+0.301599 
## [160]    train-rmse:0.190574+0.011138    test-rmse:1.099845+0.301118 
## [161]    train-rmse:0.188528+0.010469    test-rmse:1.098018+0.300023 
## [162]    train-rmse:0.186363+0.010606    test-rmse:1.099553+0.299254 
## [163]    train-rmse:0.184808+0.010966    test-rmse:1.098694+0.297857 
## [164]    train-rmse:0.182777+0.010495    test-rmse:1.098784+0.298207 
## [165]    train-rmse:0.181444+0.010725    test-rmse:1.098822+0.297880 
## [166]    train-rmse:0.179721+0.011282    test-rmse:1.098514+0.297139 
## [167]    train-rmse:0.178271+0.011103    test-rmse:1.098110+0.297119 
## Stopping. Best iteration:
## [161]    train-rmse:0.188528+0.010469    test-rmse:1.098018+0.300023
## 
## 66 
## [1]  train-rmse:77.673825+0.090310   test-rmse:77.673837+0.509215 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:56.141246+0.073781   test-rmse:56.140539+0.549016 
## [3]  train-rmse:40.670034+0.065404   test-rmse:40.668152+0.588925 
## [4]  train-rmse:29.583382+0.061017   test-rmse:29.625472+0.605847 
## [5]  train-rmse:21.613113+0.040697   test-rmse:21.729475+0.638688 
## [6]  train-rmse:15.883063+0.030328   test-rmse:16.009680+0.588402 
## [7]  train-rmse:11.759822+0.029706   test-rmse:11.858543+0.541627 
## [8]  train-rmse:8.807497+0.027265    test-rmse:8.943163+0.605963 
## [9]  train-rmse:6.692556+0.030060    test-rmse:6.812441+0.561395 
## [10] train-rmse:5.204799+0.036345    test-rmse:5.357743+0.524792 
## [11] train-rmse:4.158041+0.046026    test-rmse:4.370406+0.513374 
## [12] train-rmse:3.418593+0.033325    test-rmse:3.694005+0.407150 
## [13] train-rmse:2.915683+0.036401    test-rmse:3.221715+0.377693 
## [14] train-rmse:2.565668+0.031488    test-rmse:2.874038+0.320201 
## [15] train-rmse:2.294058+0.033645    test-rmse:2.656608+0.322368 
## [16] train-rmse:2.100477+0.032603    test-rmse:2.526729+0.281883 
## [17] train-rmse:1.951565+0.033202    test-rmse:2.391927+0.245147 
## [18] train-rmse:1.831340+0.029682    test-rmse:2.299050+0.248022 
## [19] train-rmse:1.730443+0.027680    test-rmse:2.218424+0.239313 
## [20] train-rmse:1.637961+0.027071    test-rmse:2.117803+0.230995 
## [21] train-rmse:1.556053+0.025178    test-rmse:2.064698+0.224053 
## [22] train-rmse:1.482931+0.024953    test-rmse:2.007231+0.219411 
## [23] train-rmse:1.413305+0.023003    test-rmse:1.950091+0.210235 
## [24] train-rmse:1.345053+0.020412    test-rmse:1.893619+0.216992 
## [25] train-rmse:1.286674+0.017766    test-rmse:1.836503+0.212325 
## [26] train-rmse:1.220401+0.023147    test-rmse:1.794534+0.211344 
## [27] train-rmse:1.163210+0.020160    test-rmse:1.767001+0.210432 
## [28] train-rmse:1.114025+0.018593    test-rmse:1.745336+0.212573 
## [29] train-rmse:1.071061+0.017467    test-rmse:1.710611+0.215424 
## [30] train-rmse:1.029088+0.016961    test-rmse:1.668365+0.223395 
## [31] train-rmse:0.989620+0.014136    test-rmse:1.623434+0.193804 
## [32] train-rmse:0.944219+0.018079    test-rmse:1.595907+0.194351 
## [33] train-rmse:0.908165+0.019973    test-rmse:1.564778+0.186868 
## [34] train-rmse:0.873928+0.021216    test-rmse:1.538786+0.185171 
## [35] train-rmse:0.841007+0.022047    test-rmse:1.505850+0.189704 
## [36] train-rmse:0.809784+0.021767    test-rmse:1.483448+0.185717 
## [37] train-rmse:0.777980+0.021679    test-rmse:1.463331+0.195755 
## [38] train-rmse:0.747458+0.023466    test-rmse:1.443402+0.193398 
## [39] train-rmse:0.722942+0.024757    test-rmse:1.424295+0.188947 
## [40] train-rmse:0.700305+0.024909    test-rmse:1.404276+0.192687 
## [41] train-rmse:0.678961+0.025749    test-rmse:1.393252+0.197825 
## [42] train-rmse:0.656380+0.025110    test-rmse:1.380622+0.193443 
## [43] train-rmse:0.637250+0.025083    test-rmse:1.365588+0.194574 
## [44] train-rmse:0.619167+0.023972    test-rmse:1.352057+0.194913 
## [45] train-rmse:0.600946+0.021181    test-rmse:1.340886+0.189239 
## [46] train-rmse:0.579562+0.024363    test-rmse:1.335053+0.193077 
## [47] train-rmse:0.560347+0.024017    test-rmse:1.319448+0.181068 
## [48] train-rmse:0.545520+0.023787    test-rmse:1.309837+0.182906 
## [49] train-rmse:0.529666+0.024343    test-rmse:1.296505+0.185429 
## [50] train-rmse:0.513431+0.025194    test-rmse:1.282388+0.186724 
## [51] train-rmse:0.498522+0.022579    test-rmse:1.280715+0.185765 
## [52] train-rmse:0.485438+0.020739    test-rmse:1.276416+0.189280 
## [53] train-rmse:0.472099+0.021658    test-rmse:1.262455+0.178028 
## [54] train-rmse:0.460878+0.020966    test-rmse:1.249328+0.179672 
## [55] train-rmse:0.450213+0.019604    test-rmse:1.245954+0.183209 
## [56] train-rmse:0.440616+0.019115    test-rmse:1.239842+0.180032 
## [57] train-rmse:0.429202+0.021683    test-rmse:1.237260+0.181316 
## [58] train-rmse:0.419787+0.021695    test-rmse:1.232462+0.180666 
## [59] train-rmse:0.410085+0.021381    test-rmse:1.223251+0.171895 
## [60] train-rmse:0.399748+0.019287    test-rmse:1.217190+0.173091 
## [61] train-rmse:0.387977+0.018853    test-rmse:1.209029+0.180242 
## [62] train-rmse:0.379973+0.019606    test-rmse:1.206057+0.179324 
## [63] train-rmse:0.371880+0.020488    test-rmse:1.200848+0.180473 
## [64] train-rmse:0.364933+0.020517    test-rmse:1.194422+0.175235 
## [65] train-rmse:0.356678+0.019606    test-rmse:1.192124+0.178280 
## [66] train-rmse:0.348070+0.016919    test-rmse:1.192003+0.178367 
## [67] train-rmse:0.341060+0.015690    test-rmse:1.189728+0.178422 
## [68] train-rmse:0.335032+0.016256    test-rmse:1.186438+0.181088 
## [69] train-rmse:0.328774+0.016766    test-rmse:1.179218+0.185167 
## [70] train-rmse:0.323564+0.016608    test-rmse:1.178324+0.186213 
## [71] train-rmse:0.314094+0.015970    test-rmse:1.176189+0.188721 
## [72] train-rmse:0.307028+0.015058    test-rmse:1.174493+0.189446 
## [73] train-rmse:0.302601+0.014772    test-rmse:1.173554+0.189860 
## [74] train-rmse:0.297196+0.013682    test-rmse:1.171098+0.189482 
## [75] train-rmse:0.291202+0.013027    test-rmse:1.169311+0.189960 
## [76] train-rmse:0.286420+0.013314    test-rmse:1.166243+0.190047 
## [77] train-rmse:0.279546+0.011393    test-rmse:1.165783+0.189178 
## [78] train-rmse:0.274327+0.010425    test-rmse:1.163063+0.191111 
## [79] train-rmse:0.269697+0.008613    test-rmse:1.160090+0.192289 
## [80] train-rmse:0.264853+0.009044    test-rmse:1.158785+0.194022 
## [81] train-rmse:0.259629+0.010557    test-rmse:1.157961+0.193731 
## [82] train-rmse:0.255763+0.011726    test-rmse:1.155895+0.193626 
## [83] train-rmse:0.251233+0.012187    test-rmse:1.155142+0.195004 
## [84] train-rmse:0.245453+0.010438    test-rmse:1.153917+0.196369 
## [85] train-rmse:0.240191+0.010672    test-rmse:1.153106+0.194915 
## [86] train-rmse:0.234802+0.009912    test-rmse:1.152139+0.194157 
## [87] train-rmse:0.230587+0.010207    test-rmse:1.151236+0.194126 
## [88] train-rmse:0.226897+0.010649    test-rmse:1.148588+0.196214 
## [89] train-rmse:0.222624+0.011783    test-rmse:1.147279+0.198034 
## [90] train-rmse:0.219032+0.012513    test-rmse:1.146800+0.197937 
## [91] train-rmse:0.215441+0.012222    test-rmse:1.146434+0.198006 
## [92] train-rmse:0.211259+0.013325    test-rmse:1.146639+0.198438 
## [93] train-rmse:0.207944+0.013429    test-rmse:1.146730+0.199274 
## [94] train-rmse:0.204318+0.012881    test-rmse:1.146258+0.199215 
## [95] train-rmse:0.201360+0.013538    test-rmse:1.145175+0.199029 
## [96] train-rmse:0.198418+0.013167    test-rmse:1.144669+0.199280 
## [97] train-rmse:0.195401+0.013021    test-rmse:1.144208+0.200193 
## [98] train-rmse:0.192558+0.012289    test-rmse:1.143865+0.200286 
## [99] train-rmse:0.189518+0.012684    test-rmse:1.144157+0.200120 
## [100]    train-rmse:0.187282+0.012726    test-rmse:1.143705+0.199251 
## [101]    train-rmse:0.184691+0.013193    test-rmse:1.142973+0.199051 
## [102]    train-rmse:0.182264+0.013156    test-rmse:1.140931+0.198849 
## [103]    train-rmse:0.179414+0.013436    test-rmse:1.141590+0.198296 
## [104]    train-rmse:0.175715+0.012815    test-rmse:1.139939+0.198743 
## [105]    train-rmse:0.171557+0.010528    test-rmse:1.140482+0.199136 
## [106]    train-rmse:0.168840+0.009248    test-rmse:1.140852+0.199117 
## [107]    train-rmse:0.166170+0.009486    test-rmse:1.140579+0.198364 
## [108]    train-rmse:0.163661+0.010046    test-rmse:1.140143+0.199273 
## [109]    train-rmse:0.159876+0.010339    test-rmse:1.139814+0.199383 
## [110]    train-rmse:0.156381+0.011092    test-rmse:1.139686+0.199412 
## [111]    train-rmse:0.154166+0.011709    test-rmse:1.139192+0.199706 
## [112]    train-rmse:0.152043+0.011715    test-rmse:1.138473+0.200316 
## [113]    train-rmse:0.150125+0.012271    test-rmse:1.138385+0.200463 
## [114]    train-rmse:0.147785+0.011057    test-rmse:1.138800+0.199452 
## [115]    train-rmse:0.146075+0.010500    test-rmse:1.138446+0.199377 
## [116]    train-rmse:0.143751+0.010476    test-rmse:1.138703+0.199002 
## [117]    train-rmse:0.140566+0.008869    test-rmse:1.137959+0.199144 
## [118]    train-rmse:0.139052+0.008939    test-rmse:1.137501+0.199378 
## [119]    train-rmse:0.137156+0.009119    test-rmse:1.136602+0.198695 
## [120]    train-rmse:0.134985+0.009304    test-rmse:1.136811+0.198111 
## [121]    train-rmse:0.133436+0.009749    test-rmse:1.137000+0.198208 
## [122]    train-rmse:0.131708+0.009885    test-rmse:1.137068+0.198573 
## [123]    train-rmse:0.129484+0.008978    test-rmse:1.136980+0.198607 
## [124]    train-rmse:0.127765+0.009212    test-rmse:1.136420+0.199053 
## [125]    train-rmse:0.126335+0.009069    test-rmse:1.136115+0.198748 
## [126]    train-rmse:0.123979+0.008842    test-rmse:1.135855+0.199546 
## [127]    train-rmse:0.121626+0.007772    test-rmse:1.135791+0.200021 
## [128]    train-rmse:0.120065+0.007587    test-rmse:1.134566+0.200447 
## [129]    train-rmse:0.118471+0.007704    test-rmse:1.134819+0.200436 
## [130]    train-rmse:0.116647+0.007568    test-rmse:1.135409+0.200938 
## [131]    train-rmse:0.114978+0.006999    test-rmse:1.135739+0.201210 
## [132]    train-rmse:0.113377+0.006709    test-rmse:1.135539+0.201376 
## [133]    train-rmse:0.111753+0.005771    test-rmse:1.134640+0.200729 
## [134]    train-rmse:0.110068+0.005286    test-rmse:1.134384+0.200763 
## [135]    train-rmse:0.108603+0.005242    test-rmse:1.134165+0.200889 
## [136]    train-rmse:0.106890+0.005517    test-rmse:1.134000+0.201316 
## [137]    train-rmse:0.105596+0.005548    test-rmse:1.133733+0.200322 
## [138]    train-rmse:0.103697+0.005180    test-rmse:1.134401+0.200655 
## [139]    train-rmse:0.102484+0.005190    test-rmse:1.134590+0.200776 
## [140]    train-rmse:0.100476+0.005641    test-rmse:1.134325+0.201191 
## [141]    train-rmse:0.099394+0.005847    test-rmse:1.134293+0.200921 
## [142]    train-rmse:0.098014+0.005809    test-rmse:1.134257+0.200903 
## [143]    train-rmse:0.096189+0.005288    test-rmse:1.135295+0.200585 
## Stopping. Best iteration:
## [137]    train-rmse:0.105596+0.005548    test-rmse:1.133733+0.200322
## 
## 67 
## [1]  train-rmse:77.673825+0.090310   test-rmse:77.673837+0.509215 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:56.141246+0.073781   test-rmse:56.140539+0.549016 
## [3]  train-rmse:40.670034+0.065404   test-rmse:40.668152+0.588925 
## [4]  train-rmse:29.583382+0.061017   test-rmse:29.625472+0.605847 
## [5]  train-rmse:21.613113+0.040697   test-rmse:21.729475+0.638688 
## [6]  train-rmse:15.878204+0.030275   test-rmse:16.014299+0.585968 
## [7]  train-rmse:11.738701+0.029907   test-rmse:11.849867+0.561178 
## [8]  train-rmse:8.757393+0.024250    test-rmse:8.871273+0.531719 
## [9]  train-rmse:6.612885+0.028092    test-rmse:6.742540+0.558179 
## [10] train-rmse:5.084540+0.028864    test-rmse:5.218537+0.510852 
## [11] train-rmse:3.984251+0.034690    test-rmse:4.164467+0.506388 
## [12] train-rmse:3.210263+0.040748    test-rmse:3.463201+0.424200 
## [13] train-rmse:2.658179+0.040559    test-rmse:2.969774+0.365566 
## [14] train-rmse:2.269429+0.046834    test-rmse:2.627798+0.359720 
## [15] train-rmse:1.988852+0.044589    test-rmse:2.413566+0.339725 
## [16] train-rmse:1.767291+0.056657    test-rmse:2.248707+0.277707 
## [17] train-rmse:1.613559+0.061142    test-rmse:2.116618+0.289376 
## [18] train-rmse:1.488201+0.052700    test-rmse:2.037728+0.268774 
## [19] train-rmse:1.381245+0.046636    test-rmse:1.970725+0.275070 
## [20] train-rmse:1.292870+0.045641    test-rmse:1.903966+0.269417 
## [21] train-rmse:1.214134+0.043658    test-rmse:1.851200+0.259016 
## [22] train-rmse:1.134790+0.045373    test-rmse:1.785104+0.244271 
## [23] train-rmse:1.071854+0.042236    test-rmse:1.738441+0.253163 
## [24] train-rmse:1.005227+0.043466    test-rmse:1.713611+0.249796 
## [25] train-rmse:0.947445+0.046936    test-rmse:1.671602+0.243457 
## [26] train-rmse:0.901398+0.044798    test-rmse:1.641804+0.243251 
## [27] train-rmse:0.854320+0.044857    test-rmse:1.598925+0.251271 
## [28] train-rmse:0.811253+0.038085    test-rmse:1.569700+0.246918 
## [29] train-rmse:0.771380+0.039372    test-rmse:1.534586+0.229882 
## [30] train-rmse:0.738070+0.038571    test-rmse:1.515872+0.227590 
## [31] train-rmse:0.704065+0.040977    test-rmse:1.492403+0.228977 
## [32] train-rmse:0.669843+0.033552    test-rmse:1.475409+0.242394 
## [33] train-rmse:0.639096+0.028507    test-rmse:1.452845+0.243827 
## [34] train-rmse:0.612717+0.030983    test-rmse:1.429179+0.235511 
## [35] train-rmse:0.588567+0.030754    test-rmse:1.413755+0.235734 
## [36] train-rmse:0.567881+0.029292    test-rmse:1.398248+0.236381 
## [37] train-rmse:0.544841+0.027137    test-rmse:1.383421+0.233916 
## [38] train-rmse:0.522606+0.028287    test-rmse:1.372465+0.233836 
## [39] train-rmse:0.502618+0.027438    test-rmse:1.362451+0.222394 
## [40] train-rmse:0.485970+0.027931    test-rmse:1.348358+0.224372 
## [41] train-rmse:0.465715+0.025015    test-rmse:1.334748+0.226993 
## [42] train-rmse:0.447766+0.024235    test-rmse:1.322722+0.229958 
## [43] train-rmse:0.433649+0.021149    test-rmse:1.316684+0.231108 
## [44] train-rmse:0.417285+0.022621    test-rmse:1.308414+0.231816 
## [45] train-rmse:0.405449+0.023298    test-rmse:1.304111+0.227817 
## [46] train-rmse:0.393957+0.023412    test-rmse:1.300969+0.228901 
## [47] train-rmse:0.382624+0.022330    test-rmse:1.293644+0.232315 
## [48] train-rmse:0.369402+0.021367    test-rmse:1.290384+0.236521 
## [49] train-rmse:0.358531+0.020563    test-rmse:1.287588+0.239804 
## [50] train-rmse:0.347579+0.016815    test-rmse:1.283935+0.241528 
## [51] train-rmse:0.336916+0.016762    test-rmse:1.277235+0.242919 
## [52] train-rmse:0.326626+0.016483    test-rmse:1.274713+0.246433 
## [53] train-rmse:0.317516+0.015840    test-rmse:1.273248+0.248437 
## [54] train-rmse:0.307959+0.014470    test-rmse:1.271684+0.249410 
## [55] train-rmse:0.298398+0.017141    test-rmse:1.268915+0.249415 
## [56] train-rmse:0.288469+0.015526    test-rmse:1.269077+0.251160 
## [57] train-rmse:0.281777+0.014974    test-rmse:1.264157+0.253536 
## [58] train-rmse:0.272597+0.015828    test-rmse:1.263786+0.255683 
## [59] train-rmse:0.264925+0.013404    test-rmse:1.262051+0.258751 
## [60] train-rmse:0.258278+0.012088    test-rmse:1.260887+0.260097 
## [61] train-rmse:0.251084+0.010619    test-rmse:1.257729+0.259583 
## [62] train-rmse:0.244715+0.009778    test-rmse:1.256543+0.259531 
## [63] train-rmse:0.239254+0.010260    test-rmse:1.254704+0.258688 
## [64] train-rmse:0.232789+0.009298    test-rmse:1.249312+0.260870 
## [65] train-rmse:0.226492+0.009251    test-rmse:1.247970+0.261405 
## [66] train-rmse:0.220282+0.007671    test-rmse:1.245920+0.263328 
## [67] train-rmse:0.214240+0.007757    test-rmse:1.244839+0.262989 
## [68] train-rmse:0.206797+0.011138    test-rmse:1.244831+0.263412 
## [69] train-rmse:0.201628+0.010297    test-rmse:1.243725+0.264571 
## [70] train-rmse:0.198023+0.010526    test-rmse:1.240605+0.262248 
## [71] train-rmse:0.193546+0.010528    test-rmse:1.239236+0.261500 
## [72] train-rmse:0.188060+0.009479    test-rmse:1.238119+0.261586 
## [73] train-rmse:0.183066+0.010983    test-rmse:1.236295+0.262214 
## [74] train-rmse:0.176924+0.009549    test-rmse:1.236427+0.263130 
## [75] train-rmse:0.173177+0.008793    test-rmse:1.234183+0.265458 
## [76] train-rmse:0.168891+0.007898    test-rmse:1.231714+0.267693 
## [77] train-rmse:0.165217+0.008747    test-rmse:1.230822+0.268696 
## [78] train-rmse:0.161813+0.008681    test-rmse:1.230254+0.267743 
## [79] train-rmse:0.157899+0.008900    test-rmse:1.229439+0.267582 
## [80] train-rmse:0.153959+0.008698    test-rmse:1.228169+0.267706 
## [81] train-rmse:0.149693+0.008746    test-rmse:1.228363+0.268770 
## [82] train-rmse:0.145102+0.007810    test-rmse:1.227551+0.267362 
## [83] train-rmse:0.141142+0.006958    test-rmse:1.225715+0.268310 
## [84] train-rmse:0.137438+0.005684    test-rmse:1.226192+0.268933 
## [85] train-rmse:0.134349+0.006055    test-rmse:1.225046+0.268917 
## [86] train-rmse:0.131221+0.005983    test-rmse:1.224473+0.269930 
## [87] train-rmse:0.128811+0.005808    test-rmse:1.224444+0.269987 
## [88] train-rmse:0.126358+0.005432    test-rmse:1.224359+0.269752 
## [89] train-rmse:0.123851+0.005149    test-rmse:1.224081+0.269518 
## [90] train-rmse:0.121663+0.005694    test-rmse:1.223818+0.270078 
## [91] train-rmse:0.119101+0.004808    test-rmse:1.223044+0.270418 
## [92] train-rmse:0.116862+0.004687    test-rmse:1.222283+0.270071 
## [93] train-rmse:0.114450+0.005180    test-rmse:1.221951+0.269736 
## [94] train-rmse:0.111312+0.006031    test-rmse:1.221276+0.269656 
## [95] train-rmse:0.108361+0.006267    test-rmse:1.221273+0.269349 
## [96] train-rmse:0.107240+0.006325    test-rmse:1.220666+0.269625 
## [97] train-rmse:0.104295+0.006469    test-rmse:1.221287+0.269583 
## [98] train-rmse:0.101844+0.006142    test-rmse:1.221081+0.269525 
## [99] train-rmse:0.099908+0.006182    test-rmse:1.220762+0.269225 
## [100]    train-rmse:0.097310+0.006272    test-rmse:1.221307+0.268793 
## [101]    train-rmse:0.095562+0.006655    test-rmse:1.221586+0.268529 
## [102]    train-rmse:0.093248+0.006221    test-rmse:1.221368+0.267784 
## Stopping. Best iteration:
## [96] train-rmse:0.107240+0.006325    test-rmse:1.220666+0.269625
## 
## 68 
## [1]  train-rmse:77.673825+0.090310   test-rmse:77.673837+0.509215 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:56.141246+0.073781   test-rmse:56.140539+0.549016 
## [3]  train-rmse:40.670034+0.065404   test-rmse:40.668152+0.588925 
## [4]  train-rmse:29.583382+0.061017   test-rmse:29.625472+0.605847 
## [5]  train-rmse:21.613113+0.040697   test-rmse:21.729475+0.638688 
## [6]  train-rmse:15.873245+0.030267   test-rmse:15.994896+0.594448 
## [7]  train-rmse:11.725557+0.026684   test-rmse:11.854042+0.563792 
## [8]  train-rmse:8.739105+0.031412    test-rmse:8.893071+0.601722 
## [9]  train-rmse:6.559318+0.036595    test-rmse:6.709558+0.571321 
## [10] train-rmse:4.997963+0.033527    test-rmse:5.187891+0.501544 
## [11] train-rmse:3.883372+0.030382    test-rmse:4.107251+0.452266 
## [12] train-rmse:3.074878+0.031209    test-rmse:3.377137+0.439925 
## [13] train-rmse:2.500652+0.025727    test-rmse:2.852292+0.368856 
## [14] train-rmse:2.084619+0.025118    test-rmse:2.512076+0.344768 
## [15] train-rmse:1.770017+0.022155    test-rmse:2.291106+0.284344 
## [16] train-rmse:1.553553+0.018854    test-rmse:2.124480+0.272201 
## [17] train-rmse:1.384461+0.025006    test-rmse:1.979226+0.254906 
## [18] train-rmse:1.254227+0.025828    test-rmse:1.890115+0.244814 
## [19] train-rmse:1.144497+0.024967    test-rmse:1.796031+0.215531 
## [20] train-rmse:1.048983+0.025090    test-rmse:1.711163+0.192111 
## [21] train-rmse:0.973847+0.028803    test-rmse:1.670700+0.188622 
## [22] train-rmse:0.911389+0.030071    test-rmse:1.606064+0.195728 
## [23] train-rmse:0.851487+0.031029    test-rmse:1.568394+0.202559 
## [24] train-rmse:0.785185+0.024436    test-rmse:1.548632+0.214727 
## [25] train-rmse:0.731915+0.028642    test-rmse:1.520059+0.218679 
## [26] train-rmse:0.689962+0.026488    test-rmse:1.481222+0.225216 
## [27] train-rmse:0.649401+0.026083    test-rmse:1.453130+0.224776 
## [28] train-rmse:0.615731+0.027273    test-rmse:1.432383+0.226178 
## [29] train-rmse:0.581298+0.027684    test-rmse:1.418220+0.227355 
## [30] train-rmse:0.551359+0.029120    test-rmse:1.405748+0.237744 
## [31] train-rmse:0.524418+0.028976    test-rmse:1.385097+0.237371 
## [32] train-rmse:0.498316+0.028381    test-rmse:1.373371+0.241228 
## [33] train-rmse:0.477448+0.028057    test-rmse:1.360073+0.243833 
## [34] train-rmse:0.453009+0.023723    test-rmse:1.350484+0.251026 
## [35] train-rmse:0.432361+0.021920    test-rmse:1.341037+0.249518 
## [36] train-rmse:0.415946+0.022894    test-rmse:1.335918+0.250068 
## [37] train-rmse:0.400266+0.021585    test-rmse:1.331893+0.252364 
## [38] train-rmse:0.383436+0.019859    test-rmse:1.327929+0.253602 
## [39] train-rmse:0.366352+0.019462    test-rmse:1.324380+0.256006 
## [40] train-rmse:0.349305+0.018124    test-rmse:1.319398+0.252682 
## [41] train-rmse:0.333992+0.016873    test-rmse:1.319406+0.256428 
## [42] train-rmse:0.321310+0.014435    test-rmse:1.316664+0.256694 
## [43] train-rmse:0.310546+0.015021    test-rmse:1.312023+0.258208 
## [44] train-rmse:0.300149+0.015037    test-rmse:1.306707+0.258921 
## [45] train-rmse:0.288816+0.015206    test-rmse:1.305141+0.262969 
## [46] train-rmse:0.277744+0.015689    test-rmse:1.302412+0.266588 
## [47] train-rmse:0.267442+0.015732    test-rmse:1.302369+0.267611 
## [48] train-rmse:0.257655+0.014225    test-rmse:1.301289+0.266592 
## [49] train-rmse:0.248514+0.014978    test-rmse:1.301106+0.269080 
## [50] train-rmse:0.241506+0.015399    test-rmse:1.299080+0.269484 
## [51] train-rmse:0.229905+0.014085    test-rmse:1.296214+0.271966 
## [52] train-rmse:0.218890+0.011145    test-rmse:1.294316+0.271237 
## [53] train-rmse:0.209256+0.009409    test-rmse:1.291926+0.271873 
## [54] train-rmse:0.203969+0.009770    test-rmse:1.291212+0.271804 
## [55] train-rmse:0.195904+0.009568    test-rmse:1.290374+0.271582 
## [56] train-rmse:0.189350+0.011291    test-rmse:1.289220+0.272275 
## [57] train-rmse:0.183582+0.012345    test-rmse:1.287303+0.274013 
## [58] train-rmse:0.176538+0.009872    test-rmse:1.287417+0.274800 
## [59] train-rmse:0.169719+0.007689    test-rmse:1.288361+0.275398 
## [60] train-rmse:0.165714+0.007528    test-rmse:1.287649+0.276132 
## [61] train-rmse:0.159138+0.007465    test-rmse:1.287702+0.275044 
## [62] train-rmse:0.154384+0.007351    test-rmse:1.287907+0.274793 
## [63] train-rmse:0.147792+0.006312    test-rmse:1.286821+0.276539 
## [64] train-rmse:0.142934+0.006943    test-rmse:1.287852+0.276314 
## [65] train-rmse:0.137381+0.008124    test-rmse:1.286739+0.276919 
## [66] train-rmse:0.132842+0.008106    test-rmse:1.286891+0.276855 
## [67] train-rmse:0.128455+0.007142    test-rmse:1.286689+0.275157 
## [68] train-rmse:0.123756+0.006917    test-rmse:1.285783+0.274829 
## [69] train-rmse:0.119753+0.008038    test-rmse:1.283565+0.274740 
## [70] train-rmse:0.116236+0.009003    test-rmse:1.282322+0.275372 
## [71] train-rmse:0.113012+0.007874    test-rmse:1.281710+0.274784 
## [72] train-rmse:0.110105+0.006845    test-rmse:1.281502+0.274365 
## [73] train-rmse:0.107271+0.006567    test-rmse:1.280965+0.275529 
## [74] train-rmse:0.104410+0.006371    test-rmse:1.279773+0.276214 
## [75] train-rmse:0.100439+0.007073    test-rmse:1.278926+0.277196 
## [76] train-rmse:0.098081+0.007319    test-rmse:1.278345+0.278250 
## [77] train-rmse:0.095354+0.006505    test-rmse:1.277570+0.278422 
## [78] train-rmse:0.091459+0.005687    test-rmse:1.276594+0.276949 
## [79] train-rmse:0.088818+0.005898    test-rmse:1.275275+0.275950 
## [80] train-rmse:0.085268+0.006145    test-rmse:1.274888+0.276073 
## [81] train-rmse:0.082594+0.006610    test-rmse:1.274830+0.276236 
## [82] train-rmse:0.079640+0.006727    test-rmse:1.274200+0.275582 
## [83] train-rmse:0.076720+0.005358    test-rmse:1.273020+0.275550 
## [84] train-rmse:0.074927+0.005837    test-rmse:1.272978+0.275779 
## [85] train-rmse:0.072865+0.005717    test-rmse:1.272016+0.276371 
## [86] train-rmse:0.070985+0.005801    test-rmse:1.272048+0.276607 
## [87] train-rmse:0.068549+0.005178    test-rmse:1.271551+0.276816 
## [88] train-rmse:0.066167+0.003918    test-rmse:1.271843+0.276896 
## [89] train-rmse:0.064472+0.004594    test-rmse:1.271008+0.276355 
## [90] train-rmse:0.062558+0.004085    test-rmse:1.270606+0.276642 
## [91] train-rmse:0.060524+0.003724    test-rmse:1.269579+0.275611 
## [92] train-rmse:0.058519+0.004035    test-rmse:1.269482+0.276135 
## [93] train-rmse:0.057078+0.003740    test-rmse:1.269632+0.276074 
## [94] train-rmse:0.055181+0.004114    test-rmse:1.269992+0.276069 
## [95] train-rmse:0.053506+0.004303    test-rmse:1.269641+0.276390 
## [96] train-rmse:0.052067+0.004393    test-rmse:1.269901+0.276171 
## [97] train-rmse:0.050388+0.004597    test-rmse:1.269991+0.275693 
## [98] train-rmse:0.048972+0.004422    test-rmse:1.270110+0.276250 
## Stopping. Best iteration:
## [92] train-rmse:0.058519+0.004035    test-rmse:1.269482+0.276135
## 
## 69 
## [1]  train-rmse:77.673825+0.090310   test-rmse:77.673837+0.509215 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:56.141246+0.073781   test-rmse:56.140539+0.549016 
## [3]  train-rmse:40.670034+0.065404   test-rmse:40.668152+0.588925 
## [4]  train-rmse:29.583382+0.061017   test-rmse:29.625472+0.605847 
## [5]  train-rmse:21.613113+0.040697   test-rmse:21.729475+0.638688 
## [6]  train-rmse:15.869784+0.030072   test-rmse:15.990444+0.575910 
## [7]  train-rmse:11.718226+0.025159   test-rmse:11.841952+0.558107 
## [8]  train-rmse:8.717026+0.027391    test-rmse:8.824311+0.532432 
## [9]  train-rmse:6.541765+0.021702    test-rmse:6.706908+0.556603 
## [10] train-rmse:4.956433+0.014977    test-rmse:5.134424+0.501214 
## [11] train-rmse:3.813202+0.011043    test-rmse:4.009276+0.463767 
## [12] train-rmse:2.977259+0.007196    test-rmse:3.276266+0.473450 
## [13] train-rmse:2.372147+0.006932    test-rmse:2.751477+0.416379 
## [14] train-rmse:1.940573+0.014248    test-rmse:2.361619+0.390663 
## [15] train-rmse:1.615307+0.024426    test-rmse:2.122904+0.345388 
## [16] train-rmse:1.365023+0.035442    test-rmse:1.950396+0.301701 
## [17] train-rmse:1.190933+0.036547    test-rmse:1.823231+0.293969 
## [18] train-rmse:1.051956+0.044370    test-rmse:1.733759+0.295518 
## [19] train-rmse:0.933387+0.055949    test-rmse:1.668875+0.288644 
## [20] train-rmse:0.840838+0.050931    test-rmse:1.615531+0.283769 
## [21] train-rmse:0.760493+0.040966    test-rmse:1.560905+0.278612 
## [22] train-rmse:0.697429+0.040201    test-rmse:1.527616+0.276325 
## [23] train-rmse:0.643420+0.035247    test-rmse:1.496283+0.275146 
## [24] train-rmse:0.595228+0.030201    test-rmse:1.477024+0.275191 
## [25] train-rmse:0.547095+0.031025    test-rmse:1.450678+0.285143 
## [26] train-rmse:0.512943+0.030200    test-rmse:1.431754+0.290317 
## [27] train-rmse:0.480268+0.026053    test-rmse:1.408280+0.295388 
## [28] train-rmse:0.451021+0.025779    test-rmse:1.394313+0.300611 
## [29] train-rmse:0.422679+0.026944    test-rmse:1.384894+0.294186 
## [30] train-rmse:0.399576+0.025975    test-rmse:1.370406+0.296439 
## [31] train-rmse:0.377604+0.024655    test-rmse:1.360937+0.299252 
## [32] train-rmse:0.356937+0.021988    test-rmse:1.348628+0.302312 
## [33] train-rmse:0.333206+0.022181    test-rmse:1.343053+0.302705 
## [34] train-rmse:0.316456+0.023469    test-rmse:1.332913+0.301387 
## [35] train-rmse:0.300281+0.021855    test-rmse:1.324410+0.308028 
## [36] train-rmse:0.285341+0.020227    test-rmse:1.320612+0.307169 
## [37] train-rmse:0.270761+0.015036    test-rmse:1.315965+0.307771 
## [38] train-rmse:0.259929+0.015371    test-rmse:1.310362+0.310899 
## [39] train-rmse:0.246541+0.013348    test-rmse:1.306659+0.310204 
## [40] train-rmse:0.233020+0.016914    test-rmse:1.303326+0.311764 
## [41] train-rmse:0.222630+0.017373    test-rmse:1.298060+0.312883 
## [42] train-rmse:0.211284+0.015180    test-rmse:1.294311+0.310085 
## [43] train-rmse:0.202936+0.015370    test-rmse:1.291613+0.310533 
## [44] train-rmse:0.196327+0.016294    test-rmse:1.291445+0.310726 
## [45] train-rmse:0.184276+0.013448    test-rmse:1.291747+0.309979 
## [46] train-rmse:0.176777+0.012169    test-rmse:1.291752+0.311825 
## [47] train-rmse:0.171758+0.011799    test-rmse:1.291219+0.315399 
## [48] train-rmse:0.165613+0.009710    test-rmse:1.288904+0.316355 
## [49] train-rmse:0.158030+0.010803    test-rmse:1.287328+0.316265 
## [50] train-rmse:0.150597+0.007104    test-rmse:1.287557+0.317306 
## [51] train-rmse:0.143754+0.006949    test-rmse:1.286454+0.317874 
## [52] train-rmse:0.138010+0.007259    test-rmse:1.285770+0.317209 
## [53] train-rmse:0.132139+0.008034    test-rmse:1.284774+0.316848 
## [54] train-rmse:0.126125+0.007295    test-rmse:1.283148+0.317790 
## [55] train-rmse:0.119796+0.005453    test-rmse:1.281805+0.318300 
## [56] train-rmse:0.115572+0.005303    test-rmse:1.280424+0.317617 
## [57] train-rmse:0.112059+0.004893    test-rmse:1.280067+0.317545 
## [58] train-rmse:0.108297+0.004583    test-rmse:1.279122+0.317637 
## [59] train-rmse:0.103652+0.005153    test-rmse:1.278221+0.318409 
## [60] train-rmse:0.099652+0.005439    test-rmse:1.276927+0.318338 
## [61] train-rmse:0.093688+0.004413    test-rmse:1.276871+0.318394 
## [62] train-rmse:0.090091+0.004409    test-rmse:1.276789+0.318528 
## [63] train-rmse:0.086078+0.003225    test-rmse:1.277444+0.319723 
## [64] train-rmse:0.082947+0.003316    test-rmse:1.276746+0.319697 
## [65] train-rmse:0.079920+0.003103    test-rmse:1.275817+0.319743 
## [66] train-rmse:0.077247+0.003934    test-rmse:1.275005+0.320822 
## [67] train-rmse:0.074988+0.003730    test-rmse:1.274647+0.320543 
## [68] train-rmse:0.072442+0.003956    test-rmse:1.274501+0.320617 
## [69] train-rmse:0.070334+0.003945    test-rmse:1.274223+0.320522 
## [70] train-rmse:0.067335+0.004776    test-rmse:1.274368+0.320346 
## [71] train-rmse:0.064856+0.004519    test-rmse:1.274288+0.320349 
## [72] train-rmse:0.062336+0.004239    test-rmse:1.274250+0.320179 
## [73] train-rmse:0.058685+0.002582    test-rmse:1.275229+0.319930 
## [74] train-rmse:0.056384+0.001902    test-rmse:1.274583+0.320414 
## [75] train-rmse:0.053770+0.001839    test-rmse:1.273901+0.319826 
## [76] train-rmse:0.052034+0.002765    test-rmse:1.273407+0.319989 
## [77] train-rmse:0.050145+0.002876    test-rmse:1.272909+0.319930 
## [78] train-rmse:0.048138+0.002957    test-rmse:1.272786+0.319709 
## [79] train-rmse:0.045991+0.002965    test-rmse:1.272309+0.319478 
## [80] train-rmse:0.043427+0.002598    test-rmse:1.272866+0.319843 
## [81] train-rmse:0.041864+0.002489    test-rmse:1.272800+0.319596 
## [82] train-rmse:0.040718+0.002591    test-rmse:1.272635+0.319734 
## [83] train-rmse:0.038914+0.002025    test-rmse:1.272714+0.320189 
## [84] train-rmse:0.037551+0.001689    test-rmse:1.272080+0.320546 
## [85] train-rmse:0.035919+0.001360    test-rmse:1.271847+0.320482 
## [86] train-rmse:0.034693+0.002108    test-rmse:1.271779+0.320650 
## [87] train-rmse:0.033512+0.002057    test-rmse:1.271571+0.320359 
## [88] train-rmse:0.032458+0.001894    test-rmse:1.271401+0.320284 
## [89] train-rmse:0.030946+0.001780    test-rmse:1.271257+0.320064 
## [90] train-rmse:0.029538+0.001722    test-rmse:1.271275+0.320002 
## [91] train-rmse:0.027831+0.001425    test-rmse:1.271351+0.320076 
## [92] train-rmse:0.026780+0.001452    test-rmse:1.271412+0.320223 
## [93] train-rmse:0.025713+0.001094    test-rmse:1.271089+0.320209 
## [94] train-rmse:0.024808+0.001091    test-rmse:1.271092+0.320194 
## [95] train-rmse:0.023889+0.001204    test-rmse:1.271201+0.320250 
## [96] train-rmse:0.023080+0.001273    test-rmse:1.271330+0.320297 
## [97] train-rmse:0.021818+0.000794    test-rmse:1.271259+0.320468 
## [98] train-rmse:0.020666+0.000908    test-rmse:1.271098+0.320536 
## [99] train-rmse:0.019627+0.001087    test-rmse:1.271177+0.320475 
## Stopping. Best iteration:
## [93] train-rmse:0.025713+0.001094    test-rmse:1.271089+0.320209
## 
## 70 
## [1]  train-rmse:75.538506+0.088543   test-rmse:75.538455+0.512708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:53.112974+0.071800   test-rmse:53.112100+0.555751 
## [3]  train-rmse:37.457246+0.064447   test-rmse:37.454956+0.599591 
## [4]  train-rmse:26.559660+0.061260   test-rmse:26.588342+0.611811 
## [5]  train-rmse:18.983199+0.057330   test-rmse:19.032940+0.624248 
## [6]  train-rmse:13.743705+0.053375   test-rmse:13.792476+0.581864 
## [7]  train-rmse:10.176078+0.056883   test-rmse:10.302429+0.672384 
## [8]  train-rmse:7.786917+0.062390    test-rmse:7.896483+0.650646 
## [9]  train-rmse:6.240562+0.072649    test-rmse:6.336027+0.572318 
## [10] train-rmse:5.260618+0.078588    test-rmse:5.409231+0.594989 
## [11] train-rmse:4.653557+0.077710    test-rmse:4.744061+0.520029 
## [12] train-rmse:4.266949+0.074329    test-rmse:4.431429+0.527665 
## [13] train-rmse:4.030357+0.076020    test-rmse:4.180580+0.477266 
## [14] train-rmse:3.861458+0.076394    test-rmse:4.033381+0.440634 
## [15] train-rmse:3.736975+0.069844    test-rmse:3.868390+0.370716 
## [16] train-rmse:3.637661+0.066689    test-rmse:3.840988+0.363158 
## [17] train-rmse:3.554124+0.065567    test-rmse:3.785790+0.351734 
## [18] train-rmse:3.479425+0.062027    test-rmse:3.716459+0.352961 
## [19] train-rmse:3.412139+0.061118    test-rmse:3.668886+0.367820 
## [20] train-rmse:3.346780+0.060582    test-rmse:3.624966+0.362178 
## [21] train-rmse:3.282739+0.060162    test-rmse:3.530953+0.326871 
## [22] train-rmse:3.222001+0.059473    test-rmse:3.479056+0.332148 
## [23] train-rmse:3.162812+0.057937    test-rmse:3.407530+0.336741 
## [24] train-rmse:3.106758+0.055354    test-rmse:3.323623+0.287157 
## [25] train-rmse:3.055088+0.052282    test-rmse:3.280863+0.281555 
## [26] train-rmse:3.005235+0.049265    test-rmse:3.239230+0.301019 
## [27] train-rmse:2.956962+0.048387    test-rmse:3.188709+0.299221 
## [28] train-rmse:2.913136+0.046919    test-rmse:3.161897+0.311397 
## [29] train-rmse:2.868509+0.044277    test-rmse:3.102401+0.286478 
## [30] train-rmse:2.826156+0.043451    test-rmse:3.070192+0.295540 
## [31] train-rmse:2.784955+0.043474    test-rmse:3.049468+0.302158 
## [32] train-rmse:2.744250+0.044023    test-rmse:3.014845+0.303430 
## [33] train-rmse:2.704754+0.044923    test-rmse:2.979617+0.294722 
## [34] train-rmse:2.665689+0.044867    test-rmse:2.937940+0.304999 
## [35] train-rmse:2.627304+0.045514    test-rmse:2.900734+0.305499 
## [36] train-rmse:2.589733+0.045210    test-rmse:2.859396+0.315767 
## [37] train-rmse:2.552974+0.045319    test-rmse:2.818818+0.322078 
## [38] train-rmse:2.517677+0.044415    test-rmse:2.781441+0.275636 
## [39] train-rmse:2.483161+0.044025    test-rmse:2.748490+0.276441 
## [40] train-rmse:2.450268+0.043917    test-rmse:2.714765+0.292219 
## [41] train-rmse:2.417744+0.042934    test-rmse:2.694399+0.294786 
## [42] train-rmse:2.386592+0.041270    test-rmse:2.665045+0.304954 
## [43] train-rmse:2.356969+0.040130    test-rmse:2.646674+0.300206 
## [44] train-rmse:2.328274+0.040005    test-rmse:2.613043+0.302388 
## [45] train-rmse:2.300205+0.039180    test-rmse:2.591563+0.308700 
## [46] train-rmse:2.272031+0.038805    test-rmse:2.561616+0.309816 
## [47] train-rmse:2.243403+0.038084    test-rmse:2.526966+0.288584 
## [48] train-rmse:2.215807+0.037185    test-rmse:2.495718+0.288149 
## [49] train-rmse:2.188496+0.036273    test-rmse:2.472480+0.302614 
## [50] train-rmse:2.161956+0.036092    test-rmse:2.450261+0.296803 
## [51] train-rmse:2.135876+0.035801    test-rmse:2.417279+0.276715 
## [52] train-rmse:2.110279+0.035454    test-rmse:2.384592+0.292891 
## [53] train-rmse:2.084957+0.034509    test-rmse:2.358047+0.282057 
## [54] train-rmse:2.059867+0.033941    test-rmse:2.321574+0.275856 
## [55] train-rmse:2.035550+0.033395    test-rmse:2.310122+0.277217 
## [56] train-rmse:2.012535+0.032680    test-rmse:2.288039+0.280194 
## [57] train-rmse:1.989957+0.031796    test-rmse:2.271389+0.279656 
## [58] train-rmse:1.967881+0.030901    test-rmse:2.260787+0.277673 
## [59] train-rmse:1.945345+0.029513    test-rmse:2.233721+0.275167 
## [60] train-rmse:1.924012+0.028544    test-rmse:2.207011+0.277866 
## [61] train-rmse:1.902512+0.027370    test-rmse:2.189809+0.277110 
## [62] train-rmse:1.881715+0.026932    test-rmse:2.173103+0.266741 
## [63] train-rmse:1.861499+0.026505    test-rmse:2.137903+0.245901 
## [64] train-rmse:1.841439+0.026156    test-rmse:2.120739+0.241897 
## [65] train-rmse:1.821220+0.024844    test-rmse:2.104386+0.241306 
## [66] train-rmse:1.801810+0.024054    test-rmse:2.092797+0.245120 
## [67] train-rmse:1.782784+0.023690    test-rmse:2.071601+0.247509 
## [68] train-rmse:1.764083+0.023181    test-rmse:2.053822+0.250012 
## [69] train-rmse:1.745149+0.022680    test-rmse:2.031500+0.251941 
## [70] train-rmse:1.726664+0.022627    test-rmse:2.010798+0.243617 
## [71] train-rmse:1.709039+0.022645    test-rmse:1.982332+0.238940 
## [72] train-rmse:1.691634+0.022603    test-rmse:1.973013+0.239758 
## [73] train-rmse:1.674512+0.022423    test-rmse:1.956667+0.245675 
## [74] train-rmse:1.657680+0.021841    test-rmse:1.939736+0.248356 
## [75] train-rmse:1.641533+0.021487    test-rmse:1.930950+0.247435 
## [76] train-rmse:1.625310+0.021601    test-rmse:1.925722+0.247884 
## [77] train-rmse:1.609434+0.021503    test-rmse:1.921126+0.245510 
## [78] train-rmse:1.594024+0.020905    test-rmse:1.898406+0.248735 
## [79] train-rmse:1.578845+0.020057    test-rmse:1.888113+0.250238 
## [80] train-rmse:1.563972+0.019208    test-rmse:1.864741+0.229156 
## [81] train-rmse:1.549672+0.018656    test-rmse:1.855362+0.219756 
## [82] train-rmse:1.535366+0.018118    test-rmse:1.843881+0.216598 
## [83] train-rmse:1.521038+0.017515    test-rmse:1.829431+0.218908 
## [84] train-rmse:1.507024+0.017034    test-rmse:1.812403+0.220923 
## [85] train-rmse:1.493049+0.016412    test-rmse:1.796679+0.217732 
## [86] train-rmse:1.479665+0.016153    test-rmse:1.790544+0.217286 
## [87] train-rmse:1.466206+0.015768    test-rmse:1.764673+0.207248 
## [88] train-rmse:1.453125+0.015264    test-rmse:1.758072+0.208938 
## [89] train-rmse:1.440286+0.014830    test-rmse:1.739217+0.217495 
## [90] train-rmse:1.427497+0.014507    test-rmse:1.735910+0.215368 
## [91] train-rmse:1.414843+0.014106    test-rmse:1.720269+0.214509 
## [92] train-rmse:1.402209+0.014303    test-rmse:1.712762+0.215092 
## [93] train-rmse:1.389774+0.014445    test-rmse:1.699081+0.215410 
## [94] train-rmse:1.377775+0.014117    test-rmse:1.689845+0.211850 
## [95] train-rmse:1.366062+0.013875    test-rmse:1.675860+0.216898 
## [96] train-rmse:1.354858+0.013491    test-rmse:1.660789+0.222115 
## [97] train-rmse:1.343651+0.013366    test-rmse:1.653015+0.225504 
## [98] train-rmse:1.332771+0.012909    test-rmse:1.648113+0.226559 
## [99] train-rmse:1.322147+0.012692    test-rmse:1.644746+0.222977 
## [100]    train-rmse:1.311744+0.012452    test-rmse:1.630095+0.222355 
## [101]    train-rmse:1.301587+0.012370    test-rmse:1.617592+0.224579 
## [102]    train-rmse:1.291552+0.012049    test-rmse:1.606460+0.223029 
## [103]    train-rmse:1.281773+0.011667    test-rmse:1.601222+0.224949 
## [104]    train-rmse:1.272235+0.011461    test-rmse:1.594340+0.229186 
## [105]    train-rmse:1.262670+0.011568    test-rmse:1.576818+0.211463 
## [106]    train-rmse:1.253457+0.011342    test-rmse:1.569535+0.212092 
## [107]    train-rmse:1.244204+0.011182    test-rmse:1.562931+0.210170 
## [108]    train-rmse:1.235170+0.011083    test-rmse:1.550865+0.208163 
## [109]    train-rmse:1.226427+0.010928    test-rmse:1.538275+0.206150 
## [110]    train-rmse:1.217579+0.010828    test-rmse:1.532651+0.205231 
## [111]    train-rmse:1.208746+0.010700    test-rmse:1.517231+0.204330 
## [112]    train-rmse:1.200270+0.010539    test-rmse:1.505706+0.194337 
## [113]    train-rmse:1.191840+0.010394    test-rmse:1.495083+0.195280 
## [114]    train-rmse:1.183626+0.010227    test-rmse:1.489956+0.197058 
## [115]    train-rmse:1.175391+0.010202    test-rmse:1.484113+0.200794 
## [116]    train-rmse:1.167410+0.010243    test-rmse:1.474420+0.203460 
## [117]    train-rmse:1.159501+0.010036    test-rmse:1.465822+0.206257 
## [118]    train-rmse:1.151937+0.009950    test-rmse:1.458858+0.206299 
## [119]    train-rmse:1.144499+0.009700    test-rmse:1.455752+0.202311 
## [120]    train-rmse:1.137272+0.009478    test-rmse:1.452698+0.201351 
## [121]    train-rmse:1.130064+0.009258    test-rmse:1.449171+0.203030 
## [122]    train-rmse:1.123007+0.009151    test-rmse:1.442235+0.207891 
## [123]    train-rmse:1.116143+0.008993    test-rmse:1.438339+0.210154 
## [124]    train-rmse:1.109367+0.008970    test-rmse:1.434485+0.211250 
## [125]    train-rmse:1.102501+0.008895    test-rmse:1.428006+0.209253 
## [126]    train-rmse:1.095873+0.008964    test-rmse:1.416714+0.195285 
## [127]    train-rmse:1.089447+0.008854    test-rmse:1.412014+0.197285 
## [128]    train-rmse:1.083088+0.008705    test-rmse:1.404923+0.200671 
## [129]    train-rmse:1.076932+0.008633    test-rmse:1.398531+0.198660 
## [130]    train-rmse:1.070818+0.008604    test-rmse:1.393987+0.193954 
## [131]    train-rmse:1.064824+0.008512    test-rmse:1.384403+0.200197 
## [132]    train-rmse:1.058919+0.008463    test-rmse:1.380703+0.196064 
## [133]    train-rmse:1.053195+0.008501    test-rmse:1.377356+0.196309 
## [134]    train-rmse:1.047531+0.008553    test-rmse:1.366318+0.191592 
## [135]    train-rmse:1.041939+0.008590    test-rmse:1.362498+0.187547 
## [136]    train-rmse:1.036424+0.008624    test-rmse:1.357500+0.187521 
## [137]    train-rmse:1.031148+0.008615    test-rmse:1.348511+0.187487 
## [138]    train-rmse:1.025982+0.008608    test-rmse:1.339205+0.191743 
## [139]    train-rmse:1.020811+0.008603    test-rmse:1.335263+0.191475 
## [140]    train-rmse:1.015730+0.008642    test-rmse:1.331857+0.192058 
## [141]    train-rmse:1.010738+0.008633    test-rmse:1.329575+0.191613 
## [142]    train-rmse:1.005777+0.008547    test-rmse:1.325447+0.193930 
## [143]    train-rmse:1.000852+0.008497    test-rmse:1.319007+0.197196 
## [144]    train-rmse:0.996041+0.008380    test-rmse:1.315700+0.199008 
## [145]    train-rmse:0.991356+0.008294    test-rmse:1.312195+0.200860 
## [146]    train-rmse:0.986866+0.008169    test-rmse:1.309350+0.201143 
## [147]    train-rmse:0.982441+0.008110    test-rmse:1.309641+0.199859 
## [148]    train-rmse:0.977979+0.007969    test-rmse:1.300941+0.196747 
## [149]    train-rmse:0.973648+0.007927    test-rmse:1.290328+0.189173 
## [150]    train-rmse:0.969443+0.007871    test-rmse:1.287130+0.188042 
## [151]    train-rmse:0.965358+0.007846    test-rmse:1.284885+0.186539 
## [152]    train-rmse:0.961262+0.007761    test-rmse:1.279556+0.184155 
## [153]    train-rmse:0.957185+0.007845    test-rmse:1.277231+0.184165 
## [154]    train-rmse:0.953197+0.007870    test-rmse:1.275447+0.184388 
## [155]    train-rmse:0.949355+0.007834    test-rmse:1.273706+0.184381 
## [156]    train-rmse:0.945550+0.007872    test-rmse:1.273019+0.183812 
## [157]    train-rmse:0.941704+0.007846    test-rmse:1.268168+0.177894 
## [158]    train-rmse:0.937871+0.007829    test-rmse:1.265516+0.178592 
## [159]    train-rmse:0.934196+0.007771    test-rmse:1.265004+0.178199 
## [160]    train-rmse:0.930636+0.007814    test-rmse:1.258296+0.180082 
## [161]    train-rmse:0.927060+0.007863    test-rmse:1.254390+0.178948 
## [162]    train-rmse:0.923655+0.007948    test-rmse:1.253130+0.174635 
## [163]    train-rmse:0.920350+0.008032    test-rmse:1.251374+0.175723 
## [164]    train-rmse:0.917064+0.008115    test-rmse:1.244305+0.177026 
## [165]    train-rmse:0.913891+0.008206    test-rmse:1.243084+0.176497 
## [166]    train-rmse:0.910789+0.008292    test-rmse:1.240367+0.176915 
## [167]    train-rmse:0.907702+0.008315    test-rmse:1.237205+0.178069 
## [168]    train-rmse:0.904653+0.008340    test-rmse:1.233574+0.179856 
## [169]    train-rmse:0.901700+0.008274    test-rmse:1.235399+0.179764 
## [170]    train-rmse:0.898794+0.008291    test-rmse:1.234774+0.181610 
## [171]    train-rmse:0.895873+0.008333    test-rmse:1.229594+0.184066 
## [172]    train-rmse:0.893074+0.008322    test-rmse:1.227823+0.184384 
## [173]    train-rmse:0.890237+0.008333    test-rmse:1.224794+0.183465 
## [174]    train-rmse:0.887477+0.008430    test-rmse:1.220659+0.180402 
## [175]    train-rmse:0.884749+0.008465    test-rmse:1.219138+0.178896 
## [176]    train-rmse:0.882040+0.008472    test-rmse:1.213941+0.181514 
## [177]    train-rmse:0.879377+0.008510    test-rmse:1.208890+0.182530 
## [178]    train-rmse:0.876803+0.008541    test-rmse:1.208576+0.181778 
## [179]    train-rmse:0.874261+0.008575    test-rmse:1.200826+0.175129 
## [180]    train-rmse:0.871794+0.008637    test-rmse:1.199137+0.177215 
## [181]    train-rmse:0.869309+0.008725    test-rmse:1.195680+0.179008 
## [182]    train-rmse:0.866884+0.008733    test-rmse:1.193999+0.179147 
## [183]    train-rmse:0.864530+0.008770    test-rmse:1.190839+0.176625 
## [184]    train-rmse:0.862215+0.008810    test-rmse:1.190941+0.176789 
## [185]    train-rmse:0.859957+0.008862    test-rmse:1.190363+0.177300 
## [186]    train-rmse:0.857740+0.008949    test-rmse:1.189503+0.176926 
## [187]    train-rmse:0.855598+0.008986    test-rmse:1.189616+0.176455 
## [188]    train-rmse:0.853436+0.008991    test-rmse:1.187563+0.175817 
## [189]    train-rmse:0.851368+0.009017    test-rmse:1.186708+0.176442 
## [190]    train-rmse:0.849261+0.009088    test-rmse:1.183447+0.175096 
## [191]    train-rmse:0.847276+0.009159    test-rmse:1.184266+0.173019 
## [192]    train-rmse:0.845235+0.009195    test-rmse:1.179188+0.174318 
## [193]    train-rmse:0.843263+0.009217    test-rmse:1.177751+0.175541 
## [194]    train-rmse:0.841234+0.009172    test-rmse:1.174267+0.174590 
## [195]    train-rmse:0.839351+0.009218    test-rmse:1.171243+0.175436 
## [196]    train-rmse:0.837532+0.009258    test-rmse:1.170599+0.176401 
## [197]    train-rmse:0.835603+0.009440    test-rmse:1.167648+0.179672 
## [198]    train-rmse:0.833705+0.009522    test-rmse:1.166936+0.178369 
## [199]    train-rmse:0.831907+0.009586    test-rmse:1.164651+0.179198 
## [200]    train-rmse:0.830141+0.009664    test-rmse:1.164950+0.181282 
## [201]    train-rmse:0.828404+0.009717    test-rmse:1.162843+0.184166 
## [202]    train-rmse:0.826626+0.009767    test-rmse:1.161810+0.184483 
## [203]    train-rmse:0.824948+0.009790    test-rmse:1.159022+0.185555 
## [204]    train-rmse:0.823315+0.009801    test-rmse:1.159097+0.186386 
## [205]    train-rmse:0.821670+0.009888    test-rmse:1.158457+0.186189 
## [206]    train-rmse:0.820084+0.009927    test-rmse:1.157684+0.186257 
## [207]    train-rmse:0.818490+0.009953    test-rmse:1.155859+0.187310 
## [208]    train-rmse:0.816877+0.009972    test-rmse:1.153945+0.184604 
## [209]    train-rmse:0.815370+0.010019    test-rmse:1.151988+0.181515 
## [210]    train-rmse:0.813872+0.010098    test-rmse:1.147519+0.181183 
## [211]    train-rmse:0.812379+0.010190    test-rmse:1.143045+0.174706 
## [212]    train-rmse:0.810839+0.010318    test-rmse:1.142283+0.174422 
## [213]    train-rmse:0.809374+0.010412    test-rmse:1.142533+0.173191 
## [214]    train-rmse:0.807933+0.010472    test-rmse:1.141223+0.172305 
## [215]    train-rmse:0.806533+0.010566    test-rmse:1.139532+0.171986 
## [216]    train-rmse:0.805108+0.010653    test-rmse:1.139181+0.172383 
## [217]    train-rmse:0.803707+0.010761    test-rmse:1.136805+0.173267 
## [218]    train-rmse:0.802304+0.010844    test-rmse:1.135590+0.171663 
## [219]    train-rmse:0.800960+0.010894    test-rmse:1.134354+0.172178 
## [220]    train-rmse:0.799626+0.010922    test-rmse:1.132555+0.173671 
## [221]    train-rmse:0.798318+0.010982    test-rmse:1.130841+0.174720 
## [222]    train-rmse:0.797052+0.011053    test-rmse:1.129973+0.175295 
## [223]    train-rmse:0.795758+0.011168    test-rmse:1.127397+0.175038 
## [224]    train-rmse:0.794504+0.011211    test-rmse:1.126454+0.173441 
## [225]    train-rmse:0.793285+0.011269    test-rmse:1.127808+0.173015 
## [226]    train-rmse:0.791984+0.011373    test-rmse:1.127882+0.173709 
## [227]    train-rmse:0.790787+0.011413    test-rmse:1.126429+0.174589 
## [228]    train-rmse:0.789598+0.011491    test-rmse:1.124577+0.176662 
## [229]    train-rmse:0.788363+0.011567    test-rmse:1.123598+0.173876 
## [230]    train-rmse:0.787206+0.011642    test-rmse:1.122953+0.174417 
## [231]    train-rmse:0.786055+0.011718    test-rmse:1.121939+0.174919 
## [232]    train-rmse:0.784939+0.011755    test-rmse:1.121369+0.176275 
## [233]    train-rmse:0.783835+0.011806    test-rmse:1.120730+0.177207 
## [234]    train-rmse:0.782724+0.011872    test-rmse:1.121528+0.176942 
## [235]    train-rmse:0.781565+0.011857    test-rmse:1.120633+0.176910 
## [236]    train-rmse:0.780486+0.011863    test-rmse:1.120276+0.178530 
## [237]    train-rmse:0.779289+0.011891    test-rmse:1.119668+0.174900 
## [238]    train-rmse:0.778197+0.011920    test-rmse:1.117511+0.174537 
## [239]    train-rmse:0.777152+0.011937    test-rmse:1.115542+0.173412 
## [240]    train-rmse:0.776004+0.012014    test-rmse:1.112900+0.175372 
## [241]    train-rmse:0.774965+0.012076    test-rmse:1.109006+0.171967 
## [242]    train-rmse:0.773888+0.012089    test-rmse:1.109679+0.169954 
## [243]    train-rmse:0.772917+0.012137    test-rmse:1.107267+0.170682 
## [244]    train-rmse:0.771949+0.012185    test-rmse:1.104885+0.172767 
## [245]    train-rmse:0.770964+0.012213    test-rmse:1.104075+0.171541 
## [246]    train-rmse:0.769925+0.012301    test-rmse:1.103712+0.170654 
## [247]    train-rmse:0.769010+0.012323    test-rmse:1.103160+0.170528 
## [248]    train-rmse:0.768118+0.012344    test-rmse:1.103522+0.171003 
## [249]    train-rmse:0.767224+0.012354    test-rmse:1.102526+0.172298 
## [250]    train-rmse:0.766333+0.012383    test-rmse:1.103242+0.172228 
## [251]    train-rmse:0.765386+0.012447    test-rmse:1.103425+0.171787 
## [252]    train-rmse:0.764438+0.012450    test-rmse:1.103508+0.172474 
## [253]    train-rmse:0.763547+0.012485    test-rmse:1.102552+0.174428 
## [254]    train-rmse:0.762658+0.012537    test-rmse:1.102584+0.172367 
## [255]    train-rmse:0.761775+0.012574    test-rmse:1.102138+0.173887 
## [256]    train-rmse:0.760900+0.012632    test-rmse:1.101764+0.174716 
## [257]    train-rmse:0.760053+0.012679    test-rmse:1.101836+0.175527 
## [258]    train-rmse:0.759215+0.012717    test-rmse:1.101803+0.174983 
## [259]    train-rmse:0.758397+0.012741    test-rmse:1.101378+0.174317 
## [260]    train-rmse:0.757532+0.012718    test-rmse:1.099437+0.175777 
## [261]    train-rmse:0.756702+0.012713    test-rmse:1.096935+0.176164 
## [262]    train-rmse:0.755881+0.012737    test-rmse:1.095867+0.176689 
## [263]    train-rmse:0.755073+0.012743    test-rmse:1.095826+0.176364 
## [264]    train-rmse:0.754273+0.012758    test-rmse:1.096159+0.176403 
## [265]    train-rmse:0.753453+0.012779    test-rmse:1.096210+0.177963 
## [266]    train-rmse:0.752632+0.012773    test-rmse:1.096307+0.178368 
## [267]    train-rmse:0.751860+0.012799    test-rmse:1.095211+0.176729 
## [268]    train-rmse:0.751090+0.012819    test-rmse:1.093950+0.175989 
## [269]    train-rmse:0.750315+0.012852    test-rmse:1.094738+0.175485 
## [270]    train-rmse:0.749540+0.012883    test-rmse:1.092749+0.174458 
## [271]    train-rmse:0.748780+0.012887    test-rmse:1.090207+0.171176 
## [272]    train-rmse:0.748054+0.012904    test-rmse:1.091404+0.170295 
## [273]    train-rmse:0.747319+0.012922    test-rmse:1.088985+0.172209 
## [274]    train-rmse:0.746594+0.012941    test-rmse:1.089252+0.171843 
## [275]    train-rmse:0.745880+0.012947    test-rmse:1.088440+0.170925 
## [276]    train-rmse:0.745176+0.012960    test-rmse:1.087706+0.171376 
## [277]    train-rmse:0.744492+0.012983    test-rmse:1.087047+0.171739 
## [278]    train-rmse:0.743816+0.013001    test-rmse:1.086515+0.171418 
## [279]    train-rmse:0.743141+0.013008    test-rmse:1.084861+0.170871 
## [280]    train-rmse:0.742408+0.012996    test-rmse:1.083612+0.169859 
## [281]    train-rmse:0.741711+0.013021    test-rmse:1.084097+0.169692 
## [282]    train-rmse:0.741037+0.013032    test-rmse:1.081888+0.168036 
## [283]    train-rmse:0.740353+0.013052    test-rmse:1.080870+0.166443 
## [284]    train-rmse:0.739715+0.013062    test-rmse:1.079073+0.167538 
## [285]    train-rmse:0.739083+0.013073    test-rmse:1.078550+0.167186 
## [286]    train-rmse:0.738457+0.013070    test-rmse:1.078008+0.167286 
## [287]    train-rmse:0.737810+0.013086    test-rmse:1.076593+0.168809 
## [288]    train-rmse:0.737143+0.013059    test-rmse:1.076486+0.167117 
## [289]    train-rmse:0.736525+0.013056    test-rmse:1.075992+0.165647 
## [290]    train-rmse:0.735899+0.013054    test-rmse:1.074448+0.166486 
## [291]    train-rmse:0.735270+0.013031    test-rmse:1.074914+0.165372 
## [292]    train-rmse:0.734626+0.013021    test-rmse:1.072236+0.163410 
## [293]    train-rmse:0.733981+0.013049    test-rmse:1.071104+0.164048 
## [294]    train-rmse:0.733358+0.013053    test-rmse:1.070253+0.162887 
## [295]    train-rmse:0.732751+0.013058    test-rmse:1.069701+0.163884 
## [296]    train-rmse:0.732129+0.013084    test-rmse:1.069094+0.163218 
## [297]    train-rmse:0.731531+0.013093    test-rmse:1.068790+0.163331 
## [298]    train-rmse:0.730920+0.013042    test-rmse:1.068680+0.162663 
## [299]    train-rmse:0.730328+0.013035    test-rmse:1.068219+0.161565 
## [300]    train-rmse:0.729750+0.013015    test-rmse:1.066736+0.159954 
## [301]    train-rmse:0.729181+0.013013    test-rmse:1.066013+0.160436 
## [302]    train-rmse:0.728616+0.013001    test-rmse:1.064539+0.161569 
## [303]    train-rmse:0.728053+0.012972    test-rmse:1.065588+0.159883 
## [304]    train-rmse:0.727468+0.012952    test-rmse:1.065156+0.160100 
## [305]    train-rmse:0.726891+0.012937    test-rmse:1.065370+0.159327 
## [306]    train-rmse:0.726341+0.012930    test-rmse:1.065997+0.158742 
## [307]    train-rmse:0.725748+0.012918    test-rmse:1.064277+0.160038 
## [308]    train-rmse:0.725213+0.012912    test-rmse:1.064554+0.160129 
## [309]    train-rmse:0.724669+0.012921    test-rmse:1.064737+0.159929 
## [310]    train-rmse:0.724094+0.012935    test-rmse:1.064186+0.160237 
## [311]    train-rmse:0.723557+0.012918    test-rmse:1.062548+0.160022 
## [312]    train-rmse:0.723027+0.012914    test-rmse:1.061654+0.160485 
## [313]    train-rmse:0.722511+0.012897    test-rmse:1.060971+0.160177 
## [314]    train-rmse:0.721957+0.012893    test-rmse:1.060887+0.158800 
## [315]    train-rmse:0.721413+0.012893    test-rmse:1.060130+0.158787 
## [316]    train-rmse:0.720899+0.012873    test-rmse:1.059560+0.159226 
## [317]    train-rmse:0.720372+0.012859    test-rmse:1.059720+0.159262 
## [318]    train-rmse:0.719861+0.012865    test-rmse:1.060445+0.160160 
## [319]    train-rmse:0.719360+0.012866    test-rmse:1.060716+0.160510 
## [320]    train-rmse:0.718825+0.012826    test-rmse:1.060349+0.160750 
## [321]    train-rmse:0.718347+0.012812    test-rmse:1.060722+0.160620 
## [322]    train-rmse:0.717847+0.012801    test-rmse:1.059851+0.161674 
## Stopping. Best iteration:
## [316]    train-rmse:0.720899+0.012873    test-rmse:1.059560+0.159226
## 
## 71 
## [1]  train-rmse:75.538506+0.088543   test-rmse:75.538455+0.512708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:53.112974+0.071800   test-rmse:53.112100+0.555751 
## [3]  train-rmse:37.457246+0.064447   test-rmse:37.454956+0.599591 
## [4]  train-rmse:26.556096+0.061192   test-rmse:26.606015+0.614020 
## [5]  train-rmse:18.942103+0.046909   test-rmse:18.986064+0.540532 
## [6]  train-rmse:13.646918+0.045394   test-rmse:13.735835+0.573823 
## [7]  train-rmse:10.004129+0.049418   test-rmse:10.114857+0.561768 
## [8]  train-rmse:7.528265+0.053194    test-rmse:7.679244+0.553116 
## [9]  train-rmse:5.877478+0.059423    test-rmse:6.034324+0.555193 
## [10] train-rmse:4.803570+0.065966    test-rmse:4.976334+0.506287 
## [11] train-rmse:4.114395+0.067026    test-rmse:4.356855+0.483266 
## [12] train-rmse:3.665615+0.079652    test-rmse:3.913226+0.439640 
## [13] train-rmse:3.365690+0.078224    test-rmse:3.614524+0.377732 
## [14] train-rmse:3.149371+0.065342    test-rmse:3.405066+0.322218 
## [15] train-rmse:2.990945+0.059689    test-rmse:3.281745+0.325983 
## [16] train-rmse:2.863230+0.054528    test-rmse:3.159545+0.318663 
## [17] train-rmse:2.762389+0.051370    test-rmse:3.058411+0.309883 
## [18] train-rmse:2.673248+0.049951    test-rmse:2.987907+0.313167 
## [19] train-rmse:2.593184+0.048763    test-rmse:2.938979+0.300523 
## [20] train-rmse:2.515754+0.048772    test-rmse:2.856545+0.302677 
## [21] train-rmse:2.443304+0.048655    test-rmse:2.779923+0.278810 
## [22] train-rmse:2.372921+0.047085    test-rmse:2.712657+0.286993 
## [23] train-rmse:2.303868+0.043401    test-rmse:2.635758+0.258123 
## [24] train-rmse:2.241488+0.041827    test-rmse:2.599585+0.273572 
## [25] train-rmse:2.183169+0.037715    test-rmse:2.552713+0.276887 
## [26] train-rmse:2.128716+0.035795    test-rmse:2.491779+0.264510 
## [27] train-rmse:2.068655+0.034236    test-rmse:2.452295+0.231304 
## [28] train-rmse:2.017329+0.033610    test-rmse:2.407018+0.224095 
## [29] train-rmse:1.966323+0.032639    test-rmse:2.356679+0.227397 
## [30] train-rmse:1.918773+0.031211    test-rmse:2.306620+0.224655 
## [31] train-rmse:1.870807+0.029881    test-rmse:2.263328+0.239162 
## [32] train-rmse:1.825197+0.028326    test-rmse:2.222933+0.223635 
## [33] train-rmse:1.781851+0.027123    test-rmse:2.162307+0.221723 
## [34] train-rmse:1.740592+0.023868    test-rmse:2.140151+0.224326 
## [35] train-rmse:1.701011+0.022954    test-rmse:2.105405+0.215149 
## [36] train-rmse:1.660662+0.022209    test-rmse:2.076085+0.227969 
## [37] train-rmse:1.619862+0.016948    test-rmse:2.040059+0.216356 
## [38] train-rmse:1.579924+0.016490    test-rmse:2.029615+0.221154 
## [39] train-rmse:1.544720+0.016836    test-rmse:1.997560+0.235065 
## [40] train-rmse:1.509178+0.015751    test-rmse:1.975852+0.239114 
## [41] train-rmse:1.475657+0.016149    test-rmse:1.946556+0.238834 
## [42] train-rmse:1.444427+0.015502    test-rmse:1.915269+0.239423 
## [43] train-rmse:1.413273+0.015709    test-rmse:1.887001+0.235409 
## [44] train-rmse:1.381386+0.016032    test-rmse:1.866852+0.231635 
## [45] train-rmse:1.353513+0.015243    test-rmse:1.838827+0.233139 
## [46] train-rmse:1.326298+0.015840    test-rmse:1.809209+0.218293 
## [47] train-rmse:1.299396+0.015341    test-rmse:1.791146+0.225436 
## [48] train-rmse:1.272501+0.014729    test-rmse:1.769763+0.224994 
## [49] train-rmse:1.247702+0.014660    test-rmse:1.744634+0.218742 
## [50] train-rmse:1.223865+0.015203    test-rmse:1.719460+0.222834 
## [51] train-rmse:1.199989+0.015758    test-rmse:1.697203+0.229971 
## [52] train-rmse:1.176748+0.016799    test-rmse:1.692373+0.233504 
## [53] train-rmse:1.155087+0.018271    test-rmse:1.670887+0.216037 
## [54] train-rmse:1.132096+0.017717    test-rmse:1.663699+0.212740 
## [55] train-rmse:1.111185+0.017282    test-rmse:1.642958+0.209448 
## [56] train-rmse:1.090984+0.017442    test-rmse:1.627138+0.213462 
## [57] train-rmse:1.072485+0.017520    test-rmse:1.600746+0.208879 
## [58] train-rmse:1.054900+0.017819    test-rmse:1.586102+0.216703 
## [59] train-rmse:1.037573+0.017897    test-rmse:1.559647+0.218294 
## [60] train-rmse:1.020307+0.017698    test-rmse:1.549989+0.212505 
## [61] train-rmse:1.004557+0.017558    test-rmse:1.537650+0.211290 
## [62] train-rmse:0.986290+0.017486    test-rmse:1.514180+0.194245 
## [63] train-rmse:0.969797+0.017888    test-rmse:1.503868+0.197239 
## [64] train-rmse:0.952221+0.015422    test-rmse:1.496652+0.201301 
## [65] train-rmse:0.936495+0.015817    test-rmse:1.481172+0.209710 
## [66] train-rmse:0.923087+0.016381    test-rmse:1.470327+0.218305 
## [67] train-rmse:0.906464+0.016876    test-rmse:1.454653+0.217353 
## [68] train-rmse:0.893055+0.017839    test-rmse:1.447848+0.218392 
## [69] train-rmse:0.878558+0.020350    test-rmse:1.439144+0.215925 
## [70] train-rmse:0.866116+0.019938    test-rmse:1.425196+0.216582 
## [71] train-rmse:0.853781+0.019732    test-rmse:1.418545+0.216855 
## [72] train-rmse:0.841603+0.019124    test-rmse:1.409513+0.220401 
## [73] train-rmse:0.827517+0.021545    test-rmse:1.405476+0.220068 
## [74] train-rmse:0.816777+0.021021    test-rmse:1.394004+0.207944 
## [75] train-rmse:0.805560+0.019566    test-rmse:1.384037+0.206076 
## [76] train-rmse:0.794547+0.019655    test-rmse:1.375665+0.207788 
## [77] train-rmse:0.785007+0.020025    test-rmse:1.366335+0.212647 
## [78] train-rmse:0.773717+0.017737    test-rmse:1.362853+0.213111 
## [79] train-rmse:0.763843+0.018321    test-rmse:1.358828+0.208638 
## [80] train-rmse:0.753958+0.020468    test-rmse:1.352958+0.211056 
## [81] train-rmse:0.744220+0.022085    test-rmse:1.346705+0.211468 
## [82] train-rmse:0.733807+0.021876    test-rmse:1.338741+0.207174 
## [83] train-rmse:0.721774+0.021437    test-rmse:1.329808+0.204387 
## [84] train-rmse:0.714366+0.021509    test-rmse:1.325601+0.207445 
## [85] train-rmse:0.706234+0.021524    test-rmse:1.322029+0.209028 
## [86] train-rmse:0.699160+0.021857    test-rmse:1.319191+0.206404 
## [87] train-rmse:0.692033+0.022877    test-rmse:1.306873+0.205720 
## [88] train-rmse:0.684481+0.022008    test-rmse:1.306278+0.207484 
## [89] train-rmse:0.677542+0.022188    test-rmse:1.295096+0.203764 
## [90] train-rmse:0.669913+0.022070    test-rmse:1.288416+0.207722 
## [91] train-rmse:0.661209+0.019333    test-rmse:1.280555+0.205326 
## [92] train-rmse:0.654042+0.019202    test-rmse:1.274081+0.204559 
## [93] train-rmse:0.647255+0.019657    test-rmse:1.268738+0.208880 
## [94] train-rmse:0.640466+0.019955    test-rmse:1.265913+0.209128 
## [95] train-rmse:0.634474+0.019933    test-rmse:1.260622+0.211063 
## [96] train-rmse:0.626936+0.019445    test-rmse:1.259401+0.212347 
## [97] train-rmse:0.619108+0.019032    test-rmse:1.254596+0.214996 
## [98] train-rmse:0.613524+0.019508    test-rmse:1.249825+0.214314 
## [99] train-rmse:0.608491+0.019758    test-rmse:1.249550+0.215017 
## [100]    train-rmse:0.602900+0.019783    test-rmse:1.247644+0.217331 
## [101]    train-rmse:0.597017+0.020562    test-rmse:1.244606+0.217345 
## [102]    train-rmse:0.589904+0.021744    test-rmse:1.241589+0.218063 
## [103]    train-rmse:0.584619+0.021000    test-rmse:1.238508+0.222067 
## [104]    train-rmse:0.578706+0.020520    test-rmse:1.239522+0.229498 
## [105]    train-rmse:0.572820+0.019628    test-rmse:1.234315+0.227224 
## [106]    train-rmse:0.567109+0.019515    test-rmse:1.234430+0.226909 
## [107]    train-rmse:0.562733+0.018925    test-rmse:1.230605+0.227037 
## [108]    train-rmse:0.559068+0.018786    test-rmse:1.226887+0.227024 
## [109]    train-rmse:0.554527+0.019503    test-rmse:1.220307+0.229579 
## [110]    train-rmse:0.550074+0.019479    test-rmse:1.220260+0.230264 
## [111]    train-rmse:0.544647+0.018888    test-rmse:1.217992+0.231575 
## [112]    train-rmse:0.540483+0.019054    test-rmse:1.217153+0.233397 
## [113]    train-rmse:0.535421+0.019931    test-rmse:1.213084+0.233615 
## [114]    train-rmse:0.531832+0.020190    test-rmse:1.213112+0.233033 
## [115]    train-rmse:0.528536+0.020653    test-rmse:1.212296+0.233070 
## [116]    train-rmse:0.524537+0.020841    test-rmse:1.211614+0.232585 
## [117]    train-rmse:0.520322+0.020231    test-rmse:1.208113+0.235531 
## [118]    train-rmse:0.516491+0.020957    test-rmse:1.206237+0.237342 
## [119]    train-rmse:0.512479+0.020715    test-rmse:1.206382+0.237694 
## [120]    train-rmse:0.508709+0.020921    test-rmse:1.201582+0.237151 
## [121]    train-rmse:0.505931+0.020945    test-rmse:1.199426+0.237099 
## [122]    train-rmse:0.502559+0.021394    test-rmse:1.197455+0.238886 
## [123]    train-rmse:0.498639+0.021942    test-rmse:1.197270+0.240994 
## [124]    train-rmse:0.495532+0.021698    test-rmse:1.194983+0.240741 
## [125]    train-rmse:0.491784+0.019953    test-rmse:1.193835+0.241570 
## [126]    train-rmse:0.487591+0.018639    test-rmse:1.190784+0.241436 
## [127]    train-rmse:0.485330+0.018810    test-rmse:1.188715+0.242062 
## [128]    train-rmse:0.482138+0.018612    test-rmse:1.185785+0.242330 
## [129]    train-rmse:0.478578+0.018697    test-rmse:1.186215+0.242560 
## [130]    train-rmse:0.474436+0.017368    test-rmse:1.185782+0.243089 
## [131]    train-rmse:0.471620+0.017400    test-rmse:1.183249+0.241791 
## [132]    train-rmse:0.469482+0.017587    test-rmse:1.181754+0.242088 
## [133]    train-rmse:0.467032+0.017346    test-rmse:1.182341+0.240857 
## [134]    train-rmse:0.464747+0.017545    test-rmse:1.181092+0.241907 
## [135]    train-rmse:0.462447+0.017204    test-rmse:1.179501+0.243811 
## [136]    train-rmse:0.459970+0.017909    test-rmse:1.177944+0.244224 
## [137]    train-rmse:0.456721+0.017670    test-rmse:1.177847+0.243351 
## [138]    train-rmse:0.453511+0.017142    test-rmse:1.176680+0.242653 
## [139]    train-rmse:0.449424+0.017307    test-rmse:1.173189+0.243823 
## [140]    train-rmse:0.446651+0.017389    test-rmse:1.172310+0.243708 
## [141]    train-rmse:0.444708+0.017149    test-rmse:1.170995+0.243858 
## [142]    train-rmse:0.442417+0.017636    test-rmse:1.171353+0.244209 
## [143]    train-rmse:0.439088+0.017507    test-rmse:1.173482+0.252008 
## [144]    train-rmse:0.434974+0.016417    test-rmse:1.167912+0.253193 
## [145]    train-rmse:0.432342+0.016252    test-rmse:1.165253+0.252709 
## [146]    train-rmse:0.430264+0.016268    test-rmse:1.163764+0.254480 
## [147]    train-rmse:0.426806+0.017833    test-rmse:1.161316+0.256004 
## [148]    train-rmse:0.424079+0.017919    test-rmse:1.160200+0.255770 
## [149]    train-rmse:0.422017+0.017955    test-rmse:1.154939+0.258391 
## [150]    train-rmse:0.420056+0.018448    test-rmse:1.153515+0.257246 
## [151]    train-rmse:0.418089+0.018640    test-rmse:1.153233+0.257393 
## [152]    train-rmse:0.416475+0.018388    test-rmse:1.152308+0.257438 
## [153]    train-rmse:0.414147+0.018681    test-rmse:1.152083+0.257745 
## [154]    train-rmse:0.411594+0.019042    test-rmse:1.150755+0.258475 
## [155]    train-rmse:0.409399+0.019183    test-rmse:1.150858+0.257887 
## [156]    train-rmse:0.406977+0.019801    test-rmse:1.150756+0.257335 
## [157]    train-rmse:0.404427+0.018860    test-rmse:1.150283+0.256478 
## [158]    train-rmse:0.401885+0.019319    test-rmse:1.151008+0.256839 
## [159]    train-rmse:0.400211+0.019152    test-rmse:1.150166+0.257370 
## [160]    train-rmse:0.397714+0.019247    test-rmse:1.150029+0.256909 
## [161]    train-rmse:0.395569+0.018629    test-rmse:1.148923+0.257043 
## [162]    train-rmse:0.392999+0.017599    test-rmse:1.147008+0.257192 
## [163]    train-rmse:0.390854+0.017354    test-rmse:1.146378+0.256741 
## [164]    train-rmse:0.388362+0.016955    test-rmse:1.145259+0.257425 
## [165]    train-rmse:0.385922+0.016684    test-rmse:1.145775+0.258178 
## [166]    train-rmse:0.383852+0.016506    test-rmse:1.146035+0.257810 
## [167]    train-rmse:0.381655+0.016959    test-rmse:1.145615+0.257141 
## [168]    train-rmse:0.380405+0.017070    test-rmse:1.145471+0.257060 
## [169]    train-rmse:0.378491+0.017564    test-rmse:1.144219+0.258020 
## [170]    train-rmse:0.377056+0.018017    test-rmse:1.143693+0.258724 
## [171]    train-rmse:0.375530+0.017785    test-rmse:1.143567+0.257944 
## [172]    train-rmse:0.373766+0.018445    test-rmse:1.143696+0.257305 
## [173]    train-rmse:0.372039+0.018746    test-rmse:1.143253+0.257770 
## [174]    train-rmse:0.370490+0.019238    test-rmse:1.141450+0.257095 
## [175]    train-rmse:0.369106+0.019015    test-rmse:1.140690+0.258062 
## [176]    train-rmse:0.367929+0.019096    test-rmse:1.140302+0.257566 
## [177]    train-rmse:0.365742+0.018588    test-rmse:1.138271+0.258774 
## [178]    train-rmse:0.362923+0.018877    test-rmse:1.136465+0.258173 
## [179]    train-rmse:0.361261+0.019404    test-rmse:1.135608+0.258264 
## [180]    train-rmse:0.359466+0.019312    test-rmse:1.135197+0.258429 
## [181]    train-rmse:0.358282+0.019382    test-rmse:1.136072+0.258203 
## [182]    train-rmse:0.356405+0.019781    test-rmse:1.134919+0.257720 
## [183]    train-rmse:0.354309+0.019912    test-rmse:1.135395+0.257093 
## [184]    train-rmse:0.352614+0.019777    test-rmse:1.136312+0.259249 
## [185]    train-rmse:0.350992+0.019883    test-rmse:1.135669+0.259043 
## [186]    train-rmse:0.349862+0.019722    test-rmse:1.135975+0.259659 
## [187]    train-rmse:0.348654+0.019838    test-rmse:1.135578+0.260069 
## [188]    train-rmse:0.346984+0.019425    test-rmse:1.136174+0.261161 
## Stopping. Best iteration:
## [182]    train-rmse:0.356405+0.019781    test-rmse:1.134919+0.257720
## 
## 72 
## [1]  train-rmse:75.538506+0.088543   test-rmse:75.538455+0.512708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:53.112974+0.071800   test-rmse:53.112100+0.555751 
## [3]  train-rmse:37.457246+0.064447   test-rmse:37.454956+0.599591 
## [4]  train-rmse:26.556096+0.061192   test-rmse:26.606015+0.614020 
## [5]  train-rmse:18.931370+0.044260   test-rmse:19.000555+0.544208 
## [6]  train-rmse:13.614586+0.037905   test-rmse:13.713583+0.552988 
## [7]  train-rmse:9.930088+0.037636    test-rmse:10.052425+0.540135 
## [8]  train-rmse:7.397129+0.044571    test-rmse:7.489797+0.529948 
## [9]  train-rmse:5.666951+0.051245    test-rmse:5.825230+0.518159 
## [10] train-rmse:4.513191+0.068965    test-rmse:4.699179+0.469312 
## [11] train-rmse:3.741623+0.055928    test-rmse:3.922214+0.394260 
## [12] train-rmse:3.249059+0.052094    test-rmse:3.473606+0.378965 
## [13] train-rmse:2.914551+0.042727    test-rmse:3.214684+0.386809 
## [14] train-rmse:2.681178+0.041572    test-rmse:3.002193+0.362358 
## [15] train-rmse:2.505173+0.039642    test-rmse:2.852254+0.367828 
## [16] train-rmse:2.353460+0.029173    test-rmse:2.730949+0.318511 
## [17] train-rmse:2.227183+0.031153    test-rmse:2.632187+0.290204 
## [18] train-rmse:2.125291+0.027054    test-rmse:2.561479+0.284243 
## [19] train-rmse:2.027995+0.020466    test-rmse:2.488755+0.260652 
## [20] train-rmse:1.945732+0.017893    test-rmse:2.412426+0.253231 
## [21] train-rmse:1.869947+0.017773    test-rmse:2.351217+0.265068 
## [22] train-rmse:1.796482+0.019112    test-rmse:2.294620+0.277979 
## [23] train-rmse:1.726553+0.018207    test-rmse:2.224915+0.278445 
## [24] train-rmse:1.661887+0.016096    test-rmse:2.181856+0.260739 
## [25] train-rmse:1.602101+0.016652    test-rmse:2.134091+0.270334 
## [26] train-rmse:1.543417+0.017999    test-rmse:2.079893+0.260480 
## [27] train-rmse:1.487606+0.016033    test-rmse:2.035601+0.266533 
## [28] train-rmse:1.434833+0.012288    test-rmse:2.000902+0.267759 
## [29] train-rmse:1.385709+0.011621    test-rmse:1.970511+0.263471 
## [30] train-rmse:1.339013+0.011820    test-rmse:1.923985+0.253534 
## [31] train-rmse:1.294968+0.013742    test-rmse:1.887111+0.246045 
## [32] train-rmse:1.251192+0.015001    test-rmse:1.850316+0.246521 
## [33] train-rmse:1.204121+0.015103    test-rmse:1.811310+0.228924 
## [34] train-rmse:1.166198+0.015372    test-rmse:1.775464+0.242695 
## [35] train-rmse:1.129881+0.015703    test-rmse:1.738450+0.238313 
## [36] train-rmse:1.096185+0.016630    test-rmse:1.706439+0.242801 
## [37] train-rmse:1.061245+0.015225    test-rmse:1.680262+0.250981 
## [38] train-rmse:1.030718+0.015114    test-rmse:1.656002+0.248117 
## [39] train-rmse:0.999655+0.014592    test-rmse:1.632771+0.251976 
## [40] train-rmse:0.966522+0.016281    test-rmse:1.613738+0.258199 
## [41] train-rmse:0.938367+0.018102    test-rmse:1.594126+0.255564 
## [42] train-rmse:0.911158+0.017225    test-rmse:1.570373+0.263008 
## [43] train-rmse:0.887465+0.018564    test-rmse:1.557109+0.264131 
## [44] train-rmse:0.862175+0.019761    test-rmse:1.534091+0.256430 
## [45] train-rmse:0.837634+0.017987    test-rmse:1.510171+0.259181 
## [46] train-rmse:0.816312+0.017306    test-rmse:1.493172+0.259038 
## [47] train-rmse:0.796233+0.017146    test-rmse:1.480027+0.263325 
## [48] train-rmse:0.772250+0.017936    test-rmse:1.471762+0.265614 
## [49] train-rmse:0.755124+0.018401    test-rmse:1.460493+0.265563 
## [50] train-rmse:0.734616+0.019826    test-rmse:1.450115+0.263292 
## [51] train-rmse:0.718154+0.020482    test-rmse:1.442893+0.264048 
## [52] train-rmse:0.700977+0.020540    test-rmse:1.435293+0.264595 
## [53] train-rmse:0.684932+0.019419    test-rmse:1.420703+0.266509 
## [54] train-rmse:0.667981+0.021928    test-rmse:1.412562+0.271838 
## [55] train-rmse:0.653865+0.021776    test-rmse:1.403010+0.275792 
## [56] train-rmse:0.636738+0.021549    test-rmse:1.394147+0.281371 
## [57] train-rmse:0.621876+0.023323    test-rmse:1.376270+0.276543 
## [58] train-rmse:0.609103+0.022839    test-rmse:1.368512+0.280913 
## [59] train-rmse:0.597451+0.022438    test-rmse:1.354525+0.285260 
## [60] train-rmse:0.585324+0.021277    test-rmse:1.348860+0.284595 
## [61] train-rmse:0.575147+0.020266    test-rmse:1.343874+0.284159 
## [62] train-rmse:0.565269+0.020793    test-rmse:1.339629+0.281516 
## [63] train-rmse:0.554957+0.020023    test-rmse:1.325434+0.286098 
## [64] train-rmse:0.545172+0.019260    test-rmse:1.322480+0.288098 
## [65] train-rmse:0.533874+0.020085    test-rmse:1.318429+0.288371 
## [66] train-rmse:0.520138+0.015571    test-rmse:1.312623+0.291931 
## [67] train-rmse:0.511193+0.015067    test-rmse:1.310831+0.294944 
## [68] train-rmse:0.503325+0.014673    test-rmse:1.307807+0.294910 
## [69] train-rmse:0.495882+0.013842    test-rmse:1.301839+0.295939 
## [70] train-rmse:0.487859+0.014560    test-rmse:1.294150+0.299679 
## [71] train-rmse:0.479087+0.013575    test-rmse:1.288325+0.300878 
## [72] train-rmse:0.471540+0.014776    test-rmse:1.282081+0.302430 
## [73] train-rmse:0.463568+0.014558    test-rmse:1.284258+0.307946 
## [74] train-rmse:0.456287+0.016805    test-rmse:1.281299+0.308015 
## [75] train-rmse:0.450272+0.017511    test-rmse:1.278052+0.307521 
## [76] train-rmse:0.444771+0.017621    test-rmse:1.275893+0.309198 
## [77] train-rmse:0.436912+0.014821    test-rmse:1.274417+0.310368 
## [78] train-rmse:0.429977+0.014109    test-rmse:1.269973+0.310448 
## [79] train-rmse:0.421732+0.011459    test-rmse:1.264492+0.311761 
## [80] train-rmse:0.415494+0.011006    test-rmse:1.259856+0.313261 
## [81] train-rmse:0.409233+0.010993    test-rmse:1.258961+0.312771 
## [82] train-rmse:0.404883+0.011379    test-rmse:1.257938+0.311840 
## [83] train-rmse:0.400108+0.011781    test-rmse:1.253687+0.312840 
## [84] train-rmse:0.392806+0.011916    test-rmse:1.249013+0.309334 
## [85] train-rmse:0.384947+0.009565    test-rmse:1.246016+0.313360 
## [86] train-rmse:0.378796+0.010373    test-rmse:1.246735+0.314243 
## [87] train-rmse:0.373149+0.010721    test-rmse:1.242654+0.316578 
## [88] train-rmse:0.369342+0.010950    test-rmse:1.242403+0.318096 
## [89] train-rmse:0.364695+0.009632    test-rmse:1.239071+0.318817 
## [90] train-rmse:0.360770+0.009615    test-rmse:1.238481+0.318915 
## [91] train-rmse:0.355345+0.010095    test-rmse:1.235601+0.319396 
## [92] train-rmse:0.350721+0.010460    test-rmse:1.234324+0.319235 
## [93] train-rmse:0.347084+0.011624    test-rmse:1.233354+0.320809 
## [94] train-rmse:0.342907+0.010818    test-rmse:1.230186+0.322527 
## [95] train-rmse:0.337038+0.010907    test-rmse:1.228691+0.323045 
## [96] train-rmse:0.332196+0.009241    test-rmse:1.227603+0.325648 
## [97] train-rmse:0.327368+0.009176    test-rmse:1.226640+0.327242 
## [98] train-rmse:0.322978+0.009190    test-rmse:1.225943+0.327612 
## [99] train-rmse:0.318855+0.010277    test-rmse:1.224665+0.328649 
## [100]    train-rmse:0.316308+0.010263    test-rmse:1.222353+0.328374 
## [101]    train-rmse:0.311710+0.010816    test-rmse:1.223867+0.328272 
## [102]    train-rmse:0.306951+0.011854    test-rmse:1.221865+0.328782 
## [103]    train-rmse:0.302774+0.014099    test-rmse:1.219879+0.329277 
## [104]    train-rmse:0.299613+0.013694    test-rmse:1.218955+0.330192 
## [105]    train-rmse:0.296875+0.013402    test-rmse:1.218809+0.330202 
## [106]    train-rmse:0.293584+0.013838    test-rmse:1.217031+0.331598 
## [107]    train-rmse:0.289782+0.014919    test-rmse:1.214773+0.331598 
## [108]    train-rmse:0.287196+0.014928    test-rmse:1.215348+0.331531 
## [109]    train-rmse:0.283655+0.014092    test-rmse:1.214549+0.331709 
## [110]    train-rmse:0.281067+0.014344    test-rmse:1.213342+0.332563 
## [111]    train-rmse:0.277479+0.014488    test-rmse:1.213778+0.333070 
## [112]    train-rmse:0.273253+0.014301    test-rmse:1.213191+0.333914 
## [113]    train-rmse:0.269817+0.014772    test-rmse:1.213146+0.334090 
## [114]    train-rmse:0.264540+0.013271    test-rmse:1.215450+0.340352 
## [115]    train-rmse:0.262213+0.013384    test-rmse:1.213937+0.341322 
## [116]    train-rmse:0.258408+0.013746    test-rmse:1.211025+0.342750 
## [117]    train-rmse:0.256509+0.013566    test-rmse:1.210626+0.343315 
## [118]    train-rmse:0.254036+0.013433    test-rmse:1.210190+0.343321 
## [119]    train-rmse:0.251299+0.013918    test-rmse:1.208978+0.344444 
## [120]    train-rmse:0.249553+0.013720    test-rmse:1.209389+0.344988 
## [121]    train-rmse:0.246174+0.012409    test-rmse:1.209342+0.349789 
## [122]    train-rmse:0.243018+0.012292    test-rmse:1.209253+0.349615 
## [123]    train-rmse:0.241214+0.012628    test-rmse:1.208982+0.350421 
## [124]    train-rmse:0.238918+0.012895    test-rmse:1.209081+0.349794 
## [125]    train-rmse:0.237231+0.012751    test-rmse:1.206197+0.348371 
## [126]    train-rmse:0.234697+0.012021    test-rmse:1.206945+0.347552 
## [127]    train-rmse:0.231497+0.011551    test-rmse:1.206398+0.348132 
## [128]    train-rmse:0.229545+0.012615    test-rmse:1.205743+0.348784 
## [129]    train-rmse:0.226877+0.013142    test-rmse:1.206845+0.349142 
## [130]    train-rmse:0.225230+0.013133    test-rmse:1.206172+0.349894 
## [131]    train-rmse:0.223282+0.013181    test-rmse:1.205750+0.350885 
## [132]    train-rmse:0.220129+0.012727    test-rmse:1.205952+0.351485 
## [133]    train-rmse:0.217750+0.012689    test-rmse:1.204442+0.352195 
## [134]    train-rmse:0.215637+0.012357    test-rmse:1.203401+0.353282 
## [135]    train-rmse:0.214172+0.012543    test-rmse:1.202521+0.353039 
## [136]    train-rmse:0.212497+0.012871    test-rmse:1.201224+0.351951 
## [137]    train-rmse:0.210364+0.012891    test-rmse:1.201214+0.352263 
## [138]    train-rmse:0.208081+0.012418    test-rmse:1.200634+0.352800 
## [139]    train-rmse:0.206386+0.011986    test-rmse:1.199977+0.352668 
## [140]    train-rmse:0.204735+0.012510    test-rmse:1.199072+0.352565 
## [141]    train-rmse:0.202525+0.012249    test-rmse:1.198534+0.351636 
## [142]    train-rmse:0.201013+0.012303    test-rmse:1.198091+0.351574 
## [143]    train-rmse:0.199662+0.012144    test-rmse:1.198263+0.352158 
## [144]    train-rmse:0.198104+0.011808    test-rmse:1.198536+0.351817 
## [145]    train-rmse:0.196125+0.012139    test-rmse:1.199638+0.351313 
## [146]    train-rmse:0.193245+0.011878    test-rmse:1.198673+0.352035 
## [147]    train-rmse:0.191150+0.012338    test-rmse:1.198856+0.351918 
## [148]    train-rmse:0.189216+0.011827    test-rmse:1.198745+0.352604 
## Stopping. Best iteration:
## [142]    train-rmse:0.201013+0.012303    test-rmse:1.198091+0.351574
## 
## 73 
## [1]  train-rmse:75.538506+0.088543   test-rmse:75.538455+0.512708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:53.112974+0.071800   test-rmse:53.112100+0.555751 
## [3]  train-rmse:37.457246+0.064447   test-rmse:37.454956+0.599591 
## [4]  train-rmse:26.556096+0.061192   test-rmse:26.606015+0.614020 
## [5]  train-rmse:18.931370+0.044260   test-rmse:19.000555+0.544208 
## [6]  train-rmse:13.604119+0.037057   test-rmse:13.696023+0.539023 
## [7]  train-rmse:9.889688+0.037642    test-rmse:9.976141+0.506999 
## [8]  train-rmse:7.304746+0.043756    test-rmse:7.416002+0.562837 
## [9]  train-rmse:5.535881+0.049514    test-rmse:5.652638+0.493683 
## [10] train-rmse:4.318052+0.064560    test-rmse:4.475064+0.465869 
## [11] train-rmse:3.501731+0.069356    test-rmse:3.718452+0.455531 
## [12] train-rmse:2.929605+0.047797    test-rmse:3.239643+0.353178 
## [13] train-rmse:2.559046+0.047135    test-rmse:2.903768+0.311285 
## [14] train-rmse:2.293669+0.054881    test-rmse:2.656271+0.271993 
## [15] train-rmse:2.097714+0.045736    test-rmse:2.485404+0.246345 
## [16] train-rmse:1.940867+0.041177    test-rmse:2.386935+0.230104 
## [17] train-rmse:1.819803+0.040013    test-rmse:2.265001+0.201099 
## [18] train-rmse:1.712004+0.036270    test-rmse:2.185793+0.184274 
## [19] train-rmse:1.613788+0.035804    test-rmse:2.098105+0.182880 
## [20] train-rmse:1.528602+0.030656    test-rmse:2.027174+0.211358 
## [21] train-rmse:1.450072+0.031651    test-rmse:1.960085+0.189895 
## [22] train-rmse:1.375892+0.032070    test-rmse:1.910046+0.170363 
## [23] train-rmse:1.309562+0.033620    test-rmse:1.871900+0.176094 
## [24] train-rmse:1.246976+0.032884    test-rmse:1.824361+0.179595 
## [25] train-rmse:1.191421+0.030960    test-rmse:1.770097+0.174825 
## [26] train-rmse:1.136426+0.026293    test-rmse:1.718417+0.194449 
## [27] train-rmse:1.083565+0.033606    test-rmse:1.676716+0.192751 
## [28] train-rmse:1.032663+0.032055    test-rmse:1.640297+0.182210 
## [29] train-rmse:0.988544+0.032242    test-rmse:1.613002+0.185576 
## [30] train-rmse:0.949504+0.031554    test-rmse:1.571728+0.196480 
## [31] train-rmse:0.910949+0.029023    test-rmse:1.546836+0.200956 
## [32] train-rmse:0.869568+0.021987    test-rmse:1.533606+0.204852 
## [33] train-rmse:0.837817+0.021108    test-rmse:1.511465+0.200258 
## [34] train-rmse:0.805166+0.023748    test-rmse:1.478181+0.209700 
## [35] train-rmse:0.774288+0.024738    test-rmse:1.456122+0.208942 
## [36] train-rmse:0.744265+0.023497    test-rmse:1.443651+0.208836 
## [37] train-rmse:0.716189+0.025269    test-rmse:1.431981+0.208231 
## [38] train-rmse:0.691955+0.024166    test-rmse:1.418645+0.208503 
## [39] train-rmse:0.669337+0.020809    test-rmse:1.398177+0.214798 
## [40] train-rmse:0.646264+0.021289    test-rmse:1.386123+0.213985 
## [41] train-rmse:0.627144+0.020973    test-rmse:1.375728+0.213685 
## [42] train-rmse:0.605448+0.022190    test-rmse:1.366396+0.217129 
## [43] train-rmse:0.588300+0.021454    test-rmse:1.353019+0.223200 
## [44] train-rmse:0.567044+0.022916    test-rmse:1.336262+0.228309 
## [45] train-rmse:0.550274+0.021804    test-rmse:1.326495+0.229740 
## [46] train-rmse:0.533338+0.021697    test-rmse:1.321243+0.231067 
## [47] train-rmse:0.519049+0.021767    test-rmse:1.310236+0.232801 
## [48] train-rmse:0.505839+0.022528    test-rmse:1.305802+0.236121 
## [49] train-rmse:0.491111+0.024065    test-rmse:1.298671+0.239571 
## [50] train-rmse:0.477318+0.020123    test-rmse:1.295767+0.241638 
## [51] train-rmse:0.463664+0.022414    test-rmse:1.280136+0.236589 
## [52] train-rmse:0.451526+0.023244    test-rmse:1.275470+0.235375 
## [53] train-rmse:0.441324+0.021466    test-rmse:1.271011+0.241130 
## [54] train-rmse:0.431179+0.022370    test-rmse:1.268233+0.241143 
## [55] train-rmse:0.421376+0.022813    test-rmse:1.261747+0.243749 
## [56] train-rmse:0.410210+0.023793    test-rmse:1.257180+0.243977 
## [57] train-rmse:0.397646+0.022877    test-rmse:1.254833+0.240554 
## [58] train-rmse:0.389392+0.022772    test-rmse:1.251829+0.241787 
## [59] train-rmse:0.379918+0.021237    test-rmse:1.250871+0.242800 
## [60] train-rmse:0.370275+0.020177    test-rmse:1.249587+0.242495 
## [61] train-rmse:0.361290+0.019547    test-rmse:1.247042+0.244059 
## [62] train-rmse:0.353662+0.020731    test-rmse:1.241747+0.246457 
## [63] train-rmse:0.346061+0.019680    test-rmse:1.239869+0.249723 
## [64] train-rmse:0.337575+0.019452    test-rmse:1.239340+0.252139 
## [65] train-rmse:0.329795+0.020035    test-rmse:1.234689+0.250036 
## [66] train-rmse:0.322786+0.021716    test-rmse:1.230999+0.251266 
## [67] train-rmse:0.316675+0.021995    test-rmse:1.228368+0.253191 
## [68] train-rmse:0.309789+0.022209    test-rmse:1.224936+0.250167 
## [69] train-rmse:0.301540+0.021239    test-rmse:1.223702+0.251037 
## [70] train-rmse:0.296318+0.021569    test-rmse:1.221433+0.251909 
## [71] train-rmse:0.292104+0.021561    test-rmse:1.220798+0.252110 
## [72] train-rmse:0.287187+0.019916    test-rmse:1.219834+0.252653 
## [73] train-rmse:0.278713+0.018415    test-rmse:1.218642+0.256168 
## [74] train-rmse:0.272575+0.015522    test-rmse:1.221142+0.256347 
## [75] train-rmse:0.266931+0.016347    test-rmse:1.220049+0.255973 
## [76] train-rmse:0.262520+0.016499    test-rmse:1.219340+0.256029 
## [77] train-rmse:0.258581+0.015971    test-rmse:1.217106+0.256957 
## [78] train-rmse:0.254226+0.015736    test-rmse:1.216453+0.258096 
## [79] train-rmse:0.248052+0.016411    test-rmse:1.213603+0.259157 
## [80] train-rmse:0.242260+0.015843    test-rmse:1.211266+0.261889 
## [81] train-rmse:0.237621+0.016372    test-rmse:1.210416+0.263391 
## [82] train-rmse:0.233402+0.015517    test-rmse:1.209815+0.264735 
## [83] train-rmse:0.228918+0.014360    test-rmse:1.208312+0.266717 
## [84] train-rmse:0.224653+0.014804    test-rmse:1.205349+0.267354 
## [85] train-rmse:0.220953+0.015092    test-rmse:1.203425+0.267997 
## [86] train-rmse:0.217168+0.015064    test-rmse:1.201796+0.268592 
## [87] train-rmse:0.212858+0.015851    test-rmse:1.200512+0.268374 
## [88] train-rmse:0.209738+0.015475    test-rmse:1.200617+0.268401 
## [89] train-rmse:0.205711+0.015002    test-rmse:1.201283+0.268105 
## [90] train-rmse:0.201928+0.012751    test-rmse:1.198990+0.265566 
## [91] train-rmse:0.197669+0.013148    test-rmse:1.196943+0.266534 
## [92] train-rmse:0.194079+0.012491    test-rmse:1.196642+0.267186 
## [93] train-rmse:0.191451+0.012313    test-rmse:1.197483+0.267974 
## [94] train-rmse:0.188954+0.010962    test-rmse:1.195252+0.266618 
## [95] train-rmse:0.186514+0.011152    test-rmse:1.194695+0.267184 
## [96] train-rmse:0.180393+0.009761    test-rmse:1.195786+0.266646 
## [97] train-rmse:0.177151+0.010542    test-rmse:1.194829+0.267252 
## [98] train-rmse:0.172291+0.010765    test-rmse:1.194156+0.265543 
## [99] train-rmse:0.169097+0.008623    test-rmse:1.193541+0.265026 
## [100]    train-rmse:0.165441+0.007599    test-rmse:1.195186+0.267901 
## [101]    train-rmse:0.162893+0.007152    test-rmse:1.194872+0.267609 
## [102]    train-rmse:0.160922+0.007539    test-rmse:1.195298+0.268217 
## [103]    train-rmse:0.157507+0.007881    test-rmse:1.195003+0.267013 
## [104]    train-rmse:0.154459+0.006653    test-rmse:1.194985+0.267354 
## [105]    train-rmse:0.152600+0.006367    test-rmse:1.193189+0.268654 
## [106]    train-rmse:0.150725+0.007170    test-rmse:1.192164+0.269459 
## [107]    train-rmse:0.147997+0.007395    test-rmse:1.191289+0.269895 
## [108]    train-rmse:0.145252+0.007365    test-rmse:1.192165+0.270028 
## [109]    train-rmse:0.142452+0.007241    test-rmse:1.192597+0.270825 
## [110]    train-rmse:0.140147+0.007710    test-rmse:1.191353+0.272032 
## [111]    train-rmse:0.138555+0.007646    test-rmse:1.190852+0.271592 
## [112]    train-rmse:0.135692+0.007414    test-rmse:1.191821+0.271251 
## [113]    train-rmse:0.132466+0.006616    test-rmse:1.190914+0.271055 
## [114]    train-rmse:0.129695+0.006738    test-rmse:1.190864+0.271066 
## [115]    train-rmse:0.127156+0.006678    test-rmse:1.190606+0.270828 
## [116]    train-rmse:0.125052+0.007570    test-rmse:1.189965+0.271569 
## [117]    train-rmse:0.122746+0.007994    test-rmse:1.189472+0.271667 
## [118]    train-rmse:0.121039+0.007764    test-rmse:1.188354+0.271842 
## [119]    train-rmse:0.118382+0.007309    test-rmse:1.188018+0.272226 
## [120]    train-rmse:0.116194+0.006529    test-rmse:1.188939+0.271810 
## [121]    train-rmse:0.114859+0.006112    test-rmse:1.188606+0.272696 
## [122]    train-rmse:0.113205+0.005909    test-rmse:1.188630+0.272665 
## [123]    train-rmse:0.111338+0.005643    test-rmse:1.187349+0.273050 
## [124]    train-rmse:0.109517+0.005310    test-rmse:1.186587+0.273651 
## [125]    train-rmse:0.107739+0.005963    test-rmse:1.186109+0.273888 
## [126]    train-rmse:0.106257+0.005816    test-rmse:1.185693+0.273938 
## [127]    train-rmse:0.104242+0.005706    test-rmse:1.185382+0.274519 
## [128]    train-rmse:0.102944+0.006111    test-rmse:1.185566+0.274544 
## [129]    train-rmse:0.100322+0.005704    test-rmse:1.185429+0.274816 
## [130]    train-rmse:0.098395+0.005904    test-rmse:1.186298+0.273653 
## [131]    train-rmse:0.097316+0.005655    test-rmse:1.185904+0.274315 
## [132]    train-rmse:0.095884+0.005477    test-rmse:1.185079+0.274311 
## [133]    train-rmse:0.094486+0.005931    test-rmse:1.184877+0.274783 
## [134]    train-rmse:0.092991+0.005617    test-rmse:1.184151+0.275304 
## [135]    train-rmse:0.091469+0.005647    test-rmse:1.183535+0.275562 
## [136]    train-rmse:0.089322+0.005968    test-rmse:1.183151+0.275765 
## [137]    train-rmse:0.088226+0.005822    test-rmse:1.183256+0.275907 
## [138]    train-rmse:0.086571+0.005886    test-rmse:1.182853+0.276351 
## [139]    train-rmse:0.085191+0.006264    test-rmse:1.182874+0.276278 
## [140]    train-rmse:0.084246+0.006215    test-rmse:1.183199+0.276264 
## [141]    train-rmse:0.083174+0.006331    test-rmse:1.183441+0.276714 
## [142]    train-rmse:0.081697+0.006672    test-rmse:1.183581+0.276752 
## [143]    train-rmse:0.080390+0.006372    test-rmse:1.183065+0.276658 
## [144]    train-rmse:0.078913+0.006147    test-rmse:1.182917+0.277157 
## Stopping. Best iteration:
## [138]    train-rmse:0.086571+0.005886    test-rmse:1.182853+0.276351
## 
## 74 
## [1]  train-rmse:75.538506+0.088543   test-rmse:75.538455+0.512708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:53.112974+0.071800   test-rmse:53.112100+0.555751 
## [3]  train-rmse:37.457246+0.064447   test-rmse:37.454956+0.599591 
## [4]  train-rmse:26.556096+0.061192   test-rmse:26.606015+0.614020 
## [5]  train-rmse:18.931370+0.044260   test-rmse:19.000555+0.544208 
## [6]  train-rmse:13.596779+0.038393   test-rmse:13.694289+0.534649 
## [7]  train-rmse:9.858964+0.037033    test-rmse:9.950282+0.507452 
## [8]  train-rmse:7.246851+0.038459    test-rmse:7.386817+0.542357 
## [9]  train-rmse:5.430962+0.033211    test-rmse:5.590116+0.486574 
## [10] train-rmse:4.177342+0.032761    test-rmse:4.356490+0.430576 
## [11] train-rmse:3.313640+0.022464    test-rmse:3.578235+0.408390 
## [12] train-rmse:2.705888+0.039692    test-rmse:3.038435+0.337879 
## [13] train-rmse:2.285222+0.024668    test-rmse:2.696574+0.293933 
## [14] train-rmse:2.002178+0.021009    test-rmse:2.465433+0.295951 
## [15] train-rmse:1.788309+0.031541    test-rmse:2.320457+0.252036 
## [16] train-rmse:1.618505+0.048172    test-rmse:2.200497+0.241060 
## [17] train-rmse:1.479215+0.043778    test-rmse:2.091740+0.245901 
## [18] train-rmse:1.357420+0.030432    test-rmse:2.017016+0.234636 
## [19] train-rmse:1.261398+0.032024    test-rmse:1.926019+0.218294 
## [20] train-rmse:1.182077+0.026685    test-rmse:1.875795+0.220634 
## [21] train-rmse:1.105832+0.022607    test-rmse:1.818136+0.240650 
## [22] train-rmse:1.038762+0.015707    test-rmse:1.775026+0.251838 
## [23] train-rmse:0.969323+0.022407    test-rmse:1.724490+0.245972 
## [24] train-rmse:0.916327+0.020145    test-rmse:1.693081+0.261345 
## [25] train-rmse:0.865362+0.022204    test-rmse:1.668398+0.252672 
## [26] train-rmse:0.818308+0.023329    test-rmse:1.637092+0.259820 
## [27] train-rmse:0.779833+0.022174    test-rmse:1.603781+0.254617 
## [28] train-rmse:0.735732+0.020356    test-rmse:1.558686+0.240937 
## [29] train-rmse:0.698134+0.020255    test-rmse:1.532031+0.246342 
## [30] train-rmse:0.661819+0.017876    test-rmse:1.512576+0.240655 
## [31] train-rmse:0.630238+0.016040    test-rmse:1.494122+0.242369 
## [32] train-rmse:0.602303+0.014789    test-rmse:1.479932+0.240228 
## [33] train-rmse:0.575911+0.016253    test-rmse:1.469433+0.247657 
## [34] train-rmse:0.551384+0.018368    test-rmse:1.452457+0.250029 
## [35] train-rmse:0.526096+0.019919    test-rmse:1.446345+0.247550 
## [36] train-rmse:0.506293+0.021639    test-rmse:1.436865+0.247885 
## [37] train-rmse:0.485988+0.019353    test-rmse:1.427158+0.251956 
## [38] train-rmse:0.465668+0.017585    test-rmse:1.412757+0.260102 
## [39] train-rmse:0.448122+0.017313    test-rmse:1.405536+0.259794 
## [40] train-rmse:0.430807+0.016847    test-rmse:1.397974+0.262966 
## [41] train-rmse:0.416388+0.015748    test-rmse:1.392285+0.265505 
## [42] train-rmse:0.401897+0.015493    test-rmse:1.386749+0.262936 
## [43] train-rmse:0.390141+0.014519    test-rmse:1.381189+0.267524 
## [44] train-rmse:0.374000+0.015399    test-rmse:1.379139+0.263378 
## [45] train-rmse:0.361311+0.015857    test-rmse:1.362710+0.269912 
## [46] train-rmse:0.350675+0.017885    test-rmse:1.359026+0.270733 
## [47] train-rmse:0.340061+0.016235    test-rmse:1.353046+0.273205 
## [48] train-rmse:0.328742+0.014281    test-rmse:1.337962+0.269126 
## [49] train-rmse:0.316591+0.011210    test-rmse:1.337297+0.270176 
## [50] train-rmse:0.309447+0.010980    test-rmse:1.335508+0.273147 
## [51] train-rmse:0.299919+0.010843    test-rmse:1.330877+0.273797 
## [52] train-rmse:0.290396+0.008933    test-rmse:1.329308+0.277043 
## [53] train-rmse:0.281816+0.010170    test-rmse:1.330053+0.277698 
## [54] train-rmse:0.272657+0.010628    test-rmse:1.328493+0.279851 
## [55] train-rmse:0.265561+0.008359    test-rmse:1.325281+0.280875 
## [56] train-rmse:0.259173+0.006746    test-rmse:1.320520+0.283359 
## [57] train-rmse:0.252117+0.006922    test-rmse:1.318093+0.281137 
## [58] train-rmse:0.242979+0.006630    test-rmse:1.318681+0.281862 
## [59] train-rmse:0.232421+0.006243    test-rmse:1.310916+0.282977 
## [60] train-rmse:0.226763+0.007667    test-rmse:1.308847+0.285775 
## [61] train-rmse:0.221476+0.007951    test-rmse:1.304634+0.286997 
## [62] train-rmse:0.216033+0.006474    test-rmse:1.303483+0.287982 
## [63] train-rmse:0.209826+0.006944    test-rmse:1.302085+0.290561 
## [64] train-rmse:0.205649+0.007473    test-rmse:1.301864+0.291146 
## [65] train-rmse:0.201069+0.007369    test-rmse:1.301104+0.291818 
## [66] train-rmse:0.194829+0.006071    test-rmse:1.298619+0.292272 
## [67] train-rmse:0.189869+0.005499    test-rmse:1.297147+0.292815 
## [68] train-rmse:0.180620+0.006811    test-rmse:1.293227+0.286852 
## [69] train-rmse:0.175792+0.006380    test-rmse:1.292579+0.286480 
## [70] train-rmse:0.171077+0.007183    test-rmse:1.291050+0.286695 
## [71] train-rmse:0.166088+0.006522    test-rmse:1.291192+0.289024 
## [72] train-rmse:0.162014+0.005982    test-rmse:1.289710+0.288924 
## [73] train-rmse:0.159171+0.005501    test-rmse:1.288323+0.290113 
## [74] train-rmse:0.155742+0.005808    test-rmse:1.288088+0.290644 
## [75] train-rmse:0.151006+0.005945    test-rmse:1.286952+0.291771 
## [76] train-rmse:0.147608+0.005251    test-rmse:1.285342+0.292558 
## [77] train-rmse:0.144429+0.005591    test-rmse:1.285046+0.292314 
## [78] train-rmse:0.140933+0.006018    test-rmse:1.286368+0.293500 
## [79] train-rmse:0.138209+0.006606    test-rmse:1.286326+0.294948 
## [80] train-rmse:0.134059+0.005724    test-rmse:1.286461+0.297693 
## [81] train-rmse:0.131229+0.006413    test-rmse:1.283911+0.294077 
## [82] train-rmse:0.126838+0.005866    test-rmse:1.282415+0.293776 
## [83] train-rmse:0.123576+0.005083    test-rmse:1.282031+0.293609 
## [84] train-rmse:0.120457+0.005765    test-rmse:1.281590+0.293374 
## [85] train-rmse:0.118165+0.005141    test-rmse:1.280391+0.294055 
## [86] train-rmse:0.115118+0.005689    test-rmse:1.278143+0.294818 
## [87] train-rmse:0.111678+0.005218    test-rmse:1.277665+0.294639 
## [88] train-rmse:0.108950+0.004210    test-rmse:1.278146+0.295521 
## [89] train-rmse:0.106936+0.004456    test-rmse:1.278129+0.295828 
## [90] train-rmse:0.104175+0.004356    test-rmse:1.277756+0.296079 
## [91] train-rmse:0.101522+0.004420    test-rmse:1.277972+0.296031 
## [92] train-rmse:0.099420+0.004210    test-rmse:1.276348+0.293605 
## [93] train-rmse:0.096480+0.003341    test-rmse:1.275732+0.294337 
## [94] train-rmse:0.094516+0.002556    test-rmse:1.275639+0.294258 
## [95] train-rmse:0.092071+0.002032    test-rmse:1.275340+0.292975 
## [96] train-rmse:0.090188+0.001847    test-rmse:1.275324+0.293110 
## [97] train-rmse:0.088562+0.001900    test-rmse:1.275039+0.292473 
## [98] train-rmse:0.086564+0.001676    test-rmse:1.275401+0.292749 
## [99] train-rmse:0.084540+0.001646    test-rmse:1.275120+0.293217 
## [100]    train-rmse:0.082371+0.001721    test-rmse:1.275178+0.293249 
## [101]    train-rmse:0.080313+0.001630    test-rmse:1.275197+0.293786 
## [102]    train-rmse:0.078424+0.001616    test-rmse:1.274356+0.294337 
## [103]    train-rmse:0.076553+0.001713    test-rmse:1.274063+0.294253 
## [104]    train-rmse:0.075035+0.001677    test-rmse:1.274030+0.293923 
## [105]    train-rmse:0.073429+0.001888    test-rmse:1.273796+0.294085 
## [106]    train-rmse:0.072066+0.002028    test-rmse:1.273824+0.294299 
## [107]    train-rmse:0.070277+0.002493    test-rmse:1.274047+0.294557 
## [108]    train-rmse:0.068915+0.002263    test-rmse:1.272785+0.293389 
## [109]    train-rmse:0.066911+0.002017    test-rmse:1.272838+0.293408 
## [110]    train-rmse:0.065527+0.002093    test-rmse:1.272698+0.293336 
## [111]    train-rmse:0.063936+0.002572    test-rmse:1.272457+0.293559 
## [112]    train-rmse:0.062466+0.002293    test-rmse:1.272252+0.293402 
## [113]    train-rmse:0.061637+0.002692    test-rmse:1.272393+0.294006 
## [114]    train-rmse:0.060102+0.002820    test-rmse:1.272328+0.293931 
## [115]    train-rmse:0.058974+0.002687    test-rmse:1.272204+0.294235 
## [116]    train-rmse:0.057959+0.002770    test-rmse:1.272345+0.294136 
## [117]    train-rmse:0.056279+0.002689    test-rmse:1.272843+0.294136 
## [118]    train-rmse:0.055117+0.002843    test-rmse:1.273065+0.294676 
## [119]    train-rmse:0.053488+0.002695    test-rmse:1.272577+0.294821 
## [120]    train-rmse:0.052052+0.002589    test-rmse:1.272688+0.294743 
## [121]    train-rmse:0.051091+0.002619    test-rmse:1.272530+0.295051 
## Stopping. Best iteration:
## [115]    train-rmse:0.058974+0.002687    test-rmse:1.272204+0.294235
## 
## 75 
## [1]  train-rmse:75.538506+0.088543   test-rmse:75.538455+0.512708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:53.112974+0.071800   test-rmse:53.112100+0.555751 
## [3]  train-rmse:37.457246+0.064447   test-rmse:37.454956+0.599591 
## [4]  train-rmse:26.556096+0.061192   test-rmse:26.606015+0.614020 
## [5]  train-rmse:18.931370+0.044260   test-rmse:19.000555+0.544208 
## [6]  train-rmse:13.588820+0.039821   test-rmse:13.694667+0.521432 
## [7]  train-rmse:9.834294+0.034362    test-rmse:9.930591+0.522621 
## [8]  train-rmse:7.205418+0.040331    test-rmse:7.345476+0.553645 
## [9]  train-rmse:5.363343+0.051821    test-rmse:5.537845+0.474201 
## [10] train-rmse:4.060560+0.047637    test-rmse:4.262188+0.456127 
## [11] train-rmse:3.168833+0.055964    test-rmse:3.401480+0.389668 
## [12] train-rmse:2.526289+0.045494    test-rmse:2.843091+0.390136 
## [13] train-rmse:2.080360+0.031278    test-rmse:2.472316+0.348877 
## [14] train-rmse:1.769768+0.041331    test-rmse:2.238322+0.333054 
## [15] train-rmse:1.540242+0.038547    test-rmse:2.067245+0.288078 
## [16] train-rmse:1.368084+0.038001    test-rmse:1.934947+0.280345 
## [17] train-rmse:1.225685+0.046497    test-rmse:1.835193+0.301272 
## [18] train-rmse:1.107665+0.039238    test-rmse:1.760962+0.303489 
## [19] train-rmse:1.017655+0.030117    test-rmse:1.692671+0.306197 
## [20] train-rmse:0.932400+0.025889    test-rmse:1.640032+0.315292 
## [21] train-rmse:0.863315+0.023553    test-rmse:1.591393+0.291611 
## [22] train-rmse:0.798069+0.023278    test-rmse:1.550897+0.300994 
## [23] train-rmse:0.748412+0.023158    test-rmse:1.508309+0.293257 
## [24] train-rmse:0.700823+0.023643    test-rmse:1.478095+0.293275 
## [25] train-rmse:0.655936+0.024011    test-rmse:1.459277+0.291432 
## [26] train-rmse:0.618970+0.021772    test-rmse:1.432256+0.303132 
## [27] train-rmse:0.585294+0.021334    test-rmse:1.408049+0.284410 
## [28] train-rmse:0.553088+0.019349    test-rmse:1.389341+0.291809 
## [29] train-rmse:0.525081+0.016730    test-rmse:1.373896+0.298704 
## [30] train-rmse:0.497672+0.020536    test-rmse:1.359661+0.298835 
## [31] train-rmse:0.472097+0.019706    test-rmse:1.350110+0.302042 
## [32] train-rmse:0.449846+0.018595    test-rmse:1.331170+0.291567 
## [33] train-rmse:0.423075+0.017173    test-rmse:1.311606+0.288244 
## [34] train-rmse:0.404770+0.018165    test-rmse:1.303437+0.289410 
## [35] train-rmse:0.384550+0.018008    test-rmse:1.296705+0.290567 
## [36] train-rmse:0.367210+0.016095    test-rmse:1.289398+0.291201 
## [37] train-rmse:0.349671+0.016461    test-rmse:1.279387+0.286331 
## [38] train-rmse:0.336750+0.017201    test-rmse:1.271693+0.286662 
## [39] train-rmse:0.323001+0.018294    test-rmse:1.266287+0.285000 
## [40] train-rmse:0.309692+0.015331    test-rmse:1.264970+0.285322 
## [41] train-rmse:0.296300+0.016425    test-rmse:1.260430+0.289337 
## [42] train-rmse:0.283431+0.018557    test-rmse:1.256038+0.287117 
## [43] train-rmse:0.268741+0.016935    test-rmse:1.257033+0.286467 
## [44] train-rmse:0.255092+0.018567    test-rmse:1.254112+0.290614 
## [45] train-rmse:0.245722+0.019742    test-rmse:1.250607+0.292404 
## [46] train-rmse:0.231928+0.019435    test-rmse:1.251228+0.291775 
## [47] train-rmse:0.223241+0.018380    test-rmse:1.250117+0.290684 
## [48] train-rmse:0.213152+0.017573    test-rmse:1.248027+0.290703 
## [49] train-rmse:0.205274+0.018677    test-rmse:1.244671+0.289769 
## [50] train-rmse:0.194568+0.015635    test-rmse:1.243580+0.289627 
## [51] train-rmse:0.187890+0.014385    test-rmse:1.243070+0.290342 
## [52] train-rmse:0.181242+0.015550    test-rmse:1.242564+0.290588 
## [53] train-rmse:0.174708+0.015817    test-rmse:1.241108+0.291602 
## [54] train-rmse:0.169223+0.017064    test-rmse:1.239755+0.292033 
## [55] train-rmse:0.164127+0.017116    test-rmse:1.239439+0.292878 
## [56] train-rmse:0.158447+0.016236    test-rmse:1.237087+0.289151 
## [57] train-rmse:0.152312+0.014918    test-rmse:1.235193+0.288914 
## [58] train-rmse:0.144938+0.013746    test-rmse:1.233932+0.289537 
## [59] train-rmse:0.140305+0.013300    test-rmse:1.232481+0.289710 
## [60] train-rmse:0.134988+0.012535    test-rmse:1.231516+0.289381 
## [61] train-rmse:0.131715+0.012169    test-rmse:1.231678+0.290701 
## [62] train-rmse:0.126760+0.011924    test-rmse:1.231947+0.290913 
## [63] train-rmse:0.121452+0.012195    test-rmse:1.231441+0.291289 
## [64] train-rmse:0.117967+0.012309    test-rmse:1.230047+0.292109 
## [65] train-rmse:0.111505+0.010816    test-rmse:1.229890+0.292336 
## [66] train-rmse:0.108323+0.010555    test-rmse:1.231029+0.294123 
## [67] train-rmse:0.104625+0.010868    test-rmse:1.230001+0.294152 
## [68] train-rmse:0.100493+0.011064    test-rmse:1.230486+0.295054 
## [69] train-rmse:0.097141+0.010405    test-rmse:1.230316+0.295505 
## [70] train-rmse:0.094524+0.010124    test-rmse:1.230392+0.296522 
## [71] train-rmse:0.091536+0.010113    test-rmse:1.229706+0.295813 
## [72] train-rmse:0.087554+0.009785    test-rmse:1.229632+0.296226 
## [73] train-rmse:0.084096+0.008873    test-rmse:1.229611+0.298072 
## [74] train-rmse:0.081638+0.008801    test-rmse:1.229679+0.298164 
## [75] train-rmse:0.078399+0.008265    test-rmse:1.229226+0.297920 
## [76] train-rmse:0.076493+0.008353    test-rmse:1.228106+0.297768 
## [77] train-rmse:0.074364+0.007928    test-rmse:1.227304+0.297616 
## [78] train-rmse:0.072126+0.008090    test-rmse:1.226524+0.298031 
## [79] train-rmse:0.069246+0.008627    test-rmse:1.225898+0.297559 
## [80] train-rmse:0.067459+0.008143    test-rmse:1.226010+0.297821 
## [81] train-rmse:0.064943+0.007804    test-rmse:1.226271+0.297536 
## [82] train-rmse:0.062944+0.008236    test-rmse:1.225319+0.297967 
## [83] train-rmse:0.060718+0.007783    test-rmse:1.225231+0.297578 
## [84] train-rmse:0.058594+0.007615    test-rmse:1.224789+0.298054 
## [85] train-rmse:0.056512+0.007788    test-rmse:1.224946+0.298023 
## [86] train-rmse:0.054711+0.007819    test-rmse:1.225218+0.297750 
## [87] train-rmse:0.053169+0.007367    test-rmse:1.225176+0.297302 
## [88] train-rmse:0.051513+0.007199    test-rmse:1.225284+0.297211 
## [89] train-rmse:0.049368+0.006650    test-rmse:1.224916+0.298178 
## [90] train-rmse:0.048026+0.006472    test-rmse:1.225012+0.298853 
## Stopping. Best iteration:
## [84] train-rmse:0.058594+0.007615    test-rmse:1.224789+0.298054
## 
## 76 
## [1]  train-rmse:75.538506+0.088543   test-rmse:75.538455+0.512708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:53.112974+0.071800   test-rmse:53.112100+0.555751 
## [3]  train-rmse:37.457246+0.064447   test-rmse:37.454956+0.599591 
## [4]  train-rmse:26.556096+0.061192   test-rmse:26.606015+0.614020 
## [5]  train-rmse:18.931370+0.044260   test-rmse:19.000555+0.544208 
## [6]  train-rmse:13.582443+0.039340   test-rmse:13.688913+0.540842 
## [7]  train-rmse:9.819006+0.034347    test-rmse:9.926690+0.519113 
## [8]  train-rmse:7.171198+0.042710    test-rmse:7.316840+0.550066 
## [9]  train-rmse:5.292197+0.046187    test-rmse:5.476231+0.475165 
## [10] train-rmse:3.974619+0.040684    test-rmse:4.195248+0.424486 
## [11] train-rmse:3.035787+0.041015    test-rmse:3.341221+0.426491 
## [12] train-rmse:2.379373+0.029541    test-rmse:2.780383+0.372191 
## [13] train-rmse:1.911669+0.023906    test-rmse:2.369950+0.321938 
## [14] train-rmse:1.567031+0.029773    test-rmse:2.096041+0.271574 
## [15] train-rmse:1.324865+0.035040    test-rmse:1.922096+0.244519 
## [16] train-rmse:1.138054+0.042722    test-rmse:1.796194+0.233152 
## [17] train-rmse:1.003189+0.043535    test-rmse:1.712646+0.245229 
## [18] train-rmse:0.900405+0.037924    test-rmse:1.644754+0.245638 
## [19] train-rmse:0.814246+0.035989    test-rmse:1.604693+0.253338 
## [20] train-rmse:0.741974+0.030402    test-rmse:1.560413+0.253969 
## [21] train-rmse:0.679387+0.032586    test-rmse:1.519914+0.254794 
## [22] train-rmse:0.628507+0.029291    test-rmse:1.489445+0.268065 
## [23] train-rmse:0.582633+0.025567    test-rmse:1.461330+0.270106 
## [24] train-rmse:0.539887+0.014154    test-rmse:1.434125+0.254448 
## [25] train-rmse:0.500320+0.019284    test-rmse:1.424404+0.253456 
## [26] train-rmse:0.464382+0.020717    test-rmse:1.402686+0.240895 
## [27] train-rmse:0.435775+0.021020    test-rmse:1.392924+0.240756 
## [28] train-rmse:0.410150+0.018622    test-rmse:1.379030+0.240846 
## [29] train-rmse:0.381238+0.014475    test-rmse:1.371813+0.248511 
## [30] train-rmse:0.356841+0.014932    test-rmse:1.365738+0.252832 
## [31] train-rmse:0.336067+0.016352    test-rmse:1.360031+0.252776 
## [32] train-rmse:0.319002+0.014257    test-rmse:1.351037+0.257241 
## [33] train-rmse:0.300644+0.014593    test-rmse:1.346254+0.258840 
## [34] train-rmse:0.287134+0.013108    test-rmse:1.336504+0.254746 
## [35] train-rmse:0.271802+0.012651    test-rmse:1.336554+0.255994 
## [36] train-rmse:0.257958+0.008608    test-rmse:1.332513+0.259571 
## [37] train-rmse:0.248019+0.007877    test-rmse:1.329137+0.259448 
## [38] train-rmse:0.234358+0.008975    test-rmse:1.326658+0.257380 
## [39] train-rmse:0.223782+0.007889    test-rmse:1.324077+0.258236 
## [40] train-rmse:0.210517+0.010314    test-rmse:1.323580+0.259737 
## [41] train-rmse:0.200168+0.008242    test-rmse:1.319317+0.260159 
## [42] train-rmse:0.191307+0.010695    test-rmse:1.317934+0.260267 
## [43] train-rmse:0.181618+0.009294    test-rmse:1.318691+0.261667 
## [44] train-rmse:0.173738+0.011999    test-rmse:1.319156+0.263401 
## [45] train-rmse:0.164766+0.010964    test-rmse:1.319002+0.262799 
## [46] train-rmse:0.157045+0.010727    test-rmse:1.318106+0.264053 
## [47] train-rmse:0.151273+0.010663    test-rmse:1.318369+0.263521 
## [48] train-rmse:0.144714+0.008107    test-rmse:1.318473+0.264305 
## Stopping. Best iteration:
## [42] train-rmse:0.191307+0.010695    test-rmse:1.317934+0.260267
## 
## 77 
## [1]  train-rmse:73.403534+0.086787   test-rmse:73.403435+0.516279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:50.171526+0.069998   test-rmse:50.170467+0.562705 
## [3]  train-rmse:34.428066+0.063950   test-rmse:34.425303+0.610826 
## [4]  train-rmse:23.792074+0.061275   test-rmse:23.847699+0.633078 
## [5]  train-rmse:16.620777+0.055169   test-rmse:16.658328+0.626730 
## [6]  train-rmse:11.836149+0.049475   test-rmse:11.913466+0.592000 
## [7]  train-rmse:8.703803+0.049357    test-rmse:8.830217+0.675554 
## [8]  train-rmse:6.712750+0.056075    test-rmse:6.828940+0.648417 
## [9]  train-rmse:5.489811+0.061773    test-rmse:5.609156+0.557697 
## [10] train-rmse:4.751560+0.059060    test-rmse:4.900199+0.576393 
## [11] train-rmse:4.307482+0.057625    test-rmse:4.462954+0.598807 
## [12] train-rmse:4.037358+0.063230    test-rmse:4.218333+0.486382 
## [13] train-rmse:3.860417+0.063470    test-rmse:4.069364+0.481722 
## [14] train-rmse:3.726569+0.060132    test-rmse:3.887384+0.420717 
## [15] train-rmse:3.621830+0.058525    test-rmse:3.791543+0.407890 
## [16] train-rmse:3.536263+0.056030    test-rmse:3.746319+0.382640 
## [17] train-rmse:3.458066+0.054573    test-rmse:3.717494+0.402805 
## [18] train-rmse:3.388013+0.055260    test-rmse:3.605567+0.339402 
## [19] train-rmse:3.319080+0.054667    test-rmse:3.556402+0.351679 
## [20] train-rmse:3.253530+0.052546    test-rmse:3.525792+0.339991 
## [21] train-rmse:3.189984+0.049676    test-rmse:3.464744+0.345190 
## [22] train-rmse:3.129663+0.049571    test-rmse:3.384181+0.300473 
## [23] train-rmse:3.071007+0.048676    test-rmse:3.318810+0.303857 
## [24] train-rmse:3.013785+0.046251    test-rmse:3.247995+0.293276 
## [25] train-rmse:2.962233+0.043107    test-rmse:3.213108+0.311287 
## [26] train-rmse:2.911362+0.043195    test-rmse:3.128503+0.273759 
## [27] train-rmse:2.865014+0.042056    test-rmse:3.091691+0.291100 
## [28] train-rmse:2.818808+0.041481    test-rmse:3.042150+0.306096 
## [29] train-rmse:2.775220+0.040912    test-rmse:3.005343+0.305974 
## [30] train-rmse:2.732430+0.040123    test-rmse:2.977181+0.307880 
## [31] train-rmse:2.691015+0.040736    test-rmse:2.937618+0.302817 
## [32] train-rmse:2.650866+0.041020    test-rmse:2.926440+0.304927 
## [33] train-rmse:2.610665+0.041289    test-rmse:2.897744+0.304336 
## [34] train-rmse:2.571337+0.040558    test-rmse:2.865691+0.311278 
## [35] train-rmse:2.533016+0.041231    test-rmse:2.813883+0.332863 
## [36] train-rmse:2.495212+0.040746    test-rmse:2.779973+0.298038 
## [37] train-rmse:2.458365+0.040691    test-rmse:2.748961+0.297180 
## [38] train-rmse:2.422759+0.040090    test-rmse:2.705888+0.293039 
## [39] train-rmse:2.389170+0.038604    test-rmse:2.679797+0.296430 
## [40] train-rmse:2.355671+0.037312    test-rmse:2.645961+0.298306 
## [41] train-rmse:2.323006+0.036253    test-rmse:2.607804+0.318122 
## [42] train-rmse:2.292618+0.036248    test-rmse:2.593307+0.314571 
## [43] train-rmse:2.263777+0.035778    test-rmse:2.564503+0.319153 
## [44] train-rmse:2.234630+0.035253    test-rmse:2.521825+0.306688 
## [45] train-rmse:2.205649+0.034589    test-rmse:2.495671+0.314007 
## [46] train-rmse:2.177311+0.033732    test-rmse:2.435292+0.287228 
## [47] train-rmse:2.149285+0.032420    test-rmse:2.412067+0.276700 
## [48] train-rmse:2.120796+0.031247    test-rmse:2.391918+0.277483 
## [49] train-rmse:2.093293+0.030813    test-rmse:2.368216+0.281232 
## [50] train-rmse:2.066220+0.029794    test-rmse:2.346866+0.283942 
## [51] train-rmse:2.040168+0.029100    test-rmse:2.319661+0.301598 
## [52] train-rmse:2.014360+0.028278    test-rmse:2.290936+0.293508 
## [53] train-rmse:1.989314+0.027541    test-rmse:2.262011+0.277480 
## [54] train-rmse:1.964853+0.027061    test-rmse:2.227954+0.257066 
## [55] train-rmse:1.940943+0.026436    test-rmse:2.208160+0.256930 
## [56] train-rmse:1.917771+0.025372    test-rmse:2.179736+0.261453 
## [57] train-rmse:1.895157+0.024270    test-rmse:2.163532+0.253225 
## [58] train-rmse:1.872818+0.023013    test-rmse:2.152514+0.262796 
## [59] train-rmse:1.850758+0.022732    test-rmse:2.131478+0.270052 
## [60] train-rmse:1.830043+0.022064    test-rmse:2.114346+0.267391 
## [61] train-rmse:1.809361+0.021868    test-rmse:2.096557+0.264009 
## [62] train-rmse:1.788645+0.021261    test-rmse:2.067147+0.259449 
## [63] train-rmse:1.768337+0.020378    test-rmse:2.067194+0.255539 
## [64] train-rmse:1.748685+0.019730    test-rmse:2.047903+0.254454 
## [65] train-rmse:1.729455+0.019269    test-rmse:2.035764+0.255672 
## [66] train-rmse:1.710187+0.018466    test-rmse:2.017486+0.256419 
## [67] train-rmse:1.691380+0.018584    test-rmse:1.998378+0.264212 
## [68] train-rmse:1.672892+0.018662    test-rmse:1.964507+0.244037 
## [69] train-rmse:1.654915+0.018644    test-rmse:1.938553+0.246412 
## [70] train-rmse:1.637453+0.018606    test-rmse:1.919136+0.253207 
## [71] train-rmse:1.620333+0.018217    test-rmse:1.920788+0.255272 
## [72] train-rmse:1.603499+0.017752    test-rmse:1.888424+0.243514 
## [73] train-rmse:1.586954+0.017419    test-rmse:1.875619+0.250673 
## [74] train-rmse:1.570908+0.017341    test-rmse:1.869962+0.239369 
## [75] train-rmse:1.554658+0.017298    test-rmse:1.860883+0.241089 
## [76] train-rmse:1.538998+0.016988    test-rmse:1.848865+0.241339 
## [77] train-rmse:1.523231+0.016680    test-rmse:1.829464+0.241356 
## [78] train-rmse:1.508076+0.015838    test-rmse:1.813230+0.245072 
## [79] train-rmse:1.493250+0.015220    test-rmse:1.797936+0.243422 
## [80] train-rmse:1.478473+0.014229    test-rmse:1.784387+0.237267 
## [81] train-rmse:1.464347+0.013763    test-rmse:1.769816+0.230411 
## [82] train-rmse:1.450545+0.013260    test-rmse:1.757971+0.232268 
## [83] train-rmse:1.437030+0.012932    test-rmse:1.746853+0.229801 
## [84] train-rmse:1.423524+0.012552    test-rmse:1.740575+0.225160 
## [85] train-rmse:1.409912+0.012364    test-rmse:1.731959+0.224765 
## [86] train-rmse:1.396466+0.012138    test-rmse:1.701792+0.229809 
## [87] train-rmse:1.383608+0.012179    test-rmse:1.690516+0.230612 
## [88] train-rmse:1.371138+0.012007    test-rmse:1.677555+0.232424 
## [89] train-rmse:1.359252+0.012054    test-rmse:1.669359+0.223997 
## [90] train-rmse:1.347377+0.012159    test-rmse:1.654304+0.220273 
## [91] train-rmse:1.335423+0.011846    test-rmse:1.654169+0.219714 
## [92] train-rmse:1.323855+0.011989    test-rmse:1.636572+0.223543 
## [93] train-rmse:1.312699+0.011843    test-rmse:1.626147+0.227516 
## [94] train-rmse:1.301843+0.011743    test-rmse:1.601391+0.210567 
## [95] train-rmse:1.291242+0.011416    test-rmse:1.595175+0.213907 
## [96] train-rmse:1.280826+0.011245    test-rmse:1.590059+0.215606 
## [97] train-rmse:1.270633+0.011167    test-rmse:1.582664+0.219788 
## [98] train-rmse:1.260638+0.010999    test-rmse:1.572667+0.219680 
## [99] train-rmse:1.250743+0.010759    test-rmse:1.564346+0.223482 
## [100]    train-rmse:1.240908+0.010561    test-rmse:1.559591+0.216780 
## [101]    train-rmse:1.231391+0.010595    test-rmse:1.555391+0.219350 
## [102]    train-rmse:1.222148+0.010747    test-rmse:1.542354+0.211426 
## [103]    train-rmse:1.212965+0.010817    test-rmse:1.535108+0.216392 
## [104]    train-rmse:1.203841+0.011071    test-rmse:1.521001+0.217935 
## [105]    train-rmse:1.194806+0.010925    test-rmse:1.511237+0.214759 
## [106]    train-rmse:1.186031+0.010692    test-rmse:1.503419+0.217020 
## [107]    train-rmse:1.177322+0.010600    test-rmse:1.498520+0.221511 
## [108]    train-rmse:1.168583+0.010549    test-rmse:1.490876+0.220549 
## [109]    train-rmse:1.160109+0.010527    test-rmse:1.481075+0.221293 
## [110]    train-rmse:1.151773+0.010642    test-rmse:1.474734+0.220053 
## [111]    train-rmse:1.143792+0.010725    test-rmse:1.469604+0.216433 
## [112]    train-rmse:1.136019+0.010905    test-rmse:1.461994+0.211500 
## [113]    train-rmse:1.128449+0.010920    test-rmse:1.451049+0.212283 
## [114]    train-rmse:1.121077+0.011029    test-rmse:1.432453+0.197653 
## [115]    train-rmse:1.113889+0.010965    test-rmse:1.425529+0.199043 
## [116]    train-rmse:1.106733+0.010831    test-rmse:1.412657+0.205034 
## [117]    train-rmse:1.099462+0.010528    test-rmse:1.414902+0.204947 
## [118]    train-rmse:1.092567+0.010400    test-rmse:1.405783+0.210591 
## [119]    train-rmse:1.085667+0.010435    test-rmse:1.402737+0.212762 
## [120]    train-rmse:1.078670+0.010499    test-rmse:1.395308+0.212910 
## [121]    train-rmse:1.072180+0.010604    test-rmse:1.391004+0.217336 
## [122]    train-rmse:1.065848+0.010704    test-rmse:1.384747+0.208873 
## [123]    train-rmse:1.059620+0.010728    test-rmse:1.379653+0.208571 
## [124]    train-rmse:1.053560+0.010957    test-rmse:1.373135+0.212515 
## [125]    train-rmse:1.047671+0.010989    test-rmse:1.370509+0.212936 
## [126]    train-rmse:1.041719+0.011020    test-rmse:1.367493+0.210133 
## [127]    train-rmse:1.035932+0.010910    test-rmse:1.361697+0.209280 
## [128]    train-rmse:1.030423+0.010731    test-rmse:1.356488+0.212214 
## [129]    train-rmse:1.024842+0.010511    test-rmse:1.349701+0.208514 
## [130]    train-rmse:1.019281+0.010450    test-rmse:1.337204+0.196134 
## [131]    train-rmse:1.013810+0.010291    test-rmse:1.331113+0.192763 
## [132]    train-rmse:1.008442+0.010277    test-rmse:1.326569+0.194266 
## [133]    train-rmse:1.003247+0.010407    test-rmse:1.324570+0.192438 
## [134]    train-rmse:0.998112+0.010540    test-rmse:1.318681+0.194981 
## [135]    train-rmse:0.993148+0.010773    test-rmse:1.316489+0.194377 
## [136]    train-rmse:0.988345+0.010874    test-rmse:1.309413+0.196384 
## [137]    train-rmse:0.983650+0.010991    test-rmse:1.308364+0.197234 
## [138]    train-rmse:0.978927+0.011114    test-rmse:1.304041+0.199280 
## [139]    train-rmse:0.974289+0.011236    test-rmse:1.294340+0.201018 
## [140]    train-rmse:0.969926+0.011381    test-rmse:1.293377+0.200674 
## [141]    train-rmse:0.965608+0.011543    test-rmse:1.289217+0.200158 
## [142]    train-rmse:0.961466+0.011473    test-rmse:1.284602+0.196767 
## [143]    train-rmse:0.957375+0.011457    test-rmse:1.274232+0.193969 
## [144]    train-rmse:0.953392+0.011455    test-rmse:1.271313+0.195993 
## [145]    train-rmse:0.949380+0.011494    test-rmse:1.267877+0.199046 
## [146]    train-rmse:0.945455+0.011522    test-rmse:1.267785+0.197226 
## [147]    train-rmse:0.941682+0.011590    test-rmse:1.264925+0.199428 
## [148]    train-rmse:0.937835+0.011655    test-rmse:1.264984+0.199994 
## [149]    train-rmse:0.934025+0.011776    test-rmse:1.260391+0.202019 
## [150]    train-rmse:0.930353+0.011802    test-rmse:1.258685+0.202133 
## [151]    train-rmse:0.926594+0.011904    test-rmse:1.257277+0.203650 
## [152]    train-rmse:0.922800+0.012124    test-rmse:1.248257+0.199947 
## [153]    train-rmse:0.919279+0.012217    test-rmse:1.246388+0.199110 
## [154]    train-rmse:0.915848+0.012190    test-rmse:1.244994+0.197567 
## [155]    train-rmse:0.912588+0.012143    test-rmse:1.244029+0.197173 
## [156]    train-rmse:0.909380+0.012070    test-rmse:1.241285+0.196494 
## [157]    train-rmse:0.906178+0.012034    test-rmse:1.237839+0.197633 
## [158]    train-rmse:0.902977+0.011974    test-rmse:1.237210+0.194175 
## [159]    train-rmse:0.899906+0.011965    test-rmse:1.234260+0.195646 
## [160]    train-rmse:0.896925+0.011942    test-rmse:1.229736+0.196970 
## [161]    train-rmse:0.894016+0.012001    test-rmse:1.227427+0.196671 
## [162]    train-rmse:0.891064+0.012051    test-rmse:1.227257+0.197319 
## [163]    train-rmse:0.888184+0.012149    test-rmse:1.217886+0.190265 
## [164]    train-rmse:0.885394+0.012272    test-rmse:1.216594+0.190517 
## [165]    train-rmse:0.882674+0.012382    test-rmse:1.215882+0.190088 
## [166]    train-rmse:0.879967+0.012502    test-rmse:1.206776+0.186509 
## [167]    train-rmse:0.877341+0.012640    test-rmse:1.204876+0.186583 
## [168]    train-rmse:0.874705+0.012771    test-rmse:1.203531+0.185972 
## [169]    train-rmse:0.872164+0.012958    test-rmse:1.198730+0.184708 
## [170]    train-rmse:0.869599+0.013023    test-rmse:1.197652+0.185395 
## [171]    train-rmse:0.867087+0.013191    test-rmse:1.194272+0.188380 
## [172]    train-rmse:0.864726+0.013269    test-rmse:1.191864+0.191053 
## [173]    train-rmse:0.862386+0.013326    test-rmse:1.193659+0.191212 
## [174]    train-rmse:0.860087+0.013376    test-rmse:1.192604+0.191982 
## [175]    train-rmse:0.857848+0.013455    test-rmse:1.192059+0.191317 
## [176]    train-rmse:0.855588+0.013404    test-rmse:1.191446+0.190701 
## [177]    train-rmse:0.853418+0.013401    test-rmse:1.191241+0.191614 
## [178]    train-rmse:0.851284+0.013434    test-rmse:1.188791+0.191909 
## [179]    train-rmse:0.849212+0.013494    test-rmse:1.184700+0.192068 
## [180]    train-rmse:0.847111+0.013610    test-rmse:1.183899+0.190667 
## [181]    train-rmse:0.844973+0.013725    test-rmse:1.182549+0.190305 
## [182]    train-rmse:0.842937+0.013840    test-rmse:1.181249+0.192292 
## [183]    train-rmse:0.840996+0.013935    test-rmse:1.180457+0.193248 
## [184]    train-rmse:0.839109+0.014011    test-rmse:1.177484+0.193715 
## [185]    train-rmse:0.837280+0.014133    test-rmse:1.175171+0.195499 
## [186]    train-rmse:0.835428+0.014188    test-rmse:1.173740+0.193823 
## [187]    train-rmse:0.833618+0.014317    test-rmse:1.173467+0.193887 
## [188]    train-rmse:0.831881+0.014421    test-rmse:1.171948+0.194304 
## [189]    train-rmse:0.830177+0.014537    test-rmse:1.170640+0.195446 
## [190]    train-rmse:0.828497+0.014653    test-rmse:1.169898+0.195899 
## [191]    train-rmse:0.826777+0.014714    test-rmse:1.168192+0.192138 
## [192]    train-rmse:0.825076+0.014797    test-rmse:1.166815+0.188373 
## [193]    train-rmse:0.823310+0.014869    test-rmse:1.160723+0.183195 
## [194]    train-rmse:0.821649+0.014928    test-rmse:1.157743+0.186955 
## [195]    train-rmse:0.819988+0.014974    test-rmse:1.159274+0.185392 
## [196]    train-rmse:0.818350+0.015042    test-rmse:1.156320+0.186615 
## [197]    train-rmse:0.816768+0.015104    test-rmse:1.155088+0.186780 
## [198]    train-rmse:0.815221+0.015131    test-rmse:1.153912+0.186892 
## [199]    train-rmse:0.813696+0.015143    test-rmse:1.155271+0.185442 
## [200]    train-rmse:0.812199+0.015197    test-rmse:1.153367+0.186196 
## [201]    train-rmse:0.810714+0.015262    test-rmse:1.152143+0.185998 
## [202]    train-rmse:0.809211+0.015300    test-rmse:1.150618+0.186727 
## [203]    train-rmse:0.807703+0.015323    test-rmse:1.150516+0.188218 
## [204]    train-rmse:0.806247+0.015368    test-rmse:1.148692+0.189979 
## [205]    train-rmse:0.804853+0.015444    test-rmse:1.148756+0.191307 
## [206]    train-rmse:0.803460+0.015527    test-rmse:1.147443+0.191733 
## [207]    train-rmse:0.802108+0.015629    test-rmse:1.147946+0.192053 
## [208]    train-rmse:0.800763+0.015717    test-rmse:1.146623+0.192228 
## [209]    train-rmse:0.799420+0.015801    test-rmse:1.144943+0.192393 
## [210]    train-rmse:0.798142+0.015852    test-rmse:1.143414+0.192189 
## [211]    train-rmse:0.796893+0.015889    test-rmse:1.143078+0.189006 
## [212]    train-rmse:0.795591+0.015929    test-rmse:1.139192+0.190303 
## [213]    train-rmse:0.794379+0.015991    test-rmse:1.137478+0.192262 
## [214]    train-rmse:0.793152+0.016018    test-rmse:1.137696+0.191165 
## [215]    train-rmse:0.791980+0.016034    test-rmse:1.138232+0.190694 
## [216]    train-rmse:0.790693+0.015973    test-rmse:1.137276+0.192482 
## [217]    train-rmse:0.789469+0.015993    test-rmse:1.135589+0.194024 
## [218]    train-rmse:0.788289+0.016044    test-rmse:1.134534+0.194551 
## [219]    train-rmse:0.787152+0.016075    test-rmse:1.134204+0.195016 
## [220]    train-rmse:0.785923+0.016043    test-rmse:1.131269+0.195062 
## [221]    train-rmse:0.784758+0.016076    test-rmse:1.131787+0.195830 
## [222]    train-rmse:0.783635+0.016119    test-rmse:1.129451+0.194690 
## [223]    train-rmse:0.782538+0.016165    test-rmse:1.129284+0.195063 
## [224]    train-rmse:0.781440+0.016180    test-rmse:1.127334+0.189342 
## [225]    train-rmse:0.780316+0.016174    test-rmse:1.123981+0.190333 
## [226]    train-rmse:0.779255+0.016203    test-rmse:1.122505+0.188530 
## [227]    train-rmse:0.778205+0.016270    test-rmse:1.121012+0.189707 
## [228]    train-rmse:0.777140+0.016271    test-rmse:1.119891+0.190014 
## [229]    train-rmse:0.776122+0.016289    test-rmse:1.119227+0.190449 
## [230]    train-rmse:0.775130+0.016300    test-rmse:1.118569+0.190659 
## [231]    train-rmse:0.774121+0.016369    test-rmse:1.117061+0.189190 
## [232]    train-rmse:0.773111+0.016389    test-rmse:1.116773+0.187865 
## [233]    train-rmse:0.772133+0.016383    test-rmse:1.114929+0.189909 
## [234]    train-rmse:0.771176+0.016404    test-rmse:1.114293+0.190056 
## [235]    train-rmse:0.770185+0.016402    test-rmse:1.112220+0.190616 
## [236]    train-rmse:0.769234+0.016356    test-rmse:1.111586+0.191045 
## [237]    train-rmse:0.768307+0.016347    test-rmse:1.111949+0.191106 
## [238]    train-rmse:0.767416+0.016361    test-rmse:1.110396+0.190763 
## [239]    train-rmse:0.766473+0.016389    test-rmse:1.110366+0.190052 
## [240]    train-rmse:0.765561+0.016382    test-rmse:1.108572+0.191584 
## [241]    train-rmse:0.764615+0.016414    test-rmse:1.109436+0.191574 
## [242]    train-rmse:0.763703+0.016442    test-rmse:1.109076+0.192068 
## [243]    train-rmse:0.762813+0.016441    test-rmse:1.109394+0.191866 
## [244]    train-rmse:0.761963+0.016469    test-rmse:1.107641+0.190556 
## [245]    train-rmse:0.761138+0.016481    test-rmse:1.106903+0.189947 
## [246]    train-rmse:0.760258+0.016475    test-rmse:1.107176+0.190511 
## [247]    train-rmse:0.759434+0.016476    test-rmse:1.106840+0.189981 
## [248]    train-rmse:0.758604+0.016501    test-rmse:1.105611+0.190114 
## [249]    train-rmse:0.757785+0.016495    test-rmse:1.104884+0.189133 
## [250]    train-rmse:0.756952+0.016489    test-rmse:1.102490+0.185131 
## [251]    train-rmse:0.756080+0.016462    test-rmse:1.101581+0.185274 
## [252]    train-rmse:0.755271+0.016447    test-rmse:1.101889+0.184801 
## [253]    train-rmse:0.754462+0.016462    test-rmse:1.101880+0.183426 
## [254]    train-rmse:0.753679+0.016452    test-rmse:1.102188+0.181894 
## [255]    train-rmse:0.752865+0.016487    test-rmse:1.099362+0.182912 
## [256]    train-rmse:0.752096+0.016490    test-rmse:1.098665+0.182839 
## [257]    train-rmse:0.751360+0.016496    test-rmse:1.098361+0.183197 
## [258]    train-rmse:0.750600+0.016450    test-rmse:1.096583+0.183720 
## [259]    train-rmse:0.749864+0.016454    test-rmse:1.097566+0.182933 
## [260]    train-rmse:0.749118+0.016477    test-rmse:1.096498+0.184161 
## [261]    train-rmse:0.748400+0.016448    test-rmse:1.094828+0.182021 
## [262]    train-rmse:0.747620+0.016425    test-rmse:1.092288+0.181913 
## [263]    train-rmse:0.746889+0.016410    test-rmse:1.090989+0.180437 
## [264]    train-rmse:0.746139+0.016368    test-rmse:1.089195+0.178845 
## [265]    train-rmse:0.745398+0.016321    test-rmse:1.089449+0.179550 
## [266]    train-rmse:0.744701+0.016287    test-rmse:1.087696+0.178782 
## [267]    train-rmse:0.744001+0.016252    test-rmse:1.085833+0.178239 
## [268]    train-rmse:0.743289+0.016215    test-rmse:1.085886+0.176460 
## [269]    train-rmse:0.742557+0.016213    test-rmse:1.084651+0.175823 
## [270]    train-rmse:0.741879+0.016183    test-rmse:1.083970+0.176025 
## [271]    train-rmse:0.741198+0.016173    test-rmse:1.082737+0.174326 
## [272]    train-rmse:0.740528+0.016162    test-rmse:1.081207+0.172004 
## [273]    train-rmse:0.739880+0.016143    test-rmse:1.080106+0.172928 
## [274]    train-rmse:0.739242+0.016113    test-rmse:1.078770+0.173700 
## [275]    train-rmse:0.738558+0.016126    test-rmse:1.076081+0.173234 
## [276]    train-rmse:0.737908+0.016085    test-rmse:1.075405+0.172868 
## [277]    train-rmse:0.737267+0.016064    test-rmse:1.075511+0.172222 
## [278]    train-rmse:0.736646+0.016064    test-rmse:1.074082+0.173000 
## [279]    train-rmse:0.736003+0.016039    test-rmse:1.075021+0.171929 
## [280]    train-rmse:0.735369+0.016007    test-rmse:1.075344+0.171622 
## [281]    train-rmse:0.734740+0.016010    test-rmse:1.075787+0.170823 
## [282]    train-rmse:0.734121+0.015963    test-rmse:1.075795+0.171573 
## [283]    train-rmse:0.733497+0.015931    test-rmse:1.075679+0.171289 
## [284]    train-rmse:0.732892+0.015897    test-rmse:1.075012+0.171837 
## Stopping. Best iteration:
## [278]    train-rmse:0.736646+0.016064    test-rmse:1.074082+0.173000
## 
## 78 
## [1]  train-rmse:73.403534+0.086787   test-rmse:73.403435+0.516279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:50.171526+0.069998   test-rmse:50.170467+0.562705 
## [3]  train-rmse:34.428066+0.063950   test-rmse:34.425303+0.610826 
## [4]  train-rmse:23.772392+0.053545   test-rmse:23.842593+0.630501 
## [5]  train-rmse:16.553434+0.035975   test-rmse:16.646706+0.556223 
## [6]  train-rmse:11.695168+0.029757   test-rmse:11.826321+0.567004 
## [7]  train-rmse:8.472388+0.033193    test-rmse:8.590248+0.586984 
## [8]  train-rmse:6.376988+0.037572    test-rmse:6.564277+0.602368 
## [9]  train-rmse:5.043774+0.043762    test-rmse:5.206799+0.521689 
## [10] train-rmse:4.217811+0.044845    test-rmse:4.429132+0.499954 
## [11] train-rmse:3.701687+0.048991    test-rmse:3.975679+0.478174 
## [12] train-rmse:3.375247+0.042830    test-rmse:3.656845+0.420421 
## [13] train-rmse:3.146143+0.053457    test-rmse:3.416005+0.371184 
## [14] train-rmse:2.982737+0.046914    test-rmse:3.260120+0.359112 
## [15] train-rmse:2.854904+0.042977    test-rmse:3.114308+0.333199 
## [16] train-rmse:2.732615+0.031307    test-rmse:3.041251+0.350824 
## [17] train-rmse:2.642613+0.028180    test-rmse:2.955466+0.338856 
## [18] train-rmse:2.554476+0.023025    test-rmse:2.886729+0.313958 
## [19] train-rmse:2.474675+0.021201    test-rmse:2.791735+0.305704 
## [20] train-rmse:2.394845+0.019428    test-rmse:2.743822+0.317108 
## [21] train-rmse:2.321926+0.017110    test-rmse:2.704514+0.317178 
## [22] train-rmse:2.253645+0.016014    test-rmse:2.633396+0.336758 
## [23] train-rmse:2.188370+0.016298    test-rmse:2.570696+0.303149 
## [24] train-rmse:2.127440+0.012510    test-rmse:2.506780+0.298105 
## [25] train-rmse:2.068299+0.014165    test-rmse:2.451176+0.302392 
## [26] train-rmse:2.008748+0.010190    test-rmse:2.400438+0.280167 
## [27] train-rmse:1.955189+0.010698    test-rmse:2.354865+0.289662 
## [28] train-rmse:1.901595+0.009867    test-rmse:2.295188+0.257645 
## [29] train-rmse:1.853278+0.009739    test-rmse:2.225142+0.258991 
## [30] train-rmse:1.805849+0.009500    test-rmse:2.183005+0.279153 
## [31] train-rmse:1.760830+0.008717    test-rmse:2.156443+0.279139 
## [32] train-rmse:1.715475+0.009626    test-rmse:2.114419+0.273704 
## [33] train-rmse:1.673175+0.011730    test-rmse:2.080195+0.278514 
## [34] train-rmse:1.634323+0.011675    test-rmse:2.038859+0.272772 
## [35] train-rmse:1.594254+0.013407    test-rmse:2.004397+0.283487 
## [36] train-rmse:1.555845+0.012593    test-rmse:1.973297+0.270744 
## [37] train-rmse:1.519010+0.013265    test-rmse:1.953007+0.275998 
## [38] train-rmse:1.484102+0.014133    test-rmse:1.912441+0.268168 
## [39] train-rmse:1.449189+0.014901    test-rmse:1.894776+0.273095 
## [40] train-rmse:1.413977+0.012155    test-rmse:1.866777+0.275481 
## [41] train-rmse:1.383280+0.011738    test-rmse:1.834326+0.278647 
## [42] train-rmse:1.352341+0.014146    test-rmse:1.813164+0.274964 
## [43] train-rmse:1.317711+0.014504    test-rmse:1.794390+0.268305 
## [44] train-rmse:1.289876+0.015264    test-rmse:1.781186+0.267180 
## [45] train-rmse:1.261519+0.015698    test-rmse:1.758016+0.263705 
## [46] train-rmse:1.235192+0.016450    test-rmse:1.716145+0.244379 
## [47] train-rmse:1.210630+0.016274    test-rmse:1.704328+0.244263 
## [48] train-rmse:1.186435+0.016172    test-rmse:1.667890+0.237003 
## [49] train-rmse:1.162336+0.017989    test-rmse:1.644250+0.246738 
## [50] train-rmse:1.139814+0.018827    test-rmse:1.630960+0.253554 
## [51] train-rmse:1.117660+0.018533    test-rmse:1.615925+0.254192 
## [52] train-rmse:1.097158+0.018181    test-rmse:1.595550+0.252491 
## [53] train-rmse:1.075851+0.019127    test-rmse:1.586080+0.251826 
## [54] train-rmse:1.056346+0.019216    test-rmse:1.565934+0.248390 
## [55] train-rmse:1.037133+0.019074    test-rmse:1.549595+0.254565 
## [56] train-rmse:1.015272+0.016101    test-rmse:1.536363+0.249620 
## [57] train-rmse:0.997889+0.015973    test-rmse:1.523138+0.247391 
## [58] train-rmse:0.980323+0.016743    test-rmse:1.515032+0.248836 
## [59] train-rmse:0.964944+0.017228    test-rmse:1.491025+0.251996 
## [60] train-rmse:0.949292+0.018426    test-rmse:1.473278+0.249175 
## [61] train-rmse:0.933840+0.018021    test-rmse:1.462098+0.253318 
## [62] train-rmse:0.918826+0.017889    test-rmse:1.450328+0.252776 
## [63] train-rmse:0.904749+0.017977    test-rmse:1.439629+0.254969 
## [64] train-rmse:0.891288+0.017908    test-rmse:1.428191+0.251900 
## [65] train-rmse:0.877003+0.019974    test-rmse:1.417457+0.249523 
## [66] train-rmse:0.861213+0.020391    test-rmse:1.410481+0.246326 
## [67] train-rmse:0.848046+0.019457    test-rmse:1.404311+0.240692 
## [68] train-rmse:0.835148+0.017861    test-rmse:1.393563+0.239880 
## [69] train-rmse:0.822769+0.017385    test-rmse:1.377323+0.239870 
## [70] train-rmse:0.811575+0.017484    test-rmse:1.373816+0.243455 
## [71] train-rmse:0.800868+0.017371    test-rmse:1.362049+0.244193 
## [72] train-rmse:0.787968+0.017972    test-rmse:1.348456+0.244516 
## [73] train-rmse:0.777491+0.017476    test-rmse:1.343616+0.242774 
## [74] train-rmse:0.766860+0.017907    test-rmse:1.337420+0.242544 
## [75] train-rmse:0.756633+0.017174    test-rmse:1.330761+0.238643 
## [76] train-rmse:0.746040+0.016870    test-rmse:1.325317+0.241915 
## [77] train-rmse:0.736933+0.016151    test-rmse:1.320655+0.243783 
## [78] train-rmse:0.727385+0.015892    test-rmse:1.313833+0.243418 
## [79] train-rmse:0.718157+0.016961    test-rmse:1.301425+0.244160 
## [80] train-rmse:0.709758+0.018244    test-rmse:1.296552+0.244722 
## [81] train-rmse:0.701350+0.019791    test-rmse:1.292126+0.245236 
## [82] train-rmse:0.690284+0.015997    test-rmse:1.288853+0.244618 
## [83] train-rmse:0.681681+0.015690    test-rmse:1.282482+0.234485 
## [84] train-rmse:0.675276+0.015545    test-rmse:1.277886+0.235372 
## [85] train-rmse:0.668061+0.015579    test-rmse:1.274129+0.239153 
## [86] train-rmse:0.660127+0.014903    test-rmse:1.273312+0.239709 
## [87] train-rmse:0.653088+0.014686    test-rmse:1.268456+0.242215 
## [88] train-rmse:0.646633+0.013830    test-rmse:1.262109+0.240971 
## [89] train-rmse:0.640488+0.013832    test-rmse:1.256490+0.239165 
## [90] train-rmse:0.634248+0.013549    test-rmse:1.255865+0.241755 
## [91] train-rmse:0.626054+0.015244    test-rmse:1.251083+0.240712 
## [92] train-rmse:0.620207+0.016143    test-rmse:1.245728+0.237447 
## [93] train-rmse:0.614138+0.016345    test-rmse:1.242972+0.237603 
## [94] train-rmse:0.608437+0.017044    test-rmse:1.241068+0.238405 
## [95] train-rmse:0.601014+0.015938    test-rmse:1.234025+0.231954 
## [96] train-rmse:0.595039+0.017498    test-rmse:1.235084+0.233690 
## [97] train-rmse:0.588195+0.017697    test-rmse:1.228764+0.234203 
## [98] train-rmse:0.583724+0.017820    test-rmse:1.222554+0.236377 
## [99] train-rmse:0.578604+0.019086    test-rmse:1.221927+0.236395 
## [100]    train-rmse:0.571694+0.018937    test-rmse:1.224145+0.236203 
## [101]    train-rmse:0.564233+0.015132    test-rmse:1.223124+0.239105 
## [102]    train-rmse:0.559567+0.015791    test-rmse:1.216420+0.240646 
## [103]    train-rmse:0.554612+0.016225    test-rmse:1.214700+0.241057 
## [104]    train-rmse:0.548812+0.014806    test-rmse:1.207818+0.245897 
## [105]    train-rmse:0.544964+0.014547    test-rmse:1.205395+0.244977 
## [106]    train-rmse:0.540592+0.014443    test-rmse:1.203459+0.244649 
## [107]    train-rmse:0.536615+0.014109    test-rmse:1.199284+0.242727 
## [108]    train-rmse:0.532627+0.013413    test-rmse:1.197856+0.242189 
## [109]    train-rmse:0.528780+0.013660    test-rmse:1.195300+0.242641 
## [110]    train-rmse:0.524756+0.013649    test-rmse:1.190640+0.246307 
## [111]    train-rmse:0.520730+0.012305    test-rmse:1.189789+0.246575 
## [112]    train-rmse:0.516711+0.013004    test-rmse:1.187557+0.249021 
## [113]    train-rmse:0.513091+0.014152    test-rmse:1.188417+0.251527 
## [114]    train-rmse:0.509907+0.014558    test-rmse:1.186210+0.252569 
## [115]    train-rmse:0.504826+0.015467    test-rmse:1.187121+0.253249 
## [116]    train-rmse:0.501184+0.016243    test-rmse:1.186409+0.256302 
## [117]    train-rmse:0.497921+0.016382    test-rmse:1.184194+0.258225 
## [118]    train-rmse:0.493847+0.015252    test-rmse:1.182319+0.258561 
## [119]    train-rmse:0.489587+0.014265    test-rmse:1.181931+0.257774 
## [120]    train-rmse:0.485936+0.014403    test-rmse:1.182092+0.258063 
## [121]    train-rmse:0.483095+0.014007    test-rmse:1.178238+0.257573 
## [122]    train-rmse:0.479928+0.014068    test-rmse:1.176664+0.256788 
## [123]    train-rmse:0.476174+0.014081    test-rmse:1.174756+0.258500 
## [124]    train-rmse:0.471536+0.013605    test-rmse:1.169883+0.261318 
## [125]    train-rmse:0.467743+0.010902    test-rmse:1.166859+0.259086 
## [126]    train-rmse:0.463253+0.013712    test-rmse:1.165437+0.259538 
## [127]    train-rmse:0.460545+0.014276    test-rmse:1.165642+0.259754 
## [128]    train-rmse:0.458043+0.014540    test-rmse:1.163147+0.258036 
## [129]    train-rmse:0.454923+0.014526    test-rmse:1.163105+0.258708 
## [130]    train-rmse:0.453067+0.014553    test-rmse:1.161266+0.257807 
## [131]    train-rmse:0.450249+0.014058    test-rmse:1.160937+0.260223 
## [132]    train-rmse:0.446484+0.015244    test-rmse:1.158973+0.261663 
## [133]    train-rmse:0.442656+0.014998    test-rmse:1.157581+0.264062 
## [134]    train-rmse:0.439213+0.015212    test-rmse:1.154784+0.266217 
## [135]    train-rmse:0.436331+0.015687    test-rmse:1.154011+0.265796 
## [136]    train-rmse:0.433407+0.013925    test-rmse:1.150166+0.266836 
## [137]    train-rmse:0.431305+0.014189    test-rmse:1.149665+0.267269 
## [138]    train-rmse:0.427311+0.015267    test-rmse:1.148186+0.267351 
## [139]    train-rmse:0.424589+0.015554    test-rmse:1.146867+0.267546 
## [140]    train-rmse:0.421640+0.015269    test-rmse:1.146276+0.268057 
## [141]    train-rmse:0.418936+0.015566    test-rmse:1.143386+0.267336 
## [142]    train-rmse:0.415639+0.014362    test-rmse:1.141511+0.268599 
## [143]    train-rmse:0.413008+0.014776    test-rmse:1.134513+0.261611 
## [144]    train-rmse:0.410938+0.015553    test-rmse:1.133011+0.261287 
## [145]    train-rmse:0.409197+0.015394    test-rmse:1.133067+0.261022 
## [146]    train-rmse:0.406718+0.015193    test-rmse:1.132526+0.260077 
## [147]    train-rmse:0.405182+0.015252    test-rmse:1.131342+0.260424 
## [148]    train-rmse:0.403111+0.015828    test-rmse:1.131731+0.261334 
## [149]    train-rmse:0.401700+0.015923    test-rmse:1.132324+0.260106 
## [150]    train-rmse:0.399561+0.015643    test-rmse:1.130998+0.261120 
## [151]    train-rmse:0.398074+0.015621    test-rmse:1.131609+0.261731 
## [152]    train-rmse:0.395985+0.015739    test-rmse:1.131084+0.263737 
## [153]    train-rmse:0.394484+0.015666    test-rmse:1.128761+0.262545 
## [154]    train-rmse:0.391658+0.015219    test-rmse:1.127556+0.261380 
## [155]    train-rmse:0.389903+0.015029    test-rmse:1.127699+0.261634 
## [156]    train-rmse:0.387802+0.015145    test-rmse:1.128128+0.261417 
## [157]    train-rmse:0.384282+0.015395    test-rmse:1.124673+0.262437 
## [158]    train-rmse:0.381459+0.015072    test-rmse:1.123042+0.265609 
## [159]    train-rmse:0.378445+0.016973    test-rmse:1.122877+0.265928 
## [160]    train-rmse:0.375889+0.017336    test-rmse:1.122369+0.265499 
## [161]    train-rmse:0.373763+0.016436    test-rmse:1.120183+0.266296 
## [162]    train-rmse:0.372226+0.017143    test-rmse:1.120234+0.266807 
## [163]    train-rmse:0.369358+0.017928    test-rmse:1.119200+0.267071 
## [164]    train-rmse:0.367809+0.017783    test-rmse:1.119597+0.266214 
## [165]    train-rmse:0.366160+0.018041    test-rmse:1.117680+0.266235 
## [166]    train-rmse:0.363765+0.019309    test-rmse:1.116839+0.265689 
## [167]    train-rmse:0.361817+0.019865    test-rmse:1.116577+0.266587 
## [168]    train-rmse:0.360625+0.019912    test-rmse:1.116081+0.266535 
## [169]    train-rmse:0.359442+0.020339    test-rmse:1.116474+0.266935 
## [170]    train-rmse:0.357728+0.020193    test-rmse:1.114145+0.265934 
## [171]    train-rmse:0.356192+0.020509    test-rmse:1.114502+0.265911 
## [172]    train-rmse:0.354486+0.021019    test-rmse:1.115465+0.265830 
## [173]    train-rmse:0.352199+0.020976    test-rmse:1.114893+0.265249 
## [174]    train-rmse:0.349828+0.020402    test-rmse:1.114777+0.265094 
## [175]    train-rmse:0.347907+0.020663    test-rmse:1.114737+0.264570 
## [176]    train-rmse:0.346375+0.020546    test-rmse:1.113607+0.264828 
## [177]    train-rmse:0.344736+0.019980    test-rmse:1.112478+0.265986 
## [178]    train-rmse:0.342746+0.020555    test-rmse:1.111786+0.266160 
## [179]    train-rmse:0.339079+0.018680    test-rmse:1.111402+0.267290 
## [180]    train-rmse:0.336969+0.018408    test-rmse:1.109753+0.267209 
## [181]    train-rmse:0.335382+0.018493    test-rmse:1.110936+0.267988 
## [182]    train-rmse:0.334310+0.018552    test-rmse:1.109930+0.266360 
## [183]    train-rmse:0.332789+0.018715    test-rmse:1.109935+0.265310 
## [184]    train-rmse:0.330916+0.018689    test-rmse:1.108224+0.266179 
## [185]    train-rmse:0.329554+0.018864    test-rmse:1.108693+0.266473 
## [186]    train-rmse:0.328444+0.018605    test-rmse:1.108937+0.265666 
## [187]    train-rmse:0.326214+0.018269    test-rmse:1.107805+0.265934 
## [188]    train-rmse:0.324311+0.016622    test-rmse:1.107570+0.265826 
## [189]    train-rmse:0.322410+0.016450    test-rmse:1.108216+0.266343 
## [190]    train-rmse:0.321327+0.016618    test-rmse:1.107981+0.266134 
## [191]    train-rmse:0.319299+0.015819    test-rmse:1.106896+0.265648 
## [192]    train-rmse:0.317736+0.015852    test-rmse:1.107432+0.266912 
## [193]    train-rmse:0.316603+0.016252    test-rmse:1.107236+0.266010 
## [194]    train-rmse:0.314501+0.015061    test-rmse:1.103648+0.267472 
## [195]    train-rmse:0.312626+0.014172    test-rmse:1.101714+0.267415 
## [196]    train-rmse:0.311648+0.014401    test-rmse:1.100022+0.267513 
## [197]    train-rmse:0.310422+0.014580    test-rmse:1.100533+0.267659 
## [198]    train-rmse:0.309750+0.014638    test-rmse:1.099998+0.268036 
## [199]    train-rmse:0.308542+0.014213    test-rmse:1.099326+0.268171 
## [200]    train-rmse:0.307281+0.013941    test-rmse:1.099566+0.267807 
## [201]    train-rmse:0.306193+0.014072    test-rmse:1.099081+0.268020 
## [202]    train-rmse:0.304090+0.013727    test-rmse:1.098539+0.270812 
## [203]    train-rmse:0.302324+0.014640    test-rmse:1.098443+0.270988 
## [204]    train-rmse:0.301004+0.014577    test-rmse:1.098364+0.270652 
## [205]    train-rmse:0.299535+0.014833    test-rmse:1.099205+0.271680 
## [206]    train-rmse:0.298274+0.014943    test-rmse:1.098462+0.271289 
## [207]    train-rmse:0.296204+0.014596    test-rmse:1.098511+0.270604 
## [208]    train-rmse:0.294749+0.013978    test-rmse:1.099215+0.269984 
## [209]    train-rmse:0.293628+0.013476    test-rmse:1.099064+0.270027 
## [210]    train-rmse:0.291943+0.013639    test-rmse:1.098176+0.270163 
## [211]    train-rmse:0.291288+0.013658    test-rmse:1.098412+0.271037 
## [212]    train-rmse:0.290437+0.013582    test-rmse:1.098252+0.271749 
## [213]    train-rmse:0.289319+0.013752    test-rmse:1.098551+0.271605 
## [214]    train-rmse:0.288337+0.014104    test-rmse:1.098267+0.271626 
## [215]    train-rmse:0.286761+0.013453    test-rmse:1.097927+0.270511 
## [216]    train-rmse:0.285859+0.013366    test-rmse:1.097716+0.270621 
## [217]    train-rmse:0.284354+0.013190    test-rmse:1.097067+0.271148 
## [218]    train-rmse:0.283069+0.013770    test-rmse:1.096314+0.271749 
## [219]    train-rmse:0.281319+0.013820    test-rmse:1.095612+0.271458 
## [220]    train-rmse:0.280075+0.013267    test-rmse:1.094080+0.270967 
## [221]    train-rmse:0.278914+0.013265    test-rmse:1.093519+0.271107 
## [222]    train-rmse:0.277228+0.012847    test-rmse:1.093729+0.270182 
## [223]    train-rmse:0.276325+0.012915    test-rmse:1.094019+0.270020 
## [224]    train-rmse:0.275240+0.012699    test-rmse:1.093599+0.270292 
## [225]    train-rmse:0.274331+0.012663    test-rmse:1.093588+0.270352 
## [226]    train-rmse:0.273267+0.012209    test-rmse:1.093707+0.270304 
## [227]    train-rmse:0.272278+0.012101    test-rmse:1.094594+0.269952 
## Stopping. Best iteration:
## [221]    train-rmse:0.278914+0.013265    test-rmse:1.093519+0.271107
## 
## 79 
## [1]  train-rmse:73.403534+0.086787   test-rmse:73.403435+0.516279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:50.171526+0.069998   test-rmse:50.170467+0.562705 
## [3]  train-rmse:34.428066+0.063950   test-rmse:34.425303+0.610826 
## [4]  train-rmse:23.768078+0.049908   test-rmse:23.846854+0.624752 
## [5]  train-rmse:16.530009+0.033035   test-rmse:16.637555+0.589382 
## [6]  train-rmse:11.644632+0.031538   test-rmse:11.722669+0.538413 
## [7]  train-rmse:8.360396+0.025325    test-rmse:8.499510+0.554258 
## [8]  train-rmse:6.186034+0.030270    test-rmse:6.370310+0.574419 
## [9]  train-rmse:4.771373+0.032416    test-rmse:4.972753+0.511759 
## [10] train-rmse:3.863985+0.036624    test-rmse:4.092527+0.457220 
## [11] train-rmse:3.290191+0.034063    test-rmse:3.543850+0.441395 
## [12] train-rmse:2.910822+0.039626    test-rmse:3.250616+0.436433 
## [13] train-rmse:2.662058+0.039765    test-rmse:3.022930+0.372803 
## [14] train-rmse:2.477222+0.045763    test-rmse:2.866218+0.334796 
## [15] train-rmse:2.322403+0.044159    test-rmse:2.762100+0.308012 
## [16] train-rmse:2.198896+0.040184    test-rmse:2.639916+0.292471 
## [17] train-rmse:2.096723+0.039965    test-rmse:2.547447+0.249929 
## [18] train-rmse:2.001101+0.038244    test-rmse:2.461435+0.239776 
## [19] train-rmse:1.907618+0.032003    test-rmse:2.378592+0.223973 
## [20] train-rmse:1.824472+0.035944    test-rmse:2.334564+0.229948 
## [21] train-rmse:1.750301+0.036761    test-rmse:2.280698+0.235857 
## [22] train-rmse:1.681920+0.036865    test-rmse:2.220036+0.243104 
## [23] train-rmse:1.615432+0.040064    test-rmse:2.163916+0.253383 
## [24] train-rmse:1.549801+0.041787    test-rmse:2.121722+0.251322 
## [25] train-rmse:1.490377+0.037040    test-rmse:2.076406+0.242258 
## [26] train-rmse:1.433703+0.035286    test-rmse:2.018778+0.234916 
## [27] train-rmse:1.379634+0.034405    test-rmse:1.990330+0.235274 
## [28] train-rmse:1.329571+0.035608    test-rmse:1.950391+0.230222 
## [29] train-rmse:1.283109+0.034733    test-rmse:1.904930+0.229225 
## [30] train-rmse:1.234642+0.030557    test-rmse:1.870617+0.240090 
## [31] train-rmse:1.190520+0.028116    test-rmse:1.825616+0.238819 
## [32] train-rmse:1.147681+0.030039    test-rmse:1.800953+0.237446 
## [33] train-rmse:1.110224+0.031363    test-rmse:1.775167+0.245405 
## [34] train-rmse:1.071850+0.032064    test-rmse:1.750668+0.245555 
## [35] train-rmse:1.037409+0.030664    test-rmse:1.726258+0.240363 
## [36] train-rmse:1.004019+0.032733    test-rmse:1.700350+0.246311 
## [37] train-rmse:0.972570+0.031931    test-rmse:1.663565+0.224966 
## [38] train-rmse:0.943125+0.032115    test-rmse:1.635206+0.230496 
## [39] train-rmse:0.914435+0.030976    test-rmse:1.609882+0.229839 
## [40] train-rmse:0.886814+0.030054    test-rmse:1.593636+0.232006 
## [41] train-rmse:0.860876+0.028693    test-rmse:1.570896+0.239264 
## [42] train-rmse:0.837537+0.028759    test-rmse:1.540904+0.241311 
## [43] train-rmse:0.815145+0.030073    test-rmse:1.519420+0.240696 
## [44] train-rmse:0.794430+0.030522    test-rmse:1.507573+0.236438 
## [45] train-rmse:0.773350+0.025981    test-rmse:1.493259+0.232421 
## [46] train-rmse:0.751825+0.028638    test-rmse:1.477050+0.242403 
## [47] train-rmse:0.731469+0.029349    test-rmse:1.459915+0.238879 
## [48] train-rmse:0.711775+0.030394    test-rmse:1.437003+0.241549 
## [49] train-rmse:0.696061+0.030585    test-rmse:1.424807+0.240718 
## [50] train-rmse:0.676756+0.027157    test-rmse:1.418012+0.243042 
## [51] train-rmse:0.660389+0.026934    test-rmse:1.410539+0.239467 
## [52] train-rmse:0.644549+0.027566    test-rmse:1.398255+0.236467 
## [53] train-rmse:0.629545+0.028091    test-rmse:1.392439+0.237986 
## [54] train-rmse:0.616579+0.025833    test-rmse:1.376582+0.239660 
## [55] train-rmse:0.603678+0.025851    test-rmse:1.371462+0.243869 
## [56] train-rmse:0.588600+0.023694    test-rmse:1.361417+0.247250 
## [57] train-rmse:0.576542+0.023783    test-rmse:1.354667+0.246223 
## [58] train-rmse:0.565713+0.024041    test-rmse:1.343983+0.242187 
## [59] train-rmse:0.555594+0.023780    test-rmse:1.338767+0.244834 
## [60] train-rmse:0.544220+0.024073    test-rmse:1.333074+0.248964 
## [61] train-rmse:0.533625+0.024528    test-rmse:1.327083+0.246990 
## [62] train-rmse:0.521604+0.025880    test-rmse:1.323272+0.246908 
## [63] train-rmse:0.512450+0.024907    test-rmse:1.320725+0.250004 
## [64] train-rmse:0.503112+0.024206    test-rmse:1.318011+0.253502 
## [65] train-rmse:0.495230+0.024425    test-rmse:1.312229+0.255528 
## [66] train-rmse:0.486698+0.024922    test-rmse:1.300467+0.264297 
## [67] train-rmse:0.477857+0.026140    test-rmse:1.297748+0.265163 
## [68] train-rmse:0.466529+0.023986    test-rmse:1.291765+0.264697 
## [69] train-rmse:0.458350+0.021951    test-rmse:1.284296+0.272538 
## [70] train-rmse:0.451058+0.021848    test-rmse:1.282031+0.273934 
## [71] train-rmse:0.443260+0.023103    test-rmse:1.278859+0.276274 
## [72] train-rmse:0.435325+0.024955    test-rmse:1.278198+0.275938 
## [73] train-rmse:0.428974+0.025824    test-rmse:1.266867+0.284331 
## [74] train-rmse:0.422524+0.025781    test-rmse:1.262572+0.279991 
## [75] train-rmse:0.416795+0.024869    test-rmse:1.257219+0.286949 
## [76] train-rmse:0.410644+0.024590    test-rmse:1.256444+0.285319 
## [77] train-rmse:0.405661+0.024486    test-rmse:1.252066+0.286448 
## [78] train-rmse:0.396295+0.023425    test-rmse:1.251081+0.288643 
## [79] train-rmse:0.388906+0.022656    test-rmse:1.246463+0.288392 
## [80] train-rmse:0.383254+0.023116    test-rmse:1.245305+0.287577 
## [81] train-rmse:0.376187+0.024071    test-rmse:1.240876+0.286526 
## [82] train-rmse:0.369282+0.025386    test-rmse:1.239821+0.287488 
## [83] train-rmse:0.364511+0.023342    test-rmse:1.238159+0.288643 
## [84] train-rmse:0.357140+0.023353    test-rmse:1.233444+0.295343 
## [85] train-rmse:0.352278+0.022850    test-rmse:1.230428+0.295625 
## [86] train-rmse:0.349176+0.022808    test-rmse:1.229272+0.294133 
## [87] train-rmse:0.344953+0.023145    test-rmse:1.221930+0.300877 
## [88] train-rmse:0.339308+0.023626    test-rmse:1.219576+0.301636 
## [89] train-rmse:0.334824+0.023605    test-rmse:1.218888+0.300254 
## [90] train-rmse:0.329884+0.024134    test-rmse:1.219661+0.306663 
## [91] train-rmse:0.326393+0.024316    test-rmse:1.219096+0.305995 
## [92] train-rmse:0.322375+0.023428    test-rmse:1.217669+0.307157 
## [93] train-rmse:0.315754+0.023064    test-rmse:1.216241+0.306491 
## [94] train-rmse:0.310665+0.023406    test-rmse:1.215903+0.306305 
## [95] train-rmse:0.305774+0.022478    test-rmse:1.214224+0.305535 
## [96] train-rmse:0.302353+0.022064    test-rmse:1.212479+0.304769 
## [97] train-rmse:0.299290+0.022653    test-rmse:1.210788+0.305755 
## [98] train-rmse:0.294951+0.021375    test-rmse:1.212709+0.306872 
## [99] train-rmse:0.289724+0.019214    test-rmse:1.212268+0.306392 
## [100]    train-rmse:0.286967+0.019554    test-rmse:1.211121+0.306951 
## [101]    train-rmse:0.284248+0.018656    test-rmse:1.209748+0.309300 
## [102]    train-rmse:0.281249+0.017956    test-rmse:1.209495+0.310587 
## [103]    train-rmse:0.277218+0.016977    test-rmse:1.208401+0.309161 
## [104]    train-rmse:0.275126+0.016775    test-rmse:1.207546+0.309400 
## [105]    train-rmse:0.270696+0.015208    test-rmse:1.207194+0.311685 
## [106]    train-rmse:0.266598+0.016047    test-rmse:1.207554+0.310550 
## [107]    train-rmse:0.263033+0.014204    test-rmse:1.205873+0.312297 
## [108]    train-rmse:0.260585+0.015124    test-rmse:1.205254+0.313068 
## [109]    train-rmse:0.257477+0.015371    test-rmse:1.204994+0.313300 
## [110]    train-rmse:0.253498+0.015559    test-rmse:1.202277+0.313774 
## [111]    train-rmse:0.249874+0.015610    test-rmse:1.203190+0.314348 
## [112]    train-rmse:0.246796+0.015498    test-rmse:1.204029+0.315928 
## [113]    train-rmse:0.245048+0.015823    test-rmse:1.202620+0.316378 
## [114]    train-rmse:0.242491+0.015661    test-rmse:1.201841+0.316732 
## [115]    train-rmse:0.239721+0.015690    test-rmse:1.201628+0.318709 
## [116]    train-rmse:0.237337+0.016305    test-rmse:1.200412+0.320134 
## [117]    train-rmse:0.234084+0.016275    test-rmse:1.199873+0.320017 
## [118]    train-rmse:0.231597+0.015471    test-rmse:1.199101+0.319895 
## [119]    train-rmse:0.229871+0.015135    test-rmse:1.199676+0.318867 
## [120]    train-rmse:0.228144+0.014949    test-rmse:1.198467+0.319199 
## [121]    train-rmse:0.225510+0.015105    test-rmse:1.198493+0.318854 
## [122]    train-rmse:0.222603+0.014328    test-rmse:1.198461+0.319635 
## [123]    train-rmse:0.220852+0.014375    test-rmse:1.198809+0.318763 
## [124]    train-rmse:0.218599+0.013923    test-rmse:1.198415+0.320107 
## [125]    train-rmse:0.215251+0.013072    test-rmse:1.197158+0.321104 
## [126]    train-rmse:0.212951+0.013451    test-rmse:1.197213+0.321655 
## [127]    train-rmse:0.210673+0.013295    test-rmse:1.197237+0.321334 
## [128]    train-rmse:0.207852+0.013083    test-rmse:1.195228+0.321811 
## [129]    train-rmse:0.205423+0.013639    test-rmse:1.196056+0.322106 
## [130]    train-rmse:0.203607+0.013868    test-rmse:1.195391+0.322499 
## [131]    train-rmse:0.200206+0.013534    test-rmse:1.195193+0.322983 
## [132]    train-rmse:0.198420+0.013789    test-rmse:1.195012+0.323841 
## [133]    train-rmse:0.196692+0.014140    test-rmse:1.194362+0.324148 
## [134]    train-rmse:0.194370+0.014043    test-rmse:1.193878+0.324598 
## [135]    train-rmse:0.191432+0.012937    test-rmse:1.193827+0.325745 
## [136]    train-rmse:0.189149+0.012309    test-rmse:1.193816+0.325367 
## [137]    train-rmse:0.186556+0.012700    test-rmse:1.193317+0.325905 
## [138]    train-rmse:0.184921+0.013044    test-rmse:1.193370+0.325673 
## [139]    train-rmse:0.183203+0.012714    test-rmse:1.194455+0.326349 
## [140]    train-rmse:0.181292+0.012136    test-rmse:1.194487+0.326404 
## [141]    train-rmse:0.179148+0.012139    test-rmse:1.194544+0.325787 
## [142]    train-rmse:0.177422+0.012342    test-rmse:1.194189+0.325429 
## [143]    train-rmse:0.175860+0.012385    test-rmse:1.193903+0.325171 
## Stopping. Best iteration:
## [137]    train-rmse:0.186556+0.012700    test-rmse:1.193317+0.325905
## 
## 80 
## [1]  train-rmse:73.403534+0.086787   test-rmse:73.403435+0.516279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:50.171526+0.069998   test-rmse:50.170467+0.562705 
## [3]  train-rmse:34.428066+0.063950   test-rmse:34.425303+0.610826 
## [4]  train-rmse:23.768078+0.049908   test-rmse:23.846854+0.624752 
## [5]  train-rmse:16.527301+0.033241   test-rmse:16.614856+0.580859 
## [6]  train-rmse:11.618130+0.029376   test-rmse:11.680792+0.528546 
## [7]  train-rmse:8.297184+0.014445    test-rmse:8.395584+0.545975 
## [8]  train-rmse:6.060465+0.020962    test-rmse:6.253849+0.558111 
## [9]  train-rmse:4.561811+0.019440    test-rmse:4.758599+0.481624 
## [10] train-rmse:3.590906+0.023817    test-rmse:3.847019+0.446367 
## [11] train-rmse:2.960964+0.029861    test-rmse:3.317022+0.402283 
## [12] train-rmse:2.528626+0.013407    test-rmse:2.951213+0.337432 
## [13] train-rmse:2.247867+0.012320    test-rmse:2.695644+0.308759 
## [14] train-rmse:2.038023+0.010919    test-rmse:2.521117+0.264189 
## [15] train-rmse:1.887374+0.009299    test-rmse:2.410018+0.261757 
## [16] train-rmse:1.759557+0.008808    test-rmse:2.319292+0.262059 
## [17] train-rmse:1.650165+0.013010    test-rmse:2.222980+0.230731 
## [18] train-rmse:1.558660+0.014112    test-rmse:2.149157+0.246960 
## [19] train-rmse:1.474292+0.013752    test-rmse:2.081583+0.250269 
## [20] train-rmse:1.398734+0.016058    test-rmse:2.002376+0.229642 
## [21] train-rmse:1.316938+0.011422    test-rmse:1.950695+0.238831 
## [22] train-rmse:1.250503+0.010973    test-rmse:1.913292+0.237090 
## [23] train-rmse:1.188734+0.011616    test-rmse:1.864495+0.243845 
## [24] train-rmse:1.131706+0.011179    test-rmse:1.821006+0.246813 
## [25] train-rmse:1.073662+0.015030    test-rmse:1.782037+0.253725 
## [26] train-rmse:1.023319+0.013717    test-rmse:1.739018+0.255655 
## [27] train-rmse:0.976089+0.013593    test-rmse:1.714460+0.243454 
## [28] train-rmse:0.934397+0.014944    test-rmse:1.676337+0.257510 
## [29] train-rmse:0.893571+0.012556    test-rmse:1.650067+0.262656 
## [30] train-rmse:0.854987+0.015368    test-rmse:1.620129+0.268415 
## [31] train-rmse:0.819907+0.017177    test-rmse:1.595027+0.259259 
## [32] train-rmse:0.788106+0.015714    test-rmse:1.570946+0.252130 
## [33] train-rmse:0.757255+0.015650    test-rmse:1.552421+0.257209 
## [34] train-rmse:0.727019+0.019167    test-rmse:1.545076+0.263187 
## [35] train-rmse:0.697345+0.019149    test-rmse:1.526936+0.270452 
## [36] train-rmse:0.669352+0.016301    test-rmse:1.511123+0.266098 
## [37] train-rmse:0.646245+0.014784    test-rmse:1.496916+0.265620 
## [38] train-rmse:0.625245+0.013486    test-rmse:1.480718+0.267091 
## [39] train-rmse:0.603958+0.013202    test-rmse:1.459589+0.279734 
## [40] train-rmse:0.583044+0.012243    test-rmse:1.451481+0.283117 
## [41] train-rmse:0.561987+0.011063    test-rmse:1.443646+0.286466 
## [42] train-rmse:0.544112+0.010529    test-rmse:1.431657+0.291012 
## [43] train-rmse:0.525781+0.011900    test-rmse:1.429671+0.292095 
## [44] train-rmse:0.512816+0.011637    test-rmse:1.415391+0.296008 
## [45] train-rmse:0.498512+0.011223    test-rmse:1.406972+0.296374 
## [46] train-rmse:0.483300+0.013586    test-rmse:1.404089+0.297664 
## [47] train-rmse:0.470179+0.013024    test-rmse:1.390932+0.300587 
## [48] train-rmse:0.455880+0.014295    test-rmse:1.387994+0.304625 
## [49] train-rmse:0.444312+0.015167    test-rmse:1.382135+0.311016 
## [50] train-rmse:0.431215+0.016241    test-rmse:1.377247+0.311996 
## [51] train-rmse:0.418443+0.010372    test-rmse:1.375216+0.308123 
## [52] train-rmse:0.406254+0.012052    test-rmse:1.370598+0.305812 
## [53] train-rmse:0.395630+0.013112    test-rmse:1.364705+0.309951 
## [54] train-rmse:0.382659+0.012989    test-rmse:1.363067+0.313745 
## [55] train-rmse:0.373545+0.012416    test-rmse:1.359406+0.317885 
## [56] train-rmse:0.364112+0.014181    test-rmse:1.353288+0.311186 
## [57] train-rmse:0.354834+0.015698    test-rmse:1.348144+0.315557 
## [58] train-rmse:0.346365+0.017753    test-rmse:1.344844+0.313105 
## [59] train-rmse:0.338476+0.015767    test-rmse:1.346989+0.314374 
## [60] train-rmse:0.330390+0.013805    test-rmse:1.338395+0.315716 
## [61] train-rmse:0.322802+0.015204    test-rmse:1.337419+0.319204 
## [62] train-rmse:0.316110+0.013527    test-rmse:1.337577+0.317671 
## [63] train-rmse:0.309259+0.014339    test-rmse:1.337185+0.319823 
## [64] train-rmse:0.302718+0.015618    test-rmse:1.334074+0.320366 
## [65] train-rmse:0.297526+0.015993    test-rmse:1.328565+0.321712 
## [66] train-rmse:0.289412+0.015679    test-rmse:1.328339+0.322544 
## [67] train-rmse:0.284895+0.015914    test-rmse:1.326768+0.321880 
## [68] train-rmse:0.279583+0.016653    test-rmse:1.326426+0.320413 
## [69] train-rmse:0.273234+0.017375    test-rmse:1.324947+0.322101 
## [70] train-rmse:0.267790+0.015719    test-rmse:1.324131+0.324005 
## [71] train-rmse:0.263115+0.014966    test-rmse:1.323378+0.322832 
## [72] train-rmse:0.255818+0.014886    test-rmse:1.317484+0.319652 
## [73] train-rmse:0.251658+0.015438    test-rmse:1.316046+0.320473 
## [74] train-rmse:0.247774+0.014453    test-rmse:1.316109+0.319457 
## [75] train-rmse:0.242305+0.013913    test-rmse:1.315395+0.320547 
## [76] train-rmse:0.238158+0.013678    test-rmse:1.314317+0.320973 
## [77] train-rmse:0.233252+0.011594    test-rmse:1.314088+0.321356 
## [78] train-rmse:0.228854+0.011740    test-rmse:1.313194+0.323749 
## [79] train-rmse:0.224222+0.011747    test-rmse:1.313003+0.324322 
## [80] train-rmse:0.219644+0.012391    test-rmse:1.311695+0.323308 
## [81] train-rmse:0.215224+0.013110    test-rmse:1.311778+0.323580 
## [82] train-rmse:0.210017+0.013361    test-rmse:1.308681+0.323707 
## [83] train-rmse:0.206530+0.013503    test-rmse:1.306685+0.323847 
## [84] train-rmse:0.201701+0.012749    test-rmse:1.306755+0.323374 
## [85] train-rmse:0.198381+0.012883    test-rmse:1.304912+0.323470 
## [86] train-rmse:0.194625+0.012511    test-rmse:1.305731+0.322247 
## [87] train-rmse:0.191301+0.013400    test-rmse:1.305339+0.321810 
## [88] train-rmse:0.188644+0.013074    test-rmse:1.304445+0.324280 
## [89] train-rmse:0.183149+0.012358    test-rmse:1.302401+0.322893 
## [90] train-rmse:0.180938+0.012206    test-rmse:1.301249+0.322815 
## [91] train-rmse:0.177383+0.012178    test-rmse:1.301167+0.323298 
## [92] train-rmse:0.174815+0.012172    test-rmse:1.301333+0.322313 
## [93] train-rmse:0.171834+0.012087    test-rmse:1.301434+0.322118 
## [94] train-rmse:0.168562+0.012922    test-rmse:1.303500+0.321433 
## [95] train-rmse:0.166127+0.012811    test-rmse:1.302910+0.321518 
## [96] train-rmse:0.162954+0.012199    test-rmse:1.301833+0.321393 
## [97] train-rmse:0.160065+0.012497    test-rmse:1.301737+0.321760 
## Stopping. Best iteration:
## [91] train-rmse:0.177383+0.012178    test-rmse:1.301167+0.323298
## 
## 81 
## [1]  train-rmse:73.403534+0.086787   test-rmse:73.403435+0.516279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:50.171526+0.069998   test-rmse:50.170467+0.562705 
## [3]  train-rmse:34.428066+0.063950   test-rmse:34.425303+0.610826 
## [4]  train-rmse:23.768078+0.049908   test-rmse:23.846854+0.624752 
## [5]  train-rmse:16.527301+0.033241   test-rmse:16.614856+0.580859 
## [6]  train-rmse:11.604444+0.028975   test-rmse:11.681338+0.520687 
## [7]  train-rmse:8.256470+0.009775    test-rmse:8.374769+0.567834 
## [8]  train-rmse:5.984915+0.020043    test-rmse:6.126638+0.502057 
## [9]  train-rmse:4.440862+0.024047    test-rmse:4.624830+0.446275 
## [10] train-rmse:3.413823+0.015083    test-rmse:3.683298+0.420204 
## [11] train-rmse:2.731005+0.030628    test-rmse:3.063602+0.361764 
## [12] train-rmse:2.270870+0.037634    test-rmse:2.663004+0.291442 
## [13] train-rmse:1.953868+0.027245    test-rmse:2.411785+0.266230 
## [14] train-rmse:1.729666+0.027673    test-rmse:2.220943+0.233273 
## [15] train-rmse:1.562107+0.032079    test-rmse:2.096521+0.197043 
## [16] train-rmse:1.427556+0.035765    test-rmse:2.029886+0.195952 
## [17] train-rmse:1.313049+0.033603    test-rmse:1.934710+0.221225 
## [18] train-rmse:1.215910+0.034194    test-rmse:1.862148+0.217033 
## [19] train-rmse:1.123264+0.023111    test-rmse:1.796555+0.206411 
## [20] train-rmse:1.052204+0.021540    test-rmse:1.744625+0.218288 
## [21] train-rmse:0.986048+0.021867    test-rmse:1.702913+0.217992 
## [22] train-rmse:0.926113+0.019127    test-rmse:1.672606+0.221833 
## [23] train-rmse:0.869992+0.017093    test-rmse:1.636797+0.220389 
## [24] train-rmse:0.819554+0.015162    test-rmse:1.598233+0.215329 
## [25] train-rmse:0.775199+0.013897    test-rmse:1.565724+0.218920 
## [26] train-rmse:0.732746+0.009443    test-rmse:1.547072+0.221269 
## [27] train-rmse:0.696744+0.008165    test-rmse:1.525783+0.224809 
## [28] train-rmse:0.663543+0.009751    test-rmse:1.509458+0.225159 
## [29] train-rmse:0.626111+0.015146    test-rmse:1.485236+0.228547 
## [30] train-rmse:0.590544+0.014698    test-rmse:1.469813+0.236412 
## [31] train-rmse:0.560386+0.013015    test-rmse:1.458062+0.237317 
## [32] train-rmse:0.537073+0.013288    test-rmse:1.448392+0.234850 
## [33] train-rmse:0.512416+0.013982    test-rmse:1.429235+0.238907 
## [34] train-rmse:0.491605+0.014824    test-rmse:1.425277+0.240574 
## [35] train-rmse:0.470521+0.015076    test-rmse:1.407813+0.247141 
## [36] train-rmse:0.452914+0.014980    test-rmse:1.400815+0.248234 
## [37] train-rmse:0.435433+0.015027    test-rmse:1.392908+0.251297 
## [38] train-rmse:0.418195+0.016193    test-rmse:1.389254+0.253468 
## [39] train-rmse:0.404207+0.017940    test-rmse:1.385658+0.253223 
## [40] train-rmse:0.390502+0.016252    test-rmse:1.371587+0.250827 
## [41] train-rmse:0.374991+0.017169    test-rmse:1.366667+0.247974 
## [42] train-rmse:0.364885+0.018178    test-rmse:1.361191+0.247009 
## [43] train-rmse:0.353253+0.017440    test-rmse:1.356595+0.251118 
## [44] train-rmse:0.341997+0.017666    test-rmse:1.352775+0.256335 
## [45] train-rmse:0.330470+0.017643    test-rmse:1.349339+0.257300 
## [46] train-rmse:0.321067+0.016362    test-rmse:1.350961+0.267345 
## [47] train-rmse:0.311824+0.017015    test-rmse:1.348209+0.267137 
## [48] train-rmse:0.298484+0.017234    test-rmse:1.344245+0.266439 
## [49] train-rmse:0.289265+0.015983    test-rmse:1.346054+0.270752 
## [50] train-rmse:0.279271+0.014788    test-rmse:1.345811+0.274510 
## [51] train-rmse:0.270095+0.015021    test-rmse:1.343201+0.275840 
## [52] train-rmse:0.261899+0.014016    test-rmse:1.340029+0.274938 
## [53] train-rmse:0.254117+0.014808    test-rmse:1.339207+0.275307 
## [54] train-rmse:0.242385+0.010615    test-rmse:1.341554+0.277465 
## [55] train-rmse:0.236358+0.010879    test-rmse:1.337625+0.280881 
## [56] train-rmse:0.230086+0.010391    test-rmse:1.335403+0.282016 
## [57] train-rmse:0.222549+0.011286    test-rmse:1.335808+0.283760 
## [58] train-rmse:0.217497+0.011427    test-rmse:1.335108+0.283409 
## [59] train-rmse:0.211673+0.010859    test-rmse:1.334629+0.284237 
## [60] train-rmse:0.206033+0.012280    test-rmse:1.332786+0.284096 
## [61] train-rmse:0.200556+0.013237    test-rmse:1.331069+0.284617 
## [62] train-rmse:0.193322+0.012929    test-rmse:1.331311+0.283043 
## [63] train-rmse:0.185104+0.010543    test-rmse:1.329062+0.284459 
## [64] train-rmse:0.179859+0.009385    test-rmse:1.327258+0.284027 
## [65] train-rmse:0.176018+0.010010    test-rmse:1.327674+0.283800 
## [66] train-rmse:0.170788+0.010223    test-rmse:1.326963+0.287356 
## [67] train-rmse:0.165028+0.006995    test-rmse:1.325982+0.287181 
## [68] train-rmse:0.160733+0.006127    test-rmse:1.328246+0.287140 
## [69] train-rmse:0.155726+0.006738    test-rmse:1.326038+0.285463 
## [70] train-rmse:0.152322+0.006921    test-rmse:1.326423+0.285056 
## [71] train-rmse:0.147646+0.006144    test-rmse:1.325259+0.285887 
## [72] train-rmse:0.144432+0.005430    test-rmse:1.322817+0.287682 
## [73] train-rmse:0.142127+0.005146    test-rmse:1.321822+0.287864 
## [74] train-rmse:0.136676+0.005358    test-rmse:1.321143+0.288650 
## [75] train-rmse:0.133130+0.004509    test-rmse:1.321608+0.289473 
## [76] train-rmse:0.129451+0.004170    test-rmse:1.321570+0.289387 
## [77] train-rmse:0.126388+0.003921    test-rmse:1.321494+0.289910 
## [78] train-rmse:0.122486+0.004242    test-rmse:1.322117+0.290219 
## [79] train-rmse:0.118995+0.004851    test-rmse:1.319845+0.288223 
## [80] train-rmse:0.114728+0.004011    test-rmse:1.320024+0.287161 
## [81] train-rmse:0.111259+0.004202    test-rmse:1.318991+0.287392 
## [82] train-rmse:0.108725+0.004205    test-rmse:1.319416+0.287406 
## [83] train-rmse:0.105519+0.004771    test-rmse:1.318890+0.287225 
## [84] train-rmse:0.102134+0.005331    test-rmse:1.318128+0.286540 
## [85] train-rmse:0.100126+0.005167    test-rmse:1.317222+0.285998 
## [86] train-rmse:0.097425+0.004789    test-rmse:1.316321+0.286391 
## [87] train-rmse:0.094822+0.004602    test-rmse:1.316276+0.286391 
## [88] train-rmse:0.093116+0.004566    test-rmse:1.316094+0.286127 
## [89] train-rmse:0.090943+0.003901    test-rmse:1.315779+0.286325 
## [90] train-rmse:0.089060+0.004549    test-rmse:1.314830+0.286029 
## [91] train-rmse:0.086529+0.003758    test-rmse:1.314674+0.285808 
## [92] train-rmse:0.084270+0.004037    test-rmse:1.313774+0.285051 
## [93] train-rmse:0.082199+0.003653    test-rmse:1.313809+0.285479 
## [94] train-rmse:0.080288+0.003945    test-rmse:1.314182+0.285048 
## [95] train-rmse:0.078343+0.003422    test-rmse:1.314148+0.284265 
## [96] train-rmse:0.076017+0.004363    test-rmse:1.314373+0.284591 
## [97] train-rmse:0.074557+0.004607    test-rmse:1.313795+0.284978 
## [98] train-rmse:0.072832+0.005071    test-rmse:1.313992+0.285119 
## Stopping. Best iteration:
## [92] train-rmse:0.084270+0.004037    test-rmse:1.313774+0.285051
## 
## 82 
## [1]  train-rmse:73.403534+0.086787   test-rmse:73.403435+0.516279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:50.171526+0.069998   test-rmse:50.170467+0.562705 
## [3]  train-rmse:34.428066+0.063950   test-rmse:34.425303+0.610826 
## [4]  train-rmse:23.768078+0.049908   test-rmse:23.846854+0.624752 
## [5]  train-rmse:16.527301+0.033241   test-rmse:16.614856+0.580859 
## [6]  train-rmse:11.594831+0.029443   test-rmse:11.674758+0.539187 
## [7]  train-rmse:8.229022+0.015288    test-rmse:8.394382+0.583780 
## [8]  train-rmse:5.927856+0.025414    test-rmse:6.104014+0.523683 
## [9]  train-rmse:4.368850+0.039145    test-rmse:4.560961+0.488387 
## [10] train-rmse:3.315135+0.049764    test-rmse:3.578472+0.485208 
## [11] train-rmse:2.582013+0.046744    test-rmse:2.955050+0.390031 
## [12] train-rmse:2.096748+0.050730    test-rmse:2.534167+0.362140 
## [13] train-rmse:1.758217+0.047882    test-rmse:2.226690+0.324987 
## [14] train-rmse:1.531106+0.049330    test-rmse:2.050935+0.312035 
## [15] train-rmse:1.335220+0.028077    test-rmse:1.929301+0.285143 
## [16] train-rmse:1.199822+0.022217    test-rmse:1.828215+0.310134 
## [17] train-rmse:1.083877+0.013732    test-rmse:1.731624+0.312104 
## [18] train-rmse:0.979929+0.023258    test-rmse:1.668301+0.306866 
## [19] train-rmse:0.897807+0.016420    test-rmse:1.601575+0.305659 
## [20] train-rmse:0.830695+0.019904    test-rmse:1.555415+0.322626 
## [21] train-rmse:0.769230+0.015287    test-rmse:1.517512+0.316654 
## [22] train-rmse:0.715533+0.012383    test-rmse:1.475168+0.323600 
## [23] train-rmse:0.667762+0.012893    test-rmse:1.450388+0.322044 
## [24] train-rmse:0.626143+0.013502    test-rmse:1.432196+0.325844 
## [25] train-rmse:0.584870+0.016476    test-rmse:1.405851+0.329896 
## [26] train-rmse:0.548668+0.016522    test-rmse:1.384688+0.338666 
## [27] train-rmse:0.514993+0.013725    test-rmse:1.365544+0.338711 
## [28] train-rmse:0.485697+0.011789    test-rmse:1.351939+0.339627 
## [29] train-rmse:0.456357+0.018164    test-rmse:1.335367+0.343100 
## [30] train-rmse:0.433187+0.016849    test-rmse:1.329328+0.345132 
## [31] train-rmse:0.414126+0.016003    test-rmse:1.316987+0.345426 
## [32] train-rmse:0.395798+0.016115    test-rmse:1.300478+0.350442 
## [33] train-rmse:0.377249+0.017351    test-rmse:1.293207+0.352411 
## [34] train-rmse:0.361835+0.017500    test-rmse:1.285480+0.352919 
## [35] train-rmse:0.340529+0.013607    test-rmse:1.276349+0.349006 
## [36] train-rmse:0.323253+0.015564    test-rmse:1.273191+0.353251 
## [37] train-rmse:0.310449+0.014010    test-rmse:1.268750+0.356420 
## [38] train-rmse:0.292996+0.011389    test-rmse:1.263309+0.359081 
## [39] train-rmse:0.280071+0.011406    test-rmse:1.259759+0.360553 
## [40] train-rmse:0.271642+0.011130    test-rmse:1.251893+0.362210 
## [41] train-rmse:0.254778+0.011484    test-rmse:1.245713+0.366293 
## [42] train-rmse:0.242798+0.009331    test-rmse:1.245325+0.366542 
## [43] train-rmse:0.228962+0.004636    test-rmse:1.242600+0.369236 
## [44] train-rmse:0.219545+0.004953    test-rmse:1.241022+0.371023 
## [45] train-rmse:0.210041+0.006277    test-rmse:1.239430+0.370956 
## [46] train-rmse:0.201946+0.007151    test-rmse:1.237544+0.369676 
## [47] train-rmse:0.195006+0.006709    test-rmse:1.234163+0.369624 
## [48] train-rmse:0.187180+0.006072    test-rmse:1.232661+0.370609 
## [49] train-rmse:0.179488+0.006415    test-rmse:1.231561+0.372833 
## [50] train-rmse:0.171223+0.005745    test-rmse:1.230346+0.375620 
## [51] train-rmse:0.164883+0.005926    test-rmse:1.228234+0.376075 
## [52] train-rmse:0.157370+0.006156    test-rmse:1.226590+0.376885 
## [53] train-rmse:0.152342+0.005834    test-rmse:1.226214+0.377910 
## [54] train-rmse:0.146755+0.006682    test-rmse:1.225921+0.377680 
## [55] train-rmse:0.142156+0.007303    test-rmse:1.225234+0.377489 
## [56] train-rmse:0.135524+0.007713    test-rmse:1.222613+0.379623 
## [57] train-rmse:0.131004+0.006082    test-rmse:1.222986+0.379671 
## [58] train-rmse:0.124771+0.004784    test-rmse:1.219255+0.378983 
## [59] train-rmse:0.120528+0.005019    test-rmse:1.217647+0.378326 
## [60] train-rmse:0.117366+0.004074    test-rmse:1.216069+0.376695 
## [61] train-rmse:0.112643+0.004188    test-rmse:1.214938+0.377580 
## [62] train-rmse:0.109980+0.004448    test-rmse:1.214219+0.377797 
## [63] train-rmse:0.106523+0.003919    test-rmse:1.213140+0.378697 
## [64] train-rmse:0.101749+0.003442    test-rmse:1.212193+0.378171 
## [65] train-rmse:0.096774+0.001971    test-rmse:1.210936+0.378689 
## [66] train-rmse:0.092298+0.003007    test-rmse:1.210692+0.378367 
## [67] train-rmse:0.088560+0.002277    test-rmse:1.209911+0.378298 
## [68] train-rmse:0.086194+0.002728    test-rmse:1.209649+0.379044 
## [69] train-rmse:0.082766+0.002865    test-rmse:1.208970+0.380041 
## [70] train-rmse:0.079224+0.002379    test-rmse:1.208673+0.380874 
## [71] train-rmse:0.076927+0.002471    test-rmse:1.208182+0.381337 
## [72] train-rmse:0.073454+0.002892    test-rmse:1.207058+0.381751 
## [73] train-rmse:0.070542+0.001605    test-rmse:1.206921+0.380727 
## [74] train-rmse:0.068104+0.002203    test-rmse:1.206762+0.380740 
## [75] train-rmse:0.065096+0.002540    test-rmse:1.206421+0.380620 
## [76] train-rmse:0.062444+0.002299    test-rmse:1.206088+0.380158 
## [77] train-rmse:0.060345+0.002356    test-rmse:1.205418+0.379794 
## [78] train-rmse:0.058488+0.002184    test-rmse:1.204944+0.379749 
## [79] train-rmse:0.055987+0.002480    test-rmse:1.204964+0.380078 
## [80] train-rmse:0.053898+0.002645    test-rmse:1.204368+0.379593 
## [81] train-rmse:0.052469+0.002838    test-rmse:1.204541+0.380013 
## [82] train-rmse:0.050772+0.003042    test-rmse:1.204412+0.379778 
## [83] train-rmse:0.049019+0.002760    test-rmse:1.204544+0.379778 
## [84] train-rmse:0.047128+0.002470    test-rmse:1.204676+0.380120 
## [85] train-rmse:0.045412+0.002337    test-rmse:1.204534+0.380068 
## [86] train-rmse:0.044025+0.002434    test-rmse:1.204610+0.380267 
## Stopping. Best iteration:
## [80] train-rmse:0.053898+0.002645    test-rmse:1.204368+0.379593
## 
## 83 
## [1]  train-rmse:73.403534+0.086787   test-rmse:73.403435+0.516279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:50.171526+0.069998   test-rmse:50.170467+0.562705 
## [3]  train-rmse:34.428066+0.063950   test-rmse:34.425303+0.610826 
## [4]  train-rmse:23.768078+0.049908   test-rmse:23.846854+0.624752 
## [5]  train-rmse:16.527301+0.033241   test-rmse:16.614856+0.580859 
## [6]  train-rmse:11.585745+0.030946   test-rmse:11.673044+0.530818 
## [7]  train-rmse:8.204847+0.014718    test-rmse:8.349196+0.587266 
## [8]  train-rmse:5.882313+0.026353    test-rmse:6.069472+0.526283 
## [9]  train-rmse:4.288162+0.028743    test-rmse:4.512218+0.468776 
## [10] train-rmse:3.202592+0.033952    test-rmse:3.505807+0.506332 
## [11] train-rmse:2.450884+0.029988    test-rmse:2.821346+0.443735 
## [12] train-rmse:1.944614+0.025867    test-rmse:2.378345+0.440068 
## [13] train-rmse:1.589521+0.030304    test-rmse:2.087567+0.376915 
## [14] train-rmse:1.338829+0.032732    test-rmse:1.906410+0.345096 
## [15] train-rmse:1.154348+0.028921    test-rmse:1.794547+0.352635 
## [16] train-rmse:1.009118+0.045697    test-rmse:1.711926+0.369311 
## [17] train-rmse:0.888579+0.044100    test-rmse:1.641792+0.357136 
## [18] train-rmse:0.792402+0.037251    test-rmse:1.575279+0.363185 
## [19] train-rmse:0.727065+0.036836    test-rmse:1.511064+0.360271 
## [20] train-rmse:0.664941+0.036006    test-rmse:1.474911+0.358263 
## [21] train-rmse:0.609177+0.036974    test-rmse:1.440973+0.365477 
## [22] train-rmse:0.564153+0.035580    test-rmse:1.422181+0.375601 
## [23] train-rmse:0.523334+0.029924    test-rmse:1.408899+0.372634 
## [24] train-rmse:0.481565+0.037195    test-rmse:1.395235+0.373699 
## [25] train-rmse:0.450489+0.031629    test-rmse:1.382229+0.374127 
## [26] train-rmse:0.421836+0.027996    test-rmse:1.367814+0.386171 
## [27] train-rmse:0.393308+0.030234    test-rmse:1.354505+0.387260 
## [28] train-rmse:0.367128+0.031356    test-rmse:1.346408+0.392485 
## [29] train-rmse:0.347218+0.030302    test-rmse:1.340094+0.389048 
## [30] train-rmse:0.325606+0.026497    test-rmse:1.335816+0.391459 
## [31] train-rmse:0.305943+0.021054    test-rmse:1.329642+0.391194 
## [32] train-rmse:0.290989+0.021415    test-rmse:1.319792+0.393191 
## [33] train-rmse:0.277992+0.022633    test-rmse:1.314913+0.391494 
## [34] train-rmse:0.258110+0.018478    test-rmse:1.308981+0.399227 
## [35] train-rmse:0.245121+0.015175    test-rmse:1.306354+0.399130 
## [36] train-rmse:0.232528+0.017240    test-rmse:1.299760+0.399394 
## [37] train-rmse:0.217782+0.015699    test-rmse:1.295911+0.396777 
## [38] train-rmse:0.203887+0.015479    test-rmse:1.291920+0.398177 
## [39] train-rmse:0.193837+0.015407    test-rmse:1.290183+0.397832 
## [40] train-rmse:0.183529+0.015850    test-rmse:1.288926+0.397619 
## [41] train-rmse:0.173437+0.015466    test-rmse:1.286747+0.398735 
## [42] train-rmse:0.163978+0.016646    test-rmse:1.284997+0.400531 
## [43] train-rmse:0.158768+0.016534    test-rmse:1.283845+0.401001 
## [44] train-rmse:0.152289+0.017863    test-rmse:1.282741+0.400561 
## [45] train-rmse:0.145424+0.015944    test-rmse:1.278987+0.401190 
## [46] train-rmse:0.137874+0.014137    test-rmse:1.280352+0.405159 
## [47] train-rmse:0.130639+0.015654    test-rmse:1.278001+0.404852 
## [48] train-rmse:0.122156+0.011270    test-rmse:1.276864+0.405034 
## [49] train-rmse:0.115674+0.010645    test-rmse:1.277324+0.406232 
## [50] train-rmse:0.110071+0.010887    test-rmse:1.275689+0.405233 
## [51] train-rmse:0.106245+0.010698    test-rmse:1.274018+0.404320 
## [52] train-rmse:0.100298+0.009455    test-rmse:1.274037+0.404088 
## [53] train-rmse:0.095954+0.008968    test-rmse:1.274522+0.404769 
## [54] train-rmse:0.091457+0.009796    test-rmse:1.273974+0.405100 
## [55] train-rmse:0.085720+0.009285    test-rmse:1.273582+0.405944 
## [56] train-rmse:0.080871+0.007869    test-rmse:1.273512+0.405811 
## [57] train-rmse:0.077259+0.007943    test-rmse:1.274067+0.406365 
## [58] train-rmse:0.073877+0.006952    test-rmse:1.273822+0.406805 
## [59] train-rmse:0.069914+0.006589    test-rmse:1.274125+0.407561 
## [60] train-rmse:0.066556+0.006347    test-rmse:1.274274+0.408474 
## [61] train-rmse:0.062491+0.005379    test-rmse:1.273417+0.408303 
## [62] train-rmse:0.059794+0.005163    test-rmse:1.273980+0.408536 
## [63] train-rmse:0.057582+0.005194    test-rmse:1.273358+0.409206 
## [64] train-rmse:0.054553+0.005247    test-rmse:1.273116+0.409011 
## [65] train-rmse:0.051999+0.005370    test-rmse:1.273264+0.408890 
## [66] train-rmse:0.050078+0.005435    test-rmse:1.273043+0.408609 
## [67] train-rmse:0.048271+0.005459    test-rmse:1.272798+0.408779 
## [68] train-rmse:0.045998+0.005281    test-rmse:1.272562+0.408950 
## [69] train-rmse:0.043500+0.004632    test-rmse:1.272173+0.408957 
## [70] train-rmse:0.041472+0.004568    test-rmse:1.272314+0.409059 
## [71] train-rmse:0.039490+0.004165    test-rmse:1.271727+0.408949 
## [72] train-rmse:0.037001+0.004257    test-rmse:1.271275+0.408812 
## [73] train-rmse:0.035767+0.003691    test-rmse:1.271417+0.409133 
## [74] train-rmse:0.034322+0.003634    test-rmse:1.271841+0.409511 
## [75] train-rmse:0.032959+0.003445    test-rmse:1.271823+0.409570 
## [76] train-rmse:0.031413+0.003595    test-rmse:1.271669+0.409413 
## [77] train-rmse:0.029837+0.002823    test-rmse:1.271382+0.409323 
## [78] train-rmse:0.028635+0.002988    test-rmse:1.271201+0.409253 
## [79] train-rmse:0.027364+0.002579    test-rmse:1.271381+0.409418 
## [80] train-rmse:0.026250+0.002704    test-rmse:1.271348+0.409436 
## [81] train-rmse:0.025269+0.002748    test-rmse:1.271329+0.409595 
## [82] train-rmse:0.024292+0.002606    test-rmse:1.271189+0.409674 
## [83] train-rmse:0.023551+0.002524    test-rmse:1.271298+0.409701 
## [84] train-rmse:0.022439+0.002258    test-rmse:1.271086+0.409574 
## [85] train-rmse:0.021422+0.002254    test-rmse:1.271161+0.409791 
## [86] train-rmse:0.020143+0.002032    test-rmse:1.271366+0.409707 
## [87] train-rmse:0.019480+0.001848    test-rmse:1.271246+0.409759 
## [88] train-rmse:0.018317+0.001950    test-rmse:1.271367+0.409881 
## [89] train-rmse:0.017455+0.001824    test-rmse:1.271609+0.409942 
## [90] train-rmse:0.016682+0.001718    test-rmse:1.271655+0.409866 
## Stopping. Best iteration:
## [84] train-rmse:0.022439+0.002258    test-rmse:1.271086+0.409574
## 
## 84 
## [1]  train-rmse:71.268932+0.085068   test-rmse:71.268778+0.519929 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:47.317069+0.068405   test-rmse:47.315810+0.569907 
## [3]  train-rmse:31.577981+0.063944   test-rmse:31.574661+0.622724 
## [4]  train-rmse:21.270000+0.059695   test-rmse:21.349113+0.640231 
## [5]  train-rmse:14.545765+0.051633   test-rmse:14.599417+0.639019 
## [6]  train-rmse:10.236668+0.053984   test-rmse:10.325185+0.593650 
## [7]  train-rmse:7.537961+0.057525    test-rmse:7.681887+0.677644 
## [8]  train-rmse:5.915556+0.062329    test-rmse:6.012658+0.617990 
## [9]  train-rmse:4.971713+0.068833    test-rmse:5.072588+0.522516 
## [10] train-rmse:4.423832+0.063148    test-rmse:4.566844+0.547872 
## [11] train-rmse:4.098285+0.066679    test-rmse:4.209128+0.473803 
## [12] train-rmse:3.898362+0.066919    test-rmse:4.080565+0.444477 
## [13] train-rmse:3.755117+0.064991    test-rmse:3.926346+0.425111 
## [14] train-rmse:3.640679+0.060984    test-rmse:3.758801+0.366186 
## [15] train-rmse:3.546171+0.060252    test-rmse:3.720384+0.363066 
## [16] train-rmse:3.464328+0.057100    test-rmse:3.673290+0.348728 
## [17] train-rmse:3.386064+0.056027    test-rmse:3.608508+0.364767 
## [18] train-rmse:3.316454+0.053991    test-rmse:3.578446+0.368854 
## [19] train-rmse:3.247506+0.051231    test-rmse:3.486771+0.329625 
## [20] train-rmse:3.179847+0.048165    test-rmse:3.440676+0.321662 
## [21] train-rmse:3.114966+0.047246    test-rmse:3.386578+0.322407 
## [22] train-rmse:3.053758+0.046478    test-rmse:3.331661+0.306948 
## [23] train-rmse:2.994473+0.044479    test-rmse:3.246532+0.276491 
## [24] train-rmse:2.937067+0.042919    test-rmse:3.171975+0.265718 
## [25] train-rmse:2.884367+0.040684    test-rmse:3.131520+0.281846 
## [26] train-rmse:2.834734+0.040109    test-rmse:3.083805+0.294841 
## [27] train-rmse:2.787651+0.039679    test-rmse:3.017897+0.318379 
## [28] train-rmse:2.741848+0.039797    test-rmse:2.995660+0.331070 
## [29] train-rmse:2.696533+0.040866    test-rmse:2.969803+0.323865 
## [30] train-rmse:2.652957+0.040671    test-rmse:2.929613+0.324540 
## [31] train-rmse:2.609976+0.041556    test-rmse:2.892175+0.319135 
## [32] train-rmse:2.569196+0.041576    test-rmse:2.834450+0.274927 
## [33] train-rmse:2.529032+0.040984    test-rmse:2.801827+0.285824 
## [34] train-rmse:2.490076+0.040846    test-rmse:2.765802+0.301414 
## [35] train-rmse:2.452038+0.039452    test-rmse:2.734139+0.308664 
## [36] train-rmse:2.414436+0.038031    test-rmse:2.718506+0.292038 
## [37] train-rmse:2.377447+0.037049    test-rmse:2.670805+0.300431 
## [38] train-rmse:2.341081+0.035300    test-rmse:2.612236+0.282523 
## [39] train-rmse:2.306748+0.034033    test-rmse:2.573783+0.271249 
## [40] train-rmse:2.274490+0.033163    test-rmse:2.538820+0.284409 
## [41] train-rmse:2.243240+0.033057    test-rmse:2.514893+0.296602 
## [42] train-rmse:2.212687+0.032284    test-rmse:2.483818+0.308109 
## [43] train-rmse:2.182137+0.032075    test-rmse:2.453716+0.286792 
## [44] train-rmse:2.152138+0.032004    test-rmse:2.426236+0.291891 
## [45] train-rmse:2.123380+0.032437    test-rmse:2.405484+0.291405 
## [46] train-rmse:2.094948+0.032569    test-rmse:2.382054+0.282647 
## [47] train-rmse:2.066985+0.031650    test-rmse:2.354555+0.287440 
## [48] train-rmse:2.038792+0.030931    test-rmse:2.335079+0.289574 
## [49] train-rmse:2.010856+0.029408    test-rmse:2.322828+0.290183 
## [50] train-rmse:1.983905+0.027229    test-rmse:2.291685+0.280009 
## [51] train-rmse:1.957650+0.025785    test-rmse:2.251826+0.279113 
## [52] train-rmse:1.932442+0.025235    test-rmse:2.236025+0.274868 
## [53] train-rmse:1.907296+0.024977    test-rmse:2.201260+0.266263 
## [54] train-rmse:1.882786+0.024458    test-rmse:2.169078+0.280759 
## [55] train-rmse:1.858718+0.023668    test-rmse:2.126533+0.256063 
## [56] train-rmse:1.835391+0.022372    test-rmse:2.099398+0.256534 
## [57] train-rmse:1.812801+0.021098    test-rmse:2.082043+0.256272 
## [58] train-rmse:1.790735+0.020356    test-rmse:2.053145+0.250618 
## [59] train-rmse:1.769376+0.020436    test-rmse:2.039515+0.253000 
## [60] train-rmse:1.748032+0.020071    test-rmse:2.029425+0.251918 
## [61] train-rmse:1.727690+0.019349    test-rmse:2.013509+0.243957 
## [62] train-rmse:1.707348+0.018499    test-rmse:1.986556+0.226126 
## [63] train-rmse:1.688145+0.017778    test-rmse:1.974296+0.229212 
## [64] train-rmse:1.668876+0.017268    test-rmse:1.961757+0.232696 
## [65] train-rmse:1.649582+0.016933    test-rmse:1.945402+0.228738 
## [66] train-rmse:1.630680+0.017152    test-rmse:1.927230+0.240146 
## [67] train-rmse:1.612235+0.017448    test-rmse:1.926022+0.239933 
## [68] train-rmse:1.593975+0.017772    test-rmse:1.910133+0.237350 
## [69] train-rmse:1.576048+0.017936    test-rmse:1.895237+0.234634 
## [70] train-rmse:1.559295+0.017706    test-rmse:1.869536+0.228392 
## [71] train-rmse:1.542597+0.017630    test-rmse:1.853271+0.238318 
## [72] train-rmse:1.526238+0.017234    test-rmse:1.829841+0.217353 
## [73] train-rmse:1.510212+0.016646    test-rmse:1.810456+0.220595 
## [74] train-rmse:1.494527+0.015922    test-rmse:1.795108+0.227044 
## [75] train-rmse:1.479286+0.015867    test-rmse:1.779685+0.221466 
## [76] train-rmse:1.464384+0.015941    test-rmse:1.768699+0.220821 
## [77] train-rmse:1.449579+0.015814    test-rmse:1.753463+0.222724 
## [78] train-rmse:1.435006+0.015819    test-rmse:1.747779+0.223695 
## [79] train-rmse:1.420549+0.015625    test-rmse:1.729663+0.220349 
## [80] train-rmse:1.406502+0.015014    test-rmse:1.709525+0.222807 
## [81] train-rmse:1.392716+0.014579    test-rmse:1.690998+0.213009 
## [82] train-rmse:1.379207+0.014332    test-rmse:1.682932+0.215676 
## [83] train-rmse:1.365766+0.013875    test-rmse:1.672992+0.216500 
## [84] train-rmse:1.352366+0.013570    test-rmse:1.665597+0.212926 
## [85] train-rmse:1.339409+0.013270    test-rmse:1.657303+0.208964 
## [86] train-rmse:1.326768+0.012988    test-rmse:1.649226+0.208433 
## [87] train-rmse:1.315071+0.012632    test-rmse:1.630222+0.209930 
## [88] train-rmse:1.303750+0.012489    test-rmse:1.621645+0.213169 
## [89] train-rmse:1.292642+0.012383    test-rmse:1.609896+0.216118 
## [90] train-rmse:1.281500+0.012260    test-rmse:1.608048+0.215570 
## [91] train-rmse:1.270313+0.011789    test-rmse:1.587285+0.220185 
## [92] train-rmse:1.259859+0.011713    test-rmse:1.585684+0.220321 
## [93] train-rmse:1.249463+0.011848    test-rmse:1.578308+0.221655 
## [94] train-rmse:1.239134+0.011746    test-rmse:1.569185+0.227673 
## [95] train-rmse:1.229052+0.011878    test-rmse:1.562024+0.231725 
## [96] train-rmse:1.219055+0.012048    test-rmse:1.550403+0.232698 
## [97] train-rmse:1.209140+0.012332    test-rmse:1.530665+0.213851 
## [98] train-rmse:1.199418+0.012585    test-rmse:1.523692+0.215551 
## [99] train-rmse:1.189953+0.012368    test-rmse:1.513795+0.206272 
## [100]    train-rmse:1.180647+0.012214    test-rmse:1.504588+0.200506 
## [101]    train-rmse:1.171828+0.012141    test-rmse:1.495603+0.199306 
## [102]    train-rmse:1.162977+0.012106    test-rmse:1.490194+0.202015 
## [103]    train-rmse:1.154384+0.012113    test-rmse:1.478604+0.203576 
## [104]    train-rmse:1.146053+0.011997    test-rmse:1.471010+0.201590 
## [105]    train-rmse:1.137799+0.011869    test-rmse:1.460263+0.199854 
## [106]    train-rmse:1.129531+0.011735    test-rmse:1.451789+0.197440 
## [107]    train-rmse:1.121346+0.011766    test-rmse:1.439753+0.198696 
## [108]    train-rmse:1.113316+0.011700    test-rmse:1.431098+0.203025 
## [109]    train-rmse:1.105539+0.011758    test-rmse:1.424976+0.202091 
## [110]    train-rmse:1.097945+0.011749    test-rmse:1.422706+0.203715 
## [111]    train-rmse:1.090858+0.011573    test-rmse:1.420759+0.201137 
## [112]    train-rmse:1.083798+0.011490    test-rmse:1.407483+0.198124 
## [113]    train-rmse:1.076934+0.011505    test-rmse:1.394547+0.206878 
## [114]    train-rmse:1.070162+0.011481    test-rmse:1.389082+0.208773 
## [115]    train-rmse:1.063601+0.011468    test-rmse:1.387253+0.209475 
## [116]    train-rmse:1.057085+0.011414    test-rmse:1.381816+0.209281 
## [117]    train-rmse:1.050779+0.011286    test-rmse:1.382333+0.208509 
## [118]    train-rmse:1.044591+0.011223    test-rmse:1.375166+0.214864 
## [119]    train-rmse:1.038601+0.011112    test-rmse:1.372074+0.214241 
## [120]    train-rmse:1.032587+0.010852    test-rmse:1.369042+0.213064 
## [121]    train-rmse:1.026788+0.010843    test-rmse:1.361765+0.212006 
## [122]    train-rmse:1.021049+0.010800    test-rmse:1.351961+0.209209 
## [123]    train-rmse:1.015407+0.010636    test-rmse:1.347352+0.208737 
## [124]    train-rmse:1.009895+0.010537    test-rmse:1.335659+0.197593 
## [125]    train-rmse:1.004589+0.010557    test-rmse:1.331729+0.192860 
## [126]    train-rmse:0.999313+0.010627    test-rmse:1.326117+0.191646 
## [127]    train-rmse:0.994112+0.010689    test-rmse:1.326154+0.190387 
## [128]    train-rmse:0.988945+0.010771    test-rmse:1.318827+0.184021 
## [129]    train-rmse:0.984011+0.010721    test-rmse:1.312626+0.187101 
## [130]    train-rmse:0.979157+0.010784    test-rmse:1.309560+0.186898 
## [131]    train-rmse:0.974474+0.010951    test-rmse:1.305912+0.190967 
## [132]    train-rmse:0.969896+0.011087    test-rmse:1.301304+0.193404 
## [133]    train-rmse:0.965227+0.011209    test-rmse:1.299765+0.193752 
## [134]    train-rmse:0.960752+0.011419    test-rmse:1.294335+0.192339 
## [135]    train-rmse:0.956288+0.011455    test-rmse:1.288864+0.192135 
## [136]    train-rmse:0.951889+0.011469    test-rmse:1.287329+0.194689 
## [137]    train-rmse:0.947623+0.011485    test-rmse:1.280731+0.199310 
## [138]    train-rmse:0.943451+0.011532    test-rmse:1.277950+0.199628 
## [139]    train-rmse:0.939529+0.011587    test-rmse:1.278052+0.201224 
## [140]    train-rmse:0.935666+0.011636    test-rmse:1.273402+0.200970 
## [141]    train-rmse:0.931826+0.011746    test-rmse:1.267341+0.202957 
## [142]    train-rmse:0.927913+0.011826    test-rmse:1.268230+0.200159 
## [143]    train-rmse:0.924231+0.011860    test-rmse:1.268290+0.198996 
## [144]    train-rmse:0.920541+0.011895    test-rmse:1.264189+0.201087 
## [145]    train-rmse:0.917020+0.011922    test-rmse:1.260852+0.201879 
## [146]    train-rmse:0.913594+0.011913    test-rmse:1.249358+0.196626 
## [147]    train-rmse:0.910307+0.011932    test-rmse:1.248175+0.193825 
## [148]    train-rmse:0.907036+0.011937    test-rmse:1.245532+0.195451 
## [149]    train-rmse:0.903762+0.011936    test-rmse:1.242395+0.193956 
## [150]    train-rmse:0.900619+0.012016    test-rmse:1.235831+0.190754 
## [151]    train-rmse:0.897519+0.012132    test-rmse:1.233553+0.192403 
## [152]    train-rmse:0.894437+0.012214    test-rmse:1.233007+0.194048 
## [153]    train-rmse:0.891345+0.012277    test-rmse:1.228873+0.190351 
## [154]    train-rmse:0.888454+0.012320    test-rmse:1.225084+0.191331 
## [155]    train-rmse:0.885494+0.012348    test-rmse:1.224403+0.193966 
## [156]    train-rmse:0.882656+0.012431    test-rmse:1.226385+0.192902 
## [157]    train-rmse:0.879800+0.012496    test-rmse:1.222519+0.189170 
## [158]    train-rmse:0.877015+0.012404    test-rmse:1.220343+0.188418 
## [159]    train-rmse:0.874289+0.012367    test-rmse:1.216955+0.186534 
## [160]    train-rmse:0.871684+0.012380    test-rmse:1.217341+0.186579 
## [161]    train-rmse:0.869183+0.012449    test-rmse:1.214682+0.188025 
## [162]    train-rmse:0.866748+0.012493    test-rmse:1.212307+0.188865 
## [163]    train-rmse:0.864363+0.012529    test-rmse:1.208714+0.190498 
## [164]    train-rmse:0.862047+0.012660    test-rmse:1.206990+0.191374 
## [165]    train-rmse:0.859774+0.012784    test-rmse:1.205328+0.192415 
## [166]    train-rmse:0.857506+0.012897    test-rmse:1.202624+0.189475 
## [167]    train-rmse:0.855240+0.013018    test-rmse:1.202509+0.190488 
## [168]    train-rmse:0.853029+0.013157    test-rmse:1.201907+0.188607 
## [169]    train-rmse:0.850672+0.013243    test-rmse:1.199197+0.191835 
## [170]    train-rmse:0.848528+0.013382    test-rmse:1.196373+0.192302 
## [171]    train-rmse:0.846425+0.013531    test-rmse:1.193821+0.194912 
## [172]    train-rmse:0.844342+0.013669    test-rmse:1.193608+0.195300 
## [173]    train-rmse:0.842377+0.013757    test-rmse:1.192429+0.194922 
## [174]    train-rmse:0.840426+0.013813    test-rmse:1.191531+0.196095 
## [175]    train-rmse:0.838460+0.013814    test-rmse:1.191641+0.195237 
## [176]    train-rmse:0.836584+0.013903    test-rmse:1.184984+0.194234 
## [177]    train-rmse:0.834682+0.014006    test-rmse:1.183492+0.196666 
## [178]    train-rmse:0.832773+0.014097    test-rmse:1.181754+0.192561 
## [179]    train-rmse:0.830912+0.014208    test-rmse:1.182413+0.191337 
## [180]    train-rmse:0.829119+0.014340    test-rmse:1.176692+0.194863 
## [181]    train-rmse:0.827142+0.014330    test-rmse:1.175809+0.191671 
## [182]    train-rmse:0.825371+0.014484    test-rmse:1.175187+0.191178 
## [183]    train-rmse:0.823418+0.014561    test-rmse:1.174140+0.190503 
## [184]    train-rmse:0.821650+0.014660    test-rmse:1.171542+0.191328 
## [185]    train-rmse:0.819913+0.014686    test-rmse:1.165129+0.185868 
## [186]    train-rmse:0.818193+0.014803    test-rmse:1.164194+0.186087 
## [187]    train-rmse:0.816500+0.014768    test-rmse:1.163638+0.184936 
## [188]    train-rmse:0.814919+0.014815    test-rmse:1.162719+0.186619 
## [189]    train-rmse:0.813264+0.014979    test-rmse:1.161255+0.188559 
## [190]    train-rmse:0.811658+0.015135    test-rmse:1.158490+0.190524 
## [191]    train-rmse:0.810042+0.015245    test-rmse:1.157766+0.188412 
## [192]    train-rmse:0.808532+0.015327    test-rmse:1.158002+0.189167 
## [193]    train-rmse:0.807023+0.015445    test-rmse:1.153885+0.190051 
## [194]    train-rmse:0.805552+0.015562    test-rmse:1.153461+0.190041 
## [195]    train-rmse:0.804103+0.015668    test-rmse:1.150021+0.186243 
## [196]    train-rmse:0.802640+0.015676    test-rmse:1.148947+0.187764 
## [197]    train-rmse:0.801176+0.015727    test-rmse:1.149558+0.188571 
## [198]    train-rmse:0.799794+0.015762    test-rmse:1.147284+0.190194 
## [199]    train-rmse:0.798398+0.015765    test-rmse:1.147200+0.191975 
## [200]    train-rmse:0.796947+0.015808    test-rmse:1.147685+0.191294 
## [201]    train-rmse:0.795641+0.015889    test-rmse:1.145863+0.192839 
## [202]    train-rmse:0.794318+0.015943    test-rmse:1.144811+0.187960 
## [203]    train-rmse:0.793026+0.016026    test-rmse:1.144243+0.188871 
## [204]    train-rmse:0.791756+0.016105    test-rmse:1.140005+0.183945 
## [205]    train-rmse:0.790495+0.016188    test-rmse:1.138168+0.183208 
## [206]    train-rmse:0.789242+0.016285    test-rmse:1.136170+0.182921 
## [207]    train-rmse:0.787971+0.016357    test-rmse:1.134999+0.181785 
## [208]    train-rmse:0.786686+0.016445    test-rmse:1.135062+0.182996 
## [209]    train-rmse:0.785426+0.016480    test-rmse:1.134164+0.182620 
## [210]    train-rmse:0.784196+0.016546    test-rmse:1.132604+0.183507 
## [211]    train-rmse:0.782989+0.016599    test-rmse:1.129433+0.185258 
## [212]    train-rmse:0.781748+0.016615    test-rmse:1.129622+0.186019 
## [213]    train-rmse:0.780580+0.016666    test-rmse:1.129466+0.184438 
## [214]    train-rmse:0.779453+0.016738    test-rmse:1.129921+0.184027 
## [215]    train-rmse:0.778332+0.016768    test-rmse:1.127026+0.187360 
## [216]    train-rmse:0.777217+0.016796    test-rmse:1.127420+0.187488 
## [217]    train-rmse:0.776041+0.016881    test-rmse:1.126391+0.188226 
## [218]    train-rmse:0.774948+0.016929    test-rmse:1.126477+0.189025 
## [219]    train-rmse:0.773783+0.017059    test-rmse:1.125064+0.190004 
## [220]    train-rmse:0.772746+0.017143    test-rmse:1.123999+0.189974 
## [221]    train-rmse:0.771677+0.017165    test-rmse:1.122673+0.191932 
## [222]    train-rmse:0.770643+0.017210    test-rmse:1.119143+0.191962 
## [223]    train-rmse:0.769603+0.017265    test-rmse:1.118762+0.192177 
## [224]    train-rmse:0.768585+0.017315    test-rmse:1.117872+0.192234 
## [225]    train-rmse:0.767601+0.017365    test-rmse:1.116811+0.192001 
## [226]    train-rmse:0.766622+0.017434    test-rmse:1.115642+0.195100 
## [227]    train-rmse:0.765578+0.017514    test-rmse:1.117489+0.192661 
## [228]    train-rmse:0.764572+0.017532    test-rmse:1.116110+0.193922 
## [229]    train-rmse:0.763606+0.017546    test-rmse:1.116287+0.193138 
## [230]    train-rmse:0.762647+0.017563    test-rmse:1.114445+0.192387 
## [231]    train-rmse:0.761698+0.017608    test-rmse:1.113662+0.190412 
## [232]    train-rmse:0.760750+0.017613    test-rmse:1.110624+0.191412 
## [233]    train-rmse:0.759801+0.017700    test-rmse:1.111389+0.191901 
## [234]    train-rmse:0.758894+0.017727    test-rmse:1.111268+0.192336 
## [235]    train-rmse:0.757979+0.017748    test-rmse:1.111914+0.190198 
## [236]    train-rmse:0.757082+0.017775    test-rmse:1.110538+0.190852 
## [237]    train-rmse:0.756203+0.017803    test-rmse:1.110323+0.191159 
## [238]    train-rmse:0.755316+0.017820    test-rmse:1.110597+0.192871 
## [239]    train-rmse:0.754489+0.017842    test-rmse:1.111122+0.192701 
## [240]    train-rmse:0.753652+0.017859    test-rmse:1.108165+0.193786 
## [241]    train-rmse:0.752803+0.017896    test-rmse:1.108153+0.194784 
## [242]    train-rmse:0.751968+0.017956    test-rmse:1.108994+0.193796 
## [243]    train-rmse:0.751116+0.017971    test-rmse:1.107873+0.194445 
## [244]    train-rmse:0.750201+0.018006    test-rmse:1.107873+0.192688 
## [245]    train-rmse:0.749308+0.017904    test-rmse:1.105779+0.191667 
## [246]    train-rmse:0.748474+0.017937    test-rmse:1.103843+0.188364 
## [247]    train-rmse:0.747636+0.017883    test-rmse:1.103490+0.187416 
## [248]    train-rmse:0.746836+0.017884    test-rmse:1.101717+0.186291 
## [249]    train-rmse:0.746047+0.017852    test-rmse:1.100879+0.185616 
## [250]    train-rmse:0.745274+0.017857    test-rmse:1.097713+0.184004 
## [251]    train-rmse:0.744500+0.017868    test-rmse:1.096610+0.184065 
## [252]    train-rmse:0.743765+0.017871    test-rmse:1.093774+0.185619 
## [253]    train-rmse:0.742992+0.017870    test-rmse:1.092200+0.184152 
## [254]    train-rmse:0.742280+0.017862    test-rmse:1.090327+0.183448 
## [255]    train-rmse:0.741543+0.017821    test-rmse:1.088107+0.179755 
## [256]    train-rmse:0.740805+0.017828    test-rmse:1.086807+0.178875 
## [257]    train-rmse:0.740058+0.017839    test-rmse:1.087495+0.178549 
## [258]    train-rmse:0.739321+0.017847    test-rmse:1.087177+0.178434 
## [259]    train-rmse:0.738610+0.017870    test-rmse:1.087763+0.177760 
## [260]    train-rmse:0.737910+0.017868    test-rmse:1.086893+0.178338 
## [261]    train-rmse:0.737183+0.017859    test-rmse:1.086768+0.177488 
## [262]    train-rmse:0.736488+0.017854    test-rmse:1.086071+0.177801 
## [263]    train-rmse:0.735790+0.017850    test-rmse:1.086258+0.177124 
## [264]    train-rmse:0.735084+0.017876    test-rmse:1.084528+0.175882 
## [265]    train-rmse:0.734409+0.017868    test-rmse:1.083803+0.176335 
## [266]    train-rmse:0.733746+0.017874    test-rmse:1.081464+0.176989 
## [267]    train-rmse:0.733089+0.017876    test-rmse:1.081690+0.177680 
## [268]    train-rmse:0.732413+0.017904    test-rmse:1.082167+0.176981 
## [269]    train-rmse:0.731728+0.017856    test-rmse:1.080641+0.178072 
## [270]    train-rmse:0.731068+0.017840    test-rmse:1.079878+0.176949 
## [271]    train-rmse:0.730422+0.017793    test-rmse:1.079135+0.176660 
## [272]    train-rmse:0.729792+0.017756    test-rmse:1.078101+0.177183 
## [273]    train-rmse:0.729165+0.017720    test-rmse:1.078387+0.174737 
## [274]    train-rmse:0.728549+0.017720    test-rmse:1.077772+0.175530 
## [275]    train-rmse:0.727951+0.017714    test-rmse:1.076672+0.174382 
## [276]    train-rmse:0.727329+0.017679    test-rmse:1.075583+0.174198 
## [277]    train-rmse:0.726714+0.017665    test-rmse:1.076696+0.172689 
## [278]    train-rmse:0.726096+0.017636    test-rmse:1.076443+0.173804 
## [279]    train-rmse:0.725512+0.017609    test-rmse:1.075575+0.174003 
## [280]    train-rmse:0.724912+0.017578    test-rmse:1.075623+0.173838 
## [281]    train-rmse:0.724333+0.017557    test-rmse:1.074001+0.174225 
## [282]    train-rmse:0.723690+0.017455    test-rmse:1.072599+0.172504 
## [283]    train-rmse:0.723077+0.017419    test-rmse:1.071640+0.173086 
## [284]    train-rmse:0.722471+0.017350    test-rmse:1.069498+0.172416 
## [285]    train-rmse:0.721899+0.017317    test-rmse:1.069042+0.171857 
## [286]    train-rmse:0.721339+0.017263    test-rmse:1.066660+0.171698 
## [287]    train-rmse:0.720764+0.017262    test-rmse:1.066133+0.171070 
## [288]    train-rmse:0.720225+0.017227    test-rmse:1.064904+0.169843 
## [289]    train-rmse:0.719695+0.017208    test-rmse:1.064690+0.169664 
## [290]    train-rmse:0.719119+0.017177    test-rmse:1.066359+0.169084 
## [291]    train-rmse:0.718557+0.017145    test-rmse:1.065391+0.167914 
## [292]    train-rmse:0.717971+0.017170    test-rmse:1.065230+0.166346 
## [293]    train-rmse:0.717451+0.017138    test-rmse:1.063724+0.166091 
## [294]    train-rmse:0.716895+0.017150    test-rmse:1.064107+0.165947 
## [295]    train-rmse:0.716366+0.017124    test-rmse:1.062431+0.166563 
## [296]    train-rmse:0.715850+0.017088    test-rmse:1.063207+0.166469 
## [297]    train-rmse:0.715335+0.017066    test-rmse:1.062238+0.166513 
## [298]    train-rmse:0.714820+0.017038    test-rmse:1.061758+0.165414 
## [299]    train-rmse:0.714314+0.017000    test-rmse:1.061236+0.166603 
## [300]    train-rmse:0.713812+0.016979    test-rmse:1.060760+0.165323 
## [301]    train-rmse:0.713297+0.016932    test-rmse:1.059568+0.164803 
## [302]    train-rmse:0.712781+0.016887    test-rmse:1.059604+0.164829 
## [303]    train-rmse:0.712272+0.016852    test-rmse:1.060230+0.164736 
## [304]    train-rmse:0.711770+0.016812    test-rmse:1.060587+0.164683 
## [305]    train-rmse:0.711289+0.016780    test-rmse:1.058998+0.165254 
## [306]    train-rmse:0.710751+0.016743    test-rmse:1.059889+0.164317 
## [307]    train-rmse:0.710241+0.016729    test-rmse:1.058870+0.163041 
## [308]    train-rmse:0.709717+0.016723    test-rmse:1.058525+0.163146 
## [309]    train-rmse:0.709233+0.016683    test-rmse:1.058029+0.163069 
## [310]    train-rmse:0.708751+0.016633    test-rmse:1.057698+0.163463 
## [311]    train-rmse:0.708266+0.016568    test-rmse:1.058222+0.163249 
## [312]    train-rmse:0.707772+0.016489    test-rmse:1.057263+0.162663 
## [313]    train-rmse:0.707281+0.016447    test-rmse:1.056575+0.160391 
## [314]    train-rmse:0.706807+0.016433    test-rmse:1.055776+0.160237 
## [315]    train-rmse:0.706337+0.016384    test-rmse:1.054122+0.159159 
## [316]    train-rmse:0.705892+0.016362    test-rmse:1.052677+0.158354 
## [317]    train-rmse:0.705438+0.016335    test-rmse:1.052533+0.157819 
## [318]    train-rmse:0.704987+0.016286    test-rmse:1.051072+0.156798 
## [319]    train-rmse:0.704522+0.016299    test-rmse:1.051437+0.156005 
## [320]    train-rmse:0.704064+0.016281    test-rmse:1.051220+0.154773 
## [321]    train-rmse:0.703583+0.016241    test-rmse:1.050782+0.155266 
## [322]    train-rmse:0.703125+0.016209    test-rmse:1.048450+0.151917 
## [323]    train-rmse:0.702644+0.016176    test-rmse:1.049449+0.152224 
## [324]    train-rmse:0.702227+0.016153    test-rmse:1.048916+0.152685 
## [325]    train-rmse:0.701774+0.016095    test-rmse:1.049141+0.152691 
## [326]    train-rmse:0.701324+0.016080    test-rmse:1.051051+0.151691 
## [327]    train-rmse:0.700866+0.016040    test-rmse:1.049379+0.153073 
## [328]    train-rmse:0.700424+0.015992    test-rmse:1.049098+0.152675 
## Stopping. Best iteration:
## [322]    train-rmse:0.703125+0.016209    test-rmse:1.048450+0.151917
## 
## 85 
## [1]  train-rmse:71.268932+0.085068   test-rmse:71.268778+0.519929 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:47.317069+0.068405   test-rmse:47.315810+0.569907 
## [3]  train-rmse:31.577981+0.063944   test-rmse:31.574661+0.622724 
## [4]  train-rmse:21.238430+0.046850   test-rmse:21.330363+0.644487 
## [5]  train-rmse:14.459752+0.034269   test-rmse:14.562542+0.562943 
## [6]  train-rmse:10.054961+0.032005   test-rmse:10.145731+0.549670 
## [7]  train-rmse:7.245538+0.042487    test-rmse:7.393070+0.575505 
## [8]  train-rmse:5.507863+0.044817    test-rmse:5.715325+0.576698 
## [9]  train-rmse:4.460585+0.044530    test-rmse:4.635752+0.527530 
## [10] train-rmse:3.834418+0.051651    test-rmse:4.047216+0.456605 
## [11] train-rmse:3.457668+0.055571    test-rmse:3.739311+0.456212 
## [12] train-rmse:3.212997+0.053504    test-rmse:3.516225+0.459463 
## [13] train-rmse:3.026777+0.044582    test-rmse:3.335645+0.368295 
## [14] train-rmse:2.888335+0.040015    test-rmse:3.147940+0.341751 
## [15] train-rmse:2.775835+0.041232    test-rmse:3.070961+0.357058 
## [16] train-rmse:2.676099+0.040442    test-rmse:2.992428+0.359191 
## [17] train-rmse:2.580750+0.035406    test-rmse:2.918566+0.363846 
## [18] train-rmse:2.496617+0.031219    test-rmse:2.824415+0.292460 
## [19] train-rmse:2.415958+0.029071    test-rmse:2.766954+0.298639 
## [20] train-rmse:2.336967+0.027826    test-rmse:2.701359+0.269806 
## [21] train-rmse:2.261780+0.024653    test-rmse:2.599828+0.283922 
## [22] train-rmse:2.192283+0.025234    test-rmse:2.532218+0.267796 
## [23] train-rmse:2.113940+0.022922    test-rmse:2.486792+0.292603 
## [24] train-rmse:2.050176+0.021367    test-rmse:2.424461+0.288228 
## [25] train-rmse:1.989550+0.020928    test-rmse:2.384497+0.283669 
## [26] train-rmse:1.934715+0.019397    test-rmse:2.316806+0.271411 
## [27] train-rmse:1.880816+0.017703    test-rmse:2.257953+0.275969 
## [28] train-rmse:1.827776+0.016771    test-rmse:2.192630+0.278597 
## [29] train-rmse:1.775789+0.015139    test-rmse:2.136714+0.270980 
## [30] train-rmse:1.727340+0.015436    test-rmse:2.096062+0.255934 
## [31] train-rmse:1.680835+0.014963    test-rmse:2.076046+0.263494 
## [32] train-rmse:1.637827+0.013966    test-rmse:2.038087+0.260090 
## [33] train-rmse:1.596620+0.012928    test-rmse:1.984949+0.257608 
## [34] train-rmse:1.555637+0.012341    test-rmse:1.961457+0.253911 
## [35] train-rmse:1.513199+0.009735    test-rmse:1.941603+0.258910 
## [36] train-rmse:1.472319+0.013356    test-rmse:1.905995+0.268878 
## [37] train-rmse:1.435809+0.012391    test-rmse:1.872101+0.253164 
## [38] train-rmse:1.399333+0.010779    test-rmse:1.842187+0.268076 
## [39] train-rmse:1.365034+0.008741    test-rmse:1.809526+0.259529 
## [40] train-rmse:1.331726+0.009328    test-rmse:1.785868+0.260499 
## [41] train-rmse:1.299052+0.008208    test-rmse:1.754656+0.253146 
## [42] train-rmse:1.267194+0.007970    test-rmse:1.736154+0.259806 
## [43] train-rmse:1.240932+0.007888    test-rmse:1.718384+0.258753 
## [44] train-rmse:1.212828+0.009056    test-rmse:1.695132+0.248344 
## [45] train-rmse:1.186477+0.009407    test-rmse:1.679135+0.251953 
## [46] train-rmse:1.161057+0.008836    test-rmse:1.664477+0.259013 
## [47] train-rmse:1.135885+0.009704    test-rmse:1.635112+0.256057 
## [48] train-rmse:1.112110+0.008888    test-rmse:1.614567+0.260360 
## [49] train-rmse:1.088398+0.009208    test-rmse:1.590789+0.256235 
## [50] train-rmse:1.066700+0.009190    test-rmse:1.569717+0.249400 
## [51] train-rmse:1.041126+0.006089    test-rmse:1.558196+0.249509 
## [52] train-rmse:1.021452+0.006358    test-rmse:1.541083+0.250676 
## [53] train-rmse:1.001600+0.006803    test-rmse:1.523309+0.250056 
## [54] train-rmse:0.982477+0.006009    test-rmse:1.510964+0.258997 
## [55] train-rmse:0.961150+0.007617    test-rmse:1.500172+0.261909 
## [56] train-rmse:0.943684+0.007339    test-rmse:1.488639+0.260141 
## [57] train-rmse:0.927335+0.007001    test-rmse:1.476731+0.264680 
## [58] train-rmse:0.912233+0.007077    test-rmse:1.460138+0.267530 
## [59] train-rmse:0.897663+0.006852    test-rmse:1.436069+0.255357 
## [60] train-rmse:0.881135+0.007265    test-rmse:1.412873+0.250119 
## [61] train-rmse:0.864696+0.006332    test-rmse:1.395882+0.240476 
## [62] train-rmse:0.851235+0.006887    test-rmse:1.387301+0.239019 
## [63] train-rmse:0.837415+0.007334    test-rmse:1.380336+0.244675 
## [64] train-rmse:0.821884+0.007044    test-rmse:1.378596+0.243612 
## [65] train-rmse:0.808157+0.006830    test-rmse:1.374077+0.238003 
## [66] train-rmse:0.795557+0.005337    test-rmse:1.363502+0.241833 
## [67] train-rmse:0.784397+0.005425    test-rmse:1.351640+0.243489 
## [68] train-rmse:0.773210+0.005194    test-rmse:1.344845+0.246641 
## [69] train-rmse:0.762649+0.004878    test-rmse:1.333105+0.251108 
## [70] train-rmse:0.750887+0.007826    test-rmse:1.322512+0.249497 
## [71] train-rmse:0.739733+0.008229    test-rmse:1.307523+0.247479 
## [72] train-rmse:0.728619+0.009378    test-rmse:1.304854+0.246584 
## [73] train-rmse:0.719261+0.010230    test-rmse:1.299079+0.243401 
## [74] train-rmse:0.709727+0.009576    test-rmse:1.295291+0.244342 
## [75] train-rmse:0.701401+0.009397    test-rmse:1.290519+0.244986 
## [76] train-rmse:0.693245+0.009521    test-rmse:1.289552+0.245680 
## [77] train-rmse:0.683477+0.009144    test-rmse:1.285491+0.245626 
## [78] train-rmse:0.672502+0.008265    test-rmse:1.282469+0.247506 
## [79] train-rmse:0.663671+0.006565    test-rmse:1.280750+0.248930 
## [80] train-rmse:0.655810+0.007773    test-rmse:1.274430+0.249352 
## [81] train-rmse:0.648887+0.007698    test-rmse:1.269961+0.248667 
## [82] train-rmse:0.640399+0.006806    test-rmse:1.268228+0.250763 
## [83] train-rmse:0.632039+0.009052    test-rmse:1.260759+0.251467 
## [84] train-rmse:0.625529+0.009767    test-rmse:1.251996+0.252238 
## [85] train-rmse:0.618498+0.008373    test-rmse:1.247641+0.258307 
## [86] train-rmse:0.609239+0.005955    test-rmse:1.245239+0.254558 
## [87] train-rmse:0.602856+0.005872    test-rmse:1.244766+0.255798 
## [88] train-rmse:0.597278+0.006022    test-rmse:1.243905+0.256516 
## [89] train-rmse:0.589752+0.004985    test-rmse:1.241025+0.257173 
## [90] train-rmse:0.584087+0.005654    test-rmse:1.231091+0.261500 
## [91] train-rmse:0.578791+0.005824    test-rmse:1.227375+0.262346 
## [92] train-rmse:0.573218+0.005511    test-rmse:1.225886+0.263966 
## [93] train-rmse:0.565925+0.005338    test-rmse:1.223070+0.265124 
## [94] train-rmse:0.561037+0.005256    test-rmse:1.218354+0.266301 
## [95] train-rmse:0.556136+0.005910    test-rmse:1.214593+0.265081 
## [96] train-rmse:0.549612+0.006738    test-rmse:1.214734+0.265155 
## [97] train-rmse:0.544178+0.005708    test-rmse:1.213015+0.267338 
## [98] train-rmse:0.539187+0.005492    test-rmse:1.212198+0.269417 
## [99] train-rmse:0.534361+0.005470    test-rmse:1.208601+0.266808 
## [100]    train-rmse:0.530409+0.005474    test-rmse:1.207357+0.267052 
## [101]    train-rmse:0.526617+0.004991    test-rmse:1.204086+0.269956 
## [102]    train-rmse:0.522095+0.004898    test-rmse:1.196761+0.269000 
## [103]    train-rmse:0.517956+0.004273    test-rmse:1.195878+0.269551 
## [104]    train-rmse:0.513570+0.004223    test-rmse:1.194988+0.272081 
## [105]    train-rmse:0.509313+0.004787    test-rmse:1.192522+0.275486 
## [106]    train-rmse:0.505202+0.005181    test-rmse:1.186933+0.270645 
## [107]    train-rmse:0.501984+0.004788    test-rmse:1.186130+0.269635 
## [108]    train-rmse:0.498033+0.004159    test-rmse:1.183126+0.271160 
## [109]    train-rmse:0.494460+0.004998    test-rmse:1.180623+0.272034 
## [110]    train-rmse:0.490384+0.005271    test-rmse:1.179435+0.271834 
## [111]    train-rmse:0.487423+0.004811    test-rmse:1.178757+0.272201 
## [112]    train-rmse:0.483271+0.006146    test-rmse:1.177103+0.275043 
## [113]    train-rmse:0.479063+0.007451    test-rmse:1.176182+0.274878 
## [114]    train-rmse:0.475070+0.006946    test-rmse:1.173603+0.276383 
## [115]    train-rmse:0.471801+0.007575    test-rmse:1.170488+0.276835 
## [116]    train-rmse:0.467520+0.007843    test-rmse:1.169165+0.279888 
## [117]    train-rmse:0.462792+0.007496    test-rmse:1.159647+0.271408 
## [118]    train-rmse:0.458707+0.008603    test-rmse:1.156031+0.270923 
## [119]    train-rmse:0.455949+0.008695    test-rmse:1.155856+0.270272 
## [120]    train-rmse:0.452558+0.009355    test-rmse:1.154529+0.269760 
## [121]    train-rmse:0.449862+0.009319    test-rmse:1.153934+0.268837 
## [122]    train-rmse:0.447517+0.009226    test-rmse:1.154439+0.267970 
## [123]    train-rmse:0.443202+0.006706    test-rmse:1.151099+0.268158 
## [124]    train-rmse:0.439828+0.006463    test-rmse:1.150559+0.266964 
## [125]    train-rmse:0.435388+0.006012    test-rmse:1.148561+0.265141 
## [126]    train-rmse:0.432741+0.006251    test-rmse:1.147115+0.265200 
## [127]    train-rmse:0.430486+0.006744    test-rmse:1.145461+0.265188 
## [128]    train-rmse:0.427938+0.007126    test-rmse:1.145587+0.266320 
## [129]    train-rmse:0.425085+0.007029    test-rmse:1.137572+0.257472 
## [130]    train-rmse:0.423212+0.007066    test-rmse:1.135849+0.258220 
## [131]    train-rmse:0.421406+0.006963    test-rmse:1.134625+0.257892 
## [132]    train-rmse:0.419374+0.007481    test-rmse:1.133552+0.258115 
## [133]    train-rmse:0.417285+0.007127    test-rmse:1.132256+0.257688 
## [134]    train-rmse:0.414638+0.007619    test-rmse:1.130992+0.257505 
## [135]    train-rmse:0.412598+0.007712    test-rmse:1.129765+0.258496 
## [136]    train-rmse:0.409919+0.008965    test-rmse:1.130684+0.259663 
## [137]    train-rmse:0.406997+0.010313    test-rmse:1.130063+0.260703 
## [138]    train-rmse:0.402197+0.009088    test-rmse:1.127680+0.259798 
## [139]    train-rmse:0.399938+0.009049    test-rmse:1.125787+0.260542 
## [140]    train-rmse:0.397810+0.008607    test-rmse:1.124961+0.260427 
## [141]    train-rmse:0.395059+0.007735    test-rmse:1.125283+0.261718 
## [142]    train-rmse:0.393359+0.007739    test-rmse:1.125042+0.263043 
## [143]    train-rmse:0.390941+0.008447    test-rmse:1.123734+0.264050 
## [144]    train-rmse:0.389208+0.008577    test-rmse:1.121806+0.263900 
## [145]    train-rmse:0.387336+0.008711    test-rmse:1.120440+0.264972 
## [146]    train-rmse:0.385673+0.008758    test-rmse:1.121740+0.266101 
## [147]    train-rmse:0.383440+0.009431    test-rmse:1.122488+0.264825 
## [148]    train-rmse:0.381184+0.010201    test-rmse:1.119523+0.269310 
## [149]    train-rmse:0.378073+0.009390    test-rmse:1.119206+0.270342 
## [150]    train-rmse:0.375842+0.009547    test-rmse:1.119305+0.271026 
## [151]    train-rmse:0.373763+0.009547    test-rmse:1.118028+0.270224 
## [152]    train-rmse:0.371749+0.009330    test-rmse:1.118025+0.269256 
## [153]    train-rmse:0.369073+0.008904    test-rmse:1.118262+0.269527 
## [154]    train-rmse:0.367881+0.008984    test-rmse:1.116863+0.269773 
## [155]    train-rmse:0.365857+0.009012    test-rmse:1.116512+0.267669 
## [156]    train-rmse:0.364176+0.008639    test-rmse:1.115631+0.267907 
## [157]    train-rmse:0.363206+0.008783    test-rmse:1.114265+0.267737 
## [158]    train-rmse:0.359837+0.007615    test-rmse:1.113092+0.267792 
## [159]    train-rmse:0.357370+0.007520    test-rmse:1.111883+0.268330 
## [160]    train-rmse:0.355170+0.008060    test-rmse:1.111879+0.267788 
## [161]    train-rmse:0.353401+0.008980    test-rmse:1.111185+0.268237 
## [162]    train-rmse:0.351599+0.008829    test-rmse:1.110917+0.268978 
## [163]    train-rmse:0.349746+0.008922    test-rmse:1.110235+0.267677 
## [164]    train-rmse:0.348593+0.008957    test-rmse:1.109211+0.266926 
## [165]    train-rmse:0.346187+0.009025    test-rmse:1.110269+0.268773 
## [166]    train-rmse:0.343428+0.007787    test-rmse:1.106732+0.270394 
## [167]    train-rmse:0.342028+0.008189    test-rmse:1.106315+0.270904 
## [168]    train-rmse:0.339942+0.008377    test-rmse:1.107800+0.270511 
## [169]    train-rmse:0.337978+0.009158    test-rmse:1.108041+0.271445 
## [170]    train-rmse:0.336620+0.009128    test-rmse:1.108645+0.270788 
## [171]    train-rmse:0.334140+0.009752    test-rmse:1.107108+0.271097 
## [172]    train-rmse:0.332370+0.009339    test-rmse:1.105972+0.270908 
## [173]    train-rmse:0.331016+0.009413    test-rmse:1.105105+0.270214 
## [174]    train-rmse:0.329355+0.009606    test-rmse:1.102696+0.271662 
## [175]    train-rmse:0.327425+0.009548    test-rmse:1.102217+0.271449 
## [176]    train-rmse:0.326171+0.009803    test-rmse:1.104376+0.273329 
## [177]    train-rmse:0.324124+0.010628    test-rmse:1.103301+0.273473 
## [178]    train-rmse:0.322620+0.010910    test-rmse:1.104206+0.272995 
## [179]    train-rmse:0.321730+0.011132    test-rmse:1.102499+0.272852 
## [180]    train-rmse:0.319346+0.010736    test-rmse:1.101700+0.270905 
## [181]    train-rmse:0.317776+0.010117    test-rmse:1.100975+0.271727 
## [182]    train-rmse:0.315256+0.010985    test-rmse:1.100839+0.271425 
## [183]    train-rmse:0.313238+0.010703    test-rmse:1.100647+0.271901 
## [184]    train-rmse:0.312170+0.010799    test-rmse:1.099740+0.272294 
## [185]    train-rmse:0.310517+0.010137    test-rmse:1.099458+0.272889 
## [186]    train-rmse:0.309016+0.010360    test-rmse:1.099569+0.272855 
## [187]    train-rmse:0.307073+0.010641    test-rmse:1.099928+0.273774 
## [188]    train-rmse:0.305297+0.010274    test-rmse:1.099130+0.272711 
## [189]    train-rmse:0.303668+0.010318    test-rmse:1.098308+0.273405 
## [190]    train-rmse:0.302401+0.010535    test-rmse:1.099610+0.272174 
## [191]    train-rmse:0.301460+0.010492    test-rmse:1.098497+0.271869 
## [192]    train-rmse:0.299402+0.010136    test-rmse:1.098113+0.270210 
## [193]    train-rmse:0.298392+0.010374    test-rmse:1.098614+0.270040 
## [194]    train-rmse:0.296707+0.010494    test-rmse:1.098053+0.270492 
## [195]    train-rmse:0.295250+0.010213    test-rmse:1.096652+0.270001 
## [196]    train-rmse:0.293270+0.009773    test-rmse:1.096382+0.270048 
## [197]    train-rmse:0.291380+0.009288    test-rmse:1.096116+0.270300 
## [198]    train-rmse:0.290289+0.009455    test-rmse:1.095832+0.269751 
## [199]    train-rmse:0.289065+0.009992    test-rmse:1.093623+0.271870 
## [200]    train-rmse:0.288000+0.010043    test-rmse:1.093130+0.272083 
## [201]    train-rmse:0.286963+0.009800    test-rmse:1.092264+0.272231 
## [202]    train-rmse:0.285880+0.009236    test-rmse:1.092736+0.272058 
## [203]    train-rmse:0.283138+0.010184    test-rmse:1.093167+0.271678 
## [204]    train-rmse:0.281779+0.009936    test-rmse:1.093186+0.272339 
## [205]    train-rmse:0.280083+0.009164    test-rmse:1.093837+0.273025 
## [206]    train-rmse:0.279010+0.008797    test-rmse:1.092828+0.271532 
## [207]    train-rmse:0.277421+0.008057    test-rmse:1.091564+0.272999 
## [208]    train-rmse:0.276496+0.007679    test-rmse:1.091658+0.274268 
## [209]    train-rmse:0.275144+0.007533    test-rmse:1.092493+0.273901 
## [210]    train-rmse:0.274026+0.007283    test-rmse:1.092240+0.274353 
## [211]    train-rmse:0.272838+0.007265    test-rmse:1.091765+0.273811 
## [212]    train-rmse:0.270841+0.007709    test-rmse:1.090909+0.273612 
## [213]    train-rmse:0.269835+0.007191    test-rmse:1.090798+0.273350 
## [214]    train-rmse:0.268890+0.007421    test-rmse:1.091076+0.273003 
## [215]    train-rmse:0.267542+0.006855    test-rmse:1.089526+0.274254 
## [216]    train-rmse:0.266560+0.006887    test-rmse:1.089509+0.273857 
## [217]    train-rmse:0.265233+0.006714    test-rmse:1.088475+0.273348 
## [218]    train-rmse:0.263023+0.008123    test-rmse:1.088025+0.273237 
## [219]    train-rmse:0.261772+0.008764    test-rmse:1.087564+0.273105 
## [220]    train-rmse:0.260705+0.008830    test-rmse:1.087394+0.273071 
## [221]    train-rmse:0.260051+0.008994    test-rmse:1.087632+0.273115 
## [222]    train-rmse:0.257843+0.009576    test-rmse:1.087110+0.273443 
## [223]    train-rmse:0.256742+0.009828    test-rmse:1.088060+0.273590 
## [224]    train-rmse:0.255916+0.009911    test-rmse:1.087938+0.273608 
## [225]    train-rmse:0.255134+0.009871    test-rmse:1.087662+0.273334 
## [226]    train-rmse:0.254056+0.009712    test-rmse:1.087592+0.272670 
## [227]    train-rmse:0.252455+0.009883    test-rmse:1.087820+0.274300 
## [228]    train-rmse:0.251520+0.010032    test-rmse:1.088779+0.274118 
## Stopping. Best iteration:
## [222]    train-rmse:0.257843+0.009576    test-rmse:1.087110+0.273443
## 
## 86 
## [1]  train-rmse:71.268932+0.085068   test-rmse:71.268778+0.519929 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:47.317069+0.068405   test-rmse:47.315810+0.569907 
## [3]  train-rmse:31.577981+0.063944   test-rmse:31.574661+0.622724 
## [4]  train-rmse:21.229897+0.041979   test-rmse:21.327855+0.649898 
## [5]  train-rmse:14.416945+0.027682   test-rmse:14.525238+0.609073 
## [6]  train-rmse:9.962237+0.030899    test-rmse:10.049095+0.554279 
## [7]  train-rmse:7.084963+0.032216    test-rmse:7.231692+0.559066 
## [8]  train-rmse:5.250240+0.046396    test-rmse:5.433917+0.501762 
## [9]  train-rmse:4.114151+0.054115    test-rmse:4.353346+0.479562 
## [10] train-rmse:3.395615+0.069459    test-rmse:3.664185+0.440656 
## [11] train-rmse:2.965188+0.069629    test-rmse:3.278946+0.387445 
## [12] train-rmse:2.679213+0.073835    test-rmse:3.014273+0.351389 
## [13] train-rmse:2.485071+0.068163    test-rmse:2.853608+0.341741 
## [14] train-rmse:2.329075+0.057307    test-rmse:2.730407+0.311395 
## [15] train-rmse:2.193049+0.045167    test-rmse:2.624222+0.330557 
## [16] train-rmse:2.086115+0.042121    test-rmse:2.518351+0.310542 
## [17] train-rmse:1.986065+0.035368    test-rmse:2.440209+0.300808 
## [18] train-rmse:1.894517+0.036939    test-rmse:2.345983+0.286599 
## [19] train-rmse:1.801016+0.031196    test-rmse:2.269316+0.287607 
## [20] train-rmse:1.721014+0.028569    test-rmse:2.187772+0.269427 
## [21] train-rmse:1.648746+0.026972    test-rmse:2.140115+0.273099 
## [22] train-rmse:1.571562+0.018332    test-rmse:2.108411+0.284376 
## [23] train-rmse:1.505357+0.016077    test-rmse:2.062780+0.296311 
## [24] train-rmse:1.445680+0.014143    test-rmse:2.009054+0.273723 
## [25] train-rmse:1.385737+0.009148    test-rmse:1.966446+0.266662 
## [26] train-rmse:1.326768+0.015183    test-rmse:1.933115+0.260482 
## [27] train-rmse:1.276603+0.015760    test-rmse:1.887268+0.266396 
## [28] train-rmse:1.228518+0.015970    test-rmse:1.851488+0.265221 
## [29] train-rmse:1.183826+0.016459    test-rmse:1.826886+0.276529 
## [30] train-rmse:1.136670+0.014676    test-rmse:1.792078+0.266224 
## [31] train-rmse:1.092260+0.015192    test-rmse:1.758031+0.252562 
## [32] train-rmse:1.053282+0.014340    test-rmse:1.736115+0.248322 
## [33] train-rmse:1.018570+0.014815    test-rmse:1.704296+0.252303 
## [34] train-rmse:0.985021+0.016629    test-rmse:1.666778+0.251052 
## [35] train-rmse:0.953936+0.017179    test-rmse:1.647925+0.246069 
## [36] train-rmse:0.922499+0.019510    test-rmse:1.623609+0.250372 
## [37] train-rmse:0.891877+0.021140    test-rmse:1.612204+0.258498 
## [38] train-rmse:0.864671+0.022025    test-rmse:1.597167+0.259160 
## [39] train-rmse:0.835295+0.018618    test-rmse:1.579949+0.253653 
## [40] train-rmse:0.810274+0.018516    test-rmse:1.562301+0.253937 
## [41] train-rmse:0.783612+0.013853    test-rmse:1.537724+0.263404 
## [42] train-rmse:0.759422+0.015426    test-rmse:1.527051+0.252887 
## [43] train-rmse:0.737192+0.014310    test-rmse:1.515759+0.253812 
## [44] train-rmse:0.716138+0.014645    test-rmse:1.498303+0.256169 
## [45] train-rmse:0.695962+0.015291    test-rmse:1.489298+0.260400 
## [46] train-rmse:0.679296+0.014676    test-rmse:1.482233+0.259180 
## [47] train-rmse:0.663590+0.014375    test-rmse:1.473900+0.259326 
## [48] train-rmse:0.648381+0.014732    test-rmse:1.463613+0.261881 
## [49] train-rmse:0.630978+0.013378    test-rmse:1.451750+0.249496 
## [50] train-rmse:0.614746+0.013797    test-rmse:1.435369+0.250107 
## [51] train-rmse:0.599320+0.012510    test-rmse:1.431921+0.250217 
## [52] train-rmse:0.586188+0.013291    test-rmse:1.424974+0.255759 
## [53] train-rmse:0.573212+0.015609    test-rmse:1.419979+0.261554 
## [54] train-rmse:0.561881+0.016478    test-rmse:1.416247+0.262995 
## [55] train-rmse:0.548638+0.017829    test-rmse:1.410217+0.263205 
## [56] train-rmse:0.535972+0.017886    test-rmse:1.407348+0.265394 
## [57] train-rmse:0.521679+0.016684    test-rmse:1.404656+0.265651 
## [58] train-rmse:0.509745+0.015371    test-rmse:1.401768+0.261852 
## [59] train-rmse:0.500645+0.016470    test-rmse:1.399045+0.262592 
## [60] train-rmse:0.492446+0.016682    test-rmse:1.396788+0.263526 
## [61] train-rmse:0.483059+0.016401    test-rmse:1.387455+0.264099 
## [62] train-rmse:0.474918+0.016145    test-rmse:1.383917+0.265121 
## [63] train-rmse:0.466376+0.015782    test-rmse:1.380280+0.265263 
## [64] train-rmse:0.457514+0.015943    test-rmse:1.374852+0.266503 
## [65] train-rmse:0.448252+0.015952    test-rmse:1.368816+0.266059 
## [66] train-rmse:0.440853+0.016253    test-rmse:1.365289+0.265620 
## [67] train-rmse:0.434614+0.016327    test-rmse:1.362044+0.265776 
## [68] train-rmse:0.425998+0.018019    test-rmse:1.359401+0.263522 
## [69] train-rmse:0.418632+0.016795    test-rmse:1.356141+0.266285 
## [70] train-rmse:0.411117+0.014627    test-rmse:1.350506+0.268476 
## [71] train-rmse:0.404373+0.014653    test-rmse:1.348697+0.268899 
## [72] train-rmse:0.398457+0.014605    test-rmse:1.345820+0.270222 
## [73] train-rmse:0.389901+0.016188    test-rmse:1.344379+0.274753 
## [74] train-rmse:0.383072+0.017502    test-rmse:1.336920+0.263734 
## [75] train-rmse:0.378629+0.017404    test-rmse:1.334335+0.265684 
## [76] train-rmse:0.372319+0.016309    test-rmse:1.332648+0.267129 
## [77] train-rmse:0.367310+0.017730    test-rmse:1.332696+0.268504 
## [78] train-rmse:0.361002+0.017508    test-rmse:1.331124+0.265626 
## [79] train-rmse:0.355158+0.017484    test-rmse:1.329343+0.267654 
## [80] train-rmse:0.348332+0.018976    test-rmse:1.326221+0.267890 
## [81] train-rmse:0.343459+0.018864    test-rmse:1.321826+0.269233 
## [82] train-rmse:0.340030+0.018517    test-rmse:1.320457+0.269560 
## [83] train-rmse:0.336467+0.017979    test-rmse:1.317194+0.268696 
## [84] train-rmse:0.331346+0.017735    test-rmse:1.315851+0.269749 
## [85] train-rmse:0.324257+0.015515    test-rmse:1.316947+0.269067 
## [86] train-rmse:0.319658+0.015634    test-rmse:1.316905+0.270552 
## [87] train-rmse:0.315604+0.016053    test-rmse:1.316117+0.270834 
## [88] train-rmse:0.309267+0.014933    test-rmse:1.314418+0.273357 
## [89] train-rmse:0.303551+0.012338    test-rmse:1.313253+0.273901 
## [90] train-rmse:0.299619+0.012078    test-rmse:1.313130+0.272314 
## [91] train-rmse:0.295417+0.014067    test-rmse:1.313326+0.272196 
## [92] train-rmse:0.291983+0.014019    test-rmse:1.311469+0.273258 
## [93] train-rmse:0.288262+0.014395    test-rmse:1.307752+0.274784 
## [94] train-rmse:0.285852+0.014092    test-rmse:1.306276+0.275625 
## [95] train-rmse:0.281897+0.013840    test-rmse:1.302609+0.276550 
## [96] train-rmse:0.275123+0.012038    test-rmse:1.301176+0.277189 
## [97] train-rmse:0.270854+0.011464    test-rmse:1.296269+0.273372 
## [98] train-rmse:0.266802+0.011891    test-rmse:1.296842+0.273660 
## [99] train-rmse:0.262849+0.012617    test-rmse:1.294448+0.272342 
## [100]    train-rmse:0.258153+0.012474    test-rmse:1.293175+0.270146 
## [101]    train-rmse:0.254985+0.013420    test-rmse:1.292238+0.271017 
## [102]    train-rmse:0.252411+0.014555    test-rmse:1.289983+0.270791 
## [103]    train-rmse:0.249886+0.014041    test-rmse:1.290532+0.270829 
## [104]    train-rmse:0.245983+0.014043    test-rmse:1.288616+0.268982 
## [105]    train-rmse:0.242497+0.013391    test-rmse:1.289307+0.269294 
## [106]    train-rmse:0.239476+0.013172    test-rmse:1.288174+0.267615 
## [107]    train-rmse:0.236584+0.012944    test-rmse:1.288219+0.268635 
## [108]    train-rmse:0.233892+0.012861    test-rmse:1.288070+0.268450 
## [109]    train-rmse:0.230974+0.012201    test-rmse:1.287815+0.269330 
## [110]    train-rmse:0.228517+0.011695    test-rmse:1.288215+0.269559 
## [111]    train-rmse:0.226283+0.012280    test-rmse:1.287815+0.270579 
## [112]    train-rmse:0.223780+0.011694    test-rmse:1.288115+0.271724 
## [113]    train-rmse:0.221176+0.011569    test-rmse:1.289089+0.272734 
## [114]    train-rmse:0.219225+0.011284    test-rmse:1.288618+0.272757 
## [115]    train-rmse:0.216963+0.011966    test-rmse:1.287339+0.274526 
## [116]    train-rmse:0.213578+0.011531    test-rmse:1.285212+0.274201 
## [117]    train-rmse:0.209985+0.011076    test-rmse:1.285034+0.274319 
## [118]    train-rmse:0.207914+0.011430    test-rmse:1.284740+0.275556 
## [119]    train-rmse:0.206342+0.012133    test-rmse:1.284173+0.276286 
## [120]    train-rmse:0.203397+0.012374    test-rmse:1.283014+0.276790 
## [121]    train-rmse:0.201387+0.011882    test-rmse:1.282054+0.276990 
## [122]    train-rmse:0.198647+0.012238    test-rmse:1.281898+0.277496 
## [123]    train-rmse:0.196284+0.012056    test-rmse:1.282703+0.277469 
## [124]    train-rmse:0.193330+0.012609    test-rmse:1.280577+0.276118 
## [125]    train-rmse:0.191358+0.012294    test-rmse:1.280501+0.275467 
## [126]    train-rmse:0.189142+0.012784    test-rmse:1.279892+0.274350 
## [127]    train-rmse:0.186665+0.012936    test-rmse:1.279308+0.274679 
## [128]    train-rmse:0.184365+0.012041    test-rmse:1.278657+0.274182 
## [129]    train-rmse:0.181139+0.011573    test-rmse:1.278418+0.273278 
## [130]    train-rmse:0.179123+0.011841    test-rmse:1.278444+0.273291 
## [131]    train-rmse:0.177341+0.011279    test-rmse:1.278477+0.272801 
## [132]    train-rmse:0.174691+0.011827    test-rmse:1.277127+0.272227 
## [133]    train-rmse:0.172850+0.012391    test-rmse:1.277225+0.272771 
## [134]    train-rmse:0.171366+0.012314    test-rmse:1.277191+0.272543 
## [135]    train-rmse:0.170410+0.012153    test-rmse:1.277227+0.272160 
## [136]    train-rmse:0.167931+0.012192    test-rmse:1.277214+0.271670 
## [137]    train-rmse:0.166317+0.011375    test-rmse:1.276199+0.270956 
## [138]    train-rmse:0.164895+0.011143    test-rmse:1.274406+0.271243 
## [139]    train-rmse:0.162597+0.011606    test-rmse:1.273898+0.271380 
## [140]    train-rmse:0.160924+0.010915    test-rmse:1.273725+0.271591 
## [141]    train-rmse:0.159421+0.011101    test-rmse:1.273738+0.271854 
## [142]    train-rmse:0.157493+0.010533    test-rmse:1.272540+0.273121 
## [143]    train-rmse:0.155230+0.010323    test-rmse:1.273226+0.272886 
## [144]    train-rmse:0.153833+0.010415    test-rmse:1.272841+0.272794 
## [145]    train-rmse:0.152439+0.010375    test-rmse:1.272405+0.271320 
## [146]    train-rmse:0.151253+0.010898    test-rmse:1.272410+0.270692 
## [147]    train-rmse:0.150016+0.010711    test-rmse:1.272403+0.270811 
## [148]    train-rmse:0.148702+0.011086    test-rmse:1.271250+0.269883 
## [149]    train-rmse:0.147260+0.010549    test-rmse:1.270431+0.269869 
## [150]    train-rmse:0.145987+0.009756    test-rmse:1.270798+0.269965 
## [151]    train-rmse:0.143905+0.008922    test-rmse:1.270601+0.269449 
## [152]    train-rmse:0.142276+0.008474    test-rmse:1.270313+0.269357 
## [153]    train-rmse:0.141320+0.008451    test-rmse:1.270674+0.269638 
## [154]    train-rmse:0.139529+0.008326    test-rmse:1.269868+0.271098 
## [155]    train-rmse:0.137905+0.008275    test-rmse:1.270020+0.271229 
## [156]    train-rmse:0.136945+0.008204    test-rmse:1.269897+0.271212 
## [157]    train-rmse:0.135861+0.008251    test-rmse:1.269765+0.271368 
## [158]    train-rmse:0.134312+0.009037    test-rmse:1.269720+0.271208 
## [159]    train-rmse:0.132740+0.009377    test-rmse:1.269155+0.270974 
## [160]    train-rmse:0.131115+0.009715    test-rmse:1.268587+0.271243 
## [161]    train-rmse:0.129887+0.009965    test-rmse:1.268729+0.271434 
## [162]    train-rmse:0.128591+0.009980    test-rmse:1.268429+0.271699 
## [163]    train-rmse:0.127429+0.010141    test-rmse:1.268502+0.271976 
## [164]    train-rmse:0.125875+0.010296    test-rmse:1.267616+0.271346 
## [165]    train-rmse:0.124973+0.010304    test-rmse:1.267394+0.271570 
## [166]    train-rmse:0.123604+0.010362    test-rmse:1.268525+0.273073 
## [167]    train-rmse:0.122782+0.010386    test-rmse:1.268140+0.273044 
## [168]    train-rmse:0.121549+0.010144    test-rmse:1.268301+0.273348 
## [169]    train-rmse:0.120528+0.010017    test-rmse:1.268014+0.273673 
## [170]    train-rmse:0.119771+0.010214    test-rmse:1.267256+0.274111 
## [171]    train-rmse:0.118297+0.009469    test-rmse:1.267267+0.274109 
## [172]    train-rmse:0.117065+0.009298    test-rmse:1.266765+0.274044 
## [173]    train-rmse:0.116331+0.009680    test-rmse:1.267026+0.274210 
## [174]    train-rmse:0.115399+0.009415    test-rmse:1.266906+0.273858 
## [175]    train-rmse:0.113975+0.009073    test-rmse:1.266429+0.274012 
## [176]    train-rmse:0.113104+0.008941    test-rmse:1.266388+0.273288 
## [177]    train-rmse:0.112069+0.008784    test-rmse:1.266703+0.273101 
## [178]    train-rmse:0.111078+0.008602    test-rmse:1.266251+0.273293 
## [179]    train-rmse:0.110313+0.008637    test-rmse:1.266546+0.272994 
## [180]    train-rmse:0.109735+0.008723    test-rmse:1.266206+0.273128 
## [181]    train-rmse:0.109075+0.008917    test-rmse:1.265995+0.274010 
## [182]    train-rmse:0.107862+0.009304    test-rmse:1.265903+0.273545 
## [183]    train-rmse:0.106722+0.009809    test-rmse:1.266355+0.273184 
## [184]    train-rmse:0.105624+0.009642    test-rmse:1.265686+0.273054 
## [185]    train-rmse:0.104732+0.009633    test-rmse:1.265508+0.273159 
## [186]    train-rmse:0.103526+0.009635    test-rmse:1.265219+0.272839 
## [187]    train-rmse:0.102407+0.009658    test-rmse:1.265373+0.272863 
## [188]    train-rmse:0.101247+0.009312    test-rmse:1.265013+0.272615 
## [189]    train-rmse:0.100579+0.009481    test-rmse:1.264794+0.272894 
## [190]    train-rmse:0.099476+0.009022    test-rmse:1.264660+0.273169 
## [191]    train-rmse:0.098385+0.009107    test-rmse:1.264541+0.273503 
## [192]    train-rmse:0.097476+0.008662    test-rmse:1.265759+0.273203 
## [193]    train-rmse:0.096593+0.008786    test-rmse:1.265017+0.272840 
## [194]    train-rmse:0.095916+0.008859    test-rmse:1.264870+0.273062 
## [195]    train-rmse:0.095276+0.008799    test-rmse:1.264992+0.273230 
## [196]    train-rmse:0.094403+0.008409    test-rmse:1.264271+0.272963 
## [197]    train-rmse:0.093632+0.008311    test-rmse:1.264260+0.272965 
## [198]    train-rmse:0.092900+0.008513    test-rmse:1.264382+0.273044 
## [199]    train-rmse:0.091826+0.008714    test-rmse:1.264658+0.273507 
## [200]    train-rmse:0.091165+0.008751    test-rmse:1.264120+0.273813 
## [201]    train-rmse:0.090007+0.008099    test-rmse:1.264668+0.273960 
## [202]    train-rmse:0.088915+0.007805    test-rmse:1.264562+0.273626 
## [203]    train-rmse:0.088339+0.007923    test-rmse:1.264123+0.273869 
## [204]    train-rmse:0.087614+0.007964    test-rmse:1.263736+0.273740 
## [205]    train-rmse:0.086942+0.007710    test-rmse:1.263699+0.273723 
## [206]    train-rmse:0.086019+0.007551    test-rmse:1.263760+0.273881 
## [207]    train-rmse:0.084768+0.006756    test-rmse:1.263788+0.273888 
## [208]    train-rmse:0.084263+0.006694    test-rmse:1.263900+0.274147 
## [209]    train-rmse:0.083599+0.006739    test-rmse:1.263856+0.274259 
## [210]    train-rmse:0.082967+0.006764    test-rmse:1.263753+0.274312 
## [211]    train-rmse:0.082445+0.006860    test-rmse:1.262842+0.273549 
## [212]    train-rmse:0.081756+0.007267    test-rmse:1.263140+0.273507 
## [213]    train-rmse:0.081073+0.007225    test-rmse:1.262949+0.273489 
## [214]    train-rmse:0.080250+0.007126    test-rmse:1.262822+0.273177 
## [215]    train-rmse:0.079435+0.007089    test-rmse:1.262768+0.273004 
## [216]    train-rmse:0.078576+0.006818    test-rmse:1.262916+0.273202 
## [217]    train-rmse:0.077488+0.006474    test-rmse:1.262786+0.273497 
## [218]    train-rmse:0.076730+0.006373    test-rmse:1.262846+0.273762 
## [219]    train-rmse:0.075906+0.006485    test-rmse:1.262532+0.273416 
## [220]    train-rmse:0.075206+0.006350    test-rmse:1.262358+0.273383 
## [221]    train-rmse:0.074395+0.006650    test-rmse:1.262494+0.273406 
## [222]    train-rmse:0.073804+0.006752    test-rmse:1.262053+0.273858 
## [223]    train-rmse:0.073197+0.006745    test-rmse:1.262301+0.274280 
## [224]    train-rmse:0.072359+0.006745    test-rmse:1.262313+0.274199 
## [225]    train-rmse:0.071828+0.006726    test-rmse:1.262165+0.274325 
## [226]    train-rmse:0.071037+0.006823    test-rmse:1.262402+0.274299 
## [227]    train-rmse:0.070322+0.006783    test-rmse:1.262141+0.274089 
## [228]    train-rmse:0.069913+0.006862    test-rmse:1.262248+0.273827 
## Stopping. Best iteration:
## [222]    train-rmse:0.073804+0.006752    test-rmse:1.262053+0.273858
## 
## 87 
## [1]  train-rmse:71.268932+0.085068   test-rmse:71.268778+0.519929 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:47.317069+0.068405   test-rmse:47.315810+0.569907 
## [3]  train-rmse:31.577981+0.063944   test-rmse:31.574661+0.622724 
## [4]  train-rmse:21.229897+0.041979   test-rmse:21.327855+0.649898 
## [5]  train-rmse:14.410720+0.028260   test-rmse:14.524585+0.599181 
## [6]  train-rmse:9.922825+0.028980    test-rmse:10.043284+0.529124 
## [7]  train-rmse:6.990207+0.025778    test-rmse:7.175532+0.574097 
## [8]  train-rmse:5.075617+0.028706    test-rmse:5.269135+0.549077 
## [9]  train-rmse:3.858644+0.028337    test-rmse:4.095585+0.505964 
## [10] train-rmse:3.092085+0.050927    test-rmse:3.436769+0.453970 
## [11] train-rmse:2.601005+0.044652    test-rmse:2.971732+0.347205 
## [12] train-rmse:2.282703+0.051338    test-rmse:2.693996+0.309916 
## [13] train-rmse:2.063521+0.047015    test-rmse:2.522844+0.271557 
## [14] train-rmse:1.894162+0.042698    test-rmse:2.422603+0.258727 
## [15] train-rmse:1.764367+0.038239    test-rmse:2.288702+0.256952 
## [16] train-rmse:1.638796+0.042611    test-rmse:2.189340+0.231020 
## [17] train-rmse:1.542997+0.038450    test-rmse:2.114522+0.218555 
## [18] train-rmse:1.457048+0.038588    test-rmse:2.068015+0.222854 
## [19] train-rmse:1.379365+0.036598    test-rmse:1.977771+0.220778 
## [20] train-rmse:1.292665+0.038446    test-rmse:1.933241+0.215232 
## [21] train-rmse:1.219338+0.039059    test-rmse:1.859668+0.207922 
## [22] train-rmse:1.156456+0.037305    test-rmse:1.819728+0.202366 
## [23] train-rmse:1.098696+0.035383    test-rmse:1.778773+0.217304 
## [24] train-rmse:1.048636+0.035746    test-rmse:1.719562+0.222524 
## [25] train-rmse:0.999176+0.033987    test-rmse:1.687747+0.231646 
## [26] train-rmse:0.953089+0.032409    test-rmse:1.661905+0.242858 
## [27] train-rmse:0.905060+0.036136    test-rmse:1.630898+0.242337 
## [28] train-rmse:0.860745+0.035406    test-rmse:1.602673+0.253084 
## [29] train-rmse:0.821369+0.032065    test-rmse:1.559237+0.260159 
## [30] train-rmse:0.785026+0.030477    test-rmse:1.532911+0.265845 
## [31] train-rmse:0.750559+0.031422    test-rmse:1.506785+0.253279 
## [32] train-rmse:0.723039+0.029467    test-rmse:1.491341+0.260712 
## [33] train-rmse:0.693370+0.027782    test-rmse:1.472061+0.263693 
## [34] train-rmse:0.668259+0.026370    test-rmse:1.458097+0.275028 
## [35] train-rmse:0.644294+0.024320    test-rmse:1.437119+0.279864 
## [36] train-rmse:0.621839+0.026478    test-rmse:1.434565+0.277995 
## [37] train-rmse:0.594687+0.030362    test-rmse:1.421297+0.279428 
## [38] train-rmse:0.573608+0.031454    test-rmse:1.404967+0.284116 
## [39] train-rmse:0.555926+0.031580    test-rmse:1.392616+0.289359 
## [40] train-rmse:0.533697+0.027870    test-rmse:1.368380+0.286498 
## [41] train-rmse:0.515919+0.027726    test-rmse:1.365482+0.289331 
## [42] train-rmse:0.499338+0.026584    test-rmse:1.358160+0.292181 
## [43] train-rmse:0.484496+0.027107    test-rmse:1.349766+0.293162 
## [44] train-rmse:0.470593+0.027110    test-rmse:1.338446+0.295007 
## [45] train-rmse:0.458978+0.026435    test-rmse:1.336514+0.297586 
## [46] train-rmse:0.445215+0.026351    test-rmse:1.320159+0.283516 
## [47] train-rmse:0.433044+0.024023    test-rmse:1.311049+0.290218 
## [48] train-rmse:0.418527+0.025621    test-rmse:1.309345+0.291936 
## [49] train-rmse:0.407035+0.026880    test-rmse:1.301574+0.292732 
## [50] train-rmse:0.395843+0.022946    test-rmse:1.299868+0.293354 
## [51] train-rmse:0.384801+0.022813    test-rmse:1.290961+0.299014 
## [52] train-rmse:0.374559+0.023945    test-rmse:1.291588+0.302348 
## [53] train-rmse:0.365291+0.021238    test-rmse:1.289589+0.300927 
## [54] train-rmse:0.355057+0.023588    test-rmse:1.286546+0.300989 
## [55] train-rmse:0.347048+0.024284    test-rmse:1.286281+0.303553 
## [56] train-rmse:0.337967+0.023012    test-rmse:1.284338+0.306343 
## [57] train-rmse:0.330345+0.020547    test-rmse:1.283464+0.304995 
## [58] train-rmse:0.322056+0.021654    test-rmse:1.280016+0.305321 
## [59] train-rmse:0.313704+0.022868    test-rmse:1.278675+0.305705 
## [60] train-rmse:0.305017+0.022033    test-rmse:1.277096+0.306727 
## [61] train-rmse:0.299933+0.021981    test-rmse:1.272901+0.311479 
## [62] train-rmse:0.293436+0.021005    test-rmse:1.275312+0.309290 
## [63] train-rmse:0.285893+0.018670    test-rmse:1.273566+0.309408 
## [64] train-rmse:0.278253+0.018102    test-rmse:1.272946+0.310047 
## [65] train-rmse:0.271297+0.018244    test-rmse:1.272917+0.310004 
## [66] train-rmse:0.266826+0.017439    test-rmse:1.270173+0.309064 
## [67] train-rmse:0.261994+0.015772    test-rmse:1.266900+0.309898 
## [68] train-rmse:0.255586+0.015747    test-rmse:1.265072+0.311081 
## [69] train-rmse:0.250684+0.017022    test-rmse:1.265841+0.310607 
## [70] train-rmse:0.245674+0.015781    test-rmse:1.264749+0.313276 
## [71] train-rmse:0.240622+0.014532    test-rmse:1.264889+0.313937 
## [72] train-rmse:0.236523+0.015091    test-rmse:1.264871+0.315197 
## [73] train-rmse:0.230420+0.015315    test-rmse:1.263101+0.315535 
## [74] train-rmse:0.224626+0.014259    test-rmse:1.260797+0.317522 
## [75] train-rmse:0.221097+0.014357    test-rmse:1.258219+0.318935 
## [76] train-rmse:0.216440+0.013714    test-rmse:1.257439+0.319513 
## [77] train-rmse:0.211586+0.011503    test-rmse:1.256653+0.319996 
## [78] train-rmse:0.207696+0.011369    test-rmse:1.256975+0.319278 
## [79] train-rmse:0.204298+0.012104    test-rmse:1.257079+0.319611 
## [80] train-rmse:0.201705+0.011860    test-rmse:1.255834+0.321130 
## [81] train-rmse:0.197968+0.011972    test-rmse:1.255437+0.322424 
## [82] train-rmse:0.194694+0.011142    test-rmse:1.256587+0.321254 
## [83] train-rmse:0.190304+0.010487    test-rmse:1.255926+0.321553 
## [84] train-rmse:0.187468+0.009779    test-rmse:1.255282+0.321992 
## [85] train-rmse:0.184422+0.010153    test-rmse:1.255216+0.322126 
## [86] train-rmse:0.180556+0.009737    test-rmse:1.255917+0.323930 
## [87] train-rmse:0.176960+0.009698    test-rmse:1.256384+0.323786 
## [88] train-rmse:0.174536+0.010730    test-rmse:1.254890+0.325889 
## [89] train-rmse:0.170286+0.010299    test-rmse:1.254736+0.326121 
## [90] train-rmse:0.166275+0.011099    test-rmse:1.253204+0.325925 
## [91] train-rmse:0.163781+0.011903    test-rmse:1.252711+0.326463 
## [92] train-rmse:0.159054+0.010593    test-rmse:1.253051+0.326412 
## [93] train-rmse:0.156911+0.010380    test-rmse:1.252224+0.326029 
## [94] train-rmse:0.153619+0.009619    test-rmse:1.251850+0.326650 
## [95] train-rmse:0.151966+0.009903    test-rmse:1.251639+0.326354 
## [96] train-rmse:0.148383+0.008900    test-rmse:1.250780+0.325894 
## [97] train-rmse:0.145190+0.008942    test-rmse:1.248987+0.324584 
## [98] train-rmse:0.141185+0.007372    test-rmse:1.248123+0.325434 
## [99] train-rmse:0.137342+0.006654    test-rmse:1.246759+0.325583 
## [100]    train-rmse:0.134895+0.006362    test-rmse:1.246534+0.326318 
## [101]    train-rmse:0.132350+0.005761    test-rmse:1.246804+0.326304 
## [102]    train-rmse:0.129559+0.005707    test-rmse:1.246841+0.325741 
## [103]    train-rmse:0.128462+0.005803    test-rmse:1.246315+0.325906 
## [104]    train-rmse:0.125728+0.005091    test-rmse:1.246282+0.326299 
## [105]    train-rmse:0.123978+0.005042    test-rmse:1.245850+0.327157 
## [106]    train-rmse:0.121069+0.004231    test-rmse:1.245986+0.328426 
## [107]    train-rmse:0.119394+0.004307    test-rmse:1.245923+0.329040 
## [108]    train-rmse:0.116956+0.004264    test-rmse:1.245383+0.328986 
## [109]    train-rmse:0.114743+0.003619    test-rmse:1.244769+0.328326 
## [110]    train-rmse:0.112901+0.003547    test-rmse:1.245175+0.328331 
## [111]    train-rmse:0.111321+0.003581    test-rmse:1.244661+0.327980 
## [112]    train-rmse:0.109392+0.004026    test-rmse:1.244143+0.327471 
## [113]    train-rmse:0.107222+0.003673    test-rmse:1.243692+0.327652 
## [114]    train-rmse:0.104845+0.004552    test-rmse:1.243986+0.328310 
## [115]    train-rmse:0.103072+0.004775    test-rmse:1.243575+0.328310 
## [116]    train-rmse:0.101781+0.004452    test-rmse:1.243543+0.328137 
## [117]    train-rmse:0.100162+0.004291    test-rmse:1.243499+0.328318 
## [118]    train-rmse:0.098312+0.004526    test-rmse:1.243091+0.328315 
## [119]    train-rmse:0.096335+0.004935    test-rmse:1.243075+0.328782 
## [120]    train-rmse:0.095208+0.004499    test-rmse:1.243043+0.328733 
## [121]    train-rmse:0.093159+0.003222    test-rmse:1.242909+0.329694 
## [122]    train-rmse:0.091481+0.003773    test-rmse:1.242281+0.329929 
## [123]    train-rmse:0.090319+0.003914    test-rmse:1.242684+0.330985 
## [124]    train-rmse:0.088625+0.003450    test-rmse:1.242222+0.330970 
## [125]    train-rmse:0.087568+0.003674    test-rmse:1.242248+0.331116 
## [126]    train-rmse:0.086025+0.003559    test-rmse:1.241587+0.331278 
## [127]    train-rmse:0.084317+0.003086    test-rmse:1.241706+0.331300 
## [128]    train-rmse:0.082818+0.003143    test-rmse:1.241447+0.330939 
## [129]    train-rmse:0.081673+0.003398    test-rmse:1.241986+0.332227 
## [130]    train-rmse:0.080011+0.003408    test-rmse:1.242223+0.332586 
## [131]    train-rmse:0.079118+0.003468    test-rmse:1.241976+0.332726 
## [132]    train-rmse:0.077912+0.003747    test-rmse:1.241790+0.332520 
## [133]    train-rmse:0.076740+0.003836    test-rmse:1.241777+0.332578 
## [134]    train-rmse:0.075883+0.003999    test-rmse:1.241095+0.332788 
## [135]    train-rmse:0.074540+0.003845    test-rmse:1.241030+0.332955 
## [136]    train-rmse:0.073128+0.004494    test-rmse:1.240929+0.332742 
## [137]    train-rmse:0.071803+0.004365    test-rmse:1.240866+0.332627 
## [138]    train-rmse:0.070142+0.004260    test-rmse:1.241018+0.332417 
## [139]    train-rmse:0.068980+0.004525    test-rmse:1.241188+0.332479 
## [140]    train-rmse:0.067810+0.004154    test-rmse:1.241068+0.332751 
## [141]    train-rmse:0.067035+0.004042    test-rmse:1.240674+0.333056 
## [142]    train-rmse:0.066233+0.004282    test-rmse:1.240689+0.333486 
## [143]    train-rmse:0.064785+0.003968    test-rmse:1.240694+0.333388 
## [144]    train-rmse:0.063577+0.003918    test-rmse:1.240823+0.333281 
## [145]    train-rmse:0.062776+0.004165    test-rmse:1.240301+0.333546 
## [146]    train-rmse:0.060961+0.004007    test-rmse:1.240468+0.333628 
## [147]    train-rmse:0.059736+0.003686    test-rmse:1.240247+0.333920 
## [148]    train-rmse:0.058974+0.003436    test-rmse:1.240415+0.334076 
## [149]    train-rmse:0.058312+0.003269    test-rmse:1.240182+0.334220 
## [150]    train-rmse:0.057166+0.002856    test-rmse:1.240306+0.334614 
## [151]    train-rmse:0.056154+0.002821    test-rmse:1.240552+0.335261 
## [152]    train-rmse:0.055093+0.002565    test-rmse:1.239986+0.334806 
## [153]    train-rmse:0.053831+0.002128    test-rmse:1.240015+0.334898 
## [154]    train-rmse:0.052521+0.002560    test-rmse:1.240137+0.334797 
## [155]    train-rmse:0.051561+0.002514    test-rmse:1.239880+0.334940 
## [156]    train-rmse:0.050868+0.002671    test-rmse:1.239941+0.335021 
## [157]    train-rmse:0.050224+0.002858    test-rmse:1.240052+0.334833 
## [158]    train-rmse:0.049375+0.002813    test-rmse:1.240087+0.334868 
## [159]    train-rmse:0.048510+0.002639    test-rmse:1.240277+0.334958 
## [160]    train-rmse:0.047976+0.002892    test-rmse:1.239747+0.335152 
## [161]    train-rmse:0.047036+0.002460    test-rmse:1.239693+0.335149 
## [162]    train-rmse:0.046531+0.002628    test-rmse:1.239872+0.335175 
## [163]    train-rmse:0.045810+0.002392    test-rmse:1.239734+0.335445 
## [164]    train-rmse:0.044905+0.002238    test-rmse:1.239833+0.335494 
## [165]    train-rmse:0.044367+0.002183    test-rmse:1.239448+0.335412 
## [166]    train-rmse:0.043441+0.002307    test-rmse:1.240238+0.336523 
## [167]    train-rmse:0.042661+0.002394    test-rmse:1.240306+0.336385 
## [168]    train-rmse:0.041971+0.002742    test-rmse:1.239831+0.336479 
## [169]    train-rmse:0.041162+0.002618    test-rmse:1.240068+0.336920 
## [170]    train-rmse:0.040701+0.002595    test-rmse:1.240094+0.336880 
## [171]    train-rmse:0.039906+0.002564    test-rmse:1.240168+0.336878 
## Stopping. Best iteration:
## [165]    train-rmse:0.044367+0.002183    test-rmse:1.239448+0.335412
## 
## 88 
## [1]  train-rmse:71.268932+0.085068   test-rmse:71.268778+0.519929 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:47.317069+0.068405   test-rmse:47.315810+0.569907 
## [3]  train-rmse:31.577981+0.063944   test-rmse:31.574661+0.622724 
## [4]  train-rmse:21.229897+0.041979   test-rmse:21.327855+0.649898 
## [5]  train-rmse:14.405247+0.028647   test-rmse:14.519507+0.596673 
## [6]  train-rmse:9.891010+0.027809    test-rmse:10.011839+0.565689 
## [7]  train-rmse:6.934785+0.031576    test-rmse:7.122765+0.589758 
## [8]  train-rmse:4.979566+0.021816    test-rmse:5.164149+0.545112 
## [9]  train-rmse:3.698043+0.040503    test-rmse:3.930244+0.469190 
## [10] train-rmse:2.881188+0.052369    test-rmse:3.228337+0.456684 
## [11] train-rmse:2.342153+0.056599    test-rmse:2.766304+0.359351 
## [12] train-rmse:1.981199+0.050782    test-rmse:2.466238+0.294825 
## [13] train-rmse:1.740433+0.050163    test-rmse:2.266862+0.259742 
## [14] train-rmse:1.564689+0.046820    test-rmse:2.156466+0.235712 
## [15] train-rmse:1.431192+0.037327    test-rmse:2.070872+0.211400 
## [16] train-rmse:1.313342+0.037749    test-rmse:1.985733+0.185724 
## [17] train-rmse:1.213891+0.035670    test-rmse:1.905860+0.172892 
## [18] train-rmse:1.127707+0.036598    test-rmse:1.838309+0.178704 
## [19] train-rmse:1.054238+0.034250    test-rmse:1.788775+0.191788 
## [20] train-rmse:0.985989+0.027282    test-rmse:1.738947+0.197225 
## [21] train-rmse:0.922936+0.018478    test-rmse:1.694686+0.186443 
## [22] train-rmse:0.865462+0.018078    test-rmse:1.642667+0.185864 
## [23] train-rmse:0.817172+0.020198    test-rmse:1.612766+0.195242 
## [24] train-rmse:0.760701+0.021499    test-rmse:1.575209+0.192977 
## [25] train-rmse:0.717703+0.022722    test-rmse:1.557195+0.195849 
## [26] train-rmse:0.682328+0.021324    test-rmse:1.530019+0.200241 
## [27] train-rmse:0.643774+0.016656    test-rmse:1.503146+0.200317 
## [28] train-rmse:0.613009+0.016643    test-rmse:1.489284+0.196206 
## [29] train-rmse:0.582444+0.015016    test-rmse:1.473624+0.206341 
## [30] train-rmse:0.555786+0.017664    test-rmse:1.458656+0.209510 
## [31] train-rmse:0.530013+0.017996    test-rmse:1.444632+0.219114 
## [32] train-rmse:0.507252+0.020238    test-rmse:1.427983+0.220586 
## [33] train-rmse:0.478699+0.021683    test-rmse:1.420167+0.214103 
## [34] train-rmse:0.457930+0.018712    test-rmse:1.394617+0.203965 
## [35] train-rmse:0.442655+0.020345    test-rmse:1.383655+0.209702 
## [36] train-rmse:0.426594+0.018999    test-rmse:1.377015+0.215014 
## [37] train-rmse:0.412931+0.020812    test-rmse:1.375684+0.214989 
## [38] train-rmse:0.392924+0.014590    test-rmse:1.373791+0.214180 
## [39] train-rmse:0.374692+0.015756    test-rmse:1.372035+0.223248 
## [40] train-rmse:0.364005+0.015897    test-rmse:1.370520+0.226450 
## [41] train-rmse:0.348781+0.015612    test-rmse:1.363402+0.232297 
## [42] train-rmse:0.332987+0.013529    test-rmse:1.358724+0.237089 
## [43] train-rmse:0.319812+0.014349    test-rmse:1.355263+0.238622 
## [44] train-rmse:0.308692+0.013203    test-rmse:1.352037+0.241396 
## [45] train-rmse:0.298474+0.012696    test-rmse:1.349070+0.242332 
## [46] train-rmse:0.290202+0.013404    test-rmse:1.340012+0.233055 
## [47] train-rmse:0.281092+0.016456    test-rmse:1.339060+0.237416 
## [48] train-rmse:0.273563+0.015837    test-rmse:1.338045+0.239029 
## [49] train-rmse:0.264652+0.016436    test-rmse:1.337779+0.238030 
## [50] train-rmse:0.256232+0.016568    test-rmse:1.331454+0.234765 
## [51] train-rmse:0.248512+0.017178    test-rmse:1.330031+0.234609 
## [52] train-rmse:0.240027+0.015340    test-rmse:1.328200+0.238550 
## [53] train-rmse:0.232580+0.013273    test-rmse:1.326190+0.240350 
## [54] train-rmse:0.224879+0.012203    test-rmse:1.325055+0.242561 
## [55] train-rmse:0.217292+0.011937    test-rmse:1.323070+0.242412 
## [56] train-rmse:0.209575+0.011389    test-rmse:1.320062+0.245934 
## [57] train-rmse:0.202401+0.011207    test-rmse:1.318791+0.245817 
## [58] train-rmse:0.198131+0.011695    test-rmse:1.318679+0.246421 
## [59] train-rmse:0.192263+0.012903    test-rmse:1.316836+0.249249 
## [60] train-rmse:0.188155+0.012848    test-rmse:1.315786+0.251332 
## [61] train-rmse:0.182365+0.013338    test-rmse:1.313745+0.249702 
## [62] train-rmse:0.177963+0.012366    test-rmse:1.311719+0.251322 
## [63] train-rmse:0.174281+0.013387    test-rmse:1.310857+0.253027 
## [64] train-rmse:0.170255+0.014250    test-rmse:1.310215+0.253417 
## [65] train-rmse:0.163492+0.013541    test-rmse:1.308878+0.254327 
## [66] train-rmse:0.158137+0.014229    test-rmse:1.306395+0.254123 
## [67] train-rmse:0.154123+0.014076    test-rmse:1.306774+0.254342 
## [68] train-rmse:0.148735+0.012610    test-rmse:1.306052+0.255399 
## [69] train-rmse:0.144711+0.014069    test-rmse:1.305345+0.256769 
## [70] train-rmse:0.139963+0.012894    test-rmse:1.304938+0.256663 
## [71] train-rmse:0.136393+0.012030    test-rmse:1.304524+0.257099 
## [72] train-rmse:0.130364+0.010370    test-rmse:1.304936+0.256893 
## [73] train-rmse:0.127405+0.010019    test-rmse:1.304672+0.257245 
## [74] train-rmse:0.123367+0.011115    test-rmse:1.304510+0.257641 
## [75] train-rmse:0.119689+0.010145    test-rmse:1.304837+0.258108 
## [76] train-rmse:0.116054+0.010037    test-rmse:1.303158+0.257481 
## [77] train-rmse:0.112126+0.009070    test-rmse:1.302678+0.258107 
## [78] train-rmse:0.108803+0.008402    test-rmse:1.303188+0.258130 
## [79] train-rmse:0.105532+0.009014    test-rmse:1.302560+0.257826 
## [80] train-rmse:0.102200+0.008184    test-rmse:1.302047+0.257999 
## [81] train-rmse:0.099495+0.007420    test-rmse:1.301683+0.259159 
## [82] train-rmse:0.096096+0.007435    test-rmse:1.301506+0.259987 
## [83] train-rmse:0.093602+0.007271    test-rmse:1.302007+0.261511 
## [84] train-rmse:0.090826+0.007065    test-rmse:1.300873+0.259084 
## [85] train-rmse:0.088489+0.006913    test-rmse:1.300860+0.258975 
## [86] train-rmse:0.086864+0.006642    test-rmse:1.300749+0.259608 
## [87] train-rmse:0.084447+0.006103    test-rmse:1.300323+0.259634 
## [88] train-rmse:0.081362+0.004888    test-rmse:1.300016+0.260081 
## [89] train-rmse:0.079493+0.005110    test-rmse:1.300308+0.259828 
## [90] train-rmse:0.076981+0.005383    test-rmse:1.300134+0.259489 
## [91] train-rmse:0.074967+0.005851    test-rmse:1.300482+0.260026 
## [92] train-rmse:0.072821+0.006010    test-rmse:1.301277+0.261664 
## [93] train-rmse:0.070983+0.006397    test-rmse:1.300847+0.262062 
## [94] train-rmse:0.068620+0.006582    test-rmse:1.299997+0.262263 
## [95] train-rmse:0.067331+0.006427    test-rmse:1.300050+0.263042 
## [96] train-rmse:0.065687+0.006812    test-rmse:1.299878+0.262979 
## [97] train-rmse:0.064255+0.006450    test-rmse:1.299575+0.263160 
## [98] train-rmse:0.061990+0.005607    test-rmse:1.299100+0.262760 
## [99] train-rmse:0.060521+0.005530    test-rmse:1.298981+0.262573 
## [100]    train-rmse:0.059242+0.005161    test-rmse:1.298377+0.262949 
## [101]    train-rmse:0.057996+0.004955    test-rmse:1.298579+0.263342 
## [102]    train-rmse:0.056051+0.004287    test-rmse:1.298479+0.263513 
## [103]    train-rmse:0.054484+0.004305    test-rmse:1.297812+0.264215 
## [104]    train-rmse:0.053243+0.004055    test-rmse:1.297166+0.263303 
## [105]    train-rmse:0.051151+0.003494    test-rmse:1.297542+0.263520 
## [106]    train-rmse:0.049749+0.003350    test-rmse:1.297219+0.263381 
## [107]    train-rmse:0.048871+0.002838    test-rmse:1.296790+0.263764 
## [108]    train-rmse:0.047721+0.003069    test-rmse:1.296430+0.264381 
## [109]    train-rmse:0.046609+0.002707    test-rmse:1.296577+0.264548 
## [110]    train-rmse:0.045374+0.002676    test-rmse:1.296650+0.264339 
## [111]    train-rmse:0.044568+0.002448    test-rmse:1.295758+0.264863 
## [112]    train-rmse:0.043729+0.002445    test-rmse:1.295767+0.264776 
## [113]    train-rmse:0.042507+0.002351    test-rmse:1.295521+0.264718 
## [114]    train-rmse:0.041184+0.002215    test-rmse:1.295475+0.264808 
## [115]    train-rmse:0.040509+0.002266    test-rmse:1.295319+0.265032 
## [116]    train-rmse:0.039519+0.002161    test-rmse:1.295247+0.265317 
## [117]    train-rmse:0.038402+0.001692    test-rmse:1.295258+0.265476 
## [118]    train-rmse:0.037112+0.001507    test-rmse:1.294501+0.264837 
## [119]    train-rmse:0.035986+0.001576    test-rmse:1.294276+0.265081 
## [120]    train-rmse:0.034766+0.001807    test-rmse:1.294254+0.265158 
## [121]    train-rmse:0.033892+0.001746    test-rmse:1.294328+0.265244 
## [122]    train-rmse:0.033023+0.001680    test-rmse:1.294259+0.265470 
## [123]    train-rmse:0.032263+0.001783    test-rmse:1.294377+0.265271 
## [124]    train-rmse:0.031591+0.001753    test-rmse:1.294494+0.265174 
## [125]    train-rmse:0.030722+0.001804    test-rmse:1.294583+0.265244 
## [126]    train-rmse:0.030120+0.001555    test-rmse:1.294630+0.265215 
## Stopping. Best iteration:
## [120]    train-rmse:0.034766+0.001807    test-rmse:1.294254+0.265158
## 
## 89 
## [1]  train-rmse:71.268932+0.085068   test-rmse:71.268778+0.519929 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:47.317069+0.068405   test-rmse:47.315810+0.569907 
## [3]  train-rmse:31.577981+0.063944   test-rmse:31.574661+0.622724 
## [4]  train-rmse:21.229897+0.041979   test-rmse:21.327855+0.649898 
## [5]  train-rmse:14.399559+0.028339   test-rmse:14.503341+0.589731 
## [6]  train-rmse:9.867454+0.025916    test-rmse:9.999437+0.572635 
## [7]  train-rmse:6.886197+0.033014    test-rmse:7.053607+0.585200 
## [8]  train-rmse:4.883757+0.036473    test-rmse:5.066199+0.532926 
## [9]  train-rmse:3.563478+0.044102    test-rmse:3.806181+0.460402 
## [10] train-rmse:2.695725+0.054199    test-rmse:3.011521+0.414457 
## [11] train-rmse:2.131575+0.073801    test-rmse:2.536187+0.401456 
## [12] train-rmse:1.743845+0.070664    test-rmse:2.246281+0.370623 
## [13] train-rmse:1.480701+0.068327    test-rmse:2.052067+0.316297 
## [14] train-rmse:1.291807+0.073092    test-rmse:1.931819+0.274474 
## [15] train-rmse:1.142723+0.068231    test-rmse:1.840931+0.255101 
## [16] train-rmse:1.031230+0.063338    test-rmse:1.777070+0.222537 
## [17] train-rmse:0.943761+0.058113    test-rmse:1.699598+0.186124 
## [18] train-rmse:0.864695+0.052131    test-rmse:1.635268+0.194095 
## [19] train-rmse:0.798507+0.044603    test-rmse:1.593662+0.196321 
## [20] train-rmse:0.731528+0.039711    test-rmse:1.559211+0.183693 
## [21] train-rmse:0.682749+0.033585    test-rmse:1.532024+0.193393 
## [22] train-rmse:0.637082+0.030895    test-rmse:1.492206+0.196600 
## [23] train-rmse:0.588459+0.037776    test-rmse:1.461528+0.200947 
## [24] train-rmse:0.548513+0.038056    test-rmse:1.440791+0.190180 
## [25] train-rmse:0.517002+0.033673    test-rmse:1.415425+0.193868 
## [26] train-rmse:0.483169+0.026561    test-rmse:1.407589+0.194557 
## [27] train-rmse:0.455483+0.025673    test-rmse:1.394307+0.201808 
## [28] train-rmse:0.425747+0.026760    test-rmse:1.383486+0.203492 
## [29] train-rmse:0.403812+0.028751    test-rmse:1.372628+0.207678 
## [30] train-rmse:0.379657+0.026128    test-rmse:1.358122+0.193462 
## [31] train-rmse:0.361837+0.022934    test-rmse:1.348239+0.201431 
## [32] train-rmse:0.344801+0.020691    test-rmse:1.337971+0.188349 
## [33] train-rmse:0.329203+0.020106    test-rmse:1.332788+0.190810 
## [34] train-rmse:0.315213+0.019987    test-rmse:1.325293+0.191634 
## [35] train-rmse:0.301240+0.020049    test-rmse:1.316475+0.195699 
## [36] train-rmse:0.283204+0.017828    test-rmse:1.312599+0.199181 
## [37] train-rmse:0.270935+0.018626    test-rmse:1.309968+0.198441 
## [38] train-rmse:0.260183+0.018213    test-rmse:1.308076+0.201211 
## [39] train-rmse:0.248926+0.016214    test-rmse:1.307287+0.199623 
## [40] train-rmse:0.237886+0.014574    test-rmse:1.305272+0.200033 
## [41] train-rmse:0.227061+0.014770    test-rmse:1.302066+0.202344 
## [42] train-rmse:0.216762+0.014131    test-rmse:1.301692+0.201104 
## [43] train-rmse:0.208732+0.014022    test-rmse:1.297770+0.201966 
## [44] train-rmse:0.198322+0.013797    test-rmse:1.294029+0.201262 
## [45] train-rmse:0.191590+0.013214    test-rmse:1.294169+0.203133 
## [46] train-rmse:0.181934+0.013197    test-rmse:1.293507+0.203902 
## [47] train-rmse:0.173983+0.010502    test-rmse:1.291931+0.204535 
## [48] train-rmse:0.166165+0.011689    test-rmse:1.290942+0.204946 
## [49] train-rmse:0.159023+0.011406    test-rmse:1.288109+0.205549 
## [50] train-rmse:0.151722+0.010915    test-rmse:1.286468+0.204357 
## [51] train-rmse:0.144182+0.011588    test-rmse:1.285361+0.204931 
## [52] train-rmse:0.137553+0.010579    test-rmse:1.284839+0.206227 
## [53] train-rmse:0.133595+0.009798    test-rmse:1.285421+0.206778 
## [54] train-rmse:0.127730+0.008474    test-rmse:1.284292+0.207822 
## [55] train-rmse:0.123246+0.008954    test-rmse:1.283468+0.208753 
## [56] train-rmse:0.118990+0.009075    test-rmse:1.282996+0.208735 
## [57] train-rmse:0.115093+0.009001    test-rmse:1.281857+0.208393 
## [58] train-rmse:0.111330+0.008928    test-rmse:1.281673+0.208111 
## [59] train-rmse:0.106464+0.009373    test-rmse:1.281096+0.208749 
## [60] train-rmse:0.101423+0.008897    test-rmse:1.280206+0.209678 
## [61] train-rmse:0.097790+0.008025    test-rmse:1.279552+0.209534 
## [62] train-rmse:0.094538+0.007796    test-rmse:1.278251+0.210545 
## [63] train-rmse:0.091321+0.007772    test-rmse:1.278329+0.210293 
## [64] train-rmse:0.087546+0.008097    test-rmse:1.278014+0.211445 
## [65] train-rmse:0.084981+0.007390    test-rmse:1.277541+0.210147 
## [66] train-rmse:0.081397+0.006644    test-rmse:1.277146+0.210946 
## [67] train-rmse:0.077768+0.005339    test-rmse:1.277486+0.211229 
## [68] train-rmse:0.076260+0.005299    test-rmse:1.276867+0.211156 
## [69] train-rmse:0.073077+0.005193    test-rmse:1.275659+0.211339 
## [70] train-rmse:0.070110+0.004555    test-rmse:1.276970+0.212202 
## [71] train-rmse:0.067932+0.004617    test-rmse:1.276238+0.212404 
## [72] train-rmse:0.065504+0.004824    test-rmse:1.276193+0.213009 
## [73] train-rmse:0.063275+0.004900    test-rmse:1.275901+0.213433 
## [74] train-rmse:0.059806+0.004859    test-rmse:1.275887+0.214054 
## [75] train-rmse:0.057031+0.004427    test-rmse:1.274940+0.213941 
## [76] train-rmse:0.054951+0.004327    test-rmse:1.274770+0.213755 
## [77] train-rmse:0.052307+0.003900    test-rmse:1.273676+0.213666 
## [78] train-rmse:0.050603+0.004316    test-rmse:1.274058+0.213485 
## [79] train-rmse:0.049070+0.004161    test-rmse:1.274040+0.213419 
## [80] train-rmse:0.047160+0.003381    test-rmse:1.273963+0.213877 
## [81] train-rmse:0.044779+0.003063    test-rmse:1.274138+0.213981 
## [82] train-rmse:0.043103+0.003032    test-rmse:1.274438+0.214194 
## [83] train-rmse:0.041670+0.003125    test-rmse:1.274433+0.214333 
## Stopping. Best iteration:
## [77] train-rmse:0.052307+0.003900    test-rmse:1.273676+0.213666
## 
## 90 
## [1]  train-rmse:71.268932+0.085068   test-rmse:71.268778+0.519929 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:47.317069+0.068405   test-rmse:47.315810+0.569907 
## [3]  train-rmse:31.577981+0.063944   test-rmse:31.574661+0.622724 
## [4]  train-rmse:21.229897+0.041979   test-rmse:21.327855+0.649898 
## [5]  train-rmse:14.394689+0.028024   test-rmse:14.497131+0.588484 
## [6]  train-rmse:9.849677+0.021792    test-rmse:9.972395+0.539016 
## [7]  train-rmse:6.832820+0.016877    test-rmse:6.963446+0.478993 
## [8]  train-rmse:4.811644+0.015021    test-rmse:4.996803+0.515946 
## [9]  train-rmse:3.472363+0.032865    test-rmse:3.700079+0.481195 
## [10] train-rmse:2.577830+0.037346    test-rmse:2.890858+0.480474 
## [11] train-rmse:1.984264+0.045988    test-rmse:2.363186+0.397940 
## [12] train-rmse:1.590095+0.057015    test-rmse:2.076177+0.315364 
## [13] train-rmse:1.318062+0.073103    test-rmse:1.867789+0.275218 
## [14] train-rmse:1.116586+0.072637    test-rmse:1.736272+0.251705 
## [15] train-rmse:0.963820+0.070955    test-rmse:1.661453+0.240807 
## [16] train-rmse:0.845607+0.071221    test-rmse:1.607666+0.219653 
## [17] train-rmse:0.754505+0.058959    test-rmse:1.545322+0.212539 
## [18] train-rmse:0.682568+0.062667    test-rmse:1.503297+0.208758 
## [19] train-rmse:0.619683+0.061436    test-rmse:1.482174+0.195835 
## [20] train-rmse:0.564801+0.049605    test-rmse:1.447552+0.201184 
## [21] train-rmse:0.517210+0.043377    test-rmse:1.422250+0.206580 
## [22] train-rmse:0.481284+0.042039    test-rmse:1.392634+0.210952 
## [23] train-rmse:0.444069+0.038420    test-rmse:1.373499+0.227628 
## [24] train-rmse:0.413743+0.034830    test-rmse:1.366951+0.233016 
## [25] train-rmse:0.381930+0.024309    test-rmse:1.352652+0.224814 
## [26] train-rmse:0.356336+0.021481    test-rmse:1.342572+0.237632 
## [27] train-rmse:0.331937+0.024838    test-rmse:1.333448+0.238162 
## [28] train-rmse:0.310187+0.022847    test-rmse:1.329101+0.243486 
## [29] train-rmse:0.290118+0.021259    test-rmse:1.324009+0.244498 
## [30] train-rmse:0.275355+0.020609    test-rmse:1.317264+0.244018 
## [31] train-rmse:0.259240+0.018568    test-rmse:1.313547+0.245973 
## [32] train-rmse:0.244932+0.015960    test-rmse:1.310253+0.246073 
## [33] train-rmse:0.229619+0.016502    test-rmse:1.304703+0.247311 
## [34] train-rmse:0.215093+0.013480    test-rmse:1.299902+0.248737 
## [35] train-rmse:0.202898+0.014364    test-rmse:1.293660+0.249964 
## [36] train-rmse:0.191655+0.012961    test-rmse:1.292220+0.251553 
## [37] train-rmse:0.184176+0.013252    test-rmse:1.290817+0.252980 
## [38] train-rmse:0.174425+0.009401    test-rmse:1.289877+0.255221 
## [39] train-rmse:0.166073+0.010014    test-rmse:1.286699+0.254983 
## [40] train-rmse:0.155507+0.011960    test-rmse:1.285548+0.257000 
## [41] train-rmse:0.149276+0.010901    test-rmse:1.284493+0.259614 
## [42] train-rmse:0.140960+0.012657    test-rmse:1.284230+0.259319 
## [43] train-rmse:0.131934+0.012750    test-rmse:1.284784+0.261982 
## [44] train-rmse:0.126299+0.012088    test-rmse:1.282700+0.261684 
## [45] train-rmse:0.117982+0.011247    test-rmse:1.281700+0.261421 
## [46] train-rmse:0.111534+0.010432    test-rmse:1.281518+0.259655 
## [47] train-rmse:0.104455+0.007756    test-rmse:1.281067+0.259659 
## [48] train-rmse:0.100750+0.008033    test-rmse:1.280405+0.260665 
## [49] train-rmse:0.095778+0.008244    test-rmse:1.278113+0.260721 
## [50] train-rmse:0.090228+0.006971    test-rmse:1.276708+0.261801 
## [51] train-rmse:0.085405+0.006725    test-rmse:1.276495+0.262860 
## [52] train-rmse:0.081282+0.006279    test-rmse:1.275772+0.262817 
## [53] train-rmse:0.078033+0.005595    test-rmse:1.275381+0.262999 
## [54] train-rmse:0.073198+0.006634    test-rmse:1.275000+0.263881 
## [55] train-rmse:0.069133+0.006140    test-rmse:1.274384+0.264011 
## [56] train-rmse:0.066475+0.006473    test-rmse:1.274858+0.264982 
## [57] train-rmse:0.062668+0.006244    test-rmse:1.274889+0.265267 
## [58] train-rmse:0.058888+0.005518    test-rmse:1.275091+0.265368 
## [59] train-rmse:0.055657+0.005195    test-rmse:1.274922+0.265942 
## [60] train-rmse:0.053600+0.005522    test-rmse:1.273984+0.266587 
## [61] train-rmse:0.051695+0.005282    test-rmse:1.273699+0.267195 
## [62] train-rmse:0.049243+0.005818    test-rmse:1.273808+0.267770 
## [63] train-rmse:0.047538+0.005205    test-rmse:1.273486+0.267814 
## [64] train-rmse:0.045292+0.005169    test-rmse:1.273547+0.268301 
## [65] train-rmse:0.042940+0.004588    test-rmse:1.273793+0.268484 
## [66] train-rmse:0.040861+0.003952    test-rmse:1.273389+0.268560 
## [67] train-rmse:0.038494+0.003625    test-rmse:1.273383+0.268560 
## [68] train-rmse:0.036800+0.003478    test-rmse:1.273154+0.268536 
## [69] train-rmse:0.034601+0.003911    test-rmse:1.273250+0.268275 
## [70] train-rmse:0.033012+0.003341    test-rmse:1.272762+0.267915 
## [71] train-rmse:0.031328+0.003755    test-rmse:1.272633+0.267357 
## [72] train-rmse:0.029705+0.003661    test-rmse:1.272578+0.267371 
## [73] train-rmse:0.028358+0.003434    test-rmse:1.272883+0.267838 
## [74] train-rmse:0.027076+0.003343    test-rmse:1.272928+0.267858 
## [75] train-rmse:0.026068+0.003092    test-rmse:1.272497+0.267910 
## [76] train-rmse:0.024712+0.002844    test-rmse:1.272393+0.268137 
## [77] train-rmse:0.023408+0.003062    test-rmse:1.272601+0.268089 
## [78] train-rmse:0.022199+0.002949    test-rmse:1.273141+0.268574 
## [79] train-rmse:0.021035+0.002650    test-rmse:1.272895+0.268589 
## [80] train-rmse:0.019587+0.002330    test-rmse:1.273021+0.268991 
## [81] train-rmse:0.018738+0.002061    test-rmse:1.272558+0.268573 
## [82] train-rmse:0.017633+0.001881    test-rmse:1.272616+0.268751 
## Stopping. Best iteration:
## [76] train-rmse:0.024712+0.002844    test-rmse:1.272393+0.268137
## 
## 91 
## [1]  train-rmse:69.134740+0.083369   test-rmse:69.134537+0.523672 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:44.549814+0.067020   test-rmse:44.548320+0.577398 
## [3]  train-rmse:28.902591+0.064471   test-rmse:28.898605+0.635371 
## [4]  train-rmse:18.983275+0.059510   test-rmse:19.072898+0.659096 
## [5]  train-rmse:12.735609+0.050548   test-rmse:12.797549+0.657194 
## [6]  train-rmse:8.900907+0.056620    test-rmse:9.001007+0.595841 
## [7]  train-rmse:6.617228+0.062627    test-rmse:6.779030+0.676910 
## [8]  train-rmse:5.320866+0.066017    test-rmse:5.431449+0.599056 
## [9]  train-rmse:4.606122+0.070667    test-rmse:4.716379+0.492620 
## [10] train-rmse:4.196975+0.065699    test-rmse:4.345866+0.510582 
## [11] train-rmse:3.954170+0.067734    test-rmse:4.088585+0.423972 
## [12] train-rmse:3.794484+0.063583    test-rmse:3.960193+0.441878 
## [13] train-rmse:3.667457+0.062516    test-rmse:3.821125+0.425059 
## [14] train-rmse:3.563794+0.060441    test-rmse:3.728804+0.320241 
## [15] train-rmse:3.474962+0.058170    test-rmse:3.677345+0.327584 
## [16] train-rmse:3.395159+0.056209    test-rmse:3.603821+0.324421 
## [17] train-rmse:3.315104+0.055827    test-rmse:3.553974+0.329972 
## [18] train-rmse:3.243164+0.052617    test-rmse:3.515535+0.326229 
## [19] train-rmse:3.172721+0.049533    test-rmse:3.438189+0.338604 
## [20] train-rmse:3.104742+0.046914    test-rmse:3.396945+0.357221 
## [21] train-rmse:3.039514+0.044252    test-rmse:3.309467+0.301214 
## [22] train-rmse:2.977184+0.042670    test-rmse:3.231154+0.308299 
## [23] train-rmse:2.917936+0.040870    test-rmse:3.173595+0.301779 
## [24] train-rmse:2.861844+0.039069    test-rmse:3.099588+0.276340 
## [25] train-rmse:2.807002+0.038362    test-rmse:3.049584+0.272320 
## [26] train-rmse:2.757399+0.037490    test-rmse:2.976443+0.279296 
## [27] train-rmse:2.710001+0.037781    test-rmse:2.938247+0.288659 
## [28] train-rmse:2.663287+0.039642    test-rmse:2.908704+0.297419 
## [29] train-rmse:2.617869+0.040411    test-rmse:2.879103+0.303992 
## [30] train-rmse:2.573717+0.040886    test-rmse:2.846984+0.303700 
## [31] train-rmse:2.529852+0.041955    test-rmse:2.818702+0.316167 
## [32] train-rmse:2.488879+0.042099    test-rmse:2.776077+0.327185 
## [33] train-rmse:2.448503+0.041503    test-rmse:2.755945+0.324644 
## [34] train-rmse:2.409826+0.040438    test-rmse:2.703096+0.325368 
## [35] train-rmse:2.370584+0.038439    test-rmse:2.665939+0.320291 
## [36] train-rmse:2.332865+0.036696    test-rmse:2.607278+0.273999 
## [37] train-rmse:2.296817+0.033529    test-rmse:2.589448+0.282666 
## [38] train-rmse:2.260518+0.032344    test-rmse:2.543384+0.292113 
## [39] train-rmse:2.226673+0.031117    test-rmse:2.514568+0.274813 
## [40] train-rmse:2.194100+0.030553    test-rmse:2.477048+0.292114 
## [41] train-rmse:2.161814+0.029958    test-rmse:2.461867+0.296010 
## [42] train-rmse:2.130175+0.029349    test-rmse:2.434900+0.293634 
## [43] train-rmse:2.100199+0.028534    test-rmse:2.393798+0.271699 
## [44] train-rmse:2.071967+0.028365    test-rmse:2.360806+0.281849 
## [45] train-rmse:2.043288+0.028443    test-rmse:2.326725+0.276080 
## [46] train-rmse:2.014350+0.028707    test-rmse:2.303251+0.275687 
## [47] train-rmse:1.986837+0.027318    test-rmse:2.283895+0.261872 
## [48] train-rmse:1.959062+0.025750    test-rmse:2.253618+0.266938 
## [49] train-rmse:1.931333+0.023961    test-rmse:2.225983+0.261265 
## [50] train-rmse:1.904824+0.023070    test-rmse:2.200165+0.260328 
## [51] train-rmse:1.878160+0.022026    test-rmse:2.161506+0.284629 
## [52] train-rmse:1.852806+0.020747    test-rmse:2.148442+0.283053 
## [53] train-rmse:1.827518+0.019828    test-rmse:2.124874+0.268684 
## [54] train-rmse:1.802425+0.019220    test-rmse:2.097863+0.264468 
## [55] train-rmse:1.778373+0.019181    test-rmse:2.073143+0.264212 
## [56] train-rmse:1.754911+0.018154    test-rmse:2.048909+0.253422 
## [57] train-rmse:1.732540+0.017575    test-rmse:2.013061+0.263901 
## [58] train-rmse:1.710873+0.017488    test-rmse:2.001823+0.261663 
## [59] train-rmse:1.689644+0.017105    test-rmse:1.991894+0.262600 
## [60] train-rmse:1.668503+0.016911    test-rmse:1.976062+0.254457 
## [61] train-rmse:1.648511+0.017268    test-rmse:1.931870+0.228643 
## [62] train-rmse:1.629446+0.017034    test-rmse:1.920639+0.232806 
## [63] train-rmse:1.610649+0.016542    test-rmse:1.910979+0.236529 
## [64] train-rmse:1.592052+0.016090    test-rmse:1.901472+0.236902 
## [65] train-rmse:1.573132+0.015333    test-rmse:1.892229+0.242506 
## [66] train-rmse:1.554687+0.015506    test-rmse:1.882464+0.238661 
## [67] train-rmse:1.537045+0.016058    test-rmse:1.857289+0.230243 
## [68] train-rmse:1.519566+0.016259    test-rmse:1.847229+0.226352 
## [69] train-rmse:1.502352+0.016304    test-rmse:1.827053+0.232580 
## [70] train-rmse:1.486332+0.016173    test-rmse:1.794646+0.224531 
## [71] train-rmse:1.470526+0.015719    test-rmse:1.780952+0.226196 
## [72] train-rmse:1.454546+0.015364    test-rmse:1.763636+0.234478 
## [73] train-rmse:1.438723+0.015112    test-rmse:1.740956+0.243719 
## [74] train-rmse:1.423812+0.015167    test-rmse:1.733272+0.239358 
## [75] train-rmse:1.408763+0.015360    test-rmse:1.716103+0.234926 
## [76] train-rmse:1.393486+0.015515    test-rmse:1.697300+0.240008 
## [77] train-rmse:1.378568+0.015910    test-rmse:1.682290+0.236592 
## [78] train-rmse:1.364447+0.015421    test-rmse:1.675939+0.226587 
## [79] train-rmse:1.350801+0.014678    test-rmse:1.652500+0.203387 
## [80] train-rmse:1.337100+0.013881    test-rmse:1.643208+0.200377 
## [81] train-rmse:1.323558+0.013559    test-rmse:1.633528+0.204046 
## [82] train-rmse:1.310737+0.012989    test-rmse:1.625598+0.213123 
## [83] train-rmse:1.298372+0.012467    test-rmse:1.618371+0.211021 
## [84] train-rmse:1.286131+0.012339    test-rmse:1.609102+0.213192 
## [85] train-rmse:1.274656+0.012362    test-rmse:1.612000+0.208976 
## [86] train-rmse:1.263070+0.012597    test-rmse:1.599120+0.213976 
## [87] train-rmse:1.251805+0.012758    test-rmse:1.578109+0.209256 
## [88] train-rmse:1.241211+0.012695    test-rmse:1.566529+0.215660 
## [89] train-rmse:1.230436+0.012262    test-rmse:1.551898+0.208212 
## [90] train-rmse:1.219799+0.012346    test-rmse:1.539300+0.198581 
## [91] train-rmse:1.209434+0.012667    test-rmse:1.522323+0.199937 
## [92] train-rmse:1.199249+0.013024    test-rmse:1.516137+0.202517 
## [93] train-rmse:1.189078+0.013467    test-rmse:1.506482+0.204593 
## [94] train-rmse:1.179391+0.013243    test-rmse:1.494811+0.209786 
## [95] train-rmse:1.169928+0.013114    test-rmse:1.488144+0.213979 
## [96] train-rmse:1.160897+0.012633    test-rmse:1.486252+0.212647 
## [97] train-rmse:1.151952+0.012383    test-rmse:1.471367+0.195454 
## [98] train-rmse:1.142771+0.012149    test-rmse:1.465127+0.195245 
## [99] train-rmse:1.134156+0.012119    test-rmse:1.453879+0.205918 
## [100]    train-rmse:1.125608+0.011963    test-rmse:1.443894+0.204888 
## [101]    train-rmse:1.117324+0.011821    test-rmse:1.434694+0.202278 
## [102]    train-rmse:1.109103+0.011664    test-rmse:1.425280+0.200792 
## [103]    train-rmse:1.101082+0.011698    test-rmse:1.419595+0.202382 
## [104]    train-rmse:1.093158+0.011772    test-rmse:1.407076+0.199528 
## [105]    train-rmse:1.085544+0.011923    test-rmse:1.400736+0.203573 
## [106]    train-rmse:1.077888+0.011946    test-rmse:1.394408+0.205280 
## [107]    train-rmse:1.070507+0.012012    test-rmse:1.386544+0.207928 
## [108]    train-rmse:1.063414+0.011972    test-rmse:1.380767+0.211223 
## [109]    train-rmse:1.056469+0.011988    test-rmse:1.369371+0.203189 
## [110]    train-rmse:1.049782+0.012039    test-rmse:1.362371+0.205771 
## [111]    train-rmse:1.043191+0.012075    test-rmse:1.355639+0.210334 
## [112]    train-rmse:1.036781+0.012227    test-rmse:1.346931+0.206621 
## [113]    train-rmse:1.030438+0.012317    test-rmse:1.345669+0.205115 
## [114]    train-rmse:1.024522+0.012367    test-rmse:1.342649+0.203501 
## [115]    train-rmse:1.018554+0.012166    test-rmse:1.345216+0.200841 
## [116]    train-rmse:1.012704+0.012139    test-rmse:1.340224+0.202050 
## [117]    train-rmse:1.006969+0.012261    test-rmse:1.335957+0.204375 
## [118]    train-rmse:1.001299+0.012432    test-rmse:1.334010+0.203590 
## [119]    train-rmse:0.995592+0.012523    test-rmse:1.331844+0.203554 
## [120]    train-rmse:0.990126+0.012370    test-rmse:1.329290+0.204438 
## [121]    train-rmse:0.984841+0.012164    test-rmse:1.329665+0.204491 
## [122]    train-rmse:0.979662+0.011975    test-rmse:1.315268+0.196195 
## [123]    train-rmse:0.974834+0.011968    test-rmse:1.305409+0.193355 
## [124]    train-rmse:0.970092+0.012141    test-rmse:1.302018+0.190287 
## [125]    train-rmse:0.965506+0.012165    test-rmse:1.296418+0.184835 
## [126]    train-rmse:0.960996+0.012113    test-rmse:1.297212+0.180638 
## [127]    train-rmse:0.956458+0.011969    test-rmse:1.292461+0.182695 
## [128]    train-rmse:0.951941+0.011867    test-rmse:1.285847+0.183057 
## [129]    train-rmse:0.947592+0.011889    test-rmse:1.280408+0.183880 
## [130]    train-rmse:0.943213+0.011939    test-rmse:1.274243+0.183976 
## [131]    train-rmse:0.939115+0.011917    test-rmse:1.269538+0.189500 
## [132]    train-rmse:0.935058+0.011851    test-rmse:1.268193+0.191334 
## [133]    train-rmse:0.931058+0.011789    test-rmse:1.259981+0.188375 
## [134]    train-rmse:0.927097+0.011671    test-rmse:1.258684+0.187644 
## [135]    train-rmse:0.923188+0.011632    test-rmse:1.257004+0.187953 
## [136]    train-rmse:0.919479+0.011556    test-rmse:1.253107+0.185526 
## [137]    train-rmse:0.915742+0.011540    test-rmse:1.250212+0.186255 
## [138]    train-rmse:0.912050+0.011563    test-rmse:1.243734+0.190643 
## [139]    train-rmse:0.908521+0.011596    test-rmse:1.239343+0.193638 
## [140]    train-rmse:0.905052+0.011694    test-rmse:1.236779+0.195073 
## [141]    train-rmse:0.901566+0.011889    test-rmse:1.235228+0.195057 
## [142]    train-rmse:0.898304+0.012015    test-rmse:1.230718+0.195856 
## [143]    train-rmse:0.895104+0.012250    test-rmse:1.229303+0.195999 
## [144]    train-rmse:0.892038+0.012362    test-rmse:1.229164+0.198633 
## [145]    train-rmse:0.888979+0.012464    test-rmse:1.228366+0.199770 
## [146]    train-rmse:0.886012+0.012613    test-rmse:1.227868+0.199300 
## [147]    train-rmse:0.883180+0.012717    test-rmse:1.227871+0.196689 
## [148]    train-rmse:0.880364+0.012836    test-rmse:1.226115+0.196732 
## [149]    train-rmse:0.877621+0.012863    test-rmse:1.222337+0.199792 
## [150]    train-rmse:0.874935+0.012892    test-rmse:1.222338+0.199775 
## [151]    train-rmse:0.872296+0.012839    test-rmse:1.220519+0.195754 
## [152]    train-rmse:0.869658+0.012838    test-rmse:1.217796+0.196049 
## [153]    train-rmse:0.866983+0.012709    test-rmse:1.211603+0.189596 
## [154]    train-rmse:0.864440+0.012750    test-rmse:1.210443+0.191571 
## [155]    train-rmse:0.861889+0.012804    test-rmse:1.204001+0.190655 
## [156]    train-rmse:0.859461+0.012918    test-rmse:1.203665+0.189980 
## [157]    train-rmse:0.856927+0.013112    test-rmse:1.201603+0.186515 
## [158]    train-rmse:0.854545+0.013188    test-rmse:1.204061+0.185092 
## [159]    train-rmse:0.852211+0.013249    test-rmse:1.199608+0.186780 
## [160]    train-rmse:0.849894+0.013280    test-rmse:1.194995+0.188761 
## [161]    train-rmse:0.847679+0.013284    test-rmse:1.195998+0.187364 
## [162]    train-rmse:0.845457+0.013380    test-rmse:1.192199+0.190051 
## [163]    train-rmse:0.843257+0.013480    test-rmse:1.190002+0.191111 
## [164]    train-rmse:0.841001+0.013623    test-rmse:1.191630+0.191806 
## [165]    train-rmse:0.838976+0.013695    test-rmse:1.188746+0.191106 
## [166]    train-rmse:0.836966+0.013860    test-rmse:1.188278+0.190590 
## [167]    train-rmse:0.834993+0.013992    test-rmse:1.186482+0.189974 
## [168]    train-rmse:0.833060+0.014110    test-rmse:1.184302+0.189145 
## [169]    train-rmse:0.831171+0.014200    test-rmse:1.181121+0.191113 
## [170]    train-rmse:0.829327+0.014365    test-rmse:1.178470+0.192627 
## [171]    train-rmse:0.827507+0.014462    test-rmse:1.178522+0.191680 
## [172]    train-rmse:0.825751+0.014567    test-rmse:1.172312+0.187632 
## [173]    train-rmse:0.824013+0.014668    test-rmse:1.168418+0.186134 
## [174]    train-rmse:0.822242+0.014753    test-rmse:1.164179+0.188693 
## [175]    train-rmse:0.820451+0.014852    test-rmse:1.161366+0.191186 
## [176]    train-rmse:0.818632+0.015037    test-rmse:1.160676+0.189095 
## [177]    train-rmse:0.816986+0.015135    test-rmse:1.154815+0.188089 
## [178]    train-rmse:0.815355+0.015242    test-rmse:1.155263+0.189637 
## [179]    train-rmse:0.813670+0.015265    test-rmse:1.153437+0.190433 
## [180]    train-rmse:0.812096+0.015385    test-rmse:1.149769+0.192880 
## [181]    train-rmse:0.810298+0.015455    test-rmse:1.152439+0.192063 
## [182]    train-rmse:0.808619+0.015489    test-rmse:1.150940+0.190749 
## [183]    train-rmse:0.806841+0.015553    test-rmse:1.148618+0.194408 
## [184]    train-rmse:0.805201+0.015678    test-rmse:1.147866+0.193069 
## [185]    train-rmse:0.803614+0.015829    test-rmse:1.148567+0.190478 
## [186]    train-rmse:0.802034+0.015920    test-rmse:1.146554+0.191315 
## [187]    train-rmse:0.800529+0.015967    test-rmse:1.144786+0.190025 
## [188]    train-rmse:0.799046+0.016008    test-rmse:1.145128+0.188709 
## [189]    train-rmse:0.797602+0.016055    test-rmse:1.145415+0.187787 
## [190]    train-rmse:0.796150+0.016126    test-rmse:1.142329+0.189383 
## [191]    train-rmse:0.794752+0.016145    test-rmse:1.138708+0.190692 
## [192]    train-rmse:0.793362+0.016199    test-rmse:1.140518+0.188072 
## [193]    train-rmse:0.792002+0.016256    test-rmse:1.139393+0.189697 
## [194]    train-rmse:0.790653+0.016331    test-rmse:1.138593+0.189994 
## [195]    train-rmse:0.789223+0.016410    test-rmse:1.135566+0.189026 
## [196]    train-rmse:0.787872+0.016488    test-rmse:1.133257+0.188757 
## [197]    train-rmse:0.786559+0.016459    test-rmse:1.133049+0.190573 
## [198]    train-rmse:0.785313+0.016465    test-rmse:1.130339+0.192162 
## [199]    train-rmse:0.784018+0.016508    test-rmse:1.130703+0.191395 
## [200]    train-rmse:0.782775+0.016544    test-rmse:1.131529+0.190868 
## [201]    train-rmse:0.781538+0.016566    test-rmse:1.132149+0.190241 
## [202]    train-rmse:0.780286+0.016605    test-rmse:1.129396+0.191540 
## [203]    train-rmse:0.779054+0.016758    test-rmse:1.129748+0.192157 
## [204]    train-rmse:0.777925+0.016810    test-rmse:1.128245+0.191802 
## [205]    train-rmse:0.776815+0.016885    test-rmse:1.128479+0.191211 
## [206]    train-rmse:0.775653+0.017002    test-rmse:1.129081+0.192794 
## [207]    train-rmse:0.774526+0.017091    test-rmse:1.129085+0.192752 
## [208]    train-rmse:0.773443+0.017134    test-rmse:1.129350+0.192318 
## [209]    train-rmse:0.772363+0.017155    test-rmse:1.127679+0.191879 
## [210]    train-rmse:0.771233+0.017170    test-rmse:1.124549+0.188804 
## [211]    train-rmse:0.770148+0.017185    test-rmse:1.122597+0.188609 
## [212]    train-rmse:0.769042+0.017238    test-rmse:1.120618+0.190505 
## [213]    train-rmse:0.767985+0.017271    test-rmse:1.118039+0.188306 
## [214]    train-rmse:0.766944+0.017322    test-rmse:1.115713+0.188883 
## [215]    train-rmse:0.765926+0.017371    test-rmse:1.115905+0.189194 
## [216]    train-rmse:0.764884+0.017316    test-rmse:1.115251+0.188171 
## [217]    train-rmse:0.763858+0.017333    test-rmse:1.115256+0.189706 
## [218]    train-rmse:0.762821+0.017326    test-rmse:1.112269+0.185629 
## [219]    train-rmse:0.761818+0.017353    test-rmse:1.114220+0.184090 
## [220]    train-rmse:0.760810+0.017376    test-rmse:1.115011+0.183346 
## [221]    train-rmse:0.759851+0.017394    test-rmse:1.113473+0.182806 
## [222]    train-rmse:0.758910+0.017379    test-rmse:1.113109+0.184064 
## [223]    train-rmse:0.757962+0.017369    test-rmse:1.111955+0.182370 
## [224]    train-rmse:0.757046+0.017386    test-rmse:1.111559+0.183116 
## [225]    train-rmse:0.756142+0.017411    test-rmse:1.109020+0.181382 
## [226]    train-rmse:0.755205+0.017396    test-rmse:1.108387+0.182978 
## [227]    train-rmse:0.754317+0.017383    test-rmse:1.109873+0.183058 
## [228]    train-rmse:0.753401+0.017413    test-rmse:1.108703+0.181195 
## [229]    train-rmse:0.752530+0.017402    test-rmse:1.106955+0.181724 
## [230]    train-rmse:0.751704+0.017407    test-rmse:1.105082+0.180761 
## [231]    train-rmse:0.750885+0.017419    test-rmse:1.105848+0.180780 
## [232]    train-rmse:0.750013+0.017427    test-rmse:1.104977+0.179975 
## [233]    train-rmse:0.749211+0.017436    test-rmse:1.104607+0.178891 
## [234]    train-rmse:0.748408+0.017446    test-rmse:1.102108+0.178067 
## [235]    train-rmse:0.747574+0.017465    test-rmse:1.101605+0.178300 
## [236]    train-rmse:0.746760+0.017466    test-rmse:1.100725+0.178894 
## [237]    train-rmse:0.745936+0.017407    test-rmse:1.099766+0.179319 
## [238]    train-rmse:0.745127+0.017397    test-rmse:1.098864+0.178642 
## [239]    train-rmse:0.744331+0.017367    test-rmse:1.098822+0.178834 
## [240]    train-rmse:0.743530+0.017304    test-rmse:1.097035+0.178855 
## [241]    train-rmse:0.742774+0.017278    test-rmse:1.095752+0.178742 
## [242]    train-rmse:0.742007+0.017254    test-rmse:1.093070+0.179324 
## [243]    train-rmse:0.741250+0.017230    test-rmse:1.092314+0.176695 
## [244]    train-rmse:0.740511+0.017221    test-rmse:1.091078+0.177419 
## [245]    train-rmse:0.739703+0.017203    test-rmse:1.087462+0.174458 
## [246]    train-rmse:0.738951+0.017225    test-rmse:1.088036+0.174261 
## [247]    train-rmse:0.738205+0.017243    test-rmse:1.086508+0.173922 
## [248]    train-rmse:0.737452+0.017245    test-rmse:1.086912+0.174018 
## [249]    train-rmse:0.736731+0.017238    test-rmse:1.084892+0.174161 
## [250]    train-rmse:0.736024+0.017250    test-rmse:1.085196+0.174414 
## [251]    train-rmse:0.735299+0.017249    test-rmse:1.083595+0.172778 
## [252]    train-rmse:0.734573+0.017222    test-rmse:1.082209+0.170524 
## [253]    train-rmse:0.733831+0.017213    test-rmse:1.081082+0.171059 
## [254]    train-rmse:0.733138+0.017217    test-rmse:1.082801+0.170120 
## [255]    train-rmse:0.732477+0.017209    test-rmse:1.081335+0.171690 
## [256]    train-rmse:0.731832+0.017179    test-rmse:1.081784+0.170430 
## [257]    train-rmse:0.731152+0.017139    test-rmse:1.080083+0.171818 
## [258]    train-rmse:0.730500+0.017096    test-rmse:1.080161+0.172912 
## [259]    train-rmse:0.729807+0.017077    test-rmse:1.079946+0.173010 
## [260]    train-rmse:0.729165+0.017029    test-rmse:1.078925+0.171837 
## [261]    train-rmse:0.728512+0.016978    test-rmse:1.078448+0.171356 
## [262]    train-rmse:0.727837+0.016916    test-rmse:1.077206+0.169351 
## [263]    train-rmse:0.727158+0.016879    test-rmse:1.077326+0.169540 
## [264]    train-rmse:0.726492+0.016855    test-rmse:1.077536+0.168879 
## [265]    train-rmse:0.725845+0.016788    test-rmse:1.076220+0.167438 
## [266]    train-rmse:0.725229+0.016758    test-rmse:1.077149+0.166857 
## [267]    train-rmse:0.724613+0.016723    test-rmse:1.076096+0.166242 
## [268]    train-rmse:0.723986+0.016722    test-rmse:1.074171+0.163856 
## [269]    train-rmse:0.723340+0.016719    test-rmse:1.074106+0.164044 
## [270]    train-rmse:0.722721+0.016695    test-rmse:1.074520+0.164010 
## [271]    train-rmse:0.722114+0.016654    test-rmse:1.073731+0.163278 
## [272]    train-rmse:0.721484+0.016632    test-rmse:1.074887+0.162891 
## [273]    train-rmse:0.720843+0.016604    test-rmse:1.071318+0.162691 
## [274]    train-rmse:0.720232+0.016516    test-rmse:1.071820+0.162856 
## [275]    train-rmse:0.719641+0.016484    test-rmse:1.070289+0.162779 
## [276]    train-rmse:0.719057+0.016461    test-rmse:1.069456+0.161929 
## [277]    train-rmse:0.718485+0.016455    test-rmse:1.069417+0.161304 
## [278]    train-rmse:0.717918+0.016443    test-rmse:1.069337+0.161775 
## [279]    train-rmse:0.717333+0.016442    test-rmse:1.069193+0.163192 
## [280]    train-rmse:0.716773+0.016449    test-rmse:1.069463+0.162077 
## [281]    train-rmse:0.716205+0.016429    test-rmse:1.067598+0.157714 
## [282]    train-rmse:0.715655+0.016401    test-rmse:1.066679+0.156920 
## [283]    train-rmse:0.715100+0.016387    test-rmse:1.066433+0.157843 
## [284]    train-rmse:0.714536+0.016355    test-rmse:1.066049+0.157411 
## [285]    train-rmse:0.713977+0.016304    test-rmse:1.063680+0.157079 
## [286]    train-rmse:0.713427+0.016257    test-rmse:1.063158+0.156316 
## [287]    train-rmse:0.712879+0.016196    test-rmse:1.063534+0.156586 
## [288]    train-rmse:0.712361+0.016174    test-rmse:1.062872+0.157076 
## [289]    train-rmse:0.711828+0.016117    test-rmse:1.062475+0.157394 
## [290]    train-rmse:0.711326+0.016090    test-rmse:1.063190+0.156902 
## [291]    train-rmse:0.710823+0.016055    test-rmse:1.063322+0.156487 
## [292]    train-rmse:0.710261+0.016062    test-rmse:1.062113+0.155334 
## [293]    train-rmse:0.709747+0.016063    test-rmse:1.061264+0.155210 
## [294]    train-rmse:0.709204+0.016040    test-rmse:1.061973+0.154609 
## [295]    train-rmse:0.708695+0.015982    test-rmse:1.058363+0.153866 
## [296]    train-rmse:0.708161+0.015971    test-rmse:1.059563+0.153147 
## [297]    train-rmse:0.707665+0.015967    test-rmse:1.058825+0.152914 
## [298]    train-rmse:0.707180+0.015927    test-rmse:1.058848+0.151460 
## [299]    train-rmse:0.706672+0.015940    test-rmse:1.056294+0.150160 
## [300]    train-rmse:0.706199+0.015911    test-rmse:1.056128+0.148302 
## [301]    train-rmse:0.705703+0.015874    test-rmse:1.056362+0.148706 
## [302]    train-rmse:0.705203+0.015843    test-rmse:1.055679+0.147825 
## [303]    train-rmse:0.704733+0.015819    test-rmse:1.054813+0.148318 
## [304]    train-rmse:0.704273+0.015796    test-rmse:1.055090+0.148540 
## [305]    train-rmse:0.703806+0.015783    test-rmse:1.054639+0.148875 
## [306]    train-rmse:0.703361+0.015766    test-rmse:1.053630+0.148333 
## [307]    train-rmse:0.702865+0.015769    test-rmse:1.051759+0.148089 
## [308]    train-rmse:0.702393+0.015775    test-rmse:1.053824+0.147628 
## [309]    train-rmse:0.701904+0.015731    test-rmse:1.052695+0.148532 
## [310]    train-rmse:0.701429+0.015672    test-rmse:1.053284+0.147459 
## [311]    train-rmse:0.700945+0.015644    test-rmse:1.052648+0.147090 
## [312]    train-rmse:0.700462+0.015622    test-rmse:1.050693+0.146795 
## [313]    train-rmse:0.700006+0.015596    test-rmse:1.049895+0.146250 
## [314]    train-rmse:0.699576+0.015568    test-rmse:1.047704+0.144286 
## [315]    train-rmse:0.699144+0.015553    test-rmse:1.047813+0.144074 
## [316]    train-rmse:0.698691+0.015549    test-rmse:1.047966+0.143682 
## [317]    train-rmse:0.698257+0.015517    test-rmse:1.047845+0.142294 
## [318]    train-rmse:0.697795+0.015493    test-rmse:1.047619+0.143602 
## [319]    train-rmse:0.697365+0.015469    test-rmse:1.046690+0.143882 
## [320]    train-rmse:0.696930+0.015438    test-rmse:1.046574+0.144156 
## [321]    train-rmse:0.696514+0.015422    test-rmse:1.046556+0.142201 
## [322]    train-rmse:0.696096+0.015410    test-rmse:1.046606+0.142259 
## [323]    train-rmse:0.695650+0.015358    test-rmse:1.046066+0.142264 
## [324]    train-rmse:0.695214+0.015325    test-rmse:1.046430+0.142295 
## [325]    train-rmse:0.694787+0.015299    test-rmse:1.046479+0.141392 
## [326]    train-rmse:0.694374+0.015269    test-rmse:1.046276+0.141730 
## [327]    train-rmse:0.693930+0.015262    test-rmse:1.046820+0.141655 
## [328]    train-rmse:0.693521+0.015241    test-rmse:1.047260+0.141363 
## [329]    train-rmse:0.693110+0.015231    test-rmse:1.047129+0.140032 
## Stopping. Best iteration:
## [323]    train-rmse:0.695650+0.015358    test-rmse:1.046066+0.142264
## 
## 92 
## [1]  train-rmse:69.134740+0.083369   test-rmse:69.134537+0.523672 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:44.549814+0.067020   test-rmse:44.548320+0.577398 
## [3]  train-rmse:28.902591+0.064471   test-rmse:28.898605+0.635371 
## [4]  train-rmse:18.937317+0.040829   test-rmse:19.041522+0.668695 
## [5]  train-rmse:12.624304+0.033165   test-rmse:12.736479+0.571299 
## [6]  train-rmse:8.673716+0.036045    test-rmse:8.763494+0.562853 
## [7]  train-rmse:6.270634+0.048807    test-rmse:6.406820+0.594784 
## [8]  train-rmse:4.863757+0.051246    test-rmse:5.058846+0.595578 
## [9]  train-rmse:4.055699+0.048864    test-rmse:4.238751+0.503527 
## [10] train-rmse:3.587957+0.050230    test-rmse:3.803427+0.425174 
## [11] train-rmse:3.294281+0.043854    test-rmse:3.585424+0.389489 
## [12] train-rmse:3.097735+0.038122    test-rmse:3.432272+0.400962 
## [13] train-rmse:2.935408+0.032819    test-rmse:3.229126+0.337074 
## [14] train-rmse:2.810993+0.026744    test-rmse:3.096750+0.296959 
## [15] train-rmse:2.701264+0.030403    test-rmse:2.967759+0.322367 
## [16] train-rmse:2.596609+0.027857    test-rmse:2.883300+0.291007 
## [17] train-rmse:2.507288+0.028201    test-rmse:2.792414+0.304198 
## [18] train-rmse:2.418762+0.022850    test-rmse:2.739210+0.276209 
## [19] train-rmse:2.330991+0.016318    test-rmse:2.687979+0.286119 
## [20] train-rmse:2.253156+0.015447    test-rmse:2.617707+0.283422 
## [21] train-rmse:2.170379+0.009401    test-rmse:2.545172+0.296363 
## [22] train-rmse:2.100335+0.009544    test-rmse:2.468185+0.277322 
## [23] train-rmse:2.034264+0.007940    test-rmse:2.403396+0.244917 
## [24] train-rmse:1.970669+0.008367    test-rmse:2.346489+0.265363 
## [25] train-rmse:1.912368+0.007973    test-rmse:2.295282+0.261029 
## [26] train-rmse:1.850331+0.011939    test-rmse:2.234368+0.268614 
## [27] train-rmse:1.792949+0.013708    test-rmse:2.205518+0.261134 
## [28] train-rmse:1.737561+0.014382    test-rmse:2.161303+0.250784 
## [29] train-rmse:1.688289+0.013623    test-rmse:2.101606+0.247366 
## [30] train-rmse:1.641708+0.015922    test-rmse:2.067014+0.259966 
## [31] train-rmse:1.594494+0.014490    test-rmse:2.022640+0.248199 
## [32] train-rmse:1.550680+0.013648    test-rmse:2.009630+0.254451 
## [33] train-rmse:1.509382+0.014951    test-rmse:1.978013+0.272678 
## [34] train-rmse:1.470902+0.015703    test-rmse:1.941674+0.270897 
## [35] train-rmse:1.431335+0.012058    test-rmse:1.910181+0.275221 
## [36] train-rmse:1.393137+0.010773    test-rmse:1.881973+0.278119 
## [37] train-rmse:1.355940+0.008582    test-rmse:1.843925+0.273019 
## [38] train-rmse:1.322596+0.009009    test-rmse:1.811725+0.276164 
## [39] train-rmse:1.287488+0.012293    test-rmse:1.787050+0.278196 
## [40] train-rmse:1.255293+0.012584    test-rmse:1.760997+0.281045 
## [41] train-rmse:1.223032+0.010015    test-rmse:1.719131+0.260138 
## [42] train-rmse:1.194922+0.010311    test-rmse:1.676403+0.255930 
## [43] train-rmse:1.168007+0.011974    test-rmse:1.664043+0.256879 
## [44] train-rmse:1.141320+0.013484    test-rmse:1.654440+0.250708 
## [45] train-rmse:1.115394+0.012797    test-rmse:1.625270+0.252722 
## [46] train-rmse:1.088419+0.013458    test-rmse:1.597814+0.254859 
## [47] train-rmse:1.065220+0.014586    test-rmse:1.579472+0.265118 
## [48] train-rmse:1.041419+0.014602    test-rmse:1.568747+0.270756 
## [49] train-rmse:1.020662+0.015259    test-rmse:1.555512+0.281298 
## [50] train-rmse:0.997517+0.019313    test-rmse:1.532153+0.266891 
## [51] train-rmse:0.972931+0.022081    test-rmse:1.516452+0.268307 
## [52] train-rmse:0.953730+0.022712    test-rmse:1.498963+0.269164 
## [53] train-rmse:0.935286+0.023168    test-rmse:1.480190+0.270910 
## [54] train-rmse:0.918819+0.023746    test-rmse:1.460663+0.270270 
## [55] train-rmse:0.899607+0.020685    test-rmse:1.450646+0.267449 
## [56] train-rmse:0.881563+0.020200    test-rmse:1.448842+0.269562 
## [57] train-rmse:0.866242+0.019788    test-rmse:1.438902+0.273095 
## [58] train-rmse:0.851465+0.020251    test-rmse:1.428592+0.272172 
## [59] train-rmse:0.836289+0.019052    test-rmse:1.417027+0.269621 
## [60] train-rmse:0.822781+0.019479    test-rmse:1.405407+0.271283 
## [61] train-rmse:0.809473+0.020779    test-rmse:1.398249+0.273861 
## [62] train-rmse:0.795298+0.022326    test-rmse:1.385974+0.280832 
## [63] train-rmse:0.783687+0.023117    test-rmse:1.378321+0.283496 
## [64] train-rmse:0.767365+0.022245    test-rmse:1.365960+0.276171 
## [65] train-rmse:0.756036+0.021888    test-rmse:1.354720+0.270991 
## [66] train-rmse:0.744760+0.020967    test-rmse:1.348214+0.274716 
## [67] train-rmse:0.733430+0.020048    test-rmse:1.333571+0.280006 
## [68] train-rmse:0.721351+0.018564    test-rmse:1.331043+0.283870 
## [69] train-rmse:0.711376+0.017777    test-rmse:1.323224+0.285058 
## [70] train-rmse:0.702087+0.017324    test-rmse:1.318881+0.285722 
## [71] train-rmse:0.693475+0.017013    test-rmse:1.310804+0.287836 
## [72] train-rmse:0.683178+0.016464    test-rmse:1.304539+0.284101 
## [73] train-rmse:0.675362+0.016659    test-rmse:1.300921+0.284734 
## [74] train-rmse:0.667525+0.015391    test-rmse:1.296447+0.288363 
## [75] train-rmse:0.657893+0.015857    test-rmse:1.293708+0.285886 
## [76] train-rmse:0.650962+0.016070    test-rmse:1.290288+0.284448 
## [77] train-rmse:0.644840+0.015868    test-rmse:1.280046+0.289727 
## [78] train-rmse:0.637616+0.016283    test-rmse:1.273324+0.282807 
## [79] train-rmse:0.630732+0.016737    test-rmse:1.271829+0.286136 
## [80] train-rmse:0.622826+0.015289    test-rmse:1.268618+0.282570 
## [81] train-rmse:0.615781+0.015803    test-rmse:1.263423+0.286551 
## [82] train-rmse:0.608858+0.015187    test-rmse:1.259820+0.291114 
## [83] train-rmse:0.602508+0.015940    test-rmse:1.255429+0.291171 
## [84] train-rmse:0.594665+0.014632    test-rmse:1.252911+0.293343 
## [85] train-rmse:0.588059+0.016066    test-rmse:1.249782+0.297211 
## [86] train-rmse:0.581309+0.015834    test-rmse:1.247479+0.299104 
## [87] train-rmse:0.574991+0.015733    test-rmse:1.237613+0.293684 
## [88] train-rmse:0.569141+0.015602    test-rmse:1.235786+0.293366 
## [89] train-rmse:0.563234+0.017859    test-rmse:1.230778+0.296145 
## [90] train-rmse:0.558634+0.018887    test-rmse:1.225521+0.295067 
## [91] train-rmse:0.552735+0.019160    test-rmse:1.222601+0.291193 
## [92] train-rmse:0.548469+0.019526    test-rmse:1.214707+0.294993 
## [93] train-rmse:0.544278+0.019740    test-rmse:1.212055+0.295104 
## [94] train-rmse:0.539755+0.020614    test-rmse:1.209817+0.293479 
## [95] train-rmse:0.534788+0.020372    test-rmse:1.208192+0.295844 
## [96] train-rmse:0.528290+0.019732    test-rmse:1.202988+0.296598 
## [97] train-rmse:0.524265+0.019733    test-rmse:1.202568+0.295652 
## [98] train-rmse:0.520274+0.019889    test-rmse:1.200507+0.290323 
## [99] train-rmse:0.513738+0.019182    test-rmse:1.199724+0.289384 
## [100]    train-rmse:0.508246+0.019485    test-rmse:1.200681+0.289228 
## [101]    train-rmse:0.505279+0.019796    test-rmse:1.199610+0.291449 
## [102]    train-rmse:0.501345+0.019411    test-rmse:1.198865+0.291322 
## [103]    train-rmse:0.497442+0.019925    test-rmse:1.196473+0.294205 
## [104]    train-rmse:0.494160+0.020031    test-rmse:1.192682+0.291305 
## [105]    train-rmse:0.491365+0.020227    test-rmse:1.190684+0.291330 
## [106]    train-rmse:0.485671+0.020387    test-rmse:1.188305+0.292415 
## [107]    train-rmse:0.478405+0.014090    test-rmse:1.187836+0.292490 
## [108]    train-rmse:0.472894+0.010308    test-rmse:1.180125+0.297957 
## [109]    train-rmse:0.469399+0.009248    test-rmse:1.179193+0.298720 
## [110]    train-rmse:0.465370+0.009429    test-rmse:1.172359+0.294118 
## [111]    train-rmse:0.462106+0.009697    test-rmse:1.172649+0.296084 
## [112]    train-rmse:0.458621+0.010626    test-rmse:1.171783+0.298155 
## [113]    train-rmse:0.455931+0.010489    test-rmse:1.165401+0.300763 
## [114]    train-rmse:0.453327+0.010883    test-rmse:1.162683+0.299952 
## [115]    train-rmse:0.450889+0.010401    test-rmse:1.161422+0.300832 
## [116]    train-rmse:0.447945+0.011139    test-rmse:1.160609+0.301583 
## [117]    train-rmse:0.445582+0.010493    test-rmse:1.159721+0.301524 
## [118]    train-rmse:0.440944+0.011732    test-rmse:1.155353+0.303151 
## [119]    train-rmse:0.437604+0.011601    test-rmse:1.154218+0.303750 
## [120]    train-rmse:0.434446+0.010910    test-rmse:1.152105+0.305638 
## [121]    train-rmse:0.431298+0.010718    test-rmse:1.151782+0.307340 
## [122]    train-rmse:0.429190+0.010673    test-rmse:1.151845+0.306310 
## [123]    train-rmse:0.425327+0.009877    test-rmse:1.148868+0.306703 
## [124]    train-rmse:0.423315+0.010054    test-rmse:1.147919+0.306289 
## [125]    train-rmse:0.419490+0.008967    test-rmse:1.147872+0.306578 
## [126]    train-rmse:0.417157+0.009465    test-rmse:1.147583+0.305535 
## [127]    train-rmse:0.415050+0.009759    test-rmse:1.146105+0.307238 
## [128]    train-rmse:0.412716+0.010104    test-rmse:1.145947+0.306896 
## [129]    train-rmse:0.408627+0.012441    test-rmse:1.144977+0.307934 
## [130]    train-rmse:0.406485+0.012287    test-rmse:1.143327+0.309606 
## [131]    train-rmse:0.404702+0.012571    test-rmse:1.141939+0.310702 
## [132]    train-rmse:0.399410+0.011412    test-rmse:1.139427+0.313287 
## [133]    train-rmse:0.396529+0.011003    test-rmse:1.139684+0.314291 
## [134]    train-rmse:0.395156+0.010817    test-rmse:1.138277+0.313316 
## [135]    train-rmse:0.392240+0.010689    test-rmse:1.139766+0.311721 
## [136]    train-rmse:0.388287+0.011628    test-rmse:1.139339+0.311528 
## [137]    train-rmse:0.385536+0.011742    test-rmse:1.139129+0.310712 
## [138]    train-rmse:0.381679+0.011353    test-rmse:1.136887+0.312276 
## [139]    train-rmse:0.379215+0.012107    test-rmse:1.136967+0.311708 
## [140]    train-rmse:0.376865+0.012148    test-rmse:1.135797+0.310038 
## [141]    train-rmse:0.374552+0.011847    test-rmse:1.140300+0.318137 
## [142]    train-rmse:0.372503+0.010988    test-rmse:1.140508+0.317559 
## [143]    train-rmse:0.370724+0.011325    test-rmse:1.139326+0.317979 
## [144]    train-rmse:0.369066+0.011260    test-rmse:1.139754+0.319148 
## [145]    train-rmse:0.366944+0.010658    test-rmse:1.140137+0.319246 
## [146]    train-rmse:0.365220+0.010437    test-rmse:1.138883+0.319822 
## Stopping. Best iteration:
## [140]    train-rmse:0.376865+0.012148    test-rmse:1.135797+0.310038
## 
## 93 
## [1]  train-rmse:69.134740+0.083369   test-rmse:69.134537+0.523672 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:44.549814+0.067020   test-rmse:44.548320+0.577398 
## [3]  train-rmse:28.902591+0.064471   test-rmse:28.898605+0.635371 
## [4]  train-rmse:18.923118+0.033501   test-rmse:19.032755+0.676351 
## [5]  train-rmse:12.559415+0.024442   test-rmse:12.673872+0.614188 
## [6]  train-rmse:8.549528+0.030336    test-rmse:8.665854+0.552284 
## [7]  train-rmse:6.064293+0.037220    test-rmse:6.222717+0.598755 
## [8]  train-rmse:4.553170+0.042025    test-rmse:4.750795+0.519411 
## [9]  train-rmse:3.649543+0.048170    test-rmse:3.939266+0.428767 
## [10] train-rmse:3.122135+0.046315    test-rmse:3.402935+0.396074 
## [11] train-rmse:2.790589+0.044874    test-rmse:3.128419+0.354059 
## [12] train-rmse:2.555324+0.040374    test-rmse:2.935200+0.293571 
## [13] train-rmse:2.382682+0.039892    test-rmse:2.783947+0.288727 
## [14] train-rmse:2.246209+0.034826    test-rmse:2.637756+0.252306 
## [15] train-rmse:2.121490+0.045989    test-rmse:2.547710+0.229859 
## [16] train-rmse:2.016080+0.046750    test-rmse:2.432627+0.203935 
## [17] train-rmse:1.910862+0.047047    test-rmse:2.368254+0.218453 
## [18] train-rmse:1.817777+0.043802    test-rmse:2.299274+0.210718 
## [19] train-rmse:1.729051+0.038786    test-rmse:2.225461+0.233853 
## [20] train-rmse:1.644464+0.037433    test-rmse:2.159614+0.229096 
## [21] train-rmse:1.563729+0.030569    test-rmse:2.113230+0.208876 
## [22] train-rmse:1.497851+0.030179    test-rmse:2.063081+0.191919 
## [23] train-rmse:1.433623+0.025869    test-rmse:2.022581+0.209081 
## [24] train-rmse:1.371270+0.025617    test-rmse:1.980135+0.199727 
## [25] train-rmse:1.314366+0.023056    test-rmse:1.936357+0.207057 
## [26] train-rmse:1.257272+0.020926    test-rmse:1.887654+0.210680 
## [27] train-rmse:1.206101+0.020552    test-rmse:1.844406+0.222804 
## [28] train-rmse:1.157008+0.020202    test-rmse:1.808456+0.223834 
## [29] train-rmse:1.105920+0.028168    test-rmse:1.768132+0.224294 
## [30] train-rmse:1.065080+0.027910    test-rmse:1.732670+0.219937 
## [31] train-rmse:1.024539+0.027132    test-rmse:1.689084+0.214296 
## [32] train-rmse:0.986040+0.024069    test-rmse:1.663283+0.225713 
## [33] train-rmse:0.952015+0.023194    test-rmse:1.645632+0.230873 
## [34] train-rmse:0.916058+0.024364    test-rmse:1.625707+0.225546 
## [35] train-rmse:0.884700+0.025159    test-rmse:1.603360+0.225551 
## [36] train-rmse:0.854778+0.024252    test-rmse:1.582846+0.227125 
## [37] train-rmse:0.827718+0.024895    test-rmse:1.568422+0.237026 
## [38] train-rmse:0.802047+0.023250    test-rmse:1.550756+0.240622 
## [39] train-rmse:0.778479+0.022792    test-rmse:1.542420+0.248117 
## [40] train-rmse:0.755226+0.022486    test-rmse:1.525556+0.249199 
## [41] train-rmse:0.734112+0.023845    test-rmse:1.513037+0.248645 
## [42] train-rmse:0.712097+0.024671    test-rmse:1.503155+0.262208 
## [43] train-rmse:0.690552+0.020916    test-rmse:1.482963+0.258211 
## [44] train-rmse:0.670817+0.022537    test-rmse:1.470575+0.259674 
## [45] train-rmse:0.653160+0.022095    test-rmse:1.463334+0.262929 
## [46] train-rmse:0.636922+0.021552    test-rmse:1.455020+0.267268 
## [47] train-rmse:0.619992+0.022875    test-rmse:1.442621+0.260496 
## [48] train-rmse:0.605538+0.022773    test-rmse:1.433154+0.261907 
## [49] train-rmse:0.590674+0.022723    test-rmse:1.412780+0.248237 
## [50] train-rmse:0.574619+0.021247    test-rmse:1.408734+0.250509 
## [51] train-rmse:0.561932+0.022070    test-rmse:1.402352+0.253831 
## [52] train-rmse:0.549235+0.023946    test-rmse:1.389011+0.264658 
## [53] train-rmse:0.535981+0.023817    test-rmse:1.381825+0.262757 
## [54] train-rmse:0.521903+0.022446    test-rmse:1.379532+0.265372 
## [55] train-rmse:0.510977+0.021196    test-rmse:1.372456+0.267143 
## [56] train-rmse:0.500286+0.020466    test-rmse:1.372446+0.265274 
## [57] train-rmse:0.490925+0.019858    test-rmse:1.366626+0.268556 
## [58] train-rmse:0.479741+0.018740    test-rmse:1.361846+0.271653 
## [59] train-rmse:0.468834+0.016985    test-rmse:1.360017+0.275178 
## [60] train-rmse:0.459279+0.016950    test-rmse:1.359185+0.277617 
## [61] train-rmse:0.452000+0.017641    test-rmse:1.355671+0.278974 
## [62] train-rmse:0.441993+0.015636    test-rmse:1.353496+0.276536 
## [63] train-rmse:0.434347+0.014979    test-rmse:1.352465+0.279928 
## [64] train-rmse:0.426469+0.014618    test-rmse:1.346193+0.279020 
## [65] train-rmse:0.414693+0.013557    test-rmse:1.338286+0.280409 
## [66] train-rmse:0.407287+0.013873    test-rmse:1.337208+0.281586 
## [67] train-rmse:0.401002+0.013355    test-rmse:1.335774+0.281172 
## [68] train-rmse:0.394228+0.014787    test-rmse:1.333639+0.281989 
## [69] train-rmse:0.389290+0.014752    test-rmse:1.331553+0.281469 
## [70] train-rmse:0.384320+0.014683    test-rmse:1.326998+0.281335 
## [71] train-rmse:0.378199+0.015711    test-rmse:1.324516+0.282925 
## [72] train-rmse:0.371271+0.015234    test-rmse:1.321931+0.288871 
## [73] train-rmse:0.365262+0.015277    test-rmse:1.320175+0.291143 
## [74] train-rmse:0.358722+0.015293    test-rmse:1.319655+0.291123 
## [75] train-rmse:0.351409+0.016602    test-rmse:1.314409+0.290154 
## [76] train-rmse:0.346318+0.016908    test-rmse:1.314700+0.289821 
## [77] train-rmse:0.342218+0.017424    test-rmse:1.311333+0.292174 
## [78] train-rmse:0.336347+0.016082    test-rmse:1.307002+0.295399 
## [79] train-rmse:0.331165+0.016438    test-rmse:1.300537+0.298052 
## [80] train-rmse:0.328164+0.016279    test-rmse:1.297165+0.297919 
## [81] train-rmse:0.322044+0.015258    test-rmse:1.298494+0.298596 
## [82] train-rmse:0.318423+0.015282    test-rmse:1.297787+0.302072 
## [83] train-rmse:0.314356+0.015106    test-rmse:1.296727+0.301133 
## [84] train-rmse:0.308725+0.015357    test-rmse:1.294535+0.302944 
## [85] train-rmse:0.305711+0.015099    test-rmse:1.293002+0.301071 
## [86] train-rmse:0.302813+0.015495    test-rmse:1.290080+0.300113 
## [87] train-rmse:0.299060+0.015758    test-rmse:1.289378+0.300632 
## [88] train-rmse:0.293634+0.012992    test-rmse:1.290309+0.303390 
## [89] train-rmse:0.289440+0.013093    test-rmse:1.286917+0.300981 
## [90] train-rmse:0.285310+0.013125    test-rmse:1.284467+0.301845 
## [91] train-rmse:0.279771+0.010719    test-rmse:1.284975+0.303335 
## [92] train-rmse:0.274665+0.011418    test-rmse:1.282987+0.304194 
## [93] train-rmse:0.270413+0.010758    test-rmse:1.283317+0.304177 
## [94] train-rmse:0.267680+0.010571    test-rmse:1.279628+0.305998 
## [95] train-rmse:0.264384+0.010077    test-rmse:1.277983+0.305283 
## [96] train-rmse:0.260947+0.012455    test-rmse:1.278888+0.304549 
## [97] train-rmse:0.257043+0.012991    test-rmse:1.277572+0.306288 
## [98] train-rmse:0.253788+0.013220    test-rmse:1.277897+0.307027 
## [99] train-rmse:0.250466+0.013552    test-rmse:1.278433+0.307370 
## [100]    train-rmse:0.246714+0.013058    test-rmse:1.278300+0.306221 
## [101]    train-rmse:0.244171+0.013134    test-rmse:1.276524+0.307303 
## [102]    train-rmse:0.242033+0.012779    test-rmse:1.275388+0.306659 
## [103]    train-rmse:0.237349+0.011850    test-rmse:1.274462+0.305429 
## [104]    train-rmse:0.235223+0.012188    test-rmse:1.274406+0.305293 
## [105]    train-rmse:0.230996+0.011011    test-rmse:1.274260+0.305033 
## [106]    train-rmse:0.228286+0.011361    test-rmse:1.273228+0.304296 
## [107]    train-rmse:0.224978+0.011370    test-rmse:1.272375+0.307106 
## [108]    train-rmse:0.222669+0.012088    test-rmse:1.271774+0.308518 
## [109]    train-rmse:0.219747+0.011202    test-rmse:1.271525+0.309110 
## [110]    train-rmse:0.217248+0.011599    test-rmse:1.270361+0.309465 
## [111]    train-rmse:0.215012+0.012029    test-rmse:1.269581+0.309370 
## [112]    train-rmse:0.212631+0.011939    test-rmse:1.269105+0.309162 
## [113]    train-rmse:0.210001+0.011298    test-rmse:1.268899+0.309947 
## [114]    train-rmse:0.206950+0.011363    test-rmse:1.264654+0.311209 
## [115]    train-rmse:0.204071+0.010682    test-rmse:1.264811+0.312377 
## [116]    train-rmse:0.201297+0.009971    test-rmse:1.265376+0.311432 
## [117]    train-rmse:0.198725+0.009585    test-rmse:1.265056+0.314088 
## [118]    train-rmse:0.196522+0.008942    test-rmse:1.263694+0.314974 
## [119]    train-rmse:0.193997+0.008691    test-rmse:1.262092+0.316744 
## [120]    train-rmse:0.191794+0.009140    test-rmse:1.262299+0.318317 
## [121]    train-rmse:0.189953+0.009155    test-rmse:1.262007+0.318234 
## [122]    train-rmse:0.187132+0.008897    test-rmse:1.261744+0.317467 
## [123]    train-rmse:0.184655+0.008551    test-rmse:1.262451+0.319092 
## [124]    train-rmse:0.182379+0.008431    test-rmse:1.262290+0.319854 
## [125]    train-rmse:0.179221+0.008332    test-rmse:1.262034+0.320014 
## [126]    train-rmse:0.176766+0.007495    test-rmse:1.261627+0.318692 
## [127]    train-rmse:0.174625+0.007339    test-rmse:1.260765+0.318490 
## [128]    train-rmse:0.172598+0.007454    test-rmse:1.260818+0.318137 
## [129]    train-rmse:0.170990+0.007903    test-rmse:1.261058+0.318080 
## [130]    train-rmse:0.169479+0.008039    test-rmse:1.260197+0.319075 
## [131]    train-rmse:0.167119+0.008587    test-rmse:1.261217+0.318918 
## [132]    train-rmse:0.164805+0.008603    test-rmse:1.261659+0.319089 
## [133]    train-rmse:0.163201+0.008339    test-rmse:1.260935+0.317268 
## [134]    train-rmse:0.161787+0.008387    test-rmse:1.259506+0.317822 
## [135]    train-rmse:0.160048+0.008452    test-rmse:1.258596+0.319552 
## [136]    train-rmse:0.157349+0.008349    test-rmse:1.258457+0.319831 
## [137]    train-rmse:0.155868+0.007922    test-rmse:1.257577+0.320087 
## [138]    train-rmse:0.153269+0.007309    test-rmse:1.258610+0.320133 
## [139]    train-rmse:0.151983+0.007384    test-rmse:1.257704+0.319889 
## [140]    train-rmse:0.149775+0.006768    test-rmse:1.257404+0.320175 
## [141]    train-rmse:0.147770+0.006531    test-rmse:1.256841+0.319827 
## [142]    train-rmse:0.145727+0.006750    test-rmse:1.256875+0.320275 
## [143]    train-rmse:0.143764+0.007351    test-rmse:1.257190+0.320801 
## [144]    train-rmse:0.142472+0.007562    test-rmse:1.257222+0.320659 
## [145]    train-rmse:0.140992+0.007701    test-rmse:1.257078+0.320420 
## [146]    train-rmse:0.139491+0.007425    test-rmse:1.257253+0.320322 
## [147]    train-rmse:0.137827+0.008037    test-rmse:1.257507+0.319997 
## Stopping. Best iteration:
## [141]    train-rmse:0.147770+0.006531    test-rmse:1.256841+0.319827
## 
## 94 
## [1]  train-rmse:69.134740+0.083369   test-rmse:69.134537+0.523672 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:44.549814+0.067020   test-rmse:44.548320+0.577398 
## [3]  train-rmse:28.902591+0.064471   test-rmse:28.898605+0.635371 
## [4]  train-rmse:18.923118+0.033501   test-rmse:19.032755+0.676351 
## [5]  train-rmse:12.545487+0.023366   test-rmse:12.648836+0.594647 
## [6]  train-rmse:8.492777+0.026415    test-rmse:8.580346+0.555171 
## [7]  train-rmse:5.947229+0.039065    test-rmse:6.079931+0.598170 
## [8]  train-rmse:4.349956+0.055832    test-rmse:4.543943+0.539797 
## [9]  train-rmse:3.380062+0.066649    test-rmse:3.603186+0.437574 
## [10] train-rmse:2.775626+0.068544    test-rmse:3.053749+0.344326 
## [11] train-rmse:2.393293+0.050410    test-rmse:2.719750+0.322115 
## [12] train-rmse:2.147440+0.047721    test-rmse:2.527226+0.307668 
## [13] train-rmse:1.960111+0.048816    test-rmse:2.344884+0.285466 
## [14] train-rmse:1.806622+0.045826    test-rmse:2.207646+0.282164 
## [15] train-rmse:1.671230+0.028255    test-rmse:2.129036+0.297638 
## [16] train-rmse:1.562934+0.034022    test-rmse:2.053179+0.290775 
## [17] train-rmse:1.458192+0.049234    test-rmse:1.980438+0.290003 
## [18] train-rmse:1.361542+0.038471    test-rmse:1.904291+0.277299 
## [19] train-rmse:1.281151+0.033237    test-rmse:1.838897+0.274774 
## [20] train-rmse:1.203633+0.023437    test-rmse:1.768812+0.274257 
## [21] train-rmse:1.134571+0.024480    test-rmse:1.723690+0.288810 
## [22] train-rmse:1.073303+0.025177    test-rmse:1.676058+0.283638 
## [23] train-rmse:1.019320+0.023950    test-rmse:1.626115+0.293710 
## [24] train-rmse:0.965598+0.022722    test-rmse:1.578781+0.285134 
## [25] train-rmse:0.912052+0.012923    test-rmse:1.538458+0.279274 
## [26] train-rmse:0.868582+0.012709    test-rmse:1.510892+0.280687 
## [27] train-rmse:0.820032+0.015237    test-rmse:1.476524+0.279231 
## [28] train-rmse:0.777032+0.019433    test-rmse:1.454981+0.284067 
## [29] train-rmse:0.744815+0.019336    test-rmse:1.420428+0.295782 
## [30] train-rmse:0.713771+0.021556    test-rmse:1.400712+0.300228 
## [31] train-rmse:0.678839+0.030110    test-rmse:1.385802+0.303075 
## [32] train-rmse:0.648667+0.028192    test-rmse:1.369190+0.300672 
## [33] train-rmse:0.623748+0.025386    test-rmse:1.350387+0.303988 
## [34] train-rmse:0.598244+0.021112    test-rmse:1.335609+0.301032 
## [35] train-rmse:0.576715+0.020232    test-rmse:1.324136+0.303245 
## [36] train-rmse:0.557139+0.020950    test-rmse:1.310575+0.300428 
## [37] train-rmse:0.537990+0.019612    test-rmse:1.295959+0.299987 
## [38] train-rmse:0.520922+0.018730    test-rmse:1.290343+0.305259 
## [39] train-rmse:0.505941+0.018881    test-rmse:1.278586+0.302544 
## [40] train-rmse:0.486471+0.020839    test-rmse:1.260925+0.299988 
## [41] train-rmse:0.473238+0.020993    test-rmse:1.240886+0.301117 
## [42] train-rmse:0.455967+0.022069    test-rmse:1.238308+0.303337 
## [43] train-rmse:0.438188+0.021770    test-rmse:1.237302+0.303161 
## [44] train-rmse:0.425760+0.022396    test-rmse:1.234076+0.304752 
## [45] train-rmse:0.413312+0.021597    test-rmse:1.234010+0.306016 
## [46] train-rmse:0.399018+0.018713    test-rmse:1.221567+0.300959 
## [47] train-rmse:0.385837+0.019874    test-rmse:1.214481+0.303810 
## [48] train-rmse:0.373841+0.021937    test-rmse:1.203446+0.304006 
## [49] train-rmse:0.363786+0.018331    test-rmse:1.199267+0.303780 
## [50] train-rmse:0.355480+0.017216    test-rmse:1.196792+0.303153 
## [51] train-rmse:0.344073+0.016433    test-rmse:1.194097+0.305511 
## [52] train-rmse:0.333826+0.013649    test-rmse:1.188478+0.304887 
## [53] train-rmse:0.327107+0.013883    test-rmse:1.187785+0.303407 
## [54] train-rmse:0.318474+0.015072    test-rmse:1.187325+0.299043 
## [55] train-rmse:0.309046+0.011966    test-rmse:1.187242+0.299858 
## [56] train-rmse:0.301003+0.010818    test-rmse:1.185031+0.297785 
## [57] train-rmse:0.293655+0.010454    test-rmse:1.179658+0.301565 
## [58] train-rmse:0.284507+0.013290    test-rmse:1.178842+0.301696 
## [59] train-rmse:0.277257+0.013068    test-rmse:1.179684+0.299973 
## [60] train-rmse:0.270407+0.016394    test-rmse:1.178660+0.299770 
## [61] train-rmse:0.261478+0.013244    test-rmse:1.177954+0.301069 
## [62] train-rmse:0.256813+0.013960    test-rmse:1.178087+0.302833 
## [63] train-rmse:0.250712+0.015627    test-rmse:1.176588+0.300384 
## [64] train-rmse:0.245600+0.015516    test-rmse:1.176174+0.302665 
## [65] train-rmse:0.240813+0.016390    test-rmse:1.174652+0.301718 
## [66] train-rmse:0.235981+0.016070    test-rmse:1.173507+0.301454 
## [67] train-rmse:0.231050+0.015283    test-rmse:1.174195+0.301995 
## [68] train-rmse:0.225597+0.014549    test-rmse:1.174748+0.302023 
## [69] train-rmse:0.220999+0.014131    test-rmse:1.174304+0.303929 
## [70] train-rmse:0.216510+0.015263    test-rmse:1.173931+0.304258 
## [71] train-rmse:0.211721+0.015282    test-rmse:1.173287+0.304492 
## [72] train-rmse:0.207000+0.014499    test-rmse:1.173525+0.306217 
## [73] train-rmse:0.201696+0.016141    test-rmse:1.172036+0.305768 
## [74] train-rmse:0.195596+0.016472    test-rmse:1.172731+0.306543 
## [75] train-rmse:0.191171+0.016105    test-rmse:1.171581+0.306032 
## [76] train-rmse:0.187959+0.015998    test-rmse:1.168811+0.306962 
## [77] train-rmse:0.184536+0.015347    test-rmse:1.169139+0.308273 
## [78] train-rmse:0.180744+0.016329    test-rmse:1.169419+0.307824 
## [79] train-rmse:0.177178+0.015522    test-rmse:1.167452+0.308555 
## [80] train-rmse:0.174057+0.014666    test-rmse:1.166219+0.309519 
## [81] train-rmse:0.171414+0.014518    test-rmse:1.165866+0.308218 
## [82] train-rmse:0.168633+0.015171    test-rmse:1.165031+0.308317 
## [83] train-rmse:0.164742+0.015895    test-rmse:1.162858+0.308436 
## [84] train-rmse:0.160497+0.014520    test-rmse:1.163913+0.308481 
## [85] train-rmse:0.157628+0.015406    test-rmse:1.162297+0.306887 
## [86] train-rmse:0.154952+0.015180    test-rmse:1.161419+0.307490 
## [87] train-rmse:0.151636+0.015620    test-rmse:1.159988+0.308044 
## [88] train-rmse:0.149354+0.015919    test-rmse:1.160001+0.307236 
## [89] train-rmse:0.146593+0.015741    test-rmse:1.160293+0.307762 
## [90] train-rmse:0.144601+0.015422    test-rmse:1.159924+0.307175 
## [91] train-rmse:0.141480+0.014812    test-rmse:1.160576+0.307069 
## [92] train-rmse:0.138455+0.014173    test-rmse:1.160615+0.306731 
## [93] train-rmse:0.134845+0.013651    test-rmse:1.159940+0.307589 
## [94] train-rmse:0.131139+0.012737    test-rmse:1.159124+0.306909 
## [95] train-rmse:0.128792+0.011630    test-rmse:1.159086+0.308424 
## [96] train-rmse:0.126364+0.011335    test-rmse:1.158868+0.307254 
## [97] train-rmse:0.123549+0.010772    test-rmse:1.159170+0.307580 
## [98] train-rmse:0.121190+0.010805    test-rmse:1.159301+0.308605 
## [99] train-rmse:0.118584+0.011000    test-rmse:1.159307+0.308561 
## [100]    train-rmse:0.116529+0.010943    test-rmse:1.157879+0.308934 
## [101]    train-rmse:0.113759+0.011165    test-rmse:1.156257+0.307864 
## [102]    train-rmse:0.111240+0.010792    test-rmse:1.155928+0.308305 
## [103]    train-rmse:0.108652+0.010479    test-rmse:1.155018+0.309203 
## [104]    train-rmse:0.107510+0.010616    test-rmse:1.154930+0.309264 
## [105]    train-rmse:0.105806+0.010350    test-rmse:1.154015+0.308986 
## [106]    train-rmse:0.103920+0.010217    test-rmse:1.153835+0.308624 
## [107]    train-rmse:0.102058+0.010116    test-rmse:1.153425+0.308920 
## [108]    train-rmse:0.100518+0.009863    test-rmse:1.153687+0.309131 
## [109]    train-rmse:0.098234+0.009865    test-rmse:1.152576+0.310729 
## [110]    train-rmse:0.096457+0.009957    test-rmse:1.153100+0.311092 
## [111]    train-rmse:0.093424+0.009034    test-rmse:1.152327+0.310613 
## [112]    train-rmse:0.091693+0.008500    test-rmse:1.152374+0.310230 
## [113]    train-rmse:0.089439+0.008580    test-rmse:1.151052+0.309227 
## [114]    train-rmse:0.087737+0.008163    test-rmse:1.150549+0.309398 
## [115]    train-rmse:0.086567+0.008121    test-rmse:1.150939+0.309756 
## [116]    train-rmse:0.084997+0.007456    test-rmse:1.150579+0.309094 
## [117]    train-rmse:0.083580+0.007165    test-rmse:1.149802+0.308185 
## [118]    train-rmse:0.082266+0.006893    test-rmse:1.150672+0.308735 
## [119]    train-rmse:0.079174+0.006073    test-rmse:1.150187+0.308092 
## [120]    train-rmse:0.077532+0.006152    test-rmse:1.149683+0.308498 
## [121]    train-rmse:0.076724+0.006095    test-rmse:1.149373+0.308384 
## [122]    train-rmse:0.075496+0.006216    test-rmse:1.149116+0.308519 
## [123]    train-rmse:0.074538+0.006215    test-rmse:1.149310+0.308249 
## [124]    train-rmse:0.072981+0.005734    test-rmse:1.149074+0.308278 
## [125]    train-rmse:0.071607+0.005413    test-rmse:1.147504+0.307392 
## [126]    train-rmse:0.070276+0.005361    test-rmse:1.147874+0.307220 
## [127]    train-rmse:0.068182+0.005678    test-rmse:1.146595+0.306943 
## [128]    train-rmse:0.066455+0.006124    test-rmse:1.146182+0.306602 
## [129]    train-rmse:0.065068+0.006120    test-rmse:1.146484+0.305723 
## [130]    train-rmse:0.063766+0.005978    test-rmse:1.146851+0.306440 
## [131]    train-rmse:0.062870+0.006187    test-rmse:1.146991+0.306672 
## [132]    train-rmse:0.061874+0.006345    test-rmse:1.147182+0.306371 
## [133]    train-rmse:0.060392+0.006618    test-rmse:1.147250+0.306301 
## [134]    train-rmse:0.059669+0.006801    test-rmse:1.147293+0.306315 
## Stopping. Best iteration:
## [128]    train-rmse:0.066455+0.006124    test-rmse:1.146182+0.306602
## 
## 95 
## [1]  train-rmse:69.134740+0.083369   test-rmse:69.134537+0.523672 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:44.549814+0.067020   test-rmse:44.548320+0.577398 
## [3]  train-rmse:28.902591+0.064471   test-rmse:28.898605+0.635371 
## [4]  train-rmse:18.923118+0.033501   test-rmse:19.032755+0.676351 
## [5]  train-rmse:12.535960+0.024062   test-rmse:12.648281+0.592023 
## [6]  train-rmse:8.452019+0.024821    test-rmse:8.584236+0.564181 
## [7]  train-rmse:5.857146+0.030253    test-rmse:6.049861+0.579489 
## [8]  train-rmse:4.208157+0.036637    test-rmse:4.384103+0.498485 
## [9]  train-rmse:3.135731+0.041780    test-rmse:3.387290+0.400844 
## [10] train-rmse:2.477035+0.060931    test-rmse:2.865436+0.349866 
## [11] train-rmse:2.065073+0.065327    test-rmse:2.521203+0.321769 
## [12] train-rmse:1.791927+0.055055    test-rmse:2.295269+0.295541 
## [13] train-rmse:1.597107+0.054779    test-rmse:2.136616+0.301300 
## [14] train-rmse:1.450605+0.056013    test-rmse:2.025072+0.294781 
## [15] train-rmse:1.314010+0.054537    test-rmse:1.895928+0.299272 
## [16] train-rmse:1.207437+0.047827    test-rmse:1.825172+0.308126 
## [17] train-rmse:1.116110+0.047315    test-rmse:1.764128+0.279568 
## [18] train-rmse:1.036593+0.043890    test-rmse:1.698801+0.280803 
## [19] train-rmse:0.968290+0.046109    test-rmse:1.660366+0.288885 
## [20] train-rmse:0.898414+0.037053    test-rmse:1.596312+0.285397 
## [21] train-rmse:0.835789+0.040718    test-rmse:1.559850+0.277453 
## [22] train-rmse:0.776031+0.037103    test-rmse:1.525172+0.279836 
## [23] train-rmse:0.728589+0.036541    test-rmse:1.499882+0.288555 
## [24] train-rmse:0.687031+0.035648    test-rmse:1.472596+0.287266 
## [25] train-rmse:0.646272+0.031514    test-rmse:1.440575+0.289865 
## [26] train-rmse:0.611501+0.030001    test-rmse:1.422982+0.291259 
## [27] train-rmse:0.578670+0.033234    test-rmse:1.408166+0.287494 
## [28] train-rmse:0.551072+0.030931    test-rmse:1.397423+0.280949 
## [29] train-rmse:0.525228+0.030110    test-rmse:1.384570+0.287157 
## [30] train-rmse:0.502875+0.031167    test-rmse:1.372117+0.288837 
## [31] train-rmse:0.477822+0.033956    test-rmse:1.352862+0.289773 
## [32] train-rmse:0.455028+0.028990    test-rmse:1.357914+0.300099 
## [33] train-rmse:0.437499+0.026574    test-rmse:1.349281+0.301526 
## [34] train-rmse:0.418822+0.028234    test-rmse:1.340664+0.307862 
## [35] train-rmse:0.401141+0.027274    test-rmse:1.339026+0.319288 
## [36] train-rmse:0.386147+0.029977    test-rmse:1.324493+0.319475 
## [37] train-rmse:0.372208+0.031460    test-rmse:1.316202+0.322179 
## [38] train-rmse:0.352112+0.024662    test-rmse:1.308703+0.319461 
## [39] train-rmse:0.339196+0.024017    test-rmse:1.309001+0.323741 
## [40] train-rmse:0.327821+0.023411    test-rmse:1.306833+0.328980 
## [41] train-rmse:0.317368+0.021497    test-rmse:1.305196+0.329789 
## [42] train-rmse:0.304160+0.017697    test-rmse:1.302987+0.330847 
## [43] train-rmse:0.292448+0.015850    test-rmse:1.304945+0.331382 
## [44] train-rmse:0.283530+0.017983    test-rmse:1.302588+0.329336 
## [45] train-rmse:0.272785+0.017414    test-rmse:1.297284+0.331259 
## [46] train-rmse:0.264034+0.016730    test-rmse:1.297893+0.330551 
## [47] train-rmse:0.254398+0.014164    test-rmse:1.295117+0.328797 
## [48] train-rmse:0.246085+0.014006    test-rmse:1.292249+0.328097 
## [49] train-rmse:0.238822+0.011786    test-rmse:1.289093+0.329855 
## [50] train-rmse:0.232991+0.012267    test-rmse:1.289107+0.330326 
## [51] train-rmse:0.224509+0.012084    test-rmse:1.288305+0.328764 
## [52] train-rmse:0.217154+0.015515    test-rmse:1.287008+0.331086 
## [53] train-rmse:0.211302+0.015676    test-rmse:1.282621+0.332792 
## [54] train-rmse:0.202933+0.017867    test-rmse:1.282777+0.333934 
## [55] train-rmse:0.195921+0.016833    test-rmse:1.282708+0.333409 
## [56] train-rmse:0.190977+0.017994    test-rmse:1.281763+0.334418 
## [57] train-rmse:0.185514+0.018257    test-rmse:1.280893+0.334950 
## [58] train-rmse:0.177246+0.015065    test-rmse:1.278950+0.333286 
## [59] train-rmse:0.171627+0.014891    test-rmse:1.281293+0.333070 
## [60] train-rmse:0.167672+0.015906    test-rmse:1.280401+0.332190 
## [61] train-rmse:0.162803+0.015814    test-rmse:1.279677+0.332346 
## [62] train-rmse:0.156702+0.014616    test-rmse:1.278984+0.332676 
## [63] train-rmse:0.153911+0.015218    test-rmse:1.277720+0.332839 
## [64] train-rmse:0.148495+0.015934    test-rmse:1.275384+0.332050 
## [65] train-rmse:0.142369+0.015210    test-rmse:1.274983+0.331521 
## [66] train-rmse:0.138270+0.014412    test-rmse:1.274647+0.331548 
## [67] train-rmse:0.132168+0.012408    test-rmse:1.274397+0.334594 
## [68] train-rmse:0.127575+0.010962    test-rmse:1.276956+0.341088 
## [69] train-rmse:0.123334+0.009855    test-rmse:1.277097+0.341227 
## [70] train-rmse:0.118761+0.010888    test-rmse:1.275980+0.340461 
## [71] train-rmse:0.115507+0.011563    test-rmse:1.275352+0.340774 
## [72] train-rmse:0.111613+0.011457    test-rmse:1.274043+0.341897 
## [73] train-rmse:0.108285+0.010618    test-rmse:1.273697+0.341900 
## [74] train-rmse:0.105364+0.010789    test-rmse:1.272979+0.341852 
## [75] train-rmse:0.102421+0.010609    test-rmse:1.272445+0.341768 
## [76] train-rmse:0.099929+0.009937    test-rmse:1.274606+0.346002 
## [77] train-rmse:0.096524+0.008787    test-rmse:1.275252+0.346904 
## [78] train-rmse:0.093337+0.009123    test-rmse:1.274654+0.347246 
## [79] train-rmse:0.090920+0.009240    test-rmse:1.276390+0.350312 
## [80] train-rmse:0.086952+0.008815    test-rmse:1.275680+0.348973 
## [81] train-rmse:0.084801+0.008200    test-rmse:1.275714+0.349682 
## Stopping. Best iteration:
## [75] train-rmse:0.102421+0.010609    test-rmse:1.272445+0.341768
## 
## 96 
## [1]  train-rmse:69.134740+0.083369   test-rmse:69.134537+0.523672 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:44.549814+0.067020   test-rmse:44.548320+0.577398 
## [3]  train-rmse:28.902591+0.064471   test-rmse:28.898605+0.635371 
## [4]  train-rmse:18.923118+0.033501   test-rmse:19.032755+0.676351 
## [5]  train-rmse:12.527486+0.024392   test-rmse:12.639151+0.589052 
## [6]  train-rmse:8.418325+0.024096    test-rmse:8.537720+0.550442 
## [7]  train-rmse:5.796539+0.024337    test-rmse:6.003633+0.556752 
## [8]  train-rmse:4.092730+0.015196    test-rmse:4.306307+0.494641 
## [9]  train-rmse:3.019057+0.040491    test-rmse:3.276305+0.431040 
## [10] train-rmse:2.315040+0.054978    test-rmse:2.643738+0.358174 
## [11] train-rmse:1.874558+0.067482    test-rmse:2.280548+0.311735 
## [12] train-rmse:1.559716+0.073760    test-rmse:2.074320+0.286680 
## [13] train-rmse:1.359216+0.073186    test-rmse:1.923553+0.231483 
## [14] train-rmse:1.199525+0.080975    test-rmse:1.838720+0.242677 
## [15] train-rmse:1.081772+0.069735    test-rmse:1.747337+0.217548 
## [16] train-rmse:0.982691+0.065434    test-rmse:1.646430+0.239921 
## [17] train-rmse:0.891596+0.056365    test-rmse:1.573250+0.244648 
## [18] train-rmse:0.816582+0.052417    test-rmse:1.533891+0.245854 
## [19] train-rmse:0.752073+0.044113    test-rmse:1.488760+0.247853 
## [20] train-rmse:0.695311+0.036053    test-rmse:1.445380+0.257459 
## [21] train-rmse:0.643902+0.030869    test-rmse:1.410163+0.248712 
## [22] train-rmse:0.593930+0.034377    test-rmse:1.395011+0.258931 
## [23] train-rmse:0.551967+0.036007    test-rmse:1.380221+0.255215 
## [24] train-rmse:0.511167+0.027685    test-rmse:1.350707+0.257862 
## [25] train-rmse:0.482269+0.025809    test-rmse:1.331195+0.257241 
## [26] train-rmse:0.450605+0.021555    test-rmse:1.324149+0.255931 
## [27] train-rmse:0.423430+0.023601    test-rmse:1.310108+0.266910 
## [28] train-rmse:0.400557+0.024573    test-rmse:1.301111+0.268093 
## [29] train-rmse:0.380061+0.021771    test-rmse:1.286992+0.281101 
## [30] train-rmse:0.359748+0.022056    test-rmse:1.284003+0.288520 
## [31] train-rmse:0.342589+0.022218    test-rmse:1.275947+0.298559 
## [32] train-rmse:0.327148+0.022182    test-rmse:1.272524+0.297297 
## [33] train-rmse:0.307695+0.016145    test-rmse:1.266697+0.303357 
## [34] train-rmse:0.293015+0.013402    test-rmse:1.263092+0.302052 
## [35] train-rmse:0.278328+0.014507    test-rmse:1.256521+0.306435 
## [36] train-rmse:0.260064+0.020043    test-rmse:1.263687+0.306125 
## [37] train-rmse:0.244173+0.016781    test-rmse:1.261365+0.305295 
## [38] train-rmse:0.234481+0.013599    test-rmse:1.253675+0.298202 
## [39] train-rmse:0.223686+0.011719    test-rmse:1.251064+0.296868 
## [40] train-rmse:0.214664+0.012101    test-rmse:1.248344+0.295769 
## [41] train-rmse:0.204731+0.013468    test-rmse:1.243926+0.297678 
## [42] train-rmse:0.194741+0.011173    test-rmse:1.243547+0.298993 
## [43] train-rmse:0.188253+0.010413    test-rmse:1.241413+0.301897 
## [44] train-rmse:0.178287+0.010934    test-rmse:1.239548+0.304386 
## [45] train-rmse:0.172340+0.012032    test-rmse:1.236661+0.306161 
## [46] train-rmse:0.163065+0.014288    test-rmse:1.237244+0.306623 
## [47] train-rmse:0.157261+0.014206    test-rmse:1.237020+0.308204 
## [48] train-rmse:0.150072+0.011436    test-rmse:1.234796+0.306859 
## [49] train-rmse:0.141392+0.013138    test-rmse:1.232457+0.307113 
## [50] train-rmse:0.132844+0.011453    test-rmse:1.230544+0.307569 
## [51] train-rmse:0.125171+0.008653    test-rmse:1.229517+0.308910 
## [52] train-rmse:0.119669+0.008632    test-rmse:1.229876+0.307286 
## [53] train-rmse:0.113524+0.008720    test-rmse:1.230393+0.308528 
## [54] train-rmse:0.109129+0.009681    test-rmse:1.231145+0.308036 
## [55] train-rmse:0.106087+0.009214    test-rmse:1.229507+0.306685 
## [56] train-rmse:0.100769+0.009166    test-rmse:1.228848+0.307454 
## [57] train-rmse:0.096935+0.008662    test-rmse:1.228004+0.307391 
## [58] train-rmse:0.092684+0.008103    test-rmse:1.227504+0.308375 
## [59] train-rmse:0.089501+0.007920    test-rmse:1.228181+0.308652 
## [60] train-rmse:0.085206+0.008970    test-rmse:1.227478+0.309019 
## [61] train-rmse:0.082373+0.009204    test-rmse:1.227076+0.308760 
## [62] train-rmse:0.078948+0.009120    test-rmse:1.227121+0.309547 
## [63] train-rmse:0.075390+0.009288    test-rmse:1.227232+0.309840 
## [64] train-rmse:0.072222+0.008596    test-rmse:1.227069+0.309966 
## [65] train-rmse:0.068594+0.008247    test-rmse:1.226492+0.310215 
## [66] train-rmse:0.065968+0.006932    test-rmse:1.225500+0.309925 
## [67] train-rmse:0.063630+0.006774    test-rmse:1.225259+0.309249 
## [68] train-rmse:0.061171+0.006452    test-rmse:1.224871+0.308885 
## [69] train-rmse:0.059253+0.005989    test-rmse:1.224529+0.309190 
## [70] train-rmse:0.056998+0.005651    test-rmse:1.223929+0.308493 
## [71] train-rmse:0.054681+0.005448    test-rmse:1.224447+0.308235 
## [72] train-rmse:0.052701+0.004634    test-rmse:1.224661+0.308326 
## [73] train-rmse:0.050297+0.004761    test-rmse:1.224336+0.308846 
## [74] train-rmse:0.048058+0.004572    test-rmse:1.224222+0.308714 
## [75] train-rmse:0.046382+0.004432    test-rmse:1.224229+0.308547 
## [76] train-rmse:0.044054+0.003685    test-rmse:1.223962+0.308059 
## Stopping. Best iteration:
## [70] train-rmse:0.056998+0.005651    test-rmse:1.223929+0.308493
## 
## 97 
## [1]  train-rmse:69.134740+0.083369   test-rmse:69.134537+0.523672 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:44.549814+0.067020   test-rmse:44.548320+0.577398 
## [3]  train-rmse:28.902591+0.064471   test-rmse:28.898605+0.635371 
## [4]  train-rmse:18.923118+0.033501   test-rmse:19.032755+0.676351 
## [5]  train-rmse:12.519794+0.024401   test-rmse:12.621871+0.582864 
## [6]  train-rmse:8.391540+0.019520    test-rmse:8.515625+0.562003 
## [7]  train-rmse:5.744750+0.024611    test-rmse:5.936331+0.555582 
## [8]  train-rmse:4.035052+0.024818    test-rmse:4.285416+0.487156 
## [9]  train-rmse:2.924395+0.029759    test-rmse:3.232171+0.429452 
## [10] train-rmse:2.175937+0.036411    test-rmse:2.573063+0.359926 
## [11] train-rmse:1.691516+0.038655    test-rmse:2.185756+0.305108 
## [12] train-rmse:1.353478+0.036296    test-rmse:1.967118+0.262638 
## [13] train-rmse:1.132788+0.047141    test-rmse:1.838597+0.233814 
## [14] train-rmse:0.975085+0.041747    test-rmse:1.732894+0.215279 
## [15] train-rmse:0.855319+0.035571    test-rmse:1.664623+0.224502 
## [16] train-rmse:0.765547+0.030155    test-rmse:1.616871+0.217641 
## [17] train-rmse:0.679300+0.025900    test-rmse:1.563376+0.206501 
## [18] train-rmse:0.613094+0.019498    test-rmse:1.516962+0.208726 
## [19] train-rmse:0.562431+0.021049    test-rmse:1.492137+0.213534 
## [20] train-rmse:0.517943+0.020413    test-rmse:1.472604+0.223853 
## [21] train-rmse:0.477990+0.012844    test-rmse:1.461780+0.220272 
## [22] train-rmse:0.442589+0.007714    test-rmse:1.435159+0.234034 
## [23] train-rmse:0.407318+0.006416    test-rmse:1.423968+0.234652 
## [24] train-rmse:0.379303+0.011227    test-rmse:1.407923+0.241891 
## [25] train-rmse:0.355870+0.009236    test-rmse:1.404130+0.237288 
## [26] train-rmse:0.329511+0.006440    test-rmse:1.396779+0.239394 
## [27] train-rmse:0.307761+0.006856    test-rmse:1.386270+0.245768 
## [28] train-rmse:0.291743+0.009190    test-rmse:1.380943+0.246104 
## [29] train-rmse:0.274067+0.008785    test-rmse:1.370533+0.251612 
## [30] train-rmse:0.254403+0.010543    test-rmse:1.371962+0.254469 
## [31] train-rmse:0.239463+0.011408    test-rmse:1.369900+0.256698 
## [32] train-rmse:0.220431+0.014227    test-rmse:1.365969+0.258350 
## [33] train-rmse:0.206110+0.011258    test-rmse:1.362183+0.261431 
## [34] train-rmse:0.195078+0.014074    test-rmse:1.362967+0.258631 
## [35] train-rmse:0.181951+0.015849    test-rmse:1.361770+0.257396 
## [36] train-rmse:0.170889+0.015940    test-rmse:1.360983+0.258125 
## [37] train-rmse:0.162881+0.015961    test-rmse:1.358776+0.258898 
## [38] train-rmse:0.153892+0.016093    test-rmse:1.357076+0.260236 
## [39] train-rmse:0.144660+0.014941    test-rmse:1.355262+0.261512 
## [40] train-rmse:0.136248+0.013665    test-rmse:1.355810+0.260724 
## [41] train-rmse:0.128708+0.015202    test-rmse:1.352867+0.263562 
## [42] train-rmse:0.121891+0.015662    test-rmse:1.352083+0.263470 
## [43] train-rmse:0.113765+0.014200    test-rmse:1.351504+0.263428 
## [44] train-rmse:0.109619+0.013874    test-rmse:1.351278+0.263459 
## [45] train-rmse:0.105128+0.013688    test-rmse:1.350975+0.264023 
## [46] train-rmse:0.098131+0.010196    test-rmse:1.350026+0.264369 
## [47] train-rmse:0.092027+0.008845    test-rmse:1.349981+0.263892 
## [48] train-rmse:0.086202+0.007306    test-rmse:1.348715+0.264831 
## [49] train-rmse:0.081683+0.007208    test-rmse:1.349006+0.264921 
## [50] train-rmse:0.076159+0.007540    test-rmse:1.346801+0.267408 
## [51] train-rmse:0.071767+0.008591    test-rmse:1.346594+0.267342 
## [52] train-rmse:0.069017+0.008142    test-rmse:1.346093+0.267781 
## [53] train-rmse:0.065182+0.008425    test-rmse:1.345300+0.267616 
## [54] train-rmse:0.061728+0.007175    test-rmse:1.345799+0.268845 
## [55] train-rmse:0.058394+0.006195    test-rmse:1.345540+0.269524 
## [56] train-rmse:0.056063+0.006533    test-rmse:1.345490+0.269559 
## [57] train-rmse:0.051739+0.004699    test-rmse:1.344712+0.270118 
## [58] train-rmse:0.048671+0.004451    test-rmse:1.345031+0.270672 
## [59] train-rmse:0.046485+0.004823    test-rmse:1.345313+0.271044 
## [60] train-rmse:0.044401+0.004691    test-rmse:1.344867+0.271207 
## [61] train-rmse:0.042668+0.004672    test-rmse:1.344358+0.271641 
## [62] train-rmse:0.040385+0.003892    test-rmse:1.344680+0.272496 
## [63] train-rmse:0.038066+0.003600    test-rmse:1.344474+0.272453 
## [64] train-rmse:0.036321+0.003210    test-rmse:1.344605+0.272152 
## [65] train-rmse:0.034374+0.002189    test-rmse:1.344562+0.272060 
## [66] train-rmse:0.032806+0.002302    test-rmse:1.344494+0.271901 
## [67] train-rmse:0.031344+0.002278    test-rmse:1.344506+0.271972 
## Stopping. Best iteration:
## [61] train-rmse:0.042668+0.004672    test-rmse:1.344358+0.271641
## 
## 98 
## [1]  train-rmse:67.001016+0.081705   test-rmse:67.000742+0.527509 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:41.869994+0.065854   test-rmse:41.868242+0.585212 
## [3]  train-rmse:26.397598+0.065573   test-rmse:26.392831+0.648853 
## [4]  train-rmse:16.918549+0.059492   test-rmse:17.019662+0.679812 
## [5]  train-rmse:11.167546+0.049339   test-rmse:11.238692+0.676380 
## [6]  train-rmse:7.803864+0.059623    test-rmse:7.916185+0.594368 
## [7]  train-rmse:5.908594+0.068001    test-rmse:6.088209+0.668286 
## [8]  train-rmse:4.891551+0.069415    test-rmse:5.014477+0.572964 
## [9]  train-rmse:4.350585+0.067932    test-rmse:4.434276+0.506385 
## [10] train-rmse:4.040012+0.067252    test-rmse:4.193295+0.474123 
## [11] train-rmse:3.849486+0.067030    test-rmse:4.043675+0.475350 
## [12] train-rmse:3.709875+0.061519    test-rmse:3.900535+0.458681 
## [13] train-rmse:3.592393+0.058562    test-rmse:3.720923+0.378250 
## [14] train-rmse:3.493602+0.059859    test-rmse:3.707805+0.359996 
## [15] train-rmse:3.407580+0.055422    test-rmse:3.601998+0.297234 
## [16] train-rmse:3.329015+0.054983    test-rmse:3.526592+0.318988 
## [17] train-rmse:3.249132+0.054848    test-rmse:3.487364+0.306356 
## [18] train-rmse:3.175370+0.051507    test-rmse:3.459878+0.319190 
## [19] train-rmse:3.102701+0.047468    test-rmse:3.406591+0.316657 
## [20] train-rmse:3.034497+0.044514    test-rmse:3.345017+0.289216 
## [21] train-rmse:2.967177+0.042318    test-rmse:3.263714+0.322595 
## [22] train-rmse:2.904177+0.041072    test-rmse:3.201074+0.320536 
## [23] train-rmse:2.843676+0.038204    test-rmse:3.086545+0.279396 
## [24] train-rmse:2.788195+0.036771    test-rmse:3.023549+0.285132 
## [25] train-rmse:2.735571+0.035323    test-rmse:2.991851+0.299215 
## [26] train-rmse:2.685073+0.036447    test-rmse:2.932270+0.308911 
## [27] train-rmse:2.636631+0.037126    test-rmse:2.909464+0.311513 
## [28] train-rmse:2.587968+0.039859    test-rmse:2.888980+0.318854 
## [29] train-rmse:2.542670+0.040608    test-rmse:2.828184+0.337699 
## [30] train-rmse:2.497485+0.042194    test-rmse:2.778581+0.297837 
## [31] train-rmse:2.452902+0.042054    test-rmse:2.737354+0.300206 
## [32] train-rmse:2.410919+0.041637    test-rmse:2.696341+0.310481 
## [33] train-rmse:2.371146+0.039198    test-rmse:2.669482+0.309359 
## [34] train-rmse:2.331370+0.037046    test-rmse:2.608873+0.320940 
## [35] train-rmse:2.292350+0.035529    test-rmse:2.576996+0.303318 
## [36] train-rmse:2.255000+0.032988    test-rmse:2.518138+0.285835 
## [37] train-rmse:2.217970+0.031677    test-rmse:2.493668+0.300039 
## [38] train-rmse:2.182866+0.032199    test-rmse:2.466399+0.311504 
## [39] train-rmse:2.148589+0.031717    test-rmse:2.456399+0.303281 
## [40] train-rmse:2.115258+0.031021    test-rmse:2.416985+0.301541 
## [41] train-rmse:2.082452+0.030393    test-rmse:2.394270+0.300364 
## [42] train-rmse:2.050967+0.028770    test-rmse:2.346311+0.270105 
## [43] train-rmse:2.020985+0.028308    test-rmse:2.326045+0.269360 
## [44] train-rmse:1.991993+0.027519    test-rmse:2.293489+0.274155 
## [45] train-rmse:1.963250+0.026713    test-rmse:2.263088+0.283440 
## [46] train-rmse:1.935647+0.025356    test-rmse:2.230392+0.281742 
## [47] train-rmse:1.907945+0.023811    test-rmse:2.198686+0.255505 
## [48] train-rmse:1.880497+0.022765    test-rmse:2.149125+0.257511 
## [49] train-rmse:1.853301+0.021320    test-rmse:2.124916+0.260522 
## [50] train-rmse:1.826639+0.019389    test-rmse:2.098332+0.253236 
## [51] train-rmse:1.800135+0.017497    test-rmse:2.081991+0.253936 
## [52] train-rmse:1.774990+0.016364    test-rmse:2.051127+0.260068 
## [53] train-rmse:1.749860+0.015562    test-rmse:2.032676+0.269874 
## [54] train-rmse:1.725539+0.015047    test-rmse:2.010896+0.258336 
## [55] train-rmse:1.702012+0.015461    test-rmse:2.000704+0.256546 
## [56] train-rmse:1.679471+0.015693    test-rmse:1.975094+0.244034 
## [57] train-rmse:1.657528+0.015778    test-rmse:1.961130+0.249073 
## [58] train-rmse:1.635699+0.015749    test-rmse:1.942331+0.259028 
## [59] train-rmse:1.614556+0.015750    test-rmse:1.922173+0.266104 
## [60] train-rmse:1.594356+0.015552    test-rmse:1.901578+0.274940 
## [61] train-rmse:1.574851+0.016003    test-rmse:1.897051+0.280622 
## [62] train-rmse:1.555407+0.016587    test-rmse:1.871623+0.252083 
## [63] train-rmse:1.536769+0.016279    test-rmse:1.861485+0.247830 
## [64] train-rmse:1.518832+0.015634    test-rmse:1.845862+0.241985 
## [65] train-rmse:1.501400+0.015244    test-rmse:1.833992+0.241912 
## [66] train-rmse:1.483814+0.015367    test-rmse:1.793390+0.241530 
## [67] train-rmse:1.466263+0.015312    test-rmse:1.788594+0.236826 
## [68] train-rmse:1.449130+0.015197    test-rmse:1.773095+0.237841 
## [69] train-rmse:1.432998+0.014552    test-rmse:1.744114+0.232587 
## [70] train-rmse:1.417262+0.014248    test-rmse:1.733630+0.229652 
## [71] train-rmse:1.401429+0.013959    test-rmse:1.716903+0.225176 
## [72] train-rmse:1.386432+0.014028    test-rmse:1.698222+0.223583 
## [73] train-rmse:1.371574+0.014248    test-rmse:1.681534+0.226230 
## [74] train-rmse:1.357233+0.014220    test-rmse:1.665999+0.226605 
## [75] train-rmse:1.342779+0.014204    test-rmse:1.650357+0.230810 
## [76] train-rmse:1.328356+0.014121    test-rmse:1.645136+0.229882 
## [77] train-rmse:1.314044+0.013801    test-rmse:1.630716+0.236811 
## [78] train-rmse:1.300512+0.013560    test-rmse:1.616213+0.232711 
## [79] train-rmse:1.287697+0.013301    test-rmse:1.609444+0.228034 
## [80] train-rmse:1.275207+0.012829    test-rmse:1.597030+0.229187 
## [81] train-rmse:1.262856+0.012338    test-rmse:1.585956+0.235457 
## [82] train-rmse:1.251029+0.011946    test-rmse:1.576387+0.240079 
## [83] train-rmse:1.239477+0.011790    test-rmse:1.568994+0.239841 
## [84] train-rmse:1.228141+0.011726    test-rmse:1.554655+0.245748 
## [85] train-rmse:1.217130+0.011723    test-rmse:1.545922+0.240614 
## [86] train-rmse:1.206230+0.011509    test-rmse:1.541436+0.242129 
## [87] train-rmse:1.195358+0.011299    test-rmse:1.527191+0.239690 
## [88] train-rmse:1.185151+0.011501    test-rmse:1.521577+0.242165 
## [89] train-rmse:1.174897+0.011825    test-rmse:1.489154+0.224516 
## [90] train-rmse:1.165047+0.012096    test-rmse:1.482233+0.224885 
## [91] train-rmse:1.155527+0.012375    test-rmse:1.475622+0.222146 
## [92] train-rmse:1.146233+0.012356    test-rmse:1.462321+0.230032 
## [93] train-rmse:1.137246+0.012609    test-rmse:1.461705+0.234231 
## [94] train-rmse:1.127991+0.012796    test-rmse:1.446433+0.224868 
## [95] train-rmse:1.119168+0.012750    test-rmse:1.432674+0.217231 
## [96] train-rmse:1.110528+0.012546    test-rmse:1.427951+0.214538 
## [97] train-rmse:1.101987+0.012421    test-rmse:1.419778+0.215229 
## [98] train-rmse:1.093445+0.012515    test-rmse:1.420805+0.214526 
## [99] train-rmse:1.085309+0.012266    test-rmse:1.413127+0.215172 
## [100]    train-rmse:1.077510+0.012114    test-rmse:1.405359+0.219310 
## [101]    train-rmse:1.069774+0.012042    test-rmse:1.397662+0.220267 
## [102]    train-rmse:1.062428+0.011934    test-rmse:1.388582+0.224417 
## [103]    train-rmse:1.055344+0.011989    test-rmse:1.379830+0.230044 
## [104]    train-rmse:1.048651+0.012087    test-rmse:1.368038+0.234943 
## [105]    train-rmse:1.041963+0.012131    test-rmse:1.364133+0.239016 
## [106]    train-rmse:1.035371+0.012212    test-rmse:1.357911+0.236752 
## [107]    train-rmse:1.028729+0.012161    test-rmse:1.350063+0.218402 
## [108]    train-rmse:1.022352+0.012320    test-rmse:1.344323+0.221578 
## [109]    train-rmse:1.016239+0.012328    test-rmse:1.340606+0.217536 
## [110]    train-rmse:1.010394+0.012339    test-rmse:1.335374+0.210891 
## [111]    train-rmse:1.004631+0.012368    test-rmse:1.326292+0.207766 
## [112]    train-rmse:0.998844+0.012406    test-rmse:1.321729+0.208232 
## [113]    train-rmse:0.993173+0.012497    test-rmse:1.316900+0.210754 
## [114]    train-rmse:0.987364+0.012425    test-rmse:1.317306+0.211050 
## [115]    train-rmse:0.981819+0.012513    test-rmse:1.316405+0.209550 
## [116]    train-rmse:0.976232+0.012355    test-rmse:1.314614+0.212549 
## [117]    train-rmse:0.971173+0.012204    test-rmse:1.311526+0.213568 
## [118]    train-rmse:0.966352+0.012251    test-rmse:1.303719+0.217335 
## [119]    train-rmse:0.961590+0.012432    test-rmse:1.297789+0.215428 
## [120]    train-rmse:0.956749+0.012542    test-rmse:1.290777+0.209777 
## [121]    train-rmse:0.952264+0.012483    test-rmse:1.284482+0.209437 
## [122]    train-rmse:0.947904+0.012488    test-rmse:1.281621+0.208145 
## [123]    train-rmse:0.943612+0.012524    test-rmse:1.276907+0.205370 
## [124]    train-rmse:0.939483+0.012543    test-rmse:1.275550+0.206926 
## [125]    train-rmse:0.935365+0.012589    test-rmse:1.274645+0.207236 
## [126]    train-rmse:0.931310+0.012724    test-rmse:1.273836+0.207924 
## [127]    train-rmse:0.927412+0.012752    test-rmse:1.269741+0.209503 
## [128]    train-rmse:0.923510+0.012746    test-rmse:1.264145+0.206823 
## [129]    train-rmse:0.919631+0.012830    test-rmse:1.260627+0.204352 
## [130]    train-rmse:0.915777+0.012832    test-rmse:1.258660+0.202039 
## [131]    train-rmse:0.912140+0.012862    test-rmse:1.256815+0.202376 
## [132]    train-rmse:0.908585+0.012850    test-rmse:1.249004+0.195127 
## [133]    train-rmse:0.905018+0.012771    test-rmse:1.246219+0.196941 
## [134]    train-rmse:0.901575+0.012648    test-rmse:1.242725+0.199274 
## [135]    train-rmse:0.898306+0.012637    test-rmse:1.241725+0.201018 
## [136]    train-rmse:0.895150+0.012698    test-rmse:1.237171+0.203606 
## [137]    train-rmse:0.891978+0.012781    test-rmse:1.235670+0.203603 
## [138]    train-rmse:0.888913+0.012763    test-rmse:1.231881+0.203172 
## [139]    train-rmse:0.885879+0.012802    test-rmse:1.231425+0.203037 
## [140]    train-rmse:0.882844+0.012827    test-rmse:1.226457+0.202998 
## [141]    train-rmse:0.879829+0.012711    test-rmse:1.223903+0.204619 
## [142]    train-rmse:0.876747+0.012832    test-rmse:1.224749+0.202818 
## [143]    train-rmse:0.873836+0.012943    test-rmse:1.220549+0.201559 
## [144]    train-rmse:0.871095+0.013047    test-rmse:1.216675+0.201931 
## [145]    train-rmse:0.868231+0.013220    test-rmse:1.214116+0.204165 
## [146]    train-rmse:0.865544+0.013292    test-rmse:1.213543+0.198972 
## [147]    train-rmse:0.862605+0.013508    test-rmse:1.209547+0.201832 
## [148]    train-rmse:0.860082+0.013734    test-rmse:1.208514+0.202574 
## [149]    train-rmse:0.857625+0.013863    test-rmse:1.207252+0.204784 
## [150]    train-rmse:0.855223+0.013981    test-rmse:1.206631+0.203979 
## [151]    train-rmse:0.852734+0.014163    test-rmse:1.197718+0.209877 
## [152]    train-rmse:0.850407+0.014125    test-rmse:1.199315+0.208963 
## [153]    train-rmse:0.848080+0.014255    test-rmse:1.198308+0.207795 
## [154]    train-rmse:0.845818+0.014331    test-rmse:1.199040+0.206795 
## [155]    train-rmse:0.843584+0.014348    test-rmse:1.200016+0.206582 
## [156]    train-rmse:0.841362+0.014447    test-rmse:1.196607+0.209593 
## [157]    train-rmse:0.839146+0.014510    test-rmse:1.196285+0.210273 
## [158]    train-rmse:0.837022+0.014612    test-rmse:1.197714+0.211497 
## [159]    train-rmse:0.834904+0.014674    test-rmse:1.198370+0.212464 
## [160]    train-rmse:0.832905+0.014731    test-rmse:1.192983+0.211302 
## [161]    train-rmse:0.830922+0.014784    test-rmse:1.187400+0.205136 
## [162]    train-rmse:0.828965+0.014857    test-rmse:1.187147+0.201866 
## [163]    train-rmse:0.826973+0.014901    test-rmse:1.179523+0.202542 
## [164]    train-rmse:0.825029+0.014991    test-rmse:1.177440+0.204726 
## [165]    train-rmse:0.823049+0.015142    test-rmse:1.175806+0.204871 
## [166]    train-rmse:0.821169+0.015281    test-rmse:1.171912+0.203444 
## [167]    train-rmse:0.819347+0.015403    test-rmse:1.172701+0.202785 
## [168]    train-rmse:0.817559+0.015515    test-rmse:1.173054+0.202388 
## [169]    train-rmse:0.815791+0.015594    test-rmse:1.172344+0.202178 
## [170]    train-rmse:0.814087+0.015659    test-rmse:1.169808+0.202556 
## [171]    train-rmse:0.812408+0.015745    test-rmse:1.171429+0.204551 
## [172]    train-rmse:0.810629+0.015932    test-rmse:1.169844+0.202955 
## [173]    train-rmse:0.808982+0.015976    test-rmse:1.167226+0.205542 
## [174]    train-rmse:0.807364+0.016066    test-rmse:1.166998+0.204764 
## [175]    train-rmse:0.805736+0.016125    test-rmse:1.167242+0.203848 
## [176]    train-rmse:0.804109+0.016108    test-rmse:1.165622+0.204269 
## [177]    train-rmse:0.802517+0.016200    test-rmse:1.163881+0.206067 
## [178]    train-rmse:0.800950+0.016323    test-rmse:1.162203+0.206934 
## [179]    train-rmse:0.799407+0.016449    test-rmse:1.158001+0.206625 
## [180]    train-rmse:0.797827+0.016577    test-rmse:1.159439+0.205817 
## [181]    train-rmse:0.796327+0.016682    test-rmse:1.156784+0.207270 
## [182]    train-rmse:0.794895+0.016791    test-rmse:1.154927+0.206722 
## [183]    train-rmse:0.793450+0.016864    test-rmse:1.153378+0.207956 
## [184]    train-rmse:0.792090+0.016947    test-rmse:1.152960+0.209076 
## [185]    train-rmse:0.790682+0.017044    test-rmse:1.151324+0.205185 
## [186]    train-rmse:0.789295+0.017082    test-rmse:1.148550+0.204383 
## [187]    train-rmse:0.787951+0.017124    test-rmse:1.149758+0.204029 
## [188]    train-rmse:0.786591+0.017134    test-rmse:1.146986+0.204189 
## [189]    train-rmse:0.785276+0.017150    test-rmse:1.147560+0.201896 
## [190]    train-rmse:0.783933+0.017182    test-rmse:1.146624+0.201950 
## [191]    train-rmse:0.782657+0.017261    test-rmse:1.147252+0.200532 
## [192]    train-rmse:0.781345+0.017287    test-rmse:1.146614+0.200307 
## [193]    train-rmse:0.780080+0.017283    test-rmse:1.146633+0.202090 
## [194]    train-rmse:0.778850+0.017266    test-rmse:1.145711+0.203871 
## [195]    train-rmse:0.777634+0.017324    test-rmse:1.144167+0.204267 
## [196]    train-rmse:0.776456+0.017376    test-rmse:1.139118+0.200646 
## [197]    train-rmse:0.775273+0.017427    test-rmse:1.137281+0.197281 
## [198]    train-rmse:0.774048+0.017380    test-rmse:1.134248+0.198110 
## [199]    train-rmse:0.772912+0.017419    test-rmse:1.130833+0.196348 
## [200]    train-rmse:0.771793+0.017424    test-rmse:1.129592+0.193886 
## [201]    train-rmse:0.770539+0.017422    test-rmse:1.125353+0.194295 
## [202]    train-rmse:0.769388+0.017471    test-rmse:1.124215+0.193702 
## [203]    train-rmse:0.768270+0.017539    test-rmse:1.123656+0.194967 
## [204]    train-rmse:0.767114+0.017678    test-rmse:1.125792+0.194462 
## [205]    train-rmse:0.765952+0.017678    test-rmse:1.123130+0.195385 
## [206]    train-rmse:0.764899+0.017720    test-rmse:1.121704+0.196385 
## [207]    train-rmse:0.763871+0.017771    test-rmse:1.121170+0.197294 
## [208]    train-rmse:0.762809+0.017775    test-rmse:1.120720+0.197869 
## [209]    train-rmse:0.761788+0.017797    test-rmse:1.119815+0.197392 
## [210]    train-rmse:0.760751+0.017763    test-rmse:1.117707+0.195075 
## [211]    train-rmse:0.759769+0.017766    test-rmse:1.118218+0.197152 
## [212]    train-rmse:0.758753+0.017754    test-rmse:1.117471+0.196749 
## [213]    train-rmse:0.757742+0.017746    test-rmse:1.112868+0.195066 
## [214]    train-rmse:0.756755+0.017764    test-rmse:1.108952+0.194136 
## [215]    train-rmse:0.755762+0.017770    test-rmse:1.108795+0.191154 
## [216]    train-rmse:0.754779+0.017762    test-rmse:1.108016+0.190592 
## [217]    train-rmse:0.753790+0.017713    test-rmse:1.106284+0.190344 
## [218]    train-rmse:0.752821+0.017634    test-rmse:1.106226+0.191091 
## [219]    train-rmse:0.751917+0.017590    test-rmse:1.106432+0.190488 
## [220]    train-rmse:0.750999+0.017540    test-rmse:1.106280+0.189740 
## [221]    train-rmse:0.750032+0.017546    test-rmse:1.106372+0.190309 
## [222]    train-rmse:0.749156+0.017524    test-rmse:1.106850+0.190393 
## [223]    train-rmse:0.748289+0.017480    test-rmse:1.107318+0.189995 
## [224]    train-rmse:0.747411+0.017442    test-rmse:1.106194+0.189471 
## [225]    train-rmse:0.746582+0.017429    test-rmse:1.104263+0.189497 
## [226]    train-rmse:0.745675+0.017466    test-rmse:1.104372+0.190878 
## [227]    train-rmse:0.744827+0.017438    test-rmse:1.102342+0.185005 
## [228]    train-rmse:0.744008+0.017429    test-rmse:1.101302+0.186432 
## [229]    train-rmse:0.743217+0.017421    test-rmse:1.099767+0.186571 
## [230]    train-rmse:0.742432+0.017406    test-rmse:1.097452+0.185989 
## [231]    train-rmse:0.741639+0.017353    test-rmse:1.096144+0.182569 
## [232]    train-rmse:0.740841+0.017295    test-rmse:1.095325+0.182331 
## [233]    train-rmse:0.740064+0.017251    test-rmse:1.092809+0.181107 
## [234]    train-rmse:0.739251+0.017208    test-rmse:1.094100+0.179754 
## [235]    train-rmse:0.738484+0.017180    test-rmse:1.094324+0.180877 
## [236]    train-rmse:0.737694+0.017178    test-rmse:1.092500+0.179934 
## [237]    train-rmse:0.736938+0.017155    test-rmse:1.089761+0.180804 
## [238]    train-rmse:0.736179+0.017129    test-rmse:1.088789+0.181200 
## [239]    train-rmse:0.735442+0.017098    test-rmse:1.087065+0.182341 
## [240]    train-rmse:0.734666+0.017091    test-rmse:1.083819+0.179193 
## [241]    train-rmse:0.733907+0.017064    test-rmse:1.085877+0.178906 
## [242]    train-rmse:0.733165+0.017048    test-rmse:1.084055+0.177614 
## [243]    train-rmse:0.732390+0.017036    test-rmse:1.084677+0.177559 
## [244]    train-rmse:0.731680+0.016967    test-rmse:1.085218+0.177373 
## [245]    train-rmse:0.730983+0.016964    test-rmse:1.084383+0.177382 
## [246]    train-rmse:0.730286+0.016936    test-rmse:1.082603+0.178024 
## [247]    train-rmse:0.729592+0.016923    test-rmse:1.082359+0.178012 
## [248]    train-rmse:0.728924+0.016910    test-rmse:1.082236+0.178172 
## [249]    train-rmse:0.728257+0.016874    test-rmse:1.081352+0.179541 
## [250]    train-rmse:0.727600+0.016836    test-rmse:1.079031+0.177955 
## [251]    train-rmse:0.726934+0.016801    test-rmse:1.077983+0.175211 
## [252]    train-rmse:0.726283+0.016753    test-rmse:1.078300+0.174760 
## [253]    train-rmse:0.725591+0.016736    test-rmse:1.078369+0.173368 
## [254]    train-rmse:0.724928+0.016672    test-rmse:1.079497+0.174971 
## [255]    train-rmse:0.724250+0.016624    test-rmse:1.078980+0.175591 
## [256]    train-rmse:0.723602+0.016588    test-rmse:1.080012+0.175290 
## [257]    train-rmse:0.722972+0.016550    test-rmse:1.079099+0.175829 
## Stopping. Best iteration:
## [251]    train-rmse:0.726934+0.016801    test-rmse:1.077983+0.175211
## 
## 99 
## [1]  train-rmse:67.001016+0.081705   test-rmse:67.000742+0.527509 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:41.869994+0.065854   test-rmse:41.868242+0.585212 
## [3]  train-rmse:26.397598+0.065573   test-rmse:26.392831+0.648853 
## [4]  train-rmse:16.849706+0.043896   test-rmse:16.919849+0.608783 
## [5]  train-rmse:11.020723+0.041417   test-rmse:11.187750+0.632870 
## [6]  train-rmse:7.516885+0.046723    test-rmse:7.584999+0.509094 
## [7]  train-rmse:5.493946+0.057112    test-rmse:5.630846+0.561116 
## [8]  train-rmse:4.373002+0.057797    test-rmse:4.517130+0.486306 
## [9]  train-rmse:3.749964+0.053962    test-rmse:4.011870+0.436355 
## [10] train-rmse:3.390304+0.055889    test-rmse:3.656598+0.369556 
## [11] train-rmse:3.157221+0.053349    test-rmse:3.467630+0.350106 
## [12] train-rmse:2.980780+0.049773    test-rmse:3.281611+0.314166 
## [13] train-rmse:2.834450+0.044765    test-rmse:3.187239+0.320357 
## [14] train-rmse:2.713964+0.041020    test-rmse:3.036565+0.343886 
## [15] train-rmse:2.605242+0.044431    test-rmse:2.920153+0.313992 
## [16] train-rmse:2.507691+0.042602    test-rmse:2.864950+0.311279 
## [17] train-rmse:2.416475+0.040713    test-rmse:2.775364+0.331204 
## [18] train-rmse:2.324551+0.030183    test-rmse:2.744503+0.312537 
## [19] train-rmse:2.242082+0.026940    test-rmse:2.631129+0.295917 
## [20] train-rmse:2.163429+0.024093    test-rmse:2.514097+0.275297 
## [21] train-rmse:2.085873+0.019941    test-rmse:2.487779+0.265422 
## [22] train-rmse:2.018807+0.022199    test-rmse:2.442604+0.260823 
## [23] train-rmse:1.951669+0.024260    test-rmse:2.384971+0.279823 
## [24] train-rmse:1.884381+0.024423    test-rmse:2.331499+0.272029 
## [25] train-rmse:1.823581+0.025458    test-rmse:2.247275+0.268847 
## [26] train-rmse:1.767727+0.021271    test-rmse:2.203858+0.261490 
## [27] train-rmse:1.714781+0.020076    test-rmse:2.153945+0.258085 
## [28] train-rmse:1.662185+0.019912    test-rmse:2.113236+0.261480 
## [29] train-rmse:1.609228+0.021762    test-rmse:2.068826+0.266744 
## [30] train-rmse:1.556372+0.020433    test-rmse:2.065480+0.260244 
## [31] train-rmse:1.507885+0.013710    test-rmse:2.029751+0.279818 
## [32] train-rmse:1.466197+0.012456    test-rmse:1.990828+0.271340 
## [33] train-rmse:1.426479+0.012415    test-rmse:1.946525+0.273321 
## [34] train-rmse:1.385370+0.013898    test-rmse:1.904573+0.258837 
## [35] train-rmse:1.346325+0.014678    test-rmse:1.870880+0.263241 
## [36] train-rmse:1.311292+0.015214    test-rmse:1.852527+0.270393 
## [37] train-rmse:1.276991+0.015520    test-rmse:1.829281+0.268181 
## [38] train-rmse:1.244741+0.014252    test-rmse:1.798709+0.260128 
## [39] train-rmse:1.214771+0.014816    test-rmse:1.752242+0.240033 
## [40] train-rmse:1.185734+0.015115    test-rmse:1.724654+0.237937 
## [41] train-rmse:1.156514+0.016807    test-rmse:1.692284+0.230429 
## [42] train-rmse:1.126486+0.017059    test-rmse:1.669011+0.228200 
## [43] train-rmse:1.100275+0.017135    test-rmse:1.641648+0.234642 
## [44] train-rmse:1.075109+0.018035    test-rmse:1.620083+0.227639 
## [45] train-rmse:1.048980+0.017922    test-rmse:1.603076+0.229091 
## [46] train-rmse:1.025221+0.018784    test-rmse:1.579568+0.248855 
## [47] train-rmse:1.002532+0.021375    test-rmse:1.562916+0.241369 
## [48] train-rmse:0.981534+0.023654    test-rmse:1.542927+0.234464 
## [49] train-rmse:0.959698+0.026504    test-rmse:1.536393+0.237946 
## [50] train-rmse:0.937837+0.027270    test-rmse:1.525040+0.238781 
## [51] train-rmse:0.919587+0.028914    test-rmse:1.509726+0.242039 
## [52] train-rmse:0.901126+0.029433    test-rmse:1.493786+0.243082 
## [53] train-rmse:0.884622+0.029753    test-rmse:1.480951+0.237553 
## [54] train-rmse:0.867725+0.029530    test-rmse:1.464021+0.237948 
## [55] train-rmse:0.851918+0.029147    test-rmse:1.456339+0.239906 
## [56] train-rmse:0.835879+0.028835    test-rmse:1.435301+0.246711 
## [57] train-rmse:0.819594+0.027579    test-rmse:1.425853+0.244534 
## [58] train-rmse:0.805432+0.027878    test-rmse:1.419070+0.249051 
## [59] train-rmse:0.791206+0.027185    test-rmse:1.402816+0.249861 
## [60] train-rmse:0.777812+0.028026    test-rmse:1.392445+0.251417 
## [61] train-rmse:0.765342+0.027816    test-rmse:1.379583+0.257589 
## [62] train-rmse:0.751556+0.025738    test-rmse:1.378985+0.251083 
## [63] train-rmse:0.739712+0.024502    test-rmse:1.372918+0.254937 
## [64] train-rmse:0.725965+0.023537    test-rmse:1.362402+0.255647 
## [65] train-rmse:0.714690+0.023164    test-rmse:1.359342+0.258173 
## [66] train-rmse:0.702544+0.025066    test-rmse:1.347274+0.243820 
## [67] train-rmse:0.692286+0.026173    test-rmse:1.343015+0.246190 
## [68] train-rmse:0.683813+0.026243    test-rmse:1.340551+0.247410 
## [69] train-rmse:0.669923+0.020458    test-rmse:1.331456+0.253028 
## [70] train-rmse:0.659224+0.019284    test-rmse:1.324327+0.252663 
## [71] train-rmse:0.649622+0.016788    test-rmse:1.317043+0.257644 
## [72] train-rmse:0.640950+0.017361    test-rmse:1.312416+0.258879 
## [73] train-rmse:0.633222+0.017890    test-rmse:1.307836+0.258552 
## [74] train-rmse:0.626684+0.017697    test-rmse:1.302823+0.262835 
## [75] train-rmse:0.617731+0.017516    test-rmse:1.296774+0.260400 
## [76] train-rmse:0.608863+0.019440    test-rmse:1.298449+0.263645 
## [77] train-rmse:0.602068+0.020075    test-rmse:1.296040+0.260322 
## [78] train-rmse:0.593542+0.019194    test-rmse:1.287981+0.262199 
## [79] train-rmse:0.585080+0.017427    test-rmse:1.278396+0.265252 
## [80] train-rmse:0.577215+0.018526    test-rmse:1.276571+0.263721 
## [81] train-rmse:0.570523+0.019359    test-rmse:1.272399+0.266407 
## [82] train-rmse:0.564610+0.018355    test-rmse:1.265490+0.264885 
## [83] train-rmse:0.558564+0.018889    test-rmse:1.255771+0.252551 
## [84] train-rmse:0.553334+0.018390    test-rmse:1.252260+0.253733 
## [85] train-rmse:0.548084+0.018520    test-rmse:1.250960+0.257455 
## [86] train-rmse:0.542201+0.019058    test-rmse:1.243691+0.259774 
## [87] train-rmse:0.536163+0.018087    test-rmse:1.241203+0.261921 
## [88] train-rmse:0.530517+0.018565    test-rmse:1.238769+0.263819 
## [89] train-rmse:0.525822+0.018239    test-rmse:1.238872+0.262074 
## [90] train-rmse:0.520549+0.018831    test-rmse:1.236964+0.261078 
## [91] train-rmse:0.515481+0.019006    test-rmse:1.237407+0.260938 
## [92] train-rmse:0.509278+0.018279    test-rmse:1.232374+0.260443 
## [93] train-rmse:0.504754+0.017432    test-rmse:1.230075+0.262818 
## [94] train-rmse:0.500420+0.018126    test-rmse:1.223796+0.264467 
## [95] train-rmse:0.492886+0.016162    test-rmse:1.220026+0.267714 
## [96] train-rmse:0.488784+0.016207    test-rmse:1.216396+0.268936 
## [97] train-rmse:0.483457+0.015932    test-rmse:1.214827+0.272017 
## [98] train-rmse:0.479946+0.016180    test-rmse:1.211985+0.272257 
## [99] train-rmse:0.476782+0.016205    test-rmse:1.209598+0.272422 
## [100]    train-rmse:0.473654+0.015973    test-rmse:1.208614+0.275679 
## [101]    train-rmse:0.470183+0.016535    test-rmse:1.204805+0.274840 
## [102]    train-rmse:0.466871+0.015517    test-rmse:1.201173+0.272580 
## [103]    train-rmse:0.462732+0.014712    test-rmse:1.198088+0.276481 
## [104]    train-rmse:0.459085+0.015110    test-rmse:1.194216+0.278380 
## [105]    train-rmse:0.454791+0.014688    test-rmse:1.193642+0.278477 
## [106]    train-rmse:0.451102+0.013480    test-rmse:1.191463+0.279882 
## [107]    train-rmse:0.448838+0.013429    test-rmse:1.189853+0.279001 
## [108]    train-rmse:0.445746+0.013150    test-rmse:1.189193+0.279186 
## [109]    train-rmse:0.441948+0.014454    test-rmse:1.190528+0.280978 
## [110]    train-rmse:0.437838+0.015592    test-rmse:1.184783+0.278668 
## [111]    train-rmse:0.434118+0.016506    test-rmse:1.179957+0.278262 
## [112]    train-rmse:0.428760+0.017109    test-rmse:1.181239+0.278848 
## [113]    train-rmse:0.424997+0.016505    test-rmse:1.177705+0.278921 
## [114]    train-rmse:0.422198+0.016556    test-rmse:1.174884+0.278153 
## [115]    train-rmse:0.418042+0.015913    test-rmse:1.173924+0.279695 
## [116]    train-rmse:0.413891+0.013850    test-rmse:1.174480+0.279681 
## [117]    train-rmse:0.410111+0.013529    test-rmse:1.173838+0.279449 
## [118]    train-rmse:0.407374+0.013986    test-rmse:1.167395+0.281749 
## [119]    train-rmse:0.404138+0.014472    test-rmse:1.167371+0.280982 
## [120]    train-rmse:0.400493+0.015461    test-rmse:1.164866+0.282897 
## [121]    train-rmse:0.397678+0.016361    test-rmse:1.162627+0.282284 
## [122]    train-rmse:0.394985+0.017028    test-rmse:1.161297+0.282863 
## [123]    train-rmse:0.392750+0.016745    test-rmse:1.159711+0.282251 
## [124]    train-rmse:0.391208+0.016764    test-rmse:1.158767+0.283046 
## [125]    train-rmse:0.388239+0.017128    test-rmse:1.156669+0.282216 
## [126]    train-rmse:0.385546+0.016122    test-rmse:1.153596+0.283665 
## [127]    train-rmse:0.383374+0.016066    test-rmse:1.152695+0.283742 
## [128]    train-rmse:0.380585+0.016531    test-rmse:1.150870+0.283188 
## [129]    train-rmse:0.378305+0.016686    test-rmse:1.149062+0.283925 
## [130]    train-rmse:0.376186+0.016263    test-rmse:1.148834+0.284249 
## [131]    train-rmse:0.373061+0.016035    test-rmse:1.146277+0.283248 
## [132]    train-rmse:0.369298+0.015672    test-rmse:1.146884+0.284412 
## [133]    train-rmse:0.366801+0.015621    test-rmse:1.147464+0.285461 
## [134]    train-rmse:0.362678+0.014270    test-rmse:1.145130+0.286297 
## [135]    train-rmse:0.359505+0.013589    test-rmse:1.146191+0.286439 
## [136]    train-rmse:0.357434+0.013879    test-rmse:1.145286+0.286467 
## [137]    train-rmse:0.354688+0.014404    test-rmse:1.144705+0.285623 
## [138]    train-rmse:0.352193+0.013863    test-rmse:1.145429+0.287487 
## [139]    train-rmse:0.350437+0.014306    test-rmse:1.143948+0.287465 
## [140]    train-rmse:0.347264+0.015037    test-rmse:1.144402+0.288765 
## [141]    train-rmse:0.344483+0.015012    test-rmse:1.141790+0.286615 
## [142]    train-rmse:0.341474+0.015363    test-rmse:1.138343+0.289867 
## [143]    train-rmse:0.339802+0.015561    test-rmse:1.137978+0.289596 
## [144]    train-rmse:0.338027+0.015235    test-rmse:1.138208+0.290143 
## [145]    train-rmse:0.336007+0.015196    test-rmse:1.137275+0.287985 
## [146]    train-rmse:0.335039+0.015183    test-rmse:1.135873+0.287793 
## [147]    train-rmse:0.333578+0.015304    test-rmse:1.134294+0.288879 
## [148]    train-rmse:0.331445+0.015650    test-rmse:1.134113+0.289484 
## [149]    train-rmse:0.328642+0.014076    test-rmse:1.134597+0.288844 
## [150]    train-rmse:0.324963+0.013235    test-rmse:1.130888+0.293001 
## [151]    train-rmse:0.322524+0.012276    test-rmse:1.130812+0.292960 
## [152]    train-rmse:0.320151+0.012788    test-rmse:1.129725+0.291926 
## [153]    train-rmse:0.317915+0.011620    test-rmse:1.128083+0.293831 
## [154]    train-rmse:0.315972+0.011690    test-rmse:1.127919+0.293855 
## [155]    train-rmse:0.313357+0.011513    test-rmse:1.125541+0.292736 
## [156]    train-rmse:0.311430+0.011866    test-rmse:1.125050+0.292385 
## [157]    train-rmse:0.309801+0.011997    test-rmse:1.120907+0.286942 
## [158]    train-rmse:0.308293+0.012034    test-rmse:1.118665+0.288477 
## [159]    train-rmse:0.305944+0.011611    test-rmse:1.118453+0.286779 
## [160]    train-rmse:0.304774+0.011262    test-rmse:1.117687+0.286922 
## [161]    train-rmse:0.302941+0.011971    test-rmse:1.117195+0.286631 
## [162]    train-rmse:0.301200+0.011821    test-rmse:1.115603+0.285819 
## [163]    train-rmse:0.298953+0.011647    test-rmse:1.114680+0.285612 
## [164]    train-rmse:0.297762+0.011757    test-rmse:1.114122+0.285911 
## [165]    train-rmse:0.295949+0.012134    test-rmse:1.113833+0.285546 
## [166]    train-rmse:0.293887+0.012001    test-rmse:1.112902+0.286483 
## [167]    train-rmse:0.292160+0.012068    test-rmse:1.113244+0.286499 
## [168]    train-rmse:0.290585+0.012757    test-rmse:1.112280+0.287737 
## [169]    train-rmse:0.288932+0.012691    test-rmse:1.112236+0.287810 
## [170]    train-rmse:0.287313+0.012724    test-rmse:1.112467+0.287386 
## [171]    train-rmse:0.285872+0.012496    test-rmse:1.112140+0.287560 
## [172]    train-rmse:0.283900+0.012900    test-rmse:1.112303+0.288592 
## [173]    train-rmse:0.282484+0.013360    test-rmse:1.112067+0.288342 
## [174]    train-rmse:0.281081+0.013722    test-rmse:1.112626+0.289170 
## [175]    train-rmse:0.278715+0.013134    test-rmse:1.112628+0.288183 
## [176]    train-rmse:0.277166+0.012822    test-rmse:1.111749+0.288619 
## [177]    train-rmse:0.275876+0.012843    test-rmse:1.111301+0.289244 
## [178]    train-rmse:0.274805+0.012924    test-rmse:1.110711+0.288577 
## [179]    train-rmse:0.274121+0.012739    test-rmse:1.109345+0.287625 
## [180]    train-rmse:0.272902+0.012764    test-rmse:1.109582+0.288311 
## [181]    train-rmse:0.272031+0.013103    test-rmse:1.109669+0.288029 
## [182]    train-rmse:0.270174+0.012318    test-rmse:1.109356+0.287855 
## [183]    train-rmse:0.268159+0.011938    test-rmse:1.108445+0.288095 
## [184]    train-rmse:0.266507+0.012004    test-rmse:1.108909+0.287943 
## [185]    train-rmse:0.264839+0.011886    test-rmse:1.109008+0.289398 
## [186]    train-rmse:0.264223+0.011980    test-rmse:1.108447+0.289415 
## [187]    train-rmse:0.263001+0.011305    test-rmse:1.107659+0.288862 
## [188]    train-rmse:0.262021+0.011325    test-rmse:1.107513+0.288576 
## [189]    train-rmse:0.260316+0.011728    test-rmse:1.108027+0.287409 
## [190]    train-rmse:0.258682+0.011176    test-rmse:1.108205+0.286764 
## [191]    train-rmse:0.256447+0.009935    test-rmse:1.110099+0.286299 
## [192]    train-rmse:0.255271+0.009861    test-rmse:1.110110+0.286188 
## [193]    train-rmse:0.253947+0.010583    test-rmse:1.110191+0.285716 
## [194]    train-rmse:0.252701+0.009953    test-rmse:1.109552+0.285410 
## Stopping. Best iteration:
## [188]    train-rmse:0.262021+0.011325    test-rmse:1.107513+0.288576
## 
## 100 
## [1]  train-rmse:67.001016+0.081705   test-rmse:67.000742+0.527509 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:41.869994+0.065854   test-rmse:41.868242+0.585212 
## [3]  train-rmse:26.397598+0.065573   test-rmse:26.392831+0.648853 
## [4]  train-rmse:16.827689+0.035908   test-rmse:16.891083+0.599172 
## [5]  train-rmse:10.925817+0.032545   test-rmse:10.994655+0.549852 
## [6]  train-rmse:7.356710+0.033749    test-rmse:7.474104+0.549826 
## [7]  train-rmse:5.241271+0.045483    test-rmse:5.450910+0.579151 
## [8]  train-rmse:3.994278+0.054698    test-rmse:4.264142+0.466754 
## [9]  train-rmse:3.288628+0.051081    test-rmse:3.564041+0.345138 
## [10] train-rmse:2.884923+0.046709    test-rmse:3.203227+0.323083 
## [11] train-rmse:2.602158+0.062080    test-rmse:2.972800+0.283341 
## [12] train-rmse:2.419654+0.062180    test-rmse:2.790806+0.287877 
## [13] train-rmse:2.274061+0.055380    test-rmse:2.678650+0.293081 
## [14] train-rmse:2.135140+0.042315    test-rmse:2.541365+0.244255 
## [15] train-rmse:2.012158+0.040922    test-rmse:2.422373+0.211747 
## [16] train-rmse:1.906816+0.036734    test-rmse:2.335734+0.205593 
## [17] train-rmse:1.809509+0.035129    test-rmse:2.263392+0.233277 
## [18] train-rmse:1.718419+0.030268    test-rmse:2.193558+0.224700 
## [19] train-rmse:1.632474+0.026525    test-rmse:2.103529+0.227369 
## [20] train-rmse:1.555065+0.023473    test-rmse:2.041701+0.221324 
## [21] train-rmse:1.481001+0.024018    test-rmse:1.995368+0.201584 
## [22] train-rmse:1.416564+0.021244    test-rmse:1.939761+0.218027 
## [23] train-rmse:1.348121+0.024344    test-rmse:1.895506+0.207561 
## [24] train-rmse:1.291127+0.025207    test-rmse:1.850615+0.224543 
## [25] train-rmse:1.236509+0.022702    test-rmse:1.777258+0.230902 
## [26] train-rmse:1.186447+0.019230    test-rmse:1.744889+0.248494 
## [27] train-rmse:1.138376+0.017677    test-rmse:1.711923+0.249285 
## [28] train-rmse:1.089612+0.016020    test-rmse:1.679084+0.240956 
## [29] train-rmse:1.044259+0.020228    test-rmse:1.645259+0.242347 
## [30] train-rmse:1.005104+0.021851    test-rmse:1.601131+0.247485 
## [31] train-rmse:0.967944+0.021221    test-rmse:1.569425+0.231247 
## [32] train-rmse:0.935157+0.020083    test-rmse:1.547586+0.227819 
## [33] train-rmse:0.902750+0.019063    test-rmse:1.522382+0.228129 
## [34] train-rmse:0.866084+0.020839    test-rmse:1.504479+0.229037 
## [35] train-rmse:0.836590+0.020288    test-rmse:1.484552+0.232268 
## [36] train-rmse:0.810674+0.019895    test-rmse:1.466297+0.240221 
## [37] train-rmse:0.785562+0.019106    test-rmse:1.450980+0.248561 
## [38] train-rmse:0.755559+0.020180    test-rmse:1.430375+0.238320 
## [39] train-rmse:0.730869+0.020386    test-rmse:1.417934+0.241468 
## [40] train-rmse:0.709077+0.016652    test-rmse:1.402186+0.243071 
## [41] train-rmse:0.689809+0.017041    test-rmse:1.378223+0.247331 
## [42] train-rmse:0.668626+0.019242    test-rmse:1.363403+0.245583 
## [43] train-rmse:0.652185+0.019073    test-rmse:1.360463+0.244822 
## [44] train-rmse:0.630975+0.017187    test-rmse:1.347428+0.253069 
## [45] train-rmse:0.609791+0.015445    test-rmse:1.331206+0.263881 
## [46] train-rmse:0.593888+0.014366    test-rmse:1.316094+0.268658 
## [47] train-rmse:0.577809+0.015342    test-rmse:1.305756+0.269156 
## [48] train-rmse:0.563290+0.014447    test-rmse:1.298157+0.269710 
## [49] train-rmse:0.548597+0.010661    test-rmse:1.292733+0.274978 
## [50] train-rmse:0.534922+0.010641    test-rmse:1.286908+0.281198 
## [51] train-rmse:0.522006+0.010646    test-rmse:1.278976+0.283992 
## [52] train-rmse:0.511178+0.009379    test-rmse:1.276212+0.284375 
## [53] train-rmse:0.499312+0.009883    test-rmse:1.272342+0.280155 
## [54] train-rmse:0.487421+0.011386    test-rmse:1.264606+0.285443 
## [55] train-rmse:0.478782+0.013342    test-rmse:1.261392+0.287089 
## [56] train-rmse:0.468281+0.012537    test-rmse:1.261252+0.284335 
## [57] train-rmse:0.458755+0.012767    test-rmse:1.251178+0.290978 
## [58] train-rmse:0.449486+0.012842    test-rmse:1.247041+0.290139 
## [59] train-rmse:0.440488+0.011256    test-rmse:1.243503+0.292830 
## [60] train-rmse:0.431251+0.010605    test-rmse:1.240640+0.292235 
## [61] train-rmse:0.422118+0.009212    test-rmse:1.235653+0.294935 
## [62] train-rmse:0.414368+0.007832    test-rmse:1.234698+0.294798 
## [63] train-rmse:0.408023+0.008987    test-rmse:1.228391+0.290597 
## [64] train-rmse:0.400652+0.010917    test-rmse:1.230516+0.290950 
## [65] train-rmse:0.394453+0.011884    test-rmse:1.226760+0.289712 
## [66] train-rmse:0.387578+0.012472    test-rmse:1.218225+0.294402 
## [67] train-rmse:0.382708+0.012974    test-rmse:1.217171+0.295111 
## [68] train-rmse:0.376278+0.011742    test-rmse:1.213487+0.293786 
## [69] train-rmse:0.370234+0.012848    test-rmse:1.214028+0.294376 
## [70] train-rmse:0.364483+0.013417    test-rmse:1.213866+0.293993 
## [71] train-rmse:0.357627+0.012503    test-rmse:1.210675+0.295001 
## [72] train-rmse:0.353127+0.013850    test-rmse:1.206147+0.295059 
## [73] train-rmse:0.348803+0.013554    test-rmse:1.204411+0.296757 
## [74] train-rmse:0.342159+0.013603    test-rmse:1.200491+0.298996 
## [75] train-rmse:0.334545+0.014052    test-rmse:1.196140+0.299010 
## [76] train-rmse:0.330412+0.013950    test-rmse:1.195217+0.300630 
## [77] train-rmse:0.325608+0.014144    test-rmse:1.192635+0.296844 
## [78] train-rmse:0.319851+0.013775    test-rmse:1.190814+0.297956 
## [79] train-rmse:0.314611+0.011593    test-rmse:1.188340+0.297999 
## [80] train-rmse:0.309744+0.011895    test-rmse:1.187429+0.297754 
## [81] train-rmse:0.304797+0.012883    test-rmse:1.183160+0.295552 
## [82] train-rmse:0.300169+0.012354    test-rmse:1.183136+0.298478 
## [83] train-rmse:0.296392+0.013040    test-rmse:1.181944+0.297606 
## [84] train-rmse:0.291722+0.012986    test-rmse:1.180419+0.300321 
## [85] train-rmse:0.288295+0.013486    test-rmse:1.179833+0.301201 
## [86] train-rmse:0.283073+0.011692    test-rmse:1.177958+0.298793 
## [87] train-rmse:0.280194+0.012053    test-rmse:1.178396+0.298758 
## [88] train-rmse:0.273958+0.013239    test-rmse:1.177568+0.300302 
## [89] train-rmse:0.270547+0.012956    test-rmse:1.171553+0.291390 
## [90] train-rmse:0.266980+0.012753    test-rmse:1.172184+0.291645 
## [91] train-rmse:0.263979+0.012829    test-rmse:1.171037+0.292998 
## [92] train-rmse:0.260271+0.012959    test-rmse:1.169205+0.293399 
## [93] train-rmse:0.256554+0.013087    test-rmse:1.168146+0.295811 
## [94] train-rmse:0.254023+0.013573    test-rmse:1.167887+0.296111 
## [95] train-rmse:0.249820+0.013578    test-rmse:1.167167+0.298512 
## [96] train-rmse:0.246212+0.011548    test-rmse:1.168915+0.298500 
## [97] train-rmse:0.242351+0.011162    test-rmse:1.168978+0.298966 
## [98] train-rmse:0.238440+0.011216    test-rmse:1.168424+0.299734 
## [99] train-rmse:0.234671+0.011107    test-rmse:1.168683+0.300016 
## [100]    train-rmse:0.231718+0.010329    test-rmse:1.166238+0.301609 
## [101]    train-rmse:0.228262+0.011581    test-rmse:1.165745+0.301295 
## [102]    train-rmse:0.224883+0.011978    test-rmse:1.167283+0.301199 
## [103]    train-rmse:0.222355+0.011925    test-rmse:1.167305+0.300962 
## [104]    train-rmse:0.219570+0.012174    test-rmse:1.167372+0.301125 
## [105]    train-rmse:0.217369+0.011746    test-rmse:1.166379+0.302418 
## [106]    train-rmse:0.214432+0.011998    test-rmse:1.166205+0.304291 
## [107]    train-rmse:0.212523+0.012010    test-rmse:1.166262+0.304619 
## Stopping. Best iteration:
## [101]    train-rmse:0.228262+0.011581    test-rmse:1.165745+0.301295
## 
## 101 
## [1]  train-rmse:67.001016+0.081705   test-rmse:67.000742+0.527509 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:41.869994+0.065854   test-rmse:41.868242+0.585212 
## [3]  train-rmse:26.397598+0.065573   test-rmse:26.392831+0.648853 
## [4]  train-rmse:16.826265+0.038279   test-rmse:16.885249+0.590232 
## [5]  train-rmse:10.901592+0.038279   test-rmse:10.974032+0.526604 
## [6]  train-rmse:7.276242+0.039943    test-rmse:7.367636+0.529662 
## [7]  train-rmse:5.081668+0.057807    test-rmse:5.240058+0.540646 
## [8]  train-rmse:3.780630+0.068267    test-rmse:3.954017+0.395325 
## [9]  train-rmse:3.011568+0.069916    test-rmse:3.288256+0.361397 
## [10] train-rmse:2.537753+0.103501    test-rmse:2.864497+0.291233 
## [11] train-rmse:2.224356+0.071194    test-rmse:2.616511+0.294781 
## [12] train-rmse:2.015487+0.055361    test-rmse:2.445970+0.271250 
## [13] train-rmse:1.849068+0.053824    test-rmse:2.314988+0.270283 
## [14] train-rmse:1.718854+0.052149    test-rmse:2.208973+0.264760 
## [15] train-rmse:1.596103+0.041129    test-rmse:2.120024+0.238690 
## [16] train-rmse:1.489663+0.041572    test-rmse:2.046836+0.245814 
## [17] train-rmse:1.395490+0.041312    test-rmse:1.942129+0.232081 
## [18] train-rmse:1.310280+0.038115    test-rmse:1.880652+0.226750 
## [19] train-rmse:1.231906+0.035966    test-rmse:1.813729+0.218814 
## [20] train-rmse:1.161305+0.031034    test-rmse:1.751127+0.209702 
## [21] train-rmse:1.101220+0.034726    test-rmse:1.703369+0.211517 
## [22] train-rmse:1.042765+0.032083    test-rmse:1.635879+0.221690 
## [23] train-rmse:0.983031+0.027896    test-rmse:1.595221+0.222542 
## [24] train-rmse:0.927879+0.024204    test-rmse:1.566251+0.235985 
## [25] train-rmse:0.882625+0.024835    test-rmse:1.527696+0.222837 
## [26] train-rmse:0.835088+0.015590    test-rmse:1.502626+0.214504 
## [27] train-rmse:0.794355+0.019809    test-rmse:1.477262+0.230994 
## [28] train-rmse:0.754203+0.022890    test-rmse:1.454125+0.224552 
## [29] train-rmse:0.718618+0.020570    test-rmse:1.429779+0.235545 
## [30] train-rmse:0.687373+0.019960    test-rmse:1.407395+0.223018 
## [31] train-rmse:0.659520+0.021598    test-rmse:1.386730+0.233382 
## [32] train-rmse:0.628978+0.014675    test-rmse:1.368181+0.234357 
## [33] train-rmse:0.600491+0.011620    test-rmse:1.349790+0.234411 
## [34] train-rmse:0.575677+0.009606    test-rmse:1.332488+0.229147 
## [35] train-rmse:0.554784+0.011930    test-rmse:1.314358+0.231048 
## [36] train-rmse:0.536255+0.011570    test-rmse:1.300428+0.228685 
## [37] train-rmse:0.518662+0.014495    test-rmse:1.286574+0.230166 
## [38] train-rmse:0.499979+0.015814    test-rmse:1.285293+0.233207 
## [39] train-rmse:0.481553+0.014770    test-rmse:1.272444+0.230163 
## [40] train-rmse:0.467120+0.016590    test-rmse:1.264566+0.232351 
## [41] train-rmse:0.449453+0.016658    test-rmse:1.257566+0.235877 
## [42] train-rmse:0.433590+0.014234    test-rmse:1.245651+0.223827 
## [43] train-rmse:0.422522+0.014885    test-rmse:1.241527+0.226502 
## [44] train-rmse:0.408089+0.013885    test-rmse:1.239378+0.229760 
## [45] train-rmse:0.389762+0.012846    test-rmse:1.232007+0.230313 
## [46] train-rmse:0.376886+0.011672    test-rmse:1.225404+0.231442 
## [47] train-rmse:0.367425+0.013334    test-rmse:1.223124+0.235566 
## [48] train-rmse:0.356564+0.012501    test-rmse:1.222745+0.237120 
## [49] train-rmse:0.346429+0.010599    test-rmse:1.218947+0.238454 
## [50] train-rmse:0.334900+0.010095    test-rmse:1.218426+0.240092 
## [51] train-rmse:0.326229+0.008914    test-rmse:1.213015+0.243279 
## [52] train-rmse:0.316932+0.009396    test-rmse:1.208569+0.243538 
## [53] train-rmse:0.307683+0.008785    test-rmse:1.206971+0.243642 
## [54] train-rmse:0.298506+0.008946    test-rmse:1.204302+0.242037 
## [55] train-rmse:0.293106+0.009292    test-rmse:1.200682+0.244332 
## [56] train-rmse:0.287622+0.010752    test-rmse:1.197670+0.242929 
## [57] train-rmse:0.281238+0.010566    test-rmse:1.195627+0.244867 
## [58] train-rmse:0.273110+0.009318    test-rmse:1.193984+0.247229 
## [59] train-rmse:0.268172+0.010017    test-rmse:1.191763+0.247244 
## [60] train-rmse:0.260594+0.007894    test-rmse:1.189344+0.247990 
## [61] train-rmse:0.252185+0.009016    test-rmse:1.188750+0.249205 
## [62] train-rmse:0.245735+0.009568    test-rmse:1.189994+0.251461 
## [63] train-rmse:0.240903+0.009086    test-rmse:1.189432+0.250934 
## [64] train-rmse:0.232340+0.007296    test-rmse:1.187299+0.249812 
## [65] train-rmse:0.227268+0.007873    test-rmse:1.187112+0.251402 
## [66] train-rmse:0.221283+0.008929    test-rmse:1.184732+0.251185 
## [67] train-rmse:0.216315+0.009229    test-rmse:1.183419+0.250205 
## [68] train-rmse:0.212546+0.009579    test-rmse:1.182491+0.250508 
## [69] train-rmse:0.205766+0.007169    test-rmse:1.181437+0.249981 
## [70] train-rmse:0.200935+0.006911    test-rmse:1.179535+0.246962 
## [71] train-rmse:0.197180+0.007603    test-rmse:1.178139+0.247501 
## [72] train-rmse:0.192997+0.008897    test-rmse:1.174603+0.248573 
## [73] train-rmse:0.188557+0.008765    test-rmse:1.173513+0.247952 
## [74] train-rmse:0.181527+0.004757    test-rmse:1.171565+0.249045 
## [75] train-rmse:0.177443+0.005196    test-rmse:1.171214+0.248307 
## [76] train-rmse:0.174497+0.006177    test-rmse:1.170177+0.249600 
## [77] train-rmse:0.171901+0.005489    test-rmse:1.170386+0.249597 
## [78] train-rmse:0.168899+0.005575    test-rmse:1.170573+0.250281 
## [79] train-rmse:0.166155+0.005879    test-rmse:1.170104+0.249503 
## [80] train-rmse:0.161996+0.007070    test-rmse:1.168354+0.250772 
## [81] train-rmse:0.158399+0.007987    test-rmse:1.168679+0.250672 
## [82] train-rmse:0.154458+0.007555    test-rmse:1.169110+0.250139 
## [83] train-rmse:0.150820+0.006093    test-rmse:1.170039+0.250036 
## [84] train-rmse:0.147396+0.006280    test-rmse:1.169699+0.251313 
## [85] train-rmse:0.144781+0.006031    test-rmse:1.167998+0.249815 
## [86] train-rmse:0.141930+0.007222    test-rmse:1.166956+0.252473 
## [87] train-rmse:0.138406+0.007685    test-rmse:1.166694+0.252497 
## [88] train-rmse:0.134970+0.006821    test-rmse:1.167254+0.252205 
## [89] train-rmse:0.132211+0.007187    test-rmse:1.166854+0.252412 
## [90] train-rmse:0.129586+0.007770    test-rmse:1.166177+0.253321 
## [91] train-rmse:0.126025+0.007345    test-rmse:1.166803+0.252795 
## [92] train-rmse:0.122854+0.007105    test-rmse:1.167100+0.252964 
## [93] train-rmse:0.119514+0.007729    test-rmse:1.166425+0.252541 
## [94] train-rmse:0.117692+0.007551    test-rmse:1.166159+0.252223 
## [95] train-rmse:0.114825+0.007524    test-rmse:1.165798+0.252705 
## [96] train-rmse:0.112259+0.006606    test-rmse:1.165522+0.253067 
## [97] train-rmse:0.109921+0.006412    test-rmse:1.165665+0.253220 
## [98] train-rmse:0.107048+0.005892    test-rmse:1.165027+0.253689 
## [99] train-rmse:0.104367+0.005236    test-rmse:1.165256+0.253697 
## [100]    train-rmse:0.101730+0.006077    test-rmse:1.165695+0.252904 
## [101]    train-rmse:0.099361+0.007039    test-rmse:1.165780+0.252881 
## [102]    train-rmse:0.097709+0.006716    test-rmse:1.164755+0.252990 
## [103]    train-rmse:0.095590+0.006102    test-rmse:1.164290+0.252460 
## [104]    train-rmse:0.092830+0.005408    test-rmse:1.164107+0.252678 
## [105]    train-rmse:0.091426+0.005141    test-rmse:1.163638+0.253195 
## [106]    train-rmse:0.088992+0.005803    test-rmse:1.163365+0.252579 
## [107]    train-rmse:0.087634+0.005940    test-rmse:1.162915+0.252081 
## [108]    train-rmse:0.084970+0.006218    test-rmse:1.161885+0.252505 
## [109]    train-rmse:0.083362+0.005838    test-rmse:1.161686+0.252642 
## [110]    train-rmse:0.082209+0.005701    test-rmse:1.161619+0.252576 
## [111]    train-rmse:0.080161+0.005715    test-rmse:1.162039+0.252131 
## [112]    train-rmse:0.078953+0.005481    test-rmse:1.161602+0.251622 
## [113]    train-rmse:0.077814+0.005462    test-rmse:1.162238+0.251378 
## [114]    train-rmse:0.076443+0.005757    test-rmse:1.162373+0.251269 
## [115]    train-rmse:0.075435+0.005587    test-rmse:1.162682+0.251110 
## [116]    train-rmse:0.074092+0.005141    test-rmse:1.161974+0.251102 
## [117]    train-rmse:0.072302+0.004607    test-rmse:1.161180+0.250744 
## [118]    train-rmse:0.070589+0.005057    test-rmse:1.160804+0.250425 
## [119]    train-rmse:0.068919+0.005118    test-rmse:1.161121+0.250714 
## [120]    train-rmse:0.067760+0.005347    test-rmse:1.161096+0.249844 
## [121]    train-rmse:0.066641+0.005321    test-rmse:1.160814+0.250046 
## [122]    train-rmse:0.065819+0.005145    test-rmse:1.160405+0.250265 
## [123]    train-rmse:0.064096+0.004859    test-rmse:1.160295+0.250541 
## [124]    train-rmse:0.062553+0.004768    test-rmse:1.159799+0.250635 
## [125]    train-rmse:0.061784+0.004835    test-rmse:1.160176+0.250773 
## [126]    train-rmse:0.060616+0.004733    test-rmse:1.159946+0.250776 
## [127]    train-rmse:0.059292+0.004882    test-rmse:1.159521+0.250475 
## [128]    train-rmse:0.058170+0.005049    test-rmse:1.160234+0.250648 
## [129]    train-rmse:0.057284+0.004999    test-rmse:1.160224+0.250671 
## [130]    train-rmse:0.056240+0.004943    test-rmse:1.160244+0.250671 
## [131]    train-rmse:0.055311+0.004730    test-rmse:1.159973+0.250445 
## [132]    train-rmse:0.054091+0.004586    test-rmse:1.160007+0.250144 
## [133]    train-rmse:0.053082+0.004610    test-rmse:1.159446+0.249282 
## [134]    train-rmse:0.052009+0.004910    test-rmse:1.159208+0.248756 
## [135]    train-rmse:0.050767+0.004980    test-rmse:1.159217+0.248871 
## [136]    train-rmse:0.050130+0.005147    test-rmse:1.159217+0.248878 
## [137]    train-rmse:0.048954+0.004786    test-rmse:1.158711+0.248044 
## [138]    train-rmse:0.048004+0.005336    test-rmse:1.158646+0.247984 
## [139]    train-rmse:0.047122+0.005150    test-rmse:1.158403+0.248163 
## [140]    train-rmse:0.046267+0.005180    test-rmse:1.158414+0.248202 
## [141]    train-rmse:0.045528+0.004987    test-rmse:1.158492+0.247861 
## [142]    train-rmse:0.044765+0.005164    test-rmse:1.158111+0.247883 
## [143]    train-rmse:0.044097+0.005274    test-rmse:1.158402+0.247968 
## [144]    train-rmse:0.043433+0.005380    test-rmse:1.157947+0.248043 
## [145]    train-rmse:0.042508+0.005525    test-rmse:1.157845+0.248003 
## [146]    train-rmse:0.041813+0.005294    test-rmse:1.157416+0.247976 
## [147]    train-rmse:0.041041+0.005253    test-rmse:1.157221+0.247408 
## [148]    train-rmse:0.040367+0.005090    test-rmse:1.157007+0.247258 
## [149]    train-rmse:0.039347+0.004670    test-rmse:1.157047+0.247353 
## [150]    train-rmse:0.038727+0.004749    test-rmse:1.156681+0.247467 
## [151]    train-rmse:0.038034+0.004471    test-rmse:1.156633+0.247411 
## [152]    train-rmse:0.037003+0.004439    test-rmse:1.156643+0.247173 
## [153]    train-rmse:0.036542+0.004355    test-rmse:1.156195+0.246957 
## [154]    train-rmse:0.036053+0.004266    test-rmse:1.155973+0.246977 
## [155]    train-rmse:0.035583+0.004174    test-rmse:1.155851+0.246988 
## [156]    train-rmse:0.035000+0.004383    test-rmse:1.155841+0.246858 
## [157]    train-rmse:0.034113+0.004409    test-rmse:1.155802+0.246643 
## [158]    train-rmse:0.033563+0.004328    test-rmse:1.155897+0.246538 
## [159]    train-rmse:0.033075+0.004485    test-rmse:1.156087+0.246556 
## [160]    train-rmse:0.032476+0.004341    test-rmse:1.155925+0.246579 
## [161]    train-rmse:0.031887+0.004312    test-rmse:1.155792+0.246616 
## [162]    train-rmse:0.031065+0.004056    test-rmse:1.155555+0.246592 
## [163]    train-rmse:0.030311+0.003979    test-rmse:1.155363+0.246692 
## [164]    train-rmse:0.029902+0.003955    test-rmse:1.155720+0.246459 
## [165]    train-rmse:0.029281+0.003926    test-rmse:1.155952+0.246713 
## [166]    train-rmse:0.028804+0.003903    test-rmse:1.155775+0.246462 
## [167]    train-rmse:0.028285+0.003828    test-rmse:1.155876+0.246377 
## [168]    train-rmse:0.027723+0.003873    test-rmse:1.155937+0.246306 
## [169]    train-rmse:0.027250+0.003971    test-rmse:1.155881+0.246327 
## Stopping. Best iteration:
## [163]    train-rmse:0.030311+0.003979    test-rmse:1.155363+0.246692
## 
## 102 
## [1]  train-rmse:67.001016+0.081705   test-rmse:67.000742+0.527509 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:41.869994+0.065854   test-rmse:41.868242+0.585212 
## [3]  train-rmse:26.397598+0.065573   test-rmse:26.392831+0.648853 
## [4]  train-rmse:16.826265+0.038279   test-rmse:16.885249+0.590232 
## [5]  train-rmse:10.888288+0.037416   test-rmse:10.965916+0.529293 
## [6]  train-rmse:7.214672+0.039929    test-rmse:7.328673+0.520719 
## [7]  train-rmse:4.957009+0.056387    test-rmse:5.102424+0.519320 
## [8]  train-rmse:3.559901+0.061246    test-rmse:3.785497+0.410304 
## [9]  train-rmse:2.701010+0.042329    test-rmse:3.022018+0.321690 
## [10] train-rmse:2.179537+0.033808    test-rmse:2.607268+0.249775 
## [11] train-rmse:1.857893+0.040200    test-rmse:2.330437+0.238146 
## [12] train-rmse:1.632199+0.065198    test-rmse:2.173711+0.221518 
## [13] train-rmse:1.463040+0.060431    test-rmse:2.062975+0.182088 
## [14] train-rmse:1.320004+0.045534    test-rmse:1.944567+0.156771 
## [15] train-rmse:1.209897+0.042759    test-rmse:1.887311+0.181740 
## [16] train-rmse:1.115239+0.037062    test-rmse:1.790684+0.181576 
## [17] train-rmse:1.031048+0.040516    test-rmse:1.720485+0.179981 
## [18] train-rmse:0.945406+0.045808    test-rmse:1.669789+0.194778 
## [19] train-rmse:0.880102+0.044612    test-rmse:1.623175+0.199505 
## [20] train-rmse:0.821943+0.039078    test-rmse:1.587302+0.192403 
## [21] train-rmse:0.768146+0.039825    test-rmse:1.540992+0.203086 
## [22] train-rmse:0.721084+0.035383    test-rmse:1.507801+0.181731 
## [23] train-rmse:0.675734+0.031726    test-rmse:1.467025+0.175051 
## [24] train-rmse:0.633884+0.033911    test-rmse:1.446667+0.175963 
## [25] train-rmse:0.597400+0.030406    test-rmse:1.424057+0.189773 
## [26] train-rmse:0.559035+0.025270    test-rmse:1.403303+0.188664 
## [27] train-rmse:0.530012+0.024576    test-rmse:1.381116+0.197032 
## [28] train-rmse:0.502035+0.018741    test-rmse:1.369383+0.200887 
## [29] train-rmse:0.476761+0.021600    test-rmse:1.342161+0.212269 
## [30] train-rmse:0.457521+0.021634    test-rmse:1.337862+0.212766 
## [31] train-rmse:0.433499+0.015850    test-rmse:1.330187+0.220799 
## [32] train-rmse:0.416291+0.015387    test-rmse:1.320254+0.222315 
## [33] train-rmse:0.398679+0.012088    test-rmse:1.311349+0.224971 
## [34] train-rmse:0.381791+0.013056    test-rmse:1.303238+0.235797 
## [35] train-rmse:0.364250+0.014669    test-rmse:1.295699+0.242435 
## [36] train-rmse:0.351066+0.013450    test-rmse:1.287754+0.247066 
## [37] train-rmse:0.334682+0.011114    test-rmse:1.287057+0.247944 
## [38] train-rmse:0.322039+0.008966    test-rmse:1.279388+0.259533 
## [39] train-rmse:0.308283+0.008592    test-rmse:1.273235+0.259053 
## [40] train-rmse:0.294563+0.005584    test-rmse:1.266403+0.259888 
## [41] train-rmse:0.283824+0.007045    test-rmse:1.265271+0.260826 
## [42] train-rmse:0.269375+0.005033    test-rmse:1.256547+0.259332 
## [43] train-rmse:0.258037+0.004161    test-rmse:1.253865+0.260516 
## [44] train-rmse:0.249418+0.004987    test-rmse:1.253121+0.258894 
## [45] train-rmse:0.240628+0.005874    test-rmse:1.251777+0.260568 
## [46] train-rmse:0.233331+0.004533    test-rmse:1.249021+0.261107 
## [47] train-rmse:0.223485+0.005133    test-rmse:1.249401+0.260384 
## [48] train-rmse:0.214843+0.003227    test-rmse:1.247626+0.263099 
## [49] train-rmse:0.206430+0.003466    test-rmse:1.247139+0.260754 
## [50] train-rmse:0.198217+0.002564    test-rmse:1.242537+0.253320 
## [51] train-rmse:0.190308+0.002125    test-rmse:1.243966+0.253116 
## [52] train-rmse:0.183664+0.004320    test-rmse:1.243277+0.254722 
## [53] train-rmse:0.176982+0.003462    test-rmse:1.242324+0.254790 
## [54] train-rmse:0.171666+0.004296    test-rmse:1.241531+0.255491 
## [55] train-rmse:0.167124+0.003334    test-rmse:1.239804+0.257400 
## [56] train-rmse:0.161085+0.002618    test-rmse:1.239117+0.257561 
## [57] train-rmse:0.154185+0.002177    test-rmse:1.239000+0.256772 
## [58] train-rmse:0.148647+0.003131    test-rmse:1.238413+0.256472 
## [59] train-rmse:0.142831+0.003312    test-rmse:1.236772+0.257216 
## [60] train-rmse:0.137737+0.003125    test-rmse:1.236491+0.256668 
## [61] train-rmse:0.132141+0.003068    test-rmse:1.235078+0.256202 
## [62] train-rmse:0.128986+0.003266    test-rmse:1.234977+0.255848 
## [63] train-rmse:0.123209+0.004481    test-rmse:1.234660+0.256054 
## [64] train-rmse:0.119669+0.004627    test-rmse:1.234260+0.257890 
## [65] train-rmse:0.114966+0.004627    test-rmse:1.233650+0.258110 
## [66] train-rmse:0.112625+0.004298    test-rmse:1.233696+0.258326 
## [67] train-rmse:0.108528+0.004609    test-rmse:1.233068+0.258836 
## [68] train-rmse:0.104618+0.004319    test-rmse:1.232372+0.259542 
## [69] train-rmse:0.101097+0.003339    test-rmse:1.231764+0.259319 
## [70] train-rmse:0.097753+0.004104    test-rmse:1.231391+0.259939 
## [71] train-rmse:0.094523+0.004305    test-rmse:1.229763+0.261054 
## [72] train-rmse:0.091924+0.004124    test-rmse:1.229008+0.261273 
## [73] train-rmse:0.087568+0.002596    test-rmse:1.227523+0.261619 
## [74] train-rmse:0.084732+0.002585    test-rmse:1.227655+0.261740 
## [75] train-rmse:0.081245+0.002314    test-rmse:1.227588+0.261823 
## [76] train-rmse:0.078336+0.002038    test-rmse:1.227871+0.261738 
## [77] train-rmse:0.075475+0.002312    test-rmse:1.226716+0.261723 
## [78] train-rmse:0.073090+0.003239    test-rmse:1.226072+0.261431 
## [79] train-rmse:0.070411+0.003310    test-rmse:1.226505+0.261129 
## [80] train-rmse:0.068024+0.003376    test-rmse:1.226155+0.261570 
## [81] train-rmse:0.065079+0.003731    test-rmse:1.225313+0.260619 
## [82] train-rmse:0.063199+0.003565    test-rmse:1.225323+0.260435 
## [83] train-rmse:0.061395+0.003522    test-rmse:1.225397+0.259947 
## [84] train-rmse:0.059402+0.003675    test-rmse:1.225040+0.260382 
## [85] train-rmse:0.057479+0.003696    test-rmse:1.224841+0.260133 
## [86] train-rmse:0.055971+0.003417    test-rmse:1.225037+0.260268 
## [87] train-rmse:0.054566+0.003672    test-rmse:1.224763+0.260174 
## [88] train-rmse:0.052722+0.003596    test-rmse:1.224041+0.260383 
## [89] train-rmse:0.051038+0.003391    test-rmse:1.223829+0.260286 
## [90] train-rmse:0.049248+0.003299    test-rmse:1.223695+0.260590 
## [91] train-rmse:0.048174+0.003115    test-rmse:1.223465+0.260318 
## [92] train-rmse:0.046502+0.002849    test-rmse:1.223315+0.260097 
## [93] train-rmse:0.045205+0.002992    test-rmse:1.223641+0.260728 
## [94] train-rmse:0.043627+0.002980    test-rmse:1.222997+0.261136 
## [95] train-rmse:0.042561+0.002986    test-rmse:1.222924+0.261143 
## [96] train-rmse:0.041396+0.002978    test-rmse:1.223041+0.261549 
## [97] train-rmse:0.040144+0.002992    test-rmse:1.222961+0.261557 
## [98] train-rmse:0.039033+0.002661    test-rmse:1.223189+0.262151 
## [99] train-rmse:0.037983+0.002829    test-rmse:1.222786+0.262480 
## [100]    train-rmse:0.037192+0.002910    test-rmse:1.222786+0.262404 
## [101]    train-rmse:0.036262+0.002835    test-rmse:1.222097+0.262491 
## [102]    train-rmse:0.035310+0.002490    test-rmse:1.221680+0.262027 
## [103]    train-rmse:0.034287+0.002517    test-rmse:1.221497+0.262567 
## [104]    train-rmse:0.033223+0.002876    test-rmse:1.221532+0.262373 
## [105]    train-rmse:0.032207+0.002719    test-rmse:1.221559+0.262268 
## [106]    train-rmse:0.030939+0.002197    test-rmse:1.221770+0.262048 
## [107]    train-rmse:0.029694+0.001760    test-rmse:1.221266+0.261603 
## [108]    train-rmse:0.028796+0.001885    test-rmse:1.221275+0.261787 
## [109]    train-rmse:0.027717+0.001620    test-rmse:1.221217+0.261869 
## [110]    train-rmse:0.026968+0.001637    test-rmse:1.221090+0.262031 
## [111]    train-rmse:0.026151+0.001509    test-rmse:1.220783+0.262014 
## [112]    train-rmse:0.025552+0.001415    test-rmse:1.220742+0.262182 
## [113]    train-rmse:0.024664+0.001274    test-rmse:1.220714+0.262119 
## [114]    train-rmse:0.023967+0.001337    test-rmse:1.220691+0.262212 
## [115]    train-rmse:0.023160+0.001164    test-rmse:1.220586+0.261779 
## [116]    train-rmse:0.022580+0.001217    test-rmse:1.220603+0.261664 
## [117]    train-rmse:0.021841+0.001378    test-rmse:1.220537+0.261527 
## [118]    train-rmse:0.021342+0.001229    test-rmse:1.220558+0.262021 
## [119]    train-rmse:0.020511+0.001142    test-rmse:1.220415+0.262054 
## [120]    train-rmse:0.019908+0.001028    test-rmse:1.220420+0.262073 
## [121]    train-rmse:0.019289+0.001177    test-rmse:1.220525+0.261974 
## [122]    train-rmse:0.018767+0.001072    test-rmse:1.220584+0.261988 
## [123]    train-rmse:0.018326+0.001076    test-rmse:1.220501+0.261964 
## [124]    train-rmse:0.017672+0.001040    test-rmse:1.220314+0.262055 
## [125]    train-rmse:0.017297+0.001085    test-rmse:1.220315+0.262069 
## [126]    train-rmse:0.016759+0.001270    test-rmse:1.220230+0.262086 
## [127]    train-rmse:0.016397+0.001328    test-rmse:1.220256+0.262149 
## [128]    train-rmse:0.015891+0.001438    test-rmse:1.220418+0.262133 
## [129]    train-rmse:0.015556+0.001484    test-rmse:1.220539+0.262167 
## [130]    train-rmse:0.014952+0.001286    test-rmse:1.220455+0.262174 
## [131]    train-rmse:0.014443+0.001247    test-rmse:1.220467+0.262163 
## [132]    train-rmse:0.014125+0.001170    test-rmse:1.220329+0.262160 
## Stopping. Best iteration:
## [126]    train-rmse:0.016759+0.001270    test-rmse:1.220230+0.262086
## 
## 103 
## [1]  train-rmse:67.001016+0.081705   test-rmse:67.000742+0.527509 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:41.869994+0.065854   test-rmse:41.868242+0.585212 
## [3]  train-rmse:26.397598+0.065573   test-rmse:26.392831+0.648853 
## [4]  train-rmse:16.826265+0.038279   test-rmse:16.885249+0.590232 
## [5]  train-rmse:10.876512+0.035714   test-rmse:10.964750+0.541435 
## [6]  train-rmse:7.177508+0.036860    test-rmse:7.283366+0.522607 
## [7]  train-rmse:4.893392+0.044917    test-rmse:5.044544+0.508051 
## [8]  train-rmse:3.465018+0.037064    test-rmse:3.690221+0.454791 
## [9]  train-rmse:2.558364+0.027245    test-rmse:2.894832+0.350784 
## [10] train-rmse:1.982153+0.023873    test-rmse:2.424914+0.276184 
## [11] train-rmse:1.629514+0.025912    test-rmse:2.171888+0.284067 
## [12] train-rmse:1.386975+0.024303    test-rmse:1.987161+0.248423 
## [13] train-rmse:1.213031+0.043336    test-rmse:1.882333+0.247782 
## [14] train-rmse:1.082436+0.038586    test-rmse:1.782761+0.263426 
## [15] train-rmse:0.958046+0.029729    test-rmse:1.689773+0.265798 
## [16] train-rmse:0.874647+0.031479    test-rmse:1.633993+0.276408 
## [17] train-rmse:0.791319+0.028734    test-rmse:1.562253+0.245342 
## [18] train-rmse:0.719963+0.023937    test-rmse:1.495540+0.256068 
## [19] train-rmse:0.658033+0.017709    test-rmse:1.469701+0.245363 
## [20] train-rmse:0.614389+0.018695    test-rmse:1.426721+0.264280 
## [21] train-rmse:0.562727+0.021778    test-rmse:1.402749+0.265325 
## [22] train-rmse:0.524817+0.020809    test-rmse:1.383158+0.270709 
## [23] train-rmse:0.494418+0.018825    test-rmse:1.370497+0.267721 
## [24] train-rmse:0.459530+0.014673    test-rmse:1.341374+0.252939 
## [25] train-rmse:0.430449+0.017219    test-rmse:1.324825+0.251650 
## [26] train-rmse:0.407325+0.018310    test-rmse:1.305671+0.238328 
## [27] train-rmse:0.383119+0.016896    test-rmse:1.303031+0.241497 
## [28] train-rmse:0.362285+0.016144    test-rmse:1.297617+0.241425 
## [29] train-rmse:0.338055+0.010947    test-rmse:1.287615+0.245220 
## [30] train-rmse:0.319662+0.010832    test-rmse:1.284332+0.242708 
## [31] train-rmse:0.303085+0.009759    test-rmse:1.280810+0.246666 
## [32] train-rmse:0.288139+0.010269    test-rmse:1.276311+0.244464 
## [33] train-rmse:0.273912+0.008678    test-rmse:1.274121+0.246533 
## [34] train-rmse:0.258547+0.011371    test-rmse:1.270183+0.248908 
## [35] train-rmse:0.242260+0.011470    test-rmse:1.265694+0.253982 
## [36] train-rmse:0.231335+0.012278    test-rmse:1.264116+0.251328 
## [37] train-rmse:0.219076+0.012048    test-rmse:1.259649+0.250381 
## [38] train-rmse:0.206665+0.012871    test-rmse:1.257949+0.252365 
## [39] train-rmse:0.197309+0.010044    test-rmse:1.256614+0.252700 
## [40] train-rmse:0.189743+0.009598    test-rmse:1.253217+0.253833 
## [41] train-rmse:0.180275+0.009309    test-rmse:1.252289+0.255814 
## [42] train-rmse:0.170597+0.009888    test-rmse:1.250551+0.255224 
## [43] train-rmse:0.163181+0.007927    test-rmse:1.248174+0.254225 
## [44] train-rmse:0.156624+0.007702    test-rmse:1.247701+0.252458 
## [45] train-rmse:0.150776+0.008796    test-rmse:1.244187+0.254668 
## [46] train-rmse:0.145311+0.007606    test-rmse:1.243112+0.254351 
## [47] train-rmse:0.137794+0.007689    test-rmse:1.241510+0.250134 
## [48] train-rmse:0.130670+0.008765    test-rmse:1.238193+0.250880 
## [49] train-rmse:0.126471+0.008286    test-rmse:1.237017+0.251426 
## [50] train-rmse:0.119382+0.008895    test-rmse:1.235561+0.251168 
## [51] train-rmse:0.113660+0.008319    test-rmse:1.234626+0.251336 
## [52] train-rmse:0.110197+0.008071    test-rmse:1.232997+0.252273 
## [53] train-rmse:0.105856+0.007281    test-rmse:1.231151+0.252990 
## [54] train-rmse:0.101182+0.007787    test-rmse:1.230404+0.253033 
## [55] train-rmse:0.095683+0.007974    test-rmse:1.231522+0.252886 
## [56] train-rmse:0.091694+0.008345    test-rmse:1.231795+0.252820 
## [57] train-rmse:0.088282+0.008400    test-rmse:1.231519+0.254018 
## [58] train-rmse:0.084643+0.008589    test-rmse:1.230657+0.254283 
## [59] train-rmse:0.081532+0.008580    test-rmse:1.231040+0.254400 
## [60] train-rmse:0.078151+0.009482    test-rmse:1.230957+0.254182 
## Stopping. Best iteration:
## [54] train-rmse:0.101182+0.007787    test-rmse:1.230404+0.253033
## 
## 104 
## [1]  train-rmse:67.001016+0.081705   test-rmse:67.000742+0.527509 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:41.869994+0.065854   test-rmse:41.868242+0.585212 
## [3]  train-rmse:26.397598+0.065573   test-rmse:26.392831+0.648853 
## [4]  train-rmse:16.826265+0.038279   test-rmse:16.885249+0.590232 
## [5]  train-rmse:10.865322+0.034209   test-rmse:10.951186+0.546346 
## [6]  train-rmse:7.141871+0.034426    test-rmse:7.236395+0.508049 
## [7]  train-rmse:4.827182+0.029738    test-rmse:4.997802+0.513439 
## [8]  train-rmse:3.357850+0.016458    test-rmse:3.614097+0.419643 
## [9]  train-rmse:2.422438+0.043913    test-rmse:2.757449+0.347543 
## [10] train-rmse:1.837823+0.038025    test-rmse:2.238836+0.288752 
## [11] train-rmse:1.455648+0.050702    test-rmse:1.968075+0.246246 
## [12] train-rmse:1.186732+0.053376    test-rmse:1.804255+0.231797 
## [13] train-rmse:1.011280+0.050791    test-rmse:1.693794+0.235573 
## [14] train-rmse:0.866869+0.038892    test-rmse:1.623087+0.234339 
## [15] train-rmse:0.755933+0.038383    test-rmse:1.546099+0.216580 
## [16] train-rmse:0.675363+0.034258    test-rmse:1.501589+0.205701 
## [17] train-rmse:0.608222+0.031554    test-rmse:1.461350+0.207755 
## [18] train-rmse:0.551564+0.030204    test-rmse:1.440715+0.218004 
## [19] train-rmse:0.502560+0.030086    test-rmse:1.410581+0.222567 
## [20] train-rmse:0.462519+0.021242    test-rmse:1.386655+0.222527 
## [21] train-rmse:0.425328+0.012307    test-rmse:1.369060+0.219409 
## [22] train-rmse:0.391394+0.010005    test-rmse:1.349071+0.219698 
## [23] train-rmse:0.365451+0.007432    test-rmse:1.343222+0.219806 
## [24] train-rmse:0.341342+0.009772    test-rmse:1.336288+0.220190 
## [25] train-rmse:0.319034+0.007812    test-rmse:1.327273+0.225083 
## [26] train-rmse:0.296656+0.010790    test-rmse:1.318720+0.230306 
## [27] train-rmse:0.273069+0.014326    test-rmse:1.315496+0.231174 
## [28] train-rmse:0.255535+0.011629    test-rmse:1.311210+0.232975 
## [29] train-rmse:0.239502+0.014687    test-rmse:1.309421+0.235124 
## [30] train-rmse:0.225959+0.014139    test-rmse:1.305484+0.234070 
## [31] train-rmse:0.210697+0.011698    test-rmse:1.302035+0.227547 
## [32] train-rmse:0.199637+0.011967    test-rmse:1.298364+0.227888 
## [33] train-rmse:0.185535+0.011676    test-rmse:1.297556+0.226823 
## [34] train-rmse:0.176857+0.010275    test-rmse:1.295400+0.226962 
## [35] train-rmse:0.165880+0.009181    test-rmse:1.292504+0.230482 
## [36] train-rmse:0.155521+0.010422    test-rmse:1.288281+0.233966 
## [37] train-rmse:0.145993+0.009592    test-rmse:1.285774+0.235338 
## [38] train-rmse:0.138746+0.010154    test-rmse:1.284732+0.235344 
## [39] train-rmse:0.131604+0.010637    test-rmse:1.284713+0.235178 
## [40] train-rmse:0.122607+0.009445    test-rmse:1.282191+0.234555 
## [41] train-rmse:0.115731+0.008135    test-rmse:1.280907+0.234795 
## [42] train-rmse:0.108962+0.008999    test-rmse:1.280786+0.235398 
## [43] train-rmse:0.102336+0.009665    test-rmse:1.281563+0.236300 
## [44] train-rmse:0.097162+0.008666    test-rmse:1.280100+0.235926 
## [45] train-rmse:0.091255+0.006955    test-rmse:1.279449+0.236388 
## [46] train-rmse:0.087406+0.006785    test-rmse:1.279823+0.237062 
## [47] train-rmse:0.083050+0.007175    test-rmse:1.278185+0.237808 
## [48] train-rmse:0.078094+0.007486    test-rmse:1.277716+0.239489 
## [49] train-rmse:0.073537+0.006896    test-rmse:1.276756+0.240190 
## [50] train-rmse:0.070267+0.006389    test-rmse:1.276734+0.240019 
## [51] train-rmse:0.064506+0.005457    test-rmse:1.277045+0.240030 
## [52] train-rmse:0.060559+0.003948    test-rmse:1.277196+0.241418 
## [53] train-rmse:0.057451+0.004271    test-rmse:1.277089+0.241612 
## [54] train-rmse:0.054230+0.003797    test-rmse:1.276790+0.241545 
## [55] train-rmse:0.051299+0.003946    test-rmse:1.277106+0.241425 
## [56] train-rmse:0.048884+0.004093    test-rmse:1.276631+0.242283 
## [57] train-rmse:0.046063+0.004741    test-rmse:1.276841+0.242236 
## [58] train-rmse:0.043037+0.003390    test-rmse:1.276299+0.242094 
## [59] train-rmse:0.040142+0.003166    test-rmse:1.276555+0.242147 
## [60] train-rmse:0.038712+0.003059    test-rmse:1.276171+0.242127 
## [61] train-rmse:0.036629+0.002570    test-rmse:1.276013+0.241905 
## [62] train-rmse:0.034440+0.002099    test-rmse:1.275995+0.242357 
## [63] train-rmse:0.032611+0.002278    test-rmse:1.275647+0.242089 
## [64] train-rmse:0.031439+0.002417    test-rmse:1.275797+0.242090 
## [65] train-rmse:0.030042+0.002680    test-rmse:1.275730+0.242189 
## [66] train-rmse:0.028260+0.002504    test-rmse:1.275544+0.242585 
## [67] train-rmse:0.026133+0.002218    test-rmse:1.275393+0.242748 
## [68] train-rmse:0.024832+0.002130    test-rmse:1.275183+0.242935 
## [69] train-rmse:0.023606+0.001974    test-rmse:1.275348+0.242812 
## [70] train-rmse:0.022759+0.001904    test-rmse:1.275086+0.242445 
## [71] train-rmse:0.021249+0.001930    test-rmse:1.274930+0.242185 
## [72] train-rmse:0.019900+0.001452    test-rmse:1.274984+0.242455 
## [73] train-rmse:0.018954+0.001603    test-rmse:1.274941+0.242366 
## [74] train-rmse:0.017881+0.001976    test-rmse:1.274985+0.242093 
## [75] train-rmse:0.017094+0.002222    test-rmse:1.274862+0.242138 
## [76] train-rmse:0.016007+0.002210    test-rmse:1.274516+0.241777 
## [77] train-rmse:0.015158+0.002054    test-rmse:1.274455+0.241984 
## [78] train-rmse:0.014366+0.001822    test-rmse:1.274358+0.241857 
## [79] train-rmse:0.013575+0.001713    test-rmse:1.274349+0.241898 
## [80] train-rmse:0.012780+0.001780    test-rmse:1.274392+0.242092 
## [81] train-rmse:0.012114+0.001612    test-rmse:1.274305+0.242042 
## [82] train-rmse:0.011596+0.001632    test-rmse:1.274055+0.241730 
## [83] train-rmse:0.010974+0.001596    test-rmse:1.273966+0.241513 
## [84] train-rmse:0.010417+0.001599    test-rmse:1.273900+0.241460 
## [85] train-rmse:0.009824+0.001603    test-rmse:1.273794+0.241591 
## [86] train-rmse:0.009431+0.001586    test-rmse:1.273676+0.241700 
## [87] train-rmse:0.008833+0.001548    test-rmse:1.273651+0.241669 
## [88] train-rmse:0.008495+0.001454    test-rmse:1.273657+0.241719 
## [89] train-rmse:0.008087+0.001387    test-rmse:1.273693+0.241721 
## [90] train-rmse:0.007730+0.001363    test-rmse:1.273644+0.241675 
## [91] train-rmse:0.007227+0.001290    test-rmse:1.273677+0.241716 
## [92] train-rmse:0.006851+0.001174    test-rmse:1.273592+0.241665 
## [93] train-rmse:0.006464+0.001046    test-rmse:1.273560+0.241701 
## [94] train-rmse:0.006068+0.000988    test-rmse:1.273615+0.241718 
## [95] train-rmse:0.005788+0.001018    test-rmse:1.273632+0.241697 
## [96] train-rmse:0.005454+0.000928    test-rmse:1.273683+0.241729 
## [97] train-rmse:0.005168+0.000746    test-rmse:1.273664+0.241702 
## [98] train-rmse:0.004830+0.000607    test-rmse:1.273566+0.241747 
## [99] train-rmse:0.004661+0.000654    test-rmse:1.273503+0.241764 
## [100]    train-rmse:0.004385+0.000556    test-rmse:1.273533+0.241867 
## [101]    train-rmse:0.004182+0.000519    test-rmse:1.273526+0.241839 
## [102]    train-rmse:0.004002+0.000512    test-rmse:1.273536+0.241892 
## [103]    train-rmse:0.003786+0.000576    test-rmse:1.273564+0.241825 
## [104]    train-rmse:0.003622+0.000490    test-rmse:1.273577+0.241817 
## [105]    train-rmse:0.003468+0.000485    test-rmse:1.273561+0.241828 
## Stopping. Best iteration:
## [99] train-rmse:0.004661+0.000654    test-rmse:1.273503+0.241764
## 
## 105 
## [1]  train-rmse:64.867792+0.080067   test-rmse:64.867433+0.531460 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:39.277875+0.064950   test-rmse:39.275826+0.593398 
## [3]  train-rmse:24.039232+0.061294   test-rmse:24.078998+0.616566 
## [4]  train-rmse:15.007295+0.063040   test-rmse:15.082740+0.641553 
## [5]  train-rmse:9.743448+0.062204    test-rmse:9.814983+0.574306 
## [6]  train-rmse:6.818003+0.082593    test-rmse:7.006423+0.672639 
## [7]  train-rmse:5.262240+0.088214    test-rmse:5.390812+0.593950 
## [8]  train-rmse:4.478714+0.090422    test-rmse:4.578579+0.532778 
## [9]  train-rmse:4.066379+0.086221    test-rmse:4.200116+0.494194 
## [10] train-rmse:3.843109+0.088901    test-rmse:4.049438+0.484520 
## [11] train-rmse:3.688207+0.081215    test-rmse:3.880304+0.464111 
## [12] train-rmse:3.557695+0.073338    test-rmse:3.689993+0.379638 
## [13] train-rmse:3.454053+0.074399    test-rmse:3.664340+0.373226 
## [14] train-rmse:3.365007+0.068136    test-rmse:3.586174+0.353387 
## [15] train-rmse:3.279407+0.064641    test-rmse:3.542011+0.369460 
## [16] train-rmse:3.198428+0.066570    test-rmse:3.435663+0.306595 
## [17] train-rmse:3.121284+0.065551    test-rmse:3.360941+0.307003 
## [18] train-rmse:3.051216+0.064840    test-rmse:3.277108+0.290249 
## [19] train-rmse:2.980813+0.062330    test-rmse:3.240165+0.306732 
## [20] train-rmse:2.915529+0.059151    test-rmse:3.167373+0.319074 
## [21] train-rmse:2.851848+0.057891    test-rmse:3.047358+0.274213 
## [22] train-rmse:2.793571+0.053203    test-rmse:2.987341+0.301077 
## [23] train-rmse:2.735467+0.048913    test-rmse:2.944828+0.317324 
## [24] train-rmse:2.677798+0.043875    test-rmse:2.895954+0.309338 
## [25] train-rmse:2.623917+0.043047    test-rmse:2.851273+0.308626 
## [26] train-rmse:2.574408+0.044535    test-rmse:2.827182+0.305839 
## [27] train-rmse:2.524875+0.047219    test-rmse:2.807128+0.320298 
## [28] train-rmse:2.478021+0.047599    test-rmse:2.766359+0.340141 
## [29] train-rmse:2.431137+0.048203    test-rmse:2.703248+0.300736 
## [30] train-rmse:2.386926+0.049568    test-rmse:2.673859+0.292170 
## [31] train-rmse:2.344955+0.048117    test-rmse:2.631586+0.303121 
## [32] train-rmse:2.304311+0.046621    test-rmse:2.577089+0.314150 
## [33] train-rmse:2.263940+0.043306    test-rmse:2.544424+0.296319 
## [34] train-rmse:2.225770+0.041836    test-rmse:2.494038+0.274757 
## [35] train-rmse:2.187000+0.039685    test-rmse:2.460237+0.289606 
## [36] train-rmse:2.150448+0.039012    test-rmse:2.441440+0.305047 
## [37] train-rmse:2.116033+0.038786    test-rmse:2.406931+0.315056 
## [38] train-rmse:2.081123+0.038318    test-rmse:2.370072+0.305404 
## [39] train-rmse:2.047269+0.038118    test-rmse:2.335846+0.300274 
## [40] train-rmse:2.014243+0.037492    test-rmse:2.290220+0.314701 
## [41] train-rmse:1.983263+0.035841    test-rmse:2.268463+0.317567 
## [42] train-rmse:1.953227+0.033597    test-rmse:2.218054+0.279344 
## [43] train-rmse:1.923435+0.030685    test-rmse:2.197613+0.280037 
## [44] train-rmse:1.894413+0.028751    test-rmse:2.169942+0.262894 
## [45] train-rmse:1.865635+0.027654    test-rmse:2.116514+0.272412 
## [46] train-rmse:1.837221+0.026401    test-rmse:2.092066+0.262107 
## [47] train-rmse:1.809902+0.025292    test-rmse:2.071455+0.270086 
## [48] train-rmse:1.782897+0.023935    test-rmse:2.022949+0.254177 
## [49] train-rmse:1.756524+0.021823    test-rmse:1.998949+0.247804 
## [50] train-rmse:1.730626+0.020558    test-rmse:1.972211+0.256277 
## [51] train-rmse:1.705621+0.019473    test-rmse:1.947928+0.244113 
## [52] train-rmse:1.680983+0.017647    test-rmse:1.929803+0.250955 
## [53] train-rmse:1.657198+0.017000    test-rmse:1.919253+0.253519 
## [54] train-rmse:1.634050+0.016650    test-rmse:1.908654+0.253141 
## [55] train-rmse:1.611452+0.015951    test-rmse:1.888936+0.258751 
## [56] train-rmse:1.589115+0.016028    test-rmse:1.868933+0.262421 
## [57] train-rmse:1.567604+0.015888    test-rmse:1.847208+0.253882 
## [58] train-rmse:1.547425+0.016117    test-rmse:1.827383+0.267941 
## [59] train-rmse:1.527561+0.016015    test-rmse:1.810094+0.269711 
## [60] train-rmse:1.508342+0.016064    test-rmse:1.779825+0.241248 
## [61] train-rmse:1.490154+0.015650    test-rmse:1.773975+0.239912 
## [62] train-rmse:1.472257+0.015366    test-rmse:1.759056+0.236809 
## [63] train-rmse:1.454546+0.015035    test-rmse:1.736991+0.239194 
## [64] train-rmse:1.436818+0.014694    test-rmse:1.718503+0.242793 
## [65] train-rmse:1.419447+0.014499    test-rmse:1.697629+0.244981 
## [66] train-rmse:1.402558+0.014320    test-rmse:1.695244+0.237893 
## [67] train-rmse:1.386518+0.013821    test-rmse:1.674029+0.229943 
## [68] train-rmse:1.370280+0.012919    test-rmse:1.658343+0.227727 
## [69] train-rmse:1.354973+0.012673    test-rmse:1.642834+0.223514 
## [70] train-rmse:1.340035+0.012682    test-rmse:1.621554+0.226284 
## [71] train-rmse:1.325216+0.012465    test-rmse:1.608442+0.211616 
## [72] train-rmse:1.310594+0.012312    test-rmse:1.584753+0.213895 
## [73] train-rmse:1.297065+0.011726    test-rmse:1.573230+0.218431 
## [74] train-rmse:1.283634+0.011307    test-rmse:1.563275+0.222147 
## [75] train-rmse:1.270243+0.010944    test-rmse:1.553422+0.227261 
## [76] train-rmse:1.257126+0.010298    test-rmse:1.542985+0.223212 
## [77] train-rmse:1.244561+0.009954    test-rmse:1.529139+0.229620 
## [78] train-rmse:1.232355+0.009393    test-rmse:1.519275+0.231918 
## [79] train-rmse:1.220378+0.009259    test-rmse:1.506297+0.232812 
## [80] train-rmse:1.208447+0.009293    test-rmse:1.496791+0.234421 
## [81] train-rmse:1.196315+0.009848    test-rmse:1.486605+0.238619 
## [82] train-rmse:1.184683+0.010046    test-rmse:1.478088+0.234144 
## [83] train-rmse:1.173764+0.010161    test-rmse:1.471920+0.234880 
## [84] train-rmse:1.163025+0.010406    test-rmse:1.447198+0.216227 
## [85] train-rmse:1.152644+0.010844    test-rmse:1.435668+0.217536 
## [86] train-rmse:1.142699+0.010914    test-rmse:1.428987+0.218421 
## [87] train-rmse:1.133243+0.011008    test-rmse:1.424707+0.214572 
## [88] train-rmse:1.123894+0.011115    test-rmse:1.406619+0.217960 
## [89] train-rmse:1.114784+0.011408    test-rmse:1.395620+0.219489 
## [90] train-rmse:1.106113+0.011269    test-rmse:1.391733+0.223978 
## [91] train-rmse:1.097624+0.011235    test-rmse:1.382275+0.215864 
## [92] train-rmse:1.089326+0.011094    test-rmse:1.370628+0.210104 
## [93] train-rmse:1.081052+0.011098    test-rmse:1.366639+0.214347 
## [94] train-rmse:1.072819+0.011177    test-rmse:1.356753+0.215670 
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## [303]    train-rmse:0.686203+0.009701    test-rmse:1.027179+0.141452 
## [304]    train-rmse:0.685749+0.009668    test-rmse:1.026730+0.142438 
## [305]    train-rmse:0.685315+0.009603    test-rmse:1.027731+0.142353 
## [306]    train-rmse:0.684877+0.009606    test-rmse:1.025829+0.144448 
## [307]    train-rmse:0.684449+0.009555    test-rmse:1.025981+0.142126 
## [308]    train-rmse:0.684023+0.009551    test-rmse:1.027164+0.141699 
## Stopping. Best iteration:
## [302]    train-rmse:0.686608+0.009723    test-rmse:1.025116+0.141248
## 
## 106 
## [1]  train-rmse:64.867792+0.080067   test-rmse:64.867433+0.531460 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:39.277875+0.064950   test-rmse:39.275826+0.593398 
## [3]  train-rmse:24.039232+0.061294   test-rmse:24.078998+0.616566 
## [4]  train-rmse:14.940199+0.046990   test-rmse:14.974273+0.552000 
## [5]  train-rmse:9.585281+0.053434    test-rmse:9.700390+0.570676 
## [6]  train-rmse:6.518026+0.049196    test-rmse:6.608745+0.536180 
## [7]  train-rmse:4.852392+0.059848    test-rmse:5.020800+0.515698 
## [8]  train-rmse:3.963605+0.062512    test-rmse:4.201099+0.550865 
## [9]  train-rmse:3.481845+0.063508    test-rmse:3.748229+0.430303 
## [10] train-rmse:3.189518+0.064596    test-rmse:3.462655+0.350944 
## [11] train-rmse:2.998902+0.058792    test-rmse:3.304467+0.356085 
## [12] train-rmse:2.849492+0.054193    test-rmse:3.129424+0.312733 
## [13] train-rmse:2.716292+0.035369    test-rmse:2.997260+0.288930 
## [14] train-rmse:2.598070+0.037450    test-rmse:2.893538+0.310339 
## [15] train-rmse:2.486668+0.037024    test-rmse:2.815545+0.304733 
## [16] train-rmse:2.393450+0.034839    test-rmse:2.743603+0.302085 
## [17] train-rmse:2.304607+0.034043    test-rmse:2.662338+0.320142 
## [18] train-rmse:2.220269+0.029454    test-rmse:2.571134+0.278355 
## [19] train-rmse:2.139231+0.026696    test-rmse:2.508175+0.288218 
## [20] train-rmse:2.049134+0.021026    test-rmse:2.428852+0.279661 
## [21] train-rmse:1.979563+0.023564    test-rmse:2.363170+0.257271 
## [22] train-rmse:1.912900+0.022598    test-rmse:2.298464+0.236416 
## [23] train-rmse:1.845518+0.024437    test-rmse:2.232612+0.256173 
## [24] train-rmse:1.784493+0.026761    test-rmse:2.162470+0.260747 
## [25] train-rmse:1.726320+0.027351    test-rmse:2.094732+0.244323 
## [26] train-rmse:1.671957+0.026904    test-rmse:2.061921+0.246137 
## [27] train-rmse:1.615305+0.021500    test-rmse:2.022717+0.255955 
## [28] train-rmse:1.563454+0.020352    test-rmse:1.973122+0.245889 
## [29] train-rmse:1.514731+0.023258    test-rmse:1.952453+0.246900 
## [30] train-rmse:1.468961+0.022393    test-rmse:1.924207+0.257768 
## [31] train-rmse:1.424475+0.019609    test-rmse:1.878491+0.242460 
## [32] train-rmse:1.384636+0.019717    test-rmse:1.828451+0.248430 
## [33] train-rmse:1.346729+0.019616    test-rmse:1.791499+0.243094 
## [34] train-rmse:1.307419+0.016469    test-rmse:1.746705+0.235497 
## [35] train-rmse:1.271237+0.015808    test-rmse:1.724017+0.247292 
## [36] train-rmse:1.235691+0.017543    test-rmse:1.698159+0.246115 
## [37] train-rmse:1.200589+0.016822    test-rmse:1.671004+0.243285 
## [38] train-rmse:1.171414+0.016615    test-rmse:1.640928+0.253615 
## [39] train-rmse:1.141136+0.015652    test-rmse:1.622909+0.258095 
## [40] train-rmse:1.113667+0.015621    test-rmse:1.605193+0.260400 
## [41] train-rmse:1.085740+0.015218    test-rmse:1.580520+0.246648 
## [42] train-rmse:1.057267+0.019118    test-rmse:1.567442+0.249673 
## [43] train-rmse:1.032158+0.017849    test-rmse:1.544739+0.246327 
## [44] train-rmse:1.009049+0.016443    test-rmse:1.513807+0.243017 
## [45] train-rmse:0.986758+0.017524    test-rmse:1.489545+0.244770 
## [46] train-rmse:0.966374+0.016432    test-rmse:1.472166+0.245726 
## [47] train-rmse:0.945181+0.018245    test-rmse:1.456078+0.245024 
## [48] train-rmse:0.926231+0.017514    test-rmse:1.440219+0.244402 
## [49] train-rmse:0.908266+0.019041    test-rmse:1.429783+0.247529 
## [50] train-rmse:0.890834+0.018833    test-rmse:1.417491+0.251801 
## [51] train-rmse:0.872738+0.020179    test-rmse:1.413095+0.254197 
## [52] train-rmse:0.852986+0.022012    test-rmse:1.395333+0.255737 
## [53] train-rmse:0.838485+0.022075    test-rmse:1.384499+0.256823 
## [54] train-rmse:0.824448+0.022169    test-rmse:1.370827+0.248262 
## [55] train-rmse:0.810584+0.022361    test-rmse:1.357847+0.232917 
## [56] train-rmse:0.798085+0.021663    test-rmse:1.344506+0.240934 
## [57] train-rmse:0.780886+0.021733    test-rmse:1.327375+0.242806 
## [58] train-rmse:0.768282+0.021209    test-rmse:1.316500+0.245032 
## [59] train-rmse:0.754350+0.018462    test-rmse:1.311005+0.245557 
## [60] train-rmse:0.743090+0.017898    test-rmse:1.301243+0.246717 
## [61] train-rmse:0.730031+0.015860    test-rmse:1.294025+0.243043 
## [62] train-rmse:0.716784+0.019154    test-rmse:1.285828+0.247984 
## [63] train-rmse:0.705942+0.017672    test-rmse:1.270787+0.250090 
## [64] train-rmse:0.695643+0.016678    test-rmse:1.264621+0.253794 
## [65] train-rmse:0.686671+0.016967    test-rmse:1.260152+0.254949 
## [66] train-rmse:0.676978+0.014962    test-rmse:1.251082+0.254507 
## [67] train-rmse:0.664734+0.016647    test-rmse:1.243637+0.253283 
## [68] train-rmse:0.653808+0.017514    test-rmse:1.237225+0.248664 
## [69] train-rmse:0.645685+0.016179    test-rmse:1.230497+0.250119 
## [70] train-rmse:0.636392+0.016801    test-rmse:1.226338+0.249460 
## [71] train-rmse:0.629240+0.016712    test-rmse:1.221365+0.252915 
## [72] train-rmse:0.618309+0.015986    test-rmse:1.223580+0.256851 
## [73] train-rmse:0.611418+0.016806    test-rmse:1.222977+0.258766 
## [74] train-rmse:0.603425+0.017110    test-rmse:1.220692+0.257342 
## [75] train-rmse:0.596941+0.015902    test-rmse:1.217624+0.259788 
## [76] train-rmse:0.590783+0.015302    test-rmse:1.203347+0.263832 
## [77] train-rmse:0.585553+0.015129    test-rmse:1.197653+0.263038 
## [78] train-rmse:0.579745+0.013803    test-rmse:1.194366+0.263848 
## [79] train-rmse:0.574691+0.013840    test-rmse:1.191844+0.263181 
## [80] train-rmse:0.568143+0.014791    test-rmse:1.185838+0.266110 
## [81] train-rmse:0.559537+0.014186    test-rmse:1.181399+0.267267 
## [82] train-rmse:0.554059+0.013818    test-rmse:1.180194+0.267984 
## [83] train-rmse:0.546419+0.010149    test-rmse:1.173597+0.268529 
## [84] train-rmse:0.539987+0.012696    test-rmse:1.171476+0.268226 
## [85] train-rmse:0.533965+0.011210    test-rmse:1.165224+0.271542 
## [86] train-rmse:0.528512+0.011633    test-rmse:1.161069+0.268437 
## [87] train-rmse:0.522711+0.010132    test-rmse:1.157001+0.259742 
## [88] train-rmse:0.518283+0.009716    test-rmse:1.154085+0.261866 
## [89] train-rmse:0.513010+0.012549    test-rmse:1.148808+0.246259 
## [90] train-rmse:0.509306+0.013249    test-rmse:1.146348+0.247340 
## [91] train-rmse:0.505089+0.014092    test-rmse:1.145180+0.246896 
## [92] train-rmse:0.500262+0.015991    test-rmse:1.140458+0.245442 
## [93] train-rmse:0.495835+0.016737    test-rmse:1.138630+0.247414 
## [94] train-rmse:0.491737+0.016362    test-rmse:1.135183+0.249575 
## [95] train-rmse:0.486817+0.015009    test-rmse:1.130901+0.248954 
## [96] train-rmse:0.480437+0.013360    test-rmse:1.124431+0.254615 
## [97] train-rmse:0.475586+0.013940    test-rmse:1.123760+0.257042 
## [98] train-rmse:0.470731+0.015726    test-rmse:1.122526+0.257324 
## [99] train-rmse:0.465309+0.015629    test-rmse:1.117846+0.256637 
## [100]    train-rmse:0.461805+0.015332    test-rmse:1.115308+0.260150 
## [101]    train-rmse:0.456681+0.015388    test-rmse:1.111413+0.263308 
## [102]    train-rmse:0.451790+0.013451    test-rmse:1.112219+0.262921 
## [103]    train-rmse:0.447365+0.013286    test-rmse:1.108119+0.264510 
## [104]    train-rmse:0.443979+0.013181    test-rmse:1.108597+0.265666 
## [105]    train-rmse:0.440699+0.014079    test-rmse:1.107122+0.266893 
## [106]    train-rmse:0.438107+0.014436    test-rmse:1.106895+0.265373 
## [107]    train-rmse:0.433557+0.014135    test-rmse:1.105553+0.264630 
## [108]    train-rmse:0.430680+0.013886    test-rmse:1.105091+0.264652 
## [109]    train-rmse:0.424772+0.011234    test-rmse:1.104105+0.264087 
## [110]    train-rmse:0.421962+0.011625    test-rmse:1.103299+0.263222 
## [111]    train-rmse:0.419352+0.011486    test-rmse:1.103067+0.260735 
## [112]    train-rmse:0.416387+0.012066    test-rmse:1.101594+0.261321 
## [113]    train-rmse:0.413552+0.012575    test-rmse:1.099465+0.262310 
## [114]    train-rmse:0.411560+0.012966    test-rmse:1.097232+0.263027 
## [115]    train-rmse:0.408162+0.013011    test-rmse:1.096673+0.263906 
## [116]    train-rmse:0.406136+0.013389    test-rmse:1.097169+0.268877 
## [117]    train-rmse:0.403048+0.014429    test-rmse:1.099820+0.268994 
## [118]    train-rmse:0.400348+0.014227    test-rmse:1.096535+0.268257 
## [119]    train-rmse:0.395935+0.013134    test-rmse:1.096255+0.266837 
## [120]    train-rmse:0.392753+0.012214    test-rmse:1.096906+0.267405 
## [121]    train-rmse:0.390808+0.012394    test-rmse:1.096492+0.267464 
## [122]    train-rmse:0.387082+0.012284    test-rmse:1.091299+0.268895 
## [123]    train-rmse:0.383881+0.012887    test-rmse:1.092247+0.269473 
## [124]    train-rmse:0.381233+0.012183    test-rmse:1.089531+0.269937 
## [125]    train-rmse:0.378174+0.011420    test-rmse:1.089542+0.269629 
## [126]    train-rmse:0.376345+0.011883    test-rmse:1.087966+0.268157 
## [127]    train-rmse:0.373936+0.012533    test-rmse:1.084395+0.268900 
## [128]    train-rmse:0.372239+0.012740    test-rmse:1.083591+0.269046 
## [129]    train-rmse:0.368970+0.012061    test-rmse:1.081138+0.270212 
## [130]    train-rmse:0.366751+0.011410    test-rmse:1.080402+0.270872 
## [131]    train-rmse:0.364607+0.010859    test-rmse:1.079563+0.271099 
## [132]    train-rmse:0.362998+0.010584    test-rmse:1.078169+0.271222 
## [133]    train-rmse:0.361121+0.010785    test-rmse:1.076964+0.271831 
## [134]    train-rmse:0.357207+0.011286    test-rmse:1.073106+0.270285 
## [135]    train-rmse:0.355523+0.011553    test-rmse:1.071365+0.269541 
## [136]    train-rmse:0.353940+0.011805    test-rmse:1.072485+0.269028 
## [137]    train-rmse:0.351998+0.011489    test-rmse:1.071841+0.268722 
## [138]    train-rmse:0.350031+0.011301    test-rmse:1.071314+0.268846 
## [139]    train-rmse:0.348002+0.012048    test-rmse:1.072811+0.268438 
## [140]    train-rmse:0.344329+0.012375    test-rmse:1.071306+0.267944 
## [141]    train-rmse:0.342204+0.012256    test-rmse:1.070726+0.267580 
## [142]    train-rmse:0.340321+0.012746    test-rmse:1.071338+0.267767 
## [143]    train-rmse:0.338106+0.012888    test-rmse:1.071174+0.267835 
## [144]    train-rmse:0.335755+0.012846    test-rmse:1.072043+0.268477 
## [145]    train-rmse:0.333908+0.012287    test-rmse:1.070259+0.268097 
## [146]    train-rmse:0.331699+0.011396    test-rmse:1.071108+0.267775 
## [147]    train-rmse:0.329929+0.010364    test-rmse:1.068888+0.269297 
## [148]    train-rmse:0.328012+0.009904    test-rmse:1.068122+0.268291 
## [149]    train-rmse:0.325732+0.009870    test-rmse:1.068593+0.269560 
## [150]    train-rmse:0.324649+0.009690    test-rmse:1.068970+0.269326 
## [151]    train-rmse:0.322769+0.009257    test-rmse:1.067431+0.270082 
## [152]    train-rmse:0.321299+0.009486    test-rmse:1.066250+0.269844 
## [153]    train-rmse:0.318850+0.009062    test-rmse:1.065959+0.270371 
## [154]    train-rmse:0.316480+0.009008    test-rmse:1.065632+0.270488 
## [155]    train-rmse:0.313886+0.009224    test-rmse:1.065388+0.268370 
## [156]    train-rmse:0.312430+0.008817    test-rmse:1.064245+0.267891 
## [157]    train-rmse:0.310324+0.009202    test-rmse:1.064251+0.267685 
## [158]    train-rmse:0.308687+0.009110    test-rmse:1.063350+0.268486 
## [159]    train-rmse:0.306927+0.008992    test-rmse:1.063220+0.269233 
## [160]    train-rmse:0.305271+0.008745    test-rmse:1.062284+0.269599 
## [161]    train-rmse:0.302661+0.007590    test-rmse:1.059787+0.270846 
## [162]    train-rmse:0.300666+0.006658    test-rmse:1.061121+0.271542 
## [163]    train-rmse:0.298810+0.006193    test-rmse:1.059093+0.273102 
## [164]    train-rmse:0.297145+0.007078    test-rmse:1.058768+0.273158 
## [165]    train-rmse:0.295324+0.007842    test-rmse:1.058817+0.272707 
## [166]    train-rmse:0.294381+0.007950    test-rmse:1.058262+0.273142 
## [167]    train-rmse:0.292457+0.008097    test-rmse:1.058150+0.273252 
## [168]    train-rmse:0.290744+0.007815    test-rmse:1.057769+0.273369 
## [169]    train-rmse:0.289242+0.007858    test-rmse:1.057247+0.273758 
## [170]    train-rmse:0.287982+0.008296    test-rmse:1.057243+0.274065 
## [171]    train-rmse:0.286274+0.008148    test-rmse:1.055910+0.273975 
## [172]    train-rmse:0.284567+0.008991    test-rmse:1.055111+0.274953 
## [173]    train-rmse:0.282776+0.008713    test-rmse:1.054238+0.274125 
## [174]    train-rmse:0.280536+0.009546    test-rmse:1.054593+0.275605 
## [175]    train-rmse:0.279143+0.009371    test-rmse:1.054400+0.275912 
## [176]    train-rmse:0.277397+0.009183    test-rmse:1.054819+0.275938 
## [177]    train-rmse:0.276002+0.009528    test-rmse:1.055964+0.277329 
## [178]    train-rmse:0.274435+0.009800    test-rmse:1.054983+0.275529 
## [179]    train-rmse:0.272830+0.010208    test-rmse:1.054776+0.275245 
## Stopping. Best iteration:
## [173]    train-rmse:0.282776+0.008713    test-rmse:1.054238+0.274125
## 
## 107 
## [1]  train-rmse:64.867792+0.080067   test-rmse:64.867433+0.531460 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:39.277875+0.064950   test-rmse:39.275826+0.593398 
## [3]  train-rmse:24.039232+0.061294   test-rmse:24.078998+0.616566 
## [4]  train-rmse:14.913497+0.045319   test-rmse:15.010122+0.558959 
## [5]  train-rmse:9.495933+0.040103    test-rmse:9.604309+0.555339 
## [6]  train-rmse:6.359953+0.050142    test-rmse:6.493432+0.588806 
## [7]  train-rmse:4.560693+0.057036    test-rmse:4.788800+0.550935 
## [8]  train-rmse:3.559187+0.072099    test-rmse:3.786533+0.377381 
## [9]  train-rmse:3.010500+0.048581    test-rmse:3.261072+0.315330 
## [10] train-rmse:2.687685+0.046907    test-rmse:2.982347+0.296704 
## [11] train-rmse:2.431635+0.055650    test-rmse:2.797974+0.250835 
## [12] train-rmse:2.267550+0.048824    test-rmse:2.665361+0.285845 
## [13] train-rmse:2.132495+0.047260    test-rmse:2.546679+0.294611 
## [14] train-rmse:2.006589+0.043217    test-rmse:2.417532+0.259323 
## [15] train-rmse:1.883681+0.039198    test-rmse:2.347059+0.255381 
## [16] train-rmse:1.786780+0.035558    test-rmse:2.262379+0.250112 
## [17] train-rmse:1.692538+0.034884    test-rmse:2.162034+0.223140 
## [18] train-rmse:1.605955+0.029435    test-rmse:2.087282+0.256595 
## [19] train-rmse:1.523440+0.031390    test-rmse:2.020180+0.228164 
## [20] train-rmse:1.451125+0.030971    test-rmse:1.947218+0.236609 
## [21] train-rmse:1.384015+0.027703    test-rmse:1.909020+0.226334 
## [22] train-rmse:1.322534+0.026233    test-rmse:1.861525+0.231371 
## [23] train-rmse:1.262500+0.023485    test-rmse:1.817961+0.227073 
## [24] train-rmse:1.205981+0.022579    test-rmse:1.769406+0.240294 
## [25] train-rmse:1.154739+0.022839    test-rmse:1.717176+0.233734 
## [26] train-rmse:1.103316+0.026274    test-rmse:1.669878+0.224679 
## [27] train-rmse:1.057858+0.025193    test-rmse:1.632339+0.216763 
## [28] train-rmse:1.013523+0.026276    test-rmse:1.619996+0.207156 
## [29] train-rmse:0.974896+0.029568    test-rmse:1.595090+0.213549 
## [30] train-rmse:0.938303+0.028197    test-rmse:1.555008+0.227777 
## [31] train-rmse:0.897559+0.025233    test-rmse:1.522099+0.235724 
## [32] train-rmse:0.864147+0.027961    test-rmse:1.503079+0.245483 
## [33] train-rmse:0.832648+0.027034    test-rmse:1.490651+0.243299 
## [34] train-rmse:0.805749+0.028144    test-rmse:1.457346+0.244792 
## [35] train-rmse:0.781278+0.027815    test-rmse:1.448038+0.243189 
## [36] train-rmse:0.755580+0.027030    test-rmse:1.435992+0.241506 
## [37] train-rmse:0.727049+0.029261    test-rmse:1.413419+0.235718 
## [38] train-rmse:0.705689+0.029240    test-rmse:1.395492+0.235412 
## [39] train-rmse:0.679745+0.019973    test-rmse:1.375566+0.235883 
## [40] train-rmse:0.660985+0.018660    test-rmse:1.351490+0.240557 
## [41] train-rmse:0.642632+0.017973    test-rmse:1.341149+0.244945 
## [42] train-rmse:0.625588+0.018792    test-rmse:1.339422+0.249789 
## [43] train-rmse:0.608806+0.019298    test-rmse:1.330757+0.256954 
## [44] train-rmse:0.592594+0.021679    test-rmse:1.321009+0.254992 
## [45] train-rmse:0.575680+0.019726    test-rmse:1.305861+0.260580 
## [46] train-rmse:0.561747+0.018374    test-rmse:1.296615+0.264758 
## [47] train-rmse:0.548160+0.017727    test-rmse:1.295627+0.258809 
## [48] train-rmse:0.536470+0.017875    test-rmse:1.292531+0.254050 
## [49] train-rmse:0.524465+0.017926    test-rmse:1.279551+0.261605 
## [50] train-rmse:0.513088+0.017731    test-rmse:1.266856+0.266130 
## [51] train-rmse:0.500582+0.016758    test-rmse:1.262313+0.268707 
## [52] train-rmse:0.490094+0.017300    test-rmse:1.258686+0.272900 
## [53] train-rmse:0.479184+0.018544    test-rmse:1.254360+0.272557 
## [54] train-rmse:0.468545+0.019495    test-rmse:1.248724+0.277553 
## [55] train-rmse:0.460051+0.021221    test-rmse:1.246481+0.279792 
## [56] train-rmse:0.449316+0.023758    test-rmse:1.244524+0.282910 
## [57] train-rmse:0.439577+0.025061    test-rmse:1.241083+0.286946 
## [58] train-rmse:0.431248+0.023856    test-rmse:1.234458+0.277387 
## [59] train-rmse:0.423655+0.024518    test-rmse:1.229456+0.283643 
## [60] train-rmse:0.416023+0.026814    test-rmse:1.228388+0.284606 
## [61] train-rmse:0.402854+0.017042    test-rmse:1.225778+0.284512 
## [62] train-rmse:0.396450+0.015925    test-rmse:1.225030+0.283175 
## [63] train-rmse:0.390091+0.017233    test-rmse:1.219811+0.280096 
## [64] train-rmse:0.381489+0.017690    test-rmse:1.219295+0.280799 
## [65] train-rmse:0.372848+0.016748    test-rmse:1.212614+0.284124 
## [66] train-rmse:0.366357+0.018920    test-rmse:1.202083+0.276133 
## [67] train-rmse:0.359441+0.019328    test-rmse:1.196546+0.274278 
## [68] train-rmse:0.353630+0.018637    test-rmse:1.197537+0.277488 
## [69] train-rmse:0.347024+0.016047    test-rmse:1.195464+0.278211 
## [70] train-rmse:0.342791+0.016801    test-rmse:1.190199+0.281450 
## [71] train-rmse:0.336148+0.016557    test-rmse:1.188549+0.283014 
## [72] train-rmse:0.331372+0.015501    test-rmse:1.188695+0.284182 
## [73] train-rmse:0.326479+0.015139    test-rmse:1.187519+0.283325 
## [74] train-rmse:0.321152+0.014636    test-rmse:1.187635+0.286420 
## [75] train-rmse:0.315141+0.015859    test-rmse:1.185705+0.285941 
## [76] train-rmse:0.310190+0.015292    test-rmse:1.183940+0.286749 
## [77] train-rmse:0.304101+0.015938    test-rmse:1.179864+0.286216 
## [78] train-rmse:0.298466+0.015644    test-rmse:1.181907+0.286228 
## [79] train-rmse:0.292465+0.012704    test-rmse:1.177162+0.283529 
## [80] train-rmse:0.287751+0.012686    test-rmse:1.176391+0.282954 
## [81] train-rmse:0.283952+0.013217    test-rmse:1.175928+0.283044 
## [82] train-rmse:0.280269+0.014138    test-rmse:1.174246+0.282661 
## [83] train-rmse:0.274362+0.013800    test-rmse:1.172696+0.282859 
## [84] train-rmse:0.269209+0.015225    test-rmse:1.172231+0.283629 
## [85] train-rmse:0.265352+0.014751    test-rmse:1.167542+0.284118 
## [86] train-rmse:0.260274+0.015062    test-rmse:1.165223+0.284361 
## [87] train-rmse:0.256458+0.012771    test-rmse:1.162582+0.285049 
## [88] train-rmse:0.253566+0.013604    test-rmse:1.159876+0.283863 
## [89] train-rmse:0.248291+0.011848    test-rmse:1.158394+0.284556 
## [90] train-rmse:0.243687+0.010529    test-rmse:1.156203+0.285266 
## [91] train-rmse:0.239174+0.008856    test-rmse:1.151365+0.282398 
## [92] train-rmse:0.235901+0.008752    test-rmse:1.151373+0.283430 
## [93] train-rmse:0.233192+0.007936    test-rmse:1.152250+0.283454 
## [94] train-rmse:0.230908+0.007715    test-rmse:1.151777+0.283710 
## [95] train-rmse:0.228718+0.007924    test-rmse:1.151936+0.283652 
## [96] train-rmse:0.225510+0.008583    test-rmse:1.151765+0.283461 
## [97] train-rmse:0.220954+0.009218    test-rmse:1.150460+0.283221 
## [98] train-rmse:0.218094+0.009350    test-rmse:1.151759+0.282082 
## [99] train-rmse:0.214399+0.009357    test-rmse:1.151847+0.281531 
## [100]    train-rmse:0.212430+0.010295    test-rmse:1.150991+0.282394 
## [101]    train-rmse:0.209343+0.011859    test-rmse:1.150942+0.284448 
## [102]    train-rmse:0.206253+0.012349    test-rmse:1.150518+0.282853 
## [103]    train-rmse:0.203013+0.012129    test-rmse:1.151352+0.282262 
## Stopping. Best iteration:
## [97] train-rmse:0.220954+0.009218    test-rmse:1.150460+0.283221
## 
## 108 
## [1]  train-rmse:64.867792+0.080067   test-rmse:64.867433+0.531460 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:39.277875+0.064950   test-rmse:39.275826+0.593398 
## [3]  train-rmse:24.039232+0.061294   test-rmse:24.078998+0.616566 
## [4]  train-rmse:14.908946+0.045214   test-rmse:14.982769+0.547179 
## [5]  train-rmse:9.449514+0.037624    test-rmse:9.518927+0.487140 
## [6]  train-rmse:6.238915+0.035866    test-rmse:6.388581+0.566298 
## [7]  train-rmse:4.367854+0.048289    test-rmse:4.522701+0.472314 
## [8]  train-rmse:3.265791+0.038316    test-rmse:3.592969+0.399628 
## [9]  train-rmse:2.637027+0.061847    test-rmse:2.980752+0.311731 
## [10] train-rmse:2.265219+0.057912    test-rmse:2.680453+0.242036 
## [11] train-rmse:2.020091+0.047759    test-rmse:2.458579+0.225992 
## [12] train-rmse:1.847392+0.040731    test-rmse:2.279940+0.199903 
## [13] train-rmse:1.698562+0.029493    test-rmse:2.167111+0.222190 
## [14] train-rmse:1.574502+0.024196    test-rmse:2.074591+0.214054 
## [15] train-rmse:1.469550+0.026453    test-rmse:1.979424+0.195398 
## [16] train-rmse:1.359503+0.026975    test-rmse:1.917652+0.191612 
## [17] train-rmse:1.268450+0.028423    test-rmse:1.840535+0.180490 
## [18] train-rmse:1.191121+0.023954    test-rmse:1.770778+0.186521 
## [19] train-rmse:1.113045+0.017981    test-rmse:1.690347+0.173707 
## [20] train-rmse:1.051879+0.020935    test-rmse:1.639548+0.158963 
## [21] train-rmse:0.993436+0.023816    test-rmse:1.592912+0.161793 
## [22] train-rmse:0.933015+0.026039    test-rmse:1.554765+0.156064 
## [23] train-rmse:0.883243+0.024899    test-rmse:1.522729+0.157295 
## [24] train-rmse:0.842883+0.023055    test-rmse:1.483799+0.166012 
## [25] train-rmse:0.799626+0.021840    test-rmse:1.461472+0.171365 
## [26] train-rmse:0.761565+0.022107    test-rmse:1.435630+0.177623 
## [27] train-rmse:0.723478+0.021100    test-rmse:1.420647+0.170291 
## [28] train-rmse:0.689298+0.015654    test-rmse:1.407624+0.177113 
## [29] train-rmse:0.660449+0.013013    test-rmse:1.392026+0.177760 
## [30] train-rmse:0.629639+0.013063    test-rmse:1.367559+0.180392 
## [31] train-rmse:0.603317+0.011836    test-rmse:1.360342+0.179910 
## [32] train-rmse:0.579031+0.012859    test-rmse:1.357685+0.174702 
## [33] train-rmse:0.555423+0.011239    test-rmse:1.342913+0.188469 
## [34] train-rmse:0.534486+0.010203    test-rmse:1.334490+0.193518 
## [35] train-rmse:0.515636+0.007394    test-rmse:1.318100+0.195902 
## [36] train-rmse:0.498109+0.009226    test-rmse:1.311872+0.194629 
## [37] train-rmse:0.478753+0.010785    test-rmse:1.307309+0.197705 
## [38] train-rmse:0.461470+0.008277    test-rmse:1.301409+0.201479 
## [39] train-rmse:0.443926+0.009274    test-rmse:1.296710+0.203072 
## [40] train-rmse:0.430900+0.010689    test-rmse:1.281336+0.199474 
## [41] train-rmse:0.416815+0.013353    test-rmse:1.266887+0.208012 
## [42] train-rmse:0.404988+0.012375    test-rmse:1.266511+0.208288 
## [43] train-rmse:0.390108+0.009633    test-rmse:1.258867+0.208800 
## [44] train-rmse:0.380111+0.009622    test-rmse:1.254457+0.207692 
## [45] train-rmse:0.365582+0.011558    test-rmse:1.250089+0.201497 
## [46] train-rmse:0.355087+0.014195    test-rmse:1.248140+0.202297 
## [47] train-rmse:0.343651+0.012413    test-rmse:1.243499+0.204969 
## [48] train-rmse:0.335113+0.012615    test-rmse:1.241305+0.205510 
## [49] train-rmse:0.324416+0.010865    test-rmse:1.238270+0.207187 
## [50] train-rmse:0.314763+0.011046    test-rmse:1.238648+0.210696 
## [51] train-rmse:0.306567+0.011828    test-rmse:1.231426+0.215278 
## [52] train-rmse:0.299660+0.011033    test-rmse:1.225623+0.220279 
## [53] train-rmse:0.293422+0.011585    test-rmse:1.218792+0.212586 
## [54] train-rmse:0.282483+0.011155    test-rmse:1.218371+0.212736 
## [55] train-rmse:0.275887+0.010364    test-rmse:1.216299+0.212196 
## [56] train-rmse:0.269930+0.008979    test-rmse:1.214348+0.213111 
## [57] train-rmse:0.262551+0.009772    test-rmse:1.211260+0.217556 
## [58] train-rmse:0.254061+0.009020    test-rmse:1.208850+0.218204 
## [59] train-rmse:0.248358+0.009042    test-rmse:1.208160+0.217758 
## [60] train-rmse:0.243598+0.009585    test-rmse:1.206321+0.218846 
## [61] train-rmse:0.236477+0.007630    test-rmse:1.203627+0.217546 
## [62] train-rmse:0.232277+0.007237    test-rmse:1.200159+0.219381 
## [63] train-rmse:0.224691+0.010392    test-rmse:1.199034+0.223229 
## [64] train-rmse:0.219508+0.010708    test-rmse:1.198946+0.226734 
## [65] train-rmse:0.212303+0.009132    test-rmse:1.194985+0.227478 
## [66] train-rmse:0.206391+0.007398    test-rmse:1.196966+0.226180 
## [67] train-rmse:0.201528+0.006349    test-rmse:1.195567+0.227498 
## [68] train-rmse:0.196135+0.005625    test-rmse:1.196115+0.227624 
## [69] train-rmse:0.189211+0.007796    test-rmse:1.194783+0.226418 
## [70] train-rmse:0.183402+0.008186    test-rmse:1.191767+0.226310 
## [71] train-rmse:0.179032+0.008907    test-rmse:1.191538+0.226682 
## [72] train-rmse:0.173641+0.008352    test-rmse:1.190884+0.221777 
## [73] train-rmse:0.170202+0.008387    test-rmse:1.191216+0.222525 
## [74] train-rmse:0.166479+0.009774    test-rmse:1.191383+0.222768 
## [75] train-rmse:0.161961+0.009729    test-rmse:1.189665+0.220801 
## [76] train-rmse:0.158525+0.009959    test-rmse:1.189222+0.220166 
## [77] train-rmse:0.154085+0.008623    test-rmse:1.190417+0.221989 
## [78] train-rmse:0.150544+0.008649    test-rmse:1.190999+0.223130 
## [79] train-rmse:0.147228+0.008227    test-rmse:1.190817+0.223042 
## [80] train-rmse:0.143948+0.008489    test-rmse:1.190614+0.223993 
## [81] train-rmse:0.140400+0.007965    test-rmse:1.190421+0.224416 
## [82] train-rmse:0.138691+0.007966    test-rmse:1.189646+0.224381 
## Stopping. Best iteration:
## [76] train-rmse:0.158525+0.009959    test-rmse:1.189222+0.220166
## 
## 109 
## [1]  train-rmse:64.867792+0.080067   test-rmse:64.867433+0.531460 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:39.277875+0.064950   test-rmse:39.275826+0.593398 
## [3]  train-rmse:24.039232+0.061294   test-rmse:24.078998+0.616566 
## [4]  train-rmse:14.907449+0.044271   test-rmse:14.976273+0.555419 
## [5]  train-rmse:9.427856+0.036582    test-rmse:9.514035+0.504380 
## [6]  train-rmse:6.165895+0.024853    test-rmse:6.328500+0.569241 
## [7]  train-rmse:4.218326+0.044181    test-rmse:4.401909+0.468903 
## [8]  train-rmse:3.086144+0.046185    test-rmse:3.319613+0.370007 
## [9]  train-rmse:2.417799+0.041790    test-rmse:2.785866+0.321700 
## [10] train-rmse:1.996536+0.028997    test-rmse:2.455169+0.293249 
## [11] train-rmse:1.728731+0.017316    test-rmse:2.198729+0.250665 
## [12] train-rmse:1.530977+0.008217    test-rmse:2.054464+0.250329 
## [13] train-rmse:1.369114+0.024087    test-rmse:1.966980+0.228414 
## [14] train-rmse:1.245966+0.021575    test-rmse:1.840631+0.206659 
## [15] train-rmse:1.138843+0.017737    test-rmse:1.761086+0.204768 
## [16] train-rmse:1.043501+0.013460    test-rmse:1.696021+0.198423 
## [17] train-rmse:0.957245+0.016607    test-rmse:1.651193+0.202627 
## [18] train-rmse:0.881695+0.009672    test-rmse:1.584338+0.214810 
## [19] train-rmse:0.823435+0.008531    test-rmse:1.535244+0.217390 
## [20] train-rmse:0.770902+0.009064    test-rmse:1.499250+0.212065 
## [21] train-rmse:0.715793+0.014084    test-rmse:1.460832+0.212046 
## [22] train-rmse:0.669213+0.010805    test-rmse:1.421293+0.217448 
## [23] train-rmse:0.631119+0.014043    test-rmse:1.399869+0.232648 
## [24] train-rmse:0.587112+0.023572    test-rmse:1.378831+0.237432 
## [25] train-rmse:0.555966+0.021455    test-rmse:1.355696+0.225467 
## [26] train-rmse:0.522550+0.025180    test-rmse:1.341621+0.230947 
## [27] train-rmse:0.497361+0.020359    test-rmse:1.320688+0.234057 
## [28] train-rmse:0.472380+0.016199    test-rmse:1.312676+0.244027 
## [29] train-rmse:0.446639+0.013773    test-rmse:1.299101+0.247992 
## [30] train-rmse:0.430739+0.015796    test-rmse:1.286989+0.257930 
## [31] train-rmse:0.407351+0.017404    test-rmse:1.277502+0.266302 
## [32] train-rmse:0.387608+0.016066    test-rmse:1.272602+0.264209 
## [33] train-rmse:0.371995+0.015967    test-rmse:1.268158+0.265435 
## [34] train-rmse:0.357041+0.016580    test-rmse:1.259743+0.267867 
## [35] train-rmse:0.345178+0.015409    test-rmse:1.251187+0.263101 
## [36] train-rmse:0.330845+0.012763    test-rmse:1.250716+0.263324 
## [37] train-rmse:0.317216+0.011999    test-rmse:1.246082+0.263514 
## [38] train-rmse:0.303046+0.010186    test-rmse:1.242399+0.267486 
## [39] train-rmse:0.291176+0.006928    test-rmse:1.239294+0.270533 
## [40] train-rmse:0.279526+0.008481    test-rmse:1.236631+0.271544 
## [41] train-rmse:0.269121+0.007392    test-rmse:1.236374+0.270232 
## [42] train-rmse:0.259376+0.009205    test-rmse:1.236796+0.272350 
## [43] train-rmse:0.250440+0.008672    test-rmse:1.236019+0.274405 
## [44] train-rmse:0.237333+0.008284    test-rmse:1.232240+0.275875 
## [45] train-rmse:0.228673+0.007733    test-rmse:1.231043+0.279882 
## [46] train-rmse:0.220845+0.007282    test-rmse:1.230085+0.281133 
## [47] train-rmse:0.210746+0.009125    test-rmse:1.225581+0.278873 
## [48] train-rmse:0.201909+0.007433    test-rmse:1.222636+0.279743 
## [49] train-rmse:0.192844+0.008308    test-rmse:1.223108+0.281073 
## [50] train-rmse:0.184911+0.008665    test-rmse:1.222622+0.281313 
## [51] train-rmse:0.177116+0.005337    test-rmse:1.217139+0.276040 
## [52] train-rmse:0.169210+0.007172    test-rmse:1.214916+0.277735 
## [53] train-rmse:0.161968+0.004698    test-rmse:1.214142+0.277209 
## [54] train-rmse:0.154441+0.004846    test-rmse:1.211828+0.276164 
## [55] train-rmse:0.149269+0.004385    test-rmse:1.212236+0.278168 
## [56] train-rmse:0.144868+0.005345    test-rmse:1.212440+0.278313 
## [57] train-rmse:0.140019+0.003624    test-rmse:1.211774+0.279717 
## [58] train-rmse:0.136889+0.003578    test-rmse:1.211423+0.280413 
## [59] train-rmse:0.130981+0.004346    test-rmse:1.210524+0.280586 
## [60] train-rmse:0.127967+0.003969    test-rmse:1.210592+0.280786 
## [61] train-rmse:0.122937+0.004001    test-rmse:1.210403+0.282225 
## [62] train-rmse:0.119308+0.004487    test-rmse:1.209251+0.284157 
## [63] train-rmse:0.114988+0.003676    test-rmse:1.207696+0.285813 
## [64] train-rmse:0.108687+0.003801    test-rmse:1.206719+0.286039 
## [65] train-rmse:0.105557+0.003794    test-rmse:1.204639+0.286839 
## [66] train-rmse:0.100887+0.003376    test-rmse:1.204373+0.287518 
## [67] train-rmse:0.098040+0.002970    test-rmse:1.204623+0.288178 
## [68] train-rmse:0.094810+0.002770    test-rmse:1.204315+0.288833 
## [69] train-rmse:0.091120+0.002507    test-rmse:1.202912+0.290466 
## [70] train-rmse:0.087913+0.002859    test-rmse:1.201811+0.291413 
## [71] train-rmse:0.083944+0.002592    test-rmse:1.201384+0.291418 
## [72] train-rmse:0.081299+0.001777    test-rmse:1.201760+0.291239 
## [73] train-rmse:0.078261+0.001816    test-rmse:1.200786+0.291308 
## [74] train-rmse:0.076111+0.001887    test-rmse:1.201107+0.291895 
## [75] train-rmse:0.074090+0.001665    test-rmse:1.201224+0.292527 
## [76] train-rmse:0.071619+0.001766    test-rmse:1.200457+0.292954 
## [77] train-rmse:0.068765+0.002742    test-rmse:1.199530+0.291609 
## [78] train-rmse:0.066801+0.002219    test-rmse:1.199309+0.292260 
## [79] train-rmse:0.064628+0.002613    test-rmse:1.199070+0.291669 
## [80] train-rmse:0.063381+0.002814    test-rmse:1.198980+0.291840 
## [81] train-rmse:0.061650+0.003009    test-rmse:1.198044+0.291472 
## [82] train-rmse:0.059736+0.003080    test-rmse:1.197776+0.292030 
## [83] train-rmse:0.057668+0.003505    test-rmse:1.197602+0.292721 
## [84] train-rmse:0.055349+0.003316    test-rmse:1.197324+0.292603 
## [85] train-rmse:0.053729+0.003081    test-rmse:1.196609+0.293026 
## [86] train-rmse:0.051757+0.002888    test-rmse:1.195900+0.293886 
## [87] train-rmse:0.050013+0.002264    test-rmse:1.196177+0.294180 
## [88] train-rmse:0.048438+0.001864    test-rmse:1.195801+0.293793 
## [89] train-rmse:0.047127+0.001545    test-rmse:1.195283+0.294293 
## [90] train-rmse:0.045803+0.001850    test-rmse:1.194782+0.293657 
## [91] train-rmse:0.044542+0.001684    test-rmse:1.194511+0.293757 
## [92] train-rmse:0.043284+0.001798    test-rmse:1.195038+0.294190 
## [93] train-rmse:0.041754+0.001827    test-rmse:1.194765+0.294366 
## [94] train-rmse:0.040481+0.002196    test-rmse:1.194929+0.295001 
## [95] train-rmse:0.039067+0.001919    test-rmse:1.194975+0.294901 
## [96] train-rmse:0.037794+0.001662    test-rmse:1.194932+0.295046 
## [97] train-rmse:0.037257+0.001768    test-rmse:1.194504+0.295168 
## [98] train-rmse:0.036101+0.001745    test-rmse:1.194326+0.294709 
## [99] train-rmse:0.035195+0.001790    test-rmse:1.194730+0.294406 
## [100]    train-rmse:0.034098+0.002305    test-rmse:1.194553+0.294455 
## [101]    train-rmse:0.033478+0.002346    test-rmse:1.194526+0.294675 
## [102]    train-rmse:0.032388+0.002118    test-rmse:1.194214+0.294858 
## [103]    train-rmse:0.031116+0.002043    test-rmse:1.193937+0.295179 
## [104]    train-rmse:0.030182+0.002100    test-rmse:1.193694+0.295171 
## [105]    train-rmse:0.029265+0.002164    test-rmse:1.193883+0.295403 
## [106]    train-rmse:0.028543+0.002412    test-rmse:1.193754+0.295065 
## [107]    train-rmse:0.027653+0.002382    test-rmse:1.193381+0.295083 
## [108]    train-rmse:0.026999+0.002347    test-rmse:1.193263+0.295138 
## [109]    train-rmse:0.026370+0.002520    test-rmse:1.192992+0.295235 
## [110]    train-rmse:0.025278+0.002512    test-rmse:1.192866+0.294960 
## [111]    train-rmse:0.024683+0.002577    test-rmse:1.192803+0.295049 
## [112]    train-rmse:0.023962+0.002591    test-rmse:1.192468+0.295257 
## [113]    train-rmse:0.023392+0.002452    test-rmse:1.192356+0.295006 
## [114]    train-rmse:0.022649+0.002536    test-rmse:1.192241+0.294989 
## [115]    train-rmse:0.022034+0.002570    test-rmse:1.192144+0.295063 
## [116]    train-rmse:0.021688+0.002592    test-rmse:1.192053+0.295117 
## [117]    train-rmse:0.021085+0.002382    test-rmse:1.192036+0.295031 
## [118]    train-rmse:0.020638+0.002387    test-rmse:1.191930+0.294967 
## [119]    train-rmse:0.019975+0.002222    test-rmse:1.191881+0.294965 
## [120]    train-rmse:0.019189+0.002044    test-rmse:1.191919+0.295049 
## [121]    train-rmse:0.018495+0.001839    test-rmse:1.192019+0.295220 
## [122]    train-rmse:0.017958+0.001651    test-rmse:1.191993+0.295089 
## [123]    train-rmse:0.017505+0.001682    test-rmse:1.191845+0.295102 
## [124]    train-rmse:0.017058+0.001545    test-rmse:1.191727+0.295192 
## [125]    train-rmse:0.016562+0.001601    test-rmse:1.191545+0.295048 
## [126]    train-rmse:0.016121+0.001643    test-rmse:1.191481+0.295059 
## [127]    train-rmse:0.015502+0.001351    test-rmse:1.191499+0.295154 
## [128]    train-rmse:0.015171+0.001343    test-rmse:1.191459+0.295193 
## [129]    train-rmse:0.014829+0.001366    test-rmse:1.191541+0.295201 
## [130]    train-rmse:0.014328+0.001252    test-rmse:1.191587+0.295103 
## [131]    train-rmse:0.013926+0.001310    test-rmse:1.191333+0.295225 
## [132]    train-rmse:0.013608+0.001258    test-rmse:1.191292+0.295175 
## [133]    train-rmse:0.013304+0.001233    test-rmse:1.191195+0.295293 
## [134]    train-rmse:0.012907+0.001258    test-rmse:1.191240+0.295311 
## [135]    train-rmse:0.012626+0.001365    test-rmse:1.191268+0.295320 
## [136]    train-rmse:0.012369+0.001309    test-rmse:1.191224+0.295343 
## [137]    train-rmse:0.011993+0.001175    test-rmse:1.191178+0.295457 
## [138]    train-rmse:0.011530+0.001187    test-rmse:1.191095+0.295398 
## [139]    train-rmse:0.011189+0.001198    test-rmse:1.191048+0.295387 
## [140]    train-rmse:0.010945+0.001164    test-rmse:1.191063+0.295362 
## [141]    train-rmse:0.010662+0.001209    test-rmse:1.191092+0.295375 
## [142]    train-rmse:0.010347+0.001169    test-rmse:1.191083+0.295306 
## [143]    train-rmse:0.010013+0.001139    test-rmse:1.191053+0.295268 
## [144]    train-rmse:0.009868+0.001141    test-rmse:1.191070+0.295331 
## [145]    train-rmse:0.009501+0.001092    test-rmse:1.190953+0.295247 
## [146]    train-rmse:0.009163+0.001118    test-rmse:1.190895+0.295264 
## [147]    train-rmse:0.008913+0.001004    test-rmse:1.190819+0.295272 
## [148]    train-rmse:0.008683+0.000998    test-rmse:1.190817+0.295237 
## [149]    train-rmse:0.008488+0.001012    test-rmse:1.190733+0.295257 
## [150]    train-rmse:0.008222+0.000924    test-rmse:1.190700+0.295169 
## [151]    train-rmse:0.008032+0.000909    test-rmse:1.190698+0.295159 
## [152]    train-rmse:0.007841+0.000878    test-rmse:1.190635+0.295160 
## [153]    train-rmse:0.007594+0.000831    test-rmse:1.190573+0.295166 
## [154]    train-rmse:0.007322+0.000756    test-rmse:1.190504+0.295171 
## [155]    train-rmse:0.007112+0.000694    test-rmse:1.190443+0.295184 
## [156]    train-rmse:0.006904+0.000669    test-rmse:1.190407+0.295210 
## [157]    train-rmse:0.006734+0.000637    test-rmse:1.190370+0.295230 
## [158]    train-rmse:0.006545+0.000610    test-rmse:1.190343+0.295249 
## [159]    train-rmse:0.006376+0.000552    test-rmse:1.190342+0.295274 
## [160]    train-rmse:0.006196+0.000500    test-rmse:1.190368+0.295265 
## [161]    train-rmse:0.005963+0.000443    test-rmse:1.190325+0.295265 
## [162]    train-rmse:0.005774+0.000395    test-rmse:1.190275+0.295265 
## [163]    train-rmse:0.005672+0.000385    test-rmse:1.190257+0.295254 
## [164]    train-rmse:0.005559+0.000392    test-rmse:1.190228+0.295251 
## [165]    train-rmse:0.005422+0.000373    test-rmse:1.190240+0.295307 
## [166]    train-rmse:0.005311+0.000340    test-rmse:1.190196+0.295269 
## [167]    train-rmse:0.005142+0.000373    test-rmse:1.190184+0.295258 
## [168]    train-rmse:0.005001+0.000318    test-rmse:1.190202+0.295297 
## [169]    train-rmse:0.004842+0.000298    test-rmse:1.190189+0.295275 
## [170]    train-rmse:0.004714+0.000283    test-rmse:1.190189+0.295257 
## [171]    train-rmse:0.004576+0.000287    test-rmse:1.190121+0.295242 
## [172]    train-rmse:0.004441+0.000261    test-rmse:1.190163+0.295253 
## [173]    train-rmse:0.004274+0.000252    test-rmse:1.190148+0.295251 
## [174]    train-rmse:0.004155+0.000222    test-rmse:1.190135+0.295244 
## [175]    train-rmse:0.004047+0.000195    test-rmse:1.190116+0.295238 
## [176]    train-rmse:0.003927+0.000167    test-rmse:1.190084+0.295269 
## [177]    train-rmse:0.003838+0.000189    test-rmse:1.190071+0.295289 
## [178]    train-rmse:0.003753+0.000204    test-rmse:1.190040+0.295299 
## [179]    train-rmse:0.003652+0.000171    test-rmse:1.189996+0.295278 
## [180]    train-rmse:0.003545+0.000154    test-rmse:1.189974+0.295338 
## [181]    train-rmse:0.003428+0.000151    test-rmse:1.189992+0.295342 
## [182]    train-rmse:0.003314+0.000157    test-rmse:1.190007+0.295362 
## [183]    train-rmse:0.003195+0.000186    test-rmse:1.189964+0.295395 
## [184]    train-rmse:0.003122+0.000184    test-rmse:1.189938+0.295411 
## [185]    train-rmse:0.003046+0.000197    test-rmse:1.189964+0.295460 
## [186]    train-rmse:0.002959+0.000212    test-rmse:1.189963+0.295452 
## [187]    train-rmse:0.002878+0.000222    test-rmse:1.189955+0.295451 
## [188]    train-rmse:0.002796+0.000241    test-rmse:1.189976+0.295470 
## [189]    train-rmse:0.002728+0.000230    test-rmse:1.189968+0.295486 
## [190]    train-rmse:0.002668+0.000224    test-rmse:1.189933+0.295507 
## [191]    train-rmse:0.002579+0.000218    test-rmse:1.189926+0.295481 
## [192]    train-rmse:0.002492+0.000199    test-rmse:1.189924+0.295490 
## [193]    train-rmse:0.002427+0.000197    test-rmse:1.189898+0.295494 
## [194]    train-rmse:0.002330+0.000185    test-rmse:1.189896+0.295496 
## [195]    train-rmse:0.002274+0.000170    test-rmse:1.189889+0.295492 
## [196]    train-rmse:0.002211+0.000168    test-rmse:1.189892+0.295493 
## [197]    train-rmse:0.002159+0.000182    test-rmse:1.189879+0.295486 
## [198]    train-rmse:0.002104+0.000183    test-rmse:1.189866+0.295498 
## [199]    train-rmse:0.002033+0.000168    test-rmse:1.189878+0.295515 
## [200]    train-rmse:0.001977+0.000166    test-rmse:1.189868+0.295510 
## [201]    train-rmse:0.001901+0.000163    test-rmse:1.189851+0.295491 
## [202]    train-rmse:0.001867+0.000142    test-rmse:1.189846+0.295486 
## [203]    train-rmse:0.001816+0.000138    test-rmse:1.189843+0.295493 
## [204]    train-rmse:0.001774+0.000122    test-rmse:1.189830+0.295486 
## [205]    train-rmse:0.001736+0.000111    test-rmse:1.189813+0.295494 
## [206]    train-rmse:0.001701+0.000103    test-rmse:1.189805+0.295489 
## [207]    train-rmse:0.001671+0.000108    test-rmse:1.189791+0.295488 
## [208]    train-rmse:0.001651+0.000122    test-rmse:1.189791+0.295479 
## [209]    train-rmse:0.001627+0.000122    test-rmse:1.189785+0.295478 
## [210]    train-rmse:0.001626+0.000121    test-rmse:1.189786+0.295478 
## [211]    train-rmse:0.001626+0.000121    test-rmse:1.189786+0.295478 
## [212]    train-rmse:0.001626+0.000121    test-rmse:1.189786+0.295478 
## [213]    train-rmse:0.001626+0.000121    test-rmse:1.189786+0.295478 
## [214]    train-rmse:0.001626+0.000121    test-rmse:1.189786+0.295478 
## [215]    train-rmse:0.001626+0.000121    test-rmse:1.189786+0.295478 
## Stopping. Best iteration:
## [209]    train-rmse:0.001627+0.000122    test-rmse:1.189785+0.295478
## 
## 110 
## [1]  train-rmse:64.867792+0.080067   test-rmse:64.867433+0.531460 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:39.277875+0.064950   test-rmse:39.275826+0.593398 
## [3]  train-rmse:24.039232+0.061294   test-rmse:24.078998+0.616566 
## [4]  train-rmse:14.906316+0.043679   test-rmse:14.968624+0.565349 
## [5]  train-rmse:9.408744+0.037946    test-rmse:9.503911+0.497594 
## [6]  train-rmse:6.114302+0.041175    test-rmse:6.313366+0.523175 
## [7]  train-rmse:4.120060+0.068756    test-rmse:4.297438+0.466152 
## [8]  train-rmse:2.916072+0.067775    test-rmse:3.175216+0.401677 
## [9]  train-rmse:2.163388+0.048109    test-rmse:2.555630+0.365690 
## [10] train-rmse:1.720976+0.061494    test-rmse:2.239529+0.345444 
## [11] train-rmse:1.434498+0.042978    test-rmse:2.046856+0.314088 
## [12] train-rmse:1.247738+0.051215    test-rmse:1.916630+0.323979 
## [13] train-rmse:1.107106+0.045942    test-rmse:1.779170+0.328181 
## [14] train-rmse:0.996145+0.041306    test-rmse:1.691951+0.331178 
## [15] train-rmse:0.893961+0.043701    test-rmse:1.642852+0.320894 
## [16] train-rmse:0.795788+0.029210    test-rmse:1.583221+0.337898 
## [17] train-rmse:0.728358+0.024699    test-rmse:1.537897+0.351975 
## [18] train-rmse:0.671679+0.026513    test-rmse:1.495804+0.358785 
## [19] train-rmse:0.617855+0.025435    test-rmse:1.445197+0.355287 
## [20] train-rmse:0.568104+0.030048    test-rmse:1.414507+0.343285 
## [21] train-rmse:0.531293+0.031500    test-rmse:1.403272+0.337897 
## [22] train-rmse:0.496716+0.031780    test-rmse:1.382611+0.339950 
## [23] train-rmse:0.462556+0.025947    test-rmse:1.358410+0.340186 
## [24] train-rmse:0.433945+0.027059    test-rmse:1.352237+0.349172 
## [25] train-rmse:0.407775+0.025506    test-rmse:1.343770+0.346908 
## [26] train-rmse:0.381190+0.024125    test-rmse:1.339908+0.350400 
## [27] train-rmse:0.355796+0.021432    test-rmse:1.329302+0.351114 
## [28] train-rmse:0.335426+0.021789    test-rmse:1.319295+0.356053 
## [29] train-rmse:0.318631+0.022186    test-rmse:1.318217+0.357312 
## [30] train-rmse:0.301595+0.026768    test-rmse:1.313208+0.360747 
## [31] train-rmse:0.285494+0.022709    test-rmse:1.308027+0.362710 
## [32] train-rmse:0.269113+0.026491    test-rmse:1.303068+0.362201 
## [33] train-rmse:0.256049+0.024763    test-rmse:1.297473+0.361499 
## [34] train-rmse:0.241222+0.023822    test-rmse:1.294650+0.361895 
## [35] train-rmse:0.224931+0.025223    test-rmse:1.290590+0.361486 
## [36] train-rmse:0.215628+0.025853    test-rmse:1.289315+0.361389 
## [37] train-rmse:0.205925+0.026325    test-rmse:1.284954+0.357861 
## [38] train-rmse:0.198239+0.025616    test-rmse:1.283344+0.355536 
## [39] train-rmse:0.187723+0.024424    test-rmse:1.278948+0.357072 
## [40] train-rmse:0.178280+0.023482    test-rmse:1.278416+0.358539 
## [41] train-rmse:0.167963+0.020466    test-rmse:1.276572+0.358901 
## [42] train-rmse:0.158439+0.020827    test-rmse:1.279299+0.358290 
## [43] train-rmse:0.151789+0.018821    test-rmse:1.278816+0.358987 
## [44] train-rmse:0.142304+0.015956    test-rmse:1.277201+0.360760 
## [45] train-rmse:0.137428+0.014049    test-rmse:1.277107+0.360313 
## [46] train-rmse:0.133437+0.014328    test-rmse:1.276462+0.361107 
## [47] train-rmse:0.125656+0.014470    test-rmse:1.275085+0.360651 
## [48] train-rmse:0.120178+0.013508    test-rmse:1.274460+0.360157 
## [49] train-rmse:0.115330+0.014130    test-rmse:1.274326+0.361265 
## [50] train-rmse:0.109771+0.012429    test-rmse:1.274498+0.363391 
## [51] train-rmse:0.103358+0.010428    test-rmse:1.273623+0.362359 
## [52] train-rmse:0.099473+0.010416    test-rmse:1.273706+0.363140 
## [53] train-rmse:0.094620+0.009602    test-rmse:1.273833+0.362128 
## [54] train-rmse:0.090404+0.009942    test-rmse:1.272308+0.362783 
## [55] train-rmse:0.085848+0.007001    test-rmse:1.271829+0.362459 
## [56] train-rmse:0.081805+0.007114    test-rmse:1.271006+0.362440 
## [57] train-rmse:0.078506+0.006183    test-rmse:1.270602+0.361838 
## [58] train-rmse:0.072924+0.004751    test-rmse:1.268895+0.361652 
## [59] train-rmse:0.069950+0.005531    test-rmse:1.268629+0.361406 
## [60] train-rmse:0.067669+0.005881    test-rmse:1.268266+0.362112 
## [61] train-rmse:0.064861+0.005757    test-rmse:1.267951+0.362691 
## [62] train-rmse:0.062364+0.005999    test-rmse:1.266485+0.361749 
## [63] train-rmse:0.059333+0.004719    test-rmse:1.265694+0.362339 
## [64] train-rmse:0.056856+0.003990    test-rmse:1.265104+0.362523 
## [65] train-rmse:0.055154+0.003750    test-rmse:1.265321+0.362335 
## [66] train-rmse:0.052685+0.004034    test-rmse:1.265001+0.362810 
## [67] train-rmse:0.049099+0.004127    test-rmse:1.264988+0.361937 
## [68] train-rmse:0.047753+0.004442    test-rmse:1.265071+0.361741 
## [69] train-rmse:0.045969+0.003952    test-rmse:1.264366+0.361498 
## [70] train-rmse:0.043320+0.003718    test-rmse:1.264087+0.361596 
## [71] train-rmse:0.041149+0.003671    test-rmse:1.264359+0.362045 
## [72] train-rmse:0.038625+0.003303    test-rmse:1.264694+0.361894 
## [73] train-rmse:0.037146+0.002762    test-rmse:1.264331+0.361929 
## [74] train-rmse:0.035469+0.002726    test-rmse:1.263847+0.362182 
## [75] train-rmse:0.034111+0.002831    test-rmse:1.263644+0.362003 
## [76] train-rmse:0.032223+0.002666    test-rmse:1.263469+0.362261 
## [77] train-rmse:0.031337+0.002558    test-rmse:1.263280+0.362175 
## [78] train-rmse:0.030247+0.002436    test-rmse:1.263294+0.362198 
## [79] train-rmse:0.028894+0.001909    test-rmse:1.263242+0.362133 
## [80] train-rmse:0.027545+0.001810    test-rmse:1.263304+0.362151 
## [81] train-rmse:0.026642+0.001778    test-rmse:1.263350+0.362040 
## [82] train-rmse:0.025449+0.001709    test-rmse:1.263631+0.361975 
## [83] train-rmse:0.024161+0.001652    test-rmse:1.263388+0.361969 
## [84] train-rmse:0.023141+0.001569    test-rmse:1.263263+0.362086 
## [85] train-rmse:0.022427+0.001693    test-rmse:1.263198+0.361975 
## [86] train-rmse:0.021447+0.001552    test-rmse:1.263140+0.361926 
## [87] train-rmse:0.020682+0.001500    test-rmse:1.263239+0.361729 
## [88] train-rmse:0.020180+0.001430    test-rmse:1.263133+0.361572 
## [89] train-rmse:0.019531+0.001459    test-rmse:1.263067+0.361688 
## [90] train-rmse:0.018437+0.001069    test-rmse:1.262939+0.361606 
## [91] train-rmse:0.017826+0.001122    test-rmse:1.262715+0.361632 
## [92] train-rmse:0.017319+0.001089    test-rmse:1.262604+0.361469 
## [93] train-rmse:0.016785+0.000996    test-rmse:1.262788+0.361380 
## [94] train-rmse:0.016174+0.001010    test-rmse:1.262896+0.361420 
## [95] train-rmse:0.015624+0.001084    test-rmse:1.262808+0.361456 
## [96] train-rmse:0.014997+0.000993    test-rmse:1.262765+0.361433 
## [97] train-rmse:0.014571+0.001189    test-rmse:1.262603+0.361493 
## [98] train-rmse:0.013935+0.001193    test-rmse:1.262540+0.361566 
## [99] train-rmse:0.013403+0.001226    test-rmse:1.262627+0.361592 
## [100]    train-rmse:0.012730+0.001132    test-rmse:1.262731+0.361883 
## [101]    train-rmse:0.012311+0.001158    test-rmse:1.262735+0.361814 
## [102]    train-rmse:0.011872+0.001149    test-rmse:1.262713+0.361848 
## [103]    train-rmse:0.011338+0.000968    test-rmse:1.262758+0.361726 
## [104]    train-rmse:0.010755+0.000794    test-rmse:1.262560+0.361517 
## Stopping. Best iteration:
## [98] train-rmse:0.013935+0.001193    test-rmse:1.262540+0.361566
## 
## 111 
## [1]  train-rmse:64.867792+0.080067   test-rmse:64.867433+0.531460 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:39.277875+0.064950   test-rmse:39.275826+0.593398 
## [3]  train-rmse:24.039232+0.061294   test-rmse:24.078998+0.616566 
## [4]  train-rmse:14.906316+0.043679   test-rmse:14.968624+0.565349 
## [5]  train-rmse:9.393760+0.037744    test-rmse:9.489749+0.503628 
## [6]  train-rmse:6.065604+0.028653    test-rmse:6.233963+0.574930 
## [7]  train-rmse:4.041850+0.045877    test-rmse:4.292720+0.485391 
## [8]  train-rmse:2.800419+0.047786    test-rmse:3.096050+0.416723 
## [9]  train-rmse:2.053078+0.053332    test-rmse:2.451604+0.366310 
## [10] train-rmse:1.568463+0.055516    test-rmse:2.070640+0.308184 
## [11] train-rmse:1.269586+0.047115    test-rmse:1.855932+0.224267 
## [12] train-rmse:1.066738+0.048317    test-rmse:1.732474+0.229073 
## [13] train-rmse:0.921422+0.047114    test-rmse:1.622772+0.233407 
## [14] train-rmse:0.806851+0.036733    test-rmse:1.523892+0.243518 
## [15] train-rmse:0.717689+0.029433    test-rmse:1.466992+0.264744 
## [16] train-rmse:0.642036+0.021275    test-rmse:1.434823+0.252373 
## [17] train-rmse:0.576334+0.016490    test-rmse:1.398112+0.254319 
## [18] train-rmse:0.522952+0.020219    test-rmse:1.363018+0.250961 
## [19] train-rmse:0.476009+0.012514    test-rmse:1.348078+0.259395 
## [20] train-rmse:0.437169+0.014127    test-rmse:1.324702+0.268146 
## [21] train-rmse:0.398147+0.015104    test-rmse:1.305851+0.272748 
## [22] train-rmse:0.363838+0.016768    test-rmse:1.292033+0.277472 
## [23] train-rmse:0.331926+0.017553    test-rmse:1.290529+0.279322 
## [24] train-rmse:0.309262+0.013717    test-rmse:1.277441+0.279079 
## [25] train-rmse:0.285723+0.013000    test-rmse:1.270923+0.283359 
## [26] train-rmse:0.264567+0.011334    test-rmse:1.262487+0.279957 
## [27] train-rmse:0.247293+0.012219    test-rmse:1.256551+0.284027 
## [28] train-rmse:0.228135+0.011204    test-rmse:1.252254+0.284660 
## [29] train-rmse:0.211450+0.014990    test-rmse:1.252825+0.288152 
## [30] train-rmse:0.198801+0.012843    test-rmse:1.252804+0.290289 
## [31] train-rmse:0.185150+0.012068    test-rmse:1.252841+0.294244 
## [32] train-rmse:0.173820+0.013963    test-rmse:1.250300+0.295648 
## [33] train-rmse:0.163712+0.014710    test-rmse:1.250261+0.298091 
## [34] train-rmse:0.154418+0.013626    test-rmse:1.249280+0.295069 
## [35] train-rmse:0.144651+0.011880    test-rmse:1.246140+0.296258 
## [36] train-rmse:0.136215+0.009682    test-rmse:1.246345+0.297698 
## [37] train-rmse:0.126212+0.009770    test-rmse:1.245204+0.299121 
## [38] train-rmse:0.120765+0.008751    test-rmse:1.244782+0.299102 
## [39] train-rmse:0.111574+0.009569    test-rmse:1.245854+0.299068 
## [40] train-rmse:0.104586+0.010025    test-rmse:1.246344+0.297289 
## [41] train-rmse:0.098190+0.007917    test-rmse:1.242769+0.294729 
## [42] train-rmse:0.093203+0.008632    test-rmse:1.242001+0.294841 
## [43] train-rmse:0.086070+0.006501    test-rmse:1.240597+0.294813 
## [44] train-rmse:0.080583+0.007145    test-rmse:1.239636+0.295745 
## [45] train-rmse:0.076682+0.006792    test-rmse:1.240164+0.295794 
## [46] train-rmse:0.071416+0.005420    test-rmse:1.239958+0.295703 
## [47] train-rmse:0.065990+0.004547    test-rmse:1.239986+0.295278 
## [48] train-rmse:0.062526+0.005102    test-rmse:1.239600+0.295873 
## [49] train-rmse:0.059481+0.005030    test-rmse:1.239704+0.295860 
## [50] train-rmse:0.055603+0.004290    test-rmse:1.238970+0.296317 
## [51] train-rmse:0.052226+0.004860    test-rmse:1.238487+0.296450 
## [52] train-rmse:0.049835+0.005027    test-rmse:1.238857+0.296105 
## [53] train-rmse:0.046291+0.004056    test-rmse:1.238461+0.296084 
## [54] train-rmse:0.044051+0.003910    test-rmse:1.237281+0.295283 
## [55] train-rmse:0.041464+0.003142    test-rmse:1.237031+0.295199 
## [56] train-rmse:0.038910+0.003027    test-rmse:1.236333+0.294410 
## [57] train-rmse:0.036466+0.002500    test-rmse:1.236184+0.294313 
## [58] train-rmse:0.034382+0.002354    test-rmse:1.235974+0.294831 
## [59] train-rmse:0.032243+0.002827    test-rmse:1.235994+0.294803 
## [60] train-rmse:0.030408+0.002457    test-rmse:1.236125+0.295025 
## [61] train-rmse:0.028746+0.002181    test-rmse:1.235873+0.294863 
## [62] train-rmse:0.027441+0.002310    test-rmse:1.235799+0.294992 
## [63] train-rmse:0.025867+0.001965    test-rmse:1.235970+0.294971 
## [64] train-rmse:0.024563+0.001598    test-rmse:1.235951+0.295021 
## [65] train-rmse:0.023535+0.001540    test-rmse:1.235269+0.294743 
## [66] train-rmse:0.021795+0.001636    test-rmse:1.235056+0.294873 
## [67] train-rmse:0.020539+0.001494    test-rmse:1.234896+0.295020 
## [68] train-rmse:0.019561+0.001706    test-rmse:1.234769+0.294916 
## [69] train-rmse:0.018484+0.001349    test-rmse:1.234693+0.294964 
## [70] train-rmse:0.017554+0.001222    test-rmse:1.234802+0.294888 
## [71] train-rmse:0.016601+0.001070    test-rmse:1.234763+0.294890 
## [72] train-rmse:0.015538+0.000705    test-rmse:1.234649+0.294873 
## [73] train-rmse:0.014510+0.000676    test-rmse:1.234578+0.295038 
## [74] train-rmse:0.013756+0.000903    test-rmse:1.234583+0.295094 
## [75] train-rmse:0.013086+0.000961    test-rmse:1.234709+0.294971 
## [76] train-rmse:0.012326+0.000846    test-rmse:1.234714+0.294866 
## [77] train-rmse:0.011756+0.000812    test-rmse:1.234826+0.294941 
## [78] train-rmse:0.011128+0.000923    test-rmse:1.234645+0.295058 
## [79] train-rmse:0.010439+0.000920    test-rmse:1.234521+0.295092 
## [80] train-rmse:0.009951+0.000891    test-rmse:1.234320+0.294983 
## [81] train-rmse:0.009429+0.000868    test-rmse:1.234322+0.294769 
## [82] train-rmse:0.008980+0.000805    test-rmse:1.234334+0.294731 
## [83] train-rmse:0.008511+0.000813    test-rmse:1.234219+0.294592 
## [84] train-rmse:0.007994+0.000734    test-rmse:1.234121+0.294470 
## [85] train-rmse:0.007413+0.000646    test-rmse:1.234082+0.294439 
## [86] train-rmse:0.007017+0.000634    test-rmse:1.233969+0.294446 
## [87] train-rmse:0.006614+0.000622    test-rmse:1.233990+0.294354 
## [88] train-rmse:0.006243+0.000645    test-rmse:1.234021+0.294329 
## [89] train-rmse:0.005936+0.000763    test-rmse:1.234022+0.294319 
## [90] train-rmse:0.005581+0.000750    test-rmse:1.233971+0.294215 
## [91] train-rmse:0.005319+0.000671    test-rmse:1.233974+0.294241 
## [92] train-rmse:0.005092+0.000686    test-rmse:1.233930+0.294296 
## [93] train-rmse:0.004796+0.000649    test-rmse:1.233872+0.294298 
## [94] train-rmse:0.004522+0.000638    test-rmse:1.233875+0.294326 
## [95] train-rmse:0.004318+0.000661    test-rmse:1.233927+0.294412 
## [96] train-rmse:0.004114+0.000638    test-rmse:1.233949+0.294455 
## [97] train-rmse:0.003873+0.000577    test-rmse:1.233965+0.294448 
## [98] train-rmse:0.003677+0.000582    test-rmse:1.233956+0.294458 
## [99] train-rmse:0.003508+0.000582    test-rmse:1.233990+0.294427 
## Stopping. Best iteration:
## [93] train-rmse:0.004796+0.000649    test-rmse:1.233872+0.294298
## 
## 112
##     max_depth eta
## 106         1 0.4
## [1]  train-rmse:7.594751+0.162507    test-rmse:7.574602+0.679937 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.074706+0.151635    test-rmse:7.068813+0.694239 
## [3]  train-rmse:6.616377+0.141892    test-rmse:6.625057+0.684628 
## [4]  train-rmse:6.215470+0.134075    test-rmse:6.215961+0.676572 
## [5]  train-rmse:5.864415+0.127690    test-rmse:5.899676+0.662675 
## [6]  train-rmse:5.558531+0.123021    test-rmse:5.588962+0.662945 
## [7]  train-rmse:5.291803+0.118678    test-rmse:5.325339+0.641245 
## [8]  train-rmse:5.060391+0.116615    test-rmse:5.099139+0.624768 
## [9]  train-rmse:4.858398+0.113990    test-rmse:4.923686+0.596696 
## [10] train-rmse:4.683789+0.112245    test-rmse:4.744346+0.593690 
## [11] train-rmse:4.532123+0.112080    test-rmse:4.604116+0.569895 
## [12] train-rmse:4.398900+0.111991    test-rmse:4.500157+0.560603 
## [13] train-rmse:4.283302+0.111798    test-rmse:4.379400+0.541457 
## [14] train-rmse:4.182020+0.111808    test-rmse:4.272471+0.537265 
## [15] train-rmse:4.092944+0.112648    test-rmse:4.193918+0.514793 
## [16] train-rmse:4.013913+0.113025    test-rmse:4.118055+0.514139 
## [17] train-rmse:3.943344+0.113449    test-rmse:4.056530+0.501083 
## [18] train-rmse:3.880692+0.113651    test-rmse:3.988210+0.486912 
## [19] train-rmse:3.824077+0.114399    test-rmse:3.937553+0.475025 
## [20] train-rmse:3.772590+0.115445    test-rmse:3.880207+0.468262 
## [21] train-rmse:3.725399+0.116032    test-rmse:3.851509+0.458680 
## [22] train-rmse:3.682291+0.116767    test-rmse:3.807391+0.453795 
## [23] train-rmse:3.642492+0.117248    test-rmse:3.758773+0.445939 
## [24] train-rmse:3.605473+0.117993    test-rmse:3.728706+0.440683 
## [25] train-rmse:3.570877+0.118670    test-rmse:3.690555+0.438305 
## [26] train-rmse:3.538349+0.119030    test-rmse:3.677488+0.439029 
## [27] train-rmse:3.507910+0.119397    test-rmse:3.634896+0.432734 
## [28] train-rmse:3.478659+0.119884    test-rmse:3.599907+0.425378 
## [29] train-rmse:3.451022+0.120611    test-rmse:3.580462+0.423059 
## [30] train-rmse:3.424621+0.121167    test-rmse:3.556834+0.421725 
## [31] train-rmse:3.399217+0.121547    test-rmse:3.546986+0.424192 
## [32] train-rmse:3.375033+0.121717    test-rmse:3.510791+0.423404 
## [33] train-rmse:3.351747+0.122022    test-rmse:3.478159+0.414596 
## [34] train-rmse:3.329157+0.122362    test-rmse:3.463438+0.416094 
## [35] train-rmse:3.307729+0.122797    test-rmse:3.446472+0.419127 
## [36] train-rmse:3.286836+0.122957    test-rmse:3.433917+0.428446 
## [37] train-rmse:3.266662+0.123230    test-rmse:3.411063+0.427595 
## [38] train-rmse:3.246942+0.123471    test-rmse:3.387420+0.419405 
## [39] train-rmse:3.227600+0.123901    test-rmse:3.367834+0.415701 
## [40] train-rmse:3.209005+0.124801    test-rmse:3.345942+0.419877 
## [41] train-rmse:3.191214+0.124983    test-rmse:3.334720+0.427125 
## [42] train-rmse:3.173946+0.125201    test-rmse:3.319415+0.420448 
## [43] train-rmse:3.157052+0.125401    test-rmse:3.304661+0.423112 
## [44] train-rmse:3.140385+0.125754    test-rmse:3.286286+0.423359 
## [45] train-rmse:3.124363+0.126519    test-rmse:3.269998+0.420903 
## [46] train-rmse:3.108549+0.126898    test-rmse:3.254617+0.422672 
## [47] train-rmse:3.093632+0.127160    test-rmse:3.234275+0.420079 
## [48] train-rmse:3.079001+0.127415    test-rmse:3.219310+0.422442 
## [49] train-rmse:3.064609+0.127596    test-rmse:3.210997+0.417011 
## [50] train-rmse:3.050378+0.127893    test-rmse:3.206451+0.420323 
## [51] train-rmse:3.036517+0.128411    test-rmse:3.184963+0.423739 
## [52] train-rmse:3.022969+0.128702    test-rmse:3.178289+0.425482 
## [53] train-rmse:3.010039+0.128806    test-rmse:3.160773+0.424899 
## [54] train-rmse:2.997239+0.129031    test-rmse:3.147546+0.424599 
## [55] train-rmse:2.984762+0.129276    test-rmse:3.139988+0.422296 
## [56] train-rmse:2.972277+0.129553    test-rmse:3.134301+0.429365 
## [57] train-rmse:2.960189+0.129965    test-rmse:3.123382+0.436935 
## [58] train-rmse:2.948281+0.130414    test-rmse:3.113276+0.431771 
## [59] train-rmse:2.936643+0.130790    test-rmse:3.104437+0.436939 
## [60] train-rmse:2.925199+0.131046    test-rmse:3.090205+0.441038 
## [61] train-rmse:2.914013+0.131324    test-rmse:3.082427+0.437950 
## [62] train-rmse:2.902969+0.131635    test-rmse:3.074349+0.437583 
## [63] train-rmse:2.892293+0.131899    test-rmse:3.065332+0.434036 
## [64] train-rmse:2.881744+0.132182    test-rmse:3.060968+0.438481 
## [65] train-rmse:2.871358+0.132468    test-rmse:3.053937+0.445188 
## [66] train-rmse:2.861208+0.132914    test-rmse:3.042718+0.447851 
## [67] train-rmse:2.851150+0.133263    test-rmse:3.029267+0.442005 
## [68] train-rmse:2.841302+0.133594    test-rmse:3.021756+0.437342 
## [69] train-rmse:2.831673+0.133845    test-rmse:3.013266+0.445319 
## [70] train-rmse:2.822100+0.134085    test-rmse:3.008904+0.444193 
## [71] train-rmse:2.812539+0.134335    test-rmse:2.995187+0.438204 
## [72] train-rmse:2.803207+0.134746    test-rmse:2.993830+0.442354 
## [73] train-rmse:2.793994+0.135150    test-rmse:2.980909+0.447019 
## [74] train-rmse:2.784887+0.135511    test-rmse:2.978247+0.446850 
## [75] train-rmse:2.775938+0.135869    test-rmse:2.970316+0.442954 
## [76] train-rmse:2.767030+0.136128    test-rmse:2.969193+0.447959 
## [77] train-rmse:2.758276+0.136452    test-rmse:2.958223+0.450009 
## [78] train-rmse:2.749790+0.136536    test-rmse:2.946385+0.446117 
## [79] train-rmse:2.741407+0.136661    test-rmse:2.943064+0.448836 
## [80] train-rmse:2.733153+0.136904    test-rmse:2.933253+0.448567 
## [81] train-rmse:2.724937+0.137046    test-rmse:2.921992+0.453082 
## [82] train-rmse:2.716838+0.137280    test-rmse:2.917026+0.457132 
## [83] train-rmse:2.708917+0.137534    test-rmse:2.917037+0.458851 
## [84] train-rmse:2.701075+0.137713    test-rmse:2.908359+0.451897 
## [85] train-rmse:2.693266+0.137853    test-rmse:2.902490+0.453970 
## [86] train-rmse:2.685542+0.138091    test-rmse:2.889665+0.456332 
## [87] train-rmse:2.677901+0.138297    test-rmse:2.881108+0.455436 
## [88] train-rmse:2.670323+0.138500    test-rmse:2.882232+0.458891 
## [89] train-rmse:2.662854+0.138646    test-rmse:2.878442+0.458937 
## [90] train-rmse:2.655520+0.138732    test-rmse:2.873934+0.464930 
## [91] train-rmse:2.648361+0.138743    test-rmse:2.870589+0.463750 
## [92] train-rmse:2.641286+0.138752    test-rmse:2.862602+0.460981 
## [93] train-rmse:2.634286+0.138817    test-rmse:2.854485+0.465540 
## [94] train-rmse:2.627318+0.138916    test-rmse:2.849699+0.466119 
## [95] train-rmse:2.620501+0.138959    test-rmse:2.845208+0.468145 
## [96] train-rmse:2.613681+0.139054    test-rmse:2.837181+0.466116 
## [97] train-rmse:2.607054+0.139133    test-rmse:2.830795+0.464956 
## [98] train-rmse:2.600486+0.139191    test-rmse:2.827650+0.470673 
## [99] train-rmse:2.593921+0.139259    test-rmse:2.826691+0.468871 
## [100]    train-rmse:2.587478+0.139393    test-rmse:2.820517+0.469842 
## [101]    train-rmse:2.581079+0.139469    test-rmse:2.810568+0.467906 
## [102]    train-rmse:2.574720+0.139569    test-rmse:2.806098+0.475399 
## [103]    train-rmse:2.568452+0.139621    test-rmse:2.805165+0.478250 
## [104]    train-rmse:2.562394+0.139665    test-rmse:2.800444+0.475620 
## [105]    train-rmse:2.556347+0.139660    test-rmse:2.796354+0.473366 
## [106]    train-rmse:2.550396+0.139669    test-rmse:2.789633+0.477287 
## [107]    train-rmse:2.544443+0.139651    test-rmse:2.782535+0.475373 
## [108]    train-rmse:2.538539+0.139679    test-rmse:2.773237+0.477368 
## [109]    train-rmse:2.532713+0.139713    test-rmse:2.767195+0.478153 
## [110]    train-rmse:2.526985+0.139733    test-rmse:2.761424+0.474869 
## [111]    train-rmse:2.521350+0.139819    test-rmse:2.760776+0.472684 
## [112]    train-rmse:2.515786+0.139883    test-rmse:2.758392+0.471543 
## [113]    train-rmse:2.510268+0.139952    test-rmse:2.756505+0.474136 
## [114]    train-rmse:2.504753+0.140037    test-rmse:2.753209+0.476723 
## [115]    train-rmse:2.499332+0.140154    test-rmse:2.749646+0.482538 
## [116]    train-rmse:2.493987+0.140205    test-rmse:2.743250+0.481584 
## [117]    train-rmse:2.488742+0.140191    test-rmse:2.739678+0.481409 
## [118]    train-rmse:2.483528+0.140221    test-rmse:2.733694+0.484746 
## [119]    train-rmse:2.478340+0.140285    test-rmse:2.728786+0.484421 
## [120]    train-rmse:2.473209+0.140338    test-rmse:2.721911+0.485187 
## [121]    train-rmse:2.468149+0.140385    test-rmse:2.719277+0.484457 
## [122]    train-rmse:2.463113+0.140457    test-rmse:2.710445+0.484098 
## [123]    train-rmse:2.458175+0.140482    test-rmse:2.706054+0.485678 
## [124]    train-rmse:2.453310+0.140577    test-rmse:2.702177+0.484587 
## [125]    train-rmse:2.448472+0.140629    test-rmse:2.698385+0.482874 
## [126]    train-rmse:2.443749+0.140666    test-rmse:2.697464+0.486101 
## [127]    train-rmse:2.439089+0.140699    test-rmse:2.696202+0.484027 
## [128]    train-rmse:2.434441+0.140763    test-rmse:2.693632+0.484227 
## [129]    train-rmse:2.429838+0.140792    test-rmse:2.686847+0.484311 
## [130]    train-rmse:2.425304+0.140857    test-rmse:2.681673+0.489589 
## [131]    train-rmse:2.420835+0.140879    test-rmse:2.677394+0.488036 
## [132]    train-rmse:2.416408+0.140910    test-rmse:2.669263+0.488462 
## [133]    train-rmse:2.412011+0.140975    test-rmse:2.663864+0.491607 
## [134]    train-rmse:2.407605+0.141050    test-rmse:2.662138+0.492165 
## [135]    train-rmse:2.403259+0.141170    test-rmse:2.657815+0.491460 
## [136]    train-rmse:2.398959+0.141277    test-rmse:2.656135+0.488917 
## [137]    train-rmse:2.394753+0.141392    test-rmse:2.651762+0.491378 
## [138]    train-rmse:2.390570+0.141450    test-rmse:2.649093+0.491725 
## [139]    train-rmse:2.386465+0.141533    test-rmse:2.646734+0.492137 
## [140]    train-rmse:2.382384+0.141578    test-rmse:2.640636+0.489559 
## [141]    train-rmse:2.378348+0.141625    test-rmse:2.639667+0.494177 
## [142]    train-rmse:2.374327+0.141673    test-rmse:2.635645+0.491534 
## [143]    train-rmse:2.370361+0.141695    test-rmse:2.632157+0.492591 
## [144]    train-rmse:2.366419+0.141703    test-rmse:2.628917+0.493543 
## [145]    train-rmse:2.362541+0.141767    test-rmse:2.622492+0.496465 
## [146]    train-rmse:2.358731+0.141845    test-rmse:2.618046+0.498112 
## [147]    train-rmse:2.354926+0.141932    test-rmse:2.618819+0.498301 
## [148]    train-rmse:2.351197+0.142044    test-rmse:2.613501+0.494368 
## [149]    train-rmse:2.347486+0.142151    test-rmse:2.611155+0.496345 
## [150]    train-rmse:2.343819+0.142237    test-rmse:2.608285+0.497346 
## [151]    train-rmse:2.340186+0.142352    test-rmse:2.605174+0.497013 
## [152]    train-rmse:2.336567+0.142457    test-rmse:2.601656+0.496149 
## [153]    train-rmse:2.332968+0.142572    test-rmse:2.601531+0.498429 
## [154]    train-rmse:2.329420+0.142671    test-rmse:2.597594+0.500669 
## [155]    train-rmse:2.325904+0.142759    test-rmse:2.591666+0.503392 
## [156]    train-rmse:2.322416+0.142842    test-rmse:2.590145+0.501000 
## [157]    train-rmse:2.318961+0.142910    test-rmse:2.587597+0.499546 
## [158]    train-rmse:2.315519+0.143009    test-rmse:2.587105+0.503801 
## [159]    train-rmse:2.312133+0.143094    test-rmse:2.583552+0.501095 
## [160]    train-rmse:2.308790+0.143190    test-rmse:2.582120+0.503082 
## [161]    train-rmse:2.305478+0.143333    test-rmse:2.577135+0.501730 
## [162]    train-rmse:2.302217+0.143460    test-rmse:2.571086+0.500407 
## [163]    train-rmse:2.299002+0.143584    test-rmse:2.570569+0.499831 
## [164]    train-rmse:2.295811+0.143688    test-rmse:2.569273+0.501763 
## [165]    train-rmse:2.292648+0.143794    test-rmse:2.565981+0.502637 
## [166]    train-rmse:2.289509+0.143905    test-rmse:2.562455+0.502151 
## [167]    train-rmse:2.286377+0.144021    test-rmse:2.561214+0.503462 
## [168]    train-rmse:2.283320+0.144106    test-rmse:2.557167+0.505384 
## [169]    train-rmse:2.280265+0.144208    test-rmse:2.555622+0.503650 
## [170]    train-rmse:2.277223+0.144309    test-rmse:2.553538+0.507236 
## [171]    train-rmse:2.274216+0.144385    test-rmse:2.550854+0.508089 
## [172]    train-rmse:2.271247+0.144455    test-rmse:2.547101+0.508504 
## [173]    train-rmse:2.268289+0.144541    test-rmse:2.544752+0.509417 
## [174]    train-rmse:2.265381+0.144656    test-rmse:2.545172+0.512660 
## [175]    train-rmse:2.262503+0.144750    test-rmse:2.541393+0.509931 
## [176]    train-rmse:2.259661+0.144852    test-rmse:2.539211+0.510107 
## [177]    train-rmse:2.256847+0.144936    test-rmse:2.538968+0.507789 
## [178]    train-rmse:2.254028+0.145027    test-rmse:2.534858+0.508344 
## [179]    train-rmse:2.251252+0.145117    test-rmse:2.532920+0.509023 
## [180]    train-rmse:2.248493+0.145221    test-rmse:2.527912+0.510286 
## [181]    train-rmse:2.245767+0.145321    test-rmse:2.525593+0.509833 
## [182]    train-rmse:2.243065+0.145431    test-rmse:2.526789+0.510148 
## [183]    train-rmse:2.240351+0.145534    test-rmse:2.520490+0.511490 
## [184]    train-rmse:2.237704+0.145636    test-rmse:2.518287+0.509801 
## [185]    train-rmse:2.235090+0.145731    test-rmse:2.515233+0.509220 
## [186]    train-rmse:2.232490+0.145819    test-rmse:2.514003+0.507870 
## [187]    train-rmse:2.229893+0.145882    test-rmse:2.512195+0.507556 
## [188]    train-rmse:2.227323+0.145971    test-rmse:2.509925+0.509133 
## [189]    train-rmse:2.224799+0.146056    test-rmse:2.510841+0.510887 
## [190]    train-rmse:2.222297+0.146153    test-rmse:2.508174+0.509253 
## [191]    train-rmse:2.219817+0.146250    test-rmse:2.504363+0.510354 
## [192]    train-rmse:2.217380+0.146344    test-rmse:2.503634+0.509596 
## [193]    train-rmse:2.214948+0.146439    test-rmse:2.504104+0.513923 
## [194]    train-rmse:2.212523+0.146524    test-rmse:2.503955+0.514488 
## [195]    train-rmse:2.210116+0.146622    test-rmse:2.503234+0.515521 
## [196]    train-rmse:2.207742+0.146723    test-rmse:2.498880+0.515496 
## [197]    train-rmse:2.205392+0.146800    test-rmse:2.496332+0.515791 
## [198]    train-rmse:2.203067+0.146874    test-rmse:2.497639+0.512529 
## [199]    train-rmse:2.200762+0.146943    test-rmse:2.493252+0.514322 
## [200]    train-rmse:2.198465+0.147019    test-rmse:2.491245+0.514439 
## [201]    train-rmse:2.196172+0.147105    test-rmse:2.491814+0.514814 
## [202]    train-rmse:2.193897+0.147187    test-rmse:2.489485+0.514925 
## [203]    train-rmse:2.191642+0.147269    test-rmse:2.486614+0.515073 
## [204]    train-rmse:2.189394+0.147350    test-rmse:2.481524+0.516876 
## [205]    train-rmse:2.187197+0.147438    test-rmse:2.480834+0.516373 
## [206]    train-rmse:2.185014+0.147523    test-rmse:2.477781+0.514872 
## [207]    train-rmse:2.182833+0.147593    test-rmse:2.474582+0.516981 
## [208]    train-rmse:2.180672+0.147666    test-rmse:2.472332+0.516554 
## [209]    train-rmse:2.178559+0.147762    test-rmse:2.472734+0.516290 
## [210]    train-rmse:2.176474+0.147847    test-rmse:2.472075+0.515790 
## [211]    train-rmse:2.174390+0.147919    test-rmse:2.471687+0.517710 
## [212]    train-rmse:2.172313+0.147998    test-rmse:2.469557+0.517401 
## [213]    train-rmse:2.170260+0.148086    test-rmse:2.465971+0.517558 
## [214]    train-rmse:2.168211+0.148183    test-rmse:2.466842+0.521510 
## [215]    train-rmse:2.166165+0.148271    test-rmse:2.465207+0.521426 
## [216]    train-rmse:2.164160+0.148331    test-rmse:2.464284+0.521531 
## [217]    train-rmse:2.162172+0.148397    test-rmse:2.463875+0.521384 
## [218]    train-rmse:2.160188+0.148466    test-rmse:2.463311+0.520685 
## [219]    train-rmse:2.158210+0.148522    test-rmse:2.460584+0.520530 
## [220]    train-rmse:2.156258+0.148577    test-rmse:2.458249+0.520604 
## [221]    train-rmse:2.154322+0.148644    test-rmse:2.456522+0.521154 
## [222]    train-rmse:2.152394+0.148726    test-rmse:2.453418+0.521350 
## [223]    train-rmse:2.150493+0.148828    test-rmse:2.453878+0.520339 
## [224]    train-rmse:2.148590+0.148933    test-rmse:2.452769+0.519998 
## [225]    train-rmse:2.146733+0.149030    test-rmse:2.449013+0.520759 
## [226]    train-rmse:2.144902+0.149117    test-rmse:2.446910+0.520529 
## [227]    train-rmse:2.143082+0.149212    test-rmse:2.447450+0.522024 
## [228]    train-rmse:2.141272+0.149298    test-rmse:2.446400+0.522096 
## [229]    train-rmse:2.139478+0.149382    test-rmse:2.443833+0.523373 
## [230]    train-rmse:2.137688+0.149455    test-rmse:2.443387+0.522901 
## [231]    train-rmse:2.135894+0.149539    test-rmse:2.443305+0.522766 
## [232]    train-rmse:2.134121+0.149622    test-rmse:2.441254+0.524395 
## [233]    train-rmse:2.132367+0.149715    test-rmse:2.440599+0.523361 
## [234]    train-rmse:2.130619+0.149789    test-rmse:2.439449+0.522366 
## [235]    train-rmse:2.128889+0.149859    test-rmse:2.439071+0.525082 
## [236]    train-rmse:2.127181+0.149932    test-rmse:2.438245+0.525790 
## [237]    train-rmse:2.125463+0.149996    test-rmse:2.436919+0.524541 
## [238]    train-rmse:2.123770+0.150076    test-rmse:2.437096+0.528337 
## [239]    train-rmse:2.122090+0.150157    test-rmse:2.434605+0.527688 
## [240]    train-rmse:2.120420+0.150236    test-rmse:2.431942+0.527756 
## [241]    train-rmse:2.118761+0.150298    test-rmse:2.430291+0.525351 
## [242]    train-rmse:2.117120+0.150352    test-rmse:2.429056+0.523924 
## [243]    train-rmse:2.115486+0.150431    test-rmse:2.428033+0.523674 
## [244]    train-rmse:2.113841+0.150502    test-rmse:2.428157+0.524530 
## [245]    train-rmse:2.112230+0.150583    test-rmse:2.427617+0.524018 
## [246]    train-rmse:2.110640+0.150662    test-rmse:2.425325+0.523970 
## [247]    train-rmse:2.109051+0.150749    test-rmse:2.423412+0.524148 
## [248]    train-rmse:2.107484+0.150834    test-rmse:2.421620+0.523511 
## [249]    train-rmse:2.105929+0.150929    test-rmse:2.421536+0.522507 
## [250]    train-rmse:2.104372+0.151027    test-rmse:2.417183+0.523789 
## [251]    train-rmse:2.102813+0.151129    test-rmse:2.415876+0.522897 
## [252]    train-rmse:2.101263+0.151209    test-rmse:2.414979+0.524563 
## [253]    train-rmse:2.099737+0.151293    test-rmse:2.414812+0.524216 
## [254]    train-rmse:2.098233+0.151381    test-rmse:2.414127+0.524363 
## [255]    train-rmse:2.096740+0.151445    test-rmse:2.413178+0.524042 
## [256]    train-rmse:2.095257+0.151525    test-rmse:2.412863+0.526959 
## [257]    train-rmse:2.093783+0.151610    test-rmse:2.413933+0.527386 
## [258]    train-rmse:2.092317+0.151694    test-rmse:2.413355+0.527732 
## [259]    train-rmse:2.090847+0.151759    test-rmse:2.414871+0.526098 
## [260]    train-rmse:2.089415+0.151843    test-rmse:2.413610+0.525535 
## [261]    train-rmse:2.087988+0.151925    test-rmse:2.412991+0.527058 
## [262]    train-rmse:2.086563+0.152008    test-rmse:2.413509+0.530232 
## Stopping. Best iteration:
## [256]    train-rmse:2.095257+0.151525    test-rmse:2.412863+0.526959
## 
## 1 
## [1]  train-rmse:7.552356+0.159201    test-rmse:7.544257+0.665685 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.990795+0.146166    test-rmse:6.988471+0.637084 
## [3]  train-rmse:6.491905+0.138495    test-rmse:6.522744+0.623290 
## [4]  train-rmse:6.048545+0.131937    test-rmse:6.105588+0.618054 
## [5]  train-rmse:5.658707+0.121719    test-rmse:5.735159+0.574983 
## [6]  train-rmse:5.311330+0.115922    test-rmse:5.409366+0.547563 
## [7]  train-rmse:5.001410+0.109604    test-rmse:5.110005+0.557716 
## [8]  train-rmse:4.733937+0.103101    test-rmse:4.865190+0.515065 
## [9]  train-rmse:4.492127+0.101394    test-rmse:4.637609+0.505032 
## [10] train-rmse:4.275476+0.095853    test-rmse:4.415768+0.499932 
## [11] train-rmse:4.087049+0.094763    test-rmse:4.245308+0.467333 
## [12] train-rmse:3.919655+0.088486    test-rmse:4.094864+0.470396 
## [13] train-rmse:3.759496+0.086741    test-rmse:3.942435+0.475507 
## [14] train-rmse:3.625916+0.087600    test-rmse:3.813630+0.460318 
## [15] train-rmse:3.508055+0.088847    test-rmse:3.711606+0.427745 
## [16] train-rmse:3.398520+0.087157    test-rmse:3.612967+0.439351 
## [17] train-rmse:3.294607+0.086040    test-rmse:3.508233+0.440475 
## [18] train-rmse:3.209198+0.087298    test-rmse:3.441939+0.419453 
## [19] train-rmse:3.132264+0.083799    test-rmse:3.386426+0.433250 
## [20] train-rmse:3.054394+0.083507    test-rmse:3.301097+0.436309 
## [21] train-rmse:2.988382+0.083969    test-rmse:3.249074+0.434836 
## [22] train-rmse:2.930294+0.086028    test-rmse:3.195242+0.450097 
## [23] train-rmse:2.877055+0.088851    test-rmse:3.155353+0.440852 
## [24] train-rmse:2.830423+0.086593    test-rmse:3.123364+0.448814 
## [25] train-rmse:2.783620+0.087342    test-rmse:3.088300+0.451688 
## [26] train-rmse:2.741758+0.090666    test-rmse:3.051897+0.444056 
## [27] train-rmse:2.706682+0.090964    test-rmse:3.034555+0.446403 
## [28] train-rmse:2.671426+0.091882    test-rmse:3.008900+0.447955 
## [29] train-rmse:2.635601+0.089883    test-rmse:2.982479+0.457718 
## [30] train-rmse:2.599731+0.081814    test-rmse:2.966607+0.464363 
## [31] train-rmse:2.569215+0.083391    test-rmse:2.944636+0.462505 
## [32] train-rmse:2.540765+0.081510    test-rmse:2.925351+0.468108 
## [33] train-rmse:2.514354+0.082425    test-rmse:2.904608+0.466929 
## [34] train-rmse:2.489204+0.082917    test-rmse:2.885291+0.475578 
## [35] train-rmse:2.459054+0.087218    test-rmse:2.860277+0.465431 
## [36] train-rmse:2.435400+0.088859    test-rmse:2.846735+0.457391 
## [37] train-rmse:2.411095+0.082031    test-rmse:2.830851+0.467564 
## [38] train-rmse:2.388575+0.080362    test-rmse:2.821075+0.461065 
## [39] train-rmse:2.367863+0.081688    test-rmse:2.804790+0.465557 
## [40] train-rmse:2.348684+0.082168    test-rmse:2.785207+0.461952 
## [41] train-rmse:2.328209+0.082806    test-rmse:2.772538+0.453997 
## [42] train-rmse:2.305111+0.081822    test-rmse:2.759437+0.468130 
## [43] train-rmse:2.288497+0.081729    test-rmse:2.751462+0.468365 
## [44] train-rmse:2.269563+0.082831    test-rmse:2.744291+0.474461 
## [45] train-rmse:2.248815+0.082939    test-rmse:2.735405+0.472810 
## [46] train-rmse:2.232315+0.083093    test-rmse:2.719466+0.480239 
## [47] train-rmse:2.211928+0.084448    test-rmse:2.699402+0.474891 
## [48] train-rmse:2.194280+0.082740    test-rmse:2.686323+0.483804 
## [49] train-rmse:2.179849+0.082716    test-rmse:2.681061+0.483385 
## [50] train-rmse:2.164099+0.083675    test-rmse:2.669410+0.485977 
## [51] train-rmse:2.141288+0.079216    test-rmse:2.650168+0.480489 
## [52] train-rmse:2.127651+0.079104    test-rmse:2.638710+0.484619 
## [53] train-rmse:2.113408+0.079688    test-rmse:2.631191+0.479405 
## [54] train-rmse:2.099072+0.077778    test-rmse:2.621812+0.477099 
## [55] train-rmse:2.086305+0.077751    test-rmse:2.615601+0.481007 
## [56] train-rmse:2.072616+0.078649    test-rmse:2.602747+0.484856 
## [57] train-rmse:2.058414+0.077045    test-rmse:2.594502+0.482806 
## [58] train-rmse:2.044157+0.076719    test-rmse:2.582085+0.481801 
## [59] train-rmse:2.031219+0.076893    test-rmse:2.571169+0.480160 
## [60] train-rmse:2.017488+0.078042    test-rmse:2.559112+0.482867 
## [61] train-rmse:2.005264+0.078849    test-rmse:2.558370+0.489141 
## [62] train-rmse:1.994442+0.078814    test-rmse:2.552576+0.489291 
## [63] train-rmse:1.980105+0.080428    test-rmse:2.549791+0.488274 
## [64] train-rmse:1.964283+0.074501    test-rmse:2.537712+0.489219 
## [65] train-rmse:1.953154+0.074979    test-rmse:2.535582+0.492237 
## [66] train-rmse:1.942337+0.073154    test-rmse:2.527402+0.489132 
## [67] train-rmse:1.931492+0.073160    test-rmse:2.517535+0.487969 
## [68] train-rmse:1.920734+0.073966    test-rmse:2.508425+0.486652 
## [69] train-rmse:1.909560+0.074888    test-rmse:2.502254+0.485884 
## [70] train-rmse:1.900106+0.074704    test-rmse:2.497698+0.484632 
## [71] train-rmse:1.891112+0.074822    test-rmse:2.492036+0.482955 
## [72] train-rmse:1.880711+0.075394    test-rmse:2.487302+0.484107 
## [73] train-rmse:1.869861+0.076784    test-rmse:2.481244+0.490022 
## [74] train-rmse:1.860781+0.077204    test-rmse:2.478390+0.491498 
## [75] train-rmse:1.852225+0.077156    test-rmse:2.473674+0.492566 
## [76] train-rmse:1.839707+0.070859    test-rmse:2.461351+0.495068 
## [77] train-rmse:1.830526+0.069242    test-rmse:2.457106+0.499951 
## [78] train-rmse:1.818192+0.070817    test-rmse:2.449983+0.497374 
## [79] train-rmse:1.810314+0.070934    test-rmse:2.440518+0.494621 
## [80] train-rmse:1.802320+0.070996    test-rmse:2.437204+0.492964 
## [81] train-rmse:1.792948+0.072265    test-rmse:2.430132+0.497584 
## [82] train-rmse:1.784559+0.071939    test-rmse:2.425850+0.496729 
## [83] train-rmse:1.776870+0.072207    test-rmse:2.421152+0.497073 
## [84] train-rmse:1.769307+0.072049    test-rmse:2.419958+0.497144 
## [85] train-rmse:1.759276+0.071754    test-rmse:2.417615+0.501984 
## [86] train-rmse:1.751631+0.070466    test-rmse:2.414978+0.499950 
## [87] train-rmse:1.744562+0.069940    test-rmse:2.409884+0.498814 
## [88] train-rmse:1.736660+0.070923    test-rmse:2.403178+0.499918 
## [89] train-rmse:1.729539+0.071150    test-rmse:2.396713+0.499064 
## [90] train-rmse:1.722830+0.071251    test-rmse:2.389208+0.499897 
## [91] train-rmse:1.714032+0.072457    test-rmse:2.383424+0.498615 
## [92] train-rmse:1.705383+0.073440    test-rmse:2.384586+0.498478 
## [93] train-rmse:1.698737+0.073379    test-rmse:2.383210+0.499070 
## [94] train-rmse:1.689717+0.073648    test-rmse:2.375615+0.498799 
## [95] train-rmse:1.682652+0.073400    test-rmse:2.369276+0.501744 
## [96] train-rmse:1.676768+0.073427    test-rmse:2.365203+0.502931 
## [97] train-rmse:1.670592+0.073710    test-rmse:2.361344+0.506170 
## [98] train-rmse:1.663365+0.074494    test-rmse:2.359681+0.509026 
## [99] train-rmse:1.656230+0.074640    test-rmse:2.357278+0.511948 
## [100]    train-rmse:1.649406+0.074908    test-rmse:2.351404+0.507713 
## [101]    train-rmse:1.642347+0.073795    test-rmse:2.347806+0.507685 
## [102]    train-rmse:1.634966+0.072340    test-rmse:2.342424+0.507940 
## [103]    train-rmse:1.628947+0.072565    test-rmse:2.339737+0.510644 
## [104]    train-rmse:1.620976+0.072856    test-rmse:2.335419+0.508633 
## [105]    train-rmse:1.613974+0.073219    test-rmse:2.328743+0.510239 
## [106]    train-rmse:1.606615+0.073665    test-rmse:2.324721+0.509059 
## [107]    train-rmse:1.601275+0.073040    test-rmse:2.321614+0.510781 
## [108]    train-rmse:1.596221+0.073139    test-rmse:2.318833+0.514378 
## [109]    train-rmse:1.589784+0.074335    test-rmse:2.313365+0.510801 
## [110]    train-rmse:1.579658+0.069825    test-rmse:2.310443+0.513570 
## [111]    train-rmse:1.574515+0.069675    test-rmse:2.307909+0.511906 
## [112]    train-rmse:1.568472+0.069246    test-rmse:2.307018+0.511245 
## [113]    train-rmse:1.561483+0.068117    test-rmse:2.300114+0.511361 
## [114]    train-rmse:1.554307+0.069044    test-rmse:2.295955+0.508884 
## [115]    train-rmse:1.548165+0.068524    test-rmse:2.294360+0.507938 
## [116]    train-rmse:1.543098+0.068225    test-rmse:2.290767+0.506654 
## [117]    train-rmse:1.538651+0.068341    test-rmse:2.291522+0.510692 
## [118]    train-rmse:1.534153+0.068317    test-rmse:2.289869+0.511836 
## [119]    train-rmse:1.529693+0.068276    test-rmse:2.287225+0.513949 
## [120]    train-rmse:1.525356+0.068353    test-rmse:2.283039+0.513283 
## [121]    train-rmse:1.518871+0.070118    test-rmse:2.280690+0.515742 
## [122]    train-rmse:1.514359+0.070263    test-rmse:2.278683+0.515925 
## [123]    train-rmse:1.508558+0.069761    test-rmse:2.275987+0.514823 
## [124]    train-rmse:1.503845+0.069532    test-rmse:2.273406+0.515683 
## [125]    train-rmse:1.497859+0.068882    test-rmse:2.272197+0.517406 
## [126]    train-rmse:1.490525+0.068919    test-rmse:2.271513+0.516413 
## [127]    train-rmse:1.486532+0.068515    test-rmse:2.269380+0.517200 
## [128]    train-rmse:1.482681+0.068442    test-rmse:2.268369+0.517925 
## [129]    train-rmse:1.479010+0.068441    test-rmse:2.267025+0.517701 
## [130]    train-rmse:1.474797+0.068795    test-rmse:2.263574+0.517762 
## [131]    train-rmse:1.470829+0.068755    test-rmse:2.260922+0.518613 
## [132]    train-rmse:1.465787+0.068799    test-rmse:2.259756+0.518323 
## [133]    train-rmse:1.461136+0.067651    test-rmse:2.259260+0.518035 
## [134]    train-rmse:1.457051+0.067959    test-rmse:2.256410+0.517414 
## [135]    train-rmse:1.453448+0.068172    test-rmse:2.255193+0.516877 
## [136]    train-rmse:1.448423+0.068504    test-rmse:2.253271+0.518326 
## [137]    train-rmse:1.444141+0.067388    test-rmse:2.251174+0.518019 
## [138]    train-rmse:1.439001+0.067242    test-rmse:2.248216+0.518505 
## [139]    train-rmse:1.434280+0.067643    test-rmse:2.246117+0.520353 
## [140]    train-rmse:1.430468+0.067971    test-rmse:2.246263+0.521629 
## [141]    train-rmse:1.424247+0.068233    test-rmse:2.244807+0.521720 
## [142]    train-rmse:1.420237+0.068184    test-rmse:2.245655+0.520887 
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## [144]    train-rmse:1.407037+0.063479    test-rmse:2.240793+0.523829 
## [145]    train-rmse:1.403376+0.063542    test-rmse:2.241694+0.523715 
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## [207]    train-rmse:1.196593+0.057784    test-rmse:2.170397+0.538886 
## [208]    train-rmse:1.194389+0.057214    test-rmse:2.168929+0.538833 
## [209]    train-rmse:1.191449+0.056629    test-rmse:2.167063+0.539050 
## [210]    train-rmse:1.189101+0.057044    test-rmse:2.166061+0.539199 
## [211]    train-rmse:1.186894+0.056549    test-rmse:2.163510+0.541361 
## [212]    train-rmse:1.184048+0.056451    test-rmse:2.164125+0.541131 
## [213]    train-rmse:1.180884+0.057038    test-rmse:2.164320+0.543055 
## [214]    train-rmse:1.176781+0.057524    test-rmse:2.162627+0.543880 
## [215]    train-rmse:1.174154+0.058026    test-rmse:2.160222+0.543309 
## [216]    train-rmse:1.170329+0.058828    test-rmse:2.160459+0.544252 
## [217]    train-rmse:1.168169+0.059230    test-rmse:2.159243+0.542990 
## [218]    train-rmse:1.165382+0.059498    test-rmse:2.158318+0.545129 
## [219]    train-rmse:1.163048+0.059689    test-rmse:2.157281+0.546667 
## [220]    train-rmse:1.160290+0.060085    test-rmse:2.157270+0.546673 
## [221]    train-rmse:1.158183+0.060327    test-rmse:2.157040+0.547473 
## [222]    train-rmse:1.156030+0.060247    test-rmse:2.157003+0.546933 
## [223]    train-rmse:1.153794+0.059864    test-rmse:2.157434+0.548649 
## [224]    train-rmse:1.150895+0.060086    test-rmse:2.155862+0.548462 
## [225]    train-rmse:1.148942+0.059813    test-rmse:2.154322+0.549658 
## [226]    train-rmse:1.146157+0.059277    test-rmse:2.153524+0.549460 
## [227]    train-rmse:1.143469+0.057546    test-rmse:2.153059+0.549970 
## [228]    train-rmse:1.140803+0.057065    test-rmse:2.153386+0.550856 
## [229]    train-rmse:1.138255+0.057578    test-rmse:2.152761+0.549986 
## [230]    train-rmse:1.134408+0.059590    test-rmse:2.150169+0.548320 
## [231]    train-rmse:1.131994+0.060233    test-rmse:2.148675+0.547510 
## [232]    train-rmse:1.128857+0.060786    test-rmse:2.147136+0.545893 
## [233]    train-rmse:1.126948+0.061072    test-rmse:2.147155+0.547322 
## [234]    train-rmse:1.124981+0.060987    test-rmse:2.146363+0.550697 
## [235]    train-rmse:1.123135+0.060773    test-rmse:2.146045+0.550370 
## [236]    train-rmse:1.120964+0.061228    test-rmse:2.145522+0.551279 
## [237]    train-rmse:1.119405+0.061129    test-rmse:2.144491+0.552229 
## [238]    train-rmse:1.117348+0.061231    test-rmse:2.144085+0.552295 
## [239]    train-rmse:1.112992+0.060655    test-rmse:2.146478+0.551796 
## [240]    train-rmse:1.111136+0.060654    test-rmse:2.145491+0.552109 
## [241]    train-rmse:1.109454+0.060934    test-rmse:2.143721+0.552497 
## [242]    train-rmse:1.107913+0.061190    test-rmse:2.142746+0.552158 
## [243]    train-rmse:1.104548+0.062698    test-rmse:2.141733+0.550147 
## [244]    train-rmse:1.103108+0.062610    test-rmse:2.141082+0.550313 
## [245]    train-rmse:1.101521+0.062846    test-rmse:2.141244+0.549811 
## [246]    train-rmse:1.098999+0.062196    test-rmse:2.139157+0.550435 
## [247]    train-rmse:1.096725+0.062823    test-rmse:2.138625+0.549630 
## [248]    train-rmse:1.093581+0.063455    test-rmse:2.137136+0.550967 
## [249]    train-rmse:1.092153+0.063598    test-rmse:2.137781+0.551227 
## [250]    train-rmse:1.090505+0.063804    test-rmse:2.136784+0.549890 
## [251]    train-rmse:1.087548+0.064063    test-rmse:2.138040+0.550735 
## [252]    train-rmse:1.083479+0.063981    test-rmse:2.138149+0.552073 
## [253]    train-rmse:1.081191+0.063776    test-rmse:2.136203+0.553596 
## [254]    train-rmse:1.079238+0.064136    test-rmse:2.136826+0.553731 
## [255]    train-rmse:1.076434+0.065498    test-rmse:2.135817+0.552810 
## [256]    train-rmse:1.073786+0.066343    test-rmse:2.135838+0.551271 
## [257]    train-rmse:1.071712+0.066106    test-rmse:2.134781+0.550429 
## [258]    train-rmse:1.070387+0.065975    test-rmse:2.135042+0.552341 
## [259]    train-rmse:1.068538+0.065747    test-rmse:2.133451+0.553491 
## [260]    train-rmse:1.066999+0.065999    test-rmse:2.132360+0.553899 
## [261]    train-rmse:1.064751+0.066150    test-rmse:2.132685+0.554679 
## [262]    train-rmse:1.062648+0.066334    test-rmse:2.132580+0.555075 
## [263]    train-rmse:1.061277+0.066036    test-rmse:2.134022+0.555607 
## [264]    train-rmse:1.058367+0.066130    test-rmse:2.135249+0.555382 
## [265]    train-rmse:1.056136+0.066738    test-rmse:2.135483+0.556704 
## [266]    train-rmse:1.054783+0.066889    test-rmse:2.136579+0.554902 
## Stopping. Best iteration:
## [260]    train-rmse:1.066999+0.065999    test-rmse:2.132360+0.553899
## 
## 2 
## [1]  train-rmse:7.524102+0.157920    test-rmse:7.532223+0.659216 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.932589+0.143348    test-rmse:6.960580+0.626269 
## [3]  train-rmse:6.407597+0.130462    test-rmse:6.465128+0.586887 
## [4]  train-rmse:5.938180+0.121474    test-rmse:6.018113+0.573020 
## [5]  train-rmse:5.515462+0.117045    test-rmse:5.609197+0.555504 
## [6]  train-rmse:5.140082+0.111442    test-rmse:5.251896+0.530416 
## [7]  train-rmse:4.806548+0.101371    test-rmse:4.944000+0.519963 
## [8]  train-rmse:4.503888+0.100409    test-rmse:4.658059+0.484051 
## [9]  train-rmse:4.238461+0.099917    test-rmse:4.413063+0.464314 
## [10] train-rmse:3.997083+0.092060    test-rmse:4.190250+0.445539 
## [11] train-rmse:3.784576+0.092324    test-rmse:3.991503+0.436844 
## [12] train-rmse:3.586110+0.084437    test-rmse:3.810489+0.430021 
## [13] train-rmse:3.415554+0.083086    test-rmse:3.669860+0.422960 
## [14] train-rmse:3.265791+0.079232    test-rmse:3.553022+0.419545 
## [15] train-rmse:3.132042+0.075708    test-rmse:3.436911+0.420695 
## [16] train-rmse:3.011538+0.073548    test-rmse:3.335278+0.425349 
## [17] train-rmse:2.905503+0.072169    test-rmse:3.256610+0.429906 
## [18] train-rmse:2.807567+0.072728    test-rmse:3.171009+0.435455 
## [19] train-rmse:2.720127+0.070850    test-rmse:3.109081+0.434179 
## [20] train-rmse:2.641578+0.067773    test-rmse:3.040942+0.444203 
## [21] train-rmse:2.567363+0.061168    test-rmse:2.992795+0.441517 
## [22] train-rmse:2.508925+0.062494    test-rmse:2.945692+0.442372 
## [23] train-rmse:2.449016+0.061791    test-rmse:2.912380+0.442250 
## [24] train-rmse:2.388438+0.055869    test-rmse:2.856729+0.447209 
## [25] train-rmse:2.341329+0.055770    test-rmse:2.833399+0.437475 
## [26] train-rmse:2.300087+0.055980    test-rmse:2.811515+0.443028 
## [27] train-rmse:2.255640+0.054393    test-rmse:2.784337+0.459805 
## [28] train-rmse:2.211078+0.054654    test-rmse:2.759884+0.465811 
## [29] train-rmse:2.172043+0.057638    test-rmse:2.735603+0.469137 
## [30] train-rmse:2.139896+0.054268    test-rmse:2.716693+0.468799 
## [31] train-rmse:2.103161+0.056836    test-rmse:2.690058+0.467290 
## [32] train-rmse:2.068411+0.053845    test-rmse:2.669434+0.472002 
## [33] train-rmse:2.042444+0.053454    test-rmse:2.650436+0.472404 
## [34] train-rmse:2.009681+0.053580    test-rmse:2.628589+0.461211 
## [35] train-rmse:1.981083+0.052845    test-rmse:2.613670+0.465150 
## [36] train-rmse:1.956780+0.052568    test-rmse:2.594387+0.470727 
## [37] train-rmse:1.931079+0.051560    test-rmse:2.589937+0.479097 
## [38] train-rmse:1.905284+0.049529    test-rmse:2.575817+0.473284 
## [39] train-rmse:1.880549+0.054078    test-rmse:2.551965+0.463944 
## [40] train-rmse:1.860063+0.052114    test-rmse:2.533559+0.469199 
## [41] train-rmse:1.840629+0.052274    test-rmse:2.529886+0.469376 
## [42] train-rmse:1.819556+0.054242    test-rmse:2.511848+0.463552 
## [43] train-rmse:1.798479+0.055043    test-rmse:2.510893+0.462950 
## [44] train-rmse:1.776121+0.053252    test-rmse:2.497588+0.465937 
## [45] train-rmse:1.759306+0.055017    test-rmse:2.487836+0.465638 
## [46] train-rmse:1.739587+0.052090    test-rmse:2.477012+0.468450 
## [47] train-rmse:1.720525+0.053216    test-rmse:2.462818+0.463686 
## [48] train-rmse:1.703768+0.054514    test-rmse:2.455941+0.461946 
## [49] train-rmse:1.689720+0.054552    test-rmse:2.447556+0.463209 
## [50] train-rmse:1.673476+0.054259    test-rmse:2.434719+0.468324 
## [51] train-rmse:1.660203+0.054615    test-rmse:2.425691+0.464455 
## [52] train-rmse:1.642619+0.052801    test-rmse:2.412933+0.468040 
## [53] train-rmse:1.626017+0.051665    test-rmse:2.405832+0.467793 
## [54] train-rmse:1.611364+0.052948    test-rmse:2.396749+0.472229 
## [55] train-rmse:1.595410+0.051036    test-rmse:2.384248+0.474402 
## [56] train-rmse:1.581450+0.049988    test-rmse:2.383662+0.472613 
## [57] train-rmse:1.567408+0.049024    test-rmse:2.382230+0.470299 
## [58] train-rmse:1.553978+0.047950    test-rmse:2.375275+0.470571 
## [59] train-rmse:1.540572+0.046235    test-rmse:2.371976+0.469721 
## [60] train-rmse:1.527630+0.045754    test-rmse:2.360191+0.471270 
## [61] train-rmse:1.514541+0.046579    test-rmse:2.353254+0.474193 
## [62] train-rmse:1.502882+0.046086    test-rmse:2.350247+0.475067 
## [63] train-rmse:1.491591+0.047879    test-rmse:2.342802+0.467929 
## [64] train-rmse:1.474968+0.044524    test-rmse:2.338742+0.473880 
## [65] train-rmse:1.462255+0.044490    test-rmse:2.333019+0.474514 
## [66] train-rmse:1.452961+0.044458    test-rmse:2.329612+0.476468 
## [67] train-rmse:1.437713+0.043102    test-rmse:2.318162+0.476901 
## [68] train-rmse:1.423412+0.044024    test-rmse:2.309598+0.471556 
## [69] train-rmse:1.411328+0.042939    test-rmse:2.308227+0.472581 
## [70] train-rmse:1.399922+0.042733    test-rmse:2.304807+0.470461 
## [71] train-rmse:1.391060+0.042959    test-rmse:2.300697+0.472995 
## [72] train-rmse:1.380650+0.042457    test-rmse:2.297181+0.474085 
## [73] train-rmse:1.370949+0.041892    test-rmse:2.291734+0.474943 
## [74] train-rmse:1.362877+0.043138    test-rmse:2.288110+0.474180 
## [75] train-rmse:1.353429+0.044195    test-rmse:2.288345+0.473761 
## [76] train-rmse:1.343522+0.043905    test-rmse:2.285601+0.473092 
## [77] train-rmse:1.330361+0.042842    test-rmse:2.279917+0.471283 
## [78] train-rmse:1.321889+0.043265    test-rmse:2.272878+0.468367 
## [79] train-rmse:1.311863+0.042608    test-rmse:2.275872+0.471147 
## [80] train-rmse:1.303262+0.043676    test-rmse:2.270248+0.473844 
## [81] train-rmse:1.293095+0.042353    test-rmse:2.265061+0.475382 
## [82] train-rmse:1.285114+0.041880    test-rmse:2.261941+0.474505 
## [83] train-rmse:1.276787+0.041572    test-rmse:2.259592+0.476949 
## [84] train-rmse:1.267606+0.039966    test-rmse:2.255171+0.476221 
## [85] train-rmse:1.260337+0.041975    test-rmse:2.252725+0.474067 
## [86] train-rmse:1.249199+0.039872    test-rmse:2.251816+0.475749 
## [87] train-rmse:1.242778+0.040232    test-rmse:2.246500+0.475945 
## [88] train-rmse:1.233942+0.039261    test-rmse:2.240623+0.476057 
## [89] train-rmse:1.224956+0.037300    test-rmse:2.242102+0.477452 
## [90] train-rmse:1.215462+0.039954    test-rmse:2.233784+0.474982 
## [91] train-rmse:1.206820+0.038732    test-rmse:2.230882+0.476336 
## [92] train-rmse:1.199029+0.038704    test-rmse:2.227838+0.476782 
## [93] train-rmse:1.192154+0.038346    test-rmse:2.223133+0.475914 
## [94] train-rmse:1.185049+0.038010    test-rmse:2.224080+0.478907 
## [95] train-rmse:1.176890+0.036874    test-rmse:2.223164+0.481696 
## [96] train-rmse:1.169460+0.036194    test-rmse:2.221388+0.480909 
## [97] train-rmse:1.162986+0.035151    test-rmse:2.218973+0.480149 
## [98] train-rmse:1.156215+0.035491    test-rmse:2.219587+0.483766 
## [99] train-rmse:1.151224+0.035562    test-rmse:2.216948+0.484939 
## [100]    train-rmse:1.145720+0.035536    test-rmse:2.214392+0.485831 
## [101]    train-rmse:1.139356+0.036614    test-rmse:2.211000+0.486820 
## [102]    train-rmse:1.133980+0.036551    test-rmse:2.208409+0.487713 
## [103]    train-rmse:1.127593+0.036498    test-rmse:2.206472+0.487345 
## [104]    train-rmse:1.120200+0.034617    test-rmse:2.204244+0.487605 
## [105]    train-rmse:1.113234+0.035944    test-rmse:2.201520+0.482654 
## [106]    train-rmse:1.105817+0.035976    test-rmse:2.201844+0.481564 
## [107]    train-rmse:1.098089+0.034549    test-rmse:2.200190+0.482724 
## [108]    train-rmse:1.092411+0.034669    test-rmse:2.199914+0.482679 
## [109]    train-rmse:1.086526+0.036468    test-rmse:2.197209+0.482667 
## [110]    train-rmse:1.080607+0.036055    test-rmse:2.193977+0.486606 
## [111]    train-rmse:1.075023+0.035319    test-rmse:2.194387+0.487811 
## [112]    train-rmse:1.070373+0.035041    test-rmse:2.193060+0.486998 
## [113]    train-rmse:1.063727+0.033977    test-rmse:2.191029+0.486675 
## [114]    train-rmse:1.059121+0.035011    test-rmse:2.188747+0.487807 
## [115]    train-rmse:1.053473+0.035757    test-rmse:2.186747+0.490032 
## [116]    train-rmse:1.044634+0.036049    test-rmse:2.185249+0.487381 
## [117]    train-rmse:1.034613+0.035752    test-rmse:2.183101+0.489204 
## [118]    train-rmse:1.028578+0.034218    test-rmse:2.181417+0.489392 
## [119]    train-rmse:1.021787+0.032406    test-rmse:2.177361+0.487965 
## [120]    train-rmse:1.016487+0.034047    test-rmse:2.172629+0.484652 
## [121]    train-rmse:1.012273+0.034796    test-rmse:2.171115+0.485152 
## [122]    train-rmse:1.007050+0.035659    test-rmse:2.170289+0.485571 
## [123]    train-rmse:1.002872+0.035216    test-rmse:2.169465+0.486310 
## [124]    train-rmse:0.998070+0.036095    test-rmse:2.168595+0.488551 
## [125]    train-rmse:0.993410+0.035795    test-rmse:2.164938+0.489637 
## [126]    train-rmse:0.987925+0.035386    test-rmse:2.164177+0.489993 
## [127]    train-rmse:0.983802+0.035264    test-rmse:2.162546+0.489764 
## [128]    train-rmse:0.979220+0.037195    test-rmse:2.160264+0.486417 
## [129]    train-rmse:0.972798+0.039150    test-rmse:2.158697+0.486126 
## [130]    train-rmse:0.965406+0.037797    test-rmse:2.155305+0.485507 
## [131]    train-rmse:0.960551+0.036643    test-rmse:2.152288+0.487661 
## [132]    train-rmse:0.955457+0.036585    test-rmse:2.148843+0.489504 
## [133]    train-rmse:0.950250+0.035311    test-rmse:2.145507+0.490541 
## [134]    train-rmse:0.946432+0.037100    test-rmse:2.143646+0.491321 
## [135]    train-rmse:0.942087+0.037455    test-rmse:2.143999+0.491489 
## [136]    train-rmse:0.936886+0.039651    test-rmse:2.142642+0.489940 
## [137]    train-rmse:0.931293+0.038972    test-rmse:2.141889+0.490564 
## [138]    train-rmse:0.925841+0.039459    test-rmse:2.136787+0.490266 
## [139]    train-rmse:0.920954+0.038993    test-rmse:2.138727+0.488804 
## [140]    train-rmse:0.917557+0.039039    test-rmse:2.137000+0.489513 
## [141]    train-rmse:0.909939+0.036432    test-rmse:2.134641+0.490013 
## [142]    train-rmse:0.903412+0.033847    test-rmse:2.132151+0.491320 
## [143]    train-rmse:0.898075+0.034232    test-rmse:2.128606+0.493266 
## [144]    train-rmse:0.894884+0.034652    test-rmse:2.128232+0.491952 
## [145]    train-rmse:0.889821+0.034466    test-rmse:2.127763+0.492273 
## [146]    train-rmse:0.886183+0.034569    test-rmse:2.126134+0.493181 
## [147]    train-rmse:0.883212+0.035157    test-rmse:2.123888+0.492299 
## [148]    train-rmse:0.878343+0.035875    test-rmse:2.121987+0.493098 
## [149]    train-rmse:0.872497+0.033939    test-rmse:2.118875+0.493600 
## [150]    train-rmse:0.867397+0.035354    test-rmse:2.118800+0.492018 
## [151]    train-rmse:0.863219+0.036485    test-rmse:2.118745+0.492973 
## [152]    train-rmse:0.859350+0.037229    test-rmse:2.116466+0.491792 
## [153]    train-rmse:0.855946+0.038210    test-rmse:2.113091+0.491535 
## [154]    train-rmse:0.852032+0.038819    test-rmse:2.113502+0.492900 
## [155]    train-rmse:0.849433+0.038987    test-rmse:2.113014+0.493169 
## [156]    train-rmse:0.843671+0.037993    test-rmse:2.111728+0.493642 
## [157]    train-rmse:0.839804+0.038429    test-rmse:2.111122+0.493663 
## [158]    train-rmse:0.835931+0.037805    test-rmse:2.109382+0.493743 
## [159]    train-rmse:0.831122+0.036716    test-rmse:2.106409+0.494298 
## [160]    train-rmse:0.827368+0.036301    test-rmse:2.107387+0.493611 
## [161]    train-rmse:0.825061+0.036345    test-rmse:2.106520+0.493713 
## [162]    train-rmse:0.821107+0.035730    test-rmse:2.105461+0.493207 
## [163]    train-rmse:0.814922+0.035342    test-rmse:2.102543+0.494209 
## [164]    train-rmse:0.810605+0.036337    test-rmse:2.101730+0.497159 
## [165]    train-rmse:0.805986+0.039719    test-rmse:2.100617+0.496411 
## [166]    train-rmse:0.802732+0.039903    test-rmse:2.099784+0.496848 
## [167]    train-rmse:0.799831+0.039531    test-rmse:2.098924+0.496802 
## [168]    train-rmse:0.797352+0.039527    test-rmse:2.098942+0.496119 
## [169]    train-rmse:0.792017+0.039072    test-rmse:2.098389+0.496131 
## [170]    train-rmse:0.788153+0.038472    test-rmse:2.096467+0.495781 
## [171]    train-rmse:0.784318+0.038229    test-rmse:2.095843+0.496558 
## [172]    train-rmse:0.779116+0.039655    test-rmse:2.097300+0.497115 
## [173]    train-rmse:0.775229+0.038255    test-rmse:2.095300+0.496988 
## [174]    train-rmse:0.771416+0.037503    test-rmse:2.092607+0.499009 
## [175]    train-rmse:0.767218+0.037969    test-rmse:2.090674+0.498760 
## [176]    train-rmse:0.763350+0.038164    test-rmse:2.090373+0.499708 
## [177]    train-rmse:0.760307+0.039255    test-rmse:2.090433+0.499463 
## [178]    train-rmse:0.755396+0.040304    test-rmse:2.089591+0.498113 
## [179]    train-rmse:0.751907+0.039982    test-rmse:2.088513+0.498419 
## [180]    train-rmse:0.748607+0.039006    test-rmse:2.088143+0.498720 
## [181]    train-rmse:0.746789+0.038850    test-rmse:2.087289+0.498584 
## [182]    train-rmse:0.743632+0.038612    test-rmse:2.086469+0.499507 
## [183]    train-rmse:0.740231+0.037879    test-rmse:2.085923+0.499516 
## [184]    train-rmse:0.736525+0.037980    test-rmse:2.086724+0.500874 
## [185]    train-rmse:0.731398+0.037124    test-rmse:2.082868+0.502256 
## [186]    train-rmse:0.727720+0.037038    test-rmse:2.081920+0.502713 
## [187]    train-rmse:0.724233+0.038339    test-rmse:2.081965+0.502496 
## [188]    train-rmse:0.721457+0.038525    test-rmse:2.081933+0.502075 
## [189]    train-rmse:0.718144+0.037710    test-rmse:2.081325+0.502577 
## [190]    train-rmse:0.715768+0.037173    test-rmse:2.081039+0.502801 
## [191]    train-rmse:0.713879+0.037518    test-rmse:2.080018+0.502913 
## [192]    train-rmse:0.708888+0.036546    test-rmse:2.080938+0.503727 
## [193]    train-rmse:0.706322+0.035689    test-rmse:2.080453+0.503589 
## [194]    train-rmse:0.703332+0.036435    test-rmse:2.079653+0.503170 
## [195]    train-rmse:0.699672+0.037902    test-rmse:2.078650+0.502354 
## [196]    train-rmse:0.696915+0.038330    test-rmse:2.078013+0.502813 
## [197]    train-rmse:0.695053+0.038113    test-rmse:2.079315+0.504177 
## [198]    train-rmse:0.691975+0.039162    test-rmse:2.078626+0.503943 
## [199]    train-rmse:0.687673+0.037575    test-rmse:2.076408+0.505084 
## [200]    train-rmse:0.684409+0.037688    test-rmse:2.075736+0.505057 
## [201]    train-rmse:0.681027+0.037118    test-rmse:2.074235+0.504858 
## [202]    train-rmse:0.677327+0.036874    test-rmse:2.074594+0.505606 
## [203]    train-rmse:0.674259+0.037836    test-rmse:2.074927+0.505739 
## [204]    train-rmse:0.672154+0.038169    test-rmse:2.075099+0.504529 
## [205]    train-rmse:0.669806+0.037972    test-rmse:2.074209+0.504637 
## [206]    train-rmse:0.666928+0.037482    test-rmse:2.073905+0.505566 
## [207]    train-rmse:0.664264+0.036436    test-rmse:2.073148+0.505863 
## [208]    train-rmse:0.660976+0.035825    test-rmse:2.072567+0.506081 
## [209]    train-rmse:0.658504+0.035014    test-rmse:2.072720+0.506201 
## [210]    train-rmse:0.654291+0.034382    test-rmse:2.071016+0.505905 
## [211]    train-rmse:0.651358+0.033550    test-rmse:2.070539+0.506114 
## [212]    train-rmse:0.648326+0.032485    test-rmse:2.070985+0.506255 
## [213]    train-rmse:0.645892+0.033907    test-rmse:2.071088+0.506116 
## [214]    train-rmse:0.643622+0.033988    test-rmse:2.069667+0.505997 
## [215]    train-rmse:0.641672+0.033468    test-rmse:2.069354+0.506262 
## [216]    train-rmse:0.638820+0.032699    test-rmse:2.068984+0.505989 
## [217]    train-rmse:0.636697+0.032908    test-rmse:2.068101+0.506481 
## [218]    train-rmse:0.634508+0.032585    test-rmse:2.068117+0.505938 
## [219]    train-rmse:0.631763+0.031800    test-rmse:2.067518+0.505946 
## [220]    train-rmse:0.629921+0.031560    test-rmse:2.067497+0.506079 
## [221]    train-rmse:0.627976+0.031245    test-rmse:2.067387+0.505476 
## [222]    train-rmse:0.625550+0.030743    test-rmse:2.067187+0.505910 
## [223]    train-rmse:0.622324+0.031144    test-rmse:2.067444+0.505954 
## [224]    train-rmse:0.618913+0.031653    test-rmse:2.067422+0.505883 
## [225]    train-rmse:0.616740+0.031262    test-rmse:2.068340+0.506146 
## [226]    train-rmse:0.613317+0.030796    test-rmse:2.069120+0.507111 
## [227]    train-rmse:0.610767+0.031286    test-rmse:2.069532+0.507425 
## [228]    train-rmse:0.608487+0.031009    test-rmse:2.069156+0.507616 
## Stopping. Best iteration:
## [222]    train-rmse:0.625550+0.030743    test-rmse:2.067187+0.505910
## 
## 3 
## [1]  train-rmse:7.498217+0.157191    test-rmse:7.495779+0.653569 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.883003+0.141198    test-rmse:6.909630+0.621051 
## [3]  train-rmse:6.327433+0.127156    test-rmse:6.381353+0.579564 
## [4]  train-rmse:5.832149+0.115349    test-rmse:5.911930+0.544250 
## [5]  train-rmse:5.388517+0.101483    test-rmse:5.485037+0.517168 
## [6]  train-rmse:4.989124+0.092915    test-rmse:5.109017+0.492027 
## [7]  train-rmse:4.634451+0.082004    test-rmse:4.779451+0.461339 
## [8]  train-rmse:4.317313+0.080167    test-rmse:4.487915+0.437975 
## [9]  train-rmse:4.037668+0.075008    test-rmse:4.247172+0.427507 
## [10] train-rmse:3.788615+0.074750    test-rmse:4.029248+0.418384 
## [11] train-rmse:3.562745+0.067064    test-rmse:3.832624+0.419365 
## [12] train-rmse:3.362907+0.063211    test-rmse:3.661829+0.407234 
## [13] train-rmse:3.181508+0.057212    test-rmse:3.496466+0.416126 
## [14] train-rmse:3.022375+0.056136    test-rmse:3.369013+0.411736 
## [15] train-rmse:2.885960+0.055150    test-rmse:3.260767+0.413733 
## [16] train-rmse:2.756668+0.055962    test-rmse:3.150111+0.417917 
## [17] train-rmse:2.644979+0.054831    test-rmse:3.075960+0.413619 
## [18] train-rmse:2.545051+0.053666    test-rmse:3.001214+0.418489 
## [19] train-rmse:2.455911+0.052219    test-rmse:2.940089+0.427726 
## [20] train-rmse:2.370844+0.046361    test-rmse:2.878190+0.427423 
## [21] train-rmse:2.292379+0.045091    test-rmse:2.821928+0.436555 
## [22] train-rmse:2.222442+0.050594    test-rmse:2.780650+0.429116 
## [23] train-rmse:2.159664+0.050532    test-rmse:2.744101+0.426584 
## [24] train-rmse:2.100406+0.046915    test-rmse:2.700857+0.440346 
## [25] train-rmse:2.049588+0.048975    test-rmse:2.659180+0.430757 
## [26] train-rmse:2.000465+0.049786    test-rmse:2.645671+0.434558 
## [27] train-rmse:1.953935+0.043221    test-rmse:2.616617+0.437576 
## [28] train-rmse:1.912850+0.044903    test-rmse:2.589242+0.438416 
## [29] train-rmse:1.875480+0.045684    test-rmse:2.569490+0.436473 
## [30] train-rmse:1.840923+0.044616    test-rmse:2.549971+0.424780 
## [31] train-rmse:1.806917+0.042973    test-rmse:2.530911+0.424135 
## [32] train-rmse:1.772340+0.042372    test-rmse:2.518335+0.429409 
## [33] train-rmse:1.744447+0.040535    test-rmse:2.503683+0.427037 
## [34] train-rmse:1.713594+0.043710    test-rmse:2.487451+0.427848 
## [35] train-rmse:1.689949+0.044431    test-rmse:2.475254+0.429357 
## [36] train-rmse:1.663119+0.044341    test-rmse:2.462092+0.431572 
## [37] train-rmse:1.634131+0.043307    test-rmse:2.441182+0.431565 
## [38] train-rmse:1.608164+0.043005    test-rmse:2.422491+0.422448 
## [39] train-rmse:1.588240+0.043165    test-rmse:2.414563+0.423663 
## [40] train-rmse:1.564441+0.041930    test-rmse:2.406243+0.428831 
## [41] train-rmse:1.542676+0.040358    test-rmse:2.388381+0.428039 
## [42] train-rmse:1.523894+0.037700    test-rmse:2.378162+0.430492 
## [43] train-rmse:1.501477+0.037181    test-rmse:2.368240+0.426316 
## [44] train-rmse:1.481032+0.034992    test-rmse:2.361838+0.429690 
## [45] train-rmse:1.462963+0.036947    test-rmse:2.356836+0.431396 
## [46] train-rmse:1.443363+0.033761    test-rmse:2.341781+0.431307 
## [47] train-rmse:1.426876+0.036136    test-rmse:2.332059+0.430911 
## [48] train-rmse:1.408013+0.034559    test-rmse:2.326448+0.427252 
## [49] train-rmse:1.393546+0.035815    test-rmse:2.323687+0.433296 
## [50] train-rmse:1.375748+0.035844    test-rmse:2.318044+0.437488 
## [51] train-rmse:1.354838+0.027914    test-rmse:2.309523+0.436995 
## [52] train-rmse:1.340208+0.029697    test-rmse:2.302074+0.434830 
## [53] train-rmse:1.326336+0.029101    test-rmse:2.300239+0.438407 
## [54] train-rmse:1.312435+0.030222    test-rmse:2.293017+0.438466 
## [55] train-rmse:1.297570+0.026534    test-rmse:2.284640+0.440025 
## [56] train-rmse:1.283003+0.028150    test-rmse:2.274869+0.435114 
## [57] train-rmse:1.269444+0.027615    test-rmse:2.266013+0.436920 
## [58] train-rmse:1.255390+0.023441    test-rmse:2.260389+0.440252 
## [59] train-rmse:1.243329+0.024372    test-rmse:2.258696+0.439701 
## [60] train-rmse:1.231766+0.022938    test-rmse:2.253782+0.440939 
## [61] train-rmse:1.220307+0.024690    test-rmse:2.245989+0.437828 
## [62] train-rmse:1.209295+0.025135    test-rmse:2.240922+0.438477 
## [63] train-rmse:1.192644+0.027986    test-rmse:2.238855+0.439002 
## [64] train-rmse:1.179843+0.029125    test-rmse:2.233700+0.441811 
## [65] train-rmse:1.169650+0.030763    test-rmse:2.229763+0.440170 
## [66] train-rmse:1.159547+0.031600    test-rmse:2.228543+0.444589 
## [67] train-rmse:1.146697+0.030850    test-rmse:2.228916+0.443331 
## [68] train-rmse:1.138399+0.030886    test-rmse:2.225161+0.445983 
## [69] train-rmse:1.122883+0.027840    test-rmse:2.222568+0.447112 
## [70] train-rmse:1.114924+0.028160    test-rmse:2.219547+0.444101 
## [71] train-rmse:1.103856+0.028799    test-rmse:2.216161+0.446634 
## [72] train-rmse:1.095724+0.029347    test-rmse:2.211796+0.446107 
## [73] train-rmse:1.084860+0.030254    test-rmse:2.205114+0.440764 
## [74] train-rmse:1.077053+0.030507    test-rmse:2.200812+0.442040 
## [75] train-rmse:1.068047+0.030010    test-rmse:2.194780+0.441149 
## [76] train-rmse:1.055373+0.028222    test-rmse:2.196547+0.445628 
## [77] train-rmse:1.046164+0.026794    test-rmse:2.196523+0.446184 
## [78] train-rmse:1.037394+0.026727    test-rmse:2.196098+0.449025 
## [79] train-rmse:1.029707+0.026760    test-rmse:2.196296+0.448207 
## [80] train-rmse:1.018486+0.023916    test-rmse:2.191916+0.448386 
## [81] train-rmse:1.006702+0.026441    test-rmse:2.188602+0.447502 
## [82] train-rmse:0.998400+0.027641    test-rmse:2.185982+0.446616 
## [83] train-rmse:0.992174+0.027945    test-rmse:2.184522+0.447491 
## [84] train-rmse:0.983855+0.030403    test-rmse:2.180552+0.446638 
## [85] train-rmse:0.973690+0.030715    test-rmse:2.176920+0.449527 
## [86] train-rmse:0.964126+0.032944    test-rmse:2.171015+0.452647 
## [87] train-rmse:0.955357+0.032025    test-rmse:2.166102+0.451078 
## [88] train-rmse:0.945259+0.027724    test-rmse:2.165168+0.452173 
## [89] train-rmse:0.936548+0.028867    test-rmse:2.163889+0.452799 
## [90] train-rmse:0.929197+0.026544    test-rmse:2.162411+0.451850 
## [91] train-rmse:0.920917+0.028434    test-rmse:2.162723+0.450773 
## [92] train-rmse:0.914789+0.027558    test-rmse:2.160951+0.452220 
## [93] train-rmse:0.901877+0.023841    test-rmse:2.154074+0.452232 
## [94] train-rmse:0.893733+0.027057    test-rmse:2.151476+0.450826 
## [95] train-rmse:0.886434+0.028485    test-rmse:2.150460+0.450165 
## [96] train-rmse:0.878950+0.028469    test-rmse:2.152474+0.448631 
## [97] train-rmse:0.870852+0.028586    test-rmse:2.151614+0.448579 
## [98] train-rmse:0.862297+0.026384    test-rmse:2.156256+0.448706 
## [99] train-rmse:0.855642+0.027796    test-rmse:2.154785+0.449301 
## [100]    train-rmse:0.848837+0.029340    test-rmse:2.150681+0.450985 
## [101]    train-rmse:0.841355+0.029557    test-rmse:2.147807+0.452329 
## [102]    train-rmse:0.834843+0.028892    test-rmse:2.147606+0.450215 
## [103]    train-rmse:0.829103+0.029348    test-rmse:2.144958+0.451479 
## [104]    train-rmse:0.822654+0.027523    test-rmse:2.143248+0.451670 
## [105]    train-rmse:0.815193+0.024571    test-rmse:2.139587+0.453384 
## [106]    train-rmse:0.809601+0.023258    test-rmse:2.138649+0.451732 
## [107]    train-rmse:0.802541+0.023874    test-rmse:2.135656+0.452220 
## [108]    train-rmse:0.795573+0.023134    test-rmse:2.136413+0.452111 
## [109]    train-rmse:0.789012+0.025954    test-rmse:2.135026+0.450619 
## [110]    train-rmse:0.781591+0.027489    test-rmse:2.133648+0.450964 
## [111]    train-rmse:0.777686+0.027929    test-rmse:2.130215+0.451892 
## [112]    train-rmse:0.770862+0.025944    test-rmse:2.127435+0.453585 
## [113]    train-rmse:0.764547+0.025148    test-rmse:2.126238+0.453270 
## [114]    train-rmse:0.759455+0.026864    test-rmse:2.125955+0.453502 
## [115]    train-rmse:0.750557+0.025845    test-rmse:2.125491+0.452690 
## [116]    train-rmse:0.746509+0.026098    test-rmse:2.122786+0.453804 
## [117]    train-rmse:0.741216+0.024698    test-rmse:2.124232+0.453315 
## [118]    train-rmse:0.735728+0.025070    test-rmse:2.123648+0.453566 
## [119]    train-rmse:0.728700+0.023642    test-rmse:2.122698+0.454190 
## [120]    train-rmse:0.723512+0.023209    test-rmse:2.122465+0.454096 
## [121]    train-rmse:0.718362+0.023461    test-rmse:2.120378+0.453911 
## [122]    train-rmse:0.713021+0.023199    test-rmse:2.119616+0.453460 
## [123]    train-rmse:0.707020+0.022241    test-rmse:2.118218+0.453217 
## [124]    train-rmse:0.701313+0.023609    test-rmse:2.115100+0.452750 
## [125]    train-rmse:0.696024+0.022810    test-rmse:2.114039+0.451419 
## [126]    train-rmse:0.692124+0.022324    test-rmse:2.113064+0.453412 
## [127]    train-rmse:0.687347+0.023332    test-rmse:2.111504+0.454178 
## [128]    train-rmse:0.680731+0.024018    test-rmse:2.110424+0.454742 
## [129]    train-rmse:0.671900+0.021485    test-rmse:2.112807+0.454329 
## [130]    train-rmse:0.668305+0.020671    test-rmse:2.113141+0.454710 
## [131]    train-rmse:0.663353+0.020748    test-rmse:2.112218+0.454792 
## [132]    train-rmse:0.657678+0.020731    test-rmse:2.109784+0.454802 
## [133]    train-rmse:0.654406+0.021642    test-rmse:2.106591+0.455722 
## [134]    train-rmse:0.647096+0.022371    test-rmse:2.107029+0.455873 
## [135]    train-rmse:0.642074+0.021670    test-rmse:2.106463+0.455388 
## [136]    train-rmse:0.638479+0.021568    test-rmse:2.105387+0.455926 
## [137]    train-rmse:0.632755+0.023843    test-rmse:2.103074+0.455721 
## [138]    train-rmse:0.628498+0.022827    test-rmse:2.103596+0.455228 
## [139]    train-rmse:0.625097+0.024677    test-rmse:2.102142+0.455771 
## [140]    train-rmse:0.622228+0.024415    test-rmse:2.101760+0.455647 
## [141]    train-rmse:0.618672+0.024383    test-rmse:2.101744+0.456472 
## [142]    train-rmse:0.613483+0.023882    test-rmse:2.100744+0.457307 
## [143]    train-rmse:0.609926+0.024140    test-rmse:2.100774+0.458514 
## [144]    train-rmse:0.606104+0.024088    test-rmse:2.100161+0.458647 
## [145]    train-rmse:0.601573+0.023381    test-rmse:2.099133+0.458935 
## [146]    train-rmse:0.596345+0.024787    test-rmse:2.098526+0.457744 
## [147]    train-rmse:0.592969+0.023984    test-rmse:2.097621+0.458558 
## [148]    train-rmse:0.590338+0.023547    test-rmse:2.098276+0.458212 
## [149]    train-rmse:0.585755+0.021771    test-rmse:2.098931+0.458042 
## [150]    train-rmse:0.582180+0.020519    test-rmse:2.098065+0.457015 
## [151]    train-rmse:0.576617+0.019113    test-rmse:2.097228+0.457909 
## [152]    train-rmse:0.572689+0.020506    test-rmse:2.096326+0.457953 
## [153]    train-rmse:0.568503+0.022598    test-rmse:2.096496+0.458530 
## [154]    train-rmse:0.563775+0.023331    test-rmse:2.094212+0.458266 
## [155]    train-rmse:0.560491+0.022611    test-rmse:2.093320+0.458490 
## [156]    train-rmse:0.556535+0.021929    test-rmse:2.093214+0.458952 
## [157]    train-rmse:0.554063+0.021841    test-rmse:2.093140+0.458296 
## [158]    train-rmse:0.549940+0.023524    test-rmse:2.093278+0.460130 
## [159]    train-rmse:0.546836+0.023197    test-rmse:2.092636+0.460789 
## [160]    train-rmse:0.542687+0.021775    test-rmse:2.092543+0.461167 
## [161]    train-rmse:0.539970+0.021958    test-rmse:2.092321+0.462329 
## [162]    train-rmse:0.536385+0.022853    test-rmse:2.092319+0.461930 
## [163]    train-rmse:0.531549+0.022817    test-rmse:2.091958+0.463097 
## [164]    train-rmse:0.527604+0.021833    test-rmse:2.089483+0.463665 
## [165]    train-rmse:0.524605+0.022288    test-rmse:2.089384+0.463510 
## [166]    train-rmse:0.520910+0.021910    test-rmse:2.088951+0.463885 
## [167]    train-rmse:0.517437+0.021897    test-rmse:2.088077+0.464007 
## [168]    train-rmse:0.515042+0.021915    test-rmse:2.088074+0.464084 
## [169]    train-rmse:0.512790+0.021625    test-rmse:2.088227+0.464057 
## [170]    train-rmse:0.507137+0.023042    test-rmse:2.085860+0.463255 
## [171]    train-rmse:0.503909+0.022493    test-rmse:2.087609+0.462918 
## [172]    train-rmse:0.500858+0.022183    test-rmse:2.087669+0.463322 
## [173]    train-rmse:0.496683+0.023576    test-rmse:2.087278+0.463487 
## [174]    train-rmse:0.494378+0.023227    test-rmse:2.086670+0.463153 
## [175]    train-rmse:0.492078+0.022678    test-rmse:2.086591+0.463095 
## [176]    train-rmse:0.490119+0.022418    test-rmse:2.086096+0.463730 
## Stopping. Best iteration:
## [170]    train-rmse:0.507137+0.023042    test-rmse:2.085860+0.463255
## 
## 4 
## [1]  train-rmse:7.480292+0.156033    test-rmse:7.464837+0.645716 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.844487+0.139664    test-rmse:6.849760+0.604625 
## [3]  train-rmse:6.274671+0.125253    test-rmse:6.301242+0.566609 
## [4]  train-rmse:5.761710+0.116776    test-rmse:5.814442+0.531514 
## [5]  train-rmse:5.303412+0.109975    test-rmse:5.383958+0.499849 
## [6]  train-rmse:4.893840+0.098698    test-rmse:5.014735+0.480387 
## [7]  train-rmse:4.530695+0.092866    test-rmse:4.689189+0.459591 
## [8]  train-rmse:4.203730+0.086466    test-rmse:4.400835+0.443429 
## [9]  train-rmse:3.916126+0.077837    test-rmse:4.162085+0.436074 
## [10] train-rmse:3.657283+0.071017    test-rmse:3.944800+0.437215 
## [11] train-rmse:3.429524+0.065104    test-rmse:3.767076+0.439956 
## [12] train-rmse:3.227345+0.060892    test-rmse:3.607233+0.449097 
## [13] train-rmse:3.042745+0.057772    test-rmse:3.464093+0.447624 
## [14] train-rmse:2.880842+0.055065    test-rmse:3.349097+0.451752 
## [15] train-rmse:2.740806+0.054652    test-rmse:3.254288+0.454701 
## [16] train-rmse:2.612182+0.053046    test-rmse:3.165194+0.469132 
## [17] train-rmse:2.491420+0.052421    test-rmse:3.082767+0.472423 
## [18] train-rmse:2.382060+0.048972    test-rmse:3.016578+0.489595 
## [19] train-rmse:2.284871+0.046173    test-rmse:2.947069+0.489066 
## [20] train-rmse:2.200305+0.045754    test-rmse:2.890052+0.494849 
## [21] train-rmse:2.117868+0.042690    test-rmse:2.854437+0.505753 
## [22] train-rmse:2.042597+0.037797    test-rmse:2.809266+0.510134 
## [23] train-rmse:1.974708+0.033274    test-rmse:2.764762+0.522380 
## [24] train-rmse:1.916822+0.036195    test-rmse:2.732259+0.519070 
## [25] train-rmse:1.859241+0.031595    test-rmse:2.697155+0.516632 
## [26] train-rmse:1.809393+0.032640    test-rmse:2.674062+0.516840 
## [27] train-rmse:1.756151+0.042361    test-rmse:2.650763+0.522425 
## [28] train-rmse:1.715864+0.041031    test-rmse:2.627873+0.518004 
## [29] train-rmse:1.673853+0.035042    test-rmse:2.606588+0.524521 
## [30] train-rmse:1.630690+0.036384    test-rmse:2.590344+0.524643 
## [31] train-rmse:1.595689+0.040603    test-rmse:2.575145+0.526330 
## [32] train-rmse:1.564617+0.040253    test-rmse:2.559037+0.524473 
## [33] train-rmse:1.526183+0.038412    test-rmse:2.540144+0.520344 
## [34] train-rmse:1.497952+0.040356    test-rmse:2.524702+0.523225 
## [35] train-rmse:1.470977+0.040261    test-rmse:2.514950+0.533792 
## [36] train-rmse:1.442876+0.042327    test-rmse:2.508174+0.531393 
## [37] train-rmse:1.417597+0.042056    test-rmse:2.492494+0.533082 
## [38] train-rmse:1.392887+0.039505    test-rmse:2.485184+0.535057 
## [39] train-rmse:1.363392+0.036725    test-rmse:2.473791+0.531393 
## [40] train-rmse:1.341339+0.036320    test-rmse:2.466081+0.532800 
## [41] train-rmse:1.322320+0.035956    test-rmse:2.461570+0.531085 
## [42] train-rmse:1.304057+0.036313    test-rmse:2.452808+0.534045 
## [43] train-rmse:1.279862+0.030934    test-rmse:2.446536+0.537680 
## [44] train-rmse:1.258688+0.032704    test-rmse:2.436903+0.532622 
## [45] train-rmse:1.241143+0.035817    test-rmse:2.432173+0.528360 
## [46] train-rmse:1.227153+0.036059    test-rmse:2.423906+0.531320 
## [47] train-rmse:1.211953+0.035632    test-rmse:2.422756+0.531063 
## [48] train-rmse:1.196599+0.035788    test-rmse:2.416683+0.533200 
## [49] train-rmse:1.177901+0.037656    test-rmse:2.404451+0.536330 
## [50] train-rmse:1.164223+0.037298    test-rmse:2.396622+0.538316 
## [51] train-rmse:1.151731+0.036371    test-rmse:2.392977+0.538226 
## [52] train-rmse:1.136389+0.036637    test-rmse:2.395074+0.541269 
## [53] train-rmse:1.121267+0.039410    test-rmse:2.390081+0.536776 
## [54] train-rmse:1.106636+0.041662    test-rmse:2.386274+0.536417 
## [55] train-rmse:1.092075+0.044216    test-rmse:2.384088+0.539099 
## [56] train-rmse:1.078185+0.044234    test-rmse:2.382402+0.538073 
## [57] train-rmse:1.065694+0.043958    test-rmse:2.378175+0.539175 
## [58] train-rmse:1.056291+0.044745    test-rmse:2.374906+0.540648 
## [59] train-rmse:1.040146+0.044412    test-rmse:2.364231+0.541434 
## [60] train-rmse:1.028467+0.042796    test-rmse:2.362080+0.541146 
## [61] train-rmse:1.013945+0.039867    test-rmse:2.358395+0.542219 
## [62] train-rmse:1.000586+0.040634    test-rmse:2.358393+0.543791 
## [63] train-rmse:0.985261+0.037205    test-rmse:2.351093+0.542282 
## [64] train-rmse:0.973777+0.036628    test-rmse:2.350489+0.543750 
## [65] train-rmse:0.962972+0.035800    test-rmse:2.348750+0.546892 
## [66] train-rmse:0.949475+0.032581    test-rmse:2.343201+0.549517 
## [67] train-rmse:0.938337+0.033853    test-rmse:2.340671+0.547192 
## [68] train-rmse:0.930224+0.034290    test-rmse:2.337072+0.547572 
## [69] train-rmse:0.920716+0.035533    test-rmse:2.333196+0.545243 
## [70] train-rmse:0.905205+0.033498    test-rmse:2.330846+0.544525 
## [71] train-rmse:0.895937+0.032029    test-rmse:2.328791+0.546666 
## [72] train-rmse:0.885311+0.031408    test-rmse:2.326522+0.547367 
## [73] train-rmse:0.875539+0.030983    test-rmse:2.323252+0.548800 
## [74] train-rmse:0.864368+0.029759    test-rmse:2.323875+0.546574 
## [75] train-rmse:0.853227+0.028065    test-rmse:2.322205+0.549107 
## [76] train-rmse:0.844164+0.030163    test-rmse:2.321546+0.548481 
## [77] train-rmse:0.832304+0.029185    test-rmse:2.319321+0.548882 
## [78] train-rmse:0.821510+0.026066    test-rmse:2.315496+0.552631 
## [79] train-rmse:0.814402+0.026893    test-rmse:2.314253+0.553462 
## [80] train-rmse:0.807003+0.027774    test-rmse:2.311832+0.550803 
## [81] train-rmse:0.801194+0.026998    test-rmse:2.313087+0.551625 
## [82] train-rmse:0.793810+0.026202    test-rmse:2.311818+0.551449 
## [83] train-rmse:0.783903+0.028169    test-rmse:2.310301+0.550577 
## [84] train-rmse:0.777143+0.028991    test-rmse:2.306450+0.551786 
## [85] train-rmse:0.768713+0.026031    test-rmse:2.306525+0.554715 
## [86] train-rmse:0.759919+0.023728    test-rmse:2.303248+0.556167 
## [87] train-rmse:0.751726+0.025280    test-rmse:2.301056+0.558613 
## [88] train-rmse:0.741327+0.024670    test-rmse:2.299521+0.559384 
## [89] train-rmse:0.733472+0.025166    test-rmse:2.299115+0.560077 
## [90] train-rmse:0.726036+0.027007    test-rmse:2.296911+0.562813 
## [91] train-rmse:0.717403+0.026359    test-rmse:2.296060+0.562629 
## [92] train-rmse:0.708831+0.024535    test-rmse:2.292639+0.560550 
## [93] train-rmse:0.701375+0.023478    test-rmse:2.291585+0.560905 
## [94] train-rmse:0.690558+0.023203    test-rmse:2.290490+0.561286 
## [95] train-rmse:0.684160+0.022442    test-rmse:2.288432+0.561841 
## [96] train-rmse:0.676603+0.023972    test-rmse:2.286363+0.562456 
## [97] train-rmse:0.670974+0.023627    test-rmse:2.285222+0.563292 
## [98] train-rmse:0.665693+0.024278    test-rmse:2.284296+0.562862 
## [99] train-rmse:0.656798+0.022046    test-rmse:2.281568+0.564748 
## [100]    train-rmse:0.648955+0.019678    test-rmse:2.284135+0.563501 
## [101]    train-rmse:0.641192+0.019054    test-rmse:2.283786+0.565304 
## [102]    train-rmse:0.636413+0.019293    test-rmse:2.282872+0.565285 
## [103]    train-rmse:0.629274+0.019289    test-rmse:2.280054+0.565951 
## [104]    train-rmse:0.622407+0.018371    test-rmse:2.279192+0.565634 
## [105]    train-rmse:0.616287+0.020740    test-rmse:2.279257+0.564765 
## [106]    train-rmse:0.609461+0.019620    test-rmse:2.278153+0.564242 
## [107]    train-rmse:0.603718+0.019086    test-rmse:2.277307+0.564741 
## [108]    train-rmse:0.597089+0.018303    test-rmse:2.278692+0.564560 
## [109]    train-rmse:0.590883+0.017983    test-rmse:2.276805+0.566399 
## [110]    train-rmse:0.586128+0.019267    test-rmse:2.276439+0.568179 
## [111]    train-rmse:0.581688+0.019511    test-rmse:2.276078+0.568155 
## [112]    train-rmse:0.574756+0.021649    test-rmse:2.275162+0.566947 
## [113]    train-rmse:0.570080+0.020621    test-rmse:2.273706+0.568396 
## [114]    train-rmse:0.563493+0.019204    test-rmse:2.273005+0.568125 
## [115]    train-rmse:0.559593+0.018228    test-rmse:2.272827+0.568765 
## [116]    train-rmse:0.553422+0.016017    test-rmse:2.270945+0.569626 
## [117]    train-rmse:0.547391+0.019584    test-rmse:2.269743+0.569879 
## [118]    train-rmse:0.543970+0.019486    test-rmse:2.268869+0.569240 
## [119]    train-rmse:0.539599+0.018941    test-rmse:2.268395+0.569951 
## [120]    train-rmse:0.533499+0.018028    test-rmse:2.266263+0.570999 
## [121]    train-rmse:0.528992+0.018541    test-rmse:2.265371+0.571946 
## [122]    train-rmse:0.524785+0.018025    test-rmse:2.265092+0.572430 
## [123]    train-rmse:0.521164+0.017680    test-rmse:2.264780+0.574316 
## [124]    train-rmse:0.515270+0.017586    test-rmse:2.262945+0.574427 
## [125]    train-rmse:0.512450+0.017350    test-rmse:2.262632+0.574658 
## [126]    train-rmse:0.508330+0.017817    test-rmse:2.261654+0.575845 
## [127]    train-rmse:0.503813+0.017700    test-rmse:2.261687+0.576227 
## [128]    train-rmse:0.499718+0.016352    test-rmse:2.262949+0.576762 
## [129]    train-rmse:0.493746+0.015676    test-rmse:2.261085+0.575925 
## [130]    train-rmse:0.486609+0.015534    test-rmse:2.260286+0.574403 
## [131]    train-rmse:0.483832+0.015962    test-rmse:2.259854+0.574862 
## [132]    train-rmse:0.479209+0.015866    test-rmse:2.258888+0.575739 
## [133]    train-rmse:0.474748+0.018039    test-rmse:2.259820+0.575873 
## [134]    train-rmse:0.470021+0.017525    test-rmse:2.258780+0.575972 
## [135]    train-rmse:0.466868+0.017463    test-rmse:2.258167+0.576438 
## [136]    train-rmse:0.461933+0.017055    test-rmse:2.257776+0.576966 
## [137]    train-rmse:0.457930+0.017949    test-rmse:2.257297+0.578037 
## [138]    train-rmse:0.452861+0.018720    test-rmse:2.256393+0.577949 
## [139]    train-rmse:0.449273+0.017390    test-rmse:2.256213+0.578623 
## [140]    train-rmse:0.445264+0.019309    test-rmse:2.255647+0.578069 
## [141]    train-rmse:0.440084+0.020273    test-rmse:2.255737+0.578807 
## [142]    train-rmse:0.435769+0.020532    test-rmse:2.256062+0.578621 
## [143]    train-rmse:0.433354+0.020765    test-rmse:2.255735+0.578849 
## [144]    train-rmse:0.429348+0.020257    test-rmse:2.255309+0.579561 
## [145]    train-rmse:0.425646+0.020393    test-rmse:2.254049+0.579878 
## [146]    train-rmse:0.420538+0.018834    test-rmse:2.254489+0.580736 
## [147]    train-rmse:0.417038+0.018872    test-rmse:2.254160+0.580827 
## [148]    train-rmse:0.412509+0.019848    test-rmse:2.254223+0.580654 
## [149]    train-rmse:0.409755+0.019968    test-rmse:2.255179+0.581494 
## [150]    train-rmse:0.405035+0.019280    test-rmse:2.255446+0.581796 
## [151]    train-rmse:0.401237+0.019664    test-rmse:2.254697+0.582867 
## Stopping. Best iteration:
## [145]    train-rmse:0.425646+0.020393    test-rmse:2.254049+0.579878
## 
## 5 
## [1]  train-rmse:7.468952+0.154456    test-rmse:7.456881+0.656040 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.821931+0.136667    test-rmse:6.833865+0.624351 
## [3]  train-rmse:6.240418+0.121177    test-rmse:6.282598+0.594276 
## [4]  train-rmse:5.716149+0.108191    test-rmse:5.797644+0.562780 
## [5]  train-rmse:5.246960+0.095087    test-rmse:5.364626+0.540526 
## [6]  train-rmse:4.826605+0.085235    test-rmse:4.990985+0.513716 
## [7]  train-rmse:4.450981+0.075362    test-rmse:4.661745+0.496072 
## [8]  train-rmse:4.116637+0.069040    test-rmse:4.375711+0.481237 
## [9]  train-rmse:3.817062+0.061290    test-rmse:4.126312+0.470429 
## [10] train-rmse:3.550221+0.052190    test-rmse:3.910394+0.461040 
## [11] train-rmse:3.310531+0.049984    test-rmse:3.720819+0.447930 
## [12] train-rmse:3.097968+0.041373    test-rmse:3.560931+0.450800 
## [13] train-rmse:2.906702+0.037569    test-rmse:3.424813+0.454944 
## [14] train-rmse:2.737042+0.033307    test-rmse:3.307117+0.459640 
## [15] train-rmse:2.587449+0.029810    test-rmse:3.203532+0.474006 
## [16] train-rmse:2.450597+0.030481    test-rmse:3.114630+0.486122 
## [17] train-rmse:2.324045+0.027913    test-rmse:3.031533+0.480483 
## [18] train-rmse:2.212094+0.027256    test-rmse:2.962952+0.498737 
## [19] train-rmse:2.102923+0.033437    test-rmse:2.888177+0.513664 
## [20] train-rmse:2.006187+0.037771    test-rmse:2.841489+0.526960 
## [21] train-rmse:1.922015+0.036272    test-rmse:2.789127+0.522708 
## [22] train-rmse:1.842035+0.039243    test-rmse:2.748221+0.527798 
## [23] train-rmse:1.767024+0.041153    test-rmse:2.709310+0.528186 
## [24] train-rmse:1.701595+0.043780    test-rmse:2.677479+0.533546 
## [25] train-rmse:1.643397+0.042490    test-rmse:2.645584+0.530853 
## [26] train-rmse:1.589695+0.048428    test-rmse:2.616395+0.539324 
## [27] train-rmse:1.540678+0.044733    test-rmse:2.601405+0.541963 
## [28] train-rmse:1.498456+0.042882    test-rmse:2.576376+0.542691 
## [29] train-rmse:1.452266+0.046584    test-rmse:2.563289+0.546017 
## [30] train-rmse:1.411866+0.051963    test-rmse:2.546829+0.549473 
## [31] train-rmse:1.374768+0.054920    test-rmse:2.538838+0.553107 
## [32] train-rmse:1.338841+0.051177    test-rmse:2.521367+0.554148 
## [33] train-rmse:1.308857+0.051395    test-rmse:2.511480+0.558907 
## [34] train-rmse:1.275841+0.047770    test-rmse:2.502246+0.555606 
## [35] train-rmse:1.239958+0.049341    test-rmse:2.491111+0.558596 
## [36] train-rmse:1.214661+0.047732    test-rmse:2.479850+0.567844 
## [37] train-rmse:1.190574+0.050161    test-rmse:2.470629+0.568185 
## [38] train-rmse:1.163705+0.049948    test-rmse:2.459985+0.572312 
## [39] train-rmse:1.140981+0.051229    test-rmse:2.451555+0.572510 
## [40] train-rmse:1.112214+0.047969    test-rmse:2.444252+0.571983 
## [41] train-rmse:1.089138+0.051549    test-rmse:2.436371+0.570084 
## [42] train-rmse:1.071461+0.051299    test-rmse:2.427559+0.575585 
## [43] train-rmse:1.053956+0.051053    test-rmse:2.423630+0.579807 
## [44] train-rmse:1.031514+0.054942    test-rmse:2.419026+0.582601 
## [45] train-rmse:1.011419+0.053716    test-rmse:2.411420+0.583456 
## [46] train-rmse:0.993842+0.052368    test-rmse:2.408095+0.583830 
## [47] train-rmse:0.973596+0.050036    test-rmse:2.402768+0.582157 
## [48] train-rmse:0.956649+0.049409    test-rmse:2.401019+0.583924 
## [49] train-rmse:0.938305+0.044505    test-rmse:2.392940+0.583899 
## [50] train-rmse:0.924537+0.044468    test-rmse:2.390509+0.588515 
## [51] train-rmse:0.906551+0.038386    test-rmse:2.384503+0.590074 
## [52] train-rmse:0.889317+0.037117    test-rmse:2.381595+0.590932 
## [53] train-rmse:0.872798+0.038415    test-rmse:2.378564+0.592438 
## [54] train-rmse:0.861497+0.037130    test-rmse:2.378070+0.593361 
## [55] train-rmse:0.846815+0.038327    test-rmse:2.371378+0.596385 
## [56] train-rmse:0.832304+0.040034    test-rmse:2.367208+0.597311 
## [57] train-rmse:0.821589+0.039156    test-rmse:2.363416+0.600140 
## [58] train-rmse:0.807063+0.041962    test-rmse:2.359881+0.601338 
## [59] train-rmse:0.794296+0.041029    test-rmse:2.355820+0.602574 
## [60] train-rmse:0.779016+0.037801    test-rmse:2.351698+0.603171 
## [61] train-rmse:0.768674+0.037857    test-rmse:2.349282+0.602668 
## [62] train-rmse:0.757465+0.038175    test-rmse:2.348336+0.603224 
## [63] train-rmse:0.745019+0.040595    test-rmse:2.344842+0.602998 
## [64] train-rmse:0.731138+0.037694    test-rmse:2.345691+0.604734 
## [65] train-rmse:0.720089+0.036485    test-rmse:2.342775+0.605725 
## [66] train-rmse:0.711578+0.037572    test-rmse:2.341943+0.606526 
## [67] train-rmse:0.700906+0.033228    test-rmse:2.340618+0.606662 
## [68] train-rmse:0.688072+0.031320    test-rmse:2.338311+0.607308 
## [69] train-rmse:0.677863+0.031067    test-rmse:2.336740+0.608309 
## [70] train-rmse:0.669201+0.029835    test-rmse:2.333850+0.610407 
## [71] train-rmse:0.655988+0.027783    test-rmse:2.332459+0.611420 
## [72] train-rmse:0.647766+0.027029    test-rmse:2.330523+0.611320 
## [73] train-rmse:0.638876+0.028286    test-rmse:2.330574+0.611595 
## [74] train-rmse:0.627165+0.030046    test-rmse:2.330130+0.613096 
## [75] train-rmse:0.620212+0.030827    test-rmse:2.329497+0.614036 
## [76] train-rmse:0.610391+0.031518    test-rmse:2.328843+0.614340 
## [77] train-rmse:0.601108+0.031925    test-rmse:2.327445+0.614106 
## [78] train-rmse:0.594128+0.033090    test-rmse:2.326649+0.615286 
## [79] train-rmse:0.585625+0.031833    test-rmse:2.323692+0.615289 
## [80] train-rmse:0.577910+0.032541    test-rmse:2.323147+0.614847 
## [81] train-rmse:0.567337+0.031315    test-rmse:2.323299+0.615661 
## [82] train-rmse:0.559436+0.031098    test-rmse:2.323359+0.615882 
## [83] train-rmse:0.552497+0.029749    test-rmse:2.323941+0.616725 
## [84] train-rmse:0.543879+0.027418    test-rmse:2.322028+0.617909 
## [85] train-rmse:0.536322+0.029016    test-rmse:2.322721+0.617929 
## [86] train-rmse:0.528450+0.031324    test-rmse:2.320474+0.619057 
## [87] train-rmse:0.522558+0.031305    test-rmse:2.318780+0.617930 
## [88] train-rmse:0.513864+0.029283    test-rmse:2.317505+0.619956 
## [89] train-rmse:0.507313+0.030231    test-rmse:2.316622+0.620235 
## [90] train-rmse:0.500668+0.026707    test-rmse:2.315424+0.620232 
## [91] train-rmse:0.491448+0.024416    test-rmse:2.314995+0.619995 
## [92] train-rmse:0.484400+0.024925    test-rmse:2.315194+0.621713 
## [93] train-rmse:0.477705+0.026343    test-rmse:2.315215+0.621870 
## [94] train-rmse:0.471902+0.026181    test-rmse:2.314133+0.622900 
## [95] train-rmse:0.465541+0.025205    test-rmse:2.314738+0.623810 
## [96] train-rmse:0.460041+0.024394    test-rmse:2.314028+0.625023 
## [97] train-rmse:0.455235+0.024570    test-rmse:2.313141+0.625158 
## [98] train-rmse:0.448738+0.023166    test-rmse:2.313021+0.624631 
## [99] train-rmse:0.443408+0.022045    test-rmse:2.312370+0.624635 
## [100]    train-rmse:0.437633+0.022773    test-rmse:2.310891+0.624631 
## [101]    train-rmse:0.432745+0.023767    test-rmse:2.311669+0.624141 
## [102]    train-rmse:0.427513+0.023942    test-rmse:2.310818+0.624684 
## [103]    train-rmse:0.423306+0.023741    test-rmse:2.311002+0.624874 
## [104]    train-rmse:0.416616+0.021286    test-rmse:2.310431+0.625142 
## [105]    train-rmse:0.412569+0.022130    test-rmse:2.310165+0.625569 
## [106]    train-rmse:0.406216+0.023274    test-rmse:2.308768+0.626259 
## [107]    train-rmse:0.402140+0.022428    test-rmse:2.308579+0.626437 
## [108]    train-rmse:0.397096+0.022531    test-rmse:2.309337+0.626681 
## [109]    train-rmse:0.390235+0.023711    test-rmse:2.309550+0.627039 
## [110]    train-rmse:0.383792+0.024516    test-rmse:2.309177+0.628143 
## [111]    train-rmse:0.378588+0.023592    test-rmse:2.308808+0.628750 
## [112]    train-rmse:0.374902+0.023953    test-rmse:2.308124+0.629309 
## [113]    train-rmse:0.368982+0.024931    test-rmse:2.308813+0.629563 
## [114]    train-rmse:0.364576+0.025955    test-rmse:2.308752+0.630223 
## [115]    train-rmse:0.361336+0.024858    test-rmse:2.309414+0.630784 
## [116]    train-rmse:0.357342+0.023261    test-rmse:2.307996+0.630584 
## [117]    train-rmse:0.353610+0.022935    test-rmse:2.307512+0.630847 
## [118]    train-rmse:0.349372+0.022957    test-rmse:2.306753+0.630839 
## [119]    train-rmse:0.345616+0.023132    test-rmse:2.306564+0.631347 
## [120]    train-rmse:0.342319+0.023248    test-rmse:2.306987+0.630562 
## [121]    train-rmse:0.337273+0.023670    test-rmse:2.306819+0.632430 
## [122]    train-rmse:0.333848+0.022693    test-rmse:2.306930+0.632651 
## [123]    train-rmse:0.330720+0.023254    test-rmse:2.306865+0.632852 
## [124]    train-rmse:0.326529+0.022043    test-rmse:2.307547+0.632771 
## [125]    train-rmse:0.321020+0.019966    test-rmse:2.306238+0.631480 
## [126]    train-rmse:0.317880+0.019598    test-rmse:2.304955+0.631824 
## [127]    train-rmse:0.314022+0.019375    test-rmse:2.305078+0.631691 
## [128]    train-rmse:0.310104+0.020566    test-rmse:2.305162+0.632756 
## [129]    train-rmse:0.305801+0.019636    test-rmse:2.305007+0.632647 
## [130]    train-rmse:0.301769+0.017077    test-rmse:2.304906+0.632129 
## [131]    train-rmse:0.297906+0.016883    test-rmse:2.305012+0.632416 
## [132]    train-rmse:0.294585+0.017306    test-rmse:2.304501+0.632724 
## [133]    train-rmse:0.290687+0.016101    test-rmse:2.304131+0.632860 
## [134]    train-rmse:0.286880+0.015086    test-rmse:2.304531+0.633277 
## [135]    train-rmse:0.284247+0.014795    test-rmse:2.304793+0.632974 
## [136]    train-rmse:0.280754+0.015363    test-rmse:2.304690+0.632828 
## [137]    train-rmse:0.277504+0.015632    test-rmse:2.305330+0.632518 
## [138]    train-rmse:0.273065+0.016244    test-rmse:2.305682+0.633476 
## [139]    train-rmse:0.269869+0.016404    test-rmse:2.305110+0.633476 
## Stopping. Best iteration:
## [133]    train-rmse:0.290687+0.016101    test-rmse:2.304131+0.632860
## 
## 6 
## [1]  train-rmse:7.464939+0.154128    test-rmse:7.454880+0.655764 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.813501+0.135919    test-rmse:6.828655+0.625962 
## [3]  train-rmse:6.226078+0.118472    test-rmse:6.279075+0.589763 
## [4]  train-rmse:5.696568+0.105342    test-rmse:5.781920+0.567596 
## [5]  train-rmse:5.220667+0.090739    test-rmse:5.348864+0.536774 
## [6]  train-rmse:4.792652+0.080122    test-rmse:4.969502+0.515068 
## [7]  train-rmse:4.407609+0.069823    test-rmse:4.631383+0.501701 
## [8]  train-rmse:4.064901+0.061969    test-rmse:4.343550+0.486087 
## [9]  train-rmse:3.757004+0.056705    test-rmse:4.087015+0.470485 
## [10] train-rmse:3.481640+0.052780    test-rmse:3.856126+0.463182 
## [11] train-rmse:3.235377+0.049433    test-rmse:3.668723+0.459879 
## [12] train-rmse:3.014628+0.042473    test-rmse:3.503861+0.461241 
## [13] train-rmse:2.818804+0.038484    test-rmse:3.372848+0.456257 
## [14] train-rmse:2.637717+0.033909    test-rmse:3.255240+0.456455 
## [15] train-rmse:2.478764+0.036240    test-rmse:3.154407+0.465507 
## [16] train-rmse:2.334011+0.030139    test-rmse:3.060710+0.475412 
## [17] train-rmse:2.205789+0.032772    test-rmse:2.985379+0.481613 
## [18] train-rmse:2.087452+0.030448    test-rmse:2.912924+0.480416 
## [19] train-rmse:1.981541+0.033786    test-rmse:2.857818+0.484194 
## [20] train-rmse:1.886820+0.032665    test-rmse:2.799645+0.493316 
## [21] train-rmse:1.798997+0.028604    test-rmse:2.747077+0.495739 
## [22] train-rmse:1.713617+0.028276    test-rmse:2.706072+0.508385 
## [23] train-rmse:1.640384+0.032288    test-rmse:2.664856+0.518911 
## [24] train-rmse:1.572202+0.035709    test-rmse:2.632355+0.525278 
## [25] train-rmse:1.514571+0.034974    test-rmse:2.606987+0.536802 
## [26] train-rmse:1.451490+0.041719    test-rmse:2.581201+0.542687 
## [27] train-rmse:1.402729+0.040968    test-rmse:2.565359+0.539379 
## [28] train-rmse:1.353854+0.033543    test-rmse:2.550450+0.545254 
## [29] train-rmse:1.308557+0.041203    test-rmse:2.538123+0.544258 
## [30] train-rmse:1.265621+0.048358    test-rmse:2.521549+0.543930 
## [31] train-rmse:1.223865+0.044988    test-rmse:2.507606+0.544401 
## [32] train-rmse:1.187154+0.043153    test-rmse:2.496617+0.546662 
## [33] train-rmse:1.147926+0.044744    test-rmse:2.486630+0.546951 
## [34] train-rmse:1.117098+0.041613    test-rmse:2.475291+0.547763 
## [35] train-rmse:1.083271+0.043149    test-rmse:2.469600+0.552228 
## [36] train-rmse:1.053072+0.042568    test-rmse:2.463179+0.558013 
## [37] train-rmse:1.027774+0.039952    test-rmse:2.452272+0.556816 
## [38] train-rmse:1.001175+0.037724    test-rmse:2.444160+0.559883 
## [39] train-rmse:0.976970+0.042639    test-rmse:2.437778+0.563489 
## [40] train-rmse:0.952905+0.037162    test-rmse:2.433105+0.565676 
## [41] train-rmse:0.929520+0.037949    test-rmse:2.428547+0.568753 
## [42] train-rmse:0.905389+0.041659    test-rmse:2.421295+0.572069 
## [43] train-rmse:0.884163+0.040849    test-rmse:2.412573+0.570194 
## [44] train-rmse:0.867213+0.041414    test-rmse:2.406669+0.569642 
## [45] train-rmse:0.845978+0.037894    test-rmse:2.405604+0.567934 
## [46] train-rmse:0.832006+0.038022    test-rmse:2.401545+0.567763 
## [47] train-rmse:0.813365+0.036942    test-rmse:2.397189+0.571664 
## [48] train-rmse:0.795373+0.034566    test-rmse:2.392297+0.571597 
## [49] train-rmse:0.775675+0.033963    test-rmse:2.386654+0.574293 
## [50] train-rmse:0.756724+0.030655    test-rmse:2.385267+0.573730 
## [51] train-rmse:0.743534+0.030839    test-rmse:2.384224+0.576110 
## [52] train-rmse:0.725834+0.032819    test-rmse:2.382266+0.577013 
## [53] train-rmse:0.710179+0.035275    test-rmse:2.380373+0.578209 
## [54] train-rmse:0.698572+0.033189    test-rmse:2.377363+0.579300 
## [55] train-rmse:0.683657+0.031454    test-rmse:2.374171+0.579339 
## [56] train-rmse:0.667057+0.031841    test-rmse:2.373921+0.580932 
## [57] train-rmse:0.654143+0.029229    test-rmse:2.369642+0.581905 
## [58] train-rmse:0.640304+0.033369    test-rmse:2.366945+0.584632 
## [59] train-rmse:0.624592+0.031980    test-rmse:2.362437+0.587244 
## [60] train-rmse:0.612147+0.026457    test-rmse:2.360430+0.589989 
## [61] train-rmse:0.601835+0.026117    test-rmse:2.358036+0.590568 
## [62] train-rmse:0.589345+0.024975    test-rmse:2.356566+0.591237 
## [63] train-rmse:0.578243+0.021519    test-rmse:2.355084+0.592590 
## [64] train-rmse:0.565933+0.019696    test-rmse:2.353227+0.590976 
## [65] train-rmse:0.555898+0.018825    test-rmse:2.353299+0.590169 
## [66] train-rmse:0.545859+0.019537    test-rmse:2.352555+0.592225 
## [67] train-rmse:0.536138+0.018366    test-rmse:2.350239+0.592692 
## [68] train-rmse:0.529437+0.019265    test-rmse:2.349273+0.592992 
## [69] train-rmse:0.521683+0.020204    test-rmse:2.348443+0.592874 
## [70] train-rmse:0.511375+0.019856    test-rmse:2.346937+0.593630 
## [71] train-rmse:0.502038+0.021389    test-rmse:2.347674+0.593732 
## [72] train-rmse:0.492137+0.019545    test-rmse:2.346937+0.593596 
## [73] train-rmse:0.483781+0.018361    test-rmse:2.344928+0.592360 
## [74] train-rmse:0.475243+0.017918    test-rmse:2.344121+0.593182 
## [75] train-rmse:0.465239+0.014934    test-rmse:2.342623+0.593086 
## [76] train-rmse:0.456187+0.015018    test-rmse:2.343802+0.593618 
## [77] train-rmse:0.450595+0.015508    test-rmse:2.343149+0.593880 
## [78] train-rmse:0.441467+0.014809    test-rmse:2.343096+0.593626 
## [79] train-rmse:0.432414+0.012608    test-rmse:2.344765+0.593493 
## [80] train-rmse:0.425966+0.013531    test-rmse:2.344762+0.594520 
## [81] train-rmse:0.419239+0.012343    test-rmse:2.343085+0.595450 
## Stopping. Best iteration:
## [75] train-rmse:0.465239+0.014934    test-rmse:2.342623+0.593086
## 
## 7 
## [1]  train-rmse:7.478345+0.160179    test-rmse:7.460255+0.677495 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.872826+0.146820    test-rmse:6.876525+0.701060 
## [3]  train-rmse:6.354528+0.136507    test-rmse:6.371928+0.681180 
## [4]  train-rmse:5.913547+0.128788    test-rmse:5.921723+0.668404 
## [5]  train-rmse:5.540435+0.122759    test-rmse:5.577674+0.661946 
## [6]  train-rmse:5.224933+0.118412    test-rmse:5.271653+0.646745 
## [7]  train-rmse:4.958007+0.115444    test-rmse:5.007546+0.619711 
## [8]  train-rmse:4.733526+0.112816    test-rmse:4.806486+0.614025 
## [9]  train-rmse:4.543762+0.112380    test-rmse:4.624998+0.585355 
## [10] train-rmse:4.383765+0.111584    test-rmse:4.459835+0.557254 
## [11] train-rmse:4.248086+0.111688    test-rmse:4.338996+0.542674 
## [12] train-rmse:4.132388+0.111843    test-rmse:4.227125+0.526424 
## [13] train-rmse:4.032131+0.112667    test-rmse:4.145259+0.526423 
## [14] train-rmse:3.945786+0.113064    test-rmse:4.059895+0.507849 
## [15] train-rmse:3.870111+0.114119    test-rmse:3.966962+0.486721 
## [16] train-rmse:3.803383+0.114404    test-rmse:3.912675+0.474823 
## [17] train-rmse:3.744471+0.115644    test-rmse:3.875113+0.464177 
## [18] train-rmse:3.690828+0.116544    test-rmse:3.817212+0.465619 
## [19] train-rmse:3.641929+0.117022    test-rmse:3.766722+0.455265 
## [20] train-rmse:3.597536+0.118565    test-rmse:3.709681+0.436913 
## [21] train-rmse:3.556757+0.118900    test-rmse:3.675325+0.435067 
## [22] train-rmse:3.518652+0.119378    test-rmse:3.644813+0.448572 
## [23] train-rmse:3.482671+0.119892    test-rmse:3.620644+0.439520 
## [24] train-rmse:3.448836+0.120441    test-rmse:3.579608+0.430217 
## [25] train-rmse:3.417454+0.120981    test-rmse:3.531649+0.421136 
## [26] train-rmse:3.387407+0.121277    test-rmse:3.515326+0.419005 
## [27] train-rmse:3.359116+0.121834    test-rmse:3.499635+0.424073 
## [28] train-rmse:3.331462+0.122213    test-rmse:3.468021+0.422585 
## [29] train-rmse:3.304833+0.122749    test-rmse:3.445226+0.420925 
## [30] train-rmse:3.280109+0.122934    test-rmse:3.412381+0.421528 
## [31] train-rmse:3.255836+0.123386    test-rmse:3.387401+0.412284 
## [32] train-rmse:3.232294+0.123753    test-rmse:3.361496+0.420438 
## [33] train-rmse:3.209719+0.124323    test-rmse:3.354503+0.426668 
## [34] train-rmse:3.187912+0.124634    test-rmse:3.331940+0.423067 
## [35] train-rmse:3.167082+0.125019    test-rmse:3.308345+0.429012 
## [36] train-rmse:3.147028+0.125750    test-rmse:3.282453+0.416310 
## [37] train-rmse:3.127319+0.126330    test-rmse:3.267201+0.417929 
## [38] train-rmse:3.108613+0.126627    test-rmse:3.255452+0.428322 
## [39] train-rmse:3.090301+0.127065    test-rmse:3.243815+0.428354 
## [40] train-rmse:3.072534+0.127401    test-rmse:3.225979+0.429467 
## [41] train-rmse:3.055316+0.127795    test-rmse:3.200159+0.417813 
## [42] train-rmse:3.038583+0.128158    test-rmse:3.193431+0.418828 
## [43] train-rmse:3.022292+0.128636    test-rmse:3.169783+0.422736 
## [44] train-rmse:3.006431+0.129003    test-rmse:3.159638+0.425066 
## [45] train-rmse:2.990899+0.129247    test-rmse:3.152933+0.440146 
## [46] train-rmse:2.975833+0.129556    test-rmse:3.136677+0.432615 
## [47] train-rmse:2.961319+0.129856    test-rmse:3.131416+0.433427 
## [48] train-rmse:2.946892+0.130185    test-rmse:3.109636+0.424667 
## [49] train-rmse:2.932952+0.130811    test-rmse:3.103715+0.429640 
## [50] train-rmse:2.919424+0.131283    test-rmse:3.094992+0.427089 
## [51] train-rmse:2.906126+0.131690    test-rmse:3.080061+0.432888 
## [52] train-rmse:2.893126+0.131984    test-rmse:3.068176+0.424068 
## [53] train-rmse:2.880226+0.132409    test-rmse:3.051934+0.430650 
## [54] train-rmse:2.867570+0.132583    test-rmse:3.048436+0.442570 
## [55] train-rmse:2.855172+0.132878    test-rmse:3.039868+0.445662 
## [56] train-rmse:2.843142+0.133364    test-rmse:3.032973+0.447550 
## [57] train-rmse:2.831353+0.133870    test-rmse:3.021227+0.449092 
## [58] train-rmse:2.819616+0.134456    test-rmse:3.004798+0.450538 
## [59] train-rmse:2.808291+0.134896    test-rmse:2.983600+0.450955 
## [60] train-rmse:2.797130+0.135250    test-rmse:2.975973+0.451382 
## [61] train-rmse:2.786021+0.135636    test-rmse:2.974723+0.449752 
## [62] train-rmse:2.775358+0.135985    test-rmse:2.962578+0.445993 
## [63] train-rmse:2.764766+0.136197    test-rmse:2.957186+0.447641 
## [64] train-rmse:2.754410+0.136403    test-rmse:2.946898+0.440912 
## [65] train-rmse:2.744253+0.136562    test-rmse:2.937576+0.444616 
## [66] train-rmse:2.734179+0.136761    test-rmse:2.932759+0.453333 
## [67] train-rmse:2.724227+0.137073    test-rmse:2.926606+0.447597 
## [68] train-rmse:2.714295+0.137429    test-rmse:2.919149+0.453250 
## [69] train-rmse:2.704556+0.137737    test-rmse:2.907167+0.460203 
## [70] train-rmse:2.694983+0.137921    test-rmse:2.899435+0.463618 
## [71] train-rmse:2.685683+0.138065    test-rmse:2.897539+0.469444 
## [72] train-rmse:2.676470+0.138314    test-rmse:2.890391+0.463871 
## [73] train-rmse:2.667470+0.138563    test-rmse:2.879812+0.457171 
## [74] train-rmse:2.658683+0.138726    test-rmse:2.872520+0.456910 
## [75] train-rmse:2.649878+0.138839    test-rmse:2.871588+0.461496 
## [76] train-rmse:2.641198+0.138847    test-rmse:2.863470+0.466930 
## [77] train-rmse:2.632730+0.138893    test-rmse:2.858466+0.472358 
## [78] train-rmse:2.624391+0.139048    test-rmse:2.850202+0.469205 
## [79] train-rmse:2.616139+0.139240    test-rmse:2.843569+0.464797 
## [80] train-rmse:2.607961+0.139428    test-rmse:2.835465+0.462680 
## [81] train-rmse:2.599834+0.139479    test-rmse:2.827623+0.465033 
## [82] train-rmse:2.592019+0.139609    test-rmse:2.820021+0.467112 
## [83] train-rmse:2.584312+0.139641    test-rmse:2.818413+0.465597 
## [84] train-rmse:2.576678+0.139661    test-rmse:2.809701+0.467882 
## [85] train-rmse:2.569117+0.139621    test-rmse:2.802710+0.476894 
## [86] train-rmse:2.561660+0.139620    test-rmse:2.799377+0.476795 
## [87] train-rmse:2.554336+0.139690    test-rmse:2.790881+0.476097 
## [88] train-rmse:2.547099+0.139668    test-rmse:2.784535+0.473823 
## [89] train-rmse:2.540008+0.139631    test-rmse:2.784065+0.476234 
## [90] train-rmse:2.533012+0.139731    test-rmse:2.775429+0.473644 
## [91] train-rmse:2.526082+0.139850    test-rmse:2.768006+0.476271 
## [92] train-rmse:2.519266+0.139941    test-rmse:2.765404+0.480502 
## [93] train-rmse:2.512498+0.140023    test-rmse:2.760912+0.478884 
## [94] train-rmse:2.505792+0.140084    test-rmse:2.750156+0.480546 
## [95] train-rmse:2.499305+0.140184    test-rmse:2.743096+0.484737 
## [96] train-rmse:2.492835+0.140270    test-rmse:2.738383+0.480087 
## [97] train-rmse:2.486503+0.140346    test-rmse:2.738108+0.484963 
## [98] train-rmse:2.480189+0.140381    test-rmse:2.729117+0.490633 
## [99] train-rmse:2.473964+0.140453    test-rmse:2.722582+0.488872 
## [100]    train-rmse:2.467806+0.140480    test-rmse:2.713385+0.486819 
## [101]    train-rmse:2.461798+0.140627    test-rmse:2.709639+0.487092 
## [102]    train-rmse:2.455898+0.140720    test-rmse:2.708344+0.484285 
## [103]    train-rmse:2.450071+0.140773    test-rmse:2.700690+0.483186 
## [104]    train-rmse:2.444298+0.140829    test-rmse:2.696770+0.477442 
## [105]    train-rmse:2.438634+0.140908    test-rmse:2.693025+0.478378 
## [106]    train-rmse:2.432960+0.140990    test-rmse:2.690775+0.480562 
## [107]    train-rmse:2.427364+0.140960    test-rmse:2.687970+0.481738 
## [108]    train-rmse:2.421905+0.140993    test-rmse:2.681286+0.488542 
## [109]    train-rmse:2.416519+0.140987    test-rmse:2.675504+0.485902 
## [110]    train-rmse:2.411264+0.141061    test-rmse:2.669828+0.487372 
## [111]    train-rmse:2.406071+0.141134    test-rmse:2.663510+0.490482 
## [112]    train-rmse:2.400935+0.141205    test-rmse:2.661840+0.488583 
## [113]    train-rmse:2.395849+0.141293    test-rmse:2.658227+0.489192 
## [114]    train-rmse:2.390806+0.141417    test-rmse:2.654420+0.498339 
## [115]    train-rmse:2.385812+0.141524    test-rmse:2.649537+0.498325 
## [116]    train-rmse:2.380823+0.141640    test-rmse:2.645138+0.500302 
## [117]    train-rmse:2.375905+0.141778    test-rmse:2.641089+0.497689 
## [118]    train-rmse:2.371058+0.141884    test-rmse:2.640151+0.496240 
## [119]    train-rmse:2.366282+0.141978    test-rmse:2.636613+0.493897 
## [120]    train-rmse:2.361592+0.142139    test-rmse:2.630706+0.494827 
## [121]    train-rmse:2.356950+0.142240    test-rmse:2.620724+0.492129 
## [122]    train-rmse:2.352346+0.142380    test-rmse:2.617553+0.490971 
## [123]    train-rmse:2.347832+0.142389    test-rmse:2.610891+0.487459 
## [124]    train-rmse:2.343419+0.142447    test-rmse:2.605060+0.487962 
## [125]    train-rmse:2.339028+0.142483    test-rmse:2.601566+0.489841 
## [126]    train-rmse:2.334725+0.142572    test-rmse:2.596990+0.490178 
## [127]    train-rmse:2.330455+0.142689    test-rmse:2.597175+0.491126 
## [128]    train-rmse:2.326243+0.142864    test-rmse:2.592640+0.490233 
## [129]    train-rmse:2.322069+0.143013    test-rmse:2.589335+0.490078 
## [130]    train-rmse:2.317964+0.143148    test-rmse:2.587352+0.498221 
## [131]    train-rmse:2.313903+0.143298    test-rmse:2.587948+0.502612 
## [132]    train-rmse:2.309818+0.143437    test-rmse:2.584981+0.501296 
## [133]    train-rmse:2.305787+0.143608    test-rmse:2.582077+0.501054 
## [134]    train-rmse:2.301747+0.143773    test-rmse:2.576900+0.505575 
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## [236]    train-rmse:2.055594+0.153589    test-rmse:2.390148+0.531444 
## [237]    train-rmse:2.054117+0.153647    test-rmse:2.388217+0.529875 
## [238]    train-rmse:2.052673+0.153711    test-rmse:2.388533+0.531045 
## [239]    train-rmse:2.051203+0.153776    test-rmse:2.389368+0.530420 
## [240]    train-rmse:2.049761+0.153830    test-rmse:2.389535+0.529702 
## [241]    train-rmse:2.048326+0.153874    test-rmse:2.389660+0.529684 
## [242]    train-rmse:2.046899+0.153931    test-rmse:2.388272+0.529704 
## [243]    train-rmse:2.045471+0.153986    test-rmse:2.385868+0.529381 
## [244]    train-rmse:2.044043+0.154039    test-rmse:2.384202+0.529498 
## [245]    train-rmse:2.042641+0.154097    test-rmse:2.384944+0.529797 
## [246]    train-rmse:2.041247+0.154164    test-rmse:2.383637+0.530369 
## [247]    train-rmse:2.039864+0.154228    test-rmse:2.382276+0.530372 
## [248]    train-rmse:2.038498+0.154281    test-rmse:2.381853+0.531155 
## [249]    train-rmse:2.037097+0.154334    test-rmse:2.380878+0.529842 
## [250]    train-rmse:2.035743+0.154395    test-rmse:2.377800+0.527603 
## [251]    train-rmse:2.034378+0.154436    test-rmse:2.376980+0.525780 
## [252]    train-rmse:2.033029+0.154477    test-rmse:2.374741+0.526668 
## [253]    train-rmse:2.031700+0.154532    test-rmse:2.373708+0.525934 
## [254]    train-rmse:2.030378+0.154578    test-rmse:2.373799+0.527838 
## [255]    train-rmse:2.029077+0.154632    test-rmse:2.373583+0.527181 
## [256]    train-rmse:2.027778+0.154711    test-rmse:2.372491+0.528099 
## [257]    train-rmse:2.026496+0.154783    test-rmse:2.373328+0.527852 
## [258]    train-rmse:2.025206+0.154838    test-rmse:2.372446+0.527401 
## [259]    train-rmse:2.023930+0.154892    test-rmse:2.371891+0.529080 
## [260]    train-rmse:2.022660+0.154957    test-rmse:2.370992+0.530871 
## [261]    train-rmse:2.021392+0.155004    test-rmse:2.373222+0.535140 
## [262]    train-rmse:2.020128+0.155059    test-rmse:2.371279+0.534583 
## [263]    train-rmse:2.018889+0.155111    test-rmse:2.370660+0.532332 
## [264]    train-rmse:2.017614+0.155220    test-rmse:2.370790+0.531808 
## [265]    train-rmse:2.016355+0.155245    test-rmse:2.369933+0.531439 
## [266]    train-rmse:2.015140+0.155296    test-rmse:2.369333+0.531954 
## [267]    train-rmse:2.013929+0.155347    test-rmse:2.368398+0.531222 
## [268]    train-rmse:2.012714+0.155432    test-rmse:2.369025+0.531295 
## [269]    train-rmse:2.011515+0.155485    test-rmse:2.369971+0.531331 
## [270]    train-rmse:2.010307+0.155535    test-rmse:2.368885+0.529484 
## [271]    train-rmse:2.009119+0.155580    test-rmse:2.367326+0.529984 
## [272]    train-rmse:2.007928+0.155634    test-rmse:2.367779+0.528700 
## [273]    train-rmse:2.006744+0.155719    test-rmse:2.367976+0.529678 
## [274]    train-rmse:2.005554+0.155776    test-rmse:2.368881+0.528807 
## [275]    train-rmse:2.004391+0.155843    test-rmse:2.367405+0.529646 
## [276]    train-rmse:2.003237+0.155905    test-rmse:2.364171+0.531476 
## [277]    train-rmse:2.002093+0.155951    test-rmse:2.362066+0.530649 
## [278]    train-rmse:2.000937+0.156045    test-rmse:2.360256+0.531075 
## [279]    train-rmse:1.999808+0.156102    test-rmse:2.358697+0.531267 
## [280]    train-rmse:1.998666+0.156181    test-rmse:2.357441+0.532061 
## [281]    train-rmse:1.997543+0.156243    test-rmse:2.356532+0.530880 
## [282]    train-rmse:1.996432+0.156303    test-rmse:2.354716+0.530604 
## [283]    train-rmse:1.995322+0.156367    test-rmse:2.355088+0.530614 
## [284]    train-rmse:1.994210+0.156425    test-rmse:2.355788+0.530763 
## [285]    train-rmse:1.993096+0.156476    test-rmse:2.355954+0.530433 
## [286]    train-rmse:1.991974+0.156508    test-rmse:2.355601+0.531511 
## [287]    train-rmse:1.990895+0.156562    test-rmse:2.358919+0.530237 
## [288]    train-rmse:1.989814+0.156622    test-rmse:2.359106+0.530861 
## Stopping. Best iteration:
## [282]    train-rmse:1.996432+0.156303    test-rmse:2.354716+0.530604
## 
## 8 
## [1]  train-rmse:7.427045+0.156214    test-rmse:7.423641+0.660314 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.772314+0.141401    test-rmse:6.788771+0.646805 
## [3]  train-rmse:6.206743+0.132520    test-rmse:6.263520+0.604745 
## [4]  train-rmse:5.714442+0.124377    test-rmse:5.806287+0.598579 
## [5]  train-rmse:5.298285+0.116353    test-rmse:5.395127+0.557533 
## [6]  train-rmse:4.928735+0.112765    test-rmse:5.032192+0.541685 
## [7]  train-rmse:4.615040+0.107796    test-rmse:4.746926+0.529485 
## [8]  train-rmse:4.340093+0.103797    test-rmse:4.496761+0.510019 
## [9]  train-rmse:4.105665+0.102910    test-rmse:4.257504+0.492046 
## [10] train-rmse:3.902590+0.101233    test-rmse:4.081438+0.481315 
## [11] train-rmse:3.723381+0.097824    test-rmse:3.897165+0.478054 
## [12] train-rmse:3.571643+0.098041    test-rmse:3.757026+0.453339 
## [13] train-rmse:3.431228+0.097850    test-rmse:3.630710+0.426888 
## [14] train-rmse:3.308590+0.084982    test-rmse:3.536652+0.437280 
## [15] train-rmse:3.193402+0.082876    test-rmse:3.423055+0.446760 
## [16] train-rmse:3.103225+0.085369    test-rmse:3.363751+0.444460 
## [17] train-rmse:3.020074+0.094534    test-rmse:3.290809+0.429230 
## [18] train-rmse:2.940943+0.091381    test-rmse:3.215132+0.447513 
## [19] train-rmse:2.871714+0.089476    test-rmse:3.168110+0.432315 
## [20] train-rmse:2.807974+0.076732    test-rmse:3.115852+0.443238 
## [21] train-rmse:2.749584+0.074795    test-rmse:3.070233+0.455437 
## [22] train-rmse:2.704871+0.076950    test-rmse:3.034726+0.460336 
## [23] train-rmse:2.660419+0.077945    test-rmse:3.002650+0.461770 
## [24] train-rmse:2.616802+0.067510    test-rmse:2.976789+0.461780 
## [25] train-rmse:2.574489+0.066025    test-rmse:2.942196+0.464044 
## [26] train-rmse:2.535834+0.067000    test-rmse:2.907997+0.465059 
## [27] train-rmse:2.499927+0.069028    test-rmse:2.894271+0.463745 
## [28] train-rmse:2.470732+0.069616    test-rmse:2.869609+0.473340 
## [29] train-rmse:2.439509+0.067003    test-rmse:2.846201+0.477169 
## [30] train-rmse:2.412285+0.067499    test-rmse:2.832413+0.479430 
## [31] train-rmse:2.385142+0.069744    test-rmse:2.816159+0.475276 
## [32] train-rmse:2.358003+0.070623    test-rmse:2.803666+0.472239 
## [33] train-rmse:2.329074+0.062163    test-rmse:2.781905+0.479645 
## [34] train-rmse:2.305891+0.061297    test-rmse:2.776446+0.481452 
## [35] train-rmse:2.281668+0.062197    test-rmse:2.757631+0.495280 
## [36] train-rmse:2.254772+0.063502    test-rmse:2.735220+0.485681 
## [37] train-rmse:2.234711+0.063319    test-rmse:2.719576+0.485283 
## [38] train-rmse:2.213991+0.063632    test-rmse:2.705489+0.487183 
## [39] train-rmse:2.190205+0.057100    test-rmse:2.701150+0.497828 
## [40] train-rmse:2.172129+0.056364    test-rmse:2.697120+0.498719 
## [41] train-rmse:2.148932+0.060866    test-rmse:2.674370+0.491128 
## [42] train-rmse:2.129138+0.056753    test-rmse:2.662124+0.496631 
## [43] train-rmse:2.111879+0.056246    test-rmse:2.649640+0.499513 
## [44] train-rmse:2.095599+0.056491    test-rmse:2.637794+0.494018 
## [45] train-rmse:2.077621+0.057461    test-rmse:2.635386+0.494057 
## [46] train-rmse:2.059647+0.057366    test-rmse:2.624118+0.487771 
## [47] train-rmse:2.038547+0.054981    test-rmse:2.607670+0.480526 
## [48] train-rmse:2.024177+0.055009    test-rmse:2.600079+0.486059 
## [49] train-rmse:2.007306+0.054895    test-rmse:2.590743+0.487685 
## [50] train-rmse:1.992473+0.054095    test-rmse:2.581682+0.490905 
## [51] train-rmse:1.977927+0.054521    test-rmse:2.570275+0.489398 
## [52] train-rmse:1.964646+0.054039    test-rmse:2.560058+0.489471 
## [53] train-rmse:1.949443+0.051629    test-rmse:2.554637+0.499372 
## [54] train-rmse:1.933754+0.053345    test-rmse:2.535538+0.501026 
## [55] train-rmse:1.919730+0.055505    test-rmse:2.528277+0.486561 
## [56] train-rmse:1.905202+0.054797    test-rmse:2.517030+0.489863 
## [57] train-rmse:1.891833+0.057638    test-rmse:2.508276+0.491951 
## [58] train-rmse:1.880317+0.057533    test-rmse:2.497677+0.495068 
## [59] train-rmse:1.868008+0.058077    test-rmse:2.489124+0.497341 
## [60] train-rmse:1.855431+0.055800    test-rmse:2.479059+0.500997 
## [61] train-rmse:1.842641+0.053164    test-rmse:2.471514+0.502085 
## [62] train-rmse:1.830176+0.052522    test-rmse:2.461755+0.499477 
## [63] train-rmse:1.819558+0.052604    test-rmse:2.454291+0.499361 
## [64] train-rmse:1.806405+0.054922    test-rmse:2.448383+0.489670 
## [65] train-rmse:1.792265+0.053847    test-rmse:2.438409+0.492940 
## [66] train-rmse:1.780590+0.052789    test-rmse:2.434315+0.492824 
## [67] train-rmse:1.771196+0.052582    test-rmse:2.432323+0.493036 
## [68] train-rmse:1.760353+0.053262    test-rmse:2.421732+0.498156 
## [69] train-rmse:1.751264+0.053307    test-rmse:2.415313+0.496936 
## [70] train-rmse:1.742566+0.053720    test-rmse:2.408688+0.493550 
## [71] train-rmse:1.732579+0.052234    test-rmse:2.403142+0.496674 
## [72] train-rmse:1.721109+0.049213    test-rmse:2.395632+0.492710 
## [73] train-rmse:1.712579+0.048577    test-rmse:2.391115+0.492422 
## [74] train-rmse:1.704618+0.048481    test-rmse:2.387501+0.496031 
## [75] train-rmse:1.695802+0.047888    test-rmse:2.386642+0.498595 
## [76] train-rmse:1.687422+0.047526    test-rmse:2.384771+0.499225 
## [77] train-rmse:1.678763+0.046526    test-rmse:2.381998+0.498285 
## [78] train-rmse:1.669246+0.044284    test-rmse:2.375501+0.498746 
## [79] train-rmse:1.661885+0.043967    test-rmse:2.362943+0.504140 
## [80] train-rmse:1.653276+0.041699    test-rmse:2.355946+0.503355 
## [81] train-rmse:1.644346+0.041883    test-rmse:2.349480+0.503782 
## [82] train-rmse:1.636051+0.042922    test-rmse:2.345468+0.503683 
## [83] train-rmse:1.629384+0.042832    test-rmse:2.342735+0.503710 
## [84] train-rmse:1.621229+0.042509    test-rmse:2.337128+0.499031 
## [85] train-rmse:1.613751+0.041619    test-rmse:2.331702+0.500480 
## [86] train-rmse:1.605209+0.042103    test-rmse:2.326706+0.501432 
## [87] train-rmse:1.598185+0.042201    test-rmse:2.322934+0.498675 
## [88] train-rmse:1.588790+0.040869    test-rmse:2.318523+0.499274 
## [89] train-rmse:1.581926+0.041290    test-rmse:2.313917+0.500061 
## [90] train-rmse:1.575700+0.041336    test-rmse:2.314906+0.507629 
## [91] train-rmse:1.568313+0.040821    test-rmse:2.310570+0.507360 
## [92] train-rmse:1.561265+0.040947    test-rmse:2.305396+0.508851 
## [93] train-rmse:1.553981+0.042774    test-rmse:2.301546+0.506029 
## [94] train-rmse:1.544729+0.041752    test-rmse:2.297218+0.505882 
## [95] train-rmse:1.534494+0.041275    test-rmse:2.293852+0.505764 
## [96] train-rmse:1.527100+0.043621    test-rmse:2.295236+0.506316 
## [97] train-rmse:1.520005+0.042193    test-rmse:2.294094+0.507507 
## [98] train-rmse:1.513961+0.042744    test-rmse:2.290624+0.503662 
## [99] train-rmse:1.508823+0.042543    test-rmse:2.288371+0.504474 
## [100]    train-rmse:1.503643+0.042715    test-rmse:2.285368+0.504529 
## [101]    train-rmse:1.495010+0.039869    test-rmse:2.281470+0.505447 
## [102]    train-rmse:1.489665+0.039555    test-rmse:2.279539+0.505084 
## [103]    train-rmse:1.484068+0.039642    test-rmse:2.278364+0.505295 
## [104]    train-rmse:1.477378+0.037921    test-rmse:2.275816+0.506636 
## [105]    train-rmse:1.472379+0.037772    test-rmse:2.274053+0.506021 
## [106]    train-rmse:1.465860+0.037357    test-rmse:2.266326+0.509075 
## [107]    train-rmse:1.459583+0.036447    test-rmse:2.261753+0.509716 
## [108]    train-rmse:1.453812+0.036266    test-rmse:2.264715+0.508909 
## [109]    train-rmse:1.446870+0.040841    test-rmse:2.260925+0.511486 
## [110]    train-rmse:1.442515+0.040980    test-rmse:2.258442+0.510516 
## [111]    train-rmse:1.437022+0.043334    test-rmse:2.253930+0.507320 
## [112]    train-rmse:1.431771+0.042502    test-rmse:2.250093+0.508906 
## [113]    train-rmse:1.426139+0.041957    test-rmse:2.249319+0.508663 
## [114]    train-rmse:1.420798+0.042623    test-rmse:2.245575+0.508314 
## [115]    train-rmse:1.416138+0.042492    test-rmse:2.244773+0.507648 
## [116]    train-rmse:1.408825+0.039645    test-rmse:2.242480+0.511075 
## [117]    train-rmse:1.403216+0.039421    test-rmse:2.237474+0.513220 
## [118]    train-rmse:1.398623+0.039723    test-rmse:2.237114+0.517342 
## [119]    train-rmse:1.393080+0.039852    test-rmse:2.235000+0.517554 
## [120]    train-rmse:1.387471+0.039145    test-rmse:2.234578+0.516008 
## [121]    train-rmse:1.383393+0.039321    test-rmse:2.229969+0.517656 
## [122]    train-rmse:1.377329+0.038200    test-rmse:2.229008+0.517038 
## [123]    train-rmse:1.372398+0.037682    test-rmse:2.226425+0.520353 
## [124]    train-rmse:1.368873+0.037633    test-rmse:2.224191+0.521477 
## [125]    train-rmse:1.363553+0.036143    test-rmse:2.223504+0.522948 
## [126]    train-rmse:1.357503+0.040393    test-rmse:2.220844+0.520429 
## [127]    train-rmse:1.353391+0.040628    test-rmse:2.221545+0.523459 
## [128]    train-rmse:1.348338+0.039704    test-rmse:2.220925+0.522310 
## [129]    train-rmse:1.343931+0.039048    test-rmse:2.220073+0.522948 
## [130]    train-rmse:1.339287+0.038348    test-rmse:2.216624+0.523534 
## [131]    train-rmse:1.332865+0.037009    test-rmse:2.212315+0.525534 
## [132]    train-rmse:1.328376+0.036079    test-rmse:2.211146+0.525745 
## [133]    train-rmse:1.323956+0.035912    test-rmse:2.208763+0.525529 
## [134]    train-rmse:1.319615+0.035618    test-rmse:2.206226+0.526466 
## [135]    train-rmse:1.312809+0.037213    test-rmse:2.205192+0.526879 
## [136]    train-rmse:1.308760+0.037262    test-rmse:2.204272+0.528516 
## [137]    train-rmse:1.304189+0.036974    test-rmse:2.203351+0.526227 
## [138]    train-rmse:1.300494+0.036877    test-rmse:2.204594+0.526860 
## [139]    train-rmse:1.296626+0.036400    test-rmse:2.202747+0.529033 
## [140]    train-rmse:1.289696+0.035561    test-rmse:2.196129+0.530852 
## [141]    train-rmse:1.286568+0.035648    test-rmse:2.196289+0.532503 
## [142]    train-rmse:1.283533+0.035966    test-rmse:2.195031+0.532532 
## [143]    train-rmse:1.278604+0.040049    test-rmse:2.193005+0.531914 
## [144]    train-rmse:1.275448+0.039897    test-rmse:2.192885+0.531589 
## [145]    train-rmse:1.272059+0.040440    test-rmse:2.191082+0.530252 
## [146]    train-rmse:1.268275+0.040550    test-rmse:2.192382+0.530516 
## [147]    train-rmse:1.262091+0.043877    test-rmse:2.189240+0.534208 
## [148]    train-rmse:1.258391+0.043896    test-rmse:2.189343+0.535447 
## [149]    train-rmse:1.254166+0.043478    test-rmse:2.187176+0.537546 
## [150]    train-rmse:1.250648+0.043087    test-rmse:2.186702+0.537311 
## [151]    train-rmse:1.247008+0.043281    test-rmse:2.185528+0.535473 
## [152]    train-rmse:1.242004+0.043427    test-rmse:2.184783+0.535737 
## [153]    train-rmse:1.238505+0.043271    test-rmse:2.182044+0.535507 
## [154]    train-rmse:1.233336+0.045505    test-rmse:2.182426+0.536477 
## [155]    train-rmse:1.227447+0.047564    test-rmse:2.181067+0.534722 
## [156]    train-rmse:1.223470+0.047675    test-rmse:2.181116+0.535822 
## [157]    train-rmse:1.220539+0.047656    test-rmse:2.180336+0.536959 
## [158]    train-rmse:1.216820+0.047427    test-rmse:2.178433+0.539728 
## [159]    train-rmse:1.212711+0.046881    test-rmse:2.178093+0.538893 
## [160]    train-rmse:1.208017+0.049289    test-rmse:2.183868+0.538413 
## [161]    train-rmse:1.205369+0.049212    test-rmse:2.183518+0.539675 
## [162]    train-rmse:1.202294+0.048850    test-rmse:2.181199+0.540700 
## [163]    train-rmse:1.199253+0.048431    test-rmse:2.180309+0.541489 
## [164]    train-rmse:1.196669+0.048295    test-rmse:2.178870+0.540471 
## [165]    train-rmse:1.193731+0.047569    test-rmse:2.177273+0.540756 
## [166]    train-rmse:1.190726+0.047025    test-rmse:2.175762+0.540550 
## [167]    train-rmse:1.187501+0.046590    test-rmse:2.173040+0.541692 
## [168]    train-rmse:1.182027+0.046223    test-rmse:2.172743+0.541515 
## [169]    train-rmse:1.178051+0.045119    test-rmse:2.173888+0.541442 
## [170]    train-rmse:1.175472+0.045141    test-rmse:2.174186+0.541499 
## [171]    train-rmse:1.172369+0.045579    test-rmse:2.170942+0.542551 
## [172]    train-rmse:1.169411+0.045303    test-rmse:2.168916+0.543058 
## [173]    train-rmse:1.167044+0.045458    test-rmse:2.168355+0.542942 
## [174]    train-rmse:1.161818+0.047053    test-rmse:2.167859+0.545764 
## [175]    train-rmse:1.158292+0.049424    test-rmse:2.170571+0.545244 
## [176]    train-rmse:1.155591+0.049889    test-rmse:2.169085+0.545519 
## [177]    train-rmse:1.151930+0.049159    test-rmse:2.168225+0.546309 
## [178]    train-rmse:1.148447+0.048530    test-rmse:2.168440+0.546802 
## [179]    train-rmse:1.145649+0.048119    test-rmse:2.165041+0.548479 
## [180]    train-rmse:1.142691+0.048302    test-rmse:2.166055+0.548076 
## [181]    train-rmse:1.138805+0.048595    test-rmse:2.165619+0.549081 
## [182]    train-rmse:1.134874+0.048035    test-rmse:2.164499+0.548713 
## [183]    train-rmse:1.132668+0.048183    test-rmse:2.163502+0.548642 
## [184]    train-rmse:1.130461+0.047927    test-rmse:2.162675+0.548771 
## [185]    train-rmse:1.127975+0.047682    test-rmse:2.161475+0.548673 
## [186]    train-rmse:1.125235+0.047476    test-rmse:2.163308+0.548990 
## [187]    train-rmse:1.119503+0.050272    test-rmse:2.164695+0.549514 
## [188]    train-rmse:1.116070+0.051373    test-rmse:2.160784+0.548760 
## [189]    train-rmse:1.112377+0.050677    test-rmse:2.161454+0.548431 
## [190]    train-rmse:1.108597+0.049309    test-rmse:2.161395+0.548423 
## [191]    train-rmse:1.105229+0.049557    test-rmse:2.162212+0.548772 
## [192]    train-rmse:1.102460+0.050086    test-rmse:2.160379+0.548179 
## [193]    train-rmse:1.100763+0.050177    test-rmse:2.160799+0.548848 
## [194]    train-rmse:1.097064+0.051699    test-rmse:2.159937+0.548183 
## [195]    train-rmse:1.094150+0.051679    test-rmse:2.159728+0.549418 
## [196]    train-rmse:1.091943+0.051748    test-rmse:2.161153+0.553234 
## [197]    train-rmse:1.088841+0.050927    test-rmse:2.158582+0.554293 
## [198]    train-rmse:1.085550+0.049993    test-rmse:2.156978+0.553601 
## [199]    train-rmse:1.082159+0.050211    test-rmse:2.155250+0.553550 
## [200]    train-rmse:1.079326+0.049801    test-rmse:2.154017+0.553805 
## [201]    train-rmse:1.077134+0.049906    test-rmse:2.152243+0.553496 
## [202]    train-rmse:1.074898+0.050137    test-rmse:2.150091+0.554418 
## [203]    train-rmse:1.073016+0.050196    test-rmse:2.149641+0.556752 
## [204]    train-rmse:1.069955+0.049294    test-rmse:2.150147+0.556042 
## [205]    train-rmse:1.067981+0.049300    test-rmse:2.149897+0.555706 
## [206]    train-rmse:1.065776+0.049306    test-rmse:2.146833+0.556351 
## [207]    train-rmse:1.061825+0.051338    test-rmse:2.145428+0.556692 
## [208]    train-rmse:1.059888+0.051627    test-rmse:2.145998+0.557279 
## [209]    train-rmse:1.057089+0.051929    test-rmse:2.145089+0.556372 
## [210]    train-rmse:1.055522+0.051870    test-rmse:2.145401+0.557661 
## [211]    train-rmse:1.052622+0.053879    test-rmse:2.143859+0.556745 
## [212]    train-rmse:1.050430+0.054246    test-rmse:2.144864+0.554098 
## [213]    train-rmse:1.048469+0.054459    test-rmse:2.143760+0.553602 
## [214]    train-rmse:1.045926+0.053807    test-rmse:2.143674+0.553565 
## [215]    train-rmse:1.042908+0.052996    test-rmse:2.143199+0.553030 
## [216]    train-rmse:1.040787+0.052447    test-rmse:2.143824+0.554719 
## [217]    train-rmse:1.038107+0.051797    test-rmse:2.144294+0.554330 
## [218]    train-rmse:1.035050+0.051306    test-rmse:2.144204+0.554111 
## [219]    train-rmse:1.031731+0.050797    test-rmse:2.143855+0.556146 
## [220]    train-rmse:1.029251+0.050845    test-rmse:2.143934+0.556615 
## [221]    train-rmse:1.027634+0.051010    test-rmse:2.143588+0.556306 
## Stopping. Best iteration:
## [215]    train-rmse:1.042908+0.052996    test-rmse:2.143199+0.553030
## 
## 9 
## [1]  train-rmse:7.392836+0.154580    test-rmse:7.409093+0.652744 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.701523+0.137715    test-rmse:6.742279+0.613775 
## [3]  train-rmse:6.101857+0.124332    test-rmse:6.171889+0.575258 
## [4]  train-rmse:5.578793+0.114880    test-rmse:5.670776+0.547556 
## [5]  train-rmse:5.121657+0.111845    test-rmse:5.243130+0.528957 
## [6]  train-rmse:4.724661+0.106096    test-rmse:4.871040+0.494621 
## [7]  train-rmse:4.379694+0.105752    test-rmse:4.548977+0.482958 
## [8]  train-rmse:4.074650+0.101873    test-rmse:4.255356+0.448630 
## [9]  train-rmse:3.804958+0.096251    test-rmse:3.985148+0.443789 
## [10] train-rmse:3.576328+0.095556    test-rmse:3.807137+0.423178 
## [11] train-rmse:3.367156+0.083152    test-rmse:3.613244+0.434551 
## [12] train-rmse:3.198077+0.081877    test-rmse:3.481733+0.428475 
## [13] train-rmse:3.039263+0.074926    test-rmse:3.334951+0.438521 
## [14] train-rmse:2.908970+0.073717    test-rmse:3.223505+0.435835 
## [15] train-rmse:2.797976+0.077319    test-rmse:3.148518+0.433438 
## [16] train-rmse:2.701945+0.077700    test-rmse:3.066349+0.441305 
## [17] train-rmse:2.616367+0.077486    test-rmse:3.001474+0.428525 
## [18] train-rmse:2.531938+0.072251    test-rmse:2.943923+0.438390 
## [19] train-rmse:2.463021+0.074600    test-rmse:2.889565+0.438313 
## [20] train-rmse:2.401240+0.075340    test-rmse:2.854270+0.440349 
## [21] train-rmse:2.345195+0.071910    test-rmse:2.820705+0.437669 
## [22] train-rmse:2.290781+0.068334    test-rmse:2.785360+0.431720 
## [23] train-rmse:2.235972+0.067425    test-rmse:2.738827+0.438086 
## [24] train-rmse:2.188346+0.065807    test-rmse:2.716951+0.438510 
## [25] train-rmse:2.143315+0.064962    test-rmse:2.688817+0.447199 
## [26] train-rmse:2.105943+0.067224    test-rmse:2.657953+0.452364 
## [27] train-rmse:2.068556+0.062453    test-rmse:2.637814+0.457116 
## [28] train-rmse:2.031863+0.062276    test-rmse:2.614596+0.450858 
## [29] train-rmse:2.001374+0.060866    test-rmse:2.599516+0.441493 
## [30] train-rmse:1.968356+0.059609    test-rmse:2.572925+0.450063 
## [31] train-rmse:1.938791+0.057478    test-rmse:2.559626+0.453563 
## [32] train-rmse:1.912204+0.056712    test-rmse:2.536383+0.454875 
## [33] train-rmse:1.882588+0.059230    test-rmse:2.508588+0.443636 
## [34] train-rmse:1.859845+0.059293    test-rmse:2.496597+0.445685 
## [35] train-rmse:1.834103+0.060802    test-rmse:2.481007+0.447663 
## [36] train-rmse:1.808438+0.056934    test-rmse:2.468791+0.449772 
## [37] train-rmse:1.784332+0.056975    test-rmse:2.460800+0.456059 
## [38] train-rmse:1.762177+0.059466    test-rmse:2.445884+0.445026 
## [39] train-rmse:1.741850+0.060006    test-rmse:2.437933+0.440136 
## [40] train-rmse:1.722997+0.060450    test-rmse:2.429181+0.444067 
## [41] train-rmse:1.697396+0.052148    test-rmse:2.422264+0.453191 
## [42] train-rmse:1.675778+0.048400    test-rmse:2.409217+0.457731 
## [43] train-rmse:1.652495+0.052138    test-rmse:2.394030+0.448329 
## [44] train-rmse:1.635930+0.051399    test-rmse:2.385074+0.446150 
## [45] train-rmse:1.617024+0.048148    test-rmse:2.379336+0.446889 
## [46] train-rmse:1.597332+0.049525    test-rmse:2.367399+0.448671 
## [47] train-rmse:1.582163+0.050680    test-rmse:2.363134+0.454708 
## [48] train-rmse:1.564923+0.051769    test-rmse:2.352756+0.444598 
## [49] train-rmse:1.551797+0.051834    test-rmse:2.343672+0.443341 
## [50] train-rmse:1.535690+0.050505    test-rmse:2.335053+0.446504 
## [51] train-rmse:1.520572+0.050354    test-rmse:2.323388+0.446209 
## [52] train-rmse:1.503499+0.047447    test-rmse:2.317905+0.453908 
## [53] train-rmse:1.489418+0.046041    test-rmse:2.307342+0.457834 
## [54] train-rmse:1.476549+0.048414    test-rmse:2.301304+0.456971 
## [55] train-rmse:1.462304+0.048561    test-rmse:2.297908+0.462822 
## [56] train-rmse:1.449858+0.048347    test-rmse:2.288964+0.463403 
## [57] train-rmse:1.436032+0.046903    test-rmse:2.281693+0.458488 
## [58] train-rmse:1.422980+0.045855    test-rmse:2.274828+0.458485 
## [59] train-rmse:1.408527+0.043745    test-rmse:2.271884+0.456406 
## [60] train-rmse:1.397356+0.045486    test-rmse:2.265963+0.456042 
## [61] train-rmse:1.386696+0.045610    test-rmse:2.260580+0.455330 
## [62] train-rmse:1.373443+0.045800    test-rmse:2.254096+0.458380 
## [63] train-rmse:1.362363+0.048059    test-rmse:2.250553+0.459513 
## [64] train-rmse:1.351225+0.046430    test-rmse:2.244711+0.457064 
## [65] train-rmse:1.338179+0.045841    test-rmse:2.235682+0.455888 
## [66] train-rmse:1.326020+0.043752    test-rmse:2.233204+0.455142 
## [67] train-rmse:1.317852+0.043977    test-rmse:2.230172+0.457576 
## [68] train-rmse:1.309422+0.044844    test-rmse:2.226970+0.456827 
## [69] train-rmse:1.299033+0.043289    test-rmse:2.222850+0.457409 
## [70] train-rmse:1.284245+0.043721    test-rmse:2.218247+0.456786 
## [71] train-rmse:1.274151+0.043449    test-rmse:2.217178+0.458636 
## [72] train-rmse:1.264074+0.044922    test-rmse:2.213335+0.460751 
## [73] train-rmse:1.253594+0.043034    test-rmse:2.205631+0.461411 
## [74] train-rmse:1.238588+0.042571    test-rmse:2.201641+0.461389 
## [75] train-rmse:1.230086+0.042314    test-rmse:2.197418+0.464065 
## [76] train-rmse:1.220239+0.041001    test-rmse:2.191694+0.463063 
## [77] train-rmse:1.210011+0.040134    test-rmse:2.187724+0.463091 
## [78] train-rmse:1.201691+0.043093    test-rmse:2.185038+0.460425 
## [79] train-rmse:1.194741+0.042780    test-rmse:2.185048+0.466533 
## [80] train-rmse:1.184318+0.043419    test-rmse:2.181700+0.465640 
## [81] train-rmse:1.175634+0.044114    test-rmse:2.177399+0.467647 
## [82] train-rmse:1.165886+0.043670    test-rmse:2.175017+0.464821 
## [83] train-rmse:1.157558+0.043122    test-rmse:2.170915+0.466189 
## [84] train-rmse:1.151268+0.043537    test-rmse:2.171635+0.468040 
## [85] train-rmse:1.143712+0.042264    test-rmse:2.164808+0.469923 
## [86] train-rmse:1.135273+0.041171    test-rmse:2.163840+0.471400 
## [87] train-rmse:1.128436+0.040811    test-rmse:2.160087+0.472835 
## [88] train-rmse:1.121253+0.040778    test-rmse:2.155889+0.471875 
## [89] train-rmse:1.110803+0.042648    test-rmse:2.155923+0.473650 
## [90] train-rmse:1.103539+0.044033    test-rmse:2.152825+0.471741 
## [91] train-rmse:1.094988+0.040954    test-rmse:2.146692+0.476508 
## [92] train-rmse:1.085346+0.040868    test-rmse:2.143127+0.476677 
## [93] train-rmse:1.079504+0.040546    test-rmse:2.140430+0.476616 
## [94] train-rmse:1.072385+0.042591    test-rmse:2.142170+0.476063 
## [95] train-rmse:1.062988+0.043526    test-rmse:2.140597+0.473752 
## [96] train-rmse:1.057350+0.043636    test-rmse:2.138554+0.477368 
## [97] train-rmse:1.051050+0.043631    test-rmse:2.135527+0.477220 
## [98] train-rmse:1.044464+0.043853    test-rmse:2.133684+0.476751 
## [99] train-rmse:1.035329+0.042142    test-rmse:2.131806+0.478248 
## [100]    train-rmse:1.027727+0.041642    test-rmse:2.129155+0.477948 
## [101]    train-rmse:1.019616+0.042828    test-rmse:2.128936+0.476320 
## [102]    train-rmse:1.011915+0.044468    test-rmse:2.126478+0.472661 
## [103]    train-rmse:1.004648+0.045732    test-rmse:2.124425+0.474961 
## [104]    train-rmse:0.998761+0.046242    test-rmse:2.123751+0.477999 
## [105]    train-rmse:0.992141+0.045022    test-rmse:2.121221+0.479955 
## [106]    train-rmse:0.983299+0.044014    test-rmse:2.117781+0.479373 
## [107]    train-rmse:0.975478+0.043657    test-rmse:2.115263+0.478037 
## [108]    train-rmse:0.968653+0.043330    test-rmse:2.114917+0.477257 
## [109]    train-rmse:0.964123+0.044647    test-rmse:2.113769+0.477830 
## [110]    train-rmse:0.955622+0.043578    test-rmse:2.109187+0.480167 
## [111]    train-rmse:0.950239+0.043390    test-rmse:2.107739+0.481975 
## [112]    train-rmse:0.942563+0.041509    test-rmse:2.103502+0.482949 
## [113]    train-rmse:0.936163+0.040087    test-rmse:2.104075+0.483254 
## [114]    train-rmse:0.932187+0.039791    test-rmse:2.104071+0.484177 
## [115]    train-rmse:0.927461+0.039181    test-rmse:2.102521+0.483469 
## [116]    train-rmse:0.923492+0.040451    test-rmse:2.101256+0.483634 
## [117]    train-rmse:0.919477+0.040107    test-rmse:2.100105+0.483313 
## [118]    train-rmse:0.913490+0.039703    test-rmse:2.099475+0.484389 
## [119]    train-rmse:0.907798+0.041311    test-rmse:2.095177+0.485551 
## [120]    train-rmse:0.901756+0.041095    test-rmse:2.093547+0.484859 
## [121]    train-rmse:0.896354+0.040421    test-rmse:2.090574+0.485210 
## [122]    train-rmse:0.891997+0.039739    test-rmse:2.089327+0.485900 
## [123]    train-rmse:0.887185+0.039458    test-rmse:2.086246+0.487135 
## [124]    train-rmse:0.881368+0.040903    test-rmse:2.083158+0.485466 
## [125]    train-rmse:0.875607+0.042852    test-rmse:2.083507+0.487025 
## [126]    train-rmse:0.871064+0.042573    test-rmse:2.083300+0.488096 
## [127]    train-rmse:0.867230+0.042918    test-rmse:2.081391+0.488085 
## [128]    train-rmse:0.862375+0.040744    test-rmse:2.081762+0.488100 
## [129]    train-rmse:0.857828+0.039818    test-rmse:2.080251+0.487164 
## [130]    train-rmse:0.853092+0.040038    test-rmse:2.079149+0.487102 
## [131]    train-rmse:0.847683+0.039860    test-rmse:2.077831+0.488894 
## [132]    train-rmse:0.842948+0.039738    test-rmse:2.077628+0.488379 
## [133]    train-rmse:0.836156+0.040843    test-rmse:2.076413+0.490651 
## [134]    train-rmse:0.829999+0.042134    test-rmse:2.074116+0.487253 
## [135]    train-rmse:0.825977+0.040379    test-rmse:2.072973+0.486975 
## [136]    train-rmse:0.821806+0.041087    test-rmse:2.072557+0.486438 
## [137]    train-rmse:0.817842+0.042839    test-rmse:2.072211+0.485380 
## [138]    train-rmse:0.814466+0.043720    test-rmse:2.072074+0.485849 
## [139]    train-rmse:0.810837+0.044328    test-rmse:2.072477+0.486333 
## [140]    train-rmse:0.805536+0.043613    test-rmse:2.071329+0.486251 
## [141]    train-rmse:0.801240+0.043116    test-rmse:2.069593+0.486029 
## [142]    train-rmse:0.794492+0.043566    test-rmse:2.070861+0.487545 
## [143]    train-rmse:0.788204+0.042469    test-rmse:2.067480+0.488820 
## [144]    train-rmse:0.784028+0.043654    test-rmse:2.067977+0.488568 
## [145]    train-rmse:0.780524+0.044266    test-rmse:2.068141+0.488943 
## [146]    train-rmse:0.777591+0.045585    test-rmse:2.068286+0.489564 
## [147]    train-rmse:0.774937+0.045957    test-rmse:2.067044+0.488953 
## [148]    train-rmse:0.771565+0.044891    test-rmse:2.067267+0.488634 
## [149]    train-rmse:0.767895+0.046348    test-rmse:2.066870+0.488194 
## [150]    train-rmse:0.764128+0.046223    test-rmse:2.066026+0.489061 
## [151]    train-rmse:0.759876+0.047371    test-rmse:2.064422+0.489699 
## [152]    train-rmse:0.754297+0.045925    test-rmse:2.065301+0.488760 
## [153]    train-rmse:0.750657+0.045943    test-rmse:2.062519+0.489231 
## [154]    train-rmse:0.746110+0.045546    test-rmse:2.061304+0.488642 
## [155]    train-rmse:0.742143+0.044668    test-rmse:2.058695+0.489010 
## [156]    train-rmse:0.739913+0.044960    test-rmse:2.057751+0.489272 
## [157]    train-rmse:0.736963+0.046308    test-rmse:2.058079+0.488569 
## [158]    train-rmse:0.732563+0.047956    test-rmse:2.055803+0.488279 
## [159]    train-rmse:0.726205+0.048701    test-rmse:2.056568+0.489590 
## [160]    train-rmse:0.722494+0.048683    test-rmse:2.056441+0.490858 
## [161]    train-rmse:0.719852+0.048342    test-rmse:2.055057+0.491445 
## [162]    train-rmse:0.714620+0.046437    test-rmse:2.053017+0.491822 
## [163]    train-rmse:0.709362+0.047407    test-rmse:2.051901+0.490798 
## [164]    train-rmse:0.705502+0.047811    test-rmse:2.051192+0.492218 
## [165]    train-rmse:0.702700+0.047387    test-rmse:2.049647+0.492119 
## [166]    train-rmse:0.699574+0.046813    test-rmse:2.049704+0.491802 
## [167]    train-rmse:0.697025+0.045804    test-rmse:2.049002+0.491690 
## [168]    train-rmse:0.694812+0.046238    test-rmse:2.049406+0.491728 
## [169]    train-rmse:0.691919+0.044905    test-rmse:2.046536+0.492530 
## [170]    train-rmse:0.688089+0.044412    test-rmse:2.044219+0.492057 
## [171]    train-rmse:0.684915+0.046328    test-rmse:2.044858+0.494392 
## [172]    train-rmse:0.682209+0.046372    test-rmse:2.045827+0.494676 
## [173]    train-rmse:0.679814+0.046445    test-rmse:2.045067+0.496332 
## [174]    train-rmse:0.676762+0.046456    test-rmse:2.043853+0.496547 
## [175]    train-rmse:0.673102+0.047659    test-rmse:2.043028+0.496733 
## [176]    train-rmse:0.669168+0.047510    test-rmse:2.043034+0.494755 
## [177]    train-rmse:0.664683+0.047146    test-rmse:2.042390+0.495690 
## [178]    train-rmse:0.661968+0.046879    test-rmse:2.043327+0.496150 
## [179]    train-rmse:0.659710+0.047559    test-rmse:2.042772+0.496364 
## [180]    train-rmse:0.657205+0.046348    test-rmse:2.041575+0.497306 
## [181]    train-rmse:0.654240+0.046541    test-rmse:2.043525+0.495813 
## [182]    train-rmse:0.651664+0.046187    test-rmse:2.042976+0.495987 
## [183]    train-rmse:0.648348+0.046111    test-rmse:2.041764+0.495559 
## [184]    train-rmse:0.645657+0.045572    test-rmse:2.041425+0.495657 
## [185]    train-rmse:0.643418+0.044530    test-rmse:2.042258+0.495255 
## [186]    train-rmse:0.641975+0.044579    test-rmse:2.043007+0.494614 
## [187]    train-rmse:0.638365+0.045098    test-rmse:2.042223+0.494823 
## [188]    train-rmse:0.634055+0.045854    test-rmse:2.040030+0.493717 
## [189]    train-rmse:0.629833+0.044336    test-rmse:2.038797+0.493345 
## [190]    train-rmse:0.627830+0.044282    test-rmse:2.038337+0.493455 
## [191]    train-rmse:0.624455+0.043392    test-rmse:2.040098+0.492193 
## [192]    train-rmse:0.620774+0.043786    test-rmse:2.038864+0.492743 
## [193]    train-rmse:0.617506+0.044519    test-rmse:2.037969+0.492177 
## [194]    train-rmse:0.614896+0.044102    test-rmse:2.037820+0.492120 
## [195]    train-rmse:0.612248+0.044198    test-rmse:2.037757+0.492881 
## [196]    train-rmse:0.608079+0.044218    test-rmse:2.038178+0.494753 
## [197]    train-rmse:0.606573+0.044035    test-rmse:2.037839+0.495207 
## [198]    train-rmse:0.604076+0.044448    test-rmse:2.037607+0.495747 
## [199]    train-rmse:0.601818+0.045271    test-rmse:2.036550+0.495087 
## [200]    train-rmse:0.599184+0.045119    test-rmse:2.035242+0.495269 
## [201]    train-rmse:0.596586+0.043927    test-rmse:2.035715+0.495185 
## [202]    train-rmse:0.593952+0.043372    test-rmse:2.035640+0.495917 
## [203]    train-rmse:0.590412+0.042376    test-rmse:2.034659+0.496696 
## [204]    train-rmse:0.588701+0.042625    test-rmse:2.034029+0.496709 
## [205]    train-rmse:0.585974+0.041845    test-rmse:2.034973+0.497226 
## [206]    train-rmse:0.582503+0.040576    test-rmse:2.036797+0.495513 
## [207]    train-rmse:0.579407+0.041144    test-rmse:2.037279+0.495415 
## [208]    train-rmse:0.576875+0.040660    test-rmse:2.037145+0.495899 
## [209]    train-rmse:0.574617+0.040428    test-rmse:2.037402+0.495284 
## [210]    train-rmse:0.572608+0.040311    test-rmse:2.037478+0.495462 
## Stopping. Best iteration:
## [204]    train-rmse:0.588701+0.042625    test-rmse:2.034029+0.496709
## 
## 10 
## [1]  train-rmse:7.361423+0.153731    test-rmse:7.365079+0.646034 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.641423+0.134963    test-rmse:6.679375+0.607241 
## [3]  train-rmse:6.003997+0.120160    test-rmse:6.077969+0.559640 
## [4]  train-rmse:5.452109+0.109450    test-rmse:5.561131+0.521915 
## [5]  train-rmse:4.965943+0.095549    test-rmse:5.098194+0.497686 
## [6]  train-rmse:4.545136+0.090973    test-rmse:4.709590+0.468402 
## [7]  train-rmse:4.177563+0.076825    test-rmse:4.366099+0.450636 
## [8]  train-rmse:3.857969+0.073767    test-rmse:4.084722+0.438361 
## [9]  train-rmse:3.581869+0.074859    test-rmse:3.857565+0.429685 
## [10] train-rmse:3.338801+0.064475    test-rmse:3.635352+0.439034 
## [11] train-rmse:3.130148+0.058423    test-rmse:3.470698+0.443785 
## [12] train-rmse:2.947657+0.058831    test-rmse:3.322232+0.437180 
## [13] train-rmse:2.794229+0.058997    test-rmse:3.209178+0.442887 
## [14] train-rmse:2.648940+0.046231    test-rmse:3.101333+0.463520 
## [15] train-rmse:2.530002+0.047416    test-rmse:3.032015+0.451825 
## [16] train-rmse:2.421667+0.042368    test-rmse:2.946703+0.458108 
## [17] train-rmse:2.317062+0.040171    test-rmse:2.862917+0.455209 
## [18] train-rmse:2.233450+0.041436    test-rmse:2.819844+0.455000 
## [19] train-rmse:2.157676+0.037779    test-rmse:2.769850+0.479679 
## [20] train-rmse:2.088928+0.038956    test-rmse:2.721934+0.473552 
## [21] train-rmse:2.028779+0.041632    test-rmse:2.681447+0.461868 
## [22] train-rmse:1.972203+0.040092    test-rmse:2.645804+0.459405 
## [23] train-rmse:1.921470+0.043256    test-rmse:2.620406+0.459988 
## [24] train-rmse:1.874584+0.037161    test-rmse:2.588653+0.461874 
## [25] train-rmse:1.827716+0.037161    test-rmse:2.569981+0.464147 
## [26] train-rmse:1.788192+0.036420    test-rmse:2.546411+0.459138 
## [27] train-rmse:1.751996+0.036413    test-rmse:2.535236+0.464118 
## [28] train-rmse:1.721807+0.036714    test-rmse:2.516055+0.461628 
## [29] train-rmse:1.690242+0.036081    test-rmse:2.493521+0.457861 
## [30] train-rmse:1.657985+0.037298    test-rmse:2.473666+0.457488 
## [31] train-rmse:1.630935+0.035833    test-rmse:2.459377+0.458687 
## [32] train-rmse:1.602116+0.036240    test-rmse:2.442686+0.469310 
## [33] train-rmse:1.577381+0.034864    test-rmse:2.431361+0.473723 
## [34] train-rmse:1.554051+0.033947    test-rmse:2.426125+0.468853 
## [35] train-rmse:1.522685+0.033697    test-rmse:2.406291+0.456479 
## [36] train-rmse:1.502157+0.033532    test-rmse:2.395711+0.460812 
## [37] train-rmse:1.479693+0.027257    test-rmse:2.382860+0.459693 
## [38] train-rmse:1.457640+0.028735    test-rmse:2.372442+0.458330 
## [39] train-rmse:1.426822+0.021384    test-rmse:2.359087+0.461355 
## [40] train-rmse:1.405767+0.021407    test-rmse:2.349819+0.462339 
## [41] train-rmse:1.385949+0.023093    test-rmse:2.344883+0.461093 
## [42] train-rmse:1.366454+0.021029    test-rmse:2.346292+0.466414 
## [43] train-rmse:1.346425+0.020312    test-rmse:2.332659+0.471621 
## [44] train-rmse:1.326078+0.023841    test-rmse:2.324027+0.472344 
## [45] train-rmse:1.308942+0.022756    test-rmse:2.314160+0.469328 
## [46] train-rmse:1.295748+0.023111    test-rmse:2.308946+0.466666 
## [47] train-rmse:1.278218+0.024892    test-rmse:2.299861+0.469930 
## [48] train-rmse:1.260720+0.027730    test-rmse:2.295257+0.464086 
## [49] train-rmse:1.246136+0.026976    test-rmse:2.292665+0.464997 
## [50] train-rmse:1.233459+0.028591    test-rmse:2.286082+0.466555 
## [51] train-rmse:1.217749+0.030740    test-rmse:2.281602+0.462469 
## [52] train-rmse:1.205361+0.032006    test-rmse:2.278462+0.464920 
## [53] train-rmse:1.191261+0.032174    test-rmse:2.272844+0.470275 
## [54] train-rmse:1.175080+0.035464    test-rmse:2.266068+0.467094 
## [55] train-rmse:1.153701+0.036788    test-rmse:2.264381+0.468116 
## [56] train-rmse:1.141028+0.037010    test-rmse:2.260174+0.470652 
## [57] train-rmse:1.126210+0.038285    test-rmse:2.259559+0.466545 
## [58] train-rmse:1.113789+0.034087    test-rmse:2.262094+0.471737 
## [59] train-rmse:1.102612+0.034024    test-rmse:2.260632+0.472577 
## [60] train-rmse:1.093721+0.033107    test-rmse:2.255640+0.476798 
## [61] train-rmse:1.076890+0.024020    test-rmse:2.252403+0.479958 
## [62] train-rmse:1.062825+0.023801    test-rmse:2.251627+0.479519 
## [63] train-rmse:1.052933+0.025102    test-rmse:2.247589+0.481424 
## [64] train-rmse:1.044240+0.025657    test-rmse:2.245683+0.481452 
## [65] train-rmse:1.034163+0.024562    test-rmse:2.244883+0.479492 
## [66] train-rmse:1.021304+0.025015    test-rmse:2.242899+0.483030 
## [67] train-rmse:1.004491+0.019737    test-rmse:2.236178+0.479005 
## [68] train-rmse:0.995370+0.021526    test-rmse:2.234345+0.478122 
## [69] train-rmse:0.982842+0.022001    test-rmse:2.231478+0.476820 
## [70] train-rmse:0.972367+0.022357    test-rmse:2.225675+0.480503 
## [71] train-rmse:0.963123+0.022094    test-rmse:2.223928+0.479840 
## [72] train-rmse:0.952724+0.021455    test-rmse:2.220156+0.481021 
## [73] train-rmse:0.943122+0.021253    test-rmse:2.216374+0.482361 
## [74] train-rmse:0.935058+0.023777    test-rmse:2.211364+0.480967 
## [75] train-rmse:0.925952+0.022935    test-rmse:2.209786+0.481230 
## [76] train-rmse:0.912556+0.022171    test-rmse:2.202855+0.483901 
## [77] train-rmse:0.905360+0.022369    test-rmse:2.201487+0.485139 
## [78] train-rmse:0.898228+0.023994    test-rmse:2.200848+0.484946 
## [79] train-rmse:0.887527+0.022430    test-rmse:2.199255+0.485846 
## [80] train-rmse:0.879620+0.023492    test-rmse:2.196955+0.485373 
## [81] train-rmse:0.871466+0.024331    test-rmse:2.189723+0.488553 
## [82] train-rmse:0.863356+0.021207    test-rmse:2.188727+0.488603 
## [83] train-rmse:0.854246+0.021711    test-rmse:2.184088+0.488419 
## [84] train-rmse:0.847362+0.021543    test-rmse:2.183063+0.490530 
## [85] train-rmse:0.839212+0.021715    test-rmse:2.183032+0.491124 
## [86] train-rmse:0.832292+0.022308    test-rmse:2.181712+0.493132 
## [87] train-rmse:0.825334+0.021103    test-rmse:2.180692+0.492562 
## [88] train-rmse:0.818964+0.021950    test-rmse:2.179792+0.491782 
## [89] train-rmse:0.808997+0.019615    test-rmse:2.177982+0.495403 
## [90] train-rmse:0.800081+0.021810    test-rmse:2.176068+0.494190 
## [91] train-rmse:0.791867+0.025013    test-rmse:2.172848+0.494678 
## [92] train-rmse:0.783995+0.026161    test-rmse:2.169942+0.494149 
## [93] train-rmse:0.775890+0.028983    test-rmse:2.166791+0.494151 
## [94] train-rmse:0.767920+0.026982    test-rmse:2.166636+0.495407 
## [95] train-rmse:0.760290+0.026311    test-rmse:2.164801+0.496686 
## [96] train-rmse:0.752785+0.025982    test-rmse:2.161593+0.497372 
## [97] train-rmse:0.746725+0.027429    test-rmse:2.159300+0.495700 
## [98] train-rmse:0.739108+0.024619    test-rmse:2.158432+0.495227 
## [99] train-rmse:0.731761+0.023323    test-rmse:2.158156+0.493988 
## [100]    train-rmse:0.726523+0.024166    test-rmse:2.155750+0.494877 
## [101]    train-rmse:0.717716+0.022347    test-rmse:2.155767+0.494324 
## [102]    train-rmse:0.712117+0.020841    test-rmse:2.154530+0.494847 
## [103]    train-rmse:0.703267+0.022871    test-rmse:2.153708+0.494553 
## [104]    train-rmse:0.699551+0.023361    test-rmse:2.153217+0.494259 
## [105]    train-rmse:0.694406+0.024133    test-rmse:2.153039+0.495792 
## [106]    train-rmse:0.686800+0.025290    test-rmse:2.151728+0.495009 
## [107]    train-rmse:0.680548+0.025890    test-rmse:2.148948+0.494380 
## [108]    train-rmse:0.673576+0.025830    test-rmse:2.149221+0.494228 
## [109]    train-rmse:0.666092+0.025589    test-rmse:2.150382+0.494843 
## [110]    train-rmse:0.661122+0.026110    test-rmse:2.150102+0.494614 
## [111]    train-rmse:0.656198+0.025121    test-rmse:2.149292+0.493832 
## [112]    train-rmse:0.651330+0.024303    test-rmse:2.149489+0.493833 
## [113]    train-rmse:0.645483+0.022983    test-rmse:2.148725+0.493925 
## [114]    train-rmse:0.638825+0.021490    test-rmse:2.147061+0.493160 
## [115]    train-rmse:0.634186+0.021107    test-rmse:2.146321+0.493296 
## [116]    train-rmse:0.630043+0.021216    test-rmse:2.148485+0.494656 
## [117]    train-rmse:0.622640+0.021866    test-rmse:2.147053+0.493274 
## [118]    train-rmse:0.615227+0.022217    test-rmse:2.146464+0.493365 
## [119]    train-rmse:0.612019+0.021777    test-rmse:2.146533+0.492338 
## [120]    train-rmse:0.607443+0.021120    test-rmse:2.144663+0.492244 
## [121]    train-rmse:0.603286+0.022702    test-rmse:2.143887+0.492252 
## [122]    train-rmse:0.598372+0.023545    test-rmse:2.142563+0.492155 
## [123]    train-rmse:0.594139+0.024324    test-rmse:2.141353+0.492111 
## [124]    train-rmse:0.588616+0.024313    test-rmse:2.141427+0.492829 
## [125]    train-rmse:0.584664+0.023633    test-rmse:2.141795+0.492896 
## [126]    train-rmse:0.579950+0.023444    test-rmse:2.141318+0.493243 
## [127]    train-rmse:0.573917+0.023575    test-rmse:2.140691+0.493180 
## [128]    train-rmse:0.568667+0.024768    test-rmse:2.142651+0.495214 
## [129]    train-rmse:0.561341+0.024083    test-rmse:2.139976+0.495877 
## [130]    train-rmse:0.558673+0.024147    test-rmse:2.139851+0.495746 
## [131]    train-rmse:0.555496+0.023973    test-rmse:2.139951+0.495588 
## [132]    train-rmse:0.552463+0.023543    test-rmse:2.139907+0.496066 
## [133]    train-rmse:0.548704+0.023732    test-rmse:2.140653+0.495235 
## [134]    train-rmse:0.541321+0.023990    test-rmse:2.139623+0.493970 
## [135]    train-rmse:0.537022+0.023286    test-rmse:2.138277+0.494997 
## [136]    train-rmse:0.531266+0.023313    test-rmse:2.137978+0.495378 
## [137]    train-rmse:0.526957+0.023525    test-rmse:2.137202+0.494619 
## [138]    train-rmse:0.522671+0.026259    test-rmse:2.137630+0.494429 
## [139]    train-rmse:0.519226+0.026203    test-rmse:2.137639+0.494880 
## [140]    train-rmse:0.515285+0.026270    test-rmse:2.137966+0.494642 
## [141]    train-rmse:0.510722+0.026566    test-rmse:2.137822+0.495012 
## [142]    train-rmse:0.506169+0.025077    test-rmse:2.138115+0.494376 
## [143]    train-rmse:0.501798+0.025024    test-rmse:2.136886+0.494637 
## [144]    train-rmse:0.497869+0.024327    test-rmse:2.137804+0.494573 
## [145]    train-rmse:0.493086+0.023082    test-rmse:2.137469+0.495307 
## [146]    train-rmse:0.489951+0.024325    test-rmse:2.137464+0.495317 
## [147]    train-rmse:0.486903+0.024366    test-rmse:2.137009+0.494698 
## [148]    train-rmse:0.484126+0.024270    test-rmse:2.136295+0.494165 
## [149]    train-rmse:0.481614+0.024694    test-rmse:2.136008+0.494174 
## [150]    train-rmse:0.477455+0.023639    test-rmse:2.136812+0.495163 
## [151]    train-rmse:0.474121+0.024285    test-rmse:2.136738+0.495699 
## [152]    train-rmse:0.470489+0.023823    test-rmse:2.136282+0.494970 
## [153]    train-rmse:0.466792+0.022958    test-rmse:2.137001+0.494763 
## [154]    train-rmse:0.462911+0.022686    test-rmse:2.135390+0.495861 
## [155]    train-rmse:0.461242+0.022505    test-rmse:2.136321+0.495859 
## [156]    train-rmse:0.457899+0.022562    test-rmse:2.135349+0.496207 
## [157]    train-rmse:0.454725+0.021967    test-rmse:2.134903+0.496521 
## [158]    train-rmse:0.451008+0.021457    test-rmse:2.134328+0.495786 
## [159]    train-rmse:0.448279+0.021022    test-rmse:2.133996+0.496333 
## [160]    train-rmse:0.445701+0.020793    test-rmse:2.134923+0.496270 
## [161]    train-rmse:0.443361+0.021637    test-rmse:2.134918+0.495945 
## [162]    train-rmse:0.440170+0.021428    test-rmse:2.134341+0.496208 
## [163]    train-rmse:0.436395+0.021412    test-rmse:2.133785+0.496175 
## [164]    train-rmse:0.433700+0.020931    test-rmse:2.133660+0.496304 
## [165]    train-rmse:0.429931+0.019657    test-rmse:2.133076+0.495604 
## [166]    train-rmse:0.426990+0.019515    test-rmse:2.132984+0.494969 
## [167]    train-rmse:0.424551+0.018946    test-rmse:2.132662+0.494999 
## [168]    train-rmse:0.421184+0.019180    test-rmse:2.132238+0.494823 
## [169]    train-rmse:0.417793+0.018856    test-rmse:2.132146+0.495311 
## [170]    train-rmse:0.415877+0.019040    test-rmse:2.131911+0.495686 
## [171]    train-rmse:0.412138+0.020408    test-rmse:2.132691+0.496995 
## [172]    train-rmse:0.407492+0.019666    test-rmse:2.131597+0.496658 
## [173]    train-rmse:0.403685+0.020827    test-rmse:2.132952+0.497914 
## [174]    train-rmse:0.401455+0.020766    test-rmse:2.132084+0.498104 
## [175]    train-rmse:0.398462+0.020764    test-rmse:2.132228+0.498399 
## [176]    train-rmse:0.395318+0.020190    test-rmse:2.133185+0.498237 
## [177]    train-rmse:0.392658+0.020415    test-rmse:2.132925+0.498380 
## [178]    train-rmse:0.390373+0.019985    test-rmse:2.131972+0.499093 
## Stopping. Best iteration:
## [172]    train-rmse:0.407492+0.019666    test-rmse:2.131597+0.496658
## 
## 11 
## [1]  train-rmse:7.339678+0.152357    test-rmse:7.327687+0.636508 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.594806+0.133246    test-rmse:6.608806+0.588349 
## [3]  train-rmse:5.943220+0.117104    test-rmse:5.984673+0.544810 
## [4]  train-rmse:5.370744+0.108462    test-rmse:5.445995+0.506106 
## [5]  train-rmse:4.873762+0.100796    test-rmse:4.990864+0.467336 
## [6]  train-rmse:4.439894+0.088476    test-rmse:4.606496+0.456683 
## [7]  train-rmse:4.058548+0.080058    test-rmse:4.272330+0.435994 
## [8]  train-rmse:3.732495+0.073267    test-rmse:3.997218+0.430183 
## [9]  train-rmse:3.447341+0.065313    test-rmse:3.770764+0.436544 
## [10] train-rmse:3.202197+0.062077    test-rmse:3.582563+0.431457 
## [11] train-rmse:2.983259+0.054993    test-rmse:3.408553+0.444747 
## [12] train-rmse:2.799780+0.050719    test-rmse:3.278470+0.463325 
## [13] train-rmse:2.633914+0.048831    test-rmse:3.170205+0.468494 
## [14] train-rmse:2.493712+0.053075    test-rmse:3.079114+0.488616 
## [15] train-rmse:2.357540+0.052732    test-rmse:2.992547+0.477567 
## [16] train-rmse:2.234249+0.054773    test-rmse:2.902826+0.496721 
## [17] train-rmse:2.135910+0.053475    test-rmse:2.836330+0.497117 
## [18] train-rmse:2.043249+0.049603    test-rmse:2.775573+0.496238 
## [19] train-rmse:1.965390+0.047678    test-rmse:2.726732+0.496205 
## [20] train-rmse:1.891065+0.044222    test-rmse:2.685398+0.500572 
## [21] train-rmse:1.823526+0.043483    test-rmse:2.638426+0.510480 
## [22] train-rmse:1.761121+0.038306    test-rmse:2.604071+0.517371 
## [23] train-rmse:1.710222+0.044833    test-rmse:2.578270+0.522067 
## [24] train-rmse:1.654790+0.043316    test-rmse:2.547407+0.520959 
## [25] train-rmse:1.609332+0.035572    test-rmse:2.529683+0.517195 
## [26] train-rmse:1.571743+0.037114    test-rmse:2.523492+0.524226 
## [27] train-rmse:1.534047+0.032413    test-rmse:2.512104+0.521076 
## [28] train-rmse:1.491960+0.034684    test-rmse:2.496496+0.522122 
## [29] train-rmse:1.458667+0.031265    test-rmse:2.487667+0.528341 
## [30] train-rmse:1.429285+0.036109    test-rmse:2.469989+0.533412 
## [31] train-rmse:1.399532+0.033554    test-rmse:2.468320+0.537409 
## [32] train-rmse:1.370867+0.032582    test-rmse:2.462784+0.539686 
## [33] train-rmse:1.343854+0.032216    test-rmse:2.451365+0.535429 
## [34] train-rmse:1.310645+0.033037    test-rmse:2.438640+0.542971 
## [35] train-rmse:1.288473+0.034048    test-rmse:2.427711+0.545959 
## [36] train-rmse:1.264700+0.037470    test-rmse:2.414200+0.541544 
## [37] train-rmse:1.243921+0.037074    test-rmse:2.408537+0.542099 
## [38] train-rmse:1.218329+0.032003    test-rmse:2.403028+0.538612 
## [39] train-rmse:1.199239+0.030223    test-rmse:2.394738+0.532689 
## [40] train-rmse:1.177250+0.030802    test-rmse:2.386091+0.537483 
## [41] train-rmse:1.156112+0.028224    test-rmse:2.386064+0.535027 
## [42] train-rmse:1.137401+0.029217    test-rmse:2.377632+0.536225 
## [43] train-rmse:1.120324+0.032539    test-rmse:2.370717+0.536439 
## [44] train-rmse:1.097749+0.028851    test-rmse:2.364540+0.538688 
## [45] train-rmse:1.079500+0.026479    test-rmse:2.361953+0.541265 
## [46] train-rmse:1.061690+0.025831    test-rmse:2.359483+0.542678 
## [47] train-rmse:1.046077+0.025807    test-rmse:2.351864+0.538247 
## [48] train-rmse:1.031559+0.024838    test-rmse:2.348439+0.538572 
## [49] train-rmse:1.012450+0.027562    test-rmse:2.344632+0.534920 
## [50] train-rmse:0.998399+0.030638    test-rmse:2.339563+0.534135 
## [51] train-rmse:0.983435+0.029731    test-rmse:2.334335+0.540209 
## [52] train-rmse:0.966952+0.031015    test-rmse:2.334877+0.540185 
## [53] train-rmse:0.949348+0.034098    test-rmse:2.330178+0.543054 
## [54] train-rmse:0.938123+0.034244    test-rmse:2.325820+0.540722 
## [55] train-rmse:0.921680+0.027707    test-rmse:2.323284+0.541902 
## [56] train-rmse:0.906082+0.030100    test-rmse:2.315699+0.542766 
## [57] train-rmse:0.890357+0.031150    test-rmse:2.315756+0.546877 
## [58] train-rmse:0.878511+0.031821    test-rmse:2.311657+0.548233 
## [59] train-rmse:0.865573+0.027666    test-rmse:2.311010+0.549824 
## [60] train-rmse:0.852064+0.029979    test-rmse:2.308300+0.549936 
## [61] train-rmse:0.839349+0.031049    test-rmse:2.308670+0.551812 
## [62] train-rmse:0.828793+0.033628    test-rmse:2.302291+0.549396 
## [63] train-rmse:0.820518+0.031370    test-rmse:2.300141+0.550372 
## [64] train-rmse:0.810518+0.032266    test-rmse:2.297912+0.551179 
## [65] train-rmse:0.798904+0.031304    test-rmse:2.293756+0.553060 
## [66] train-rmse:0.787475+0.031510    test-rmse:2.291402+0.554221 
## [67] train-rmse:0.773392+0.028139    test-rmse:2.288530+0.555154 
## [68] train-rmse:0.765240+0.027497    test-rmse:2.284660+0.556135 
## [69] train-rmse:0.753688+0.030315    test-rmse:2.284506+0.557695 
## [70] train-rmse:0.740862+0.029629    test-rmse:2.281166+0.559190 
## [71] train-rmse:0.730447+0.029854    test-rmse:2.281289+0.558519 
## [72] train-rmse:0.720512+0.027321    test-rmse:2.281199+0.559372 
## [73] train-rmse:0.710795+0.028717    test-rmse:2.281045+0.559418 
## [74] train-rmse:0.703480+0.030259    test-rmse:2.278664+0.561074 
## [75] train-rmse:0.695528+0.028434    test-rmse:2.277861+0.559531 
## [76] train-rmse:0.683150+0.026288    test-rmse:2.276440+0.559309 
## [77] train-rmse:0.673824+0.026068    test-rmse:2.274843+0.562409 
## [78] train-rmse:0.666171+0.026777    test-rmse:2.274708+0.563954 
## [79] train-rmse:0.656480+0.026890    test-rmse:2.273315+0.565751 
## [80] train-rmse:0.646609+0.024575    test-rmse:2.269866+0.566164 
## [81] train-rmse:0.638620+0.026045    test-rmse:2.269026+0.568067 
## [82] train-rmse:0.630960+0.026689    test-rmse:2.268385+0.567758 
## [83] train-rmse:0.622775+0.026221    test-rmse:2.268211+0.568501 
## [84] train-rmse:0.616866+0.027631    test-rmse:2.267145+0.567649 
## [85] train-rmse:0.608992+0.026814    test-rmse:2.267155+0.567209 
## [86] train-rmse:0.602047+0.026510    test-rmse:2.265488+0.568162 
## [87] train-rmse:0.596320+0.027135    test-rmse:2.263928+0.568538 
## [88] train-rmse:0.587528+0.027615    test-rmse:2.263276+0.567488 
## [89] train-rmse:0.580739+0.026883    test-rmse:2.263990+0.567745 
## [90] train-rmse:0.574054+0.028210    test-rmse:2.264023+0.568662 
## [91] train-rmse:0.565339+0.026591    test-rmse:2.261071+0.570485 
## [92] train-rmse:0.560958+0.026659    test-rmse:2.259466+0.570751 
## [93] train-rmse:0.555760+0.026479    test-rmse:2.259138+0.570607 
## [94] train-rmse:0.549906+0.027363    test-rmse:2.259139+0.571537 
## [95] train-rmse:0.543848+0.026729    test-rmse:2.258787+0.571579 
## [96] train-rmse:0.536615+0.026641    test-rmse:2.258063+0.571880 
## [97] train-rmse:0.528952+0.028177    test-rmse:2.256875+0.572214 
## [98] train-rmse:0.522860+0.025861    test-rmse:2.255370+0.571827 
## [99] train-rmse:0.515251+0.024244    test-rmse:2.254549+0.572651 
## [100]    train-rmse:0.508353+0.025113    test-rmse:2.254487+0.572033 
## [101]    train-rmse:0.501546+0.023698    test-rmse:2.254971+0.572356 
## [102]    train-rmse:0.494243+0.022713    test-rmse:2.255139+0.573381 
## [103]    train-rmse:0.487982+0.021556    test-rmse:2.254903+0.573337 
## [104]    train-rmse:0.482302+0.019957    test-rmse:2.255230+0.573930 
## [105]    train-rmse:0.476111+0.016917    test-rmse:2.253939+0.573801 
## [106]    train-rmse:0.470479+0.016997    test-rmse:2.254016+0.573582 
## [107]    train-rmse:0.463883+0.018527    test-rmse:2.254771+0.573391 
## [108]    train-rmse:0.460242+0.019267    test-rmse:2.254213+0.574211 
## [109]    train-rmse:0.455093+0.019019    test-rmse:2.253809+0.575319 
## [110]    train-rmse:0.449900+0.018084    test-rmse:2.255155+0.575160 
## [111]    train-rmse:0.445272+0.016832    test-rmse:2.255644+0.575613 
## [112]    train-rmse:0.442108+0.016881    test-rmse:2.255032+0.576258 
## [113]    train-rmse:0.437526+0.018052    test-rmse:2.254154+0.576368 
## [114]    train-rmse:0.434719+0.018406    test-rmse:2.253930+0.576299 
## [115]    train-rmse:0.430191+0.019067    test-rmse:2.253304+0.578340 
## [116]    train-rmse:0.425376+0.019135    test-rmse:2.254058+0.578179 
## [117]    train-rmse:0.421631+0.019723    test-rmse:2.253313+0.578216 
## [118]    train-rmse:0.416725+0.017544    test-rmse:2.254670+0.577790 
## [119]    train-rmse:0.413943+0.017154    test-rmse:2.254475+0.577723 
## [120]    train-rmse:0.409228+0.016919    test-rmse:2.254710+0.578765 
## [121]    train-rmse:0.405440+0.018606    test-rmse:2.255110+0.580111 
## Stopping. Best iteration:
## [115]    train-rmse:0.430191+0.019067    test-rmse:2.253304+0.578340
## 
## 12 
## [1]  train-rmse:7.325933+0.150419    test-rmse:7.318150+0.649059 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.567494+0.129616    test-rmse:6.590112+0.612137 
## [3]  train-rmse:5.900850+0.111510    test-rmse:5.966409+0.573609 
## [4]  train-rmse:5.316454+0.097950    test-rmse:5.427718+0.538418 
## [5]  train-rmse:4.805128+0.086822    test-rmse:4.964424+0.514004 
## [6]  train-rmse:4.359337+0.076060    test-rmse:4.577300+0.491854 
## [7]  train-rmse:3.969306+0.068382    test-rmse:4.251144+0.468226 
## [8]  train-rmse:3.633462+0.061661    test-rmse:3.968558+0.463627 
## [9]  train-rmse:3.335277+0.052790    test-rmse:3.734351+0.449334 
## [10] train-rmse:3.078657+0.049377    test-rmse:3.539811+0.431375 
## [11] train-rmse:2.852289+0.038924    test-rmse:3.368227+0.449062 
## [12] train-rmse:2.659658+0.035858    test-rmse:3.248479+0.451920 
## [13] train-rmse:2.487021+0.032910    test-rmse:3.125633+0.452147 
## [14] train-rmse:2.336598+0.025241    test-rmse:3.020079+0.464726 
## [15] train-rmse:2.206782+0.020020    test-rmse:2.937773+0.483299 
## [16] train-rmse:2.077833+0.024935    test-rmse:2.864792+0.485220 
## [17] train-rmse:1.971005+0.022372    test-rmse:2.800350+0.497784 
## [18] train-rmse:1.875602+0.025179    test-rmse:2.759533+0.501661 
## [19] train-rmse:1.788757+0.032972    test-rmse:2.713660+0.504364 
## [20] train-rmse:1.714240+0.033318    test-rmse:2.672262+0.505261 
## [21] train-rmse:1.643274+0.036187    test-rmse:2.639912+0.513883 
## [22] train-rmse:1.579828+0.038166    test-rmse:2.611769+0.519718 
## [23] train-rmse:1.520552+0.038179    test-rmse:2.578265+0.521689 
## [24] train-rmse:1.469237+0.038777    test-rmse:2.559452+0.528644 
## [25] train-rmse:1.413125+0.033350    test-rmse:2.536308+0.524118 
## [26] train-rmse:1.367903+0.030899    test-rmse:2.513155+0.526777 
## [27] train-rmse:1.324194+0.033849    test-rmse:2.489354+0.533185 
## [28] train-rmse:1.281447+0.025289    test-rmse:2.476357+0.534219 
## [29] train-rmse:1.245789+0.023209    test-rmse:2.463965+0.534854 
## [30] train-rmse:1.213017+0.026584    test-rmse:2.455936+0.535362 
## [31] train-rmse:1.176208+0.024117    test-rmse:2.448795+0.533841 
## [32] train-rmse:1.150791+0.024179    test-rmse:2.436380+0.539451 
## [33] train-rmse:1.125001+0.023873    test-rmse:2.427468+0.541755 
## [34] train-rmse:1.096844+0.028013    test-rmse:2.419386+0.543307 
## [35] train-rmse:1.068598+0.026906    test-rmse:2.409155+0.547374 
## [36] train-rmse:1.046811+0.026042    test-rmse:2.408266+0.548826 
## [37] train-rmse:1.022042+0.022417    test-rmse:2.404007+0.549333 
## [38] train-rmse:0.994657+0.023081    test-rmse:2.401658+0.554448 
## [39] train-rmse:0.974523+0.025630    test-rmse:2.399241+0.554249 
## [40] train-rmse:0.952567+0.024494    test-rmse:2.399230+0.555693 
## [41] train-rmse:0.936673+0.021589    test-rmse:2.395694+0.557374 
## [42] train-rmse:0.919990+0.021313    test-rmse:2.391207+0.560258 
## [43] train-rmse:0.899683+0.020080    test-rmse:2.390867+0.558453 
## [44] train-rmse:0.877568+0.019956    test-rmse:2.381507+0.559868 
## [45] train-rmse:0.858949+0.021423    test-rmse:2.379868+0.559505 
## [46] train-rmse:0.845682+0.021661    test-rmse:2.377516+0.561145 
## [47] train-rmse:0.832858+0.020599    test-rmse:2.375008+0.559425 
## [48] train-rmse:0.814283+0.022696    test-rmse:2.369996+0.564309 
## [49] train-rmse:0.792940+0.020938    test-rmse:2.368441+0.561542 
## [50] train-rmse:0.779677+0.019175    test-rmse:2.366366+0.558044 
## [51] train-rmse:0.769183+0.019347    test-rmse:2.360958+0.561526 
## [52] train-rmse:0.751296+0.022436    test-rmse:2.355761+0.565831 
## [53] train-rmse:0.736578+0.022261    test-rmse:2.353566+0.566334 
## [54] train-rmse:0.719438+0.024709    test-rmse:2.349893+0.567467 
## [55] train-rmse:0.703823+0.022951    test-rmse:2.346047+0.567312 
## [56] train-rmse:0.689581+0.025690    test-rmse:2.344269+0.566901 
## [57] train-rmse:0.677575+0.028227    test-rmse:2.341389+0.568512 
## [58] train-rmse:0.665830+0.029002    test-rmse:2.338711+0.566596 
## [59] train-rmse:0.652588+0.031198    test-rmse:2.334695+0.567783 
## [60] train-rmse:0.639766+0.028632    test-rmse:2.332474+0.565913 
## [61] train-rmse:0.628770+0.029032    test-rmse:2.332357+0.566439 
## [62] train-rmse:0.618114+0.026778    test-rmse:2.332851+0.565782 
## [63] train-rmse:0.608499+0.024109    test-rmse:2.330153+0.566525 
## [64] train-rmse:0.594923+0.025676    test-rmse:2.327730+0.568506 
## [65] train-rmse:0.585878+0.024967    test-rmse:2.326990+0.568240 
## [66] train-rmse:0.577049+0.024644    test-rmse:2.327689+0.566801 
## [67] train-rmse:0.566017+0.021415    test-rmse:2.322555+0.569249 
## [68] train-rmse:0.557401+0.022889    test-rmse:2.321446+0.570048 
## [69] train-rmse:0.548870+0.022954    test-rmse:2.320805+0.572559 
## [70] train-rmse:0.538157+0.025680    test-rmse:2.319937+0.570801 
## [71] train-rmse:0.529061+0.021928    test-rmse:2.317616+0.572121 
## [72] train-rmse:0.518333+0.022154    test-rmse:2.317539+0.572294 
## [73] train-rmse:0.509713+0.023976    test-rmse:2.317685+0.570742 
## [74] train-rmse:0.499536+0.022654    test-rmse:2.317214+0.571425 
## [75] train-rmse:0.493341+0.020721    test-rmse:2.317759+0.572361 
## [76] train-rmse:0.483916+0.023048    test-rmse:2.316367+0.572988 
## [77] train-rmse:0.477582+0.024615    test-rmse:2.316308+0.575120 
## [78] train-rmse:0.469268+0.022296    test-rmse:2.316687+0.574025 
## [79] train-rmse:0.460991+0.020723    test-rmse:2.315126+0.574409 
## [80] train-rmse:0.454973+0.019164    test-rmse:2.313739+0.575431 
## [81] train-rmse:0.448280+0.018990    test-rmse:2.314930+0.573641 
## [82] train-rmse:0.439789+0.022127    test-rmse:2.314630+0.573845 
## [83] train-rmse:0.432988+0.023238    test-rmse:2.311903+0.575255 
## [84] train-rmse:0.426430+0.021876    test-rmse:2.311359+0.575963 
## [85] train-rmse:0.421587+0.021769    test-rmse:2.311884+0.574824 
## [86] train-rmse:0.414557+0.021636    test-rmse:2.311014+0.575649 
## [87] train-rmse:0.408982+0.021645    test-rmse:2.310328+0.576376 
## [88] train-rmse:0.403278+0.020185    test-rmse:2.310689+0.576245 
## [89] train-rmse:0.398257+0.020519    test-rmse:2.311542+0.575542 
## [90] train-rmse:0.391962+0.019800    test-rmse:2.311897+0.576079 
## [91] train-rmse:0.383340+0.018295    test-rmse:2.310776+0.577341 
## [92] train-rmse:0.379689+0.018557    test-rmse:2.310731+0.577669 
## [93] train-rmse:0.374589+0.017840    test-rmse:2.310554+0.579164 
## Stopping. Best iteration:
## [87] train-rmse:0.408982+0.021645    test-rmse:2.310328+0.576376
## 
## 13 
## [1]  train-rmse:7.321069+0.150020    test-rmse:7.315670+0.648743 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.557470+0.129025    test-rmse:6.590933+0.607365 
## [3]  train-rmse:5.883455+0.108388    test-rmse:5.960360+0.572809 
## [4]  train-rmse:5.289537+0.093907    test-rmse:5.413071+0.542775 
## [5]  train-rmse:4.767195+0.079961    test-rmse:4.946051+0.517304 
## [6]  train-rmse:4.312666+0.067633    test-rmse:4.546784+0.498988 
## [7]  train-rmse:3.912351+0.059488    test-rmse:4.211136+0.477891 
## [8]  train-rmse:3.563159+0.053792    test-rmse:3.920117+0.464382 
## [9]  train-rmse:3.258227+0.049100    test-rmse:3.685397+0.453042 
## [10] train-rmse:2.991211+0.045166    test-rmse:3.489380+0.455090 
## [11] train-rmse:2.758803+0.041985    test-rmse:3.332664+0.450574 
## [12] train-rmse:2.549476+0.035655    test-rmse:3.191431+0.467151 
## [13] train-rmse:2.369422+0.033239    test-rmse:3.079823+0.470701 
## [14] train-rmse:2.211440+0.027863    test-rmse:2.993374+0.472217 
## [15] train-rmse:2.074503+0.027516    test-rmse:2.913227+0.494572 
## [16] train-rmse:1.952356+0.025785    test-rmse:2.841531+0.500140 
## [17] train-rmse:1.839607+0.023865    test-rmse:2.781730+0.510665 
## [18] train-rmse:1.741432+0.024169    test-rmse:2.721482+0.521261 
## [19] train-rmse:1.649612+0.025008    test-rmse:2.676397+0.524480 
## [20] train-rmse:1.571254+0.028640    test-rmse:2.642206+0.533386 
## [21] train-rmse:1.496100+0.029595    test-rmse:2.605817+0.542402 
## [22] train-rmse:1.431559+0.028151    test-rmse:2.587565+0.544703 
## [23] train-rmse:1.373882+0.029665    test-rmse:2.565904+0.549654 
## [24] train-rmse:1.316726+0.032533    test-rmse:2.552291+0.548876 
## [25] train-rmse:1.270111+0.032818    test-rmse:2.537971+0.552240 
## [26] train-rmse:1.223147+0.028463    test-rmse:2.520548+0.557200 
## [27] train-rmse:1.184158+0.027800    test-rmse:2.508513+0.563341 
## [28] train-rmse:1.141218+0.032147    test-rmse:2.496563+0.569138 
## [29] train-rmse:1.104860+0.035358    test-rmse:2.482987+0.569844 
## [30] train-rmse:1.068138+0.027377    test-rmse:2.471464+0.569196 
## [31] train-rmse:1.030065+0.032660    test-rmse:2.464367+0.572704 
## [32] train-rmse:0.994525+0.033184    test-rmse:2.457964+0.566676 
## [33] train-rmse:0.970808+0.035273    test-rmse:2.452124+0.566119 
## [34] train-rmse:0.942143+0.037480    test-rmse:2.449797+0.573090 
## [35] train-rmse:0.914455+0.036365    test-rmse:2.438845+0.571766 
## [36] train-rmse:0.887530+0.037643    test-rmse:2.431922+0.575127 
## [37] train-rmse:0.860533+0.033450    test-rmse:2.426387+0.580559 
## [38] train-rmse:0.839069+0.034546    test-rmse:2.422916+0.578552 
## [39] train-rmse:0.819521+0.031579    test-rmse:2.416419+0.576672 
## [40] train-rmse:0.801602+0.029065    test-rmse:2.412674+0.580770 
## [41] train-rmse:0.777996+0.031926    test-rmse:2.410336+0.585238 
## [42] train-rmse:0.761755+0.032345    test-rmse:2.405714+0.591274 
## [43] train-rmse:0.741685+0.028909    test-rmse:2.403249+0.595115 
## [44] train-rmse:0.726677+0.030180    test-rmse:2.399754+0.595082 
## [45] train-rmse:0.713404+0.031005    test-rmse:2.394352+0.596262 
## [46] train-rmse:0.694746+0.030423    test-rmse:2.390836+0.597979 
## [47] train-rmse:0.681159+0.029737    test-rmse:2.387235+0.600527 
## [48] train-rmse:0.662402+0.029677    test-rmse:2.386296+0.597690 
## [49] train-rmse:0.647916+0.025376    test-rmse:2.382497+0.597711 
## [50] train-rmse:0.626932+0.026247    test-rmse:2.381198+0.596879 
## [51] train-rmse:0.609364+0.030538    test-rmse:2.378602+0.595396 
## [52] train-rmse:0.599170+0.029841    test-rmse:2.375945+0.596227 
## [53] train-rmse:0.585973+0.026853    test-rmse:2.375556+0.598922 
## [54] train-rmse:0.574039+0.026469    test-rmse:2.374547+0.597743 
## [55] train-rmse:0.555226+0.027697    test-rmse:2.373077+0.597779 
## [56] train-rmse:0.543291+0.029166    test-rmse:2.369163+0.600439 
## [57] train-rmse:0.533164+0.029554    test-rmse:2.367123+0.602852 
## [58] train-rmse:0.519027+0.027687    test-rmse:2.365670+0.603806 
## [59] train-rmse:0.507575+0.024746    test-rmse:2.368307+0.607192 
## [60] train-rmse:0.499779+0.025435    test-rmse:2.366473+0.607234 
## [61] train-rmse:0.488950+0.027173    test-rmse:2.366688+0.607105 
## [62] train-rmse:0.477193+0.024501    test-rmse:2.364502+0.608678 
## [63] train-rmse:0.467944+0.025523    test-rmse:2.363181+0.609009 
## [64] train-rmse:0.458078+0.025127    test-rmse:2.362847+0.611116 
## [65] train-rmse:0.447676+0.027522    test-rmse:2.361784+0.609267 
## [66] train-rmse:0.438310+0.027722    test-rmse:2.360894+0.609357 
## [67] train-rmse:0.427692+0.026984    test-rmse:2.359467+0.608341 
## [68] train-rmse:0.419773+0.028266    test-rmse:2.359247+0.608338 
## [69] train-rmse:0.412927+0.027750    test-rmse:2.358500+0.607690 
## [70] train-rmse:0.402711+0.025380    test-rmse:2.357229+0.607072 
## [71] train-rmse:0.392988+0.026793    test-rmse:2.355788+0.607856 
## [72] train-rmse:0.384667+0.028403    test-rmse:2.354140+0.609068 
## [73] train-rmse:0.380189+0.027875    test-rmse:2.353298+0.608829 
## [74] train-rmse:0.371570+0.025487    test-rmse:2.352394+0.608690 
## [75] train-rmse:0.363249+0.027439    test-rmse:2.352426+0.608356 
## [76] train-rmse:0.357063+0.025699    test-rmse:2.351855+0.609148 
## [77] train-rmse:0.349988+0.023600    test-rmse:2.351461+0.609669 
## [78] train-rmse:0.343143+0.022830    test-rmse:2.350813+0.608585 
## [79] train-rmse:0.337884+0.023049    test-rmse:2.350624+0.609314 
## [80] train-rmse:0.332151+0.021278    test-rmse:2.350101+0.609880 
## [81] train-rmse:0.324904+0.020738    test-rmse:2.349027+0.611618 
## [82] train-rmse:0.318988+0.020881    test-rmse:2.348477+0.610989 
## [83] train-rmse:0.313278+0.019417    test-rmse:2.347788+0.611055 
## [84] train-rmse:0.305711+0.020541    test-rmse:2.348680+0.611407 
## [85] train-rmse:0.299894+0.019887    test-rmse:2.348601+0.611371 
## [86] train-rmse:0.293524+0.021377    test-rmse:2.348293+0.611549 
## [87] train-rmse:0.288466+0.021627    test-rmse:2.347806+0.611885 
## [88] train-rmse:0.284517+0.021822    test-rmse:2.349062+0.613243 
## [89] train-rmse:0.279683+0.021990    test-rmse:2.348432+0.613677 
## Stopping. Best iteration:
## [83] train-rmse:0.313278+0.019417    test-rmse:2.347788+0.611055
## 
## 14 
## [1]  train-rmse:7.362714+0.157883    test-rmse:7.346713+0.675021 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.677497+0.142627    test-rmse:6.687488+0.701404 
## [3]  train-rmse:6.109435+0.131477    test-rmse:6.148116+0.679669 
## [4]  train-rmse:5.639260+0.124456    test-rmse:5.655526+0.658513 
## [5]  train-rmse:5.255033+0.118438    test-rmse:5.305093+0.649560 
## [6]  train-rmse:4.941120+0.115207    test-rmse:5.004346+0.628903 
## [7]  train-rmse:4.682516+0.113511    test-rmse:4.761688+0.581907 
## [8]  train-rmse:4.471146+0.110895    test-rmse:4.562138+0.585569 
## [9]  train-rmse:4.299058+0.111890    test-rmse:4.403525+0.554862 
## [10] train-rmse:4.155777+0.112308    test-rmse:4.260425+0.517351 
## [11] train-rmse:4.035693+0.112761    test-rmse:4.148392+0.507262 
## [12] train-rmse:3.934361+0.112676    test-rmse:4.048001+0.512759 
## [13] train-rmse:3.848814+0.113749    test-rmse:3.962275+0.501561 
## [14] train-rmse:3.774041+0.115536    test-rmse:3.897261+0.474586 
## [15] train-rmse:3.708136+0.116721    test-rmse:3.832287+0.440698 
## [16] train-rmse:3.649073+0.116663    test-rmse:3.778973+0.443644 
## [17] train-rmse:3.596321+0.117529    test-rmse:3.722310+0.451778 
## [18] train-rmse:3.549188+0.118334    test-rmse:3.669631+0.432498 
## [19] train-rmse:3.505167+0.120061    test-rmse:3.636722+0.434882 
## [20] train-rmse:3.463825+0.121254    test-rmse:3.590446+0.413705 
## [21] train-rmse:3.425860+0.120767    test-rmse:3.560267+0.420916 
## [22] train-rmse:3.390116+0.120708    test-rmse:3.530015+0.430903 
## [23] train-rmse:3.356777+0.121289    test-rmse:3.501512+0.446144 
## [24] train-rmse:3.325180+0.122307    test-rmse:3.470324+0.423223 
## [25] train-rmse:3.294136+0.123441    test-rmse:3.430746+0.415160 
## [26] train-rmse:3.264677+0.123203    test-rmse:3.412263+0.418589 
## [27] train-rmse:3.237047+0.123078    test-rmse:3.385323+0.430745 
## [28] train-rmse:3.210948+0.123607    test-rmse:3.339290+0.419835 
## [29] train-rmse:3.185751+0.124597    test-rmse:3.314850+0.416407 
## [30] train-rmse:3.161094+0.125666    test-rmse:3.303617+0.419397 
## [31] train-rmse:3.136747+0.126421    test-rmse:3.278073+0.420619 
## [32] train-rmse:3.114252+0.126164    test-rmse:3.260615+0.429923 
## [33] train-rmse:3.092968+0.126404    test-rmse:3.243760+0.425304 
## [34] train-rmse:3.072261+0.126956    test-rmse:3.211661+0.413731 
## [35] train-rmse:3.052265+0.127738    test-rmse:3.203530+0.426401 
## [36] train-rmse:3.032485+0.128681    test-rmse:3.182882+0.415142 
## [37] train-rmse:3.013733+0.129075    test-rmse:3.165471+0.429605 
## [38] train-rmse:2.995386+0.129274    test-rmse:3.161474+0.427745 
## [39] train-rmse:2.977521+0.129478    test-rmse:3.148714+0.434765 
## [40] train-rmse:2.959888+0.129669    test-rmse:3.128348+0.429525 
## [41] train-rmse:2.943017+0.130231    test-rmse:3.106513+0.427797 
## [42] train-rmse:2.926729+0.130604    test-rmse:3.092499+0.441190 
## [43] train-rmse:2.911032+0.131177    test-rmse:3.084843+0.438300 
## [44] train-rmse:2.895466+0.131801    test-rmse:3.068502+0.440235 
## [45] train-rmse:2.880234+0.132412    test-rmse:3.052090+0.443668 
## [46] train-rmse:2.865592+0.132761    test-rmse:3.041906+0.433129 
## [47] train-rmse:2.851261+0.133145    test-rmse:3.037169+0.440262 
## [48] train-rmse:2.837243+0.133337    test-rmse:3.023626+0.440770 
## [49] train-rmse:2.823648+0.133882    test-rmse:3.005650+0.449554 
## [50] train-rmse:2.810310+0.134402    test-rmse:2.997442+0.457615 
## [51] train-rmse:2.797054+0.134972    test-rmse:2.973679+0.452051 
## [52] train-rmse:2.783945+0.135334    test-rmse:2.959893+0.448064 
## [53] train-rmse:2.771121+0.135752    test-rmse:2.961986+0.456042 
## [54] train-rmse:2.758761+0.136237    test-rmse:2.952058+0.454535 
## [55] train-rmse:2.746596+0.136682    test-rmse:2.939800+0.450784 
## [56] train-rmse:2.734687+0.137067    test-rmse:2.934541+0.447587 
## [57] train-rmse:2.722856+0.137542    test-rmse:2.924814+0.460647 
## [58] train-rmse:2.711596+0.137654    test-rmse:2.922338+0.462313 
## [59] train-rmse:2.700418+0.137964    test-rmse:2.908041+0.453749 
## [60] train-rmse:2.689460+0.138115    test-rmse:2.899799+0.450007 
## [61] train-rmse:2.678649+0.138306    test-rmse:2.890296+0.456731 
## [62] train-rmse:2.668005+0.138338    test-rmse:2.884646+0.459320 
## [63] train-rmse:2.657452+0.138360    test-rmse:2.875083+0.470182 
## [64] train-rmse:2.647139+0.138542    test-rmse:2.860815+0.468326 
## [65] train-rmse:2.637111+0.138666    test-rmse:2.851550+0.463351 
## [66] train-rmse:2.627115+0.138934    test-rmse:2.846071+0.471011 
## [67] train-rmse:2.617231+0.139195    test-rmse:2.838383+0.470843 
## [68] train-rmse:2.607601+0.139433    test-rmse:2.833954+0.472235 
## [69] train-rmse:2.598083+0.139651    test-rmse:2.829111+0.472077 
## [70] train-rmse:2.588928+0.139775    test-rmse:2.822820+0.469163 
## [71] train-rmse:2.579912+0.139967    test-rmse:2.810491+0.470439 
## [72] train-rmse:2.571057+0.140043    test-rmse:2.799865+0.472973 
## [73] train-rmse:2.562367+0.139923    test-rmse:2.789803+0.468072 
## [74] train-rmse:2.553832+0.139892    test-rmse:2.792343+0.462720 
## [75] train-rmse:2.545387+0.139863    test-rmse:2.782148+0.467018 
## [76] train-rmse:2.537071+0.139873    test-rmse:2.780306+0.471179 
## [77] train-rmse:2.528765+0.139856    test-rmse:2.770276+0.481866 
## [78] train-rmse:2.520623+0.139843    test-rmse:2.759613+0.481699 
## [79] train-rmse:2.512694+0.139999    test-rmse:2.757639+0.486012 
## [80] train-rmse:2.504865+0.140152    test-rmse:2.748462+0.484631 
## [81] train-rmse:2.497238+0.140271    test-rmse:2.736612+0.482301 
## [82] train-rmse:2.489633+0.140398    test-rmse:2.733504+0.483880 
## [83] train-rmse:2.482212+0.140534    test-rmse:2.726859+0.482735 
## [84] train-rmse:2.474805+0.140629    test-rmse:2.724635+0.483002 
## [85] train-rmse:2.467536+0.140750    test-rmse:2.714021+0.486892 
## [86] train-rmse:2.460374+0.140827    test-rmse:2.707092+0.483945 
## [87] train-rmse:2.453425+0.140988    test-rmse:2.700956+0.483917 
## [88] train-rmse:2.446582+0.141040    test-rmse:2.689069+0.485446 
## [89] train-rmse:2.439777+0.141071    test-rmse:2.685147+0.485175 
## [90] train-rmse:2.433198+0.141101    test-rmse:2.677326+0.489356 
## [91] train-rmse:2.426735+0.141131    test-rmse:2.679081+0.488562 
## [92] train-rmse:2.420279+0.141201    test-rmse:2.678098+0.491138 
## [93] train-rmse:2.414007+0.141294    test-rmse:2.671925+0.487665 
## [94] train-rmse:2.407868+0.141434    test-rmse:2.668963+0.486100 
## [95] train-rmse:2.401733+0.141551    test-rmse:2.661520+0.489115 
## [96] train-rmse:2.395736+0.141601    test-rmse:2.657614+0.491123 
## [97] train-rmse:2.389814+0.141701    test-rmse:2.654878+0.495874 
## [98] train-rmse:2.383934+0.141785    test-rmse:2.642542+0.491489 
## [99] train-rmse:2.378119+0.141812    test-rmse:2.637786+0.492289 
## [100]    train-rmse:2.372384+0.141844    test-rmse:2.631128+0.495046 
## [101]    train-rmse:2.366751+0.141933    test-rmse:2.625142+0.495241 
## [102]    train-rmse:2.361150+0.142071    test-rmse:2.620170+0.493715 
## [103]    train-rmse:2.355733+0.142228    test-rmse:2.613349+0.491961 
## [104]    train-rmse:2.350346+0.142418    test-rmse:2.605943+0.490744 
## [105]    train-rmse:2.345108+0.142555    test-rmse:2.598018+0.493773 
## [106]    train-rmse:2.339950+0.142705    test-rmse:2.596120+0.499781 
## [107]    train-rmse:2.334886+0.142843    test-rmse:2.593792+0.501320 
## [108]    train-rmse:2.329864+0.142984    test-rmse:2.593921+0.503369 
## [109]    train-rmse:2.324840+0.143182    test-rmse:2.590216+0.498517 
## [110]    train-rmse:2.319852+0.143332    test-rmse:2.587842+0.497265 
## [111]    train-rmse:2.314947+0.143537    test-rmse:2.584385+0.496400 
## [112]    train-rmse:2.310129+0.143693    test-rmse:2.580134+0.497739 
## [113]    train-rmse:2.305416+0.143828    test-rmse:2.573878+0.501031 
## [114]    train-rmse:2.300735+0.143902    test-rmse:2.570357+0.509487 
## [115]    train-rmse:2.296151+0.144010    test-rmse:2.569302+0.511683 
## [116]    train-rmse:2.291630+0.144095    test-rmse:2.560597+0.507497 
## [117]    train-rmse:2.287166+0.144222    test-rmse:2.561373+0.506281 
## [118]    train-rmse:2.282815+0.144416    test-rmse:2.557107+0.506150 
## [119]    train-rmse:2.278542+0.144588    test-rmse:2.553142+0.505589 
## [120]    train-rmse:2.274253+0.144769    test-rmse:2.550447+0.505950 
## [121]    train-rmse:2.270059+0.144937    test-rmse:2.548159+0.501138 
## [122]    train-rmse:2.265914+0.145083    test-rmse:2.541009+0.500198 
## [123]    train-rmse:2.261801+0.145277    test-rmse:2.540188+0.505706 
## [124]    train-rmse:2.257750+0.145435    test-rmse:2.534369+0.505757 
## [125]    train-rmse:2.253713+0.145599    test-rmse:2.529944+0.504148 
## [126]    train-rmse:2.249760+0.145739    test-rmse:2.530310+0.504766 
## [127]    train-rmse:2.245843+0.145865    test-rmse:2.524136+0.508069 
## [128]    train-rmse:2.241968+0.146020    test-rmse:2.519892+0.508423 
## [129]    train-rmse:2.238138+0.146208    test-rmse:2.516313+0.509698 
## [130]    train-rmse:2.234360+0.146375    test-rmse:2.514190+0.510411 
## [131]    train-rmse:2.230653+0.146435    test-rmse:2.512911+0.508481 
## [132]    train-rmse:2.226982+0.146586    test-rmse:2.507428+0.508943 
## [133]    train-rmse:2.223392+0.146706    test-rmse:2.503156+0.510676 
## [134]    train-rmse:2.219831+0.146796    test-rmse:2.501275+0.508848 
## [135]    train-rmse:2.216301+0.146852    test-rmse:2.495789+0.512907 
## [136]    train-rmse:2.212857+0.146899    test-rmse:2.492258+0.513597 
## [137]    train-rmse:2.209439+0.146996    test-rmse:2.490875+0.513410 
## [138]    train-rmse:2.206107+0.147098    test-rmse:2.492827+0.514713 
## [139]    train-rmse:2.202796+0.147176    test-rmse:2.492287+0.515190 
## [140]    train-rmse:2.199526+0.147273    test-rmse:2.487580+0.512416 
## [141]    train-rmse:2.196314+0.147384    test-rmse:2.486686+0.512434 
## [142]    train-rmse:2.193135+0.147492    test-rmse:2.486481+0.518963 
## [143]    train-rmse:2.190013+0.147612    test-rmse:2.483843+0.517528 
## [144]    train-rmse:2.186917+0.147732    test-rmse:2.482465+0.515760 
## [145]    train-rmse:2.183820+0.147826    test-rmse:2.475094+0.519877 
## [146]    train-rmse:2.180736+0.147929    test-rmse:2.474277+0.520684 
## [147]    train-rmse:2.177693+0.148038    test-rmse:2.473059+0.520143 
## [148]    train-rmse:2.174678+0.148170    test-rmse:2.467713+0.521336 
## [149]    train-rmse:2.171684+0.148314    test-rmse:2.461691+0.517851 
## [150]    train-rmse:2.168754+0.148468    test-rmse:2.460521+0.518298 
## [151]    train-rmse:2.165853+0.148604    test-rmse:2.457714+0.518665 
## [152]    train-rmse:2.162965+0.148765    test-rmse:2.457842+0.519027 
## [153]    train-rmse:2.160139+0.148868    test-rmse:2.455817+0.518504 
## [154]    train-rmse:2.157305+0.148972    test-rmse:2.454807+0.516722 
## [155]    train-rmse:2.154536+0.149091    test-rmse:2.450391+0.516555 
## [156]    train-rmse:2.151820+0.149144    test-rmse:2.451625+0.520144 
## [157]    train-rmse:2.149114+0.149183    test-rmse:2.449075+0.521939 
## [158]    train-rmse:2.146456+0.149244    test-rmse:2.450372+0.520331 
## [159]    train-rmse:2.143852+0.149330    test-rmse:2.447356+0.518632 
## [160]    train-rmse:2.141253+0.149440    test-rmse:2.442312+0.518059 
## [161]    train-rmse:2.138709+0.149567    test-rmse:2.439347+0.523931 
## [162]    train-rmse:2.136209+0.149659    test-rmse:2.437744+0.524667 
## [163]    train-rmse:2.133717+0.149751    test-rmse:2.435416+0.524885 
## [164]    train-rmse:2.131245+0.149884    test-rmse:2.435064+0.524686 
## [165]    train-rmse:2.128814+0.149997    test-rmse:2.432976+0.523716 
## [166]    train-rmse:2.126403+0.150115    test-rmse:2.434076+0.522749 
## [167]    train-rmse:2.123947+0.150211    test-rmse:2.430960+0.523846 
## [168]    train-rmse:2.121556+0.150307    test-rmse:2.426878+0.526465 
## [169]    train-rmse:2.119163+0.150392    test-rmse:2.424605+0.526256 
## [170]    train-rmse:2.116796+0.150500    test-rmse:2.425965+0.527422 
## [171]    train-rmse:2.114470+0.150622    test-rmse:2.424055+0.525232 
## [172]    train-rmse:2.112162+0.150754    test-rmse:2.422708+0.525389 
## [173]    train-rmse:2.109871+0.150890    test-rmse:2.421701+0.527940 
## [174]    train-rmse:2.107596+0.151057    test-rmse:2.419569+0.526123 
## [175]    train-rmse:2.105317+0.151210    test-rmse:2.419278+0.524711 
## [176]    train-rmse:2.103085+0.151384    test-rmse:2.416886+0.523452 
## [177]    train-rmse:2.100857+0.151537    test-rmse:2.415800+0.524362 
## [178]    train-rmse:2.098664+0.151637    test-rmse:2.412683+0.524259 
## [179]    train-rmse:2.096531+0.151745    test-rmse:2.410701+0.524964 
## [180]    train-rmse:2.094459+0.151818    test-rmse:2.413515+0.525441 
## [181]    train-rmse:2.092408+0.151905    test-rmse:2.414615+0.527160 
## [182]    train-rmse:2.090360+0.151979    test-rmse:2.410912+0.529567 
## [183]    train-rmse:2.088318+0.152068    test-rmse:2.408517+0.526726 
## [184]    train-rmse:2.086275+0.152163    test-rmse:2.404716+0.527010 
## [185]    train-rmse:2.084269+0.152271    test-rmse:2.407343+0.529618 
## [186]    train-rmse:2.082274+0.152364    test-rmse:2.404947+0.530093 
## [187]    train-rmse:2.080280+0.152488    test-rmse:2.402306+0.529831 
## [188]    train-rmse:2.078341+0.152594    test-rmse:2.401450+0.530116 
## [189]    train-rmse:2.076425+0.152670    test-rmse:2.398813+0.529710 
## [190]    train-rmse:2.074541+0.152783    test-rmse:2.398214+0.529699 
## [191]    train-rmse:2.072657+0.152858    test-rmse:2.398990+0.528691 
## [192]    train-rmse:2.070785+0.152944    test-rmse:2.396597+0.527832 
## [193]    train-rmse:2.068915+0.153011    test-rmse:2.394503+0.527456 
## [194]    train-rmse:2.067063+0.153112    test-rmse:2.392127+0.528057 
## [195]    train-rmse:2.065209+0.153216    test-rmse:2.389945+0.527753 
## [196]    train-rmse:2.063370+0.153333    test-rmse:2.391145+0.529472 
## [197]    train-rmse:2.061531+0.153448    test-rmse:2.388114+0.530285 
## [198]    train-rmse:2.059712+0.153528    test-rmse:2.387381+0.532520 
## [199]    train-rmse:2.057934+0.153593    test-rmse:2.386608+0.532362 
## [200]    train-rmse:2.056173+0.153666    test-rmse:2.385842+0.529364 
## [201]    train-rmse:2.054388+0.153790    test-rmse:2.383080+0.530953 
## [202]    train-rmse:2.052643+0.153850    test-rmse:2.382249+0.529784 
## [203]    train-rmse:2.050899+0.153924    test-rmse:2.381628+0.530919 
## [204]    train-rmse:2.049194+0.153986    test-rmse:2.379667+0.530545 
## [205]    train-rmse:2.047495+0.154012    test-rmse:2.380056+0.531941 
## [206]    train-rmse:2.045810+0.154049    test-rmse:2.379730+0.530651 
## [207]    train-rmse:2.044136+0.154136    test-rmse:2.378553+0.531082 
## [208]    train-rmse:2.042488+0.154206    test-rmse:2.377490+0.533252 
## [209]    train-rmse:2.040831+0.154255    test-rmse:2.377580+0.529718 
## [210]    train-rmse:2.039205+0.154289    test-rmse:2.376237+0.528738 
## [211]    train-rmse:2.037567+0.154342    test-rmse:2.378003+0.526982 
## [212]    train-rmse:2.035977+0.154414    test-rmse:2.375974+0.525372 
## [213]    train-rmse:2.034380+0.154518    test-rmse:2.373421+0.528745 
## [214]    train-rmse:2.032825+0.154597    test-rmse:2.373177+0.528797 
## [215]    train-rmse:2.031272+0.154664    test-rmse:2.372406+0.528592 
## [216]    train-rmse:2.029715+0.154722    test-rmse:2.370781+0.527992 
## [217]    train-rmse:2.028173+0.154775    test-rmse:2.369406+0.526904 
## [218]    train-rmse:2.026652+0.154842    test-rmse:2.368644+0.527702 
## [219]    train-rmse:2.025140+0.154910    test-rmse:2.369526+0.526655 
## [220]    train-rmse:2.023617+0.154971    test-rmse:2.369968+0.525694 
## [221]    train-rmse:2.022111+0.155039    test-rmse:2.370661+0.530137 
## [222]    train-rmse:2.020625+0.155107    test-rmse:2.368034+0.529808 
## [223]    train-rmse:2.019153+0.155159    test-rmse:2.366246+0.530488 
## [224]    train-rmse:2.017653+0.155255    test-rmse:2.368966+0.529497 
## [225]    train-rmse:2.016186+0.155320    test-rmse:2.368170+0.529685 
## [226]    train-rmse:2.014735+0.155416    test-rmse:2.370775+0.531703 
## [227]    train-rmse:2.013319+0.155488    test-rmse:2.368263+0.530533 
## [228]    train-rmse:2.011910+0.155599    test-rmse:2.368567+0.530915 
## [229]    train-rmse:2.010453+0.155641    test-rmse:2.367336+0.531971 
## Stopping. Best iteration:
## [223]    train-rmse:2.019153+0.155159    test-rmse:2.366246+0.530488
## 
## 15 
## [1]  train-rmse:7.302359+0.153266    test-rmse:7.303759+0.654899 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.557549+0.138125    test-rmse:6.582337+0.638434 
## [3]  train-rmse:5.933481+0.125342    test-rmse:5.996466+0.619396 
## [4]  train-rmse:5.407163+0.116097    test-rmse:5.528855+0.584077 
## [5]  train-rmse:4.965383+0.108223    test-rmse:5.111464+0.538515 
## [6]  train-rmse:4.588944+0.100477    test-rmse:4.734727+0.533114 
## [7]  train-rmse:4.281612+0.094538    test-rmse:4.456604+0.514180 
## [8]  train-rmse:4.020448+0.089985    test-rmse:4.206346+0.491365 
## [9]  train-rmse:3.788032+0.083754    test-rmse:3.972400+0.493440 
## [10] train-rmse:3.598253+0.081286    test-rmse:3.796995+0.484133 
## [11] train-rmse:3.432665+0.081471    test-rmse:3.652839+0.462501 
## [12] train-rmse:3.294856+0.084099    test-rmse:3.552069+0.440319 
## [13] train-rmse:3.167819+0.081117    test-rmse:3.430531+0.445420 
## [14] train-rmse:3.062289+0.075596    test-rmse:3.340590+0.455699 
## [15] train-rmse:2.968197+0.079468    test-rmse:3.262021+0.463136 
## [16] train-rmse:2.886077+0.087393    test-rmse:3.196438+0.446579 
## [17] train-rmse:2.813718+0.085326    test-rmse:3.125683+0.463556 
## [18] train-rmse:2.753416+0.083002    test-rmse:3.094822+0.461665 
## [19] train-rmse:2.702059+0.082058    test-rmse:3.072621+0.475106 
## [20] train-rmse:2.643433+0.079092    test-rmse:3.013243+0.478327 
## [21] train-rmse:2.593968+0.081901    test-rmse:2.971800+0.482570 
## [22] train-rmse:2.553657+0.083637    test-rmse:2.955168+0.486444 
## [23] train-rmse:2.516638+0.084365    test-rmse:2.932496+0.485611 
## [24] train-rmse:2.481500+0.085978    test-rmse:2.915594+0.490680 
## [25] train-rmse:2.444682+0.088957    test-rmse:2.886632+0.501891 
## [26] train-rmse:2.407398+0.091981    test-rmse:2.865747+0.498211 
## [27] train-rmse:2.372674+0.091969    test-rmse:2.845484+0.504796 
## [28] train-rmse:2.336437+0.085160    test-rmse:2.826106+0.509361 
## [29] train-rmse:2.309915+0.085685    test-rmse:2.813142+0.509601 
## [30] train-rmse:2.279548+0.089932    test-rmse:2.793363+0.486914 
## [31] train-rmse:2.253291+0.091295    test-rmse:2.778608+0.500372 
## [32] train-rmse:2.228033+0.092074    test-rmse:2.754188+0.501851 
## [33] train-rmse:2.206376+0.091368    test-rmse:2.741340+0.503867 
## [34] train-rmse:2.179349+0.088977    test-rmse:2.726383+0.503503 
## [35] train-rmse:2.157682+0.089979    test-rmse:2.718987+0.499930 
## [36] train-rmse:2.129507+0.082319    test-rmse:2.708351+0.512412 
## [37] train-rmse:2.108840+0.079953    test-rmse:2.689215+0.504369 
## [38] train-rmse:2.088265+0.079681    test-rmse:2.686927+0.502314 
## [39] train-rmse:2.070461+0.079249    test-rmse:2.671927+0.508407 
## [40] train-rmse:2.050359+0.079782    test-rmse:2.657103+0.516653 
## [41] train-rmse:2.032576+0.080068    test-rmse:2.648011+0.512585 
## [42] train-rmse:2.012234+0.072611    test-rmse:2.640019+0.514874 
## [43] train-rmse:1.995633+0.073487    test-rmse:2.624902+0.518423 
## [44] train-rmse:1.977997+0.073221    test-rmse:2.610560+0.511100 
## [45] train-rmse:1.959162+0.077028    test-rmse:2.596229+0.500523 
## [46] train-rmse:1.944662+0.077025    test-rmse:2.597572+0.513824 
## [47] train-rmse:1.929475+0.075330    test-rmse:2.585032+0.520545 
## [48] train-rmse:1.908862+0.077558    test-rmse:2.570755+0.520848 
## [49] train-rmse:1.895067+0.078222    test-rmse:2.567654+0.518711 
## [50] train-rmse:1.881330+0.079244    test-rmse:2.555994+0.517967 
## [51] train-rmse:1.867619+0.080084    test-rmse:2.550637+0.515789 
## [52] train-rmse:1.854343+0.080185    test-rmse:2.539199+0.513743 
## [53] train-rmse:1.838140+0.084169    test-rmse:2.521977+0.505408 
## [54] train-rmse:1.825940+0.082833    test-rmse:2.515644+0.507827 
## [55] train-rmse:1.814010+0.082157    test-rmse:2.509217+0.506453 
## [56] train-rmse:1.793816+0.074208    test-rmse:2.497233+0.514136 
## [57] train-rmse:1.780745+0.074258    test-rmse:2.491409+0.516904 
## [58] train-rmse:1.767548+0.075036    test-rmse:2.485533+0.513934 
## [59] train-rmse:1.754708+0.074797    test-rmse:2.473691+0.515182 
## [60] train-rmse:1.744028+0.073450    test-rmse:2.466167+0.512461 
## [61] train-rmse:1.734042+0.073279    test-rmse:2.458392+0.514387 
## [62] train-rmse:1.724366+0.073189    test-rmse:2.456384+0.515323 
## [63] train-rmse:1.714512+0.072793    test-rmse:2.451085+0.515564 
## [64] train-rmse:1.700526+0.073450    test-rmse:2.451319+0.519448 
## [65] train-rmse:1.689523+0.074040    test-rmse:2.440164+0.523665 
## [66] train-rmse:1.678047+0.075833    test-rmse:2.425775+0.525594 
## [67] train-rmse:1.669697+0.075382    test-rmse:2.420081+0.524298 
## [68] train-rmse:1.659261+0.075533    test-rmse:2.416232+0.525216 
## [69] train-rmse:1.647040+0.067821    test-rmse:2.407626+0.530022 
## [70] train-rmse:1.638450+0.067995    test-rmse:2.406648+0.525504 
## [71] train-rmse:1.629682+0.068671    test-rmse:2.400450+0.526448 
## [72] train-rmse:1.619600+0.067582    test-rmse:2.398666+0.521783 
## [73] train-rmse:1.605961+0.067263    test-rmse:2.393300+0.524204 
## [74] train-rmse:1.597903+0.067397    test-rmse:2.392859+0.529466 
## [75] train-rmse:1.589588+0.068324    test-rmse:2.383933+0.534829 
## [76] train-rmse:1.582277+0.067889    test-rmse:2.377314+0.535457 
## [77] train-rmse:1.573814+0.068769    test-rmse:2.371077+0.537657 
## [78] train-rmse:1.567157+0.068730    test-rmse:2.367269+0.536700 
## [79] train-rmse:1.560416+0.068636    test-rmse:2.362807+0.537365 
## [80] train-rmse:1.553003+0.067970    test-rmse:2.362814+0.536264 
## [81] train-rmse:1.541246+0.063066    test-rmse:2.351503+0.534507 
## [82] train-rmse:1.534254+0.063016    test-rmse:2.352449+0.535458 
## [83] train-rmse:1.525698+0.063022    test-rmse:2.346259+0.533771 
## [84] train-rmse:1.518548+0.061661    test-rmse:2.346522+0.536052 
## [85] train-rmse:1.510679+0.062079    test-rmse:2.335757+0.533912 
## [86] train-rmse:1.503000+0.063425    test-rmse:2.333906+0.534968 
## [87] train-rmse:1.494646+0.067064    test-rmse:2.328666+0.533910 
## [88] train-rmse:1.487835+0.068184    test-rmse:2.326290+0.531737 
## [89] train-rmse:1.482347+0.068518    test-rmse:2.320169+0.534949 
## [90] train-rmse:1.476264+0.067912    test-rmse:2.319703+0.537709 
## [91] train-rmse:1.470007+0.068402    test-rmse:2.315964+0.536200 
## [92] train-rmse:1.463901+0.067772    test-rmse:2.315818+0.533503 
## [93] train-rmse:1.457786+0.067026    test-rmse:2.318081+0.535880 
## [94] train-rmse:1.447301+0.062052    test-rmse:2.316936+0.540597 
## [95] train-rmse:1.436744+0.059120    test-rmse:2.311204+0.541006 
## [96] train-rmse:1.428865+0.060977    test-rmse:2.309000+0.543800 
## [97] train-rmse:1.423059+0.062248    test-rmse:2.305272+0.543006 
## [98] train-rmse:1.416744+0.061740    test-rmse:2.297206+0.543788 
## [99] train-rmse:1.411816+0.062154    test-rmse:2.293357+0.540505 
## [100]    train-rmse:1.406515+0.062346    test-rmse:2.291294+0.541166 
## [101]    train-rmse:1.398999+0.066239    test-rmse:2.286675+0.539236 
## [102]    train-rmse:1.393204+0.064504    test-rmse:2.284836+0.538567 
## [103]    train-rmse:1.387665+0.065224    test-rmse:2.279805+0.537567 
## [104]    train-rmse:1.383033+0.065661    test-rmse:2.277371+0.538093 
## [105]    train-rmse:1.375736+0.068221    test-rmse:2.272355+0.540140 
## [106]    train-rmse:1.370913+0.068708    test-rmse:2.270065+0.541131 
## [107]    train-rmse:1.365838+0.068295    test-rmse:2.268623+0.542836 
## [108]    train-rmse:1.360031+0.068809    test-rmse:2.267082+0.540462 
## [109]    train-rmse:1.352621+0.066558    test-rmse:2.265617+0.540996 
## [110]    train-rmse:1.347740+0.066326    test-rmse:2.263778+0.539619 
## [111]    train-rmse:1.343886+0.066135    test-rmse:2.262524+0.539379 
## [112]    train-rmse:1.338705+0.065454    test-rmse:2.256127+0.542795 
## [113]    train-rmse:1.333193+0.064328    test-rmse:2.255629+0.542595 
## [114]    train-rmse:1.327414+0.064847    test-rmse:2.251749+0.541999 
## [115]    train-rmse:1.320860+0.064584    test-rmse:2.251098+0.544072 
## [116]    train-rmse:1.317407+0.064666    test-rmse:2.249967+0.544533 
## [117]    train-rmse:1.313703+0.064943    test-rmse:2.246854+0.545751 
## [118]    train-rmse:1.308349+0.065393    test-rmse:2.241977+0.548600 
## [119]    train-rmse:1.304451+0.065769    test-rmse:2.238662+0.548384 
## [120]    train-rmse:1.298636+0.065402    test-rmse:2.234582+0.549272 
## [121]    train-rmse:1.290301+0.066731    test-rmse:2.232070+0.549562 
## [122]    train-rmse:1.285126+0.068530    test-rmse:2.232435+0.549484 
## [123]    train-rmse:1.280345+0.069298    test-rmse:2.230083+0.549314 
## [124]    train-rmse:1.275113+0.069281    test-rmse:2.230865+0.547092 
## [125]    train-rmse:1.269685+0.069307    test-rmse:2.225961+0.547568 
## [126]    train-rmse:1.266119+0.069234    test-rmse:2.227125+0.550341 
## [127]    train-rmse:1.261638+0.068319    test-rmse:2.225725+0.549872 
## [128]    train-rmse:1.256425+0.066983    test-rmse:2.224842+0.550432 
## [129]    train-rmse:1.251755+0.066758    test-rmse:2.224125+0.554767 
## [130]    train-rmse:1.248228+0.065876    test-rmse:2.224239+0.553659 
## [131]    train-rmse:1.242084+0.068968    test-rmse:2.223897+0.551636 
## [132]    train-rmse:1.238659+0.068812    test-rmse:2.224395+0.553193 
## [133]    train-rmse:1.235313+0.068098    test-rmse:2.221360+0.553776 
## [134]    train-rmse:1.230822+0.070028    test-rmse:2.220049+0.555259 
## [135]    train-rmse:1.223344+0.065484    test-rmse:2.215745+0.556856 
## [136]    train-rmse:1.219892+0.065731    test-rmse:2.217041+0.554700 
## [137]    train-rmse:1.216818+0.065280    test-rmse:2.215628+0.555484 
## [138]    train-rmse:1.213931+0.065375    test-rmse:2.214039+0.554785 
## [139]    train-rmse:1.210617+0.064717    test-rmse:2.212797+0.554613 
## [140]    train-rmse:1.207551+0.064595    test-rmse:2.211521+0.555841 
## [141]    train-rmse:1.203539+0.063573    test-rmse:2.210110+0.556169 
## [142]    train-rmse:1.199243+0.061967    test-rmse:2.205746+0.557098 
## [143]    train-rmse:1.195078+0.061391    test-rmse:2.205530+0.557928 
## [144]    train-rmse:1.191365+0.062339    test-rmse:2.203947+0.558449 
## [145]    train-rmse:1.187423+0.063350    test-rmse:2.202578+0.556624 
## [146]    train-rmse:1.184031+0.064137    test-rmse:2.200607+0.556086 
## [147]    train-rmse:1.179505+0.064379    test-rmse:2.202645+0.556706 
## [148]    train-rmse:1.175488+0.064476    test-rmse:2.200207+0.558110 
## [149]    train-rmse:1.173013+0.064472    test-rmse:2.200036+0.558847 
## [150]    train-rmse:1.168731+0.063855    test-rmse:2.199518+0.558411 
## [151]    train-rmse:1.165080+0.065255    test-rmse:2.196520+0.555208 
## [152]    train-rmse:1.162623+0.065346    test-rmse:2.196738+0.555071 
## [153]    train-rmse:1.158989+0.065433    test-rmse:2.195475+0.556393 
## [154]    train-rmse:1.155290+0.064950    test-rmse:2.191533+0.560425 
## [155]    train-rmse:1.149849+0.064857    test-rmse:2.190980+0.561942 
## [156]    train-rmse:1.144462+0.066173    test-rmse:2.188970+0.561944 
## [157]    train-rmse:1.140974+0.065182    test-rmse:2.187497+0.561749 
## [158]    train-rmse:1.137291+0.065067    test-rmse:2.187210+0.561467 
## [159]    train-rmse:1.132244+0.064328    test-rmse:2.189346+0.559868 
## [160]    train-rmse:1.129102+0.063772    test-rmse:2.188658+0.559719 
## [161]    train-rmse:1.126530+0.063801    test-rmse:2.186883+0.560572 
## [162]    train-rmse:1.123754+0.063677    test-rmse:2.186048+0.559996 
## [163]    train-rmse:1.119772+0.062969    test-rmse:2.183836+0.562684 
## [164]    train-rmse:1.116248+0.063025    test-rmse:2.182244+0.565148 
## [165]    train-rmse:1.114179+0.063043    test-rmse:2.181856+0.564985 
## [166]    train-rmse:1.110878+0.063549    test-rmse:2.180102+0.565814 
## [167]    train-rmse:1.108314+0.064057    test-rmse:2.178536+0.566405 
## [168]    train-rmse:1.104516+0.064366    test-rmse:2.175633+0.565664 
## [169]    train-rmse:1.098863+0.067247    test-rmse:2.174564+0.564016 
## [170]    train-rmse:1.096256+0.067311    test-rmse:2.173243+0.565226 
## [171]    train-rmse:1.093749+0.067513    test-rmse:2.174667+0.564912 
## [172]    train-rmse:1.090708+0.067786    test-rmse:2.175534+0.566751 
## [173]    train-rmse:1.088483+0.067551    test-rmse:2.174958+0.568118 
## [174]    train-rmse:1.085956+0.067730    test-rmse:2.175491+0.570342 
## [175]    train-rmse:1.084134+0.067594    test-rmse:2.173014+0.571938 
## [176]    train-rmse:1.081246+0.067921    test-rmse:2.171269+0.573729 
## [177]    train-rmse:1.078751+0.067499    test-rmse:2.168544+0.574329 
## [178]    train-rmse:1.076564+0.066879    test-rmse:2.169237+0.574477 
## [179]    train-rmse:1.074934+0.066836    test-rmse:2.168964+0.574441 
## [180]    train-rmse:1.072859+0.066835    test-rmse:2.170134+0.572561 
## [181]    train-rmse:1.069902+0.067381    test-rmse:2.172077+0.572176 
## [182]    train-rmse:1.068044+0.067498    test-rmse:2.171352+0.573903 
## [183]    train-rmse:1.064514+0.070077    test-rmse:2.169526+0.573043 
## Stopping. Best iteration:
## [177]    train-rmse:1.078751+0.067499    test-rmse:2.168544+0.574329
## 
## 16 
## [1]  train-rmse:7.262084+0.151238    test-rmse:7.286660+0.646293 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.477224+0.132304    test-rmse:6.531172+0.601479 
## [3]  train-rmse:5.814509+0.117745    test-rmse:5.895394+0.568621 
## [4]  train-rmse:5.249486+0.108750    test-rmse:5.352182+0.526497 
## [5]  train-rmse:4.765412+0.106716    test-rmse:4.896590+0.471986 
## [6]  train-rmse:4.357643+0.099567    test-rmse:4.517075+0.455093 
## [7]  train-rmse:4.009706+0.099604    test-rmse:4.202342+0.436380 
## [8]  train-rmse:3.715881+0.089868    test-rmse:3.928009+0.416798 
## [9]  train-rmse:3.455244+0.086505    test-rmse:3.671752+0.429488 
## [10] train-rmse:3.235614+0.081231    test-rmse:3.485752+0.420856 
## [11] train-rmse:3.054792+0.073223    test-rmse:3.335632+0.432720 
## [12] train-rmse:2.901011+0.068998    test-rmse:3.216229+0.416783 
## [13] train-rmse:2.767844+0.067066    test-rmse:3.104452+0.433788 
## [14] train-rmse:2.659617+0.067844    test-rmse:3.025322+0.440972 
## [15] train-rmse:2.566854+0.066967    test-rmse:2.959828+0.433670 
## [16] train-rmse:2.479332+0.070348    test-rmse:2.876791+0.430078 
## [17] train-rmse:2.402343+0.067523    test-rmse:2.823459+0.426173 
## [18] train-rmse:2.332736+0.061792    test-rmse:2.778785+0.437829 
## [19] train-rmse:2.271152+0.054195    test-rmse:2.751842+0.458211 
## [20] train-rmse:2.211196+0.057815    test-rmse:2.719187+0.454529 
## [21] train-rmse:2.160604+0.062622    test-rmse:2.676530+0.462802 
## [22] train-rmse:2.116812+0.058721    test-rmse:2.657584+0.464422 
## [23] train-rmse:2.067696+0.050730    test-rmse:2.631946+0.464630 
## [24] train-rmse:2.030995+0.048625    test-rmse:2.606429+0.455586 
## [25] train-rmse:1.994131+0.042098    test-rmse:2.594495+0.456625 
## [26] train-rmse:1.956934+0.042245    test-rmse:2.559564+0.459699 
## [27] train-rmse:1.922932+0.044731    test-rmse:2.548995+0.466432 
## [28] train-rmse:1.894492+0.042786    test-rmse:2.535192+0.458950 
## [29] train-rmse:1.857300+0.042774    test-rmse:2.506109+0.452780 
## [30] train-rmse:1.830498+0.043168    test-rmse:2.494064+0.455503 
## [31] train-rmse:1.803359+0.039482    test-rmse:2.473396+0.457133 
## [32] train-rmse:1.776477+0.036792    test-rmse:2.466882+0.466621 
## [33] train-rmse:1.752306+0.036208    test-rmse:2.453844+0.467906 
## [34] train-rmse:1.730719+0.035545    test-rmse:2.445797+0.457154 
## [35] train-rmse:1.701662+0.037924    test-rmse:2.427485+0.450908 
## [36] train-rmse:1.677657+0.037931    test-rmse:2.411111+0.455862 
## [37] train-rmse:1.657501+0.041847    test-rmse:2.399434+0.461575 
## [38] train-rmse:1.637841+0.040672    test-rmse:2.391702+0.459896 
## [39] train-rmse:1.618319+0.038534    test-rmse:2.387620+0.460689 
## [40] train-rmse:1.593294+0.033613    test-rmse:2.369021+0.462559 
## [41] train-rmse:1.574793+0.032908    test-rmse:2.360825+0.463052 
## [42] train-rmse:1.554389+0.034431    test-rmse:2.349039+0.467031 
## [43] train-rmse:1.537018+0.037043    test-rmse:2.342831+0.470940 
## [44] train-rmse:1.516805+0.041097    test-rmse:2.327129+0.463404 
## [45] train-rmse:1.496852+0.037695    test-rmse:2.321327+0.459417 
## [46] train-rmse:1.478710+0.035813    test-rmse:2.312231+0.459481 
## [47] train-rmse:1.466169+0.035781    test-rmse:2.302401+0.463535 
## [48] train-rmse:1.448072+0.034433    test-rmse:2.300707+0.465300 
## [49] train-rmse:1.429896+0.034335    test-rmse:2.288693+0.466099 
## [50] train-rmse:1.415473+0.039631    test-rmse:2.279745+0.456885 
## [51] train-rmse:1.401603+0.040456    test-rmse:2.276037+0.459522 
## [52] train-rmse:1.389531+0.041691    test-rmse:2.265170+0.462951 
## [53] train-rmse:1.376607+0.040753    test-rmse:2.259422+0.465238 
## [54] train-rmse:1.364873+0.040118    test-rmse:2.253156+0.465059 
## [55] train-rmse:1.349500+0.038342    test-rmse:2.241007+0.468092 
## [56] train-rmse:1.335113+0.037837    test-rmse:2.235733+0.466786 
## [57] train-rmse:1.318531+0.039908    test-rmse:2.233130+0.462917 
## [58] train-rmse:1.307800+0.042409    test-rmse:2.229589+0.464851 
## [59] train-rmse:1.296130+0.041954    test-rmse:2.227055+0.464935 
## [60] train-rmse:1.285166+0.040693    test-rmse:2.223006+0.466042 
## [61] train-rmse:1.271686+0.040870    test-rmse:2.217115+0.466254 
## [62] train-rmse:1.260802+0.039333    test-rmse:2.216148+0.463203 
## [63] train-rmse:1.250682+0.038315    test-rmse:2.212604+0.464602 
## [64] train-rmse:1.241644+0.037919    test-rmse:2.211387+0.467260 
## [65] train-rmse:1.232392+0.038166    test-rmse:2.207739+0.468131 
## [66] train-rmse:1.220189+0.037168    test-rmse:2.210431+0.468759 
## [67] train-rmse:1.207440+0.039621    test-rmse:2.205063+0.462271 
## [68] train-rmse:1.198299+0.041782    test-rmse:2.202657+0.467140 
## [69] train-rmse:1.189663+0.043623    test-rmse:2.201185+0.469538 
## [70] train-rmse:1.180754+0.044516    test-rmse:2.198583+0.471806 
## [71] train-rmse:1.172978+0.044671    test-rmse:2.194318+0.469821 
## [72] train-rmse:1.161553+0.043369    test-rmse:2.188897+0.466859 
## [73] train-rmse:1.151180+0.042598    test-rmse:2.186086+0.471185 
## [74] train-rmse:1.144008+0.042237    test-rmse:2.184564+0.472363 
## [75] train-rmse:1.136667+0.041502    test-rmse:2.181281+0.471549 
## [76] train-rmse:1.121681+0.039727    test-rmse:2.181019+0.473032 
## [77] train-rmse:1.114418+0.042110    test-rmse:2.177011+0.472714 
## [78] train-rmse:1.103871+0.039698    test-rmse:2.174895+0.474571 
## [79] train-rmse:1.092637+0.042201    test-rmse:2.170019+0.468650 
## [80] train-rmse:1.083715+0.039353    test-rmse:2.162945+0.469785 
## [81] train-rmse:1.076436+0.040439    test-rmse:2.161756+0.469611 
## [82] train-rmse:1.068291+0.039674    test-rmse:2.159839+0.468438 
## [83] train-rmse:1.060850+0.040393    test-rmse:2.158722+0.473272 
## [84] train-rmse:1.053493+0.040347    test-rmse:2.157559+0.474773 
## [85] train-rmse:1.044652+0.039545    test-rmse:2.156209+0.476212 
## [86] train-rmse:1.035602+0.039138    test-rmse:2.157158+0.476631 
## [87] train-rmse:1.026876+0.036236    test-rmse:2.152222+0.476751 
## [88] train-rmse:1.016962+0.036682    test-rmse:2.151143+0.473670 
## [89] train-rmse:1.009993+0.038252    test-rmse:2.146109+0.473113 
## [90] train-rmse:1.003641+0.038918    test-rmse:2.143913+0.473852 
## [91] train-rmse:0.998625+0.039675    test-rmse:2.142360+0.474476 
## [92] train-rmse:0.991794+0.038879    test-rmse:2.136885+0.476787 
## [93] train-rmse:0.986147+0.038113    test-rmse:2.133505+0.482169 
## [94] train-rmse:0.977115+0.037060    test-rmse:2.132197+0.480982 
## [95] train-rmse:0.971546+0.036084    test-rmse:2.131130+0.481986 
## [96] train-rmse:0.963491+0.036452    test-rmse:2.131259+0.478392 
## [97] train-rmse:0.957445+0.036838    test-rmse:2.130597+0.479418 
## [98] train-rmse:0.951263+0.036063    test-rmse:2.125045+0.481270 
## [99] train-rmse:0.945165+0.038525    test-rmse:2.124442+0.481694 
## [100]    train-rmse:0.934288+0.041908    test-rmse:2.122512+0.479863 
## [101]    train-rmse:0.929291+0.041504    test-rmse:2.120080+0.480422 
## [102]    train-rmse:0.920895+0.042586    test-rmse:2.119149+0.479878 
## [103]    train-rmse:0.914774+0.042199    test-rmse:2.120392+0.478930 
## [104]    train-rmse:0.907121+0.040159    test-rmse:2.117430+0.480859 
## [105]    train-rmse:0.902546+0.040839    test-rmse:2.115738+0.480960 
## [106]    train-rmse:0.896141+0.040721    test-rmse:2.114569+0.482563 
## [107]    train-rmse:0.887262+0.042302    test-rmse:2.111525+0.481813 
## [108]    train-rmse:0.881270+0.040350    test-rmse:2.108712+0.481431 
## [109]    train-rmse:0.874108+0.038544    test-rmse:2.107334+0.482138 
## [110]    train-rmse:0.870086+0.039187    test-rmse:2.106349+0.484362 
## [111]    train-rmse:0.861951+0.043007    test-rmse:2.105286+0.483711 
## [112]    train-rmse:0.856066+0.042323    test-rmse:2.104847+0.485180 
## [113]    train-rmse:0.847691+0.044180    test-rmse:2.102942+0.483461 
## [114]    train-rmse:0.841143+0.043927    test-rmse:2.101123+0.484656 
## [115]    train-rmse:0.836660+0.045258    test-rmse:2.098999+0.484678 
## [116]    train-rmse:0.830473+0.045423    test-rmse:2.097649+0.485274 
## [117]    train-rmse:0.826079+0.044105    test-rmse:2.097719+0.484848 
## [118]    train-rmse:0.818713+0.044685    test-rmse:2.097869+0.486626 
## [119]    train-rmse:0.811243+0.042146    test-rmse:2.097098+0.486530 
## [120]    train-rmse:0.807654+0.043156    test-rmse:2.095150+0.488535 
## [121]    train-rmse:0.799830+0.041967    test-rmse:2.092021+0.488100 
## [122]    train-rmse:0.794594+0.041206    test-rmse:2.090173+0.488693 
## [123]    train-rmse:0.788644+0.039978    test-rmse:2.088584+0.487745 
## [124]    train-rmse:0.781798+0.042165    test-rmse:2.086306+0.486911 
## [125]    train-rmse:0.774925+0.040717    test-rmse:2.085634+0.488435 
## [126]    train-rmse:0.769592+0.039842    test-rmse:2.085122+0.490483 
## [127]    train-rmse:0.765087+0.040572    test-rmse:2.084527+0.490718 
## [128]    train-rmse:0.759300+0.039593    test-rmse:2.085441+0.489420 
## [129]    train-rmse:0.755254+0.038078    test-rmse:2.085443+0.491485 
## [130]    train-rmse:0.750116+0.037097    test-rmse:2.085432+0.491982 
## [131]    train-rmse:0.746096+0.036857    test-rmse:2.084862+0.490991 
## [132]    train-rmse:0.742066+0.037827    test-rmse:2.083729+0.492434 
## [133]    train-rmse:0.737024+0.037243    test-rmse:2.080852+0.493378 
## [134]    train-rmse:0.733494+0.037561    test-rmse:2.080840+0.493177 
## [135]    train-rmse:0.726297+0.037318    test-rmse:2.078495+0.492942 
## [136]    train-rmse:0.722290+0.037669    test-rmse:2.077708+0.492497 
## [137]    train-rmse:0.717637+0.036497    test-rmse:2.077233+0.493116 
## [138]    train-rmse:0.712322+0.037024    test-rmse:2.076393+0.492830 
## [139]    train-rmse:0.708370+0.036826    test-rmse:2.077136+0.494454 
## [140]    train-rmse:0.704139+0.036604    test-rmse:2.077178+0.494431 
## [141]    train-rmse:0.701065+0.037689    test-rmse:2.076706+0.494211 
## [142]    train-rmse:0.697659+0.036211    test-rmse:2.075269+0.495731 
## [143]    train-rmse:0.693021+0.035160    test-rmse:2.073900+0.495433 
## [144]    train-rmse:0.688757+0.034348    test-rmse:2.074040+0.492938 
## [145]    train-rmse:0.683505+0.033564    test-rmse:2.072679+0.493285 
## [146]    train-rmse:0.679384+0.033816    test-rmse:2.073622+0.493531 
## [147]    train-rmse:0.675415+0.033800    test-rmse:2.074913+0.491850 
## [148]    train-rmse:0.671368+0.033751    test-rmse:2.072759+0.493155 
## [149]    train-rmse:0.667740+0.032635    test-rmse:2.072195+0.492447 
## [150]    train-rmse:0.663023+0.034889    test-rmse:2.071440+0.491729 
## [151]    train-rmse:0.659714+0.034749    test-rmse:2.070541+0.492182 
## [152]    train-rmse:0.655625+0.034106    test-rmse:2.070759+0.492260 
## [153]    train-rmse:0.652235+0.034025    test-rmse:2.069826+0.492867 
## [154]    train-rmse:0.648100+0.033970    test-rmse:2.069156+0.493669 
## [155]    train-rmse:0.643066+0.036532    test-rmse:2.067532+0.491717 
## [156]    train-rmse:0.639685+0.036060    test-rmse:2.066788+0.492733 
## [157]    train-rmse:0.637946+0.036126    test-rmse:2.065714+0.492653 
## [158]    train-rmse:0.635187+0.035982    test-rmse:2.064122+0.492520 
## [159]    train-rmse:0.631710+0.035307    test-rmse:2.064509+0.493798 
## [160]    train-rmse:0.626651+0.035703    test-rmse:2.064499+0.493559 
## [161]    train-rmse:0.624942+0.035304    test-rmse:2.064914+0.493587 
## [162]    train-rmse:0.621111+0.036431    test-rmse:2.065128+0.493933 
## [163]    train-rmse:0.616983+0.036143    test-rmse:2.065561+0.493978 
## [164]    train-rmse:0.614271+0.034804    test-rmse:2.064515+0.494167 
## Stopping. Best iteration:
## [158]    train-rmse:0.635187+0.035982    test-rmse:2.064122+0.492520
## 
## 17 
## [1]  train-rmse:7.225012+0.150279    test-rmse:7.234975+0.638548 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.403515+0.124922    test-rmse:6.442823+0.571983 
## [3]  train-rmse:5.700224+0.108045    test-rmse:5.785383+0.536862 
## [4]  train-rmse:5.102880+0.097591    test-rmse:5.222339+0.505123 
## [5]  train-rmse:4.586350+0.077869    test-rmse:4.736256+0.475903 
## [6]  train-rmse:4.151434+0.075703    test-rmse:4.355784+0.448784 
## [7]  train-rmse:3.785353+0.075337    test-rmse:4.038899+0.431733 
## [8]  train-rmse:3.477598+0.063084    test-rmse:3.764715+0.427504 
## [9]  train-rmse:3.209392+0.062219    test-rmse:3.545549+0.436035 
## [10] train-rmse:2.988145+0.056388    test-rmse:3.338867+0.447415 
## [11] train-rmse:2.794297+0.058036    test-rmse:3.190156+0.437924 
## [12] train-rmse:2.637375+0.056925    test-rmse:3.076134+0.447165 
## [13] train-rmse:2.508053+0.054535    test-rmse:2.988712+0.449752 
## [14] train-rmse:2.385086+0.049980    test-rmse:2.919593+0.456885 
## [15] train-rmse:2.270820+0.042431    test-rmse:2.827917+0.456132 
## [16] train-rmse:2.175022+0.042037    test-rmse:2.782868+0.465571 
## [17] train-rmse:2.103080+0.040871    test-rmse:2.728091+0.473782 
## [18] train-rmse:2.022641+0.033529    test-rmse:2.676390+0.465310 
## [19] train-rmse:1.958561+0.033065    test-rmse:2.637832+0.447559 
## [20] train-rmse:1.907395+0.032712    test-rmse:2.606420+0.461886 
## [21] train-rmse:1.854007+0.027770    test-rmse:2.580061+0.468193 
## [22] train-rmse:1.806637+0.030169    test-rmse:2.554036+0.466638 
## [23] train-rmse:1.759858+0.027096    test-rmse:2.531495+0.472361 
## [24] train-rmse:1.721945+0.028484    test-rmse:2.510318+0.466869 
## [25] train-rmse:1.685534+0.028844    test-rmse:2.496599+0.466550 
## [26] train-rmse:1.649396+0.023239    test-rmse:2.487583+0.471109 
## [27] train-rmse:1.616681+0.027133    test-rmse:2.467461+0.484400 
## [28] train-rmse:1.587405+0.029706    test-rmse:2.448909+0.484403 
## [29] train-rmse:1.554720+0.029992    test-rmse:2.429633+0.481118 
## [30] train-rmse:1.519571+0.029289    test-rmse:2.417249+0.483241 
## [31] train-rmse:1.492314+0.025018    test-rmse:2.406047+0.486832 
## [32] train-rmse:1.462245+0.025998    test-rmse:2.389994+0.487205 
## [33] train-rmse:1.435558+0.026643    test-rmse:2.372311+0.476636 
## [34] train-rmse:1.407661+0.026477    test-rmse:2.365513+0.479540 
## [35] train-rmse:1.386628+0.023798    test-rmse:2.354865+0.481798 
## [36] train-rmse:1.362355+0.019518    test-rmse:2.350906+0.481232 
## [37] train-rmse:1.342361+0.018534    test-rmse:2.342147+0.481790 
## [38] train-rmse:1.319741+0.024370    test-rmse:2.342892+0.480547 
## [39] train-rmse:1.302682+0.025234    test-rmse:2.337312+0.492666 
## [40] train-rmse:1.285380+0.024730    test-rmse:2.327850+0.482766 
## [41] train-rmse:1.265959+0.026081    test-rmse:2.319108+0.481538 
## [42] train-rmse:1.244862+0.027261    test-rmse:2.310201+0.481399 
## [43] train-rmse:1.224978+0.026393    test-rmse:2.299558+0.475948 
## [44] train-rmse:1.209930+0.024693    test-rmse:2.294964+0.475878 
## [45] train-rmse:1.195412+0.025982    test-rmse:2.286568+0.480359 
## [46] train-rmse:1.180576+0.028782    test-rmse:2.285241+0.482260 
## [47] train-rmse:1.161955+0.029639    test-rmse:2.282792+0.482815 
## [48] train-rmse:1.143613+0.035174    test-rmse:2.274977+0.478528 
## [49] train-rmse:1.128773+0.030554    test-rmse:2.270163+0.479660 
## [50] train-rmse:1.114198+0.029885    test-rmse:2.262039+0.487799 
## [51] train-rmse:1.095600+0.027275    test-rmse:2.260268+0.483644 
## [52] train-rmse:1.084465+0.026499    test-rmse:2.255625+0.485046 
## [53] train-rmse:1.073594+0.025419    test-rmse:2.252017+0.486498 
## [54] train-rmse:1.062495+0.026040    test-rmse:2.250458+0.488134 
## [55] train-rmse:1.050204+0.025298    test-rmse:2.238269+0.490885 
## [56] train-rmse:1.038060+0.024181    test-rmse:2.236330+0.493762 
## [57] train-rmse:1.026725+0.021698    test-rmse:2.232824+0.492309 
## [58] train-rmse:1.014123+0.021253    test-rmse:2.225533+0.490811 
## [59] train-rmse:1.001761+0.018111    test-rmse:2.221550+0.491473 
## [60] train-rmse:0.991191+0.019062    test-rmse:2.222635+0.492611 
## [61] train-rmse:0.976829+0.022112    test-rmse:2.222485+0.496404 
## [62] train-rmse:0.967318+0.023130    test-rmse:2.217894+0.493292 
## [63] train-rmse:0.957187+0.021682    test-rmse:2.212330+0.493505 
## [64] train-rmse:0.944807+0.027727    test-rmse:2.214760+0.492793 
## [65] train-rmse:0.934578+0.028541    test-rmse:2.210769+0.493851 
## [66] train-rmse:0.923331+0.028309    test-rmse:2.206462+0.497465 
## [67] train-rmse:0.914787+0.029365    test-rmse:2.203420+0.497100 
## [68] train-rmse:0.903126+0.023816    test-rmse:2.200411+0.498721 
## [69] train-rmse:0.893784+0.023720    test-rmse:2.193814+0.498323 
## [70] train-rmse:0.884248+0.026604    test-rmse:2.191443+0.499608 
## [71] train-rmse:0.872531+0.027772    test-rmse:2.192584+0.497890 
## [72] train-rmse:0.856688+0.027420    test-rmse:2.190057+0.499518 
## [73] train-rmse:0.845268+0.024369    test-rmse:2.189802+0.499577 
## [74] train-rmse:0.837231+0.025076    test-rmse:2.187134+0.500522 
## [75] train-rmse:0.825962+0.027079    test-rmse:2.185113+0.498223 
## [76] train-rmse:0.816705+0.025856    test-rmse:2.182926+0.497908 
## [77] train-rmse:0.807374+0.026761    test-rmse:2.180892+0.499093 
## [78] train-rmse:0.802021+0.026047    test-rmse:2.179804+0.499273 
## [79] train-rmse:0.792433+0.023449    test-rmse:2.176646+0.499865 
## [80] train-rmse:0.782849+0.022471    test-rmse:2.173787+0.500619 
## [81] train-rmse:0.773337+0.022292    test-rmse:2.175012+0.500810 
## [82] train-rmse:0.765247+0.022933    test-rmse:2.174259+0.500128 
## [83] train-rmse:0.758153+0.025510    test-rmse:2.172978+0.499070 
## [84] train-rmse:0.747206+0.023976    test-rmse:2.170627+0.500304 
## [85] train-rmse:0.735547+0.023278    test-rmse:2.169346+0.498515 
## [86] train-rmse:0.725034+0.019945    test-rmse:2.165886+0.501531 
## [87] train-rmse:0.715608+0.018329    test-rmse:2.165460+0.501278 
## [88] train-rmse:0.709689+0.019804    test-rmse:2.164254+0.501489 
## [89] train-rmse:0.701398+0.019129    test-rmse:2.162329+0.499647 
## [90] train-rmse:0.695308+0.017938    test-rmse:2.160661+0.498971 
## [91] train-rmse:0.685787+0.014957    test-rmse:2.161085+0.500417 
## [92] train-rmse:0.679391+0.015303    test-rmse:2.160226+0.500691 
## [93] train-rmse:0.673847+0.015779    test-rmse:2.159069+0.500209 
## [94] train-rmse:0.666989+0.014798    test-rmse:2.158675+0.500599 
## [95] train-rmse:0.662817+0.015292    test-rmse:2.157270+0.500826 
## [96] train-rmse:0.658186+0.015684    test-rmse:2.156935+0.500611 
## [97] train-rmse:0.648084+0.014106    test-rmse:2.154663+0.499806 
## [98] train-rmse:0.641141+0.017018    test-rmse:2.154855+0.501049 
## [99] train-rmse:0.636011+0.016473    test-rmse:2.154359+0.499990 
## [100]    train-rmse:0.629926+0.015238    test-rmse:2.152636+0.500595 
## [101]    train-rmse:0.623555+0.016316    test-rmse:2.152695+0.499812 
## [102]    train-rmse:0.613828+0.014429    test-rmse:2.150317+0.499252 
## [103]    train-rmse:0.605960+0.014426    test-rmse:2.149056+0.499867 
## [104]    train-rmse:0.600689+0.014157    test-rmse:2.148713+0.500468 
## [105]    train-rmse:0.596560+0.015513    test-rmse:2.146858+0.499448 
## [106]    train-rmse:0.592488+0.016665    test-rmse:2.146226+0.499310 
## [107]    train-rmse:0.588457+0.015943    test-rmse:2.145805+0.498796 
## [108]    train-rmse:0.583202+0.015174    test-rmse:2.146316+0.499499 
## [109]    train-rmse:0.577080+0.013760    test-rmse:2.148895+0.498816 
## [110]    train-rmse:0.571028+0.012710    test-rmse:2.148456+0.499238 
## [111]    train-rmse:0.566214+0.012832    test-rmse:2.147648+0.498660 
## [112]    train-rmse:0.561431+0.012184    test-rmse:2.148825+0.498536 
## [113]    train-rmse:0.551829+0.012311    test-rmse:2.148682+0.498035 
## Stopping. Best iteration:
## [107]    train-rmse:0.588457+0.015943    test-rmse:2.145805+0.498796
## 
## 18 
## [1]  train-rmse:7.199357+0.148698    test-rmse:7.191038+0.627313 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.351764+0.126697    test-rmse:6.374904+0.572541 
## [3]  train-rmse:5.624545+0.114901    test-rmse:5.683823+0.521093 
## [4]  train-rmse:5.001475+0.101428    test-rmse:5.103561+0.486326 
## [5]  train-rmse:4.479184+0.091436    test-rmse:4.646187+0.455898 
## [6]  train-rmse:4.031603+0.082680    test-rmse:4.257207+0.435467 
## [7]  train-rmse:3.657011+0.074932    test-rmse:3.948711+0.432949 
## [8]  train-rmse:3.336265+0.068664    test-rmse:3.686642+0.429647 
## [9]  train-rmse:3.065662+0.057245    test-rmse:3.468182+0.440982 
## [10] train-rmse:2.833910+0.054108    test-rmse:3.308921+0.450030 
## [11] train-rmse:2.640864+0.050470    test-rmse:3.161948+0.466208 
## [12] train-rmse:2.475109+0.052973    test-rmse:3.056496+0.479356 
## [13] train-rmse:2.327554+0.062036    test-rmse:2.962739+0.489894 
## [14] train-rmse:2.198984+0.060613    test-rmse:2.873803+0.502032 
## [15] train-rmse:2.083933+0.066548    test-rmse:2.799072+0.509777 
## [16] train-rmse:1.989560+0.072864    test-rmse:2.749934+0.512415 
## [17] train-rmse:1.907080+0.070988    test-rmse:2.707853+0.506420 
## [18] train-rmse:1.827077+0.063838    test-rmse:2.680065+0.523835 
## [19] train-rmse:1.755624+0.057601    test-rmse:2.637410+0.524971 
## [20] train-rmse:1.688732+0.055613    test-rmse:2.604841+0.529340 
## [21] train-rmse:1.634388+0.049429    test-rmse:2.587606+0.527518 
## [22] train-rmse:1.583802+0.049980    test-rmse:2.562896+0.523117 
## [23] train-rmse:1.537064+0.053017    test-rmse:2.538048+0.527931 
## [24] train-rmse:1.494621+0.051590    test-rmse:2.521279+0.537550 
## [25] train-rmse:1.456070+0.051982    test-rmse:2.497542+0.529307 
## [26] train-rmse:1.422521+0.053591    test-rmse:2.477164+0.537484 
## [27] train-rmse:1.383778+0.059782    test-rmse:2.463522+0.529813 
## [28] train-rmse:1.348355+0.051295    test-rmse:2.450899+0.525546 
## [29] train-rmse:1.318399+0.051494    test-rmse:2.438937+0.528656 
## [30] train-rmse:1.287668+0.056850    test-rmse:2.430809+0.524470 
## [31] train-rmse:1.259654+0.052443    test-rmse:2.430641+0.533490 
## [32] train-rmse:1.234624+0.053316    test-rmse:2.418619+0.532925 
## [33] train-rmse:1.212420+0.055231    test-rmse:2.408362+0.535078 
## [34] train-rmse:1.185104+0.056195    test-rmse:2.406617+0.530530 
## [35] train-rmse:1.164996+0.053706    test-rmse:2.398901+0.530480 
## [36] train-rmse:1.142149+0.047479    test-rmse:2.391335+0.533316 
## [37] train-rmse:1.111467+0.054078    test-rmse:2.376881+0.533495 
## [38] train-rmse:1.090665+0.052036    test-rmse:2.372864+0.534980 
## [39] train-rmse:1.067671+0.047014    test-rmse:2.372932+0.538572 
## [40] train-rmse:1.046942+0.049000    test-rmse:2.372200+0.542342 
## [41] train-rmse:1.030702+0.049746    test-rmse:2.367213+0.541792 
## [42] train-rmse:1.010735+0.050440    test-rmse:2.361619+0.542711 
## [43] train-rmse:0.991689+0.043705    test-rmse:2.356052+0.541600 
## [44] train-rmse:0.973950+0.047549    test-rmse:2.347760+0.541864 
## [45] train-rmse:0.956280+0.050419    test-rmse:2.347252+0.539026 
## [46] train-rmse:0.944520+0.051405    test-rmse:2.342651+0.540862 
## [47] train-rmse:0.924240+0.046234    test-rmse:2.341038+0.542195 
## [48] train-rmse:0.901840+0.046467    test-rmse:2.334222+0.538393 
## [49] train-rmse:0.886780+0.043798    test-rmse:2.334643+0.541301 
## [50] train-rmse:0.872243+0.044610    test-rmse:2.330578+0.546762 
## [51] train-rmse:0.859726+0.044786    test-rmse:2.326754+0.547699 
## [52] train-rmse:0.846015+0.043835    test-rmse:2.328283+0.547374 
## [53] train-rmse:0.831805+0.042877    test-rmse:2.323684+0.551726 
## [54] train-rmse:0.815064+0.041543    test-rmse:2.319890+0.550655 
## [55] train-rmse:0.800894+0.038933    test-rmse:2.316987+0.554724 
## [56] train-rmse:0.786587+0.040057    test-rmse:2.317999+0.556384 
## [57] train-rmse:0.771711+0.037974    test-rmse:2.314765+0.557758 
## [58] train-rmse:0.761979+0.039683    test-rmse:2.313523+0.557539 
## [59] train-rmse:0.747796+0.037933    test-rmse:2.310305+0.558012 
## [60] train-rmse:0.735184+0.037576    test-rmse:2.305300+0.560277 
## [61] train-rmse:0.723552+0.038499    test-rmse:2.309207+0.563118 
## [62] train-rmse:0.712514+0.040379    test-rmse:2.307673+0.562401 
## [63] train-rmse:0.705547+0.040756    test-rmse:2.303902+0.565172 
## [64] train-rmse:0.692907+0.040915    test-rmse:2.301684+0.567082 
## [65] train-rmse:0.679850+0.041345    test-rmse:2.301764+0.568135 
## [66] train-rmse:0.670651+0.044118    test-rmse:2.299856+0.567867 
## [67] train-rmse:0.661344+0.041810    test-rmse:2.298700+0.567977 
## [68] train-rmse:0.645849+0.034764    test-rmse:2.297224+0.570509 
## [69] train-rmse:0.636741+0.032498    test-rmse:2.295330+0.572088 
## [70] train-rmse:0.624205+0.032123    test-rmse:2.293888+0.571558 
## [71] train-rmse:0.616145+0.033590    test-rmse:2.292649+0.572325 
## [72] train-rmse:0.605452+0.036084    test-rmse:2.292533+0.572530 
## [73] train-rmse:0.597193+0.036926    test-rmse:2.290907+0.572486 
## [74] train-rmse:0.590004+0.035250    test-rmse:2.291295+0.572247 
## [75] train-rmse:0.579953+0.032738    test-rmse:2.290212+0.571561 
## [76] train-rmse:0.572740+0.032831    test-rmse:2.289344+0.573035 
## [77] train-rmse:0.560709+0.035316    test-rmse:2.286873+0.573611 
## [78] train-rmse:0.550467+0.031842    test-rmse:2.287968+0.573122 
## [79] train-rmse:0.541819+0.033872    test-rmse:2.287986+0.572964 
## [80] train-rmse:0.535458+0.032354    test-rmse:2.286824+0.574090 
## [81] train-rmse:0.524656+0.033466    test-rmse:2.285950+0.576678 
## [82] train-rmse:0.515762+0.031344    test-rmse:2.287123+0.576015 
## [83] train-rmse:0.509884+0.029961    test-rmse:2.286722+0.576491 
## [84] train-rmse:0.502101+0.028994    test-rmse:2.288172+0.577714 
## [85] train-rmse:0.496884+0.029878    test-rmse:2.287266+0.578582 
## [86] train-rmse:0.488681+0.032929    test-rmse:2.287884+0.578977 
## [87] train-rmse:0.481732+0.033232    test-rmse:2.286854+0.578865 
## Stopping. Best iteration:
## [81] train-rmse:0.524656+0.033466    test-rmse:2.285950+0.576678
## 
## 19 
## [1]  train-rmse:7.183159+0.146381    test-rmse:7.179923+0.642150 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.319248+0.122719    test-rmse:6.356838+0.597715 
## [3]  train-rmse:5.576071+0.104891    test-rmse:5.667550+0.555917 
## [4]  train-rmse:4.942387+0.089122    test-rmse:5.093068+0.522494 
## [5]  train-rmse:4.402224+0.077357    test-rmse:4.617486+0.494779 
## [6]  train-rmse:3.941854+0.066093    test-rmse:4.227172+0.481053 
## [7]  train-rmse:3.553105+0.059418    test-rmse:3.905723+0.464738 
## [8]  train-rmse:3.218095+0.048693    test-rmse:3.651784+0.458349 
## [9]  train-rmse:2.933121+0.038463    test-rmse:3.444711+0.465582 
## [10] train-rmse:2.697828+0.035052    test-rmse:3.275127+0.477373 
## [11] train-rmse:2.495183+0.030474    test-rmse:3.135219+0.494510 
## [12] train-rmse:2.314490+0.027877    test-rmse:3.005481+0.493147 
## [13] train-rmse:2.165099+0.027129    test-rmse:2.900567+0.506106 
## [14] train-rmse:2.029410+0.025843    test-rmse:2.827872+0.512832 
## [15] train-rmse:1.912052+0.026458    test-rmse:2.766182+0.506298 
## [16] train-rmse:1.810372+0.024195    test-rmse:2.707215+0.518656 
## [17] train-rmse:1.713444+0.015967    test-rmse:2.651602+0.531628 
## [18] train-rmse:1.622846+0.014184    test-rmse:2.611447+0.529042 
## [19] train-rmse:1.551210+0.019990    test-rmse:2.578625+0.531407 
## [20] train-rmse:1.490772+0.020439    test-rmse:2.556806+0.537061 
## [21] train-rmse:1.433835+0.022187    test-rmse:2.530422+0.540834 
## [22] train-rmse:1.379867+0.019060    test-rmse:2.513972+0.552386 
## [23] train-rmse:1.334974+0.018349    test-rmse:2.494047+0.545211 
## [24] train-rmse:1.288483+0.021884    test-rmse:2.476113+0.550023 
## [25] train-rmse:1.249764+0.019679    test-rmse:2.462680+0.549526 
## [26] train-rmse:1.200567+0.008338    test-rmse:2.446245+0.548953 
## [27] train-rmse:1.158761+0.014454    test-rmse:2.443108+0.552058 
## [28] train-rmse:1.122397+0.022000    test-rmse:2.431707+0.549871 
## [29] train-rmse:1.094908+0.022620    test-rmse:2.422180+0.553617 
## [30] train-rmse:1.064064+0.019040    test-rmse:2.420759+0.556401 
## [31] train-rmse:1.035612+0.023169    test-rmse:2.410230+0.562126 
## [32] train-rmse:1.011413+0.020465    test-rmse:2.405487+0.564978 
## [33] train-rmse:0.983640+0.022833    test-rmse:2.402731+0.562588 
## [34] train-rmse:0.953754+0.018164    test-rmse:2.400052+0.566890 
## [35] train-rmse:0.932104+0.020789    test-rmse:2.389747+0.570768 
## [36] train-rmse:0.908808+0.021721    test-rmse:2.383838+0.569037 
## [37] train-rmse:0.893255+0.020110    test-rmse:2.379547+0.567850 
## [38] train-rmse:0.877116+0.019271    test-rmse:2.372879+0.571558 
## [39] train-rmse:0.855197+0.020962    test-rmse:2.367550+0.574695 
## [40] train-rmse:0.832672+0.022736    test-rmse:2.359799+0.575598 
## [41] train-rmse:0.811879+0.017633    test-rmse:2.356579+0.575267 
## [42] train-rmse:0.793018+0.017380    test-rmse:2.352940+0.572362 
## [43] train-rmse:0.776735+0.017503    test-rmse:2.348123+0.574382 
## [44] train-rmse:0.754810+0.013234    test-rmse:2.342665+0.578545 
## [45] train-rmse:0.739236+0.009740    test-rmse:2.342702+0.576169 
## [46] train-rmse:0.724208+0.009560    test-rmse:2.339748+0.574489 
## [47] train-rmse:0.710974+0.010266    test-rmse:2.335367+0.576929 
## [48] train-rmse:0.694578+0.012261    test-rmse:2.333818+0.577046 
## [49] train-rmse:0.680703+0.009186    test-rmse:2.330947+0.579386 
## [50] train-rmse:0.661494+0.008205    test-rmse:2.328356+0.581129 
## [51] train-rmse:0.646651+0.005058    test-rmse:2.326553+0.581308 
## [52] train-rmse:0.631982+0.008422    test-rmse:2.322791+0.581304 
## [53] train-rmse:0.619683+0.007670    test-rmse:2.323474+0.582026 
## [54] train-rmse:0.605911+0.006271    test-rmse:2.319943+0.585503 
## [55] train-rmse:0.595386+0.007977    test-rmse:2.321532+0.588284 
## [56] train-rmse:0.584093+0.009480    test-rmse:2.319975+0.586919 
## [57] train-rmse:0.568968+0.010991    test-rmse:2.320350+0.587625 
## [58] train-rmse:0.553285+0.014021    test-rmse:2.318597+0.587502 
## [59] train-rmse:0.540105+0.013736    test-rmse:2.317932+0.586143 
## [60] train-rmse:0.527467+0.014954    test-rmse:2.315209+0.587577 
## [61] train-rmse:0.517147+0.014318    test-rmse:2.316287+0.586947 
## [62] train-rmse:0.508671+0.012616    test-rmse:2.314016+0.587571 
## [63] train-rmse:0.498039+0.016984    test-rmse:2.312804+0.585749 
## [64] train-rmse:0.486315+0.018161    test-rmse:2.314469+0.583767 
## [65] train-rmse:0.477347+0.016345    test-rmse:2.314165+0.582801 
## [66] train-rmse:0.471236+0.016632    test-rmse:2.313786+0.583236 
## [67] train-rmse:0.460207+0.016776    test-rmse:2.311551+0.583729 
## [68] train-rmse:0.451829+0.016862    test-rmse:2.311265+0.583382 
## [69] train-rmse:0.446281+0.017766    test-rmse:2.310863+0.583173 
## [70] train-rmse:0.438535+0.020417    test-rmse:2.308679+0.580995 
## [71] train-rmse:0.430971+0.020083    test-rmse:2.308487+0.580374 
## [72] train-rmse:0.421955+0.018152    test-rmse:2.308003+0.581466 
## [73] train-rmse:0.412374+0.016680    test-rmse:2.307292+0.580682 
## [74] train-rmse:0.403759+0.017559    test-rmse:2.306670+0.582003 
## [75] train-rmse:0.395106+0.016875    test-rmse:2.305853+0.583788 
## [76] train-rmse:0.387984+0.016157    test-rmse:2.305147+0.583257 
## [77] train-rmse:0.382285+0.014835    test-rmse:2.304723+0.583374 
## [78] train-rmse:0.375359+0.014934    test-rmse:2.304733+0.582833 
## [79] train-rmse:0.367880+0.014674    test-rmse:2.304647+0.582516 
## [80] train-rmse:0.360854+0.014019    test-rmse:2.303734+0.584348 
## [81] train-rmse:0.353576+0.016281    test-rmse:2.304292+0.586609 
## [82] train-rmse:0.348562+0.017187    test-rmse:2.302586+0.588051 
## [83] train-rmse:0.344103+0.017537    test-rmse:2.302265+0.587935 
## [84] train-rmse:0.338313+0.017552    test-rmse:2.302112+0.588012 
## [85] train-rmse:0.328514+0.017149    test-rmse:2.301236+0.587608 
## [86] train-rmse:0.322195+0.016638    test-rmse:2.301197+0.589534 
## [87] train-rmse:0.316190+0.015909    test-rmse:2.300968+0.589108 
## [88] train-rmse:0.309536+0.014786    test-rmse:2.301809+0.589380 
## [89] train-rmse:0.305519+0.014347    test-rmse:2.301432+0.589265 
## [90] train-rmse:0.299217+0.013687    test-rmse:2.301680+0.589546 
## [91] train-rmse:0.294887+0.013604    test-rmse:2.300618+0.589433 
## [92] train-rmse:0.290179+0.012429    test-rmse:2.300257+0.589572 
## [93] train-rmse:0.284838+0.014723    test-rmse:2.299443+0.588090 
## [94] train-rmse:0.280524+0.015220    test-rmse:2.298968+0.588076 
## [95] train-rmse:0.275095+0.013678    test-rmse:2.299285+0.588563 
## [96] train-rmse:0.270022+0.011594    test-rmse:2.298011+0.588938 
## [97] train-rmse:0.266075+0.013076    test-rmse:2.297792+0.588907 
## [98] train-rmse:0.262806+0.012786    test-rmse:2.297436+0.588882 
## [99] train-rmse:0.258974+0.013276    test-rmse:2.298142+0.590632 
## [100]    train-rmse:0.255136+0.012674    test-rmse:2.298593+0.591681 
## [101]    train-rmse:0.250224+0.013666    test-rmse:2.297446+0.591875 
## [102]    train-rmse:0.245814+0.012437    test-rmse:2.296864+0.591644 
## [103]    train-rmse:0.241700+0.013159    test-rmse:2.297355+0.592079 
## [104]    train-rmse:0.236673+0.012262    test-rmse:2.296197+0.592164 
## [105]    train-rmse:0.233775+0.012921    test-rmse:2.295231+0.591339 
## [106]    train-rmse:0.229186+0.012387    test-rmse:2.296011+0.591628 
## [107]    train-rmse:0.224521+0.011390    test-rmse:2.296094+0.591661 
## [108]    train-rmse:0.221489+0.011262    test-rmse:2.296176+0.591486 
## [109]    train-rmse:0.218112+0.011061    test-rmse:2.295541+0.591493 
## [110]    train-rmse:0.214548+0.011126    test-rmse:2.296054+0.592324 
## [111]    train-rmse:0.209023+0.009868    test-rmse:2.296004+0.592110 
## Stopping. Best iteration:
## [105]    train-rmse:0.233775+0.012921    test-rmse:2.295231+0.591339
## 
## 20 
## [1]  train-rmse:7.177423+0.145908    test-rmse:7.176935+0.641800 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.306112+0.120606    test-rmse:6.359445+0.588621 
## [3]  train-rmse:5.554681+0.101197    test-rmse:5.654394+0.560183 
## [4]  train-rmse:4.910183+0.082959    test-rmse:5.072406+0.523030 
## [5]  train-rmse:4.356136+0.068813    test-rmse:4.584057+0.497601 
## [6]  train-rmse:3.887063+0.059781    test-rmse:4.189745+0.483410 
## [7]  train-rmse:3.481601+0.051564    test-rmse:3.862261+0.462391 
## [8]  train-rmse:3.138203+0.045392    test-rmse:3.598128+0.455426 
## [9]  train-rmse:2.844624+0.037423    test-rmse:3.395686+0.453448 
## [10] train-rmse:2.593727+0.031964    test-rmse:3.221624+0.460787 
## [11] train-rmse:2.374748+0.026468    test-rmse:3.098856+0.466440 
## [12] train-rmse:2.191721+0.023808    test-rmse:2.977415+0.486496 
## [13] train-rmse:2.032642+0.021461    test-rmse:2.885154+0.497091 
## [14] train-rmse:1.898181+0.021124    test-rmse:2.811304+0.509902 
## [15] train-rmse:1.769129+0.029212    test-rmse:2.746924+0.514778 
## [16] train-rmse:1.666593+0.029146    test-rmse:2.696358+0.522856 
## [17] train-rmse:1.567670+0.038577    test-rmse:2.653109+0.540185 
## [18] train-rmse:1.483099+0.046880    test-rmse:2.614415+0.547741 
## [19] train-rmse:1.403061+0.046970    test-rmse:2.578932+0.552165 
## [20] train-rmse:1.336959+0.049485    test-rmse:2.557865+0.555127 
## [21] train-rmse:1.272560+0.044952    test-rmse:2.538870+0.564082 
## [22] train-rmse:1.218550+0.042105    test-rmse:2.521086+0.572326 
## [23] train-rmse:1.167884+0.036136    test-rmse:2.507702+0.565325 
## [24] train-rmse:1.121030+0.035487    test-rmse:2.487960+0.564008 
## [25] train-rmse:1.082532+0.038029    test-rmse:2.474240+0.569898 
## [26] train-rmse:1.033738+0.039754    test-rmse:2.464826+0.575793 
## [27] train-rmse:0.999795+0.033943    test-rmse:2.463672+0.579097 
## [28] train-rmse:0.966832+0.032755    test-rmse:2.453081+0.576688 
## [29] train-rmse:0.929552+0.032875    test-rmse:2.449901+0.580602 
## [30] train-rmse:0.904642+0.034077    test-rmse:2.440454+0.584852 
## [31] train-rmse:0.879401+0.034235    test-rmse:2.431626+0.584571 
## [32] train-rmse:0.858813+0.034681    test-rmse:2.426321+0.586202 
## [33] train-rmse:0.832500+0.033005    test-rmse:2.420266+0.589114 
## [34] train-rmse:0.803142+0.033563    test-rmse:2.415758+0.590308 
## [35] train-rmse:0.777775+0.034640    test-rmse:2.412758+0.593797 
## [36] train-rmse:0.756928+0.033544    test-rmse:2.406990+0.597410 
## [37] train-rmse:0.727429+0.037981    test-rmse:2.401726+0.598743 
## [38] train-rmse:0.706753+0.032999    test-rmse:2.397172+0.599077 
## [39] train-rmse:0.686150+0.031333    test-rmse:2.392145+0.601849 
## [40] train-rmse:0.669503+0.027445    test-rmse:2.390074+0.603513 
## [41] train-rmse:0.648806+0.028395    test-rmse:2.385461+0.601360 
## [42] train-rmse:0.635564+0.027908    test-rmse:2.384236+0.603067 
## [43] train-rmse:0.618052+0.032837    test-rmse:2.381282+0.604924 
## [44] train-rmse:0.600561+0.030752    test-rmse:2.377912+0.605655 
## [45] train-rmse:0.585607+0.031961    test-rmse:2.374815+0.606105 
## [46] train-rmse:0.570626+0.036155    test-rmse:2.375097+0.605904 
## [47] train-rmse:0.553827+0.034294    test-rmse:2.372989+0.607171 
## [48] train-rmse:0.540919+0.036019    test-rmse:2.371945+0.609043 
## [49] train-rmse:0.525402+0.034481    test-rmse:2.368529+0.609655 
## [50] train-rmse:0.511721+0.036296    test-rmse:2.367436+0.610687 
## [51] train-rmse:0.498339+0.038150    test-rmse:2.367044+0.609845 
## [52] train-rmse:0.483148+0.035932    test-rmse:2.364143+0.610417 
## [53] train-rmse:0.469772+0.039171    test-rmse:2.364377+0.608633 
## [54] train-rmse:0.457786+0.035709    test-rmse:2.363463+0.607861 
## [55] train-rmse:0.448146+0.034193    test-rmse:2.361727+0.608719 
## [56] train-rmse:0.439061+0.034416    test-rmse:2.359966+0.609072 
## [57] train-rmse:0.423625+0.034669    test-rmse:2.358948+0.607520 
## [58] train-rmse:0.412526+0.031831    test-rmse:2.358973+0.607670 
## [59] train-rmse:0.402940+0.030596    test-rmse:2.359938+0.606734 
## [60] train-rmse:0.394974+0.029133    test-rmse:2.357537+0.606807 
## [61] train-rmse:0.386732+0.029391    test-rmse:2.355366+0.608203 
## [62] train-rmse:0.376284+0.027127    test-rmse:2.353007+0.607481 
## [63] train-rmse:0.369933+0.024240    test-rmse:2.352110+0.607269 
## [64] train-rmse:0.361481+0.023100    test-rmse:2.351531+0.608313 
## [65] train-rmse:0.350742+0.022264    test-rmse:2.351427+0.608219 
## [66] train-rmse:0.345210+0.021728    test-rmse:2.351135+0.608743 
## [67] train-rmse:0.337284+0.023201    test-rmse:2.350485+0.607902 
## [68] train-rmse:0.328247+0.023211    test-rmse:2.351496+0.608252 
## [69] train-rmse:0.319603+0.024727    test-rmse:2.350487+0.607842 
## [70] train-rmse:0.311080+0.023466    test-rmse:2.350292+0.608087 
## [71] train-rmse:0.303247+0.024626    test-rmse:2.348090+0.607078 
## [72] train-rmse:0.296906+0.023394    test-rmse:2.347990+0.607333 
## [73] train-rmse:0.289526+0.022763    test-rmse:2.349413+0.606245 
## [74] train-rmse:0.283703+0.022711    test-rmse:2.349766+0.606493 
## [75] train-rmse:0.275615+0.023509    test-rmse:2.347474+0.606929 
## [76] train-rmse:0.268512+0.022784    test-rmse:2.346218+0.605089 
## [77] train-rmse:0.262459+0.022107    test-rmse:2.345475+0.605160 
## [78] train-rmse:0.258149+0.022025    test-rmse:2.345317+0.605204 
## [79] train-rmse:0.252621+0.019782    test-rmse:2.345424+0.605762 
## [80] train-rmse:0.247374+0.020244    test-rmse:2.344298+0.605860 
## [81] train-rmse:0.241223+0.018594    test-rmse:2.343123+0.606153 
## [82] train-rmse:0.236263+0.019593    test-rmse:2.343555+0.605700 
## [83] train-rmse:0.230584+0.019031    test-rmse:2.343978+0.605353 
## [84] train-rmse:0.225482+0.020077    test-rmse:2.344638+0.605483 
## [85] train-rmse:0.220981+0.019734    test-rmse:2.344413+0.605400 
## [86] train-rmse:0.216246+0.019263    test-rmse:2.343467+0.605773 
## [87] train-rmse:0.212330+0.019646    test-rmse:2.343115+0.606286 
## [88] train-rmse:0.206943+0.019025    test-rmse:2.342729+0.606119 
## [89] train-rmse:0.202391+0.017166    test-rmse:2.342746+0.606244 
## [90] train-rmse:0.197811+0.018016    test-rmse:2.342584+0.605658 
## [91] train-rmse:0.194192+0.016644    test-rmse:2.342366+0.605859 
## [92] train-rmse:0.189891+0.017882    test-rmse:2.341997+0.605828 
## [93] train-rmse:0.187119+0.018157    test-rmse:2.342076+0.606036 
## [94] train-rmse:0.183616+0.016863    test-rmse:2.341836+0.606133 
## [95] train-rmse:0.180171+0.017405    test-rmse:2.341440+0.605580 
## [96] train-rmse:0.175291+0.016540    test-rmse:2.341001+0.606066 
## [97] train-rmse:0.171219+0.015818    test-rmse:2.340876+0.606288 
## [98] train-rmse:0.167709+0.015310    test-rmse:2.340956+0.606305 
## [99] train-rmse:0.164513+0.014753    test-rmse:2.340681+0.606304 
## [100]    train-rmse:0.161006+0.015285    test-rmse:2.340651+0.606216 
## [101]    train-rmse:0.157820+0.015624    test-rmse:2.340873+0.606133 
## [102]    train-rmse:0.154411+0.015987    test-rmse:2.340958+0.606098 
## [103]    train-rmse:0.151801+0.015662    test-rmse:2.340032+0.605930 
## [104]    train-rmse:0.149119+0.015487    test-rmse:2.340020+0.606222 
## [105]    train-rmse:0.146710+0.015482    test-rmse:2.339487+0.606690 
## [106]    train-rmse:0.144001+0.014873    test-rmse:2.338812+0.606399 
## [107]    train-rmse:0.141797+0.015225    test-rmse:2.338732+0.606216 
## [108]    train-rmse:0.138432+0.015266    test-rmse:2.338541+0.606458 
## [109]    train-rmse:0.136089+0.015325    test-rmse:2.338761+0.606220 
## [110]    train-rmse:0.133056+0.014720    test-rmse:2.338693+0.606538 
## [111]    train-rmse:0.130033+0.013587    test-rmse:2.338920+0.606463 
## [112]    train-rmse:0.127237+0.013776    test-rmse:2.338445+0.606966 
## [113]    train-rmse:0.125387+0.013485    test-rmse:2.338418+0.606981 
## [114]    train-rmse:0.122734+0.012808    test-rmse:2.338071+0.607272 
## [115]    train-rmse:0.120267+0.012622    test-rmse:2.337731+0.607425 
## [116]    train-rmse:0.117261+0.011996    test-rmse:2.337428+0.607565 
## [117]    train-rmse:0.115150+0.012284    test-rmse:2.337410+0.607574 
## [118]    train-rmse:0.112913+0.011971    test-rmse:2.337362+0.607635 
## [119]    train-rmse:0.110600+0.011917    test-rmse:2.337160+0.607451 
## [120]    train-rmse:0.108536+0.012823    test-rmse:2.336981+0.607170 
## [121]    train-rmse:0.106724+0.013005    test-rmse:2.337206+0.608206 
## [122]    train-rmse:0.105174+0.012695    test-rmse:2.337139+0.608387 
## [123]    train-rmse:0.102394+0.012161    test-rmse:2.337035+0.608346 
## [124]    train-rmse:0.100930+0.012011    test-rmse:2.336763+0.608236 
## [125]    train-rmse:0.099112+0.011655    test-rmse:2.336968+0.608330 
## [126]    train-rmse:0.097428+0.011621    test-rmse:2.336954+0.608188 
## [127]    train-rmse:0.095036+0.010823    test-rmse:2.336978+0.608132 
## [128]    train-rmse:0.093283+0.011220    test-rmse:2.336968+0.608091 
## [129]    train-rmse:0.091415+0.010962    test-rmse:2.336876+0.607864 
## [130]    train-rmse:0.089572+0.010723    test-rmse:2.336873+0.607920 
## Stopping. Best iteration:
## [124]    train-rmse:0.100930+0.012011    test-rmse:2.336763+0.608236
## 
## 21 
## [1]  train-rmse:7.247898+0.155625    test-rmse:7.234017+0.672503 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.489066+0.138569    test-rmse:6.505553+0.701370 
## [3]  train-rmse:5.879890+0.127137    test-rmse:5.928813+0.674843 
## [4]  train-rmse:5.391158+0.121112    test-rmse:5.416146+0.647033 
## [5]  train-rmse:5.005461+0.115215    test-rmse:5.068700+0.635681 
## [6]  train-rmse:4.700549+0.112254    test-rmse:4.783698+0.613222 
## [7]  train-rmse:4.455932+0.112007    test-rmse:4.538013+0.572926 
## [8]  train-rmse:4.261161+0.111033    test-rmse:4.351160+0.549164 
## [9]  train-rmse:4.106283+0.111661    test-rmse:4.225950+0.530984 
## [10] train-rmse:3.979821+0.112596    test-rmse:4.085371+0.506015 
## [11] train-rmse:3.874582+0.113924    test-rmse:3.998060+0.492468 
## [12] train-rmse:3.784503+0.115677    test-rmse:3.920281+0.484078 
## [13] train-rmse:3.707828+0.115884    test-rmse:3.837151+0.474387 
## [14] train-rmse:3.642064+0.116734    test-rmse:3.777057+0.455412 
## [15] train-rmse:3.582779+0.118110    test-rmse:3.704208+0.437703 
## [16] train-rmse:3.528668+0.119520    test-rmse:3.667276+0.442181 
## [17] train-rmse:3.479379+0.120559    test-rmse:3.625312+0.433044 
## [18] train-rmse:3.434281+0.120156    test-rmse:3.570856+0.438001 
## [19] train-rmse:3.393204+0.120809    test-rmse:3.534429+0.429069 
## [20] train-rmse:3.353932+0.122134    test-rmse:3.482670+0.416796 
## [21] train-rmse:3.317506+0.123145    test-rmse:3.461728+0.423285 
## [22] train-rmse:3.283459+0.123087    test-rmse:3.414147+0.400310 
## [23] train-rmse:3.250377+0.122519    test-rmse:3.392014+0.419689 
## [24] train-rmse:3.219721+0.123299    test-rmse:3.370204+0.429740 
## [25] train-rmse:3.189508+0.124562    test-rmse:3.331948+0.430405 
## [26] train-rmse:3.160559+0.125622    test-rmse:3.313249+0.418899 
## [27] train-rmse:3.133359+0.125962    test-rmse:3.287508+0.423187 
## [28] train-rmse:3.107159+0.126438    test-rmse:3.245080+0.422494 
## [29] train-rmse:3.083085+0.126948    test-rmse:3.230988+0.424317 
## [30] train-rmse:3.059494+0.127556    test-rmse:3.213540+0.437843 
## [31] train-rmse:3.036399+0.128588    test-rmse:3.184060+0.429734 
## [32] train-rmse:3.014963+0.129130    test-rmse:3.168381+0.417048 
## [33] train-rmse:2.993684+0.129574    test-rmse:3.158980+0.430617 
## [34] train-rmse:2.972983+0.129737    test-rmse:3.141020+0.419687 
## [35] train-rmse:2.953390+0.130171    test-rmse:3.125279+0.428017 
## [36] train-rmse:2.934441+0.130585    test-rmse:3.103769+0.430245 
## [37] train-rmse:2.915597+0.131314    test-rmse:3.084217+0.435822 
## [38] train-rmse:2.897729+0.132145    test-rmse:3.076797+0.450977 
## [39] train-rmse:2.880254+0.132612    test-rmse:3.060939+0.458297 
## [40] train-rmse:2.863182+0.133114    test-rmse:3.044274+0.452643 
## [41] train-rmse:2.846555+0.133738    test-rmse:3.029000+0.444245 
## [42] train-rmse:2.830560+0.133686    test-rmse:3.012038+0.447293 
## [43] train-rmse:2.814934+0.133963    test-rmse:2.999973+0.439095 
## [44] train-rmse:2.799500+0.134400    test-rmse:2.987809+0.444192 
## [45] train-rmse:2.784600+0.135146    test-rmse:2.971798+0.444628 
## [46] train-rmse:2.769971+0.135926    test-rmse:2.959166+0.439782 
## [47] train-rmse:2.755507+0.136648    test-rmse:2.949081+0.448135 
## [48] train-rmse:2.741393+0.136955    test-rmse:2.939809+0.444109 
## [49] train-rmse:2.727651+0.137152    test-rmse:2.925366+0.453621 
## [50] train-rmse:2.714165+0.137505    test-rmse:2.919052+0.463716 
## [51] train-rmse:2.701182+0.138011    test-rmse:2.909260+0.460922 
## [52] train-rmse:2.688600+0.138345    test-rmse:2.906073+0.464794 
## [53] train-rmse:2.676337+0.138474    test-rmse:2.893458+0.471420 
## [54] train-rmse:2.664196+0.138580    test-rmse:2.876725+0.462783 
## [55] train-rmse:2.652347+0.138742    test-rmse:2.864903+0.469903 
## [56] train-rmse:2.640402+0.138986    test-rmse:2.862961+0.468942 
## [57] train-rmse:2.628807+0.139040    test-rmse:2.851211+0.473771 
## [58] train-rmse:2.617466+0.139152    test-rmse:2.841174+0.465332 
## [59] train-rmse:2.606338+0.139182    test-rmse:2.831289+0.463201 
## [60] train-rmse:2.595405+0.139303    test-rmse:2.820547+0.466962 
## [61] train-rmse:2.584693+0.139309    test-rmse:2.813801+0.470747 
## [62] train-rmse:2.574194+0.139427    test-rmse:2.810424+0.475466 
## [63] train-rmse:2.563877+0.139670    test-rmse:2.801505+0.473756 
## [64] train-rmse:2.553921+0.140044    test-rmse:2.800764+0.474308 
## [65] train-rmse:2.543997+0.140228    test-rmse:2.793628+0.488458 
## [66] train-rmse:2.534375+0.140171    test-rmse:2.783914+0.489377 
## [67] train-rmse:2.524922+0.140029    test-rmse:2.773823+0.486080 
## [68] train-rmse:2.515711+0.139985    test-rmse:2.765218+0.480067 
## [69] train-rmse:2.506714+0.140013    test-rmse:2.755015+0.484170 
## [70] train-rmse:2.497937+0.140076    test-rmse:2.745893+0.485221 
## [71] train-rmse:2.489317+0.140097    test-rmse:2.742028+0.484833 
## [72] train-rmse:2.480811+0.140289    test-rmse:2.726925+0.486595 
## [73] train-rmse:2.472448+0.140401    test-rmse:2.721911+0.483537 
## [74] train-rmse:2.464121+0.140530    test-rmse:2.713620+0.485355 
## [75] train-rmse:2.455989+0.140595    test-rmse:2.702143+0.482771 
## [76] train-rmse:2.447907+0.140643    test-rmse:2.696724+0.477970 
## [77] train-rmse:2.439996+0.140687    test-rmse:2.692458+0.482386 
## [78] train-rmse:2.432355+0.140854    test-rmse:2.694648+0.493430 
## [79] train-rmse:2.424752+0.141053    test-rmse:2.691489+0.494792 
## [80] train-rmse:2.417333+0.141364    test-rmse:2.683043+0.493042 
## [81] train-rmse:2.410004+0.141483    test-rmse:2.672878+0.495794 
## [82] train-rmse:2.402745+0.141645    test-rmse:2.665693+0.497693 
## [83] train-rmse:2.395835+0.141742    test-rmse:2.660579+0.498133 
## [84] train-rmse:2.389147+0.141806    test-rmse:2.655969+0.499619 
## [85] train-rmse:2.382559+0.141901    test-rmse:2.656481+0.500800 
## [86] train-rmse:2.376069+0.141984    test-rmse:2.653741+0.501938 
## [87] train-rmse:2.369659+0.142079    test-rmse:2.646665+0.499989 
## [88] train-rmse:2.363297+0.142233    test-rmse:2.638178+0.498619 
## [89] train-rmse:2.357008+0.142300    test-rmse:2.624685+0.499083 
## [90] train-rmse:2.350764+0.142338    test-rmse:2.618386+0.496531 
## [91] train-rmse:2.344571+0.142404    test-rmse:2.607830+0.492633 
## [92] train-rmse:2.338526+0.142481    test-rmse:2.604122+0.489094 
## [93] train-rmse:2.332487+0.142583    test-rmse:2.599154+0.492196 
## [94] train-rmse:2.326614+0.142663    test-rmse:2.591843+0.495923 
## [95] train-rmse:2.320793+0.142814    test-rmse:2.589549+0.504379 
## [96] train-rmse:2.315104+0.143000    test-rmse:2.588501+0.503242 
## [97] train-rmse:2.309473+0.143145    test-rmse:2.582268+0.510392 
## [98] train-rmse:2.304046+0.143457    test-rmse:2.580261+0.509612 
## [99] train-rmse:2.298637+0.143757    test-rmse:2.578613+0.508060 
## [100]    train-rmse:2.293440+0.143955    test-rmse:2.578336+0.505334 
## [101]    train-rmse:2.288349+0.144054    test-rmse:2.573125+0.506358 
## [102]    train-rmse:2.283323+0.144234    test-rmse:2.568627+0.507729 
## [103]    train-rmse:2.278347+0.144360    test-rmse:2.562463+0.510341 
## [104]    train-rmse:2.273454+0.144527    test-rmse:2.559299+0.511201 
## [105]    train-rmse:2.268653+0.144697    test-rmse:2.553100+0.512074 
## [106]    train-rmse:2.263907+0.144857    test-rmse:2.546409+0.508141 
## [107]    train-rmse:2.259227+0.145023    test-rmse:2.540691+0.510778 
## [108]    train-rmse:2.254607+0.145193    test-rmse:2.534675+0.510640 
## [109]    train-rmse:2.250045+0.145402    test-rmse:2.528033+0.510287 
## [110]    train-rmse:2.245509+0.145627    test-rmse:2.522713+0.510767 
## [111]    train-rmse:2.241068+0.145812    test-rmse:2.519166+0.511449 
## [112]    train-rmse:2.236659+0.145988    test-rmse:2.516315+0.510296 
## [113]    train-rmse:2.232301+0.146171    test-rmse:2.514793+0.515146 
## [114]    train-rmse:2.227976+0.146313    test-rmse:2.515935+0.514802 
## [115]    train-rmse:2.223684+0.146465    test-rmse:2.517711+0.511883 
## [116]    train-rmse:2.219548+0.146629    test-rmse:2.515492+0.513871 
## [117]    train-rmse:2.215493+0.146777    test-rmse:2.506546+0.514637 
## [118]    train-rmse:2.211514+0.146866    test-rmse:2.505661+0.515862 
## [119]    train-rmse:2.207650+0.146989    test-rmse:2.502421+0.518836 
## [120]    train-rmse:2.203811+0.147139    test-rmse:2.501413+0.517725 
## [121]    train-rmse:2.199995+0.147289    test-rmse:2.499459+0.519214 
## [122]    train-rmse:2.196233+0.147404    test-rmse:2.495717+0.520110 
## [123]    train-rmse:2.192591+0.147535    test-rmse:2.489575+0.522003 
## [124]    train-rmse:2.188963+0.147681    test-rmse:2.486507+0.522618 
## [125]    train-rmse:2.185383+0.147833    test-rmse:2.484494+0.521490 
## [126]    train-rmse:2.181825+0.147938    test-rmse:2.482282+0.516431 
## [127]    train-rmse:2.178282+0.148058    test-rmse:2.480746+0.516114 
## [128]    train-rmse:2.174783+0.148189    test-rmse:2.477462+0.514576 
## [129]    train-rmse:2.171331+0.148341    test-rmse:2.476001+0.513853 
## [130]    train-rmse:2.167855+0.148487    test-rmse:2.475815+0.519998 
## [131]    train-rmse:2.164481+0.148640    test-rmse:2.472860+0.524740 
## [132]    train-rmse:2.161188+0.148811    test-rmse:2.466629+0.525907 
## [133]    train-rmse:2.157902+0.148988    test-rmse:2.461050+0.526393 
## [134]    train-rmse:2.154697+0.149124    test-rmse:2.456250+0.529552 
## [135]    train-rmse:2.151551+0.149300    test-rmse:2.456522+0.529230 
## [136]    train-rmse:2.148420+0.149465    test-rmse:2.455826+0.523744 
## [137]    train-rmse:2.145399+0.149594    test-rmse:2.451257+0.525065 
## [138]    train-rmse:2.142375+0.149727    test-rmse:2.448918+0.526203 
## [139]    train-rmse:2.139383+0.149870    test-rmse:2.450086+0.523304 
## [140]    train-rmse:2.136470+0.150001    test-rmse:2.445753+0.525060 
## [141]    train-rmse:2.133560+0.150143    test-rmse:2.446107+0.524380 
## [142]    train-rmse:2.130685+0.150260    test-rmse:2.446810+0.526758 
## [143]    train-rmse:2.127833+0.150394    test-rmse:2.444063+0.527875 
## [144]    train-rmse:2.125005+0.150536    test-rmse:2.442068+0.528068 
## [145]    train-rmse:2.122209+0.150669    test-rmse:2.439566+0.528425 
## [146]    train-rmse:2.119446+0.150813    test-rmse:2.436383+0.528061 
## [147]    train-rmse:2.116736+0.150945    test-rmse:2.432160+0.529196 
## [148]    train-rmse:2.114034+0.151093    test-rmse:2.432304+0.529397 
## [149]    train-rmse:2.111354+0.151232    test-rmse:2.430698+0.528637 
## [150]    train-rmse:2.108726+0.151380    test-rmse:2.430293+0.527039 
## [151]    train-rmse:2.106082+0.151454    test-rmse:2.425014+0.527308 
## [152]    train-rmse:2.103528+0.151577    test-rmse:2.424618+0.524837 
## [153]    train-rmse:2.101016+0.151706    test-rmse:2.422567+0.526147 
## [154]    train-rmse:2.098522+0.151867    test-rmse:2.420695+0.523888 
## [155]    train-rmse:2.096096+0.152023    test-rmse:2.418501+0.523087 
## [156]    train-rmse:2.093643+0.152171    test-rmse:2.417006+0.522386 
## [157]    train-rmse:2.091149+0.152344    test-rmse:2.411961+0.525026 
## [158]    train-rmse:2.088757+0.152523    test-rmse:2.409165+0.522854 
## [159]    train-rmse:2.086385+0.152679    test-rmse:2.410875+0.528639 
## [160]    train-rmse:2.084075+0.152806    test-rmse:2.404807+0.529265 
## [161]    train-rmse:2.081782+0.152939    test-rmse:2.403611+0.530766 
## [162]    train-rmse:2.079487+0.153011    test-rmse:2.407229+0.534377 
## [163]    train-rmse:2.077259+0.153121    test-rmse:2.407287+0.536607 
## [164]    train-rmse:2.074970+0.153208    test-rmse:2.404821+0.537342 
## [165]    train-rmse:2.072759+0.153294    test-rmse:2.402378+0.536206 
## [166]    train-rmse:2.070559+0.153389    test-rmse:2.400768+0.533558 
## [167]    train-rmse:2.068382+0.153499    test-rmse:2.402267+0.535987 
## [168]    train-rmse:2.066228+0.153602    test-rmse:2.403427+0.534063 
## [169]    train-rmse:2.064108+0.153693    test-rmse:2.402850+0.532436 
## [170]    train-rmse:2.062008+0.153785    test-rmse:2.400805+0.530890 
## [171]    train-rmse:2.059906+0.153841    test-rmse:2.399236+0.530578 
## [172]    train-rmse:2.057855+0.153909    test-rmse:2.400665+0.528660 
## [173]    train-rmse:2.055764+0.154019    test-rmse:2.398138+0.528475 
## [174]    train-rmse:2.053757+0.154135    test-rmse:2.394872+0.528667 
## [175]    train-rmse:2.051768+0.154240    test-rmse:2.391706+0.528676 
## [176]    train-rmse:2.049822+0.154311    test-rmse:2.392523+0.527620 
## [177]    train-rmse:2.047906+0.154388    test-rmse:2.388551+0.528498 
## [178]    train-rmse:2.045970+0.154483    test-rmse:2.388785+0.528276 
## [179]    train-rmse:2.044049+0.154577    test-rmse:2.386789+0.525252 
## [180]    train-rmse:2.042159+0.154683    test-rmse:2.382654+0.526911 
## [181]    train-rmse:2.040257+0.154779    test-rmse:2.381282+0.527313 
## [182]    train-rmse:2.038358+0.154849    test-rmse:2.381257+0.527078 
## [183]    train-rmse:2.036501+0.154949    test-rmse:2.385777+0.531268 
## [184]    train-rmse:2.034597+0.154995    test-rmse:2.385481+0.531041 
## [185]    train-rmse:2.032773+0.155055    test-rmse:2.387096+0.534175 
## [186]    train-rmse:2.030914+0.155151    test-rmse:2.385719+0.534742 
## [187]    train-rmse:2.029064+0.155322    test-rmse:2.383506+0.532211 
## [188]    train-rmse:2.027296+0.155408    test-rmse:2.382096+0.533759 
## Stopping. Best iteration:
## [182]    train-rmse:2.038358+0.154849    test-rmse:2.381257+0.527078
## 
## 22 
## [1]  train-rmse:7.178327+0.150359    test-rmse:7.184649+0.649439 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.350962+0.136618    test-rmse:6.381575+0.622884 
## [3]  train-rmse:5.677065+0.125382    test-rmse:5.763054+0.618154 
## [4]  train-rmse:5.124782+0.111402    test-rmse:5.253037+0.562582 
## [5]  train-rmse:4.669347+0.106677    test-rmse:4.807962+0.528011 
## [6]  train-rmse:4.303987+0.094587    test-rmse:4.454397+0.491240 
## [7]  train-rmse:3.995436+0.091817    test-rmse:4.159776+0.476454 
## [8]  train-rmse:3.751162+0.094873    test-rmse:3.948332+0.437497 
## [9]  train-rmse:3.542886+0.087636    test-rmse:3.759655+0.430970 
## [10] train-rmse:3.369486+0.080523    test-rmse:3.594040+0.446601 
## [11] train-rmse:3.214775+0.093844    test-rmse:3.460094+0.419039 
## [12] train-rmse:3.086609+0.090108    test-rmse:3.342002+0.404390 
## [13] train-rmse:2.976575+0.071461    test-rmse:3.253906+0.432518 
## [14] train-rmse:2.883868+0.080525    test-rmse:3.171719+0.419288 
## [15] train-rmse:2.809082+0.076755    test-rmse:3.117480+0.420906 
## [16] train-rmse:2.741676+0.072951    test-rmse:3.080606+0.444922 
## [17] train-rmse:2.672010+0.074902    test-rmse:3.014434+0.436748 
## [18] train-rmse:2.620696+0.078610    test-rmse:2.984416+0.437905 
## [19] train-rmse:2.571754+0.078833    test-rmse:2.960315+0.448699 
## [20] train-rmse:2.525193+0.067819    test-rmse:2.924065+0.469510 
## [21] train-rmse:2.484666+0.064433    test-rmse:2.895987+0.464486 
## [22] train-rmse:2.442390+0.066374    test-rmse:2.864945+0.472606 
## [23] train-rmse:2.400102+0.062385    test-rmse:2.834076+0.468938 
## [24] train-rmse:2.358170+0.058629    test-rmse:2.808448+0.482987 
## [25] train-rmse:2.323627+0.057871    test-rmse:2.792068+0.485463 
## [26] train-rmse:2.286598+0.057886    test-rmse:2.774521+0.473668 
## [27] train-rmse:2.259243+0.058096    test-rmse:2.746186+0.471715 
## [28] train-rmse:2.224558+0.060833    test-rmse:2.730309+0.468656 
## [29] train-rmse:2.196800+0.063721    test-rmse:2.710824+0.478330 
## [30] train-rmse:2.169492+0.058896    test-rmse:2.689983+0.483328 
## [31] train-rmse:2.141861+0.051256    test-rmse:2.674082+0.490783 
## [32] train-rmse:2.117548+0.049626    test-rmse:2.650650+0.493432 
## [33] train-rmse:2.093324+0.051066    test-rmse:2.628672+0.494544 
## [34] train-rmse:2.071678+0.053063    test-rmse:2.616355+0.484723 
## [35] train-rmse:2.049623+0.054298    test-rmse:2.599876+0.480446 
## [36] train-rmse:2.027037+0.053928    test-rmse:2.580818+0.496114 
## [37] train-rmse:2.005468+0.056597    test-rmse:2.575274+0.489389 
## [38] train-rmse:1.985053+0.053027    test-rmse:2.556349+0.490896 
## [39] train-rmse:1.963566+0.045816    test-rmse:2.547079+0.501045 
## [40] train-rmse:1.945183+0.046633    test-rmse:2.530685+0.494916 
## [41] train-rmse:1.928740+0.046854    test-rmse:2.515116+0.496400 
## [42] train-rmse:1.905353+0.048835    test-rmse:2.505647+0.494268 
## [43] train-rmse:1.887845+0.049673    test-rmse:2.497586+0.493822 
## [44] train-rmse:1.872365+0.049817    test-rmse:2.487340+0.494158 
## [45] train-rmse:1.858277+0.049925    test-rmse:2.479603+0.497595 
## [46] train-rmse:1.843418+0.048446    test-rmse:2.474849+0.491716 
## [47] train-rmse:1.825815+0.048600    test-rmse:2.463598+0.490704 
## [48] train-rmse:1.811867+0.048635    test-rmse:2.454426+0.497482 
## [49] train-rmse:1.797299+0.048481    test-rmse:2.445587+0.502994 
## [50] train-rmse:1.778726+0.045059    test-rmse:2.423116+0.506732 
## [51] train-rmse:1.765125+0.046160    test-rmse:2.420460+0.505222 
## [52] train-rmse:1.752698+0.044620    test-rmse:2.419224+0.504717 
## [53] train-rmse:1.741468+0.044404    test-rmse:2.410592+0.508153 
## [54] train-rmse:1.729412+0.042941    test-rmse:2.403393+0.504666 
## [55] train-rmse:1.715880+0.043062    test-rmse:2.394872+0.508202 
## [56] train-rmse:1.701268+0.039126    test-rmse:2.392701+0.518366 
## [57] train-rmse:1.689794+0.038937    test-rmse:2.388741+0.513243 
## [58] train-rmse:1.679012+0.039465    test-rmse:2.380510+0.515702 
## [59] train-rmse:1.664398+0.039660    test-rmse:2.370755+0.515533 
## [60] train-rmse:1.652856+0.037601    test-rmse:2.364671+0.511411 
## [61] train-rmse:1.640339+0.032628    test-rmse:2.359774+0.520604 
## [62] train-rmse:1.629442+0.033432    test-rmse:2.351852+0.517690 
## [63] train-rmse:1.618950+0.033410    test-rmse:2.341127+0.512746 
## [64] train-rmse:1.608606+0.034949    test-rmse:2.340298+0.511055 
## [65] train-rmse:1.599885+0.035343    test-rmse:2.330191+0.519082 
## [66] train-rmse:1.591869+0.035435    test-rmse:2.329086+0.518999 
## [67] train-rmse:1.581023+0.033988    test-rmse:2.324331+0.522255 
## [68] train-rmse:1.572497+0.034171    test-rmse:2.322081+0.527384 
## [69] train-rmse:1.559017+0.033990    test-rmse:2.316837+0.527510 
## [70] train-rmse:1.548211+0.037234    test-rmse:2.318845+0.528637 
## [71] train-rmse:1.540695+0.037332    test-rmse:2.315416+0.531726 
## [72] train-rmse:1.527207+0.036069    test-rmse:2.309552+0.531130 
## [73] train-rmse:1.517420+0.036034    test-rmse:2.301097+0.526535 
## [74] train-rmse:1.509516+0.034748    test-rmse:2.297063+0.525385 
## [75] train-rmse:1.498937+0.032253    test-rmse:2.293553+0.527560 
## [76] train-rmse:1.492136+0.032754    test-rmse:2.291191+0.533556 
## [77] train-rmse:1.481235+0.035192    test-rmse:2.282629+0.530782 
## [78] train-rmse:1.474701+0.034990    test-rmse:2.279150+0.530028 
## [79] train-rmse:1.468031+0.035955    test-rmse:2.274401+0.528134 
## [80] train-rmse:1.458269+0.033609    test-rmse:2.273526+0.528933 
## [81] train-rmse:1.451418+0.032889    test-rmse:2.272123+0.528968 
## [82] train-rmse:1.444254+0.031885    test-rmse:2.269641+0.530563 
## [83] train-rmse:1.436248+0.030462    test-rmse:2.271375+0.527188 
## [84] train-rmse:1.428243+0.028747    test-rmse:2.270637+0.537896 
## [85] train-rmse:1.420033+0.027546    test-rmse:2.267688+0.538479 
## [86] train-rmse:1.413707+0.028350    test-rmse:2.264280+0.535092 
## [87] train-rmse:1.405502+0.027455    test-rmse:2.259284+0.534005 
## [88] train-rmse:1.399894+0.028130    test-rmse:2.254683+0.535702 
## [89] train-rmse:1.394111+0.028928    test-rmse:2.250254+0.533591 
## [90] train-rmse:1.388909+0.028774    test-rmse:2.247471+0.534448 
## [91] train-rmse:1.383766+0.028473    test-rmse:2.245219+0.537073 
## [92] train-rmse:1.375369+0.027435    test-rmse:2.241216+0.536949 
## [93] train-rmse:1.369332+0.026985    test-rmse:2.236873+0.537307 
## [94] train-rmse:1.362228+0.026579    test-rmse:2.232161+0.535283 
## [95] train-rmse:1.350957+0.028457    test-rmse:2.232951+0.535621 
## [96] train-rmse:1.342644+0.032193    test-rmse:2.234157+0.533037 
## [97] train-rmse:1.334575+0.031732    test-rmse:2.232999+0.533153 
## [98] train-rmse:1.326470+0.028965    test-rmse:2.226148+0.535254 
## [99] train-rmse:1.318645+0.028628    test-rmse:2.224105+0.533370 
## [100]    train-rmse:1.313923+0.029259    test-rmse:2.221951+0.532093 
## [101]    train-rmse:1.309288+0.029602    test-rmse:2.220910+0.531290 
## [102]    train-rmse:1.303468+0.029465    test-rmse:2.214125+0.531075 
## [103]    train-rmse:1.299217+0.029308    test-rmse:2.215410+0.532486 
## [104]    train-rmse:1.292145+0.030310    test-rmse:2.210740+0.532965 
## [105]    train-rmse:1.286597+0.029982    test-rmse:2.210305+0.533100 
## [106]    train-rmse:1.280337+0.030220    test-rmse:2.209525+0.534015 
## [107]    train-rmse:1.276276+0.030078    test-rmse:2.209171+0.535526 
## [108]    train-rmse:1.267924+0.026657    test-rmse:2.204884+0.539029 
## [109]    train-rmse:1.261967+0.025330    test-rmse:2.204953+0.538284 
## [110]    train-rmse:1.256763+0.025043    test-rmse:2.200864+0.538988 
## [111]    train-rmse:1.251670+0.025395    test-rmse:2.199733+0.543289 
## [112]    train-rmse:1.245906+0.027176    test-rmse:2.196066+0.541047 
## [113]    train-rmse:1.240025+0.028635    test-rmse:2.195925+0.542586 
## [114]    train-rmse:1.236077+0.029006    test-rmse:2.193620+0.542551 
## [115]    train-rmse:1.230052+0.029588    test-rmse:2.186256+0.542987 
## [116]    train-rmse:1.223668+0.033129    test-rmse:2.180926+0.542685 
## [117]    train-rmse:1.216897+0.032756    test-rmse:2.183531+0.539440 
## [118]    train-rmse:1.211572+0.033735    test-rmse:2.179800+0.537051 
## [119]    train-rmse:1.206284+0.033968    test-rmse:2.180529+0.538398 
## [120]    train-rmse:1.201282+0.032975    test-rmse:2.178946+0.537602 
## [121]    train-rmse:1.197037+0.033333    test-rmse:2.176663+0.540317 
## [122]    train-rmse:1.193934+0.033276    test-rmse:2.174178+0.541566 
## [123]    train-rmse:1.189577+0.033980    test-rmse:2.173165+0.543619 
## [124]    train-rmse:1.186246+0.034393    test-rmse:2.172442+0.543583 
## [125]    train-rmse:1.182465+0.034944    test-rmse:2.173980+0.543278 
## [126]    train-rmse:1.178269+0.035520    test-rmse:2.170664+0.543766 
## [127]    train-rmse:1.175172+0.035169    test-rmse:2.169354+0.548007 
## [128]    train-rmse:1.170754+0.035017    test-rmse:2.166537+0.547764 
## [129]    train-rmse:1.165917+0.037958    test-rmse:2.163242+0.546594 
## [130]    train-rmse:1.160961+0.036768    test-rmse:2.161295+0.545611 
## [131]    train-rmse:1.158244+0.036622    test-rmse:2.162047+0.549552 
## [132]    train-rmse:1.154107+0.035603    test-rmse:2.160251+0.551316 
## [133]    train-rmse:1.149590+0.035139    test-rmse:2.160404+0.550823 
## [134]    train-rmse:1.143906+0.037574    test-rmse:2.158402+0.552508 
## [135]    train-rmse:1.139334+0.038323    test-rmse:2.159229+0.553401 
## [136]    train-rmse:1.132462+0.034214    test-rmse:2.164001+0.553797 
## [137]    train-rmse:1.127918+0.034299    test-rmse:2.164132+0.553164 
## [138]    train-rmse:1.123477+0.036663    test-rmse:2.163435+0.556956 
## [139]    train-rmse:1.118243+0.039018    test-rmse:2.161484+0.558794 
## [140]    train-rmse:1.112226+0.039158    test-rmse:2.164391+0.558826 
## Stopping. Best iteration:
## [134]    train-rmse:1.143906+0.037574    test-rmse:2.158402+0.552508
## 
## 23 
## [1]  train-rmse:7.131872+0.147895    test-rmse:7.164958+0.639863 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.259756+0.127128    test-rmse:6.327340+0.589379 
## [3]  train-rmse:5.531664+0.122050    test-rmse:5.614142+0.546927 
## [4]  train-rmse:4.934974+0.116936    test-rmse:5.074223+0.497883 
## [5]  train-rmse:4.434169+0.103635    test-rmse:4.609648+0.480716 
## [6]  train-rmse:4.028579+0.100514    test-rmse:4.248003+0.460212 
## [7]  train-rmse:3.675831+0.091855    test-rmse:3.904237+0.449057 
## [8]  train-rmse:3.394752+0.088095    test-rmse:3.671323+0.432327 
## [9]  train-rmse:3.156632+0.079098    test-rmse:3.452481+0.462385 
## [10] train-rmse:2.962154+0.075193    test-rmse:3.282481+0.448333 
## [11] train-rmse:2.803380+0.078719    test-rmse:3.153545+0.443865 
## [12] train-rmse:2.675108+0.072697    test-rmse:3.077821+0.443579 
## [13] train-rmse:2.549250+0.060445    test-rmse:2.986044+0.444082 
## [14] train-rmse:2.462150+0.060994    test-rmse:2.915003+0.450929 
## [15] train-rmse:2.372241+0.059862    test-rmse:2.859645+0.446498 
## [16] train-rmse:2.290126+0.064727    test-rmse:2.799401+0.444087 
## [17] train-rmse:2.218720+0.056452    test-rmse:2.765039+0.445455 
## [18] train-rmse:2.154161+0.052591    test-rmse:2.727759+0.454264 
## [19] train-rmse:2.101025+0.047694    test-rmse:2.700944+0.466702 
## [20] train-rmse:2.054537+0.048174    test-rmse:2.675825+0.469149 
## [21] train-rmse:2.007381+0.054551    test-rmse:2.637640+0.448662 
## [22] train-rmse:1.966712+0.054611    test-rmse:2.616312+0.440646 
## [23] train-rmse:1.927649+0.052558    test-rmse:2.601814+0.431846 
## [24] train-rmse:1.888746+0.052224    test-rmse:2.574481+0.424298 
## [25] train-rmse:1.855228+0.055748    test-rmse:2.564074+0.438246 
## [26] train-rmse:1.817097+0.046635    test-rmse:2.531188+0.453429 
## [27] train-rmse:1.783738+0.044048    test-rmse:2.508177+0.450110 
## [28] train-rmse:1.758194+0.044405    test-rmse:2.495235+0.452641 
## [29] train-rmse:1.728762+0.045930    test-rmse:2.487200+0.453002 
## [30] train-rmse:1.701241+0.045612    test-rmse:2.466759+0.454699 
## [31] train-rmse:1.672736+0.048305    test-rmse:2.448188+0.464133 
## [32] train-rmse:1.647360+0.050539    test-rmse:2.427923+0.456459 
## [33] train-rmse:1.621282+0.052583    test-rmse:2.413661+0.452131 
## [34] train-rmse:1.596563+0.047715    test-rmse:2.402837+0.460492 
## [35] train-rmse:1.569704+0.043030    test-rmse:2.393662+0.461346 
## [36] train-rmse:1.548735+0.042364    test-rmse:2.380911+0.465542 
## [37] train-rmse:1.529542+0.043816    test-rmse:2.374117+0.458827 
## [38] train-rmse:1.512434+0.042851    test-rmse:2.364031+0.453949 
## [39] train-rmse:1.496274+0.042977    test-rmse:2.351819+0.457126 
## [40] train-rmse:1.476517+0.041762    test-rmse:2.347585+0.463647 
## [41] train-rmse:1.454966+0.038664    test-rmse:2.341702+0.473359 
## [42] train-rmse:1.436867+0.038891    test-rmse:2.335613+0.471111 
## [43] train-rmse:1.415281+0.038215    test-rmse:2.334656+0.468746 
## [44] train-rmse:1.397972+0.035423    test-rmse:2.330707+0.472809 
## [45] train-rmse:1.382362+0.041464    test-rmse:2.322230+0.470739 
## [46] train-rmse:1.366350+0.040397    test-rmse:2.312508+0.467958 
## [47] train-rmse:1.350787+0.038603    test-rmse:2.303284+0.469313 
## [48] train-rmse:1.337081+0.039618    test-rmse:2.300506+0.473885 
## [49] train-rmse:1.322728+0.041351    test-rmse:2.297097+0.478511 
## [50] train-rmse:1.307905+0.044161    test-rmse:2.294451+0.481049 
## [51] train-rmse:1.290090+0.047180    test-rmse:2.286050+0.478416 
## [52] train-rmse:1.276727+0.046607    test-rmse:2.279597+0.478257 
## [53] train-rmse:1.262306+0.045150    test-rmse:2.269754+0.479711 
## [54] train-rmse:1.247175+0.044503    test-rmse:2.264857+0.481212 
## [55] train-rmse:1.231971+0.047572    test-rmse:2.253674+0.474595 
## [56] train-rmse:1.218811+0.044032    test-rmse:2.252214+0.480426 
## [57] train-rmse:1.206457+0.045096    test-rmse:2.244898+0.478665 
## [58] train-rmse:1.193221+0.045777    test-rmse:2.242510+0.478714 
## [59] train-rmse:1.184017+0.046021    test-rmse:2.241229+0.478584 
## [60] train-rmse:1.171228+0.046331    test-rmse:2.237170+0.479797 
## [61] train-rmse:1.158134+0.047111    test-rmse:2.233596+0.480300 
## [62] train-rmse:1.146636+0.046413    test-rmse:2.231100+0.478284 
## [63] train-rmse:1.135812+0.047520    test-rmse:2.229247+0.482513 
## [64] train-rmse:1.124288+0.046450    test-rmse:2.225704+0.483593 
## [65] train-rmse:1.114281+0.049789    test-rmse:2.221363+0.477499 
## [66] train-rmse:1.104559+0.050541    test-rmse:2.218361+0.480475 
## [67] train-rmse:1.097186+0.050348    test-rmse:2.217031+0.480201 
## [68] train-rmse:1.084794+0.051311    test-rmse:2.212355+0.480775 
## [69] train-rmse:1.074652+0.049129    test-rmse:2.210083+0.480800 
## [70] train-rmse:1.065128+0.049452    test-rmse:2.210285+0.480876 
## [71] train-rmse:1.053990+0.049764    test-rmse:2.205725+0.484435 
## [72] train-rmse:1.046715+0.049080    test-rmse:2.200927+0.486518 
## [73] train-rmse:1.037464+0.048969    test-rmse:2.198892+0.486067 
## [74] train-rmse:1.029519+0.049324    test-rmse:2.197743+0.486573 
## [75] train-rmse:1.017829+0.046401    test-rmse:2.194999+0.487929 
## [76] train-rmse:1.010880+0.047211    test-rmse:2.192158+0.489187 
## [77] train-rmse:1.000927+0.044444    test-rmse:2.185569+0.484813 
## [78] train-rmse:0.988929+0.043254    test-rmse:2.187651+0.485453 
## [79] train-rmse:0.980934+0.043943    test-rmse:2.187176+0.487140 
## [80] train-rmse:0.971042+0.044448    test-rmse:2.186623+0.492985 
## [81] train-rmse:0.961435+0.041607    test-rmse:2.185108+0.489523 
## [82] train-rmse:0.950827+0.040415    test-rmse:2.184179+0.489555 
## [83] train-rmse:0.945787+0.040853    test-rmse:2.182272+0.487580 
## [84] train-rmse:0.937877+0.040915    test-rmse:2.180315+0.486121 
## [85] train-rmse:0.932222+0.040817    test-rmse:2.177408+0.492693 
## [86] train-rmse:0.926525+0.041415    test-rmse:2.175662+0.493047 
## [87] train-rmse:0.919180+0.040566    test-rmse:2.173436+0.492853 
## [88] train-rmse:0.910830+0.039562    test-rmse:2.171725+0.495138 
## [89] train-rmse:0.903199+0.041453    test-rmse:2.166786+0.493191 
## [90] train-rmse:0.893553+0.042169    test-rmse:2.161294+0.491264 
## [91] train-rmse:0.884912+0.041843    test-rmse:2.161715+0.488877 
## [92] train-rmse:0.877500+0.040869    test-rmse:2.160090+0.486234 
## [93] train-rmse:0.869969+0.039592    test-rmse:2.156786+0.489642 
## [94] train-rmse:0.862856+0.040084    test-rmse:2.156457+0.490272 
## [95] train-rmse:0.855245+0.041681    test-rmse:2.150914+0.490888 
## [96] train-rmse:0.849088+0.040321    test-rmse:2.146115+0.491236 
## [97] train-rmse:0.842109+0.043910    test-rmse:2.144883+0.489291 
## [98] train-rmse:0.837241+0.044315    test-rmse:2.144248+0.488815 
## [99] train-rmse:0.831158+0.044303    test-rmse:2.141669+0.491778 
## [100]    train-rmse:0.824705+0.044095    test-rmse:2.140581+0.490173 
## [101]    train-rmse:0.820188+0.043673    test-rmse:2.138748+0.490285 
## [102]    train-rmse:0.815289+0.045626    test-rmse:2.138499+0.492070 
## [103]    train-rmse:0.808198+0.043275    test-rmse:2.138020+0.493387 
## [104]    train-rmse:0.800768+0.041355    test-rmse:2.135970+0.494407 
## [105]    train-rmse:0.793703+0.038278    test-rmse:2.135820+0.496380 
## [106]    train-rmse:0.784233+0.039068    test-rmse:2.134666+0.491915 
## [107]    train-rmse:0.775818+0.036047    test-rmse:2.132801+0.491926 
## [108]    train-rmse:0.771066+0.034898    test-rmse:2.132480+0.491924 
## [109]    train-rmse:0.767196+0.034358    test-rmse:2.132354+0.492359 
## [110]    train-rmse:0.762515+0.038174    test-rmse:2.130727+0.490499 
## [111]    train-rmse:0.756983+0.039950    test-rmse:2.129362+0.492686 
## [112]    train-rmse:0.752160+0.042419    test-rmse:2.127873+0.492175 
## [113]    train-rmse:0.745875+0.043982    test-rmse:2.126905+0.491189 
## [114]    train-rmse:0.742129+0.043614    test-rmse:2.125721+0.491699 
## [115]    train-rmse:0.737567+0.045085    test-rmse:2.122805+0.490034 
## [116]    train-rmse:0.733894+0.044183    test-rmse:2.121371+0.491414 
## [117]    train-rmse:0.729488+0.043381    test-rmse:2.120233+0.492631 
## [118]    train-rmse:0.722960+0.043910    test-rmse:2.118328+0.492980 
## [119]    train-rmse:0.716468+0.045091    test-rmse:2.116773+0.493061 
## [120]    train-rmse:0.709605+0.043487    test-rmse:2.112703+0.493224 
## [121]    train-rmse:0.703205+0.043947    test-rmse:2.110610+0.495999 
## [122]    train-rmse:0.696606+0.043384    test-rmse:2.109784+0.497163 
## [123]    train-rmse:0.690277+0.044081    test-rmse:2.110282+0.496786 
## [124]    train-rmse:0.684703+0.044885    test-rmse:2.108681+0.494895 
## [125]    train-rmse:0.679992+0.044940    test-rmse:2.107093+0.495017 
## [126]    train-rmse:0.677132+0.045304    test-rmse:2.105686+0.493642 
## [127]    train-rmse:0.674173+0.046554    test-rmse:2.104273+0.494290 
## [128]    train-rmse:0.668757+0.045563    test-rmse:2.104464+0.494995 
## [129]    train-rmse:0.661670+0.043251    test-rmse:2.102522+0.495408 
## [130]    train-rmse:0.659087+0.043441    test-rmse:2.101979+0.494919 
## [131]    train-rmse:0.654679+0.043594    test-rmse:2.102850+0.496663 
## [132]    train-rmse:0.649380+0.043467    test-rmse:2.104443+0.496542 
## [133]    train-rmse:0.645156+0.042486    test-rmse:2.103821+0.497339 
## [134]    train-rmse:0.641732+0.043019    test-rmse:2.103106+0.496699 
## [135]    train-rmse:0.637016+0.041331    test-rmse:2.103211+0.496467 
## [136]    train-rmse:0.632889+0.042398    test-rmse:2.104540+0.495016 
## Stopping. Best iteration:
## [130]    train-rmse:0.659087+0.043441    test-rmse:2.101979+0.494919
## 
## 24 
## [1]  train-rmse:7.089002+0.146837    test-rmse:7.105498+0.631113 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.173838+0.118719    test-rmse:6.225084+0.556294 
## [3]  train-rmse:5.410551+0.100999    test-rmse:5.515960+0.518813 
## [4]  train-rmse:4.771150+0.088723    test-rmse:4.916378+0.487324 
## [5]  train-rmse:4.246106+0.069349    test-rmse:4.439460+0.467862 
## [6]  train-rmse:3.806750+0.068697    test-rmse:4.058476+0.435204 
## [7]  train-rmse:3.453122+0.068715    test-rmse:3.750990+0.433773 
## [8]  train-rmse:3.157423+0.067788    test-rmse:3.489111+0.429642 
## [9]  train-rmse:2.909985+0.060416    test-rmse:3.285996+0.445077 
## [10] train-rmse:2.701908+0.063936    test-rmse:3.133449+0.437253 
## [11] train-rmse:2.533387+0.056389    test-rmse:3.011715+0.464599 
## [12] train-rmse:2.382947+0.054370    test-rmse:2.910438+0.457067 
## [13] train-rmse:2.256686+0.062185    test-rmse:2.822444+0.443489 
## [14] train-rmse:2.152103+0.052746    test-rmse:2.749071+0.464814 
## [15] train-rmse:2.063645+0.052445    test-rmse:2.709210+0.475192 
## [16] train-rmse:1.986041+0.057499    test-rmse:2.659983+0.452053 
## [17] train-rmse:1.919559+0.057711    test-rmse:2.623794+0.436570 
## [18] train-rmse:1.853252+0.050634    test-rmse:2.587695+0.442690 
## [19] train-rmse:1.799199+0.050989    test-rmse:2.545064+0.450662 
## [20] train-rmse:1.750576+0.049118    test-rmse:2.512078+0.453123 
## [21] train-rmse:1.705400+0.046090    test-rmse:2.491355+0.454075 
## [22] train-rmse:1.664716+0.049022    test-rmse:2.465903+0.453287 
## [23] train-rmse:1.622204+0.037805    test-rmse:2.441742+0.456311 
## [24] train-rmse:1.577036+0.041457    test-rmse:2.411260+0.461269 
## [25] train-rmse:1.546507+0.038852    test-rmse:2.405212+0.459780 
## [26] train-rmse:1.507086+0.037919    test-rmse:2.392220+0.465877 
## [27] train-rmse:1.475451+0.039631    test-rmse:2.388795+0.469563 
## [28] train-rmse:1.442408+0.038070    test-rmse:2.375277+0.471101 
## [29] train-rmse:1.414662+0.033454    test-rmse:2.375863+0.475194 
## [30] train-rmse:1.382284+0.032613    test-rmse:2.367506+0.475884 
## [31] train-rmse:1.360407+0.030756    test-rmse:2.362532+0.478617 
## [32] train-rmse:1.333750+0.026565    test-rmse:2.347744+0.476299 
## [33] train-rmse:1.313371+0.025767    test-rmse:2.337964+0.477749 
## [34] train-rmse:1.284025+0.035293    test-rmse:2.328111+0.465205 
## [35] train-rmse:1.264494+0.034728    test-rmse:2.321016+0.469752 
## [36] train-rmse:1.244479+0.036634    test-rmse:2.313344+0.470451 
## [37] train-rmse:1.221811+0.031767    test-rmse:2.309500+0.476821 
## [38] train-rmse:1.202202+0.030562    test-rmse:2.307628+0.480537 
## [39] train-rmse:1.185522+0.028435    test-rmse:2.303094+0.480172 
## [40] train-rmse:1.166390+0.031896    test-rmse:2.294711+0.473039 
## [41] train-rmse:1.147187+0.030190    test-rmse:2.288497+0.471998 
## [42] train-rmse:1.130850+0.032121    test-rmse:2.282595+0.472548 
## [43] train-rmse:1.114643+0.034005    test-rmse:2.274894+0.477356 
## [44] train-rmse:1.089889+0.028332    test-rmse:2.270650+0.474377 
## [45] train-rmse:1.073340+0.030019    test-rmse:2.265876+0.477023 
## [46] train-rmse:1.057816+0.024918    test-rmse:2.262267+0.476651 
## [47] train-rmse:1.041239+0.030699    test-rmse:2.260473+0.476503 
## [48] train-rmse:1.024778+0.037067    test-rmse:2.254005+0.477310 
## [49] train-rmse:1.009633+0.041158    test-rmse:2.245503+0.474739 
## [50] train-rmse:0.996254+0.040707    test-rmse:2.243090+0.471504 
## [51] train-rmse:0.982219+0.041824    test-rmse:2.242623+0.468818 
## [52] train-rmse:0.963371+0.042622    test-rmse:2.242664+0.470472 
## [53] train-rmse:0.942414+0.030419    test-rmse:2.241934+0.470406 
## [54] train-rmse:0.928075+0.031616    test-rmse:2.237898+0.470981 
## [55] train-rmse:0.913460+0.030991    test-rmse:2.234928+0.473518 
## [56] train-rmse:0.901596+0.034801    test-rmse:2.232717+0.472012 
## [57] train-rmse:0.892282+0.032970    test-rmse:2.228542+0.470787 
## [58] train-rmse:0.880270+0.029596    test-rmse:2.230415+0.471902 
## [59] train-rmse:0.862870+0.033944    test-rmse:2.220439+0.474676 
## [60] train-rmse:0.852256+0.036724    test-rmse:2.217367+0.475136 
## [61] train-rmse:0.844346+0.036818    test-rmse:2.214590+0.477304 
## [62] train-rmse:0.829288+0.030838    test-rmse:2.214804+0.475532 
## [63] train-rmse:0.816735+0.033223    test-rmse:2.211549+0.475462 
## [64] train-rmse:0.808834+0.033064    test-rmse:2.207400+0.477220 
## [65] train-rmse:0.798626+0.030883    test-rmse:2.205963+0.477538 
## [66] train-rmse:0.781865+0.026654    test-rmse:2.202213+0.478456 
## [67] train-rmse:0.770186+0.026025    test-rmse:2.201195+0.479302 
## [68] train-rmse:0.763053+0.027686    test-rmse:2.201866+0.481830 
## [69] train-rmse:0.753615+0.026030    test-rmse:2.202879+0.484484 
## [70] train-rmse:0.743907+0.029202    test-rmse:2.199241+0.486731 
## [71] train-rmse:0.732060+0.030907    test-rmse:2.195426+0.487075 
## [72] train-rmse:0.719840+0.026716    test-rmse:2.194308+0.486493 
## [73] train-rmse:0.713587+0.026873    test-rmse:2.194869+0.486467 
## [74] train-rmse:0.705450+0.027186    test-rmse:2.189120+0.485069 
## [75] train-rmse:0.696916+0.032256    test-rmse:2.184885+0.483347 
## [76] train-rmse:0.691947+0.032402    test-rmse:2.186216+0.483334 
## [77] train-rmse:0.680661+0.031302    test-rmse:2.185276+0.483813 
## [78] train-rmse:0.671041+0.032081    test-rmse:2.183984+0.482717 
## [79] train-rmse:0.663853+0.030875    test-rmse:2.182886+0.483463 
## [80] train-rmse:0.655415+0.030841    test-rmse:2.177338+0.485595 
## [81] train-rmse:0.646608+0.033916    test-rmse:2.175810+0.486002 
## [82] train-rmse:0.639307+0.033613    test-rmse:2.172030+0.486774 
## [83] train-rmse:0.632279+0.032869    test-rmse:2.169331+0.487516 
## [84] train-rmse:0.625345+0.032680    test-rmse:2.171456+0.488969 
## [85] train-rmse:0.620088+0.031655    test-rmse:2.170697+0.489433 
## [86] train-rmse:0.614683+0.031405    test-rmse:2.168043+0.489518 
## [87] train-rmse:0.606900+0.031944    test-rmse:2.167624+0.491654 
## [88] train-rmse:0.601424+0.032247    test-rmse:2.167431+0.490529 
## [89] train-rmse:0.595394+0.032225    test-rmse:2.166034+0.490268 
## [90] train-rmse:0.588980+0.031319    test-rmse:2.166953+0.490013 
## [91] train-rmse:0.582788+0.030243    test-rmse:2.168453+0.491589 
## [92] train-rmse:0.576695+0.033070    test-rmse:2.167338+0.490572 
## [93] train-rmse:0.571098+0.033342    test-rmse:2.166923+0.489922 
## [94] train-rmse:0.565687+0.032655    test-rmse:2.166164+0.490464 
## [95] train-rmse:0.560729+0.032580    test-rmse:2.164401+0.491045 
## [96] train-rmse:0.552145+0.032064    test-rmse:2.164830+0.491743 
## [97] train-rmse:0.544123+0.032644    test-rmse:2.165875+0.492141 
## [98] train-rmse:0.537708+0.032115    test-rmse:2.164817+0.491965 
## [99] train-rmse:0.533020+0.031439    test-rmse:2.163919+0.491504 
## [100]    train-rmse:0.529604+0.031825    test-rmse:2.164404+0.493151 
## [101]    train-rmse:0.523231+0.032852    test-rmse:2.163076+0.493701 
## [102]    train-rmse:0.517476+0.033064    test-rmse:2.162099+0.493553 
## [103]    train-rmse:0.514212+0.032901    test-rmse:2.161299+0.494113 
## [104]    train-rmse:0.508815+0.033492    test-rmse:2.160475+0.494421 
## [105]    train-rmse:0.503423+0.034994    test-rmse:2.159410+0.494517 
## [106]    train-rmse:0.496334+0.034899    test-rmse:2.159464+0.494715 
## [107]    train-rmse:0.491865+0.036959    test-rmse:2.158839+0.497826 
## [108]    train-rmse:0.486773+0.036074    test-rmse:2.155934+0.499524 
## [109]    train-rmse:0.483289+0.036021    test-rmse:2.153663+0.500780 
## [110]    train-rmse:0.477941+0.035220    test-rmse:2.154127+0.501497 
## [111]    train-rmse:0.472631+0.036465    test-rmse:2.153203+0.501208 
## [112]    train-rmse:0.468494+0.035917    test-rmse:2.152910+0.501024 
## [113]    train-rmse:0.464008+0.035279    test-rmse:2.153013+0.500796 
## [114]    train-rmse:0.459394+0.036617    test-rmse:2.151562+0.500609 
## [115]    train-rmse:0.455079+0.035577    test-rmse:2.151381+0.500646 
## [116]    train-rmse:0.450731+0.034925    test-rmse:2.150830+0.501094 
## [117]    train-rmse:0.446466+0.034179    test-rmse:2.149858+0.501989 
## [118]    train-rmse:0.442363+0.033922    test-rmse:2.150558+0.501038 
## [119]    train-rmse:0.437560+0.033185    test-rmse:2.149293+0.501475 
## [120]    train-rmse:0.432807+0.032004    test-rmse:2.149279+0.501476 
## [121]    train-rmse:0.428015+0.032781    test-rmse:2.149833+0.500863 
## [122]    train-rmse:0.423667+0.033565    test-rmse:2.149080+0.500603 
## [123]    train-rmse:0.419865+0.034866    test-rmse:2.150282+0.500770 
## [124]    train-rmse:0.415470+0.033603    test-rmse:2.149166+0.500980 
## [125]    train-rmse:0.411395+0.034055    test-rmse:2.150294+0.502541 
## [126]    train-rmse:0.407168+0.034623    test-rmse:2.149127+0.502815 
## [127]    train-rmse:0.404022+0.034456    test-rmse:2.149199+0.502582 
## [128]    train-rmse:0.399402+0.033624    test-rmse:2.149544+0.501987 
## Stopping. Best iteration:
## [122]    train-rmse:0.423667+0.033565    test-rmse:2.149080+0.500603
## 
## 25 
## [1]  train-rmse:7.059347+0.145057    test-rmse:7.054918+0.618135 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.114990+0.120688    test-rmse:6.148119+0.557187 
## [3]  train-rmse:5.325287+0.108786    test-rmse:5.402932+0.500971 
## [4]  train-rmse:4.672923+0.098485    test-rmse:4.810322+0.453120 
## [5]  train-rmse:4.130717+0.081002    test-rmse:4.338176+0.442502 
## [6]  train-rmse:3.682514+0.072992    test-rmse:3.949441+0.426776 
## [7]  train-rmse:3.314366+0.062961    test-rmse:3.659630+0.413788 
## [8]  train-rmse:3.003798+0.056192    test-rmse:3.406990+0.428267 
## [9]  train-rmse:2.744439+0.049857    test-rmse:3.217855+0.427506 
## [10] train-rmse:2.531948+0.054645    test-rmse:3.064752+0.437872 
## [11] train-rmse:2.357983+0.055417    test-rmse:2.954838+0.453092 
## [12] train-rmse:2.201387+0.047861    test-rmse:2.861868+0.487634 
## [13] train-rmse:2.073245+0.046044    test-rmse:2.776578+0.470869 
## [14] train-rmse:1.969590+0.045017    test-rmse:2.714603+0.481009 
## [15] train-rmse:1.870860+0.037887    test-rmse:2.651045+0.491484 
## [16] train-rmse:1.781225+0.041446    test-rmse:2.612877+0.496704 
## [17] train-rmse:1.709390+0.041490    test-rmse:2.567072+0.487957 
## [18] train-rmse:1.648245+0.037322    test-rmse:2.532318+0.489103 
## [19] train-rmse:1.590057+0.036774    test-rmse:2.508009+0.492975 
## [20] train-rmse:1.531638+0.029260    test-rmse:2.488174+0.495477 
## [21] train-rmse:1.492667+0.029649    test-rmse:2.464091+0.492192 
## [22] train-rmse:1.447372+0.029072    test-rmse:2.441480+0.494002 
## [23] train-rmse:1.398586+0.032874    test-rmse:2.417893+0.495596 
## [24] train-rmse:1.359334+0.028718    test-rmse:2.392447+0.505401 
## [25] train-rmse:1.321567+0.037696    test-rmse:2.375978+0.512463 
## [26] train-rmse:1.288577+0.035761    test-rmse:2.361232+0.504680 
## [27] train-rmse:1.256424+0.040427    test-rmse:2.351111+0.508504 
## [28] train-rmse:1.226432+0.045349    test-rmse:2.342037+0.510647 
## [29] train-rmse:1.202290+0.044678    test-rmse:2.340706+0.510702 
## [30] train-rmse:1.173647+0.044053    test-rmse:2.331392+0.506274 
## [31] train-rmse:1.147203+0.041701    test-rmse:2.323261+0.508011 
## [32] train-rmse:1.112202+0.038286    test-rmse:2.319703+0.513414 
## [33] train-rmse:1.089530+0.037146    test-rmse:2.313408+0.519787 
## [34] train-rmse:1.066978+0.034450    test-rmse:2.307134+0.517949 
## [35] train-rmse:1.049715+0.034583    test-rmse:2.300884+0.519395 
## [36] train-rmse:1.021683+0.034224    test-rmse:2.298552+0.512872 
## [37] train-rmse:1.000656+0.031938    test-rmse:2.284740+0.515578 
## [38] train-rmse:0.986741+0.031409    test-rmse:2.283307+0.521284 
## [39] train-rmse:0.967066+0.036926    test-rmse:2.282071+0.522986 
## [40] train-rmse:0.945633+0.039439    test-rmse:2.279374+0.526984 
## [41] train-rmse:0.927047+0.039331    test-rmse:2.277643+0.528609 
## [42] train-rmse:0.909980+0.037252    test-rmse:2.268231+0.530595 
## [43] train-rmse:0.894632+0.040722    test-rmse:2.271933+0.532955 
## [44] train-rmse:0.877602+0.033445    test-rmse:2.266560+0.535044 
## [45] train-rmse:0.861447+0.030402    test-rmse:2.258506+0.532661 
## [46] train-rmse:0.845218+0.029450    test-rmse:2.253544+0.533742 
## [47] train-rmse:0.828719+0.030954    test-rmse:2.257374+0.530802 
## [48] train-rmse:0.807978+0.034467    test-rmse:2.252831+0.533468 
## [49] train-rmse:0.792556+0.035136    test-rmse:2.247318+0.531470 
## [50] train-rmse:0.777134+0.033233    test-rmse:2.244986+0.535454 
## [51] train-rmse:0.761533+0.031977    test-rmse:2.243766+0.535282 
## [52] train-rmse:0.748446+0.032989    test-rmse:2.241130+0.537618 
## [53] train-rmse:0.737388+0.028885    test-rmse:2.238672+0.538846 
## [54] train-rmse:0.721276+0.030967    test-rmse:2.235796+0.536813 
## [55] train-rmse:0.708911+0.028408    test-rmse:2.238543+0.537626 
## [56] train-rmse:0.694745+0.030040    test-rmse:2.232717+0.535470 
## [57] train-rmse:0.682121+0.029606    test-rmse:2.233839+0.533361 
## [58] train-rmse:0.668711+0.026852    test-rmse:2.231877+0.530770 
## [59] train-rmse:0.660019+0.028325    test-rmse:2.228979+0.532403 
## [60] train-rmse:0.646941+0.026893    test-rmse:2.227702+0.532747 
## [61] train-rmse:0.632330+0.022990    test-rmse:2.228777+0.533549 
## [62] train-rmse:0.618648+0.028882    test-rmse:2.228163+0.533398 
## [63] train-rmse:0.610956+0.028911    test-rmse:2.226019+0.534168 
## [64] train-rmse:0.601360+0.028355    test-rmse:2.222842+0.532420 
## [65] train-rmse:0.592833+0.028333    test-rmse:2.221071+0.532042 
## [66] train-rmse:0.582511+0.028235    test-rmse:2.221284+0.533225 
## [67] train-rmse:0.573928+0.028264    test-rmse:2.220677+0.534669 
## [68] train-rmse:0.562950+0.029945    test-rmse:2.217144+0.533921 
## [69] train-rmse:0.554343+0.028073    test-rmse:2.216134+0.531767 
## [70] train-rmse:0.543704+0.027021    test-rmse:2.216842+0.531362 
## [71] train-rmse:0.536022+0.027268    test-rmse:2.215535+0.531364 
## [72] train-rmse:0.526484+0.025055    test-rmse:2.211727+0.531298 
## [73] train-rmse:0.518849+0.025889    test-rmse:2.211979+0.531243 
## [74] train-rmse:0.511291+0.027442    test-rmse:2.210370+0.532914 
## [75] train-rmse:0.503629+0.027659    test-rmse:2.208995+0.533420 
## [76] train-rmse:0.496922+0.028363    test-rmse:2.209643+0.534979 
## [77] train-rmse:0.491519+0.028353    test-rmse:2.208577+0.534950 
## [78] train-rmse:0.483699+0.027580    test-rmse:2.208151+0.535618 
## [79] train-rmse:0.476004+0.028720    test-rmse:2.208793+0.534512 
## [80] train-rmse:0.469453+0.029245    test-rmse:2.208124+0.535628 
## [81] train-rmse:0.462471+0.030913    test-rmse:2.206755+0.535983 
## [82] train-rmse:0.456504+0.029791    test-rmse:2.204896+0.535998 
## [83] train-rmse:0.448112+0.025315    test-rmse:2.201049+0.537526 
## [84] train-rmse:0.438082+0.026286    test-rmse:2.198508+0.537617 
## [85] train-rmse:0.432727+0.025863    test-rmse:2.198882+0.537623 
## [86] train-rmse:0.425831+0.023598    test-rmse:2.197991+0.538017 
## [87] train-rmse:0.418214+0.021996    test-rmse:2.195328+0.538157 
## [88] train-rmse:0.412772+0.020367    test-rmse:2.195507+0.539595 
## [89] train-rmse:0.406038+0.019124    test-rmse:2.194933+0.538540 
## [90] train-rmse:0.400416+0.016476    test-rmse:2.193846+0.539332 
## [91] train-rmse:0.393907+0.017899    test-rmse:2.192666+0.538918 
## [92] train-rmse:0.390181+0.017309    test-rmse:2.192603+0.539185 
## [93] train-rmse:0.384119+0.018411    test-rmse:2.192899+0.540040 
## [94] train-rmse:0.378760+0.018549    test-rmse:2.194155+0.541043 
## [95] train-rmse:0.373638+0.019406    test-rmse:2.194115+0.542306 
## [96] train-rmse:0.368116+0.020898    test-rmse:2.195291+0.543293 
## [97] train-rmse:0.364355+0.021430    test-rmse:2.194201+0.543765 
## [98] train-rmse:0.359832+0.019916    test-rmse:2.194178+0.542867 
## Stopping. Best iteration:
## [92] train-rmse:0.390181+0.017309    test-rmse:2.192603+0.539185
## 
## 26 
## [1]  train-rmse:7.040642+0.142340    test-rmse:7.042231+0.635318 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.077864+0.116610    test-rmse:6.128688+0.585199 
## [3]  train-rmse:5.268170+0.096771    test-rmse:5.387365+0.538158 
## [4]  train-rmse:4.598136+0.081755    test-rmse:4.791172+0.497027 
## [5]  train-rmse:4.039333+0.065811    test-rmse:4.307015+0.488508 
## [6]  train-rmse:3.576997+0.056929    test-rmse:3.928501+0.471612 
## [7]  train-rmse:3.192923+0.047491    test-rmse:3.624270+0.467888 
## [8]  train-rmse:2.869447+0.037009    test-rmse:3.378209+0.463700 
## [9]  train-rmse:2.611312+0.034338    test-rmse:3.205346+0.466625 
## [10] train-rmse:2.391351+0.035910    test-rmse:3.057973+0.485098 
## [11] train-rmse:2.203568+0.034288    test-rmse:2.933431+0.501677 
## [12] train-rmse:2.039663+0.036319    test-rmse:2.837911+0.521864 
## [13] train-rmse:1.905090+0.032585    test-rmse:2.771692+0.534376 
## [14] train-rmse:1.795451+0.030084    test-rmse:2.710914+0.542965 
## [15] train-rmse:1.689366+0.046389    test-rmse:2.647371+0.555050 
## [16] train-rmse:1.591682+0.057569    test-rmse:2.604953+0.559025 
## [17] train-rmse:1.509804+0.050556    test-rmse:2.571176+0.554499 
## [18] train-rmse:1.438391+0.063004    test-rmse:2.540808+0.565584 
## [19] train-rmse:1.372464+0.064279    test-rmse:2.519499+0.554695 
## [20] train-rmse:1.322882+0.064973    test-rmse:2.497331+0.554561 
## [21] train-rmse:1.274589+0.064601    test-rmse:2.478388+0.556313 
## [22] train-rmse:1.224083+0.059379    test-rmse:2.469807+0.564952 
## [23] train-rmse:1.187460+0.060765    test-rmse:2.460762+0.564905 
## [24] train-rmse:1.151977+0.059528    test-rmse:2.451336+0.561391 
## [25] train-rmse:1.117656+0.056791    test-rmse:2.437388+0.569852 
## [26] train-rmse:1.074476+0.049425    test-rmse:2.428502+0.571457 
## [27] train-rmse:1.038917+0.046099    test-rmse:2.414759+0.573933 
## [28] train-rmse:1.010463+0.048338    test-rmse:2.413057+0.577741 
## [29] train-rmse:0.984742+0.044581    test-rmse:2.404601+0.585820 
## [30] train-rmse:0.960179+0.044392    test-rmse:2.397739+0.586070 
## [31] train-rmse:0.936899+0.046955    test-rmse:2.389236+0.591785 
## [32] train-rmse:0.903637+0.043451    test-rmse:2.383729+0.590945 
## [33] train-rmse:0.884443+0.046590    test-rmse:2.383463+0.588522 
## [34] train-rmse:0.857609+0.044234    test-rmse:2.386977+0.590607 
## [35] train-rmse:0.830196+0.036023    test-rmse:2.376389+0.583459 
## [36] train-rmse:0.811569+0.035902    test-rmse:2.368859+0.587627 
## [37] train-rmse:0.792761+0.038485    test-rmse:2.364617+0.589537 
## [38] train-rmse:0.767801+0.031520    test-rmse:2.366526+0.585125 
## [39] train-rmse:0.748662+0.026343    test-rmse:2.363834+0.584478 
## [40] train-rmse:0.727486+0.025314    test-rmse:2.356460+0.587122 
## [41] train-rmse:0.707602+0.024199    test-rmse:2.354718+0.585500 
## [42] train-rmse:0.688627+0.025254    test-rmse:2.349927+0.587888 
## [43] train-rmse:0.672556+0.023527    test-rmse:2.343360+0.588991 
## [44] train-rmse:0.660478+0.021856    test-rmse:2.340069+0.589258 
## [45] train-rmse:0.646298+0.021445    test-rmse:2.339565+0.589132 
## [46] train-rmse:0.631466+0.019597    test-rmse:2.337300+0.586921 
## [47] train-rmse:0.617948+0.021752    test-rmse:2.335148+0.588107 
## [48] train-rmse:0.601617+0.021688    test-rmse:2.335311+0.589435 
## [49] train-rmse:0.581548+0.021875    test-rmse:2.333887+0.587242 
## [50] train-rmse:0.570168+0.023766    test-rmse:2.331442+0.586539 
## [51] train-rmse:0.554262+0.019398    test-rmse:2.327827+0.585894 
## [52] train-rmse:0.539857+0.024619    test-rmse:2.323463+0.588056 
## [53] train-rmse:0.527888+0.023675    test-rmse:2.320956+0.586176 
## [54] train-rmse:0.515092+0.018452    test-rmse:2.319438+0.587970 
## [55] train-rmse:0.503298+0.019451    test-rmse:2.319938+0.587406 
## [56] train-rmse:0.488081+0.017400    test-rmse:2.318777+0.587981 
## [57] train-rmse:0.477699+0.019355    test-rmse:2.320321+0.591111 
## [58] train-rmse:0.466345+0.020273    test-rmse:2.318949+0.590576 
## [59] train-rmse:0.457723+0.020996    test-rmse:2.316684+0.591695 
## [60] train-rmse:0.448673+0.018691    test-rmse:2.316078+0.591280 
## [61] train-rmse:0.438580+0.021626    test-rmse:2.314192+0.592470 
## [62] train-rmse:0.429902+0.020687    test-rmse:2.313423+0.592823 
## [63] train-rmse:0.421040+0.020853    test-rmse:2.312360+0.593585 
## [64] train-rmse:0.413102+0.022035    test-rmse:2.314360+0.593923 
## [65] train-rmse:0.403405+0.022554    test-rmse:2.314433+0.594212 
## [66] train-rmse:0.396521+0.022931    test-rmse:2.312433+0.594115 
## [67] train-rmse:0.386793+0.023768    test-rmse:2.313141+0.593306 
## [68] train-rmse:0.381659+0.023061    test-rmse:2.311674+0.594349 
## [69] train-rmse:0.374844+0.022942    test-rmse:2.310813+0.594196 
## [70] train-rmse:0.368388+0.021006    test-rmse:2.309001+0.594347 
## [71] train-rmse:0.361032+0.020745    test-rmse:2.308238+0.595089 
## [72] train-rmse:0.352864+0.021827    test-rmse:2.308578+0.592078 
## [73] train-rmse:0.346953+0.023384    test-rmse:2.307696+0.591868 
## [74] train-rmse:0.340281+0.025061    test-rmse:2.307204+0.591321 
## [75] train-rmse:0.334660+0.025441    test-rmse:2.307711+0.591203 
## [76] train-rmse:0.328279+0.022761    test-rmse:2.307683+0.591512 
## [77] train-rmse:0.321064+0.023820    test-rmse:2.306589+0.589069 
## [78] train-rmse:0.315331+0.023570    test-rmse:2.305630+0.589360 
## [79] train-rmse:0.308059+0.021657    test-rmse:2.305822+0.589453 
## [80] train-rmse:0.302647+0.019397    test-rmse:2.304392+0.591081 
## [81] train-rmse:0.296384+0.020235    test-rmse:2.303783+0.591243 
## [82] train-rmse:0.289326+0.019681    test-rmse:2.302324+0.590233 
## [83] train-rmse:0.284290+0.019782    test-rmse:2.301957+0.589992 
## [84] train-rmse:0.279915+0.019764    test-rmse:2.303166+0.592131 
## [85] train-rmse:0.274215+0.019625    test-rmse:2.303180+0.591410 
## [86] train-rmse:0.270472+0.018278    test-rmse:2.303954+0.591683 
## [87] train-rmse:0.265642+0.019098    test-rmse:2.304032+0.591993 
## [88] train-rmse:0.261214+0.020303    test-rmse:2.303484+0.592156 
## [89] train-rmse:0.256662+0.020165    test-rmse:2.303264+0.593345 
## Stopping. Best iteration:
## [83] train-rmse:0.284290+0.019782    test-rmse:2.301957+0.589992
## 
## 27 
## [1]  train-rmse:7.034015+0.141791    test-rmse:7.038703+0.634939 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.061716+0.113241    test-rmse:6.130397+0.576066 
## [3]  train-rmse:5.242670+0.092471    test-rmse:5.366739+0.545563 
## [4]  train-rmse:4.557776+0.072914    test-rmse:4.758155+0.503809 
## [5]  train-rmse:3.985135+0.059884    test-rmse:4.275058+0.477956 
## [6]  train-rmse:3.511777+0.052689    test-rmse:3.886045+0.470013 
## [7]  train-rmse:3.116612+0.047024    test-rmse:3.591654+0.461325 
## [8]  train-rmse:2.786628+0.039361    test-rmse:3.346741+0.462125 
## [9]  train-rmse:2.505884+0.034252    test-rmse:3.173404+0.462703 
## [10] train-rmse:2.278792+0.028393    test-rmse:3.037062+0.468362 
## [11] train-rmse:2.087524+0.025347    test-rmse:2.925869+0.492161 
## [12] train-rmse:1.918984+0.027908    test-rmse:2.809087+0.505899 
## [13] train-rmse:1.778623+0.026528    test-rmse:2.728832+0.508324 
## [14] train-rmse:1.660383+0.026674    test-rmse:2.652740+0.521430 
## [15] train-rmse:1.554598+0.020770    test-rmse:2.601143+0.532048 
## [16] train-rmse:1.456308+0.024156    test-rmse:2.553336+0.528249 
## [17] train-rmse:1.366589+0.015877    test-rmse:2.523990+0.530041 
## [18] train-rmse:1.290824+0.020052    test-rmse:2.500889+0.547622 
## [19] train-rmse:1.237438+0.018798    test-rmse:2.479496+0.554532 
## [20] train-rmse:1.173223+0.027131    test-rmse:2.457420+0.557167 
## [21] train-rmse:1.121646+0.025836    test-rmse:2.436218+0.558053 
## [22] train-rmse:1.076045+0.031612    test-rmse:2.424925+0.565687 
## [23] train-rmse:1.031057+0.037671    test-rmse:2.412832+0.560910 
## [24] train-rmse:0.985872+0.021662    test-rmse:2.408791+0.569599 
## [25] train-rmse:0.938449+0.026104    test-rmse:2.399609+0.569636 
## [26] train-rmse:0.901290+0.023716    test-rmse:2.389415+0.572273 
## [27] train-rmse:0.875605+0.025047    test-rmse:2.379249+0.568156 
## [28] train-rmse:0.847380+0.024728    test-rmse:2.371645+0.573602 
## [29] train-rmse:0.819045+0.027581    test-rmse:2.362541+0.570290 
## [30] train-rmse:0.793295+0.027569    test-rmse:2.354113+0.573339 
## [31] train-rmse:0.760135+0.022500    test-rmse:2.352129+0.578994 
## [32] train-rmse:0.732240+0.026720    test-rmse:2.350768+0.582007 
## [33] train-rmse:0.706988+0.031573    test-rmse:2.343329+0.581759 
## [34] train-rmse:0.682276+0.024115    test-rmse:2.340110+0.583324 
## [35] train-rmse:0.663674+0.023400    test-rmse:2.336777+0.585364 
## [36] train-rmse:0.642104+0.022691    test-rmse:2.330974+0.587188 
## [37] train-rmse:0.619581+0.013733    test-rmse:2.329552+0.587746 
## [38] train-rmse:0.599173+0.013260    test-rmse:2.330029+0.589930 
## [39] train-rmse:0.579921+0.011441    test-rmse:2.325508+0.590494 
## [40] train-rmse:0.561177+0.011039    test-rmse:2.320260+0.591660 
## [41] train-rmse:0.544026+0.015182    test-rmse:2.319368+0.593653 
## [42] train-rmse:0.528332+0.014287    test-rmse:2.316788+0.592180 
## [43] train-rmse:0.510220+0.014629    test-rmse:2.317619+0.591377 
## [44] train-rmse:0.496397+0.015722    test-rmse:2.313248+0.593227 
## [45] train-rmse:0.482243+0.015208    test-rmse:2.310199+0.592470 
## [46] train-rmse:0.466195+0.018448    test-rmse:2.310461+0.589580 
## [47] train-rmse:0.450046+0.017446    test-rmse:2.306969+0.589198 
## [48] train-rmse:0.435342+0.015837    test-rmse:2.307999+0.590212 
## [49] train-rmse:0.420938+0.018074    test-rmse:2.307694+0.591182 
## [50] train-rmse:0.408179+0.016576    test-rmse:2.307452+0.591161 
## [51] train-rmse:0.397102+0.013631    test-rmse:2.307012+0.591674 
## [52] train-rmse:0.385339+0.012031    test-rmse:2.307087+0.590471 
## [53] train-rmse:0.374387+0.013715    test-rmse:2.305509+0.590756 
## [54] train-rmse:0.364492+0.015838    test-rmse:2.303975+0.590235 
## [55] train-rmse:0.351291+0.014280    test-rmse:2.300861+0.590493 
## [56] train-rmse:0.342574+0.015457    test-rmse:2.300090+0.591239 
## [57] train-rmse:0.334068+0.013729    test-rmse:2.300013+0.590234 
## [58] train-rmse:0.324441+0.013346    test-rmse:2.300079+0.590506 
## [59] train-rmse:0.316527+0.013573    test-rmse:2.298868+0.591116 
## [60] train-rmse:0.308843+0.013144    test-rmse:2.297892+0.591519 
## [61] train-rmse:0.299111+0.011235    test-rmse:2.298674+0.592205 
## [62] train-rmse:0.292144+0.011475    test-rmse:2.297576+0.591806 
## [63] train-rmse:0.284145+0.011025    test-rmse:2.298261+0.592051 
## [64] train-rmse:0.274745+0.009330    test-rmse:2.297220+0.592347 
## [65] train-rmse:0.269390+0.007481    test-rmse:2.296531+0.592569 
## [66] train-rmse:0.261655+0.009420    test-rmse:2.294990+0.594207 
## [67] train-rmse:0.254691+0.009465    test-rmse:2.294532+0.593459 
## [68] train-rmse:0.248241+0.008925    test-rmse:2.294263+0.592846 
## [69] train-rmse:0.242084+0.008806    test-rmse:2.294359+0.593568 
## [70] train-rmse:0.236361+0.009182    test-rmse:2.294308+0.592688 
## [71] train-rmse:0.230315+0.009015    test-rmse:2.293860+0.593070 
## [72] train-rmse:0.222209+0.006741    test-rmse:2.293165+0.592884 
## [73] train-rmse:0.216726+0.006957    test-rmse:2.292747+0.592822 
## [74] train-rmse:0.209800+0.004950    test-rmse:2.293803+0.592927 
## [75] train-rmse:0.204324+0.003989    test-rmse:2.294860+0.593266 
## [76] train-rmse:0.198245+0.004100    test-rmse:2.295353+0.593918 
## [77] train-rmse:0.194106+0.003875    test-rmse:2.294920+0.593554 
## [78] train-rmse:0.189834+0.005473    test-rmse:2.294129+0.593545 
## [79] train-rmse:0.185958+0.004932    test-rmse:2.294051+0.594435 
## Stopping. Best iteration:
## [73] train-rmse:0.216726+0.006957    test-rmse:2.292747+0.592822
## 
## 28 
## [1]  train-rmse:7.133930+0.153405    test-rmse:7.122205+0.669932 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.307565+0.134648    test-rmse:6.330760+0.700947 
## [3]  train-rmse:5.666044+0.123332    test-rmse:5.725269+0.669224 
## [4]  train-rmse:5.167706+0.118752    test-rmse:5.201904+0.634073 
## [5]  train-rmse:4.788475+0.112875    test-rmse:4.869480+0.627714 
## [6]  train-rmse:4.497036+0.109900    test-rmse:4.585152+0.605757 
## [7]  train-rmse:4.270124+0.110954    test-rmse:4.370555+0.547813 
## [8]  train-rmse:4.092416+0.112631    test-rmse:4.194915+0.520971 
## [9]  train-rmse:3.953541+0.111815    test-rmse:4.091990+0.505242 
## [10] train-rmse:3.840487+0.112515    test-rmse:3.952154+0.493788 
## [11] train-rmse:3.746542+0.115042    test-rmse:3.858419+0.445776 
## [12] train-rmse:3.665046+0.118479    test-rmse:3.784529+0.427064 
## [13] train-rmse:3.594833+0.117868    test-rmse:3.740703+0.450759 
## [14] train-rmse:3.534673+0.118002    test-rmse:3.686961+0.441000 
## [15] train-rmse:3.479740+0.118635    test-rmse:3.603612+0.427360 
## [16] train-rmse:3.429188+0.120476    test-rmse:3.561683+0.417778 
## [17] train-rmse:3.381415+0.122482    test-rmse:3.512160+0.407188 
## [18] train-rmse:3.337651+0.121702    test-rmse:3.486370+0.432013 
## [19] train-rmse:3.298400+0.121455    test-rmse:3.449130+0.420608 
## [20] train-rmse:3.260639+0.122295    test-rmse:3.394738+0.423982 
## [21] train-rmse:3.224529+0.123685    test-rmse:3.352954+0.421974 
## [22] train-rmse:3.190110+0.125218    test-rmse:3.323837+0.402382 
## [23] train-rmse:3.156781+0.125707    test-rmse:3.298005+0.418976 
## [24] train-rmse:3.126803+0.125573    test-rmse:3.283151+0.435361 
## [25] train-rmse:3.098305+0.125946    test-rmse:3.256855+0.439652 
## [26] train-rmse:3.070759+0.126810    test-rmse:3.225475+0.429508 
## [27] train-rmse:3.044850+0.128029    test-rmse:3.186185+0.417122 
## [28] train-rmse:3.019376+0.129434    test-rmse:3.171639+0.414741 
## [29] train-rmse:2.995484+0.129375    test-rmse:3.153551+0.426623 
## [30] train-rmse:2.972464+0.129236    test-rmse:3.142510+0.427409 
## [31] train-rmse:2.950310+0.129215    test-rmse:3.123822+0.431412 
## [32] train-rmse:2.928777+0.129808    test-rmse:3.095307+0.424942 
## [33] train-rmse:2.907859+0.130616    test-rmse:3.082637+0.424388 
## [34] train-rmse:2.887255+0.131517    test-rmse:3.064413+0.442844 
## [35] train-rmse:2.867793+0.132725    test-rmse:3.047655+0.441326 
## [36] train-rmse:2.848872+0.133559    test-rmse:3.042787+0.443252 
## [37] train-rmse:2.830568+0.134021    test-rmse:3.027384+0.440017 
## [38] train-rmse:2.812888+0.134218    test-rmse:2.996967+0.437954 
## [39] train-rmse:2.795862+0.134627    test-rmse:2.986461+0.445028 
## [40] train-rmse:2.778974+0.134885    test-rmse:2.969696+0.452250 
## [41] train-rmse:2.762470+0.135604    test-rmse:2.960391+0.454599 
## [42] train-rmse:2.746313+0.136381    test-rmse:2.951071+0.457579 
## [43] train-rmse:2.730659+0.136825    test-rmse:2.943817+0.460632 
## [44] train-rmse:2.715398+0.137320    test-rmse:2.932593+0.453475 
## [45] train-rmse:2.700382+0.137749    test-rmse:2.904043+0.450021 
## [46] train-rmse:2.685887+0.138068    test-rmse:2.889312+0.451339 
## [47] train-rmse:2.671811+0.138410    test-rmse:2.881938+0.453108 
## [48] train-rmse:2.658202+0.138565    test-rmse:2.875879+0.457849 
## [49] train-rmse:2.645037+0.138660    test-rmse:2.879412+0.472088 
## [50] train-rmse:2.632147+0.138665    test-rmse:2.865973+0.468852 
## [51] train-rmse:2.619458+0.138526    test-rmse:2.838947+0.468731 
## [52] train-rmse:2.606772+0.138611    test-rmse:2.825546+0.476843 
## [53] train-rmse:2.594260+0.138667    test-rmse:2.818010+0.475968 
## [54] train-rmse:2.582018+0.138868    test-rmse:2.814758+0.473158 
## [55] train-rmse:2.569993+0.139060    test-rmse:2.807458+0.474026 
## [56] train-rmse:2.558412+0.139190    test-rmse:2.801151+0.478703 
## [57] train-rmse:2.547217+0.139406    test-rmse:2.788598+0.479707 
## [58] train-rmse:2.536405+0.139589    test-rmse:2.779332+0.490520 
## [59] train-rmse:2.525821+0.139688    test-rmse:2.772556+0.492751 
## [60] train-rmse:2.515383+0.139653    test-rmse:2.761183+0.479646 
## [61] train-rmse:2.505022+0.139734    test-rmse:2.747084+0.476707 
## [62] train-rmse:2.494919+0.139807    test-rmse:2.746366+0.482679 
## [63] train-rmse:2.485167+0.139812    test-rmse:2.739013+0.482184 
## [64] train-rmse:2.475657+0.139750    test-rmse:2.732098+0.486115 
## [65] train-rmse:2.466414+0.139772    test-rmse:2.722988+0.489579 
## [66] train-rmse:2.457234+0.139864    test-rmse:2.713598+0.488874 
## [67] train-rmse:2.448176+0.139972    test-rmse:2.698643+0.495391 
## [68] train-rmse:2.439137+0.140176    test-rmse:2.689882+0.493609 
## [69] train-rmse:2.430389+0.140150    test-rmse:2.679300+0.484794 
## [70] train-rmse:2.421810+0.140175    test-rmse:2.676982+0.485474 
## [71] train-rmse:2.413366+0.140246    test-rmse:2.667163+0.483032 
## [72] train-rmse:2.405282+0.140460    test-rmse:2.665348+0.482771 
## [73] train-rmse:2.397350+0.140707    test-rmse:2.655114+0.482021 
## [74] train-rmse:2.389550+0.140887    test-rmse:2.646922+0.493777 
## [75] train-rmse:2.381985+0.140928    test-rmse:2.642512+0.494120 
## [76] train-rmse:2.374394+0.140975    test-rmse:2.650725+0.500460 
## [77] train-rmse:2.367162+0.140988    test-rmse:2.645641+0.506701 
## [78] train-rmse:2.360121+0.141160    test-rmse:2.639917+0.504078 
## [79] train-rmse:2.353141+0.141275    test-rmse:2.638216+0.502630 
## [80] train-rmse:2.346331+0.141465    test-rmse:2.626656+0.496806 
## [81] train-rmse:2.339589+0.141664    test-rmse:2.615763+0.496587 
## [82] train-rmse:2.332899+0.141870    test-rmse:2.605770+0.498551 
## [83] train-rmse:2.326328+0.142132    test-rmse:2.597497+0.500188 
## [84] train-rmse:2.319808+0.142382    test-rmse:2.592340+0.500119 
## [85] train-rmse:2.313480+0.142503    test-rmse:2.586707+0.495801 
## [86] train-rmse:2.307220+0.142723    test-rmse:2.580521+0.498993 
## [87] train-rmse:2.301054+0.142813    test-rmse:2.578566+0.498350 
## [88] train-rmse:2.294998+0.142939    test-rmse:2.571757+0.496948 
## [89] train-rmse:2.289056+0.143148    test-rmse:2.567042+0.493072 
## [90] train-rmse:2.283175+0.143415    test-rmse:2.558376+0.495234 
## [91] train-rmse:2.277472+0.143598    test-rmse:2.556618+0.502001 
## [92] train-rmse:2.271891+0.143753    test-rmse:2.553474+0.501822 
## [93] train-rmse:2.266393+0.143815    test-rmse:2.553906+0.515438 
## [94] train-rmse:2.260970+0.143922    test-rmse:2.549712+0.508285 
## [95] train-rmse:2.255665+0.144180    test-rmse:2.545852+0.507472 
## [96] train-rmse:2.250586+0.144330    test-rmse:2.541020+0.509952 
## [97] train-rmse:2.245583+0.144417    test-rmse:2.536007+0.510834 
## [98] train-rmse:2.240749+0.144567    test-rmse:2.529027+0.511265 
## [99] train-rmse:2.235890+0.144735    test-rmse:2.521479+0.514278 
## [100]    train-rmse:2.231050+0.144924    test-rmse:2.519717+0.511382 
## [101]    train-rmse:2.226143+0.145006    test-rmse:2.520811+0.513714 
## [102]    train-rmse:2.221488+0.145218    test-rmse:2.511847+0.515614 
## [103]    train-rmse:2.216903+0.145391    test-rmse:2.511108+0.516416 
## [104]    train-rmse:2.212405+0.145533    test-rmse:2.502366+0.512167 
## [105]    train-rmse:2.208004+0.145730    test-rmse:2.497382+0.514389 
## [106]    train-rmse:2.203664+0.145923    test-rmse:2.494012+0.512813 
## [107]    train-rmse:2.199336+0.146037    test-rmse:2.489012+0.514080 
## [108]    train-rmse:2.195075+0.146195    test-rmse:2.486050+0.517840 
## [109]    train-rmse:2.190947+0.146310    test-rmse:2.485084+0.521196 
## [110]    train-rmse:2.186875+0.146439    test-rmse:2.483984+0.525317 
## [111]    train-rmse:2.182824+0.146548    test-rmse:2.477796+0.521998 
## [112]    train-rmse:2.178815+0.146744    test-rmse:2.471385+0.525795 
## [113]    train-rmse:2.174869+0.146937    test-rmse:2.468573+0.521683 
## [114]    train-rmse:2.170991+0.147115    test-rmse:2.468454+0.528630 
## [115]    train-rmse:2.167177+0.147282    test-rmse:2.468231+0.526665 
## [116]    train-rmse:2.163353+0.147380    test-rmse:2.471162+0.525841 
## [117]    train-rmse:2.159593+0.147467    test-rmse:2.473244+0.522228 
## [118]    train-rmse:2.155893+0.147576    test-rmse:2.469017+0.523810 
## [119]    train-rmse:2.152284+0.147663    test-rmse:2.468745+0.524821 
## [120]    train-rmse:2.148772+0.147806    test-rmse:2.465204+0.524445 
## [121]    train-rmse:2.145299+0.147942    test-rmse:2.458045+0.525767 
## [122]    train-rmse:2.141933+0.148062    test-rmse:2.456611+0.524789 
## [123]    train-rmse:2.138581+0.148183    test-rmse:2.455735+0.526400 
## [124]    train-rmse:2.135280+0.148278    test-rmse:2.444937+0.524081 
## [125]    train-rmse:2.131990+0.148368    test-rmse:2.441349+0.527578 
## [126]    train-rmse:2.128792+0.148556    test-rmse:2.440240+0.526874 
## [127]    train-rmse:2.125631+0.148722    test-rmse:2.437098+0.523927 
## [128]    train-rmse:2.122483+0.148887    test-rmse:2.437387+0.526811 
## [129]    train-rmse:2.119396+0.149084    test-rmse:2.433453+0.525432 
## [130]    train-rmse:2.116395+0.149220    test-rmse:2.430429+0.528636 
## [131]    train-rmse:2.113390+0.149386    test-rmse:2.428461+0.527890 
## [132]    train-rmse:2.110438+0.149553    test-rmse:2.427594+0.527095 
## [133]    train-rmse:2.107372+0.149728    test-rmse:2.423021+0.524856 
## [134]    train-rmse:2.104504+0.149887    test-rmse:2.421757+0.523571 
## [135]    train-rmse:2.101679+0.150032    test-rmse:2.420652+0.523539 
## [136]    train-rmse:2.098897+0.150197    test-rmse:2.419336+0.524271 
## [137]    train-rmse:2.096080+0.150364    test-rmse:2.420270+0.522980 
## [138]    train-rmse:2.093346+0.150495    test-rmse:2.418289+0.522232 
## [139]    train-rmse:2.090642+0.150626    test-rmse:2.419074+0.523441 
## [140]    train-rmse:2.087935+0.150760    test-rmse:2.417612+0.522782 
## [141]    train-rmse:2.085284+0.150857    test-rmse:2.413014+0.522847 
## [142]    train-rmse:2.082617+0.150968    test-rmse:2.414321+0.528774 
## [143]    train-rmse:2.080032+0.151090    test-rmse:2.409479+0.528871 
## [144]    train-rmse:2.077467+0.151219    test-rmse:2.407400+0.533245 
## [145]    train-rmse:2.074965+0.151380    test-rmse:2.407065+0.530620 
## [146]    train-rmse:2.072496+0.151508    test-rmse:2.405757+0.532073 
## [147]    train-rmse:2.070034+0.151635    test-rmse:2.407050+0.529360 
## [148]    train-rmse:2.067600+0.151771    test-rmse:2.404096+0.530708 
## [149]    train-rmse:2.065177+0.151895    test-rmse:2.401248+0.528864 
## [150]    train-rmse:2.062816+0.152025    test-rmse:2.399349+0.526956 
## [151]    train-rmse:2.060483+0.152091    test-rmse:2.398262+0.524101 
## [152]    train-rmse:2.058148+0.152172    test-rmse:2.393151+0.525707 
## [153]    train-rmse:2.055833+0.152313    test-rmse:2.390090+0.524095 
## [154]    train-rmse:2.053517+0.152384    test-rmse:2.389617+0.523073 
## [155]    train-rmse:2.051304+0.152497    test-rmse:2.390136+0.525321 
## [156]    train-rmse:2.049069+0.152630    test-rmse:2.387902+0.525666 
## [157]    train-rmse:2.046844+0.152755    test-rmse:2.385483+0.525949 
## [158]    train-rmse:2.044612+0.152889    test-rmse:2.384957+0.523408 
## [159]    train-rmse:2.042452+0.153004    test-rmse:2.383058+0.520714 
## [160]    train-rmse:2.040307+0.153080    test-rmse:2.385045+0.521998 
## [161]    train-rmse:2.038175+0.153156    test-rmse:2.381733+0.522931 
## [162]    train-rmse:2.036105+0.153234    test-rmse:2.382134+0.525800 
## [163]    train-rmse:2.034043+0.153323    test-rmse:2.380760+0.526743 
## [164]    train-rmse:2.032005+0.153406    test-rmse:2.381357+0.526480 
## [165]    train-rmse:2.029976+0.153509    test-rmse:2.383380+0.531916 
## [166]    train-rmse:2.027944+0.153603    test-rmse:2.385565+0.532304 
## [167]    train-rmse:2.025945+0.153677    test-rmse:2.383592+0.532752 
## [168]    train-rmse:2.023945+0.153744    test-rmse:2.379103+0.534096 
## [169]    train-rmse:2.021978+0.153822    test-rmse:2.378687+0.531469 
## [170]    train-rmse:2.020017+0.153888    test-rmse:2.378087+0.530279 
## [171]    train-rmse:2.018093+0.154032    test-rmse:2.376758+0.530474 
## [172]    train-rmse:2.016160+0.154158    test-rmse:2.373004+0.529311 
## [173]    train-rmse:2.014221+0.154249    test-rmse:2.371262+0.530037 
## [174]    train-rmse:2.012342+0.154347    test-rmse:2.371735+0.528084 
## [175]    train-rmse:2.010497+0.154449    test-rmse:2.370382+0.527482 
## [176]    train-rmse:2.008643+0.154547    test-rmse:2.369624+0.526917 
## [177]    train-rmse:2.006844+0.154628    test-rmse:2.370076+0.527143 
## [178]    train-rmse:2.004997+0.154657    test-rmse:2.370820+0.525886 
## [179]    train-rmse:2.003221+0.154736    test-rmse:2.368941+0.527500 
## [180]    train-rmse:2.001450+0.154801    test-rmse:2.366986+0.527938 
## [181]    train-rmse:1.999686+0.154840    test-rmse:2.365940+0.530054 
## [182]    train-rmse:1.997946+0.154885    test-rmse:2.363460+0.532203 
## [183]    train-rmse:1.996235+0.154985    test-rmse:2.359923+0.531827 
## [184]    train-rmse:1.994542+0.155063    test-rmse:2.359089+0.531248 
## [185]    train-rmse:1.992863+0.155154    test-rmse:2.355420+0.532752 
## [186]    train-rmse:1.991205+0.155248    test-rmse:2.357090+0.532648 
## [187]    train-rmse:1.989564+0.155342    test-rmse:2.354423+0.531462 
## [188]    train-rmse:1.987870+0.155537    test-rmse:2.354242+0.532717 
## [189]    train-rmse:1.986206+0.155662    test-rmse:2.354100+0.531401 
## [190]    train-rmse:1.984538+0.155739    test-rmse:2.354507+0.532542 
## [191]    train-rmse:1.982907+0.155881    test-rmse:2.354038+0.531698 
## [192]    train-rmse:1.981304+0.155979    test-rmse:2.354300+0.530835 
## [193]    train-rmse:1.979686+0.156082    test-rmse:2.354886+0.530328 
## [194]    train-rmse:1.978101+0.156189    test-rmse:2.353718+0.530652 
## [195]    train-rmse:1.976530+0.156283    test-rmse:2.351158+0.532352 
## [196]    train-rmse:1.974992+0.156351    test-rmse:2.349230+0.531587 
## [197]    train-rmse:1.973449+0.156438    test-rmse:2.350651+0.531386 
## [198]    train-rmse:1.971909+0.156537    test-rmse:2.348514+0.531683 
## [199]    train-rmse:1.970343+0.156635    test-rmse:2.346917+0.534129 
## [200]    train-rmse:1.968829+0.156725    test-rmse:2.343846+0.533198 
## [201]    train-rmse:1.967298+0.156825    test-rmse:2.343096+0.534493 
## [202]    train-rmse:1.965787+0.156955    test-rmse:2.342374+0.534613 
## [203]    train-rmse:1.964279+0.157044    test-rmse:2.339756+0.534850 
## [204]    train-rmse:1.962802+0.157151    test-rmse:2.340096+0.532569 
## [205]    train-rmse:1.961330+0.157207    test-rmse:2.338490+0.534918 
## [206]    train-rmse:1.959825+0.157265    test-rmse:2.340562+0.539086 
## [207]    train-rmse:1.958366+0.157316    test-rmse:2.344285+0.539525 
## [208]    train-rmse:1.956932+0.157387    test-rmse:2.344399+0.538720 
## [209]    train-rmse:1.955496+0.157460    test-rmse:2.342357+0.537808 
## [210]    train-rmse:1.954054+0.157561    test-rmse:2.341481+0.537243 
## [211]    train-rmse:1.952648+0.157643    test-rmse:2.337873+0.535894 
## [212]    train-rmse:1.951215+0.157816    test-rmse:2.341618+0.536435 
## [213]    train-rmse:1.949823+0.157908    test-rmse:2.343074+0.535164 
## [214]    train-rmse:1.948458+0.158001    test-rmse:2.339283+0.536382 
## [215]    train-rmse:1.947104+0.158098    test-rmse:2.336724+0.537257 
## [216]    train-rmse:1.945769+0.158185    test-rmse:2.336215+0.538943 
## [217]    train-rmse:1.944388+0.158309    test-rmse:2.334559+0.536793 
## [218]    train-rmse:1.942986+0.158348    test-rmse:2.336147+0.539861 
## [219]    train-rmse:1.941659+0.158406    test-rmse:2.335108+0.537768 
## [220]    train-rmse:1.940326+0.158470    test-rmse:2.333186+0.536714 
## [221]    train-rmse:1.938963+0.158580    test-rmse:2.333496+0.536982 
## [222]    train-rmse:1.937697+0.158650    test-rmse:2.332213+0.536421 
## [223]    train-rmse:1.936343+0.158675    test-rmse:2.329267+0.538664 
## [224]    train-rmse:1.935084+0.158748    test-rmse:2.328698+0.537848 
## [225]    train-rmse:1.933785+0.158822    test-rmse:2.329075+0.536963 
## [226]    train-rmse:1.932532+0.158899    test-rmse:2.327509+0.537200 
## [227]    train-rmse:1.931292+0.158991    test-rmse:2.325932+0.538889 
## [228]    train-rmse:1.930050+0.159074    test-rmse:2.323339+0.537815 
## [229]    train-rmse:1.928770+0.159145    test-rmse:2.323783+0.537248 
## [230]    train-rmse:1.927534+0.159242    test-rmse:2.324479+0.536810 
## [231]    train-rmse:1.926309+0.159337    test-rmse:2.324159+0.535975 
## [232]    train-rmse:1.925086+0.159421    test-rmse:2.321759+0.536479 
## [233]    train-rmse:1.923830+0.159475    test-rmse:2.320959+0.536968 
## [234]    train-rmse:1.922627+0.159553    test-rmse:2.321020+0.535223 
## [235]    train-rmse:1.921412+0.159645    test-rmse:2.323192+0.535380 
## [236]    train-rmse:1.920221+0.159732    test-rmse:2.320985+0.533813 
## [237]    train-rmse:1.918989+0.159853    test-rmse:2.317402+0.536645 
## [238]    train-rmse:1.917784+0.159944    test-rmse:2.317270+0.534379 
## [239]    train-rmse:1.916591+0.159998    test-rmse:2.321314+0.537461 
## [240]    train-rmse:1.915391+0.160039    test-rmse:2.320560+0.535391 
## [241]    train-rmse:1.914245+0.160107    test-rmse:2.321958+0.539146 
## [242]    train-rmse:1.913059+0.160163    test-rmse:2.319559+0.540857 
## [243]    train-rmse:1.911879+0.160218    test-rmse:2.320556+0.541294 
## [244]    train-rmse:1.910734+0.160275    test-rmse:2.320558+0.541065 
## Stopping. Best iteration:
## [238]    train-rmse:1.917784+0.159944    test-rmse:2.317270+0.534379
## 
## 29 
## [1]  train-rmse:7.054984+0.147494    test-rmse:7.066349+0.643934 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.152403+0.134059    test-rmse:6.191756+0.613155 
## [3]  train-rmse:5.439161+0.122675    test-rmse:5.540505+0.607670 
## [4]  train-rmse:4.869488+0.108595    test-rmse:5.019526+0.545521 
## [5]  train-rmse:4.411689+0.099955    test-rmse:4.539756+0.494193 
## [6]  train-rmse:4.043589+0.099714    test-rmse:4.194044+0.474193 
## [7]  train-rmse:3.752366+0.100757    test-rmse:3.944945+0.442297 
## [8]  train-rmse:3.509086+0.093285    test-rmse:3.719139+0.442265 
## [9]  train-rmse:3.312676+0.093989    test-rmse:3.531100+0.418105 
## [10] train-rmse:3.155002+0.092467    test-rmse:3.409589+0.432059 
## [11] train-rmse:3.019426+0.094902    test-rmse:3.270727+0.445003 
## [12] train-rmse:2.895757+0.086306    test-rmse:3.173362+0.441083 
## [13] train-rmse:2.815626+0.087734    test-rmse:3.145801+0.448676 
## [14] train-rmse:2.739443+0.087778    test-rmse:3.078044+0.461969 
## [15] train-rmse:2.671244+0.090193    test-rmse:3.028112+0.457379 
## [16] train-rmse:2.595706+0.082681    test-rmse:2.976490+0.466522 
## [17] train-rmse:2.542061+0.081249    test-rmse:2.946379+0.480789 
## [18] train-rmse:2.491883+0.082722    test-rmse:2.916326+0.472912 
## [19] train-rmse:2.448608+0.082612    test-rmse:2.895544+0.471869 
## [20] train-rmse:2.401574+0.090036    test-rmse:2.863302+0.488851 
## [21] train-rmse:2.361256+0.091552    test-rmse:2.848406+0.487721 
## [22] train-rmse:2.324018+0.093487    test-rmse:2.822494+0.490745 
## [23] train-rmse:2.284393+0.091640    test-rmse:2.795218+0.492209 
## [24] train-rmse:2.248290+0.081036    test-rmse:2.777591+0.503124 
## [25] train-rmse:2.212695+0.084396    test-rmse:2.758152+0.511959 
## [26] train-rmse:2.180847+0.086656    test-rmse:2.738927+0.513121 
## [27] train-rmse:2.146849+0.091412    test-rmse:2.698029+0.504723 
## [28] train-rmse:2.120642+0.091201    test-rmse:2.678693+0.503070 
## [29] train-rmse:2.089028+0.090437    test-rmse:2.669030+0.498966 
## [30] train-rmse:2.059700+0.088977    test-rmse:2.644305+0.501124 
## [31] train-rmse:2.032792+0.081180    test-rmse:2.626072+0.509160 
## [32] train-rmse:2.009254+0.082546    test-rmse:2.615460+0.504615 
## [33] train-rmse:1.979508+0.081016    test-rmse:2.603192+0.505379 
## [34] train-rmse:1.957930+0.080240    test-rmse:2.597071+0.519656 
## [35] train-rmse:1.935168+0.074708    test-rmse:2.577942+0.524926 
## [36] train-rmse:1.914210+0.074317    test-rmse:2.555652+0.511130 
## [37] train-rmse:1.887615+0.071132    test-rmse:2.528548+0.519809 
## [38] train-rmse:1.866632+0.072896    test-rmse:2.522914+0.521060 
## [39] train-rmse:1.850233+0.072951    test-rmse:2.512903+0.518176 
## [40] train-rmse:1.832887+0.073691    test-rmse:2.504083+0.529130 
## [41] train-rmse:1.814091+0.074037    test-rmse:2.491862+0.526791 
## [42] train-rmse:1.794716+0.075290    test-rmse:2.484539+0.528708 
## [43] train-rmse:1.771985+0.076552    test-rmse:2.470285+0.519287 
## [44] train-rmse:1.756963+0.077490    test-rmse:2.455633+0.519965 
## [45] train-rmse:1.742249+0.078328    test-rmse:2.443613+0.520839 
## [46] train-rmse:1.724994+0.079459    test-rmse:2.434475+0.522812 
## [47] train-rmse:1.711856+0.080533    test-rmse:2.422038+0.522148 
## [48] train-rmse:1.697303+0.079850    test-rmse:2.420602+0.524146 
## [49] train-rmse:1.684400+0.080316    test-rmse:2.415046+0.523583 
## [50] train-rmse:1.666625+0.070704    test-rmse:2.397970+0.524152 
## [51] train-rmse:1.654235+0.069708    test-rmse:2.394338+0.523018 
## [52] train-rmse:1.640883+0.070019    test-rmse:2.386967+0.520299 
## [53] train-rmse:1.624695+0.064117    test-rmse:2.377742+0.531555 
## [54] train-rmse:1.611876+0.061475    test-rmse:2.377495+0.529240 
## [55] train-rmse:1.602208+0.061376    test-rmse:2.372907+0.527997 
## [56] train-rmse:1.587555+0.061266    test-rmse:2.359332+0.534664 
## [57] train-rmse:1.574382+0.060820    test-rmse:2.348253+0.527545 
## [58] train-rmse:1.562514+0.060419    test-rmse:2.343529+0.526713 
## [59] train-rmse:1.551520+0.058976    test-rmse:2.340126+0.524744 
## [60] train-rmse:1.541796+0.058288    test-rmse:2.335086+0.529302 
## [61] train-rmse:1.529789+0.059242    test-rmse:2.322820+0.534597 
## [62] train-rmse:1.520934+0.058949    test-rmse:2.318604+0.536864 
## [63] train-rmse:1.512634+0.059306    test-rmse:2.314635+0.535446 
## [64] train-rmse:1.504181+0.058990    test-rmse:2.312965+0.539408 
## [65] train-rmse:1.495708+0.060279    test-rmse:2.313300+0.537428 
## [66] train-rmse:1.486374+0.059336    test-rmse:2.303395+0.537986 
## [67] train-rmse:1.473878+0.056986    test-rmse:2.295360+0.538069 
## [68] train-rmse:1.464534+0.058128    test-rmse:2.288538+0.537339 
## [69] train-rmse:1.456862+0.058073    test-rmse:2.286406+0.534053 
## [70] train-rmse:1.445278+0.056011    test-rmse:2.282973+0.533760 
## [71] train-rmse:1.437505+0.054634    test-rmse:2.277995+0.532528 
## [72] train-rmse:1.430261+0.054707    test-rmse:2.275341+0.531027 
## [73] train-rmse:1.422484+0.054924    test-rmse:2.269355+0.528697 
## [74] train-rmse:1.411128+0.054822    test-rmse:2.270959+0.536509 
## [75] train-rmse:1.404454+0.054627    test-rmse:2.267833+0.539633 
## [76] train-rmse:1.396513+0.058035    test-rmse:2.261975+0.534220 
## [77] train-rmse:1.383224+0.051165    test-rmse:2.257643+0.534881 
## [78] train-rmse:1.376927+0.050812    test-rmse:2.253853+0.535496 
## [79] train-rmse:1.370905+0.050705    test-rmse:2.253234+0.536897 
## [80] train-rmse:1.363676+0.049483    test-rmse:2.249855+0.537631 
## [81] train-rmse:1.354601+0.049608    test-rmse:2.247148+0.537679 
## [82] train-rmse:1.347244+0.048653    test-rmse:2.240336+0.539654 
## [83] train-rmse:1.336698+0.047077    test-rmse:2.236745+0.538218 
## [84] train-rmse:1.331013+0.046619    test-rmse:2.230321+0.541379 
## [85] train-rmse:1.323220+0.048069    test-rmse:2.231682+0.540829 
## [86] train-rmse:1.317831+0.048446    test-rmse:2.223790+0.537896 
## [87] train-rmse:1.312603+0.049071    test-rmse:2.222060+0.535069 
## [88] train-rmse:1.307389+0.049455    test-rmse:2.220350+0.537473 
## [89] train-rmse:1.302072+0.049049    test-rmse:2.220722+0.540073 
## [90] train-rmse:1.292485+0.048321    test-rmse:2.217400+0.539940 
## [91] train-rmse:1.284059+0.048407    test-rmse:2.211922+0.543486 
## [92] train-rmse:1.279650+0.048516    test-rmse:2.209047+0.545410 
## [93] train-rmse:1.273030+0.048713    test-rmse:2.206583+0.542978 
## [94] train-rmse:1.268651+0.048737    test-rmse:2.207301+0.542057 
## [95] train-rmse:1.262918+0.049406    test-rmse:2.205550+0.542147 
## [96] train-rmse:1.256301+0.048118    test-rmse:2.198641+0.543472 
## [97] train-rmse:1.251374+0.047154    test-rmse:2.198402+0.545307 
## [98] train-rmse:1.243102+0.050697    test-rmse:2.198519+0.546042 
## [99] train-rmse:1.238391+0.050246    test-rmse:2.194647+0.542279 
## [100]    train-rmse:1.234301+0.050024    test-rmse:2.193870+0.547075 
## [101]    train-rmse:1.229861+0.049894    test-rmse:2.192944+0.549063 
## [102]    train-rmse:1.222643+0.050509    test-rmse:2.188740+0.547069 
## [103]    train-rmse:1.217584+0.050811    test-rmse:2.186171+0.547187 
## [104]    train-rmse:1.212044+0.052952    test-rmse:2.184312+0.546873 
## [105]    train-rmse:1.205809+0.053767    test-rmse:2.181828+0.547922 
## [106]    train-rmse:1.202081+0.054083    test-rmse:2.181177+0.546624 
## [107]    train-rmse:1.192615+0.052059    test-rmse:2.175357+0.549305 
## [108]    train-rmse:1.185794+0.054076    test-rmse:2.174021+0.552783 
## [109]    train-rmse:1.181790+0.054226    test-rmse:2.172762+0.552434 
## [110]    train-rmse:1.171876+0.051411    test-rmse:2.171906+0.556629 
## [111]    train-rmse:1.165874+0.050597    test-rmse:2.167673+0.555427 
## [112]    train-rmse:1.161475+0.050130    test-rmse:2.164667+0.556894 
## [113]    train-rmse:1.156208+0.049736    test-rmse:2.165143+0.557036 
## [114]    train-rmse:1.152478+0.049134    test-rmse:2.162741+0.555621 
## [115]    train-rmse:1.146152+0.048764    test-rmse:2.163329+0.555566 
## [116]    train-rmse:1.142777+0.048663    test-rmse:2.164466+0.556180 
## [117]    train-rmse:1.138953+0.049508    test-rmse:2.162923+0.554725 
## [118]    train-rmse:1.133310+0.052020    test-rmse:2.161943+0.556719 
## [119]    train-rmse:1.128994+0.051930    test-rmse:2.160838+0.559711 
## [120]    train-rmse:1.122503+0.054107    test-rmse:2.160087+0.561999 
## [121]    train-rmse:1.119370+0.054355    test-rmse:2.159077+0.562504 
## [122]    train-rmse:1.116455+0.054114    test-rmse:2.156816+0.564061 
## [123]    train-rmse:1.112721+0.055363    test-rmse:2.154004+0.562552 
## [124]    train-rmse:1.110136+0.055298    test-rmse:2.152738+0.562406 
## [125]    train-rmse:1.107281+0.055687    test-rmse:2.152877+0.562391 
## [126]    train-rmse:1.103949+0.056080    test-rmse:2.152368+0.563351 
## [127]    train-rmse:1.100449+0.056150    test-rmse:2.150930+0.561146 
## [128]    train-rmse:1.094566+0.055800    test-rmse:2.150901+0.563678 
## [129]    train-rmse:1.090283+0.054093    test-rmse:2.150158+0.563735 
## [130]    train-rmse:1.087235+0.054522    test-rmse:2.150140+0.561337 
## [131]    train-rmse:1.079413+0.052141    test-rmse:2.146544+0.562482 
## [132]    train-rmse:1.073388+0.055968    test-rmse:2.143188+0.561091 
## [133]    train-rmse:1.068594+0.055087    test-rmse:2.143063+0.564486 
## [134]    train-rmse:1.064363+0.054069    test-rmse:2.138887+0.565638 
## [135]    train-rmse:1.059532+0.057670    test-rmse:2.139725+0.571168 
## [136]    train-rmse:1.054740+0.054958    test-rmse:2.140091+0.570795 
## [137]    train-rmse:1.052224+0.054937    test-rmse:2.141102+0.568260 
## [138]    train-rmse:1.049581+0.054512    test-rmse:2.138659+0.569039 
## [139]    train-rmse:1.042884+0.053664    test-rmse:2.134734+0.565816 
## [140]    train-rmse:1.040417+0.053601    test-rmse:2.135491+0.563780 
## [141]    train-rmse:1.037714+0.053062    test-rmse:2.133403+0.565109 
## [142]    train-rmse:1.033095+0.055190    test-rmse:2.135788+0.571448 
## [143]    train-rmse:1.028572+0.053468    test-rmse:2.132803+0.573528 
## [144]    train-rmse:1.026294+0.053573    test-rmse:2.132395+0.573721 
## [145]    train-rmse:1.022299+0.052977    test-rmse:2.130052+0.575935 
## [146]    train-rmse:1.018994+0.054989    test-rmse:2.130867+0.577158 
## [147]    train-rmse:1.013330+0.054888    test-rmse:2.129205+0.579462 
## [148]    train-rmse:1.007271+0.054905    test-rmse:2.130963+0.579699 
## [149]    train-rmse:1.002800+0.055142    test-rmse:2.129942+0.579109 
## [150]    train-rmse:0.998551+0.056403    test-rmse:2.130281+0.579014 
## [151]    train-rmse:0.996041+0.056457    test-rmse:2.130219+0.579593 
## [152]    train-rmse:0.993386+0.056313    test-rmse:2.128443+0.579253 
## [153]    train-rmse:0.990391+0.056486    test-rmse:2.128685+0.578459 
## [154]    train-rmse:0.987205+0.056764    test-rmse:2.130068+0.579354 
## [155]    train-rmse:0.983078+0.056494    test-rmse:2.128347+0.580410 
## [156]    train-rmse:0.979438+0.057852    test-rmse:2.130401+0.581204 
## [157]    train-rmse:0.976601+0.057254    test-rmse:2.129366+0.582610 
## [158]    train-rmse:0.974077+0.058253    test-rmse:2.127330+0.582493 
## [159]    train-rmse:0.971275+0.058662    test-rmse:2.127880+0.581059 
## [160]    train-rmse:0.964151+0.056545    test-rmse:2.130746+0.579792 
## [161]    train-rmse:0.960729+0.055805    test-rmse:2.129928+0.580230 
## [162]    train-rmse:0.957357+0.054311    test-rmse:2.129386+0.579964 
## [163]    train-rmse:0.953887+0.054818    test-rmse:2.129003+0.580061 
## [164]    train-rmse:0.951743+0.054423    test-rmse:2.128200+0.582035 
## Stopping. Best iteration:
## [158]    train-rmse:0.974077+0.058253    test-rmse:2.127330+0.582493
## 
## 30 
## [1]  train-rmse:7.002229+0.144550    test-rmse:7.044027+0.633452 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.049186+0.122199    test-rmse:6.130863+0.577481 
## [3]  train-rmse:5.272001+0.116752    test-rmse:5.371749+0.530281 
## [4]  train-rmse:4.653323+0.111070    test-rmse:4.818312+0.481641 
## [5]  train-rmse:4.145921+0.111094    test-rmse:4.307833+0.439735 
## [6]  train-rmse:3.742079+0.093507    test-rmse:3.937917+0.449572 
## [7]  train-rmse:3.407126+0.082939    test-rmse:3.663092+0.427351 
## [8]  train-rmse:3.139602+0.074709    test-rmse:3.414347+0.429215 
## [9]  train-rmse:2.928445+0.066804    test-rmse:3.242121+0.431775 
## [10] train-rmse:2.752708+0.066415    test-rmse:3.106670+0.451938 
## [11] train-rmse:2.613788+0.061662    test-rmse:3.027551+0.467243 
## [12] train-rmse:2.497924+0.059466    test-rmse:2.936171+0.473822 
## [13] train-rmse:2.404912+0.059787    test-rmse:2.880996+0.467149 
## [14] train-rmse:2.310574+0.060716    test-rmse:2.821321+0.471765 
## [15] train-rmse:2.230004+0.063817    test-rmse:2.756484+0.483467 
## [16] train-rmse:2.164662+0.062170    test-rmse:2.731955+0.500372 
## [17] train-rmse:2.099832+0.068330    test-rmse:2.681717+0.483974 
## [18] train-rmse:2.040971+0.065457    test-rmse:2.646652+0.503794 
## [19] train-rmse:1.991086+0.064780    test-rmse:2.621964+0.495047 
## [20] train-rmse:1.942462+0.060295    test-rmse:2.576507+0.492478 
## [21] train-rmse:1.898624+0.056835    test-rmse:2.562660+0.487464 
## [22] train-rmse:1.857845+0.058106    test-rmse:2.540430+0.491667 
## [23] train-rmse:1.820369+0.057719    test-rmse:2.521143+0.492317 
## [24] train-rmse:1.775103+0.060272    test-rmse:2.490858+0.474748 
## [25] train-rmse:1.738907+0.063559    test-rmse:2.467938+0.475041 
## [26] train-rmse:1.706055+0.060157    test-rmse:2.452962+0.483647 
## [27] train-rmse:1.679268+0.060607    test-rmse:2.432553+0.485683 
## [28] train-rmse:1.650114+0.060688    test-rmse:2.417225+0.486646 
## [29] train-rmse:1.618623+0.064143    test-rmse:2.395916+0.489170 
## [30] train-rmse:1.588234+0.061671    test-rmse:2.389565+0.493178 
## [31] train-rmse:1.560532+0.055219    test-rmse:2.372888+0.494249 
## [32] train-rmse:1.534994+0.051906    test-rmse:2.365126+0.495819 
## [33] train-rmse:1.508595+0.057504    test-rmse:2.348552+0.483026 
## [34] train-rmse:1.490337+0.057755    test-rmse:2.334873+0.483379 
## [35] train-rmse:1.471243+0.057816    test-rmse:2.320665+0.476910 
## [36] train-rmse:1.446422+0.056519    test-rmse:2.311313+0.489683 
## [37] train-rmse:1.423558+0.053704    test-rmse:2.302718+0.492845 
## [38] train-rmse:1.408075+0.053226    test-rmse:2.298584+0.488422 
## [39] train-rmse:1.387863+0.054318    test-rmse:2.293909+0.488642 
## [40] train-rmse:1.362270+0.050819    test-rmse:2.287772+0.503969 
## [41] train-rmse:1.346694+0.051802    test-rmse:2.281343+0.500710 
## [42] train-rmse:1.332173+0.052261    test-rmse:2.275369+0.497714 
## [43] train-rmse:1.312000+0.045427    test-rmse:2.266490+0.501257 
## [44] train-rmse:1.296349+0.048011    test-rmse:2.260200+0.503572 
## [45] train-rmse:1.281223+0.046984    test-rmse:2.261082+0.500935 
## [46] train-rmse:1.268387+0.046304    test-rmse:2.254350+0.505124 
## [47] train-rmse:1.252784+0.044604    test-rmse:2.249286+0.506353 
## [48] train-rmse:1.242162+0.044478    test-rmse:2.244079+0.505531 
## [49] train-rmse:1.224314+0.045058    test-rmse:2.239097+0.507066 
## [50] train-rmse:1.213402+0.044638    test-rmse:2.233794+0.511089 
## [51] train-rmse:1.199811+0.048400    test-rmse:2.224751+0.505874 
## [52] train-rmse:1.186708+0.050297    test-rmse:2.214237+0.503058 
## [53] train-rmse:1.169921+0.053603    test-rmse:2.213502+0.506291 
## [54] train-rmse:1.153113+0.054279    test-rmse:2.210600+0.514210 
## [55] train-rmse:1.140463+0.054072    test-rmse:2.210632+0.518123 
## [56] train-rmse:1.127533+0.054073    test-rmse:2.205775+0.520525 
## [57] train-rmse:1.118367+0.054063    test-rmse:2.202380+0.520614 
## [58] train-rmse:1.106644+0.053717    test-rmse:2.199797+0.522544 
## [59] train-rmse:1.096033+0.052661    test-rmse:2.197332+0.519993 
## [60] train-rmse:1.077797+0.055379    test-rmse:2.193332+0.515776 
## [61] train-rmse:1.067470+0.056427    test-rmse:2.188467+0.514444 
## [62] train-rmse:1.055775+0.057537    test-rmse:2.187815+0.515652 
## [63] train-rmse:1.044350+0.054967    test-rmse:2.183051+0.512993 
## [64] train-rmse:1.034785+0.054128    test-rmse:2.178573+0.514452 
## [65] train-rmse:1.025279+0.056030    test-rmse:2.171057+0.513008 
## [66] train-rmse:1.017809+0.056332    test-rmse:2.168047+0.512400 
## [67] train-rmse:1.008663+0.058194    test-rmse:2.165388+0.512495 
## [68] train-rmse:1.001462+0.058632    test-rmse:2.165910+0.517581 
## [69] train-rmse:0.992266+0.058796    test-rmse:2.159998+0.519730 
## [70] train-rmse:0.984539+0.059765    test-rmse:2.158624+0.519582 
## [71] train-rmse:0.976052+0.056908    test-rmse:2.157389+0.520953 
## [72] train-rmse:0.967541+0.056510    test-rmse:2.152806+0.522182 
## [73] train-rmse:0.958985+0.058309    test-rmse:2.150956+0.525424 
## [74] train-rmse:0.951573+0.060476    test-rmse:2.151749+0.526522 
## [75] train-rmse:0.941916+0.062805    test-rmse:2.152069+0.529387 
## [76] train-rmse:0.934000+0.065006    test-rmse:2.146605+0.523501 
## [77] train-rmse:0.924283+0.063214    test-rmse:2.143908+0.523360 
## [78] train-rmse:0.919551+0.063841    test-rmse:2.141100+0.524256 
## [79] train-rmse:0.909693+0.064143    test-rmse:2.136974+0.524860 
## [80] train-rmse:0.902966+0.065722    test-rmse:2.135484+0.526424 
## [81] train-rmse:0.890503+0.064729    test-rmse:2.131867+0.527847 
## [82] train-rmse:0.884635+0.064282    test-rmse:2.132310+0.529522 
## [83] train-rmse:0.879102+0.063630    test-rmse:2.132438+0.533297 
## [84] train-rmse:0.873365+0.064529    test-rmse:2.133623+0.532861 
## [85] train-rmse:0.865156+0.066764    test-rmse:2.127802+0.526541 
## [86] train-rmse:0.853000+0.062126    test-rmse:2.125518+0.526053 
## [87] train-rmse:0.843356+0.062545    test-rmse:2.124617+0.525809 
## [88] train-rmse:0.835684+0.062500    test-rmse:2.120499+0.525537 
## [89] train-rmse:0.827961+0.061972    test-rmse:2.117199+0.527906 
## [90] train-rmse:0.819121+0.058051    test-rmse:2.115623+0.529802 
## [91] train-rmse:0.813229+0.059237    test-rmse:2.115185+0.527658 
## [92] train-rmse:0.807087+0.060339    test-rmse:2.114667+0.528356 
## [93] train-rmse:0.801291+0.061786    test-rmse:2.112056+0.527573 
## [94] train-rmse:0.796299+0.061273    test-rmse:2.112511+0.527005 
## [95] train-rmse:0.789575+0.060818    test-rmse:2.113712+0.528738 
## [96] train-rmse:0.783559+0.058525    test-rmse:2.113029+0.530110 
## [97] train-rmse:0.773564+0.059240    test-rmse:2.111143+0.529842 
## [98] train-rmse:0.767222+0.061289    test-rmse:2.110846+0.530332 
## [99] train-rmse:0.760634+0.063342    test-rmse:2.108269+0.530545 
## [100]    train-rmse:0.751803+0.063974    test-rmse:2.106414+0.528647 
## [101]    train-rmse:0.745800+0.063413    test-rmse:2.106493+0.528711 
## [102]    train-rmse:0.740362+0.061956    test-rmse:2.106688+0.526798 
## [103]    train-rmse:0.734630+0.061456    test-rmse:2.104217+0.528126 
## [104]    train-rmse:0.731621+0.061730    test-rmse:2.103402+0.528578 
## [105]    train-rmse:0.725518+0.063588    test-rmse:2.099999+0.528616 
## [106]    train-rmse:0.720565+0.061718    test-rmse:2.100420+0.528295 
## [107]    train-rmse:0.714721+0.063835    test-rmse:2.099952+0.528017 
## [108]    train-rmse:0.709304+0.062495    test-rmse:2.098789+0.527489 
## [109]    train-rmse:0.702215+0.061995    test-rmse:2.098601+0.528197 
## [110]    train-rmse:0.695661+0.059843    test-rmse:2.098825+0.530629 
## [111]    train-rmse:0.692151+0.059745    test-rmse:2.099466+0.531294 
## [112]    train-rmse:0.687519+0.061432    test-rmse:2.098899+0.530445 
## [113]    train-rmse:0.681480+0.059466    test-rmse:2.097750+0.528624 
## [114]    train-rmse:0.677289+0.057692    test-rmse:2.096878+0.529316 
## [115]    train-rmse:0.673669+0.058688    test-rmse:2.099060+0.530558 
## [116]    train-rmse:0.668765+0.060849    test-rmse:2.096082+0.529108 
## [117]    train-rmse:0.664071+0.060786    test-rmse:2.098386+0.527339 
## [118]    train-rmse:0.657262+0.060735    test-rmse:2.096624+0.527522 
## [119]    train-rmse:0.653962+0.059562    test-rmse:2.096086+0.527898 
## [120]    train-rmse:0.647835+0.059763    test-rmse:2.098929+0.526496 
## [121]    train-rmse:0.642720+0.059667    test-rmse:2.096169+0.528444 
## [122]    train-rmse:0.637400+0.057714    test-rmse:2.096014+0.528617 
## [123]    train-rmse:0.632102+0.060597    test-rmse:2.097849+0.530932 
## [124]    train-rmse:0.628242+0.060103    test-rmse:2.095773+0.531182 
## [125]    train-rmse:0.624323+0.058656    test-rmse:2.093555+0.532477 
## [126]    train-rmse:0.617026+0.056468    test-rmse:2.096193+0.536142 
## [127]    train-rmse:0.613499+0.057345    test-rmse:2.096536+0.535742 
## [128]    train-rmse:0.610044+0.057001    test-rmse:2.096835+0.537038 
## [129]    train-rmse:0.605660+0.057643    test-rmse:2.098614+0.538053 
## [130]    train-rmse:0.600679+0.057323    test-rmse:2.099097+0.537004 
## [131]    train-rmse:0.598027+0.056619    test-rmse:2.097893+0.535424 
## Stopping. Best iteration:
## [125]    train-rmse:0.624323+0.058656    test-rmse:2.093555+0.532477
## 
## 31 
## [1]  train-rmse:6.953420+0.143406    test-rmse:6.976686+0.623730 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.947071+0.118375    test-rmse:6.020649+0.550811 
## [3]  train-rmse:5.132520+0.100589    test-rmse:5.257560+0.493455 
## [4]  train-rmse:4.471991+0.083341    test-rmse:4.630360+0.454070 
## [5]  train-rmse:3.940485+0.083899    test-rmse:4.158635+0.428202 
## [6]  train-rmse:3.505586+0.071107    test-rmse:3.798733+0.426947 
## [7]  train-rmse:3.168228+0.059062    test-rmse:3.494644+0.430373 
## [8]  train-rmse:2.892689+0.059381    test-rmse:3.274583+0.437298 
## [9]  train-rmse:2.662402+0.058940    test-rmse:3.091678+0.444964 
## [10] train-rmse:2.477384+0.064022    test-rmse:2.956497+0.445585 
## [11] train-rmse:2.328453+0.058677    test-rmse:2.862206+0.465633 
## [12] train-rmse:2.209503+0.067080    test-rmse:2.782383+0.472118 
## [13] train-rmse:2.099627+0.071770    test-rmse:2.692639+0.447910 
## [14] train-rmse:2.005290+0.061265    test-rmse:2.642816+0.463544 
## [15] train-rmse:1.916357+0.061125    test-rmse:2.572941+0.447453 
## [16] train-rmse:1.846034+0.046955    test-rmse:2.522694+0.460052 
## [17] train-rmse:1.782634+0.052619    test-rmse:2.504978+0.455025 
## [18] train-rmse:1.732600+0.050063    test-rmse:2.478490+0.451854 
## [19] train-rmse:1.679398+0.054707    test-rmse:2.460287+0.462151 
## [20] train-rmse:1.624162+0.048107    test-rmse:2.429902+0.468840 
## [21] train-rmse:1.583706+0.047517    test-rmse:2.411277+0.467635 
## [22] train-rmse:1.546613+0.040457    test-rmse:2.390274+0.465691 
## [23] train-rmse:1.510989+0.045746    test-rmse:2.369404+0.463249 
## [24] train-rmse:1.467357+0.048284    test-rmse:2.348269+0.446433 
## [25] train-rmse:1.436717+0.045793    test-rmse:2.339318+0.449406 
## [26] train-rmse:1.402903+0.041093    test-rmse:2.326079+0.452910 
## [27] train-rmse:1.369948+0.033546    test-rmse:2.308805+0.458324 
## [28] train-rmse:1.344985+0.036234    test-rmse:2.299720+0.458883 
## [29] train-rmse:1.322072+0.034291    test-rmse:2.292143+0.449302 
## [30] train-rmse:1.291038+0.031406    test-rmse:2.279289+0.460308 
## [31] train-rmse:1.261974+0.022191    test-rmse:2.271308+0.462985 
## [32] train-rmse:1.238948+0.027119    test-rmse:2.256490+0.458646 
## [33] train-rmse:1.216378+0.030920    test-rmse:2.247782+0.461723 
## [34] train-rmse:1.195259+0.030339    test-rmse:2.241321+0.461273 
## [35] train-rmse:1.169872+0.029658    test-rmse:2.238771+0.463516 
## [36] train-rmse:1.148076+0.032566    test-rmse:2.237114+0.464525 
## [37] train-rmse:1.130161+0.030839    test-rmse:2.228009+0.464182 
## [38] train-rmse:1.108258+0.028627    test-rmse:2.222696+0.464714 
## [39] train-rmse:1.088386+0.032496    test-rmse:2.213847+0.457586 
## [40] train-rmse:1.071252+0.031882    test-rmse:2.204584+0.459086 
## [41] train-rmse:1.056104+0.031983    test-rmse:2.199152+0.467102 
## [42] train-rmse:1.029976+0.032638    test-rmse:2.198695+0.467951 
## [43] train-rmse:1.011893+0.027074    test-rmse:2.194808+0.469329 
## [44] train-rmse:0.993455+0.028940    test-rmse:2.188101+0.469464 
## [45] train-rmse:0.978221+0.026568    test-rmse:2.182231+0.473627 
## [46] train-rmse:0.966196+0.025530    test-rmse:2.181285+0.478264 
## [47] train-rmse:0.949114+0.025467    test-rmse:2.179564+0.479595 
## [48] train-rmse:0.938882+0.026331    test-rmse:2.174763+0.475687 
## [49] train-rmse:0.925595+0.026511    test-rmse:2.169915+0.474418 
## [50] train-rmse:0.906846+0.024467    test-rmse:2.166453+0.472640 
## [51] train-rmse:0.893308+0.022358    test-rmse:2.167592+0.474078 
## [52] train-rmse:0.880297+0.018915    test-rmse:2.161378+0.476912 
## [53] train-rmse:0.867960+0.019841    test-rmse:2.156018+0.479147 
## [54] train-rmse:0.849863+0.016925    test-rmse:2.155898+0.476083 
## [55] train-rmse:0.841379+0.018500    test-rmse:2.155245+0.474534 
## [56] train-rmse:0.827944+0.019857    test-rmse:2.155063+0.475545 
## [57] train-rmse:0.816523+0.015538    test-rmse:2.151975+0.476843 
## [58] train-rmse:0.802367+0.013729    test-rmse:2.143914+0.479522 
## [59] train-rmse:0.787710+0.017338    test-rmse:2.139513+0.478838 
## [60] train-rmse:0.774302+0.015623    test-rmse:2.140445+0.479510 
## [61] train-rmse:0.762092+0.012781    test-rmse:2.139053+0.480024 
## [62] train-rmse:0.747424+0.015607    test-rmse:2.136419+0.482479 
## [63] train-rmse:0.737490+0.016415    test-rmse:2.133245+0.482442 
## [64] train-rmse:0.728720+0.013397    test-rmse:2.131966+0.481960 
## [65] train-rmse:0.714639+0.015216    test-rmse:2.130074+0.481677 
## [66] train-rmse:0.706693+0.015783    test-rmse:2.130250+0.482400 
## [67] train-rmse:0.698572+0.013143    test-rmse:2.125244+0.484131 
## [68] train-rmse:0.688883+0.015816    test-rmse:2.124738+0.485357 
## [69] train-rmse:0.679914+0.013448    test-rmse:2.123782+0.485078 
## [70] train-rmse:0.671035+0.014358    test-rmse:2.120456+0.485590 
## [71] train-rmse:0.662736+0.013434    test-rmse:2.118840+0.484443 
## [72] train-rmse:0.653808+0.010916    test-rmse:2.118733+0.483157 
## [73] train-rmse:0.644588+0.011621    test-rmse:2.117653+0.484269 
## [74] train-rmse:0.637940+0.012107    test-rmse:2.117530+0.487463 
## [75] train-rmse:0.628953+0.011773    test-rmse:2.113426+0.488360 
## [76] train-rmse:0.622004+0.010996    test-rmse:2.113288+0.485683 
## [77] train-rmse:0.612725+0.012944    test-rmse:2.112875+0.485736 
## [78] train-rmse:0.603918+0.013794    test-rmse:2.110676+0.486125 
## [79] train-rmse:0.595094+0.011946    test-rmse:2.112139+0.485688 
## [80] train-rmse:0.587797+0.011996    test-rmse:2.113115+0.483331 
## [81] train-rmse:0.580445+0.012278    test-rmse:2.114381+0.482883 
## [82] train-rmse:0.575546+0.012552    test-rmse:2.112445+0.481917 
## [83] train-rmse:0.571602+0.013124    test-rmse:2.112167+0.482722 
## [84] train-rmse:0.560706+0.010725    test-rmse:2.112235+0.482940 
## Stopping. Best iteration:
## [78] train-rmse:0.603918+0.013794    test-rmse:2.110676+0.486125
## 
## 32 
## [1]  train-rmse:6.919667+0.141436    test-rmse:6.919358+0.608975 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.884742+0.114933    test-rmse:5.928558+0.542345 
## [3]  train-rmse:5.042087+0.103278    test-rmse:5.137363+0.484859 
## [4]  train-rmse:4.365350+0.093480    test-rmse:4.533268+0.438321 
## [5]  train-rmse:3.816176+0.076011    test-rmse:4.064793+0.426976 
## [6]  train-rmse:3.377665+0.070688    test-rmse:3.715258+0.411704 
## [7]  train-rmse:3.025002+0.059898    test-rmse:3.429907+0.409614 
## [8]  train-rmse:2.731257+0.049476    test-rmse:3.209269+0.433815 
## [9]  train-rmse:2.488367+0.066396    test-rmse:3.037619+0.445772 
## [10] train-rmse:2.289638+0.069382    test-rmse:2.911497+0.474995 
## [11] train-rmse:2.112738+0.059479    test-rmse:2.799055+0.495940 
## [12] train-rmse:1.984640+0.057339    test-rmse:2.718941+0.496715 
## [13] train-rmse:1.876640+0.060279    test-rmse:2.651459+0.484267 
## [14] train-rmse:1.783111+0.058176    test-rmse:2.598274+0.474604 
## [15] train-rmse:1.693796+0.057016    test-rmse:2.561226+0.479914 
## [16] train-rmse:1.630726+0.055417    test-rmse:2.527513+0.487881 
## [17] train-rmse:1.563642+0.060689    test-rmse:2.502839+0.496864 
## [18] train-rmse:1.504881+0.062251    test-rmse:2.489784+0.498439 
## [19] train-rmse:1.448410+0.068613    test-rmse:2.467069+0.494224 
## [20] train-rmse:1.393221+0.065685    test-rmse:2.445006+0.495598 
## [21] train-rmse:1.343874+0.053469    test-rmse:2.421174+0.503990 
## [22] train-rmse:1.307779+0.050121    test-rmse:2.417783+0.498612 
## [23] train-rmse:1.274521+0.053912    test-rmse:2.416256+0.511243 
## [24] train-rmse:1.231706+0.058542    test-rmse:2.410120+0.506891 
## [25] train-rmse:1.194473+0.053969    test-rmse:2.400492+0.516276 
## [26] train-rmse:1.170414+0.054064    test-rmse:2.391773+0.518307 
## [27] train-rmse:1.133802+0.058250    test-rmse:2.378408+0.514916 
## [28] train-rmse:1.100087+0.059531    test-rmse:2.372772+0.517352 
## [29] train-rmse:1.076202+0.053830    test-rmse:2.365284+0.517854 
## [30] train-rmse:1.046741+0.049268    test-rmse:2.367314+0.523506 
## [31] train-rmse:1.023014+0.055432    test-rmse:2.362105+0.523791 
## [32] train-rmse:0.995333+0.058652    test-rmse:2.366730+0.530045 
## [33] train-rmse:0.969486+0.055044    test-rmse:2.362706+0.531725 
## [34] train-rmse:0.944265+0.056334    test-rmse:2.358366+0.535542 
## [35] train-rmse:0.917869+0.046674    test-rmse:2.347514+0.533744 
## [36] train-rmse:0.897927+0.046072    test-rmse:2.340683+0.539682 
## [37] train-rmse:0.871840+0.044637    test-rmse:2.338063+0.535747 
## [38] train-rmse:0.854569+0.047757    test-rmse:2.335218+0.527365 
## [39] train-rmse:0.840208+0.044865    test-rmse:2.330830+0.529905 
## [40] train-rmse:0.823028+0.045207    test-rmse:2.324853+0.531687 
## [41] train-rmse:0.801307+0.043327    test-rmse:2.318257+0.536147 
## [42] train-rmse:0.788880+0.042801    test-rmse:2.314189+0.535337 
## [43] train-rmse:0.773031+0.040953    test-rmse:2.317660+0.536914 
## [44] train-rmse:0.755281+0.039349    test-rmse:2.312350+0.534067 
## [45] train-rmse:0.743730+0.039759    test-rmse:2.311084+0.534624 
## [46] train-rmse:0.727259+0.046644    test-rmse:2.308754+0.535854 
## [47] train-rmse:0.715035+0.048766    test-rmse:2.306452+0.535154 
## [48] train-rmse:0.699171+0.046883    test-rmse:2.301883+0.533287 
## [49] train-rmse:0.684164+0.042004    test-rmse:2.301440+0.536467 
## [50] train-rmse:0.668635+0.043731    test-rmse:2.296522+0.536004 
## [51] train-rmse:0.651416+0.045827    test-rmse:2.292039+0.537022 
## [52] train-rmse:0.639363+0.046740    test-rmse:2.292288+0.536138 
## [53] train-rmse:0.626063+0.045294    test-rmse:2.291925+0.537718 
## [54] train-rmse:0.609482+0.044034    test-rmse:2.284863+0.541092 
## [55] train-rmse:0.598191+0.045851    test-rmse:2.285780+0.538692 
## [56] train-rmse:0.590277+0.044746    test-rmse:2.286031+0.540088 
## [57] train-rmse:0.579911+0.041157    test-rmse:2.282092+0.540425 
## [58] train-rmse:0.567113+0.043918    test-rmse:2.279918+0.540479 
## [59] train-rmse:0.553841+0.042716    test-rmse:2.278650+0.542272 
## [60] train-rmse:0.545199+0.043457    test-rmse:2.278697+0.542577 
## [61] train-rmse:0.537940+0.045271    test-rmse:2.277353+0.545563 
## [62] train-rmse:0.527902+0.044220    test-rmse:2.276691+0.544910 
## [63] train-rmse:0.515758+0.046923    test-rmse:2.277014+0.546697 
## [64] train-rmse:0.508751+0.044741    test-rmse:2.276816+0.544652 
## [65] train-rmse:0.500605+0.043250    test-rmse:2.274198+0.545653 
## [66] train-rmse:0.492587+0.043059    test-rmse:2.273026+0.546009 
## [67] train-rmse:0.480851+0.041268    test-rmse:2.269261+0.548727 
## [68] train-rmse:0.474546+0.041485    test-rmse:2.268569+0.549631 
## [69] train-rmse:0.466562+0.042543    test-rmse:2.270345+0.549652 
## [70] train-rmse:0.456500+0.042281    test-rmse:2.269945+0.548605 
## [71] train-rmse:0.447734+0.039642    test-rmse:2.269005+0.550082 
## [72] train-rmse:0.441905+0.040342    test-rmse:2.268970+0.550557 
## [73] train-rmse:0.433397+0.040104    test-rmse:2.270945+0.550011 
## [74] train-rmse:0.425160+0.037254    test-rmse:2.270872+0.550653 
## Stopping. Best iteration:
## [68] train-rmse:0.474546+0.041485    test-rmse:2.268569+0.549631
## 
## 33 
## [1]  train-rmse:6.898398+0.138297    test-rmse:6.905104+0.628567 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.842233+0.110125    test-rmse:5.906915+0.574035 
## [3]  train-rmse:4.979517+0.090046    test-rmse:5.121473+0.518143 
## [4]  train-rmse:4.279084+0.074094    test-rmse:4.511843+0.480155 
## [5]  train-rmse:3.714147+0.060267    test-rmse:4.046897+0.458245 
## [6]  train-rmse:3.262461+0.051210    test-rmse:3.683436+0.451712 
## [7]  train-rmse:2.885386+0.040820    test-rmse:3.370228+0.444094 
## [8]  train-rmse:2.595014+0.037641    test-rmse:3.178712+0.446765 
## [9]  train-rmse:2.345298+0.031876    test-rmse:3.012453+0.468687 
## [10] train-rmse:2.147126+0.023961    test-rmse:2.887478+0.497883 
## [11] train-rmse:1.975171+0.036027    test-rmse:2.789038+0.499738 
## [12] train-rmse:1.823165+0.038399    test-rmse:2.709485+0.490431 
## [13] train-rmse:1.700524+0.040938    test-rmse:2.639163+0.487199 
## [14] train-rmse:1.600758+0.041608    test-rmse:2.593891+0.495866 
## [15] train-rmse:1.517368+0.044998    test-rmse:2.557410+0.516628 
## [16] train-rmse:1.424314+0.038762    test-rmse:2.523306+0.516917 
## [17] train-rmse:1.359312+0.033901    test-rmse:2.490494+0.529626 
## [18] train-rmse:1.292921+0.039817    test-rmse:2.472515+0.528781 
## [19] train-rmse:1.233217+0.037899    test-rmse:2.451969+0.530240 
## [20] train-rmse:1.176088+0.031175    test-rmse:2.438369+0.524570 
## [21] train-rmse:1.133577+0.032274    test-rmse:2.421754+0.527532 
## [22] train-rmse:1.092140+0.034314    test-rmse:2.404993+0.528608 
## [23] train-rmse:1.056256+0.037470    test-rmse:2.391331+0.533862 
## [24] train-rmse:1.020356+0.027378    test-rmse:2.384658+0.536657 
## [25] train-rmse:0.982225+0.033310    test-rmse:2.384100+0.542501 
## [26] train-rmse:0.952675+0.028376    test-rmse:2.384548+0.540942 
## [27] train-rmse:0.919607+0.032975    test-rmse:2.373684+0.541849 
## [28] train-rmse:0.890084+0.029203    test-rmse:2.365817+0.547521 
## [29] train-rmse:0.862534+0.031840    test-rmse:2.357673+0.546738 
## [30] train-rmse:0.830877+0.038490    test-rmse:2.352921+0.546282 
## [31] train-rmse:0.804991+0.039085    test-rmse:2.345246+0.543051 
## [32] train-rmse:0.789495+0.038194    test-rmse:2.339780+0.540393 
## [33] train-rmse:0.762443+0.037993    test-rmse:2.338375+0.542599 
## [34] train-rmse:0.737397+0.035688    test-rmse:2.336586+0.549911 
## [35] train-rmse:0.717153+0.033117    test-rmse:2.332809+0.551335 
## [36] train-rmse:0.694993+0.035484    test-rmse:2.329182+0.554074 
## [37] train-rmse:0.680915+0.035612    test-rmse:2.325232+0.549371 
## [38] train-rmse:0.666105+0.033734    test-rmse:2.320678+0.553587 
## [39] train-rmse:0.648697+0.035482    test-rmse:2.317150+0.553106 
## [40] train-rmse:0.628187+0.034381    test-rmse:2.313574+0.552503 
## [41] train-rmse:0.610406+0.032396    test-rmse:2.308486+0.553849 
## [42] train-rmse:0.590647+0.033282    test-rmse:2.303964+0.554645 
## [43] train-rmse:0.578010+0.033492    test-rmse:2.302667+0.553676 
## [44] train-rmse:0.562314+0.030535    test-rmse:2.298789+0.550465 
## [45] train-rmse:0.546941+0.027925    test-rmse:2.296493+0.552156 
## [46] train-rmse:0.531391+0.029525    test-rmse:2.296010+0.552461 
## [47] train-rmse:0.519989+0.032942    test-rmse:2.295901+0.554579 
## [48] train-rmse:0.509434+0.033401    test-rmse:2.295626+0.554404 
## [49] train-rmse:0.494412+0.035743    test-rmse:2.292333+0.556383 
## [50] train-rmse:0.479452+0.032855    test-rmse:2.292864+0.558148 
## [51] train-rmse:0.470390+0.032340    test-rmse:2.291728+0.556946 
## [52] train-rmse:0.461184+0.029611    test-rmse:2.287774+0.558623 
## [53] train-rmse:0.446668+0.032662    test-rmse:2.285989+0.559186 
## [54] train-rmse:0.436267+0.029511    test-rmse:2.286393+0.557772 
## [55] train-rmse:0.427939+0.030745    test-rmse:2.285778+0.559003 
## [56] train-rmse:0.419310+0.031184    test-rmse:2.286687+0.558382 
## [57] train-rmse:0.411737+0.028552    test-rmse:2.286953+0.558299 
## [58] train-rmse:0.402561+0.026196    test-rmse:2.285491+0.559382 
## [59] train-rmse:0.390554+0.021272    test-rmse:2.284371+0.558507 
## [60] train-rmse:0.382216+0.021253    test-rmse:2.281567+0.558697 
## [61] train-rmse:0.373697+0.021203    test-rmse:2.279669+0.562315 
## [62] train-rmse:0.365695+0.021851    test-rmse:2.279656+0.563019 
## [63] train-rmse:0.357983+0.019793    test-rmse:2.279349+0.563607 
## [64] train-rmse:0.347820+0.017650    test-rmse:2.277701+0.562921 
## [65] train-rmse:0.337654+0.016780    test-rmse:2.278496+0.564704 
## [66] train-rmse:0.329578+0.013913    test-rmse:2.277038+0.564058 
## [67] train-rmse:0.322643+0.013178    test-rmse:2.277624+0.563964 
## [68] train-rmse:0.316849+0.014237    test-rmse:2.275825+0.564241 
## [69] train-rmse:0.310287+0.015407    test-rmse:2.275808+0.564619 
## [70] train-rmse:0.302731+0.015966    test-rmse:2.273595+0.565221 
## [71] train-rmse:0.295768+0.015196    test-rmse:2.273914+0.565542 
## [72] train-rmse:0.289834+0.014760    test-rmse:2.274295+0.566020 
## [73] train-rmse:0.283446+0.015293    test-rmse:2.273037+0.564947 
## [74] train-rmse:0.275490+0.016713    test-rmse:2.272072+0.565414 
## [75] train-rmse:0.269378+0.016528    test-rmse:2.271357+0.565672 
## [76] train-rmse:0.262570+0.014830    test-rmse:2.270863+0.565242 
## [77] train-rmse:0.257766+0.014246    test-rmse:2.270138+0.565391 
## [78] train-rmse:0.253218+0.013127    test-rmse:2.269928+0.564884 
## [79] train-rmse:0.248759+0.013469    test-rmse:2.269144+0.564604 
## [80] train-rmse:0.244377+0.013786    test-rmse:2.268779+0.564708 
## [81] train-rmse:0.239183+0.011351    test-rmse:2.268783+0.563616 
## [82] train-rmse:0.235109+0.011649    test-rmse:2.268845+0.563694 
## [83] train-rmse:0.231489+0.010965    test-rmse:2.269409+0.564814 
## [84] train-rmse:0.226825+0.009971    test-rmse:2.270666+0.564547 
## [85] train-rmse:0.221043+0.011921    test-rmse:2.270165+0.564132 
## [86] train-rmse:0.216651+0.011925    test-rmse:2.269663+0.564255 
## Stopping. Best iteration:
## [80] train-rmse:0.244377+0.013786    test-rmse:2.268779+0.564708
## 
## 34 
## [1]  train-rmse:6.890859+0.137669    test-rmse:6.901004+0.628164 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.823396+0.105995    test-rmse:5.907981+0.563901 
## [3]  train-rmse:4.946100+0.083952    test-rmse:5.098408+0.533450 
## [4]  train-rmse:4.230242+0.066896    test-rmse:4.478139+0.491980 
## [5]  train-rmse:3.654618+0.053095    test-rmse:3.998118+0.472720 
## [6]  train-rmse:3.180511+0.046763    test-rmse:3.616437+0.456734 
## [7]  train-rmse:2.797419+0.043084    test-rmse:3.342851+0.464842 
## [8]  train-rmse:2.489957+0.036070    test-rmse:3.132930+0.477684 
## [9]  train-rmse:2.230510+0.029504    test-rmse:2.983506+0.481250 
## [10] train-rmse:2.017286+0.023504    test-rmse:2.851487+0.497475 
## [11] train-rmse:1.844934+0.016807    test-rmse:2.741228+0.519595 
## [12] train-rmse:1.698699+0.019631    test-rmse:2.663423+0.528696 
## [13] train-rmse:1.573257+0.020835    test-rmse:2.611666+0.526529 
## [14] train-rmse:1.461724+0.017472    test-rmse:2.550229+0.530877 
## [15] train-rmse:1.375204+0.020668    test-rmse:2.509807+0.538617 
## [16] train-rmse:1.283393+0.020719    test-rmse:2.474870+0.535389 
## [17] train-rmse:1.206757+0.032129    test-rmse:2.443543+0.539779 
## [18] train-rmse:1.144042+0.028416    test-rmse:2.420079+0.540182 
## [19] train-rmse:1.083838+0.030404    test-rmse:2.397732+0.551190 
## [20] train-rmse:1.030021+0.030431    test-rmse:2.388909+0.550506 
## [21] train-rmse:0.988949+0.027939    test-rmse:2.376829+0.552105 
## [22] train-rmse:0.949220+0.029677    test-rmse:2.370330+0.558204 
## [23] train-rmse:0.911285+0.021844    test-rmse:2.361523+0.563000 
## [24] train-rmse:0.869744+0.024094    test-rmse:2.352738+0.565814 
## [25] train-rmse:0.837034+0.030145    test-rmse:2.346367+0.569692 
## [26] train-rmse:0.797286+0.026431    test-rmse:2.339040+0.571216 
## [27] train-rmse:0.770638+0.027377    test-rmse:2.329374+0.576354 
## [28] train-rmse:0.738796+0.024132    test-rmse:2.323403+0.575871 
## [29] train-rmse:0.709628+0.026032    test-rmse:2.315322+0.579632 
## [30] train-rmse:0.680565+0.024231    test-rmse:2.310521+0.580850 
## [31] train-rmse:0.658490+0.022286    test-rmse:2.304516+0.582465 
## [32] train-rmse:0.633886+0.020901    test-rmse:2.302007+0.584143 
## [33] train-rmse:0.610632+0.026262    test-rmse:2.298664+0.585706 
## [34] train-rmse:0.590134+0.024130    test-rmse:2.297330+0.586406 
## [35] train-rmse:0.568092+0.021564    test-rmse:2.299866+0.592312 
## [36] train-rmse:0.551777+0.016942    test-rmse:2.293014+0.598944 
## [37] train-rmse:0.530504+0.017580    test-rmse:2.288709+0.598150 
## [38] train-rmse:0.511914+0.017919    test-rmse:2.289418+0.598830 
## [39] train-rmse:0.496306+0.016061    test-rmse:2.285254+0.596129 
## [40] train-rmse:0.481013+0.012854    test-rmse:2.282711+0.597430 
## [41] train-rmse:0.472358+0.012334    test-rmse:2.280205+0.599617 
## [42] train-rmse:0.454313+0.014486    test-rmse:2.281242+0.600452 
## [43] train-rmse:0.441082+0.016613    test-rmse:2.281390+0.597359 
## [44] train-rmse:0.429442+0.016958    test-rmse:2.279221+0.596547 
## [45] train-rmse:0.414594+0.012358    test-rmse:2.276870+0.595452 
## [46] train-rmse:0.401926+0.015397    test-rmse:2.276171+0.595933 
## [47] train-rmse:0.380817+0.014512    test-rmse:2.275724+0.596565 
## [48] train-rmse:0.373228+0.014477    test-rmse:2.273860+0.597165 
## [49] train-rmse:0.361346+0.017038    test-rmse:2.272384+0.597145 
## [50] train-rmse:0.351193+0.015669    test-rmse:2.273079+0.599489 
## [51] train-rmse:0.336768+0.016728    test-rmse:2.273655+0.599224 
## [52] train-rmse:0.327635+0.016501    test-rmse:2.271657+0.599682 
## [53] train-rmse:0.318856+0.014697    test-rmse:2.271609+0.599998 
## [54] train-rmse:0.309639+0.014436    test-rmse:2.269903+0.600689 
## [55] train-rmse:0.299305+0.016661    test-rmse:2.270211+0.598770 
## [56] train-rmse:0.292018+0.017184    test-rmse:2.269445+0.598727 
## [57] train-rmse:0.284564+0.016202    test-rmse:2.268865+0.598772 
## [58] train-rmse:0.275810+0.019002    test-rmse:2.265824+0.600044 
## [59] train-rmse:0.266206+0.016400    test-rmse:2.266213+0.600306 
## [60] train-rmse:0.257517+0.015525    test-rmse:2.265827+0.600438 
## [61] train-rmse:0.250050+0.016123    test-rmse:2.264936+0.600994 
## [62] train-rmse:0.244015+0.016553    test-rmse:2.264038+0.601130 
## [63] train-rmse:0.236053+0.016564    test-rmse:2.262783+0.600728 
## [64] train-rmse:0.228512+0.016784    test-rmse:2.263761+0.600177 
## [65] train-rmse:0.222223+0.015067    test-rmse:2.262838+0.599660 
## [66] train-rmse:0.218449+0.014239    test-rmse:2.262940+0.600719 
## [67] train-rmse:0.213580+0.014190    test-rmse:2.262991+0.600848 
## [68] train-rmse:0.205801+0.012598    test-rmse:2.262689+0.602990 
## [69] train-rmse:0.201377+0.011728    test-rmse:2.262464+0.602222 
## [70] train-rmse:0.195671+0.012178    test-rmse:2.262301+0.602651 
## [71] train-rmse:0.190205+0.012143    test-rmse:2.261875+0.602867 
## [72] train-rmse:0.184337+0.012298    test-rmse:2.261770+0.602162 
## [73] train-rmse:0.178947+0.011651    test-rmse:2.261591+0.602758 
## [74] train-rmse:0.173452+0.013469    test-rmse:2.261176+0.603535 
## [75] train-rmse:0.169929+0.013045    test-rmse:2.260166+0.604855 
## [76] train-rmse:0.164855+0.012343    test-rmse:2.259632+0.605429 
## [77] train-rmse:0.160592+0.011599    test-rmse:2.259375+0.605763 
## [78] train-rmse:0.155313+0.012982    test-rmse:2.259131+0.605620 
## [79] train-rmse:0.151690+0.013464    test-rmse:2.258728+0.605211 
## [80] train-rmse:0.146272+0.011636    test-rmse:2.257770+0.605150 
## [81] train-rmse:0.141824+0.010811    test-rmse:2.258127+0.605736 
## [82] train-rmse:0.139595+0.010905    test-rmse:2.258331+0.605457 
## [83] train-rmse:0.136702+0.010528    test-rmse:2.258432+0.605070 
## [84] train-rmse:0.133147+0.011314    test-rmse:2.259114+0.604804 
## [85] train-rmse:0.129329+0.008940    test-rmse:2.258296+0.605028 
## [86] train-rmse:0.126932+0.009319    test-rmse:2.258061+0.604942 
## Stopping. Best iteration:
## [80] train-rmse:0.146272+0.011636    test-rmse:2.257770+0.605150
## 
## 35 
## [1]  train-rmse:7.020857+0.151224    test-rmse:7.011322+0.667298 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.133026+0.130870    test-rmse:6.163142+0.700124 
## [3]  train-rmse:5.467186+0.120456    test-rmse:5.520561+0.636339 
## [4]  train-rmse:4.967329+0.117357    test-rmse:5.011418+0.620207 
## [5]  train-rmse:4.599764+0.112494    test-rmse:4.687296+0.600426 
## [6]  train-rmse:4.324923+0.108772    test-rmse:4.408095+0.556098 
## [7]  train-rmse:4.117441+0.111234    test-rmse:4.228306+0.511353 
## [8]  train-rmse:3.955229+0.113652    test-rmse:4.067093+0.503782 
## [9]  train-rmse:3.829867+0.113487    test-rmse:3.924159+0.472808 
## [10] train-rmse:3.726972+0.113553    test-rmse:3.856724+0.477603 
## [11] train-rmse:3.641656+0.116722    test-rmse:3.763231+0.440414 
## [12] train-rmse:3.566801+0.119760    test-rmse:3.696251+0.415112 
## [13] train-rmse:3.500866+0.120120    test-rmse:3.627931+0.439138 
## [14] train-rmse:3.443704+0.120283    test-rmse:3.573330+0.435819 
## [15] train-rmse:3.391068+0.119896    test-rmse:3.531261+0.426981 
## [16] train-rmse:3.341029+0.120828    test-rmse:3.457412+0.420710 
## [17] train-rmse:3.295408+0.123079    test-rmse:3.445521+0.415164 
## [18] train-rmse:3.252170+0.123234    test-rmse:3.402098+0.429884 
## [19] train-rmse:3.214103+0.123009    test-rmse:3.336580+0.408590 
## [20] train-rmse:3.176821+0.122960    test-rmse:3.328264+0.431891 
## [21] train-rmse:3.141424+0.124013    test-rmse:3.286595+0.428893 
## [22] train-rmse:3.108438+0.126302    test-rmse:3.246028+0.425259 
## [23] train-rmse:3.075532+0.128596    test-rmse:3.225919+0.427892 
## [24] train-rmse:3.046779+0.127847    test-rmse:3.187237+0.429496 
## [25] train-rmse:3.019111+0.127198    test-rmse:3.179711+0.425775 
## [26] train-rmse:2.992891+0.127336    test-rmse:3.157303+0.426206 
## [27] train-rmse:2.967438+0.128049    test-rmse:3.128395+0.418899 
## [28] train-rmse:2.942233+0.129276    test-rmse:3.108854+0.421341 
## [29] train-rmse:2.918042+0.130827    test-rmse:3.079860+0.437477 
## [30] train-rmse:2.895120+0.131830    test-rmse:3.071542+0.442452 
## [31] train-rmse:2.873144+0.132617    test-rmse:3.043920+0.439687 
## [32] train-rmse:2.851580+0.132971    test-rmse:3.039176+0.442009 
## [33] train-rmse:2.830810+0.133677    test-rmse:3.020748+0.442108 
## [34] train-rmse:2.811542+0.134019    test-rmse:3.005151+0.447258 
## [35] train-rmse:2.792372+0.134459    test-rmse:2.998262+0.465856 
## [36] train-rmse:2.773955+0.134937    test-rmse:2.977794+0.458431 
## [37] train-rmse:2.755834+0.135547    test-rmse:2.971237+0.455461 
## [38] train-rmse:2.737921+0.136088    test-rmse:2.950778+0.445793 
## [39] train-rmse:2.720219+0.136974    test-rmse:2.926555+0.457414 
## [40] train-rmse:2.703339+0.137710    test-rmse:2.921053+0.454964 
## [41] train-rmse:2.686917+0.138298    test-rmse:2.900851+0.464015 
## [42] train-rmse:2.670882+0.138306    test-rmse:2.886082+0.460676 
## [43] train-rmse:2.655725+0.138240    test-rmse:2.871013+0.461374 
## [44] train-rmse:2.641074+0.138371    test-rmse:2.856911+0.456716 
## [45] train-rmse:2.626585+0.138431    test-rmse:2.854210+0.464772 
## [46] train-rmse:2.612589+0.138428    test-rmse:2.837901+0.465008 
## [47] train-rmse:2.598750+0.138567    test-rmse:2.828332+0.476802 
## [48] train-rmse:2.584879+0.138818    test-rmse:2.822529+0.479215 
## [49] train-rmse:2.571653+0.138899    test-rmse:2.807358+0.488307 
## [50] train-rmse:2.558579+0.139087    test-rmse:2.795778+0.486037 
## [51] train-rmse:2.545852+0.139414    test-rmse:2.793900+0.488247 
## [52] train-rmse:2.533192+0.139625    test-rmse:2.776682+0.489273 
## [53] train-rmse:2.520941+0.139740    test-rmse:2.765112+0.489573 
## [54] train-rmse:2.509227+0.139599    test-rmse:2.753281+0.478707 
## [55] train-rmse:2.498013+0.139849    test-rmse:2.736242+0.474753 
## [56] train-rmse:2.486879+0.140092    test-rmse:2.724830+0.480989 
## [57] train-rmse:2.476183+0.140224    test-rmse:2.726432+0.485590 
## [58] train-rmse:2.465751+0.140047    test-rmse:2.719341+0.482487 
## [59] train-rmse:2.455595+0.139811    test-rmse:2.706976+0.475706 
## [60] train-rmse:2.445627+0.139928    test-rmse:2.700701+0.482776 
## [61] train-rmse:2.435818+0.140075    test-rmse:2.688710+0.486889 
## [62] train-rmse:2.426269+0.140391    test-rmse:2.679790+0.489806 
## [63] train-rmse:2.416881+0.140663    test-rmse:2.669032+0.486453 
## [64] train-rmse:2.407578+0.140899    test-rmse:2.665265+0.493216 
## [65] train-rmse:2.398672+0.141013    test-rmse:2.659924+0.499009 
## [66] train-rmse:2.389871+0.141090    test-rmse:2.652664+0.497525 
## [67] train-rmse:2.381193+0.141253    test-rmse:2.655652+0.499701 
## [68] train-rmse:2.372663+0.141481    test-rmse:2.638973+0.494004 
## [69] train-rmse:2.364408+0.141629    test-rmse:2.639662+0.494724 
## [70] train-rmse:2.356413+0.141762    test-rmse:2.628252+0.498124 
## [71] train-rmse:2.348736+0.141947    test-rmse:2.618702+0.496638 
## [72] train-rmse:2.341204+0.142188    test-rmse:2.606651+0.493912 
## [73] train-rmse:2.333702+0.142387    test-rmse:2.608331+0.497456 
## [74] train-rmse:2.326236+0.142610    test-rmse:2.596271+0.494147 
## [75] train-rmse:2.319072+0.142833    test-rmse:2.592472+0.491653 
## [76] train-rmse:2.312037+0.143005    test-rmse:2.583052+0.491960 
## [77] train-rmse:2.305074+0.143280    test-rmse:2.576193+0.494089 
## [78] train-rmse:2.298232+0.143522    test-rmse:2.571313+0.494308 
## [79] train-rmse:2.291604+0.143787    test-rmse:2.565256+0.491835 
## [80] train-rmse:2.285024+0.144022    test-rmse:2.560518+0.492742 
## [81] train-rmse:2.278625+0.144250    test-rmse:2.559965+0.497697 
## [82] train-rmse:2.272325+0.144508    test-rmse:2.559037+0.500545 
## [83] train-rmse:2.266099+0.144747    test-rmse:2.555572+0.496778 
## [84] train-rmse:2.259916+0.144988    test-rmse:2.547512+0.498361 
## [85] train-rmse:2.254022+0.145165    test-rmse:2.542008+0.501968 
## [86] train-rmse:2.248212+0.145353    test-rmse:2.525369+0.501927 
## [87] train-rmse:2.242599+0.145573    test-rmse:2.521887+0.508750 
## [88] train-rmse:2.237136+0.145819    test-rmse:2.521329+0.521495 
## [89] train-rmse:2.231702+0.146054    test-rmse:2.515556+0.520537 
## [90] train-rmse:2.226326+0.146290    test-rmse:2.508148+0.516096 
## [91] train-rmse:2.221096+0.146434    test-rmse:2.506590+0.515685 
## [92] train-rmse:2.215969+0.146474    test-rmse:2.505071+0.510840 
## [93] train-rmse:2.210974+0.146572    test-rmse:2.501949+0.511216 
## [94] train-rmse:2.205977+0.146715    test-rmse:2.499386+0.511441 
## [95] train-rmse:2.201103+0.146927    test-rmse:2.496905+0.511818 
## [96] train-rmse:2.196367+0.147089    test-rmse:2.491203+0.513648 
## [97] train-rmse:2.191674+0.147251    test-rmse:2.485256+0.511291 
## [98] train-rmse:2.187059+0.147429    test-rmse:2.483830+0.509756 
## [99] train-rmse:2.182575+0.147647    test-rmse:2.483913+0.511272 
## [100]    train-rmse:2.178131+0.147909    test-rmse:2.479997+0.509573 
## [101]    train-rmse:2.173785+0.148128    test-rmse:2.470002+0.507448 
## [102]    train-rmse:2.169450+0.148273    test-rmse:2.466489+0.504768 
## [103]    train-rmse:2.165192+0.148463    test-rmse:2.463834+0.504916 
## [104]    train-rmse:2.160818+0.148613    test-rmse:2.467371+0.505415 
## [105]    train-rmse:2.156725+0.148757    test-rmse:2.463289+0.508423 
## [106]    train-rmse:2.152656+0.148891    test-rmse:2.458879+0.511684 
## [107]    train-rmse:2.148739+0.149019    test-rmse:2.458795+0.514330 
## [108]    train-rmse:2.144874+0.149154    test-rmse:2.453149+0.520267 
## [109]    train-rmse:2.140995+0.149327    test-rmse:2.447521+0.522846 
## [110]    train-rmse:2.137156+0.149452    test-rmse:2.449210+0.532022 
## [111]    train-rmse:2.133472+0.149541    test-rmse:2.444691+0.525474 
## [112]    train-rmse:2.129853+0.149669    test-rmse:2.442249+0.524142 
## [113]    train-rmse:2.126246+0.149766    test-rmse:2.434719+0.524330 
## [114]    train-rmse:2.122697+0.149917    test-rmse:2.430685+0.522764 
## [115]    train-rmse:2.119210+0.150081    test-rmse:2.427342+0.521615 
## [116]    train-rmse:2.115781+0.150216    test-rmse:2.424525+0.520490 
## [117]    train-rmse:2.112380+0.150325    test-rmse:2.423313+0.521273 
## [118]    train-rmse:2.108953+0.150476    test-rmse:2.422517+0.516549 
## [119]    train-rmse:2.105677+0.150671    test-rmse:2.420423+0.515395 
## [120]    train-rmse:2.102398+0.150870    test-rmse:2.416531+0.514400 
## [121]    train-rmse:2.099180+0.151084    test-rmse:2.414416+0.516497 
## [122]    train-rmse:2.095931+0.151305    test-rmse:2.413859+0.515465 
## [123]    train-rmse:2.092699+0.151551    test-rmse:2.415142+0.518183 
## [124]    train-rmse:2.089620+0.151842    test-rmse:2.417587+0.518068 
## [125]    train-rmse:2.086565+0.151936    test-rmse:2.412923+0.517969 
## [126]    train-rmse:2.083606+0.152129    test-rmse:2.415516+0.516895 
## [127]    train-rmse:2.080646+0.152285    test-rmse:2.414026+0.516319 
## [128]    train-rmse:2.077733+0.152407    test-rmse:2.406086+0.519584 
## [129]    train-rmse:2.074980+0.152500    test-rmse:2.406851+0.521420 
## [130]    train-rmse:2.072238+0.152639    test-rmse:2.403601+0.523102 
## [131]    train-rmse:2.069519+0.152775    test-rmse:2.402458+0.524990 
## [132]    train-rmse:2.066787+0.152869    test-rmse:2.403949+0.522883 
## [133]    train-rmse:2.064115+0.152952    test-rmse:2.401448+0.533114 
## [134]    train-rmse:2.061528+0.153016    test-rmse:2.402942+0.529777 
## [135]    train-rmse:2.058834+0.153060    test-rmse:2.398523+0.528256 
## [136]    train-rmse:2.056233+0.153109    test-rmse:2.398027+0.527354 
## [137]    train-rmse:2.053633+0.153204    test-rmse:2.393847+0.526627 
## [138]    train-rmse:2.051063+0.153345    test-rmse:2.388441+0.526304 
## [139]    train-rmse:2.048539+0.153456    test-rmse:2.387279+0.526589 
## [140]    train-rmse:2.046014+0.153694    test-rmse:2.388353+0.527490 
## [141]    train-rmse:2.043577+0.153856    test-rmse:2.383595+0.527727 
## [142]    train-rmse:2.041164+0.154017    test-rmse:2.381066+0.528620 
## [143]    train-rmse:2.038784+0.154151    test-rmse:2.380500+0.527006 
## [144]    train-rmse:2.036412+0.154258    test-rmse:2.380707+0.524190 
## [145]    train-rmse:2.034086+0.154383    test-rmse:2.382855+0.525310 
## [146]    train-rmse:2.031708+0.154510    test-rmse:2.381313+0.523398 
## [147]    train-rmse:2.029437+0.154596    test-rmse:2.377989+0.521358 
## [148]    train-rmse:2.027164+0.154667    test-rmse:2.377713+0.523390 
## [149]    train-rmse:2.024944+0.154760    test-rmse:2.380080+0.526565 
## [150]    train-rmse:2.022743+0.154846    test-rmse:2.378791+0.524722 
## [151]    train-rmse:2.020549+0.154917    test-rmse:2.373761+0.525836 
## [152]    train-rmse:2.018370+0.155006    test-rmse:2.371462+0.527314 
## [153]    train-rmse:2.016199+0.155087    test-rmse:2.370762+0.525444 
## [154]    train-rmse:2.014107+0.155174    test-rmse:2.370955+0.524255 
## [155]    train-rmse:2.012066+0.155269    test-rmse:2.366714+0.525832 
## [156]    train-rmse:2.010023+0.155362    test-rmse:2.366091+0.526830 
## [157]    train-rmse:2.007958+0.155413    test-rmse:2.367146+0.525370 
## [158]    train-rmse:2.005879+0.155480    test-rmse:2.364436+0.526104 
## [159]    train-rmse:2.003824+0.155545    test-rmse:2.361291+0.524049 
## [160]    train-rmse:2.001776+0.155635    test-rmse:2.362336+0.528869 
## [161]    train-rmse:1.999808+0.155720    test-rmse:2.361522+0.528155 
## [162]    train-rmse:1.997834+0.155888    test-rmse:2.360490+0.528503 
## [163]    train-rmse:1.995891+0.155981    test-rmse:2.360227+0.527082 
## [164]    train-rmse:1.993944+0.156203    test-rmse:2.359318+0.529094 
## [165]    train-rmse:1.992013+0.156315    test-rmse:2.355058+0.528883 
## [166]    train-rmse:1.990086+0.156395    test-rmse:2.355479+0.531203 
## [167]    train-rmse:1.988190+0.156496    test-rmse:2.355700+0.531603 
## [168]    train-rmse:1.986364+0.156577    test-rmse:2.354811+0.534733 
## [169]    train-rmse:1.984567+0.156696    test-rmse:2.353075+0.534619 
## [170]    train-rmse:1.982740+0.156802    test-rmse:2.349891+0.536204 
## [171]    train-rmse:1.980942+0.156927    test-rmse:2.348468+0.535927 
## [172]    train-rmse:1.979123+0.157019    test-rmse:2.345923+0.532890 
## [173]    train-rmse:1.977309+0.157149    test-rmse:2.347406+0.529356 
## [174]    train-rmse:1.975521+0.157252    test-rmse:2.347165+0.528543 
## [175]    train-rmse:1.973758+0.157376    test-rmse:2.348810+0.534322 
## [176]    train-rmse:1.972048+0.157502    test-rmse:2.347994+0.535805 
## [177]    train-rmse:1.970275+0.157571    test-rmse:2.345786+0.537026 
## [178]    train-rmse:1.968585+0.157688    test-rmse:2.340637+0.538317 
## [179]    train-rmse:1.966871+0.157781    test-rmse:2.339436+0.535071 
## [180]    train-rmse:1.965097+0.157910    test-rmse:2.339688+0.535998 
## [181]    train-rmse:1.963429+0.158043    test-rmse:2.338828+0.534407 
## [182]    train-rmse:1.961795+0.158157    test-rmse:2.336076+0.533151 
## [183]    train-rmse:1.960176+0.158236    test-rmse:2.338860+0.533141 
## [184]    train-rmse:1.958556+0.158299    test-rmse:2.340356+0.531334 
## [185]    train-rmse:1.956942+0.158377    test-rmse:2.338940+0.530650 
## [186]    train-rmse:1.955335+0.158452    test-rmse:2.336943+0.533177 
## [187]    train-rmse:1.953766+0.158532    test-rmse:2.335884+0.536629 
## [188]    train-rmse:1.952214+0.158609    test-rmse:2.333962+0.536070 
## [189]    train-rmse:1.950630+0.158675    test-rmse:2.334178+0.535876 
## [190]    train-rmse:1.949058+0.158731    test-rmse:2.335226+0.536310 
## [191]    train-rmse:1.947415+0.158978    test-rmse:2.333988+0.536476 
## [192]    train-rmse:1.945876+0.159055    test-rmse:2.330258+0.537619 
## [193]    train-rmse:1.944293+0.159082    test-rmse:2.332427+0.537416 
## [194]    train-rmse:1.942784+0.159173    test-rmse:2.332998+0.536321 
## [195]    train-rmse:1.941312+0.159270    test-rmse:2.332104+0.534513 
## [196]    train-rmse:1.939859+0.159354    test-rmse:2.332857+0.536478 
## [197]    train-rmse:1.938401+0.159436    test-rmse:2.331459+0.536515 
## [198]    train-rmse:1.936919+0.159557    test-rmse:2.332298+0.535629 
## Stopping. Best iteration:
## [192]    train-rmse:1.945876+0.159055    test-rmse:2.330258+0.537619
## 
## 36 
## [1]  train-rmse:6.932368+0.144678    test-rmse:6.948900+0.638384 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.960930+0.132064    test-rmse:6.009314+0.603108 
## [3]  train-rmse:5.217190+0.120717    test-rmse:5.333999+0.597041 
## [4]  train-rmse:4.632193+0.107249    test-rmse:4.761541+0.523701 
## [5]  train-rmse:4.172349+0.102847    test-rmse:4.350363+0.510949 
## [6]  train-rmse:3.820777+0.100616    test-rmse:3.987368+0.472047 
## [7]  train-rmse:3.541376+0.104645    test-rmse:3.761096+0.438442 
## [8]  train-rmse:3.315102+0.093705    test-rmse:3.544378+0.437818 
## [9]  train-rmse:3.134909+0.094960    test-rmse:3.358410+0.426933 
## [10] train-rmse:2.994146+0.093114    test-rmse:3.259494+0.432694 
## [11] train-rmse:2.873252+0.105784    test-rmse:3.165052+0.412729 
## [12] train-rmse:2.772546+0.110619    test-rmse:3.083968+0.417230 
## [13] train-rmse:2.688733+0.102054    test-rmse:3.019434+0.439719 
## [14] train-rmse:2.614284+0.101320    test-rmse:2.960815+0.440843 
## [15] train-rmse:2.557544+0.103587    test-rmse:2.931617+0.434901 
## [16] train-rmse:2.501668+0.104218    test-rmse:2.905082+0.422913 
## [17] train-rmse:2.443318+0.096359    test-rmse:2.858084+0.450914 
## [18] train-rmse:2.399498+0.097629    test-rmse:2.822579+0.445394 
## [19] train-rmse:2.350989+0.099720    test-rmse:2.782550+0.455621 
## [20] train-rmse:2.300617+0.096117    test-rmse:2.765617+0.471741 
## [21] train-rmse:2.257967+0.080737    test-rmse:2.724085+0.487521 
## [22] train-rmse:2.221718+0.080336    test-rmse:2.718665+0.483453 
## [23] train-rmse:2.183713+0.084979    test-rmse:2.692612+0.484784 
## [24] train-rmse:2.146676+0.086994    test-rmse:2.679839+0.478677 
## [25] train-rmse:2.112171+0.088351    test-rmse:2.658507+0.477091 
## [26] train-rmse:2.079940+0.088733    test-rmse:2.618220+0.474514 
## [27] train-rmse:2.049980+0.084308    test-rmse:2.599014+0.481244 
## [28] train-rmse:2.024194+0.084475    test-rmse:2.575188+0.484927 
## [29] train-rmse:1.993286+0.090416    test-rmse:2.554229+0.459441 
## [30] train-rmse:1.969235+0.092466    test-rmse:2.539198+0.452194 
## [31] train-rmse:1.943258+0.092581    test-rmse:2.529917+0.460876 
## [32] train-rmse:1.918618+0.086182    test-rmse:2.508788+0.475919 
## [33] train-rmse:1.897261+0.085511    test-rmse:2.495852+0.475380 
## [34] train-rmse:1.876872+0.086135    test-rmse:2.477607+0.474169 
## [35] train-rmse:1.852989+0.086534    test-rmse:2.465972+0.474735 
## [36] train-rmse:1.832367+0.087249    test-rmse:2.452559+0.478302 
## [37] train-rmse:1.814393+0.087592    test-rmse:2.439940+0.480655 
## [38] train-rmse:1.796205+0.084936    test-rmse:2.423716+0.474868 
## [39] train-rmse:1.774511+0.084898    test-rmse:2.416745+0.471650 
## [40] train-rmse:1.753984+0.085647    test-rmse:2.404946+0.478217 
## [41] train-rmse:1.739172+0.085534    test-rmse:2.393415+0.474226 
## [42] train-rmse:1.719766+0.076087    test-rmse:2.382278+0.482949 
## [43] train-rmse:1.703880+0.075767    test-rmse:2.369026+0.490223 
## [44] train-rmse:1.688352+0.075823    test-rmse:2.371133+0.490340 
## [45] train-rmse:1.674993+0.075124    test-rmse:2.364192+0.487659 
## [46] train-rmse:1.662735+0.075391    test-rmse:2.358972+0.491236 
## [47] train-rmse:1.646929+0.070680    test-rmse:2.354664+0.487409 
## [48] train-rmse:1.631974+0.071570    test-rmse:2.343816+0.483900 
## [49] train-rmse:1.618673+0.073604    test-rmse:2.329918+0.477203 
## [50] train-rmse:1.604996+0.076603    test-rmse:2.322255+0.474303 
## [51] train-rmse:1.594216+0.076819    test-rmse:2.311492+0.476949 
## [52] train-rmse:1.583071+0.076832    test-rmse:2.306387+0.474344 
## [53] train-rmse:1.572596+0.077661    test-rmse:2.297399+0.482079 
## [54] train-rmse:1.558507+0.075413    test-rmse:2.291613+0.488037 
## [55] train-rmse:1.546594+0.077897    test-rmse:2.286965+0.487815 
## [56] train-rmse:1.533224+0.077140    test-rmse:2.283836+0.486460 
## [57] train-rmse:1.523463+0.078654    test-rmse:2.279273+0.487383 
## [58] train-rmse:1.514777+0.078985    test-rmse:2.275328+0.490189 
## [59] train-rmse:1.504078+0.079716    test-rmse:2.267245+0.494605 
## [60] train-rmse:1.495642+0.080045    test-rmse:2.262791+0.493932 
## [61] train-rmse:1.485486+0.077693    test-rmse:2.261814+0.490007 
## [62] train-rmse:1.475526+0.078345    test-rmse:2.255104+0.492607 
## [63] train-rmse:1.465624+0.080095    test-rmse:2.249943+0.491045 
## [64] train-rmse:1.456883+0.081637    test-rmse:2.244923+0.491300 
## [65] train-rmse:1.447754+0.081002    test-rmse:2.235853+0.486071 
## [66] train-rmse:1.438205+0.084597    test-rmse:2.228555+0.484824 
## [67] train-rmse:1.426062+0.085165    test-rmse:2.230783+0.487990 
## [68] train-rmse:1.417445+0.085621    test-rmse:2.224017+0.489033 
## [69] train-rmse:1.409484+0.087160    test-rmse:2.221438+0.486635 
## [70] train-rmse:1.401369+0.086157    test-rmse:2.217621+0.488227 
## [71] train-rmse:1.394841+0.085994    test-rmse:2.211330+0.491837 
## [72] train-rmse:1.388105+0.085991    test-rmse:2.211572+0.492345 
## [73] train-rmse:1.379040+0.086613    test-rmse:2.204750+0.495232 
## [74] train-rmse:1.369356+0.084658    test-rmse:2.209238+0.498602 
## [75] train-rmse:1.362826+0.083562    test-rmse:2.204709+0.496283 
## [76] train-rmse:1.353906+0.084797    test-rmse:2.203435+0.499806 
## [77] train-rmse:1.345995+0.088389    test-rmse:2.203698+0.504078 
## [78] train-rmse:1.340085+0.089436    test-rmse:2.198607+0.504425 
## [79] train-rmse:1.333927+0.090153    test-rmse:2.194902+0.502295 
## [80] train-rmse:1.325417+0.089340    test-rmse:2.193418+0.500386 
## [81] train-rmse:1.318534+0.089209    test-rmse:2.195257+0.507729 
## [82] train-rmse:1.304860+0.081703    test-rmse:2.188145+0.507938 
## [83] train-rmse:1.296955+0.082084    test-rmse:2.185450+0.506828 
## [84] train-rmse:1.291012+0.082306    test-rmse:2.189875+0.509275 
## [85] train-rmse:1.285424+0.082810    test-rmse:2.184789+0.509571 
## [86] train-rmse:1.274281+0.084543    test-rmse:2.181022+0.512912 
## [87] train-rmse:1.267525+0.084919    test-rmse:2.175475+0.515342 
## [88] train-rmse:1.261861+0.085175    test-rmse:2.177839+0.518486 
## [89] train-rmse:1.256871+0.085227    test-rmse:2.176884+0.521333 
## [90] train-rmse:1.241579+0.076628    test-rmse:2.174904+0.523918 
## [91] train-rmse:1.235705+0.076812    test-rmse:2.174457+0.523676 
## [92] train-rmse:1.229613+0.076142    test-rmse:2.169570+0.529301 
## [93] train-rmse:1.223546+0.078652    test-rmse:2.166143+0.526765 
## [94] train-rmse:1.219323+0.078717    test-rmse:2.170284+0.531505 
## [95] train-rmse:1.214490+0.079405    test-rmse:2.165263+0.531329 
## [96] train-rmse:1.210113+0.079244    test-rmse:2.166867+0.532194 
## [97] train-rmse:1.202248+0.082209    test-rmse:2.165016+0.531605 
## [98] train-rmse:1.194936+0.082938    test-rmse:2.156735+0.532155 
## [99] train-rmse:1.186828+0.084103    test-rmse:2.153170+0.530948 
## [100]    train-rmse:1.180448+0.084422    test-rmse:2.159408+0.531872 
## [101]    train-rmse:1.175184+0.083963    test-rmse:2.157500+0.533123 
## [102]    train-rmse:1.168124+0.085059    test-rmse:2.154980+0.534581 
## [103]    train-rmse:1.163901+0.085376    test-rmse:2.152431+0.535148 
## [104]    train-rmse:1.158269+0.084566    test-rmse:2.150454+0.535971 
## [105]    train-rmse:1.153766+0.083609    test-rmse:2.150946+0.538055 
## [106]    train-rmse:1.148857+0.084824    test-rmse:2.148765+0.537785 
## [107]    train-rmse:1.145456+0.084860    test-rmse:2.146697+0.535753 
## [108]    train-rmse:1.142130+0.084506    test-rmse:2.145912+0.538151 
## [109]    train-rmse:1.138447+0.085911    test-rmse:2.145402+0.537239 
## [110]    train-rmse:1.130063+0.088307    test-rmse:2.141796+0.536584 
## [111]    train-rmse:1.125370+0.088914    test-rmse:2.138590+0.540375 
## [112]    train-rmse:1.121218+0.089297    test-rmse:2.135880+0.541098 
## [113]    train-rmse:1.116837+0.089407    test-rmse:2.135064+0.540099 
## [114]    train-rmse:1.112619+0.088739    test-rmse:2.134390+0.538982 
## [115]    train-rmse:1.109150+0.087733    test-rmse:2.135616+0.536948 
## [116]    train-rmse:1.104572+0.088248    test-rmse:2.134600+0.535167 
## [117]    train-rmse:1.101718+0.088456    test-rmse:2.134942+0.532726 
## [118]    train-rmse:1.097428+0.089356    test-rmse:2.130199+0.534286 
## [119]    train-rmse:1.086895+0.088802    test-rmse:2.128277+0.533148 
## [120]    train-rmse:1.083223+0.089311    test-rmse:2.125704+0.532608 
## [121]    train-rmse:1.079126+0.088750    test-rmse:2.125272+0.532860 
## [122]    train-rmse:1.076342+0.089497    test-rmse:2.123581+0.534442 
## [123]    train-rmse:1.073005+0.089505    test-rmse:2.123237+0.534435 
## [124]    train-rmse:1.067983+0.090072    test-rmse:2.117647+0.535960 
## [125]    train-rmse:1.061596+0.089883    test-rmse:2.118196+0.536020 
## [126]    train-rmse:1.056086+0.090150    test-rmse:2.116709+0.540291 
## [127]    train-rmse:1.051812+0.090887    test-rmse:2.117630+0.540437 
## [128]    train-rmse:1.047762+0.091957    test-rmse:2.116967+0.541242 
## [129]    train-rmse:1.043289+0.093020    test-rmse:2.120046+0.542576 
## [130]    train-rmse:1.037626+0.096904    test-rmse:2.128147+0.549438 
## [131]    train-rmse:1.031101+0.089673    test-rmse:2.127820+0.548931 
## [132]    train-rmse:1.026427+0.089725    test-rmse:2.126916+0.549573 
## Stopping. Best iteration:
## [126]    train-rmse:1.056086+0.090150    test-rmse:2.116709+0.540291
## 
## 37 
## [1]  train-rmse:6.873187+0.141205    test-rmse:6.923907+0.627060 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.845586+0.117532    test-rmse:5.941820+0.565792 
## [3]  train-rmse:5.031875+0.110097    test-rmse:5.158349+0.530847 
## [4]  train-rmse:4.401799+0.099738    test-rmse:4.582974+0.489461 
## [5]  train-rmse:3.898768+0.087936    test-rmse:4.104075+0.468481 
## [6]  train-rmse:3.505076+0.075707    test-rmse:3.725996+0.453056 
## [7]  train-rmse:3.185096+0.064339    test-rmse:3.470070+0.435278 
## [8]  train-rmse:2.944323+0.071192    test-rmse:3.263966+0.445111 
## [9]  train-rmse:2.761156+0.075885    test-rmse:3.125208+0.434905 
## [10] train-rmse:2.598909+0.072264    test-rmse:3.017163+0.441081 
## [11] train-rmse:2.472128+0.059423    test-rmse:2.938055+0.441379 
## [12] train-rmse:2.357320+0.056177    test-rmse:2.838296+0.435737 
## [13] train-rmse:2.271224+0.058741    test-rmse:2.771466+0.452940 
## [14] train-rmse:2.186756+0.063000    test-rmse:2.738349+0.456068 
## [15] train-rmse:2.119589+0.065531    test-rmse:2.707483+0.457565 
## [16] train-rmse:2.059498+0.066696    test-rmse:2.654932+0.464343 
## [17] train-rmse:2.003859+0.073246    test-rmse:2.630036+0.467551 
## [18] train-rmse:1.944147+0.065650    test-rmse:2.594395+0.469575 
## [19] train-rmse:1.901348+0.067719    test-rmse:2.564277+0.471075 
## [20] train-rmse:1.854998+0.063200    test-rmse:2.545658+0.466922 
## [21] train-rmse:1.808558+0.053948    test-rmse:2.525368+0.475366 
## [22] train-rmse:1.774673+0.054461    test-rmse:2.506615+0.481601 
## [23] train-rmse:1.735492+0.056159    test-rmse:2.478889+0.480174 
## [24] train-rmse:1.699336+0.060669    test-rmse:2.453451+0.467978 
## [25] train-rmse:1.664512+0.057033    test-rmse:2.433634+0.480090 
## [26] train-rmse:1.630121+0.051936    test-rmse:2.414527+0.475167 
## [27] train-rmse:1.602252+0.051574    test-rmse:2.396770+0.477524 
## [28] train-rmse:1.574712+0.051630    test-rmse:2.387147+0.466854 
## [29] train-rmse:1.546198+0.052681    test-rmse:2.371674+0.485452 
## [30] train-rmse:1.520611+0.051739    test-rmse:2.363258+0.490211 
## [31] train-rmse:1.491543+0.051100    test-rmse:2.338805+0.487482 
## [32] train-rmse:1.471469+0.052483    test-rmse:2.328170+0.495257 
## [33] train-rmse:1.449007+0.050316    test-rmse:2.323195+0.494275 
## [34] train-rmse:1.426246+0.047895    test-rmse:2.309392+0.488138 
## [35] train-rmse:1.406436+0.049499    test-rmse:2.304607+0.488640 
## [36] train-rmse:1.385906+0.054298    test-rmse:2.301725+0.487918 
## [37] train-rmse:1.364993+0.054892    test-rmse:2.296562+0.493620 
## [38] train-rmse:1.348528+0.056248    test-rmse:2.288466+0.497076 
## [39] train-rmse:1.331639+0.062710    test-rmse:2.274176+0.485923 
## [40] train-rmse:1.308744+0.059046    test-rmse:2.267297+0.486002 
## [41] train-rmse:1.276985+0.048886    test-rmse:2.262756+0.484196 
## [42] train-rmse:1.262250+0.048238    test-rmse:2.252942+0.489250 
## [43] train-rmse:1.248140+0.048942    test-rmse:2.244313+0.488795 
## [44] train-rmse:1.234040+0.049178    test-rmse:2.236555+0.488280 
## [45] train-rmse:1.219457+0.047263    test-rmse:2.233880+0.493056 
## [46] train-rmse:1.203548+0.045039    test-rmse:2.229750+0.489297 
## [47] train-rmse:1.190031+0.044930    test-rmse:2.226502+0.494911 
## [48] train-rmse:1.179203+0.045694    test-rmse:2.219859+0.490330 
## [49] train-rmse:1.166827+0.043728    test-rmse:2.211033+0.491696 
## [50] train-rmse:1.154739+0.044255    test-rmse:2.208421+0.492177 
## [51] train-rmse:1.140122+0.044010    test-rmse:2.202983+0.499920 
## [52] train-rmse:1.124023+0.044757    test-rmse:2.195426+0.501164 
## [53] train-rmse:1.110570+0.040947    test-rmse:2.191910+0.499832 
## [54] train-rmse:1.099215+0.042421    test-rmse:2.186980+0.501258 
## [55] train-rmse:1.089441+0.043119    test-rmse:2.184896+0.496821 
## [56] train-rmse:1.077291+0.038206    test-rmse:2.180157+0.496944 
## [57] train-rmse:1.069623+0.038635    test-rmse:2.177773+0.498501 
## [58] train-rmse:1.055187+0.040544    test-rmse:2.173264+0.498169 
## [59] train-rmse:1.041799+0.043715    test-rmse:2.173010+0.502659 
## [60] train-rmse:1.018570+0.044418    test-rmse:2.165041+0.504028 
## [61] train-rmse:1.008859+0.045922    test-rmse:2.168476+0.510516 
## [62] train-rmse:0.995506+0.044828    test-rmse:2.163911+0.509976 
## [63] train-rmse:0.984158+0.045535    test-rmse:2.154667+0.513364 
## [64] train-rmse:0.976505+0.046865    test-rmse:2.150597+0.508906 
## [65] train-rmse:0.966147+0.048427    test-rmse:2.150687+0.509612 
## [66] train-rmse:0.954143+0.051473    test-rmse:2.145442+0.506913 
## [67] train-rmse:0.945818+0.053558    test-rmse:2.144345+0.510661 
## [68] train-rmse:0.933262+0.053279    test-rmse:2.144373+0.508863 
## [69] train-rmse:0.924863+0.053299    test-rmse:2.141399+0.507863 
## [70] train-rmse:0.915497+0.051065    test-rmse:2.135640+0.511315 
## [71] train-rmse:0.909486+0.050993    test-rmse:2.134112+0.512339 
## [72] train-rmse:0.895644+0.046599    test-rmse:2.136675+0.511065 
## [73] train-rmse:0.886791+0.046809    test-rmse:2.137395+0.508248 
## [74] train-rmse:0.879058+0.048422    test-rmse:2.131334+0.508591 
## [75] train-rmse:0.870995+0.048556    test-rmse:2.130394+0.510016 
## [76] train-rmse:0.863197+0.048330    test-rmse:2.131336+0.511868 
## [77] train-rmse:0.855828+0.046613    test-rmse:2.126343+0.515031 
## [78] train-rmse:0.847089+0.045353    test-rmse:2.126329+0.514536 
## [79] train-rmse:0.839421+0.046314    test-rmse:2.122782+0.515846 
## [80] train-rmse:0.833676+0.049094    test-rmse:2.117987+0.509981 
## [81] train-rmse:0.821740+0.049607    test-rmse:2.117735+0.507077 
## [82] train-rmse:0.812126+0.049264    test-rmse:2.118570+0.507869 
## [83] train-rmse:0.805735+0.050776    test-rmse:2.120008+0.506258 
## [84] train-rmse:0.800079+0.050958    test-rmse:2.119268+0.506288 
## [85] train-rmse:0.790699+0.049955    test-rmse:2.115227+0.507599 
## [86] train-rmse:0.782835+0.049242    test-rmse:2.115715+0.507327 
## [87] train-rmse:0.776087+0.053456    test-rmse:2.114062+0.507384 
## [88] train-rmse:0.769401+0.052578    test-rmse:2.112144+0.509899 
## [89] train-rmse:0.762185+0.051825    test-rmse:2.112157+0.510341 
## [90] train-rmse:0.757888+0.051323    test-rmse:2.109726+0.510211 
## [91] train-rmse:0.749627+0.049339    test-rmse:2.109440+0.511698 
## [92] train-rmse:0.743573+0.049094    test-rmse:2.106570+0.511151 
## [93] train-rmse:0.737455+0.049214    test-rmse:2.109972+0.510767 
## [94] train-rmse:0.728534+0.049548    test-rmse:2.107169+0.510088 
## [95] train-rmse:0.720916+0.051815    test-rmse:2.104894+0.506721 
## [96] train-rmse:0.714940+0.055090    test-rmse:2.102786+0.507445 
## [97] train-rmse:0.705318+0.050388    test-rmse:2.100948+0.508343 
## [98] train-rmse:0.699455+0.049596    test-rmse:2.100064+0.507118 
## [99] train-rmse:0.693449+0.049519    test-rmse:2.097147+0.509534 
## [100]    train-rmse:0.687004+0.050279    test-rmse:2.096730+0.506136 
## [101]    train-rmse:0.682272+0.051256    test-rmse:2.096909+0.506372 
## [102]    train-rmse:0.675366+0.054042    test-rmse:2.096011+0.507547 
## [103]    train-rmse:0.668620+0.049474    test-rmse:2.099646+0.509631 
## [104]    train-rmse:0.661565+0.044621    test-rmse:2.100384+0.507870 
## [105]    train-rmse:0.657063+0.043601    test-rmse:2.098581+0.507862 
## [106]    train-rmse:0.649524+0.041159    test-rmse:2.100128+0.506328 
## [107]    train-rmse:0.645075+0.040574    test-rmse:2.097832+0.505509 
## [108]    train-rmse:0.639654+0.040190    test-rmse:2.098090+0.505159 
## Stopping. Best iteration:
## [102]    train-rmse:0.675366+0.054042    test-rmse:2.096011+0.507547
## 
## 38 
## [1]  train-rmse:6.818291+0.139987    test-rmse:6.848573+0.616402 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.729816+0.113479    test-rmse:5.817880+0.537260 
## [3]  train-rmse:4.877239+0.093316    test-rmse:5.017484+0.475151 
## [4]  train-rmse:4.202226+0.076251    test-rmse:4.382041+0.436443 
## [5]  train-rmse:3.677508+0.077628    test-rmse:3.917839+0.414104 
## [6]  train-rmse:3.258510+0.063765    test-rmse:3.546266+0.437854 
## [7]  train-rmse:2.940209+0.059571    test-rmse:3.289734+0.414108 
## [8]  train-rmse:2.684328+0.043655    test-rmse:3.088219+0.449895 
## [9]  train-rmse:2.484211+0.045756    test-rmse:2.932888+0.435031 
## [10] train-rmse:2.302582+0.043822    test-rmse:2.821557+0.441524 
## [11] train-rmse:2.168963+0.048011    test-rmse:2.756432+0.440256 
## [12] train-rmse:2.061138+0.052897    test-rmse:2.686088+0.459346 
## [13] train-rmse:1.955771+0.044581    test-rmse:2.616788+0.440721 
## [14] train-rmse:1.875167+0.047922    test-rmse:2.568852+0.443375 
## [15] train-rmse:1.799660+0.042055    test-rmse:2.540031+0.445754 
## [16] train-rmse:1.741960+0.048189    test-rmse:2.503362+0.448098 
## [17] train-rmse:1.683005+0.049674    test-rmse:2.474824+0.453347 
## [18] train-rmse:1.628794+0.044644    test-rmse:2.446407+0.457903 
## [19] train-rmse:1.570498+0.037601    test-rmse:2.426248+0.467053 
## [20] train-rmse:1.529634+0.035234    test-rmse:2.412393+0.470095 
## [21] train-rmse:1.488275+0.033434    test-rmse:2.376922+0.469004 
## [22] train-rmse:1.451013+0.038208    test-rmse:2.365222+0.463112 
## [23] train-rmse:1.414726+0.038503    test-rmse:2.347251+0.471591 
## [24] train-rmse:1.377377+0.039466    test-rmse:2.331198+0.465156 
## [25] train-rmse:1.341886+0.030049    test-rmse:2.316671+0.469484 
## [26] train-rmse:1.312324+0.027322    test-rmse:2.323159+0.464882 
## [27] train-rmse:1.281233+0.031365    test-rmse:2.304777+0.473550 
## [28] train-rmse:1.252088+0.030666    test-rmse:2.297430+0.468241 
## [29] train-rmse:1.225216+0.029710    test-rmse:2.288479+0.469705 
## [30] train-rmse:1.206175+0.028565    test-rmse:2.278923+0.469857 
## [31] train-rmse:1.175408+0.029658    test-rmse:2.270532+0.476171 
## [32] train-rmse:1.145561+0.035161    test-rmse:2.265915+0.477262 
## [33] train-rmse:1.127005+0.036172    test-rmse:2.256977+0.479503 
## [34] train-rmse:1.099781+0.033098    test-rmse:2.246680+0.478149 
## [35] train-rmse:1.076759+0.026408    test-rmse:2.248043+0.478376 
## [36] train-rmse:1.060565+0.027431    test-rmse:2.238927+0.482337 
## [37] train-rmse:1.040400+0.027433    test-rmse:2.226920+0.474665 
## [38] train-rmse:1.017591+0.031103    test-rmse:2.223311+0.475793 
## [39] train-rmse:1.000382+0.029108    test-rmse:2.216984+0.477006 
## [40] train-rmse:0.982854+0.021933    test-rmse:2.213223+0.479995 
## [41] train-rmse:0.965134+0.025970    test-rmse:2.211004+0.473960 
## [42] train-rmse:0.950016+0.024949    test-rmse:2.212455+0.475329 
## [43] train-rmse:0.929038+0.015778    test-rmse:2.208684+0.478534 
## [44] train-rmse:0.915627+0.013736    test-rmse:2.200528+0.480997 
## [45] train-rmse:0.903240+0.014388    test-rmse:2.197140+0.484175 
## [46] train-rmse:0.889532+0.013729    test-rmse:2.192518+0.487195 
## [47] train-rmse:0.872580+0.010595    test-rmse:2.190087+0.485694 
## [48] train-rmse:0.859698+0.012990    test-rmse:2.185426+0.483900 
## [49] train-rmse:0.846699+0.013909    test-rmse:2.184881+0.483214 
## [50] train-rmse:0.829777+0.017376    test-rmse:2.180442+0.482671 
## [51] train-rmse:0.812179+0.018072    test-rmse:2.169228+0.486528 
## [52] train-rmse:0.800903+0.019493    test-rmse:2.163404+0.489601 
## [53] train-rmse:0.789898+0.022027    test-rmse:2.161865+0.489653 
## [54] train-rmse:0.778330+0.024524    test-rmse:2.159622+0.491847 
## [55] train-rmse:0.759866+0.023662    test-rmse:2.152578+0.491707 
## [56] train-rmse:0.750555+0.024915    test-rmse:2.152184+0.490077 
## [57] train-rmse:0.737573+0.024240    test-rmse:2.151024+0.491605 
## [58] train-rmse:0.724152+0.028729    test-rmse:2.152935+0.489500 
## [59] train-rmse:0.712928+0.025400    test-rmse:2.150774+0.494783 
## [60] train-rmse:0.703301+0.026650    test-rmse:2.149374+0.493431 
## [61] train-rmse:0.694232+0.029782    test-rmse:2.146377+0.494680 
## [62] train-rmse:0.684345+0.029594    test-rmse:2.147085+0.495865 
## [63] train-rmse:0.673308+0.026119    test-rmse:2.148166+0.495122 
## [64] train-rmse:0.667158+0.025160    test-rmse:2.144898+0.495195 
## [65] train-rmse:0.658794+0.027724    test-rmse:2.144708+0.492884 
## [66] train-rmse:0.648964+0.027866    test-rmse:2.143985+0.492366 
## [67] train-rmse:0.633700+0.025188    test-rmse:2.142272+0.489477 
## [68] train-rmse:0.625981+0.023937    test-rmse:2.140752+0.491203 
## [69] train-rmse:0.615607+0.022111    test-rmse:2.139711+0.489457 
## [70] train-rmse:0.608125+0.020248    test-rmse:2.140158+0.489859 
## [71] train-rmse:0.602314+0.019957    test-rmse:2.140797+0.490697 
## [72] train-rmse:0.595332+0.019405    test-rmse:2.143353+0.492286 
## [73] train-rmse:0.588811+0.023262    test-rmse:2.142503+0.492850 
## [74] train-rmse:0.581426+0.022385    test-rmse:2.139373+0.493536 
## [75] train-rmse:0.574912+0.023078    test-rmse:2.138023+0.494226 
## [76] train-rmse:0.566854+0.022195    test-rmse:2.140606+0.493291 
## [77] train-rmse:0.560321+0.023985    test-rmse:2.140086+0.492973 
## [78] train-rmse:0.552861+0.023075    test-rmse:2.139384+0.492226 
## [79] train-rmse:0.546331+0.021181    test-rmse:2.137108+0.492459 
## [80] train-rmse:0.535089+0.023973    test-rmse:2.135313+0.495995 
## [81] train-rmse:0.529006+0.022369    test-rmse:2.137391+0.495003 
## [82] train-rmse:0.520025+0.023344    test-rmse:2.136322+0.494871 
## [83] train-rmse:0.512886+0.023265    test-rmse:2.135624+0.495889 
## [84] train-rmse:0.504468+0.024202    test-rmse:2.133344+0.493471 
## [85] train-rmse:0.500768+0.025658    test-rmse:2.133029+0.495410 
## [86] train-rmse:0.496111+0.024650    test-rmse:2.131913+0.495327 
## [87] train-rmse:0.486774+0.023699    test-rmse:2.128515+0.496928 
## [88] train-rmse:0.481350+0.021287    test-rmse:2.129712+0.494925 
## [89] train-rmse:0.474543+0.019645    test-rmse:2.127846+0.495329 
## [90] train-rmse:0.469142+0.022820    test-rmse:2.128322+0.497536 
## [91] train-rmse:0.463828+0.021982    test-rmse:2.128594+0.496815 
## [92] train-rmse:0.457655+0.020965    test-rmse:2.126172+0.497676 
## [93] train-rmse:0.450990+0.021148    test-rmse:2.124425+0.497641 
## [94] train-rmse:0.444833+0.020994    test-rmse:2.122745+0.499101 
## [95] train-rmse:0.439162+0.023033    test-rmse:2.122524+0.498537 
## [96] train-rmse:0.434103+0.023879    test-rmse:2.122169+0.497419 
## [97] train-rmse:0.427740+0.023398    test-rmse:2.120672+0.498839 
## [98] train-rmse:0.422041+0.022634    test-rmse:2.121713+0.498822 
## [99] train-rmse:0.418728+0.022858    test-rmse:2.121503+0.500449 
## [100]    train-rmse:0.414865+0.023130    test-rmse:2.120386+0.501765 
## [101]    train-rmse:0.408878+0.022183    test-rmse:2.119202+0.501808 
## [102]    train-rmse:0.403069+0.021875    test-rmse:2.117561+0.502610 
## [103]    train-rmse:0.398000+0.020643    test-rmse:2.119450+0.501822 
## [104]    train-rmse:0.392589+0.020716    test-rmse:2.118662+0.502037 
## [105]    train-rmse:0.386722+0.022379    test-rmse:2.117412+0.503348 
## [106]    train-rmse:0.381221+0.024883    test-rmse:2.117341+0.503008 
## [107]    train-rmse:0.376961+0.024230    test-rmse:2.119109+0.503235 
## [108]    train-rmse:0.373681+0.024532    test-rmse:2.119224+0.502859 
## [109]    train-rmse:0.370277+0.024218    test-rmse:2.118944+0.503039 
## [110]    train-rmse:0.365079+0.023452    test-rmse:2.118218+0.505007 
## [111]    train-rmse:0.361541+0.023215    test-rmse:2.118487+0.504544 
## [112]    train-rmse:0.357337+0.022360    test-rmse:2.117654+0.505242 
## Stopping. Best iteration:
## [106]    train-rmse:0.381221+0.024883    test-rmse:2.117341+0.503008
## 
## 39 
## [1]  train-rmse:6.780337+0.137838    test-rmse:6.784392+0.599835 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.661086+0.109457    test-rmse:5.716318+0.528060 
## [3]  train-rmse:4.774709+0.098440    test-rmse:4.890532+0.468780 
## [4]  train-rmse:4.083732+0.090183    test-rmse:4.282429+0.427162 
## [5]  train-rmse:3.541519+0.075597    test-rmse:3.833585+0.415869 
## [6]  train-rmse:3.114368+0.065117    test-rmse:3.474975+0.410436 
## [7]  train-rmse:2.788242+0.068632    test-rmse:3.243584+0.434338 
## [8]  train-rmse:2.515182+0.077688    test-rmse:3.056926+0.451066 
## [9]  train-rmse:2.287994+0.064712    test-rmse:2.919292+0.487915 
## [10] train-rmse:2.109889+0.062051    test-rmse:2.803378+0.500704 
## [11] train-rmse:1.972600+0.061231    test-rmse:2.712741+0.497798 
## [12] train-rmse:1.852090+0.051872    test-rmse:2.638607+0.482408 
## [13] train-rmse:1.753749+0.053174    test-rmse:2.582517+0.501580 
## [14] train-rmse:1.671817+0.048291    test-rmse:2.535929+0.503943 
## [15] train-rmse:1.604641+0.049177    test-rmse:2.495408+0.514371 
## [16] train-rmse:1.532636+0.055384    test-rmse:2.471850+0.517670 
## [17] train-rmse:1.479706+0.053326    test-rmse:2.444597+0.525051 
## [18] train-rmse:1.418067+0.043326    test-rmse:2.427980+0.536852 
## [19] train-rmse:1.363446+0.029619    test-rmse:2.411202+0.539784 
## [20] train-rmse:1.316902+0.020542    test-rmse:2.385063+0.527264 
## [21] train-rmse:1.274847+0.022361    test-rmse:2.374361+0.518882 
## [22] train-rmse:1.234302+0.017898    test-rmse:2.359947+0.528761 
## [23] train-rmse:1.195559+0.022440    test-rmse:2.334444+0.534786 
## [24] train-rmse:1.158915+0.023297    test-rmse:2.325141+0.535015 
## [25] train-rmse:1.125916+0.026291    test-rmse:2.319434+0.531681 
## [26] train-rmse:1.094708+0.017843    test-rmse:2.311426+0.523389 
## [27] train-rmse:1.065840+0.018238    test-rmse:2.306788+0.528150 
## [28] train-rmse:1.036756+0.024270    test-rmse:2.297735+0.534956 
## [29] train-rmse:1.014469+0.021723    test-rmse:2.290323+0.538706 
## [30] train-rmse:0.993744+0.020031    test-rmse:2.287868+0.540313 
## [31] train-rmse:0.958637+0.018862    test-rmse:2.282139+0.546384 
## [32] train-rmse:0.930256+0.011530    test-rmse:2.280092+0.546228 
## [33] train-rmse:0.904624+0.007150    test-rmse:2.277252+0.547492 
## [34] train-rmse:0.884638+0.005394    test-rmse:2.273134+0.547959 
## [35] train-rmse:0.857258+0.006283    test-rmse:2.259632+0.544751 
## [36] train-rmse:0.840186+0.007595    test-rmse:2.255930+0.550456 
## [37] train-rmse:0.817710+0.011092    test-rmse:2.248706+0.550283 
## [38] train-rmse:0.796015+0.009160    test-rmse:2.246922+0.554666 
## [39] train-rmse:0.778187+0.011813    test-rmse:2.240307+0.556113 
## [40] train-rmse:0.760478+0.008995    test-rmse:2.241584+0.557228 
## [41] train-rmse:0.742971+0.012696    test-rmse:2.240581+0.560723 
## [42] train-rmse:0.730238+0.011649    test-rmse:2.240506+0.558687 
## [43] train-rmse:0.709898+0.009922    test-rmse:2.234733+0.560032 
## [44] train-rmse:0.692029+0.015070    test-rmse:2.235278+0.567145 
## [45] train-rmse:0.676599+0.016277    test-rmse:2.227688+0.573922 
## [46] train-rmse:0.658572+0.022901    test-rmse:2.225621+0.576442 
## [47] train-rmse:0.637221+0.019173    test-rmse:2.223000+0.578231 
## [48] train-rmse:0.626890+0.020072    test-rmse:2.221635+0.577736 
## [49] train-rmse:0.613461+0.019820    test-rmse:2.220987+0.578013 
## [50] train-rmse:0.597232+0.016996    test-rmse:2.222581+0.579022 
## [51] train-rmse:0.582072+0.016578    test-rmse:2.219060+0.579843 
## [52] train-rmse:0.569477+0.017075    test-rmse:2.220369+0.582098 
## [53] train-rmse:0.557682+0.018159    test-rmse:2.220091+0.584836 
## [54] train-rmse:0.549364+0.019177    test-rmse:2.220946+0.584229 
## [55] train-rmse:0.539950+0.019759    test-rmse:2.222214+0.582857 
## [56] train-rmse:0.527663+0.019607    test-rmse:2.223316+0.581543 
## [57] train-rmse:0.517409+0.020474    test-rmse:2.219941+0.584412 
## Stopping. Best iteration:
## [51] train-rmse:0.582072+0.016578    test-rmse:2.219060+0.579843
## 
## 40 
## [1]  train-rmse:6.756445+0.134251    test-rmse:6.768577+0.621900 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.612353+0.103080    test-rmse:5.698829+0.555902 
## [3]  train-rmse:4.702491+0.082601    test-rmse:4.874663+0.503077 
## [4]  train-rmse:3.988679+0.069556    test-rmse:4.267409+0.469333 
## [5]  train-rmse:3.421554+0.053490    test-rmse:3.806016+0.462387 
## [6]  train-rmse:2.977623+0.040967    test-rmse:3.473957+0.465101 
## [7]  train-rmse:2.626471+0.034889    test-rmse:3.210907+0.443614 
## [8]  train-rmse:2.356031+0.033120    test-rmse:3.043130+0.471956 
## [9]  train-rmse:2.127491+0.027808    test-rmse:2.892477+0.490815 
## [10] train-rmse:1.948554+0.034279    test-rmse:2.772286+0.500296 
## [11] train-rmse:1.785377+0.032742    test-rmse:2.687467+0.513964 
## [12] train-rmse:1.661071+0.023393    test-rmse:2.623265+0.528898 
## [13] train-rmse:1.555597+0.030277    test-rmse:2.574465+0.522365 
## [14] train-rmse:1.467641+0.030820    test-rmse:2.520164+0.526235 
## [15] train-rmse:1.378061+0.037402    test-rmse:2.505105+0.528722 
## [16] train-rmse:1.309230+0.030709    test-rmse:2.485888+0.519314 
## [17] train-rmse:1.252221+0.028084    test-rmse:2.464866+0.524292 
## [18] train-rmse:1.200518+0.026588    test-rmse:2.443092+0.528930 
## [19] train-rmse:1.145571+0.026786    test-rmse:2.425013+0.521019 
## [20] train-rmse:1.096860+0.030715    test-rmse:2.414079+0.524261 
## [21] train-rmse:1.057321+0.034762    test-rmse:2.406415+0.528087 
## [22] train-rmse:1.019963+0.031685    test-rmse:2.390804+0.521982 
## [23] train-rmse:0.981374+0.026931    test-rmse:2.389253+0.526327 
## [24] train-rmse:0.944972+0.026383    test-rmse:2.384134+0.529623 
## [25] train-rmse:0.905601+0.026368    test-rmse:2.372369+0.531248 
## [26] train-rmse:0.876434+0.030857    test-rmse:2.367186+0.536044 
## [27] train-rmse:0.839216+0.031781    test-rmse:2.355919+0.536576 
## [28] train-rmse:0.819925+0.029881    test-rmse:2.351581+0.537209 
## [29] train-rmse:0.792510+0.020923    test-rmse:2.348693+0.537326 
## [30] train-rmse:0.761068+0.020401    test-rmse:2.340715+0.534141 
## [31] train-rmse:0.740091+0.021111    test-rmse:2.337008+0.532693 
## [32] train-rmse:0.710837+0.018048    test-rmse:2.331936+0.534073 
## [33] train-rmse:0.687657+0.021798    test-rmse:2.319823+0.536956 
## [34] train-rmse:0.670694+0.018504    test-rmse:2.314150+0.541572 
## [35] train-rmse:0.649471+0.020174    test-rmse:2.312637+0.541473 
## [36] train-rmse:0.627752+0.022001    test-rmse:2.309710+0.542434 
## [37] train-rmse:0.608201+0.025330    test-rmse:2.306280+0.541792 
## [38] train-rmse:0.590621+0.025579    test-rmse:2.304785+0.541983 
## [39] train-rmse:0.573040+0.028715    test-rmse:2.302733+0.545559 
## [40] train-rmse:0.556793+0.033409    test-rmse:2.300409+0.545940 
## [41] train-rmse:0.540174+0.028909    test-rmse:2.299220+0.545501 
## [42] train-rmse:0.523969+0.027439    test-rmse:2.297997+0.544583 
## [43] train-rmse:0.508096+0.028186    test-rmse:2.297893+0.543164 
## [44] train-rmse:0.492941+0.026129    test-rmse:2.296778+0.544507 
## [45] train-rmse:0.479698+0.026942    test-rmse:2.296822+0.543670 
## [46] train-rmse:0.466194+0.029799    test-rmse:2.299225+0.543740 
## [47] train-rmse:0.453118+0.027389    test-rmse:2.300025+0.545047 
## [48] train-rmse:0.440115+0.023685    test-rmse:2.297383+0.544861 
## [49] train-rmse:0.427576+0.025274    test-rmse:2.297657+0.545695 
## [50] train-rmse:0.417506+0.024334    test-rmse:2.297998+0.546437 
## Stopping. Best iteration:
## [44] train-rmse:0.492941+0.026129    test-rmse:2.296778+0.544507
## 
## 41 
## [1]  train-rmse:6.747972+0.133543    test-rmse:6.763870+0.621481 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.590975+0.099311    test-rmse:5.693064+0.552228 
## [3]  train-rmse:4.664117+0.076531    test-rmse:4.844877+0.521489 
## [4]  train-rmse:3.928588+0.061056    test-rmse:4.226565+0.481514 
## [5]  train-rmse:3.350335+0.053686    test-rmse:3.739994+0.468990 
## [6]  train-rmse:2.889823+0.043367    test-rmse:3.417381+0.462848 
## [7]  train-rmse:2.522680+0.036905    test-rmse:3.174228+0.472837 
## [8]  train-rmse:2.233585+0.031215    test-rmse:3.004834+0.483231 
## [9]  train-rmse:1.992898+0.028167    test-rmse:2.855119+0.483278 
## [10] train-rmse:1.797334+0.022591    test-rmse:2.743056+0.506061 
## [11] train-rmse:1.644811+0.023120    test-rmse:2.670551+0.519490 
## [12] train-rmse:1.516462+0.023867    test-rmse:2.606308+0.529038 
## [13] train-rmse:1.417752+0.022253    test-rmse:2.564559+0.539769 
## [14] train-rmse:1.326908+0.019367    test-rmse:2.523724+0.533249 
## [15] train-rmse:1.245360+0.026287    test-rmse:2.504667+0.543397 
## [16] train-rmse:1.176188+0.026551    test-rmse:2.481616+0.553351 
## [17] train-rmse:1.097133+0.025236    test-rmse:2.462222+0.542592 
## [18] train-rmse:1.053035+0.023564    test-rmse:2.442971+0.544165 
## [19] train-rmse:0.999522+0.028496    test-rmse:2.432810+0.548792 
## [20] train-rmse:0.956169+0.034265    test-rmse:2.420077+0.555685 
## [21] train-rmse:0.905908+0.033700    test-rmse:2.420908+0.560251 
## [22] train-rmse:0.864743+0.027617    test-rmse:2.415364+0.565524 
## [23] train-rmse:0.827957+0.029450    test-rmse:2.412784+0.566509 
## [24] train-rmse:0.795298+0.026717    test-rmse:2.401788+0.564012 
## [25] train-rmse:0.760853+0.033658    test-rmse:2.401373+0.564481 
## [26] train-rmse:0.731774+0.026412    test-rmse:2.396474+0.565019 
## [27] train-rmse:0.694407+0.033259    test-rmse:2.392898+0.564930 
## [28] train-rmse:0.662400+0.026076    test-rmse:2.393634+0.568260 
## [29] train-rmse:0.638631+0.020441    test-rmse:2.388057+0.566323 
## [30] train-rmse:0.614545+0.017540    test-rmse:2.384220+0.569191 
## [31] train-rmse:0.589853+0.013719    test-rmse:2.383722+0.573083 
## [32] train-rmse:0.565897+0.011976    test-rmse:2.384209+0.574166 
## [33] train-rmse:0.544722+0.010073    test-rmse:2.381302+0.569416 
## [34] train-rmse:0.528457+0.014675    test-rmse:2.377292+0.570099 
## [35] train-rmse:0.504569+0.010048    test-rmse:2.374371+0.574967 
## [36] train-rmse:0.485136+0.011944    test-rmse:2.372597+0.575994 
## [37] train-rmse:0.466653+0.015778    test-rmse:2.368947+0.575908 
## [38] train-rmse:0.445252+0.014217    test-rmse:2.362491+0.575197 
## [39] train-rmse:0.431877+0.011885    test-rmse:2.361413+0.576462 
## [40] train-rmse:0.417132+0.015215    test-rmse:2.360019+0.574812 
## [41] train-rmse:0.405736+0.010930    test-rmse:2.359980+0.575831 
## [42] train-rmse:0.389867+0.010403    test-rmse:2.361032+0.577004 
## [43] train-rmse:0.377229+0.007720    test-rmse:2.358099+0.577310 
## [44] train-rmse:0.365270+0.004599    test-rmse:2.359392+0.578720 
## [45] train-rmse:0.353710+0.004939    test-rmse:2.358807+0.577921 
## [46] train-rmse:0.340119+0.006852    test-rmse:2.357289+0.578104 
## [47] train-rmse:0.330308+0.005266    test-rmse:2.357376+0.578894 
## [48] train-rmse:0.316989+0.004143    test-rmse:2.357652+0.579996 
## [49] train-rmse:0.307377+0.007437    test-rmse:2.357570+0.580574 
## [50] train-rmse:0.298501+0.008761    test-rmse:2.356966+0.580209 
## [51] train-rmse:0.287618+0.010188    test-rmse:2.357844+0.581245 
## [52] train-rmse:0.279091+0.010662    test-rmse:2.357271+0.581498 
## [53] train-rmse:0.268451+0.011270    test-rmse:2.357632+0.581204 
## [54] train-rmse:0.262004+0.013016    test-rmse:2.357824+0.582110 
## [55] train-rmse:0.254611+0.012529    test-rmse:2.356263+0.582988 
## [56] train-rmse:0.245557+0.012788    test-rmse:2.355625+0.582609 
## [57] train-rmse:0.238705+0.011153    test-rmse:2.354251+0.582712 
## [58] train-rmse:0.231494+0.011397    test-rmse:2.353588+0.583100 
## [59] train-rmse:0.225365+0.012418    test-rmse:2.353585+0.582627 
## [60] train-rmse:0.217737+0.011222    test-rmse:2.352142+0.583531 
## [61] train-rmse:0.211034+0.010016    test-rmse:2.351284+0.584011 
## [62] train-rmse:0.202218+0.011289    test-rmse:2.351597+0.585201 
## [63] train-rmse:0.197957+0.011474    test-rmse:2.350708+0.584996 
## [64] train-rmse:0.190347+0.010816    test-rmse:2.349619+0.584459 
## [65] train-rmse:0.185183+0.010997    test-rmse:2.348528+0.584085 
## [66] train-rmse:0.180039+0.010566    test-rmse:2.348755+0.585692 
## [67] train-rmse:0.174300+0.008962    test-rmse:2.347121+0.585426 
## [68] train-rmse:0.169499+0.009485    test-rmse:2.346664+0.586198 
## [69] train-rmse:0.163630+0.009072    test-rmse:2.347091+0.587195 
## [70] train-rmse:0.158688+0.008606    test-rmse:2.346707+0.587351 
## [71] train-rmse:0.155678+0.008398    test-rmse:2.346585+0.587668 
## [72] train-rmse:0.151413+0.009826    test-rmse:2.345290+0.587209 
## [73] train-rmse:0.145506+0.009732    test-rmse:2.344495+0.588106 
## [74] train-rmse:0.140878+0.010022    test-rmse:2.344149+0.588020 
## [75] train-rmse:0.135675+0.009634    test-rmse:2.343529+0.587561 
## [76] train-rmse:0.132032+0.009490    test-rmse:2.343431+0.587613 
## [77] train-rmse:0.128576+0.008517    test-rmse:2.343840+0.588218 
## [78] train-rmse:0.125560+0.008305    test-rmse:2.344078+0.588025 
## [79] train-rmse:0.122221+0.008518    test-rmse:2.344134+0.588327 
## [80] train-rmse:0.118135+0.008960    test-rmse:2.343597+0.587940 
## [81] train-rmse:0.114477+0.008634    test-rmse:2.343195+0.587513 
## [82] train-rmse:0.111417+0.009450    test-rmse:2.342464+0.586401 
## [83] train-rmse:0.108453+0.009869    test-rmse:2.342460+0.586222 
## [84] train-rmse:0.105583+0.009853    test-rmse:2.341583+0.586483 
## [85] train-rmse:0.102631+0.008929    test-rmse:2.341560+0.586405 
## [86] train-rmse:0.100306+0.008639    test-rmse:2.341454+0.586380 
## [87] train-rmse:0.097715+0.008666    test-rmse:2.341613+0.586288 
## [88] train-rmse:0.094100+0.007858    test-rmse:2.340624+0.585942 
## [89] train-rmse:0.091638+0.007165    test-rmse:2.340073+0.585997 
## [90] train-rmse:0.088392+0.007236    test-rmse:2.340035+0.586321 
## [91] train-rmse:0.086722+0.007288    test-rmse:2.340121+0.586420 
## [92] train-rmse:0.084506+0.006952    test-rmse:2.339802+0.586479 
## [93] train-rmse:0.082135+0.006455    test-rmse:2.339449+0.586712 
## [94] train-rmse:0.079143+0.006978    test-rmse:2.339019+0.586006 
## [95] train-rmse:0.076714+0.006408    test-rmse:2.338926+0.585932 
## [96] train-rmse:0.074358+0.006468    test-rmse:2.338420+0.585833 
## [97] train-rmse:0.071995+0.006667    test-rmse:2.338677+0.585501 
## [98] train-rmse:0.069956+0.006938    test-rmse:2.338258+0.584702 
## [99] train-rmse:0.067806+0.006193    test-rmse:2.337951+0.584905 
## [100]    train-rmse:0.065765+0.006249    test-rmse:2.337590+0.584945 
## [101]    train-rmse:0.064132+0.006153    test-rmse:2.337566+0.584778 
## [102]    train-rmse:0.062063+0.006146    test-rmse:2.337370+0.584806 
## [103]    train-rmse:0.060550+0.006154    test-rmse:2.337267+0.584803 
## [104]    train-rmse:0.059246+0.006338    test-rmse:2.337250+0.585407 
## [105]    train-rmse:0.057625+0.005993    test-rmse:2.337356+0.585386 
## [106]    train-rmse:0.056456+0.005734    test-rmse:2.337451+0.585221 
## [107]    train-rmse:0.055029+0.005754    test-rmse:2.337123+0.585407 
## [108]    train-rmse:0.053712+0.005266    test-rmse:2.337051+0.585337 
## [109]    train-rmse:0.052223+0.005319    test-rmse:2.336700+0.585144 
## [110]    train-rmse:0.051002+0.005132    test-rmse:2.336587+0.585287 
## [111]    train-rmse:0.049901+0.005150    test-rmse:2.336569+0.585252 
## [112]    train-rmse:0.048849+0.004974    test-rmse:2.336594+0.585189 
## [113]    train-rmse:0.047831+0.004728    test-rmse:2.336559+0.585192 
## [114]    train-rmse:0.046560+0.004725    test-rmse:2.336431+0.585213 
## [115]    train-rmse:0.045244+0.004300    test-rmse:2.336365+0.585165 
## [116]    train-rmse:0.043899+0.004092    test-rmse:2.336194+0.585207 
## [117]    train-rmse:0.043078+0.004006    test-rmse:2.336138+0.585220 
## [118]    train-rmse:0.042009+0.003816    test-rmse:2.336069+0.585290 
## [119]    train-rmse:0.040985+0.003726    test-rmse:2.336091+0.585338 
## [120]    train-rmse:0.039728+0.003737    test-rmse:2.335917+0.585379 
## [121]    train-rmse:0.038900+0.003460    test-rmse:2.335968+0.585339 
## [122]    train-rmse:0.038030+0.003495    test-rmse:2.335852+0.585480 
## [123]    train-rmse:0.037214+0.003328    test-rmse:2.335636+0.585543 
## [124]    train-rmse:0.036382+0.003116    test-rmse:2.335878+0.585700 
## [125]    train-rmse:0.035157+0.003016    test-rmse:2.335613+0.585594 
## [126]    train-rmse:0.034535+0.002969    test-rmse:2.335682+0.585919 
## [127]    train-rmse:0.033657+0.002957    test-rmse:2.335764+0.586133 
## [128]    train-rmse:0.032853+0.002936    test-rmse:2.335687+0.586163 
## [129]    train-rmse:0.032129+0.002892    test-rmse:2.335755+0.586335 
## [130]    train-rmse:0.031296+0.003034    test-rmse:2.335782+0.586382 
## [131]    train-rmse:0.030645+0.003083    test-rmse:2.335759+0.586391 
## Stopping. Best iteration:
## [125]    train-rmse:0.035157+0.003016    test-rmse:2.335613+0.585594
## 
## 42 
## [1]  train-rmse:6.908719+0.149088    test-rmse:6.901413+0.664591 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.965472+0.127239    test-rmse:6.002722+0.698894 
## [3]  train-rmse:5.282677+0.118691    test-rmse:5.344873+0.625739 
## [4]  train-rmse:4.788383+0.116810    test-rmse:4.846487+0.600713 
## [5]  train-rmse:4.436466+0.113082    test-rmse:4.515644+0.606276 
## [6]  train-rmse:4.179301+0.108282    test-rmse:4.269683+0.534041 
## [7]  train-rmse:3.990712+0.111347    test-rmse:4.107927+0.502917 
## [8]  train-rmse:3.842770+0.114844    test-rmse:3.971128+0.481905 
## [9]  train-rmse:3.726462+0.115833    test-rmse:3.833982+0.449473 
## [10] train-rmse:3.631225+0.115092    test-rmse:3.768724+0.461066 
## [11] train-rmse:3.552916+0.118315    test-rmse:3.674067+0.432608 
## [12] train-rmse:3.482914+0.121572    test-rmse:3.633062+0.415648 
## [13] train-rmse:3.419154+0.122810    test-rmse:3.558275+0.431777 
## [14] train-rmse:3.362985+0.122265    test-rmse:3.475954+0.409804 
## [15] train-rmse:3.311708+0.121941    test-rmse:3.457299+0.421083 
## [16] train-rmse:3.262177+0.121528    test-rmse:3.396585+0.418858 
## [17] train-rmse:3.218208+0.123568    test-rmse:3.374098+0.420156 
## [18] train-rmse:3.175853+0.124920    test-rmse:3.318282+0.443889 
## [19] train-rmse:3.137568+0.125216    test-rmse:3.272449+0.415844 
## [20] train-rmse:3.101304+0.125569    test-rmse:3.246260+0.424484 
## [21] train-rmse:3.066689+0.126035    test-rmse:3.217016+0.433003 
## [22] train-rmse:3.034979+0.127860    test-rmse:3.190269+0.421662 
## [23] train-rmse:3.003725+0.129834    test-rmse:3.156344+0.432779 
## [24] train-rmse:2.975726+0.130367    test-rmse:3.136135+0.443302 
## [25] train-rmse:2.948482+0.130314    test-rmse:3.127800+0.444357 
## [26] train-rmse:2.921801+0.130338    test-rmse:3.096985+0.421027 
## [27] train-rmse:2.896803+0.130635    test-rmse:3.079999+0.426692 
## [28] train-rmse:2.872415+0.131201    test-rmse:3.047384+0.422012 
## [29] train-rmse:2.849053+0.131734    test-rmse:3.046067+0.434252 
## [30] train-rmse:2.826216+0.132674    test-rmse:3.021975+0.440493 
## [31] train-rmse:2.804000+0.133427    test-rmse:3.020096+0.453691 
## [32] train-rmse:2.782039+0.134231    test-rmse:2.977851+0.456493 
## [33] train-rmse:2.761093+0.135469    test-rmse:2.954339+0.467783 
## [34] train-rmse:2.741103+0.136279    test-rmse:2.943865+0.465023 
## [35] train-rmse:2.721960+0.136751    test-rmse:2.934760+0.465086 
## [36] train-rmse:2.703653+0.137094    test-rmse:2.926833+0.458133 
## [37] train-rmse:2.685554+0.137436    test-rmse:2.912040+0.445821 
## [38] train-rmse:2.668215+0.137471    test-rmse:2.890873+0.444708 
## [39] train-rmse:2.651348+0.137612    test-rmse:2.881548+0.448962 
## [40] train-rmse:2.635175+0.137430    test-rmse:2.869156+0.453790 
## [41] train-rmse:2.619516+0.137754    test-rmse:2.852632+0.468605 
## [42] train-rmse:2.603922+0.138267    test-rmse:2.831447+0.473120 
## [43] train-rmse:2.588633+0.138634    test-rmse:2.821449+0.468616 
## [44] train-rmse:2.573908+0.138511    test-rmse:2.807237+0.479505 
## [45] train-rmse:2.559288+0.138471    test-rmse:2.789782+0.469221 
## [46] train-rmse:2.544937+0.138487    test-rmse:2.766766+0.476053 
## [47] train-rmse:2.530980+0.138567    test-rmse:2.763872+0.479415 
## [48] train-rmse:2.517721+0.138764    test-rmse:2.757067+0.483048 
## [49] train-rmse:2.504829+0.138711    test-rmse:2.755577+0.490601 
## [50] train-rmse:2.492487+0.138837    test-rmse:2.741045+0.484700 
## [51] train-rmse:2.480558+0.138862    test-rmse:2.729719+0.491185 
## [52] train-rmse:2.468978+0.139005    test-rmse:2.713415+0.480831 
## [53] train-rmse:2.457595+0.139266    test-rmse:2.702217+0.481203 
## [54] train-rmse:2.446429+0.139513    test-rmse:2.693179+0.471759 
## [55] train-rmse:2.435584+0.139725    test-rmse:2.688377+0.471359 
## [56] train-rmse:2.424883+0.139814    test-rmse:2.684443+0.479447 
## [57] train-rmse:2.414276+0.139916    test-rmse:2.674758+0.481687 
## [58] train-rmse:2.403994+0.140015    test-rmse:2.661706+0.486863 
## [59] train-rmse:2.394073+0.140031    test-rmse:2.650662+0.490889 
## [60] train-rmse:2.384413+0.140244    test-rmse:2.646223+0.490152 
## [61] train-rmse:2.374932+0.140368    test-rmse:2.638114+0.494091 
## [62] train-rmse:2.365625+0.140393    test-rmse:2.625071+0.494775 
## [63] train-rmse:2.356629+0.140558    test-rmse:2.622859+0.495815 
## [64] train-rmse:2.347968+0.140765    test-rmse:2.615182+0.497192 
## [65] train-rmse:2.339702+0.141027    test-rmse:2.609439+0.497264 
## [66] train-rmse:2.331491+0.141206    test-rmse:2.606929+0.506180 
## [67] train-rmse:2.323532+0.141609    test-rmse:2.597382+0.499550 
## [68] train-rmse:2.315797+0.141857    test-rmse:2.593126+0.501696 
## [69] train-rmse:2.308208+0.142186    test-rmse:2.584593+0.507575 
## [70] train-rmse:2.300786+0.142500    test-rmse:2.579267+0.506149 
## [71] train-rmse:2.293302+0.142683    test-rmse:2.575230+0.500161 
## [72] train-rmse:2.286040+0.143043    test-rmse:2.570597+0.500648 
## [73] train-rmse:2.278839+0.143338    test-rmse:2.567689+0.499826 
## [74] train-rmse:2.271827+0.143564    test-rmse:2.563260+0.507362 
## [75] train-rmse:2.264813+0.143764    test-rmse:2.556522+0.507933 
## [76] train-rmse:2.258086+0.143871    test-rmse:2.551531+0.503624 
## [77] train-rmse:2.251731+0.144001    test-rmse:2.540690+0.499261 
## [78] train-rmse:2.245404+0.144262    test-rmse:2.535035+0.493918 
## [79] train-rmse:2.239069+0.144606    test-rmse:2.520905+0.497447 
## [80] train-rmse:2.232789+0.144942    test-rmse:2.516360+0.499411 
## [81] train-rmse:2.226695+0.145224    test-rmse:2.510312+0.500327 
## [82] train-rmse:2.220636+0.145445    test-rmse:2.516719+0.511288 
## [83] train-rmse:2.214888+0.145645    test-rmse:2.503745+0.512965 
## [84] train-rmse:2.209386+0.145693    test-rmse:2.500325+0.512715 
## [85] train-rmse:2.203925+0.145869    test-rmse:2.504472+0.512001 
## [86] train-rmse:2.198616+0.146075    test-rmse:2.502052+0.513105 
## [87] train-rmse:2.193471+0.146255    test-rmse:2.496581+0.517482 
## [88] train-rmse:2.188412+0.146457    test-rmse:2.492335+0.516525 
## [89] train-rmse:2.183387+0.146700    test-rmse:2.487133+0.519108 
## [90] train-rmse:2.178511+0.146874    test-rmse:2.484014+0.517281 
## [91] train-rmse:2.173647+0.147082    test-rmse:2.481335+0.517604 
## [92] train-rmse:2.168854+0.147236    test-rmse:2.474468+0.516432 
## [93] train-rmse:2.164178+0.147510    test-rmse:2.474135+0.516577 
## [94] train-rmse:2.159642+0.147703    test-rmse:2.467194+0.514733 
## [95] train-rmse:2.155126+0.147887    test-rmse:2.465354+0.507642 
## [96] train-rmse:2.150620+0.148086    test-rmse:2.456686+0.511769 
## [97] train-rmse:2.146204+0.148312    test-rmse:2.454821+0.515759 
## [98] train-rmse:2.141855+0.148514    test-rmse:2.456630+0.513958 
## [99] train-rmse:2.137555+0.148717    test-rmse:2.453513+0.521843 
## [100]    train-rmse:2.133346+0.148944    test-rmse:2.450303+0.520082 
## [101]    train-rmse:2.129322+0.149097    test-rmse:2.443763+0.524859 
## [102]    train-rmse:2.125335+0.149198    test-rmse:2.443368+0.522174 
## [103]    train-rmse:2.121387+0.149293    test-rmse:2.442611+0.518373 
## [104]    train-rmse:2.117561+0.149400    test-rmse:2.437124+0.523096 
## [105]    train-rmse:2.113762+0.149490    test-rmse:2.435645+0.520331 
## [106]    train-rmse:2.110088+0.149642    test-rmse:2.429916+0.519273 
## [107]    train-rmse:2.106571+0.149844    test-rmse:2.430776+0.526826 
## [108]    train-rmse:2.103062+0.150042    test-rmse:2.430055+0.528349 
## [109]    train-rmse:2.099444+0.150234    test-rmse:2.423650+0.524656 
## [110]    train-rmse:2.096027+0.150455    test-rmse:2.419041+0.521503 
## [111]    train-rmse:2.092610+0.150704    test-rmse:2.414201+0.521575 
## [112]    train-rmse:2.089211+0.150960    test-rmse:2.415295+0.522977 
## [113]    train-rmse:2.085952+0.151126    test-rmse:2.411531+0.521317 
## [114]    train-rmse:2.082687+0.151296    test-rmse:2.411274+0.524354 
## [115]    train-rmse:2.079445+0.151487    test-rmse:2.408327+0.522867 
## [116]    train-rmse:2.076256+0.151713    test-rmse:2.408660+0.521048 
## [117]    train-rmse:2.073100+0.151894    test-rmse:2.405482+0.519193 
## [118]    train-rmse:2.070030+0.152093    test-rmse:2.404561+0.518108 
## [119]    train-rmse:2.066910+0.152239    test-rmse:2.398429+0.520975 
## [120]    train-rmse:2.063932+0.152386    test-rmse:2.395757+0.522377 
## [121]    train-rmse:2.060954+0.152512    test-rmse:2.394735+0.524006 
## [122]    train-rmse:2.058016+0.152680    test-rmse:2.395157+0.522805 
## [123]    train-rmse:2.055104+0.152777    test-rmse:2.394829+0.519353 
## [124]    train-rmse:2.052232+0.152844    test-rmse:2.393576+0.519203 
## [125]    train-rmse:2.049430+0.152968    test-rmse:2.392444+0.522608 
## [126]    train-rmse:2.046720+0.153047    test-rmse:2.390369+0.522898 
## [127]    train-rmse:2.044038+0.153132    test-rmse:2.399053+0.517842 
## [128]    train-rmse:2.041333+0.153199    test-rmse:2.391914+0.522245 
## [129]    train-rmse:2.038669+0.153283    test-rmse:2.393674+0.529058 
## [130]    train-rmse:2.036006+0.153356    test-rmse:2.389041+0.526394 
## [131]    train-rmse:2.033313+0.153443    test-rmse:2.389230+0.528803 
## [132]    train-rmse:2.030761+0.153558    test-rmse:2.386643+0.527871 
## [133]    train-rmse:2.028252+0.153680    test-rmse:2.385422+0.529437 
## [134]    train-rmse:2.025714+0.153770    test-rmse:2.382152+0.525881 
## [135]    train-rmse:2.023264+0.153900    test-rmse:2.380258+0.524462 
## [136]    train-rmse:2.020843+0.154017    test-rmse:2.378088+0.525221 
## [137]    train-rmse:2.018457+0.154148    test-rmse:2.377597+0.528194 
## [138]    train-rmse:2.016061+0.154363    test-rmse:2.380545+0.529079 
## [139]    train-rmse:2.013689+0.154523    test-rmse:2.378190+0.528617 
## [140]    train-rmse:2.011395+0.154627    test-rmse:2.381968+0.531018 
## [141]    train-rmse:2.009156+0.154752    test-rmse:2.378235+0.532273 
## [142]    train-rmse:2.006912+0.154823    test-rmse:2.378604+0.530278 
## [143]    train-rmse:2.004711+0.154906    test-rmse:2.376005+0.527087 
## [144]    train-rmse:2.002424+0.155224    test-rmse:2.374776+0.528094 
## [145]    train-rmse:2.000166+0.155343    test-rmse:2.370788+0.529172 
## [146]    train-rmse:1.997992+0.155465    test-rmse:2.367130+0.530193 
## [147]    train-rmse:1.995798+0.155567    test-rmse:2.370577+0.527010 
## [148]    train-rmse:1.993684+0.155704    test-rmse:2.370827+0.528924 
## [149]    train-rmse:1.991528+0.155843    test-rmse:2.366462+0.529236 
## [150]    train-rmse:1.989486+0.155915    test-rmse:2.362371+0.532480 
## [151]    train-rmse:1.987411+0.156035    test-rmse:2.363595+0.530652 
## [152]    train-rmse:1.985396+0.156162    test-rmse:2.362878+0.528917 
## [153]    train-rmse:1.983335+0.156304    test-rmse:2.360987+0.532416 
## [154]    train-rmse:1.981345+0.156439    test-rmse:2.356589+0.533758 
## [155]    train-rmse:1.979345+0.156600    test-rmse:2.354282+0.533276 
## [156]    train-rmse:1.977347+0.156749    test-rmse:2.350558+0.531356 
## [157]    train-rmse:1.975347+0.156986    test-rmse:2.350945+0.533825 
## [158]    train-rmse:1.973400+0.157095    test-rmse:2.350966+0.535294 
## [159]    train-rmse:1.971469+0.157162    test-rmse:2.346752+0.533842 
## [160]    train-rmse:1.969584+0.157270    test-rmse:2.347229+0.531650 
## [161]    train-rmse:1.967736+0.157393    test-rmse:2.343579+0.529631 
## [162]    train-rmse:1.965906+0.157514    test-rmse:2.344747+0.531235 
## [163]    train-rmse:1.964060+0.157610    test-rmse:2.345044+0.529788 
## [164]    train-rmse:1.962155+0.157729    test-rmse:2.342615+0.528766 
## [165]    train-rmse:1.960344+0.157860    test-rmse:2.345073+0.531474 
## [166]    train-rmse:1.958464+0.157901    test-rmse:2.343650+0.531726 
## [167]    train-rmse:1.956679+0.158019    test-rmse:2.342652+0.531675 
## [168]    train-rmse:1.954906+0.158112    test-rmse:2.340009+0.531834 
## [169]    train-rmse:1.953153+0.158206    test-rmse:2.338331+0.534062 
## [170]    train-rmse:1.951391+0.158299    test-rmse:2.334932+0.538790 
## [171]    train-rmse:1.949687+0.158405    test-rmse:2.333953+0.541691 
## [172]    train-rmse:1.947951+0.158476    test-rmse:2.333297+0.541659 
## [173]    train-rmse:1.946227+0.158570    test-rmse:2.336728+0.537682 
## [174]    train-rmse:1.944539+0.158656    test-rmse:2.334532+0.539022 
## [175]    train-rmse:1.942854+0.158735    test-rmse:2.334502+0.540709 
## [176]    train-rmse:1.941078+0.158948    test-rmse:2.333080+0.538141 
## [177]    train-rmse:1.939362+0.159036    test-rmse:2.331795+0.538367 
## [178]    train-rmse:1.937711+0.159136    test-rmse:2.330097+0.538512 
## [179]    train-rmse:1.936081+0.159299    test-rmse:2.326369+0.539432 
## [180]    train-rmse:1.934416+0.159376    test-rmse:2.327100+0.539376 
## [181]    train-rmse:1.932831+0.159469    test-rmse:2.324345+0.538817 
## [182]    train-rmse:1.931273+0.159547    test-rmse:2.321745+0.536430 
## [183]    train-rmse:1.929700+0.159650    test-rmse:2.322228+0.536751 
## [184]    train-rmse:1.928118+0.159749    test-rmse:2.321462+0.535864 
## [185]    train-rmse:1.926441+0.159732    test-rmse:2.319511+0.533427 
## [186]    train-rmse:1.924901+0.159871    test-rmse:2.318885+0.537086 
## [187]    train-rmse:1.923391+0.160018    test-rmse:2.316943+0.536917 
## [188]    train-rmse:1.921806+0.160130    test-rmse:2.319160+0.537313 
## [189]    train-rmse:1.920289+0.160222    test-rmse:2.316727+0.534764 
## [190]    train-rmse:1.918829+0.160323    test-rmse:2.315254+0.534723 
## [191]    train-rmse:1.917376+0.160409    test-rmse:2.316302+0.536123 
## [192]    train-rmse:1.915917+0.160517    test-rmse:2.315883+0.533944 
## [193]    train-rmse:1.914508+0.160585    test-rmse:2.313055+0.535175 
## [194]    train-rmse:1.913054+0.160714    test-rmse:2.313206+0.534993 
## [195]    train-rmse:1.911575+0.160756    test-rmse:2.312737+0.534276 
## [196]    train-rmse:1.910160+0.160862    test-rmse:2.316426+0.536475 
## [197]    train-rmse:1.908685+0.160924    test-rmse:2.314977+0.539043 
## [198]    train-rmse:1.907256+0.161115    test-rmse:2.312596+0.539010 
## [199]    train-rmse:1.905832+0.161180    test-rmse:2.311310+0.536795 
## [200]    train-rmse:1.904411+0.161305    test-rmse:2.313492+0.535280 
## [201]    train-rmse:1.903047+0.161402    test-rmse:2.313461+0.535869 
## [202]    train-rmse:1.901692+0.161457    test-rmse:2.314810+0.540783 
## [203]    train-rmse:1.900348+0.161516    test-rmse:2.315808+0.537130 
## [204]    train-rmse:1.898981+0.161570    test-rmse:2.314528+0.538839 
## [205]    train-rmse:1.897686+0.161654    test-rmse:2.312619+0.538623 
## Stopping. Best iteration:
## [199]    train-rmse:1.905832+0.161180    test-rmse:2.311310+0.536795
## 
## 43 
## [1]  train-rmse:6.810517+0.141913    test-rmse:6.832346+0.632789 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.772934+0.135773    test-rmse:5.848137+0.566532 
## [3]  train-rmse:5.005476+0.115587    test-rmse:5.126354+0.529097 
## [4]  train-rmse:4.423219+0.110446    test-rmse:4.519824+0.494154 
## [5]  train-rmse:3.977086+0.109633    test-rmse:4.141602+0.456627 
## [6]  train-rmse:3.636140+0.100792    test-rmse:3.823061+0.425142 
## [7]  train-rmse:3.370854+0.102619    test-rmse:3.592913+0.419405 
## [8]  train-rmse:3.156614+0.108868    test-rmse:3.383404+0.403184 
## [9]  train-rmse:2.992849+0.098692    test-rmse:3.235494+0.401273 
## [10] train-rmse:2.863775+0.088305    test-rmse:3.125469+0.417734 
## [11] train-rmse:2.751871+0.091879    test-rmse:3.031169+0.430809 
## [12] train-rmse:2.673811+0.092021    test-rmse:2.986589+0.447652 
## [13] train-rmse:2.593827+0.094909    test-rmse:2.922839+0.435495 
## [14] train-rmse:2.526879+0.088386    test-rmse:2.864958+0.456627 
## [15] train-rmse:2.465951+0.088998    test-rmse:2.843596+0.460546 
## [16] train-rmse:2.414024+0.092681    test-rmse:2.815279+0.443483 
## [17] train-rmse:2.369115+0.092678    test-rmse:2.783286+0.461963 
## [18] train-rmse:2.316573+0.097228    test-rmse:2.757174+0.454717 
## [19] train-rmse:2.273671+0.098687    test-rmse:2.726763+0.446616 
## [20] train-rmse:2.233844+0.097897    test-rmse:2.708490+0.449874 
## [21] train-rmse:2.197137+0.092484    test-rmse:2.696875+0.454809 
## [22] train-rmse:2.151950+0.101034    test-rmse:2.661716+0.432973 
## [23] train-rmse:2.114120+0.093408    test-rmse:2.633532+0.449237 
## [24] train-rmse:2.073694+0.094883    test-rmse:2.587821+0.458766 
## [25] train-rmse:2.043055+0.093251    test-rmse:2.572809+0.449915 
## [26] train-rmse:2.014295+0.094072    test-rmse:2.549510+0.446930 
## [27] train-rmse:1.987279+0.095541    test-rmse:2.530083+0.459528 
## [28] train-rmse:1.956190+0.086998    test-rmse:2.507591+0.472118 
## [29] train-rmse:1.932295+0.087281    test-rmse:2.489789+0.475452 
## [30] train-rmse:1.906966+0.091040    test-rmse:2.466843+0.454798 
## [31] train-rmse:1.883928+0.087389    test-rmse:2.461247+0.465019 
## [32] train-rmse:1.854934+0.079731    test-rmse:2.445575+0.470490 
## [33] train-rmse:1.833351+0.079604    test-rmse:2.428985+0.468383 
## [34] train-rmse:1.813843+0.080211    test-rmse:2.418364+0.459417 
## [35] train-rmse:1.791352+0.078342    test-rmse:2.399587+0.461805 
## [36] train-rmse:1.772183+0.078059    test-rmse:2.379294+0.460611 
## [37] train-rmse:1.753082+0.080955    test-rmse:2.363128+0.450222 
## [38] train-rmse:1.737090+0.080979    test-rmse:2.357162+0.459593 
## [39] train-rmse:1.719718+0.082720    test-rmse:2.345619+0.452201 
## [40] train-rmse:1.704057+0.082296    test-rmse:2.339482+0.463239 
## [41] train-rmse:1.685310+0.083177    test-rmse:2.326920+0.459350 
## [42] train-rmse:1.670726+0.081471    test-rmse:2.314570+0.464685 
## [43] train-rmse:1.654189+0.081686    test-rmse:2.311431+0.468821 
## [44] train-rmse:1.636944+0.075076    test-rmse:2.307066+0.465067 
## [45] train-rmse:1.620465+0.076536    test-rmse:2.298356+0.458100 
## [46] train-rmse:1.604539+0.078383    test-rmse:2.285292+0.461961 
## [47] train-rmse:1.589100+0.080721    test-rmse:2.273719+0.453202 
## [48] train-rmse:1.577195+0.081045    test-rmse:2.268611+0.456785 
## [49] train-rmse:1.563531+0.078315    test-rmse:2.267577+0.453004 
## [50] train-rmse:1.552108+0.079173    test-rmse:2.263552+0.455172 
## [51] train-rmse:1.541576+0.079248    test-rmse:2.258781+0.459222 
## [52] train-rmse:1.532196+0.079552    test-rmse:2.257817+0.468592 
## [53] train-rmse:1.515521+0.073948    test-rmse:2.250106+0.472442 
## [54] train-rmse:1.501226+0.075142    test-rmse:2.241901+0.474087 
## [55] train-rmse:1.491070+0.074989    test-rmse:2.245902+0.473858 
## [56] train-rmse:1.480573+0.075629    test-rmse:2.239545+0.478951 
## [57] train-rmse:1.469446+0.076819    test-rmse:2.234909+0.479022 
## [58] train-rmse:1.458511+0.078394    test-rmse:2.229630+0.476180 
## [59] train-rmse:1.442294+0.082511    test-rmse:2.216792+0.470327 
## [60] train-rmse:1.434757+0.082820    test-rmse:2.217281+0.468678 
## [61] train-rmse:1.422338+0.081899    test-rmse:2.212578+0.468141 
## [62] train-rmse:1.403078+0.066173    test-rmse:2.200706+0.473833 
## [63] train-rmse:1.394011+0.064255    test-rmse:2.197171+0.472106 
## [64] train-rmse:1.386423+0.064723    test-rmse:2.193260+0.471477 
## [65] train-rmse:1.376883+0.062591    test-rmse:2.194133+0.480099 
## [66] train-rmse:1.365939+0.067182    test-rmse:2.193342+0.477225 
## [67] train-rmse:1.356845+0.068458    test-rmse:2.188830+0.482212 
## [68] train-rmse:1.349331+0.069818    test-rmse:2.188663+0.482217 
## [69] train-rmse:1.341048+0.069576    test-rmse:2.185349+0.484458 
## [70] train-rmse:1.331552+0.066878    test-rmse:2.181932+0.486828 
## [71] train-rmse:1.322194+0.064989    test-rmse:2.179049+0.488374 
## [72] train-rmse:1.313153+0.068416    test-rmse:2.173975+0.486806 
## [73] train-rmse:1.305040+0.071839    test-rmse:2.168388+0.480843 
## [74] train-rmse:1.299546+0.072029    test-rmse:2.164674+0.480137 
## [75] train-rmse:1.292719+0.072357    test-rmse:2.162548+0.478286 
## [76] train-rmse:1.284930+0.072606    test-rmse:2.156807+0.478976 
## [77] train-rmse:1.272128+0.074402    test-rmse:2.154618+0.483004 
## [78] train-rmse:1.260612+0.075616    test-rmse:2.147959+0.492678 
## [79] train-rmse:1.250206+0.075793    test-rmse:2.150073+0.491947 
## [80] train-rmse:1.238648+0.071016    test-rmse:2.147923+0.491372 
## [81] train-rmse:1.234081+0.071281    test-rmse:2.144162+0.492191 
## [82] train-rmse:1.228724+0.071401    test-rmse:2.142399+0.494044 
## [83] train-rmse:1.223268+0.069337    test-rmse:2.143518+0.492507 
## [84] train-rmse:1.215938+0.067850    test-rmse:2.144510+0.498230 
## [85] train-rmse:1.209775+0.068058    test-rmse:2.141048+0.498235 
## [86] train-rmse:1.202918+0.067592    test-rmse:2.131775+0.503526 
## [87] train-rmse:1.193040+0.072307    test-rmse:2.135863+0.506336 
## [88] train-rmse:1.186113+0.076097    test-rmse:2.129004+0.504564 
## [89] train-rmse:1.179839+0.077268    test-rmse:2.127058+0.502917 
## [90] train-rmse:1.171699+0.078326    test-rmse:2.128330+0.506756 
## [91] train-rmse:1.167676+0.078063    test-rmse:2.125607+0.508020 
## [92] train-rmse:1.163811+0.077858    test-rmse:2.124400+0.507104 
## [93] train-rmse:1.158627+0.078099    test-rmse:2.121644+0.507464 
## [94] train-rmse:1.152976+0.077601    test-rmse:2.118154+0.508771 
## [95] train-rmse:1.147366+0.078183    test-rmse:2.118413+0.509396 
## [96] train-rmse:1.141444+0.079207    test-rmse:2.119326+0.510062 
## [97] train-rmse:1.135632+0.077829    test-rmse:2.118040+0.510539 
## [98] train-rmse:1.130524+0.079187    test-rmse:2.115821+0.508190 
## [99] train-rmse:1.124867+0.076317    test-rmse:2.114849+0.510506 
## [100]    train-rmse:1.119347+0.077710    test-rmse:2.110795+0.510074 
## [101]    train-rmse:1.112013+0.077344    test-rmse:2.114623+0.508065 
## [102]    train-rmse:1.105325+0.077910    test-rmse:2.109653+0.508366 
## [103]    train-rmse:1.096422+0.075149    test-rmse:2.109411+0.508027 
## [104]    train-rmse:1.092888+0.075763    test-rmse:2.107774+0.508716 
## [105]    train-rmse:1.088616+0.076048    test-rmse:2.103794+0.508235 
## [106]    train-rmse:1.083098+0.075716    test-rmse:2.102087+0.510914 
## [107]    train-rmse:1.078718+0.074438    test-rmse:2.104238+0.514030 
## [108]    train-rmse:1.072421+0.075412    test-rmse:2.108556+0.511274 
## [109]    train-rmse:1.066507+0.075874    test-rmse:2.107165+0.514991 
## [110]    train-rmse:1.060901+0.076807    test-rmse:2.105214+0.512684 
## [111]    train-rmse:1.050442+0.074972    test-rmse:2.109606+0.520405 
## [112]    train-rmse:1.047272+0.075692    test-rmse:2.107698+0.519398 
## Stopping. Best iteration:
## [106]    train-rmse:1.083098+0.075716    test-rmse:2.102087+0.510914
## 
## 44 
## [1]  train-rmse:6.744784+0.137858    test-rmse:6.804640+0.620685 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.649021+0.113145    test-rmse:5.760286+0.554329 
## [3]  train-rmse:4.794825+0.098419    test-rmse:4.934679+0.530009 
## [4]  train-rmse:4.148064+0.089692    test-rmse:4.332180+0.491567 
## [5]  train-rmse:3.661203+0.078324    test-rmse:3.886157+0.466245 
## [6]  train-rmse:3.277400+0.059029    test-rmse:3.538540+0.452029 
## [7]  train-rmse:2.981581+0.055028    test-rmse:3.276110+0.458588 
## [8]  train-rmse:2.763237+0.069405    test-rmse:3.114076+0.440381 
## [9]  train-rmse:2.591715+0.072049    test-rmse:2.977888+0.463596 
## [10] train-rmse:2.452875+0.061097    test-rmse:2.901688+0.442512 
## [11] train-rmse:2.333101+0.067325    test-rmse:2.822950+0.458210 
## [12] train-rmse:2.244742+0.064587    test-rmse:2.756746+0.478941 
## [13] train-rmse:2.156345+0.054382    test-rmse:2.719124+0.485340 
## [14] train-rmse:2.088062+0.060833    test-rmse:2.680196+0.496875 
## [15] train-rmse:2.018423+0.056908    test-rmse:2.637144+0.519894 
## [16] train-rmse:1.958458+0.059750    test-rmse:2.592751+0.518004 
## [17] train-rmse:1.900508+0.055821    test-rmse:2.549753+0.501191 
## [18] train-rmse:1.846878+0.055663    test-rmse:2.538770+0.507555 
## [19] train-rmse:1.799795+0.051929    test-rmse:2.514351+0.506098 
## [20] train-rmse:1.763824+0.051442    test-rmse:2.495058+0.517671 
## [21] train-rmse:1.723950+0.057495    test-rmse:2.454052+0.495869 
## [22] train-rmse:1.688694+0.051580    test-rmse:2.435754+0.503352 
## [23] train-rmse:1.655578+0.053738    test-rmse:2.430171+0.497367 
## [24] train-rmse:1.620413+0.047649    test-rmse:2.418571+0.500048 
## [25] train-rmse:1.595421+0.047420    test-rmse:2.399097+0.502696 
## [26] train-rmse:1.560621+0.052555    test-rmse:2.374060+0.498248 
## [27] train-rmse:1.529180+0.049695    test-rmse:2.352743+0.510664 
## [28] train-rmse:1.501717+0.049585    test-rmse:2.338647+0.518503 
## [29] train-rmse:1.475817+0.051197    test-rmse:2.333374+0.518940 
## [30] train-rmse:1.439272+0.051768    test-rmse:2.329671+0.523948 
## [31] train-rmse:1.413981+0.051970    test-rmse:2.317977+0.512422 
## [32] train-rmse:1.391035+0.052610    test-rmse:2.305984+0.510224 
## [33] train-rmse:1.371602+0.053405    test-rmse:2.301203+0.507311 
## [34] train-rmse:1.351565+0.055492    test-rmse:2.289278+0.516645 
## [35] train-rmse:1.331067+0.059458    test-rmse:2.284451+0.509300 
## [36] train-rmse:1.309049+0.055736    test-rmse:2.270677+0.515304 
## [37] train-rmse:1.291486+0.051561    test-rmse:2.271337+0.516990 
## [38] train-rmse:1.269400+0.053066    test-rmse:2.272014+0.517819 
## [39] train-rmse:1.254281+0.055329    test-rmse:2.259634+0.516988 
## [40] train-rmse:1.239791+0.052208    test-rmse:2.252911+0.524199 
## [41] train-rmse:1.224280+0.055410    test-rmse:2.241416+0.527844 
## [42] train-rmse:1.205017+0.057108    test-rmse:2.233540+0.530466 
## [43] train-rmse:1.190790+0.054034    test-rmse:2.242780+0.540485 
## [44] train-rmse:1.170749+0.043259    test-rmse:2.241600+0.537888 
## [45] train-rmse:1.156960+0.042948    test-rmse:2.240303+0.538553 
## [46] train-rmse:1.141671+0.041981    test-rmse:2.229953+0.538300 
## [47] train-rmse:1.126148+0.048745    test-rmse:2.222478+0.539220 
## [48] train-rmse:1.111367+0.042335    test-rmse:2.218412+0.537736 
## [49] train-rmse:1.095959+0.036631    test-rmse:2.216949+0.539000 
## [50] train-rmse:1.081632+0.035878    test-rmse:2.211396+0.539912 
## [51] train-rmse:1.067939+0.038491    test-rmse:2.207625+0.535026 
## [52] train-rmse:1.053885+0.039085    test-rmse:2.200525+0.536676 
## [53] train-rmse:1.036673+0.041691    test-rmse:2.197764+0.543411 
## [54] train-rmse:1.026546+0.046012    test-rmse:2.197707+0.548752 
## [55] train-rmse:1.015403+0.042054    test-rmse:2.194278+0.548749 
## [56] train-rmse:1.001880+0.042940    test-rmse:2.187863+0.551627 
## [57] train-rmse:0.993710+0.042465    test-rmse:2.182702+0.547549 
## [58] train-rmse:0.973757+0.034113    test-rmse:2.183964+0.557300 
## [59] train-rmse:0.963422+0.034002    test-rmse:2.181132+0.557064 
## [60] train-rmse:0.952851+0.032794    test-rmse:2.177557+0.559363 
## [61] train-rmse:0.942804+0.034095    test-rmse:2.173784+0.557621 
## [62] train-rmse:0.935987+0.034582    test-rmse:2.173461+0.557229 
## [63] train-rmse:0.923528+0.039055    test-rmse:2.170113+0.557499 
## [64] train-rmse:0.910127+0.040595    test-rmse:2.171084+0.556972 
## [65] train-rmse:0.895842+0.037498    test-rmse:2.165527+0.560513 
## [66] train-rmse:0.884720+0.040710    test-rmse:2.157379+0.557725 
## [67] train-rmse:0.874640+0.038597    test-rmse:2.153948+0.559227 
## [68] train-rmse:0.864502+0.039939    test-rmse:2.146875+0.554151 
## [69] train-rmse:0.856088+0.040391    test-rmse:2.144336+0.553776 
## [70] train-rmse:0.845118+0.038165    test-rmse:2.146250+0.549614 
## [71] train-rmse:0.837696+0.036437    test-rmse:2.144862+0.553785 
## [72] train-rmse:0.831845+0.035465    test-rmse:2.142793+0.553597 
## [73] train-rmse:0.821262+0.033580    test-rmse:2.142878+0.553615 
## [74] train-rmse:0.809965+0.032594    test-rmse:2.136958+0.549881 
## [75] train-rmse:0.801386+0.035874    test-rmse:2.134129+0.548134 
## [76] train-rmse:0.791633+0.033240    test-rmse:2.136528+0.547433 
## [77] train-rmse:0.783243+0.031441    test-rmse:2.132673+0.545820 
## [78] train-rmse:0.777335+0.031490    test-rmse:2.131511+0.545886 
## [79] train-rmse:0.764784+0.032367    test-rmse:2.127333+0.544754 
## [80] train-rmse:0.757176+0.032570    test-rmse:2.124845+0.543381 
## [81] train-rmse:0.751201+0.033819    test-rmse:2.124823+0.547581 
## [82] train-rmse:0.744966+0.035734    test-rmse:2.129434+0.549819 
## [83] train-rmse:0.739385+0.037892    test-rmse:2.128703+0.550243 
## [84] train-rmse:0.731968+0.035777    test-rmse:2.127130+0.550059 
## [85] train-rmse:0.721969+0.035786    test-rmse:2.129617+0.555196 
## [86] train-rmse:0.718039+0.035299    test-rmse:2.129590+0.557186 
## [87] train-rmse:0.713116+0.035288    test-rmse:2.126345+0.557080 
## Stopping. Best iteration:
## [81] train-rmse:0.751201+0.033819    test-rmse:2.124823+0.547581
## 
## 45 
## [1]  train-rmse:6.683643+0.136581    test-rmse:6.721201+0.609132 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.518563+0.109197    test-rmse:5.621737+0.524024 
## [3]  train-rmse:4.623669+0.081647    test-rmse:4.777244+0.482100 
## [4]  train-rmse:3.941335+0.061203    test-rmse:4.149943+0.444138 
## [5]  train-rmse:3.430624+0.057166    test-rmse:3.722842+0.428355 
## [6]  train-rmse:3.034445+0.047415    test-rmse:3.394865+0.454033 
## [7]  train-rmse:2.726078+0.044546    test-rmse:3.164268+0.463492 
## [8]  train-rmse:2.499990+0.044044    test-rmse:2.998935+0.464642 
## [9]  train-rmse:2.312436+0.051265    test-rmse:2.889999+0.471037 
## [10] train-rmse:2.144733+0.052560    test-rmse:2.780357+0.453449 
## [11] train-rmse:2.025882+0.039982    test-rmse:2.703027+0.487540 
## [12] train-rmse:1.922372+0.049736    test-rmse:2.643686+0.470178 
## [13] train-rmse:1.838030+0.047431    test-rmse:2.598091+0.437577 
## [14] train-rmse:1.762000+0.056076    test-rmse:2.546540+0.445706 
## [15] train-rmse:1.696742+0.053730    test-rmse:2.505031+0.439705 
## [16] train-rmse:1.636801+0.050744    test-rmse:2.471228+0.442590 
## [17] train-rmse:1.581444+0.047065    test-rmse:2.444299+0.445927 
## [18] train-rmse:1.536260+0.051005    test-rmse:2.432674+0.455996 
## [19] train-rmse:1.492087+0.051404    test-rmse:2.410477+0.462738 
## [20] train-rmse:1.452448+0.047454    test-rmse:2.406577+0.459735 
## [21] train-rmse:1.406776+0.038951    test-rmse:2.402673+0.466443 
## [22] train-rmse:1.366927+0.036887    test-rmse:2.390186+0.471436 
## [23] train-rmse:1.327462+0.031446    test-rmse:2.367743+0.463245 
## [24] train-rmse:1.298062+0.033492    test-rmse:2.356625+0.462757 
## [25] train-rmse:1.261048+0.032706    test-rmse:2.350414+0.466523 
## [26] train-rmse:1.235598+0.030662    test-rmse:2.340715+0.473346 
## [27] train-rmse:1.198569+0.028821    test-rmse:2.325557+0.465344 
## [28] train-rmse:1.170279+0.032200    test-rmse:2.316229+0.466488 
## [29] train-rmse:1.141764+0.027441    test-rmse:2.305286+0.464682 
## [30] train-rmse:1.117138+0.031708    test-rmse:2.292193+0.468448 
## [31] train-rmse:1.089780+0.036129    test-rmse:2.282472+0.472898 
## [32] train-rmse:1.072270+0.036558    test-rmse:2.276698+0.470636 
## [33] train-rmse:1.052084+0.032801    test-rmse:2.274452+0.472090 
## [34] train-rmse:1.027577+0.030661    test-rmse:2.268305+0.469141 
## [35] train-rmse:1.007648+0.031442    test-rmse:2.268126+0.464076 
## [36] train-rmse:0.990038+0.032880    test-rmse:2.259850+0.465152 
## [37] train-rmse:0.972745+0.022833    test-rmse:2.257921+0.473680 
## [38] train-rmse:0.947435+0.022174    test-rmse:2.249530+0.476623 
## [39] train-rmse:0.923016+0.018844    test-rmse:2.237364+0.476029 
## [40] train-rmse:0.901740+0.021303    test-rmse:2.234069+0.481801 
## [41] train-rmse:0.887390+0.020027    test-rmse:2.232760+0.478807 
## [42] train-rmse:0.873808+0.019434    test-rmse:2.232359+0.481154 
## [43] train-rmse:0.858312+0.022892    test-rmse:2.224244+0.484324 
## [44] train-rmse:0.839999+0.021906    test-rmse:2.215191+0.486921 
## [45] train-rmse:0.820936+0.023792    test-rmse:2.211810+0.491555 
## [46] train-rmse:0.806179+0.023496    test-rmse:2.207988+0.488627 
## [47] train-rmse:0.795281+0.019241    test-rmse:2.203649+0.489722 
## [48] train-rmse:0.776470+0.015164    test-rmse:2.196485+0.490453 
## [49] train-rmse:0.762847+0.013672    test-rmse:2.191053+0.489993 
## [50] train-rmse:0.749929+0.017368    test-rmse:2.189785+0.491056 
## [51] train-rmse:0.739408+0.017238    test-rmse:2.184820+0.491853 
## [52] train-rmse:0.723468+0.024900    test-rmse:2.186695+0.493231 
## [53] train-rmse:0.712974+0.026949    test-rmse:2.181685+0.495320 
## [54] train-rmse:0.693535+0.027791    test-rmse:2.174585+0.494504 
## [55] train-rmse:0.684574+0.030525    test-rmse:2.177654+0.497811 
## [56] train-rmse:0.670133+0.028686    test-rmse:2.177407+0.496578 
## [57] train-rmse:0.655891+0.027580    test-rmse:2.175435+0.501084 
## [58] train-rmse:0.640061+0.026589    test-rmse:2.174916+0.500789 
## [59] train-rmse:0.631961+0.027627    test-rmse:2.173356+0.500273 
## [60] train-rmse:0.625416+0.027445    test-rmse:2.171122+0.501777 
## [61] train-rmse:0.615963+0.028932    test-rmse:2.169605+0.503200 
## [62] train-rmse:0.602883+0.028298    test-rmse:2.169670+0.503348 
## [63] train-rmse:0.594192+0.029490    test-rmse:2.169883+0.501761 
## [64] train-rmse:0.585018+0.028752    test-rmse:2.168065+0.503004 
## [65] train-rmse:0.575770+0.027695    test-rmse:2.173040+0.505186 
## [66] train-rmse:0.566364+0.030075    test-rmse:2.172513+0.504998 
## [67] train-rmse:0.560012+0.031486    test-rmse:2.169847+0.504489 
## [68] train-rmse:0.551657+0.033052    test-rmse:2.170812+0.505633 
## [69] train-rmse:0.543084+0.034397    test-rmse:2.169389+0.506742 
## [70] train-rmse:0.535399+0.034874    test-rmse:2.173129+0.509359 
## Stopping. Best iteration:
## [64] train-rmse:0.585018+0.028752    test-rmse:2.168065+0.503004
## 
## 46 
## [1]  train-rmse:6.641380+0.134266    test-rmse:6.650056+0.590720 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.444091+0.104284    test-rmse:5.511503+0.514386 
## [3]  train-rmse:4.522743+0.093897    test-rmse:4.672128+0.441376 
## [4]  train-rmse:3.814346+0.070253    test-rmse:4.058290+0.430485 
## [5]  train-rmse:3.286395+0.067599    test-rmse:3.622711+0.422477 
## [6]  train-rmse:2.882163+0.060745    test-rmse:3.330696+0.404020 
## [7]  train-rmse:2.557907+0.056568    test-rmse:3.106325+0.453606 
## [8]  train-rmse:2.326375+0.054603    test-rmse:2.957393+0.475934 
## [9]  train-rmse:2.119225+0.058647    test-rmse:2.833944+0.495787 
## [10] train-rmse:1.973698+0.071827    test-rmse:2.765160+0.496221 
## [11] train-rmse:1.853267+0.067632    test-rmse:2.700551+0.497501 
## [12] train-rmse:1.749073+0.058295    test-rmse:2.651167+0.523994 
## [13] train-rmse:1.661700+0.060516    test-rmse:2.592275+0.525595 
## [14] train-rmse:1.588407+0.064401    test-rmse:2.555380+0.538268 
## [15] train-rmse:1.513711+0.058191    test-rmse:2.518840+0.525968 
## [16] train-rmse:1.435134+0.055187    test-rmse:2.486310+0.545864 
## [17] train-rmse:1.381707+0.060916    test-rmse:2.465295+0.546463 
## [18] train-rmse:1.335005+0.055581    test-rmse:2.441957+0.538801 
## [19] train-rmse:1.279967+0.049765    test-rmse:2.429186+0.534316 
## [20] train-rmse:1.236176+0.059110    test-rmse:2.412465+0.532835 
## [21] train-rmse:1.191826+0.054845    test-rmse:2.396571+0.528162 
## [22] train-rmse:1.155466+0.059817    test-rmse:2.382991+0.539328 
## [23] train-rmse:1.116322+0.057935    test-rmse:2.374851+0.539259 
## [24] train-rmse:1.078304+0.052621    test-rmse:2.362381+0.539958 
## [25] train-rmse:1.042954+0.057452    test-rmse:2.365659+0.535846 
## [26] train-rmse:1.019326+0.055545    test-rmse:2.357747+0.540869 
## [27] train-rmse:0.996315+0.056232    test-rmse:2.354156+0.540125 
## [28] train-rmse:0.972162+0.053159    test-rmse:2.345021+0.541483 
## [29] train-rmse:0.950676+0.052088    test-rmse:2.343177+0.543674 
## [30] train-rmse:0.920266+0.056139    test-rmse:2.330673+0.553232 
## [31] train-rmse:0.888845+0.046670    test-rmse:2.325767+0.558084 
## [32] train-rmse:0.863470+0.041442    test-rmse:2.322845+0.557003 
## [33] train-rmse:0.839418+0.040756    test-rmse:2.315331+0.564575 
## [34] train-rmse:0.819955+0.034807    test-rmse:2.314103+0.568153 
## [35] train-rmse:0.790224+0.030750    test-rmse:2.309365+0.568084 
## [36] train-rmse:0.768339+0.029697    test-rmse:2.308584+0.570450 
## [37] train-rmse:0.750547+0.028769    test-rmse:2.299953+0.579644 
## [38] train-rmse:0.724774+0.022940    test-rmse:2.295719+0.580318 
## [39] train-rmse:0.705425+0.019857    test-rmse:2.299430+0.584049 
## [40] train-rmse:0.692146+0.021319    test-rmse:2.296158+0.583194 
## [41] train-rmse:0.673408+0.018788    test-rmse:2.295071+0.584458 
## [42] train-rmse:0.654230+0.016814    test-rmse:2.291928+0.587705 
## [43] train-rmse:0.638048+0.019221    test-rmse:2.288096+0.589356 
## [44] train-rmse:0.624515+0.016300    test-rmse:2.285444+0.589668 
## [45] train-rmse:0.608471+0.013599    test-rmse:2.283407+0.592442 
## [46] train-rmse:0.596440+0.012240    test-rmse:2.280540+0.593010 
## [47] train-rmse:0.583197+0.011051    test-rmse:2.282100+0.592559 
## [48] train-rmse:0.570866+0.008726    test-rmse:2.282571+0.592412 
## [49] train-rmse:0.556387+0.007481    test-rmse:2.281207+0.594699 
## [50] train-rmse:0.546929+0.009350    test-rmse:2.276588+0.596066 
## [51] train-rmse:0.533475+0.007563    test-rmse:2.271703+0.591416 
## [52] train-rmse:0.521074+0.007159    test-rmse:2.272534+0.592201 
## [53] train-rmse:0.509797+0.011937    test-rmse:2.271277+0.591133 
## [54] train-rmse:0.498137+0.008589    test-rmse:2.271205+0.593594 
## [55] train-rmse:0.486305+0.007019    test-rmse:2.272455+0.592185 
## [56] train-rmse:0.475427+0.006145    test-rmse:2.272593+0.593753 
## [57] train-rmse:0.466332+0.006354    test-rmse:2.270348+0.595172 
## [58] train-rmse:0.457629+0.007173    test-rmse:2.268313+0.597452 
## [59] train-rmse:0.446935+0.007366    test-rmse:2.265756+0.597747 
## [60] train-rmse:0.436528+0.007786    test-rmse:2.265626+0.596846 
## [61] train-rmse:0.424004+0.008001    test-rmse:2.263975+0.596658 
## [62] train-rmse:0.415138+0.008545    test-rmse:2.261388+0.602012 
## [63] train-rmse:0.403846+0.011832    test-rmse:2.257819+0.600820 
## [64] train-rmse:0.397426+0.011204    test-rmse:2.255674+0.602022 
## [65] train-rmse:0.392335+0.011466    test-rmse:2.254748+0.601035 
## [66] train-rmse:0.387764+0.010404    test-rmse:2.254481+0.600758 
## [67] train-rmse:0.380839+0.009721    test-rmse:2.254554+0.600514 
## [68] train-rmse:0.371630+0.007542    test-rmse:2.252613+0.600627 
## [69] train-rmse:0.363615+0.007711    test-rmse:2.251848+0.597333 
## [70] train-rmse:0.354001+0.004952    test-rmse:2.250078+0.597582 
## [71] train-rmse:0.343964+0.007337    test-rmse:2.249327+0.596290 
## [72] train-rmse:0.336815+0.009021    test-rmse:2.247708+0.598451 
## [73] train-rmse:0.329454+0.010353    test-rmse:2.247776+0.599186 
## [74] train-rmse:0.322448+0.009377    test-rmse:2.246655+0.600288 
## [75] train-rmse:0.315843+0.008995    test-rmse:2.244706+0.600024 
## [76] train-rmse:0.306648+0.006608    test-rmse:2.243835+0.600301 
## [77] train-rmse:0.300736+0.004582    test-rmse:2.245195+0.601267 
## [78] train-rmse:0.295635+0.005695    test-rmse:2.244071+0.600053 
## [79] train-rmse:0.289562+0.004348    test-rmse:2.244107+0.599476 
## [80] train-rmse:0.283821+0.006103    test-rmse:2.243393+0.597988 
## [81] train-rmse:0.279179+0.005782    test-rmse:2.244650+0.599999 
## [82] train-rmse:0.274124+0.005673    test-rmse:2.244506+0.600414 
## [83] train-rmse:0.266409+0.005354    test-rmse:2.243368+0.599687 
## [84] train-rmse:0.260717+0.005148    test-rmse:2.243487+0.600105 
## [85] train-rmse:0.255997+0.007317    test-rmse:2.242917+0.601950 
## [86] train-rmse:0.252177+0.006716    test-rmse:2.243559+0.601952 
## [87] train-rmse:0.246267+0.005784    test-rmse:2.243252+0.602966 
## [88] train-rmse:0.241238+0.007325    test-rmse:2.242923+0.602306 
## [89] train-rmse:0.235733+0.007748    test-rmse:2.244025+0.602128 
## [90] train-rmse:0.231947+0.007853    test-rmse:2.244086+0.601911 
## [91] train-rmse:0.228304+0.009424    test-rmse:2.244384+0.602646 
## Stopping. Best iteration:
## [85] train-rmse:0.255997+0.007317    test-rmse:2.242917+0.601950
## 
## 47 
## [1]  train-rmse:6.614803+0.130204    test-rmse:6.632687+0.615322 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.389400+0.096789    test-rmse:5.492629+0.545107 
## [3]  train-rmse:4.442451+0.075529    test-rmse:4.648604+0.493068 
## [4]  train-rmse:3.722341+0.062329    test-rmse:4.052704+0.468397 
## [5]  train-rmse:3.163644+0.046145    test-rmse:3.599831+0.454502 
## [6]  train-rmse:2.730568+0.040521    test-rmse:3.284674+0.467864 
## [7]  train-rmse:2.399318+0.040449    test-rmse:3.087921+0.496319 
## [8]  train-rmse:2.146514+0.038568    test-rmse:2.909270+0.515880 
## [9]  train-rmse:1.935152+0.036383    test-rmse:2.780548+0.545440 
## [10] train-rmse:1.770054+0.030803    test-rmse:2.710815+0.532045 
## [11] train-rmse:1.646145+0.029538    test-rmse:2.651384+0.539589 
## [12] train-rmse:1.523235+0.040308    test-rmse:2.609097+0.563022 
## [13] train-rmse:1.416974+0.035626    test-rmse:2.569320+0.555049 
## [14] train-rmse:1.335395+0.035582    test-rmse:2.528176+0.562654 
## [15] train-rmse:1.266817+0.027491    test-rmse:2.498198+0.564679 
## [16] train-rmse:1.210330+0.032523    test-rmse:2.482908+0.564521 
## [17] train-rmse:1.150708+0.030190    test-rmse:2.464861+0.561081 
## [18] train-rmse:1.100778+0.027695    test-rmse:2.446822+0.564086 
## [19] train-rmse:1.053424+0.024764    test-rmse:2.433931+0.577426 
## [20] train-rmse:1.009976+0.015829    test-rmse:2.431004+0.574866 
## [21] train-rmse:0.972946+0.014739    test-rmse:2.422936+0.568459 
## [22] train-rmse:0.940603+0.021006    test-rmse:2.411835+0.569695 
## [23] train-rmse:0.904130+0.020515    test-rmse:2.399890+0.574075 
## [24] train-rmse:0.857563+0.018144    test-rmse:2.389750+0.559200 
## [25] train-rmse:0.822807+0.015490    test-rmse:2.383304+0.566928 
## [26] train-rmse:0.793514+0.016040    test-rmse:2.378609+0.570624 
## [27] train-rmse:0.766210+0.016379    test-rmse:2.363851+0.574110 
## [28] train-rmse:0.732495+0.021398    test-rmse:2.361413+0.572215 
## [29] train-rmse:0.704888+0.023471    test-rmse:2.357560+0.573128 
## [30] train-rmse:0.682036+0.017309    test-rmse:2.354049+0.576975 
## [31] train-rmse:0.656685+0.019640    test-rmse:2.344452+0.580741 
## [32] train-rmse:0.628811+0.026023    test-rmse:2.343135+0.579108 
## [33] train-rmse:0.611742+0.022322    test-rmse:2.336427+0.584337 
## [34] train-rmse:0.593254+0.021748    test-rmse:2.335343+0.582668 
## [35] train-rmse:0.571288+0.018849    test-rmse:2.338260+0.581080 
## [36] train-rmse:0.557669+0.020182    test-rmse:2.337204+0.582680 
## [37] train-rmse:0.540887+0.024717    test-rmse:2.333565+0.584003 
## [38] train-rmse:0.525241+0.022439    test-rmse:2.333540+0.581699 
## [39] train-rmse:0.508796+0.016109    test-rmse:2.330802+0.584322 
## [40] train-rmse:0.489656+0.018878    test-rmse:2.329139+0.585089 
## [41] train-rmse:0.474599+0.019281    test-rmse:2.325102+0.585730 
## [42] train-rmse:0.457299+0.018419    test-rmse:2.321956+0.586026 
## [43] train-rmse:0.443600+0.023577    test-rmse:2.319653+0.587096 
## [44] train-rmse:0.429277+0.020679    test-rmse:2.319062+0.584658 
## [45] train-rmse:0.418636+0.020502    test-rmse:2.318075+0.586130 
## [46] train-rmse:0.405539+0.022222    test-rmse:2.317001+0.586315 
## [47] train-rmse:0.394769+0.025132    test-rmse:2.316383+0.586578 
## [48] train-rmse:0.383152+0.021693    test-rmse:2.314561+0.586423 
## [49] train-rmse:0.371603+0.019969    test-rmse:2.315388+0.585128 
## [50] train-rmse:0.361926+0.019233    test-rmse:2.315120+0.584737 
## [51] train-rmse:0.350090+0.012059    test-rmse:2.315100+0.585197 
## [52] train-rmse:0.340128+0.013052    test-rmse:2.310905+0.585716 
## [53] train-rmse:0.327318+0.013701    test-rmse:2.309630+0.588654 
## [54] train-rmse:0.320514+0.013087    test-rmse:2.308830+0.589384 
## [55] train-rmse:0.312382+0.013151    test-rmse:2.305285+0.591858 
## [56] train-rmse:0.301550+0.016448    test-rmse:2.303720+0.592720 
## [57] train-rmse:0.293806+0.017956    test-rmse:2.302357+0.592141 
## [58] train-rmse:0.287205+0.020551    test-rmse:2.300755+0.592651 
## [59] train-rmse:0.280395+0.019014    test-rmse:2.302295+0.593037 
## [60] train-rmse:0.273394+0.018583    test-rmse:2.304933+0.593338 
## [61] train-rmse:0.266001+0.020117    test-rmse:2.303989+0.593311 
## [62] train-rmse:0.259066+0.020055    test-rmse:2.303666+0.593710 
## [63] train-rmse:0.254543+0.021735    test-rmse:2.304094+0.593850 
## [64] train-rmse:0.247608+0.022981    test-rmse:2.304607+0.590792 
## Stopping. Best iteration:
## [58] train-rmse:0.287205+0.020551    test-rmse:2.300755+0.592651
## 
## 48 
## [1]  train-rmse:6.605372+0.129411    test-rmse:6.627336+0.614893 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.364980+0.092460    test-rmse:5.486353+0.542474 
## [3]  train-rmse:4.397326+0.069133    test-rmse:4.610540+0.511803 
## [4]  train-rmse:3.651502+0.054260    test-rmse:3.984206+0.470766 
## [5]  train-rmse:3.079821+0.046775    test-rmse:3.529958+0.462593 
## [6]  train-rmse:2.630044+0.032222    test-rmse:3.241158+0.461753 
## [7]  train-rmse:2.283090+0.030394    test-rmse:3.049533+0.471323 
## [8]  train-rmse:2.014165+0.027417    test-rmse:2.871664+0.502362 
## [9]  train-rmse:1.800260+0.025173    test-rmse:2.759805+0.519740 
## [10] train-rmse:1.630590+0.029723    test-rmse:2.661061+0.528888 
## [11] train-rmse:1.487380+0.041293    test-rmse:2.620993+0.536551 
## [12] train-rmse:1.363548+0.039605    test-rmse:2.561693+0.544782 
## [13] train-rmse:1.268792+0.034770    test-rmse:2.526781+0.552982 
## [14] train-rmse:1.173755+0.029101    test-rmse:2.489465+0.572441 
## [15] train-rmse:1.107555+0.028543    test-rmse:2.461112+0.571959 
## [16] train-rmse:1.038215+0.030318    test-rmse:2.442757+0.573554 
## [17] train-rmse:0.974435+0.038784    test-rmse:2.419342+0.556779 
## [18] train-rmse:0.922678+0.025895    test-rmse:2.411258+0.563069 
## [19] train-rmse:0.879462+0.016052    test-rmse:2.396847+0.564644 
## [20] train-rmse:0.833796+0.023525    test-rmse:2.387017+0.567234 
## [21] train-rmse:0.791648+0.014342    test-rmse:2.389714+0.565536 
## [22] train-rmse:0.751691+0.018222    test-rmse:2.383010+0.572251 
## [23] train-rmse:0.704901+0.022684    test-rmse:2.377352+0.574710 
## [24] train-rmse:0.674993+0.018589    test-rmse:2.371460+0.578374 
## [25] train-rmse:0.650770+0.024350    test-rmse:2.366241+0.579671 
## [26] train-rmse:0.621180+0.019206    test-rmse:2.361067+0.583664 
## [27] train-rmse:0.583899+0.028622    test-rmse:2.360142+0.582813 
## [28] train-rmse:0.555405+0.033957    test-rmse:2.356271+0.585533 
## [29] train-rmse:0.531736+0.041485    test-rmse:2.351019+0.586187 
## [30] train-rmse:0.510038+0.035899    test-rmse:2.351914+0.590623 
## [31] train-rmse:0.477996+0.027060    test-rmse:2.348296+0.594368 
## [32] train-rmse:0.461944+0.024181    test-rmse:2.348917+0.595227 
## [33] train-rmse:0.447008+0.028196    test-rmse:2.347519+0.598289 
## [34] train-rmse:0.429877+0.031546    test-rmse:2.343853+0.597622 
## [35] train-rmse:0.409089+0.030790    test-rmse:2.340566+0.596182 
## [36] train-rmse:0.389022+0.025969    test-rmse:2.341019+0.594567 
## [37] train-rmse:0.374087+0.026316    test-rmse:2.342766+0.592784 
## [38] train-rmse:0.358553+0.022814    test-rmse:2.342586+0.592611 
## [39] train-rmse:0.345944+0.020831    test-rmse:2.342642+0.593248 
## [40] train-rmse:0.331422+0.016399    test-rmse:2.341306+0.593934 
## [41] train-rmse:0.321593+0.016729    test-rmse:2.340978+0.593992 
## Stopping. Best iteration:
## [35] train-rmse:0.409089+0.030790    test-rmse:2.340566+0.596182
## 
## 49 
## [1]  train-rmse:6.797565+0.146997    test-rmse:6.792528+0.661795 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.804918+0.123759    test-rmse:5.849516+0.697252 
## [3]  train-rmse:5.112160+0.117511    test-rmse:5.161033+0.637943 
## [4]  train-rmse:4.628672+0.116246    test-rmse:4.698430+0.583709 
## [5]  train-rmse:4.294678+0.113334    test-rmse:4.387655+0.589833 
## [6]  train-rmse:4.055825+0.108021    test-rmse:4.159332+0.514392 
## [7]  train-rmse:3.883506+0.112369    test-rmse:4.014231+0.482297 
## [8]  train-rmse:3.748805+0.115313    test-rmse:3.865371+0.487509 
## [9]  train-rmse:3.639320+0.117630    test-rmse:3.745139+0.450909 
## [10] train-rmse:3.549929+0.116633    test-rmse:3.699899+0.449062 
## [11] train-rmse:3.474950+0.118688    test-rmse:3.625488+0.442485 
## [12] train-rmse:3.408084+0.122023    test-rmse:3.562385+0.421732 
## [13] train-rmse:3.345903+0.123683    test-rmse:3.484106+0.435353 
## [14] train-rmse:3.290726+0.123757    test-rmse:3.407789+0.414711 
## [15] train-rmse:3.239678+0.122270    test-rmse:3.356226+0.411235 
## [16] train-rmse:3.191476+0.121811    test-rmse:3.322598+0.420363 
## [17] train-rmse:3.148930+0.124120    test-rmse:3.314904+0.428107 
## [18] train-rmse:3.107623+0.126397    test-rmse:3.267151+0.440829 
## [19] train-rmse:3.069278+0.127796    test-rmse:3.214608+0.420616 
## [20] train-rmse:3.034254+0.128387    test-rmse:3.189172+0.430881 
## [21] train-rmse:3.001120+0.128425    test-rmse:3.160634+0.440733 
## [22] train-rmse:2.969780+0.129156    test-rmse:3.135608+0.430057 
## [23] train-rmse:2.939696+0.130177    test-rmse:3.128014+0.429595 
## [24] train-rmse:2.910928+0.131140    test-rmse:3.093432+0.436047 
## [25] train-rmse:2.883839+0.132391    test-rmse:3.065620+0.453666 
## [26] train-rmse:2.857836+0.133129    test-rmse:3.048687+0.442235 
## [27] train-rmse:2.832098+0.133913    test-rmse:3.033541+0.448355 
## [28] train-rmse:2.807549+0.134304    test-rmse:3.005326+0.433086 
## [29] train-rmse:2.785201+0.134688    test-rmse:2.973215+0.420609 
## [30] train-rmse:2.762974+0.135253    test-rmse:2.946103+0.435128 
## [31] train-rmse:2.741353+0.135710    test-rmse:2.927934+0.432321 
## [32] train-rmse:2.720305+0.136364    test-rmse:2.920089+0.447381 
## [33] train-rmse:2.698880+0.137207    test-rmse:2.913423+0.463535 
## [34] train-rmse:2.678070+0.138345    test-rmse:2.896316+0.454438 
## [35] train-rmse:2.658380+0.138910    test-rmse:2.883433+0.471390 
## [36] train-rmse:2.639754+0.139138    test-rmse:2.868267+0.473848 
## [37] train-rmse:2.621975+0.138965    test-rmse:2.858968+0.469431 
## [38] train-rmse:2.605200+0.139314    test-rmse:2.842302+0.474841 
## [39] train-rmse:2.589047+0.138980    test-rmse:2.820550+0.460450 
## [40] train-rmse:2.573139+0.138918    test-rmse:2.798365+0.463936 
## [41] train-rmse:2.557534+0.138391    test-rmse:2.798564+0.473482 
## [42] train-rmse:2.542415+0.138005    test-rmse:2.784300+0.473379 
## [43] train-rmse:2.527977+0.137796    test-rmse:2.770441+0.468079 
## [44] train-rmse:2.513791+0.138126    test-rmse:2.764919+0.471908 
## [45] train-rmse:2.499977+0.138509    test-rmse:2.751390+0.474355 
## [46] train-rmse:2.486215+0.138934    test-rmse:2.731929+0.470177 
## [47] train-rmse:2.472558+0.139354    test-rmse:2.728962+0.482264 
## [48] train-rmse:2.459600+0.139821    test-rmse:2.714584+0.483600 
## [49] train-rmse:2.446797+0.140413    test-rmse:2.701513+0.490711 
## [50] train-rmse:2.434453+0.140718    test-rmse:2.688879+0.490079 
## [51] train-rmse:2.422696+0.140950    test-rmse:2.678971+0.487051 
## [52] train-rmse:2.411183+0.141231    test-rmse:2.668731+0.485863 
## [53] train-rmse:2.400226+0.141186    test-rmse:2.663890+0.482866 
## [54] train-rmse:2.389718+0.141023    test-rmse:2.659978+0.494658 
## [55] train-rmse:2.379430+0.140736    test-rmse:2.654333+0.496317 
## [56] train-rmse:2.369363+0.140596    test-rmse:2.638330+0.497166 
## [57] train-rmse:2.359547+0.140561    test-rmse:2.625087+0.489790 
## [58] train-rmse:2.349957+0.140486    test-rmse:2.618266+0.497360 
## [59] train-rmse:2.340761+0.140635    test-rmse:2.606821+0.495012 
## [60] train-rmse:2.331430+0.140839    test-rmse:2.601597+0.496784 
## [61] train-rmse:2.322588+0.141269    test-rmse:2.603287+0.500104 
## [62] train-rmse:2.314052+0.141477    test-rmse:2.590888+0.500954 
## [63] train-rmse:2.305603+0.141756    test-rmse:2.583703+0.501746 
## [64] train-rmse:2.297351+0.142198    test-rmse:2.571129+0.501215 
## [65] train-rmse:2.289426+0.142518    test-rmse:2.558872+0.502847 
## [66] train-rmse:2.281677+0.142921    test-rmse:2.549023+0.498453 
## [67] train-rmse:2.274024+0.143393    test-rmse:2.547472+0.505763 
## [68] train-rmse:2.266292+0.143770    test-rmse:2.543130+0.512340 
## [69] train-rmse:2.258832+0.144066    test-rmse:2.536907+0.512286 
## [70] train-rmse:2.251432+0.144293    test-rmse:2.533647+0.508338 
## [71] train-rmse:2.244315+0.144639    test-rmse:2.525681+0.509806 
## [72] train-rmse:2.237486+0.145078    test-rmse:2.516885+0.509978 
## [73] train-rmse:2.230853+0.145314    test-rmse:2.521072+0.516480 
## [74] train-rmse:2.224310+0.145567    test-rmse:2.517769+0.512502 
## [75] train-rmse:2.218081+0.145622    test-rmse:2.507802+0.518409 
## [76] train-rmse:2.211958+0.145732    test-rmse:2.498766+0.517412 
## [77] train-rmse:2.206010+0.145877    test-rmse:2.497425+0.515294 
## [78] train-rmse:2.200083+0.145918    test-rmse:2.493602+0.511456 
## [79] train-rmse:2.194363+0.145950    test-rmse:2.491481+0.510203 
## [80] train-rmse:2.188685+0.145974    test-rmse:2.484711+0.511428 
## [81] train-rmse:2.183123+0.146109    test-rmse:2.486162+0.510961 
## [82] train-rmse:2.177710+0.146219    test-rmse:2.481888+0.510748 
## [83] train-rmse:2.172502+0.146501    test-rmse:2.477724+0.513214 
## [84] train-rmse:2.167410+0.146727    test-rmse:2.470564+0.506041 
## [85] train-rmse:2.162369+0.146943    test-rmse:2.466479+0.505007 
## [86] train-rmse:2.157343+0.147176    test-rmse:2.458594+0.505831 
## [87] train-rmse:2.152379+0.147419    test-rmse:2.456852+0.512293 
## [88] train-rmse:2.147494+0.147649    test-rmse:2.447676+0.513357 
## [89] train-rmse:2.142738+0.147941    test-rmse:2.450536+0.517523 
## [90] train-rmse:2.137943+0.148187    test-rmse:2.449699+0.522463 
## [91] train-rmse:2.133337+0.148466    test-rmse:2.448716+0.522007 
## [92] train-rmse:2.128880+0.148650    test-rmse:2.442707+0.519943 
## [93] train-rmse:2.124496+0.148871    test-rmse:2.440390+0.520786 
## [94] train-rmse:2.120168+0.149041    test-rmse:2.440119+0.522057 
## [95] train-rmse:2.115969+0.149163    test-rmse:2.434855+0.524760 
## [96] train-rmse:2.111892+0.149313    test-rmse:2.425362+0.529392 
## [97] train-rmse:2.107854+0.149486    test-rmse:2.426831+0.526686 
## [98] train-rmse:2.103917+0.149596    test-rmse:2.423046+0.527507 
## [99] train-rmse:2.099996+0.149786    test-rmse:2.418200+0.524623 
## [100]    train-rmse:2.096225+0.149925    test-rmse:2.415402+0.520724 
## [101]    train-rmse:2.092534+0.150119    test-rmse:2.411949+0.520119 
## [102]    train-rmse:2.088904+0.150382    test-rmse:2.417156+0.524928 
## [103]    train-rmse:2.085333+0.150573    test-rmse:2.411870+0.523298 
## [104]    train-rmse:2.081806+0.150732    test-rmse:2.409814+0.521718 
## [105]    train-rmse:2.078333+0.150901    test-rmse:2.409615+0.524584 
## [106]    train-rmse:2.074936+0.151031    test-rmse:2.408561+0.525805 
## [107]    train-rmse:2.071550+0.151178    test-rmse:2.408661+0.523253 
## [108]    train-rmse:2.068217+0.151335    test-rmse:2.402614+0.523003 
## [109]    train-rmse:2.064914+0.151511    test-rmse:2.397947+0.525072 
## [110]    train-rmse:2.061646+0.151606    test-rmse:2.396026+0.526239 
## [111]    train-rmse:2.058481+0.151727    test-rmse:2.392946+0.523503 
## [112]    train-rmse:2.055357+0.151831    test-rmse:2.388553+0.523008 
## [113]    train-rmse:2.052303+0.151948    test-rmse:2.388762+0.525132 
## [114]    train-rmse:2.049260+0.152029    test-rmse:2.386048+0.525412 
## [115]    train-rmse:2.046272+0.152183    test-rmse:2.389537+0.526957 
## [116]    train-rmse:2.043144+0.152227    test-rmse:2.390012+0.526896 
## [117]    train-rmse:2.040061+0.152589    test-rmse:2.387494+0.522591 
## [118]    train-rmse:2.037106+0.152725    test-rmse:2.385785+0.521053 
## [119]    train-rmse:2.034159+0.152855    test-rmse:2.380226+0.524636 
## [120]    train-rmse:2.031340+0.152999    test-rmse:2.377579+0.523334 
## [121]    train-rmse:2.028538+0.153154    test-rmse:2.376434+0.523858 
## [122]    train-rmse:2.025736+0.153221    test-rmse:2.376999+0.523493 
## [123]    train-rmse:2.023049+0.153350    test-rmse:2.377370+0.523485 
## [124]    train-rmse:2.020387+0.153414    test-rmse:2.375625+0.523716 
## [125]    train-rmse:2.017782+0.153459    test-rmse:2.373544+0.524039 
## [126]    train-rmse:2.015214+0.153515    test-rmse:2.371341+0.524759 
## [127]    train-rmse:2.012682+0.153593    test-rmse:2.370155+0.522923 
## [128]    train-rmse:2.010124+0.153684    test-rmse:2.367256+0.523989 
## [129]    train-rmse:2.007606+0.153812    test-rmse:2.366250+0.525162 
## [130]    train-rmse:2.005091+0.153963    test-rmse:2.368386+0.531250 
## [131]    train-rmse:2.002644+0.154125    test-rmse:2.366219+0.530538 
## [132]    train-rmse:2.000236+0.154310    test-rmse:2.362667+0.534918 
## [133]    train-rmse:1.997773+0.154463    test-rmse:2.362844+0.533110 
## [134]    train-rmse:1.995168+0.154859    test-rmse:2.363307+0.530867 
## [135]    train-rmse:1.992812+0.154929    test-rmse:2.365345+0.531473 
## [136]    train-rmse:1.990511+0.155097    test-rmse:2.363292+0.532840 
## [137]    train-rmse:1.988252+0.155191    test-rmse:2.359766+0.532416 
## [138]    train-rmse:1.986031+0.155297    test-rmse:2.354227+0.536610 
## [139]    train-rmse:1.983708+0.155431    test-rmse:2.350290+0.537598 
## [140]    train-rmse:1.981536+0.155563    test-rmse:2.349218+0.536011 
## [141]    train-rmse:1.979385+0.155706    test-rmse:2.348971+0.535509 
## [142]    train-rmse:1.977218+0.155802    test-rmse:2.349460+0.536846 
## [143]    train-rmse:1.975058+0.155898    test-rmse:2.347230+0.536321 
## [144]    train-rmse:1.972942+0.156010    test-rmse:2.345208+0.535377 
## [145]    train-rmse:1.970842+0.156134    test-rmse:2.343029+0.533466 
## [146]    train-rmse:1.968700+0.156341    test-rmse:2.346489+0.533554 
## [147]    train-rmse:1.966647+0.156562    test-rmse:2.347159+0.532229 
## [148]    train-rmse:1.964551+0.156625    test-rmse:2.344174+0.531946 
## [149]    train-rmse:1.962546+0.156739    test-rmse:2.342246+0.531640 
## [150]    train-rmse:1.960514+0.156927    test-rmse:2.343568+0.533408 
## [151]    train-rmse:1.958475+0.157027    test-rmse:2.345629+0.532574 
## [152]    train-rmse:1.956484+0.157135    test-rmse:2.342456+0.531678 
## [153]    train-rmse:1.954550+0.157256    test-rmse:2.340096+0.529605 
## [154]    train-rmse:1.952648+0.157392    test-rmse:2.336673+0.529686 
## [155]    train-rmse:1.950774+0.157492    test-rmse:2.335910+0.527220 
## [156]    train-rmse:1.948904+0.157688    test-rmse:2.337516+0.530835 
## [157]    train-rmse:1.947025+0.157860    test-rmse:2.333898+0.533865 
## [158]    train-rmse:1.945108+0.157930    test-rmse:2.333350+0.533957 
## [159]    train-rmse:1.943280+0.158046    test-rmse:2.332085+0.534053 
## [160]    train-rmse:1.941497+0.158165    test-rmse:2.333609+0.536069 
## [161]    train-rmse:1.939630+0.158264    test-rmse:2.335172+0.533916 
## [162]    train-rmse:1.937834+0.158365    test-rmse:2.334845+0.532556 
## [163]    train-rmse:1.936073+0.158507    test-rmse:2.330572+0.531952 
## [164]    train-rmse:1.934324+0.158634    test-rmse:2.327987+0.529828 
## [165]    train-rmse:1.932523+0.158802    test-rmse:2.327959+0.533101 
## [166]    train-rmse:1.930690+0.158936    test-rmse:2.325900+0.530602 
## [167]    train-rmse:1.928972+0.159022    test-rmse:2.326464+0.529902 
## [168]    train-rmse:1.927269+0.159112    test-rmse:2.326849+0.531414 
## [169]    train-rmse:1.925571+0.159230    test-rmse:2.324801+0.531341 
## [170]    train-rmse:1.923884+0.159243    test-rmse:2.323442+0.530630 
## [171]    train-rmse:1.922255+0.159313    test-rmse:2.321030+0.530837 
## [172]    train-rmse:1.920560+0.159371    test-rmse:2.316268+0.527989 
## [173]    train-rmse:1.918964+0.159481    test-rmse:2.314126+0.529735 
## [174]    train-rmse:1.917361+0.159573    test-rmse:2.315521+0.530180 
## [175]    train-rmse:1.915704+0.159590    test-rmse:2.315371+0.528615 
## [176]    train-rmse:1.914143+0.159659    test-rmse:2.317093+0.527480 
## [177]    train-rmse:1.912606+0.159704    test-rmse:2.318911+0.527070 
## [178]    train-rmse:1.910999+0.159741    test-rmse:2.317667+0.527147 
## [179]    train-rmse:1.909464+0.159795    test-rmse:2.315478+0.530953 
## Stopping. Best iteration:
## [173]    train-rmse:1.918964+0.159481    test-rmse:2.314126+0.529735
## 
## 50 
## [1]  train-rmse:6.689475+0.139207    test-rmse:6.716734+0.627150 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.590473+0.128846    test-rmse:5.634431+0.601013 
## [3]  train-rmse:4.803106+0.108284    test-rmse:4.926579+0.532729 
## [4]  train-rmse:4.224839+0.110653    test-rmse:4.362254+0.519654 
## [5]  train-rmse:3.788401+0.111728    test-rmse:3.999339+0.470621 
## [6]  train-rmse:3.467759+0.107259    test-rmse:3.658930+0.455451 
## [7]  train-rmse:3.233454+0.117652    test-rmse:3.494705+0.450240 
## [8]  train-rmse:3.038215+0.124964    test-rmse:3.314933+0.393244 
## [9]  train-rmse:2.882830+0.104265    test-rmse:3.163223+0.403421 
## [10] train-rmse:2.772856+0.111430    test-rmse:3.099616+0.413305 
## [11] train-rmse:2.685303+0.108169    test-rmse:3.041307+0.419231 
## [12] train-rmse:2.606729+0.117731    test-rmse:2.983619+0.417322 
## [13] train-rmse:2.528770+0.119135    test-rmse:2.911773+0.423789 
## [14] train-rmse:2.469874+0.120896    test-rmse:2.887453+0.430204 
## [15] train-rmse:2.400465+0.125026    test-rmse:2.799956+0.435402 
## [16] train-rmse:2.344074+0.127659    test-rmse:2.786002+0.450936 
## [17] train-rmse:2.296671+0.124101    test-rmse:2.765279+0.440962 
## [18] train-rmse:2.236868+0.114435    test-rmse:2.719961+0.460420 
## [19] train-rmse:2.197760+0.111430    test-rmse:2.694153+0.453220 
## [20] train-rmse:2.157427+0.110662    test-rmse:2.674749+0.457945 
## [21] train-rmse:2.118183+0.111606    test-rmse:2.652080+0.454551 
## [22] train-rmse:2.083999+0.111991    test-rmse:2.623515+0.452642 
## [23] train-rmse:2.041208+0.103465    test-rmse:2.603470+0.468553 
## [24] train-rmse:2.004697+0.107941    test-rmse:2.587856+0.466723 
## [25] train-rmse:1.971680+0.105966    test-rmse:2.574333+0.457160 
## [26] train-rmse:1.937388+0.094705    test-rmse:2.543196+0.464513 
## [27] train-rmse:1.906125+0.093189    test-rmse:2.509196+0.472877 
## [28] train-rmse:1.882244+0.092943    test-rmse:2.501231+0.475166 
## [29] train-rmse:1.859711+0.092110    test-rmse:2.496156+0.473564 
## [30] train-rmse:1.835349+0.093741    test-rmse:2.476249+0.461286 
## [31] train-rmse:1.814470+0.093734    test-rmse:2.465881+0.463234 
## [32] train-rmse:1.794139+0.094004    test-rmse:2.454857+0.468346 
## [33] train-rmse:1.774515+0.094066    test-rmse:2.437027+0.473906 
## [34] train-rmse:1.755817+0.093506    test-rmse:2.423069+0.475959 
## [35] train-rmse:1.732086+0.095672    test-rmse:2.408271+0.478080 
## [36] train-rmse:1.714421+0.095579    test-rmse:2.399240+0.474792 
## [37] train-rmse:1.693734+0.085993    test-rmse:2.387233+0.479073 
## [38] train-rmse:1.672197+0.088847    test-rmse:2.371112+0.483182 
## [39] train-rmse:1.655397+0.089200    test-rmse:2.379624+0.495021 
## [40] train-rmse:1.637579+0.088863    test-rmse:2.361189+0.498684 
## [41] train-rmse:1.624045+0.088266    test-rmse:2.347043+0.489605 
## [42] train-rmse:1.609879+0.087114    test-rmse:2.345775+0.493890 
## [43] train-rmse:1.594066+0.088236    test-rmse:2.350774+0.494637 
## [44] train-rmse:1.576239+0.092106    test-rmse:2.335125+0.488664 
## [45] train-rmse:1.559029+0.091276    test-rmse:2.315104+0.485915 
## [46] train-rmse:1.544045+0.091850    test-rmse:2.307891+0.486150 
## [47] train-rmse:1.532750+0.091464    test-rmse:2.301326+0.486747 
## [48] train-rmse:1.522390+0.091837    test-rmse:2.299081+0.484277 
## [49] train-rmse:1.510753+0.088805    test-rmse:2.296475+0.484471 
## [50] train-rmse:1.494066+0.089549    test-rmse:2.294290+0.490794 
## [51] train-rmse:1.473069+0.082255    test-rmse:2.280627+0.494050 
## [52] train-rmse:1.461385+0.083962    test-rmse:2.279352+0.489685 
## [53] train-rmse:1.450352+0.085229    test-rmse:2.277097+0.494185 
## [54] train-rmse:1.439912+0.086834    test-rmse:2.280928+0.496301 
## [55] train-rmse:1.429722+0.088380    test-rmse:2.271699+0.492820 
## [56] train-rmse:1.420238+0.088588    test-rmse:2.265536+0.499206 
## [57] train-rmse:1.407779+0.092975    test-rmse:2.262011+0.496428 
## [58] train-rmse:1.393267+0.091148    test-rmse:2.254039+0.491716 
## [59] train-rmse:1.384470+0.090697    test-rmse:2.251817+0.490744 
## [60] train-rmse:1.369601+0.089383    test-rmse:2.243726+0.492041 
## [61] train-rmse:1.361723+0.090084    test-rmse:2.239981+0.489517 
## [62] train-rmse:1.349935+0.089917    test-rmse:2.238565+0.493835 
## [63] train-rmse:1.341442+0.090525    test-rmse:2.232540+0.497980 
## [64] train-rmse:1.325731+0.084395    test-rmse:2.225120+0.498896 
## [65] train-rmse:1.318249+0.082252    test-rmse:2.222736+0.498849 
## [66] train-rmse:1.309662+0.079851    test-rmse:2.222285+0.500251 
## [67] train-rmse:1.301969+0.081150    test-rmse:2.217448+0.501632 
## [68] train-rmse:1.293960+0.082065    test-rmse:2.212837+0.502540 
## [69] train-rmse:1.284275+0.084748    test-rmse:2.213661+0.505846 
## [70] train-rmse:1.274597+0.088423    test-rmse:2.206515+0.498085 
## [71] train-rmse:1.264873+0.089003    test-rmse:2.199637+0.497844 
## [72] train-rmse:1.258088+0.089882    test-rmse:2.199420+0.499436 
## [73] train-rmse:1.250535+0.090094    test-rmse:2.196027+0.500117 
## [74] train-rmse:1.242896+0.090370    test-rmse:2.187660+0.506219 
## [75] train-rmse:1.236583+0.090986    test-rmse:2.185426+0.506178 
## [76] train-rmse:1.227106+0.093824    test-rmse:2.184339+0.502529 
## [77] train-rmse:1.219768+0.093242    test-rmse:2.179949+0.503542 
## [78] train-rmse:1.213860+0.093965    test-rmse:2.175750+0.503009 
## [79] train-rmse:1.208964+0.094608    test-rmse:2.173366+0.506429 
## [80] train-rmse:1.203951+0.094456    test-rmse:2.168072+0.508892 
## [81] train-rmse:1.198929+0.094314    test-rmse:2.164428+0.509974 
## [82] train-rmse:1.193956+0.095210    test-rmse:2.164598+0.511006 
## [83] train-rmse:1.187168+0.095004    test-rmse:2.168325+0.510837 
## [84] train-rmse:1.181102+0.097673    test-rmse:2.167054+0.512437 
## [85] train-rmse:1.172974+0.101565    test-rmse:2.165287+0.514579 
## [86] train-rmse:1.164401+0.103961    test-rmse:2.164621+0.513972 
## [87] train-rmse:1.159461+0.103349    test-rmse:2.162443+0.515614 
## [88] train-rmse:1.153793+0.103803    test-rmse:2.159179+0.516420 
## [89] train-rmse:1.146468+0.103235    test-rmse:2.158903+0.515621 
## [90] train-rmse:1.139728+0.104515    test-rmse:2.153148+0.516217 
## [91] train-rmse:1.134545+0.104733    test-rmse:2.151521+0.514931 
## [92] train-rmse:1.129407+0.106770    test-rmse:2.149894+0.517986 
## [93] train-rmse:1.125169+0.107608    test-rmse:2.150154+0.518714 
## [94] train-rmse:1.119937+0.108061    test-rmse:2.146372+0.522361 
## [95] train-rmse:1.112525+0.108795    test-rmse:2.142283+0.522188 
## [96] train-rmse:1.108403+0.108007    test-rmse:2.139490+0.521148 
## [97] train-rmse:1.101659+0.107898    test-rmse:2.138463+0.519502 
## [98] train-rmse:1.097024+0.107930    test-rmse:2.137115+0.517197 
## [99] train-rmse:1.089382+0.106858    test-rmse:2.135837+0.516573 
## [100]    train-rmse:1.083935+0.104009    test-rmse:2.132891+0.515811 
## [101]    train-rmse:1.078942+0.104449    test-rmse:2.134547+0.515430 
## [102]    train-rmse:1.075251+0.103017    test-rmse:2.132850+0.516285 
## [103]    train-rmse:1.071988+0.103027    test-rmse:2.132478+0.519467 
## [104]    train-rmse:1.065077+0.102152    test-rmse:2.136031+0.521433 
## [105]    train-rmse:1.057167+0.103071    test-rmse:2.134787+0.524991 
## [106]    train-rmse:1.048862+0.102271    test-rmse:2.137549+0.523718 
## [107]    train-rmse:1.042574+0.104008    test-rmse:2.138427+0.522454 
## [108]    train-rmse:1.035296+0.104264    test-rmse:2.139008+0.519264 
## [109]    train-rmse:1.028234+0.104420    test-rmse:2.138163+0.518278 
## Stopping. Best iteration:
## [103]    train-rmse:1.071988+0.103027    test-rmse:2.132478+0.519467
## 
## 51 
## [1]  train-rmse:6.617054+0.134512    test-rmse:6.686273+0.614327 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.460306+0.107898    test-rmse:5.574853+0.566844 
## [3]  train-rmse:4.585308+0.109282    test-rmse:4.758756+0.520692 
## [4]  train-rmse:3.929438+0.078423    test-rmse:4.132677+0.509172 
## [5]  train-rmse:3.457237+0.068121    test-rmse:3.713109+0.468441 
## [6]  train-rmse:3.098534+0.069861    test-rmse:3.424975+0.488981 
## [7]  train-rmse:2.821229+0.066699    test-rmse:3.195712+0.497946 
## [8]  train-rmse:2.612606+0.059041    test-rmse:3.033104+0.507174 
## [9]  train-rmse:2.446168+0.052783    test-rmse:2.928528+0.494007 
## [10] train-rmse:2.327197+0.048128    test-rmse:2.842233+0.491089 
## [11] train-rmse:2.221184+0.044671    test-rmse:2.788062+0.482876 
## [12] train-rmse:2.124936+0.037367    test-rmse:2.741610+0.495191 
## [13] train-rmse:2.050900+0.042815    test-rmse:2.693444+0.495741 
## [14] train-rmse:1.975667+0.042135    test-rmse:2.655026+0.488199 
## [15] train-rmse:1.918242+0.042152    test-rmse:2.624159+0.486789 
## [16] train-rmse:1.858586+0.046358    test-rmse:2.589943+0.486843 
## [17] train-rmse:1.809563+0.045048    test-rmse:2.559269+0.496985 
## [18] train-rmse:1.765813+0.048165    test-rmse:2.522992+0.503164 
## [19] train-rmse:1.721549+0.044929    test-rmse:2.494716+0.493917 
## [20] train-rmse:1.674351+0.051838    test-rmse:2.485241+0.505222 
## [21] train-rmse:1.634438+0.048023    test-rmse:2.462289+0.494877 
## [22] train-rmse:1.596398+0.044394    test-rmse:2.439482+0.492524 
## [23] train-rmse:1.568727+0.043637    test-rmse:2.417704+0.493461 
## [24] train-rmse:1.537904+0.042643    test-rmse:2.406454+0.503869 
## [25] train-rmse:1.500207+0.038053    test-rmse:2.381573+0.506682 
## [26] train-rmse:1.471202+0.039679    test-rmse:2.362515+0.508134 
## [27] train-rmse:1.445138+0.042634    test-rmse:2.348961+0.514698 
## [28] train-rmse:1.417746+0.045933    test-rmse:2.346937+0.507978 
## [29] train-rmse:1.391187+0.051149    test-rmse:2.342861+0.505968 
## [30] train-rmse:1.371028+0.052916    test-rmse:2.332137+0.506677 
## [31] train-rmse:1.349399+0.052823    test-rmse:2.327241+0.508145 
## [32] train-rmse:1.330089+0.052471    test-rmse:2.321048+0.509680 
## [33] train-rmse:1.300165+0.050348    test-rmse:2.313544+0.514140 
## [34] train-rmse:1.282855+0.049456    test-rmse:2.306501+0.512514 
## [35] train-rmse:1.257868+0.056269    test-rmse:2.294313+0.513310 
## [36] train-rmse:1.236435+0.058150    test-rmse:2.290768+0.521327 
## [37] train-rmse:1.217525+0.050843    test-rmse:2.299494+0.530032 
## [38] train-rmse:1.193346+0.040484    test-rmse:2.294212+0.528719 
## [39] train-rmse:1.172576+0.043152    test-rmse:2.289748+0.531805 
## [40] train-rmse:1.150481+0.043837    test-rmse:2.277979+0.526246 
## [41] train-rmse:1.135348+0.043936    test-rmse:2.269025+0.534712 
## [42] train-rmse:1.115944+0.047847    test-rmse:2.265828+0.539165 
## [43] train-rmse:1.103533+0.046337    test-rmse:2.256069+0.535246 
## [44] train-rmse:1.088083+0.049070    test-rmse:2.248888+0.536237 
## [45] train-rmse:1.074994+0.049398    test-rmse:2.241965+0.533424 
## [46] train-rmse:1.059093+0.042551    test-rmse:2.233110+0.533112 
## [47] train-rmse:1.041250+0.036467    test-rmse:2.228269+0.535176 
## [48] train-rmse:1.024706+0.037290    test-rmse:2.223099+0.529593 
## [49] train-rmse:1.010440+0.037688    test-rmse:2.225950+0.535682 
## [50] train-rmse:0.997525+0.038606    test-rmse:2.223589+0.535520 
## [51] train-rmse:0.978825+0.033800    test-rmse:2.216089+0.540541 
## [52] train-rmse:0.964866+0.035027    test-rmse:2.212254+0.540278 
## [53] train-rmse:0.953810+0.030874    test-rmse:2.209272+0.542072 
## [54] train-rmse:0.938608+0.026626    test-rmse:2.203859+0.543255 
## [55] train-rmse:0.924986+0.030173    test-rmse:2.198202+0.553023 
## [56] train-rmse:0.913623+0.026278    test-rmse:2.199033+0.555014 
## [57] train-rmse:0.900337+0.031778    test-rmse:2.195770+0.557501 
## [58] train-rmse:0.889828+0.030965    test-rmse:2.193232+0.560783 
## [59] train-rmse:0.881434+0.029691    test-rmse:2.194222+0.558592 
## [60] train-rmse:0.868730+0.027628    test-rmse:2.186002+0.559442 
## [61] train-rmse:0.861802+0.026795    test-rmse:2.179947+0.563192 
## [62] train-rmse:0.849697+0.026175    test-rmse:2.178624+0.564860 
## [63] train-rmse:0.839954+0.026543    test-rmse:2.175239+0.559361 
## [64] train-rmse:0.827921+0.026291    test-rmse:2.173952+0.558535 
## [65] train-rmse:0.820116+0.025023    test-rmse:2.171293+0.559321 
## [66] train-rmse:0.813877+0.024445    test-rmse:2.170464+0.557442 
## [67] train-rmse:0.803112+0.023219    test-rmse:2.165728+0.557805 
## [68] train-rmse:0.791048+0.024727    test-rmse:2.165455+0.558364 
## [69] train-rmse:0.780157+0.026132    test-rmse:2.165512+0.558936 
## [70] train-rmse:0.765423+0.020008    test-rmse:2.161315+0.555663 
## [71] train-rmse:0.752998+0.023202    test-rmse:2.163819+0.561077 
## [72] train-rmse:0.746493+0.023256    test-rmse:2.166018+0.560455 
## [73] train-rmse:0.737110+0.024739    test-rmse:2.162163+0.561215 
## [74] train-rmse:0.730652+0.025065    test-rmse:2.161715+0.562378 
## [75] train-rmse:0.722220+0.023455    test-rmse:2.162942+0.563490 
## [76] train-rmse:0.713274+0.026218    test-rmse:2.160830+0.564137 
## [77] train-rmse:0.704669+0.023586    test-rmse:2.159314+0.565980 
## [78] train-rmse:0.693986+0.027483    test-rmse:2.161928+0.563351 
## [79] train-rmse:0.688168+0.028817    test-rmse:2.161799+0.564153 
## [80] train-rmse:0.683385+0.029194    test-rmse:2.160139+0.563034 
## [81] train-rmse:0.674992+0.025700    test-rmse:2.159733+0.563756 
## [82] train-rmse:0.665954+0.026373    test-rmse:2.159319+0.562409 
## [83] train-rmse:0.659932+0.027304    test-rmse:2.155611+0.563366 
## [84] train-rmse:0.652231+0.027233    test-rmse:2.153515+0.564389 
## [85] train-rmse:0.644491+0.031550    test-rmse:2.157056+0.568855 
## [86] train-rmse:0.637605+0.030948    test-rmse:2.155445+0.568727 
## [87] train-rmse:0.628184+0.031141    test-rmse:2.155391+0.572142 
## [88] train-rmse:0.622859+0.030003    test-rmse:2.155769+0.574643 
## [89] train-rmse:0.617484+0.027539    test-rmse:2.160235+0.575012 
## [90] train-rmse:0.612337+0.026794    test-rmse:2.160945+0.575028 
## Stopping. Best iteration:
## [84] train-rmse:0.652231+0.027233    test-rmse:2.153515+0.564389
## 
## 52 
## [1]  train-rmse:6.549503+0.133190    test-rmse:6.594613+0.601921 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.314227+0.105095    test-rmse:5.433249+0.511508 
## [3]  train-rmse:4.404696+0.085791    test-rmse:4.582514+0.469573 
## [4]  train-rmse:3.716019+0.080232    test-rmse:3.984650+0.451045 
## [5]  train-rmse:3.226349+0.081956    test-rmse:3.527210+0.415101 
## [6]  train-rmse:2.844361+0.065870    test-rmse:3.204261+0.455998 
## [7]  train-rmse:2.569962+0.058877    test-rmse:3.027993+0.446830 
## [8]  train-rmse:2.357007+0.060132    test-rmse:2.865904+0.421634 
## [9]  train-rmse:2.184888+0.050953    test-rmse:2.739486+0.448669 
## [10] train-rmse:2.035429+0.044928    test-rmse:2.619229+0.415981 
## [11] train-rmse:1.933266+0.040609    test-rmse:2.557826+0.422390 
## [12] train-rmse:1.843945+0.033315    test-rmse:2.512777+0.405222 
## [13] train-rmse:1.765249+0.035251    test-rmse:2.476412+0.420943 
## [14] train-rmse:1.706219+0.034692    test-rmse:2.445998+0.415779 
## [15] train-rmse:1.635217+0.039541    test-rmse:2.390692+0.406094 
## [16] train-rmse:1.580831+0.040208    test-rmse:2.388860+0.424771 
## [17] train-rmse:1.527736+0.021788    test-rmse:2.373096+0.420557 
## [18] train-rmse:1.481749+0.025596    test-rmse:2.339173+0.425066 
## [19] train-rmse:1.432786+0.031095    test-rmse:2.313526+0.427812 
## [20] train-rmse:1.392352+0.032614    test-rmse:2.298320+0.430314 
## [21] train-rmse:1.359280+0.030866    test-rmse:2.289174+0.428126 
## [22] train-rmse:1.317643+0.030848    test-rmse:2.271330+0.434847 
## [23] train-rmse:1.274861+0.030453    test-rmse:2.262729+0.432628 
## [24] train-rmse:1.241593+0.027963    test-rmse:2.246246+0.436025 
## [25] train-rmse:1.214883+0.027906    test-rmse:2.249670+0.438580 
## [26] train-rmse:1.179138+0.021418    test-rmse:2.238681+0.441372 
## [27] train-rmse:1.154393+0.022990    test-rmse:2.234164+0.444524 
## [28] train-rmse:1.134424+0.022709    test-rmse:2.220583+0.451524 
## [29] train-rmse:1.106923+0.019892    test-rmse:2.210465+0.457100 
## [30] train-rmse:1.075545+0.026341    test-rmse:2.208380+0.453255 
## [31] train-rmse:1.045616+0.034797    test-rmse:2.207362+0.447976 
## [32] train-rmse:1.023918+0.029665    test-rmse:2.197037+0.449876 
## [33] train-rmse:1.006354+0.030386    test-rmse:2.195711+0.452245 
## [34] train-rmse:0.982256+0.024383    test-rmse:2.189713+0.453926 
## [35] train-rmse:0.957716+0.024954    test-rmse:2.195176+0.457747 
## [36] train-rmse:0.928930+0.025731    test-rmse:2.186749+0.459780 
## [37] train-rmse:0.914075+0.026033    test-rmse:2.185367+0.458480 
## [38] train-rmse:0.888713+0.024820    test-rmse:2.172571+0.466683 
## [39] train-rmse:0.871997+0.017598    test-rmse:2.159817+0.458705 
## [40] train-rmse:0.855571+0.015121    test-rmse:2.153716+0.459046 
## [41] train-rmse:0.832891+0.019828    test-rmse:2.151688+0.458172 
## [42] train-rmse:0.817943+0.025392    test-rmse:2.149048+0.460439 
## [43] train-rmse:0.800366+0.026095    test-rmse:2.143509+0.459516 
## [44] train-rmse:0.782224+0.019432    test-rmse:2.137882+0.453590 
## [45] train-rmse:0.763596+0.017217    test-rmse:2.134408+0.461722 
## [46] train-rmse:0.749372+0.019725    test-rmse:2.131914+0.465661 
## [47] train-rmse:0.728116+0.019260    test-rmse:2.131257+0.465037 
## [48] train-rmse:0.710867+0.018289    test-rmse:2.125448+0.461341 
## [49] train-rmse:0.700024+0.016308    test-rmse:2.123043+0.464662 
## [50] train-rmse:0.690383+0.012838    test-rmse:2.122321+0.463448 
## [51] train-rmse:0.676678+0.014331    test-rmse:2.122553+0.467104 
## [52] train-rmse:0.663167+0.009508    test-rmse:2.122552+0.464539 
## [53] train-rmse:0.648830+0.012130    test-rmse:2.125268+0.464719 
## [54] train-rmse:0.638828+0.013171    test-rmse:2.126429+0.463075 
## [55] train-rmse:0.628868+0.013186    test-rmse:2.125609+0.466042 
## [56] train-rmse:0.616734+0.010303    test-rmse:2.121268+0.463229 
## [57] train-rmse:0.606656+0.009327    test-rmse:2.118889+0.462912 
## [58] train-rmse:0.598388+0.008859    test-rmse:2.118325+0.463795 
## [59] train-rmse:0.590555+0.010284    test-rmse:2.114590+0.466151 
## [60] train-rmse:0.581572+0.008153    test-rmse:2.114574+0.467616 
## [61] train-rmse:0.572353+0.008872    test-rmse:2.112450+0.470261 
## [62] train-rmse:0.560355+0.007353    test-rmse:2.112436+0.471398 
## [63] train-rmse:0.550906+0.014345    test-rmse:2.111616+0.474113 
## [64] train-rmse:0.543669+0.013253    test-rmse:2.112400+0.472751 
## [65] train-rmse:0.535826+0.012903    test-rmse:2.110265+0.470902 
## [66] train-rmse:0.525490+0.015758    test-rmse:2.107811+0.471942 
## [67] train-rmse:0.517800+0.016185    test-rmse:2.107580+0.469713 
## [68] train-rmse:0.507047+0.017879    test-rmse:2.110889+0.468397 
## [69] train-rmse:0.499343+0.017891    test-rmse:2.109493+0.470119 
## [70] train-rmse:0.490346+0.015839    test-rmse:2.109671+0.467511 
## [71] train-rmse:0.485917+0.017292    test-rmse:2.109009+0.467808 
## [72] train-rmse:0.476880+0.016502    test-rmse:2.107509+0.466746 
## [73] train-rmse:0.467633+0.016454    test-rmse:2.104607+0.467135 
## [74] train-rmse:0.460939+0.013937    test-rmse:2.105619+0.467118 
## [75] train-rmse:0.451862+0.012793    test-rmse:2.105332+0.467873 
## [76] train-rmse:0.443812+0.014480    test-rmse:2.105346+0.467392 
## [77] train-rmse:0.438570+0.015368    test-rmse:2.102587+0.465704 
## [78] train-rmse:0.430485+0.016098    test-rmse:2.102102+0.467494 
## [79] train-rmse:0.426030+0.016982    test-rmse:2.101515+0.467174 
## [80] train-rmse:0.420398+0.020338    test-rmse:2.102722+0.466785 
## [81] train-rmse:0.413091+0.022128    test-rmse:2.101356+0.467746 
## [82] train-rmse:0.407679+0.022164    test-rmse:2.102299+0.467584 
## [83] train-rmse:0.403899+0.021025    test-rmse:2.101158+0.468076 
## [84] train-rmse:0.398388+0.022867    test-rmse:2.099484+0.469053 
## [85] train-rmse:0.392486+0.021309    test-rmse:2.098554+0.468447 
## [86] train-rmse:0.387756+0.021898    test-rmse:2.099975+0.469657 
## [87] train-rmse:0.383251+0.024626    test-rmse:2.100244+0.471076 
## [88] train-rmse:0.375682+0.024594    test-rmse:2.097847+0.471038 
## [89] train-rmse:0.369626+0.023261    test-rmse:2.096922+0.471307 
## [90] train-rmse:0.363908+0.022459    test-rmse:2.098327+0.469459 
## [91] train-rmse:0.356194+0.020934    test-rmse:2.099137+0.473697 
## [92] train-rmse:0.348973+0.020074    test-rmse:2.099759+0.473210 
## [93] train-rmse:0.342315+0.017777    test-rmse:2.098857+0.472698 
## [94] train-rmse:0.337793+0.015881    test-rmse:2.097951+0.472585 
## [95] train-rmse:0.331975+0.017846    test-rmse:2.095437+0.470359 
## [96] train-rmse:0.325156+0.019993    test-rmse:2.095754+0.470273 
## [97] train-rmse:0.322018+0.019922    test-rmse:2.096300+0.469524 
## [98] train-rmse:0.318607+0.018262    test-rmse:2.096821+0.470104 
## [99] train-rmse:0.314147+0.017800    test-rmse:2.097951+0.470709 
## [100]    train-rmse:0.309609+0.019020    test-rmse:2.098070+0.471338 
## [101]    train-rmse:0.302922+0.019699    test-rmse:2.096942+0.472892 
## Stopping. Best iteration:
## [95] train-rmse:0.331975+0.017846    test-rmse:2.095437+0.470359
## 
## 53 
## [1]  train-rmse:6.502818+0.130723    test-rmse:6.516388+0.581632 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.226718+0.108989    test-rmse:5.310894+0.496629 
## [3]  train-rmse:4.285477+0.091279    test-rmse:4.476299+0.446932 
## [4]  train-rmse:3.590032+0.061733    test-rmse:3.902146+0.446118 
## [5]  train-rmse:3.074015+0.050391    test-rmse:3.506593+0.433664 
## [6]  train-rmse:2.677677+0.055352    test-rmse:3.195923+0.464987 
## [7]  train-rmse:2.379216+0.053755    test-rmse:3.012836+0.470247 
## [8]  train-rmse:2.147546+0.050569    test-rmse:2.874964+0.526493 
## [9]  train-rmse:1.966707+0.054747    test-rmse:2.768101+0.546506 
## [10] train-rmse:1.836436+0.044615    test-rmse:2.702850+0.541894 
## [11] train-rmse:1.707095+0.053322    test-rmse:2.618446+0.552838 
## [12] train-rmse:1.598992+0.049426    test-rmse:2.591382+0.540309 
## [13] train-rmse:1.520837+0.048569    test-rmse:2.556404+0.538381 
## [14] train-rmse:1.452733+0.044208    test-rmse:2.533797+0.528329 
## [15] train-rmse:1.383956+0.031425    test-rmse:2.509149+0.542290 
## [16] train-rmse:1.332587+0.033165    test-rmse:2.493190+0.548461 
## [17] train-rmse:1.282997+0.038196    test-rmse:2.469246+0.552945 
## [18] train-rmse:1.233460+0.045589    test-rmse:2.438169+0.542321 
## [19] train-rmse:1.188195+0.041014    test-rmse:2.422154+0.547661 
## [20] train-rmse:1.152779+0.045976    test-rmse:2.411646+0.546949 
## [21] train-rmse:1.112435+0.048753    test-rmse:2.403509+0.551056 
## [22] train-rmse:1.073539+0.039622    test-rmse:2.393201+0.552164 
## [23] train-rmse:1.037268+0.030612    test-rmse:2.381715+0.557265 
## [24] train-rmse:1.000275+0.032379    test-rmse:2.372376+0.564871 
## [25] train-rmse:0.960516+0.023248    test-rmse:2.357646+0.565662 
## [26] train-rmse:0.940222+0.022694    test-rmse:2.354078+0.562004 
## [27] train-rmse:0.911935+0.020600    test-rmse:2.352121+0.565226 
## [28] train-rmse:0.877110+0.021023    test-rmse:2.343398+0.566286 
## [29] train-rmse:0.855177+0.014252    test-rmse:2.335873+0.567432 
## [30] train-rmse:0.834834+0.018498    test-rmse:2.330462+0.571404 
## [31] train-rmse:0.809876+0.016017    test-rmse:2.320037+0.577311 
## [32] train-rmse:0.791948+0.014586    test-rmse:2.316145+0.577616 
## [33] train-rmse:0.765409+0.019662    test-rmse:2.314602+0.572406 
## [34] train-rmse:0.742560+0.026684    test-rmse:2.310658+0.573624 
## [35] train-rmse:0.722273+0.021189    test-rmse:2.308645+0.576650 
## [36] train-rmse:0.698564+0.021515    test-rmse:2.303994+0.577046 
## [37] train-rmse:0.677425+0.020283    test-rmse:2.299412+0.577835 
## [38] train-rmse:0.662471+0.023057    test-rmse:2.299899+0.583007 
## [39] train-rmse:0.644722+0.020302    test-rmse:2.294477+0.583529 
## [40] train-rmse:0.622495+0.020044    test-rmse:2.291801+0.584847 
## [41] train-rmse:0.603888+0.021158    test-rmse:2.284066+0.584083 
## [42] train-rmse:0.586027+0.019843    test-rmse:2.284294+0.585807 
## [43] train-rmse:0.573166+0.017830    test-rmse:2.281689+0.588744 
## [44] train-rmse:0.559906+0.018704    test-rmse:2.278475+0.588329 
## [45] train-rmse:0.543578+0.017001    test-rmse:2.270464+0.590863 
## [46] train-rmse:0.534227+0.017370    test-rmse:2.266202+0.591798 
## [47] train-rmse:0.522240+0.017385    test-rmse:2.266348+0.594159 
## [48] train-rmse:0.505841+0.018471    test-rmse:2.264729+0.595333 
## [49] train-rmse:0.493237+0.014237    test-rmse:2.265002+0.595849 
## [50] train-rmse:0.478155+0.020929    test-rmse:2.263966+0.591672 
## [51] train-rmse:0.470641+0.022412    test-rmse:2.264226+0.592737 
## [52] train-rmse:0.455939+0.019951    test-rmse:2.259293+0.593598 
## [53] train-rmse:0.445615+0.021021    test-rmse:2.257915+0.593921 
## [54] train-rmse:0.432224+0.018026    test-rmse:2.257241+0.592841 
## [55] train-rmse:0.422045+0.017983    test-rmse:2.256988+0.591778 
## [56] train-rmse:0.413498+0.016685    test-rmse:2.257658+0.592853 
## [57] train-rmse:0.403640+0.014359    test-rmse:2.256916+0.593219 
## [58] train-rmse:0.392339+0.016307    test-rmse:2.257537+0.596618 
## [59] train-rmse:0.384258+0.017468    test-rmse:2.261391+0.597784 
## [60] train-rmse:0.374576+0.019694    test-rmse:2.262118+0.597938 
## [61] train-rmse:0.365863+0.019971    test-rmse:2.260384+0.597456 
## [62] train-rmse:0.356370+0.017732    test-rmse:2.256608+0.596097 
## [63] train-rmse:0.346931+0.016966    test-rmse:2.258342+0.597014 
## [64] train-rmse:0.338793+0.013271    test-rmse:2.258321+0.596709 
## [65] train-rmse:0.332294+0.016062    test-rmse:2.256621+0.594140 
## [66] train-rmse:0.324531+0.017553    test-rmse:2.256929+0.593798 
## [67] train-rmse:0.319519+0.017675    test-rmse:2.256433+0.593178 
## [68] train-rmse:0.313806+0.017174    test-rmse:2.255412+0.593129 
## [69] train-rmse:0.305497+0.015544    test-rmse:2.257565+0.592916 
## [70] train-rmse:0.300962+0.016159    test-rmse:2.258173+0.593855 
## [71] train-rmse:0.293381+0.016233    test-rmse:2.256624+0.594545 
## [72] train-rmse:0.287580+0.016532    test-rmse:2.256460+0.595298 
## [73] train-rmse:0.280901+0.016413    test-rmse:2.254865+0.594974 
## [74] train-rmse:0.275850+0.016993    test-rmse:2.254885+0.594982 
## [75] train-rmse:0.272213+0.017206    test-rmse:2.255171+0.594662 
## [76] train-rmse:0.265249+0.018763    test-rmse:2.253556+0.595195 
## [77] train-rmse:0.259360+0.021048    test-rmse:2.253845+0.597924 
## [78] train-rmse:0.255312+0.019970    test-rmse:2.253970+0.597628 
## [79] train-rmse:0.250613+0.021730    test-rmse:2.254027+0.596813 
## [80] train-rmse:0.243240+0.017095    test-rmse:2.254247+0.596547 
## [81] train-rmse:0.238654+0.017716    test-rmse:2.252988+0.595749 
## [82] train-rmse:0.234195+0.018293    test-rmse:2.253077+0.594964 
## [83] train-rmse:0.229968+0.016960    test-rmse:2.252602+0.595881 
## [84] train-rmse:0.225466+0.018086    test-rmse:2.252416+0.597969 
## [85] train-rmse:0.220020+0.018326    test-rmse:2.251638+0.598212 
## [86] train-rmse:0.216553+0.018996    test-rmse:2.252011+0.597708 
## [87] train-rmse:0.209872+0.017419    test-rmse:2.252073+0.598070 
## [88] train-rmse:0.204291+0.016858    test-rmse:2.252710+0.598266 
## [89] train-rmse:0.198676+0.015111    test-rmse:2.252109+0.598909 
## [90] train-rmse:0.195204+0.014447    test-rmse:2.252292+0.598535 
## [91] train-rmse:0.191496+0.012999    test-rmse:2.252061+0.599119 
## Stopping. Best iteration:
## [85] train-rmse:0.220020+0.018326    test-rmse:2.251638+0.598212
## 
## 54 
## [1]  train-rmse:6.473490+0.126154    test-rmse:6.497475+0.608838 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.172935+0.090673    test-rmse:5.294049+0.534920 
## [3]  train-rmse:4.197416+0.071022    test-rmse:4.438633+0.478238 
## [4]  train-rmse:3.469591+0.053089    test-rmse:3.852354+0.454741 
## [5]  train-rmse:2.936457+0.045322    test-rmse:3.449489+0.459231 
## [6]  train-rmse:2.527151+0.040619    test-rmse:3.150183+0.484144 
## [7]  train-rmse:2.219824+0.031130    test-rmse:2.938074+0.519406 
## [8]  train-rmse:1.971829+0.044888    test-rmse:2.800262+0.541210 
## [9]  train-rmse:1.773216+0.045399    test-rmse:2.691808+0.533051 
## [10] train-rmse:1.628520+0.038173    test-rmse:2.614374+0.538455 
## [11] train-rmse:1.489073+0.023202    test-rmse:2.577403+0.537130 
## [12] train-rmse:1.393331+0.016332    test-rmse:2.537852+0.535981 
## [13] train-rmse:1.300821+0.024501    test-rmse:2.505769+0.546149 
## [14] train-rmse:1.233193+0.026379    test-rmse:2.482313+0.549815 
## [15] train-rmse:1.159117+0.014986    test-rmse:2.462136+0.553701 
## [16] train-rmse:1.111014+0.015325    test-rmse:2.447215+0.545731 
## [17] train-rmse:1.056616+0.007220    test-rmse:2.428658+0.543255 
## [18] train-rmse:1.011616+0.020168    test-rmse:2.415240+0.551649 
## [19] train-rmse:0.967648+0.017573    test-rmse:2.402582+0.550503 
## [20] train-rmse:0.923404+0.022960    test-rmse:2.395638+0.550245 
## [21] train-rmse:0.873413+0.025865    test-rmse:2.395233+0.549739 
## [22] train-rmse:0.839054+0.027323    test-rmse:2.391203+0.545033 
## [23] train-rmse:0.810377+0.023921    test-rmse:2.381577+0.546538 
## [24] train-rmse:0.775808+0.025590    test-rmse:2.373726+0.545636 
## [25] train-rmse:0.741719+0.029196    test-rmse:2.370240+0.548466 
## [26] train-rmse:0.708484+0.021557    test-rmse:2.368248+0.546552 
## [27] train-rmse:0.683334+0.021621    test-rmse:2.357485+0.549711 
## [28] train-rmse:0.656345+0.019691    test-rmse:2.354541+0.555283 
## [29] train-rmse:0.633299+0.026707    test-rmse:2.351763+0.553356 
## [30] train-rmse:0.608821+0.029222    test-rmse:2.349080+0.550326 
## [31] train-rmse:0.587876+0.030890    test-rmse:2.346158+0.553089 
## [32] train-rmse:0.564677+0.027097    test-rmse:2.343778+0.552708 
## [33] train-rmse:0.547759+0.021133    test-rmse:2.343734+0.552656 
## [34] train-rmse:0.531650+0.024365    test-rmse:2.343445+0.549279 
## [35] train-rmse:0.508628+0.026188    test-rmse:2.337918+0.551673 
## [36] train-rmse:0.494830+0.029709    test-rmse:2.335137+0.553095 
## [37] train-rmse:0.482184+0.023922    test-rmse:2.332325+0.552349 
## [38] train-rmse:0.466968+0.017250    test-rmse:2.329084+0.552698 
## [39] train-rmse:0.448769+0.021650    test-rmse:2.329811+0.550400 
## [40] train-rmse:0.436857+0.021842    test-rmse:2.323235+0.552535 
## [41] train-rmse:0.419591+0.023066    test-rmse:2.316468+0.553230 
## [42] train-rmse:0.407117+0.021266    test-rmse:2.315532+0.553843 
## [43] train-rmse:0.393698+0.022054    test-rmse:2.315656+0.553937 
## [44] train-rmse:0.383869+0.023905    test-rmse:2.314046+0.551758 
## [45] train-rmse:0.369552+0.021478    test-rmse:2.312583+0.552359 
## [46] train-rmse:0.360392+0.023804    test-rmse:2.314699+0.555564 
## [47] train-rmse:0.346585+0.024402    test-rmse:2.315171+0.555355 
## [48] train-rmse:0.336768+0.021314    test-rmse:2.314095+0.555262 
## [49] train-rmse:0.325822+0.019482    test-rmse:2.313385+0.555608 
## [50] train-rmse:0.310318+0.020409    test-rmse:2.313877+0.554281 
## [51] train-rmse:0.298951+0.019480    test-rmse:2.313618+0.556368 
## Stopping. Best iteration:
## [45] train-rmse:0.369552+0.021478    test-rmse:2.312583+0.552359
## 
## 55 
## [1]  train-rmse:6.463075+0.125274    test-rmse:6.491441+0.608406 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.145252+0.085780    test-rmse:5.286403+0.532263 
## [3]  train-rmse:4.145247+0.062125    test-rmse:4.393345+0.503767 
## [4]  train-rmse:3.394963+0.047503    test-rmse:3.784525+0.458850 
## [5]  train-rmse:2.837559+0.041056    test-rmse:3.366648+0.460859 
## [6]  train-rmse:2.409586+0.032826    test-rmse:3.099088+0.481660 
## [7]  train-rmse:2.093663+0.030944    test-rmse:2.919930+0.497713 
## [8]  train-rmse:1.829140+0.033228    test-rmse:2.751609+0.522339 
## [9]  train-rmse:1.632322+0.045349    test-rmse:2.674564+0.526898 
## [10] train-rmse:1.471179+0.031620    test-rmse:2.586650+0.551979 
## [11] train-rmse:1.347532+0.053679    test-rmse:2.536142+0.552471 
## [12] train-rmse:1.233615+0.049121    test-rmse:2.507848+0.551654 
## [13] train-rmse:1.125676+0.049198    test-rmse:2.482377+0.569033 
## [14] train-rmse:1.042259+0.042901    test-rmse:2.454395+0.565681 
## [15] train-rmse:0.986977+0.049542    test-rmse:2.436897+0.567609 
## [16] train-rmse:0.928236+0.052395    test-rmse:2.430817+0.564355 
## [17] train-rmse:0.876126+0.046740    test-rmse:2.425270+0.573918 
## [18] train-rmse:0.830474+0.043364    test-rmse:2.416245+0.576183 
## [19] train-rmse:0.783159+0.048419    test-rmse:2.410698+0.581692 
## [20] train-rmse:0.739399+0.048867    test-rmse:2.406429+0.580485 
## [21] train-rmse:0.696750+0.040528    test-rmse:2.402143+0.582488 
## [22] train-rmse:0.662282+0.037660    test-rmse:2.398502+0.580067 
## [23] train-rmse:0.632867+0.037540    test-rmse:2.396595+0.583297 
## [24] train-rmse:0.605903+0.034553    test-rmse:2.391914+0.590896 
## [25] train-rmse:0.575972+0.032562    test-rmse:2.389173+0.589285 
## [26] train-rmse:0.550218+0.025991    test-rmse:2.385902+0.593324 
## [27] train-rmse:0.526576+0.029160    test-rmse:2.382419+0.593834 
## [28] train-rmse:0.501106+0.031986    test-rmse:2.380727+0.590689 
## [29] train-rmse:0.480628+0.030907    test-rmse:2.384925+0.590307 
## [30] train-rmse:0.460336+0.033215    test-rmse:2.380151+0.591458 
## [31] train-rmse:0.439793+0.035180    test-rmse:2.379593+0.591000 
## [32] train-rmse:0.425356+0.033422    test-rmse:2.376716+0.592732 
## [33] train-rmse:0.405078+0.029739    test-rmse:2.375066+0.594080 
## [34] train-rmse:0.387650+0.031830    test-rmse:2.377186+0.597051 
## [35] train-rmse:0.369811+0.028983    test-rmse:2.372789+0.598511 
## [36] train-rmse:0.356428+0.028127    test-rmse:2.373317+0.596454 
## [37] train-rmse:0.338393+0.028396    test-rmse:2.372047+0.598409 
## [38] train-rmse:0.322830+0.025935    test-rmse:2.370473+0.600279 
## [39] train-rmse:0.310485+0.024463    test-rmse:2.369020+0.602569 
## [40] train-rmse:0.298767+0.019707    test-rmse:2.368119+0.602487 
## [41] train-rmse:0.285367+0.017286    test-rmse:2.365700+0.600283 
## [42] train-rmse:0.272607+0.013808    test-rmse:2.365552+0.600993 
## [43] train-rmse:0.260419+0.016304    test-rmse:2.362324+0.600617 
## [44] train-rmse:0.250776+0.014084    test-rmse:2.360859+0.600844 
## [45] train-rmse:0.239445+0.013183    test-rmse:2.359454+0.602899 
## [46] train-rmse:0.230776+0.012135    test-rmse:2.357453+0.605070 
## [47] train-rmse:0.223005+0.013081    test-rmse:2.356182+0.603695 
## [48] train-rmse:0.215877+0.013170    test-rmse:2.354976+0.604421 
## [49] train-rmse:0.207541+0.011158    test-rmse:2.353911+0.603968 
## [50] train-rmse:0.201255+0.010374    test-rmse:2.354999+0.604894 
## [51] train-rmse:0.194329+0.010989    test-rmse:2.354316+0.605352 
## [52] train-rmse:0.188180+0.010502    test-rmse:2.354766+0.605377 
## [53] train-rmse:0.181982+0.008297    test-rmse:2.354908+0.605598 
## [54] train-rmse:0.175998+0.009289    test-rmse:2.353327+0.605909 
## [55] train-rmse:0.170004+0.009398    test-rmse:2.352083+0.606255 
## [56] train-rmse:0.163067+0.008595    test-rmse:2.351012+0.606252 
## [57] train-rmse:0.159040+0.008104    test-rmse:2.349780+0.606931 
## [58] train-rmse:0.154038+0.008625    test-rmse:2.348672+0.607705 
## [59] train-rmse:0.148737+0.007470    test-rmse:2.347978+0.609501 
## [60] train-rmse:0.142594+0.006252    test-rmse:2.347299+0.610887 
## [61] train-rmse:0.138226+0.006107    test-rmse:2.347197+0.611241 
## [62] train-rmse:0.132725+0.005868    test-rmse:2.346836+0.611063 
## [63] train-rmse:0.129052+0.004938    test-rmse:2.346504+0.611366 
## [64] train-rmse:0.125556+0.003894    test-rmse:2.345945+0.610480 
## [65] train-rmse:0.121244+0.003811    test-rmse:2.345070+0.610601 
## [66] train-rmse:0.117741+0.003936    test-rmse:2.345433+0.611800 
## [67] train-rmse:0.114215+0.003638    test-rmse:2.344905+0.611294 
## [68] train-rmse:0.110987+0.003634    test-rmse:2.344574+0.611550 
## [69] train-rmse:0.105288+0.003275    test-rmse:2.344327+0.612635 
## [70] train-rmse:0.101910+0.003559    test-rmse:2.344013+0.612352 
## [71] train-rmse:0.097031+0.002913    test-rmse:2.343332+0.612774 
## [72] train-rmse:0.094820+0.003269    test-rmse:2.342679+0.612965 
## [73] train-rmse:0.091636+0.003514    test-rmse:2.342502+0.612813 
## [74] train-rmse:0.088514+0.004396    test-rmse:2.341705+0.612433 
## [75] train-rmse:0.084903+0.004702    test-rmse:2.341385+0.612308 
## [76] train-rmse:0.082858+0.004711    test-rmse:2.341124+0.612533 
## [77] train-rmse:0.079909+0.004868    test-rmse:2.340952+0.612698 
## [78] train-rmse:0.077019+0.004606    test-rmse:2.341009+0.612429 
## [79] train-rmse:0.073974+0.005312    test-rmse:2.340432+0.612427 
## [80] train-rmse:0.071690+0.004928    test-rmse:2.340238+0.612017 
## [81] train-rmse:0.069106+0.004559    test-rmse:2.340073+0.612678 
## [82] train-rmse:0.066894+0.005322    test-rmse:2.339934+0.613227 
## [83] train-rmse:0.065002+0.005421    test-rmse:2.339769+0.613201 
## [84] train-rmse:0.063036+0.005665    test-rmse:2.339596+0.613336 
## [85] train-rmse:0.060696+0.005023    test-rmse:2.339133+0.613132 
## [86] train-rmse:0.058833+0.005257    test-rmse:2.338776+0.613461 
## [87] train-rmse:0.057391+0.004974    test-rmse:2.338515+0.613545 
## [88] train-rmse:0.055337+0.004343    test-rmse:2.338308+0.613650 
## [89] train-rmse:0.053370+0.003837    test-rmse:2.338244+0.613710 
## [90] train-rmse:0.051607+0.003629    test-rmse:2.337776+0.613953 
## [91] train-rmse:0.050113+0.002992    test-rmse:2.337905+0.614092 
## [92] train-rmse:0.047897+0.003089    test-rmse:2.337285+0.613580 
## [93] train-rmse:0.046124+0.002772    test-rmse:2.336917+0.613532 
## [94] train-rmse:0.044407+0.002712    test-rmse:2.336956+0.613329 
## [95] train-rmse:0.043206+0.002767    test-rmse:2.336720+0.613550 
## [96] train-rmse:0.041893+0.002955    test-rmse:2.336618+0.613633 
## [97] train-rmse:0.040270+0.003385    test-rmse:2.336618+0.613855 
## [98] train-rmse:0.038768+0.003144    test-rmse:2.336443+0.613999 
## [99] train-rmse:0.037232+0.002885    test-rmse:2.336567+0.614222 
## [100]    train-rmse:0.036345+0.003117    test-rmse:2.336764+0.614090 
## [101]    train-rmse:0.035238+0.002632    test-rmse:2.336783+0.614093 
## [102]    train-rmse:0.034058+0.002506    test-rmse:2.336949+0.614076 
## [103]    train-rmse:0.032884+0.002033    test-rmse:2.336870+0.614006 
## [104]    train-rmse:0.031787+0.002250    test-rmse:2.336909+0.613966 
## Stopping. Best iteration:
## [98] train-rmse:0.038768+0.003144    test-rmse:2.336443+0.613999
## 
## 56 
## [1]  train-rmse:6.687441+0.144953    test-rmse:6.684716+0.658902 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.651374+0.120436    test-rmse:5.703527+0.695198 
## [3]  train-rmse:4.954580+0.116228    test-rmse:5.011616+0.627811 
## [4]  train-rmse:4.487085+0.116316    test-rmse:4.568521+0.566369 
## [5]  train-rmse:4.172085+0.113421    test-rmse:4.303594+0.544574 
## [6]  train-rmse:3.951523+0.108237    test-rmse:4.067139+0.497110 
## [7]  train-rmse:3.792603+0.112679    test-rmse:3.928840+0.490421 
## [8]  train-rmse:3.667134+0.115491    test-rmse:3.803994+0.480972 
## [9]  train-rmse:3.563280+0.119497    test-rmse:3.693030+0.410171 
## [10] train-rmse:3.479325+0.118552    test-rmse:3.639991+0.441515 
## [11] train-rmse:3.406085+0.119472    test-rmse:3.580736+0.437309 
## [12] train-rmse:3.339965+0.121815    test-rmse:3.491888+0.434963 
## [13] train-rmse:3.279554+0.123646    test-rmse:3.428056+0.436397 
## [14] train-rmse:3.224747+0.124899    test-rmse:3.359668+0.394268 
## [15] train-rmse:3.174402+0.123523    test-rmse:3.313703+0.430942 
## [16] train-rmse:3.127658+0.122284    test-rmse:3.279151+0.446991 
## [17] train-rmse:3.085397+0.125157    test-rmse:3.261314+0.433891 
## [18] train-rmse:3.045678+0.127123    test-rmse:3.193144+0.426175 
## [19] train-rmse:3.008471+0.129802    test-rmse:3.168328+0.421476 
## [20] train-rmse:2.974149+0.131043    test-rmse:3.128969+0.431567 
## [21] train-rmse:2.941448+0.130852    test-rmse:3.110148+0.433721 
## [22] train-rmse:2.909367+0.130601    test-rmse:3.090725+0.445358 
## [23] train-rmse:2.879408+0.130548    test-rmse:3.065244+0.456909 
## [24] train-rmse:2.851038+0.131428    test-rmse:3.036976+0.460805 
## [25] train-rmse:2.823917+0.132525    test-rmse:3.028390+0.463935 
## [26] train-rmse:2.797695+0.133383    test-rmse:3.002879+0.444749 
## [27] train-rmse:2.772168+0.134982    test-rmse:2.975122+0.463791 
## [28] train-rmse:2.748423+0.135559    test-rmse:2.956614+0.476480 
## [29] train-rmse:2.725703+0.136338    test-rmse:2.932557+0.459146 
## [30] train-rmse:2.703093+0.137371    test-rmse:2.906531+0.454973 
## [31] train-rmse:2.681454+0.137705    test-rmse:2.896120+0.455839 
## [32] train-rmse:2.660273+0.138301    test-rmse:2.873208+0.453899 
## [33] train-rmse:2.639815+0.138436    test-rmse:2.866140+0.458229 
## [34] train-rmse:2.620253+0.138200    test-rmse:2.854439+0.468442 
## [35] train-rmse:2.601556+0.138213    test-rmse:2.859698+0.477373 
## [36] train-rmse:2.583187+0.138619    test-rmse:2.835901+0.470955 
## [37] train-rmse:2.565640+0.138990    test-rmse:2.809573+0.486031 
## [38] train-rmse:2.548446+0.139421    test-rmse:2.802652+0.485988 
## [39] train-rmse:2.531753+0.139436    test-rmse:2.783484+0.483130 
## [40] train-rmse:2.515848+0.139016    test-rmse:2.759015+0.483219 
## [41] train-rmse:2.500385+0.138830    test-rmse:2.751799+0.485690 
## [42] train-rmse:2.485472+0.138829    test-rmse:2.734449+0.488593 
## [43] train-rmse:2.471517+0.138919    test-rmse:2.723699+0.494974 
## [44] train-rmse:2.457707+0.138734    test-rmse:2.702998+0.483518 
## [45] train-rmse:2.444286+0.139166    test-rmse:2.688344+0.479596 
## [46] train-rmse:2.431647+0.139535    test-rmse:2.681991+0.472098 
## [47] train-rmse:2.419252+0.140005    test-rmse:2.670112+0.478168 
## [48] train-rmse:2.407190+0.140288    test-rmse:2.649227+0.485399 
## [49] train-rmse:2.395054+0.140483    test-rmse:2.646863+0.499571 
## [50] train-rmse:2.383318+0.140799    test-rmse:2.637552+0.494267 
## [51] train-rmse:2.372183+0.141222    test-rmse:2.632073+0.489063 
## [52] train-rmse:2.361449+0.141501    test-rmse:2.625728+0.499476 
## [53] train-rmse:2.350762+0.141817    test-rmse:2.626207+0.491377 
## [54] train-rmse:2.340305+0.141760    test-rmse:2.615666+0.495401 
## [55] train-rmse:2.330192+0.141819    test-rmse:2.607278+0.493600 
## [56] train-rmse:2.320108+0.141766    test-rmse:2.594682+0.502830 
## [57] train-rmse:2.310576+0.141791    test-rmse:2.577929+0.500641 
## [58] train-rmse:2.301408+0.141842    test-rmse:2.572372+0.501349 
## [59] train-rmse:2.292375+0.141868    test-rmse:2.564086+0.500802 
## [60] train-rmse:2.283612+0.142061    test-rmse:2.561005+0.500664 
## [61] train-rmse:2.275287+0.142472    test-rmse:2.557414+0.504742 
## [62] train-rmse:2.267065+0.142650    test-rmse:2.552579+0.505919 
## [63] train-rmse:2.259077+0.143000    test-rmse:2.539892+0.500596 
## [64] train-rmse:2.251430+0.143437    test-rmse:2.540780+0.511115 
## [65] train-rmse:2.244036+0.143894    test-rmse:2.528655+0.509801 
## [66] train-rmse:2.236546+0.144289    test-rmse:2.530498+0.513050 
## [67] train-rmse:2.229385+0.144630    test-rmse:2.521732+0.515305 
## [68] train-rmse:2.222256+0.144936    test-rmse:2.522232+0.518232 
## [69] train-rmse:2.215328+0.145295    test-rmse:2.509959+0.521759 
## [70] train-rmse:2.208632+0.145614    test-rmse:2.496686+0.519975 
## [71] train-rmse:2.202038+0.145763    test-rmse:2.493055+0.516492 
## [72] train-rmse:2.195671+0.146035    test-rmse:2.487343+0.509832 
## [73] train-rmse:2.189380+0.146259    test-rmse:2.485777+0.515299 
## [74] train-rmse:2.183158+0.146285    test-rmse:2.477195+0.511947 
## [75] train-rmse:2.177172+0.146354    test-rmse:2.472471+0.510849 
## [76] train-rmse:2.171336+0.146446    test-rmse:2.474210+0.507006 
## [77] train-rmse:2.165549+0.146593    test-rmse:2.470332+0.513882 
## [78] train-rmse:2.159920+0.146865    test-rmse:2.466274+0.514437 
## [79] train-rmse:2.154353+0.147092    test-rmse:2.467333+0.511702 
## [80] train-rmse:2.148995+0.147269    test-rmse:2.461612+0.510836 
## [81] train-rmse:2.143897+0.147497    test-rmse:2.456824+0.510466 
## [82] train-rmse:2.138889+0.147764    test-rmse:2.455045+0.510145 
## [83] train-rmse:2.133999+0.147908    test-rmse:2.449848+0.510146 
## [84] train-rmse:2.129191+0.148074    test-rmse:2.442736+0.510121 
## [85] train-rmse:2.124496+0.148242    test-rmse:2.438793+0.511390 
## [86] train-rmse:2.119886+0.148504    test-rmse:2.431393+0.511500 
## [87] train-rmse:2.115265+0.148791    test-rmse:2.425247+0.514857 
## [88] train-rmse:2.110842+0.148852    test-rmse:2.420504+0.517199 
## [89] train-rmse:2.106546+0.149055    test-rmse:2.424465+0.529433 
## [90] train-rmse:2.102291+0.149255    test-rmse:2.421746+0.531114 
## [91] train-rmse:2.097992+0.149427    test-rmse:2.421230+0.533425 
## [92] train-rmse:2.093693+0.149577    test-rmse:2.415366+0.530842 
## [93] train-rmse:2.089548+0.149787    test-rmse:2.404010+0.529895 
## [94] train-rmse:2.085433+0.150011    test-rmse:2.407483+0.531939 
## [95] train-rmse:2.081233+0.150331    test-rmse:2.402401+0.528267 
## [96] train-rmse:2.077352+0.150444    test-rmse:2.407380+0.532196 
## [97] train-rmse:2.073615+0.150568    test-rmse:2.405666+0.530358 
## [98] train-rmse:2.069929+0.150759    test-rmse:2.402504+0.526933 
## [99] train-rmse:2.066188+0.150927    test-rmse:2.399685+0.527912 
## [100]    train-rmse:2.062657+0.151071    test-rmse:2.396482+0.528051 
## [101]    train-rmse:2.059186+0.151240    test-rmse:2.395426+0.527472 
## [102]    train-rmse:2.055765+0.151397    test-rmse:2.395755+0.529630 
## [103]    train-rmse:2.052355+0.151562    test-rmse:2.396590+0.530529 
## [104]    train-rmse:2.049032+0.151742    test-rmse:2.391539+0.529115 
## [105]    train-rmse:2.045689+0.151882    test-rmse:2.392396+0.521897 
## [106]    train-rmse:2.042512+0.152013    test-rmse:2.388430+0.523734 
## [107]    train-rmse:2.039216+0.152089    test-rmse:2.387545+0.523075 
## [108]    train-rmse:2.036095+0.152225    test-rmse:2.385367+0.520385 
## [109]    train-rmse:2.033021+0.152382    test-rmse:2.381868+0.525237 
## [110]    train-rmse:2.029961+0.152470    test-rmse:2.376745+0.522837 
## [111]    train-rmse:2.027003+0.152600    test-rmse:2.372880+0.523518 
## [112]    train-rmse:2.023985+0.152741    test-rmse:2.372171+0.526331 
## [113]    train-rmse:2.021029+0.152847    test-rmse:2.366423+0.531233 
## [114]    train-rmse:2.018114+0.152981    test-rmse:2.366521+0.524872 
## [115]    train-rmse:2.015213+0.153183    test-rmse:2.365641+0.525541 
## [116]    train-rmse:2.012356+0.153302    test-rmse:2.367411+0.531019 
## [117]    train-rmse:2.009573+0.153432    test-rmse:2.367646+0.531705 
## [118]    train-rmse:2.006828+0.153601    test-rmse:2.364771+0.532538 
## [119]    train-rmse:2.004081+0.153819    test-rmse:2.360146+0.532683 
## [120]    train-rmse:2.001391+0.153942    test-rmse:2.360235+0.529769 
## [121]    train-rmse:1.998687+0.154022    test-rmse:2.358118+0.530250 
## [122]    train-rmse:1.996005+0.154187    test-rmse:2.360783+0.528886 
## [123]    train-rmse:1.993358+0.154317    test-rmse:2.362955+0.530777 
## [124]    train-rmse:1.990826+0.154426    test-rmse:2.364316+0.533891 
## [125]    train-rmse:1.988352+0.154560    test-rmse:2.364896+0.538814 
## [126]    train-rmse:1.985904+0.154689    test-rmse:2.363998+0.540368 
## [127]    train-rmse:1.983517+0.154799    test-rmse:2.363529+0.538514 
## Stopping. Best iteration:
## [121]    train-rmse:1.998687+0.154022    test-rmse:2.358118+0.530250
## 
## 57 
## [1]  train-rmse:6.569283+0.136561    test-rmse:6.602112+0.621469 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.418914+0.127330    test-rmse:5.471840+0.592572 
## [3]  train-rmse:4.614884+0.100071    test-rmse:4.757809+0.519990 
## [4]  train-rmse:4.036789+0.091227    test-rmse:4.210301+0.518890 
## [5]  train-rmse:3.630496+0.091440    test-rmse:3.876645+0.475578 
## [6]  train-rmse:3.317602+0.087628    test-rmse:3.560825+0.471853 
## [7]  train-rmse:3.089982+0.078861    test-rmse:3.353337+0.486838 
## [8]  train-rmse:2.920944+0.085080    test-rmse:3.229155+0.465328 
## [9]  train-rmse:2.780596+0.100363    test-rmse:3.125924+0.477684 
## [10] train-rmse:2.679150+0.098009    test-rmse:3.057447+0.481284 
## [11] train-rmse:2.593781+0.097240    test-rmse:3.001948+0.473549 
## [12] train-rmse:2.523815+0.095540    test-rmse:2.947980+0.484010 
## [13] train-rmse:2.451646+0.093337    test-rmse:2.905375+0.500070 
## [14] train-rmse:2.395973+0.095086    test-rmse:2.853245+0.513290 
## [15] train-rmse:2.341035+0.095594    test-rmse:2.821842+0.487558 
## [16] train-rmse:2.286753+0.089968    test-rmse:2.804646+0.499487 
## [17] train-rmse:2.235662+0.093708    test-rmse:2.793034+0.492211 
## [18] train-rmse:2.193740+0.093156    test-rmse:2.763967+0.496015 
## [19] train-rmse:2.156856+0.091171    test-rmse:2.740874+0.503161 
## [20] train-rmse:2.113839+0.094249    test-rmse:2.703444+0.509475 
## [21] train-rmse:2.060216+0.084415    test-rmse:2.646210+0.529689 
## [22] train-rmse:2.025225+0.085676    test-rmse:2.632587+0.520719 
## [23] train-rmse:1.986144+0.085120    test-rmse:2.607646+0.512552 
## [24] train-rmse:1.954433+0.087749    test-rmse:2.596597+0.505878 
## [25] train-rmse:1.923618+0.088076    test-rmse:2.584597+0.520272 
## [26] train-rmse:1.894577+0.087636    test-rmse:2.570513+0.512003 
## [27] train-rmse:1.866077+0.086124    test-rmse:2.553931+0.516333 
## [28] train-rmse:1.842019+0.086756    test-rmse:2.533641+0.518716 
## [29] train-rmse:1.818080+0.085845    test-rmse:2.521169+0.527081 
## [30] train-rmse:1.790087+0.075708    test-rmse:2.500487+0.539839 
## [31] train-rmse:1.769441+0.075640    test-rmse:2.484560+0.539953 
## [32] train-rmse:1.749624+0.075480    test-rmse:2.477153+0.538475 
## [33] train-rmse:1.731606+0.075302    test-rmse:2.485060+0.551379 
## [34] train-rmse:1.712436+0.073130    test-rmse:2.479495+0.542765 
## [35] train-rmse:1.694604+0.074124    test-rmse:2.464560+0.533870 
## [36] train-rmse:1.671126+0.078425    test-rmse:2.439212+0.529492 
## [37] train-rmse:1.652281+0.075770    test-rmse:2.437324+0.537785 
## [38] train-rmse:1.627513+0.056424    test-rmse:2.423673+0.543183 
## [39] train-rmse:1.610166+0.057532    test-rmse:2.420674+0.549974 
## [40] train-rmse:1.592846+0.056494    test-rmse:2.406872+0.548000 
## [41] train-rmse:1.578232+0.055691    test-rmse:2.402359+0.548730 
## [42] train-rmse:1.565033+0.056128    test-rmse:2.391843+0.545766 
## [43] train-rmse:1.545964+0.051297    test-rmse:2.377792+0.552986 
## [44] train-rmse:1.532115+0.050435    test-rmse:2.375253+0.555535 
## [45] train-rmse:1.518971+0.052747    test-rmse:2.382170+0.556145 
## [46] train-rmse:1.502472+0.055794    test-rmse:2.378109+0.561851 
## [47] train-rmse:1.488659+0.055961    test-rmse:2.363803+0.562660 
## [48] train-rmse:1.476712+0.053463    test-rmse:2.363042+0.564822 
## [49] train-rmse:1.463465+0.054226    test-rmse:2.350416+0.566245 
## [50] train-rmse:1.452992+0.054251    test-rmse:2.347762+0.564368 
## [51] train-rmse:1.439036+0.057411    test-rmse:2.344321+0.558541 
## [52] train-rmse:1.428835+0.056647    test-rmse:2.339987+0.555882 
## [53] train-rmse:1.420044+0.056346    test-rmse:2.332507+0.559147 
## [54] train-rmse:1.410957+0.057027    test-rmse:2.336130+0.568105 
## [55] train-rmse:1.403011+0.057119    test-rmse:2.336031+0.575686 
## [56] train-rmse:1.393438+0.054689    test-rmse:2.334934+0.576944 
## [57] train-rmse:1.377052+0.050322    test-rmse:2.322867+0.579558 
## [58] train-rmse:1.366301+0.051868    test-rmse:2.318404+0.580500 
## [59] train-rmse:1.351294+0.053627    test-rmse:2.313660+0.579101 
## [60] train-rmse:1.340760+0.055970    test-rmse:2.304524+0.573349 
## [61] train-rmse:1.331194+0.056821    test-rmse:2.299685+0.567147 
## [62] train-rmse:1.323759+0.057788    test-rmse:2.291814+0.568922 
## [63] train-rmse:1.316191+0.058078    test-rmse:2.292989+0.565144 
## [64] train-rmse:1.309658+0.057687    test-rmse:2.288048+0.569083 
## [65] train-rmse:1.298758+0.053768    test-rmse:2.287351+0.572752 
## [66] train-rmse:1.291656+0.054483    test-rmse:2.281338+0.573327 
## [67] train-rmse:1.275105+0.054998    test-rmse:2.275575+0.574960 
## [68] train-rmse:1.267576+0.054035    test-rmse:2.276251+0.576004 
## [69] train-rmse:1.255104+0.053885    test-rmse:2.272600+0.581126 
## [70] train-rmse:1.249123+0.053897    test-rmse:2.273879+0.578682 
## [71] train-rmse:1.242631+0.054243    test-rmse:2.271574+0.576976 
## [72] train-rmse:1.236420+0.052698    test-rmse:2.268315+0.576866 
## [73] train-rmse:1.229478+0.053375    test-rmse:2.267042+0.576507 
## [74] train-rmse:1.222317+0.054619    test-rmse:2.271311+0.580992 
## [75] train-rmse:1.211972+0.057599    test-rmse:2.270444+0.579758 
## [76] train-rmse:1.203678+0.058473    test-rmse:2.268498+0.578099 
## [77] train-rmse:1.190037+0.062537    test-rmse:2.267962+0.578895 
## [78] train-rmse:1.182642+0.063482    test-rmse:2.273190+0.584531 
## [79] train-rmse:1.175233+0.062788    test-rmse:2.270400+0.589672 
## Stopping. Best iteration:
## [73] train-rmse:1.229478+0.053375    test-rmse:2.267042+0.576507
## 
## 58 
## [1]  train-rmse:6.490036+0.131167    test-rmse:6.568854+0.607984 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.277575+0.103559    test-rmse:5.407343+0.557951 
## [3]  train-rmse:4.384502+0.108251    test-rmse:4.559982+0.531291 
## [4]  train-rmse:3.735139+0.082818    test-rmse:3.932710+0.483911 
## [5]  train-rmse:3.287611+0.081290    test-rmse:3.542297+0.442411 
## [6]  train-rmse:2.938094+0.059767    test-rmse:3.282502+0.440409 
## [7]  train-rmse:2.694420+0.064585    test-rmse:3.091641+0.471090 
## [8]  train-rmse:2.504700+0.064096    test-rmse:2.951463+0.477353 
## [9]  train-rmse:2.363321+0.074334    test-rmse:2.867442+0.475121 
## [10] train-rmse:2.250638+0.074579    test-rmse:2.789310+0.471542 
## [11] train-rmse:2.152273+0.067108    test-rmse:2.722029+0.473426 
## [12] train-rmse:2.060046+0.067883    test-rmse:2.691512+0.482068 
## [13] train-rmse:1.989493+0.050595    test-rmse:2.649402+0.485018 
## [14] train-rmse:1.918831+0.059524    test-rmse:2.597833+0.485715 
## [15] train-rmse:1.862578+0.065154    test-rmse:2.562136+0.476807 
## [16] train-rmse:1.806886+0.057825    test-rmse:2.526450+0.477973 
## [17] train-rmse:1.751338+0.060182    test-rmse:2.504270+0.471667 
## [18] train-rmse:1.713280+0.058568    test-rmse:2.480280+0.481145 
## [19] train-rmse:1.668713+0.062049    test-rmse:2.455102+0.479647 
## [20] train-rmse:1.635107+0.061711    test-rmse:2.435398+0.485961 
## [21] train-rmse:1.591315+0.058052    test-rmse:2.416426+0.486551 
## [22] train-rmse:1.551108+0.049067    test-rmse:2.397274+0.488091 
## [23] train-rmse:1.518965+0.046611    test-rmse:2.380391+0.466304 
## [24] train-rmse:1.484116+0.045850    test-rmse:2.369384+0.480854 
## [25] train-rmse:1.452750+0.053162    test-rmse:2.345666+0.483720 
## [26] train-rmse:1.421069+0.054139    test-rmse:2.339256+0.491752 
## [27] train-rmse:1.387850+0.046689    test-rmse:2.337868+0.501480 
## [28] train-rmse:1.364724+0.049500    test-rmse:2.328127+0.502444 
## [29] train-rmse:1.342066+0.051809    test-rmse:2.319010+0.504630 
## [30] train-rmse:1.321681+0.051015    test-rmse:2.307372+0.505084 
## [31] train-rmse:1.293521+0.043879    test-rmse:2.296565+0.504102 
## [32] train-rmse:1.274683+0.042107    test-rmse:2.283560+0.500418 
## [33] train-rmse:1.249992+0.036779    test-rmse:2.266904+0.501241 
## [34] train-rmse:1.229119+0.029171    test-rmse:2.260707+0.507346 
## [35] train-rmse:1.212313+0.028672    test-rmse:2.252736+0.504642 
## [36] train-rmse:1.181812+0.032795    test-rmse:2.248318+0.507332 
## [37] train-rmse:1.162648+0.030172    test-rmse:2.239616+0.513845 
## [38] train-rmse:1.150279+0.030903    test-rmse:2.238862+0.513174 
## [39] train-rmse:1.132061+0.025975    test-rmse:2.240024+0.511607 
## [40] train-rmse:1.111476+0.022366    test-rmse:2.232806+0.520642 
## [41] train-rmse:1.095492+0.019889    test-rmse:2.235214+0.523142 
## [42] train-rmse:1.082110+0.023735    test-rmse:2.227631+0.528153 
## [43] train-rmse:1.062539+0.031029    test-rmse:2.220107+0.516876 
## [44] train-rmse:1.049602+0.032048    test-rmse:2.214478+0.513413 
## [45] train-rmse:1.034538+0.036441    test-rmse:2.208136+0.517364 
## [46] train-rmse:1.017165+0.036667    test-rmse:2.201422+0.519048 
## [47] train-rmse:1.002672+0.034179    test-rmse:2.194613+0.518107 
## [48] train-rmse:0.987314+0.036361    test-rmse:2.190395+0.516510 
## [49] train-rmse:0.969795+0.035181    test-rmse:2.186791+0.518746 
## [50] train-rmse:0.959349+0.035529    test-rmse:2.185279+0.516125 
## [51] train-rmse:0.946164+0.031401    test-rmse:2.179830+0.520093 
## [52] train-rmse:0.933721+0.031774    test-rmse:2.176420+0.521131 
## [53] train-rmse:0.923120+0.028271    test-rmse:2.173125+0.524564 
## [54] train-rmse:0.907578+0.028365    test-rmse:2.165221+0.519784 
## [55] train-rmse:0.897103+0.030128    test-rmse:2.161461+0.519073 
## [56] train-rmse:0.887085+0.032930    test-rmse:2.159088+0.518842 
## [57] train-rmse:0.872356+0.029629    test-rmse:2.156303+0.520309 
## [58] train-rmse:0.862144+0.030971    test-rmse:2.156246+0.517255 
## [59] train-rmse:0.851704+0.030413    test-rmse:2.157742+0.515515 
## [60] train-rmse:0.840061+0.030503    test-rmse:2.150426+0.513371 
## [61] train-rmse:0.830194+0.030889    test-rmse:2.157279+0.512946 
## [62] train-rmse:0.822088+0.034087    test-rmse:2.159880+0.516564 
## [63] train-rmse:0.813924+0.038890    test-rmse:2.156318+0.515185 
## [64] train-rmse:0.806144+0.039147    test-rmse:2.155648+0.512881 
## [65] train-rmse:0.794199+0.037511    test-rmse:2.155920+0.513112 
## [66] train-rmse:0.781817+0.036553    test-rmse:2.163344+0.515141 
## Stopping. Best iteration:
## [60] train-rmse:0.840061+0.030503    test-rmse:2.150426+0.513371
## 
## 59 
## [1]  train-rmse:6.415906+0.129817    test-rmse:6.468854+0.594774 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.109404+0.093325    test-rmse:5.245815+0.506427 
## [3]  train-rmse:4.171723+0.065037    test-rmse:4.385567+0.449039 
## [4]  train-rmse:3.491542+0.059242    test-rmse:3.770254+0.458426 
## [5]  train-rmse:3.016733+0.037984    test-rmse:3.373566+0.478591 
## [6]  train-rmse:2.652694+0.035478    test-rmse:3.084696+0.489667 
## [7]  train-rmse:2.409218+0.034596    test-rmse:2.921998+0.502008 
## [8]  train-rmse:2.212742+0.034616    test-rmse:2.810904+0.511992 
## [9]  train-rmse:2.052031+0.043198    test-rmse:2.737361+0.534425 
## [10] train-rmse:1.928580+0.042228    test-rmse:2.674702+0.543563 
## [11] train-rmse:1.825779+0.041365    test-rmse:2.596653+0.531023 
## [12] train-rmse:1.729761+0.042395    test-rmse:2.544652+0.543692 
## [13] train-rmse:1.664767+0.041226    test-rmse:2.508432+0.537189 
## [14] train-rmse:1.599303+0.039730    test-rmse:2.459452+0.544662 
## [15] train-rmse:1.541299+0.042204    test-rmse:2.432270+0.547368 
## [16] train-rmse:1.483559+0.043751    test-rmse:2.403875+0.558720 
## [17] train-rmse:1.424712+0.040589    test-rmse:2.386203+0.572452 
## [18] train-rmse:1.380408+0.039509    test-rmse:2.376931+0.571853 
## [19] train-rmse:1.341235+0.043691    test-rmse:2.346149+0.563112 
## [20] train-rmse:1.292445+0.048890    test-rmse:2.334376+0.556232 
## [21] train-rmse:1.240344+0.036961    test-rmse:2.323032+0.558364 
## [22] train-rmse:1.209578+0.038345    test-rmse:2.309444+0.561610 
## [23] train-rmse:1.173583+0.031797    test-rmse:2.293999+0.558406 
## [24] train-rmse:1.140197+0.033819    test-rmse:2.293798+0.565059 
## [25] train-rmse:1.108028+0.026566    test-rmse:2.275092+0.564652 
## [26] train-rmse:1.086096+0.024107    test-rmse:2.268452+0.564759 
## [27] train-rmse:1.061610+0.028043    test-rmse:2.261365+0.565663 
## [28] train-rmse:1.029334+0.035015    test-rmse:2.250759+0.574210 
## [29] train-rmse:1.003893+0.041229    test-rmse:2.247487+0.569689 
## [30] train-rmse:0.978554+0.040065    test-rmse:2.226844+0.573806 
## [31] train-rmse:0.960048+0.038744    test-rmse:2.218368+0.576445 
## [32] train-rmse:0.940245+0.039276    test-rmse:2.210325+0.579277 
## [33] train-rmse:0.918224+0.029162    test-rmse:2.202846+0.579415 
## [34] train-rmse:0.891404+0.032979    test-rmse:2.200032+0.579098 
## [35] train-rmse:0.872880+0.032120    test-rmse:2.197188+0.582286 
## [36] train-rmse:0.853710+0.030521    test-rmse:2.198193+0.578600 
## [37] train-rmse:0.835197+0.030253    test-rmse:2.202412+0.585134 
## [38] train-rmse:0.813440+0.028045    test-rmse:2.196986+0.582736 
## [39] train-rmse:0.796153+0.030624    test-rmse:2.194644+0.587851 
## [40] train-rmse:0.777048+0.022199    test-rmse:2.194115+0.587569 
## [41] train-rmse:0.760501+0.024063    test-rmse:2.185054+0.593302 
## [42] train-rmse:0.740240+0.023218    test-rmse:2.180926+0.596098 
## [43] train-rmse:0.721704+0.023348    test-rmse:2.176798+0.593716 
## [44] train-rmse:0.706366+0.026792    test-rmse:2.173132+0.589601 
## [45] train-rmse:0.690977+0.020979    test-rmse:2.166695+0.592860 
## [46] train-rmse:0.677104+0.026715    test-rmse:2.169058+0.591397 
## [47] train-rmse:0.665751+0.027031    test-rmse:2.166523+0.591360 
## [48] train-rmse:0.653181+0.023656    test-rmse:2.163472+0.592441 
## [49] train-rmse:0.641208+0.022453    test-rmse:2.161879+0.593386 
## [50] train-rmse:0.623940+0.021471    test-rmse:2.159436+0.597276 
## [51] train-rmse:0.612172+0.024629    test-rmse:2.156746+0.596634 
## [52] train-rmse:0.601380+0.022196    test-rmse:2.157939+0.599237 
## [53] train-rmse:0.589197+0.022706    test-rmse:2.153245+0.602802 
## [54] train-rmse:0.581224+0.022441    test-rmse:2.152216+0.601026 
## [55] train-rmse:0.566349+0.024307    test-rmse:2.151649+0.599267 
## [56] train-rmse:0.549941+0.019211    test-rmse:2.149589+0.600847 
## [57] train-rmse:0.541748+0.018698    test-rmse:2.151506+0.601124 
## [58] train-rmse:0.532492+0.019274    test-rmse:2.150064+0.600603 
## [59] train-rmse:0.524991+0.016918    test-rmse:2.151569+0.598315 
## [60] train-rmse:0.516260+0.016150    test-rmse:2.148451+0.600239 
## [61] train-rmse:0.501408+0.017124    test-rmse:2.145110+0.597374 
## [62] train-rmse:0.493709+0.018922    test-rmse:2.143934+0.597754 
## [63] train-rmse:0.487161+0.019028    test-rmse:2.143389+0.597303 
## [64] train-rmse:0.478164+0.017460    test-rmse:2.138791+0.598769 
## [65] train-rmse:0.470344+0.019390    test-rmse:2.138927+0.599143 
## [66] train-rmse:0.461260+0.017854    test-rmse:2.135550+0.599473 
## [67] train-rmse:0.454421+0.017931    test-rmse:2.134179+0.598696 
## [68] train-rmse:0.446422+0.018062    test-rmse:2.134466+0.598429 
## [69] train-rmse:0.439319+0.018046    test-rmse:2.132990+0.600908 
## [70] train-rmse:0.432158+0.018359    test-rmse:2.135215+0.603628 
## [71] train-rmse:0.421421+0.014028    test-rmse:2.132419+0.605803 
## [72] train-rmse:0.414575+0.014354    test-rmse:2.131565+0.605572 
## [73] train-rmse:0.408743+0.012898    test-rmse:2.133279+0.605197 
## [74] train-rmse:0.400075+0.013621    test-rmse:2.132515+0.607225 
## [75] train-rmse:0.394966+0.013309    test-rmse:2.133445+0.606740 
## [76] train-rmse:0.389364+0.012661    test-rmse:2.131696+0.608725 
## [77] train-rmse:0.382830+0.011872    test-rmse:2.132609+0.608652 
## [78] train-rmse:0.373061+0.013590    test-rmse:2.130309+0.610845 
## [79] train-rmse:0.368290+0.015485    test-rmse:2.130319+0.610926 
## [80] train-rmse:0.362422+0.013991    test-rmse:2.128796+0.611839 
## [81] train-rmse:0.355942+0.012858    test-rmse:2.124516+0.614363 
## [82] train-rmse:0.350123+0.012400    test-rmse:2.125173+0.614982 
## [83] train-rmse:0.344838+0.011021    test-rmse:2.124178+0.614635 
## [84] train-rmse:0.339952+0.011093    test-rmse:2.124060+0.613942 
## [85] train-rmse:0.334667+0.011628    test-rmse:2.123657+0.612924 
## [86] train-rmse:0.327835+0.010859    test-rmse:2.123521+0.613748 
## [87] train-rmse:0.324476+0.011780    test-rmse:2.124425+0.613721 
## [88] train-rmse:0.316552+0.011959    test-rmse:2.124862+0.614420 
## [89] train-rmse:0.313162+0.013357    test-rmse:2.125096+0.614457 
## [90] train-rmse:0.307796+0.013649    test-rmse:2.124715+0.614026 
## [91] train-rmse:0.302727+0.013849    test-rmse:2.123602+0.614103 
## [92] train-rmse:0.297690+0.011552    test-rmse:2.122577+0.613923 
## [93] train-rmse:0.290095+0.011122    test-rmse:2.123088+0.613727 
## [94] train-rmse:0.285060+0.011349    test-rmse:2.122764+0.614531 
## [95] train-rmse:0.280588+0.012488    test-rmse:2.123350+0.615054 
## [96] train-rmse:0.276506+0.012621    test-rmse:2.123425+0.615756 
## [97] train-rmse:0.271780+0.012990    test-rmse:2.123867+0.616619 
## [98] train-rmse:0.267850+0.011985    test-rmse:2.122750+0.617056 
## Stopping. Best iteration:
## [92] train-rmse:0.297690+0.011552    test-rmse:2.122577+0.613923
## 
## 60 
## [1]  train-rmse:6.364678+0.127213    test-rmse:6.383430+0.572577 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.015577+0.097944    test-rmse:5.113946+0.492625 
## [3]  train-rmse:4.054524+0.080137    test-rmse:4.278192+0.446993 
## [4]  train-rmse:3.366107+0.064885    test-rmse:3.708720+0.439486 
## [5]  train-rmse:2.871056+0.059961    test-rmse:3.336974+0.418063 
## [6]  train-rmse:2.485830+0.049804    test-rmse:3.057499+0.451011 
## [7]  train-rmse:2.225924+0.056034    test-rmse:2.906835+0.461847 
## [8]  train-rmse:2.009428+0.044329    test-rmse:2.751725+0.483337 
## [9]  train-rmse:1.820510+0.049691    test-rmse:2.664617+0.495325 
## [10] train-rmse:1.672718+0.058551    test-rmse:2.596202+0.498325 
## [11] train-rmse:1.572912+0.062561    test-rmse:2.531600+0.506131 
## [12] train-rmse:1.488711+0.057793    test-rmse:2.498226+0.515962 
## [13] train-rmse:1.421035+0.059758    test-rmse:2.463634+0.504029 
## [14] train-rmse:1.349644+0.063463    test-rmse:2.433833+0.527622 
## [15] train-rmse:1.287927+0.057416    test-rmse:2.430652+0.530665 
## [16] train-rmse:1.233671+0.058713    test-rmse:2.405848+0.532375 
## [17] train-rmse:1.177385+0.052010    test-rmse:2.390611+0.532403 
## [18] train-rmse:1.122913+0.048638    test-rmse:2.364671+0.515169 
## [19] train-rmse:1.080304+0.046446    test-rmse:2.361183+0.508080 
## [20] train-rmse:1.047602+0.049329    test-rmse:2.339690+0.516211 
## [21] train-rmse:1.001439+0.061397    test-rmse:2.332760+0.520386 
## [22] train-rmse:0.968707+0.050595    test-rmse:2.327821+0.520644 
## [23] train-rmse:0.932984+0.039884    test-rmse:2.308860+0.511026 
## [24] train-rmse:0.904682+0.039016    test-rmse:2.298212+0.508762 
## [25] train-rmse:0.870798+0.049520    test-rmse:2.292985+0.511188 
## [26] train-rmse:0.839069+0.048899    test-rmse:2.276169+0.516236 
## [27] train-rmse:0.818433+0.046034    test-rmse:2.265877+0.521821 
## [28] train-rmse:0.792320+0.044302    test-rmse:2.260826+0.519679 
## [29] train-rmse:0.774729+0.041355    test-rmse:2.254851+0.517772 
## [30] train-rmse:0.751103+0.047529    test-rmse:2.248174+0.522325 
## [31] train-rmse:0.730111+0.046445    test-rmse:2.245618+0.520525 
## [32] train-rmse:0.705003+0.037207    test-rmse:2.252407+0.531601 
## [33] train-rmse:0.686795+0.034706    test-rmse:2.245609+0.533991 
## [34] train-rmse:0.669149+0.034855    test-rmse:2.242685+0.526967 
## [35] train-rmse:0.646460+0.038002    test-rmse:2.238498+0.530029 
## [36] train-rmse:0.626944+0.031489    test-rmse:2.234294+0.536776 
## [37] train-rmse:0.607998+0.032533    test-rmse:2.232672+0.536232 
## [38] train-rmse:0.591196+0.031240    test-rmse:2.233458+0.538269 
## [39] train-rmse:0.571780+0.028622    test-rmse:2.234405+0.539240 
## [40] train-rmse:0.557079+0.028269    test-rmse:2.230794+0.541297 
## [41] train-rmse:0.545986+0.025657    test-rmse:2.227531+0.541554 
## [42] train-rmse:0.529752+0.026444    test-rmse:2.229166+0.542843 
## [43] train-rmse:0.514055+0.024688    test-rmse:2.227807+0.540644 
## [44] train-rmse:0.501021+0.026836    test-rmse:2.229592+0.539941 
## [45] train-rmse:0.490435+0.026851    test-rmse:2.229235+0.540540 
## [46] train-rmse:0.478460+0.023848    test-rmse:2.230583+0.541223 
## [47] train-rmse:0.468215+0.025374    test-rmse:2.227267+0.541152 
## [48] train-rmse:0.458823+0.024361    test-rmse:2.225527+0.542401 
## [49] train-rmse:0.444220+0.025787    test-rmse:2.226472+0.541556 
## [50] train-rmse:0.435834+0.029207    test-rmse:2.225300+0.538296 
## [51] train-rmse:0.427534+0.027702    test-rmse:2.224795+0.538509 
## [52] train-rmse:0.420984+0.029709    test-rmse:2.223731+0.537733 
## [53] train-rmse:0.406984+0.030042    test-rmse:2.222307+0.537100 
## [54] train-rmse:0.391860+0.028614    test-rmse:2.224221+0.538217 
## [55] train-rmse:0.378559+0.028861    test-rmse:2.221722+0.539345 
## [56] train-rmse:0.370191+0.029313    test-rmse:2.221375+0.539035 
## [57] train-rmse:0.362920+0.028916    test-rmse:2.221485+0.539369 
## [58] train-rmse:0.352468+0.033564    test-rmse:2.220723+0.540190 
## [59] train-rmse:0.342797+0.035382    test-rmse:2.216121+0.539791 
## [60] train-rmse:0.334297+0.035921    test-rmse:2.216073+0.539592 
## [61] train-rmse:0.327236+0.035584    test-rmse:2.215492+0.538805 
## [62] train-rmse:0.320365+0.036283    test-rmse:2.215238+0.539413 
## [63] train-rmse:0.311783+0.032946    test-rmse:2.213996+0.539534 
## [64] train-rmse:0.304829+0.033611    test-rmse:2.213101+0.539809 
## [65] train-rmse:0.295073+0.027932    test-rmse:2.212256+0.539594 
## [66] train-rmse:0.287176+0.029169    test-rmse:2.213487+0.540785 
## [67] train-rmse:0.280646+0.026002    test-rmse:2.211899+0.540596 
## [68] train-rmse:0.275166+0.027852    test-rmse:2.211557+0.541052 
## [69] train-rmse:0.269039+0.030317    test-rmse:2.212287+0.539982 
## [70] train-rmse:0.265251+0.028714    test-rmse:2.210472+0.538906 
## [71] train-rmse:0.260110+0.028882    test-rmse:2.211441+0.538726 
## [72] train-rmse:0.254032+0.030941    test-rmse:2.210991+0.540063 
## [73] train-rmse:0.248272+0.030491    test-rmse:2.211560+0.540598 
## [74] train-rmse:0.240972+0.027805    test-rmse:2.210750+0.541585 
## [75] train-rmse:0.234841+0.028454    test-rmse:2.209737+0.541581 
## [76] train-rmse:0.228071+0.028240    test-rmse:2.208337+0.541018 
## [77] train-rmse:0.223306+0.025732    test-rmse:2.207866+0.540341 
## [78] train-rmse:0.218653+0.024090    test-rmse:2.207068+0.540747 
## [79] train-rmse:0.214380+0.024264    test-rmse:2.207085+0.541006 
## [80] train-rmse:0.207363+0.022611    test-rmse:2.207880+0.540388 
## [81] train-rmse:0.202743+0.021764    test-rmse:2.206948+0.540494 
## [82] train-rmse:0.198121+0.019747    test-rmse:2.206299+0.539998 
## [83] train-rmse:0.192581+0.018618    test-rmse:2.205859+0.539689 
## [84] train-rmse:0.187586+0.018837    test-rmse:2.206788+0.539134 
## [85] train-rmse:0.183219+0.018046    test-rmse:2.206518+0.539592 
## [86] train-rmse:0.179644+0.016770    test-rmse:2.206385+0.539509 
## [87] train-rmse:0.176084+0.016973    test-rmse:2.207168+0.539236 
## [88] train-rmse:0.173103+0.017258    test-rmse:2.206164+0.539130 
## [89] train-rmse:0.170177+0.017687    test-rmse:2.205736+0.538774 
## [90] train-rmse:0.166241+0.017744    test-rmse:2.206434+0.538970 
## [91] train-rmse:0.162668+0.018545    test-rmse:2.206008+0.537119 
## [92] train-rmse:0.160371+0.019036    test-rmse:2.206084+0.537079 
## [93] train-rmse:0.156824+0.019723    test-rmse:2.206314+0.536884 
## [94] train-rmse:0.154409+0.019976    test-rmse:2.207104+0.538335 
## [95] train-rmse:0.152123+0.020359    test-rmse:2.207292+0.537946 
## Stopping. Best iteration:
## [89] train-rmse:0.170177+0.017687    test-rmse:2.205736+0.538774
## 
## 61 
## [1]  train-rmse:6.332529+0.122103    test-rmse:6.362981+0.602454 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.959661+0.089450    test-rmse:5.108104+0.528266 
## [3]  train-rmse:3.963252+0.066947    test-rmse:4.245030+0.476484 
## [4]  train-rmse:3.243744+0.046971    test-rmse:3.674488+0.454703 
## [5]  train-rmse:2.712789+0.036895    test-rmse:3.286455+0.473490 
## [6]  train-rmse:2.337488+0.038450    test-rmse:3.035434+0.483029 
## [7]  train-rmse:2.041846+0.030997    test-rmse:2.848928+0.490511 
## [8]  train-rmse:1.826872+0.055223    test-rmse:2.737139+0.507125 
## [9]  train-rmse:1.667513+0.048895    test-rmse:2.667324+0.542676 
## [10] train-rmse:1.510694+0.050910    test-rmse:2.616539+0.555163 
## [11] train-rmse:1.398603+0.048805    test-rmse:2.557766+0.558208 
## [12] train-rmse:1.318525+0.043215    test-rmse:2.528902+0.561474 
## [13] train-rmse:1.250050+0.039769    test-rmse:2.510139+0.541565 
## [14] train-rmse:1.171601+0.039848    test-rmse:2.491682+0.531755 
## [15] train-rmse:1.110135+0.036891    test-rmse:2.481105+0.535398 
## [16] train-rmse:1.049751+0.038441    test-rmse:2.458549+0.537800 
## [17] train-rmse:0.993175+0.030646    test-rmse:2.450213+0.534380 
## [18] train-rmse:0.940014+0.035678    test-rmse:2.436340+0.536498 
## [19] train-rmse:0.893037+0.030698    test-rmse:2.429416+0.547499 
## [20] train-rmse:0.864134+0.030054    test-rmse:2.419054+0.550231 
## [21] train-rmse:0.839956+0.028630    test-rmse:2.413444+0.552128 
## [22] train-rmse:0.798887+0.029535    test-rmse:2.401724+0.555934 
## [23] train-rmse:0.755692+0.036059    test-rmse:2.395452+0.553406 
## [24] train-rmse:0.720298+0.027626    test-rmse:2.392344+0.553469 
## [25] train-rmse:0.691822+0.020370    test-rmse:2.389862+0.560164 
## [26] train-rmse:0.659309+0.027635    test-rmse:2.382539+0.562121 
## [27] train-rmse:0.638041+0.031728    test-rmse:2.378550+0.563314 
## [28] train-rmse:0.614709+0.032055    test-rmse:2.378835+0.563624 
## [29] train-rmse:0.587039+0.028389    test-rmse:2.375377+0.562312 
## [30] train-rmse:0.559316+0.034469    test-rmse:2.365878+0.567182 
## [31] train-rmse:0.537865+0.033453    test-rmse:2.357744+0.572002 
## [32] train-rmse:0.515087+0.034615    test-rmse:2.354981+0.573017 
## [33] train-rmse:0.493408+0.033416    test-rmse:2.351713+0.572479 
## [34] train-rmse:0.477848+0.032688    test-rmse:2.348894+0.574930 
## [35] train-rmse:0.465615+0.031802    test-rmse:2.346889+0.573077 
## [36] train-rmse:0.443077+0.033905    test-rmse:2.342088+0.579196 
## [37] train-rmse:0.425439+0.033103    test-rmse:2.336490+0.580859 
## [38] train-rmse:0.410083+0.029544    test-rmse:2.338894+0.582191 
## [39] train-rmse:0.394207+0.035553    test-rmse:2.337939+0.584072 
## [40] train-rmse:0.380199+0.037670    test-rmse:2.336190+0.584364 
## [41] train-rmse:0.361659+0.033548    test-rmse:2.330536+0.583231 
## [42] train-rmse:0.350075+0.030712    test-rmse:2.328750+0.581874 
## [43] train-rmse:0.340250+0.030480    test-rmse:2.328709+0.580971 
## [44] train-rmse:0.329976+0.028628    test-rmse:2.326516+0.582110 
## [45] train-rmse:0.319981+0.025880    test-rmse:2.322469+0.582515 
## [46] train-rmse:0.305875+0.024151    test-rmse:2.318963+0.584553 
## [47] train-rmse:0.297637+0.024262    test-rmse:2.317329+0.584541 
## [48] train-rmse:0.286284+0.024877    test-rmse:2.315839+0.585779 
## [49] train-rmse:0.276991+0.023644    test-rmse:2.311503+0.586432 
## [50] train-rmse:0.268415+0.027489    test-rmse:2.311478+0.587276 
## [51] train-rmse:0.261278+0.028177    test-rmse:2.310386+0.586969 
## [52] train-rmse:0.251492+0.026818    test-rmse:2.307638+0.585735 
## [53] train-rmse:0.240437+0.027306    test-rmse:2.307890+0.585979 
## [54] train-rmse:0.231711+0.026814    test-rmse:2.307708+0.588132 
## [55] train-rmse:0.222720+0.025965    test-rmse:2.306928+0.588511 
## [56] train-rmse:0.213948+0.025644    test-rmse:2.307120+0.588043 
## [57] train-rmse:0.206733+0.024259    test-rmse:2.304854+0.587937 
## [58] train-rmse:0.199919+0.022856    test-rmse:2.305928+0.589272 
## [59] train-rmse:0.194770+0.024057    test-rmse:2.305487+0.589467 
## [60] train-rmse:0.189689+0.023066    test-rmse:2.305703+0.590051 
## [61] train-rmse:0.183688+0.021641    test-rmse:2.305581+0.591451 
## [62] train-rmse:0.178300+0.020675    test-rmse:2.305543+0.590842 
## [63] train-rmse:0.173900+0.020155    test-rmse:2.304350+0.590313 
## [64] train-rmse:0.166580+0.020543    test-rmse:2.303127+0.590547 
## [65] train-rmse:0.160522+0.018316    test-rmse:2.302797+0.589831 
## [66] train-rmse:0.156295+0.018617    test-rmse:2.302349+0.589216 
## [67] train-rmse:0.151982+0.019682    test-rmse:2.301654+0.589502 
## [68] train-rmse:0.147902+0.018441    test-rmse:2.302753+0.590213 
## [69] train-rmse:0.144070+0.017564    test-rmse:2.303199+0.589828 
## [70] train-rmse:0.139933+0.017410    test-rmse:2.303808+0.589814 
## [71] train-rmse:0.135672+0.016130    test-rmse:2.302622+0.590560 
## [72] train-rmse:0.130329+0.015265    test-rmse:2.301479+0.589595 
## [73] train-rmse:0.126309+0.014187    test-rmse:2.301442+0.589964 
## [74] train-rmse:0.123286+0.013844    test-rmse:2.301203+0.590109 
## [75] train-rmse:0.119676+0.013482    test-rmse:2.301222+0.590721 
## [76] train-rmse:0.115584+0.013563    test-rmse:2.301006+0.590717 
## [77] train-rmse:0.111528+0.012923    test-rmse:2.300378+0.590993 
## [78] train-rmse:0.107678+0.012485    test-rmse:2.299609+0.590439 
## [79] train-rmse:0.103218+0.011143    test-rmse:2.298705+0.591256 
## [80] train-rmse:0.100799+0.010848    test-rmse:2.298244+0.591611 
## [81] train-rmse:0.098795+0.010746    test-rmse:2.298740+0.591318 
## [82] train-rmse:0.096420+0.011045    test-rmse:2.298765+0.591169 
## [83] train-rmse:0.093279+0.010491    test-rmse:2.298708+0.591379 
## [84] train-rmse:0.090629+0.010134    test-rmse:2.298872+0.591229 
## [85] train-rmse:0.087590+0.009083    test-rmse:2.299583+0.591930 
## [86] train-rmse:0.084505+0.009185    test-rmse:2.299568+0.591849 
## Stopping. Best iteration:
## [80] train-rmse:0.100799+0.010848    test-rmse:2.298244+0.591611
## 
## 62 
## [1]  train-rmse:6.321105+0.121135    test-rmse:6.356225+0.602026 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.928922+0.083572    test-rmse:5.093405+0.522361 
## [3]  train-rmse:3.908484+0.054822    test-rmse:4.195993+0.497491 
## [4]  train-rmse:3.165500+0.037355    test-rmse:3.607269+0.459955 
## [5]  train-rmse:2.627419+0.031569    test-rmse:3.232138+0.473721 
## [6]  train-rmse:2.218879+0.025751    test-rmse:2.990703+0.497128 
## [7]  train-rmse:1.927006+0.028433    test-rmse:2.794064+0.527698 
## [8]  train-rmse:1.673460+0.029521    test-rmse:2.678747+0.508064 
## [9]  train-rmse:1.483222+0.041500    test-rmse:2.603113+0.499435 
## [10] train-rmse:1.342000+0.050011    test-rmse:2.541802+0.522661 
## [11] train-rmse:1.222198+0.053306    test-rmse:2.509289+0.531037 
## [12] train-rmse:1.115003+0.057430    test-rmse:2.501442+0.545277 
## [13] train-rmse:1.036385+0.052882    test-rmse:2.478581+0.541199 
## [14] train-rmse:0.963379+0.058841    test-rmse:2.466426+0.555770 
## [15] train-rmse:0.911822+0.054435    test-rmse:2.455007+0.553386 
## [16] train-rmse:0.858084+0.049536    test-rmse:2.452626+0.559931 
## [17] train-rmse:0.793837+0.054615    test-rmse:2.446271+0.557346 
## [18] train-rmse:0.749732+0.056371    test-rmse:2.439644+0.564007 
## [19] train-rmse:0.701899+0.054787    test-rmse:2.422581+0.571338 
## [20] train-rmse:0.660098+0.045173    test-rmse:2.411732+0.581492 
## [21] train-rmse:0.627674+0.051141    test-rmse:2.408698+0.578076 
## [22] train-rmse:0.592869+0.045311    test-rmse:2.404771+0.582936 
## [23] train-rmse:0.568192+0.044144    test-rmse:2.395843+0.584366 
## [24] train-rmse:0.543004+0.041687    test-rmse:2.393324+0.588563 
## [25] train-rmse:0.505972+0.041701    test-rmse:2.388081+0.588185 
## [26] train-rmse:0.486924+0.041662    test-rmse:2.383647+0.586471 
## [27] train-rmse:0.465508+0.041448    test-rmse:2.380078+0.586943 
## [28] train-rmse:0.442245+0.037908    test-rmse:2.372760+0.591021 
## [29] train-rmse:0.419591+0.038072    test-rmse:2.366993+0.591425 
## [30] train-rmse:0.403429+0.039760    test-rmse:2.365042+0.593195 
## [31] train-rmse:0.385265+0.037399    test-rmse:2.360633+0.592989 
## [32] train-rmse:0.363704+0.037870    test-rmse:2.359760+0.594404 
## [33] train-rmse:0.347369+0.038752    test-rmse:2.358375+0.594365 
## [34] train-rmse:0.330438+0.041161    test-rmse:2.358051+0.592947 
## [35] train-rmse:0.315453+0.040922    test-rmse:2.355728+0.595367 
## [36] train-rmse:0.302258+0.036601    test-rmse:2.355182+0.594164 
## [37] train-rmse:0.290086+0.036146    test-rmse:2.352490+0.593777 
## [38] train-rmse:0.280327+0.033266    test-rmse:2.353062+0.595334 
## [39] train-rmse:0.270158+0.033229    test-rmse:2.352397+0.596400 
## [40] train-rmse:0.258165+0.032189    test-rmse:2.355340+0.599243 
## [41] train-rmse:0.247334+0.029735    test-rmse:2.355639+0.601106 
## [42] train-rmse:0.237402+0.030859    test-rmse:2.355081+0.602435 
## [43] train-rmse:0.225433+0.026290    test-rmse:2.354241+0.602189 
## [44] train-rmse:0.217181+0.024206    test-rmse:2.353488+0.601271 
## [45] train-rmse:0.209601+0.025242    test-rmse:2.353197+0.601214 
## Stopping. Best iteration:
## [39] train-rmse:0.270158+0.033229    test-rmse:2.352397+0.596400
## 
## 63 
## [1]  train-rmse:6.578402+0.142961    test-rmse:6.578032+0.655895 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.504833+0.117276    test-rmse:5.564744+0.692737 
## [3]  train-rmse:4.809794+0.115411    test-rmse:4.875141+0.617034 
## [4]  train-rmse:4.361862+0.116750    test-rmse:4.452130+0.552455 
## [5]  train-rmse:4.066091+0.113320    test-rmse:4.212805+0.527158 
## [6]  train-rmse:3.861677+0.107634    test-rmse:3.981688+0.492928 
## [7]  train-rmse:3.714085+0.112329    test-rmse:3.823922+0.511313 
## [8]  train-rmse:3.595106+0.116980    test-rmse:3.716457+0.441245 
## [9]  train-rmse:3.495933+0.121137    test-rmse:3.636803+0.399047 
## [10] train-rmse:3.415747+0.120305    test-rmse:3.581670+0.440613 
## [11] train-rmse:3.343779+0.119957    test-rmse:3.513129+0.445376 
## [12] train-rmse:3.277144+0.121962    test-rmse:3.414070+0.451685 
## [13] train-rmse:3.218236+0.123686    test-rmse:3.360408+0.427657 
## [14] train-rmse:3.163696+0.126154    test-rmse:3.285381+0.399197 
## [15] train-rmse:3.114323+0.126526    test-rmse:3.265557+0.414274 
## [16] train-rmse:3.068605+0.124831    test-rmse:3.218963+0.446551 
## [17] train-rmse:3.027156+0.126298    test-rmse:3.205160+0.423711 
## [18] train-rmse:2.988046+0.128593    test-rmse:3.143202+0.402857 
## [19] train-rmse:2.952534+0.131152    test-rmse:3.125360+0.411619 
## [20] train-rmse:2.918062+0.133008    test-rmse:3.085990+0.419781 
## [21] train-rmse:2.885256+0.133971    test-rmse:3.051863+0.435625 
## [22] train-rmse:2.854002+0.135301    test-rmse:3.045877+0.439416 
## [23] train-rmse:2.824364+0.135902    test-rmse:3.025166+0.449196 
## [24] train-rmse:2.796404+0.135721    test-rmse:3.015478+0.469798 
## [25] train-rmse:2.769398+0.135915    test-rmse:2.982155+0.459011 
## [26] train-rmse:2.742339+0.135925    test-rmse:2.942097+0.445176 
## [27] train-rmse:2.717194+0.136474    test-rmse:2.920735+0.454949 
## [28] train-rmse:2.693053+0.137078    test-rmse:2.915805+0.465510 
## [29] train-rmse:2.669466+0.138656    test-rmse:2.910078+0.468177 
## [30] train-rmse:2.647195+0.139840    test-rmse:2.876189+0.467322 
## [31] train-rmse:2.625307+0.140487    test-rmse:2.857108+0.463777 
## [32] train-rmse:2.604997+0.140721    test-rmse:2.848373+0.465374 
## [33] train-rmse:2.585547+0.140949    test-rmse:2.828324+0.475953 
## [34] train-rmse:2.566607+0.140910    test-rmse:2.820837+0.473964 
## [35] train-rmse:2.548245+0.140951    test-rmse:2.804510+0.472252 
## [36] train-rmse:2.530680+0.141072    test-rmse:2.785936+0.464491 
## [37] train-rmse:2.513539+0.140450    test-rmse:2.766319+0.482872 
## [38] train-rmse:2.496926+0.140126    test-rmse:2.744971+0.480073 
## [39] train-rmse:2.480265+0.139927    test-rmse:2.745477+0.488532 
## [40] train-rmse:2.464529+0.139997    test-rmse:2.728881+0.499997 
## [41] train-rmse:2.449573+0.140258    test-rmse:2.712029+0.494750 
## [42] train-rmse:2.435180+0.140673    test-rmse:2.695945+0.485030 
## [43] train-rmse:2.420649+0.141080    test-rmse:2.688447+0.489726 
## [44] train-rmse:2.407379+0.141423    test-rmse:2.670026+0.496811 
## [45] train-rmse:2.394385+0.141956    test-rmse:2.666342+0.501258 
## [46] train-rmse:2.381978+0.142366    test-rmse:2.660351+0.505730 
## [47] train-rmse:2.370082+0.142595    test-rmse:2.647471+0.491497 
## [48] train-rmse:2.358709+0.142811    test-rmse:2.639923+0.496139 
## [49] train-rmse:2.347420+0.143063    test-rmse:2.628246+0.502275 
## [50] train-rmse:2.336401+0.143339    test-rmse:2.617249+0.508089 
## [51] train-rmse:2.325575+0.143601    test-rmse:2.604600+0.506730 
## [52] train-rmse:2.315415+0.143748    test-rmse:2.596079+0.505999 
## [53] train-rmse:2.305636+0.144025    test-rmse:2.587030+0.497995 
## [54] train-rmse:2.296067+0.144267    test-rmse:2.586340+0.508116 
## [55] train-rmse:2.286512+0.144521    test-rmse:2.574498+0.503229 
## [56] train-rmse:2.277001+0.144800    test-rmse:2.564966+0.503782 
## [57] train-rmse:2.267857+0.145012    test-rmse:2.556225+0.502396 
## [58] train-rmse:2.258908+0.145304    test-rmse:2.551810+0.505225 
## [59] train-rmse:2.250256+0.145779    test-rmse:2.541201+0.508618 
## [60] train-rmse:2.241810+0.146362    test-rmse:2.538586+0.516811 
## [61] train-rmse:2.233749+0.146643    test-rmse:2.530116+0.521862 
## [62] train-rmse:2.225849+0.146980    test-rmse:2.517433+0.522166 
## [63] train-rmse:2.218135+0.147271    test-rmse:2.510073+0.529671 
## [64] train-rmse:2.210578+0.147419    test-rmse:2.510728+0.525813 
## [65] train-rmse:2.203431+0.147533    test-rmse:2.506061+0.524706 
## [66] train-rmse:2.196506+0.147760    test-rmse:2.500068+0.518999 
## [67] train-rmse:2.189778+0.147913    test-rmse:2.495010+0.521179 
## [68] train-rmse:2.183168+0.148191    test-rmse:2.485568+0.519682 
## [69] train-rmse:2.176546+0.148419    test-rmse:2.474841+0.518736 
## [70] train-rmse:2.170212+0.148585    test-rmse:2.468775+0.522377 
## [71] train-rmse:2.164041+0.148740    test-rmse:2.463160+0.521960 
## [72] train-rmse:2.158200+0.148908    test-rmse:2.465192+0.532395 
## [73] train-rmse:2.152439+0.149099    test-rmse:2.465734+0.527552 
## [74] train-rmse:2.146889+0.149383    test-rmse:2.456181+0.532483 
## [75] train-rmse:2.141412+0.149711    test-rmse:2.447068+0.528939 
## [76] train-rmse:2.136039+0.150105    test-rmse:2.449704+0.525182 
## [77] train-rmse:2.130647+0.150555    test-rmse:2.441549+0.524675 
## [78] train-rmse:2.125412+0.150885    test-rmse:2.438143+0.518972 
## [79] train-rmse:2.120278+0.151111    test-rmse:2.445763+0.520824 
## [80] train-rmse:2.115217+0.151354    test-rmse:2.439588+0.521449 
## [81] train-rmse:2.110139+0.151624    test-rmse:2.432452+0.521502 
## [82] train-rmse:2.105181+0.151829    test-rmse:2.428736+0.521053 
## [83] train-rmse:2.100390+0.152020    test-rmse:2.424470+0.525771 
## [84] train-rmse:2.095758+0.152216    test-rmse:2.418315+0.533343 
## [85] train-rmse:2.091285+0.152507    test-rmse:2.417183+0.535672 
## [86] train-rmse:2.086843+0.152779    test-rmse:2.421566+0.532742 
## [87] train-rmse:2.082466+0.153046    test-rmse:2.416256+0.534949 
## [88] train-rmse:2.078139+0.153206    test-rmse:2.413885+0.539953 
## [89] train-rmse:2.073962+0.153366    test-rmse:2.410898+0.538152 
## [90] train-rmse:2.070011+0.153514    test-rmse:2.408785+0.534602 
## [91] train-rmse:2.066125+0.153604    test-rmse:2.405892+0.536352 
## [92] train-rmse:2.062242+0.153605    test-rmse:2.402207+0.534286 
## [93] train-rmse:2.058440+0.153682    test-rmse:2.404867+0.543051 
## [94] train-rmse:2.054849+0.153782    test-rmse:2.398498+0.541383 
## [95] train-rmse:2.050963+0.153736    test-rmse:2.393910+0.539104 
## [96] train-rmse:2.047418+0.153861    test-rmse:2.388939+0.535287 
## [97] train-rmse:2.043806+0.154113    test-rmse:2.394158+0.536907 
## [98] train-rmse:2.040361+0.154276    test-rmse:2.388798+0.536622 
## [99] train-rmse:2.036891+0.154477    test-rmse:2.384439+0.533187 
## [100]    train-rmse:2.033456+0.154704    test-rmse:2.382088+0.530798 
## [101]    train-rmse:2.030076+0.154849    test-rmse:2.381993+0.528728 
## [102]    train-rmse:2.026785+0.155093    test-rmse:2.378779+0.530399 
## [103]    train-rmse:2.023459+0.155266    test-rmse:2.376184+0.533358 
## [104]    train-rmse:2.020176+0.155398    test-rmse:2.378629+0.535592 
## [105]    train-rmse:2.016962+0.155572    test-rmse:2.378829+0.529445 
## [106]    train-rmse:2.013773+0.155701    test-rmse:2.379004+0.528960 
## [107]    train-rmse:2.010733+0.155843    test-rmse:2.371327+0.532160 
## [108]    train-rmse:2.007794+0.155977    test-rmse:2.370556+0.532141 
## [109]    train-rmse:2.004841+0.156103    test-rmse:2.367822+0.531466 
## [110]    train-rmse:2.001973+0.156174    test-rmse:2.363575+0.532603 
## [111]    train-rmse:1.999179+0.156213    test-rmse:2.364610+0.531964 
## [112]    train-rmse:1.996372+0.156184    test-rmse:2.361956+0.535265 
## [113]    train-rmse:1.993545+0.156401    test-rmse:2.361391+0.539091 
## [114]    train-rmse:1.990738+0.156557    test-rmse:2.362532+0.540810 
## [115]    train-rmse:1.988041+0.156616    test-rmse:2.357765+0.537155 
## [116]    train-rmse:1.985354+0.156647    test-rmse:2.362309+0.538836 
## [117]    train-rmse:1.982769+0.156701    test-rmse:2.356345+0.536874 
## [118]    train-rmse:1.980179+0.156974    test-rmse:2.356634+0.537689 
## [119]    train-rmse:1.977716+0.157120    test-rmse:2.353986+0.539470 
## [120]    train-rmse:1.975140+0.157153    test-rmse:2.356545+0.537161 
## [121]    train-rmse:1.972713+0.157266    test-rmse:2.352705+0.538134 
## [122]    train-rmse:1.970161+0.157397    test-rmse:2.349735+0.539132 
## [123]    train-rmse:1.967723+0.157582    test-rmse:2.350158+0.540533 
## [124]    train-rmse:1.965239+0.157651    test-rmse:2.350005+0.537507 
## [125]    train-rmse:1.962705+0.157805    test-rmse:2.347755+0.535537 
## [126]    train-rmse:1.960339+0.157929    test-rmse:2.350875+0.536675 
## [127]    train-rmse:1.957977+0.158076    test-rmse:2.357252+0.538924 
## [128]    train-rmse:1.955670+0.158171    test-rmse:2.355211+0.540292 
## [129]    train-rmse:1.953407+0.158305    test-rmse:2.353330+0.540851 
## [130]    train-rmse:1.951171+0.158443    test-rmse:2.350637+0.542111 
## [131]    train-rmse:1.948818+0.158513    test-rmse:2.348995+0.543486 
## Stopping. Best iteration:
## [125]    train-rmse:1.962705+0.157805    test-rmse:2.347755+0.535537
## 
## 64 
## [1]  train-rmse:6.449990+0.133982    test-rmse:6.488533+0.615748 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.254494+0.126157    test-rmse:5.316845+0.584046 
## [3]  train-rmse:4.444581+0.098716    test-rmse:4.603486+0.507011 
## [4]  train-rmse:3.871451+0.091185    test-rmse:4.045843+0.484052 
## [5]  train-rmse:3.480923+0.098573    test-rmse:3.722003+0.451836 
## [6]  train-rmse:3.197647+0.089165    test-rmse:3.438326+0.451626 
## [7]  train-rmse:2.985875+0.080154    test-rmse:3.242007+0.472346 
## [8]  train-rmse:2.829852+0.089521    test-rmse:3.128689+0.456697 
## [9]  train-rmse:2.706836+0.102323    test-rmse:3.026155+0.449087 
## [10] train-rmse:2.612015+0.109977    test-rmse:2.964633+0.453865 
## [11] train-rmse:2.525919+0.096064    test-rmse:2.894507+0.467840 
## [12] train-rmse:2.456150+0.093194    test-rmse:2.867286+0.464824 
## [13] train-rmse:2.383468+0.092832    test-rmse:2.817861+0.462796 
## [14] train-rmse:2.323014+0.094522    test-rmse:2.776569+0.464677 
## [15] train-rmse:2.260150+0.091463    test-rmse:2.731972+0.466886 
## [16] train-rmse:2.208817+0.096584    test-rmse:2.713778+0.467727 
## [17] train-rmse:2.156195+0.086228    test-rmse:2.667863+0.480304 
## [18] train-rmse:2.116086+0.085050    test-rmse:2.632814+0.501013 
## [19] train-rmse:2.066475+0.087348    test-rmse:2.630594+0.490112 
## [20] train-rmse:2.027804+0.086818    test-rmse:2.591422+0.499452 
## [21] train-rmse:1.983850+0.091629    test-rmse:2.566408+0.463170 
## [22] train-rmse:1.949239+0.092914    test-rmse:2.544341+0.460468 
## [23] train-rmse:1.913260+0.091165    test-rmse:2.537191+0.468231 
## [24] train-rmse:1.885727+0.091039    test-rmse:2.520577+0.466790 
## [25] train-rmse:1.855902+0.092522    test-rmse:2.515318+0.469981 
## [26] train-rmse:1.831259+0.092197    test-rmse:2.496844+0.472123 
## [27] train-rmse:1.805588+0.092815    test-rmse:2.477255+0.485275 
## [28] train-rmse:1.777776+0.082781    test-rmse:2.455806+0.491529 
## [29] train-rmse:1.749426+0.086141    test-rmse:2.432361+0.505969 
## [30] train-rmse:1.723338+0.084766    test-rmse:2.419355+0.499126 
## [31] train-rmse:1.698180+0.082652    test-rmse:2.406572+0.500112 
## [32] train-rmse:1.670870+0.081892    test-rmse:2.388192+0.495407 
## [33] train-rmse:1.644053+0.086343    test-rmse:2.369121+0.473341 
## [34] train-rmse:1.620587+0.088224    test-rmse:2.360990+0.475481 
## [35] train-rmse:1.601161+0.089240    test-rmse:2.361281+0.475822 
## [36] train-rmse:1.585347+0.089928    test-rmse:2.349746+0.482599 
## [37] train-rmse:1.571008+0.089656    test-rmse:2.344236+0.482007 
## [38] train-rmse:1.553782+0.090122    test-rmse:2.333140+0.482844 
## [39] train-rmse:1.538744+0.089365    test-rmse:2.326235+0.487493 
## [40] train-rmse:1.526451+0.089317    test-rmse:2.321253+0.488830 
## [41] train-rmse:1.510941+0.090325    test-rmse:2.313813+0.488728 
## [42] train-rmse:1.492716+0.088097    test-rmse:2.294533+0.489229 
## [43] train-rmse:1.470211+0.089317    test-rmse:2.273909+0.490408 
## [44] train-rmse:1.452733+0.078770    test-rmse:2.273350+0.496221 
## [45] train-rmse:1.432618+0.062439    test-rmse:2.262751+0.495482 
## [46] train-rmse:1.420794+0.062875    test-rmse:2.261201+0.500408 
## [47] train-rmse:1.410515+0.062592    test-rmse:2.257697+0.501803 
## [48] train-rmse:1.395552+0.059945    test-rmse:2.257069+0.505333 
## [49] train-rmse:1.386189+0.059549    test-rmse:2.253481+0.504896 
## [50] train-rmse:1.375405+0.060565    test-rmse:2.244142+0.494256 
## [51] train-rmse:1.364945+0.061554    test-rmse:2.239009+0.502331 
## [52] train-rmse:1.357082+0.061418    test-rmse:2.234187+0.500681 
## [53] train-rmse:1.343687+0.065274    test-rmse:2.233022+0.502133 
## [54] train-rmse:1.333446+0.065383    test-rmse:2.226800+0.506267 
## [55] train-rmse:1.322435+0.062664    test-rmse:2.223532+0.507393 
## [56] train-rmse:1.311444+0.063861    test-rmse:2.220789+0.500145 
## [57] train-rmse:1.292006+0.059662    test-rmse:2.216596+0.499178 
## [58] train-rmse:1.281099+0.052725    test-rmse:2.214095+0.509450 
## [59] train-rmse:1.269535+0.053575    test-rmse:2.211424+0.521356 
## [60] train-rmse:1.259189+0.050157    test-rmse:2.203515+0.516507 
## [61] train-rmse:1.249702+0.052080    test-rmse:2.196758+0.514326 
## [62] train-rmse:1.242252+0.052469    test-rmse:2.195368+0.514379 
## [63] train-rmse:1.236048+0.052752    test-rmse:2.192755+0.513327 
## [64] train-rmse:1.230522+0.052363    test-rmse:2.189606+0.514678 
## [65] train-rmse:1.223679+0.051233    test-rmse:2.184189+0.518080 
## [66] train-rmse:1.214007+0.054192    test-rmse:2.178388+0.525279 
## [67] train-rmse:1.206514+0.050760    test-rmse:2.172060+0.530719 
## [68] train-rmse:1.200795+0.051038    test-rmse:2.163328+0.534493 
## [69] train-rmse:1.187718+0.050972    test-rmse:2.156660+0.536565 
## [70] train-rmse:1.180717+0.051509    test-rmse:2.156941+0.538399 
## [71] train-rmse:1.175854+0.052012    test-rmse:2.156058+0.538484 
## [72] train-rmse:1.166157+0.055138    test-rmse:2.146458+0.534348 
## [73] train-rmse:1.155009+0.056448    test-rmse:2.145495+0.536055 
## [74] train-rmse:1.147554+0.057710    test-rmse:2.147594+0.539059 
## [75] train-rmse:1.139114+0.058412    test-rmse:2.145904+0.537367 
## [76] train-rmse:1.130634+0.054378    test-rmse:2.140778+0.532208 
## [77] train-rmse:1.124093+0.054875    test-rmse:2.138462+0.537528 
## [78] train-rmse:1.114218+0.059196    test-rmse:2.137736+0.528911 
## [79] train-rmse:1.107843+0.058334    test-rmse:2.140399+0.525233 
## [80] train-rmse:1.096376+0.059537    test-rmse:2.144154+0.522433 
## [81] train-rmse:1.089514+0.060599    test-rmse:2.141099+0.529690 
## [82] train-rmse:1.084350+0.059666    test-rmse:2.135708+0.533767 
## [83] train-rmse:1.080535+0.060153    test-rmse:2.135717+0.535157 
## [84] train-rmse:1.072346+0.063300    test-rmse:2.138167+0.543236 
## [85] train-rmse:1.066091+0.065614    test-rmse:2.138097+0.543681 
## [86] train-rmse:1.058662+0.066215    test-rmse:2.136473+0.543032 
## [87] train-rmse:1.053217+0.065660    test-rmse:2.132578+0.536873 
## [88] train-rmse:1.046521+0.065896    test-rmse:2.130823+0.538281 
## [89] train-rmse:1.041535+0.066253    test-rmse:2.126512+0.540377 
## [90] train-rmse:1.032699+0.069982    test-rmse:2.124670+0.539246 
## [91] train-rmse:1.024266+0.068131    test-rmse:2.120668+0.531530 
## [92] train-rmse:1.009610+0.071373    test-rmse:2.128789+0.528206 
## [93] train-rmse:0.998933+0.069199    test-rmse:2.132800+0.531236 
## [94] train-rmse:0.994436+0.068751    test-rmse:2.129216+0.530865 
## [95] train-rmse:0.991196+0.068780    test-rmse:2.132870+0.526992 
## [96] train-rmse:0.987898+0.068493    test-rmse:2.130096+0.526071 
## [97] train-rmse:0.983337+0.069001    test-rmse:2.128317+0.529823 
## Stopping. Best iteration:
## [91] train-rmse:1.024266+0.068131    test-rmse:2.120668+0.531530
## 
## 65 
## [1]  train-rmse:6.363775+0.127822    test-rmse:6.452436+0.601658 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.101991+0.099441    test-rmse:5.247493+0.549418 
## [3]  train-rmse:4.186012+0.093999    test-rmse:4.412160+0.470104 
## [4]  train-rmse:3.551254+0.070236    test-rmse:3.768714+0.480013 
## [5]  train-rmse:3.115832+0.075342    test-rmse:3.412238+0.441406 
## [6]  train-rmse:2.799575+0.074800    test-rmse:3.204582+0.439272 
## [7]  train-rmse:2.568228+0.063707    test-rmse:3.018973+0.488273 
## [8]  train-rmse:2.383946+0.053990    test-rmse:2.891512+0.471840 
## [9]  train-rmse:2.241081+0.046402    test-rmse:2.805768+0.463495 
## [10] train-rmse:2.143572+0.048369    test-rmse:2.747188+0.478277 
## [11] train-rmse:2.031208+0.042367    test-rmse:2.663463+0.490091 
## [12] train-rmse:1.956696+0.042262    test-rmse:2.615062+0.503511 
## [13] train-rmse:1.886310+0.045250    test-rmse:2.593117+0.483193 
## [14] train-rmse:1.820791+0.048072    test-rmse:2.551752+0.497947 
## [15] train-rmse:1.755242+0.049615    test-rmse:2.533369+0.511646 
## [16] train-rmse:1.705121+0.052466    test-rmse:2.511945+0.512943 
## [17] train-rmse:1.658831+0.046036    test-rmse:2.481574+0.511772 
## [18] train-rmse:1.611818+0.047480    test-rmse:2.444999+0.507069 
## [19] train-rmse:1.568577+0.050068    test-rmse:2.420378+0.498690 
## [20] train-rmse:1.530784+0.046992    test-rmse:2.400402+0.498874 
## [21] train-rmse:1.493944+0.042997    test-rmse:2.396597+0.501953 
## [22] train-rmse:1.452037+0.037004    test-rmse:2.375792+0.502679 
## [23] train-rmse:1.416115+0.032977    test-rmse:2.349741+0.497059 
## [24] train-rmse:1.384119+0.036871    test-rmse:2.338190+0.492467 
## [25] train-rmse:1.350757+0.035083    test-rmse:2.321613+0.495839 
## [26] train-rmse:1.322640+0.027314    test-rmse:2.308138+0.495990 
## [27] train-rmse:1.300682+0.027592    test-rmse:2.304559+0.512812 
## [28] train-rmse:1.271177+0.027293    test-rmse:2.300205+0.512616 
## [29] train-rmse:1.246399+0.028016    test-rmse:2.285290+0.511452 
## [30] train-rmse:1.224362+0.027402    test-rmse:2.273742+0.510886 
## [31] train-rmse:1.199812+0.032721    test-rmse:2.267422+0.512909 
## [32] train-rmse:1.180752+0.038301    test-rmse:2.261754+0.505935 
## [33] train-rmse:1.163273+0.036228    test-rmse:2.251932+0.509935 
## [34] train-rmse:1.144897+0.035363    test-rmse:2.250231+0.506783 
## [35] train-rmse:1.129365+0.037883    test-rmse:2.245521+0.515735 
## [36] train-rmse:1.111547+0.037155    test-rmse:2.233221+0.516201 
## [37] train-rmse:1.092422+0.044377    test-rmse:2.227624+0.510775 
## [38] train-rmse:1.077464+0.041863    test-rmse:2.225480+0.520498 
## [39] train-rmse:1.051696+0.042656    test-rmse:2.222903+0.517625 
## [40] train-rmse:1.036444+0.040387    test-rmse:2.220440+0.515880 
## [41] train-rmse:1.013421+0.036275    test-rmse:2.219890+0.515423 
## [42] train-rmse:1.000404+0.039057    test-rmse:2.216527+0.518330 
## [43] train-rmse:0.988820+0.040964    test-rmse:2.210837+0.522040 
## [44] train-rmse:0.974836+0.042319    test-rmse:2.206261+0.522796 
## [45] train-rmse:0.962495+0.040816    test-rmse:2.203901+0.524498 
## [46] train-rmse:0.948251+0.038804    test-rmse:2.194861+0.515069 
## [47] train-rmse:0.935283+0.039178    test-rmse:2.191276+0.519762 
## [48] train-rmse:0.922209+0.042309    test-rmse:2.187446+0.520402 
## [49] train-rmse:0.899767+0.042756    test-rmse:2.177837+0.520173 
## [50] train-rmse:0.884705+0.042988    test-rmse:2.172895+0.522594 
## [51] train-rmse:0.875806+0.043842    test-rmse:2.170500+0.525530 
## [52] train-rmse:0.866529+0.043515    test-rmse:2.170237+0.524752 
## [53] train-rmse:0.849122+0.047307    test-rmse:2.164931+0.524843 
## [54] train-rmse:0.834777+0.050358    test-rmse:2.159087+0.522382 
## [55] train-rmse:0.819590+0.052037    test-rmse:2.157457+0.526517 
## [56] train-rmse:0.805956+0.049276    test-rmse:2.155911+0.525815 
## [57] train-rmse:0.794558+0.051700    test-rmse:2.154171+0.531824 
## [58] train-rmse:0.785655+0.052494    test-rmse:2.154622+0.532424 
## [59] train-rmse:0.772173+0.053917    test-rmse:2.160075+0.525553 
## [60] train-rmse:0.760841+0.050624    test-rmse:2.152812+0.526217 
## [61] train-rmse:0.754661+0.051077    test-rmse:2.151780+0.525913 
## [62] train-rmse:0.748546+0.050395    test-rmse:2.149112+0.530366 
## [63] train-rmse:0.733301+0.047212    test-rmse:2.147319+0.531031 
## [64] train-rmse:0.722769+0.050601    test-rmse:2.144341+0.530458 
## [65] train-rmse:0.714691+0.051579    test-rmse:2.144244+0.530626 
## [66] train-rmse:0.704240+0.051754    test-rmse:2.140947+0.524099 
## [67] train-rmse:0.687672+0.042262    test-rmse:2.139989+0.522219 
## [68] train-rmse:0.680386+0.043817    test-rmse:2.141469+0.523793 
## [69] train-rmse:0.671099+0.039450    test-rmse:2.139991+0.524624 
## [70] train-rmse:0.662863+0.040478    test-rmse:2.145015+0.526335 
## [71] train-rmse:0.656059+0.040446    test-rmse:2.142003+0.528430 
## [72] train-rmse:0.647043+0.042821    test-rmse:2.139984+0.526004 
## [73] train-rmse:0.638689+0.042750    test-rmse:2.139317+0.530028 
## [74] train-rmse:0.627345+0.041492    test-rmse:2.135507+0.522442 
## [75] train-rmse:0.618574+0.045878    test-rmse:2.134642+0.520701 
## [76] train-rmse:0.609755+0.042832    test-rmse:2.135188+0.521657 
## [77] train-rmse:0.604856+0.042973    test-rmse:2.136614+0.522478 
## [78] train-rmse:0.596817+0.040708    test-rmse:2.140376+0.525239 
## [79] train-rmse:0.587988+0.037871    test-rmse:2.139605+0.524723 
## [80] train-rmse:0.578214+0.036711    test-rmse:2.138045+0.523947 
## [81] train-rmse:0.569368+0.039329    test-rmse:2.133766+0.523399 
## [82] train-rmse:0.560556+0.038588    test-rmse:2.128909+0.527548 
## [83] train-rmse:0.555672+0.040068    test-rmse:2.129049+0.524507 
## [84] train-rmse:0.550048+0.038775    test-rmse:2.129520+0.523441 
## [85] train-rmse:0.541052+0.037494    test-rmse:2.126271+0.521448 
## [86] train-rmse:0.534488+0.038092    test-rmse:2.127908+0.520921 
## [87] train-rmse:0.529309+0.039380    test-rmse:2.127565+0.521588 
## [88] train-rmse:0.524100+0.038107    test-rmse:2.127969+0.521010 
## [89] train-rmse:0.518154+0.036003    test-rmse:2.127983+0.520581 
## [90] train-rmse:0.514292+0.035418    test-rmse:2.128210+0.520878 
## [91] train-rmse:0.511049+0.036215    test-rmse:2.126377+0.521989 
## Stopping. Best iteration:
## [85] train-rmse:0.541052+0.037494    test-rmse:2.126271+0.521448
## 
## 66 
## [1]  train-rmse:6.282884+0.126464    test-rmse:6.343974+0.587695 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.917502+0.088835    test-rmse:5.071497+0.496095 
## [3]  train-rmse:3.968587+0.060445    test-rmse:4.198036+0.434413 
## [4]  train-rmse:3.313811+0.055478    test-rmse:3.630963+0.430879 
## [5]  train-rmse:2.848794+0.055579    test-rmse:3.230414+0.434091 
## [6]  train-rmse:2.517420+0.044870    test-rmse:2.987600+0.462611 
## [7]  train-rmse:2.284541+0.050839    test-rmse:2.836231+0.440749 
## [8]  train-rmse:2.108184+0.061196    test-rmse:2.714707+0.441275 
## [9]  train-rmse:1.965287+0.064374    test-rmse:2.653011+0.433986 
## [10] train-rmse:1.850825+0.057181    test-rmse:2.572442+0.450364 
## [11] train-rmse:1.726183+0.075905    test-rmse:2.509186+0.474996 
## [12] train-rmse:1.646969+0.066615    test-rmse:2.481292+0.478824 
## [13] train-rmse:1.581582+0.069637    test-rmse:2.446142+0.480930 
## [14] train-rmse:1.520227+0.068227    test-rmse:2.414615+0.471490 
## [15] train-rmse:1.461972+0.061117    test-rmse:2.411181+0.474498 
## [16] train-rmse:1.406208+0.044678    test-rmse:2.394384+0.481909 
## [17] train-rmse:1.351505+0.039579    test-rmse:2.365854+0.493226 
## [18] train-rmse:1.302410+0.032079    test-rmse:2.364618+0.497527 
## [19] train-rmse:1.262011+0.031735    test-rmse:2.350311+0.497626 
## [20] train-rmse:1.220131+0.042351    test-rmse:2.321336+0.482426 
## [21] train-rmse:1.177965+0.044921    test-rmse:2.310353+0.490682 
## [22] train-rmse:1.137196+0.036947    test-rmse:2.285665+0.481152 
## [23] train-rmse:1.101909+0.040274    test-rmse:2.257870+0.477021 
## [24] train-rmse:1.070593+0.042611    test-rmse:2.245885+0.475334 
## [25] train-rmse:1.036128+0.043423    test-rmse:2.245069+0.477688 
## [26] train-rmse:1.015095+0.043620    test-rmse:2.235854+0.469066 
## [27] train-rmse:0.994128+0.042303    test-rmse:2.233045+0.475685 
## [28] train-rmse:0.966792+0.041621    test-rmse:2.221176+0.478182 
## [29] train-rmse:0.943505+0.041785    test-rmse:2.205563+0.481523 
## [30] train-rmse:0.914190+0.044585    test-rmse:2.202106+0.478508 
## [31] train-rmse:0.891594+0.046910    test-rmse:2.200131+0.476879 
## [32] train-rmse:0.872861+0.043596    test-rmse:2.191236+0.480109 
## [33] train-rmse:0.853378+0.041325    test-rmse:2.193236+0.491280 
## [34] train-rmse:0.825315+0.042164    test-rmse:2.187595+0.486584 
## [35] train-rmse:0.808488+0.041639    test-rmse:2.185074+0.487121 
## [36] train-rmse:0.785849+0.036547    test-rmse:2.185802+0.489168 
## [37] train-rmse:0.764639+0.029948    test-rmse:2.183257+0.488354 
## [38] train-rmse:0.750441+0.032824    test-rmse:2.183902+0.489024 
## [39] train-rmse:0.724989+0.035803    test-rmse:2.175851+0.491426 
## [40] train-rmse:0.707799+0.037337    test-rmse:2.175373+0.493223 
## [41] train-rmse:0.688838+0.034070    test-rmse:2.175666+0.498214 
## [42] train-rmse:0.672448+0.027475    test-rmse:2.171892+0.493481 
## [43] train-rmse:0.659847+0.028063    test-rmse:2.166747+0.495915 
## [44] train-rmse:0.642814+0.027458    test-rmse:2.166765+0.502675 
## [45] train-rmse:0.630438+0.032111    test-rmse:2.163900+0.502663 
## [46] train-rmse:0.617419+0.031938    test-rmse:2.165048+0.502888 
## [47] train-rmse:0.601654+0.026217    test-rmse:2.156879+0.504942 
## [48] train-rmse:0.591632+0.026109    test-rmse:2.151390+0.508164 
## [49] train-rmse:0.580321+0.026865    test-rmse:2.148745+0.509437 
## [50] train-rmse:0.572306+0.023846    test-rmse:2.148278+0.510855 
## [51] train-rmse:0.560648+0.025664    test-rmse:2.149526+0.508096 
## [52] train-rmse:0.548558+0.025198    test-rmse:2.147412+0.509711 
## [53] train-rmse:0.535193+0.023051    test-rmse:2.144214+0.511140 
## [54] train-rmse:0.523447+0.026408    test-rmse:2.146395+0.510259 
## [55] train-rmse:0.508958+0.026480    test-rmse:2.147661+0.513543 
## [56] train-rmse:0.498577+0.028337    test-rmse:2.146460+0.513745 
## [57] train-rmse:0.488757+0.030097    test-rmse:2.144975+0.511416 
## [58] train-rmse:0.478794+0.028695    test-rmse:2.142615+0.514212 
## [59] train-rmse:0.469592+0.031535    test-rmse:2.141164+0.514617 
## [60] train-rmse:0.463188+0.030444    test-rmse:2.139950+0.515911 
## [61] train-rmse:0.453335+0.030166    test-rmse:2.139394+0.515214 
## [62] train-rmse:0.445262+0.028544    test-rmse:2.139921+0.515905 
## [63] train-rmse:0.434367+0.028500    test-rmse:2.138784+0.517481 
## [64] train-rmse:0.425564+0.030187    test-rmse:2.139210+0.518820 
## [65] train-rmse:0.420221+0.031123    test-rmse:2.138110+0.519474 
## [66] train-rmse:0.409387+0.033346    test-rmse:2.138745+0.519689 
## [67] train-rmse:0.398077+0.029787    test-rmse:2.139245+0.519388 
## [68] train-rmse:0.389736+0.030250    test-rmse:2.139346+0.518889 
## [69] train-rmse:0.383254+0.029557    test-rmse:2.140647+0.519737 
## [70] train-rmse:0.375742+0.028752    test-rmse:2.137390+0.521530 
## [71] train-rmse:0.367618+0.029250    test-rmse:2.136437+0.522861 
## [72] train-rmse:0.360858+0.028062    test-rmse:2.138322+0.524477 
## [73] train-rmse:0.355509+0.027579    test-rmse:2.137677+0.525126 
## [74] train-rmse:0.346757+0.027590    test-rmse:2.135525+0.524127 
## [75] train-rmse:0.341681+0.027406    test-rmse:2.135042+0.521265 
## [76] train-rmse:0.333994+0.028119    test-rmse:2.133278+0.522013 
## [77] train-rmse:0.329536+0.029900    test-rmse:2.133548+0.521707 
## [78] train-rmse:0.320287+0.026685    test-rmse:2.136158+0.523207 
## [79] train-rmse:0.314876+0.026279    test-rmse:2.134581+0.524125 
## [80] train-rmse:0.309211+0.024992    test-rmse:2.134896+0.524097 
## [81] train-rmse:0.302081+0.022445    test-rmse:2.134423+0.524760 
## [82] train-rmse:0.296779+0.021504    test-rmse:2.136342+0.526287 
## Stopping. Best iteration:
## [76] train-rmse:0.333994+0.028119    test-rmse:2.133278+0.522013
## 
## 67 
## [1]  train-rmse:6.226988+0.123739    test-rmse:6.251227+0.563562 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.817585+0.093929    test-rmse:4.931225+0.481509 
## [3]  train-rmse:3.845969+0.078012    test-rmse:4.099317+0.431754 
## [4]  train-rmse:3.156992+0.071543    test-rmse:3.541626+0.390891 
## [5]  train-rmse:2.677789+0.053535    test-rmse:3.154217+0.424681 
## [6]  train-rmse:2.330258+0.052710    test-rmse:2.931498+0.466891 
## [7]  train-rmse:2.085723+0.075665    test-rmse:2.765879+0.482449 
## [8]  train-rmse:1.882495+0.070337    test-rmse:2.641236+0.492397 
## [9]  train-rmse:1.741179+0.067734    test-rmse:2.562060+0.513407 
## [10] train-rmse:1.610251+0.074155    test-rmse:2.512194+0.529090 
## [11] train-rmse:1.499878+0.071303    test-rmse:2.462095+0.516807 
## [12] train-rmse:1.410170+0.061715    test-rmse:2.419460+0.516822 
## [13] train-rmse:1.331244+0.044324    test-rmse:2.394085+0.526637 
## [14] train-rmse:1.269192+0.047905    test-rmse:2.368299+0.524497 
## [15] train-rmse:1.201180+0.045935    test-rmse:2.364469+0.516783 
## [16] train-rmse:1.152317+0.051496    test-rmse:2.348048+0.515478 
## [17] train-rmse:1.099768+0.041505    test-rmse:2.333376+0.522075 
## [18] train-rmse:1.058206+0.040186    test-rmse:2.313811+0.534197 
## [19] train-rmse:1.014006+0.037028    test-rmse:2.286773+0.552379 
## [20] train-rmse:0.975658+0.029858    test-rmse:2.283396+0.563085 
## [21] train-rmse:0.936894+0.031726    test-rmse:2.285104+0.567666 
## [22] train-rmse:0.907658+0.032338    test-rmse:2.272044+0.561344 
## [23] train-rmse:0.872874+0.031840    test-rmse:2.261880+0.560876 
## [24] train-rmse:0.845350+0.030347    test-rmse:2.254179+0.554100 
## [25] train-rmse:0.814405+0.038774    test-rmse:2.249046+0.555510 
## [26] train-rmse:0.787003+0.026938    test-rmse:2.240185+0.559467 
## [27] train-rmse:0.749485+0.034475    test-rmse:2.236178+0.560301 
## [28] train-rmse:0.726943+0.032084    test-rmse:2.226541+0.558098 
## [29] train-rmse:0.700578+0.031390    test-rmse:2.223427+0.564088 
## [30] train-rmse:0.682566+0.035411    test-rmse:2.219539+0.567068 
## [31] train-rmse:0.664902+0.041452    test-rmse:2.219273+0.565245 
## [32] train-rmse:0.646202+0.039277    test-rmse:2.218133+0.561814 
## [33] train-rmse:0.628318+0.036999    test-rmse:2.219057+0.569095 
## [34] train-rmse:0.603620+0.034229    test-rmse:2.214868+0.568859 
## [35] train-rmse:0.588637+0.031051    test-rmse:2.212351+0.570106 
## [36] train-rmse:0.570759+0.031637    test-rmse:2.214908+0.570340 
## [37] train-rmse:0.557946+0.032353    test-rmse:2.208813+0.574220 
## [38] train-rmse:0.539783+0.032920    test-rmse:2.204598+0.574298 
## [39] train-rmse:0.517133+0.031453    test-rmse:2.211970+0.574180 
## [40] train-rmse:0.502387+0.029878    test-rmse:2.211413+0.573891 
## [41] train-rmse:0.486870+0.031280    test-rmse:2.212478+0.579586 
## [42] train-rmse:0.473867+0.031790    test-rmse:2.210179+0.581805 
## [43] train-rmse:0.461694+0.033225    test-rmse:2.209088+0.581772 
## [44] train-rmse:0.447109+0.031955    test-rmse:2.209426+0.585808 
## Stopping. Best iteration:
## [38] train-rmse:0.539783+0.032920    test-rmse:2.204598+0.574298
## 
## 68 
## [1]  train-rmse:6.191944+0.118052    test-rmse:6.229254+0.596176 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.759855+0.078544    test-rmse:4.924746+0.518467 
## [3]  train-rmse:3.746651+0.058419    test-rmse:4.070847+0.462192 
## [4]  train-rmse:3.031844+0.034192    test-rmse:3.506471+0.467142 
## [5]  train-rmse:2.539297+0.033876    test-rmse:3.149099+0.497508 
## [6]  train-rmse:2.182567+0.023958    test-rmse:2.921154+0.500446 
## [7]  train-rmse:1.903779+0.043782    test-rmse:2.773727+0.522067 
## [8]  train-rmse:1.710549+0.035902    test-rmse:2.693127+0.539383 
## [9]  train-rmse:1.558019+0.036100    test-rmse:2.595704+0.545674 
## [10] train-rmse:1.429581+0.039997    test-rmse:2.534688+0.520049 
## [11] train-rmse:1.322778+0.041059    test-rmse:2.510901+0.537844 
## [12] train-rmse:1.252042+0.042421    test-rmse:2.482020+0.538354 
## [13] train-rmse:1.175829+0.038403    test-rmse:2.445163+0.538820 
## [14] train-rmse:1.115761+0.047414    test-rmse:2.414594+0.542080 
## [15] train-rmse:1.036951+0.050482    test-rmse:2.407566+0.557376 
## [16] train-rmse:0.984050+0.059027    test-rmse:2.389147+0.557764 
## [17] train-rmse:0.935399+0.053796    test-rmse:2.377426+0.560224 
## [18] train-rmse:0.891694+0.058054    test-rmse:2.378678+0.569523 
## [19] train-rmse:0.846516+0.053727    test-rmse:2.363693+0.568003 
## [20] train-rmse:0.814283+0.054941    test-rmse:2.359312+0.567964 
## [21] train-rmse:0.779428+0.059995    test-rmse:2.358024+0.569758 
## [22] train-rmse:0.742920+0.056319    test-rmse:2.350513+0.577285 
## [23] train-rmse:0.706500+0.039209    test-rmse:2.349320+0.578565 
## [24] train-rmse:0.663957+0.033405    test-rmse:2.349586+0.588239 
## [25] train-rmse:0.642339+0.034991    test-rmse:2.339734+0.589919 
## [26] train-rmse:0.621168+0.039065    test-rmse:2.331825+0.590190 
## [27] train-rmse:0.593157+0.042613    test-rmse:2.325842+0.595882 
## [28] train-rmse:0.561646+0.039142    test-rmse:2.328542+0.595202 
## [29] train-rmse:0.533997+0.033701    test-rmse:2.330606+0.590297 
## [30] train-rmse:0.502107+0.025371    test-rmse:2.326042+0.590843 
## [31] train-rmse:0.486888+0.028677    test-rmse:2.324620+0.593644 
## [32] train-rmse:0.467533+0.024498    test-rmse:2.322144+0.592471 
## [33] train-rmse:0.451785+0.025796    test-rmse:2.321088+0.593664 
## [34] train-rmse:0.432130+0.026432    test-rmse:2.320402+0.594666 
## [35] train-rmse:0.417682+0.025760    test-rmse:2.318753+0.594324 
## [36] train-rmse:0.399340+0.027440    test-rmse:2.319237+0.595938 
## [37] train-rmse:0.387998+0.029986    test-rmse:2.317447+0.595330 
## [38] train-rmse:0.369681+0.031780    test-rmse:2.316185+0.600498 
## [39] train-rmse:0.357567+0.028481    test-rmse:2.316157+0.599379 
## [40] train-rmse:0.339485+0.025583    test-rmse:2.316693+0.597947 
## [41] train-rmse:0.326793+0.025830    test-rmse:2.315730+0.599978 
## [42] train-rmse:0.315419+0.025784    test-rmse:2.316102+0.601864 
## [43] train-rmse:0.303480+0.021904    test-rmse:2.316583+0.600339 
## [44] train-rmse:0.295475+0.022441    test-rmse:2.316368+0.602139 
## [45] train-rmse:0.287002+0.020960    test-rmse:2.318327+0.601952 
## [46] train-rmse:0.271936+0.018270    test-rmse:2.317350+0.601439 
## [47] train-rmse:0.263421+0.019878    test-rmse:2.318074+0.600451 
## Stopping. Best iteration:
## [41] train-rmse:0.326793+0.025830    test-rmse:2.315730+0.599978
## 
## 69 
## [1]  train-rmse:6.179482+0.116989    test-rmse:6.221731+0.595762 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.721513+0.077695    test-rmse:4.908799+0.513744 
## [3]  train-rmse:3.685379+0.049483    test-rmse:4.011809+0.492989 
## [4]  train-rmse:2.951940+0.033669    test-rmse:3.456626+0.470173 
## [5]  train-rmse:2.432226+0.031156    test-rmse:3.108776+0.485577 
## [6]  train-rmse:2.055290+0.025124    test-rmse:2.881255+0.540135 
## [7]  train-rmse:1.764409+0.036606    test-rmse:2.741384+0.541238 
## [8]  train-rmse:1.555568+0.043720    test-rmse:2.633843+0.556852 
## [9]  train-rmse:1.395560+0.036800    test-rmse:2.565288+0.549285 
## [10] train-rmse:1.256542+0.038764    test-rmse:2.500382+0.549921 
## [11] train-rmse:1.160316+0.043725    test-rmse:2.466345+0.551706 
## [12] train-rmse:1.085642+0.038960    test-rmse:2.443390+0.551002 
## [13] train-rmse:1.001023+0.023363    test-rmse:2.437156+0.578294 
## [14] train-rmse:0.941527+0.023949    test-rmse:2.427006+0.578136 
## [15] train-rmse:0.892451+0.026392    test-rmse:2.418261+0.577117 
## [16] train-rmse:0.845703+0.025770    test-rmse:2.413343+0.580086 
## [17] train-rmse:0.793098+0.037139    test-rmse:2.396167+0.591096 
## [18] train-rmse:0.741148+0.037577    test-rmse:2.389840+0.598046 
## [19] train-rmse:0.693721+0.024023    test-rmse:2.376130+0.594941 
## [20] train-rmse:0.647180+0.021870    test-rmse:2.364763+0.601816 
## [21] train-rmse:0.613777+0.021797    test-rmse:2.360481+0.608575 
## [22] train-rmse:0.579298+0.017670    test-rmse:2.354993+0.605296 
## [23] train-rmse:0.543742+0.016550    test-rmse:2.357971+0.617800 
## [24] train-rmse:0.513425+0.014026    test-rmse:2.353714+0.618689 
## [25] train-rmse:0.483716+0.012507    test-rmse:2.350869+0.616394 
## [26] train-rmse:0.458617+0.013528    test-rmse:2.350872+0.618864 
## [27] train-rmse:0.433339+0.014888    test-rmse:2.348239+0.618467 
## [28] train-rmse:0.408669+0.016479    test-rmse:2.348992+0.622145 
## [29] train-rmse:0.387698+0.011929    test-rmse:2.348505+0.621996 
## [30] train-rmse:0.370080+0.010663    test-rmse:2.342527+0.618384 
## [31] train-rmse:0.351544+0.012799    test-rmse:2.340691+0.620219 
## [32] train-rmse:0.336098+0.017699    test-rmse:2.340191+0.622158 
## [33] train-rmse:0.321369+0.020682    test-rmse:2.340080+0.621220 
## [34] train-rmse:0.307832+0.019438    test-rmse:2.339627+0.623345 
## [35] train-rmse:0.288962+0.018672    test-rmse:2.335773+0.623716 
## [36] train-rmse:0.278143+0.017966    test-rmse:2.332723+0.622567 
## [37] train-rmse:0.263744+0.015533    test-rmse:2.330707+0.619360 
## [38] train-rmse:0.254341+0.017504    test-rmse:2.329101+0.619970 
## [39] train-rmse:0.243014+0.019379    test-rmse:2.328903+0.619706 
## [40] train-rmse:0.228984+0.019807    test-rmse:2.330986+0.622556 
## [41] train-rmse:0.221268+0.019066    test-rmse:2.329102+0.622296 
## [42] train-rmse:0.212489+0.017563    test-rmse:2.330183+0.622874 
## [43] train-rmse:0.203740+0.019303    test-rmse:2.329751+0.623697 
## [44] train-rmse:0.192349+0.016010    test-rmse:2.331107+0.624842 
## [45] train-rmse:0.185815+0.017911    test-rmse:2.330837+0.625895 
## Stopping. Best iteration:
## [39] train-rmse:0.243014+0.019379    test-rmse:2.328903+0.619706
## 
## 70 
## [1]  train-rmse:6.470501+0.141022    test-rmse:6.472533+0.652763 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.365281+0.114281    test-rmse:5.433145+0.689876 
## [3]  train-rmse:4.677165+0.115025    test-rmse:4.750931+0.605718 
## [4]  train-rmse:4.250548+0.116217    test-rmse:4.353208+0.535730 
## [5]  train-rmse:3.974725+0.113072    test-rmse:4.095233+0.485979 
## [6]  train-rmse:3.783335+0.107477    test-rmse:3.914839+0.478034 
## [7]  train-rmse:3.644152+0.110771    test-rmse:3.764581+0.501077 
## [8]  train-rmse:3.529926+0.118490    test-rmse:3.651138+0.428241 
## [9]  train-rmse:3.435646+0.122795    test-rmse:3.585578+0.391064 
## [10] train-rmse:3.358102+0.122108    test-rmse:3.533948+0.437469 
## [11] train-rmse:3.286100+0.120284    test-rmse:3.465546+0.443876 
## [12] train-rmse:3.218793+0.121984    test-rmse:3.364489+0.452234 
## [13] train-rmse:3.161000+0.124141    test-rmse:3.276457+0.409100 
## [14] train-rmse:3.106969+0.126771    test-rmse:3.228908+0.412998 
## [15] train-rmse:3.058629+0.128772    test-rmse:3.203318+0.441751 
## [16] train-rmse:3.014562+0.127339    test-rmse:3.185093+0.461357 
## [17] train-rmse:2.973921+0.127499    test-rmse:3.143873+0.439563 
## [18] train-rmse:2.935331+0.129383    test-rmse:3.123402+0.419573 
## [19] train-rmse:2.900616+0.131283    test-rmse:3.083716+0.413350 
## [20] train-rmse:2.866909+0.133145    test-rmse:3.061531+0.426430 
## [21] train-rmse:2.834028+0.134650    test-rmse:3.039103+0.439728 
## [22] train-rmse:2.802805+0.136680    test-rmse:2.998017+0.457600 
## [23] train-rmse:2.773506+0.138661    test-rmse:2.979802+0.454104 
## [24] train-rmse:2.745783+0.138796    test-rmse:2.963797+0.452995 
## [25] train-rmse:2.718668+0.138488    test-rmse:2.935990+0.437139 
## [26] train-rmse:2.692566+0.137983    test-rmse:2.902814+0.446688 
## [27] train-rmse:2.666843+0.138014    test-rmse:2.887079+0.478852 
## [28] train-rmse:2.642200+0.137731    test-rmse:2.861612+0.467804 
## [29] train-rmse:2.618545+0.137818    test-rmse:2.860294+0.487553 
## [30] train-rmse:2.596537+0.139094    test-rmse:2.845934+0.468981 
## [31] train-rmse:2.575686+0.140327    test-rmse:2.816337+0.472892 
## [32] train-rmse:2.555797+0.140465    test-rmse:2.806379+0.472660 
## [33] train-rmse:2.536442+0.141101    test-rmse:2.785884+0.485008 
## [34] train-rmse:2.517624+0.141287    test-rmse:2.776045+0.477378 
## [35] train-rmse:2.499376+0.141895    test-rmse:2.771741+0.480093 
## [36] train-rmse:2.481679+0.141615    test-rmse:2.751845+0.470165 
## [37] train-rmse:2.464730+0.141292    test-rmse:2.729331+0.480963 
## [38] train-rmse:2.448693+0.141126    test-rmse:2.705486+0.482852 
## [39] train-rmse:2.433502+0.141198    test-rmse:2.692550+0.488808 
## [40] train-rmse:2.418575+0.141289    test-rmse:2.669684+0.487026 
## [41] train-rmse:2.404319+0.141142    test-rmse:2.659710+0.474994 
## [42] train-rmse:2.390083+0.140900    test-rmse:2.637725+0.478002 
## [43] train-rmse:2.376360+0.140885    test-rmse:2.644983+0.495215 
## [44] train-rmse:2.363373+0.141227    test-rmse:2.628308+0.497821 
## [45] train-rmse:2.351090+0.141892    test-rmse:2.629646+0.507638 
## [46] train-rmse:2.339171+0.142390    test-rmse:2.630291+0.507167 
## [47] train-rmse:2.327778+0.142765    test-rmse:2.622107+0.505389 
## [48] train-rmse:2.316631+0.143276    test-rmse:2.613891+0.509715 
## [49] train-rmse:2.305685+0.143855    test-rmse:2.599720+0.515960 
## [50] train-rmse:2.295235+0.144319    test-rmse:2.590509+0.510227 
## [51] train-rmse:2.284908+0.144753    test-rmse:2.573754+0.507631 
## [52] train-rmse:2.274771+0.145080    test-rmse:2.560706+0.511332 
## [53] train-rmse:2.264975+0.145540    test-rmse:2.545972+0.510386 
## [54] train-rmse:2.255734+0.145729    test-rmse:2.544904+0.503993 
## [55] train-rmse:2.246731+0.145909    test-rmse:2.533655+0.500163 
## [56] train-rmse:2.237839+0.146319    test-rmse:2.531533+0.505302 
## [57] train-rmse:2.229373+0.146639    test-rmse:2.515600+0.509018 
## [58] train-rmse:2.221046+0.146904    test-rmse:2.515438+0.512109 
## [59] train-rmse:2.212840+0.147189    test-rmse:2.513446+0.526645 
## [60] train-rmse:2.205002+0.147458    test-rmse:2.500516+0.527411 
## [61] train-rmse:2.197454+0.147747    test-rmse:2.496545+0.527678 
## [62] train-rmse:2.190189+0.147894    test-rmse:2.488810+0.525438 
## [63] train-rmse:2.183052+0.148183    test-rmse:2.486322+0.528950 
## [64] train-rmse:2.176014+0.148310    test-rmse:2.479018+0.527392 
## [65] train-rmse:2.169168+0.148525    test-rmse:2.478795+0.526464 
## [66] train-rmse:2.162423+0.148794    test-rmse:2.483392+0.526960 
## [67] train-rmse:2.156003+0.149162    test-rmse:2.482856+0.526524 
## [68] train-rmse:2.149672+0.149568    test-rmse:2.472699+0.530554 
## [69] train-rmse:2.143537+0.149824    test-rmse:2.463180+0.538618 
## [70] train-rmse:2.137519+0.150245    test-rmse:2.456220+0.532349 
## [71] train-rmse:2.131651+0.150537    test-rmse:2.448735+0.537217 
## [72] train-rmse:2.125887+0.150820    test-rmse:2.434405+0.534520 
## [73] train-rmse:2.120230+0.151100    test-rmse:2.431671+0.535272 
## [74] train-rmse:2.114718+0.151461    test-rmse:2.432306+0.534863 
## [75] train-rmse:2.109426+0.151587    test-rmse:2.433970+0.531204 
## [76] train-rmse:2.104309+0.151780    test-rmse:2.425605+0.533264 
## [77] train-rmse:2.099347+0.152009    test-rmse:2.422092+0.531816 
## [78] train-rmse:2.094389+0.152040    test-rmse:2.414399+0.530344 
## [79] train-rmse:2.089659+0.152193    test-rmse:2.411142+0.529300 
## [80] train-rmse:2.085078+0.152430    test-rmse:2.408537+0.524507 
## [81] train-rmse:2.080431+0.152709    test-rmse:2.402038+0.527468 
## [82] train-rmse:2.075933+0.152903    test-rmse:2.397269+0.529087 
## [83] train-rmse:2.071427+0.152985    test-rmse:2.398281+0.530982 
## [84] train-rmse:2.066869+0.153119    test-rmse:2.396316+0.542081 
## [85] train-rmse:2.062599+0.153414    test-rmse:2.390344+0.539734 
## [86] train-rmse:2.058319+0.153749    test-rmse:2.398371+0.536547 
## [87] train-rmse:2.054181+0.153857    test-rmse:2.397028+0.535440 
## [88] train-rmse:2.050269+0.153944    test-rmse:2.395175+0.532742 
## [89] train-rmse:2.046202+0.153930    test-rmse:2.392380+0.533361 
## [90] train-rmse:2.042403+0.154049    test-rmse:2.395157+0.535865 
## [91] train-rmse:2.038740+0.154183    test-rmse:2.390569+0.539764 
## Stopping. Best iteration:
## [85] train-rmse:2.062599+0.153414    test-rmse:2.390344+0.539734
## 
## 71 
## [1]  train-rmse:6.331649+0.131476    test-rmse:6.376052+0.609992 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.097252+0.125319    test-rmse:5.169473+0.575475 
## [3]  train-rmse:4.286609+0.097273    test-rmse:4.450465+0.505373 
## [4]  train-rmse:3.724956+0.092913    test-rmse:3.906456+0.475079 
## [5]  train-rmse:3.368766+0.093410    test-rmse:3.626746+0.451694 
## [6]  train-rmse:3.103441+0.090107    test-rmse:3.341920+0.448946 
## [7]  train-rmse:2.900352+0.076964    test-rmse:3.169079+0.477636 
## [8]  train-rmse:2.757636+0.085514    test-rmse:3.083382+0.463128 
## [9]  train-rmse:2.656853+0.089230    test-rmse:2.994827+0.449432 
## [10] train-rmse:2.561735+0.096386    test-rmse:2.945867+0.442048 
## [11] train-rmse:2.486776+0.089983    test-rmse:2.882566+0.471371 
## [12] train-rmse:2.410134+0.090819    test-rmse:2.832444+0.475749 
## [13] train-rmse:2.338298+0.080097    test-rmse:2.801241+0.458697 
## [14] train-rmse:2.266999+0.087014    test-rmse:2.725161+0.483466 
## [15] train-rmse:2.217943+0.088302    test-rmse:2.686263+0.481449 
## [16] train-rmse:2.172890+0.088795    test-rmse:2.659925+0.488965 
## [17] train-rmse:2.129777+0.083847    test-rmse:2.642202+0.484099 
## [18] train-rmse:2.078382+0.064194    test-rmse:2.599992+0.495627 
## [19] train-rmse:2.037219+0.065154    test-rmse:2.575085+0.495494 
## [20] train-rmse:2.000460+0.065017    test-rmse:2.537849+0.486660 
## [21] train-rmse:1.957609+0.066849    test-rmse:2.533420+0.488702 
## [22] train-rmse:1.925233+0.065416    test-rmse:2.525745+0.497221 
## [23] train-rmse:1.893830+0.066895    test-rmse:2.511351+0.505767 
## [24] train-rmse:1.866022+0.067500    test-rmse:2.494554+0.509841 
## [25] train-rmse:1.827043+0.074686    test-rmse:2.474950+0.501552 
## [26] train-rmse:1.799937+0.076912    test-rmse:2.440224+0.492071 
## [27] train-rmse:1.773675+0.076711    test-rmse:2.419963+0.489228 
## [28] train-rmse:1.743857+0.075362    test-rmse:2.410620+0.494210 
## [29] train-rmse:1.720569+0.074480    test-rmse:2.407935+0.490946 
## [30] train-rmse:1.695446+0.076339    test-rmse:2.403164+0.499605 
## [31] train-rmse:1.677873+0.075902    test-rmse:2.397053+0.498953 
## [32] train-rmse:1.659571+0.074720    test-rmse:2.385273+0.491368 
## [33] train-rmse:1.642775+0.074520    test-rmse:2.381204+0.494926 
## [34] train-rmse:1.624150+0.076596    test-rmse:2.371448+0.507533 
## [35] train-rmse:1.604742+0.074544    test-rmse:2.358295+0.508874 
## [36] train-rmse:1.574302+0.062258    test-rmse:2.329429+0.516153 
## [37] train-rmse:1.558734+0.063867    test-rmse:2.313625+0.512896 
## [38] train-rmse:1.542185+0.064931    test-rmse:2.309121+0.512343 
## [39] train-rmse:1.525361+0.056840    test-rmse:2.299665+0.520038 
## [40] train-rmse:1.503454+0.059846    test-rmse:2.296523+0.522448 
## [41] train-rmse:1.488077+0.060862    test-rmse:2.296274+0.523686 
## [42] train-rmse:1.472889+0.063355    test-rmse:2.297148+0.515060 
## [43] train-rmse:1.451726+0.071394    test-rmse:2.291997+0.507891 
## [44] train-rmse:1.439838+0.071885    test-rmse:2.291049+0.508450 
## [45] train-rmse:1.428553+0.072294    test-rmse:2.295022+0.514735 
## [46] train-rmse:1.413197+0.069837    test-rmse:2.278671+0.513371 
## [47] train-rmse:1.393160+0.070295    test-rmse:2.271006+0.507551 
## [48] train-rmse:1.379014+0.071292    test-rmse:2.263196+0.510120 
## [49] train-rmse:1.368986+0.068824    test-rmse:2.259698+0.509880 
## [50] train-rmse:1.357302+0.072331    test-rmse:2.260746+0.521537 
## [51] train-rmse:1.349262+0.072630    test-rmse:2.256299+0.520980 
## [52] train-rmse:1.336885+0.075434    test-rmse:2.260199+0.528851 
## [53] train-rmse:1.324098+0.074856    test-rmse:2.261531+0.539033 
## [54] train-rmse:1.310027+0.071772    test-rmse:2.263858+0.536305 
## [55] train-rmse:1.300618+0.072465    test-rmse:2.261184+0.533121 
## [56] train-rmse:1.292968+0.072195    test-rmse:2.259086+0.533464 
## [57] train-rmse:1.281919+0.071209    test-rmse:2.251858+0.535762 
## [58] train-rmse:1.273492+0.071598    test-rmse:2.252448+0.541885 
## [59] train-rmse:1.260651+0.064437    test-rmse:2.244774+0.544293 
## [60] train-rmse:1.248905+0.062783    test-rmse:2.244449+0.540326 
## [61] train-rmse:1.240531+0.062756    test-rmse:2.244803+0.539661 
## [62] train-rmse:1.227870+0.064124    test-rmse:2.244912+0.543480 
## [63] train-rmse:1.219425+0.067161    test-rmse:2.234122+0.536934 
## [64] train-rmse:1.206362+0.073533    test-rmse:2.228148+0.529175 
## [65] train-rmse:1.198973+0.076540    test-rmse:2.227006+0.527203 
## [66] train-rmse:1.189497+0.080718    test-rmse:2.221010+0.528266 
## [67] train-rmse:1.181652+0.079716    test-rmse:2.213721+0.534712 
## [68] train-rmse:1.174229+0.081953    test-rmse:2.210671+0.528691 
## [69] train-rmse:1.163576+0.079321    test-rmse:2.210224+0.529956 
## [70] train-rmse:1.140941+0.063360    test-rmse:2.206547+0.531790 
## [71] train-rmse:1.133270+0.061005    test-rmse:2.200072+0.534412 
## [72] train-rmse:1.124425+0.059291    test-rmse:2.197727+0.533007 
## [73] train-rmse:1.118245+0.060201    test-rmse:2.191395+0.535344 
## [74] train-rmse:1.111304+0.058057    test-rmse:2.193192+0.532260 
## [75] train-rmse:1.101931+0.062504    test-rmse:2.188374+0.529785 
## [76] train-rmse:1.095222+0.062423    test-rmse:2.189866+0.532589 
## [77] train-rmse:1.086669+0.060187    test-rmse:2.190727+0.531711 
## [78] train-rmse:1.076240+0.057583    test-rmse:2.190676+0.534360 
## [79] train-rmse:1.066514+0.057019    test-rmse:2.195794+0.542083 
## [80] train-rmse:1.060688+0.057797    test-rmse:2.193117+0.541523 
## [81] train-rmse:1.050020+0.058220    test-rmse:2.187467+0.544162 
## [82] train-rmse:1.040236+0.062567    test-rmse:2.176585+0.550333 
## [83] train-rmse:1.035349+0.063055    test-rmse:2.174954+0.548049 
## [84] train-rmse:1.027780+0.059947    test-rmse:2.174755+0.550494 
## [85] train-rmse:1.019833+0.061367    test-rmse:2.176118+0.547831 
## [86] train-rmse:1.012090+0.062133    test-rmse:2.178117+0.547119 
## [87] train-rmse:1.006675+0.062405    test-rmse:2.175217+0.546269 
## [88] train-rmse:0.999090+0.064218    test-rmse:2.173574+0.547426 
## [89] train-rmse:0.993248+0.063591    test-rmse:2.173612+0.549533 
## [90] train-rmse:0.990093+0.063745    test-rmse:2.169633+0.552487 
## [91] train-rmse:0.982681+0.067333    test-rmse:2.165817+0.552136 
## [92] train-rmse:0.978637+0.068613    test-rmse:2.165967+0.553749 
## [93] train-rmse:0.974169+0.068908    test-rmse:2.164111+0.554810 
## [94] train-rmse:0.970047+0.066199    test-rmse:2.163088+0.555490 
## [95] train-rmse:0.962599+0.064547    test-rmse:2.159502+0.554752 
## [96] train-rmse:0.958599+0.064484    test-rmse:2.160988+0.555481 
## [97] train-rmse:0.946441+0.065968    test-rmse:2.168855+0.554691 
## [98] train-rmse:0.939841+0.068823    test-rmse:2.169357+0.556118 
## [99] train-rmse:0.936062+0.068456    test-rmse:2.166268+0.558924 
## [100]    train-rmse:0.930709+0.068770    test-rmse:2.171364+0.555251 
## [101]    train-rmse:0.927388+0.069498    test-rmse:2.172070+0.556180 
## Stopping. Best iteration:
## [95] train-rmse:0.962599+0.064547    test-rmse:2.159502+0.554752
## 
## 72 
## [1]  train-rmse:6.238318+0.124478    test-rmse:6.337073+0.595347 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.920812+0.095565    test-rmse:5.043284+0.546065 
## [3]  train-rmse:3.995546+0.095326    test-rmse:4.225340+0.451571 
## [4]  train-rmse:3.365972+0.053137    test-rmse:3.604617+0.473353 
## [5]  train-rmse:2.965920+0.051241    test-rmse:3.285966+0.447124 
## [6]  train-rmse:2.690571+0.053284    test-rmse:3.054163+0.434947 
## [7]  train-rmse:2.471338+0.042269    test-rmse:2.922731+0.461803 
## [8]  train-rmse:2.307275+0.054338    test-rmse:2.829853+0.431199 
## [9]  train-rmse:2.186638+0.055885    test-rmse:2.735474+0.449473 
## [10] train-rmse:2.076936+0.063556    test-rmse:2.669237+0.435688 
## [11] train-rmse:1.985975+0.070251    test-rmse:2.587283+0.430443 
## [12] train-rmse:1.901575+0.068138    test-rmse:2.535998+0.444801 
## [13] train-rmse:1.830597+0.059694    test-rmse:2.488765+0.440902 
## [14] train-rmse:1.758037+0.059928    test-rmse:2.469396+0.427452 
## [15] train-rmse:1.710441+0.061087    test-rmse:2.438688+0.425128 
## [16] train-rmse:1.647379+0.066545    test-rmse:2.407068+0.444454 
## [17] train-rmse:1.605871+0.066231    test-rmse:2.386837+0.442288 
## [18] train-rmse:1.567384+0.065178    test-rmse:2.365639+0.451319 
## [19] train-rmse:1.525885+0.060958    test-rmse:2.344302+0.434323 
## [20] train-rmse:1.484502+0.053690    test-rmse:2.323093+0.435500 
## [21] train-rmse:1.444422+0.053058    test-rmse:2.302674+0.435932 
## [22] train-rmse:1.410299+0.052961    test-rmse:2.271262+0.423651 
## [23] train-rmse:1.373004+0.057257    test-rmse:2.261867+0.421466 
## [24] train-rmse:1.343870+0.061874    test-rmse:2.241710+0.430020 
## [25] train-rmse:1.318135+0.065647    test-rmse:2.232946+0.436625 
## [26] train-rmse:1.290760+0.063396    test-rmse:2.216691+0.445826 
## [27] train-rmse:1.261445+0.057021    test-rmse:2.210295+0.452901 
## [28] train-rmse:1.226592+0.049889    test-rmse:2.211040+0.459977 
## [29] train-rmse:1.199399+0.046495    test-rmse:2.200042+0.451709 
## [30] train-rmse:1.182085+0.047818    test-rmse:2.200243+0.454483 
## [31] train-rmse:1.159774+0.047277    test-rmse:2.193343+0.457307 
## [32] train-rmse:1.138667+0.045267    test-rmse:2.183088+0.460974 
## [33] train-rmse:1.118191+0.052659    test-rmse:2.162072+0.461560 
## [34] train-rmse:1.096394+0.054885    test-rmse:2.159061+0.460459 
## [35] train-rmse:1.080215+0.059148    test-rmse:2.151528+0.456267 
## [36] train-rmse:1.058930+0.055918    test-rmse:2.143743+0.456218 
## [37] train-rmse:1.039006+0.061747    test-rmse:2.140951+0.454638 
## [38] train-rmse:1.016216+0.056755    test-rmse:2.120788+0.450965 
## [39] train-rmse:0.992704+0.051527    test-rmse:2.116036+0.453874 
## [40] train-rmse:0.975746+0.053251    test-rmse:2.109775+0.457322 
## [41] train-rmse:0.964905+0.053204    test-rmse:2.106447+0.453031 
## [42] train-rmse:0.942375+0.052838    test-rmse:2.100605+0.452509 
## [43] train-rmse:0.928081+0.051605    test-rmse:2.100732+0.457948 
## [44] train-rmse:0.917477+0.048749    test-rmse:2.093238+0.460854 
## [45] train-rmse:0.905787+0.046800    test-rmse:2.091989+0.460226 
## [46] train-rmse:0.891721+0.049455    test-rmse:2.091350+0.458433 
## [47] train-rmse:0.877254+0.046421    test-rmse:2.087043+0.457446 
## [48] train-rmse:0.863296+0.049271    test-rmse:2.080679+0.462751 
## [49] train-rmse:0.849715+0.053839    test-rmse:2.082111+0.460249 
## [50] train-rmse:0.836757+0.056681    test-rmse:2.080925+0.462555 
## [51] train-rmse:0.825916+0.056194    test-rmse:2.078135+0.466233 
## [52] train-rmse:0.817776+0.055841    test-rmse:2.078105+0.468578 
## [53] train-rmse:0.804276+0.055360    test-rmse:2.073974+0.471935 
## [54] train-rmse:0.789257+0.054512    test-rmse:2.069468+0.474564 
## [55] train-rmse:0.779753+0.055029    test-rmse:2.068242+0.474840 
## [56] train-rmse:0.770221+0.055003    test-rmse:2.066719+0.472385 
## [57] train-rmse:0.759991+0.051887    test-rmse:2.062198+0.475128 
## [58] train-rmse:0.744155+0.049778    test-rmse:2.060002+0.474444 
## [59] train-rmse:0.734052+0.050377    test-rmse:2.062992+0.476124 
## [60] train-rmse:0.722865+0.053142    test-rmse:2.058853+0.474727 
## [61] train-rmse:0.714174+0.053942    test-rmse:2.059557+0.475110 
## [62] train-rmse:0.704971+0.054408    test-rmse:2.060008+0.474217 
## [63] train-rmse:0.696876+0.056737    test-rmse:2.063268+0.472744 
## [64] train-rmse:0.687161+0.060609    test-rmse:2.059260+0.470927 
## [65] train-rmse:0.679234+0.059942    test-rmse:2.054967+0.475533 
## [66] train-rmse:0.671869+0.056164    test-rmse:2.051285+0.477436 
## [67] train-rmse:0.658273+0.055788    test-rmse:2.053017+0.477347 
## [68] train-rmse:0.646488+0.057265    test-rmse:2.053423+0.479040 
## [69] train-rmse:0.638454+0.059411    test-rmse:2.056338+0.484220 
## [70] train-rmse:0.626434+0.062161    test-rmse:2.057126+0.490629 
## [71] train-rmse:0.615305+0.058848    test-rmse:2.056148+0.493171 
## [72] train-rmse:0.610216+0.058043    test-rmse:2.055165+0.493279 
## Stopping. Best iteration:
## [66] train-rmse:0.671869+0.056164    test-rmse:2.051285+0.477436
## 
## 73 
## [1]  train-rmse:6.150475+0.123131    test-rmse:6.220024+0.580687 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.732291+0.084681    test-rmse:4.904651+0.486618 
## [3]  train-rmse:3.780101+0.056657    test-rmse:4.035358+0.426462 
## [4]  train-rmse:3.140692+0.057446    test-rmse:3.483480+0.418962 
## [5]  train-rmse:2.711016+0.047660    test-rmse:3.106470+0.457232 
## [6]  train-rmse:2.419819+0.048861    test-rmse:2.909540+0.438220 
## [7]  train-rmse:2.183098+0.043177    test-rmse:2.776891+0.478332 
## [8]  train-rmse:2.016147+0.047149    test-rmse:2.685580+0.472298 
## [9]  train-rmse:1.882975+0.044296    test-rmse:2.597573+0.463885 
## [10] train-rmse:1.779264+0.039236    test-rmse:2.522911+0.475561 
## [11] train-rmse:1.671183+0.039358    test-rmse:2.453639+0.455788 
## [12] train-rmse:1.601580+0.042894    test-rmse:2.417463+0.462821 
## [13] train-rmse:1.529978+0.047298    test-rmse:2.359642+0.459251 
## [14] train-rmse:1.471285+0.037761    test-rmse:2.335634+0.470924 
## [15] train-rmse:1.412907+0.032853    test-rmse:2.308711+0.474931 
## [16] train-rmse:1.357557+0.034699    test-rmse:2.277386+0.443014 
## [17] train-rmse:1.302130+0.034016    test-rmse:2.268024+0.454087 
## [18] train-rmse:1.258709+0.031424    test-rmse:2.252548+0.455470 
## [19] train-rmse:1.222990+0.028070    test-rmse:2.238228+0.452563 
## [20] train-rmse:1.181424+0.032958    test-rmse:2.226833+0.460844 
## [21] train-rmse:1.150967+0.036022    test-rmse:2.214339+0.470967 
## [22] train-rmse:1.112221+0.039904    test-rmse:2.210187+0.452073 
## [23] train-rmse:1.069129+0.050121    test-rmse:2.186436+0.443611 
## [24] train-rmse:1.043867+0.046026    test-rmse:2.183169+0.447580 
## [25] train-rmse:1.005791+0.035488    test-rmse:2.179498+0.450341 
## [26] train-rmse:0.984163+0.036266    test-rmse:2.177091+0.450857 
## [27] train-rmse:0.959109+0.036667    test-rmse:2.158981+0.457067 
## [28] train-rmse:0.934109+0.031058    test-rmse:2.150911+0.452526 
## [29] train-rmse:0.904973+0.023984    test-rmse:2.144266+0.452201 
## [30] train-rmse:0.881709+0.030958    test-rmse:2.139176+0.456421 
## [31] train-rmse:0.860371+0.032742    test-rmse:2.137174+0.447591 
## [32] train-rmse:0.840658+0.032709    test-rmse:2.135624+0.453118 
## [33] train-rmse:0.813339+0.032608    test-rmse:2.126959+0.456952 
## [34] train-rmse:0.798025+0.031771    test-rmse:2.123233+0.455069 
## [35] train-rmse:0.779044+0.035179    test-rmse:2.117826+0.452197 
## [36] train-rmse:0.765437+0.033323    test-rmse:2.116804+0.453048 
## [37] train-rmse:0.740549+0.028150    test-rmse:2.110605+0.448947 
## [38] train-rmse:0.727211+0.030730    test-rmse:2.106372+0.447166 
## [39] train-rmse:0.711216+0.028718    test-rmse:2.103537+0.446421 
## [40] train-rmse:0.687766+0.034202    test-rmse:2.100930+0.446646 
## [41] train-rmse:0.667508+0.037205    test-rmse:2.101393+0.442896 
## [42] train-rmse:0.647442+0.036550    test-rmse:2.102979+0.450645 
## [43] train-rmse:0.635508+0.035277    test-rmse:2.100229+0.447410 
## [44] train-rmse:0.619927+0.035732    test-rmse:2.109942+0.451442 
## [45] train-rmse:0.600401+0.039658    test-rmse:2.113847+0.452654 
## [46] train-rmse:0.587450+0.037159    test-rmse:2.110429+0.450111 
## [47] train-rmse:0.578668+0.035731    test-rmse:2.110958+0.449742 
## [48] train-rmse:0.562060+0.033763    test-rmse:2.106721+0.449543 
## [49] train-rmse:0.549247+0.034995    test-rmse:2.102713+0.454353 
## Stopping. Best iteration:
## [43] train-rmse:0.635508+0.035277    test-rmse:2.100229+0.447410
## 
## 74 
## [1]  train-rmse:6.089779+0.120308    test-rmse:6.119827+0.554591 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.626522+0.090243    test-rmse:4.756353+0.471317 
## [3]  train-rmse:3.650652+0.076005    test-rmse:3.935621+0.426962 
## [4]  train-rmse:2.983212+0.076861    test-rmse:3.402458+0.406868 
## [5]  train-rmse:2.515084+0.061969    test-rmse:3.046408+0.463181 
## [6]  train-rmse:2.203351+0.072193    test-rmse:2.864374+0.486371 
## [7]  train-rmse:1.976028+0.057388    test-rmse:2.716593+0.490040 
## [8]  train-rmse:1.815772+0.059805    test-rmse:2.647659+0.474452 
## [9]  train-rmse:1.664704+0.062563    test-rmse:2.575376+0.505888 
## [10] train-rmse:1.566754+0.075823    test-rmse:2.525069+0.515376 
## [11] train-rmse:1.468273+0.057067    test-rmse:2.480516+0.528460 
## [12] train-rmse:1.381059+0.076073    test-rmse:2.450964+0.531720 
## [13] train-rmse:1.306794+0.079513    test-rmse:2.425084+0.537690 
## [14] train-rmse:1.236700+0.064375    test-rmse:2.400619+0.546503 
## [15] train-rmse:1.178912+0.075507    test-rmse:2.379058+0.551973 
## [16] train-rmse:1.140541+0.078657    test-rmse:2.366544+0.559427 
## [17] train-rmse:1.087615+0.072191    test-rmse:2.356419+0.565737 
## [18] train-rmse:1.038739+0.063896    test-rmse:2.339721+0.561465 
## [19] train-rmse:0.993822+0.063246    test-rmse:2.334214+0.568720 
## [20] train-rmse:0.953609+0.042563    test-rmse:2.327115+0.572257 
## [21] train-rmse:0.925481+0.047913    test-rmse:2.318957+0.568073 
## [22] train-rmse:0.876801+0.038846    test-rmse:2.308136+0.565879 
## [23] train-rmse:0.843917+0.036377    test-rmse:2.296713+0.565782 
## [24] train-rmse:0.800278+0.046718    test-rmse:2.287932+0.575783 
## [25] train-rmse:0.769363+0.048163    test-rmse:2.279876+0.582320 
## [26] train-rmse:0.751662+0.049113    test-rmse:2.273616+0.582574 
## [27] train-rmse:0.724257+0.040897    test-rmse:2.264870+0.591516 
## [28] train-rmse:0.693393+0.030777    test-rmse:2.258801+0.586130 
## [29] train-rmse:0.667789+0.028233    test-rmse:2.258191+0.597302 
## [30] train-rmse:0.648556+0.032674    test-rmse:2.256661+0.594890 
## [31] train-rmse:0.631612+0.035396    test-rmse:2.250377+0.590034 
## [32] train-rmse:0.610583+0.030604    test-rmse:2.245318+0.594148 
## [33] train-rmse:0.590124+0.032964    test-rmse:2.241029+0.591521 
## [34] train-rmse:0.574462+0.027378    test-rmse:2.238520+0.594565 
## [35] train-rmse:0.556344+0.025865    test-rmse:2.234208+0.595934 
## [36] train-rmse:0.537940+0.033313    test-rmse:2.231558+0.595896 
## [37] train-rmse:0.524997+0.033603    test-rmse:2.233500+0.597776 
## [38] train-rmse:0.509181+0.033476    test-rmse:2.229159+0.592461 
## [39] train-rmse:0.494123+0.031811    test-rmse:2.229700+0.592897 
## [40] train-rmse:0.484495+0.031592    test-rmse:2.227224+0.592632 
## [41] train-rmse:0.468773+0.029763    test-rmse:2.228782+0.588515 
## [42] train-rmse:0.449297+0.027559    test-rmse:2.230645+0.591695 
## [43] train-rmse:0.433893+0.026986    test-rmse:2.232519+0.589549 
## [44] train-rmse:0.422185+0.029554    test-rmse:2.236282+0.593865 
## [45] train-rmse:0.406422+0.029782    test-rmse:2.235334+0.595049 
## [46] train-rmse:0.398834+0.028569    test-rmse:2.233153+0.595737 
## Stopping. Best iteration:
## [40] train-rmse:0.484495+0.031592    test-rmse:2.227224+0.592632
## 
## 75 
## [1]  train-rmse:6.051761+0.114000    test-rmse:6.096343+0.590011 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.563180+0.073078    test-rmse:4.750107+0.510557 
## [3]  train-rmse:3.542101+0.053168    test-rmse:3.905836+0.457469 
## [4]  train-rmse:2.845972+0.030370    test-rmse:3.374253+0.472945 
## [5]  train-rmse:2.377848+0.037986    test-rmse:3.043595+0.508443 
## [6]  train-rmse:2.035738+0.031255    test-rmse:2.834654+0.510281 
## [7]  train-rmse:1.782895+0.034437    test-rmse:2.688540+0.529856 
## [8]  train-rmse:1.603770+0.036404    test-rmse:2.610327+0.547821 
## [9]  train-rmse:1.466574+0.043898    test-rmse:2.527113+0.556742 
## [10] train-rmse:1.330890+0.052245    test-rmse:2.469725+0.575027 
## [11] train-rmse:1.226033+0.051776    test-rmse:2.460969+0.589931 
## [12] train-rmse:1.146959+0.060052    test-rmse:2.437531+0.598242 
## [13] train-rmse:1.087356+0.058495    test-rmse:2.415933+0.588786 
## [14] train-rmse:1.031822+0.055421    test-rmse:2.399070+0.589524 
## [15] train-rmse:0.971734+0.052129    test-rmse:2.385093+0.592730 
## [16] train-rmse:0.916849+0.050799    test-rmse:2.379448+0.591372 
## [17] train-rmse:0.877210+0.054572    test-rmse:2.365998+0.590355 
## [18] train-rmse:0.837808+0.050017    test-rmse:2.355029+0.595072 
## [19] train-rmse:0.792057+0.031314    test-rmse:2.355180+0.596325 
## [20] train-rmse:0.741488+0.035572    test-rmse:2.344169+0.601248 
## [21] train-rmse:0.698714+0.030983    test-rmse:2.337877+0.595971 
## [22] train-rmse:0.673379+0.027661    test-rmse:2.332336+0.603270 
## [23] train-rmse:0.644322+0.021411    test-rmse:2.333019+0.601770 
## [24] train-rmse:0.613514+0.021886    test-rmse:2.327259+0.600735 
## [25] train-rmse:0.593460+0.020521    test-rmse:2.322639+0.606884 
## [26] train-rmse:0.568846+0.023527    test-rmse:2.316378+0.604457 
## [27] train-rmse:0.541659+0.018714    test-rmse:2.313857+0.605225 
## [28] train-rmse:0.516895+0.023401    test-rmse:2.307541+0.605098 
## [29] train-rmse:0.490269+0.030320    test-rmse:2.309131+0.607495 
## [30] train-rmse:0.471523+0.030764    test-rmse:2.306097+0.601750 
## [31] train-rmse:0.447543+0.026246    test-rmse:2.310241+0.602299 
## [32] train-rmse:0.429840+0.027509    test-rmse:2.308829+0.604056 
## [33] train-rmse:0.408913+0.026096    test-rmse:2.307212+0.603398 
## [34] train-rmse:0.396426+0.019966    test-rmse:2.305927+0.603071 
## [35] train-rmse:0.380823+0.019722    test-rmse:2.299493+0.606616 
## [36] train-rmse:0.364641+0.019524    test-rmse:2.298794+0.606977 
## [37] train-rmse:0.351708+0.020729    test-rmse:2.299920+0.607354 
## [38] train-rmse:0.335527+0.025370    test-rmse:2.298594+0.608580 
## [39] train-rmse:0.322481+0.025027    test-rmse:2.297763+0.608953 
## [40] train-rmse:0.310191+0.025789    test-rmse:2.298117+0.609967 
## [41] train-rmse:0.298306+0.023777    test-rmse:2.297060+0.609694 
## [42] train-rmse:0.285524+0.020983    test-rmse:2.295131+0.607888 
## [43] train-rmse:0.277636+0.020436    test-rmse:2.296051+0.606291 
## [44] train-rmse:0.268755+0.020242    test-rmse:2.295320+0.606309 
## [45] train-rmse:0.254990+0.016059    test-rmse:2.290636+0.610549 
## [46] train-rmse:0.246236+0.017105    test-rmse:2.290601+0.611577 
## [47] train-rmse:0.237280+0.016210    test-rmse:2.290176+0.611518 
## [48] train-rmse:0.228500+0.017510    test-rmse:2.288236+0.610075 
## [49] train-rmse:0.221499+0.018375    test-rmse:2.288690+0.609619 
## [50] train-rmse:0.212129+0.016991    test-rmse:2.288212+0.610677 
## [51] train-rmse:0.203113+0.018956    test-rmse:2.289780+0.609671 
## [52] train-rmse:0.197168+0.017772    test-rmse:2.288468+0.608970 
## [53] train-rmse:0.187903+0.015630    test-rmse:2.287683+0.608400 
## [54] train-rmse:0.179296+0.014689    test-rmse:2.285547+0.608266 
## [55] train-rmse:0.173188+0.013756    test-rmse:2.285432+0.608192 
## [56] train-rmse:0.167003+0.011394    test-rmse:2.285606+0.608614 
## [57] train-rmse:0.159729+0.012795    test-rmse:2.282935+0.608357 
## [58] train-rmse:0.151956+0.012019    test-rmse:2.282848+0.606605 
## [59] train-rmse:0.147681+0.011112    test-rmse:2.282707+0.606669 
## [60] train-rmse:0.142078+0.010032    test-rmse:2.281901+0.607506 
## [61] train-rmse:0.137519+0.009789    test-rmse:2.281986+0.605617 
## [62] train-rmse:0.131991+0.009696    test-rmse:2.282065+0.605860 
## [63] train-rmse:0.127134+0.010365    test-rmse:2.281567+0.604961 
## [64] train-rmse:0.124184+0.009474    test-rmse:2.281605+0.604758 
## [65] train-rmse:0.118747+0.007662    test-rmse:2.281816+0.604311 
## [66] train-rmse:0.114572+0.007886    test-rmse:2.281472+0.604243 
## [67] train-rmse:0.110772+0.007772    test-rmse:2.281156+0.604488 
## [68] train-rmse:0.106359+0.007957    test-rmse:2.280935+0.604490 
## [69] train-rmse:0.102975+0.008095    test-rmse:2.280708+0.604804 
## [70] train-rmse:0.099034+0.009271    test-rmse:2.280501+0.604914 
## [71] train-rmse:0.095305+0.009943    test-rmse:2.280635+0.604760 
## [72] train-rmse:0.092913+0.010447    test-rmse:2.280684+0.604757 
## [73] train-rmse:0.089737+0.010970    test-rmse:2.281019+0.604584 
## [74] train-rmse:0.086638+0.010797    test-rmse:2.280662+0.605030 
## [75] train-rmse:0.082772+0.010598    test-rmse:2.280118+0.604770 
## [76] train-rmse:0.078772+0.009489    test-rmse:2.280353+0.604975 
## [77] train-rmse:0.076174+0.008880    test-rmse:2.280491+0.605162 
## [78] train-rmse:0.073944+0.009260    test-rmse:2.280596+0.605172 
## [79] train-rmse:0.070939+0.008176    test-rmse:2.280036+0.605127 
## [80] train-rmse:0.068016+0.007712    test-rmse:2.280500+0.604769 
## [81] train-rmse:0.066239+0.008173    test-rmse:2.280112+0.604497 
## [82] train-rmse:0.064528+0.008353    test-rmse:2.280069+0.604630 
## [83] train-rmse:0.062605+0.008012    test-rmse:2.279425+0.604571 
## [84] train-rmse:0.060419+0.007348    test-rmse:2.279075+0.604684 
## [85] train-rmse:0.058635+0.007051    test-rmse:2.279189+0.604594 
## [86] train-rmse:0.056658+0.007270    test-rmse:2.279044+0.604619 
## [87] train-rmse:0.054669+0.007043    test-rmse:2.279404+0.604680 
## [88] train-rmse:0.053174+0.006443    test-rmse:2.279206+0.604575 
## [89] train-rmse:0.051708+0.006326    test-rmse:2.278624+0.605109 
## [90] train-rmse:0.050468+0.006262    test-rmse:2.278619+0.604662 
## [91] train-rmse:0.048516+0.005666    test-rmse:2.278645+0.605285 
## [92] train-rmse:0.046859+0.005312    test-rmse:2.278952+0.605296 
## [93] train-rmse:0.045494+0.005295    test-rmse:2.278886+0.605297 
## [94] train-rmse:0.044358+0.005442    test-rmse:2.278700+0.605756 
## [95] train-rmse:0.042691+0.005615    test-rmse:2.278575+0.605891 
## [96] train-rmse:0.041570+0.005152    test-rmse:2.278614+0.605873 
## [97] train-rmse:0.040682+0.005128    test-rmse:2.278019+0.605352 
## [98] train-rmse:0.039789+0.005173    test-rmse:2.277846+0.605313 
## [99] train-rmse:0.038555+0.004995    test-rmse:2.277903+0.605301 
## [100]    train-rmse:0.037556+0.004916    test-rmse:2.277891+0.605205 
## [101]    train-rmse:0.036410+0.004511    test-rmse:2.277778+0.605242 
## [102]    train-rmse:0.035573+0.004350    test-rmse:2.277849+0.605189 
## [103]    train-rmse:0.034338+0.004522    test-rmse:2.277784+0.605302 
## [104]    train-rmse:0.033217+0.004517    test-rmse:2.277513+0.605139 
## [105]    train-rmse:0.032641+0.004748    test-rmse:2.277751+0.605025 
## [106]    train-rmse:0.031674+0.004790    test-rmse:2.277782+0.604951 
## [107]    train-rmse:0.030497+0.004294    test-rmse:2.277500+0.605017 
## [108]    train-rmse:0.029405+0.004027    test-rmse:2.277490+0.604884 
## [109]    train-rmse:0.028267+0.003573    test-rmse:2.277266+0.605098 
## [110]    train-rmse:0.027726+0.003489    test-rmse:2.277170+0.605160 
## [111]    train-rmse:0.026670+0.003476    test-rmse:2.277256+0.605150 
## [112]    train-rmse:0.025700+0.003509    test-rmse:2.277283+0.605014 
## [113]    train-rmse:0.025176+0.003618    test-rmse:2.277304+0.604965 
## [114]    train-rmse:0.024466+0.003502    test-rmse:2.277277+0.605066 
## [115]    train-rmse:0.023879+0.003549    test-rmse:2.277194+0.605092 
## [116]    train-rmse:0.023000+0.003086    test-rmse:2.277271+0.605077 
## Stopping. Best iteration:
## [110]    train-rmse:0.027726+0.003489    test-rmse:2.277170+0.605160
## 
## 76 
## [1]  train-rmse:6.038232+0.112841    test-rmse:6.088006+0.589619 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.520708+0.071716    test-rmse:4.732437+0.506595 
## [3]  train-rmse:3.477441+0.049009    test-rmse:3.838164+0.479654 
## [4]  train-rmse:2.765773+0.032800    test-rmse:3.330830+0.467369 
## [5]  train-rmse:2.262445+0.031523    test-rmse:3.035095+0.514645 
## [6]  train-rmse:1.916161+0.034448    test-rmse:2.833195+0.494681 
## [7]  train-rmse:1.671654+0.020353    test-rmse:2.713328+0.534666 
## [8]  train-rmse:1.479945+0.022461    test-rmse:2.604444+0.548247 
## [9]  train-rmse:1.336489+0.027099    test-rmse:2.543261+0.554841 
## [10] train-rmse:1.230381+0.022103    test-rmse:2.508360+0.564687 
## [11] train-rmse:1.136846+0.026466    test-rmse:2.492106+0.576256 
## [12] train-rmse:1.054494+0.032359    test-rmse:2.470515+0.586533 
## [13] train-rmse:0.965244+0.037138    test-rmse:2.455398+0.597539 
## [14] train-rmse:0.918239+0.037237    test-rmse:2.438933+0.597554 
## [15] train-rmse:0.849192+0.038625    test-rmse:2.418293+0.603289 
## [16] train-rmse:0.802941+0.029952    test-rmse:2.402499+0.616044 
## [17] train-rmse:0.746494+0.043128    test-rmse:2.402481+0.614934 
## [18] train-rmse:0.708438+0.040434    test-rmse:2.391358+0.617838 
## [19] train-rmse:0.667226+0.031399    test-rmse:2.384334+0.622289 
## [20] train-rmse:0.622442+0.023730    test-rmse:2.384533+0.620261 
## [21] train-rmse:0.582980+0.020363    test-rmse:2.382266+0.616522 
## [22] train-rmse:0.539595+0.022513    test-rmse:2.376322+0.618318 
## [23] train-rmse:0.511605+0.022437    test-rmse:2.377502+0.622480 
## [24] train-rmse:0.486115+0.021387    test-rmse:2.368706+0.621276 
## [25] train-rmse:0.459581+0.022250    test-rmse:2.365899+0.619411 
## [26] train-rmse:0.433498+0.026157    test-rmse:2.370087+0.624036 
## [27] train-rmse:0.406445+0.022302    test-rmse:2.362976+0.616754 
## [28] train-rmse:0.386488+0.018043    test-rmse:2.362241+0.616976 
## [29] train-rmse:0.358652+0.015028    test-rmse:2.357201+0.613551 
## [30] train-rmse:0.337892+0.016003    test-rmse:2.356389+0.611776 
## [31] train-rmse:0.322440+0.014260    test-rmse:2.353920+0.610013 
## [32] train-rmse:0.304542+0.013675    test-rmse:2.349484+0.611524 
## [33] train-rmse:0.292763+0.010681    test-rmse:2.347794+0.611776 
## [34] train-rmse:0.279979+0.013512    test-rmse:2.348545+0.611573 
## [35] train-rmse:0.269061+0.014931    test-rmse:2.350265+0.608398 
## [36] train-rmse:0.257127+0.011579    test-rmse:2.349781+0.608189 
## [37] train-rmse:0.242903+0.009893    test-rmse:2.347662+0.608398 
## [38] train-rmse:0.232269+0.009230    test-rmse:2.347359+0.608697 
## [39] train-rmse:0.220409+0.010072    test-rmse:2.347235+0.609880 
## [40] train-rmse:0.211350+0.011746    test-rmse:2.346089+0.608967 
## [41] train-rmse:0.204427+0.012129    test-rmse:2.346228+0.609301 
## [42] train-rmse:0.198010+0.010769    test-rmse:2.346542+0.609337 
## [43] train-rmse:0.184835+0.010733    test-rmse:2.346405+0.610753 
## [44] train-rmse:0.177664+0.010972    test-rmse:2.347255+0.610607 
## [45] train-rmse:0.165971+0.006732    test-rmse:2.344773+0.609829 
## [46] train-rmse:0.159428+0.004969    test-rmse:2.344293+0.610427 
## [47] train-rmse:0.153716+0.006734    test-rmse:2.346536+0.611447 
## [48] train-rmse:0.145686+0.006370    test-rmse:2.345948+0.611365 
## [49] train-rmse:0.137777+0.006497    test-rmse:2.346177+0.610795 
## [50] train-rmse:0.134107+0.007200    test-rmse:2.345213+0.611068 
## [51] train-rmse:0.128346+0.006003    test-rmse:2.345647+0.611135 
## [52] train-rmse:0.122569+0.005762    test-rmse:2.345201+0.611115 
## Stopping. Best iteration:
## [46] train-rmse:0.159428+0.004969    test-rmse:2.344293+0.610427
## 
## 77 
## [1]  train-rmse:6.363796+0.139139    test-rmse:6.368280+0.649489 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.232691+0.111457    test-rmse:5.308688+0.686633 
## [3]  train-rmse:4.556026+0.115034    test-rmse:4.638275+0.593986 
## [4]  train-rmse:4.152690+0.116060    test-rmse:4.245878+0.540345 
## [5]  train-rmse:3.893952+0.113966    test-rmse:4.018195+0.466463 
## [6]  train-rmse:3.714861+0.107471    test-rmse:3.845173+0.450971 
## [7]  train-rmse:3.579942+0.109627    test-rmse:3.646706+0.439389 
## [8]  train-rmse:3.468678+0.119718    test-rmse:3.591259+0.416433 
## [9]  train-rmse:3.380414+0.123869    test-rmse:3.521102+0.399376 
## [10] train-rmse:3.303574+0.122204    test-rmse:3.486803+0.418643 
## [11] train-rmse:3.231460+0.119522    test-rmse:3.400830+0.445330 
## [12] train-rmse:3.165281+0.121207    test-rmse:3.317344+0.455046 
## [13] train-rmse:3.107017+0.124765    test-rmse:3.225932+0.410972 
## [14] train-rmse:3.054910+0.126617    test-rmse:3.180892+0.418478 
## [15] train-rmse:3.007293+0.130474    test-rmse:3.169548+0.447105 
## [16] train-rmse:2.964778+0.129447    test-rmse:3.152780+0.465833 
## [17] train-rmse:2.924032+0.128988    test-rmse:3.118863+0.463812 
## [18] train-rmse:2.886690+0.129126    test-rmse:3.079654+0.439654 
## [19] train-rmse:2.851238+0.131693    test-rmse:3.054462+0.428764 
## [20] train-rmse:2.817082+0.134587    test-rmse:3.015174+0.425617 
## [21] train-rmse:2.784612+0.136331    test-rmse:2.999552+0.430667 
## [22] train-rmse:2.754573+0.138149    test-rmse:2.961155+0.437266 
## [23] train-rmse:2.724766+0.140131    test-rmse:2.944061+0.461658 
## [24] train-rmse:2.696586+0.141116    test-rmse:2.926374+0.468923 
## [25] train-rmse:2.669086+0.141307    test-rmse:2.880884+0.466967 
## [26] train-rmse:2.643852+0.140352    test-rmse:2.868501+0.476619 
## [27] train-rmse:2.618449+0.138784    test-rmse:2.837310+0.466176 
## [28] train-rmse:2.594398+0.138580    test-rmse:2.815770+0.464189 
## [29] train-rmse:2.571835+0.137682    test-rmse:2.795526+0.474869 
## [30] train-rmse:2.550843+0.138156    test-rmse:2.793116+0.467078 
## [31] train-rmse:2.530390+0.138625    test-rmse:2.784730+0.490436 
## [32] train-rmse:2.510389+0.138637    test-rmse:2.785744+0.484454 
## [33] train-rmse:2.490978+0.139103    test-rmse:2.761502+0.480147 
## [34] train-rmse:2.472293+0.140039    test-rmse:2.751039+0.476899 
## [35] train-rmse:2.454228+0.140945    test-rmse:2.730683+0.489235 
## [36] train-rmse:2.437045+0.141675    test-rmse:2.706900+0.483855 
## [37] train-rmse:2.420686+0.141792    test-rmse:2.687568+0.492794 
## [38] train-rmse:2.405143+0.142322    test-rmse:2.676502+0.486163 
## [39] train-rmse:2.389782+0.142445    test-rmse:2.658956+0.496292 
## [40] train-rmse:2.375328+0.142584    test-rmse:2.645517+0.493801 
## [41] train-rmse:2.361565+0.142421    test-rmse:2.619676+0.492386 
## [42] train-rmse:2.348649+0.142556    test-rmse:2.605560+0.493591 
## [43] train-rmse:2.336152+0.142592    test-rmse:2.599720+0.495208 
## [44] train-rmse:2.323878+0.142682    test-rmse:2.597922+0.498442 
## [45] train-rmse:2.311955+0.143380    test-rmse:2.585405+0.494375 
## [46] train-rmse:2.300449+0.143674    test-rmse:2.585429+0.497934 
## [47] train-rmse:2.289114+0.144026    test-rmse:2.580394+0.495732 
## [48] train-rmse:2.278380+0.144052    test-rmse:2.566397+0.503045 
## [49] train-rmse:2.268026+0.144261    test-rmse:2.553466+0.496761 
## [50] train-rmse:2.257754+0.144576    test-rmse:2.541720+0.495998 
## [51] train-rmse:2.247630+0.145188    test-rmse:2.543272+0.507740 
## [52] train-rmse:2.238062+0.145877    test-rmse:2.538274+0.515891 
## [53] train-rmse:2.228416+0.146581    test-rmse:2.530390+0.516180 
## [54] train-rmse:2.219547+0.146869    test-rmse:2.516612+0.511478 
## [55] train-rmse:2.210842+0.147215    test-rmse:2.502443+0.517923 
## [56] train-rmse:2.202328+0.147525    test-rmse:2.508586+0.522063 
## [57] train-rmse:2.194186+0.147925    test-rmse:2.503170+0.520522 
## [58] train-rmse:2.186488+0.148155    test-rmse:2.496002+0.526667 
## [59] train-rmse:2.178959+0.148613    test-rmse:2.490682+0.524854 
## [60] train-rmse:2.171586+0.149007    test-rmse:2.490940+0.529451 
## [61] train-rmse:2.164569+0.149122    test-rmse:2.486921+0.526153 
## [62] train-rmse:2.157640+0.149375    test-rmse:2.483440+0.521511 
## [63] train-rmse:2.150980+0.149598    test-rmse:2.473948+0.522430 
## [64] train-rmse:2.144347+0.149857    test-rmse:2.462521+0.526370 
## [65] train-rmse:2.137967+0.150027    test-rmse:2.457130+0.526874 
## [66] train-rmse:2.131670+0.150297    test-rmse:2.455206+0.521557 
## [67] train-rmse:2.125430+0.150668    test-rmse:2.441629+0.517828 
## [68] train-rmse:2.119440+0.151059    test-rmse:2.443516+0.530187 
## [69] train-rmse:2.113514+0.151495    test-rmse:2.443093+0.535225 
## [70] train-rmse:2.107738+0.151691    test-rmse:2.438845+0.535959 
## [71] train-rmse:2.102001+0.151887    test-rmse:2.429845+0.539288 
## [72] train-rmse:2.096473+0.152240    test-rmse:2.426480+0.538878 
## [73] train-rmse:2.091176+0.152353    test-rmse:2.422005+0.544350 
## [74] train-rmse:2.085962+0.152517    test-rmse:2.425595+0.547829 
## [75] train-rmse:2.080612+0.152639    test-rmse:2.417830+0.542247 
## [76] train-rmse:2.075601+0.152861    test-rmse:2.412268+0.542439 
## [77] train-rmse:2.070793+0.153302    test-rmse:2.406108+0.538898 
## [78] train-rmse:2.066177+0.153545    test-rmse:2.402348+0.540118 
## [79] train-rmse:2.061611+0.153696    test-rmse:2.405686+0.536646 
## [80] train-rmse:2.057230+0.153946    test-rmse:2.406958+0.535036 
## [81] train-rmse:2.052768+0.154009    test-rmse:2.399909+0.539173 
## [82] train-rmse:2.048492+0.154009    test-rmse:2.399663+0.537343 
## [83] train-rmse:2.044324+0.154135    test-rmse:2.396338+0.539004 
## [84] train-rmse:2.040232+0.154135    test-rmse:2.392330+0.531976 
## [85] train-rmse:2.035997+0.154340    test-rmse:2.393995+0.539955 
## [86] train-rmse:2.032060+0.154528    test-rmse:2.390246+0.537410 
## [87] train-rmse:2.028156+0.154676    test-rmse:2.384134+0.539094 
## [88] train-rmse:2.024318+0.154809    test-rmse:2.379773+0.534890 
## [89] train-rmse:2.020596+0.154911    test-rmse:2.377911+0.533438 
## [90] train-rmse:2.016932+0.155095    test-rmse:2.372961+0.533457 
## [91] train-rmse:2.013246+0.155112    test-rmse:2.375144+0.530826 
## [92] train-rmse:2.009627+0.155230    test-rmse:2.386769+0.539681 
## [93] train-rmse:2.006008+0.155314    test-rmse:2.378426+0.539490 
## [94] train-rmse:2.002483+0.155494    test-rmse:2.376906+0.538544 
## [95] train-rmse:1.999074+0.155704    test-rmse:2.375023+0.539084 
## [96] train-rmse:1.995753+0.155878    test-rmse:2.368074+0.538053 
## [97] train-rmse:1.992509+0.156084    test-rmse:2.365259+0.543618 
## [98] train-rmse:1.989243+0.156277    test-rmse:2.365872+0.543790 
## [99] train-rmse:1.986110+0.156355    test-rmse:2.359234+0.544096 
## [100]    train-rmse:1.982878+0.156596    test-rmse:2.356578+0.545301 
## [101]    train-rmse:1.979872+0.156777    test-rmse:2.358516+0.549864 
## [102]    train-rmse:1.976818+0.156959    test-rmse:2.356254+0.549551 
## [103]    train-rmse:1.973807+0.157220    test-rmse:2.353430+0.549583 
## [104]    train-rmse:1.970899+0.157398    test-rmse:2.348757+0.547292 
## [105]    train-rmse:1.967965+0.157745    test-rmse:2.348129+0.551794 
## [106]    train-rmse:1.965177+0.157902    test-rmse:2.352408+0.547791 
## [107]    train-rmse:1.962297+0.158016    test-rmse:2.350336+0.551766 
## [108]    train-rmse:1.959542+0.158152    test-rmse:2.347268+0.553417 
## [109]    train-rmse:1.956665+0.158239    test-rmse:2.346453+0.553098 
## [110]    train-rmse:1.953981+0.158334    test-rmse:2.341311+0.555740 
## [111]    train-rmse:1.951290+0.158540    test-rmse:2.340085+0.559278 
## [112]    train-rmse:1.948722+0.158600    test-rmse:2.337790+0.556157 
## [113]    train-rmse:1.945956+0.158691    test-rmse:2.333192+0.554680 
## [114]    train-rmse:1.943174+0.158714    test-rmse:2.334157+0.554780 
## [115]    train-rmse:1.940679+0.158887    test-rmse:2.336475+0.557175 
## [116]    train-rmse:1.938219+0.158983    test-rmse:2.330136+0.557267 
## [117]    train-rmse:1.935651+0.158937    test-rmse:2.329304+0.559434 
## [118]    train-rmse:1.933266+0.159067    test-rmse:2.329380+0.555868 
## [119]    train-rmse:1.930802+0.159056    test-rmse:2.325597+0.555018 
## [120]    train-rmse:1.928501+0.159137    test-rmse:2.326531+0.555112 
## [121]    train-rmse:1.926116+0.159429    test-rmse:2.323204+0.552988 
## [122]    train-rmse:1.923793+0.159561    test-rmse:2.324695+0.549967 
## [123]    train-rmse:1.921389+0.159535    test-rmse:2.323617+0.549106 
## [124]    train-rmse:1.919192+0.159637    test-rmse:2.323279+0.547627 
## [125]    train-rmse:1.917017+0.159784    test-rmse:2.326062+0.544772 
## [126]    train-rmse:1.914834+0.159937    test-rmse:2.324001+0.545052 
## [127]    train-rmse:1.912673+0.160050    test-rmse:2.328109+0.547426 
## Stopping. Best iteration:
## [121]    train-rmse:1.926116+0.159429    test-rmse:2.323204+0.552988
## 
## 78 
## [1]  train-rmse:6.214311+0.129047    test-rmse:6.264728+0.604203 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.949299+0.121857    test-rmse:5.031736+0.570197 
## [3]  train-rmse:4.132590+0.097088    test-rmse:4.250730+0.474621 
## [4]  train-rmse:3.592045+0.090063    test-rmse:3.764424+0.462262 
## [5]  train-rmse:3.251185+0.097737    test-rmse:3.496949+0.438217 
## [6]  train-rmse:3.008328+0.092729    test-rmse:3.241106+0.449737 
## [7]  train-rmse:2.822797+0.074921    test-rmse:3.084713+0.482502 
## [8]  train-rmse:2.690180+0.081513    test-rmse:2.997680+0.472678 
## [9]  train-rmse:2.593382+0.083635    test-rmse:2.920205+0.463957 
## [10] train-rmse:2.499453+0.090814    test-rmse:2.870366+0.438153 
## [11] train-rmse:2.410600+0.090387    test-rmse:2.803848+0.471672 
## [12] train-rmse:2.328520+0.099969    test-rmse:2.746487+0.476789 
## [13] train-rmse:2.265303+0.100277    test-rmse:2.705709+0.478050 
## [14] train-rmse:2.197751+0.078198    test-rmse:2.657618+0.512842 
## [15] train-rmse:2.150642+0.078414    test-rmse:2.652172+0.540604 
## [16] train-rmse:2.106671+0.078118    test-rmse:2.618756+0.549762 
## [17] train-rmse:2.061760+0.082482    test-rmse:2.583214+0.545735 
## [18] train-rmse:2.013977+0.069612    test-rmse:2.547829+0.554302 
## [19] train-rmse:1.968135+0.076793    test-rmse:2.503020+0.555228 
## [20] train-rmse:1.925756+0.075806    test-rmse:2.500766+0.550159 
## [21] train-rmse:1.891022+0.074991    test-rmse:2.491685+0.547798 
## [22] train-rmse:1.849241+0.065595    test-rmse:2.486882+0.550184 
## [23] train-rmse:1.814106+0.063998    test-rmse:2.468451+0.551453 
## [24] train-rmse:1.780579+0.066338    test-rmse:2.449799+0.550274 
## [25] train-rmse:1.756030+0.065613    test-rmse:2.430905+0.567578 
## [26] train-rmse:1.729523+0.068291    test-rmse:2.406331+0.551090 
## [27] train-rmse:1.706005+0.068605    test-rmse:2.377693+0.558867 
## [28] train-rmse:1.680247+0.070654    test-rmse:2.362886+0.549727 
## [29] train-rmse:1.657058+0.072657    test-rmse:2.349591+0.538611 
## [30] train-rmse:1.621656+0.058158    test-rmse:2.330506+0.534672 
## [31] train-rmse:1.591569+0.054230    test-rmse:2.313523+0.548537 
## [32] train-rmse:1.570608+0.054909    test-rmse:2.307226+0.560863 
## [33] train-rmse:1.553023+0.058348    test-rmse:2.291009+0.549433 
## [34] train-rmse:1.535904+0.058854    test-rmse:2.281809+0.558697 
## [35] train-rmse:1.519673+0.059036    test-rmse:2.276730+0.557531 
## [36] train-rmse:1.501798+0.060999    test-rmse:2.275917+0.554893 
## [37] train-rmse:1.482137+0.057386    test-rmse:2.266114+0.563699 
## [38] train-rmse:1.465140+0.060182    test-rmse:2.270949+0.556345 
## [39] train-rmse:1.447181+0.050783    test-rmse:2.263654+0.552913 
## [40] train-rmse:1.435140+0.051291    test-rmse:2.261718+0.545389 
## [41] train-rmse:1.418528+0.052460    test-rmse:2.255275+0.541307 
## [42] train-rmse:1.404623+0.053902    test-rmse:2.245992+0.535027 
## [43] train-rmse:1.384935+0.050678    test-rmse:2.243616+0.535743 
## [44] train-rmse:1.372174+0.049679    test-rmse:2.237259+0.538767 
## [45] train-rmse:1.360991+0.051114    test-rmse:2.244612+0.544397 
## [46] train-rmse:1.351510+0.052046    test-rmse:2.241158+0.545231 
## [47] train-rmse:1.338348+0.055447    test-rmse:2.233186+0.541768 
## [48] train-rmse:1.327040+0.054527    test-rmse:2.227774+0.536135 
## [49] train-rmse:1.317331+0.056767    test-rmse:2.223798+0.544581 
## [50] train-rmse:1.308567+0.057014    test-rmse:2.217367+0.549011 
## [51] train-rmse:1.300782+0.056802    test-rmse:2.210843+0.548318 
## [52] train-rmse:1.288772+0.055592    test-rmse:2.203588+0.555185 
## [53] train-rmse:1.280085+0.055467    test-rmse:2.200173+0.551738 
## [54] train-rmse:1.271149+0.056495    test-rmse:2.196312+0.555199 
## [55] train-rmse:1.259780+0.061106    test-rmse:2.193486+0.554314 
## [56] train-rmse:1.245617+0.057931    test-rmse:2.195229+0.562121 
## [57] train-rmse:1.239303+0.058662    test-rmse:2.189092+0.560281 
## [58] train-rmse:1.226057+0.058041    test-rmse:2.192111+0.562389 
## [59] train-rmse:1.212991+0.055403    test-rmse:2.185325+0.566218 
## [60] train-rmse:1.202999+0.059041    test-rmse:2.189433+0.569403 
## [61] train-rmse:1.193238+0.059563    test-rmse:2.188106+0.569111 
## [62] train-rmse:1.183726+0.061245    test-rmse:2.193549+0.565047 
## [63] train-rmse:1.173815+0.064819    test-rmse:2.190062+0.571837 
## [64] train-rmse:1.167086+0.066229    test-rmse:2.189985+0.570176 
## [65] train-rmse:1.160728+0.065836    test-rmse:2.188575+0.570680 
## Stopping. Best iteration:
## [59] train-rmse:1.212991+0.055403    test-rmse:2.185325+0.566218
## 
## 79 
## [1]  train-rmse:6.113711+0.121138    test-rmse:6.222824+0.589053 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.758180+0.091976    test-rmse:4.894820+0.539192 
## [3]  train-rmse:3.829946+0.095494    test-rmse:4.080573+0.438391 
## [4]  train-rmse:3.213847+0.048186    test-rmse:3.476400+0.469847 
## [5]  train-rmse:2.834305+0.066084    test-rmse:3.183145+0.425033 
## [6]  train-rmse:2.567432+0.052731    test-rmse:2.952621+0.429645 
## [7]  train-rmse:2.374622+0.054209    test-rmse:2.831875+0.476524 
## [8]  train-rmse:2.216490+0.045175    test-rmse:2.723317+0.459392 
## [9]  train-rmse:2.110269+0.046746    test-rmse:2.679055+0.467296 
## [10] train-rmse:2.010670+0.048323    test-rmse:2.609863+0.462904 
## [11] train-rmse:1.905226+0.052218    test-rmse:2.560287+0.433967 
## [12] train-rmse:1.835114+0.044627    test-rmse:2.518494+0.415540 
## [13] train-rmse:1.755589+0.042732    test-rmse:2.478891+0.417917 
## [14] train-rmse:1.685781+0.042180    test-rmse:2.443544+0.420526 
## [15] train-rmse:1.634933+0.045276    test-rmse:2.422312+0.434389 
## [16] train-rmse:1.592521+0.044669    test-rmse:2.389067+0.438819 
## [17] train-rmse:1.544321+0.049283    test-rmse:2.362923+0.448462 
## [18] train-rmse:1.499284+0.031890    test-rmse:2.337471+0.444448 
## [19] train-rmse:1.446938+0.026828    test-rmse:2.329117+0.434297 
## [20] train-rmse:1.410082+0.030058    test-rmse:2.310936+0.437049 
## [21] train-rmse:1.373110+0.033761    test-rmse:2.283550+0.437982 
## [22] train-rmse:1.337386+0.037373    test-rmse:2.277681+0.450511 
## [23] train-rmse:1.308359+0.029653    test-rmse:2.265094+0.443505 
## [24] train-rmse:1.279910+0.033459    test-rmse:2.260662+0.438735 
## [25] train-rmse:1.245147+0.029252    test-rmse:2.256720+0.438471 
## [26] train-rmse:1.219957+0.031334    test-rmse:2.246801+0.436280 
## [27] train-rmse:1.194868+0.029101    test-rmse:2.245169+0.442170 
## [28] train-rmse:1.164481+0.032449    test-rmse:2.226410+0.449048 
## [29] train-rmse:1.143772+0.034048    test-rmse:2.213237+0.450368 
## [30] train-rmse:1.118195+0.034381    test-rmse:2.213129+0.454887 
## [31] train-rmse:1.093344+0.031109    test-rmse:2.209571+0.461275 
## [32] train-rmse:1.074814+0.033387    test-rmse:2.207329+0.457320 
## [33] train-rmse:1.051021+0.023062    test-rmse:2.199139+0.458049 
## [34] train-rmse:1.032607+0.023196    test-rmse:2.193118+0.459692 
## [35] train-rmse:1.017547+0.017873    test-rmse:2.183333+0.464073 
## [36] train-rmse:0.996639+0.022940    test-rmse:2.165859+0.450915 
## [37] train-rmse:0.979446+0.021048    test-rmse:2.155964+0.453154 
## [38] train-rmse:0.961931+0.022074    test-rmse:2.153117+0.461979 
## [39] train-rmse:0.943167+0.026558    test-rmse:2.153587+0.470262 
## [40] train-rmse:0.930463+0.026089    test-rmse:2.153702+0.471615 
## [41] train-rmse:0.911466+0.027256    test-rmse:2.143324+0.470379 
## [42] train-rmse:0.898205+0.025508    test-rmse:2.143989+0.469630 
## [43] train-rmse:0.884227+0.027729    test-rmse:2.136888+0.466015 
## [44] train-rmse:0.870421+0.027090    test-rmse:2.138632+0.466858 
## [45] train-rmse:0.847654+0.032443    test-rmse:2.132236+0.469499 
## [46] train-rmse:0.835303+0.032551    test-rmse:2.128367+0.475923 
## [47] train-rmse:0.817510+0.034296    test-rmse:2.124024+0.474812 
## [48] train-rmse:0.803177+0.036155    test-rmse:2.113151+0.479569 
## [49] train-rmse:0.793260+0.034706    test-rmse:2.113179+0.484113 
## [50] train-rmse:0.783170+0.035400    test-rmse:2.109418+0.483992 
## [51] train-rmse:0.772315+0.036291    test-rmse:2.106836+0.481526 
## [52] train-rmse:0.761697+0.038903    test-rmse:2.105360+0.481933 
## [53] train-rmse:0.748254+0.039995    test-rmse:2.102434+0.481738 
## [54] train-rmse:0.737581+0.031530    test-rmse:2.104588+0.481088 
## [55] train-rmse:0.725869+0.032460    test-rmse:2.104572+0.485093 
## [56] train-rmse:0.714201+0.032860    test-rmse:2.102129+0.485288 
## [57] train-rmse:0.705086+0.034279    test-rmse:2.101914+0.485447 
## [58] train-rmse:0.697725+0.034271    test-rmse:2.101007+0.487440 
## [59] train-rmse:0.689252+0.034768    test-rmse:2.100147+0.484896 
## [60] train-rmse:0.679079+0.033292    test-rmse:2.098705+0.483981 
## [61] train-rmse:0.667823+0.032123    test-rmse:2.094395+0.487032 
## [62] train-rmse:0.656062+0.028306    test-rmse:2.091112+0.485718 
## [63] train-rmse:0.646499+0.022355    test-rmse:2.088104+0.484418 
## [64] train-rmse:0.635422+0.020160    test-rmse:2.082800+0.481667 
## [65] train-rmse:0.623609+0.024207    test-rmse:2.076391+0.479835 
## [66] train-rmse:0.615766+0.024114    test-rmse:2.076710+0.481380 
## [67] train-rmse:0.606071+0.020974    test-rmse:2.076894+0.484543 
## [68] train-rmse:0.597238+0.021599    test-rmse:2.077898+0.484001 
## [69] train-rmse:0.590959+0.018188    test-rmse:2.082154+0.485034 
## [70] train-rmse:0.587650+0.017829    test-rmse:2.081775+0.485955 
## [71] train-rmse:0.578212+0.017163    test-rmse:2.079719+0.484150 
## Stopping. Best iteration:
## [65] train-rmse:0.623609+0.024207    test-rmse:2.076391+0.479835
## 
## 80 
## [1]  train-rmse:6.018722+0.119823    test-rmse:6.097063+0.573756 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.563952+0.093388    test-rmse:4.750587+0.471730 
## [3]  train-rmse:3.608392+0.058406    test-rmse:3.894321+0.453316 
## [4]  train-rmse:2.993446+0.066228    test-rmse:3.384016+0.431604 
## [5]  train-rmse:2.578013+0.050995    test-rmse:3.055002+0.472135 
## [6]  train-rmse:2.294621+0.049077    test-rmse:2.884130+0.457388 
## [7]  train-rmse:2.085643+0.043693    test-rmse:2.721847+0.437522 
## [8]  train-rmse:1.918341+0.043871    test-rmse:2.642145+0.442970 
## [9]  train-rmse:1.807880+0.043041    test-rmse:2.557215+0.463154 
## [10] train-rmse:1.708469+0.042463    test-rmse:2.521852+0.448165 
## [11] train-rmse:1.606400+0.030919    test-rmse:2.495378+0.464680 
## [12] train-rmse:1.532528+0.031739    test-rmse:2.475445+0.466473 
## [13] train-rmse:1.468978+0.026244    test-rmse:2.434070+0.472302 
## [14] train-rmse:1.412642+0.025377    test-rmse:2.423899+0.463823 
## [15] train-rmse:1.361185+0.034813    test-rmse:2.397147+0.471033 
## [16] train-rmse:1.316013+0.027383    test-rmse:2.379050+0.465259 
## [17] train-rmse:1.262724+0.025133    test-rmse:2.349034+0.460876 
## [18] train-rmse:1.226023+0.029057    test-rmse:2.332884+0.467739 
## [19] train-rmse:1.191468+0.036256    test-rmse:2.328214+0.476389 
## [20] train-rmse:1.147312+0.031454    test-rmse:2.317561+0.487776 
## [21] train-rmse:1.104847+0.026424    test-rmse:2.310551+0.490497 
## [22] train-rmse:1.065705+0.036054    test-rmse:2.302346+0.485999 
## [23] train-rmse:1.037611+0.041209    test-rmse:2.290548+0.492160 
## [24] train-rmse:1.002810+0.041899    test-rmse:2.291192+0.502092 
## [25] train-rmse:0.960594+0.046836    test-rmse:2.284995+0.504577 
## [26] train-rmse:0.932639+0.044986    test-rmse:2.270268+0.497596 
## [27] train-rmse:0.902760+0.036416    test-rmse:2.246803+0.499636 
## [28] train-rmse:0.878839+0.039608    test-rmse:2.240829+0.507940 
## [29] train-rmse:0.862097+0.039018    test-rmse:2.240822+0.512894 
## [30] train-rmse:0.842554+0.041863    test-rmse:2.238433+0.508742 
## [31] train-rmse:0.821839+0.040920    test-rmse:2.229001+0.505582 
## [32] train-rmse:0.803211+0.039167    test-rmse:2.226164+0.506711 
## [33] train-rmse:0.779364+0.030557    test-rmse:2.218274+0.506769 
## [34] train-rmse:0.750901+0.021408    test-rmse:2.221471+0.506321 
## [35] train-rmse:0.728443+0.032100    test-rmse:2.212892+0.508330 
## [36] train-rmse:0.711750+0.037585    test-rmse:2.205437+0.509361 
## [37] train-rmse:0.694821+0.031709    test-rmse:2.203038+0.509087 
## [38] train-rmse:0.678084+0.031089    test-rmse:2.199778+0.515230 
## [39] train-rmse:0.663463+0.032263    test-rmse:2.196745+0.516296 
## [40] train-rmse:0.640108+0.028503    test-rmse:2.193907+0.516018 
## [41] train-rmse:0.624243+0.028742    test-rmse:2.193025+0.516784 
## [42] train-rmse:0.612502+0.026654    test-rmse:2.194466+0.516250 
## [43] train-rmse:0.596971+0.025731    test-rmse:2.198674+0.525016 
## [44] train-rmse:0.582539+0.028686    test-rmse:2.195062+0.524265 
## [45] train-rmse:0.561022+0.023363    test-rmse:2.186488+0.526596 
## [46] train-rmse:0.550819+0.024782    test-rmse:2.184830+0.525208 
## [47] train-rmse:0.537289+0.027010    test-rmse:2.179161+0.523865 
## [48] train-rmse:0.523694+0.021653    test-rmse:2.178621+0.525183 
## [49] train-rmse:0.508402+0.016994    test-rmse:2.178252+0.524941 
## [50] train-rmse:0.496348+0.022914    test-rmse:2.175169+0.523890 
## [51] train-rmse:0.489214+0.021768    test-rmse:2.178238+0.522363 
## [52] train-rmse:0.479075+0.021527    test-rmse:2.178347+0.523344 
## [53] train-rmse:0.466226+0.019161    test-rmse:2.178242+0.521783 
## [54] train-rmse:0.457318+0.021219    test-rmse:2.178146+0.520819 
## [55] train-rmse:0.450296+0.021498    test-rmse:2.176310+0.520686 
## [56] train-rmse:0.438395+0.018453    test-rmse:2.179174+0.522228 
## Stopping. Best iteration:
## [50] train-rmse:0.496348+0.022914    test-rmse:2.175169+0.523890
## 
## 81 
## [1]  train-rmse:5.953084+0.116924    test-rmse:5.989284+0.545674 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.442407+0.086882    test-rmse:4.589337+0.462092 
## [3]  train-rmse:3.466519+0.068847    test-rmse:3.777814+0.443144 
## [4]  train-rmse:2.819646+0.059843    test-rmse:3.290062+0.427409 
## [5]  train-rmse:2.385736+0.052110    test-rmse:3.001219+0.503987 
## [6]  train-rmse:2.083462+0.065209    test-rmse:2.840872+0.491270 
## [7]  train-rmse:1.853174+0.056359    test-rmse:2.696515+0.506714 
## [8]  train-rmse:1.706597+0.057758    test-rmse:2.618051+0.503100 
## [9]  train-rmse:1.560463+0.077449    test-rmse:2.586950+0.508754 
## [10] train-rmse:1.473147+0.075164    test-rmse:2.549518+0.527799 
## [11] train-rmse:1.382980+0.069231    test-rmse:2.502665+0.519738 
## [12] train-rmse:1.290967+0.049930    test-rmse:2.483564+0.532124 
## [13] train-rmse:1.229340+0.049483    test-rmse:2.473062+0.542216 
## [14] train-rmse:1.164904+0.060702    test-rmse:2.441055+0.540423 
## [15] train-rmse:1.122622+0.063224    test-rmse:2.430268+0.539828 
## [16] train-rmse:1.063622+0.055711    test-rmse:2.419163+0.561920 
## [17] train-rmse:1.019729+0.057001    test-rmse:2.418869+0.566679 
## [18] train-rmse:0.957455+0.042460    test-rmse:2.416889+0.571174 
## [19] train-rmse:0.919058+0.041843    test-rmse:2.409096+0.577196 
## [20] train-rmse:0.887362+0.046039    test-rmse:2.399654+0.573899 
## [21] train-rmse:0.854605+0.044307    test-rmse:2.391789+0.565146 
## [22] train-rmse:0.820700+0.038303    test-rmse:2.385872+0.574623 
## [23] train-rmse:0.783529+0.037221    test-rmse:2.377561+0.581329 
## [24] train-rmse:0.759142+0.038258    test-rmse:2.370058+0.586308 
## [25] train-rmse:0.729926+0.048549    test-rmse:2.354491+0.581150 
## [26] train-rmse:0.702246+0.046105    test-rmse:2.353566+0.585412 
## [27] train-rmse:0.677489+0.050275    test-rmse:2.354998+0.582078 
## [28] train-rmse:0.645155+0.042573    test-rmse:2.348162+0.583936 
## [29] train-rmse:0.627052+0.045783    test-rmse:2.340344+0.581364 
## [30] train-rmse:0.605442+0.035890    test-rmse:2.343547+0.586086 
## [31] train-rmse:0.591949+0.035832    test-rmse:2.340543+0.588080 
## [32] train-rmse:0.575655+0.033360    test-rmse:2.335633+0.589955 
## [33] train-rmse:0.550186+0.040147    test-rmse:2.336748+0.592881 
## [34] train-rmse:0.526316+0.036786    test-rmse:2.331213+0.594956 
## [35] train-rmse:0.508228+0.039131    test-rmse:2.328169+0.598954 
## [36] train-rmse:0.495843+0.036344    test-rmse:2.326967+0.597823 
## [37] train-rmse:0.483760+0.040021    test-rmse:2.327715+0.598866 
## [38] train-rmse:0.468003+0.038031    test-rmse:2.319894+0.599852 
## [39] train-rmse:0.455220+0.039297    test-rmse:2.314333+0.601286 
## [40] train-rmse:0.443894+0.039236    test-rmse:2.311902+0.602516 
## [41] train-rmse:0.427765+0.036791    test-rmse:2.313415+0.605507 
## [42] train-rmse:0.412121+0.036782    test-rmse:2.311736+0.605983 
## [43] train-rmse:0.400382+0.039201    test-rmse:2.309820+0.609241 
## [44] train-rmse:0.385544+0.043840    test-rmse:2.304464+0.613310 
## [45] train-rmse:0.375710+0.043534    test-rmse:2.305431+0.617116 
## [46] train-rmse:0.362991+0.039192    test-rmse:2.302774+0.618408 
## [47] train-rmse:0.354623+0.040179    test-rmse:2.302853+0.616447 
## [48] train-rmse:0.342368+0.042534    test-rmse:2.302695+0.618615 
## [49] train-rmse:0.335982+0.043575    test-rmse:2.302994+0.619333 
## [50] train-rmse:0.328150+0.045793    test-rmse:2.300477+0.619946 
## [51] train-rmse:0.319217+0.046728    test-rmse:2.299721+0.621467 
## [52] train-rmse:0.307405+0.044763    test-rmse:2.296325+0.621676 
## [53] train-rmse:0.299177+0.044169    test-rmse:2.294380+0.622928 
## [54] train-rmse:0.292529+0.044881    test-rmse:2.293435+0.624756 
## [55] train-rmse:0.283332+0.044691    test-rmse:2.291749+0.624434 
## [56] train-rmse:0.272885+0.042542    test-rmse:2.292145+0.623691 
## [57] train-rmse:0.265519+0.040972    test-rmse:2.291667+0.624402 
## [58] train-rmse:0.258032+0.039928    test-rmse:2.292738+0.626058 
## [59] train-rmse:0.251012+0.037387    test-rmse:2.291685+0.626235 
## [60] train-rmse:0.243896+0.038410    test-rmse:2.291240+0.627602 
## [61] train-rmse:0.237675+0.035170    test-rmse:2.292183+0.627021 
## [62] train-rmse:0.231820+0.035400    test-rmse:2.291652+0.626599 
## [63] train-rmse:0.228243+0.035927    test-rmse:2.291434+0.627032 
## [64] train-rmse:0.221267+0.033463    test-rmse:2.289291+0.627470 
## [65] train-rmse:0.215040+0.031971    test-rmse:2.288277+0.628121 
## [66] train-rmse:0.210309+0.033278    test-rmse:2.287827+0.628845 
## [67] train-rmse:0.204241+0.033705    test-rmse:2.289534+0.629424 
## [68] train-rmse:0.199605+0.032138    test-rmse:2.289412+0.629522 
## [69] train-rmse:0.194549+0.033146    test-rmse:2.288280+0.630063 
## [70] train-rmse:0.187531+0.030892    test-rmse:2.286556+0.630677 
## [71] train-rmse:0.183146+0.030452    test-rmse:2.285495+0.630675 
## [72] train-rmse:0.176922+0.030230    test-rmse:2.285539+0.629841 
## [73] train-rmse:0.169921+0.030385    test-rmse:2.285675+0.630497 
## [74] train-rmse:0.165454+0.028400    test-rmse:2.285741+0.630425 
## [75] train-rmse:0.162263+0.028537    test-rmse:2.285999+0.631293 
## [76] train-rmse:0.157770+0.028325    test-rmse:2.285829+0.631354 
## [77] train-rmse:0.153460+0.029469    test-rmse:2.285306+0.630960 
## [78] train-rmse:0.148910+0.028719    test-rmse:2.285452+0.630827 
## [79] train-rmse:0.144453+0.028018    test-rmse:2.286238+0.630800 
## [80] train-rmse:0.141427+0.027537    test-rmse:2.285342+0.630588 
## [81] train-rmse:0.137320+0.025018    test-rmse:2.286438+0.630825 
## [82] train-rmse:0.135339+0.025021    test-rmse:2.286672+0.631140 
## [83] train-rmse:0.131411+0.022542    test-rmse:2.286887+0.630680 
## Stopping. Best iteration:
## [77] train-rmse:0.153460+0.029469    test-rmse:2.285306+0.630960
## 
## 82 
## [1]  train-rmse:5.912009+0.109948    test-rmse:5.964302+0.583969 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.373770+0.075732    test-rmse:4.596368+0.480254 
## [3]  train-rmse:3.348215+0.041786    test-rmse:3.740059+0.461112 
## [4]  train-rmse:2.689438+0.028100    test-rmse:3.257480+0.488619 
## [5]  train-rmse:2.258582+0.028491    test-rmse:2.975851+0.512920 
## [6]  train-rmse:1.897324+0.045276    test-rmse:2.757454+0.525526 
## [7]  train-rmse:1.675749+0.052034    test-rmse:2.684120+0.520464 
## [8]  train-rmse:1.513901+0.053951    test-rmse:2.610266+0.545766 
## [9]  train-rmse:1.387497+0.047458    test-rmse:2.532152+0.541832 
## [10] train-rmse:1.282042+0.047654    test-rmse:2.494235+0.555510 
## [11] train-rmse:1.192874+0.048413    test-rmse:2.477591+0.568430 
## [12] train-rmse:1.123464+0.050719    test-rmse:2.465118+0.576300 
## [13] train-rmse:1.058898+0.064667    test-rmse:2.446116+0.585173 
## [14] train-rmse:0.983316+0.064090    test-rmse:2.433843+0.576303 
## [15] train-rmse:0.939366+0.059230    test-rmse:2.422073+0.577934 
## [16] train-rmse:0.899684+0.057729    test-rmse:2.409625+0.585583 
## [17] train-rmse:0.848518+0.054662    test-rmse:2.404123+0.589196 
## [18] train-rmse:0.811145+0.049385    test-rmse:2.384518+0.591811 
## [19] train-rmse:0.764362+0.050054    test-rmse:2.385589+0.587915 
## [20] train-rmse:0.723633+0.050468    test-rmse:2.374997+0.593466 
## [21] train-rmse:0.679844+0.025452    test-rmse:2.370308+0.597670 
## [22] train-rmse:0.640197+0.026566    test-rmse:2.370190+0.590135 
## [23] train-rmse:0.613079+0.022260    test-rmse:2.359238+0.594294 
## [24] train-rmse:0.583221+0.017805    test-rmse:2.346409+0.598824 
## [25] train-rmse:0.551019+0.015462    test-rmse:2.345525+0.599633 
## [26] train-rmse:0.525165+0.011906    test-rmse:2.349516+0.603355 
## [27] train-rmse:0.499046+0.009135    test-rmse:2.344403+0.606041 
## [28] train-rmse:0.471159+0.011845    test-rmse:2.341939+0.606012 
## [29] train-rmse:0.450140+0.012509    test-rmse:2.336376+0.609105 
## [30] train-rmse:0.431030+0.016602    test-rmse:2.333189+0.605110 
## [31] train-rmse:0.417918+0.018311    test-rmse:2.332856+0.605576 
## [32] train-rmse:0.394807+0.013652    test-rmse:2.333413+0.610408 
## [33] train-rmse:0.381503+0.016028    test-rmse:2.330612+0.609704 
## [34] train-rmse:0.364487+0.018291    test-rmse:2.329016+0.610876 
## [35] train-rmse:0.346722+0.020780    test-rmse:2.327329+0.606871 
## [36] train-rmse:0.331190+0.024950    test-rmse:2.325011+0.608749 
## [37] train-rmse:0.316983+0.022856    test-rmse:2.323286+0.607479 
## [38] train-rmse:0.305452+0.020311    test-rmse:2.323501+0.606340 
## [39] train-rmse:0.293064+0.013899    test-rmse:2.323253+0.606156 
## [40] train-rmse:0.273302+0.012006    test-rmse:2.322457+0.607200 
## [41] train-rmse:0.260826+0.011055    test-rmse:2.320089+0.607455 
## [42] train-rmse:0.250402+0.010431    test-rmse:2.321936+0.608312 
## [43] train-rmse:0.240410+0.010123    test-rmse:2.322839+0.607680 
## [44] train-rmse:0.228863+0.009113    test-rmse:2.321855+0.608634 
## [45] train-rmse:0.221440+0.006597    test-rmse:2.320500+0.610644 
## [46] train-rmse:0.212789+0.009171    test-rmse:2.320329+0.611132 
## [47] train-rmse:0.201683+0.007082    test-rmse:2.318959+0.611610 
## [48] train-rmse:0.195035+0.006078    test-rmse:2.318029+0.612353 
## [49] train-rmse:0.188715+0.006621    test-rmse:2.316102+0.611053 
## [50] train-rmse:0.180588+0.006537    test-rmse:2.314795+0.611300 
## [51] train-rmse:0.174998+0.006287    test-rmse:2.314081+0.611655 
## [52] train-rmse:0.165493+0.008300    test-rmse:2.316286+0.612406 
## [53] train-rmse:0.155510+0.008493    test-rmse:2.314239+0.612913 
## [54] train-rmse:0.150545+0.009629    test-rmse:2.313855+0.613357 
## [55] train-rmse:0.146148+0.009746    test-rmse:2.314602+0.614734 
## [56] train-rmse:0.140890+0.010114    test-rmse:2.314618+0.615373 
## [57] train-rmse:0.134235+0.010217    test-rmse:2.313581+0.614759 
## [58] train-rmse:0.130852+0.010017    test-rmse:2.313505+0.615485 
## [59] train-rmse:0.125559+0.010234    test-rmse:2.312850+0.615427 
## [60] train-rmse:0.120377+0.011167    test-rmse:2.312166+0.615708 
## [61] train-rmse:0.115579+0.012217    test-rmse:2.311813+0.617019 
## [62] train-rmse:0.111577+0.011315    test-rmse:2.311629+0.616818 
## [63] train-rmse:0.108473+0.011406    test-rmse:2.311637+0.616455 
## [64] train-rmse:0.105182+0.011081    test-rmse:2.311028+0.616252 
## [65] train-rmse:0.100336+0.011784    test-rmse:2.310738+0.617258 
## [66] train-rmse:0.097447+0.011338    test-rmse:2.310864+0.617417 
## [67] train-rmse:0.094062+0.010312    test-rmse:2.310742+0.617448 
## [68] train-rmse:0.090745+0.009364    test-rmse:2.310154+0.617200 
## [69] train-rmse:0.087911+0.008837    test-rmse:2.309852+0.618496 
## [70] train-rmse:0.084593+0.008268    test-rmse:2.309330+0.619201 
## [71] train-rmse:0.081220+0.007563    test-rmse:2.309522+0.618971 
## [72] train-rmse:0.079293+0.007611    test-rmse:2.309484+0.619059 
## [73] train-rmse:0.077049+0.007127    test-rmse:2.309594+0.619631 
## [74] train-rmse:0.074387+0.006810    test-rmse:2.310111+0.619391 
## [75] train-rmse:0.071509+0.006440    test-rmse:2.309440+0.619202 
## [76] train-rmse:0.068246+0.007016    test-rmse:2.309160+0.619003 
## [77] train-rmse:0.066269+0.006886    test-rmse:2.309573+0.618914 
## [78] train-rmse:0.064077+0.006257    test-rmse:2.309225+0.619013 
## [79] train-rmse:0.062074+0.006548    test-rmse:2.309238+0.618980 
## [80] train-rmse:0.060559+0.006872    test-rmse:2.309997+0.619373 
## [81] train-rmse:0.058975+0.006715    test-rmse:2.310023+0.619448 
## [82] train-rmse:0.056229+0.006940    test-rmse:2.310330+0.619455 
## Stopping. Best iteration:
## [76] train-rmse:0.068246+0.007016    test-rmse:2.309160+0.619003
## 
## 83 
## [1]  train-rmse:5.897381+0.108690    test-rmse:5.955104+0.583608 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.326158+0.066152    test-rmse:4.563501+0.499676 
## [3]  train-rmse:3.282441+0.042109    test-rmse:3.685376+0.481236 
## [4]  train-rmse:2.580196+0.035548    test-rmse:3.203017+0.470335 
## [5]  train-rmse:2.106816+0.034433    test-rmse:2.913691+0.507415 
## [6]  train-rmse:1.782385+0.031384    test-rmse:2.711689+0.524151 
## [7]  train-rmse:1.531420+0.040872    test-rmse:2.631895+0.516670 
## [8]  train-rmse:1.375508+0.041668    test-rmse:2.570713+0.537026 
## [9]  train-rmse:1.222311+0.037190    test-rmse:2.508234+0.541518 
## [10] train-rmse:1.123082+0.027388    test-rmse:2.466122+0.557043 
## [11] train-rmse:1.034499+0.034550    test-rmse:2.440597+0.576658 
## [12] train-rmse:0.953125+0.035270    test-rmse:2.423875+0.573499 
## [13] train-rmse:0.889292+0.041633    test-rmse:2.400730+0.584354 
## [14] train-rmse:0.826033+0.045702    test-rmse:2.389435+0.585004 
## [15] train-rmse:0.757872+0.031405    test-rmse:2.375741+0.600722 
## [16] train-rmse:0.704846+0.022828    test-rmse:2.371727+0.606807 
## [17] train-rmse:0.662102+0.032971    test-rmse:2.368928+0.610679 
## [18] train-rmse:0.617141+0.039242    test-rmse:2.365440+0.612716 
## [19] train-rmse:0.577241+0.039233    test-rmse:2.357184+0.618029 
## [20] train-rmse:0.543789+0.023191    test-rmse:2.355567+0.618640 
## [21] train-rmse:0.509670+0.024385    test-rmse:2.343790+0.617675 
## [22] train-rmse:0.484802+0.029237    test-rmse:2.339497+0.616706 
## [23] train-rmse:0.456159+0.028517    test-rmse:2.335965+0.620202 
## [24] train-rmse:0.418740+0.030723    test-rmse:2.335977+0.624133 
## [25] train-rmse:0.395661+0.032630    test-rmse:2.332054+0.623684 
## [26] train-rmse:0.371378+0.031888    test-rmse:2.329022+0.626209 
## [27] train-rmse:0.351852+0.028132    test-rmse:2.324672+0.629050 
## [28] train-rmse:0.336238+0.031080    test-rmse:2.322546+0.629197 
## [29] train-rmse:0.314712+0.031197    test-rmse:2.319444+0.630224 
## [30] train-rmse:0.300316+0.029156    test-rmse:2.319946+0.631922 
## [31] train-rmse:0.286312+0.027654    test-rmse:2.319964+0.631864 
## [32] train-rmse:0.271554+0.027091    test-rmse:2.319744+0.631130 
## [33] train-rmse:0.255333+0.027834    test-rmse:2.317343+0.630234 
## [34] train-rmse:0.240635+0.024267    test-rmse:2.315364+0.631141 
## [35] train-rmse:0.230136+0.023245    test-rmse:2.315584+0.630209 
## [36] train-rmse:0.219940+0.023554    test-rmse:2.312649+0.631878 
## [37] train-rmse:0.207271+0.022518    test-rmse:2.309882+0.630729 
## [38] train-rmse:0.197226+0.024084    test-rmse:2.308786+0.630915 
## [39] train-rmse:0.189544+0.023944    test-rmse:2.310096+0.631336 
## [40] train-rmse:0.181722+0.023411    test-rmse:2.310486+0.629629 
## [41] train-rmse:0.169179+0.022488    test-rmse:2.309259+0.628422 
## [42] train-rmse:0.162588+0.020542    test-rmse:2.307658+0.629016 
## [43] train-rmse:0.154161+0.020308    test-rmse:2.309190+0.629249 
## [44] train-rmse:0.149082+0.021399    test-rmse:2.307548+0.629511 
## [45] train-rmse:0.139583+0.018247    test-rmse:2.306329+0.633606 
## [46] train-rmse:0.132611+0.016156    test-rmse:2.304972+0.634391 
## [47] train-rmse:0.127134+0.016064    test-rmse:2.305265+0.634748 
## [48] train-rmse:0.122271+0.014193    test-rmse:2.305682+0.633925 
## [49] train-rmse:0.115225+0.013621    test-rmse:2.305307+0.635300 
## [50] train-rmse:0.110588+0.013394    test-rmse:2.305869+0.635464 
## [51] train-rmse:0.107019+0.013453    test-rmse:2.306076+0.635546 
## [52] train-rmse:0.102201+0.013723    test-rmse:2.307182+0.636163 
## Stopping. Best iteration:
## [46] train-rmse:0.132611+0.016156    test-rmse:2.304972+0.634391
## 
## 84 
## [1]  train-rmse:6.258348+0.137319    test-rmse:6.265335+0.646059 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.107018+0.108807    test-rmse:5.191316+0.683026 
## [3]  train-rmse:4.445692+0.115397    test-rmse:4.536440+0.581968 
## [4]  train-rmse:4.065281+0.115025    test-rmse:4.169625+0.526251 
## [5]  train-rmse:3.823033+0.115091    test-rmse:3.958753+0.449597 
## [6]  train-rmse:3.654032+0.108255    test-rmse:3.793918+0.438675 
## [7]  train-rmse:3.521724+0.109398    test-rmse:3.610125+0.449892 
## [8]  train-rmse:3.413172+0.121018    test-rmse:3.541989+0.409317 
## [9]  train-rmse:3.328456+0.123957    test-rmse:3.477147+0.397829 
## [10] train-rmse:3.253016+0.122198    test-rmse:3.430699+0.428206 
## [11] train-rmse:3.181662+0.120097    test-rmse:3.378793+0.474113 
## [12] train-rmse:3.117480+0.118547    test-rmse:3.279072+0.464019 
## [13] train-rmse:3.059257+0.122987    test-rmse:3.194845+0.433072 
## [14] train-rmse:3.006950+0.127629    test-rmse:3.138755+0.425193 
## [15] train-rmse:2.960850+0.131956    test-rmse:3.141335+0.435132 
## [16] train-rmse:2.919943+0.132134    test-rmse:3.109013+0.454885 
## [17] train-rmse:2.879010+0.132256    test-rmse:3.085585+0.456963 
## [18] train-rmse:2.840310+0.131932    test-rmse:3.024772+0.436953 
## [19] train-rmse:2.805456+0.133998    test-rmse:3.006502+0.450778 
## [20] train-rmse:2.771163+0.135162    test-rmse:3.002704+0.469816 
## [21] train-rmse:2.739160+0.137084    test-rmse:2.933256+0.471067 
## [22] train-rmse:2.708613+0.138803    test-rmse:2.904252+0.469003 
## [23] train-rmse:2.678978+0.140442    test-rmse:2.898513+0.475937 
## [24] train-rmse:2.650575+0.141534    test-rmse:2.868100+0.463156 
## [25] train-rmse:2.622214+0.143366    test-rmse:2.861271+0.462198 
## [26] train-rmse:2.596828+0.143112    test-rmse:2.840869+0.460915 
## [27] train-rmse:2.572379+0.141442    test-rmse:2.811849+0.480914 
## [28] train-rmse:2.548990+0.140591    test-rmse:2.791906+0.486374 
## [29] train-rmse:2.527041+0.141038    test-rmse:2.772669+0.479916 
## [30] train-rmse:2.506267+0.140887    test-rmse:2.767084+0.477516 
## [31] train-rmse:2.486393+0.140819    test-rmse:2.743061+0.499542 
## [32] train-rmse:2.467146+0.141185    test-rmse:2.725522+0.490499 
## [33] train-rmse:2.448319+0.141378    test-rmse:2.722014+0.497485 
## [34] train-rmse:2.430014+0.142025    test-rmse:2.715744+0.481956 
## [35] train-rmse:2.412208+0.142618    test-rmse:2.680480+0.474790 
## [36] train-rmse:2.395074+0.142788    test-rmse:2.665949+0.498912 
## [37] train-rmse:2.378843+0.143128    test-rmse:2.656935+0.496824 
## [38] train-rmse:2.363723+0.144323    test-rmse:2.646014+0.495460 
## [39] train-rmse:2.348623+0.144883    test-rmse:2.628523+0.492493 
## [40] train-rmse:2.334837+0.144887    test-rmse:2.613590+0.502069 
## [41] train-rmse:2.321760+0.145201    test-rmse:2.597450+0.509954 
## [42] train-rmse:2.309489+0.145556    test-rmse:2.590496+0.511890 
## [43] train-rmse:2.297633+0.146047    test-rmse:2.579734+0.499603 
## [44] train-rmse:2.286226+0.146410    test-rmse:2.571801+0.498004 
## [45] train-rmse:2.274946+0.146642    test-rmse:2.559359+0.501929 
## [46] train-rmse:2.263842+0.146974    test-rmse:2.555782+0.504131 
## [47] train-rmse:2.253020+0.147308    test-rmse:2.536848+0.501961 
## [48] train-rmse:2.242375+0.147899    test-rmse:2.522934+0.501159 
## [49] train-rmse:2.232096+0.148200    test-rmse:2.513132+0.508464 
## [50] train-rmse:2.222471+0.148538    test-rmse:2.513325+0.512426 
## [51] train-rmse:2.213109+0.149184    test-rmse:2.495395+0.508822 
## [52] train-rmse:2.204086+0.149583    test-rmse:2.492616+0.524155 
## [53] train-rmse:2.195377+0.150098    test-rmse:2.497844+0.533826 
## [54] train-rmse:2.186720+0.150442    test-rmse:2.480452+0.530161 
## [55] train-rmse:2.178408+0.150700    test-rmse:2.478961+0.528257 
## [56] train-rmse:2.170334+0.151050    test-rmse:2.473680+0.525844 
## [57] train-rmse:2.162327+0.151187    test-rmse:2.465949+0.522652 
## [58] train-rmse:2.155024+0.151614    test-rmse:2.468296+0.518284 
## [59] train-rmse:2.147896+0.152064    test-rmse:2.456452+0.523278 
## [60] train-rmse:2.141072+0.152430    test-rmse:2.451838+0.523817 
## [61] train-rmse:2.134180+0.152783    test-rmse:2.457161+0.528311 
## [62] train-rmse:2.127709+0.153034    test-rmse:2.452097+0.535088 
## [63] train-rmse:2.121320+0.153397    test-rmse:2.446900+0.526758 
## [64] train-rmse:2.114900+0.153709    test-rmse:2.442429+0.519429 
## [65] train-rmse:2.108757+0.154032    test-rmse:2.436136+0.519186 
## [66] train-rmse:2.102870+0.154253    test-rmse:2.432714+0.522910 
## [67] train-rmse:2.097021+0.154414    test-rmse:2.430584+0.524967 
## [68] train-rmse:2.091362+0.154628    test-rmse:2.430951+0.527621 
## [69] train-rmse:2.085831+0.154798    test-rmse:2.426568+0.523956 
## [70] train-rmse:2.080199+0.154943    test-rmse:2.418467+0.524784 
## [71] train-rmse:2.074701+0.155337    test-rmse:2.411808+0.522341 
## [72] train-rmse:2.069476+0.155489    test-rmse:2.404583+0.528196 
## [73] train-rmse:2.064441+0.155782    test-rmse:2.399679+0.530235 
## [74] train-rmse:2.059392+0.155945    test-rmse:2.395619+0.530838 
## [75] train-rmse:2.054739+0.156166    test-rmse:2.393531+0.532165 
## [76] train-rmse:2.050290+0.156511    test-rmse:2.395481+0.543763 
## [77] train-rmse:2.045875+0.156843    test-rmse:2.396326+0.549438 
## [78] train-rmse:2.041654+0.156964    test-rmse:2.391427+0.549853 
## [79] train-rmse:2.037486+0.157155    test-rmse:2.394164+0.547807 
## [80] train-rmse:2.033410+0.157256    test-rmse:2.387442+0.545659 
## [81] train-rmse:2.029281+0.157424    test-rmse:2.388309+0.550894 
## [82] train-rmse:2.025105+0.157482    test-rmse:2.388633+0.544955 
## [83] train-rmse:2.021074+0.157618    test-rmse:2.388501+0.544321 
## [84] train-rmse:2.017095+0.157770    test-rmse:2.382600+0.544864 
## [85] train-rmse:2.013259+0.157846    test-rmse:2.377701+0.544815 
## [86] train-rmse:2.009575+0.157865    test-rmse:2.377826+0.543533 
## [87] train-rmse:2.005783+0.158016    test-rmse:2.376376+0.535560 
## [88] train-rmse:2.002180+0.158007    test-rmse:2.368255+0.540453 
## [89] train-rmse:1.998809+0.158063    test-rmse:2.368332+0.539629 
## [90] train-rmse:1.995241+0.158019    test-rmse:2.371422+0.544187 
## [91] train-rmse:1.991604+0.158201    test-rmse:2.366389+0.543849 
## [92] train-rmse:1.988113+0.158243    test-rmse:2.360391+0.540686 
## [93] train-rmse:1.984792+0.158368    test-rmse:2.361631+0.540180 
## [94] train-rmse:1.981365+0.158589    test-rmse:2.365420+0.543050 
## [95] train-rmse:1.978072+0.158857    test-rmse:2.359799+0.544659 
## [96] train-rmse:1.974727+0.159118    test-rmse:2.356663+0.543088 
## [97] train-rmse:1.971484+0.159281    test-rmse:2.356644+0.545054 
## [98] train-rmse:1.968279+0.159500    test-rmse:2.352332+0.544667 
## [99] train-rmse:1.965183+0.159692    test-rmse:2.350227+0.541494 
## [100]    train-rmse:1.962088+0.159837    test-rmse:2.348399+0.542492 
## [101]    train-rmse:1.958914+0.160293    test-rmse:2.343142+0.536956 
## [102]    train-rmse:1.955992+0.160444    test-rmse:2.340536+0.537086 
## [103]    train-rmse:1.953140+0.160610    test-rmse:2.337815+0.535845 
## [104]    train-rmse:1.950128+0.160711    test-rmse:2.336531+0.540399 
## [105]    train-rmse:1.947262+0.161118    test-rmse:2.335328+0.543872 
## [106]    train-rmse:1.944431+0.161303    test-rmse:2.331884+0.543481 
## [107]    train-rmse:1.941643+0.161395    test-rmse:2.331738+0.541868 
## [108]    train-rmse:1.938826+0.161516    test-rmse:2.330568+0.545597 
## [109]    train-rmse:1.935915+0.161757    test-rmse:2.333501+0.540329 
## [110]    train-rmse:1.933373+0.161826    test-rmse:2.333565+0.533911 
## [111]    train-rmse:1.930879+0.161903    test-rmse:2.330359+0.533802 
## [112]    train-rmse:1.928391+0.162120    test-rmse:2.327794+0.534951 
## [113]    train-rmse:1.925884+0.162261    test-rmse:2.331761+0.535964 
## [114]    train-rmse:1.923450+0.162444    test-rmse:2.331960+0.536398 
## [115]    train-rmse:1.921062+0.162561    test-rmse:2.332668+0.538587 
## [116]    train-rmse:1.918607+0.162618    test-rmse:2.332241+0.542422 
## [117]    train-rmse:1.916152+0.162789    test-rmse:2.333894+0.551395 
## [118]    train-rmse:1.913539+0.162719    test-rmse:2.327696+0.551890 
## [119]    train-rmse:1.911261+0.162728    test-rmse:2.324768+0.552713 
## [120]    train-rmse:1.908965+0.162747    test-rmse:2.323758+0.548149 
## [121]    train-rmse:1.906728+0.162780    test-rmse:2.320685+0.548921 
## [122]    train-rmse:1.904459+0.162900    test-rmse:2.317743+0.549075 
## [123]    train-rmse:1.901999+0.162880    test-rmse:2.317901+0.549324 
## [124]    train-rmse:1.899872+0.162939    test-rmse:2.314546+0.548078 
## [125]    train-rmse:1.897745+0.162996    test-rmse:2.312169+0.547630 
## [126]    train-rmse:1.895545+0.163022    test-rmse:2.311424+0.547827 
## [127]    train-rmse:1.893210+0.163357    test-rmse:2.309380+0.554353 
## [128]    train-rmse:1.891095+0.163448    test-rmse:2.309217+0.551184 
## [129]    train-rmse:1.889095+0.163526    test-rmse:2.306977+0.552391 
## [130]    train-rmse:1.887053+0.163547    test-rmse:2.307193+0.553880 
## [131]    train-rmse:1.885017+0.163617    test-rmse:2.312939+0.548161 
## [132]    train-rmse:1.883006+0.163725    test-rmse:2.311848+0.546188 
## [133]    train-rmse:1.880970+0.163836    test-rmse:2.311475+0.545237 
## [134]    train-rmse:1.879040+0.163980    test-rmse:2.311222+0.542417 
## [135]    train-rmse:1.877092+0.164048    test-rmse:2.310688+0.540527 
## Stopping. Best iteration:
## [129]    train-rmse:1.889095+0.163526    test-rmse:2.306977+0.552391
## 
## 85 
## [1]  train-rmse:6.098036+0.126702    test-rmse:6.154623+0.598390 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.806645+0.121511    test-rmse:4.899701+0.561781 
## [3]  train-rmse:3.990774+0.091815    test-rmse:4.112025+0.474889 
## [4]  train-rmse:3.466699+0.086599    test-rmse:3.672438+0.458029 
## [5]  train-rmse:3.145123+0.099689    test-rmse:3.425164+0.429134 
## [6]  train-rmse:2.899085+0.102578    test-rmse:3.173734+0.445520 
## [7]  train-rmse:2.727567+0.085864    test-rmse:3.053257+0.510412 
## [8]  train-rmse:2.604248+0.099051    test-rmse:2.952427+0.485778 
## [9]  train-rmse:2.504931+0.098657    test-rmse:2.903762+0.486023 
## [10] train-rmse:2.414698+0.103975    test-rmse:2.836096+0.488037 
## [11] train-rmse:2.327426+0.108951    test-rmse:2.779535+0.487322 
## [12] train-rmse:2.262892+0.109316    test-rmse:2.718219+0.487299 
## [13] train-rmse:2.191435+0.086714    test-rmse:2.667157+0.498384 
## [14] train-rmse:2.140211+0.084510    test-rmse:2.637691+0.496459 
## [15] train-rmse:2.090775+0.085503    test-rmse:2.611762+0.502532 
## [16] train-rmse:2.045106+0.085850    test-rmse:2.574885+0.488754 
## [17] train-rmse:1.999794+0.088025    test-rmse:2.546966+0.501427 
## [18] train-rmse:1.962251+0.085993    test-rmse:2.527004+0.525151 
## [19] train-rmse:1.914748+0.088561    test-rmse:2.515982+0.524830 
## [20] train-rmse:1.877361+0.091274    test-rmse:2.492333+0.520551 
## [21] train-rmse:1.833990+0.085131    test-rmse:2.465641+0.515073 
## [22] train-rmse:1.804952+0.084359    test-rmse:2.457167+0.520335 
## [23] train-rmse:1.774456+0.087040    test-rmse:2.442096+0.521919 
## [24] train-rmse:1.748195+0.087691    test-rmse:2.414681+0.525332 
## [25] train-rmse:1.718959+0.089073    test-rmse:2.412735+0.510279 
## [26] train-rmse:1.695175+0.086907    test-rmse:2.395326+0.518186 
## [27] train-rmse:1.653586+0.063228    test-rmse:2.366529+0.533003 
## [28] train-rmse:1.631063+0.057290    test-rmse:2.379222+0.557534 
## [29] train-rmse:1.606218+0.061817    test-rmse:2.369058+0.548998 
## [30] train-rmse:1.579390+0.061410    test-rmse:2.356804+0.547557 
## [31] train-rmse:1.561225+0.061804    test-rmse:2.344024+0.550546 
## [32] train-rmse:1.541602+0.055920    test-rmse:2.326979+0.559401 
## [33] train-rmse:1.522865+0.055906    test-rmse:2.321816+0.555797 
## [34] train-rmse:1.509279+0.056096    test-rmse:2.318718+0.561489 
## [35] train-rmse:1.489043+0.059849    test-rmse:2.310945+0.571135 
## [36] train-rmse:1.475670+0.058874    test-rmse:2.307311+0.573430 
## [37] train-rmse:1.462748+0.058451    test-rmse:2.294140+0.575133 
## [38] train-rmse:1.443595+0.059218    test-rmse:2.296521+0.569417 
## [39] train-rmse:1.431284+0.059803    test-rmse:2.293180+0.572885 
## [40] train-rmse:1.408263+0.061072    test-rmse:2.275618+0.552771 
## [41] train-rmse:1.392608+0.061149    test-rmse:2.261070+0.563175 
## [42] train-rmse:1.372320+0.043183    test-rmse:2.243086+0.567283 
## [43] train-rmse:1.361697+0.043822    test-rmse:2.245014+0.560146 
## [44] train-rmse:1.347714+0.049341    test-rmse:2.233696+0.556986 
## [45] train-rmse:1.328260+0.058221    test-rmse:2.231634+0.564329 
## [46] train-rmse:1.313009+0.057202    test-rmse:2.232553+0.567816 
## [47] train-rmse:1.303021+0.057592    test-rmse:2.222073+0.574018 
## [48] train-rmse:1.294612+0.057508    test-rmse:2.220869+0.577880 
## [49] train-rmse:1.279075+0.056071    test-rmse:2.217192+0.578462 
## [50] train-rmse:1.266380+0.055820    test-rmse:2.220616+0.578649 
## [51] train-rmse:1.255089+0.052913    test-rmse:2.219655+0.580410 
## [52] train-rmse:1.239397+0.050687    test-rmse:2.214182+0.591833 
## [53] train-rmse:1.228716+0.051388    test-rmse:2.207489+0.589982 
## [54] train-rmse:1.214260+0.051190    test-rmse:2.209977+0.599269 
## [55] train-rmse:1.204854+0.050221    test-rmse:2.208136+0.597479 
## [56] train-rmse:1.194402+0.049378    test-rmse:2.205866+0.594473 
## [57] train-rmse:1.186913+0.050227    test-rmse:2.207898+0.599299 
## [58] train-rmse:1.178734+0.050058    test-rmse:2.205372+0.604740 
## [59] train-rmse:1.166107+0.053463    test-rmse:2.203683+0.602686 
## [60] train-rmse:1.157729+0.054752    test-rmse:2.203763+0.599625 
## [61] train-rmse:1.141480+0.056987    test-rmse:2.209031+0.594728 
## [62] train-rmse:1.129187+0.057888    test-rmse:2.213208+0.601514 
## [63] train-rmse:1.121632+0.057387    test-rmse:2.202499+0.607576 
## [64] train-rmse:1.109034+0.065277    test-rmse:2.208063+0.604101 
## [65] train-rmse:1.103023+0.067173    test-rmse:2.205637+0.599115 
## [66] train-rmse:1.097480+0.067518    test-rmse:2.204241+0.598090 
## [67] train-rmse:1.091204+0.068465    test-rmse:2.197182+0.599667 
## [68] train-rmse:1.086363+0.068531    test-rmse:2.197288+0.605334 
## [69] train-rmse:1.078632+0.066207    test-rmse:2.193111+0.606439 
## [70] train-rmse:1.072736+0.068972    test-rmse:2.188485+0.606759 
## [71] train-rmse:1.065853+0.067465    test-rmse:2.190872+0.610852 
## [72] train-rmse:1.053620+0.064405    test-rmse:2.185408+0.609616 
## [73] train-rmse:1.044070+0.065239    test-rmse:2.186489+0.609974 
## [74] train-rmse:1.039475+0.063617    test-rmse:2.186174+0.611749 
## [75] train-rmse:1.033749+0.064395    test-rmse:2.190943+0.610747 
## [76] train-rmse:1.028623+0.064145    test-rmse:2.188169+0.610021 
## [77] train-rmse:1.015266+0.069043    test-rmse:2.185514+0.612565 
## [78] train-rmse:1.003046+0.072006    test-rmse:2.179145+0.615548 
## [79] train-rmse:0.991469+0.061817    test-rmse:2.171155+0.618384 
## [80] train-rmse:0.986217+0.062192    test-rmse:2.163633+0.624572 
## [81] train-rmse:0.980997+0.064450    test-rmse:2.164278+0.627206 
## [82] train-rmse:0.976704+0.064566    test-rmse:2.164879+0.622812 
## [83] train-rmse:0.967524+0.068266    test-rmse:2.166514+0.617289 
## [84] train-rmse:0.961529+0.070417    test-rmse:2.164642+0.621259 
## [85] train-rmse:0.954770+0.073880    test-rmse:2.168101+0.627272 
## [86] train-rmse:0.950099+0.072032    test-rmse:2.164268+0.627197 
## Stopping. Best iteration:
## [80] train-rmse:0.986217+0.062192    test-rmse:2.163633+0.624572
## 
## 86 
## [1]  train-rmse:5.990009+0.117801    test-rmse:6.109752+0.582776 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.602781+0.088683    test-rmse:4.754133+0.532722 
## [3]  train-rmse:3.675899+0.096563    test-rmse:3.930270+0.401487 
## [4]  train-rmse:3.067499+0.056806    test-rmse:3.334836+0.438249 
## [5]  train-rmse:2.727643+0.082506    test-rmse:3.080883+0.407764 
## [6]  train-rmse:2.464599+0.073891    test-rmse:2.877985+0.425796 
## [7]  train-rmse:2.292133+0.066086    test-rmse:2.770847+0.438302 
## [8]  train-rmse:2.153923+0.066629    test-rmse:2.673402+0.434985 
## [9]  train-rmse:2.047428+0.069403    test-rmse:2.617572+0.428771 
## [10] train-rmse:1.959158+0.074863    test-rmse:2.569652+0.435032 
## [11] train-rmse:1.851630+0.066721    test-rmse:2.510427+0.431225 
## [12] train-rmse:1.777273+0.060234    test-rmse:2.472373+0.450744 
## [13] train-rmse:1.712530+0.070448    test-rmse:2.430446+0.430426 
## [14] train-rmse:1.651712+0.062893    test-rmse:2.392013+0.439721 
## [15] train-rmse:1.596837+0.069283    test-rmse:2.368427+0.450279 
## [16] train-rmse:1.548852+0.066137    test-rmse:2.339065+0.440379 
## [17] train-rmse:1.499998+0.064242    test-rmse:2.320038+0.447180 
## [18] train-rmse:1.461627+0.060765    test-rmse:2.303864+0.445994 
## [19] train-rmse:1.423532+0.065458    test-rmse:2.277214+0.459963 
## [20] train-rmse:1.390673+0.070496    test-rmse:2.270502+0.457516 
## [21] train-rmse:1.342865+0.056164    test-rmse:2.267194+0.456350 
## [22] train-rmse:1.315428+0.062214    test-rmse:2.240329+0.438041 
## [23] train-rmse:1.281959+0.062315    test-rmse:2.221110+0.441540 
## [24] train-rmse:1.239542+0.067800    test-rmse:2.204387+0.450247 
## [25] train-rmse:1.204175+0.062946    test-rmse:2.191898+0.455735 
## [26] train-rmse:1.181406+0.063826    test-rmse:2.187795+0.456185 
## [27] train-rmse:1.154326+0.058729    test-rmse:2.188280+0.474776 
## [28] train-rmse:1.123764+0.053675    test-rmse:2.194437+0.458194 
## [29] train-rmse:1.100637+0.052169    test-rmse:2.183191+0.456027 
## [30] train-rmse:1.068409+0.054328    test-rmse:2.177448+0.459755 
## [31] train-rmse:1.049158+0.054486    test-rmse:2.162583+0.467131 
## [32] train-rmse:1.027295+0.047862    test-rmse:2.151082+0.472584 
## [33] train-rmse:1.008823+0.053590    test-rmse:2.149008+0.470246 
## [34] train-rmse:0.993934+0.056856    test-rmse:2.137604+0.467982 
## [35] train-rmse:0.974832+0.059321    test-rmse:2.128767+0.471875 
## [36] train-rmse:0.954469+0.056781    test-rmse:2.118576+0.473971 
## [37] train-rmse:0.928660+0.045560    test-rmse:2.102384+0.474550 
## [38] train-rmse:0.915074+0.043335    test-rmse:2.106291+0.470198 
## [39] train-rmse:0.892091+0.049039    test-rmse:2.101934+0.478830 
## [40] train-rmse:0.876836+0.045587    test-rmse:2.099251+0.476497 
## [41] train-rmse:0.865331+0.045153    test-rmse:2.094253+0.479854 
## [42] train-rmse:0.845864+0.041715    test-rmse:2.090414+0.479857 
## [43] train-rmse:0.834466+0.044689    test-rmse:2.091519+0.478377 
## [44] train-rmse:0.822056+0.040727    test-rmse:2.088962+0.477487 
## [45] train-rmse:0.809733+0.041454    test-rmse:2.084195+0.476728 
## [46] train-rmse:0.791515+0.039784    test-rmse:2.079740+0.483981 
## [47] train-rmse:0.776410+0.036690    test-rmse:2.082077+0.480159 
## [48] train-rmse:0.759944+0.030450    test-rmse:2.077942+0.480600 
## [49] train-rmse:0.750151+0.030530    test-rmse:2.073589+0.483758 
## [50] train-rmse:0.735267+0.037900    test-rmse:2.067159+0.485388 
## [51] train-rmse:0.719669+0.043119    test-rmse:2.063252+0.483462 
## [52] train-rmse:0.712810+0.043284    test-rmse:2.061272+0.482044 
## [53] train-rmse:0.701105+0.042302    test-rmse:2.065817+0.485324 
## [54] train-rmse:0.689349+0.039663    test-rmse:2.060814+0.483065 
## [55] train-rmse:0.679605+0.042120    test-rmse:2.062053+0.485366 
## [56] train-rmse:0.662825+0.037412    test-rmse:2.067409+0.485525 
## [57] train-rmse:0.655581+0.036408    test-rmse:2.064497+0.485189 
## [58] train-rmse:0.648187+0.033960    test-rmse:2.063665+0.488053 
## [59] train-rmse:0.640831+0.033380    test-rmse:2.059281+0.490600 
## [60] train-rmse:0.634720+0.034177    test-rmse:2.059407+0.490601 
## [61] train-rmse:0.625348+0.035705    test-rmse:2.056188+0.490425 
## [62] train-rmse:0.613528+0.031427    test-rmse:2.056930+0.488082 
## [63] train-rmse:0.603388+0.031232    test-rmse:2.057595+0.486853 
## [64] train-rmse:0.587576+0.029384    test-rmse:2.063854+0.490377 
## [65] train-rmse:0.578883+0.029403    test-rmse:2.061120+0.488724 
## [66] train-rmse:0.572214+0.028120    test-rmse:2.062262+0.485428 
## [67] train-rmse:0.568982+0.028062    test-rmse:2.060856+0.486585 
## Stopping. Best iteration:
## [61] train-rmse:0.625348+0.035705    test-rmse:2.056188+0.490425
## 
## 87 
## [1]  train-rmse:5.887665+0.116545    test-rmse:5.975150+0.566910 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.393037+0.091246    test-rmse:4.598958+0.463657 
## [3]  train-rmse:3.445792+0.055726    test-rmse:3.758691+0.452329 
## [4]  train-rmse:2.847512+0.054150    test-rmse:3.256411+0.474657 
## [5]  train-rmse:2.450876+0.048054    test-rmse:2.951318+0.485297 
## [6]  train-rmse:2.192755+0.038559    test-rmse:2.789348+0.490540 
## [7]  train-rmse:1.998170+0.055314    test-rmse:2.673023+0.493047 
## [8]  train-rmse:1.853884+0.056093    test-rmse:2.572005+0.498302 
## [9]  train-rmse:1.740677+0.047892    test-rmse:2.507404+0.494013 
## [10] train-rmse:1.640952+0.058209    test-rmse:2.486838+0.508594 
## [11] train-rmse:1.539180+0.039545    test-rmse:2.473099+0.508135 
## [12] train-rmse:1.470966+0.043954    test-rmse:2.449984+0.492919 
## [13] train-rmse:1.407752+0.035285    test-rmse:2.417008+0.487883 
## [14] train-rmse:1.342082+0.036068    test-rmse:2.373677+0.477715 
## [15] train-rmse:1.291831+0.041334    test-rmse:2.360129+0.480440 
## [16] train-rmse:1.239936+0.034208    test-rmse:2.345861+0.490462 
## [17] train-rmse:1.198618+0.031524    test-rmse:2.329869+0.490056 
## [18] train-rmse:1.157828+0.029691    test-rmse:2.311293+0.501614 
## [19] train-rmse:1.110141+0.034133    test-rmse:2.294910+0.500009 
## [20] train-rmse:1.077740+0.033970    test-rmse:2.283375+0.498149 
## [21] train-rmse:1.037698+0.025202    test-rmse:2.277974+0.498982 
## [22] train-rmse:1.005109+0.029493    test-rmse:2.266461+0.498869 
## [23] train-rmse:0.973122+0.032783    test-rmse:2.261725+0.506073 
## [24] train-rmse:0.945736+0.024887    test-rmse:2.250604+0.510470 
## [25] train-rmse:0.917696+0.034139    test-rmse:2.242748+0.518337 
## [26] train-rmse:0.883474+0.030854    test-rmse:2.219926+0.507335 
## [27] train-rmse:0.856758+0.028559    test-rmse:2.214802+0.509250 
## [28] train-rmse:0.828315+0.029657    test-rmse:2.202182+0.514474 
## [29] train-rmse:0.805286+0.026199    test-rmse:2.207621+0.519459 
## [30] train-rmse:0.783152+0.026155    test-rmse:2.202543+0.528455 
## [31] train-rmse:0.754572+0.022893    test-rmse:2.198151+0.529396 
## [32] train-rmse:0.736974+0.022647    test-rmse:2.198290+0.522981 
## [33] train-rmse:0.720835+0.019501    test-rmse:2.195221+0.528097 
## [34] train-rmse:0.699297+0.020436    test-rmse:2.195737+0.525634 
## [35] train-rmse:0.680143+0.020493    test-rmse:2.194232+0.525526 
## [36] train-rmse:0.663306+0.025325    test-rmse:2.183522+0.525968 
## [37] train-rmse:0.649989+0.028261    test-rmse:2.179803+0.527418 
## [38] train-rmse:0.631401+0.021155    test-rmse:2.180350+0.525173 
## [39] train-rmse:0.618007+0.019730    test-rmse:2.179538+0.529095 
## [40] train-rmse:0.595307+0.020338    test-rmse:2.171910+0.524340 
## [41] train-rmse:0.583251+0.019446    test-rmse:2.172390+0.520090 
## [42] train-rmse:0.571489+0.016096    test-rmse:2.171140+0.521343 
## [43] train-rmse:0.553543+0.010494    test-rmse:2.170908+0.524469 
## [44] train-rmse:0.543487+0.013867    test-rmse:2.169234+0.525408 
## [45] train-rmse:0.529696+0.015085    test-rmse:2.173559+0.530587 
## [46] train-rmse:0.514777+0.019024    test-rmse:2.170751+0.525704 
## [47] train-rmse:0.501182+0.014950    test-rmse:2.169479+0.525669 
## [48] train-rmse:0.488212+0.016670    test-rmse:2.159599+0.527137 
## [49] train-rmse:0.476072+0.013222    test-rmse:2.159075+0.526631 
## [50] train-rmse:0.466077+0.018837    test-rmse:2.163556+0.529054 
## [51] train-rmse:0.455501+0.018258    test-rmse:2.160935+0.530805 
## [52] train-rmse:0.439553+0.011391    test-rmse:2.160446+0.531188 
## [53] train-rmse:0.429925+0.018204    test-rmse:2.155446+0.531581 
## [54] train-rmse:0.416271+0.015415    test-rmse:2.154266+0.533595 
## [55] train-rmse:0.406948+0.016016    test-rmse:2.153434+0.534567 
## [56] train-rmse:0.396112+0.016516    test-rmse:2.154846+0.532699 
## [57] train-rmse:0.389629+0.017416    test-rmse:2.152086+0.533477 
## [58] train-rmse:0.382254+0.015574    test-rmse:2.151764+0.531628 
## [59] train-rmse:0.375659+0.015306    test-rmse:2.151856+0.531605 
## [60] train-rmse:0.364852+0.016512    test-rmse:2.153236+0.531620 
## [61] train-rmse:0.359904+0.016450    test-rmse:2.152411+0.532172 
## [62] train-rmse:0.350377+0.017900    test-rmse:2.152694+0.532008 
## [63] train-rmse:0.342530+0.017086    test-rmse:2.150886+0.530333 
## [64] train-rmse:0.335675+0.014614    test-rmse:2.150967+0.530117 
## [65] train-rmse:0.330207+0.015116    test-rmse:2.150157+0.530262 
## [66] train-rmse:0.324350+0.014494    test-rmse:2.151743+0.530296 
## [67] train-rmse:0.317832+0.014777    test-rmse:2.152017+0.530958 
## [68] train-rmse:0.310074+0.013099    test-rmse:2.151338+0.532175 
## [69] train-rmse:0.304398+0.015326    test-rmse:2.151271+0.532747 
## [70] train-rmse:0.300374+0.014665    test-rmse:2.149440+0.532998 
## [71] train-rmse:0.293427+0.017625    test-rmse:2.149704+0.532370 
## [72] train-rmse:0.286991+0.015706    test-rmse:2.149399+0.534304 
## [73] train-rmse:0.280589+0.015205    test-rmse:2.147597+0.536112 
## [74] train-rmse:0.275479+0.016563    test-rmse:2.147085+0.536135 
## [75] train-rmse:0.271077+0.015302    test-rmse:2.147123+0.537072 
## [76] train-rmse:0.263019+0.014692    test-rmse:2.148936+0.539551 
## [77] train-rmse:0.256397+0.011357    test-rmse:2.144489+0.537846 
## [78] train-rmse:0.250927+0.010846    test-rmse:2.143272+0.538126 
## [79] train-rmse:0.245969+0.010249    test-rmse:2.143107+0.538730 
## [80] train-rmse:0.241286+0.010593    test-rmse:2.138988+0.534225 
## [81] train-rmse:0.236799+0.010958    test-rmse:2.137650+0.534351 
## [82] train-rmse:0.230774+0.009444    test-rmse:2.135370+0.533927 
## [83] train-rmse:0.227771+0.008635    test-rmse:2.136243+0.534854 
## [84] train-rmse:0.223460+0.007950    test-rmse:2.134678+0.535049 
## [85] train-rmse:0.218353+0.009278    test-rmse:2.136525+0.536429 
## [86] train-rmse:0.214614+0.009442    test-rmse:2.137729+0.535922 
## [87] train-rmse:0.211193+0.009386    test-rmse:2.136832+0.536679 
## [88] train-rmse:0.208523+0.009336    test-rmse:2.136821+0.536348 
## [89] train-rmse:0.202531+0.008075    test-rmse:2.137454+0.536137 
## [90] train-rmse:0.197626+0.008537    test-rmse:2.137632+0.535066 
## Stopping. Best iteration:
## [84] train-rmse:0.223460+0.007950    test-rmse:2.134678+0.535049
## 
## 88 
## [1]  train-rmse:5.816938+0.113593    test-rmse:5.859651+0.536821 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.265359+0.083927    test-rmse:4.430262+0.453872 
## [3]  train-rmse:3.303932+0.062074    test-rmse:3.647927+0.448092 
## [4]  train-rmse:2.686322+0.064787    test-rmse:3.175680+0.463684 
## [5]  train-rmse:2.280472+0.047571    test-rmse:2.944715+0.529810 
## [6]  train-rmse:1.985083+0.059112    test-rmse:2.751999+0.534026 
## [7]  train-rmse:1.767921+0.054373    test-rmse:2.629759+0.560343 
## [8]  train-rmse:1.628301+0.067389    test-rmse:2.574780+0.552119 
## [9]  train-rmse:1.502199+0.048411    test-rmse:2.515469+0.564981 
## [10] train-rmse:1.403624+0.042795    test-rmse:2.482563+0.557101 
## [11] train-rmse:1.319831+0.035821    test-rmse:2.452568+0.566814 
## [12] train-rmse:1.247433+0.027219    test-rmse:2.447064+0.564061 
## [13] train-rmse:1.173204+0.037178    test-rmse:2.422561+0.542447 
## [14] train-rmse:1.119880+0.046000    test-rmse:2.394241+0.551909 
## [15] train-rmse:1.056815+0.048427    test-rmse:2.385766+0.560826 
## [16] train-rmse:0.994032+0.035579    test-rmse:2.369099+0.564731 
## [17] train-rmse:0.945325+0.042410    test-rmse:2.350500+0.555132 
## [18] train-rmse:0.899367+0.038473    test-rmse:2.337051+0.564033 
## [19] train-rmse:0.870265+0.038380    test-rmse:2.330822+0.563447 
## [20] train-rmse:0.828740+0.027276    test-rmse:2.324638+0.567235 
## [21] train-rmse:0.795777+0.027081    test-rmse:2.318999+0.573709 
## [22] train-rmse:0.754560+0.023926    test-rmse:2.311142+0.578522 
## [23] train-rmse:0.731509+0.028433    test-rmse:2.300848+0.580476 
## [24] train-rmse:0.706153+0.032174    test-rmse:2.298767+0.575682 
## [25] train-rmse:0.663499+0.020028    test-rmse:2.293705+0.578958 
## [26] train-rmse:0.637430+0.017948    test-rmse:2.293889+0.583135 
## [27] train-rmse:0.617832+0.017540    test-rmse:2.295367+0.582698 
## [28] train-rmse:0.594172+0.020996    test-rmse:2.299398+0.586819 
## [29] train-rmse:0.563825+0.025604    test-rmse:2.293892+0.580192 
## [30] train-rmse:0.543975+0.024062    test-rmse:2.292861+0.581856 
## [31] train-rmse:0.528137+0.024850    test-rmse:2.288944+0.581547 
## [32] train-rmse:0.511506+0.032231    test-rmse:2.283510+0.576218 
## [33] train-rmse:0.485078+0.035282    test-rmse:2.283550+0.579326 
## [34] train-rmse:0.466132+0.029809    test-rmse:2.283697+0.578978 
## [35] train-rmse:0.450942+0.032815    test-rmse:2.282252+0.581784 
## [36] train-rmse:0.433283+0.030260    test-rmse:2.275856+0.583959 
## [37] train-rmse:0.418767+0.032489    test-rmse:2.275171+0.581571 
## [38] train-rmse:0.405962+0.030886    test-rmse:2.273861+0.581814 
## [39] train-rmse:0.394269+0.030119    test-rmse:2.277324+0.581149 
## [40] train-rmse:0.384107+0.027626    test-rmse:2.276304+0.581431 
## [41] train-rmse:0.369908+0.022031    test-rmse:2.279484+0.583595 
## [42] train-rmse:0.352264+0.023185    test-rmse:2.279223+0.583456 
## [43] train-rmse:0.336260+0.024780    test-rmse:2.279792+0.584600 
## [44] train-rmse:0.328671+0.022981    test-rmse:2.278602+0.583224 
## Stopping. Best iteration:
## [38] train-rmse:0.405962+0.030886    test-rmse:2.273861+0.581814
## 
## 89 
## [1]  train-rmse:5.772721+0.105898    test-rmse:5.833190+0.578057 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.190005+0.070889    test-rmse:4.438178+0.472560 
## [3]  train-rmse:3.173839+0.032762    test-rmse:3.604450+0.462355 
## [4]  train-rmse:2.527110+0.025218    test-rmse:3.167487+0.508070 
## [5]  train-rmse:2.104102+0.026616    test-rmse:2.897756+0.547119 
## [6]  train-rmse:1.798567+0.030294    test-rmse:2.766129+0.547237 
## [7]  train-rmse:1.602864+0.031185    test-rmse:2.651332+0.560323 
## [8]  train-rmse:1.456670+0.046904    test-rmse:2.582459+0.558598 
## [9]  train-rmse:1.334104+0.035049    test-rmse:2.549698+0.585999 
## [10] train-rmse:1.202837+0.024005    test-rmse:2.516260+0.606063 
## [11] train-rmse:1.132005+0.027236    test-rmse:2.484821+0.615412 
## [12] train-rmse:1.060064+0.029096    test-rmse:2.465315+0.621444 
## [13] train-rmse:1.004761+0.033673    test-rmse:2.459884+0.623321 
## [14] train-rmse:0.939716+0.035419    test-rmse:2.444532+0.629493 
## [15] train-rmse:0.879545+0.030350    test-rmse:2.424074+0.641318 
## [16] train-rmse:0.829858+0.040261    test-rmse:2.402357+0.631019 
## [17] train-rmse:0.779663+0.032714    test-rmse:2.402006+0.624330 
## [18] train-rmse:0.733898+0.025705    test-rmse:2.390623+0.639498 
## [19] train-rmse:0.696377+0.021498    test-rmse:2.389039+0.633230 
## [20] train-rmse:0.660065+0.028922    test-rmse:2.385623+0.642815 
## [21] train-rmse:0.625600+0.027242    test-rmse:2.376541+0.649890 
## [22] train-rmse:0.588667+0.021643    test-rmse:2.373869+0.657701 
## [23] train-rmse:0.558714+0.022908    test-rmse:2.364530+0.660189 
## [24] train-rmse:0.529148+0.021129    test-rmse:2.362601+0.659207 
## [25] train-rmse:0.504759+0.019815    test-rmse:2.355407+0.664778 
## [26] train-rmse:0.483007+0.022379    test-rmse:2.351856+0.663027 
## [27] train-rmse:0.463428+0.021872    test-rmse:2.352284+0.665065 
## [28] train-rmse:0.432365+0.015219    test-rmse:2.355488+0.672727 
## [29] train-rmse:0.415140+0.014944    test-rmse:2.352939+0.673762 
## [30] train-rmse:0.397392+0.015878    test-rmse:2.353609+0.674810 
## [31] train-rmse:0.372088+0.023283    test-rmse:2.353654+0.676042 
## [32] train-rmse:0.357763+0.019980    test-rmse:2.355278+0.678412 
## Stopping. Best iteration:
## [26] train-rmse:0.483007+0.022379    test-rmse:2.351856+0.663027
## 
## 90 
## [1]  train-rmse:5.756959+0.104536    test-rmse:5.823078+0.577737 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.141547+0.065599    test-rmse:4.406630+0.488690 
## [3]  train-rmse:3.101539+0.043212    test-rmse:3.557647+0.489337 
## [4]  train-rmse:2.422264+0.039706    test-rmse:3.099901+0.500869 
## [5]  train-rmse:1.977510+0.035495    test-rmse:2.858321+0.504474 
## [6]  train-rmse:1.675442+0.024605    test-rmse:2.671049+0.542849 
## [7]  train-rmse:1.435245+0.042357    test-rmse:2.589790+0.512889 
## [8]  train-rmse:1.283277+0.039057    test-rmse:2.549258+0.533712 
## [9]  train-rmse:1.131869+0.036355    test-rmse:2.508589+0.551264 
## [10] train-rmse:1.029947+0.045042    test-rmse:2.462805+0.544704 
## [11] train-rmse:0.943579+0.048278    test-rmse:2.452017+0.538217 
## [12] train-rmse:0.861446+0.051689    test-rmse:2.425530+0.556906 
## [13] train-rmse:0.801353+0.047300    test-rmse:2.406223+0.575619 
## [14] train-rmse:0.737982+0.027522    test-rmse:2.403857+0.561439 
## [15] train-rmse:0.688864+0.032130    test-rmse:2.396574+0.571378 
## [16] train-rmse:0.646079+0.038545    test-rmse:2.394646+0.569342 
## [17] train-rmse:0.589952+0.037103    test-rmse:2.390508+0.570143 
## [18] train-rmse:0.562991+0.031679    test-rmse:2.383490+0.577035 
## [19] train-rmse:0.518941+0.036625    test-rmse:2.377424+0.574340 
## [20] train-rmse:0.496262+0.032337    test-rmse:2.376111+0.571484 
## [21] train-rmse:0.458209+0.033867    test-rmse:2.374749+0.575619 
## [22] train-rmse:0.426567+0.035911    test-rmse:2.371193+0.572927 
## [23] train-rmse:0.403670+0.035036    test-rmse:2.367275+0.574673 
## [24] train-rmse:0.376773+0.031115    test-rmse:2.370433+0.578645 
## [25] train-rmse:0.355803+0.031073    test-rmse:2.369350+0.577185 
## [26] train-rmse:0.333438+0.034241    test-rmse:2.367772+0.574296 
## [27] train-rmse:0.316680+0.025600    test-rmse:2.366412+0.577176 
## [28] train-rmse:0.298956+0.023895    test-rmse:2.362585+0.578853 
## [29] train-rmse:0.287336+0.020843    test-rmse:2.361743+0.580313 
## [30] train-rmse:0.269314+0.023674    test-rmse:2.362191+0.585522 
## [31] train-rmse:0.255110+0.022142    test-rmse:2.362807+0.583736 
## [32] train-rmse:0.242992+0.022799    test-rmse:2.362561+0.585660 
## [33] train-rmse:0.226657+0.022775    test-rmse:2.363298+0.587588 
## [34] train-rmse:0.208770+0.020978    test-rmse:2.363920+0.592620 
## [35] train-rmse:0.199445+0.020508    test-rmse:2.365099+0.590355 
## Stopping. Best iteration:
## [29] train-rmse:0.287336+0.020843    test-rmse:2.361743+0.580313
## 
## 91 
## [1]  train-rmse:6.154224+0.135559    test-rmse:6.163765+0.642457 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.988204+0.106334    test-rmse:5.080952+0.679084 
## [3]  train-rmse:4.345461+0.116078    test-rmse:4.444678+0.569806 
## [4]  train-rmse:3.987750+0.114263    test-rmse:4.105137+0.515501 
## [5]  train-rmse:3.760637+0.116352    test-rmse:3.907244+0.434196 
## [6]  train-rmse:3.598736+0.110743    test-rmse:3.717654+0.397094 
## [7]  train-rmse:3.468133+0.109739    test-rmse:3.562675+0.444969 
## [8]  train-rmse:3.361815+0.122303    test-rmse:3.496777+0.404849 
## [9]  train-rmse:3.278803+0.124294    test-rmse:3.426076+0.427208 
## [10] train-rmse:3.204829+0.123457    test-rmse:3.380149+0.419018 
## [11] train-rmse:3.135242+0.121177    test-rmse:3.339485+0.479496 
## [12] train-rmse:3.070277+0.120221    test-rmse:3.229276+0.460619 
## [13] train-rmse:3.012521+0.124104    test-rmse:3.147421+0.455811 
## [14] train-rmse:2.961307+0.129392    test-rmse:3.120647+0.437229 
## [15] train-rmse:2.916419+0.132454    test-rmse:3.102023+0.450623 
## [16] train-rmse:2.875215+0.133415    test-rmse:3.078352+0.446069 
## [17] train-rmse:2.834499+0.134765    test-rmse:3.034477+0.474011 
## [18] train-rmse:2.797619+0.135775    test-rmse:2.984980+0.465475 
## [19] train-rmse:2.761832+0.138132    test-rmse:2.979965+0.468332 
## [20] train-rmse:2.728053+0.137839    test-rmse:2.961896+0.449577 
## [21] train-rmse:2.696908+0.138523    test-rmse:2.933828+0.455738 
## [22] train-rmse:2.666237+0.139076    test-rmse:2.890304+0.463610 
## [23] train-rmse:2.635637+0.140095    test-rmse:2.863298+0.449835 
## [24] train-rmse:2.607191+0.141009    test-rmse:2.837030+0.437586 
## [25] train-rmse:2.578545+0.142720    test-rmse:2.826193+0.468436 
## [26] train-rmse:2.553162+0.142774    test-rmse:2.810814+0.470804 
## [27] train-rmse:2.529423+0.142339    test-rmse:2.788442+0.490464 
## [28] train-rmse:2.506858+0.142361    test-rmse:2.765849+0.487398 
## [29] train-rmse:2.485543+0.142587    test-rmse:2.745946+0.499959 
## [30] train-rmse:2.464734+0.143154    test-rmse:2.727509+0.503926 
## [31] train-rmse:2.445321+0.143502    test-rmse:2.712368+0.485821 
## [32] train-rmse:2.426451+0.144185    test-rmse:2.694791+0.490280 
## [33] train-rmse:2.407977+0.144184    test-rmse:2.676000+0.485217 
## [34] train-rmse:2.390752+0.143695    test-rmse:2.657803+0.473319 
## [35] train-rmse:2.373584+0.143732    test-rmse:2.634180+0.483008 
## [36] train-rmse:2.357511+0.143734    test-rmse:2.615522+0.482044 
## [37] train-rmse:2.342236+0.144049    test-rmse:2.621794+0.490744 
## [38] train-rmse:2.327866+0.143950    test-rmse:2.605968+0.490211 
## [39] train-rmse:2.313881+0.144175    test-rmse:2.596102+0.503974 
## [40] train-rmse:2.300948+0.145035    test-rmse:2.576710+0.490791 
## [41] train-rmse:2.288414+0.145976    test-rmse:2.571941+0.500702 
## [42] train-rmse:2.276146+0.146775    test-rmse:2.568964+0.500281 
## [43] train-rmse:2.264238+0.147273    test-rmse:2.562058+0.502872 
## [44] train-rmse:2.252902+0.147681    test-rmse:2.560333+0.500657 
## [45] train-rmse:2.242049+0.148520    test-rmse:2.541600+0.507707 
## [46] train-rmse:2.231585+0.149273    test-rmse:2.533717+0.513318 
## [47] train-rmse:2.221441+0.149693    test-rmse:2.512916+0.516780 
## [48] train-rmse:2.211267+0.150083    test-rmse:2.509740+0.522371 
## [49] train-rmse:2.201603+0.150585    test-rmse:2.496993+0.516735 
## [50] train-rmse:2.192358+0.151002    test-rmse:2.495925+0.516872 
## [51] train-rmse:2.183563+0.151109    test-rmse:2.481628+0.516051 
## [52] train-rmse:2.175228+0.151155    test-rmse:2.474938+0.517461 
## [53] train-rmse:2.167086+0.151313    test-rmse:2.464341+0.518596 
## [54] train-rmse:2.158566+0.151420    test-rmse:2.468461+0.517039 
## [55] train-rmse:2.150786+0.151450    test-rmse:2.462561+0.516413 
## [56] train-rmse:2.143138+0.151486    test-rmse:2.452169+0.514128 
## [57] train-rmse:2.135883+0.151990    test-rmse:2.439350+0.508280 
## [58] train-rmse:2.128779+0.152314    test-rmse:2.444256+0.514766 
## [59] train-rmse:2.121797+0.152706    test-rmse:2.438848+0.533340 
## [60] train-rmse:2.115133+0.153131    test-rmse:2.437541+0.544025 
## [61] train-rmse:2.108702+0.153434    test-rmse:2.436380+0.535458 
## [62] train-rmse:2.102484+0.153834    test-rmse:2.429943+0.521838 
## [63] train-rmse:2.096227+0.154155    test-rmse:2.426411+0.521284 
## [64] train-rmse:2.090253+0.154434    test-rmse:2.420892+0.518051 
## [65] train-rmse:2.084433+0.154740    test-rmse:2.420844+0.521328 
## [66] train-rmse:2.078754+0.155092    test-rmse:2.417206+0.522025 
## [67] train-rmse:2.073138+0.155426    test-rmse:2.408272+0.520401 
## [68] train-rmse:2.067498+0.155693    test-rmse:2.410486+0.525195 
## [69] train-rmse:2.061966+0.156039    test-rmse:2.411554+0.532000 
## [70] train-rmse:2.056611+0.156326    test-rmse:2.404705+0.533215 
## [71] train-rmse:2.051500+0.156559    test-rmse:2.398632+0.536523 
## [72] train-rmse:2.046707+0.156812    test-rmse:2.402089+0.536572 
## [73] train-rmse:2.041928+0.156985    test-rmse:2.398055+0.533693 
## [74] train-rmse:2.037556+0.157112    test-rmse:2.394478+0.532834 
## [75] train-rmse:2.033104+0.157007    test-rmse:2.391172+0.529774 
## [76] train-rmse:2.028793+0.157054    test-rmse:2.391080+0.530890 
## [77] train-rmse:2.024321+0.157463    test-rmse:2.389300+0.528922 
## [78] train-rmse:2.020014+0.157641    test-rmse:2.382661+0.527741 
## [79] train-rmse:2.016048+0.157825    test-rmse:2.382021+0.531203 
## [80] train-rmse:2.011995+0.157883    test-rmse:2.377183+0.529842 
## [81] train-rmse:2.008056+0.158009    test-rmse:2.372551+0.530485 
## [82] train-rmse:2.004212+0.158119    test-rmse:2.369464+0.535157 
## [83] train-rmse:2.000355+0.158255    test-rmse:2.369499+0.539020 
## [84] train-rmse:1.996552+0.158437    test-rmse:2.372151+0.535726 
## [85] train-rmse:1.992753+0.158532    test-rmse:2.367538+0.534765 
## [86] train-rmse:1.989079+0.158654    test-rmse:2.374914+0.544122 
## [87] train-rmse:1.985433+0.158824    test-rmse:2.366063+0.545459 
## [88] train-rmse:1.981823+0.159016    test-rmse:2.357139+0.543632 
## [89] train-rmse:1.978153+0.159094    test-rmse:2.360868+0.544650 
## [90] train-rmse:1.974723+0.159237    test-rmse:2.356544+0.547644 
## [91] train-rmse:1.971383+0.159472    test-rmse:2.353402+0.543684 
## [92] train-rmse:1.968135+0.159664    test-rmse:2.351700+0.545871 
## [93] train-rmse:1.964740+0.159721    test-rmse:2.352686+0.545100 
## [94] train-rmse:1.961424+0.160115    test-rmse:2.351809+0.545841 
## [95] train-rmse:1.958277+0.160276    test-rmse:2.346343+0.545828 
## [96] train-rmse:1.954942+0.160336    test-rmse:2.352509+0.543320 
## [97] train-rmse:1.951966+0.160518    test-rmse:2.352692+0.544136 
## [98] train-rmse:1.948875+0.160583    test-rmse:2.351888+0.544032 
## [99] train-rmse:1.945941+0.160712    test-rmse:2.350996+0.547321 
## [100]    train-rmse:1.943042+0.160893    test-rmse:2.347088+0.544180 
## [101]    train-rmse:1.940186+0.161063    test-rmse:2.346266+0.540938 
## [102]    train-rmse:1.937266+0.161062    test-rmse:2.340768+0.545719 
## [103]    train-rmse:1.934487+0.161153    test-rmse:2.338222+0.545398 
## [104]    train-rmse:1.931832+0.161214    test-rmse:2.338764+0.545383 
## [105]    train-rmse:1.929076+0.161302    test-rmse:2.338054+0.542881 
## [106]    train-rmse:1.926312+0.161450    test-rmse:2.337387+0.542840 
## [107]    train-rmse:1.923667+0.161555    test-rmse:2.333591+0.545794 
## [108]    train-rmse:1.920907+0.161634    test-rmse:2.333940+0.546672 
## [109]    train-rmse:1.918122+0.161677    test-rmse:2.331527+0.548478 
## [110]    train-rmse:1.915602+0.161753    test-rmse:2.331534+0.540742 
## [111]    train-rmse:1.912806+0.162346    test-rmse:2.330727+0.543518 
## [112]    train-rmse:1.910437+0.162406    test-rmse:2.328915+0.541696 
## [113]    train-rmse:1.908052+0.162439    test-rmse:2.333329+0.540582 
## [114]    train-rmse:1.905529+0.162601    test-rmse:2.333783+0.542238 
## [115]    train-rmse:1.903136+0.162700    test-rmse:2.329449+0.543766 
## [116]    train-rmse:1.900736+0.162712    test-rmse:2.325684+0.545004 
## [117]    train-rmse:1.898425+0.162765    test-rmse:2.325707+0.544129 
## [118]    train-rmse:1.896016+0.162828    test-rmse:2.329527+0.546920 
## [119]    train-rmse:1.893650+0.163101    test-rmse:2.323801+0.549463 
## [120]    train-rmse:1.891478+0.163224    test-rmse:2.320452+0.548075 
## [121]    train-rmse:1.889213+0.163419    test-rmse:2.321563+0.547387 
## [122]    train-rmse:1.887113+0.163537    test-rmse:2.320729+0.547901 
## [123]    train-rmse:1.884951+0.163644    test-rmse:2.321391+0.553745 
## [124]    train-rmse:1.882866+0.163770    test-rmse:2.313121+0.551534 
## [125]    train-rmse:1.880827+0.163835    test-rmse:2.312320+0.551132 
## [126]    train-rmse:1.878702+0.163845    test-rmse:2.309490+0.549172 
## [127]    train-rmse:1.876708+0.163956    test-rmse:2.304447+0.548615 
## [128]    train-rmse:1.874626+0.164036    test-rmse:2.307032+0.548409 
## [129]    train-rmse:1.872576+0.164205    test-rmse:2.305365+0.545688 
## [130]    train-rmse:1.870594+0.164230    test-rmse:2.304025+0.544146 
## [131]    train-rmse:1.868692+0.164306    test-rmse:2.299714+0.542721 
## [132]    train-rmse:1.866685+0.164317    test-rmse:2.301419+0.543473 
## [133]    train-rmse:1.864796+0.164357    test-rmse:2.301917+0.540398 
## [134]    train-rmse:1.862873+0.164376    test-rmse:2.296541+0.542797 
## [135]    train-rmse:1.860886+0.164449    test-rmse:2.296827+0.544270 
## [136]    train-rmse:1.859039+0.164551    test-rmse:2.291943+0.543187 
## [137]    train-rmse:1.857150+0.164649    test-rmse:2.296032+0.539044 
## [138]    train-rmse:1.855269+0.164736    test-rmse:2.298840+0.538512 
## [139]    train-rmse:1.853387+0.164764    test-rmse:2.297807+0.541138 
## [140]    train-rmse:1.851585+0.164881    test-rmse:2.296383+0.540660 
## [141]    train-rmse:1.849868+0.164932    test-rmse:2.292887+0.543581 
## [142]    train-rmse:1.847944+0.165191    test-rmse:2.289106+0.541094 
## [143]    train-rmse:1.846042+0.165200    test-rmse:2.289921+0.540913 
## [144]    train-rmse:1.844289+0.165304    test-rmse:2.287491+0.542896 
## [145]    train-rmse:1.842486+0.165298    test-rmse:2.289667+0.541773 
## [146]    train-rmse:1.840818+0.165350    test-rmse:2.287819+0.543128 
## [147]    train-rmse:1.839061+0.165282    test-rmse:2.291595+0.542009 
## [148]    train-rmse:1.837435+0.165289    test-rmse:2.291021+0.542420 
## [149]    train-rmse:1.835746+0.165308    test-rmse:2.290948+0.540248 
## [150]    train-rmse:1.834128+0.165354    test-rmse:2.289925+0.542365 
## Stopping. Best iteration:
## [144]    train-rmse:1.844289+0.165304    test-rmse:2.287491+0.542896
## 
## 92 
## [1]  train-rmse:5.982886+0.124450    test-rmse:6.045802+0.592558 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.671218+0.121490    test-rmse:4.775261+0.553464 
## [3]  train-rmse:3.863615+0.091562    test-rmse:4.002803+0.443587 
## [4]  train-rmse:3.364456+0.081311    test-rmse:3.559374+0.425868 
## [5]  train-rmse:3.061184+0.097876    test-rmse:3.315610+0.390400 
## [6]  train-rmse:2.836654+0.102064    test-rmse:3.080077+0.440411 
## [7]  train-rmse:2.699231+0.107793    test-rmse:2.996107+0.432250 
## [8]  train-rmse:2.584055+0.102431    test-rmse:2.944647+0.449546 
## [9]  train-rmse:2.477249+0.079988    test-rmse:2.859602+0.488745 
## [10] train-rmse:2.396291+0.081828    test-rmse:2.827479+0.456702 
## [11] train-rmse:2.303992+0.095482    test-rmse:2.748844+0.452439 
## [12] train-rmse:2.223753+0.075238    test-rmse:2.722339+0.511500 
## [13] train-rmse:2.153038+0.080599    test-rmse:2.681872+0.516152 
## [14] train-rmse:2.089519+0.072700    test-rmse:2.598127+0.539542 
## [15] train-rmse:2.038350+0.070129    test-rmse:2.558207+0.520785 
## [16] train-rmse:1.993187+0.069812    test-rmse:2.531576+0.523816 
## [17] train-rmse:1.943812+0.070397    test-rmse:2.507423+0.522410 
## [18] train-rmse:1.897446+0.070977    test-rmse:2.496343+0.518335 
## [19] train-rmse:1.861666+0.070162    test-rmse:2.469317+0.504411 
## [20] train-rmse:1.820628+0.055407    test-rmse:2.457993+0.552117 
## [21] train-rmse:1.786098+0.050036    test-rmse:2.447631+0.553191 
## [22] train-rmse:1.757223+0.044378    test-rmse:2.432908+0.550910 
## [23] train-rmse:1.730836+0.041671    test-rmse:2.416251+0.543953 
## [24] train-rmse:1.705133+0.040704    test-rmse:2.398971+0.551707 
## [25] train-rmse:1.682243+0.039448    test-rmse:2.387920+0.555257 
## [26] train-rmse:1.655499+0.037453    test-rmse:2.357716+0.568315 
## [27] train-rmse:1.632925+0.035995    test-rmse:2.347596+0.566063 
## [28] train-rmse:1.602997+0.039468    test-rmse:2.349879+0.572614 
## [29] train-rmse:1.574267+0.040744    test-rmse:2.334684+0.562242 
## [30] train-rmse:1.552498+0.042503    test-rmse:2.325295+0.548513 
## [31] train-rmse:1.531122+0.044589    test-rmse:2.325699+0.567482 
## [32] train-rmse:1.512451+0.036833    test-rmse:2.316932+0.574971 
## [33] train-rmse:1.492951+0.029616    test-rmse:2.304436+0.577241 
## [34] train-rmse:1.474720+0.025749    test-rmse:2.287822+0.579360 
## [35] train-rmse:1.457619+0.021857    test-rmse:2.281165+0.581123 
## [36] train-rmse:1.439525+0.025096    test-rmse:2.278747+0.581277 
## [37] train-rmse:1.415715+0.034397    test-rmse:2.271003+0.577328 
## [38] train-rmse:1.400430+0.037002    test-rmse:2.268105+0.578737 
## [39] train-rmse:1.388443+0.037847    test-rmse:2.261818+0.577160 
## [40] train-rmse:1.366037+0.043388    test-rmse:2.245429+0.563141 
## [41] train-rmse:1.353124+0.046581    test-rmse:2.242851+0.567350 
## [42] train-rmse:1.339797+0.048157    test-rmse:2.236828+0.565031 
## [43] train-rmse:1.327113+0.048434    test-rmse:2.235243+0.575686 
## [44] train-rmse:1.306471+0.048410    test-rmse:2.236762+0.574675 
## [45] train-rmse:1.294620+0.052012    test-rmse:2.237972+0.573358 
## [46] train-rmse:1.284210+0.053641    test-rmse:2.228328+0.562000 
## [47] train-rmse:1.270693+0.051534    test-rmse:2.234026+0.557751 
## [48] train-rmse:1.258551+0.051822    test-rmse:2.233735+0.560482 
## [49] train-rmse:1.244230+0.050110    test-rmse:2.233111+0.558194 
## [50] train-rmse:1.231256+0.050555    test-rmse:2.215908+0.570845 
## [51] train-rmse:1.221006+0.047820    test-rmse:2.209171+0.570984 
## [52] train-rmse:1.208839+0.044714    test-rmse:2.208618+0.574688 
## [53] train-rmse:1.200752+0.045314    test-rmse:2.204702+0.579032 
## [54] train-rmse:1.192041+0.042864    test-rmse:2.207523+0.577445 
## [55] train-rmse:1.177847+0.042549    test-rmse:2.200397+0.580622 
## [56] train-rmse:1.163061+0.047409    test-rmse:2.198776+0.563769 
## [57] train-rmse:1.151746+0.049586    test-rmse:2.192797+0.566871 
## [58] train-rmse:1.144287+0.048337    test-rmse:2.191072+0.565619 
## [59] train-rmse:1.135278+0.051467    test-rmse:2.188567+0.565547 
## [60] train-rmse:1.125720+0.048746    test-rmse:2.179899+0.569469 
## [61] train-rmse:1.117396+0.047193    test-rmse:2.176664+0.572134 
## [62] train-rmse:1.111991+0.047335    test-rmse:2.175672+0.570713 
## [63] train-rmse:1.100319+0.045350    test-rmse:2.179050+0.567416 
## [64] train-rmse:1.094555+0.044238    test-rmse:2.181537+0.568243 
## [65] train-rmse:1.088671+0.045049    test-rmse:2.181016+0.570052 
## [66] train-rmse:1.083312+0.046186    test-rmse:2.182471+0.564665 
## [67] train-rmse:1.074227+0.043431    test-rmse:2.180832+0.569292 
## [68] train-rmse:1.058966+0.045580    test-rmse:2.181809+0.565625 
## Stopping. Best iteration:
## [62] train-rmse:1.111991+0.047335    test-rmse:2.175672+0.570713
## 
## 93 
## [1]  train-rmse:5.867268+0.114469    test-rmse:5.997922+0.576519 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.454293+0.085837    test-rmse:4.601266+0.513754 
## [3]  train-rmse:3.524120+0.071849    test-rmse:3.754776+0.486958 
## [4]  train-rmse:2.967254+0.050992    test-rmse:3.265786+0.458348 
## [5]  train-rmse:2.645502+0.083268    test-rmse:3.032049+0.471942 
## [6]  train-rmse:2.420327+0.073922    test-rmse:2.851969+0.459181 
## [7]  train-rmse:2.252709+0.065601    test-rmse:2.798602+0.442637 
## [8]  train-rmse:2.121442+0.071718    test-rmse:2.710172+0.476761 
## [9]  train-rmse:1.997641+0.050427    test-rmse:2.621106+0.461547 
## [10] train-rmse:1.913521+0.049571    test-rmse:2.579103+0.462926 
## [11] train-rmse:1.835627+0.041145    test-rmse:2.524997+0.451594 
## [12] train-rmse:1.759317+0.039972    test-rmse:2.467214+0.456442 
## [13] train-rmse:1.687825+0.038650    test-rmse:2.449212+0.479507 
## [14] train-rmse:1.631731+0.035267    test-rmse:2.415070+0.482958 
## [15] train-rmse:1.571155+0.040662    test-rmse:2.386455+0.488834 
## [16] train-rmse:1.513114+0.052848    test-rmse:2.352756+0.473267 
## [17] train-rmse:1.466990+0.055879    test-rmse:2.347008+0.473245 
## [18] train-rmse:1.430456+0.057806    test-rmse:2.339526+0.473603 
## [19] train-rmse:1.395349+0.058131    test-rmse:2.316894+0.475964 
## [20] train-rmse:1.352813+0.047090    test-rmse:2.313148+0.478593 
## [21] train-rmse:1.318206+0.058490    test-rmse:2.293068+0.477208 
## [22] train-rmse:1.287958+0.060824    test-rmse:2.276181+0.481005 
## [23] train-rmse:1.257248+0.056917    test-rmse:2.270124+0.473316 
## [24] train-rmse:1.229324+0.055703    test-rmse:2.266403+0.465649 
## [25] train-rmse:1.191571+0.062795    test-rmse:2.246706+0.467933 
## [26] train-rmse:1.160057+0.057515    test-rmse:2.232078+0.469581 
## [27] train-rmse:1.138574+0.062564    test-rmse:2.224186+0.469898 
## [28] train-rmse:1.113212+0.058321    test-rmse:2.211674+0.477211 
## [29] train-rmse:1.088215+0.055731    test-rmse:2.203264+0.477419 
## [30] train-rmse:1.070266+0.056351    test-rmse:2.193715+0.485617 
## [31] train-rmse:1.053299+0.054981    test-rmse:2.189120+0.497501 
## [32] train-rmse:1.028376+0.048908    test-rmse:2.185544+0.494604 
## [33] train-rmse:1.013798+0.047545    test-rmse:2.177661+0.495261 
## [34] train-rmse:0.986846+0.053043    test-rmse:2.174077+0.493060 
## [35] train-rmse:0.967624+0.059548    test-rmse:2.172809+0.493800 
## [36] train-rmse:0.949167+0.063842    test-rmse:2.166493+0.481998 
## [37] train-rmse:0.920932+0.051564    test-rmse:2.165782+0.478872 
## [38] train-rmse:0.904563+0.054366    test-rmse:2.158550+0.481763 
## [39] train-rmse:0.890057+0.051835    test-rmse:2.157156+0.482285 
## [40] train-rmse:0.871871+0.049326    test-rmse:2.148227+0.490872 
## [41] train-rmse:0.862045+0.050887    test-rmse:2.142189+0.488419 
## [42] train-rmse:0.852655+0.051372    test-rmse:2.138220+0.486384 
## [43] train-rmse:0.835473+0.052528    test-rmse:2.129464+0.485535 
## [44] train-rmse:0.820503+0.054150    test-rmse:2.120759+0.471712 
## [45] train-rmse:0.806792+0.055317    test-rmse:2.120615+0.475712 
## [46] train-rmse:0.789391+0.058871    test-rmse:2.118446+0.479050 
## [47] train-rmse:0.771166+0.051931    test-rmse:2.124700+0.482262 
## [48] train-rmse:0.757365+0.057976    test-rmse:2.120390+0.491942 
## [49] train-rmse:0.748322+0.059740    test-rmse:2.114858+0.495380 
## [50] train-rmse:0.736394+0.061964    test-rmse:2.130017+0.498595 
## [51] train-rmse:0.723478+0.059899    test-rmse:2.128477+0.499825 
## [52] train-rmse:0.709955+0.055261    test-rmse:2.128380+0.500238 
## [53] train-rmse:0.697221+0.050860    test-rmse:2.127990+0.500792 
## [54] train-rmse:0.682228+0.052610    test-rmse:2.122437+0.500990 
## [55] train-rmse:0.669312+0.047975    test-rmse:2.120629+0.499695 
## Stopping. Best iteration:
## [49] train-rmse:0.748322+0.059740    test-rmse:2.114858+0.495380
## 
## 94 
## [1]  train-rmse:5.757354+0.113297    test-rmse:5.854349+0.560155 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.218347+0.089434    test-rmse:4.432214+0.446689 
## [3]  train-rmse:3.282075+0.057017    test-rmse:3.622836+0.442871 
## [4]  train-rmse:2.715809+0.056456    test-rmse:3.163267+0.476956 
## [5]  train-rmse:2.351926+0.039526    test-rmse:2.877200+0.499205 
## [6]  train-rmse:2.100558+0.040043    test-rmse:2.768968+0.508926 
## [7]  train-rmse:1.932421+0.045440    test-rmse:2.679064+0.509007 
## [8]  train-rmse:1.811407+0.047449    test-rmse:2.617381+0.513235 
## [9]  train-rmse:1.678415+0.053864    test-rmse:2.580146+0.506148 
## [10] train-rmse:1.593321+0.052575    test-rmse:2.530973+0.501973 
## [11] train-rmse:1.503181+0.042390    test-rmse:2.477239+0.516780 
## [12] train-rmse:1.438104+0.047521    test-rmse:2.446609+0.521657 
## [13] train-rmse:1.371760+0.054106    test-rmse:2.431021+0.514286 
## [14] train-rmse:1.318595+0.058230    test-rmse:2.411541+0.540078 
## [15] train-rmse:1.265991+0.048332    test-rmse:2.385635+0.536391 
## [16] train-rmse:1.225547+0.046927    test-rmse:2.369980+0.548939 
## [17] train-rmse:1.178231+0.033990    test-rmse:2.362673+0.549684 
## [18] train-rmse:1.135725+0.047225    test-rmse:2.339833+0.537342 
## [19] train-rmse:1.081991+0.037890    test-rmse:2.333106+0.540948 
## [20] train-rmse:1.044644+0.041489    test-rmse:2.319683+0.530994 
## [21] train-rmse:1.019146+0.040042    test-rmse:2.315793+0.529205 
## [22] train-rmse:0.985677+0.042878    test-rmse:2.292050+0.548122 
## [23] train-rmse:0.954878+0.028559    test-rmse:2.280713+0.549691 
## [24] train-rmse:0.913753+0.031251    test-rmse:2.270481+0.550304 
## [25] train-rmse:0.879526+0.039348    test-rmse:2.256571+0.535523 
## [26] train-rmse:0.853664+0.037821    test-rmse:2.249059+0.532803 
## [27] train-rmse:0.837257+0.038421    test-rmse:2.248492+0.539146 
## [28] train-rmse:0.802841+0.032894    test-rmse:2.241002+0.544545 
## [29] train-rmse:0.786793+0.036511    test-rmse:2.237517+0.548618 
## [30] train-rmse:0.763190+0.041570    test-rmse:2.230361+0.550022 
## [31] train-rmse:0.737505+0.037047    test-rmse:2.222876+0.549614 
## [32] train-rmse:0.720629+0.038814    test-rmse:2.223530+0.549417 
## [33] train-rmse:0.697102+0.038291    test-rmse:2.220208+0.553095 
## [34] train-rmse:0.674646+0.037574    test-rmse:2.218671+0.550465 
## [35] train-rmse:0.648256+0.037078    test-rmse:2.218693+0.550460 
## [36] train-rmse:0.628064+0.037481    test-rmse:2.214539+0.549654 
## [37] train-rmse:0.608485+0.031724    test-rmse:2.217647+0.550041 
## [38] train-rmse:0.596039+0.029855    test-rmse:2.215056+0.548147 
## [39] train-rmse:0.581593+0.027398    test-rmse:2.214333+0.546124 
## [40] train-rmse:0.562652+0.027606    test-rmse:2.214397+0.546289 
## [41] train-rmse:0.548371+0.027327    test-rmse:2.209350+0.549097 
## [42] train-rmse:0.536242+0.025553    test-rmse:2.209862+0.546182 
## [43] train-rmse:0.527136+0.025249    test-rmse:2.207615+0.545361 
## [44] train-rmse:0.514911+0.027341    test-rmse:2.206504+0.545356 
## [45] train-rmse:0.499679+0.023742    test-rmse:2.204484+0.550024 
## [46] train-rmse:0.484748+0.021870    test-rmse:2.199197+0.549912 
## [47] train-rmse:0.474624+0.019999    test-rmse:2.197184+0.551699 
## [48] train-rmse:0.466745+0.020959    test-rmse:2.198343+0.546501 
## [49] train-rmse:0.454869+0.018292    test-rmse:2.199549+0.547214 
## [50] train-rmse:0.443982+0.023117    test-rmse:2.195423+0.546217 
## [51] train-rmse:0.432350+0.027080    test-rmse:2.193499+0.547264 
## [52] train-rmse:0.423506+0.029417    test-rmse:2.193600+0.547162 
## [53] train-rmse:0.406417+0.027671    test-rmse:2.184364+0.538181 
## [54] train-rmse:0.394833+0.021830    test-rmse:2.180515+0.539582 
## [55] train-rmse:0.382999+0.020131    test-rmse:2.179983+0.543077 
## [56] train-rmse:0.372504+0.019040    test-rmse:2.179502+0.542403 
## [57] train-rmse:0.363209+0.019827    test-rmse:2.181725+0.541730 
## [58] train-rmse:0.357737+0.020867    test-rmse:2.178596+0.537029 
## [59] train-rmse:0.351174+0.022722    test-rmse:2.178749+0.537710 
## [60] train-rmse:0.346347+0.021996    test-rmse:2.179565+0.537196 
## [61] train-rmse:0.335228+0.018578    test-rmse:2.178043+0.535876 
## [62] train-rmse:0.324791+0.018510    test-rmse:2.174011+0.529934 
## [63] train-rmse:0.317614+0.019033    test-rmse:2.171846+0.531588 
## [64] train-rmse:0.308337+0.015347    test-rmse:2.168685+0.532422 
## [65] train-rmse:0.302964+0.014492    test-rmse:2.169389+0.533511 
## [66] train-rmse:0.297740+0.014399    test-rmse:2.169611+0.536103 
## [67] train-rmse:0.292777+0.015378    test-rmse:2.169341+0.536175 
## [68] train-rmse:0.288316+0.014779    test-rmse:2.169233+0.535110 
## [69] train-rmse:0.281208+0.014367    test-rmse:2.170087+0.535369 
## [70] train-rmse:0.276297+0.013036    test-rmse:2.170352+0.535084 
## Stopping. Best iteration:
## [64] train-rmse:0.308337+0.015347    test-rmse:2.168685+0.532422
## 
## 95 
## [1]  train-rmse:5.681381+0.110323    test-rmse:5.730993+0.528044 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.095520+0.081347    test-rmse:4.279301+0.446865 
## [3]  train-rmse:3.152176+0.060855    test-rmse:3.533389+0.454069 
## [4]  train-rmse:2.570234+0.064375    test-rmse:3.127618+0.450363 
## [5]  train-rmse:2.167044+0.060248    test-rmse:2.897721+0.502461 
## [6]  train-rmse:1.918014+0.055513    test-rmse:2.765406+0.505018 
## [7]  train-rmse:1.736023+0.056621    test-rmse:2.632380+0.489788 
## [8]  train-rmse:1.594819+0.082711    test-rmse:2.579448+0.491181 
## [9]  train-rmse:1.479169+0.083571    test-rmse:2.530867+0.508138 
## [10] train-rmse:1.386896+0.082832    test-rmse:2.500874+0.521620 
## [11] train-rmse:1.277954+0.053090    test-rmse:2.461408+0.520539 
## [12] train-rmse:1.211643+0.059819    test-rmse:2.463328+0.518526 
## [13] train-rmse:1.159610+0.068090    test-rmse:2.455721+0.530186 
## [14] train-rmse:1.067303+0.055013    test-rmse:2.418073+0.524245 
## [15] train-rmse:1.006118+0.040924    test-rmse:2.412809+0.524533 
## [16] train-rmse:0.974563+0.041366    test-rmse:2.392297+0.534263 
## [17] train-rmse:0.915514+0.042328    test-rmse:2.378769+0.528144 
## [18] train-rmse:0.882580+0.043194    test-rmse:2.366937+0.536956 
## [19] train-rmse:0.842090+0.039112    test-rmse:2.356176+0.532732 
## [20] train-rmse:0.810613+0.042632    test-rmse:2.348166+0.536295 
## [21] train-rmse:0.765907+0.034530    test-rmse:2.339917+0.538313 
## [22] train-rmse:0.736070+0.027890    test-rmse:2.328843+0.535536 
## [23] train-rmse:0.704888+0.027322    test-rmse:2.321558+0.537821 
## [24] train-rmse:0.679959+0.027316    test-rmse:2.321978+0.532837 
## [25] train-rmse:0.648078+0.026330    test-rmse:2.323865+0.530684 
## [26] train-rmse:0.615856+0.023295    test-rmse:2.318669+0.533976 
## [27] train-rmse:0.594153+0.021593    test-rmse:2.315663+0.534549 
## [28] train-rmse:0.571236+0.021444    test-rmse:2.311797+0.539071 
## [29] train-rmse:0.548131+0.015589    test-rmse:2.311794+0.535834 
## [30] train-rmse:0.526139+0.021048    test-rmse:2.311201+0.539048 
## [31] train-rmse:0.504582+0.023739    test-rmse:2.313505+0.547703 
## [32] train-rmse:0.478689+0.020629    test-rmse:2.307723+0.548648 
## [33] train-rmse:0.458016+0.021696    test-rmse:2.316015+0.560758 
## [34] train-rmse:0.442484+0.023283    test-rmse:2.313016+0.558295 
## [35] train-rmse:0.429465+0.019945    test-rmse:2.314998+0.561736 
## [36] train-rmse:0.418039+0.022202    test-rmse:2.312440+0.560977 
## [37] train-rmse:0.403415+0.019781    test-rmse:2.307736+0.562233 
## [38] train-rmse:0.388327+0.010609    test-rmse:2.308506+0.563384 
## Stopping. Best iteration:
## [32] train-rmse:0.478689+0.020629    test-rmse:2.307723+0.548648
## 
## 96 
## [1]  train-rmse:5.633926+0.101851    test-rmse:5.703070+0.572286 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.013067+0.066211    test-rmse:4.288174+0.465830 
## [3]  train-rmse:3.015585+0.034300    test-rmse:3.492429+0.465507 
## [4]  train-rmse:2.404612+0.030049    test-rmse:3.076868+0.526766 
## [5]  train-rmse:1.995673+0.045912    test-rmse:2.808664+0.586047 
## [6]  train-rmse:1.734649+0.050363    test-rmse:2.653564+0.588303 
## [7]  train-rmse:1.515776+0.046566    test-rmse:2.579832+0.568648 
## [8]  train-rmse:1.380143+0.041624    test-rmse:2.528047+0.575792 
## [9]  train-rmse:1.260775+0.059264    test-rmse:2.503334+0.587651 
## [10] train-rmse:1.162878+0.044316    test-rmse:2.479199+0.591426 
## [11] train-rmse:1.080418+0.039055    test-rmse:2.460945+0.609240 
## [12] train-rmse:1.016082+0.042202    test-rmse:2.441035+0.610754 
## [13] train-rmse:0.950221+0.052508    test-rmse:2.429851+0.618926 
## [14] train-rmse:0.881152+0.052632    test-rmse:2.417913+0.611373 
## [15] train-rmse:0.811434+0.029477    test-rmse:2.394011+0.617752 
## [16] train-rmse:0.752898+0.034244    test-rmse:2.386608+0.633725 
## [17] train-rmse:0.712439+0.031910    test-rmse:2.380855+0.639946 
## [18] train-rmse:0.670500+0.039877    test-rmse:2.370345+0.638562 
## [19] train-rmse:0.637536+0.039299    test-rmse:2.371624+0.642060 
## [20] train-rmse:0.601373+0.032160    test-rmse:2.363982+0.641400 
## [21] train-rmse:0.566271+0.031261    test-rmse:2.356497+0.644896 
## [22] train-rmse:0.538024+0.024655    test-rmse:2.358894+0.642265 
## [23] train-rmse:0.507777+0.030106    test-rmse:2.350307+0.639105 
## [24] train-rmse:0.486140+0.030268    test-rmse:2.347003+0.642706 
## [25] train-rmse:0.457264+0.040612    test-rmse:2.348775+0.645551 
## [26] train-rmse:0.438560+0.034627    test-rmse:2.347940+0.645932 
## [27] train-rmse:0.415217+0.029428    test-rmse:2.342550+0.648034 
## [28] train-rmse:0.392859+0.030243    test-rmse:2.344910+0.646004 
## [29] train-rmse:0.366414+0.029958    test-rmse:2.344562+0.645727 
## [30] train-rmse:0.350293+0.030121    test-rmse:2.344259+0.647669 
## [31] train-rmse:0.334432+0.027296    test-rmse:2.342975+0.645056 
## [32] train-rmse:0.311444+0.026678    test-rmse:2.338050+0.646711 
## [33] train-rmse:0.293815+0.029137    test-rmse:2.341518+0.652570 
## [34] train-rmse:0.283134+0.025617    test-rmse:2.340746+0.652033 
## [35] train-rmse:0.267283+0.024131    test-rmse:2.342502+0.648081 
## [36] train-rmse:0.255499+0.020677    test-rmse:2.343362+0.648156 
## [37] train-rmse:0.243018+0.018171    test-rmse:2.340793+0.648889 
## [38] train-rmse:0.230854+0.017916    test-rmse:2.337975+0.647504 
## [39] train-rmse:0.221158+0.017236    test-rmse:2.338123+0.646011 
## [40] train-rmse:0.211634+0.015215    test-rmse:2.337428+0.646951 
## [41] train-rmse:0.199551+0.013413    test-rmse:2.334213+0.643796 
## [42] train-rmse:0.191172+0.012848    test-rmse:2.333176+0.644028 
## [43] train-rmse:0.184988+0.012292    test-rmse:2.333061+0.644178 
## [44] train-rmse:0.177862+0.012734    test-rmse:2.334687+0.643121 
## [45] train-rmse:0.165610+0.009184    test-rmse:2.331444+0.642561 
## [46] train-rmse:0.159131+0.009907    test-rmse:2.331358+0.642833 
## [47] train-rmse:0.152854+0.009964    test-rmse:2.331262+0.642852 
## [48] train-rmse:0.146346+0.010424    test-rmse:2.331416+0.642754 
## [49] train-rmse:0.139624+0.007745    test-rmse:2.330596+0.642516 
## [50] train-rmse:0.133060+0.006640    test-rmse:2.330119+0.643498 
## [51] train-rmse:0.126229+0.008075    test-rmse:2.329793+0.642995 
## [52] train-rmse:0.119718+0.007806    test-rmse:2.329662+0.643239 
## [53] train-rmse:0.114996+0.007239    test-rmse:2.327989+0.643733 
## [54] train-rmse:0.111435+0.006437    test-rmse:2.327418+0.643870 
## [55] train-rmse:0.107748+0.005599    test-rmse:2.327202+0.644137 
## [56] train-rmse:0.103578+0.006115    test-rmse:2.326895+0.644888 
## [57] train-rmse:0.099173+0.007444    test-rmse:2.326904+0.643792 
## [58] train-rmse:0.095439+0.007365    test-rmse:2.326373+0.644174 
## [59] train-rmse:0.091989+0.006505    test-rmse:2.325656+0.644796 
## [60] train-rmse:0.088237+0.006261    test-rmse:2.325888+0.644854 
## [61] train-rmse:0.084134+0.007008    test-rmse:2.325528+0.644964 
## [62] train-rmse:0.081244+0.006735    test-rmse:2.325108+0.645293 
## [63] train-rmse:0.077573+0.006117    test-rmse:2.325777+0.645142 
## [64] train-rmse:0.073671+0.005795    test-rmse:2.325442+0.646076 
## [65] train-rmse:0.069731+0.004185    test-rmse:2.325696+0.645647 
## [66] train-rmse:0.066599+0.002937    test-rmse:2.326056+0.645320 
## [67] train-rmse:0.063882+0.003502    test-rmse:2.325979+0.645490 
## [68] train-rmse:0.061760+0.003346    test-rmse:2.326189+0.645722 
## Stopping. Best iteration:
## [62] train-rmse:0.081244+0.006735    test-rmse:2.325108+0.645293
## 
## 97 
## [1]  train-rmse:5.616997+0.100380    test-rmse:5.691990+0.572019 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.960246+0.060911    test-rmse:4.253966+0.483147 
## [3]  train-rmse:2.936114+0.038264    test-rmse:3.436436+0.495715 
## [4]  train-rmse:2.282579+0.040483    test-rmse:3.024076+0.519414 
## [5]  train-rmse:1.852717+0.051435    test-rmse:2.760657+0.544639 
## [6]  train-rmse:1.546085+0.039717    test-rmse:2.657452+0.549452 
## [7]  train-rmse:1.345678+0.049982    test-rmse:2.564750+0.550903 
## [8]  train-rmse:1.187476+0.035566    test-rmse:2.516628+0.527335 
## [9]  train-rmse:1.055303+0.029655    test-rmse:2.502455+0.540834 
## [10] train-rmse:0.945141+0.033348    test-rmse:2.466197+0.532294 
## [11] train-rmse:0.869212+0.031745    test-rmse:2.441266+0.543724 
## [12] train-rmse:0.815275+0.028991    test-rmse:2.433883+0.544141 
## [13] train-rmse:0.745860+0.030240    test-rmse:2.407464+0.549943 
## [14] train-rmse:0.690820+0.033711    test-rmse:2.413176+0.559499 
## [15] train-rmse:0.638616+0.033350    test-rmse:2.411526+0.568711 
## [16] train-rmse:0.584178+0.020746    test-rmse:2.398402+0.571290 
## [17] train-rmse:0.534858+0.028055    test-rmse:2.390161+0.571208 
## [18] train-rmse:0.501277+0.024968    test-rmse:2.386829+0.574985 
## [19] train-rmse:0.471900+0.028345    test-rmse:2.384313+0.576550 
## [20] train-rmse:0.435164+0.018757    test-rmse:2.374755+0.578305 
## [21] train-rmse:0.408151+0.021743    test-rmse:2.374082+0.579651 
## [22] train-rmse:0.383653+0.015203    test-rmse:2.367198+0.578075 
## [23] train-rmse:0.360654+0.017613    test-rmse:2.367341+0.577356 
## [24] train-rmse:0.342292+0.013592    test-rmse:2.366069+0.580954 
## [25] train-rmse:0.318981+0.019039    test-rmse:2.359071+0.581328 
## [26] train-rmse:0.300298+0.018674    test-rmse:2.360336+0.583714 
## [27] train-rmse:0.280866+0.017477    test-rmse:2.357206+0.584263 
## [28] train-rmse:0.260587+0.014129    test-rmse:2.353783+0.584676 
## [29] train-rmse:0.244830+0.014273    test-rmse:2.349661+0.587606 
## [30] train-rmse:0.230451+0.011271    test-rmse:2.348061+0.587546 
## [31] train-rmse:0.215340+0.014520    test-rmse:2.344227+0.589134 
## [32] train-rmse:0.197728+0.020929    test-rmse:2.346714+0.591115 
## [33] train-rmse:0.189499+0.019545    test-rmse:2.346831+0.590656 
## [34] train-rmse:0.178803+0.021059    test-rmse:2.346422+0.590326 
## [35] train-rmse:0.168418+0.019488    test-rmse:2.346302+0.591140 
## [36] train-rmse:0.157362+0.014936    test-rmse:2.348022+0.594039 
## [37] train-rmse:0.148278+0.015785    test-rmse:2.347683+0.593549 
## Stopping. Best iteration:
## [31] train-rmse:0.215340+0.014520    test-rmse:2.344227+0.589134
## 
## 98 
## [1]  train-rmse:6.051489+0.133867    test-rmse:6.063642+0.638666 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.876173+0.104040    test-rmse:4.977498+0.674841 
## [3]  train-rmse:4.254621+0.117041    test-rmse:4.362234+0.557640 
## [4]  train-rmse:3.918233+0.114223    test-rmse:4.047886+0.503617 
## [5]  train-rmse:3.705315+0.117784    test-rmse:3.862625+0.420934 
## [6]  train-rmse:3.548559+0.113787    test-rmse:3.675694+0.385543 
## [7]  train-rmse:3.418487+0.110226    test-rmse:3.518610+0.441510 
## [8]  train-rmse:3.313822+0.123658    test-rmse:3.454294+0.401862 
## [9]  train-rmse:3.232002+0.124133    test-rmse:3.387119+0.429739 
## [10] train-rmse:3.158780+0.124546    test-rmse:3.331282+0.429490 
## [11] train-rmse:3.091429+0.123102    test-rmse:3.230457+0.450111 
## [12] train-rmse:3.026294+0.122336    test-rmse:3.191912+0.464351 
## [13] train-rmse:2.968807+0.126039    test-rmse:3.109700+0.460335 
## [14] train-rmse:2.918828+0.130826    test-rmse:3.075041+0.434942 
## [15] train-rmse:2.874675+0.132932    test-rmse:3.069446+0.457255 
## [16] train-rmse:2.833154+0.133676    test-rmse:3.042020+0.464491 
## [17] train-rmse:2.792685+0.136370    test-rmse:2.995707+0.482984 
## [18] train-rmse:2.755482+0.138320    test-rmse:2.978718+0.494770 
## [19] train-rmse:2.719819+0.141012    test-rmse:2.950746+0.476432 
## [20] train-rmse:2.685846+0.141304    test-rmse:2.932068+0.455901 
## [21] train-rmse:2.655062+0.140612    test-rmse:2.899131+0.463161 
## [22] train-rmse:2.624341+0.140240    test-rmse:2.885838+0.477562 
## [23] train-rmse:2.594447+0.139937    test-rmse:2.853609+0.467551 
## [24] train-rmse:2.565703+0.140040    test-rmse:2.825803+0.456586 
## [25] train-rmse:2.538492+0.140733    test-rmse:2.776797+0.463781 
## [26] train-rmse:2.513889+0.141464    test-rmse:2.782348+0.481857 
## [27] train-rmse:2.490778+0.142151    test-rmse:2.765653+0.494879 
## [28] train-rmse:2.468342+0.143008    test-rmse:2.740476+0.494702 
## [29] train-rmse:2.447741+0.143682    test-rmse:2.719947+0.509326 
## [30] train-rmse:2.427546+0.144512    test-rmse:2.708673+0.499667 
## [31] train-rmse:2.408096+0.145386    test-rmse:2.673534+0.495309 
## [32] train-rmse:2.389673+0.146423    test-rmse:2.666502+0.495234 
## [33] train-rmse:2.371912+0.146885    test-rmse:2.657872+0.485405 
## [34] train-rmse:2.354630+0.146360    test-rmse:2.633208+0.498666 
## [35] train-rmse:2.337787+0.145715    test-rmse:2.607217+0.492064 
## [36] train-rmse:2.322328+0.145179    test-rmse:2.596577+0.491267 
## [37] train-rmse:2.308030+0.144637    test-rmse:2.578237+0.496252 
## [38] train-rmse:2.294075+0.144446    test-rmse:2.573982+0.497809 
## [39] train-rmse:2.281087+0.144628    test-rmse:2.568958+0.500320 
## [40] train-rmse:2.268668+0.145006    test-rmse:2.559299+0.490978 
## [41] train-rmse:2.256646+0.145650    test-rmse:2.546117+0.491434 
## [42] train-rmse:2.244724+0.146448    test-rmse:2.535807+0.496216 
## [43] train-rmse:2.233041+0.147416    test-rmse:2.522052+0.492311 
## [44] train-rmse:2.221817+0.148606    test-rmse:2.531091+0.506360 
## [45] train-rmse:2.210794+0.149370    test-rmse:2.523975+0.514615 
## [46] train-rmse:2.200579+0.150035    test-rmse:2.512348+0.514678 
## [47] train-rmse:2.190885+0.150777    test-rmse:2.502736+0.520520 
## [48] train-rmse:2.181521+0.151472    test-rmse:2.493180+0.523120 
## [49] train-rmse:2.172420+0.151732    test-rmse:2.475288+0.529906 
## [50] train-rmse:2.163566+0.152134    test-rmse:2.471093+0.528748 
## [51] train-rmse:2.155057+0.152274    test-rmse:2.471668+0.531215 
## [52] train-rmse:2.146722+0.152395    test-rmse:2.466795+0.525732 
## [53] train-rmse:2.138429+0.152509    test-rmse:2.458746+0.525916 
## [54] train-rmse:2.130960+0.152698    test-rmse:2.445294+0.533634 
## [55] train-rmse:2.123789+0.153132    test-rmse:2.440688+0.532502 
## [56] train-rmse:2.116845+0.153467    test-rmse:2.428947+0.528963 
## [57] train-rmse:2.109850+0.153856    test-rmse:2.420067+0.534705 
## [58] train-rmse:2.103191+0.154157    test-rmse:2.416252+0.532527 
## [59] train-rmse:2.096638+0.154508    test-rmse:2.418817+0.532113 
## [60] train-rmse:2.090025+0.154568    test-rmse:2.421745+0.524364 
## [61] train-rmse:2.083577+0.154729    test-rmse:2.417320+0.524374 
## [62] train-rmse:2.077349+0.154975    test-rmse:2.405534+0.528032 
## [63] train-rmse:2.071531+0.155467    test-rmse:2.401676+0.523719 
## [64] train-rmse:2.065831+0.155821    test-rmse:2.396119+0.520271 
## [65] train-rmse:2.060317+0.156069    test-rmse:2.394636+0.517952 
## [66] train-rmse:2.054856+0.156355    test-rmse:2.394446+0.516378 
## [67] train-rmse:2.049545+0.156607    test-rmse:2.394120+0.523462 
## [68] train-rmse:2.044371+0.157008    test-rmse:2.398929+0.536313 
## [69] train-rmse:2.039333+0.157356    test-rmse:2.400660+0.530201 
## [70] train-rmse:2.034418+0.157546    test-rmse:2.390050+0.538042 
## [71] train-rmse:2.029528+0.157752    test-rmse:2.387445+0.536782 
## [72] train-rmse:2.024818+0.157837    test-rmse:2.385320+0.533043 
## [73] train-rmse:2.020526+0.158000    test-rmse:2.378716+0.535998 
## [74] train-rmse:2.016373+0.158157    test-rmse:2.383158+0.536240 
## [75] train-rmse:2.011849+0.158602    test-rmse:2.378048+0.533334 
## [76] train-rmse:2.007701+0.158774    test-rmse:2.383343+0.534094 
## [77] train-rmse:2.003574+0.158782    test-rmse:2.383864+0.529649 
## [78] train-rmse:1.999371+0.158870    test-rmse:2.384962+0.532945 
## [79] train-rmse:1.995076+0.159028    test-rmse:2.383971+0.536963 
## [80] train-rmse:1.991155+0.159249    test-rmse:2.368862+0.546281 
## [81] train-rmse:1.987313+0.159241    test-rmse:2.367956+0.544445 
## [82] train-rmse:1.983510+0.159298    test-rmse:2.359374+0.540902 
## [83] train-rmse:1.979710+0.159348    test-rmse:2.358672+0.540042 
## [84] train-rmse:1.975993+0.159459    test-rmse:2.356598+0.543228 
## [85] train-rmse:1.972367+0.159680    test-rmse:2.358733+0.535091 
## [86] train-rmse:1.968878+0.159838    test-rmse:2.359022+0.542352 
## [87] train-rmse:1.965369+0.160023    test-rmse:2.357745+0.539971 
## [88] train-rmse:1.962020+0.160292    test-rmse:2.357810+0.540440 
## [89] train-rmse:1.958589+0.160356    test-rmse:2.358505+0.535734 
## [90] train-rmse:1.955196+0.160627    test-rmse:2.358304+0.535096 
## Stopping. Best iteration:
## [84] train-rmse:1.975993+0.159459    test-rmse:2.356598+0.543228
## 
## 99 
## [1]  train-rmse:5.868926+0.122296    test-rmse:5.938335+0.586717 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.541394+0.119339    test-rmse:4.692044+0.500737 
## [3]  train-rmse:3.761516+0.120444    test-rmse:3.909705+0.414142 
## [4]  train-rmse:3.281873+0.110026    test-rmse:3.489448+0.402600 
## [5]  train-rmse:2.978171+0.091227    test-rmse:3.255584+0.391469 
## [6]  train-rmse:2.765356+0.093205    test-rmse:3.034054+0.442525 
## [7]  train-rmse:2.625377+0.084316    test-rmse:2.932925+0.437814 
## [8]  train-rmse:2.518301+0.089847    test-rmse:2.907190+0.426425 
## [9]  train-rmse:2.414858+0.058214    test-rmse:2.809354+0.452135 
## [10] train-rmse:2.314491+0.070882    test-rmse:2.735665+0.386488 
## [11] train-rmse:2.234737+0.065702    test-rmse:2.674605+0.415638 
## [12] train-rmse:2.161401+0.070077    test-rmse:2.639536+0.438214 
## [13] train-rmse:2.094406+0.076762    test-rmse:2.599441+0.457259 
## [14] train-rmse:2.042467+0.072443    test-rmse:2.577319+0.454161 
## [15] train-rmse:1.993435+0.072084    test-rmse:2.528533+0.429797 
## [16] train-rmse:1.944754+0.076118    test-rmse:2.463743+0.461549 
## [17] train-rmse:1.900469+0.074039    test-rmse:2.439099+0.453212 
## [18] train-rmse:1.862945+0.075554    test-rmse:2.444026+0.431906 
## [19] train-rmse:1.809544+0.057885    test-rmse:2.437878+0.457796 
## [20] train-rmse:1.773354+0.059714    test-rmse:2.417624+0.480621 
## [21] train-rmse:1.743278+0.056453    test-rmse:2.403968+0.483802 
## [22] train-rmse:1.703797+0.052656    test-rmse:2.377791+0.493701 
## [23] train-rmse:1.678724+0.049803    test-rmse:2.352689+0.490086 
## [24] train-rmse:1.651475+0.044180    test-rmse:2.333172+0.495431 
## [25] train-rmse:1.628289+0.043127    test-rmse:2.318281+0.499101 
## [26] train-rmse:1.606633+0.041159    test-rmse:2.312603+0.499196 
## [27] train-rmse:1.583534+0.042577    test-rmse:2.300730+0.498295 
## [28] train-rmse:1.563498+0.043300    test-rmse:2.275031+0.501575 
## [29] train-rmse:1.524475+0.053625    test-rmse:2.256047+0.501678 
## [30] train-rmse:1.492207+0.033859    test-rmse:2.247596+0.517693 
## [31] train-rmse:1.472551+0.030456    test-rmse:2.241368+0.535505 
## [32] train-rmse:1.445040+0.035185    test-rmse:2.237240+0.520794 
## [33] train-rmse:1.427854+0.033701    test-rmse:2.241741+0.525197 
## [34] train-rmse:1.412104+0.029941    test-rmse:2.235419+0.526134 
## [35] train-rmse:1.393281+0.033636    test-rmse:2.220469+0.514344 
## [36] train-rmse:1.372800+0.033493    test-rmse:2.221927+0.513410 
## [37] train-rmse:1.358630+0.033966    test-rmse:2.215599+0.517742 
## [38] train-rmse:1.344271+0.034026    test-rmse:2.211350+0.514278 
## [39] train-rmse:1.331372+0.034756    test-rmse:2.206775+0.514326 
## [40] train-rmse:1.319523+0.033866    test-rmse:2.200651+0.514949 
## [41] train-rmse:1.306725+0.033727    test-rmse:2.199495+0.520257 
## [42] train-rmse:1.294158+0.030763    test-rmse:2.187964+0.523649 
## [43] train-rmse:1.285194+0.031163    test-rmse:2.180974+0.528604 
## [44] train-rmse:1.267803+0.036411    test-rmse:2.167986+0.536960 
## [45] train-rmse:1.252801+0.040594    test-rmse:2.156643+0.533200 
## [46] train-rmse:1.241309+0.043394    test-rmse:2.156974+0.537692 
## [47] train-rmse:1.232056+0.045981    test-rmse:2.160270+0.544435 
## [48] train-rmse:1.219738+0.049277    test-rmse:2.147983+0.540738 
## [49] train-rmse:1.201128+0.057945    test-rmse:2.139344+0.531690 
## [50] train-rmse:1.188173+0.058041    test-rmse:2.136945+0.533891 
## [51] train-rmse:1.180677+0.057911    test-rmse:2.135491+0.533749 
## [52] train-rmse:1.171360+0.058195    test-rmse:2.136210+0.531753 
## [53] train-rmse:1.160950+0.055209    test-rmse:2.133135+0.527105 
## [54] train-rmse:1.154238+0.055440    test-rmse:2.129627+0.527983 
## [55] train-rmse:1.147379+0.056717    test-rmse:2.121976+0.531634 
## [56] train-rmse:1.135024+0.055451    test-rmse:2.116950+0.532207 
## [57] train-rmse:1.114881+0.063967    test-rmse:2.108563+0.540799 
## [58] train-rmse:1.102541+0.065196    test-rmse:2.115068+0.530741 
## [59] train-rmse:1.092796+0.067890    test-rmse:2.113836+0.527794 
## [60] train-rmse:1.085005+0.069280    test-rmse:2.111151+0.524510 
## [61] train-rmse:1.072602+0.068755    test-rmse:2.099815+0.528435 
## [62] train-rmse:1.060521+0.064977    test-rmse:2.103149+0.545332 
## [63] train-rmse:1.042446+0.049554    test-rmse:2.097232+0.550112 
## [64] train-rmse:1.035977+0.048951    test-rmse:2.096880+0.550359 
## [65] train-rmse:1.026474+0.047501    test-rmse:2.093659+0.551707 
## [66] train-rmse:1.012549+0.043931    test-rmse:2.086478+0.538735 
## [67] train-rmse:1.002412+0.044319    test-rmse:2.073956+0.544217 
## [68] train-rmse:0.995459+0.043709    test-rmse:2.076718+0.544564 
## [69] train-rmse:0.977946+0.040382    test-rmse:2.082012+0.543126 
## [70] train-rmse:0.970080+0.042133    test-rmse:2.080563+0.545035 
## [71] train-rmse:0.959813+0.041105    test-rmse:2.085438+0.540672 
## [72] train-rmse:0.954082+0.041619    test-rmse:2.078424+0.534242 
## [73] train-rmse:0.949307+0.041962    test-rmse:2.075599+0.539849 
## Stopping. Best iteration:
## [67] train-rmse:1.002412+0.044319    test-rmse:2.073956+0.544217
## 
## 100 
## [1]  train-rmse:5.745553+0.111143    test-rmse:5.887407+0.570284 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.311638+0.084252    test-rmse:4.454818+0.476018 
## [3]  train-rmse:3.391970+0.068739    test-rmse:3.606303+0.442296 
## [4]  train-rmse:2.861174+0.049448    test-rmse:3.180983+0.460256 
## [5]  train-rmse:2.550873+0.076516    test-rmse:2.924376+0.460459 
## [6]  train-rmse:2.327835+0.071863    test-rmse:2.822275+0.465678 
## [7]  train-rmse:2.173747+0.055498    test-rmse:2.724882+0.470068 
## [8]  train-rmse:2.035308+0.060728    test-rmse:2.668566+0.492396 
## [9]  train-rmse:1.938757+0.069501    test-rmse:2.607254+0.496103 
## [10] train-rmse:1.835729+0.062596    test-rmse:2.564521+0.474799 
## [11] train-rmse:1.746425+0.061082    test-rmse:2.514831+0.453534 
## [12] train-rmse:1.686619+0.058752    test-rmse:2.463747+0.462837 
## [13] train-rmse:1.608085+0.057011    test-rmse:2.418540+0.459550 
## [14] train-rmse:1.548275+0.059286    test-rmse:2.405773+0.465578 
## [15] train-rmse:1.496141+0.063786    test-rmse:2.371892+0.456470 
## [16] train-rmse:1.454323+0.064401    test-rmse:2.355466+0.456557 
## [17] train-rmse:1.416120+0.066782    test-rmse:2.335753+0.460818 
## [18] train-rmse:1.378801+0.068043    test-rmse:2.316625+0.463688 
## [19] train-rmse:1.341817+0.067168    test-rmse:2.298643+0.469942 
## [20] train-rmse:1.310550+0.073844    test-rmse:2.281129+0.475690 
## [21] train-rmse:1.262267+0.062139    test-rmse:2.253750+0.478691 
## [22] train-rmse:1.231168+0.069639    test-rmse:2.253104+0.470409 
## [23] train-rmse:1.198401+0.060662    test-rmse:2.241069+0.473964 
## [24] train-rmse:1.176112+0.060334    test-rmse:2.235231+0.474707 
## [25] train-rmse:1.152396+0.063620    test-rmse:2.230042+0.469173 
## [26] train-rmse:1.123827+0.065954    test-rmse:2.222728+0.468230 
## [27] train-rmse:1.096210+0.070911    test-rmse:2.225947+0.475045 
## [28] train-rmse:1.068612+0.070799    test-rmse:2.220011+0.474350 
## [29] train-rmse:1.040789+0.063636    test-rmse:2.210990+0.470352 
## [30] train-rmse:1.022797+0.065035    test-rmse:2.209683+0.474908 
## [31] train-rmse:1.008989+0.066076    test-rmse:2.205946+0.471502 
## [32] train-rmse:0.976923+0.083669    test-rmse:2.194152+0.468107 
## [33] train-rmse:0.946454+0.080402    test-rmse:2.186083+0.465857 
## [34] train-rmse:0.926891+0.076895    test-rmse:2.178285+0.463935 
## [35] train-rmse:0.911264+0.080198    test-rmse:2.171706+0.465945 
## [36] train-rmse:0.890313+0.077774    test-rmse:2.165474+0.464945 
## [37] train-rmse:0.868400+0.070930    test-rmse:2.168247+0.466327 
## [38] train-rmse:0.855324+0.068268    test-rmse:2.171801+0.482191 
## [39] train-rmse:0.832742+0.074110    test-rmse:2.174458+0.476816 
## [40] train-rmse:0.823408+0.072757    test-rmse:2.173485+0.477103 
## [41] train-rmse:0.803338+0.073501    test-rmse:2.169567+0.481110 
## [42] train-rmse:0.785479+0.075611    test-rmse:2.168300+0.479434 
## Stopping. Best iteration:
## [36] train-rmse:0.890313+0.077774    test-rmse:2.165474+0.464945
## 
## 101 
## [1]  train-rmse:5.627841+0.110084    test-rmse:5.734733+0.553501 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.061201+0.086269    test-rmse:4.278806+0.434229 
## [3]  train-rmse:3.143977+0.066681    test-rmse:3.538810+0.404271 
## [4]  train-rmse:2.609172+0.058490    test-rmse:3.085606+0.475817 
## [5]  train-rmse:2.278053+0.065457    test-rmse:2.848483+0.446006 
## [6]  train-rmse:2.032900+0.063915    test-rmse:2.694802+0.457644 
## [7]  train-rmse:1.869186+0.077546    test-rmse:2.635403+0.457613 
## [8]  train-rmse:1.737703+0.044240    test-rmse:2.550153+0.451260 
## [9]  train-rmse:1.624805+0.030917    test-rmse:2.497636+0.460864 
## [10] train-rmse:1.518675+0.048109    test-rmse:2.465554+0.465765 
## [11] train-rmse:1.428945+0.057386    test-rmse:2.388344+0.438854 
## [12] train-rmse:1.360554+0.050611    test-rmse:2.351289+0.443769 
## [13] train-rmse:1.295789+0.047197    test-rmse:2.335701+0.453021 
## [14] train-rmse:1.245678+0.054747    test-rmse:2.329020+0.449329 
## [15] train-rmse:1.194398+0.053243    test-rmse:2.303803+0.457424 
## [16] train-rmse:1.148056+0.051493    test-rmse:2.286928+0.457860 
## [17] train-rmse:1.095945+0.035567    test-rmse:2.281417+0.480195 
## [18] train-rmse:1.046216+0.046941    test-rmse:2.258382+0.481964 
## [19] train-rmse:1.017438+0.049519    test-rmse:2.244058+0.482409 
## [20] train-rmse:0.972524+0.040995    test-rmse:2.241855+0.493952 
## [21] train-rmse:0.929620+0.043474    test-rmse:2.244479+0.500768 
## [22] train-rmse:0.899656+0.041866    test-rmse:2.239555+0.494958 
## [23] train-rmse:0.876691+0.039407    test-rmse:2.229335+0.497590 
## [24] train-rmse:0.836886+0.043751    test-rmse:2.237709+0.515667 
## [25] train-rmse:0.809307+0.043869    test-rmse:2.227068+0.519475 
## [26] train-rmse:0.779286+0.040253    test-rmse:2.211027+0.525749 
## [27] train-rmse:0.756590+0.039964    test-rmse:2.209432+0.524754 
## [28] train-rmse:0.732963+0.050214    test-rmse:2.210933+0.525900 
## [29] train-rmse:0.713450+0.048584    test-rmse:2.217013+0.536806 
## [30] train-rmse:0.694350+0.050635    test-rmse:2.217754+0.535952 
## [31] train-rmse:0.666517+0.049263    test-rmse:2.207797+0.529202 
## [32] train-rmse:0.648255+0.048008    test-rmse:2.204933+0.533582 
## [33] train-rmse:0.634281+0.050191    test-rmse:2.201310+0.531591 
## [34] train-rmse:0.611609+0.038887    test-rmse:2.200086+0.534561 
## [35] train-rmse:0.598136+0.038407    test-rmse:2.197821+0.536198 
## [36] train-rmse:0.576810+0.031829    test-rmse:2.189857+0.534058 
## [37] train-rmse:0.561684+0.035447    test-rmse:2.188662+0.527218 
## [38] train-rmse:0.540565+0.036675    test-rmse:2.177980+0.531892 
## [39] train-rmse:0.522468+0.032795    test-rmse:2.177184+0.535110 
## [40] train-rmse:0.509545+0.031174    test-rmse:2.175287+0.535340 
## [41] train-rmse:0.492603+0.028730    test-rmse:2.182651+0.541689 
## [42] train-rmse:0.480430+0.028272    test-rmse:2.173194+0.550682 
## [43] train-rmse:0.468664+0.029518    test-rmse:2.168562+0.549398 
## [44] train-rmse:0.451507+0.034390    test-rmse:2.164846+0.545155 
## [45] train-rmse:0.441506+0.034028    test-rmse:2.162918+0.545899 
## [46] train-rmse:0.428986+0.029840    test-rmse:2.159214+0.547098 
## [47] train-rmse:0.417876+0.026641    test-rmse:2.160428+0.550411 
## [48] train-rmse:0.410818+0.027856    test-rmse:2.163246+0.549301 
## [49] train-rmse:0.399464+0.027104    test-rmse:2.165315+0.553254 
## [50] train-rmse:0.390958+0.028006    test-rmse:2.164053+0.553576 
## [51] train-rmse:0.382929+0.028479    test-rmse:2.164248+0.553618 
## [52] train-rmse:0.372383+0.027762    test-rmse:2.169539+0.559774 
## Stopping. Best iteration:
## [46] train-rmse:0.428986+0.029840    test-rmse:2.159214+0.547098
## 
## 102 
## [1]  train-rmse:5.546457+0.107123    test-rmse:5.603375+0.519358 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.931178+0.076405    test-rmse:4.137944+0.439356 
## [3]  train-rmse:3.018279+0.067661    test-rmse:3.419430+0.450491 
## [4]  train-rmse:2.433757+0.065985    test-rmse:3.000361+0.462758 
## [5]  train-rmse:2.078055+0.071136    test-rmse:2.841404+0.500968 
## [6]  train-rmse:1.821529+0.080725    test-rmse:2.714874+0.508790 
## [7]  train-rmse:1.627516+0.089134    test-rmse:2.611620+0.545631 
## [8]  train-rmse:1.500621+0.073725    test-rmse:2.557860+0.522565 
## [9]  train-rmse:1.401847+0.080749    test-rmse:2.524923+0.535711 
## [10] train-rmse:1.317482+0.076564    test-rmse:2.507455+0.528598 
## [11] train-rmse:1.234413+0.073480    test-rmse:2.489337+0.536009 
## [12] train-rmse:1.142572+0.062433    test-rmse:2.436302+0.517520 
## [13] train-rmse:1.089632+0.068817    test-rmse:2.426070+0.527767 
## [14] train-rmse:1.038595+0.060281    test-rmse:2.408024+0.527458 
## [15] train-rmse:0.990457+0.055974    test-rmse:2.380882+0.521212 
## [16] train-rmse:0.942534+0.047991    test-rmse:2.369744+0.517446 
## [17] train-rmse:0.896058+0.045130    test-rmse:2.360707+0.519325 
## [18] train-rmse:0.847427+0.041705    test-rmse:2.350304+0.524008 
## [19] train-rmse:0.806489+0.029201    test-rmse:2.338295+0.532523 
## [20] train-rmse:0.778802+0.032704    test-rmse:2.326951+0.535408 
## [21] train-rmse:0.739995+0.034489    test-rmse:2.323923+0.527317 
## [22] train-rmse:0.708608+0.033739    test-rmse:2.307675+0.547314 
## [23] train-rmse:0.677743+0.030541    test-rmse:2.303571+0.552354 
## [24] train-rmse:0.634028+0.034826    test-rmse:2.289771+0.557835 
## [25] train-rmse:0.609689+0.026022    test-rmse:2.284662+0.557274 
## [26] train-rmse:0.587693+0.022791    test-rmse:2.284708+0.557415 
## [27] train-rmse:0.560706+0.019040    test-rmse:2.285907+0.560566 
## [28] train-rmse:0.533476+0.017025    test-rmse:2.288865+0.563774 
## [29] train-rmse:0.508282+0.024493    test-rmse:2.278956+0.569727 
## [30] train-rmse:0.491892+0.021856    test-rmse:2.278558+0.574298 
## [31] train-rmse:0.475039+0.013512    test-rmse:2.276183+0.574430 
## [32] train-rmse:0.458295+0.013890    test-rmse:2.274001+0.577382 
## [33] train-rmse:0.436080+0.021049    test-rmse:2.275299+0.576183 
## [34] train-rmse:0.417825+0.021249    test-rmse:2.272581+0.579535 
## [35] train-rmse:0.395867+0.021429    test-rmse:2.271345+0.580223 
## [36] train-rmse:0.383176+0.023245    test-rmse:2.271473+0.579028 
## [37] train-rmse:0.370401+0.019386    test-rmse:2.275924+0.575226 
## [38] train-rmse:0.356385+0.019738    test-rmse:2.273655+0.576463 
## [39] train-rmse:0.345697+0.019964    test-rmse:2.274266+0.577339 
## [40] train-rmse:0.334019+0.017198    test-rmse:2.273444+0.576185 
## [41] train-rmse:0.314368+0.012704    test-rmse:2.274854+0.577052 
## Stopping. Best iteration:
## [35] train-rmse:0.395867+0.021429    test-rmse:2.271345+0.580223
## 
## 103 
## [1]  train-rmse:5.495667+0.097807    test-rmse:5.574011+0.566667 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.855312+0.067994    test-rmse:4.142708+0.471284 
## [3]  train-rmse:2.872532+0.036365    test-rmse:3.377454+0.467900 
## [4]  train-rmse:2.276823+0.029986    test-rmse:2.961833+0.506308 
## [5]  train-rmse:1.887726+0.050679    test-rmse:2.767639+0.525706 
## [6]  train-rmse:1.610820+0.082522    test-rmse:2.623046+0.582208 
## [7]  train-rmse:1.427987+0.087886    test-rmse:2.561907+0.582826 
## [8]  train-rmse:1.271683+0.082583    test-rmse:2.500888+0.577081 
## [9]  train-rmse:1.152218+0.063280    test-rmse:2.464399+0.582137 
## [10] train-rmse:1.060739+0.068035    test-rmse:2.446951+0.577275 
## [11] train-rmse:0.987430+0.065940    test-rmse:2.440691+0.593647 
## [12] train-rmse:0.908631+0.078496    test-rmse:2.422815+0.599468 
## [13] train-rmse:0.850646+0.089924    test-rmse:2.401504+0.609757 
## [14] train-rmse:0.794920+0.075198    test-rmse:2.376522+0.597250 
## [15] train-rmse:0.755997+0.073870    test-rmse:2.371630+0.598312 
## [16] train-rmse:0.713234+0.076451    test-rmse:2.354527+0.597786 
## [17] train-rmse:0.661529+0.049912    test-rmse:2.347448+0.597555 
## [18] train-rmse:0.623793+0.052485    test-rmse:2.339957+0.596056 
## [19] train-rmse:0.579275+0.043722    test-rmse:2.331341+0.600423 
## [20] train-rmse:0.543085+0.038651    test-rmse:2.336004+0.605720 
## [21] train-rmse:0.510783+0.046026    test-rmse:2.331341+0.603926 
## [22] train-rmse:0.488908+0.043736    test-rmse:2.326031+0.606076 
## [23] train-rmse:0.466008+0.041028    test-rmse:2.326005+0.605543 
## [24] train-rmse:0.438282+0.036597    test-rmse:2.324754+0.603910 
## [25] train-rmse:0.414894+0.035492    test-rmse:2.324200+0.600428 
## [26] train-rmse:0.389733+0.029435    test-rmse:2.319665+0.602567 
## [27] train-rmse:0.372991+0.028812    test-rmse:2.314244+0.604480 
## [28] train-rmse:0.349802+0.023700    test-rmse:2.312024+0.604036 
## [29] train-rmse:0.332451+0.016790    test-rmse:2.313333+0.608293 
## [30] train-rmse:0.312053+0.016061    test-rmse:2.314258+0.610447 
## [31] train-rmse:0.295793+0.014581    test-rmse:2.309718+0.612888 
## [32] train-rmse:0.282180+0.013466    test-rmse:2.310180+0.611639 
## [33] train-rmse:0.270386+0.014829    test-rmse:2.309347+0.610712 
## [34] train-rmse:0.253836+0.011792    test-rmse:2.308320+0.611390 
## [35] train-rmse:0.238887+0.010623    test-rmse:2.308977+0.611821 
## [36] train-rmse:0.228270+0.011652    test-rmse:2.306685+0.613634 
## [37] train-rmse:0.212026+0.011168    test-rmse:2.308492+0.613339 
## [38] train-rmse:0.201850+0.009736    test-rmse:2.307705+0.611692 
## [39] train-rmse:0.190126+0.012367    test-rmse:2.306336+0.609399 
## [40] train-rmse:0.179808+0.009806    test-rmse:2.304865+0.610721 
## [41] train-rmse:0.171372+0.009640    test-rmse:2.304651+0.610439 
## [42] train-rmse:0.165722+0.010427    test-rmse:2.305540+0.610382 
## [43] train-rmse:0.157447+0.011776    test-rmse:2.304607+0.609572 
## [44] train-rmse:0.149739+0.008053    test-rmse:2.304174+0.608737 
## [45] train-rmse:0.141017+0.006603    test-rmse:2.303770+0.609496 
## [46] train-rmse:0.135778+0.008863    test-rmse:2.302697+0.609779 
## [47] train-rmse:0.130199+0.008833    test-rmse:2.301122+0.609016 
## [48] train-rmse:0.124113+0.010001    test-rmse:2.299999+0.609301 
## [49] train-rmse:0.118942+0.009513    test-rmse:2.300282+0.608660 
## [50] train-rmse:0.114204+0.007991    test-rmse:2.300696+0.609309 
## [51] train-rmse:0.109656+0.007249    test-rmse:2.299866+0.607838 
## [52] train-rmse:0.105453+0.006919    test-rmse:2.299572+0.608050 
## [53] train-rmse:0.099706+0.004869    test-rmse:2.299949+0.608564 
## [54] train-rmse:0.095295+0.004628    test-rmse:2.299351+0.607838 
## [55] train-rmse:0.091953+0.004889    test-rmse:2.299895+0.607458 
## [56] train-rmse:0.088008+0.005663    test-rmse:2.299714+0.606772 
## [57] train-rmse:0.084405+0.005969    test-rmse:2.299012+0.606689 
## [58] train-rmse:0.081584+0.006084    test-rmse:2.298107+0.606211 
## [59] train-rmse:0.077758+0.005882    test-rmse:2.298179+0.605828 
## [60] train-rmse:0.074231+0.005368    test-rmse:2.298710+0.605612 
## [61] train-rmse:0.070749+0.006707    test-rmse:2.298090+0.605782 
## [62] train-rmse:0.066527+0.006712    test-rmse:2.298037+0.604964 
## [63] train-rmse:0.064054+0.006527    test-rmse:2.297840+0.604997 
## [64] train-rmse:0.060736+0.006225    test-rmse:2.297641+0.604299 
## [65] train-rmse:0.058590+0.005855    test-rmse:2.297017+0.603760 
## [66] train-rmse:0.056854+0.005613    test-rmse:2.296770+0.603746 
## [67] train-rmse:0.055071+0.005529    test-rmse:2.297197+0.603406 
## [68] train-rmse:0.052136+0.006070    test-rmse:2.296059+0.604326 
## [69] train-rmse:0.050123+0.005568    test-rmse:2.296017+0.604376 
## [70] train-rmse:0.046820+0.005428    test-rmse:2.295781+0.604148 
## [71] train-rmse:0.044770+0.005124    test-rmse:2.295787+0.604322 
## [72] train-rmse:0.043011+0.005304    test-rmse:2.295498+0.604130 
## [73] train-rmse:0.041460+0.004808    test-rmse:2.295413+0.604405 
## [74] train-rmse:0.040154+0.004758    test-rmse:2.295431+0.604440 
## [75] train-rmse:0.038533+0.004556    test-rmse:2.295688+0.604085 
## [76] train-rmse:0.037127+0.004194    test-rmse:2.295244+0.604444 
## [77] train-rmse:0.035794+0.003994    test-rmse:2.295041+0.604178 
## [78] train-rmse:0.034289+0.003904    test-rmse:2.295288+0.604269 
## [79] train-rmse:0.032840+0.003737    test-rmse:2.295045+0.604501 
## [80] train-rmse:0.031490+0.003864    test-rmse:2.295006+0.604554 
## [81] train-rmse:0.030503+0.003724    test-rmse:2.295064+0.604635 
## [82] train-rmse:0.029620+0.003698    test-rmse:2.294990+0.604678 
## [83] train-rmse:0.028244+0.003535    test-rmse:2.295117+0.604792 
## [84] train-rmse:0.027159+0.003486    test-rmse:2.295133+0.604888 
## [85] train-rmse:0.026042+0.003086    test-rmse:2.295139+0.604823 
## [86] train-rmse:0.025349+0.002976    test-rmse:2.294945+0.604915 
## [87] train-rmse:0.024307+0.002637    test-rmse:2.294709+0.605063 
## [88] train-rmse:0.023318+0.002705    test-rmse:2.294579+0.605096 
## [89] train-rmse:0.022735+0.002742    test-rmse:2.294538+0.605242 
## [90] train-rmse:0.022056+0.002600    test-rmse:2.294604+0.605088 
## [91] train-rmse:0.020874+0.002394    test-rmse:2.294595+0.605372 
## [92] train-rmse:0.020125+0.002170    test-rmse:2.294731+0.605705 
## [93] train-rmse:0.019422+0.002103    test-rmse:2.294656+0.605607 
## [94] train-rmse:0.018729+0.002055    test-rmse:2.294774+0.605694 
## [95] train-rmse:0.018248+0.001953    test-rmse:2.294893+0.605604 
## Stopping. Best iteration:
## [89] train-rmse:0.022735+0.002742    test-rmse:2.294538+0.605242
## 
## 104 
## [1]  train-rmse:5.477530+0.096224    test-rmse:5.561905+0.566466 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.785644+0.056499    test-rmse:4.109492+0.478658 
## [3]  train-rmse:2.779084+0.034952    test-rmse:3.321672+0.503710 
## [4]  train-rmse:2.151117+0.035089    test-rmse:2.958275+0.502166 
## [5]  train-rmse:1.742654+0.056462    test-rmse:2.718995+0.519232 
## [6]  train-rmse:1.459789+0.038238    test-rmse:2.591811+0.506458 
## [7]  train-rmse:1.270320+0.031011    test-rmse:2.507335+0.534815 
## [8]  train-rmse:1.157586+0.040200    test-rmse:2.462801+0.532907 
## [9]  train-rmse:1.034391+0.025013    test-rmse:2.437770+0.538020 
## [10] train-rmse:0.944694+0.033946    test-rmse:2.408891+0.546647 
## [11] train-rmse:0.848146+0.031936    test-rmse:2.398292+0.549311 
## [12] train-rmse:0.775395+0.020191    test-rmse:2.388900+0.549584 
## [13] train-rmse:0.710711+0.019869    test-rmse:2.387252+0.537349 
## [14] train-rmse:0.655035+0.018135    test-rmse:2.363463+0.549294 
## [15] train-rmse:0.608394+0.016371    test-rmse:2.371278+0.562137 
## [16] train-rmse:0.554495+0.008874    test-rmse:2.374350+0.571080 
## [17] train-rmse:0.523698+0.007005    test-rmse:2.374695+0.571978 
## [18] train-rmse:0.490444+0.007167    test-rmse:2.370578+0.569045 
## [19] train-rmse:0.460148+0.011110    test-rmse:2.363446+0.566438 
## [20] train-rmse:0.415625+0.012572    test-rmse:2.361071+0.560188 
## [21] train-rmse:0.395690+0.008574    test-rmse:2.352067+0.567107 
## [22] train-rmse:0.370618+0.011381    test-rmse:2.352436+0.571824 
## [23] train-rmse:0.344038+0.004488    test-rmse:2.346950+0.574126 
## [24] train-rmse:0.322904+0.008066    test-rmse:2.348424+0.572598 
## [25] train-rmse:0.302459+0.011528    test-rmse:2.349909+0.573074 
## [26] train-rmse:0.283321+0.009452    test-rmse:2.342912+0.572380 
## [27] train-rmse:0.263987+0.010889    test-rmse:2.341898+0.572351 
## [28] train-rmse:0.246119+0.012587    test-rmse:2.342234+0.572251 
## [29] train-rmse:0.225199+0.011941    test-rmse:2.340988+0.574641 
## [30] train-rmse:0.212591+0.012507    test-rmse:2.339524+0.575396 
## [31] train-rmse:0.200077+0.013317    test-rmse:2.337900+0.575010 
## [32] train-rmse:0.189185+0.011543    test-rmse:2.338155+0.575381 
## [33] train-rmse:0.178193+0.009837    test-rmse:2.336880+0.575383 
## [34] train-rmse:0.169007+0.010499    test-rmse:2.336346+0.575975 
## [35] train-rmse:0.158816+0.009145    test-rmse:2.336795+0.575992 
## [36] train-rmse:0.144650+0.006542    test-rmse:2.334339+0.575761 
## [37] train-rmse:0.135317+0.006489    test-rmse:2.336446+0.578080 
## [38] train-rmse:0.125910+0.005000    test-rmse:2.332960+0.578485 
## [39] train-rmse:0.119125+0.002726    test-rmse:2.333591+0.578781 
## [40] train-rmse:0.111499+0.004421    test-rmse:2.331395+0.578071 
## [41] train-rmse:0.106879+0.004499    test-rmse:2.331526+0.578995 
## [42] train-rmse:0.101120+0.004437    test-rmse:2.331162+0.579227 
## [43] train-rmse:0.096739+0.003635    test-rmse:2.332715+0.579877 
## [44] train-rmse:0.092991+0.004205    test-rmse:2.332160+0.579261 
## [45] train-rmse:0.088008+0.004089    test-rmse:2.332550+0.579142 
## [46] train-rmse:0.083155+0.004258    test-rmse:2.332422+0.580115 
## [47] train-rmse:0.078859+0.004796    test-rmse:2.332041+0.580129 
## [48] train-rmse:0.073821+0.003928    test-rmse:2.331441+0.578956 
## Stopping. Best iteration:
## [42] train-rmse:0.101120+0.004437    test-rmse:2.331162+0.579227
## 
## 105 
## [1]  train-rmse:5.950215+0.132245    test-rmse:5.965040+0.634669 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.770830+0.101927    test-rmse:4.880836+0.670336 
## [3]  train-rmse:4.173035+0.118733    test-rmse:4.252701+0.579564 
## [4]  train-rmse:3.857214+0.112667    test-rmse:3.995632+0.491239 
## [5]  train-rmse:3.653895+0.118027    test-rmse:3.809205+0.420071 
## [6]  train-rmse:3.502018+0.117137    test-rmse:3.637375+0.375184 
## [7]  train-rmse:3.371395+0.110901    test-rmse:3.477143+0.438689 
## [8]  train-rmse:3.268243+0.125319    test-rmse:3.413991+0.399551 
## [9]  train-rmse:3.187359+0.124381    test-rmse:3.343530+0.432766 
## [10] train-rmse:3.114704+0.126033    test-rmse:3.294261+0.433663 
## [11] train-rmse:3.048112+0.125960    test-rmse:3.204229+0.446591 
## [12] train-rmse:2.984730+0.125005    test-rmse:3.150037+0.464188 
## [13] train-rmse:2.928363+0.128285    test-rmse:3.067451+0.460962 
## [14] train-rmse:2.878254+0.132225    test-rmse:3.030333+0.437330 
## [15] train-rmse:2.834775+0.133248    test-rmse:3.016482+0.437555 
## [16] train-rmse:2.793450+0.135400    test-rmse:2.991880+0.450450 
## [17] train-rmse:2.754563+0.138062    test-rmse:2.981030+0.464207 
## [18] train-rmse:2.717321+0.141026    test-rmse:2.938888+0.500154 
## [19] train-rmse:2.681402+0.143266    test-rmse:2.891994+0.492877 
## [20] train-rmse:2.646758+0.144683    test-rmse:2.861007+0.475728 
## [21] train-rmse:2.614611+0.142801    test-rmse:2.859632+0.482353 
## [22] train-rmse:2.584359+0.141044    test-rmse:2.853734+0.509411 
## [23] train-rmse:2.555432+0.139863    test-rmse:2.825510+0.498728 
## [24] train-rmse:2.528059+0.139635    test-rmse:2.797985+0.476418 
## [25] train-rmse:2.502087+0.139947    test-rmse:2.746024+0.473830 
## [26] train-rmse:2.478094+0.140342    test-rmse:2.735336+0.468472 
## [27] train-rmse:2.455104+0.141475    test-rmse:2.722377+0.479039 
## [28] train-rmse:2.432861+0.142795    test-rmse:2.708557+0.505716 
## [29] train-rmse:2.412305+0.143298    test-rmse:2.700183+0.499672 
## [30] train-rmse:2.392686+0.144564    test-rmse:2.673186+0.503993 
## [31] train-rmse:2.373835+0.146372    test-rmse:2.660185+0.498520 
## [32] train-rmse:2.355568+0.147224    test-rmse:2.650610+0.496932 
## [33] train-rmse:2.338342+0.148397    test-rmse:2.618812+0.504278 
## [34] train-rmse:2.322028+0.148201    test-rmse:2.602308+0.508990 
## [35] train-rmse:2.306077+0.147960    test-rmse:2.590162+0.512696 
## [36] train-rmse:2.290513+0.147144    test-rmse:2.558140+0.506374 
## [37] train-rmse:2.276031+0.146096    test-rmse:2.544167+0.496614 
## [38] train-rmse:2.262722+0.146434    test-rmse:2.544327+0.502881 
## [39] train-rmse:2.250135+0.146945    test-rmse:2.533359+0.505865 
## [40] train-rmse:2.238153+0.147368    test-rmse:2.525372+0.507765 
## [41] train-rmse:2.226233+0.147857    test-rmse:2.505368+0.500441 
## [42] train-rmse:2.214906+0.148127    test-rmse:2.492642+0.492801 
## [43] train-rmse:2.204012+0.148690    test-rmse:2.469282+0.503299 
## [44] train-rmse:2.193807+0.149299    test-rmse:2.476817+0.497654 
## [45] train-rmse:2.183548+0.149965    test-rmse:2.486869+0.502340 
## [46] train-rmse:2.173225+0.150483    test-rmse:2.493751+0.519056 
## [47] train-rmse:2.163905+0.151493    test-rmse:2.493593+0.519993 
## [48] train-rmse:2.154936+0.152072    test-rmse:2.490054+0.526012 
## [49] train-rmse:2.146216+0.152357    test-rmse:2.476314+0.526642 
## Stopping. Best iteration:
## [43] train-rmse:2.204012+0.148690    test-rmse:2.469282+0.503299
## 
## 106 
## [1]  train-rmse:5.756227+0.120247    test-rmse:5.832297+0.580880 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.418148+0.117279    test-rmse:4.576134+0.495612 
## [3]  train-rmse:3.667526+0.128529    test-rmse:3.874231+0.442382 
## [4]  train-rmse:3.172498+0.124794    test-rmse:3.364661+0.429020 
## [5]  train-rmse:2.877486+0.094651    test-rmse:3.112249+0.426054 
## [6]  train-rmse:2.678909+0.107582    test-rmse:2.953518+0.454939 
## [7]  train-rmse:2.563237+0.108750    test-rmse:2.904313+0.468179 
## [8]  train-rmse:2.439136+0.113796    test-rmse:2.811877+0.464603 
## [9]  train-rmse:2.339335+0.118757    test-rmse:2.767737+0.474046 
## [10] train-rmse:2.249976+0.117040    test-rmse:2.698185+0.482096 
## [11] train-rmse:2.170492+0.115540    test-rmse:2.648386+0.517592 
## [12] train-rmse:2.093002+0.117368    test-rmse:2.581785+0.524381 
## [13] train-rmse:2.032165+0.118287    test-rmse:2.557719+0.515416 
## [14] train-rmse:1.971926+0.126439    test-rmse:2.492804+0.481732 
## [15] train-rmse:1.921134+0.127336    test-rmse:2.457509+0.474682 
## [16] train-rmse:1.877951+0.125282    test-rmse:2.428111+0.480080 
## [17] train-rmse:1.836756+0.127462    test-rmse:2.426125+0.514730 
## [18] train-rmse:1.787710+0.107934    test-rmse:2.359453+0.525014 
## [19] train-rmse:1.754547+0.109308    test-rmse:2.338260+0.523929 
## [20] train-rmse:1.721407+0.109986    test-rmse:2.326569+0.535123 
## [21] train-rmse:1.680053+0.111799    test-rmse:2.316827+0.536121 
## [22] train-rmse:1.655018+0.111317    test-rmse:2.298013+0.537333 
## [23] train-rmse:1.625699+0.110107    test-rmse:2.275926+0.564074 
## [24] train-rmse:1.598773+0.112157    test-rmse:2.246726+0.558918 
## [25] train-rmse:1.565996+0.118058    test-rmse:2.233358+0.539444 
## [26] train-rmse:1.543941+0.116988    test-rmse:2.225509+0.556166 
## [27] train-rmse:1.523264+0.113879    test-rmse:2.210894+0.560926 
## [28] train-rmse:1.499288+0.119766    test-rmse:2.195654+0.559940 
## [29] train-rmse:1.477375+0.112950    test-rmse:2.201311+0.561464 
## [30] train-rmse:1.458467+0.115639    test-rmse:2.187932+0.562840 
## [31] train-rmse:1.439196+0.117640    test-rmse:2.200000+0.568090 
## [32] train-rmse:1.423447+0.118919    test-rmse:2.193932+0.572028 
## [33] train-rmse:1.403689+0.121471    test-rmse:2.181053+0.573403 
## [34] train-rmse:1.389006+0.123192    test-rmse:2.186365+0.573104 
## [35] train-rmse:1.365297+0.128974    test-rmse:2.186225+0.579268 
## [36] train-rmse:1.344969+0.134092    test-rmse:2.170755+0.579733 
## [37] train-rmse:1.332793+0.134939    test-rmse:2.160129+0.579760 
## [38] train-rmse:1.316572+0.133318    test-rmse:2.147024+0.585573 
## [39] train-rmse:1.304933+0.133515    test-rmse:2.138181+0.589497 
## [40] train-rmse:1.266658+0.098631    test-rmse:2.130912+0.597122 
## [41] train-rmse:1.242002+0.089817    test-rmse:2.117647+0.590963 
## [42] train-rmse:1.230681+0.090896    test-rmse:2.106177+0.587173 
## [43] train-rmse:1.221249+0.090920    test-rmse:2.105861+0.590409 
## [44] train-rmse:1.207736+0.093869    test-rmse:2.102030+0.594978 
## [45] train-rmse:1.197020+0.092741    test-rmse:2.096887+0.592977 
## [46] train-rmse:1.184122+0.097134    test-rmse:2.095061+0.580369 
## [47] train-rmse:1.168646+0.098467    test-rmse:2.084044+0.588334 
## [48] train-rmse:1.154068+0.099244    test-rmse:2.074051+0.592796 
## [49] train-rmse:1.143860+0.099980    test-rmse:2.070410+0.595699 
## [50] train-rmse:1.133414+0.099770    test-rmse:2.071300+0.589747 
## [51] train-rmse:1.109699+0.079653    test-rmse:2.072290+0.597625 
## [52] train-rmse:1.097787+0.081281    test-rmse:2.068661+0.603694 
## [53] train-rmse:1.088060+0.077980    test-rmse:2.067566+0.604318 
## [54] train-rmse:1.078391+0.076323    test-rmse:2.062152+0.608895 
## [55] train-rmse:1.068041+0.075309    test-rmse:2.049333+0.613430 
## [56] train-rmse:1.058845+0.078524    test-rmse:2.049115+0.610438 
## [57] train-rmse:1.052353+0.079675    test-rmse:2.048236+0.615649 
## [58] train-rmse:1.044878+0.081211    test-rmse:2.044435+0.616042 
## [59] train-rmse:1.029598+0.082710    test-rmse:2.037672+0.617212 
## [60] train-rmse:1.018609+0.086052    test-rmse:2.043851+0.620819 
## [61] train-rmse:1.002553+0.088596    test-rmse:2.053068+0.626623 
## [62] train-rmse:0.990427+0.081186    test-rmse:2.054344+0.621346 
## [63] train-rmse:0.985201+0.079925    test-rmse:2.048678+0.626811 
## [64] train-rmse:0.977717+0.078553    test-rmse:2.044954+0.625174 
## [65] train-rmse:0.968334+0.079604    test-rmse:2.041024+0.630893 
## Stopping. Best iteration:
## [59] train-rmse:1.029598+0.082710    test-rmse:2.037672+0.617212
## 
## 107 
## [1]  train-rmse:5.624925+0.107826    test-rmse:5.778279+0.564075 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.154124+0.074725    test-rmse:4.272764+0.513544 
## [3]  train-rmse:3.279401+0.066854    test-rmse:3.484895+0.412927 
## [4]  train-rmse:2.763757+0.051288    test-rmse:3.081835+0.439658 
## [5]  train-rmse:2.465066+0.054353    test-rmse:2.824640+0.466026 
## [6]  train-rmse:2.260589+0.050966    test-rmse:2.730530+0.466485 
## [7]  train-rmse:2.088660+0.062918    test-rmse:2.612652+0.451348 
## [8]  train-rmse:1.965654+0.064735    test-rmse:2.564613+0.473524 
## [9]  train-rmse:1.860514+0.060322    test-rmse:2.498157+0.465330 
## [10] train-rmse:1.782118+0.061455    test-rmse:2.454904+0.459639 
## [11] train-rmse:1.692172+0.052466    test-rmse:2.427568+0.463964 
## [12] train-rmse:1.631112+0.055412    test-rmse:2.403484+0.461147 
## [13] train-rmse:1.568551+0.055630    test-rmse:2.369049+0.462468 
## [14] train-rmse:1.504121+0.048825    test-rmse:2.347692+0.469937 
## [15] train-rmse:1.452471+0.050979    test-rmse:2.298646+0.471436 
## [16] train-rmse:1.389233+0.053904    test-rmse:2.259063+0.466887 
## [17] train-rmse:1.348877+0.059746    test-rmse:2.264019+0.473074 
## [18] train-rmse:1.312721+0.066875    test-rmse:2.241068+0.460111 
## [19] train-rmse:1.274864+0.067422    test-rmse:2.230837+0.454721 
## [20] train-rmse:1.229738+0.053133    test-rmse:2.209618+0.462154 
## [21] train-rmse:1.198168+0.053693    test-rmse:2.184226+0.451701 
## [22] train-rmse:1.171333+0.053691    test-rmse:2.170246+0.456853 
## [23] train-rmse:1.141432+0.054696    test-rmse:2.164489+0.456695 
## [24] train-rmse:1.111025+0.056964    test-rmse:2.156797+0.453233 
## [25] train-rmse:1.079323+0.061309    test-rmse:2.152459+0.453185 
## [26] train-rmse:1.054337+0.052256    test-rmse:2.158758+0.464206 
## [27] train-rmse:1.021050+0.039095    test-rmse:2.177312+0.474521 
## [28] train-rmse:1.003189+0.038756    test-rmse:2.175994+0.477839 
## [29] train-rmse:0.980969+0.033885    test-rmse:2.162906+0.474362 
## [30] train-rmse:0.953015+0.024610    test-rmse:2.139179+0.468363 
## [31] train-rmse:0.937519+0.027018    test-rmse:2.129197+0.465733 
## [32] train-rmse:0.921451+0.031516    test-rmse:2.121015+0.472809 
## [33] train-rmse:0.894781+0.027884    test-rmse:2.109572+0.491205 
## [34] train-rmse:0.869854+0.026265    test-rmse:2.106907+0.487532 
## [35] train-rmse:0.850398+0.024816    test-rmse:2.104545+0.485688 
## [36] train-rmse:0.835824+0.023182    test-rmse:2.097698+0.492675 
## [37] train-rmse:0.813285+0.022340    test-rmse:2.087675+0.495994 
## [38] train-rmse:0.803685+0.021366    test-rmse:2.083032+0.497940 
## [39] train-rmse:0.786804+0.022493    test-rmse:2.079311+0.499451 
## [40] train-rmse:0.768003+0.022100    test-rmse:2.074053+0.501740 
## [41] train-rmse:0.752480+0.014255    test-rmse:2.077531+0.503890 
## [42] train-rmse:0.736190+0.012409    test-rmse:2.073054+0.504151 
## [43] train-rmse:0.725926+0.012933    test-rmse:2.071832+0.504644 
## [44] train-rmse:0.714605+0.013466    test-rmse:2.069865+0.501615 
## [45] train-rmse:0.700391+0.010514    test-rmse:2.072999+0.504468 
## [46] train-rmse:0.688182+0.015005    test-rmse:2.068311+0.506181 
## [47] train-rmse:0.676323+0.010032    test-rmse:2.069618+0.506263 
## [48] train-rmse:0.664651+0.007929    test-rmse:2.067710+0.505867 
## [49] train-rmse:0.648595+0.009784    test-rmse:2.063477+0.507335 
## [50] train-rmse:0.635057+0.010546    test-rmse:2.062764+0.510235 
## [51] train-rmse:0.624882+0.010263    test-rmse:2.061150+0.509933 
## [52] train-rmse:0.605719+0.012703    test-rmse:2.060764+0.505017 
## [53] train-rmse:0.597697+0.014920    test-rmse:2.062712+0.504915 
## [54] train-rmse:0.589102+0.013022    test-rmse:2.067249+0.512483 
## [55] train-rmse:0.579426+0.014206    test-rmse:2.072927+0.514731 
## [56] train-rmse:0.569967+0.013196    test-rmse:2.069985+0.515801 
## [57] train-rmse:0.561766+0.011692    test-rmse:2.067025+0.516076 
## [58] train-rmse:0.553689+0.012677    test-rmse:2.067276+0.516915 
## Stopping. Best iteration:
## [52] train-rmse:0.605719+0.012703    test-rmse:2.060764+0.505017
## 
## 108 
## [1]  train-rmse:5.499180+0.106910    test-rmse:5.616375+0.546957 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.910424+0.085138    test-rmse:4.147719+0.426867 
## [3]  train-rmse:3.006484+0.063702    test-rmse:3.378792+0.376636 
## [4]  train-rmse:2.494177+0.051336    test-rmse:2.991538+0.421633 
## [5]  train-rmse:2.182996+0.058691    test-rmse:2.808057+0.408081 
## [6]  train-rmse:1.962717+0.039150    test-rmse:2.714177+0.450594 
## [7]  train-rmse:1.790056+0.059781    test-rmse:2.631938+0.427292 
## [8]  train-rmse:1.678616+0.055264    test-rmse:2.557894+0.438257 
## [9]  train-rmse:1.563124+0.067950    test-rmse:2.511798+0.427101 
## [10] train-rmse:1.482189+0.059944    test-rmse:2.466711+0.450437 
## [11] train-rmse:1.410227+0.061381    test-rmse:2.432293+0.448289 
## [12] train-rmse:1.335730+0.071050    test-rmse:2.393907+0.448295 
## [13] train-rmse:1.274217+0.061143    test-rmse:2.381606+0.436138 
## [14] train-rmse:1.201407+0.053481    test-rmse:2.342641+0.429591 
## [15] train-rmse:1.154007+0.054580    test-rmse:2.328724+0.424625 
## [16] train-rmse:1.111657+0.063915    test-rmse:2.322916+0.441344 
## [17] train-rmse:1.051448+0.044570    test-rmse:2.303047+0.429601 
## [18] train-rmse:0.991970+0.030396    test-rmse:2.287728+0.427304 
## [19] train-rmse:0.954970+0.035191    test-rmse:2.283872+0.431691 
## [20] train-rmse:0.924627+0.031045    test-rmse:2.275116+0.440614 
## [21] train-rmse:0.883842+0.041493    test-rmse:2.262263+0.444539 
## [22] train-rmse:0.850877+0.041570    test-rmse:2.256889+0.439743 
## [23] train-rmse:0.819986+0.037854    test-rmse:2.251990+0.440625 
## [24] train-rmse:0.789792+0.033698    test-rmse:2.263087+0.453944 
## [25] train-rmse:0.762564+0.030431    test-rmse:2.260091+0.455282 
## [26] train-rmse:0.738363+0.022127    test-rmse:2.251488+0.457199 
## [27] train-rmse:0.707849+0.018612    test-rmse:2.246388+0.461692 
## [28] train-rmse:0.682552+0.016968    test-rmse:2.239140+0.466210 
## [29] train-rmse:0.666452+0.018980    test-rmse:2.234024+0.468582 
## [30] train-rmse:0.647652+0.019754    test-rmse:2.231646+0.468227 
## [31] train-rmse:0.630509+0.021071    test-rmse:2.244860+0.475692 
## [32] train-rmse:0.607043+0.026924    test-rmse:2.237346+0.477236 
## [33] train-rmse:0.589302+0.021926    test-rmse:2.230190+0.480417 
## [34] train-rmse:0.567979+0.027188    test-rmse:2.226762+0.493297 
## [35] train-rmse:0.544229+0.028877    test-rmse:2.218618+0.495034 
## [36] train-rmse:0.521891+0.023992    test-rmse:2.218100+0.499653 
## [37] train-rmse:0.504415+0.023502    test-rmse:2.212546+0.501629 
## [38] train-rmse:0.490148+0.020573    test-rmse:2.209230+0.498148 
## [39] train-rmse:0.473464+0.023282    test-rmse:2.207346+0.499161 
## [40] train-rmse:0.457557+0.024444    test-rmse:2.204563+0.496060 
## [41] train-rmse:0.443914+0.022325    test-rmse:2.207409+0.500850 
## [42] train-rmse:0.431599+0.019268    test-rmse:2.204959+0.496047 
## [43] train-rmse:0.421254+0.021267    test-rmse:2.204630+0.497534 
## [44] train-rmse:0.406047+0.022293    test-rmse:2.205277+0.497015 
## [45] train-rmse:0.392973+0.023453    test-rmse:2.202161+0.495425 
## [46] train-rmse:0.384607+0.023727    test-rmse:2.199922+0.496039 
## [47] train-rmse:0.376193+0.023443    test-rmse:2.199617+0.493206 
## [48] train-rmse:0.367940+0.020542    test-rmse:2.197215+0.492399 
## [49] train-rmse:0.358226+0.018549    test-rmse:2.196413+0.493351 
## [50] train-rmse:0.347004+0.016962    test-rmse:2.193938+0.493300 
## [51] train-rmse:0.329673+0.017029    test-rmse:2.191785+0.491620 
## [52] train-rmse:0.321667+0.017108    test-rmse:2.190773+0.492870 
## [53] train-rmse:0.313469+0.015285    test-rmse:2.188029+0.493468 
## [54] train-rmse:0.303095+0.016709    test-rmse:2.186689+0.495822 
## [55] train-rmse:0.295585+0.017422    test-rmse:2.187626+0.495555 
## [56] train-rmse:0.287059+0.017399    test-rmse:2.187487+0.494632 
## [57] train-rmse:0.278892+0.016386    test-rmse:2.187835+0.495032 
## [58] train-rmse:0.272266+0.014845    test-rmse:2.187838+0.495272 
## [59] train-rmse:0.265064+0.013630    test-rmse:2.187828+0.495051 
## [60] train-rmse:0.257095+0.010758    test-rmse:2.186289+0.496186 
## [61] train-rmse:0.250642+0.010810    test-rmse:2.183063+0.498368 
## [62] train-rmse:0.244173+0.014529    test-rmse:2.181862+0.498013 
## [63] train-rmse:0.239403+0.014246    test-rmse:2.182669+0.497098 
## [64] train-rmse:0.234494+0.013045    test-rmse:2.183999+0.496792 
## [65] train-rmse:0.228426+0.012940    test-rmse:2.184431+0.496935 
## [66] train-rmse:0.223341+0.011408    test-rmse:2.186359+0.497799 
## [67] train-rmse:0.218784+0.010190    test-rmse:2.185965+0.496615 
## [68] train-rmse:0.214231+0.009793    test-rmse:2.185166+0.495491 
## Stopping. Best iteration:
## [62] train-rmse:0.244173+0.014529    test-rmse:2.181862+0.498013
## 
## 109 
## [1]  train-rmse:5.412213+0.104001    test-rmse:5.476866+0.510779 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.775840+0.074201    test-rmse:4.003196+0.435319 
## [3]  train-rmse:2.883938+0.063261    test-rmse:3.304095+0.484817 
## [4]  train-rmse:2.300578+0.063836    test-rmse:2.877624+0.514337 
## [5]  train-rmse:1.975414+0.066890    test-rmse:2.737961+0.570176 
## [6]  train-rmse:1.748899+0.097613    test-rmse:2.632839+0.595618 
## [7]  train-rmse:1.585824+0.096707    test-rmse:2.566244+0.591605 
## [8]  train-rmse:1.445456+0.080853    test-rmse:2.508075+0.588816 
## [9]  train-rmse:1.339979+0.066911    test-rmse:2.481131+0.577231 
## [10] train-rmse:1.258379+0.070095    test-rmse:2.441235+0.579090 
## [11] train-rmse:1.171211+0.070300    test-rmse:2.412688+0.578014 
## [12] train-rmse:1.101837+0.074320    test-rmse:2.417761+0.582499 
## [13] train-rmse:1.045796+0.062804    test-rmse:2.388298+0.586291 
## [14] train-rmse:0.984861+0.051668    test-rmse:2.371970+0.577347 
## [15] train-rmse:0.934007+0.056311    test-rmse:2.364803+0.585448 
## [16] train-rmse:0.890397+0.060332    test-rmse:2.352457+0.575010 
## [17] train-rmse:0.849951+0.056510    test-rmse:2.350950+0.573759 
## [18] train-rmse:0.815510+0.056480    test-rmse:2.345644+0.581068 
## [19] train-rmse:0.771208+0.050448    test-rmse:2.343376+0.575182 
## [20] train-rmse:0.731636+0.043871    test-rmse:2.333891+0.580893 
## [21] train-rmse:0.699698+0.033069    test-rmse:2.323078+0.590566 
## [22] train-rmse:0.664068+0.020282    test-rmse:2.326452+0.598831 
## [23] train-rmse:0.630131+0.024845    test-rmse:2.322783+0.599037 
## [24] train-rmse:0.603055+0.024114    test-rmse:2.331119+0.609339 
## [25] train-rmse:0.576780+0.021674    test-rmse:2.321402+0.609711 
## [26] train-rmse:0.552763+0.018716    test-rmse:2.308822+0.612100 
## [27] train-rmse:0.519475+0.016838    test-rmse:2.302717+0.611270 
## [28] train-rmse:0.489348+0.018050    test-rmse:2.314587+0.614924 
## [29] train-rmse:0.475583+0.018563    test-rmse:2.314655+0.615233 
## [30] train-rmse:0.458497+0.025215    test-rmse:2.304903+0.607849 
## [31] train-rmse:0.442379+0.027345    test-rmse:2.305069+0.610161 
## [32] train-rmse:0.431105+0.025774    test-rmse:2.300139+0.615656 
## [33] train-rmse:0.413349+0.024206    test-rmse:2.297259+0.616791 
## [34] train-rmse:0.390815+0.023697    test-rmse:2.291348+0.616171 
## [35] train-rmse:0.380126+0.021360    test-rmse:2.292225+0.618794 
## [36] train-rmse:0.361507+0.018148    test-rmse:2.287944+0.618390 
## [37] train-rmse:0.351075+0.017069    test-rmse:2.286452+0.620779 
## [38] train-rmse:0.335053+0.024159    test-rmse:2.287990+0.623923 
## [39] train-rmse:0.320999+0.025588    test-rmse:2.287045+0.625535 
## [40] train-rmse:0.309670+0.029209    test-rmse:2.286944+0.622350 
## [41] train-rmse:0.296414+0.024386    test-rmse:2.283197+0.623778 
## [42] train-rmse:0.283465+0.025902    test-rmse:2.278986+0.626346 
## [43] train-rmse:0.272912+0.026062    test-rmse:2.278770+0.627011 
## [44] train-rmse:0.263864+0.026547    test-rmse:2.280161+0.628065 
## [45] train-rmse:0.250645+0.025622    test-rmse:2.278847+0.630128 
## [46] train-rmse:0.241776+0.023977    test-rmse:2.282533+0.626273 
## [47] train-rmse:0.234692+0.023613    test-rmse:2.281614+0.628411 
## [48] train-rmse:0.226885+0.023480    test-rmse:2.281091+0.630154 
## [49] train-rmse:0.218065+0.025020    test-rmse:2.278905+0.630666 
## Stopping. Best iteration:
## [43] train-rmse:0.272912+0.026062    test-rmse:2.278770+0.627011
## 
## 110 
## [1]  train-rmse:5.357983+0.093768    test-rmse:5.446087+0.561213 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.693181+0.063796    test-rmse:4.009262+0.467678 
## [3]  train-rmse:2.739141+0.033189    test-rmse:3.285400+0.489625 
## [4]  train-rmse:2.165486+0.024889    test-rmse:2.875536+0.556486 
## [5]  train-rmse:1.785500+0.048594    test-rmse:2.681458+0.578015 
## [6]  train-rmse:1.550391+0.022686    test-rmse:2.575895+0.557276 
## [7]  train-rmse:1.362336+0.054788    test-rmse:2.488441+0.585022 
## [8]  train-rmse:1.230784+0.046756    test-rmse:2.424439+0.564774 
## [9]  train-rmse:1.139054+0.055443    test-rmse:2.394543+0.558852 
## [10] train-rmse:1.021930+0.052595    test-rmse:2.358169+0.548121 
## [11] train-rmse:0.948505+0.050197    test-rmse:2.347541+0.545104 
## [12] train-rmse:0.865019+0.045855    test-rmse:2.331010+0.558165 
## [13] train-rmse:0.804959+0.050300    test-rmse:2.314861+0.561951 
## [14] train-rmse:0.761804+0.055557    test-rmse:2.302363+0.555430 
## [15] train-rmse:0.707470+0.043983    test-rmse:2.285064+0.551810 
## [16] train-rmse:0.662452+0.038748    test-rmse:2.278979+0.556831 
## [17] train-rmse:0.606503+0.024815    test-rmse:2.283153+0.561574 
## [18] train-rmse:0.578203+0.021419    test-rmse:2.284731+0.564556 
## [19] train-rmse:0.537969+0.008135    test-rmse:2.283907+0.569258 
## [20] train-rmse:0.514124+0.010223    test-rmse:2.278262+0.571543 
## [21] train-rmse:0.472307+0.016839    test-rmse:2.262772+0.562136 
## [22] train-rmse:0.449277+0.013409    test-rmse:2.260295+0.561611 
## [23] train-rmse:0.421006+0.018365    test-rmse:2.255417+0.563122 
## [24] train-rmse:0.395492+0.014460    test-rmse:2.245477+0.562508 
## [25] train-rmse:0.379262+0.016374    test-rmse:2.244457+0.561395 
## [26] train-rmse:0.360525+0.023295    test-rmse:2.241255+0.560790 
## [27] train-rmse:0.343274+0.019845    test-rmse:2.242403+0.561255 
## [28] train-rmse:0.324063+0.020310    test-rmse:2.240569+0.561601 
## [29] train-rmse:0.307052+0.018329    test-rmse:2.239626+0.560379 
## [30] train-rmse:0.288863+0.013277    test-rmse:2.237503+0.562153 
## [31] train-rmse:0.278281+0.014004    test-rmse:2.238239+0.563053 
## [32] train-rmse:0.264657+0.012756    test-rmse:2.238701+0.562275 
## [33] train-rmse:0.249207+0.009974    test-rmse:2.237683+0.562661 
## [34] train-rmse:0.233979+0.011900    test-rmse:2.237460+0.561505 
## [35] train-rmse:0.222679+0.008353    test-rmse:2.237673+0.557982 
## [36] train-rmse:0.210783+0.008475    test-rmse:2.236304+0.556908 
## [37] train-rmse:0.197570+0.009022    test-rmse:2.235087+0.557486 
## [38] train-rmse:0.189176+0.008642    test-rmse:2.233170+0.555370 
## [39] train-rmse:0.178715+0.007630    test-rmse:2.234017+0.555625 
## [40] train-rmse:0.170670+0.009715    test-rmse:2.234437+0.554669 
## [41] train-rmse:0.163062+0.010100    test-rmse:2.235010+0.553836 
## [42] train-rmse:0.158690+0.010330    test-rmse:2.235420+0.552906 
## [43] train-rmse:0.150728+0.008570    test-rmse:2.234705+0.552087 
## [44] train-rmse:0.143876+0.009468    test-rmse:2.235291+0.551158 
## Stopping. Best iteration:
## [38] train-rmse:0.189176+0.008642    test-rmse:2.233170+0.555370
## 
## 111 
## [1]  train-rmse:5.338599+0.092070    test-rmse:5.432893+0.561093 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.618019+0.052290    test-rmse:3.973736+0.475476 
## [3]  train-rmse:2.632378+0.033575    test-rmse:3.212463+0.502699 
## [4]  train-rmse:2.035932+0.034793    test-rmse:2.849638+0.521042 
## [5]  train-rmse:1.633744+0.049469    test-rmse:2.658568+0.512480 
## [6]  train-rmse:1.384138+0.052795    test-rmse:2.543886+0.512309 
## [7]  train-rmse:1.215447+0.035256    test-rmse:2.479673+0.549347 
## [8]  train-rmse:1.085299+0.023828    test-rmse:2.456031+0.568558 
## [9]  train-rmse:0.976554+0.037289    test-rmse:2.434782+0.572216 
## [10] train-rmse:0.886327+0.039378    test-rmse:2.399720+0.569817 
## [11] train-rmse:0.813370+0.049617    test-rmse:2.386607+0.576436 
## [12] train-rmse:0.728870+0.035646    test-rmse:2.376159+0.576722 
## [13] train-rmse:0.660396+0.036183    test-rmse:2.371677+0.579821 
## [14] train-rmse:0.610030+0.032332    test-rmse:2.366411+0.577984 
## [15] train-rmse:0.572836+0.034055    test-rmse:2.362878+0.582540 
## [16] train-rmse:0.515243+0.030936    test-rmse:2.361196+0.585628 
## [17] train-rmse:0.469478+0.027029    test-rmse:2.358107+0.582375 
## [18] train-rmse:0.435088+0.027602    test-rmse:2.354078+0.586954 
## [19] train-rmse:0.407267+0.028170    test-rmse:2.350805+0.591906 
## [20] train-rmse:0.381880+0.026200    test-rmse:2.348951+0.596132 
## [21] train-rmse:0.353893+0.022598    test-rmse:2.352544+0.595746 
## [22] train-rmse:0.333476+0.021562    test-rmse:2.354872+0.592300 
## [23] train-rmse:0.314003+0.023557    test-rmse:2.353490+0.593715 
## [24] train-rmse:0.287174+0.027463    test-rmse:2.347748+0.595234 
## [25] train-rmse:0.265266+0.031738    test-rmse:2.349423+0.595472 
## [26] train-rmse:0.246799+0.030090    test-rmse:2.348133+0.594093 
## [27] train-rmse:0.233421+0.028157    test-rmse:2.344989+0.594008 
## [28] train-rmse:0.217045+0.027757    test-rmse:2.344328+0.594163 
## [29] train-rmse:0.203458+0.025243    test-rmse:2.340587+0.591717 
## [30] train-rmse:0.191251+0.025357    test-rmse:2.340211+0.593050 
## [31] train-rmse:0.180666+0.021568    test-rmse:2.337618+0.592289 
## [32] train-rmse:0.169705+0.023677    test-rmse:2.337239+0.591845 
## [33] train-rmse:0.159614+0.022816    test-rmse:2.336596+0.592128 
## [34] train-rmse:0.151800+0.021396    test-rmse:2.336743+0.592107 
## [35] train-rmse:0.141695+0.017709    test-rmse:2.334749+0.593656 
## [36] train-rmse:0.134108+0.016544    test-rmse:2.334890+0.592276 
## [37] train-rmse:0.125124+0.017679    test-rmse:2.334013+0.592388 
## [38] train-rmse:0.116147+0.017471    test-rmse:2.332909+0.593439 
## [39] train-rmse:0.110512+0.017452    test-rmse:2.331009+0.593117 
## [40] train-rmse:0.104423+0.017739    test-rmse:2.330995+0.593557 
## [41] train-rmse:0.097236+0.017614    test-rmse:2.330885+0.594962 
## [42] train-rmse:0.091999+0.016947    test-rmse:2.330279+0.594459 
## [43] train-rmse:0.085769+0.016462    test-rmse:2.330300+0.594119 
## [44] train-rmse:0.080405+0.015696    test-rmse:2.332014+0.595283 
## [45] train-rmse:0.075991+0.015196    test-rmse:2.332940+0.594114 
## [46] train-rmse:0.071710+0.014324    test-rmse:2.333249+0.594230 
## [47] train-rmse:0.068046+0.012908    test-rmse:2.332913+0.593527 
## [48] train-rmse:0.063699+0.010624    test-rmse:2.333394+0.592966 
## Stopping. Best iteration:
## [42] train-rmse:0.091999+0.016947    test-rmse:2.330279+0.594459
## 
## 112
##    max_depth  eta
## 10         3 0.12
## [1]  train-rmse:1.296633+0.005288    test-rmse:1.296483+0.023806 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.172200+0.004322    test-rmse:1.172116+0.022422 
## [3]  train-rmse:1.060667+0.003414    test-rmse:1.060655+0.021101 
## [4]  train-rmse:0.960789+0.002564    test-rmse:0.960914+0.019836 
## [5]  train-rmse:0.871446+0.001784    test-rmse:0.871740+0.018630 
## [6]  train-rmse:0.791639+0.001140    test-rmse:0.792117+0.017447 
## [7]  train-rmse:0.720471+0.000866    test-rmse:0.721139+0.016305 
## [8]  train-rmse:0.657130+0.001188    test-rmse:0.658775+0.015618 
## [9]  train-rmse:0.600742+0.001670    test-rmse:0.603127+0.014642 
## [10] train-rmse:0.550441+0.002167    test-rmse:0.552952+0.013272 
## [11] train-rmse:0.505859+0.002593    test-rmse:0.509293+0.012049 
## [12] train-rmse:0.466247+0.002981    test-rmse:0.469968+0.010849 
## [13] train-rmse:0.431255+0.003347    test-rmse:0.435189+0.011419 
## [14] train-rmse:0.400296+0.003687    test-rmse:0.405147+0.011459 
## [15] train-rmse:0.373125+0.003998    test-rmse:0.377762+0.012111 
## [16] train-rmse:0.349219+0.004288    test-rmse:0.354480+0.012700 
## [17] train-rmse:0.328382+0.004545    test-rmse:0.333978+0.013910 
## [18] train-rmse:0.310156+0.004791    test-rmse:0.316315+0.015038 
## [19] train-rmse:0.294381+0.004999    test-rmse:0.300607+0.015915 
## [20] train-rmse:0.280668+0.005203    test-rmse:0.287067+0.017095 
## [21] train-rmse:0.268889+0.005360    test-rmse:0.275309+0.017827 
## [22] train-rmse:0.258685+0.005496    test-rmse:0.265244+0.019098 
## [23] train-rmse:0.249976+0.005613    test-rmse:0.257027+0.019896 
## [24] train-rmse:0.242465+0.005706    test-rmse:0.249720+0.021044 
## [25] train-rmse:0.236091+0.005783    test-rmse:0.243379+0.021321 
## [26] train-rmse:0.230588+0.005826    test-rmse:0.237950+0.022358 
## [27] train-rmse:0.225944+0.005877    test-rmse:0.233352+0.022456 
## [28] train-rmse:0.221935+0.005892    test-rmse:0.229361+0.023341 
## [29] train-rmse:0.218477+0.005921    test-rmse:0.226935+0.023213 
## [30] train-rmse:0.215479+0.005924    test-rmse:0.224339+0.024068 
## [31] train-rmse:0.212866+0.005936    test-rmse:0.222786+0.024395 
## [32] train-rmse:0.210564+0.005922    test-rmse:0.220520+0.025001 
## [33] train-rmse:0.208522+0.005894    test-rmse:0.219730+0.024832 
## [34] train-rmse:0.206683+0.005827    test-rmse:0.218487+0.024896 
## [35] train-rmse:0.205047+0.005780    test-rmse:0.216878+0.025039 
## [36] train-rmse:0.203561+0.005713    test-rmse:0.216046+0.025925 
## [37] train-rmse:0.202211+0.005659    test-rmse:0.215390+0.025933 
## [38] train-rmse:0.200979+0.005614    test-rmse:0.215198+0.025573 
## [39] train-rmse:0.199853+0.005554    test-rmse:0.214357+0.025569 
## [40] train-rmse:0.198796+0.005493    test-rmse:0.213645+0.026329 
## [41] train-rmse:0.197809+0.005455    test-rmse:0.213048+0.026209 
## [42] train-rmse:0.196883+0.005413    test-rmse:0.213310+0.026757 
## [43] train-rmse:0.196024+0.005374    test-rmse:0.213188+0.026838 
## [44] train-rmse:0.195208+0.005359    test-rmse:0.213260+0.026287 
## [45] train-rmse:0.194446+0.005338    test-rmse:0.211850+0.026502 
## [46] train-rmse:0.193698+0.005302    test-rmse:0.212265+0.026869 
## [47] train-rmse:0.193001+0.005293    test-rmse:0.212228+0.026868 
## [48] train-rmse:0.192325+0.005297    test-rmse:0.211900+0.027194 
## [49] train-rmse:0.191693+0.005291    test-rmse:0.212405+0.027504 
## [50] train-rmse:0.191075+0.005302    test-rmse:0.212307+0.026724 
## [51] train-rmse:0.190486+0.005313    test-rmse:0.211759+0.026764 
## [52] train-rmse:0.189911+0.005322    test-rmse:0.211669+0.026793 
## [53] train-rmse:0.189356+0.005335    test-rmse:0.211809+0.027336 
## [54] train-rmse:0.188808+0.005356    test-rmse:0.211729+0.027208 
## [55] train-rmse:0.188289+0.005385    test-rmse:0.211670+0.027222 
## [56] train-rmse:0.187801+0.005402    test-rmse:0.211121+0.027119 
## [57] train-rmse:0.187323+0.005422    test-rmse:0.211076+0.026387 
## [58] train-rmse:0.186833+0.005460    test-rmse:0.211596+0.026643 
## [59] train-rmse:0.186378+0.005491    test-rmse:0.211474+0.026577 
## [60] train-rmse:0.185926+0.005520    test-rmse:0.211044+0.026649 
## [61] train-rmse:0.185492+0.005558    test-rmse:0.211631+0.026902 
## [62] train-rmse:0.185073+0.005598    test-rmse:0.211645+0.026311 
## [63] train-rmse:0.184664+0.005633    test-rmse:0.211719+0.026558 
## [64] train-rmse:0.184267+0.005667    test-rmse:0.211394+0.026361 
## [65] train-rmse:0.183882+0.005699    test-rmse:0.211020+0.026175 
## [66] train-rmse:0.183491+0.005735    test-rmse:0.211437+0.026754 
## [67] train-rmse:0.183109+0.005762    test-rmse:0.211440+0.026686 
## [68] train-rmse:0.182742+0.005798    test-rmse:0.211061+0.026391 
## [69] train-rmse:0.182388+0.005831    test-rmse:0.211219+0.026982 
## [70] train-rmse:0.182047+0.005869    test-rmse:0.211473+0.026433 
## [71] train-rmse:0.181711+0.005903    test-rmse:0.211223+0.026307 
## Stopping. Best iteration:
## [65] train-rmse:0.183882+0.005699    test-rmse:0.211020+0.026175
## 
## 1 
## [1]  train-rmse:1.295044+0.005591    test-rmse:1.295424+0.023218 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.168947+0.004923    test-rmse:1.169990+0.021363 
## [3]  train-rmse:1.055671+0.004315    test-rmse:1.057340+0.019648 
## [4]  train-rmse:0.953912+0.003760    test-rmse:0.956423+0.018095 
## [5]  train-rmse:0.862568+0.003283    test-rmse:0.866159+0.016804 
## [6]  train-rmse:0.780637+0.002847    test-rmse:0.785200+0.015563 
## [7]  train-rmse:0.707191+0.002489    test-rmse:0.712900+0.014531 
## [8]  train-rmse:0.641426+0.002180    test-rmse:0.648371+0.013638 
## [9]  train-rmse:0.582617+0.001941    test-rmse:0.590889+0.012914 
## [10] train-rmse:0.530126+0.001750    test-rmse:0.540543+0.012229 
## [11] train-rmse:0.483269+0.001647    test-rmse:0.495290+0.011839 
## [12] train-rmse:0.441573+0.001586    test-rmse:0.455698+0.011546 
## [13] train-rmse:0.404527+0.001572    test-rmse:0.420519+0.011491 
## [14] train-rmse:0.371731+0.001641    test-rmse:0.390025+0.011847 
## [15] train-rmse:0.342665+0.001683    test-rmse:0.363274+0.012444 
## [16] train-rmse:0.317063+0.001784    test-rmse:0.339857+0.013150 
## [17] train-rmse:0.294515+0.001833    test-rmse:0.319714+0.013802 
## [18] train-rmse:0.274761+0.001922    test-rmse:0.302881+0.014999 
## [19] train-rmse:0.257516+0.001956    test-rmse:0.287602+0.016061 
## [20] train-rmse:0.242494+0.002017    test-rmse:0.275381+0.017061 
## [21] train-rmse:0.229480+0.002034    test-rmse:0.264492+0.018456 
## [22] train-rmse:0.218208+0.002040    test-rmse:0.255876+0.019669 
## [23] train-rmse:0.207818+0.001660    test-rmse:0.247592+0.021198 
## [24] train-rmse:0.199468+0.001717    test-rmse:0.241305+0.022060 
## [25] train-rmse:0.191834+0.001328    test-rmse:0.235754+0.023097 
## [26] train-rmse:0.185722+0.001305    test-rmse:0.231008+0.023708 
## [27] train-rmse:0.180255+0.001651    test-rmse:0.226954+0.024096 
## [28] train-rmse:0.175310+0.002127    test-rmse:0.223361+0.024429 
## [29] train-rmse:0.171292+0.002408    test-rmse:0.220835+0.025113 
## [30] train-rmse:0.167896+0.002810    test-rmse:0.218436+0.025512 
## [31] train-rmse:0.164627+0.002938    test-rmse:0.217158+0.025645 
## [32] train-rmse:0.161584+0.003021    test-rmse:0.215901+0.025548 
## [33] train-rmse:0.159616+0.003136    test-rmse:0.214875+0.026146 
## [34] train-rmse:0.157218+0.003399    test-rmse:0.214194+0.026197 
## [35] train-rmse:0.155232+0.003640    test-rmse:0.213487+0.026348 
## [36] train-rmse:0.153950+0.003795    test-rmse:0.212946+0.026696 
## [37] train-rmse:0.152914+0.003832    test-rmse:0.212967+0.026732 
## [38] train-rmse:0.151043+0.003752    test-rmse:0.212873+0.026576 
## [39] train-rmse:0.149857+0.003770    test-rmse:0.212677+0.027051 
## [40] train-rmse:0.148906+0.003811    test-rmse:0.212571+0.027315 
## [41] train-rmse:0.147819+0.004269    test-rmse:0.212253+0.027269 
## [42] train-rmse:0.147042+0.004588    test-rmse:0.212110+0.027438 
## [43] train-rmse:0.145357+0.004096    test-rmse:0.212451+0.027559 
## [44] train-rmse:0.144055+0.004346    test-rmse:0.211932+0.027617 
## [45] train-rmse:0.143215+0.004129    test-rmse:0.212046+0.027888 
## [46] train-rmse:0.142645+0.004373    test-rmse:0.211937+0.028042 
## [47] train-rmse:0.141427+0.004062    test-rmse:0.211986+0.028224 
## [48] train-rmse:0.140730+0.004360    test-rmse:0.211874+0.028196 
## [49] train-rmse:0.139795+0.004282    test-rmse:0.211848+0.028181 
## [50] train-rmse:0.139351+0.004480    test-rmse:0.211788+0.028208 
## [51] train-rmse:0.138580+0.004284    test-rmse:0.211957+0.028169 
## [52] train-rmse:0.137827+0.004529    test-rmse:0.211817+0.028243 
## [53] train-rmse:0.137231+0.004177    test-rmse:0.212032+0.028448 
## [54] train-rmse:0.136691+0.004077    test-rmse:0.212253+0.028771 
## [55] train-rmse:0.136247+0.004107    test-rmse:0.212203+0.028881 
## [56] train-rmse:0.135651+0.004420    test-rmse:0.211983+0.028954 
## Stopping. Best iteration:
## [50] train-rmse:0.139351+0.004480    test-rmse:0.211788+0.028208
## 
## 2 
## [1]  train-rmse:1.294731+0.005579    test-rmse:1.295658+0.022907 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.168290+0.004887    test-rmse:1.170480+0.020743 
## [3]  train-rmse:1.054596+0.004256    test-rmse:1.058041+0.018688 
## [4]  train-rmse:0.952392+0.003702    test-rmse:0.957428+0.016848 
## [5]  train-rmse:0.860542+0.003212    test-rmse:0.867444+0.015163 
## [6]  train-rmse:0.778011+0.002760    test-rmse:0.787100+0.013692 
## [7]  train-rmse:0.703921+0.002360    test-rmse:0.715147+0.012360 
## [8]  train-rmse:0.637427+0.002026    test-rmse:0.651209+0.011003 
## [9]  train-rmse:0.577806+0.001781    test-rmse:0.594603+0.009942 
## [10] train-rmse:0.524387+0.001650    test-rmse:0.544003+0.009011 
## [11] train-rmse:0.476391+0.001620    test-rmse:0.499173+0.008715 
## [12] train-rmse:0.433450+0.001893    test-rmse:0.459293+0.009168 
## [13] train-rmse:0.395235+0.002197    test-rmse:0.425165+0.008667 
## [14] train-rmse:0.360994+0.002799    test-rmse:0.394866+0.009286 
## [15] train-rmse:0.330526+0.003403    test-rmse:0.369024+0.009807 
## [16] train-rmse:0.303157+0.004019    test-rmse:0.345405+0.010721 
## [17] train-rmse:0.278698+0.004565    test-rmse:0.325966+0.011557 
## [18] train-rmse:0.256777+0.004615    test-rmse:0.308607+0.012782 
## [19] train-rmse:0.237323+0.004834    test-rmse:0.294310+0.014064 
## [20] train-rmse:0.220155+0.004948    test-rmse:0.281765+0.015071 
## [21] train-rmse:0.204891+0.005218    test-rmse:0.271596+0.016829 
## [22] train-rmse:0.191680+0.005311    test-rmse:0.262668+0.017944 
## [23] train-rmse:0.180024+0.005703    test-rmse:0.255620+0.019492 
## [24] train-rmse:0.169699+0.006045    test-rmse:0.248931+0.020536 
## [25] train-rmse:0.160733+0.006182    test-rmse:0.244625+0.021833 
## [26] train-rmse:0.152676+0.006675    test-rmse:0.240333+0.022980 
## [27] train-rmse:0.145905+0.006865    test-rmse:0.237072+0.024025 
## [28] train-rmse:0.139671+0.007379    test-rmse:0.234164+0.024781 
## [29] train-rmse:0.134180+0.007831    test-rmse:0.232115+0.025465 
## [30] train-rmse:0.129521+0.007897    test-rmse:0.230311+0.026312 
## [31] train-rmse:0.125522+0.007918    test-rmse:0.228682+0.026828 
## [32] train-rmse:0.122077+0.007959    test-rmse:0.227478+0.027562 
## [33] train-rmse:0.118943+0.008101    test-rmse:0.226389+0.027708 
## [34] train-rmse:0.116313+0.008186    test-rmse:0.225733+0.028265 
## [35] train-rmse:0.113767+0.008376    test-rmse:0.224917+0.028034 
## [36] train-rmse:0.111796+0.008475    test-rmse:0.224559+0.028563 
## [37] train-rmse:0.109905+0.008759    test-rmse:0.224288+0.028764 
## [38] train-rmse:0.108479+0.008666    test-rmse:0.224172+0.028912 
## [39] train-rmse:0.107064+0.008718    test-rmse:0.223841+0.029085 
## [40] train-rmse:0.105954+0.008633    test-rmse:0.223838+0.029419 
## [41] train-rmse:0.104627+0.008632    test-rmse:0.224069+0.029752 
## [42] train-rmse:0.103052+0.008658    test-rmse:0.224156+0.029826 
## [43] train-rmse:0.101656+0.008667    test-rmse:0.224537+0.030216 
## [44] train-rmse:0.100487+0.008843    test-rmse:0.224513+0.030348 
## [45] train-rmse:0.099332+0.008868    test-rmse:0.224990+0.030830 
## [46] train-rmse:0.098298+0.008973    test-rmse:0.225416+0.031192 
## Stopping. Best iteration:
## [40] train-rmse:0.105954+0.008633    test-rmse:0.223838+0.029419
## 
## 3 
## [1]  train-rmse:1.294618+0.005588    test-rmse:1.295658+0.022888 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.168039+0.004903    test-rmse:1.170497+0.020703 
## [3]  train-rmse:1.054194+0.004286    test-rmse:1.058135+0.018641 
## [4]  train-rmse:0.951832+0.003713    test-rmse:0.957463+0.016740 
## [5]  train-rmse:0.859843+0.003190    test-rmse:0.867359+0.014987 
## [6]  train-rmse:0.777154+0.002772    test-rmse:0.787185+0.013524 
## [7]  train-rmse:0.702591+0.002129    test-rmse:0.715588+0.012312 
## [8]  train-rmse:0.635468+0.001671    test-rmse:0.651769+0.011118 
## [9]  train-rmse:0.575030+0.001330    test-rmse:0.594867+0.009898 
## [10] train-rmse:0.520903+0.001261    test-rmse:0.544544+0.008825 
## [11] train-rmse:0.471928+0.001043    test-rmse:0.499951+0.008066 
## [12] train-rmse:0.427915+0.001093    test-rmse:0.460913+0.007630 
## [13] train-rmse:0.388425+0.001178    test-rmse:0.426594+0.007923 
## [14] train-rmse:0.353003+0.001467    test-rmse:0.396772+0.008532 
## [15] train-rmse:0.321218+0.001806    test-rmse:0.371196+0.009463 
## [16] train-rmse:0.292776+0.002189    test-rmse:0.349031+0.010566 
## [17] train-rmse:0.267394+0.002520    test-rmse:0.329804+0.011814 
## [18] train-rmse:0.244364+0.002933    test-rmse:0.313385+0.013219 
## [19] train-rmse:0.223759+0.003396    test-rmse:0.299066+0.014754 
## [20] train-rmse:0.205354+0.003755    test-rmse:0.287259+0.016365 
## [21] train-rmse:0.188881+0.004042    test-rmse:0.277082+0.018180 
## [22] train-rmse:0.174263+0.004374    test-rmse:0.268567+0.019749 
## [23] train-rmse:0.161220+0.004681    test-rmse:0.261581+0.021382 
## [24] train-rmse:0.149579+0.004892    test-rmse:0.256070+0.022911 
## [25] train-rmse:0.139311+0.005163    test-rmse:0.251309+0.024471 
## [26] train-rmse:0.130099+0.005544    test-rmse:0.247811+0.025696 
## [27] train-rmse:0.121969+0.005912    test-rmse:0.244702+0.027016 
## [28] train-rmse:0.114788+0.006289    test-rmse:0.242153+0.027804 
## [29] train-rmse:0.108502+0.006607    test-rmse:0.240258+0.028716 
## [30] train-rmse:0.103075+0.007025    test-rmse:0.238430+0.029577 
## [31] train-rmse:0.098291+0.007442    test-rmse:0.236724+0.030044 
## [32] train-rmse:0.094127+0.007836    test-rmse:0.235693+0.030534 
## [33] train-rmse:0.090458+0.008150    test-rmse:0.235439+0.031372 
## [34] train-rmse:0.087256+0.008196    test-rmse:0.234811+0.031839 
## [35] train-rmse:0.084299+0.008148    test-rmse:0.234244+0.032219 
## [36] train-rmse:0.081977+0.008297    test-rmse:0.233953+0.032558 
## [37] train-rmse:0.079811+0.008365    test-rmse:0.233743+0.032955 
## [38] train-rmse:0.077735+0.008585    test-rmse:0.233346+0.033041 
## [39] train-rmse:0.075841+0.008768    test-rmse:0.233080+0.033165 
## [40] train-rmse:0.074167+0.008965    test-rmse:0.233160+0.033500 
## [41] train-rmse:0.072679+0.008934    test-rmse:0.232962+0.033652 
## [42] train-rmse:0.071354+0.009060    test-rmse:0.232808+0.033721 
## [43] train-rmse:0.070143+0.008799    test-rmse:0.232595+0.033774 
## [44] train-rmse:0.069029+0.008670    test-rmse:0.232270+0.033721 
## [45] train-rmse:0.067950+0.008592    test-rmse:0.232358+0.033860 
## [46] train-rmse:0.067052+0.008553    test-rmse:0.232418+0.034031 
## [47] train-rmse:0.065977+0.008527    test-rmse:0.232289+0.034004 
## [48] train-rmse:0.065189+0.008550    test-rmse:0.232417+0.034122 
## [49] train-rmse:0.064239+0.008665    test-rmse:0.232567+0.034221 
## [50] train-rmse:0.063271+0.008489    test-rmse:0.232633+0.034243 
## Stopping. Best iteration:
## [44] train-rmse:0.069029+0.008670    test-rmse:0.232270+0.033721
## 
## 4 
## [1]  train-rmse:1.294546+0.005583    test-rmse:1.295654+0.022882 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.167897+0.004892    test-rmse:1.170503+0.020686 
## [3]  train-rmse:1.053953+0.004274    test-rmse:1.058156+0.018606 
## [4]  train-rmse:0.951495+0.003702    test-rmse:0.957579+0.016707 
## [5]  train-rmse:0.859398+0.003175    test-rmse:0.867532+0.014892 
## [6]  train-rmse:0.776614+0.002735    test-rmse:0.787417+0.013404 
## [7]  train-rmse:0.701862+0.002037    test-rmse:0.715712+0.012202 
## [8]  train-rmse:0.634457+0.001410    test-rmse:0.651849+0.011091 
## [9]  train-rmse:0.573558+0.001044    test-rmse:0.595315+0.009828 
## [10] train-rmse:0.519092+0.000929    test-rmse:0.545144+0.008604 
## [11] train-rmse:0.469819+0.000920    test-rmse:0.501404+0.007850 
## [12] train-rmse:0.425461+0.000993    test-rmse:0.462439+0.007479 
## [13] train-rmse:0.385615+0.001188    test-rmse:0.428245+0.008007 
## [14] train-rmse:0.349621+0.001382    test-rmse:0.398909+0.008750 
## [15] train-rmse:0.317266+0.001698    test-rmse:0.373398+0.009959 
## [16] train-rmse:0.288167+0.001931    test-rmse:0.351494+0.011430 
## [17] train-rmse:0.261860+0.002178    test-rmse:0.332677+0.013267 
## [18] train-rmse:0.238190+0.002485    test-rmse:0.316693+0.015152 
## [19] train-rmse:0.217210+0.002757    test-rmse:0.303243+0.017075 
## [20] train-rmse:0.198040+0.002927    test-rmse:0.291675+0.019215 
## [21] train-rmse:0.181022+0.003081    test-rmse:0.282155+0.021323 
## [22] train-rmse:0.165649+0.003307    test-rmse:0.273957+0.023158 
## [23] train-rmse:0.151748+0.003530    test-rmse:0.267380+0.025037 
## [24] train-rmse:0.139384+0.003775    test-rmse:0.262305+0.027071 
## [25] train-rmse:0.128332+0.003978    test-rmse:0.257838+0.028507 
## [26] train-rmse:0.118494+0.004265    test-rmse:0.254038+0.029932 
## [27] train-rmse:0.109636+0.004364    test-rmse:0.251017+0.031160 
## [28] train-rmse:0.101800+0.004457    test-rmse:0.248809+0.032486 
## [29] train-rmse:0.094560+0.004580    test-rmse:0.247266+0.033647 
## [30] train-rmse:0.088449+0.004918    test-rmse:0.245963+0.034715 
## [31] train-rmse:0.082961+0.005262    test-rmse:0.245050+0.035551 
## [32] train-rmse:0.078013+0.005600    test-rmse:0.244595+0.036331 
## [33] train-rmse:0.073559+0.005618    test-rmse:0.243739+0.036946 
## [34] train-rmse:0.069710+0.005965    test-rmse:0.243398+0.037420 
## [35] train-rmse:0.066348+0.006113    test-rmse:0.242995+0.037730 
## [36] train-rmse:0.063359+0.006344    test-rmse:0.242454+0.038021 
## [37] train-rmse:0.060644+0.006637    test-rmse:0.242321+0.038377 
## [38] train-rmse:0.058238+0.006800    test-rmse:0.241965+0.038585 
## [39] train-rmse:0.056020+0.006838    test-rmse:0.241768+0.038784 
## [40] train-rmse:0.053912+0.006978    test-rmse:0.241662+0.038912 
## [41] train-rmse:0.052160+0.006994    test-rmse:0.241696+0.039240 
## [42] train-rmse:0.050524+0.007069    test-rmse:0.241583+0.039299 
## [43] train-rmse:0.049160+0.007079    test-rmse:0.241516+0.039478 
## [44] train-rmse:0.047906+0.007108    test-rmse:0.241614+0.039601 
## [45] train-rmse:0.046874+0.007096    test-rmse:0.241542+0.039648 
## [46] train-rmse:0.045868+0.007018    test-rmse:0.241603+0.039765 
## [47] train-rmse:0.044889+0.006986    test-rmse:0.241611+0.039790 
## [48] train-rmse:0.044035+0.006961    test-rmse:0.241874+0.039970 
## [49] train-rmse:0.043237+0.006998    test-rmse:0.242036+0.040067 
## Stopping. Best iteration:
## [43] train-rmse:0.049160+0.007079    test-rmse:0.241516+0.039478
## 
## 5 
## [1]  train-rmse:1.294510+0.005599    test-rmse:1.295658+0.022870 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.167823+0.004926    test-rmse:1.170516+0.020662 
## [3]  train-rmse:1.053823+0.004333    test-rmse:1.058176+0.018565 
## [4]  train-rmse:0.951308+0.003784    test-rmse:0.957557+0.016592 
## [5]  train-rmse:0.859149+0.003280    test-rmse:0.867526+0.014764 
## [6]  train-rmse:0.776296+0.002870    test-rmse:0.787351+0.013213 
## [7]  train-rmse:0.701462+0.002179    test-rmse:0.715485+0.011887 
## [8]  train-rmse:0.633962+0.001578    test-rmse:0.651580+0.010800 
## [9]  train-rmse:0.573258+0.001386    test-rmse:0.594872+0.009321 
## [10] train-rmse:0.518311+0.000985    test-rmse:0.544456+0.008252 
## [11] train-rmse:0.468623+0.000867    test-rmse:0.500034+0.007314 
## [12] train-rmse:0.424025+0.000808    test-rmse:0.460979+0.007299 
## [13] train-rmse:0.383932+0.000942    test-rmse:0.426701+0.007756 
## [14] train-rmse:0.347648+0.001038    test-rmse:0.397103+0.008449 
## [15] train-rmse:0.315174+0.001028    test-rmse:0.371823+0.009580 
## [16] train-rmse:0.285595+0.001200    test-rmse:0.349659+0.010980 
## [17] train-rmse:0.258988+0.001235    test-rmse:0.331049+0.012636 
## [18] train-rmse:0.235356+0.001262    test-rmse:0.315065+0.014415 
## [19] train-rmse:0.213932+0.001469    test-rmse:0.301942+0.016048 
## [20] train-rmse:0.194408+0.001570    test-rmse:0.290477+0.018212 
## [21] train-rmse:0.176751+0.001633    test-rmse:0.280783+0.020211 
## [22] train-rmse:0.161053+0.001707    test-rmse:0.272894+0.022176 
## [23] train-rmse:0.146921+0.001795    test-rmse:0.266238+0.024019 
## [24] train-rmse:0.134359+0.002024    test-rmse:0.260962+0.025980 
## [25] train-rmse:0.123051+0.002230    test-rmse:0.256690+0.027588 
## [26] train-rmse:0.112746+0.002391    test-rmse:0.253188+0.029062 
## [27] train-rmse:0.103495+0.002430    test-rmse:0.250328+0.030380 
## [28] train-rmse:0.095292+0.002581    test-rmse:0.248116+0.031580 
## [29] train-rmse:0.087921+0.002727    test-rmse:0.246516+0.032783 
## [30] train-rmse:0.081283+0.002982    test-rmse:0.245124+0.033862 
## [31] train-rmse:0.075320+0.003193    test-rmse:0.244187+0.034927 
## [32] train-rmse:0.069940+0.003303    test-rmse:0.243528+0.035853 
## [33] train-rmse:0.065149+0.003379    test-rmse:0.242857+0.036389 
## [34] train-rmse:0.060902+0.003484    test-rmse:0.242329+0.036870 
## [35] train-rmse:0.057075+0.003584    test-rmse:0.241918+0.037302 
## [36] train-rmse:0.053603+0.003544    test-rmse:0.241589+0.037759 
## [37] train-rmse:0.050568+0.003725    test-rmse:0.241489+0.038095 
## [38] train-rmse:0.047998+0.003855    test-rmse:0.241425+0.038445 
## [39] train-rmse:0.045691+0.004003    test-rmse:0.241478+0.038758 
## [40] train-rmse:0.043560+0.004153    test-rmse:0.241336+0.038968 
## [41] train-rmse:0.041572+0.004329    test-rmse:0.241107+0.039068 
## [42] train-rmse:0.039842+0.004382    test-rmse:0.241094+0.039227 
## [43] train-rmse:0.038369+0.004387    test-rmse:0.241222+0.039473 
## [44] train-rmse:0.037079+0.004390    test-rmse:0.241539+0.039599 
## [45] train-rmse:0.035904+0.004383    test-rmse:0.241635+0.039751 
## [46] train-rmse:0.034842+0.004391    test-rmse:0.241629+0.039852 
## [47] train-rmse:0.033784+0.004231    test-rmse:0.241647+0.039942 
## [48] train-rmse:0.032876+0.004154    test-rmse:0.241622+0.039979 
## Stopping. Best iteration:
## [42] train-rmse:0.039842+0.004382    test-rmse:0.241094+0.039227
## 
## 6 
## [1]  train-rmse:1.294494+0.005618    test-rmse:1.295659+0.022866 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.167789+0.004966    test-rmse:1.170521+0.020650 
## [3]  train-rmse:1.053766+0.004397    test-rmse:1.058186+0.018541 
## [4]  train-rmse:0.951223+0.003873    test-rmse:0.957578+0.016548 
## [5]  train-rmse:0.859035+0.003393    test-rmse:0.867574+0.014716 
## [6]  train-rmse:0.776156+0.002995    test-rmse:0.787422+0.013141 
## [7]  train-rmse:0.701280+0.002302    test-rmse:0.715537+0.011859 
## [8]  train-rmse:0.633924+0.001944    test-rmse:0.651775+0.010590 
## [9]  train-rmse:0.572933+0.001450    test-rmse:0.594922+0.009192 
## [10] train-rmse:0.517868+0.000949    test-rmse:0.544622+0.008047 
## [11] train-rmse:0.468061+0.000747    test-rmse:0.500151+0.007154 
## [12] train-rmse:0.423358+0.000458    test-rmse:0.461167+0.006889 
## [13] train-rmse:0.383026+0.000661    test-rmse:0.426788+0.007235 
## [14] train-rmse:0.346630+0.000752    test-rmse:0.397190+0.008070 
## [15] train-rmse:0.314020+0.000824    test-rmse:0.371835+0.009189 
## [16] train-rmse:0.284528+0.000720    test-rmse:0.349747+0.010806 
## [17] train-rmse:0.257703+0.000814    test-rmse:0.330996+0.012367 
## [18] train-rmse:0.233782+0.000963    test-rmse:0.314961+0.014159 
## [19] train-rmse:0.212113+0.001054    test-rmse:0.301829+0.015957 
## [20] train-rmse:0.192802+0.001166    test-rmse:0.290643+0.017715 
## [21] train-rmse:0.175125+0.001217    test-rmse:0.281364+0.019769 
## [22] train-rmse:0.159440+0.001190    test-rmse:0.273843+0.021793 
## [23] train-rmse:0.145373+0.001421    test-rmse:0.267409+0.023581 
## [24] train-rmse:0.132447+0.001488    test-rmse:0.262556+0.025438 
## [25] train-rmse:0.120907+0.001433    test-rmse:0.258255+0.027004 
## [26] train-rmse:0.110506+0.001492    test-rmse:0.254933+0.028554 
## [27] train-rmse:0.101098+0.001521    test-rmse:0.252532+0.029969 
## [28] train-rmse:0.092703+0.001559    test-rmse:0.250612+0.031310 
## [29] train-rmse:0.085049+0.001665    test-rmse:0.248983+0.032457 
## [30] train-rmse:0.078137+0.001685    test-rmse:0.247848+0.033498 
## [31] train-rmse:0.071932+0.001840    test-rmse:0.247046+0.034456 
## [32] train-rmse:0.066281+0.001827    test-rmse:0.246635+0.035171 
## [33] train-rmse:0.061224+0.001828    test-rmse:0.246286+0.035750 
## [34] train-rmse:0.056646+0.001818    test-rmse:0.246074+0.036268 
## [35] train-rmse:0.052675+0.001875    test-rmse:0.245903+0.036844 
## [36] train-rmse:0.049044+0.001941    test-rmse:0.246067+0.037300 
## [37] train-rmse:0.045834+0.002023    test-rmse:0.246171+0.037783 
## [38] train-rmse:0.042746+0.002056    test-rmse:0.246447+0.038256 
## [39] train-rmse:0.040109+0.002102    test-rmse:0.246647+0.038635 
## [40] train-rmse:0.037721+0.002147    test-rmse:0.246932+0.039023 
## [41] train-rmse:0.035585+0.002183    test-rmse:0.247143+0.039319 
## Stopping. Best iteration:
## [35] train-rmse:0.052675+0.001875    test-rmse:0.245903+0.036844
## 
## 7 
## [1]  train-rmse:1.268950+0.005076    test-rmse:1.268814+0.023505 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.123131+0.003928    test-rmse:1.123077+0.021852 
## [3]  train-rmse:0.995505+0.002865    test-rmse:0.995543+0.020281 
## [4]  train-rmse:0.883964+0.001890    test-rmse:0.884264+0.018823 
## [5]  train-rmse:0.786670+0.001107    test-rmse:0.787194+0.017386 
## [6]  train-rmse:0.702017+0.000902    test-rmse:0.702744+0.015994 
## [7]  train-rmse:0.628569+0.001469    test-rmse:0.630492+0.015211 
## [8]  train-rmse:0.564648+0.001990    test-rmse:0.567277+0.013743 
## [9]  train-rmse:0.509270+0.002609    test-rmse:0.512344+0.012318 
## [10] train-rmse:0.461399+0.002990    test-rmse:0.465500+0.010664 
## [11] train-rmse:0.420115+0.003515    test-rmse:0.423709+0.011850 
## [12] train-rmse:0.384707+0.003829    test-rmse:0.389401+0.011004 
## [13] train-rmse:0.354399+0.004264    test-rmse:0.359224+0.013495 
## [14] train-rmse:0.328681+0.004513    test-rmse:0.334564+0.013432 
## [15] train-rmse:0.306852+0.004872    test-rmse:0.312030+0.015667 
## [16] train-rmse:0.288525+0.005058    test-rmse:0.294793+0.015863 
## [17] train-rmse:0.273095+0.005337    test-rmse:0.279174+0.018521 
## [18] train-rmse:0.260293+0.005457    test-rmse:0.267358+0.018754 
## [19] train-rmse:0.249551+0.005638    test-rmse:0.255821+0.020689 
## [20] train-rmse:0.240732+0.005703    test-rmse:0.248072+0.020709 
## [21] train-rmse:0.233369+0.005829    test-rmse:0.239938+0.022373 
## [22] train-rmse:0.227336+0.005842    test-rmse:0.234854+0.022281 
## [23] train-rmse:0.222282+0.005900    test-rmse:0.230456+0.023653 
## [24] train-rmse:0.218134+0.005907    test-rmse:0.227082+0.023262 
## [25] train-rmse:0.214612+0.005939    test-rmse:0.224172+0.024456 
## [26] train-rmse:0.211598+0.005922    test-rmse:0.221276+0.024757 
## [27] train-rmse:0.209018+0.005906    test-rmse:0.219825+0.024446 
## [28] train-rmse:0.206788+0.005862    test-rmse:0.218003+0.025314 
## [29] train-rmse:0.204795+0.005763    test-rmse:0.217074+0.025452 
## [30] train-rmse:0.203057+0.005697    test-rmse:0.215481+0.025748 
## [31] train-rmse:0.201529+0.005642    test-rmse:0.214059+0.026309 
## [32] train-rmse:0.200085+0.005568    test-rmse:0.213640+0.026367 
## [33] train-rmse:0.198803+0.005505    test-rmse:0.214044+0.026239 
## [34] train-rmse:0.197618+0.005455    test-rmse:0.213395+0.026088 
## [35] train-rmse:0.196530+0.005388    test-rmse:0.213117+0.026257 
## [36] train-rmse:0.195517+0.005363    test-rmse:0.212829+0.026681 
## [37] train-rmse:0.194578+0.005344    test-rmse:0.212130+0.027091 
## [38] train-rmse:0.193673+0.005303    test-rmse:0.211998+0.027098 
## [39] train-rmse:0.192839+0.005287    test-rmse:0.212512+0.026702 
## [40] train-rmse:0.192040+0.005291    test-rmse:0.212198+0.026496 
## [41] train-rmse:0.191279+0.005283    test-rmse:0.212123+0.027032 
## [42] train-rmse:0.190552+0.005302    test-rmse:0.211597+0.027155 
## [43] train-rmse:0.189870+0.005319    test-rmse:0.211177+0.026954 
## [44] train-rmse:0.189198+0.005350    test-rmse:0.211999+0.026708 
## [45] train-rmse:0.188556+0.005371    test-rmse:0.211743+0.026527 
## [46] train-rmse:0.187948+0.005385    test-rmse:0.211380+0.026801 
## [47] train-rmse:0.187340+0.005432    test-rmse:0.211301+0.027032 
## [48] train-rmse:0.186784+0.005464    test-rmse:0.211254+0.026882 
## [49] train-rmse:0.186227+0.005504    test-rmse:0.210880+0.026626 
## [50] train-rmse:0.185690+0.005542    test-rmse:0.211628+0.027050 
## [51] train-rmse:0.185179+0.005588    test-rmse:0.212013+0.026443 
## [52] train-rmse:0.184680+0.005636    test-rmse:0.211406+0.026456 
## [53] train-rmse:0.184194+0.005663    test-rmse:0.212028+0.026806 
## [54] train-rmse:0.183724+0.005712    test-rmse:0.211916+0.026842 
## [55] train-rmse:0.183263+0.005749    test-rmse:0.211000+0.026498 
## Stopping. Best iteration:
## [49] train-rmse:0.186227+0.005504    test-rmse:0.210880+0.026626
## 
## 8 
## [1]  train-rmse:1.267019+0.005444    test-rmse:1.267530+0.022805 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.119151+0.004657    test-rmse:1.120495+0.020628 
## [3]  train-rmse:0.989335+0.003954    test-rmse:0.991580+0.018656 
## [4]  train-rmse:0.875398+0.003349    test-rmse:0.878748+0.016985 
## [5]  train-rmse:0.775548+0.002826    test-rmse:0.780073+0.015493 
## [6]  train-rmse:0.688093+0.002395    test-rmse:0.694003+0.014261 
## [7]  train-rmse:0.611668+0.002054    test-rmse:0.619077+0.013260 
## [8]  train-rmse:0.545008+0.001796    test-rmse:0.554810+0.012400 
## [9]  train-rmse:0.486907+0.001662    test-rmse:0.498623+0.011885 
## [10] train-rmse:0.436444+0.001588    test-rmse:0.450192+0.011662 
## [11] train-rmse:0.392814+0.001645    test-rmse:0.409033+0.012006 
## [12] train-rmse:0.355124+0.001684    test-rmse:0.374670+0.012360 
## [13] train-rmse:0.322669+0.001760    test-rmse:0.344854+0.012960 
## [14] train-rmse:0.294887+0.001871    test-rmse:0.319631+0.014299 
## [15] train-rmse:0.271232+0.001925    test-rmse:0.298865+0.015021 
## [16] train-rmse:0.251118+0.002016    test-rmse:0.281633+0.016898 
## [17] train-rmse:0.234221+0.002032    test-rmse:0.267991+0.018036 
## [18] train-rmse:0.219985+0.002090    test-rmse:0.256234+0.019411 
## [19] train-rmse:0.208138+0.002101    test-rmse:0.247267+0.021151 
## [20] train-rmse:0.197400+0.001703    test-rmse:0.238878+0.022785 
## [21] train-rmse:0.188670+0.001315    test-rmse:0.232370+0.023838 
## [22] train-rmse:0.181631+0.001713    test-rmse:0.226787+0.024013 
## [23] train-rmse:0.175846+0.002221    test-rmse:0.222773+0.024981 
## [24] train-rmse:0.171126+0.002759    test-rmse:0.219383+0.025186 
## [25] train-rmse:0.166883+0.002589    test-rmse:0.216841+0.025581 
## [26] train-rmse:0.163199+0.003165    test-rmse:0.214502+0.025701 
## [27] train-rmse:0.160238+0.003386    test-rmse:0.213063+0.025505 
## [28] train-rmse:0.158093+0.003610    test-rmse:0.212216+0.026361 
## [29] train-rmse:0.156087+0.003890    test-rmse:0.211386+0.026247 
## [30] train-rmse:0.153949+0.003986    test-rmse:0.210824+0.026715 
## [31] train-rmse:0.152476+0.003795    test-rmse:0.210815+0.027392 
## [32] train-rmse:0.150912+0.003625    test-rmse:0.210860+0.027523 
## [33] train-rmse:0.149612+0.003594    test-rmse:0.210919+0.027737 
## [34] train-rmse:0.148676+0.003978    test-rmse:0.210506+0.027837 
## [35] train-rmse:0.147583+0.003655    test-rmse:0.210665+0.028262 
## [36] train-rmse:0.146234+0.004123    test-rmse:0.210505+0.028162 
## [37] train-rmse:0.145287+0.004180    test-rmse:0.210439+0.028338 
## [38] train-rmse:0.143964+0.004178    test-rmse:0.210315+0.028031 
## [39] train-rmse:0.143394+0.003932    test-rmse:0.210521+0.028272 
## [40] train-rmse:0.142681+0.004181    test-rmse:0.210386+0.028436 
## [41] train-rmse:0.141342+0.003978    test-rmse:0.209967+0.028506 
## [42] train-rmse:0.140601+0.004208    test-rmse:0.209745+0.028371 
## [43] train-rmse:0.139837+0.003934    test-rmse:0.209745+0.028327 
## [44] train-rmse:0.138665+0.004513    test-rmse:0.209741+0.028305 
## [45] train-rmse:0.137809+0.004166    test-rmse:0.210070+0.028426 
## [46] train-rmse:0.137236+0.004524    test-rmse:0.209941+0.028476 
## [47] train-rmse:0.136747+0.004545    test-rmse:0.209886+0.028531 
## [48] train-rmse:0.135611+0.004462    test-rmse:0.210351+0.027996 
## [49] train-rmse:0.134977+0.004616    test-rmse:0.210463+0.027954 
## [50] train-rmse:0.134346+0.004820    test-rmse:0.210524+0.027963 
## Stopping. Best iteration:
## [44] train-rmse:0.138665+0.004513    test-rmse:0.209741+0.028305
## 
## 9 
## [1]  train-rmse:1.266636+0.005428    test-rmse:1.267817+0.022428 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.118339+0.004608    test-rmse:1.121102+0.019875 
## [3]  train-rmse:0.988004+0.003870    test-rmse:0.992376+0.017486 
## [4]  train-rmse:0.873480+0.003231    test-rmse:0.880349+0.015490 
## [5]  train-rmse:0.772919+0.002688    test-rmse:0.782069+0.013611 
## [6]  train-rmse:0.684670+0.002220    test-rmse:0.696832+0.012051 
## [7]  train-rmse:0.607303+0.001856    test-rmse:0.622776+0.010537 
## [8]  train-rmse:0.539547+0.001640    test-rmse:0.558419+0.009328 
## [9]  train-rmse:0.480297+0.001625    test-rmse:0.503043+0.008369 
## [10] train-rmse:0.428316+0.001827    test-rmse:0.454850+0.008930 
## [11] train-rmse:0.383218+0.002201    test-rmse:0.414860+0.008415 
## [12] train-rmse:0.343736+0.002945    test-rmse:0.380162+0.009282 
## [13] train-rmse:0.309464+0.003741    test-rmse:0.351374+0.009942 
## [14] train-rmse:0.279403+0.004277    test-rmse:0.326126+0.011451 
## [15] train-rmse:0.253482+0.004747    test-rmse:0.306467+0.012693 
## [16] train-rmse:0.230800+0.004729    test-rmse:0.289163+0.014381 
## [17] train-rmse:0.211104+0.005110    test-rmse:0.275395+0.015751 
## [18] train-rmse:0.194169+0.005455    test-rmse:0.264388+0.017824 
## [19] train-rmse:0.179690+0.005822    test-rmse:0.255613+0.019782 
## [20] train-rmse:0.167458+0.006337    test-rmse:0.247986+0.021084 
## [21] train-rmse:0.157084+0.006440    test-rmse:0.242725+0.022563 
## [22] train-rmse:0.148353+0.006919    test-rmse:0.238023+0.023473 
## [23] train-rmse:0.140867+0.007427    test-rmse:0.234301+0.024558 
## [24] train-rmse:0.134359+0.008013    test-rmse:0.231638+0.025331 
## [25] train-rmse:0.129128+0.008228    test-rmse:0.229572+0.026542 
## [26] train-rmse:0.124491+0.008185    test-rmse:0.227763+0.027050 
## [27] train-rmse:0.120808+0.008357    test-rmse:0.226070+0.027478 
## [28] train-rmse:0.117682+0.008676    test-rmse:0.225205+0.028013 
## [29] train-rmse:0.114909+0.008583    test-rmse:0.224618+0.028131 
## [30] train-rmse:0.112613+0.008857    test-rmse:0.223787+0.028374 
## [31] train-rmse:0.110589+0.008761    test-rmse:0.223423+0.028391 
## [32] train-rmse:0.108682+0.009028    test-rmse:0.223164+0.028754 
## [33] train-rmse:0.107117+0.009111    test-rmse:0.223049+0.028873 
## [34] train-rmse:0.105408+0.009294    test-rmse:0.223188+0.029166 
## [35] train-rmse:0.103418+0.009340    test-rmse:0.223502+0.029434 
## [36] train-rmse:0.101947+0.009285    test-rmse:0.224167+0.030145 
## [37] train-rmse:0.100673+0.009462    test-rmse:0.224166+0.030242 
## [38] train-rmse:0.099055+0.009352    test-rmse:0.224883+0.030864 
## [39] train-rmse:0.098101+0.009375    test-rmse:0.225199+0.030909 
## Stopping. Best iteration:
## [33] train-rmse:0.107117+0.009111    test-rmse:0.223049+0.028873
## 
## 10 
## [1]  train-rmse:1.266498+0.005439    test-rmse:1.267819+0.022404 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.118027+0.004628    test-rmse:1.121132+0.019825 
## [3]  train-rmse:0.987491+0.003910    test-rmse:0.992530+0.017425 
## [4]  train-rmse:0.872795+0.003260    test-rmse:0.880049+0.015247 
## [5]  train-rmse:0.772064+0.002710    test-rmse:0.782325+0.013471 
## [6]  train-rmse:0.683157+0.002004    test-rmse:0.696984+0.011990 
## [7]  train-rmse:0.605050+0.001486    test-rmse:0.622788+0.010605 
## [8]  train-rmse:0.536038+0.001153    test-rmse:0.558503+0.009150 
## [9]  train-rmse:0.475558+0.001076    test-rmse:0.503320+0.008166 
## [10] train-rmse:0.422505+0.001015    test-rmse:0.455958+0.007666 
## [11] train-rmse:0.375771+0.001297    test-rmse:0.415521+0.008256 
## [12] train-rmse:0.334835+0.001691    test-rmse:0.382060+0.009064 
## [13] train-rmse:0.299050+0.002121    test-rmse:0.353535+0.010340 
## [14] train-rmse:0.267814+0.002628    test-rmse:0.330227+0.011957 
## [15] train-rmse:0.240365+0.003098    test-rmse:0.310717+0.013736 
## [16] train-rmse:0.216134+0.003394    test-rmse:0.294276+0.015941 
## [17] train-rmse:0.195013+0.003661    test-rmse:0.280955+0.017852 
## [18] train-rmse:0.176720+0.004095    test-rmse:0.269952+0.019937 
## [19] train-rmse:0.160783+0.004387    test-rmse:0.261818+0.021888 
## [20] train-rmse:0.146723+0.004839    test-rmse:0.254327+0.023536 
## [21] train-rmse:0.134768+0.005065    test-rmse:0.249395+0.024977 
## [22] train-rmse:0.124298+0.005446    test-rmse:0.245738+0.026389 
## [23] train-rmse:0.115195+0.005925    test-rmse:0.242766+0.027775 
## [24] train-rmse:0.107628+0.006254    test-rmse:0.240045+0.028669 
## [25] train-rmse:0.101142+0.006744    test-rmse:0.238294+0.029506 
## [26] train-rmse:0.095569+0.007202    test-rmse:0.237174+0.030181 
## [27] train-rmse:0.090816+0.007541    test-rmse:0.236001+0.030799 
## [28] train-rmse:0.086800+0.007672    test-rmse:0.235234+0.031201 
## [29] train-rmse:0.083415+0.007906    test-rmse:0.234484+0.031731 
## [30] train-rmse:0.080520+0.008187    test-rmse:0.234070+0.032187 
## [31] train-rmse:0.078109+0.008333    test-rmse:0.233858+0.032623 
## [32] train-rmse:0.075744+0.008473    test-rmse:0.233673+0.032861 
## [33] train-rmse:0.073582+0.008461    test-rmse:0.233541+0.033080 
## [34] train-rmse:0.071519+0.008369    test-rmse:0.233477+0.033237 
## [35] train-rmse:0.069963+0.008345    test-rmse:0.233667+0.033492 
## [36] train-rmse:0.068587+0.008349    test-rmse:0.233737+0.033748 
## [37] train-rmse:0.067427+0.008300    test-rmse:0.233950+0.033871 
## [38] train-rmse:0.066273+0.008256    test-rmse:0.233723+0.033849 
## [39] train-rmse:0.065210+0.008150    test-rmse:0.233632+0.033982 
## [40] train-rmse:0.063937+0.008301    test-rmse:0.233756+0.034084 
## Stopping. Best iteration:
## [34] train-rmse:0.071519+0.008369    test-rmse:0.233477+0.033237
## 
## 11 
## [1]  train-rmse:1.266411+0.005432    test-rmse:1.267816+0.022396 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.117853+0.004614    test-rmse:1.121147+0.019802 
## [3]  train-rmse:0.987199+0.003892    test-rmse:0.992557+0.017377 
## [4]  train-rmse:0.872378+0.003239    test-rmse:0.880117+0.015174 
## [5]  train-rmse:0.771495+0.002693    test-rmse:0.782344+0.013305 
## [6]  train-rmse:0.682475+0.001826    test-rmse:0.696857+0.011905 
## [7]  train-rmse:0.604007+0.001154    test-rmse:0.622811+0.010574 
## [8]  train-rmse:0.534612+0.000867    test-rmse:0.558882+0.009030 
## [9]  train-rmse:0.473617+0.000961    test-rmse:0.503791+0.007979 
## [10] train-rmse:0.420109+0.001138    test-rmse:0.457027+0.007429 
## [11] train-rmse:0.372944+0.001411    test-rmse:0.416777+0.008239 
## [12] train-rmse:0.331382+0.001681    test-rmse:0.383377+0.009233 
## [13] train-rmse:0.294599+0.002011    test-rmse:0.355219+0.010902 
## [14] train-rmse:0.262274+0.002329    test-rmse:0.331886+0.013027 
## [15] train-rmse:0.233959+0.002598    test-rmse:0.312793+0.015267 
## [16] train-rmse:0.208956+0.002845    test-rmse:0.297103+0.017321 
## [17] train-rmse:0.187100+0.003134    test-rmse:0.284363+0.019810 
## [18] train-rmse:0.167987+0.003220    test-rmse:0.273974+0.021871 
## [19] train-rmse:0.151145+0.003430    test-rmse:0.265780+0.024164 
## [20] train-rmse:0.136320+0.003807    test-rmse:0.259312+0.026029 
## [21] train-rmse:0.123428+0.004077    test-rmse:0.254314+0.027608 
## [22] train-rmse:0.112300+0.004261    test-rmse:0.250825+0.029324 
## [23] train-rmse:0.102493+0.004461    test-rmse:0.247617+0.030525 
## [24] train-rmse:0.094207+0.004847    test-rmse:0.245535+0.031813 
## [25] train-rmse:0.086835+0.004982    test-rmse:0.243644+0.032707 
## [26] train-rmse:0.080332+0.005190    test-rmse:0.242720+0.033743 
## [27] train-rmse:0.074960+0.005687    test-rmse:0.241940+0.034384 
## [28] train-rmse:0.070198+0.005927    test-rmse:0.241652+0.035100 
## [29] train-rmse:0.066092+0.006375    test-rmse:0.241173+0.035450 
## [30] train-rmse:0.062558+0.006569    test-rmse:0.240594+0.035782 
## [31] train-rmse:0.059715+0.006786    test-rmse:0.240062+0.035975 
## [32] train-rmse:0.056937+0.006964    test-rmse:0.239998+0.036311 
## [33] train-rmse:0.054556+0.007280    test-rmse:0.239923+0.036549 
## [34] train-rmse:0.052354+0.007240    test-rmse:0.239787+0.036596 
## [35] train-rmse:0.050756+0.007359    test-rmse:0.239673+0.036801 
## [36] train-rmse:0.049147+0.007378    test-rmse:0.239863+0.037067 
## [37] train-rmse:0.047785+0.007399    test-rmse:0.239742+0.037151 
## [38] train-rmse:0.046523+0.007367    test-rmse:0.239877+0.037256 
## [39] train-rmse:0.045350+0.007341    test-rmse:0.239981+0.037428 
## [40] train-rmse:0.044291+0.007308    test-rmse:0.240276+0.037655 
## [41] train-rmse:0.043222+0.007147    test-rmse:0.240301+0.037717 
## Stopping. Best iteration:
## [35] train-rmse:0.050756+0.007359    test-rmse:0.239673+0.036801
## 
## 12 
## [1]  train-rmse:1.266367+0.005453    test-rmse:1.267821+0.022383 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.117762+0.004655    test-rmse:1.121165+0.019772 
## [3]  train-rmse:0.987040+0.003963    test-rmse:0.992590+0.017328 
## [4]  train-rmse:0.872147+0.003336    test-rmse:0.880202+0.015060 
## [5]  train-rmse:0.771181+0.002822    test-rmse:0.782470+0.013166 
## [6]  train-rmse:0.682024+0.002014    test-rmse:0.697014+0.011519 
## [7]  train-rmse:0.603404+0.001409    test-rmse:0.622900+0.010282 
## [8]  train-rmse:0.534277+0.001131    test-rmse:0.558783+0.008527 
## [9]  train-rmse:0.473063+0.000975    test-rmse:0.503593+0.007710 
## [10] train-rmse:0.418897+0.001055    test-rmse:0.456524+0.007257 
## [11] train-rmse:0.371486+0.001231    test-rmse:0.416463+0.008283 
## [12] train-rmse:0.329344+0.001334    test-rmse:0.382767+0.009094 
## [13] train-rmse:0.292293+0.001310    test-rmse:0.354858+0.010570 
## [14] train-rmse:0.259898+0.001535    test-rmse:0.331485+0.012697 
## [15] train-rmse:0.231297+0.001741    test-rmse:0.312826+0.014818 
## [16] train-rmse:0.205800+0.001784    test-rmse:0.297049+0.017534 
## [17] train-rmse:0.183990+0.002013    test-rmse:0.284552+0.019592 
## [18] train-rmse:0.164228+0.002096    test-rmse:0.274246+0.021880 
## [19] train-rmse:0.146999+0.002124    test-rmse:0.266219+0.024252 
## [20] train-rmse:0.131793+0.002173    test-rmse:0.259943+0.026297 
## [21] train-rmse:0.118608+0.002278    test-rmse:0.255061+0.028202 
## [22] train-rmse:0.107018+0.002515    test-rmse:0.251462+0.029964 
## [23] train-rmse:0.096749+0.002717    test-rmse:0.248904+0.031563 
## [24] train-rmse:0.087854+0.002915    test-rmse:0.246840+0.032797 
## [25] train-rmse:0.079846+0.003075    test-rmse:0.245436+0.033935 
## [26] train-rmse:0.073006+0.003303    test-rmse:0.244399+0.035133 
## [27] train-rmse:0.066916+0.003546    test-rmse:0.243691+0.036309 
## [28] train-rmse:0.061707+0.003597    test-rmse:0.242926+0.036914 
## [29] train-rmse:0.057110+0.003652    test-rmse:0.242523+0.037431 
## [30] train-rmse:0.053063+0.003750    test-rmse:0.242133+0.037908 
## [31] train-rmse:0.049549+0.003681    test-rmse:0.241744+0.038356 
## [32] train-rmse:0.046657+0.003831    test-rmse:0.241623+0.038652 
## [33] train-rmse:0.043959+0.004014    test-rmse:0.241423+0.038864 
## [34] train-rmse:0.041624+0.004219    test-rmse:0.241368+0.039123 
## [35] train-rmse:0.039625+0.004295    test-rmse:0.241193+0.039237 
## [36] train-rmse:0.037882+0.004286    test-rmse:0.241536+0.039514 
## [37] train-rmse:0.036406+0.004326    test-rmse:0.241754+0.039757 
## [38] train-rmse:0.035046+0.004360    test-rmse:0.241837+0.039927 
## [39] train-rmse:0.033743+0.004175    test-rmse:0.242021+0.040038 
## [40] train-rmse:0.032534+0.004206    test-rmse:0.242023+0.040135 
## [41] train-rmse:0.031485+0.004280    test-rmse:0.242203+0.040230 
## Stopping. Best iteration:
## [35] train-rmse:0.039625+0.004295    test-rmse:0.241193+0.039237
## 
## 13 
## [1]  train-rmse:1.266347+0.005475    test-rmse:1.267823+0.022377 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.117720+0.004705    test-rmse:1.121173+0.019755 
## [3]  train-rmse:0.986966+0.004040    test-rmse:0.992637+0.017245 
## [4]  train-rmse:0.872039+0.003445    test-rmse:0.880258+0.014961 
## [5]  train-rmse:0.771041+0.002953    test-rmse:0.782532+0.013112 
## [6]  train-rmse:0.681833+0.002135    test-rmse:0.697007+0.011543 
## [7]  train-rmse:0.603383+0.001768    test-rmse:0.623145+0.010016 
## [8]  train-rmse:0.533822+0.001093    test-rmse:0.558930+0.008409 
## [9]  train-rmse:0.472451+0.000714    test-rmse:0.503430+0.007549 
## [10] train-rmse:0.418221+0.000548    test-rmse:0.456293+0.006954 
## [11] train-rmse:0.370518+0.000821    test-rmse:0.416047+0.007627 
## [12] train-rmse:0.328206+0.000860    test-rmse:0.382289+0.008566 
## [13] train-rmse:0.291298+0.000831    test-rmse:0.354487+0.010296 
## [14] train-rmse:0.258458+0.000905    test-rmse:0.330943+0.012319 
## [15] train-rmse:0.229821+0.001141    test-rmse:0.312359+0.014360 
## [16] train-rmse:0.204471+0.001184    test-rmse:0.296580+0.016949 
## [17] train-rmse:0.182017+0.001186    test-rmse:0.284277+0.019547 
## [18] train-rmse:0.162328+0.001620    test-rmse:0.274379+0.021811 
## [19] train-rmse:0.145056+0.001489    test-rmse:0.266549+0.024188 
## [20] train-rmse:0.129657+0.001473    test-rmse:0.260312+0.026233 
## [21] train-rmse:0.116345+0.001492    test-rmse:0.255521+0.028211 
## [22] train-rmse:0.104445+0.001584    test-rmse:0.252058+0.030107 
## [23] train-rmse:0.093813+0.001636    test-rmse:0.249498+0.031696 
## [24] train-rmse:0.084612+0.001726    test-rmse:0.247591+0.033014 
## [25] train-rmse:0.076549+0.001881    test-rmse:0.246272+0.034270 
## [26] train-rmse:0.069386+0.002019    test-rmse:0.245681+0.034894 
## [27] train-rmse:0.063008+0.002011    test-rmse:0.245184+0.035590 
## [28] train-rmse:0.057441+0.001999    test-rmse:0.244934+0.036178 
## [29] train-rmse:0.052519+0.001989    test-rmse:0.244785+0.036958 
## [30] train-rmse:0.048247+0.002057    test-rmse:0.244753+0.037565 
## [31] train-rmse:0.044575+0.002106    test-rmse:0.244713+0.038058 
## [32] train-rmse:0.041144+0.002109    test-rmse:0.244895+0.038176 
## [33] train-rmse:0.038289+0.002131    test-rmse:0.245012+0.038539 
## [34] train-rmse:0.035640+0.002154    test-rmse:0.245072+0.038802 
## [35] train-rmse:0.033417+0.002283    test-rmse:0.245307+0.039086 
## [36] train-rmse:0.031356+0.002475    test-rmse:0.245542+0.039355 
## [37] train-rmse:0.029759+0.002576    test-rmse:0.245717+0.039560 
## Stopping. Best iteration:
## [31] train-rmse:0.044575+0.002106    test-rmse:0.244713+0.038058
## 
## 14 
## [1]  train-rmse:1.241290+0.004863    test-rmse:1.241168+0.023200 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.075260+0.003536    test-rmse:1.075238+0.021280 
## [3]  train-rmse:0.933471+0.002325    test-rmse:0.933645+0.019475 
## [4]  train-rmse:0.812655+0.001294    test-rmse:0.813110+0.017784 
## [5]  train-rmse:0.710043+0.000879    test-rmse:0.710750+0.016140 
## [6]  train-rmse:0.623225+0.001528    test-rmse:0.625337+0.015215 
## [7]  train-rmse:0.549575+0.002137    test-rmse:0.552557+0.013463 
## [8]  train-rmse:0.487437+0.002828    test-rmse:0.490955+0.011804 
## [9]  train-rmse:0.435181+0.003261    test-rmse:0.438789+0.012134 
## [10] train-rmse:0.391427+0.003842    test-rmse:0.395619+0.011983 
## [11] train-rmse:0.355076+0.004181    test-rmse:0.359306+0.012999 
## [12] train-rmse:0.324993+0.004637    test-rmse:0.330537+0.014679 
## [13] train-rmse:0.300374+0.004885    test-rmse:0.305904+0.015905 
## [14] train-rmse:0.280199+0.005245    test-rmse:0.286079+0.017576 
## [15] train-rmse:0.263969+0.005406    test-rmse:0.269793+0.018541 
## [16] train-rmse:0.250786+0.005629    test-rmse:0.256846+0.020343 
## [17] train-rmse:0.240327+0.005705    test-rmse:0.247654+0.020410 
## [18] train-rmse:0.231840+0.005847    test-rmse:0.238884+0.022659 
## [19] train-rmse:0.225173+0.005863    test-rmse:0.233210+0.022487 
## [20] train-rmse:0.219727+0.005908    test-rmse:0.228087+0.023856 
## [21] train-rmse:0.215390+0.005923    test-rmse:0.223900+0.023607 
## [22] train-rmse:0.211795+0.005950    test-rmse:0.221269+0.024964 
## [23] train-rmse:0.208724+0.005876    test-rmse:0.219180+0.025061 
## [24] train-rmse:0.206151+0.005815    test-rmse:0.217560+0.025288 
## [25] train-rmse:0.203976+0.005754    test-rmse:0.215721+0.026298 
## [26] train-rmse:0.202048+0.005662    test-rmse:0.215509+0.025854 
## [27] train-rmse:0.200307+0.005584    test-rmse:0.213833+0.025848 
## [28] train-rmse:0.198806+0.005505    test-rmse:0.213332+0.026062 
## [29] train-rmse:0.197465+0.005437    test-rmse:0.212646+0.026177 
## [30] train-rmse:0.196167+0.005390    test-rmse:0.212905+0.026424 
## [31] train-rmse:0.195045+0.005358    test-rmse:0.212032+0.026991 
## [32] train-rmse:0.193961+0.005312    test-rmse:0.213237+0.026827 
## [33] train-rmse:0.192930+0.005296    test-rmse:0.212361+0.026861 
## [34] train-rmse:0.191996+0.005294    test-rmse:0.211702+0.026550 
## [35] train-rmse:0.191119+0.005287    test-rmse:0.212152+0.027385 
## [36] train-rmse:0.190272+0.005304    test-rmse:0.211977+0.027351 
## [37] train-rmse:0.189489+0.005326    test-rmse:0.211571+0.027192 
## [38] train-rmse:0.188715+0.005360    test-rmse:0.211730+0.026805 
## [39] train-rmse:0.187983+0.005398    test-rmse:0.211776+0.026887 
## [40] train-rmse:0.187273+0.005429    test-rmse:0.211397+0.027037 
## [41] train-rmse:0.186608+0.005474    test-rmse:0.211746+0.027194 
## [42] train-rmse:0.185989+0.005521    test-rmse:0.211778+0.027074 
## [43] train-rmse:0.185381+0.005569    test-rmse:0.211443+0.025977 
## [44] train-rmse:0.184781+0.005618    test-rmse:0.210841+0.026310 
## [45] train-rmse:0.184191+0.005674    test-rmse:0.211436+0.026681 
## [46] train-rmse:0.183644+0.005726    test-rmse:0.211184+0.026674 
## [47] train-rmse:0.183108+0.005765    test-rmse:0.211048+0.026604 
## [48] train-rmse:0.182596+0.005813    test-rmse:0.211171+0.026435 
## [49] train-rmse:0.182096+0.005850    test-rmse:0.211728+0.026817 
## [50] train-rmse:0.181612+0.005908    test-rmse:0.211058+0.026553 
## Stopping. Best iteration:
## [44] train-rmse:0.184781+0.005618    test-rmse:0.210841+0.026310
## 
## 15 
## [1]  train-rmse:1.239008+0.005298    test-rmse:1.239654+0.022392 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.070525+0.004395    test-rmse:1.072189+0.019908 
## [3]  train-rmse:0.926063+0.003605    test-rmse:0.928925+0.017699 
## [4]  train-rmse:0.802282+0.002965    test-rmse:0.806491+0.015891 
## [5]  train-rmse:0.696466+0.002429    test-rmse:0.702207+0.014350 
## [6]  train-rmse:0.606095+0.002029    test-rmse:0.613664+0.013157 
## [7]  train-rmse:0.529257+0.001764    test-rmse:0.539965+0.012236 
## [8]  train-rmse:0.463947+0.001616    test-rmse:0.476897+0.011614 
## [9]  train-rmse:0.408789+0.001602    test-rmse:0.425097+0.011483 
## [10] train-rmse:0.362341+0.001713    test-rmse:0.380975+0.011989 
## [11] train-rmse:0.323392+0.001764    test-rmse:0.345414+0.013028 
## [12] train-rmse:0.290937+0.001859    test-rmse:0.316531+0.014035 
## [13] train-rmse:0.264113+0.002005    test-rmse:0.292743+0.015869 
## [14] train-rmse:0.242064+0.002035    test-rmse:0.274825+0.017395 
## [15] train-rmse:0.224096+0.002098    test-rmse:0.259781+0.019158 
## [16] train-rmse:0.209050+0.002366    test-rmse:0.247754+0.021147 
## [17] train-rmse:0.196855+0.001706    test-rmse:0.238961+0.023088 
## [18] train-rmse:0.186836+0.001336    test-rmse:0.231428+0.023815 
## [19] train-rmse:0.178996+0.001724    test-rmse:0.225096+0.024119 
## [20] train-rmse:0.172659+0.001996    test-rmse:0.221170+0.025379 
## [21] train-rmse:0.167271+0.002777    test-rmse:0.217888+0.025666 
## [22] train-rmse:0.163082+0.003436    test-rmse:0.214843+0.025838 
## [23] train-rmse:0.158934+0.003773    test-rmse:0.213537+0.025737 
## [24] train-rmse:0.155845+0.003691    test-rmse:0.212519+0.026383 
## [25] train-rmse:0.153930+0.003806    test-rmse:0.211796+0.027357 
## [26] train-rmse:0.150979+0.003657    test-rmse:0.211501+0.027105 
## [27] train-rmse:0.149735+0.003676    test-rmse:0.211317+0.027622 
## [28] train-rmse:0.147886+0.003347    test-rmse:0.211386+0.028266 
## [29] train-rmse:0.146264+0.003497    test-rmse:0.211043+0.027992 
## [30] train-rmse:0.145365+0.003382    test-rmse:0.210751+0.028431 
## [31] train-rmse:0.144220+0.003384    test-rmse:0.210664+0.028560 
## [32] train-rmse:0.143452+0.003692    test-rmse:0.210711+0.028798 
## [33] train-rmse:0.142386+0.003919    test-rmse:0.210579+0.028478 
## [34] train-rmse:0.140894+0.003843    test-rmse:0.210637+0.028276 
## [35] train-rmse:0.140051+0.003797    test-rmse:0.210513+0.028469 
## [36] train-rmse:0.139211+0.003852    test-rmse:0.210400+0.028455 
## [37] train-rmse:0.138223+0.003915    test-rmse:0.210412+0.028486 
## [38] train-rmse:0.137463+0.003472    test-rmse:0.210404+0.028709 
## [39] train-rmse:0.136606+0.003663    test-rmse:0.210680+0.029094 
## [40] train-rmse:0.135795+0.003256    test-rmse:0.210882+0.028861 
## [41] train-rmse:0.134872+0.003198    test-rmse:0.210828+0.029019 
## [42] train-rmse:0.134219+0.003439    test-rmse:0.210865+0.029014 
## Stopping. Best iteration:
## [36] train-rmse:0.139211+0.003852    test-rmse:0.210400+0.028455
## 
## 16 
## [1]  train-rmse:1.238552+0.005278    test-rmse:1.239996+0.021947 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.069550+0.004330    test-rmse:1.072919+0.019018 
## [3]  train-rmse:0.924471+0.003481    test-rmse:0.930130+0.016407 
## [4]  train-rmse:0.799918+0.002809    test-rmse:0.808494+0.014125 
## [5]  train-rmse:0.693148+0.002249    test-rmse:0.705157+0.012136 
## [6]  train-rmse:0.601725+0.001820    test-rmse:0.617151+0.010211 
## [7]  train-rmse:0.523561+0.001627    test-rmse:0.543511+0.008909 
## [8]  train-rmse:0.456606+0.001697    test-rmse:0.481285+0.008627 
## [9]  train-rmse:0.399718+0.002153    test-rmse:0.428701+0.009096 
## [10] train-rmse:0.351440+0.002762    test-rmse:0.387153+0.009096 
## [11] train-rmse:0.310029+0.003674    test-rmse:0.351686+0.010753 
## [12] train-rmse:0.275099+0.004451    test-rmse:0.323663+0.012331 
## [13] train-rmse:0.245198+0.004584    test-rmse:0.300174+0.013760 
## [14] train-rmse:0.219884+0.004949    test-rmse:0.283055+0.015735 
## [15] train-rmse:0.198697+0.005426    test-rmse:0.268093+0.018073 
## [16] train-rmse:0.181459+0.005563    test-rmse:0.256981+0.019571 
## [17] train-rmse:0.166930+0.006302    test-rmse:0.248132+0.021360 
## [18] train-rmse:0.154830+0.006777    test-rmse:0.241550+0.022939 
## [19] train-rmse:0.145261+0.007062    test-rmse:0.236781+0.023778 
## [20] train-rmse:0.137038+0.007738    test-rmse:0.232768+0.024794 
## [21] train-rmse:0.129907+0.007629    test-rmse:0.229658+0.025458 
## [22] train-rmse:0.124519+0.007861    test-rmse:0.227904+0.026547 
## [23] train-rmse:0.119931+0.008039    test-rmse:0.226262+0.026877 
## [24] train-rmse:0.116270+0.008111    test-rmse:0.224898+0.027621 
## [25] train-rmse:0.113018+0.008073    test-rmse:0.224320+0.027706 
## [26] train-rmse:0.110162+0.008300    test-rmse:0.223971+0.027926 
## [27] train-rmse:0.108079+0.008376    test-rmse:0.223164+0.027940 
## [28] train-rmse:0.106153+0.008372    test-rmse:0.222918+0.028218 
## [29] train-rmse:0.103874+0.008750    test-rmse:0.222998+0.028448 
## [30] train-rmse:0.102228+0.009068    test-rmse:0.222797+0.028535 
## [31] train-rmse:0.100670+0.008990    test-rmse:0.223738+0.029434 
## [32] train-rmse:0.099167+0.008984    test-rmse:0.224251+0.029916 
## [33] train-rmse:0.097791+0.009163    test-rmse:0.224522+0.030355 
## [34] train-rmse:0.096563+0.008949    test-rmse:0.224238+0.030335 
## [35] train-rmse:0.095379+0.008474    test-rmse:0.224528+0.030522 
## [36] train-rmse:0.094295+0.008500    test-rmse:0.224874+0.030974 
## Stopping. Best iteration:
## [30] train-rmse:0.102228+0.009068    test-rmse:0.222797+0.028535
## 
## 17 
## [1]  train-rmse:1.238388+0.005291    test-rmse:1.240002+0.021919 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.069168+0.004355    test-rmse:1.072875+0.018921 
## [3]  train-rmse:0.923823+0.003545    test-rmse:0.929952+0.016212 
## [4]  train-rmse:0.799092+0.002829    test-rmse:0.808073+0.013802 
## [5]  train-rmse:0.691881+0.002269    test-rmse:0.704979+0.011857 
## [6]  train-rmse:0.599719+0.001669    test-rmse:0.617562+0.010256 
## [7]  train-rmse:0.520046+0.001227    test-rmse:0.543554+0.008650 
## [8]  train-rmse:0.451809+0.001286    test-rmse:0.481942+0.007758 
## [9]  train-rmse:0.393187+0.001292    test-rmse:0.430071+0.008056 
## [10] train-rmse:0.343052+0.001524    test-rmse:0.387658+0.009154 
## [11] train-rmse:0.300099+0.002044    test-rmse:0.353718+0.010898 
## [12] train-rmse:0.263442+0.002675    test-rmse:0.326363+0.012242 
## [13] train-rmse:0.232118+0.003294    test-rmse:0.304598+0.014440 
## [14] train-rmse:0.205140+0.003622    test-rmse:0.286527+0.017122 
## [15] train-rmse:0.182278+0.003997    test-rmse:0.272743+0.019417 
## [16] train-rmse:0.163024+0.004465    test-rmse:0.262123+0.021677 
## [17] train-rmse:0.146899+0.004841    test-rmse:0.254123+0.023820 
## [18] train-rmse:0.132753+0.005256    test-rmse:0.247513+0.025896 
## [19] train-rmse:0.121230+0.005782    test-rmse:0.243333+0.027541 
## [20] train-rmse:0.111525+0.006216    test-rmse:0.240348+0.028570 
## [21] train-rmse:0.103449+0.006860    test-rmse:0.237995+0.029454 
## [22] train-rmse:0.097047+0.007214    test-rmse:0.236077+0.030351 
## [23] train-rmse:0.091219+0.007549    test-rmse:0.234620+0.030777 
## [24] train-rmse:0.086605+0.007937    test-rmse:0.233730+0.031485 
## [25] train-rmse:0.082356+0.007750    test-rmse:0.232865+0.031935 
## [26] train-rmse:0.079099+0.007875    test-rmse:0.232743+0.032569 
## [27] train-rmse:0.076262+0.007861    test-rmse:0.232494+0.032984 
## [28] train-rmse:0.073439+0.008067    test-rmse:0.232411+0.033109 
## [29] train-rmse:0.071146+0.007755    test-rmse:0.232454+0.033442 
## [30] train-rmse:0.069415+0.007678    test-rmse:0.232789+0.033798 
## [31] train-rmse:0.068008+0.007581    test-rmse:0.233087+0.034066 
## [32] train-rmse:0.066607+0.007553    test-rmse:0.232832+0.034192 
## [33] train-rmse:0.065436+0.007497    test-rmse:0.233174+0.034435 
## [34] train-rmse:0.064032+0.007580    test-rmse:0.233292+0.034522 
## Stopping. Best iteration:
## [28] train-rmse:0.073439+0.008067    test-rmse:0.232411+0.033109
## 
## 18 
## [1]  train-rmse:1.238285+0.005283    test-rmse:1.240000+0.021909 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.068901+0.004374    test-rmse:1.072893+0.018888 
## [3]  train-rmse:0.923475+0.003518    test-rmse:0.930226+0.016216 
## [4]  train-rmse:0.798594+0.002798    test-rmse:0.808429+0.013743 
## [5]  train-rmse:0.691198+0.002240    test-rmse:0.705166+0.011715 
## [6]  train-rmse:0.598691+0.001355    test-rmse:0.617725+0.010207 
## [7]  train-rmse:0.518474+0.000903    test-rmse:0.544172+0.008462 
## [8]  train-rmse:0.449562+0.001069    test-rmse:0.482759+0.007497 
## [9]  train-rmse:0.390591+0.001213    test-rmse:0.431166+0.007992 
## [10] train-rmse:0.339633+0.001452    test-rmse:0.389721+0.009096 
## [11] train-rmse:0.295766+0.001757    test-rmse:0.356111+0.010782 
## [12] train-rmse:0.257883+0.002180    test-rmse:0.328882+0.013254 
## [13] train-rmse:0.225615+0.002481    test-rmse:0.307405+0.015913 
## [14] train-rmse:0.197815+0.002674    test-rmse:0.290321+0.018908 
## [15] train-rmse:0.173962+0.002851    test-rmse:0.276608+0.021813 
## [16] train-rmse:0.153439+0.003107    test-rmse:0.266742+0.024586 
## [17] train-rmse:0.135981+0.003440    test-rmse:0.259005+0.027194 
## [18] train-rmse:0.121222+0.003869    test-rmse:0.253113+0.029095 
## [19] train-rmse:0.108425+0.004125    test-rmse:0.249367+0.030880 
## [20] train-rmse:0.097843+0.004774    test-rmse:0.246394+0.032670 
## [21] train-rmse:0.088986+0.005283    test-rmse:0.244212+0.033791 
## [22] train-rmse:0.081284+0.005524    test-rmse:0.242911+0.035105 
## [23] train-rmse:0.074824+0.005604    test-rmse:0.241922+0.036051 
## [24] train-rmse:0.069338+0.005884    test-rmse:0.240954+0.036497 
## [25] train-rmse:0.064683+0.006062    test-rmse:0.240629+0.037170 
## [26] train-rmse:0.060838+0.006349    test-rmse:0.240419+0.037465 
## [27] train-rmse:0.057560+0.006468    test-rmse:0.239945+0.037765 
## [28] train-rmse:0.054816+0.006816    test-rmse:0.239939+0.038051 
## [29] train-rmse:0.052378+0.006985    test-rmse:0.239888+0.038245 
## [30] train-rmse:0.050419+0.006971    test-rmse:0.239727+0.038353 
## [31] train-rmse:0.048645+0.006989    test-rmse:0.239880+0.038549 
## [32] train-rmse:0.047048+0.007087    test-rmse:0.240022+0.038767 
## [33] train-rmse:0.045534+0.007081    test-rmse:0.240139+0.038915 
## [34] train-rmse:0.044343+0.006972    test-rmse:0.240382+0.039107 
## [35] train-rmse:0.043109+0.007011    test-rmse:0.240698+0.039254 
## [36] train-rmse:0.041945+0.006972    test-rmse:0.240967+0.039448 
## Stopping. Best iteration:
## [30] train-rmse:0.050419+0.006971    test-rmse:0.239727+0.038353
## 
## 19 
## [1]  train-rmse:1.238232+0.005307    test-rmse:1.240007+0.021893 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.068774+0.004433    test-rmse:1.072875+0.018835 
## [3]  train-rmse:0.923204+0.003655    test-rmse:0.930129+0.016055 
## [4]  train-rmse:0.798229+0.002987    test-rmse:0.808510+0.013536 
## [5]  train-rmse:0.690684+0.002521    test-rmse:0.705243+0.011558 
## [6]  train-rmse:0.598035+0.001655    test-rmse:0.618047+0.009755 
## [7]  train-rmse:0.517610+0.001060    test-rmse:0.543771+0.007991 
## [8]  train-rmse:0.448401+0.001196    test-rmse:0.482731+0.007088 
## [9]  train-rmse:0.389189+0.001350    test-rmse:0.431211+0.007545 
## [10] train-rmse:0.337910+0.001565    test-rmse:0.389581+0.008577 
## [11] train-rmse:0.293441+0.001716    test-rmse:0.355779+0.010368 
## [12] train-rmse:0.255453+0.001774    test-rmse:0.328662+0.013029 
## [13] train-rmse:0.222752+0.001814    test-rmse:0.307275+0.015667 
## [14] train-rmse:0.194452+0.002074    test-rmse:0.290626+0.018576 
## [15] train-rmse:0.170135+0.002649    test-rmse:0.278039+0.020759 
## [16] train-rmse:0.149407+0.002815    test-rmse:0.268376+0.023691 
## [17] train-rmse:0.131638+0.002982    test-rmse:0.260919+0.026065 
## [18] train-rmse:0.116391+0.003160    test-rmse:0.255549+0.028298 
## [19] train-rmse:0.103193+0.003291    test-rmse:0.251628+0.030242 
## [20] train-rmse:0.091920+0.003513    test-rmse:0.248896+0.031977 
## [21] train-rmse:0.082393+0.003920    test-rmse:0.247018+0.033636 
## [22] train-rmse:0.074278+0.004287    test-rmse:0.245741+0.034783 
## [23] train-rmse:0.067246+0.004596    test-rmse:0.244988+0.035831 
## [24] train-rmse:0.061352+0.004632    test-rmse:0.244347+0.036545 
## [25] train-rmse:0.056187+0.004529    test-rmse:0.243876+0.037215 
## [26] train-rmse:0.051998+0.004784    test-rmse:0.243600+0.037738 
## [27] train-rmse:0.048309+0.004953    test-rmse:0.243599+0.038178 
## [28] train-rmse:0.045091+0.005143    test-rmse:0.243646+0.038450 
## [29] train-rmse:0.042454+0.005217    test-rmse:0.243727+0.038793 
## [30] train-rmse:0.040308+0.005306    test-rmse:0.243653+0.038991 
## [31] train-rmse:0.038482+0.005373    test-rmse:0.243590+0.039139 
## [32] train-rmse:0.036827+0.005268    test-rmse:0.243568+0.039324 
## [33] train-rmse:0.035255+0.005398    test-rmse:0.243739+0.039474 
## [34] train-rmse:0.033891+0.005240    test-rmse:0.243785+0.039623 
## [35] train-rmse:0.032628+0.005081    test-rmse:0.244013+0.039912 
## [36] train-rmse:0.031367+0.004694    test-rmse:0.244279+0.039969 
## [37] train-rmse:0.030280+0.004509    test-rmse:0.244486+0.040229 
## [38] train-rmse:0.029312+0.004184    test-rmse:0.244659+0.040358 
## Stopping. Best iteration:
## [32] train-rmse:0.036827+0.005268    test-rmse:0.243568+0.039324
## 
## 20 
## [1]  train-rmse:1.238209+0.005334    test-rmse:1.240010+0.021885 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.068722+0.004492    test-rmse:1.072887+0.018815 
## [3]  train-rmse:0.923118+0.003744    test-rmse:0.930161+0.016004 
## [4]  train-rmse:0.798110+0.003097    test-rmse:0.808591+0.013482 
## [5]  train-rmse:0.690514+0.002628    test-rmse:0.705333+0.011445 
## [6]  train-rmse:0.597785+0.001657    test-rmse:0.618080+0.009794 
## [7]  train-rmse:0.517161+0.000951    test-rmse:0.543687+0.007916 
## [8]  train-rmse:0.447708+0.000895    test-rmse:0.482528+0.006791 
## [9]  train-rmse:0.388085+0.000706    test-rmse:0.431602+0.006563 
## [10] train-rmse:0.336738+0.001230    test-rmse:0.389496+0.007810 
## [11] train-rmse:0.292467+0.001045    test-rmse:0.355975+0.010094 
## [12] train-rmse:0.254200+0.001119    test-rmse:0.328962+0.012758 
## [13] train-rmse:0.221008+0.001131    test-rmse:0.307862+0.015230 
## [14] train-rmse:0.193033+0.001422    test-rmse:0.291554+0.017916 
## [15] train-rmse:0.168341+0.001453    test-rmse:0.278634+0.020942 
## [16] train-rmse:0.147240+0.001490    test-rmse:0.269128+0.023745 
## [17] train-rmse:0.129080+0.001415    test-rmse:0.261860+0.026392 
## [18] train-rmse:0.113395+0.001504    test-rmse:0.256634+0.028694 
## [19] train-rmse:0.099810+0.001531    test-rmse:0.252804+0.030806 
## [20] train-rmse:0.088259+0.001593    test-rmse:0.250473+0.032259 
## [21] train-rmse:0.078201+0.001758    test-rmse:0.248796+0.033750 
## [22] train-rmse:0.069692+0.001920    test-rmse:0.248150+0.034568 
## [23] train-rmse:0.062177+0.001924    test-rmse:0.247725+0.035497 
## [24] train-rmse:0.055791+0.001907    test-rmse:0.247627+0.036279 
## [25] train-rmse:0.050314+0.001880    test-rmse:0.247663+0.037229 
## [26] train-rmse:0.045486+0.001915    test-rmse:0.247683+0.037976 
## [27] train-rmse:0.041345+0.001955    test-rmse:0.247910+0.038719 
## [28] train-rmse:0.037921+0.002053    test-rmse:0.248149+0.039194 
## [29] train-rmse:0.035012+0.002141    test-rmse:0.248441+0.039593 
## [30] train-rmse:0.032483+0.002265    test-rmse:0.248744+0.039971 
## Stopping. Best iteration:
## [24] train-rmse:0.055791+0.001907    test-rmse:0.247627+0.036279
## 
## 21 
## [1]  train-rmse:1.213655+0.004647    test-rmse:1.213547+0.022892 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.028596+0.003145    test-rmse:1.028608+0.020702 
## [3]  train-rmse:0.874518+0.001806    test-rmse:0.874826+0.018655 
## [4]  train-rmse:0.746664+0.000901    test-rmse:0.747258+0.016732 
## [5]  train-rmse:0.641110+0.001341    test-rmse:0.642020+0.014957 
## [6]  train-rmse:0.554075+0.002090    test-rmse:0.555937+0.012977 
## [7]  train-rmse:0.482209+0.002850    test-rmse:0.485926+0.011596 
## [8]  train-rmse:0.423732+0.003380    test-rmse:0.427817+0.012413 
## [9]  train-rmse:0.375882+0.003991    test-rmse:0.380362+0.012092 
## [10] train-rmse:0.337658+0.004392    test-rmse:0.342407+0.013741 
## [11] train-rmse:0.306819+0.004853    test-rmse:0.312776+0.015614 
## [12] train-rmse:0.282727+0.005124    test-rmse:0.288196+0.016812 
## [13] train-rmse:0.263485+0.005462    test-rmse:0.269782+0.018954 
## [14] train-rmse:0.248820+0.005610    test-rmse:0.255416+0.020204 
## [15] train-rmse:0.237188+0.005771    test-rmse:0.244469+0.022125 
## [16] train-rmse:0.228425+0.005837    test-rmse:0.237212+0.021995 
## [17] train-rmse:0.221495+0.005911    test-rmse:0.229106+0.023781 
## [18] train-rmse:0.216202+0.005934    test-rmse:0.225089+0.023351 
## [19] train-rmse:0.211923+0.005945    test-rmse:0.221722+0.024762 
## [20] train-rmse:0.208412+0.005855    test-rmse:0.219516+0.025067 
## [21] train-rmse:0.205559+0.005803    test-rmse:0.217557+0.025162 
## [22] train-rmse:0.203157+0.005703    test-rmse:0.215557+0.026321 
## [23] train-rmse:0.201065+0.005637    test-rmse:0.214276+0.025746 
## [24] train-rmse:0.199206+0.005538    test-rmse:0.214270+0.026130 
## [25] train-rmse:0.197623+0.005464    test-rmse:0.213558+0.026455 
## [26] train-rmse:0.196151+0.005382    test-rmse:0.213337+0.026500 
## [27] train-rmse:0.194841+0.005337    test-rmse:0.212892+0.026575 
## [28] train-rmse:0.193629+0.005317    test-rmse:0.210777+0.026881 
## [29] train-rmse:0.192506+0.005300    test-rmse:0.212158+0.026470 
## [30] train-rmse:0.191438+0.005291    test-rmse:0.212163+0.026614 
## [31] train-rmse:0.190496+0.005307    test-rmse:0.211401+0.026849 
## [32] train-rmse:0.189542+0.005311    test-rmse:0.212058+0.027283 
## [33] train-rmse:0.188656+0.005347    test-rmse:0.212227+0.027368 
## [34] train-rmse:0.187838+0.005386    test-rmse:0.210730+0.026910 
## [35] train-rmse:0.187018+0.005455    test-rmse:0.211721+0.026459 
## [36] train-rmse:0.186287+0.005489    test-rmse:0.211655+0.026809 
## [37] train-rmse:0.185532+0.005548    test-rmse:0.211384+0.026623 
## [38] train-rmse:0.184872+0.005603    test-rmse:0.212169+0.027075 
## [39] train-rmse:0.184227+0.005667    test-rmse:0.211848+0.026802 
## [40] train-rmse:0.183592+0.005721    test-rmse:0.211378+0.026376 
## Stopping. Best iteration:
## [34] train-rmse:0.187838+0.005386    test-rmse:0.210730+0.026910
## 
## 22 
## [1]  train-rmse:1.211010+0.005150    test-rmse:1.211798+0.021979 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.023067+0.004138    test-rmse:1.024893+0.019134 
## [3]  train-rmse:0.865765+0.003297    test-rmse:0.869373+0.016894 
## [4]  train-rmse:0.734333+0.002621    test-rmse:0.739537+0.014900 
## [5]  train-rmse:0.624817+0.002110    test-rmse:0.632049+0.013377 
## [6]  train-rmse:0.533777+0.001779    test-rmse:0.543404+0.012390 
## [7]  train-rmse:0.458543+0.001638    test-rmse:0.471624+0.011638 
## [8]  train-rmse:0.396542+0.001573    test-rmse:0.412708+0.011536 
## [9]  train-rmse:0.345836+0.001775    test-rmse:0.365584+0.012269 
## [10] train-rmse:0.304542+0.001816    test-rmse:0.328060+0.013723 
## [11] train-rmse:0.271343+0.002016    test-rmse:0.299302+0.015368 
## [12] train-rmse:0.244708+0.002062    test-rmse:0.276202+0.017200 
## [13] train-rmse:0.223740+0.002132    test-rmse:0.259509+0.019293 
## [14] train-rmse:0.207293+0.002123    test-rmse:0.247326+0.020914 
## [15] train-rmse:0.193143+0.002006    test-rmse:0.236211+0.022179 
## [16] train-rmse:0.182393+0.001780    test-rmse:0.228171+0.023026 
## [17] train-rmse:0.174617+0.001921    test-rmse:0.222683+0.024126 
## [18] train-rmse:0.168517+0.002534    test-rmse:0.218197+0.024476 
## [19] train-rmse:0.163555+0.002500    test-rmse:0.215710+0.025368 
## [20] train-rmse:0.159895+0.003186    test-rmse:0.213210+0.025677 
## [21] train-rmse:0.156663+0.003703    test-rmse:0.212278+0.025504 
## [22] train-rmse:0.154522+0.003824    test-rmse:0.211732+0.026207 
## [23] train-rmse:0.152153+0.004480    test-rmse:0.210625+0.026375 
## [24] train-rmse:0.150015+0.004498    test-rmse:0.210503+0.026415 
## [25] train-rmse:0.148499+0.004320    test-rmse:0.210781+0.026917 
## [26] train-rmse:0.146976+0.004047    test-rmse:0.210772+0.027392 
## [27] train-rmse:0.145601+0.004420    test-rmse:0.210617+0.027473 
## [28] train-rmse:0.143962+0.004545    test-rmse:0.210172+0.027687 
## [29] train-rmse:0.141891+0.004207    test-rmse:0.210496+0.027753 
## [30] train-rmse:0.140779+0.004751    test-rmse:0.210513+0.027726 
## [31] train-rmse:0.139821+0.005063    test-rmse:0.210794+0.028089 
## [32] train-rmse:0.138230+0.004886    test-rmse:0.210882+0.028130 
## [33] train-rmse:0.137147+0.004762    test-rmse:0.211075+0.028401 
## [34] train-rmse:0.136012+0.005013    test-rmse:0.211285+0.028891 
## Stopping. Best iteration:
## [28] train-rmse:0.143962+0.004545    test-rmse:0.210172+0.027687
## 
## 23 
## [1]  train-rmse:1.210478+0.005126    test-rmse:1.212198+0.021465 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.021920+0.004053    test-rmse:1.025941+0.018170 
## [3]  train-rmse:0.863764+0.003199    test-rmse:0.870898+0.015306 
## [4]  train-rmse:0.731347+0.002477    test-rmse:0.741689+0.012815 
## [5]  train-rmse:0.620623+0.001910    test-rmse:0.635183+0.010600 
## [6]  train-rmse:0.528207+0.001627    test-rmse:0.547619+0.008828 
## [7]  train-rmse:0.450932+0.001679    test-rmse:0.476077+0.008851 
## [8]  train-rmse:0.386978+0.002244    test-rmse:0.417105+0.009577 
## [9]  train-rmse:0.334100+0.002966    test-rmse:0.371027+0.009782 
## [10] train-rmse:0.289954+0.003994    test-rmse:0.334297+0.010966 
## [11] train-rmse:0.253422+0.004961    test-rmse:0.304893+0.013002 
## [12] train-rmse:0.223134+0.005362    test-rmse:0.283362+0.014913 
## [13] train-rmse:0.198887+0.005547    test-rmse:0.265830+0.017120 
## [14] train-rmse:0.178789+0.006008    test-rmse:0.253625+0.019749 
## [15] train-rmse:0.163071+0.006211    test-rmse:0.244241+0.021285 
## [16] train-rmse:0.150139+0.006827    test-rmse:0.237913+0.023279 
## [17] train-rmse:0.139823+0.007355    test-rmse:0.233294+0.024908 
## [18] train-rmse:0.131417+0.008004    test-rmse:0.229758+0.025519 
## [19] train-rmse:0.124949+0.008060    test-rmse:0.226676+0.026004 
## [20] train-rmse:0.120167+0.008357    test-rmse:0.224594+0.026655 
## [21] train-rmse:0.116079+0.008605    test-rmse:0.223302+0.027455 
## [22] train-rmse:0.112719+0.008736    test-rmse:0.223053+0.027673 
## [23] train-rmse:0.109697+0.008745    test-rmse:0.222276+0.027963 
## [24] train-rmse:0.107576+0.009033    test-rmse:0.222170+0.028597 
## [25] train-rmse:0.105609+0.009700    test-rmse:0.222324+0.029010 
## [26] train-rmse:0.103822+0.010013    test-rmse:0.222211+0.029255 
## [27] train-rmse:0.101852+0.010058    test-rmse:0.222614+0.029719 
## [28] train-rmse:0.100037+0.009999    test-rmse:0.223394+0.030106 
## [29] train-rmse:0.098601+0.010423    test-rmse:0.223983+0.030485 
## [30] train-rmse:0.096853+0.010850    test-rmse:0.223486+0.030299 
## Stopping. Best iteration:
## [24] train-rmse:0.107576+0.009033    test-rmse:0.222170+0.028597
## 
## 24 
## [1]  train-rmse:1.210288+0.005141    test-rmse:1.212207+0.021432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.021466+0.004084    test-rmse:1.025897+0.018057 
## [3]  train-rmse:0.863125+0.003192    test-rmse:0.870539+0.015056 
## [4]  train-rmse:0.730424+0.002532    test-rmse:0.741947+0.012658 
## [5]  train-rmse:0.618629+0.001699    test-rmse:0.635538+0.010731 
## [6]  train-rmse:0.524474+0.001119    test-rmse:0.547695+0.008985 
## [7]  train-rmse:0.445840+0.000955    test-rmse:0.476911+0.007783 
## [8]  train-rmse:0.379674+0.001181    test-rmse:0.419317+0.008175 
## [9]  train-rmse:0.324551+0.001661    test-rmse:0.373681+0.009692 
## [10] train-rmse:0.278597+0.002118    test-rmse:0.337811+0.011932 
## [11] train-rmse:0.240181+0.002820    test-rmse:0.310050+0.013878 
## [12] train-rmse:0.208205+0.003314    test-rmse:0.288523+0.016822 
## [13] train-rmse:0.181646+0.003706    test-rmse:0.272493+0.019362 
## [14] train-rmse:0.159648+0.004177    test-rmse:0.260336+0.021510 
## [15] train-rmse:0.141475+0.004715    test-rmse:0.252292+0.023584 
## [16] train-rmse:0.126535+0.005267    test-rmse:0.245955+0.025360 
## [17] train-rmse:0.113905+0.005562    test-rmse:0.241900+0.027097 
## [18] train-rmse:0.104021+0.005692    test-rmse:0.239240+0.028644 
## [19] train-rmse:0.096081+0.006145    test-rmse:0.236881+0.029522 
## [20] train-rmse:0.089800+0.006591    test-rmse:0.235513+0.030306 
## [21] train-rmse:0.084863+0.006560    test-rmse:0.234806+0.031137 
## [22] train-rmse:0.080803+0.006796    test-rmse:0.234019+0.031640 
## [23] train-rmse:0.077340+0.006935    test-rmse:0.233818+0.032078 
## [24] train-rmse:0.074534+0.006862    test-rmse:0.233892+0.032613 
## [25] train-rmse:0.072263+0.007155    test-rmse:0.233430+0.032586 
## [26] train-rmse:0.069963+0.007284    test-rmse:0.233543+0.032918 
## [27] train-rmse:0.068365+0.007224    test-rmse:0.233582+0.033272 
## [28] train-rmse:0.066868+0.007280    test-rmse:0.234191+0.033643 
## [29] train-rmse:0.065696+0.007352    test-rmse:0.234021+0.033618 
## [30] train-rmse:0.064580+0.007326    test-rmse:0.234076+0.033771 
## [31] train-rmse:0.063378+0.007653    test-rmse:0.234069+0.033792 
## Stopping. Best iteration:
## [25] train-rmse:0.072263+0.007155    test-rmse:0.233430+0.032586
## 
## 25 
## [1]  train-rmse:1.210167+0.005131    test-rmse:1.212208+0.021420 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.021152+0.004107    test-rmse:1.025930+0.018017 
## [3]  train-rmse:0.862642+0.003214    test-rmse:0.870780+0.015018 
## [4]  train-rmse:0.729801+0.002523    test-rmse:0.742311+0.012572 
## [5]  train-rmse:0.617564+0.001452    test-rmse:0.636009+0.010723 
## [6]  train-rmse:0.522959+0.000850    test-rmse:0.548684+0.008746 
## [7]  train-rmse:0.443636+0.001076    test-rmse:0.477738+0.007634 
## [8]  train-rmse:0.376990+0.001273    test-rmse:0.420607+0.008469 
## [9]  train-rmse:0.320776+0.001634    test-rmse:0.375828+0.009853 
## [10] train-rmse:0.273542+0.001942    test-rmse:0.340536+0.012385 
## [11] train-rmse:0.234063+0.002234    test-rmse:0.313700+0.015522 
## [12] train-rmse:0.200844+0.002195    test-rmse:0.292913+0.019127 
## [13] train-rmse:0.173055+0.002388    test-rmse:0.276947+0.022310 
## [14] train-rmse:0.149820+0.002447    test-rmse:0.266073+0.025773 
## [15] train-rmse:0.130400+0.002813    test-rmse:0.257798+0.028558 
## [16] train-rmse:0.114437+0.003198    test-rmse:0.251965+0.031046 
## [17] train-rmse:0.100898+0.003442    test-rmse:0.248024+0.033095 
## [18] train-rmse:0.090249+0.003772    test-rmse:0.245260+0.034865 
## [19] train-rmse:0.081312+0.004271    test-rmse:0.243208+0.035819 
## [20] train-rmse:0.074141+0.004706    test-rmse:0.242050+0.036933 
## [21] train-rmse:0.067919+0.005223    test-rmse:0.241385+0.037496 
## [22] train-rmse:0.063056+0.005611    test-rmse:0.240547+0.037927 
## [23] train-rmse:0.058941+0.006099    test-rmse:0.240848+0.038465 
## [24] train-rmse:0.055522+0.006027    test-rmse:0.240698+0.039029 
## [25] train-rmse:0.052721+0.006072    test-rmse:0.240343+0.039267 
## [26] train-rmse:0.050211+0.006061    test-rmse:0.240025+0.039313 
## [27] train-rmse:0.048121+0.005816    test-rmse:0.240080+0.039536 
## [28] train-rmse:0.046258+0.005892    test-rmse:0.240215+0.039668 
## [29] train-rmse:0.044819+0.005808    test-rmse:0.240226+0.039831 
## [30] train-rmse:0.043408+0.005821    test-rmse:0.240191+0.039761 
## [31] train-rmse:0.042213+0.005688    test-rmse:0.240535+0.039891 
## [32] train-rmse:0.041079+0.005601    test-rmse:0.240663+0.040009 
## Stopping. Best iteration:
## [26] train-rmse:0.050211+0.006061    test-rmse:0.240025+0.039313
## 
## 26 
## [1]  train-rmse:1.210106+0.005159    test-rmse:1.212217+0.021400 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.021003+0.004175    test-rmse:1.025911+0.017956 
## [3]  train-rmse:0.862395+0.003320    test-rmse:0.870784+0.014866 
## [4]  train-rmse:0.729432+0.002681    test-rmse:0.742403+0.012329 
## [5]  train-rmse:0.617076+0.001567    test-rmse:0.635823+0.010281 
## [6]  train-rmse:0.522135+0.000958    test-rmse:0.548045+0.008174 
## [7]  train-rmse:0.442515+0.001138    test-rmse:0.477095+0.007366 
## [8]  train-rmse:0.375482+0.001339    test-rmse:0.419653+0.008158 
## [9]  train-rmse:0.318960+0.001611    test-rmse:0.374736+0.009633 
## [10] train-rmse:0.271078+0.001765    test-rmse:0.339897+0.011920 
## [11] train-rmse:0.231330+0.001961    test-rmse:0.312794+0.014894 
## [12] train-rmse:0.197527+0.002298    test-rmse:0.292561+0.018282 
## [13] train-rmse:0.169342+0.002513    test-rmse:0.277429+0.021755 
## [14] train-rmse:0.145643+0.002984    test-rmse:0.266248+0.025221 
## [15] train-rmse:0.126003+0.003268    test-rmse:0.258354+0.028036 
## [16] train-rmse:0.109394+0.003424    test-rmse:0.252676+0.030368 
## [17] train-rmse:0.095192+0.003423    test-rmse:0.248665+0.032514 
## [18] train-rmse:0.083625+0.003618    test-rmse:0.245829+0.034061 
## [19] train-rmse:0.074106+0.003951    test-rmse:0.244297+0.035799 
## [20] train-rmse:0.066248+0.004211    test-rmse:0.242924+0.037023 
## [21] train-rmse:0.059384+0.004257    test-rmse:0.242678+0.038115 
## [22] train-rmse:0.053909+0.004520    test-rmse:0.242159+0.038701 
## [23] train-rmse:0.049451+0.004761    test-rmse:0.241883+0.039111 
## [24] train-rmse:0.045595+0.005110    test-rmse:0.241885+0.039488 
## [25] train-rmse:0.042399+0.005127    test-rmse:0.241573+0.039662 
## [26] train-rmse:0.039782+0.005022    test-rmse:0.241751+0.039905 
## [27] train-rmse:0.037513+0.005152    test-rmse:0.241880+0.040144 
## [28] train-rmse:0.035798+0.005150    test-rmse:0.242208+0.040510 
## [29] train-rmse:0.034123+0.005168    test-rmse:0.242444+0.040547 
## [30] train-rmse:0.032698+0.005212    test-rmse:0.242543+0.040690 
## [31] train-rmse:0.031480+0.005317    test-rmse:0.242770+0.040868 
## Stopping. Best iteration:
## [25] train-rmse:0.042399+0.005127    test-rmse:0.241573+0.039662
## 
## 27 
## [1]  train-rmse:1.210079+0.005190    test-rmse:1.212221+0.021391 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.020942+0.004244    test-rmse:1.025926+0.017930 
## [3]  train-rmse:0.862293+0.003423    test-rmse:0.870827+0.014803 
## [4]  train-rmse:0.729284+0.002801    test-rmse:0.742501+0.012256 
## [5]  train-rmse:0.616839+0.001594    test-rmse:0.635879+0.010249 
## [6]  train-rmse:0.521704+0.000889    test-rmse:0.548050+0.008059 
## [7]  train-rmse:0.442100+0.001126    test-rmse:0.476958+0.006456 
## [8]  train-rmse:0.374694+0.001046    test-rmse:0.420133+0.006589 
## [9]  train-rmse:0.317883+0.001125    test-rmse:0.374842+0.008526 
## [10] train-rmse:0.270247+0.001017    test-rmse:0.339774+0.011488 
## [11] train-rmse:0.230195+0.001121    test-rmse:0.312496+0.014630 
## [12] train-rmse:0.196207+0.001284    test-rmse:0.292013+0.018181 
## [13] train-rmse:0.168000+0.001568    test-rmse:0.277173+0.021028 
## [14] train-rmse:0.143801+0.001695    test-rmse:0.266285+0.024489 
## [15] train-rmse:0.123637+0.001598    test-rmse:0.258582+0.027402 
## [16] train-rmse:0.106679+0.001668    test-rmse:0.253201+0.030026 
## [17] train-rmse:0.092589+0.001785    test-rmse:0.249602+0.032199 
## [18] train-rmse:0.080468+0.001721    test-rmse:0.247080+0.034015 
## [19] train-rmse:0.070344+0.001834    test-rmse:0.246311+0.034893 
## [20] train-rmse:0.061812+0.001721    test-rmse:0.245689+0.035909 
## [21] train-rmse:0.054664+0.001631    test-rmse:0.245610+0.036756 
## [22] train-rmse:0.048750+0.001574    test-rmse:0.245815+0.037433 
## [23] train-rmse:0.043676+0.001610    test-rmse:0.246013+0.037903 
## [24] train-rmse:0.039446+0.001620    test-rmse:0.246261+0.038402 
## [25] train-rmse:0.035995+0.001710    test-rmse:0.246579+0.038842 
## [26] train-rmse:0.032918+0.001773    test-rmse:0.246876+0.039164 
## [27] train-rmse:0.030286+0.001947    test-rmse:0.247179+0.039486 
## Stopping. Best iteration:
## [21] train-rmse:0.054664+0.001631    test-rmse:0.245610+0.036756
## 
## 28 
## [1]  train-rmse:1.186046+0.004431    test-rmse:1.185953+0.022581 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.983144+0.002759    test-rmse:0.983192+0.020120 
## [3]  train-rmse:0.818590+0.001334    test-rmse:0.819047+0.017885 
## [4]  train-rmse:0.685803+0.000980    test-rmse:0.686552+0.015707 
## [5]  train-rmse:0.579410+0.002060    test-rmse:0.581999+0.014647 
## [6]  train-rmse:0.493486+0.002693    test-rmse:0.497450+0.012060 
## [7]  train-rmse:0.425612+0.003535    test-rmse:0.428824+0.013350 
## [8]  train-rmse:0.371422+0.004002    test-rmse:0.376252+0.012264 
## [9]  train-rmse:0.329635+0.004646    test-rmse:0.334725+0.013857 
## [10] train-rmse:0.296887+0.004953    test-rmse:0.302290+0.015551 
## [11] train-rmse:0.272274+0.005415    test-rmse:0.279233+0.018644 
## [12] train-rmse:0.253346+0.005583    test-rmse:0.260143+0.019731 
## [13] train-rmse:0.239413+0.005790    test-rmse:0.246748+0.022104 
## [14] train-rmse:0.228872+0.005836    test-rmse:0.236095+0.022333 
## [15] train-rmse:0.221138+0.005938    test-rmse:0.229357+0.024570 
## [16] train-rmse:0.215200+0.005921    test-rmse:0.225257+0.024128 
## [17] train-rmse:0.210689+0.005939    test-rmse:0.222063+0.024655 
## [18] train-rmse:0.207034+0.005850    test-rmse:0.217056+0.025664 
## [19] train-rmse:0.204046+0.005753    test-rmse:0.215843+0.026081 
## [20] train-rmse:0.201569+0.005652    test-rmse:0.214865+0.025347 
## [21] train-rmse:0.199489+0.005546    test-rmse:0.215105+0.025882 
## [22] train-rmse:0.197609+0.005457    test-rmse:0.213966+0.026803 
## [23] train-rmse:0.195981+0.005373    test-rmse:0.213476+0.027020 
## [24] train-rmse:0.194518+0.005360    test-rmse:0.211818+0.027179 
## [25] train-rmse:0.193195+0.005332    test-rmse:0.212700+0.027644 
## [26] train-rmse:0.191939+0.005331    test-rmse:0.212424+0.026316 
## [27] train-rmse:0.190797+0.005330    test-rmse:0.211861+0.026903 
## [28] train-rmse:0.189750+0.005311    test-rmse:0.212234+0.027021 
## [29] train-rmse:0.188755+0.005347    test-rmse:0.213080+0.027585 
## [30] train-rmse:0.187799+0.005390    test-rmse:0.211525+0.027219 
## [31] train-rmse:0.186880+0.005471    test-rmse:0.211033+0.027600 
## [32] train-rmse:0.186053+0.005518    test-rmse:0.212658+0.027233 
## [33] train-rmse:0.185194+0.005580    test-rmse:0.211831+0.026600 
## [34] train-rmse:0.184471+0.005645    test-rmse:0.211273+0.026544 
## [35] train-rmse:0.183772+0.005707    test-rmse:0.211156+0.026415 
## [36] train-rmse:0.183084+0.005763    test-rmse:0.211308+0.026705 
## [37] train-rmse:0.182384+0.005830    test-rmse:0.211097+0.026951 
## Stopping. Best iteration:
## [31] train-rmse:0.186880+0.005471    test-rmse:0.211033+0.027600
## 
## 29 
## [1]  train-rmse:1.183027+0.005002    test-rmse:1.183964+0.021566 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.976783+0.003886    test-rmse:0.978946+0.018439 
## [3]  train-rmse:0.808415+0.003003    test-rmse:0.812447+0.015986 
## [4]  train-rmse:0.671352+0.002315    test-rmse:0.677432+0.014029 
## [5]  train-rmse:0.560167+0.001881    test-rmse:0.568715+0.012714 
## [6]  train-rmse:0.470461+0.001638    test-rmse:0.483175+0.011700 
## [7]  train-rmse:0.398472+0.001601    test-rmse:0.414376+0.011397 
## [8]  train-rmse:0.341144+0.001785    test-rmse:0.361265+0.012256 
## [9]  train-rmse:0.295819+0.001913    test-rmse:0.320419+0.014268 
## [10] train-rmse:0.260531+0.002040    test-rmse:0.290389+0.015738 
## [11] train-rmse:0.233267+0.002142    test-rmse:0.267123+0.017987 
## [12] train-rmse:0.212521+0.002107    test-rmse:0.250823+0.020376 
## [13] train-rmse:0.195678+0.001740    test-rmse:0.238499+0.023280 
## [14] train-rmse:0.183634+0.001917    test-rmse:0.229209+0.024171 
## [15] train-rmse:0.174532+0.002378    test-rmse:0.221910+0.024572 
## [16] train-rmse:0.167823+0.003051    test-rmse:0.217231+0.025320 
## [17] train-rmse:0.163172+0.003300    test-rmse:0.214873+0.026050 
## [18] train-rmse:0.158003+0.003549    test-rmse:0.213215+0.025971 
## [19] train-rmse:0.154824+0.003936    test-rmse:0.212386+0.026063 
## [20] train-rmse:0.152358+0.003188    test-rmse:0.211906+0.026985 
## [21] train-rmse:0.149856+0.003608    test-rmse:0.212079+0.027441 
## [22] train-rmse:0.146691+0.003367    test-rmse:0.211930+0.026743 
## [23] train-rmse:0.145743+0.003366    test-rmse:0.212029+0.027297 
## [24] train-rmse:0.143786+0.003120    test-rmse:0.211913+0.027455 
## [25] train-rmse:0.142369+0.002639    test-rmse:0.212722+0.027594 
## [26] train-rmse:0.141170+0.003347    test-rmse:0.212480+0.027638 
## Stopping. Best iteration:
## [20] train-rmse:0.152358+0.003188    test-rmse:0.211906+0.026985
## 
## 30 
## [1]  train-rmse:1.182416+0.004972    test-rmse:1.184423+0.020980 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.975455+0.003777    test-rmse:0.980173+0.017333 
## [3]  train-rmse:0.806092+0.002859    test-rmse:0.814472+0.014191 
## [4]  train-rmse:0.667817+0.002125    test-rmse:0.680279+0.011605 
## [5]  train-rmse:0.555111+0.001653    test-rmse:0.573008+0.009537 
## [6]  train-rmse:0.463558+0.001663    test-rmse:0.487455+0.007984 
## [7]  train-rmse:0.389029+0.002243    test-rmse:0.418999+0.009053 
## [8]  train-rmse:0.328915+0.003434    test-rmse:0.367738+0.010038 
## [9]  train-rmse:0.280002+0.004467    test-rmse:0.326387+0.012297 
## [10] train-rmse:0.240365+0.004929    test-rmse:0.297306+0.014483 
## [11] train-rmse:0.209277+0.005293    test-rmse:0.275481+0.017151 
## [12] train-rmse:0.184401+0.005744    test-rmse:0.258724+0.019469 
## [13] train-rmse:0.165150+0.006437    test-rmse:0.248298+0.022200 
## [14] train-rmse:0.150425+0.006708    test-rmse:0.239780+0.023671 
## [15] train-rmse:0.138767+0.007353    test-rmse:0.234347+0.025845 
## [16] train-rmse:0.129675+0.007461    test-rmse:0.230996+0.027859 
## [17] train-rmse:0.122792+0.007335    test-rmse:0.227805+0.028523 
## [18] train-rmse:0.117397+0.007909    test-rmse:0.225836+0.029035 
## [19] train-rmse:0.113105+0.008035    test-rmse:0.225210+0.029498 
## [20] train-rmse:0.109998+0.008333    test-rmse:0.224543+0.030118 
## [21] train-rmse:0.106871+0.008132    test-rmse:0.224416+0.030716 
## [22] train-rmse:0.104823+0.008275    test-rmse:0.224247+0.031049 
## [23] train-rmse:0.102532+0.008329    test-rmse:0.224447+0.031436 
## [24] train-rmse:0.100163+0.008456    test-rmse:0.225240+0.032067 
## [25] train-rmse:0.098588+0.008211    test-rmse:0.225760+0.031620 
## [26] train-rmse:0.097049+0.008174    test-rmse:0.226155+0.032011 
## [27] train-rmse:0.095828+0.008037    test-rmse:0.226566+0.032256 
## [28] train-rmse:0.094114+0.008415    test-rmse:0.226342+0.032325 
## Stopping. Best iteration:
## [22] train-rmse:0.104823+0.008275    test-rmse:0.224247+0.031049
## 
## 31 
## [1]  train-rmse:1.182198+0.004990    test-rmse:1.184438+0.020942 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.974871+0.003851    test-rmse:0.979988+0.017158 
## [3]  train-rmse:0.805337+0.002851    test-rmse:0.814153+0.013930 
## [4]  train-rmse:0.666649+0.002309    test-rmse:0.680580+0.011352 
## [5]  train-rmse:0.552565+0.001613    test-rmse:0.573337+0.009470 
## [6]  train-rmse:0.458890+0.001161    test-rmse:0.487609+0.007948 
## [7]  train-rmse:0.382318+0.001371    test-rmse:0.420480+0.008509 
## [8]  train-rmse:0.319879+0.001904    test-rmse:0.368871+0.009754 
## [9]  train-rmse:0.269049+0.002627    test-rmse:0.329398+0.012095 
## [10] train-rmse:0.227649+0.003192    test-rmse:0.300060+0.014798 
## [11] train-rmse:0.194225+0.003627    test-rmse:0.278533+0.018011 
## [12] train-rmse:0.167245+0.004123    test-rmse:0.262915+0.020997 
## [13] train-rmse:0.145584+0.004712    test-rmse:0.252363+0.023862 
## [14] train-rmse:0.128237+0.005355    test-rmse:0.245805+0.026200 
## [15] train-rmse:0.114030+0.005572    test-rmse:0.241126+0.028007 
## [16] train-rmse:0.103048+0.006310    test-rmse:0.237306+0.029660 
## [17] train-rmse:0.094590+0.006849    test-rmse:0.235318+0.030789 
## [18] train-rmse:0.088077+0.007293    test-rmse:0.233772+0.031569 
## [19] train-rmse:0.082724+0.007642    test-rmse:0.233568+0.032063 
## [20] train-rmse:0.078663+0.007995    test-rmse:0.233048+0.032793 
## [21] train-rmse:0.075265+0.008396    test-rmse:0.232496+0.032995 
## [22] train-rmse:0.072106+0.008538    test-rmse:0.232127+0.033159 
## [23] train-rmse:0.069722+0.008875    test-rmse:0.232036+0.033479 
## [24] train-rmse:0.067608+0.008998    test-rmse:0.232210+0.033827 
## [25] train-rmse:0.065975+0.008909    test-rmse:0.232446+0.034164 
## [26] train-rmse:0.064131+0.009224    test-rmse:0.232557+0.034192 
## [27] train-rmse:0.062514+0.008853    test-rmse:0.232599+0.034218 
## [28] train-rmse:0.060986+0.008754    test-rmse:0.232543+0.034175 
## [29] train-rmse:0.059419+0.008779    test-rmse:0.232568+0.034293 
## Stopping. Best iteration:
## [23] train-rmse:0.069722+0.008875    test-rmse:0.232036+0.033479
## 
## 32 
## [1]  train-rmse:1.182059+0.004978    test-rmse:1.184442+0.020927 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.974561+0.003841    test-rmse:0.980029+0.017108 
## [3]  train-rmse:0.804856+0.002807    test-rmse:0.814450+0.013798 
## [4]  train-rmse:0.666074+0.002130    test-rmse:0.680978+0.011170 
## [5]  train-rmse:0.550984+0.001149    test-rmse:0.573312+0.009014 
## [6]  train-rmse:0.456636+0.000592    test-rmse:0.487938+0.007507 
## [7]  train-rmse:0.379238+0.000856    test-rmse:0.421446+0.007605 
## [8]  train-rmse:0.315715+0.001462    test-rmse:0.370320+0.009566 
## [9]  train-rmse:0.263521+0.001984    test-rmse:0.331609+0.012670 
## [10] train-rmse:0.220994+0.002485    test-rmse:0.303424+0.016176 
## [11] train-rmse:0.186074+0.002946    test-rmse:0.282292+0.019800 
## [12] train-rmse:0.157937+0.003093    test-rmse:0.267710+0.023436 
## [13] train-rmse:0.134633+0.003674    test-rmse:0.257253+0.026562 
## [14] train-rmse:0.115856+0.003959    test-rmse:0.250332+0.028868 
## [15] train-rmse:0.100745+0.004213    test-rmse:0.245619+0.030859 
## [16] train-rmse:0.088539+0.004340    test-rmse:0.243522+0.032794 
## [17] train-rmse:0.078767+0.004766    test-rmse:0.241936+0.034327 
## [18] train-rmse:0.071004+0.005303    test-rmse:0.241079+0.035656 
## [19] train-rmse:0.064467+0.005845    test-rmse:0.240509+0.036226 
## [20] train-rmse:0.059531+0.006096    test-rmse:0.240124+0.036620 
## [21] train-rmse:0.055331+0.006326    test-rmse:0.239758+0.037231 
## [22] train-rmse:0.052065+0.006666    test-rmse:0.239414+0.037534 
## [23] train-rmse:0.049399+0.006982    test-rmse:0.239489+0.037705 
## [24] train-rmse:0.047182+0.007241    test-rmse:0.239400+0.037838 
## [25] train-rmse:0.045342+0.007301    test-rmse:0.239334+0.037899 
## [26] train-rmse:0.043661+0.007382    test-rmse:0.239709+0.038234 
## [27] train-rmse:0.042353+0.007371    test-rmse:0.239574+0.038141 
## [28] train-rmse:0.040909+0.007544    test-rmse:0.240017+0.038412 
## [29] train-rmse:0.039798+0.007505    test-rmse:0.240270+0.038693 
## [30] train-rmse:0.038551+0.007368    test-rmse:0.240673+0.039139 
## [31] train-rmse:0.037228+0.007118    test-rmse:0.240810+0.039218 
## Stopping. Best iteration:
## [25] train-rmse:0.045342+0.007301    test-rmse:0.239334+0.037899
## 
## 33 
## [1]  train-rmse:1.181989+0.005009    test-rmse:1.184453+0.020905 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.974388+0.003921    test-rmse:0.980164+0.017086 
## [3]  train-rmse:0.804487+0.003000    test-rmse:0.814351+0.013689 
## [4]  train-rmse:0.665573+0.002406    test-rmse:0.680991+0.010997 
## [5]  train-rmse:0.550786+0.001523    test-rmse:0.573834+0.008969 
## [6]  train-rmse:0.455859+0.001234    test-rmse:0.488081+0.007124 
## [7]  train-rmse:0.378107+0.001837    test-rmse:0.421777+0.007463 
## [8]  train-rmse:0.314315+0.001635    test-rmse:0.370190+0.009624 
## [9]  train-rmse:0.261610+0.002099    test-rmse:0.331764+0.012278 
## [10] train-rmse:0.218214+0.002090    test-rmse:0.302964+0.016458 
## [11] train-rmse:0.183320+0.002611    test-rmse:0.282884+0.019788 
## [12] train-rmse:0.154184+0.002589    test-rmse:0.268190+0.023238 
## [13] train-rmse:0.130584+0.002917    test-rmse:0.258213+0.026516 
## [14] train-rmse:0.111427+0.003076    test-rmse:0.251568+0.029288 
## [15] train-rmse:0.095712+0.003526    test-rmse:0.247021+0.031881 
## [16] train-rmse:0.082621+0.003871    test-rmse:0.244513+0.033700 
## [17] train-rmse:0.072343+0.004241    test-rmse:0.242905+0.035080 
## [18] train-rmse:0.063953+0.004036    test-rmse:0.241767+0.036163 
## [19] train-rmse:0.057026+0.004161    test-rmse:0.241184+0.036855 
## [20] train-rmse:0.051576+0.004327    test-rmse:0.240924+0.037209 
## [21] train-rmse:0.047091+0.004437    test-rmse:0.241219+0.037695 
## [22] train-rmse:0.043603+0.004598    test-rmse:0.240922+0.037788 
## [23] train-rmse:0.040599+0.004706    test-rmse:0.240991+0.038120 
## [24] train-rmse:0.038227+0.004830    test-rmse:0.241267+0.038460 
## [25] train-rmse:0.036235+0.005017    test-rmse:0.241670+0.038769 
## [26] train-rmse:0.034155+0.005133    test-rmse:0.241631+0.038831 
## [27] train-rmse:0.032478+0.005001    test-rmse:0.241766+0.038911 
## [28] train-rmse:0.030679+0.005217    test-rmse:0.242101+0.039102 
## Stopping. Best iteration:
## [22] train-rmse:0.043603+0.004598    test-rmse:0.240922+0.037788
## 
## 34 
## [1]  train-rmse:1.181959+0.005046    test-rmse:1.184458+0.020894 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.974316+0.004001    test-rmse:0.980183+0.017054 
## [3]  train-rmse:0.804363+0.003119    test-rmse:0.814423+0.013649 
## [4]  train-rmse:0.665389+0.002533    test-rmse:0.681119+0.010915 
## [5]  train-rmse:0.550484+0.001405    test-rmse:0.573915+0.008921 
## [6]  train-rmse:0.455258+0.000819    test-rmse:0.488170+0.006913 
## [7]  train-rmse:0.377314+0.001268    test-rmse:0.421811+0.006792 
## [8]  train-rmse:0.313123+0.001025    test-rmse:0.370404+0.008926 
## [9]  train-rmse:0.260213+0.001353    test-rmse:0.331799+0.011937 
## [10] train-rmse:0.217339+0.001661    test-rmse:0.303817+0.015408 
## [11] train-rmse:0.181629+0.001518    test-rmse:0.283237+0.019406 
## [12] train-rmse:0.152310+0.001404    test-rmse:0.268885+0.023215 
## [13] train-rmse:0.128229+0.001496    test-rmse:0.258756+0.026486 
## [14] train-rmse:0.108657+0.001559    test-rmse:0.252425+0.029432 
## [15] train-rmse:0.092207+0.001556    test-rmse:0.248161+0.031657 
## [16] train-rmse:0.079023+0.001775    test-rmse:0.245774+0.033632 
## [17] train-rmse:0.067996+0.002041    test-rmse:0.244986+0.034483 
## [18] train-rmse:0.058970+0.001959    test-rmse:0.244304+0.035957 
## [19] train-rmse:0.051670+0.001869    test-rmse:0.244073+0.037242 
## [20] train-rmse:0.045538+0.001998    test-rmse:0.244313+0.038087 
## [21] train-rmse:0.040515+0.002165    test-rmse:0.244595+0.038677 
## [22] train-rmse:0.036534+0.002449    test-rmse:0.245132+0.039352 
## [23] train-rmse:0.033253+0.002577    test-rmse:0.245464+0.039734 
## [24] train-rmse:0.030232+0.002472    test-rmse:0.245792+0.040083 
## [25] train-rmse:0.027969+0.002266    test-rmse:0.245946+0.040316 
## Stopping. Best iteration:
## [19] train-rmse:0.051670+0.001869    test-rmse:0.244073+0.037242
## 
## 35 
## [1]  train-rmse:1.158465+0.004212    test-rmse:1.158388+0.022265 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.938913+0.002376    test-rmse:0.939001+0.019531 
## [3]  train-rmse:0.765642+0.000973    test-rmse:0.766244+0.017073 
## [4]  train-rmse:0.629888+0.001456    test-rmse:0.630892+0.014798 
## [5]  train-rmse:0.523882+0.002391    test-rmse:0.526195+0.012128 
## [6]  train-rmse:0.441209+0.003269    test-rmse:0.445986+0.010762 
## [7]  train-rmse:0.377947+0.003907    test-rmse:0.382761+0.012267 
## [8]  train-rmse:0.329612+0.004563    test-rmse:0.335249+0.013465 
## [9]  train-rmse:0.293790+0.004957    test-rmse:0.299309+0.015384 
## [10] train-rmse:0.267046+0.005423    test-rmse:0.274293+0.018717 
## [11] train-rmse:0.247918+0.005610    test-rmse:0.254199+0.019440 
## [12] train-rmse:0.233772+0.005822    test-rmse:0.241423+0.022225 
## [13] train-rmse:0.224003+0.005857    test-rmse:0.231736+0.022511 
## [14] train-rmse:0.216650+0.005938    test-rmse:0.224467+0.024120 
## [15] train-rmse:0.211273+0.005984    test-rmse:0.221671+0.023927 
## [16] train-rmse:0.207104+0.005917    test-rmse:0.218694+0.023925 
## [17] train-rmse:0.203803+0.005742    test-rmse:0.216747+0.025653 
## [18] train-rmse:0.201058+0.005585    test-rmse:0.215662+0.026074 
## [19] train-rmse:0.198816+0.005520    test-rmse:0.213017+0.025924 
## [20] train-rmse:0.196908+0.005374    test-rmse:0.211497+0.026792 
## [21] train-rmse:0.195110+0.005389    test-rmse:0.212992+0.026519 
## [22] train-rmse:0.193556+0.005312    test-rmse:0.212126+0.026309 
## [23] train-rmse:0.192155+0.005296    test-rmse:0.212006+0.026954 
## [24] train-rmse:0.190871+0.005283    test-rmse:0.211748+0.026724 
## [25] train-rmse:0.189681+0.005317    test-rmse:0.211360+0.026641 
## [26] train-rmse:0.188577+0.005364    test-rmse:0.211142+0.026547 
## [27] train-rmse:0.187506+0.005405    test-rmse:0.211710+0.027867 
## [28] train-rmse:0.186530+0.005486    test-rmse:0.212256+0.026603 
## [29] train-rmse:0.185578+0.005549    test-rmse:0.211960+0.026312 
## [30] train-rmse:0.184749+0.005610    test-rmse:0.212745+0.027228 
## [31] train-rmse:0.183908+0.005679    test-rmse:0.211496+0.026856 
## [32] train-rmse:0.183117+0.005753    test-rmse:0.210808+0.026564 
## [33] train-rmse:0.182356+0.005825    test-rmse:0.211509+0.027005 
## [34] train-rmse:0.181639+0.005894    test-rmse:0.210903+0.026464 
## [35] train-rmse:0.180949+0.005977    test-rmse:0.211149+0.025301 
## [36] train-rmse:0.180294+0.006032    test-rmse:0.212426+0.025954 
## [37] train-rmse:0.179677+0.006090    test-rmse:0.211108+0.025963 
## [38] train-rmse:0.179103+0.006126    test-rmse:0.210830+0.025768 
## Stopping. Best iteration:
## [32] train-rmse:0.183117+0.005753    test-rmse:0.210808+0.026564
## 
## 36 
## [1]  train-rmse:1.155062+0.004854    test-rmse:1.156151+0.021152 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.931679+0.003639    test-rmse:0.934204+0.017760 
## [3]  train-rmse:0.753955+0.002729    test-rmse:0.758694+0.015183 
## [4]  train-rmse:0.613141+0.002057    test-rmse:0.620374+0.013271 
## [5]  train-rmse:0.502124+0.001728    test-rmse:0.512386+0.012200 
## [6]  train-rmse:0.415259+0.001627    test-rmse:0.430450+0.011532 
## [7]  train-rmse:0.347965+0.001852    test-rmse:0.367558+0.012453 
## [8]  train-rmse:0.296100+0.001932    test-rmse:0.321129+0.014013 
## [9]  train-rmse:0.256956+0.002047    test-rmse:0.286970+0.015912 
## [10] train-rmse:0.227767+0.002157    test-rmse:0.263013+0.018674 
## [11] train-rmse:0.206385+0.002162    test-rmse:0.245805+0.021039 
## [12] train-rmse:0.190300+0.000983    test-rmse:0.234562+0.023689 
## [13] train-rmse:0.178852+0.002033    test-rmse:0.225565+0.024203 
## [14] train-rmse:0.170080+0.001900    test-rmse:0.219970+0.024900 
## [15] train-rmse:0.163503+0.003185    test-rmse:0.215678+0.025538 
## [16] train-rmse:0.158240+0.003044    test-rmse:0.215465+0.025058 
## [17] train-rmse:0.154878+0.003815    test-rmse:0.214328+0.025269 
## [18] train-rmse:0.151457+0.003962    test-rmse:0.212725+0.025520 
## [19] train-rmse:0.148620+0.003575    test-rmse:0.212141+0.025368 
## [20] train-rmse:0.147103+0.003416    test-rmse:0.212085+0.026280 
## [21] train-rmse:0.145411+0.004056    test-rmse:0.212204+0.026257 
## [22] train-rmse:0.143391+0.003571    test-rmse:0.212686+0.026506 
## [23] train-rmse:0.141774+0.004431    test-rmse:0.212388+0.026729 
## [24] train-rmse:0.140372+0.005095    test-rmse:0.212339+0.026692 
## [25] train-rmse:0.138497+0.004497    test-rmse:0.213264+0.026665 
## [26] train-rmse:0.136638+0.004687    test-rmse:0.213389+0.026855 
## Stopping. Best iteration:
## [20] train-rmse:0.147103+0.003416    test-rmse:0.212085+0.026280
## 
## 37 
## [1]  train-rmse:1.154369+0.004818    test-rmse:1.156674+0.020493 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.930089+0.003545    test-rmse:0.935199+0.016376 
## [3]  train-rmse:0.751230+0.002520    test-rmse:0.761124+0.013168 
## [4]  train-rmse:0.608933+0.001757    test-rmse:0.624317+0.010476 
## [5]  train-rmse:0.496029+0.001575    test-rmse:0.517628+0.008506 
## [6]  train-rmse:0.406453+0.002064    test-rmse:0.434961+0.008622 
## [7]  train-rmse:0.336003+0.003374    test-rmse:0.373662+0.010073 
## [8]  train-rmse:0.280138+0.004528    test-rmse:0.327086+0.011719 
## [9]  train-rmse:0.236548+0.004741    test-rmse:0.294392+0.014106 
## [10] train-rmse:0.202837+0.005243    test-rmse:0.269986+0.016704 
## [11] train-rmse:0.177210+0.005979    test-rmse:0.253452+0.019270 
## [12] train-rmse:0.157482+0.006694    test-rmse:0.242780+0.021449 
## [13] train-rmse:0.143145+0.007138    test-rmse:0.235300+0.023067 
## [14] train-rmse:0.132132+0.007842    test-rmse:0.230831+0.024910 
## [15] train-rmse:0.123836+0.007844    test-rmse:0.227602+0.026054 
## [16] train-rmse:0.117933+0.008256    test-rmse:0.225798+0.027508 
## [17] train-rmse:0.113282+0.008599    test-rmse:0.224386+0.027463 
## [18] train-rmse:0.109469+0.008131    test-rmse:0.222965+0.027548 
## [19] train-rmse:0.106618+0.007737    test-rmse:0.222938+0.028529 
## [20] train-rmse:0.103994+0.007679    test-rmse:0.222912+0.028832 
## [21] train-rmse:0.101885+0.007407    test-rmse:0.222745+0.029238 
## [22] train-rmse:0.100191+0.007673    test-rmse:0.222788+0.029424 
## [23] train-rmse:0.097895+0.007772    test-rmse:0.223767+0.029949 
## [24] train-rmse:0.096113+0.007846    test-rmse:0.224406+0.030399 
## [25] train-rmse:0.095091+0.007821    test-rmse:0.224003+0.030418 
## [26] train-rmse:0.093548+0.007870    test-rmse:0.223904+0.030399 
## [27] train-rmse:0.091739+0.007973    test-rmse:0.224452+0.030550 
## Stopping. Best iteration:
## [21] train-rmse:0.101885+0.007407    test-rmse:0.222745+0.029238
## 
## 38 
## [1]  train-rmse:1.154122+0.004838    test-rmse:1.156695+0.020450 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.929478+0.003593    test-rmse:0.935420+0.016310 
## [3]  train-rmse:0.750310+0.002623    test-rmse:0.760586+0.012815 
## [4]  train-rmse:0.606748+0.001470    test-rmse:0.623970+0.010226 
## [5]  train-rmse:0.491593+0.000942    test-rmse:0.517606+0.008144 
## [6]  train-rmse:0.399970+0.001049    test-rmse:0.436653+0.007110 
## [7]  train-rmse:0.326856+0.001610    test-rmse:0.375319+0.009488 
## [8]  train-rmse:0.268798+0.002424    test-rmse:0.330754+0.011943 
## [9]  train-rmse:0.222895+0.003245    test-rmse:0.298549+0.015213 
## [10] train-rmse:0.186785+0.003584    test-rmse:0.275410+0.018893 
## [11] train-rmse:0.158423+0.004065    test-rmse:0.259123+0.022245 
## [12] train-rmse:0.136540+0.004858    test-rmse:0.249176+0.025340 
## [13] train-rmse:0.119752+0.005672    test-rmse:0.241401+0.027918 
## [14] train-rmse:0.106450+0.006316    test-rmse:0.237892+0.029770 
## [15] train-rmse:0.096843+0.006847    test-rmse:0.234977+0.030488 
## [16] train-rmse:0.089141+0.007121    test-rmse:0.233407+0.031372 
## [17] train-rmse:0.083713+0.007512    test-rmse:0.232107+0.032191 
## [18] train-rmse:0.079526+0.007847    test-rmse:0.231489+0.032694 
## [19] train-rmse:0.075788+0.007797    test-rmse:0.231182+0.033020 
## [20] train-rmse:0.072517+0.007835    test-rmse:0.231350+0.033425 
## [21] train-rmse:0.070176+0.007623    test-rmse:0.231367+0.033521 
## [22] train-rmse:0.068383+0.007818    test-rmse:0.231550+0.033837 
## [23] train-rmse:0.066467+0.007786    test-rmse:0.231939+0.034077 
## [24] train-rmse:0.064399+0.007857    test-rmse:0.232536+0.034291 
## [25] train-rmse:0.062182+0.007564    test-rmse:0.232412+0.034327 
## Stopping. Best iteration:
## [19] train-rmse:0.075788+0.007797    test-rmse:0.231182+0.033020
## 
## 39 
## [1]  train-rmse:1.153965+0.004825    test-rmse:1.156703+0.020432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.929126+0.003578    test-rmse:0.935478+0.016252 
## [3]  train-rmse:0.749711+0.002607    test-rmse:0.760978+0.012656 
## [4]  train-rmse:0.605861+0.001354    test-rmse:0.624063+0.010416 
## [5]  train-rmse:0.490186+0.001016    test-rmse:0.518434+0.008310 
## [6]  train-rmse:0.397487+0.001370    test-rmse:0.437801+0.007832 
## [7]  train-rmse:0.323798+0.001597    test-rmse:0.377177+0.010298 
## [8]  train-rmse:0.264589+0.002049    test-rmse:0.332993+0.013440 
## [9]  train-rmse:0.217158+0.002663    test-rmse:0.301560+0.017056 
## [10] train-rmse:0.179452+0.002937    test-rmse:0.279528+0.021482 
## [11] train-rmse:0.149286+0.003383    test-rmse:0.264118+0.025359 
## [12] train-rmse:0.125490+0.003779    test-rmse:0.254546+0.028713 
## [13] train-rmse:0.106589+0.003997    test-rmse:0.247900+0.031151 
## [14] train-rmse:0.091979+0.004830    test-rmse:0.243851+0.033288 
## [15] train-rmse:0.080764+0.005539    test-rmse:0.242013+0.035113 
## [16] train-rmse:0.071300+0.005656    test-rmse:0.241123+0.036247 
## [17] train-rmse:0.064606+0.006230    test-rmse:0.240257+0.036813 
## [18] train-rmse:0.059125+0.006589    test-rmse:0.240017+0.037357 
## [19] train-rmse:0.055036+0.006774    test-rmse:0.239680+0.037967 
## [20] train-rmse:0.051564+0.007120    test-rmse:0.239678+0.038409 
## [21] train-rmse:0.048894+0.007211    test-rmse:0.239633+0.038760 
## [22] train-rmse:0.046643+0.006930    test-rmse:0.239738+0.038955 
## [23] train-rmse:0.044767+0.007076    test-rmse:0.240015+0.039099 
## [24] train-rmse:0.043124+0.006940    test-rmse:0.240244+0.039426 
## [25] train-rmse:0.041598+0.007032    test-rmse:0.240926+0.039764 
## [26] train-rmse:0.040246+0.006960    test-rmse:0.240709+0.039674 
## [27] train-rmse:0.039228+0.006864    test-rmse:0.240905+0.039896 
## Stopping. Best iteration:
## [21] train-rmse:0.048894+0.007211    test-rmse:0.239633+0.038760
## 
## 40 
## [1]  train-rmse:1.153886+0.004860    test-rmse:1.156717+0.020406 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.928927+0.003671    test-rmse:0.935517+0.016192 
## [3]  train-rmse:0.749419+0.002700    test-rmse:0.760869+0.012561 
## [4]  train-rmse:0.605328+0.001622    test-rmse:0.624189+0.009979 
## [5]  train-rmse:0.489444+0.001279    test-rmse:0.517653+0.007937 
## [6]  train-rmse:0.396835+0.001455    test-rmse:0.437157+0.007367 
## [7]  train-rmse:0.322313+0.001828    test-rmse:0.376039+0.009982 
## [8]  train-rmse:0.262297+0.002365    test-rmse:0.331746+0.012792 
## [9]  train-rmse:0.214020+0.002268    test-rmse:0.299604+0.017114 
## [10] train-rmse:0.175372+0.002187    test-rmse:0.277765+0.021458 
## [11] train-rmse:0.144914+0.002448    test-rmse:0.263213+0.025548 
## [12] train-rmse:0.120485+0.002147    test-rmse:0.253435+0.028819 
## [13] train-rmse:0.100985+0.002381    test-rmse:0.247689+0.031609 
## [14] train-rmse:0.085457+0.002499    test-rmse:0.244734+0.034059 
## [15] train-rmse:0.073198+0.002882    test-rmse:0.242605+0.036218 
## [16] train-rmse:0.063412+0.002972    test-rmse:0.241121+0.037266 
## [17] train-rmse:0.056081+0.003271    test-rmse:0.240138+0.038027 
## [18] train-rmse:0.049951+0.003704    test-rmse:0.240109+0.038682 
## [19] train-rmse:0.045463+0.003844    test-rmse:0.240133+0.039191 
## [20] train-rmse:0.041800+0.003926    test-rmse:0.239718+0.039596 
## [21] train-rmse:0.038849+0.003834    test-rmse:0.239848+0.039794 
## [22] train-rmse:0.036285+0.003582    test-rmse:0.240307+0.040143 
## [23] train-rmse:0.034002+0.003130    test-rmse:0.240645+0.040346 
## [24] train-rmse:0.032466+0.003097    test-rmse:0.240716+0.040381 
## [25] train-rmse:0.030761+0.003120    test-rmse:0.240745+0.040581 
## [26] train-rmse:0.029094+0.003112    test-rmse:0.241068+0.040621 
## Stopping. Best iteration:
## [20] train-rmse:0.041800+0.003926    test-rmse:0.239718+0.039596
## 
## 41 
## [1]  train-rmse:1.153851+0.004901    test-rmse:1.156723+0.020394 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.928840+0.003765    test-rmse:0.935535+0.016152 
## [3]  train-rmse:0.749273+0.002831    test-rmse:0.760951+0.012517 
## [4]  train-rmse:0.605072+0.001663    test-rmse:0.624151+0.010039 
## [5]  train-rmse:0.489329+0.001755    test-rmse:0.517810+0.007562 
## [6]  train-rmse:0.396445+0.001553    test-rmse:0.437350+0.007307 
## [7]  train-rmse:0.320917+0.001405    test-rmse:0.375983+0.008983 
## [8]  train-rmse:0.260865+0.001332    test-rmse:0.331820+0.012497 
## [9]  train-rmse:0.212330+0.001386    test-rmse:0.300362+0.016335 
## [10] train-rmse:0.173635+0.001481    test-rmse:0.278289+0.020651 
## [11] train-rmse:0.143121+0.001739    test-rmse:0.263837+0.024816 
## [12] train-rmse:0.118402+0.001588    test-rmse:0.254435+0.028226 
## [13] train-rmse:0.098550+0.001744    test-rmse:0.248887+0.031271 
## [14] train-rmse:0.082365+0.001643    test-rmse:0.245988+0.033980 
## [15] train-rmse:0.069444+0.001618    test-rmse:0.244758+0.035296 
## [16] train-rmse:0.059127+0.001548    test-rmse:0.244212+0.036544 
## [17] train-rmse:0.050824+0.001499    test-rmse:0.244227+0.037536 
## [18] train-rmse:0.044054+0.001504    test-rmse:0.244432+0.038452 
## [19] train-rmse:0.038528+0.001537    test-rmse:0.244794+0.038992 
## [20] train-rmse:0.034123+0.001690    test-rmse:0.245453+0.039722 
## [21] train-rmse:0.030748+0.001808    test-rmse:0.245714+0.040034 
## [22] train-rmse:0.027968+0.001874    test-rmse:0.246023+0.040436 
## Stopping. Best iteration:
## [16] train-rmse:0.059127+0.001548    test-rmse:0.244212+0.036544
## 
## 42 
## [1]  train-rmse:1.130914+0.003991    test-rmse:1.130854+0.021944 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.895912+0.001999    test-rmse:0.896043+0.018933 
## [3]  train-rmse:0.715628+0.000869    test-rmse:0.716344+0.016241 
## [4]  train-rmse:0.578718+0.002069    test-rmse:0.581739+0.014965 
## [5]  train-rmse:0.474361+0.002867    test-rmse:0.479179+0.011861 
## [6]  train-rmse:0.396449+0.003862    test-rmse:0.400312+0.013912 
## [7]  train-rmse:0.338454+0.004393    test-rmse:0.344397+0.013220 
## [8]  train-rmse:0.296704+0.005074    test-rmse:0.301249+0.017411 
## [9]  train-rmse:0.266615+0.005362    test-rmse:0.273110+0.017839 
## [10] train-rmse:0.245711+0.005747    test-rmse:0.251828+0.022430 
## [11] train-rmse:0.231075+0.005812    test-rmse:0.238929+0.022296 
## [12] train-rmse:0.221027+0.005944    test-rmse:0.229464+0.024497 
## [13] train-rmse:0.213931+0.005924    test-rmse:0.224620+0.023982 
## [14] train-rmse:0.208791+0.005910    test-rmse:0.219330+0.024629 
## [15] train-rmse:0.204807+0.005724    test-rmse:0.215580+0.025747 
## [16] train-rmse:0.201579+0.005637    test-rmse:0.214376+0.026113 
## [17] train-rmse:0.198996+0.005566    test-rmse:0.214398+0.025473 
## [18] train-rmse:0.196908+0.005491    test-rmse:0.212983+0.026381 
## [19] train-rmse:0.194932+0.005348    test-rmse:0.213765+0.027037 
## [20] train-rmse:0.193256+0.005281    test-rmse:0.211758+0.026948 
## [21] train-rmse:0.191719+0.005275    test-rmse:0.210906+0.026881 
## [22] train-rmse:0.190321+0.005303    test-rmse:0.212268+0.027496 
## [23] train-rmse:0.189073+0.005366    test-rmse:0.212189+0.026044 
## [24] train-rmse:0.187874+0.005419    test-rmse:0.212983+0.026467 
## [25] train-rmse:0.186790+0.005474    test-rmse:0.212028+0.027332 
## [26] train-rmse:0.185686+0.005523    test-rmse:0.211025+0.026555 
## [27] train-rmse:0.184730+0.005601    test-rmse:0.210917+0.026314 
## Stopping. Best iteration:
## [21] train-rmse:0.191719+0.005275    test-rmse:0.210906+0.026881
## 
## 43 
## [1]  train-rmse:1.127112+0.004705    test-rmse:1.128363+0.020738 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.887733+0.003396    test-rmse:0.890830+0.017110 
## [3]  train-rmse:0.702325+0.002481    test-rmse:0.707882+0.014447 
## [4]  train-rmse:0.559514+0.001849    test-rmse:0.568012+0.012607 
## [5]  train-rmse:0.450362+0.001672    test-rmse:0.463775+0.011534 
## [6]  train-rmse:0.367619+0.001626    test-rmse:0.385569+0.011629 
## [7]  train-rmse:0.305789+0.001973    test-rmse:0.329605+0.013571 
## [8]  train-rmse:0.260186+0.002050    test-rmse:0.289481+0.016186 
## [9]  train-rmse:0.227366+0.002207    test-rmse:0.261132+0.019199 
## [10] train-rmse:0.204058+0.002195    test-rmse:0.244260+0.021218 
## [11] train-rmse:0.187402+0.001121    test-rmse:0.231955+0.023099 
## [12] train-rmse:0.175824+0.000850    test-rmse:0.224023+0.024953 
## [13] train-rmse:0.166992+0.002158    test-rmse:0.218373+0.025604 
## [14] train-rmse:0.160406+0.003149    test-rmse:0.214406+0.025915 
## [15] train-rmse:0.155194+0.004114    test-rmse:0.212109+0.025915 
## [16] train-rmse:0.151809+0.004663    test-rmse:0.211452+0.026405 
## [17] train-rmse:0.149160+0.004189    test-rmse:0.211442+0.026956 
## [18] train-rmse:0.146626+0.004187    test-rmse:0.212370+0.027791 
## [19] train-rmse:0.144759+0.004787    test-rmse:0.211967+0.028146 
## [20] train-rmse:0.142371+0.004545    test-rmse:0.212150+0.028323 
## [21] train-rmse:0.140875+0.004983    test-rmse:0.211379+0.027765 
## [22] train-rmse:0.139027+0.005383    test-rmse:0.211183+0.027825 
## [23] train-rmse:0.138334+0.005388    test-rmse:0.211649+0.027489 
## [24] train-rmse:0.136182+0.005844    test-rmse:0.212364+0.027536 
## [25] train-rmse:0.134626+0.005654    test-rmse:0.213281+0.028171 
## [26] train-rmse:0.133255+0.006166    test-rmse:0.212941+0.028470 
## [27] train-rmse:0.132660+0.006374    test-rmse:0.213058+0.028487 
## [28] train-rmse:0.131126+0.006649    test-rmse:0.213232+0.028056 
## Stopping. Best iteration:
## [22] train-rmse:0.139027+0.005383    test-rmse:0.211183+0.027825
## 
## 44 
## [1]  train-rmse:1.126333+0.004662    test-rmse:1.128952+0.020004 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.885948+0.003281    test-rmse:0.891806+0.015548 
## [3]  train-rmse:0.699204+0.002227    test-rmse:0.710793+0.012369 
## [4]  train-rmse:0.554505+0.001572    test-rmse:0.572547+0.009339 
## [5]  train-rmse:0.442957+0.001686    test-rmse:0.468304+0.007671 
## [6]  train-rmse:0.356988+0.002545    test-rmse:0.392092+0.008980 
## [7]  train-rmse:0.291523+0.004220    test-rmse:0.336051+0.010671 
## [8]  train-rmse:0.240476+0.004877    test-rmse:0.296062+0.013709 
## [9]  train-rmse:0.202346+0.005359    test-rmse:0.269424+0.017212 
## [10] train-rmse:0.174159+0.005951    test-rmse:0.251673+0.019686 
## [11] train-rmse:0.153687+0.006272    test-rmse:0.240814+0.022689 
## [12] train-rmse:0.138572+0.007286    test-rmse:0.233830+0.025378 
## [13] train-rmse:0.127034+0.007976    test-rmse:0.229657+0.026344 
## [14] train-rmse:0.119527+0.008367    test-rmse:0.225754+0.027009 
## [15] train-rmse:0.113955+0.008749    test-rmse:0.224752+0.028368 
## [16] train-rmse:0.109795+0.008615    test-rmse:0.224787+0.028903 
## [17] train-rmse:0.106658+0.008882    test-rmse:0.224276+0.029542 
## [18] train-rmse:0.103755+0.008693    test-rmse:0.224242+0.029909 
## [19] train-rmse:0.101093+0.009161    test-rmse:0.224537+0.030460 
## [20] train-rmse:0.098944+0.009194    test-rmse:0.225274+0.031259 
## [21] train-rmse:0.097348+0.009775    test-rmse:0.225163+0.031579 
## [22] train-rmse:0.095365+0.010137    test-rmse:0.226174+0.032250 
## [23] train-rmse:0.093055+0.010001    test-rmse:0.226530+0.032874 
## [24] train-rmse:0.090612+0.009951    test-rmse:0.226865+0.033144 
## Stopping. Best iteration:
## [18] train-rmse:0.103755+0.008693    test-rmse:0.224242+0.029909
## 
## 45 
## [1]  train-rmse:1.126056+0.004685    test-rmse:1.128981+0.019955 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.885248+0.003340    test-rmse:0.892079+0.015471 
## [3]  train-rmse:0.698066+0.002365    test-rmse:0.710999+0.012092 
## [4]  train-rmse:0.551883+0.001279    test-rmse:0.572653+0.009599 
## [5]  train-rmse:0.437639+0.000962    test-rmse:0.468928+0.007810 
## [6]  train-rmse:0.348857+0.001584    test-rmse:0.392622+0.009597 
## [7]  train-rmse:0.280509+0.002218    test-rmse:0.339162+0.011443 
## [8]  train-rmse:0.227238+0.002852    test-rmse:0.300922+0.014694 
## [9]  train-rmse:0.186629+0.003634    test-rmse:0.274457+0.018419 
## [10] train-rmse:0.155547+0.004711    test-rmse:0.257888+0.022272 
## [11] train-rmse:0.132134+0.005428    test-rmse:0.248391+0.025100 
## [12] train-rmse:0.114460+0.006360    test-rmse:0.242170+0.027450 
## [13] train-rmse:0.101336+0.007260    test-rmse:0.239506+0.029328 
## [14] train-rmse:0.091964+0.007958    test-rmse:0.237298+0.030538 
## [15] train-rmse:0.085141+0.008499    test-rmse:0.236116+0.031714 
## [16] train-rmse:0.080064+0.008645    test-rmse:0.235088+0.032390 
## [17] train-rmse:0.075774+0.008796    test-rmse:0.234579+0.032709 
## [18] train-rmse:0.072612+0.008996    test-rmse:0.234498+0.033184 
## [19] train-rmse:0.069631+0.008431    test-rmse:0.234008+0.033353 
## [20] train-rmse:0.066940+0.008280    test-rmse:0.234204+0.033639 
## [21] train-rmse:0.064811+0.008528    test-rmse:0.234485+0.034093 
## [22] train-rmse:0.062923+0.008422    test-rmse:0.235003+0.034483 
## [23] train-rmse:0.060668+0.008036    test-rmse:0.235038+0.034526 
## [24] train-rmse:0.058439+0.007962    test-rmse:0.234935+0.034361 
## [25] train-rmse:0.056999+0.007867    test-rmse:0.235500+0.034609 
## Stopping. Best iteration:
## [19] train-rmse:0.069631+0.008431    test-rmse:0.234008+0.033353
## 
## 46 
## [1]  train-rmse:1.125879+0.004670    test-rmse:1.128993+0.019934 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.884853+0.003320    test-rmse:0.892149+0.015398 
## [3]  train-rmse:0.697448+0.002346    test-rmse:0.711229+0.011884 
## [4]  train-rmse:0.550486+0.001034    test-rmse:0.572559+0.009318 
## [5]  train-rmse:0.435467+0.001064    test-rmse:0.469685+0.007425 
## [6]  train-rmse:0.345797+0.001595    test-rmse:0.394132+0.009213 
## [7]  train-rmse:0.275781+0.002040    test-rmse:0.340826+0.012065 
## [8]  train-rmse:0.221081+0.002278    test-rmse:0.303787+0.017165 
## [9]  train-rmse:0.178514+0.002366    test-rmse:0.278828+0.021973 
## [10] train-rmse:0.145536+0.002702    test-rmse:0.262884+0.026743 
## [11] train-rmse:0.120396+0.003332    test-rmse:0.253403+0.030000 
## [12] train-rmse:0.101041+0.004436    test-rmse:0.247136+0.032482 
## [13] train-rmse:0.086126+0.005134    test-rmse:0.244298+0.034702 
## [14] train-rmse:0.075411+0.005615    test-rmse:0.242208+0.036392 
## [15] train-rmse:0.066625+0.005699    test-rmse:0.240930+0.036923 
## [16] train-rmse:0.060331+0.006224    test-rmse:0.241111+0.037701 
## [17] train-rmse:0.055437+0.006750    test-rmse:0.241040+0.038428 
## [18] train-rmse:0.051720+0.006807    test-rmse:0.240848+0.039078 
## [19] train-rmse:0.049065+0.007117    test-rmse:0.241489+0.039759 
## [20] train-rmse:0.046692+0.007148    test-rmse:0.241585+0.039976 
## [21] train-rmse:0.044531+0.007255    test-rmse:0.241577+0.040021 
## [22] train-rmse:0.042676+0.007179    test-rmse:0.241777+0.040083 
## [23] train-rmse:0.040836+0.006770    test-rmse:0.241891+0.040111 
## [24] train-rmse:0.039376+0.006886    test-rmse:0.242030+0.040229 
## Stopping. Best iteration:
## [18] train-rmse:0.051720+0.006807    test-rmse:0.240848+0.039078
## 
## 47 
## [1]  train-rmse:1.125791+0.004708    test-rmse:1.129010+0.019905 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.884630+0.003424    test-rmse:0.892208+0.015338 
## [3]  train-rmse:0.697069+0.002496    test-rmse:0.711117+0.011765 
## [4]  train-rmse:0.549942+0.001346    test-rmse:0.572854+0.009097 
## [5]  train-rmse:0.434381+0.001183    test-rmse:0.468859+0.007113 
## [6]  train-rmse:0.344377+0.001773    test-rmse:0.393135+0.009112 
## [7]  train-rmse:0.273411+0.002138    test-rmse:0.339539+0.011639 
## [8]  train-rmse:0.217974+0.002328    test-rmse:0.303150+0.016335 
## [9]  train-rmse:0.174598+0.002487    test-rmse:0.278285+0.021177 
## [10] train-rmse:0.141104+0.002554    test-rmse:0.262535+0.025719 
## [11] train-rmse:0.115404+0.003076    test-rmse:0.253098+0.028601 
## [12] train-rmse:0.095230+0.003294    test-rmse:0.247313+0.031585 
## [13] train-rmse:0.079674+0.003849    test-rmse:0.244698+0.033941 
## [14] train-rmse:0.067847+0.004251    test-rmse:0.243324+0.035624 
## [15] train-rmse:0.058600+0.004311    test-rmse:0.242347+0.036613 
## [16] train-rmse:0.051677+0.004625    test-rmse:0.241887+0.037219 
## [17] train-rmse:0.046367+0.004779    test-rmse:0.241950+0.037597 
## [18] train-rmse:0.042335+0.004963    test-rmse:0.241764+0.037912 
## [19] train-rmse:0.038861+0.004854    test-rmse:0.242168+0.038388 
## [20] train-rmse:0.035992+0.004527    test-rmse:0.242336+0.038938 
## [21] train-rmse:0.033395+0.004505    test-rmse:0.242388+0.038979 
## [22] train-rmse:0.031579+0.004370    test-rmse:0.242836+0.039270 
## [23] train-rmse:0.029948+0.004308    test-rmse:0.242855+0.039391 
## [24] train-rmse:0.028447+0.004367    test-rmse:0.242746+0.039395 
## Stopping. Best iteration:
## [18] train-rmse:0.042335+0.004963    test-rmse:0.241764+0.037912
## 
## 48 
## [1]  train-rmse:1.125752+0.004755    test-rmse:1.129018+0.019890 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.884526+0.003522    test-rmse:0.892299+0.015185 
## [3]  train-rmse:0.696901+0.002634    test-rmse:0.711223+0.011694 
## [4]  train-rmse:0.549629+0.001209    test-rmse:0.572858+0.009185 
## [5]  train-rmse:0.433690+0.000796    test-rmse:0.468751+0.007008 
## [6]  train-rmse:0.343352+0.001185    test-rmse:0.393023+0.008496 
## [7]  train-rmse:0.271933+0.001349    test-rmse:0.339458+0.011228 
## [8]  train-rmse:0.216655+0.001381    test-rmse:0.302817+0.016160 
## [9]  train-rmse:0.173529+0.001905    test-rmse:0.278665+0.020325 
## [10] train-rmse:0.139601+0.002022    test-rmse:0.263104+0.024958 
## [11] train-rmse:0.113016+0.001927    test-rmse:0.253403+0.028515 
## [12] train-rmse:0.092413+0.002160    test-rmse:0.247733+0.031466 
## [13] train-rmse:0.076236+0.002466    test-rmse:0.245588+0.032846 
## [14] train-rmse:0.063501+0.002701    test-rmse:0.244845+0.033899 
## [15] train-rmse:0.053409+0.002802    test-rmse:0.244765+0.035551 
## [16] train-rmse:0.045414+0.002781    test-rmse:0.244954+0.037038 
## [17] train-rmse:0.039363+0.002844    test-rmse:0.245612+0.037956 
## [18] train-rmse:0.034710+0.002716    test-rmse:0.246116+0.038627 
## [19] train-rmse:0.030817+0.002648    test-rmse:0.246346+0.039411 
## [20] train-rmse:0.027982+0.002553    test-rmse:0.246974+0.039948 
## [21] train-rmse:0.025463+0.002458    test-rmse:0.247416+0.040262 
## Stopping. Best iteration:
## [15] train-rmse:0.053409+0.002802    test-rmse:0.244765+0.035551
## 
## 49 
## [1]  train-rmse:1.103394+0.003767    test-rmse:1.103352+0.021618 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.854147+0.001633    test-rmse:0.854465+0.018376 
## [3]  train-rmse:0.668510+0.001097    test-rmse:0.669385+0.015450 
## [4]  train-rmse:0.531996+0.002560    test-rmse:0.534835+0.012880 
## [5]  train-rmse:0.430672+0.003319    test-rmse:0.436205+0.010684 
## [6]  train-rmse:0.358424+0.004313    test-rmse:0.364392+0.012908 
## [7]  train-rmse:0.306456+0.004830    test-rmse:0.313164+0.014628 
## [8]  train-rmse:0.271206+0.005418    test-rmse:0.277899+0.016862 
## [9]  train-rmse:0.246656+0.005691    test-rmse:0.253708+0.020006 
## [10] train-rmse:0.230718+0.005884    test-rmse:0.237146+0.024328 
## [11] train-rmse:0.219757+0.005900    test-rmse:0.228243+0.023637 
## [12] train-rmse:0.212559+0.005977    test-rmse:0.221426+0.025710 
## [13] train-rmse:0.207266+0.005928    test-rmse:0.218447+0.026179 
## [14] train-rmse:0.203271+0.005714    test-rmse:0.216957+0.025312 
## [15] train-rmse:0.200177+0.005537    test-rmse:0.213945+0.025766 
## [16] train-rmse:0.197601+0.005438    test-rmse:0.211876+0.026639 
## [17] train-rmse:0.195380+0.005340    test-rmse:0.213086+0.027546 
## [18] train-rmse:0.193495+0.005363    test-rmse:0.212693+0.025954 
## [19] train-rmse:0.191869+0.005306    test-rmse:0.213030+0.027117 
## [20] train-rmse:0.190302+0.005323    test-rmse:0.212604+0.027019 
## [21] train-rmse:0.188925+0.005363    test-rmse:0.211841+0.026336 
## [22] train-rmse:0.187635+0.005393    test-rmse:0.211411+0.026175 
## [23] train-rmse:0.186382+0.005460    test-rmse:0.211954+0.027596 
## [24] train-rmse:0.185260+0.005541    test-rmse:0.211310+0.026903 
## [25] train-rmse:0.184190+0.005691    test-rmse:0.212176+0.025722 
## [26] train-rmse:0.183288+0.005761    test-rmse:0.211152+0.025831 
## [27] train-rmse:0.182416+0.005823    test-rmse:0.210762+0.026195 
## [28] train-rmse:0.181510+0.005899    test-rmse:0.211744+0.026876 
## [29] train-rmse:0.180648+0.005980    test-rmse:0.211275+0.026146 
## [30] train-rmse:0.179882+0.006052    test-rmse:0.211876+0.026435 
## [31] train-rmse:0.179129+0.006135    test-rmse:0.211276+0.025943 
## [32] train-rmse:0.178465+0.006208    test-rmse:0.211370+0.026349 
## [33] train-rmse:0.177831+0.006258    test-rmse:0.211443+0.026329 
## Stopping. Best iteration:
## [27] train-rmse:0.182416+0.005823    test-rmse:0.210762+0.026195
## 
## 50 
## [1]  train-rmse:1.099182+0.004556    test-rmse:1.100602+0.020324 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.844994+0.003161    test-rmse:0.848531+0.016474 
## [3]  train-rmse:0.653477+0.002262    test-rmse:0.659892+0.013766 
## [4]  train-rmse:0.510237+0.001727    test-rmse:0.520358+0.012243 
## [5]  train-rmse:0.404248+0.001688    test-rmse:0.419883+0.011503 
## [6]  train-rmse:0.326835+0.001917    test-rmse:0.347218+0.013168 
## [7]  train-rmse:0.271154+0.002067    test-rmse:0.298410+0.015302 
## [8]  train-rmse:0.232075+0.002132    test-rmse:0.264873+0.018418 
## [9]  train-rmse:0.205344+0.002233    test-rmse:0.244857+0.020999 
## [10] train-rmse:0.185400+0.002275    test-rmse:0.230574+0.023388 
## [11] train-rmse:0.172316+0.003072    test-rmse:0.220876+0.023763 
## [12] train-rmse:0.164470+0.003302    test-rmse:0.215731+0.025543 
## [13] train-rmse:0.158144+0.004175    test-rmse:0.212683+0.025387 
## [14] train-rmse:0.154505+0.004636    test-rmse:0.211152+0.025995 
## [15] train-rmse:0.150838+0.004412    test-rmse:0.210375+0.027083 
## [16] train-rmse:0.148143+0.004773    test-rmse:0.209794+0.027687 
## [17] train-rmse:0.146414+0.004323    test-rmse:0.211086+0.026891 
## [18] train-rmse:0.143761+0.005749    test-rmse:0.210711+0.027050 
## [19] train-rmse:0.141736+0.006119    test-rmse:0.210221+0.027173 
## [20] train-rmse:0.140456+0.005982    test-rmse:0.210291+0.027225 
## [21] train-rmse:0.138103+0.005428    test-rmse:0.211277+0.026834 
## [22] train-rmse:0.135268+0.005521    test-rmse:0.210458+0.026506 
## Stopping. Best iteration:
## [16] train-rmse:0.148143+0.004773    test-rmse:0.209794+0.027687
## 
## 51 
## [1]  train-rmse:1.098313+0.004505    test-rmse:1.101260+0.019511 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.842984+0.003020    test-rmse:0.849642+0.014730 
## [3]  train-rmse:0.649909+0.001946    test-rmse:0.663269+0.011357 
## [4]  train-rmse:0.504402+0.001465    test-rmse:0.525737+0.008660 
## [5]  train-rmse:0.395036+0.002222    test-rmse:0.423970+0.009214 
## [6]  train-rmse:0.314054+0.003418    test-rmse:0.355737+0.009774 
## [7]  train-rmse:0.253244+0.005104    test-rmse:0.306957+0.012412 
## [8]  train-rmse:0.208282+0.005555    test-rmse:0.275038+0.015232 
## [9]  train-rmse:0.176348+0.005945    test-rmse:0.254192+0.019434 
## [10] train-rmse:0.153350+0.006842    test-rmse:0.241809+0.021999 
## [11] train-rmse:0.137339+0.007848    test-rmse:0.235310+0.024624 
## [12] train-rmse:0.125812+0.008805    test-rmse:0.229807+0.025525 
## [13] train-rmse:0.118597+0.009186    test-rmse:0.227757+0.027141 
## [14] train-rmse:0.112726+0.009536    test-rmse:0.226362+0.026997 
## [15] train-rmse:0.107794+0.010562    test-rmse:0.225262+0.027243 
## [16] train-rmse:0.104000+0.011312    test-rmse:0.225097+0.027576 
## [17] train-rmse:0.100804+0.011288    test-rmse:0.225077+0.028227 
## [18] train-rmse:0.098718+0.011279    test-rmse:0.225850+0.028888 
## [19] train-rmse:0.096186+0.011390    test-rmse:0.226758+0.029192 
## [20] train-rmse:0.094641+0.011379    test-rmse:0.227689+0.029829 
## [21] train-rmse:0.092122+0.011390    test-rmse:0.226952+0.029580 
## [22] train-rmse:0.090524+0.011039    test-rmse:0.227167+0.029959 
## [23] train-rmse:0.088308+0.011637    test-rmse:0.227996+0.030124 
## Stopping. Best iteration:
## [17] train-rmse:0.100804+0.011288    test-rmse:0.225077+0.028227
## 
## 52 
## [1]  train-rmse:1.098005+0.004531    test-rmse:1.101298+0.019457 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.842177+0.003090    test-rmse:0.850011+0.014646 
## [3]  train-rmse:0.648609+0.002138    test-rmse:0.663499+0.011062 
## [4]  train-rmse:0.501122+0.001475    test-rmse:0.526071+0.008654 
## [5]  train-rmse:0.389149+0.001770    test-rmse:0.426564+0.007554 
## [6]  train-rmse:0.304443+0.002232    test-rmse:0.357211+0.010249 
## [7]  train-rmse:0.240751+0.003350    test-rmse:0.309874+0.013289 
## [8]  train-rmse:0.192942+0.004130    test-rmse:0.278715+0.018450 
## [9]  train-rmse:0.157469+0.004609    test-rmse:0.258239+0.022148 
## [10] train-rmse:0.131586+0.005158    test-rmse:0.246333+0.025826 
## [11] train-rmse:0.112898+0.006373    test-rmse:0.239305+0.028673 
## [12] train-rmse:0.099216+0.006968    test-rmse:0.235049+0.030302 
## [13] train-rmse:0.089952+0.007926    test-rmse:0.232893+0.031139 
## [14] train-rmse:0.082975+0.008325    test-rmse:0.231333+0.031778 
## [15] train-rmse:0.077631+0.008091    test-rmse:0.230341+0.032169 
## [16] train-rmse:0.073423+0.008174    test-rmse:0.230143+0.032544 
## [17] train-rmse:0.070463+0.008219    test-rmse:0.230800+0.033459 
## [18] train-rmse:0.067510+0.007788    test-rmse:0.230174+0.033454 
## [19] train-rmse:0.064773+0.008273    test-rmse:0.230795+0.033813 
## [20] train-rmse:0.062062+0.007902    test-rmse:0.231086+0.033984 
## [21] train-rmse:0.060340+0.007767    test-rmse:0.231308+0.034231 
## [22] train-rmse:0.058469+0.007481    test-rmse:0.231907+0.034649 
## Stopping. Best iteration:
## [16] train-rmse:0.073423+0.008174    test-rmse:0.230143+0.032544
## 
## 53 
## [1]  train-rmse:1.097807+0.004513    test-rmse:1.101315+0.019433 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.841745+0.003067    test-rmse:0.850070+0.014559 
## [3]  train-rmse:0.647943+0.002138    test-rmse:0.663508+0.010845 
## [4]  train-rmse:0.498962+0.001004    test-rmse:0.526350+0.008150 
## [5]  train-rmse:0.385744+0.001322    test-rmse:0.427634+0.008404 
## [6]  train-rmse:0.300151+0.001935    test-rmse:0.358585+0.011673 
## [7]  train-rmse:0.234746+0.002478    test-rmse:0.312110+0.015763 
## [8]  train-rmse:0.184835+0.002521    test-rmse:0.281731+0.021070 
## [9]  train-rmse:0.147316+0.002639    test-rmse:0.263151+0.026142 
## [10] train-rmse:0.119104+0.002701    test-rmse:0.251649+0.029965 
## [11] train-rmse:0.098084+0.003423    test-rmse:0.246139+0.033174 
## [12] train-rmse:0.082727+0.003532    test-rmse:0.241978+0.034891 
## [13] train-rmse:0.071557+0.004016    test-rmse:0.239692+0.036350 
## [14] train-rmse:0.063558+0.004676    test-rmse:0.238639+0.036900 
## [15] train-rmse:0.057599+0.005025    test-rmse:0.237982+0.037680 
## [16] train-rmse:0.053091+0.005334    test-rmse:0.237745+0.038214 
## [17] train-rmse:0.049606+0.005255    test-rmse:0.237443+0.038388 
## [18] train-rmse:0.046731+0.005078    test-rmse:0.237964+0.038917 
## [19] train-rmse:0.044198+0.005627    test-rmse:0.238170+0.039233 
## [20] train-rmse:0.042080+0.006052    test-rmse:0.238670+0.039357 
## [21] train-rmse:0.040357+0.006190    test-rmse:0.238715+0.039789 
## [22] train-rmse:0.038813+0.006355    test-rmse:0.239571+0.040260 
## [23] train-rmse:0.037256+0.006221    test-rmse:0.239558+0.040123 
## Stopping. Best iteration:
## [17] train-rmse:0.049606+0.005255    test-rmse:0.237443+0.038388
## 
## 54 
## [1]  train-rmse:1.097710+0.004556    test-rmse:1.101335+0.019400 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.841497+0.003176    test-rmse:0.850205+0.014394 
## [3]  train-rmse:0.647516+0.002293    test-rmse:0.663750+0.010624 
## [4]  train-rmse:0.498545+0.001049    test-rmse:0.525817+0.007930 
## [5]  train-rmse:0.384479+0.000641    test-rmse:0.426174+0.007957 
## [6]  train-rmse:0.298059+0.000781    test-rmse:0.357170+0.011551 
## [7]  train-rmse:0.232195+0.001438    test-rmse:0.311216+0.014812 
## [8]  train-rmse:0.181860+0.001514    test-rmse:0.281029+0.020105 
## [9]  train-rmse:0.143587+0.001894    test-rmse:0.262474+0.024792 
## [10] train-rmse:0.114570+0.002030    test-rmse:0.251697+0.028987 
## [11] train-rmse:0.092860+0.002482    test-rmse:0.246262+0.032157 
## [12] train-rmse:0.076355+0.002723    test-rmse:0.243560+0.035312 
## [13] train-rmse:0.063816+0.002768    test-rmse:0.241769+0.036639 
## [14] train-rmse:0.054686+0.003171    test-rmse:0.241149+0.037757 
## [15] train-rmse:0.047980+0.003587    test-rmse:0.241478+0.038587 
## [16] train-rmse:0.042918+0.003770    test-rmse:0.241202+0.038651 
## [17] train-rmse:0.038490+0.003449    test-rmse:0.241749+0.039350 
## [18] train-rmse:0.035207+0.003567    test-rmse:0.241852+0.039471 
## [19] train-rmse:0.032887+0.003700    test-rmse:0.241941+0.039659 
## [20] train-rmse:0.030957+0.003575    test-rmse:0.242121+0.039844 
## Stopping. Best iteration:
## [14] train-rmse:0.054686+0.003171    test-rmse:0.241149+0.037757
## 
## 55 
## [1]  train-rmse:1.097666+0.004607    test-rmse:1.101345+0.019383 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.841381+0.003288    test-rmse:0.850248+0.014318 
## [3]  train-rmse:0.647323+0.002434    test-rmse:0.663856+0.010570 
## [4]  train-rmse:0.498173+0.001091    test-rmse:0.525672+0.008068 
## [5]  train-rmse:0.383780+0.000654    test-rmse:0.425976+0.007879 
## [6]  train-rmse:0.297030+0.000409    test-rmse:0.356996+0.010930 
## [7]  train-rmse:0.230607+0.000861    test-rmse:0.311106+0.014703 
## [8]  train-rmse:0.180254+0.000808    test-rmse:0.281541+0.019188 
## [9]  train-rmse:0.142077+0.001282    test-rmse:0.263414+0.023947 
## [10] train-rmse:0.112594+0.001260    test-rmse:0.252642+0.027871 
## [11] train-rmse:0.090237+0.001511    test-rmse:0.246918+0.030924 
## [12] train-rmse:0.073379+0.001553    test-rmse:0.244423+0.033500 
## [13] train-rmse:0.060461+0.001658    test-rmse:0.243612+0.035440 
## [14] train-rmse:0.050440+0.001858    test-rmse:0.243524+0.036845 
## [15] train-rmse:0.043016+0.002215    test-rmse:0.244073+0.038014 
## [16] train-rmse:0.037273+0.002278    test-rmse:0.244426+0.039130 
## [17] train-rmse:0.032726+0.002295    test-rmse:0.245356+0.039816 
## [18] train-rmse:0.029303+0.002400    test-rmse:0.246185+0.040490 
## [19] train-rmse:0.026264+0.002395    test-rmse:0.246871+0.040889 
## [20] train-rmse:0.024297+0.002449    test-rmse:0.247349+0.041148 
## Stopping. Best iteration:
## [14] train-rmse:0.050440+0.001858    test-rmse:0.243524+0.036845
## 
## 56 
## [1]  train-rmse:1.075910+0.003541    test-rmse:1.075886+0.021288 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.813630+0.001291    test-rmse:0.814012+0.017773 
## [3]  train-rmse:0.624247+0.001520    test-rmse:0.625259+0.014681 
## [4]  train-rmse:0.488892+0.002767    test-rmse:0.491822+0.011021 
## [5]  train-rmse:0.392413+0.003788    test-rmse:0.396183+0.013942 
## [6]  train-rmse:0.326174+0.004535    test-rmse:0.332369+0.013588 
## [7]  train-rmse:0.280884+0.005230    test-rmse:0.285530+0.018159 
## [8]  train-rmse:0.251652+0.005530    test-rmse:0.258403+0.018696 
## [9]  train-rmse:0.232243+0.005864    test-rmse:0.239927+0.022090 
## [10] train-rmse:0.220349+0.005876    test-rmse:0.228208+0.022496 
## [11] train-rmse:0.212214+0.005915    test-rmse:0.221138+0.024693 
## [12] train-rmse:0.206527+0.005799    test-rmse:0.217873+0.025190 
## [13] train-rmse:0.202493+0.005774    test-rmse:0.214183+0.026665 
## [14] train-rmse:0.199296+0.005607    test-rmse:0.213453+0.028074 
## [15] train-rmse:0.196628+0.005450    test-rmse:0.213544+0.026843 
## [16] train-rmse:0.194411+0.005299    test-rmse:0.212320+0.026378 
## [17] train-rmse:0.192454+0.005285    test-rmse:0.211076+0.027317 
## [18] train-rmse:0.190712+0.005311    test-rmse:0.211756+0.027687 
## [19] train-rmse:0.189181+0.005403    test-rmse:0.212569+0.028377 
## [20] train-rmse:0.187755+0.005490    test-rmse:0.211677+0.027762 
## [21] train-rmse:0.186461+0.005524    test-rmse:0.211800+0.026664 
## [22] train-rmse:0.185182+0.005562    test-rmse:0.211201+0.026134 
## [23] train-rmse:0.184084+0.005630    test-rmse:0.211955+0.026437 
## Stopping. Best iteration:
## [17] train-rmse:0.192454+0.005285    test-rmse:0.211076+0.027317
## 
## 57 
## [1]  train-rmse:1.071273+0.004407    test-rmse:1.072868+0.019909 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.803429+0.002934    test-rmse:0.807583+0.015840 
## [3]  train-rmse:0.607357+0.002075    test-rmse:0.614751+0.013160 
## [4]  train-rmse:0.465325+0.001670    test-rmse:0.479101+0.011742 
## [5]  train-rmse:0.363636+0.001712    test-rmse:0.382867+0.011463 
## [6]  train-rmse:0.292177+0.002047    test-rmse:0.317506+0.013972 
## [7]  train-rmse:0.243160+0.002186    test-rmse:0.275870+0.017025 
## [8]  train-rmse:0.210573+0.002204    test-rmse:0.248980+0.020460 
## [9]  train-rmse:0.188165+0.001380    test-rmse:0.233067+0.023128 
## [10] train-rmse:0.174790+0.001375    test-rmse:0.223263+0.025475 
## [11] train-rmse:0.164968+0.002403    test-rmse:0.218405+0.026157 
## [12] train-rmse:0.156817+0.003678    test-rmse:0.214066+0.025852 
## [13] train-rmse:0.152039+0.003870    test-rmse:0.212127+0.026081 
## [14] train-rmse:0.148962+0.003916    test-rmse:0.211553+0.027925 
## [15] train-rmse:0.145764+0.004727    test-rmse:0.211210+0.026020 
## [16] train-rmse:0.143745+0.003951    test-rmse:0.211448+0.026994 
## [17] train-rmse:0.141138+0.003991    test-rmse:0.210811+0.026837 
## [18] train-rmse:0.139726+0.004234    test-rmse:0.210959+0.026595 
## [19] train-rmse:0.137990+0.003435    test-rmse:0.210669+0.026890 
## [20] train-rmse:0.135782+0.003638    test-rmse:0.211345+0.026453 
## [21] train-rmse:0.134564+0.004017    test-rmse:0.211970+0.027122 
## [22] train-rmse:0.131685+0.003885    test-rmse:0.212719+0.026997 
## [23] train-rmse:0.129483+0.004142    test-rmse:0.213277+0.027185 
## [24] train-rmse:0.128522+0.004160    test-rmse:0.213412+0.027154 
## [25] train-rmse:0.126526+0.005055    test-rmse:0.214767+0.027544 
## Stopping. Best iteration:
## [19] train-rmse:0.137990+0.003435    test-rmse:0.210669+0.026890
## 
## 58 
## [1]  train-rmse:1.070310+0.004347    test-rmse:1.073598+0.019016 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.801237+0.002731    test-rmse:0.809365+0.014053 
## [3]  train-rmse:0.603250+0.001774    test-rmse:0.618619+0.010372 
## [4]  train-rmse:0.457823+0.001798    test-rmse:0.482345+0.009374 
## [5]  train-rmse:0.352624+0.003073    test-rmse:0.385984+0.010503 
## [6]  train-rmse:0.276043+0.005069    test-rmse:0.324114+0.012688 
## [7]  train-rmse:0.220614+0.005699    test-rmse:0.282524+0.016887 
## [8]  train-rmse:0.182350+0.006200    test-rmse:0.255781+0.020040 
## [9]  train-rmse:0.155596+0.007156    test-rmse:0.242225+0.024149 
## [10] train-rmse:0.137752+0.008085    test-rmse:0.234061+0.026604 
## [11] train-rmse:0.125750+0.008721    test-rmse:0.229488+0.028305 
## [12] train-rmse:0.116793+0.008441    test-rmse:0.226389+0.028835 
## [13] train-rmse:0.110613+0.008936    test-rmse:0.225200+0.030024 
## [14] train-rmse:0.105362+0.008264    test-rmse:0.223692+0.030159 
## [15] train-rmse:0.102714+0.008153    test-rmse:0.223245+0.031062 
## [16] train-rmse:0.100373+0.008477    test-rmse:0.223903+0.031809 
## [17] train-rmse:0.097181+0.009171    test-rmse:0.224301+0.031954 
## [18] train-rmse:0.094664+0.009144    test-rmse:0.224671+0.032433 
## [19] train-rmse:0.092032+0.009798    test-rmse:0.224898+0.032784 
## [20] train-rmse:0.090326+0.010424    test-rmse:0.225926+0.033102 
## [21] train-rmse:0.088086+0.010316    test-rmse:0.226074+0.033285 
## Stopping. Best iteration:
## [15] train-rmse:0.102714+0.008153    test-rmse:0.223245+0.031062
## 
## 59 
## [1]  train-rmse:1.069969+0.004376    test-rmse:1.073647+0.018956 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.800285+0.002847    test-rmse:0.809171+0.013824 
## [3]  train-rmse:0.601882+0.001984    test-rmse:0.618950+0.010129 
## [4]  train-rmse:0.454152+0.001324    test-rmse:0.482602+0.009224 
## [5]  train-rmse:0.345143+0.002008    test-rmse:0.388173+0.010125 
## [6]  train-rmse:0.265687+0.002846    test-rmse:0.326007+0.013210 
## [7]  train-rmse:0.207173+0.003918    test-rmse:0.285293+0.017285 
## [8]  train-rmse:0.165442+0.005005    test-rmse:0.260539+0.020914 
## [9]  train-rmse:0.135552+0.005654    test-rmse:0.247005+0.024657 
## [10] train-rmse:0.113979+0.006352    test-rmse:0.238629+0.028114 
## [11] train-rmse:0.099682+0.006998    test-rmse:0.234299+0.029678 
## [12] train-rmse:0.089233+0.006948    test-rmse:0.232066+0.030700 
## [13] train-rmse:0.082026+0.006881    test-rmse:0.230970+0.031065 
## [14] train-rmse:0.076935+0.007017    test-rmse:0.231434+0.032279 
## [15] train-rmse:0.072238+0.006890    test-rmse:0.231246+0.032581 
## [16] train-rmse:0.069503+0.006929    test-rmse:0.231298+0.033051 
## [17] train-rmse:0.066846+0.007170    test-rmse:0.232172+0.033540 
## [18] train-rmse:0.064205+0.007125    test-rmse:0.232127+0.033680 
## [19] train-rmse:0.061681+0.006671    test-rmse:0.232598+0.033717 
## Stopping. Best iteration:
## [13] train-rmse:0.082026+0.006881    test-rmse:0.230970+0.031065
## 
## 60 
## [1]  train-rmse:1.069750+0.004355    test-rmse:1.073670+0.018928 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.799805+0.002819    test-rmse:0.809243+0.013729 
## [3]  train-rmse:0.601071+0.001984    test-rmse:0.618922+0.009876 
## [4]  train-rmse:0.452006+0.000846    test-rmse:0.483528+0.008283 
## [5]  train-rmse:0.341691+0.001832    test-rmse:0.389841+0.009547 
## [6]  train-rmse:0.260212+0.002242    test-rmse:0.327931+0.013747 
## [7]  train-rmse:0.200058+0.002516    test-rmse:0.288610+0.019173 
## [8]  train-rmse:0.155736+0.003146    test-rmse:0.265262+0.024300 
## [9]  train-rmse:0.123330+0.003775    test-rmse:0.251506+0.028961 
## [10] train-rmse:0.100310+0.005120    test-rmse:0.244665+0.032232 
## [11] train-rmse:0.084248+0.005872    test-rmse:0.240762+0.034233 
## [12] train-rmse:0.072659+0.005855    test-rmse:0.238743+0.035932 
## [13] train-rmse:0.064331+0.005926    test-rmse:0.237813+0.036933 
## [14] train-rmse:0.058635+0.005875    test-rmse:0.236810+0.037437 
## [15] train-rmse:0.054043+0.006008    test-rmse:0.236463+0.037681 
## [16] train-rmse:0.050150+0.006498    test-rmse:0.236105+0.037798 
## [17] train-rmse:0.047743+0.006335    test-rmse:0.236522+0.038198 
## [18] train-rmse:0.045419+0.006816    test-rmse:0.237092+0.038396 
## [19] train-rmse:0.043534+0.007283    test-rmse:0.237608+0.038608 
## [20] train-rmse:0.041889+0.007110    test-rmse:0.237800+0.038752 
## [21] train-rmse:0.039996+0.006790    test-rmse:0.238273+0.039009 
## [22] train-rmse:0.037812+0.006958    test-rmse:0.239114+0.039438 
## Stopping. Best iteration:
## [16] train-rmse:0.050150+0.006498    test-rmse:0.236105+0.037798
## 
## 61 
## [1]  train-rmse:1.069642+0.004403    test-rmse:1.073695+0.018891 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.799532+0.002934    test-rmse:0.809401+0.013547 
## [3]  train-rmse:0.600597+0.002133    test-rmse:0.619212+0.009646 
## [4]  train-rmse:0.451180+0.000930    test-rmse:0.482590+0.008424 
## [5]  train-rmse:0.340054+0.001776    test-rmse:0.388481+0.009376 
## [6]  train-rmse:0.257968+0.002073    test-rmse:0.326277+0.013036 
## [7]  train-rmse:0.197544+0.002709    test-rmse:0.287652+0.017850 
## [8]  train-rmse:0.151883+0.002702    test-rmse:0.263793+0.022957 
## [9]  train-rmse:0.118858+0.002719    test-rmse:0.250601+0.027834 
## [10] train-rmse:0.094370+0.002728    test-rmse:0.243276+0.031280 
## [11] train-rmse:0.076712+0.003334    test-rmse:0.240334+0.034279 
## [12] train-rmse:0.064022+0.003730    test-rmse:0.238468+0.035772 
## [13] train-rmse:0.054716+0.003982    test-rmse:0.238186+0.036988 
## [14] train-rmse:0.048211+0.004123    test-rmse:0.237917+0.037688 
## [15] train-rmse:0.043076+0.004588    test-rmse:0.238349+0.038344 
## [16] train-rmse:0.038571+0.004404    test-rmse:0.238370+0.038358 
## [17] train-rmse:0.035556+0.004636    test-rmse:0.238562+0.038648 
## [18] train-rmse:0.032779+0.004416    test-rmse:0.238447+0.038679 
## [19] train-rmse:0.030246+0.004488    test-rmse:0.238427+0.038835 
## [20] train-rmse:0.028132+0.004365    test-rmse:0.238660+0.039092 
## Stopping. Best iteration:
## [14] train-rmse:0.048211+0.004123    test-rmse:0.237917+0.037688
## 
## 62 
## [1]  train-rmse:1.069594+0.004459    test-rmse:1.073706+0.018871 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.799401+0.003057    test-rmse:0.809452+0.013459 
## [3]  train-rmse:0.600366+0.002277    test-rmse:0.619342+0.009608 
## [4]  train-rmse:0.450677+0.001229    test-rmse:0.482603+0.008308 
## [5]  train-rmse:0.339294+0.000965    test-rmse:0.388903+0.009233 
## [6]  train-rmse:0.256858+0.000759    test-rmse:0.326916+0.013132 
## [7]  train-rmse:0.195506+0.001366    test-rmse:0.287922+0.017964 
## [8]  train-rmse:0.149930+0.001293    test-rmse:0.264539+0.023259 
## [9]  train-rmse:0.116429+0.001271    test-rmse:0.251919+0.028156 
## [10] train-rmse:0.091302+0.001557    test-rmse:0.245199+0.032050 
## [11] train-rmse:0.073170+0.001327    test-rmse:0.242693+0.033829 
## [12] train-rmse:0.059476+0.001119    test-rmse:0.241319+0.036022 
## [13] train-rmse:0.049320+0.001194    test-rmse:0.241588+0.037837 
## [14] train-rmse:0.041835+0.001872    test-rmse:0.241962+0.038321 
## [15] train-rmse:0.036294+0.002411    test-rmse:0.242527+0.039127 
## [16] train-rmse:0.032082+0.002601    test-rmse:0.243233+0.039842 
## [17] train-rmse:0.028700+0.002375    test-rmse:0.243427+0.040077 
## [18] train-rmse:0.025325+0.002009    test-rmse:0.243842+0.040538 
## Stopping. Best iteration:
## [12] train-rmse:0.059476+0.001119    test-rmse:0.241319+0.036022
## 
## 63 
## [1]  train-rmse:1.048463+0.003312    test-rmse:1.048459+0.020951 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.774373+0.001006    test-rmse:0.774825+0.017165 
## [3]  train-rmse:0.582799+0.002022    test-rmse:0.583958+0.013986 
## [4]  train-rmse:0.449744+0.003108    test-rmse:0.453309+0.009627 
## [5]  train-rmse:0.359094+0.004285    test-rmse:0.363168+0.014653 
## [6]  train-rmse:0.299237+0.004881    test-rmse:0.306054+0.014722 
## [7]  train-rmse:0.260734+0.005567    test-rmse:0.265597+0.020109 
## [8]  train-rmse:0.236828+0.005760    test-rmse:0.244027+0.020367 
## [9]  train-rmse:0.221931+0.005951    test-rmse:0.228585+0.024883 
## [10] train-rmse:0.212929+0.005946    test-rmse:0.222493+0.023942 
## [11] train-rmse:0.206779+0.005902    test-rmse:0.217099+0.023760 
## [12] train-rmse:0.202272+0.005586    test-rmse:0.214258+0.025864 
## [13] train-rmse:0.198742+0.005487    test-rmse:0.213599+0.026568 
## [14] train-rmse:0.196082+0.005462    test-rmse:0.211714+0.026614 
## [15] train-rmse:0.193718+0.005372    test-rmse:0.211806+0.027686 
## [16] train-rmse:0.191787+0.005324    test-rmse:0.212366+0.026256 
## [17] train-rmse:0.190039+0.005313    test-rmse:0.212044+0.026082 
## [18] train-rmse:0.188421+0.005309    test-rmse:0.210422+0.026385 
## [19] train-rmse:0.186882+0.005414    test-rmse:0.211434+0.026770 
## [20] train-rmse:0.185508+0.005550    test-rmse:0.211875+0.026750 
## [21] train-rmse:0.184227+0.005701    test-rmse:0.213346+0.025728 
## [22] train-rmse:0.183124+0.005770    test-rmse:0.211665+0.026160 
## [23] train-rmse:0.182034+0.005833    test-rmse:0.211087+0.025705 
## [24] train-rmse:0.181032+0.005948    test-rmse:0.210607+0.025483 
## Stopping. Best iteration:
## [18] train-rmse:0.188421+0.005309    test-rmse:0.210422+0.026385
## 
## 64 
## [1]  train-rmse:1.043383+0.004258    test-rmse:1.045165+0.019494 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.763058+0.002722    test-rmse:0.767819+0.015258 
## [3]  train-rmse:0.563913+0.001928    test-rmse:0.572368+0.012634 
## [4]  train-rmse:0.424275+0.001654    test-rmse:0.439411+0.011433 
## [5]  train-rmse:0.328096+0.002038    test-rmse:0.348743+0.013074 
## [6]  train-rmse:0.263169+0.002131    test-rmse:0.292250+0.015705 
## [7]  train-rmse:0.220957+0.002248    test-rmse:0.256406+0.019444 
## [8]  train-rmse:0.193546+0.000881    test-rmse:0.235837+0.023604 
## [9]  train-rmse:0.176124+0.002369    test-rmse:0.222604+0.025219 
## [10] train-rmse:0.164848+0.003617    test-rmse:0.215244+0.026096 
## [11] train-rmse:0.157242+0.003618    test-rmse:0.212602+0.026873 
## [12] train-rmse:0.152520+0.004320    test-rmse:0.212042+0.027399 
## [13] train-rmse:0.149252+0.003596    test-rmse:0.211784+0.028477 
## [14] train-rmse:0.146487+0.003975    test-rmse:0.211110+0.028915 
## [15] train-rmse:0.143590+0.003683    test-rmse:0.211196+0.028759 
## [16] train-rmse:0.141172+0.003346    test-rmse:0.210800+0.028821 
## [17] train-rmse:0.138578+0.002891    test-rmse:0.210368+0.028770 
## [18] train-rmse:0.136782+0.002841    test-rmse:0.210628+0.029268 
## [19] train-rmse:0.134948+0.003037    test-rmse:0.210634+0.029647 
## [20] train-rmse:0.133759+0.003108    test-rmse:0.210507+0.029757 
## [21] train-rmse:0.131204+0.003162    test-rmse:0.210974+0.029129 
## [22] train-rmse:0.130061+0.003233    test-rmse:0.212114+0.027693 
## [23] train-rmse:0.127676+0.004776    test-rmse:0.212769+0.027405 
## Stopping. Best iteration:
## [17] train-rmse:0.138578+0.002891    test-rmse:0.210368+0.028770
## 
## 65 
## [1]  train-rmse:1.042322+0.004187    test-rmse:1.045972+0.018517 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.760448+0.002674    test-rmse:0.768975+0.013101 
## [3]  train-rmse:0.559068+0.001713    test-rmse:0.575867+0.009572 
## [4]  train-rmse:0.416266+0.001916    test-rmse:0.443754+0.008252 
## [5]  train-rmse:0.315938+0.003081    test-rmse:0.353613+0.011190 
## [6]  train-rmse:0.245358+0.005185    test-rmse:0.298345+0.013815 
## [7]  train-rmse:0.197238+0.005405    test-rmse:0.266422+0.017246 
## [8]  train-rmse:0.163749+0.006938    test-rmse:0.245870+0.021043 
## [9]  train-rmse:0.141848+0.007836    test-rmse:0.233538+0.023736 
## [10] train-rmse:0.128276+0.007665    test-rmse:0.227905+0.023737 
## [11] train-rmse:0.120021+0.008100    test-rmse:0.225780+0.025895 
## [12] train-rmse:0.113869+0.009014    test-rmse:0.225520+0.027116 
## [13] train-rmse:0.108947+0.009734    test-rmse:0.226571+0.028948 
## [14] train-rmse:0.105978+0.009469    test-rmse:0.225810+0.029225 
## [15] train-rmse:0.102722+0.009381    test-rmse:0.226319+0.029838 
## [16] train-rmse:0.099942+0.009692    test-rmse:0.226560+0.029569 
## [17] train-rmse:0.097107+0.008869    test-rmse:0.227495+0.030439 
## [18] train-rmse:0.094383+0.008857    test-rmse:0.227873+0.031219 
## Stopping. Best iteration:
## [12] train-rmse:0.113869+0.009014    test-rmse:0.225520+0.027116
## 
## 66 
## [1]  train-rmse:1.041947+0.004220    test-rmse:1.046033+0.018451 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.759570+0.002612    test-rmse:0.769586+0.013014 
## [3]  train-rmse:0.556372+0.001462    test-rmse:0.576481+0.009686 
## [4]  train-rmse:0.410497+0.001310    test-rmse:0.445175+0.007369 
## [5]  train-rmse:0.305931+0.002226    test-rmse:0.357606+0.009905 
## [6]  train-rmse:0.231715+0.003101    test-rmse:0.303255+0.014962 
## [7]  train-rmse:0.179012+0.003763    test-rmse:0.269811+0.020389 
## [8]  train-rmse:0.142647+0.004894    test-rmse:0.252198+0.024792 
## [9]  train-rmse:0.117685+0.005404    test-rmse:0.242457+0.028841 
## [10] train-rmse:0.101369+0.006454    test-rmse:0.236240+0.030639 
## [11] train-rmse:0.089851+0.006937    test-rmse:0.234649+0.031552 
## [12] train-rmse:0.082131+0.006572    test-rmse:0.233941+0.033104 
## [13] train-rmse:0.076567+0.006853    test-rmse:0.233157+0.033304 
## [14] train-rmse:0.072635+0.006939    test-rmse:0.232840+0.033728 
## [15] train-rmse:0.069102+0.006464    test-rmse:0.233540+0.034187 
## [16] train-rmse:0.066666+0.006908    test-rmse:0.234180+0.034445 
## [17] train-rmse:0.064122+0.007434    test-rmse:0.233361+0.034541 
## [18] train-rmse:0.062107+0.007874    test-rmse:0.234099+0.034673 
## [19] train-rmse:0.059651+0.007223    test-rmse:0.234762+0.034762 
## [20] train-rmse:0.057264+0.006952    test-rmse:0.235333+0.035394 
## Stopping. Best iteration:
## [14] train-rmse:0.072635+0.006939    test-rmse:0.232840+0.033728
## 
## 67 
## [1]  train-rmse:1.041706+0.004196    test-rmse:1.046062+0.018419 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.759040+0.002577    test-rmse:0.769681+0.012909 
## [3]  train-rmse:0.555400+0.001312    test-rmse:0.576566+0.009632 
## [4]  train-rmse:0.408058+0.001573    test-rmse:0.445761+0.007752 
## [5]  train-rmse:0.302417+0.002045    test-rmse:0.359526+0.011201 
## [6]  train-rmse:0.225622+0.001897    test-rmse:0.305670+0.017633 
## [7]  train-rmse:0.170728+0.002217    test-rmse:0.273824+0.023917 
## [8]  train-rmse:0.131886+0.002648    test-rmse:0.256869+0.029742 
## [9]  train-rmse:0.104347+0.003487    test-rmse:0.247657+0.033591 
## [10] train-rmse:0.085117+0.004287    test-rmse:0.243070+0.036826 
## [11] train-rmse:0.072268+0.004602    test-rmse:0.240940+0.037908 
## [12] train-rmse:0.063279+0.005114    test-rmse:0.239793+0.038979 
## [13] train-rmse:0.056910+0.005270    test-rmse:0.239670+0.039577 
## [14] train-rmse:0.052190+0.005301    test-rmse:0.239768+0.040359 
## [15] train-rmse:0.048081+0.005097    test-rmse:0.240409+0.040714 
## [16] train-rmse:0.045699+0.005163    test-rmse:0.240873+0.041098 
## [17] train-rmse:0.043326+0.005186    test-rmse:0.241462+0.041558 
## [18] train-rmse:0.041179+0.004997    test-rmse:0.241668+0.041973 
## [19] train-rmse:0.039197+0.005450    test-rmse:0.242693+0.042179 
## Stopping. Best iteration:
## [13] train-rmse:0.056910+0.005270    test-rmse:0.239670+0.039577
## 
## 68 
## [1]  train-rmse:1.041588+0.004248    test-rmse:1.046092+0.018378 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.758740+0.002697    test-rmse:0.769865+0.012709 
## [3]  train-rmse:0.554726+0.001643    test-rmse:0.577100+0.008902 
## [4]  train-rmse:0.408101+0.001080    test-rmse:0.445551+0.007208 
## [5]  train-rmse:0.300850+0.001942    test-rmse:0.358341+0.010506 
## [6]  train-rmse:0.223590+0.002218    test-rmse:0.304659+0.015622 
## [7]  train-rmse:0.167986+0.002989    test-rmse:0.272935+0.021701 
## [8]  train-rmse:0.128299+0.003644    test-rmse:0.255361+0.026982 
## [9]  train-rmse:0.099958+0.003583    test-rmse:0.246506+0.030969 
## [10] train-rmse:0.079817+0.004467    test-rmse:0.242926+0.033911 
## [11] train-rmse:0.065333+0.005018    test-rmse:0.241112+0.035242 
## [12] train-rmse:0.054957+0.004802    test-rmse:0.240651+0.036248 
## [13] train-rmse:0.047645+0.005282    test-rmse:0.241237+0.037410 
## [14] train-rmse:0.042433+0.005538    test-rmse:0.241156+0.037824 
## [15] train-rmse:0.038775+0.005747    test-rmse:0.241208+0.038008 
## [16] train-rmse:0.035580+0.005697    test-rmse:0.240982+0.038122 
## [17] train-rmse:0.032634+0.004963    test-rmse:0.241475+0.038189 
## [18] train-rmse:0.030392+0.004834    test-rmse:0.242074+0.038630 
## Stopping. Best iteration:
## [12] train-rmse:0.054957+0.004802    test-rmse:0.240651+0.036248
## 
## 69 
## [1]  train-rmse:1.041534+0.004310    test-rmse:1.046105+0.018355 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.758595+0.002830    test-rmse:0.769926+0.012611 
## [3]  train-rmse:0.554418+0.001599    test-rmse:0.577047+0.009068 
## [4]  train-rmse:0.407426+0.001222    test-rmse:0.445248+0.007365 
## [5]  train-rmse:0.299972+0.001163    test-rmse:0.358025+0.010487 
## [6]  train-rmse:0.222268+0.000699    test-rmse:0.304572+0.015841 
## [7]  train-rmse:0.166072+0.000810    test-rmse:0.272647+0.021754 
## [8]  train-rmse:0.125496+0.001042    test-rmse:0.256354+0.027336 
## [9]  train-rmse:0.097264+0.000995    test-rmse:0.248684+0.030325 
## [10] train-rmse:0.076151+0.001011    test-rmse:0.245377+0.032312 
## [11] train-rmse:0.060961+0.001148    test-rmse:0.244009+0.033861 
## [12] train-rmse:0.049911+0.001760    test-rmse:0.244518+0.035527 
## [13] train-rmse:0.041599+0.001879    test-rmse:0.245016+0.036691 
## [14] train-rmse:0.035762+0.002348    test-rmse:0.245665+0.037429 
## [15] train-rmse:0.031384+0.002559    test-rmse:0.245968+0.037699 
## [16] train-rmse:0.028136+0.002399    test-rmse:0.246231+0.038159 
## [17] train-rmse:0.025052+0.001677    test-rmse:0.246441+0.038349 
## Stopping. Best iteration:
## [11] train-rmse:0.060961+0.001148    test-rmse:0.244009+0.033861
## 
## 70 
## [1]  train-rmse:1.021057+0.003081    test-rmse:1.021073+0.020608 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.736387+0.000848    test-rmse:0.737026+0.016579 
## [3]  train-rmse:0.544127+0.002563    test-rmse:0.545473+0.013440 
## [4]  train-rmse:0.414513+0.003453    test-rmse:0.418788+0.008838 
## [5]  train-rmse:0.330497+0.004741    test-rmse:0.334840+0.015770 
## [6]  train-rmse:0.277205+0.005219    test-rmse:0.284498+0.016369 
## [7]  train-rmse:0.245141+0.005809    test-rmse:0.250048+0.022225 
## [8]  train-rmse:0.225916+0.005876    test-rmse:0.233385+0.021937 
## [9]  train-rmse:0.214703+0.005966    test-rmse:0.221462+0.025931 
## [10] train-rmse:0.207424+0.005990    test-rmse:0.217781+0.024838 
## [11] train-rmse:0.202592+0.005598    test-rmse:0.214798+0.024840 
## [12] train-rmse:0.198952+0.005559    test-rmse:0.211928+0.026555 
## [13] train-rmse:0.195889+0.005294    test-rmse:0.213197+0.027376 
## [14] train-rmse:0.193327+0.005290    test-rmse:0.212928+0.027637 
## [15] train-rmse:0.191300+0.005220    test-rmse:0.212537+0.027695 
## [16] train-rmse:0.189436+0.005371    test-rmse:0.213086+0.026964 
## [17] train-rmse:0.187803+0.005362    test-rmse:0.212586+0.026391 
## [18] train-rmse:0.186202+0.005458    test-rmse:0.212646+0.026460 
## Stopping. Best iteration:
## [12] train-rmse:0.198952+0.005559    test-rmse:0.211928+0.026555
## 
## 71 
## [1]  train-rmse:1.015519+0.004109    test-rmse:1.017495+0.019079 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.723878+0.002545    test-rmse:0.729335+0.014828 
## [3]  train-rmse:0.523096+0.001825    test-rmse:0.532734+0.012243 
## [4]  train-rmse:0.387132+0.001697    test-rmse:0.404179+0.011447 
## [5]  train-rmse:0.297113+0.002194    test-rmse:0.321282+0.014705 
## [6]  train-rmse:0.239224+0.002192    test-rmse:0.272355+0.017630 
## [7]  train-rmse:0.203609+0.002263    test-rmse:0.242752+0.021228 
## [8]  train-rmse:0.181705+0.000699    test-rmse:0.227182+0.025228 
## [9]  train-rmse:0.168770+0.002332    test-rmse:0.218597+0.026386 
## [10] train-rmse:0.160167+0.003749    test-rmse:0.213515+0.027416 
## [11] train-rmse:0.154865+0.005018    test-rmse:0.211521+0.027240 
## [12] train-rmse:0.150617+0.004629    test-rmse:0.212701+0.028301 
## [13] train-rmse:0.145175+0.004392    test-rmse:0.211084+0.027062 
## [14] train-rmse:0.143714+0.004457    test-rmse:0.211585+0.027836 
## [15] train-rmse:0.141441+0.005059    test-rmse:0.211466+0.027812 
## [16] train-rmse:0.139986+0.005389    test-rmse:0.211163+0.028175 
## [17] train-rmse:0.138054+0.005024    test-rmse:0.212231+0.028425 
## [18] train-rmse:0.136258+0.004743    test-rmse:0.211905+0.028193 
## [19] train-rmse:0.133266+0.005149    test-rmse:0.213012+0.027448 
## Stopping. Best iteration:
## [13] train-rmse:0.145175+0.004392    test-rmse:0.211084+0.027062
## 
## 72 
## [1]  train-rmse:1.014355+0.004027    test-rmse:1.018382+0.018015 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.721003+0.002465    test-rmse:0.731553+0.012452 
## [3]  train-rmse:0.517587+0.001612    test-rmse:0.536509+0.008392 
## [4]  train-rmse:0.377276+0.002609    test-rmse:0.408301+0.009268 
## [5]  train-rmse:0.281697+0.004298    test-rmse:0.327661+0.011900 
## [6]  train-rmse:0.216392+0.005111    test-rmse:0.277821+0.016845 
## [7]  train-rmse:0.174145+0.006468    test-rmse:0.250311+0.020884 
## [8]  train-rmse:0.147105+0.006979    test-rmse:0.236834+0.025140 
## [9]  train-rmse:0.130457+0.007446    test-rmse:0.228699+0.027144 
## [10] train-rmse:0.119295+0.007870    test-rmse:0.226204+0.029712 
## [11] train-rmse:0.112612+0.008596    test-rmse:0.224257+0.030305 
## [12] train-rmse:0.107212+0.008194    test-rmse:0.224063+0.029404 
## [13] train-rmse:0.103086+0.008895    test-rmse:0.225167+0.030607 
## [14] train-rmse:0.099860+0.008750    test-rmse:0.225357+0.031396 
## [15] train-rmse:0.096816+0.008390    test-rmse:0.225105+0.031536 
## [16] train-rmse:0.093751+0.009375    test-rmse:0.226408+0.031732 
## [17] train-rmse:0.091150+0.009311    test-rmse:0.227475+0.032451 
## [18] train-rmse:0.088694+0.010079    test-rmse:0.228106+0.032136 
## Stopping. Best iteration:
## [12] train-rmse:0.107212+0.008194    test-rmse:0.224063+0.029404
## 
## 73 
## [1]  train-rmse:1.013944+0.004063    test-rmse:1.018456+0.017942 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.720036+0.002386    test-rmse:0.731276+0.012216 
## [3]  train-rmse:0.514376+0.001453    test-rmse:0.537559+0.009046 
## [4]  train-rmse:0.370673+0.001532    test-rmse:0.410334+0.007976 
## [5]  train-rmse:0.271145+0.002537    test-rmse:0.330544+0.012446 
## [6]  train-rmse:0.202379+0.003243    test-rmse:0.284152+0.017503 
## [7]  train-rmse:0.155561+0.004185    test-rmse:0.257304+0.022600 
## [8]  train-rmse:0.124012+0.004756    test-rmse:0.244129+0.027969 
## [9]  train-rmse:0.103512+0.005589    test-rmse:0.238197+0.030246 
## [10] train-rmse:0.091148+0.006245    test-rmse:0.234350+0.031395 
## [11] train-rmse:0.082727+0.006820    test-rmse:0.232111+0.032248 
## [12] train-rmse:0.077249+0.006558    test-rmse:0.231723+0.033464 
## [13] train-rmse:0.072760+0.006224    test-rmse:0.231470+0.034063 
## [14] train-rmse:0.069102+0.006513    test-rmse:0.232107+0.034374 
## [15] train-rmse:0.066811+0.006224    test-rmse:0.232225+0.034543 
## [16] train-rmse:0.064071+0.006836    test-rmse:0.233454+0.034852 
## [17] train-rmse:0.060886+0.006182    test-rmse:0.233334+0.034480 
## [18] train-rmse:0.058027+0.006681    test-rmse:0.233364+0.034562 
## [19] train-rmse:0.056054+0.006239    test-rmse:0.234454+0.035183 
## Stopping. Best iteration:
## [13] train-rmse:0.072760+0.006224    test-rmse:0.231470+0.034063
## 
## 74 
## [1]  train-rmse:1.013679+0.004036    test-rmse:1.018494+0.017905 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.719453+0.002343    test-rmse:0.731398+0.012102 
## [3]  train-rmse:0.512921+0.001417    test-rmse:0.537538+0.008841 
## [4]  train-rmse:0.367434+0.001722    test-rmse:0.411301+0.007637 
## [5]  train-rmse:0.266435+0.002374    test-rmse:0.332903+0.012982 
## [6]  train-rmse:0.195126+0.002909    test-rmse:0.288225+0.019854 
## [7]  train-rmse:0.145847+0.003727    test-rmse:0.264057+0.027030 
## [8]  train-rmse:0.111375+0.004194    test-rmse:0.250454+0.031960 
## [9]  train-rmse:0.088234+0.005618    test-rmse:0.244846+0.035121 
## [10] train-rmse:0.073220+0.006340    test-rmse:0.242037+0.037103 
## [11] train-rmse:0.062701+0.006857    test-rmse:0.241563+0.038071 
## [12] train-rmse:0.054986+0.006768    test-rmse:0.241496+0.038549 
## [13] train-rmse:0.049851+0.007219    test-rmse:0.241420+0.039327 
## [14] train-rmse:0.046168+0.007344    test-rmse:0.242593+0.040398 
## [15] train-rmse:0.042982+0.007587    test-rmse:0.242477+0.040669 
## [16] train-rmse:0.040705+0.007469    test-rmse:0.243777+0.041568 
## [17] train-rmse:0.037854+0.006551    test-rmse:0.244486+0.042028 
## [18] train-rmse:0.036131+0.006249    test-rmse:0.245143+0.042361 
## [19] train-rmse:0.034504+0.006225    test-rmse:0.245682+0.042811 
## Stopping. Best iteration:
## [13] train-rmse:0.049851+0.007219    test-rmse:0.241420+0.039327
## 
## 75 
## [1]  train-rmse:1.013549+0.004093    test-rmse:1.018529+0.017860 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.719125+0.002466    test-rmse:0.731612+0.011885 
## [3]  train-rmse:0.512156+0.001551    test-rmse:0.538139+0.008105 
## [4]  train-rmse:0.366082+0.002048    test-rmse:0.410441+0.007637 
## [5]  train-rmse:0.264391+0.002982    test-rmse:0.331422+0.012154 
## [6]  train-rmse:0.192543+0.002613    test-rmse:0.285802+0.019555 
## [7]  train-rmse:0.141958+0.002752    test-rmse:0.260211+0.025421 
## [8]  train-rmse:0.107127+0.002929    test-rmse:0.247879+0.030647 
## [9]  train-rmse:0.082969+0.003186    test-rmse:0.242323+0.034681 
## [10] train-rmse:0.066544+0.003500    test-rmse:0.239706+0.037004 
## [11] train-rmse:0.055468+0.004190    test-rmse:0.239523+0.039072 
## [12] train-rmse:0.047623+0.004372    test-rmse:0.240051+0.039889 
## [13] train-rmse:0.041890+0.004641    test-rmse:0.240397+0.040439 
## [14] train-rmse:0.037613+0.004066    test-rmse:0.240310+0.041963 
## [15] train-rmse:0.034138+0.003430    test-rmse:0.240359+0.042247 
## [16] train-rmse:0.031120+0.003203    test-rmse:0.240522+0.042249 
## [17] train-rmse:0.028790+0.003061    test-rmse:0.240548+0.042237 
## Stopping. Best iteration:
## [11] train-rmse:0.055468+0.004190    test-rmse:0.239523+0.039072
## 
## 76 
## [1]  train-rmse:1.013491+0.004161    test-rmse:1.018544+0.017835 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.718964+0.002608    test-rmse:0.731682+0.011774 
## [3]  train-rmse:0.511787+0.001266    test-rmse:0.538094+0.008288 
## [4]  train-rmse:0.365389+0.001190    test-rmse:0.410158+0.007437 
## [5]  train-rmse:0.262612+0.002154    test-rmse:0.331266+0.012247 
## [6]  train-rmse:0.189966+0.002266    test-rmse:0.286219+0.019189 
## [7]  train-rmse:0.139379+0.002026    test-rmse:0.261328+0.025370 
## [8]  train-rmse:0.103918+0.002512    test-rmse:0.250909+0.030867 
## [9]  train-rmse:0.078974+0.003048    test-rmse:0.245718+0.034101 
## [10] train-rmse:0.061657+0.002917    test-rmse:0.244793+0.036507 
## [11] train-rmse:0.049723+0.002700    test-rmse:0.245196+0.038054 
## [12] train-rmse:0.041274+0.002793    test-rmse:0.245790+0.039297 
## [13] train-rmse:0.035278+0.003062    test-rmse:0.246369+0.040177 
## [14] train-rmse:0.030336+0.002980    test-rmse:0.246992+0.040996 
## [15] train-rmse:0.026648+0.002568    test-rmse:0.247176+0.041534 
## [16] train-rmse:0.023564+0.002094    test-rmse:0.247712+0.042217 
## Stopping. Best iteration:
## [10] train-rmse:0.061657+0.002917    test-rmse:0.244793+0.036507
## 
## 77 
## [1]  train-rmse:0.993695+0.002849    test-rmse:0.993733+0.020258 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.699684+0.000902    test-rmse:0.700514+0.016041 
## [3]  train-rmse:0.508195+0.003128    test-rmse:0.509734+0.013059 
## [4]  train-rmse:0.383027+0.003828    test-rmse:0.388073+0.008995 
## [5]  train-rmse:0.306249+0.005140    test-rmse:0.310862+0.017428 
## [6]  train-rmse:0.259462+0.005482    test-rmse:0.267320+0.018310 
## [7]  train-rmse:0.233233+0.005958    test-rmse:0.238449+0.024080 
## [8]  train-rmse:0.217917+0.005942    test-rmse:0.226074+0.023216 
## [9]  train-rmse:0.209304+0.006063    test-rmse:0.219115+0.026518 
## [10] train-rmse:0.203432+0.005811    test-rmse:0.213978+0.026799 
## [11] train-rmse:0.199341+0.005618    test-rmse:0.214004+0.025845 
## [12] train-rmse:0.196053+0.005317    test-rmse:0.212232+0.025642 
## [13] train-rmse:0.193504+0.005262    test-rmse:0.212918+0.026573 
## [14] train-rmse:0.191065+0.005311    test-rmse:0.212222+0.026035 
## [15] train-rmse:0.189124+0.005447    test-rmse:0.212774+0.027914 
## [16] train-rmse:0.187354+0.005471    test-rmse:0.211103+0.027814 
## [17] train-rmse:0.185784+0.005504    test-rmse:0.211659+0.027927 
## [18] train-rmse:0.184277+0.005657    test-rmse:0.211831+0.025604 
## [19] train-rmse:0.182973+0.005719    test-rmse:0.213165+0.026369 
## [20] train-rmse:0.181703+0.005914    test-rmse:0.211719+0.026278 
## [21] train-rmse:0.180561+0.005988    test-rmse:0.210982+0.025913 
## [22] train-rmse:0.179535+0.006079    test-rmse:0.209838+0.025387 
## [23] train-rmse:0.178529+0.006202    test-rmse:0.211229+0.026057 
## [24] train-rmse:0.177682+0.006295    test-rmse:0.211277+0.025806 
## [25] train-rmse:0.176904+0.006327    test-rmse:0.211697+0.023948 
## [26] train-rmse:0.176106+0.006461    test-rmse:0.212580+0.025380 
## [27] train-rmse:0.175400+0.006433    test-rmse:0.212802+0.025159 
## [28] train-rmse:0.174757+0.006488    test-rmse:0.212798+0.025090 
## Stopping. Best iteration:
## [22] train-rmse:0.179535+0.006079    test-rmse:0.209838+0.025387
## 
## 78 
## [1]  train-rmse:0.987681+0.003958    test-rmse:0.989860+0.018663 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.685926+0.002366    test-rmse:0.692013+0.014281 
## [3]  train-rmse:0.484870+0.001764    test-rmse:0.495766+0.011931 
## [4]  train-rmse:0.353658+0.001781    test-rmse:0.373067+0.011499 
## [5]  train-rmse:0.270505+0.002362    test-rmse:0.297625+0.015564 
## [6]  train-rmse:0.219738+0.002282    test-rmse:0.256696+0.019397 
## [7]  train-rmse:0.189633+0.001038    test-rmse:0.234230+0.023812 
## [8]  train-rmse:0.171878+0.002738    test-rmse:0.220907+0.025035 
## [9]  train-rmse:0.161190+0.004036    test-rmse:0.214497+0.025203 
## [10] train-rmse:0.153388+0.004764    test-rmse:0.214426+0.022909 
## [11] train-rmse:0.149133+0.005479    test-rmse:0.214332+0.024326 
## [12] train-rmse:0.146649+0.005656    test-rmse:0.213920+0.025815 
## [13] train-rmse:0.143480+0.004298    test-rmse:0.213391+0.025496 
## [14] train-rmse:0.140066+0.005125    test-rmse:0.212985+0.025336 
## [15] train-rmse:0.137039+0.005130    test-rmse:0.213177+0.025233 
## [16] train-rmse:0.134554+0.004530    test-rmse:0.214780+0.024102 
## [17] train-rmse:0.132149+0.004474    test-rmse:0.214846+0.024168 
## [18] train-rmse:0.130080+0.004713    test-rmse:0.214467+0.024359 
## [19] train-rmse:0.129196+0.004662    test-rmse:0.215109+0.024837 
## [20] train-rmse:0.126901+0.004373    test-rmse:0.215322+0.024381 
## Stopping. Best iteration:
## [14] train-rmse:0.140066+0.005125    test-rmse:0.212985+0.025336
## 
## 79 
## [1]  train-rmse:0.986407+0.003864    test-rmse:0.990832+0.017508 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.682771+0.002270    test-rmse:0.694396+0.011649 
## [3]  train-rmse:0.478623+0.001614    test-rmse:0.499990+0.007757 
## [4]  train-rmse:0.342425+0.003166    test-rmse:0.376688+0.009641 
## [5]  train-rmse:0.251622+0.003395    test-rmse:0.305306+0.012349 
## [6]  train-rmse:0.193169+0.004684    test-rmse:0.264040+0.017159 
## [7]  train-rmse:0.157218+0.006211    test-rmse:0.243265+0.021304 
## [8]  train-rmse:0.135174+0.008172    test-rmse:0.233144+0.023802 
## [9]  train-rmse:0.122218+0.008435    test-rmse:0.227821+0.022890 
## [10] train-rmse:0.113318+0.008163    test-rmse:0.225797+0.024911 
## [11] train-rmse:0.107625+0.007846    test-rmse:0.224207+0.025476 
## [12] train-rmse:0.104093+0.007887    test-rmse:0.224270+0.026491 
## [13] train-rmse:0.100871+0.007288    test-rmse:0.224462+0.026666 
## [14] train-rmse:0.097374+0.006983    test-rmse:0.226059+0.026109 
## [15] train-rmse:0.094670+0.007354    test-rmse:0.226794+0.026673 
## [16] train-rmse:0.092585+0.007613    test-rmse:0.226847+0.026091 
## [17] train-rmse:0.089165+0.008374    test-rmse:0.227341+0.026432 
## Stopping. Best iteration:
## [11] train-rmse:0.107625+0.007846    test-rmse:0.224207+0.025476
## 
## 80 
## [1]  train-rmse:0.985959+0.003903    test-rmse:0.990922+0.017428 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.681689+0.002173    test-rmse:0.694257+0.011431 
## [3]  train-rmse:0.474916+0.001661    test-rmse:0.501363+0.008530 
## [4]  train-rmse:0.334544+0.002082    test-rmse:0.379883+0.009719 
## [5]  train-rmse:0.240531+0.003569    test-rmse:0.308737+0.014498 
## [6]  train-rmse:0.177782+0.004552    test-rmse:0.269031+0.020140 
## [7]  train-rmse:0.137228+0.005555    test-rmse:0.249640+0.025433 
## [8]  train-rmse:0.110609+0.006108    test-rmse:0.239238+0.030029 
## [9]  train-rmse:0.094677+0.007907    test-rmse:0.236746+0.031817 
## [10] train-rmse:0.084646+0.008781    test-rmse:0.236281+0.034291 
## [11] train-rmse:0.077285+0.008783    test-rmse:0.236456+0.035337 
## [12] train-rmse:0.072815+0.009101    test-rmse:0.235917+0.035173 
## [13] train-rmse:0.068786+0.009162    test-rmse:0.236101+0.035436 
## [14] train-rmse:0.065348+0.009076    test-rmse:0.235655+0.034944 
## [15] train-rmse:0.061999+0.009085    test-rmse:0.235129+0.034478 
## [16] train-rmse:0.059538+0.008608    test-rmse:0.235217+0.034490 
## [17] train-rmse:0.057113+0.007877    test-rmse:0.234690+0.034230 
## [18] train-rmse:0.054214+0.007443    test-rmse:0.235206+0.034377 
## [19] train-rmse:0.052030+0.007124    test-rmse:0.235367+0.034506 
## [20] train-rmse:0.049401+0.007442    test-rmse:0.236375+0.035363 
## [21] train-rmse:0.047305+0.007326    test-rmse:0.236762+0.035381 
## [22] train-rmse:0.045393+0.007319    test-rmse:0.236621+0.035468 
## [23] train-rmse:0.043893+0.007063    test-rmse:0.236720+0.035579 
## Stopping. Best iteration:
## [17] train-rmse:0.057113+0.007877    test-rmse:0.234690+0.034230
## 
## 81 
## [1]  train-rmse:0.985669+0.003873    test-rmse:0.990969+0.017388 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.681049+0.002120    test-rmse:0.694410+0.011310 
## [3]  train-rmse:0.473147+0.002034    test-rmse:0.501361+0.008207 
## [4]  train-rmse:0.331070+0.002297    test-rmse:0.379944+0.009890 
## [5]  train-rmse:0.234832+0.003212    test-rmse:0.309967+0.015469 
## [6]  train-rmse:0.169728+0.004017    test-rmse:0.271966+0.023031 
## [7]  train-rmse:0.126497+0.005107    test-rmse:0.253379+0.029030 
## [8]  train-rmse:0.096831+0.006021    test-rmse:0.245756+0.033770 
## [9]  train-rmse:0.078628+0.007615    test-rmse:0.242544+0.036085 
## [10] train-rmse:0.066862+0.008616    test-rmse:0.241954+0.037808 
## [11] train-rmse:0.058619+0.008484    test-rmse:0.240643+0.038547 
## [12] train-rmse:0.052580+0.008683    test-rmse:0.240402+0.039107 
## [13] train-rmse:0.049063+0.008892    test-rmse:0.241293+0.039914 
## [14] train-rmse:0.045338+0.008621    test-rmse:0.241094+0.039983 
## [15] train-rmse:0.042449+0.008512    test-rmse:0.241788+0.039987 
## [16] train-rmse:0.040040+0.007655    test-rmse:0.242785+0.039486 
## [17] train-rmse:0.038046+0.007596    test-rmse:0.242928+0.039824 
## [18] train-rmse:0.036109+0.007232    test-rmse:0.242911+0.039881 
## Stopping. Best iteration:
## [12] train-rmse:0.052580+0.008683    test-rmse:0.240402+0.039107
## 
## 82 
## [1]  train-rmse:0.985529+0.003934    test-rmse:0.991010+0.017338 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.680693+0.002244    test-rmse:0.694656+0.011078 
## [3]  train-rmse:0.472491+0.002015    test-rmse:0.502141+0.007506 
## [4]  train-rmse:0.329348+0.002047    test-rmse:0.380318+0.009299 
## [5]  train-rmse:0.232359+0.002662    test-rmse:0.310716+0.014146 
## [6]  train-rmse:0.165629+0.002575    test-rmse:0.273727+0.021722 
## [7]  train-rmse:0.121332+0.002895    test-rmse:0.255122+0.028425 
## [8]  train-rmse:0.090891+0.002906    test-rmse:0.247193+0.033177 
## [9]  train-rmse:0.071272+0.003625    test-rmse:0.243947+0.035784 
## [10] train-rmse:0.058324+0.004063    test-rmse:0.242543+0.037265 
## [11] train-rmse:0.049030+0.004489    test-rmse:0.242208+0.038122 
## [12] train-rmse:0.042881+0.004200    test-rmse:0.241873+0.040100 
## [13] train-rmse:0.038716+0.003904    test-rmse:0.242033+0.040572 
## [14] train-rmse:0.035424+0.003980    test-rmse:0.242166+0.040813 
## [15] train-rmse:0.032535+0.003913    test-rmse:0.242391+0.041954 
## [16] train-rmse:0.029986+0.003687    test-rmse:0.242537+0.042593 
## [17] train-rmse:0.027351+0.003844    test-rmse:0.242518+0.043070 
## [18] train-rmse:0.024849+0.003876    test-rmse:0.242954+0.043326 
## Stopping. Best iteration:
## [12] train-rmse:0.042881+0.004200    test-rmse:0.241873+0.040100
## 
## 83 
## [1]  train-rmse:0.985465+0.004009    test-rmse:0.991028+0.017309 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.680515+0.002392    test-rmse:0.694737+0.010954 
## [3]  train-rmse:0.472055+0.001664    test-rmse:0.501984+0.007699 
## [4]  train-rmse:0.328781+0.001289    test-rmse:0.380107+0.009394 
## [5]  train-rmse:0.230750+0.002502    test-rmse:0.310262+0.014238 
## [6]  train-rmse:0.163487+0.002442    test-rmse:0.273432+0.022040 
## [7]  train-rmse:0.118330+0.002411    test-rmse:0.254601+0.028148 
## [8]  train-rmse:0.087424+0.002587    test-rmse:0.247835+0.031973 
## [9]  train-rmse:0.066318+0.002858    test-rmse:0.244818+0.035137 
## [10] train-rmse:0.052268+0.002738    test-rmse:0.244918+0.035990 
## [11] train-rmse:0.042793+0.002835    test-rmse:0.245327+0.037191 
## [12] train-rmse:0.036306+0.003559    test-rmse:0.245849+0.038131 
## [13] train-rmse:0.031282+0.003453    test-rmse:0.246114+0.038456 
## [14] train-rmse:0.027179+0.003697    test-rmse:0.246692+0.038949 
## [15] train-rmse:0.023913+0.003542    test-rmse:0.247386+0.039191 
## Stopping. Best iteration:
## [9]  train-rmse:0.066318+0.002858    test-rmse:0.244818+0.035137
## 
## 84 
## [1]  train-rmse:0.966378+0.002614    test-rmse:0.966441+0.019899 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.664279+0.001134    test-rmse:0.665211+0.015433 
## [3]  train-rmse:0.474664+0.003429    test-rmse:0.478843+0.011510 
## [4]  train-rmse:0.354846+0.004207    test-rmse:0.356962+0.013525 
## [5]  train-rmse:0.285716+0.005286    test-rmse:0.293251+0.015075 
## [6]  train-rmse:0.245300+0.005695    test-rmse:0.250295+0.020911 
## [7]  train-rmse:0.224200+0.005950    test-rmse:0.231278+0.026124 
## [8]  train-rmse:0.212024+0.005894    test-rmse:0.221848+0.024646 
## [9]  train-rmse:0.204937+0.005784    test-rmse:0.218523+0.025727 
## [10] train-rmse:0.200263+0.005586    test-rmse:0.214174+0.027338 
## [11] train-rmse:0.196489+0.005525    test-rmse:0.213138+0.028877 
## [12] train-rmse:0.193734+0.005344    test-rmse:0.213694+0.026968 
## [13] train-rmse:0.191247+0.005272    test-rmse:0.212874+0.026857 
## [14] train-rmse:0.189088+0.005271    test-rmse:0.211832+0.026166 
## [15] train-rmse:0.187197+0.005498    test-rmse:0.213330+0.026771 
## [16] train-rmse:0.185498+0.005606    test-rmse:0.212585+0.026228 
## [17] train-rmse:0.183927+0.005765    test-rmse:0.210924+0.027348 
## [18] train-rmse:0.182515+0.005850    test-rmse:0.212261+0.027992 
## [19] train-rmse:0.181250+0.005948    test-rmse:0.211884+0.025439 
## [20] train-rmse:0.180085+0.006052    test-rmse:0.211201+0.024991 
## [21] train-rmse:0.178971+0.006125    test-rmse:0.211252+0.024620 
## [22] train-rmse:0.178047+0.006231    test-rmse:0.212313+0.026122 
## [23] train-rmse:0.177093+0.006344    test-rmse:0.211084+0.025631 
## Stopping. Best iteration:
## [17] train-rmse:0.183927+0.005765    test-rmse:0.210924+0.027348
## 
## 85 
## [1]  train-rmse:0.959869+0.003808    test-rmse:0.962264+0.018247 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.649195+0.002205    test-rmse:0.655965+0.013764 
## [3]  train-rmse:0.449186+0.001752    test-rmse:0.461455+0.011746 
## [4]  train-rmse:0.323784+0.001825    test-rmse:0.345564+0.011895 
## [5]  train-rmse:0.247858+0.002393    test-rmse:0.278076+0.016723 
## [6]  train-rmse:0.204138+0.002392    test-rmse:0.243494+0.021802 
## [7]  train-rmse:0.179956+0.002865    test-rmse:0.227495+0.022696 
## [8]  train-rmse:0.166773+0.003780    test-rmse:0.218151+0.024092 
## [9]  train-rmse:0.156632+0.006061    test-rmse:0.213182+0.023968 
## [10] train-rmse:0.151916+0.007395    test-rmse:0.211312+0.025014 
## [11] train-rmse:0.147219+0.007062    test-rmse:0.210335+0.024163 
## [12] train-rmse:0.144543+0.007196    test-rmse:0.211839+0.025445 
## [13] train-rmse:0.141111+0.008327    test-rmse:0.210802+0.025618 
## [14] train-rmse:0.139948+0.008372    test-rmse:0.210818+0.026552 
## [15] train-rmse:0.138053+0.008322    test-rmse:0.211489+0.026977 
## [16] train-rmse:0.135776+0.007967    test-rmse:0.211504+0.027309 
## [17] train-rmse:0.133156+0.008093    test-rmse:0.210880+0.027849 
## Stopping. Best iteration:
## [11] train-rmse:0.147219+0.007062    test-rmse:0.210335+0.024163
## 
## 86 
## [1]  train-rmse:0.958481+0.003699    test-rmse:0.963326+0.016998 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.645729+0.002088    test-rmse:0.658581+0.010838 
## [3]  train-rmse:0.442126+0.001713    test-rmse:0.466596+0.007772 
## [4]  train-rmse:0.310314+0.003601    test-rmse:0.349589+0.011693 
## [5]  train-rmse:0.225725+0.004603    test-rmse:0.285419+0.014330 
## [6]  train-rmse:0.174524+0.005242    test-rmse:0.251771+0.017676 
## [7]  train-rmse:0.144206+0.006908    test-rmse:0.236010+0.022175 
## [8]  train-rmse:0.126522+0.008347    test-rmse:0.228317+0.025299 
## [9]  train-rmse:0.116042+0.008223    test-rmse:0.224821+0.026079 
## [10] train-rmse:0.109115+0.008693    test-rmse:0.223883+0.026938 
## [11] train-rmse:0.104055+0.008556    test-rmse:0.223799+0.027327 
## [12] train-rmse:0.100302+0.008500    test-rmse:0.224339+0.028028 
## [13] train-rmse:0.095558+0.010094    test-rmse:0.225232+0.028199 
## [14] train-rmse:0.092410+0.011486    test-rmse:0.225576+0.028770 
## [15] train-rmse:0.089733+0.011299    test-rmse:0.225656+0.029022 
## [16] train-rmse:0.086320+0.011347    test-rmse:0.226199+0.028833 
## [17] train-rmse:0.083963+0.012461    test-rmse:0.227857+0.029431 
## Stopping. Best iteration:
## [11] train-rmse:0.104055+0.008556    test-rmse:0.223799+0.027327
## 
## 87 
## [1]  train-rmse:0.957993+0.003743    test-rmse:0.963433+0.016911 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.644537+0.001981    test-rmse:0.658545+0.010664 
## [3]  train-rmse:0.437901+0.001890    test-rmse:0.467790+0.008219 
## [4]  train-rmse:0.301888+0.002512    test-rmse:0.352996+0.010958 
## [5]  train-rmse:0.214179+0.004150    test-rmse:0.292388+0.015561 
## [6]  train-rmse:0.157863+0.005288    test-rmse:0.261282+0.022744 
## [7]  train-rmse:0.121929+0.006037    test-rmse:0.244853+0.027700 
## [8]  train-rmse:0.101080+0.007076    test-rmse:0.237923+0.030300 
## [9]  train-rmse:0.088676+0.008172    test-rmse:0.235269+0.032119 
## [10] train-rmse:0.081613+0.008229    test-rmse:0.235342+0.033573 
## [11] train-rmse:0.076214+0.007812    test-rmse:0.235450+0.033087 
## [12] train-rmse:0.072826+0.007624    test-rmse:0.235350+0.033761 
## [13] train-rmse:0.069194+0.007846    test-rmse:0.235463+0.033685 
## [14] train-rmse:0.065369+0.006807    test-rmse:0.235544+0.033569 
## [15] train-rmse:0.061320+0.007832    test-rmse:0.236977+0.033974 
## Stopping. Best iteration:
## [9]  train-rmse:0.088676+0.008172    test-rmse:0.235269+0.032119
## 
## 88 
## [1]  train-rmse:0.957677+0.003709    test-rmse:0.963491+0.016865 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.643837+0.001915    test-rmse:0.658734+0.010541 
## [3]  train-rmse:0.434948+0.000588    test-rmse:0.467562+0.007660 
## [4]  train-rmse:0.297275+0.002424    test-rmse:0.353972+0.011517 
## [5]  train-rmse:0.206609+0.002944    test-rmse:0.292621+0.016727 
## [6]  train-rmse:0.146592+0.002956    test-rmse:0.261676+0.023792 
## [7]  train-rmse:0.108416+0.004190    test-rmse:0.246911+0.029213 
## [8]  train-rmse:0.084470+0.004918    test-rmse:0.240056+0.032539 
## [9]  train-rmse:0.069748+0.005769    test-rmse:0.238425+0.034738 
## [10] train-rmse:0.060148+0.005919    test-rmse:0.237560+0.035835 
## [11] train-rmse:0.053759+0.006431    test-rmse:0.237159+0.036401 
## [12] train-rmse:0.049216+0.007139    test-rmse:0.237813+0.036580 
## [13] train-rmse:0.046067+0.007177    test-rmse:0.238028+0.037005 
## [14] train-rmse:0.043684+0.007150    test-rmse:0.239248+0.038026 
## [15] train-rmse:0.040058+0.005753    test-rmse:0.239602+0.038250 
## [16] train-rmse:0.037680+0.006057    test-rmse:0.240461+0.038595 
## [17] train-rmse:0.035354+0.005944    test-rmse:0.240904+0.039406 
## Stopping. Best iteration:
## [11] train-rmse:0.053759+0.006431    test-rmse:0.237159+0.036401
## 
## 89 
## [1]  train-rmse:0.957524+0.003775    test-rmse:0.963537+0.016811 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.643450+0.002036    test-rmse:0.659015+0.010294 
## [3]  train-rmse:0.433991+0.000859    test-rmse:0.468102+0.006997 
## [4]  train-rmse:0.295416+0.002136    test-rmse:0.354232+0.011053 
## [5]  train-rmse:0.204336+0.002970    test-rmse:0.292494+0.017106 
## [6]  train-rmse:0.143611+0.002971    test-rmse:0.261141+0.024343 
## [7]  train-rmse:0.103909+0.003016    test-rmse:0.246974+0.030126 
## [8]  train-rmse:0.078317+0.003528    test-rmse:0.241630+0.033685 
## [9]  train-rmse:0.062044+0.003282    test-rmse:0.240063+0.035906 
## [10] train-rmse:0.051692+0.003428    test-rmse:0.238919+0.038707 
## [11] train-rmse:0.044521+0.003792    test-rmse:0.238602+0.039181 
## [12] train-rmse:0.039074+0.003668    test-rmse:0.239209+0.039577 
## [13] train-rmse:0.035158+0.004247    test-rmse:0.239922+0.039895 
## [14] train-rmse:0.031647+0.004020    test-rmse:0.240627+0.039875 
## [15] train-rmse:0.029159+0.004199    test-rmse:0.240688+0.039630 
## [16] train-rmse:0.027456+0.004257    test-rmse:0.241064+0.040042 
## [17] train-rmse:0.025356+0.004231    test-rmse:0.241389+0.040159 
## Stopping. Best iteration:
## [11] train-rmse:0.044521+0.003792    test-rmse:0.238602+0.039181
## 
## 90 
## [1]  train-rmse:0.957455+0.003855    test-rmse:0.963558+0.016778 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.643244+0.002196    test-rmse:0.659103+0.010152 
## [3]  train-rmse:0.433325+0.001245    test-rmse:0.467809+0.007150 
## [4]  train-rmse:0.293955+0.001490    test-rmse:0.353958+0.010994 
## [5]  train-rmse:0.201872+0.001880    test-rmse:0.293340+0.017272 
## [6]  train-rmse:0.140444+0.001649    test-rmse:0.264399+0.025893 
## [7]  train-rmse:0.099639+0.002102    test-rmse:0.250130+0.031620 
## [8]  train-rmse:0.073445+0.002634    test-rmse:0.244959+0.035685 
## [9]  train-rmse:0.056090+0.002422    test-rmse:0.243816+0.038801 
## [10] train-rmse:0.043734+0.002986    test-rmse:0.244482+0.040223 
## [11] train-rmse:0.035717+0.003213    test-rmse:0.244994+0.041664 
## [12] train-rmse:0.030469+0.003394    test-rmse:0.245332+0.042260 
## [13] train-rmse:0.026106+0.003412    test-rmse:0.245979+0.043113 
## [14] train-rmse:0.022425+0.003423    test-rmse:0.246528+0.043447 
## [15] train-rmse:0.019162+0.003128    test-rmse:0.247145+0.043754 
## Stopping. Best iteration:
## [9]  train-rmse:0.056090+0.002422    test-rmse:0.243816+0.038801
## 
## 91 
## [1]  train-rmse:0.939115+0.002375    test-rmse:0.939202+0.019533 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.630192+0.001467    test-rmse:0.631296+0.014867 
## [3]  train-rmse:0.443079+0.003411    test-rmse:0.447164+0.009637 
## [4]  train-rmse:0.329890+0.004560    test-rmse:0.335066+0.016216 
## [5]  train-rmse:0.268418+0.005316    test-rmse:0.275885+0.016536 
## [6]  train-rmse:0.234076+0.005910    test-rmse:0.240127+0.022794 
## [7]  train-rmse:0.217471+0.005999    test-rmse:0.226130+0.021835 
## [8]  train-rmse:0.207668+0.005830    test-rmse:0.217132+0.024843 
## [9]  train-rmse:0.201549+0.005542    test-rmse:0.215121+0.025670 
## [10] train-rmse:0.197460+0.005484    test-rmse:0.214317+0.026578 
## [11] train-rmse:0.194251+0.005380    test-rmse:0.210074+0.027860 
## [12] train-rmse:0.191495+0.005471    test-rmse:0.212757+0.027048 
## [13] train-rmse:0.189296+0.005272    test-rmse:0.211081+0.025927 
## [14] train-rmse:0.187313+0.005336    test-rmse:0.212693+0.027022 
## [15] train-rmse:0.185426+0.005528    test-rmse:0.211907+0.026196 
## [16] train-rmse:0.183776+0.005654    test-rmse:0.210474+0.025897 
## [17] train-rmse:0.182328+0.005918    test-rmse:0.211888+0.026788 
## Stopping. Best iteration:
## [11] train-rmse:0.194251+0.005380    test-rmse:0.210074+0.027860
## 
## 92 
## [1]  train-rmse:0.932088+0.003658    test-rmse:0.934710+0.017831 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.613698+0.002063    test-rmse:0.621207+0.013280 
## [3]  train-rmse:0.416205+0.001831    test-rmse:0.432401+0.011500 
## [4]  train-rmse:0.297323+0.002192    test-rmse:0.321452+0.013684 
## [5]  train-rmse:0.228865+0.002368    test-rmse:0.262500+0.018429 
## [6]  train-rmse:0.190219+0.002421    test-rmse:0.233461+0.024452 
## [7]  train-rmse:0.170697+0.003654    test-rmse:0.220518+0.023853 
## [8]  train-rmse:0.159390+0.004965    test-rmse:0.213273+0.024642 
## [9]  train-rmse:0.153930+0.004449    test-rmse:0.212526+0.026271 
## [10] train-rmse:0.151035+0.004464    test-rmse:0.211465+0.026217 
## [11] train-rmse:0.147512+0.005922    test-rmse:0.210604+0.026519 
## [12] train-rmse:0.143859+0.006239    test-rmse:0.211360+0.026134 
## [13] train-rmse:0.142272+0.006665    test-rmse:0.210556+0.025619 
## [14] train-rmse:0.138940+0.006995    test-rmse:0.211030+0.025588 
## [15] train-rmse:0.136229+0.006493    test-rmse:0.210629+0.025406 
## [16] train-rmse:0.134360+0.006029    test-rmse:0.210943+0.026251 
## [17] train-rmse:0.132136+0.006686    test-rmse:0.210752+0.026169 
## [18] train-rmse:0.129771+0.006654    test-rmse:0.211670+0.026490 
## [19] train-rmse:0.127267+0.007809    test-rmse:0.212758+0.024477 
## Stopping. Best iteration:
## [13] train-rmse:0.142272+0.006665    test-rmse:0.210556+0.025619
## 
## 93 
## [1]  train-rmse:0.930579+0.003532    test-rmse:0.935866+0.016482 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.609903+0.001927    test-rmse:0.624173+0.010171 
## [3]  train-rmse:0.407241+0.002685    test-rmse:0.434524+0.009603 
## [4]  train-rmse:0.281066+0.004509    test-rmse:0.327414+0.012102 
## [5]  train-rmse:0.204443+0.007424    test-rmse:0.268202+0.016103 
## [6]  train-rmse:0.159904+0.007867    test-rmse:0.242551+0.022362 
## [7]  train-rmse:0.133341+0.009794    test-rmse:0.229400+0.025603 
## [8]  train-rmse:0.118731+0.009828    test-rmse:0.224138+0.027326 
## [9]  train-rmse:0.110114+0.010871    test-rmse:0.221983+0.029238 
## [10] train-rmse:0.103687+0.010518    test-rmse:0.220612+0.029742 
## [11] train-rmse:0.098931+0.010731    test-rmse:0.221778+0.030410 
## [12] train-rmse:0.094826+0.011595    test-rmse:0.223556+0.030622 
## [13] train-rmse:0.091618+0.011996    test-rmse:0.224090+0.031116 
## [14] train-rmse:0.088676+0.012072    test-rmse:0.224970+0.029697 
## [15] train-rmse:0.086312+0.012638    test-rmse:0.226097+0.029897 
## [16] train-rmse:0.083426+0.012154    test-rmse:0.227689+0.031384 
## Stopping. Best iteration:
## [10] train-rmse:0.103687+0.010518    test-rmse:0.220612+0.029742
## 
## 94 
## [1]  train-rmse:0.930050+0.003580    test-rmse:0.935993+0.016388 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.608587+0.001814    test-rmse:0.624159+0.009922 
## [3]  train-rmse:0.402954+0.002455    test-rmse:0.435755+0.009892 
## [4]  train-rmse:0.272354+0.003475    test-rmse:0.329278+0.013116 
## [5]  train-rmse:0.190883+0.005009    test-rmse:0.273782+0.018272 
## [6]  train-rmse:0.141332+0.006181    test-rmse:0.248447+0.024974 
## [7]  train-rmse:0.110371+0.006798    test-rmse:0.240174+0.029645 
## [8]  train-rmse:0.093633+0.008066    test-rmse:0.236576+0.030903 
## [9]  train-rmse:0.084345+0.008699    test-rmse:0.234596+0.032214 
## [10] train-rmse:0.078266+0.009285    test-rmse:0.234227+0.032899 
## [11] train-rmse:0.073737+0.008998    test-rmse:0.233644+0.033223 
## [12] train-rmse:0.069739+0.009262    test-rmse:0.233626+0.033138 
## [13] train-rmse:0.066081+0.009324    test-rmse:0.234122+0.033154 
## [14] train-rmse:0.062844+0.009172    test-rmse:0.234392+0.033309 
## [15] train-rmse:0.059815+0.009511    test-rmse:0.235285+0.033563 
## [16] train-rmse:0.056934+0.009501    test-rmse:0.236331+0.033961 
## [17] train-rmse:0.054551+0.009313    test-rmse:0.237025+0.034536 
## [18] train-rmse:0.051205+0.009232    test-rmse:0.237846+0.035428 
## Stopping. Best iteration:
## [12] train-rmse:0.069739+0.009262    test-rmse:0.233626+0.033138
## 
## 95 
## [1]  train-rmse:0.929707+0.003542    test-rmse:0.936063+0.016337 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.607743+0.001788    test-rmse:0.624383+0.009796 
## [3]  train-rmse:0.399774+0.000686    test-rmse:0.436084+0.008712 
## [4]  train-rmse:0.267204+0.002457    test-rmse:0.329776+0.013320 
## [5]  train-rmse:0.181737+0.002736    test-rmse:0.276055+0.021019 
## [6]  train-rmse:0.127768+0.003379    test-rmse:0.250186+0.028123 
## [7]  train-rmse:0.095053+0.005067    test-rmse:0.239943+0.032816 
## [8]  train-rmse:0.075750+0.005743    test-rmse:0.236981+0.036119 
## [9]  train-rmse:0.063694+0.006805    test-rmse:0.236307+0.036913 
## [10] train-rmse:0.055803+0.006958    test-rmse:0.236066+0.038259 
## [11] train-rmse:0.051517+0.007007    test-rmse:0.236304+0.038820 
## [12] train-rmse:0.047106+0.007486    test-rmse:0.237623+0.038941 
## [13] train-rmse:0.043825+0.007149    test-rmse:0.237872+0.039233 
## [14] train-rmse:0.040589+0.007332    test-rmse:0.238765+0.039698 
## [15] train-rmse:0.038172+0.006933    test-rmse:0.239112+0.040125 
## [16] train-rmse:0.036576+0.006780    test-rmse:0.239246+0.040660 
## Stopping. Best iteration:
## [10] train-rmse:0.055803+0.006958    test-rmse:0.236066+0.038259
## 
## 96 
## [1]  train-rmse:0.929541+0.003613    test-rmse:0.936116+0.016279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.607403+0.001846    test-rmse:0.624708+0.009545 
## [3]  train-rmse:0.398701+0.001051    test-rmse:0.436063+0.008910 
## [4]  train-rmse:0.265296+0.002209    test-rmse:0.330612+0.013498 
## [5]  train-rmse:0.178989+0.002020    test-rmse:0.276038+0.021472 
## [6]  train-rmse:0.123988+0.002674    test-rmse:0.251274+0.028393 
## [7]  train-rmse:0.088825+0.002436    test-rmse:0.242668+0.033671 
## [8]  train-rmse:0.067228+0.003619    test-rmse:0.239481+0.036465 
## [9]  train-rmse:0.053640+0.003606    test-rmse:0.238722+0.037866 
## [10] train-rmse:0.045148+0.004037    test-rmse:0.238477+0.038527 
## [11] train-rmse:0.039649+0.004404    test-rmse:0.238637+0.038958 
## [12] train-rmse:0.035194+0.004436    test-rmse:0.239007+0.039313 
## [13] train-rmse:0.031885+0.004043    test-rmse:0.238930+0.039344 
## [14] train-rmse:0.027969+0.004324    test-rmse:0.239285+0.039324 
## [15] train-rmse:0.025724+0.004614    test-rmse:0.239821+0.039746 
## [16] train-rmse:0.023463+0.004532    test-rmse:0.240071+0.039902 
## Stopping. Best iteration:
## [10] train-rmse:0.045148+0.004037    test-rmse:0.238477+0.038527
## 
## 97 
## [1]  train-rmse:0.929466+0.003700    test-rmse:0.936141+0.016242 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.607176+0.001999    test-rmse:0.624810+0.009391 
## [3]  train-rmse:0.398028+0.001311    test-rmse:0.435988+0.008728 
## [4]  train-rmse:0.264192+0.000800    test-rmse:0.330644+0.013189 
## [5]  train-rmse:0.178030+0.001701    test-rmse:0.277258+0.019943 
## [6]  train-rmse:0.121885+0.002098    test-rmse:0.253870+0.027177 
## [7]  train-rmse:0.086323+0.001825    test-rmse:0.244818+0.032703 
## [8]  train-rmse:0.063246+0.002301    test-rmse:0.241849+0.036108 
## [9]  train-rmse:0.048386+0.001933    test-rmse:0.241646+0.038789 
## [10] train-rmse:0.038851+0.002771    test-rmse:0.242596+0.039867 
## [11] train-rmse:0.031852+0.002509    test-rmse:0.243917+0.040569 
## [12] train-rmse:0.027019+0.002756    test-rmse:0.244828+0.041484 
## [13] train-rmse:0.023339+0.002601    test-rmse:0.245005+0.041976 
## [14] train-rmse:0.019772+0.002590    test-rmse:0.245397+0.042552 
## [15] train-rmse:0.017453+0.002838    test-rmse:0.245663+0.042821 
## Stopping. Best iteration:
## [9]  train-rmse:0.048386+0.001933    test-rmse:0.241646+0.038789
## 
## 98 
## [1]  train-rmse:0.911908+0.002138    test-rmse:0.912021+0.019158 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.597438+0.001852    test-rmse:0.598658+0.014306 
## [3]  train-rmse:0.413727+0.003594    test-rmse:0.418482+0.008492 
## [4]  train-rmse:0.308132+0.004896    test-rmse:0.313527+0.017250 
## [5]  train-rmse:0.253918+0.005506    test-rmse:0.262763+0.018400 
## [6]  train-rmse:0.225323+0.005981    test-rmse:0.231451+0.024117 
## [7]  train-rmse:0.212283+0.005904    test-rmse:0.221080+0.022783 
## [8]  train-rmse:0.204377+0.005879    test-rmse:0.213949+0.025096 
## [9]  train-rmse:0.198925+0.005368    test-rmse:0.213168+0.026203 
## [10] train-rmse:0.195117+0.005340    test-rmse:0.214808+0.027554 
## [11] train-rmse:0.192252+0.005314    test-rmse:0.210268+0.028368 
## [12] train-rmse:0.189572+0.005381    test-rmse:0.210893+0.028729 
## [13] train-rmse:0.187549+0.005416    test-rmse:0.211774+0.026205 
## [14] train-rmse:0.185640+0.005505    test-rmse:0.213654+0.027429 
## [15] train-rmse:0.183810+0.005700    test-rmse:0.211441+0.025713 
## [16] train-rmse:0.182158+0.005750    test-rmse:0.211966+0.025807 
## [17] train-rmse:0.180792+0.005964    test-rmse:0.213434+0.027082 
## Stopping. Best iteration:
## [11] train-rmse:0.192252+0.005314    test-rmse:0.210268+0.028368
## 
## 99 
## [1]  train-rmse:0.904341+0.003510    test-rmse:0.907202+0.017414 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.579447+0.001944    test-rmse:0.587757+0.012836 
## [3]  train-rmse:0.385437+0.001889    test-rmse:0.403270+0.011436 
## [4]  train-rmse:0.273777+0.002309    test-rmse:0.301408+0.015434 
## [5]  train-rmse:0.213141+0.002401    test-rmse:0.251148+0.020111 
## [6]  train-rmse:0.180946+0.001854    test-rmse:0.229591+0.021610 
## [7]  train-rmse:0.165062+0.002720    test-rmse:0.217531+0.022255 
## [8]  train-rmse:0.155913+0.002828    test-rmse:0.215755+0.024633 
## [9]  train-rmse:0.150936+0.003493    test-rmse:0.215034+0.025735 
## [10] train-rmse:0.146477+0.005501    test-rmse:0.213729+0.025338 
## [11] train-rmse:0.141620+0.004850    test-rmse:0.214441+0.025653 
## [12] train-rmse:0.139981+0.005330    test-rmse:0.214588+0.026094 
## [13] train-rmse:0.137572+0.005642    test-rmse:0.214711+0.026385 
## [14] train-rmse:0.135302+0.006224    test-rmse:0.215864+0.023853 
## [15] train-rmse:0.132625+0.005439    test-rmse:0.218463+0.024746 
## [16] train-rmse:0.129878+0.006860    test-rmse:0.218051+0.024379 
## Stopping. Best iteration:
## [10] train-rmse:0.146477+0.005501    test-rmse:0.213729+0.025338
## 
## 100 
## [1]  train-rmse:0.902704+0.003364    test-rmse:0.908460+0.015961 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.575284+0.001814    test-rmse:0.590985+0.009375 
## [3]  train-rmse:0.375452+0.003171    test-rmse:0.405886+0.009557 
## [4]  train-rmse:0.255580+0.005499    test-rmse:0.306955+0.013175 
## [5]  train-rmse:0.185375+0.006910    test-rmse:0.257153+0.017897 
## [6]  train-rmse:0.146515+0.008832    test-rmse:0.235711+0.023175 
## [7]  train-rmse:0.124746+0.008300    test-rmse:0.225203+0.025712 
## [8]  train-rmse:0.114490+0.009238    test-rmse:0.221646+0.027976 
## [9]  train-rmse:0.108114+0.009324    test-rmse:0.221698+0.029864 
## [10] train-rmse:0.104453+0.009309    test-rmse:0.221139+0.029751 
## [11] train-rmse:0.099849+0.009283    test-rmse:0.221677+0.030296 
## [12] train-rmse:0.096413+0.008701    test-rmse:0.221347+0.029844 
## [13] train-rmse:0.093097+0.009525    test-rmse:0.223101+0.030650 
## [14] train-rmse:0.089943+0.009077    test-rmse:0.223868+0.031335 
## [15] train-rmse:0.087134+0.010051    test-rmse:0.224239+0.030816 
## [16] train-rmse:0.083750+0.010601    test-rmse:0.225020+0.030873 
## Stopping. Best iteration:
## [10] train-rmse:0.104453+0.009309    test-rmse:0.221139+0.029751
## 
## 101 
## [1]  train-rmse:0.902131+0.003418    test-rmse:0.908608+0.015860 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.573849+0.001686    test-rmse:0.591120+0.009214 
## [3]  train-rmse:0.369365+0.001523    test-rmse:0.406782+0.009499 
## [4]  train-rmse:0.244473+0.003688    test-rmse:0.309745+0.014830 
## [5]  train-rmse:0.169426+0.005021    test-rmse:0.262117+0.021409 
## [6]  train-rmse:0.125493+0.006362    test-rmse:0.241333+0.026476 
## [7]  train-rmse:0.100238+0.006759    test-rmse:0.232691+0.028705 
## [8]  train-rmse:0.086888+0.007694    test-rmse:0.231179+0.030487 
## [9]  train-rmse:0.079809+0.007970    test-rmse:0.230692+0.032650 
## [10] train-rmse:0.073654+0.007318    test-rmse:0.230848+0.033004 
## [11] train-rmse:0.069596+0.008178    test-rmse:0.230025+0.032824 
## [12] train-rmse:0.065864+0.008103    test-rmse:0.230876+0.033739 
## [13] train-rmse:0.061923+0.007881    test-rmse:0.232205+0.033620 
## [14] train-rmse:0.059424+0.007608    test-rmse:0.233220+0.034243 
## [15] train-rmse:0.056001+0.006881    test-rmse:0.233430+0.034313 
## [16] train-rmse:0.053107+0.007199    test-rmse:0.233554+0.034721 
## [17] train-rmse:0.050084+0.006069    test-rmse:0.233736+0.035033 
## Stopping. Best iteration:
## [11] train-rmse:0.069596+0.008178    test-rmse:0.230025+0.032824
## 
## 102 
## [1]  train-rmse:0.901758+0.003375    test-rmse:0.908691+0.015803 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.572926+0.001627    test-rmse:0.591388+0.009101 
## [3]  train-rmse:0.366738+0.001212    test-rmse:0.407763+0.009012 
## [4]  train-rmse:0.239401+0.003341    test-rmse:0.310296+0.015042 
## [5]  train-rmse:0.160350+0.003010    test-rmse:0.264070+0.023930 
## [6]  train-rmse:0.112547+0.003933    test-rmse:0.246900+0.030609 
## [7]  train-rmse:0.085302+0.004784    test-rmse:0.239043+0.034005 
## [8]  train-rmse:0.069067+0.005778    test-rmse:0.236907+0.036170 
## [9]  train-rmse:0.058887+0.006008    test-rmse:0.236234+0.037088 
## [10] train-rmse:0.052598+0.006991    test-rmse:0.236470+0.037534 
## [11] train-rmse:0.048177+0.006983    test-rmse:0.237072+0.037865 
## [12] train-rmse:0.043944+0.006596    test-rmse:0.237608+0.038291 
## [13] train-rmse:0.041505+0.006666    test-rmse:0.238024+0.038868 
## [14] train-rmse:0.038453+0.007119    test-rmse:0.239104+0.039460 
## [15] train-rmse:0.036522+0.006427    test-rmse:0.239303+0.040291 
## Stopping. Best iteration:
## [9]  train-rmse:0.058887+0.006008    test-rmse:0.236234+0.037088
## 
## 103 
## [1]  train-rmse:0.901580+0.003451    test-rmse:0.908752+0.015740 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.572541+0.001695    test-rmse:0.591751+0.008839 
## [3]  train-rmse:0.365582+0.001276    test-rmse:0.407908+0.009793 
## [4]  train-rmse:0.237240+0.002799    test-rmse:0.310730+0.015247 
## [5]  train-rmse:0.157411+0.002355    test-rmse:0.264599+0.024973 
## [6]  train-rmse:0.107575+0.002946    test-rmse:0.246539+0.031823 
## [7]  train-rmse:0.078149+0.003650    test-rmse:0.240529+0.036821 
## [8]  train-rmse:0.060084+0.004568    test-rmse:0.239230+0.038915 
## [9]  train-rmse:0.048700+0.005366    test-rmse:0.240320+0.040156 
## [10] train-rmse:0.041013+0.005053    test-rmse:0.239974+0.040705 
## [11] train-rmse:0.035820+0.005333    test-rmse:0.239732+0.040812 
## [12] train-rmse:0.031931+0.005850    test-rmse:0.240340+0.041242 
## [13] train-rmse:0.028784+0.006093    test-rmse:0.240896+0.041501 
## [14] train-rmse:0.025840+0.006313    test-rmse:0.240995+0.041395 
## Stopping. Best iteration:
## [8]  train-rmse:0.060084+0.004568    test-rmse:0.239230+0.038915
## 
## 104 
## [1]  train-rmse:0.901499+0.003545    test-rmse:0.908780+0.015699 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.572307+0.001824    test-rmse:0.591843+0.008694 
## [3]  train-rmse:0.364669+0.001252    test-rmse:0.407662+0.009253 
## [4]  train-rmse:0.236106+0.001505    test-rmse:0.310948+0.014894 
## [5]  train-rmse:0.155384+0.001711    test-rmse:0.265628+0.023920 
## [6]  train-rmse:0.105245+0.002021    test-rmse:0.247670+0.031041 
## [7]  train-rmse:0.074007+0.003102    test-rmse:0.243059+0.035866 
## [8]  train-rmse:0.054793+0.003210    test-rmse:0.243009+0.038941 
## [9]  train-rmse:0.042347+0.003724    test-rmse:0.243923+0.040396 
## [10] train-rmse:0.034112+0.003993    test-rmse:0.244820+0.041079 
## [11] train-rmse:0.028408+0.004194    test-rmse:0.245380+0.041752 
## [12] train-rmse:0.024230+0.003590    test-rmse:0.245340+0.042284 
## [13] train-rmse:0.020751+0.003307    test-rmse:0.245562+0.042697 
## [14] train-rmse:0.018222+0.003039    test-rmse:0.246003+0.042793 
## Stopping. Best iteration:
## [8]  train-rmse:0.054793+0.003210    test-rmse:0.243009+0.038941
## 
## 105 
## [1]  train-rmse:0.884763+0.001900    test-rmse:0.884903+0.018774 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.566042+0.002268    test-rmse:0.567385+0.013802 
## [3]  train-rmse:0.386784+0.003804    test-rmse:0.392261+0.007962 
## [4]  train-rmse:0.289322+0.005203    test-rmse:0.294895+0.018510 
## [5]  train-rmse:0.242020+0.005668    test-rmse:0.251254+0.019598 
## [6]  train-rmse:0.218596+0.005991    test-rmse:0.224915+0.025128 
## [7]  train-rmse:0.207895+0.005998    test-rmse:0.218125+0.023470 
## [8]  train-rmse:0.201398+0.005668    test-rmse:0.214597+0.023833 
## [9]  train-rmse:0.196829+0.005257    test-rmse:0.212372+0.026689 
## [10] train-rmse:0.193124+0.005213    test-rmse:0.214198+0.027817 
## [11] train-rmse:0.190447+0.005369    test-rmse:0.212477+0.027159 
## [12] train-rmse:0.187889+0.005374    test-rmse:0.210952+0.027256 
## [13] train-rmse:0.185917+0.005501    test-rmse:0.212631+0.028221 
## [14] train-rmse:0.183999+0.005732    test-rmse:0.212625+0.025206 
## [15] train-rmse:0.182342+0.005773    test-rmse:0.211775+0.024152 
## [16] train-rmse:0.180833+0.005875    test-rmse:0.213540+0.026658 
## [17] train-rmse:0.179426+0.006065    test-rmse:0.212695+0.026078 
## [18] train-rmse:0.178059+0.006267    test-rmse:0.211149+0.025476 
## Stopping. Best iteration:
## [12] train-rmse:0.187889+0.005374    test-rmse:0.210952+0.027256
## 
## 106 
## [1]  train-rmse:0.876630+0.003360    test-rmse:0.879745+0.016997 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.546455+0.001852    test-rmse:0.555634+0.012438 
## [3]  train-rmse:0.357079+0.001972    test-rmse:0.376660+0.011550 
## [4]  train-rmse:0.253217+0.002412    test-rmse:0.282840+0.017046 
## [5]  train-rmse:0.200301+0.002465    test-rmse:0.241351+0.021613 
## [6]  train-rmse:0.174106+0.002009    test-rmse:0.223446+0.023222 
## [7]  train-rmse:0.160535+0.003444    test-rmse:0.215535+0.024316 
## [8]  train-rmse:0.151626+0.005279    test-rmse:0.211840+0.023755 
## [9]  train-rmse:0.146584+0.004406    test-rmse:0.212872+0.026075 
## [10] train-rmse:0.142669+0.004957    test-rmse:0.211631+0.026430 
## [11] train-rmse:0.140323+0.005089    test-rmse:0.212443+0.026139 
## [12] train-rmse:0.135783+0.005267    test-rmse:0.214617+0.025945 
## [13] train-rmse:0.133442+0.005411    test-rmse:0.214653+0.025686 
## [14] train-rmse:0.131306+0.005860    test-rmse:0.214928+0.025397 
## [15] train-rmse:0.129004+0.005161    test-rmse:0.216929+0.023870 
## [16] train-rmse:0.127767+0.005664    test-rmse:0.216975+0.024225 
## Stopping. Best iteration:
## [10] train-rmse:0.142669+0.004957    test-rmse:0.211631+0.026430
## 
## 107 
## [1]  train-rmse:0.874858+0.003191    test-rmse:0.881108+0.015435 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.541890+0.001771    test-rmse:0.559520+0.009126 
## [3]  train-rmse:0.345951+0.003662    test-rmse:0.379462+0.009796 
## [4]  train-rmse:0.232214+0.004712    test-rmse:0.288663+0.016108 
## [5]  train-rmse:0.169295+0.006373    test-rmse:0.247963+0.021834 
## [6]  train-rmse:0.136403+0.007527    test-rmse:0.231074+0.026926 
## [7]  train-rmse:0.119513+0.008503    test-rmse:0.222466+0.027372 
## [8]  train-rmse:0.110353+0.008774    test-rmse:0.220950+0.028873 
## [9]  train-rmse:0.104763+0.009349    test-rmse:0.220277+0.029053 
## [10] train-rmse:0.100068+0.008636    test-rmse:0.219888+0.030269 
## [11] train-rmse:0.096382+0.008676    test-rmse:0.220615+0.030424 
## [12] train-rmse:0.092120+0.010064    test-rmse:0.224897+0.031943 
## [13] train-rmse:0.088920+0.009768    test-rmse:0.224708+0.031759 
## [14] train-rmse:0.086216+0.010589    test-rmse:0.226868+0.032473 
## [15] train-rmse:0.082469+0.010824    test-rmse:0.227007+0.032589 
## [16] train-rmse:0.079797+0.011073    test-rmse:0.227301+0.032673 
## Stopping. Best iteration:
## [10] train-rmse:0.100068+0.008636    test-rmse:0.219888+0.030269
## 
## 108 
## [1]  train-rmse:0.874239+0.003251    test-rmse:0.881281+0.015326 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.540035+0.001809    test-rmse:0.560702+0.008977 
## [3]  train-rmse:0.338317+0.002291    test-rmse:0.382204+0.009515 
## [4]  train-rmse:0.219854+0.004083    test-rmse:0.292413+0.014853 
## [5]  train-rmse:0.151107+0.005070    test-rmse:0.253108+0.022385 
## [6]  train-rmse:0.112935+0.006501    test-rmse:0.237333+0.027117 
## [7]  train-rmse:0.093394+0.006818    test-rmse:0.231339+0.029902 
## [8]  train-rmse:0.082781+0.007305    test-rmse:0.230368+0.031545 
## [9]  train-rmse:0.076258+0.007613    test-rmse:0.231139+0.033186 
## [10] train-rmse:0.071128+0.007576    test-rmse:0.231155+0.033881 
## [11] train-rmse:0.066949+0.007391    test-rmse:0.231515+0.034613 
## [12] train-rmse:0.062437+0.005983    test-rmse:0.232888+0.033886 
## [13] train-rmse:0.058393+0.006611    test-rmse:0.234130+0.034462 
## [14] train-rmse:0.054860+0.005757    test-rmse:0.235499+0.034734 
## Stopping. Best iteration:
## [8]  train-rmse:0.082781+0.007305    test-rmse:0.230368+0.031545
## 
## 109 
## [1]  train-rmse:0.873835+0.003203    test-rmse:0.881379+0.015263 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.539125+0.001754    test-rmse:0.561019+0.008803 
## [3]  train-rmse:0.335995+0.001398    test-rmse:0.382576+0.009117 
## [4]  train-rmse:0.214364+0.002634    test-rmse:0.295707+0.015901 
## [5]  train-rmse:0.142265+0.003408    test-rmse:0.257992+0.025191 
## [6]  train-rmse:0.101522+0.003532    test-rmse:0.243743+0.031529 
## [7]  train-rmse:0.077754+0.004933    test-rmse:0.240537+0.034780 
## [8]  train-rmse:0.064948+0.005723    test-rmse:0.239555+0.035979 
## [9]  train-rmse:0.057314+0.006788    test-rmse:0.238979+0.036912 
## [10] train-rmse:0.052246+0.006545    test-rmse:0.238649+0.037081 
## [11] train-rmse:0.047495+0.006487    test-rmse:0.239422+0.037950 
## [12] train-rmse:0.043251+0.005616    test-rmse:0.239881+0.038749 
## [13] train-rmse:0.040248+0.005365    test-rmse:0.240167+0.038765 
## [14] train-rmse:0.037812+0.004910    test-rmse:0.240953+0.039333 
## [15] train-rmse:0.034660+0.004560    test-rmse:0.241606+0.039659 
## [16] train-rmse:0.031976+0.004710    test-rmse:0.242145+0.040500 
## Stopping. Best iteration:
## [10] train-rmse:0.052246+0.006545    test-rmse:0.238649+0.037081
## 
## 110 
## [1]  train-rmse:0.873643+0.003285    test-rmse:0.881449+0.015195 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.538712+0.001808    test-rmse:0.561431+0.008482 
## [3]  train-rmse:0.334614+0.001448    test-rmse:0.382665+0.009060 
## [4]  train-rmse:0.213038+0.003169    test-rmse:0.294750+0.015837 
## [5]  train-rmse:0.138904+0.003428    test-rmse:0.257327+0.025098 
## [6]  train-rmse:0.095284+0.004352    test-rmse:0.244255+0.031801 
## [7]  train-rmse:0.069737+0.004788    test-rmse:0.241443+0.035461 
## [8]  train-rmse:0.054679+0.006246    test-rmse:0.242094+0.037503 
## [9]  train-rmse:0.044654+0.005358    test-rmse:0.241098+0.037653 
## [10] train-rmse:0.037759+0.005510    test-rmse:0.241660+0.038063 
## [11] train-rmse:0.033516+0.005764    test-rmse:0.241909+0.038311 
## [12] train-rmse:0.029633+0.005554    test-rmse:0.243032+0.038759 
## [13] train-rmse:0.026779+0.005584    test-rmse:0.243776+0.039295 
## [14] train-rmse:0.023929+0.004884    test-rmse:0.244187+0.039491 
## [15] train-rmse:0.021751+0.004530    test-rmse:0.244644+0.040208 
## Stopping. Best iteration:
## [9]  train-rmse:0.044654+0.005358    test-rmse:0.241098+0.037653
## 
## 111 
## [1]  train-rmse:0.873555+0.003386    test-rmse:0.881480+0.015150 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.538453+0.001909    test-rmse:0.561536+0.008295 
## [3]  train-rmse:0.333392+0.001354    test-rmse:0.382619+0.008797 
## [4]  train-rmse:0.211629+0.001246    test-rmse:0.295465+0.016178 
## [5]  train-rmse:0.136559+0.001508    test-rmse:0.257723+0.025138 
## [6]  train-rmse:0.091947+0.001730    test-rmse:0.245336+0.031795 
## [7]  train-rmse:0.064945+0.002435    test-rmse:0.242621+0.036039 
## [8]  train-rmse:0.048540+0.002886    test-rmse:0.243805+0.037346 
## [9]  train-rmse:0.038455+0.003462    test-rmse:0.245263+0.038438 
## [10] train-rmse:0.031950+0.003814    test-rmse:0.246339+0.039163 
## [11] train-rmse:0.027653+0.004301    test-rmse:0.246630+0.039572 
## [12] train-rmse:0.023864+0.004417    test-rmse:0.247611+0.039953 
## [13] train-rmse:0.021320+0.004472    test-rmse:0.248509+0.040343 
## Stopping. Best iteration:
## [7]  train-rmse:0.064945+0.002435    test-rmse:0.242621+0.036039
## 
## 112
##   max_depth  eta
## 9         2 0.12
## [1]  train-rmse:0.950535+0.015707    test-rmse:0.950472+0.062012 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.904098+0.015142    test-rmse:0.906141+0.061347 
## [3]  train-rmse:0.864501+0.014709    test-rmse:0.868585+0.060575 
## [4]  train-rmse:0.830876+0.014292    test-rmse:0.836579+0.060433 
## [5]  train-rmse:0.802054+0.014131    test-rmse:0.813605+0.060356 
## [6]  train-rmse:0.777366+0.013749    test-rmse:0.789947+0.059168 
## [7]  train-rmse:0.756105+0.013560    test-rmse:0.772680+0.059365 
## [8]  train-rmse:0.737584+0.013150    test-rmse:0.755543+0.059540 
## [9]  train-rmse:0.721680+0.012893    test-rmse:0.740246+0.058033 
## [10] train-rmse:0.707530+0.012640    test-rmse:0.726395+0.057833 
## [11] train-rmse:0.695081+0.012420    test-rmse:0.715595+0.058321 
## [12] train-rmse:0.684238+0.012240    test-rmse:0.703656+0.057416 
## [13] train-rmse:0.674537+0.012053    test-rmse:0.696872+0.055942 
## [14] train-rmse:0.665951+0.011817    test-rmse:0.688636+0.054610 
## [15] train-rmse:0.658311+0.011736    test-rmse:0.681259+0.053380 
## [16] train-rmse:0.651429+0.011586    test-rmse:0.675685+0.054500 
## [17] train-rmse:0.645412+0.011487    test-rmse:0.669925+0.052484 
## [18] train-rmse:0.639889+0.011420    test-rmse:0.665527+0.052821 
## [19] train-rmse:0.634975+0.011282    test-rmse:0.661395+0.052634 
## [20] train-rmse:0.630547+0.011211    test-rmse:0.655911+0.052355 
## [21] train-rmse:0.626488+0.011181    test-rmse:0.653250+0.051493 
## [22] train-rmse:0.622871+0.011116    test-rmse:0.650014+0.052265 
## [23] train-rmse:0.619506+0.011084    test-rmse:0.646946+0.052171 
## [24] train-rmse:0.616363+0.011104    test-rmse:0.645445+0.051704 
## [25] train-rmse:0.613386+0.011093    test-rmse:0.642974+0.052665 
## [26] train-rmse:0.610555+0.011096    test-rmse:0.640737+0.053211 
## [27] train-rmse:0.607916+0.011092    test-rmse:0.639087+0.052250 
## [28] train-rmse:0.605394+0.011119    test-rmse:0.637059+0.052721 
## [29] train-rmse:0.602938+0.011132    test-rmse:0.634704+0.052773 
## [30] train-rmse:0.600633+0.011103    test-rmse:0.632691+0.052604 
## [31] train-rmse:0.598436+0.011126    test-rmse:0.631053+0.053325 
## [32] train-rmse:0.596315+0.011146    test-rmse:0.628964+0.053723 
## [33] train-rmse:0.594308+0.011126    test-rmse:0.627251+0.054276 
## [34] train-rmse:0.592347+0.011120    test-rmse:0.624904+0.054472 
## [35] train-rmse:0.590461+0.011101    test-rmse:0.623661+0.054691 
## [36] train-rmse:0.588597+0.011092    test-rmse:0.622282+0.053342 
## [37] train-rmse:0.586830+0.011107    test-rmse:0.620688+0.054476 
## [38] train-rmse:0.585099+0.011119    test-rmse:0.619699+0.054050 
## [39] train-rmse:0.583426+0.011120    test-rmse:0.618314+0.053994 
## [40] train-rmse:0.581784+0.011129    test-rmse:0.616510+0.053972 
## [41] train-rmse:0.580215+0.011128    test-rmse:0.615429+0.054754 
## [42] train-rmse:0.578682+0.011114    test-rmse:0.613655+0.054882 
## [43] train-rmse:0.577185+0.011093    test-rmse:0.613406+0.055109 
## [44] train-rmse:0.575730+0.011116    test-rmse:0.612478+0.055023 
## [45] train-rmse:0.574302+0.011120    test-rmse:0.611108+0.055031 
## [46] train-rmse:0.572918+0.011118    test-rmse:0.610379+0.054315 
## [47] train-rmse:0.571550+0.011119    test-rmse:0.609721+0.054298 
## [48] train-rmse:0.570213+0.011132    test-rmse:0.608246+0.054872 
## [49] train-rmse:0.568891+0.011140    test-rmse:0.606328+0.054862 
## [50] train-rmse:0.567583+0.011155    test-rmse:0.604717+0.055423 
## [51] train-rmse:0.566319+0.011154    test-rmse:0.603698+0.055297 
## [52] train-rmse:0.565083+0.011141    test-rmse:0.602630+0.055161 
## [53] train-rmse:0.563883+0.011150    test-rmse:0.601698+0.054575 
## [54] train-rmse:0.562695+0.011153    test-rmse:0.601156+0.054998 
## [55] train-rmse:0.561538+0.011149    test-rmse:0.600702+0.055334 
## [56] train-rmse:0.560389+0.011153    test-rmse:0.600351+0.054927 
## [57] train-rmse:0.559260+0.011155    test-rmse:0.599322+0.054406 
## [58] train-rmse:0.558143+0.011154    test-rmse:0.598936+0.053884 
## [59] train-rmse:0.557051+0.011144    test-rmse:0.597756+0.054677 
## [60] train-rmse:0.555977+0.011137    test-rmse:0.596991+0.054486 
## [61] train-rmse:0.554920+0.011127    test-rmse:0.595683+0.054753 
## [62] train-rmse:0.553866+0.011120    test-rmse:0.594462+0.055120 
## [63] train-rmse:0.552827+0.011122    test-rmse:0.593622+0.055113 
## [64] train-rmse:0.551800+0.011120    test-rmse:0.592896+0.055765 
## [65] train-rmse:0.550796+0.011120    test-rmse:0.591979+0.054951 
## [66] train-rmse:0.549823+0.011112    test-rmse:0.591438+0.054607 
## [67] train-rmse:0.548860+0.011103    test-rmse:0.591048+0.054572 
## [68] train-rmse:0.547920+0.011101    test-rmse:0.590547+0.054449 
## [69] train-rmse:0.546988+0.011093    test-rmse:0.589753+0.054153 
## [70] train-rmse:0.546065+0.011069    test-rmse:0.588637+0.054366 
## [71] train-rmse:0.545161+0.011056    test-rmse:0.588599+0.054174 
## [72] train-rmse:0.544266+0.011052    test-rmse:0.588069+0.054365 
## [73] train-rmse:0.543380+0.011053    test-rmse:0.587051+0.054560 
## [74] train-rmse:0.542510+0.011053    test-rmse:0.586561+0.054391 
## [75] train-rmse:0.541655+0.011051    test-rmse:0.585903+0.055396 
## [76] train-rmse:0.540815+0.011042    test-rmse:0.585029+0.054890 
## [77] train-rmse:0.539984+0.011031    test-rmse:0.584382+0.055230 
## [78] train-rmse:0.539166+0.011026    test-rmse:0.583868+0.055078 
## [79] train-rmse:0.538358+0.011017    test-rmse:0.583603+0.054941 
## [80] train-rmse:0.537557+0.011003    test-rmse:0.583114+0.055344 
## [81] train-rmse:0.536766+0.010983    test-rmse:0.582646+0.054772 
## [82] train-rmse:0.535982+0.010971    test-rmse:0.581567+0.055023 
## [83] train-rmse:0.535198+0.010958    test-rmse:0.580920+0.054700 
## [84] train-rmse:0.534430+0.010944    test-rmse:0.580944+0.054937 
## [85] train-rmse:0.533678+0.010936    test-rmse:0.580306+0.054611 
## [86] train-rmse:0.532937+0.010939    test-rmse:0.580036+0.054581 
## [87] train-rmse:0.532219+0.010929    test-rmse:0.579947+0.055445 
## [88] train-rmse:0.531511+0.010917    test-rmse:0.579080+0.055254 
## [89] train-rmse:0.530803+0.010902    test-rmse:0.578070+0.055408 
## [90] train-rmse:0.530107+0.010892    test-rmse:0.577591+0.055464 
## [91] train-rmse:0.529415+0.010884    test-rmse:0.577194+0.054914 
## [92] train-rmse:0.528731+0.010870    test-rmse:0.576545+0.054977 
## [93] train-rmse:0.528063+0.010858    test-rmse:0.576413+0.054926 
## [94] train-rmse:0.527400+0.010849    test-rmse:0.575851+0.055158 
## [95] train-rmse:0.526736+0.010836    test-rmse:0.575755+0.055269 
## [96] train-rmse:0.526084+0.010822    test-rmse:0.575236+0.054820 
## [97] train-rmse:0.525442+0.010808    test-rmse:0.575051+0.054667 
## [98] train-rmse:0.524809+0.010791    test-rmse:0.574633+0.054444 
## [99] train-rmse:0.524186+0.010775    test-rmse:0.573677+0.054599 
## [100]    train-rmse:0.523567+0.010755    test-rmse:0.572881+0.054254 
## [101]    train-rmse:0.522954+0.010735    test-rmse:0.572448+0.054232 
## [102]    train-rmse:0.522352+0.010727    test-rmse:0.571796+0.054133 
## [103]    train-rmse:0.521752+0.010720    test-rmse:0.571302+0.054780 
## [104]    train-rmse:0.521165+0.010707    test-rmse:0.570890+0.054259 
## [105]    train-rmse:0.520573+0.010697    test-rmse:0.570781+0.054727 
## [106]    train-rmse:0.519990+0.010689    test-rmse:0.570497+0.055009 
## [107]    train-rmse:0.519414+0.010676    test-rmse:0.570028+0.055361 
## [108]    train-rmse:0.518848+0.010671    test-rmse:0.569559+0.055479 
## [109]    train-rmse:0.518291+0.010663    test-rmse:0.569135+0.055477 
## [110]    train-rmse:0.517736+0.010653    test-rmse:0.568770+0.055154 
## [111]    train-rmse:0.517183+0.010648    test-rmse:0.568559+0.054829 
## [112]    train-rmse:0.516634+0.010639    test-rmse:0.567978+0.054506 
## [113]    train-rmse:0.516096+0.010624    test-rmse:0.567321+0.054648 
## [114]    train-rmse:0.515561+0.010612    test-rmse:0.566919+0.054606 
## [115]    train-rmse:0.515035+0.010600    test-rmse:0.566591+0.054557 
## [116]    train-rmse:0.514508+0.010585    test-rmse:0.566091+0.054884 
## [117]    train-rmse:0.513986+0.010574    test-rmse:0.566135+0.054643 
## [118]    train-rmse:0.513473+0.010560    test-rmse:0.565659+0.054303 
## [119]    train-rmse:0.512965+0.010545    test-rmse:0.565480+0.054594 
## [120]    train-rmse:0.512461+0.010533    test-rmse:0.564863+0.053923 
## [121]    train-rmse:0.511963+0.010523    test-rmse:0.564095+0.053825 
## [122]    train-rmse:0.511478+0.010517    test-rmse:0.563999+0.054205 
## [123]    train-rmse:0.510995+0.010509    test-rmse:0.563268+0.054238 
## [124]    train-rmse:0.510515+0.010500    test-rmse:0.562904+0.054673 
## [125]    train-rmse:0.510037+0.010492    test-rmse:0.562249+0.054792 
## [126]    train-rmse:0.509569+0.010489    test-rmse:0.561755+0.054665 
## [127]    train-rmse:0.509102+0.010485    test-rmse:0.561600+0.054835 
## [128]    train-rmse:0.508640+0.010479    test-rmse:0.561287+0.054747 
## [129]    train-rmse:0.508179+0.010470    test-rmse:0.561244+0.054697 
## [130]    train-rmse:0.507721+0.010465    test-rmse:0.560996+0.054683 
## [131]    train-rmse:0.507268+0.010461    test-rmse:0.560416+0.054473 
## [132]    train-rmse:0.506823+0.010453    test-rmse:0.560330+0.054060 
## [133]    train-rmse:0.506381+0.010446    test-rmse:0.560202+0.054188 
## [134]    train-rmse:0.505949+0.010436    test-rmse:0.560129+0.054510 
## [135]    train-rmse:0.505522+0.010429    test-rmse:0.559606+0.054339 
## [136]    train-rmse:0.505098+0.010420    test-rmse:0.559191+0.054035 
## [137]    train-rmse:0.504680+0.010416    test-rmse:0.558460+0.054017 
## [138]    train-rmse:0.504264+0.010412    test-rmse:0.557692+0.053789 
## [139]    train-rmse:0.503852+0.010410    test-rmse:0.557770+0.053756 
## [140]    train-rmse:0.503443+0.010407    test-rmse:0.557337+0.053593 
## [141]    train-rmse:0.503033+0.010398    test-rmse:0.557417+0.053865 
## [142]    train-rmse:0.502631+0.010397    test-rmse:0.557064+0.053627 
## [143]    train-rmse:0.502228+0.010397    test-rmse:0.556891+0.053802 
## [144]    train-rmse:0.501827+0.010396    test-rmse:0.556631+0.053770 
## [145]    train-rmse:0.501435+0.010394    test-rmse:0.556782+0.053536 
## [146]    train-rmse:0.501045+0.010389    test-rmse:0.556387+0.053654 
## [147]    train-rmse:0.500655+0.010386    test-rmse:0.556013+0.053822 
## [148]    train-rmse:0.500272+0.010380    test-rmse:0.555672+0.054163 
## [149]    train-rmse:0.499895+0.010377    test-rmse:0.555301+0.054073 
## [150]    train-rmse:0.499521+0.010370    test-rmse:0.554688+0.053772 
## [151]    train-rmse:0.499153+0.010366    test-rmse:0.554927+0.053601 
## [152]    train-rmse:0.498784+0.010361    test-rmse:0.554830+0.053502 
## [153]    train-rmse:0.498418+0.010355    test-rmse:0.554445+0.053840 
## [154]    train-rmse:0.498056+0.010349    test-rmse:0.554253+0.054062 
## [155]    train-rmse:0.497698+0.010345    test-rmse:0.553727+0.053937 
## [156]    train-rmse:0.497345+0.010345    test-rmse:0.553326+0.053909 
## [157]    train-rmse:0.496992+0.010345    test-rmse:0.552848+0.053983 
## [158]    train-rmse:0.496643+0.010345    test-rmse:0.552686+0.053780 
## [159]    train-rmse:0.496297+0.010347    test-rmse:0.552360+0.053518 
## [160]    train-rmse:0.495954+0.010347    test-rmse:0.551945+0.053439 
## [161]    train-rmse:0.495615+0.010349    test-rmse:0.551802+0.053099 
## [162]    train-rmse:0.495282+0.010345    test-rmse:0.551361+0.052556 
## [163]    train-rmse:0.494950+0.010342    test-rmse:0.551361+0.052576 
## [164]    train-rmse:0.494620+0.010339    test-rmse:0.551468+0.053131 
## [165]    train-rmse:0.494295+0.010335    test-rmse:0.551657+0.053090 
## [166]    train-rmse:0.493968+0.010333    test-rmse:0.551194+0.053221 
## [167]    train-rmse:0.493648+0.010326    test-rmse:0.551228+0.053405 
## [168]    train-rmse:0.493329+0.010315    test-rmse:0.550818+0.053510 
## [169]    train-rmse:0.493012+0.010308    test-rmse:0.550411+0.053591 
## [170]    train-rmse:0.492698+0.010302    test-rmse:0.550037+0.053778 
## [171]    train-rmse:0.492387+0.010295    test-rmse:0.550005+0.053903 
## [172]    train-rmse:0.492078+0.010291    test-rmse:0.549954+0.053865 
## [173]    train-rmse:0.491772+0.010288    test-rmse:0.549840+0.053580 
## [174]    train-rmse:0.491467+0.010286    test-rmse:0.549558+0.053451 
## [175]    train-rmse:0.491164+0.010286    test-rmse:0.549210+0.053341 
## [176]    train-rmse:0.490864+0.010285    test-rmse:0.548706+0.053266 
## [177]    train-rmse:0.490565+0.010284    test-rmse:0.548742+0.053081 
## [178]    train-rmse:0.490269+0.010282    test-rmse:0.548137+0.053115 
## [179]    train-rmse:0.489977+0.010280    test-rmse:0.547873+0.053100 
## [180]    train-rmse:0.489681+0.010284    test-rmse:0.547821+0.052981 
## [181]    train-rmse:0.489394+0.010280    test-rmse:0.547899+0.052855 
## [182]    train-rmse:0.489107+0.010276    test-rmse:0.547726+0.052582 
## [183]    train-rmse:0.488824+0.010272    test-rmse:0.547817+0.052596 
## [184]    train-rmse:0.488540+0.010269    test-rmse:0.547540+0.052464 
## [185]    train-rmse:0.488262+0.010264    test-rmse:0.547378+0.052488 
## [186]    train-rmse:0.487985+0.010257    test-rmse:0.547488+0.052612 
## [187]    train-rmse:0.487712+0.010253    test-rmse:0.547362+0.052600 
## [188]    train-rmse:0.487440+0.010248    test-rmse:0.547314+0.052567 
## [189]    train-rmse:0.487170+0.010245    test-rmse:0.547203+0.052557 
## [190]    train-rmse:0.486901+0.010241    test-rmse:0.546983+0.052722 
## [191]    train-rmse:0.486638+0.010239    test-rmse:0.547049+0.052899 
## [192]    train-rmse:0.486375+0.010236    test-rmse:0.546799+0.052788 
## [193]    train-rmse:0.486115+0.010233    test-rmse:0.546384+0.053035 
## [194]    train-rmse:0.485857+0.010228    test-rmse:0.546441+0.052812 
## [195]    train-rmse:0.485601+0.010225    test-rmse:0.546014+0.052900 
## [196]    train-rmse:0.485348+0.010222    test-rmse:0.546129+0.052801 
## [197]    train-rmse:0.485095+0.010219    test-rmse:0.545904+0.052676 
## [198]    train-rmse:0.484847+0.010215    test-rmse:0.545775+0.052524 
## [199]    train-rmse:0.484597+0.010213    test-rmse:0.545235+0.052276 
## [200]    train-rmse:0.484352+0.010212    test-rmse:0.544795+0.052273 
## [201]    train-rmse:0.484107+0.010209    test-rmse:0.544759+0.052112 
## [202]    train-rmse:0.483864+0.010206    test-rmse:0.544716+0.052037 
## [203]    train-rmse:0.483624+0.010205    test-rmse:0.544689+0.052310 
## [204]    train-rmse:0.483383+0.010203    test-rmse:0.544786+0.052075 
## [205]    train-rmse:0.483145+0.010200    test-rmse:0.544969+0.051970 
## [206]    train-rmse:0.482909+0.010198    test-rmse:0.544497+0.051992 
## [207]    train-rmse:0.482675+0.010195    test-rmse:0.544410+0.051939 
## [208]    train-rmse:0.482442+0.010192    test-rmse:0.544405+0.052042 
## [209]    train-rmse:0.482211+0.010188    test-rmse:0.544206+0.051976 
## [210]    train-rmse:0.481982+0.010185    test-rmse:0.543754+0.052087 
## [211]    train-rmse:0.481754+0.010180    test-rmse:0.543968+0.052140 
## [212]    train-rmse:0.481527+0.010177    test-rmse:0.543709+0.051895 
## [213]    train-rmse:0.481303+0.010174    test-rmse:0.543365+0.052038 
## [214]    train-rmse:0.481080+0.010174    test-rmse:0.543289+0.051888 
## [215]    train-rmse:0.480858+0.010174    test-rmse:0.543520+0.052306 
## [216]    train-rmse:0.480639+0.010175    test-rmse:0.543522+0.052379 
## [217]    train-rmse:0.480421+0.010174    test-rmse:0.543251+0.052386 
## [218]    train-rmse:0.480206+0.010173    test-rmse:0.542944+0.052305 
## [219]    train-rmse:0.479993+0.010172    test-rmse:0.542710+0.052137 
## [220]    train-rmse:0.479782+0.010170    test-rmse:0.542546+0.051773 
## [221]    train-rmse:0.479571+0.010167    test-rmse:0.542525+0.051641 
## [222]    train-rmse:0.479363+0.010164    test-rmse:0.542617+0.051608 
## [223]    train-rmse:0.479156+0.010162    test-rmse:0.542616+0.051279 
## [224]    train-rmse:0.478952+0.010160    test-rmse:0.542343+0.051254 
## [225]    train-rmse:0.478747+0.010158    test-rmse:0.542086+0.051090 
## [226]    train-rmse:0.478543+0.010156    test-rmse:0.541927+0.051121 
## [227]    train-rmse:0.478340+0.010152    test-rmse:0.541907+0.051215 
## [228]    train-rmse:0.478140+0.010150    test-rmse:0.541695+0.051208 
## [229]    train-rmse:0.477940+0.010148    test-rmse:0.541639+0.051008 
## [230]    train-rmse:0.477742+0.010147    test-rmse:0.541237+0.051226 
## [231]    train-rmse:0.477542+0.010142    test-rmse:0.541100+0.051015 
## [232]    train-rmse:0.477348+0.010141    test-rmse:0.540875+0.051028 
## [233]    train-rmse:0.477154+0.010141    test-rmse:0.540994+0.051017 
## [234]    train-rmse:0.476963+0.010141    test-rmse:0.540845+0.051129 
## [235]    train-rmse:0.476772+0.010139    test-rmse:0.540650+0.051135 
## [236]    train-rmse:0.476584+0.010138    test-rmse:0.540698+0.051166 
## [237]    train-rmse:0.476398+0.010137    test-rmse:0.540855+0.051042 
## [238]    train-rmse:0.476211+0.010135    test-rmse:0.540724+0.050826 
## [239]    train-rmse:0.476025+0.010134    test-rmse:0.540604+0.050874 
## [240]    train-rmse:0.475840+0.010132    test-rmse:0.540744+0.050835 
## [241]    train-rmse:0.475657+0.010130    test-rmse:0.540550+0.050813 
## [242]    train-rmse:0.475475+0.010130    test-rmse:0.540562+0.051009 
## [243]    train-rmse:0.475293+0.010129    test-rmse:0.540409+0.050907 
## [244]    train-rmse:0.475113+0.010127    test-rmse:0.540356+0.050785 
## [245]    train-rmse:0.474936+0.010123    test-rmse:0.540420+0.050818 
## [246]    train-rmse:0.474760+0.010120    test-rmse:0.540291+0.050772 
## [247]    train-rmse:0.474585+0.010118    test-rmse:0.540322+0.050839 
## [248]    train-rmse:0.474410+0.010118    test-rmse:0.540061+0.050765 
## [249]    train-rmse:0.474235+0.010116    test-rmse:0.539871+0.050613 
## [250]    train-rmse:0.474062+0.010114    test-rmse:0.539411+0.050578 
## [251]    train-rmse:0.473891+0.010111    test-rmse:0.539641+0.050525 
## [252]    train-rmse:0.473722+0.010109    test-rmse:0.539898+0.050527 
## [253]    train-rmse:0.473553+0.010108    test-rmse:0.539843+0.050694 
## [254]    train-rmse:0.473385+0.010106    test-rmse:0.539525+0.050748 
## [255]    train-rmse:0.473219+0.010104    test-rmse:0.539315+0.050562 
## [256]    train-rmse:0.473053+0.010103    test-rmse:0.539166+0.050420 
## [257]    train-rmse:0.472889+0.010102    test-rmse:0.538815+0.050361 
## [258]    train-rmse:0.472722+0.010100    test-rmse:0.538750+0.050348 
## [259]    train-rmse:0.472560+0.010097    test-rmse:0.538622+0.050250 
## [260]    train-rmse:0.472400+0.010094    test-rmse:0.538788+0.050464 
## [261]    train-rmse:0.472241+0.010091    test-rmse:0.538589+0.050454 
## [262]    train-rmse:0.472081+0.010088    test-rmse:0.538687+0.050503 
## [263]    train-rmse:0.471923+0.010085    test-rmse:0.538509+0.050628 
## [264]    train-rmse:0.471766+0.010082    test-rmse:0.538354+0.050653 
## [265]    train-rmse:0.471610+0.010079    test-rmse:0.538258+0.050376 
## [266]    train-rmse:0.471456+0.010075    test-rmse:0.538337+0.050481 
## [267]    train-rmse:0.471302+0.010072    test-rmse:0.538284+0.050230 
## [268]    train-rmse:0.471150+0.010070    test-rmse:0.538006+0.050064 
## [269]    train-rmse:0.470999+0.010068    test-rmse:0.537844+0.050079 
## [270]    train-rmse:0.470849+0.010065    test-rmse:0.537727+0.050025 
## [271]    train-rmse:0.470700+0.010062    test-rmse:0.537580+0.050092 
## [272]    train-rmse:0.470551+0.010060    test-rmse:0.537409+0.050095 
## [273]    train-rmse:0.470402+0.010058    test-rmse:0.537472+0.050000 
## [274]    train-rmse:0.470254+0.010055    test-rmse:0.537666+0.049934 
## [275]    train-rmse:0.470109+0.010052    test-rmse:0.537667+0.049988 
## [276]    train-rmse:0.469964+0.010050    test-rmse:0.537649+0.049976 
## [277]    train-rmse:0.469821+0.010047    test-rmse:0.537494+0.049800 
## [278]    train-rmse:0.469677+0.010044    test-rmse:0.537732+0.049686 
## Stopping. Best iteration:
## [272]    train-rmse:0.470551+0.010060    test-rmse:0.537409+0.050095
## 
## 1 
## [1]  train-rmse:0.942777+0.015656    test-rmse:0.943340+0.061874 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.888937+0.015034    test-rmse:0.892500+0.060611 
## [3]  train-rmse:0.842375+0.014689    test-rmse:0.849457+0.061717 
## [4]  train-rmse:0.801994+0.014317    test-rmse:0.811827+0.061382 
## [5]  train-rmse:0.767111+0.014009    test-rmse:0.780992+0.062166 
## [6]  train-rmse:0.736613+0.013608    test-rmse:0.754973+0.061565 
## [7]  train-rmse:0.710398+0.013037    test-rmse:0.730969+0.064138 
## [8]  train-rmse:0.687724+0.012541    test-rmse:0.709552+0.065229 
## [9]  train-rmse:0.667694+0.012331    test-rmse:0.690887+0.062939 
## [10] train-rmse:0.650296+0.011912    test-rmse:0.675465+0.063506 
## [11] train-rmse:0.634879+0.011542    test-rmse:0.660992+0.063550 
## [12] train-rmse:0.620922+0.011565    test-rmse:0.651554+0.064446 
## [13] train-rmse:0.609495+0.011792    test-rmse:0.641382+0.063005 
## [14] train-rmse:0.598596+0.012302    test-rmse:0.634378+0.063144 
## [15] train-rmse:0.589416+0.012318    test-rmse:0.627082+0.063420 
## [16] train-rmse:0.581551+0.012431    test-rmse:0.620859+0.062458 
## [17] train-rmse:0.573012+0.013365    test-rmse:0.613941+0.061157 
## [18] train-rmse:0.566903+0.013717    test-rmse:0.608293+0.060269 
## [19] train-rmse:0.560624+0.014222    test-rmse:0.605751+0.060321 
## [20] train-rmse:0.554933+0.013713    test-rmse:0.601498+0.060745 
## [21] train-rmse:0.550454+0.013675    test-rmse:0.598730+0.060196 
## [22] train-rmse:0.545681+0.013736    test-rmse:0.593859+0.058992 
## [23] train-rmse:0.542073+0.013992    test-rmse:0.590860+0.057888 
## [24] train-rmse:0.538072+0.014458    test-rmse:0.588838+0.058417 
## [25] train-rmse:0.534673+0.014615    test-rmse:0.586851+0.058201 
## [26] train-rmse:0.531343+0.014358    test-rmse:0.584416+0.057882 
## [27] train-rmse:0.528288+0.014583    test-rmse:0.581414+0.058437 
## [28] train-rmse:0.525510+0.014582    test-rmse:0.580112+0.057766 
## [29] train-rmse:0.522741+0.014616    test-rmse:0.579029+0.057590 
## [30] train-rmse:0.520121+0.014464    test-rmse:0.577807+0.058001 
## [31] train-rmse:0.517215+0.014798    test-rmse:0.575322+0.057118 
## [32] train-rmse:0.514960+0.014907    test-rmse:0.574676+0.056137 
## [33] train-rmse:0.512340+0.014895    test-rmse:0.572412+0.055947 
## [34] train-rmse:0.510202+0.014788    test-rmse:0.570715+0.056073 
## [35] train-rmse:0.507847+0.014676    test-rmse:0.569006+0.056692 
## [36] train-rmse:0.505739+0.014719    test-rmse:0.568491+0.056510 
## [37] train-rmse:0.503628+0.014729    test-rmse:0.566655+0.058371 
## [38] train-rmse:0.500798+0.014708    test-rmse:0.564223+0.058606 
## [39] train-rmse:0.498448+0.014761    test-rmse:0.562285+0.058478 
## [40] train-rmse:0.496606+0.014589    test-rmse:0.560681+0.057626 
## [41] train-rmse:0.494888+0.014643    test-rmse:0.559942+0.058141 
## [42] train-rmse:0.493001+0.014836    test-rmse:0.559146+0.057982 
## [43] train-rmse:0.491239+0.014870    test-rmse:0.557637+0.058732 
## [44] train-rmse:0.489500+0.014879    test-rmse:0.556513+0.058451 
## [45] train-rmse:0.487014+0.014789    test-rmse:0.555065+0.058386 
## [46] train-rmse:0.485258+0.014852    test-rmse:0.554731+0.058471 
## [47] train-rmse:0.483528+0.014652    test-rmse:0.553625+0.059277 
## [48] train-rmse:0.481919+0.014612    test-rmse:0.553206+0.059799 
## [49] train-rmse:0.480003+0.014268    test-rmse:0.550334+0.060115 
## [50] train-rmse:0.478409+0.014317    test-rmse:0.548999+0.060132 
## [51] train-rmse:0.476443+0.014145    test-rmse:0.548081+0.059529 
## [52] train-rmse:0.475008+0.014383    test-rmse:0.546469+0.059280 
## [53] train-rmse:0.473221+0.014790    test-rmse:0.546063+0.059531 
## [54] train-rmse:0.471831+0.014681    test-rmse:0.545440+0.059674 
## [55] train-rmse:0.470259+0.014873    test-rmse:0.544392+0.058517 
## [56] train-rmse:0.468802+0.014727    test-rmse:0.543469+0.058740 
## [57] train-rmse:0.467257+0.014518    test-rmse:0.542590+0.058946 
## [58] train-rmse:0.465936+0.014473    test-rmse:0.542519+0.059330 
## [59] train-rmse:0.464559+0.014618    test-rmse:0.541121+0.059061 
## [60] train-rmse:0.463290+0.014637    test-rmse:0.540498+0.058714 
## [61] train-rmse:0.461851+0.014580    test-rmse:0.539124+0.059207 
## [62] train-rmse:0.460735+0.014624    test-rmse:0.537668+0.059211 
## [63] train-rmse:0.459458+0.014616    test-rmse:0.536645+0.058684 
## [64] train-rmse:0.458268+0.014504    test-rmse:0.536280+0.058667 
## [65] train-rmse:0.456999+0.014562    test-rmse:0.536062+0.058097 
## [66] train-rmse:0.455643+0.014648    test-rmse:0.535105+0.058594 
## [67] train-rmse:0.454418+0.014698    test-rmse:0.534825+0.058894 
## [68] train-rmse:0.453398+0.014768    test-rmse:0.534833+0.059202 
## [69] train-rmse:0.452244+0.014697    test-rmse:0.534261+0.059193 
## [70] train-rmse:0.450936+0.014434    test-rmse:0.533184+0.059915 
## [71] train-rmse:0.449631+0.014268    test-rmse:0.531975+0.060906 
## [72] train-rmse:0.448239+0.014388    test-rmse:0.531332+0.061826 
## [73] train-rmse:0.447243+0.014332    test-rmse:0.530481+0.061238 
## [74] train-rmse:0.446367+0.014351    test-rmse:0.530492+0.060799 
## [75] train-rmse:0.445463+0.014369    test-rmse:0.530120+0.060237 
## [76] train-rmse:0.444354+0.014611    test-rmse:0.529744+0.059737 
## [77] train-rmse:0.443254+0.014382    test-rmse:0.528990+0.060334 
## [78] train-rmse:0.442264+0.014450    test-rmse:0.528686+0.060062 
## [79] train-rmse:0.441312+0.014369    test-rmse:0.528301+0.059861 
## [80] train-rmse:0.440141+0.014530    test-rmse:0.527500+0.060236 
## [81] train-rmse:0.438981+0.014073    test-rmse:0.526581+0.060545 
## [82] train-rmse:0.437856+0.013664    test-rmse:0.525864+0.061837 
## [83] train-rmse:0.437036+0.013673    test-rmse:0.526025+0.061927 
## [84] train-rmse:0.436030+0.013949    test-rmse:0.525904+0.061503 
## [85] train-rmse:0.435111+0.013977    test-rmse:0.525594+0.060790 
## [86] train-rmse:0.434009+0.014037    test-rmse:0.524790+0.061436 
## [87] train-rmse:0.433070+0.014220    test-rmse:0.524491+0.061340 
## [88] train-rmse:0.432138+0.014269    test-rmse:0.523949+0.061781 
## [89] train-rmse:0.431197+0.014325    test-rmse:0.523636+0.061728 
## [90] train-rmse:0.430041+0.013993    test-rmse:0.522861+0.061558 
## [91] train-rmse:0.429350+0.013935    test-rmse:0.522619+0.061468 
## [92] train-rmse:0.428481+0.013984    test-rmse:0.522568+0.061507 
## [93] train-rmse:0.427770+0.014011    test-rmse:0.522931+0.061718 
## [94] train-rmse:0.426702+0.014281    test-rmse:0.522431+0.061038 
## [95] train-rmse:0.425895+0.014187    test-rmse:0.522123+0.062029 
## [96] train-rmse:0.424813+0.014365    test-rmse:0.521514+0.061980 
## [97] train-rmse:0.424050+0.014376    test-rmse:0.521089+0.061391 
## [98] train-rmse:0.423025+0.014440    test-rmse:0.520655+0.061846 
## [99] train-rmse:0.422307+0.014579    test-rmse:0.520055+0.061819 
## [100]    train-rmse:0.421572+0.014738    test-rmse:0.519811+0.061680 
## [101]    train-rmse:0.420714+0.014812    test-rmse:0.519447+0.061316 
## [102]    train-rmse:0.419976+0.014983    test-rmse:0.518961+0.060964 
## [103]    train-rmse:0.419187+0.014996    test-rmse:0.519007+0.060871 
## [104]    train-rmse:0.418427+0.014964    test-rmse:0.518706+0.060475 
## [105]    train-rmse:0.417612+0.014784    test-rmse:0.518292+0.060760 
## [106]    train-rmse:0.416820+0.014782    test-rmse:0.517858+0.061545 
## [107]    train-rmse:0.416185+0.014984    test-rmse:0.517425+0.061579 
## [108]    train-rmse:0.415398+0.015090    test-rmse:0.516973+0.061275 
## [109]    train-rmse:0.414683+0.014983    test-rmse:0.516946+0.061009 
## [110]    train-rmse:0.413906+0.014989    test-rmse:0.516317+0.060999 
## [111]    train-rmse:0.413418+0.015020    test-rmse:0.516580+0.061285 
## [112]    train-rmse:0.412519+0.015097    test-rmse:0.516366+0.061537 
## [113]    train-rmse:0.411928+0.015115    test-rmse:0.516506+0.061622 
## [114]    train-rmse:0.411209+0.015212    test-rmse:0.516238+0.061370 
## [115]    train-rmse:0.410471+0.015291    test-rmse:0.516279+0.061564 
## [116]    train-rmse:0.409666+0.015331    test-rmse:0.516060+0.061368 
## [117]    train-rmse:0.408934+0.015272    test-rmse:0.515352+0.061854 
## [118]    train-rmse:0.408333+0.015334    test-rmse:0.515418+0.062053 
## [119]    train-rmse:0.407597+0.015436    test-rmse:0.514746+0.062276 
## [120]    train-rmse:0.406924+0.015355    test-rmse:0.514290+0.062672 
## [121]    train-rmse:0.406361+0.015435    test-rmse:0.514102+0.062293 
## [122]    train-rmse:0.405541+0.015368    test-rmse:0.513799+0.062564 
## [123]    train-rmse:0.404930+0.015318    test-rmse:0.514185+0.062848 
## [124]    train-rmse:0.404469+0.015260    test-rmse:0.513925+0.062944 
## [125]    train-rmse:0.403767+0.015412    test-rmse:0.513800+0.062783 
## [126]    train-rmse:0.403287+0.015371    test-rmse:0.513872+0.062621 
## [127]    train-rmse:0.402728+0.015360    test-rmse:0.513764+0.062520 
## [128]    train-rmse:0.402329+0.015354    test-rmse:0.513439+0.062256 
## [129]    train-rmse:0.401692+0.015333    test-rmse:0.513411+0.062494 
## [130]    train-rmse:0.401065+0.015316    test-rmse:0.513120+0.062797 
## [131]    train-rmse:0.400371+0.015468    test-rmse:0.513113+0.062591 
## [132]    train-rmse:0.399785+0.015549    test-rmse:0.512789+0.062466 
## [133]    train-rmse:0.399041+0.015280    test-rmse:0.512618+0.062310 
## [134]    train-rmse:0.398290+0.015399    test-rmse:0.512548+0.061995 
## [135]    train-rmse:0.397661+0.015270    test-rmse:0.511944+0.061963 
## [136]    train-rmse:0.397134+0.015403    test-rmse:0.511933+0.061867 
## [137]    train-rmse:0.396513+0.015364    test-rmse:0.511534+0.061764 
## [138]    train-rmse:0.395939+0.015328    test-rmse:0.511389+0.061601 
## [139]    train-rmse:0.395262+0.015492    test-rmse:0.511130+0.061341 
## [140]    train-rmse:0.394769+0.015382    test-rmse:0.511337+0.061422 
## [141]    train-rmse:0.394158+0.015150    test-rmse:0.511123+0.061270 
## [142]    train-rmse:0.393686+0.015317    test-rmse:0.510924+0.061407 
## [143]    train-rmse:0.393153+0.015142    test-rmse:0.510569+0.061307 
## [144]    train-rmse:0.392638+0.015153    test-rmse:0.510257+0.061503 
## [145]    train-rmse:0.392155+0.015075    test-rmse:0.510164+0.061218 
## [146]    train-rmse:0.391513+0.015021    test-rmse:0.509942+0.061451 
## [147]    train-rmse:0.391051+0.015085    test-rmse:0.509831+0.061586 
## [148]    train-rmse:0.390380+0.015001    test-rmse:0.509517+0.061568 
## [149]    train-rmse:0.389917+0.014909    test-rmse:0.509109+0.061368 
## [150]    train-rmse:0.389426+0.014837    test-rmse:0.509599+0.061498 
## [151]    train-rmse:0.388850+0.014630    test-rmse:0.509332+0.061461 
## [152]    train-rmse:0.388341+0.014638    test-rmse:0.509071+0.061247 
## [153]    train-rmse:0.387832+0.014681    test-rmse:0.508665+0.061344 
## [154]    train-rmse:0.387409+0.014616    test-rmse:0.508713+0.061493 
## [155]    train-rmse:0.386979+0.014519    test-rmse:0.508616+0.061646 
## [156]    train-rmse:0.386524+0.014405    test-rmse:0.508684+0.061776 
## [157]    train-rmse:0.386053+0.014347    test-rmse:0.508565+0.061686 
## [158]    train-rmse:0.385318+0.014605    test-rmse:0.508098+0.061172 
## [159]    train-rmse:0.384730+0.014760    test-rmse:0.508012+0.060959 
## [160]    train-rmse:0.383992+0.014733    test-rmse:0.507700+0.060917 
## [161]    train-rmse:0.383557+0.014677    test-rmse:0.507501+0.061065 
## [162]    train-rmse:0.383113+0.014583    test-rmse:0.507608+0.061131 
## [163]    train-rmse:0.382741+0.014521    test-rmse:0.507566+0.060882 
## [164]    train-rmse:0.382425+0.014515    test-rmse:0.507302+0.060809 
## [165]    train-rmse:0.382070+0.014510    test-rmse:0.507303+0.061473 
## [166]    train-rmse:0.381586+0.014496    test-rmse:0.506969+0.061427 
## [167]    train-rmse:0.380925+0.014541    test-rmse:0.506946+0.061214 
## [168]    train-rmse:0.380379+0.014632    test-rmse:0.506485+0.061127 
## [169]    train-rmse:0.379978+0.014624    test-rmse:0.506446+0.061281 
## [170]    train-rmse:0.379413+0.014621    test-rmse:0.506579+0.061618 
## [171]    train-rmse:0.379095+0.014589    test-rmse:0.506495+0.061590 
## [172]    train-rmse:0.378562+0.014503    test-rmse:0.506518+0.061662 
## [173]    train-rmse:0.378005+0.014629    test-rmse:0.506620+0.061217 
## [174]    train-rmse:0.377592+0.014584    test-rmse:0.506524+0.061708 
## [175]    train-rmse:0.377091+0.014702    test-rmse:0.506317+0.061231 
## [176]    train-rmse:0.376736+0.014706    test-rmse:0.506587+0.061190 
## [177]    train-rmse:0.376238+0.014751    test-rmse:0.506251+0.061079 
## [178]    train-rmse:0.375762+0.014900    test-rmse:0.506158+0.060919 
## [179]    train-rmse:0.375315+0.014790    test-rmse:0.505760+0.060930 
## [180]    train-rmse:0.374812+0.014749    test-rmse:0.505719+0.060900 
## [181]    train-rmse:0.374263+0.014859    test-rmse:0.505426+0.060898 
## [182]    train-rmse:0.373729+0.014680    test-rmse:0.505050+0.060788 
## [183]    train-rmse:0.373242+0.014524    test-rmse:0.505132+0.060694 
## [184]    train-rmse:0.372736+0.014428    test-rmse:0.504977+0.060724 
## [185]    train-rmse:0.372154+0.014492    test-rmse:0.504834+0.061047 
## [186]    train-rmse:0.371582+0.014452    test-rmse:0.504615+0.061096 
## [187]    train-rmse:0.371123+0.014252    test-rmse:0.504381+0.061088 
## [188]    train-rmse:0.370724+0.014081    test-rmse:0.504272+0.060824 
## [189]    train-rmse:0.370347+0.014118    test-rmse:0.503692+0.060942 
## [190]    train-rmse:0.369970+0.014181    test-rmse:0.503844+0.061128 
## [191]    train-rmse:0.369613+0.014151    test-rmse:0.503668+0.061200 
## [192]    train-rmse:0.369268+0.014040    test-rmse:0.503696+0.061116 
## [193]    train-rmse:0.368927+0.014126    test-rmse:0.503653+0.060995 
## [194]    train-rmse:0.368625+0.014115    test-rmse:0.503636+0.060943 
## [195]    train-rmse:0.368131+0.014234    test-rmse:0.503423+0.060923 
## [196]    train-rmse:0.367597+0.014081    test-rmse:0.503682+0.061116 
## [197]    train-rmse:0.367300+0.014024    test-rmse:0.503510+0.061297 
## [198]    train-rmse:0.366792+0.013896    test-rmse:0.503133+0.061468 
## [199]    train-rmse:0.366154+0.014179    test-rmse:0.502822+0.061333 
## [200]    train-rmse:0.365818+0.014264    test-rmse:0.502659+0.061086 
## [201]    train-rmse:0.365579+0.014250    test-rmse:0.502313+0.061165 
## [202]    train-rmse:0.365211+0.014221    test-rmse:0.502327+0.060852 
## [203]    train-rmse:0.364886+0.014227    test-rmse:0.501875+0.061083 
## [204]    train-rmse:0.364364+0.014194    test-rmse:0.501687+0.061009 
## [205]    train-rmse:0.364076+0.014237    test-rmse:0.501851+0.060972 
## [206]    train-rmse:0.363700+0.014178    test-rmse:0.501512+0.060993 
## [207]    train-rmse:0.363321+0.014236    test-rmse:0.501504+0.060907 
## [208]    train-rmse:0.362869+0.014012    test-rmse:0.501614+0.060808 
## [209]    train-rmse:0.362471+0.013889    test-rmse:0.501587+0.061037 
## [210]    train-rmse:0.362047+0.013924    test-rmse:0.501276+0.061113 
## [211]    train-rmse:0.361407+0.013883    test-rmse:0.501151+0.061086 
## [212]    train-rmse:0.360931+0.013874    test-rmse:0.501244+0.060749 
## [213]    train-rmse:0.360409+0.013966    test-rmse:0.500902+0.060810 
## [214]    train-rmse:0.360000+0.013795    test-rmse:0.501051+0.060676 
## [215]    train-rmse:0.359405+0.013630    test-rmse:0.501166+0.060688 
## [216]    train-rmse:0.358955+0.013525    test-rmse:0.500921+0.060766 
## [217]    train-rmse:0.358369+0.013366    test-rmse:0.500701+0.060627 
## [218]    train-rmse:0.357800+0.013365    test-rmse:0.500572+0.060604 
## [219]    train-rmse:0.357453+0.013419    test-rmse:0.500469+0.060315 
## [220]    train-rmse:0.357176+0.013398    test-rmse:0.500862+0.060347 
## [221]    train-rmse:0.356726+0.013287    test-rmse:0.500823+0.060333 
## [222]    train-rmse:0.356361+0.013312    test-rmse:0.500622+0.060256 
## [223]    train-rmse:0.355997+0.013259    test-rmse:0.500813+0.060313 
## [224]    train-rmse:0.355345+0.013364    test-rmse:0.500815+0.060273 
## [225]    train-rmse:0.354972+0.013247    test-rmse:0.500607+0.060322 
## Stopping. Best iteration:
## [219]    train-rmse:0.357453+0.013419    test-rmse:0.500469+0.060315
## 
## 2 
## [1]  train-rmse:0.939318+0.015788    test-rmse:0.941241+0.062436 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.881898+0.015193    test-rmse:0.887996+0.062277 
## [3]  train-rmse:0.831766+0.014944    test-rmse:0.841348+0.062224 
## [4]  train-rmse:0.787849+0.014567    test-rmse:0.801028+0.063325 
## [5]  train-rmse:0.749559+0.013968    test-rmse:0.766483+0.064071 
## [6]  train-rmse:0.715768+0.013623    test-rmse:0.735948+0.064151 
## [7]  train-rmse:0.685750+0.013214    test-rmse:0.711027+0.065893 
## [8]  train-rmse:0.660451+0.012924    test-rmse:0.688365+0.065751 
## [9]  train-rmse:0.637325+0.012661    test-rmse:0.667572+0.064223 
## [10] train-rmse:0.617278+0.013116    test-rmse:0.651103+0.062967 
## [11] train-rmse:0.599675+0.013056    test-rmse:0.636931+0.062729 
## [12] train-rmse:0.584125+0.012509    test-rmse:0.624400+0.063180 
## [13] train-rmse:0.570505+0.012161    test-rmse:0.614593+0.062446 
## [14] train-rmse:0.558731+0.012361    test-rmse:0.607382+0.062143 
## [15] train-rmse:0.548074+0.012477    test-rmse:0.600132+0.059703 
## [16] train-rmse:0.537777+0.013118    test-rmse:0.592690+0.059010 
## [17] train-rmse:0.528983+0.013018    test-rmse:0.586648+0.057456 
## [18] train-rmse:0.520789+0.013619    test-rmse:0.581039+0.056024 
## [19] train-rmse:0.512792+0.013930    test-rmse:0.575937+0.054192 
## [20] train-rmse:0.504916+0.014387    test-rmse:0.571260+0.054148 
## [21] train-rmse:0.499455+0.014721    test-rmse:0.568667+0.054538 
## [22] train-rmse:0.494729+0.014571    test-rmse:0.565508+0.053173 
## [23] train-rmse:0.490406+0.014552    test-rmse:0.562484+0.053560 
## [24] train-rmse:0.485259+0.014844    test-rmse:0.559489+0.053154 
## [25] train-rmse:0.480685+0.014284    test-rmse:0.556417+0.053529 
## [26] train-rmse:0.476619+0.014458    test-rmse:0.554120+0.053853 
## [27] train-rmse:0.473439+0.014431    test-rmse:0.552035+0.053067 
## [28] train-rmse:0.469895+0.013951    test-rmse:0.550380+0.053247 
## [29] train-rmse:0.466334+0.014260    test-rmse:0.549997+0.053908 
## [30] train-rmse:0.463262+0.013831    test-rmse:0.547304+0.054628 
## [31] train-rmse:0.459918+0.013694    test-rmse:0.545814+0.054678 
## [32] train-rmse:0.456947+0.013798    test-rmse:0.544346+0.054499 
## [33] train-rmse:0.453546+0.013460    test-rmse:0.541791+0.055051 
## [34] train-rmse:0.450511+0.013500    test-rmse:0.541238+0.054889 
## [35] train-rmse:0.447467+0.013568    test-rmse:0.538580+0.054527 
## [36] train-rmse:0.444903+0.013678    test-rmse:0.537181+0.053772 
## [37] train-rmse:0.442655+0.013775    test-rmse:0.536355+0.053686 
## [38] train-rmse:0.439614+0.012571    test-rmse:0.533999+0.055530 
## [39] train-rmse:0.437386+0.012728    test-rmse:0.532217+0.055480 
## [40] train-rmse:0.434885+0.012605    test-rmse:0.530881+0.054917 
## [41] train-rmse:0.432516+0.012215    test-rmse:0.530973+0.055376 
## [42] train-rmse:0.429823+0.012683    test-rmse:0.529892+0.055470 
## [43] train-rmse:0.427200+0.013269    test-rmse:0.529952+0.056267 
## [44] train-rmse:0.424771+0.012981    test-rmse:0.529140+0.055623 
## [45] train-rmse:0.422884+0.012730    test-rmse:0.528143+0.056175 
## [46] train-rmse:0.420864+0.012659    test-rmse:0.526772+0.056428 
## [47] train-rmse:0.418554+0.013369    test-rmse:0.526156+0.056048 
## [48] train-rmse:0.416088+0.013730    test-rmse:0.524243+0.056327 
## [49] train-rmse:0.414440+0.013821    test-rmse:0.524588+0.056539 
## [50] train-rmse:0.412751+0.013789    test-rmse:0.523900+0.055785 
## [51] train-rmse:0.410640+0.013842    test-rmse:0.522547+0.055649 
## [52] train-rmse:0.408938+0.013930    test-rmse:0.521926+0.055834 
## [53] train-rmse:0.407201+0.013928    test-rmse:0.521495+0.056285 
## [54] train-rmse:0.405721+0.014105    test-rmse:0.521782+0.056353 
## [55] train-rmse:0.404020+0.014325    test-rmse:0.520898+0.056566 
## [56] train-rmse:0.402220+0.014752    test-rmse:0.519718+0.057122 
## [57] train-rmse:0.400376+0.014249    test-rmse:0.519418+0.057064 
## [58] train-rmse:0.399145+0.014270    test-rmse:0.519310+0.057383 
## [59] train-rmse:0.397093+0.014448    test-rmse:0.518288+0.056786 
## [60] train-rmse:0.395115+0.014295    test-rmse:0.517043+0.057214 
## [61] train-rmse:0.393446+0.014404    test-rmse:0.516210+0.056955 
## [62] train-rmse:0.391714+0.014495    test-rmse:0.515656+0.057193 
## [63] train-rmse:0.390253+0.014850    test-rmse:0.515738+0.057280 
## [64] train-rmse:0.388869+0.015089    test-rmse:0.515273+0.057103 
## [65] train-rmse:0.386964+0.015127    test-rmse:0.515199+0.056844 
## [66] train-rmse:0.385671+0.015154    test-rmse:0.514625+0.056219 
## [67] train-rmse:0.384442+0.015034    test-rmse:0.514575+0.056401 
## [68] train-rmse:0.382331+0.015251    test-rmse:0.514335+0.056444 
## [69] train-rmse:0.380968+0.015428    test-rmse:0.514039+0.055969 
## [70] train-rmse:0.379945+0.015409    test-rmse:0.513194+0.056023 
## [71] train-rmse:0.378692+0.014990    test-rmse:0.513118+0.055882 
## [72] train-rmse:0.377212+0.014989    test-rmse:0.512911+0.056391 
## [73] train-rmse:0.375847+0.014864    test-rmse:0.512641+0.056175 
## [74] train-rmse:0.374708+0.014826    test-rmse:0.511756+0.056502 
## [75] train-rmse:0.373248+0.014951    test-rmse:0.511410+0.056543 
## [76] train-rmse:0.371984+0.014598    test-rmse:0.511419+0.056792 
## [77] train-rmse:0.370637+0.014534    test-rmse:0.510784+0.057254 
## [78] train-rmse:0.369426+0.014480    test-rmse:0.510380+0.057253 
## [79] train-rmse:0.368247+0.014473    test-rmse:0.510265+0.056931 
## [80] train-rmse:0.367246+0.014374    test-rmse:0.509731+0.057025 
## [81] train-rmse:0.365950+0.014307    test-rmse:0.508589+0.057281 
## [82] train-rmse:0.364422+0.014503    test-rmse:0.508150+0.056983 
## [83] train-rmse:0.363088+0.014685    test-rmse:0.507483+0.056458 
## [84] train-rmse:0.361882+0.015094    test-rmse:0.507402+0.056510 
## [85] train-rmse:0.360983+0.015133    test-rmse:0.507345+0.056514 
## [86] train-rmse:0.359739+0.014856    test-rmse:0.507110+0.055885 
## [87] train-rmse:0.358559+0.014592    test-rmse:0.506573+0.055731 
## [88] train-rmse:0.357479+0.014700    test-rmse:0.506377+0.055625 
## [89] train-rmse:0.356532+0.014656    test-rmse:0.505734+0.055301 
## [90] train-rmse:0.355345+0.014489    test-rmse:0.506129+0.055501 
## [91] train-rmse:0.354291+0.014349    test-rmse:0.505910+0.055602 
## [92] train-rmse:0.353193+0.014152    test-rmse:0.505370+0.055689 
## [93] train-rmse:0.352469+0.013925    test-rmse:0.504632+0.055513 
## [94] train-rmse:0.351266+0.014222    test-rmse:0.504801+0.054928 
## [95] train-rmse:0.350333+0.014287    test-rmse:0.504243+0.054807 
## [96] train-rmse:0.349182+0.014413    test-rmse:0.504341+0.054741 
## [97] train-rmse:0.347942+0.014399    test-rmse:0.504347+0.054749 
## [98] train-rmse:0.346887+0.014428    test-rmse:0.503840+0.054575 
## [99] train-rmse:0.345916+0.014279    test-rmse:0.502961+0.054455 
## [100]    train-rmse:0.344577+0.014404    test-rmse:0.502746+0.054754 
## [101]    train-rmse:0.343381+0.014367    test-rmse:0.502570+0.054449 
## [102]    train-rmse:0.342475+0.014028    test-rmse:0.502691+0.054484 
## [103]    train-rmse:0.341658+0.014026    test-rmse:0.502754+0.054490 
## [104]    train-rmse:0.340679+0.014106    test-rmse:0.502533+0.054466 
## [105]    train-rmse:0.339737+0.014038    test-rmse:0.502633+0.054240 
## [106]    train-rmse:0.338996+0.013830    test-rmse:0.502307+0.054548 
## [107]    train-rmse:0.337964+0.014024    test-rmse:0.502398+0.054167 
## [108]    train-rmse:0.336651+0.014661    test-rmse:0.501721+0.054033 
## [109]    train-rmse:0.335651+0.014356    test-rmse:0.501477+0.053993 
## [110]    train-rmse:0.334640+0.014104    test-rmse:0.500992+0.054162 
## [111]    train-rmse:0.333977+0.014060    test-rmse:0.501041+0.054354 
## [112]    train-rmse:0.333016+0.014269    test-rmse:0.500927+0.053954 
## [113]    train-rmse:0.332274+0.014160    test-rmse:0.500766+0.053921 
## [114]    train-rmse:0.331203+0.014172    test-rmse:0.500742+0.054039 
## [115]    train-rmse:0.330437+0.013999    test-rmse:0.501064+0.053579 
## [116]    train-rmse:0.329591+0.014132    test-rmse:0.500718+0.053507 
## [117]    train-rmse:0.328838+0.014115    test-rmse:0.500158+0.053335 
## [118]    train-rmse:0.327967+0.014109    test-rmse:0.499935+0.053484 
## [119]    train-rmse:0.327117+0.014226    test-rmse:0.500147+0.053385 
## [120]    train-rmse:0.326133+0.013807    test-rmse:0.499995+0.053239 
## [121]    train-rmse:0.325510+0.013728    test-rmse:0.500267+0.053102 
## [122]    train-rmse:0.324717+0.013700    test-rmse:0.500039+0.052724 
## [123]    train-rmse:0.323907+0.013442    test-rmse:0.499814+0.053023 
## [124]    train-rmse:0.323129+0.013433    test-rmse:0.499518+0.053060 
## [125]    train-rmse:0.322258+0.013485    test-rmse:0.499841+0.052833 
## [126]    train-rmse:0.321639+0.013570    test-rmse:0.500002+0.052827 
## [127]    train-rmse:0.320905+0.013898    test-rmse:0.499724+0.052711 
## [128]    train-rmse:0.319994+0.013735    test-rmse:0.499613+0.052695 
## [129]    train-rmse:0.319095+0.013846    test-rmse:0.499207+0.052507 
## [130]    train-rmse:0.317919+0.014042    test-rmse:0.499897+0.052250 
## [131]    train-rmse:0.316716+0.013835    test-rmse:0.499193+0.052661 
## [132]    train-rmse:0.315917+0.014035    test-rmse:0.499485+0.052570 
## [133]    train-rmse:0.315057+0.014312    test-rmse:0.499491+0.052613 
## [134]    train-rmse:0.314354+0.014401    test-rmse:0.499172+0.052332 
## [135]    train-rmse:0.313715+0.014407    test-rmse:0.499270+0.052619 
## [136]    train-rmse:0.312903+0.014616    test-rmse:0.499345+0.052315 
## [137]    train-rmse:0.312179+0.014417    test-rmse:0.498967+0.052519 
## [138]    train-rmse:0.311456+0.014230    test-rmse:0.499176+0.052656 
## [139]    train-rmse:0.310963+0.014234    test-rmse:0.499421+0.052720 
## [140]    train-rmse:0.310135+0.014576    test-rmse:0.499502+0.052508 
## [141]    train-rmse:0.309547+0.014518    test-rmse:0.499388+0.052537 
## [142]    train-rmse:0.308998+0.014470    test-rmse:0.499109+0.052211 
## [143]    train-rmse:0.308055+0.014570    test-rmse:0.499042+0.052256 
## Stopping. Best iteration:
## [137]    train-rmse:0.312179+0.014417    test-rmse:0.498967+0.052519
## 
## 3 
## [1]  train-rmse:0.936366+0.015681    test-rmse:0.939452+0.061849 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.875983+0.014909    test-rmse:0.884276+0.061468 
## [3]  train-rmse:0.822956+0.014902    test-rmse:0.836286+0.061101 
## [4]  train-rmse:0.776023+0.014453    test-rmse:0.794012+0.062174 
## [5]  train-rmse:0.735251+0.014330    test-rmse:0.757502+0.062845 
## [6]  train-rmse:0.699066+0.014106    test-rmse:0.727364+0.063637 
## [7]  train-rmse:0.666978+0.013511    test-rmse:0.699669+0.063450 
## [8]  train-rmse:0.638071+0.013204    test-rmse:0.677362+0.064880 
## [9]  train-rmse:0.612587+0.013462    test-rmse:0.657617+0.065331 
## [10] train-rmse:0.589363+0.013067    test-rmse:0.639791+0.064115 
## [11] train-rmse:0.568952+0.012435    test-rmse:0.623917+0.062916 
## [12] train-rmse:0.551051+0.013043    test-rmse:0.611108+0.060392 
## [13] train-rmse:0.534921+0.012320    test-rmse:0.599946+0.059906 
## [14] train-rmse:0.519816+0.012590    test-rmse:0.588166+0.059034 
## [15] train-rmse:0.507458+0.012256    test-rmse:0.580151+0.058939 
## [16] train-rmse:0.495910+0.012155    test-rmse:0.573062+0.058377 
## [17] train-rmse:0.485157+0.011902    test-rmse:0.566306+0.059391 
## [18] train-rmse:0.475677+0.012648    test-rmse:0.563097+0.057820 
## [19] train-rmse:0.467093+0.012553    test-rmse:0.557989+0.056932 
## [20] train-rmse:0.459190+0.012870    test-rmse:0.554545+0.055887 
## [21] train-rmse:0.450796+0.013653    test-rmse:0.550242+0.055709 
## [22] train-rmse:0.443826+0.013797    test-rmse:0.544849+0.053117 
## [23] train-rmse:0.437916+0.013727    test-rmse:0.542070+0.053559 
## [24] train-rmse:0.433307+0.013474    test-rmse:0.538645+0.053561 
## [25] train-rmse:0.428998+0.013711    test-rmse:0.536137+0.053321 
## [26] train-rmse:0.423858+0.012788    test-rmse:0.534156+0.052998 
## [27] train-rmse:0.419027+0.013950    test-rmse:0.532831+0.053413 
## [28] train-rmse:0.415083+0.013736    test-rmse:0.530388+0.052417 
## [29] train-rmse:0.410789+0.013518    test-rmse:0.527665+0.054106 
## [30] train-rmse:0.406992+0.013067    test-rmse:0.525419+0.055398 
## [31] train-rmse:0.403225+0.013424    test-rmse:0.523265+0.054388 
## [32] train-rmse:0.399851+0.013449    test-rmse:0.522346+0.054142 
## [33] train-rmse:0.397195+0.013644    test-rmse:0.520928+0.055138 
## [34] train-rmse:0.393948+0.013220    test-rmse:0.518218+0.055667 
## [35] train-rmse:0.391024+0.012960    test-rmse:0.517505+0.055344 
## [36] train-rmse:0.388464+0.012912    test-rmse:0.516833+0.054505 
## [37] train-rmse:0.385728+0.013192    test-rmse:0.516151+0.054547 
## [38] train-rmse:0.383341+0.013107    test-rmse:0.515809+0.054408 
## [39] train-rmse:0.380572+0.012740    test-rmse:0.514125+0.054225 
## [40] train-rmse:0.377877+0.012232    test-rmse:0.512977+0.053852 
## [41] train-rmse:0.375424+0.011614    test-rmse:0.511687+0.054516 
## [42] train-rmse:0.372822+0.011277    test-rmse:0.510148+0.055640 
## [43] train-rmse:0.370751+0.011514    test-rmse:0.510056+0.055309 
## [44] train-rmse:0.368876+0.011424    test-rmse:0.509210+0.054904 
## [45] train-rmse:0.366475+0.011268    test-rmse:0.508077+0.054996 
## [46] train-rmse:0.364513+0.011381    test-rmse:0.507716+0.054389 
## [47] train-rmse:0.362668+0.010817    test-rmse:0.507270+0.054530 
## [48] train-rmse:0.359918+0.011264    test-rmse:0.505914+0.053976 
## [49] train-rmse:0.357618+0.011718    test-rmse:0.505258+0.054065 
## [50] train-rmse:0.355856+0.012007    test-rmse:0.504909+0.054003 
## [51] train-rmse:0.353572+0.012148    test-rmse:0.504399+0.054152 
## [52] train-rmse:0.351272+0.012424    test-rmse:0.503685+0.054465 
## [53] train-rmse:0.349027+0.011854    test-rmse:0.503091+0.054823 
## [54] train-rmse:0.347346+0.011861    test-rmse:0.502669+0.054744 
## [55] train-rmse:0.345604+0.012188    test-rmse:0.501675+0.054630 
## [56] train-rmse:0.343669+0.011713    test-rmse:0.501332+0.054714 
## [57] train-rmse:0.342193+0.011596    test-rmse:0.501090+0.054828 
## [58] train-rmse:0.340446+0.011748    test-rmse:0.500664+0.054469 
## [59] train-rmse:0.339051+0.011583    test-rmse:0.500355+0.054261 
## [60] train-rmse:0.336676+0.012061    test-rmse:0.500348+0.054230 
## [61] train-rmse:0.334635+0.012093    test-rmse:0.499431+0.054494 
## [62] train-rmse:0.332750+0.011877    test-rmse:0.498875+0.054237 
## [63] train-rmse:0.330535+0.011416    test-rmse:0.498654+0.054421 
## [64] train-rmse:0.328932+0.011825    test-rmse:0.498079+0.054333 
## [65] train-rmse:0.326997+0.012039    test-rmse:0.498052+0.054955 
## [66] train-rmse:0.324961+0.012090    test-rmse:0.496982+0.055610 
## [67] train-rmse:0.323129+0.011804    test-rmse:0.496508+0.055443 
## [68] train-rmse:0.321099+0.011381    test-rmse:0.495942+0.055008 
## [69] train-rmse:0.319326+0.011541    test-rmse:0.495936+0.054572 
## [70] train-rmse:0.317091+0.011665    test-rmse:0.496143+0.054023 
## [71] train-rmse:0.315274+0.011303    test-rmse:0.495697+0.054361 
## [72] train-rmse:0.313932+0.011226    test-rmse:0.495842+0.054356 
## [73] train-rmse:0.312406+0.011087    test-rmse:0.495903+0.053955 
## [74] train-rmse:0.311015+0.011344    test-rmse:0.496199+0.053592 
## [75] train-rmse:0.309557+0.011023    test-rmse:0.495607+0.053318 
## [76] train-rmse:0.308299+0.010859    test-rmse:0.495252+0.053283 
## [77] train-rmse:0.307264+0.010851    test-rmse:0.495135+0.053134 
## [78] train-rmse:0.305743+0.010783    test-rmse:0.494846+0.053344 
## [79] train-rmse:0.304288+0.010979    test-rmse:0.494837+0.053203 
## [80] train-rmse:0.302988+0.010717    test-rmse:0.494668+0.052569 
## [81] train-rmse:0.300830+0.010984    test-rmse:0.495034+0.052244 
## [82] train-rmse:0.299359+0.011281    test-rmse:0.494992+0.052431 
## [83] train-rmse:0.297693+0.011344    test-rmse:0.494811+0.051866 
## [84] train-rmse:0.296554+0.011163    test-rmse:0.494998+0.051724 
## [85] train-rmse:0.294956+0.011330    test-rmse:0.494709+0.051551 
## [86] train-rmse:0.293930+0.011262    test-rmse:0.494992+0.051480 
## Stopping. Best iteration:
## [80] train-rmse:0.302988+0.010717    test-rmse:0.494668+0.052569
## 
## 4 
## [1]  train-rmse:0.933751+0.015431    test-rmse:0.937872+0.062279 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.870737+0.014650    test-rmse:0.880491+0.060623 
## [3]  train-rmse:0.815214+0.013888    test-rmse:0.831760+0.060483 
## [4]  train-rmse:0.765006+0.013257    test-rmse:0.788666+0.060596 
## [5]  train-rmse:0.721266+0.012522    test-rmse:0.751119+0.061712 
## [6]  train-rmse:0.682410+0.011818    test-rmse:0.719011+0.062529 
## [7]  train-rmse:0.648443+0.011574    test-rmse:0.691437+0.063442 
## [8]  train-rmse:0.616995+0.011069    test-rmse:0.666723+0.061618 
## [9]  train-rmse:0.589508+0.011743    test-rmse:0.646918+0.061809 
## [10] train-rmse:0.564273+0.011512    test-rmse:0.627170+0.061549 
## [11] train-rmse:0.542168+0.011514    test-rmse:0.611665+0.060240 
## [12] train-rmse:0.522354+0.011363    test-rmse:0.597008+0.059623 
## [13] train-rmse:0.504962+0.011067    test-rmse:0.586024+0.059393 
## [14] train-rmse:0.488738+0.012220    test-rmse:0.576593+0.058696 
## [15] train-rmse:0.475113+0.012735    test-rmse:0.569583+0.058019 
## [16] train-rmse:0.461823+0.012253    test-rmse:0.562500+0.059698 
## [17] train-rmse:0.449482+0.013175    test-rmse:0.555757+0.057791 
## [18] train-rmse:0.438346+0.013598    test-rmse:0.549800+0.056349 
## [19] train-rmse:0.427915+0.013963    test-rmse:0.546263+0.055794 
## [20] train-rmse:0.419406+0.014250    test-rmse:0.542990+0.056286 
## [21] train-rmse:0.411968+0.014507    test-rmse:0.541071+0.056107 
## [22] train-rmse:0.404235+0.015116    test-rmse:0.538063+0.055055 
## [23] train-rmse:0.397729+0.015007    test-rmse:0.534918+0.055298 
## [24] train-rmse:0.392177+0.014257    test-rmse:0.531944+0.055543 
## [25] train-rmse:0.386735+0.014840    test-rmse:0.529645+0.055232 
## [26] train-rmse:0.380932+0.013823    test-rmse:0.525993+0.055137 
## [27] train-rmse:0.376075+0.014186    test-rmse:0.523221+0.055400 
## [28] train-rmse:0.371111+0.014515    test-rmse:0.521775+0.054307 
## [29] train-rmse:0.366573+0.014515    test-rmse:0.520163+0.053941 
## [30] train-rmse:0.360274+0.012626    test-rmse:0.516964+0.054917 
## [31] train-rmse:0.357177+0.012679    test-rmse:0.516162+0.055478 
## [32] train-rmse:0.353818+0.012690    test-rmse:0.514143+0.055533 
## [33] train-rmse:0.350412+0.013113    test-rmse:0.512968+0.055731 
## [34] train-rmse:0.347299+0.012670    test-rmse:0.511676+0.055997 
## [35] train-rmse:0.343641+0.013232    test-rmse:0.511784+0.055882 
## [36] train-rmse:0.340713+0.013103    test-rmse:0.510740+0.056189 
## [37] train-rmse:0.336975+0.012101    test-rmse:0.509523+0.056403 
## [38] train-rmse:0.334131+0.012357    test-rmse:0.508484+0.056824 
## [39] train-rmse:0.331393+0.012110    test-rmse:0.507641+0.056136 
## [40] train-rmse:0.328549+0.011669    test-rmse:0.507284+0.056912 
## [41] train-rmse:0.325921+0.011578    test-rmse:0.506578+0.057305 
## [42] train-rmse:0.323645+0.011832    test-rmse:0.505782+0.056838 
## [43] train-rmse:0.321642+0.011693    test-rmse:0.505507+0.056534 
## [44] train-rmse:0.319253+0.011816    test-rmse:0.504145+0.056193 
## [45] train-rmse:0.316384+0.011091    test-rmse:0.503699+0.056474 
## [46] train-rmse:0.314056+0.010746    test-rmse:0.503432+0.056525 
## [47] train-rmse:0.311817+0.011023    test-rmse:0.502458+0.056225 
## [48] train-rmse:0.309208+0.011109    test-rmse:0.502175+0.056636 
## [49] train-rmse:0.306881+0.011218    test-rmse:0.501673+0.056450 
## [50] train-rmse:0.304888+0.010623    test-rmse:0.501599+0.056564 
## [51] train-rmse:0.302852+0.010649    test-rmse:0.501345+0.056641 
## [52] train-rmse:0.300409+0.010682    test-rmse:0.501167+0.057149 
## [53] train-rmse:0.298371+0.010288    test-rmse:0.500397+0.056864 
## [54] train-rmse:0.296448+0.010497    test-rmse:0.500361+0.056971 
## [55] train-rmse:0.294306+0.010637    test-rmse:0.500307+0.056891 
## [56] train-rmse:0.292076+0.011017    test-rmse:0.499806+0.057196 
## [57] train-rmse:0.289649+0.011200    test-rmse:0.498856+0.056971 
## [58] train-rmse:0.287760+0.011157    test-rmse:0.498980+0.056989 
## [59] train-rmse:0.286153+0.011056    test-rmse:0.498883+0.056612 
## [60] train-rmse:0.283764+0.011229    test-rmse:0.498471+0.056787 
## [61] train-rmse:0.281591+0.011139    test-rmse:0.498793+0.056705 
## [62] train-rmse:0.279798+0.010544    test-rmse:0.498704+0.056519 
## [63] train-rmse:0.278493+0.010239    test-rmse:0.498513+0.056396 
## [64] train-rmse:0.277235+0.010360    test-rmse:0.498788+0.056415 
## [65] train-rmse:0.275568+0.010481    test-rmse:0.497987+0.056136 
## [66] train-rmse:0.273436+0.010203    test-rmse:0.497883+0.055990 
## [67] train-rmse:0.272019+0.010615    test-rmse:0.497962+0.056057 
## [68] train-rmse:0.269951+0.010142    test-rmse:0.498560+0.056086 
## [69] train-rmse:0.268539+0.010091    test-rmse:0.498629+0.056189 
## [70] train-rmse:0.266844+0.010272    test-rmse:0.499533+0.056387 
## [71] train-rmse:0.265220+0.010550    test-rmse:0.499443+0.055941 
## [72] train-rmse:0.263072+0.010858    test-rmse:0.499797+0.056551 
## Stopping. Best iteration:
## [66] train-rmse:0.273436+0.010203    test-rmse:0.497883+0.055990
## 
## 5 
## [1]  train-rmse:0.931377+0.015188    test-rmse:0.936786+0.062453 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.866138+0.014435    test-rmse:0.877439+0.060932 
## [3]  train-rmse:0.808295+0.013628    test-rmse:0.826722+0.061482 
## [4]  train-rmse:0.756371+0.013028    test-rmse:0.783404+0.060434 
## [5]  train-rmse:0.709846+0.011760    test-rmse:0.744886+0.062234 
## [6]  train-rmse:0.669151+0.010640    test-rmse:0.711016+0.062736 
## [7]  train-rmse:0.632871+0.010333    test-rmse:0.683069+0.064688 
## [8]  train-rmse:0.599709+0.010786    test-rmse:0.658924+0.064422 
## [9]  train-rmse:0.571152+0.011090    test-rmse:0.637384+0.062656 
## [10] train-rmse:0.544482+0.010910    test-rmse:0.619640+0.064126 
## [11] train-rmse:0.520324+0.010748    test-rmse:0.602135+0.063196 
## [12] train-rmse:0.498993+0.010032    test-rmse:0.587486+0.061752 
## [13] train-rmse:0.478185+0.011113    test-rmse:0.576328+0.060840 
## [14] train-rmse:0.460658+0.011543    test-rmse:0.566030+0.061547 
## [15] train-rmse:0.445451+0.011705    test-rmse:0.557082+0.061015 
## [16] train-rmse:0.430077+0.011643    test-rmse:0.548886+0.061058 
## [17] train-rmse:0.416991+0.010816    test-rmse:0.541862+0.060486 
## [18] train-rmse:0.404339+0.011454    test-rmse:0.536537+0.059910 
## [19] train-rmse:0.393339+0.012312    test-rmse:0.532603+0.059729 
## [20] train-rmse:0.383628+0.012601    test-rmse:0.529779+0.060441 
## [21] train-rmse:0.374346+0.012480    test-rmse:0.525758+0.060052 
## [22] train-rmse:0.366680+0.012214    test-rmse:0.524012+0.059720 
## [23] train-rmse:0.359487+0.012479    test-rmse:0.522196+0.060084 
## [24] train-rmse:0.352780+0.011859    test-rmse:0.519593+0.059044 
## [25] train-rmse:0.346299+0.012125    test-rmse:0.517672+0.058650 
## [26] train-rmse:0.340895+0.011991    test-rmse:0.515962+0.058509 
## [27] train-rmse:0.336118+0.011654    test-rmse:0.513899+0.057765 
## [28] train-rmse:0.331449+0.011465    test-rmse:0.512119+0.058263 
## [29] train-rmse:0.326665+0.011086    test-rmse:0.509710+0.057346 
## [30] train-rmse:0.321975+0.011999    test-rmse:0.507926+0.057272 
## [31] train-rmse:0.317168+0.012876    test-rmse:0.506895+0.056882 
## [32] train-rmse:0.313589+0.012495    test-rmse:0.505581+0.056329 
## [33] train-rmse:0.309894+0.012453    test-rmse:0.505528+0.055428 
## [34] train-rmse:0.305698+0.012251    test-rmse:0.504748+0.054046 
## [35] train-rmse:0.302311+0.012120    test-rmse:0.503571+0.054472 
## [36] train-rmse:0.299756+0.012112    test-rmse:0.502935+0.053942 
## [37] train-rmse:0.297192+0.011766    test-rmse:0.503299+0.053043 
## [38] train-rmse:0.293630+0.012547    test-rmse:0.502565+0.052416 
## [39] train-rmse:0.291217+0.012198    test-rmse:0.502114+0.053143 
## [40] train-rmse:0.287011+0.012965    test-rmse:0.501285+0.053500 
## [41] train-rmse:0.284286+0.012540    test-rmse:0.500416+0.053559 
## [42] train-rmse:0.280870+0.013534    test-rmse:0.499525+0.053389 
## [43] train-rmse:0.278267+0.014382    test-rmse:0.499265+0.052412 
## [44] train-rmse:0.275460+0.014372    test-rmse:0.498798+0.051865 
## [45] train-rmse:0.272833+0.014234    test-rmse:0.498204+0.050971 
## [46] train-rmse:0.270452+0.014251    test-rmse:0.498491+0.050499 
## [47] train-rmse:0.268024+0.013979    test-rmse:0.498035+0.051231 
## [48] train-rmse:0.266082+0.014088    test-rmse:0.497497+0.051420 
## [49] train-rmse:0.263236+0.013027    test-rmse:0.497116+0.051551 
## [50] train-rmse:0.261447+0.012842    test-rmse:0.496868+0.051132 
## [51] train-rmse:0.259742+0.012813    test-rmse:0.497208+0.050979 
## [52] train-rmse:0.257187+0.012791    test-rmse:0.496800+0.050673 
## [53] train-rmse:0.255416+0.012500    test-rmse:0.496163+0.050440 
## [54] train-rmse:0.253592+0.012289    test-rmse:0.495634+0.050318 
## [55] train-rmse:0.251388+0.011511    test-rmse:0.495969+0.050299 
## [56] train-rmse:0.249766+0.010920    test-rmse:0.495784+0.050059 
## [57] train-rmse:0.247351+0.011301    test-rmse:0.495974+0.049855 
## [58] train-rmse:0.244855+0.011279    test-rmse:0.495307+0.049664 
## [59] train-rmse:0.242784+0.011145    test-rmse:0.495926+0.049861 
## [60] train-rmse:0.240525+0.010806    test-rmse:0.496374+0.050859 
## [61] train-rmse:0.238898+0.010844    test-rmse:0.496356+0.051152 
## [62] train-rmse:0.237222+0.010879    test-rmse:0.496513+0.050949 
## [63] train-rmse:0.234524+0.011245    test-rmse:0.496890+0.051081 
## [64] train-rmse:0.232644+0.011426    test-rmse:0.497094+0.051132 
## Stopping. Best iteration:
## [58] train-rmse:0.244855+0.011279    test-rmse:0.495307+0.049664
## 
## 6 
## [1]  train-rmse:0.929060+0.014970    test-rmse:0.935159+0.061703 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.861826+0.013902    test-rmse:0.875932+0.062111 
## [3]  train-rmse:0.801902+0.012559    test-rmse:0.824709+0.062944 
## [4]  train-rmse:0.748275+0.012009    test-rmse:0.778961+0.060760 
## [5]  train-rmse:0.699442+0.011264    test-rmse:0.739207+0.061100 
## [6]  train-rmse:0.656453+0.010784    test-rmse:0.706012+0.062182 
## [7]  train-rmse:0.617553+0.010674    test-rmse:0.677371+0.063698 
## [8]  train-rmse:0.583711+0.010209    test-rmse:0.653441+0.065398 
## [9]  train-rmse:0.553438+0.010641    test-rmse:0.632994+0.065476 
## [10] train-rmse:0.525783+0.011272    test-rmse:0.614904+0.065181 
## [11] train-rmse:0.500942+0.012434    test-rmse:0.600426+0.066588 
## [12] train-rmse:0.477735+0.011868    test-rmse:0.585660+0.066499 
## [13] train-rmse:0.456312+0.012619    test-rmse:0.572876+0.065226 
## [14] train-rmse:0.437213+0.013139    test-rmse:0.562902+0.066767 
## [15] train-rmse:0.419779+0.013871    test-rmse:0.554695+0.065761 
## [16] train-rmse:0.404681+0.014177    test-rmse:0.548796+0.064861 
## [17] train-rmse:0.390580+0.015052    test-rmse:0.543247+0.064381 
## [18] train-rmse:0.377322+0.014911    test-rmse:0.536978+0.062248 
## [19] train-rmse:0.365156+0.015557    test-rmse:0.531791+0.062908 
## [20] train-rmse:0.353447+0.016038    test-rmse:0.528897+0.061356 
## [21] train-rmse:0.343738+0.016362    test-rmse:0.526122+0.061388 
## [22] train-rmse:0.334176+0.016674    test-rmse:0.523816+0.061606 
## [23] train-rmse:0.326144+0.017143    test-rmse:0.520974+0.059852 
## [24] train-rmse:0.317307+0.017591    test-rmse:0.520093+0.059411 
## [25] train-rmse:0.310816+0.017880    test-rmse:0.519758+0.059055 
## [26] train-rmse:0.304585+0.018851    test-rmse:0.518731+0.058274 
## [27] train-rmse:0.298977+0.018751    test-rmse:0.517582+0.057416 
## [28] train-rmse:0.293908+0.018862    test-rmse:0.516441+0.056571 
## [29] train-rmse:0.288239+0.018596    test-rmse:0.514671+0.055169 
## [30] train-rmse:0.284545+0.018724    test-rmse:0.513901+0.054379 
## [31] train-rmse:0.279990+0.018690    test-rmse:0.512273+0.052958 
## [32] train-rmse:0.275551+0.018526    test-rmse:0.511625+0.051973 
## [33] train-rmse:0.271202+0.018265    test-rmse:0.510971+0.051347 
## [34] train-rmse:0.265459+0.018199    test-rmse:0.510002+0.051292 
## [35] train-rmse:0.261223+0.019259    test-rmse:0.509137+0.051137 
## [36] train-rmse:0.258003+0.018933    test-rmse:0.507716+0.050579 
## [37] train-rmse:0.255200+0.018210    test-rmse:0.507720+0.049952 
## [38] train-rmse:0.252136+0.018757    test-rmse:0.507514+0.049469 
## [39] train-rmse:0.249162+0.018386    test-rmse:0.507005+0.049888 
## [40] train-rmse:0.246095+0.018783    test-rmse:0.506491+0.050070 
## [41] train-rmse:0.243784+0.018932    test-rmse:0.506659+0.049855 
## [42] train-rmse:0.240432+0.018846    test-rmse:0.507302+0.050360 
## [43] train-rmse:0.237581+0.018790    test-rmse:0.506574+0.050670 
## [44] train-rmse:0.234251+0.017756    test-rmse:0.506384+0.050186 
## [45] train-rmse:0.232023+0.017313    test-rmse:0.505881+0.050448 
## [46] train-rmse:0.229556+0.017514    test-rmse:0.506430+0.051208 
## [47] train-rmse:0.227554+0.017654    test-rmse:0.506674+0.051258 
## [48] train-rmse:0.225391+0.017465    test-rmse:0.506917+0.050818 
## [49] train-rmse:0.223618+0.017140    test-rmse:0.506564+0.050916 
## [50] train-rmse:0.220866+0.016605    test-rmse:0.506099+0.050814 
## [51] train-rmse:0.217554+0.016482    test-rmse:0.507074+0.050744 
## Stopping. Best iteration:
## [45] train-rmse:0.232023+0.017313    test-rmse:0.505881+0.050448
## 
## 7 
## [1]  train-rmse:0.940023+0.015582    test-rmse:0.940346+0.061765 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.886440+0.014946    test-rmse:0.889287+0.061102 
## [3]  train-rmse:0.842376+0.014462    test-rmse:0.847455+0.060569 
## [4]  train-rmse:0.806133+0.014169    test-rmse:0.816460+0.061665 
## [5]  train-rmse:0.775931+0.013782    test-rmse:0.788190+0.059536 
## [6]  train-rmse:0.750755+0.013475    test-rmse:0.767247+0.060158 
## [7]  train-rmse:0.729628+0.012969    test-rmse:0.749284+0.059455 
## [8]  train-rmse:0.711624+0.012653    test-rmse:0.730728+0.059365 
## [9]  train-rmse:0.696120+0.012462    test-rmse:0.715891+0.058622 
## [10] train-rmse:0.682989+0.012160    test-rmse:0.705552+0.056988 
## [11] train-rmse:0.671548+0.012060    test-rmse:0.692833+0.055827 
## [12] train-rmse:0.661670+0.011759    test-rmse:0.683458+0.054921 
## [13] train-rmse:0.653138+0.011579    test-rmse:0.676868+0.055366 
## [14] train-rmse:0.645567+0.011525    test-rmse:0.668302+0.054628 
## [15] train-rmse:0.639039+0.011360    test-rmse:0.663984+0.054822 
## [16] train-rmse:0.633213+0.011270    test-rmse:0.659411+0.053496 
## [17] train-rmse:0.628112+0.011229    test-rmse:0.654489+0.052396 
## [18] train-rmse:0.623593+0.011132    test-rmse:0.651348+0.052590 
## [19] train-rmse:0.619494+0.011063    test-rmse:0.647020+0.052774 
## [20] train-rmse:0.615646+0.011099    test-rmse:0.645344+0.052378 
## [21] train-rmse:0.612098+0.011084    test-rmse:0.642376+0.053564 
## [22] train-rmse:0.608816+0.011067    test-rmse:0.640144+0.053355 
## [23] train-rmse:0.605749+0.011070    test-rmse:0.636830+0.053504 
## [24] train-rmse:0.602855+0.011055    test-rmse:0.635027+0.054180 
## [25] train-rmse:0.600065+0.011091    test-rmse:0.632039+0.053991 
## [26] train-rmse:0.597396+0.011058    test-rmse:0.628991+0.053756 
## [27] train-rmse:0.594883+0.011067    test-rmse:0.627156+0.054588 
## [28] train-rmse:0.592498+0.011048    test-rmse:0.625977+0.054244 
## [29] train-rmse:0.590215+0.011048    test-rmse:0.624487+0.054433 
## [30] train-rmse:0.588023+0.011036    test-rmse:0.622664+0.054879 
## [31] train-rmse:0.585889+0.011020    test-rmse:0.619769+0.054978 
## [32] train-rmse:0.583861+0.011055    test-rmse:0.618632+0.054690 
## [33] train-rmse:0.581879+0.011077    test-rmse:0.618051+0.054668 
## [34] train-rmse:0.579972+0.011094    test-rmse:0.616703+0.054000 
## [35] train-rmse:0.578155+0.011060    test-rmse:0.614157+0.053929 
## [36] train-rmse:0.576366+0.011052    test-rmse:0.613346+0.054582 
## [37] train-rmse:0.574599+0.011037    test-rmse:0.611563+0.055073 
## [38] train-rmse:0.572879+0.011050    test-rmse:0.609997+0.055081 
## [39] train-rmse:0.571238+0.011041    test-rmse:0.609026+0.054667 
## [40] train-rmse:0.569647+0.011059    test-rmse:0.606965+0.054840 
## [41] train-rmse:0.568068+0.011081    test-rmse:0.605645+0.054605 
## [42] train-rmse:0.566519+0.011084    test-rmse:0.604299+0.054753 
## [43] train-rmse:0.565010+0.011075    test-rmse:0.602701+0.055142 
## [44] train-rmse:0.563572+0.011073    test-rmse:0.601066+0.055158 
## [45] train-rmse:0.562141+0.011072    test-rmse:0.600199+0.056266 
## [46] train-rmse:0.560740+0.011070    test-rmse:0.600203+0.056297 
## [47] train-rmse:0.559375+0.011068    test-rmse:0.599663+0.054986 
## [48] train-rmse:0.558050+0.011066    test-rmse:0.597752+0.055240 
## [49] train-rmse:0.556731+0.011062    test-rmse:0.596363+0.054336 
## [50] train-rmse:0.555432+0.011065    test-rmse:0.595370+0.054901 
## [51] train-rmse:0.554142+0.011063    test-rmse:0.595121+0.054666 
## [52] train-rmse:0.552878+0.011066    test-rmse:0.593938+0.053946 
## [53] train-rmse:0.551638+0.011064    test-rmse:0.592685+0.053818 
## [54] train-rmse:0.550439+0.011065    test-rmse:0.591815+0.054673 
## [55] train-rmse:0.549257+0.011057    test-rmse:0.591498+0.055104 
## [56] train-rmse:0.548110+0.011035    test-rmse:0.590962+0.054282 
## [57] train-rmse:0.546993+0.011013    test-rmse:0.590351+0.054192 
## [58] train-rmse:0.545888+0.010997    test-rmse:0.589787+0.054651 
## [59] train-rmse:0.544803+0.010980    test-rmse:0.588227+0.054939 
## [60] train-rmse:0.543728+0.010978    test-rmse:0.587383+0.054694 
## [61] train-rmse:0.542681+0.010977    test-rmse:0.586764+0.054514 
## [62] train-rmse:0.541641+0.010966    test-rmse:0.585765+0.054527 
## [63] train-rmse:0.540602+0.010958    test-rmse:0.585212+0.054074 
## [64] train-rmse:0.539587+0.010950    test-rmse:0.583811+0.053832 
## [65] train-rmse:0.538594+0.010948    test-rmse:0.583236+0.054691 
## [66] train-rmse:0.537610+0.010949    test-rmse:0.582951+0.054358 
## [67] train-rmse:0.536660+0.010948    test-rmse:0.582818+0.055061 
## [68] train-rmse:0.535718+0.010937    test-rmse:0.582513+0.054998 
## [69] train-rmse:0.534788+0.010928    test-rmse:0.581407+0.055129 
## [70] train-rmse:0.533878+0.010901    test-rmse:0.580643+0.054503 
## [71] train-rmse:0.532987+0.010876    test-rmse:0.580532+0.053872 
## [72] train-rmse:0.532104+0.010855    test-rmse:0.579760+0.054165 
## [73] train-rmse:0.531247+0.010839    test-rmse:0.579015+0.054201 
## [74] train-rmse:0.530389+0.010817    test-rmse:0.578068+0.054794 
## [75] train-rmse:0.529557+0.010812    test-rmse:0.578080+0.054630 
## [76] train-rmse:0.528728+0.010807    test-rmse:0.576994+0.054883 
## [77] train-rmse:0.527917+0.010800    test-rmse:0.576008+0.055132 
## [78] train-rmse:0.527108+0.010791    test-rmse:0.576234+0.055547 
## [79] train-rmse:0.526316+0.010776    test-rmse:0.575574+0.054994 
## [80] train-rmse:0.525537+0.010761    test-rmse:0.574908+0.055076 
## [81] train-rmse:0.524774+0.010747    test-rmse:0.573920+0.055059 
## [82] train-rmse:0.524025+0.010730    test-rmse:0.573164+0.055099 
## [83] train-rmse:0.523281+0.010716    test-rmse:0.572262+0.055262 
## [84] train-rmse:0.522538+0.010698    test-rmse:0.571499+0.055463 
## [85] train-rmse:0.521811+0.010683    test-rmse:0.571475+0.054644 
## [86] train-rmse:0.521099+0.010675    test-rmse:0.571586+0.054662 
## [87] train-rmse:0.520385+0.010668    test-rmse:0.570793+0.054061 
## [88] train-rmse:0.519686+0.010649    test-rmse:0.570240+0.054176 
## [89] train-rmse:0.518990+0.010640    test-rmse:0.570449+0.054111 
## [90] train-rmse:0.518311+0.010627    test-rmse:0.570127+0.054275 
## [91] train-rmse:0.517639+0.010605    test-rmse:0.569390+0.054710 
## [92] train-rmse:0.516980+0.010585    test-rmse:0.568768+0.054652 
## [93] train-rmse:0.516327+0.010566    test-rmse:0.568716+0.054540 
## [94] train-rmse:0.515679+0.010553    test-rmse:0.567807+0.054032 
## [95] train-rmse:0.515042+0.010546    test-rmse:0.567317+0.054194 
## [96] train-rmse:0.514408+0.010537    test-rmse:0.567150+0.054908 
## [97] train-rmse:0.513787+0.010527    test-rmse:0.566647+0.054664 
## [98] train-rmse:0.513172+0.010519    test-rmse:0.565719+0.054663 
## [99] train-rmse:0.512562+0.010510    test-rmse:0.565024+0.054807 
## [100]    train-rmse:0.511958+0.010500    test-rmse:0.564283+0.055001 
## [101]    train-rmse:0.511365+0.010489    test-rmse:0.563488+0.054339 
## [102]    train-rmse:0.510775+0.010480    test-rmse:0.563185+0.054226 
## [103]    train-rmse:0.510189+0.010465    test-rmse:0.562070+0.054141 
## [104]    train-rmse:0.509623+0.010469    test-rmse:0.562174+0.054039 
## [105]    train-rmse:0.509059+0.010468    test-rmse:0.562002+0.054263 
## [106]    train-rmse:0.508503+0.010455    test-rmse:0.561631+0.054370 
## [107]    train-rmse:0.507956+0.010454    test-rmse:0.561385+0.053958 
## [108]    train-rmse:0.507415+0.010443    test-rmse:0.560823+0.053656 
## [109]    train-rmse:0.506877+0.010436    test-rmse:0.560456+0.053539 
## [110]    train-rmse:0.506347+0.010432    test-rmse:0.560353+0.054238 
## [111]    train-rmse:0.505819+0.010426    test-rmse:0.560092+0.054051 
## [112]    train-rmse:0.505298+0.010421    test-rmse:0.559658+0.054223 
## [113]    train-rmse:0.504787+0.010411    test-rmse:0.559273+0.054505 
## [114]    train-rmse:0.504275+0.010404    test-rmse:0.558824+0.054301 
## [115]    train-rmse:0.503770+0.010398    test-rmse:0.558757+0.054055 
## [116]    train-rmse:0.503270+0.010392    test-rmse:0.558305+0.054020 
## [117]    train-rmse:0.502786+0.010384    test-rmse:0.557970+0.054217 
## [118]    train-rmse:0.502303+0.010380    test-rmse:0.557528+0.054200 
## [119]    train-rmse:0.501827+0.010377    test-rmse:0.557203+0.053851 
## [120]    train-rmse:0.501356+0.010375    test-rmse:0.556828+0.054348 
## [121]    train-rmse:0.500886+0.010372    test-rmse:0.556625+0.053959 
## [122]    train-rmse:0.500423+0.010363    test-rmse:0.555728+0.053775 
## [123]    train-rmse:0.499965+0.010361    test-rmse:0.555395+0.053398 
## [124]    train-rmse:0.499515+0.010355    test-rmse:0.555257+0.053625 
## [125]    train-rmse:0.499067+0.010349    test-rmse:0.554578+0.053697 
## [126]    train-rmse:0.498626+0.010349    test-rmse:0.554353+0.053673 
## [127]    train-rmse:0.498188+0.010352    test-rmse:0.554487+0.053471 
## [128]    train-rmse:0.497752+0.010350    test-rmse:0.553840+0.053254 
## [129]    train-rmse:0.497319+0.010348    test-rmse:0.553562+0.053368 
## [130]    train-rmse:0.496896+0.010346    test-rmse:0.553574+0.053045 
## [131]    train-rmse:0.496479+0.010343    test-rmse:0.553468+0.053351 
## [132]    train-rmse:0.496066+0.010339    test-rmse:0.552929+0.053521 
## [133]    train-rmse:0.495654+0.010338    test-rmse:0.552538+0.053465 
## [134]    train-rmse:0.495250+0.010337    test-rmse:0.552361+0.053619 
## [135]    train-rmse:0.494852+0.010330    test-rmse:0.552013+0.053484 
## [136]    train-rmse:0.494458+0.010326    test-rmse:0.552144+0.053142 
## [137]    train-rmse:0.494067+0.010322    test-rmse:0.551735+0.052966 
## [138]    train-rmse:0.493674+0.010317    test-rmse:0.551267+0.053102 
## [139]    train-rmse:0.493284+0.010312    test-rmse:0.551040+0.053331 
## [140]    train-rmse:0.492901+0.010308    test-rmse:0.550810+0.053626 
## [141]    train-rmse:0.492523+0.010305    test-rmse:0.550614+0.053491 
## [142]    train-rmse:0.492148+0.010299    test-rmse:0.550315+0.053842 
## [143]    train-rmse:0.491776+0.010293    test-rmse:0.550171+0.053275 
## [144]    train-rmse:0.491408+0.010289    test-rmse:0.549819+0.053237 
## [145]    train-rmse:0.491043+0.010287    test-rmse:0.549521+0.053151 
## [146]    train-rmse:0.490685+0.010284    test-rmse:0.549334+0.053125 
## [147]    train-rmse:0.490330+0.010279    test-rmse:0.549283+0.052998 
## [148]    train-rmse:0.489979+0.010273    test-rmse:0.549241+0.052710 
## [149]    train-rmse:0.489631+0.010267    test-rmse:0.548850+0.052502 
## [150]    train-rmse:0.489287+0.010259    test-rmse:0.548622+0.052697 
## [151]    train-rmse:0.488943+0.010253    test-rmse:0.548409+0.052639 
## [152]    train-rmse:0.488602+0.010251    test-rmse:0.548378+0.052585 
## [153]    train-rmse:0.488264+0.010245    test-rmse:0.548239+0.052289 
## [154]    train-rmse:0.487929+0.010239    test-rmse:0.547680+0.052457 
## [155]    train-rmse:0.487599+0.010236    test-rmse:0.547680+0.052550 
## [156]    train-rmse:0.487272+0.010236    test-rmse:0.547840+0.052487 
## [157]    train-rmse:0.486947+0.010232    test-rmse:0.547295+0.052388 
## [158]    train-rmse:0.486627+0.010222    test-rmse:0.547403+0.052417 
## [159]    train-rmse:0.486309+0.010214    test-rmse:0.546985+0.052150 
## [160]    train-rmse:0.485995+0.010205    test-rmse:0.546603+0.052365 
## [161]    train-rmse:0.485676+0.010209    test-rmse:0.546341+0.052541 
## [162]    train-rmse:0.485372+0.010208    test-rmse:0.546594+0.052625 
## [163]    train-rmse:0.485070+0.010205    test-rmse:0.546014+0.052678 
## [164]    train-rmse:0.484770+0.010204    test-rmse:0.546251+0.052874 
## [165]    train-rmse:0.484469+0.010200    test-rmse:0.546324+0.052470 
## [166]    train-rmse:0.484174+0.010198    test-rmse:0.545898+0.052110 
## [167]    train-rmse:0.483879+0.010194    test-rmse:0.545640+0.052260 
## [168]    train-rmse:0.483588+0.010190    test-rmse:0.545351+0.052134 
## [169]    train-rmse:0.483298+0.010185    test-rmse:0.545137+0.052201 
## [170]    train-rmse:0.483010+0.010179    test-rmse:0.544707+0.052106 
## [171]    train-rmse:0.482729+0.010174    test-rmse:0.544582+0.051601 
## [172]    train-rmse:0.482444+0.010176    test-rmse:0.544621+0.052003 
## [173]    train-rmse:0.482166+0.010173    test-rmse:0.544611+0.051848 
## [174]    train-rmse:0.481893+0.010172    test-rmse:0.544253+0.052033 
## [175]    train-rmse:0.481622+0.010168    test-rmse:0.544296+0.051800 
## [176]    train-rmse:0.481354+0.010163    test-rmse:0.544198+0.051861 
## [177]    train-rmse:0.481085+0.010162    test-rmse:0.543906+0.051979 
## [178]    train-rmse:0.480819+0.010155    test-rmse:0.543524+0.051878 
## [179]    train-rmse:0.480556+0.010149    test-rmse:0.543397+0.051851 
## [180]    train-rmse:0.480287+0.010148    test-rmse:0.543504+0.051529 
## [181]    train-rmse:0.480026+0.010141    test-rmse:0.543062+0.051261 
## [182]    train-rmse:0.479774+0.010141    test-rmse:0.543098+0.051074 
## [183]    train-rmse:0.479522+0.010140    test-rmse:0.543109+0.051365 
## [184]    train-rmse:0.479273+0.010140    test-rmse:0.542958+0.051334 
## [185]    train-rmse:0.479024+0.010137    test-rmse:0.542661+0.051212 
## [186]    train-rmse:0.478778+0.010136    test-rmse:0.542343+0.051136 
## [187]    train-rmse:0.478534+0.010135    test-rmse:0.542065+0.051199 
## [188]    train-rmse:0.478290+0.010133    test-rmse:0.541949+0.051200 
## [189]    train-rmse:0.478050+0.010132    test-rmse:0.542045+0.051068 
## [190]    train-rmse:0.477810+0.010130    test-rmse:0.542026+0.051077 
## [191]    train-rmse:0.477570+0.010127    test-rmse:0.541910+0.051355 
## [192]    train-rmse:0.477330+0.010125    test-rmse:0.541636+0.051482 
## [193]    train-rmse:0.477096+0.010122    test-rmse:0.541502+0.051185 
## [194]    train-rmse:0.476863+0.010118    test-rmse:0.541358+0.051079 
## [195]    train-rmse:0.476632+0.010114    test-rmse:0.541296+0.051258 
## [196]    train-rmse:0.476406+0.010110    test-rmse:0.541073+0.051116 
## [197]    train-rmse:0.476179+0.010105    test-rmse:0.540985+0.050956 
## [198]    train-rmse:0.475957+0.010103    test-rmse:0.541132+0.051026 
## [199]    train-rmse:0.475736+0.010103    test-rmse:0.540615+0.051104 
## [200]    train-rmse:0.475518+0.010100    test-rmse:0.540284+0.050904 
## [201]    train-rmse:0.475302+0.010098    test-rmse:0.540316+0.050455 
## [202]    train-rmse:0.475087+0.010098    test-rmse:0.539912+0.050468 
## [203]    train-rmse:0.474872+0.010100    test-rmse:0.539713+0.050311 
## [204]    train-rmse:0.474660+0.010099    test-rmse:0.539856+0.050127 
## [205]    train-rmse:0.474449+0.010100    test-rmse:0.539814+0.050480 
## [206]    train-rmse:0.474242+0.010098    test-rmse:0.539572+0.050595 
## [207]    train-rmse:0.474034+0.010095    test-rmse:0.539201+0.050515 
## [208]    train-rmse:0.473831+0.010093    test-rmse:0.539329+0.050484 
## [209]    train-rmse:0.473628+0.010088    test-rmse:0.539030+0.050447 
## [210]    train-rmse:0.473426+0.010083    test-rmse:0.539153+0.050465 
## [211]    train-rmse:0.473227+0.010077    test-rmse:0.539037+0.050552 
## [212]    train-rmse:0.473027+0.010069    test-rmse:0.539100+0.050613 
## [213]    train-rmse:0.472827+0.010063    test-rmse:0.539061+0.050640 
## [214]    train-rmse:0.472628+0.010061    test-rmse:0.538885+0.050655 
## [215]    train-rmse:0.472431+0.010056    test-rmse:0.538642+0.050595 
## [216]    train-rmse:0.472240+0.010053    test-rmse:0.538639+0.050356 
## [217]    train-rmse:0.472050+0.010050    test-rmse:0.538366+0.050043 
## [218]    train-rmse:0.471854+0.010045    test-rmse:0.538506+0.050269 
## [219]    train-rmse:0.471665+0.010045    test-rmse:0.538430+0.050094 
## [220]    train-rmse:0.471477+0.010045    test-rmse:0.538473+0.050094 
## [221]    train-rmse:0.471292+0.010045    test-rmse:0.538498+0.049832 
## [222]    train-rmse:0.471108+0.010042    test-rmse:0.538552+0.049792 
## [223]    train-rmse:0.470928+0.010039    test-rmse:0.538460+0.049929 
## Stopping. Best iteration:
## [217]    train-rmse:0.472050+0.010050    test-rmse:0.538366+0.050043
## 
## 8 
## [1]  train-rmse:0.930677+0.015519    test-rmse:0.931749+0.061626 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.868286+0.014803    test-rmse:0.872913+0.060306 
## [3]  train-rmse:0.816006+0.014517    test-rmse:0.825073+0.061678 
## [4]  train-rmse:0.772089+0.013978    test-rmse:0.784462+0.062519 
## [5]  train-rmse:0.735456+0.013849    test-rmse:0.752787+0.063114 
## [6]  train-rmse:0.704214+0.013160    test-rmse:0.725060+0.063995 
## [7]  train-rmse:0.677811+0.012377    test-rmse:0.699489+0.065930 
## [8]  train-rmse:0.655940+0.012025    test-rmse:0.679743+0.062669 
## [9]  train-rmse:0.636488+0.011097    test-rmse:0.660896+0.063047 
## [10] train-rmse:0.619701+0.011193    test-rmse:0.648382+0.063025 
## [11] train-rmse:0.605551+0.011681    test-rmse:0.638381+0.063431 
## [12] train-rmse:0.594229+0.011456    test-rmse:0.629048+0.063512 
## [13] train-rmse:0.583310+0.012251    test-rmse:0.621614+0.063308 
## [14] train-rmse:0.574785+0.012033    test-rmse:0.614934+0.063750 
## [15] train-rmse:0.567564+0.012216    test-rmse:0.609554+0.062019 
## [16] train-rmse:0.560016+0.013091    test-rmse:0.604359+0.061637 
## [17] train-rmse:0.554188+0.013858    test-rmse:0.600767+0.060093 
## [18] train-rmse:0.548524+0.013622    test-rmse:0.596359+0.060268 
## [19] train-rmse:0.543859+0.013630    test-rmse:0.592655+0.060940 
## [20] train-rmse:0.539193+0.014202    test-rmse:0.588561+0.059430 
## [21] train-rmse:0.534869+0.015071    test-rmse:0.587401+0.059205 
## [22] train-rmse:0.530485+0.015002    test-rmse:0.583505+0.058185 
## [23] train-rmse:0.526905+0.014833    test-rmse:0.581322+0.056953 
## [24] train-rmse:0.523293+0.014186    test-rmse:0.579295+0.057508 
## [25] train-rmse:0.519784+0.014802    test-rmse:0.576629+0.056525 
## [26] train-rmse:0.516657+0.014837    test-rmse:0.575323+0.056132 
## [27] train-rmse:0.513863+0.015026    test-rmse:0.573571+0.057238 
## [28] train-rmse:0.511194+0.014955    test-rmse:0.572953+0.056837 
## [29] train-rmse:0.508056+0.014377    test-rmse:0.570322+0.057885 
## [30] train-rmse:0.505136+0.014531    test-rmse:0.568260+0.057993 
## [31] train-rmse:0.502433+0.014743    test-rmse:0.565633+0.058148 
## [32] train-rmse:0.499915+0.014682    test-rmse:0.565508+0.058026 
## [33] train-rmse:0.497367+0.014971    test-rmse:0.563983+0.056989 
## [34] train-rmse:0.494994+0.015109    test-rmse:0.561982+0.057358 
## [35] train-rmse:0.492554+0.015133    test-rmse:0.560681+0.058086 
## [36] train-rmse:0.490233+0.015243    test-rmse:0.558966+0.057235 
## [37] train-rmse:0.487819+0.014698    test-rmse:0.556617+0.056927 
## [38] train-rmse:0.485794+0.014768    test-rmse:0.555334+0.057691 
## [39] train-rmse:0.483764+0.014798    test-rmse:0.553972+0.057517 
## [40] train-rmse:0.481976+0.014716    test-rmse:0.553380+0.057867 
## [41] train-rmse:0.479754+0.014295    test-rmse:0.552354+0.058291 
## [42] train-rmse:0.477900+0.014543    test-rmse:0.550622+0.058904 
## [43] train-rmse:0.476031+0.014581    test-rmse:0.550047+0.059087 
## [44] train-rmse:0.474120+0.014299    test-rmse:0.548712+0.058784 
## [45] train-rmse:0.472291+0.014441    test-rmse:0.546826+0.059133 
## [46] train-rmse:0.470163+0.014311    test-rmse:0.543988+0.058629 
## [47] train-rmse:0.468179+0.014572    test-rmse:0.542895+0.058172 
## [48] train-rmse:0.466527+0.014597    test-rmse:0.542452+0.058214 
## [49] train-rmse:0.464743+0.014439    test-rmse:0.541344+0.059038 
## [50] train-rmse:0.463062+0.014023    test-rmse:0.541315+0.058719 
## [51] train-rmse:0.461417+0.014480    test-rmse:0.540765+0.058798 
## [52] train-rmse:0.460034+0.014553    test-rmse:0.539784+0.059259 
## [53] train-rmse:0.458462+0.014680    test-rmse:0.537747+0.058802 
## [54] train-rmse:0.456335+0.014750    test-rmse:0.536755+0.059050 
## [55] train-rmse:0.454618+0.014559    test-rmse:0.536019+0.060190 
## [56] train-rmse:0.453346+0.014446    test-rmse:0.534346+0.059746 
## [57] train-rmse:0.451499+0.014313    test-rmse:0.533948+0.059751 
## [58] train-rmse:0.450183+0.014301    test-rmse:0.534578+0.060061 
## [59] train-rmse:0.448838+0.014150    test-rmse:0.533986+0.059687 
## [60] train-rmse:0.447325+0.013986    test-rmse:0.532729+0.061069 
## [61] train-rmse:0.445943+0.013744    test-rmse:0.531728+0.061990 
## [62] train-rmse:0.444843+0.013925    test-rmse:0.530794+0.061777 
## [63] train-rmse:0.443850+0.013863    test-rmse:0.530098+0.061646 
## [64] train-rmse:0.442183+0.013990    test-rmse:0.529014+0.061742 
## [65] train-rmse:0.441019+0.014019    test-rmse:0.528007+0.061670 
## [66] train-rmse:0.439503+0.014248    test-rmse:0.527386+0.060565 
## [67] train-rmse:0.438145+0.014238    test-rmse:0.527119+0.060318 
## [68] train-rmse:0.436982+0.014029    test-rmse:0.526853+0.060553 
## [69] train-rmse:0.435677+0.013844    test-rmse:0.526484+0.060771 
## [70] train-rmse:0.434559+0.013892    test-rmse:0.525842+0.061400 
## [71] train-rmse:0.433707+0.013913    test-rmse:0.525171+0.061896 
## [72] train-rmse:0.432715+0.014095    test-rmse:0.524233+0.061772 
## [73] train-rmse:0.431660+0.013928    test-rmse:0.523524+0.061364 
## [74] train-rmse:0.430814+0.014009    test-rmse:0.523665+0.060909 
## [75] train-rmse:0.429634+0.013925    test-rmse:0.522913+0.061407 
## [76] train-rmse:0.428641+0.014077    test-rmse:0.522883+0.061949 
## [77] train-rmse:0.427550+0.014126    test-rmse:0.522830+0.062025 
## [78] train-rmse:0.426425+0.014119    test-rmse:0.522874+0.061447 
## [79] train-rmse:0.425397+0.014096    test-rmse:0.522248+0.061581 
## [80] train-rmse:0.424397+0.014234    test-rmse:0.521633+0.061939 
## [81] train-rmse:0.423181+0.014347    test-rmse:0.521227+0.061143 
## [82] train-rmse:0.422344+0.014436    test-rmse:0.521433+0.061399 
## [83] train-rmse:0.421480+0.014462    test-rmse:0.521383+0.061439 
## [84] train-rmse:0.420358+0.014498    test-rmse:0.520245+0.060880 
## [85] train-rmse:0.419562+0.014412    test-rmse:0.519815+0.061452 
## [86] train-rmse:0.418696+0.014488    test-rmse:0.519520+0.061771 
## [87] train-rmse:0.417769+0.014478    test-rmse:0.518686+0.061672 
## [88] train-rmse:0.416934+0.014304    test-rmse:0.519026+0.061941 
## [89] train-rmse:0.416104+0.014396    test-rmse:0.518836+0.061915 
## [90] train-rmse:0.414941+0.014515    test-rmse:0.517993+0.061406 
## [91] train-rmse:0.414192+0.014532    test-rmse:0.518069+0.061286 
## [92] train-rmse:0.413120+0.015016    test-rmse:0.517674+0.061339 
## [93] train-rmse:0.412257+0.015174    test-rmse:0.517323+0.061192 
## [94] train-rmse:0.411550+0.015289    test-rmse:0.516943+0.060527 
## [95] train-rmse:0.410829+0.015259    test-rmse:0.516716+0.061094 
## [96] train-rmse:0.410100+0.015292    test-rmse:0.516854+0.061330 
## [97] train-rmse:0.409533+0.015286    test-rmse:0.516554+0.061582 
## [98] train-rmse:0.408722+0.015220    test-rmse:0.515684+0.062304 
## [99] train-rmse:0.407887+0.015287    test-rmse:0.515509+0.062052 
## [100]    train-rmse:0.407107+0.015555    test-rmse:0.515547+0.061848 
## [101]    train-rmse:0.406073+0.015424    test-rmse:0.515865+0.061943 
## [102]    train-rmse:0.405300+0.015334    test-rmse:0.515328+0.061728 
## [103]    train-rmse:0.404707+0.015322    test-rmse:0.515300+0.061904 
## [104]    train-rmse:0.403836+0.015272    test-rmse:0.515132+0.062138 
## [105]    train-rmse:0.403007+0.015231    test-rmse:0.514624+0.062527 
## [106]    train-rmse:0.402303+0.015227    test-rmse:0.514648+0.062649 
## [107]    train-rmse:0.401444+0.015211    test-rmse:0.514389+0.062672 
## [108]    train-rmse:0.400530+0.015301    test-rmse:0.514124+0.062714 
## [109]    train-rmse:0.399654+0.015082    test-rmse:0.513573+0.062555 
## [110]    train-rmse:0.399065+0.015070    test-rmse:0.512915+0.062376 
## [111]    train-rmse:0.398132+0.014940    test-rmse:0.513057+0.062589 
## [112]    train-rmse:0.397567+0.014835    test-rmse:0.512567+0.062655 
## [113]    train-rmse:0.396924+0.014868    test-rmse:0.513150+0.062609 
## [114]    train-rmse:0.396438+0.014886    test-rmse:0.513280+0.062493 
## [115]    train-rmse:0.395635+0.014860    test-rmse:0.513111+0.062670 
## [116]    train-rmse:0.394849+0.014978    test-rmse:0.513031+0.062514 
## [117]    train-rmse:0.394270+0.014976    test-rmse:0.513040+0.062510 
## [118]    train-rmse:0.393492+0.014933    test-rmse:0.512421+0.062469 
## [119]    train-rmse:0.392787+0.014743    test-rmse:0.512025+0.062744 
## [120]    train-rmse:0.392109+0.014788    test-rmse:0.511794+0.063296 
## [121]    train-rmse:0.391467+0.014627    test-rmse:0.511434+0.063263 
## [122]    train-rmse:0.391022+0.014656    test-rmse:0.511095+0.063025 
## [123]    train-rmse:0.390514+0.014760    test-rmse:0.510977+0.062733 
## [124]    train-rmse:0.389941+0.014750    test-rmse:0.510493+0.063141 
## [125]    train-rmse:0.389341+0.014707    test-rmse:0.510636+0.062710 
## [126]    train-rmse:0.388550+0.014693    test-rmse:0.510195+0.062944 
## [127]    train-rmse:0.387876+0.014824    test-rmse:0.509910+0.063490 
## [128]    train-rmse:0.387332+0.014990    test-rmse:0.509702+0.063320 
## [129]    train-rmse:0.386628+0.014893    test-rmse:0.509352+0.063308 
## [130]    train-rmse:0.386091+0.014787    test-rmse:0.509083+0.063538 
## [131]    train-rmse:0.385272+0.014606    test-rmse:0.508854+0.063437 
## [132]    train-rmse:0.384577+0.014522    test-rmse:0.508324+0.063365 
## [133]    train-rmse:0.383725+0.014199    test-rmse:0.507645+0.063756 
## [134]    train-rmse:0.383113+0.014191    test-rmse:0.507820+0.064041 
## [135]    train-rmse:0.382458+0.014339    test-rmse:0.507470+0.063939 
## [136]    train-rmse:0.381647+0.014445    test-rmse:0.507505+0.063415 
## [137]    train-rmse:0.381165+0.014534    test-rmse:0.507546+0.063034 
## [138]    train-rmse:0.380735+0.014480    test-rmse:0.507738+0.062675 
## [139]    train-rmse:0.380258+0.014398    test-rmse:0.507347+0.062435 
## [140]    train-rmse:0.379602+0.014240    test-rmse:0.507692+0.062328 
## [141]    train-rmse:0.379258+0.014210    test-rmse:0.507604+0.062660 
## [142]    train-rmse:0.378580+0.014118    test-rmse:0.507197+0.062939 
## [143]    train-rmse:0.378168+0.014091    test-rmse:0.506811+0.062585 
## [144]    train-rmse:0.377399+0.014011    test-rmse:0.506100+0.062551 
## [145]    train-rmse:0.376697+0.013898    test-rmse:0.505705+0.062591 
## [146]    train-rmse:0.375975+0.014142    test-rmse:0.505701+0.062436 
## [147]    train-rmse:0.375464+0.014021    test-rmse:0.505443+0.062874 
## [148]    train-rmse:0.374951+0.013976    test-rmse:0.505449+0.063177 
## [149]    train-rmse:0.374424+0.013878    test-rmse:0.505391+0.062918 
## [150]    train-rmse:0.373839+0.013710    test-rmse:0.505247+0.062953 
## [151]    train-rmse:0.373105+0.014000    test-rmse:0.505425+0.063182 
## [152]    train-rmse:0.372632+0.013915    test-rmse:0.505617+0.062829 
## [153]    train-rmse:0.372234+0.013820    test-rmse:0.505217+0.062845 
## [154]    train-rmse:0.371797+0.013848    test-rmse:0.505044+0.063290 
## [155]    train-rmse:0.371382+0.013942    test-rmse:0.504483+0.062906 
## [156]    train-rmse:0.370609+0.013962    test-rmse:0.504294+0.062567 
## [157]    train-rmse:0.370119+0.013798    test-rmse:0.504051+0.062348 
## [158]    train-rmse:0.369747+0.013751    test-rmse:0.503810+0.062244 
## [159]    train-rmse:0.369123+0.013765    test-rmse:0.502984+0.061943 
## [160]    train-rmse:0.368552+0.013607    test-rmse:0.502832+0.062450 
## [161]    train-rmse:0.368030+0.013546    test-rmse:0.502442+0.062507 
## [162]    train-rmse:0.367707+0.013579    test-rmse:0.502364+0.062538 
## [163]    train-rmse:0.367082+0.013778    test-rmse:0.502138+0.062546 
## [164]    train-rmse:0.366389+0.013675    test-rmse:0.501885+0.062109 
## [165]    train-rmse:0.365971+0.013547    test-rmse:0.501782+0.062323 
## [166]    train-rmse:0.365335+0.013341    test-rmse:0.501685+0.062631 
## [167]    train-rmse:0.364689+0.013375    test-rmse:0.501412+0.062388 
## [168]    train-rmse:0.364145+0.013440    test-rmse:0.501669+0.062192 
## [169]    train-rmse:0.363622+0.013294    test-rmse:0.501562+0.062267 
## [170]    train-rmse:0.362836+0.012828    test-rmse:0.501182+0.062080 
## [171]    train-rmse:0.362466+0.012739    test-rmse:0.501598+0.062548 
## [172]    train-rmse:0.362144+0.012718    test-rmse:0.501515+0.062082 
## [173]    train-rmse:0.361431+0.012683    test-rmse:0.501161+0.062180 
## [174]    train-rmse:0.360790+0.012671    test-rmse:0.500798+0.062452 
## [175]    train-rmse:0.360230+0.012576    test-rmse:0.500497+0.062589 
## [176]    train-rmse:0.359798+0.012706    test-rmse:0.500400+0.062456 
## [177]    train-rmse:0.359395+0.012777    test-rmse:0.500210+0.062267 
## [178]    train-rmse:0.359100+0.012801    test-rmse:0.500209+0.062321 
## [179]    train-rmse:0.358732+0.012618    test-rmse:0.499858+0.062330 
## [180]    train-rmse:0.357958+0.012171    test-rmse:0.500204+0.062273 
## [181]    train-rmse:0.357518+0.012138    test-rmse:0.500223+0.062159 
## [182]    train-rmse:0.356850+0.012500    test-rmse:0.500209+0.061937 
## [183]    train-rmse:0.356486+0.012354    test-rmse:0.499948+0.062083 
## [184]    train-rmse:0.355925+0.012135    test-rmse:0.500033+0.062051 
## [185]    train-rmse:0.355488+0.012033    test-rmse:0.500094+0.062011 
## Stopping. Best iteration:
## [179]    train-rmse:0.358732+0.012618    test-rmse:0.499858+0.062330
## 
## 9 
## [1]  train-rmse:0.926509+0.015679    test-rmse:0.929240+0.062334 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.859816+0.015009    test-rmse:0.867786+0.062697 
## [3]  train-rmse:0.803126+0.014849    test-rmse:0.815278+0.062581 
## [4]  train-rmse:0.755010+0.014255    test-rmse:0.771613+0.063374 
## [5]  train-rmse:0.714187+0.014312    test-rmse:0.734609+0.062922 
## [6]  train-rmse:0.678511+0.013797    test-rmse:0.704843+0.065389 
## [7]  train-rmse:0.648671+0.013487    test-rmse:0.679433+0.064526 
## [8]  train-rmse:0.623096+0.013775    test-rmse:0.657730+0.061576 
## [9]  train-rmse:0.601681+0.013541    test-rmse:0.640044+0.061938 
## [10] train-rmse:0.582217+0.012875    test-rmse:0.624679+0.061588 
## [11] train-rmse:0.565963+0.013194    test-rmse:0.612828+0.059767 
## [12] train-rmse:0.551955+0.013299    test-rmse:0.603025+0.060676 
## [13] train-rmse:0.539864+0.013284    test-rmse:0.593508+0.059565 
## [14] train-rmse:0.529375+0.013034    test-rmse:0.586248+0.058338 
## [15] train-rmse:0.517805+0.014041    test-rmse:0.578929+0.056424 
## [16] train-rmse:0.509540+0.013322    test-rmse:0.572886+0.057273 
## [17] train-rmse:0.500845+0.014954    test-rmse:0.567100+0.056237 
## [18] train-rmse:0.494745+0.014767    test-rmse:0.563999+0.056129 
## [19] train-rmse:0.487936+0.014387    test-rmse:0.558060+0.057921 
## [20] train-rmse:0.482741+0.014267    test-rmse:0.556350+0.058054 
## [21] train-rmse:0.478186+0.014362    test-rmse:0.553578+0.057468 
## [22] train-rmse:0.473773+0.014420    test-rmse:0.551269+0.056798 
## [23] train-rmse:0.469376+0.013874    test-rmse:0.548979+0.056711 
## [24] train-rmse:0.464421+0.014026    test-rmse:0.545590+0.056972 
## [25] train-rmse:0.460850+0.014057    test-rmse:0.542734+0.055100 
## [26] train-rmse:0.457255+0.013947    test-rmse:0.542310+0.056720 
## [27] train-rmse:0.453247+0.013866    test-rmse:0.539711+0.055398 
## [28] train-rmse:0.449346+0.014785    test-rmse:0.538280+0.055154 
## [29] train-rmse:0.446556+0.014757    test-rmse:0.537052+0.055322 
## [30] train-rmse:0.443574+0.014632    test-rmse:0.535792+0.056125 
## [31] train-rmse:0.440951+0.014703    test-rmse:0.534348+0.055420 
## [32] train-rmse:0.437438+0.013981    test-rmse:0.532822+0.054665 
## [33] train-rmse:0.434521+0.014346    test-rmse:0.531588+0.054601 
## [34] train-rmse:0.432092+0.014268    test-rmse:0.530247+0.055285 
## [35] train-rmse:0.428964+0.014206    test-rmse:0.529575+0.054946 
## [36] train-rmse:0.426324+0.013858    test-rmse:0.527756+0.055106 
## [37] train-rmse:0.423278+0.014378    test-rmse:0.526219+0.055119 
## [38] train-rmse:0.420857+0.014690    test-rmse:0.525188+0.055123 
## [39] train-rmse:0.418538+0.014680    test-rmse:0.524445+0.055816 
## [40] train-rmse:0.415938+0.014339    test-rmse:0.522654+0.055949 
## [41] train-rmse:0.414098+0.014318    test-rmse:0.521848+0.055470 
## [42] train-rmse:0.411946+0.014295    test-rmse:0.520851+0.056177 
## [43] train-rmse:0.409620+0.014121    test-rmse:0.521334+0.055773 
## [44] train-rmse:0.407370+0.013869    test-rmse:0.519597+0.055978 
## [45] train-rmse:0.405104+0.014042    test-rmse:0.518228+0.055310 
## [46] train-rmse:0.402879+0.014150    test-rmse:0.517468+0.054758 
## [47] train-rmse:0.400690+0.013863    test-rmse:0.515833+0.055073 
## [48] train-rmse:0.398498+0.013970    test-rmse:0.514952+0.055856 
## [49] train-rmse:0.396503+0.014143    test-rmse:0.514291+0.056754 
## [50] train-rmse:0.395061+0.014101    test-rmse:0.514672+0.057279 
## [51] train-rmse:0.392749+0.013770    test-rmse:0.514070+0.057417 
## [52] train-rmse:0.390774+0.013678    test-rmse:0.513484+0.057294 
## [53] train-rmse:0.389420+0.013436    test-rmse:0.512796+0.057750 
## [54] train-rmse:0.387336+0.013880    test-rmse:0.512293+0.057549 
## [55] train-rmse:0.385462+0.013898    test-rmse:0.512234+0.057353 
## [56] train-rmse:0.383684+0.014199    test-rmse:0.511588+0.058375 
## [57] train-rmse:0.382022+0.013786    test-rmse:0.511193+0.059714 
## [58] train-rmse:0.380474+0.014039    test-rmse:0.510920+0.060047 
## [59] train-rmse:0.378899+0.013960    test-rmse:0.510574+0.059687 
## [60] train-rmse:0.377146+0.013784    test-rmse:0.509332+0.059559 
## [61] train-rmse:0.375742+0.013719    test-rmse:0.508887+0.059863 
## [62] train-rmse:0.373984+0.013591    test-rmse:0.508230+0.059566 
## [63] train-rmse:0.372661+0.013535    test-rmse:0.508055+0.058909 
## [64] train-rmse:0.371406+0.013385    test-rmse:0.508080+0.059110 
## [65] train-rmse:0.370257+0.013272    test-rmse:0.508071+0.059305 
## [66] train-rmse:0.368816+0.013446    test-rmse:0.507882+0.059311 
## [67] train-rmse:0.367438+0.013357    test-rmse:0.507485+0.058644 
## [68] train-rmse:0.366129+0.013217    test-rmse:0.506640+0.058674 
## [69] train-rmse:0.364289+0.012761    test-rmse:0.506058+0.059268 
## [70] train-rmse:0.362967+0.012435    test-rmse:0.505406+0.058567 
## [71] train-rmse:0.361565+0.012362    test-rmse:0.504990+0.058217 
## [72] train-rmse:0.359849+0.012695    test-rmse:0.504283+0.058735 
## [73] train-rmse:0.358386+0.012427    test-rmse:0.504283+0.058775 
## [74] train-rmse:0.356762+0.012574    test-rmse:0.503598+0.058770 
## [75] train-rmse:0.355652+0.012532    test-rmse:0.503441+0.058755 
## [76] train-rmse:0.353883+0.012831    test-rmse:0.502650+0.058813 
## [77] train-rmse:0.352677+0.012680    test-rmse:0.502164+0.059327 
## [78] train-rmse:0.351313+0.012596    test-rmse:0.501212+0.058684 
## [79] train-rmse:0.350178+0.012636    test-rmse:0.500752+0.057913 
## [80] train-rmse:0.348719+0.012504    test-rmse:0.500495+0.058181 
## [81] train-rmse:0.347150+0.012689    test-rmse:0.500885+0.057860 
## [82] train-rmse:0.345425+0.013182    test-rmse:0.500125+0.057304 
## [83] train-rmse:0.344368+0.013518    test-rmse:0.499571+0.057357 
## [84] train-rmse:0.343138+0.013519    test-rmse:0.499217+0.056801 
## [85] train-rmse:0.341931+0.013384    test-rmse:0.499271+0.056248 
## [86] train-rmse:0.340626+0.013098    test-rmse:0.499264+0.056186 
## [87] train-rmse:0.339310+0.012766    test-rmse:0.499290+0.056321 
## [88] train-rmse:0.337478+0.013681    test-rmse:0.499297+0.055867 
## [89] train-rmse:0.336253+0.014041    test-rmse:0.498616+0.055753 
## [90] train-rmse:0.335059+0.014158    test-rmse:0.498754+0.055862 
## [91] train-rmse:0.334082+0.014127    test-rmse:0.498473+0.055556 
## [92] train-rmse:0.333198+0.013914    test-rmse:0.498240+0.055514 
## [93] train-rmse:0.331953+0.014003    test-rmse:0.498295+0.055309 
## [94] train-rmse:0.331157+0.014028    test-rmse:0.498605+0.055227 
## [95] train-rmse:0.329941+0.013786    test-rmse:0.498667+0.055125 
## [96] train-rmse:0.328717+0.014257    test-rmse:0.498166+0.055186 
## [97] train-rmse:0.327475+0.014612    test-rmse:0.498293+0.054038 
## [98] train-rmse:0.326353+0.014495    test-rmse:0.497882+0.053978 
## [99] train-rmse:0.324963+0.014740    test-rmse:0.497929+0.054222 
## [100]    train-rmse:0.323822+0.014828    test-rmse:0.497561+0.054442 
## [101]    train-rmse:0.322445+0.015163    test-rmse:0.497812+0.053867 
## [102]    train-rmse:0.321331+0.015570    test-rmse:0.497253+0.053638 
## [103]    train-rmse:0.319869+0.015747    test-rmse:0.498053+0.053581 
## [104]    train-rmse:0.318790+0.015180    test-rmse:0.497257+0.053817 
## [105]    train-rmse:0.317669+0.015079    test-rmse:0.497462+0.052941 
## [106]    train-rmse:0.317022+0.015079    test-rmse:0.497277+0.052282 
## [107]    train-rmse:0.316229+0.014942    test-rmse:0.497186+0.052533 
## [108]    train-rmse:0.315424+0.014932    test-rmse:0.497308+0.052172 
## [109]    train-rmse:0.314780+0.014821    test-rmse:0.496914+0.052113 
## [110]    train-rmse:0.314044+0.014883    test-rmse:0.496714+0.051958 
## [111]    train-rmse:0.313062+0.014767    test-rmse:0.497210+0.051796 
## [112]    train-rmse:0.311978+0.014325    test-rmse:0.496305+0.051699 
## [113]    train-rmse:0.311085+0.014100    test-rmse:0.495657+0.052572 
## [114]    train-rmse:0.309837+0.014395    test-rmse:0.495514+0.052529 
## [115]    train-rmse:0.308087+0.014571    test-rmse:0.495185+0.052334 
## [116]    train-rmse:0.307113+0.014451    test-rmse:0.495118+0.052428 
## [117]    train-rmse:0.306085+0.014228    test-rmse:0.494675+0.052766 
## [118]    train-rmse:0.305289+0.014123    test-rmse:0.494127+0.052530 
## [119]    train-rmse:0.304143+0.013963    test-rmse:0.493513+0.052806 
## [120]    train-rmse:0.302879+0.013436    test-rmse:0.493416+0.053024 
## [121]    train-rmse:0.301904+0.013372    test-rmse:0.493817+0.052917 
## [122]    train-rmse:0.301132+0.013434    test-rmse:0.493617+0.052823 
## [123]    train-rmse:0.300178+0.013331    test-rmse:0.493558+0.052752 
## [124]    train-rmse:0.299623+0.013355    test-rmse:0.493324+0.052547 
## [125]    train-rmse:0.298736+0.013518    test-rmse:0.493484+0.052183 
## [126]    train-rmse:0.297438+0.013093    test-rmse:0.492988+0.052268 
## [127]    train-rmse:0.296073+0.012744    test-rmse:0.492858+0.052281 
## [128]    train-rmse:0.295395+0.012668    test-rmse:0.492983+0.052160 
## [129]    train-rmse:0.294624+0.012783    test-rmse:0.492883+0.052055 
## [130]    train-rmse:0.294070+0.012838    test-rmse:0.492821+0.052049 
## [131]    train-rmse:0.293231+0.012844    test-rmse:0.492832+0.052437 
## [132]    train-rmse:0.292648+0.012817    test-rmse:0.493232+0.052037 
## [133]    train-rmse:0.291437+0.012852    test-rmse:0.493097+0.051572 
## [134]    train-rmse:0.289837+0.012808    test-rmse:0.493137+0.051560 
## [135]    train-rmse:0.288822+0.012452    test-rmse:0.493373+0.051166 
## [136]    train-rmse:0.288090+0.012584    test-rmse:0.493096+0.051266 
## Stopping. Best iteration:
## [130]    train-rmse:0.294070+0.012838    test-rmse:0.492821+0.052049
## 
## 10 
## [1]  train-rmse:0.922950+0.015553    test-rmse:0.927087+0.061616 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.852703+0.014659    test-rmse:0.863120+0.061397 
## [3]  train-rmse:0.792436+0.014403    test-rmse:0.809195+0.061264 
## [4]  train-rmse:0.740812+0.014232    test-rmse:0.763242+0.061656 
## [5]  train-rmse:0.696402+0.013656    test-rmse:0.724529+0.063647 
## [6]  train-rmse:0.658108+0.012565    test-rmse:0.693453+0.063708 
## [7]  train-rmse:0.624988+0.012679    test-rmse:0.667180+0.065838 
## [8]  train-rmse:0.595366+0.012354    test-rmse:0.643034+0.064233 
## [9]  train-rmse:0.570450+0.012919    test-rmse:0.625509+0.061815 
## [10] train-rmse:0.548468+0.012702    test-rmse:0.608608+0.060865 
## [11] train-rmse:0.529275+0.012498    test-rmse:0.594891+0.059977 
## [12] train-rmse:0.512634+0.012337    test-rmse:0.583324+0.059258 
## [13] train-rmse:0.497567+0.012738    test-rmse:0.573830+0.058662 
## [14] train-rmse:0.484328+0.013660    test-rmse:0.566743+0.057470 
## [15] train-rmse:0.473165+0.013426    test-rmse:0.560603+0.056813 
## [16] train-rmse:0.463767+0.012879    test-rmse:0.554778+0.056726 
## [17] train-rmse:0.453916+0.015111    test-rmse:0.551087+0.055889 
## [18] train-rmse:0.446221+0.014896    test-rmse:0.547157+0.054284 
## [19] train-rmse:0.437165+0.015224    test-rmse:0.540939+0.051877 
## [20] train-rmse:0.431312+0.015563    test-rmse:0.537344+0.051864 
## [21] train-rmse:0.425216+0.015321    test-rmse:0.534023+0.051225 
## [22] train-rmse:0.419827+0.015488    test-rmse:0.530720+0.051732 
## [23] train-rmse:0.415285+0.015590    test-rmse:0.528413+0.051354 
## [24] train-rmse:0.410565+0.014899    test-rmse:0.526355+0.052229 
## [25] train-rmse:0.405676+0.014439    test-rmse:0.525083+0.052587 
## [26] train-rmse:0.401130+0.014669    test-rmse:0.522956+0.052662 
## [27] train-rmse:0.397074+0.014279    test-rmse:0.521006+0.051661 
## [28] train-rmse:0.393082+0.013883    test-rmse:0.518181+0.052668 
## [29] train-rmse:0.390118+0.013959    test-rmse:0.517098+0.051711 
## [30] train-rmse:0.384966+0.012765    test-rmse:0.514926+0.054220 
## [31] train-rmse:0.381812+0.012845    test-rmse:0.513571+0.054408 
## [32] train-rmse:0.379276+0.012787    test-rmse:0.512968+0.054145 
## [33] train-rmse:0.376181+0.012205    test-rmse:0.511294+0.055026 
## [34] train-rmse:0.373107+0.012020    test-rmse:0.510183+0.055358 
## [35] train-rmse:0.370806+0.011878    test-rmse:0.509428+0.054861 
## [36] train-rmse:0.366855+0.012849    test-rmse:0.507662+0.054538 
## [37] train-rmse:0.364527+0.012565    test-rmse:0.506768+0.054960 
## [38] train-rmse:0.361516+0.012798    test-rmse:0.506209+0.055030 
## [39] train-rmse:0.358490+0.013916    test-rmse:0.505896+0.054867 
## [40] train-rmse:0.355368+0.013521    test-rmse:0.505050+0.055469 
## [41] train-rmse:0.352389+0.013499    test-rmse:0.504364+0.055669 
## [42] train-rmse:0.349688+0.014373    test-rmse:0.503456+0.055628 
## [43] train-rmse:0.347396+0.013814    test-rmse:0.503353+0.056254 
## [44] train-rmse:0.345154+0.013354    test-rmse:0.503128+0.056580 
## [45] train-rmse:0.342986+0.013880    test-rmse:0.503300+0.056827 
## [46] train-rmse:0.340231+0.014235    test-rmse:0.503102+0.056447 
## [47] train-rmse:0.338175+0.013826    test-rmse:0.502013+0.057302 
## [48] train-rmse:0.335857+0.013808    test-rmse:0.501959+0.056589 
## [49] train-rmse:0.333600+0.014006    test-rmse:0.501654+0.055953 
## [50] train-rmse:0.331727+0.014127    test-rmse:0.500893+0.056445 
## [51] train-rmse:0.329314+0.014401    test-rmse:0.500477+0.056819 
## [52] train-rmse:0.327372+0.014201    test-rmse:0.499755+0.056186 
## [53] train-rmse:0.324406+0.014879    test-rmse:0.500250+0.055572 
## [54] train-rmse:0.322655+0.014601    test-rmse:0.499382+0.056112 
## [55] train-rmse:0.320749+0.014761    test-rmse:0.498839+0.055985 
## [56] train-rmse:0.318968+0.014762    test-rmse:0.498188+0.055658 
## [57] train-rmse:0.316827+0.014484    test-rmse:0.497777+0.055638 
## [58] train-rmse:0.315068+0.014446    test-rmse:0.498049+0.055562 
## [59] train-rmse:0.313451+0.014103    test-rmse:0.497512+0.055478 
## [60] train-rmse:0.311389+0.014248    test-rmse:0.497489+0.055563 
## [61] train-rmse:0.309539+0.014432    test-rmse:0.497147+0.056015 
## [62] train-rmse:0.307520+0.014572    test-rmse:0.496308+0.055688 
## [63] train-rmse:0.305809+0.014718    test-rmse:0.495837+0.055053 
## [64] train-rmse:0.303914+0.014408    test-rmse:0.495299+0.055447 
## [65] train-rmse:0.301454+0.014151    test-rmse:0.495642+0.054662 
## [66] train-rmse:0.299232+0.014087    test-rmse:0.495734+0.054437 
## [67] train-rmse:0.297816+0.014299    test-rmse:0.495957+0.054551 
## [68] train-rmse:0.295881+0.014301    test-rmse:0.494961+0.054767 
## [69] train-rmse:0.294863+0.014022    test-rmse:0.495399+0.054867 
## [70] train-rmse:0.292676+0.013830    test-rmse:0.495730+0.053844 
## [71] train-rmse:0.291485+0.013893    test-rmse:0.495656+0.053434 
## [72] train-rmse:0.289835+0.013798    test-rmse:0.495902+0.053385 
## [73] train-rmse:0.288577+0.013515    test-rmse:0.495609+0.054305 
## [74] train-rmse:0.287148+0.013510    test-rmse:0.495454+0.054540 
## Stopping. Best iteration:
## [68] train-rmse:0.295881+0.014301    test-rmse:0.494961+0.054767
## 
## 11 
## [1]  train-rmse:0.919793+0.015253    test-rmse:0.925195+0.062117 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.846332+0.014344    test-rmse:0.858437+0.060651 
## [3]  train-rmse:0.782855+0.012911    test-rmse:0.802885+0.061455 
## [4]  train-rmse:0.727553+0.012390    test-rmse:0.755104+0.062189 
## [5]  train-rmse:0.679748+0.011860    test-rmse:0.715971+0.062505 
## [6]  train-rmse:0.639625+0.011696    test-rmse:0.685200+0.062895 
## [7]  train-rmse:0.602706+0.010999    test-rmse:0.655731+0.061671 
## [8]  train-rmse:0.571413+0.011992    test-rmse:0.633861+0.062190 
## [9]  train-rmse:0.543849+0.012024    test-rmse:0.613514+0.060669 
## [10] train-rmse:0.519920+0.012187    test-rmse:0.595314+0.059659 
## [11] train-rmse:0.499050+0.012342    test-rmse:0.582498+0.058093 
## [12] train-rmse:0.479912+0.013030    test-rmse:0.570600+0.056958 
## [13] train-rmse:0.463430+0.013441    test-rmse:0.561910+0.055526 
## [14] train-rmse:0.448554+0.013308    test-rmse:0.552645+0.055410 
## [15] train-rmse:0.435090+0.013867    test-rmse:0.545435+0.056240 
## [16] train-rmse:0.423599+0.013724    test-rmse:0.540190+0.055427 
## [17] train-rmse:0.413347+0.014000    test-rmse:0.536605+0.054705 
## [18] train-rmse:0.404597+0.013137    test-rmse:0.532221+0.055608 
## [19] train-rmse:0.397239+0.013170    test-rmse:0.529368+0.054783 
## [20] train-rmse:0.390082+0.012863    test-rmse:0.527358+0.054663 
## [21] train-rmse:0.383488+0.013306    test-rmse:0.524742+0.055166 
## [22] train-rmse:0.377605+0.012295    test-rmse:0.521648+0.055395 
## [23] train-rmse:0.371349+0.014353    test-rmse:0.519156+0.054279 
## [24] train-rmse:0.366367+0.014049    test-rmse:0.517786+0.053931 
## [25] train-rmse:0.361860+0.014565    test-rmse:0.515717+0.052920 
## [26] train-rmse:0.358133+0.015408    test-rmse:0.515012+0.053636 
## [27] train-rmse:0.353784+0.014895    test-rmse:0.512776+0.054249 
## [28] train-rmse:0.350108+0.014495    test-rmse:0.510789+0.054818 
## [29] train-rmse:0.345362+0.014156    test-rmse:0.508626+0.056109 
## [30] train-rmse:0.341696+0.014030    test-rmse:0.507216+0.055814 
## [31] train-rmse:0.338151+0.013706    test-rmse:0.505876+0.054760 
## [32] train-rmse:0.333439+0.014499    test-rmse:0.505371+0.055390 
## [33] train-rmse:0.330389+0.014535    test-rmse:0.504043+0.054935 
## [34] train-rmse:0.327659+0.014045    test-rmse:0.504199+0.055241 
## [35] train-rmse:0.324156+0.014546    test-rmse:0.503965+0.055850 
## [36] train-rmse:0.321511+0.014180    test-rmse:0.502959+0.055310 
## [37] train-rmse:0.318245+0.013729    test-rmse:0.502623+0.055140 
## [38] train-rmse:0.314782+0.013713    test-rmse:0.501434+0.055697 
## [39] train-rmse:0.311823+0.013626    test-rmse:0.500978+0.056100 
## [40] train-rmse:0.308894+0.014510    test-rmse:0.501501+0.056008 
## [41] train-rmse:0.306679+0.014261    test-rmse:0.501355+0.055472 
## [42] train-rmse:0.303858+0.013754    test-rmse:0.501068+0.055966 
## [43] train-rmse:0.301236+0.013289    test-rmse:0.500905+0.055590 
## [44] train-rmse:0.299003+0.013644    test-rmse:0.500619+0.056105 
## [45] train-rmse:0.296855+0.013563    test-rmse:0.500525+0.056649 
## [46] train-rmse:0.294555+0.014288    test-rmse:0.499860+0.056033 
## [47] train-rmse:0.292356+0.013479    test-rmse:0.499859+0.055653 
## [48] train-rmse:0.288923+0.013684    test-rmse:0.498853+0.055311 
## [49] train-rmse:0.285909+0.013286    test-rmse:0.497884+0.056193 
## [50] train-rmse:0.283452+0.013132    test-rmse:0.497587+0.056250 
## [51] train-rmse:0.281131+0.013027    test-rmse:0.497039+0.056864 
## [52] train-rmse:0.279210+0.013126    test-rmse:0.496760+0.057216 
## [53] train-rmse:0.277195+0.013353    test-rmse:0.496653+0.056803 
## [54] train-rmse:0.274302+0.013001    test-rmse:0.496183+0.056051 
## [55] train-rmse:0.272521+0.012811    test-rmse:0.495828+0.055709 
## [56] train-rmse:0.271037+0.013211    test-rmse:0.495826+0.055557 
## [57] train-rmse:0.268223+0.012686    test-rmse:0.495054+0.055542 
## [58] train-rmse:0.266417+0.012180    test-rmse:0.494939+0.055543 
## [59] train-rmse:0.264409+0.012335    test-rmse:0.494453+0.054961 
## [60] train-rmse:0.261240+0.013237    test-rmse:0.494751+0.055114 
## [61] train-rmse:0.258959+0.012637    test-rmse:0.494660+0.055181 
## [62] train-rmse:0.256782+0.012237    test-rmse:0.493735+0.054926 
## [63] train-rmse:0.255440+0.012253    test-rmse:0.493587+0.054787 
## [64] train-rmse:0.253208+0.013063    test-rmse:0.493025+0.054701 
## [65] train-rmse:0.251139+0.013235    test-rmse:0.492664+0.054853 
## [66] train-rmse:0.249013+0.013597    test-rmse:0.492967+0.054923 
## [67] train-rmse:0.246959+0.013984    test-rmse:0.494147+0.055683 
## [68] train-rmse:0.245013+0.014246    test-rmse:0.494423+0.056297 
## [69] train-rmse:0.243708+0.014391    test-rmse:0.494176+0.055915 
## [70] train-rmse:0.241102+0.014242    test-rmse:0.493588+0.056174 
## [71] train-rmse:0.237980+0.014585    test-rmse:0.493817+0.055964 
## Stopping. Best iteration:
## [65] train-rmse:0.251139+0.013235    test-rmse:0.492664+0.054853
## 
## 12 
## [1]  train-rmse:0.916924+0.014959    test-rmse:0.923903+0.062312 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.840699+0.014175    test-rmse:0.854880+0.060994 
## [3]  train-rmse:0.774489+0.013092    test-rmse:0.798714+0.060641 
## [4]  train-rmse:0.716351+0.011521    test-rmse:0.749800+0.062103 
## [5]  train-rmse:0.665880+0.011665    test-rmse:0.708748+0.062414 
## [6]  train-rmse:0.623102+0.011422    test-rmse:0.676090+0.064120 
## [7]  train-rmse:0.585921+0.011244    test-rmse:0.648817+0.065204 
## [8]  train-rmse:0.552683+0.011504    test-rmse:0.626730+0.065418 
## [9]  train-rmse:0.522393+0.011978    test-rmse:0.606468+0.062029 
## [10] train-rmse:0.495472+0.012838    test-rmse:0.590792+0.061422 
## [11] train-rmse:0.472745+0.013103    test-rmse:0.575885+0.059271 
## [12] train-rmse:0.452129+0.013514    test-rmse:0.564362+0.058694 
## [13] train-rmse:0.434083+0.012778    test-rmse:0.554159+0.059070 
## [14] train-rmse:0.418333+0.012113    test-rmse:0.547578+0.060403 
## [15] train-rmse:0.403366+0.011452    test-rmse:0.539619+0.060441 
## [16] train-rmse:0.391156+0.010650    test-rmse:0.534692+0.061579 
## [17] train-rmse:0.380302+0.010601    test-rmse:0.530880+0.059770 
## [18] train-rmse:0.369844+0.011558    test-rmse:0.527926+0.060999 
## [19] train-rmse:0.362119+0.011207    test-rmse:0.525397+0.060784 
## [20] train-rmse:0.354201+0.011461    test-rmse:0.522647+0.060247 
## [21] train-rmse:0.347703+0.011421    test-rmse:0.521422+0.059363 
## [22] train-rmse:0.341316+0.010977    test-rmse:0.518685+0.057941 
## [23] train-rmse:0.335073+0.010773    test-rmse:0.517177+0.057526 
## [24] train-rmse:0.330776+0.011128    test-rmse:0.516603+0.057072 
## [25] train-rmse:0.324255+0.011147    test-rmse:0.515915+0.056799 
## [26] train-rmse:0.317847+0.012037    test-rmse:0.514455+0.055952 
## [27] train-rmse:0.313051+0.011393    test-rmse:0.512657+0.055285 
## [28] train-rmse:0.308631+0.012621    test-rmse:0.511561+0.054302 
## [29] train-rmse:0.304578+0.011870    test-rmse:0.510206+0.053213 
## [30] train-rmse:0.299605+0.010366    test-rmse:0.507636+0.052414 
## [31] train-rmse:0.294343+0.012613    test-rmse:0.506259+0.052135 
## [32] train-rmse:0.291037+0.012376    test-rmse:0.505339+0.051646 
## [33] train-rmse:0.287321+0.012845    test-rmse:0.504441+0.051440 
## [34] train-rmse:0.284411+0.013014    test-rmse:0.504621+0.051435 
## [35] train-rmse:0.279231+0.012697    test-rmse:0.504054+0.051988 
## [36] train-rmse:0.276269+0.012594    test-rmse:0.502828+0.051439 
## [37] train-rmse:0.273426+0.012440    test-rmse:0.502094+0.051312 
## [38] train-rmse:0.269878+0.013523    test-rmse:0.501246+0.051464 
## [39] train-rmse:0.267059+0.013586    test-rmse:0.501437+0.051445 
## [40] train-rmse:0.264465+0.013266    test-rmse:0.501317+0.051813 
## [41] train-rmse:0.261414+0.014222    test-rmse:0.501526+0.051820 
## [42] train-rmse:0.257229+0.014696    test-rmse:0.501041+0.052439 
## [43] train-rmse:0.255002+0.014749    test-rmse:0.500396+0.052025 
## [44] train-rmse:0.251458+0.015350    test-rmse:0.500345+0.051769 
## [45] train-rmse:0.249457+0.015208    test-rmse:0.500122+0.051385 
## [46] train-rmse:0.246732+0.015435    test-rmse:0.499831+0.051718 
## [47] train-rmse:0.244627+0.015158    test-rmse:0.500174+0.051788 
## [48] train-rmse:0.242043+0.015175    test-rmse:0.500107+0.052041 
## [49] train-rmse:0.239635+0.015566    test-rmse:0.499560+0.052490 
## [50] train-rmse:0.237402+0.015463    test-rmse:0.499364+0.051887 
## [51] train-rmse:0.234216+0.014400    test-rmse:0.499380+0.052747 
## [52] train-rmse:0.232071+0.014614    test-rmse:0.499119+0.052750 
## [53] train-rmse:0.229411+0.013516    test-rmse:0.499375+0.053058 
## [54] train-rmse:0.226537+0.013214    test-rmse:0.500383+0.053799 
## [55] train-rmse:0.223942+0.013953    test-rmse:0.499906+0.053574 
## [56] train-rmse:0.222006+0.013890    test-rmse:0.499401+0.053449 
## [57] train-rmse:0.220320+0.013801    test-rmse:0.499596+0.053574 
## [58] train-rmse:0.218042+0.013130    test-rmse:0.499619+0.053699 
## Stopping. Best iteration:
## [52] train-rmse:0.232071+0.014614    test-rmse:0.499119+0.052750
## 
## 13 
## [1]  train-rmse:0.914122+0.014697    test-rmse:0.921960+0.061395 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.835441+0.013279    test-rmse:0.853048+0.062286 
## [3]  train-rmse:0.766863+0.012661    test-rmse:0.795199+0.062477 
## [4]  train-rmse:0.706652+0.012431    test-rmse:0.745790+0.061744 
## [5]  train-rmse:0.653848+0.011160    test-rmse:0.704273+0.063211 
## [6]  train-rmse:0.608106+0.010381    test-rmse:0.669866+0.063062 
## [7]  train-rmse:0.568036+0.011152    test-rmse:0.644015+0.064257 
## [8]  train-rmse:0.533343+0.012343    test-rmse:0.621725+0.065266 
## [9]  train-rmse:0.501386+0.012315    test-rmse:0.600928+0.064643 
## [10] train-rmse:0.474471+0.012534    test-rmse:0.583877+0.064913 
## [11] train-rmse:0.449699+0.011295    test-rmse:0.568359+0.063205 
## [12] train-rmse:0.428610+0.012377    test-rmse:0.557847+0.062988 
## [13] train-rmse:0.409146+0.012663    test-rmse:0.547232+0.062680 
## [14] train-rmse:0.391547+0.012587    test-rmse:0.538145+0.062375 
## [15] train-rmse:0.376335+0.012736    test-rmse:0.530975+0.062812 
## [16] train-rmse:0.361851+0.012264    test-rmse:0.525966+0.062367 
## [17] train-rmse:0.349581+0.012444    test-rmse:0.524129+0.062490 
## [18] train-rmse:0.337862+0.013399    test-rmse:0.521570+0.062471 
## [19] train-rmse:0.326824+0.013574    test-rmse:0.518886+0.063598 
## [20] train-rmse:0.317417+0.014416    test-rmse:0.516687+0.063226 
## [21] train-rmse:0.309570+0.015102    test-rmse:0.515184+0.062753 
## [22] train-rmse:0.302737+0.014899    test-rmse:0.513313+0.061843 
## [23] train-rmse:0.294721+0.014851    test-rmse:0.512121+0.060810 
## [24] train-rmse:0.289639+0.014654    test-rmse:0.510679+0.059566 
## [25] train-rmse:0.283131+0.014187    test-rmse:0.508344+0.058437 
## [26] train-rmse:0.276521+0.014998    test-rmse:0.507615+0.056825 
## [27] train-rmse:0.271308+0.014841    test-rmse:0.506586+0.055981 
## [28] train-rmse:0.267578+0.014664    test-rmse:0.505300+0.056295 
## [29] train-rmse:0.262805+0.013810    test-rmse:0.504970+0.054879 
## [30] train-rmse:0.258870+0.013427    test-rmse:0.504066+0.054056 
## [31] train-rmse:0.253884+0.014176    test-rmse:0.502896+0.053433 
## [32] train-rmse:0.250338+0.013898    test-rmse:0.501235+0.052945 
## [33] train-rmse:0.247159+0.013780    test-rmse:0.500677+0.052452 
## [34] train-rmse:0.242240+0.014840    test-rmse:0.499939+0.052686 
## [35] train-rmse:0.238422+0.014894    test-rmse:0.498922+0.051866 
## [36] train-rmse:0.236071+0.014492    test-rmse:0.498131+0.051965 
## [37] train-rmse:0.231944+0.013885    test-rmse:0.497199+0.051637 
## [38] train-rmse:0.228804+0.013126    test-rmse:0.497026+0.049923 
## [39] train-rmse:0.226154+0.013230    test-rmse:0.496685+0.049332 
## [40] train-rmse:0.224100+0.013396    test-rmse:0.496757+0.049314 
## [41] train-rmse:0.220949+0.013348    test-rmse:0.496768+0.048700 
## [42] train-rmse:0.218263+0.013322    test-rmse:0.496706+0.048310 
## [43] train-rmse:0.216269+0.013335    test-rmse:0.496742+0.047665 
## [44] train-rmse:0.213916+0.012928    test-rmse:0.497284+0.047783 
## [45] train-rmse:0.210696+0.012496    test-rmse:0.497011+0.048355 
## Stopping. Best iteration:
## [39] train-rmse:0.226154+0.013230    test-rmse:0.496685+0.049332
## 
## 14 
## [1]  train-rmse:0.929630+0.015462    test-rmse:0.930346+0.061537 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.869603+0.014764    test-rmse:0.873258+0.060906 
## [3]  train-rmse:0.822048+0.014210    test-rmse:0.828264+0.060508 
## [4]  train-rmse:0.783919+0.013994    test-rmse:0.798234+0.060355 
## [5]  train-rmse:0.753267+0.013489    test-rmse:0.769235+0.058183 
## [6]  train-rmse:0.728374+0.013052    test-rmse:0.745350+0.059582 
## [7]  train-rmse:0.707717+0.012619    test-rmse:0.728624+0.057513 
## [8]  train-rmse:0.690179+0.012440    test-rmse:0.710675+0.057446 
## [9]  train-rmse:0.675983+0.012090    test-rmse:0.697114+0.057045 
## [10] train-rmse:0.663690+0.011827    test-rmse:0.685120+0.055089 
## [11] train-rmse:0.653386+0.011719    test-rmse:0.677381+0.055831 
## [12] train-rmse:0.644789+0.011525    test-rmse:0.670482+0.052474 
## [13] train-rmse:0.637167+0.011400    test-rmse:0.662790+0.053638 
## [14] train-rmse:0.630691+0.011232    test-rmse:0.655941+0.053156 
## [15] train-rmse:0.625078+0.011169    test-rmse:0.652555+0.053006 
## [16] train-rmse:0.620091+0.011103    test-rmse:0.647285+0.051275 
## [17] train-rmse:0.615684+0.011191    test-rmse:0.643408+0.053120 
## [18] train-rmse:0.611535+0.011230    test-rmse:0.642131+0.054325 
## [19] train-rmse:0.607723+0.011207    test-rmse:0.638477+0.054618 
## [20] train-rmse:0.604092+0.011181    test-rmse:0.635346+0.053557 
## [21] train-rmse:0.600817+0.011182    test-rmse:0.633234+0.054130 
## [22] train-rmse:0.597672+0.011226    test-rmse:0.631240+0.053335 
## [23] train-rmse:0.594677+0.011177    test-rmse:0.626506+0.053705 
## [24] train-rmse:0.591926+0.011165    test-rmse:0.625521+0.054013 
## [25] train-rmse:0.589265+0.011146    test-rmse:0.623146+0.054799 
## [26] train-rmse:0.586709+0.011182    test-rmse:0.621156+0.053904 
## [27] train-rmse:0.584306+0.011184    test-rmse:0.618361+0.054742 
## [28] train-rmse:0.581947+0.011225    test-rmse:0.616095+0.055679 
## [29] train-rmse:0.579712+0.011238    test-rmse:0.615500+0.054260 
## [30] train-rmse:0.577530+0.011180    test-rmse:0.614541+0.053792 
## [31] train-rmse:0.575449+0.011128    test-rmse:0.612364+0.054356 
## [32] train-rmse:0.573445+0.011156    test-rmse:0.610671+0.054991 
## [33] train-rmse:0.571474+0.011167    test-rmse:0.608569+0.055719 
## [34] train-rmse:0.569589+0.011177    test-rmse:0.607825+0.055748 
## [35] train-rmse:0.567737+0.011203    test-rmse:0.605502+0.055390 
## [36] train-rmse:0.565943+0.011194    test-rmse:0.603113+0.055423 
## [37] train-rmse:0.564207+0.011189    test-rmse:0.601325+0.054800 
## [38] train-rmse:0.562530+0.011188    test-rmse:0.601474+0.053705 
## [39] train-rmse:0.560873+0.011208    test-rmse:0.601157+0.054192 
## [40] train-rmse:0.559280+0.011225    test-rmse:0.599033+0.054403 
## [41] train-rmse:0.557709+0.011231    test-rmse:0.597192+0.054565 
## [42] train-rmse:0.556174+0.011216    test-rmse:0.595835+0.054995 
## [43] train-rmse:0.554688+0.011194    test-rmse:0.594541+0.054237 
## [44] train-rmse:0.553222+0.011186    test-rmse:0.593332+0.054479 
## [45] train-rmse:0.551768+0.011185    test-rmse:0.592756+0.054160 
## [46] train-rmse:0.550340+0.011185    test-rmse:0.591356+0.054351 
## [47] train-rmse:0.548935+0.011193    test-rmse:0.590776+0.053479 
## [48] train-rmse:0.547566+0.011202    test-rmse:0.589810+0.053652 
## [49] train-rmse:0.546253+0.011191    test-rmse:0.587986+0.054491 
## [50] train-rmse:0.544941+0.011173    test-rmse:0.587993+0.054048 
## [51] train-rmse:0.543660+0.011146    test-rmse:0.587319+0.054319 
## [52] train-rmse:0.542416+0.011146    test-rmse:0.586331+0.054662 
## [53] train-rmse:0.541218+0.011117    test-rmse:0.585521+0.053967 
## [54] train-rmse:0.540028+0.011074    test-rmse:0.584658+0.054088 
## [55] train-rmse:0.538863+0.011068    test-rmse:0.584276+0.054822 
## [56] train-rmse:0.537721+0.011068    test-rmse:0.583054+0.054486 
## [57] train-rmse:0.536589+0.011076    test-rmse:0.582432+0.054739 
## [58] train-rmse:0.535479+0.011080    test-rmse:0.581496+0.053970 
## [59] train-rmse:0.534396+0.011082    test-rmse:0.580421+0.054887 
## [60] train-rmse:0.533352+0.011078    test-rmse:0.579962+0.054646 
## [61] train-rmse:0.532321+0.011067    test-rmse:0.578378+0.054274 
## [62] train-rmse:0.531299+0.011054    test-rmse:0.578239+0.053596 
## [63] train-rmse:0.530301+0.011040    test-rmse:0.577736+0.053399 
## [64] train-rmse:0.529317+0.011023    test-rmse:0.577325+0.053036 
## [65] train-rmse:0.528340+0.010996    test-rmse:0.577099+0.053013 
## [66] train-rmse:0.527385+0.010970    test-rmse:0.576393+0.053931 
## [67] train-rmse:0.526453+0.010942    test-rmse:0.575557+0.053983 
## [68] train-rmse:0.525539+0.010907    test-rmse:0.574578+0.054113 
## [69] train-rmse:0.524638+0.010885    test-rmse:0.575060+0.054867 
## [70] train-rmse:0.523751+0.010877    test-rmse:0.573805+0.054902 
## [71] train-rmse:0.522878+0.010860    test-rmse:0.572785+0.054335 
## [72] train-rmse:0.522013+0.010839    test-rmse:0.572342+0.054860 
## [73] train-rmse:0.521176+0.010840    test-rmse:0.571608+0.055528 
## [74] train-rmse:0.520353+0.010832    test-rmse:0.571109+0.054793 
## [75] train-rmse:0.519536+0.010828    test-rmse:0.570630+0.054034 
## [76] train-rmse:0.518729+0.010817    test-rmse:0.569207+0.054216 
## [77] train-rmse:0.517929+0.010797    test-rmse:0.568507+0.053773 
## [78] train-rmse:0.517148+0.010786    test-rmse:0.568235+0.053607 
## [79] train-rmse:0.516370+0.010774    test-rmse:0.567495+0.053359 
## [80] train-rmse:0.515607+0.010761    test-rmse:0.566922+0.053977 
## [81] train-rmse:0.514854+0.010752    test-rmse:0.566158+0.054354 
## [82] train-rmse:0.514113+0.010745    test-rmse:0.565099+0.054239 
## [83] train-rmse:0.513380+0.010735    test-rmse:0.564780+0.053609 
## [84] train-rmse:0.512661+0.010725    test-rmse:0.564234+0.053584 
## [85] train-rmse:0.511964+0.010712    test-rmse:0.563938+0.052908 
## [86] train-rmse:0.511274+0.010702    test-rmse:0.564140+0.053265 
## [87] train-rmse:0.510590+0.010687    test-rmse:0.564009+0.054221 
## [88] train-rmse:0.509917+0.010666    test-rmse:0.563232+0.054424 
## [89] train-rmse:0.509242+0.010645    test-rmse:0.562397+0.054207 
## [90] train-rmse:0.508582+0.010636    test-rmse:0.561691+0.054094 
## [91] train-rmse:0.507929+0.010638    test-rmse:0.560945+0.054027 
## [92] train-rmse:0.507284+0.010638    test-rmse:0.560621+0.053753 
## [93] train-rmse:0.506652+0.010632    test-rmse:0.560020+0.053345 
## [94] train-rmse:0.506038+0.010620    test-rmse:0.559242+0.053389 
## [95] train-rmse:0.505430+0.010613    test-rmse:0.558715+0.053436 
## [96] train-rmse:0.504823+0.010600    test-rmse:0.558348+0.052814 
## [97] train-rmse:0.504237+0.010597    test-rmse:0.558030+0.053194 
## [98] train-rmse:0.503650+0.010589    test-rmse:0.557434+0.053306 
## [99] train-rmse:0.503069+0.010582    test-rmse:0.557461+0.053626 
## [100]    train-rmse:0.502500+0.010576    test-rmse:0.556975+0.054255 
## [101]    train-rmse:0.501943+0.010569    test-rmse:0.556576+0.054230 
## [102]    train-rmse:0.501386+0.010556    test-rmse:0.556336+0.054034 
## [103]    train-rmse:0.500833+0.010546    test-rmse:0.555882+0.053698 
## [104]    train-rmse:0.500291+0.010538    test-rmse:0.555057+0.053789 
## [105]    train-rmse:0.499754+0.010544    test-rmse:0.554665+0.053768 
## [106]    train-rmse:0.499227+0.010548    test-rmse:0.554738+0.053027 
## [107]    train-rmse:0.498702+0.010549    test-rmse:0.554222+0.053243 
## [108]    train-rmse:0.498183+0.010551    test-rmse:0.553991+0.053481 
## [109]    train-rmse:0.497672+0.010555    test-rmse:0.553915+0.053199 
## [110]    train-rmse:0.497171+0.010558    test-rmse:0.553702+0.053037 
## [111]    train-rmse:0.496678+0.010560    test-rmse:0.553951+0.052354 
## [112]    train-rmse:0.496193+0.010559    test-rmse:0.553030+0.052278 
## [113]    train-rmse:0.495716+0.010558    test-rmse:0.552517+0.052194 
## [114]    train-rmse:0.495243+0.010553    test-rmse:0.552371+0.052019 
## [115]    train-rmse:0.494773+0.010548    test-rmse:0.551717+0.052376 
## [116]    train-rmse:0.494306+0.010536    test-rmse:0.551597+0.052396 
## [117]    train-rmse:0.493846+0.010520    test-rmse:0.551260+0.052144 
## [118]    train-rmse:0.493390+0.010508    test-rmse:0.550902+0.052510 
## [119]    train-rmse:0.492940+0.010503    test-rmse:0.550777+0.052200 
## [120]    train-rmse:0.492498+0.010499    test-rmse:0.550888+0.052672 
## [121]    train-rmse:0.492057+0.010496    test-rmse:0.550085+0.052599 
## [122]    train-rmse:0.491622+0.010494    test-rmse:0.549870+0.052721 
## [123]    train-rmse:0.491191+0.010491    test-rmse:0.549970+0.052693 
## [124]    train-rmse:0.490768+0.010488    test-rmse:0.549646+0.052612 
## [125]    train-rmse:0.490346+0.010478    test-rmse:0.549187+0.052731 
## [126]    train-rmse:0.489928+0.010474    test-rmse:0.548604+0.052778 
## [127]    train-rmse:0.489519+0.010470    test-rmse:0.547875+0.053003 
## [128]    train-rmse:0.489114+0.010461    test-rmse:0.547866+0.052251 
## [129]    train-rmse:0.488717+0.010455    test-rmse:0.547186+0.051956 
## [130]    train-rmse:0.488320+0.010453    test-rmse:0.547108+0.051987 
## [131]    train-rmse:0.487930+0.010451    test-rmse:0.547111+0.051717 
## [132]    train-rmse:0.487542+0.010445    test-rmse:0.547260+0.052172 
## [133]    train-rmse:0.487159+0.010446    test-rmse:0.547185+0.052201 
## [134]    train-rmse:0.486779+0.010450    test-rmse:0.546933+0.052057 
## [135]    train-rmse:0.486408+0.010451    test-rmse:0.546754+0.052068 
## [136]    train-rmse:0.486039+0.010451    test-rmse:0.546878+0.052078 
## [137]    train-rmse:0.485675+0.010448    test-rmse:0.546548+0.052022 
## [138]    train-rmse:0.485315+0.010442    test-rmse:0.546270+0.051838 
## [139]    train-rmse:0.484958+0.010435    test-rmse:0.546105+0.052106 
## [140]    train-rmse:0.484606+0.010427    test-rmse:0.545590+0.052028 
## [141]    train-rmse:0.484259+0.010419    test-rmse:0.545312+0.052369 
## [142]    train-rmse:0.483913+0.010411    test-rmse:0.545022+0.052423 
## [143]    train-rmse:0.483574+0.010404    test-rmse:0.545100+0.052081 
## [144]    train-rmse:0.483237+0.010397    test-rmse:0.544896+0.052168 
## [145]    train-rmse:0.482908+0.010388    test-rmse:0.545253+0.051980 
## [146]    train-rmse:0.482577+0.010383    test-rmse:0.544941+0.051994 
## [147]    train-rmse:0.482252+0.010379    test-rmse:0.544780+0.051901 
## [148]    train-rmse:0.481932+0.010380    test-rmse:0.544547+0.051893 
## [149]    train-rmse:0.481610+0.010382    test-rmse:0.544625+0.051621 
## [150]    train-rmse:0.481291+0.010374    test-rmse:0.544274+0.051354 
## [151]    train-rmse:0.480980+0.010372    test-rmse:0.543673+0.051356 
## [152]    train-rmse:0.480672+0.010373    test-rmse:0.543421+0.051042 
## [153]    train-rmse:0.480367+0.010375    test-rmse:0.543071+0.051294 
## [154]    train-rmse:0.480064+0.010373    test-rmse:0.543099+0.050929 
## [155]    train-rmse:0.479763+0.010374    test-rmse:0.542613+0.050653 
## [156]    train-rmse:0.479465+0.010373    test-rmse:0.542818+0.050964 
## [157]    train-rmse:0.479172+0.010370    test-rmse:0.542842+0.051252 
## [158]    train-rmse:0.478883+0.010366    test-rmse:0.542494+0.051115 
## [159]    train-rmse:0.478596+0.010362    test-rmse:0.542005+0.050832 
## [160]    train-rmse:0.478314+0.010363    test-rmse:0.541768+0.050564 
## [161]    train-rmse:0.478031+0.010363    test-rmse:0.541599+0.050789 
## [162]    train-rmse:0.477753+0.010368    test-rmse:0.541512+0.050868 
## [163]    train-rmse:0.477476+0.010367    test-rmse:0.540952+0.050778 
## [164]    train-rmse:0.477205+0.010365    test-rmse:0.540855+0.050960 
## [165]    train-rmse:0.476935+0.010367    test-rmse:0.540886+0.051039 
## [166]    train-rmse:0.476670+0.010368    test-rmse:0.540650+0.050839 
## [167]    train-rmse:0.476406+0.010367    test-rmse:0.540550+0.051156 
## [168]    train-rmse:0.476144+0.010365    test-rmse:0.540498+0.050972 
## [169]    train-rmse:0.475881+0.010364    test-rmse:0.540500+0.050590 
## [170]    train-rmse:0.475622+0.010360    test-rmse:0.540097+0.050589 
## [171]    train-rmse:0.475362+0.010352    test-rmse:0.539951+0.050280 
## [172]    train-rmse:0.475109+0.010348    test-rmse:0.539866+0.050430 
## [173]    train-rmse:0.474859+0.010343    test-rmse:0.539758+0.050277 
## [174]    train-rmse:0.474609+0.010338    test-rmse:0.539731+0.050079 
## [175]    train-rmse:0.474364+0.010335    test-rmse:0.539650+0.049813 
## [176]    train-rmse:0.474121+0.010332    test-rmse:0.539744+0.049597 
## [177]    train-rmse:0.473881+0.010328    test-rmse:0.539811+0.049920 
## [178]    train-rmse:0.473643+0.010325    test-rmse:0.540119+0.049970 
## [179]    train-rmse:0.473408+0.010319    test-rmse:0.539535+0.050011 
## [180]    train-rmse:0.473173+0.010314    test-rmse:0.539307+0.050056 
## [181]    train-rmse:0.472940+0.010306    test-rmse:0.539459+0.049715 
## [182]    train-rmse:0.472709+0.010300    test-rmse:0.539007+0.049699 
## [183]    train-rmse:0.472482+0.010298    test-rmse:0.538952+0.049564 
## [184]    train-rmse:0.472258+0.010298    test-rmse:0.538769+0.049406 
## [185]    train-rmse:0.472037+0.010297    test-rmse:0.538352+0.049538 
## [186]    train-rmse:0.471819+0.010293    test-rmse:0.538158+0.049546 
## [187]    train-rmse:0.471603+0.010290    test-rmse:0.538084+0.049676 
## [188]    train-rmse:0.471389+0.010286    test-rmse:0.537983+0.049727 
## [189]    train-rmse:0.471173+0.010282    test-rmse:0.537761+0.049548 
## [190]    train-rmse:0.470964+0.010277    test-rmse:0.537502+0.049489 
## [191]    train-rmse:0.470754+0.010274    test-rmse:0.537471+0.049674 
## [192]    train-rmse:0.470546+0.010272    test-rmse:0.537435+0.049646 
## [193]    train-rmse:0.470339+0.010272    test-rmse:0.537396+0.049498 
## [194]    train-rmse:0.470134+0.010267    test-rmse:0.537107+0.049531 
## [195]    train-rmse:0.469930+0.010264    test-rmse:0.536556+0.049257 
## [196]    train-rmse:0.469727+0.010259    test-rmse:0.536811+0.049338 
## [197]    train-rmse:0.469524+0.010256    test-rmse:0.537060+0.049235 
## [198]    train-rmse:0.469326+0.010251    test-rmse:0.536671+0.049164 
## [199]    train-rmse:0.469130+0.010247    test-rmse:0.536643+0.049050 
## [200]    train-rmse:0.468934+0.010243    test-rmse:0.536520+0.048931 
## [201]    train-rmse:0.468740+0.010237    test-rmse:0.536533+0.048860 
## [202]    train-rmse:0.468550+0.010233    test-rmse:0.536452+0.049130 
## [203]    train-rmse:0.468363+0.010227    test-rmse:0.536363+0.048990 
## [204]    train-rmse:0.468175+0.010222    test-rmse:0.536632+0.048750 
## [205]    train-rmse:0.467988+0.010218    test-rmse:0.536629+0.048589 
## [206]    train-rmse:0.467804+0.010211    test-rmse:0.536443+0.048603 
## [207]    train-rmse:0.467621+0.010204    test-rmse:0.536297+0.048726 
## [208]    train-rmse:0.467438+0.010200    test-rmse:0.536258+0.048322 
## [209]    train-rmse:0.467260+0.010195    test-rmse:0.536102+0.048479 
## [210]    train-rmse:0.467077+0.010190    test-rmse:0.536499+0.048459 
## [211]    train-rmse:0.466901+0.010186    test-rmse:0.536411+0.048663 
## [212]    train-rmse:0.466727+0.010182    test-rmse:0.536291+0.048582 
## [213]    train-rmse:0.466551+0.010173    test-rmse:0.535842+0.048569 
## [214]    train-rmse:0.466380+0.010166    test-rmse:0.536086+0.048557 
## [215]    train-rmse:0.466210+0.010161    test-rmse:0.536050+0.048497 
## [216]    train-rmse:0.466042+0.010155    test-rmse:0.535778+0.048449 
## [217]    train-rmse:0.465878+0.010150    test-rmse:0.535742+0.048145 
## [218]    train-rmse:0.465715+0.010143    test-rmse:0.535589+0.048311 
## [219]    train-rmse:0.465552+0.010137    test-rmse:0.535533+0.048289 
## [220]    train-rmse:0.465390+0.010132    test-rmse:0.535302+0.048077 
## [221]    train-rmse:0.465229+0.010126    test-rmse:0.535156+0.048118 
## [222]    train-rmse:0.465069+0.010121    test-rmse:0.535121+0.047988 
## [223]    train-rmse:0.464906+0.010109    test-rmse:0.534936+0.048095 
## [224]    train-rmse:0.464749+0.010101    test-rmse:0.534838+0.047981 
## [225]    train-rmse:0.464591+0.010094    test-rmse:0.534906+0.047737 
## [226]    train-rmse:0.464434+0.010088    test-rmse:0.534841+0.047825 
## [227]    train-rmse:0.464278+0.010082    test-rmse:0.535015+0.047662 
## [228]    train-rmse:0.464123+0.010078    test-rmse:0.535073+0.047629 
## [229]    train-rmse:0.463968+0.010073    test-rmse:0.535091+0.047499 
## [230]    train-rmse:0.463815+0.010068    test-rmse:0.534774+0.047502 
## [231]    train-rmse:0.463660+0.010061    test-rmse:0.534718+0.047479 
## [232]    train-rmse:0.463510+0.010054    test-rmse:0.534673+0.047596 
## [233]    train-rmse:0.463362+0.010045    test-rmse:0.534553+0.047453 
## [234]    train-rmse:0.463215+0.010038    test-rmse:0.534507+0.047459 
## [235]    train-rmse:0.463067+0.010030    test-rmse:0.534526+0.047514 
## [236]    train-rmse:0.462920+0.010024    test-rmse:0.534341+0.047432 
## [237]    train-rmse:0.462776+0.010017    test-rmse:0.534085+0.047372 
## [238]    train-rmse:0.462631+0.010007    test-rmse:0.534244+0.047356 
## [239]    train-rmse:0.462488+0.010000    test-rmse:0.534142+0.047306 
## [240]    train-rmse:0.462346+0.009995    test-rmse:0.534084+0.047314 
## [241]    train-rmse:0.462207+0.009989    test-rmse:0.533940+0.047306 
## [242]    train-rmse:0.462067+0.009981    test-rmse:0.533944+0.047437 
## [243]    train-rmse:0.461929+0.009976    test-rmse:0.533653+0.047610 
## [244]    train-rmse:0.461791+0.009970    test-rmse:0.533611+0.047354 
## [245]    train-rmse:0.461655+0.009961    test-rmse:0.533589+0.047360 
## [246]    train-rmse:0.461520+0.009953    test-rmse:0.533625+0.047353 
## [247]    train-rmse:0.461384+0.009946    test-rmse:0.533590+0.047136 
## [248]    train-rmse:0.461252+0.009939    test-rmse:0.533700+0.047261 
## [249]    train-rmse:0.461121+0.009932    test-rmse:0.533672+0.047246 
## [250]    train-rmse:0.460991+0.009925    test-rmse:0.533277+0.047482 
## [251]    train-rmse:0.460859+0.009918    test-rmse:0.533265+0.047236 
## [252]    train-rmse:0.460728+0.009911    test-rmse:0.533223+0.047306 
## [253]    train-rmse:0.460596+0.009903    test-rmse:0.533050+0.047133 
## [254]    train-rmse:0.460464+0.009890    test-rmse:0.533363+0.047049 
## [255]    train-rmse:0.460335+0.009884    test-rmse:0.533388+0.047141 
## [256]    train-rmse:0.460207+0.009877    test-rmse:0.533294+0.047183 
## [257]    train-rmse:0.460080+0.009869    test-rmse:0.533179+0.047129 
## [258]    train-rmse:0.459953+0.009861    test-rmse:0.533402+0.047069 
## [259]    train-rmse:0.459827+0.009854    test-rmse:0.533364+0.046856 
## Stopping. Best iteration:
## [253]    train-rmse:0.460596+0.009903    test-rmse:0.533050+0.047133
## 
## 15 
## [1]  train-rmse:0.918684+0.015385    test-rmse:0.920272+0.061406 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.848454+0.014596    test-rmse:0.855857+0.061568 
## [3]  train-rmse:0.791507+0.014384    test-rmse:0.802442+0.061999 
## [4]  train-rmse:0.745111+0.013706    test-rmse:0.761539+0.063275 
## [5]  train-rmse:0.707041+0.013033    test-rmse:0.727695+0.064847 
## [6]  train-rmse:0.676141+0.012104    test-rmse:0.699621+0.066239 
## [7]  train-rmse:0.651050+0.012039    test-rmse:0.676100+0.063396 
## [8]  train-rmse:0.630253+0.011938    test-rmse:0.657060+0.062310 
## [9]  train-rmse:0.612043+0.012495    test-rmse:0.644429+0.061005 
## [10] train-rmse:0.597482+0.012739    test-rmse:0.633882+0.061137 
## [11] train-rmse:0.584814+0.013092    test-rmse:0.623683+0.060387 
## [12] train-rmse:0.574596+0.013907    test-rmse:0.616722+0.060285 
## [13] train-rmse:0.565783+0.013767    test-rmse:0.610710+0.059593 
## [14] train-rmse:0.558011+0.013766    test-rmse:0.604115+0.059958 
## [15] train-rmse:0.551507+0.014686    test-rmse:0.600712+0.058578 
## [16] train-rmse:0.545197+0.014995    test-rmse:0.594631+0.057391 
## [17] train-rmse:0.540002+0.014709    test-rmse:0.592122+0.059174 
## [18] train-rmse:0.534626+0.015698    test-rmse:0.589815+0.057929 
## [19] train-rmse:0.529968+0.015193    test-rmse:0.585114+0.057594 
## [20] train-rmse:0.525884+0.015122    test-rmse:0.582562+0.057101 
## [21] train-rmse:0.522296+0.015101    test-rmse:0.580262+0.057310 
## [22] train-rmse:0.518150+0.014340    test-rmse:0.577975+0.059074 
## [23] train-rmse:0.514746+0.014406    test-rmse:0.575782+0.058841 
## [24] train-rmse:0.511496+0.014397    test-rmse:0.573862+0.059421 
## [25] train-rmse:0.507452+0.014854    test-rmse:0.572229+0.060231 
## [26] train-rmse:0.504763+0.014896    test-rmse:0.570821+0.060403 
## [27] train-rmse:0.501322+0.015256    test-rmse:0.567366+0.060005 
## [28] train-rmse:0.498372+0.015360    test-rmse:0.565159+0.058818 
## [29] train-rmse:0.495747+0.015161    test-rmse:0.562546+0.060375 
## [30] train-rmse:0.492674+0.015399    test-rmse:0.559396+0.060652 
## [31] train-rmse:0.490366+0.015305    test-rmse:0.558794+0.060660 
## [32] train-rmse:0.488003+0.015633    test-rmse:0.558054+0.060634 
## [33] train-rmse:0.485485+0.015628    test-rmse:0.555863+0.060474 
## [34] train-rmse:0.482557+0.015020    test-rmse:0.554053+0.060980 
## [35] train-rmse:0.480381+0.015068    test-rmse:0.552285+0.060472 
## [36] train-rmse:0.478181+0.015367    test-rmse:0.550882+0.061347 
## [37] train-rmse:0.475935+0.015235    test-rmse:0.549733+0.060831 
## [38] train-rmse:0.473411+0.015460    test-rmse:0.547086+0.059794 
## [39] train-rmse:0.471604+0.015379    test-rmse:0.546427+0.060660 
## [40] train-rmse:0.469665+0.015091    test-rmse:0.545711+0.061154 
## [41] train-rmse:0.467406+0.015180    test-rmse:0.543791+0.060052 
## [42] train-rmse:0.465206+0.014974    test-rmse:0.541887+0.061065 
## [43] train-rmse:0.463162+0.015124    test-rmse:0.539546+0.061479 
## [44] train-rmse:0.461521+0.015281    test-rmse:0.539078+0.061292 
## [45] train-rmse:0.459550+0.015430    test-rmse:0.537866+0.061484 
## [46] train-rmse:0.458137+0.015376    test-rmse:0.536752+0.061561 
## [47] train-rmse:0.456305+0.014948    test-rmse:0.535296+0.061655 
## [48] train-rmse:0.454923+0.015053    test-rmse:0.535778+0.061771 
## [49] train-rmse:0.453312+0.015144    test-rmse:0.534814+0.060617 
## [50] train-rmse:0.451822+0.015326    test-rmse:0.534496+0.060720 
## [51] train-rmse:0.450217+0.015548    test-rmse:0.533610+0.060693 
## [52] train-rmse:0.448738+0.015480    test-rmse:0.532378+0.060822 
## [53] train-rmse:0.447138+0.015224    test-rmse:0.531465+0.061489 
## [54] train-rmse:0.445451+0.015145    test-rmse:0.530568+0.061384 
## [55] train-rmse:0.443789+0.014765    test-rmse:0.530095+0.061311 
## [56] train-rmse:0.442439+0.014589    test-rmse:0.529175+0.061529 
## [57] train-rmse:0.440818+0.014761    test-rmse:0.528133+0.060559 
## [58] train-rmse:0.439625+0.014888    test-rmse:0.527390+0.060663 
## [59] train-rmse:0.437956+0.015414    test-rmse:0.527002+0.060080 
## [60] train-rmse:0.436721+0.015595    test-rmse:0.526988+0.060542 
## [61] train-rmse:0.435455+0.015432    test-rmse:0.525800+0.061858 
## [62] train-rmse:0.433877+0.015283    test-rmse:0.525653+0.062667 
## [63] train-rmse:0.432758+0.015415    test-rmse:0.525316+0.062598 
## [64] train-rmse:0.431265+0.015390    test-rmse:0.524784+0.062642 
## [65] train-rmse:0.430161+0.015349    test-rmse:0.523576+0.062109 
## [66] train-rmse:0.428854+0.015004    test-rmse:0.522610+0.062293 
## [67] train-rmse:0.427720+0.015262    test-rmse:0.522434+0.062545 
## [68] train-rmse:0.426565+0.015232    test-rmse:0.521514+0.062598 
## [69] train-rmse:0.425659+0.015231    test-rmse:0.521358+0.062528 
## [70] train-rmse:0.424193+0.015532    test-rmse:0.520379+0.062862 
## [71] train-rmse:0.422931+0.015429    test-rmse:0.519233+0.064055 
## [72] train-rmse:0.421876+0.015563    test-rmse:0.518787+0.063371 
## [73] train-rmse:0.420781+0.015638    test-rmse:0.518355+0.063344 
## [74] train-rmse:0.419583+0.015895    test-rmse:0.517868+0.062729 
## [75] train-rmse:0.418464+0.015634    test-rmse:0.517967+0.063205 
## [76] train-rmse:0.417168+0.015545    test-rmse:0.517451+0.063513 
## [77] train-rmse:0.416092+0.015502    test-rmse:0.517471+0.063019 
## [78] train-rmse:0.415289+0.015621    test-rmse:0.517079+0.063488 
## [79] train-rmse:0.414387+0.015978    test-rmse:0.516611+0.063455 
## [80] train-rmse:0.413166+0.015880    test-rmse:0.515953+0.064096 
## [81] train-rmse:0.412001+0.015896    test-rmse:0.514994+0.064761 
## [82] train-rmse:0.410952+0.015918    test-rmse:0.515248+0.066059 
## [83] train-rmse:0.409941+0.015867    test-rmse:0.514820+0.065504 
## [84] train-rmse:0.409203+0.015805    test-rmse:0.514444+0.064866 
## [85] train-rmse:0.408219+0.015683    test-rmse:0.514380+0.064791 
## [86] train-rmse:0.407276+0.015702    test-rmse:0.513853+0.064434 
## [87] train-rmse:0.406298+0.015680    test-rmse:0.513454+0.064247 
## [88] train-rmse:0.405340+0.015757    test-rmse:0.513701+0.064211 
## [89] train-rmse:0.404522+0.015767    test-rmse:0.513659+0.064248 
## [90] train-rmse:0.403660+0.015658    test-rmse:0.512839+0.064453 
## [91] train-rmse:0.402931+0.015820    test-rmse:0.512988+0.064534 
## [92] train-rmse:0.402195+0.015972    test-rmse:0.512551+0.064382 
## [93] train-rmse:0.401420+0.015784    test-rmse:0.512326+0.064513 
## [94] train-rmse:0.400843+0.015702    test-rmse:0.512151+0.064345 
## [95] train-rmse:0.400074+0.015494    test-rmse:0.512471+0.064561 
## [96] train-rmse:0.398975+0.015205    test-rmse:0.512275+0.064367 
## [97] train-rmse:0.398210+0.015061    test-rmse:0.512272+0.065012 
## [98] train-rmse:0.397267+0.014960    test-rmse:0.512143+0.064461 
## [99] train-rmse:0.396327+0.015109    test-rmse:0.511832+0.064235 
## [100]    train-rmse:0.395567+0.015228    test-rmse:0.511736+0.063980 
## [101]    train-rmse:0.394791+0.014996    test-rmse:0.512182+0.063928 
## [102]    train-rmse:0.394098+0.015143    test-rmse:0.511608+0.063858 
## [103]    train-rmse:0.393099+0.014603    test-rmse:0.511263+0.063599 
## [104]    train-rmse:0.392093+0.014559    test-rmse:0.511009+0.063866 
## [105]    train-rmse:0.391585+0.014470    test-rmse:0.510719+0.063958 
## [106]    train-rmse:0.390794+0.014619    test-rmse:0.510295+0.064061 
## [107]    train-rmse:0.390135+0.014572    test-rmse:0.509722+0.064277 
## [108]    train-rmse:0.389250+0.014290    test-rmse:0.509336+0.064181 
## [109]    train-rmse:0.388266+0.014152    test-rmse:0.509516+0.064089 
## [110]    train-rmse:0.387724+0.014118    test-rmse:0.509410+0.063668 
## [111]    train-rmse:0.386842+0.013894    test-rmse:0.508714+0.064519 
## [112]    train-rmse:0.386024+0.013966    test-rmse:0.508186+0.064034 
## [113]    train-rmse:0.385422+0.013926    test-rmse:0.507992+0.064007 
## [114]    train-rmse:0.384943+0.013803    test-rmse:0.507857+0.064058 
## [115]    train-rmse:0.384265+0.013906    test-rmse:0.508053+0.064110 
## [116]    train-rmse:0.383338+0.013915    test-rmse:0.508042+0.063930 
## [117]    train-rmse:0.382565+0.013980    test-rmse:0.508340+0.063901 
## [118]    train-rmse:0.382057+0.013996    test-rmse:0.508321+0.063400 
## [119]    train-rmse:0.381614+0.013920    test-rmse:0.508324+0.063454 
## [120]    train-rmse:0.380997+0.013781    test-rmse:0.508273+0.063083 
## Stopping. Best iteration:
## [114]    train-rmse:0.384943+0.013803    test-rmse:0.507857+0.064058
## 
## 16 
## [1]  train-rmse:0.913802+0.015576    test-rmse:0.917358+0.062270 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.838521+0.014851    test-rmse:0.848449+0.062729 
## [3]  train-rmse:0.776225+0.014511    test-rmse:0.791286+0.062910 
## [4]  train-rmse:0.725288+0.014232    test-rmse:0.745189+0.064450 
## [5]  train-rmse:0.682533+0.014057    test-rmse:0.708034+0.063697 
## [6]  train-rmse:0.647036+0.013139    test-rmse:0.677924+0.064726 
## [7]  train-rmse:0.617980+0.013451    test-rmse:0.652916+0.063518 
## [8]  train-rmse:0.593824+0.013543    test-rmse:0.633394+0.061050 
## [9]  train-rmse:0.572574+0.012718    test-rmse:0.618171+0.060975 
## [10] train-rmse:0.555487+0.013053    test-rmse:0.603762+0.059822 
## [11] train-rmse:0.540385+0.012230    test-rmse:0.594856+0.058188 
## [12] train-rmse:0.527675+0.013391    test-rmse:0.585497+0.054817 
## [13] train-rmse:0.516429+0.012668    test-rmse:0.576645+0.054260 
## [14] train-rmse:0.505484+0.014085    test-rmse:0.570252+0.054915 
## [15] train-rmse:0.497360+0.013478    test-rmse:0.565115+0.055251 
## [16] train-rmse:0.490389+0.013301    test-rmse:0.561401+0.054755 
## [17] train-rmse:0.483095+0.014064    test-rmse:0.558956+0.056670 
## [18] train-rmse:0.477799+0.014001    test-rmse:0.555594+0.056008 
## [19] train-rmse:0.472591+0.013901    test-rmse:0.553566+0.055801 
## [20] train-rmse:0.467966+0.013957    test-rmse:0.549771+0.055262 
## [21] train-rmse:0.463453+0.013248    test-rmse:0.547892+0.055994 
## [22] train-rmse:0.459283+0.012797    test-rmse:0.545546+0.055254 
## [23] train-rmse:0.454841+0.012673    test-rmse:0.542929+0.055801 
## [24] train-rmse:0.450751+0.012323    test-rmse:0.539799+0.055753 
## [25] train-rmse:0.446842+0.012873    test-rmse:0.537692+0.055922 
## [26] train-rmse:0.442886+0.012772    test-rmse:0.536685+0.055556 
## [27] train-rmse:0.439169+0.012340    test-rmse:0.534942+0.056964 
## [28] train-rmse:0.435901+0.012177    test-rmse:0.534636+0.057381 
## [29] train-rmse:0.432366+0.013076    test-rmse:0.532979+0.057378 
## [30] train-rmse:0.429037+0.013007    test-rmse:0.530976+0.057864 
## [31] train-rmse:0.426307+0.013070    test-rmse:0.529276+0.056883 
## [32] train-rmse:0.422992+0.012858    test-rmse:0.528692+0.057605 
## [33] train-rmse:0.420603+0.012971    test-rmse:0.528198+0.057060 
## [34] train-rmse:0.416088+0.013575    test-rmse:0.525420+0.056822 
## [35] train-rmse:0.413537+0.013566    test-rmse:0.524569+0.057972 
## [36] train-rmse:0.410192+0.014890    test-rmse:0.523799+0.058386 
## [37] train-rmse:0.407680+0.014913    test-rmse:0.523134+0.058568 
## [38] train-rmse:0.405032+0.015179    test-rmse:0.522682+0.059338 
## [39] train-rmse:0.402863+0.015297    test-rmse:0.522022+0.059236 
## [40] train-rmse:0.400847+0.014930    test-rmse:0.521039+0.060408 
## [41] train-rmse:0.398646+0.014989    test-rmse:0.520183+0.058829 
## [42] train-rmse:0.396447+0.014748    test-rmse:0.519998+0.058543 
## [43] train-rmse:0.394439+0.014596    test-rmse:0.518980+0.059398 
## [44] train-rmse:0.391539+0.015127    test-rmse:0.518151+0.059464 
## [45] train-rmse:0.389378+0.015100    test-rmse:0.516953+0.060310 
## [46] train-rmse:0.387382+0.015073    test-rmse:0.516687+0.060742 
## [47] train-rmse:0.385759+0.015253    test-rmse:0.515858+0.060444 
## [48] train-rmse:0.383814+0.015325    test-rmse:0.514050+0.060207 
## [49] train-rmse:0.381765+0.014864    test-rmse:0.513401+0.059978 
## [50] train-rmse:0.380046+0.014887    test-rmse:0.513638+0.059950 
## [51] train-rmse:0.378070+0.014565    test-rmse:0.512991+0.059726 
## [52] train-rmse:0.376244+0.014656    test-rmse:0.512104+0.059492 
## [53] train-rmse:0.374068+0.014797    test-rmse:0.511453+0.059688 
## [54] train-rmse:0.371998+0.013895    test-rmse:0.511177+0.059566 
## [55] train-rmse:0.369678+0.014029    test-rmse:0.511122+0.058902 
## [56] train-rmse:0.367851+0.014094    test-rmse:0.509905+0.059414 
## [57] train-rmse:0.366382+0.014216    test-rmse:0.509367+0.058980 
## [58] train-rmse:0.364554+0.014121    test-rmse:0.509094+0.058247 
## [59] train-rmse:0.362579+0.013976    test-rmse:0.507780+0.058845 
## [60] train-rmse:0.360750+0.013887    test-rmse:0.507701+0.058265 
## [61] train-rmse:0.359182+0.013875    test-rmse:0.508400+0.058474 
## [62] train-rmse:0.358057+0.013664    test-rmse:0.508018+0.058856 
## [63] train-rmse:0.356578+0.013309    test-rmse:0.507543+0.059011 
## [64] train-rmse:0.355082+0.013133    test-rmse:0.506437+0.059002 
## [65] train-rmse:0.353808+0.012991    test-rmse:0.505492+0.059097 
## [66] train-rmse:0.352153+0.013105    test-rmse:0.505563+0.059139 
## [67] train-rmse:0.350155+0.013311    test-rmse:0.504352+0.058702 
## [68] train-rmse:0.348533+0.013408    test-rmse:0.503672+0.058696 
## [69] train-rmse:0.346844+0.012968    test-rmse:0.502737+0.057452 
## [70] train-rmse:0.345078+0.013089    test-rmse:0.502220+0.057783 
## [71] train-rmse:0.343303+0.013491    test-rmse:0.501498+0.057831 
## [72] train-rmse:0.342138+0.013538    test-rmse:0.501360+0.058478 
## [73] train-rmse:0.340744+0.013807    test-rmse:0.501028+0.058349 
## [74] train-rmse:0.339337+0.013660    test-rmse:0.500564+0.058170 
## [75] train-rmse:0.337668+0.013899    test-rmse:0.499894+0.057857 
## [76] train-rmse:0.336753+0.013936    test-rmse:0.500443+0.057648 
## [77] train-rmse:0.335200+0.014050    test-rmse:0.499967+0.057321 
## [78] train-rmse:0.333667+0.013826    test-rmse:0.500449+0.056665 
## [79] train-rmse:0.331968+0.013594    test-rmse:0.499956+0.056528 
## [80] train-rmse:0.330775+0.013734    test-rmse:0.499799+0.056769 
## [81] train-rmse:0.328635+0.013641    test-rmse:0.498270+0.056870 
## [82] train-rmse:0.327302+0.013999    test-rmse:0.498204+0.057223 
## [83] train-rmse:0.326109+0.014147    test-rmse:0.498897+0.057198 
## [84] train-rmse:0.324941+0.014077    test-rmse:0.498801+0.057053 
## [85] train-rmse:0.323839+0.014056    test-rmse:0.499030+0.056799 
## [86] train-rmse:0.322731+0.014264    test-rmse:0.498310+0.056883 
## [87] train-rmse:0.321860+0.014153    test-rmse:0.498357+0.057534 
## [88] train-rmse:0.320490+0.014257    test-rmse:0.497642+0.057670 
## [89] train-rmse:0.318949+0.013981    test-rmse:0.497253+0.057803 
## [90] train-rmse:0.317161+0.013775    test-rmse:0.497196+0.057643 
## [91] train-rmse:0.315906+0.013676    test-rmse:0.497482+0.057196 
## [92] train-rmse:0.314739+0.013980    test-rmse:0.497472+0.057675 
## [93] train-rmse:0.313850+0.013942    test-rmse:0.497369+0.057518 
## [94] train-rmse:0.312485+0.014273    test-rmse:0.496972+0.057441 
## [95] train-rmse:0.310924+0.014032    test-rmse:0.497103+0.057470 
## [96] train-rmse:0.309834+0.014054    test-rmse:0.497333+0.057106 
## [97] train-rmse:0.308395+0.014647    test-rmse:0.497166+0.056709 
## [98] train-rmse:0.307106+0.014307    test-rmse:0.496880+0.056340 
## [99] train-rmse:0.305601+0.014216    test-rmse:0.496556+0.055887 
## [100]    train-rmse:0.304726+0.014286    test-rmse:0.496148+0.056033 
## [101]    train-rmse:0.303518+0.014149    test-rmse:0.496138+0.056167 
## [102]    train-rmse:0.302187+0.014126    test-rmse:0.495934+0.055984 
## [103]    train-rmse:0.301052+0.013906    test-rmse:0.495519+0.056493 
## [104]    train-rmse:0.300242+0.013807    test-rmse:0.495444+0.056170 
## [105]    train-rmse:0.298980+0.013671    test-rmse:0.495753+0.056150 
## [106]    train-rmse:0.298164+0.013585    test-rmse:0.495435+0.056071 
## [107]    train-rmse:0.297096+0.013343    test-rmse:0.494626+0.056565 
## [108]    train-rmse:0.296538+0.013246    test-rmse:0.495184+0.056658 
## [109]    train-rmse:0.295515+0.013581    test-rmse:0.495366+0.056978 
## [110]    train-rmse:0.294217+0.013786    test-rmse:0.494873+0.057629 
## [111]    train-rmse:0.293206+0.014010    test-rmse:0.495200+0.056948 
## [112]    train-rmse:0.292581+0.013885    test-rmse:0.495308+0.057105 
## [113]    train-rmse:0.291484+0.013615    test-rmse:0.494962+0.057167 
## Stopping. Best iteration:
## [107]    train-rmse:0.297096+0.013343    test-rmse:0.494626+0.056565
## 
## 17 
## [1]  train-rmse:0.909629+0.015428    test-rmse:0.914837+0.061417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.830261+0.014427    test-rmse:0.843112+0.061833 
## [3]  train-rmse:0.764051+0.014260    test-rmse:0.783004+0.062567 
## [4]  train-rmse:0.708528+0.014140    test-rmse:0.733618+0.062279 
## [5]  train-rmse:0.662331+0.013543    test-rmse:0.696138+0.064147 
## [6]  train-rmse:0.622659+0.012289    test-rmse:0.665119+0.064762 
## [7]  train-rmse:0.589046+0.012378    test-rmse:0.638672+0.064057 
## [8]  train-rmse:0.561365+0.012792    test-rmse:0.619496+0.064040 
## [9]  train-rmse:0.536753+0.012302    test-rmse:0.602214+0.061865 
## [10] train-rmse:0.516782+0.012465    test-rmse:0.588266+0.061570 
## [11] train-rmse:0.498421+0.012933    test-rmse:0.576671+0.058931 
## [12] train-rmse:0.483950+0.012356    test-rmse:0.567526+0.059269 
## [13] train-rmse:0.470791+0.012473    test-rmse:0.560845+0.058396 
## [14] train-rmse:0.460139+0.011537    test-rmse:0.555754+0.057559 
## [15] train-rmse:0.449526+0.011399    test-rmse:0.551224+0.059901 
## [16] train-rmse:0.440629+0.012211    test-rmse:0.544226+0.059290 
## [17] train-rmse:0.430392+0.013698    test-rmse:0.540734+0.058760 
## [18] train-rmse:0.423990+0.013397    test-rmse:0.538188+0.058355 
## [19] train-rmse:0.417372+0.012976    test-rmse:0.533696+0.056154 
## [20] train-rmse:0.411847+0.012505    test-rmse:0.529680+0.056749 
## [21] train-rmse:0.405841+0.013143    test-rmse:0.525405+0.057283 
## [22] train-rmse:0.401342+0.012869    test-rmse:0.524010+0.057235 
## [23] train-rmse:0.395634+0.014088    test-rmse:0.522896+0.057608 
## [24] train-rmse:0.391156+0.013584    test-rmse:0.520608+0.059442 
## [25] train-rmse:0.386353+0.013373    test-rmse:0.518127+0.059725 
## [26] train-rmse:0.381853+0.014281    test-rmse:0.517050+0.060180 
## [27] train-rmse:0.377757+0.013955    test-rmse:0.515074+0.058957 
## [28] train-rmse:0.374283+0.013728    test-rmse:0.514829+0.059299 
## [29] train-rmse:0.370739+0.013262    test-rmse:0.511897+0.060885 
## [30] train-rmse:0.366705+0.013708    test-rmse:0.510515+0.062098 
## [31] train-rmse:0.363235+0.013503    test-rmse:0.509001+0.061642 
## [32] train-rmse:0.360255+0.013650    test-rmse:0.508383+0.061111 
## [33] train-rmse:0.357370+0.013495    test-rmse:0.505821+0.062665 
## [34] train-rmse:0.353836+0.013340    test-rmse:0.504754+0.063179 
## [35] train-rmse:0.351188+0.013251    test-rmse:0.504593+0.062354 
## [36] train-rmse:0.348089+0.012791    test-rmse:0.501901+0.062634 
## [37] train-rmse:0.345249+0.013465    test-rmse:0.500363+0.062579 
## [38] train-rmse:0.342455+0.014129    test-rmse:0.500452+0.062510 
## [39] train-rmse:0.340092+0.013689    test-rmse:0.499367+0.063016 
## [40] train-rmse:0.338097+0.013283    test-rmse:0.499718+0.063512 
## [41] train-rmse:0.335472+0.012972    test-rmse:0.499323+0.063432 
## [42] train-rmse:0.332897+0.013295    test-rmse:0.499544+0.062762 
## [43] train-rmse:0.330071+0.013213    test-rmse:0.498350+0.062393 
## [44] train-rmse:0.328237+0.012903    test-rmse:0.498313+0.061915 
## [45] train-rmse:0.326723+0.012893    test-rmse:0.498347+0.061900 
## [46] train-rmse:0.324460+0.013110    test-rmse:0.497336+0.062128 
## [47] train-rmse:0.322110+0.013319    test-rmse:0.497388+0.061419 
## [48] train-rmse:0.320121+0.012980    test-rmse:0.496340+0.061017 
## [49] train-rmse:0.316585+0.013883    test-rmse:0.495349+0.060841 
## [50] train-rmse:0.314269+0.014364    test-rmse:0.494671+0.060076 
## [51] train-rmse:0.311977+0.013730    test-rmse:0.494297+0.059644 
## [52] train-rmse:0.309279+0.013718    test-rmse:0.493742+0.061012 
## [53] train-rmse:0.307330+0.014500    test-rmse:0.493204+0.059983 
## [54] train-rmse:0.305501+0.014690    test-rmse:0.493508+0.059787 
## [55] train-rmse:0.303053+0.015046    test-rmse:0.493508+0.059064 
## [56] train-rmse:0.300845+0.015075    test-rmse:0.493405+0.059871 
## [57] train-rmse:0.298834+0.015276    test-rmse:0.493389+0.060340 
## [58] train-rmse:0.296256+0.014675    test-rmse:0.493212+0.059598 
## [59] train-rmse:0.294165+0.015168    test-rmse:0.493786+0.059992 
## Stopping. Best iteration:
## [53] train-rmse:0.307330+0.014500    test-rmse:0.493204+0.059983
## 
## 18 
## [1]  train-rmse:0.905925+0.015078    test-rmse:0.912635+0.061985 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.822729+0.014053    test-rmse:0.838154+0.060169 
## [3]  train-rmse:0.752874+0.012509    test-rmse:0.778177+0.061554 
## [4]  train-rmse:0.693337+0.012053    test-rmse:0.727489+0.062078 
## [5]  train-rmse:0.643436+0.011356    test-rmse:0.689122+0.063202 
## [6]  train-rmse:0.600661+0.010391    test-rmse:0.653206+0.059214 
## [7]  train-rmse:0.564635+0.010349    test-rmse:0.628721+0.061161 
## [8]  train-rmse:0.533765+0.010744    test-rmse:0.605354+0.060280 
## [9]  train-rmse:0.507381+0.010676    test-rmse:0.587810+0.060294 
## [10] train-rmse:0.484403+0.011943    test-rmse:0.573674+0.059773 
## [11] train-rmse:0.465144+0.012881    test-rmse:0.560898+0.059829 
## [12] train-rmse:0.447528+0.011907    test-rmse:0.553395+0.059497 
## [13] train-rmse:0.433285+0.011958    test-rmse:0.545712+0.057453 
## [14] train-rmse:0.419872+0.012911    test-rmse:0.539230+0.059150 
## [15] train-rmse:0.409297+0.012616    test-rmse:0.535812+0.057865 
## [16] train-rmse:0.401499+0.012420    test-rmse:0.531296+0.056362 
## [17] train-rmse:0.392815+0.013255    test-rmse:0.526740+0.058427 
## [18] train-rmse:0.386246+0.013693    test-rmse:0.524000+0.058193 
## [19] train-rmse:0.379482+0.012713    test-rmse:0.521226+0.057482 
## [20] train-rmse:0.372981+0.012085    test-rmse:0.518246+0.058810 
## [21] train-rmse:0.366157+0.016137    test-rmse:0.516747+0.058263 
## [22] train-rmse:0.359047+0.015322    test-rmse:0.514256+0.057613 
## [23] train-rmse:0.353945+0.014164    test-rmse:0.511090+0.058461 
## [24] train-rmse:0.348853+0.013744    test-rmse:0.510142+0.058128 
## [25] train-rmse:0.344900+0.014068    test-rmse:0.509831+0.058370 
## [26] train-rmse:0.340738+0.013517    test-rmse:0.509183+0.058474 
## [27] train-rmse:0.336790+0.013170    test-rmse:0.507578+0.058523 
## [28] train-rmse:0.331755+0.013514    test-rmse:0.506057+0.058138 
## [29] train-rmse:0.327505+0.012641    test-rmse:0.504723+0.057705 
## [30] train-rmse:0.324165+0.012615    test-rmse:0.503822+0.057649 
## [31] train-rmse:0.321386+0.011964    test-rmse:0.503613+0.058139 
## [32] train-rmse:0.318663+0.011806    test-rmse:0.502892+0.057795 
## [33] train-rmse:0.315304+0.011549    test-rmse:0.502223+0.057824 
## [34] train-rmse:0.312364+0.011039    test-rmse:0.501479+0.057073 
## [35] train-rmse:0.307196+0.011720    test-rmse:0.498786+0.057319 
## [36] train-rmse:0.303980+0.011581    test-rmse:0.498725+0.057530 
## [37] train-rmse:0.301247+0.012530    test-rmse:0.499198+0.057978 
## [38] train-rmse:0.298206+0.012053    test-rmse:0.498799+0.057127 
## [39] train-rmse:0.294923+0.012249    test-rmse:0.497739+0.057774 
## [40] train-rmse:0.292146+0.011885    test-rmse:0.497176+0.057788 
## [41] train-rmse:0.289507+0.011863    test-rmse:0.496650+0.058405 
## [42] train-rmse:0.286808+0.011614    test-rmse:0.496928+0.058368 
## [43] train-rmse:0.284498+0.011051    test-rmse:0.496698+0.058297 
## [44] train-rmse:0.282218+0.011693    test-rmse:0.496084+0.057629 
## [45] train-rmse:0.279948+0.012353    test-rmse:0.496459+0.057430 
## [46] train-rmse:0.277445+0.011977    test-rmse:0.496123+0.058224 
## [47] train-rmse:0.273820+0.012168    test-rmse:0.496144+0.059153 
## [48] train-rmse:0.271764+0.011954    test-rmse:0.496459+0.059653 
## [49] train-rmse:0.268627+0.012047    test-rmse:0.496179+0.060313 
## [50] train-rmse:0.266195+0.011742    test-rmse:0.496084+0.060090 
## [51] train-rmse:0.263969+0.011838    test-rmse:0.495863+0.059480 
## [52] train-rmse:0.261849+0.011795    test-rmse:0.496356+0.059796 
## [53] train-rmse:0.259004+0.010965    test-rmse:0.496020+0.059833 
## [54] train-rmse:0.255291+0.011517    test-rmse:0.495663+0.058836 
## [55] train-rmse:0.253012+0.010266    test-rmse:0.495763+0.058974 
## [56] train-rmse:0.250758+0.009866    test-rmse:0.494861+0.058593 
## [57] train-rmse:0.248698+0.009671    test-rmse:0.494625+0.058963 
## [58] train-rmse:0.246484+0.009654    test-rmse:0.494786+0.059464 
## [59] train-rmse:0.243516+0.010089    test-rmse:0.495589+0.058609 
## [60] train-rmse:0.241811+0.010536    test-rmse:0.495517+0.058653 
## [61] train-rmse:0.239765+0.010316    test-rmse:0.495703+0.058323 
## [62] train-rmse:0.238049+0.010088    test-rmse:0.495400+0.058831 
## [63] train-rmse:0.235829+0.010763    test-rmse:0.495382+0.058960 
## Stopping. Best iteration:
## [57] train-rmse:0.248698+0.009671    test-rmse:0.494625+0.058963
## 
## 19 
## [1]  train-rmse:0.902555+0.014735    test-rmse:0.911144+0.062193 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.816060+0.013850    test-rmse:0.833691+0.060646 
## [3]  train-rmse:0.741955+0.011860    test-rmse:0.772533+0.061414 
## [4]  train-rmse:0.679992+0.010572    test-rmse:0.719476+0.062598 
## [5]  train-rmse:0.626863+0.010791    test-rmse:0.677557+0.063431 
## [6]  train-rmse:0.581852+0.010260    test-rmse:0.644800+0.066486 
## [7]  train-rmse:0.544110+0.010326    test-rmse:0.615643+0.062908 
## [8]  train-rmse:0.511098+0.009607    test-rmse:0.594710+0.059758 
## [9]  train-rmse:0.480629+0.011386    test-rmse:0.576109+0.058331 
## [10] train-rmse:0.456315+0.011382    test-rmse:0.561587+0.059839 
## [11] train-rmse:0.434325+0.011803    test-rmse:0.552234+0.057818 
## [12] train-rmse:0.415245+0.012198    test-rmse:0.543092+0.056559 
## [13] train-rmse:0.397731+0.011404    test-rmse:0.536738+0.056063 
## [14] train-rmse:0.383125+0.012549    test-rmse:0.531948+0.054075 
## [15] train-rmse:0.371267+0.012528    test-rmse:0.526439+0.054678 
## [16] train-rmse:0.359753+0.012557    test-rmse:0.522260+0.054089 
## [17] train-rmse:0.350186+0.012533    test-rmse:0.519222+0.053353 
## [18] train-rmse:0.342087+0.013317    test-rmse:0.516725+0.053023 
## [19] train-rmse:0.333265+0.014910    test-rmse:0.514525+0.052276 
## [20] train-rmse:0.326356+0.015062    test-rmse:0.513275+0.052121 
## [21] train-rmse:0.320291+0.015040    test-rmse:0.511502+0.052115 
## [22] train-rmse:0.313759+0.013806    test-rmse:0.509297+0.052218 
## [23] train-rmse:0.307933+0.015433    test-rmse:0.507101+0.051002 
## [24] train-rmse:0.301805+0.015522    test-rmse:0.503902+0.052349 
## [25] train-rmse:0.296747+0.015457    test-rmse:0.502490+0.053227 
## [26] train-rmse:0.293003+0.015222    test-rmse:0.502762+0.052877 
## [27] train-rmse:0.288545+0.016124    test-rmse:0.502026+0.051788 
## [28] train-rmse:0.283721+0.015970    test-rmse:0.501571+0.051614 
## [29] train-rmse:0.278827+0.016152    test-rmse:0.501200+0.051978 
## [30] train-rmse:0.275483+0.015562    test-rmse:0.500662+0.051664 
## [31] train-rmse:0.272672+0.015038    test-rmse:0.500382+0.052126 
## [32] train-rmse:0.269407+0.014768    test-rmse:0.499843+0.052261 
## [33] train-rmse:0.266180+0.014385    test-rmse:0.499208+0.052982 
## [34] train-rmse:0.262977+0.014323    test-rmse:0.498939+0.052517 
## [35] train-rmse:0.259374+0.013516    test-rmse:0.499182+0.053025 
## [36] train-rmse:0.256342+0.013377    test-rmse:0.499585+0.052905 
## [37] train-rmse:0.253417+0.013652    test-rmse:0.498817+0.052987 
## [38] train-rmse:0.250158+0.014271    test-rmse:0.498913+0.053472 
## [39] train-rmse:0.246775+0.013779    test-rmse:0.498041+0.053863 
## [40] train-rmse:0.244242+0.013448    test-rmse:0.498411+0.054198 
## [41] train-rmse:0.241297+0.013748    test-rmse:0.498809+0.054913 
## [42] train-rmse:0.238795+0.013839    test-rmse:0.499268+0.054689 
## [43] train-rmse:0.236011+0.013982    test-rmse:0.499810+0.054359 
## [44] train-rmse:0.233034+0.013384    test-rmse:0.500341+0.054282 
## [45] train-rmse:0.231169+0.013594    test-rmse:0.500489+0.054226 
## Stopping. Best iteration:
## [39] train-rmse:0.246775+0.013779    test-rmse:0.498041+0.053863
## 
## 20 
## [1]  train-rmse:0.899258+0.014429    test-rmse:0.908888+0.061105 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.809918+0.012851    test-rmse:0.831511+0.062654 
## [3]  train-rmse:0.733415+0.012344    test-rmse:0.767685+0.062618 
## [4]  train-rmse:0.668761+0.011567    test-rmse:0.716598+0.062059 
## [5]  train-rmse:0.612240+0.011535    test-rmse:0.673251+0.062134 
## [6]  train-rmse:0.565139+0.011558    test-rmse:0.640239+0.065511 
## [7]  train-rmse:0.525239+0.012347    test-rmse:0.613735+0.066481 
## [8]  train-rmse:0.490127+0.012506    test-rmse:0.591477+0.065934 
## [9]  train-rmse:0.458880+0.013022    test-rmse:0.573747+0.064168 
## [10] train-rmse:0.433413+0.012827    test-rmse:0.558641+0.063908 
## [11] train-rmse:0.409796+0.014257    test-rmse:0.547456+0.064128 
## [12] train-rmse:0.388202+0.013077    test-rmse:0.540481+0.064952 
## [13] train-rmse:0.371645+0.013694    test-rmse:0.533465+0.065135 
## [14] train-rmse:0.355769+0.013853    test-rmse:0.530119+0.065467 
## [15] train-rmse:0.341692+0.015179    test-rmse:0.526224+0.065196 
## [16] train-rmse:0.326794+0.016694    test-rmse:0.523187+0.062064 
## [17] train-rmse:0.314307+0.016579    test-rmse:0.519829+0.060428 
## [18] train-rmse:0.304181+0.017016    test-rmse:0.517330+0.060146 
## [19] train-rmse:0.295962+0.016747    test-rmse:0.515101+0.059057 
## [20] train-rmse:0.288156+0.015459    test-rmse:0.512900+0.058360 
## [21] train-rmse:0.281541+0.014329    test-rmse:0.510568+0.057528 
## [22] train-rmse:0.274482+0.016352    test-rmse:0.509058+0.055786 
## [23] train-rmse:0.267816+0.015319    test-rmse:0.507103+0.056584 
## [24] train-rmse:0.263702+0.014662    test-rmse:0.505483+0.056372 
## [25] train-rmse:0.258977+0.015559    test-rmse:0.504665+0.055249 
## [26] train-rmse:0.253100+0.017461    test-rmse:0.503591+0.054601 
## [27] train-rmse:0.247876+0.016910    test-rmse:0.501927+0.054068 
## [28] train-rmse:0.244376+0.016822    test-rmse:0.500853+0.052723 
## [29] train-rmse:0.239175+0.017452    test-rmse:0.500298+0.052710 
## [30] train-rmse:0.236635+0.017359    test-rmse:0.499743+0.052230 
## [31] train-rmse:0.232824+0.016584    test-rmse:0.499348+0.051803 
## [32] train-rmse:0.229998+0.015563    test-rmse:0.499523+0.051632 
## [33] train-rmse:0.226202+0.014531    test-rmse:0.499564+0.050926 
## [34] train-rmse:0.223007+0.014926    test-rmse:0.499386+0.050313 
## [35] train-rmse:0.219787+0.014082    test-rmse:0.499107+0.050098 
## [36] train-rmse:0.216254+0.013107    test-rmse:0.499612+0.050518 
## [37] train-rmse:0.211450+0.015101    test-rmse:0.499189+0.050152 
## [38] train-rmse:0.208382+0.014915    test-rmse:0.498987+0.049508 
## [39] train-rmse:0.205739+0.013758    test-rmse:0.498663+0.049724 
## [40] train-rmse:0.202752+0.013480    test-rmse:0.498465+0.049779 
## [41] train-rmse:0.198464+0.013411    test-rmse:0.498060+0.049041 
## [42] train-rmse:0.195837+0.013141    test-rmse:0.497690+0.048935 
## [43] train-rmse:0.193178+0.013441    test-rmse:0.497782+0.049323 
## [44] train-rmse:0.190312+0.013015    test-rmse:0.497963+0.049602 
## [45] train-rmse:0.186588+0.012386    test-rmse:0.498296+0.049557 
## [46] train-rmse:0.183527+0.011954    test-rmse:0.498733+0.049957 
## [47] train-rmse:0.180476+0.011740    test-rmse:0.498489+0.049020 
## [48] train-rmse:0.177736+0.010624    test-rmse:0.498233+0.048656 
## Stopping. Best iteration:
## [42] train-rmse:0.195837+0.013141    test-rmse:0.497690+0.048935
## 
## 21 
## [1]  train-rmse:0.919360+0.015345    test-rmse:0.920477+0.061327 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.853561+0.014587    test-rmse:0.858482+0.060449 
## [3]  train-rmse:0.803421+0.014031    test-rmse:0.812910+0.063826 
## [4]  train-rmse:0.764078+0.013719    test-rmse:0.778645+0.059088 
## [5]  train-rmse:0.733522+0.013141    test-rmse:0.753400+0.059664 
## [6]  train-rmse:0.709211+0.012476    test-rmse:0.729911+0.056733 
## [7]  train-rmse:0.688816+0.012154    test-rmse:0.710129+0.057458 
## [8]  train-rmse:0.672764+0.011991    test-rmse:0.693394+0.056931 
## [9]  train-rmse:0.659530+0.011783    test-rmse:0.682176+0.054215 
## [10] train-rmse:0.648258+0.011602    test-rmse:0.672336+0.056312 
## [11] train-rmse:0.639122+0.011315    test-rmse:0.664313+0.053099 
## [12] train-rmse:0.631276+0.011197    test-rmse:0.656814+0.051740 
## [13] train-rmse:0.624717+0.011073    test-rmse:0.651102+0.053196 
## [14] train-rmse:0.619278+0.010996    test-rmse:0.645775+0.052689 
## [15] train-rmse:0.614235+0.010985    test-rmse:0.645182+0.051718 
## [16] train-rmse:0.609603+0.011017    test-rmse:0.640362+0.053496 
## [17] train-rmse:0.605270+0.011076    test-rmse:0.636667+0.053060 
## [18] train-rmse:0.601380+0.011067    test-rmse:0.632544+0.052438 
## [19] train-rmse:0.597857+0.011061    test-rmse:0.630287+0.052050 
## [20] train-rmse:0.594428+0.011091    test-rmse:0.627001+0.054041 
## [21] train-rmse:0.591196+0.011032    test-rmse:0.623756+0.054110 
## [22] train-rmse:0.588070+0.010986    test-rmse:0.621432+0.053772 
## [23] train-rmse:0.585127+0.010997    test-rmse:0.620304+0.053806 
## [24] train-rmse:0.582401+0.011057    test-rmse:0.618384+0.053591 
## [25] train-rmse:0.579871+0.011059    test-rmse:0.615528+0.054242 
## [26] train-rmse:0.577402+0.011070    test-rmse:0.613031+0.054210 
## [27] train-rmse:0.575055+0.011047    test-rmse:0.611601+0.054119 
## [28] train-rmse:0.572752+0.011018    test-rmse:0.609332+0.054722 
## [29] train-rmse:0.570517+0.011001    test-rmse:0.607516+0.054033 
## [30] train-rmse:0.568392+0.011042    test-rmse:0.605907+0.055314 
## [31] train-rmse:0.566290+0.011048    test-rmse:0.604951+0.054085 
## [32] train-rmse:0.564253+0.011084    test-rmse:0.602668+0.054543 
## [33] train-rmse:0.562272+0.011089    test-rmse:0.601349+0.054626 
## [34] train-rmse:0.560313+0.011075    test-rmse:0.600052+0.055281 
## [35] train-rmse:0.558470+0.011070    test-rmse:0.598718+0.056137 
## [36] train-rmse:0.556697+0.011031    test-rmse:0.596492+0.055410 
## [37] train-rmse:0.554982+0.011023    test-rmse:0.595722+0.055541 
## [38] train-rmse:0.553294+0.010999    test-rmse:0.594702+0.055319 
## [39] train-rmse:0.551638+0.010992    test-rmse:0.593381+0.055988 
## [40] train-rmse:0.550004+0.010999    test-rmse:0.592725+0.054402 
## [41] train-rmse:0.548403+0.011021    test-rmse:0.590574+0.054345 
## [42] train-rmse:0.546829+0.011054    test-rmse:0.589224+0.054131 
## [43] train-rmse:0.545306+0.011044    test-rmse:0.587760+0.054741 
## [44] train-rmse:0.543828+0.011030    test-rmse:0.586876+0.055695 
## [45] train-rmse:0.542391+0.011002    test-rmse:0.586906+0.055805 
## [46] train-rmse:0.540987+0.010982    test-rmse:0.585553+0.055518 
## [47] train-rmse:0.539620+0.010976    test-rmse:0.585000+0.054974 
## [48] train-rmse:0.538287+0.010944    test-rmse:0.584104+0.055726 
## [49] train-rmse:0.536974+0.010892    test-rmse:0.582920+0.055620 
## [50] train-rmse:0.535681+0.010874    test-rmse:0.581639+0.056191 
## [51] train-rmse:0.534427+0.010870    test-rmse:0.581195+0.055046 
## [52] train-rmse:0.533210+0.010841    test-rmse:0.579339+0.054371 
## [53] train-rmse:0.532014+0.010829    test-rmse:0.579415+0.055065 
## [54] train-rmse:0.530838+0.010806    test-rmse:0.579035+0.055945 
## [55] train-rmse:0.529693+0.010811    test-rmse:0.578150+0.056285 
## [56] train-rmse:0.528587+0.010810    test-rmse:0.577067+0.056278 
## [57] train-rmse:0.527497+0.010807    test-rmse:0.575469+0.056769 
## [58] train-rmse:0.526421+0.010787    test-rmse:0.574562+0.056741 
## [59] train-rmse:0.525372+0.010760    test-rmse:0.574111+0.055846 
## [60] train-rmse:0.524325+0.010729    test-rmse:0.573704+0.054999 
## [61] train-rmse:0.523320+0.010702    test-rmse:0.572970+0.054989 
## [62] train-rmse:0.522322+0.010679    test-rmse:0.572114+0.055130 
## [63] train-rmse:0.521344+0.010666    test-rmse:0.571595+0.054484 
## [64] train-rmse:0.520374+0.010640    test-rmse:0.570800+0.054469 
## [65] train-rmse:0.519414+0.010622    test-rmse:0.570336+0.055525 
## [66] train-rmse:0.518476+0.010588    test-rmse:0.570159+0.055566 
## [67] train-rmse:0.517574+0.010564    test-rmse:0.569278+0.055518 
## [68] train-rmse:0.516688+0.010546    test-rmse:0.567820+0.054950 
## [69] train-rmse:0.515815+0.010534    test-rmse:0.567406+0.055437 
## [70] train-rmse:0.514946+0.010526    test-rmse:0.567486+0.055401 
## [71] train-rmse:0.514091+0.010527    test-rmse:0.566861+0.054691 
## [72] train-rmse:0.513242+0.010525    test-rmse:0.565618+0.054619 
## [73] train-rmse:0.512414+0.010512    test-rmse:0.565168+0.054687 
## [74] train-rmse:0.511588+0.010498    test-rmse:0.564486+0.054207 
## [75] train-rmse:0.510789+0.010480    test-rmse:0.563456+0.054087 
## [76] train-rmse:0.510014+0.010473    test-rmse:0.562442+0.054301 
## [77] train-rmse:0.509260+0.010456    test-rmse:0.561462+0.054258 
## [78] train-rmse:0.508524+0.010443    test-rmse:0.561162+0.054411 
## [79] train-rmse:0.507787+0.010429    test-rmse:0.560966+0.054414 
## [80] train-rmse:0.507051+0.010422    test-rmse:0.561299+0.054734 
## [81] train-rmse:0.506340+0.010414    test-rmse:0.561118+0.055309 
## [82] train-rmse:0.505630+0.010406    test-rmse:0.560024+0.054677 
## [83] train-rmse:0.504939+0.010402    test-rmse:0.559061+0.054758 
## [84] train-rmse:0.504252+0.010400    test-rmse:0.558285+0.054653 
## [85] train-rmse:0.503568+0.010394    test-rmse:0.558304+0.054497 
## [86] train-rmse:0.502882+0.010390    test-rmse:0.558046+0.054322 
## [87] train-rmse:0.502212+0.010396    test-rmse:0.557846+0.054019 
## [88] train-rmse:0.501554+0.010403    test-rmse:0.557813+0.054095 
## [89] train-rmse:0.500916+0.010395    test-rmse:0.557645+0.053563 
## [90] train-rmse:0.500294+0.010383    test-rmse:0.556677+0.053598 
## [91] train-rmse:0.499675+0.010368    test-rmse:0.556461+0.054165 
## [92] train-rmse:0.499069+0.010350    test-rmse:0.555341+0.054135 
## [93] train-rmse:0.498472+0.010334    test-rmse:0.554729+0.053850 
## [94] train-rmse:0.497882+0.010329    test-rmse:0.554161+0.053932 
## [95] train-rmse:0.497299+0.010314    test-rmse:0.553346+0.053386 
## [96] train-rmse:0.496732+0.010306    test-rmse:0.553050+0.053512 
## [97] train-rmse:0.496164+0.010309    test-rmse:0.552903+0.053909 
## [98] train-rmse:0.495605+0.010306    test-rmse:0.552840+0.053913 
## [99] train-rmse:0.495061+0.010296    test-rmse:0.552755+0.053815 
## [100]    train-rmse:0.494524+0.010295    test-rmse:0.552375+0.053761 
## [101]    train-rmse:0.493998+0.010288    test-rmse:0.552260+0.053926 
## [102]    train-rmse:0.493470+0.010281    test-rmse:0.551359+0.054374 
## [103]    train-rmse:0.492942+0.010286    test-rmse:0.552330+0.054328 
## [104]    train-rmse:0.492433+0.010279    test-rmse:0.551845+0.054316 
## [105]    train-rmse:0.491930+0.010273    test-rmse:0.551422+0.054040 
## [106]    train-rmse:0.491426+0.010270    test-rmse:0.550338+0.053570 
## [107]    train-rmse:0.490932+0.010266    test-rmse:0.549812+0.053263 
## [108]    train-rmse:0.490443+0.010266    test-rmse:0.549698+0.053080 
## [109]    train-rmse:0.489962+0.010260    test-rmse:0.549710+0.053134 
## [110]    train-rmse:0.489484+0.010249    test-rmse:0.548403+0.053109 
## [111]    train-rmse:0.489019+0.010249    test-rmse:0.548145+0.053175 
## [112]    train-rmse:0.488557+0.010241    test-rmse:0.548000+0.053469 
## [113]    train-rmse:0.488093+0.010239    test-rmse:0.548088+0.053064 
## [114]    train-rmse:0.487641+0.010232    test-rmse:0.547899+0.053487 
## [115]    train-rmse:0.487204+0.010233    test-rmse:0.547727+0.053708 
## [116]    train-rmse:0.486769+0.010232    test-rmse:0.548025+0.053086 
## [117]    train-rmse:0.486339+0.010234    test-rmse:0.547471+0.052922 
## [118]    train-rmse:0.485915+0.010236    test-rmse:0.547146+0.052373 
## [119]    train-rmse:0.485500+0.010226    test-rmse:0.546968+0.052612 
## [120]    train-rmse:0.485092+0.010222    test-rmse:0.546497+0.052653 
## [121]    train-rmse:0.484686+0.010218    test-rmse:0.546425+0.052992 
## [122]    train-rmse:0.484290+0.010210    test-rmse:0.546609+0.053437 
## [123]    train-rmse:0.483896+0.010205    test-rmse:0.545996+0.053278 
## [124]    train-rmse:0.483505+0.010195    test-rmse:0.545884+0.052964 
## [125]    train-rmse:0.483121+0.010190    test-rmse:0.545138+0.052616 
## [126]    train-rmse:0.482744+0.010180    test-rmse:0.544992+0.052098 
## [127]    train-rmse:0.482364+0.010170    test-rmse:0.544727+0.052165 
## [128]    train-rmse:0.481990+0.010166    test-rmse:0.544522+0.052017 
## [129]    train-rmse:0.481615+0.010159    test-rmse:0.544223+0.051798 
## [130]    train-rmse:0.481248+0.010154    test-rmse:0.544220+0.051512 
## [131]    train-rmse:0.480878+0.010149    test-rmse:0.543746+0.051882 
## [132]    train-rmse:0.480519+0.010146    test-rmse:0.543486+0.051844 
## [133]    train-rmse:0.480165+0.010143    test-rmse:0.543271+0.051799 
## [134]    train-rmse:0.479814+0.010144    test-rmse:0.543366+0.052235 
## [135]    train-rmse:0.479472+0.010148    test-rmse:0.543597+0.051759 
## [136]    train-rmse:0.479132+0.010153    test-rmse:0.543712+0.051945 
## [137]    train-rmse:0.478796+0.010159    test-rmse:0.543213+0.051925 
## [138]    train-rmse:0.478464+0.010160    test-rmse:0.543142+0.052114 
## [139]    train-rmse:0.478137+0.010163    test-rmse:0.542906+0.052153 
## [140]    train-rmse:0.477818+0.010164    test-rmse:0.543024+0.051964 
## [141]    train-rmse:0.477502+0.010164    test-rmse:0.542821+0.052393 
## [142]    train-rmse:0.477189+0.010162    test-rmse:0.542813+0.052176 
## [143]    train-rmse:0.476880+0.010159    test-rmse:0.542515+0.051468 
## [144]    train-rmse:0.476578+0.010152    test-rmse:0.542011+0.051192 
## [145]    train-rmse:0.476278+0.010148    test-rmse:0.541894+0.051445 
## [146]    train-rmse:0.475982+0.010145    test-rmse:0.541296+0.051399 
## [147]    train-rmse:0.475688+0.010139    test-rmse:0.541004+0.051357 
## [148]    train-rmse:0.475393+0.010136    test-rmse:0.541068+0.051354 
## [149]    train-rmse:0.475100+0.010130    test-rmse:0.541004+0.051262 
## [150]    train-rmse:0.474810+0.010126    test-rmse:0.541053+0.051049 
## [151]    train-rmse:0.474524+0.010119    test-rmse:0.540943+0.051152 
## [152]    train-rmse:0.474241+0.010115    test-rmse:0.540741+0.050926 
## [153]    train-rmse:0.473963+0.010110    test-rmse:0.540519+0.050656 
## [154]    train-rmse:0.473683+0.010105    test-rmse:0.540456+0.050809 
## [155]    train-rmse:0.473405+0.010102    test-rmse:0.540839+0.051074 
## [156]    train-rmse:0.473129+0.010102    test-rmse:0.540930+0.051149 
## [157]    train-rmse:0.472858+0.010096    test-rmse:0.540516+0.051223 
## [158]    train-rmse:0.472592+0.010094    test-rmse:0.540364+0.051146 
## [159]    train-rmse:0.472327+0.010092    test-rmse:0.540028+0.050804 
## [160]    train-rmse:0.472070+0.010088    test-rmse:0.540045+0.050774 
## [161]    train-rmse:0.471816+0.010085    test-rmse:0.539759+0.050735 
## [162]    train-rmse:0.471565+0.010082    test-rmse:0.539452+0.050698 
## [163]    train-rmse:0.471319+0.010080    test-rmse:0.539142+0.050544 
## [164]    train-rmse:0.471074+0.010077    test-rmse:0.538758+0.050552 
## [165]    train-rmse:0.470830+0.010072    test-rmse:0.538508+0.050400 
## [166]    train-rmse:0.470588+0.010066    test-rmse:0.538576+0.050153 
## [167]    train-rmse:0.470349+0.010060    test-rmse:0.538747+0.050035 
## [168]    train-rmse:0.470108+0.010053    test-rmse:0.538576+0.050198 
## [169]    train-rmse:0.469872+0.010052    test-rmse:0.538180+0.049904 
## [170]    train-rmse:0.469643+0.010046    test-rmse:0.537928+0.050022 
## [171]    train-rmse:0.469416+0.010042    test-rmse:0.538023+0.050031 
## [172]    train-rmse:0.469192+0.010039    test-rmse:0.538155+0.049813 
## [173]    train-rmse:0.468968+0.010036    test-rmse:0.537654+0.049778 
## [174]    train-rmse:0.468741+0.010028    test-rmse:0.537578+0.049664 
## [175]    train-rmse:0.468522+0.010024    test-rmse:0.537737+0.049714 
## [176]    train-rmse:0.468307+0.010019    test-rmse:0.537632+0.049740 
## [177]    train-rmse:0.468093+0.010015    test-rmse:0.537696+0.049644 
## [178]    train-rmse:0.467880+0.010010    test-rmse:0.537530+0.049565 
## [179]    train-rmse:0.467670+0.010007    test-rmse:0.537462+0.049818 
## [180]    train-rmse:0.467462+0.010004    test-rmse:0.537660+0.049678 
## [181]    train-rmse:0.467255+0.010002    test-rmse:0.537582+0.049451 
## [182]    train-rmse:0.467046+0.009997    test-rmse:0.537416+0.049347 
## [183]    train-rmse:0.466840+0.009987    test-rmse:0.537193+0.049518 
## [184]    train-rmse:0.466634+0.009979    test-rmse:0.537358+0.049324 
## [185]    train-rmse:0.466433+0.009971    test-rmse:0.537081+0.049231 
## [186]    train-rmse:0.466236+0.009963    test-rmse:0.537164+0.049328 
## [187]    train-rmse:0.466040+0.009955    test-rmse:0.537214+0.049644 
## [188]    train-rmse:0.465846+0.009948    test-rmse:0.537147+0.049717 
## [189]    train-rmse:0.465659+0.009941    test-rmse:0.536964+0.049472 
## [190]    train-rmse:0.465471+0.009935    test-rmse:0.536638+0.049446 
## [191]    train-rmse:0.465284+0.009928    test-rmse:0.536483+0.049271 
## [192]    train-rmse:0.465099+0.009919    test-rmse:0.536680+0.049202 
## [193]    train-rmse:0.464915+0.009911    test-rmse:0.536557+0.049464 
## [194]    train-rmse:0.464729+0.009900    test-rmse:0.536448+0.049059 
## [195]    train-rmse:0.464547+0.009893    test-rmse:0.536134+0.048875 
## [196]    train-rmse:0.464361+0.009881    test-rmse:0.535861+0.048737 
## [197]    train-rmse:0.464182+0.009872    test-rmse:0.535983+0.048551 
## [198]    train-rmse:0.464003+0.009863    test-rmse:0.535611+0.048780 
## [199]    train-rmse:0.463826+0.009853    test-rmse:0.535548+0.048836 
## [200]    train-rmse:0.463653+0.009844    test-rmse:0.535352+0.048768 
## [201]    train-rmse:0.463479+0.009833    test-rmse:0.535188+0.048588 
## [202]    train-rmse:0.463311+0.009825    test-rmse:0.535235+0.048274 
## [203]    train-rmse:0.463145+0.009815    test-rmse:0.534971+0.048223 
## [204]    train-rmse:0.462980+0.009807    test-rmse:0.534978+0.048388 
## [205]    train-rmse:0.462815+0.009799    test-rmse:0.534725+0.048249 
## [206]    train-rmse:0.462644+0.009783    test-rmse:0.534687+0.047953 
## [207]    train-rmse:0.462481+0.009777    test-rmse:0.534837+0.048031 
## [208]    train-rmse:0.462315+0.009767    test-rmse:0.534835+0.048001 
## [209]    train-rmse:0.462155+0.009762    test-rmse:0.534729+0.047861 
## [210]    train-rmse:0.461995+0.009754    test-rmse:0.534439+0.047823 
## [211]    train-rmse:0.461831+0.009743    test-rmse:0.534636+0.047692 
## [212]    train-rmse:0.461670+0.009733    test-rmse:0.534797+0.047839 
## [213]    train-rmse:0.461510+0.009725    test-rmse:0.534718+0.047908 
## [214]    train-rmse:0.461351+0.009716    test-rmse:0.534758+0.048040 
## [215]    train-rmse:0.461194+0.009708    test-rmse:0.534438+0.047858 
## [216]    train-rmse:0.461040+0.009699    test-rmse:0.534473+0.047771 
## [217]    train-rmse:0.460884+0.009690    test-rmse:0.534282+0.048068 
## [218]    train-rmse:0.460728+0.009680    test-rmse:0.534177+0.048068 
## [219]    train-rmse:0.460577+0.009670    test-rmse:0.533925+0.048001 
## [220]    train-rmse:0.460428+0.009661    test-rmse:0.533957+0.048065 
## [221]    train-rmse:0.460280+0.009654    test-rmse:0.534061+0.048115 
## [222]    train-rmse:0.460133+0.009646    test-rmse:0.534280+0.048092 
## [223]    train-rmse:0.459987+0.009639    test-rmse:0.534220+0.048022 
## [224]    train-rmse:0.459842+0.009633    test-rmse:0.534498+0.048050 
## [225]    train-rmse:0.459697+0.009626    test-rmse:0.534285+0.047852 
## Stopping. Best iteration:
## [219]    train-rmse:0.460577+0.009670    test-rmse:0.533925+0.048001
## 
## 22 
## [1]  train-rmse:0.906803+0.015255    test-rmse:0.908912+0.061214 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.829422+0.014450    test-rmse:0.838675+0.062420 
## [3]  train-rmse:0.768692+0.014090    test-rmse:0.781779+0.062562 
## [4]  train-rmse:0.720625+0.013411    test-rmse:0.742023+0.062713 
## [5]  train-rmse:0.682527+0.012339    test-rmse:0.706248+0.065381 
## [6]  train-rmse:0.652543+0.011836    test-rmse:0.676267+0.064624 
## [7]  train-rmse:0.627725+0.011007    test-rmse:0.655167+0.066204 
## [8]  train-rmse:0.607545+0.011295    test-rmse:0.641455+0.066332 
## [9]  train-rmse:0.591376+0.011171    test-rmse:0.626803+0.065081 
## [10] train-rmse:0.578515+0.011730    test-rmse:0.617035+0.064008 
## [11] train-rmse:0.567607+0.012583    test-rmse:0.609414+0.064072 
## [12] train-rmse:0.557506+0.012825    test-rmse:0.603463+0.066156 
## [13] train-rmse:0.548722+0.013928    test-rmse:0.595481+0.062196 
## [14] train-rmse:0.541200+0.012672    test-rmse:0.590719+0.062468 
## [15] train-rmse:0.535011+0.012635    test-rmse:0.586337+0.063044 
## [16] train-rmse:0.529224+0.013772    test-rmse:0.583356+0.063293 
## [17] train-rmse:0.524162+0.013732    test-rmse:0.579325+0.061744 
## [18] train-rmse:0.519732+0.013558    test-rmse:0.575877+0.058967 
## [19] train-rmse:0.515720+0.013870    test-rmse:0.572639+0.060171 
## [20] train-rmse:0.512088+0.013872    test-rmse:0.573159+0.061674 
## [21] train-rmse:0.508180+0.014076    test-rmse:0.570241+0.061561 
## [22] train-rmse:0.504778+0.014100    test-rmse:0.568123+0.061626 
## [23] train-rmse:0.501446+0.014251    test-rmse:0.567203+0.061640 
## [24] train-rmse:0.497881+0.014133    test-rmse:0.564021+0.060637 
## [25] train-rmse:0.494880+0.014582    test-rmse:0.561686+0.062664 
## [26] train-rmse:0.491891+0.014540    test-rmse:0.560701+0.061368 
## [27] train-rmse:0.488136+0.015053    test-rmse:0.557544+0.059210 
## [28] train-rmse:0.484967+0.014994    test-rmse:0.555055+0.059683 
## [29] train-rmse:0.482021+0.015310    test-rmse:0.552906+0.057572 
## [30] train-rmse:0.479737+0.015221    test-rmse:0.552260+0.058247 
## [31] train-rmse:0.476871+0.015383    test-rmse:0.550133+0.058451 
## [32] train-rmse:0.474644+0.015413    test-rmse:0.548681+0.057560 
## [33] train-rmse:0.472081+0.015274    test-rmse:0.546772+0.058138 
## [34] train-rmse:0.469676+0.015198    test-rmse:0.546820+0.058974 
## [35] train-rmse:0.467471+0.015291    test-rmse:0.545107+0.060100 
## [36] train-rmse:0.465299+0.015529    test-rmse:0.544766+0.059196 
## [37] train-rmse:0.463052+0.015177    test-rmse:0.542484+0.059094 
## [38] train-rmse:0.460146+0.014956    test-rmse:0.539844+0.058545 
## [39] train-rmse:0.458030+0.015211    test-rmse:0.539300+0.057951 
## [40] train-rmse:0.455938+0.014885    test-rmse:0.538867+0.057938 
## [41] train-rmse:0.453692+0.015385    test-rmse:0.537374+0.057876 
## [42] train-rmse:0.451583+0.015227    test-rmse:0.536203+0.058999 
## [43] train-rmse:0.449519+0.014932    test-rmse:0.536268+0.058805 
## [44] train-rmse:0.447693+0.014904    test-rmse:0.534646+0.059828 
## [45] train-rmse:0.445397+0.014878    test-rmse:0.533644+0.059322 
## [46] train-rmse:0.443542+0.015298    test-rmse:0.532567+0.059748 
## [47] train-rmse:0.441989+0.015439    test-rmse:0.532384+0.059302 
## [48] train-rmse:0.440695+0.015322    test-rmse:0.531213+0.058720 
## [49] train-rmse:0.438965+0.015254    test-rmse:0.529706+0.059197 
## [50] train-rmse:0.437476+0.015123    test-rmse:0.529317+0.058641 
## [51] train-rmse:0.435673+0.015222    test-rmse:0.528315+0.058424 
## [52] train-rmse:0.433660+0.015321    test-rmse:0.527186+0.060064 
## [53] train-rmse:0.432143+0.015084    test-rmse:0.525949+0.059666 
## [54] train-rmse:0.430615+0.015011    test-rmse:0.525230+0.059215 
## [55] train-rmse:0.428999+0.015160    test-rmse:0.524167+0.059844 
## [56] train-rmse:0.427696+0.015286    test-rmse:0.524552+0.059270 
## [57] train-rmse:0.426406+0.015514    test-rmse:0.524433+0.059470 
## [58] train-rmse:0.425195+0.015478    test-rmse:0.524234+0.060247 
## [59] train-rmse:0.424094+0.015484    test-rmse:0.523854+0.059907 
## [60] train-rmse:0.422766+0.015644    test-rmse:0.523316+0.059298 
## [61] train-rmse:0.421148+0.015537    test-rmse:0.521330+0.059280 
## [62] train-rmse:0.419796+0.015724    test-rmse:0.520516+0.058812 
## [63] train-rmse:0.418646+0.015991    test-rmse:0.520199+0.058501 
## [64] train-rmse:0.417209+0.016147    test-rmse:0.519211+0.058207 
## [65] train-rmse:0.415951+0.015946    test-rmse:0.518211+0.058318 
## [66] train-rmse:0.414905+0.015954    test-rmse:0.517712+0.058740 
## [67] train-rmse:0.413639+0.016229    test-rmse:0.517420+0.058861 
## [68] train-rmse:0.412167+0.016100    test-rmse:0.516530+0.059045 
## [69] train-rmse:0.410798+0.016086    test-rmse:0.515321+0.059102 
## [70] train-rmse:0.409839+0.016227    test-rmse:0.515161+0.059286 
## [71] train-rmse:0.408870+0.016365    test-rmse:0.514770+0.058944 
## [72] train-rmse:0.407767+0.016672    test-rmse:0.514509+0.058755 
## [73] train-rmse:0.406643+0.016663    test-rmse:0.514632+0.058159 
## [74] train-rmse:0.405721+0.016512    test-rmse:0.514592+0.058665 
## [75] train-rmse:0.404701+0.016310    test-rmse:0.514092+0.058287 
## [76] train-rmse:0.403955+0.016225    test-rmse:0.514008+0.058071 
## [77] train-rmse:0.402952+0.016404    test-rmse:0.513247+0.058647 
## [78] train-rmse:0.401938+0.016371    test-rmse:0.513062+0.058740 
## [79] train-rmse:0.400635+0.016397    test-rmse:0.512010+0.059237 
## [80] train-rmse:0.399646+0.016301    test-rmse:0.511462+0.059898 
## [81] train-rmse:0.398858+0.016395    test-rmse:0.510605+0.059991 
## [82] train-rmse:0.397835+0.016575    test-rmse:0.510166+0.060110 
## [83] train-rmse:0.396996+0.016570    test-rmse:0.509943+0.059949 
## [84] train-rmse:0.395882+0.016679    test-rmse:0.509264+0.059165 
## [85] train-rmse:0.394897+0.016616    test-rmse:0.509382+0.058982 
## [86] train-rmse:0.394170+0.016646    test-rmse:0.509787+0.059084 
## [87] train-rmse:0.393359+0.016825    test-rmse:0.509296+0.058951 
## [88] train-rmse:0.392451+0.016498    test-rmse:0.509399+0.059418 
## [89] train-rmse:0.391521+0.016356    test-rmse:0.508099+0.059556 
## [90] train-rmse:0.390679+0.016294    test-rmse:0.507642+0.059852 
## [91] train-rmse:0.390044+0.016306    test-rmse:0.507623+0.060005 
## [92] train-rmse:0.389013+0.016112    test-rmse:0.507338+0.059786 
## [93] train-rmse:0.387992+0.016100    test-rmse:0.506878+0.060269 
## [94] train-rmse:0.387238+0.016070    test-rmse:0.506987+0.060540 
## [95] train-rmse:0.386431+0.015861    test-rmse:0.506614+0.060407 
## [96] train-rmse:0.385698+0.015943    test-rmse:0.506340+0.060082 
## [97] train-rmse:0.384850+0.016022    test-rmse:0.505861+0.059730 
## [98] train-rmse:0.384270+0.015979    test-rmse:0.506273+0.059684 
## [99] train-rmse:0.383240+0.016326    test-rmse:0.505221+0.060094 
## [100]    train-rmse:0.382155+0.015991    test-rmse:0.505017+0.060494 
## [101]    train-rmse:0.381532+0.015872    test-rmse:0.504548+0.060277 
## [102]    train-rmse:0.381007+0.015795    test-rmse:0.504878+0.060201 
## [103]    train-rmse:0.380202+0.015684    test-rmse:0.504675+0.060605 
## [104]    train-rmse:0.379629+0.015751    test-rmse:0.504349+0.060701 
## [105]    train-rmse:0.379027+0.015812    test-rmse:0.504703+0.060537 
## [106]    train-rmse:0.378007+0.015644    test-rmse:0.504783+0.060375 
## [107]    train-rmse:0.377300+0.015564    test-rmse:0.504366+0.060077 
## [108]    train-rmse:0.376423+0.015615    test-rmse:0.503819+0.059438 
## [109]    train-rmse:0.375796+0.015715    test-rmse:0.503766+0.059368 
## [110]    train-rmse:0.375192+0.015641    test-rmse:0.503290+0.058999 
## [111]    train-rmse:0.374469+0.015392    test-rmse:0.503411+0.058876 
## [112]    train-rmse:0.373319+0.015773    test-rmse:0.502890+0.058524 
## [113]    train-rmse:0.372724+0.015772    test-rmse:0.502772+0.059458 
## [114]    train-rmse:0.371989+0.015865    test-rmse:0.502699+0.059875 
## [115]    train-rmse:0.371318+0.015914    test-rmse:0.502119+0.060304 
## [116]    train-rmse:0.370390+0.015647    test-rmse:0.501859+0.059948 
## [117]    train-rmse:0.369760+0.015499    test-rmse:0.501321+0.059755 
## [118]    train-rmse:0.368545+0.015845    test-rmse:0.501260+0.059709 
## [119]    train-rmse:0.367874+0.015635    test-rmse:0.500893+0.059497 
## [120]    train-rmse:0.367242+0.015807    test-rmse:0.500709+0.059424 
## [121]    train-rmse:0.366733+0.015745    test-rmse:0.500996+0.059342 
## [122]    train-rmse:0.366036+0.015756    test-rmse:0.501129+0.059418 
## [123]    train-rmse:0.365413+0.015862    test-rmse:0.501327+0.059680 
## [124]    train-rmse:0.364699+0.015947    test-rmse:0.500981+0.059430 
## [125]    train-rmse:0.363946+0.015960    test-rmse:0.500164+0.059828 
## [126]    train-rmse:0.363245+0.015706    test-rmse:0.500072+0.059507 
## [127]    train-rmse:0.362703+0.015506    test-rmse:0.499987+0.059408 
## [128]    train-rmse:0.361940+0.015318    test-rmse:0.500264+0.059477 
## [129]    train-rmse:0.361458+0.015243    test-rmse:0.500350+0.059389 
## [130]    train-rmse:0.360767+0.015066    test-rmse:0.500293+0.059902 
## [131]    train-rmse:0.360211+0.015156    test-rmse:0.500196+0.060045 
## [132]    train-rmse:0.359338+0.015198    test-rmse:0.499547+0.060089 
## [133]    train-rmse:0.358326+0.014887    test-rmse:0.499716+0.059725 
## [134]    train-rmse:0.357769+0.014807    test-rmse:0.499788+0.059641 
## [135]    train-rmse:0.357180+0.014733    test-rmse:0.499594+0.059614 
## [136]    train-rmse:0.356606+0.014533    test-rmse:0.499501+0.059405 
## [137]    train-rmse:0.356068+0.014503    test-rmse:0.499431+0.059201 
## [138]    train-rmse:0.355280+0.014768    test-rmse:0.498953+0.059341 
## [139]    train-rmse:0.354065+0.015130    test-rmse:0.499054+0.059424 
## [140]    train-rmse:0.353536+0.015177    test-rmse:0.498563+0.058930 
## [141]    train-rmse:0.352979+0.015197    test-rmse:0.498234+0.058955 
## [142]    train-rmse:0.352518+0.015028    test-rmse:0.498212+0.058677 
## [143]    train-rmse:0.351932+0.015113    test-rmse:0.497816+0.058261 
## [144]    train-rmse:0.351592+0.015073    test-rmse:0.498085+0.058317 
## [145]    train-rmse:0.350862+0.014662    test-rmse:0.498124+0.058048 
## [146]    train-rmse:0.350467+0.014576    test-rmse:0.498255+0.058018 
## [147]    train-rmse:0.349980+0.014391    test-rmse:0.498330+0.057850 
## [148]    train-rmse:0.349061+0.014152    test-rmse:0.498578+0.057730 
## [149]    train-rmse:0.347969+0.014071    test-rmse:0.497839+0.057927 
## Stopping. Best iteration:
## [143]    train-rmse:0.351932+0.015113    test-rmse:0.497816+0.058261
## 
## 23 
## [1]  train-rmse:0.901201+0.015476    test-rmse:0.905599+0.062243 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.817973+0.014871    test-rmse:0.829010+0.062305 
## [3]  train-rmse:0.751323+0.014372    test-rmse:0.769136+0.063694 
## [4]  train-rmse:0.697549+0.013517    test-rmse:0.722634+0.065467 
## [5]  train-rmse:0.654301+0.013699    test-rmse:0.686725+0.066611 
## [6]  train-rmse:0.619352+0.012970    test-rmse:0.656170+0.065738 
## [7]  train-rmse:0.591734+0.013389    test-rmse:0.633624+0.063978 
## [8]  train-rmse:0.568419+0.012651    test-rmse:0.614978+0.062727 
## [9]  train-rmse:0.548234+0.014161    test-rmse:0.599251+0.062221 
## [10] train-rmse:0.532181+0.013992    test-rmse:0.589565+0.060115 
## [11] train-rmse:0.518409+0.013103    test-rmse:0.581185+0.059375 
## [12] train-rmse:0.508296+0.013457    test-rmse:0.576021+0.057659 
## [13] train-rmse:0.497712+0.014431    test-rmse:0.568751+0.054966 
## [14] train-rmse:0.488552+0.015157    test-rmse:0.564788+0.053346 
## [15] train-rmse:0.481678+0.014655    test-rmse:0.560508+0.053579 
## [16] train-rmse:0.475152+0.013853    test-rmse:0.557587+0.054640 
## [17] train-rmse:0.469757+0.014404    test-rmse:0.556251+0.054534 
## [18] train-rmse:0.464749+0.014387    test-rmse:0.552209+0.054838 
## [19] train-rmse:0.459124+0.013392    test-rmse:0.548098+0.055908 
## [20] train-rmse:0.453773+0.014436    test-rmse:0.546252+0.055730 
## [21] train-rmse:0.448729+0.014487    test-rmse:0.543942+0.056855 
## [22] train-rmse:0.443997+0.013582    test-rmse:0.541234+0.055685 
## [23] train-rmse:0.440435+0.013567    test-rmse:0.539674+0.054919 
## [24] train-rmse:0.436235+0.014113    test-rmse:0.537225+0.054273 
## [25] train-rmse:0.431866+0.013666    test-rmse:0.534938+0.056656 
## [26] train-rmse:0.428155+0.013659    test-rmse:0.533900+0.056301 
## [27] train-rmse:0.424746+0.013697    test-rmse:0.531866+0.056256 
## [28] train-rmse:0.421623+0.013489    test-rmse:0.530901+0.057220 
## [29] train-rmse:0.418602+0.013956    test-rmse:0.530119+0.056158 
## [30] train-rmse:0.415324+0.013661    test-rmse:0.529022+0.055803 
## [31] train-rmse:0.412787+0.013565    test-rmse:0.528939+0.055526 
## [32] train-rmse:0.410203+0.014107    test-rmse:0.528140+0.055180 
## [33] train-rmse:0.406657+0.014259    test-rmse:0.526708+0.054411 
## [34] train-rmse:0.403905+0.014685    test-rmse:0.525802+0.053873 
## [35] train-rmse:0.401305+0.015050    test-rmse:0.525589+0.053618 
## [36] train-rmse:0.398853+0.015313    test-rmse:0.525678+0.053089 
## [37] train-rmse:0.396280+0.015319    test-rmse:0.523939+0.053565 
## [38] train-rmse:0.393735+0.015430    test-rmse:0.523487+0.053464 
## [39] train-rmse:0.391154+0.015205    test-rmse:0.523200+0.053527 
## [40] train-rmse:0.388585+0.014844    test-rmse:0.522373+0.054361 
## [41] train-rmse:0.385937+0.014778    test-rmse:0.521844+0.054682 
## [42] train-rmse:0.383867+0.015113    test-rmse:0.521366+0.054880 
## [43] train-rmse:0.381817+0.015204    test-rmse:0.520816+0.055274 
## [44] train-rmse:0.379912+0.015387    test-rmse:0.520596+0.055284 
## [45] train-rmse:0.377637+0.014855    test-rmse:0.519980+0.055102 
## [46] train-rmse:0.375249+0.014605    test-rmse:0.518586+0.055660 
## [47] train-rmse:0.373364+0.014646    test-rmse:0.518599+0.055529 
## [48] train-rmse:0.371840+0.014196    test-rmse:0.518226+0.055850 
## [49] train-rmse:0.369486+0.014191    test-rmse:0.517588+0.055412 
## [50] train-rmse:0.367769+0.014097    test-rmse:0.516389+0.055992 
## [51] train-rmse:0.365284+0.013334    test-rmse:0.515865+0.055470 
## [52] train-rmse:0.363368+0.013191    test-rmse:0.516160+0.054691 
## [53] train-rmse:0.361684+0.012829    test-rmse:0.515575+0.054947 
## [54] train-rmse:0.359617+0.012897    test-rmse:0.514963+0.054484 
## [55] train-rmse:0.357464+0.012314    test-rmse:0.514815+0.054783 
## [56] train-rmse:0.355348+0.012192    test-rmse:0.514606+0.054154 
## [57] train-rmse:0.353000+0.012070    test-rmse:0.515123+0.053337 
## [58] train-rmse:0.351396+0.012448    test-rmse:0.514283+0.052401 
## [59] train-rmse:0.349498+0.012960    test-rmse:0.513716+0.052684 
## [60] train-rmse:0.347616+0.012573    test-rmse:0.512579+0.053077 
## [61] train-rmse:0.346050+0.012676    test-rmse:0.512107+0.053157 
## [62] train-rmse:0.344869+0.012452    test-rmse:0.511748+0.053309 
## [63] train-rmse:0.342328+0.011950    test-rmse:0.510810+0.053563 
## [64] train-rmse:0.340253+0.011618    test-rmse:0.510818+0.053574 
## [65] train-rmse:0.338823+0.011436    test-rmse:0.511138+0.053614 
## [66] train-rmse:0.337278+0.011737    test-rmse:0.510930+0.054006 
## [67] train-rmse:0.335952+0.011991    test-rmse:0.511099+0.054104 
## [68] train-rmse:0.334182+0.011639    test-rmse:0.509745+0.054890 
## [69] train-rmse:0.332604+0.011361    test-rmse:0.509390+0.054764 
## [70] train-rmse:0.330713+0.012004    test-rmse:0.509019+0.054350 
## [71] train-rmse:0.329439+0.012310    test-rmse:0.508965+0.053643 
## [72] train-rmse:0.327627+0.012294    test-rmse:0.508884+0.053412 
## [73] train-rmse:0.324836+0.012359    test-rmse:0.507886+0.053140 
## [74] train-rmse:0.323149+0.012325    test-rmse:0.507329+0.052432 
## [75] train-rmse:0.321363+0.012198    test-rmse:0.507423+0.051515 
## [76] train-rmse:0.319931+0.012156    test-rmse:0.506576+0.051113 
## [77] train-rmse:0.318944+0.012098    test-rmse:0.506463+0.050727 
## [78] train-rmse:0.317348+0.011951    test-rmse:0.504967+0.050762 
## [79] train-rmse:0.315637+0.011995    test-rmse:0.505451+0.050235 
## [80] train-rmse:0.314509+0.012264    test-rmse:0.504937+0.049962 
## [81] train-rmse:0.312662+0.013213    test-rmse:0.504765+0.049841 
## [82] train-rmse:0.311432+0.013327    test-rmse:0.505215+0.049425 
## [83] train-rmse:0.309753+0.012898    test-rmse:0.505009+0.049616 
## [84] train-rmse:0.308462+0.012825    test-rmse:0.504708+0.049600 
## [85] train-rmse:0.307133+0.012680    test-rmse:0.505705+0.050095 
## [86] train-rmse:0.306138+0.012669    test-rmse:0.505638+0.050145 
## [87] train-rmse:0.305161+0.012552    test-rmse:0.505776+0.050127 
## [88] train-rmse:0.304340+0.012563    test-rmse:0.505782+0.049864 
## [89] train-rmse:0.302249+0.012814    test-rmse:0.505683+0.049337 
## [90] train-rmse:0.300789+0.012551    test-rmse:0.505019+0.049408 
## Stopping. Best iteration:
## [84] train-rmse:0.308462+0.012825    test-rmse:0.504708+0.049600
## 
## 24 
## [1]  train-rmse:0.896408+0.015307    test-rmse:0.902709+0.061253 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.808636+0.014256    test-rmse:0.822852+0.060719 
## [3]  train-rmse:0.737302+0.013856    test-rmse:0.758518+0.060871 
## [4]  train-rmse:0.679151+0.012463    test-rmse:0.707432+0.063670 
## [5]  train-rmse:0.631686+0.012486    test-rmse:0.669759+0.066778 
## [6]  train-rmse:0.591935+0.012406    test-rmse:0.637494+0.063548 
## [7]  train-rmse:0.559208+0.013057    test-rmse:0.612711+0.060430 
## [8]  train-rmse:0.532904+0.012482    test-rmse:0.594018+0.058657 
## [9]  train-rmse:0.511136+0.013264    test-rmse:0.580277+0.057189 
## [10] train-rmse:0.491277+0.014186    test-rmse:0.569283+0.055316 
## [11] train-rmse:0.475590+0.014590    test-rmse:0.559585+0.055777 
## [12] train-rmse:0.462545+0.014157    test-rmse:0.552739+0.054277 
## [13] train-rmse:0.451100+0.013483    test-rmse:0.546316+0.052795 
## [14] train-rmse:0.440990+0.014756    test-rmse:0.540606+0.052020 
## [15] train-rmse:0.431174+0.014383    test-rmse:0.532672+0.052610 
## [16] train-rmse:0.423806+0.014009    test-rmse:0.528982+0.052880 
## [17] train-rmse:0.417843+0.014008    test-rmse:0.527538+0.053133 
## [18] train-rmse:0.410640+0.015698    test-rmse:0.524292+0.053366 
## [19] train-rmse:0.403608+0.015332    test-rmse:0.520277+0.053162 
## [20] train-rmse:0.397831+0.015340    test-rmse:0.517495+0.055376 
## [21] train-rmse:0.393310+0.015399    test-rmse:0.516355+0.055036 
## [22] train-rmse:0.388786+0.015045    test-rmse:0.513865+0.054820 
## [23] train-rmse:0.384642+0.014568    test-rmse:0.512724+0.054220 
## [24] train-rmse:0.380043+0.015824    test-rmse:0.511151+0.053934 
## [25] train-rmse:0.375592+0.015600    test-rmse:0.509507+0.053155 
## [26] train-rmse:0.370923+0.015033    test-rmse:0.508193+0.054215 
## [27] train-rmse:0.367996+0.015247    test-rmse:0.506431+0.053608 
## [28] train-rmse:0.363603+0.014757    test-rmse:0.505202+0.053843 
## [29] train-rmse:0.359547+0.015750    test-rmse:0.504005+0.053840 
## [30] train-rmse:0.355186+0.014376    test-rmse:0.503581+0.055092 
## [31] train-rmse:0.352329+0.014636    test-rmse:0.502826+0.054698 
## [32] train-rmse:0.349359+0.014385    test-rmse:0.502360+0.054207 
## [33] train-rmse:0.345816+0.014548    test-rmse:0.502628+0.054912 
## [34] train-rmse:0.342805+0.015101    test-rmse:0.501768+0.054446 
## [35] train-rmse:0.339231+0.014619    test-rmse:0.500364+0.054574 
## [36] train-rmse:0.335708+0.015317    test-rmse:0.499033+0.054457 
## [37] train-rmse:0.333211+0.014717    test-rmse:0.498804+0.053911 
## [38] train-rmse:0.330424+0.014175    test-rmse:0.497579+0.053641 
## [39] train-rmse:0.327086+0.014576    test-rmse:0.497225+0.053213 
## [40] train-rmse:0.325044+0.014580    test-rmse:0.497600+0.052934 
## [41] train-rmse:0.322144+0.014972    test-rmse:0.497106+0.052756 
## [42] train-rmse:0.319346+0.014804    test-rmse:0.496639+0.053029 
## [43] train-rmse:0.315680+0.015845    test-rmse:0.496228+0.053056 
## [44] train-rmse:0.313129+0.015416    test-rmse:0.495765+0.053642 
## [45] train-rmse:0.310373+0.014584    test-rmse:0.494905+0.053893 
## [46] train-rmse:0.308429+0.014503    test-rmse:0.494149+0.054367 
## [47] train-rmse:0.306239+0.014571    test-rmse:0.493813+0.054354 
## [48] train-rmse:0.303324+0.014336    test-rmse:0.493635+0.054175 
## [49] train-rmse:0.300852+0.014056    test-rmse:0.492957+0.054606 
## [50] train-rmse:0.298413+0.014162    test-rmse:0.493064+0.054931 
## [51] train-rmse:0.296573+0.014089    test-rmse:0.492864+0.055035 
## [52] train-rmse:0.294882+0.014193    test-rmse:0.492519+0.055358 
## [53] train-rmse:0.292242+0.014408    test-rmse:0.491445+0.055674 
## [54] train-rmse:0.289825+0.014631    test-rmse:0.490892+0.055502 
## [55] train-rmse:0.288011+0.015280    test-rmse:0.490701+0.055451 
## [56] train-rmse:0.285942+0.015540    test-rmse:0.490673+0.055145 
## [57] train-rmse:0.283854+0.015047    test-rmse:0.490962+0.054952 
## [58] train-rmse:0.281936+0.015410    test-rmse:0.490572+0.053858 
## [59] train-rmse:0.280458+0.015019    test-rmse:0.490977+0.054132 
## [60] train-rmse:0.279005+0.014667    test-rmse:0.490754+0.054269 
## [61] train-rmse:0.276668+0.014183    test-rmse:0.490918+0.054247 
## [62] train-rmse:0.274776+0.014307    test-rmse:0.491246+0.054207 
## [63] train-rmse:0.272840+0.014032    test-rmse:0.491133+0.053339 
## [64] train-rmse:0.270804+0.014208    test-rmse:0.490668+0.053126 
## Stopping. Best iteration:
## [58] train-rmse:0.281936+0.015410    test-rmse:0.490572+0.053858
## 
## 25 
## [1]  train-rmse:0.892150+0.014908    test-rmse:0.900199+0.061882 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.800099+0.013338    test-rmse:0.819359+0.062005 
## [3]  train-rmse:0.723935+0.012228    test-rmse:0.753901+0.061953 
## [4]  train-rmse:0.662179+0.012171    test-rmse:0.700912+0.062575 
## [5]  train-rmse:0.609815+0.011075    test-rmse:0.662188+0.063422 
## [6]  train-rmse:0.567082+0.010876    test-rmse:0.630515+0.060590 
## [7]  train-rmse:0.531804+0.011478    test-rmse:0.603242+0.059611 
## [8]  train-rmse:0.502297+0.011467    test-rmse:0.584370+0.058630 
## [9]  train-rmse:0.477280+0.010554    test-rmse:0.572070+0.058038 
## [10] train-rmse:0.456045+0.012278    test-rmse:0.560395+0.057146 
## [11] train-rmse:0.437858+0.012251    test-rmse:0.551548+0.059402 
## [12] train-rmse:0.422664+0.012019    test-rmse:0.544238+0.061717 
## [13] train-rmse:0.409588+0.011523    test-rmse:0.539585+0.059783 
## [14] train-rmse:0.398649+0.011577    test-rmse:0.534531+0.059852 
## [15] train-rmse:0.389411+0.012295    test-rmse:0.530025+0.059996 
## [16] train-rmse:0.381642+0.013151    test-rmse:0.526797+0.059114 
## [17] train-rmse:0.372565+0.010685    test-rmse:0.524000+0.060050 
## [18] train-rmse:0.364583+0.009786    test-rmse:0.519365+0.059570 
## [19] train-rmse:0.359418+0.009687    test-rmse:0.516701+0.058108 
## [20] train-rmse:0.351887+0.009107    test-rmse:0.512871+0.059827 
## [21] train-rmse:0.346625+0.008955    test-rmse:0.511442+0.061279 
## [22] train-rmse:0.342622+0.008820    test-rmse:0.510508+0.059481 
## [23] train-rmse:0.336831+0.011800    test-rmse:0.509066+0.060117 
## [24] train-rmse:0.332009+0.011026    test-rmse:0.506841+0.060701 
## [25] train-rmse:0.327750+0.010970    test-rmse:0.505079+0.061665 
## [26] train-rmse:0.323570+0.009761    test-rmse:0.503112+0.062434 
## [27] train-rmse:0.319979+0.010120    test-rmse:0.502493+0.062292 
## [28] train-rmse:0.315655+0.010962    test-rmse:0.501196+0.061492 
## [29] train-rmse:0.311257+0.010992    test-rmse:0.499220+0.062108 
## [30] train-rmse:0.307622+0.010925    test-rmse:0.498159+0.062452 
## [31] train-rmse:0.303918+0.010326    test-rmse:0.498896+0.062562 
## [32] train-rmse:0.299540+0.011092    test-rmse:0.497119+0.063221 
## [33] train-rmse:0.296881+0.011095    test-rmse:0.496744+0.063811 
## [34] train-rmse:0.293476+0.010935    test-rmse:0.495884+0.063587 
## [35] train-rmse:0.290092+0.011497    test-rmse:0.495405+0.064759 
## [36] train-rmse:0.286020+0.010157    test-rmse:0.495351+0.064575 
## [37] train-rmse:0.283172+0.010809    test-rmse:0.495354+0.064412 
## [38] train-rmse:0.279440+0.010860    test-rmse:0.495292+0.063670 
## [39] train-rmse:0.275909+0.010503    test-rmse:0.493938+0.064123 
## [40] train-rmse:0.272381+0.009796    test-rmse:0.493665+0.065328 
## [41] train-rmse:0.269892+0.010832    test-rmse:0.494739+0.066944 
## [42] train-rmse:0.267395+0.010698    test-rmse:0.494450+0.067254 
## [43] train-rmse:0.263993+0.010674    test-rmse:0.493159+0.067786 
## [44] train-rmse:0.261076+0.010053    test-rmse:0.493107+0.067268 
## [45] train-rmse:0.257458+0.009712    test-rmse:0.492780+0.067093 
## [46] train-rmse:0.254963+0.009289    test-rmse:0.492094+0.067022 
## [47] train-rmse:0.252141+0.009545    test-rmse:0.491855+0.067280 
## [48] train-rmse:0.249408+0.008741    test-rmse:0.491512+0.067299 
## [49] train-rmse:0.245937+0.008946    test-rmse:0.491388+0.066907 
## [50] train-rmse:0.242944+0.008373    test-rmse:0.491696+0.066524 
## [51] train-rmse:0.240512+0.007708    test-rmse:0.491446+0.066811 
## [52] train-rmse:0.238556+0.007897    test-rmse:0.491436+0.066474 
## [53] train-rmse:0.235423+0.007043    test-rmse:0.490281+0.066240 
## [54] train-rmse:0.232344+0.007024    test-rmse:0.490246+0.065940 
## [55] train-rmse:0.229526+0.006932    test-rmse:0.489968+0.065678 
## [56] train-rmse:0.227772+0.006456    test-rmse:0.489991+0.065431 
## [57] train-rmse:0.225382+0.006844    test-rmse:0.490685+0.065738 
## [58] train-rmse:0.222819+0.007073    test-rmse:0.491669+0.065998 
## [59] train-rmse:0.218892+0.008046    test-rmse:0.491406+0.065624 
## [60] train-rmse:0.216745+0.008973    test-rmse:0.490843+0.065174 
## [61] train-rmse:0.214545+0.008806    test-rmse:0.491628+0.065563 
## Stopping. Best iteration:
## [55] train-rmse:0.229526+0.006932    test-rmse:0.489968+0.065678
## 
## 26 
## [1]  train-rmse:0.888270+0.014515    test-rmse:0.898512+0.062098 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.791976+0.013479    test-rmse:0.812073+0.061711 
## [3]  train-rmse:0.711514+0.011140    test-rmse:0.746252+0.062511 
## [4]  train-rmse:0.645987+0.010109    test-rmse:0.694738+0.062169 
## [5]  train-rmse:0.591399+0.011355    test-rmse:0.653344+0.065036 
## [6]  train-rmse:0.544979+0.010802    test-rmse:0.618589+0.060904 
## [7]  train-rmse:0.507482+0.012418    test-rmse:0.592635+0.057810 
## [8]  train-rmse:0.474746+0.012210    test-rmse:0.571015+0.057286 
## [9]  train-rmse:0.447145+0.012485    test-rmse:0.554725+0.055534 
## [10] train-rmse:0.423254+0.012655    test-rmse:0.543077+0.056936 
## [11] train-rmse:0.404573+0.011691    test-rmse:0.533329+0.056122 
## [12] train-rmse:0.386729+0.012049    test-rmse:0.526115+0.057329 
## [13] train-rmse:0.372849+0.011655    test-rmse:0.520650+0.058665 
## [14] train-rmse:0.361612+0.011564    test-rmse:0.515473+0.058178 
## [15] train-rmse:0.352153+0.010965    test-rmse:0.511556+0.057699 
## [16] train-rmse:0.342408+0.011805    test-rmse:0.509262+0.057316 
## [17] train-rmse:0.335133+0.011920    test-rmse:0.505922+0.057916 
## [18] train-rmse:0.326959+0.011570    test-rmse:0.503424+0.058454 
## [19] train-rmse:0.319871+0.012581    test-rmse:0.502186+0.055883 
## [20] train-rmse:0.312677+0.013533    test-rmse:0.499567+0.056911 
## [21] train-rmse:0.307948+0.013919    test-rmse:0.499402+0.056281 
## [22] train-rmse:0.302299+0.013348    test-rmse:0.498408+0.054785 
## [23] train-rmse:0.298053+0.013773    test-rmse:0.497466+0.054968 
## [24] train-rmse:0.293643+0.014164    test-rmse:0.498104+0.054458 
## [25] train-rmse:0.288672+0.014875    test-rmse:0.495418+0.052203 
## [26] train-rmse:0.283355+0.012144    test-rmse:0.494624+0.052782 
## [27] train-rmse:0.278294+0.012368    test-rmse:0.494361+0.052007 
## [28] train-rmse:0.274712+0.012373    test-rmse:0.494125+0.052951 
## [29] train-rmse:0.270798+0.011719    test-rmse:0.492538+0.052084 
## [30] train-rmse:0.265501+0.012378    test-rmse:0.492208+0.051534 
## [31] train-rmse:0.260842+0.012257    test-rmse:0.491391+0.052387 
## [32] train-rmse:0.258224+0.011924    test-rmse:0.491326+0.052427 
## [33] train-rmse:0.254762+0.012490    test-rmse:0.490978+0.053078 
## [34] train-rmse:0.250319+0.012321    test-rmse:0.491394+0.052910 
## [35] train-rmse:0.246884+0.012593    test-rmse:0.490577+0.052872 
## [36] train-rmse:0.243447+0.012534    test-rmse:0.491713+0.053453 
## [37] train-rmse:0.239754+0.013172    test-rmse:0.491831+0.054837 
## [38] train-rmse:0.236011+0.014170    test-rmse:0.491799+0.054562 
## [39] train-rmse:0.232018+0.015322    test-rmse:0.492118+0.053638 
## [40] train-rmse:0.228241+0.015306    test-rmse:0.492221+0.054441 
## [41] train-rmse:0.225354+0.016248    test-rmse:0.493900+0.054919 
## Stopping. Best iteration:
## [35] train-rmse:0.246884+0.012593    test-rmse:0.490577+0.052872
## 
## 27 
## [1]  train-rmse:0.884472+0.014166    test-rmse:0.895948+0.060832 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.785201+0.012367    test-rmse:0.810700+0.062904 
## [3]  train-rmse:0.701634+0.011080    test-rmse:0.741279+0.062773 
## [4]  train-rmse:0.633393+0.010128    test-rmse:0.689086+0.062593 
## [5]  train-rmse:0.574638+0.010760    test-rmse:0.646054+0.061324 
## [6]  train-rmse:0.527131+0.011766    test-rmse:0.616656+0.064756 
## [7]  train-rmse:0.487213+0.011560    test-rmse:0.592717+0.065144 
## [8]  train-rmse:0.451631+0.013285    test-rmse:0.571445+0.063475 
## [9]  train-rmse:0.422634+0.014164    test-rmse:0.556452+0.061189 
## [10] train-rmse:0.398244+0.015201    test-rmse:0.545527+0.060554 
## [11] train-rmse:0.376424+0.015203    test-rmse:0.535918+0.060819 
## [12] train-rmse:0.356909+0.015042    test-rmse:0.527788+0.060320 
## [13] train-rmse:0.340834+0.014732    test-rmse:0.522994+0.059737 
## [14] train-rmse:0.326815+0.015257    test-rmse:0.519192+0.060198 
## [15] train-rmse:0.313651+0.016238    test-rmse:0.516286+0.057642 
## [16] train-rmse:0.304302+0.015862    test-rmse:0.514934+0.057070 
## [17] train-rmse:0.296705+0.016621    test-rmse:0.513102+0.055820 
## [18] train-rmse:0.286772+0.016714    test-rmse:0.512914+0.054881 
## [19] train-rmse:0.277569+0.014935    test-rmse:0.509948+0.054049 
## [20] train-rmse:0.270422+0.013034    test-rmse:0.507717+0.054034 
## [21] train-rmse:0.262281+0.014121    test-rmse:0.505202+0.054622 
## [22] train-rmse:0.256307+0.013682    test-rmse:0.502778+0.054117 
## [23] train-rmse:0.251905+0.013560    test-rmse:0.501495+0.053905 
## [24] train-rmse:0.245271+0.014396    test-rmse:0.500774+0.054177 
## [25] train-rmse:0.241710+0.014108    test-rmse:0.500363+0.053399 
## [26] train-rmse:0.237810+0.013659    test-rmse:0.499954+0.053742 
## [27] train-rmse:0.233361+0.013492    test-rmse:0.499388+0.052346 
## [28] train-rmse:0.228480+0.014192    test-rmse:0.499879+0.052101 
## [29] train-rmse:0.223631+0.015334    test-rmse:0.499608+0.051591 
## [30] train-rmse:0.218518+0.014118    test-rmse:0.497082+0.051754 
## [31] train-rmse:0.215756+0.014243    test-rmse:0.497467+0.050841 
## [32] train-rmse:0.211561+0.012719    test-rmse:0.496815+0.051279 
## [33] train-rmse:0.207328+0.013177    test-rmse:0.497931+0.052787 
## [34] train-rmse:0.204053+0.012582    test-rmse:0.497754+0.053107 
## [35] train-rmse:0.199657+0.012291    test-rmse:0.497348+0.052111 
## [36] train-rmse:0.196299+0.012838    test-rmse:0.497309+0.051337 
## [37] train-rmse:0.194225+0.012911    test-rmse:0.498341+0.051152 
## [38] train-rmse:0.190509+0.012434    test-rmse:0.498634+0.052328 
## Stopping. Best iteration:
## [32] train-rmse:0.211561+0.012719    test-rmse:0.496815+0.051279
## 
## 28 
## [1]  train-rmse:0.909216+0.015233    test-rmse:0.910743+0.061135 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.838320+0.014421    test-rmse:0.844083+0.060324 
## [3]  train-rmse:0.786199+0.014072    test-rmse:0.799262+0.062303 
## [4]  train-rmse:0.746369+0.013524    test-rmse:0.762957+0.058604 
## [5]  train-rmse:0.716401+0.012765    test-rmse:0.733642+0.059832 
## [6]  train-rmse:0.692415+0.012424    test-rmse:0.710091+0.056472 
## [7]  train-rmse:0.673271+0.012049    test-rmse:0.696385+0.056535 
## [8]  train-rmse:0.658563+0.011760    test-rmse:0.682017+0.054878 
## [9]  train-rmse:0.646023+0.011573    test-rmse:0.669714+0.052676 
## [10] train-rmse:0.636255+0.011492    test-rmse:0.662508+0.053715 
## [11] train-rmse:0.628177+0.011373    test-rmse:0.654488+0.050553 
## [12] train-rmse:0.621166+0.011144    test-rmse:0.648361+0.050154 
## [13] train-rmse:0.615394+0.011124    test-rmse:0.643776+0.051766 
## [14] train-rmse:0.610155+0.011151    test-rmse:0.638658+0.052828 
## [15] train-rmse:0.605375+0.011161    test-rmse:0.636848+0.053971 
## [16] train-rmse:0.600905+0.011212    test-rmse:0.633701+0.053488 
## [17] train-rmse:0.596754+0.011233    test-rmse:0.630241+0.051256 
## [18] train-rmse:0.593093+0.011175    test-rmse:0.626916+0.052737 
## [19] train-rmse:0.589502+0.011169    test-rmse:0.623911+0.054578 
## [20] train-rmse:0.586168+0.011095    test-rmse:0.621001+0.055308 
## [21] train-rmse:0.583017+0.011135    test-rmse:0.619426+0.056005 
## [22] train-rmse:0.580066+0.011177    test-rmse:0.616834+0.054664 
## [23] train-rmse:0.577238+0.011207    test-rmse:0.613869+0.053379 
## [24] train-rmse:0.574527+0.011159    test-rmse:0.610447+0.054939 
## [25] train-rmse:0.571941+0.011144    test-rmse:0.606917+0.054987 
## [26] train-rmse:0.569441+0.011109    test-rmse:0.605649+0.053011 
## [27] train-rmse:0.567024+0.011144    test-rmse:0.604203+0.053473 
## [28] train-rmse:0.564678+0.011189    test-rmse:0.603108+0.054291 
## [29] train-rmse:0.562481+0.011172    test-rmse:0.600885+0.054120 
## [30] train-rmse:0.560331+0.011197    test-rmse:0.600518+0.054652 
## [31] train-rmse:0.558222+0.011223    test-rmse:0.598513+0.055751 
## [32] train-rmse:0.556173+0.011232    test-rmse:0.595656+0.056003 
## [33] train-rmse:0.554199+0.011179    test-rmse:0.594306+0.056039 
## [34] train-rmse:0.552283+0.011131    test-rmse:0.594107+0.055112 
## [35] train-rmse:0.550443+0.011133    test-rmse:0.592755+0.054941 
## [36] train-rmse:0.548644+0.011144    test-rmse:0.589888+0.054469 
## [37] train-rmse:0.546883+0.011133    test-rmse:0.588481+0.054020 
## [38] train-rmse:0.545196+0.011096    test-rmse:0.587635+0.053346 
## [39] train-rmse:0.543532+0.011089    test-rmse:0.586224+0.054016 
## [40] train-rmse:0.541892+0.011062    test-rmse:0.585443+0.053279 
## [41] train-rmse:0.540295+0.011077    test-rmse:0.584201+0.055126 
## [42] train-rmse:0.538756+0.011083    test-rmse:0.583150+0.054899 
## [43] train-rmse:0.537286+0.011098    test-rmse:0.581326+0.055309 
## [44] train-rmse:0.535843+0.011086    test-rmse:0.581209+0.055339 
## [45] train-rmse:0.534415+0.011070    test-rmse:0.580021+0.054794 
## [46] train-rmse:0.533016+0.011043    test-rmse:0.579213+0.053592 
## [47] train-rmse:0.531666+0.010992    test-rmse:0.577953+0.054216 
## [48] train-rmse:0.530341+0.010952    test-rmse:0.576570+0.054152 
## [49] train-rmse:0.529066+0.010939    test-rmse:0.576405+0.054736 
## [50] train-rmse:0.527800+0.010915    test-rmse:0.576823+0.054493 
## [51] train-rmse:0.526598+0.010901    test-rmse:0.575526+0.054065 
## [52] train-rmse:0.525421+0.010862    test-rmse:0.575151+0.053641 
## [53] train-rmse:0.524259+0.010822    test-rmse:0.574143+0.053830 
## [54] train-rmse:0.523109+0.010798    test-rmse:0.572588+0.053936 
## [55] train-rmse:0.521994+0.010780    test-rmse:0.571520+0.054306 
## [56] train-rmse:0.520892+0.010768    test-rmse:0.571238+0.055242 
## [57] train-rmse:0.519816+0.010751    test-rmse:0.570730+0.054914 
## [58] train-rmse:0.518757+0.010719    test-rmse:0.569764+0.054985 
## [59] train-rmse:0.517706+0.010699    test-rmse:0.568386+0.055587 
## [60] train-rmse:0.516691+0.010686    test-rmse:0.567375+0.055466 
## [61] train-rmse:0.515701+0.010675    test-rmse:0.566652+0.055011 
## [62] train-rmse:0.514709+0.010659    test-rmse:0.566319+0.055420 
## [63] train-rmse:0.513721+0.010642    test-rmse:0.565471+0.054043 
## [64] train-rmse:0.512746+0.010631    test-rmse:0.564605+0.054449 
## [65] train-rmse:0.511801+0.010623    test-rmse:0.563701+0.055047 
## [66] train-rmse:0.510891+0.010620    test-rmse:0.563532+0.054996 
## [67] train-rmse:0.510004+0.010609    test-rmse:0.562868+0.054741 
## [68] train-rmse:0.509148+0.010584    test-rmse:0.561937+0.054563 
## [69] train-rmse:0.508314+0.010574    test-rmse:0.561039+0.054679 
## [70] train-rmse:0.507491+0.010564    test-rmse:0.560444+0.053903 
## [71] train-rmse:0.506668+0.010543    test-rmse:0.560440+0.054228 
## [72] train-rmse:0.505855+0.010530    test-rmse:0.559635+0.053644 
## [73] train-rmse:0.505058+0.010504    test-rmse:0.559364+0.053916 
## [74] train-rmse:0.504272+0.010486    test-rmse:0.558483+0.053482 
## [75] train-rmse:0.503509+0.010486    test-rmse:0.557720+0.053363 
## [76] train-rmse:0.502759+0.010476    test-rmse:0.556892+0.052800 
## [77] train-rmse:0.502028+0.010483    test-rmse:0.555458+0.052671 
## [78] train-rmse:0.501310+0.010486    test-rmse:0.555329+0.052628 
## [79] train-rmse:0.500597+0.010487    test-rmse:0.554616+0.052788 
## [80] train-rmse:0.499897+0.010479    test-rmse:0.554357+0.052713 
## [81] train-rmse:0.499203+0.010476    test-rmse:0.554145+0.053365 
## [82] train-rmse:0.498508+0.010473    test-rmse:0.554057+0.053603 
## [83] train-rmse:0.497828+0.010461    test-rmse:0.553482+0.053505 
## [84] train-rmse:0.497165+0.010439    test-rmse:0.553745+0.052629 
## [85] train-rmse:0.496510+0.010427    test-rmse:0.552980+0.053048 
## [86] train-rmse:0.495870+0.010423    test-rmse:0.553246+0.052434 
## [87] train-rmse:0.495234+0.010424    test-rmse:0.552840+0.052309 
## [88] train-rmse:0.494599+0.010422    test-rmse:0.552373+0.052134 
## [89] train-rmse:0.493980+0.010421    test-rmse:0.551626+0.052030 
## [90] train-rmse:0.493385+0.010416    test-rmse:0.551154+0.051913 
## [91] train-rmse:0.492795+0.010419    test-rmse:0.550320+0.051824 
## [92] train-rmse:0.492214+0.010422    test-rmse:0.550042+0.052384 
## [93] train-rmse:0.491648+0.010421    test-rmse:0.549889+0.052357 
## [94] train-rmse:0.491093+0.010412    test-rmse:0.548931+0.052584 
## [95] train-rmse:0.490545+0.010397    test-rmse:0.548613+0.052462 
## [96] train-rmse:0.490009+0.010382    test-rmse:0.547909+0.051709 
## [97] train-rmse:0.489479+0.010371    test-rmse:0.547821+0.051630 
## [98] train-rmse:0.488952+0.010359    test-rmse:0.547318+0.051943 
## [99] train-rmse:0.488432+0.010350    test-rmse:0.547389+0.052448 
## [100]    train-rmse:0.487922+0.010337    test-rmse:0.547243+0.051995 
## [101]    train-rmse:0.487417+0.010334    test-rmse:0.548138+0.052079 
## [102]    train-rmse:0.486919+0.010327    test-rmse:0.547815+0.052386 
## [103]    train-rmse:0.486432+0.010324    test-rmse:0.546858+0.052429 
## [104]    train-rmse:0.485953+0.010324    test-rmse:0.546393+0.052260 
## [105]    train-rmse:0.485476+0.010330    test-rmse:0.546169+0.052024 
## [106]    train-rmse:0.485007+0.010327    test-rmse:0.545572+0.052677 
## [107]    train-rmse:0.484542+0.010325    test-rmse:0.546053+0.052089 
## [108]    train-rmse:0.484092+0.010323    test-rmse:0.545421+0.052144 
## [109]    train-rmse:0.483644+0.010322    test-rmse:0.545211+0.051415 
## [110]    train-rmse:0.483202+0.010323    test-rmse:0.544607+0.051216 
## [111]    train-rmse:0.482762+0.010317    test-rmse:0.544225+0.050966 
## [112]    train-rmse:0.482333+0.010308    test-rmse:0.544347+0.051150 
## [113]    train-rmse:0.481906+0.010299    test-rmse:0.544086+0.051142 
## [114]    train-rmse:0.481484+0.010299    test-rmse:0.543942+0.050891 
## [115]    train-rmse:0.481070+0.010297    test-rmse:0.543843+0.050923 
## [116]    train-rmse:0.480662+0.010299    test-rmse:0.543387+0.051249 
## [117]    train-rmse:0.480261+0.010296    test-rmse:0.542934+0.050814 
## [118]    train-rmse:0.479861+0.010290    test-rmse:0.542937+0.051312 
## [119]    train-rmse:0.479474+0.010290    test-rmse:0.542490+0.051449 
## [120]    train-rmse:0.479092+0.010284    test-rmse:0.542094+0.051209 
## [121]    train-rmse:0.478715+0.010282    test-rmse:0.541601+0.051084 
## [122]    train-rmse:0.478339+0.010276    test-rmse:0.541223+0.050521 
## [123]    train-rmse:0.477965+0.010268    test-rmse:0.541468+0.050519 
## [124]    train-rmse:0.477599+0.010261    test-rmse:0.541487+0.049970 
## [125]    train-rmse:0.477237+0.010251    test-rmse:0.541528+0.050430 
## [126]    train-rmse:0.476878+0.010241    test-rmse:0.541069+0.050507 
## [127]    train-rmse:0.476527+0.010230    test-rmse:0.541032+0.050650 
## [128]    train-rmse:0.476184+0.010227    test-rmse:0.540707+0.050941 
## [129]    train-rmse:0.475845+0.010224    test-rmse:0.540769+0.050960 
## [130]    train-rmse:0.475509+0.010225    test-rmse:0.540595+0.050678 
## [131]    train-rmse:0.475175+0.010229    test-rmse:0.540383+0.050486 
## [132]    train-rmse:0.474842+0.010231    test-rmse:0.540106+0.050653 
## [133]    train-rmse:0.474519+0.010232    test-rmse:0.539559+0.050807 
## [134]    train-rmse:0.474204+0.010231    test-rmse:0.539511+0.050819 
## [135]    train-rmse:0.473890+0.010224    test-rmse:0.539214+0.050752 
## [136]    train-rmse:0.473573+0.010217    test-rmse:0.539122+0.050360 
## [137]    train-rmse:0.473261+0.010213    test-rmse:0.538725+0.050355 
## [138]    train-rmse:0.472955+0.010213    test-rmse:0.538715+0.050051 
## [139]    train-rmse:0.472657+0.010210    test-rmse:0.538372+0.049767 
## [140]    train-rmse:0.472359+0.010210    test-rmse:0.538499+0.049461 
## [141]    train-rmse:0.472068+0.010209    test-rmse:0.537972+0.049449 
## [142]    train-rmse:0.471780+0.010202    test-rmse:0.538045+0.048975 
## [143]    train-rmse:0.471492+0.010194    test-rmse:0.537510+0.049220 
## [144]    train-rmse:0.471210+0.010191    test-rmse:0.537825+0.049522 
## [145]    train-rmse:0.470932+0.010192    test-rmse:0.537901+0.049469 
## [146]    train-rmse:0.470657+0.010189    test-rmse:0.537868+0.049028 
## [147]    train-rmse:0.470385+0.010186    test-rmse:0.537791+0.048668 
## [148]    train-rmse:0.470119+0.010183    test-rmse:0.537808+0.048522 
## [149]    train-rmse:0.469857+0.010179    test-rmse:0.537646+0.048507 
## Stopping. Best iteration:
## [143]    train-rmse:0.471492+0.010194    test-rmse:0.537510+0.049220
## 
## 29 
## [1]  train-rmse:0.895038+0.015127    test-rmse:0.897676+0.061050 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.811215+0.014338    test-rmse:0.821871+0.062638 
## [3]  train-rmse:0.747562+0.013835    test-rmse:0.763539+0.061872 
## [4]  train-rmse:0.699005+0.013347    test-rmse:0.723113+0.063332 
## [5]  train-rmse:0.661455+0.012247    test-rmse:0.687966+0.065833 
## [6]  train-rmse:0.632640+0.011996    test-rmse:0.662201+0.065475 
## [7]  train-rmse:0.608419+0.011900    test-rmse:0.643490+0.065775 
## [8]  train-rmse:0.590754+0.013062    test-rmse:0.630536+0.065200 
## [9]  train-rmse:0.576258+0.012268    test-rmse:0.616235+0.064981 
## [10] train-rmse:0.562360+0.015077    test-rmse:0.608539+0.062988 
## [11] train-rmse:0.552960+0.015768    test-rmse:0.602833+0.062385 
## [12] train-rmse:0.544475+0.015535    test-rmse:0.594987+0.061491 
## [13] train-rmse:0.537181+0.015755    test-rmse:0.589834+0.059523 
## [14] train-rmse:0.531489+0.015914    test-rmse:0.586109+0.059148 
## [15] train-rmse:0.525877+0.015948    test-rmse:0.583400+0.058417 
## [16] train-rmse:0.521027+0.015680    test-rmse:0.579116+0.058766 
## [17] train-rmse:0.515303+0.015296    test-rmse:0.575930+0.058761 
## [18] train-rmse:0.510807+0.015998    test-rmse:0.572924+0.059209 
## [19] train-rmse:0.506870+0.016085    test-rmse:0.570845+0.059879 
## [20] train-rmse:0.503136+0.016085    test-rmse:0.569133+0.060625 
## [21] train-rmse:0.499303+0.015784    test-rmse:0.566994+0.060004 
## [22] train-rmse:0.495642+0.015984    test-rmse:0.564796+0.060563 
## [23] train-rmse:0.491940+0.016153    test-rmse:0.563669+0.060659 
## [24] train-rmse:0.488852+0.016267    test-rmse:0.562659+0.059131 
## [25] train-rmse:0.485582+0.016784    test-rmse:0.560844+0.061160 
## [26] train-rmse:0.482420+0.016388    test-rmse:0.558903+0.061369 
## [27] train-rmse:0.478524+0.016767    test-rmse:0.554437+0.061173 
## [28] train-rmse:0.475578+0.016556    test-rmse:0.553537+0.062442 
## [29] train-rmse:0.472532+0.016411    test-rmse:0.550701+0.062207 
## [30] train-rmse:0.469927+0.016589    test-rmse:0.550052+0.061793 
## [31] train-rmse:0.467461+0.016472    test-rmse:0.548096+0.060994 
## [32] train-rmse:0.465233+0.016536    test-rmse:0.547827+0.061151 
## [33] train-rmse:0.462904+0.016194    test-rmse:0.546885+0.062521 
## [34] train-rmse:0.460797+0.015942    test-rmse:0.545450+0.062463 
## [35] train-rmse:0.458603+0.016086    test-rmse:0.543356+0.062968 
## [36] train-rmse:0.456228+0.015947    test-rmse:0.540042+0.063760 
## [37] train-rmse:0.453664+0.015901    test-rmse:0.538747+0.062728 
## [38] train-rmse:0.451696+0.015931    test-rmse:0.537482+0.064372 
## [39] train-rmse:0.449476+0.016339    test-rmse:0.536512+0.063837 
## [40] train-rmse:0.446886+0.016332    test-rmse:0.534584+0.063064 
## [41] train-rmse:0.444601+0.016395    test-rmse:0.532669+0.062257 
## [42] train-rmse:0.442355+0.015977    test-rmse:0.531195+0.062420 
## [43] train-rmse:0.440734+0.015996    test-rmse:0.530571+0.061847 
## [44] train-rmse:0.438927+0.016105    test-rmse:0.529070+0.062933 
## [45] train-rmse:0.437294+0.015916    test-rmse:0.528491+0.062161 
## [46] train-rmse:0.435461+0.015685    test-rmse:0.528057+0.062632 
## [47] train-rmse:0.433695+0.015845    test-rmse:0.528260+0.062406 
## [48] train-rmse:0.431665+0.016263    test-rmse:0.527930+0.061725 
## [49] train-rmse:0.429801+0.016023    test-rmse:0.527546+0.061205 
## [50] train-rmse:0.428291+0.015832    test-rmse:0.527244+0.061116 
## [51] train-rmse:0.426008+0.015204    test-rmse:0.525112+0.060309 
## [52] train-rmse:0.424564+0.015069    test-rmse:0.523922+0.059678 
## [53] train-rmse:0.422891+0.015242    test-rmse:0.522346+0.059953 
## [54] train-rmse:0.421495+0.015205    test-rmse:0.521968+0.059795 
## [55] train-rmse:0.419698+0.014896    test-rmse:0.521061+0.059927 
## [56] train-rmse:0.418568+0.015041    test-rmse:0.520778+0.060759 
## [57] train-rmse:0.416934+0.014842    test-rmse:0.520261+0.060456 
## [58] train-rmse:0.415518+0.014808    test-rmse:0.519927+0.060504 
## [59] train-rmse:0.414224+0.014957    test-rmse:0.519606+0.060794 
## [60] train-rmse:0.412854+0.015369    test-rmse:0.518403+0.060890 
## [61] train-rmse:0.411606+0.015301    test-rmse:0.518983+0.061436 
## [62] train-rmse:0.410462+0.015409    test-rmse:0.519661+0.061298 
## [63] train-rmse:0.408890+0.015065    test-rmse:0.519180+0.061282 
## [64] train-rmse:0.407388+0.015008    test-rmse:0.519033+0.061208 
## [65] train-rmse:0.406433+0.014956    test-rmse:0.518303+0.060275 
## [66] train-rmse:0.405039+0.014961    test-rmse:0.517580+0.060412 
## [67] train-rmse:0.403930+0.015160    test-rmse:0.517440+0.060408 
## [68] train-rmse:0.402740+0.015381    test-rmse:0.516695+0.060287 
## [69] train-rmse:0.401457+0.015264    test-rmse:0.515676+0.060425 
## [70] train-rmse:0.400431+0.015404    test-rmse:0.515311+0.060545 
## [71] train-rmse:0.399426+0.015371    test-rmse:0.515647+0.060082 
## [72] train-rmse:0.398554+0.015266    test-rmse:0.515573+0.059979 
## [73] train-rmse:0.397570+0.015086    test-rmse:0.515482+0.059554 
## [74] train-rmse:0.396614+0.015189    test-rmse:0.515596+0.059342 
## [75] train-rmse:0.395501+0.015196    test-rmse:0.515545+0.059415 
## [76] train-rmse:0.394465+0.015159    test-rmse:0.515214+0.059731 
## [77] train-rmse:0.393152+0.014756    test-rmse:0.514635+0.060358 
## [78] train-rmse:0.392213+0.014611    test-rmse:0.514196+0.060056 
## [79] train-rmse:0.391145+0.014739    test-rmse:0.513832+0.059583 
## [80] train-rmse:0.390092+0.014722    test-rmse:0.513992+0.059596 
## [81] train-rmse:0.388680+0.014737    test-rmse:0.513867+0.059376 
## [82] train-rmse:0.387871+0.014740    test-rmse:0.513884+0.059111 
## [83] train-rmse:0.387098+0.014885    test-rmse:0.513694+0.058389 
## [84] train-rmse:0.386368+0.014744    test-rmse:0.513682+0.058212 
## [85] train-rmse:0.384893+0.014449    test-rmse:0.512772+0.058527 
## [86] train-rmse:0.383978+0.014198    test-rmse:0.512285+0.059380 
## [87] train-rmse:0.383026+0.014333    test-rmse:0.511777+0.059582 
## [88] train-rmse:0.382108+0.014134    test-rmse:0.511139+0.059970 
## [89] train-rmse:0.381413+0.014144    test-rmse:0.510886+0.060180 
## [90] train-rmse:0.380666+0.014190    test-rmse:0.510083+0.059997 
## [91] train-rmse:0.379914+0.014020    test-rmse:0.509858+0.059841 
## [92] train-rmse:0.378793+0.013857    test-rmse:0.510264+0.060016 
## [93] train-rmse:0.377678+0.013627    test-rmse:0.510432+0.059844 
## [94] train-rmse:0.376847+0.013474    test-rmse:0.510506+0.059652 
## [95] train-rmse:0.375758+0.013639    test-rmse:0.509973+0.059407 
## [96] train-rmse:0.374459+0.012830    test-rmse:0.509703+0.060116 
## [97] train-rmse:0.373607+0.012691    test-rmse:0.508892+0.060013 
## [98] train-rmse:0.372970+0.012734    test-rmse:0.508873+0.059763 
## [99] train-rmse:0.372214+0.012520    test-rmse:0.508892+0.059636 
## [100]    train-rmse:0.371584+0.012487    test-rmse:0.508267+0.060206 
## [101]    train-rmse:0.370606+0.012465    test-rmse:0.507873+0.059984 
## [102]    train-rmse:0.368905+0.012861    test-rmse:0.506831+0.060205 
## [103]    train-rmse:0.368296+0.012781    test-rmse:0.506635+0.059934 
## [104]    train-rmse:0.367443+0.012953    test-rmse:0.506198+0.059889 
## [105]    train-rmse:0.366688+0.013031    test-rmse:0.505495+0.059506 
## [106]    train-rmse:0.366041+0.012951    test-rmse:0.504956+0.059670 
## [107]    train-rmse:0.365548+0.012925    test-rmse:0.505414+0.059380 
## [108]    train-rmse:0.364991+0.013065    test-rmse:0.505984+0.059104 
## [109]    train-rmse:0.364320+0.013071    test-rmse:0.505626+0.058836 
## [110]    train-rmse:0.363325+0.013341    test-rmse:0.505258+0.058897 
## [111]    train-rmse:0.362167+0.012752    test-rmse:0.505301+0.058188 
## [112]    train-rmse:0.361395+0.012692    test-rmse:0.504676+0.058174 
## [113]    train-rmse:0.360879+0.012825    test-rmse:0.504699+0.057931 
## [114]    train-rmse:0.359996+0.012938    test-rmse:0.504574+0.057804 
## [115]    train-rmse:0.359275+0.012791    test-rmse:0.504656+0.057685 
## [116]    train-rmse:0.358524+0.012464    test-rmse:0.504971+0.057988 
## [117]    train-rmse:0.357901+0.012539    test-rmse:0.504384+0.057877 
## [118]    train-rmse:0.357351+0.012512    test-rmse:0.504315+0.058025 
## [119]    train-rmse:0.356414+0.012666    test-rmse:0.504168+0.057731 
## [120]    train-rmse:0.355827+0.012592    test-rmse:0.504089+0.057884 
## [121]    train-rmse:0.354741+0.012479    test-rmse:0.504145+0.058010 
## [122]    train-rmse:0.353665+0.012777    test-rmse:0.504040+0.057835 
## [123]    train-rmse:0.352962+0.012507    test-rmse:0.503641+0.057886 
## [124]    train-rmse:0.351707+0.012054    test-rmse:0.503617+0.057314 
## [125]    train-rmse:0.350818+0.011687    test-rmse:0.503204+0.057574 
## [126]    train-rmse:0.350157+0.011508    test-rmse:0.503083+0.057549 
## [127]    train-rmse:0.349609+0.011380    test-rmse:0.503120+0.057533 
## [128]    train-rmse:0.348801+0.011253    test-rmse:0.503161+0.057481 
## [129]    train-rmse:0.347451+0.010428    test-rmse:0.502768+0.057831 
## [130]    train-rmse:0.346685+0.010044    test-rmse:0.502610+0.058197 
## [131]    train-rmse:0.346111+0.010168    test-rmse:0.502412+0.057908 
## [132]    train-rmse:0.345375+0.010396    test-rmse:0.502131+0.057670 
## [133]    train-rmse:0.344409+0.010971    test-rmse:0.501650+0.057049 
## [134]    train-rmse:0.343756+0.011084    test-rmse:0.501922+0.056872 
## [135]    train-rmse:0.342931+0.010877    test-rmse:0.501516+0.057075 
## [136]    train-rmse:0.342526+0.010923    test-rmse:0.501601+0.056588 
## [137]    train-rmse:0.342069+0.010856    test-rmse:0.501411+0.056965 
## [138]    train-rmse:0.341097+0.010762    test-rmse:0.500685+0.056514 
## [139]    train-rmse:0.340092+0.010824    test-rmse:0.500755+0.056273 
## [140]    train-rmse:0.339160+0.010886    test-rmse:0.500573+0.056926 
## [141]    train-rmse:0.338025+0.010994    test-rmse:0.500094+0.056610 
## [142]    train-rmse:0.337599+0.011025    test-rmse:0.499932+0.057030 
## [143]    train-rmse:0.336750+0.011263    test-rmse:0.500319+0.057539 
## [144]    train-rmse:0.335697+0.011172    test-rmse:0.500188+0.057197 
## [145]    train-rmse:0.335139+0.011194    test-rmse:0.500418+0.057089 
## [146]    train-rmse:0.334365+0.011540    test-rmse:0.500494+0.057264 
## [147]    train-rmse:0.333862+0.011322    test-rmse:0.500676+0.057126 
## [148]    train-rmse:0.333237+0.011124    test-rmse:0.500683+0.056798 
## Stopping. Best iteration:
## [142]    train-rmse:0.337599+0.011025    test-rmse:0.499932+0.057030
## 
## 30 
## [1]  train-rmse:0.888710+0.015381    test-rmse:0.893968+0.062253 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.798250+0.014789    test-rmse:0.809907+0.062934 
## [3]  train-rmse:0.728347+0.014288    test-rmse:0.747639+0.064820 
## [4]  train-rmse:0.672773+0.013236    test-rmse:0.698362+0.063568 
## [5]  train-rmse:0.630337+0.013064    test-rmse:0.662809+0.064136 
## [6]  train-rmse:0.595324+0.012353    test-rmse:0.634988+0.062794 
## [7]  train-rmse:0.567550+0.011497    test-rmse:0.612739+0.061670 
## [8]  train-rmse:0.546294+0.011099    test-rmse:0.598691+0.061186 
## [9]  train-rmse:0.529239+0.011225    test-rmse:0.586631+0.059607 
## [10] train-rmse:0.513501+0.011252    test-rmse:0.577469+0.055761 
## [11] train-rmse:0.501290+0.011467    test-rmse:0.568180+0.055858 
## [12] train-rmse:0.491469+0.011610    test-rmse:0.563162+0.053932 
## [13] train-rmse:0.481009+0.010862    test-rmse:0.554023+0.054710 
## [14] train-rmse:0.473708+0.010991    test-rmse:0.550371+0.053530 
## [15] train-rmse:0.466824+0.010616    test-rmse:0.548966+0.055239 
## [16] train-rmse:0.461629+0.010783    test-rmse:0.546806+0.057453 
## [17] train-rmse:0.456290+0.010628    test-rmse:0.542411+0.056773 
## [18] train-rmse:0.450526+0.010546    test-rmse:0.539047+0.055865 
## [19] train-rmse:0.444040+0.012284    test-rmse:0.537186+0.056297 
## [20] train-rmse:0.439487+0.011730    test-rmse:0.535116+0.057162 
## [21] train-rmse:0.435666+0.011554    test-rmse:0.533090+0.056928 
## [22] train-rmse:0.431690+0.011164    test-rmse:0.529866+0.057963 
## [23] train-rmse:0.427973+0.011113    test-rmse:0.527880+0.056994 
## [24] train-rmse:0.423891+0.010205    test-rmse:0.527556+0.055826 
## [25] train-rmse:0.420044+0.010401    test-rmse:0.524696+0.055519 
## [26] train-rmse:0.416319+0.010003    test-rmse:0.522244+0.056560 
## [27] train-rmse:0.412283+0.010082    test-rmse:0.520569+0.055762 
## [28] train-rmse:0.408297+0.009537    test-rmse:0.518880+0.056074 
## [29] train-rmse:0.405043+0.009293    test-rmse:0.517608+0.057306 
## [30] train-rmse:0.402156+0.008743    test-rmse:0.516081+0.058704 
## [31] train-rmse:0.398957+0.009083    test-rmse:0.515116+0.058068 
## [32] train-rmse:0.395903+0.009773    test-rmse:0.513879+0.058073 
## [33] train-rmse:0.393467+0.009815    test-rmse:0.513404+0.057465 
## [34] train-rmse:0.389409+0.011546    test-rmse:0.513129+0.057537 
## [35] train-rmse:0.386789+0.012008    test-rmse:0.512450+0.058315 
## [36] train-rmse:0.384023+0.012138    test-rmse:0.512026+0.058352 
## [37] train-rmse:0.381326+0.011818    test-rmse:0.510314+0.058676 
## [38] train-rmse:0.378997+0.011745    test-rmse:0.509159+0.058907 
## [39] train-rmse:0.376641+0.011963    test-rmse:0.508520+0.059689 
## [40] train-rmse:0.373913+0.010835    test-rmse:0.509166+0.059443 
## [41] train-rmse:0.371280+0.011168    test-rmse:0.508197+0.058486 
## [42] train-rmse:0.368963+0.012002    test-rmse:0.507322+0.059047 
## [43] train-rmse:0.366909+0.011905    test-rmse:0.506197+0.058496 
## [44] train-rmse:0.364278+0.012610    test-rmse:0.505647+0.056998 
## [45] train-rmse:0.362154+0.012977    test-rmse:0.505722+0.058480 
## [46] train-rmse:0.360188+0.012891    test-rmse:0.505647+0.059364 
## [47] train-rmse:0.357919+0.012442    test-rmse:0.506121+0.059236 
## [48] train-rmse:0.355694+0.012863    test-rmse:0.505058+0.058914 
## [49] train-rmse:0.353936+0.012437    test-rmse:0.504560+0.057891 
## [50] train-rmse:0.351855+0.012398    test-rmse:0.504813+0.058283 
## [51] train-rmse:0.350125+0.012369    test-rmse:0.503554+0.058279 
## [52] train-rmse:0.347717+0.012028    test-rmse:0.502999+0.059031 
## [53] train-rmse:0.345523+0.011547    test-rmse:0.503320+0.058560 
## [54] train-rmse:0.342435+0.010676    test-rmse:0.502618+0.058835 
## [55] train-rmse:0.340390+0.011120    test-rmse:0.501876+0.058953 
## [56] train-rmse:0.338689+0.010690    test-rmse:0.500251+0.060074 
## [57] train-rmse:0.337307+0.010740    test-rmse:0.500412+0.060112 
## [58] train-rmse:0.335850+0.010662    test-rmse:0.500291+0.060368 
## [59] train-rmse:0.334590+0.010882    test-rmse:0.500245+0.060131 
## [60] train-rmse:0.333013+0.010472    test-rmse:0.500575+0.059451 
## [61] train-rmse:0.331031+0.010097    test-rmse:0.500969+0.059395 
## [62] train-rmse:0.329343+0.010122    test-rmse:0.501792+0.058473 
## [63] train-rmse:0.327690+0.010429    test-rmse:0.501453+0.057861 
## [64] train-rmse:0.326575+0.010427    test-rmse:0.500610+0.057641 
## [65] train-rmse:0.325042+0.010118    test-rmse:0.500615+0.057159 
## Stopping. Best iteration:
## [59] train-rmse:0.334590+0.010882    test-rmse:0.500245+0.060131
## 
## 31 
## [1]  train-rmse:0.883290+0.015192    test-rmse:0.890706+0.061123 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.787709+0.014195    test-rmse:0.803573+0.061011 
## [3]  train-rmse:0.712700+0.013792    test-rmse:0.736328+0.062959 
## [4]  train-rmse:0.651887+0.012731    test-rmse:0.683397+0.062988 
## [5]  train-rmse:0.604713+0.011574    test-rmse:0.647193+0.062883 
## [6]  train-rmse:0.566881+0.012279    test-rmse:0.620195+0.058593 
## [7]  train-rmse:0.534337+0.013780    test-rmse:0.596740+0.057875 
## [8]  train-rmse:0.509707+0.013846    test-rmse:0.580237+0.056280 
## [9]  train-rmse:0.489259+0.013984    test-rmse:0.569216+0.056800 
## [10] train-rmse:0.471471+0.013895    test-rmse:0.558690+0.056129 
## [11] train-rmse:0.458587+0.012972    test-rmse:0.553544+0.054012 
## [12] train-rmse:0.446094+0.012049    test-rmse:0.544664+0.052639 
## [13] train-rmse:0.436819+0.012343    test-rmse:0.542256+0.052461 
## [14] train-rmse:0.428264+0.012361    test-rmse:0.534875+0.050967 
## [15] train-rmse:0.420120+0.012316    test-rmse:0.530554+0.050017 
## [16] train-rmse:0.410052+0.012226    test-rmse:0.525932+0.051790 
## [17] train-rmse:0.404099+0.012933    test-rmse:0.522031+0.049901 
## [18] train-rmse:0.398316+0.012286    test-rmse:0.520897+0.050334 
## [19] train-rmse:0.393205+0.011401    test-rmse:0.518255+0.051113 
## [20] train-rmse:0.385888+0.011458    test-rmse:0.515296+0.051584 
## [21] train-rmse:0.380437+0.012268    test-rmse:0.513028+0.051771 
## [22] train-rmse:0.376695+0.011948    test-rmse:0.511258+0.053263 
## [23] train-rmse:0.372251+0.012161    test-rmse:0.510170+0.052152 
## [24] train-rmse:0.367897+0.012903    test-rmse:0.509083+0.052124 
## [25] train-rmse:0.363706+0.012497    test-rmse:0.508187+0.052909 
## [26] train-rmse:0.360041+0.011887    test-rmse:0.507712+0.051723 
## [27] train-rmse:0.355762+0.013009    test-rmse:0.506713+0.052011 
## [28] train-rmse:0.351918+0.012458    test-rmse:0.507390+0.052245 
## [29] train-rmse:0.346915+0.011283    test-rmse:0.505745+0.052981 
## [30] train-rmse:0.342976+0.012093    test-rmse:0.504251+0.052885 
## [31] train-rmse:0.339546+0.012167    test-rmse:0.502052+0.053804 
## [32] train-rmse:0.336340+0.011898    test-rmse:0.501307+0.052543 
## [33] train-rmse:0.331975+0.012092    test-rmse:0.501607+0.051951 
## [34] train-rmse:0.329263+0.011536    test-rmse:0.501128+0.052588 
## [35] train-rmse:0.326167+0.010650    test-rmse:0.500287+0.052053 
## [36] train-rmse:0.322252+0.010944    test-rmse:0.499361+0.050893 
## [37] train-rmse:0.318745+0.010053    test-rmse:0.498293+0.052285 
## [38] train-rmse:0.315655+0.010056    test-rmse:0.498093+0.052328 
## [39] train-rmse:0.312668+0.009984    test-rmse:0.497221+0.052119 
## [40] train-rmse:0.308416+0.010793    test-rmse:0.496587+0.052566 
## [41] train-rmse:0.304886+0.011459    test-rmse:0.495675+0.053907 
## [42] train-rmse:0.302366+0.011980    test-rmse:0.495404+0.054053 
## [43] train-rmse:0.300331+0.011989    test-rmse:0.494823+0.055068 
## [44] train-rmse:0.298263+0.011677    test-rmse:0.494620+0.054805 
## [45] train-rmse:0.295718+0.011697    test-rmse:0.493694+0.053849 
## [46] train-rmse:0.293299+0.010798    test-rmse:0.493732+0.054078 
## [47] train-rmse:0.290809+0.011184    test-rmse:0.493874+0.053928 
## [48] train-rmse:0.288352+0.011073    test-rmse:0.493689+0.054997 
## [49] train-rmse:0.286190+0.010460    test-rmse:0.493231+0.055184 
## [50] train-rmse:0.283743+0.009407    test-rmse:0.493224+0.055705 
## [51] train-rmse:0.280109+0.010187    test-rmse:0.493706+0.054965 
## [52] train-rmse:0.277677+0.010193    test-rmse:0.494525+0.055288 
## [53] train-rmse:0.274775+0.010910    test-rmse:0.494415+0.055771 
## [54] train-rmse:0.272246+0.011443    test-rmse:0.494300+0.056220 
## [55] train-rmse:0.270208+0.010779    test-rmse:0.495027+0.057077 
## [56] train-rmse:0.268156+0.010743    test-rmse:0.494312+0.057481 
## Stopping. Best iteration:
## [50] train-rmse:0.283743+0.009407    test-rmse:0.493224+0.055705
## 
## 32 
## [1]  train-rmse:0.878473+0.014743    test-rmse:0.887890+0.061805 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.777812+0.013442    test-rmse:0.799791+0.060388 
## [3]  train-rmse:0.696852+0.012630    test-rmse:0.731348+0.061895 
## [4]  train-rmse:0.633893+0.012234    test-rmse:0.678858+0.063578 
## [5]  train-rmse:0.582224+0.010548    test-rmse:0.640818+0.065365 
## [6]  train-rmse:0.539959+0.011472    test-rmse:0.607231+0.063899 
## [7]  train-rmse:0.506062+0.012089    test-rmse:0.583805+0.061151 
## [8]  train-rmse:0.477241+0.013816    test-rmse:0.568143+0.059129 
## [9]  train-rmse:0.455574+0.013743    test-rmse:0.557393+0.058767 
## [10] train-rmse:0.434991+0.015310    test-rmse:0.546045+0.057941 
## [11] train-rmse:0.419189+0.014891    test-rmse:0.537181+0.057767 
## [12] train-rmse:0.405000+0.017601    test-rmse:0.533699+0.055208 
## [13] train-rmse:0.393641+0.016104    test-rmse:0.527868+0.057627 
## [14] train-rmse:0.384323+0.017874    test-rmse:0.523815+0.058563 
## [15] train-rmse:0.376179+0.018189    test-rmse:0.520674+0.056749 
## [16] train-rmse:0.367734+0.016690    test-rmse:0.517507+0.058238 
## [17] train-rmse:0.359608+0.018581    test-rmse:0.514638+0.058066 
## [18] train-rmse:0.352878+0.018470    test-rmse:0.513238+0.060417 
## [19] train-rmse:0.346983+0.018046    test-rmse:0.511757+0.059614 
## [20] train-rmse:0.339702+0.017800    test-rmse:0.508615+0.059797 
## [21] train-rmse:0.334143+0.016978    test-rmse:0.508141+0.059367 
## [22] train-rmse:0.327141+0.018894    test-rmse:0.506841+0.059965 
## [23] train-rmse:0.321174+0.017217    test-rmse:0.505815+0.061624 
## [24] train-rmse:0.317159+0.017123    test-rmse:0.504226+0.062365 
## [25] train-rmse:0.313507+0.016760    test-rmse:0.502561+0.061518 
## [26] train-rmse:0.309091+0.016298    test-rmse:0.501478+0.061633 
## [27] train-rmse:0.303942+0.017101    test-rmse:0.500579+0.061260 
## [28] train-rmse:0.301048+0.016802    test-rmse:0.500467+0.060342 
## [29] train-rmse:0.296904+0.017105    test-rmse:0.500363+0.061515 
## [30] train-rmse:0.293307+0.016750    test-rmse:0.500240+0.061850 
## [31] train-rmse:0.288752+0.017220    test-rmse:0.498937+0.061582 
## [32] train-rmse:0.283268+0.017640    test-rmse:0.498855+0.061551 
## [33] train-rmse:0.279439+0.016294    test-rmse:0.498137+0.061056 
## [34] train-rmse:0.275741+0.015902    test-rmse:0.498093+0.059891 
## [35] train-rmse:0.271303+0.015310    test-rmse:0.498554+0.059887 
## [36] train-rmse:0.267148+0.014653    test-rmse:0.497988+0.059491 
## [37] train-rmse:0.264559+0.014536    test-rmse:0.498686+0.059318 
## [38] train-rmse:0.259432+0.015354    test-rmse:0.498307+0.059161 
## [39] train-rmse:0.256102+0.014147    test-rmse:0.498391+0.059825 
## [40] train-rmse:0.252471+0.015137    test-rmse:0.499493+0.059799 
## [41] train-rmse:0.249537+0.013933    test-rmse:0.499629+0.059255 
## [42] train-rmse:0.246881+0.013438    test-rmse:0.499426+0.059072 
## Stopping. Best iteration:
## [36] train-rmse:0.267148+0.014653    test-rmse:0.497988+0.059491
## 
## 33 
## [1]  train-rmse:0.874077+0.014300    test-rmse:0.886014+0.062025 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.769062+0.012942    test-rmse:0.792795+0.062427 
## [3]  train-rmse:0.683592+0.010650    test-rmse:0.722704+0.061715 
## [4]  train-rmse:0.614723+0.011764    test-rmse:0.670350+0.060840 
## [5]  train-rmse:0.560315+0.012216    test-rmse:0.631259+0.062821 
## [6]  train-rmse:0.514443+0.011787    test-rmse:0.599874+0.065258 
## [7]  train-rmse:0.477494+0.012546    test-rmse:0.575076+0.062776 
## [8]  train-rmse:0.448105+0.012789    test-rmse:0.560133+0.060264 
## [9]  train-rmse:0.421044+0.013696    test-rmse:0.545026+0.058293 
## [10] train-rmse:0.399902+0.013674    test-rmse:0.533869+0.058791 
## [11] train-rmse:0.381265+0.012237    test-rmse:0.526759+0.059755 
## [12] train-rmse:0.366464+0.012657    test-rmse:0.518870+0.058121 
## [13] train-rmse:0.353791+0.014386    test-rmse:0.513120+0.056960 
## [14] train-rmse:0.343872+0.013581    test-rmse:0.508979+0.056725 
## [15] train-rmse:0.334221+0.013455    test-rmse:0.508052+0.056993 
## [16] train-rmse:0.323029+0.015681    test-rmse:0.505327+0.056885 
## [17] train-rmse:0.317402+0.015282    test-rmse:0.504686+0.055926 
## [18] train-rmse:0.306353+0.017592    test-rmse:0.501992+0.055638 
## [19] train-rmse:0.299131+0.017064    test-rmse:0.499911+0.055456 
## [20] train-rmse:0.293487+0.016356    test-rmse:0.497720+0.054811 
## [21] train-rmse:0.288066+0.016396    test-rmse:0.498481+0.054928 
## [22] train-rmse:0.282955+0.016398    test-rmse:0.496515+0.054288 
## [23] train-rmse:0.275718+0.013491    test-rmse:0.494140+0.054629 
## [24] train-rmse:0.269224+0.014535    test-rmse:0.493632+0.054536 
## [25] train-rmse:0.264456+0.014926    test-rmse:0.493479+0.054113 
## [26] train-rmse:0.260386+0.014459    test-rmse:0.493409+0.054329 
## [27] train-rmse:0.254531+0.017356    test-rmse:0.492871+0.053547 
## [28] train-rmse:0.250974+0.017326    test-rmse:0.493098+0.051904 
## [29] train-rmse:0.246800+0.018150    test-rmse:0.493126+0.051688 
## [30] train-rmse:0.242653+0.017347    test-rmse:0.493072+0.052395 
## [31] train-rmse:0.239055+0.017767    test-rmse:0.493625+0.052337 
## [32] train-rmse:0.235365+0.017467    test-rmse:0.493881+0.053273 
## [33] train-rmse:0.231802+0.017561    test-rmse:0.493566+0.052674 
## Stopping. Best iteration:
## [27] train-rmse:0.254531+0.017356    test-rmse:0.492871+0.053547
## 
## 34 
## [1]  train-rmse:0.869768+0.013907    test-rmse:0.883145+0.060577 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.761501+0.011523    test-rmse:0.790081+0.061972 
## [3]  train-rmse:0.672586+0.009428    test-rmse:0.718189+0.060876 
## [4]  train-rmse:0.601437+0.008563    test-rmse:0.665731+0.063822 
## [5]  train-rmse:0.544294+0.008656    test-rmse:0.625102+0.066194 
## [6]  train-rmse:0.496372+0.010217    test-rmse:0.595570+0.066728 
## [7]  train-rmse:0.456368+0.008774    test-rmse:0.569627+0.065349 
## [8]  train-rmse:0.422527+0.009065    test-rmse:0.552921+0.064745 
## [9]  train-rmse:0.394262+0.008984    test-rmse:0.540747+0.066435 
## [10] train-rmse:0.368189+0.010718    test-rmse:0.531509+0.065815 
## [11] train-rmse:0.348018+0.011764    test-rmse:0.525916+0.062165 
## [12] train-rmse:0.330687+0.011341    test-rmse:0.521283+0.062584 
## [13] train-rmse:0.315584+0.010719    test-rmse:0.519683+0.061414 
## [14] train-rmse:0.303380+0.011123    test-rmse:0.517146+0.062421 
## [15] train-rmse:0.292899+0.012610    test-rmse:0.514377+0.061211 
## [16] train-rmse:0.283774+0.013807    test-rmse:0.513198+0.061835 
## [17] train-rmse:0.276897+0.014121    test-rmse:0.510695+0.060043 
## [18] train-rmse:0.269621+0.014020    test-rmse:0.510901+0.059064 
## [19] train-rmse:0.262920+0.014685    test-rmse:0.508739+0.058098 
## [20] train-rmse:0.257506+0.014618    test-rmse:0.507017+0.057597 
## [21] train-rmse:0.248033+0.014609    test-rmse:0.501721+0.055930 
## [22] train-rmse:0.243979+0.015096    test-rmse:0.501964+0.055961 
## [23] train-rmse:0.238082+0.014809    test-rmse:0.502416+0.055146 
## [24] train-rmse:0.232557+0.014041    test-rmse:0.501279+0.056597 
## [25] train-rmse:0.225189+0.015507    test-rmse:0.500905+0.056118 
## [26] train-rmse:0.220311+0.015824    test-rmse:0.499761+0.055956 
## [27] train-rmse:0.216595+0.015992    test-rmse:0.499745+0.055784 
## [28] train-rmse:0.213045+0.016770    test-rmse:0.499503+0.055893 
## [29] train-rmse:0.207110+0.015427    test-rmse:0.499073+0.055015 
## [30] train-rmse:0.203817+0.015277    test-rmse:0.498623+0.053416 
## [31] train-rmse:0.199927+0.015985    test-rmse:0.499346+0.054062 
## [32] train-rmse:0.196465+0.015655    test-rmse:0.499486+0.054179 
## [33] train-rmse:0.192466+0.014897    test-rmse:0.500923+0.055308 
## [34] train-rmse:0.187625+0.014251    test-rmse:0.500622+0.055258 
## [35] train-rmse:0.184329+0.013739    test-rmse:0.500345+0.054961 
## [36] train-rmse:0.180794+0.013802    test-rmse:0.500741+0.054055 
## Stopping. Best iteration:
## [30] train-rmse:0.203817+0.015277    test-rmse:0.498623+0.053416
## 
## 35 
## [1]  train-rmse:0.899205+0.015124    test-rmse:0.901148+0.060961 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.823877+0.014266    test-rmse:0.830474+0.060235 
## [3]  train-rmse:0.770133+0.013875    test-rmse:0.788072+0.060306 
## [4]  train-rmse:0.730719+0.013284    test-rmse:0.749328+0.058321 
## [5]  train-rmse:0.701322+0.012489    test-rmse:0.720658+0.059597 
## [6]  train-rmse:0.677774+0.012243    test-rmse:0.697480+0.055812 
## [7]  train-rmse:0.660389+0.011918    test-rmse:0.685547+0.055902 
## [8]  train-rmse:0.646725+0.011788    test-rmse:0.672021+0.053610 
## [9]  train-rmse:0.635133+0.011354    test-rmse:0.661014+0.051990 
## [10] train-rmse:0.626460+0.011330    test-rmse:0.652786+0.052156 
## [11] train-rmse:0.619163+0.011278    test-rmse:0.643958+0.051676 
## [12] train-rmse:0.613067+0.011319    test-rmse:0.643032+0.050057 
## [13] train-rmse:0.607289+0.011282    test-rmse:0.637294+0.052928 
## [14] train-rmse:0.602172+0.011370    test-rmse:0.633270+0.052476 
## [15] train-rmse:0.597553+0.011339    test-rmse:0.630313+0.052403 
## [16] train-rmse:0.593404+0.011345    test-rmse:0.626515+0.052222 
## [17] train-rmse:0.589509+0.011294    test-rmse:0.622333+0.054379 
## [18] train-rmse:0.585698+0.011263    test-rmse:0.617195+0.053789 
## [19] train-rmse:0.582124+0.011189    test-rmse:0.616422+0.054005 
## [20] train-rmse:0.578846+0.011218    test-rmse:0.616368+0.053960 
## [21] train-rmse:0.575796+0.011186    test-rmse:0.613834+0.052307 
## [22] train-rmse:0.572868+0.011154    test-rmse:0.610459+0.054223 
## [23] train-rmse:0.570130+0.011180    test-rmse:0.607991+0.055251 
## [24] train-rmse:0.567395+0.011229    test-rmse:0.605157+0.054850 
## [25] train-rmse:0.564799+0.011269    test-rmse:0.602630+0.054576 
## [26] train-rmse:0.562264+0.011306    test-rmse:0.600751+0.055008 
## [27] train-rmse:0.559756+0.011328    test-rmse:0.598691+0.055436 
## [28] train-rmse:0.557419+0.011376    test-rmse:0.598341+0.055706 
## [29] train-rmse:0.555216+0.011366    test-rmse:0.596035+0.054778 
## [30] train-rmse:0.553055+0.011334    test-rmse:0.593771+0.054224 
## [31] train-rmse:0.550952+0.011310    test-rmse:0.591839+0.054050 
## [32] train-rmse:0.548925+0.011254    test-rmse:0.591123+0.055368 
## [33] train-rmse:0.547010+0.011203    test-rmse:0.589735+0.053762 
## [34] train-rmse:0.545124+0.011151    test-rmse:0.587536+0.053922 
## [35] train-rmse:0.543267+0.011104    test-rmse:0.586460+0.053062 
## [36] train-rmse:0.541463+0.011107    test-rmse:0.584241+0.054344 
## [37] train-rmse:0.539681+0.011110    test-rmse:0.582160+0.054485 
## [38] train-rmse:0.537928+0.011094    test-rmse:0.581542+0.054157 
## [39] train-rmse:0.536248+0.011110    test-rmse:0.580773+0.055529 
## [40] train-rmse:0.534630+0.011109    test-rmse:0.581014+0.054762 
## [41] train-rmse:0.533074+0.011116    test-rmse:0.580988+0.054859 
## [42] train-rmse:0.531565+0.011086    test-rmse:0.579834+0.054400 
## [43] train-rmse:0.530084+0.011086    test-rmse:0.579434+0.054101 
## [44] train-rmse:0.528650+0.011050    test-rmse:0.577605+0.054899 
## [45] train-rmse:0.527259+0.011022    test-rmse:0.576546+0.053922 
## [46] train-rmse:0.525907+0.010989    test-rmse:0.575337+0.054160 
## [47] train-rmse:0.524573+0.010971    test-rmse:0.574325+0.054858 
## [48] train-rmse:0.523289+0.010930    test-rmse:0.572122+0.054645 
## [49] train-rmse:0.522031+0.010899    test-rmse:0.571426+0.054031 
## [50] train-rmse:0.520805+0.010851    test-rmse:0.570560+0.054701 
## [51] train-rmse:0.519613+0.010837    test-rmse:0.569383+0.054669 
## [52] train-rmse:0.518452+0.010811    test-rmse:0.568740+0.054010 
## [53] train-rmse:0.517304+0.010785    test-rmse:0.568518+0.054873 
## [54] train-rmse:0.516162+0.010743    test-rmse:0.567551+0.054087 
## [55] train-rmse:0.515071+0.010732    test-rmse:0.566221+0.054029 
## [56] train-rmse:0.513993+0.010724    test-rmse:0.565770+0.054939 
## [57] train-rmse:0.512936+0.010704    test-rmse:0.565302+0.054461 
## [58] train-rmse:0.511901+0.010684    test-rmse:0.563923+0.054474 
## [59] train-rmse:0.510868+0.010653    test-rmse:0.563866+0.054354 
## [60] train-rmse:0.509842+0.010601    test-rmse:0.563106+0.054245 
## [61] train-rmse:0.508862+0.010584    test-rmse:0.562058+0.053687 
## [62] train-rmse:0.507885+0.010584    test-rmse:0.561368+0.053639 
## [63] train-rmse:0.506944+0.010597    test-rmse:0.560672+0.053235 
## [64] train-rmse:0.506022+0.010618    test-rmse:0.559470+0.053640 
## [65] train-rmse:0.505120+0.010623    test-rmse:0.559741+0.054501 
## [66] train-rmse:0.504261+0.010618    test-rmse:0.558782+0.054134 
## [67] train-rmse:0.503416+0.010605    test-rmse:0.557867+0.053914 
## [68] train-rmse:0.502590+0.010597    test-rmse:0.557184+0.053880 
## [69] train-rmse:0.501773+0.010592    test-rmse:0.556800+0.054278 
## [70] train-rmse:0.500968+0.010577    test-rmse:0.556354+0.053769 
## [71] train-rmse:0.500185+0.010574    test-rmse:0.555380+0.053176 
## [72] train-rmse:0.499415+0.010571    test-rmse:0.554843+0.054031 
## [73] train-rmse:0.498654+0.010559    test-rmse:0.554409+0.054230 
## [74] train-rmse:0.497905+0.010556    test-rmse:0.554178+0.053292 
## [75] train-rmse:0.497169+0.010546    test-rmse:0.553944+0.053487 
## [76] train-rmse:0.496453+0.010540    test-rmse:0.553947+0.052489 
## [77] train-rmse:0.495746+0.010539    test-rmse:0.553734+0.052250 
## [78] train-rmse:0.495050+0.010541    test-rmse:0.553467+0.051495 
## [79] train-rmse:0.494363+0.010527    test-rmse:0.552539+0.052120 
## [80] train-rmse:0.493687+0.010518    test-rmse:0.552440+0.053047 
## [81] train-rmse:0.493023+0.010510    test-rmse:0.552120+0.052663 
## [82] train-rmse:0.492371+0.010503    test-rmse:0.551452+0.052450 
## [83] train-rmse:0.491733+0.010503    test-rmse:0.550612+0.052750 
## [84] train-rmse:0.491101+0.010497    test-rmse:0.550091+0.052695 
## [85] train-rmse:0.490479+0.010493    test-rmse:0.550312+0.052866 
## [86] train-rmse:0.489866+0.010487    test-rmse:0.549774+0.052554 
## [87] train-rmse:0.489265+0.010477    test-rmse:0.548802+0.052829 
## [88] train-rmse:0.488686+0.010475    test-rmse:0.548152+0.052651 
## [89] train-rmse:0.488111+0.010472    test-rmse:0.547235+0.052526 
## [90] train-rmse:0.487543+0.010462    test-rmse:0.547362+0.051781 
## [91] train-rmse:0.486977+0.010447    test-rmse:0.546686+0.051390 
## [92] train-rmse:0.486424+0.010444    test-rmse:0.546743+0.051417 
## [93] train-rmse:0.485882+0.010437    test-rmse:0.546611+0.051904 
## [94] train-rmse:0.485355+0.010424    test-rmse:0.546453+0.052153 
## [95] train-rmse:0.484835+0.010419    test-rmse:0.546273+0.052197 
## [96] train-rmse:0.484322+0.010425    test-rmse:0.546514+0.051747 
## [97] train-rmse:0.483828+0.010422    test-rmse:0.546392+0.051419 
## [98] train-rmse:0.483339+0.010425    test-rmse:0.545962+0.051090 
## [99] train-rmse:0.482857+0.010424    test-rmse:0.545577+0.051212 
## [100]    train-rmse:0.482384+0.010416    test-rmse:0.545166+0.051110 
## [101]    train-rmse:0.481916+0.010414    test-rmse:0.544962+0.051721 
## [102]    train-rmse:0.481451+0.010410    test-rmse:0.544274+0.051230 
## [103]    train-rmse:0.480999+0.010405    test-rmse:0.544719+0.051719 
## [104]    train-rmse:0.480548+0.010394    test-rmse:0.543985+0.051567 
## [105]    train-rmse:0.480104+0.010387    test-rmse:0.543446+0.051394 
## [106]    train-rmse:0.479668+0.010389    test-rmse:0.543341+0.051153 
## [107]    train-rmse:0.479237+0.010388    test-rmse:0.542918+0.051113 
## [108]    train-rmse:0.478813+0.010394    test-rmse:0.543186+0.050743 
## [109]    train-rmse:0.478398+0.010403    test-rmse:0.542988+0.050233 
## [110]    train-rmse:0.477992+0.010401    test-rmse:0.542594+0.050032 
## [111]    train-rmse:0.477585+0.010393    test-rmse:0.542115+0.050277 
## [112]    train-rmse:0.477184+0.010377    test-rmse:0.541932+0.050332 
## [113]    train-rmse:0.476781+0.010362    test-rmse:0.541721+0.050345 
## [114]    train-rmse:0.476397+0.010353    test-rmse:0.541785+0.050499 
## [115]    train-rmse:0.476012+0.010356    test-rmse:0.541299+0.050174 
## [116]    train-rmse:0.475633+0.010350    test-rmse:0.541391+0.049493 
## [117]    train-rmse:0.475262+0.010345    test-rmse:0.541322+0.049344 
## [118]    train-rmse:0.474894+0.010340    test-rmse:0.541252+0.049948 
## [119]    train-rmse:0.474536+0.010345    test-rmse:0.540969+0.049746 
## [120]    train-rmse:0.474181+0.010345    test-rmse:0.540598+0.049807 
## [121]    train-rmse:0.473834+0.010343    test-rmse:0.540871+0.050376 
## [122]    train-rmse:0.473487+0.010344    test-rmse:0.540652+0.050489 
## [123]    train-rmse:0.473139+0.010345    test-rmse:0.540523+0.050365 
## [124]    train-rmse:0.472799+0.010339    test-rmse:0.540178+0.050442 
## [125]    train-rmse:0.472474+0.010333    test-rmse:0.539899+0.050424 
## [126]    train-rmse:0.472150+0.010326    test-rmse:0.539252+0.050378 
## [127]    train-rmse:0.471833+0.010320    test-rmse:0.538923+0.050202 
## [128]    train-rmse:0.471518+0.010314    test-rmse:0.538975+0.050079 
## [129]    train-rmse:0.471203+0.010309    test-rmse:0.538406+0.050261 
## [130]    train-rmse:0.470894+0.010302    test-rmse:0.538319+0.049968 
## [131]    train-rmse:0.470583+0.010298    test-rmse:0.538305+0.049916 
## [132]    train-rmse:0.470279+0.010298    test-rmse:0.538323+0.049888 
## [133]    train-rmse:0.469981+0.010298    test-rmse:0.538670+0.049822 
## [134]    train-rmse:0.469691+0.010291    test-rmse:0.538102+0.050019 
## [135]    train-rmse:0.469394+0.010272    test-rmse:0.537632+0.049863 
## [136]    train-rmse:0.469109+0.010264    test-rmse:0.537297+0.049122 
## [137]    train-rmse:0.468827+0.010254    test-rmse:0.536940+0.048800 
## [138]    train-rmse:0.468549+0.010244    test-rmse:0.536990+0.048535 
## [139]    train-rmse:0.468275+0.010238    test-rmse:0.536910+0.048752 
## [140]    train-rmse:0.468000+0.010230    test-rmse:0.536820+0.048631 
## [141]    train-rmse:0.467730+0.010227    test-rmse:0.536267+0.048616 
## [142]    train-rmse:0.467454+0.010218    test-rmse:0.536217+0.048299 
## [143]    train-rmse:0.467188+0.010209    test-rmse:0.536645+0.048063 
## [144]    train-rmse:0.466930+0.010202    test-rmse:0.536344+0.048216 
## [145]    train-rmse:0.466683+0.010197    test-rmse:0.536731+0.048364 
## [146]    train-rmse:0.466437+0.010192    test-rmse:0.536332+0.048301 
## [147]    train-rmse:0.466190+0.010183    test-rmse:0.536285+0.047814 
## [148]    train-rmse:0.465953+0.010174    test-rmse:0.536099+0.047451 
## [149]    train-rmse:0.465717+0.010164    test-rmse:0.535708+0.047378 
## [150]    train-rmse:0.465478+0.010157    test-rmse:0.535469+0.047297 
## [151]    train-rmse:0.465241+0.010147    test-rmse:0.535693+0.047122 
## [152]    train-rmse:0.465011+0.010142    test-rmse:0.535842+0.047105 
## [153]    train-rmse:0.464780+0.010131    test-rmse:0.535983+0.047102 
## [154]    train-rmse:0.464548+0.010118    test-rmse:0.535910+0.047056 
## [155]    train-rmse:0.464313+0.010099    test-rmse:0.536119+0.047094 
## [156]    train-rmse:0.464084+0.010089    test-rmse:0.536052+0.047072 
## Stopping. Best iteration:
## [150]    train-rmse:0.465478+0.010157    test-rmse:0.535469+0.047297
## 
## 36 
## [1]  train-rmse:0.883394+0.015003    test-rmse:0.886568+0.060912 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.793891+0.014278    test-rmse:0.806241+0.062660 
## [3]  train-rmse:0.728390+0.013610    test-rmse:0.746023+0.062418 
## [4]  train-rmse:0.679701+0.013054    test-rmse:0.704097+0.065371 
## [5]  train-rmse:0.643096+0.011712    test-rmse:0.671287+0.065154 
## [6]  train-rmse:0.615213+0.011700    test-rmse:0.645675+0.063562 
## [7]  train-rmse:0.592756+0.012331    test-rmse:0.629606+0.063699 
## [8]  train-rmse:0.575698+0.011941    test-rmse:0.614792+0.064046 
## [9]  train-rmse:0.562786+0.011777    test-rmse:0.604449+0.062900 
## [10] train-rmse:0.550832+0.013293    test-rmse:0.594957+0.061163 
## [11] train-rmse:0.542785+0.013489    test-rmse:0.592471+0.059648 
## [12] train-rmse:0.536038+0.013176    test-rmse:0.587326+0.059949 
## [13] train-rmse:0.529509+0.013585    test-rmse:0.583747+0.058953 
## [14] train-rmse:0.523559+0.013545    test-rmse:0.580406+0.056482 
## [15] train-rmse:0.518206+0.013398    test-rmse:0.577180+0.055830 
## [16] train-rmse:0.513098+0.013460    test-rmse:0.572541+0.056107 
## [17] train-rmse:0.508550+0.013760    test-rmse:0.569502+0.057020 
## [18] train-rmse:0.502586+0.013585    test-rmse:0.566809+0.059724 
## [19] train-rmse:0.498341+0.013283    test-rmse:0.562935+0.059055 
## [20] train-rmse:0.494379+0.013231    test-rmse:0.560553+0.060841 
## [21] train-rmse:0.490092+0.013348    test-rmse:0.558329+0.061808 
## [22] train-rmse:0.486960+0.013288    test-rmse:0.556305+0.062055 
## [23] train-rmse:0.483631+0.013237    test-rmse:0.555578+0.061675 
## [24] train-rmse:0.479953+0.013853    test-rmse:0.552812+0.061620 
## [25] train-rmse:0.476864+0.014087    test-rmse:0.549850+0.062849 
## [26] train-rmse:0.474348+0.014188    test-rmse:0.548329+0.061793 
## [27] train-rmse:0.470812+0.014195    test-rmse:0.546489+0.060986 
## [28] train-rmse:0.467010+0.013515    test-rmse:0.542003+0.061480 
## [29] train-rmse:0.463936+0.013592    test-rmse:0.539832+0.062782 
## [30] train-rmse:0.461242+0.013464    test-rmse:0.537883+0.063050 
## [31] train-rmse:0.458676+0.013555    test-rmse:0.537679+0.062546 
## [32] train-rmse:0.456031+0.012957    test-rmse:0.536984+0.061280 
## [33] train-rmse:0.453700+0.013358    test-rmse:0.536373+0.061432 
## [34] train-rmse:0.451747+0.013413    test-rmse:0.534929+0.060460 
## [35] train-rmse:0.449208+0.013833    test-rmse:0.534035+0.060833 
## [36] train-rmse:0.446865+0.013800    test-rmse:0.531423+0.061057 
## [37] train-rmse:0.444592+0.014107    test-rmse:0.530328+0.060738 
## [38] train-rmse:0.442414+0.013823    test-rmse:0.528753+0.061283 
## [39] train-rmse:0.440318+0.014012    test-rmse:0.526914+0.061469 
## [40] train-rmse:0.438589+0.014145    test-rmse:0.526116+0.062258 
## [41] train-rmse:0.436760+0.014035    test-rmse:0.525091+0.062373 
## [42] train-rmse:0.434736+0.014156    test-rmse:0.523961+0.063366 
## [43] train-rmse:0.432902+0.014352    test-rmse:0.524429+0.063723 
## [44] train-rmse:0.431184+0.014564    test-rmse:0.523040+0.063288 
## [45] train-rmse:0.428770+0.014943    test-rmse:0.522711+0.062361 
## [46] train-rmse:0.427323+0.015109    test-rmse:0.522506+0.062451 
## [47] train-rmse:0.425690+0.015379    test-rmse:0.521820+0.061718 
## [48] train-rmse:0.423948+0.015410    test-rmse:0.521125+0.060998 
## [49] train-rmse:0.422443+0.015263    test-rmse:0.520325+0.059409 
## [50] train-rmse:0.421099+0.015480    test-rmse:0.518498+0.059493 
## [51] train-rmse:0.419364+0.015489    test-rmse:0.517333+0.061042 
## [52] train-rmse:0.417255+0.015431    test-rmse:0.515373+0.061007 
## [53] train-rmse:0.415986+0.015372    test-rmse:0.515217+0.061147 
## [54] train-rmse:0.414862+0.015339    test-rmse:0.515214+0.061180 
## [55] train-rmse:0.413435+0.015309    test-rmse:0.515333+0.061566 
## [56] train-rmse:0.412064+0.015993    test-rmse:0.514798+0.060967 
## [57] train-rmse:0.410684+0.015878    test-rmse:0.515179+0.060775 
## [58] train-rmse:0.409029+0.015991    test-rmse:0.515448+0.060003 
## [59] train-rmse:0.407671+0.016438    test-rmse:0.515105+0.059938 
## [60] train-rmse:0.406465+0.016337    test-rmse:0.515065+0.060617 
## [61] train-rmse:0.405163+0.015933    test-rmse:0.514291+0.059926 
## [62] train-rmse:0.403889+0.015864    test-rmse:0.513266+0.060641 
## [63] train-rmse:0.402283+0.015788    test-rmse:0.511753+0.060398 
## [64] train-rmse:0.401212+0.015866    test-rmse:0.511831+0.060464 
## [65] train-rmse:0.399402+0.015774    test-rmse:0.511158+0.060279 
## [66] train-rmse:0.398517+0.015834    test-rmse:0.511664+0.059532 
## [67] train-rmse:0.397494+0.015834    test-rmse:0.511259+0.059629 
## [68] train-rmse:0.396432+0.015596    test-rmse:0.510749+0.059636 
## [69] train-rmse:0.395181+0.015786    test-rmse:0.510789+0.059646 
## [70] train-rmse:0.394165+0.015600    test-rmse:0.510551+0.059617 
## [71] train-rmse:0.392702+0.015804    test-rmse:0.509943+0.059600 
## [72] train-rmse:0.391795+0.015584    test-rmse:0.508936+0.059754 
## [73] train-rmse:0.390309+0.015775    test-rmse:0.508354+0.059915 
## [74] train-rmse:0.389428+0.015651    test-rmse:0.508232+0.060813 
## [75] train-rmse:0.388533+0.015639    test-rmse:0.508030+0.061157 
## [76] train-rmse:0.387302+0.015421    test-rmse:0.507519+0.061129 
## [77] train-rmse:0.386399+0.015248    test-rmse:0.508144+0.060776 
## [78] train-rmse:0.385470+0.015525    test-rmse:0.508174+0.060440 
## [79] train-rmse:0.384345+0.015079    test-rmse:0.507618+0.060495 
## [80] train-rmse:0.383036+0.014858    test-rmse:0.506653+0.060295 
## [81] train-rmse:0.381654+0.015309    test-rmse:0.506535+0.060201 
## [82] train-rmse:0.380565+0.015273    test-rmse:0.506582+0.060987 
## [83] train-rmse:0.379692+0.015158    test-rmse:0.506323+0.061347 
## [84] train-rmse:0.378670+0.014831    test-rmse:0.506349+0.060753 
## [85] train-rmse:0.377685+0.014819    test-rmse:0.506462+0.061193 
## [86] train-rmse:0.376177+0.015075    test-rmse:0.505332+0.061403 
## [87] train-rmse:0.375174+0.014785    test-rmse:0.505203+0.061841 
## [88] train-rmse:0.374311+0.014705    test-rmse:0.504230+0.061952 
## [89] train-rmse:0.373478+0.014584    test-rmse:0.503764+0.061743 
## [90] train-rmse:0.372334+0.015004    test-rmse:0.502921+0.062232 
## [91] train-rmse:0.371312+0.014784    test-rmse:0.502927+0.062181 
## [92] train-rmse:0.370291+0.014875    test-rmse:0.502471+0.061686 
## [93] train-rmse:0.369644+0.014923    test-rmse:0.502981+0.061693 
## [94] train-rmse:0.368966+0.014793    test-rmse:0.503099+0.061475 
## [95] train-rmse:0.368167+0.014798    test-rmse:0.503126+0.061664 
## [96] train-rmse:0.367089+0.015022    test-rmse:0.502654+0.061567 
## [97] train-rmse:0.366196+0.014909    test-rmse:0.502220+0.060984 
## [98] train-rmse:0.365090+0.014918    test-rmse:0.501969+0.060977 
## [99] train-rmse:0.364121+0.014604    test-rmse:0.502395+0.061112 
## [100]    train-rmse:0.363454+0.014734    test-rmse:0.502171+0.061219 
## [101]    train-rmse:0.362171+0.014665    test-rmse:0.502068+0.060764 
## [102]    train-rmse:0.361189+0.014617    test-rmse:0.502029+0.060263 
## [103]    train-rmse:0.358828+0.014163    test-rmse:0.501375+0.059621 
## [104]    train-rmse:0.358036+0.013964    test-rmse:0.500738+0.059129 
## [105]    train-rmse:0.357219+0.013870    test-rmse:0.500607+0.058991 
## [106]    train-rmse:0.356121+0.013978    test-rmse:0.500662+0.059313 
## [107]    train-rmse:0.355301+0.014043    test-rmse:0.500121+0.058855 
## [108]    train-rmse:0.354197+0.013919    test-rmse:0.499683+0.058213 
## [109]    train-rmse:0.353448+0.014010    test-rmse:0.499671+0.057796 
## [110]    train-rmse:0.352490+0.013980    test-rmse:0.499814+0.057075 
## [111]    train-rmse:0.351869+0.013920    test-rmse:0.499497+0.057217 
## [112]    train-rmse:0.351440+0.013941    test-rmse:0.499662+0.057123 
## [113]    train-rmse:0.350605+0.013795    test-rmse:0.499478+0.057202 
## [114]    train-rmse:0.349765+0.014004    test-rmse:0.499097+0.057056 
## [115]    train-rmse:0.348979+0.014125    test-rmse:0.498792+0.056961 
## [116]    train-rmse:0.348122+0.013863    test-rmse:0.498649+0.057195 
## [117]    train-rmse:0.347458+0.013810    test-rmse:0.498443+0.056758 
## [118]    train-rmse:0.346904+0.013781    test-rmse:0.499015+0.056712 
## [119]    train-rmse:0.346321+0.013802    test-rmse:0.498272+0.056431 
## [120]    train-rmse:0.344332+0.015063    test-rmse:0.497392+0.056263 
## [121]    train-rmse:0.343187+0.015793    test-rmse:0.497507+0.055920 
## [122]    train-rmse:0.342562+0.015709    test-rmse:0.497083+0.056218 
## [123]    train-rmse:0.341760+0.015541    test-rmse:0.496288+0.056420 
## [124]    train-rmse:0.341105+0.015455    test-rmse:0.496312+0.056359 
## [125]    train-rmse:0.340375+0.015573    test-rmse:0.496353+0.056070 
## [126]    train-rmse:0.339862+0.015520    test-rmse:0.496630+0.056250 
## [127]    train-rmse:0.339286+0.015227    test-rmse:0.496632+0.055902 
## [128]    train-rmse:0.338642+0.015499    test-rmse:0.496696+0.055600 
## [129]    train-rmse:0.337698+0.015129    test-rmse:0.496134+0.055497 
## [130]    train-rmse:0.337248+0.015051    test-rmse:0.495935+0.055395 
## [131]    train-rmse:0.336559+0.014777    test-rmse:0.496216+0.055305 
## [132]    train-rmse:0.335901+0.014764    test-rmse:0.496426+0.055622 
## [133]    train-rmse:0.335234+0.014807    test-rmse:0.496381+0.055023 
## [134]    train-rmse:0.334294+0.015084    test-rmse:0.496827+0.054697 
## [135]    train-rmse:0.333807+0.014922    test-rmse:0.496237+0.054588 
## [136]    train-rmse:0.333095+0.014967    test-rmse:0.495852+0.053964 
## [137]    train-rmse:0.331956+0.015739    test-rmse:0.495845+0.053843 
## [138]    train-rmse:0.331140+0.015227    test-rmse:0.495511+0.053404 
## [139]    train-rmse:0.330688+0.015188    test-rmse:0.495285+0.053362 
## [140]    train-rmse:0.330284+0.015113    test-rmse:0.495067+0.053268 
## [141]    train-rmse:0.328992+0.015364    test-rmse:0.494859+0.053613 
## [142]    train-rmse:0.327952+0.015660    test-rmse:0.494849+0.053097 
## [143]    train-rmse:0.326401+0.016048    test-rmse:0.495385+0.052956 
## [144]    train-rmse:0.325523+0.016516    test-rmse:0.495099+0.052645 
## [145]    train-rmse:0.325015+0.016405    test-rmse:0.495586+0.052622 
## [146]    train-rmse:0.324311+0.015944    test-rmse:0.495151+0.052848 
## [147]    train-rmse:0.323527+0.015671    test-rmse:0.494474+0.052830 
## [148]    train-rmse:0.322752+0.015408    test-rmse:0.494900+0.052673 
## [149]    train-rmse:0.322006+0.015154    test-rmse:0.495168+0.052455 
## [150]    train-rmse:0.321407+0.015488    test-rmse:0.495192+0.052464 
## [151]    train-rmse:0.320822+0.015266    test-rmse:0.495383+0.052247 
## [152]    train-rmse:0.320045+0.015474    test-rmse:0.495397+0.052290 
## [153]    train-rmse:0.319391+0.015394    test-rmse:0.495227+0.052329 
## Stopping. Best iteration:
## [147]    train-rmse:0.323527+0.015671    test-rmse:0.494474+0.052830
## 
## 37 
## [1]  train-rmse:0.876334+0.015290    test-rmse:0.882471+0.062297 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.779260+0.014677    test-rmse:0.793522+0.062428 
## [3]  train-rmse:0.706732+0.014052    test-rmse:0.730748+0.064549 
## [4]  train-rmse:0.651406+0.013736    test-rmse:0.682268+0.063058 
## [5]  train-rmse:0.609442+0.013948    test-rmse:0.647169+0.061142 
## [6]  train-rmse:0.577918+0.014209    test-rmse:0.623875+0.058332 
## [7]  train-rmse:0.551839+0.013470    test-rmse:0.607150+0.059953 
## [8]  train-rmse:0.532535+0.012826    test-rmse:0.592713+0.058036 
## [9]  train-rmse:0.515643+0.012572    test-rmse:0.583315+0.055209 
## [10] train-rmse:0.503526+0.014446    test-rmse:0.573736+0.053847 
## [11] train-rmse:0.491147+0.015094    test-rmse:0.568480+0.054000 
## [12] train-rmse:0.482749+0.015490    test-rmse:0.564087+0.052782 
## [13] train-rmse:0.474622+0.014120    test-rmse:0.558750+0.056500 
## [14] train-rmse:0.466960+0.013842    test-rmse:0.554720+0.055044 
## [15] train-rmse:0.460175+0.013813    test-rmse:0.551508+0.055183 
## [16] train-rmse:0.454434+0.013264    test-rmse:0.547143+0.055408 
## [17] train-rmse:0.448225+0.014928    test-rmse:0.544408+0.057967 
## [18] train-rmse:0.442486+0.014773    test-rmse:0.541109+0.057199 
## [19] train-rmse:0.437343+0.014304    test-rmse:0.539505+0.057637 
## [20] train-rmse:0.431831+0.012655    test-rmse:0.538187+0.058362 
## [21] train-rmse:0.426724+0.012078    test-rmse:0.533800+0.056928 
## [22] train-rmse:0.422481+0.012173    test-rmse:0.532562+0.058255 
## [23] train-rmse:0.418206+0.012425    test-rmse:0.530754+0.059342 
## [24] train-rmse:0.414448+0.012315    test-rmse:0.530526+0.058790 
## [25] train-rmse:0.410418+0.012623    test-rmse:0.529878+0.056825 
## [26] train-rmse:0.406462+0.012349    test-rmse:0.527596+0.054864 
## [27] train-rmse:0.402970+0.011240    test-rmse:0.526416+0.056504 
## [28] train-rmse:0.398864+0.011866    test-rmse:0.525469+0.057488 
## [29] train-rmse:0.395666+0.011575    test-rmse:0.523472+0.057036 
## [30] train-rmse:0.392217+0.011976    test-rmse:0.523425+0.056690 
## [31] train-rmse:0.388464+0.012026    test-rmse:0.522319+0.056314 
## [32] train-rmse:0.385652+0.011716    test-rmse:0.521257+0.055725 
## [33] train-rmse:0.382590+0.011341    test-rmse:0.520103+0.056177 
## [34] train-rmse:0.379513+0.011535    test-rmse:0.520054+0.055554 
## [35] train-rmse:0.377140+0.011571    test-rmse:0.519078+0.057001 
## [36] train-rmse:0.374362+0.011796    test-rmse:0.518457+0.056980 
## [37] train-rmse:0.371880+0.011392    test-rmse:0.517646+0.058306 
## [38] train-rmse:0.370171+0.011252    test-rmse:0.517356+0.057726 
## [39] train-rmse:0.367196+0.011341    test-rmse:0.516099+0.057290 
## [40] train-rmse:0.364938+0.011097    test-rmse:0.515442+0.056887 
## [41] train-rmse:0.361726+0.011278    test-rmse:0.514398+0.056857 
## [42] train-rmse:0.358136+0.012224    test-rmse:0.513471+0.056678 
## [43] train-rmse:0.355675+0.012032    test-rmse:0.512329+0.057723 
## [44] train-rmse:0.353443+0.011110    test-rmse:0.512579+0.057987 
## [45] train-rmse:0.351005+0.011294    test-rmse:0.512013+0.057677 
## [46] train-rmse:0.348597+0.011464    test-rmse:0.512060+0.056770 
## [47] train-rmse:0.346481+0.011257    test-rmse:0.511432+0.057231 
## [48] train-rmse:0.344645+0.010596    test-rmse:0.511020+0.056726 
## [49] train-rmse:0.342363+0.010591    test-rmse:0.511080+0.056114 
## [50] train-rmse:0.339993+0.010795    test-rmse:0.510579+0.055471 
## [51] train-rmse:0.337651+0.010536    test-rmse:0.510338+0.055349 
## [52] train-rmse:0.335652+0.010328    test-rmse:0.508984+0.055518 
## [53] train-rmse:0.334128+0.010324    test-rmse:0.508210+0.055570 
## [54] train-rmse:0.331778+0.009958    test-rmse:0.507908+0.056378 
## [55] train-rmse:0.329565+0.009229    test-rmse:0.506897+0.057121 
## [56] train-rmse:0.327362+0.009557    test-rmse:0.506882+0.055481 
## [57] train-rmse:0.325108+0.009565    test-rmse:0.506395+0.055061 
## [58] train-rmse:0.322911+0.009393    test-rmse:0.506251+0.055047 
## [59] train-rmse:0.321123+0.009123    test-rmse:0.506388+0.055178 
## [60] train-rmse:0.319494+0.009257    test-rmse:0.506675+0.054429 
## [61] train-rmse:0.317992+0.008922    test-rmse:0.507019+0.053330 
## [62] train-rmse:0.316075+0.009808    test-rmse:0.507668+0.054070 
## [63] train-rmse:0.314222+0.009525    test-rmse:0.506747+0.053869 
## [64] train-rmse:0.311914+0.009689    test-rmse:0.505987+0.053953 
## [65] train-rmse:0.309129+0.010616    test-rmse:0.506505+0.054212 
## [66] train-rmse:0.307404+0.010364    test-rmse:0.506203+0.054330 
## [67] train-rmse:0.305161+0.010552    test-rmse:0.506071+0.053876 
## [68] train-rmse:0.303831+0.010034    test-rmse:0.506579+0.053489 
## [69] train-rmse:0.301886+0.009907    test-rmse:0.506683+0.053285 
## [70] train-rmse:0.300491+0.010218    test-rmse:0.506901+0.052999 
## Stopping. Best iteration:
## [64] train-rmse:0.311914+0.009689    test-rmse:0.505987+0.053953
## 
## 38 
## [1]  train-rmse:0.870282+0.015080    test-rmse:0.878834+0.061027 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.767545+0.014684    test-rmse:0.787841+0.061970 
## [3]  train-rmse:0.688772+0.014424    test-rmse:0.720376+0.064549 
## [4]  train-rmse:0.628409+0.012750    test-rmse:0.665873+0.063349 
## [5]  train-rmse:0.581334+0.012921    test-rmse:0.627848+0.062170 
## [6]  train-rmse:0.543310+0.013267    test-rmse:0.600363+0.060309 
## [7]  train-rmse:0.515008+0.014044    test-rmse:0.580250+0.057362 
## [8]  train-rmse:0.491754+0.013625    test-rmse:0.566904+0.054773 
## [9]  train-rmse:0.472750+0.013512    test-rmse:0.556423+0.055343 
## [10] train-rmse:0.457468+0.015876    test-rmse:0.549549+0.052867 
## [11] train-rmse:0.444431+0.014732    test-rmse:0.541495+0.052978 
## [12] train-rmse:0.433435+0.016242    test-rmse:0.537002+0.051442 
## [13] train-rmse:0.424594+0.015538    test-rmse:0.531878+0.052578 
## [14] train-rmse:0.417066+0.016517    test-rmse:0.526999+0.054509 
## [15] train-rmse:0.409070+0.015254    test-rmse:0.525793+0.053849 
## [16] train-rmse:0.401506+0.014024    test-rmse:0.522023+0.053567 
## [17] train-rmse:0.395592+0.013440    test-rmse:0.520436+0.053177 
## [18] train-rmse:0.389984+0.014316    test-rmse:0.517723+0.054236 
## [19] train-rmse:0.383506+0.012851    test-rmse:0.514702+0.053960 
## [20] train-rmse:0.376709+0.012477    test-rmse:0.511700+0.054907 
## [21] train-rmse:0.371651+0.013163    test-rmse:0.510829+0.053592 
## [22] train-rmse:0.366335+0.012651    test-rmse:0.509399+0.053562 
## [23] train-rmse:0.361959+0.013001    test-rmse:0.509256+0.053706 
## [24] train-rmse:0.357099+0.013288    test-rmse:0.508602+0.053118 
## [25] train-rmse:0.352524+0.012115    test-rmse:0.507304+0.055099 
## [26] train-rmse:0.348327+0.012598    test-rmse:0.503619+0.056568 
## [27] train-rmse:0.343958+0.011647    test-rmse:0.501642+0.056685 
## [28] train-rmse:0.339329+0.012157    test-rmse:0.500980+0.056596 
## [29] train-rmse:0.336301+0.012610    test-rmse:0.500772+0.057003 
## [30] train-rmse:0.332185+0.012805    test-rmse:0.500295+0.057790 
## [31] train-rmse:0.329208+0.012577    test-rmse:0.499300+0.057406 
## [32] train-rmse:0.324411+0.011804    test-rmse:0.498446+0.057468 
## [33] train-rmse:0.321729+0.011602    test-rmse:0.498611+0.056802 
## [34] train-rmse:0.318770+0.011290    test-rmse:0.499557+0.056277 
## [35] train-rmse:0.315657+0.012251    test-rmse:0.499553+0.056404 
## [36] train-rmse:0.311854+0.012128    test-rmse:0.497706+0.056913 
## [37] train-rmse:0.309098+0.012038    test-rmse:0.497716+0.057655 
## [38] train-rmse:0.305139+0.012440    test-rmse:0.498202+0.056336 
## [39] train-rmse:0.302165+0.011946    test-rmse:0.497294+0.056896 
## [40] train-rmse:0.299253+0.012181    test-rmse:0.497637+0.056446 
## [41] train-rmse:0.296473+0.011986    test-rmse:0.496316+0.056287 
## [42] train-rmse:0.292520+0.011747    test-rmse:0.495154+0.057369 
## [43] train-rmse:0.289061+0.012813    test-rmse:0.493977+0.056625 
## [44] train-rmse:0.285982+0.013077    test-rmse:0.493489+0.057368 
## [45] train-rmse:0.283200+0.013033    test-rmse:0.492637+0.056462 
## [46] train-rmse:0.280531+0.012658    test-rmse:0.492493+0.056250 
## [47] train-rmse:0.276946+0.013478    test-rmse:0.494067+0.054905 
## [48] train-rmse:0.274642+0.013204    test-rmse:0.494141+0.054701 
## [49] train-rmse:0.272712+0.013124    test-rmse:0.494315+0.054805 
## [50] train-rmse:0.268863+0.013687    test-rmse:0.493657+0.055626 
## [51] train-rmse:0.264792+0.014674    test-rmse:0.494528+0.055732 
## [52] train-rmse:0.263121+0.014404    test-rmse:0.495237+0.055885 
## Stopping. Best iteration:
## [46] train-rmse:0.280531+0.012658    test-rmse:0.492493+0.056250
## 
## 39 
## [1]  train-rmse:0.864898+0.014583    test-rmse:0.875716+0.061755 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.756324+0.013266    test-rmse:0.780888+0.060708 
## [3]  train-rmse:0.671702+0.011987    test-rmse:0.709515+0.060778 
## [4]  train-rmse:0.607460+0.010911    test-rmse:0.658650+0.064485 
## [5]  train-rmse:0.554511+0.010447    test-rmse:0.618505+0.062058 
## [6]  train-rmse:0.513310+0.009951    test-rmse:0.589848+0.061913 
## [7]  train-rmse:0.480981+0.010593    test-rmse:0.570135+0.060713 
## [8]  train-rmse:0.453401+0.011888    test-rmse:0.553354+0.061212 
## [9]  train-rmse:0.431987+0.013225    test-rmse:0.544764+0.059331 
## [10] train-rmse:0.413852+0.013365    test-rmse:0.537124+0.060786 
## [11] train-rmse:0.398504+0.015026    test-rmse:0.530367+0.058663 
## [12] train-rmse:0.388697+0.015681    test-rmse:0.526573+0.057200 
## [13] train-rmse:0.378425+0.014864    test-rmse:0.520734+0.057536 
## [14] train-rmse:0.368289+0.015549    test-rmse:0.518571+0.057157 
## [15] train-rmse:0.359903+0.014301    test-rmse:0.515253+0.058946 
## [16] train-rmse:0.352567+0.015822    test-rmse:0.512895+0.059691 
## [17] train-rmse:0.345041+0.013955    test-rmse:0.509482+0.061779 
## [18] train-rmse:0.338177+0.012361    test-rmse:0.506658+0.059708 
## [19] train-rmse:0.333351+0.012447    test-rmse:0.505449+0.060461 
## [20] train-rmse:0.329220+0.012656    test-rmse:0.504533+0.059169 
## [21] train-rmse:0.322202+0.013546    test-rmse:0.502077+0.059350 
## [22] train-rmse:0.317482+0.013595    test-rmse:0.499032+0.061069 
## [23] train-rmse:0.312062+0.013905    test-rmse:0.498244+0.061642 
## [24] train-rmse:0.307917+0.013541    test-rmse:0.497992+0.061662 
## [25] train-rmse:0.301791+0.011559    test-rmse:0.496914+0.061634 
## [26] train-rmse:0.297007+0.012095    test-rmse:0.496149+0.060730 
## [27] train-rmse:0.292521+0.012447    test-rmse:0.495958+0.061420 
## [28] train-rmse:0.288715+0.011783    test-rmse:0.495171+0.060429 
## [29] train-rmse:0.284156+0.011506    test-rmse:0.494503+0.061244 
## [30] train-rmse:0.279866+0.012170    test-rmse:0.494862+0.061364 
## [31] train-rmse:0.275974+0.012319    test-rmse:0.493780+0.059558 
## [32] train-rmse:0.273179+0.012017    test-rmse:0.493525+0.059435 
## [33] train-rmse:0.268727+0.012149    test-rmse:0.493503+0.058617 
## [34] train-rmse:0.264678+0.012718    test-rmse:0.492651+0.058046 
## [35] train-rmse:0.262022+0.012098    test-rmse:0.493009+0.057445 
## [36] train-rmse:0.259553+0.011641    test-rmse:0.492835+0.057351 
## [37] train-rmse:0.256838+0.012611    test-rmse:0.492913+0.056891 
## [38] train-rmse:0.254453+0.013238    test-rmse:0.492979+0.056720 
## [39] train-rmse:0.251074+0.012687    test-rmse:0.492064+0.056789 
## [40] train-rmse:0.247783+0.012436    test-rmse:0.492472+0.056870 
## [41] train-rmse:0.243991+0.012860    test-rmse:0.492943+0.057164 
## [42] train-rmse:0.240155+0.013859    test-rmse:0.493133+0.057481 
## [43] train-rmse:0.236952+0.014032    test-rmse:0.493282+0.056394 
## [44] train-rmse:0.233360+0.012964    test-rmse:0.492471+0.056891 
## [45] train-rmse:0.231770+0.012549    test-rmse:0.492498+0.056704 
## Stopping. Best iteration:
## [39] train-rmse:0.251074+0.012687    test-rmse:0.492064+0.056789
## 
## 40 
## [1]  train-rmse:0.859978+0.014089    test-rmse:0.873655+0.061973 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.746628+0.012653    test-rmse:0.773269+0.061905 
## [3]  train-rmse:0.656349+0.011594    test-rmse:0.700390+0.063620 
## [4]  train-rmse:0.586671+0.011651    test-rmse:0.649035+0.064069 
## [5]  train-rmse:0.531570+0.012662    test-rmse:0.612658+0.063558 
## [6]  train-rmse:0.486911+0.013428    test-rmse:0.579947+0.063592 
## [7]  train-rmse:0.449590+0.014386    test-rmse:0.558671+0.063192 
## [8]  train-rmse:0.419475+0.013773    test-rmse:0.545227+0.062216 
## [9]  train-rmse:0.395951+0.013272    test-rmse:0.535467+0.062109 
## [10] train-rmse:0.375342+0.014496    test-rmse:0.526667+0.060138 
## [11] train-rmse:0.360190+0.015283    test-rmse:0.522843+0.059828 
## [12] train-rmse:0.347033+0.015270    test-rmse:0.518310+0.057087 
## [13] train-rmse:0.336486+0.015504    test-rmse:0.515119+0.056943 
## [14] train-rmse:0.325711+0.014477    test-rmse:0.513216+0.058480 
## [15] train-rmse:0.316416+0.015758    test-rmse:0.511835+0.057809 
## [16] train-rmse:0.307594+0.015216    test-rmse:0.509067+0.057741 
## [17] train-rmse:0.301516+0.014003    test-rmse:0.504935+0.059590 
## [18] train-rmse:0.291703+0.013177    test-rmse:0.500549+0.061170 
## [19] train-rmse:0.284300+0.014978    test-rmse:0.499986+0.059392 
## [20] train-rmse:0.276566+0.014194    test-rmse:0.498997+0.056610 
## [21] train-rmse:0.271113+0.013737    test-rmse:0.496672+0.056339 
## [22] train-rmse:0.266696+0.013125    test-rmse:0.496608+0.055684 
## [23] train-rmse:0.260834+0.010676    test-rmse:0.495303+0.057082 
## [24] train-rmse:0.256094+0.010109    test-rmse:0.495701+0.056093 
## [25] train-rmse:0.249907+0.011963    test-rmse:0.495546+0.054026 
## [26] train-rmse:0.245006+0.011373    test-rmse:0.496836+0.053654 
## [27] train-rmse:0.240687+0.012369    test-rmse:0.496202+0.054853 
## [28] train-rmse:0.237589+0.011425    test-rmse:0.495598+0.054256 
## [29] train-rmse:0.234003+0.010866    test-rmse:0.495821+0.053164 
## Stopping. Best iteration:
## [23] train-rmse:0.260834+0.010676    test-rmse:0.495303+0.057082
## 
## 41 
## [1]  train-rmse:0.855149+0.013654    test-rmse:0.870487+0.060339 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.737973+0.011068    test-rmse:0.771088+0.061269 
## [3]  train-rmse:0.643977+0.009287    test-rmse:0.694575+0.061759 
## [4]  train-rmse:0.571350+0.009278    test-rmse:0.642376+0.062212 
## [5]  train-rmse:0.512515+0.009017    test-rmse:0.600919+0.066565 
## [6]  train-rmse:0.465482+0.009165    test-rmse:0.572023+0.067134 
## [7]  train-rmse:0.429237+0.011211    test-rmse:0.552038+0.067806 
## [8]  train-rmse:0.396520+0.009741    test-rmse:0.541642+0.066313 
## [9]  train-rmse:0.369139+0.009774    test-rmse:0.529574+0.065078 
## [10] train-rmse:0.349323+0.009119    test-rmse:0.521711+0.065135 
## [11] train-rmse:0.331770+0.007677    test-rmse:0.518159+0.065834 
## [12] train-rmse:0.316059+0.010878    test-rmse:0.514986+0.064856 
## [13] train-rmse:0.302959+0.011193    test-rmse:0.510929+0.062762 
## [14] train-rmse:0.291161+0.014142    test-rmse:0.507631+0.060835 
## [15] train-rmse:0.283214+0.014979    test-rmse:0.505335+0.061467 
## [16] train-rmse:0.273411+0.015322    test-rmse:0.502602+0.059081 
## [17] train-rmse:0.262864+0.014373    test-rmse:0.500384+0.058827 
## [18] train-rmse:0.255333+0.011313    test-rmse:0.498391+0.060336 
## [19] train-rmse:0.248568+0.011945    test-rmse:0.497562+0.059815 
## [20] train-rmse:0.243772+0.011207    test-rmse:0.496485+0.059562 
## [21] train-rmse:0.236219+0.010266    test-rmse:0.495581+0.060560 
## [22] train-rmse:0.232032+0.010891    test-rmse:0.495454+0.061031 
## [23] train-rmse:0.226923+0.011120    test-rmse:0.495005+0.060271 
## [24] train-rmse:0.222232+0.011769    test-rmse:0.494547+0.059848 
## [25] train-rmse:0.216233+0.011783    test-rmse:0.492981+0.059765 
## [26] train-rmse:0.211286+0.014004    test-rmse:0.493558+0.060442 
## [27] train-rmse:0.205937+0.012880    test-rmse:0.492884+0.058846 
## [28] train-rmse:0.200109+0.012561    test-rmse:0.494403+0.060726 
## [29] train-rmse:0.196971+0.012096    test-rmse:0.494174+0.061030 
## [30] train-rmse:0.192203+0.010097    test-rmse:0.494727+0.060395 
## [31] train-rmse:0.188436+0.010293    test-rmse:0.495300+0.061738 
## [32] train-rmse:0.183273+0.011193    test-rmse:0.496345+0.061511 
## [33] train-rmse:0.178721+0.011890    test-rmse:0.496349+0.062247 
## Stopping. Best iteration:
## [27] train-rmse:0.205937+0.012880    test-rmse:0.492884+0.058846
## 
## 42 
## [1]  train-rmse:0.889329+0.015020    test-rmse:0.891698+0.060802 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.810221+0.014118    test-rmse:0.817282+0.060559 
## [3]  train-rmse:0.755511+0.013729    test-rmse:0.775066+0.060259 
## [4]  train-rmse:0.716624+0.012852    test-rmse:0.734230+0.060566 
## [5]  train-rmse:0.688053+0.012215    test-rmse:0.705499+0.062296 
## [6]  train-rmse:0.665451+0.012097    test-rmse:0.686575+0.055662 
## [7]  train-rmse:0.649580+0.011757    test-rmse:0.675379+0.053537 
## [8]  train-rmse:0.636798+0.011816    test-rmse:0.660653+0.052258 
## [9]  train-rmse:0.626362+0.011280    test-rmse:0.655039+0.053577 
## [10] train-rmse:0.618604+0.011228    test-rmse:0.646375+0.051956 
## [11] train-rmse:0.611905+0.011188    test-rmse:0.641678+0.054039 
## [12] train-rmse:0.605636+0.011246    test-rmse:0.634981+0.053518 
## [13] train-rmse:0.600176+0.011289    test-rmse:0.631739+0.054473 
## [14] train-rmse:0.595194+0.011439    test-rmse:0.627953+0.054657 
## [15] train-rmse:0.590937+0.011385    test-rmse:0.624262+0.054644 
## [16] train-rmse:0.586852+0.011395    test-rmse:0.621681+0.055786 
## [17] train-rmse:0.582832+0.011311    test-rmse:0.615364+0.056009 
## [18] train-rmse:0.579084+0.011270    test-rmse:0.613251+0.056942 
## [19] train-rmse:0.575585+0.011291    test-rmse:0.613160+0.054231 
## [20] train-rmse:0.572366+0.011172    test-rmse:0.612448+0.054709 
## [21] train-rmse:0.569290+0.011107    test-rmse:0.607706+0.055185 
## [22] train-rmse:0.566400+0.011180    test-rmse:0.605330+0.056818 
## [23] train-rmse:0.563574+0.011202    test-rmse:0.604190+0.055924 
## [24] train-rmse:0.560873+0.011176    test-rmse:0.599967+0.055881 
## [25] train-rmse:0.558242+0.011254    test-rmse:0.597249+0.056148 
## [26] train-rmse:0.555698+0.011285    test-rmse:0.596359+0.056754 
## [27] train-rmse:0.553271+0.011294    test-rmse:0.593860+0.056562 
## [28] train-rmse:0.550895+0.011290    test-rmse:0.592709+0.054846 
## [29] train-rmse:0.548590+0.011258    test-rmse:0.589814+0.053926 
## [30] train-rmse:0.546404+0.011206    test-rmse:0.588870+0.053518 
## [31] train-rmse:0.544347+0.011203    test-rmse:0.587746+0.053160 
## [32] train-rmse:0.542292+0.011151    test-rmse:0.585646+0.053179 
## [33] train-rmse:0.540306+0.011101    test-rmse:0.583840+0.053048 
## [34] train-rmse:0.538414+0.011025    test-rmse:0.582705+0.054061 
## [35] train-rmse:0.536572+0.010971    test-rmse:0.581062+0.052660 
## [36] train-rmse:0.534800+0.010920    test-rmse:0.580126+0.053956 
## [37] train-rmse:0.533089+0.010906    test-rmse:0.578827+0.055920 
## [38] train-rmse:0.531453+0.010907    test-rmse:0.580054+0.056148 
## [39] train-rmse:0.529848+0.010896    test-rmse:0.577908+0.055932 
## [40] train-rmse:0.528276+0.010874    test-rmse:0.576749+0.055383 
## [41] train-rmse:0.526727+0.010871    test-rmse:0.574812+0.055899 
## [42] train-rmse:0.525222+0.010866    test-rmse:0.573487+0.055330 
## [43] train-rmse:0.523735+0.010840    test-rmse:0.572732+0.056090 
## [44] train-rmse:0.522322+0.010809    test-rmse:0.571819+0.054739 
## [45] train-rmse:0.520926+0.010769    test-rmse:0.571504+0.056306 
## [46] train-rmse:0.519597+0.010740    test-rmse:0.569825+0.056267 
## [47] train-rmse:0.518298+0.010701    test-rmse:0.569172+0.055263 
## [48] train-rmse:0.517049+0.010647    test-rmse:0.568931+0.054751 
## [49] train-rmse:0.515834+0.010598    test-rmse:0.567365+0.054653 
## [50] train-rmse:0.514648+0.010558    test-rmse:0.566439+0.054230 
## [51] train-rmse:0.513479+0.010525    test-rmse:0.565569+0.054124 
## [52] train-rmse:0.512329+0.010502    test-rmse:0.564438+0.053076 
## [53] train-rmse:0.511198+0.010483    test-rmse:0.564375+0.054017 
## [54] train-rmse:0.510083+0.010456    test-rmse:0.562806+0.054674 
## [55] train-rmse:0.508985+0.010473    test-rmse:0.562185+0.053912 
## [56] train-rmse:0.507929+0.010479    test-rmse:0.561441+0.053984 
## [57] train-rmse:0.506903+0.010451    test-rmse:0.560093+0.053834 
## [58] train-rmse:0.505893+0.010452    test-rmse:0.557978+0.053962 
## [59] train-rmse:0.504895+0.010451    test-rmse:0.557477+0.053900 
## [60] train-rmse:0.503908+0.010456    test-rmse:0.557282+0.053894 
## [61] train-rmse:0.502957+0.010447    test-rmse:0.556758+0.053450 
## [62] train-rmse:0.502038+0.010423    test-rmse:0.556121+0.053799 
## [63] train-rmse:0.501133+0.010408    test-rmse:0.555679+0.053385 
## [64] train-rmse:0.500258+0.010389    test-rmse:0.555326+0.053645 
## [65] train-rmse:0.499387+0.010356    test-rmse:0.553837+0.053724 
## [66] train-rmse:0.498568+0.010358    test-rmse:0.553670+0.053082 
## [67] train-rmse:0.497767+0.010361    test-rmse:0.552517+0.053005 
## [68] train-rmse:0.496964+0.010358    test-rmse:0.551349+0.053007 
## [69] train-rmse:0.496183+0.010360    test-rmse:0.550951+0.052965 
## [70] train-rmse:0.495403+0.010361    test-rmse:0.551362+0.052804 
## [71] train-rmse:0.494634+0.010362    test-rmse:0.551343+0.052188 
## [72] train-rmse:0.493880+0.010366    test-rmse:0.551895+0.052198 
## [73] train-rmse:0.493154+0.010361    test-rmse:0.551813+0.051251 
## [74] train-rmse:0.492454+0.010340    test-rmse:0.551597+0.051208 
## [75] train-rmse:0.491766+0.010323    test-rmse:0.552150+0.051790 
## Stopping. Best iteration:
## [69] train-rmse:0.496183+0.010360    test-rmse:0.550951+0.052965
## 
## 43 
## [1]  train-rmse:0.871874+0.014883    test-rmse:0.875592+0.060800 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.777325+0.014219    test-rmse:0.791071+0.062952 
## [3]  train-rmse:0.710398+0.013414    test-rmse:0.730235+0.063237 
## [4]  train-rmse:0.662426+0.012410    test-rmse:0.688073+0.065082 
## [5]  train-rmse:0.626503+0.011220    test-rmse:0.655212+0.065779 
## [6]  train-rmse:0.598952+0.011911    test-rmse:0.635318+0.063951 
## [7]  train-rmse:0.578974+0.011591    test-rmse:0.618716+0.065636 
## [8]  train-rmse:0.563172+0.013148    test-rmse:0.605318+0.061807 
## [9]  train-rmse:0.549664+0.013051    test-rmse:0.597864+0.064347 
## [10] train-rmse:0.540556+0.013244    test-rmse:0.592049+0.062021 
## [11] train-rmse:0.532388+0.013240    test-rmse:0.584991+0.063132 
## [12] train-rmse:0.525652+0.012795    test-rmse:0.581810+0.063654 
## [13] train-rmse:0.518788+0.013940    test-rmse:0.577801+0.061742 
## [14] train-rmse:0.512846+0.013818    test-rmse:0.573403+0.060743 
## [15] train-rmse:0.507869+0.013817    test-rmse:0.567787+0.061273 
## [16] train-rmse:0.503198+0.013999    test-rmse:0.565713+0.062793 
## [17] train-rmse:0.498843+0.013923    test-rmse:0.564935+0.061732 
## [18] train-rmse:0.493837+0.013898    test-rmse:0.559804+0.060618 
## [19] train-rmse:0.488622+0.012822    test-rmse:0.556162+0.061803 
## [20] train-rmse:0.484585+0.013120    test-rmse:0.552960+0.061166 
## [21] train-rmse:0.480718+0.012927    test-rmse:0.550957+0.061739 
## [22] train-rmse:0.476680+0.013888    test-rmse:0.549022+0.061102 
## [23] train-rmse:0.473372+0.013594    test-rmse:0.548189+0.060572 
## [24] train-rmse:0.470419+0.013474    test-rmse:0.545879+0.059067 
## [25] train-rmse:0.466890+0.013870    test-rmse:0.544300+0.058831 
## [26] train-rmse:0.463863+0.014092    test-rmse:0.542042+0.058861 
## [27] train-rmse:0.460284+0.014152    test-rmse:0.540217+0.058766 
## [28] train-rmse:0.457361+0.013771    test-rmse:0.539132+0.058797 
## [29] train-rmse:0.454531+0.013821    test-rmse:0.538489+0.059107 
## [30] train-rmse:0.451678+0.013799    test-rmse:0.537123+0.059749 
## [31] train-rmse:0.448654+0.013718    test-rmse:0.535954+0.059007 
## [32] train-rmse:0.446100+0.014035    test-rmse:0.534177+0.058508 
## [33] train-rmse:0.443459+0.013921    test-rmse:0.533071+0.060220 
## [34] train-rmse:0.441439+0.014251    test-rmse:0.531272+0.060393 
## [35] train-rmse:0.439056+0.014336    test-rmse:0.530913+0.060648 
## [36] train-rmse:0.436723+0.013903    test-rmse:0.530816+0.062022 
## [37] train-rmse:0.434451+0.014307    test-rmse:0.528536+0.061476 
## [38] train-rmse:0.432303+0.014168    test-rmse:0.527462+0.060975 
## [39] train-rmse:0.429961+0.014758    test-rmse:0.526519+0.062152 
## [40] train-rmse:0.428118+0.014958    test-rmse:0.525377+0.060737 
## [41] train-rmse:0.425486+0.014999    test-rmse:0.523076+0.061276 
## [42] train-rmse:0.423713+0.015433    test-rmse:0.522587+0.060862 
## [43] train-rmse:0.421706+0.015247    test-rmse:0.521796+0.060088 
## [44] train-rmse:0.419834+0.015343    test-rmse:0.521270+0.060854 
## [45] train-rmse:0.417649+0.014750    test-rmse:0.520004+0.061444 
## [46] train-rmse:0.415657+0.014413    test-rmse:0.517615+0.062389 
## [47] train-rmse:0.413932+0.014517    test-rmse:0.517748+0.062983 
## [48] train-rmse:0.412509+0.014371    test-rmse:0.516880+0.064195 
## [49] train-rmse:0.410923+0.014461    test-rmse:0.516832+0.063415 
## [50] train-rmse:0.409729+0.014577    test-rmse:0.516164+0.063342 
## [51] train-rmse:0.408397+0.014737    test-rmse:0.515869+0.062114 
## [52] train-rmse:0.406592+0.014444    test-rmse:0.513609+0.062868 
## [53] train-rmse:0.405169+0.014470    test-rmse:0.513252+0.063327 
## [54] train-rmse:0.403477+0.014722    test-rmse:0.512466+0.063017 
## [55] train-rmse:0.402037+0.014832    test-rmse:0.511744+0.063661 
## [56] train-rmse:0.400706+0.014971    test-rmse:0.511171+0.063692 
## [57] train-rmse:0.398859+0.014216    test-rmse:0.511357+0.064550 
## [58] train-rmse:0.397383+0.014134    test-rmse:0.509908+0.064938 
## [59] train-rmse:0.396287+0.014271    test-rmse:0.510930+0.065466 
## [60] train-rmse:0.395293+0.014511    test-rmse:0.510759+0.065362 
## [61] train-rmse:0.393920+0.014514    test-rmse:0.509786+0.064852 
## [62] train-rmse:0.393001+0.014573    test-rmse:0.509972+0.064825 
## [63] train-rmse:0.392026+0.014577    test-rmse:0.509560+0.064147 
## [64] train-rmse:0.390654+0.014529    test-rmse:0.508789+0.063573 
## [65] train-rmse:0.389354+0.014230    test-rmse:0.508721+0.063947 
## [66] train-rmse:0.388441+0.014123    test-rmse:0.508303+0.064592 
## [67] train-rmse:0.387418+0.014047    test-rmse:0.508737+0.064698 
## [68] train-rmse:0.386655+0.014199    test-rmse:0.509027+0.064636 
## [69] train-rmse:0.385280+0.014412    test-rmse:0.508172+0.064309 
## [70] train-rmse:0.384025+0.014546    test-rmse:0.507706+0.063896 
## [71] train-rmse:0.383066+0.014427    test-rmse:0.506867+0.064656 
## [72] train-rmse:0.381641+0.014644    test-rmse:0.506168+0.063980 
## [73] train-rmse:0.380262+0.014576    test-rmse:0.506132+0.063890 
## [74] train-rmse:0.379136+0.014744    test-rmse:0.506219+0.063355 
## [75] train-rmse:0.378178+0.014527    test-rmse:0.505938+0.064119 
## [76] train-rmse:0.377147+0.014373    test-rmse:0.506030+0.063652 
## [77] train-rmse:0.376110+0.014386    test-rmse:0.505866+0.062865 
## [78] train-rmse:0.374755+0.014605    test-rmse:0.505682+0.062423 
## [79] train-rmse:0.373878+0.014557    test-rmse:0.505638+0.062161 
## [80] train-rmse:0.372444+0.015156    test-rmse:0.505228+0.062157 
## [81] train-rmse:0.371025+0.014764    test-rmse:0.504590+0.063239 
## [82] train-rmse:0.370206+0.014722    test-rmse:0.504304+0.064022 
## [83] train-rmse:0.369455+0.014878    test-rmse:0.503355+0.063328 
## [84] train-rmse:0.368793+0.014819    test-rmse:0.503414+0.062941 
## [85] train-rmse:0.367703+0.014945    test-rmse:0.503067+0.062325 
## [86] train-rmse:0.366717+0.014555    test-rmse:0.502897+0.062035 
## [87] train-rmse:0.365112+0.014230    test-rmse:0.502656+0.062108 
## [88] train-rmse:0.363860+0.013910    test-rmse:0.502192+0.062478 
## [89] train-rmse:0.362895+0.013908    test-rmse:0.501698+0.062072 
## [90] train-rmse:0.362165+0.014013    test-rmse:0.501891+0.062142 
## [91] train-rmse:0.360980+0.013865    test-rmse:0.501003+0.061820 
## [92] train-rmse:0.360434+0.013884    test-rmse:0.500937+0.061297 
## [93] train-rmse:0.359520+0.013836    test-rmse:0.500609+0.061071 
## [94] train-rmse:0.358289+0.014045    test-rmse:0.500034+0.061409 
## [95] train-rmse:0.357109+0.013800    test-rmse:0.499965+0.061685 
## [96] train-rmse:0.355850+0.014081    test-rmse:0.500433+0.061538 
## [97] train-rmse:0.355249+0.014200    test-rmse:0.500832+0.061317 
## [98] train-rmse:0.354334+0.014006    test-rmse:0.500868+0.062048 
## [99] train-rmse:0.353401+0.014399    test-rmse:0.499753+0.061413 
## [100]    train-rmse:0.352475+0.014347    test-rmse:0.498894+0.060963 
## [101]    train-rmse:0.351454+0.014123    test-rmse:0.498739+0.061098 
## [102]    train-rmse:0.350692+0.014120    test-rmse:0.498596+0.060440 
## [103]    train-rmse:0.349669+0.014153    test-rmse:0.498634+0.060530 
## [104]    train-rmse:0.348454+0.013664    test-rmse:0.498903+0.060034 
## [105]    train-rmse:0.347440+0.013768    test-rmse:0.498868+0.060004 
## [106]    train-rmse:0.346217+0.013529    test-rmse:0.499504+0.059676 
## [107]    train-rmse:0.344976+0.013439    test-rmse:0.498879+0.059997 
## [108]    train-rmse:0.343890+0.013485    test-rmse:0.498829+0.059850 
## Stopping. Best iteration:
## [102]    train-rmse:0.350692+0.014120    test-rmse:0.498596+0.060440
## 
## 44 
## [1]  train-rmse:0.864077+0.015204    test-rmse:0.871112+0.062374 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.761195+0.014662    test-rmse:0.777223+0.062682 
## [3]  train-rmse:0.686763+0.014304    test-rmse:0.710511+0.063844 
## [4]  train-rmse:0.631556+0.013861    test-rmse:0.663813+0.064706 
## [5]  train-rmse:0.590376+0.013964    test-rmse:0.630353+0.062897 
## [6]  train-rmse:0.559433+0.013335    test-rmse:0.609579+0.059891 
## [7]  train-rmse:0.536613+0.013637    test-rmse:0.594126+0.057149 
## [8]  train-rmse:0.517645+0.014527    test-rmse:0.580301+0.054502 
## [9]  train-rmse:0.504224+0.014860    test-rmse:0.572413+0.054062 
## [10] train-rmse:0.491726+0.014913    test-rmse:0.563477+0.051593 
## [11] train-rmse:0.483237+0.015047    test-rmse:0.558553+0.050518 
## [12] train-rmse:0.474141+0.012973    test-rmse:0.555110+0.054343 
## [13] train-rmse:0.466856+0.012118    test-rmse:0.551968+0.056211 
## [14] train-rmse:0.458942+0.012461    test-rmse:0.547573+0.055106 
## [15] train-rmse:0.453150+0.012476    test-rmse:0.544777+0.056020 
## [16] train-rmse:0.445925+0.012485    test-rmse:0.541398+0.054113 
## [17] train-rmse:0.439712+0.011336    test-rmse:0.537803+0.055454 
## [18] train-rmse:0.435043+0.011035    test-rmse:0.534440+0.055573 
## [19] train-rmse:0.430681+0.011074    test-rmse:0.532068+0.055885 
## [20] train-rmse:0.424871+0.010654    test-rmse:0.529395+0.057048 
## [21] train-rmse:0.420624+0.011394    test-rmse:0.527640+0.056482 
## [22] train-rmse:0.415652+0.011275    test-rmse:0.524098+0.055395 
## [23] train-rmse:0.411762+0.011336    test-rmse:0.521898+0.058178 
## [24] train-rmse:0.407829+0.010909    test-rmse:0.520563+0.058719 
## [25] train-rmse:0.404418+0.010932    test-rmse:0.520939+0.058113 
## [26] train-rmse:0.400721+0.010229    test-rmse:0.519314+0.056808 
## [27] train-rmse:0.397011+0.010641    test-rmse:0.517164+0.056647 
## [28] train-rmse:0.393573+0.011278    test-rmse:0.515045+0.057153 
## [29] train-rmse:0.389711+0.011836    test-rmse:0.514090+0.056462 
## [30] train-rmse:0.386190+0.011360    test-rmse:0.512379+0.057290 
## [31] train-rmse:0.382974+0.011319    test-rmse:0.511869+0.056750 
## [32] train-rmse:0.380426+0.011417    test-rmse:0.510911+0.056891 
## [33] train-rmse:0.377476+0.011589    test-rmse:0.509751+0.056653 
## [34] train-rmse:0.374696+0.011092    test-rmse:0.509758+0.057133 
## [35] train-rmse:0.372043+0.010860    test-rmse:0.509093+0.057370 
## [36] train-rmse:0.369513+0.011582    test-rmse:0.508507+0.057386 
## [37] train-rmse:0.366509+0.011691    test-rmse:0.507990+0.056864 
## [38] train-rmse:0.363268+0.011182    test-rmse:0.506291+0.057545 
## [39] train-rmse:0.360784+0.011009    test-rmse:0.506385+0.057172 
## [40] train-rmse:0.357066+0.011693    test-rmse:0.504931+0.056800 
## [41] train-rmse:0.354882+0.011117    test-rmse:0.504490+0.057119 
## [42] train-rmse:0.352437+0.010843    test-rmse:0.504162+0.055966 
## [43] train-rmse:0.350133+0.010267    test-rmse:0.504012+0.055757 
## [44] train-rmse:0.347688+0.009728    test-rmse:0.503006+0.054873 
## [45] train-rmse:0.345238+0.009778    test-rmse:0.502546+0.054106 
## [46] train-rmse:0.343031+0.010040    test-rmse:0.502030+0.054917 
## [47] train-rmse:0.341076+0.009507    test-rmse:0.501279+0.055218 
## [48] train-rmse:0.338580+0.009591    test-rmse:0.500817+0.054784 
## [49] train-rmse:0.336678+0.009212    test-rmse:0.501910+0.053694 
## [50] train-rmse:0.335132+0.009261    test-rmse:0.501599+0.052900 
## [51] train-rmse:0.332775+0.009198    test-rmse:0.500882+0.053328 
## [52] train-rmse:0.330524+0.008933    test-rmse:0.501830+0.053173 
## [53] train-rmse:0.328078+0.008744    test-rmse:0.500691+0.054350 
## [54] train-rmse:0.325334+0.009284    test-rmse:0.499377+0.054253 
## [55] train-rmse:0.323766+0.008965    test-rmse:0.499015+0.054274 
## [56] train-rmse:0.322239+0.009080    test-rmse:0.499135+0.054490 
## [57] train-rmse:0.319674+0.009559    test-rmse:0.498382+0.054005 
## [58] train-rmse:0.317296+0.009639    test-rmse:0.499284+0.053335 
## [59] train-rmse:0.313823+0.011269    test-rmse:0.499583+0.053814 
## [60] train-rmse:0.311713+0.010639    test-rmse:0.499721+0.053299 
## [61] train-rmse:0.309905+0.011280    test-rmse:0.499646+0.053112 
## [62] train-rmse:0.307930+0.011323    test-rmse:0.499880+0.052365 
## [63] train-rmse:0.306107+0.011614    test-rmse:0.499485+0.052118 
## Stopping. Best iteration:
## [57] train-rmse:0.319674+0.009559    test-rmse:0.498382+0.054005
## 
## 45 
## [1]  train-rmse:0.857388+0.014974    test-rmse:0.867099+0.060963 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.747853+0.013948    test-rmse:0.770470+0.061352 
## [3]  train-rmse:0.665472+0.013055    test-rmse:0.698917+0.063377 
## [4]  train-rmse:0.606642+0.012589    test-rmse:0.651457+0.068215 
## [5]  train-rmse:0.558833+0.013882    test-rmse:0.613285+0.061543 
## [6]  train-rmse:0.520938+0.014301    test-rmse:0.587787+0.057387 
## [7]  train-rmse:0.493039+0.015195    test-rmse:0.569702+0.054441 
## [8]  train-rmse:0.472053+0.014476    test-rmse:0.558285+0.055137 
## [9]  train-rmse:0.456181+0.015405    test-rmse:0.551276+0.050496 
## [10] train-rmse:0.440612+0.014814    test-rmse:0.543874+0.051752 
## [11] train-rmse:0.428005+0.015965    test-rmse:0.537260+0.047726 
## [12] train-rmse:0.417368+0.014097    test-rmse:0.532273+0.050430 
## [13] train-rmse:0.408870+0.013836    test-rmse:0.527022+0.052305 
## [14] train-rmse:0.401145+0.013253    test-rmse:0.522744+0.053147 
## [15] train-rmse:0.392516+0.011950    test-rmse:0.517236+0.054977 
## [16] train-rmse:0.386405+0.011102    test-rmse:0.513325+0.055025 
## [17] train-rmse:0.380839+0.011030    test-rmse:0.512735+0.055392 
## [18] train-rmse:0.374197+0.009776    test-rmse:0.510404+0.056300 
## [19] train-rmse:0.368435+0.010332    test-rmse:0.508676+0.055396 
## [20] train-rmse:0.361838+0.011203    test-rmse:0.509210+0.053864 
## [21] train-rmse:0.355046+0.012280    test-rmse:0.506928+0.053668 
## [22] train-rmse:0.350792+0.012728    test-rmse:0.505539+0.054031 
## [23] train-rmse:0.345316+0.013765    test-rmse:0.505468+0.054443 
## [24] train-rmse:0.340818+0.012635    test-rmse:0.504435+0.056191 
## [25] train-rmse:0.337278+0.012360    test-rmse:0.503725+0.055788 
## [26] train-rmse:0.333005+0.012303    test-rmse:0.503931+0.054973 
## [27] train-rmse:0.329425+0.012777    test-rmse:0.503172+0.055285 
## [28] train-rmse:0.324988+0.013140    test-rmse:0.501902+0.055223 
## [29] train-rmse:0.319868+0.012790    test-rmse:0.500758+0.056518 
## [30] train-rmse:0.316650+0.013070    test-rmse:0.499285+0.057303 
## [31] train-rmse:0.312424+0.012938    test-rmse:0.498839+0.055762 
## [32] train-rmse:0.309771+0.012698    test-rmse:0.499036+0.055425 
## [33] train-rmse:0.305373+0.011942    test-rmse:0.499403+0.055445 
## [34] train-rmse:0.300221+0.012879    test-rmse:0.499263+0.056381 
## [35] train-rmse:0.298372+0.012486    test-rmse:0.499451+0.056267 
## [36] train-rmse:0.295340+0.012240    test-rmse:0.498954+0.056018 
## [37] train-rmse:0.292569+0.012675    test-rmse:0.498932+0.055176 
## Stopping. Best iteration:
## [31] train-rmse:0.312424+0.012938    test-rmse:0.498839+0.055762
## 
## 46 
## [1]  train-rmse:0.851431+0.014428    test-rmse:0.863680+0.061730 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.735948+0.013384    test-rmse:0.763291+0.060418 
## [3]  train-rmse:0.649030+0.012607    test-rmse:0.690905+0.061700 
## [4]  train-rmse:0.582802+0.010468    test-rmse:0.635669+0.057028 
## [5]  train-rmse:0.532043+0.009810    test-rmse:0.598532+0.055558 
## [6]  train-rmse:0.492377+0.009852    test-rmse:0.571410+0.053985 
## [7]  train-rmse:0.460222+0.010960    test-rmse:0.551810+0.055405 
## [8]  train-rmse:0.435894+0.010250    test-rmse:0.539398+0.054761 
## [9]  train-rmse:0.416430+0.009993    test-rmse:0.529485+0.052533 
## [10] train-rmse:0.398348+0.013251    test-rmse:0.525185+0.050280 
## [11] train-rmse:0.385636+0.011699    test-rmse:0.517889+0.050866 
## [12] train-rmse:0.374467+0.010910    test-rmse:0.513332+0.050849 
## [13] train-rmse:0.364383+0.010591    test-rmse:0.508521+0.051322 
## [14] train-rmse:0.356307+0.009760    test-rmse:0.506170+0.051375 
## [15] train-rmse:0.348254+0.008796    test-rmse:0.504142+0.050895 
## [16] train-rmse:0.339230+0.009353    test-rmse:0.500692+0.051285 
## [17] train-rmse:0.334495+0.009479    test-rmse:0.499738+0.052048 
## [18] train-rmse:0.328392+0.008891    test-rmse:0.497168+0.052786 
## [19] train-rmse:0.321298+0.012496    test-rmse:0.496917+0.053534 
## [20] train-rmse:0.315891+0.011387    test-rmse:0.494826+0.053947 
## [21] train-rmse:0.311890+0.011109    test-rmse:0.495954+0.053456 
## [22] train-rmse:0.305144+0.011173    test-rmse:0.494470+0.052672 
## [23] train-rmse:0.297874+0.010913    test-rmse:0.492811+0.051923 
## [24] train-rmse:0.293000+0.010451    test-rmse:0.491324+0.051187 
## [25] train-rmse:0.287802+0.010330    test-rmse:0.490931+0.052266 
## [26] train-rmse:0.283259+0.010338    test-rmse:0.489137+0.049570 
## [27] train-rmse:0.280321+0.010256    test-rmse:0.489166+0.050227 
## [28] train-rmse:0.276037+0.010386    test-rmse:0.487948+0.051071 
## [29] train-rmse:0.272072+0.008463    test-rmse:0.486335+0.051904 
## [30] train-rmse:0.267456+0.007538    test-rmse:0.486428+0.051466 
## [31] train-rmse:0.263877+0.007207    test-rmse:0.487037+0.051773 
## [32] train-rmse:0.259311+0.006819    test-rmse:0.488322+0.051619 
## [33] train-rmse:0.255505+0.008714    test-rmse:0.487832+0.052345 
## [34] train-rmse:0.251010+0.008136    test-rmse:0.487918+0.052048 
## [35] train-rmse:0.245722+0.007981    test-rmse:0.487692+0.052709 
## Stopping. Best iteration:
## [29] train-rmse:0.272072+0.008463    test-rmse:0.486335+0.051904
## 
## 47 
## [1]  train-rmse:0.845978+0.013885    test-rmse:0.861442+0.061941 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.724792+0.012240    test-rmse:0.756510+0.060742 
## [3]  train-rmse:0.630968+0.011791    test-rmse:0.681634+0.060706 
## [4]  train-rmse:0.560797+0.012422    test-rmse:0.631831+0.063696 
## [5]  train-rmse:0.504689+0.012828    test-rmse:0.591551+0.061727 
## [6]  train-rmse:0.462327+0.014089    test-rmse:0.562954+0.060813 
## [7]  train-rmse:0.426849+0.014500    test-rmse:0.540976+0.059602 
## [8]  train-rmse:0.398714+0.015244    test-rmse:0.530779+0.056860 
## [9]  train-rmse:0.375887+0.013564    test-rmse:0.523243+0.055823 
## [10] train-rmse:0.355044+0.018489    test-rmse:0.519746+0.057026 
## [11] train-rmse:0.342615+0.018549    test-rmse:0.514141+0.054730 
## [12] train-rmse:0.330822+0.019028    test-rmse:0.509622+0.056208 
## [13] train-rmse:0.319929+0.018074    test-rmse:0.508439+0.055173 
## [14] train-rmse:0.310025+0.019740    test-rmse:0.505967+0.055046 
## [15] train-rmse:0.301795+0.018789    test-rmse:0.500982+0.053620 
## [16] train-rmse:0.294856+0.018657    test-rmse:0.500310+0.053216 
## [17] train-rmse:0.288495+0.017595    test-rmse:0.499374+0.051051 
## [18] train-rmse:0.280936+0.017503    test-rmse:0.498861+0.051138 
## [19] train-rmse:0.276223+0.019082    test-rmse:0.499175+0.050758 
## [20] train-rmse:0.269506+0.018175    test-rmse:0.496257+0.051229 
## [21] train-rmse:0.264640+0.017568    test-rmse:0.496844+0.049822 
## [22] train-rmse:0.257577+0.015165    test-rmse:0.496003+0.050974 
## [23] train-rmse:0.253507+0.014154    test-rmse:0.493731+0.051739 
## [24] train-rmse:0.248065+0.014540    test-rmse:0.493958+0.051192 
## [25] train-rmse:0.244749+0.014494    test-rmse:0.493809+0.050321 
## [26] train-rmse:0.239480+0.014430    test-rmse:0.492973+0.050479 
## [27] train-rmse:0.235736+0.013918    test-rmse:0.492766+0.050152 
## [28] train-rmse:0.229803+0.012954    test-rmse:0.492771+0.049327 
## [29] train-rmse:0.222678+0.011850    test-rmse:0.494413+0.051153 
## [30] train-rmse:0.219518+0.011401    test-rmse:0.494995+0.051580 
## [31] train-rmse:0.215596+0.010637    test-rmse:0.495867+0.052105 
## [32] train-rmse:0.211560+0.011508    test-rmse:0.495839+0.051303 
## [33] train-rmse:0.207922+0.011812    test-rmse:0.495227+0.050300 
## Stopping. Best iteration:
## [27] train-rmse:0.235736+0.013918    test-rmse:0.492766+0.050152
## 
## 48 
## [1]  train-rmse:0.840622+0.013406    test-rmse:0.857979+0.060116 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.715628+0.010753    test-rmse:0.754138+0.060101 
## [3]  train-rmse:0.618197+0.009126    test-rmse:0.675485+0.061546 
## [4]  train-rmse:0.543358+0.009676    test-rmse:0.623861+0.059534 
## [5]  train-rmse:0.484314+0.008697    test-rmse:0.586134+0.058760 
## [6]  train-rmse:0.439965+0.009375    test-rmse:0.560089+0.058910 
## [7]  train-rmse:0.401467+0.007883    test-rmse:0.542401+0.059588 
## [8]  train-rmse:0.371122+0.009076    test-rmse:0.530426+0.059615 
## [9]  train-rmse:0.346392+0.009655    test-rmse:0.526038+0.057803 
## [10] train-rmse:0.325875+0.008613    test-rmse:0.522018+0.058378 
## [11] train-rmse:0.311265+0.008985    test-rmse:0.520231+0.056353 
## [12] train-rmse:0.298421+0.010040    test-rmse:0.518783+0.057599 
## [13] train-rmse:0.287655+0.011040    test-rmse:0.516722+0.058516 
## [14] train-rmse:0.275842+0.015876    test-rmse:0.514677+0.058994 
## [15] train-rmse:0.264246+0.017031    test-rmse:0.510007+0.056346 
## [16] train-rmse:0.255649+0.014780    test-rmse:0.508288+0.056764 
## [17] train-rmse:0.245120+0.013186    test-rmse:0.507063+0.055764 
## [18] train-rmse:0.240115+0.014355    test-rmse:0.505604+0.054271 
## [19] train-rmse:0.232693+0.011519    test-rmse:0.502395+0.055770 
## [20] train-rmse:0.225004+0.012081    test-rmse:0.501798+0.056804 
## [21] train-rmse:0.219232+0.011350    test-rmse:0.500954+0.055078 
## [22] train-rmse:0.215105+0.011258    test-rmse:0.499946+0.056205 
## [23] train-rmse:0.211392+0.011248    test-rmse:0.499260+0.057347 
## [24] train-rmse:0.205737+0.009634    test-rmse:0.499134+0.057046 
## [25] train-rmse:0.201520+0.009514    test-rmse:0.499097+0.057345 
## [26] train-rmse:0.196488+0.009101    test-rmse:0.498250+0.057157 
## [27] train-rmse:0.190830+0.008840    test-rmse:0.498783+0.058645 
## [28] train-rmse:0.185866+0.009561    test-rmse:0.498960+0.058486 
## [29] train-rmse:0.178997+0.009362    test-rmse:0.499520+0.056439 
## [30] train-rmse:0.172248+0.010218    test-rmse:0.499149+0.056816 
## [31] train-rmse:0.167490+0.010945    test-rmse:0.499849+0.056280 
## [32] train-rmse:0.162269+0.011603    test-rmse:0.500028+0.056462 
## Stopping. Best iteration:
## [26] train-rmse:0.196488+0.009101    test-rmse:0.498250+0.057157
## 
## 49 
## [1]  train-rmse:0.879593+0.014920    test-rmse:0.882397+0.060659 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.797308+0.013945    test-rmse:0.805163+0.060589 
## [3]  train-rmse:0.742045+0.013707    test-rmse:0.761250+0.057251 
## [4]  train-rmse:0.704265+0.012303    test-rmse:0.727531+0.061672 
## [5]  train-rmse:0.676222+0.012064    test-rmse:0.696299+0.059138 
## [6]  train-rmse:0.655092+0.012005    test-rmse:0.680119+0.054017 
## [7]  train-rmse:0.640317+0.011695    test-rmse:0.667588+0.055899 
## [8]  train-rmse:0.628236+0.011649    test-rmse:0.656010+0.053608 
## [9]  train-rmse:0.619117+0.011256    test-rmse:0.649536+0.052806 
## [10] train-rmse:0.611887+0.011316    test-rmse:0.644026+0.055799 
## [11] train-rmse:0.605266+0.011370    test-rmse:0.637836+0.053602 
## [12] train-rmse:0.599168+0.011353    test-rmse:0.630156+0.053110 
## [13] train-rmse:0.593650+0.011417    test-rmse:0.627401+0.054033 
## [14] train-rmse:0.588984+0.011459    test-rmse:0.624696+0.054295 
## [15] train-rmse:0.584562+0.011404    test-rmse:0.620741+0.055631 
## [16] train-rmse:0.580297+0.011373    test-rmse:0.616118+0.057168 
## [17] train-rmse:0.576481+0.011405    test-rmse:0.612977+0.056411 
## [18] train-rmse:0.572916+0.011348    test-rmse:0.611110+0.056100 
## [19] train-rmse:0.569519+0.011305    test-rmse:0.607585+0.056433 
## [20] train-rmse:0.566316+0.011257    test-rmse:0.603501+0.057941 
## [21] train-rmse:0.563239+0.011363    test-rmse:0.600936+0.054938 
## [22] train-rmse:0.560299+0.011295    test-rmse:0.598190+0.056360 
## [23] train-rmse:0.557405+0.011270    test-rmse:0.597225+0.057369 
## [24] train-rmse:0.554636+0.011302    test-rmse:0.595881+0.057215 
## [25] train-rmse:0.551947+0.011433    test-rmse:0.594284+0.058468 
## [26] train-rmse:0.549423+0.011451    test-rmse:0.593262+0.056986 
## [27] train-rmse:0.546968+0.011455    test-rmse:0.590352+0.055493 
## [28] train-rmse:0.544611+0.011437    test-rmse:0.589827+0.054126 
## [29] train-rmse:0.542345+0.011374    test-rmse:0.588041+0.053099 
## [30] train-rmse:0.540271+0.011340    test-rmse:0.585743+0.053408 
## [31] train-rmse:0.538183+0.011293    test-rmse:0.583949+0.053168 
## [32] train-rmse:0.536212+0.011264    test-rmse:0.583659+0.053049 
## [33] train-rmse:0.534315+0.011202    test-rmse:0.583923+0.054630 
## [34] train-rmse:0.532457+0.011121    test-rmse:0.580890+0.054570 
## [35] train-rmse:0.530621+0.011116    test-rmse:0.580157+0.054786 
## [36] train-rmse:0.528859+0.011048    test-rmse:0.577325+0.055534 
## [37] train-rmse:0.527144+0.011012    test-rmse:0.576043+0.055665 
## [38] train-rmse:0.525452+0.010981    test-rmse:0.574340+0.055566 
## [39] train-rmse:0.523820+0.010936    test-rmse:0.573320+0.056624 
## [40] train-rmse:0.522273+0.010877    test-rmse:0.572401+0.055930 
## [41] train-rmse:0.520758+0.010864    test-rmse:0.571729+0.055092 
## [42] train-rmse:0.519309+0.010850    test-rmse:0.570554+0.054678 
## [43] train-rmse:0.517905+0.010825    test-rmse:0.570119+0.055884 
## [44] train-rmse:0.516527+0.010813    test-rmse:0.569920+0.054836 
## [45] train-rmse:0.515155+0.010790    test-rmse:0.567936+0.053770 
## [46] train-rmse:0.513805+0.010743    test-rmse:0.566873+0.055059 
## [47] train-rmse:0.512531+0.010712    test-rmse:0.565205+0.054460 
## [48] train-rmse:0.511316+0.010651    test-rmse:0.564037+0.054749 
## [49] train-rmse:0.510137+0.010626    test-rmse:0.562642+0.054686 
## [50] train-rmse:0.508995+0.010576    test-rmse:0.562975+0.056164 
## [51] train-rmse:0.507862+0.010527    test-rmse:0.561856+0.056470 
## [52] train-rmse:0.506747+0.010508    test-rmse:0.560645+0.055161 
## [53] train-rmse:0.505651+0.010497    test-rmse:0.561040+0.054928 
## [54] train-rmse:0.504597+0.010509    test-rmse:0.561102+0.054412 
## [55] train-rmse:0.503568+0.010532    test-rmse:0.560024+0.054537 
## [56] train-rmse:0.502578+0.010529    test-rmse:0.559386+0.054110 
## [57] train-rmse:0.501576+0.010520    test-rmse:0.557499+0.054001 
## [58] train-rmse:0.500602+0.010516    test-rmse:0.556984+0.053899 
## [59] train-rmse:0.499628+0.010520    test-rmse:0.555603+0.054266 
## [60] train-rmse:0.498671+0.010502    test-rmse:0.554921+0.054439 
## [61] train-rmse:0.497748+0.010505    test-rmse:0.554135+0.054454 
## [62] train-rmse:0.496848+0.010480    test-rmse:0.552844+0.054432 
## [63] train-rmse:0.495986+0.010435    test-rmse:0.551969+0.054055 
## [64] train-rmse:0.495146+0.010417    test-rmse:0.551484+0.053592 
## [65] train-rmse:0.494334+0.010411    test-rmse:0.551338+0.054267 
## [66] train-rmse:0.493532+0.010401    test-rmse:0.551368+0.053549 
## [67] train-rmse:0.492742+0.010391    test-rmse:0.550476+0.053491 
## [68] train-rmse:0.491992+0.010397    test-rmse:0.551205+0.052862 
## [69] train-rmse:0.491252+0.010406    test-rmse:0.550769+0.052419 
## [70] train-rmse:0.490524+0.010398    test-rmse:0.550709+0.052398 
## [71] train-rmse:0.489813+0.010398    test-rmse:0.549650+0.052296 
## [72] train-rmse:0.489115+0.010395    test-rmse:0.549447+0.052382 
## [73] train-rmse:0.488438+0.010377    test-rmse:0.548307+0.052640 
## [74] train-rmse:0.487762+0.010363    test-rmse:0.548137+0.052072 
## [75] train-rmse:0.487076+0.010375    test-rmse:0.547927+0.052578 
## [76] train-rmse:0.486419+0.010370    test-rmse:0.547666+0.052599 
## [77] train-rmse:0.485774+0.010360    test-rmse:0.547431+0.052161 
## [78] train-rmse:0.485148+0.010341    test-rmse:0.547085+0.052463 
## [79] train-rmse:0.484537+0.010333    test-rmse:0.545963+0.052569 
## [80] train-rmse:0.483918+0.010322    test-rmse:0.545684+0.052844 
## [81] train-rmse:0.483312+0.010310    test-rmse:0.546395+0.052606 
## [82] train-rmse:0.482722+0.010290    test-rmse:0.546508+0.053700 
## [83] train-rmse:0.482148+0.010283    test-rmse:0.546409+0.053881 
## [84] train-rmse:0.481593+0.010286    test-rmse:0.546000+0.053713 
## [85] train-rmse:0.481058+0.010284    test-rmse:0.544972+0.053230 
## [86] train-rmse:0.480529+0.010284    test-rmse:0.544235+0.053207 
## [87] train-rmse:0.480013+0.010277    test-rmse:0.544003+0.052751 
## [88] train-rmse:0.479484+0.010255    test-rmse:0.542919+0.052209 
## [89] train-rmse:0.478989+0.010246    test-rmse:0.542182+0.052564 
## [90] train-rmse:0.478473+0.010224    test-rmse:0.542377+0.052223 
## [91] train-rmse:0.477986+0.010225    test-rmse:0.542025+0.051570 
## [92] train-rmse:0.477510+0.010224    test-rmse:0.542588+0.051580 
## [93] train-rmse:0.477033+0.010229    test-rmse:0.542207+0.051343 
## [94] train-rmse:0.476573+0.010240    test-rmse:0.541847+0.051229 
## [95] train-rmse:0.476114+0.010244    test-rmse:0.541215+0.050948 
## [96] train-rmse:0.475667+0.010238    test-rmse:0.541186+0.050506 
## [97] train-rmse:0.475233+0.010235    test-rmse:0.541248+0.050421 
## [98] train-rmse:0.474804+0.010227    test-rmse:0.541195+0.050368 
## [99] train-rmse:0.474381+0.010235    test-rmse:0.540790+0.050368 
## [100]    train-rmse:0.473971+0.010241    test-rmse:0.540819+0.050514 
## [101]    train-rmse:0.473569+0.010236    test-rmse:0.540640+0.050699 
## [102]    train-rmse:0.473162+0.010236    test-rmse:0.540192+0.051235 
## [103]    train-rmse:0.472767+0.010221    test-rmse:0.540132+0.051336 
## [104]    train-rmse:0.472373+0.010209    test-rmse:0.539884+0.052250 
## [105]    train-rmse:0.471988+0.010196    test-rmse:0.539718+0.051577 
## [106]    train-rmse:0.471606+0.010171    test-rmse:0.539396+0.050889 
## [107]    train-rmse:0.471222+0.010154    test-rmse:0.538877+0.050322 
## [108]    train-rmse:0.470850+0.010131    test-rmse:0.538190+0.050245 
## [109]    train-rmse:0.470488+0.010113    test-rmse:0.537778+0.050206 
## [110]    train-rmse:0.470141+0.010095    test-rmse:0.537507+0.050149 
## [111]    train-rmse:0.469799+0.010084    test-rmse:0.537475+0.050459 
## [112]    train-rmse:0.469465+0.010078    test-rmse:0.537020+0.050405 
## [113]    train-rmse:0.469135+0.010066    test-rmse:0.536445+0.050187 
## [114]    train-rmse:0.468809+0.010059    test-rmse:0.536557+0.049989 
## [115]    train-rmse:0.468487+0.010051    test-rmse:0.536689+0.049656 
## [116]    train-rmse:0.468172+0.010044    test-rmse:0.536727+0.049606 
## [117]    train-rmse:0.467852+0.010039    test-rmse:0.536677+0.049610 
## [118]    train-rmse:0.467542+0.010027    test-rmse:0.536482+0.049712 
## [119]    train-rmse:0.467220+0.010017    test-rmse:0.536758+0.049485 
## Stopping. Best iteration:
## [113]    train-rmse:0.469135+0.010066    test-rmse:0.536445+0.050187
## 
## 50 
## [1]  train-rmse:0.860486+0.014766    test-rmse:0.864754+0.060712 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.761515+0.014150    test-rmse:0.776586+0.063324 
## [3]  train-rmse:0.693869+0.013184    test-rmse:0.714958+0.064139 
## [4]  train-rmse:0.646752+0.012176    test-rmse:0.675296+0.066420 
## [5]  train-rmse:0.612037+0.011190    test-rmse:0.644288+0.066395 
## [6]  train-rmse:0.586941+0.011901    test-rmse:0.627227+0.066273 
## [7]  train-rmse:0.568162+0.011760    test-rmse:0.612731+0.068944 
## [8]  train-rmse:0.555781+0.011727    test-rmse:0.603013+0.069735 
## [9]  train-rmse:0.543510+0.014344    test-rmse:0.595191+0.065773 
## [10] train-rmse:0.534023+0.013089    test-rmse:0.584613+0.067682 
## [11] train-rmse:0.525559+0.012879    test-rmse:0.579931+0.065437 
## [12] train-rmse:0.518659+0.012868    test-rmse:0.577296+0.063971 
## [13] train-rmse:0.512153+0.013300    test-rmse:0.572233+0.061522 
## [14] train-rmse:0.506486+0.013601    test-rmse:0.569865+0.062043 
## [15] train-rmse:0.501460+0.013585    test-rmse:0.568508+0.063432 
## [16] train-rmse:0.495659+0.014165    test-rmse:0.561547+0.061986 
## [17] train-rmse:0.490763+0.014015    test-rmse:0.557932+0.060011 
## [18] train-rmse:0.485876+0.014129    test-rmse:0.556284+0.061231 
## [19] train-rmse:0.481043+0.013885    test-rmse:0.552800+0.059350 
## [20] train-rmse:0.476396+0.014126    test-rmse:0.550933+0.059542 
## [21] train-rmse:0.472550+0.014624    test-rmse:0.550454+0.059030 
## [22] train-rmse:0.468960+0.014390    test-rmse:0.547068+0.061151 
## [23] train-rmse:0.465394+0.014208    test-rmse:0.544477+0.059577 
## [24] train-rmse:0.462085+0.013656    test-rmse:0.542304+0.058840 
## [25] train-rmse:0.458821+0.013072    test-rmse:0.539935+0.062077 
## [26] train-rmse:0.455572+0.013593    test-rmse:0.538078+0.060317 
## [27] train-rmse:0.452383+0.013078    test-rmse:0.536700+0.061859 
## [28] train-rmse:0.449871+0.013212    test-rmse:0.537221+0.062782 
## [29] train-rmse:0.447466+0.013500    test-rmse:0.536532+0.061787 
## [30] train-rmse:0.444948+0.013726    test-rmse:0.534426+0.061488 
## [31] train-rmse:0.441620+0.014194    test-rmse:0.532467+0.062020 
## [32] train-rmse:0.439094+0.013607    test-rmse:0.532123+0.061254 
## [33] train-rmse:0.436818+0.013373    test-rmse:0.530580+0.060552 
## [34] train-rmse:0.434351+0.013095    test-rmse:0.529877+0.059531 
## [35] train-rmse:0.431955+0.013900    test-rmse:0.529794+0.060065 
## [36] train-rmse:0.429170+0.013578    test-rmse:0.529479+0.060957 
## [37] train-rmse:0.427145+0.013383    test-rmse:0.529399+0.059660 
## [38] train-rmse:0.424493+0.013684    test-rmse:0.528474+0.059015 
## [39] train-rmse:0.422418+0.013845    test-rmse:0.528316+0.058667 
## [40] train-rmse:0.420338+0.014341    test-rmse:0.526990+0.057401 
## [41] train-rmse:0.418397+0.014637    test-rmse:0.524622+0.058494 
## [42] train-rmse:0.416154+0.014487    test-rmse:0.523672+0.058957 
## [43] train-rmse:0.414285+0.014047    test-rmse:0.522051+0.061175 
## [44] train-rmse:0.412147+0.014044    test-rmse:0.521343+0.061349 
## [45] train-rmse:0.410542+0.014128    test-rmse:0.521172+0.061036 
## [46] train-rmse:0.408262+0.014346    test-rmse:0.520310+0.060976 
## [47] train-rmse:0.405745+0.013626    test-rmse:0.519900+0.060461 
## [48] train-rmse:0.404076+0.013476    test-rmse:0.518841+0.059352 
## [49] train-rmse:0.402127+0.013241    test-rmse:0.518728+0.060079 
## [50] train-rmse:0.399956+0.013728    test-rmse:0.518231+0.061103 
## [51] train-rmse:0.398748+0.013602    test-rmse:0.517818+0.060638 
## [52] train-rmse:0.397150+0.014078    test-rmse:0.516183+0.060584 
## [53] train-rmse:0.395464+0.014222    test-rmse:0.516132+0.060657 
## [54] train-rmse:0.393779+0.014425    test-rmse:0.515412+0.061518 
## [55] train-rmse:0.391994+0.014266    test-rmse:0.514185+0.062231 
## [56] train-rmse:0.390666+0.014025    test-rmse:0.514208+0.062662 
## [57] train-rmse:0.389464+0.013974    test-rmse:0.514433+0.062301 
## [58] train-rmse:0.388518+0.014100    test-rmse:0.513790+0.062680 
## [59] train-rmse:0.387537+0.014053    test-rmse:0.513556+0.061721 
## [60] train-rmse:0.386398+0.013926    test-rmse:0.512708+0.061061 
## [61] train-rmse:0.384593+0.013863    test-rmse:0.512649+0.060089 
## [62] train-rmse:0.382578+0.013873    test-rmse:0.511295+0.060287 
## [63] train-rmse:0.380981+0.014093    test-rmse:0.510786+0.059939 
## [64] train-rmse:0.379308+0.013952    test-rmse:0.511408+0.060388 
## [65] train-rmse:0.377959+0.013847    test-rmse:0.512338+0.060106 
## [66] train-rmse:0.376395+0.013220    test-rmse:0.510226+0.061607 
## [67] train-rmse:0.375400+0.013460    test-rmse:0.510395+0.061290 
## [68] train-rmse:0.374315+0.013382    test-rmse:0.510088+0.061002 
## [69] train-rmse:0.373278+0.013366    test-rmse:0.510853+0.060168 
## [70] train-rmse:0.372280+0.013230    test-rmse:0.510285+0.060707 
## [71] train-rmse:0.371139+0.013552    test-rmse:0.510310+0.060202 
## [72] train-rmse:0.370398+0.013614    test-rmse:0.509953+0.059878 
## [73] train-rmse:0.369066+0.014005    test-rmse:0.509565+0.059093 
## [74] train-rmse:0.368012+0.013858    test-rmse:0.508715+0.058826 
## [75] train-rmse:0.366548+0.014554    test-rmse:0.507557+0.058886 
## [76] train-rmse:0.364698+0.015251    test-rmse:0.507029+0.058920 
## [77] train-rmse:0.363429+0.015059    test-rmse:0.506434+0.059546 
## [78] train-rmse:0.362475+0.014984    test-rmse:0.506010+0.059736 
## [79] train-rmse:0.361420+0.015058    test-rmse:0.506671+0.060022 
## [80] train-rmse:0.360015+0.015652    test-rmse:0.507099+0.058490 
## [81] train-rmse:0.358710+0.014684    test-rmse:0.507423+0.057680 
## [82] train-rmse:0.357882+0.014557    test-rmse:0.507175+0.056827 
## [83] train-rmse:0.356268+0.015322    test-rmse:0.506311+0.055995 
## [84] train-rmse:0.355135+0.014957    test-rmse:0.505648+0.055888 
## [85] train-rmse:0.354493+0.014961    test-rmse:0.505440+0.055560 
## [86] train-rmse:0.353553+0.015066    test-rmse:0.505664+0.055391 
## [87] train-rmse:0.352383+0.015054    test-rmse:0.505368+0.055731 
## [88] train-rmse:0.351182+0.014522    test-rmse:0.504050+0.055700 
## [89] train-rmse:0.349461+0.014250    test-rmse:0.504678+0.055446 
## [90] train-rmse:0.348189+0.013697    test-rmse:0.504143+0.055890 
## [91] train-rmse:0.347396+0.013578    test-rmse:0.504018+0.055563 
## [92] train-rmse:0.346464+0.013589    test-rmse:0.503921+0.054542 
## [93] train-rmse:0.345430+0.013477    test-rmse:0.503367+0.054886 
## [94] train-rmse:0.344715+0.013236    test-rmse:0.502823+0.054056 
## [95] train-rmse:0.343972+0.013026    test-rmse:0.502645+0.053934 
## [96] train-rmse:0.342481+0.013644    test-rmse:0.502633+0.054456 
## [97] train-rmse:0.341791+0.013493    test-rmse:0.502385+0.054552 
## [98] train-rmse:0.340330+0.013519    test-rmse:0.502239+0.054199 
## [99] train-rmse:0.338645+0.013675    test-rmse:0.502431+0.053540 
## [100]    train-rmse:0.337733+0.013690    test-rmse:0.501999+0.053408 
## [101]    train-rmse:0.336869+0.013379    test-rmse:0.502305+0.053903 
## [102]    train-rmse:0.336182+0.013145    test-rmse:0.502095+0.054567 
## [103]    train-rmse:0.335620+0.012938    test-rmse:0.502243+0.054541 
## [104]    train-rmse:0.333821+0.012184    test-rmse:0.502387+0.054801 
## [105]    train-rmse:0.332804+0.012526    test-rmse:0.502261+0.054639 
## [106]    train-rmse:0.331809+0.012908    test-rmse:0.502712+0.054525 
## Stopping. Best iteration:
## [100]    train-rmse:0.337733+0.013690    test-rmse:0.501999+0.053408
## 
## 51 
## [1]  train-rmse:0.851946+0.015123    test-rmse:0.859898+0.062482 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.743928+0.014671    test-rmse:0.762900+0.062243 
## [3]  train-rmse:0.667582+0.014085    test-rmse:0.692543+0.064229 
## [4]  train-rmse:0.611739+0.013795    test-rmse:0.647505+0.064270 
## [5]  train-rmse:0.571522+0.013749    test-rmse:0.616073+0.060140 
## [6]  train-rmse:0.543243+0.013690    test-rmse:0.597855+0.055587 
## [7]  train-rmse:0.521356+0.014272    test-rmse:0.578729+0.054712 
## [8]  train-rmse:0.505652+0.014531    test-rmse:0.566828+0.051684 
## [9]  train-rmse:0.492029+0.013798    test-rmse:0.560339+0.051018 
## [10] train-rmse:0.480578+0.012421    test-rmse:0.554641+0.054588 
## [11] train-rmse:0.470459+0.014030    test-rmse:0.552287+0.056850 
## [12] train-rmse:0.461985+0.013755    test-rmse:0.546514+0.055523 
## [13] train-rmse:0.454535+0.014819    test-rmse:0.542899+0.056531 
## [14] train-rmse:0.447208+0.015382    test-rmse:0.539345+0.056862 
## [15] train-rmse:0.440514+0.014643    test-rmse:0.536319+0.054768 
## [16] train-rmse:0.433415+0.013457    test-rmse:0.532000+0.053348 
## [17] train-rmse:0.427691+0.013979    test-rmse:0.529272+0.054207 
## [18] train-rmse:0.423001+0.013686    test-rmse:0.525327+0.054928 
## [19] train-rmse:0.417946+0.015137    test-rmse:0.523111+0.053766 
## [20] train-rmse:0.413313+0.015055    test-rmse:0.523097+0.053264 
## [21] train-rmse:0.408849+0.014504    test-rmse:0.521620+0.052844 
## [22] train-rmse:0.403640+0.014664    test-rmse:0.517496+0.054432 
## [23] train-rmse:0.400012+0.014469    test-rmse:0.516803+0.056067 
## [24] train-rmse:0.396416+0.014563    test-rmse:0.514173+0.054871 
## [25] train-rmse:0.392246+0.014168    test-rmse:0.511907+0.055244 
## [26] train-rmse:0.387869+0.013727    test-rmse:0.510687+0.056384 
## [27] train-rmse:0.384473+0.013077    test-rmse:0.509548+0.057579 
## [28] train-rmse:0.380206+0.012797    test-rmse:0.506388+0.058415 
## [29] train-rmse:0.377051+0.013076    test-rmse:0.505813+0.057464 
## [30] train-rmse:0.373531+0.012246    test-rmse:0.505581+0.057728 
## [31] train-rmse:0.370685+0.012314    test-rmse:0.505919+0.058512 
## [32] train-rmse:0.368023+0.012712    test-rmse:0.506034+0.058293 
## [33] train-rmse:0.364475+0.013455    test-rmse:0.504500+0.057400 
## [34] train-rmse:0.362121+0.013609    test-rmse:0.505397+0.056361 
## [35] train-rmse:0.359091+0.013047    test-rmse:0.503402+0.057891 
## [36] train-rmse:0.356801+0.012538    test-rmse:0.503753+0.057213 
## [37] train-rmse:0.352567+0.013570    test-rmse:0.502693+0.057986 
## [38] train-rmse:0.349747+0.013042    test-rmse:0.500979+0.057597 
## [39] train-rmse:0.347458+0.012921    test-rmse:0.501499+0.058447 
## [40] train-rmse:0.344481+0.013613    test-rmse:0.500773+0.058796 
## [41] train-rmse:0.341616+0.013538    test-rmse:0.501047+0.059084 
## [42] train-rmse:0.339711+0.013030    test-rmse:0.499856+0.060019 
## [43] train-rmse:0.336912+0.013154    test-rmse:0.499011+0.059354 
## [44] train-rmse:0.334299+0.012344    test-rmse:0.498517+0.059141 
## [45] train-rmse:0.331097+0.011833    test-rmse:0.496615+0.059638 
## [46] train-rmse:0.328872+0.011697    test-rmse:0.495655+0.059226 
## [47] train-rmse:0.326429+0.011565    test-rmse:0.495393+0.058634 
## [48] train-rmse:0.323710+0.010916    test-rmse:0.493957+0.058566 
## [49] train-rmse:0.320790+0.010237    test-rmse:0.493877+0.057304 
## [50] train-rmse:0.318314+0.010732    test-rmse:0.494960+0.057387 
## [51] train-rmse:0.316308+0.010291    test-rmse:0.495881+0.056487 
## [52] train-rmse:0.313592+0.010094    test-rmse:0.495330+0.056638 
## [53] train-rmse:0.311268+0.010680    test-rmse:0.495110+0.056223 
## [54] train-rmse:0.308623+0.011069    test-rmse:0.493731+0.055862 
## [55] train-rmse:0.306283+0.010825    test-rmse:0.493793+0.056295 
## [56] train-rmse:0.304471+0.010936    test-rmse:0.493504+0.056796 
## [57] train-rmse:0.303062+0.011077    test-rmse:0.493251+0.056473 
## [58] train-rmse:0.300350+0.011907    test-rmse:0.493298+0.057295 
## [59] train-rmse:0.297638+0.011036    test-rmse:0.492384+0.056854 
## [60] train-rmse:0.295258+0.010715    test-rmse:0.492746+0.055101 
## [61] train-rmse:0.292872+0.010953    test-rmse:0.493219+0.053786 
## [62] train-rmse:0.291374+0.010467    test-rmse:0.493515+0.052615 
## [63] train-rmse:0.288639+0.010649    test-rmse:0.493303+0.051869 
## [64] train-rmse:0.286982+0.010716    test-rmse:0.492551+0.051389 
## [65] train-rmse:0.284783+0.011268    test-rmse:0.492379+0.050344 
## [66] train-rmse:0.282689+0.012038    test-rmse:0.493100+0.050215 
## [67] train-rmse:0.280112+0.011645    test-rmse:0.493038+0.050447 
## [68] train-rmse:0.277464+0.011928    test-rmse:0.492906+0.050284 
## [69] train-rmse:0.275569+0.010786    test-rmse:0.492374+0.049943 
## [70] train-rmse:0.274233+0.010712    test-rmse:0.492452+0.050215 
## [71] train-rmse:0.272924+0.010467    test-rmse:0.492296+0.050493 
## [72] train-rmse:0.271482+0.010471    test-rmse:0.491778+0.049911 
## [73] train-rmse:0.269462+0.010588    test-rmse:0.491332+0.049343 
## [74] train-rmse:0.267859+0.010478    test-rmse:0.491050+0.049116 
## [75] train-rmse:0.266112+0.010543    test-rmse:0.490836+0.049503 
## [76] train-rmse:0.263071+0.011346    test-rmse:0.491475+0.049035 
## [77] train-rmse:0.261799+0.011256    test-rmse:0.492099+0.047963 
## [78] train-rmse:0.259672+0.011724    test-rmse:0.491932+0.048215 
## [79] train-rmse:0.257700+0.011791    test-rmse:0.490814+0.048201 
## [80] train-rmse:0.255871+0.011965    test-rmse:0.490968+0.049605 
## [81] train-rmse:0.254005+0.012395    test-rmse:0.490960+0.049359 
## [82] train-rmse:0.252210+0.011891    test-rmse:0.491667+0.048533 
## [83] train-rmse:0.250811+0.011999    test-rmse:0.491147+0.048372 
## [84] train-rmse:0.249471+0.012206    test-rmse:0.490537+0.048692 
## [85] train-rmse:0.247540+0.011878    test-rmse:0.490662+0.048934 
## [86] train-rmse:0.245180+0.011470    test-rmse:0.489763+0.048861 
## [87] train-rmse:0.243628+0.012168    test-rmse:0.489497+0.047601 
## [88] train-rmse:0.242791+0.012138    test-rmse:0.489360+0.047532 
## [89] train-rmse:0.240964+0.013274    test-rmse:0.489641+0.047576 
## [90] train-rmse:0.239441+0.013243    test-rmse:0.489538+0.047364 
## [91] train-rmse:0.238123+0.013704    test-rmse:0.488886+0.047277 
## [92] train-rmse:0.237076+0.013671    test-rmse:0.488479+0.047223 
## [93] train-rmse:0.235319+0.013449    test-rmse:0.487820+0.046595 
## [94] train-rmse:0.233781+0.013131    test-rmse:0.487480+0.046761 
## [95] train-rmse:0.231702+0.012849    test-rmse:0.487487+0.047281 
## [96] train-rmse:0.229825+0.012591    test-rmse:0.487541+0.046846 
## [97] train-rmse:0.228855+0.012472    test-rmse:0.487779+0.046618 
## [98] train-rmse:0.227829+0.012650    test-rmse:0.488828+0.047644 
## [99] train-rmse:0.226865+0.012520    test-rmse:0.488996+0.047272 
## [100]    train-rmse:0.225235+0.012463    test-rmse:0.488707+0.047754 
## Stopping. Best iteration:
## [94] train-rmse:0.233781+0.013131    test-rmse:0.487480+0.046761
## 
## 52 
## [1]  train-rmse:0.844613+0.014873    test-rmse:0.855506+0.060931 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.729408+0.013858    test-rmse:0.753988+0.061850 
## [3]  train-rmse:0.645135+0.012445    test-rmse:0.682335+0.063728 
## [4]  train-rmse:0.587801+0.012787    test-rmse:0.636252+0.064860 
## [5]  train-rmse:0.538456+0.015221    test-rmse:0.597047+0.057233 
## [6]  train-rmse:0.504955+0.015780    test-rmse:0.578062+0.057877 
## [7]  train-rmse:0.480670+0.014314    test-rmse:0.565297+0.057891 
## [8]  train-rmse:0.459299+0.015193    test-rmse:0.552934+0.057717 
## [9]  train-rmse:0.443870+0.016082    test-rmse:0.543374+0.052526 
## [10] train-rmse:0.430510+0.017373    test-rmse:0.534849+0.053857 
## [11] train-rmse:0.420303+0.015580    test-rmse:0.529471+0.055197 
## [12] train-rmse:0.409652+0.016412    test-rmse:0.525328+0.054381 
## [13] train-rmse:0.401856+0.017090    test-rmse:0.521608+0.055078 
## [14] train-rmse:0.394494+0.016727    test-rmse:0.518634+0.055295 
## [15] train-rmse:0.387123+0.014803    test-rmse:0.514837+0.054855 
## [16] train-rmse:0.381076+0.015038    test-rmse:0.512937+0.053797 
## [17] train-rmse:0.373987+0.015810    test-rmse:0.512121+0.053128 
## [18] train-rmse:0.369168+0.014787    test-rmse:0.511828+0.051990 
## [19] train-rmse:0.362885+0.014126    test-rmse:0.512034+0.052608 
## [20] train-rmse:0.356127+0.013885    test-rmse:0.510545+0.053421 
## [21] train-rmse:0.351299+0.014439    test-rmse:0.510052+0.052895 
## [22] train-rmse:0.346601+0.013694    test-rmse:0.507056+0.053797 
## [23] train-rmse:0.341644+0.013586    test-rmse:0.504017+0.053507 
## [24] train-rmse:0.338312+0.013447    test-rmse:0.505317+0.053269 
## [25] train-rmse:0.334205+0.013149    test-rmse:0.503711+0.051807 
## [26] train-rmse:0.329194+0.013481    test-rmse:0.501099+0.053881 
## [27] train-rmse:0.324657+0.014737    test-rmse:0.500358+0.054310 
## [28] train-rmse:0.320218+0.015105    test-rmse:0.498073+0.054588 
## [29] train-rmse:0.316018+0.014880    test-rmse:0.497338+0.053131 
## [30] train-rmse:0.311336+0.013458    test-rmse:0.495786+0.055407 
## [31] train-rmse:0.308191+0.013652    test-rmse:0.494529+0.053439 
## [32] train-rmse:0.304634+0.013396    test-rmse:0.494707+0.052412 
## [33] train-rmse:0.301045+0.012604    test-rmse:0.492768+0.053458 
## [34] train-rmse:0.296927+0.012931    test-rmse:0.492646+0.051953 
## [35] train-rmse:0.292397+0.012387    test-rmse:0.492616+0.051738 
## [36] train-rmse:0.287336+0.014267    test-rmse:0.492517+0.051284 
## [37] train-rmse:0.283486+0.014593    test-rmse:0.493005+0.051167 
## [38] train-rmse:0.279941+0.013490    test-rmse:0.493745+0.050799 
## [39] train-rmse:0.275760+0.014264    test-rmse:0.493086+0.047444 
## [40] train-rmse:0.272738+0.013428    test-rmse:0.493998+0.046940 
## [41] train-rmse:0.269635+0.012838    test-rmse:0.493643+0.047124 
## [42] train-rmse:0.265875+0.013512    test-rmse:0.493667+0.047070 
## Stopping. Best iteration:
## [36] train-rmse:0.287336+0.014267    test-rmse:0.492517+0.051284
## 
## 53 
## [1]  train-rmse:0.838076+0.014280    test-rmse:0.851791+0.061729 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.715451+0.011992    test-rmse:0.743849+0.063197 
## [3]  train-rmse:0.625217+0.012478    test-rmse:0.670229+0.063508 
## [4]  train-rmse:0.557164+0.010947    test-rmse:0.619570+0.058938 
## [5]  train-rmse:0.507851+0.011329    test-rmse:0.583156+0.056536 
## [6]  train-rmse:0.469859+0.009973    test-rmse:0.562983+0.057264 
## [7]  train-rmse:0.439634+0.010623    test-rmse:0.544200+0.055171 
## [8]  train-rmse:0.416391+0.010664    test-rmse:0.535256+0.054764 
## [9]  train-rmse:0.399288+0.011072    test-rmse:0.531262+0.053100 
## [10] train-rmse:0.385672+0.012425    test-rmse:0.527530+0.052193 
## [11] train-rmse:0.371817+0.010036    test-rmse:0.520633+0.054948 
## [12] train-rmse:0.362819+0.010194    test-rmse:0.514770+0.057612 
## [13] train-rmse:0.351050+0.012045    test-rmse:0.509819+0.059050 
## [14] train-rmse:0.342101+0.010281    test-rmse:0.505776+0.059697 
## [15] train-rmse:0.333279+0.008434    test-rmse:0.504098+0.058453 
## [16] train-rmse:0.325953+0.010502    test-rmse:0.502211+0.056720 
## [17] train-rmse:0.319443+0.009181    test-rmse:0.500411+0.056507 
## [18] train-rmse:0.311676+0.007747    test-rmse:0.497411+0.059646 
## [19] train-rmse:0.306837+0.007541    test-rmse:0.495944+0.059428 
## [20] train-rmse:0.300164+0.009753    test-rmse:0.496306+0.058898 
## [21] train-rmse:0.294757+0.009581    test-rmse:0.494994+0.058425 
## [22] train-rmse:0.290207+0.009449    test-rmse:0.493564+0.058159 
## [23] train-rmse:0.284914+0.009485    test-rmse:0.492218+0.059136 
## [24] train-rmse:0.281268+0.010525    test-rmse:0.491150+0.058451 
## [25] train-rmse:0.275929+0.009265    test-rmse:0.491778+0.058362 
## [26] train-rmse:0.270666+0.009449    test-rmse:0.490702+0.058818 
## [27] train-rmse:0.265844+0.010322    test-rmse:0.491254+0.059801 
## [28] train-rmse:0.259310+0.010354    test-rmse:0.490992+0.059346 
## [29] train-rmse:0.254124+0.012776    test-rmse:0.490676+0.059175 
## [30] train-rmse:0.249435+0.012041    test-rmse:0.490424+0.058945 
## [31] train-rmse:0.244535+0.010880    test-rmse:0.490882+0.058494 
## [32] train-rmse:0.240772+0.010749    test-rmse:0.490943+0.058318 
## [33] train-rmse:0.236503+0.012397    test-rmse:0.490093+0.057913 
## [34] train-rmse:0.233331+0.013355    test-rmse:0.490478+0.057934 
## [35] train-rmse:0.228750+0.014617    test-rmse:0.491286+0.058590 
## [36] train-rmse:0.223606+0.013501    test-rmse:0.491303+0.061335 
## [37] train-rmse:0.219921+0.014145    test-rmse:0.491868+0.061282 
## [38] train-rmse:0.213563+0.014622    test-rmse:0.493390+0.060955 
## [39] train-rmse:0.209126+0.013901    test-rmse:0.492952+0.061011 
## Stopping. Best iteration:
## [33] train-rmse:0.236503+0.012397    test-rmse:0.490093+0.057913
## 
## 54 
## [1]  train-rmse:0.832084+0.013686    test-rmse:0.849380+0.061929 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.702200+0.010678    test-rmse:0.739139+0.062265 
## [3]  train-rmse:0.606798+0.010986    test-rmse:0.663288+0.063283 
## [4]  train-rmse:0.534979+0.014022    test-rmse:0.612313+0.059275 
## [5]  train-rmse:0.480925+0.014536    test-rmse:0.575066+0.058156 
## [6]  train-rmse:0.438578+0.014122    test-rmse:0.553692+0.056349 
## [7]  train-rmse:0.407474+0.014745    test-rmse:0.536344+0.057959 
## [8]  train-rmse:0.383566+0.015513    test-rmse:0.526759+0.059004 
## [9]  train-rmse:0.361388+0.014330    test-rmse:0.516933+0.058247 
## [10] train-rmse:0.347410+0.015278    test-rmse:0.513088+0.057746 
## [11] train-rmse:0.335638+0.017995    test-rmse:0.511558+0.055357 
## [12] train-rmse:0.322332+0.014179    test-rmse:0.508889+0.053671 
## [13] train-rmse:0.310975+0.016031    test-rmse:0.504734+0.053108 
## [14] train-rmse:0.301326+0.013115    test-rmse:0.501565+0.055749 
## [15] train-rmse:0.291570+0.015876    test-rmse:0.499456+0.057123 
## [16] train-rmse:0.284542+0.014172    test-rmse:0.496102+0.054574 
## [17] train-rmse:0.277008+0.012861    test-rmse:0.493996+0.053783 
## [18] train-rmse:0.270996+0.013534    test-rmse:0.493467+0.052208 
## [19] train-rmse:0.264225+0.012619    test-rmse:0.491380+0.051109 
## [20] train-rmse:0.258727+0.012680    test-rmse:0.490632+0.051908 
## [21] train-rmse:0.250579+0.013663    test-rmse:0.489435+0.051928 
## [22] train-rmse:0.246154+0.013709    test-rmse:0.489560+0.052642 
## [23] train-rmse:0.241680+0.012434    test-rmse:0.489588+0.051659 
## [24] train-rmse:0.236680+0.013933    test-rmse:0.491241+0.051958 
## [25] train-rmse:0.232390+0.013825    test-rmse:0.491470+0.050991 
## [26] train-rmse:0.227729+0.013935    test-rmse:0.491880+0.049787 
## [27] train-rmse:0.222348+0.013051    test-rmse:0.492471+0.047566 
## Stopping. Best iteration:
## [21] train-rmse:0.250579+0.013663    test-rmse:0.489435+0.051928
## 
## 55 
## [1]  train-rmse:0.826189+0.013165    test-rmse:0.845628+0.059907 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.692883+0.012080    test-rmse:0.738116+0.060657 
## [3]  train-rmse:0.592191+0.010949    test-rmse:0.660227+0.061868 
## [4]  train-rmse:0.516996+0.011083    test-rmse:0.610140+0.061096 
## [5]  train-rmse:0.461497+0.011603    test-rmse:0.578689+0.060432 
## [6]  train-rmse:0.415959+0.011691    test-rmse:0.553622+0.063040 
## [7]  train-rmse:0.380435+0.014417    test-rmse:0.541029+0.063310 
## [8]  train-rmse:0.351527+0.012092    test-rmse:0.529385+0.060639 
## [9]  train-rmse:0.328702+0.014519    test-rmse:0.525328+0.061103 
## [10] train-rmse:0.309474+0.014335    test-rmse:0.522044+0.060547 
## [11] train-rmse:0.296800+0.014529    test-rmse:0.520534+0.059842 
## [12] train-rmse:0.281759+0.014669    test-rmse:0.517763+0.058432 
## [13] train-rmse:0.272043+0.015925    test-rmse:0.516433+0.058169 
## [14] train-rmse:0.261880+0.015636    test-rmse:0.514463+0.056671 
## [15] train-rmse:0.251933+0.012991    test-rmse:0.510551+0.059025 
## [16] train-rmse:0.239990+0.015205    test-rmse:0.508344+0.056087 
## [17] train-rmse:0.233098+0.013976    test-rmse:0.507312+0.054991 
## [18] train-rmse:0.226542+0.015680    test-rmse:0.506310+0.054434 
## [19] train-rmse:0.221469+0.015927    test-rmse:0.506120+0.054243 
## [20] train-rmse:0.216479+0.014922    test-rmse:0.503407+0.054397 
## [21] train-rmse:0.211658+0.014847    test-rmse:0.502811+0.055959 
## [22] train-rmse:0.204636+0.012811    test-rmse:0.501468+0.053979 
## [23] train-rmse:0.199597+0.012371    test-rmse:0.502219+0.054665 
## [24] train-rmse:0.193143+0.013784    test-rmse:0.502082+0.054463 
## [25] train-rmse:0.188322+0.015192    test-rmse:0.501834+0.054051 
## [26] train-rmse:0.181883+0.017116    test-rmse:0.502218+0.054337 
## [27] train-rmse:0.176832+0.017401    test-rmse:0.504031+0.056920 
## [28] train-rmse:0.172004+0.017298    test-rmse:0.503503+0.058120 
## Stopping. Best iteration:
## [22] train-rmse:0.204636+0.012811    test-rmse:0.501468+0.053979
## 
## 56 
## [1]  train-rmse:0.870003+0.014825    test-rmse:0.873249+0.060532 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.785158+0.013776    test-rmse:0.793799+0.060642 
## [3]  train-rmse:0.729647+0.013608    test-rmse:0.750328+0.057082 
## [4]  train-rmse:0.692899+0.012433    test-rmse:0.713306+0.059447 
## [5]  train-rmse:0.665710+0.012407    test-rmse:0.684232+0.056165 
## [6]  train-rmse:0.646057+0.011692    test-rmse:0.672047+0.050999 
## [7]  train-rmse:0.632082+0.011397    test-rmse:0.658146+0.049616 
## [8]  train-rmse:0.620880+0.011315    test-rmse:0.649707+0.050396 
## [9]  train-rmse:0.612918+0.011229    test-rmse:0.643819+0.048901 
## [10] train-rmse:0.605401+0.011201    test-rmse:0.639413+0.051049 
## [11] train-rmse:0.599013+0.011336    test-rmse:0.634192+0.052572 
## [12] train-rmse:0.593051+0.011462    test-rmse:0.625930+0.052507 
## [13] train-rmse:0.587761+0.011495    test-rmse:0.619666+0.052810 
## [14] train-rmse:0.582976+0.011405    test-rmse:0.617050+0.053357 
## [15] train-rmse:0.578696+0.011300    test-rmse:0.613288+0.054255 
## [16] train-rmse:0.574344+0.011196    test-rmse:0.612095+0.054963 
## [17] train-rmse:0.570615+0.011133    test-rmse:0.611197+0.053114 
## [18] train-rmse:0.567190+0.011028    test-rmse:0.607039+0.055020 
## [19] train-rmse:0.563726+0.011004    test-rmse:0.602715+0.055271 
## [20] train-rmse:0.560493+0.010971    test-rmse:0.600261+0.055823 
## [21] train-rmse:0.557301+0.011006    test-rmse:0.596251+0.056517 
## [22] train-rmse:0.554321+0.011094    test-rmse:0.593322+0.058644 
## [23] train-rmse:0.551475+0.011142    test-rmse:0.592634+0.058615 
## [24] train-rmse:0.548714+0.011155    test-rmse:0.591113+0.058153 
## [25] train-rmse:0.546034+0.011229    test-rmse:0.590542+0.056707 
## [26] train-rmse:0.543496+0.011255    test-rmse:0.588609+0.053652 
## [27] train-rmse:0.541070+0.011298    test-rmse:0.588180+0.053576 
## [28] train-rmse:0.538770+0.011290    test-rmse:0.585127+0.053813 
## [29] train-rmse:0.536599+0.011163    test-rmse:0.582957+0.054184 
## [30] train-rmse:0.534470+0.011055    test-rmse:0.581941+0.053138 
## [31] train-rmse:0.532422+0.010995    test-rmse:0.582470+0.053427 
## [32] train-rmse:0.530432+0.011000    test-rmse:0.581762+0.056403 
## [33] train-rmse:0.528504+0.011004    test-rmse:0.578567+0.055927 
## [34] train-rmse:0.526652+0.010952    test-rmse:0.577473+0.054264 
## [35] train-rmse:0.524859+0.010926    test-rmse:0.574662+0.054699 
## [36] train-rmse:0.523142+0.010882    test-rmse:0.572761+0.055287 
## [37] train-rmse:0.521452+0.010883    test-rmse:0.571192+0.056083 
## [38] train-rmse:0.519808+0.010842    test-rmse:0.571087+0.055467 
## [39] train-rmse:0.518287+0.010848    test-rmse:0.569927+0.054398 
## [40] train-rmse:0.516771+0.010832    test-rmse:0.569669+0.054896 
## [41] train-rmse:0.515286+0.010793    test-rmse:0.567649+0.054630 
## [42] train-rmse:0.513841+0.010727    test-rmse:0.566925+0.054213 
## [43] train-rmse:0.512434+0.010711    test-rmse:0.565158+0.053950 
## [44] train-rmse:0.511051+0.010688    test-rmse:0.563451+0.053563 
## [45] train-rmse:0.509722+0.010664    test-rmse:0.563302+0.054803 
## [46] train-rmse:0.508408+0.010656    test-rmse:0.562476+0.054408 
## [47] train-rmse:0.507154+0.010617    test-rmse:0.562994+0.054752 
## [48] train-rmse:0.505936+0.010633    test-rmse:0.562408+0.053975 
## [49] train-rmse:0.504776+0.010588    test-rmse:0.561902+0.054064 
## [50] train-rmse:0.503631+0.010590    test-rmse:0.560184+0.053697 
## [51] train-rmse:0.502508+0.010576    test-rmse:0.560119+0.054184 
## [52] train-rmse:0.501430+0.010587    test-rmse:0.559829+0.054536 
## [53] train-rmse:0.500387+0.010610    test-rmse:0.558572+0.054973 
## [54] train-rmse:0.499361+0.010624    test-rmse:0.557061+0.055596 
## [55] train-rmse:0.498351+0.010659    test-rmse:0.556259+0.054891 
## [56] train-rmse:0.497367+0.010678    test-rmse:0.554882+0.054177 
## [57] train-rmse:0.496415+0.010691    test-rmse:0.554641+0.052486 
## [58] train-rmse:0.495465+0.010683    test-rmse:0.554285+0.053029 
## [59] train-rmse:0.494532+0.010673    test-rmse:0.553210+0.052974 
## [60] train-rmse:0.493622+0.010665    test-rmse:0.552413+0.052298 
## [61] train-rmse:0.492744+0.010626    test-rmse:0.551737+0.051944 
## [62] train-rmse:0.491865+0.010612    test-rmse:0.551889+0.051819 
## [63] train-rmse:0.490995+0.010580    test-rmse:0.550092+0.052013 
## [64] train-rmse:0.490187+0.010576    test-rmse:0.550820+0.052655 
## [65] train-rmse:0.489399+0.010578    test-rmse:0.550133+0.052446 
## [66] train-rmse:0.488636+0.010549    test-rmse:0.549677+0.052258 
## [67] train-rmse:0.487905+0.010522    test-rmse:0.548871+0.051874 
## [68] train-rmse:0.487179+0.010527    test-rmse:0.548490+0.051548 
## [69] train-rmse:0.486465+0.010518    test-rmse:0.547581+0.051619 
## [70] train-rmse:0.485766+0.010511    test-rmse:0.547338+0.051050 
## [71] train-rmse:0.485080+0.010510    test-rmse:0.547678+0.051394 
## [72] train-rmse:0.484404+0.010492    test-rmse:0.547474+0.051539 
## [73] train-rmse:0.483737+0.010485    test-rmse:0.546582+0.051490 
## [74] train-rmse:0.483082+0.010484    test-rmse:0.546527+0.051991 
## [75] train-rmse:0.482432+0.010473    test-rmse:0.546612+0.052065 
## [76] train-rmse:0.481798+0.010480    test-rmse:0.546657+0.052607 
## [77] train-rmse:0.481187+0.010473    test-rmse:0.546354+0.051975 
## [78] train-rmse:0.480590+0.010461    test-rmse:0.546600+0.051576 
## [79] train-rmse:0.480007+0.010452    test-rmse:0.545833+0.051535 
## [80] train-rmse:0.479445+0.010445    test-rmse:0.545755+0.051355 
## [81] train-rmse:0.478885+0.010437    test-rmse:0.545351+0.051611 
## [82] train-rmse:0.478328+0.010424    test-rmse:0.545167+0.052419 
## [83] train-rmse:0.477790+0.010412    test-rmse:0.544336+0.052102 
## [84] train-rmse:0.477258+0.010401    test-rmse:0.543707+0.052554 
## [85] train-rmse:0.476756+0.010412    test-rmse:0.543691+0.052399 
## [86] train-rmse:0.476259+0.010416    test-rmse:0.543253+0.052402 
## [87] train-rmse:0.475774+0.010419    test-rmse:0.543060+0.051833 
## [88] train-rmse:0.475287+0.010420    test-rmse:0.542777+0.051599 
## [89] train-rmse:0.474810+0.010421    test-rmse:0.542554+0.051590 
## [90] train-rmse:0.474334+0.010412    test-rmse:0.542040+0.051456 
## [91] train-rmse:0.473861+0.010414    test-rmse:0.541914+0.050856 
## [92] train-rmse:0.473403+0.010417    test-rmse:0.541327+0.050089 
## [93] train-rmse:0.472949+0.010416    test-rmse:0.540942+0.049304 
## [94] train-rmse:0.472504+0.010417    test-rmse:0.540852+0.048588 
## [95] train-rmse:0.472059+0.010414    test-rmse:0.540736+0.049102 
## [96] train-rmse:0.471636+0.010411    test-rmse:0.539609+0.049220 
## [97] train-rmse:0.471223+0.010404    test-rmse:0.539431+0.049159 
## [98] train-rmse:0.470805+0.010399    test-rmse:0.539713+0.049131 
## [99] train-rmse:0.470410+0.010381    test-rmse:0.539682+0.048799 
## [100]    train-rmse:0.470014+0.010360    test-rmse:0.539324+0.049235 
## [101]    train-rmse:0.469644+0.010351    test-rmse:0.539598+0.048830 
## [102]    train-rmse:0.469277+0.010339    test-rmse:0.539170+0.048730 
## [103]    train-rmse:0.468918+0.010334    test-rmse:0.539016+0.048487 
## [104]    train-rmse:0.468551+0.010322    test-rmse:0.538951+0.049337 
## [105]    train-rmse:0.468202+0.010314    test-rmse:0.538651+0.049443 
## [106]    train-rmse:0.467847+0.010307    test-rmse:0.538351+0.049095 
## [107]    train-rmse:0.467492+0.010295    test-rmse:0.538459+0.049541 
## [108]    train-rmse:0.467150+0.010279    test-rmse:0.537482+0.049654 
## [109]    train-rmse:0.466803+0.010247    test-rmse:0.537415+0.049615 
## [110]    train-rmse:0.466463+0.010219    test-rmse:0.537618+0.049168 
## [111]    train-rmse:0.466129+0.010194    test-rmse:0.537549+0.049131 
## [112]    train-rmse:0.465809+0.010171    test-rmse:0.537679+0.049178 
## [113]    train-rmse:0.465490+0.010145    test-rmse:0.537806+0.048327 
## [114]    train-rmse:0.465180+0.010126    test-rmse:0.538162+0.048402 
## [115]    train-rmse:0.464873+0.010109    test-rmse:0.537631+0.048376 
## Stopping. Best iteration:
## [109]    train-rmse:0.466803+0.010247    test-rmse:0.537415+0.049615
## 
## 57 
## [1]  train-rmse:0.849232+0.014653    test-rmse:0.854059+0.060649 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.746461+0.014038    test-rmse:0.762971+0.063659 
## [3]  train-rmse:0.678635+0.012757    test-rmse:0.701965+0.064495 
## [4]  train-rmse:0.632580+0.010753    test-rmse:0.661462+0.065841 
## [5]  train-rmse:0.600467+0.011113    test-rmse:0.635500+0.065476 
## [6]  train-rmse:0.576857+0.012796    test-rmse:0.619197+0.063124 
## [7]  train-rmse:0.558250+0.014978    test-rmse:0.600433+0.061305 
## [8]  train-rmse:0.547086+0.014993    test-rmse:0.593488+0.059787 
## [9]  train-rmse:0.537519+0.014636    test-rmse:0.589278+0.059442 
## [10] train-rmse:0.529646+0.014444    test-rmse:0.583538+0.057621 
## [11] train-rmse:0.521750+0.015143    test-rmse:0.581329+0.057768 
## [12] train-rmse:0.514023+0.014256    test-rmse:0.574993+0.055048 
## [13] train-rmse:0.506678+0.015462    test-rmse:0.570011+0.054016 
## [14] train-rmse:0.501676+0.015767    test-rmse:0.570913+0.056647 
## [15] train-rmse:0.496442+0.016145    test-rmse:0.566837+0.058014 
## [16] train-rmse:0.490956+0.015843    test-rmse:0.561369+0.060220 
## [17] train-rmse:0.486441+0.015843    test-rmse:0.558533+0.058956 
## [18] train-rmse:0.482194+0.015935    test-rmse:0.557531+0.058214 
## [19] train-rmse:0.477489+0.017126    test-rmse:0.554284+0.057053 
## [20] train-rmse:0.472714+0.015809    test-rmse:0.551344+0.059435 
## [21] train-rmse:0.468839+0.016190    test-rmse:0.549999+0.060731 
## [22] train-rmse:0.464800+0.015962    test-rmse:0.544983+0.060185 
## [23] train-rmse:0.460659+0.016547    test-rmse:0.543629+0.060496 
## [24] train-rmse:0.457455+0.016316    test-rmse:0.541748+0.060071 
## [25] train-rmse:0.454411+0.016712    test-rmse:0.539986+0.061586 
## [26] train-rmse:0.450850+0.016541    test-rmse:0.538233+0.062355 
## [27] train-rmse:0.447283+0.015430    test-rmse:0.537409+0.062937 
## [28] train-rmse:0.444561+0.015292    test-rmse:0.535517+0.062997 
## [29] train-rmse:0.441956+0.015381    test-rmse:0.533571+0.062247 
## [30] train-rmse:0.438768+0.014908    test-rmse:0.532898+0.062591 
## [31] train-rmse:0.436225+0.014844    test-rmse:0.531383+0.064474 
## [32] train-rmse:0.434021+0.014601    test-rmse:0.529972+0.063825 
## [33] train-rmse:0.431958+0.014945    test-rmse:0.529982+0.062980 
## [34] train-rmse:0.429441+0.014426    test-rmse:0.528047+0.061631 
## [35] train-rmse:0.426997+0.014380    test-rmse:0.526758+0.062052 
## [36] train-rmse:0.423756+0.014557    test-rmse:0.525305+0.063317 
## [37] train-rmse:0.421793+0.014561    test-rmse:0.524964+0.063167 
## [38] train-rmse:0.419761+0.014971    test-rmse:0.523878+0.062705 
## [39] train-rmse:0.417865+0.015135    test-rmse:0.522390+0.063070 
## [40] train-rmse:0.415845+0.014900    test-rmse:0.520537+0.064360 
## [41] train-rmse:0.414400+0.014922    test-rmse:0.519511+0.063254 
## [42] train-rmse:0.412553+0.015013    test-rmse:0.519960+0.062423 
## [43] train-rmse:0.410964+0.014943    test-rmse:0.520477+0.062220 
## [44] train-rmse:0.408840+0.014528    test-rmse:0.518446+0.063464 
## [45] train-rmse:0.407503+0.014321    test-rmse:0.519087+0.063721 
## [46] train-rmse:0.405697+0.014048    test-rmse:0.519361+0.063856 
## [47] train-rmse:0.404056+0.013898    test-rmse:0.519774+0.063394 
## [48] train-rmse:0.401892+0.014070    test-rmse:0.518651+0.063206 
## [49] train-rmse:0.400041+0.014247    test-rmse:0.517732+0.062984 
## [50] train-rmse:0.398700+0.014488    test-rmse:0.517704+0.063099 
## [51] train-rmse:0.396751+0.014120    test-rmse:0.518118+0.063497 
## [52] train-rmse:0.394820+0.014655    test-rmse:0.516945+0.062329 
## [53] train-rmse:0.393406+0.014653    test-rmse:0.515282+0.062387 
## [54] train-rmse:0.391989+0.014481    test-rmse:0.515320+0.063207 
## [55] train-rmse:0.390275+0.014258    test-rmse:0.515878+0.062934 
## [56] train-rmse:0.388309+0.013859    test-rmse:0.516695+0.062324 
## [57] train-rmse:0.386641+0.014244    test-rmse:0.515921+0.062121 
## [58] train-rmse:0.385066+0.014010    test-rmse:0.516455+0.062081 
## [59] train-rmse:0.384159+0.013860    test-rmse:0.516259+0.062683 
## Stopping. Best iteration:
## [53] train-rmse:0.393406+0.014653    test-rmse:0.515282+0.062387
## 
## 58 
## [1]  train-rmse:0.839946+0.015046    test-rmse:0.848834+0.062620 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.727010+0.014133    test-rmse:0.748779+0.062153 
## [3]  train-rmse:0.650108+0.013800    test-rmse:0.680336+0.062405 
## [4]  train-rmse:0.595550+0.014548    test-rmse:0.635581+0.060806 
## [5]  train-rmse:0.557781+0.013836    test-rmse:0.607040+0.057656 
## [6]  train-rmse:0.531161+0.013694    test-rmse:0.584248+0.057954 
## [7]  train-rmse:0.509859+0.015285    test-rmse:0.570138+0.053716 
## [8]  train-rmse:0.494304+0.016840    test-rmse:0.562246+0.050508 
## [9]  train-rmse:0.482652+0.016727    test-rmse:0.556482+0.049966 
## [10] train-rmse:0.473184+0.017284    test-rmse:0.552708+0.050352 
## [11] train-rmse:0.462096+0.016279    test-rmse:0.549869+0.048545 
## [12] train-rmse:0.451632+0.013552    test-rmse:0.542784+0.047135 
## [13] train-rmse:0.445005+0.013437    test-rmse:0.538972+0.048304 
## [14] train-rmse:0.437295+0.014745    test-rmse:0.536912+0.050041 
## [15] train-rmse:0.430676+0.014311    test-rmse:0.533543+0.049720 
## [16] train-rmse:0.423567+0.013496    test-rmse:0.530652+0.050041 
## [17] train-rmse:0.418737+0.013922    test-rmse:0.529858+0.048814 
## [18] train-rmse:0.413149+0.012964    test-rmse:0.526681+0.051319 
## [19] train-rmse:0.408372+0.012999    test-rmse:0.523975+0.052136 
## [20] train-rmse:0.403790+0.012883    test-rmse:0.522498+0.051524 
## [21] train-rmse:0.399583+0.013196    test-rmse:0.521339+0.052680 
## [22] train-rmse:0.396004+0.013245    test-rmse:0.518569+0.051601 
## [23] train-rmse:0.392064+0.012959    test-rmse:0.517586+0.051575 
## [24] train-rmse:0.388404+0.012221    test-rmse:0.516551+0.050847 
## [25] train-rmse:0.383776+0.011380    test-rmse:0.514447+0.051873 
## [26] train-rmse:0.380427+0.011397    test-rmse:0.512860+0.050436 
## [27] train-rmse:0.376161+0.011590    test-rmse:0.511591+0.050595 
## [28] train-rmse:0.373254+0.011834    test-rmse:0.510920+0.049690 
## [29] train-rmse:0.369662+0.012474    test-rmse:0.509038+0.049775 
## [30] train-rmse:0.364936+0.012957    test-rmse:0.506313+0.050884 
## [31] train-rmse:0.361918+0.012598    test-rmse:0.506217+0.051757 
## [32] train-rmse:0.358631+0.013148    test-rmse:0.505277+0.050856 
## [33] train-rmse:0.355018+0.012897    test-rmse:0.504809+0.050437 
## [34] train-rmse:0.351400+0.012568    test-rmse:0.502375+0.050160 
## [35] train-rmse:0.347524+0.012965    test-rmse:0.502782+0.050658 
## [36] train-rmse:0.344003+0.014347    test-rmse:0.503262+0.049602 
## [37] train-rmse:0.341668+0.014103    test-rmse:0.504205+0.049951 
## [38] train-rmse:0.339742+0.013850    test-rmse:0.504260+0.049691 
## [39] train-rmse:0.336684+0.014101    test-rmse:0.501886+0.051058 
## [40] train-rmse:0.332766+0.012834    test-rmse:0.501646+0.050371 
## [41] train-rmse:0.329356+0.012766    test-rmse:0.501009+0.049844 
## [42] train-rmse:0.326220+0.012512    test-rmse:0.498932+0.049319 
## [43] train-rmse:0.323088+0.012478    test-rmse:0.498875+0.048755 
## [44] train-rmse:0.320278+0.012628    test-rmse:0.499428+0.047857 
## [45] train-rmse:0.316382+0.012106    test-rmse:0.499445+0.047702 
## [46] train-rmse:0.313649+0.011329    test-rmse:0.498659+0.047389 
## [47] train-rmse:0.311490+0.011006    test-rmse:0.498681+0.046537 
## [48] train-rmse:0.307398+0.013450    test-rmse:0.498031+0.045434 
## [49] train-rmse:0.303599+0.012165    test-rmse:0.496024+0.044343 
## [50] train-rmse:0.301958+0.011630    test-rmse:0.495009+0.044632 
## [51] train-rmse:0.299963+0.011613    test-rmse:0.495335+0.046267 
## [52] train-rmse:0.297646+0.011296    test-rmse:0.494523+0.046352 
## [53] train-rmse:0.294950+0.011278    test-rmse:0.495321+0.045184 
## [54] train-rmse:0.292073+0.011742    test-rmse:0.494055+0.045110 
## [55] train-rmse:0.290723+0.011481    test-rmse:0.493514+0.044657 
## [56] train-rmse:0.289032+0.011350    test-rmse:0.493798+0.043826 
## [57] train-rmse:0.286729+0.011506    test-rmse:0.493016+0.043545 
## [58] train-rmse:0.284488+0.012465    test-rmse:0.491817+0.042232 
## [59] train-rmse:0.282664+0.012514    test-rmse:0.491230+0.042289 
## [60] train-rmse:0.279766+0.013060    test-rmse:0.491858+0.042898 
## [61] train-rmse:0.277787+0.012277    test-rmse:0.492499+0.043131 
## [62] train-rmse:0.274426+0.012924    test-rmse:0.491606+0.043258 
## [63] train-rmse:0.271304+0.013700    test-rmse:0.490667+0.042337 
## [64] train-rmse:0.269107+0.013934    test-rmse:0.490318+0.042633 
## [65] train-rmse:0.266659+0.014189    test-rmse:0.489182+0.042195 
## [66] train-rmse:0.264656+0.014335    test-rmse:0.488657+0.041566 
## [67] train-rmse:0.261756+0.013624    test-rmse:0.488484+0.042093 
## [68] train-rmse:0.259345+0.012676    test-rmse:0.487569+0.042679 
## [69] train-rmse:0.258151+0.012812    test-rmse:0.487474+0.042076 
## [70] train-rmse:0.256552+0.013030    test-rmse:0.486568+0.041813 
## [71] train-rmse:0.254856+0.012398    test-rmse:0.487904+0.042023 
## [72] train-rmse:0.252671+0.012223    test-rmse:0.487352+0.042081 
## [73] train-rmse:0.250632+0.012510    test-rmse:0.487367+0.041694 
## [74] train-rmse:0.249356+0.012615    test-rmse:0.487119+0.041118 
## [75] train-rmse:0.247976+0.012513    test-rmse:0.487506+0.040713 
## [76] train-rmse:0.246090+0.012830    test-rmse:0.488877+0.040234 
## Stopping. Best iteration:
## [70] train-rmse:0.256552+0.013030    test-rmse:0.486568+0.041813
## 
## 59 
## [1]  train-rmse:0.831962+0.014776    test-rmse:0.844061+0.060930 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.711769+0.013811    test-rmse:0.738197+0.064485 
## [3]  train-rmse:0.626535+0.011723    test-rmse:0.663425+0.063690 
## [4]  train-rmse:0.568232+0.010936    test-rmse:0.619424+0.065515 
## [5]  train-rmse:0.522834+0.012622    test-rmse:0.586078+0.063385 
## [6]  train-rmse:0.489720+0.015077    test-rmse:0.564424+0.057603 
## [7]  train-rmse:0.465654+0.013402    test-rmse:0.552976+0.057306 
## [8]  train-rmse:0.444748+0.014123    test-rmse:0.542036+0.053829 
## [9]  train-rmse:0.432459+0.014635    test-rmse:0.535664+0.053289 
## [10] train-rmse:0.417666+0.017062    test-rmse:0.528243+0.049610 
## [11] train-rmse:0.405686+0.016272    test-rmse:0.520238+0.051371 
## [12] train-rmse:0.397222+0.016492    test-rmse:0.517850+0.052870 
## [13] train-rmse:0.389372+0.016817    test-rmse:0.513489+0.049634 
## [14] train-rmse:0.382734+0.017218    test-rmse:0.512935+0.051290 
## [15] train-rmse:0.375712+0.017125    test-rmse:0.510060+0.051031 
## [16] train-rmse:0.369633+0.017631    test-rmse:0.507542+0.051104 
## [17] train-rmse:0.363812+0.017788    test-rmse:0.504480+0.050589 
## [18] train-rmse:0.357999+0.018091    test-rmse:0.501820+0.052738 
## [19] train-rmse:0.349567+0.017712    test-rmse:0.499803+0.053081 
## [20] train-rmse:0.342723+0.017415    test-rmse:0.497718+0.052922 
## [21] train-rmse:0.338738+0.018069    test-rmse:0.496628+0.052790 
## [22] train-rmse:0.333245+0.016627    test-rmse:0.496629+0.052606 
## [23] train-rmse:0.328103+0.016568    test-rmse:0.494596+0.051886 
## [24] train-rmse:0.323691+0.015668    test-rmse:0.494934+0.051790 
## [25] train-rmse:0.318797+0.015423    test-rmse:0.495215+0.053704 
## [26] train-rmse:0.314571+0.015420    test-rmse:0.495696+0.052704 
## [27] train-rmse:0.310423+0.015069    test-rmse:0.495237+0.051395 
## [28] train-rmse:0.306287+0.015280    test-rmse:0.494969+0.051782 
## [29] train-rmse:0.301058+0.013795    test-rmse:0.494042+0.050749 
## [30] train-rmse:0.297311+0.013385    test-rmse:0.494044+0.050397 
## [31] train-rmse:0.293174+0.011817    test-rmse:0.492122+0.051808 
## [32] train-rmse:0.289159+0.012535    test-rmse:0.490547+0.050697 
## [33] train-rmse:0.285052+0.012445    test-rmse:0.491199+0.050733 
## [34] train-rmse:0.281164+0.013541    test-rmse:0.489730+0.050598 
## [35] train-rmse:0.277299+0.013378    test-rmse:0.488871+0.050530 
## [36] train-rmse:0.273545+0.014109    test-rmse:0.489070+0.050974 
## [37] train-rmse:0.268945+0.014191    test-rmse:0.489490+0.051420 
## [38] train-rmse:0.265000+0.013974    test-rmse:0.490140+0.051944 
## [39] train-rmse:0.261376+0.015083    test-rmse:0.489589+0.052207 
## [40] train-rmse:0.258659+0.014973    test-rmse:0.490520+0.051911 
## [41] train-rmse:0.255300+0.014470    test-rmse:0.491300+0.050930 
## Stopping. Best iteration:
## [35] train-rmse:0.277299+0.013378    test-rmse:0.488871+0.050530
## 
## 60 
## [1]  train-rmse:0.824840+0.014137    test-rmse:0.840053+0.061749 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.696366+0.011590    test-rmse:0.729001+0.062850 
## [3]  train-rmse:0.603919+0.012552    test-rmse:0.656182+0.061727 
## [4]  train-rmse:0.538388+0.011602    test-rmse:0.608575+0.055225 
## [5]  train-rmse:0.490184+0.010022    test-rmse:0.574740+0.053405 
## [6]  train-rmse:0.453658+0.009548    test-rmse:0.556566+0.052830 
## [7]  train-rmse:0.428205+0.008077    test-rmse:0.540938+0.057663 
## [8]  train-rmse:0.408073+0.008925    test-rmse:0.533880+0.056139 
## [9]  train-rmse:0.389516+0.010081    test-rmse:0.526966+0.057418 
## [10] train-rmse:0.378083+0.009446    test-rmse:0.519688+0.056605 
## [11] train-rmse:0.367155+0.009450    test-rmse:0.515807+0.057952 
## [12] train-rmse:0.356060+0.010880    test-rmse:0.509997+0.058226 
## [13] train-rmse:0.347263+0.010732    test-rmse:0.506632+0.056284 
## [14] train-rmse:0.334290+0.012457    test-rmse:0.503369+0.056880 
## [15] train-rmse:0.329771+0.012861    test-rmse:0.502339+0.056924 
## [16] train-rmse:0.321291+0.010105    test-rmse:0.499971+0.057599 
## [17] train-rmse:0.314913+0.009650    test-rmse:0.497902+0.057390 
## [18] train-rmse:0.308073+0.010630    test-rmse:0.497150+0.059228 
## [19] train-rmse:0.301952+0.009155    test-rmse:0.496652+0.058047 
## [20] train-rmse:0.297943+0.009949    test-rmse:0.496739+0.057549 
## [21] train-rmse:0.290767+0.010663    test-rmse:0.494034+0.056224 
## [22] train-rmse:0.284928+0.010004    test-rmse:0.492723+0.056733 
## [23] train-rmse:0.278308+0.010945    test-rmse:0.492235+0.055816 
## [24] train-rmse:0.270944+0.008979    test-rmse:0.492028+0.055771 
## [25] train-rmse:0.266839+0.008960    test-rmse:0.493094+0.054577 
## [26] train-rmse:0.261377+0.009923    test-rmse:0.492222+0.053902 
## [27] train-rmse:0.253909+0.013470    test-rmse:0.491357+0.052521 
## [28] train-rmse:0.251312+0.013841    test-rmse:0.491209+0.052174 
## [29] train-rmse:0.247053+0.012356    test-rmse:0.491273+0.050700 
## [30] train-rmse:0.240760+0.011171    test-rmse:0.490816+0.051494 
## [31] train-rmse:0.235525+0.012014    test-rmse:0.491807+0.050326 
## [32] train-rmse:0.231262+0.010819    test-rmse:0.491274+0.050448 
## [33] train-rmse:0.227627+0.011363    test-rmse:0.492067+0.051123 
## [34] train-rmse:0.222663+0.010700    test-rmse:0.493090+0.049595 
## [35] train-rmse:0.217632+0.010940    test-rmse:0.493349+0.049603 
## [36] train-rmse:0.213347+0.008812    test-rmse:0.493543+0.048843 
## Stopping. Best iteration:
## [30] train-rmse:0.240760+0.011171    test-rmse:0.490816+0.051494
## 
## 61 
## [1]  train-rmse:0.818299+0.013493    test-rmse:0.837477+0.061934 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.682390+0.010611    test-rmse:0.722901+0.062642 
## [3]  train-rmse:0.585841+0.011997    test-rmse:0.646950+0.062208 
## [4]  train-rmse:0.511660+0.013512    test-rmse:0.595398+0.056997 
## [5]  train-rmse:0.460108+0.013856    test-rmse:0.563481+0.052128 
## [6]  train-rmse:0.418687+0.013108    test-rmse:0.542120+0.050638 
## [7]  train-rmse:0.388356+0.013990    test-rmse:0.532410+0.049236 
## [8]  train-rmse:0.365121+0.012472    test-rmse:0.522733+0.052564 
## [9]  train-rmse:0.348507+0.013319    test-rmse:0.514307+0.047890 
## [10] train-rmse:0.334952+0.012105    test-rmse:0.509123+0.049093 
## [11] train-rmse:0.319554+0.010539    test-rmse:0.505099+0.048467 
## [12] train-rmse:0.309874+0.010590    test-rmse:0.502016+0.050476 
## [13] train-rmse:0.300653+0.008216    test-rmse:0.500811+0.048928 
## [14] train-rmse:0.293271+0.007042    test-rmse:0.498575+0.049812 
## [15] train-rmse:0.286710+0.006713    test-rmse:0.498036+0.050323 
## [16] train-rmse:0.279408+0.007513    test-rmse:0.497398+0.050414 
## [17] train-rmse:0.272914+0.006871    test-rmse:0.498644+0.049271 
## [18] train-rmse:0.265917+0.007614    test-rmse:0.497855+0.049480 
## [19] train-rmse:0.259076+0.009085    test-rmse:0.496537+0.049510 
## [20] train-rmse:0.252829+0.008040    test-rmse:0.497692+0.050924 
## [21] train-rmse:0.246715+0.008476    test-rmse:0.496483+0.050456 
## [22] train-rmse:0.240674+0.010165    test-rmse:0.496400+0.050270 
## [23] train-rmse:0.234694+0.013299    test-rmse:0.495190+0.050214 
## [24] train-rmse:0.226726+0.011373    test-rmse:0.496476+0.048760 
## [25] train-rmse:0.218278+0.011540    test-rmse:0.496279+0.047463 
## [26] train-rmse:0.211356+0.012241    test-rmse:0.498930+0.048730 
## [27] train-rmse:0.205574+0.009685    test-rmse:0.498786+0.048945 
## [28] train-rmse:0.202038+0.008628    test-rmse:0.498046+0.048516 
## [29] train-rmse:0.196272+0.007899    test-rmse:0.497813+0.048858 
## Stopping. Best iteration:
## [23] train-rmse:0.234694+0.013299    test-rmse:0.495190+0.050214
## 
## 62 
## [1]  train-rmse:0.811857+0.012932    test-rmse:0.833441+0.059712 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.672109+0.011890    test-rmse:0.721639+0.060942 
## [3]  train-rmse:0.568846+0.010857    test-rmse:0.642445+0.060857 
## [4]  train-rmse:0.495628+0.011943    test-rmse:0.596647+0.061305 
## [5]  train-rmse:0.437896+0.014321    test-rmse:0.566277+0.058210 
## [6]  train-rmse:0.394251+0.015624    test-rmse:0.546869+0.062388 
## [7]  train-rmse:0.357876+0.013920    test-rmse:0.535245+0.060499 
## [8]  train-rmse:0.331036+0.014741    test-rmse:0.528795+0.061632 
## [9]  train-rmse:0.309149+0.014889    test-rmse:0.524854+0.061134 
## [10] train-rmse:0.292196+0.014688    test-rmse:0.523196+0.061874 
## [11] train-rmse:0.279352+0.014102    test-rmse:0.519812+0.062183 
## [12] train-rmse:0.265191+0.011147    test-rmse:0.517437+0.062740 
## [13] train-rmse:0.254540+0.011089    test-rmse:0.515620+0.061119 
## [14] train-rmse:0.246294+0.010602    test-rmse:0.514590+0.060681 
## [15] train-rmse:0.236974+0.011324    test-rmse:0.513392+0.060407 
## [16] train-rmse:0.230046+0.011828    test-rmse:0.513840+0.060774 
## [17] train-rmse:0.222409+0.013672    test-rmse:0.514681+0.058610 
## [18] train-rmse:0.215800+0.012107    test-rmse:0.514167+0.059810 
## [19] train-rmse:0.207756+0.012964    test-rmse:0.513705+0.059068 
## [20] train-rmse:0.202529+0.013511    test-rmse:0.514860+0.059498 
## [21] train-rmse:0.197928+0.013331    test-rmse:0.515414+0.059112 
## Stopping. Best iteration:
## [15] train-rmse:0.236974+0.011324    test-rmse:0.513392+0.060407
## 
## 63 
## [1]  train-rmse:0.860562+0.014734    test-rmse:0.864260+0.060418 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.773753+0.013612    test-rmse:0.785582+0.059119 
## [3]  train-rmse:0.718804+0.013609    test-rmse:0.743482+0.055230 
## [4]  train-rmse:0.682526+0.012621    test-rmse:0.703809+0.053265 
## [5]  train-rmse:0.656202+0.012307    test-rmse:0.680444+0.053777 
## [6]  train-rmse:0.638457+0.011604    test-rmse:0.666800+0.050216 
## [7]  train-rmse:0.625227+0.011362    test-rmse:0.653732+0.049014 
## [8]  train-rmse:0.614935+0.011209    test-rmse:0.646150+0.049820 
## [9]  train-rmse:0.606848+0.011237    test-rmse:0.639992+0.053502 
## [10] train-rmse:0.599909+0.011339    test-rmse:0.634302+0.053220 
## [11] train-rmse:0.593542+0.011471    test-rmse:0.632405+0.051708 
## [12] train-rmse:0.587860+0.011561    test-rmse:0.625598+0.051121 
## [13] train-rmse:0.582733+0.011598    test-rmse:0.618655+0.051925 
## [14] train-rmse:0.577761+0.011606    test-rmse:0.614651+0.052976 
## [15] train-rmse:0.573173+0.011495    test-rmse:0.610221+0.054647 
## [16] train-rmse:0.569158+0.011346    test-rmse:0.609483+0.052545 
## [17] train-rmse:0.565453+0.011205    test-rmse:0.607549+0.051739 
## [18] train-rmse:0.561837+0.011094    test-rmse:0.603117+0.054240 
## [19] train-rmse:0.558435+0.011122    test-rmse:0.599406+0.055642 
## [20] train-rmse:0.555175+0.011176    test-rmse:0.598253+0.057714 
## [21] train-rmse:0.552051+0.011195    test-rmse:0.595487+0.057294 
## [22] train-rmse:0.549117+0.011172    test-rmse:0.594126+0.057799 
## [23] train-rmse:0.546268+0.011172    test-rmse:0.589708+0.057714 
## [24] train-rmse:0.543440+0.011241    test-rmse:0.585726+0.056280 
## [25] train-rmse:0.540690+0.011330    test-rmse:0.584319+0.055780 
## [26] train-rmse:0.538194+0.011321    test-rmse:0.583168+0.055160 
## [27] train-rmse:0.535777+0.011358    test-rmse:0.581037+0.055357 
## [28] train-rmse:0.533542+0.011259    test-rmse:0.580208+0.057129 
## [29] train-rmse:0.531297+0.011127    test-rmse:0.581471+0.056564 
## [30] train-rmse:0.529213+0.011073    test-rmse:0.580388+0.055836 
## [31] train-rmse:0.527196+0.011009    test-rmse:0.579284+0.054533 
## [32] train-rmse:0.525243+0.010969    test-rmse:0.577724+0.055493 
## [33] train-rmse:0.523317+0.010945    test-rmse:0.574792+0.056562 
## [34] train-rmse:0.521461+0.010945    test-rmse:0.572620+0.056434 
## [35] train-rmse:0.519701+0.010943    test-rmse:0.571287+0.056201 
## [36] train-rmse:0.517999+0.010947    test-rmse:0.569752+0.056200 
## [37] train-rmse:0.516378+0.010933    test-rmse:0.567223+0.056474 
## [38] train-rmse:0.514779+0.010892    test-rmse:0.565813+0.056070 
## [39] train-rmse:0.513232+0.010848    test-rmse:0.564478+0.055801 
## [40] train-rmse:0.511711+0.010796    test-rmse:0.562242+0.055163 
## [41] train-rmse:0.510263+0.010737    test-rmse:0.559926+0.054785 
## [42] train-rmse:0.508851+0.010687    test-rmse:0.560398+0.055427 
## [43] train-rmse:0.507497+0.010639    test-rmse:0.561049+0.053902 
## [44] train-rmse:0.506191+0.010608    test-rmse:0.560246+0.053116 
## [45] train-rmse:0.504949+0.010600    test-rmse:0.559671+0.054188 
## [46] train-rmse:0.503728+0.010625    test-rmse:0.560171+0.054301 
## [47] train-rmse:0.502528+0.010637    test-rmse:0.560306+0.053793 
## [48] train-rmse:0.501322+0.010646    test-rmse:0.560142+0.054534 
## [49] train-rmse:0.500165+0.010664    test-rmse:0.559329+0.054863 
## [50] train-rmse:0.499028+0.010692    test-rmse:0.558029+0.054673 
## [51] train-rmse:0.497943+0.010736    test-rmse:0.556974+0.054610 
## [52] train-rmse:0.496920+0.010752    test-rmse:0.556527+0.054344 
## [53] train-rmse:0.495913+0.010756    test-rmse:0.555307+0.054822 
## [54] train-rmse:0.494922+0.010770    test-rmse:0.553302+0.055153 
## [55] train-rmse:0.493938+0.010762    test-rmse:0.553042+0.054082 
## [56] train-rmse:0.492983+0.010769    test-rmse:0.552735+0.053477 
## [57] train-rmse:0.492063+0.010744    test-rmse:0.551798+0.054034 
## [58] train-rmse:0.491148+0.010737    test-rmse:0.551180+0.053503 
## [59] train-rmse:0.490268+0.010700    test-rmse:0.549702+0.053226 
## [60] train-rmse:0.489395+0.010712    test-rmse:0.549928+0.053547 
## [61] train-rmse:0.488546+0.010716    test-rmse:0.549550+0.052857 
## [62] train-rmse:0.487741+0.010706    test-rmse:0.549000+0.053868 
## [63] train-rmse:0.486942+0.010680    test-rmse:0.548443+0.053440 
## [64] train-rmse:0.486159+0.010658    test-rmse:0.548462+0.052140 
## [65] train-rmse:0.485374+0.010631    test-rmse:0.548327+0.052861 
## [66] train-rmse:0.484613+0.010577    test-rmse:0.547567+0.053305 
## [67] train-rmse:0.483905+0.010572    test-rmse:0.546569+0.053103 
## [68] train-rmse:0.483223+0.010565    test-rmse:0.545725+0.052987 
## [69] train-rmse:0.482555+0.010574    test-rmse:0.545557+0.052411 
## [70] train-rmse:0.481895+0.010575    test-rmse:0.545042+0.051333 
## [71] train-rmse:0.481246+0.010576    test-rmse:0.545267+0.052492 
## [72] train-rmse:0.480608+0.010589    test-rmse:0.544619+0.051256 
## [73] train-rmse:0.479987+0.010596    test-rmse:0.544132+0.050973 
## [74] train-rmse:0.479372+0.010600    test-rmse:0.543783+0.051078 
## [75] train-rmse:0.478730+0.010624    test-rmse:0.544001+0.051401 
## [76] train-rmse:0.478143+0.010620    test-rmse:0.543891+0.051279 
## [77] train-rmse:0.477565+0.010610    test-rmse:0.543493+0.050790 
## [78] train-rmse:0.477000+0.010581    test-rmse:0.543333+0.051125 
## [79] train-rmse:0.476449+0.010559    test-rmse:0.542770+0.050826 
## [80] train-rmse:0.475907+0.010547    test-rmse:0.541746+0.050911 
## [81] train-rmse:0.475370+0.010534    test-rmse:0.541973+0.051000 
## [82] train-rmse:0.474864+0.010531    test-rmse:0.541215+0.051306 
## [83] train-rmse:0.474356+0.010525    test-rmse:0.540956+0.051311 
## [84] train-rmse:0.473864+0.010524    test-rmse:0.541915+0.051589 
## [85] train-rmse:0.473380+0.010521    test-rmse:0.541548+0.051346 
## [86] train-rmse:0.472914+0.010519    test-rmse:0.541021+0.051711 
## [87] train-rmse:0.472422+0.010533    test-rmse:0.541158+0.051313 
## [88] train-rmse:0.471967+0.010529    test-rmse:0.540864+0.051241 
## [89] train-rmse:0.471524+0.010523    test-rmse:0.540552+0.051076 
## [90] train-rmse:0.471087+0.010514    test-rmse:0.539914+0.051511 
## [91] train-rmse:0.470662+0.010505    test-rmse:0.540032+0.050721 
## [92] train-rmse:0.470236+0.010504    test-rmse:0.539916+0.050563 
## [93] train-rmse:0.469816+0.010498    test-rmse:0.539661+0.049754 
## [94] train-rmse:0.469396+0.010484    test-rmse:0.539045+0.049646 
## [95] train-rmse:0.468980+0.010480    test-rmse:0.539122+0.049751 
## [96] train-rmse:0.468584+0.010463    test-rmse:0.538515+0.049508 
## [97] train-rmse:0.468203+0.010454    test-rmse:0.538053+0.049951 
## [98] train-rmse:0.467824+0.010438    test-rmse:0.538236+0.050024 
## [99] train-rmse:0.467462+0.010424    test-rmse:0.537452+0.050079 
## [100]    train-rmse:0.467098+0.010403    test-rmse:0.537263+0.049438 
## [101]    train-rmse:0.466746+0.010388    test-rmse:0.537142+0.049289 
## [102]    train-rmse:0.466399+0.010381    test-rmse:0.536937+0.049532 
## [103]    train-rmse:0.466055+0.010374    test-rmse:0.537572+0.049916 
## [104]    train-rmse:0.465717+0.010371    test-rmse:0.537246+0.049454 
## [105]    train-rmse:0.465393+0.010354    test-rmse:0.537163+0.049182 
## [106]    train-rmse:0.465049+0.010321    test-rmse:0.537184+0.049893 
## [107]    train-rmse:0.464720+0.010302    test-rmse:0.537436+0.049430 
## [108]    train-rmse:0.464397+0.010284    test-rmse:0.537480+0.049235 
## Stopping. Best iteration:
## [102]    train-rmse:0.466399+0.010381    test-rmse:0.536937+0.049532
## 
## 64 
## [1]  train-rmse:0.838120+0.014544    test-rmse:0.843513+0.060609 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.732185+0.013921    test-rmse:0.750047+0.064114 
## [3]  train-rmse:0.664425+0.011689    test-rmse:0.688328+0.068453 
## [4]  train-rmse:0.620951+0.011757    test-rmse:0.651423+0.064469 
## [5]  train-rmse:0.590642+0.011458    test-rmse:0.622212+0.064871 
## [6]  train-rmse:0.568826+0.013148    test-rmse:0.608221+0.064849 
## [7]  train-rmse:0.552021+0.015769    test-rmse:0.594758+0.059594 
## [8]  train-rmse:0.540875+0.014871    test-rmse:0.586631+0.061177 
## [9]  train-rmse:0.530429+0.014269    test-rmse:0.578793+0.065937 
## [10] train-rmse:0.520749+0.016407    test-rmse:0.576454+0.062492 
## [11] train-rmse:0.513354+0.016949    test-rmse:0.573267+0.060292 
## [12] train-rmse:0.505457+0.015519    test-rmse:0.564882+0.058419 
## [13] train-rmse:0.499462+0.015841    test-rmse:0.561408+0.058933 
## [14] train-rmse:0.494035+0.016238    test-rmse:0.561108+0.061020 
## [15] train-rmse:0.488424+0.017017    test-rmse:0.555589+0.061106 
## [16] train-rmse:0.483415+0.016788    test-rmse:0.552250+0.061830 
## [17] train-rmse:0.478712+0.016960    test-rmse:0.551631+0.061829 
## [18] train-rmse:0.474736+0.017451    test-rmse:0.548188+0.058990 
## [19] train-rmse:0.470842+0.017313    test-rmse:0.546111+0.060290 
## [20] train-rmse:0.465518+0.016948    test-rmse:0.543830+0.058592 
## [21] train-rmse:0.461548+0.017631    test-rmse:0.540882+0.058389 
## [22] train-rmse:0.457496+0.017475    test-rmse:0.540294+0.060974 
## [23] train-rmse:0.453504+0.017455    test-rmse:0.536346+0.060309 
## [24] train-rmse:0.449703+0.016982    test-rmse:0.533103+0.061597 
## [25] train-rmse:0.446665+0.017171    test-rmse:0.529674+0.061169 
## [26] train-rmse:0.443700+0.017045    test-rmse:0.529258+0.059835 
## [27] train-rmse:0.440507+0.016634    test-rmse:0.529316+0.061210 
## [28] train-rmse:0.437224+0.016952    test-rmse:0.529994+0.060110 
## [29] train-rmse:0.434240+0.016842    test-rmse:0.528624+0.058961 
## [30] train-rmse:0.432022+0.016841    test-rmse:0.527110+0.061394 
## [31] train-rmse:0.429750+0.017050    test-rmse:0.526348+0.061188 
## [32] train-rmse:0.426997+0.017014    test-rmse:0.524838+0.059904 
## [33] train-rmse:0.424762+0.017127    test-rmse:0.525286+0.059033 
## [34] train-rmse:0.422010+0.017845    test-rmse:0.524290+0.058688 
## [35] train-rmse:0.419310+0.017942    test-rmse:0.523245+0.058967 
## [36] train-rmse:0.416647+0.018388    test-rmse:0.521175+0.059420 
## [37] train-rmse:0.414016+0.017535    test-rmse:0.520240+0.057925 
## [38] train-rmse:0.412087+0.017548    test-rmse:0.520965+0.057808 
## [39] train-rmse:0.410329+0.017443    test-rmse:0.520791+0.058405 
## [40] train-rmse:0.408600+0.017606    test-rmse:0.519232+0.058812 
## [41] train-rmse:0.406892+0.017614    test-rmse:0.516527+0.059718 
## [42] train-rmse:0.404815+0.017514    test-rmse:0.515663+0.060050 
## [43] train-rmse:0.402976+0.017515    test-rmse:0.515393+0.059208 
## [44] train-rmse:0.401320+0.017410    test-rmse:0.516318+0.059630 
## [45] train-rmse:0.399415+0.017645    test-rmse:0.516469+0.059461 
## [46] train-rmse:0.397430+0.017409    test-rmse:0.515805+0.059758 
## [47] train-rmse:0.396104+0.017525    test-rmse:0.515299+0.059831 
## [48] train-rmse:0.393804+0.016458    test-rmse:0.515385+0.059945 
## [49] train-rmse:0.391537+0.016422    test-rmse:0.514118+0.059891 
## [50] train-rmse:0.390069+0.016715    test-rmse:0.512259+0.059802 
## [51] train-rmse:0.388565+0.016859    test-rmse:0.511450+0.058618 
## [52] train-rmse:0.386493+0.016962    test-rmse:0.510903+0.058010 
## [53] train-rmse:0.385083+0.017341    test-rmse:0.510174+0.057995 
## [54] train-rmse:0.383791+0.017354    test-rmse:0.508995+0.056364 
## [55] train-rmse:0.382365+0.017303    test-rmse:0.509201+0.055729 
## [56] train-rmse:0.381098+0.017381    test-rmse:0.508518+0.055183 
## [57] train-rmse:0.379135+0.017437    test-rmse:0.507699+0.054803 
## [58] train-rmse:0.378189+0.017191    test-rmse:0.506786+0.054205 
## [59] train-rmse:0.376290+0.016754    test-rmse:0.506932+0.055157 
## [60] train-rmse:0.374688+0.016557    test-rmse:0.506054+0.055175 
## [61] train-rmse:0.373411+0.016778    test-rmse:0.505575+0.055731 
## [62] train-rmse:0.371751+0.016310    test-rmse:0.505618+0.055009 
## [63] train-rmse:0.369427+0.016984    test-rmse:0.504490+0.055446 
## [64] train-rmse:0.368646+0.016950    test-rmse:0.504462+0.055906 
## [65] train-rmse:0.366825+0.016337    test-rmse:0.504339+0.055459 
## [66] train-rmse:0.365585+0.016140    test-rmse:0.503320+0.055795 
## [67] train-rmse:0.364272+0.016301    test-rmse:0.503487+0.055179 
## [68] train-rmse:0.363258+0.016052    test-rmse:0.504572+0.054681 
## [69] train-rmse:0.362234+0.016063    test-rmse:0.503798+0.054409 
## [70] train-rmse:0.360674+0.015655    test-rmse:0.502706+0.055306 
## [71] train-rmse:0.359965+0.015515    test-rmse:0.501381+0.054915 
## [72] train-rmse:0.358732+0.016068    test-rmse:0.501100+0.054486 
## [73] train-rmse:0.357483+0.015833    test-rmse:0.501263+0.054161 
## [74] train-rmse:0.355847+0.015170    test-rmse:0.498823+0.055380 
## [75] train-rmse:0.355256+0.015139    test-rmse:0.499171+0.054474 
## [76] train-rmse:0.353611+0.014475    test-rmse:0.500984+0.054695 
## [77] train-rmse:0.352424+0.014158    test-rmse:0.500489+0.054645 
## [78] train-rmse:0.351658+0.014363    test-rmse:0.500501+0.055619 
## [79] train-rmse:0.349884+0.014107    test-rmse:0.498604+0.056030 
## [80] train-rmse:0.348678+0.013898    test-rmse:0.498331+0.055152 
## [81] train-rmse:0.347602+0.013267    test-rmse:0.498911+0.055276 
## [82] train-rmse:0.346085+0.012506    test-rmse:0.498829+0.054136 
## [83] train-rmse:0.345185+0.012262    test-rmse:0.498803+0.053754 
## [84] train-rmse:0.344128+0.012350    test-rmse:0.498452+0.053536 
## [85] train-rmse:0.343132+0.012565    test-rmse:0.497996+0.053247 
## [86] train-rmse:0.342079+0.012640    test-rmse:0.498295+0.053437 
## [87] train-rmse:0.340498+0.012070    test-rmse:0.497278+0.053694 
## [88] train-rmse:0.339597+0.011722    test-rmse:0.497970+0.053647 
## [89] train-rmse:0.338806+0.011885    test-rmse:0.497399+0.053130 
## [90] train-rmse:0.337204+0.012369    test-rmse:0.495775+0.051237 
## [91] train-rmse:0.336053+0.012278    test-rmse:0.495886+0.050711 
## [92] train-rmse:0.334584+0.013228    test-rmse:0.496218+0.050627 
## [93] train-rmse:0.333214+0.013636    test-rmse:0.497201+0.050860 
## [94] train-rmse:0.332288+0.013236    test-rmse:0.498012+0.051030 
## [95] train-rmse:0.331282+0.013189    test-rmse:0.497001+0.051468 
## [96] train-rmse:0.328666+0.013727    test-rmse:0.496016+0.052000 
## Stopping. Best iteration:
## [90] train-rmse:0.337204+0.012369    test-rmse:0.495775+0.051237
## 
## 65 
## [1]  train-rmse:0.828081+0.014975    test-rmse:0.837926+0.062786 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.711245+0.014043    test-rmse:0.734970+0.062525 
## [3]  train-rmse:0.633517+0.014088    test-rmse:0.669581+0.062509 
## [4]  train-rmse:0.581571+0.014031    test-rmse:0.628038+0.062310 
## [5]  train-rmse:0.545360+0.013164    test-rmse:0.598489+0.056978 
## [6]  train-rmse:0.518640+0.015421    test-rmse:0.584873+0.056221 
## [7]  train-rmse:0.500044+0.016975    test-rmse:0.570975+0.053686 
## [8]  train-rmse:0.484856+0.015964    test-rmse:0.562260+0.049288 
## [9]  train-rmse:0.472552+0.015166    test-rmse:0.553233+0.047770 
## [10] train-rmse:0.462732+0.015407    test-rmse:0.550877+0.047690 
## [11] train-rmse:0.454277+0.014789    test-rmse:0.545968+0.046989 
## [12] train-rmse:0.444456+0.012980    test-rmse:0.541355+0.046269 
## [13] train-rmse:0.437300+0.012704    test-rmse:0.537736+0.047080 
## [14] train-rmse:0.430467+0.013128    test-rmse:0.535010+0.050198 
## [15] train-rmse:0.424255+0.011870    test-rmse:0.531521+0.052513 
## [16] train-rmse:0.417923+0.011798    test-rmse:0.527927+0.052408 
## [17] train-rmse:0.412217+0.011085    test-rmse:0.525796+0.053341 
## [18] train-rmse:0.407420+0.011363    test-rmse:0.524909+0.055036 
## [19] train-rmse:0.402704+0.011469    test-rmse:0.524381+0.054552 
## [20] train-rmse:0.397224+0.011933    test-rmse:0.521789+0.054579 
## [21] train-rmse:0.391719+0.013105    test-rmse:0.521401+0.055031 
## [22] train-rmse:0.385535+0.013813    test-rmse:0.519887+0.053369 
## [23] train-rmse:0.381755+0.013090    test-rmse:0.518538+0.054928 
## [24] train-rmse:0.377944+0.012495    test-rmse:0.518376+0.052649 
## [25] train-rmse:0.375304+0.012722    test-rmse:0.516667+0.052826 
## [26] train-rmse:0.372194+0.013126    test-rmse:0.514667+0.052164 
## [27] train-rmse:0.367877+0.012808    test-rmse:0.513699+0.051861 
## [28] train-rmse:0.363882+0.011605    test-rmse:0.513162+0.051913 
## [29] train-rmse:0.359135+0.012511    test-rmse:0.513524+0.051448 
## [30] train-rmse:0.354474+0.012772    test-rmse:0.513190+0.051219 
## [31] train-rmse:0.351527+0.012648    test-rmse:0.513414+0.050470 
## [32] train-rmse:0.348222+0.012297    test-rmse:0.511043+0.049619 
## [33] train-rmse:0.345560+0.012460    test-rmse:0.510007+0.050402 
## [34] train-rmse:0.342899+0.012061    test-rmse:0.509106+0.050645 
## [35] train-rmse:0.339652+0.012127    test-rmse:0.509100+0.049346 
## [36] train-rmse:0.335869+0.010958    test-rmse:0.507757+0.050252 
## [37] train-rmse:0.332450+0.011761    test-rmse:0.507027+0.049772 
## [38] train-rmse:0.329899+0.011305    test-rmse:0.507628+0.049282 
## [39] train-rmse:0.326662+0.012103    test-rmse:0.507627+0.049812 
## [40] train-rmse:0.323537+0.011976    test-rmse:0.509441+0.049128 
## [41] train-rmse:0.321196+0.011617    test-rmse:0.507959+0.049080 
## [42] train-rmse:0.317861+0.010678    test-rmse:0.508935+0.048297 
## [43] train-rmse:0.314909+0.012313    test-rmse:0.508375+0.049325 
## Stopping. Best iteration:
## [37] train-rmse:0.332450+0.011761    test-rmse:0.507027+0.049772
## 
## 66 
## [1]  train-rmse:0.819441+0.014685    test-rmse:0.832770+0.060959 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.694665+0.013581    test-rmse:0.720940+0.065231 
## [3]  train-rmse:0.609348+0.012249    test-rmse:0.653547+0.067749 
## [4]  train-rmse:0.549083+0.013178    test-rmse:0.604369+0.059374 
## [5]  train-rmse:0.504340+0.013624    test-rmse:0.572296+0.057190 
## [6]  train-rmse:0.474738+0.014661    test-rmse:0.553737+0.052769 
## [7]  train-rmse:0.450637+0.017145    test-rmse:0.538486+0.053956 
## [8]  train-rmse:0.435342+0.017936    test-rmse:0.530604+0.051394 
## [9]  train-rmse:0.420574+0.016847    test-rmse:0.525419+0.056141 
## [10] train-rmse:0.408161+0.015763    test-rmse:0.520305+0.055110 
## [11] train-rmse:0.397075+0.013903    test-rmse:0.514817+0.057999 
## [12] train-rmse:0.388493+0.013589    test-rmse:0.508546+0.059665 
## [13] train-rmse:0.381143+0.013569    test-rmse:0.507019+0.059333 
## [14] train-rmse:0.373295+0.012482    test-rmse:0.502477+0.058493 
## [15] train-rmse:0.366322+0.013297    test-rmse:0.504086+0.059422 
## [16] train-rmse:0.358749+0.013646    test-rmse:0.501626+0.059607 
## [17] train-rmse:0.351812+0.015867    test-rmse:0.499095+0.057982 
## [18] train-rmse:0.345099+0.015839    test-rmse:0.498607+0.058756 
## [19] train-rmse:0.337855+0.016134    test-rmse:0.499112+0.058527 
## [20] train-rmse:0.331856+0.014833    test-rmse:0.499783+0.057949 
## [21] train-rmse:0.327463+0.014722    test-rmse:0.500363+0.057869 
## [22] train-rmse:0.323178+0.014416    test-rmse:0.499817+0.056377 
## [23] train-rmse:0.319162+0.013522    test-rmse:0.499763+0.056638 
## [24] train-rmse:0.314438+0.014210    test-rmse:0.500868+0.055515 
## Stopping. Best iteration:
## [18] train-rmse:0.345099+0.015839    test-rmse:0.498607+0.058756
## 
## 67 
## [1]  train-rmse:0.811726+0.013999    test-rmse:0.828473+0.061791 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.678290+0.011330    test-rmse:0.714800+0.062399 
## [3]  train-rmse:0.583642+0.010787    test-rmse:0.640410+0.063570 
## [4]  train-rmse:0.518860+0.012693    test-rmse:0.594138+0.059191 
## [5]  train-rmse:0.473694+0.012724    test-rmse:0.564118+0.056322 
## [6]  train-rmse:0.438421+0.012150    test-rmse:0.546732+0.053725 
## [7]  train-rmse:0.415609+0.012551    test-rmse:0.538522+0.055447 
## [8]  train-rmse:0.397615+0.011855    test-rmse:0.531390+0.053573 
## [9]  train-rmse:0.381127+0.016143    test-rmse:0.523589+0.055404 
## [10] train-rmse:0.368867+0.016104    test-rmse:0.515480+0.053297 
## [11] train-rmse:0.356930+0.014385    test-rmse:0.511756+0.054777 
## [12] train-rmse:0.343642+0.018432    test-rmse:0.509075+0.051704 
## [13] train-rmse:0.334929+0.017093    test-rmse:0.506250+0.052144 
## [14] train-rmse:0.326985+0.014847    test-rmse:0.502929+0.052632 
## [15] train-rmse:0.317441+0.014382    test-rmse:0.499318+0.055407 
## [16] train-rmse:0.311271+0.014945    test-rmse:0.497513+0.055460 
## [17] train-rmse:0.304457+0.014593    test-rmse:0.496490+0.057822 
## [18] train-rmse:0.296859+0.013544    test-rmse:0.494729+0.057389 
## [19] train-rmse:0.288668+0.014016    test-rmse:0.492023+0.056936 
## [20] train-rmse:0.282620+0.012881    test-rmse:0.491896+0.058648 
## [21] train-rmse:0.277985+0.013199    test-rmse:0.490137+0.058656 
## [22] train-rmse:0.272118+0.013907    test-rmse:0.490294+0.059731 
## [23] train-rmse:0.264611+0.014223    test-rmse:0.490652+0.060773 
## [24] train-rmse:0.257582+0.017032    test-rmse:0.490598+0.060469 
## [25] train-rmse:0.252224+0.017421    test-rmse:0.489360+0.060720 
## [26] train-rmse:0.245529+0.016923    test-rmse:0.488344+0.061988 
## [27] train-rmse:0.240710+0.015882    test-rmse:0.489899+0.063415 
## [28] train-rmse:0.235577+0.013258    test-rmse:0.492518+0.064342 
## [29] train-rmse:0.231507+0.013529    test-rmse:0.491782+0.064201 
## [30] train-rmse:0.227378+0.012673    test-rmse:0.491574+0.064113 
## [31] train-rmse:0.222765+0.015642    test-rmse:0.491576+0.063750 
## [32] train-rmse:0.218601+0.015558    test-rmse:0.492253+0.062327 
## Stopping. Best iteration:
## [26] train-rmse:0.245529+0.016923    test-rmse:0.488344+0.061988
## 
## 68 
## [1]  train-rmse:0.804630+0.013307    test-rmse:0.825739+0.061956 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.662732+0.010509    test-rmse:0.707200+0.062612 
## [3]  train-rmse:0.564880+0.012642    test-rmse:0.631900+0.063395 
## [4]  train-rmse:0.493177+0.013954    test-rmse:0.584289+0.056583 
## [5]  train-rmse:0.443573+0.012351    test-rmse:0.551608+0.054362 
## [6]  train-rmse:0.404988+0.012083    test-rmse:0.530721+0.051088 
## [7]  train-rmse:0.374805+0.011372    test-rmse:0.521177+0.054550 
## [8]  train-rmse:0.353875+0.010748    test-rmse:0.514091+0.053793 
## [9]  train-rmse:0.338671+0.011175    test-rmse:0.510367+0.052600 
## [10] train-rmse:0.319048+0.009398    test-rmse:0.505590+0.049756 
## [11] train-rmse:0.307544+0.012573    test-rmse:0.503286+0.048602 
## [12] train-rmse:0.299066+0.011381    test-rmse:0.499328+0.048541 
## [13] train-rmse:0.287724+0.011890    test-rmse:0.494999+0.051851 
## [14] train-rmse:0.280479+0.010021    test-rmse:0.495325+0.050932 
## [15] train-rmse:0.272905+0.010392    test-rmse:0.493883+0.052613 
## [16] train-rmse:0.266030+0.012313    test-rmse:0.492831+0.052111 
## [17] train-rmse:0.259254+0.011067    test-rmse:0.491810+0.052241 
## [18] train-rmse:0.252518+0.014009    test-rmse:0.491369+0.052809 
## [19] train-rmse:0.246398+0.013961    test-rmse:0.491498+0.053392 
## [20] train-rmse:0.238775+0.013639    test-rmse:0.491478+0.052473 
## [21] train-rmse:0.232492+0.012459    test-rmse:0.494677+0.053238 
## [22] train-rmse:0.225430+0.014922    test-rmse:0.492332+0.051642 
## [23] train-rmse:0.218570+0.015529    test-rmse:0.492198+0.051738 
## [24] train-rmse:0.211431+0.016167    test-rmse:0.492723+0.049671 
## Stopping. Best iteration:
## [18] train-rmse:0.252518+0.014009    test-rmse:0.491369+0.052809
## 
## 69 
## [1]  train-rmse:0.797631+0.012704    test-rmse:0.821427+0.059529 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.649896+0.010060    test-rmse:0.702971+0.061650 
## [3]  train-rmse:0.548629+0.009022    test-rmse:0.625251+0.062967 
## [4]  train-rmse:0.475848+0.008859    test-rmse:0.582559+0.059503 
## [5]  train-rmse:0.417331+0.013371    test-rmse:0.547996+0.062324 
## [6]  train-rmse:0.375375+0.013124    test-rmse:0.528764+0.061055 
## [7]  train-rmse:0.344524+0.012790    test-rmse:0.518175+0.056203 
## [8]  train-rmse:0.317653+0.017361    test-rmse:0.512937+0.058287 
## [9]  train-rmse:0.302338+0.017736    test-rmse:0.511007+0.057391 
## [10] train-rmse:0.288101+0.017920    test-rmse:0.507654+0.053762 
## [11] train-rmse:0.276314+0.016278    test-rmse:0.506125+0.053418 
## [12] train-rmse:0.264469+0.013749    test-rmse:0.503927+0.054071 
## [13] train-rmse:0.251783+0.015102    test-rmse:0.502626+0.052296 
## [14] train-rmse:0.241683+0.014651    test-rmse:0.501523+0.050410 
## [15] train-rmse:0.234712+0.013208    test-rmse:0.499048+0.049457 
## [16] train-rmse:0.226416+0.011000    test-rmse:0.498036+0.049781 
## [17] train-rmse:0.219975+0.012051    test-rmse:0.498765+0.050619 
## [18] train-rmse:0.214304+0.011230    test-rmse:0.498455+0.050450 
## [19] train-rmse:0.208532+0.009770    test-rmse:0.499420+0.050480 
## [20] train-rmse:0.197507+0.010983    test-rmse:0.499703+0.052823 
## [21] train-rmse:0.191383+0.011555    test-rmse:0.501155+0.052338 
## [22] train-rmse:0.186155+0.011138    test-rmse:0.501643+0.051789 
## Stopping. Best iteration:
## [16] train-rmse:0.226416+0.011000    test-rmse:0.498036+0.049781
## 
## 70 
## [1]  train-rmse:0.851277+0.014647    test-rmse:0.855434+0.060317 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.762903+0.013586    test-rmse:0.779237+0.064284 
## [3]  train-rmse:0.708413+0.013379    test-rmse:0.731249+0.059236 
## [4]  train-rmse:0.672401+0.012276    test-rmse:0.691567+0.057835 
## [5]  train-rmse:0.648010+0.012022    test-rmse:0.673332+0.054964 
## [6]  train-rmse:0.631771+0.011468    test-rmse:0.661088+0.051150 
## [7]  train-rmse:0.619350+0.011292    test-rmse:0.648963+0.049969 
## [8]  train-rmse:0.609882+0.011102    test-rmse:0.641361+0.049664 
## [9]  train-rmse:0.601671+0.011070    test-rmse:0.637053+0.052725 
## [10] train-rmse:0.594655+0.011016    test-rmse:0.630337+0.054826 
## [11] train-rmse:0.588467+0.011138    test-rmse:0.621926+0.055472 
## [12] train-rmse:0.582979+0.011022    test-rmse:0.616404+0.055640 
## [13] train-rmse:0.577839+0.011017    test-rmse:0.613343+0.056176 
## [14] train-rmse:0.572999+0.011214    test-rmse:0.608236+0.057899 
## [15] train-rmse:0.568576+0.011301    test-rmse:0.604193+0.059038 
## [16] train-rmse:0.564551+0.011130    test-rmse:0.600966+0.057145 
## [17] train-rmse:0.560713+0.011127    test-rmse:0.596944+0.056700 
## [18] train-rmse:0.557091+0.011120    test-rmse:0.594944+0.059289 
## [19] train-rmse:0.553581+0.011078    test-rmse:0.595851+0.057519 
## [20] train-rmse:0.550226+0.010883    test-rmse:0.593670+0.058797 
## [21] train-rmse:0.547177+0.010876    test-rmse:0.591993+0.056378 
## [22] train-rmse:0.544214+0.010894    test-rmse:0.590580+0.056284 
## [23] train-rmse:0.541416+0.010961    test-rmse:0.587823+0.056792 
## [24] train-rmse:0.538670+0.011023    test-rmse:0.585268+0.057040 
## [25] train-rmse:0.536009+0.011061    test-rmse:0.582177+0.056090 
## [26] train-rmse:0.533468+0.011082    test-rmse:0.579919+0.055656 
## [27] train-rmse:0.531105+0.010998    test-rmse:0.581935+0.055635 
## [28] train-rmse:0.528792+0.010914    test-rmse:0.579626+0.057523 
## [29] train-rmse:0.526620+0.010884    test-rmse:0.579754+0.057500 
## [30] train-rmse:0.524576+0.010804    test-rmse:0.577899+0.056340 
## [31] train-rmse:0.522578+0.010731    test-rmse:0.576244+0.055434 
## [32] train-rmse:0.520675+0.010670    test-rmse:0.574792+0.055937 
## [33] train-rmse:0.518842+0.010660    test-rmse:0.572164+0.056686 
## [34] train-rmse:0.517046+0.010654    test-rmse:0.570743+0.057169 
## [35] train-rmse:0.515298+0.010629    test-rmse:0.567866+0.056668 
## [36] train-rmse:0.513595+0.010558    test-rmse:0.566111+0.056908 
## [37] train-rmse:0.511957+0.010492    test-rmse:0.564036+0.056606 
## [38] train-rmse:0.510383+0.010471    test-rmse:0.562288+0.055328 
## [39] train-rmse:0.508815+0.010437    test-rmse:0.560585+0.055781 
## [40] train-rmse:0.507345+0.010355    test-rmse:0.559306+0.055685 
## [41] train-rmse:0.505925+0.010305    test-rmse:0.557319+0.056043 
## [42] train-rmse:0.504553+0.010273    test-rmse:0.556202+0.056162 
## [43] train-rmse:0.503244+0.010234    test-rmse:0.557619+0.056001 
## [44] train-rmse:0.501969+0.010191    test-rmse:0.557368+0.054787 
## [45] train-rmse:0.500750+0.010239    test-rmse:0.556916+0.055070 
## [46] train-rmse:0.499563+0.010256    test-rmse:0.558456+0.055811 
## [47] train-rmse:0.498403+0.010294    test-rmse:0.557787+0.055099 
## [48] train-rmse:0.497244+0.010336    test-rmse:0.556786+0.055299 
## Stopping. Best iteration:
## [42] train-rmse:0.504553+0.010273    test-rmse:0.556202+0.056162
## 
## 71 
## [1]  train-rmse:0.827155+0.014439    test-rmse:0.833121+0.060589 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.718581+0.013679    test-rmse:0.737013+0.065688 
## [3]  train-rmse:0.652180+0.012414    test-rmse:0.680361+0.063820 
## [4]  train-rmse:0.610260+0.012049    test-rmse:0.644727+0.061006 
## [5]  train-rmse:0.582667+0.010908    test-rmse:0.625894+0.065368 
## [6]  train-rmse:0.560776+0.012784    test-rmse:0.610263+0.060827 
## [7]  train-rmse:0.547130+0.013102    test-rmse:0.599643+0.058236 
## [8]  train-rmse:0.535256+0.014019    test-rmse:0.590662+0.056670 
## [9]  train-rmse:0.526394+0.014301    test-rmse:0.582556+0.060126 
## [10] train-rmse:0.517911+0.014214    test-rmse:0.576738+0.059241 
## [11] train-rmse:0.509579+0.013619    test-rmse:0.571919+0.058979 
## [12] train-rmse:0.502240+0.013224    test-rmse:0.569486+0.057994 
## [13] train-rmse:0.494432+0.014076    test-rmse:0.561483+0.055752 
## [14] train-rmse:0.487889+0.014809    test-rmse:0.560239+0.057336 
## [15] train-rmse:0.482956+0.014844    test-rmse:0.556931+0.057537 
## [16] train-rmse:0.477521+0.014970    test-rmse:0.552730+0.055338 
## [17] train-rmse:0.472686+0.015564    test-rmse:0.549954+0.056966 
## [18] train-rmse:0.467554+0.015446    test-rmse:0.545709+0.055528 
## [19] train-rmse:0.463268+0.015229    test-rmse:0.543843+0.055857 
## [20] train-rmse:0.459138+0.015251    test-rmse:0.543281+0.055755 
## [21] train-rmse:0.454519+0.016068    test-rmse:0.540152+0.055778 
## [22] train-rmse:0.450936+0.015365    test-rmse:0.537306+0.056316 
## [23] train-rmse:0.446999+0.015311    test-rmse:0.534914+0.059467 
## [24] train-rmse:0.443894+0.015539    test-rmse:0.533292+0.060199 
## [25] train-rmse:0.440375+0.015106    test-rmse:0.530739+0.058333 
## [26] train-rmse:0.437073+0.015363    test-rmse:0.528596+0.058813 
## [27] train-rmse:0.434527+0.015484    test-rmse:0.529301+0.058391 
## [28] train-rmse:0.431337+0.014874    test-rmse:0.526574+0.060734 
## [29] train-rmse:0.427471+0.015397    test-rmse:0.525315+0.061494 
## [30] train-rmse:0.424701+0.015340    test-rmse:0.525113+0.062238 
## [31] train-rmse:0.422141+0.014970    test-rmse:0.523372+0.062604 
## [32] train-rmse:0.420050+0.014599    test-rmse:0.521597+0.063111 
## [33] train-rmse:0.417227+0.013820    test-rmse:0.521647+0.063722 
## [34] train-rmse:0.414708+0.014094    test-rmse:0.521112+0.063878 
## [35] train-rmse:0.412732+0.013619    test-rmse:0.519430+0.064909 
## [36] train-rmse:0.410299+0.013893    test-rmse:0.517904+0.064875 
## [37] train-rmse:0.408207+0.014126    test-rmse:0.517436+0.063580 
## [38] train-rmse:0.406079+0.014062    test-rmse:0.516958+0.062165 
## [39] train-rmse:0.403854+0.014309    test-rmse:0.517268+0.061965 
## [40] train-rmse:0.402139+0.014287    test-rmse:0.516611+0.061104 
## [41] train-rmse:0.400561+0.014326    test-rmse:0.517044+0.060989 
## [42] train-rmse:0.398307+0.013583    test-rmse:0.515534+0.060470 
## [43] train-rmse:0.396634+0.013730    test-rmse:0.514646+0.060286 
## [44] train-rmse:0.394554+0.013510    test-rmse:0.514578+0.059599 
## [45] train-rmse:0.392897+0.013762    test-rmse:0.514079+0.059779 
## [46] train-rmse:0.391150+0.013630    test-rmse:0.512040+0.061723 
## [47] train-rmse:0.389680+0.013632    test-rmse:0.511594+0.060427 
## [48] train-rmse:0.388113+0.013721    test-rmse:0.511735+0.061000 
## [49] train-rmse:0.386748+0.013476    test-rmse:0.511147+0.061491 
## [50] train-rmse:0.385013+0.012990    test-rmse:0.509936+0.061835 
## [51] train-rmse:0.383558+0.013102    test-rmse:0.510827+0.060898 
## [52] train-rmse:0.382285+0.012856    test-rmse:0.510947+0.060397 
## [53] train-rmse:0.380910+0.013238    test-rmse:0.510485+0.060692 
## [54] train-rmse:0.379162+0.013256    test-rmse:0.509190+0.061699 
## [55] train-rmse:0.378039+0.013182    test-rmse:0.508485+0.061609 
## [56] train-rmse:0.376364+0.012651    test-rmse:0.507324+0.061965 
## [57] train-rmse:0.375123+0.012226    test-rmse:0.507041+0.062918 
## [58] train-rmse:0.373983+0.012200    test-rmse:0.507181+0.062541 
## [59] train-rmse:0.372390+0.012781    test-rmse:0.506612+0.062578 
## [60] train-rmse:0.370367+0.012567    test-rmse:0.506446+0.063758 
## [61] train-rmse:0.369269+0.012340    test-rmse:0.506227+0.064481 
## [62] train-rmse:0.368010+0.012204    test-rmse:0.506191+0.063447 
## [63] train-rmse:0.365741+0.011988    test-rmse:0.504206+0.065294 
## [64] train-rmse:0.364900+0.011908    test-rmse:0.503372+0.065517 
## [65] train-rmse:0.362793+0.012463    test-rmse:0.501725+0.065451 
## [66] train-rmse:0.361412+0.013020    test-rmse:0.502218+0.065807 
## [67] train-rmse:0.360240+0.012931    test-rmse:0.501922+0.065905 
## [68] train-rmse:0.359221+0.013041    test-rmse:0.500247+0.065841 
## [69] train-rmse:0.357730+0.013689    test-rmse:0.499737+0.066273 
## [70] train-rmse:0.355714+0.013503    test-rmse:0.498635+0.064974 
## [71] train-rmse:0.354295+0.013423    test-rmse:0.498975+0.064335 
## [72] train-rmse:0.352131+0.012377    test-rmse:0.498630+0.064168 
## [73] train-rmse:0.349937+0.011981    test-rmse:0.498549+0.063255 
## [74] train-rmse:0.348403+0.012439    test-rmse:0.498739+0.063591 
## [75] train-rmse:0.346629+0.012296    test-rmse:0.498893+0.062191 
## [76] train-rmse:0.345682+0.012106    test-rmse:0.498440+0.061654 
## [77] train-rmse:0.344996+0.012141    test-rmse:0.498651+0.062212 
## [78] train-rmse:0.343934+0.012463    test-rmse:0.498504+0.062265 
## [79] train-rmse:0.343031+0.012348    test-rmse:0.499189+0.061503 
## [80] train-rmse:0.341716+0.013048    test-rmse:0.498501+0.061152 
## [81] train-rmse:0.340019+0.013053    test-rmse:0.498966+0.061864 
## [82] train-rmse:0.338391+0.012611    test-rmse:0.497532+0.060793 
## [83] train-rmse:0.337096+0.012656    test-rmse:0.497437+0.060371 
## [84] train-rmse:0.336309+0.012589    test-rmse:0.497634+0.059528 
## [85] train-rmse:0.334846+0.012475    test-rmse:0.498184+0.058717 
## [86] train-rmse:0.333296+0.012844    test-rmse:0.497523+0.058146 
## [87] train-rmse:0.332245+0.012796    test-rmse:0.497531+0.058199 
## [88] train-rmse:0.331408+0.012769    test-rmse:0.496531+0.058043 
## [89] train-rmse:0.330592+0.012746    test-rmse:0.496372+0.058360 
## [90] train-rmse:0.329121+0.012526    test-rmse:0.495588+0.058298 
## [91] train-rmse:0.327356+0.012948    test-rmse:0.495832+0.058411 
## [92] train-rmse:0.326391+0.013167    test-rmse:0.495469+0.058745 
## [93] train-rmse:0.325026+0.014082    test-rmse:0.494822+0.057974 
## [94] train-rmse:0.323605+0.014273    test-rmse:0.494689+0.058380 
## [95] train-rmse:0.322552+0.014128    test-rmse:0.495466+0.058597 
## [96] train-rmse:0.320932+0.012825    test-rmse:0.495617+0.057922 
## [97] train-rmse:0.319008+0.012655    test-rmse:0.495696+0.057931 
## [98] train-rmse:0.317026+0.012129    test-rmse:0.495339+0.057786 
## [99] train-rmse:0.315828+0.011682    test-rmse:0.494652+0.058263 
## [100]    train-rmse:0.314679+0.011540    test-rmse:0.496064+0.056987 
## [101]    train-rmse:0.313558+0.011902    test-rmse:0.496396+0.056832 
## [102]    train-rmse:0.312412+0.012210    test-rmse:0.496595+0.056429 
## [103]    train-rmse:0.311558+0.012855    test-rmse:0.497577+0.057593 
## [104]    train-rmse:0.310547+0.013102    test-rmse:0.497861+0.057851 
## [105]    train-rmse:0.308823+0.012888    test-rmse:0.496883+0.058453 
## Stopping. Best iteration:
## [99] train-rmse:0.315828+0.011682    test-rmse:0.494652+0.058263
## 
## 72 
## [1]  train-rmse:0.816359+0.014908    test-rmse:0.827181+0.062976 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.696234+0.013941    test-rmse:0.722136+0.062807 
## [3]  train-rmse:0.619414+0.014513    test-rmse:0.655841+0.059839 
## [4]  train-rmse:0.568968+0.014356    test-rmse:0.615593+0.059154 
## [5]  train-rmse:0.533701+0.013635    test-rmse:0.591393+0.053214 
## [6]  train-rmse:0.511347+0.012534    test-rmse:0.573743+0.056424 
## [7]  train-rmse:0.492970+0.015236    test-rmse:0.563117+0.049775 
## [8]  train-rmse:0.480670+0.014862    test-rmse:0.557357+0.048569 
## [9]  train-rmse:0.469118+0.016228    test-rmse:0.552132+0.049622 
## [10] train-rmse:0.460008+0.016739    test-rmse:0.546610+0.048120 
## [11] train-rmse:0.449853+0.014722    test-rmse:0.542960+0.050222 
## [12] train-rmse:0.441152+0.012663    test-rmse:0.535461+0.053170 
## [13] train-rmse:0.432471+0.012129    test-rmse:0.533077+0.054311 
## [14] train-rmse:0.425952+0.013055    test-rmse:0.529055+0.053799 
## [15] train-rmse:0.418741+0.013784    test-rmse:0.527116+0.054923 
## [16] train-rmse:0.413063+0.013551    test-rmse:0.525326+0.057021 
## [17] train-rmse:0.405181+0.013441    test-rmse:0.521892+0.056356 
## [18] train-rmse:0.400472+0.013308    test-rmse:0.522169+0.056372 
## [19] train-rmse:0.394863+0.013775    test-rmse:0.519698+0.056747 
## [20] train-rmse:0.390335+0.013641    test-rmse:0.518228+0.058422 
## [21] train-rmse:0.387194+0.013253    test-rmse:0.518440+0.057691 
## [22] train-rmse:0.381933+0.012509    test-rmse:0.516953+0.058002 
## [23] train-rmse:0.377772+0.013486    test-rmse:0.513584+0.057799 
## [24] train-rmse:0.373369+0.013155    test-rmse:0.513225+0.057089 
## [25] train-rmse:0.368800+0.012823    test-rmse:0.513286+0.056800 
## [26] train-rmse:0.362511+0.015122    test-rmse:0.511323+0.058464 
## [27] train-rmse:0.358939+0.014770    test-rmse:0.510046+0.059812 
## [28] train-rmse:0.355214+0.014915    test-rmse:0.507938+0.060353 
## [29] train-rmse:0.351776+0.013967    test-rmse:0.507938+0.059387 
## [30] train-rmse:0.347965+0.013170    test-rmse:0.508063+0.058429 
## [31] train-rmse:0.344330+0.013463    test-rmse:0.506690+0.058808 
## [32] train-rmse:0.340839+0.012989    test-rmse:0.504961+0.058131 
## [33] train-rmse:0.337270+0.012994    test-rmse:0.504227+0.060195 
## [34] train-rmse:0.335029+0.012754    test-rmse:0.503940+0.059125 
## [35] train-rmse:0.331884+0.013064    test-rmse:0.504485+0.059617 
## [36] train-rmse:0.329242+0.012775    test-rmse:0.504892+0.058429 
## [37] train-rmse:0.326711+0.012557    test-rmse:0.503848+0.058383 
## [38] train-rmse:0.323630+0.013565    test-rmse:0.505350+0.056769 
## [39] train-rmse:0.319110+0.015074    test-rmse:0.505082+0.055336 
## [40] train-rmse:0.316206+0.015488    test-rmse:0.504719+0.056450 
## [41] train-rmse:0.312844+0.014694    test-rmse:0.503458+0.057259 
## [42] train-rmse:0.308582+0.014125    test-rmse:0.502363+0.057243 
## [43] train-rmse:0.305493+0.014129    test-rmse:0.501350+0.054836 
## [44] train-rmse:0.302944+0.015009    test-rmse:0.500463+0.052953 
## [45] train-rmse:0.299412+0.015440    test-rmse:0.500898+0.052459 
## [46] train-rmse:0.296652+0.015044    test-rmse:0.499940+0.052137 
## [47] train-rmse:0.294390+0.015533    test-rmse:0.500186+0.053709 
## [48] train-rmse:0.291988+0.016454    test-rmse:0.499799+0.054607 
## [49] train-rmse:0.289085+0.015241    test-rmse:0.499881+0.054247 
## [50] train-rmse:0.286857+0.015360    test-rmse:0.500763+0.052545 
## [51] train-rmse:0.283287+0.015119    test-rmse:0.500561+0.053353 
## [52] train-rmse:0.281180+0.015449    test-rmse:0.500761+0.053999 
## [53] train-rmse:0.278603+0.015245    test-rmse:0.500242+0.053976 
## [54] train-rmse:0.275754+0.016583    test-rmse:0.499394+0.054014 
## [55] train-rmse:0.273651+0.016365    test-rmse:0.499623+0.053547 
## [56] train-rmse:0.270874+0.015877    test-rmse:0.500377+0.053445 
## [57] train-rmse:0.268453+0.015217    test-rmse:0.499953+0.053657 
## [58] train-rmse:0.266829+0.015022    test-rmse:0.500628+0.053538 
## [59] train-rmse:0.264998+0.015029    test-rmse:0.500392+0.053505 
## [60] train-rmse:0.263642+0.014908    test-rmse:0.499478+0.053973 
## Stopping. Best iteration:
## [54] train-rmse:0.275754+0.016583    test-rmse:0.499394+0.054014
## 
## 73 
## [1]  train-rmse:0.807057+0.014600    test-rmse:0.821640+0.061016 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.678475+0.013422    test-rmse:0.707165+0.065401 
## [3]  train-rmse:0.593705+0.012368    test-rmse:0.641087+0.061203 
## [4]  train-rmse:0.533152+0.013501    test-rmse:0.597362+0.052002 
## [5]  train-rmse:0.492895+0.015720    test-rmse:0.570084+0.048848 
## [6]  train-rmse:0.465821+0.016339    test-rmse:0.556258+0.046803 
## [7]  train-rmse:0.440623+0.017576    test-rmse:0.543738+0.051314 
## [8]  train-rmse:0.426849+0.018093    test-rmse:0.536189+0.051577 
## [9]  train-rmse:0.415120+0.018017    test-rmse:0.528208+0.052295 
## [10] train-rmse:0.405440+0.016393    test-rmse:0.523300+0.051726 
## [11] train-rmse:0.394682+0.016437    test-rmse:0.520461+0.053215 
## [12] train-rmse:0.384508+0.016256    test-rmse:0.514686+0.053801 
## [13] train-rmse:0.375650+0.016534    test-rmse:0.514700+0.052807 
## [14] train-rmse:0.366342+0.017075    test-rmse:0.509129+0.053355 
## [15] train-rmse:0.360924+0.016016    test-rmse:0.507525+0.054650 
## [16] train-rmse:0.354420+0.016239    test-rmse:0.507928+0.053954 
## [17] train-rmse:0.349218+0.016370    test-rmse:0.508858+0.054445 
## [18] train-rmse:0.343472+0.015661    test-rmse:0.509410+0.054683 
## [19] train-rmse:0.336151+0.014184    test-rmse:0.505211+0.056945 
## [20] train-rmse:0.330950+0.013112    test-rmse:0.502480+0.055256 
## [21] train-rmse:0.323408+0.012050    test-rmse:0.502327+0.055055 
## [22] train-rmse:0.317111+0.013423    test-rmse:0.503542+0.057044 
## [23] train-rmse:0.310602+0.012449    test-rmse:0.502622+0.056116 
## [24] train-rmse:0.305690+0.012102    test-rmse:0.502670+0.056293 
## [25] train-rmse:0.301296+0.012840    test-rmse:0.501560+0.055082 
## [26] train-rmse:0.296677+0.013107    test-rmse:0.498976+0.054220 
## [27] train-rmse:0.292267+0.013388    test-rmse:0.499324+0.053643 
## [28] train-rmse:0.287962+0.013103    test-rmse:0.499756+0.053923 
## [29] train-rmse:0.282739+0.011404    test-rmse:0.499420+0.055664 
## [30] train-rmse:0.278699+0.010967    test-rmse:0.498820+0.053955 
## [31] train-rmse:0.275543+0.011478    test-rmse:0.498105+0.053274 
## [32] train-rmse:0.270640+0.011754    test-rmse:0.498614+0.052845 
## [33] train-rmse:0.266444+0.012404    test-rmse:0.498275+0.051873 
## [34] train-rmse:0.261436+0.012874    test-rmse:0.497450+0.051079 
## [35] train-rmse:0.258439+0.012009    test-rmse:0.497518+0.051326 
## [36] train-rmse:0.254549+0.011705    test-rmse:0.498128+0.051528 
## [37] train-rmse:0.249728+0.011987    test-rmse:0.500305+0.051752 
## [38] train-rmse:0.245180+0.010763    test-rmse:0.500143+0.053203 
## [39] train-rmse:0.240926+0.011096    test-rmse:0.501521+0.050878 
## [40] train-rmse:0.236699+0.011703    test-rmse:0.502426+0.051656 
## Stopping. Best iteration:
## [34] train-rmse:0.261436+0.012874    test-rmse:0.497450+0.051079
## 
## 74 
## [1]  train-rmse:0.798743+0.013868    test-rmse:0.817058+0.061851 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.660980+0.011075    test-rmse:0.700722+0.062722 
## [3]  train-rmse:0.565182+0.010739    test-rmse:0.623347+0.057973 
## [4]  train-rmse:0.502400+0.012518    test-rmse:0.582197+0.055908 
## [5]  train-rmse:0.458196+0.012330    test-rmse:0.556860+0.054747 
## [6]  train-rmse:0.424255+0.012363    test-rmse:0.545099+0.055594 
## [7]  train-rmse:0.402661+0.011810    test-rmse:0.534513+0.054352 
## [8]  train-rmse:0.382550+0.011896    test-rmse:0.525279+0.052740 
## [9]  train-rmse:0.366361+0.012204    test-rmse:0.521367+0.051377 
## [10] train-rmse:0.353166+0.009270    test-rmse:0.516605+0.047895 
## [11] train-rmse:0.341353+0.013648    test-rmse:0.515472+0.047375 
## [12] train-rmse:0.330672+0.013360    test-rmse:0.512097+0.046505 
## [13] train-rmse:0.322185+0.012515    test-rmse:0.510007+0.049828 
## [14] train-rmse:0.312727+0.012707    test-rmse:0.508277+0.050344 
## [15] train-rmse:0.305354+0.012033    test-rmse:0.508413+0.049832 
## [16] train-rmse:0.296197+0.013883    test-rmse:0.504875+0.050498 
## [17] train-rmse:0.289238+0.013962    test-rmse:0.505045+0.048106 
## [18] train-rmse:0.282866+0.013742    test-rmse:0.503638+0.049491 
## [19] train-rmse:0.276776+0.012475    test-rmse:0.502212+0.049712 
## [20] train-rmse:0.270108+0.013795    test-rmse:0.500835+0.046672 
## [21] train-rmse:0.265064+0.013598    test-rmse:0.500837+0.046894 
## [22] train-rmse:0.259599+0.013783    test-rmse:0.500516+0.045765 
## [23] train-rmse:0.252000+0.013746    test-rmse:0.499998+0.045947 
## [24] train-rmse:0.247720+0.013763    test-rmse:0.497969+0.045791 
## [25] train-rmse:0.242237+0.013666    test-rmse:0.499662+0.043936 
## [26] train-rmse:0.234511+0.012247    test-rmse:0.501404+0.043594 
## [27] train-rmse:0.228732+0.012261    test-rmse:0.502023+0.042798 
## [28] train-rmse:0.223918+0.013579    test-rmse:0.503154+0.042997 
## [29] train-rmse:0.218202+0.011567    test-rmse:0.502799+0.042756 
## [30] train-rmse:0.213418+0.011440    test-rmse:0.503359+0.041549 
## Stopping. Best iteration:
## [24] train-rmse:0.247720+0.013763    test-rmse:0.497969+0.045791
## 
## 75 
## [1]  train-rmse:0.791083+0.013128    test-rmse:0.814174+0.061993 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.644200+0.010318    test-rmse:0.692631+0.062932 
## [3]  train-rmse:0.541870+0.012471    test-rmse:0.614305+0.057112 
## [4]  train-rmse:0.472131+0.014132    test-rmse:0.565407+0.054548 
## [5]  train-rmse:0.425058+0.014678    test-rmse:0.541100+0.054129 
## [6]  train-rmse:0.389032+0.014042    test-rmse:0.525849+0.061480 
## [7]  train-rmse:0.363002+0.012011    test-rmse:0.517615+0.065455 
## [8]  train-rmse:0.343249+0.011197    test-rmse:0.509640+0.064189 
## [9]  train-rmse:0.324875+0.009953    test-rmse:0.505676+0.058993 
## [10] train-rmse:0.314259+0.008450    test-rmse:0.501950+0.057814 
## [11] train-rmse:0.301155+0.015225    test-rmse:0.497390+0.056097 
## [12] train-rmse:0.290118+0.013299    test-rmse:0.496241+0.054358 
## [13] train-rmse:0.280252+0.013741    test-rmse:0.492986+0.056328 
## [14] train-rmse:0.272833+0.012350    test-rmse:0.492806+0.055816 
## [15] train-rmse:0.262970+0.012905    test-rmse:0.490399+0.056398 
## [16] train-rmse:0.254688+0.014247    test-rmse:0.492052+0.055893 
## [17] train-rmse:0.246366+0.013253    test-rmse:0.492524+0.055625 
## [18] train-rmse:0.240318+0.014248    test-rmse:0.491264+0.053726 
## [19] train-rmse:0.233235+0.015940    test-rmse:0.492093+0.053490 
## [20] train-rmse:0.225207+0.014123    test-rmse:0.489713+0.053075 
## [21] train-rmse:0.218006+0.012348    test-rmse:0.491503+0.053555 
## [22] train-rmse:0.210317+0.012461    test-rmse:0.492173+0.052939 
## [23] train-rmse:0.205422+0.012553    test-rmse:0.492256+0.053215 
## [24] train-rmse:0.200763+0.012509    test-rmse:0.492336+0.053285 
## [25] train-rmse:0.195689+0.012364    test-rmse:0.493631+0.053649 
## [26] train-rmse:0.188544+0.011375    test-rmse:0.495827+0.054958 
## Stopping. Best iteration:
## [20] train-rmse:0.225207+0.014123    test-rmse:0.489713+0.053075
## 
## 76 
## [1]  train-rmse:0.783517+0.012486    test-rmse:0.809591+0.059358 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.629781+0.009612    test-rmse:0.688438+0.061649 
## [3]  train-rmse:0.528181+0.009002    test-rmse:0.614232+0.061480 
## [4]  train-rmse:0.453978+0.009717    test-rmse:0.567129+0.061051 
## [5]  train-rmse:0.401338+0.011712    test-rmse:0.541311+0.059700 
## [6]  train-rmse:0.356401+0.010399    test-rmse:0.524129+0.057749 
## [7]  train-rmse:0.326802+0.014464    test-rmse:0.514363+0.056722 
## [8]  train-rmse:0.300909+0.014038    test-rmse:0.506012+0.048669 
## [9]  train-rmse:0.283580+0.012303    test-rmse:0.504361+0.042984 
## [10] train-rmse:0.266709+0.012012    test-rmse:0.497721+0.043221 
## [11] train-rmse:0.255761+0.012824    test-rmse:0.496664+0.044170 
## [12] train-rmse:0.245650+0.009803    test-rmse:0.491999+0.043926 
## [13] train-rmse:0.237632+0.008204    test-rmse:0.491088+0.042652 
## [14] train-rmse:0.230179+0.009524    test-rmse:0.487915+0.040372 
## [15] train-rmse:0.219153+0.012137    test-rmse:0.489344+0.041675 
## [16] train-rmse:0.210623+0.011981    test-rmse:0.490778+0.041525 
## [17] train-rmse:0.204151+0.012234    test-rmse:0.490939+0.040742 
## [18] train-rmse:0.198981+0.012366    test-rmse:0.491248+0.040781 
## [19] train-rmse:0.189926+0.013199    test-rmse:0.492638+0.038169 
## [20] train-rmse:0.183026+0.012868    test-rmse:0.492851+0.039293 
## Stopping. Best iteration:
## [14] train-rmse:0.230179+0.009524    test-rmse:0.487915+0.040372
## 
## 77 
## [1]  train-rmse:0.842152+0.014565    test-rmse:0.846778+0.060229 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.752670+0.013708    test-rmse:0.770034+0.064207 
## [3]  train-rmse:0.698969+0.013111    test-rmse:0.723345+0.059289 
## [4]  train-rmse:0.663647+0.012005    test-rmse:0.684078+0.057812 
## [5]  train-rmse:0.640807+0.011974    test-rmse:0.668890+0.054358 
## [6]  train-rmse:0.625957+0.011595    test-rmse:0.654008+0.048660 
## [7]  train-rmse:0.614492+0.011346    test-rmse:0.643344+0.048010 
## [8]  train-rmse:0.605400+0.011254    test-rmse:0.638235+0.049995 
## [9]  train-rmse:0.596966+0.011163    test-rmse:0.633589+0.052769 
## [10] train-rmse:0.589826+0.011162    test-rmse:0.628899+0.055414 
## [11] train-rmse:0.583885+0.011008    test-rmse:0.623521+0.055665 
## [12] train-rmse:0.578323+0.011043    test-rmse:0.617395+0.057526 
## [13] train-rmse:0.573237+0.010917    test-rmse:0.613502+0.058043 
## [14] train-rmse:0.568551+0.010968    test-rmse:0.607265+0.058386 
## [15] train-rmse:0.564103+0.011099    test-rmse:0.603687+0.057531 
## [16] train-rmse:0.560075+0.011110    test-rmse:0.598598+0.056055 
## [17] train-rmse:0.556167+0.011089    test-rmse:0.593032+0.057063 
## [18] train-rmse:0.552422+0.011102    test-rmse:0.592686+0.057371 
## [19] train-rmse:0.548838+0.011017    test-rmse:0.588503+0.059422 
## [20] train-rmse:0.545568+0.010929    test-rmse:0.588650+0.058816 
## [21] train-rmse:0.542408+0.010808    test-rmse:0.588483+0.057185 
## [22] train-rmse:0.539489+0.010910    test-rmse:0.584913+0.057806 
## [23] train-rmse:0.536707+0.010974    test-rmse:0.582859+0.057995 
## [24] train-rmse:0.534059+0.010949    test-rmse:0.580998+0.055476 
## [25] train-rmse:0.531511+0.010957    test-rmse:0.577310+0.056311 
## [26] train-rmse:0.528965+0.010972    test-rmse:0.573665+0.056839 
## [27] train-rmse:0.526590+0.010965    test-rmse:0.575055+0.058172 
## [28] train-rmse:0.524338+0.010934    test-rmse:0.574550+0.059134 
## [29] train-rmse:0.522236+0.010883    test-rmse:0.571652+0.059175 
## [30] train-rmse:0.520180+0.010738    test-rmse:0.570318+0.058017 
## [31] train-rmse:0.518279+0.010712    test-rmse:0.570420+0.057104 
## [32] train-rmse:0.516420+0.010712    test-rmse:0.569379+0.055598 
## [33] train-rmse:0.514658+0.010709    test-rmse:0.568152+0.054690 
## [34] train-rmse:0.512904+0.010658    test-rmse:0.565412+0.054329 
## [35] train-rmse:0.511196+0.010598    test-rmse:0.564065+0.055716 
## [36] train-rmse:0.509471+0.010605    test-rmse:0.562044+0.056071 
## [37] train-rmse:0.507827+0.010600    test-rmse:0.561559+0.056489 
## [38] train-rmse:0.506246+0.010561    test-rmse:0.558816+0.055849 
## [39] train-rmse:0.504742+0.010540    test-rmse:0.557435+0.055250 
## [40] train-rmse:0.503296+0.010453    test-rmse:0.555795+0.054715 
## [41] train-rmse:0.501954+0.010420    test-rmse:0.555440+0.054725 
## [42] train-rmse:0.500633+0.010366    test-rmse:0.554271+0.054674 
## [43] train-rmse:0.499336+0.010384    test-rmse:0.553627+0.054050 
## [44] train-rmse:0.498114+0.010379    test-rmse:0.553384+0.053322 
## [45] train-rmse:0.496918+0.010414    test-rmse:0.553823+0.054707 
## [46] train-rmse:0.495760+0.010454    test-rmse:0.553986+0.056041 
## [47] train-rmse:0.494620+0.010482    test-rmse:0.552950+0.056202 
## [48] train-rmse:0.493532+0.010519    test-rmse:0.552928+0.054505 
## [49] train-rmse:0.492471+0.010511    test-rmse:0.552431+0.054830 
## [50] train-rmse:0.491399+0.010566    test-rmse:0.552142+0.055183 
## [51] train-rmse:0.490383+0.010592    test-rmse:0.550550+0.054678 
## [52] train-rmse:0.489378+0.010587    test-rmse:0.549896+0.054403 
## [53] train-rmse:0.488402+0.010576    test-rmse:0.548769+0.054133 
## [54] train-rmse:0.487491+0.010557    test-rmse:0.547971+0.053778 
## [55] train-rmse:0.486588+0.010538    test-rmse:0.547457+0.053378 
## [56] train-rmse:0.485746+0.010513    test-rmse:0.546566+0.053009 
## [57] train-rmse:0.484907+0.010494    test-rmse:0.546423+0.053262 
## [58] train-rmse:0.484070+0.010473    test-rmse:0.545364+0.053963 
## [59] train-rmse:0.483257+0.010464    test-rmse:0.545582+0.054016 
## [60] train-rmse:0.482460+0.010419    test-rmse:0.544937+0.053948 
## [61] train-rmse:0.481683+0.010390    test-rmse:0.544846+0.053032 
## [62] train-rmse:0.480926+0.010389    test-rmse:0.544352+0.052423 
## [63] train-rmse:0.480196+0.010387    test-rmse:0.543821+0.051876 
## [64] train-rmse:0.479484+0.010393    test-rmse:0.542913+0.051923 
## [65] train-rmse:0.478782+0.010406    test-rmse:0.542036+0.052002 
## [66] train-rmse:0.478098+0.010417    test-rmse:0.541407+0.051761 
## [67] train-rmse:0.477427+0.010442    test-rmse:0.540824+0.051455 
## [68] train-rmse:0.476776+0.010465    test-rmse:0.540629+0.051540 
## [69] train-rmse:0.476125+0.010486    test-rmse:0.540827+0.052525 
## [70] train-rmse:0.475496+0.010484    test-rmse:0.539431+0.052118 
## [71] train-rmse:0.474883+0.010480    test-rmse:0.540232+0.050530 
## [72] train-rmse:0.474287+0.010486    test-rmse:0.540797+0.051544 
## [73] train-rmse:0.473712+0.010480    test-rmse:0.541170+0.051284 
## [74] train-rmse:0.473162+0.010467    test-rmse:0.541112+0.051655 
## [75] train-rmse:0.472638+0.010459    test-rmse:0.540655+0.051513 
## [76] train-rmse:0.472119+0.010452    test-rmse:0.540579+0.051774 
## Stopping. Best iteration:
## [70] train-rmse:0.475496+0.010484    test-rmse:0.539431+0.052118
## 
## 78 
## [1]  train-rmse:0.816342+0.014337    test-rmse:0.822889+0.060591 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.705705+0.013489    test-rmse:0.725489+0.066285 
## [3]  train-rmse:0.640308+0.012367    test-rmse:0.668346+0.061907 
## [4]  train-rmse:0.600021+0.012627    test-rmse:0.638002+0.061183 
## [5]  train-rmse:0.571549+0.013352    test-rmse:0.615545+0.064352 
## [6]  train-rmse:0.552567+0.011999    test-rmse:0.599766+0.067497 
## [7]  train-rmse:0.539373+0.013830    test-rmse:0.589231+0.058917 
## [8]  train-rmse:0.529673+0.013680    test-rmse:0.582397+0.059930 
## [9]  train-rmse:0.520040+0.014079    test-rmse:0.569559+0.061724 
## [10] train-rmse:0.512133+0.014669    test-rmse:0.565595+0.057992 
## [11] train-rmse:0.505406+0.014680    test-rmse:0.563224+0.058009 
## [12] train-rmse:0.498501+0.015022    test-rmse:0.558542+0.063695 
## [13] train-rmse:0.491852+0.015811    test-rmse:0.556786+0.061860 
## [14] train-rmse:0.485459+0.016161    test-rmse:0.552128+0.063086 
## [15] train-rmse:0.479384+0.015940    test-rmse:0.550365+0.064688 
## [16] train-rmse:0.474317+0.015138    test-rmse:0.544786+0.064548 
## [17] train-rmse:0.469411+0.014831    test-rmse:0.541186+0.063809 
## [18] train-rmse:0.464469+0.014322    test-rmse:0.537179+0.062068 
## [19] train-rmse:0.460595+0.014253    test-rmse:0.538331+0.064079 
## [20] train-rmse:0.456800+0.014513    test-rmse:0.536514+0.065563 
## [21] train-rmse:0.453320+0.015085    test-rmse:0.535766+0.065559 
## [22] train-rmse:0.449618+0.015018    test-rmse:0.534548+0.063907 
## [23] train-rmse:0.445665+0.015066    test-rmse:0.531377+0.065369 
## [24] train-rmse:0.442253+0.015407    test-rmse:0.529888+0.065399 
## [25] train-rmse:0.438595+0.015020    test-rmse:0.530396+0.065356 
## [26] train-rmse:0.435600+0.014688    test-rmse:0.530445+0.063716 
## [27] train-rmse:0.432335+0.015107    test-rmse:0.529452+0.064172 
## [28] train-rmse:0.429544+0.014844    test-rmse:0.529420+0.065352 
## [29] train-rmse:0.427017+0.014583    test-rmse:0.527856+0.065998 
## [30] train-rmse:0.423948+0.013845    test-rmse:0.527021+0.067912 
## [31] train-rmse:0.421386+0.013993    test-rmse:0.526566+0.066556 
## [32] train-rmse:0.418674+0.014291    test-rmse:0.524972+0.065791 
## [33] train-rmse:0.414886+0.014958    test-rmse:0.523282+0.065873 
## [34] train-rmse:0.412521+0.014677    test-rmse:0.521215+0.066201 
## [35] train-rmse:0.410480+0.015001    test-rmse:0.522660+0.066599 
## [36] train-rmse:0.408319+0.014901    test-rmse:0.521249+0.066116 
## [37] train-rmse:0.405165+0.013405    test-rmse:0.519971+0.066365 
## [38] train-rmse:0.403099+0.013381    test-rmse:0.519255+0.064928 
## [39] train-rmse:0.401043+0.012961    test-rmse:0.518518+0.065131 
## [40] train-rmse:0.398931+0.013476    test-rmse:0.517330+0.065327 
## [41] train-rmse:0.397379+0.013195    test-rmse:0.516883+0.065905 
## [42] train-rmse:0.395153+0.013456    test-rmse:0.516307+0.065083 
## [43] train-rmse:0.393446+0.013495    test-rmse:0.516529+0.065138 
## [44] train-rmse:0.391023+0.013464    test-rmse:0.515406+0.064252 
## [45] train-rmse:0.388899+0.012975    test-rmse:0.514907+0.064283 
## [46] train-rmse:0.387328+0.012435    test-rmse:0.515649+0.063988 
## [47] train-rmse:0.385524+0.011886    test-rmse:0.515516+0.063308 
## [48] train-rmse:0.383579+0.012165    test-rmse:0.514224+0.062984 
## [49] train-rmse:0.382339+0.011953    test-rmse:0.513525+0.063670 
## [50] train-rmse:0.379649+0.011836    test-rmse:0.512621+0.063197 
## [51] train-rmse:0.377953+0.011802    test-rmse:0.510603+0.063564 
## [52] train-rmse:0.375732+0.011838    test-rmse:0.508919+0.063709 
## [53] train-rmse:0.374395+0.011648    test-rmse:0.509810+0.063274 
## [54] train-rmse:0.372382+0.011937    test-rmse:0.509211+0.062469 
## [55] train-rmse:0.371064+0.011926    test-rmse:0.509557+0.062637 
## [56] train-rmse:0.369535+0.012157    test-rmse:0.508652+0.062334 
## [57] train-rmse:0.368486+0.011838    test-rmse:0.508201+0.062311 
## [58] train-rmse:0.366760+0.012058    test-rmse:0.508257+0.061052 
## [59] train-rmse:0.365860+0.011970    test-rmse:0.508330+0.061351 
## [60] train-rmse:0.364485+0.011895    test-rmse:0.508073+0.061401 
## [61] train-rmse:0.362921+0.012018    test-rmse:0.507839+0.059973 
## [62] train-rmse:0.361469+0.011858    test-rmse:0.506896+0.059267 
## [63] train-rmse:0.360151+0.011765    test-rmse:0.506734+0.058619 
## [64] train-rmse:0.358678+0.012054    test-rmse:0.506177+0.058644 
## [65] train-rmse:0.357431+0.011928    test-rmse:0.506281+0.058408 
## [66] train-rmse:0.355866+0.012971    test-rmse:0.505690+0.057562 
## [67] train-rmse:0.353942+0.011516    test-rmse:0.504903+0.057157 
## [68] train-rmse:0.351506+0.012331    test-rmse:0.504227+0.055928 
## [69] train-rmse:0.350279+0.012031    test-rmse:0.503650+0.056154 
## [70] train-rmse:0.349381+0.011979    test-rmse:0.503332+0.055589 
## [71] train-rmse:0.347091+0.011057    test-rmse:0.501455+0.054729 
## [72] train-rmse:0.346154+0.011069    test-rmse:0.500877+0.053876 
## [73] train-rmse:0.344622+0.010421    test-rmse:0.500843+0.054207 
## [74] train-rmse:0.343120+0.010158    test-rmse:0.500230+0.053874 
## [75] train-rmse:0.341173+0.010439    test-rmse:0.500045+0.052850 
## [76] train-rmse:0.339880+0.011023    test-rmse:0.499268+0.052104 
## [77] train-rmse:0.339140+0.011251    test-rmse:0.499626+0.052868 
## [78] train-rmse:0.338272+0.010817    test-rmse:0.499815+0.053142 
## [79] train-rmse:0.336731+0.011340    test-rmse:0.497866+0.053763 
## [80] train-rmse:0.335064+0.011920    test-rmse:0.496850+0.053722 
## [81] train-rmse:0.334250+0.011939    test-rmse:0.496514+0.053287 
## [82] train-rmse:0.332438+0.010686    test-rmse:0.495616+0.054168 
## [83] train-rmse:0.331222+0.010960    test-rmse:0.495360+0.054356 
## [84] train-rmse:0.330567+0.010736    test-rmse:0.495122+0.054572 
## [85] train-rmse:0.328603+0.009514    test-rmse:0.494882+0.054269 
## [86] train-rmse:0.326705+0.010492    test-rmse:0.495299+0.054457 
## [87] train-rmse:0.324925+0.010064    test-rmse:0.495977+0.052763 
## [88] train-rmse:0.323151+0.008915    test-rmse:0.494220+0.053083 
## [89] train-rmse:0.321862+0.009074    test-rmse:0.494790+0.053233 
## [90] train-rmse:0.320424+0.009326    test-rmse:0.494299+0.053286 
## [91] train-rmse:0.318925+0.009666    test-rmse:0.493321+0.052949 
## [92] train-rmse:0.317820+0.010000    test-rmse:0.493643+0.052909 
## [93] train-rmse:0.316928+0.010196    test-rmse:0.493050+0.052478 
## [94] train-rmse:0.315402+0.010651    test-rmse:0.493306+0.054384 
## [95] train-rmse:0.314357+0.010446    test-rmse:0.493572+0.053975 
## [96] train-rmse:0.313461+0.010704    test-rmse:0.493423+0.053650 
## [97] train-rmse:0.312318+0.010528    test-rmse:0.492790+0.054330 
## [98] train-rmse:0.311398+0.011035    test-rmse:0.492420+0.054077 
## [99] train-rmse:0.309552+0.012193    test-rmse:0.492444+0.054326 
## [100]    train-rmse:0.307955+0.012437    test-rmse:0.492522+0.054695 
## [101]    train-rmse:0.306781+0.013015    test-rmse:0.492374+0.055921 
## [102]    train-rmse:0.304871+0.013755    test-rmse:0.491702+0.054797 
## [103]    train-rmse:0.302942+0.014025    test-rmse:0.492701+0.053849 
## [104]    train-rmse:0.301679+0.014139    test-rmse:0.492239+0.054005 
## [105]    train-rmse:0.300249+0.014838    test-rmse:0.492573+0.054578 
## [106]    train-rmse:0.298938+0.014314    test-rmse:0.492621+0.055063 
## [107]    train-rmse:0.297489+0.013355    test-rmse:0.492499+0.055383 
## [108]    train-rmse:0.296333+0.012705    test-rmse:0.492299+0.055430 
## Stopping. Best iteration:
## [102]    train-rmse:0.304871+0.013755    test-rmse:0.491702+0.054797
## 
## 79 
## [1]  train-rmse:0.804785+0.014847    test-rmse:0.816605+0.063189 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.681967+0.013819    test-rmse:0.707786+0.064209 
## [3]  train-rmse:0.606060+0.014675    test-rmse:0.643084+0.061256 
## [4]  train-rmse:0.556836+0.013956    test-rmse:0.605621+0.059956 
## [5]  train-rmse:0.525198+0.015209    test-rmse:0.584844+0.054795 
## [6]  train-rmse:0.502074+0.015500    test-rmse:0.572270+0.055336 
## [7]  train-rmse:0.487088+0.015869    test-rmse:0.564769+0.052286 
## [8]  train-rmse:0.474083+0.018067    test-rmse:0.557464+0.049532 
## [9]  train-rmse:0.463816+0.019679    test-rmse:0.553961+0.049032 
## [10] train-rmse:0.452692+0.020568    test-rmse:0.550552+0.048554 
## [11] train-rmse:0.444045+0.020695    test-rmse:0.544795+0.047316 
## [12] train-rmse:0.434418+0.020740    test-rmse:0.541055+0.048917 
## [13] train-rmse:0.425594+0.020055    test-rmse:0.540486+0.048845 
## [14] train-rmse:0.418863+0.019174    test-rmse:0.533680+0.052477 
## [15] train-rmse:0.412115+0.020977    test-rmse:0.530318+0.050844 
## [16] train-rmse:0.405235+0.020584    test-rmse:0.527452+0.049182 
## [17] train-rmse:0.400359+0.019793    test-rmse:0.525498+0.048459 
## [18] train-rmse:0.394177+0.018894    test-rmse:0.524155+0.048416 
## [19] train-rmse:0.387717+0.020822    test-rmse:0.523602+0.048660 
## [20] train-rmse:0.382790+0.019601    test-rmse:0.521603+0.048663 
## [21] train-rmse:0.377362+0.019098    test-rmse:0.521374+0.050042 
## [22] train-rmse:0.373250+0.018228    test-rmse:0.520868+0.048932 
## [23] train-rmse:0.369090+0.017569    test-rmse:0.517901+0.049094 
## [24] train-rmse:0.364708+0.018004    test-rmse:0.514949+0.049006 
## [25] train-rmse:0.360434+0.018267    test-rmse:0.514296+0.049748 
## [26] train-rmse:0.356574+0.018499    test-rmse:0.512934+0.050911 
## [27] train-rmse:0.349695+0.019409    test-rmse:0.513456+0.050469 
## [28] train-rmse:0.346771+0.019076    test-rmse:0.512344+0.050469 
## [29] train-rmse:0.342953+0.018129    test-rmse:0.512922+0.050668 
## [30] train-rmse:0.339086+0.018745    test-rmse:0.512141+0.049779 
## [31] train-rmse:0.336142+0.018025    test-rmse:0.512019+0.049493 
## [32] train-rmse:0.332496+0.018515    test-rmse:0.512356+0.049274 
## [33] train-rmse:0.329116+0.017342    test-rmse:0.511782+0.049018 
## [34] train-rmse:0.326075+0.017098    test-rmse:0.510815+0.050092 
## [35] train-rmse:0.323426+0.017520    test-rmse:0.510750+0.050036 
## [36] train-rmse:0.320222+0.017531    test-rmse:0.510408+0.050927 
## [37] train-rmse:0.317887+0.017960    test-rmse:0.510474+0.050543 
## [38] train-rmse:0.314564+0.016020    test-rmse:0.510254+0.050141 
## [39] train-rmse:0.312328+0.016463    test-rmse:0.512201+0.049325 
## [40] train-rmse:0.308865+0.016824    test-rmse:0.511337+0.049970 
## [41] train-rmse:0.305306+0.015948    test-rmse:0.513885+0.048562 
## [42] train-rmse:0.302213+0.016044    test-rmse:0.515241+0.047506 
## [43] train-rmse:0.298843+0.016426    test-rmse:0.514762+0.045867 
## [44] train-rmse:0.294187+0.018715    test-rmse:0.513389+0.044603 
## Stopping. Best iteration:
## [38] train-rmse:0.314564+0.016020    test-rmse:0.510254+0.050141
## 
## 80 
## [1]  train-rmse:0.794816+0.014520    test-rmse:0.810678+0.061101 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.663265+0.013405    test-rmse:0.695174+0.064685 
## [3]  train-rmse:0.578163+0.012281    test-rmse:0.626935+0.059956 
## [4]  train-rmse:0.522927+0.012518    test-rmse:0.587295+0.060098 
## [5]  train-rmse:0.482878+0.016858    test-rmse:0.564566+0.060490 
## [6]  train-rmse:0.459254+0.015814    test-rmse:0.555320+0.056935 
## [7]  train-rmse:0.439319+0.016931    test-rmse:0.540754+0.060203 
## [8]  train-rmse:0.420828+0.016119    test-rmse:0.533265+0.057472 
## [9]  train-rmse:0.409978+0.016609    test-rmse:0.524811+0.056996 
## [10] train-rmse:0.399561+0.015552    test-rmse:0.517424+0.061138 
## [11] train-rmse:0.389189+0.015132    test-rmse:0.513732+0.063068 
## [12] train-rmse:0.378766+0.014075    test-rmse:0.509767+0.062889 
## [13] train-rmse:0.369570+0.014144    test-rmse:0.503547+0.060344 
## [14] train-rmse:0.363353+0.015054    test-rmse:0.500930+0.059672 
## [15] train-rmse:0.356395+0.013330    test-rmse:0.500654+0.060208 
## [16] train-rmse:0.349378+0.011681    test-rmse:0.497828+0.062087 
## [17] train-rmse:0.343460+0.010594    test-rmse:0.498187+0.060485 
## [18] train-rmse:0.336997+0.012342    test-rmse:0.497025+0.058716 
## [19] train-rmse:0.331744+0.011657    test-rmse:0.496056+0.059926 
## [20] train-rmse:0.327172+0.010967    test-rmse:0.496044+0.061539 
## [21] train-rmse:0.321435+0.010945    test-rmse:0.494029+0.059197 
## [22] train-rmse:0.314657+0.011318    test-rmse:0.493738+0.056877 
## [23] train-rmse:0.308315+0.009997    test-rmse:0.494342+0.054525 
## [24] train-rmse:0.300457+0.012241    test-rmse:0.494972+0.053833 
## [25] train-rmse:0.295277+0.011822    test-rmse:0.493631+0.052931 
## [26] train-rmse:0.290874+0.012781    test-rmse:0.493862+0.053859 
## [27] train-rmse:0.287090+0.013061    test-rmse:0.493703+0.054817 
## [28] train-rmse:0.280265+0.011167    test-rmse:0.492262+0.055767 
## [29] train-rmse:0.276147+0.010788    test-rmse:0.491959+0.055583 
## [30] train-rmse:0.271446+0.009679    test-rmse:0.491968+0.054190 
## [31] train-rmse:0.267982+0.008791    test-rmse:0.493657+0.053494 
## [32] train-rmse:0.261111+0.008905    test-rmse:0.493207+0.053230 
## [33] train-rmse:0.258050+0.008310    test-rmse:0.492693+0.053980 
## [34] train-rmse:0.252462+0.009340    test-rmse:0.491841+0.052130 
## [35] train-rmse:0.249189+0.009484    test-rmse:0.492965+0.050996 
## [36] train-rmse:0.245810+0.008803    test-rmse:0.492506+0.051171 
## [37] train-rmse:0.242584+0.008578    test-rmse:0.490920+0.049732 
## [38] train-rmse:0.237393+0.008519    test-rmse:0.492348+0.048844 
## [39] train-rmse:0.232238+0.008042    test-rmse:0.493576+0.047442 
## [40] train-rmse:0.228168+0.008961    test-rmse:0.495313+0.048409 
## [41] train-rmse:0.225098+0.008737    test-rmse:0.495855+0.047588 
## [42] train-rmse:0.218122+0.009087    test-rmse:0.497172+0.046710 
## [43] train-rmse:0.213899+0.008538    test-rmse:0.496298+0.045244 
## Stopping. Best iteration:
## [37] train-rmse:0.242584+0.008578    test-rmse:0.490920+0.049732
## 
## 81 
## [1]  train-rmse:0.785896+0.013743    test-rmse:0.805816+0.061928 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.644559+0.010752    test-rmse:0.687860+0.063327 
## [3]  train-rmse:0.547974+0.011866    test-rmse:0.611168+0.057270 
## [4]  train-rmse:0.487199+0.014256    test-rmse:0.574677+0.055612 
## [5]  train-rmse:0.445943+0.013801    test-rmse:0.553267+0.059191 
## [6]  train-rmse:0.415932+0.012495    test-rmse:0.542386+0.056677 
## [7]  train-rmse:0.393890+0.012015    test-rmse:0.533558+0.059309 
## [8]  train-rmse:0.377937+0.011544    test-rmse:0.526345+0.058395 
## [9]  train-rmse:0.362547+0.019199    test-rmse:0.523627+0.058779 
## [10] train-rmse:0.347199+0.015922    test-rmse:0.516632+0.056161 
## [11] train-rmse:0.333567+0.017017    test-rmse:0.511229+0.058098 
## [12] train-rmse:0.321546+0.014967    test-rmse:0.504854+0.060256 
## [13] train-rmse:0.314101+0.014654    test-rmse:0.502533+0.059570 
## [14] train-rmse:0.305901+0.015255    test-rmse:0.500047+0.058455 
## [15] train-rmse:0.297679+0.015532    test-rmse:0.497463+0.057282 
## [16] train-rmse:0.290764+0.014800    test-rmse:0.495281+0.059534 
## [17] train-rmse:0.283085+0.012372    test-rmse:0.494066+0.059676 
## [18] train-rmse:0.276920+0.010799    test-rmse:0.493666+0.058364 
## [19] train-rmse:0.269472+0.014974    test-rmse:0.489606+0.057225 
## [20] train-rmse:0.260812+0.015052    test-rmse:0.491199+0.058238 
## [21] train-rmse:0.252279+0.014267    test-rmse:0.493531+0.058306 
## [22] train-rmse:0.246287+0.016061    test-rmse:0.495252+0.059064 
## [23] train-rmse:0.238177+0.018294    test-rmse:0.495943+0.060204 
## [24] train-rmse:0.233302+0.017349    test-rmse:0.495957+0.059919 
## [25] train-rmse:0.229563+0.017766    test-rmse:0.496662+0.059077 
## Stopping. Best iteration:
## [19] train-rmse:0.269472+0.014974    test-rmse:0.489606+0.057225
## 
## 82 
## [1]  train-rmse:0.777663+0.012957    test-rmse:0.802789+0.062044 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.626199+0.009776    test-rmse:0.678669+0.063281 
## [3]  train-rmse:0.524353+0.012553    test-rmse:0.603113+0.055227 
## [4]  train-rmse:0.459456+0.012103    test-rmse:0.555831+0.056411 
## [5]  train-rmse:0.413099+0.012129    test-rmse:0.527943+0.059266 
## [6]  train-rmse:0.379674+0.011110    test-rmse:0.520106+0.057586 
## [7]  train-rmse:0.356388+0.010905    test-rmse:0.514007+0.053920 
## [8]  train-rmse:0.337822+0.012405    test-rmse:0.509272+0.052734 
## [9]  train-rmse:0.321287+0.015286    test-rmse:0.500979+0.052073 
## [10] train-rmse:0.305052+0.013876    test-rmse:0.499657+0.055459 
## [11] train-rmse:0.294745+0.014389    test-rmse:0.499280+0.053675 
## [12] train-rmse:0.284406+0.013847    test-rmse:0.497210+0.051536 
## [13] train-rmse:0.274852+0.010191    test-rmse:0.497906+0.048846 
## [14] train-rmse:0.265464+0.012931    test-rmse:0.496583+0.050210 
## [15] train-rmse:0.257189+0.012314    test-rmse:0.493928+0.052385 
## [16] train-rmse:0.248105+0.013840    test-rmse:0.492574+0.053513 
## [17] train-rmse:0.239824+0.014207    test-rmse:0.494266+0.054124 
## [18] train-rmse:0.234001+0.013744    test-rmse:0.495768+0.053052 
## [19] train-rmse:0.228460+0.011570    test-rmse:0.496471+0.053734 
## [20] train-rmse:0.220011+0.011843    test-rmse:0.495605+0.054933 
## [21] train-rmse:0.212863+0.011040    test-rmse:0.496085+0.056308 
## [22] train-rmse:0.204745+0.013738    test-rmse:0.497693+0.057384 
## Stopping. Best iteration:
## [16] train-rmse:0.248105+0.013840    test-rmse:0.492574+0.053513
## 
## 83 
## [1]  train-rmse:0.769520+0.012276    test-rmse:0.797942+0.059197 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.611052+0.009374    test-rmse:0.675532+0.061973 
## [3]  train-rmse:0.509405+0.011474    test-rmse:0.605700+0.061039 
## [4]  train-rmse:0.436092+0.013114    test-rmse:0.562092+0.061387 
## [5]  train-rmse:0.383215+0.010303    test-rmse:0.541091+0.060386 
## [6]  train-rmse:0.344379+0.013174    test-rmse:0.531217+0.059534 
## [7]  train-rmse:0.316437+0.013625    test-rmse:0.522120+0.061012 
## [8]  train-rmse:0.299313+0.014240    test-rmse:0.516319+0.061156 
## [9]  train-rmse:0.281383+0.011793    test-rmse:0.509913+0.057876 
## [10] train-rmse:0.260903+0.012509    test-rmse:0.505321+0.053455 
## [11] train-rmse:0.249857+0.013742    test-rmse:0.507899+0.052203 
## [12] train-rmse:0.235672+0.012085    test-rmse:0.508384+0.051017 
## [13] train-rmse:0.227180+0.013458    test-rmse:0.507849+0.051838 
## [14] train-rmse:0.216353+0.013083    test-rmse:0.503775+0.049207 
## [15] train-rmse:0.209526+0.013095    test-rmse:0.503969+0.048872 
## [16] train-rmse:0.198476+0.012704    test-rmse:0.505658+0.050419 
## [17] train-rmse:0.190518+0.013509    test-rmse:0.507136+0.050119 
## [18] train-rmse:0.182997+0.013863    test-rmse:0.506989+0.052020 
## [19] train-rmse:0.176640+0.012785    test-rmse:0.505264+0.051773 
## [20] train-rmse:0.169703+0.011407    test-rmse:0.505407+0.050988 
## Stopping. Best iteration:
## [14] train-rmse:0.216353+0.013083    test-rmse:0.503775+0.049207
## 
## 84 
## [1]  train-rmse:0.833191+0.014487    test-rmse:0.838295+0.060151 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.743021+0.013835    test-rmse:0.762316+0.063636 
## [3]  train-rmse:0.690266+0.012789    test-rmse:0.712962+0.060743 
## [4]  train-rmse:0.655852+0.011737    test-rmse:0.674196+0.058553 
## [5]  train-rmse:0.634118+0.011827    test-rmse:0.660228+0.054809 
## [6]  train-rmse:0.620130+0.011600    test-rmse:0.646669+0.053096 
## [7]  train-rmse:0.609825+0.011391    test-rmse:0.634423+0.051284 
## [8]  train-rmse:0.601027+0.011318    test-rmse:0.629515+0.054998 
## [9]  train-rmse:0.593203+0.011300    test-rmse:0.621401+0.056270 
## [10] train-rmse:0.585934+0.011414    test-rmse:0.620964+0.053661 
## [11] train-rmse:0.580143+0.011568    test-rmse:0.615548+0.053856 
## [12] train-rmse:0.574709+0.011622    test-rmse:0.611589+0.053470 
## [13] train-rmse:0.569710+0.011391    test-rmse:0.605130+0.053216 
## [14] train-rmse:0.565051+0.011234    test-rmse:0.599111+0.053189 
## [15] train-rmse:0.560702+0.011179    test-rmse:0.594071+0.055264 
## [16] train-rmse:0.556435+0.011067    test-rmse:0.591368+0.054854 
## [17] train-rmse:0.552281+0.011160    test-rmse:0.587494+0.056574 
## [18] train-rmse:0.548532+0.010969    test-rmse:0.585754+0.057205 
## [19] train-rmse:0.544921+0.010860    test-rmse:0.582610+0.057454 
## [20] train-rmse:0.541611+0.010651    test-rmse:0.581899+0.056110 
## [21] train-rmse:0.538487+0.010536    test-rmse:0.582391+0.055206 
## [22] train-rmse:0.535604+0.010597    test-rmse:0.581911+0.054686 
## [23] train-rmse:0.532801+0.010727    test-rmse:0.578161+0.054381 
## [24] train-rmse:0.530144+0.010808    test-rmse:0.576425+0.054412 
## [25] train-rmse:0.527631+0.010751    test-rmse:0.572298+0.054687 
## [26] train-rmse:0.525216+0.010689    test-rmse:0.571675+0.057433 
## [27] train-rmse:0.522925+0.010630    test-rmse:0.570365+0.056447 
## [28] train-rmse:0.520732+0.010544    test-rmse:0.567854+0.055494 
## [29] train-rmse:0.518618+0.010515    test-rmse:0.567500+0.056428 
## [30] train-rmse:0.516543+0.010455    test-rmse:0.564643+0.056071 
## [31] train-rmse:0.514651+0.010404    test-rmse:0.564031+0.055021 
## [32] train-rmse:0.512784+0.010351    test-rmse:0.562693+0.055905 
## [33] train-rmse:0.510907+0.010301    test-rmse:0.560766+0.054710 
## [34] train-rmse:0.509161+0.010257    test-rmse:0.557666+0.054373 
## [35] train-rmse:0.507419+0.010234    test-rmse:0.555345+0.052599 
## [36] train-rmse:0.505747+0.010267    test-rmse:0.553485+0.053608 
## [37] train-rmse:0.504171+0.010208    test-rmse:0.553347+0.054394 
## [38] train-rmse:0.502636+0.010174    test-rmse:0.553125+0.055127 
## [39] train-rmse:0.501197+0.010140    test-rmse:0.552092+0.055041 
## [40] train-rmse:0.499782+0.010094    test-rmse:0.551615+0.054074 
## [41] train-rmse:0.498419+0.010025    test-rmse:0.549861+0.053995 
## [42] train-rmse:0.497173+0.010006    test-rmse:0.548531+0.053372 
## [43] train-rmse:0.495953+0.009995    test-rmse:0.548916+0.052988 
## [44] train-rmse:0.494778+0.009990    test-rmse:0.547641+0.053233 
## [45] train-rmse:0.493642+0.010007    test-rmse:0.547548+0.053606 
## [46] train-rmse:0.492505+0.010034    test-rmse:0.547736+0.054860 
## [47] train-rmse:0.491364+0.010003    test-rmse:0.547630+0.052757 
## [48] train-rmse:0.490275+0.010026    test-rmse:0.546843+0.052255 
## [49] train-rmse:0.489203+0.010046    test-rmse:0.545547+0.052756 
## [50] train-rmse:0.488173+0.010054    test-rmse:0.545187+0.052202 
## [51] train-rmse:0.487176+0.010075    test-rmse:0.544847+0.052218 
## [52] train-rmse:0.486200+0.010103    test-rmse:0.543935+0.051683 
## [53] train-rmse:0.485273+0.010096    test-rmse:0.543563+0.052146 
## [54] train-rmse:0.484391+0.010065    test-rmse:0.542906+0.051509 
## [55] train-rmse:0.483553+0.010037    test-rmse:0.541566+0.051035 
## [56] train-rmse:0.482714+0.009976    test-rmse:0.541591+0.051051 
## [57] train-rmse:0.481910+0.009928    test-rmse:0.540960+0.051049 
## [58] train-rmse:0.481142+0.009915    test-rmse:0.540550+0.051309 
## [59] train-rmse:0.480369+0.009910    test-rmse:0.539589+0.051361 
## [60] train-rmse:0.479634+0.009914    test-rmse:0.539027+0.050814 
## [61] train-rmse:0.478914+0.009907    test-rmse:0.538926+0.050963 
## [62] train-rmse:0.478197+0.009909    test-rmse:0.538160+0.051027 
## [63] train-rmse:0.477480+0.009911    test-rmse:0.537404+0.050463 
## [64] train-rmse:0.476793+0.009920    test-rmse:0.535763+0.050058 
## [65] train-rmse:0.476117+0.009953    test-rmse:0.536152+0.049691 
## [66] train-rmse:0.475483+0.009971    test-rmse:0.536288+0.049280 
## [67] train-rmse:0.474854+0.009977    test-rmse:0.535663+0.049283 
## [68] train-rmse:0.474241+0.009990    test-rmse:0.534464+0.048898 
## [69] train-rmse:0.473625+0.010003    test-rmse:0.534901+0.049714 
## [70] train-rmse:0.473024+0.010019    test-rmse:0.535592+0.049314 
## [71] train-rmse:0.472455+0.010010    test-rmse:0.535576+0.049711 
## [72] train-rmse:0.471902+0.009982    test-rmse:0.535845+0.049864 
## [73] train-rmse:0.471374+0.009983    test-rmse:0.536442+0.049648 
## [74] train-rmse:0.470872+0.009975    test-rmse:0.536656+0.050256 
## Stopping. Best iteration:
## [68] train-rmse:0.474241+0.009990    test-rmse:0.534464+0.048898
## 
## 85 
## [1]  train-rmse:0.805688+0.014240    test-rmse:0.812823+0.060611 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.693661+0.013420    test-rmse:0.716673+0.064414 
## [3]  train-rmse:0.630268+0.012126    test-rmse:0.659808+0.063470 
## [4]  train-rmse:0.591624+0.012570    test-rmse:0.633679+0.065150 
## [5]  train-rmse:0.565989+0.016134    test-rmse:0.615566+0.064777 
## [6]  train-rmse:0.547023+0.015171    test-rmse:0.596593+0.063958 
## [7]  train-rmse:0.534407+0.015362    test-rmse:0.589488+0.061383 
## [8]  train-rmse:0.523967+0.015402    test-rmse:0.580703+0.059996 
## [9]  train-rmse:0.515704+0.015420    test-rmse:0.570292+0.064578 
## [10] train-rmse:0.507779+0.015173    test-rmse:0.566286+0.062953 
## [11] train-rmse:0.498474+0.012775    test-rmse:0.558495+0.063807 
## [12] train-rmse:0.491845+0.013418    test-rmse:0.559401+0.066653 
## [13] train-rmse:0.485290+0.013345    test-rmse:0.557577+0.064037 
## [14] train-rmse:0.479040+0.013171    test-rmse:0.549401+0.062890 
## [15] train-rmse:0.473738+0.012963    test-rmse:0.544941+0.064168 
## [16] train-rmse:0.468826+0.013908    test-rmse:0.543962+0.063371 
## [17] train-rmse:0.462842+0.013156    test-rmse:0.543554+0.062556 
## [18] train-rmse:0.458218+0.012918    test-rmse:0.538105+0.064954 
## [19] train-rmse:0.454294+0.012194    test-rmse:0.537348+0.065143 
## [20] train-rmse:0.450284+0.012599    test-rmse:0.533287+0.066861 
## [21] train-rmse:0.446742+0.013285    test-rmse:0.529976+0.064251 
## [22] train-rmse:0.442408+0.012223    test-rmse:0.527260+0.063231 
## [23] train-rmse:0.438758+0.012226    test-rmse:0.525730+0.065915 
## [24] train-rmse:0.435446+0.013059    test-rmse:0.524912+0.066145 
## [25] train-rmse:0.431269+0.013075    test-rmse:0.524089+0.062973 
## [26] train-rmse:0.428614+0.013172    test-rmse:0.523826+0.062698 
## [27] train-rmse:0.425184+0.013454    test-rmse:0.522812+0.061439 
## [28] train-rmse:0.422402+0.013971    test-rmse:0.522906+0.060216 
## [29] train-rmse:0.419104+0.013979    test-rmse:0.521535+0.060779 
## [30] train-rmse:0.416121+0.013909    test-rmse:0.518915+0.061364 
## [31] train-rmse:0.411841+0.014439    test-rmse:0.515408+0.060984 
## [32] train-rmse:0.409389+0.014528    test-rmse:0.513668+0.060176 
## [33] train-rmse:0.407564+0.014208    test-rmse:0.513056+0.059317 
## [34] train-rmse:0.405089+0.014584    test-rmse:0.510230+0.059714 
## [35] train-rmse:0.403033+0.014587    test-rmse:0.509930+0.059102 
## [36] train-rmse:0.400878+0.014501    test-rmse:0.509412+0.059061 
## [37] train-rmse:0.398894+0.014711    test-rmse:0.508696+0.059411 
## [38] train-rmse:0.396930+0.014159    test-rmse:0.508221+0.061246 
## [39] train-rmse:0.394722+0.013690    test-rmse:0.507410+0.061637 
## [40] train-rmse:0.392058+0.013397    test-rmse:0.507018+0.060713 
## [41] train-rmse:0.390487+0.013484    test-rmse:0.507393+0.059578 
## [42] train-rmse:0.388464+0.013547    test-rmse:0.506130+0.059091 
## [43] train-rmse:0.386842+0.013817    test-rmse:0.505619+0.058681 
## [44] train-rmse:0.384832+0.013736    test-rmse:0.504891+0.059609 
## [45] train-rmse:0.383433+0.013800    test-rmse:0.505160+0.059574 
## [46] train-rmse:0.381250+0.012890    test-rmse:0.505701+0.056988 
## [47] train-rmse:0.379440+0.013045    test-rmse:0.505121+0.055750 
## [48] train-rmse:0.377130+0.012739    test-rmse:0.502463+0.055140 
## [49] train-rmse:0.375545+0.013017    test-rmse:0.502613+0.056574 
## [50] train-rmse:0.373623+0.012542    test-rmse:0.502725+0.056939 
## [51] train-rmse:0.371750+0.013462    test-rmse:0.502650+0.057517 
## [52] train-rmse:0.368975+0.014133    test-rmse:0.501649+0.058287 
## [53] train-rmse:0.366685+0.014675    test-rmse:0.501555+0.058596 
## [54] train-rmse:0.364589+0.013774    test-rmse:0.501550+0.057796 
## [55] train-rmse:0.362128+0.012872    test-rmse:0.499451+0.057621 
## [56] train-rmse:0.360174+0.012911    test-rmse:0.498936+0.057133 
## [57] train-rmse:0.357714+0.013240    test-rmse:0.498701+0.056077 
## [58] train-rmse:0.356449+0.012943    test-rmse:0.499026+0.057706 
## [59] train-rmse:0.355444+0.012987    test-rmse:0.498130+0.057114 
## [60] train-rmse:0.352724+0.013963    test-rmse:0.497147+0.055436 
## [61] train-rmse:0.350684+0.013619    test-rmse:0.495299+0.055578 
## [62] train-rmse:0.347898+0.013606    test-rmse:0.496395+0.054544 
## [63] train-rmse:0.345935+0.012238    test-rmse:0.496784+0.053721 
## [64] train-rmse:0.344697+0.012260    test-rmse:0.496157+0.054474 
## [65] train-rmse:0.342699+0.011324    test-rmse:0.496057+0.055046 
## [66] train-rmse:0.340411+0.011257    test-rmse:0.495981+0.055263 
## [67] train-rmse:0.338119+0.010117    test-rmse:0.495840+0.054317 
## Stopping. Best iteration:
## [61] train-rmse:0.350684+0.013619    test-rmse:0.495299+0.055578
## 
## 86 
## [1]  train-rmse:0.793366+0.014790    test-rmse:0.806205+0.063424 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.668474+0.013762    test-rmse:0.696139+0.064678 
## [3]  train-rmse:0.593694+0.014804    test-rmse:0.633295+0.061541 
## [4]  train-rmse:0.544940+0.014228    test-rmse:0.594063+0.054827 
## [5]  train-rmse:0.512878+0.019287    test-rmse:0.581318+0.048615 
## [6]  train-rmse:0.491308+0.017334    test-rmse:0.569132+0.046276 
## [7]  train-rmse:0.477106+0.016910    test-rmse:0.559198+0.043224 
## [8]  train-rmse:0.461767+0.013811    test-rmse:0.550355+0.045743 
## [9]  train-rmse:0.451143+0.014315    test-rmse:0.549198+0.049773 
## [10] train-rmse:0.443220+0.013917    test-rmse:0.540937+0.050630 
## [11] train-rmse:0.433151+0.012992    test-rmse:0.533875+0.048442 
## [12] train-rmse:0.426031+0.013677    test-rmse:0.531151+0.050472 
## [13] train-rmse:0.418303+0.014640    test-rmse:0.527937+0.049488 
## [14] train-rmse:0.412792+0.015143    test-rmse:0.524813+0.049228 
## [15] train-rmse:0.406577+0.015791    test-rmse:0.522178+0.048002 
## [16] train-rmse:0.400261+0.015220    test-rmse:0.520569+0.047169 
## [17] train-rmse:0.393040+0.014556    test-rmse:0.519721+0.045914 
## [18] train-rmse:0.387228+0.014782    test-rmse:0.518016+0.048641 
## [19] train-rmse:0.380988+0.014042    test-rmse:0.513196+0.049878 
## [20] train-rmse:0.376940+0.014818    test-rmse:0.513541+0.049577 
## [21] train-rmse:0.373203+0.015278    test-rmse:0.511536+0.051679 
## [22] train-rmse:0.369248+0.014744    test-rmse:0.510244+0.051320 
## [23] train-rmse:0.364457+0.015584    test-rmse:0.508228+0.050456 
## [24] train-rmse:0.360717+0.015603    test-rmse:0.507385+0.050976 
## [25] train-rmse:0.356281+0.017122    test-rmse:0.507062+0.050274 
## [26] train-rmse:0.352475+0.016536    test-rmse:0.508473+0.049319 
## [27] train-rmse:0.348209+0.017075    test-rmse:0.505796+0.048298 
## [28] train-rmse:0.343765+0.017869    test-rmse:0.505633+0.048673 
## [29] train-rmse:0.339563+0.017279    test-rmse:0.503623+0.051147 
## [30] train-rmse:0.336042+0.016703    test-rmse:0.503147+0.050928 
## [31] train-rmse:0.332266+0.017562    test-rmse:0.501340+0.049439 
## [32] train-rmse:0.330015+0.017899    test-rmse:0.501683+0.047907 
## [33] train-rmse:0.327224+0.017119    test-rmse:0.500365+0.048546 
## [34] train-rmse:0.324112+0.017241    test-rmse:0.500715+0.047964 
## [35] train-rmse:0.320027+0.015888    test-rmse:0.501093+0.047558 
## [36] train-rmse:0.317333+0.016067    test-rmse:0.502853+0.046125 
## [37] train-rmse:0.312848+0.013972    test-rmse:0.501157+0.046378 
## [38] train-rmse:0.309449+0.013104    test-rmse:0.500697+0.047674 
## [39] train-rmse:0.305249+0.013645    test-rmse:0.502110+0.046549 
## Stopping. Best iteration:
## [33] train-rmse:0.327224+0.017119    test-rmse:0.500365+0.048546
## 
## 87 
## [1]  train-rmse:0.782724+0.014445    test-rmse:0.799890+0.061211 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.648730+0.013412    test-rmse:0.681875+0.063862 
## [3]  train-rmse:0.565034+0.012554    test-rmse:0.619460+0.055431 
## [4]  train-rmse:0.510178+0.012701    test-rmse:0.580889+0.053872 
## [5]  train-rmse:0.472679+0.016417    test-rmse:0.559354+0.052042 
## [6]  train-rmse:0.450041+0.015446    test-rmse:0.546497+0.051574 
## [7]  train-rmse:0.432654+0.017129    test-rmse:0.541530+0.053888 
## [8]  train-rmse:0.419276+0.015225    test-rmse:0.535959+0.049155 
## [9]  train-rmse:0.402812+0.013351    test-rmse:0.525426+0.051808 
## [10] train-rmse:0.393440+0.015007    test-rmse:0.521956+0.051315 
## [11] train-rmse:0.382845+0.013906    test-rmse:0.518546+0.055260 
## [12] train-rmse:0.371507+0.016547    test-rmse:0.516949+0.054825 
## [13] train-rmse:0.362613+0.015230    test-rmse:0.512721+0.056463 
## [14] train-rmse:0.355643+0.014225    test-rmse:0.512138+0.057051 
## [15] train-rmse:0.347539+0.015166    test-rmse:0.509206+0.056999 
## [16] train-rmse:0.340660+0.015470    test-rmse:0.506043+0.054154 
## [17] train-rmse:0.333445+0.015367    test-rmse:0.504207+0.055987 
## [18] train-rmse:0.327958+0.015841    test-rmse:0.502121+0.055875 
## [19] train-rmse:0.322767+0.015699    test-rmse:0.504128+0.056023 
## [20] train-rmse:0.316492+0.016059    test-rmse:0.504238+0.056594 
## [21] train-rmse:0.310742+0.017401    test-rmse:0.503832+0.057434 
## [22] train-rmse:0.302836+0.016939    test-rmse:0.502401+0.055956 
## [23] train-rmse:0.297248+0.014968    test-rmse:0.498761+0.054703 
## [24] train-rmse:0.293091+0.015772    test-rmse:0.501063+0.054955 
## [25] train-rmse:0.288215+0.017535    test-rmse:0.500638+0.055745 
## [26] train-rmse:0.282768+0.015636    test-rmse:0.499612+0.055088 
## [27] train-rmse:0.279103+0.015129    test-rmse:0.500608+0.056913 
## [28] train-rmse:0.272405+0.015658    test-rmse:0.500443+0.057390 
## [29] train-rmse:0.268313+0.014831    test-rmse:0.500446+0.057129 
## Stopping. Best iteration:
## [23] train-rmse:0.297248+0.014968    test-rmse:0.498761+0.054703
## 
## 88 
## [1]  train-rmse:0.773192+0.013625    test-rmse:0.794753+0.062020 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.628594+0.010415    test-rmse:0.674695+0.062670 
## [3]  train-rmse:0.534982+0.013324    test-rmse:0.598702+0.058952 
## [4]  train-rmse:0.474872+0.015709    test-rmse:0.565293+0.058050 
## [5]  train-rmse:0.434435+0.014421    test-rmse:0.540730+0.056301 
## [6]  train-rmse:0.408939+0.013129    test-rmse:0.526852+0.052749 
## [7]  train-rmse:0.389454+0.014575    test-rmse:0.520004+0.053227 
## [8]  train-rmse:0.369767+0.021199    test-rmse:0.517113+0.061090 
## [9]  train-rmse:0.357722+0.020455    test-rmse:0.512357+0.060638 
## [10] train-rmse:0.346220+0.019033    test-rmse:0.510118+0.058777 
## [11] train-rmse:0.336395+0.017625    test-rmse:0.512375+0.057794 
## [12] train-rmse:0.328762+0.016194    test-rmse:0.508698+0.062346 
## [13] train-rmse:0.318022+0.017233    test-rmse:0.505554+0.061123 
## [14] train-rmse:0.308667+0.015280    test-rmse:0.507249+0.060283 
## [15] train-rmse:0.299750+0.016030    test-rmse:0.509026+0.061685 
## [16] train-rmse:0.292956+0.014327    test-rmse:0.507313+0.061023 
## [17] train-rmse:0.287149+0.013469    test-rmse:0.506640+0.059907 
## [18] train-rmse:0.279683+0.012148    test-rmse:0.506666+0.061865 
## [19] train-rmse:0.273351+0.011160    test-rmse:0.505630+0.062402 
## Stopping. Best iteration:
## [13] train-rmse:0.318022+0.017233    test-rmse:0.505554+0.061123
## 
## 89 
## [1]  train-rmse:0.764378+0.012794    test-rmse:0.791591+0.062106 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.609210+0.009910    test-rmse:0.665074+0.063365 
## [3]  train-rmse:0.508265+0.012537    test-rmse:0.594777+0.060348 
## [4]  train-rmse:0.445781+0.013173    test-rmse:0.557763+0.059349 
## [5]  train-rmse:0.400942+0.015074    test-rmse:0.536072+0.059675 
## [6]  train-rmse:0.370979+0.013408    test-rmse:0.521277+0.061840 
## [7]  train-rmse:0.349932+0.013727    test-rmse:0.519454+0.064081 
## [8]  train-rmse:0.328410+0.010351    test-rmse:0.511186+0.064085 
## [9]  train-rmse:0.313581+0.010390    test-rmse:0.501441+0.063099 
## [10] train-rmse:0.299729+0.009643    test-rmse:0.498345+0.060569 
## [11] train-rmse:0.286114+0.012597    test-rmse:0.496013+0.062673 
## [12] train-rmse:0.276430+0.010356    test-rmse:0.493022+0.064349 
## [13] train-rmse:0.264269+0.012787    test-rmse:0.494618+0.066462 
## [14] train-rmse:0.255584+0.015560    test-rmse:0.492205+0.064215 
## [15] train-rmse:0.246693+0.015353    test-rmse:0.492314+0.065256 
## [16] train-rmse:0.237770+0.014633    test-rmse:0.492821+0.067661 
## [17] train-rmse:0.227351+0.013089    test-rmse:0.490657+0.066950 
## [18] train-rmse:0.221141+0.015317    test-rmse:0.490075+0.066045 
## [19] train-rmse:0.213633+0.015476    test-rmse:0.490362+0.065667 
## [20] train-rmse:0.207484+0.015060    test-rmse:0.491113+0.065160 
## [21] train-rmse:0.200046+0.014201    test-rmse:0.492725+0.067217 
## [22] train-rmse:0.193067+0.014714    test-rmse:0.493016+0.066284 
## [23] train-rmse:0.185891+0.013200    test-rmse:0.493991+0.064790 
## [24] train-rmse:0.180478+0.014676    test-rmse:0.494601+0.065155 
## Stopping. Best iteration:
## [18] train-rmse:0.221141+0.015317    test-rmse:0.490075+0.066045
## 
## 90 
## [1]  train-rmse:0.755647+0.012075    test-rmse:0.786490+0.059045 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.594194+0.007239    test-rmse:0.659861+0.060596 
## [3]  train-rmse:0.491670+0.010297    test-rmse:0.594609+0.064603 
## [4]  train-rmse:0.419016+0.011965    test-rmse:0.550797+0.063542 
## [5]  train-rmse:0.367043+0.006428    test-rmse:0.532116+0.062758 
## [6]  train-rmse:0.330678+0.006828    test-rmse:0.520525+0.059324 
## [7]  train-rmse:0.304489+0.012174    test-rmse:0.511756+0.048967 
## [8]  train-rmse:0.289053+0.013941    test-rmse:0.505879+0.045186 
## [9]  train-rmse:0.268641+0.014450    test-rmse:0.506652+0.048532 
## [10] train-rmse:0.251422+0.017627    test-rmse:0.501688+0.043462 
## [11] train-rmse:0.241139+0.017781    test-rmse:0.502211+0.045519 
## [12] train-rmse:0.231363+0.015196    test-rmse:0.496560+0.047313 
## [13] train-rmse:0.220172+0.015025    test-rmse:0.493504+0.047893 
## [14] train-rmse:0.209484+0.018580    test-rmse:0.493721+0.049417 
## [15] train-rmse:0.198726+0.017023    test-rmse:0.497204+0.056455 
## [16] train-rmse:0.191171+0.016813    test-rmse:0.497275+0.055998 
## [17] train-rmse:0.183162+0.014266    test-rmse:0.498094+0.056292 
## [18] train-rmse:0.176094+0.014293    test-rmse:0.498130+0.056393 
## [19] train-rmse:0.169860+0.012933    test-rmse:0.498066+0.055315 
## Stopping. Best iteration:
## [13] train-rmse:0.220172+0.015025    test-rmse:0.493504+0.047893
## 
## 91 
## [1]  train-rmse:0.824402+0.014414    test-rmse:0.829993+0.060083 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.734014+0.013995    test-rmse:0.754311+0.063563 
## [3]  train-rmse:0.682694+0.012539    test-rmse:0.706753+0.060760 
## [4]  train-rmse:0.649009+0.011502    test-rmse:0.668430+0.058475 
## [5]  train-rmse:0.628593+0.011771    test-rmse:0.656117+0.054106 
## [6]  train-rmse:0.615384+0.011645    test-rmse:0.643404+0.052398 
## [7]  train-rmse:0.605637+0.011566    test-rmse:0.634720+0.055231 
## [8]  train-rmse:0.596724+0.011371    test-rmse:0.632076+0.053962 
## [9]  train-rmse:0.588854+0.011290    test-rmse:0.624764+0.057396 
## [10] train-rmse:0.581930+0.011096    test-rmse:0.618328+0.058167 
## [11] train-rmse:0.575969+0.011240    test-rmse:0.615958+0.058099 
## [12] train-rmse:0.570646+0.010960    test-rmse:0.611110+0.056225 
## [13] train-rmse:0.565656+0.010840    test-rmse:0.606075+0.057854 
## [14] train-rmse:0.560980+0.010715    test-rmse:0.602220+0.055485 
## [15] train-rmse:0.556564+0.010762    test-rmse:0.595731+0.055103 
## [16] train-rmse:0.552277+0.010819    test-rmse:0.592883+0.056812 
## [17] train-rmse:0.548156+0.010953    test-rmse:0.587867+0.055977 
## [18] train-rmse:0.544271+0.010958    test-rmse:0.583028+0.056079 
## [19] train-rmse:0.540617+0.011006    test-rmse:0.583359+0.055773 
## [20] train-rmse:0.537406+0.010921    test-rmse:0.579507+0.053909 
## [21] train-rmse:0.534336+0.010866    test-rmse:0.577530+0.055489 
## [22] train-rmse:0.531525+0.010878    test-rmse:0.575870+0.056036 
## [23] train-rmse:0.528815+0.010973    test-rmse:0.575033+0.055961 
## [24] train-rmse:0.526210+0.010996    test-rmse:0.575447+0.055255 
## [25] train-rmse:0.523737+0.010925    test-rmse:0.575393+0.056507 
## [26] train-rmse:0.521376+0.010913    test-rmse:0.572823+0.056338 
## [27] train-rmse:0.518985+0.010875    test-rmse:0.570783+0.056554 
## [28] train-rmse:0.516768+0.010774    test-rmse:0.568523+0.055661 
## [29] train-rmse:0.514684+0.010705    test-rmse:0.567137+0.056891 
## [30] train-rmse:0.512704+0.010698    test-rmse:0.564454+0.056421 
## [31] train-rmse:0.510801+0.010683    test-rmse:0.562944+0.054893 
## [32] train-rmse:0.508939+0.010657    test-rmse:0.559554+0.054232 
## [33] train-rmse:0.507072+0.010668    test-rmse:0.557304+0.053785 
## [34] train-rmse:0.505331+0.010646    test-rmse:0.555029+0.053185 
## [35] train-rmse:0.503665+0.010569    test-rmse:0.553679+0.052293 
## [36] train-rmse:0.502071+0.010546    test-rmse:0.552860+0.051964 
## [37] train-rmse:0.500517+0.010523    test-rmse:0.550533+0.053275 
## [38] train-rmse:0.499016+0.010555    test-rmse:0.550337+0.053941 
## [39] train-rmse:0.497606+0.010555    test-rmse:0.551087+0.053684 
## [40] train-rmse:0.496267+0.010494    test-rmse:0.550256+0.052887 
## [41] train-rmse:0.494991+0.010475    test-rmse:0.549973+0.052644 
## [42] train-rmse:0.493705+0.010489    test-rmse:0.550473+0.052040 
## [43] train-rmse:0.492498+0.010444    test-rmse:0.549141+0.051529 
## [44] train-rmse:0.491356+0.010440    test-rmse:0.547956+0.051603 
## [45] train-rmse:0.490266+0.010461    test-rmse:0.545969+0.051503 
## [46] train-rmse:0.489188+0.010464    test-rmse:0.545473+0.051860 
## [47] train-rmse:0.488133+0.010484    test-rmse:0.545134+0.051547 
## [48] train-rmse:0.487071+0.010529    test-rmse:0.545478+0.052201 
## [49] train-rmse:0.486057+0.010575    test-rmse:0.545205+0.051107 
## [50] train-rmse:0.485082+0.010598    test-rmse:0.544077+0.050901 
## [51] train-rmse:0.484133+0.010588    test-rmse:0.543254+0.051763 
## [52] train-rmse:0.483189+0.010569    test-rmse:0.543394+0.052061 
## [53] train-rmse:0.482296+0.010527    test-rmse:0.543573+0.050578 
## [54] train-rmse:0.481420+0.010495    test-rmse:0.542934+0.049462 
## [55] train-rmse:0.480595+0.010469    test-rmse:0.542180+0.050231 
## [56] train-rmse:0.479820+0.010428    test-rmse:0.541755+0.051101 
## [57] train-rmse:0.479038+0.010369    test-rmse:0.540617+0.050768 
## [58] train-rmse:0.478272+0.010371    test-rmse:0.539751+0.050634 
## [59] train-rmse:0.477552+0.010370    test-rmse:0.539184+0.050317 
## [60] train-rmse:0.476849+0.010364    test-rmse:0.539109+0.050098 
## [61] train-rmse:0.476175+0.010367    test-rmse:0.539046+0.049840 
## [62] train-rmse:0.475515+0.010369    test-rmse:0.538210+0.049288 
## [63] train-rmse:0.474882+0.010373    test-rmse:0.536681+0.049805 
## [64] train-rmse:0.474242+0.010364    test-rmse:0.536974+0.049780 
## [65] train-rmse:0.473626+0.010366    test-rmse:0.536629+0.049117 
## [66] train-rmse:0.473021+0.010379    test-rmse:0.536425+0.049200 
## [67] train-rmse:0.472431+0.010370    test-rmse:0.535710+0.049325 
## [68] train-rmse:0.471836+0.010402    test-rmse:0.535696+0.048527 
## [69] train-rmse:0.471270+0.010400    test-rmse:0.534980+0.048285 
## [70] train-rmse:0.470705+0.010388    test-rmse:0.534830+0.049319 
## [71] train-rmse:0.470159+0.010383    test-rmse:0.535185+0.049381 
## [72] train-rmse:0.469626+0.010375    test-rmse:0.535581+0.049481 
## [73] train-rmse:0.469110+0.010359    test-rmse:0.535545+0.049916 
## [74] train-rmse:0.468595+0.010311    test-rmse:0.536890+0.049200 
## [75] train-rmse:0.468122+0.010290    test-rmse:0.536227+0.048721 
## [76] train-rmse:0.467673+0.010274    test-rmse:0.536273+0.048276 
## Stopping. Best iteration:
## [70] train-rmse:0.470705+0.010388    test-rmse:0.534830+0.049319
## 
## 92 
## [1]  train-rmse:0.795200+0.014147    test-rmse:0.802931+0.060649 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.682208+0.013164    test-rmse:0.706643+0.064943 
## [3]  train-rmse:0.620541+0.012079    test-rmse:0.651882+0.063906 
## [4]  train-rmse:0.582296+0.014234    test-rmse:0.627466+0.063756 
## [5]  train-rmse:0.559315+0.017242    test-rmse:0.610527+0.063750 
## [6]  train-rmse:0.543886+0.017375    test-rmse:0.593578+0.059280 
## [7]  train-rmse:0.532217+0.018027    test-rmse:0.588399+0.063239 
## [8]  train-rmse:0.519973+0.016137    test-rmse:0.574245+0.065775 
## [9]  train-rmse:0.511324+0.016038    test-rmse:0.570623+0.066840 
## [10] train-rmse:0.503503+0.016793    test-rmse:0.565191+0.066291 
## [11] train-rmse:0.494098+0.014965    test-rmse:0.559649+0.063783 
## [12] train-rmse:0.486735+0.014676    test-rmse:0.555096+0.060759 
## [13] train-rmse:0.479739+0.014973    test-rmse:0.551776+0.064625 
## [14] train-rmse:0.473942+0.015365    test-rmse:0.547979+0.062867 
## [15] train-rmse:0.468067+0.014541    test-rmse:0.547085+0.061668 
## [16] train-rmse:0.463254+0.014456    test-rmse:0.544532+0.064568 
## [17] train-rmse:0.457695+0.015433    test-rmse:0.538926+0.063042 
## [18] train-rmse:0.453237+0.015462    test-rmse:0.534111+0.061194 
## [19] train-rmse:0.449053+0.014252    test-rmse:0.533172+0.060565 
## [20] train-rmse:0.445028+0.013529    test-rmse:0.529175+0.061303 
## [21] train-rmse:0.440876+0.013328    test-rmse:0.527800+0.059213 
## [22] train-rmse:0.436955+0.014156    test-rmse:0.527221+0.059997 
## [23] train-rmse:0.432634+0.014665    test-rmse:0.526159+0.059070 
## [24] train-rmse:0.429067+0.015633    test-rmse:0.523740+0.059825 
## [25] train-rmse:0.425918+0.015329    test-rmse:0.521920+0.057637 
## [26] train-rmse:0.422428+0.015583    test-rmse:0.519519+0.055454 
## [27] train-rmse:0.419283+0.015658    test-rmse:0.518319+0.056348 
## [28] train-rmse:0.416767+0.015316    test-rmse:0.517517+0.056901 
## [29] train-rmse:0.414044+0.015336    test-rmse:0.515562+0.057071 
## [30] train-rmse:0.409331+0.016269    test-rmse:0.513797+0.055847 
## [31] train-rmse:0.406828+0.015993    test-rmse:0.513316+0.055632 
## [32] train-rmse:0.405153+0.016043    test-rmse:0.513219+0.053631 
## [33] train-rmse:0.402504+0.015919    test-rmse:0.511124+0.054309 
## [34] train-rmse:0.400376+0.015452    test-rmse:0.510927+0.054060 
## [35] train-rmse:0.398385+0.015669    test-rmse:0.510958+0.054766 
## [36] train-rmse:0.395605+0.015622    test-rmse:0.509568+0.057705 
## [37] train-rmse:0.393642+0.015333    test-rmse:0.508177+0.059793 
## [38] train-rmse:0.391711+0.015579    test-rmse:0.508774+0.058200 
## [39] train-rmse:0.390038+0.015743    test-rmse:0.509151+0.057528 
## [40] train-rmse:0.388175+0.015828    test-rmse:0.508225+0.057081 
## [41] train-rmse:0.385661+0.016222    test-rmse:0.507756+0.058855 
## [42] train-rmse:0.384149+0.015870    test-rmse:0.507219+0.057315 
## [43] train-rmse:0.382113+0.016094    test-rmse:0.507127+0.055688 
## [44] train-rmse:0.378987+0.016018    test-rmse:0.504850+0.055732 
## [45] train-rmse:0.376878+0.015796    test-rmse:0.505512+0.055566 
## [46] train-rmse:0.374273+0.016245    test-rmse:0.503825+0.054455 
## [47] train-rmse:0.372230+0.016155    test-rmse:0.503186+0.053291 
## [48] train-rmse:0.370350+0.015973    test-rmse:0.502670+0.052777 
## [49] train-rmse:0.369016+0.016112    test-rmse:0.503509+0.052426 
## [50] train-rmse:0.366393+0.017916    test-rmse:0.502242+0.051837 
## [51] train-rmse:0.364166+0.017003    test-rmse:0.502783+0.051264 
## [52] train-rmse:0.362543+0.016831    test-rmse:0.502584+0.051426 
## [53] train-rmse:0.361229+0.016596    test-rmse:0.501184+0.051284 
## [54] train-rmse:0.359487+0.015784    test-rmse:0.500555+0.051681 
## [55] train-rmse:0.356933+0.014889    test-rmse:0.501195+0.050259 
## [56] train-rmse:0.354788+0.015635    test-rmse:0.499779+0.050990 
## [57] train-rmse:0.352832+0.015182    test-rmse:0.500320+0.051950 
## [58] train-rmse:0.351583+0.015067    test-rmse:0.500613+0.052322 
## [59] train-rmse:0.348691+0.014552    test-rmse:0.497954+0.053456 
## [60] train-rmse:0.346540+0.014721    test-rmse:0.496769+0.054659 
## [61] train-rmse:0.345240+0.014688    test-rmse:0.496513+0.053298 
## [62] train-rmse:0.344047+0.014650    test-rmse:0.496410+0.052861 
## [63] train-rmse:0.341631+0.016167    test-rmse:0.495410+0.051287 
## [64] train-rmse:0.339923+0.016230    test-rmse:0.494919+0.051641 
## [65] train-rmse:0.338611+0.016152    test-rmse:0.493810+0.051457 
## [66] train-rmse:0.337501+0.016038    test-rmse:0.494745+0.051431 
## [67] train-rmse:0.336065+0.016723    test-rmse:0.495912+0.052369 
## [68] train-rmse:0.334737+0.016548    test-rmse:0.495931+0.053004 
## [69] train-rmse:0.333770+0.016867    test-rmse:0.494417+0.053388 
## [70] train-rmse:0.332394+0.017383    test-rmse:0.494302+0.052023 
## [71] train-rmse:0.330677+0.018204    test-rmse:0.494319+0.050917 
## Stopping. Best iteration:
## [65] train-rmse:0.338611+0.016152    test-rmse:0.493810+0.051457
## 
## 93 
## [1]  train-rmse:0.782108+0.014739    test-rmse:0.795988+0.063676 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.655360+0.013727    test-rmse:0.684134+0.065668 
## [3]  train-rmse:0.583106+0.014773    test-rmse:0.624744+0.061855 
## [4]  train-rmse:0.535598+0.014808    test-rmse:0.589187+0.056820 
## [5]  train-rmse:0.508361+0.017169    test-rmse:0.572777+0.048847 
## [6]  train-rmse:0.485882+0.017406    test-rmse:0.562017+0.043733 
## [7]  train-rmse:0.471757+0.016491    test-rmse:0.555010+0.044540 
## [8]  train-rmse:0.458579+0.015814    test-rmse:0.547587+0.047599 
## [9]  train-rmse:0.446327+0.016542    test-rmse:0.543065+0.043372 
## [10] train-rmse:0.436259+0.015866    test-rmse:0.537764+0.042217 
## [11] train-rmse:0.427497+0.014225    test-rmse:0.534403+0.041522 
## [12] train-rmse:0.419863+0.013529    test-rmse:0.534385+0.041184 
## [13] train-rmse:0.412289+0.013429    test-rmse:0.531026+0.040882 
## [14] train-rmse:0.405550+0.012876    test-rmse:0.527436+0.042851 
## [15] train-rmse:0.398817+0.011739    test-rmse:0.525116+0.041962 
## [16] train-rmse:0.393338+0.012050    test-rmse:0.522412+0.043576 
## [17] train-rmse:0.387691+0.012898    test-rmse:0.522534+0.041897 
## [18] train-rmse:0.381282+0.012854    test-rmse:0.521499+0.040483 
## [19] train-rmse:0.375613+0.010788    test-rmse:0.518265+0.043945 
## [20] train-rmse:0.370094+0.011621    test-rmse:0.514360+0.044814 
## [21] train-rmse:0.364394+0.013280    test-rmse:0.511483+0.044409 
## [22] train-rmse:0.359813+0.012889    test-rmse:0.509345+0.045400 
## [23] train-rmse:0.355785+0.013785    test-rmse:0.506877+0.045698 
## [24] train-rmse:0.352075+0.013071    test-rmse:0.507056+0.045757 
## [25] train-rmse:0.346465+0.013790    test-rmse:0.507410+0.046131 
## [26] train-rmse:0.341958+0.014030    test-rmse:0.506180+0.048476 
## [27] train-rmse:0.337863+0.012994    test-rmse:0.504426+0.049980 
## [28] train-rmse:0.333706+0.012560    test-rmse:0.502438+0.047003 
## [29] train-rmse:0.329748+0.012190    test-rmse:0.503123+0.044811 
## [30] train-rmse:0.324771+0.012995    test-rmse:0.503047+0.046349 
## [31] train-rmse:0.321668+0.012230    test-rmse:0.502727+0.045759 
## [32] train-rmse:0.316233+0.010192    test-rmse:0.502914+0.045897 
## [33] train-rmse:0.311558+0.011933    test-rmse:0.503588+0.041767 
## [34] train-rmse:0.309148+0.012529    test-rmse:0.504892+0.042610 
## Stopping. Best iteration:
## [28] train-rmse:0.333706+0.012560    test-rmse:0.502438+0.047003
## 
## 94 
## [1]  train-rmse:0.770788+0.014377    test-rmse:0.789284+0.061346 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.635003+0.013420    test-rmse:0.668914+0.061639 
## [3]  train-rmse:0.552819+0.012608    test-rmse:0.614816+0.059395 
## [4]  train-rmse:0.498482+0.015406    test-rmse:0.575557+0.056739 
## [5]  train-rmse:0.467326+0.014902    test-rmse:0.554362+0.054671 
## [6]  train-rmse:0.446244+0.014782    test-rmse:0.541752+0.055596 
## [7]  train-rmse:0.424321+0.016974    test-rmse:0.530091+0.056788 
## [8]  train-rmse:0.407364+0.018047    test-rmse:0.524746+0.058498 
## [9]  train-rmse:0.394852+0.016644    test-rmse:0.520176+0.057139 
## [10] train-rmse:0.385002+0.015363    test-rmse:0.516045+0.059236 
## [11] train-rmse:0.375820+0.016282    test-rmse:0.514786+0.059212 
## [12] train-rmse:0.365341+0.017935    test-rmse:0.511395+0.060717 
## [13] train-rmse:0.353290+0.018534    test-rmse:0.505812+0.061544 
## [14] train-rmse:0.346567+0.017990    test-rmse:0.506516+0.060932 
## [15] train-rmse:0.337338+0.019738    test-rmse:0.503647+0.060914 
## [16] train-rmse:0.329929+0.020691    test-rmse:0.499656+0.061185 
## [17] train-rmse:0.323394+0.020076    test-rmse:0.497467+0.059875 
## [18] train-rmse:0.313472+0.018295    test-rmse:0.497912+0.060421 
## [19] train-rmse:0.309457+0.017786    test-rmse:0.498473+0.059727 
## [20] train-rmse:0.303937+0.017022    test-rmse:0.498457+0.060027 
## [21] train-rmse:0.295781+0.014553    test-rmse:0.497733+0.059295 
## [22] train-rmse:0.288867+0.015719    test-rmse:0.496152+0.054107 
## [23] train-rmse:0.281059+0.014937    test-rmse:0.497632+0.054221 
## [24] train-rmse:0.275787+0.015582    test-rmse:0.496766+0.052054 
## [25] train-rmse:0.271346+0.014802    test-rmse:0.496726+0.053452 
## [26] train-rmse:0.267612+0.013856    test-rmse:0.496639+0.051441 
## [27] train-rmse:0.263175+0.015313    test-rmse:0.497568+0.050777 
## [28] train-rmse:0.258050+0.017504    test-rmse:0.498187+0.052248 
## Stopping. Best iteration:
## [22] train-rmse:0.288867+0.015719    test-rmse:0.496152+0.054107
## 
## 95 
## [1]  train-rmse:0.760638+0.013514    test-rmse:0.783878+0.062125 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.612383+0.010833    test-rmse:0.661360+0.062640 
## [3]  train-rmse:0.520642+0.013622    test-rmse:0.589110+0.055828 
## [4]  train-rmse:0.463170+0.013179    test-rmse:0.554718+0.054038 
## [5]  train-rmse:0.423270+0.012722    test-rmse:0.536801+0.052975 
## [6]  train-rmse:0.398246+0.013718    test-rmse:0.523868+0.048773 
## [7]  train-rmse:0.379174+0.014229    test-rmse:0.518206+0.054472 
## [8]  train-rmse:0.365012+0.014927    test-rmse:0.511207+0.056839 
## [9]  train-rmse:0.352560+0.015973    test-rmse:0.501993+0.057970 
## [10] train-rmse:0.337511+0.010417    test-rmse:0.500444+0.058218 
## [11] train-rmse:0.327551+0.009821    test-rmse:0.496608+0.060090 
## [12] train-rmse:0.317855+0.009581    test-rmse:0.495256+0.063859 
## [13] train-rmse:0.308065+0.009487    test-rmse:0.492307+0.064523 
## [14] train-rmse:0.298724+0.008253    test-rmse:0.491332+0.063568 
## [15] train-rmse:0.291923+0.007615    test-rmse:0.490071+0.063750 
## [16] train-rmse:0.283160+0.008046    test-rmse:0.488727+0.063264 
## [17] train-rmse:0.274244+0.008280    test-rmse:0.488291+0.063327 
## [18] train-rmse:0.266462+0.007645    test-rmse:0.488074+0.060409 
## [19] train-rmse:0.258723+0.009078    test-rmse:0.487401+0.058785 
## [20] train-rmse:0.251189+0.008693    test-rmse:0.486687+0.058384 
## [21] train-rmse:0.243344+0.006992    test-rmse:0.490052+0.060464 
## [22] train-rmse:0.234005+0.008771    test-rmse:0.490572+0.057443 
## [23] train-rmse:0.229508+0.009711    test-rmse:0.491198+0.055478 
## [24] train-rmse:0.222833+0.012079    test-rmse:0.490696+0.055621 
## [25] train-rmse:0.218080+0.011177    test-rmse:0.490727+0.055831 
## [26] train-rmse:0.211966+0.010037    test-rmse:0.492223+0.054030 
## Stopping. Best iteration:
## [20] train-rmse:0.251189+0.008693    test-rmse:0.486687+0.058384
## 
## 96 
## [1]  train-rmse:0.751235+0.012639    test-rmse:0.780591+0.062179 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.592926+0.009722    test-rmse:0.654456+0.065147 
## [3]  train-rmse:0.492699+0.012491    test-rmse:0.586868+0.063260 
## [4]  train-rmse:0.433404+0.011339    test-rmse:0.552356+0.054501 
## [5]  train-rmse:0.392374+0.009704    test-rmse:0.533512+0.053369 
## [6]  train-rmse:0.362426+0.012331    test-rmse:0.521923+0.051038 
## [7]  train-rmse:0.340441+0.011806    test-rmse:0.515050+0.051036 
## [8]  train-rmse:0.315497+0.011798    test-rmse:0.503534+0.060218 
## [9]  train-rmse:0.300265+0.011141    test-rmse:0.503324+0.060186 
## [10] train-rmse:0.287775+0.010692    test-rmse:0.499526+0.059559 
## [11] train-rmse:0.275782+0.011477    test-rmse:0.499021+0.060796 
## [12] train-rmse:0.264322+0.010029    test-rmse:0.496289+0.062819 
## [13] train-rmse:0.252166+0.010877    test-rmse:0.495492+0.066109 
## [14] train-rmse:0.244805+0.011537    test-rmse:0.499074+0.068294 
## [15] train-rmse:0.235046+0.010454    test-rmse:0.497743+0.068434 
## [16] train-rmse:0.225860+0.008831    test-rmse:0.498873+0.067740 
## [17] train-rmse:0.217061+0.011606    test-rmse:0.499608+0.066810 
## [18] train-rmse:0.211206+0.010802    test-rmse:0.502424+0.067578 
## [19] train-rmse:0.205941+0.012071    test-rmse:0.503441+0.067792 
## Stopping. Best iteration:
## [13] train-rmse:0.252166+0.010877    test-rmse:0.495492+0.066109
## 
## 97 
## [1]  train-rmse:0.741906+0.011885    test-rmse:0.775242+0.058900 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.577092+0.007029    test-rmse:0.649824+0.063617 
## [3]  train-rmse:0.474742+0.007853    test-rmse:0.581782+0.065174 
## [4]  train-rmse:0.403616+0.012140    test-rmse:0.540460+0.063915 
## [5]  train-rmse:0.355476+0.010114    test-rmse:0.516958+0.065412 
## [6]  train-rmse:0.317574+0.013790    test-rmse:0.506607+0.064901 
## [7]  train-rmse:0.297645+0.016740    test-rmse:0.500614+0.064392 
## [8]  train-rmse:0.281007+0.014682    test-rmse:0.496918+0.062414 
## [9]  train-rmse:0.264577+0.012087    test-rmse:0.495063+0.058653 
## [10] train-rmse:0.251226+0.011032    test-rmse:0.492052+0.061000 
## [11] train-rmse:0.238668+0.011539    test-rmse:0.489665+0.054994 
## [12] train-rmse:0.229936+0.011920    test-rmse:0.489271+0.054798 
## [13] train-rmse:0.216966+0.012463    test-rmse:0.487688+0.053543 
## [14] train-rmse:0.206891+0.014214    test-rmse:0.484978+0.051632 
## [15] train-rmse:0.194539+0.012458    test-rmse:0.488520+0.054346 
## [16] train-rmse:0.186835+0.012687    test-rmse:0.487561+0.051768 
## [17] train-rmse:0.179792+0.011235    test-rmse:0.487815+0.052421 
## [18] train-rmse:0.171203+0.011334    test-rmse:0.489175+0.050631 
## [19] train-rmse:0.163585+0.009586    test-rmse:0.489385+0.052406 
## [20] train-rmse:0.156901+0.011162    test-rmse:0.488652+0.051103 
## Stopping. Best iteration:
## [14] train-rmse:0.206891+0.014214    test-rmse:0.484978+0.051632
## 
## 98 
## [1]  train-rmse:0.815789+0.014346    test-rmse:0.821875+0.060024 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.725158+0.013734    test-rmse:0.752851+0.060067 
## [3]  train-rmse:0.675931+0.012586    test-rmse:0.702077+0.059746 
## [4]  train-rmse:0.643008+0.011447    test-rmse:0.664612+0.057021 
## [5]  train-rmse:0.623515+0.011283    test-rmse:0.654376+0.051346 
## [6]  train-rmse:0.611122+0.011172    test-rmse:0.642865+0.049053 
## [7]  train-rmse:0.601318+0.011081    test-rmse:0.635457+0.052616 
## [8]  train-rmse:0.592373+0.010831    test-rmse:0.628902+0.055127 
## [9]  train-rmse:0.584445+0.010789    test-rmse:0.619494+0.055946 
## [10] train-rmse:0.577787+0.010729    test-rmse:0.614217+0.057220 
## [11] train-rmse:0.571825+0.010789    test-rmse:0.610910+0.057601 
## [12] train-rmse:0.566608+0.010936    test-rmse:0.604086+0.057240 
## [13] train-rmse:0.561787+0.010900    test-rmse:0.602471+0.054135 
## [14] train-rmse:0.557233+0.010949    test-rmse:0.599062+0.052942 
## [15] train-rmse:0.552678+0.010796    test-rmse:0.593741+0.052809 
## [16] train-rmse:0.548374+0.010788    test-rmse:0.588806+0.054322 
## [17] train-rmse:0.544305+0.010794    test-rmse:0.584610+0.053965 
## [18] train-rmse:0.540425+0.010663    test-rmse:0.581493+0.055390 
## [19] train-rmse:0.536786+0.010582    test-rmse:0.581274+0.054460 
## [20] train-rmse:0.533588+0.010538    test-rmse:0.578865+0.052672 
## [21] train-rmse:0.530595+0.010601    test-rmse:0.577125+0.051896 
## [22] train-rmse:0.527756+0.010565    test-rmse:0.575040+0.052194 
## [23] train-rmse:0.525084+0.010562    test-rmse:0.574087+0.052518 
## [24] train-rmse:0.522559+0.010593    test-rmse:0.573313+0.053648 
## [25] train-rmse:0.520090+0.010571    test-rmse:0.572761+0.056063 
## [26] train-rmse:0.517719+0.010611    test-rmse:0.570038+0.055942 
## [27] train-rmse:0.515501+0.010494    test-rmse:0.569517+0.055354 
## [28] train-rmse:0.513419+0.010507    test-rmse:0.567515+0.054823 
## [29] train-rmse:0.511421+0.010437    test-rmse:0.565889+0.054901 
## [30] train-rmse:0.509467+0.010395    test-rmse:0.562440+0.054684 
## [31] train-rmse:0.507567+0.010346    test-rmse:0.559826+0.053781 
## [32] train-rmse:0.505687+0.010314    test-rmse:0.557148+0.052314 
## [33] train-rmse:0.503908+0.010256    test-rmse:0.555763+0.053341 
## [34] train-rmse:0.502200+0.010167    test-rmse:0.553829+0.051996 
## [35] train-rmse:0.500556+0.010100    test-rmse:0.551713+0.051024 
## [36] train-rmse:0.498991+0.010113    test-rmse:0.552156+0.051531 
## [37] train-rmse:0.497507+0.010101    test-rmse:0.551940+0.051056 
## [38] train-rmse:0.496055+0.010113    test-rmse:0.550347+0.052119 
## [39] train-rmse:0.494675+0.010125    test-rmse:0.549459+0.052373 
## [40] train-rmse:0.493380+0.010141    test-rmse:0.548768+0.052857 
## [41] train-rmse:0.492160+0.010136    test-rmse:0.547591+0.053519 
## [42] train-rmse:0.490983+0.010138    test-rmse:0.547322+0.053523 
## [43] train-rmse:0.489827+0.010172    test-rmse:0.547252+0.052685 
## [44] train-rmse:0.488703+0.010146    test-rmse:0.546422+0.052360 
## [45] train-rmse:0.487586+0.010108    test-rmse:0.547423+0.051726 
## [46] train-rmse:0.486515+0.010102    test-rmse:0.547123+0.051937 
## [47] train-rmse:0.485479+0.010113    test-rmse:0.545711+0.051523 
## [48] train-rmse:0.484488+0.010093    test-rmse:0.545892+0.052292 
## [49] train-rmse:0.483535+0.010098    test-rmse:0.546138+0.052497 
## [50] train-rmse:0.482582+0.010117    test-rmse:0.543952+0.053257 
## [51] train-rmse:0.481669+0.010127    test-rmse:0.543913+0.052210 
## [52] train-rmse:0.480793+0.010161    test-rmse:0.543275+0.051518 
## [53] train-rmse:0.479948+0.010158    test-rmse:0.542658+0.051387 
## [54] train-rmse:0.479123+0.010141    test-rmse:0.542426+0.050307 
## [55] train-rmse:0.478347+0.010102    test-rmse:0.540896+0.050197 
## [56] train-rmse:0.477590+0.010070    test-rmse:0.539808+0.050435 
## [57] train-rmse:0.476835+0.010069    test-rmse:0.540115+0.049722 
## [58] train-rmse:0.476104+0.010060    test-rmse:0.539637+0.049596 
## [59] train-rmse:0.475400+0.010050    test-rmse:0.538761+0.048569 
## [60] train-rmse:0.474725+0.010021    test-rmse:0.537671+0.047336 
## [61] train-rmse:0.474069+0.009987    test-rmse:0.537428+0.046903 
## [62] train-rmse:0.473441+0.009960    test-rmse:0.537528+0.047186 
## [63] train-rmse:0.472827+0.009941    test-rmse:0.537372+0.047978 
## [64] train-rmse:0.472238+0.009936    test-rmse:0.537255+0.046760 
## [65] train-rmse:0.471656+0.009923    test-rmse:0.537540+0.047563 
## [66] train-rmse:0.471106+0.009914    test-rmse:0.537197+0.047388 
## [67] train-rmse:0.470571+0.009913    test-rmse:0.537210+0.047423 
## [68] train-rmse:0.470028+0.009909    test-rmse:0.537319+0.046863 
## [69] train-rmse:0.469459+0.009854    test-rmse:0.537136+0.047603 
## [70] train-rmse:0.468928+0.009856    test-rmse:0.536700+0.046471 
## [71] train-rmse:0.468423+0.009844    test-rmse:0.536709+0.047302 
## [72] train-rmse:0.467934+0.009848    test-rmse:0.537542+0.047687 
## [73] train-rmse:0.467461+0.009818    test-rmse:0.537262+0.047747 
## [74] train-rmse:0.466970+0.009808    test-rmse:0.536860+0.048181 
## [75] train-rmse:0.466475+0.009753    test-rmse:0.535500+0.048361 
## [76] train-rmse:0.466037+0.009741    test-rmse:0.535486+0.048107 
## [77] train-rmse:0.465621+0.009728    test-rmse:0.535156+0.047299 
## [78] train-rmse:0.465202+0.009727    test-rmse:0.535569+0.047071 
## [79] train-rmse:0.464776+0.009737    test-rmse:0.534735+0.046792 
## [80] train-rmse:0.464346+0.009711    test-rmse:0.534309+0.046918 
## [81] train-rmse:0.463889+0.009709    test-rmse:0.534271+0.046820 
## [82] train-rmse:0.463459+0.009712    test-rmse:0.533912+0.046718 
## [83] train-rmse:0.463047+0.009706    test-rmse:0.533222+0.046645 
## [84] train-rmse:0.462655+0.009687    test-rmse:0.533697+0.046701 
## [85] train-rmse:0.462225+0.009667    test-rmse:0.533056+0.046500 
## [86] train-rmse:0.461852+0.009658    test-rmse:0.533666+0.045587 
## [87] train-rmse:0.461481+0.009640    test-rmse:0.533226+0.045093 
## [88] train-rmse:0.461114+0.009631    test-rmse:0.533421+0.045063 
## [89] train-rmse:0.460759+0.009620    test-rmse:0.533779+0.044597 
## [90] train-rmse:0.460414+0.009611    test-rmse:0.533270+0.044293 
## [91] train-rmse:0.460075+0.009594    test-rmse:0.533028+0.044711 
## [92] train-rmse:0.459725+0.009571    test-rmse:0.533163+0.045077 
## [93] train-rmse:0.459398+0.009549    test-rmse:0.533007+0.044243 
## [94] train-rmse:0.459051+0.009570    test-rmse:0.532595+0.044957 
## [95] train-rmse:0.458723+0.009545    test-rmse:0.532508+0.045071 
## [96] train-rmse:0.458405+0.009533    test-rmse:0.532318+0.045448 
## [97] train-rmse:0.458080+0.009528    test-rmse:0.532694+0.045869 
## [98] train-rmse:0.457762+0.009520    test-rmse:0.532659+0.045440 
## [99] train-rmse:0.457441+0.009509    test-rmse:0.533320+0.045490 
## [100]    train-rmse:0.457117+0.009485    test-rmse:0.532993+0.045409 
## [101]    train-rmse:0.456818+0.009490    test-rmse:0.532974+0.045478 
## [102]    train-rmse:0.456533+0.009481    test-rmse:0.532648+0.045545 
## Stopping. Best iteration:
## [96] train-rmse:0.458405+0.009533    test-rmse:0.532318+0.045448
## 
## 99 
## [1]  train-rmse:0.784883+0.014058    test-rmse:0.793218+0.060702 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.671373+0.013039    test-rmse:0.696318+0.064368 
## [3]  train-rmse:0.611127+0.012739    test-rmse:0.641752+0.066116 
## [4]  train-rmse:0.574491+0.013719    test-rmse:0.617014+0.066322 
## [5]  train-rmse:0.552766+0.016618    test-rmse:0.602430+0.065888 
## [6]  train-rmse:0.536869+0.017332    test-rmse:0.588183+0.060915 
## [7]  train-rmse:0.525677+0.018304    test-rmse:0.577541+0.060430 
## [8]  train-rmse:0.515780+0.017452    test-rmse:0.573002+0.059353 
## [9]  train-rmse:0.506394+0.017795    test-rmse:0.563607+0.057823 
## [10] train-rmse:0.497146+0.017663    test-rmse:0.560895+0.059546 
## [11] train-rmse:0.489807+0.017591    test-rmse:0.553091+0.061420 
## [12] train-rmse:0.483843+0.017809    test-rmse:0.547931+0.061404 
## [13] train-rmse:0.476543+0.017315    test-rmse:0.545131+0.063741 
## [14] train-rmse:0.470598+0.017649    test-rmse:0.543107+0.062905 
## [15] train-rmse:0.464994+0.017010    test-rmse:0.540602+0.063876 
## [16] train-rmse:0.459751+0.016362    test-rmse:0.537890+0.063958 
## [17] train-rmse:0.455347+0.017033    test-rmse:0.537391+0.061873 
## [18] train-rmse:0.450160+0.018164    test-rmse:0.532364+0.064046 
## [19] train-rmse:0.446015+0.017493    test-rmse:0.529165+0.062636 
## [20] train-rmse:0.442573+0.017142    test-rmse:0.526793+0.061547 
## [21] train-rmse:0.438962+0.017275    test-rmse:0.526235+0.060558 
## [22] train-rmse:0.435399+0.016699    test-rmse:0.524963+0.061951 
## [23] train-rmse:0.430941+0.017193    test-rmse:0.523114+0.060379 
## [24] train-rmse:0.428068+0.016943    test-rmse:0.520432+0.060601 
## [25] train-rmse:0.424680+0.017581    test-rmse:0.521195+0.059583 
## [26] train-rmse:0.421189+0.018189    test-rmse:0.516851+0.059659 
## [27] train-rmse:0.418557+0.018633    test-rmse:0.516829+0.059761 
## [28] train-rmse:0.415929+0.018709    test-rmse:0.515614+0.059303 
## [29] train-rmse:0.413494+0.018437    test-rmse:0.513934+0.059653 
## [30] train-rmse:0.409994+0.018199    test-rmse:0.512969+0.059391 
## [31] train-rmse:0.407155+0.017864    test-rmse:0.510873+0.059202 
## [32] train-rmse:0.403744+0.017473    test-rmse:0.511777+0.059126 
## [33] train-rmse:0.401306+0.017911    test-rmse:0.511713+0.059793 
## [34] train-rmse:0.399282+0.018179    test-rmse:0.512505+0.058025 
## [35] train-rmse:0.396710+0.017035    test-rmse:0.512857+0.058254 
## [36] train-rmse:0.394341+0.016814    test-rmse:0.512081+0.058291 
## [37] train-rmse:0.391580+0.017372    test-rmse:0.511509+0.058476 
## Stopping. Best iteration:
## [31] train-rmse:0.407155+0.017864    test-rmse:0.510873+0.059202
## 
## 100 
## [1]  train-rmse:0.771020+0.014694    test-rmse:0.785961+0.063944 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.643289+0.013648    test-rmse:0.675108+0.065527 
## [3]  train-rmse:0.573236+0.014885    test-rmse:0.619489+0.062162 
## [4]  train-rmse:0.529824+0.013013    test-rmse:0.586999+0.058637 
## [5]  train-rmse:0.500688+0.018435    test-rmse:0.571772+0.050510 
## [6]  train-rmse:0.481870+0.016438    test-rmse:0.556956+0.052123 
## [7]  train-rmse:0.467773+0.016412    test-rmse:0.554775+0.052065 
## [8]  train-rmse:0.453727+0.016422    test-rmse:0.547967+0.053937 
## [9]  train-rmse:0.441218+0.016125    test-rmse:0.540175+0.055694 
## [10] train-rmse:0.431360+0.015924    test-rmse:0.535945+0.058894 
## [11] train-rmse:0.421703+0.017441    test-rmse:0.533677+0.059703 
## [12] train-rmse:0.414709+0.017188    test-rmse:0.529336+0.059385 
## [13] train-rmse:0.406265+0.016467    test-rmse:0.525024+0.061162 
## [14] train-rmse:0.399266+0.017178    test-rmse:0.520522+0.061438 
## [15] train-rmse:0.392070+0.016763    test-rmse:0.516944+0.058997 
## [16] train-rmse:0.385984+0.017676    test-rmse:0.517192+0.058725 
## [17] train-rmse:0.381661+0.017567    test-rmse:0.517975+0.059422 
## [18] train-rmse:0.376569+0.017419    test-rmse:0.517971+0.058589 
## [19] train-rmse:0.369859+0.017094    test-rmse:0.516667+0.058201 
## [20] train-rmse:0.364891+0.015989    test-rmse:0.514530+0.059906 
## [21] train-rmse:0.361002+0.016046    test-rmse:0.517084+0.057829 
## [22] train-rmse:0.354117+0.015387    test-rmse:0.513368+0.056520 
## [23] train-rmse:0.347250+0.015117    test-rmse:0.511972+0.055233 
## [24] train-rmse:0.344098+0.014621    test-rmse:0.511249+0.055121 
## [25] train-rmse:0.338269+0.015415    test-rmse:0.512498+0.054364 
## [26] train-rmse:0.334805+0.016107    test-rmse:0.510623+0.053255 
## [27] train-rmse:0.331861+0.015909    test-rmse:0.510093+0.051840 
## [28] train-rmse:0.327420+0.015190    test-rmse:0.510070+0.052019 
## [29] train-rmse:0.321430+0.017262    test-rmse:0.506392+0.053845 
## [30] train-rmse:0.317531+0.017328    test-rmse:0.506353+0.055440 
## [31] train-rmse:0.312825+0.017414    test-rmse:0.507895+0.054117 
## [32] train-rmse:0.310529+0.017639    test-rmse:0.509311+0.053150 
## [33] train-rmse:0.306475+0.014833    test-rmse:0.508560+0.052174 
## [34] train-rmse:0.303125+0.013557    test-rmse:0.509064+0.052957 
## [35] train-rmse:0.299693+0.014094    test-rmse:0.508440+0.054418 
## [36] train-rmse:0.294787+0.015070    test-rmse:0.506959+0.054548 
## Stopping. Best iteration:
## [30] train-rmse:0.317531+0.017328    test-rmse:0.506353+0.055440
## 
## 101 
## [1]  train-rmse:0.759017+0.014314    test-rmse:0.778867+0.061503 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.621372+0.013069    test-rmse:0.658333+0.063686 
## [3]  train-rmse:0.541201+0.014276    test-rmse:0.602441+0.058611 
## [4]  train-rmse:0.490081+0.016503    test-rmse:0.568169+0.053792 
## [5]  train-rmse:0.457781+0.017631    test-rmse:0.552053+0.054633 
## [6]  train-rmse:0.439903+0.018301    test-rmse:0.540068+0.049844 
## [7]  train-rmse:0.418235+0.015726    test-rmse:0.527924+0.049203 
## [8]  train-rmse:0.403032+0.017448    test-rmse:0.523957+0.047863 
## [9]  train-rmse:0.392272+0.016813    test-rmse:0.519280+0.049310 
## [10] train-rmse:0.379346+0.017510    test-rmse:0.513821+0.054764 
## [11] train-rmse:0.367663+0.016943    test-rmse:0.511191+0.055740 
## [12] train-rmse:0.359942+0.016650    test-rmse:0.509392+0.056957 
## [13] train-rmse:0.351778+0.016334    test-rmse:0.505466+0.059682 
## [14] train-rmse:0.343628+0.014990    test-rmse:0.505141+0.059618 
## [15] train-rmse:0.335221+0.014791    test-rmse:0.504052+0.058935 
## [16] train-rmse:0.328210+0.015005    test-rmse:0.503035+0.060944 
## [17] train-rmse:0.321142+0.012500    test-rmse:0.504959+0.059306 
## [18] train-rmse:0.315654+0.012565    test-rmse:0.503228+0.060162 
## [19] train-rmse:0.308086+0.013074    test-rmse:0.502507+0.058036 
## [20] train-rmse:0.302555+0.013632    test-rmse:0.500583+0.056165 
## [21] train-rmse:0.295190+0.012466    test-rmse:0.499118+0.058753 
## [22] train-rmse:0.287844+0.014276    test-rmse:0.499915+0.058479 
## [23] train-rmse:0.281954+0.014356    test-rmse:0.501046+0.058479 
## [24] train-rmse:0.274409+0.013992    test-rmse:0.496501+0.057647 
## [25] train-rmse:0.269795+0.013684    test-rmse:0.495663+0.058551 
## [26] train-rmse:0.263907+0.013746    test-rmse:0.491481+0.060143 
## [27] train-rmse:0.257785+0.013120    test-rmse:0.490280+0.060713 
## [28] train-rmse:0.253240+0.011312    test-rmse:0.490139+0.059468 
## [29] train-rmse:0.246398+0.010656    test-rmse:0.491654+0.060318 
## [30] train-rmse:0.241826+0.007823    test-rmse:0.491643+0.060747 
## [31] train-rmse:0.237439+0.008026    test-rmse:0.492020+0.061484 
## [32] train-rmse:0.234058+0.008575    test-rmse:0.492787+0.060095 
## [33] train-rmse:0.229039+0.009153    test-rmse:0.492860+0.059752 
## [34] train-rmse:0.223158+0.009097    test-rmse:0.491740+0.059074 
## Stopping. Best iteration:
## [28] train-rmse:0.253240+0.011312    test-rmse:0.490139+0.059468
## 
## 102 
## [1]  train-rmse:0.748242+0.013411    test-rmse:0.773198+0.062241 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.597605+0.011310    test-rmse:0.650481+0.062549 
## [3]  train-rmse:0.507203+0.014242    test-rmse:0.585064+0.050113 
## [4]  train-rmse:0.451491+0.012362    test-rmse:0.547656+0.053354 
## [5]  train-rmse:0.413643+0.011652    test-rmse:0.529532+0.056146 
## [6]  train-rmse:0.383909+0.017866    test-rmse:0.517601+0.055648 
## [7]  train-rmse:0.369217+0.018294    test-rmse:0.514586+0.053233 
## [8]  train-rmse:0.355404+0.016258    test-rmse:0.508341+0.054392 
## [9]  train-rmse:0.341437+0.019041    test-rmse:0.509187+0.055584 
## [10] train-rmse:0.328634+0.016451    test-rmse:0.503280+0.059470 
## [11] train-rmse:0.317396+0.016714    test-rmse:0.501633+0.058440 
## [12] train-rmse:0.306634+0.016821    test-rmse:0.501609+0.058121 
## [13] train-rmse:0.297289+0.015509    test-rmse:0.500232+0.059637 
## [14] train-rmse:0.287011+0.015725    test-rmse:0.499899+0.058667 
## [15] train-rmse:0.276783+0.013410    test-rmse:0.500391+0.058408 
## [16] train-rmse:0.269117+0.013124    test-rmse:0.500636+0.055924 
## [17] train-rmse:0.258633+0.015163    test-rmse:0.497109+0.056792 
## [18] train-rmse:0.251633+0.016066    test-rmse:0.497299+0.055273 
## [19] train-rmse:0.244411+0.015766    test-rmse:0.495184+0.055177 
## [20] train-rmse:0.237180+0.012486    test-rmse:0.496070+0.054016 
## [21] train-rmse:0.229717+0.011527    test-rmse:0.496142+0.055956 
## [22] train-rmse:0.222255+0.009198    test-rmse:0.496599+0.055535 
## [23] train-rmse:0.215755+0.009870    test-rmse:0.497040+0.053421 
## [24] train-rmse:0.209120+0.010619    test-rmse:0.496919+0.051931 
## [25] train-rmse:0.204805+0.010670    test-rmse:0.497605+0.051337 
## Stopping. Best iteration:
## [19] train-rmse:0.244411+0.015766    test-rmse:0.495184+0.055177
## 
## 103 
## [1]  train-rmse:0.738241+0.012492    test-rmse:0.769795+0.062261 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.577245+0.009675    test-rmse:0.643638+0.061818 
## [3]  train-rmse:0.475754+0.011709    test-rmse:0.572813+0.056134 
## [4]  train-rmse:0.415930+0.010550    test-rmse:0.539342+0.052929 
## [5]  train-rmse:0.373070+0.010181    test-rmse:0.522005+0.053298 
## [6]  train-rmse:0.348700+0.012163    test-rmse:0.514098+0.051143 
## [7]  train-rmse:0.329080+0.011178    test-rmse:0.508062+0.047895 
## [8]  train-rmse:0.313356+0.012558    test-rmse:0.506485+0.047215 
## [9]  train-rmse:0.295777+0.012635    test-rmse:0.502137+0.049423 
## [10] train-rmse:0.286193+0.013174    test-rmse:0.499487+0.049764 
## [11] train-rmse:0.272700+0.011458    test-rmse:0.495090+0.049246 
## [12] train-rmse:0.264396+0.013477    test-rmse:0.495786+0.050205 
## [13] train-rmse:0.250393+0.010210    test-rmse:0.493573+0.048797 
## [14] train-rmse:0.242408+0.009887    test-rmse:0.493621+0.049159 
## [15] train-rmse:0.231693+0.010025    test-rmse:0.494261+0.051475 
## [16] train-rmse:0.223447+0.008934    test-rmse:0.494226+0.051381 
## [17] train-rmse:0.216083+0.007482    test-rmse:0.496396+0.052494 
## [18] train-rmse:0.210852+0.006789    test-rmse:0.496940+0.053024 
## [19] train-rmse:0.200118+0.005711    test-rmse:0.498875+0.050813 
## Stopping. Best iteration:
## [13] train-rmse:0.250393+0.010210    test-rmse:0.493573+0.048797
## 
## 104 
## [1]  train-rmse:0.728303+0.011705    test-rmse:0.764208+0.058762 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.561099+0.006742    test-rmse:0.635151+0.061308 
## [3]  train-rmse:0.455936+0.006970    test-rmse:0.570704+0.063714 
## [4]  train-rmse:0.388361+0.011775    test-rmse:0.537328+0.068286 
## [5]  train-rmse:0.346618+0.009342    test-rmse:0.518241+0.065829 
## [6]  train-rmse:0.316056+0.012775    test-rmse:0.512460+0.064588 
## [7]  train-rmse:0.292904+0.016588    test-rmse:0.509843+0.061292 
## [8]  train-rmse:0.271723+0.015630    test-rmse:0.504341+0.054756 
## [9]  train-rmse:0.254461+0.016639    test-rmse:0.500291+0.053771 
## [10] train-rmse:0.240463+0.018322    test-rmse:0.496192+0.051869 
## [11] train-rmse:0.228330+0.017438    test-rmse:0.493912+0.049871 
## [12] train-rmse:0.220121+0.016141    test-rmse:0.493638+0.048915 
## [13] train-rmse:0.209676+0.014608    test-rmse:0.490508+0.049486 
## [14] train-rmse:0.198408+0.014019    test-rmse:0.491847+0.053301 
## [15] train-rmse:0.190607+0.014106    test-rmse:0.490461+0.053450 
## [16] train-rmse:0.181244+0.012206    test-rmse:0.492591+0.050425 
## [17] train-rmse:0.168961+0.015336    test-rmse:0.495342+0.051985 
## [18] train-rmse:0.163697+0.016149    test-rmse:0.494793+0.051227 
## [19] train-rmse:0.153302+0.015322    test-rmse:0.496000+0.051516 
## [20] train-rmse:0.146364+0.012828    test-rmse:0.497097+0.053283 
## [21] train-rmse:0.140313+0.013302    test-rmse:0.497576+0.055744 
## Stopping. Best iteration:
## [15] train-rmse:0.190607+0.014106    test-rmse:0.490461+0.053450
## 
## 105 
## [1]  train-rmse:0.807358+0.014282    test-rmse:0.813948+0.059973 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.717000+0.013574    test-rmse:0.741531+0.056261 
## [3]  train-rmse:0.669187+0.012352    test-rmse:0.689499+0.051873 
## [4]  train-rmse:0.638328+0.010567    test-rmse:0.660489+0.056998 
## [5]  train-rmse:0.619244+0.011279    test-rmse:0.650353+0.050186 
## [6]  train-rmse:0.607596+0.011059    test-rmse:0.639807+0.048223 
## [7]  train-rmse:0.597497+0.010899    test-rmse:0.634665+0.052003 
## [8]  train-rmse:0.588708+0.010971    test-rmse:0.625840+0.054412 
## [9]  train-rmse:0.580851+0.010704    test-rmse:0.616334+0.055373 
## [10] train-rmse:0.574190+0.010708    test-rmse:0.611617+0.057207 
## [11] train-rmse:0.568160+0.010802    test-rmse:0.606680+0.055226 
## [12] train-rmse:0.562951+0.010833    test-rmse:0.602057+0.052967 
## [13] train-rmse:0.558073+0.010832    test-rmse:0.597988+0.052948 
## [14] train-rmse:0.553337+0.010915    test-rmse:0.591759+0.053048 
## [15] train-rmse:0.548864+0.010967    test-rmse:0.588548+0.054716 
## [16] train-rmse:0.544708+0.010945    test-rmse:0.587455+0.053273 
## [17] train-rmse:0.540656+0.010837    test-rmse:0.584324+0.054125 
## [18] train-rmse:0.536856+0.010785    test-rmse:0.578912+0.054502 
## [19] train-rmse:0.533249+0.010657    test-rmse:0.578044+0.053082 
## [20] train-rmse:0.530116+0.010589    test-rmse:0.576390+0.051936 
## [21] train-rmse:0.527046+0.010640    test-rmse:0.576781+0.050339 
## [22] train-rmse:0.524168+0.010607    test-rmse:0.573269+0.051703 
## [23] train-rmse:0.521609+0.010679    test-rmse:0.570792+0.052302 
## [24] train-rmse:0.519046+0.010699    test-rmse:0.570833+0.054832 
## [25] train-rmse:0.516607+0.010716    test-rmse:0.570190+0.054859 
## [26] train-rmse:0.514215+0.010779    test-rmse:0.568278+0.053469 
## [27] train-rmse:0.512052+0.010768    test-rmse:0.566153+0.053539 
## [28] train-rmse:0.509961+0.010705    test-rmse:0.563868+0.051981 
## [29] train-rmse:0.507962+0.010670    test-rmse:0.561906+0.053229 
## [30] train-rmse:0.506009+0.010614    test-rmse:0.560436+0.051606 
## [31] train-rmse:0.504111+0.010506    test-rmse:0.558167+0.051716 
## [32] train-rmse:0.502336+0.010447    test-rmse:0.555350+0.052189 
## [33] train-rmse:0.500567+0.010381    test-rmse:0.554004+0.051694 
## [34] train-rmse:0.498866+0.010350    test-rmse:0.551915+0.051524 
## [35] train-rmse:0.497193+0.010325    test-rmse:0.550503+0.051557 
## [36] train-rmse:0.495686+0.010321    test-rmse:0.548941+0.050315 
## [37] train-rmse:0.494248+0.010280    test-rmse:0.550022+0.048955 
## [38] train-rmse:0.492869+0.010232    test-rmse:0.549148+0.049547 
## [39] train-rmse:0.491491+0.010215    test-rmse:0.547147+0.050857 
## [40] train-rmse:0.490182+0.010224    test-rmse:0.547081+0.050671 
## [41] train-rmse:0.488906+0.010266    test-rmse:0.545050+0.051036 
## [42] train-rmse:0.487739+0.010230    test-rmse:0.544172+0.051548 
## [43] train-rmse:0.486602+0.010242    test-rmse:0.544280+0.051650 
## [44] train-rmse:0.485455+0.010229    test-rmse:0.544368+0.049914 
## [45] train-rmse:0.484412+0.010234    test-rmse:0.545740+0.049730 
## [46] train-rmse:0.483376+0.010228    test-rmse:0.544055+0.049355 
## [47] train-rmse:0.482376+0.010214    test-rmse:0.544565+0.049230 
## [48] train-rmse:0.481430+0.010199    test-rmse:0.543899+0.049793 
## [49] train-rmse:0.480502+0.010181    test-rmse:0.543779+0.051283 
## [50] train-rmse:0.479580+0.010154    test-rmse:0.543801+0.050602 
## [51] train-rmse:0.478714+0.010151    test-rmse:0.542078+0.050168 
## [52] train-rmse:0.477888+0.010171    test-rmse:0.541598+0.050235 
## [53] train-rmse:0.477086+0.010173    test-rmse:0.541016+0.049421 
## [54] train-rmse:0.476317+0.010165    test-rmse:0.540013+0.048832 
## [55] train-rmse:0.475545+0.010142    test-rmse:0.540797+0.048708 
## [56] train-rmse:0.474808+0.010130    test-rmse:0.539860+0.048627 
## [57] train-rmse:0.474101+0.010116    test-rmse:0.539996+0.048286 
## [58] train-rmse:0.473395+0.010096    test-rmse:0.539270+0.047353 
## [59] train-rmse:0.472716+0.010051    test-rmse:0.538260+0.046882 
## [60] train-rmse:0.472055+0.010008    test-rmse:0.536968+0.045814 
## [61] train-rmse:0.471412+0.010007    test-rmse:0.536524+0.045217 
## [62] train-rmse:0.470800+0.009997    test-rmse:0.536518+0.045245 
## [63] train-rmse:0.470203+0.009965    test-rmse:0.536492+0.044703 
## [64] train-rmse:0.469629+0.009943    test-rmse:0.535316+0.046106 
## [65] train-rmse:0.469059+0.009913    test-rmse:0.535742+0.046308 
## [66] train-rmse:0.468509+0.009879    test-rmse:0.535446+0.046041 
## [67] train-rmse:0.467976+0.009867    test-rmse:0.535703+0.045176 
## [68] train-rmse:0.467462+0.009849    test-rmse:0.534994+0.045763 
## [69] train-rmse:0.466965+0.009819    test-rmse:0.535180+0.045440 
## [70] train-rmse:0.466440+0.009816    test-rmse:0.535465+0.045398 
## [71] train-rmse:0.465957+0.009781    test-rmse:0.535203+0.045501 
## [72] train-rmse:0.465462+0.009734    test-rmse:0.535151+0.045877 
## [73] train-rmse:0.465002+0.009714    test-rmse:0.535394+0.046129 
## [74] train-rmse:0.464537+0.009698    test-rmse:0.534591+0.045856 
## [75] train-rmse:0.464093+0.009678    test-rmse:0.534621+0.045183 
## [76] train-rmse:0.463651+0.009663    test-rmse:0.534477+0.045193 
## [77] train-rmse:0.463218+0.009647    test-rmse:0.535111+0.045737 
## [78] train-rmse:0.462793+0.009636    test-rmse:0.534661+0.044963 
## [79] train-rmse:0.462379+0.009635    test-rmse:0.533139+0.045473 
## [80] train-rmse:0.461962+0.009632    test-rmse:0.532965+0.045472 
## [81] train-rmse:0.461554+0.009642    test-rmse:0.532757+0.045499 
## [82] train-rmse:0.461153+0.009632    test-rmse:0.532397+0.045533 
## [83] train-rmse:0.460758+0.009619    test-rmse:0.532152+0.045553 
## [84] train-rmse:0.460371+0.009606    test-rmse:0.532318+0.045848 
## [85] train-rmse:0.459988+0.009603    test-rmse:0.532136+0.045474 
## [86] train-rmse:0.459597+0.009599    test-rmse:0.532817+0.044852 
## [87] train-rmse:0.459252+0.009583    test-rmse:0.532444+0.044699 
## [88] train-rmse:0.458875+0.009573    test-rmse:0.532577+0.044295 
## [89] train-rmse:0.458526+0.009546    test-rmse:0.532136+0.044890 
## [90] train-rmse:0.458194+0.009529    test-rmse:0.531819+0.044751 
## [91] train-rmse:0.457862+0.009506    test-rmse:0.531156+0.044188 
## [92] train-rmse:0.457536+0.009488    test-rmse:0.531826+0.044420 
## [93] train-rmse:0.457204+0.009479    test-rmse:0.530914+0.044912 
## [94] train-rmse:0.456871+0.009452    test-rmse:0.530858+0.044551 
## [95] train-rmse:0.456551+0.009418    test-rmse:0.531067+0.044412 
## [96] train-rmse:0.456232+0.009419    test-rmse:0.531355+0.044633 
## [97] train-rmse:0.455930+0.009400    test-rmse:0.530894+0.045740 
## [98] train-rmse:0.455624+0.009386    test-rmse:0.530924+0.045232 
## [99] train-rmse:0.455301+0.009363    test-rmse:0.530458+0.045317 
## [100]    train-rmse:0.454961+0.009344    test-rmse:0.531148+0.045008 
## [101]    train-rmse:0.454668+0.009337    test-rmse:0.530315+0.044712 
## [102]    train-rmse:0.454365+0.009326    test-rmse:0.529863+0.045271 
## [103]    train-rmse:0.454056+0.009325    test-rmse:0.529556+0.045449 
## [104]    train-rmse:0.453757+0.009314    test-rmse:0.529472+0.045606 
## [105]    train-rmse:0.453468+0.009297    test-rmse:0.529721+0.045688 
## [106]    train-rmse:0.453184+0.009286    test-rmse:0.529586+0.045244 
## [107]    train-rmse:0.452875+0.009241    test-rmse:0.529169+0.045021 
## [108]    train-rmse:0.452592+0.009234    test-rmse:0.529685+0.045037 
## [109]    train-rmse:0.452317+0.009227    test-rmse:0.529259+0.044742 
## [110]    train-rmse:0.452042+0.009219    test-rmse:0.529022+0.045009 
## [111]    train-rmse:0.451744+0.009225    test-rmse:0.528823+0.044996 
## [112]    train-rmse:0.451458+0.009223    test-rmse:0.529181+0.044612 
## [113]    train-rmse:0.451189+0.009215    test-rmse:0.528897+0.044125 
## [114]    train-rmse:0.450933+0.009209    test-rmse:0.528615+0.043119 
## [115]    train-rmse:0.450646+0.009173    test-rmse:0.528583+0.043619 
## [116]    train-rmse:0.450376+0.009174    test-rmse:0.529284+0.043875 
## [117]    train-rmse:0.450119+0.009157    test-rmse:0.528843+0.044439 
## [118]    train-rmse:0.449862+0.009145    test-rmse:0.528862+0.044357 
## [119]    train-rmse:0.449597+0.009137    test-rmse:0.529422+0.044190 
## [120]    train-rmse:0.449337+0.009114    test-rmse:0.529813+0.044260 
## [121]    train-rmse:0.449081+0.009122    test-rmse:0.529725+0.044801 
## Stopping. Best iteration:
## [115]    train-rmse:0.450646+0.009173    test-rmse:0.528583+0.043619
## 
## 106 
## [1]  train-rmse:0.774745+0.013973    test-rmse:0.783691+0.060769 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.661252+0.012883    test-rmse:0.687540+0.064771 
## [3]  train-rmse:0.602165+0.011712    test-rmse:0.632278+0.069915 
## [4]  train-rmse:0.568067+0.014390    test-rmse:0.609976+0.067620 
## [5]  train-rmse:0.546730+0.018214    test-rmse:0.591453+0.061900 
## [6]  train-rmse:0.532945+0.017802    test-rmse:0.579766+0.059109 
## [7]  train-rmse:0.521159+0.017898    test-rmse:0.570561+0.058894 
## [8]  train-rmse:0.511141+0.016874    test-rmse:0.565441+0.058778 
## [9]  train-rmse:0.501410+0.016948    test-rmse:0.556358+0.057255 
## [10] train-rmse:0.492686+0.017786    test-rmse:0.554302+0.054931 
## [11] train-rmse:0.486234+0.017383    test-rmse:0.547806+0.060994 
## [12] train-rmse:0.479502+0.017794    test-rmse:0.540470+0.057236 
## [13] train-rmse:0.473267+0.016447    test-rmse:0.538424+0.061450 
## [14] train-rmse:0.465405+0.018810    test-rmse:0.535117+0.057752 
## [15] train-rmse:0.460273+0.018659    test-rmse:0.533180+0.055534 
## [16] train-rmse:0.455117+0.018239    test-rmse:0.531119+0.056980 
## [17] train-rmse:0.449025+0.017634    test-rmse:0.529569+0.059146 
## [18] train-rmse:0.443642+0.018064    test-rmse:0.528216+0.059147 
## [19] train-rmse:0.440152+0.017964    test-rmse:0.527673+0.056878 
## [20] train-rmse:0.436141+0.017697    test-rmse:0.528425+0.057835 
## [21] train-rmse:0.431736+0.016970    test-rmse:0.527278+0.056613 
## [22] train-rmse:0.427673+0.017544    test-rmse:0.525005+0.058078 
## [23] train-rmse:0.423212+0.018960    test-rmse:0.524421+0.058542 
## [24] train-rmse:0.419957+0.018947    test-rmse:0.523346+0.055424 
## [25] train-rmse:0.416199+0.018461    test-rmse:0.521145+0.055377 
## [26] train-rmse:0.413110+0.018505    test-rmse:0.518792+0.055340 
## [27] train-rmse:0.409910+0.018408    test-rmse:0.518627+0.055403 
## [28] train-rmse:0.406752+0.019500    test-rmse:0.517452+0.055699 
## [29] train-rmse:0.403942+0.018850    test-rmse:0.514108+0.057282 
## [30] train-rmse:0.401932+0.018568    test-rmse:0.512547+0.057348 
## [31] train-rmse:0.399879+0.018306    test-rmse:0.514013+0.057679 
## [32] train-rmse:0.396590+0.018274    test-rmse:0.512426+0.059777 
## [33] train-rmse:0.394077+0.017679    test-rmse:0.511806+0.059850 
## [34] train-rmse:0.390916+0.016826    test-rmse:0.509187+0.060115 
## [35] train-rmse:0.389048+0.016271    test-rmse:0.507517+0.061503 
## [36] train-rmse:0.387054+0.016131    test-rmse:0.507005+0.062036 
## [37] train-rmse:0.384418+0.016559    test-rmse:0.506822+0.061725 
## [38] train-rmse:0.382438+0.016952    test-rmse:0.505758+0.060195 
## [39] train-rmse:0.380187+0.017308    test-rmse:0.506704+0.059841 
## [40] train-rmse:0.377902+0.017024    test-rmse:0.505175+0.059503 
## [41] train-rmse:0.376227+0.016983    test-rmse:0.504955+0.060040 
## [42] train-rmse:0.374779+0.017136    test-rmse:0.503492+0.058008 
## [43] train-rmse:0.372405+0.018293    test-rmse:0.503176+0.056719 
## [44] train-rmse:0.369645+0.018184    test-rmse:0.502470+0.058132 
## [45] train-rmse:0.367936+0.018051    test-rmse:0.503138+0.057137 
## [46] train-rmse:0.366233+0.017521    test-rmse:0.504306+0.057238 
## [47] train-rmse:0.363922+0.017807    test-rmse:0.504128+0.057805 
## [48] train-rmse:0.361651+0.018293    test-rmse:0.504086+0.058196 
## [49] train-rmse:0.360052+0.018353    test-rmse:0.504530+0.056820 
## [50] train-rmse:0.357465+0.017308    test-rmse:0.503614+0.059021 
## Stopping. Best iteration:
## [44] train-rmse:0.369645+0.018184    test-rmse:0.502470+0.058132
## 
## 107 
## [1]  train-rmse:0.760107+0.014654    test-rmse:0.776133+0.064224 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.631484+0.013748    test-rmse:0.664209+0.064834 
## [3]  train-rmse:0.563744+0.015189    test-rmse:0.611069+0.060250 
## [4]  train-rmse:0.524491+0.017984    test-rmse:0.586396+0.057102 
## [5]  train-rmse:0.497345+0.017912    test-rmse:0.566613+0.054297 
## [6]  train-rmse:0.480632+0.016800    test-rmse:0.555208+0.053106 
## [7]  train-rmse:0.465744+0.016592    test-rmse:0.549702+0.052537 
## [8]  train-rmse:0.454871+0.016495    test-rmse:0.540533+0.054564 
## [9]  train-rmse:0.443255+0.017674    test-rmse:0.536084+0.051719 
## [10] train-rmse:0.429844+0.018243    test-rmse:0.531929+0.056362 
## [11] train-rmse:0.421248+0.018823    test-rmse:0.529701+0.057236 
## [12] train-rmse:0.412225+0.016297    test-rmse:0.527001+0.055087 
## [13] train-rmse:0.404254+0.018456    test-rmse:0.526509+0.054810 
## [14] train-rmse:0.395408+0.020393    test-rmse:0.523167+0.058533 
## [15] train-rmse:0.389489+0.019169    test-rmse:0.520708+0.058037 
## [16] train-rmse:0.382256+0.018660    test-rmse:0.518677+0.059534 
## [17] train-rmse:0.376710+0.018676    test-rmse:0.516101+0.062359 
## [18] train-rmse:0.371029+0.017647    test-rmse:0.515025+0.062245 
## [19] train-rmse:0.364426+0.019903    test-rmse:0.516414+0.060513 
## [20] train-rmse:0.359787+0.019563    test-rmse:0.514301+0.060440 
## [21] train-rmse:0.353532+0.017726    test-rmse:0.513670+0.058521 
## [22] train-rmse:0.348734+0.018120    test-rmse:0.511007+0.059043 
## [23] train-rmse:0.343144+0.017007    test-rmse:0.507402+0.061068 
## [24] train-rmse:0.335983+0.018562    test-rmse:0.506448+0.059024 
## [25] train-rmse:0.330158+0.019627    test-rmse:0.508040+0.058280 
## [26] train-rmse:0.325089+0.018415    test-rmse:0.507581+0.057122 
## [27] train-rmse:0.320950+0.018153    test-rmse:0.507132+0.055044 
## [28] train-rmse:0.316217+0.017661    test-rmse:0.506412+0.053318 
## [29] train-rmse:0.311769+0.018312    test-rmse:0.505613+0.051399 
## [30] train-rmse:0.308002+0.019300    test-rmse:0.503558+0.050739 
## [31] train-rmse:0.305056+0.019058    test-rmse:0.502978+0.049950 
## [32] train-rmse:0.301490+0.018755    test-rmse:0.502261+0.048568 
## [33] train-rmse:0.297553+0.019283    test-rmse:0.503861+0.048106 
## [34] train-rmse:0.294893+0.018587    test-rmse:0.503641+0.047850 
## [35] train-rmse:0.291152+0.018142    test-rmse:0.504126+0.049190 
## [36] train-rmse:0.285663+0.018607    test-rmse:0.500058+0.047218 
## [37] train-rmse:0.281443+0.019100    test-rmse:0.501098+0.046230 
## [38] train-rmse:0.277369+0.017895    test-rmse:0.500640+0.046047 
## [39] train-rmse:0.274520+0.018154    test-rmse:0.499980+0.045069 
## [40] train-rmse:0.271505+0.017242    test-rmse:0.500023+0.045770 
## [41] train-rmse:0.267655+0.016144    test-rmse:0.499460+0.048013 
## [42] train-rmse:0.264730+0.015588    test-rmse:0.500732+0.049296 
## [43] train-rmse:0.260698+0.015840    test-rmse:0.499965+0.048324 
## [44] train-rmse:0.257029+0.016893    test-rmse:0.500914+0.047501 
## [45] train-rmse:0.253657+0.016299    test-rmse:0.501599+0.047087 
## [46] train-rmse:0.250333+0.016375    test-rmse:0.500072+0.045949 
## [47] train-rmse:0.247992+0.016429    test-rmse:0.500107+0.044377 
## Stopping. Best iteration:
## [41] train-rmse:0.267655+0.016144    test-rmse:0.499460+0.048013
## 
## 108 
## [1]  train-rmse:0.747417+0.014258    test-rmse:0.768647+0.061680 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.607708+0.013616    test-rmse:0.648085+0.063261 
## [3]  train-rmse:0.531868+0.013512    test-rmse:0.598520+0.057230 
## [4]  train-rmse:0.484321+0.011137    test-rmse:0.563595+0.052729 
## [5]  train-rmse:0.454923+0.011283    test-rmse:0.546047+0.051032 
## [6]  train-rmse:0.431791+0.014196    test-rmse:0.540327+0.049538 
## [7]  train-rmse:0.413085+0.012689    test-rmse:0.534298+0.054971 
## [8]  train-rmse:0.397872+0.012688    test-rmse:0.527981+0.053665 
## [9]  train-rmse:0.386273+0.012912    test-rmse:0.520782+0.055120 
## [10] train-rmse:0.375072+0.011914    test-rmse:0.521259+0.053757 
## [11] train-rmse:0.364454+0.012761    test-rmse:0.520291+0.056176 
## [12] train-rmse:0.355116+0.014874    test-rmse:0.515656+0.056813 
## [13] train-rmse:0.346744+0.014351    test-rmse:0.515805+0.053247 
## [14] train-rmse:0.338428+0.012798    test-rmse:0.514828+0.055010 
## [15] train-rmse:0.330940+0.010816    test-rmse:0.514387+0.053484 
## [16] train-rmse:0.323223+0.010597    test-rmse:0.513721+0.057008 
## [17] train-rmse:0.316450+0.010169    test-rmse:0.515849+0.057234 
## [18] train-rmse:0.309638+0.009651    test-rmse:0.514476+0.056068 
## [19] train-rmse:0.302640+0.010673    test-rmse:0.512567+0.054906 
## [20] train-rmse:0.293238+0.008915    test-rmse:0.513083+0.055418 
## [21] train-rmse:0.289099+0.007897    test-rmse:0.512720+0.057370 
## [22] train-rmse:0.279719+0.009457    test-rmse:0.508859+0.059338 
## [23] train-rmse:0.275351+0.010064    test-rmse:0.508407+0.061266 
## [24] train-rmse:0.269540+0.012442    test-rmse:0.506651+0.060075 
## [25] train-rmse:0.263636+0.013311    test-rmse:0.507180+0.058506 
## [26] train-rmse:0.258227+0.012504    test-rmse:0.507671+0.058840 
## [27] train-rmse:0.252399+0.013131    test-rmse:0.507624+0.057534 
## [28] train-rmse:0.247872+0.013078    test-rmse:0.508029+0.058558 
## [29] train-rmse:0.242672+0.013683    test-rmse:0.508869+0.059156 
## [30] train-rmse:0.237804+0.014018    test-rmse:0.507876+0.057581 
## Stopping. Best iteration:
## [24] train-rmse:0.269540+0.012442    test-rmse:0.506651+0.060075
## 
## 109 
## [1]  train-rmse:0.736012+0.013314    test-rmse:0.762723+0.062366 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.584481+0.011053    test-rmse:0.640493+0.062654 
## [3]  train-rmse:0.495923+0.014250    test-rmse:0.577449+0.049557 
## [4]  train-rmse:0.442165+0.011673    test-rmse:0.552155+0.059202 
## [5]  train-rmse:0.412143+0.011641    test-rmse:0.536789+0.059107 
## [6]  train-rmse:0.382917+0.017960    test-rmse:0.523955+0.062226 
## [7]  train-rmse:0.364416+0.016649    test-rmse:0.519657+0.061509 
## [8]  train-rmse:0.349759+0.014350    test-rmse:0.513488+0.057357 
## [9]  train-rmse:0.334141+0.017649    test-rmse:0.515783+0.058429 
## [10] train-rmse:0.322535+0.018272    test-rmse:0.513030+0.059651 
## [11] train-rmse:0.312042+0.017135    test-rmse:0.508074+0.063153 
## [12] train-rmse:0.300917+0.017550    test-rmse:0.505062+0.064625 
## [13] train-rmse:0.291643+0.016353    test-rmse:0.501638+0.064581 
## [14] train-rmse:0.280009+0.017369    test-rmse:0.499444+0.063494 
## [15] train-rmse:0.268485+0.019036    test-rmse:0.496816+0.063079 
## [16] train-rmse:0.255182+0.017848    test-rmse:0.497210+0.060489 
## [17] train-rmse:0.245220+0.017241    test-rmse:0.498355+0.059437 
## [18] train-rmse:0.236643+0.017515    test-rmse:0.499378+0.059359 
## [19] train-rmse:0.230104+0.018002    test-rmse:0.497640+0.059124 
## [20] train-rmse:0.225609+0.018983    test-rmse:0.497309+0.058550 
## [21] train-rmse:0.218740+0.018617    test-rmse:0.496598+0.055432 
## [22] train-rmse:0.211488+0.020765    test-rmse:0.498367+0.056222 
## [23] train-rmse:0.203882+0.021420    test-rmse:0.500040+0.057153 
## [24] train-rmse:0.196681+0.021972    test-rmse:0.504156+0.058110 
## [25] train-rmse:0.191542+0.018576    test-rmse:0.504186+0.057582 
## [26] train-rmse:0.183551+0.016618    test-rmse:0.505099+0.058257 
## [27] train-rmse:0.178865+0.017364    test-rmse:0.506403+0.059355 
## Stopping. Best iteration:
## [21] train-rmse:0.218740+0.018617    test-rmse:0.496598+0.055432
## 
## 110 
## [1]  train-rmse:0.725404+0.012356    test-rmse:0.759212+0.062350 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.562336+0.009455    test-rmse:0.632981+0.062164 
## [3]  train-rmse:0.462422+0.011152    test-rmse:0.563447+0.057108 
## [4]  train-rmse:0.405655+0.006801    test-rmse:0.534809+0.053303 
## [5]  train-rmse:0.367629+0.013082    test-rmse:0.526878+0.050095 
## [6]  train-rmse:0.334774+0.014231    test-rmse:0.520510+0.049920 
## [7]  train-rmse:0.318643+0.013230    test-rmse:0.512006+0.049932 
## [8]  train-rmse:0.303200+0.011799    test-rmse:0.506564+0.047211 
## [9]  train-rmse:0.283598+0.019149    test-rmse:0.502054+0.045804 
## [10] train-rmse:0.270475+0.016091    test-rmse:0.496954+0.048231 
## [11] train-rmse:0.257924+0.017602    test-rmse:0.494418+0.048467 
## [12] train-rmse:0.246025+0.015111    test-rmse:0.493681+0.046280 
## [13] train-rmse:0.236575+0.014088    test-rmse:0.492414+0.046150 
## [14] train-rmse:0.228030+0.013550    test-rmse:0.493839+0.046514 
## [15] train-rmse:0.216027+0.012430    test-rmse:0.492237+0.044778 
## [16] train-rmse:0.207453+0.009605    test-rmse:0.491457+0.044249 
## [17] train-rmse:0.198460+0.009608    test-rmse:0.490600+0.042333 
## [18] train-rmse:0.188311+0.010890    test-rmse:0.489445+0.042172 
## [19] train-rmse:0.181472+0.013088    test-rmse:0.490780+0.044653 
## [20] train-rmse:0.175106+0.013595    test-rmse:0.492062+0.045477 
## [21] train-rmse:0.169468+0.012751    test-rmse:0.492959+0.044378 
## [22] train-rmse:0.163491+0.010647    test-rmse:0.494192+0.044579 
## [23] train-rmse:0.156610+0.010752    test-rmse:0.495349+0.044211 
## [24] train-rmse:0.148900+0.008083    test-rmse:0.497846+0.044066 
## Stopping. Best iteration:
## [18] train-rmse:0.188311+0.010890    test-rmse:0.489445+0.042172
## 
## 111 
## [1]  train-rmse:0.714847+0.011536    test-rmse:0.753398+0.058629 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.545134+0.006454    test-rmse:0.624695+0.060813 
## [3]  train-rmse:0.442349+0.006937    test-rmse:0.555810+0.060216 
## [4]  train-rmse:0.378426+0.007206    test-rmse:0.522271+0.062161 
## [5]  train-rmse:0.334287+0.009469    test-rmse:0.508678+0.064733 
## [6]  train-rmse:0.303208+0.005725    test-rmse:0.502667+0.060561 
## [7]  train-rmse:0.287526+0.007787    test-rmse:0.499740+0.057848 
## [8]  train-rmse:0.266179+0.007373    test-rmse:0.491349+0.055074 
## [9]  train-rmse:0.246272+0.012374    test-rmse:0.492561+0.055719 
## [10] train-rmse:0.236381+0.011172    test-rmse:0.492984+0.054388 
## [11] train-rmse:0.226088+0.009980    test-rmse:0.493491+0.057585 
## [12] train-rmse:0.216157+0.009118    test-rmse:0.490955+0.055857 
## [13] train-rmse:0.203148+0.006435    test-rmse:0.493673+0.058777 
## [14] train-rmse:0.191692+0.007088    test-rmse:0.493223+0.058484 
## [15] train-rmse:0.180719+0.007475    test-rmse:0.494607+0.054568 
## [16] train-rmse:0.170878+0.007199    test-rmse:0.495601+0.056192 
## [17] train-rmse:0.163430+0.008751    test-rmse:0.496427+0.057354 
## [18] train-rmse:0.151682+0.006470    test-rmse:0.495096+0.056308 
## Stopping. Best iteration:
## [12] train-rmse:0.216157+0.009118    test-rmse:0.490955+0.055857
## 
## 112
##    max_depth  eta
## 98         7 0.36
## [1]  train-rmse:1.482418+0.015245    test-rmse:1.482465+0.068789 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.374356+0.013741    test-rmse:1.375676+0.069946 
## [3]  train-rmse:1.280008+0.012388    test-rmse:1.282001+0.069717 
## [4]  train-rmse:1.198026+0.011212    test-rmse:1.201141+0.070062 
## [5]  train-rmse:1.127156+0.010179    test-rmse:1.131071+0.069015 
## [6]  train-rmse:1.066209+0.009309    test-rmse:1.071277+0.068499 
## [7]  train-rmse:1.013879+0.008670    test-rmse:1.022203+0.067685 
## [8]  train-rmse:0.969057+0.008182    test-rmse:0.978466+0.065470 
## [9]  train-rmse:0.930692+0.007743    test-rmse:0.941915+0.063792 
## [10] train-rmse:0.898027+0.007377    test-rmse:0.911898+0.061915 
## [11] train-rmse:0.870189+0.007106    test-rmse:0.885571+0.059447 
## [12] train-rmse:0.846570+0.006837    test-rmse:0.863350+0.058010 
## [13] train-rmse:0.826545+0.006701    test-rmse:0.845984+0.055831 
## [14] train-rmse:0.809521+0.006525    test-rmse:0.830524+0.052735 
## [15] train-rmse:0.795133+0.006381    test-rmse:0.818341+0.051176 
## [16] train-rmse:0.782907+0.006151    test-rmse:0.807748+0.048804 
## [17] train-rmse:0.772489+0.006018    test-rmse:0.799427+0.048186 
## [18] train-rmse:0.763529+0.005886    test-rmse:0.790461+0.047713 
## [19] train-rmse:0.755869+0.005749    test-rmse:0.783401+0.044856 
## [20] train-rmse:0.749183+0.005625    test-rmse:0.778109+0.042128 
## [21] train-rmse:0.743326+0.005507    test-rmse:0.773552+0.041227 
## [22] train-rmse:0.738198+0.005430    test-rmse:0.768730+0.040003 
## [23] train-rmse:0.733656+0.005341    test-rmse:0.764679+0.040312 
## [24] train-rmse:0.729642+0.005262    test-rmse:0.761408+0.038615 
## [25] train-rmse:0.726026+0.005168    test-rmse:0.759667+0.037969 
## [26] train-rmse:0.722769+0.005083    test-rmse:0.756612+0.036371 
## [27] train-rmse:0.719797+0.005077    test-rmse:0.754036+0.036529 
## [28] train-rmse:0.717092+0.005037    test-rmse:0.752788+0.035814 
## [29] train-rmse:0.714596+0.004987    test-rmse:0.751555+0.034642 
## [30] train-rmse:0.712254+0.004952    test-rmse:0.749818+0.033563 
## [31] train-rmse:0.710079+0.004937    test-rmse:0.748380+0.032951 
## [32] train-rmse:0.708026+0.004954    test-rmse:0.746792+0.032111 
## [33] train-rmse:0.706116+0.004970    test-rmse:0.746924+0.031737 
## [34] train-rmse:0.704304+0.004953    test-rmse:0.745932+0.031324 
## [35] train-rmse:0.702550+0.004927    test-rmse:0.744233+0.031532 
## [36] train-rmse:0.700847+0.004917    test-rmse:0.743097+0.031108 
## [37] train-rmse:0.699275+0.004940    test-rmse:0.741147+0.030288 
## [38] train-rmse:0.697738+0.004962    test-rmse:0.740188+0.030271 
## [39] train-rmse:0.696241+0.005006    test-rmse:0.739675+0.029143 
## [40] train-rmse:0.694813+0.005017    test-rmse:0.739267+0.028584 
## [41] train-rmse:0.693425+0.005029    test-rmse:0.738753+0.028647 
## [42] train-rmse:0.692062+0.005037    test-rmse:0.739394+0.028981 
## [43] train-rmse:0.690728+0.005033    test-rmse:0.738671+0.029026 
## [44] train-rmse:0.689439+0.005036    test-rmse:0.737592+0.029116 
## [45] train-rmse:0.688181+0.005027    test-rmse:0.736704+0.029802 
## [46] train-rmse:0.686963+0.005054    test-rmse:0.735733+0.029934 
## [47] train-rmse:0.685762+0.005073    test-rmse:0.734294+0.029646 
## [48] train-rmse:0.684578+0.005092    test-rmse:0.733705+0.029457 
## [49] train-rmse:0.683428+0.005095    test-rmse:0.733659+0.028622 
## [50] train-rmse:0.682303+0.005095    test-rmse:0.733913+0.028909 
## [51] train-rmse:0.681203+0.005100    test-rmse:0.733560+0.029376 
## [52] train-rmse:0.680123+0.005103    test-rmse:0.732729+0.029160 
## [53] train-rmse:0.679055+0.005107    test-rmse:0.732224+0.029342 
## [54] train-rmse:0.677996+0.005115    test-rmse:0.731975+0.030007 
## [55] train-rmse:0.676960+0.005129    test-rmse:0.731584+0.029939 
## [56] train-rmse:0.675940+0.005129    test-rmse:0.730798+0.029958 
## [57] train-rmse:0.674949+0.005137    test-rmse:0.730371+0.030331 
## [58] train-rmse:0.673975+0.005138    test-rmse:0.730091+0.030803 
## [59] train-rmse:0.673004+0.005132    test-rmse:0.729252+0.030832 
## [60] train-rmse:0.672043+0.005142    test-rmse:0.728785+0.030767 
## [61] train-rmse:0.671105+0.005137    test-rmse:0.728798+0.030609 
## [62] train-rmse:0.670171+0.005136    test-rmse:0.729493+0.031122 
## [63] train-rmse:0.669252+0.005152    test-rmse:0.728175+0.030877 
## [64] train-rmse:0.668357+0.005172    test-rmse:0.728558+0.031679 
## [65] train-rmse:0.667479+0.005194    test-rmse:0.728095+0.031597 
## [66] train-rmse:0.666609+0.005219    test-rmse:0.727415+0.031792 
## [67] train-rmse:0.665755+0.005234    test-rmse:0.727009+0.031839 
## [68] train-rmse:0.664909+0.005245    test-rmse:0.726239+0.031315 
## [69] train-rmse:0.664081+0.005253    test-rmse:0.725806+0.031313 
## [70] train-rmse:0.663260+0.005267    test-rmse:0.725020+0.031174 
## [71] train-rmse:0.662446+0.005285    test-rmse:0.724144+0.031504 
## [72] train-rmse:0.661641+0.005295    test-rmse:0.724342+0.032235 
## [73] train-rmse:0.660848+0.005304    test-rmse:0.724578+0.032355 
## [74] train-rmse:0.660069+0.005317    test-rmse:0.724287+0.032768 
## [75] train-rmse:0.659292+0.005331    test-rmse:0.723248+0.031600 
## [76] train-rmse:0.658523+0.005340    test-rmse:0.723551+0.031909 
## [77] train-rmse:0.657763+0.005352    test-rmse:0.723691+0.032455 
## [78] train-rmse:0.657012+0.005369    test-rmse:0.723627+0.032371 
## [79] train-rmse:0.656267+0.005382    test-rmse:0.723142+0.032313 
## [80] train-rmse:0.655535+0.005405    test-rmse:0.722174+0.032507 
## [81] train-rmse:0.654823+0.005426    test-rmse:0.722324+0.032765 
## [82] train-rmse:0.654125+0.005437    test-rmse:0.721766+0.032425 
## [83] train-rmse:0.653427+0.005445    test-rmse:0.721396+0.032214 
## [84] train-rmse:0.652735+0.005454    test-rmse:0.721206+0.032106 
## [85] train-rmse:0.652049+0.005469    test-rmse:0.720449+0.032096 
## [86] train-rmse:0.651371+0.005494    test-rmse:0.720269+0.032813 
## [87] train-rmse:0.650698+0.005514    test-rmse:0.720686+0.033392 
## [88] train-rmse:0.650039+0.005525    test-rmse:0.720577+0.033524 
## [89] train-rmse:0.649388+0.005535    test-rmse:0.719993+0.032947 
## [90] train-rmse:0.648743+0.005548    test-rmse:0.720115+0.033208 
## [91] train-rmse:0.648107+0.005556    test-rmse:0.719892+0.033058 
## [92] train-rmse:0.647478+0.005564    test-rmse:0.719639+0.033355 
## [93] train-rmse:0.646860+0.005573    test-rmse:0.719840+0.033704 
## [94] train-rmse:0.646244+0.005582    test-rmse:0.719264+0.033997 
## [95] train-rmse:0.645636+0.005586    test-rmse:0.719811+0.034406 
## [96] train-rmse:0.645031+0.005596    test-rmse:0.719133+0.034266 
## [97] train-rmse:0.644433+0.005610    test-rmse:0.718825+0.033648 
## [98] train-rmse:0.643839+0.005623    test-rmse:0.717923+0.033680 
## [99] train-rmse:0.643254+0.005632    test-rmse:0.718344+0.034070 
## [100]    train-rmse:0.642675+0.005640    test-rmse:0.718010+0.033924 
## [101]    train-rmse:0.642100+0.005650    test-rmse:0.717897+0.034437 
## [102]    train-rmse:0.641535+0.005659    test-rmse:0.717701+0.034369 
## [103]    train-rmse:0.640964+0.005661    test-rmse:0.717403+0.034543 
## [104]    train-rmse:0.640408+0.005672    test-rmse:0.717367+0.034426 
## [105]    train-rmse:0.639850+0.005684    test-rmse:0.717023+0.034404 
## [106]    train-rmse:0.639304+0.005691    test-rmse:0.717201+0.034608 
## [107]    train-rmse:0.638764+0.005695    test-rmse:0.716801+0.034552 
## [108]    train-rmse:0.638224+0.005701    test-rmse:0.716468+0.034388 
## [109]    train-rmse:0.637690+0.005705    test-rmse:0.716596+0.034662 
## [110]    train-rmse:0.637166+0.005712    test-rmse:0.715903+0.034834 
## [111]    train-rmse:0.636643+0.005721    test-rmse:0.715479+0.033962 
## [112]    train-rmse:0.636125+0.005728    test-rmse:0.715124+0.033992 
## [113]    train-rmse:0.635603+0.005730    test-rmse:0.715112+0.034386 
## [114]    train-rmse:0.635084+0.005739    test-rmse:0.714305+0.034743 
## [115]    train-rmse:0.634566+0.005748    test-rmse:0.713790+0.034475 
## [116]    train-rmse:0.634063+0.005751    test-rmse:0.713361+0.034325 
## [117]    train-rmse:0.633565+0.005755    test-rmse:0.713251+0.034486 
## [118]    train-rmse:0.633072+0.005759    test-rmse:0.712753+0.034383 
## [119]    train-rmse:0.632576+0.005759    test-rmse:0.712313+0.034195 
## [120]    train-rmse:0.632086+0.005765    test-rmse:0.711942+0.034006 
## [121]    train-rmse:0.631604+0.005765    test-rmse:0.711917+0.033909 
## [122]    train-rmse:0.631123+0.005768    test-rmse:0.711552+0.034248 
## [123]    train-rmse:0.630645+0.005772    test-rmse:0.711656+0.034267 
## [124]    train-rmse:0.630172+0.005779    test-rmse:0.711445+0.034001 
## [125]    train-rmse:0.629699+0.005777    test-rmse:0.711710+0.034395 
## [126]    train-rmse:0.629225+0.005780    test-rmse:0.711579+0.034808 
## [127]    train-rmse:0.628754+0.005790    test-rmse:0.711508+0.034547 
## [128]    train-rmse:0.628287+0.005799    test-rmse:0.711326+0.034157 
## [129]    train-rmse:0.627829+0.005806    test-rmse:0.710510+0.033833 
## [130]    train-rmse:0.627369+0.005814    test-rmse:0.711337+0.034253 
## [131]    train-rmse:0.626918+0.005817    test-rmse:0.710666+0.034047 
## [132]    train-rmse:0.626464+0.005830    test-rmse:0.710514+0.033992 
## [133]    train-rmse:0.626018+0.005839    test-rmse:0.710415+0.033728 
## [134]    train-rmse:0.625578+0.005844    test-rmse:0.710708+0.034182 
## [135]    train-rmse:0.625141+0.005847    test-rmse:0.709925+0.034490 
## [136]    train-rmse:0.624704+0.005851    test-rmse:0.709281+0.034421 
## [137]    train-rmse:0.624270+0.005855    test-rmse:0.709171+0.034051 
## [138]    train-rmse:0.623842+0.005863    test-rmse:0.708861+0.034015 
## [139]    train-rmse:0.623412+0.005869    test-rmse:0.708493+0.033658 
## [140]    train-rmse:0.622988+0.005875    test-rmse:0.707873+0.033600 
## [141]    train-rmse:0.622573+0.005880    test-rmse:0.707627+0.033774 
## [142]    train-rmse:0.622160+0.005887    test-rmse:0.707588+0.033557 
## [143]    train-rmse:0.621750+0.005896    test-rmse:0.707744+0.033964 
## [144]    train-rmse:0.621344+0.005906    test-rmse:0.707200+0.033363 
## [145]    train-rmse:0.620936+0.005913    test-rmse:0.706826+0.033328 
## [146]    train-rmse:0.620528+0.005918    test-rmse:0.706694+0.033381 
## [147]    train-rmse:0.620127+0.005923    test-rmse:0.706607+0.033719 
## [148]    train-rmse:0.619730+0.005924    test-rmse:0.705990+0.033621 
## [149]    train-rmse:0.619337+0.005929    test-rmse:0.705978+0.033253 
## [150]    train-rmse:0.618946+0.005931    test-rmse:0.705601+0.033399 
## [151]    train-rmse:0.618553+0.005932    test-rmse:0.705779+0.033690 
## [152]    train-rmse:0.618161+0.005935    test-rmse:0.705344+0.033414 
## [153]    train-rmse:0.617769+0.005942    test-rmse:0.704796+0.033742 
## [154]    train-rmse:0.617379+0.005954    test-rmse:0.704981+0.033504 
## [155]    train-rmse:0.616989+0.005969    test-rmse:0.705289+0.033770 
## [156]    train-rmse:0.616613+0.005970    test-rmse:0.705249+0.033825 
## [157]    train-rmse:0.616237+0.005973    test-rmse:0.705212+0.033852 
## [158]    train-rmse:0.615860+0.005976    test-rmse:0.704738+0.033888 
## [159]    train-rmse:0.615490+0.005983    test-rmse:0.704283+0.033853 
## [160]    train-rmse:0.615119+0.005985    test-rmse:0.703776+0.034526 
## [161]    train-rmse:0.614756+0.005987    test-rmse:0.703968+0.034922 
## [162]    train-rmse:0.614391+0.005988    test-rmse:0.703865+0.034844 
## [163]    train-rmse:0.614027+0.005990    test-rmse:0.704042+0.035186 
## [164]    train-rmse:0.613667+0.005996    test-rmse:0.703907+0.034892 
## [165]    train-rmse:0.613309+0.006001    test-rmse:0.703653+0.034522 
## [166]    train-rmse:0.612952+0.006004    test-rmse:0.703373+0.034191 
## [167]    train-rmse:0.612596+0.006007    test-rmse:0.703404+0.034137 
## [168]    train-rmse:0.612242+0.006011    test-rmse:0.702951+0.034048 
## [169]    train-rmse:0.611889+0.006017    test-rmse:0.702617+0.033746 
## [170]    train-rmse:0.611540+0.006023    test-rmse:0.702859+0.033853 
## [171]    train-rmse:0.611194+0.006027    test-rmse:0.702650+0.034637 
## [172]    train-rmse:0.610851+0.006032    test-rmse:0.702260+0.034557 
## [173]    train-rmse:0.610510+0.006037    test-rmse:0.701713+0.034147 
## [174]    train-rmse:0.610170+0.006040    test-rmse:0.701237+0.034363 
## [175]    train-rmse:0.609833+0.006043    test-rmse:0.700950+0.034304 
## [176]    train-rmse:0.609497+0.006046    test-rmse:0.700537+0.033961 
## [177]    train-rmse:0.609156+0.006053    test-rmse:0.700054+0.033993 
## [178]    train-rmse:0.608820+0.006056    test-rmse:0.700414+0.034078 
## [179]    train-rmse:0.608486+0.006056    test-rmse:0.700665+0.034386 
## [180]    train-rmse:0.608155+0.006060    test-rmse:0.700502+0.034342 
## [181]    train-rmse:0.607828+0.006061    test-rmse:0.700312+0.034464 
## [182]    train-rmse:0.607500+0.006066    test-rmse:0.700482+0.033925 
## [183]    train-rmse:0.607175+0.006069    test-rmse:0.700417+0.033451 
## Stopping. Best iteration:
## [177]    train-rmse:0.609156+0.006053    test-rmse:0.700054+0.033993
## 
## 1 
## [1]  train-rmse:1.479685+0.015198    test-rmse:1.481571+0.068923 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.368784+0.013576    test-rmse:1.373915+0.070725 
## [3]  train-rmse:1.271519+0.012119    test-rmse:1.278585+0.071907 
## [4]  train-rmse:1.186707+0.010866    test-rmse:1.197442+0.072464 
## [5]  train-rmse:1.112669+0.009693    test-rmse:1.126912+0.073130 
## [6]  train-rmse:1.048541+0.008693    test-rmse:1.065686+0.071902 
## [7]  train-rmse:0.993063+0.007802    test-rmse:1.013915+0.070707 
## [8]  train-rmse:0.945319+0.007080    test-rmse:0.969956+0.070714 
## [9]  train-rmse:0.904526+0.006539    test-rmse:0.932153+0.069411 
## [10] train-rmse:0.869111+0.006341    test-rmse:0.900941+0.067870 
## [11] train-rmse:0.838590+0.005075    test-rmse:0.872467+0.065895 
## [12] train-rmse:0.812626+0.004419    test-rmse:0.850278+0.064940 
## [13] train-rmse:0.788775+0.004230    test-rmse:0.828218+0.063840 
## [14] train-rmse:0.768873+0.005632    test-rmse:0.810572+0.060179 
## [15] train-rmse:0.751635+0.004593    test-rmse:0.795195+0.059122 
## [16] train-rmse:0.736810+0.004741    test-rmse:0.782233+0.058887 
## [17] train-rmse:0.724463+0.005031    test-rmse:0.771719+0.055766 
## [18] train-rmse:0.713217+0.004218    test-rmse:0.763380+0.053985 
## [19] train-rmse:0.703986+0.004239    test-rmse:0.756479+0.052146 
## [20] train-rmse:0.696030+0.004434    test-rmse:0.751822+0.051691 
## [21] train-rmse:0.687665+0.004464    test-rmse:0.745001+0.051101 
## [22] train-rmse:0.681389+0.004570    test-rmse:0.740452+0.050736 
## [23] train-rmse:0.675757+0.004879    test-rmse:0.735618+0.049451 
## [24] train-rmse:0.669935+0.005557    test-rmse:0.731952+0.047809 
## [25] train-rmse:0.663495+0.005897    test-rmse:0.726743+0.044259 
## [26] train-rmse:0.659495+0.006095    test-rmse:0.724575+0.044400 
## [27] train-rmse:0.655708+0.006191    test-rmse:0.722636+0.042961 
## [28] train-rmse:0.651948+0.005808    test-rmse:0.719426+0.041305 
## [29] train-rmse:0.648651+0.005879    test-rmse:0.718836+0.042053 
## [30] train-rmse:0.645768+0.005917    test-rmse:0.717744+0.040485 
## [31] train-rmse:0.642618+0.005889    test-rmse:0.715631+0.043251 
## [32] train-rmse:0.639725+0.006379    test-rmse:0.714525+0.042347 
## [33] train-rmse:0.637095+0.006460    test-rmse:0.713837+0.042250 
## [34] train-rmse:0.633722+0.005262    test-rmse:0.711510+0.043178 
## [35] train-rmse:0.630515+0.005421    test-rmse:0.710701+0.043988 
## [36] train-rmse:0.627941+0.005980    test-rmse:0.709224+0.043823 
## [37] train-rmse:0.625634+0.005974    test-rmse:0.707634+0.043462 
## [38] train-rmse:0.622975+0.006170    test-rmse:0.705447+0.042076 
## [39] train-rmse:0.620811+0.005793    test-rmse:0.705437+0.042137 
## [40] train-rmse:0.618897+0.005816    test-rmse:0.705663+0.042616 
## [41] train-rmse:0.616968+0.005792    test-rmse:0.704930+0.042363 
## [42] train-rmse:0.615127+0.005855    test-rmse:0.703002+0.041528 
## [43] train-rmse:0.613052+0.006439    test-rmse:0.702140+0.041247 
## [44] train-rmse:0.611206+0.006563    test-rmse:0.700838+0.041632 
## [45] train-rmse:0.608758+0.005709    test-rmse:0.699436+0.042275 
## [46] train-rmse:0.606827+0.005610    test-rmse:0.697797+0.042022 
## [47] train-rmse:0.604899+0.005429    test-rmse:0.697875+0.042931 
## [48] train-rmse:0.602448+0.005968    test-rmse:0.696513+0.042526 
## [49] train-rmse:0.600271+0.005295    test-rmse:0.696222+0.041408 
## [50] train-rmse:0.598930+0.005156    test-rmse:0.695240+0.041237 
## [51] train-rmse:0.597260+0.005042    test-rmse:0.695679+0.041861 
## [52] train-rmse:0.595815+0.004873    test-rmse:0.694606+0.042175 
## [53] train-rmse:0.594154+0.005238    test-rmse:0.693847+0.042341 
## [54] train-rmse:0.592878+0.005266    test-rmse:0.695038+0.043308 
## [55] train-rmse:0.590411+0.005837    test-rmse:0.692556+0.042556 
## [56] train-rmse:0.588545+0.005473    test-rmse:0.692383+0.043072 
## [57] train-rmse:0.587228+0.005420    test-rmse:0.693187+0.042705 
## [58] train-rmse:0.585638+0.005266    test-rmse:0.691957+0.041845 
## [59] train-rmse:0.584207+0.005148    test-rmse:0.691444+0.041913 
## [60] train-rmse:0.581627+0.005732    test-rmse:0.690548+0.040637 
## [61] train-rmse:0.580249+0.005665    test-rmse:0.689931+0.040775 
## [62] train-rmse:0.579085+0.005525    test-rmse:0.691003+0.041760 
## [63] train-rmse:0.577562+0.005822    test-rmse:0.690443+0.042011 
## [64] train-rmse:0.576500+0.005811    test-rmse:0.689992+0.042213 
## [65] train-rmse:0.574739+0.005736    test-rmse:0.688755+0.041788 
## [66] train-rmse:0.573135+0.005536    test-rmse:0.686764+0.042346 
## [67] train-rmse:0.571894+0.005456    test-rmse:0.686153+0.041617 
## [68] train-rmse:0.570594+0.005401    test-rmse:0.685824+0.041809 
## [69] train-rmse:0.569050+0.005100    test-rmse:0.685694+0.042695 
## [70] train-rmse:0.567830+0.005138    test-rmse:0.684980+0.042233 
## [71] train-rmse:0.566356+0.005038    test-rmse:0.684785+0.042945 
## [72] train-rmse:0.565117+0.004931    test-rmse:0.684448+0.042624 
## [73] train-rmse:0.563595+0.005591    test-rmse:0.683854+0.042607 
## [74] train-rmse:0.561807+0.005059    test-rmse:0.682416+0.040978 
## [75] train-rmse:0.560649+0.005119    test-rmse:0.681711+0.041205 
## [76] train-rmse:0.559212+0.005278    test-rmse:0.681095+0.040773 
## [77] train-rmse:0.557608+0.005891    test-rmse:0.679871+0.040887 
## [78] train-rmse:0.556163+0.006084    test-rmse:0.679026+0.041130 
## [79] train-rmse:0.555224+0.006091    test-rmse:0.679127+0.041264 
## [80] train-rmse:0.554122+0.005993    test-rmse:0.678982+0.041206 
## [81] train-rmse:0.552802+0.006230    test-rmse:0.678937+0.041125 
## [82] train-rmse:0.551753+0.006108    test-rmse:0.678485+0.040856 
## [83] train-rmse:0.550676+0.005919    test-rmse:0.678683+0.041149 
## [84] train-rmse:0.549170+0.006357    test-rmse:0.677442+0.041053 
## [85] train-rmse:0.548287+0.006265    test-rmse:0.677568+0.041425 
## [86] train-rmse:0.547315+0.006341    test-rmse:0.676727+0.040999 
## [87] train-rmse:0.544279+0.006445    test-rmse:0.675486+0.041641 
## [88] train-rmse:0.543256+0.006608    test-rmse:0.675112+0.041546 
## [89] train-rmse:0.541538+0.006301    test-rmse:0.673737+0.040416 
## [90] train-rmse:0.540260+0.005965    test-rmse:0.673072+0.040606 
## [91] train-rmse:0.539373+0.005922    test-rmse:0.672809+0.040770 
## [92] train-rmse:0.537319+0.005511    test-rmse:0.671171+0.040944 
## [93] train-rmse:0.535962+0.005515    test-rmse:0.670272+0.042477 
## [94] train-rmse:0.534668+0.005989    test-rmse:0.670049+0.042654 
## [95] train-rmse:0.533317+0.006038    test-rmse:0.669785+0.042191 
## [96] train-rmse:0.531698+0.006708    test-rmse:0.668312+0.042188 
## [97] train-rmse:0.530222+0.006383    test-rmse:0.667974+0.042105 
## [98] train-rmse:0.528753+0.007162    test-rmse:0.667270+0.042411 
## [99] train-rmse:0.527805+0.007143    test-rmse:0.666835+0.042338 
## [100]    train-rmse:0.526733+0.006966    test-rmse:0.667101+0.042505 
## [101]    train-rmse:0.525885+0.006962    test-rmse:0.666749+0.042322 
## [102]    train-rmse:0.524808+0.007086    test-rmse:0.666561+0.042757 
## [103]    train-rmse:0.523959+0.006957    test-rmse:0.666422+0.042348 
## [104]    train-rmse:0.523166+0.006863    test-rmse:0.666415+0.043226 
## [105]    train-rmse:0.521576+0.007778    test-rmse:0.665998+0.043944 
## [106]    train-rmse:0.520693+0.007987    test-rmse:0.665323+0.044402 
## [107]    train-rmse:0.519426+0.008256    test-rmse:0.664415+0.043257 
## [108]    train-rmse:0.518660+0.008221    test-rmse:0.664233+0.043725 
## [109]    train-rmse:0.517952+0.008242    test-rmse:0.663831+0.043468 
## [110]    train-rmse:0.517175+0.008324    test-rmse:0.663849+0.043807 
## [111]    train-rmse:0.516123+0.008468    test-rmse:0.663553+0.043821 
## [112]    train-rmse:0.514707+0.007958    test-rmse:0.662524+0.043162 
## [113]    train-rmse:0.513556+0.008303    test-rmse:0.662315+0.042911 
## [114]    train-rmse:0.512083+0.008783    test-rmse:0.660624+0.042987 
## [115]    train-rmse:0.511138+0.008580    test-rmse:0.660741+0.043369 
## [116]    train-rmse:0.510021+0.008148    test-rmse:0.660690+0.043564 
## [117]    train-rmse:0.509282+0.008076    test-rmse:0.660554+0.043339 
## [118]    train-rmse:0.508083+0.007586    test-rmse:0.659898+0.043652 
## [119]    train-rmse:0.507354+0.007646    test-rmse:0.659690+0.043369 
## [120]    train-rmse:0.506479+0.007749    test-rmse:0.659377+0.044052 
## [121]    train-rmse:0.505273+0.008502    test-rmse:0.658771+0.043561 
## [122]    train-rmse:0.504278+0.008806    test-rmse:0.658345+0.043602 
## [123]    train-rmse:0.503212+0.009160    test-rmse:0.657362+0.043051 
## [124]    train-rmse:0.502539+0.009098    test-rmse:0.656779+0.043460 
## [125]    train-rmse:0.501730+0.009091    test-rmse:0.657062+0.043638 
## [126]    train-rmse:0.500950+0.009458    test-rmse:0.657126+0.043685 
## [127]    train-rmse:0.500216+0.009385    test-rmse:0.656189+0.043615 
## [128]    train-rmse:0.499504+0.009461    test-rmse:0.655550+0.043311 
## [129]    train-rmse:0.498944+0.009493    test-rmse:0.655541+0.043016 
## [130]    train-rmse:0.498269+0.009504    test-rmse:0.655314+0.042664 
## [131]    train-rmse:0.497590+0.009453    test-rmse:0.655457+0.042586 
## [132]    train-rmse:0.496998+0.009372    test-rmse:0.655641+0.042907 
## [133]    train-rmse:0.495880+0.010053    test-rmse:0.655416+0.043538 
## [134]    train-rmse:0.495206+0.009899    test-rmse:0.655252+0.043105 
## [135]    train-rmse:0.494156+0.009992    test-rmse:0.655160+0.043083 
## [136]    train-rmse:0.492903+0.010041    test-rmse:0.654866+0.042756 
## [137]    train-rmse:0.491953+0.009907    test-rmse:0.654925+0.043000 
## [138]    train-rmse:0.491300+0.010074    test-rmse:0.653732+0.042985 
## [139]    train-rmse:0.490705+0.010084    test-rmse:0.653274+0.043318 
## [140]    train-rmse:0.490118+0.010089    test-rmse:0.653523+0.043970 
## [141]    train-rmse:0.489598+0.010108    test-rmse:0.653200+0.044168 
## [142]    train-rmse:0.488706+0.010033    test-rmse:0.652901+0.044181 
## [143]    train-rmse:0.487974+0.010036    test-rmse:0.653127+0.044271 
## [144]    train-rmse:0.486664+0.009806    test-rmse:0.652740+0.045334 
## [145]    train-rmse:0.485873+0.009928    test-rmse:0.652283+0.045464 
## [146]    train-rmse:0.485312+0.010057    test-rmse:0.651544+0.045148 
## [147]    train-rmse:0.484578+0.010102    test-rmse:0.651576+0.044885 
## [148]    train-rmse:0.483749+0.010320    test-rmse:0.651614+0.044717 
## [149]    train-rmse:0.482739+0.009860    test-rmse:0.651418+0.045713 
## [150]    train-rmse:0.482044+0.009785    test-rmse:0.651251+0.045364 
## [151]    train-rmse:0.481251+0.010025    test-rmse:0.651258+0.045192 
## [152]    train-rmse:0.480600+0.009887    test-rmse:0.651318+0.046093 
## [153]    train-rmse:0.479618+0.009359    test-rmse:0.650839+0.044781 
## [154]    train-rmse:0.478809+0.009402    test-rmse:0.651294+0.044695 
## [155]    train-rmse:0.478094+0.009260    test-rmse:0.651501+0.045070 
## [156]    train-rmse:0.476906+0.009066    test-rmse:0.650216+0.045466 
## [157]    train-rmse:0.476328+0.009112    test-rmse:0.649426+0.045354 
## [158]    train-rmse:0.475435+0.009142    test-rmse:0.648846+0.045129 
## [159]    train-rmse:0.474814+0.009033    test-rmse:0.648637+0.045092 
## [160]    train-rmse:0.473888+0.009708    test-rmse:0.648832+0.045415 
## [161]    train-rmse:0.473320+0.009611    test-rmse:0.648329+0.045068 
## [162]    train-rmse:0.472366+0.009366    test-rmse:0.648168+0.045523 
## [163]    train-rmse:0.471899+0.009319    test-rmse:0.648411+0.045623 
## [164]    train-rmse:0.471430+0.009236    test-rmse:0.648151+0.045648 
## [165]    train-rmse:0.470938+0.009222    test-rmse:0.648165+0.045791 
## [166]    train-rmse:0.469760+0.008788    test-rmse:0.648220+0.045919 
## [167]    train-rmse:0.468978+0.008706    test-rmse:0.647638+0.045604 
## [168]    train-rmse:0.468355+0.008866    test-rmse:0.647157+0.045615 
## [169]    train-rmse:0.467734+0.008982    test-rmse:0.646791+0.045920 
## [170]    train-rmse:0.467287+0.008975    test-rmse:0.646740+0.045943 
## [171]    train-rmse:0.466264+0.008889    test-rmse:0.646331+0.045332 
## [172]    train-rmse:0.465578+0.008929    test-rmse:0.645677+0.044928 
## [173]    train-rmse:0.465012+0.008812    test-rmse:0.645393+0.045400 
## [174]    train-rmse:0.464546+0.008809    test-rmse:0.645793+0.045658 
## [175]    train-rmse:0.463995+0.008939    test-rmse:0.645796+0.045704 
## [176]    train-rmse:0.463241+0.009627    test-rmse:0.645591+0.045806 
## [177]    train-rmse:0.462693+0.009576    test-rmse:0.645649+0.045915 
## [178]    train-rmse:0.462158+0.009544    test-rmse:0.645978+0.045880 
## [179]    train-rmse:0.461424+0.009596    test-rmse:0.645569+0.045786 
## Stopping. Best iteration:
## [173]    train-rmse:0.465012+0.008812    test-rmse:0.645393+0.045400
## 
## 2 
## [1]  train-rmse:1.477838+0.015045    test-rmse:1.480565+0.068366 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.364903+0.013404    test-rmse:1.371795+0.068488 
## [3]  train-rmse:1.265014+0.011982    test-rmse:1.276348+0.069526 
## [4]  train-rmse:1.177354+0.010747    test-rmse:1.191375+0.071495 
## [5]  train-rmse:1.100984+0.009428    test-rmse:1.119790+0.071356 
## [6]  train-rmse:1.034451+0.008599    test-rmse:1.058333+0.071913 
## [7]  train-rmse:0.976362+0.007934    test-rmse:1.004832+0.070978 
## [8]  train-rmse:0.926041+0.007733    test-rmse:0.958081+0.069040 
## [9]  train-rmse:0.881791+0.006648    test-rmse:0.916375+0.069758 
## [10] train-rmse:0.843975+0.005932    test-rmse:0.883271+0.067584 
## [11] train-rmse:0.811574+0.005128    test-rmse:0.852873+0.067122 
## [12] train-rmse:0.783457+0.005248    test-rmse:0.829048+0.066565 
## [13] train-rmse:0.758840+0.005392    test-rmse:0.808258+0.063521 
## [14] train-rmse:0.737375+0.005879    test-rmse:0.792207+0.061621 
## [15] train-rmse:0.715929+0.005855    test-rmse:0.773560+0.059155 
## [16] train-rmse:0.698053+0.005477    test-rmse:0.758322+0.057111 
## [17] train-rmse:0.683264+0.006097    test-rmse:0.747651+0.054988 
## [18] train-rmse:0.668553+0.005810    test-rmse:0.734128+0.053599 
## [19] train-rmse:0.657257+0.005901    test-rmse:0.726707+0.053389 
## [20] train-rmse:0.646828+0.005844    test-rmse:0.719769+0.051488 
## [21] train-rmse:0.638128+0.006234    test-rmse:0.714366+0.049038 
## [22] train-rmse:0.630802+0.006419    test-rmse:0.711958+0.049151 
## [23] train-rmse:0.624023+0.006208    test-rmse:0.707576+0.050942 
## [24] train-rmse:0.618048+0.005894    test-rmse:0.703605+0.050406 
## [25] train-rmse:0.611302+0.005872    test-rmse:0.699734+0.049640 
## [26] train-rmse:0.605721+0.005898    test-rmse:0.696318+0.048031 
## [27] train-rmse:0.601348+0.005857    test-rmse:0.694151+0.048902 
## [28] train-rmse:0.596772+0.006389    test-rmse:0.693419+0.049521 
## [29] train-rmse:0.592168+0.005906    test-rmse:0.692125+0.050358 
## [30] train-rmse:0.588150+0.005692    test-rmse:0.690435+0.049975 
## [31] train-rmse:0.584106+0.005951    test-rmse:0.688250+0.050654 
## [32] train-rmse:0.579581+0.005308    test-rmse:0.685638+0.048511 
## [33] train-rmse:0.576203+0.005299    test-rmse:0.684903+0.048168 
## [34] train-rmse:0.573212+0.005407    test-rmse:0.684676+0.049255 
## [35] train-rmse:0.569559+0.005713    test-rmse:0.680819+0.048294 
## [36] train-rmse:0.566014+0.006511    test-rmse:0.680832+0.048990 
## [37] train-rmse:0.562080+0.004961    test-rmse:0.680347+0.050207 
## [38] train-rmse:0.558621+0.005061    test-rmse:0.678762+0.050644 
## [39] train-rmse:0.555532+0.005336    test-rmse:0.676627+0.049211 
## [40] train-rmse:0.553222+0.005419    test-rmse:0.676433+0.049517 
## [41] train-rmse:0.550705+0.005214    test-rmse:0.675986+0.050581 
## [42] train-rmse:0.548143+0.005746    test-rmse:0.673969+0.050364 
## [43] train-rmse:0.545470+0.005831    test-rmse:0.672619+0.050920 
## [44] train-rmse:0.542050+0.005515    test-rmse:0.669949+0.049794 
## [45] train-rmse:0.538560+0.005520    test-rmse:0.668560+0.049798 
## [46] train-rmse:0.535871+0.004966    test-rmse:0.668439+0.050912 
## [47] train-rmse:0.533827+0.004702    test-rmse:0.667715+0.051337 
## [48] train-rmse:0.530755+0.005345    test-rmse:0.666967+0.051185 
## [49] train-rmse:0.528235+0.005001    test-rmse:0.665798+0.052953 
## [50] train-rmse:0.525626+0.005327    test-rmse:0.664087+0.053450 
## [51] train-rmse:0.522598+0.006282    test-rmse:0.663531+0.051770 
## [52] train-rmse:0.519590+0.005646    test-rmse:0.661914+0.051884 
## [53] train-rmse:0.517697+0.005373    test-rmse:0.662267+0.052038 
## [54] train-rmse:0.515817+0.005406    test-rmse:0.661613+0.051845 
## [55] train-rmse:0.513900+0.005781    test-rmse:0.660838+0.052478 
## [56] train-rmse:0.511312+0.006548    test-rmse:0.659271+0.051585 
## [57] train-rmse:0.509464+0.006714    test-rmse:0.658629+0.052303 
## [58] train-rmse:0.506023+0.006224    test-rmse:0.655817+0.050722 
## [59] train-rmse:0.504020+0.006681    test-rmse:0.655636+0.050481 
## [60] train-rmse:0.501551+0.007075    test-rmse:0.655333+0.050876 
## [61] train-rmse:0.498255+0.006677    test-rmse:0.652690+0.050418 
## [62] train-rmse:0.496598+0.006497    test-rmse:0.652432+0.051068 
## [63] train-rmse:0.494658+0.006406    test-rmse:0.652531+0.050945 
## [64] train-rmse:0.493007+0.006402    test-rmse:0.651927+0.052677 
## [65] train-rmse:0.490914+0.006597    test-rmse:0.650198+0.051818 
## [66] train-rmse:0.488631+0.006993    test-rmse:0.649357+0.051146 
## [67] train-rmse:0.485998+0.007634    test-rmse:0.648951+0.051329 
## [68] train-rmse:0.483665+0.005818    test-rmse:0.648708+0.052674 
## [69] train-rmse:0.482441+0.005853    test-rmse:0.648836+0.052809 
## [70] train-rmse:0.480341+0.005861    test-rmse:0.649174+0.052969 
## [71] train-rmse:0.478063+0.005984    test-rmse:0.647540+0.053460 
## [72] train-rmse:0.476437+0.005753    test-rmse:0.646747+0.053118 
## [73] train-rmse:0.474509+0.006078    test-rmse:0.644981+0.053116 
## [74] train-rmse:0.472742+0.006283    test-rmse:0.644996+0.052870 
## [75] train-rmse:0.471407+0.006178    test-rmse:0.644130+0.053282 
## [76] train-rmse:0.469006+0.005405    test-rmse:0.644632+0.054617 
## [77] train-rmse:0.467408+0.005093    test-rmse:0.645057+0.054101 
## [78] train-rmse:0.465721+0.005241    test-rmse:0.646008+0.054408 
## [79] train-rmse:0.463887+0.005582    test-rmse:0.644432+0.054389 
## [80] train-rmse:0.461471+0.006215    test-rmse:0.642628+0.054409 
## [81] train-rmse:0.460173+0.006333    test-rmse:0.642815+0.054967 
## [82] train-rmse:0.457255+0.006792    test-rmse:0.641243+0.053549 
## [83] train-rmse:0.455639+0.006993    test-rmse:0.641279+0.053664 
## [84] train-rmse:0.454375+0.006624    test-rmse:0.640792+0.053669 
## [85] train-rmse:0.452954+0.006765    test-rmse:0.640676+0.054000 
## [86] train-rmse:0.451389+0.006845    test-rmse:0.640459+0.054582 
## [87] train-rmse:0.449280+0.006186    test-rmse:0.639702+0.053725 
## [88] train-rmse:0.447209+0.006557    test-rmse:0.639360+0.053178 
## [89] train-rmse:0.445522+0.006765    test-rmse:0.638228+0.052696 
## [90] train-rmse:0.444103+0.006689    test-rmse:0.637463+0.053207 
## [91] train-rmse:0.442953+0.006878    test-rmse:0.637607+0.053107 
## [92] train-rmse:0.441539+0.006950    test-rmse:0.637744+0.052293 
## [93] train-rmse:0.439975+0.006478    test-rmse:0.637484+0.052097 
## [94] train-rmse:0.438594+0.006843    test-rmse:0.636601+0.052068 
## [95] train-rmse:0.436685+0.007311    test-rmse:0.636623+0.052108 
## [96] train-rmse:0.434753+0.007823    test-rmse:0.636621+0.052017 
## [97] train-rmse:0.433436+0.007634    test-rmse:0.636171+0.052447 
## [98] train-rmse:0.431503+0.008256    test-rmse:0.636132+0.053228 
## [99] train-rmse:0.430409+0.008546    test-rmse:0.636477+0.053315 
## [100]    train-rmse:0.428432+0.008765    test-rmse:0.635593+0.052511 
## [101]    train-rmse:0.427229+0.008826    test-rmse:0.635763+0.052483 
## [102]    train-rmse:0.425522+0.008460    test-rmse:0.635205+0.052034 
## [103]    train-rmse:0.424082+0.008694    test-rmse:0.634499+0.052661 
## [104]    train-rmse:0.423142+0.008917    test-rmse:0.634288+0.052470 
## [105]    train-rmse:0.422069+0.009041    test-rmse:0.633968+0.052130 
## [106]    train-rmse:0.420574+0.009304    test-rmse:0.634408+0.052261 
## [107]    train-rmse:0.419331+0.009265    test-rmse:0.634125+0.052079 
## [108]    train-rmse:0.417963+0.009257    test-rmse:0.634180+0.052512 
## [109]    train-rmse:0.417083+0.009310    test-rmse:0.634338+0.052488 
## [110]    train-rmse:0.415544+0.009574    test-rmse:0.633727+0.052889 
## [111]    train-rmse:0.414562+0.009440    test-rmse:0.633034+0.052484 
## [112]    train-rmse:0.413225+0.009575    test-rmse:0.633658+0.053255 
## [113]    train-rmse:0.412020+0.009922    test-rmse:0.633337+0.052988 
## [114]    train-rmse:0.410491+0.010690    test-rmse:0.633141+0.052367 
## [115]    train-rmse:0.408966+0.009649    test-rmse:0.633183+0.053131 
## [116]    train-rmse:0.407324+0.009570    test-rmse:0.632895+0.052474 
## [117]    train-rmse:0.406322+0.009391    test-rmse:0.632539+0.053002 
## [118]    train-rmse:0.404979+0.009633    test-rmse:0.632326+0.053487 
## [119]    train-rmse:0.403801+0.009804    test-rmse:0.632403+0.053602 
## [120]    train-rmse:0.402849+0.009461    test-rmse:0.632925+0.053311 
## [121]    train-rmse:0.401421+0.009837    test-rmse:0.632356+0.052858 
## [122]    train-rmse:0.400359+0.009470    test-rmse:0.631898+0.052762 
## [123]    train-rmse:0.399273+0.009890    test-rmse:0.631847+0.052698 
## [124]    train-rmse:0.398411+0.010032    test-rmse:0.631954+0.052700 
## [125]    train-rmse:0.397405+0.009803    test-rmse:0.632101+0.052764 
## [126]    train-rmse:0.396627+0.010043    test-rmse:0.632071+0.052943 
## [127]    train-rmse:0.394562+0.009467    test-rmse:0.631750+0.052879 
## [128]    train-rmse:0.392833+0.009740    test-rmse:0.630881+0.053012 
## [129]    train-rmse:0.391762+0.010035    test-rmse:0.630827+0.052888 
## [130]    train-rmse:0.390541+0.010104    test-rmse:0.630716+0.053245 
## [131]    train-rmse:0.389903+0.010240    test-rmse:0.630643+0.053131 
## [132]    train-rmse:0.388500+0.010490    test-rmse:0.631418+0.053619 
## [133]    train-rmse:0.387785+0.010727    test-rmse:0.631686+0.053704 
## [134]    train-rmse:0.387039+0.010816    test-rmse:0.631294+0.053625 
## [135]    train-rmse:0.385732+0.011357    test-rmse:0.630248+0.053535 
## [136]    train-rmse:0.384484+0.012233    test-rmse:0.629771+0.053251 
## [137]    train-rmse:0.383638+0.012340    test-rmse:0.629502+0.053147 
## [138]    train-rmse:0.382964+0.012500    test-rmse:0.629991+0.053529 
## [139]    train-rmse:0.381445+0.012958    test-rmse:0.629038+0.052769 
## [140]    train-rmse:0.379622+0.013446    test-rmse:0.628603+0.053777 
## [141]    train-rmse:0.378179+0.013432    test-rmse:0.628298+0.053505 
## [142]    train-rmse:0.377117+0.013378    test-rmse:0.628219+0.053628 
## [143]    train-rmse:0.376100+0.013276    test-rmse:0.628518+0.054543 
## [144]    train-rmse:0.375061+0.013224    test-rmse:0.628470+0.054754 
## [145]    train-rmse:0.373945+0.013627    test-rmse:0.627963+0.054294 
## [146]    train-rmse:0.372613+0.013494    test-rmse:0.627847+0.053649 
## [147]    train-rmse:0.371672+0.013401    test-rmse:0.626759+0.053120 
## [148]    train-rmse:0.370683+0.013838    test-rmse:0.626872+0.052940 
## [149]    train-rmse:0.369608+0.013542    test-rmse:0.626697+0.053227 
## [150]    train-rmse:0.368524+0.013723    test-rmse:0.626674+0.053131 
## [151]    train-rmse:0.367614+0.013621    test-rmse:0.626974+0.053129 
## [152]    train-rmse:0.366073+0.013450    test-rmse:0.626799+0.053338 
## [153]    train-rmse:0.364875+0.013684    test-rmse:0.627311+0.053740 
## [154]    train-rmse:0.364236+0.013765    test-rmse:0.627454+0.053935 
## [155]    train-rmse:0.363613+0.013567    test-rmse:0.627293+0.054581 
## [156]    train-rmse:0.362591+0.013499    test-rmse:0.626845+0.054785 
## Stopping. Best iteration:
## [150]    train-rmse:0.368524+0.013723    test-rmse:0.626674+0.053131
## 
## 3 
## [1]  train-rmse:1.475959+0.015073    test-rmse:1.481760+0.068506 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.360566+0.013445    test-rmse:1.371688+0.068621 
## [3]  train-rmse:1.258160+0.011471    test-rmse:1.274546+0.069832 
## [4]  train-rmse:1.167672+0.009986    test-rmse:1.189086+0.070415 
## [5]  train-rmse:1.088055+0.008741    test-rmse:1.117199+0.071140 
## [6]  train-rmse:1.018042+0.007774    test-rmse:1.052780+0.072685 
## [7]  train-rmse:0.957543+0.006860    test-rmse:0.999625+0.072386 
## [8]  train-rmse:0.903907+0.005862    test-rmse:0.953168+0.071655 
## [9]  train-rmse:0.858069+0.005441    test-rmse:0.911469+0.072088 
## [10] train-rmse:0.818247+0.005144    test-rmse:0.878222+0.070380 
## [11] train-rmse:0.783059+0.005396    test-rmse:0.846990+0.068551 
## [12] train-rmse:0.753221+0.005337    test-rmse:0.822690+0.068414 
## [13] train-rmse:0.726923+0.004495    test-rmse:0.801361+0.067490 
## [14] train-rmse:0.704042+0.004318    test-rmse:0.784560+0.066335 
## [15] train-rmse:0.684053+0.004580    test-rmse:0.768247+0.064906 
## [16] train-rmse:0.666253+0.004157    test-rmse:0.755479+0.063359 
## [17] train-rmse:0.646167+0.005146    test-rmse:0.741225+0.060119 
## [18] train-rmse:0.630331+0.008077    test-rmse:0.731875+0.060923 
## [19] train-rmse:0.616810+0.007159    test-rmse:0.724018+0.059981 
## [20] train-rmse:0.603682+0.005583    test-rmse:0.716489+0.056668 
## [21] train-rmse:0.594255+0.005403    test-rmse:0.710505+0.054733 
## [22] train-rmse:0.584600+0.005897    test-rmse:0.705552+0.056834 
## [23] train-rmse:0.576323+0.006064    test-rmse:0.701968+0.058514 
## [24] train-rmse:0.567968+0.006684    test-rmse:0.696982+0.056485 
## [25] train-rmse:0.561304+0.007048    test-rmse:0.694540+0.056842 
## [26] train-rmse:0.554723+0.006121    test-rmse:0.691036+0.056072 
## [27] train-rmse:0.549153+0.007417    test-rmse:0.687435+0.057259 
## [28] train-rmse:0.544584+0.007278    test-rmse:0.685768+0.057106 
## [29] train-rmse:0.538853+0.006701    test-rmse:0.684325+0.057375 
## [30] train-rmse:0.533808+0.006674    test-rmse:0.681166+0.058042 
## [31] train-rmse:0.529030+0.007276    test-rmse:0.678057+0.056681 
## [32] train-rmse:0.523183+0.006724    test-rmse:0.675760+0.057107 
## [33] train-rmse:0.518801+0.006695    test-rmse:0.675568+0.057697 
## [34] train-rmse:0.515519+0.007135    test-rmse:0.675111+0.057473 
## [35] train-rmse:0.511278+0.006679    test-rmse:0.674230+0.057918 
## [36] train-rmse:0.507416+0.006344    test-rmse:0.672689+0.057677 
## [37] train-rmse:0.502076+0.004866    test-rmse:0.668117+0.057571 
## [38] train-rmse:0.497667+0.005705    test-rmse:0.667903+0.058450 
## [39] train-rmse:0.494229+0.005709    test-rmse:0.666881+0.058683 
## [40] train-rmse:0.489933+0.005467    test-rmse:0.665418+0.058960 
## [41] train-rmse:0.486773+0.005519    test-rmse:0.663938+0.057989 
## [42] train-rmse:0.484135+0.005652    test-rmse:0.663306+0.059064 
## [43] train-rmse:0.480899+0.005983    test-rmse:0.661854+0.060030 
## [44] train-rmse:0.478054+0.006203    test-rmse:0.660646+0.061527 
## [45] train-rmse:0.475094+0.006386    test-rmse:0.660815+0.061262 
## [46] train-rmse:0.471731+0.007257    test-rmse:0.661509+0.060545 
## [47] train-rmse:0.468694+0.007174    test-rmse:0.660241+0.060699 
## [48] train-rmse:0.465598+0.007241    test-rmse:0.659360+0.062012 
## [49] train-rmse:0.462781+0.008021    test-rmse:0.659075+0.061762 
## [50] train-rmse:0.459902+0.008308    test-rmse:0.659961+0.062036 
## [51] train-rmse:0.455672+0.008693    test-rmse:0.658501+0.061751 
## [52] train-rmse:0.452587+0.009380    test-rmse:0.657564+0.062815 
## [53] train-rmse:0.450088+0.009558    test-rmse:0.657055+0.063331 
## [54] train-rmse:0.446767+0.009332    test-rmse:0.655675+0.063281 
## [55] train-rmse:0.443124+0.008694    test-rmse:0.654056+0.063643 
## [56] train-rmse:0.440973+0.008967    test-rmse:0.654469+0.064621 
## [57] train-rmse:0.436852+0.007709    test-rmse:0.651911+0.062419 
## [58] train-rmse:0.433745+0.007157    test-rmse:0.651801+0.061585 
## [59] train-rmse:0.431487+0.007027    test-rmse:0.651105+0.062091 
## [60] train-rmse:0.428683+0.007586    test-rmse:0.650798+0.062410 
## [61] train-rmse:0.425522+0.008853    test-rmse:0.649916+0.061871 
## [62] train-rmse:0.423309+0.009400    test-rmse:0.649277+0.062127 
## [63] train-rmse:0.420858+0.009720    test-rmse:0.647273+0.061145 
## [64] train-rmse:0.418603+0.010157    test-rmse:0.646836+0.062185 
## [65] train-rmse:0.416515+0.009799    test-rmse:0.645996+0.061964 
## [66] train-rmse:0.414602+0.010070    test-rmse:0.646426+0.060697 
## [67] train-rmse:0.412673+0.009836    test-rmse:0.646385+0.061447 
## [68] train-rmse:0.410940+0.009679    test-rmse:0.645940+0.062076 
## [69] train-rmse:0.408809+0.010645    test-rmse:0.646019+0.062777 
## [70] train-rmse:0.406084+0.010118    test-rmse:0.645370+0.062619 
## [71] train-rmse:0.403943+0.009260    test-rmse:0.643260+0.062176 
## [72] train-rmse:0.401235+0.010627    test-rmse:0.643081+0.062387 
## [73] train-rmse:0.399367+0.010866    test-rmse:0.642930+0.062378 
## [74] train-rmse:0.396337+0.011540    test-rmse:0.641883+0.061559 
## [75] train-rmse:0.394715+0.011429    test-rmse:0.642028+0.062292 
## [76] train-rmse:0.392428+0.012378    test-rmse:0.641138+0.061770 
## [77] train-rmse:0.389528+0.012080    test-rmse:0.638961+0.061284 
## [78] train-rmse:0.387774+0.012248    test-rmse:0.637809+0.060869 
## [79] train-rmse:0.386078+0.012175    test-rmse:0.637786+0.060764 
## [80] train-rmse:0.383882+0.012701    test-rmse:0.637453+0.060674 
## [81] train-rmse:0.381525+0.013282    test-rmse:0.637243+0.060623 
## [82] train-rmse:0.379682+0.013605    test-rmse:0.637064+0.060712 
## [83] train-rmse:0.378055+0.013696    test-rmse:0.637619+0.061328 
## [84] train-rmse:0.375908+0.013726    test-rmse:0.637294+0.060789 
## [85] train-rmse:0.373389+0.014300    test-rmse:0.637040+0.060914 
## [86] train-rmse:0.371371+0.014864    test-rmse:0.637011+0.061154 
## [87] train-rmse:0.369395+0.014759    test-rmse:0.636675+0.061397 
## [88] train-rmse:0.367695+0.014068    test-rmse:0.635793+0.062613 
## [89] train-rmse:0.365843+0.013725    test-rmse:0.636182+0.062480 
## [90] train-rmse:0.364401+0.013654    test-rmse:0.635810+0.062114 
## [91] train-rmse:0.362571+0.014076    test-rmse:0.634949+0.061992 
## [92] train-rmse:0.360601+0.013998    test-rmse:0.633792+0.061684 
## [93] train-rmse:0.358773+0.013933    test-rmse:0.633982+0.062058 
## [94] train-rmse:0.357108+0.013591    test-rmse:0.633980+0.062407 
## [95] train-rmse:0.354548+0.012646    test-rmse:0.633440+0.062558 
## [96] train-rmse:0.352061+0.012143    test-rmse:0.633091+0.062736 
## [97] train-rmse:0.350232+0.012522    test-rmse:0.633044+0.062665 
## [98] train-rmse:0.348588+0.012077    test-rmse:0.632677+0.062699 
## [99] train-rmse:0.347103+0.012408    test-rmse:0.632321+0.062169 
## [100]    train-rmse:0.345612+0.012285    test-rmse:0.632751+0.062981 
## [101]    train-rmse:0.343224+0.012430    test-rmse:0.632022+0.062067 
## [102]    train-rmse:0.341499+0.012400    test-rmse:0.631803+0.061378 
## [103]    train-rmse:0.340053+0.011977    test-rmse:0.631187+0.061041 
## [104]    train-rmse:0.338050+0.011839    test-rmse:0.632093+0.061184 
## [105]    train-rmse:0.336593+0.012014    test-rmse:0.632072+0.061228 
## [106]    train-rmse:0.334359+0.012288    test-rmse:0.630813+0.061222 
## [107]    train-rmse:0.332774+0.012152    test-rmse:0.629771+0.060812 
## [108]    train-rmse:0.330914+0.011734    test-rmse:0.629994+0.060379 
## [109]    train-rmse:0.329610+0.011511    test-rmse:0.629309+0.060025 
## [110]    train-rmse:0.328148+0.012403    test-rmse:0.629070+0.059391 
## [111]    train-rmse:0.326500+0.012999    test-rmse:0.629632+0.059453 
## [112]    train-rmse:0.324861+0.012768    test-rmse:0.629788+0.059267 
## [113]    train-rmse:0.322667+0.012556    test-rmse:0.629483+0.060265 
## [114]    train-rmse:0.321318+0.012356    test-rmse:0.629254+0.060195 
## [115]    train-rmse:0.320332+0.012462    test-rmse:0.629695+0.060479 
## [116]    train-rmse:0.318571+0.012549    test-rmse:0.629798+0.060356 
## Stopping. Best iteration:
## [110]    train-rmse:0.328148+0.012403    test-rmse:0.629070+0.059391
## 
## 4 
## [1]  train-rmse:1.474693+0.015160    test-rmse:1.479880+0.069533 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.357830+0.013637    test-rmse:1.371845+0.070502 
## [3]  train-rmse:1.254204+0.012017    test-rmse:1.271961+0.071076 
## [4]  train-rmse:1.161403+0.010222    test-rmse:1.186564+0.070605 
## [5]  train-rmse:1.079184+0.008756    test-rmse:1.112165+0.072367 
## [6]  train-rmse:1.006218+0.007643    test-rmse:1.046624+0.070856 
## [7]  train-rmse:0.942274+0.006419    test-rmse:0.988097+0.071675 
## [8]  train-rmse:0.886460+0.006317    test-rmse:0.938545+0.070425 
## [9]  train-rmse:0.836848+0.006308    test-rmse:0.898174+0.069954 
## [10] train-rmse:0.793648+0.006201    test-rmse:0.862833+0.069420 
## [11] train-rmse:0.756745+0.006443    test-rmse:0.832617+0.067531 
## [12] train-rmse:0.724249+0.006720    test-rmse:0.807463+0.065972 
## [13] train-rmse:0.696292+0.006917    test-rmse:0.785355+0.063938 
## [14] train-rmse:0.671562+0.007574    test-rmse:0.768567+0.063315 
## [15] train-rmse:0.649342+0.007350    test-rmse:0.753828+0.060878 
## [16] train-rmse:0.630873+0.007964    test-rmse:0.741109+0.059899 
## [17] train-rmse:0.613419+0.007159    test-rmse:0.729103+0.056637 
## [18] train-rmse:0.595064+0.010531    test-rmse:0.720988+0.053809 
## [19] train-rmse:0.579941+0.009089    test-rmse:0.712062+0.051492 
## [20] train-rmse:0.564439+0.011210    test-rmse:0.701710+0.047749 
## [21] train-rmse:0.553980+0.010990    test-rmse:0.696224+0.045784 
## [22] train-rmse:0.544206+0.011320    test-rmse:0.690232+0.044731 
## [23] train-rmse:0.532686+0.010764    test-rmse:0.685838+0.045226 
## [24] train-rmse:0.522102+0.013094    test-rmse:0.681571+0.044415 
## [25] train-rmse:0.514768+0.013219    test-rmse:0.678887+0.044906 
## [26] train-rmse:0.506580+0.013242    test-rmse:0.677020+0.045641 
## [27] train-rmse:0.500492+0.013384    test-rmse:0.674445+0.045466 
## [28] train-rmse:0.493968+0.014223    test-rmse:0.673669+0.046140 
## [29] train-rmse:0.486169+0.012435    test-rmse:0.669909+0.046827 
## [30] train-rmse:0.480631+0.012752    test-rmse:0.667619+0.047927 
## [31] train-rmse:0.475167+0.013580    test-rmse:0.666130+0.049193 
## [32] train-rmse:0.469095+0.014168    test-rmse:0.664240+0.047846 
## [33] train-rmse:0.463247+0.015368    test-rmse:0.661756+0.047239 
## [34] train-rmse:0.458392+0.013157    test-rmse:0.658130+0.047405 
## [35] train-rmse:0.454092+0.012963    test-rmse:0.658805+0.048297 
## [36] train-rmse:0.449799+0.013553    test-rmse:0.657968+0.048789 
## [37] train-rmse:0.445349+0.012075    test-rmse:0.656415+0.050090 
## [38] train-rmse:0.442199+0.011778    test-rmse:0.656543+0.050597 
## [39] train-rmse:0.437175+0.013068    test-rmse:0.654093+0.050428 
## [40] train-rmse:0.433505+0.012628    test-rmse:0.653978+0.051874 
## [41] train-rmse:0.429117+0.013874    test-rmse:0.653670+0.051600 
## [42] train-rmse:0.422294+0.013590    test-rmse:0.651807+0.051323 
## [43] train-rmse:0.418518+0.013507    test-rmse:0.651561+0.051755 
## [44] train-rmse:0.415229+0.014235    test-rmse:0.650525+0.053440 
## [45] train-rmse:0.412502+0.014480    test-rmse:0.650332+0.054094 
## [46] train-rmse:0.408379+0.014705    test-rmse:0.650027+0.053836 
## [47] train-rmse:0.405146+0.014817    test-rmse:0.650818+0.054901 
## [48] train-rmse:0.401346+0.013425    test-rmse:0.649355+0.054043 
## [49] train-rmse:0.397211+0.012796    test-rmse:0.645272+0.053931 
## [50] train-rmse:0.393192+0.013560    test-rmse:0.645645+0.054344 
## [51] train-rmse:0.389494+0.014467    test-rmse:0.644293+0.054690 
## [52] train-rmse:0.386472+0.015253    test-rmse:0.644004+0.054713 
## [53] train-rmse:0.383240+0.015968    test-rmse:0.642504+0.055065 
## [54] train-rmse:0.379676+0.015920    test-rmse:0.641823+0.055601 
## [55] train-rmse:0.376556+0.016498    test-rmse:0.642416+0.055434 
## [56] train-rmse:0.373049+0.015654    test-rmse:0.641968+0.058153 
## [57] train-rmse:0.370213+0.016083    test-rmse:0.641628+0.058303 
## [58] train-rmse:0.367422+0.016258    test-rmse:0.641430+0.058588 
## [59] train-rmse:0.365043+0.015736    test-rmse:0.641606+0.058649 
## [60] train-rmse:0.361732+0.015273    test-rmse:0.640682+0.057721 
## [61] train-rmse:0.358375+0.014903    test-rmse:0.640683+0.058290 
## [62] train-rmse:0.355514+0.015148    test-rmse:0.640535+0.058159 
## [63] train-rmse:0.352943+0.015344    test-rmse:0.640796+0.058632 
## [64] train-rmse:0.349059+0.016469    test-rmse:0.639910+0.058500 
## [65] train-rmse:0.346367+0.017292    test-rmse:0.640090+0.058664 
## [66] train-rmse:0.343646+0.016907    test-rmse:0.640182+0.059232 
## [67] train-rmse:0.340468+0.016735    test-rmse:0.637817+0.058777 
## [68] train-rmse:0.337354+0.015809    test-rmse:0.636331+0.058733 
## [69] train-rmse:0.335287+0.016019    test-rmse:0.636163+0.059540 
## [70] train-rmse:0.333255+0.015480    test-rmse:0.636247+0.059704 
## [71] train-rmse:0.331398+0.015357    test-rmse:0.636614+0.059880 
## [72] train-rmse:0.328666+0.014955    test-rmse:0.634958+0.059058 
## [73] train-rmse:0.326740+0.015279    test-rmse:0.634323+0.059114 
## [74] train-rmse:0.324272+0.015420    test-rmse:0.634906+0.059658 
## [75] train-rmse:0.321264+0.014944    test-rmse:0.635521+0.059322 
## [76] train-rmse:0.319299+0.015211    test-rmse:0.634987+0.058854 
## [77] train-rmse:0.317351+0.015401    test-rmse:0.635149+0.058373 
## [78] train-rmse:0.314885+0.015232    test-rmse:0.634827+0.058716 
## [79] train-rmse:0.312805+0.015589    test-rmse:0.634442+0.058430 
## Stopping. Best iteration:
## [73] train-rmse:0.326740+0.015279    test-rmse:0.634323+0.059114
## 
## 5 
## [1]  train-rmse:1.473412+0.014950    test-rmse:1.480683+0.070400 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.355648+0.013614    test-rmse:1.371093+0.072067 
## [3]  train-rmse:1.250746+0.011873    test-rmse:1.272139+0.071626 
## [4]  train-rmse:1.156885+0.010454    test-rmse:1.183928+0.071858 
## [5]  train-rmse:1.072888+0.008526    test-rmse:1.108442+0.073613 
## [6]  train-rmse:0.997724+0.007514    test-rmse:1.041720+0.073965 
## [7]  train-rmse:0.931169+0.006927    test-rmse:0.985631+0.072026 
## [8]  train-rmse:0.872457+0.006433    test-rmse:0.936231+0.070728 
## [9]  train-rmse:0.820188+0.005368    test-rmse:0.894130+0.070628 
## [10] train-rmse:0.774216+0.005171    test-rmse:0.856151+0.070249 
## [11] train-rmse:0.733701+0.005324    test-rmse:0.825362+0.072126 
## [12] train-rmse:0.698553+0.005975    test-rmse:0.799095+0.070839 
## [13] train-rmse:0.667706+0.007281    test-rmse:0.777789+0.068324 
## [14] train-rmse:0.641024+0.007878    test-rmse:0.757834+0.066357 
## [15] train-rmse:0.618253+0.008461    test-rmse:0.743060+0.064791 
## [16] train-rmse:0.597179+0.008879    test-rmse:0.728071+0.062603 
## [17] train-rmse:0.577592+0.008422    test-rmse:0.717502+0.061897 
## [18] train-rmse:0.562023+0.007900    test-rmse:0.706822+0.060496 
## [19] train-rmse:0.543089+0.010186    test-rmse:0.699328+0.059031 
## [20] train-rmse:0.528861+0.009791    test-rmse:0.691194+0.057416 
## [21] train-rmse:0.513387+0.012689    test-rmse:0.686657+0.055096 
## [22] train-rmse:0.500587+0.014200    test-rmse:0.681941+0.053083 
## [23] train-rmse:0.487327+0.012249    test-rmse:0.676330+0.052356 
## [24] train-rmse:0.477682+0.012781    test-rmse:0.672960+0.052602 
## [25] train-rmse:0.467927+0.010941    test-rmse:0.670545+0.051928 
## [26] train-rmse:0.459454+0.011704    test-rmse:0.668921+0.052407 
## [27] train-rmse:0.451665+0.013252    test-rmse:0.665778+0.053055 
## [28] train-rmse:0.444344+0.014015    test-rmse:0.664242+0.051959 
## [29] train-rmse:0.437548+0.015592    test-rmse:0.663451+0.051334 
## [30] train-rmse:0.431002+0.015859    test-rmse:0.661879+0.050349 
## [31] train-rmse:0.425570+0.015809    test-rmse:0.661203+0.049633 
## [32] train-rmse:0.419088+0.016304    test-rmse:0.660515+0.049274 
## [33] train-rmse:0.414462+0.016578    test-rmse:0.659466+0.050997 
## [34] train-rmse:0.408800+0.016460    test-rmse:0.659448+0.051039 
## [35] train-rmse:0.403598+0.016294    test-rmse:0.657592+0.053137 
## [36] train-rmse:0.397840+0.016532    test-rmse:0.656874+0.054649 
## [37] train-rmse:0.393562+0.016345    test-rmse:0.655950+0.053466 
## [38] train-rmse:0.388609+0.016197    test-rmse:0.652695+0.053064 
## [39] train-rmse:0.383666+0.016190    test-rmse:0.651695+0.053010 
## [40] train-rmse:0.379574+0.016847    test-rmse:0.651619+0.053745 
## [41] train-rmse:0.375947+0.016838    test-rmse:0.651660+0.054407 
## [42] train-rmse:0.371198+0.015601    test-rmse:0.649622+0.054908 
## [43] train-rmse:0.366610+0.016465    test-rmse:0.648306+0.055682 
## [44] train-rmse:0.363137+0.016676    test-rmse:0.648143+0.054816 
## [45] train-rmse:0.358855+0.016641    test-rmse:0.647553+0.054846 
## [46] train-rmse:0.355750+0.015630    test-rmse:0.647746+0.055223 
## [47] train-rmse:0.350783+0.016252    test-rmse:0.645926+0.054299 
## [48] train-rmse:0.347453+0.016419    test-rmse:0.646042+0.054300 
## [49] train-rmse:0.341930+0.017362    test-rmse:0.645641+0.055461 
## [50] train-rmse:0.339186+0.017322    test-rmse:0.645354+0.055427 
## [51] train-rmse:0.336133+0.016410    test-rmse:0.644872+0.055397 
## [52] train-rmse:0.331635+0.017437    test-rmse:0.644672+0.056095 
## [53] train-rmse:0.328450+0.017428    test-rmse:0.645519+0.057106 
## [54] train-rmse:0.325051+0.017936    test-rmse:0.645517+0.057448 
## [55] train-rmse:0.320961+0.016590    test-rmse:0.643514+0.056765 
## [56] train-rmse:0.318035+0.016364    test-rmse:0.643003+0.056671 
## [57] train-rmse:0.313882+0.016675    test-rmse:0.643443+0.055793 
## [58] train-rmse:0.310412+0.016997    test-rmse:0.642065+0.055961 
## [59] train-rmse:0.306545+0.015801    test-rmse:0.641885+0.055963 
## [60] train-rmse:0.304383+0.015903    test-rmse:0.641727+0.056220 
## [61] train-rmse:0.300758+0.016191    test-rmse:0.640614+0.056414 
## [62] train-rmse:0.298002+0.016650    test-rmse:0.641313+0.057264 
## [63] train-rmse:0.294839+0.016384    test-rmse:0.641226+0.057320 
## [64] train-rmse:0.292526+0.016378    test-rmse:0.641030+0.057396 
## [65] train-rmse:0.289378+0.017013    test-rmse:0.640919+0.057793 
## [66] train-rmse:0.284635+0.016006    test-rmse:0.638189+0.058289 
## [67] train-rmse:0.282340+0.015626    test-rmse:0.637993+0.058004 
## [68] train-rmse:0.280238+0.015565    test-rmse:0.638369+0.057686 
## [69] train-rmse:0.277299+0.016520    test-rmse:0.637325+0.057406 
## [70] train-rmse:0.274591+0.017105    test-rmse:0.637059+0.057047 
## [71] train-rmse:0.271891+0.016426    test-rmse:0.636445+0.056589 
## [72] train-rmse:0.269631+0.015941    test-rmse:0.636395+0.056746 
## [73] train-rmse:0.266560+0.016024    test-rmse:0.635532+0.056885 
## [74] train-rmse:0.264125+0.016283    test-rmse:0.635658+0.057061 
## [75] train-rmse:0.261633+0.017097    test-rmse:0.635659+0.056923 
## [76] train-rmse:0.258834+0.016649    test-rmse:0.635478+0.056978 
## [77] train-rmse:0.255626+0.016526    test-rmse:0.634799+0.055775 
## [78] train-rmse:0.253282+0.016353    test-rmse:0.634914+0.056402 
## [79] train-rmse:0.250895+0.016538    test-rmse:0.634981+0.056816 
## [80] train-rmse:0.249146+0.017315    test-rmse:0.635531+0.057214 
## [81] train-rmse:0.246291+0.017148    test-rmse:0.635148+0.056967 
## [82] train-rmse:0.244322+0.016962    test-rmse:0.635636+0.056576 
## [83] train-rmse:0.242713+0.017157    test-rmse:0.635635+0.057140 
## Stopping. Best iteration:
## [77] train-rmse:0.255626+0.016526    test-rmse:0.634799+0.055775
## 
## 6 
## [1]  train-rmse:1.472470+0.014761    test-rmse:1.480154+0.070464 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.353573+0.013280    test-rmse:1.371050+0.070484 
## [3]  train-rmse:1.247286+0.011732    test-rmse:1.270536+0.071970 
## [4]  train-rmse:1.151638+0.009662    test-rmse:1.182283+0.070489 
## [5]  train-rmse:1.066852+0.008036    test-rmse:1.105216+0.072041 
## [6]  train-rmse:0.991054+0.006484    test-rmse:1.038717+0.073000 
## [7]  train-rmse:0.922815+0.005949    test-rmse:0.983394+0.071043 
## [8]  train-rmse:0.861822+0.004916    test-rmse:0.933361+0.071659 
## [9]  train-rmse:0.807729+0.005120    test-rmse:0.891929+0.072728 
## [10] train-rmse:0.760797+0.005920    test-rmse:0.856557+0.072640 
## [11] train-rmse:0.717136+0.005258    test-rmse:0.823264+0.070803 
## [12] train-rmse:0.679615+0.005644    test-rmse:0.797979+0.071502 
## [13] train-rmse:0.646551+0.007105    test-rmse:0.775471+0.071513 
## [14] train-rmse:0.618156+0.007902    test-rmse:0.756045+0.070078 
## [15] train-rmse:0.592762+0.008337    test-rmse:0.738041+0.067871 
## [16] train-rmse:0.570533+0.010710    test-rmse:0.726103+0.066948 
## [17] train-rmse:0.548371+0.009818    test-rmse:0.712657+0.065220 
## [18] train-rmse:0.530692+0.010378    test-rmse:0.702610+0.064160 
## [19] train-rmse:0.512268+0.009235    test-rmse:0.694686+0.062164 
## [20] train-rmse:0.499388+0.009433    test-rmse:0.688775+0.062127 
## [21] train-rmse:0.483648+0.012703    test-rmse:0.683043+0.061434 
## [22] train-rmse:0.468281+0.015185    test-rmse:0.676955+0.059335 
## [23] train-rmse:0.458478+0.015253    test-rmse:0.671048+0.056724 
## [24] train-rmse:0.443779+0.015050    test-rmse:0.663256+0.053698 
## [25] train-rmse:0.433233+0.016148    test-rmse:0.660892+0.051706 
## [26] train-rmse:0.422788+0.014978    test-rmse:0.657560+0.051365 
## [27] train-rmse:0.412353+0.015909    test-rmse:0.655739+0.051289 
## [28] train-rmse:0.402551+0.014192    test-rmse:0.653078+0.049253 
## [29] train-rmse:0.395122+0.013910    test-rmse:0.651437+0.049341 
## [30] train-rmse:0.387429+0.015500    test-rmse:0.649395+0.050225 
## [31] train-rmse:0.380094+0.015325    test-rmse:0.649034+0.049682 
## [32] train-rmse:0.373416+0.014364    test-rmse:0.647913+0.048483 
## [33] train-rmse:0.367771+0.015074    test-rmse:0.646499+0.048031 
## [34] train-rmse:0.362135+0.015765    test-rmse:0.644622+0.048295 
## [35] train-rmse:0.355726+0.016127    test-rmse:0.644609+0.048911 
## [36] train-rmse:0.350681+0.016782    test-rmse:0.643537+0.048406 
## [37] train-rmse:0.346115+0.017490    test-rmse:0.643153+0.049520 
## [38] train-rmse:0.341523+0.017145    test-rmse:0.643051+0.048798 
## [39] train-rmse:0.337245+0.017174    test-rmse:0.643833+0.049388 
## [40] train-rmse:0.333031+0.017772    test-rmse:0.644396+0.049905 
## [41] train-rmse:0.328734+0.016890    test-rmse:0.642370+0.050275 
## [42] train-rmse:0.324580+0.016919    test-rmse:0.641693+0.050241 
## [43] train-rmse:0.319984+0.017245    test-rmse:0.640787+0.049693 
## [44] train-rmse:0.315962+0.017469    test-rmse:0.641211+0.049707 
## [45] train-rmse:0.312197+0.018098    test-rmse:0.641170+0.049925 
## [46] train-rmse:0.308225+0.016996    test-rmse:0.639454+0.048888 
## [47] train-rmse:0.304593+0.017110    test-rmse:0.638836+0.050171 
## [48] train-rmse:0.298216+0.015422    test-rmse:0.637810+0.050875 
## [49] train-rmse:0.293521+0.016724    test-rmse:0.637248+0.050075 
## [50] train-rmse:0.289231+0.015230    test-rmse:0.635286+0.050615 
## [51] train-rmse:0.285055+0.013845    test-rmse:0.635028+0.050465 
## [52] train-rmse:0.280884+0.013774    test-rmse:0.634692+0.051158 
## [53] train-rmse:0.277061+0.012487    test-rmse:0.634960+0.051579 
## [54] train-rmse:0.272405+0.012840    test-rmse:0.634568+0.051244 
## [55] train-rmse:0.268524+0.014009    test-rmse:0.634370+0.050576 
## [56] train-rmse:0.264668+0.014363    test-rmse:0.634507+0.050177 
## [57] train-rmse:0.261595+0.014489    test-rmse:0.635511+0.050972 
## [58] train-rmse:0.258017+0.014307    test-rmse:0.634845+0.050685 
## [59] train-rmse:0.253864+0.014349    test-rmse:0.635042+0.051325 
## [60] train-rmse:0.250446+0.014769    test-rmse:0.634998+0.050665 
## [61] train-rmse:0.246977+0.015023    test-rmse:0.634412+0.051922 
## Stopping. Best iteration:
## [55] train-rmse:0.268524+0.014009    test-rmse:0.634370+0.050576
## 
## 7 
## [1]  train-rmse:1.458151+0.014904    test-rmse:1.458404+0.069082 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.332529+0.013149    test-rmse:1.334732+0.070086 
## [3]  train-rmse:1.226210+0.011606    test-rmse:1.228638+0.069914 
## [4]  train-rmse:1.136934+0.010329    test-rmse:1.141404+0.069568 
## [5]  train-rmse:1.062490+0.009248    test-rmse:1.067398+0.068302 
## [6]  train-rmse:1.000671+0.008521    test-rmse:1.009153+0.067369 
## [7]  train-rmse:0.949414+0.007956    test-rmse:0.959168+0.064551 
## [8]  train-rmse:0.907145+0.007518    test-rmse:0.921295+0.063375 
## [9]  train-rmse:0.872331+0.007094    test-rmse:0.887450+0.059497 
## [10] train-rmse:0.843833+0.006866    test-rmse:0.861182+0.056848 
## [11] train-rmse:0.820407+0.006595    test-rmse:0.839329+0.055092 
## [12] train-rmse:0.801227+0.006491    test-rmse:0.823234+0.052088 
## [13] train-rmse:0.785623+0.006280    test-rmse:0.809090+0.048626 
## [14] train-rmse:0.772702+0.006029    test-rmse:0.798513+0.047984 
## [15] train-rmse:0.761940+0.005892    test-rmse:0.788457+0.046928 
## [16] train-rmse:0.753019+0.005690    test-rmse:0.780546+0.043494 
## [17] train-rmse:0.745452+0.005553    test-rmse:0.773813+0.041241 
## [18] train-rmse:0.739064+0.005396    test-rmse:0.769350+0.039236 
## [19] train-rmse:0.733500+0.005324    test-rmse:0.765403+0.039358 
## [20] train-rmse:0.728671+0.005265    test-rmse:0.760222+0.038855 
## [21] train-rmse:0.724484+0.005181    test-rmse:0.757599+0.037676 
## [22] train-rmse:0.720762+0.005038    test-rmse:0.754377+0.035606 
## [23] train-rmse:0.717429+0.005002    test-rmse:0.751904+0.034225 
## [24] train-rmse:0.714401+0.004976    test-rmse:0.750288+0.032709 
## [25] train-rmse:0.711596+0.004959    test-rmse:0.749326+0.032451 
## [26] train-rmse:0.709010+0.004928    test-rmse:0.747401+0.033115 
## [27] train-rmse:0.706623+0.004934    test-rmse:0.746121+0.032431 
## [28] train-rmse:0.704380+0.004943    test-rmse:0.744304+0.031569 
## [29] train-rmse:0.702273+0.004958    test-rmse:0.742924+0.030955 
## [30] train-rmse:0.700260+0.004939    test-rmse:0.742033+0.030081 
## [31] train-rmse:0.698396+0.004934    test-rmse:0.742083+0.030231 
## [32] train-rmse:0.696581+0.004936    test-rmse:0.742063+0.030313 
## [33] train-rmse:0.694821+0.004948    test-rmse:0.739794+0.029508 
## [34] train-rmse:0.693138+0.004963    test-rmse:0.738309+0.029439 
## [35] train-rmse:0.691508+0.004987    test-rmse:0.737102+0.029675 
## [36] train-rmse:0.689921+0.005021    test-rmse:0.736981+0.029053 
## [37] train-rmse:0.688396+0.005028    test-rmse:0.736541+0.029001 
## [38] train-rmse:0.686894+0.005039    test-rmse:0.735494+0.029138 
## [39] train-rmse:0.685436+0.005065    test-rmse:0.734326+0.029189 
## [40] train-rmse:0.684016+0.005074    test-rmse:0.733589+0.029588 
## [41] train-rmse:0.682642+0.005079    test-rmse:0.733636+0.029645 
## [42] train-rmse:0.681309+0.005082    test-rmse:0.732603+0.029161 
## [43] train-rmse:0.679996+0.005087    test-rmse:0.731689+0.028728 
## [44] train-rmse:0.678712+0.005095    test-rmse:0.731327+0.029386 
## [45] train-rmse:0.677455+0.005110    test-rmse:0.730357+0.030169 
## [46] train-rmse:0.676210+0.005125    test-rmse:0.729999+0.030233 
## [47] train-rmse:0.674995+0.005126    test-rmse:0.729565+0.030169 
## [48] train-rmse:0.673808+0.005114    test-rmse:0.729168+0.029575 
## [49] train-rmse:0.672642+0.005114    test-rmse:0.728808+0.029689 
## [50] train-rmse:0.671492+0.005120    test-rmse:0.728953+0.030049 
## [51] train-rmse:0.670380+0.005119    test-rmse:0.728254+0.031159 
## [52] train-rmse:0.669278+0.005124    test-rmse:0.728038+0.031224 
## [53] train-rmse:0.668187+0.005142    test-rmse:0.727747+0.031584 
## [54] train-rmse:0.667111+0.005155    test-rmse:0.727241+0.031915 
## [55] train-rmse:0.666044+0.005184    test-rmse:0.727575+0.032260 
## [56] train-rmse:0.665025+0.005190    test-rmse:0.727017+0.032553 
## [57] train-rmse:0.664015+0.005202    test-rmse:0.726225+0.032312 
## [58] train-rmse:0.663023+0.005203    test-rmse:0.725696+0.032356 
## [59] train-rmse:0.662044+0.005216    test-rmse:0.725209+0.032967 
## [60] train-rmse:0.661067+0.005231    test-rmse:0.724358+0.032317 
## [61] train-rmse:0.660107+0.005259    test-rmse:0.724495+0.031624 
## [62] train-rmse:0.659164+0.005274    test-rmse:0.723618+0.031888 
## [63] train-rmse:0.658250+0.005298    test-rmse:0.723647+0.031456 
## [64] train-rmse:0.657333+0.005323    test-rmse:0.722669+0.032006 
## [65] train-rmse:0.656431+0.005337    test-rmse:0.722883+0.032280 
## [66] train-rmse:0.655547+0.005355    test-rmse:0.722726+0.033011 
## [67] train-rmse:0.654669+0.005380    test-rmse:0.721574+0.032688 
## [68] train-rmse:0.653793+0.005407    test-rmse:0.720850+0.032932 
## [69] train-rmse:0.652947+0.005423    test-rmse:0.720374+0.032596 
## [70] train-rmse:0.652126+0.005439    test-rmse:0.720449+0.033035 
## [71] train-rmse:0.651306+0.005455    test-rmse:0.720239+0.033680 
## [72] train-rmse:0.650503+0.005462    test-rmse:0.720394+0.033766 
## [73] train-rmse:0.649711+0.005474    test-rmse:0.719905+0.033525 
## [74] train-rmse:0.648925+0.005491    test-rmse:0.718909+0.033071 
## [75] train-rmse:0.648150+0.005506    test-rmse:0.719230+0.033292 
## [76] train-rmse:0.647380+0.005518    test-rmse:0.719504+0.033419 
## [77] train-rmse:0.646627+0.005534    test-rmse:0.719826+0.033276 
## [78] train-rmse:0.645886+0.005546    test-rmse:0.719552+0.033080 
## [79] train-rmse:0.645158+0.005554    test-rmse:0.720106+0.034049 
## [80] train-rmse:0.644424+0.005561    test-rmse:0.720015+0.034235 
## Stopping. Best iteration:
## [74] train-rmse:0.648925+0.005491    test-rmse:0.718909+0.033071
## 
## 8 
## [1]  train-rmse:1.454841+0.014847    test-rmse:1.457328+0.069239 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.325743+0.012935    test-rmse:1.332190+0.071220 
## [3]  train-rmse:1.216019+0.011333    test-rmse:1.226377+0.072718 
## [4]  train-rmse:1.123029+0.009843    test-rmse:1.137426+0.073119 
## [5]  train-rmse:1.044643+0.008593    test-rmse:1.062095+0.071928 
## [6]  train-rmse:0.979128+0.007596    test-rmse:1.000799+0.071046 
## [7]  train-rmse:0.924493+0.006726    test-rmse:0.950839+0.069385 
## [8]  train-rmse:0.879325+0.006134    test-rmse:0.909391+0.068816 
## [9]  train-rmse:0.841572+0.006058    test-rmse:0.875245+0.068399 
## [10] train-rmse:0.809516+0.005205    test-rmse:0.844920+0.066359 
## [11] train-rmse:0.782143+0.004883    test-rmse:0.820941+0.063673 
## [12] train-rmse:0.758017+0.004133    test-rmse:0.799435+0.061719 
## [13] train-rmse:0.740643+0.004105    test-rmse:0.784443+0.058454 
## [14] train-rmse:0.724990+0.004486    test-rmse:0.772037+0.056234 
## [15] train-rmse:0.711711+0.004274    test-rmse:0.762540+0.053903 
## [16] train-rmse:0.701080+0.004266    test-rmse:0.754133+0.052426 
## [17] train-rmse:0.691621+0.004232    test-rmse:0.748184+0.052390 
## [18] train-rmse:0.683695+0.004493    test-rmse:0.743578+0.050799 
## [19] train-rmse:0.674992+0.003973    test-rmse:0.737437+0.048803 
## [20] train-rmse:0.668840+0.003920    test-rmse:0.734787+0.047361 
## [21] train-rmse:0.663368+0.003616    test-rmse:0.729655+0.045423 
## [22] train-rmse:0.657644+0.004865    test-rmse:0.726736+0.042929 
## [23] train-rmse:0.651903+0.004524    test-rmse:0.724665+0.043268 
## [24] train-rmse:0.648047+0.004453    test-rmse:0.723068+0.043441 
## [25] train-rmse:0.643120+0.004794    test-rmse:0.718659+0.040461 
## [26] train-rmse:0.639698+0.004758    test-rmse:0.715185+0.039284 
## [27] train-rmse:0.636531+0.004396    test-rmse:0.714368+0.040133 
## [28] train-rmse:0.633102+0.004455    test-rmse:0.713676+0.039822 
## [29] train-rmse:0.629144+0.004940    test-rmse:0.711789+0.039154 
## [30] train-rmse:0.625417+0.005639    test-rmse:0.710514+0.038109 
## [31] train-rmse:0.622586+0.005628    test-rmse:0.708484+0.039661 
## [32] train-rmse:0.620015+0.005453    test-rmse:0.707528+0.039653 
## [33] train-rmse:0.617668+0.005673    test-rmse:0.705769+0.038623 
## [34] train-rmse:0.615184+0.005691    test-rmse:0.704113+0.038950 
## [35] train-rmse:0.612771+0.005991    test-rmse:0.704205+0.038357 
## [36] train-rmse:0.610516+0.006080    test-rmse:0.704196+0.039102 
## [37] train-rmse:0.608408+0.005955    test-rmse:0.703807+0.038549 
## [38] train-rmse:0.606210+0.005997    test-rmse:0.703767+0.039081 
## [39] train-rmse:0.604190+0.006181    test-rmse:0.701572+0.038722 
## [40] train-rmse:0.602156+0.005817    test-rmse:0.700683+0.039388 
## [41] train-rmse:0.599589+0.005671    test-rmse:0.699621+0.038454 
## [42] train-rmse:0.597085+0.005755    test-rmse:0.699401+0.038452 
## [43] train-rmse:0.595072+0.005619    test-rmse:0.698480+0.037953 
## [44] train-rmse:0.593407+0.005418    test-rmse:0.699078+0.039314 
## [45] train-rmse:0.591443+0.005585    test-rmse:0.697384+0.038641 
## [46] train-rmse:0.589432+0.005495    test-rmse:0.696942+0.038972 
## [47] train-rmse:0.587451+0.005668    test-rmse:0.697747+0.039543 
## [48] train-rmse:0.585584+0.005599    test-rmse:0.696853+0.039503 
## [49] train-rmse:0.583423+0.005611    test-rmse:0.696136+0.038230 
## [50] train-rmse:0.581764+0.005819    test-rmse:0.695893+0.036604 
## [51] train-rmse:0.580238+0.005662    test-rmse:0.695536+0.037658 
## [52] train-rmse:0.578616+0.005733    test-rmse:0.694898+0.036884 
## [53] train-rmse:0.576876+0.005882    test-rmse:0.693762+0.036712 
## [54] train-rmse:0.575066+0.005781    test-rmse:0.693389+0.036790 
## [55] train-rmse:0.573534+0.005248    test-rmse:0.694604+0.038246 
## [56] train-rmse:0.571221+0.005954    test-rmse:0.693342+0.038724 
## [57] train-rmse:0.569541+0.006034    test-rmse:0.692810+0.038635 
## [58] train-rmse:0.567586+0.006307    test-rmse:0.691400+0.038524 
## [59] train-rmse:0.566198+0.006376    test-rmse:0.691435+0.038157 
## [60] train-rmse:0.564668+0.006628    test-rmse:0.690437+0.037522 
## [61] train-rmse:0.563102+0.006770    test-rmse:0.688947+0.036115 
## [62] train-rmse:0.561581+0.006873    test-rmse:0.688635+0.036261 
## [63] train-rmse:0.560324+0.006636    test-rmse:0.688290+0.037878 
## [64] train-rmse:0.559122+0.006645    test-rmse:0.687414+0.038058 
## [65] train-rmse:0.558008+0.006467    test-rmse:0.687094+0.038907 
## [66] train-rmse:0.556489+0.006608    test-rmse:0.686101+0.039120 
## [67] train-rmse:0.554352+0.007179    test-rmse:0.685738+0.039091 
## [68] train-rmse:0.552004+0.005617    test-rmse:0.683708+0.039205 
## [69] train-rmse:0.550540+0.005386    test-rmse:0.684065+0.038910 
## [70] train-rmse:0.549280+0.005560    test-rmse:0.684080+0.040670 
## [71] train-rmse:0.547211+0.005671    test-rmse:0.682119+0.040042 
## [72] train-rmse:0.545877+0.005891    test-rmse:0.682002+0.039325 
## [73] train-rmse:0.544246+0.005869    test-rmse:0.681571+0.038839 
## [74] train-rmse:0.543036+0.006046    test-rmse:0.681428+0.038646 
## [75] train-rmse:0.541583+0.006381    test-rmse:0.682228+0.038458 
## [76] train-rmse:0.539999+0.006206    test-rmse:0.681960+0.039212 
## [77] train-rmse:0.538047+0.004945    test-rmse:0.680499+0.040084 
## [78] train-rmse:0.536821+0.005019    test-rmse:0.678701+0.039479 
## [79] train-rmse:0.535907+0.005098    test-rmse:0.678578+0.040224 
## [80] train-rmse:0.534647+0.005357    test-rmse:0.677951+0.039721 
## [81] train-rmse:0.532986+0.005448    test-rmse:0.676489+0.038900 
## [82] train-rmse:0.531706+0.005528    test-rmse:0.676193+0.038714 
## [83] train-rmse:0.529749+0.005052    test-rmse:0.675019+0.038779 
## [84] train-rmse:0.528554+0.004969    test-rmse:0.674548+0.038984 
## [85] train-rmse:0.526832+0.005187    test-rmse:0.673379+0.039130 
## [86] train-rmse:0.525442+0.005231    test-rmse:0.673177+0.039900 
## [87] train-rmse:0.524388+0.005472    test-rmse:0.673713+0.039622 
## [88] train-rmse:0.523443+0.005527    test-rmse:0.673611+0.039594 
## [89] train-rmse:0.522035+0.005744    test-rmse:0.673677+0.039254 
## [90] train-rmse:0.520412+0.006607    test-rmse:0.672786+0.038561 
## [91] train-rmse:0.519283+0.006923    test-rmse:0.672654+0.038656 
## [92] train-rmse:0.517575+0.006803    test-rmse:0.672033+0.038610 
## [93] train-rmse:0.516910+0.006769    test-rmse:0.671814+0.038418 
## [94] train-rmse:0.516152+0.006813    test-rmse:0.671342+0.038221 
## [95] train-rmse:0.514642+0.006994    test-rmse:0.669351+0.037704 
## [96] train-rmse:0.512928+0.005938    test-rmse:0.668651+0.038281 
## [97] train-rmse:0.511777+0.006143    test-rmse:0.667832+0.037817 
## [98] train-rmse:0.510662+0.006341    test-rmse:0.667059+0.037702 
## [99] train-rmse:0.509503+0.006129    test-rmse:0.666039+0.037336 
## [100]    train-rmse:0.508388+0.006465    test-rmse:0.665618+0.037196 
## [101]    train-rmse:0.507092+0.006413    test-rmse:0.664954+0.036447 
## [102]    train-rmse:0.505571+0.007222    test-rmse:0.664627+0.036701 
## [103]    train-rmse:0.504871+0.007293    test-rmse:0.664864+0.037848 
## [104]    train-rmse:0.503949+0.007330    test-rmse:0.665160+0.038191 
## [105]    train-rmse:0.502519+0.007614    test-rmse:0.664525+0.038066 
## [106]    train-rmse:0.501595+0.007537    test-rmse:0.664317+0.038551 
## [107]    train-rmse:0.500224+0.007158    test-rmse:0.663950+0.038990 
## [108]    train-rmse:0.499422+0.007252    test-rmse:0.663680+0.038596 
## [109]    train-rmse:0.498104+0.007106    test-rmse:0.662403+0.037284 
## [110]    train-rmse:0.497317+0.007130    test-rmse:0.662205+0.036961 
## [111]    train-rmse:0.496190+0.007366    test-rmse:0.661522+0.037329 
## [112]    train-rmse:0.495239+0.007675    test-rmse:0.660934+0.037419 
## [113]    train-rmse:0.494177+0.008089    test-rmse:0.661136+0.036928 
## [114]    train-rmse:0.492853+0.007409    test-rmse:0.659986+0.038204 
## [115]    train-rmse:0.492127+0.007374    test-rmse:0.659504+0.038542 
## [116]    train-rmse:0.490930+0.007458    test-rmse:0.658389+0.038018 
## [117]    train-rmse:0.489711+0.007540    test-rmse:0.657771+0.037815 
## [118]    train-rmse:0.488548+0.007244    test-rmse:0.657148+0.037432 
## [119]    train-rmse:0.487602+0.007388    test-rmse:0.656803+0.036877 
## [120]    train-rmse:0.486941+0.007459    test-rmse:0.657034+0.037293 
## [121]    train-rmse:0.485841+0.007503    test-rmse:0.656494+0.037228 
## [122]    train-rmse:0.484950+0.007673    test-rmse:0.656418+0.037570 
## [123]    train-rmse:0.484092+0.007854    test-rmse:0.656166+0.037333 
## [124]    train-rmse:0.482981+0.007623    test-rmse:0.655751+0.036983 
## [125]    train-rmse:0.481874+0.007415    test-rmse:0.655050+0.037299 
## [126]    train-rmse:0.481106+0.007466    test-rmse:0.655738+0.037670 
## [127]    train-rmse:0.480461+0.007660    test-rmse:0.655526+0.037247 
## [128]    train-rmse:0.479974+0.007687    test-rmse:0.655302+0.037658 
## [129]    train-rmse:0.479257+0.007704    test-rmse:0.654412+0.037320 
## [130]    train-rmse:0.477959+0.007174    test-rmse:0.654105+0.037665 
## [131]    train-rmse:0.477294+0.007221    test-rmse:0.653571+0.037873 
## [132]    train-rmse:0.476413+0.007446    test-rmse:0.653592+0.037675 
## [133]    train-rmse:0.475240+0.007312    test-rmse:0.653034+0.037034 
## [134]    train-rmse:0.473943+0.008062    test-rmse:0.652473+0.036420 
## [135]    train-rmse:0.473155+0.008354    test-rmse:0.652764+0.035752 
## [136]    train-rmse:0.472476+0.008115    test-rmse:0.653105+0.036784 
## [137]    train-rmse:0.471403+0.007967    test-rmse:0.652485+0.036619 
## [138]    train-rmse:0.470846+0.008094    test-rmse:0.651861+0.036205 
## [139]    train-rmse:0.469831+0.008371    test-rmse:0.652885+0.036432 
## [140]    train-rmse:0.468700+0.007816    test-rmse:0.651811+0.037140 
## [141]    train-rmse:0.468114+0.007767    test-rmse:0.651803+0.037152 
## [142]    train-rmse:0.467022+0.007393    test-rmse:0.651690+0.037156 
## [143]    train-rmse:0.465639+0.007951    test-rmse:0.651358+0.037723 
## [144]    train-rmse:0.464873+0.008128    test-rmse:0.651774+0.037820 
## [145]    train-rmse:0.464022+0.008170    test-rmse:0.651056+0.037788 
## [146]    train-rmse:0.463475+0.008092    test-rmse:0.650748+0.037766 
## [147]    train-rmse:0.462901+0.008314    test-rmse:0.650391+0.037848 
## [148]    train-rmse:0.461642+0.008754    test-rmse:0.650149+0.037940 
## [149]    train-rmse:0.460823+0.008904    test-rmse:0.649931+0.037868 
## [150]    train-rmse:0.460223+0.008963    test-rmse:0.650157+0.037974 
## [151]    train-rmse:0.459532+0.009135    test-rmse:0.650317+0.038425 
## [152]    train-rmse:0.458976+0.009139    test-rmse:0.649785+0.038023 
## [153]    train-rmse:0.458316+0.009125    test-rmse:0.648982+0.037867 
## [154]    train-rmse:0.457796+0.009147    test-rmse:0.649286+0.037883 
## [155]    train-rmse:0.457090+0.009437    test-rmse:0.649236+0.037877 
## [156]    train-rmse:0.456377+0.009594    test-rmse:0.648940+0.037769 
## [157]    train-rmse:0.455732+0.009648    test-rmse:0.649480+0.037643 
## [158]    train-rmse:0.454982+0.009785    test-rmse:0.649586+0.038099 
## [159]    train-rmse:0.454049+0.010505    test-rmse:0.649066+0.038351 
## [160]    train-rmse:0.453453+0.010518    test-rmse:0.648977+0.038458 
## [161]    train-rmse:0.452353+0.009907    test-rmse:0.648672+0.038086 
## [162]    train-rmse:0.451477+0.009764    test-rmse:0.648246+0.037945 
## [163]    train-rmse:0.451014+0.009796    test-rmse:0.648645+0.038237 
## [164]    train-rmse:0.450369+0.009932    test-rmse:0.648505+0.038282 
## [165]    train-rmse:0.449622+0.009837    test-rmse:0.648691+0.038391 
## [166]    train-rmse:0.448966+0.009643    test-rmse:0.648609+0.038750 
## [167]    train-rmse:0.448472+0.009673    test-rmse:0.648734+0.038944 
## [168]    train-rmse:0.447440+0.010469    test-rmse:0.648940+0.038803 
## Stopping. Best iteration:
## [162]    train-rmse:0.451477+0.009764    test-rmse:0.648246+0.037945
## 
## 9 
## [1]  train-rmse:1.452603+0.014661    test-rmse:1.456115+0.068570 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.320775+0.012586    test-rmse:1.328903+0.068973 
## [3]  train-rmse:1.207949+0.011201    test-rmse:1.222013+0.069931 
## [4]  train-rmse:1.111988+0.009743    test-rmse:1.131049+0.069322 
## [5]  train-rmse:1.030540+0.008833    test-rmse:1.054890+0.068710 
## [6]  train-rmse:0.961654+0.007909    test-rmse:0.992380+0.067755 
## [7]  train-rmse:0.903635+0.006435    test-rmse:0.936948+0.068541 
## [8]  train-rmse:0.854588+0.006316    test-rmse:0.890370+0.067342 
## [9]  train-rmse:0.814065+0.005334    test-rmse:0.855588+0.065427 
## [10] train-rmse:0.779932+0.005941    test-rmse:0.827359+0.063855 
## [11] train-rmse:0.751761+0.005779    test-rmse:0.802066+0.063653 
## [12] train-rmse:0.726117+0.006487    test-rmse:0.784010+0.061548 
## [13] train-rmse:0.702819+0.006029    test-rmse:0.765548+0.060173 
## [14] train-rmse:0.685034+0.005351    test-rmse:0.751233+0.058027 
## [15] train-rmse:0.668581+0.005914    test-rmse:0.736470+0.053317 
## [16] train-rmse:0.655504+0.005789    test-rmse:0.726184+0.050597 
## [17] train-rmse:0.644061+0.005689    test-rmse:0.719562+0.049296 
## [18] train-rmse:0.633669+0.006161    test-rmse:0.714110+0.050575 
## [19] train-rmse:0.625218+0.006121    test-rmse:0.710834+0.050790 
## [20] train-rmse:0.617658+0.006615    test-rmse:0.707673+0.050463 
## [21] train-rmse:0.610851+0.006404    test-rmse:0.702416+0.050025 
## [22] train-rmse:0.604652+0.006439    test-rmse:0.700624+0.049473 
## [23] train-rmse:0.599222+0.007592    test-rmse:0.698889+0.050044 
## [24] train-rmse:0.594356+0.008225    test-rmse:0.698174+0.049495 
## [25] train-rmse:0.589129+0.007863    test-rmse:0.695401+0.050943 
## [26] train-rmse:0.584343+0.007854    test-rmse:0.694023+0.051571 
## [27] train-rmse:0.579144+0.007162    test-rmse:0.691065+0.049615 
## [28] train-rmse:0.573802+0.007845    test-rmse:0.689857+0.050179 
## [29] train-rmse:0.570111+0.007676    test-rmse:0.687704+0.051176 
## [30] train-rmse:0.566179+0.008171    test-rmse:0.686891+0.051535 
## [31] train-rmse:0.562139+0.007912    test-rmse:0.686057+0.051886 
## [32] train-rmse:0.557850+0.008289    test-rmse:0.685081+0.051182 
## [33] train-rmse:0.554385+0.008924    test-rmse:0.684609+0.050635 
## [34] train-rmse:0.551409+0.008562    test-rmse:0.682548+0.051488 
## [35] train-rmse:0.548100+0.009040    test-rmse:0.681617+0.051568 
## [36] train-rmse:0.545563+0.009115    test-rmse:0.680917+0.052527 
## [37] train-rmse:0.543152+0.009566    test-rmse:0.681260+0.052364 
## [38] train-rmse:0.539084+0.010330    test-rmse:0.680231+0.052980 
## [39] train-rmse:0.535222+0.009118    test-rmse:0.678376+0.054293 
## [40] train-rmse:0.530369+0.008053    test-rmse:0.675915+0.054505 
## [41] train-rmse:0.527458+0.008177    test-rmse:0.675264+0.054040 
## [42] train-rmse:0.524401+0.008416    test-rmse:0.675817+0.055242 
## [43] train-rmse:0.521059+0.008809    test-rmse:0.675343+0.055870 
## [44] train-rmse:0.518588+0.008503    test-rmse:0.674249+0.056954 
## [45] train-rmse:0.515981+0.008108    test-rmse:0.674563+0.057989 
## [46] train-rmse:0.512957+0.008346    test-rmse:0.673932+0.059940 
## [47] train-rmse:0.509579+0.009695    test-rmse:0.672380+0.057622 
## [48] train-rmse:0.507296+0.010243    test-rmse:0.672179+0.058267 
## [49] train-rmse:0.504655+0.010960    test-rmse:0.671930+0.059567 
## [50] train-rmse:0.501698+0.011430    test-rmse:0.670397+0.058013 
## [51] train-rmse:0.498503+0.010243    test-rmse:0.668395+0.057977 
## [52] train-rmse:0.495805+0.010600    test-rmse:0.667824+0.059391 
## [53] train-rmse:0.493499+0.009679    test-rmse:0.666889+0.060193 
## [54] train-rmse:0.491601+0.009778    test-rmse:0.666603+0.061844 
## [55] train-rmse:0.488345+0.010626    test-rmse:0.665389+0.061426 
## [56] train-rmse:0.485722+0.011101    test-rmse:0.665428+0.061616 
## [57] train-rmse:0.482679+0.010518    test-rmse:0.664198+0.060883 
## [58] train-rmse:0.480820+0.010338    test-rmse:0.663646+0.061408 
## [59] train-rmse:0.478763+0.010087    test-rmse:0.663498+0.062295 
## [60] train-rmse:0.476270+0.010553    test-rmse:0.662575+0.062515 
## [61] train-rmse:0.474469+0.010281    test-rmse:0.662664+0.062915 
## [62] train-rmse:0.471900+0.010861    test-rmse:0.660414+0.061870 
## [63] train-rmse:0.469086+0.009532    test-rmse:0.658686+0.060899 
## [64] train-rmse:0.466965+0.009985    test-rmse:0.658492+0.061384 
## [65] train-rmse:0.464592+0.009555    test-rmse:0.658263+0.061895 
## [66] train-rmse:0.462469+0.009524    test-rmse:0.657367+0.060897 
## [67] train-rmse:0.460449+0.009741    test-rmse:0.658163+0.061259 
## [68] train-rmse:0.458173+0.010797    test-rmse:0.657208+0.061575 
## [69] train-rmse:0.455703+0.009601    test-rmse:0.655135+0.062714 
## [70] train-rmse:0.453558+0.009523    test-rmse:0.655061+0.062588 
## [71] train-rmse:0.451178+0.009785    test-rmse:0.654929+0.062602 
## [72] train-rmse:0.449300+0.009648    test-rmse:0.655152+0.063177 
## [73] train-rmse:0.446481+0.009500    test-rmse:0.653627+0.063017 
## [74] train-rmse:0.444584+0.009442    test-rmse:0.652577+0.062434 
## [75] train-rmse:0.442714+0.009627    test-rmse:0.651846+0.062079 
## [76] train-rmse:0.441340+0.009510    test-rmse:0.651112+0.062154 
## [77] train-rmse:0.440040+0.009820    test-rmse:0.651029+0.062549 
## [78] train-rmse:0.438703+0.009782    test-rmse:0.650732+0.062995 
## [79] train-rmse:0.437122+0.010159    test-rmse:0.650920+0.063202 
## [80] train-rmse:0.434919+0.010795    test-rmse:0.651212+0.063296 
## [81] train-rmse:0.432511+0.010936    test-rmse:0.650285+0.063852 
## [82] train-rmse:0.430387+0.012001    test-rmse:0.649754+0.063202 
## [83] train-rmse:0.428199+0.011960    test-rmse:0.648495+0.062890 
## [84] train-rmse:0.425931+0.011012    test-rmse:0.648779+0.063479 
## [85] train-rmse:0.424694+0.011144    test-rmse:0.647770+0.063529 
## [86] train-rmse:0.423148+0.011861    test-rmse:0.647271+0.063268 
## [87] train-rmse:0.421067+0.012373    test-rmse:0.646810+0.062768 
## [88] train-rmse:0.418824+0.011680    test-rmse:0.646262+0.062552 
## [89] train-rmse:0.416575+0.012080    test-rmse:0.644633+0.062615 
## [90] train-rmse:0.414345+0.012146    test-rmse:0.644293+0.062281 
## [91] train-rmse:0.412717+0.012364    test-rmse:0.644701+0.062839 
## [92] train-rmse:0.410629+0.011794    test-rmse:0.643834+0.062912 
## [93] train-rmse:0.409125+0.012105    test-rmse:0.643563+0.063045 
## [94] train-rmse:0.407842+0.012279    test-rmse:0.643901+0.062786 
## [95] train-rmse:0.406338+0.012132    test-rmse:0.644046+0.063603 
## [96] train-rmse:0.404942+0.012217    test-rmse:0.643952+0.063206 
## [97] train-rmse:0.403140+0.011381    test-rmse:0.643241+0.064273 
## [98] train-rmse:0.400288+0.012302    test-rmse:0.641952+0.064398 
## [99] train-rmse:0.398718+0.012440    test-rmse:0.641352+0.063578 
## [100]    train-rmse:0.396955+0.012868    test-rmse:0.640915+0.063098 
## [101]    train-rmse:0.395774+0.013033    test-rmse:0.641311+0.062911 
## [102]    train-rmse:0.394738+0.013010    test-rmse:0.640956+0.062920 
## [103]    train-rmse:0.392980+0.013766    test-rmse:0.640474+0.062660 
## [104]    train-rmse:0.391495+0.014471    test-rmse:0.640501+0.063245 
## [105]    train-rmse:0.390511+0.014594    test-rmse:0.640178+0.062635 
## [106]    train-rmse:0.388770+0.014575    test-rmse:0.639552+0.062869 
## [107]    train-rmse:0.387194+0.014032    test-rmse:0.639842+0.062553 
## [108]    train-rmse:0.385164+0.013690    test-rmse:0.638254+0.061577 
## [109]    train-rmse:0.383421+0.013571    test-rmse:0.637345+0.061058 
## [110]    train-rmse:0.382099+0.013549    test-rmse:0.637262+0.061480 
## [111]    train-rmse:0.380640+0.013568    test-rmse:0.636955+0.060778 
## [112]    train-rmse:0.379334+0.013942    test-rmse:0.636611+0.060028 
## [113]    train-rmse:0.377881+0.013739    test-rmse:0.636477+0.060205 
## [114]    train-rmse:0.376948+0.014062    test-rmse:0.636790+0.060371 
## [115]    train-rmse:0.375795+0.013867    test-rmse:0.636573+0.060097 
## [116]    train-rmse:0.374371+0.013997    test-rmse:0.636686+0.060657 
## [117]    train-rmse:0.372813+0.013681    test-rmse:0.636556+0.060255 
## [118]    train-rmse:0.371458+0.013638    test-rmse:0.636120+0.060742 
## [119]    train-rmse:0.369883+0.014195    test-rmse:0.635576+0.060675 
## [120]    train-rmse:0.368825+0.014378    test-rmse:0.635286+0.060447 
## [121]    train-rmse:0.367878+0.014783    test-rmse:0.635488+0.060350 
## [122]    train-rmse:0.366396+0.015044    test-rmse:0.634787+0.060188 
## [123]    train-rmse:0.365241+0.015056    test-rmse:0.635550+0.061064 
## [124]    train-rmse:0.364210+0.015205    test-rmse:0.635347+0.060609 
## [125]    train-rmse:0.363094+0.015235    test-rmse:0.635142+0.060551 
## [126]    train-rmse:0.361914+0.015453    test-rmse:0.634497+0.059756 
## [127]    train-rmse:0.360832+0.015207    test-rmse:0.634477+0.059788 
## [128]    train-rmse:0.359992+0.015329    test-rmse:0.634427+0.059846 
## [129]    train-rmse:0.358481+0.015958    test-rmse:0.634562+0.059930 
## [130]    train-rmse:0.357390+0.015780    test-rmse:0.634663+0.060167 
## [131]    train-rmse:0.356487+0.015646    test-rmse:0.634907+0.060368 
## [132]    train-rmse:0.355292+0.015152    test-rmse:0.635034+0.060533 
## [133]    train-rmse:0.354335+0.014890    test-rmse:0.635475+0.060538 
## [134]    train-rmse:0.352776+0.014881    test-rmse:0.634887+0.061262 
## Stopping. Best iteration:
## [128]    train-rmse:0.359992+0.015329    test-rmse:0.634427+0.059846
## 
## 10 
## [1]  train-rmse:1.450321+0.014694    test-rmse:1.457560+0.068773 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.315655+0.012777    test-rmse:1.329399+0.068835 
## [3]  train-rmse:1.198966+0.010584    test-rmse:1.219167+0.068962 
## [4]  train-rmse:1.099360+0.008995    test-rmse:1.129506+0.071686 
## [5]  train-rmse:1.014129+0.007778    test-rmse:1.049510+0.072084 
## [6]  train-rmse:0.941377+0.006258    test-rmse:0.983879+0.071088 
## [7]  train-rmse:0.880894+0.005112    test-rmse:0.933500+0.070333 
## [8]  train-rmse:0.829402+0.004360    test-rmse:0.886623+0.070100 
## [9]  train-rmse:0.786383+0.003835    test-rmse:0.850676+0.067456 
## [10] train-rmse:0.750424+0.004089    test-rmse:0.821285+0.066342 
## [11] train-rmse:0.718797+0.003148    test-rmse:0.795662+0.063897 
## [12] train-rmse:0.693397+0.003800    test-rmse:0.775636+0.062368 
## [13] train-rmse:0.667039+0.006970    test-rmse:0.756267+0.062397 
## [14] train-rmse:0.647627+0.006379    test-rmse:0.742379+0.060586 
## [15] train-rmse:0.631762+0.006018    test-rmse:0.730307+0.058981 
## [16] train-rmse:0.612910+0.008465    test-rmse:0.718736+0.058282 
## [17] train-rmse:0.599361+0.009998    test-rmse:0.710009+0.056610 
## [18] train-rmse:0.585629+0.011709    test-rmse:0.700639+0.051577 
## [19] train-rmse:0.575175+0.011823    test-rmse:0.696257+0.051829 
## [20] train-rmse:0.566186+0.011538    test-rmse:0.693033+0.052783 
## [21] train-rmse:0.558112+0.011404    test-rmse:0.690993+0.053816 
## [22] train-rmse:0.550451+0.011560    test-rmse:0.686955+0.055401 
## [23] train-rmse:0.544156+0.011644    test-rmse:0.685424+0.054444 
## [24] train-rmse:0.536955+0.012022    test-rmse:0.682314+0.053807 
## [25] train-rmse:0.530697+0.011311    test-rmse:0.679718+0.053422 
## [26] train-rmse:0.523563+0.012468    test-rmse:0.676595+0.054285 
## [27] train-rmse:0.518476+0.012706    test-rmse:0.676853+0.054857 
## [28] train-rmse:0.513330+0.013099    test-rmse:0.675758+0.053680 
## [29] train-rmse:0.509158+0.012958    test-rmse:0.673274+0.053768 
## [30] train-rmse:0.504030+0.014466    test-rmse:0.670173+0.052904 
## [31] train-rmse:0.499012+0.015436    test-rmse:0.670334+0.055134 
## [32] train-rmse:0.495630+0.015217    test-rmse:0.670337+0.055392 
## [33] train-rmse:0.491879+0.015960    test-rmse:0.669816+0.056414 
## [34] train-rmse:0.487031+0.016879    test-rmse:0.667590+0.057336 
## [35] train-rmse:0.483231+0.015309    test-rmse:0.666336+0.056620 
## [36] train-rmse:0.478370+0.015219    test-rmse:0.666079+0.055187 
## [37] train-rmse:0.473707+0.015986    test-rmse:0.663979+0.056774 
## [38] train-rmse:0.468061+0.014637    test-rmse:0.658986+0.054895 
## [39] train-rmse:0.463734+0.014625    test-rmse:0.657229+0.054930 
## [40] train-rmse:0.460464+0.014520    test-rmse:0.657369+0.055147 
## [41] train-rmse:0.455338+0.014770    test-rmse:0.655203+0.055070 
## [42] train-rmse:0.452255+0.015326    test-rmse:0.655681+0.055631 
## [43] train-rmse:0.448535+0.015173    test-rmse:0.654777+0.055268 
## [44] train-rmse:0.445154+0.015621    test-rmse:0.654916+0.055952 
## [45] train-rmse:0.441881+0.015480    test-rmse:0.653092+0.055870 
## [46] train-rmse:0.438886+0.015885    test-rmse:0.651980+0.056432 
## [47] train-rmse:0.435558+0.015945    test-rmse:0.650608+0.056228 
## [48] train-rmse:0.432004+0.016826    test-rmse:0.650315+0.055874 
## [49] train-rmse:0.428402+0.016131    test-rmse:0.650036+0.056293 
## [50] train-rmse:0.426033+0.016055    test-rmse:0.650017+0.057268 
## [51] train-rmse:0.423621+0.016378    test-rmse:0.649892+0.058585 
## [52] train-rmse:0.420930+0.016035    test-rmse:0.648996+0.058711 
## [53] train-rmse:0.418078+0.015813    test-rmse:0.649312+0.058936 
## [54] train-rmse:0.414875+0.015981    test-rmse:0.649469+0.058620 
## [55] train-rmse:0.411209+0.015864    test-rmse:0.648743+0.058621 
## [56] train-rmse:0.407723+0.016985    test-rmse:0.649262+0.058926 
## [57] train-rmse:0.405169+0.016696    test-rmse:0.649004+0.059925 
## [58] train-rmse:0.402933+0.016666    test-rmse:0.648219+0.059601 
## [59] train-rmse:0.400144+0.017474    test-rmse:0.648346+0.060210 
## [60] train-rmse:0.397697+0.017068    test-rmse:0.647078+0.059882 
## [61] train-rmse:0.394525+0.016934    test-rmse:0.646334+0.060447 
## [62] train-rmse:0.391458+0.017511    test-rmse:0.643805+0.060175 
## [63] train-rmse:0.389047+0.017803    test-rmse:0.642737+0.060713 
## [64] train-rmse:0.386781+0.018139    test-rmse:0.642884+0.061729 
## [65] train-rmse:0.384380+0.018598    test-rmse:0.643027+0.062503 
## [66] train-rmse:0.381761+0.018965    test-rmse:0.643457+0.062476 
## [67] train-rmse:0.379449+0.018832    test-rmse:0.642808+0.063693 
## [68] train-rmse:0.376791+0.018550    test-rmse:0.641069+0.063021 
## [69] train-rmse:0.374536+0.017917    test-rmse:0.640948+0.063259 
## [70] train-rmse:0.371740+0.017871    test-rmse:0.640842+0.063914 
## [71] train-rmse:0.369903+0.018195    test-rmse:0.641341+0.064403 
## [72] train-rmse:0.366143+0.017502    test-rmse:0.641721+0.064230 
## [73] train-rmse:0.363599+0.017245    test-rmse:0.641978+0.064112 
## [74] train-rmse:0.361161+0.017206    test-rmse:0.642085+0.064154 
## [75] train-rmse:0.359083+0.016969    test-rmse:0.642185+0.064355 
## [76] train-rmse:0.356744+0.016662    test-rmse:0.642500+0.065113 
## Stopping. Best iteration:
## [70] train-rmse:0.371740+0.017871    test-rmse:0.640842+0.063914
## 
## 11 
## [1]  train-rmse:1.448782+0.014799    test-rmse:1.455295+0.070036 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.312288+0.013083    test-rmse:1.328540+0.072409 
## [3]  train-rmse:1.194099+0.010974    test-rmse:1.214616+0.072760 
## [4]  train-rmse:1.091303+0.009035    test-rmse:1.120256+0.072043 
## [5]  train-rmse:1.003214+0.007059    test-rmse:1.040349+0.072379 
## [6]  train-rmse:0.927138+0.006345    test-rmse:0.972610+0.073375 
## [7]  train-rmse:0.861778+0.005902    test-rmse:0.914440+0.072396 
## [8]  train-rmse:0.806364+0.006632    test-rmse:0.870092+0.070951 
## [9]  train-rmse:0.759400+0.006862    test-rmse:0.830341+0.067563 
## [10] train-rmse:0.719366+0.006806    test-rmse:0.799134+0.066491 
## [11] train-rmse:0.686168+0.007855    test-rmse:0.774965+0.064168 
## [12] train-rmse:0.658040+0.008254    test-rmse:0.755928+0.063147 
## [13] train-rmse:0.634327+0.008904    test-rmse:0.738095+0.059529 
## [14] train-rmse:0.613101+0.008351    test-rmse:0.721599+0.055764 
## [15] train-rmse:0.591529+0.012335    test-rmse:0.711972+0.054729 
## [16] train-rmse:0.575582+0.011325    test-rmse:0.702247+0.053657 
## [17] train-rmse:0.560968+0.009990    test-rmse:0.693421+0.052115 
## [18] train-rmse:0.545858+0.013643    test-rmse:0.685691+0.050426 
## [19] train-rmse:0.534734+0.013243    test-rmse:0.683040+0.051172 
## [20] train-rmse:0.524113+0.014275    test-rmse:0.679898+0.050321 
## [21] train-rmse:0.514062+0.012221    test-rmse:0.675753+0.051524 
## [22] train-rmse:0.504431+0.010976    test-rmse:0.671514+0.051997 
## [23] train-rmse:0.496956+0.009723    test-rmse:0.667256+0.054074 
## [24] train-rmse:0.489964+0.009115    test-rmse:0.663757+0.052916 
## [25] train-rmse:0.482856+0.010586    test-rmse:0.662075+0.054720 
## [26] train-rmse:0.478045+0.009936    test-rmse:0.660025+0.056080 
## [27] train-rmse:0.471648+0.010884    test-rmse:0.658792+0.058327 
## [28] train-rmse:0.464309+0.013132    test-rmse:0.657692+0.056418 
## [29] train-rmse:0.457041+0.014835    test-rmse:0.655109+0.054916 
## [30] train-rmse:0.449849+0.014053    test-rmse:0.652454+0.054564 
## [31] train-rmse:0.444424+0.016139    test-rmse:0.652627+0.055647 
## [32] train-rmse:0.438890+0.016916    test-rmse:0.651947+0.057326 
## [33] train-rmse:0.433276+0.015128    test-rmse:0.648687+0.059060 
## [34] train-rmse:0.428998+0.015030    test-rmse:0.647754+0.058433 
## [35] train-rmse:0.423691+0.013957    test-rmse:0.645797+0.058256 
## [36] train-rmse:0.418650+0.014485    test-rmse:0.644382+0.058230 
## [37] train-rmse:0.414386+0.015200    test-rmse:0.642630+0.059158 
## [38] train-rmse:0.409097+0.014863    test-rmse:0.641586+0.059116 
## [39] train-rmse:0.404872+0.014706    test-rmse:0.641179+0.060125 
## [40] train-rmse:0.399778+0.014770    test-rmse:0.641031+0.060332 
## [41] train-rmse:0.395474+0.014246    test-rmse:0.639625+0.059816 
## [42] train-rmse:0.391313+0.014193    test-rmse:0.640506+0.058432 
## [43] train-rmse:0.386066+0.014720    test-rmse:0.637802+0.058063 
## [44] train-rmse:0.380509+0.013310    test-rmse:0.636265+0.058348 
## [45] train-rmse:0.377117+0.013073    test-rmse:0.635664+0.060023 
## [46] train-rmse:0.373875+0.013290    test-rmse:0.635099+0.059989 
## [47] train-rmse:0.369974+0.014014    test-rmse:0.633806+0.059944 
## [48] train-rmse:0.366703+0.014393    test-rmse:0.633957+0.060439 
## [49] train-rmse:0.362575+0.014677    test-rmse:0.632886+0.060361 
## [50] train-rmse:0.358899+0.014635    test-rmse:0.631754+0.059808 
## [51] train-rmse:0.355298+0.015381    test-rmse:0.631506+0.060993 
## [52] train-rmse:0.352344+0.015706    test-rmse:0.631587+0.060459 
## [53] train-rmse:0.349084+0.015903    test-rmse:0.631497+0.060545 
## [54] train-rmse:0.344838+0.016471    test-rmse:0.629899+0.060985 
## [55] train-rmse:0.341964+0.016518    test-rmse:0.629553+0.061143 
## [56] train-rmse:0.339201+0.016157    test-rmse:0.629065+0.060973 
## [57] train-rmse:0.334095+0.015056    test-rmse:0.626140+0.062518 
## [58] train-rmse:0.331873+0.014964    test-rmse:0.626397+0.062707 
## [59] train-rmse:0.328489+0.015212    test-rmse:0.626149+0.063984 
## [60] train-rmse:0.325371+0.015555    test-rmse:0.626053+0.064172 
## [61] train-rmse:0.322393+0.015533    test-rmse:0.627205+0.064881 
## [62] train-rmse:0.319881+0.015785    test-rmse:0.627110+0.064692 
## [63] train-rmse:0.316985+0.016641    test-rmse:0.626227+0.065130 
## [64] train-rmse:0.314906+0.016576    test-rmse:0.626071+0.065070 
## [65] train-rmse:0.312063+0.015539    test-rmse:0.624946+0.065103 
## [66] train-rmse:0.309671+0.015650    test-rmse:0.625469+0.065211 
## [67] train-rmse:0.307040+0.016428    test-rmse:0.625387+0.065209 
## [68] train-rmse:0.304882+0.016018    test-rmse:0.624194+0.065234 
## [69] train-rmse:0.302275+0.016223    test-rmse:0.623963+0.065759 
## [70] train-rmse:0.299456+0.016479    test-rmse:0.623888+0.066013 
## [71] train-rmse:0.297157+0.017262    test-rmse:0.623598+0.065232 
## [72] train-rmse:0.294543+0.017819    test-rmse:0.623695+0.065844 
## [73] train-rmse:0.291452+0.017639    test-rmse:0.622605+0.065880 
## [74] train-rmse:0.288113+0.016686    test-rmse:0.623126+0.066272 
## [75] train-rmse:0.286087+0.016958    test-rmse:0.623534+0.066724 
## [76] train-rmse:0.282770+0.017052    test-rmse:0.623750+0.066517 
## [77] train-rmse:0.280774+0.017498    test-rmse:0.623622+0.066306 
## [78] train-rmse:0.278175+0.017018    test-rmse:0.622767+0.065946 
## [79] train-rmse:0.276208+0.016374    test-rmse:0.622470+0.066149 
## [80] train-rmse:0.274162+0.017033    test-rmse:0.622245+0.065639 
## [81] train-rmse:0.272218+0.016803    test-rmse:0.622375+0.065490 
## [82] train-rmse:0.270448+0.016784    test-rmse:0.622532+0.065532 
## [83] train-rmse:0.268431+0.017142    test-rmse:0.622711+0.065511 
## [84] train-rmse:0.266266+0.017484    test-rmse:0.623407+0.065633 
## [85] train-rmse:0.263753+0.017529    test-rmse:0.623449+0.065057 
## [86] train-rmse:0.260870+0.017182    test-rmse:0.624006+0.065168 
## Stopping. Best iteration:
## [80] train-rmse:0.274162+0.017033    test-rmse:0.622245+0.065639
## 
## 12 
## [1]  train-rmse:1.447224+0.014544    test-rmse:1.456261+0.071061 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.309635+0.013071    test-rmse:1.329631+0.073465 
## [3]  train-rmse:1.189639+0.010827    test-rmse:1.217443+0.073480 
## [4]  train-rmse:1.084639+0.008437    test-rmse:1.121012+0.075846 
## [5]  train-rmse:0.993806+0.006487    test-rmse:1.040973+0.076313 
## [6]  train-rmse:0.915628+0.006040    test-rmse:0.973552+0.075247 
## [7]  train-rmse:0.848037+0.005887    test-rmse:0.918842+0.071594 
## [8]  train-rmse:0.789052+0.005158    test-rmse:0.871154+0.072138 
## [9]  train-rmse:0.738827+0.006097    test-rmse:0.831196+0.068338 
## [10] train-rmse:0.696198+0.006617    test-rmse:0.797760+0.069558 
## [11] train-rmse:0.660215+0.006839    test-rmse:0.770433+0.068632 
## [12] train-rmse:0.627822+0.009878    test-rmse:0.750015+0.064999 
## [13] train-rmse:0.602360+0.010681    test-rmse:0.732608+0.062751 
## [14] train-rmse:0.577757+0.010137    test-rmse:0.717095+0.060662 
## [15] train-rmse:0.557687+0.010220    test-rmse:0.707714+0.061948 
## [16] train-rmse:0.539469+0.009980    test-rmse:0.698661+0.060871 
## [17] train-rmse:0.522755+0.010305    test-rmse:0.689500+0.057161 
## [18] train-rmse:0.505546+0.013780    test-rmse:0.684204+0.057367 
## [19] train-rmse:0.490235+0.014331    test-rmse:0.679498+0.057775 
## [20] train-rmse:0.476064+0.014326    test-rmse:0.674051+0.057152 
## [21] train-rmse:0.465379+0.016279    test-rmse:0.670496+0.059293 
## [22] train-rmse:0.456541+0.017087    test-rmse:0.666080+0.060234 
## [23] train-rmse:0.447205+0.016804    test-rmse:0.662925+0.058945 
## [24] train-rmse:0.439796+0.016375    test-rmse:0.661079+0.057890 
## [25] train-rmse:0.431755+0.016465    test-rmse:0.659277+0.058501 
## [26] train-rmse:0.424821+0.015949    test-rmse:0.657776+0.059540 
## [27] train-rmse:0.419169+0.016583    test-rmse:0.654854+0.060001 
## [28] train-rmse:0.412161+0.018780    test-rmse:0.653966+0.060510 
## [29] train-rmse:0.406682+0.019771    test-rmse:0.654697+0.061928 
## [30] train-rmse:0.401159+0.019329    test-rmse:0.653934+0.062669 
## [31] train-rmse:0.395663+0.019929    test-rmse:0.651673+0.064034 
## [32] train-rmse:0.389760+0.018656    test-rmse:0.650970+0.064843 
## [33] train-rmse:0.383815+0.020459    test-rmse:0.650127+0.064827 
## [34] train-rmse:0.378811+0.020728    test-rmse:0.648874+0.063456 
## [35] train-rmse:0.374175+0.021977    test-rmse:0.647720+0.064175 
## [36] train-rmse:0.368080+0.021582    test-rmse:0.647325+0.064834 
## [37] train-rmse:0.363876+0.021424    test-rmse:0.647478+0.065065 
## [38] train-rmse:0.358135+0.021757    test-rmse:0.646323+0.065883 
## [39] train-rmse:0.352702+0.020567    test-rmse:0.644009+0.067886 
## [40] train-rmse:0.347044+0.020374    test-rmse:0.644128+0.068816 
## [41] train-rmse:0.340659+0.019105    test-rmse:0.642577+0.066595 
## [42] train-rmse:0.336886+0.019181    test-rmse:0.641852+0.065642 
## [43] train-rmse:0.332744+0.019683    test-rmse:0.641909+0.065907 
## [44] train-rmse:0.328889+0.019612    test-rmse:0.640855+0.064631 
## [45] train-rmse:0.323404+0.019380    test-rmse:0.640442+0.065477 
## [46] train-rmse:0.320181+0.019016    test-rmse:0.640502+0.064669 
## [47] train-rmse:0.315242+0.018798    test-rmse:0.639315+0.064326 
## [48] train-rmse:0.309927+0.016961    test-rmse:0.637836+0.065141 
## [49] train-rmse:0.307098+0.016932    test-rmse:0.637759+0.066378 
## [50] train-rmse:0.302331+0.016570    test-rmse:0.636416+0.065704 
## [51] train-rmse:0.299422+0.016399    test-rmse:0.636655+0.066078 
## [52] train-rmse:0.296163+0.016113    test-rmse:0.636953+0.066931 
## [53] train-rmse:0.292638+0.015464    test-rmse:0.636913+0.067003 
## [54] train-rmse:0.289391+0.015931    test-rmse:0.636032+0.066271 
## [55] train-rmse:0.284805+0.015632    test-rmse:0.634461+0.066783 
## [56] train-rmse:0.281216+0.015001    test-rmse:0.633489+0.066571 
## [57] train-rmse:0.277494+0.014782    test-rmse:0.632822+0.066332 
## [58] train-rmse:0.273180+0.014857    test-rmse:0.632511+0.067238 
## [59] train-rmse:0.270667+0.014506    test-rmse:0.632541+0.067004 
## [60] train-rmse:0.267599+0.014215    test-rmse:0.632163+0.065930 
## [61] train-rmse:0.264312+0.014113    test-rmse:0.631914+0.065973 
## [62] train-rmse:0.261167+0.014507    test-rmse:0.632257+0.065568 
## [63] train-rmse:0.257260+0.014950    test-rmse:0.630467+0.064741 
## [64] train-rmse:0.254975+0.015123    test-rmse:0.630841+0.064887 
## [65] train-rmse:0.251603+0.016302    test-rmse:0.630922+0.064632 
## [66] train-rmse:0.248666+0.015585    test-rmse:0.630406+0.064124 
## [67] train-rmse:0.246597+0.015994    test-rmse:0.629746+0.064575 
## [68] train-rmse:0.243711+0.016001    test-rmse:0.629641+0.064331 
## [69] train-rmse:0.241292+0.015419    test-rmse:0.629340+0.064181 
## [70] train-rmse:0.239701+0.015262    test-rmse:0.629455+0.064891 
## [71] train-rmse:0.236743+0.014944    test-rmse:0.628938+0.065131 
## [72] train-rmse:0.233351+0.014124    test-rmse:0.628410+0.064327 
## [73] train-rmse:0.230786+0.014070    test-rmse:0.628378+0.064950 
## [74] train-rmse:0.228474+0.014557    test-rmse:0.628943+0.064887 
## [75] train-rmse:0.226065+0.014029    test-rmse:0.627622+0.064055 
## [76] train-rmse:0.223471+0.012613    test-rmse:0.627519+0.064040 
## [77] train-rmse:0.221387+0.013590    test-rmse:0.628085+0.064424 
## [78] train-rmse:0.219365+0.014257    test-rmse:0.628580+0.065180 
## [79] train-rmse:0.216661+0.012746    test-rmse:0.627825+0.064964 
## [80] train-rmse:0.214055+0.011754    test-rmse:0.627840+0.065476 
## [81] train-rmse:0.211646+0.011989    test-rmse:0.627828+0.065145 
## [82] train-rmse:0.208492+0.011763    test-rmse:0.627794+0.065175 
## Stopping. Best iteration:
## [76] train-rmse:0.223471+0.012613    test-rmse:0.627519+0.064040
## 
## 13 
## [1]  train-rmse:1.446079+0.014316    test-rmse:1.455624+0.071141 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.307152+0.012644    test-rmse:1.327866+0.071313 
## [3]  train-rmse:1.184869+0.010313    test-rmse:1.216100+0.071942 
## [4]  train-rmse:1.079294+0.008509    test-rmse:1.117726+0.070914 
## [5]  train-rmse:0.986488+0.006404    test-rmse:1.036011+0.073947 
## [6]  train-rmse:0.905415+0.006081    test-rmse:0.968326+0.072681 
## [7]  train-rmse:0.835077+0.005267    test-rmse:0.910939+0.072865 
## [8]  train-rmse:0.774059+0.005637    test-rmse:0.864107+0.073042 
## [9]  train-rmse:0.720984+0.005702    test-rmse:0.822060+0.070466 
## [10] train-rmse:0.676115+0.006540    test-rmse:0.790402+0.071596 
## [11] train-rmse:0.637913+0.008510    test-rmse:0.764782+0.071462 
## [12] train-rmse:0.606089+0.009256    test-rmse:0.743006+0.070380 
## [13] train-rmse:0.575478+0.010345    test-rmse:0.726937+0.066735 
## [14] train-rmse:0.551108+0.010826    test-rmse:0.711938+0.064832 
## [15] train-rmse:0.530403+0.011265    test-rmse:0.700781+0.063203 
## [16] train-rmse:0.509162+0.009893    test-rmse:0.689484+0.063583 
## [17] train-rmse:0.491369+0.009333    test-rmse:0.681698+0.062785 
## [18] train-rmse:0.472630+0.012208    test-rmse:0.671187+0.060535 
## [19] train-rmse:0.454677+0.012781    test-rmse:0.665202+0.059314 
## [20] train-rmse:0.440471+0.014310    test-rmse:0.660113+0.059569 
## [21] train-rmse:0.428069+0.015327    test-rmse:0.655724+0.059710 
## [22] train-rmse:0.413599+0.015035    test-rmse:0.652890+0.059405 
## [23] train-rmse:0.402703+0.016080    test-rmse:0.650225+0.058307 
## [24] train-rmse:0.393752+0.016209    test-rmse:0.649262+0.056788 
## [25] train-rmse:0.385598+0.017546    test-rmse:0.647965+0.057337 
## [26] train-rmse:0.376808+0.017645    test-rmse:0.646067+0.056203 
## [27] train-rmse:0.368807+0.018136    test-rmse:0.644978+0.056120 
## [28] train-rmse:0.362383+0.018162    test-rmse:0.644986+0.055682 
## [29] train-rmse:0.356042+0.017155    test-rmse:0.643017+0.054982 
## [30] train-rmse:0.348267+0.017273    test-rmse:0.642366+0.056276 
## [31] train-rmse:0.342188+0.018763    test-rmse:0.641474+0.055728 
## [32] train-rmse:0.336615+0.018595    test-rmse:0.640663+0.057114 
## [33] train-rmse:0.331075+0.019358    test-rmse:0.641788+0.057029 
## [34] train-rmse:0.326725+0.019264    test-rmse:0.642176+0.056769 
## [35] train-rmse:0.320188+0.020147    test-rmse:0.641639+0.056795 
## [36] train-rmse:0.315126+0.021199    test-rmse:0.641998+0.057086 
## [37] train-rmse:0.309914+0.019464    test-rmse:0.639649+0.056752 
## [38] train-rmse:0.305046+0.019898    test-rmse:0.639086+0.055804 
## [39] train-rmse:0.300166+0.018975    test-rmse:0.638916+0.056232 
## [40] train-rmse:0.296688+0.018666    test-rmse:0.638293+0.055691 
## [41] train-rmse:0.291949+0.018521    test-rmse:0.638313+0.055962 
## [42] train-rmse:0.286412+0.017214    test-rmse:0.637445+0.056658 
## [43] train-rmse:0.281733+0.016453    test-rmse:0.636387+0.056259 
## [44] train-rmse:0.277323+0.015678    test-rmse:0.635698+0.054701 
## [45] train-rmse:0.272139+0.015018    test-rmse:0.635417+0.055690 
## [46] train-rmse:0.267264+0.015523    test-rmse:0.636004+0.055230 
## [47] train-rmse:0.263477+0.015825    test-rmse:0.637102+0.055666 
## [48] train-rmse:0.259305+0.016532    test-rmse:0.636612+0.054998 
## [49] train-rmse:0.256134+0.016599    test-rmse:0.635991+0.055449 
## [50] train-rmse:0.251389+0.016146    test-rmse:0.634742+0.055368 
## [51] train-rmse:0.246935+0.015879    test-rmse:0.635205+0.056126 
## [52] train-rmse:0.242916+0.016819    test-rmse:0.634636+0.056028 
## [53] train-rmse:0.238662+0.016512    test-rmse:0.634227+0.056285 
## [54] train-rmse:0.234217+0.015584    test-rmse:0.634631+0.056179 
## [55] train-rmse:0.229977+0.016304    test-rmse:0.635069+0.055396 
## [56] train-rmse:0.226141+0.016312    test-rmse:0.634548+0.055598 
## [57] train-rmse:0.222100+0.016595    test-rmse:0.634486+0.056320 
## [58] train-rmse:0.219421+0.015959    test-rmse:0.634280+0.056626 
## [59] train-rmse:0.214980+0.014544    test-rmse:0.632798+0.057261 
## [60] train-rmse:0.211893+0.014698    test-rmse:0.632080+0.056993 
## [61] train-rmse:0.208996+0.014945    test-rmse:0.631730+0.056932 
## [62] train-rmse:0.204269+0.015190    test-rmse:0.630300+0.055977 
## [63] train-rmse:0.201925+0.015432    test-rmse:0.630483+0.056377 
## [64] train-rmse:0.198762+0.015730    test-rmse:0.630517+0.056443 
## [65] train-rmse:0.195204+0.015495    test-rmse:0.630029+0.055655 
## [66] train-rmse:0.191734+0.014431    test-rmse:0.630317+0.055351 
## [67] train-rmse:0.189198+0.014733    test-rmse:0.629909+0.055352 
## [68] train-rmse:0.185680+0.015044    test-rmse:0.630515+0.055816 
## [69] train-rmse:0.182527+0.014077    test-rmse:0.629892+0.055081 
## [70] train-rmse:0.179851+0.013259    test-rmse:0.630373+0.055644 
## [71] train-rmse:0.177059+0.013139    test-rmse:0.630744+0.055584 
## [72] train-rmse:0.174125+0.012180    test-rmse:0.630388+0.056192 
## [73] train-rmse:0.171446+0.012544    test-rmse:0.630746+0.056539 
## [74] train-rmse:0.168727+0.012384    test-rmse:0.630185+0.056620 
## [75] train-rmse:0.166063+0.012141    test-rmse:0.630084+0.056698 
## Stopping. Best iteration:
## [69] train-rmse:0.182527+0.014077    test-rmse:0.629892+0.055081
## 
## 14 
## [1]  train-rmse:1.434029+0.014566    test-rmse:1.434496+0.069361 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.292215+0.012571    test-rmse:1.295052+0.070304 
## [3]  train-rmse:1.176080+0.010880    test-rmse:1.179201+0.069804 
## [4]  train-rmse:1.082032+0.009534    test-rmse:1.087562+0.069004 
## [5]  train-rmse:1.006514+0.008544    test-rmse:1.014525+0.067222 
## [6]  train-rmse:0.945823+0.007959    test-rmse:0.956609+0.064803 
## [7]  train-rmse:0.897587+0.007370    test-rmse:0.910627+0.062740 
## [8]  train-rmse:0.859124+0.006999    test-rmse:0.874055+0.057534 
## [9]  train-rmse:0.828912+0.006696    test-rmse:0.846519+0.055630 
## [10] train-rmse:0.804885+0.006480    test-rmse:0.825001+0.052501 
## [11] train-rmse:0.785922+0.006209    test-rmse:0.810099+0.050162 
## [12] train-rmse:0.771016+0.005995    test-rmse:0.797051+0.048192 
## [13] train-rmse:0.758941+0.005877    test-rmse:0.787997+0.046154 
## [14] train-rmse:0.749089+0.005633    test-rmse:0.778010+0.044378 
## [15] train-rmse:0.740981+0.005440    test-rmse:0.770927+0.041148 
## [16] train-rmse:0.734205+0.005382    test-rmse:0.765072+0.039197 
## [17] train-rmse:0.728521+0.005201    test-rmse:0.761274+0.038650 
## [18] train-rmse:0.723642+0.005147    test-rmse:0.757023+0.037964 
## [19] train-rmse:0.719397+0.005136    test-rmse:0.753558+0.037125 
## [20] train-rmse:0.715648+0.005023    test-rmse:0.751649+0.036143 
## [21] train-rmse:0.712305+0.004960    test-rmse:0.749725+0.035518 
## [22] train-rmse:0.709212+0.004869    test-rmse:0.747385+0.033305 
## [23] train-rmse:0.706388+0.004898    test-rmse:0.745073+0.030985 
## [24] train-rmse:0.703809+0.004920    test-rmse:0.743756+0.031976 
## [25] train-rmse:0.701383+0.004940    test-rmse:0.742347+0.031632 
## [26] train-rmse:0.699082+0.004950    test-rmse:0.741341+0.030150 
## [27] train-rmse:0.696888+0.004975    test-rmse:0.740613+0.030247 
## [28] train-rmse:0.694831+0.005005    test-rmse:0.739403+0.030170 
## [29] train-rmse:0.692853+0.004987    test-rmse:0.737898+0.029419 
## [30] train-rmse:0.690965+0.005022    test-rmse:0.736322+0.028085 
## [31] train-rmse:0.689163+0.005050    test-rmse:0.736251+0.028329 
## [32] train-rmse:0.687395+0.005087    test-rmse:0.735840+0.028758 
## [33] train-rmse:0.685676+0.005136    test-rmse:0.735127+0.028600 
## [34] train-rmse:0.684009+0.005155    test-rmse:0.734194+0.028441 
## [35] train-rmse:0.682389+0.005171    test-rmse:0.733861+0.028669 
## [36] train-rmse:0.680818+0.005182    test-rmse:0.732506+0.029445 
## [37] train-rmse:0.679278+0.005183    test-rmse:0.731467+0.028626 
## [38] train-rmse:0.677779+0.005159    test-rmse:0.730726+0.029047 
## [39] train-rmse:0.676315+0.005119    test-rmse:0.730565+0.029921 
## [40] train-rmse:0.674883+0.005104    test-rmse:0.729420+0.029775 
## [41] train-rmse:0.673486+0.005122    test-rmse:0.728887+0.029885 
## [42] train-rmse:0.672118+0.005161    test-rmse:0.729152+0.030456 
## [43] train-rmse:0.670771+0.005183    test-rmse:0.728882+0.030880 
## [44] train-rmse:0.669474+0.005198    test-rmse:0.728652+0.031291 
## [45] train-rmse:0.668184+0.005207    test-rmse:0.727626+0.031056 
## [46] train-rmse:0.666937+0.005232    test-rmse:0.727821+0.031521 
## [47] train-rmse:0.665709+0.005282    test-rmse:0.728223+0.031890 
## [48] train-rmse:0.664491+0.005319    test-rmse:0.727207+0.031383 
## [49] train-rmse:0.663302+0.005319    test-rmse:0.726487+0.032327 
## [50] train-rmse:0.662142+0.005315    test-rmse:0.725621+0.032276 
## [51] train-rmse:0.660994+0.005302    test-rmse:0.724530+0.032471 
## [52] train-rmse:0.659869+0.005295    test-rmse:0.723289+0.031839 
## [53] train-rmse:0.658773+0.005297    test-rmse:0.722773+0.031462 
## [54] train-rmse:0.657684+0.005301    test-rmse:0.721900+0.031881 
## [55] train-rmse:0.656640+0.005327    test-rmse:0.721792+0.032768 
## [56] train-rmse:0.655614+0.005354    test-rmse:0.722332+0.033855 
## [57] train-rmse:0.654591+0.005385    test-rmse:0.721588+0.033280 
## [58] train-rmse:0.653590+0.005406    test-rmse:0.720618+0.032896 
## [59] train-rmse:0.652612+0.005439    test-rmse:0.720660+0.033526 
## [60] train-rmse:0.651639+0.005464    test-rmse:0.720113+0.034052 
## [61] train-rmse:0.650677+0.005500    test-rmse:0.720111+0.033756 
## [62] train-rmse:0.649725+0.005529    test-rmse:0.719689+0.033889 
## [63] train-rmse:0.648794+0.005552    test-rmse:0.719056+0.034241 
## [64] train-rmse:0.647884+0.005579    test-rmse:0.718893+0.033794 
## [65] train-rmse:0.646987+0.005592    test-rmse:0.718515+0.032890 
## [66] train-rmse:0.646089+0.005591    test-rmse:0.718333+0.032973 
## [67] train-rmse:0.645221+0.005590    test-rmse:0.718087+0.032963 
## [68] train-rmse:0.644349+0.005597    test-rmse:0.719193+0.033894 
## [69] train-rmse:0.643500+0.005608    test-rmse:0.719543+0.034740 
## [70] train-rmse:0.642640+0.005609    test-rmse:0.719467+0.034769 
## [71] train-rmse:0.641824+0.005623    test-rmse:0.718506+0.034361 
## [72] train-rmse:0.641015+0.005645    test-rmse:0.716550+0.034389 
## [73] train-rmse:0.640225+0.005656    test-rmse:0.716625+0.034222 
## [74] train-rmse:0.639446+0.005673    test-rmse:0.715301+0.033900 
## [75] train-rmse:0.638683+0.005689    test-rmse:0.713801+0.033996 
## [76] train-rmse:0.637928+0.005711    test-rmse:0.713205+0.033962 
## [77] train-rmse:0.637177+0.005724    test-rmse:0.713038+0.033782 
## [78] train-rmse:0.636423+0.005731    test-rmse:0.712658+0.033530 
## [79] train-rmse:0.635685+0.005739    test-rmse:0.713241+0.034230 
## [80] train-rmse:0.634962+0.005744    test-rmse:0.712475+0.034089 
## [81] train-rmse:0.634236+0.005754    test-rmse:0.712405+0.033768 
## [82] train-rmse:0.633521+0.005755    test-rmse:0.712523+0.034175 
## [83] train-rmse:0.632814+0.005760    test-rmse:0.711779+0.033559 
## [84] train-rmse:0.632110+0.005761    test-rmse:0.711308+0.033432 
## [85] train-rmse:0.631408+0.005769    test-rmse:0.711665+0.033556 
## [86] train-rmse:0.630709+0.005778    test-rmse:0.711077+0.032944 
## [87] train-rmse:0.630021+0.005787    test-rmse:0.710669+0.033231 
## [88] train-rmse:0.629334+0.005795    test-rmse:0.711163+0.033853 
## [89] train-rmse:0.628665+0.005805    test-rmse:0.710524+0.034238 
## [90] train-rmse:0.628011+0.005816    test-rmse:0.709210+0.034286 
## [91] train-rmse:0.627362+0.005821    test-rmse:0.709736+0.034394 
## [92] train-rmse:0.626709+0.005823    test-rmse:0.710352+0.034230 
## [93] train-rmse:0.626073+0.005825    test-rmse:0.709671+0.033817 
## [94] train-rmse:0.625435+0.005827    test-rmse:0.709573+0.033495 
## [95] train-rmse:0.624811+0.005839    test-rmse:0.708187+0.033483 
## [96] train-rmse:0.624202+0.005856    test-rmse:0.707660+0.033144 
## [97] train-rmse:0.623595+0.005856    test-rmse:0.708199+0.034099 
## [98] train-rmse:0.622983+0.005859    test-rmse:0.708350+0.034296 
## [99] train-rmse:0.622382+0.005870    test-rmse:0.708483+0.033991 
## [100]    train-rmse:0.621783+0.005899    test-rmse:0.707472+0.033666 
## [101]    train-rmse:0.621194+0.005906    test-rmse:0.706624+0.033797 
## [102]    train-rmse:0.620616+0.005914    test-rmse:0.706680+0.033853 
## [103]    train-rmse:0.620044+0.005922    test-rmse:0.705809+0.032993 
## [104]    train-rmse:0.619474+0.005930    test-rmse:0.705532+0.033214 
## [105]    train-rmse:0.618913+0.005933    test-rmse:0.705573+0.032926 
## [106]    train-rmse:0.618359+0.005944    test-rmse:0.704976+0.033080 
## [107]    train-rmse:0.617808+0.005951    test-rmse:0.705816+0.034090 
## [108]    train-rmse:0.617252+0.005969    test-rmse:0.705382+0.034403 
## [109]    train-rmse:0.616706+0.005972    test-rmse:0.705044+0.034347 
## [110]    train-rmse:0.616174+0.005973    test-rmse:0.704395+0.034120 
## [111]    train-rmse:0.615643+0.005966    test-rmse:0.704916+0.033786 
## [112]    train-rmse:0.615112+0.005953    test-rmse:0.704406+0.033723 
## [113]    train-rmse:0.614591+0.005959    test-rmse:0.704534+0.033438 
## [114]    train-rmse:0.614066+0.005963    test-rmse:0.703918+0.033735 
## [115]    train-rmse:0.613553+0.005967    test-rmse:0.704438+0.034386 
## [116]    train-rmse:0.613043+0.005977    test-rmse:0.704068+0.033942 
## [117]    train-rmse:0.612541+0.005986    test-rmse:0.703080+0.034618 
## [118]    train-rmse:0.612038+0.005991    test-rmse:0.702725+0.034966 
## [119]    train-rmse:0.611529+0.005985    test-rmse:0.702828+0.034793 
## [120]    train-rmse:0.611028+0.005984    test-rmse:0.702128+0.034422 
## [121]    train-rmse:0.610544+0.005993    test-rmse:0.701462+0.034194 
## [122]    train-rmse:0.610066+0.005996    test-rmse:0.700716+0.034239 
## [123]    train-rmse:0.609588+0.006006    test-rmse:0.699873+0.034295 
## [124]    train-rmse:0.609115+0.006015    test-rmse:0.699568+0.034312 
## [125]    train-rmse:0.608640+0.006024    test-rmse:0.698884+0.034794 
## [126]    train-rmse:0.608168+0.006022    test-rmse:0.699176+0.034504 
## [127]    train-rmse:0.607706+0.006030    test-rmse:0.698954+0.034718 
## [128]    train-rmse:0.607249+0.006033    test-rmse:0.698522+0.034140 
## [129]    train-rmse:0.606790+0.006036    test-rmse:0.698621+0.034288 
## [130]    train-rmse:0.606333+0.006037    test-rmse:0.698839+0.033993 
## [131]    train-rmse:0.605878+0.006044    test-rmse:0.698660+0.034137 
## [132]    train-rmse:0.605415+0.006053    test-rmse:0.698927+0.034357 
## [133]    train-rmse:0.604967+0.006060    test-rmse:0.698853+0.034651 
## [134]    train-rmse:0.604522+0.006064    test-rmse:0.699209+0.034416 
## Stopping. Best iteration:
## [128]    train-rmse:0.607249+0.006033    test-rmse:0.698522+0.034140
## 
## 15 
## [1]  train-rmse:1.430133+0.014496    test-rmse:1.433238+0.069539 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.284193+0.012304    test-rmse:1.292016+0.071648 
## [3]  train-rmse:1.164001+0.010509    test-rmse:1.177016+0.072750 
## [4]  train-rmse:1.065366+0.008973    test-rmse:1.082791+0.073108 
## [5]  train-rmse:0.985188+0.007678    test-rmse:1.005894+0.071462 
## [6]  train-rmse:0.920739+0.006709    test-rmse:0.946769+0.069044 
## [7]  train-rmse:0.869109+0.005969    test-rmse:0.900903+0.068170 
## [8]  train-rmse:0.827456+0.005961    test-rmse:0.864033+0.065318 
## [9]  train-rmse:0.792050+0.005295    test-rmse:0.829641+0.063358 
## [10] train-rmse:0.763687+0.005274    test-rmse:0.802660+0.060145 
## [11] train-rmse:0.740869+0.004794    test-rmse:0.782272+0.059854 
## [12] train-rmse:0.723360+0.004670    test-rmse:0.770694+0.056179 
## [13] train-rmse:0.709491+0.004756    test-rmse:0.760662+0.051749 
## [14] train-rmse:0.695870+0.005085    test-rmse:0.750514+0.050664 
## [15] train-rmse:0.686180+0.005044    test-rmse:0.744865+0.050152 
## [16] train-rmse:0.677437+0.004566    test-rmse:0.739574+0.047655 
## [17] train-rmse:0.669535+0.005938    test-rmse:0.733582+0.044829 
## [18] train-rmse:0.662969+0.005248    test-rmse:0.729472+0.043316 
## [19] train-rmse:0.656139+0.006028    test-rmse:0.726339+0.042927 
## [20] train-rmse:0.650765+0.005914    test-rmse:0.723308+0.043087 
## [21] train-rmse:0.646337+0.005884    test-rmse:0.722376+0.043363 
## [22] train-rmse:0.642204+0.005801    test-rmse:0.719789+0.042324 
## [23] train-rmse:0.638361+0.005601    test-rmse:0.717726+0.040448 
## [24] train-rmse:0.633552+0.006132    test-rmse:0.716351+0.039388 
## [25] train-rmse:0.628890+0.006764    test-rmse:0.713432+0.040395 
## [26] train-rmse:0.625351+0.006328    test-rmse:0.711992+0.038948 
## [27] train-rmse:0.622408+0.006380    test-rmse:0.711366+0.040337 
## [28] train-rmse:0.618816+0.005775    test-rmse:0.708957+0.044016 
## [29] train-rmse:0.615317+0.005783    test-rmse:0.707948+0.042945 
## [30] train-rmse:0.612136+0.005952    test-rmse:0.705697+0.041318 
## [31] train-rmse:0.609447+0.006007    test-rmse:0.705657+0.042053 
## [32] train-rmse:0.607049+0.005904    test-rmse:0.703897+0.041356 
## [33] train-rmse:0.603479+0.005784    test-rmse:0.704439+0.040887 
## [34] train-rmse:0.600038+0.005293    test-rmse:0.702398+0.041696 
## [35] train-rmse:0.597799+0.005432    test-rmse:0.702226+0.042665 
## [36] train-rmse:0.595438+0.005492    test-rmse:0.700262+0.042850 
## [37] train-rmse:0.593507+0.005487    test-rmse:0.700049+0.042613 
## [38] train-rmse:0.590162+0.004653    test-rmse:0.699202+0.044812 
## [39] train-rmse:0.586903+0.004844    test-rmse:0.696549+0.046209 
## [40] train-rmse:0.585077+0.004759    test-rmse:0.696488+0.046067 
## [41] train-rmse:0.582856+0.004862    test-rmse:0.694601+0.044163 
## [42] train-rmse:0.580379+0.004872    test-rmse:0.693635+0.043269 
## [43] train-rmse:0.578509+0.004998    test-rmse:0.693064+0.043110 
## [44] train-rmse:0.576335+0.005068    test-rmse:0.692563+0.042568 
## [45] train-rmse:0.574313+0.005013    test-rmse:0.692182+0.041216 
## [46] train-rmse:0.572250+0.005495    test-rmse:0.691620+0.041003 
## [47] train-rmse:0.570711+0.005522    test-rmse:0.691465+0.040429 
## [48] train-rmse:0.568531+0.005433    test-rmse:0.689388+0.044036 
## [49] train-rmse:0.566683+0.005017    test-rmse:0.687457+0.044684 
## [50] train-rmse:0.564768+0.005320    test-rmse:0.686544+0.044403 
## [51] train-rmse:0.562537+0.005953    test-rmse:0.684872+0.043259 
## [52] train-rmse:0.560593+0.006470    test-rmse:0.684524+0.043157 
## [53] train-rmse:0.558011+0.007613    test-rmse:0.683120+0.042572 
## [54] train-rmse:0.556338+0.007906    test-rmse:0.682644+0.042021 
## [55] train-rmse:0.554491+0.007986    test-rmse:0.682865+0.043041 
## [56] train-rmse:0.552857+0.007919    test-rmse:0.683147+0.043445 
## [57] train-rmse:0.551368+0.007951    test-rmse:0.681637+0.043163 
## [58] train-rmse:0.549619+0.007918    test-rmse:0.679518+0.042972 
## [59] train-rmse:0.547530+0.008301    test-rmse:0.678638+0.042478 
## [60] train-rmse:0.545855+0.008756    test-rmse:0.677910+0.042647 
## [61] train-rmse:0.544252+0.009174    test-rmse:0.678684+0.042953 
## [62] train-rmse:0.542121+0.010397    test-rmse:0.677846+0.042635 
## [63] train-rmse:0.540269+0.009877    test-rmse:0.677362+0.042350 
## [64] train-rmse:0.538936+0.009830    test-rmse:0.676441+0.043031 
## [65] train-rmse:0.537593+0.009795    test-rmse:0.676754+0.043211 
## [66] train-rmse:0.536300+0.009676    test-rmse:0.676052+0.044071 
## [67] train-rmse:0.534025+0.010810    test-rmse:0.674535+0.042090 
## [68] train-rmse:0.532755+0.011022    test-rmse:0.674208+0.041908 
## [69] train-rmse:0.531522+0.011026    test-rmse:0.673254+0.042460 
## [70] train-rmse:0.528950+0.010118    test-rmse:0.671441+0.043914 
## [71] train-rmse:0.527337+0.010613    test-rmse:0.671512+0.044155 
## [72] train-rmse:0.525929+0.010451    test-rmse:0.671121+0.044435 
## [73] train-rmse:0.524183+0.010018    test-rmse:0.671306+0.044108 
## [74] train-rmse:0.523034+0.009943    test-rmse:0.671170+0.044373 
## [75] train-rmse:0.521027+0.011196    test-rmse:0.671278+0.044870 
## [76] train-rmse:0.519007+0.011801    test-rmse:0.669680+0.043894 
## [77] train-rmse:0.517633+0.011893    test-rmse:0.669333+0.043853 
## [78] train-rmse:0.516250+0.012117    test-rmse:0.667263+0.042514 
## [79] train-rmse:0.515177+0.012004    test-rmse:0.668104+0.043632 
## [80] train-rmse:0.514050+0.012188    test-rmse:0.667442+0.044459 
## [81] train-rmse:0.512336+0.012139    test-rmse:0.667143+0.044279 
## [82] train-rmse:0.511086+0.011898    test-rmse:0.666220+0.043854 
## [83] train-rmse:0.509769+0.012360    test-rmse:0.665375+0.043592 
## [84] train-rmse:0.508006+0.013449    test-rmse:0.664394+0.043351 
## [85] train-rmse:0.507084+0.013485    test-rmse:0.664142+0.043812 
## [86] train-rmse:0.505445+0.014007    test-rmse:0.665229+0.043920 
## [87] train-rmse:0.504241+0.013728    test-rmse:0.665387+0.044346 
## [88] train-rmse:0.503347+0.013662    test-rmse:0.664873+0.044035 
## [89] train-rmse:0.502102+0.013893    test-rmse:0.663578+0.044509 
## [90] train-rmse:0.501022+0.013816    test-rmse:0.663089+0.045076 
## [91] train-rmse:0.499081+0.014449    test-rmse:0.662655+0.044733 
## [92] train-rmse:0.497954+0.014327    test-rmse:0.662327+0.045430 
## [93] train-rmse:0.496693+0.014678    test-rmse:0.661621+0.045189 
## [94] train-rmse:0.495713+0.014726    test-rmse:0.661226+0.045577 
## [95] train-rmse:0.494628+0.014867    test-rmse:0.660616+0.046374 
## [96] train-rmse:0.492797+0.013784    test-rmse:0.659245+0.044642 
## [97] train-rmse:0.491753+0.014108    test-rmse:0.658442+0.044725 
## [98] train-rmse:0.490455+0.013936    test-rmse:0.657177+0.043021 
## [99] train-rmse:0.489070+0.013683    test-rmse:0.657064+0.043062 
## [100]    train-rmse:0.488106+0.013638    test-rmse:0.657058+0.043580 
## [101]    train-rmse:0.486848+0.014524    test-rmse:0.656353+0.043406 
## [102]    train-rmse:0.485948+0.014560    test-rmse:0.656171+0.043082 
## [103]    train-rmse:0.484025+0.013994    test-rmse:0.655621+0.043860 
## [104]    train-rmse:0.483342+0.014117    test-rmse:0.655492+0.044589 
## [105]    train-rmse:0.481682+0.014374    test-rmse:0.654729+0.044699 
## [106]    train-rmse:0.480952+0.014300    test-rmse:0.654830+0.044755 
## [107]    train-rmse:0.480238+0.014548    test-rmse:0.654357+0.044019 
## [108]    train-rmse:0.479417+0.014407    test-rmse:0.653496+0.043958 
## [109]    train-rmse:0.478719+0.014349    test-rmse:0.653563+0.044310 
## [110]    train-rmse:0.477667+0.014230    test-rmse:0.653100+0.044410 
## [111]    train-rmse:0.476251+0.013889    test-rmse:0.652269+0.045014 
## [112]    train-rmse:0.474699+0.014701    test-rmse:0.651390+0.045564 
## [113]    train-rmse:0.473569+0.014828    test-rmse:0.650934+0.045496 
## [114]    train-rmse:0.472935+0.014865    test-rmse:0.650845+0.045450 
## [115]    train-rmse:0.471461+0.014983    test-rmse:0.651113+0.046100 
## [116]    train-rmse:0.470743+0.014943    test-rmse:0.651698+0.046360 
## [117]    train-rmse:0.469137+0.014657    test-rmse:0.650525+0.046531 
## [118]    train-rmse:0.468376+0.014904    test-rmse:0.650431+0.046248 
## [119]    train-rmse:0.467443+0.015350    test-rmse:0.651348+0.046944 
## [120]    train-rmse:0.466406+0.015239    test-rmse:0.651824+0.046425 
## [121]    train-rmse:0.465614+0.015537    test-rmse:0.651320+0.046219 
## [122]    train-rmse:0.464810+0.015619    test-rmse:0.650260+0.045974 
## [123]    train-rmse:0.463743+0.015327    test-rmse:0.650151+0.046227 
## [124]    train-rmse:0.462448+0.015027    test-rmse:0.650849+0.046173 
## [125]    train-rmse:0.460914+0.014249    test-rmse:0.649812+0.044978 
## [126]    train-rmse:0.459653+0.014306    test-rmse:0.648889+0.044829 
## [127]    train-rmse:0.458337+0.014914    test-rmse:0.648738+0.045341 
## [128]    train-rmse:0.456874+0.014289    test-rmse:0.647637+0.044219 
## [129]    train-rmse:0.456004+0.014474    test-rmse:0.647033+0.044211 
## [130]    train-rmse:0.455408+0.014452    test-rmse:0.646539+0.044614 
## [131]    train-rmse:0.454401+0.014516    test-rmse:0.646070+0.044504 
## [132]    train-rmse:0.453572+0.014388    test-rmse:0.646020+0.045223 
## [133]    train-rmse:0.452979+0.014506    test-rmse:0.645505+0.045570 
## [134]    train-rmse:0.452413+0.014481    test-rmse:0.645797+0.045752 
## [135]    train-rmse:0.451325+0.014089    test-rmse:0.646445+0.045671 
## [136]    train-rmse:0.450511+0.014260    test-rmse:0.645786+0.045410 
## [137]    train-rmse:0.449787+0.014288    test-rmse:0.646053+0.045853 
## [138]    train-rmse:0.448669+0.014489    test-rmse:0.646325+0.045801 
## [139]    train-rmse:0.448177+0.014462    test-rmse:0.646060+0.045514 
## Stopping. Best iteration:
## [133]    train-rmse:0.452979+0.014506    test-rmse:0.645505+0.045570
## 
## 16 
## [1]  train-rmse:1.427495+0.014276    test-rmse:1.431815+0.068759 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.277798+0.012117    test-rmse:1.288845+0.069458 
## [3]  train-rmse:1.154346+0.010416    test-rmse:1.171697+0.070063 
## [4]  train-rmse:1.052043+0.008823    test-rmse:1.075647+0.069606 
## [5]  train-rmse:0.967552+0.007405    test-rmse:0.995347+0.070068 
## [6]  train-rmse:0.899671+0.006630    test-rmse:0.934931+0.070384 
## [7]  train-rmse:0.843250+0.005448    test-rmse:0.883334+0.070721 
## [8]  train-rmse:0.798537+0.005704    test-rmse:0.843942+0.067432 
## [9]  train-rmse:0.761767+0.006642    test-rmse:0.813953+0.067187 
## [10] train-rmse:0.728960+0.008198    test-rmse:0.784351+0.061006 
## [11] train-rmse:0.702963+0.006382    test-rmse:0.762875+0.061425 
## [12] train-rmse:0.679357+0.006379    test-rmse:0.746147+0.057077 
## [13] train-rmse:0.660712+0.006001    test-rmse:0.733313+0.052972 
## [14] train-rmse:0.646383+0.006262    test-rmse:0.724449+0.052503 
## [15] train-rmse:0.634191+0.006264    test-rmse:0.716431+0.052152 
## [16] train-rmse:0.622973+0.005878    test-rmse:0.713046+0.049785 
## [17] train-rmse:0.614804+0.006106    test-rmse:0.708196+0.048796 
## [18] train-rmse:0.604580+0.006333    test-rmse:0.700508+0.046112 
## [19] train-rmse:0.597770+0.006276    test-rmse:0.698683+0.047863 
## [20] train-rmse:0.591624+0.006559    test-rmse:0.695302+0.048437 
## [21] train-rmse:0.585863+0.006676    test-rmse:0.692470+0.048768 
## [22] train-rmse:0.581129+0.006755    test-rmse:0.692206+0.048175 
## [23] train-rmse:0.576346+0.006358    test-rmse:0.688547+0.047131 
## [24] train-rmse:0.571905+0.007140    test-rmse:0.686554+0.047869 
## [25] train-rmse:0.566897+0.006203    test-rmse:0.684787+0.049800 
## [26] train-rmse:0.560854+0.006828    test-rmse:0.681955+0.050650 
## [27] train-rmse:0.556467+0.006370    test-rmse:0.681324+0.052101 
## [28] train-rmse:0.552730+0.006581    test-rmse:0.680272+0.052176 
## [29] train-rmse:0.547578+0.006776    test-rmse:0.679489+0.053736 
## [30] train-rmse:0.544144+0.007192    test-rmse:0.677876+0.054571 
## [31] train-rmse:0.540494+0.006924    test-rmse:0.676709+0.055538 
## [32] train-rmse:0.537457+0.007118    test-rmse:0.675996+0.055006 
## [33] train-rmse:0.534550+0.007406    test-rmse:0.674386+0.055143 
## [34] train-rmse:0.530212+0.006640    test-rmse:0.672224+0.056317 
## [35] train-rmse:0.527363+0.006095    test-rmse:0.672479+0.056556 
## [36] train-rmse:0.523190+0.005296    test-rmse:0.670152+0.058557 
## [37] train-rmse:0.519492+0.003487    test-rmse:0.670491+0.058303 
## [38] train-rmse:0.517126+0.003686    test-rmse:0.671481+0.058747 
## [39] train-rmse:0.514133+0.003769    test-rmse:0.670701+0.058437 
## [40] train-rmse:0.510521+0.005046    test-rmse:0.668744+0.057778 
## [41] train-rmse:0.507612+0.005078    test-rmse:0.667387+0.058414 
## [42] train-rmse:0.503248+0.005977    test-rmse:0.665592+0.057869 
## [43] train-rmse:0.500380+0.006053    test-rmse:0.664868+0.059172 
## [44] train-rmse:0.497399+0.005981    test-rmse:0.664725+0.059161 
## [45] train-rmse:0.493568+0.006696    test-rmse:0.664972+0.059750 
## [46] train-rmse:0.490554+0.007411    test-rmse:0.664549+0.059815 
## [47] train-rmse:0.487140+0.007820    test-rmse:0.661625+0.057494 
## [48] train-rmse:0.483049+0.005794    test-rmse:0.659640+0.057219 
## [49] train-rmse:0.480660+0.006738    test-rmse:0.658040+0.058246 
## [50] train-rmse:0.477312+0.006453    test-rmse:0.657864+0.058931 
## [51] train-rmse:0.474752+0.006738    test-rmse:0.656731+0.059106 
## [52] train-rmse:0.471403+0.008127    test-rmse:0.654703+0.058501 
## [53] train-rmse:0.469004+0.008011    test-rmse:0.655268+0.059012 
## [54] train-rmse:0.466154+0.007449    test-rmse:0.653337+0.057888 
## [55] train-rmse:0.464047+0.007256    test-rmse:0.653551+0.057867 
## [56] train-rmse:0.461711+0.006492    test-rmse:0.651449+0.058179 
## [57] train-rmse:0.459117+0.006733    test-rmse:0.649598+0.057829 
## [58] train-rmse:0.457082+0.006875    test-rmse:0.649615+0.058594 
## [59] train-rmse:0.455097+0.006672    test-rmse:0.648905+0.058308 
## [60] train-rmse:0.451256+0.008074    test-rmse:0.648268+0.058586 
## [61] train-rmse:0.449049+0.008829    test-rmse:0.648031+0.058148 
## [62] train-rmse:0.445918+0.009713    test-rmse:0.648122+0.057965 
## [63] train-rmse:0.443436+0.009369    test-rmse:0.647719+0.057997 
## [64] train-rmse:0.440877+0.009102    test-rmse:0.648083+0.058212 
## [65] train-rmse:0.438720+0.009015    test-rmse:0.646596+0.057950 
## [66] train-rmse:0.437245+0.009035    test-rmse:0.645548+0.058098 
## [67] train-rmse:0.435541+0.009640    test-rmse:0.646069+0.058943 
## [68] train-rmse:0.433411+0.009732    test-rmse:0.645301+0.058975 
## [69] train-rmse:0.430953+0.009971    test-rmse:0.644362+0.058778 
## [70] train-rmse:0.428372+0.011393    test-rmse:0.644721+0.058943 
## [71] train-rmse:0.426485+0.012124    test-rmse:0.644495+0.059293 
## [72] train-rmse:0.424431+0.012751    test-rmse:0.644753+0.059182 
## [73] train-rmse:0.422538+0.012631    test-rmse:0.644796+0.059130 
## [74] train-rmse:0.419990+0.012727    test-rmse:0.643069+0.059688 
## [75] train-rmse:0.418477+0.012470    test-rmse:0.643490+0.059335 
## [76] train-rmse:0.416709+0.013189    test-rmse:0.642691+0.058875 
## [77] train-rmse:0.414696+0.013674    test-rmse:0.641936+0.058023 
## [78] train-rmse:0.413357+0.013759    test-rmse:0.642496+0.058135 
## [79] train-rmse:0.411507+0.013512    test-rmse:0.641681+0.058771 
## [80] train-rmse:0.409417+0.012774    test-rmse:0.640307+0.057665 
## [81] train-rmse:0.407022+0.012334    test-rmse:0.639935+0.057921 
## [82] train-rmse:0.405119+0.011932    test-rmse:0.639905+0.057970 
## [83] train-rmse:0.403288+0.012042    test-rmse:0.639520+0.058567 
## [84] train-rmse:0.401167+0.012662    test-rmse:0.639148+0.058680 
## [85] train-rmse:0.399608+0.012943    test-rmse:0.638863+0.058338 
## [86] train-rmse:0.398203+0.013042    test-rmse:0.639637+0.058765 
## [87] train-rmse:0.395438+0.012509    test-rmse:0.638848+0.058956 
## [88] train-rmse:0.393534+0.012698    test-rmse:0.638351+0.059300 
## [89] train-rmse:0.392454+0.012684    test-rmse:0.638173+0.059446 
## [90] train-rmse:0.391038+0.012761    test-rmse:0.637059+0.059201 
## [91] train-rmse:0.388821+0.013284    test-rmse:0.635696+0.058209 
## [92] train-rmse:0.387264+0.013626    test-rmse:0.635232+0.058420 
## [93] train-rmse:0.385553+0.012552    test-rmse:0.634891+0.059099 
## [94] train-rmse:0.383687+0.012137    test-rmse:0.635679+0.059047 
## [95] train-rmse:0.381821+0.012200    test-rmse:0.635076+0.059326 
## [96] train-rmse:0.380333+0.012749    test-rmse:0.634913+0.058920 
## [97] train-rmse:0.379079+0.013159    test-rmse:0.634985+0.058889 
## [98] train-rmse:0.378105+0.013300    test-rmse:0.635685+0.059341 
## [99] train-rmse:0.375761+0.013783    test-rmse:0.635717+0.058836 
## Stopping. Best iteration:
## [93] train-rmse:0.385553+0.012552    test-rmse:0.634891+0.059099
## 
## 17 
## [1]  train-rmse:1.424802+0.014316    test-rmse:1.433513+0.069038 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.272152+0.012118    test-rmse:1.288329+0.068584 
## [3]  train-rmse:1.143628+0.009722    test-rmse:1.167931+0.068865 
## [4]  train-rmse:1.037414+0.007238    test-rmse:1.071376+0.068430 
## [5]  train-rmse:0.949057+0.006510    test-rmse:0.988004+0.066948 
## [6]  train-rmse:0.875453+0.004853    test-rmse:0.922911+0.068083 
## [7]  train-rmse:0.816772+0.004825    test-rmse:0.873417+0.066923 
## [8]  train-rmse:0.768370+0.005059    test-rmse:0.832370+0.065407 
## [9]  train-rmse:0.729384+0.005515    test-rmse:0.798619+0.063468 
## [10] train-rmse:0.697463+0.004692    test-rmse:0.774174+0.062394 
## [11] train-rmse:0.668471+0.009815    test-rmse:0.754481+0.060817 
## [12] train-rmse:0.645513+0.008779    test-rmse:0.735934+0.059782 
## [13] train-rmse:0.623439+0.011195    test-rmse:0.720320+0.058797 
## [14] train-rmse:0.605865+0.009785    test-rmse:0.709207+0.053796 
## [15] train-rmse:0.587695+0.009738    test-rmse:0.700147+0.054691 
## [16] train-rmse:0.575210+0.009021    test-rmse:0.694668+0.055003 
## [17] train-rmse:0.565654+0.009009    test-rmse:0.692738+0.055198 
## [18] train-rmse:0.556960+0.009397    test-rmse:0.686994+0.053311 
## [19] train-rmse:0.548465+0.009506    test-rmse:0.683373+0.053921 
## [20] train-rmse:0.539562+0.010234    test-rmse:0.681078+0.051814 
## [21] train-rmse:0.532816+0.010312    test-rmse:0.679101+0.054881 
## [22] train-rmse:0.526959+0.010012    test-rmse:0.675811+0.056050 
## [23] train-rmse:0.520707+0.009440    test-rmse:0.674176+0.057470 
## [24] train-rmse:0.515134+0.010764    test-rmse:0.674359+0.057487 
## [25] train-rmse:0.509397+0.012364    test-rmse:0.672635+0.055954 
## [26] train-rmse:0.504811+0.012771    test-rmse:0.671135+0.056602 
## [27] train-rmse:0.497010+0.012073    test-rmse:0.667761+0.055853 
## [28] train-rmse:0.491311+0.013081    test-rmse:0.666268+0.056585 
## [29] train-rmse:0.485331+0.013708    test-rmse:0.665990+0.058252 
## [30] train-rmse:0.480558+0.015581    test-rmse:0.664209+0.060269 
## [31] train-rmse:0.476087+0.015688    test-rmse:0.661962+0.061924 
## [32] train-rmse:0.471275+0.015618    test-rmse:0.662324+0.061601 
## [33] train-rmse:0.466472+0.014667    test-rmse:0.661525+0.062916 
## [34] train-rmse:0.461821+0.014110    test-rmse:0.660981+0.062383 
## [35] train-rmse:0.456981+0.014697    test-rmse:0.659051+0.062569 
## [36] train-rmse:0.452690+0.015649    test-rmse:0.657350+0.064339 
## [37] train-rmse:0.447537+0.014476    test-rmse:0.654831+0.063479 
## [38] train-rmse:0.443599+0.014593    test-rmse:0.654474+0.065094 
## [39] train-rmse:0.439814+0.014413    test-rmse:0.654830+0.065348 
## [40] train-rmse:0.436259+0.014869    test-rmse:0.653592+0.066187 
## [41] train-rmse:0.432592+0.015374    test-rmse:0.653650+0.065271 
## [42] train-rmse:0.428722+0.015224    test-rmse:0.652681+0.065988 
## [43] train-rmse:0.424731+0.014208    test-rmse:0.651681+0.067984 
## [44] train-rmse:0.421676+0.014054    test-rmse:0.651041+0.068796 
## [45] train-rmse:0.418012+0.014988    test-rmse:0.649545+0.070151 
## [46] train-rmse:0.414870+0.014953    test-rmse:0.649289+0.070561 
## [47] train-rmse:0.411980+0.015447    test-rmse:0.649981+0.069817 
## [48] train-rmse:0.409301+0.015529    test-rmse:0.648627+0.070294 
## [49] train-rmse:0.406425+0.015590    test-rmse:0.648358+0.069461 
## [50] train-rmse:0.402306+0.016550    test-rmse:0.647419+0.070449 
## [51] train-rmse:0.397585+0.017022    test-rmse:0.646726+0.069386 
## [52] train-rmse:0.394740+0.017078    test-rmse:0.646453+0.069761 
## [53] train-rmse:0.392671+0.016966    test-rmse:0.646570+0.071084 
## [54] train-rmse:0.389998+0.016648    test-rmse:0.645439+0.071665 
## [55] train-rmse:0.385936+0.016545    test-rmse:0.643787+0.071112 
## [56] train-rmse:0.382671+0.016978    test-rmse:0.644198+0.070204 
## [57] train-rmse:0.378778+0.017096    test-rmse:0.643545+0.071125 
## [58] train-rmse:0.375099+0.016692    test-rmse:0.643639+0.071421 
## [59] train-rmse:0.372332+0.016086    test-rmse:0.642611+0.070787 
## [60] train-rmse:0.370342+0.016187    test-rmse:0.642871+0.071497 
## [61] train-rmse:0.368448+0.016126    test-rmse:0.642421+0.071581 
## [62] train-rmse:0.366185+0.016182    test-rmse:0.642602+0.071649 
## [63] train-rmse:0.363422+0.016023    test-rmse:0.641509+0.071041 
## [64] train-rmse:0.359466+0.014935    test-rmse:0.641723+0.070868 
## [65] train-rmse:0.356853+0.015603    test-rmse:0.641910+0.071555 
## [66] train-rmse:0.354067+0.015315    test-rmse:0.641061+0.070174 
## [67] train-rmse:0.351992+0.015354    test-rmse:0.640074+0.069727 
## [68] train-rmse:0.349187+0.015690    test-rmse:0.639032+0.070175 
## [69] train-rmse:0.346263+0.015565    test-rmse:0.638903+0.069803 
## [70] train-rmse:0.343460+0.015562    test-rmse:0.637682+0.069715 
## [71] train-rmse:0.340938+0.016091    test-rmse:0.636796+0.070574 
## [72] train-rmse:0.338912+0.016281    test-rmse:0.637183+0.070104 
## [73] train-rmse:0.337109+0.016407    test-rmse:0.637103+0.070205 
## [74] train-rmse:0.335305+0.016416    test-rmse:0.636220+0.070495 
## [75] train-rmse:0.333059+0.016492    test-rmse:0.635821+0.069843 
## [76] train-rmse:0.331239+0.016359    test-rmse:0.635295+0.070226 
## [77] train-rmse:0.327678+0.014797    test-rmse:0.634166+0.069571 
## [78] train-rmse:0.325730+0.014980    test-rmse:0.633440+0.069517 
## [79] train-rmse:0.323462+0.015552    test-rmse:0.634532+0.069636 
## [80] train-rmse:0.321148+0.015729    test-rmse:0.634787+0.069743 
## [81] train-rmse:0.318382+0.015554    test-rmse:0.634348+0.070398 
## [82] train-rmse:0.315358+0.016210    test-rmse:0.633350+0.070778 
## [83] train-rmse:0.312423+0.015139    test-rmse:0.632245+0.071150 
## [84] train-rmse:0.310211+0.015290    test-rmse:0.632733+0.071069 
## [85] train-rmse:0.308349+0.015564    test-rmse:0.632834+0.071436 
## [86] train-rmse:0.306458+0.015848    test-rmse:0.632926+0.071651 
## [87] train-rmse:0.304298+0.016350    test-rmse:0.632804+0.070584 
## [88] train-rmse:0.302290+0.016825    test-rmse:0.633079+0.071331 
## [89] train-rmse:0.300880+0.016833    test-rmse:0.633069+0.071202 
## Stopping. Best iteration:
## [83] train-rmse:0.312423+0.015139    test-rmse:0.632245+0.071150
## 
## 18 
## [1]  train-rmse:1.422982+0.014439    test-rmse:1.430862+0.070547 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.267872+0.012282    test-rmse:1.286119+0.073797 
## [3]  train-rmse:1.136801+0.009734    test-rmse:1.164443+0.074193 
## [4]  train-rmse:1.026257+0.008009    test-rmse:1.062840+0.075054 
## [5]  train-rmse:0.933549+0.007073    test-rmse:0.979713+0.074090 
## [6]  train-rmse:0.857473+0.006765    test-rmse:0.912963+0.073471 
## [7]  train-rmse:0.794908+0.006381    test-rmse:0.860844+0.071100 
## [8]  train-rmse:0.742086+0.005360    test-rmse:0.819291+0.072942 
## [9]  train-rmse:0.699498+0.004919    test-rmse:0.785670+0.070980 
## [10] train-rmse:0.663626+0.004637    test-rmse:0.760243+0.069325 
## [11] train-rmse:0.634938+0.005198    test-rmse:0.739115+0.065902 
## [12] train-rmse:0.608782+0.006145    test-rmse:0.724461+0.063385 
## [13] train-rmse:0.585415+0.009045    test-rmse:0.710455+0.060046 
## [14] train-rmse:0.565647+0.008941    test-rmse:0.697918+0.060750 
## [15] train-rmse:0.546575+0.012604    test-rmse:0.690580+0.055292 
## [16] train-rmse:0.534436+0.013054    test-rmse:0.684338+0.054502 
## [17] train-rmse:0.523325+0.012929    test-rmse:0.680685+0.056215 
## [18] train-rmse:0.511670+0.012699    test-rmse:0.675253+0.056461 
## [19] train-rmse:0.501296+0.013612    test-rmse:0.671689+0.057300 
## [20] train-rmse:0.491982+0.014056    test-rmse:0.669439+0.059250 
## [21] train-rmse:0.482735+0.014759    test-rmse:0.664449+0.058345 
## [22] train-rmse:0.475275+0.014942    test-rmse:0.661841+0.058684 
## [23] train-rmse:0.467400+0.016124    test-rmse:0.658699+0.055681 
## [24] train-rmse:0.458654+0.013618    test-rmse:0.654823+0.053430 
## [25] train-rmse:0.453399+0.013876    test-rmse:0.653487+0.053482 
## [26] train-rmse:0.446443+0.014573    test-rmse:0.651748+0.054428 
## [27] train-rmse:0.439138+0.013559    test-rmse:0.650569+0.055648 
## [28] train-rmse:0.431489+0.015175    test-rmse:0.648110+0.052954 
## [29] train-rmse:0.425537+0.014990    test-rmse:0.647040+0.054738 
## [30] train-rmse:0.419525+0.013328    test-rmse:0.646236+0.054452 
## [31] train-rmse:0.414690+0.014925    test-rmse:0.645151+0.054082 
## [32] train-rmse:0.408820+0.017133    test-rmse:0.643962+0.054144 
## [33] train-rmse:0.403719+0.015446    test-rmse:0.640555+0.055391 
## [34] train-rmse:0.398381+0.014699    test-rmse:0.642255+0.054772 
## [35] train-rmse:0.391461+0.014743    test-rmse:0.639176+0.054490 
## [36] train-rmse:0.387107+0.013944    test-rmse:0.638060+0.055815 
## [37] train-rmse:0.383109+0.014930    test-rmse:0.638943+0.056462 
## [38] train-rmse:0.378461+0.014756    test-rmse:0.637295+0.056647 
## [39] train-rmse:0.373804+0.015189    test-rmse:0.636212+0.055453 
## [40] train-rmse:0.368552+0.014701    test-rmse:0.633211+0.056078 
## [41] train-rmse:0.363446+0.014279    test-rmse:0.634517+0.056817 
## [42] train-rmse:0.359300+0.013931    test-rmse:0.633236+0.058335 
## [43] train-rmse:0.355688+0.014167    test-rmse:0.632336+0.058958 
## [44] train-rmse:0.351848+0.014917    test-rmse:0.632940+0.059070 
## [45] train-rmse:0.346971+0.015495    test-rmse:0.633786+0.057731 
## [46] train-rmse:0.343299+0.016003    test-rmse:0.633142+0.057477 
## [47] train-rmse:0.339296+0.017126    test-rmse:0.632582+0.056875 
## [48] train-rmse:0.335744+0.018059    test-rmse:0.632564+0.057452 
## [49] train-rmse:0.331367+0.019115    test-rmse:0.632923+0.057123 
## Stopping. Best iteration:
## [43] train-rmse:0.355688+0.014167    test-rmse:0.632336+0.058958
## 
## 19 
## [1]  train-rmse:1.421139+0.014137    test-rmse:1.431990+0.071723 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.264685+0.012239    test-rmse:1.287568+0.071484 
## [3]  train-rmse:1.131523+0.009313    test-rmse:1.165222+0.073741 
## [4]  train-rmse:1.018267+0.006964    test-rmse:1.065529+0.074472 
## [5]  train-rmse:0.923204+0.005982    test-rmse:0.984606+0.074278 
## [6]  train-rmse:0.842479+0.004805    test-rmse:0.917508+0.072175 
## [7]  train-rmse:0.776190+0.004924    test-rmse:0.861885+0.070319 
## [8]  train-rmse:0.720507+0.005125    test-rmse:0.817948+0.068115 
## [9]  train-rmse:0.672958+0.006693    test-rmse:0.784232+0.066292 
## [10] train-rmse:0.634158+0.006518    test-rmse:0.753545+0.061800 
## [11] train-rmse:0.602732+0.006011    test-rmse:0.734396+0.061564 
## [12] train-rmse:0.577503+0.005483    test-rmse:0.719634+0.059425 
## [13] train-rmse:0.551475+0.003249    test-rmse:0.704960+0.058697 
## [14] train-rmse:0.528240+0.008824    test-rmse:0.696738+0.056780 
## [15] train-rmse:0.510561+0.008396    test-rmse:0.688060+0.055377 
## [16] train-rmse:0.493101+0.011012    test-rmse:0.681963+0.055694 
## [17] train-rmse:0.477933+0.012060    test-rmse:0.676039+0.056550 
## [18] train-rmse:0.462052+0.015515    test-rmse:0.670789+0.057261 
## [19] train-rmse:0.450913+0.014936    test-rmse:0.667767+0.057505 
## [20] train-rmse:0.442443+0.014385    test-rmse:0.664278+0.058192 
## [21] train-rmse:0.430656+0.015927    test-rmse:0.660898+0.058403 
## [22] train-rmse:0.423118+0.015438    test-rmse:0.659349+0.058348 
## [23] train-rmse:0.414811+0.016905    test-rmse:0.656659+0.057758 
## [24] train-rmse:0.407431+0.019115    test-rmse:0.655394+0.056937 
## [25] train-rmse:0.399810+0.019476    test-rmse:0.655010+0.056210 
## [26] train-rmse:0.391917+0.019961    test-rmse:0.653404+0.057373 
## [27] train-rmse:0.382002+0.016962    test-rmse:0.647781+0.054720 
## [28] train-rmse:0.375673+0.017109    test-rmse:0.646093+0.054329 
## [29] train-rmse:0.370170+0.019137    test-rmse:0.644167+0.054004 
## [30] train-rmse:0.365506+0.019663    test-rmse:0.642520+0.054740 
## [31] train-rmse:0.358728+0.020751    test-rmse:0.640126+0.054763 
## [32] train-rmse:0.351359+0.019015    test-rmse:0.639559+0.056994 
## [33] train-rmse:0.347357+0.018954    test-rmse:0.638547+0.057470 
## [34] train-rmse:0.342753+0.019905    test-rmse:0.637660+0.056884 
## [35] train-rmse:0.334926+0.019773    test-rmse:0.637382+0.056982 
## [36] train-rmse:0.329343+0.020295    test-rmse:0.637807+0.058288 
## [37] train-rmse:0.323598+0.018460    test-rmse:0.636479+0.057201 
## [38] train-rmse:0.318966+0.019203    test-rmse:0.635869+0.057940 
## [39] train-rmse:0.313544+0.019152    test-rmse:0.634883+0.057002 
## [40] train-rmse:0.309553+0.019314    test-rmse:0.633573+0.057243 
## [41] train-rmse:0.305472+0.018339    test-rmse:0.632735+0.056803 
## [42] train-rmse:0.300987+0.018737    test-rmse:0.632445+0.056125 
## [43] train-rmse:0.296216+0.019476    test-rmse:0.632772+0.057225 
## [44] train-rmse:0.292188+0.018671    test-rmse:0.631075+0.058013 
## [45] train-rmse:0.288629+0.019127    test-rmse:0.631141+0.058368 
## [46] train-rmse:0.286228+0.019610    test-rmse:0.631108+0.058355 
## [47] train-rmse:0.281498+0.019596    test-rmse:0.629626+0.056087 
## [48] train-rmse:0.277079+0.019029    test-rmse:0.628992+0.057315 
## [49] train-rmse:0.272218+0.017792    test-rmse:0.629996+0.057248 
## [50] train-rmse:0.268595+0.016370    test-rmse:0.630867+0.056760 
## [51] train-rmse:0.264079+0.015679    test-rmse:0.629176+0.056013 
## [52] train-rmse:0.260098+0.015866    test-rmse:0.629097+0.057333 
## [53] train-rmse:0.256235+0.015223    test-rmse:0.628221+0.056764 
## [54] train-rmse:0.252483+0.015329    test-rmse:0.628152+0.056210 
## [55] train-rmse:0.250169+0.015310    test-rmse:0.627898+0.056071 
## [56] train-rmse:0.246795+0.015409    test-rmse:0.628318+0.056105 
## [57] train-rmse:0.243053+0.015685    test-rmse:0.627943+0.056754 
## [58] train-rmse:0.239915+0.015414    test-rmse:0.627166+0.056462 
## [59] train-rmse:0.236257+0.014003    test-rmse:0.627458+0.056867 
## [60] train-rmse:0.232289+0.015046    test-rmse:0.626616+0.056190 
## [61] train-rmse:0.229319+0.014900    test-rmse:0.626710+0.056679 
## [62] train-rmse:0.226082+0.014915    test-rmse:0.626612+0.058070 
## [63] train-rmse:0.224022+0.014606    test-rmse:0.626858+0.058186 
## [64] train-rmse:0.220137+0.014842    test-rmse:0.624795+0.057247 
## [65] train-rmse:0.217003+0.014394    test-rmse:0.625651+0.057440 
## [66] train-rmse:0.213839+0.014033    test-rmse:0.626146+0.057900 
## [67] train-rmse:0.210639+0.012733    test-rmse:0.625285+0.057685 
## [68] train-rmse:0.208217+0.012684    test-rmse:0.625548+0.058077 
## [69] train-rmse:0.204166+0.011685    test-rmse:0.625820+0.057211 
## [70] train-rmse:0.202046+0.011481    test-rmse:0.626160+0.056912 
## Stopping. Best iteration:
## [64] train-rmse:0.220137+0.014842    test-rmse:0.624795+0.057247
## 
## 20 
## [1]  train-rmse:1.419784+0.013869    test-rmse:1.431245+0.071819 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.261420+0.011744    test-rmse:1.284268+0.071821 
## [3]  train-rmse:1.126356+0.009235    test-rmse:1.159489+0.070161 
## [4]  train-rmse:1.011705+0.007220    test-rmse:1.059072+0.072533 
## [5]  train-rmse:0.913295+0.006339    test-rmse:0.976391+0.071057 
## [6]  train-rmse:0.830142+0.004993    test-rmse:0.908875+0.071232 
## [7]  train-rmse:0.759353+0.004191    test-rmse:0.856440+0.070111 
## [8]  train-rmse:0.699755+0.004384    test-rmse:0.811470+0.070372 
## [9]  train-rmse:0.651362+0.005258    test-rmse:0.782570+0.073585 
## [10] train-rmse:0.609956+0.005563    test-rmse:0.755234+0.071336 
## [11] train-rmse:0.574887+0.008809    test-rmse:0.735878+0.068717 
## [12] train-rmse:0.545869+0.009327    test-rmse:0.719770+0.068132 
## [13] train-rmse:0.518522+0.009505    test-rmse:0.704055+0.065163 
## [14] train-rmse:0.495447+0.010416    test-rmse:0.693934+0.063190 
## [15] train-rmse:0.474145+0.011210    test-rmse:0.681858+0.061932 
## [16] train-rmse:0.456934+0.011178    test-rmse:0.674614+0.057931 
## [17] train-rmse:0.437673+0.013403    test-rmse:0.669627+0.056766 
## [18] train-rmse:0.421554+0.015063    test-rmse:0.664951+0.055719 
## [19] train-rmse:0.409988+0.015220    test-rmse:0.662984+0.055238 
## [20] train-rmse:0.397849+0.017545    test-rmse:0.662055+0.055283 
## [21] train-rmse:0.388255+0.018492    test-rmse:0.659030+0.054760 
## [22] train-rmse:0.376522+0.016946    test-rmse:0.657987+0.054856 
## [23] train-rmse:0.368810+0.015319    test-rmse:0.656789+0.055569 
## [24] train-rmse:0.359514+0.015541    test-rmse:0.654985+0.054852 
## [25] train-rmse:0.351416+0.013734    test-rmse:0.654405+0.054202 
## [26] train-rmse:0.343732+0.014247    test-rmse:0.652749+0.051571 
## [27] train-rmse:0.337680+0.014867    test-rmse:0.651984+0.051076 
## [28] train-rmse:0.331703+0.012611    test-rmse:0.649714+0.052248 
## [29] train-rmse:0.326636+0.012252    test-rmse:0.650290+0.052244 
## [30] train-rmse:0.319490+0.013907    test-rmse:0.650216+0.052121 
## [31] train-rmse:0.313576+0.015860    test-rmse:0.648709+0.052529 
## [32] train-rmse:0.307764+0.015444    test-rmse:0.648233+0.052887 
## [33] train-rmse:0.301357+0.016505    test-rmse:0.647657+0.051616 
## [34] train-rmse:0.296942+0.016993    test-rmse:0.648166+0.053001 
## [35] train-rmse:0.291235+0.017400    test-rmse:0.648139+0.054197 
## [36] train-rmse:0.284366+0.016485    test-rmse:0.645648+0.054176 
## [37] train-rmse:0.279817+0.016101    test-rmse:0.646102+0.055650 
## [38] train-rmse:0.275625+0.016150    test-rmse:0.646473+0.054351 
## [39] train-rmse:0.269445+0.015912    test-rmse:0.646043+0.052959 
## [40] train-rmse:0.263118+0.017498    test-rmse:0.645806+0.055135 
## [41] train-rmse:0.259649+0.017063    test-rmse:0.645740+0.055612 
## [42] train-rmse:0.253036+0.016298    test-rmse:0.644794+0.056731 
## [43] train-rmse:0.248197+0.015538    test-rmse:0.643578+0.057011 
## [44] train-rmse:0.243856+0.016432    test-rmse:0.643416+0.057347 
## [45] train-rmse:0.240138+0.017244    test-rmse:0.643404+0.057603 
## [46] train-rmse:0.234917+0.016926    test-rmse:0.643894+0.058373 
## [47] train-rmse:0.230582+0.015541    test-rmse:0.644686+0.058731 
## [48] train-rmse:0.227149+0.015935    test-rmse:0.645333+0.058925 
## [49] train-rmse:0.222433+0.015662    test-rmse:0.645415+0.058683 
## [50] train-rmse:0.218628+0.015512    test-rmse:0.644330+0.058418 
## [51] train-rmse:0.214603+0.015365    test-rmse:0.644289+0.057247 
## Stopping. Best iteration:
## [45] train-rmse:0.240138+0.017244    test-rmse:0.643404+0.057603
## 
## 21 
## [1]  train-rmse:1.410061+0.014228    test-rmse:1.410749+0.069625 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.253427+0.012009    test-rmse:1.256917+0.070470 
## [3]  train-rmse:1.129539+0.010207    test-rmse:1.133471+0.069642 
## [4]  train-rmse:1.033016+0.008840    test-rmse:1.038305+0.067174 
## [5]  train-rmse:0.958134+0.008147    test-rmse:0.967865+0.065539 
## [6]  train-rmse:0.900358+0.007363    test-rmse:0.911929+0.061670 
## [7]  train-rmse:0.856175+0.007032    test-rmse:0.871101+0.056623 
## [8]  train-rmse:0.822456+0.006607    test-rmse:0.840072+0.053525 
## [9]  train-rmse:0.796715+0.006443    test-rmse:0.817753+0.051085 
## [10] train-rmse:0.777195+0.006067    test-rmse:0.802232+0.047744 
## [11] train-rmse:0.762195+0.005840    test-rmse:0.789382+0.046763 
## [12] train-rmse:0.750342+0.005696    test-rmse:0.778796+0.045886 
## [13] train-rmse:0.740881+0.005507    test-rmse:0.770462+0.042207 
## [14] train-rmse:0.733148+0.005313    test-rmse:0.762698+0.038735 
## [15] train-rmse:0.726806+0.005222    test-rmse:0.757703+0.035892 
## [16] train-rmse:0.721590+0.005168    test-rmse:0.754962+0.035772 
## [17] train-rmse:0.717037+0.005108    test-rmse:0.750810+0.035497 
## [18] train-rmse:0.713061+0.005051    test-rmse:0.750123+0.033420 
## [19] train-rmse:0.709459+0.004966    test-rmse:0.748192+0.033568 
## [20] train-rmse:0.706195+0.004913    test-rmse:0.746070+0.033290 
## [21] train-rmse:0.703228+0.004912    test-rmse:0.743811+0.032300 
## [22] train-rmse:0.700462+0.004961    test-rmse:0.740842+0.030098 
## [23] train-rmse:0.697922+0.004987    test-rmse:0.739180+0.030838 
## [24] train-rmse:0.695484+0.005013    test-rmse:0.738402+0.029396 
## [25] train-rmse:0.693164+0.005025    test-rmse:0.738479+0.030039 
## [26] train-rmse:0.690942+0.005073    test-rmse:0.736313+0.028583 
## [27] train-rmse:0.688850+0.005084    test-rmse:0.735775+0.029681 
## [28] train-rmse:0.686831+0.005109    test-rmse:0.735135+0.030058 
## [29] train-rmse:0.684885+0.005105    test-rmse:0.734278+0.030410 
## [30] train-rmse:0.683001+0.005116    test-rmse:0.732667+0.030384 
## [31] train-rmse:0.681168+0.005115    test-rmse:0.731513+0.029705 
## [32] train-rmse:0.679383+0.005108    test-rmse:0.730653+0.030238 
## [33] train-rmse:0.677676+0.005118    test-rmse:0.730457+0.030241 
## [34] train-rmse:0.675994+0.005124    test-rmse:0.728729+0.029616 
## [35] train-rmse:0.674338+0.005158    test-rmse:0.729537+0.030797 
## [36] train-rmse:0.672719+0.005178    test-rmse:0.729496+0.030467 
## [37] train-rmse:0.671148+0.005164    test-rmse:0.728911+0.031418 
## [38] train-rmse:0.669625+0.005155    test-rmse:0.727243+0.031068 
## [39] train-rmse:0.668140+0.005167    test-rmse:0.728147+0.032295 
## [40] train-rmse:0.666717+0.005192    test-rmse:0.726693+0.031649 
## [41] train-rmse:0.665324+0.005203    test-rmse:0.725726+0.031492 
## [42] train-rmse:0.663962+0.005212    test-rmse:0.725172+0.032836 
## [43] train-rmse:0.662612+0.005225    test-rmse:0.725372+0.034308 
## [44] train-rmse:0.661286+0.005231    test-rmse:0.724482+0.034164 
## [45] train-rmse:0.659986+0.005262    test-rmse:0.723064+0.033799 
## [46] train-rmse:0.658721+0.005316    test-rmse:0.723335+0.033529 
## [47] train-rmse:0.657474+0.005372    test-rmse:0.722775+0.033247 
## [48] train-rmse:0.656253+0.005430    test-rmse:0.721891+0.032726 
## [49] train-rmse:0.655055+0.005451    test-rmse:0.721142+0.032445 
## [50] train-rmse:0.653870+0.005443    test-rmse:0.722092+0.033308 
## [51] train-rmse:0.652735+0.005445    test-rmse:0.722409+0.033156 
## [52] train-rmse:0.651607+0.005452    test-rmse:0.720613+0.032889 
## [53] train-rmse:0.650490+0.005462    test-rmse:0.720589+0.033677 
## [54] train-rmse:0.649400+0.005487    test-rmse:0.720254+0.033261 
## [55] train-rmse:0.648356+0.005504    test-rmse:0.719726+0.032970 
## [56] train-rmse:0.647331+0.005520    test-rmse:0.718592+0.033556 
## [57] train-rmse:0.646330+0.005543    test-rmse:0.717439+0.033010 
## [58] train-rmse:0.645336+0.005561    test-rmse:0.717919+0.033994 
## [59] train-rmse:0.644347+0.005578    test-rmse:0.717623+0.033588 
## [60] train-rmse:0.643374+0.005575    test-rmse:0.717372+0.033983 
## [61] train-rmse:0.642409+0.005573    test-rmse:0.718211+0.034291 
## [62] train-rmse:0.641462+0.005591    test-rmse:0.717721+0.034014 
## [63] train-rmse:0.640535+0.005624    test-rmse:0.716208+0.034471 
## [64] train-rmse:0.639612+0.005664    test-rmse:0.715563+0.034423 
## [65] train-rmse:0.638719+0.005693    test-rmse:0.716158+0.035563 
## [66] train-rmse:0.637832+0.005715    test-rmse:0.715093+0.035124 
## [67] train-rmse:0.636952+0.005702    test-rmse:0.714803+0.035220 
## [68] train-rmse:0.636078+0.005689    test-rmse:0.714202+0.034362 
## [69] train-rmse:0.635220+0.005691    test-rmse:0.713911+0.034641 
## [70] train-rmse:0.634374+0.005697    test-rmse:0.713211+0.034421 
## [71] train-rmse:0.633553+0.005715    test-rmse:0.712268+0.034204 
## [72] train-rmse:0.632743+0.005737    test-rmse:0.711441+0.033975 
## [73] train-rmse:0.631950+0.005739    test-rmse:0.711512+0.034407 
## [74] train-rmse:0.631168+0.005752    test-rmse:0.711037+0.033914 
## [75] train-rmse:0.630384+0.005760    test-rmse:0.710122+0.032750 
## [76] train-rmse:0.629589+0.005751    test-rmse:0.709856+0.033086 
## [77] train-rmse:0.628812+0.005763    test-rmse:0.709591+0.033297 
## [78] train-rmse:0.628060+0.005772    test-rmse:0.707586+0.033734 
## [79] train-rmse:0.627312+0.005783    test-rmse:0.707307+0.034062 
## [80] train-rmse:0.626571+0.005799    test-rmse:0.708116+0.034623 
## [81] train-rmse:0.625848+0.005803    test-rmse:0.708409+0.035074 
## [82] train-rmse:0.625129+0.005818    test-rmse:0.708513+0.035125 
## [83] train-rmse:0.624410+0.005832    test-rmse:0.707942+0.035035 
## [84] train-rmse:0.623703+0.005847    test-rmse:0.707855+0.034880 
## [85] train-rmse:0.622984+0.005858    test-rmse:0.707586+0.035629 
## Stopping. Best iteration:
## [79] train-rmse:0.627312+0.005783    test-rmse:0.707307+0.034062
## 
## 22 
## [1]  train-rmse:1.405568+0.014145    test-rmse:1.409307+0.069823 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.244094+0.011707    test-rmse:1.251897+0.071075 
## [3]  train-rmse:1.115494+0.009676    test-rmse:1.129172+0.071833 
## [4]  train-rmse:1.013484+0.008091    test-rmse:1.032714+0.072667 
## [5]  train-rmse:0.933734+0.006908    test-rmse:0.957179+0.069902 
## [6]  train-rmse:0.872274+0.006028    test-rmse:0.901326+0.066919 
## [7]  train-rmse:0.824359+0.005993    test-rmse:0.859582+0.067902 
## [8]  train-rmse:0.785574+0.004460    test-rmse:0.824878+0.064137 
## [9]  train-rmse:0.755086+0.003850    test-rmse:0.795336+0.062594 
## [10] train-rmse:0.731164+0.004909    test-rmse:0.774452+0.058513 
## [11] train-rmse:0.713368+0.004934    test-rmse:0.761381+0.053792 
## [12] train-rmse:0.697864+0.003272    test-rmse:0.750332+0.051433 
## [13] train-rmse:0.685183+0.004839    test-rmse:0.741831+0.046834 
## [14] train-rmse:0.675699+0.004886    test-rmse:0.736884+0.046431 
## [15] train-rmse:0.667072+0.005110    test-rmse:0.729192+0.042571 
## [16] train-rmse:0.660426+0.004982    test-rmse:0.725779+0.042208 
## [17] train-rmse:0.654272+0.004687    test-rmse:0.721968+0.039473 
## [18] train-rmse:0.647853+0.004717    test-rmse:0.718018+0.042746 
## [19] train-rmse:0.641393+0.005168    test-rmse:0.715360+0.043001 
## [20] train-rmse:0.636673+0.004920    test-rmse:0.715942+0.043331 
## [21] train-rmse:0.632695+0.004668    test-rmse:0.713037+0.042250 
## [22] train-rmse:0.628723+0.004220    test-rmse:0.712436+0.042011 
## [23] train-rmse:0.624319+0.004264    test-rmse:0.708629+0.045044 
## [24] train-rmse:0.619984+0.005428    test-rmse:0.705973+0.043313 
## [25] train-rmse:0.616397+0.005437    test-rmse:0.705983+0.043735 
## [26] train-rmse:0.613637+0.005737    test-rmse:0.703905+0.044010 
## [27] train-rmse:0.610457+0.006162    test-rmse:0.702798+0.044372 
## [28] train-rmse:0.607102+0.005644    test-rmse:0.699123+0.045415 
## [29] train-rmse:0.604378+0.005309    test-rmse:0.697164+0.045306 
## [30] train-rmse:0.600845+0.004737    test-rmse:0.697561+0.044663 
## [31] train-rmse:0.598132+0.004458    test-rmse:0.696965+0.044147 
## [32] train-rmse:0.595328+0.005154    test-rmse:0.696757+0.044899 
## [33] train-rmse:0.591575+0.005040    test-rmse:0.695011+0.046539 
## [34] train-rmse:0.588950+0.005588    test-rmse:0.694483+0.045943 
## [35] train-rmse:0.585722+0.006129    test-rmse:0.693395+0.046036 
## [36] train-rmse:0.583217+0.006087    test-rmse:0.692974+0.045955 
## [37] train-rmse:0.580501+0.005722    test-rmse:0.689854+0.045671 
## [38] train-rmse:0.578149+0.005401    test-rmse:0.689350+0.044497 
## [39] train-rmse:0.576181+0.005230    test-rmse:0.689040+0.044715 
## [40] train-rmse:0.573828+0.005258    test-rmse:0.688532+0.044236 
## [41] train-rmse:0.570966+0.006540    test-rmse:0.687318+0.043509 
## [42] train-rmse:0.568788+0.007062    test-rmse:0.687143+0.043492 
## [43] train-rmse:0.566946+0.007079    test-rmse:0.686473+0.043605 
## [44] train-rmse:0.564864+0.007081    test-rmse:0.684261+0.043812 
## [45] train-rmse:0.561696+0.007528    test-rmse:0.683210+0.043787 
## [46] train-rmse:0.558268+0.006064    test-rmse:0.682068+0.043031 
## [47] train-rmse:0.556271+0.006129    test-rmse:0.681808+0.042837 
## [48] train-rmse:0.554264+0.006528    test-rmse:0.680102+0.042993 
## [49] train-rmse:0.552326+0.006214    test-rmse:0.679407+0.043254 
## [50] train-rmse:0.550503+0.006630    test-rmse:0.678736+0.043357 
## [51] train-rmse:0.548491+0.006928    test-rmse:0.679175+0.043748 
## [52] train-rmse:0.545699+0.007847    test-rmse:0.678723+0.043949 
## [53] train-rmse:0.543824+0.008497    test-rmse:0.677644+0.043871 
## [54] train-rmse:0.542272+0.008613    test-rmse:0.676808+0.043469 
## [55] train-rmse:0.537523+0.006703    test-rmse:0.672448+0.042351 
## [56] train-rmse:0.536139+0.006953    test-rmse:0.672538+0.042088 
## [57] train-rmse:0.533500+0.006989    test-rmse:0.672833+0.042258 
## [58] train-rmse:0.531279+0.007246    test-rmse:0.672182+0.043209 
## [59] train-rmse:0.529800+0.007441    test-rmse:0.670517+0.043525 
## [60] train-rmse:0.527279+0.008232    test-rmse:0.668578+0.043319 
## [61] train-rmse:0.525784+0.008365    test-rmse:0.667993+0.043525 
## [62] train-rmse:0.523966+0.008739    test-rmse:0.668217+0.044166 
## [63] train-rmse:0.522491+0.008624    test-rmse:0.668941+0.045854 
## [64] train-rmse:0.520819+0.008737    test-rmse:0.667590+0.045304 
## [65] train-rmse:0.519027+0.009344    test-rmse:0.666890+0.044124 
## [66] train-rmse:0.517570+0.009459    test-rmse:0.665745+0.043805 
## [67] train-rmse:0.516112+0.009663    test-rmse:0.665633+0.044245 
## [68] train-rmse:0.514101+0.008843    test-rmse:0.666251+0.043513 
## [69] train-rmse:0.511798+0.008565    test-rmse:0.663740+0.041980 
## [70] train-rmse:0.510595+0.008304    test-rmse:0.662519+0.042393 
## [71] train-rmse:0.508480+0.008674    test-rmse:0.661707+0.041798 
## [72] train-rmse:0.507061+0.008646    test-rmse:0.661228+0.041893 
## [73] train-rmse:0.505597+0.008622    test-rmse:0.661186+0.041864 
## [74] train-rmse:0.504389+0.008383    test-rmse:0.660848+0.042467 
## [75] train-rmse:0.502206+0.008467    test-rmse:0.659270+0.042402 
## [76] train-rmse:0.500268+0.009595    test-rmse:0.659266+0.042439 
## [77] train-rmse:0.498632+0.010079    test-rmse:0.658122+0.041638 
## [78] train-rmse:0.497242+0.010337    test-rmse:0.656592+0.041288 
## [79] train-rmse:0.496243+0.010330    test-rmse:0.655876+0.040990 
## [80] train-rmse:0.493874+0.008918    test-rmse:0.653869+0.042614 
## [81] train-rmse:0.492113+0.009749    test-rmse:0.653363+0.042387 
## [82] train-rmse:0.490777+0.009858    test-rmse:0.652674+0.042723 
## [83] train-rmse:0.488311+0.009151    test-rmse:0.651260+0.040452 
## [84] train-rmse:0.487129+0.009020    test-rmse:0.650893+0.040012 
## [85] train-rmse:0.486077+0.009087    test-rmse:0.649696+0.040337 
## [86] train-rmse:0.484738+0.009183    test-rmse:0.649953+0.040451 
## [87] train-rmse:0.483439+0.008959    test-rmse:0.650455+0.040216 
## [88] train-rmse:0.482635+0.008923    test-rmse:0.650615+0.040561 
## [89] train-rmse:0.481926+0.008934    test-rmse:0.650822+0.040119 
## [90] train-rmse:0.480810+0.009178    test-rmse:0.650635+0.041047 
## [91] train-rmse:0.479475+0.009284    test-rmse:0.650293+0.041271 
## Stopping. Best iteration:
## [85] train-rmse:0.486077+0.009087    test-rmse:0.649696+0.040337
## 
## 23 
## [1]  train-rmse:1.402522+0.013891    test-rmse:1.407672+0.068930 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.236641+0.011495    test-rmse:1.249426+0.069343 
## [3]  train-rmse:1.104095+0.009660    test-rmse:1.124998+0.069702 
## [4]  train-rmse:0.998325+0.008020    test-rmse:1.026557+0.068917 
## [5]  train-rmse:0.912818+0.006156    test-rmse:0.945636+0.069942 
## [6]  train-rmse:0.847401+0.005187    test-rmse:0.886261+0.067967 
## [7]  train-rmse:0.795121+0.004272    test-rmse:0.840111+0.069459 
## [8]  train-rmse:0.754311+0.005218    test-rmse:0.807721+0.065587 
## [9]  train-rmse:0.721080+0.005612    test-rmse:0.779147+0.064504 
## [10] train-rmse:0.691478+0.004964    test-rmse:0.754979+0.057352 
## [11] train-rmse:0.666203+0.005917    test-rmse:0.736061+0.051734 
## [12] train-rmse:0.649607+0.006604    test-rmse:0.725889+0.051535 
## [13] train-rmse:0.636079+0.006748    test-rmse:0.715768+0.049476 
## [14] train-rmse:0.624336+0.007259    test-rmse:0.710851+0.047017 
## [15] train-rmse:0.614286+0.007249    test-rmse:0.705178+0.046807 
## [16] train-rmse:0.605259+0.008340    test-rmse:0.698684+0.047621 
## [17] train-rmse:0.598004+0.008307    test-rmse:0.695589+0.047826 
## [18] train-rmse:0.590735+0.007771    test-rmse:0.693539+0.050632 
## [19] train-rmse:0.583163+0.007449    test-rmse:0.690040+0.049716 
## [20] train-rmse:0.577001+0.006780    test-rmse:0.688993+0.049096 
## [21] train-rmse:0.571057+0.007015    test-rmse:0.688165+0.048498 
## [22] train-rmse:0.566976+0.006706    test-rmse:0.686881+0.050122 
## [23] train-rmse:0.562823+0.006965    test-rmse:0.684545+0.050796 
## [24] train-rmse:0.557837+0.006496    test-rmse:0.683500+0.050755 
## [25] train-rmse:0.553577+0.006799    test-rmse:0.684154+0.050594 
## [26] train-rmse:0.546256+0.007765    test-rmse:0.679826+0.048903 
## [27] train-rmse:0.541123+0.007702    test-rmse:0.677986+0.049776 
## [28] train-rmse:0.536824+0.006942    test-rmse:0.676249+0.050864 
## [29] train-rmse:0.532015+0.007812    test-rmse:0.673664+0.051465 
## [30] train-rmse:0.527140+0.007503    test-rmse:0.672856+0.049879 
## [31] train-rmse:0.522889+0.008383    test-rmse:0.671504+0.052248 
## [32] train-rmse:0.519332+0.009259    test-rmse:0.670055+0.050714 
## [33] train-rmse:0.514393+0.009402    test-rmse:0.669597+0.051595 
## [34] train-rmse:0.511309+0.009301    test-rmse:0.668798+0.051049 
## [35] train-rmse:0.507248+0.009852    test-rmse:0.668412+0.050555 
## [36] train-rmse:0.504777+0.009915    test-rmse:0.667798+0.051897 
## [37] train-rmse:0.498093+0.009935    test-rmse:0.663048+0.048849 
## [38] train-rmse:0.495076+0.009871    test-rmse:0.662531+0.048784 
## [39] train-rmse:0.491880+0.010463    test-rmse:0.660892+0.048433 
## [40] train-rmse:0.487183+0.007343    test-rmse:0.661019+0.051233 
## [41] train-rmse:0.482066+0.006548    test-rmse:0.657648+0.052381 
## [42] train-rmse:0.479480+0.006849    test-rmse:0.658149+0.052210 
## [43] train-rmse:0.476486+0.007272    test-rmse:0.657729+0.053786 
## [44] train-rmse:0.472636+0.008091    test-rmse:0.655618+0.053061 
## [45] train-rmse:0.470181+0.008964    test-rmse:0.655359+0.054525 
## [46] train-rmse:0.467839+0.009211    test-rmse:0.655429+0.056313 
## [47] train-rmse:0.463226+0.007716    test-rmse:0.653387+0.056489 
## [48] train-rmse:0.460247+0.007955    test-rmse:0.653309+0.056187 
## [49] train-rmse:0.457641+0.007870    test-rmse:0.650816+0.055155 
## [50] train-rmse:0.454778+0.007384    test-rmse:0.649784+0.055916 
## [51] train-rmse:0.452863+0.007250    test-rmse:0.649325+0.055631 
## [52] train-rmse:0.449772+0.007017    test-rmse:0.647880+0.055705 
## [53] train-rmse:0.447049+0.007629    test-rmse:0.648710+0.056436 
## [54] train-rmse:0.445031+0.007563    test-rmse:0.647903+0.056802 
## [55] train-rmse:0.442984+0.008171    test-rmse:0.648155+0.057494 
## [56] train-rmse:0.440438+0.008223    test-rmse:0.648602+0.057673 
## [57] train-rmse:0.438356+0.008663    test-rmse:0.647616+0.056504 
## [58] train-rmse:0.435272+0.007327    test-rmse:0.646951+0.059231 
## [59] train-rmse:0.432996+0.007758    test-rmse:0.645861+0.059531 
## [60] train-rmse:0.430530+0.007589    test-rmse:0.644130+0.060638 
## [61] train-rmse:0.428417+0.008199    test-rmse:0.644799+0.061574 
## [62] train-rmse:0.425266+0.007735    test-rmse:0.644909+0.062035 
## [63] train-rmse:0.423192+0.007937    test-rmse:0.643746+0.062661 
## [64] train-rmse:0.420835+0.009152    test-rmse:0.643158+0.062131 
## [65] train-rmse:0.418344+0.009098    test-rmse:0.642644+0.062575 
## [66] train-rmse:0.416070+0.009406    test-rmse:0.641600+0.061291 
## [67] train-rmse:0.414460+0.009626    test-rmse:0.641374+0.061527 
## [68] train-rmse:0.411391+0.009051    test-rmse:0.640725+0.062137 
## [69] train-rmse:0.408830+0.009685    test-rmse:0.640382+0.060634 
## [70] train-rmse:0.407192+0.009406    test-rmse:0.641156+0.060903 
## [71] train-rmse:0.405285+0.009852    test-rmse:0.641433+0.061169 
## [72] train-rmse:0.402298+0.009457    test-rmse:0.641320+0.060387 
## [73] train-rmse:0.400251+0.010149    test-rmse:0.641155+0.060399 
## [74] train-rmse:0.398675+0.010348    test-rmse:0.641059+0.060960 
## [75] train-rmse:0.396247+0.009907    test-rmse:0.638938+0.060291 
## [76] train-rmse:0.394401+0.010111    test-rmse:0.638405+0.059985 
## [77] train-rmse:0.392826+0.010629    test-rmse:0.637658+0.059382 
## [78] train-rmse:0.391063+0.010785    test-rmse:0.637543+0.060214 
## [79] train-rmse:0.388967+0.011270    test-rmse:0.637437+0.059579 
## [80] train-rmse:0.387336+0.011376    test-rmse:0.636615+0.060412 
## [81] train-rmse:0.385777+0.010869    test-rmse:0.636325+0.060205 
## [82] train-rmse:0.383632+0.009680    test-rmse:0.635574+0.058963 
## [83] train-rmse:0.381425+0.010179    test-rmse:0.636731+0.059522 
## [84] train-rmse:0.379762+0.011156    test-rmse:0.636537+0.059503 
## [85] train-rmse:0.377680+0.011266    test-rmse:0.636254+0.059705 
## [86] train-rmse:0.376293+0.011369    test-rmse:0.635841+0.059501 
## [87] train-rmse:0.374871+0.011863    test-rmse:0.634985+0.058095 
## [88] train-rmse:0.373383+0.012240    test-rmse:0.635581+0.058225 
## [89] train-rmse:0.371390+0.011752    test-rmse:0.635257+0.057941 
## [90] train-rmse:0.368897+0.011787    test-rmse:0.635336+0.057768 
## [91] train-rmse:0.366865+0.011802    test-rmse:0.634767+0.057419 
## [92] train-rmse:0.365874+0.011775    test-rmse:0.635351+0.057908 
## [93] train-rmse:0.364173+0.012279    test-rmse:0.634418+0.057397 
## [94] train-rmse:0.362237+0.011667    test-rmse:0.634603+0.057625 
## [95] train-rmse:0.360002+0.012106    test-rmse:0.634683+0.057412 
## [96] train-rmse:0.358743+0.012294    test-rmse:0.634802+0.057454 
## [97] train-rmse:0.357291+0.012623    test-rmse:0.634353+0.057716 
## [98] train-rmse:0.356134+0.012634    test-rmse:0.634425+0.057649 
## [99] train-rmse:0.354677+0.012781    test-rmse:0.634243+0.057253 
## [100]    train-rmse:0.352753+0.012335    test-rmse:0.633299+0.056767 
## [101]    train-rmse:0.351637+0.012540    test-rmse:0.633052+0.056694 
## [102]    train-rmse:0.349496+0.013124    test-rmse:0.632740+0.056763 
## [103]    train-rmse:0.348787+0.013153    test-rmse:0.632905+0.056630 
## [104]    train-rmse:0.346476+0.012790    test-rmse:0.630580+0.056374 
## [105]    train-rmse:0.344508+0.012537    test-rmse:0.629714+0.057357 
## [106]    train-rmse:0.343597+0.012546    test-rmse:0.629154+0.057281 
## [107]    train-rmse:0.342752+0.012549    test-rmse:0.628913+0.057646 
## [108]    train-rmse:0.341362+0.012234    test-rmse:0.628554+0.057557 
## [109]    train-rmse:0.339933+0.012701    test-rmse:0.629120+0.057886 
## [110]    train-rmse:0.338563+0.012661    test-rmse:0.628505+0.057616 
## [111]    train-rmse:0.336106+0.012599    test-rmse:0.627711+0.056959 
## [112]    train-rmse:0.333851+0.012302    test-rmse:0.626202+0.057136 
## [113]    train-rmse:0.332184+0.012149    test-rmse:0.626050+0.056870 
## [114]    train-rmse:0.330187+0.012428    test-rmse:0.627108+0.057039 
## [115]    train-rmse:0.328121+0.011705    test-rmse:0.626754+0.056970 
## [116]    train-rmse:0.326423+0.011414    test-rmse:0.626957+0.056614 
## [117]    train-rmse:0.325187+0.011496    test-rmse:0.626312+0.056300 
## [118]    train-rmse:0.324314+0.011686    test-rmse:0.626194+0.056292 
## [119]    train-rmse:0.323275+0.011564    test-rmse:0.626227+0.056337 
## Stopping. Best iteration:
## [113]    train-rmse:0.332184+0.012149    test-rmse:0.626050+0.056870
## 
## 24 
## [1]  train-rmse:1.399406+0.013937    test-rmse:1.409628+0.069298 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.229500+0.010848    test-rmse:1.248725+0.070128 
## [3]  train-rmse:1.091599+0.008706    test-rmse:1.117608+0.070936 
## [4]  train-rmse:0.980335+0.006229    test-rmse:1.016304+0.070508 
## [5]  train-rmse:0.892466+0.004627    test-rmse:0.938576+0.074474 
## [6]  train-rmse:0.819198+0.003670    test-rmse:0.874619+0.070120 
## [7]  train-rmse:0.763801+0.003461    test-rmse:0.828527+0.066701 
## [8]  train-rmse:0.718665+0.004298    test-rmse:0.792227+0.067962 
## [9]  train-rmse:0.685064+0.003968    test-rmse:0.766333+0.065128 
## [10] train-rmse:0.655555+0.004383    test-rmse:0.741869+0.062623 
## [11] train-rmse:0.627703+0.005829    test-rmse:0.725432+0.061146 
## [12] train-rmse:0.607278+0.006801    test-rmse:0.710307+0.059114 
## [13] train-rmse:0.591283+0.008678    test-rmse:0.701054+0.056903 
## [14] train-rmse:0.577654+0.008339    test-rmse:0.695819+0.057396 
## [15] train-rmse:0.565767+0.009279    test-rmse:0.691829+0.058398 
## [16] train-rmse:0.556792+0.010097    test-rmse:0.685564+0.057865 
## [17] train-rmse:0.545756+0.009014    test-rmse:0.681836+0.057676 
## [18] train-rmse:0.535744+0.006766    test-rmse:0.679126+0.056627 
## [19] train-rmse:0.529216+0.006723    test-rmse:0.678960+0.059235 
## [20] train-rmse:0.522881+0.006891    test-rmse:0.675792+0.061724 
## [21] train-rmse:0.517001+0.007659    test-rmse:0.674111+0.060550 
## [22] train-rmse:0.510421+0.007970    test-rmse:0.672259+0.060228 
## [23] train-rmse:0.503934+0.007792    test-rmse:0.667171+0.059195 
## [24] train-rmse:0.499042+0.008173    test-rmse:0.663254+0.060038 
## [25] train-rmse:0.493604+0.009398    test-rmse:0.661122+0.061745 
## [26] train-rmse:0.486241+0.006170    test-rmse:0.657307+0.061680 
## [27] train-rmse:0.480556+0.005718    test-rmse:0.655897+0.063315 
## [28] train-rmse:0.474731+0.004511    test-rmse:0.654248+0.064538 
## [29] train-rmse:0.469797+0.003216    test-rmse:0.654927+0.064481 
## [30] train-rmse:0.463114+0.005974    test-rmse:0.654790+0.063901 
## [31] train-rmse:0.459274+0.006305    test-rmse:0.651669+0.064651 
## [32] train-rmse:0.455723+0.006040    test-rmse:0.652110+0.064257 
## [33] train-rmse:0.451530+0.006040    test-rmse:0.651523+0.067045 
## [34] train-rmse:0.446700+0.006143    test-rmse:0.650618+0.068082 
## [35] train-rmse:0.442576+0.005990    test-rmse:0.651692+0.068660 
## [36] train-rmse:0.434714+0.008450    test-rmse:0.649327+0.066924 
## [37] train-rmse:0.429579+0.009107    test-rmse:0.647086+0.066120 
## [38] train-rmse:0.424914+0.009022    test-rmse:0.646559+0.067218 
## [39] train-rmse:0.420276+0.010766    test-rmse:0.645151+0.065787 
## [40] train-rmse:0.416045+0.010395    test-rmse:0.644430+0.066795 
## [41] train-rmse:0.413039+0.011126    test-rmse:0.644540+0.067099 
## [42] train-rmse:0.408394+0.011420    test-rmse:0.642598+0.066766 
## [43] train-rmse:0.405011+0.011238    test-rmse:0.642333+0.069004 
## [44] train-rmse:0.401977+0.011615    test-rmse:0.640730+0.069511 
## [45] train-rmse:0.397507+0.011517    test-rmse:0.638417+0.068239 
## [46] train-rmse:0.393334+0.010751    test-rmse:0.637964+0.068339 
## [47] train-rmse:0.389239+0.010979    test-rmse:0.637053+0.066925 
## [48] train-rmse:0.385502+0.011537    test-rmse:0.637038+0.067179 
## [49] train-rmse:0.383067+0.010831    test-rmse:0.635105+0.068350 
## [50] train-rmse:0.379703+0.010835    test-rmse:0.634459+0.068715 
## [51] train-rmse:0.376357+0.010158    test-rmse:0.633509+0.068566 
## [52] train-rmse:0.371760+0.011049    test-rmse:0.632401+0.067038 
## [53] train-rmse:0.368531+0.012030    test-rmse:0.632355+0.067696 
## [54] train-rmse:0.365806+0.012392    test-rmse:0.632768+0.067892 
## [55] train-rmse:0.361023+0.012553    test-rmse:0.632707+0.068749 
## [56] train-rmse:0.358299+0.013985    test-rmse:0.632504+0.070013 
## [57] train-rmse:0.355720+0.013543    test-rmse:0.632830+0.069349 
## [58] train-rmse:0.352821+0.013322    test-rmse:0.631569+0.068311 
## [59] train-rmse:0.349735+0.013880    test-rmse:0.632069+0.067539 
## [60] train-rmse:0.346360+0.013554    test-rmse:0.631056+0.067205 
## [61] train-rmse:0.344096+0.013001    test-rmse:0.630567+0.067797 
## [62] train-rmse:0.340375+0.013157    test-rmse:0.628887+0.068185 
## [63] train-rmse:0.337784+0.012826    test-rmse:0.628470+0.067765 
## [64] train-rmse:0.334258+0.011537    test-rmse:0.629807+0.067855 
## [65] train-rmse:0.331267+0.012566    test-rmse:0.630513+0.068566 
## [66] train-rmse:0.326903+0.012322    test-rmse:0.629856+0.067164 
## [67] train-rmse:0.323894+0.012301    test-rmse:0.629522+0.068109 
## [68] train-rmse:0.321701+0.012678    test-rmse:0.629542+0.068588 
## [69] train-rmse:0.318831+0.013492    test-rmse:0.628977+0.069559 
## Stopping. Best iteration:
## [63] train-rmse:0.337784+0.012826    test-rmse:0.628470+0.067765
## 
## 25 
## [1]  train-rmse:1.397298+0.014078    test-rmse:1.406588+0.071064 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.224898+0.011115    test-rmse:1.246934+0.076237 
## [3]  train-rmse:1.082947+0.008956    test-rmse:1.111611+0.074477 
## [4]  train-rmse:0.966795+0.006856    test-rmse:1.007595+0.074487 
## [5]  train-rmse:0.873620+0.007455    test-rmse:0.926386+0.071355 
## [6]  train-rmse:0.798559+0.006658    test-rmse:0.865619+0.070943 
## [7]  train-rmse:0.738960+0.005642    test-rmse:0.817784+0.067687 
## [8]  train-rmse:0.690320+0.005299    test-rmse:0.780703+0.067657 
## [9]  train-rmse:0.651571+0.004086    test-rmse:0.751829+0.064759 
## [10] train-rmse:0.620556+0.005042    test-rmse:0.731499+0.063968 
## [11] train-rmse:0.594921+0.004583    test-rmse:0.715066+0.057624 
## [12] train-rmse:0.569317+0.007718    test-rmse:0.701181+0.055194 
## [13] train-rmse:0.550500+0.006306    test-rmse:0.691554+0.054635 
## [14] train-rmse:0.535416+0.008347    test-rmse:0.687520+0.051822 
## [15] train-rmse:0.520033+0.009617    test-rmse:0.682312+0.052614 
## [16] train-rmse:0.508222+0.010746    test-rmse:0.677147+0.051713 
## [17] train-rmse:0.497271+0.013563    test-rmse:0.672993+0.050311 
## [18] train-rmse:0.484959+0.012911    test-rmse:0.666203+0.047001 
## [19] train-rmse:0.475322+0.014590    test-rmse:0.661184+0.047972 
## [20] train-rmse:0.465257+0.011732    test-rmse:0.654734+0.047418 
## [21] train-rmse:0.458061+0.013531    test-rmse:0.656205+0.046993 
## [22] train-rmse:0.449745+0.013940    test-rmse:0.655719+0.046639 
## [23] train-rmse:0.443126+0.013180    test-rmse:0.655243+0.046912 
## [24] train-rmse:0.434722+0.013972    test-rmse:0.652203+0.046237 
## [25] train-rmse:0.427762+0.011369    test-rmse:0.647870+0.046978 
## [26] train-rmse:0.421102+0.013331    test-rmse:0.645596+0.044715 
## [27] train-rmse:0.415408+0.013927    test-rmse:0.645697+0.047157 
## [28] train-rmse:0.409708+0.015501    test-rmse:0.646284+0.047723 
## [29] train-rmse:0.403960+0.016421    test-rmse:0.645035+0.047322 
## [30] train-rmse:0.397280+0.015840    test-rmse:0.642707+0.048524 
## [31] train-rmse:0.391341+0.016300    test-rmse:0.643350+0.049762 
## [32] train-rmse:0.385865+0.017729    test-rmse:0.641803+0.049661 
## [33] train-rmse:0.380306+0.016872    test-rmse:0.642468+0.049715 
## [34] train-rmse:0.374738+0.018214    test-rmse:0.642051+0.049141 
## [35] train-rmse:0.367420+0.018921    test-rmse:0.639096+0.049199 
## [36] train-rmse:0.362445+0.018718    test-rmse:0.636929+0.050295 
## [37] train-rmse:0.358546+0.018450    test-rmse:0.636619+0.050064 
## [38] train-rmse:0.352856+0.017397    test-rmse:0.635002+0.047976 
## [39] train-rmse:0.347347+0.017022    test-rmse:0.633663+0.048856 
## [40] train-rmse:0.343260+0.016194    test-rmse:0.633175+0.049593 
## [41] train-rmse:0.337617+0.015915    test-rmse:0.629957+0.049275 
## [42] train-rmse:0.333505+0.015313    test-rmse:0.628559+0.046720 
## [43] train-rmse:0.330456+0.016267    test-rmse:0.629070+0.046781 
## [44] train-rmse:0.326344+0.017060    test-rmse:0.629066+0.047135 
## [45] train-rmse:0.320982+0.015880    test-rmse:0.627885+0.047455 
## [46] train-rmse:0.316490+0.015918    test-rmse:0.627430+0.046397 
## [47] train-rmse:0.313111+0.016132    test-rmse:0.628175+0.046488 
## [48] train-rmse:0.309584+0.015243    test-rmse:0.627937+0.047391 
## [49] train-rmse:0.304993+0.015841    test-rmse:0.627268+0.046710 
## [50] train-rmse:0.299277+0.014563    test-rmse:0.627730+0.047840 
## [51] train-rmse:0.296108+0.014885    test-rmse:0.628507+0.048174 
## [52] train-rmse:0.291970+0.013987    test-rmse:0.627207+0.049089 
## [53] train-rmse:0.288392+0.015254    test-rmse:0.626658+0.047424 
## [54] train-rmse:0.285915+0.015407    test-rmse:0.627264+0.047533 
## [55] train-rmse:0.282347+0.016058    test-rmse:0.627586+0.048788 
## [56] train-rmse:0.278848+0.016058    test-rmse:0.628295+0.048939 
## [57] train-rmse:0.274467+0.015367    test-rmse:0.628484+0.049275 
## [58] train-rmse:0.270019+0.015014    test-rmse:0.628577+0.048087 
## [59] train-rmse:0.266117+0.013800    test-rmse:0.628378+0.047616 
## Stopping. Best iteration:
## [53] train-rmse:0.288392+0.015254    test-rmse:0.626658+0.047424
## 
## 26 
## [1]  train-rmse:1.395162+0.013729    test-rmse:1.407879+0.072383 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.220971+0.010717    test-rmse:1.246479+0.073746 
## [3]  train-rmse:1.077040+0.008235    test-rmse:1.116410+0.072808 
## [4]  train-rmse:0.957725+0.006109    test-rmse:1.012360+0.072264 
## [5]  train-rmse:0.859981+0.004881    test-rmse:0.933101+0.069176 
## [6]  train-rmse:0.781442+0.004871    test-rmse:0.868817+0.067286 
## [7]  train-rmse:0.717303+0.003039    test-rmse:0.816589+0.065690 
## [8]  train-rmse:0.665024+0.003749    test-rmse:0.781578+0.065321 
## [9]  train-rmse:0.622640+0.009030    test-rmse:0.753571+0.063797 
## [10] train-rmse:0.589253+0.010481    test-rmse:0.733380+0.062900 
## [11] train-rmse:0.560728+0.009522    test-rmse:0.714798+0.058987 
## [12] train-rmse:0.539343+0.010564    test-rmse:0.702139+0.058141 
## [13] train-rmse:0.512629+0.012726    test-rmse:0.689362+0.054208 
## [14] train-rmse:0.491397+0.017862    test-rmse:0.682735+0.055044 
## [15] train-rmse:0.475678+0.017109    test-rmse:0.676123+0.054435 
## [16] train-rmse:0.460075+0.017145    test-rmse:0.669233+0.055590 
## [17] train-rmse:0.445971+0.020611    test-rmse:0.667167+0.057557 
## [18] train-rmse:0.433859+0.020328    test-rmse:0.663415+0.058191 
## [19] train-rmse:0.426295+0.020241    test-rmse:0.658984+0.057283 
## [20] train-rmse:0.415954+0.021339    test-rmse:0.657027+0.057535 
## [21] train-rmse:0.409595+0.021509    test-rmse:0.654682+0.058912 
## [22] train-rmse:0.399954+0.023864    test-rmse:0.653627+0.059201 
## [23] train-rmse:0.394179+0.022162    test-rmse:0.651297+0.059960 
## [24] train-rmse:0.383972+0.018627    test-rmse:0.643857+0.057344 
## [25] train-rmse:0.375578+0.016399    test-rmse:0.642204+0.058193 
## [26] train-rmse:0.367376+0.018714    test-rmse:0.640506+0.059484 
## [27] train-rmse:0.359583+0.019196    test-rmse:0.638736+0.059462 
## [28] train-rmse:0.354700+0.018605    test-rmse:0.638165+0.059419 
## [29] train-rmse:0.348070+0.019844    test-rmse:0.638561+0.059356 
## [30] train-rmse:0.343654+0.021210    test-rmse:0.638465+0.059444 
## [31] train-rmse:0.335669+0.018507    test-rmse:0.637086+0.060285 
## [32] train-rmse:0.328121+0.018965    test-rmse:0.634418+0.058697 
## [33] train-rmse:0.320323+0.018923    test-rmse:0.633706+0.058539 
## [34] train-rmse:0.314590+0.018510    test-rmse:0.633227+0.058045 
## [35] train-rmse:0.309490+0.018482    test-rmse:0.633442+0.057953 
## [36] train-rmse:0.303975+0.017895    test-rmse:0.634050+0.058779 
## [37] train-rmse:0.296875+0.015486    test-rmse:0.632113+0.055868 
## [38] train-rmse:0.291925+0.015651    test-rmse:0.631153+0.055632 
## [39] train-rmse:0.286209+0.014942    test-rmse:0.631223+0.057161 
## [40] train-rmse:0.281331+0.013138    test-rmse:0.631782+0.058725 
## [41] train-rmse:0.274952+0.012878    test-rmse:0.629513+0.057295 
## [42] train-rmse:0.270235+0.013739    test-rmse:0.627885+0.055551 
## [43] train-rmse:0.264505+0.012731    test-rmse:0.626514+0.055707 
## [44] train-rmse:0.259462+0.012295    test-rmse:0.625197+0.054837 
## [45] train-rmse:0.255314+0.013216    test-rmse:0.624635+0.053582 
## [46] train-rmse:0.251029+0.012370    test-rmse:0.624364+0.053897 
## [47] train-rmse:0.247416+0.012906    test-rmse:0.624507+0.054923 
## [48] train-rmse:0.244861+0.013163    test-rmse:0.624906+0.055139 
## [49] train-rmse:0.240742+0.013273    test-rmse:0.625219+0.056273 
## [50] train-rmse:0.236423+0.012530    test-rmse:0.625151+0.054945 
## [51] train-rmse:0.232833+0.012084    test-rmse:0.624747+0.054394 
## [52] train-rmse:0.228977+0.012396    test-rmse:0.624869+0.055021 
## Stopping. Best iteration:
## [46] train-rmse:0.251029+0.012370    test-rmse:0.624364+0.053897
## 
## 27 
## [1]  train-rmse:1.393592+0.013421    test-rmse:1.407025+0.072496 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.217450+0.011069    test-rmse:1.244622+0.072097 
## [3]  train-rmse:1.070584+0.007859    test-rmse:1.107442+0.070160 
## [4]  train-rmse:0.949626+0.005864    test-rmse:1.003642+0.073205 
## [5]  train-rmse:0.847605+0.004381    test-rmse:0.918806+0.073670 
## [6]  train-rmse:0.764955+0.005275    test-rmse:0.855522+0.072682 
## [7]  train-rmse:0.697635+0.006103    test-rmse:0.805025+0.071753 
## [8]  train-rmse:0.643761+0.007689    test-rmse:0.767905+0.070282 
## [9]  train-rmse:0.598798+0.009797    test-rmse:0.738930+0.068658 
## [10] train-rmse:0.562308+0.011138    test-rmse:0.715797+0.064932 
## [11] train-rmse:0.529939+0.008076    test-rmse:0.696913+0.058894 
## [12] train-rmse:0.502961+0.007014    test-rmse:0.682108+0.055392 
## [13] train-rmse:0.480272+0.012402    test-rmse:0.676954+0.055713 
## [14] train-rmse:0.456788+0.016193    test-rmse:0.669097+0.054061 
## [15] train-rmse:0.439501+0.015870    test-rmse:0.658949+0.051397 
## [16] train-rmse:0.422519+0.018152    test-rmse:0.656095+0.052616 
## [17] train-rmse:0.405353+0.017786    test-rmse:0.649950+0.054029 
## [18] train-rmse:0.389291+0.017281    test-rmse:0.645281+0.053840 
## [19] train-rmse:0.375539+0.018087    test-rmse:0.645357+0.054690 
## [20] train-rmse:0.365502+0.016010    test-rmse:0.642319+0.051630 
## [21] train-rmse:0.356461+0.014603    test-rmse:0.639528+0.049003 
## [22] train-rmse:0.348522+0.013379    test-rmse:0.638855+0.049842 
## [23] train-rmse:0.341075+0.013919    test-rmse:0.637261+0.051737 
## [24] train-rmse:0.331914+0.013623    test-rmse:0.637766+0.052130 
## [25] train-rmse:0.322718+0.012720    test-rmse:0.637076+0.051922 
## [26] train-rmse:0.312983+0.014543    test-rmse:0.636345+0.049798 
## [27] train-rmse:0.306305+0.014729    test-rmse:0.634600+0.048781 
## [28] train-rmse:0.297833+0.013578    test-rmse:0.632596+0.049481 
## [29] train-rmse:0.289577+0.014195    test-rmse:0.630363+0.050877 
## [30] train-rmse:0.284043+0.015184    test-rmse:0.631836+0.052099 
## [31] train-rmse:0.277895+0.015386    test-rmse:0.631870+0.053316 
## [32] train-rmse:0.271804+0.015261    test-rmse:0.630715+0.054354 
## [33] train-rmse:0.265073+0.015726    test-rmse:0.630551+0.053589 
## [34] train-rmse:0.259868+0.014093    test-rmse:0.629749+0.053552 
## [35] train-rmse:0.254741+0.013644    test-rmse:0.627941+0.053625 
## [36] train-rmse:0.250274+0.013524    test-rmse:0.628588+0.053166 
## [37] train-rmse:0.245466+0.013455    test-rmse:0.627096+0.053050 
## [38] train-rmse:0.241907+0.013158    test-rmse:0.627387+0.052597 
## [39] train-rmse:0.237542+0.013572    test-rmse:0.628887+0.051942 
## [40] train-rmse:0.231679+0.013689    test-rmse:0.628274+0.053182 
## [41] train-rmse:0.225927+0.013314    test-rmse:0.626771+0.052030 
## [42] train-rmse:0.222452+0.012674    test-rmse:0.627001+0.053402 
## [43] train-rmse:0.217188+0.013125    test-rmse:0.626413+0.052920 
## [44] train-rmse:0.212496+0.013276    test-rmse:0.625373+0.052872 
## [45] train-rmse:0.209174+0.012899    test-rmse:0.625863+0.053421 
## [46] train-rmse:0.204770+0.013139    test-rmse:0.625829+0.052756 
## [47] train-rmse:0.202028+0.013099    test-rmse:0.625933+0.053414 
## [48] train-rmse:0.197399+0.013184    test-rmse:0.626336+0.053326 
## [49] train-rmse:0.194447+0.013436    test-rmse:0.626101+0.052779 
## [50] train-rmse:0.190124+0.012548    test-rmse:0.625262+0.052810 
## [51] train-rmse:0.184348+0.011791    test-rmse:0.625243+0.053851 
## [52] train-rmse:0.180245+0.012017    test-rmse:0.625884+0.053607 
## [53] train-rmse:0.176334+0.011416    test-rmse:0.626518+0.052614 
## [54] train-rmse:0.173294+0.011809    test-rmse:0.626566+0.053037 
## [55] train-rmse:0.168944+0.012006    test-rmse:0.626717+0.053765 
## [56] train-rmse:0.165889+0.012245    test-rmse:0.627353+0.053864 
## [57] train-rmse:0.162842+0.012306    test-rmse:0.628050+0.053288 
## Stopping. Best iteration:
## [51] train-rmse:0.184348+0.011791    test-rmse:0.625243+0.053851
## 
## 28 
## [1]  train-rmse:1.386254+0.013890    test-rmse:1.387172+0.069872 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.216168+0.011470    test-rmse:1.218427+0.069355 
## [3]  train-rmse:1.086512+0.009589    test-rmse:1.090883+0.068957 
## [4]  train-rmse:0.989465+0.008291    test-rmse:0.998564+0.068221 
## [5]  train-rmse:0.916574+0.007675    test-rmse:0.928967+0.062798 
## [6]  train-rmse:0.863109+0.007065    test-rmse:0.876894+0.059258 
## [7]  train-rmse:0.823445+0.006652    test-rmse:0.842076+0.054188 
## [8]  train-rmse:0.794657+0.006343    test-rmse:0.819324+0.051636 
## [9]  train-rmse:0.773300+0.006141    test-rmse:0.800514+0.047242 
## [10] train-rmse:0.757361+0.005769    test-rmse:0.786547+0.045825 
## [11] train-rmse:0.745263+0.005512    test-rmse:0.775279+0.043841 
## [12] train-rmse:0.735782+0.005301    test-rmse:0.767505+0.040253 
## [13] train-rmse:0.728136+0.005196    test-rmse:0.761116+0.037856 
## [14] train-rmse:0.721942+0.005120    test-rmse:0.755740+0.035158 
## [15] train-rmse:0.716737+0.005073    test-rmse:0.750453+0.033912 
## [16] train-rmse:0.712294+0.005100    test-rmse:0.748685+0.033387 
## [17] train-rmse:0.708342+0.005047    test-rmse:0.747839+0.031177 
## [18] train-rmse:0.704706+0.004947    test-rmse:0.745026+0.032503 
## [19] train-rmse:0.701522+0.004885    test-rmse:0.743263+0.031816 
## [20] train-rmse:0.698570+0.004981    test-rmse:0.742122+0.031182 
## [21] train-rmse:0.695811+0.005013    test-rmse:0.739517+0.029702 
## [22] train-rmse:0.693203+0.005067    test-rmse:0.736836+0.028270 
## [23] train-rmse:0.690720+0.005122    test-rmse:0.735573+0.026990 
## [24] train-rmse:0.688337+0.005147    test-rmse:0.735010+0.028163 
## [25] train-rmse:0.686041+0.005184    test-rmse:0.734282+0.029038 
## [26] train-rmse:0.683835+0.005229    test-rmse:0.733126+0.029456 
## [27] train-rmse:0.681693+0.005218    test-rmse:0.732648+0.029103 
## [28] train-rmse:0.679706+0.005181    test-rmse:0.731229+0.028511 
## [29] train-rmse:0.677772+0.005142    test-rmse:0.729950+0.028617 
## [30] train-rmse:0.675877+0.005122    test-rmse:0.729291+0.028439 
## [31] train-rmse:0.673989+0.005099    test-rmse:0.728808+0.030725 
## [32] train-rmse:0.672204+0.005100    test-rmse:0.727263+0.031030 
## [33] train-rmse:0.670445+0.005104    test-rmse:0.726812+0.030081 
## [34] train-rmse:0.668713+0.005122    test-rmse:0.726932+0.031033 
## [35] train-rmse:0.667040+0.005140    test-rmse:0.727690+0.031673 
## [36] train-rmse:0.665434+0.005196    test-rmse:0.727941+0.033779 
## [37] train-rmse:0.663880+0.005247    test-rmse:0.726393+0.033144 
## [38] train-rmse:0.662375+0.005320    test-rmse:0.724751+0.032068 
## [39] train-rmse:0.660889+0.005367    test-rmse:0.724453+0.032007 
## [40] train-rmse:0.659436+0.005382    test-rmse:0.723734+0.031627 
## [41] train-rmse:0.658001+0.005396    test-rmse:0.723228+0.031026 
## [42] train-rmse:0.656589+0.005433    test-rmse:0.723000+0.032523 
## [43] train-rmse:0.655220+0.005456    test-rmse:0.721882+0.032543 
## [44] train-rmse:0.653879+0.005437    test-rmse:0.721372+0.033002 
## [45] train-rmse:0.652575+0.005441    test-rmse:0.720096+0.032962 
## [46] train-rmse:0.651292+0.005469    test-rmse:0.719258+0.032951 
## [47] train-rmse:0.650068+0.005498    test-rmse:0.719818+0.032513 
## [48] train-rmse:0.648851+0.005530    test-rmse:0.720158+0.033933 
## [49] train-rmse:0.647664+0.005553    test-rmse:0.720759+0.034892 
## [50] train-rmse:0.646516+0.005577    test-rmse:0.720412+0.035151 
## [51] train-rmse:0.645374+0.005603    test-rmse:0.718786+0.034140 
## [52] train-rmse:0.644239+0.005626    test-rmse:0.717744+0.034202 
## [53] train-rmse:0.643136+0.005644    test-rmse:0.717968+0.034042 
## [54] train-rmse:0.642059+0.005633    test-rmse:0.715889+0.033958 
## [55] train-rmse:0.641014+0.005646    test-rmse:0.715932+0.035664 
## [56] train-rmse:0.639993+0.005669    test-rmse:0.715540+0.035597 
## [57] train-rmse:0.638968+0.005675    test-rmse:0.716411+0.036466 
## [58] train-rmse:0.637959+0.005687    test-rmse:0.715665+0.035613 
## [59] train-rmse:0.636976+0.005695    test-rmse:0.715017+0.034822 
## [60] train-rmse:0.635982+0.005728    test-rmse:0.715719+0.035052 
## [61] train-rmse:0.634993+0.005718    test-rmse:0.714681+0.035500 
## [62] train-rmse:0.634042+0.005720    test-rmse:0.714550+0.035866 
## [63] train-rmse:0.633111+0.005724    test-rmse:0.714076+0.035978 
## [64] train-rmse:0.632200+0.005720    test-rmse:0.713151+0.035917 
## [65] train-rmse:0.631296+0.005755    test-rmse:0.711778+0.035484 
## [66] train-rmse:0.630395+0.005788    test-rmse:0.711601+0.035119 
## [67] train-rmse:0.629503+0.005801    test-rmse:0.711868+0.035408 
## [68] train-rmse:0.628628+0.005809    test-rmse:0.712657+0.035950 
## [69] train-rmse:0.627760+0.005829    test-rmse:0.712150+0.036128 
## [70] train-rmse:0.626901+0.005830    test-rmse:0.711869+0.035423 
## [71] train-rmse:0.626059+0.005824    test-rmse:0.711035+0.033936 
## [72] train-rmse:0.625238+0.005826    test-rmse:0.710714+0.032817 
## [73] train-rmse:0.624421+0.005847    test-rmse:0.709653+0.033400 
## [74] train-rmse:0.623628+0.005864    test-rmse:0.708539+0.032721 
## [75] train-rmse:0.622847+0.005863    test-rmse:0.707703+0.032734 
## [76] train-rmse:0.622058+0.005859    test-rmse:0.707193+0.032807 
## [77] train-rmse:0.621292+0.005863    test-rmse:0.706839+0.032292 
## [78] train-rmse:0.620537+0.005863    test-rmse:0.706051+0.032474 
## [79] train-rmse:0.619795+0.005857    test-rmse:0.705820+0.033348 
## [80] train-rmse:0.619069+0.005872    test-rmse:0.705437+0.033496 
## [81] train-rmse:0.618346+0.005899    test-rmse:0.704932+0.033750 
## [82] train-rmse:0.617614+0.005943    test-rmse:0.702929+0.033821 
## [83] train-rmse:0.616892+0.005962    test-rmse:0.702020+0.033611 
## [84] train-rmse:0.616182+0.005966    test-rmse:0.703292+0.033732 
## [85] train-rmse:0.615479+0.005971    test-rmse:0.703141+0.033931 
## [86] train-rmse:0.614769+0.005971    test-rmse:0.702747+0.033885 
## [87] train-rmse:0.614075+0.005973    test-rmse:0.703666+0.034455 
## [88] train-rmse:0.613387+0.005967    test-rmse:0.701904+0.034528 
## [89] train-rmse:0.612719+0.005969    test-rmse:0.702169+0.035440 
## [90] train-rmse:0.612062+0.005973    test-rmse:0.701745+0.035258 
## [91] train-rmse:0.611420+0.005992    test-rmse:0.701699+0.035146 
## [92] train-rmse:0.610797+0.006009    test-rmse:0.701403+0.035232 
## [93] train-rmse:0.610178+0.006018    test-rmse:0.701847+0.035258 
## [94] train-rmse:0.609562+0.006024    test-rmse:0.701704+0.035102 
## [95] train-rmse:0.608946+0.006026    test-rmse:0.700213+0.035006 
## [96] train-rmse:0.608321+0.006025    test-rmse:0.699378+0.034811 
## [97] train-rmse:0.607713+0.006033    test-rmse:0.699348+0.034937 
## [98] train-rmse:0.607094+0.006054    test-rmse:0.699364+0.035213 
## [99] train-rmse:0.606483+0.006051    test-rmse:0.699241+0.035088 
## [100]    train-rmse:0.605881+0.006056    test-rmse:0.699096+0.034797 
## [101]    train-rmse:0.605294+0.006073    test-rmse:0.699064+0.034712 
## [102]    train-rmse:0.604716+0.006077    test-rmse:0.698852+0.034818 
## [103]    train-rmse:0.604141+0.006089    test-rmse:0.698445+0.034651 
## [104]    train-rmse:0.603561+0.006079    test-rmse:0.698568+0.034368 
## [105]    train-rmse:0.602989+0.006077    test-rmse:0.698331+0.035074 
## [106]    train-rmse:0.602426+0.006092    test-rmse:0.698404+0.034735 
## [107]    train-rmse:0.601868+0.006103    test-rmse:0.698027+0.034732 
## [108]    train-rmse:0.601326+0.006113    test-rmse:0.696931+0.034822 
## [109]    train-rmse:0.600794+0.006125    test-rmse:0.696293+0.036232 
## [110]    train-rmse:0.600262+0.006137    test-rmse:0.696492+0.036413 
## [111]    train-rmse:0.599736+0.006148    test-rmse:0.695943+0.035746 
## [112]    train-rmse:0.599198+0.006157    test-rmse:0.695242+0.035929 
## [113]    train-rmse:0.598668+0.006164    test-rmse:0.695176+0.034966 
## [114]    train-rmse:0.598147+0.006162    test-rmse:0.695062+0.034514 
## [115]    train-rmse:0.597644+0.006164    test-rmse:0.694483+0.034345 
## [116]    train-rmse:0.597149+0.006168    test-rmse:0.693429+0.034010 
## [117]    train-rmse:0.596662+0.006173    test-rmse:0.693735+0.034815 
## [118]    train-rmse:0.596162+0.006171    test-rmse:0.693557+0.034445 
## [119]    train-rmse:0.595661+0.006172    test-rmse:0.693282+0.034549 
## [120]    train-rmse:0.595165+0.006179    test-rmse:0.693085+0.034979 
## [121]    train-rmse:0.594668+0.006201    test-rmse:0.693266+0.035327 
## [122]    train-rmse:0.594187+0.006227    test-rmse:0.692464+0.035036 
## [123]    train-rmse:0.593707+0.006229    test-rmse:0.693157+0.034668 
## [124]    train-rmse:0.593238+0.006240    test-rmse:0.692885+0.034857 
## [125]    train-rmse:0.592771+0.006252    test-rmse:0.692599+0.034899 
## [126]    train-rmse:0.592304+0.006259    test-rmse:0.691296+0.034455 
## [127]    train-rmse:0.591850+0.006267    test-rmse:0.691446+0.034482 
## [128]    train-rmse:0.591398+0.006276    test-rmse:0.691240+0.034880 
## [129]    train-rmse:0.590949+0.006291    test-rmse:0.691619+0.034896 
## [130]    train-rmse:0.590500+0.006306    test-rmse:0.691300+0.034942 
## [131]    train-rmse:0.590045+0.006332    test-rmse:0.690502+0.035015 
## [132]    train-rmse:0.589596+0.006344    test-rmse:0.690436+0.034774 
## [133]    train-rmse:0.589156+0.006359    test-rmse:0.690155+0.034389 
## [134]    train-rmse:0.588716+0.006374    test-rmse:0.690363+0.033846 
## [135]    train-rmse:0.588273+0.006383    test-rmse:0.690404+0.034608 
## [136]    train-rmse:0.587849+0.006400    test-rmse:0.689864+0.034712 
## [137]    train-rmse:0.587428+0.006404    test-rmse:0.689933+0.034698 
## [138]    train-rmse:0.587011+0.006407    test-rmse:0.689830+0.034778 
## [139]    train-rmse:0.586605+0.006414    test-rmse:0.689027+0.035465 
## [140]    train-rmse:0.586197+0.006426    test-rmse:0.688852+0.035568 
## [141]    train-rmse:0.585786+0.006432    test-rmse:0.688562+0.036033 
## [142]    train-rmse:0.585377+0.006438    test-rmse:0.688852+0.035966 
## [143]    train-rmse:0.584984+0.006454    test-rmse:0.688303+0.036058 
## [144]    train-rmse:0.584592+0.006469    test-rmse:0.687963+0.036383 
## [145]    train-rmse:0.584201+0.006483    test-rmse:0.688647+0.036717 
## [146]    train-rmse:0.583808+0.006494    test-rmse:0.688575+0.036570 
## [147]    train-rmse:0.583414+0.006519    test-rmse:0.687991+0.036486 
## [148]    train-rmse:0.583021+0.006529    test-rmse:0.688394+0.036054 
## [149]    train-rmse:0.582632+0.006544    test-rmse:0.688149+0.036072 
## [150]    train-rmse:0.582248+0.006556    test-rmse:0.688487+0.035893 
## Stopping. Best iteration:
## [144]    train-rmse:0.584592+0.006469    test-rmse:0.687963+0.036383
## 
## 29 
## [1]  train-rmse:1.381152+0.013794    test-rmse:1.385545+0.070089 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.205546+0.011106    test-rmse:1.214618+0.071177 
## [3]  train-rmse:1.070370+0.008999    test-rmse:1.086819+0.072655 
## [4]  train-rmse:0.967096+0.007298    test-rmse:0.988236+0.070515 
## [5]  train-rmse:0.889777+0.006269    test-rmse:0.917055+0.068005 
## [6]  train-rmse:0.830374+0.004944    test-rmse:0.865415+0.067411 
## [7]  train-rmse:0.786727+0.004515    test-rmse:0.823906+0.062739 
## [8]  train-rmse:0.752379+0.005424    test-rmse:0.792678+0.059628 
## [9]  train-rmse:0.725312+0.002875    test-rmse:0.771246+0.059403 
## [10] train-rmse:0.706320+0.003313    test-rmse:0.756835+0.055024 
## [11] train-rmse:0.690827+0.005467    test-rmse:0.744696+0.051441 
## [12] train-rmse:0.678368+0.004819    test-rmse:0.738996+0.050089 
## [13] train-rmse:0.668778+0.004373    test-rmse:0.732669+0.046767 
## [14] train-rmse:0.661203+0.004135    test-rmse:0.727145+0.042348 
## [15] train-rmse:0.654784+0.004035    test-rmse:0.723435+0.042446 
## [16] train-rmse:0.648016+0.004088    test-rmse:0.717961+0.045922 
## [17] train-rmse:0.642913+0.004134    test-rmse:0.715700+0.044575 
## [18] train-rmse:0.637134+0.005851    test-rmse:0.713817+0.043296 
## [19] train-rmse:0.632994+0.006354    test-rmse:0.712612+0.043865 
## [20] train-rmse:0.627287+0.005095    test-rmse:0.709299+0.043454 
## [21] train-rmse:0.623194+0.004763    test-rmse:0.707318+0.042584 
## [22] train-rmse:0.618689+0.006066    test-rmse:0.704645+0.043437 
## [23] train-rmse:0.615181+0.006158    test-rmse:0.702498+0.042934 
## [24] train-rmse:0.611689+0.005660    test-rmse:0.702274+0.043369 
## [25] train-rmse:0.608516+0.005633    test-rmse:0.700738+0.042341 
## [26] train-rmse:0.604494+0.005305    test-rmse:0.699777+0.042496 
## [27] train-rmse:0.601011+0.004562    test-rmse:0.699997+0.043271 
## [28] train-rmse:0.598160+0.004687    test-rmse:0.700299+0.043993 
## [29] train-rmse:0.594562+0.004534    test-rmse:0.697993+0.042587 
## [30] train-rmse:0.590728+0.005017    test-rmse:0.697783+0.044498 
## [31] train-rmse:0.587521+0.005793    test-rmse:0.697504+0.044488 
## [32] train-rmse:0.584953+0.005921    test-rmse:0.697248+0.043401 
## [33] train-rmse:0.582346+0.005526    test-rmse:0.695976+0.042695 
## [34] train-rmse:0.578594+0.004415    test-rmse:0.694102+0.041517 
## [35] train-rmse:0.576103+0.004165    test-rmse:0.695033+0.043180 
## [36] train-rmse:0.572771+0.005000    test-rmse:0.691752+0.043293 
## [37] train-rmse:0.570438+0.005418    test-rmse:0.689661+0.043278 
## [38] train-rmse:0.566458+0.004903    test-rmse:0.687791+0.040593 
## [39] train-rmse:0.564179+0.005037    test-rmse:0.687049+0.041009 
## [40] train-rmse:0.559871+0.004843    test-rmse:0.684979+0.040844 
## [41] train-rmse:0.556697+0.005037    test-rmse:0.682767+0.040466 
## [42] train-rmse:0.554724+0.004973    test-rmse:0.681667+0.042077 
## [43] train-rmse:0.552855+0.005194    test-rmse:0.681252+0.040983 
## [44] train-rmse:0.550355+0.005236    test-rmse:0.680396+0.041330 
## [45] train-rmse:0.547745+0.005439    test-rmse:0.679157+0.040883 
## [46] train-rmse:0.544175+0.003673    test-rmse:0.677037+0.041713 
## [47] train-rmse:0.542435+0.003524    test-rmse:0.677541+0.042440 
## [48] train-rmse:0.539075+0.004918    test-rmse:0.676432+0.042537 
## [49] train-rmse:0.537355+0.004901    test-rmse:0.676721+0.043074 
## [50] train-rmse:0.535595+0.005103    test-rmse:0.675703+0.041712 
## [51] train-rmse:0.533000+0.004696    test-rmse:0.673199+0.041084 
## [52] train-rmse:0.530137+0.003742    test-rmse:0.671255+0.040879 
## [53] train-rmse:0.527750+0.003751    test-rmse:0.669868+0.044059 
## [54] train-rmse:0.524692+0.005300    test-rmse:0.669369+0.043711 
## [55] train-rmse:0.522590+0.005064    test-rmse:0.669095+0.043031 
## [56] train-rmse:0.521021+0.004948    test-rmse:0.669094+0.043434 
## [57] train-rmse:0.518889+0.006033    test-rmse:0.668772+0.042817 
## [58] train-rmse:0.517499+0.006124    test-rmse:0.668738+0.043662 
## [59] train-rmse:0.515891+0.006407    test-rmse:0.666942+0.043333 
## [60] train-rmse:0.514625+0.006539    test-rmse:0.666467+0.043914 
## [61] train-rmse:0.512952+0.006500    test-rmse:0.664941+0.043964 
## [62] train-rmse:0.510572+0.007904    test-rmse:0.665344+0.043631 
## [63] train-rmse:0.508968+0.007813    test-rmse:0.665755+0.043692 
## [64] train-rmse:0.507492+0.008420    test-rmse:0.665305+0.043645 
## [65] train-rmse:0.506048+0.008213    test-rmse:0.665448+0.044466 
## [66] train-rmse:0.503193+0.008770    test-rmse:0.662994+0.042446 
## [67] train-rmse:0.501717+0.008599    test-rmse:0.661510+0.042643 
## [68] train-rmse:0.500096+0.009169    test-rmse:0.660838+0.042311 
## [69] train-rmse:0.498601+0.009167    test-rmse:0.660233+0.042876 
## [70] train-rmse:0.496605+0.009142    test-rmse:0.658681+0.042878 
## [71] train-rmse:0.495387+0.009051    test-rmse:0.659409+0.042725 
## [72] train-rmse:0.493136+0.009480    test-rmse:0.657787+0.041955 
## [73] train-rmse:0.491563+0.009193    test-rmse:0.656972+0.042401 
## [74] train-rmse:0.489832+0.008805    test-rmse:0.656140+0.043518 
## [75] train-rmse:0.487744+0.009393    test-rmse:0.654178+0.041762 
## [76] train-rmse:0.486014+0.009298    test-rmse:0.653260+0.042161 
## [77] train-rmse:0.484672+0.009749    test-rmse:0.651855+0.041505 
## [78] train-rmse:0.483490+0.009862    test-rmse:0.650946+0.041476 
## [79] train-rmse:0.481782+0.009490    test-rmse:0.650423+0.040993 
## [80] train-rmse:0.479950+0.008795    test-rmse:0.649715+0.040595 
## [81] train-rmse:0.478767+0.009187    test-rmse:0.648408+0.040322 
## [82] train-rmse:0.477814+0.009180    test-rmse:0.647894+0.040163 
## [83] train-rmse:0.476336+0.008973    test-rmse:0.647929+0.040395 
## [84] train-rmse:0.474426+0.008681    test-rmse:0.647780+0.040663 
## [85] train-rmse:0.473503+0.008640    test-rmse:0.647856+0.040856 
## [86] train-rmse:0.472595+0.008700    test-rmse:0.648709+0.040961 
## [87] train-rmse:0.471782+0.008760    test-rmse:0.649093+0.041462 
## [88] train-rmse:0.470762+0.008888    test-rmse:0.649824+0.042191 
## [89] train-rmse:0.469704+0.008904    test-rmse:0.648775+0.042019 
## [90] train-rmse:0.468241+0.008523    test-rmse:0.646478+0.041458 
## [91] train-rmse:0.466348+0.009685    test-rmse:0.646263+0.042014 
## [92] train-rmse:0.464751+0.009538    test-rmse:0.645191+0.042281 
## [93] train-rmse:0.463306+0.009886    test-rmse:0.645527+0.041400 
## [94] train-rmse:0.462196+0.010130    test-rmse:0.644410+0.040499 
## [95] train-rmse:0.460361+0.010351    test-rmse:0.643281+0.039760 
## [96] train-rmse:0.459493+0.010335    test-rmse:0.642911+0.039778 
## [97] train-rmse:0.457975+0.009714    test-rmse:0.642624+0.041013 
## [98] train-rmse:0.456905+0.010253    test-rmse:0.642600+0.041527 
## [99] train-rmse:0.455754+0.009935    test-rmse:0.642848+0.041551 
## [100]    train-rmse:0.455004+0.010049    test-rmse:0.642186+0.041289 
## [101]    train-rmse:0.453723+0.010382    test-rmse:0.641875+0.041602 
## [102]    train-rmse:0.452093+0.010608    test-rmse:0.641259+0.040711 
## [103]    train-rmse:0.449737+0.010891    test-rmse:0.640597+0.040998 
## [104]    train-rmse:0.447937+0.011277    test-rmse:0.640544+0.040691 
## [105]    train-rmse:0.446599+0.010890    test-rmse:0.640005+0.041288 
## [106]    train-rmse:0.445230+0.011505    test-rmse:0.639909+0.041170 
## [107]    train-rmse:0.444375+0.011595    test-rmse:0.639396+0.041663 
## [108]    train-rmse:0.443162+0.011569    test-rmse:0.638341+0.040901 
## [109]    train-rmse:0.442235+0.011887    test-rmse:0.637447+0.041069 
## [110]    train-rmse:0.441526+0.011884    test-rmse:0.638646+0.041126 
## [111]    train-rmse:0.440876+0.011840    test-rmse:0.639135+0.041754 
## [112]    train-rmse:0.440197+0.011936    test-rmse:0.639794+0.042115 
## [113]    train-rmse:0.439367+0.012206    test-rmse:0.640127+0.042390 
## [114]    train-rmse:0.438178+0.011820    test-rmse:0.639944+0.042429 
## [115]    train-rmse:0.437096+0.011883    test-rmse:0.638592+0.043672 
## Stopping. Best iteration:
## [109]    train-rmse:0.442235+0.011887    test-rmse:0.637447+0.041069
## 
## 30 
## [1]  train-rmse:1.377689+0.013505    test-rmse:1.383696+0.069084 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.196955+0.010890    test-rmse:1.211703+0.069233 
## [3]  train-rmse:1.057188+0.008910    test-rmse:1.080950+0.069180 
## [4]  train-rmse:0.949260+0.007848    test-rmse:0.981128+0.067817 
## [5]  train-rmse:0.865711+0.005727    test-rmse:0.901350+0.068007 
## [6]  train-rmse:0.804283+0.004988    test-rmse:0.849308+0.066754 
## [7]  train-rmse:0.755389+0.004763    test-rmse:0.807006+0.067751 
## [8]  train-rmse:0.717349+0.006294    test-rmse:0.775613+0.060277 
## [9]  train-rmse:0.684490+0.004345    test-rmse:0.752724+0.060496 
## [10] train-rmse:0.659674+0.004608    test-rmse:0.734018+0.054336 
## [11] train-rmse:0.641251+0.004593    test-rmse:0.721917+0.053745 
## [12] train-rmse:0.627161+0.005627    test-rmse:0.712229+0.052537 
## [13] train-rmse:0.615472+0.006454    test-rmse:0.708165+0.053020 
## [14] train-rmse:0.604697+0.005190    test-rmse:0.701570+0.052975 
## [15] train-rmse:0.595836+0.006018    test-rmse:0.697212+0.049975 
## [16] train-rmse:0.588803+0.005803    test-rmse:0.695068+0.054905 
## [17] train-rmse:0.582087+0.006586    test-rmse:0.691844+0.054201 
## [18] train-rmse:0.574600+0.005666    test-rmse:0.690409+0.052098 
## [19] train-rmse:0.567517+0.006011    test-rmse:0.685341+0.051740 
## [20] train-rmse:0.559710+0.005847    test-rmse:0.680551+0.051985 
## [21] train-rmse:0.551681+0.005761    test-rmse:0.678494+0.053722 
## [22] train-rmse:0.546622+0.005142    test-rmse:0.677212+0.055172 
## [23] train-rmse:0.542335+0.005035    test-rmse:0.675585+0.054924 
## [24] train-rmse:0.537238+0.005613    test-rmse:0.675267+0.053796 
## [25] train-rmse:0.530844+0.002332    test-rmse:0.673708+0.055517 
## [26] train-rmse:0.526897+0.002317    test-rmse:0.669051+0.057532 
## [27] train-rmse:0.521344+0.002385    test-rmse:0.668508+0.057055 
## [28] train-rmse:0.516885+0.002386    test-rmse:0.667070+0.054223 
## [29] train-rmse:0.510896+0.002144    test-rmse:0.666412+0.055325 
## [30] train-rmse:0.507661+0.002408    test-rmse:0.664615+0.056703 
## [31] train-rmse:0.503688+0.003448    test-rmse:0.665651+0.056609 
## [32] train-rmse:0.499886+0.003245    test-rmse:0.663934+0.056922 
## [33] train-rmse:0.495424+0.002552    test-rmse:0.661493+0.057234 
## [34] train-rmse:0.491305+0.003867    test-rmse:0.659735+0.057035 
## [35] train-rmse:0.487567+0.004994    test-rmse:0.658760+0.056682 
## [36] train-rmse:0.482773+0.004181    test-rmse:0.656536+0.055673 
## [37] train-rmse:0.478964+0.003648    test-rmse:0.656562+0.055020 
## [38] train-rmse:0.475311+0.003707    test-rmse:0.655973+0.055075 
## [39] train-rmse:0.470998+0.004235    test-rmse:0.655620+0.055714 
## [40] train-rmse:0.467651+0.005391    test-rmse:0.654725+0.058920 
## [41] train-rmse:0.463389+0.005202    test-rmse:0.652133+0.057947 
## [42] train-rmse:0.460566+0.005605    test-rmse:0.651532+0.058015 
## [43] train-rmse:0.458499+0.005785    test-rmse:0.651623+0.058521 
## [44] train-rmse:0.455107+0.005110    test-rmse:0.651286+0.059391 
## [45] train-rmse:0.449961+0.005796    test-rmse:0.648788+0.057543 
## [46] train-rmse:0.447185+0.005541    test-rmse:0.647899+0.057186 
## [47] train-rmse:0.444272+0.005631    test-rmse:0.648217+0.058244 
## [48] train-rmse:0.442560+0.005808    test-rmse:0.646672+0.057749 
## [49] train-rmse:0.440059+0.006056    test-rmse:0.645924+0.058248 
## [50] train-rmse:0.435990+0.005665    test-rmse:0.645116+0.059402 
## [51] train-rmse:0.433789+0.005492    test-rmse:0.645592+0.059262 
## [52] train-rmse:0.431527+0.005375    test-rmse:0.644916+0.058384 
## [53] train-rmse:0.428256+0.006475    test-rmse:0.644604+0.058317 
## [54] train-rmse:0.426060+0.006601    test-rmse:0.644415+0.059294 
## [55] train-rmse:0.424228+0.006196    test-rmse:0.643309+0.059425 
## [56] train-rmse:0.421406+0.006717    test-rmse:0.642671+0.059876 
## [57] train-rmse:0.418043+0.007923    test-rmse:0.642604+0.058548 
## [58] train-rmse:0.416234+0.008309    test-rmse:0.642483+0.058775 
## [59] train-rmse:0.410913+0.007329    test-rmse:0.639091+0.057384 
## [60] train-rmse:0.408938+0.008470    test-rmse:0.639250+0.057453 
## [61] train-rmse:0.406954+0.008554    test-rmse:0.639399+0.057180 
## [62] train-rmse:0.405414+0.008822    test-rmse:0.639140+0.057018 
## [63] train-rmse:0.403392+0.008605    test-rmse:0.640857+0.057661 
## [64] train-rmse:0.399817+0.007513    test-rmse:0.638527+0.056725 
## [65] train-rmse:0.397755+0.007278    test-rmse:0.636585+0.056991 
## [66] train-rmse:0.393746+0.006660    test-rmse:0.636644+0.056797 
## [67] train-rmse:0.390815+0.007144    test-rmse:0.636564+0.056882 
## [68] train-rmse:0.388395+0.007244    test-rmse:0.636082+0.057967 
## [69] train-rmse:0.385942+0.007891    test-rmse:0.637157+0.058299 
## [70] train-rmse:0.383760+0.008863    test-rmse:0.638445+0.058479 
## [71] train-rmse:0.381746+0.009583    test-rmse:0.638472+0.058447 
## [72] train-rmse:0.380036+0.009568    test-rmse:0.637697+0.058878 
## [73] train-rmse:0.376688+0.009897    test-rmse:0.635977+0.057916 
## [74] train-rmse:0.374820+0.010302    test-rmse:0.636674+0.059067 
## [75] train-rmse:0.373227+0.010446    test-rmse:0.635886+0.058843 
## [76] train-rmse:0.371546+0.011413    test-rmse:0.634814+0.057871 
## [77] train-rmse:0.369672+0.011753    test-rmse:0.635472+0.058511 
## [78] train-rmse:0.368488+0.012002    test-rmse:0.635570+0.058686 
## [79] train-rmse:0.365714+0.012327    test-rmse:0.634646+0.058606 
## [80] train-rmse:0.363539+0.012451    test-rmse:0.635519+0.058697 
## [81] train-rmse:0.361989+0.013308    test-rmse:0.634660+0.058489 
## [82] train-rmse:0.359874+0.013607    test-rmse:0.634355+0.059217 
## [83] train-rmse:0.356694+0.013263    test-rmse:0.631489+0.057770 
## [84] train-rmse:0.354205+0.011399    test-rmse:0.631486+0.058245 
## [85] train-rmse:0.351628+0.010556    test-rmse:0.629813+0.057917 
## [86] train-rmse:0.349725+0.010145    test-rmse:0.630127+0.058503 
## [87] train-rmse:0.348152+0.010730    test-rmse:0.630763+0.058683 
## [88] train-rmse:0.345999+0.011348    test-rmse:0.631369+0.058778 
## [89] train-rmse:0.343922+0.011421    test-rmse:0.631355+0.057896 
## [90] train-rmse:0.342914+0.011463    test-rmse:0.631891+0.058483 
## [91] train-rmse:0.341803+0.011510    test-rmse:0.632389+0.059210 
## Stopping. Best iteration:
## [85] train-rmse:0.351628+0.010556    test-rmse:0.629813+0.057917
## 
## 31 
## [1]  train-rmse:1.374140+0.013558    test-rmse:1.385912+0.069554 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.188718+0.010164    test-rmse:1.210333+0.070713 
## [3]  train-rmse:1.042281+0.007696    test-rmse:1.072393+0.071886 
## [4]  train-rmse:0.929049+0.005695    test-rmse:0.972185+0.073727 
## [5]  train-rmse:0.842377+0.004845    test-rmse:0.896957+0.073317 
## [6]  train-rmse:0.775246+0.005049    test-rmse:0.836303+0.072514 
## [7]  train-rmse:0.724517+0.004479    test-rmse:0.793059+0.072289 
## [8]  train-rmse:0.684845+0.005078    test-rmse:0.764155+0.068944 
## [9]  train-rmse:0.652293+0.005858    test-rmse:0.744665+0.069025 
## [10] train-rmse:0.623760+0.009413    test-rmse:0.727359+0.067284 
## [11] train-rmse:0.601540+0.008411    test-rmse:0.711324+0.064888 
## [12] train-rmse:0.584144+0.007282    test-rmse:0.700986+0.061470 
## [13] train-rmse:0.570866+0.007720    test-rmse:0.696907+0.061795 
## [14] train-rmse:0.557208+0.004032    test-rmse:0.687709+0.061509 
## [15] train-rmse:0.546925+0.003963    test-rmse:0.684691+0.064238 
## [16] train-rmse:0.539361+0.003805    test-rmse:0.681121+0.064887 
## [17] train-rmse:0.528390+0.005729    test-rmse:0.675703+0.059815 
## [18] train-rmse:0.521994+0.005455    test-rmse:0.677699+0.061597 
## [19] train-rmse:0.511626+0.006696    test-rmse:0.673847+0.063449 
## [20] train-rmse:0.504913+0.007794    test-rmse:0.672306+0.063992 
## [21] train-rmse:0.497965+0.008132    test-rmse:0.670806+0.061571 
## [22] train-rmse:0.490490+0.008719    test-rmse:0.669223+0.063025 
## [23] train-rmse:0.483388+0.008804    test-rmse:0.664858+0.063086 
## [24] train-rmse:0.475947+0.011473    test-rmse:0.665131+0.063838 
## [25] train-rmse:0.470878+0.011239    test-rmse:0.665010+0.064709 
## [26] train-rmse:0.465056+0.013001    test-rmse:0.665033+0.064601 
## [27] train-rmse:0.457351+0.012252    test-rmse:0.662420+0.064474 
## [28] train-rmse:0.451477+0.011691    test-rmse:0.659641+0.063008 
## [29] train-rmse:0.445987+0.012168    test-rmse:0.656363+0.063122 
## [30] train-rmse:0.441604+0.012725    test-rmse:0.654328+0.064195 
## [31] train-rmse:0.436571+0.012837    test-rmse:0.654702+0.065466 
## [32] train-rmse:0.430653+0.014293    test-rmse:0.654931+0.065674 
## [33] train-rmse:0.426434+0.013631    test-rmse:0.653207+0.066948 
## [34] train-rmse:0.422087+0.014593    test-rmse:0.651638+0.066108 
## [35] train-rmse:0.417386+0.015324    test-rmse:0.650708+0.064304 
## [36] train-rmse:0.410227+0.012043    test-rmse:0.645464+0.063680 
## [37] train-rmse:0.407283+0.012427    test-rmse:0.645264+0.062202 
## [38] train-rmse:0.401626+0.010892    test-rmse:0.643265+0.061582 
## [39] train-rmse:0.396583+0.011729    test-rmse:0.641825+0.060999 
## [40] train-rmse:0.392175+0.010871    test-rmse:0.642463+0.061703 
## [41] train-rmse:0.387745+0.011531    test-rmse:0.640945+0.061034 
## [42] train-rmse:0.383739+0.012606    test-rmse:0.639412+0.062458 
## [43] train-rmse:0.380912+0.012810    test-rmse:0.639562+0.063157 
## [44] train-rmse:0.376963+0.012620    test-rmse:0.638993+0.063228 
## [45] train-rmse:0.373420+0.011834    test-rmse:0.638380+0.062470 
## [46] train-rmse:0.371016+0.012128    test-rmse:0.638118+0.062686 
## [47] train-rmse:0.366327+0.012006    test-rmse:0.636172+0.061978 
## [48] train-rmse:0.361457+0.012686    test-rmse:0.635203+0.061146 
## [49] train-rmse:0.357562+0.013299    test-rmse:0.634971+0.062134 
## [50] train-rmse:0.354347+0.013591    test-rmse:0.633323+0.061135 
## [51] train-rmse:0.351554+0.013825    test-rmse:0.633324+0.061417 
## [52] train-rmse:0.348981+0.014386    test-rmse:0.632570+0.062233 
## [53] train-rmse:0.345340+0.013872    test-rmse:0.629997+0.061840 
## [54] train-rmse:0.340726+0.013302    test-rmse:0.629607+0.061441 
## [55] train-rmse:0.338107+0.012473    test-rmse:0.628619+0.062336 
## [56] train-rmse:0.335217+0.011675    test-rmse:0.629485+0.063422 
## [57] train-rmse:0.332360+0.013322    test-rmse:0.629641+0.064510 
## [58] train-rmse:0.328627+0.014894    test-rmse:0.631894+0.064639 
## [59] train-rmse:0.324957+0.015297    test-rmse:0.630870+0.064693 
## [60] train-rmse:0.322270+0.015805    test-rmse:0.631598+0.064941 
## [61] train-rmse:0.320012+0.015228    test-rmse:0.632042+0.065564 
## Stopping. Best iteration:
## [55] train-rmse:0.338107+0.012473    test-rmse:0.628619+0.062336
## 
## 32 
## [1]  train-rmse:1.371737+0.013717    test-rmse:1.382481+0.071587 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.183367+0.010510    test-rmse:1.207745+0.074495 
## [3]  train-rmse:1.032495+0.007790    test-rmse:1.066764+0.074552 
## [4]  train-rmse:0.912958+0.006297    test-rmse:0.961372+0.073879 
## [5]  train-rmse:0.821172+0.007355    test-rmse:0.884816+0.071666 
## [6]  train-rmse:0.747688+0.007194    test-rmse:0.825300+0.069340 
## [7]  train-rmse:0.693169+0.006934    test-rmse:0.780451+0.061799 
## [8]  train-rmse:0.648722+0.005008    test-rmse:0.747707+0.058756 
## [9]  train-rmse:0.612444+0.004479    test-rmse:0.726888+0.061119 
## [10] train-rmse:0.584197+0.004008    test-rmse:0.710457+0.058063 
## [11] train-rmse:0.557037+0.007411    test-rmse:0.696638+0.052394 
## [12] train-rmse:0.534293+0.005528    test-rmse:0.685138+0.049363 
## [13] train-rmse:0.515706+0.008566    test-rmse:0.678902+0.050943 
## [14] train-rmse:0.501525+0.007629    test-rmse:0.670562+0.049742 
## [15] train-rmse:0.489179+0.006250    test-rmse:0.667505+0.051957 
## [16] train-rmse:0.479152+0.006768    test-rmse:0.666465+0.050580 
## [17] train-rmse:0.467723+0.004642    test-rmse:0.661479+0.049330 
## [18] train-rmse:0.460187+0.005378    test-rmse:0.659784+0.049302 
## [19] train-rmse:0.449206+0.006335    test-rmse:0.656405+0.049220 
## [20] train-rmse:0.440679+0.007968    test-rmse:0.655979+0.051583 
## [21] train-rmse:0.430767+0.009980    test-rmse:0.654517+0.049805 
## [22] train-rmse:0.422652+0.011403    test-rmse:0.652675+0.049463 
## [23] train-rmse:0.415193+0.011557    test-rmse:0.652215+0.049935 
## [24] train-rmse:0.409285+0.012574    test-rmse:0.652541+0.050850 
## [25] train-rmse:0.402894+0.012842    test-rmse:0.651906+0.050225 
## [26] train-rmse:0.394552+0.014502    test-rmse:0.650712+0.053811 
## [27] train-rmse:0.389485+0.014033    test-rmse:0.650481+0.053728 
## [28] train-rmse:0.379188+0.015438    test-rmse:0.646474+0.054554 
## [29] train-rmse:0.372778+0.015101    test-rmse:0.643435+0.054544 
## [30] train-rmse:0.365566+0.014217    test-rmse:0.643744+0.055029 
## [31] train-rmse:0.360492+0.014257    test-rmse:0.643133+0.056445 
## [32] train-rmse:0.354554+0.015952    test-rmse:0.642396+0.054888 
## [33] train-rmse:0.349701+0.013523    test-rmse:0.640813+0.053216 
## [34] train-rmse:0.346310+0.013729    test-rmse:0.641401+0.053543 
## [35] train-rmse:0.341144+0.013063    test-rmse:0.640131+0.055245 
## [36] train-rmse:0.335960+0.014085    test-rmse:0.640243+0.053939 
## [37] train-rmse:0.329935+0.015815    test-rmse:0.639653+0.053145 
## [38] train-rmse:0.325517+0.015624    test-rmse:0.640412+0.053748 
## [39] train-rmse:0.319122+0.015240    test-rmse:0.636577+0.051406 
## [40] train-rmse:0.316782+0.015275    test-rmse:0.636310+0.051786 
## [41] train-rmse:0.310148+0.014387    test-rmse:0.635039+0.053372 
## [42] train-rmse:0.305117+0.015544    test-rmse:0.634478+0.054274 
## [43] train-rmse:0.301945+0.015328    test-rmse:0.634364+0.054558 
## [44] train-rmse:0.298224+0.015972    test-rmse:0.633512+0.053698 
## [45] train-rmse:0.293601+0.016383    test-rmse:0.633899+0.053881 
## [46] train-rmse:0.289560+0.016025    test-rmse:0.633460+0.054676 
## [47] train-rmse:0.285736+0.016592    test-rmse:0.634294+0.055193 
## [48] train-rmse:0.280758+0.016205    test-rmse:0.632801+0.055237 
## [49] train-rmse:0.276395+0.016907    test-rmse:0.632937+0.055948 
## [50] train-rmse:0.271796+0.016081    test-rmse:0.632371+0.056472 
## [51] train-rmse:0.268237+0.015480    test-rmse:0.631903+0.056473 
## [52] train-rmse:0.264742+0.015862    test-rmse:0.632877+0.055720 
## [53] train-rmse:0.262415+0.015455    test-rmse:0.631892+0.056135 
## [54] train-rmse:0.258875+0.015382    test-rmse:0.632812+0.056227 
## [55] train-rmse:0.255568+0.016314    test-rmse:0.632165+0.055800 
## [56] train-rmse:0.252056+0.016297    test-rmse:0.631189+0.055437 
## [57] train-rmse:0.248848+0.015936    test-rmse:0.630166+0.054508 
## [58] train-rmse:0.244375+0.015694    test-rmse:0.630487+0.055080 
## [59] train-rmse:0.241701+0.015122    test-rmse:0.629913+0.055957 
## [60] train-rmse:0.238718+0.016491    test-rmse:0.631690+0.056014 
## [61] train-rmse:0.235604+0.015872    test-rmse:0.632148+0.056278 
## [62] train-rmse:0.232156+0.015727    test-rmse:0.631975+0.055947 
## [63] train-rmse:0.230021+0.015377    test-rmse:0.632156+0.056369 
## [64] train-rmse:0.227286+0.016396    test-rmse:0.632138+0.054912 
## [65] train-rmse:0.224661+0.015789    test-rmse:0.631243+0.054896 
## Stopping. Best iteration:
## [59] train-rmse:0.241701+0.015122    test-rmse:0.629913+0.055957
## 
## 33 
## [1]  train-rmse:1.369299+0.013319    test-rmse:1.383935+0.073041 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.178851+0.010051    test-rmse:1.208476+0.074081 
## [3]  train-rmse:1.026902+0.008393    test-rmse:1.076419+0.072766 
## [4]  train-rmse:0.902467+0.007175    test-rmse:0.967013+0.072959 
## [5]  train-rmse:0.804315+0.006455    test-rmse:0.885117+0.073808 
## [6]  train-rmse:0.727730+0.008016    test-rmse:0.825039+0.072557 
## [7]  train-rmse:0.666528+0.008146    test-rmse:0.780462+0.073575 
## [8]  train-rmse:0.619954+0.010243    test-rmse:0.748307+0.072161 
## [9]  train-rmse:0.583766+0.011088    test-rmse:0.722945+0.068779 
## [10] train-rmse:0.552749+0.009930    test-rmse:0.704124+0.065431 
## [11] train-rmse:0.521366+0.015466    test-rmse:0.693212+0.065520 
## [12] train-rmse:0.497657+0.018374    test-rmse:0.681722+0.066955 
## [13] train-rmse:0.474107+0.016365    test-rmse:0.672163+0.065281 
## [14] train-rmse:0.455821+0.017481    test-rmse:0.663661+0.064288 
## [15] train-rmse:0.440288+0.015399    test-rmse:0.658535+0.063045 
## [16] train-rmse:0.429912+0.014612    test-rmse:0.656167+0.062215 
## [17] train-rmse:0.416841+0.015496    test-rmse:0.652886+0.061560 
## [18] train-rmse:0.405797+0.016383    test-rmse:0.651818+0.060676 
## [19] train-rmse:0.397221+0.017985    test-rmse:0.648600+0.062021 
## [20] train-rmse:0.389143+0.017539    test-rmse:0.647018+0.061777 
## [21] train-rmse:0.380174+0.016638    test-rmse:0.644117+0.060432 
## [22] train-rmse:0.368289+0.016985    test-rmse:0.641106+0.059281 
## [23] train-rmse:0.361678+0.016819    test-rmse:0.638180+0.059360 
## [24] train-rmse:0.355966+0.016096    test-rmse:0.638778+0.060102 
## [25] train-rmse:0.349219+0.016293    test-rmse:0.639196+0.061899 
## [26] train-rmse:0.342142+0.015904    test-rmse:0.637909+0.063006 
## [27] train-rmse:0.336531+0.015815    test-rmse:0.636201+0.061839 
## [28] train-rmse:0.330706+0.016099    test-rmse:0.637369+0.062835 
## [29] train-rmse:0.323052+0.015276    test-rmse:0.636075+0.062369 
## [30] train-rmse:0.316004+0.014719    test-rmse:0.633884+0.061514 
## [31] train-rmse:0.309232+0.015304    test-rmse:0.632045+0.062732 
## [32] train-rmse:0.304256+0.015534    test-rmse:0.631714+0.062613 
## [33] train-rmse:0.298424+0.016621    test-rmse:0.632398+0.062708 
## [34] train-rmse:0.293360+0.017264    test-rmse:0.631649+0.063608 
## [35] train-rmse:0.288013+0.016833    test-rmse:0.631635+0.064272 
## [36] train-rmse:0.282974+0.016649    test-rmse:0.631968+0.062668 
## [37] train-rmse:0.277625+0.016213    test-rmse:0.631444+0.062743 
## [38] train-rmse:0.273908+0.016830    test-rmse:0.632122+0.062879 
## [39] train-rmse:0.268171+0.017098    test-rmse:0.631465+0.062164 
## [40] train-rmse:0.262965+0.016163    test-rmse:0.630774+0.061318 
## [41] train-rmse:0.259204+0.016268    test-rmse:0.629965+0.061029 
## [42] train-rmse:0.253680+0.017570    test-rmse:0.627604+0.059937 
## [43] train-rmse:0.249258+0.018379    test-rmse:0.628720+0.059700 
## [44] train-rmse:0.243864+0.019265    test-rmse:0.628863+0.059117 
## [45] train-rmse:0.238500+0.018627    test-rmse:0.627106+0.058563 
## [46] train-rmse:0.235347+0.018646    test-rmse:0.626499+0.059253 
## [47] train-rmse:0.230570+0.018190    test-rmse:0.626704+0.059787 
## [48] train-rmse:0.224877+0.016785    test-rmse:0.625881+0.058474 
## [49] train-rmse:0.220649+0.015456    test-rmse:0.625600+0.058211 
## [50] train-rmse:0.216888+0.015571    test-rmse:0.624922+0.057607 
## [51] train-rmse:0.212282+0.013998    test-rmse:0.625286+0.056536 
## [52] train-rmse:0.208674+0.014599    test-rmse:0.624914+0.056100 
## [53] train-rmse:0.205358+0.014579    test-rmse:0.624765+0.055750 
## [54] train-rmse:0.202831+0.013845    test-rmse:0.624363+0.055615 
## [55] train-rmse:0.198349+0.014129    test-rmse:0.625510+0.056523 
## [56] train-rmse:0.194406+0.013398    test-rmse:0.624571+0.056977 
## [57] train-rmse:0.190231+0.013389    test-rmse:0.624544+0.057701 
## [58] train-rmse:0.187534+0.013805    test-rmse:0.624954+0.057558 
## [59] train-rmse:0.185145+0.014186    test-rmse:0.624524+0.057107 
## [60] train-rmse:0.181940+0.014133    test-rmse:0.623582+0.056850 
## [61] train-rmse:0.179003+0.013914    test-rmse:0.623995+0.056889 
## [62] train-rmse:0.175315+0.013276    test-rmse:0.624975+0.056433 
## [63] train-rmse:0.171953+0.012572    test-rmse:0.625553+0.055630 
## [64] train-rmse:0.168803+0.013079    test-rmse:0.625687+0.055942 
## [65] train-rmse:0.165987+0.013212    test-rmse:0.625522+0.056613 
## [66] train-rmse:0.163609+0.013671    test-rmse:0.625341+0.057017 
## Stopping. Best iteration:
## [60] train-rmse:0.181940+0.014133    test-rmse:0.623582+0.056850
## 
## 34 
## [1]  train-rmse:1.367508+0.012971    test-rmse:1.382972+0.073171 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.174925+0.010492    test-rmse:1.205867+0.072856 
## [3]  train-rmse:1.018375+0.006844    test-rmse:1.063852+0.072783 
## [4]  train-rmse:0.891502+0.006216    test-rmse:0.956367+0.073611 
## [5]  train-rmse:0.788993+0.004389    test-rmse:0.874497+0.074849 
## [6]  train-rmse:0.706740+0.003714    test-rmse:0.810637+0.068332 
## [7]  train-rmse:0.643878+0.006598    test-rmse:0.763809+0.064901 
## [8]  train-rmse:0.590340+0.009281    test-rmse:0.732095+0.063686 
## [9]  train-rmse:0.549580+0.009640    test-rmse:0.708588+0.060282 
## [10] train-rmse:0.514029+0.007851    test-rmse:0.691000+0.055955 
## [11] train-rmse:0.487112+0.007434    test-rmse:0.676693+0.053916 
## [12] train-rmse:0.463121+0.007039    test-rmse:0.669117+0.053909 
## [13] train-rmse:0.436171+0.005407    test-rmse:0.655606+0.049824 
## [14] train-rmse:0.418980+0.008763    test-rmse:0.650225+0.049764 
## [15] train-rmse:0.402616+0.012518    test-rmse:0.646746+0.049313 
## [16] train-rmse:0.391259+0.012018    test-rmse:0.643775+0.048931 
## [17] train-rmse:0.380675+0.010168    test-rmse:0.642887+0.049582 
## [18] train-rmse:0.365791+0.013522    test-rmse:0.641708+0.052245 
## [19] train-rmse:0.355346+0.017153    test-rmse:0.640823+0.052548 
## [20] train-rmse:0.346446+0.018281    test-rmse:0.640830+0.053732 
## [21] train-rmse:0.336795+0.018207    test-rmse:0.639939+0.052044 
## [22] train-rmse:0.326902+0.018084    test-rmse:0.640433+0.053178 
## [23] train-rmse:0.319370+0.018371    test-rmse:0.641315+0.053130 
## [24] train-rmse:0.307753+0.018352    test-rmse:0.641528+0.051347 
## [25] train-rmse:0.299336+0.017028    test-rmse:0.641650+0.053532 
## [26] train-rmse:0.291170+0.017519    test-rmse:0.641668+0.052333 
## [27] train-rmse:0.282842+0.017983    test-rmse:0.638759+0.052555 
## [28] train-rmse:0.277609+0.019193    test-rmse:0.639485+0.053123 
## [29] train-rmse:0.268603+0.016627    test-rmse:0.638162+0.052858 
## [30] train-rmse:0.261750+0.016500    test-rmse:0.639315+0.053694 
## [31] train-rmse:0.255907+0.017319    test-rmse:0.640741+0.052999 
## [32] train-rmse:0.247124+0.016348    test-rmse:0.637399+0.051426 
## [33] train-rmse:0.240828+0.017095    test-rmse:0.635835+0.051582 
## [34] train-rmse:0.234026+0.016034    test-rmse:0.634394+0.052542 
## [35] train-rmse:0.229210+0.016209    test-rmse:0.633535+0.051958 
## [36] train-rmse:0.225336+0.015688    test-rmse:0.634296+0.051681 
## [37] train-rmse:0.220409+0.016659    test-rmse:0.635244+0.053069 
## [38] train-rmse:0.215420+0.015577    test-rmse:0.635134+0.053546 
## [39] train-rmse:0.211252+0.017188    test-rmse:0.634491+0.053165 
## [40] train-rmse:0.205625+0.017471    test-rmse:0.634556+0.053617 
## [41] train-rmse:0.200205+0.017932    test-rmse:0.634921+0.055068 
## Stopping. Best iteration:
## [35] train-rmse:0.229210+0.016209    test-rmse:0.633535+0.051958
## 
## 35 
## [1]  train-rmse:1.362617+0.013555    test-rmse:1.363772+0.070103 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.180459+0.010953    test-rmse:1.183166+0.069207 
## [3]  train-rmse:1.046911+0.009026    test-rmse:1.052008+0.068259 
## [4]  train-rmse:0.950642+0.008004    test-rmse:0.962382+0.065886 
## [5]  train-rmse:0.881354+0.007228    test-rmse:0.895477+0.060690 
## [6]  train-rmse:0.832616+0.006823    test-rmse:0.850384+0.054310 
## [7]  train-rmse:0.797906+0.006443    test-rmse:0.818925+0.050598 
## [8]  train-rmse:0.773390+0.006098    test-rmse:0.799472+0.045724 
## [9]  train-rmse:0.755905+0.005820    test-rmse:0.786088+0.045563 
## [10] train-rmse:0.742987+0.005520    test-rmse:0.774645+0.044245 
## [11] train-rmse:0.733081+0.005351    test-rmse:0.765991+0.040916 
## [12] train-rmse:0.725167+0.005159    test-rmse:0.759541+0.038007 
## [13] train-rmse:0.718873+0.005059    test-rmse:0.753248+0.034833 
## [14] train-rmse:0.713443+0.005034    test-rmse:0.750018+0.033610 
## [15] train-rmse:0.708960+0.005083    test-rmse:0.748903+0.031344 
## [16] train-rmse:0.704923+0.005073    test-rmse:0.746701+0.032115 
## [17] train-rmse:0.701241+0.004995    test-rmse:0.744931+0.031933 
## [18] train-rmse:0.698020+0.004965    test-rmse:0.743561+0.032007 
## [19] train-rmse:0.695037+0.004990    test-rmse:0.741521+0.031176 
## [20] train-rmse:0.692198+0.005031    test-rmse:0.737136+0.029274 
## [21] train-rmse:0.689457+0.005073    test-rmse:0.735294+0.028794 
## [22] train-rmse:0.686842+0.005134    test-rmse:0.734353+0.027562 
## [23] train-rmse:0.684349+0.005181    test-rmse:0.734801+0.027177 
## [24] train-rmse:0.681978+0.005249    test-rmse:0.734829+0.027513 
## [25] train-rmse:0.679676+0.005316    test-rmse:0.733585+0.028375 
## [26] train-rmse:0.677454+0.005319    test-rmse:0.732493+0.028827 
## [27] train-rmse:0.675373+0.005266    test-rmse:0.730182+0.029997 
## [28] train-rmse:0.673314+0.005215    test-rmse:0.729966+0.031968 
## [29] train-rmse:0.671299+0.005141    test-rmse:0.728548+0.031389 
## [30] train-rmse:0.669364+0.005059    test-rmse:0.726978+0.030843 
## [31] train-rmse:0.667534+0.005061    test-rmse:0.727751+0.032130 
## [32] train-rmse:0.665735+0.005079    test-rmse:0.727583+0.031872 
## [33] train-rmse:0.663995+0.005083    test-rmse:0.726769+0.032039 
## [34] train-rmse:0.662269+0.005136    test-rmse:0.725206+0.032113 
## [35] train-rmse:0.660579+0.005237    test-rmse:0.723371+0.031954 
## [36] train-rmse:0.658947+0.005283    test-rmse:0.722558+0.033314 
## [37] train-rmse:0.657347+0.005357    test-rmse:0.723701+0.034127 
## [38] train-rmse:0.655798+0.005437    test-rmse:0.722952+0.034210 
## [39] train-rmse:0.654280+0.005517    test-rmse:0.722908+0.035598 
## [40] train-rmse:0.652807+0.005550    test-rmse:0.721836+0.034977 
## [41] train-rmse:0.651363+0.005568    test-rmse:0.720318+0.034812 
## [42] train-rmse:0.649945+0.005580    test-rmse:0.719700+0.034589 
## [43] train-rmse:0.648577+0.005564    test-rmse:0.719488+0.033934 
## [44] train-rmse:0.647242+0.005577    test-rmse:0.719895+0.034609 
## [45] train-rmse:0.645932+0.005615    test-rmse:0.718373+0.034652 
## [46] train-rmse:0.644685+0.005638    test-rmse:0.717453+0.033839 
## [47] train-rmse:0.643452+0.005638    test-rmse:0.717911+0.035222 
## [48] train-rmse:0.642231+0.005630    test-rmse:0.718082+0.035928 
## [49] train-rmse:0.641055+0.005656    test-rmse:0.717401+0.033992 
## [50] train-rmse:0.639903+0.005710    test-rmse:0.717387+0.034149 
## [51] train-rmse:0.638755+0.005711    test-rmse:0.717362+0.033238 
## [52] train-rmse:0.637627+0.005723    test-rmse:0.716634+0.033113 
## [53] train-rmse:0.636528+0.005743    test-rmse:0.714618+0.033827 
## [54] train-rmse:0.635459+0.005762    test-rmse:0.714444+0.034733 
## [55] train-rmse:0.634410+0.005781    test-rmse:0.715445+0.036700 
## [56] train-rmse:0.633377+0.005777    test-rmse:0.713632+0.036719 
## [57] train-rmse:0.632307+0.005775    test-rmse:0.713589+0.036656 
## [58] train-rmse:0.631299+0.005787    test-rmse:0.713303+0.036130 
## [59] train-rmse:0.630297+0.005787    test-rmse:0.713170+0.036320 
## [60] train-rmse:0.629299+0.005811    test-rmse:0.712390+0.036240 
## [61] train-rmse:0.628328+0.005832    test-rmse:0.711922+0.035254 
## [62] train-rmse:0.627371+0.005849    test-rmse:0.711459+0.034949 
## [63] train-rmse:0.626423+0.005869    test-rmse:0.710996+0.035546 
## [64] train-rmse:0.625474+0.005882    test-rmse:0.709829+0.034742 
## [65] train-rmse:0.624541+0.005901    test-rmse:0.709124+0.033965 
## [66] train-rmse:0.623625+0.005933    test-rmse:0.708903+0.034277 
## [67] train-rmse:0.622728+0.005946    test-rmse:0.708111+0.034288 
## [68] train-rmse:0.621863+0.005961    test-rmse:0.707995+0.034256 
## [69] train-rmse:0.621003+0.005971    test-rmse:0.708211+0.033875 
## [70] train-rmse:0.620142+0.005987    test-rmse:0.707775+0.033295 
## [71] train-rmse:0.619283+0.005990    test-rmse:0.706943+0.032853 
## [72] train-rmse:0.618439+0.005992    test-rmse:0.706426+0.033682 
## [73] train-rmse:0.617631+0.005992    test-rmse:0.706466+0.033761 
## [74] train-rmse:0.616840+0.006002    test-rmse:0.706114+0.034493 
## [75] train-rmse:0.616058+0.006007    test-rmse:0.705270+0.034266 
## [76] train-rmse:0.615279+0.006002    test-rmse:0.705361+0.034413 
## [77] train-rmse:0.614493+0.005994    test-rmse:0.703667+0.034093 
## [78] train-rmse:0.613719+0.006027    test-rmse:0.702894+0.033886 
## [79] train-rmse:0.612970+0.006049    test-rmse:0.703203+0.034180 
## [80] train-rmse:0.612244+0.006052    test-rmse:0.703633+0.034259 
## [81] train-rmse:0.611523+0.006059    test-rmse:0.703764+0.034028 
## [82] train-rmse:0.610800+0.006068    test-rmse:0.703258+0.033782 
## [83] train-rmse:0.610098+0.006072    test-rmse:0.703171+0.034058 
## [84] train-rmse:0.609384+0.006086    test-rmse:0.701750+0.033403 
## [85] train-rmse:0.608668+0.006096    test-rmse:0.701623+0.033263 
## [86] train-rmse:0.607988+0.006103    test-rmse:0.700208+0.034287 
## [87] train-rmse:0.607311+0.006111    test-rmse:0.699940+0.033864 
## [88] train-rmse:0.606638+0.006119    test-rmse:0.699379+0.034689 
## [89] train-rmse:0.605975+0.006131    test-rmse:0.699583+0.035895 
## [90] train-rmse:0.605317+0.006144    test-rmse:0.698581+0.035611 
## [91] train-rmse:0.604647+0.006154    test-rmse:0.697929+0.035417 
## [92] train-rmse:0.603996+0.006159    test-rmse:0.697070+0.034855 
## [93] train-rmse:0.603356+0.006173    test-rmse:0.697903+0.034881 
## [94] train-rmse:0.602716+0.006177    test-rmse:0.697059+0.034721 
## [95] train-rmse:0.602074+0.006198    test-rmse:0.696783+0.034057 
## [96] train-rmse:0.601446+0.006208    test-rmse:0.696721+0.033653 
## [97] train-rmse:0.600833+0.006220    test-rmse:0.696257+0.033832 
## [98] train-rmse:0.600222+0.006234    test-rmse:0.696107+0.034200 
## [99] train-rmse:0.599619+0.006258    test-rmse:0.696199+0.034947 
## [100]    train-rmse:0.599036+0.006262    test-rmse:0.695891+0.034869 
## [101]    train-rmse:0.598455+0.006284    test-rmse:0.695418+0.033717 
## [102]    train-rmse:0.597872+0.006299    test-rmse:0.694826+0.033499 
## [103]    train-rmse:0.597305+0.006310    test-rmse:0.694741+0.033150 
## [104]    train-rmse:0.596739+0.006324    test-rmse:0.694465+0.032948 
## [105]    train-rmse:0.596161+0.006332    test-rmse:0.694505+0.034143 
## [106]    train-rmse:0.595602+0.006336    test-rmse:0.694231+0.034480 
## [107]    train-rmse:0.595058+0.006337    test-rmse:0.694941+0.035392 
## [108]    train-rmse:0.594526+0.006351    test-rmse:0.695226+0.035699 
## [109]    train-rmse:0.594002+0.006355    test-rmse:0.695505+0.036142 
## [110]    train-rmse:0.593476+0.006364    test-rmse:0.694839+0.036088 
## [111]    train-rmse:0.592956+0.006377    test-rmse:0.694415+0.036659 
## [112]    train-rmse:0.592439+0.006388    test-rmse:0.694421+0.036068 
## Stopping. Best iteration:
## [106]    train-rmse:0.595602+0.006336    test-rmse:0.694231+0.034480
## 
## 36 
## [1]  train-rmse:1.356895+0.013444    test-rmse:1.361960+0.070337 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.168812+0.010668    test-rmse:1.179462+0.070859 
## [3]  train-rmse:1.028553+0.008334    test-rmse:1.047919+0.072059 
## [4]  train-rmse:0.925826+0.006647    test-rmse:0.950084+0.069312 
## [5]  train-rmse:0.852024+0.005829    test-rmse:0.882045+0.065858 
## [6]  train-rmse:0.797778+0.007197    test-rmse:0.836036+0.060099 
## [7]  train-rmse:0.757156+0.005364    test-rmse:0.795247+0.060279 
## [8]  train-rmse:0.727446+0.006050    test-rmse:0.768911+0.057203 
## [9]  train-rmse:0.706721+0.006222    test-rmse:0.755506+0.050408 
## [10] train-rmse:0.688975+0.007868    test-rmse:0.742860+0.048673 
## [11] train-rmse:0.676671+0.007819    test-rmse:0.735128+0.047361 
## [12] train-rmse:0.666616+0.006772    test-rmse:0.728686+0.045920 
## [13] train-rmse:0.656952+0.008243    test-rmse:0.724118+0.042794 
## [14] train-rmse:0.648417+0.005655    test-rmse:0.721368+0.044232 
## [15] train-rmse:0.642279+0.005330    test-rmse:0.720075+0.042853 
## [16] train-rmse:0.636598+0.005281    test-rmse:0.716039+0.042560 
## [17] train-rmse:0.629596+0.006128    test-rmse:0.709645+0.039164 
## [18] train-rmse:0.624735+0.006608    test-rmse:0.706144+0.038503 
## [19] train-rmse:0.619820+0.006248    test-rmse:0.704424+0.044180 
## [20] train-rmse:0.614534+0.006249    test-rmse:0.702813+0.042590 
## [21] train-rmse:0.609488+0.006259    test-rmse:0.698812+0.041762 
## [22] train-rmse:0.605593+0.005582    test-rmse:0.697463+0.041070 
## [23] train-rmse:0.600903+0.005615    test-rmse:0.693960+0.042165 
## [24] train-rmse:0.596656+0.005460    test-rmse:0.693569+0.042937 
## [25] train-rmse:0.592993+0.005838    test-rmse:0.690266+0.041874 
## [26] train-rmse:0.589129+0.003949    test-rmse:0.690027+0.041876 
## [27] train-rmse:0.586188+0.003873    test-rmse:0.692281+0.044517 
## [28] train-rmse:0.581515+0.002523    test-rmse:0.690558+0.045559 
## [29] train-rmse:0.578997+0.002642    test-rmse:0.688642+0.045434 
## [30] train-rmse:0.574965+0.003222    test-rmse:0.686962+0.043292 
## [31] train-rmse:0.571334+0.003682    test-rmse:0.685720+0.042234 
## [32] train-rmse:0.567777+0.004934    test-rmse:0.682468+0.041381 
## [33] train-rmse:0.565250+0.005432    test-rmse:0.681899+0.040991 
## [34] train-rmse:0.562890+0.005574    test-rmse:0.681328+0.041291 
## [35] train-rmse:0.559321+0.005831    test-rmse:0.680531+0.040260 
## [36] train-rmse:0.557064+0.005783    test-rmse:0.681389+0.041128 
## [37] train-rmse:0.551611+0.003759    test-rmse:0.676911+0.043476 
## [38] train-rmse:0.549016+0.004295    test-rmse:0.676189+0.042222 
## [39] train-rmse:0.546473+0.004133    test-rmse:0.675397+0.042506 
## [40] train-rmse:0.544114+0.004186    test-rmse:0.673719+0.041451 
## [41] train-rmse:0.540767+0.005316    test-rmse:0.670424+0.041379 
## [42] train-rmse:0.538344+0.004946    test-rmse:0.669696+0.041786 
## [43] train-rmse:0.534851+0.006262    test-rmse:0.669957+0.041765 
## [44] train-rmse:0.532181+0.007607    test-rmse:0.669315+0.041124 
## [45] train-rmse:0.530057+0.007882    test-rmse:0.669019+0.041885 
## [46] train-rmse:0.527074+0.007445    test-rmse:0.668121+0.041980 
## [47] train-rmse:0.525009+0.007079    test-rmse:0.665175+0.041377 
## [48] train-rmse:0.522889+0.007136    test-rmse:0.663888+0.040424 
## [49] train-rmse:0.521438+0.007195    test-rmse:0.664307+0.040954 
## [50] train-rmse:0.518259+0.006328    test-rmse:0.663774+0.039101 
## [51] train-rmse:0.516244+0.006822    test-rmse:0.662091+0.041013 
## [52] train-rmse:0.513941+0.007184    test-rmse:0.661583+0.040154 
## [53] train-rmse:0.512339+0.006877    test-rmse:0.660755+0.040733 
## [54] train-rmse:0.510507+0.007207    test-rmse:0.659601+0.040836 
## [55] train-rmse:0.508006+0.007362    test-rmse:0.658825+0.039742 
## [56] train-rmse:0.505199+0.009091    test-rmse:0.657361+0.039560 
## [57] train-rmse:0.502599+0.008844    test-rmse:0.654198+0.038264 
## [58] train-rmse:0.500852+0.009619    test-rmse:0.654557+0.038412 
## [59] train-rmse:0.498808+0.010035    test-rmse:0.654118+0.039040 
## [60] train-rmse:0.497094+0.010346    test-rmse:0.653654+0.039306 
## [61] train-rmse:0.495506+0.010492    test-rmse:0.653658+0.039833 
## [62] train-rmse:0.493584+0.010451    test-rmse:0.652222+0.038120 
## [63] train-rmse:0.492051+0.010541    test-rmse:0.650630+0.036388 
## [64] train-rmse:0.490965+0.010447    test-rmse:0.650511+0.037215 
## [65] train-rmse:0.489294+0.010612    test-rmse:0.649922+0.037036 
## [66] train-rmse:0.487566+0.010015    test-rmse:0.648864+0.036512 
## [67] train-rmse:0.485611+0.011014    test-rmse:0.648197+0.035969 
## [68] train-rmse:0.483815+0.011242    test-rmse:0.648306+0.036147 
## [69] train-rmse:0.482182+0.011287    test-rmse:0.647998+0.037491 
## [70] train-rmse:0.481083+0.011275    test-rmse:0.648314+0.037277 
## [71] train-rmse:0.479923+0.011111    test-rmse:0.649399+0.037818 
## [72] train-rmse:0.477688+0.011998    test-rmse:0.649450+0.037641 
## [73] train-rmse:0.475584+0.012463    test-rmse:0.647355+0.035357 
## [74] train-rmse:0.472958+0.010899    test-rmse:0.646019+0.037127 
## [75] train-rmse:0.472104+0.010870    test-rmse:0.645842+0.037231 
## [76] train-rmse:0.470803+0.010936    test-rmse:0.645446+0.037848 
## [77] train-rmse:0.469210+0.011688    test-rmse:0.645938+0.038034 
## [78] train-rmse:0.467885+0.011525    test-rmse:0.645779+0.038207 
## [79] train-rmse:0.465149+0.010798    test-rmse:0.643995+0.038966 
## [80] train-rmse:0.463786+0.010962    test-rmse:0.642777+0.038548 
## [81] train-rmse:0.462506+0.010933    test-rmse:0.641978+0.040359 
## [82] train-rmse:0.461300+0.010788    test-rmse:0.641586+0.040800 
## [83] train-rmse:0.460406+0.010686    test-rmse:0.642220+0.040367 
## [84] train-rmse:0.458874+0.011933    test-rmse:0.643366+0.040879 
## [85] train-rmse:0.457402+0.011871    test-rmse:0.642937+0.041397 
## [86] train-rmse:0.455740+0.011748    test-rmse:0.642343+0.040618 
## [87] train-rmse:0.454801+0.011844    test-rmse:0.641111+0.040498 
## [88] train-rmse:0.453348+0.011269    test-rmse:0.641320+0.040494 
## [89] train-rmse:0.452153+0.011099    test-rmse:0.641091+0.040829 
## [90] train-rmse:0.450651+0.012104    test-rmse:0.640792+0.040892 
## [91] train-rmse:0.449735+0.012146    test-rmse:0.641094+0.041508 
## [92] train-rmse:0.448706+0.012320    test-rmse:0.639670+0.041256 
## [93] train-rmse:0.447770+0.012513    test-rmse:0.639973+0.041013 
## [94] train-rmse:0.446365+0.012419    test-rmse:0.640801+0.041378 
## [95] train-rmse:0.445474+0.012367    test-rmse:0.638969+0.041086 
## [96] train-rmse:0.444209+0.013021    test-rmse:0.639559+0.041383 
## [97] train-rmse:0.443126+0.012957    test-rmse:0.639195+0.042188 
## [98] train-rmse:0.442227+0.013023    test-rmse:0.639022+0.042006 
## [99] train-rmse:0.440581+0.013593    test-rmse:0.639341+0.042114 
## [100]    train-rmse:0.439378+0.013532    test-rmse:0.638559+0.041808 
## [101]    train-rmse:0.438477+0.013731    test-rmse:0.638658+0.041861 
## [102]    train-rmse:0.436786+0.013275    test-rmse:0.637610+0.040445 
## [103]    train-rmse:0.435004+0.012828    test-rmse:0.636885+0.040749 
## [104]    train-rmse:0.433646+0.013245    test-rmse:0.636053+0.040127 
## [105]    train-rmse:0.432396+0.013480    test-rmse:0.635199+0.039760 
## [106]    train-rmse:0.431611+0.013879    test-rmse:0.635507+0.039988 
## [107]    train-rmse:0.430249+0.013977    test-rmse:0.635278+0.040126 
## [108]    train-rmse:0.429179+0.014296    test-rmse:0.634966+0.039686 
## [109]    train-rmse:0.427906+0.013739    test-rmse:0.634441+0.040968 
## [110]    train-rmse:0.427008+0.013910    test-rmse:0.633566+0.040268 
## [111]    train-rmse:0.425833+0.014174    test-rmse:0.633310+0.040246 
## [112]    train-rmse:0.424952+0.014443    test-rmse:0.633285+0.041149 
## [113]    train-rmse:0.423872+0.015304    test-rmse:0.633364+0.041190 
## [114]    train-rmse:0.422700+0.015293    test-rmse:0.632258+0.042431 
## [115]    train-rmse:0.421399+0.015213    test-rmse:0.632249+0.042484 
## [116]    train-rmse:0.419971+0.015294    test-rmse:0.632178+0.042514 
## [117]    train-rmse:0.419223+0.015270    test-rmse:0.631793+0.042594 
## [118]    train-rmse:0.418483+0.015350    test-rmse:0.631998+0.042647 
## [119]    train-rmse:0.417670+0.015638    test-rmse:0.632178+0.042840 
## [120]    train-rmse:0.415928+0.015893    test-rmse:0.630641+0.043304 
## [121]    train-rmse:0.414879+0.015838    test-rmse:0.631138+0.043658 
## [122]    train-rmse:0.414040+0.015830    test-rmse:0.631658+0.043900 
## [123]    train-rmse:0.412729+0.015652    test-rmse:0.632136+0.044020 
## [124]    train-rmse:0.411957+0.015674    test-rmse:0.632374+0.044588 
## [125]    train-rmse:0.410179+0.016240    test-rmse:0.631984+0.044203 
## [126]    train-rmse:0.409558+0.016312    test-rmse:0.632468+0.044345 
## Stopping. Best iteration:
## [120]    train-rmse:0.415928+0.015893    test-rmse:0.630641+0.043304
## 
## 37 
## [1]  train-rmse:1.353005+0.013120    test-rmse:1.359893+0.069217 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.158752+0.010306    test-rmse:1.175521+0.069019 
## [3]  train-rmse:1.013746+0.008100    test-rmse:1.041091+0.069593 
## [4]  train-rmse:0.905462+0.007325    test-rmse:0.940201+0.068773 
## [5]  train-rmse:0.826923+0.006751    test-rmse:0.870292+0.064649 
## [6]  train-rmse:0.766591+0.007055    test-rmse:0.816185+0.066928 
## [7]  train-rmse:0.722433+0.008429    test-rmse:0.782924+0.059899 
## [8]  train-rmse:0.685903+0.007468    test-rmse:0.755817+0.060718 
## [9]  train-rmse:0.658260+0.008350    test-rmse:0.733118+0.056240 
## [10] train-rmse:0.638997+0.006550    test-rmse:0.718215+0.052800 
## [11] train-rmse:0.623382+0.007200    test-rmse:0.707900+0.051047 
## [12] train-rmse:0.611479+0.007757    test-rmse:0.703996+0.053923 
## [13] train-rmse:0.600599+0.007678    test-rmse:0.697805+0.051350 
## [14] train-rmse:0.592934+0.007989    test-rmse:0.695374+0.050866 
## [15] train-rmse:0.584350+0.008376    test-rmse:0.690627+0.052049 
## [16] train-rmse:0.575266+0.009486    test-rmse:0.687921+0.050085 
## [17] train-rmse:0.569173+0.009407    test-rmse:0.684856+0.049200 
## [18] train-rmse:0.562285+0.009021    test-rmse:0.682928+0.049403 
## [19] train-rmse:0.556630+0.009136    test-rmse:0.682763+0.049688 
## [20] train-rmse:0.548013+0.007629    test-rmse:0.680675+0.051786 
## [21] train-rmse:0.543504+0.007716    test-rmse:0.679087+0.052241 
## [22] train-rmse:0.537463+0.007051    test-rmse:0.677084+0.053502 
## [23] train-rmse:0.532650+0.007197    test-rmse:0.678689+0.054174 
## [24] train-rmse:0.528685+0.007439    test-rmse:0.677295+0.055041 
## [25] train-rmse:0.523521+0.008614    test-rmse:0.675542+0.053920 
## [26] train-rmse:0.518398+0.008078    test-rmse:0.672802+0.054432 
## [27] train-rmse:0.512241+0.005385    test-rmse:0.673265+0.058202 
## [28] train-rmse:0.506293+0.006880    test-rmse:0.668098+0.060038 
## [29] train-rmse:0.502296+0.006878    test-rmse:0.667977+0.060926 
## [30] train-rmse:0.495684+0.006598    test-rmse:0.662390+0.058974 
## [31] train-rmse:0.491811+0.007388    test-rmse:0.661106+0.058077 
## [32] train-rmse:0.488249+0.007964    test-rmse:0.661432+0.055571 
## [33] train-rmse:0.485067+0.007471    test-rmse:0.658976+0.058291 
## [34] train-rmse:0.481929+0.007793    test-rmse:0.659129+0.058698 
## [35] train-rmse:0.478148+0.008660    test-rmse:0.659405+0.060083 
## [36] train-rmse:0.473965+0.009483    test-rmse:0.657356+0.059051 
## [37] train-rmse:0.469219+0.009125    test-rmse:0.653236+0.058156 
## [38] train-rmse:0.465177+0.010023    test-rmse:0.649984+0.057848 
## [39] train-rmse:0.461802+0.011123    test-rmse:0.651148+0.058879 
## [40] train-rmse:0.456875+0.009486    test-rmse:0.651347+0.060789 
## [41] train-rmse:0.453721+0.009928    test-rmse:0.651852+0.060513 
## [42] train-rmse:0.450667+0.010237    test-rmse:0.650024+0.061932 
## [43] train-rmse:0.447808+0.010482    test-rmse:0.650665+0.062492 
## [44] train-rmse:0.444607+0.010838    test-rmse:0.650604+0.062993 
## Stopping. Best iteration:
## [38] train-rmse:0.465177+0.010023    test-rmse:0.649984+0.057848
## 
## 38 
## [1]  train-rmse:1.349013+0.013180    test-rmse:1.362375+0.069803 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.149346+0.009499    test-rmse:1.173852+0.071047 
## [3]  train-rmse:0.997080+0.008023    test-rmse:1.034565+0.070884 
## [4]  train-rmse:0.882568+0.005508    test-rmse:0.934415+0.074274 
## [5]  train-rmse:0.798535+0.005619    test-rmse:0.860470+0.070500 
## [6]  train-rmse:0.734612+0.005211    test-rmse:0.807380+0.069358 
## [7]  train-rmse:0.688838+0.004958    test-rmse:0.766739+0.069017 
## [8]  train-rmse:0.652915+0.007757    test-rmse:0.738971+0.063894 
## [9]  train-rmse:0.621246+0.008627    test-rmse:0.722909+0.061109 
## [10] train-rmse:0.597341+0.013343    test-rmse:0.710545+0.060890 
## [11] train-rmse:0.577193+0.010275    test-rmse:0.697454+0.057700 
## [12] train-rmse:0.561870+0.009444    test-rmse:0.688337+0.057817 
## [13] train-rmse:0.548695+0.009080    test-rmse:0.679248+0.057339 
## [14] train-rmse:0.537885+0.008713    test-rmse:0.679036+0.058374 
## [15] train-rmse:0.528269+0.010198    test-rmse:0.672722+0.056376 
## [16] train-rmse:0.518954+0.009587    test-rmse:0.669290+0.058652 
## [17] train-rmse:0.511248+0.009730    test-rmse:0.667987+0.058931 
## [18] train-rmse:0.502427+0.010785    test-rmse:0.666146+0.058823 
## [19] train-rmse:0.494224+0.010732    test-rmse:0.663037+0.058847 
## [20] train-rmse:0.486602+0.008653    test-rmse:0.658235+0.060266 
## [21] train-rmse:0.480344+0.009425    test-rmse:0.656564+0.063477 
## [22] train-rmse:0.470415+0.011806    test-rmse:0.653383+0.059318 
## [23] train-rmse:0.463175+0.013040    test-rmse:0.651863+0.058593 
## [24] train-rmse:0.455980+0.014487    test-rmse:0.652389+0.057350 
## [25] train-rmse:0.449284+0.015883    test-rmse:0.651627+0.057875 
## [26] train-rmse:0.444296+0.015859    test-rmse:0.650569+0.060641 
## [27] train-rmse:0.435953+0.013537    test-rmse:0.645203+0.058928 
## [28] train-rmse:0.429981+0.015008    test-rmse:0.641550+0.059648 
## [29] train-rmse:0.424230+0.014086    test-rmse:0.637301+0.059050 
## [30] train-rmse:0.420038+0.013569    test-rmse:0.636374+0.060488 
## [31] train-rmse:0.414354+0.014814    test-rmse:0.636589+0.061522 
## [32] train-rmse:0.408719+0.014981    test-rmse:0.634707+0.063367 
## [33] train-rmse:0.403487+0.013803    test-rmse:0.635379+0.062299 
## [34] train-rmse:0.398358+0.013800    test-rmse:0.635889+0.062174 
## [35] train-rmse:0.393688+0.014621    test-rmse:0.633942+0.061053 
## [36] train-rmse:0.390005+0.015026    test-rmse:0.632081+0.062588 
## [37] train-rmse:0.384824+0.017602    test-rmse:0.630636+0.063684 
## [38] train-rmse:0.380376+0.018366    test-rmse:0.631554+0.063244 
## [39] train-rmse:0.374774+0.016397    test-rmse:0.630483+0.062563 
## [40] train-rmse:0.370646+0.015682    test-rmse:0.630281+0.062858 
## [41] train-rmse:0.366435+0.015660    test-rmse:0.628500+0.063141 
## [42] train-rmse:0.362904+0.015286    test-rmse:0.628328+0.062784 
## [43] train-rmse:0.358062+0.015654    test-rmse:0.626991+0.061785 
## [44] train-rmse:0.354934+0.015827    test-rmse:0.625799+0.062568 
## [45] train-rmse:0.352004+0.015846    test-rmse:0.625838+0.063308 
## [46] train-rmse:0.349421+0.015525    test-rmse:0.626685+0.063830 
## [47] train-rmse:0.343982+0.013870    test-rmse:0.626333+0.064346 
## [48] train-rmse:0.341179+0.014454    test-rmse:0.627929+0.064729 
## [49] train-rmse:0.337157+0.014663    test-rmse:0.627966+0.066121 
## [50] train-rmse:0.333894+0.016023    test-rmse:0.627851+0.066274 
## Stopping. Best iteration:
## [44] train-rmse:0.354934+0.015827    test-rmse:0.625799+0.062568
## 
## 39 
## [1]  train-rmse:1.346306+0.013357    test-rmse:1.358549+0.072114 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.143226+0.009909    test-rmse:1.168804+0.073706 
## [3]  train-rmse:0.986725+0.007708    test-rmse:1.028688+0.070100 
## [4]  train-rmse:0.864947+0.005471    test-rmse:0.920941+0.070762 
## [5]  train-rmse:0.773996+0.005952    test-rmse:0.846102+0.066445 
## [6]  train-rmse:0.706742+0.006369    test-rmse:0.792736+0.062144 
## [7]  train-rmse:0.653524+0.007165    test-rmse:0.758426+0.058845 
## [8]  train-rmse:0.615501+0.006865    test-rmse:0.734933+0.059671 
## [9]  train-rmse:0.585027+0.005866    test-rmse:0.718423+0.056692 
## [10] train-rmse:0.554404+0.011815    test-rmse:0.701059+0.051029 
## [11] train-rmse:0.530556+0.006959    test-rmse:0.687881+0.051058 
## [12] train-rmse:0.515361+0.007200    test-rmse:0.680591+0.050674 
## [13] train-rmse:0.501979+0.008065    test-rmse:0.674489+0.052837 
## [14] train-rmse:0.485256+0.008458    test-rmse:0.668150+0.050973 
## [15] train-rmse:0.471606+0.011109    test-rmse:0.665906+0.052930 
## [16] train-rmse:0.462062+0.013278    test-rmse:0.662850+0.054317 
## [17] train-rmse:0.450953+0.014645    test-rmse:0.659492+0.055941 
## [18] train-rmse:0.441357+0.015077    test-rmse:0.657958+0.053216 
## [19] train-rmse:0.433232+0.015799    test-rmse:0.657773+0.056152 
## [20] train-rmse:0.425284+0.016172    test-rmse:0.656454+0.056080 
## [21] train-rmse:0.417885+0.017205    test-rmse:0.653881+0.055755 
## [22] train-rmse:0.406824+0.017733    test-rmse:0.651870+0.052825 
## [23] train-rmse:0.400607+0.017466    test-rmse:0.650058+0.053688 
## [24] train-rmse:0.395539+0.017531    test-rmse:0.647038+0.051835 
## [25] train-rmse:0.387046+0.015381    test-rmse:0.643293+0.050085 
## [26] train-rmse:0.382058+0.015369    test-rmse:0.645240+0.051779 
## [27] train-rmse:0.374475+0.017239    test-rmse:0.641384+0.052136 
## [28] train-rmse:0.365086+0.016543    test-rmse:0.639399+0.052195 
## [29] train-rmse:0.360978+0.016220    test-rmse:0.637893+0.052159 
## [30] train-rmse:0.353736+0.015885    test-rmse:0.638161+0.053135 
## [31] train-rmse:0.346140+0.014639    test-rmse:0.636649+0.051852 
## [32] train-rmse:0.341608+0.015052    test-rmse:0.637159+0.051985 
## [33] train-rmse:0.333756+0.015257    test-rmse:0.636885+0.051162 
## [34] train-rmse:0.328062+0.014578    test-rmse:0.635010+0.051033 
## [35] train-rmse:0.322880+0.013886    test-rmse:0.634970+0.050956 
## [36] train-rmse:0.316822+0.014163    test-rmse:0.633817+0.050694 
## [37] train-rmse:0.311599+0.015275    test-rmse:0.633604+0.050581 
## [38] train-rmse:0.306197+0.014092    test-rmse:0.632100+0.050576 
## [39] train-rmse:0.302324+0.015345    test-rmse:0.632596+0.051776 
## [40] train-rmse:0.298545+0.014236    test-rmse:0.632722+0.049965 
## [41] train-rmse:0.293335+0.014908    test-rmse:0.634703+0.050401 
## [42] train-rmse:0.288853+0.015121    test-rmse:0.634262+0.050778 
## [43] train-rmse:0.285774+0.015186    test-rmse:0.633746+0.050781 
## [44] train-rmse:0.278162+0.014347    test-rmse:0.631620+0.048789 
## [45] train-rmse:0.273310+0.014656    test-rmse:0.631537+0.047802 
## [46] train-rmse:0.268844+0.014733    test-rmse:0.631867+0.047366 
## [47] train-rmse:0.265052+0.016015    test-rmse:0.630339+0.047498 
## [48] train-rmse:0.261519+0.016044    test-rmse:0.631469+0.047917 
## [49] train-rmse:0.259119+0.015915    test-rmse:0.631326+0.048172 
## [50] train-rmse:0.254984+0.015760    test-rmse:0.632404+0.048132 
## [51] train-rmse:0.251294+0.016936    test-rmse:0.633098+0.048859 
## [52] train-rmse:0.246836+0.016095    test-rmse:0.633692+0.048796 
## [53] train-rmse:0.242594+0.015344    test-rmse:0.632766+0.050251 
## Stopping. Best iteration:
## [47] train-rmse:0.265052+0.016015    test-rmse:0.630339+0.047498
## 
## 40 
## [1]  train-rmse:1.343557+0.012909    test-rmse:1.360168+0.073694 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.137865+0.009688    test-rmse:1.168087+0.074641 
## [3]  train-rmse:0.976928+0.006893    test-rmse:1.029855+0.076886 
## [4]  train-rmse:0.850436+0.006659    test-rmse:0.918278+0.071821 
## [5]  train-rmse:0.753048+0.004697    test-rmse:0.841593+0.072101 
## [6]  train-rmse:0.680636+0.003383    test-rmse:0.783278+0.068707 
## [7]  train-rmse:0.626051+0.005521    test-rmse:0.745083+0.066422 
## [8]  train-rmse:0.583577+0.005127    test-rmse:0.716307+0.058961 
## [9]  train-rmse:0.550321+0.008440    test-rmse:0.696883+0.057336 
## [10] train-rmse:0.522362+0.007935    test-rmse:0.680822+0.054622 
## [11] train-rmse:0.495414+0.012316    test-rmse:0.674820+0.051521 
## [12] train-rmse:0.474096+0.010820    test-rmse:0.670351+0.052945 
## [13] train-rmse:0.457894+0.013645    test-rmse:0.667681+0.053017 
## [14] train-rmse:0.441044+0.012290    test-rmse:0.664061+0.056418 
## [15] train-rmse:0.426288+0.011360    test-rmse:0.658692+0.057420 
## [16] train-rmse:0.415122+0.014605    test-rmse:0.657633+0.058449 
## [17] train-rmse:0.403243+0.014958    test-rmse:0.656134+0.058574 
## [18] train-rmse:0.391118+0.013580    test-rmse:0.651733+0.058648 
## [19] train-rmse:0.381162+0.013612    test-rmse:0.646583+0.058463 
## [20] train-rmse:0.374038+0.012892    test-rmse:0.643301+0.059700 
## [21] train-rmse:0.362945+0.010350    test-rmse:0.642320+0.059320 
## [22] train-rmse:0.355972+0.010541    test-rmse:0.640288+0.059987 
## [23] train-rmse:0.348958+0.011621    test-rmse:0.639572+0.063040 
## [24] train-rmse:0.341701+0.010183    test-rmse:0.636934+0.062849 
## [25] train-rmse:0.333925+0.010513    test-rmse:0.636020+0.063410 
## [26] train-rmse:0.329098+0.010614    test-rmse:0.635285+0.065259 
## [27] train-rmse:0.322430+0.010459    test-rmse:0.634641+0.066131 
## [28] train-rmse:0.314635+0.011235    test-rmse:0.632656+0.067025 
## [29] train-rmse:0.305813+0.014296    test-rmse:0.632455+0.065398 
## [30] train-rmse:0.299257+0.013892    test-rmse:0.632586+0.064805 
## [31] train-rmse:0.293324+0.016134    test-rmse:0.631544+0.065846 
## [32] train-rmse:0.286567+0.015430    test-rmse:0.632366+0.063426 
## [33] train-rmse:0.281733+0.016203    test-rmse:0.632514+0.062861 
## [34] train-rmse:0.273878+0.014820    test-rmse:0.634292+0.064188 
## [35] train-rmse:0.266138+0.013336    test-rmse:0.631935+0.064790 
## [36] train-rmse:0.259552+0.013404    test-rmse:0.631924+0.064588 
## [37] train-rmse:0.254281+0.013784    test-rmse:0.630393+0.064757 
## [38] train-rmse:0.249418+0.014452    test-rmse:0.630632+0.065345 
## [39] train-rmse:0.245345+0.014691    test-rmse:0.630090+0.065808 
## [40] train-rmse:0.237929+0.012275    test-rmse:0.627049+0.066010 
## [41] train-rmse:0.232795+0.013409    test-rmse:0.626761+0.065341 
## [42] train-rmse:0.228011+0.012790    test-rmse:0.627818+0.065264 
## [43] train-rmse:0.223443+0.012943    test-rmse:0.626591+0.064496 
## [44] train-rmse:0.219952+0.011755    test-rmse:0.626664+0.063996 
## [45] train-rmse:0.215231+0.011863    test-rmse:0.626189+0.062255 
## [46] train-rmse:0.211370+0.011610    test-rmse:0.626690+0.062009 
## [47] train-rmse:0.207213+0.011828    test-rmse:0.627194+0.061175 
## [48] train-rmse:0.201664+0.011770    test-rmse:0.625347+0.059370 
## [49] train-rmse:0.198732+0.011525    test-rmse:0.625872+0.059167 
## [50] train-rmse:0.194147+0.011721    test-rmse:0.626947+0.059687 
## [51] train-rmse:0.189488+0.011342    test-rmse:0.626397+0.060234 
## [52] train-rmse:0.186261+0.010845    test-rmse:0.626686+0.059441 
## [53] train-rmse:0.183425+0.011722    test-rmse:0.626928+0.059220 
## [54] train-rmse:0.180347+0.011936    test-rmse:0.626902+0.059164 
## Stopping. Best iteration:
## [48] train-rmse:0.201664+0.011770    test-rmse:0.625347+0.059370
## 
## 41 
## [1]  train-rmse:1.341538+0.012519    test-rmse:1.359095+0.073842 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.133456+0.009374    test-rmse:1.170481+0.075899 
## [3]  train-rmse:0.968743+0.005510    test-rmse:1.022548+0.079154 
## [4]  train-rmse:0.839134+0.005908    test-rmse:0.921145+0.076643 
## [5]  train-rmse:0.738168+0.004778    test-rmse:0.844240+0.079017 
## [6]  train-rmse:0.660717+0.006936    test-rmse:0.788713+0.078503 
## [7]  train-rmse:0.602067+0.010283    test-rmse:0.750838+0.076877 
## [8]  train-rmse:0.557536+0.009932    test-rmse:0.725070+0.078065 
## [9]  train-rmse:0.521446+0.010668    test-rmse:0.704565+0.074782 
## [10] train-rmse:0.490626+0.009476    test-rmse:0.690530+0.072403 
## [11] train-rmse:0.461882+0.014007    test-rmse:0.675730+0.069459 
## [12] train-rmse:0.437611+0.011617    test-rmse:0.667737+0.067812 
## [13] train-rmse:0.421114+0.011706    test-rmse:0.663000+0.067476 
## [14] train-rmse:0.398992+0.014851    test-rmse:0.660371+0.065655 
## [15] train-rmse:0.383303+0.018139    test-rmse:0.659098+0.065622 
## [16] train-rmse:0.371801+0.018935    test-rmse:0.659402+0.064015 
## [17] train-rmse:0.355599+0.021266    test-rmse:0.657420+0.063709 
## [18] train-rmse:0.343270+0.022671    test-rmse:0.656732+0.066046 
## [19] train-rmse:0.334344+0.021612    test-rmse:0.655458+0.063849 
## [20] train-rmse:0.325037+0.021874    test-rmse:0.656170+0.063466 
## [21] train-rmse:0.317311+0.021253    test-rmse:0.654266+0.061698 
## [22] train-rmse:0.310207+0.021123    test-rmse:0.653151+0.060702 
## [23] train-rmse:0.300019+0.023097    test-rmse:0.655027+0.060834 
## [24] train-rmse:0.293283+0.022886    test-rmse:0.654673+0.061637 
## [25] train-rmse:0.286022+0.021998    test-rmse:0.653316+0.058417 
## [26] train-rmse:0.277531+0.019773    test-rmse:0.651919+0.058798 
## [27] train-rmse:0.268808+0.019988    test-rmse:0.650608+0.057987 
## [28] train-rmse:0.260570+0.019184    test-rmse:0.649486+0.058840 
## [29] train-rmse:0.251545+0.016102    test-rmse:0.648275+0.058021 
## [30] train-rmse:0.243959+0.015102    test-rmse:0.646806+0.056612 
## [31] train-rmse:0.236871+0.015617    test-rmse:0.649029+0.057005 
## [32] train-rmse:0.231976+0.014348    test-rmse:0.646958+0.057642 
## [33] train-rmse:0.227019+0.012422    test-rmse:0.645757+0.057687 
## [34] train-rmse:0.222841+0.011527    test-rmse:0.645800+0.057893 
## [35] train-rmse:0.215564+0.012043    test-rmse:0.642408+0.055584 
## [36] train-rmse:0.209871+0.012944    test-rmse:0.642819+0.055574 
## [37] train-rmse:0.205016+0.014473    test-rmse:0.643018+0.055728 
## [38] train-rmse:0.199893+0.015622    test-rmse:0.643082+0.055487 
## [39] train-rmse:0.195712+0.016178    test-rmse:0.644033+0.055476 
## [40] train-rmse:0.190403+0.015956    test-rmse:0.643551+0.055384 
## [41] train-rmse:0.185367+0.014837    test-rmse:0.643594+0.054846 
## Stopping. Best iteration:
## [35] train-rmse:0.215564+0.012043    test-rmse:0.642408+0.055584
## 
## 42 
## [1]  train-rmse:1.339159+0.013221    test-rmse:1.340559+0.070314 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.146312+0.010458    test-rmse:1.149488+0.068966 
## [3]  train-rmse:1.010638+0.008521    test-rmse:1.016484+0.067374 
## [4]  train-rmse:0.916494+0.007828    test-rmse:0.928994+0.064439 
## [5]  train-rmse:0.851763+0.006866    test-rmse:0.865127+0.059213 
## [6]  train-rmse:0.807980+0.006748    test-rmse:0.826024+0.053601 
## [7]  train-rmse:0.778225+0.006385    test-rmse:0.802375+0.047665 
## [8]  train-rmse:0.757552+0.005994    test-rmse:0.786002+0.045208 
## [9]  train-rmse:0.742920+0.005716    test-rmse:0.773832+0.041695 
## [10] train-rmse:0.732005+0.005420    test-rmse:0.762776+0.040952 
## [11] train-rmse:0.723562+0.005150    test-rmse:0.756143+0.037642 
## [12] train-rmse:0.716928+0.005179    test-rmse:0.751076+0.035302 
## [13] train-rmse:0.711568+0.005077    test-rmse:0.750611+0.033449 
## [14] train-rmse:0.706913+0.005093    test-rmse:0.747481+0.032073 
## [15] train-rmse:0.702756+0.005099    test-rmse:0.743469+0.032353 
## [16] train-rmse:0.698839+0.005152    test-rmse:0.742108+0.031597 
## [17] train-rmse:0.695321+0.005073    test-rmse:0.739102+0.031576 
## [18] train-rmse:0.692190+0.005098    test-rmse:0.737904+0.031925 
## [19] train-rmse:0.689309+0.005156    test-rmse:0.737285+0.029914 
## [20] train-rmse:0.686487+0.005161    test-rmse:0.735317+0.030335 
## [21] train-rmse:0.683731+0.005198    test-rmse:0.733710+0.029059 
## [22] train-rmse:0.681203+0.005233    test-rmse:0.732467+0.028242 
## [23] train-rmse:0.678713+0.005301    test-rmse:0.731548+0.027877 
## [24] train-rmse:0.676303+0.005346    test-rmse:0.731611+0.028176 
## [25] train-rmse:0.673939+0.005367    test-rmse:0.730142+0.029075 
## [26] train-rmse:0.671749+0.005343    test-rmse:0.729715+0.031467 
## [27] train-rmse:0.669612+0.005284    test-rmse:0.728206+0.032141 
## [28] train-rmse:0.667505+0.005246    test-rmse:0.728107+0.033908 
## [29] train-rmse:0.665484+0.005196    test-rmse:0.727928+0.035677 
## [30] train-rmse:0.663531+0.005217    test-rmse:0.727592+0.035421 
## [31] train-rmse:0.661623+0.005259    test-rmse:0.725622+0.034366 
## [32] train-rmse:0.659797+0.005294    test-rmse:0.725116+0.033324 
## [33] train-rmse:0.658033+0.005300    test-rmse:0.724251+0.033150 
## [34] train-rmse:0.656294+0.005327    test-rmse:0.722852+0.031972 
## [35] train-rmse:0.654630+0.005401    test-rmse:0.721874+0.032910 
## [36] train-rmse:0.652957+0.005440    test-rmse:0.721649+0.031978 
## [37] train-rmse:0.651382+0.005528    test-rmse:0.721803+0.034078 
## [38] train-rmse:0.649851+0.005593    test-rmse:0.721308+0.034733 
## [39] train-rmse:0.648347+0.005643    test-rmse:0.720635+0.035152 
## [40] train-rmse:0.646896+0.005659    test-rmse:0.720319+0.035608 
## [41] train-rmse:0.645464+0.005646    test-rmse:0.720322+0.036666 
## [42] train-rmse:0.644062+0.005666    test-rmse:0.719256+0.037192 
## [43] train-rmse:0.642724+0.005697    test-rmse:0.717881+0.036456 
## [44] train-rmse:0.641386+0.005718    test-rmse:0.716691+0.036211 
## [45] train-rmse:0.640029+0.005710    test-rmse:0.716218+0.035431 
## [46] train-rmse:0.638726+0.005736    test-rmse:0.715457+0.035239 
## [47] train-rmse:0.637460+0.005751    test-rmse:0.714345+0.035324 
## [48] train-rmse:0.636234+0.005751    test-rmse:0.713326+0.034757 
## [49] train-rmse:0.635054+0.005767    test-rmse:0.713325+0.034619 
## [50] train-rmse:0.633908+0.005761    test-rmse:0.712790+0.035929 
## [51] train-rmse:0.632760+0.005787    test-rmse:0.712232+0.035421 
## [52] train-rmse:0.631657+0.005794    test-rmse:0.711986+0.034085 
## [53] train-rmse:0.630552+0.005816    test-rmse:0.712177+0.034327 
## [54] train-rmse:0.629471+0.005824    test-rmse:0.712343+0.034498 
## [55] train-rmse:0.628394+0.005849    test-rmse:0.713505+0.035176 
## [56] train-rmse:0.627308+0.005828    test-rmse:0.712243+0.035801 
## [57] train-rmse:0.626235+0.005824    test-rmse:0.712087+0.035856 
## [58] train-rmse:0.625224+0.005822    test-rmse:0.711861+0.035069 
## [59] train-rmse:0.624205+0.005855    test-rmse:0.710800+0.036343 
## [60] train-rmse:0.623213+0.005887    test-rmse:0.710531+0.037039 
## [61] train-rmse:0.622235+0.005919    test-rmse:0.710788+0.037820 
## [62] train-rmse:0.621256+0.005959    test-rmse:0.708564+0.037312 
## [63] train-rmse:0.620313+0.005972    test-rmse:0.708251+0.036638 
## [64] train-rmse:0.619369+0.005983    test-rmse:0.706466+0.036407 
## [65] train-rmse:0.618442+0.006004    test-rmse:0.705581+0.036025 
## [66] train-rmse:0.617530+0.006021    test-rmse:0.705710+0.035621 
## [67] train-rmse:0.616642+0.006036    test-rmse:0.705669+0.035409 
## [68] train-rmse:0.615748+0.006049    test-rmse:0.704690+0.034847 
## [69] train-rmse:0.614860+0.006055    test-rmse:0.704104+0.036090 
## [70] train-rmse:0.614007+0.006054    test-rmse:0.703276+0.035491 
## [71] train-rmse:0.613147+0.006068    test-rmse:0.703046+0.034591 
## [72] train-rmse:0.612300+0.006074    test-rmse:0.701785+0.034081 
## [73] train-rmse:0.611485+0.006072    test-rmse:0.701064+0.033744 
## [74] train-rmse:0.610696+0.006072    test-rmse:0.701458+0.035106 
## [75] train-rmse:0.609899+0.006073    test-rmse:0.701430+0.035778 
## [76] train-rmse:0.609124+0.006062    test-rmse:0.700643+0.036132 
## [77] train-rmse:0.608368+0.006055    test-rmse:0.701786+0.036790 
## [78] train-rmse:0.607621+0.006048    test-rmse:0.700565+0.036814 
## [79] train-rmse:0.606875+0.006052    test-rmse:0.699979+0.037865 
## [80] train-rmse:0.606144+0.006062    test-rmse:0.699227+0.036817 
## [81] train-rmse:0.605395+0.006078    test-rmse:0.699246+0.037565 
## [82] train-rmse:0.604672+0.006091    test-rmse:0.698414+0.037293 
## [83] train-rmse:0.603962+0.006102    test-rmse:0.698226+0.038293 
## [84] train-rmse:0.603246+0.006132    test-rmse:0.697313+0.037467 
## [85] train-rmse:0.602537+0.006159    test-rmse:0.697230+0.036978 
## [86] train-rmse:0.601849+0.006168    test-rmse:0.696808+0.036609 
## [87] train-rmse:0.601172+0.006195    test-rmse:0.697190+0.036343 
## [88] train-rmse:0.600512+0.006218    test-rmse:0.696555+0.036130 
## [89] train-rmse:0.599846+0.006209    test-rmse:0.696737+0.035231 
## [90] train-rmse:0.599174+0.006192    test-rmse:0.696203+0.034920 
## [91] train-rmse:0.598507+0.006192    test-rmse:0.694763+0.034634 
## [92] train-rmse:0.597852+0.006221    test-rmse:0.695897+0.035532 
## [93] train-rmse:0.597201+0.006246    test-rmse:0.696018+0.035991 
## [94] train-rmse:0.596567+0.006261    test-rmse:0.694864+0.035778 
## [95] train-rmse:0.595940+0.006286    test-rmse:0.694027+0.036354 
## [96] train-rmse:0.595320+0.006309    test-rmse:0.693803+0.036304 
## [97] train-rmse:0.594711+0.006318    test-rmse:0.693589+0.035498 
## [98] train-rmse:0.594103+0.006308    test-rmse:0.692501+0.035414 
## [99] train-rmse:0.593500+0.006320    test-rmse:0.691121+0.035865 
## [100]    train-rmse:0.592918+0.006342    test-rmse:0.692130+0.035710 
## [101]    train-rmse:0.592342+0.006357    test-rmse:0.692020+0.035945 
## [102]    train-rmse:0.591781+0.006375    test-rmse:0.691927+0.036562 
## [103]    train-rmse:0.591222+0.006396    test-rmse:0.691864+0.036655 
## [104]    train-rmse:0.590662+0.006416    test-rmse:0.692191+0.036321 
## [105]    train-rmse:0.590099+0.006421    test-rmse:0.690179+0.035891 
## [106]    train-rmse:0.589541+0.006438    test-rmse:0.689962+0.035996 
## [107]    train-rmse:0.588985+0.006448    test-rmse:0.689759+0.035484 
## [108]    train-rmse:0.588440+0.006471    test-rmse:0.689638+0.036080 
## [109]    train-rmse:0.587908+0.006490    test-rmse:0.689444+0.035549 
## [110]    train-rmse:0.587374+0.006511    test-rmse:0.689910+0.034972 
## [111]    train-rmse:0.586848+0.006529    test-rmse:0.689956+0.034683 
## [112]    train-rmse:0.586328+0.006554    test-rmse:0.689707+0.035243 
## [113]    train-rmse:0.585811+0.006572    test-rmse:0.689695+0.035764 
## [114]    train-rmse:0.585293+0.006586    test-rmse:0.689074+0.035835 
## [115]    train-rmse:0.584786+0.006608    test-rmse:0.688788+0.035267 
## [116]    train-rmse:0.584287+0.006619    test-rmse:0.687930+0.035377 
## [117]    train-rmse:0.583793+0.006627    test-rmse:0.688271+0.035112 
## [118]    train-rmse:0.583313+0.006647    test-rmse:0.688145+0.034230 
## [119]    train-rmse:0.582836+0.006656    test-rmse:0.687979+0.034742 
## [120]    train-rmse:0.582368+0.006661    test-rmse:0.688185+0.035456 
## [121]    train-rmse:0.581909+0.006671    test-rmse:0.688035+0.035471 
## [122]    train-rmse:0.581443+0.006676    test-rmse:0.688203+0.035657 
## Stopping. Best iteration:
## [116]    train-rmse:0.584287+0.006619    test-rmse:0.687930+0.035377
## 
## 43 
## [1]  train-rmse:1.332805+0.013095    test-rmse:1.338561+0.070564 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.133350+0.010097    test-rmse:1.145376+0.070734 
## [3]  train-rmse:0.990041+0.007729    test-rmse:1.011774+0.071559 
## [4]  train-rmse:0.889367+0.006062    test-rmse:0.916441+0.068034 
## [5]  train-rmse:0.819589+0.006101    test-rmse:0.856042+0.065251 
## [6]  train-rmse:0.767072+0.004264    test-rmse:0.804954+0.063929 
## [7]  train-rmse:0.732624+0.006318    test-rmse:0.774164+0.056579 
## [8]  train-rmse:0.706018+0.005975    test-rmse:0.754923+0.048448 
## [9]  train-rmse:0.686676+0.005694    test-rmse:0.737073+0.049691 
## [10] train-rmse:0.671096+0.005735    test-rmse:0.729938+0.048902 
## [11] train-rmse:0.660404+0.005660    test-rmse:0.723987+0.045002 
## [12] train-rmse:0.652329+0.005407    test-rmse:0.719536+0.044486 
## [13] train-rmse:0.644881+0.005386    test-rmse:0.717727+0.047337 
## [14] train-rmse:0.638240+0.004794    test-rmse:0.713359+0.048901 
## [15] train-rmse:0.632149+0.004265    test-rmse:0.710314+0.046550 
## [16] train-rmse:0.626035+0.004042    test-rmse:0.709458+0.045757 
## [17] train-rmse:0.620915+0.003482    test-rmse:0.707688+0.045511 
## [18] train-rmse:0.616368+0.003645    test-rmse:0.705341+0.046457 
## [19] train-rmse:0.611063+0.003476    test-rmse:0.705743+0.046465 
## [20] train-rmse:0.606495+0.004629    test-rmse:0.705545+0.046111 
## [21] train-rmse:0.601745+0.005417    test-rmse:0.703140+0.045820 
## [22] train-rmse:0.596998+0.005208    test-rmse:0.701720+0.045989 
## [23] train-rmse:0.593831+0.005403    test-rmse:0.699451+0.046837 
## [24] train-rmse:0.589898+0.004985    test-rmse:0.697346+0.046895 
## [25] train-rmse:0.586137+0.006144    test-rmse:0.695680+0.046468 
## [26] train-rmse:0.582812+0.005539    test-rmse:0.695153+0.048458 
## [27] train-rmse:0.578483+0.006901    test-rmse:0.694389+0.046050 
## [28] train-rmse:0.573272+0.008123    test-rmse:0.691640+0.045337 
## [29] train-rmse:0.570053+0.007851    test-rmse:0.689392+0.047479 
## [30] train-rmse:0.567579+0.007770    test-rmse:0.688484+0.047185 
## [31] train-rmse:0.564663+0.008264    test-rmse:0.689167+0.046638 
## [32] train-rmse:0.561879+0.008085    test-rmse:0.686757+0.045088 
## [33] train-rmse:0.559264+0.008142    test-rmse:0.686375+0.044608 
## [34] train-rmse:0.556231+0.008285    test-rmse:0.685103+0.046161 
## [35] train-rmse:0.553508+0.007795    test-rmse:0.682041+0.046403 
## [36] train-rmse:0.549664+0.007894    test-rmse:0.681598+0.047207 
## [37] train-rmse:0.546654+0.008592    test-rmse:0.680907+0.045283 
## [38] train-rmse:0.542459+0.010078    test-rmse:0.677293+0.043506 
## [39] train-rmse:0.539630+0.010288    test-rmse:0.677054+0.045185 
## [40] train-rmse:0.535416+0.008329    test-rmse:0.676100+0.048223 
## [41] train-rmse:0.532597+0.008330    test-rmse:0.675465+0.049305 
## [42] train-rmse:0.529416+0.007826    test-rmse:0.676437+0.049421 
## [43] train-rmse:0.527188+0.007313    test-rmse:0.675340+0.049546 
## [44] train-rmse:0.523594+0.008247    test-rmse:0.674977+0.047655 
## [45] train-rmse:0.521555+0.008504    test-rmse:0.674593+0.047368 
## [46] train-rmse:0.519884+0.008583    test-rmse:0.674693+0.048174 
## [47] train-rmse:0.517510+0.009094    test-rmse:0.674028+0.048067 
## [48] train-rmse:0.514923+0.008759    test-rmse:0.671532+0.047143 
## [49] train-rmse:0.512764+0.008083    test-rmse:0.671207+0.046856 
## [50] train-rmse:0.510241+0.008659    test-rmse:0.669421+0.046086 
## [51] train-rmse:0.507293+0.007908    test-rmse:0.667362+0.047203 
## [52] train-rmse:0.505498+0.008128    test-rmse:0.666200+0.046538 
## [53] train-rmse:0.503516+0.008559    test-rmse:0.664897+0.046542 
## [54] train-rmse:0.501869+0.008512    test-rmse:0.663187+0.047664 
## [55] train-rmse:0.499091+0.009386    test-rmse:0.664149+0.048500 
## [56] train-rmse:0.496725+0.009131    test-rmse:0.663659+0.048572 
## [57] train-rmse:0.494174+0.008935    test-rmse:0.662136+0.047121 
## [58] train-rmse:0.492347+0.008592    test-rmse:0.661378+0.047760 
## [59] train-rmse:0.490286+0.008714    test-rmse:0.660755+0.048717 
## [60] train-rmse:0.488966+0.008823    test-rmse:0.660290+0.048985 
## [61] train-rmse:0.487458+0.009051    test-rmse:0.660857+0.048697 
## [62] train-rmse:0.486019+0.009017    test-rmse:0.659799+0.048540 
## [63] train-rmse:0.484180+0.009310    test-rmse:0.659746+0.048328 
## [64] train-rmse:0.482767+0.009430    test-rmse:0.659896+0.048662 
## [65] train-rmse:0.481231+0.009032    test-rmse:0.660291+0.049219 
## [66] train-rmse:0.479744+0.008958    test-rmse:0.659140+0.049724 
## [67] train-rmse:0.478168+0.008829    test-rmse:0.658276+0.050560 
## [68] train-rmse:0.475001+0.006501    test-rmse:0.656797+0.047383 
## [69] train-rmse:0.472558+0.005183    test-rmse:0.656331+0.046126 
## [70] train-rmse:0.470648+0.005876    test-rmse:0.654690+0.045753 
## [71] train-rmse:0.468953+0.006173    test-rmse:0.654345+0.045954 
## [72] train-rmse:0.467558+0.006206    test-rmse:0.653970+0.046101 
## [73] train-rmse:0.466494+0.006302    test-rmse:0.653844+0.045300 
## [74] train-rmse:0.465253+0.006043    test-rmse:0.653056+0.043919 
## [75] train-rmse:0.463897+0.006035    test-rmse:0.653375+0.044870 
## [76] train-rmse:0.462745+0.006125    test-rmse:0.652648+0.044884 
## [77] train-rmse:0.460986+0.005266    test-rmse:0.651124+0.045812 
## [78] train-rmse:0.459022+0.006156    test-rmse:0.650970+0.046981 
## [79] train-rmse:0.457273+0.007331    test-rmse:0.651286+0.048116 
## [80] train-rmse:0.455477+0.006729    test-rmse:0.650216+0.046867 
## [81] train-rmse:0.453569+0.006887    test-rmse:0.650823+0.046900 
## [82] train-rmse:0.452623+0.006759    test-rmse:0.650221+0.046241 
## [83] train-rmse:0.451374+0.006994    test-rmse:0.649788+0.046837 
## [84] train-rmse:0.450145+0.006904    test-rmse:0.650111+0.047062 
## [85] train-rmse:0.448923+0.007038    test-rmse:0.650422+0.046936 
## [86] train-rmse:0.447823+0.007350    test-rmse:0.650559+0.046983 
## [87] train-rmse:0.445837+0.006813    test-rmse:0.649164+0.047259 
## [88] train-rmse:0.444866+0.007048    test-rmse:0.648710+0.047165 
## [89] train-rmse:0.443663+0.007105    test-rmse:0.648311+0.047972 
## [90] train-rmse:0.441687+0.006742    test-rmse:0.648979+0.048270 
## [91] train-rmse:0.440524+0.006507    test-rmse:0.649708+0.048515 
## [92] train-rmse:0.438778+0.006522    test-rmse:0.650325+0.049410 
## [93] train-rmse:0.437929+0.006506    test-rmse:0.649150+0.049843 
## [94] train-rmse:0.436996+0.006369    test-rmse:0.650233+0.049028 
## [95] train-rmse:0.435133+0.007915    test-rmse:0.649796+0.049232 
## Stopping. Best iteration:
## [89] train-rmse:0.443663+0.007105    test-rmse:0.648311+0.047972
## 
## 44 
## [1]  train-rmse:1.328479+0.012733    test-rmse:1.336276+0.069330 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.122337+0.009889    test-rmse:1.141824+0.068217 
## [3]  train-rmse:0.973411+0.007476    test-rmse:1.003928+0.068650 
## [4]  train-rmse:0.866238+0.005224    test-rmse:0.903406+0.070091 
## [5]  train-rmse:0.790640+0.006177    test-rmse:0.838866+0.065803 
## [6]  train-rmse:0.734798+0.006672    test-rmse:0.795028+0.061360 
## [7]  train-rmse:0.691310+0.006285    test-rmse:0.761727+0.055548 
## [8]  train-rmse:0.663040+0.004912    test-rmse:0.737865+0.053375 
## [9]  train-rmse:0.636982+0.005972    test-rmse:0.719731+0.044142 
## [10] train-rmse:0.620287+0.006304    test-rmse:0.707200+0.040536 
## [11] train-rmse:0.606054+0.005540    test-rmse:0.703512+0.040852 
## [12] train-rmse:0.595100+0.004873    test-rmse:0.697128+0.046911 
## [13] train-rmse:0.585834+0.006583    test-rmse:0.696019+0.047096 
## [14] train-rmse:0.576729+0.007236    test-rmse:0.692437+0.045935 
## [15] train-rmse:0.569579+0.006895    test-rmse:0.689894+0.049524 
## [16] train-rmse:0.562781+0.007528    test-rmse:0.685213+0.049053 
## [17] train-rmse:0.555906+0.006385    test-rmse:0.680895+0.050714 
## [18] train-rmse:0.547469+0.006342    test-rmse:0.677277+0.050195 
## [19] train-rmse:0.541811+0.006538    test-rmse:0.675160+0.049567 
## [20] train-rmse:0.536156+0.006964    test-rmse:0.673587+0.047578 
## [21] train-rmse:0.531673+0.007624    test-rmse:0.673465+0.048073 
## [22] train-rmse:0.526978+0.007798    test-rmse:0.673017+0.047432 
## [23] train-rmse:0.522468+0.008040    test-rmse:0.673296+0.048857 
## [24] train-rmse:0.516473+0.009339    test-rmse:0.673402+0.050862 
## [25] train-rmse:0.512119+0.008716    test-rmse:0.672861+0.053923 
## [26] train-rmse:0.507328+0.009793    test-rmse:0.668654+0.051908 
## [27] train-rmse:0.501038+0.010820    test-rmse:0.661989+0.048654 
## [28] train-rmse:0.493316+0.008237    test-rmse:0.657014+0.048142 
## [29] train-rmse:0.488657+0.007694    test-rmse:0.657202+0.049256 
## [30] train-rmse:0.481952+0.005912    test-rmse:0.653507+0.052033 
## [31] train-rmse:0.478029+0.005978    test-rmse:0.652639+0.053529 
## [32] train-rmse:0.472155+0.007829    test-rmse:0.650643+0.053472 
## [33] train-rmse:0.468733+0.006993    test-rmse:0.652113+0.053437 
## [34] train-rmse:0.464656+0.006269    test-rmse:0.651010+0.055082 
## [35] train-rmse:0.458346+0.008768    test-rmse:0.649506+0.054059 
## [36] train-rmse:0.455004+0.009040    test-rmse:0.649507+0.054166 
## [37] train-rmse:0.451758+0.008459    test-rmse:0.650670+0.057056 
## [38] train-rmse:0.447885+0.008465    test-rmse:0.650301+0.056160 
## [39] train-rmse:0.445055+0.009220    test-rmse:0.649811+0.055169 
## [40] train-rmse:0.440783+0.009092    test-rmse:0.647378+0.055943 
## [41] train-rmse:0.437531+0.009471    test-rmse:0.646870+0.058622 
## [42] train-rmse:0.433862+0.010628    test-rmse:0.644020+0.057140 
## [43] train-rmse:0.429613+0.010848    test-rmse:0.643392+0.058189 
## [44] train-rmse:0.425972+0.011350    test-rmse:0.644910+0.058806 
## [45] train-rmse:0.421001+0.010436    test-rmse:0.641814+0.055946 
## [46] train-rmse:0.416867+0.008576    test-rmse:0.642055+0.056549 
## [47] train-rmse:0.413155+0.009033    test-rmse:0.638496+0.054483 
## [48] train-rmse:0.410090+0.008791    test-rmse:0.636508+0.054502 
## [49] train-rmse:0.408040+0.008879    test-rmse:0.636427+0.054633 
## [50] train-rmse:0.405587+0.008477    test-rmse:0.636208+0.056297 
## [51] train-rmse:0.400641+0.008633    test-rmse:0.635890+0.054577 
## [52] train-rmse:0.398718+0.008731    test-rmse:0.634312+0.053679 
## [53] train-rmse:0.396295+0.008984    test-rmse:0.632659+0.053459 
## [54] train-rmse:0.393663+0.008261    test-rmse:0.631197+0.052942 
## [55] train-rmse:0.391391+0.009646    test-rmse:0.631870+0.053082 
## [56] train-rmse:0.388634+0.009227    test-rmse:0.632320+0.054082 
## [57] train-rmse:0.386227+0.009847    test-rmse:0.632550+0.054084 
## [58] train-rmse:0.384021+0.009740    test-rmse:0.631366+0.054799 
## [59] train-rmse:0.382025+0.010180    test-rmse:0.630878+0.054998 
## [60] train-rmse:0.379285+0.009154    test-rmse:0.630390+0.055406 
## [61] train-rmse:0.376623+0.008820    test-rmse:0.629389+0.057350 
## [62] train-rmse:0.374723+0.009399    test-rmse:0.629252+0.057001 
## [63] train-rmse:0.372028+0.010226    test-rmse:0.628619+0.056285 
## [64] train-rmse:0.368786+0.010054    test-rmse:0.628279+0.055794 
## [65] train-rmse:0.366094+0.010455    test-rmse:0.628985+0.056026 
## [66] train-rmse:0.364099+0.009932    test-rmse:0.628672+0.056041 
## [67] train-rmse:0.362177+0.010645    test-rmse:0.628407+0.055705 
## [68] train-rmse:0.358636+0.011573    test-rmse:0.626353+0.056593 
## [69] train-rmse:0.357033+0.012060    test-rmse:0.626483+0.056828 
## [70] train-rmse:0.354255+0.013206    test-rmse:0.625141+0.056761 
## [71] train-rmse:0.351311+0.012979    test-rmse:0.626329+0.058697 
## [72] train-rmse:0.347233+0.013770    test-rmse:0.626545+0.059565 
## [73] train-rmse:0.345414+0.014470    test-rmse:0.627910+0.059716 
## [74] train-rmse:0.343179+0.013855    test-rmse:0.628552+0.060029 
## [75] train-rmse:0.340799+0.014079    test-rmse:0.628510+0.061067 
## [76] train-rmse:0.337502+0.012176    test-rmse:0.628419+0.061621 
## Stopping. Best iteration:
## [70] train-rmse:0.354255+0.013206    test-rmse:0.625141+0.056761
## 
## 45 
## [1]  train-rmse:1.324034+0.012802    test-rmse:1.339027+0.070047 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.110725+0.008923    test-rmse:1.139776+0.071979 
## [3]  train-rmse:0.955540+0.005997    test-rmse:0.999421+0.072971 
## [4]  train-rmse:0.841470+0.003345    test-rmse:0.897967+0.076816 
## [5]  train-rmse:0.760174+0.002623    test-rmse:0.828080+0.071403 
## [6]  train-rmse:0.700785+0.002915    test-rmse:0.777174+0.062826 
## [7]  train-rmse:0.656179+0.004774    test-rmse:0.746205+0.063712 
## [8]  train-rmse:0.619707+0.006703    test-rmse:0.721205+0.061022 
## [9]  train-rmse:0.593708+0.008156    test-rmse:0.705547+0.059075 
## [10] train-rmse:0.576491+0.008247    test-rmse:0.696748+0.058211 
## [11] train-rmse:0.560841+0.007863    test-rmse:0.686649+0.056780 
## [12] train-rmse:0.547045+0.006754    test-rmse:0.680454+0.057583 
## [13] train-rmse:0.534365+0.005859    test-rmse:0.675490+0.058411 
## [14] train-rmse:0.523426+0.007769    test-rmse:0.671412+0.055789 
## [15] train-rmse:0.514224+0.007851    test-rmse:0.670373+0.056083 
## [16] train-rmse:0.504588+0.010989    test-rmse:0.667550+0.056900 
## [17] train-rmse:0.496798+0.011308    test-rmse:0.664896+0.054848 
## [18] train-rmse:0.486761+0.011132    test-rmse:0.661333+0.056647 
## [19] train-rmse:0.480007+0.010629    test-rmse:0.657692+0.060423 
## [20] train-rmse:0.470371+0.008134    test-rmse:0.655489+0.059446 
## [21] train-rmse:0.461304+0.010256    test-rmse:0.656326+0.056185 
## [22] train-rmse:0.455430+0.011850    test-rmse:0.653420+0.053216 
## [23] train-rmse:0.448356+0.010925    test-rmse:0.654141+0.052966 
## [24] train-rmse:0.440684+0.013228    test-rmse:0.650600+0.051726 
## [25] train-rmse:0.432166+0.012721    test-rmse:0.647061+0.050681 
## [26] train-rmse:0.427703+0.012215    test-rmse:0.647429+0.049357 
## [27] train-rmse:0.421985+0.011092    test-rmse:0.645477+0.050728 
## [28] train-rmse:0.416346+0.011074    test-rmse:0.644581+0.051428 
## [29] train-rmse:0.411927+0.012797    test-rmse:0.645079+0.051681 
## [30] train-rmse:0.405803+0.014651    test-rmse:0.646339+0.053444 
## [31] train-rmse:0.398415+0.015726    test-rmse:0.641734+0.048987 
## [32] train-rmse:0.393080+0.015542    test-rmse:0.641485+0.048878 
## [33] train-rmse:0.389247+0.016057    test-rmse:0.639184+0.049330 
## [34] train-rmse:0.385776+0.015781    test-rmse:0.639756+0.049127 
## [35] train-rmse:0.381980+0.015684    test-rmse:0.640218+0.051269 
## [36] train-rmse:0.376329+0.017002    test-rmse:0.639701+0.051980 
## [37] train-rmse:0.371354+0.014874    test-rmse:0.638465+0.052789 
## [38] train-rmse:0.366591+0.016512    test-rmse:0.636579+0.051209 
## [39] train-rmse:0.360895+0.016158    test-rmse:0.634489+0.052782 
## [40] train-rmse:0.356948+0.015599    test-rmse:0.634320+0.053388 
## [41] train-rmse:0.351137+0.015913    test-rmse:0.635814+0.056551 
## [42] train-rmse:0.347768+0.016372    test-rmse:0.634476+0.056008 
## [43] train-rmse:0.345196+0.016810    test-rmse:0.634045+0.056085 
## [44] train-rmse:0.341369+0.017394    test-rmse:0.632932+0.054932 
## [45] train-rmse:0.336254+0.016931    test-rmse:0.632036+0.053980 
## [46] train-rmse:0.331819+0.017093    test-rmse:0.631116+0.053722 
## [47] train-rmse:0.328311+0.017448    test-rmse:0.631910+0.054442 
## [48] train-rmse:0.323996+0.015825    test-rmse:0.632836+0.053265 
## [49] train-rmse:0.320724+0.015539    test-rmse:0.633746+0.053466 
## [50] train-rmse:0.316593+0.014761    test-rmse:0.633062+0.053029 
## [51] train-rmse:0.314544+0.015232    test-rmse:0.632385+0.053389 
## [52] train-rmse:0.312312+0.015007    test-rmse:0.632863+0.053311 
## Stopping. Best iteration:
## [46] train-rmse:0.331819+0.017093    test-rmse:0.631116+0.053722
## 
## 46 
## [1]  train-rmse:1.321013+0.012997    test-rmse:1.334804+0.072645 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.104558+0.009486    test-rmse:1.133973+0.073323 
## [3]  train-rmse:0.941711+0.007697    test-rmse:0.987567+0.074588 
## [4]  train-rmse:0.821931+0.005514    test-rmse:0.885955+0.071940 
## [5]  train-rmse:0.736701+0.005671    test-rmse:0.815098+0.068405 
## [6]  train-rmse:0.674366+0.005624    test-rmse:0.769987+0.066216 
## [7]  train-rmse:0.627899+0.005798    test-rmse:0.736623+0.055029 
## [8]  train-rmse:0.591736+0.007180    test-rmse:0.716595+0.051536 
## [9]  train-rmse:0.557310+0.012373    test-rmse:0.697833+0.049037 
## [10] train-rmse:0.528937+0.009698    test-rmse:0.688985+0.047545 
## [11] train-rmse:0.508450+0.010817    test-rmse:0.682907+0.047890 
## [12] train-rmse:0.492965+0.010823    test-rmse:0.677908+0.047662 
## [13] train-rmse:0.478542+0.011662    test-rmse:0.673647+0.045185 
## [14] train-rmse:0.466691+0.013676    test-rmse:0.672391+0.047412 
## [15] train-rmse:0.453516+0.013176    test-rmse:0.665392+0.050221 
## [16] train-rmse:0.441482+0.009543    test-rmse:0.657967+0.044493 
## [17] train-rmse:0.432847+0.011824    test-rmse:0.655672+0.045974 
## [18] train-rmse:0.421617+0.007546    test-rmse:0.650429+0.044169 
## [19] train-rmse:0.409299+0.011572    test-rmse:0.646645+0.046677 
## [20] train-rmse:0.402458+0.013085    test-rmse:0.647454+0.046264 
## [21] train-rmse:0.395239+0.011630    test-rmse:0.646800+0.047104 
## [22] train-rmse:0.388595+0.011484    test-rmse:0.648198+0.048110 
## [23] train-rmse:0.381642+0.010842    test-rmse:0.646943+0.048134 
## [24] train-rmse:0.374861+0.011739    test-rmse:0.646858+0.048262 
## [25] train-rmse:0.367049+0.010466    test-rmse:0.645647+0.048207 
## [26] train-rmse:0.361574+0.010921    test-rmse:0.646119+0.047858 
## [27] train-rmse:0.353624+0.012015    test-rmse:0.642597+0.047995 
## [28] train-rmse:0.346033+0.011177    test-rmse:0.639349+0.048146 
## [29] train-rmse:0.341865+0.011332    test-rmse:0.638527+0.047726 
## [30] train-rmse:0.336057+0.012133    test-rmse:0.637614+0.047629 
## [31] train-rmse:0.331266+0.010452    test-rmse:0.636997+0.046942 
## [32] train-rmse:0.326916+0.010621    test-rmse:0.636598+0.048484 
## [33] train-rmse:0.322575+0.009485    test-rmse:0.636424+0.050197 
## [34] train-rmse:0.316788+0.009041    test-rmse:0.636178+0.046457 
## [35] train-rmse:0.312093+0.010395    test-rmse:0.636644+0.046009 
## [36] train-rmse:0.306417+0.010929    test-rmse:0.637188+0.047410 
## [37] train-rmse:0.301003+0.008511    test-rmse:0.635585+0.045686 
## [38] train-rmse:0.295687+0.010028    test-rmse:0.633909+0.045314 
## [39] train-rmse:0.291950+0.010321    test-rmse:0.633245+0.044582 
## [40] train-rmse:0.286823+0.008677    test-rmse:0.633113+0.045267 
## [41] train-rmse:0.277945+0.009105    test-rmse:0.632245+0.047206 
## [42] train-rmse:0.273968+0.009045    test-rmse:0.631878+0.047209 
## [43] train-rmse:0.269150+0.009086    test-rmse:0.630639+0.046585 
## [44] train-rmse:0.265555+0.009229    test-rmse:0.627756+0.045799 
## [45] train-rmse:0.260260+0.008619    test-rmse:0.626368+0.045334 
## [46] train-rmse:0.255822+0.007681    test-rmse:0.625817+0.045803 
## [47] train-rmse:0.250292+0.006595    test-rmse:0.625717+0.046187 
## [48] train-rmse:0.245716+0.005551    test-rmse:0.625302+0.045709 
## [49] train-rmse:0.241876+0.005766    test-rmse:0.624884+0.046168 
## [50] train-rmse:0.237862+0.006068    test-rmse:0.624138+0.045504 
## [51] train-rmse:0.233970+0.005351    test-rmse:0.625471+0.046005 
## [52] train-rmse:0.231855+0.005663    test-rmse:0.625182+0.046865 
## [53] train-rmse:0.229404+0.005995    test-rmse:0.625934+0.047896 
## [54] train-rmse:0.226027+0.005709    test-rmse:0.625878+0.048259 
## [55] train-rmse:0.222643+0.006765    test-rmse:0.626317+0.048414 
## [56] train-rmse:0.219135+0.007240    test-rmse:0.626766+0.048889 
## Stopping. Best iteration:
## [50] train-rmse:0.237862+0.006068    test-rmse:0.624138+0.045504
## 
## 47 
## [1]  train-rmse:1.317944+0.012497    test-rmse:1.336587+0.074340 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.098281+0.008934    test-rmse:1.132505+0.075169 
## [3]  train-rmse:0.930867+0.007295    test-rmse:0.988027+0.073821 
## [4]  train-rmse:0.804201+0.005482    test-rmse:0.880658+0.071867 
## [5]  train-rmse:0.710858+0.004608    test-rmse:0.810543+0.068446 
## [6]  train-rmse:0.642871+0.006091    test-rmse:0.760514+0.066678 
## [7]  train-rmse:0.588211+0.011186    test-rmse:0.727960+0.065535 
## [8]  train-rmse:0.547800+0.010122    test-rmse:0.703771+0.060452 
## [9]  train-rmse:0.513527+0.011205    test-rmse:0.688430+0.057508 
## [10] train-rmse:0.486395+0.016089    test-rmse:0.676893+0.051462 
## [11] train-rmse:0.468160+0.015267    test-rmse:0.671071+0.052424 
## [12] train-rmse:0.451389+0.014986    test-rmse:0.664576+0.054352 
## [13] train-rmse:0.432510+0.015729    test-rmse:0.659781+0.057334 
## [14] train-rmse:0.416647+0.018229    test-rmse:0.657461+0.056543 
## [15] train-rmse:0.404887+0.019073    test-rmse:0.654504+0.059353 
## [16] train-rmse:0.388829+0.016875    test-rmse:0.647769+0.054785 
## [17] train-rmse:0.377940+0.018996    test-rmse:0.646580+0.055976 
## [18] train-rmse:0.366659+0.018637    test-rmse:0.645293+0.053352 
## [19] train-rmse:0.355531+0.015164    test-rmse:0.641625+0.054068 
## [20] train-rmse:0.344956+0.015833    test-rmse:0.640443+0.053853 
## [21] train-rmse:0.335075+0.013613    test-rmse:0.638759+0.055436 
## [22] train-rmse:0.325445+0.017677    test-rmse:0.636628+0.053234 
## [23] train-rmse:0.316744+0.017236    test-rmse:0.639304+0.056066 
## [24] train-rmse:0.311144+0.017046    test-rmse:0.638590+0.057517 
## [25] train-rmse:0.302271+0.018840    test-rmse:0.639017+0.056079 
## [26] train-rmse:0.293284+0.017934    test-rmse:0.634636+0.053703 
## [27] train-rmse:0.286631+0.017592    test-rmse:0.635144+0.054414 
## [28] train-rmse:0.280189+0.015677    test-rmse:0.636264+0.054786 
## [29] train-rmse:0.274117+0.015739    test-rmse:0.636396+0.055556 
## [30] train-rmse:0.268008+0.015752    test-rmse:0.635330+0.054169 
## [31] train-rmse:0.260725+0.014871    test-rmse:0.631975+0.054684 
## [32] train-rmse:0.253846+0.014308    test-rmse:0.631967+0.055470 
## [33] train-rmse:0.247225+0.015294    test-rmse:0.632816+0.056412 
## [34] train-rmse:0.241374+0.015932    test-rmse:0.632464+0.056075 
## [35] train-rmse:0.236018+0.016362    test-rmse:0.631827+0.055155 
## [36] train-rmse:0.230450+0.013282    test-rmse:0.631808+0.054301 
## [37] train-rmse:0.225938+0.013178    test-rmse:0.632402+0.053283 
## [38] train-rmse:0.220227+0.013459    test-rmse:0.633171+0.053016 
## [39] train-rmse:0.216364+0.013119    test-rmse:0.632166+0.052969 
## [40] train-rmse:0.212494+0.012929    test-rmse:0.632538+0.052855 
## [41] train-rmse:0.208687+0.013198    test-rmse:0.632292+0.053460 
## [42] train-rmse:0.205489+0.013654    test-rmse:0.632736+0.053946 
## Stopping. Best iteration:
## [36] train-rmse:0.230450+0.013282    test-rmse:0.631808+0.054301
## 
## 48 
## [1]  train-rmse:1.315690+0.012066    test-rmse:1.335404+0.074507 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.093464+0.008860    test-rmse:1.137688+0.074651 
## [3]  train-rmse:0.922206+0.005430    test-rmse:0.986358+0.078090 
## [4]  train-rmse:0.790585+0.007182    test-rmse:0.882324+0.073716 
## [5]  train-rmse:0.690562+0.008215    test-rmse:0.809352+0.074680 
## [6]  train-rmse:0.618371+0.009671    test-rmse:0.757248+0.075679 
## [7]  train-rmse:0.565353+0.012214    test-rmse:0.727695+0.071640 
## [8]  train-rmse:0.517674+0.009632    test-rmse:0.701196+0.066266 
## [9]  train-rmse:0.486159+0.010349    test-rmse:0.686549+0.065421 
## [10] train-rmse:0.456609+0.008854    test-rmse:0.677191+0.060940 
## [11] train-rmse:0.424670+0.010258    test-rmse:0.665901+0.057431 
## [12] train-rmse:0.404125+0.012237    test-rmse:0.660419+0.055360 
## [13] train-rmse:0.385914+0.015309    test-rmse:0.656881+0.053402 
## [14] train-rmse:0.371024+0.014201    test-rmse:0.653014+0.052325 
## [15] train-rmse:0.354482+0.015050    test-rmse:0.648035+0.050890 
## [16] train-rmse:0.344776+0.014845    test-rmse:0.643851+0.049730 
## [17] train-rmse:0.331724+0.015272    test-rmse:0.638750+0.050696 
## [18] train-rmse:0.319699+0.013720    test-rmse:0.641084+0.049825 
## [19] train-rmse:0.308349+0.015809    test-rmse:0.640784+0.050022 
## [20] train-rmse:0.298180+0.015012    test-rmse:0.641627+0.048185 
## [21] train-rmse:0.289827+0.014562    test-rmse:0.640311+0.047134 
## [22] train-rmse:0.277730+0.013108    test-rmse:0.640452+0.049829 
## [23] train-rmse:0.271568+0.013341    test-rmse:0.641022+0.050579 
## Stopping. Best iteration:
## [17] train-rmse:0.331724+0.015272    test-rmse:0.638750+0.050696
## 
## 49 
## [1]  train-rmse:1.315890+0.012887    test-rmse:1.317544+0.070505 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.113738+0.009987    test-rmse:1.117403+0.068629 
## [3]  train-rmse:0.977582+0.008073    test-rmse:0.984194+0.066309 
## [4]  train-rmse:0.886404+0.007556    test-rmse:0.898710+0.060480 
## [5]  train-rmse:0.827035+0.006755    test-rmse:0.841880+0.054926 
## [6]  train-rmse:0.787837+0.006393    test-rmse:0.808665+0.048167 
## [7]  train-rmse:0.762346+0.005764    test-rmse:0.789827+0.043885 
## [8]  train-rmse:0.745203+0.005531    test-rmse:0.774005+0.043564 
## [9]  train-rmse:0.732934+0.005344    test-rmse:0.764308+0.041103 
## [10] train-rmse:0.723546+0.005191    test-rmse:0.755119+0.037148 
## [11] train-rmse:0.716179+0.004925    test-rmse:0.749276+0.035410 
## [12] train-rmse:0.710251+0.005041    test-rmse:0.746066+0.030572 
## [13] train-rmse:0.705450+0.005036    test-rmse:0.744156+0.030675 
## [14] train-rmse:0.701107+0.005107    test-rmse:0.741423+0.029062 
## [15] train-rmse:0.697018+0.005158    test-rmse:0.739630+0.029941 
## [16] train-rmse:0.693313+0.005096    test-rmse:0.738294+0.030407 
## [17] train-rmse:0.690021+0.005099    test-rmse:0.734282+0.028824 
## [18] train-rmse:0.686880+0.005117    test-rmse:0.732968+0.029171 
## [19] train-rmse:0.683869+0.005154    test-rmse:0.733849+0.029645 
## [20] train-rmse:0.681045+0.005180    test-rmse:0.733042+0.027571 
## [21] train-rmse:0.678290+0.005225    test-rmse:0.731145+0.027424 
## [22] train-rmse:0.675654+0.005345    test-rmse:0.729012+0.027272 
## [23] train-rmse:0.673142+0.005350    test-rmse:0.727682+0.029730 
## [24] train-rmse:0.670686+0.005329    test-rmse:0.728091+0.032107 
## [25] train-rmse:0.668315+0.005329    test-rmse:0.727423+0.032723 
## [26] train-rmse:0.666054+0.005304    test-rmse:0.725774+0.032572 
## [27] train-rmse:0.663843+0.005324    test-rmse:0.726022+0.033202 
## [28] train-rmse:0.661734+0.005351    test-rmse:0.723649+0.032418 
## [29] train-rmse:0.659696+0.005381    test-rmse:0.722493+0.032527 
## [30] train-rmse:0.657758+0.005432    test-rmse:0.720233+0.030230 
## [31] train-rmse:0.655892+0.005431    test-rmse:0.720589+0.031472 
## [32] train-rmse:0.654095+0.005441    test-rmse:0.720500+0.032130 
## [33] train-rmse:0.652323+0.005490    test-rmse:0.721934+0.032150 
## [34] train-rmse:0.650538+0.005526    test-rmse:0.719780+0.030809 
## [35] train-rmse:0.648882+0.005591    test-rmse:0.719030+0.031080 
## [36] train-rmse:0.647246+0.005643    test-rmse:0.719881+0.032829 
## [37] train-rmse:0.645664+0.005692    test-rmse:0.718505+0.034148 
## [38] train-rmse:0.644113+0.005716    test-rmse:0.718282+0.034845 
## [39] train-rmse:0.642588+0.005762    test-rmse:0.718812+0.034863 
## [40] train-rmse:0.641122+0.005789    test-rmse:0.718681+0.035429 
## [41] train-rmse:0.639714+0.005787    test-rmse:0.716537+0.034656 
## [42] train-rmse:0.638327+0.005785    test-rmse:0.715975+0.035061 
## [43] train-rmse:0.636965+0.005773    test-rmse:0.714461+0.035018 
## [44] train-rmse:0.635625+0.005800    test-rmse:0.713615+0.035479 
## [45] train-rmse:0.634290+0.005808    test-rmse:0.712902+0.033717 
## [46] train-rmse:0.632990+0.005811    test-rmse:0.712070+0.033845 
## [47] train-rmse:0.631717+0.005863    test-rmse:0.710856+0.035066 
## [48] train-rmse:0.630481+0.005862    test-rmse:0.709024+0.034507 
## [49] train-rmse:0.629282+0.005852    test-rmse:0.709020+0.035572 
## [50] train-rmse:0.628091+0.005839    test-rmse:0.709851+0.036211 
## [51] train-rmse:0.626913+0.005860    test-rmse:0.709295+0.035355 
## [52] train-rmse:0.625757+0.005854    test-rmse:0.707974+0.035399 
## [53] train-rmse:0.624637+0.005865    test-rmse:0.709099+0.035644 
## [54] train-rmse:0.623543+0.005867    test-rmse:0.710186+0.035704 
## [55] train-rmse:0.622465+0.005869    test-rmse:0.708644+0.034881 
## [56] train-rmse:0.621382+0.005909    test-rmse:0.707593+0.033037 
## [57] train-rmse:0.620335+0.005936    test-rmse:0.708005+0.032742 
## [58] train-rmse:0.619304+0.005972    test-rmse:0.708598+0.033803 
## [59] train-rmse:0.618303+0.005996    test-rmse:0.708244+0.033255 
## [60] train-rmse:0.617320+0.006006    test-rmse:0.706907+0.033224 
## [61] train-rmse:0.616339+0.006037    test-rmse:0.705922+0.033191 
## [62] train-rmse:0.615373+0.006065    test-rmse:0.707497+0.033493 
## [63] train-rmse:0.614399+0.006060    test-rmse:0.705368+0.035124 
## [64] train-rmse:0.613456+0.006065    test-rmse:0.704429+0.034680 
## [65] train-rmse:0.612543+0.006073    test-rmse:0.703026+0.033562 
## [66] train-rmse:0.611633+0.006101    test-rmse:0.702797+0.033605 
## [67] train-rmse:0.610740+0.006097    test-rmse:0.701457+0.033767 
## [68] train-rmse:0.609853+0.006087    test-rmse:0.701979+0.033623 
## [69] train-rmse:0.608995+0.006075    test-rmse:0.700926+0.033811 
## [70] train-rmse:0.608153+0.006086    test-rmse:0.700877+0.034137 
## [71] train-rmse:0.607318+0.006085    test-rmse:0.700580+0.034196 
## [72] train-rmse:0.606499+0.006096    test-rmse:0.699602+0.034537 
## [73] train-rmse:0.605681+0.006105    test-rmse:0.699012+0.034097 
## [74] train-rmse:0.604888+0.006099    test-rmse:0.697652+0.035379 
## [75] train-rmse:0.604095+0.006115    test-rmse:0.697307+0.035055 
## [76] train-rmse:0.603300+0.006145    test-rmse:0.696267+0.034624 
## [77] train-rmse:0.602517+0.006173    test-rmse:0.695468+0.033740 
## [78] train-rmse:0.601741+0.006197    test-rmse:0.695443+0.034028 
## [79] train-rmse:0.600998+0.006202    test-rmse:0.695903+0.034948 
## [80] train-rmse:0.600260+0.006224    test-rmse:0.695642+0.034393 
## [81] train-rmse:0.599528+0.006225    test-rmse:0.696506+0.033802 
## [82] train-rmse:0.598797+0.006225    test-rmse:0.697504+0.035064 
## [83] train-rmse:0.598079+0.006217    test-rmse:0.696632+0.035505 
## [84] train-rmse:0.597365+0.006240    test-rmse:0.695724+0.036397 
## Stopping. Best iteration:
## [78] train-rmse:0.601741+0.006197    test-rmse:0.695443+0.034028
## 
## 50 
## [1]  train-rmse:1.308891+0.012745    test-rmse:1.315359+0.070771 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.099438+0.009538    test-rmse:1.112881+0.070501 
## [3]  train-rmse:0.954720+0.007183    test-rmse:0.979794+0.069721 
## [4]  train-rmse:0.857385+0.005544    test-rmse:0.888279+0.065288 
## [5]  train-rmse:0.792684+0.005931    test-rmse:0.830967+0.063979 
## [6]  train-rmse:0.744643+0.003650    test-rmse:0.787513+0.057729 
## [7]  train-rmse:0.714367+0.004621    test-rmse:0.762643+0.051285 
## [8]  train-rmse:0.690852+0.002215    test-rmse:0.742088+0.051339 
## [9]  train-rmse:0.676178+0.002027    test-rmse:0.733439+0.050555 
## [10] train-rmse:0.664697+0.002169    test-rmse:0.728237+0.046007 
## [11] train-rmse:0.655548+0.002276    test-rmse:0.723305+0.043110 
## [12] train-rmse:0.648329+0.002299    test-rmse:0.719667+0.042217 
## [13] train-rmse:0.640115+0.003327    test-rmse:0.714165+0.046875 
## [14] train-rmse:0.633258+0.003652    test-rmse:0.706544+0.045266 
## [15] train-rmse:0.627669+0.004468    test-rmse:0.703882+0.043811 
## [16] train-rmse:0.619173+0.002961    test-rmse:0.699901+0.044356 
## [17] train-rmse:0.614003+0.003948    test-rmse:0.698425+0.044095 
## [18] train-rmse:0.609294+0.003495    test-rmse:0.700569+0.047441 
## [19] train-rmse:0.605010+0.003176    test-rmse:0.699961+0.048113 
## [20] train-rmse:0.600404+0.002830    test-rmse:0.699307+0.047939 
## [21] train-rmse:0.595444+0.002669    test-rmse:0.700035+0.046338 
## [22] train-rmse:0.591000+0.002345    test-rmse:0.699611+0.047818 
## [23] train-rmse:0.585698+0.003946    test-rmse:0.693954+0.046012 
## [24] train-rmse:0.581240+0.003295    test-rmse:0.692549+0.045058 
## [25] train-rmse:0.576542+0.003578    test-rmse:0.691857+0.043641 
## [26] train-rmse:0.573364+0.003088    test-rmse:0.690311+0.042875 
## [27] train-rmse:0.570387+0.003543    test-rmse:0.690308+0.044879 
## [28] train-rmse:0.564411+0.005547    test-rmse:0.685045+0.042418 
## [29] train-rmse:0.560902+0.005461    test-rmse:0.681812+0.040520 
## [30] train-rmse:0.557504+0.004777    test-rmse:0.680443+0.041768 
## [31] train-rmse:0.554712+0.005221    test-rmse:0.679122+0.040924 
## [32] train-rmse:0.550416+0.006021    test-rmse:0.678497+0.041594 
## [33] train-rmse:0.546889+0.006803    test-rmse:0.677563+0.040994 
## [34] train-rmse:0.544013+0.006700    test-rmse:0.676324+0.041237 
## [35] train-rmse:0.541775+0.007146    test-rmse:0.677040+0.043103 
## [36] train-rmse:0.538438+0.008070    test-rmse:0.675065+0.042416 
## [37] train-rmse:0.535729+0.007704    test-rmse:0.675646+0.041556 
## [38] train-rmse:0.533621+0.007697    test-rmse:0.675660+0.041784 
## [39] train-rmse:0.530035+0.008029    test-rmse:0.672426+0.039578 
## [40] train-rmse:0.526726+0.007125    test-rmse:0.669518+0.036557 
## [41] train-rmse:0.523710+0.006969    test-rmse:0.668896+0.034861 
## [42] train-rmse:0.521088+0.006843    test-rmse:0.668053+0.036785 
## [43] train-rmse:0.518827+0.007312    test-rmse:0.667572+0.038270 
## [44] train-rmse:0.516668+0.007597    test-rmse:0.666731+0.039089 
## [45] train-rmse:0.514805+0.007726    test-rmse:0.666742+0.039692 
## [46] train-rmse:0.511995+0.007256    test-rmse:0.667268+0.040201 
## [47] train-rmse:0.508533+0.004231    test-rmse:0.666380+0.044274 
## [48] train-rmse:0.506426+0.003843    test-rmse:0.666131+0.043681 
## [49] train-rmse:0.504240+0.003859    test-rmse:0.666207+0.044824 
## [50] train-rmse:0.502373+0.003247    test-rmse:0.665497+0.047074 
## [51] train-rmse:0.499495+0.005639    test-rmse:0.664057+0.047834 
## [52] train-rmse:0.497090+0.006437    test-rmse:0.662959+0.046977 
## [53] train-rmse:0.494744+0.006014    test-rmse:0.662019+0.046642 
## [54] train-rmse:0.491025+0.005628    test-rmse:0.661295+0.045194 
## [55] train-rmse:0.488860+0.004913    test-rmse:0.660985+0.043987 
## [56] train-rmse:0.486283+0.006771    test-rmse:0.660171+0.043889 
## [57] train-rmse:0.483359+0.008308    test-rmse:0.660496+0.044047 
## [58] train-rmse:0.481354+0.009075    test-rmse:0.661120+0.043203 
## [59] train-rmse:0.479414+0.008985    test-rmse:0.660116+0.042740 
## [60] train-rmse:0.478063+0.008975    test-rmse:0.657139+0.043137 
## [61] train-rmse:0.476530+0.009191    test-rmse:0.655444+0.043520 
## [62] train-rmse:0.474808+0.009288    test-rmse:0.656462+0.043707 
## [63] train-rmse:0.473304+0.009232    test-rmse:0.656427+0.044202 
## [64] train-rmse:0.470580+0.008430    test-rmse:0.655425+0.046025 
## [65] train-rmse:0.468946+0.008216    test-rmse:0.655180+0.046413 
## [66] train-rmse:0.467099+0.008201    test-rmse:0.653242+0.047363 
## [67] train-rmse:0.465974+0.008363    test-rmse:0.652861+0.047368 
## [68] train-rmse:0.464659+0.008990    test-rmse:0.654497+0.049383 
## [69] train-rmse:0.462126+0.009754    test-rmse:0.654385+0.049566 
## [70] train-rmse:0.460456+0.010052    test-rmse:0.652600+0.049460 
## [71] train-rmse:0.459181+0.009869    test-rmse:0.653095+0.049160 
## [72] train-rmse:0.457345+0.010399    test-rmse:0.650842+0.048962 
## [73] train-rmse:0.454802+0.009965    test-rmse:0.648046+0.047633 
## [74] train-rmse:0.452965+0.010180    test-rmse:0.647660+0.047953 
## [75] train-rmse:0.451485+0.010462    test-rmse:0.647409+0.047898 
## [76] train-rmse:0.450265+0.010417    test-rmse:0.646753+0.047608 
## [77] train-rmse:0.449162+0.010524    test-rmse:0.645606+0.046658 
## [78] train-rmse:0.447865+0.010628    test-rmse:0.645655+0.047295 
## [79] train-rmse:0.446700+0.010683    test-rmse:0.645296+0.047053 
## [80] train-rmse:0.443881+0.009786    test-rmse:0.644353+0.047147 
## [81] train-rmse:0.441565+0.009512    test-rmse:0.644335+0.046901 
## [82] train-rmse:0.439483+0.009153    test-rmse:0.642401+0.046573 
## [83] train-rmse:0.437979+0.009524    test-rmse:0.643598+0.045457 
## [84] train-rmse:0.436921+0.009829    test-rmse:0.642604+0.044830 
## [85] train-rmse:0.435143+0.011350    test-rmse:0.642658+0.044542 
## [86] train-rmse:0.433860+0.012004    test-rmse:0.642765+0.044018 
## [87] train-rmse:0.432922+0.011963    test-rmse:0.642730+0.044991 
## [88] train-rmse:0.431555+0.011693    test-rmse:0.641454+0.044141 
## [89] train-rmse:0.430303+0.011294    test-rmse:0.640935+0.043907 
## [90] train-rmse:0.428775+0.011621    test-rmse:0.641567+0.044410 
## [91] train-rmse:0.427893+0.011632    test-rmse:0.640431+0.043774 
## [92] train-rmse:0.427106+0.011690    test-rmse:0.640452+0.043701 
## [93] train-rmse:0.425693+0.011795    test-rmse:0.640815+0.043974 
## [94] train-rmse:0.424620+0.011920    test-rmse:0.640631+0.044034 
## [95] train-rmse:0.423434+0.012557    test-rmse:0.640632+0.044824 
## [96] train-rmse:0.422251+0.012707    test-rmse:0.641897+0.044295 
## [97] train-rmse:0.420619+0.012591    test-rmse:0.642099+0.044204 
## Stopping. Best iteration:
## [91] train-rmse:0.427893+0.011632    test-rmse:0.640431+0.043774
## 
## 51 
## [1]  train-rmse:1.304120+0.012347    test-rmse:1.312853+0.069419 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.087154+0.009351    test-rmse:1.108824+0.067761 
## [3]  train-rmse:0.936170+0.006935    test-rmse:0.970044+0.067728 
## [4]  train-rmse:0.831798+0.004655    test-rmse:0.873167+0.069957 
## [5]  train-rmse:0.759816+0.005479    test-rmse:0.815723+0.066027 
## [6]  train-rmse:0.711141+0.008698    test-rmse:0.777318+0.058962 
## [7]  train-rmse:0.668828+0.005733    test-rmse:0.744899+0.055307 
## [8]  train-rmse:0.644714+0.004454    test-rmse:0.724089+0.052169 
## [9]  train-rmse:0.623914+0.005132    test-rmse:0.714400+0.050054 
## [10] train-rmse:0.610059+0.006081    test-rmse:0.708183+0.048631 
## [11] train-rmse:0.598453+0.006705    test-rmse:0.704158+0.048846 
## [12] train-rmse:0.583973+0.006754    test-rmse:0.699228+0.051301 
## [13] train-rmse:0.575541+0.007046    test-rmse:0.696404+0.054779 
## [14] train-rmse:0.567714+0.007427    test-rmse:0.695596+0.052239 
## [15] train-rmse:0.560784+0.008264    test-rmse:0.692382+0.051879 
## [16] train-rmse:0.551976+0.006425    test-rmse:0.688311+0.051296 
## [17] train-rmse:0.546747+0.006380    test-rmse:0.685555+0.055060 
## [18] train-rmse:0.539581+0.006561    test-rmse:0.686185+0.056954 
## [19] train-rmse:0.533007+0.008329    test-rmse:0.687186+0.057675 
## [20] train-rmse:0.527569+0.009281    test-rmse:0.683450+0.056366 
## [21] train-rmse:0.519548+0.010959    test-rmse:0.682719+0.058287 
## [22] train-rmse:0.513001+0.011540    test-rmse:0.680401+0.057713 
## [23] train-rmse:0.506406+0.012588    test-rmse:0.673925+0.053050 
## [24] train-rmse:0.499358+0.009191    test-rmse:0.666846+0.051472 
## [25] train-rmse:0.493308+0.007849    test-rmse:0.666480+0.054425 
## [26] train-rmse:0.488235+0.008140    test-rmse:0.667279+0.056584 
## [27] train-rmse:0.482714+0.007960    test-rmse:0.662412+0.054412 
## [28] train-rmse:0.478566+0.007655    test-rmse:0.661542+0.055745 
## [29] train-rmse:0.474130+0.009102    test-rmse:0.657887+0.054909 
## [30] train-rmse:0.468862+0.010596    test-rmse:0.654666+0.053880 
## [31] train-rmse:0.463433+0.009999    test-rmse:0.653953+0.053355 
## [32] train-rmse:0.459136+0.010346    test-rmse:0.651980+0.052891 
## [33] train-rmse:0.455318+0.010889    test-rmse:0.650611+0.052642 
## [34] train-rmse:0.451554+0.010512    test-rmse:0.650017+0.052033 
## [35] train-rmse:0.446789+0.010031    test-rmse:0.650763+0.054916 
## [36] train-rmse:0.443576+0.009943    test-rmse:0.649908+0.056052 
## [37] train-rmse:0.440628+0.010562    test-rmse:0.648594+0.056974 
## [38] train-rmse:0.436306+0.009717    test-rmse:0.646367+0.056862 
## [39] train-rmse:0.432387+0.010894    test-rmse:0.647777+0.055568 
## [40] train-rmse:0.427887+0.010286    test-rmse:0.645537+0.058844 
## [41] train-rmse:0.423775+0.010312    test-rmse:0.643856+0.059097 
## [42] train-rmse:0.418944+0.012601    test-rmse:0.642842+0.061340 
## [43] train-rmse:0.415968+0.013304    test-rmse:0.643515+0.062567 
## [44] train-rmse:0.413025+0.013410    test-rmse:0.642769+0.063912 
## [45] train-rmse:0.410151+0.013600    test-rmse:0.641911+0.061756 
## [46] train-rmse:0.407861+0.013679    test-rmse:0.642480+0.061750 
## [47] train-rmse:0.404865+0.013681    test-rmse:0.641617+0.061111 
## [48] train-rmse:0.401538+0.013915    test-rmse:0.641290+0.061141 
## [49] train-rmse:0.399181+0.014540    test-rmse:0.640240+0.063177 
## [50] train-rmse:0.396354+0.015151    test-rmse:0.639629+0.064148 
## [51] train-rmse:0.392263+0.013831    test-rmse:0.640265+0.065031 
## [52] train-rmse:0.388678+0.013494    test-rmse:0.639806+0.065410 
## [53] train-rmse:0.386321+0.013486    test-rmse:0.640314+0.066043 
## [54] train-rmse:0.382987+0.013503    test-rmse:0.641362+0.066513 
## [55] train-rmse:0.380991+0.013194    test-rmse:0.640919+0.066116 
## [56] train-rmse:0.378957+0.013205    test-rmse:0.641069+0.066855 
## Stopping. Best iteration:
## [50] train-rmse:0.396354+0.015151    test-rmse:0.639629+0.064148
## 
## 52 
## [1]  train-rmse:1.299208+0.012425    test-rmse:1.315877+0.070282 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.074396+0.008521    test-rmse:1.105152+0.073050 
## [3]  train-rmse:0.916632+0.006306    test-rmse:0.958205+0.077652 
## [4]  train-rmse:0.805732+0.005516    test-rmse:0.863112+0.073598 
## [5]  train-rmse:0.727958+0.006274    test-rmse:0.798766+0.069591 
## [6]  train-rmse:0.671201+0.003486    test-rmse:0.748758+0.064084 
## [7]  train-rmse:0.633052+0.004583    test-rmse:0.727520+0.061274 
## [8]  train-rmse:0.600431+0.004748    test-rmse:0.706210+0.055031 
## [9]  train-rmse:0.576697+0.003846    test-rmse:0.693092+0.052566 
## [10] train-rmse:0.556749+0.007249    test-rmse:0.685811+0.050714 
## [11] train-rmse:0.541989+0.007708    test-rmse:0.678157+0.053105 
## [12] train-rmse:0.527802+0.008859    test-rmse:0.675938+0.052960 
## [13] train-rmse:0.517404+0.008900    test-rmse:0.669168+0.051326 
## [14] train-rmse:0.506640+0.008506    test-rmse:0.664779+0.054373 
## [15] train-rmse:0.497208+0.010722    test-rmse:0.666246+0.054434 
## [16] train-rmse:0.487844+0.010777    test-rmse:0.662103+0.049815 
## [17] train-rmse:0.476818+0.005119    test-rmse:0.657487+0.046980 
## [18] train-rmse:0.467282+0.007295    test-rmse:0.654529+0.046424 
## [19] train-rmse:0.457942+0.007830    test-rmse:0.654951+0.048055 
## [20] train-rmse:0.449692+0.006577    test-rmse:0.648639+0.047403 
## [21] train-rmse:0.443932+0.007503    test-rmse:0.649939+0.050405 
## [22] train-rmse:0.437171+0.008173    test-rmse:0.648047+0.051553 
## [23] train-rmse:0.428368+0.008518    test-rmse:0.644121+0.053851 
## [24] train-rmse:0.418810+0.007039    test-rmse:0.642360+0.053299 
## [25] train-rmse:0.411797+0.004669    test-rmse:0.642063+0.055161 
## [26] train-rmse:0.406471+0.005979    test-rmse:0.639404+0.055509 
## [27] train-rmse:0.402270+0.006381    test-rmse:0.640429+0.055618 
## [28] train-rmse:0.397564+0.007121    test-rmse:0.641459+0.055855 
## [29] train-rmse:0.391000+0.006919    test-rmse:0.639579+0.055170 
## [30] train-rmse:0.383807+0.008511    test-rmse:0.641087+0.056179 
## [31] train-rmse:0.379149+0.009932    test-rmse:0.642194+0.056193 
## [32] train-rmse:0.374163+0.010043    test-rmse:0.642630+0.056257 
## Stopping. Best iteration:
## [26] train-rmse:0.406471+0.005979    test-rmse:0.639404+0.055509
## 
## 53 
## [1]  train-rmse:1.295865+0.012639    test-rmse:1.311254+0.073177 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.067190+0.008991    test-rmse:1.100080+0.073706 
## [3]  train-rmse:0.900889+0.007038    test-rmse:0.949939+0.072493 
## [4]  train-rmse:0.783862+0.005836    test-rmse:0.854883+0.072168 
## [5]  train-rmse:0.701708+0.006952    test-rmse:0.786549+0.064615 
## [6]  train-rmse:0.642902+0.005385    test-rmse:0.745342+0.058394 
## [7]  train-rmse:0.600939+0.004035    test-rmse:0.720495+0.056126 
## [8]  train-rmse:0.566994+0.004436    test-rmse:0.699419+0.052353 
## [9]  train-rmse:0.539384+0.004753    test-rmse:0.686298+0.052769 
## [10] train-rmse:0.514008+0.007387    test-rmse:0.679085+0.052521 
## [11] train-rmse:0.489076+0.008561    test-rmse:0.668363+0.054331 
## [12] train-rmse:0.473043+0.010061    test-rmse:0.667935+0.056635 
## [13] train-rmse:0.461754+0.007917    test-rmse:0.663239+0.056992 
## [14] train-rmse:0.450012+0.007361    test-rmse:0.659458+0.055075 
## [15] train-rmse:0.434873+0.003459    test-rmse:0.653941+0.050531 
## [16] train-rmse:0.424217+0.005809    test-rmse:0.654167+0.049771 
## [17] train-rmse:0.415079+0.006298    test-rmse:0.651595+0.048821 
## [18] train-rmse:0.406636+0.007853    test-rmse:0.650329+0.049715 
## [19] train-rmse:0.397028+0.010620    test-rmse:0.648515+0.050847 
## [20] train-rmse:0.386773+0.011015    test-rmse:0.645666+0.050635 
## [21] train-rmse:0.378823+0.009133    test-rmse:0.643729+0.052515 
## [22] train-rmse:0.370106+0.007388    test-rmse:0.642516+0.053840 
## [23] train-rmse:0.361833+0.008677    test-rmse:0.641523+0.052784 
## [24] train-rmse:0.355551+0.009173    test-rmse:0.641616+0.056775 
## [25] train-rmse:0.347486+0.008513    test-rmse:0.638688+0.055683 
## [26] train-rmse:0.339945+0.010391    test-rmse:0.637531+0.053733 
## [27] train-rmse:0.332639+0.010923    test-rmse:0.633689+0.052288 
## [28] train-rmse:0.327260+0.010291    test-rmse:0.634932+0.051002 
## [29] train-rmse:0.321862+0.009706    test-rmse:0.635939+0.049698 
## [30] train-rmse:0.315415+0.009724    test-rmse:0.634441+0.049262 
## [31] train-rmse:0.308442+0.008244    test-rmse:0.632919+0.049967 
## [32] train-rmse:0.301480+0.009107    test-rmse:0.633212+0.052224 
## [33] train-rmse:0.295859+0.009453    test-rmse:0.634256+0.052586 
## [34] train-rmse:0.289589+0.007739    test-rmse:0.632575+0.052113 
## [35] train-rmse:0.284771+0.009117    test-rmse:0.632762+0.053811 
## [36] train-rmse:0.279541+0.009461    test-rmse:0.631652+0.054123 
## [37] train-rmse:0.273966+0.009528    test-rmse:0.631047+0.054571 
## [38] train-rmse:0.268529+0.010314    test-rmse:0.631401+0.054205 
## [39] train-rmse:0.264987+0.010397    test-rmse:0.632747+0.053293 
## [40] train-rmse:0.259673+0.009999    test-rmse:0.633931+0.055018 
## [41] train-rmse:0.255042+0.011841    test-rmse:0.634917+0.053694 
## [42] train-rmse:0.250153+0.012062    test-rmse:0.634960+0.053622 
## [43] train-rmse:0.244135+0.011471    test-rmse:0.633761+0.056965 
## Stopping. Best iteration:
## [37] train-rmse:0.273966+0.009528    test-rmse:0.631047+0.054571
## 
## 54 
## [1]  train-rmse:1.292466+0.012085    test-rmse:1.313203+0.074978 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.060121+0.008388    test-rmse:1.098735+0.075871 
## [3]  train-rmse:0.886153+0.006848    test-rmse:0.944895+0.074737 
## [4]  train-rmse:0.761714+0.005996    test-rmse:0.847918+0.073385 
## [5]  train-rmse:0.672224+0.007791    test-rmse:0.776482+0.065922 
## [6]  train-rmse:0.609005+0.007774    test-rmse:0.730398+0.068054 
## [7]  train-rmse:0.561921+0.008619    test-rmse:0.701820+0.063026 
## [8]  train-rmse:0.526592+0.008590    test-rmse:0.687631+0.061092 
## [9]  train-rmse:0.493657+0.014657    test-rmse:0.677120+0.060821 
## [10] train-rmse:0.471492+0.016544    test-rmse:0.669495+0.059920 
## [11] train-rmse:0.449980+0.018965    test-rmse:0.661253+0.057637 
## [12] train-rmse:0.430423+0.018050    test-rmse:0.653383+0.055406 
## [13] train-rmse:0.415448+0.019605    test-rmse:0.649652+0.056139 
## [14] train-rmse:0.402963+0.019946    test-rmse:0.646089+0.054992 
## [15] train-rmse:0.386874+0.017783    test-rmse:0.640588+0.049639 
## [16] train-rmse:0.376590+0.018264    test-rmse:0.637080+0.048119 
## [17] train-rmse:0.365339+0.017965    test-rmse:0.636889+0.046935 
## [18] train-rmse:0.358570+0.019029    test-rmse:0.636333+0.047503 
## [19] train-rmse:0.349709+0.016047    test-rmse:0.635539+0.045799 
## [20] train-rmse:0.338226+0.015453    test-rmse:0.633137+0.047065 
## [21] train-rmse:0.328678+0.018655    test-rmse:0.633112+0.047584 
## [22] train-rmse:0.319053+0.018250    test-rmse:0.630058+0.048874 
## [23] train-rmse:0.307343+0.016340    test-rmse:0.628689+0.048993 
## [24] train-rmse:0.298621+0.015930    test-rmse:0.627749+0.050099 
## [25] train-rmse:0.291140+0.016892    test-rmse:0.627502+0.048175 
## [26] train-rmse:0.286043+0.016557    test-rmse:0.626121+0.047911 
## [27] train-rmse:0.278124+0.016927    test-rmse:0.625853+0.047879 
## [28] train-rmse:0.272625+0.016940    test-rmse:0.626830+0.048415 
## [29] train-rmse:0.266049+0.017105    test-rmse:0.626590+0.048207 
## [30] train-rmse:0.258153+0.016194    test-rmse:0.623741+0.048768 
## [31] train-rmse:0.250575+0.015947    test-rmse:0.622282+0.049536 
## [32] train-rmse:0.244645+0.013651    test-rmse:0.622498+0.051126 
## [33] train-rmse:0.238266+0.014914    test-rmse:0.622710+0.048415 
## [34] train-rmse:0.232589+0.016093    test-rmse:0.621727+0.049399 
## [35] train-rmse:0.226171+0.014826    test-rmse:0.620258+0.048819 
## [36] train-rmse:0.220483+0.016258    test-rmse:0.621012+0.048479 
## [37] train-rmse:0.214752+0.015284    test-rmse:0.620588+0.048308 
## [38] train-rmse:0.209968+0.015194    test-rmse:0.620609+0.049588 
## [39] train-rmse:0.204286+0.014515    test-rmse:0.620807+0.048856 
## [40] train-rmse:0.199429+0.015267    test-rmse:0.621428+0.049127 
## [41] train-rmse:0.193931+0.014958    test-rmse:0.620452+0.048798 
## Stopping. Best iteration:
## [35] train-rmse:0.226171+0.014826    test-rmse:0.620258+0.048819
## 
## 55 
## [1]  train-rmse:1.289971+0.011612    test-rmse:1.311908+0.075164 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.054713+0.008133    test-rmse:1.101450+0.077715 
## [3]  train-rmse:0.877898+0.006587    test-rmse:0.949771+0.083471 
## [4]  train-rmse:0.746734+0.007289    test-rmse:0.847587+0.079723 
## [5]  train-rmse:0.650482+0.010656    test-rmse:0.778157+0.079093 
## [6]  train-rmse:0.582575+0.012331    test-rmse:0.732293+0.076890 
## [7]  train-rmse:0.532380+0.015982    test-rmse:0.705280+0.072358 
## [8]  train-rmse:0.491438+0.015792    test-rmse:0.682534+0.066567 
## [9]  train-rmse:0.457605+0.018602    test-rmse:0.668848+0.059853 
## [10] train-rmse:0.431108+0.019719    test-rmse:0.660890+0.060661 
## [11] train-rmse:0.409445+0.019133    test-rmse:0.657681+0.061602 
## [12] train-rmse:0.391589+0.017806    test-rmse:0.654951+0.062243 
## [13] train-rmse:0.374981+0.017874    test-rmse:0.652577+0.059942 
## [14] train-rmse:0.357962+0.018813    test-rmse:0.652929+0.058631 
## [15] train-rmse:0.342389+0.017829    test-rmse:0.654247+0.050525 
## [16] train-rmse:0.329752+0.016987    test-rmse:0.656011+0.051014 
## [17] train-rmse:0.320235+0.017753    test-rmse:0.654141+0.052451 
## [18] train-rmse:0.303670+0.017638    test-rmse:0.649618+0.048111 
## [19] train-rmse:0.295075+0.018038    test-rmse:0.650583+0.047567 
## [20] train-rmse:0.282293+0.017628    test-rmse:0.652574+0.048927 
## [21] train-rmse:0.273560+0.015914    test-rmse:0.650263+0.047163 
## [22] train-rmse:0.265742+0.014784    test-rmse:0.651609+0.047862 
## [23] train-rmse:0.258557+0.016085    test-rmse:0.653012+0.049396 
## [24] train-rmse:0.248550+0.016803    test-rmse:0.653415+0.048401 
## Stopping. Best iteration:
## [18] train-rmse:0.303670+0.017638    test-rmse:0.649618+0.048111
## 
## 56 
## [1]  train-rmse:1.292819+0.012557    test-rmse:1.294737+0.070674 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.082743+0.009540    test-rmse:1.086919+0.068193 
## [3]  train-rmse:0.947429+0.007836    test-rmse:0.957209+0.067019 
## [4]  train-rmse:0.860153+0.007180    test-rmse:0.874910+0.058684 
## [5]  train-rmse:0.806376+0.006581    test-rmse:0.824667+0.051093 
## [6]  train-rmse:0.771956+0.006217    test-rmse:0.795951+0.045363 
## [7]  train-rmse:0.749900+0.005599    test-rmse:0.779459+0.041713 
## [8]  train-rmse:0.735327+0.005199    test-rmse:0.765939+0.039113 
## [9]  train-rmse:0.724634+0.004908    test-rmse:0.756581+0.035902 
## [10] train-rmse:0.716488+0.004762    test-rmse:0.751133+0.033433 
## [11] train-rmse:0.710061+0.004771    test-rmse:0.747695+0.031567 
## [12] train-rmse:0.704771+0.004787    test-rmse:0.745696+0.029557 
## [13] train-rmse:0.700148+0.004851    test-rmse:0.743440+0.029557 
## [14] train-rmse:0.695895+0.004929    test-rmse:0.740220+0.027284 
## [15] train-rmse:0.691773+0.004964    test-rmse:0.737950+0.029368 
## [16] train-rmse:0.688123+0.005024    test-rmse:0.734510+0.028762 
## [17] train-rmse:0.684850+0.004999    test-rmse:0.733333+0.027744 
## [18] train-rmse:0.681797+0.005025    test-rmse:0.732427+0.027959 
## [19] train-rmse:0.678902+0.005097    test-rmse:0.731670+0.028545 
## [20] train-rmse:0.676081+0.005159    test-rmse:0.729283+0.027531 
## [21] train-rmse:0.673297+0.005245    test-rmse:0.728580+0.028652 
## [22] train-rmse:0.670686+0.005185    test-rmse:0.730024+0.030088 
## [23] train-rmse:0.668154+0.005142    test-rmse:0.728039+0.030232 
## [24] train-rmse:0.665658+0.005111    test-rmse:0.726383+0.030413 
## [25] train-rmse:0.663313+0.005160    test-rmse:0.727488+0.031418 
## [26] train-rmse:0.661010+0.005246    test-rmse:0.725895+0.032636 
## [27] train-rmse:0.658785+0.005326    test-rmse:0.724120+0.031216 
## [28] train-rmse:0.656597+0.005370    test-rmse:0.724082+0.032746 
## [29] train-rmse:0.654528+0.005390    test-rmse:0.722657+0.033345 
## [30] train-rmse:0.652576+0.005451    test-rmse:0.719933+0.031874 
## [31] train-rmse:0.650683+0.005426    test-rmse:0.718367+0.031102 
## [32] train-rmse:0.648849+0.005450    test-rmse:0.717011+0.032714 
## [33] train-rmse:0.647103+0.005509    test-rmse:0.716622+0.031121 
## [34] train-rmse:0.645400+0.005547    test-rmse:0.715780+0.030948 
## [35] train-rmse:0.643738+0.005597    test-rmse:0.716613+0.032446 
## [36] train-rmse:0.642109+0.005642    test-rmse:0.715657+0.032697 
## [37] train-rmse:0.640545+0.005656    test-rmse:0.715308+0.031452 
## [38] train-rmse:0.639046+0.005705    test-rmse:0.715679+0.032383 
## [39] train-rmse:0.637523+0.005741    test-rmse:0.716066+0.033263 
## [40] train-rmse:0.636070+0.005792    test-rmse:0.717457+0.033481 
## [41] train-rmse:0.634613+0.005791    test-rmse:0.715892+0.034895 
## [42] train-rmse:0.633199+0.005816    test-rmse:0.713584+0.035297 
## [43] train-rmse:0.631817+0.005863    test-rmse:0.711588+0.034681 
## [44] train-rmse:0.630464+0.005930    test-rmse:0.709398+0.033974 
## [45] train-rmse:0.629115+0.005966    test-rmse:0.708841+0.033640 
## [46] train-rmse:0.627802+0.005947    test-rmse:0.708189+0.033736 
## [47] train-rmse:0.626530+0.005920    test-rmse:0.707033+0.034136 
## [48] train-rmse:0.625309+0.005904    test-rmse:0.706969+0.033096 
## [49] train-rmse:0.624116+0.005890    test-rmse:0.707473+0.033854 
## [50] train-rmse:0.622951+0.005866    test-rmse:0.706300+0.033781 
## [51] train-rmse:0.621773+0.005855    test-rmse:0.706015+0.034067 
## [52] train-rmse:0.620581+0.005860    test-rmse:0.706530+0.034052 
## [53] train-rmse:0.619442+0.005873    test-rmse:0.707480+0.033973 
## [54] train-rmse:0.618337+0.005886    test-rmse:0.706475+0.034567 
## [55] train-rmse:0.617283+0.005924    test-rmse:0.707219+0.034466 
## [56] train-rmse:0.616248+0.005952    test-rmse:0.705010+0.034863 
## [57] train-rmse:0.615177+0.005983    test-rmse:0.703701+0.033447 
## [58] train-rmse:0.614159+0.005997    test-rmse:0.702368+0.032550 
## [59] train-rmse:0.613156+0.006006    test-rmse:0.704126+0.033584 
## [60] train-rmse:0.612157+0.006025    test-rmse:0.704614+0.033005 
## [61] train-rmse:0.611166+0.006042    test-rmse:0.702557+0.033691 
## [62] train-rmse:0.610196+0.006059    test-rmse:0.701860+0.033958 
## [63] train-rmse:0.609244+0.006070    test-rmse:0.700985+0.034240 
## [64] train-rmse:0.608294+0.006062    test-rmse:0.700514+0.035604 
## [65] train-rmse:0.607369+0.006068    test-rmse:0.700285+0.035191 
## [66] train-rmse:0.606453+0.006068    test-rmse:0.699065+0.035112 
## [67] train-rmse:0.605571+0.006089    test-rmse:0.699055+0.034976 
## [68] train-rmse:0.604684+0.006104    test-rmse:0.697310+0.034811 
## [69] train-rmse:0.603801+0.006123    test-rmse:0.696845+0.034767 
## [70] train-rmse:0.602920+0.006166    test-rmse:0.696099+0.035288 
## [71] train-rmse:0.602096+0.006176    test-rmse:0.695791+0.035687 
## [72] train-rmse:0.601295+0.006171    test-rmse:0.695929+0.035593 
## [73] train-rmse:0.600491+0.006171    test-rmse:0.694780+0.035232 
## [74] train-rmse:0.599656+0.006173    test-rmse:0.694337+0.035253 
## [75] train-rmse:0.598854+0.006175    test-rmse:0.693103+0.034276 
## [76] train-rmse:0.598071+0.006182    test-rmse:0.692575+0.034851 
## [77] train-rmse:0.597294+0.006198    test-rmse:0.692503+0.035873 
## [78] train-rmse:0.596542+0.006198    test-rmse:0.693534+0.035761 
## [79] train-rmse:0.595788+0.006198    test-rmse:0.693147+0.035556 
## [80] train-rmse:0.595070+0.006214    test-rmse:0.693187+0.035798 
## [81] train-rmse:0.594362+0.006226    test-rmse:0.692603+0.034427 
## [82] train-rmse:0.593643+0.006250    test-rmse:0.690993+0.034614 
## [83] train-rmse:0.592943+0.006291    test-rmse:0.691165+0.034081 
## [84] train-rmse:0.592252+0.006322    test-rmse:0.691090+0.034143 
## [85] train-rmse:0.591554+0.006338    test-rmse:0.691547+0.033642 
## [86] train-rmse:0.590854+0.006335    test-rmse:0.689854+0.033863 
## [87] train-rmse:0.590161+0.006359    test-rmse:0.689966+0.033658 
## [88] train-rmse:0.589500+0.006380    test-rmse:0.688691+0.034426 
## [89] train-rmse:0.588849+0.006390    test-rmse:0.687409+0.033934 
## [90] train-rmse:0.588208+0.006393    test-rmse:0.687673+0.033913 
## [91] train-rmse:0.587550+0.006452    test-rmse:0.687149+0.032869 
## [92] train-rmse:0.586916+0.006484    test-rmse:0.687369+0.032485 
## [93] train-rmse:0.586307+0.006503    test-rmse:0.687242+0.032501 
## [94] train-rmse:0.585703+0.006504    test-rmse:0.687062+0.032528 
## [95] train-rmse:0.585107+0.006520    test-rmse:0.687478+0.033611 
## [96] train-rmse:0.584514+0.006540    test-rmse:0.686972+0.033109 
## [97] train-rmse:0.583922+0.006574    test-rmse:0.687040+0.033356 
## [98] train-rmse:0.583321+0.006589    test-rmse:0.687322+0.033549 
## [99] train-rmse:0.582760+0.006600    test-rmse:0.686040+0.034101 
## [100]    train-rmse:0.582197+0.006619    test-rmse:0.685861+0.034264 
## [101]    train-rmse:0.581627+0.006625    test-rmse:0.685605+0.035059 
## [102]    train-rmse:0.581057+0.006638    test-rmse:0.684815+0.035086 
## [103]    train-rmse:0.580478+0.006669    test-rmse:0.684626+0.034909 
## [104]    train-rmse:0.579922+0.006694    test-rmse:0.684436+0.034523 
## [105]    train-rmse:0.579372+0.006687    test-rmse:0.684793+0.034458 
## [106]    train-rmse:0.578814+0.006673    test-rmse:0.684594+0.034890 
## [107]    train-rmse:0.578282+0.006671    test-rmse:0.684362+0.035847 
## [108]    train-rmse:0.577782+0.006685    test-rmse:0.683502+0.035899 
## [109]    train-rmse:0.577272+0.006701    test-rmse:0.683877+0.035728 
## [110]    train-rmse:0.576760+0.006724    test-rmse:0.683182+0.035931 
## [111]    train-rmse:0.576243+0.006734    test-rmse:0.682719+0.035939 
## [112]    train-rmse:0.575741+0.006760    test-rmse:0.683369+0.035971 
## [113]    train-rmse:0.575247+0.006771    test-rmse:0.683466+0.035862 
## [114]    train-rmse:0.574746+0.006787    test-rmse:0.682331+0.035355 
## [115]    train-rmse:0.574265+0.006809    test-rmse:0.682596+0.035169 
## [116]    train-rmse:0.573776+0.006823    test-rmse:0.682820+0.035647 
## [117]    train-rmse:0.573299+0.006835    test-rmse:0.682713+0.035645 
## [118]    train-rmse:0.572823+0.006854    test-rmse:0.682011+0.036173 
## [119]    train-rmse:0.572352+0.006855    test-rmse:0.682702+0.036341 
## [120]    train-rmse:0.571888+0.006862    test-rmse:0.682993+0.036540 
## [121]    train-rmse:0.571429+0.006871    test-rmse:0.683008+0.036310 
## [122]    train-rmse:0.570966+0.006863    test-rmse:0.682134+0.036032 
## [123]    train-rmse:0.570531+0.006877    test-rmse:0.682539+0.035448 
## [124]    train-rmse:0.570098+0.006882    test-rmse:0.681192+0.035197 
## [125]    train-rmse:0.569659+0.006898    test-rmse:0.681035+0.035053 
## [126]    train-rmse:0.569215+0.006920    test-rmse:0.681558+0.034695 
## [127]    train-rmse:0.568783+0.006944    test-rmse:0.681407+0.034807 
## [128]    train-rmse:0.568345+0.006957    test-rmse:0.681944+0.035767 
## [129]    train-rmse:0.567919+0.006982    test-rmse:0.681341+0.035547 
## [130]    train-rmse:0.567504+0.006990    test-rmse:0.681062+0.035259 
## [131]    train-rmse:0.567091+0.007015    test-rmse:0.680793+0.034999 
## [132]    train-rmse:0.566694+0.007038    test-rmse:0.680713+0.035262 
## [133]    train-rmse:0.566293+0.007051    test-rmse:0.680055+0.035772 
## [134]    train-rmse:0.565870+0.007070    test-rmse:0.679626+0.035722 
## [135]    train-rmse:0.565469+0.007082    test-rmse:0.679482+0.036013 
## [136]    train-rmse:0.565073+0.007093    test-rmse:0.678733+0.036058 
## [137]    train-rmse:0.564677+0.007100    test-rmse:0.678235+0.036129 
## [138]    train-rmse:0.564280+0.007111    test-rmse:0.678794+0.036226 
## [139]    train-rmse:0.563881+0.007126    test-rmse:0.678563+0.035143 
## [140]    train-rmse:0.563502+0.007140    test-rmse:0.678161+0.035524 
## [141]    train-rmse:0.563131+0.007150    test-rmse:0.677281+0.035386 
## [142]    train-rmse:0.562758+0.007160    test-rmse:0.677770+0.035700 
## [143]    train-rmse:0.562390+0.007170    test-rmse:0.677587+0.035265 
## [144]    train-rmse:0.562008+0.007190    test-rmse:0.676470+0.035256 
## [145]    train-rmse:0.561634+0.007207    test-rmse:0.677187+0.035529 
## [146]    train-rmse:0.561261+0.007218    test-rmse:0.676168+0.035750 
## [147]    train-rmse:0.560891+0.007223    test-rmse:0.675865+0.035784 
## [148]    train-rmse:0.560540+0.007238    test-rmse:0.675991+0.035659 
## [149]    train-rmse:0.560203+0.007250    test-rmse:0.675746+0.035331 
## [150]    train-rmse:0.559856+0.007251    test-rmse:0.675935+0.035422 
## [151]    train-rmse:0.559517+0.007275    test-rmse:0.675673+0.035421 
## [152]    train-rmse:0.559172+0.007286    test-rmse:0.675855+0.035829 
## [153]    train-rmse:0.558829+0.007299    test-rmse:0.676601+0.036407 
## [154]    train-rmse:0.558502+0.007306    test-rmse:0.676830+0.036189 
## [155]    train-rmse:0.558172+0.007322    test-rmse:0.676792+0.035711 
## [156]    train-rmse:0.557844+0.007344    test-rmse:0.676735+0.035761 
## [157]    train-rmse:0.557516+0.007370    test-rmse:0.676664+0.035746 
## Stopping. Best iteration:
## [151]    train-rmse:0.559517+0.007275    test-rmse:0.675673+0.035421
## 
## 57 
## [1]  train-rmse:1.285162+0.012397    test-rmse:1.292365+0.070954 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.067065+0.008981    test-rmse:1.082941+0.069582 
## [3]  train-rmse:0.922388+0.006565    test-rmse:0.950040+0.068722 
## [4]  train-rmse:0.827948+0.003420    test-rmse:0.861524+0.065691 
## [5]  train-rmse:0.766932+0.004576    test-rmse:0.808533+0.062406 
## [6]  train-rmse:0.727324+0.005671    test-rmse:0.774157+0.054102 
## [7]  train-rmse:0.698856+0.004299    test-rmse:0.751730+0.047366 
## [8]  train-rmse:0.679305+0.002780    test-rmse:0.736007+0.049159 
## [9]  train-rmse:0.665499+0.001514    test-rmse:0.731056+0.046763 
## [10] train-rmse:0.654850+0.002203    test-rmse:0.725033+0.044979 
## [11] train-rmse:0.646643+0.002083    test-rmse:0.719656+0.044452 
## [12] train-rmse:0.638853+0.002546    test-rmse:0.716802+0.046239 
## [13] train-rmse:0.631855+0.003736    test-rmse:0.716102+0.045036 
## [14] train-rmse:0.625554+0.003864    test-rmse:0.711578+0.042995 
## [15] train-rmse:0.619925+0.004501    test-rmse:0.708427+0.042113 
## [16] train-rmse:0.613559+0.003826    test-rmse:0.710050+0.046288 
## [17] train-rmse:0.607940+0.004327    test-rmse:0.705982+0.044917 
## [18] train-rmse:0.599502+0.003258    test-rmse:0.701512+0.043747 
## [19] train-rmse:0.593401+0.000877    test-rmse:0.698175+0.045997 
## [20] train-rmse:0.589106+0.001378    test-rmse:0.697791+0.045878 
## [21] train-rmse:0.583647+0.002828    test-rmse:0.694229+0.043298 
## [22] train-rmse:0.579657+0.003006    test-rmse:0.692152+0.042179 
## [23] train-rmse:0.574740+0.003635    test-rmse:0.684190+0.040620 
## [24] train-rmse:0.570926+0.003591    test-rmse:0.683341+0.040712 
## [25] train-rmse:0.567266+0.003917    test-rmse:0.686238+0.040115 
## [26] train-rmse:0.563915+0.003782    test-rmse:0.683011+0.041813 
## [27] train-rmse:0.558703+0.005507    test-rmse:0.679194+0.040388 
## [28] train-rmse:0.555042+0.005058    test-rmse:0.677926+0.040479 
## [29] train-rmse:0.551035+0.005660    test-rmse:0.676896+0.039980 
## [30] train-rmse:0.547462+0.004597    test-rmse:0.674926+0.044156 
## [31] train-rmse:0.543789+0.005970    test-rmse:0.671900+0.041552 
## [32] train-rmse:0.540469+0.006097    test-rmse:0.671171+0.040638 
## [33] train-rmse:0.537503+0.006224    test-rmse:0.669570+0.039026 
## [34] train-rmse:0.534876+0.006116    test-rmse:0.668396+0.040348 
## [35] train-rmse:0.530623+0.006377    test-rmse:0.668908+0.041044 
## [36] train-rmse:0.526010+0.009237    test-rmse:0.665992+0.038801 
## [37] train-rmse:0.522828+0.007721    test-rmse:0.666546+0.038316 
## [38] train-rmse:0.520617+0.007640    test-rmse:0.664192+0.039832 
## [39] train-rmse:0.518081+0.007388    test-rmse:0.664900+0.041130 
## [40] train-rmse:0.515849+0.006922    test-rmse:0.664147+0.042270 
## [41] train-rmse:0.513514+0.006834    test-rmse:0.663500+0.044249 
## [42] train-rmse:0.510677+0.006804    test-rmse:0.663066+0.044749 
## [43] train-rmse:0.508372+0.006971    test-rmse:0.662038+0.044937 
## [44] train-rmse:0.504690+0.007433    test-rmse:0.660188+0.042769 
## [45] train-rmse:0.501939+0.008071    test-rmse:0.659436+0.042442 
## [46] train-rmse:0.497903+0.004747    test-rmse:0.657691+0.046044 
## [47] train-rmse:0.495853+0.003985    test-rmse:0.657452+0.046676 
## [48] train-rmse:0.492534+0.007204    test-rmse:0.656079+0.045411 
## [49] train-rmse:0.490999+0.007314    test-rmse:0.654754+0.046272 
## [50] train-rmse:0.488026+0.007553    test-rmse:0.655334+0.046626 
## [51] train-rmse:0.486351+0.007724    test-rmse:0.654305+0.047205 
## [52] train-rmse:0.483014+0.006669    test-rmse:0.654064+0.049264 
## [53] train-rmse:0.481447+0.006803    test-rmse:0.651887+0.047655 
## [54] train-rmse:0.478077+0.007609    test-rmse:0.649750+0.047914 
## [55] train-rmse:0.476534+0.007502    test-rmse:0.649510+0.048359 
## [56] train-rmse:0.474241+0.008460    test-rmse:0.649695+0.048150 
## [57] train-rmse:0.471563+0.008470    test-rmse:0.647466+0.046843 
## [58] train-rmse:0.468997+0.009557    test-rmse:0.646965+0.046854 
## [59] train-rmse:0.466962+0.008880    test-rmse:0.646525+0.047038 
## [60] train-rmse:0.464446+0.008143    test-rmse:0.646602+0.046871 
## [61] train-rmse:0.462179+0.007659    test-rmse:0.647059+0.046697 
## [62] train-rmse:0.460693+0.007444    test-rmse:0.647021+0.047525 
## [63] train-rmse:0.458787+0.008060    test-rmse:0.646791+0.048582 
## [64] train-rmse:0.457406+0.008295    test-rmse:0.646595+0.049232 
## [65] train-rmse:0.455797+0.008330    test-rmse:0.645318+0.049047 
## [66] train-rmse:0.453624+0.009777    test-rmse:0.644042+0.049083 
## [67] train-rmse:0.452457+0.009809    test-rmse:0.643854+0.049086 
## [68] train-rmse:0.450522+0.011343    test-rmse:0.644705+0.049048 
## [69] train-rmse:0.449581+0.011486    test-rmse:0.644640+0.048584 
## [70] train-rmse:0.447657+0.012465    test-rmse:0.643780+0.048327 
## [71] train-rmse:0.446492+0.012518    test-rmse:0.643190+0.048320 
## [72] train-rmse:0.444447+0.012240    test-rmse:0.642383+0.049439 
## [73] train-rmse:0.442520+0.011626    test-rmse:0.641110+0.049935 
## [74] train-rmse:0.441172+0.012208    test-rmse:0.640966+0.049742 
## [75] train-rmse:0.439789+0.012791    test-rmse:0.641345+0.049352 
## [76] train-rmse:0.438141+0.012153    test-rmse:0.639632+0.048740 
## [77] train-rmse:0.436328+0.012434    test-rmse:0.641200+0.049564 
## [78] train-rmse:0.434802+0.013123    test-rmse:0.641559+0.049740 
## [79] train-rmse:0.432435+0.013403    test-rmse:0.640717+0.050545 
## [80] train-rmse:0.431385+0.013861    test-rmse:0.640388+0.050411 
## [81] train-rmse:0.430007+0.013703    test-rmse:0.641598+0.051463 
## [82] train-rmse:0.428992+0.013660    test-rmse:0.642347+0.051830 
## Stopping. Best iteration:
## [76] train-rmse:0.438141+0.012153    test-rmse:0.639632+0.048740
## 
## 58 
## [1]  train-rmse:1.279937+0.011961    test-rmse:1.289635+0.069484 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.053490+0.008836    test-rmse:1.077395+0.067191 
## [3]  train-rmse:0.902000+0.006452    test-rmse:0.939725+0.066992 
## [4]  train-rmse:0.801675+0.003911    test-rmse:0.847964+0.069695 
## [5]  train-rmse:0.735052+0.004844    test-rmse:0.797103+0.065940 
## [6]  train-rmse:0.681336+0.005591    test-rmse:0.755190+0.055914 
## [7]  train-rmse:0.646672+0.006064    test-rmse:0.731862+0.046981 
## [8]  train-rmse:0.622226+0.008366    test-rmse:0.718981+0.044741 
## [9]  train-rmse:0.605289+0.007701    test-rmse:0.709337+0.043387 
## [10] train-rmse:0.592736+0.008747    test-rmse:0.702680+0.042100 
## [11] train-rmse:0.583163+0.008956    test-rmse:0.698455+0.044195 
## [12] train-rmse:0.570613+0.009896    test-rmse:0.693062+0.044768 
## [13] train-rmse:0.561874+0.010896    test-rmse:0.686431+0.046695 
## [14] train-rmse:0.553069+0.010145    test-rmse:0.690428+0.046318 
## [15] train-rmse:0.544663+0.008307    test-rmse:0.689333+0.047618 
## [16] train-rmse:0.534912+0.004874    test-rmse:0.684903+0.047518 
## [17] train-rmse:0.529371+0.004529    test-rmse:0.682864+0.048003 
## [18] train-rmse:0.523726+0.004160    test-rmse:0.681241+0.050091 
## [19] train-rmse:0.516498+0.005640    test-rmse:0.673620+0.047216 
## [20] train-rmse:0.510769+0.005042    test-rmse:0.672392+0.049221 
## [21] train-rmse:0.505939+0.005362    test-rmse:0.671880+0.051689 
## [22] train-rmse:0.500182+0.007614    test-rmse:0.672733+0.052740 
## [23] train-rmse:0.494400+0.005540    test-rmse:0.669706+0.051302 
## [24] train-rmse:0.489454+0.005417    test-rmse:0.667568+0.050799 
## [25] train-rmse:0.485495+0.005300    test-rmse:0.666822+0.050781 
## [26] train-rmse:0.478735+0.007010    test-rmse:0.664961+0.052916 
## [27] train-rmse:0.472040+0.007716    test-rmse:0.664719+0.052741 
## [28] train-rmse:0.466119+0.006952    test-rmse:0.661129+0.054676 
## [29] train-rmse:0.460651+0.010366    test-rmse:0.657050+0.054361 
## [30] train-rmse:0.454420+0.011432    test-rmse:0.654550+0.053271 
## [31] train-rmse:0.450275+0.010419    test-rmse:0.653496+0.056672 
## [32] train-rmse:0.446641+0.010927    test-rmse:0.653939+0.055253 
## [33] train-rmse:0.442432+0.011975    test-rmse:0.653148+0.053121 
## [34] train-rmse:0.437499+0.012615    test-rmse:0.653661+0.054697 
## [35] train-rmse:0.433057+0.013019    test-rmse:0.654014+0.054690 
## [36] train-rmse:0.428269+0.012303    test-rmse:0.652628+0.055475 
## [37] train-rmse:0.424943+0.012427    test-rmse:0.651021+0.055155 
## [38] train-rmse:0.419387+0.010631    test-rmse:0.648408+0.055251 
## [39] train-rmse:0.415234+0.011338    test-rmse:0.645742+0.055192 
## [40] train-rmse:0.412590+0.012151    test-rmse:0.646418+0.054197 
## [41] train-rmse:0.408139+0.009812    test-rmse:0.643876+0.053796 
## [42] train-rmse:0.403341+0.008998    test-rmse:0.641158+0.052750 
## [43] train-rmse:0.397880+0.010801    test-rmse:0.638318+0.051639 
## [44] train-rmse:0.394832+0.009663    test-rmse:0.635073+0.051311 
## [45] train-rmse:0.390746+0.009828    test-rmse:0.633854+0.051076 
## [46] train-rmse:0.386723+0.011309    test-rmse:0.631153+0.049072 
## [47] train-rmse:0.381700+0.010174    test-rmse:0.631406+0.049413 
## [48] train-rmse:0.377638+0.010919    test-rmse:0.634358+0.050770 
## [49] train-rmse:0.374787+0.010718    test-rmse:0.635009+0.051249 
## [50] train-rmse:0.371312+0.010118    test-rmse:0.633944+0.051719 
## [51] train-rmse:0.367275+0.009622    test-rmse:0.631571+0.051861 
## [52] train-rmse:0.364657+0.009527    test-rmse:0.631464+0.051543 
## Stopping. Best iteration:
## [46] train-rmse:0.386723+0.011309    test-rmse:0.631153+0.049072
## 
## 59 
## [1]  train-rmse:1.274546+0.012050    test-rmse:1.292936+0.070508 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.040457+0.008088    test-rmse:1.075544+0.073882 
## [3]  train-rmse:0.880630+0.006340    test-rmse:0.924848+0.075285 
## [4]  train-rmse:0.775143+0.006166    test-rmse:0.836574+0.071266 
## [5]  train-rmse:0.703875+0.006124    test-rmse:0.778657+0.066156 
## [6]  train-rmse:0.649337+0.004853    test-rmse:0.738067+0.062245 
## [7]  train-rmse:0.612905+0.007848    test-rmse:0.713832+0.060478 
## [8]  train-rmse:0.581570+0.008690    test-rmse:0.699279+0.055048 
## [9]  train-rmse:0.562936+0.008338    test-rmse:0.690308+0.055089 
## [10] train-rmse:0.547619+0.011365    test-rmse:0.684610+0.057147 
## [11] train-rmse:0.532653+0.011736    test-rmse:0.678956+0.065678 
## [12] train-rmse:0.519325+0.008833    test-rmse:0.675235+0.064872 
## [13] train-rmse:0.508820+0.010163    test-rmse:0.673069+0.061752 
## [14] train-rmse:0.495149+0.006670    test-rmse:0.665119+0.057792 
## [15] train-rmse:0.484078+0.009209    test-rmse:0.662706+0.057284 
## [16] train-rmse:0.475997+0.009486    test-rmse:0.662742+0.057574 
## [17] train-rmse:0.463396+0.009876    test-rmse:0.655133+0.058835 
## [18] train-rmse:0.456605+0.010919    test-rmse:0.654997+0.058103 
## [19] train-rmse:0.446290+0.012465    test-rmse:0.653126+0.057727 
## [20] train-rmse:0.439634+0.013491    test-rmse:0.650271+0.060169 
## [21] train-rmse:0.431233+0.010166    test-rmse:0.646526+0.061309 
## [22] train-rmse:0.423919+0.010836    test-rmse:0.646401+0.061031 
## [23] train-rmse:0.415672+0.011322    test-rmse:0.641053+0.064614 
## [24] train-rmse:0.408650+0.014062    test-rmse:0.639843+0.061511 
## [25] train-rmse:0.400936+0.012316    test-rmse:0.634603+0.064280 
## [26] train-rmse:0.394963+0.013526    test-rmse:0.634885+0.063732 
## [27] train-rmse:0.390479+0.013474    test-rmse:0.634339+0.064767 
## [28] train-rmse:0.383261+0.015862    test-rmse:0.631216+0.062553 
## [29] train-rmse:0.378840+0.016327    test-rmse:0.630638+0.062908 
## [30] train-rmse:0.373364+0.017562    test-rmse:0.630777+0.062803 
## [31] train-rmse:0.369488+0.017894    test-rmse:0.630925+0.063557 
## [32] train-rmse:0.363834+0.019735    test-rmse:0.633299+0.064306 
## [33] train-rmse:0.357710+0.019995    test-rmse:0.632082+0.063501 
## [34] train-rmse:0.352962+0.019606    test-rmse:0.630380+0.063643 
## [35] train-rmse:0.348055+0.019115    test-rmse:0.631016+0.065230 
## [36] train-rmse:0.344971+0.019255    test-rmse:0.631391+0.064107 
## [37] train-rmse:0.340539+0.019636    test-rmse:0.629584+0.064004 
## [38] train-rmse:0.335709+0.018834    test-rmse:0.628827+0.061979 
## [39] train-rmse:0.329985+0.020015    test-rmse:0.629346+0.062481 
## [40] train-rmse:0.326973+0.020152    test-rmse:0.629627+0.061976 
## [41] train-rmse:0.321994+0.018469    test-rmse:0.628832+0.061502 
## [42] train-rmse:0.319171+0.018666    test-rmse:0.630149+0.062030 
## [43] train-rmse:0.314081+0.017772    test-rmse:0.630527+0.062335 
## [44] train-rmse:0.309788+0.017344    test-rmse:0.629775+0.061936 
## Stopping. Best iteration:
## [38] train-rmse:0.335709+0.018834    test-rmse:0.628827+0.061979
## 
## 60 
## [1]  train-rmse:1.270872+0.012282    test-rmse:1.287911+0.073711 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.031544+0.008460    test-rmse:1.071309+0.074769 
## [3]  train-rmse:0.863498+0.006121    test-rmse:0.919478+0.072177 
## [4]  train-rmse:0.750644+0.006189    test-rmse:0.824068+0.065419 
## [5]  train-rmse:0.673297+0.006049    test-rmse:0.759542+0.058545 
## [6]  train-rmse:0.618618+0.002928    test-rmse:0.726542+0.050823 
## [7]  train-rmse:0.576234+0.005695    test-rmse:0.700913+0.051820 
## [8]  train-rmse:0.542264+0.010973    test-rmse:0.686594+0.051289 
## [9]  train-rmse:0.518633+0.013490    test-rmse:0.681766+0.054743 
## [10] train-rmse:0.501625+0.013486    test-rmse:0.676952+0.052600 
## [11] train-rmse:0.485352+0.013386    test-rmse:0.670099+0.052438 
## [12] train-rmse:0.467590+0.006687    test-rmse:0.663324+0.053943 
## [13] train-rmse:0.453703+0.011157    test-rmse:0.660245+0.055548 
## [14] train-rmse:0.440159+0.011556    test-rmse:0.655302+0.056136 
## [15] train-rmse:0.421668+0.008747    test-rmse:0.649523+0.054022 
## [16] train-rmse:0.409293+0.007549    test-rmse:0.648205+0.058413 
## [17] train-rmse:0.400814+0.007358    test-rmse:0.648748+0.056539 
## [18] train-rmse:0.390773+0.006746    test-rmse:0.646277+0.060452 
## [19] train-rmse:0.380892+0.005984    test-rmse:0.641931+0.062701 
## [20] train-rmse:0.371814+0.005158    test-rmse:0.639818+0.063361 
## [21] train-rmse:0.359864+0.004494    test-rmse:0.637435+0.064768 
## [22] train-rmse:0.352617+0.005790    test-rmse:0.633496+0.063414 
## [23] train-rmse:0.343868+0.003089    test-rmse:0.631220+0.063759 
## [24] train-rmse:0.336881+0.003007    test-rmse:0.631639+0.064521 
## [25] train-rmse:0.328689+0.002709    test-rmse:0.628647+0.064643 
## [26] train-rmse:0.322341+0.001522    test-rmse:0.628830+0.063880 
## [27] train-rmse:0.315250+0.003311    test-rmse:0.631723+0.064203 
## [28] train-rmse:0.306266+0.004458    test-rmse:0.626495+0.065279 
## [29] train-rmse:0.300342+0.005435    test-rmse:0.624108+0.063552 
## [30] train-rmse:0.294549+0.006266    test-rmse:0.623331+0.064723 
## [31] train-rmse:0.285277+0.006523    test-rmse:0.621533+0.063374 
## [32] train-rmse:0.278321+0.005796    test-rmse:0.622994+0.063876 
## [33] train-rmse:0.274338+0.006058    test-rmse:0.622862+0.063751 
## [34] train-rmse:0.268580+0.006472    test-rmse:0.623521+0.062953 
## [35] train-rmse:0.263206+0.006335    test-rmse:0.623168+0.061052 
## [36] train-rmse:0.257436+0.006298    test-rmse:0.624131+0.061266 
## [37] train-rmse:0.250580+0.005420    test-rmse:0.623280+0.062866 
## Stopping. Best iteration:
## [31] train-rmse:0.285277+0.006523    test-rmse:0.621533+0.063374
## 
## 61 
## [1]  train-rmse:1.267132+0.011673    test-rmse:1.290025+0.075605 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.023211+0.007329    test-rmse:1.066440+0.075720 
## [3]  train-rmse:0.847898+0.005751    test-rmse:0.917303+0.070521 
## [4]  train-rmse:0.724438+0.006862    test-rmse:0.819546+0.062867 
## [5]  train-rmse:0.641834+0.006354    test-rmse:0.759137+0.056453 
## [6]  train-rmse:0.581862+0.006937    test-rmse:0.720747+0.055950 
## [7]  train-rmse:0.538357+0.012507    test-rmse:0.697015+0.056790 
## [8]  train-rmse:0.500064+0.017870    test-rmse:0.678350+0.055315 
## [9]  train-rmse:0.468901+0.014555    test-rmse:0.667149+0.051166 
## [10] train-rmse:0.448178+0.013200    test-rmse:0.661283+0.052297 
## [11] train-rmse:0.433118+0.012665    test-rmse:0.654254+0.050713 
## [12] train-rmse:0.417859+0.009687    test-rmse:0.652208+0.052648 
## [13] train-rmse:0.399394+0.011271    test-rmse:0.641467+0.052516 
## [14] train-rmse:0.383436+0.015634    test-rmse:0.643605+0.056401 
## [15] train-rmse:0.370834+0.015720    test-rmse:0.639916+0.054375 
## [16] train-rmse:0.355565+0.014682    test-rmse:0.635143+0.055782 
## [17] train-rmse:0.340875+0.017617    test-rmse:0.632547+0.051548 
## [18] train-rmse:0.329479+0.014638    test-rmse:0.633337+0.051942 
## [19] train-rmse:0.318597+0.015866    test-rmse:0.634415+0.051474 
## [20] train-rmse:0.308529+0.016138    test-rmse:0.631635+0.048963 
## [21] train-rmse:0.301245+0.016177    test-rmse:0.632195+0.048212 
## [22] train-rmse:0.293395+0.017256    test-rmse:0.633778+0.051972 
## [23] train-rmse:0.286314+0.018372    test-rmse:0.633345+0.052072 
## [24] train-rmse:0.279935+0.018577    test-rmse:0.633871+0.051317 
## [25] train-rmse:0.270137+0.016242    test-rmse:0.633278+0.052648 
## [26] train-rmse:0.262206+0.017211    test-rmse:0.630743+0.052637 
## [27] train-rmse:0.252732+0.019643    test-rmse:0.631023+0.050542 
## [28] train-rmse:0.242024+0.017432    test-rmse:0.630473+0.054364 
## [29] train-rmse:0.237004+0.018106    test-rmse:0.630665+0.054040 
## [30] train-rmse:0.232437+0.019509    test-rmse:0.632141+0.054211 
## [31] train-rmse:0.225962+0.015914    test-rmse:0.632768+0.053829 
## [32] train-rmse:0.219424+0.014605    test-rmse:0.630530+0.052540 
## [33] train-rmse:0.214927+0.015466    test-rmse:0.631454+0.052719 
## [34] train-rmse:0.208534+0.015702    test-rmse:0.631520+0.052389 
## Stopping. Best iteration:
## [28] train-rmse:0.242024+0.017432    test-rmse:0.630473+0.054364
## 
## 62 
## [1]  train-rmse:1.264388+0.011158    test-rmse:1.288620+0.075810 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.017094+0.007475    test-rmse:1.065270+0.075639 
## [3]  train-rmse:0.835856+0.006245    test-rmse:0.919040+0.082174 
## [4]  train-rmse:0.705900+0.008903    test-rmse:0.819859+0.077021 
## [5]  train-rmse:0.618293+0.012947    test-rmse:0.763338+0.072046 
## [6]  train-rmse:0.551936+0.015207    test-rmse:0.719438+0.071767 
## [7]  train-rmse:0.502914+0.014463    test-rmse:0.692313+0.068573 
## [8]  train-rmse:0.459679+0.010365    test-rmse:0.673249+0.059177 
## [9]  train-rmse:0.423507+0.014152    test-rmse:0.662686+0.054649 
## [10] train-rmse:0.402807+0.015819    test-rmse:0.657716+0.057929 
## [11] train-rmse:0.380424+0.016003    test-rmse:0.654129+0.056140 
## [12] train-rmse:0.364785+0.013924    test-rmse:0.648662+0.053468 
## [13] train-rmse:0.350446+0.016417    test-rmse:0.643594+0.051328 
## [14] train-rmse:0.334036+0.011540    test-rmse:0.636998+0.048480 
## [15] train-rmse:0.320596+0.012362    test-rmse:0.631840+0.047357 
## [16] train-rmse:0.310072+0.012106    test-rmse:0.630296+0.043146 
## [17] train-rmse:0.302344+0.012992    test-rmse:0.627997+0.044449 
## [18] train-rmse:0.293072+0.015603    test-rmse:0.627732+0.043480 
## [19] train-rmse:0.278981+0.014193    test-rmse:0.628222+0.044172 
## [20] train-rmse:0.268234+0.015606    test-rmse:0.630346+0.040541 
## [21] train-rmse:0.257671+0.015100    test-rmse:0.628896+0.042540 
## [22] train-rmse:0.247742+0.012885    test-rmse:0.628406+0.039343 
## [23] train-rmse:0.239240+0.014763    test-rmse:0.627814+0.041106 
## [24] train-rmse:0.231799+0.016167    test-rmse:0.624527+0.043545 
## [25] train-rmse:0.223529+0.015599    test-rmse:0.623120+0.044067 
## [26] train-rmse:0.215589+0.017245    test-rmse:0.621506+0.042271 
## [27] train-rmse:0.206617+0.017762    test-rmse:0.621849+0.041386 
## [28] train-rmse:0.199626+0.017591    test-rmse:0.622555+0.043013 
## [29] train-rmse:0.191606+0.015922    test-rmse:0.623013+0.043756 
## [30] train-rmse:0.185336+0.015336    test-rmse:0.624633+0.043953 
## [31] train-rmse:0.177988+0.012820    test-rmse:0.624734+0.045763 
## [32] train-rmse:0.172449+0.014407    test-rmse:0.625940+0.044882 
## Stopping. Best iteration:
## [26] train-rmse:0.215589+0.017245    test-rmse:0.621506+0.042271
## 
## 63 
## [1]  train-rmse:1.269958+0.012227    test-rmse:1.272150+0.070819 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.053331+0.009121    test-rmse:1.058038+0.067655 
## [3]  train-rmse:0.919766+0.007692    test-rmse:0.933246+0.064084 
## [4]  train-rmse:0.837551+0.006956    test-rmse:0.851627+0.057362 
## [5]  train-rmse:0.789412+0.006697    test-rmse:0.809949+0.049673 
## [6]  train-rmse:0.759632+0.006364    test-rmse:0.785392+0.043792 
## [7]  train-rmse:0.740605+0.005953    test-rmse:0.770959+0.042089 
## [8]  train-rmse:0.727895+0.005746    test-rmse:0.758315+0.040559 
## [9]  train-rmse:0.718426+0.005400    test-rmse:0.750674+0.035113 
## [10] train-rmse:0.710972+0.004961    test-rmse:0.745606+0.034598 
## [11] train-rmse:0.705064+0.004947    test-rmse:0.745634+0.032231 
## [12] train-rmse:0.700218+0.005146    test-rmse:0.742828+0.030191 
## [13] train-rmse:0.695846+0.005257    test-rmse:0.739280+0.031824 
## [14] train-rmse:0.691692+0.005285    test-rmse:0.737006+0.031054 
## [15] train-rmse:0.687791+0.005357    test-rmse:0.736528+0.032506 
## [16] train-rmse:0.684094+0.005420    test-rmse:0.732024+0.031783 
## [17] train-rmse:0.680637+0.005308    test-rmse:0.731003+0.030692 
## [18] train-rmse:0.677403+0.005242    test-rmse:0.730446+0.031638 
## [19] train-rmse:0.674416+0.005250    test-rmse:0.729460+0.031330 
## [20] train-rmse:0.671513+0.005243    test-rmse:0.730235+0.032527 
## [21] train-rmse:0.668709+0.005279    test-rmse:0.729757+0.030515 
## [22] train-rmse:0.666078+0.005207    test-rmse:0.728196+0.031761 
## [23] train-rmse:0.663522+0.005146    test-rmse:0.724339+0.031768 
## [24] train-rmse:0.661060+0.005130    test-rmse:0.725068+0.033955 
## [25] train-rmse:0.658640+0.005188    test-rmse:0.722772+0.033334 
## [26] train-rmse:0.656375+0.005366    test-rmse:0.722361+0.034613 
## [27] train-rmse:0.654185+0.005485    test-rmse:0.722849+0.036389 
## [28] train-rmse:0.652099+0.005595    test-rmse:0.721710+0.034826 
## [29] train-rmse:0.650113+0.005594    test-rmse:0.719901+0.034262 
## [30] train-rmse:0.648131+0.005549    test-rmse:0.719909+0.033354 
## [31] train-rmse:0.646265+0.005552    test-rmse:0.716620+0.033695 
## [32] train-rmse:0.644445+0.005595    test-rmse:0.716316+0.034519 
## [33] train-rmse:0.642677+0.005633    test-rmse:0.715147+0.034382 
## [34] train-rmse:0.640938+0.005634    test-rmse:0.714377+0.033121 
## [35] train-rmse:0.639234+0.005669    test-rmse:0.714870+0.034327 
## [36] train-rmse:0.637591+0.005726    test-rmse:0.713214+0.033559 
## [37] train-rmse:0.635944+0.005755    test-rmse:0.711674+0.034247 
## [38] train-rmse:0.634382+0.005810    test-rmse:0.712590+0.034398 
## [39] train-rmse:0.632862+0.005874    test-rmse:0.711919+0.035011 
## [40] train-rmse:0.631360+0.005863    test-rmse:0.712673+0.035126 
## [41] train-rmse:0.629840+0.005862    test-rmse:0.711530+0.035229 
## [42] train-rmse:0.628343+0.005936    test-rmse:0.708999+0.036190 
## [43] train-rmse:0.626919+0.005982    test-rmse:0.707103+0.035247 
## [44] train-rmse:0.625537+0.005992    test-rmse:0.707942+0.035829 
## [45] train-rmse:0.624224+0.005989    test-rmse:0.707970+0.036681 
## [46] train-rmse:0.622965+0.005978    test-rmse:0.706661+0.036547 
## [47] train-rmse:0.621714+0.005980    test-rmse:0.707402+0.035415 
## [48] train-rmse:0.620512+0.005965    test-rmse:0.706774+0.035562 
## [49] train-rmse:0.619287+0.005933    test-rmse:0.706162+0.034469 
## [50] train-rmse:0.618099+0.005933    test-rmse:0.706920+0.034672 
## [51] train-rmse:0.616890+0.005920    test-rmse:0.707457+0.035657 
## [52] train-rmse:0.615741+0.005963    test-rmse:0.706651+0.035712 
## [53] train-rmse:0.614609+0.005992    test-rmse:0.705857+0.034895 
## [54] train-rmse:0.613520+0.006037    test-rmse:0.702747+0.034843 
## [55] train-rmse:0.612414+0.006052    test-rmse:0.703639+0.034682 
## [56] train-rmse:0.611341+0.006058    test-rmse:0.703339+0.034864 
## [57] train-rmse:0.610302+0.006057    test-rmse:0.702220+0.035753 
## [58] train-rmse:0.609274+0.006066    test-rmse:0.702616+0.036250 
## [59] train-rmse:0.608280+0.006067    test-rmse:0.701769+0.036692 
## [60] train-rmse:0.607266+0.006075    test-rmse:0.699809+0.037031 
## [61] train-rmse:0.606276+0.006107    test-rmse:0.699971+0.035558 
## [62] train-rmse:0.605286+0.006141    test-rmse:0.699136+0.036161 
## [63] train-rmse:0.604328+0.006163    test-rmse:0.698405+0.035019 
## [64] train-rmse:0.603385+0.006187    test-rmse:0.698792+0.036224 
## [65] train-rmse:0.602458+0.006206    test-rmse:0.698736+0.036553 
## [66] train-rmse:0.601564+0.006227    test-rmse:0.698317+0.036679 
## [67] train-rmse:0.600672+0.006248    test-rmse:0.697011+0.036755 
## [68] train-rmse:0.599779+0.006302    test-rmse:0.695005+0.036026 
## [69] train-rmse:0.598911+0.006331    test-rmse:0.695295+0.036270 
## [70] train-rmse:0.598061+0.006360    test-rmse:0.694892+0.036478 
## [71] train-rmse:0.597221+0.006386    test-rmse:0.694512+0.035910 
## [72] train-rmse:0.596410+0.006398    test-rmse:0.694483+0.035301 
## [73] train-rmse:0.595591+0.006405    test-rmse:0.695288+0.036652 
## [74] train-rmse:0.594800+0.006394    test-rmse:0.695266+0.036587 
## [75] train-rmse:0.594034+0.006393    test-rmse:0.695912+0.037750 
## [76] train-rmse:0.593290+0.006395    test-rmse:0.695235+0.037502 
## [77] train-rmse:0.592537+0.006385    test-rmse:0.695092+0.036027 
## [78] train-rmse:0.591789+0.006389    test-rmse:0.693238+0.036783 
## [79] train-rmse:0.591055+0.006407    test-rmse:0.692145+0.036953 
## [80] train-rmse:0.590289+0.006427    test-rmse:0.690494+0.035841 
## [81] train-rmse:0.589544+0.006464    test-rmse:0.689880+0.037899 
## [82] train-rmse:0.588821+0.006485    test-rmse:0.690448+0.037355 
## [83] train-rmse:0.588105+0.006515    test-rmse:0.690411+0.037437 
## [84] train-rmse:0.587402+0.006541    test-rmse:0.689711+0.037127 
## [85] train-rmse:0.586716+0.006562    test-rmse:0.688952+0.036901 
## [86] train-rmse:0.586031+0.006576    test-rmse:0.688239+0.036824 
## [87] train-rmse:0.585377+0.006578    test-rmse:0.687548+0.036541 
## [88] train-rmse:0.584708+0.006619    test-rmse:0.687943+0.036082 
## [89] train-rmse:0.584043+0.006608    test-rmse:0.687857+0.036606 
## [90] train-rmse:0.583407+0.006620    test-rmse:0.687124+0.036961 
## [91] train-rmse:0.582780+0.006647    test-rmse:0.686483+0.036629 
## [92] train-rmse:0.582162+0.006674    test-rmse:0.686651+0.035711 
## [93] train-rmse:0.581562+0.006687    test-rmse:0.687349+0.034817 
## [94] train-rmse:0.580960+0.006695    test-rmse:0.687808+0.035449 
## [95] train-rmse:0.580362+0.006706    test-rmse:0.687801+0.035730 
## [96] train-rmse:0.579785+0.006718    test-rmse:0.687115+0.035576 
## [97] train-rmse:0.579203+0.006718    test-rmse:0.686959+0.035218 
## Stopping. Best iteration:
## [91] train-rmse:0.582780+0.006647    test-rmse:0.686483+0.036629
## 
## 64 
## [1]  train-rmse:1.261631+0.012050    test-rmse:1.269588+0.071112 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.036213+0.008412    test-rmse:1.053695+0.069092 
## [3]  train-rmse:0.893056+0.006046    test-rmse:0.922176+0.068303 
## [4]  train-rmse:0.801789+0.002580    test-rmse:0.841895+0.065073 
## [5]  train-rmse:0.744965+0.003050    test-rmse:0.788383+0.057309 
## [6]  train-rmse:0.709678+0.003524    test-rmse:0.757919+0.055499 
## [7]  train-rmse:0.684247+0.004385    test-rmse:0.739151+0.046765 
## [8]  train-rmse:0.667572+0.003645    test-rmse:0.729000+0.042767 
## [9]  train-rmse:0.655706+0.004144    test-rmse:0.724427+0.041154 
## [10] train-rmse:0.644192+0.005253    test-rmse:0.715573+0.031477 
## [11] train-rmse:0.636132+0.005332    test-rmse:0.712340+0.031556 
## [12] train-rmse:0.628115+0.005627    test-rmse:0.710566+0.032841 
## [13] train-rmse:0.621916+0.006341    test-rmse:0.708893+0.030824 
## [14] train-rmse:0.615857+0.006548    test-rmse:0.705485+0.030980 
## [15] train-rmse:0.609533+0.006939    test-rmse:0.705225+0.028660 
## [16] train-rmse:0.604453+0.006406    test-rmse:0.704557+0.030719 
## [17] train-rmse:0.600142+0.006636    test-rmse:0.705012+0.033129 
## [18] train-rmse:0.595155+0.007310    test-rmse:0.700844+0.033110 
## [19] train-rmse:0.589456+0.008388    test-rmse:0.696489+0.031211 
## [20] train-rmse:0.581378+0.006267    test-rmse:0.692171+0.033586 
## [21] train-rmse:0.576793+0.005320    test-rmse:0.690564+0.034584 
## [22] train-rmse:0.571487+0.006473    test-rmse:0.685645+0.030933 
## [23] train-rmse:0.568424+0.006021    test-rmse:0.683553+0.035942 
## [24] train-rmse:0.563169+0.006481    test-rmse:0.681326+0.036359 
## [25] train-rmse:0.558330+0.007230    test-rmse:0.683658+0.037452 
## [26] train-rmse:0.552353+0.009098    test-rmse:0.677708+0.032068 
## [27] train-rmse:0.549218+0.009586    test-rmse:0.675678+0.032007 
## [28] train-rmse:0.546430+0.008973    test-rmse:0.674384+0.030877 
## [29] train-rmse:0.543861+0.009255    test-rmse:0.674256+0.031073 
## [30] train-rmse:0.540100+0.008108    test-rmse:0.673116+0.030714 
## [31] train-rmse:0.536755+0.008336    test-rmse:0.671620+0.031000 
## [32] train-rmse:0.534050+0.007960    test-rmse:0.671840+0.031707 
## [33] train-rmse:0.529657+0.007540    test-rmse:0.669789+0.029605 
## [34] train-rmse:0.527104+0.007563    test-rmse:0.670735+0.030517 
## [35] train-rmse:0.522920+0.008116    test-rmse:0.668152+0.030452 
## [36] train-rmse:0.518994+0.006994    test-rmse:0.666795+0.032770 
## [37] train-rmse:0.516130+0.006353    test-rmse:0.665222+0.032435 
## [38] train-rmse:0.511560+0.007019    test-rmse:0.663099+0.032455 
## [39] train-rmse:0.509634+0.006687    test-rmse:0.662573+0.034748 
## [40] train-rmse:0.506488+0.007172    test-rmse:0.660177+0.034944 
## [41] train-rmse:0.503547+0.007910    test-rmse:0.661309+0.036374 
## [42] train-rmse:0.501509+0.008066    test-rmse:0.660148+0.035524 
## [43] train-rmse:0.498460+0.007806    test-rmse:0.659455+0.033313 
## [44] train-rmse:0.495334+0.007787    test-rmse:0.657523+0.033446 
## [45] train-rmse:0.492083+0.008604    test-rmse:0.656812+0.033230 
## [46] train-rmse:0.489332+0.007431    test-rmse:0.654410+0.037340 
## [47] train-rmse:0.487651+0.007469    test-rmse:0.655583+0.037300 
## [48] train-rmse:0.485614+0.007599    test-rmse:0.656737+0.038163 
## [49] train-rmse:0.483289+0.008118    test-rmse:0.655391+0.038535 
## [50] train-rmse:0.480970+0.008142    test-rmse:0.655618+0.038479 
## [51] train-rmse:0.478047+0.007895    test-rmse:0.653632+0.037451 
## [52] train-rmse:0.476361+0.007632    test-rmse:0.651522+0.036909 
## [53] train-rmse:0.473221+0.008918    test-rmse:0.649086+0.037281 
## [54] train-rmse:0.470789+0.010184    test-rmse:0.650211+0.036409 
## [55] train-rmse:0.469294+0.009999    test-rmse:0.650567+0.036930 
## [56] train-rmse:0.467175+0.009903    test-rmse:0.649870+0.036994 
## [57] train-rmse:0.465181+0.009055    test-rmse:0.651174+0.037730 
## [58] train-rmse:0.463773+0.008551    test-rmse:0.652269+0.038175 
## [59] train-rmse:0.462141+0.008616    test-rmse:0.652801+0.037716 
## Stopping. Best iteration:
## [53] train-rmse:0.473221+0.008918    test-rmse:0.649086+0.037281
## 
## 65 
## [1]  train-rmse:1.255940+0.011576    test-rmse:1.266633+0.069522 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.021237+0.008278    test-rmse:1.045885+0.067538 
## [3]  train-rmse:0.869783+0.005820    test-rmse:0.909758+0.067308 
## [4]  train-rmse:0.773087+0.004890    test-rmse:0.822430+0.069589 
## [5]  train-rmse:0.707903+0.008242    test-rmse:0.771993+0.061441 
## [6]  train-rmse:0.664410+0.005751    test-rmse:0.741426+0.061788 
## [7]  train-rmse:0.636669+0.006952    test-rmse:0.719709+0.056060 
## [8]  train-rmse:0.618082+0.006653    test-rmse:0.709866+0.054730 
## [9]  train-rmse:0.600706+0.007023    test-rmse:0.700044+0.053111 
## [10] train-rmse:0.586036+0.008242    test-rmse:0.692987+0.054967 
## [11] train-rmse:0.574590+0.009455    test-rmse:0.687379+0.053292 
## [12] train-rmse:0.565619+0.008506    test-rmse:0.681826+0.053542 
## [13] train-rmse:0.558341+0.008114    test-rmse:0.682958+0.055012 
## [14] train-rmse:0.551227+0.007617    test-rmse:0.678492+0.058116 
## [15] train-rmse:0.541765+0.009266    test-rmse:0.677855+0.056876 
## [16] train-rmse:0.532381+0.008854    test-rmse:0.675681+0.053837 
## [17] train-rmse:0.525948+0.008818    test-rmse:0.673903+0.055212 
## [18] train-rmse:0.519248+0.009193    test-rmse:0.671794+0.053803 
## [19] train-rmse:0.511706+0.012266    test-rmse:0.669207+0.055061 
## [20] train-rmse:0.505571+0.011402    test-rmse:0.667235+0.054917 
## [21] train-rmse:0.501454+0.011677    test-rmse:0.663941+0.057351 
## [22] train-rmse:0.495811+0.012653    test-rmse:0.662299+0.057087 
## [23] train-rmse:0.484952+0.013290    test-rmse:0.659570+0.056402 
## [24] train-rmse:0.479999+0.012623    test-rmse:0.657321+0.057333 
## [25] train-rmse:0.473259+0.014470    test-rmse:0.653942+0.055943 
## [26] train-rmse:0.466094+0.016519    test-rmse:0.653416+0.056369 
## [27] train-rmse:0.460662+0.015479    test-rmse:0.651645+0.056801 
## [28] train-rmse:0.456253+0.015921    test-rmse:0.653922+0.056261 
## [29] train-rmse:0.450819+0.016673    test-rmse:0.648134+0.055739 
## [30] train-rmse:0.446570+0.018528    test-rmse:0.647525+0.055440 
## [31] train-rmse:0.440568+0.018274    test-rmse:0.647231+0.054911 
## [32] train-rmse:0.434616+0.015416    test-rmse:0.643025+0.052032 
## [33] train-rmse:0.429083+0.014955    test-rmse:0.639293+0.050477 
## [34] train-rmse:0.424922+0.014640    test-rmse:0.637415+0.050586 
## [35] train-rmse:0.418710+0.015042    test-rmse:0.639159+0.049771 
## [36] train-rmse:0.415232+0.014734    test-rmse:0.639846+0.052669 
## [37] train-rmse:0.409800+0.014777    test-rmse:0.638221+0.050829 
## [38] train-rmse:0.405514+0.015010    test-rmse:0.635858+0.051171 
## [39] train-rmse:0.401512+0.015100    test-rmse:0.635409+0.052939 
## [40] train-rmse:0.397870+0.014661    test-rmse:0.636402+0.052244 
## [41] train-rmse:0.394450+0.014241    test-rmse:0.635916+0.051183 
## [42] train-rmse:0.391127+0.013506    test-rmse:0.635347+0.051630 
## [43] train-rmse:0.387536+0.013603    test-rmse:0.635960+0.051002 
## [44] train-rmse:0.384441+0.014000    test-rmse:0.636331+0.050658 
## [45] train-rmse:0.380808+0.015042    test-rmse:0.635257+0.050092 
## [46] train-rmse:0.377694+0.016178    test-rmse:0.635795+0.049821 
## [47] train-rmse:0.374565+0.016141    test-rmse:0.635061+0.051033 
## [48] train-rmse:0.371555+0.017541    test-rmse:0.633938+0.050308 
## [49] train-rmse:0.365849+0.016289    test-rmse:0.634476+0.048157 
## [50] train-rmse:0.361907+0.016410    test-rmse:0.633982+0.046522 
## [51] train-rmse:0.359745+0.016287    test-rmse:0.635017+0.047231 
## [52] train-rmse:0.355793+0.017480    test-rmse:0.631844+0.046409 
## [53] train-rmse:0.352524+0.016833    test-rmse:0.633510+0.044312 
## [54] train-rmse:0.347948+0.016380    test-rmse:0.629822+0.045579 
## [55] train-rmse:0.345717+0.016467    test-rmse:0.630412+0.045192 
## [56] train-rmse:0.342467+0.015191    test-rmse:0.629997+0.046874 
## [57] train-rmse:0.339384+0.015334    test-rmse:0.629369+0.047076 
## [58] train-rmse:0.335433+0.014406    test-rmse:0.629902+0.046374 
## [59] train-rmse:0.332072+0.015451    test-rmse:0.629334+0.045288 
## [60] train-rmse:0.328638+0.017032    test-rmse:0.629729+0.046122 
## [61] train-rmse:0.324989+0.017086    test-rmse:0.630427+0.045756 
## [62] train-rmse:0.322879+0.017063    test-rmse:0.630395+0.045418 
## [63] train-rmse:0.320021+0.016684    test-rmse:0.630214+0.044849 
## [64] train-rmse:0.317972+0.017391    test-rmse:0.630557+0.044766 
## [65] train-rmse:0.315074+0.017600    test-rmse:0.630909+0.043614 
## Stopping. Best iteration:
## [59] train-rmse:0.332072+0.015451    test-rmse:0.629334+0.045288
## 
## 66 
## [1]  train-rmse:1.250057+0.011678    test-rmse:1.270215+0.070724 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.006952+0.007657    test-rmse:1.045207+0.074528 
## [3]  train-rmse:0.845568+0.005991    test-rmse:0.900196+0.070338 
## [4]  train-rmse:0.742601+0.005169    test-rmse:0.809974+0.072330 
## [5]  train-rmse:0.674831+0.005335    test-rmse:0.756998+0.068317 
## [6]  train-rmse:0.630077+0.008669    test-rmse:0.729986+0.067793 
## [7]  train-rmse:0.592013+0.010513    test-rmse:0.708272+0.062404 
## [8]  train-rmse:0.570014+0.009379    test-rmse:0.697579+0.059812 
## [9]  train-rmse:0.545636+0.013776    test-rmse:0.691457+0.057690 
## [10] train-rmse:0.531710+0.013926    test-rmse:0.686867+0.060085 
## [11] train-rmse:0.517804+0.012620    test-rmse:0.679328+0.057609 
## [12] train-rmse:0.504987+0.014406    test-rmse:0.673677+0.057248 
## [13] train-rmse:0.493941+0.015335    test-rmse:0.675245+0.056575 
## [14] train-rmse:0.486589+0.015641    test-rmse:0.676930+0.060526 
## [15] train-rmse:0.473047+0.014892    test-rmse:0.668221+0.058332 
## [16] train-rmse:0.460031+0.012304    test-rmse:0.662422+0.058311 
## [17] train-rmse:0.451022+0.013242    test-rmse:0.657735+0.057216 
## [18] train-rmse:0.440238+0.011730    test-rmse:0.653633+0.057952 
## [19] train-rmse:0.432508+0.012798    test-rmse:0.651461+0.057294 
## [20] train-rmse:0.425027+0.013149    test-rmse:0.651029+0.058533 
## [21] train-rmse:0.416419+0.010194    test-rmse:0.651348+0.063078 
## [22] train-rmse:0.407861+0.011359    test-rmse:0.648428+0.064159 
## [23] train-rmse:0.403007+0.011833    test-rmse:0.648975+0.065587 
## [24] train-rmse:0.393315+0.013545    test-rmse:0.647403+0.065073 
## [25] train-rmse:0.384939+0.014462    test-rmse:0.644535+0.065283 
## [26] train-rmse:0.381249+0.014336    test-rmse:0.642225+0.064532 
## [27] train-rmse:0.373729+0.014289    test-rmse:0.637534+0.064406 
## [28] train-rmse:0.364253+0.011329    test-rmse:0.634583+0.064359 
## [29] train-rmse:0.358026+0.013553    test-rmse:0.634968+0.064052 
## [30] train-rmse:0.351231+0.012299    test-rmse:0.630882+0.062591 
## [31] train-rmse:0.344729+0.010349    test-rmse:0.630871+0.062187 
## [32] train-rmse:0.339042+0.008800    test-rmse:0.630588+0.062439 
## [33] train-rmse:0.333700+0.011584    test-rmse:0.631361+0.062600 
## [34] train-rmse:0.328776+0.011244    test-rmse:0.629592+0.062813 
## [35] train-rmse:0.324649+0.012599    test-rmse:0.630706+0.063755 
## [36] train-rmse:0.319480+0.012819    test-rmse:0.630514+0.064210 
## [37] train-rmse:0.315601+0.012422    test-rmse:0.628936+0.063737 
## [38] train-rmse:0.309281+0.012366    test-rmse:0.627326+0.061669 
## [39] train-rmse:0.303782+0.014456    test-rmse:0.627903+0.061441 
## [40] train-rmse:0.299875+0.013914    test-rmse:0.628000+0.061064 
## [41] train-rmse:0.296955+0.013960    test-rmse:0.628243+0.061062 
## [42] train-rmse:0.292627+0.013663    test-rmse:0.630327+0.062788 
## [43] train-rmse:0.285960+0.011598    test-rmse:0.632524+0.061955 
## [44] train-rmse:0.281942+0.012036    test-rmse:0.634418+0.063472 
## Stopping. Best iteration:
## [38] train-rmse:0.309281+0.012366    test-rmse:0.627326+0.061669
## 
## 67 
## [1]  train-rmse:1.246041+0.011926    test-rmse:1.264787+0.074243 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.996652+0.007515    test-rmse:1.040071+0.075990 
## [3]  train-rmse:0.827052+0.005146    test-rmse:0.887840+0.073590 
## [4]  train-rmse:0.716928+0.006765    test-rmse:0.800933+0.066496 
## [5]  train-rmse:0.644560+0.008431    test-rmse:0.745464+0.058647 
## [6]  train-rmse:0.594591+0.008576    test-rmse:0.716082+0.048716 
## [7]  train-rmse:0.556336+0.007140    test-rmse:0.698079+0.044285 
## [8]  train-rmse:0.529516+0.003931    test-rmse:0.680245+0.048183 
## [9]  train-rmse:0.506181+0.008200    test-rmse:0.674895+0.047751 
## [10] train-rmse:0.483676+0.013679    test-rmse:0.661806+0.047251 
## [11] train-rmse:0.469840+0.014340    test-rmse:0.660669+0.048345 
## [12] train-rmse:0.454912+0.012620    test-rmse:0.656664+0.050442 
## [13] train-rmse:0.442957+0.013658    test-rmse:0.649216+0.053611 
## [14] train-rmse:0.429484+0.012438    test-rmse:0.645558+0.056224 
## [15] train-rmse:0.417963+0.010897    test-rmse:0.641357+0.053478 
## [16] train-rmse:0.405575+0.013853    test-rmse:0.641582+0.055859 
## [17] train-rmse:0.391898+0.021540    test-rmse:0.638620+0.054809 
## [18] train-rmse:0.380494+0.016301    test-rmse:0.633752+0.055362 
## [19] train-rmse:0.370674+0.021568    test-rmse:0.632592+0.051617 
## [20] train-rmse:0.361975+0.023229    test-rmse:0.630165+0.050053 
## [21] train-rmse:0.350152+0.023367    test-rmse:0.630763+0.053727 
## [22] train-rmse:0.337745+0.018078    test-rmse:0.625804+0.053331 
## [23] train-rmse:0.332292+0.017895    test-rmse:0.624191+0.053486 
## [24] train-rmse:0.324833+0.014871    test-rmse:0.618843+0.052876 
## [25] train-rmse:0.316252+0.015295    test-rmse:0.617814+0.055108 
## [26] train-rmse:0.307106+0.016182    test-rmse:0.616124+0.056186 
## [27] train-rmse:0.298966+0.017888    test-rmse:0.615464+0.055516 
## [28] train-rmse:0.291503+0.014762    test-rmse:0.614762+0.055972 
## [29] train-rmse:0.285058+0.016907    test-rmse:0.615199+0.055815 
## [30] train-rmse:0.278736+0.016325    test-rmse:0.613282+0.056940 
## [31] train-rmse:0.271863+0.017917    test-rmse:0.612309+0.057981 
## [32] train-rmse:0.264732+0.019710    test-rmse:0.612100+0.057525 
## [33] train-rmse:0.258566+0.018302    test-rmse:0.613636+0.057726 
## [34] train-rmse:0.252455+0.017331    test-rmse:0.615272+0.058428 
## [35] train-rmse:0.246038+0.014519    test-rmse:0.614911+0.058323 
## [36] train-rmse:0.241573+0.013542    test-rmse:0.614581+0.059238 
## [37] train-rmse:0.236347+0.012277    test-rmse:0.611415+0.059004 
## [38] train-rmse:0.231763+0.013297    test-rmse:0.611646+0.060249 
## [39] train-rmse:0.228420+0.014128    test-rmse:0.610945+0.059405 
## [40] train-rmse:0.223693+0.015169    test-rmse:0.612521+0.060605 
## [41] train-rmse:0.219599+0.014708    test-rmse:0.613198+0.060470 
## [42] train-rmse:0.212668+0.012454    test-rmse:0.612093+0.059665 
## [43] train-rmse:0.209517+0.012840    test-rmse:0.610817+0.059092 
## [44] train-rmse:0.204227+0.013476    test-rmse:0.611516+0.059583 
## [45] train-rmse:0.199779+0.014664    test-rmse:0.612522+0.060719 
## [46] train-rmse:0.196188+0.015138    test-rmse:0.614054+0.062095 
## [47] train-rmse:0.192800+0.014056    test-rmse:0.614771+0.062639 
## [48] train-rmse:0.189342+0.014780    test-rmse:0.614901+0.064530 
## [49] train-rmse:0.185260+0.013903    test-rmse:0.613470+0.064765 
## Stopping. Best iteration:
## [43] train-rmse:0.209517+0.012840    test-rmse:0.610817+0.059092
## 
## 68 
## [1]  train-rmse:1.241950+0.011262    test-rmse:1.267066+0.076219 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.987731+0.006668    test-rmse:1.035023+0.076188 
## [3]  train-rmse:0.811042+0.005537    test-rmse:0.888337+0.069253 
## [4]  train-rmse:0.692345+0.006329    test-rmse:0.798672+0.068520 
## [5]  train-rmse:0.613622+0.005712    test-rmse:0.740762+0.059388 
## [6]  train-rmse:0.559684+0.007565    test-rmse:0.710319+0.057424 
## [7]  train-rmse:0.511308+0.013996    test-rmse:0.693168+0.051688 
## [8]  train-rmse:0.474145+0.009185    test-rmse:0.674826+0.049209 
## [9]  train-rmse:0.452077+0.008635    test-rmse:0.660375+0.045657 
## [10] train-rmse:0.426990+0.012683    test-rmse:0.659560+0.043996 
## [11] train-rmse:0.411366+0.014160    test-rmse:0.658097+0.045211 
## [12] train-rmse:0.396275+0.013308    test-rmse:0.658609+0.046281 
## [13] train-rmse:0.381030+0.016521    test-rmse:0.660238+0.046284 
## [14] train-rmse:0.359675+0.009719    test-rmse:0.647937+0.043446 
## [15] train-rmse:0.350112+0.009894    test-rmse:0.645163+0.041823 
## [16] train-rmse:0.337733+0.011803    test-rmse:0.647246+0.042274 
## [17] train-rmse:0.325315+0.013994    test-rmse:0.643738+0.042138 
## [18] train-rmse:0.316361+0.012914    test-rmse:0.642359+0.045407 
## [19] train-rmse:0.305352+0.012772    test-rmse:0.635745+0.045741 
## [20] train-rmse:0.295477+0.011086    test-rmse:0.633317+0.043800 
## [21] train-rmse:0.284630+0.011446    test-rmse:0.635022+0.044812 
## [22] train-rmse:0.276945+0.010877    test-rmse:0.634007+0.046352 
## [23] train-rmse:0.268586+0.010907    test-rmse:0.633485+0.046885 
## [24] train-rmse:0.259169+0.009143    test-rmse:0.632175+0.046295 
## [25] train-rmse:0.251638+0.008516    test-rmse:0.631670+0.047425 
## [26] train-rmse:0.242844+0.007269    test-rmse:0.629991+0.046555 
## [27] train-rmse:0.233638+0.005579    test-rmse:0.629860+0.047958 
## [28] train-rmse:0.226614+0.005867    test-rmse:0.631646+0.048258 
## [29] train-rmse:0.218024+0.006640    test-rmse:0.631525+0.049491 
## [30] train-rmse:0.211922+0.008292    test-rmse:0.631956+0.050555 
## [31] train-rmse:0.205918+0.006633    test-rmse:0.632907+0.050108 
## [32] train-rmse:0.202029+0.006565    test-rmse:0.632829+0.051872 
## [33] train-rmse:0.197863+0.007330    test-rmse:0.631912+0.051543 
## Stopping. Best iteration:
## [27] train-rmse:0.233638+0.005579    test-rmse:0.629860+0.047958
## 
## 69 
## [1]  train-rmse:1.238950+0.010704    test-rmse:1.265550+0.076443 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.981544+0.006885    test-rmse:1.029839+0.077037 
## [3]  train-rmse:0.799180+0.007153    test-rmse:0.884443+0.081521 
## [4]  train-rmse:0.677705+0.008302    test-rmse:0.798316+0.076444 
## [5]  train-rmse:0.586886+0.011915    test-rmse:0.744175+0.066196 
## [6]  train-rmse:0.525934+0.013337    test-rmse:0.710444+0.065448 
## [7]  train-rmse:0.480974+0.010293    test-rmse:0.684653+0.052727 
## [8]  train-rmse:0.439238+0.016176    test-rmse:0.670366+0.046795 
## [9]  train-rmse:0.412847+0.015127    test-rmse:0.663829+0.047126 
## [10] train-rmse:0.387020+0.014341    test-rmse:0.658864+0.049519 
## [11] train-rmse:0.368405+0.015710    test-rmse:0.654749+0.052713 
## [12] train-rmse:0.350500+0.016872    test-rmse:0.650021+0.052672 
## [13] train-rmse:0.332955+0.014151    test-rmse:0.645156+0.048197 
## [14] train-rmse:0.318821+0.012918    test-rmse:0.644193+0.049540 
## [15] train-rmse:0.307339+0.012754    test-rmse:0.641759+0.051487 
## [16] train-rmse:0.295108+0.012166    test-rmse:0.644498+0.051856 
## [17] train-rmse:0.281827+0.011172    test-rmse:0.639950+0.051402 
## [18] train-rmse:0.272129+0.010158    test-rmse:0.640630+0.052994 
## [19] train-rmse:0.258280+0.014428    test-rmse:0.642166+0.053409 
## [20] train-rmse:0.247844+0.016581    test-rmse:0.644366+0.053877 
## [21] train-rmse:0.234890+0.013918    test-rmse:0.642168+0.051552 
## [22] train-rmse:0.227136+0.013593    test-rmse:0.642207+0.049986 
## [23] train-rmse:0.219464+0.012719    test-rmse:0.642726+0.053358 
## Stopping. Best iteration:
## [17] train-rmse:0.281827+0.011172    test-rmse:0.639950+0.051402
## 
## 70 
## [1]  train-rmse:1.247318+0.011900    test-rmse:1.249793+0.070938 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.025503+0.008729    test-rmse:1.030761+0.067016 
## [3]  train-rmse:0.894820+0.007647    test-rmse:0.909440+0.062506 
## [4]  train-rmse:0.818060+0.006764    test-rmse:0.834111+0.055296 
## [5]  train-rmse:0.774856+0.006485    test-rmse:0.800010+0.048643 
## [6]  train-rmse:0.749346+0.006009    test-rmse:0.779585+0.043449 
## [7]  train-rmse:0.733097+0.005805    test-rmse:0.764840+0.040107 
## [8]  train-rmse:0.721699+0.005637    test-rmse:0.753281+0.039253 
## [9]  train-rmse:0.713050+0.005181    test-rmse:0.746075+0.034895 
## [10] train-rmse:0.706244+0.004937    test-rmse:0.741949+0.034017 
## [11] train-rmse:0.700570+0.004765    test-rmse:0.742663+0.032019 
## [12] train-rmse:0.695728+0.005004    test-rmse:0.739934+0.030091 
## [13] train-rmse:0.691344+0.005187    test-rmse:0.736807+0.031495 
## [14] train-rmse:0.687344+0.005333    test-rmse:0.736019+0.031410 
## [15] train-rmse:0.683581+0.005401    test-rmse:0.734050+0.030519 
## [16] train-rmse:0.680035+0.005298    test-rmse:0.732386+0.030865 
## [17] train-rmse:0.676669+0.005207    test-rmse:0.731374+0.032810 
## [18] train-rmse:0.673397+0.005218    test-rmse:0.728201+0.032407 
## [19] train-rmse:0.670287+0.005177    test-rmse:0.726523+0.031644 
## [20] train-rmse:0.667310+0.005246    test-rmse:0.728846+0.032580 
## [21] train-rmse:0.664479+0.005310    test-rmse:0.725802+0.032484 
## [22] train-rmse:0.661759+0.005321    test-rmse:0.723960+0.032854 
## [23] train-rmse:0.659100+0.005352    test-rmse:0.724871+0.034468 
## [24] train-rmse:0.656548+0.005366    test-rmse:0.722025+0.032912 
## [25] train-rmse:0.654171+0.005372    test-rmse:0.720646+0.033854 
## [26] train-rmse:0.651890+0.005446    test-rmse:0.721010+0.035004 
## [27] train-rmse:0.649748+0.005519    test-rmse:0.720132+0.035191 
## [28] train-rmse:0.647667+0.005644    test-rmse:0.720375+0.034369 
## [29] train-rmse:0.645660+0.005596    test-rmse:0.719354+0.034580 
## [30] train-rmse:0.643749+0.005593    test-rmse:0.717931+0.034664 
## [31] train-rmse:0.641804+0.005566    test-rmse:0.716118+0.033983 
## [32] train-rmse:0.639992+0.005603    test-rmse:0.713909+0.032494 
## [33] train-rmse:0.638226+0.005633    test-rmse:0.713680+0.033109 
## [34] train-rmse:0.636505+0.005676    test-rmse:0.713290+0.033608 
## [35] train-rmse:0.634813+0.005707    test-rmse:0.713166+0.032985 
## [36] train-rmse:0.633140+0.005729    test-rmse:0.711730+0.032208 
## [37] train-rmse:0.631465+0.005710    test-rmse:0.711328+0.034877 
## [38] train-rmse:0.629885+0.005696    test-rmse:0.710861+0.035041 
## [39] train-rmse:0.628348+0.005667    test-rmse:0.708346+0.033883 
## [40] train-rmse:0.626796+0.005762    test-rmse:0.710018+0.033736 
## [41] train-rmse:0.625199+0.005860    test-rmse:0.709952+0.032355 
## [42] train-rmse:0.623682+0.005974    test-rmse:0.710139+0.034317 
## [43] train-rmse:0.622288+0.005946    test-rmse:0.708513+0.035191 
## [44] train-rmse:0.620947+0.005963    test-rmse:0.708805+0.035076 
## [45] train-rmse:0.619623+0.005939    test-rmse:0.708494+0.035863 
## Stopping. Best iteration:
## [39] train-rmse:0.628348+0.005667    test-rmse:0.708346+0.033883
## 
## 71 
## [1]  train-rmse:1.238307+0.011705    test-rmse:1.247043+0.071244 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.006899+0.007845    test-rmse:1.026014+0.068470 
## [3]  train-rmse:0.866435+0.005549    test-rmse:0.898007+0.066934 
## [4]  train-rmse:0.780319+0.002415    test-rmse:0.823037+0.063289 
## [5]  train-rmse:0.726876+0.005122    test-rmse:0.775286+0.051731 
## [6]  train-rmse:0.694929+0.004964    test-rmse:0.747115+0.047719 
## [7]  train-rmse:0.673382+0.002982    test-rmse:0.733001+0.042036 
## [8]  train-rmse:0.658645+0.002505    test-rmse:0.724350+0.041348 
## [9]  train-rmse:0.645858+0.005360    test-rmse:0.717274+0.036028 
## [10] train-rmse:0.637457+0.005599    test-rmse:0.714433+0.032522 
## [11] train-rmse:0.629868+0.005827    test-rmse:0.713339+0.038061 
## [12] train-rmse:0.620472+0.004639    test-rmse:0.708074+0.037685 
## [13] train-rmse:0.613633+0.005430    test-rmse:0.707580+0.037787 
## [14] train-rmse:0.607550+0.004495    test-rmse:0.701524+0.034585 
## [15] train-rmse:0.598179+0.004908    test-rmse:0.697402+0.034215 
## [16] train-rmse:0.593737+0.004438    test-rmse:0.698742+0.039790 
## [17] train-rmse:0.587452+0.003187    test-rmse:0.694971+0.036751 
## [18] train-rmse:0.582307+0.004156    test-rmse:0.692072+0.038490 
## [19] train-rmse:0.577654+0.003666    test-rmse:0.688709+0.039485 
## [20] train-rmse:0.573099+0.003295    test-rmse:0.687000+0.039763 
## [21] train-rmse:0.568118+0.005319    test-rmse:0.682659+0.036258 
## [22] train-rmse:0.562742+0.003702    test-rmse:0.675867+0.039158 
## [23] train-rmse:0.558335+0.004460    test-rmse:0.673764+0.038121 
## [24] train-rmse:0.555048+0.004550    test-rmse:0.672125+0.036908 
## [25] train-rmse:0.552079+0.004489    test-rmse:0.672673+0.038571 
## [26] train-rmse:0.548180+0.004707    test-rmse:0.673096+0.039835 
## [27] train-rmse:0.543138+0.003829    test-rmse:0.669839+0.044047 
## [28] train-rmse:0.538716+0.003976    test-rmse:0.666381+0.042871 
## [29] train-rmse:0.534266+0.004147    test-rmse:0.663358+0.042622 
## [30] train-rmse:0.531170+0.003701    test-rmse:0.662227+0.043837 
## [31] train-rmse:0.527937+0.003896    test-rmse:0.659536+0.043078 
## [32] train-rmse:0.523993+0.004455    test-rmse:0.657410+0.042943 
## [33] train-rmse:0.519058+0.004199    test-rmse:0.657809+0.041463 
## [34] train-rmse:0.515975+0.004599    test-rmse:0.658085+0.043086 
## [35] train-rmse:0.511813+0.005135    test-rmse:0.658665+0.043102 
## [36] train-rmse:0.509492+0.005278    test-rmse:0.659105+0.040705 
## [37] train-rmse:0.506850+0.005161    test-rmse:0.656631+0.043618 
## [38] train-rmse:0.504552+0.005098    test-rmse:0.656138+0.044768 
## [39] train-rmse:0.502191+0.005307    test-rmse:0.657627+0.045367 
## [40] train-rmse:0.497878+0.006613    test-rmse:0.655002+0.043955 
## [41] train-rmse:0.495227+0.006624    test-rmse:0.655538+0.045167 
## [42] train-rmse:0.492921+0.006875    test-rmse:0.654835+0.044972 
## [43] train-rmse:0.490297+0.006962    test-rmse:0.654878+0.045343 
## [44] train-rmse:0.487535+0.006348    test-rmse:0.654130+0.044777 
## [45] train-rmse:0.485379+0.006815    test-rmse:0.653361+0.045892 
## [46] train-rmse:0.483197+0.007523    test-rmse:0.653345+0.047977 
## [47] train-rmse:0.480664+0.006661    test-rmse:0.650715+0.046420 
## [48] train-rmse:0.476416+0.005440    test-rmse:0.647867+0.042850 
## [49] train-rmse:0.473816+0.004829    test-rmse:0.646107+0.041968 
## [50] train-rmse:0.470491+0.004878    test-rmse:0.645900+0.044027 
## [51] train-rmse:0.468857+0.005415    test-rmse:0.646249+0.045618 
## [52] train-rmse:0.466097+0.006215    test-rmse:0.646555+0.043806 
## [53] train-rmse:0.464330+0.006025    test-rmse:0.646272+0.043738 
## [54] train-rmse:0.462360+0.006684    test-rmse:0.644219+0.043292 
## [55] train-rmse:0.459572+0.008001    test-rmse:0.644671+0.042739 
## [56] train-rmse:0.456721+0.007131    test-rmse:0.642022+0.042431 
## [57] train-rmse:0.454584+0.006815    test-rmse:0.643188+0.044279 
## [58] train-rmse:0.453000+0.006269    test-rmse:0.643273+0.043544 
## [59] train-rmse:0.451274+0.006362    test-rmse:0.643351+0.043687 
## [60] train-rmse:0.448860+0.006659    test-rmse:0.643445+0.044115 
## [61] train-rmse:0.446944+0.006327    test-rmse:0.642716+0.042535 
## [62] train-rmse:0.443402+0.008792    test-rmse:0.641945+0.042429 
## [63] train-rmse:0.442014+0.008821    test-rmse:0.641461+0.042677 
## [64] train-rmse:0.440187+0.008918    test-rmse:0.642718+0.042985 
## [65] train-rmse:0.439128+0.009069    test-rmse:0.642714+0.043225 
## [66] train-rmse:0.437918+0.009547    test-rmse:0.642401+0.042811 
## [67] train-rmse:0.435556+0.011321    test-rmse:0.641658+0.043518 
## [68] train-rmse:0.434103+0.011486    test-rmse:0.641489+0.044072 
## [69] train-rmse:0.431589+0.013001    test-rmse:0.641692+0.043711 
## Stopping. Best iteration:
## [63] train-rmse:0.442014+0.008821    test-rmse:0.641461+0.042677
## 
## 72 
## [1]  train-rmse:1.232139+0.011191    test-rmse:1.243859+0.069532 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.990580+0.007784    test-rmse:1.017445+0.066840 
## [3]  train-rmse:0.840968+0.005562    test-rmse:0.878514+0.068448 
## [4]  train-rmse:0.750661+0.006005    test-rmse:0.800758+0.068332 
## [5]  train-rmse:0.688566+0.008020    test-rmse:0.755380+0.058774 
## [6]  train-rmse:0.651283+0.009270    test-rmse:0.726875+0.048282 
## [7]  train-rmse:0.624975+0.009466    test-rmse:0.713498+0.041135 
## [8]  train-rmse:0.608526+0.008401    test-rmse:0.704858+0.039529 
## [9]  train-rmse:0.592254+0.009505    test-rmse:0.694793+0.038216 
## [10] train-rmse:0.580189+0.010698    test-rmse:0.691216+0.040127 
## [11] train-rmse:0.569788+0.011536    test-rmse:0.688577+0.038685 
## [12] train-rmse:0.560547+0.012304    test-rmse:0.687909+0.039857 
## [13] train-rmse:0.548455+0.011206    test-rmse:0.683302+0.041301 
## [14] train-rmse:0.537858+0.013593    test-rmse:0.682815+0.041221 
## [15] train-rmse:0.525874+0.010936    test-rmse:0.676194+0.045641 
## [16] train-rmse:0.516787+0.008682    test-rmse:0.671233+0.046435 
## [17] train-rmse:0.510646+0.009116    test-rmse:0.668508+0.048479 
## [18] train-rmse:0.505747+0.009600    test-rmse:0.667255+0.048390 
## [19] train-rmse:0.500183+0.008924    test-rmse:0.664931+0.051234 
## [20] train-rmse:0.495614+0.008929    test-rmse:0.664782+0.054150 
## [21] train-rmse:0.488573+0.008079    test-rmse:0.660992+0.051446 
## [22] train-rmse:0.482525+0.009667    test-rmse:0.656039+0.048188 
## [23] train-rmse:0.474588+0.010599    test-rmse:0.653794+0.051060 
## [24] train-rmse:0.468837+0.012118    test-rmse:0.652445+0.050041 
## [25] train-rmse:0.462130+0.012989    test-rmse:0.650328+0.051544 
## [26] train-rmse:0.454857+0.012796    test-rmse:0.651920+0.052386 
## [27] train-rmse:0.450660+0.012720    test-rmse:0.650944+0.054530 
## [28] train-rmse:0.442439+0.009462    test-rmse:0.644579+0.051558 
## [29] train-rmse:0.438206+0.009894    test-rmse:0.642572+0.052529 
## [30] train-rmse:0.434441+0.011444    test-rmse:0.644841+0.052720 
## [31] train-rmse:0.429685+0.010619    test-rmse:0.645879+0.053818 
## [32] train-rmse:0.423515+0.012803    test-rmse:0.642725+0.054428 
## [33] train-rmse:0.419889+0.013608    test-rmse:0.640674+0.051644 
## [34] train-rmse:0.416237+0.013793    test-rmse:0.639182+0.050295 
## [35] train-rmse:0.409637+0.012544    test-rmse:0.640628+0.051831 
## [36] train-rmse:0.405369+0.014731    test-rmse:0.640029+0.050523 
## [37] train-rmse:0.400954+0.015599    test-rmse:0.639564+0.050429 
## [38] train-rmse:0.397607+0.015501    test-rmse:0.638250+0.051633 
## [39] train-rmse:0.393014+0.014366    test-rmse:0.635268+0.053134 
## [40] train-rmse:0.388951+0.014723    test-rmse:0.634587+0.055215 
## [41] train-rmse:0.385215+0.015086    test-rmse:0.635222+0.054865 
## [42] train-rmse:0.379990+0.012659    test-rmse:0.633836+0.053856 
## [43] train-rmse:0.374773+0.011143    test-rmse:0.632544+0.053614 
## [44] train-rmse:0.369147+0.011526    test-rmse:0.630019+0.051317 
## [45] train-rmse:0.364364+0.010676    test-rmse:0.631418+0.047953 
## [46] train-rmse:0.362028+0.011353    test-rmse:0.631229+0.046675 
## [47] train-rmse:0.358997+0.011291    test-rmse:0.631288+0.046497 
## [48] train-rmse:0.354573+0.012254    test-rmse:0.630088+0.045155 
## [49] train-rmse:0.352042+0.012435    test-rmse:0.628292+0.046340 
## [50] train-rmse:0.349509+0.012845    test-rmse:0.629323+0.046435 
## [51] train-rmse:0.346832+0.013797    test-rmse:0.628834+0.046390 
## [52] train-rmse:0.345379+0.013969    test-rmse:0.629047+0.046967 
## [53] train-rmse:0.341930+0.015996    test-rmse:0.630372+0.048316 
## [54] train-rmse:0.338320+0.014952    test-rmse:0.629913+0.047679 
## [55] train-rmse:0.335703+0.015526    test-rmse:0.629931+0.047731 
## Stopping. Best iteration:
## [49] train-rmse:0.352042+0.012435    test-rmse:0.628292+0.046340
## 
## 73 
## [1]  train-rmse:1.225752+0.011308    test-rmse:1.247727+0.070928 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.974896+0.007277    test-rmse:1.016078+0.074301 
## [3]  train-rmse:0.816407+0.004887    test-rmse:0.877666+0.070632 
## [4]  train-rmse:0.718130+0.004160    test-rmse:0.792177+0.072065 
## [5]  train-rmse:0.650806+0.011164    test-rmse:0.748778+0.064923 
## [6]  train-rmse:0.604551+0.008507    test-rmse:0.718601+0.063335 
## [7]  train-rmse:0.575215+0.008209    test-rmse:0.700500+0.065137 
## [8]  train-rmse:0.555480+0.007385    test-rmse:0.693002+0.064499 
## [9]  train-rmse:0.536284+0.006856    test-rmse:0.678456+0.061007 
## [10] train-rmse:0.522379+0.007619    test-rmse:0.670624+0.059063 
## [11] train-rmse:0.506516+0.010003    test-rmse:0.665297+0.060746 
## [12] train-rmse:0.493731+0.011062    test-rmse:0.661809+0.062710 
## [13] train-rmse:0.480576+0.014456    test-rmse:0.666374+0.066278 
## [14] train-rmse:0.471301+0.016958    test-rmse:0.662436+0.064579 
## [15] train-rmse:0.461260+0.016968    test-rmse:0.656243+0.060740 
## [16] train-rmse:0.451457+0.015848    test-rmse:0.654997+0.058888 
## [17] train-rmse:0.442101+0.014457    test-rmse:0.653756+0.058792 
## [18] train-rmse:0.428198+0.011272    test-rmse:0.650498+0.061838 
## [19] train-rmse:0.421189+0.014517    test-rmse:0.650256+0.059249 
## [20] train-rmse:0.411616+0.012156    test-rmse:0.648810+0.059635 
## [21] train-rmse:0.403668+0.010218    test-rmse:0.643702+0.062739 
## [22] train-rmse:0.392263+0.011913    test-rmse:0.640883+0.060739 
## [23] train-rmse:0.386671+0.013492    test-rmse:0.641770+0.061807 
## [24] train-rmse:0.378668+0.015320    test-rmse:0.640306+0.060926 
## [25] train-rmse:0.372115+0.015205    test-rmse:0.640086+0.061688 
## [26] train-rmse:0.363619+0.015327    test-rmse:0.642142+0.064265 
## [27] train-rmse:0.355594+0.013163    test-rmse:0.640997+0.064230 
## [28] train-rmse:0.349573+0.013590    test-rmse:0.640471+0.062875 
## [29] train-rmse:0.342121+0.014208    test-rmse:0.640296+0.062057 
## [30] train-rmse:0.336226+0.013250    test-rmse:0.636102+0.061543 
## [31] train-rmse:0.330615+0.014615    test-rmse:0.636066+0.061674 
## [32] train-rmse:0.324148+0.015411    test-rmse:0.633727+0.062620 
## [33] train-rmse:0.317241+0.017480    test-rmse:0.632740+0.062151 
## [34] train-rmse:0.312034+0.016768    test-rmse:0.633022+0.062486 
## [35] train-rmse:0.308591+0.017170    test-rmse:0.633520+0.062244 
## [36] train-rmse:0.303674+0.018738    test-rmse:0.631449+0.063475 
## [37] train-rmse:0.300892+0.018246    test-rmse:0.631518+0.062642 
## [38] train-rmse:0.296578+0.016323    test-rmse:0.631004+0.062420 
## [39] train-rmse:0.289874+0.016494    test-rmse:0.631305+0.062561 
## [40] train-rmse:0.284932+0.015603    test-rmse:0.631959+0.063337 
## [41] train-rmse:0.281360+0.016669    test-rmse:0.631083+0.062610 
## [42] train-rmse:0.276711+0.015478    test-rmse:0.631618+0.062193 
## [43] train-rmse:0.271214+0.016287    test-rmse:0.633795+0.062583 
## [44] train-rmse:0.265470+0.016895    test-rmse:0.636258+0.062775 
## Stopping. Best iteration:
## [38] train-rmse:0.296578+0.016323    test-rmse:0.631004+0.062420
## 
## 74 
## [1]  train-rmse:1.221384+0.011574    test-rmse:1.241892+0.074774 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.963560+0.007063    test-rmse:1.008906+0.078872 
## [3]  train-rmse:0.794513+0.005675    test-rmse:0.859339+0.075442 
## [4]  train-rmse:0.688333+0.007434    test-rmse:0.775365+0.068892 
## [5]  train-rmse:0.622346+0.009313    test-rmse:0.733780+0.066110 
## [6]  train-rmse:0.570984+0.007659    test-rmse:0.705610+0.058161 
## [7]  train-rmse:0.531032+0.005286    test-rmse:0.682980+0.053197 
## [8]  train-rmse:0.502840+0.010663    test-rmse:0.677197+0.050302 
## [9]  train-rmse:0.482398+0.007954    test-rmse:0.666801+0.051112 
## [10] train-rmse:0.468898+0.009314    test-rmse:0.666270+0.053249 
## [11] train-rmse:0.452821+0.014493    test-rmse:0.664455+0.059903 
## [12] train-rmse:0.439282+0.015452    test-rmse:0.658306+0.053139 
## [13] train-rmse:0.421581+0.012664    test-rmse:0.649481+0.052320 
## [14] train-rmse:0.406286+0.006916    test-rmse:0.641477+0.052669 
## [15] train-rmse:0.396612+0.006393    test-rmse:0.644449+0.054893 
## [16] train-rmse:0.378923+0.011767    test-rmse:0.640330+0.054327 
## [17] train-rmse:0.366524+0.009879    test-rmse:0.634292+0.050834 
## [18] train-rmse:0.358320+0.011426    test-rmse:0.634028+0.052998 
## [19] train-rmse:0.348317+0.013590    test-rmse:0.632228+0.052354 
## [20] train-rmse:0.340415+0.013737    test-rmse:0.633412+0.055206 
## [21] train-rmse:0.332551+0.015995    test-rmse:0.630939+0.054268 
## [22] train-rmse:0.323099+0.013728    test-rmse:0.631157+0.058136 
## [23] train-rmse:0.316332+0.012935    test-rmse:0.631769+0.058557 
## [24] train-rmse:0.309649+0.012658    test-rmse:0.631172+0.057945 
## [25] train-rmse:0.302539+0.013311    test-rmse:0.632223+0.057965 
## [26] train-rmse:0.292445+0.012822    test-rmse:0.634689+0.057611 
## [27] train-rmse:0.284758+0.014672    test-rmse:0.631452+0.054498 
## Stopping. Best iteration:
## [21] train-rmse:0.332551+0.015995    test-rmse:0.630939+0.054268
## 
## 75 
## [1]  train-rmse:1.216931+0.010852    test-rmse:1.244339+0.076818 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.953690+0.005933    test-rmse:1.007138+0.075607 
## [3]  train-rmse:0.778111+0.004295    test-rmse:0.861624+0.070449 
## [4]  train-rmse:0.662877+0.005609    test-rmse:0.777028+0.069318 
## [5]  train-rmse:0.591393+0.006690    test-rmse:0.726199+0.059994 
## [6]  train-rmse:0.534517+0.016639    test-rmse:0.701302+0.058290 
## [7]  train-rmse:0.492200+0.010443    test-rmse:0.684029+0.048531 
## [8]  train-rmse:0.462980+0.012731    test-rmse:0.668651+0.048849 
## [9]  train-rmse:0.436588+0.009159    test-rmse:0.663854+0.056053 
## [10] train-rmse:0.414958+0.014340    test-rmse:0.662054+0.054715 
## [11] train-rmse:0.398048+0.016915    test-rmse:0.654520+0.061773 
## [12] train-rmse:0.383777+0.016741    test-rmse:0.652457+0.062726 
## [13] train-rmse:0.364689+0.013750    test-rmse:0.642353+0.063541 
## [14] train-rmse:0.347154+0.013596    test-rmse:0.641076+0.062577 
## [15] train-rmse:0.338429+0.015005    test-rmse:0.640903+0.062860 
## [16] train-rmse:0.325977+0.012218    test-rmse:0.638406+0.061000 
## [17] train-rmse:0.315393+0.010030    test-rmse:0.638751+0.062068 
## [18] train-rmse:0.306262+0.010009    test-rmse:0.639420+0.061630 
## [19] train-rmse:0.295558+0.010436    test-rmse:0.640070+0.059926 
## [20] train-rmse:0.287800+0.011570    test-rmse:0.641616+0.061636 
## [21] train-rmse:0.278955+0.013193    test-rmse:0.644459+0.063259 
## [22] train-rmse:0.269993+0.014392    test-rmse:0.643968+0.064863 
## Stopping. Best iteration:
## [16] train-rmse:0.325977+0.012218    test-rmse:0.638406+0.061000
## 
## 76 
## [1]  train-rmse:1.213666+0.010250    test-rmse:1.242711+0.077061 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.945476+0.005893    test-rmse:1.000468+0.075469 
## [3]  train-rmse:0.762549+0.006224    test-rmse:0.858699+0.081059 
## [4]  train-rmse:0.638715+0.006704    test-rmse:0.771773+0.077423 
## [5]  train-rmse:0.552305+0.010093    test-rmse:0.723148+0.067201 
## [6]  train-rmse:0.494989+0.013107    test-rmse:0.688106+0.061032 
## [7]  train-rmse:0.446898+0.021899    test-rmse:0.671482+0.058891 
## [8]  train-rmse:0.415077+0.023276    test-rmse:0.663815+0.060483 
## [9]  train-rmse:0.396076+0.021812    test-rmse:0.661583+0.061493 
## [10] train-rmse:0.373044+0.018777    test-rmse:0.649961+0.059835 
## [11] train-rmse:0.354813+0.023100    test-rmse:0.651473+0.056669 
## [12] train-rmse:0.337981+0.026010    test-rmse:0.643391+0.053330 
## [13] train-rmse:0.323009+0.023790    test-rmse:0.639461+0.054091 
## [14] train-rmse:0.307083+0.017154    test-rmse:0.639173+0.053542 
## [15] train-rmse:0.295422+0.017765    test-rmse:0.635465+0.051521 
## [16] train-rmse:0.284005+0.017246    test-rmse:0.634122+0.051894 
## [17] train-rmse:0.272977+0.019482    test-rmse:0.633049+0.051579 
## [18] train-rmse:0.261221+0.016859    test-rmse:0.631334+0.046165 
## [19] train-rmse:0.247930+0.015683    test-rmse:0.626399+0.042990 
## [20] train-rmse:0.235347+0.016691    test-rmse:0.628175+0.044672 
## [21] train-rmse:0.226783+0.017287    test-rmse:0.628438+0.042420 
## [22] train-rmse:0.216136+0.015388    test-rmse:0.627359+0.041326 
## [23] train-rmse:0.205217+0.013734    test-rmse:0.626296+0.041307 
## [24] train-rmse:0.195474+0.015500    test-rmse:0.627381+0.042342 
## [25] train-rmse:0.189454+0.015653    test-rmse:0.627019+0.041998 
## [26] train-rmse:0.182360+0.017287    test-rmse:0.627739+0.043149 
## [27] train-rmse:0.174764+0.015167    test-rmse:0.626054+0.045582 
## [28] train-rmse:0.166379+0.014698    test-rmse:0.626067+0.046211 
## [29] train-rmse:0.158795+0.014243    test-rmse:0.625882+0.045006 
## [30] train-rmse:0.152496+0.012800    test-rmse:0.626046+0.045366 
## [31] train-rmse:0.148162+0.011825    test-rmse:0.625551+0.044742 
## [32] train-rmse:0.143160+0.011345    test-rmse:0.625712+0.045623 
## [33] train-rmse:0.137308+0.010098    test-rmse:0.626236+0.046387 
## [34] train-rmse:0.130737+0.009758    test-rmse:0.627631+0.046410 
## [35] train-rmse:0.125791+0.009659    test-rmse:0.628853+0.045808 
## [36] train-rmse:0.120201+0.009817    test-rmse:0.628896+0.046062 
## [37] train-rmse:0.115792+0.010942    test-rmse:0.629180+0.045656 
## Stopping. Best iteration:
## [31] train-rmse:0.148162+0.011825    test-rmse:0.625551+0.044742
## 
## 77 
## [1]  train-rmse:1.224911+0.011575    test-rmse:1.227681+0.071031 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.999257+0.008367    test-rmse:1.005085+0.066272 
## [3]  train-rmse:0.872193+0.007607    test-rmse:0.888733+0.060056 
## [4]  train-rmse:0.801572+0.006780    test-rmse:0.820629+0.050630 
## [5]  train-rmse:0.762759+0.006048    test-rmse:0.791248+0.044080 
## [6]  train-rmse:0.740432+0.005397    test-rmse:0.775399+0.039885 
## [7]  train-rmse:0.726328+0.004866    test-rmse:0.761034+0.038489 
## [8]  train-rmse:0.716014+0.004639    test-rmse:0.750998+0.032435 
## [9]  train-rmse:0.708146+0.004488    test-rmse:0.744082+0.030282 
## [10] train-rmse:0.701875+0.004599    test-rmse:0.742240+0.028122 
## [11] train-rmse:0.696437+0.004526    test-rmse:0.742029+0.027985 
## [12] train-rmse:0.691661+0.004684    test-rmse:0.740261+0.028292 
## [13] train-rmse:0.687204+0.004883    test-rmse:0.737328+0.027629 
## [14] train-rmse:0.683174+0.004974    test-rmse:0.734985+0.025703 
## [15] train-rmse:0.679415+0.004990    test-rmse:0.736171+0.029206 
## [16] train-rmse:0.675890+0.004980    test-rmse:0.734417+0.029303 
## [17] train-rmse:0.672460+0.005024    test-rmse:0.730684+0.030715 
## [18] train-rmse:0.669186+0.005021    test-rmse:0.728536+0.030618 
## [19] train-rmse:0.666125+0.005000    test-rmse:0.725984+0.029613 
## [20] train-rmse:0.663053+0.004987    test-rmse:0.725254+0.031280 
## [21] train-rmse:0.660263+0.005032    test-rmse:0.726148+0.031793 
## [22] train-rmse:0.657454+0.005105    test-rmse:0.724630+0.033261 
## [23] train-rmse:0.654852+0.005264    test-rmse:0.722726+0.032287 
## [24] train-rmse:0.652347+0.005306    test-rmse:0.720755+0.031269 
## [25] train-rmse:0.650015+0.005409    test-rmse:0.722470+0.031125 
## [26] train-rmse:0.647763+0.005509    test-rmse:0.722376+0.032808 
## [27] train-rmse:0.645600+0.005482    test-rmse:0.720632+0.032072 
## [28] train-rmse:0.643525+0.005551    test-rmse:0.720201+0.033913 
## [29] train-rmse:0.641525+0.005600    test-rmse:0.718356+0.033780 
## [30] train-rmse:0.639606+0.005577    test-rmse:0.716729+0.035016 
## [31] train-rmse:0.637702+0.005569    test-rmse:0.715753+0.033454 
## [32] train-rmse:0.635886+0.005575    test-rmse:0.713530+0.033472 
## [33] train-rmse:0.634181+0.005608    test-rmse:0.712419+0.032564 
## [34] train-rmse:0.632494+0.005671    test-rmse:0.713521+0.034355 
## [35] train-rmse:0.630809+0.005727    test-rmse:0.713107+0.033533 
## [36] train-rmse:0.629120+0.005715    test-rmse:0.712981+0.034682 
## [37] train-rmse:0.627485+0.005678    test-rmse:0.713429+0.033756 
## [38] train-rmse:0.625799+0.005764    test-rmse:0.709984+0.034270 
## [39] train-rmse:0.624149+0.005825    test-rmse:0.708371+0.032875 
## [40] train-rmse:0.622548+0.005865    test-rmse:0.707073+0.033858 
## [41] train-rmse:0.621024+0.005918    test-rmse:0.707909+0.033555 
## [42] train-rmse:0.619523+0.005970    test-rmse:0.709801+0.033241 
## [43] train-rmse:0.618118+0.005994    test-rmse:0.708361+0.033920 
## [44] train-rmse:0.616758+0.006018    test-rmse:0.707616+0.034411 
## [45] train-rmse:0.615461+0.006027    test-rmse:0.707142+0.034724 
## [46] train-rmse:0.614158+0.006029    test-rmse:0.705915+0.036829 
## [47] train-rmse:0.612894+0.006056    test-rmse:0.704843+0.034694 
## [48] train-rmse:0.611682+0.006079    test-rmse:0.703724+0.034610 
## [49] train-rmse:0.610461+0.006075    test-rmse:0.702732+0.033411 
## [50] train-rmse:0.609223+0.006051    test-rmse:0.702368+0.033304 
## [51] train-rmse:0.608075+0.006064    test-rmse:0.700877+0.032807 
## [52] train-rmse:0.606954+0.006073    test-rmse:0.698938+0.034601 
## [53] train-rmse:0.605838+0.006101    test-rmse:0.699437+0.034634 
## [54] train-rmse:0.604745+0.006107    test-rmse:0.699400+0.033925 
## [55] train-rmse:0.603683+0.006177    test-rmse:0.698649+0.033163 
## [56] train-rmse:0.602660+0.006211    test-rmse:0.698295+0.033585 
## [57] train-rmse:0.601616+0.006246    test-rmse:0.696639+0.034264 
## [58] train-rmse:0.600569+0.006304    test-rmse:0.695508+0.033384 
## [59] train-rmse:0.599539+0.006327    test-rmse:0.696059+0.033893 
## [60] train-rmse:0.598554+0.006332    test-rmse:0.696057+0.034415 
## [61] train-rmse:0.597598+0.006334    test-rmse:0.696207+0.033232 
## [62] train-rmse:0.596605+0.006304    test-rmse:0.696205+0.035326 
## [63] train-rmse:0.595657+0.006312    test-rmse:0.696468+0.035345 
## [64] train-rmse:0.594712+0.006355    test-rmse:0.695775+0.036187 
## Stopping. Best iteration:
## [58] train-rmse:0.600569+0.006304    test-rmse:0.695508+0.033384
## 
## 78 
## [1]  train-rmse:1.215203+0.011360    test-rmse:1.224742+0.071347 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.979037+0.007506    test-rmse:1.001241+0.069918 
## [3]  train-rmse:0.842539+0.005036    test-rmse:0.874872+0.063138 
## [4]  train-rmse:0.760371+0.003855    test-rmse:0.804098+0.057562 
## [5]  train-rmse:0.714742+0.003188    test-rmse:0.766875+0.051847 
## [6]  train-rmse:0.687507+0.002904    test-rmse:0.749213+0.044523 
## [7]  train-rmse:0.666865+0.004430    test-rmse:0.731598+0.045792 
## [8]  train-rmse:0.653729+0.005352    test-rmse:0.720839+0.043849 
## [9]  train-rmse:0.643137+0.006817    test-rmse:0.714897+0.043540 
## [10] train-rmse:0.632528+0.004667    test-rmse:0.714696+0.044384 
## [11] train-rmse:0.623583+0.005077    test-rmse:0.708548+0.044601 
## [12] train-rmse:0.615737+0.004360    test-rmse:0.706495+0.044210 
## [13] train-rmse:0.609764+0.004501    test-rmse:0.702873+0.045954 
## [14] train-rmse:0.603009+0.005710    test-rmse:0.702588+0.044407 
## [15] train-rmse:0.597320+0.004476    test-rmse:0.702994+0.047320 
## [16] train-rmse:0.590057+0.004307    test-rmse:0.701045+0.044181 
## [17] train-rmse:0.584453+0.005389    test-rmse:0.696592+0.042675 
## [18] train-rmse:0.579627+0.004870    test-rmse:0.693441+0.043110 
## [19] train-rmse:0.573099+0.007580    test-rmse:0.687560+0.040917 
## [20] train-rmse:0.567861+0.006533    test-rmse:0.684118+0.041236 
## [21] train-rmse:0.560679+0.005482    test-rmse:0.680897+0.035593 
## [22] train-rmse:0.556726+0.005968    test-rmse:0.679565+0.037171 
## [23] train-rmse:0.552306+0.005166    test-rmse:0.676190+0.038215 
## [24] train-rmse:0.548632+0.005214    test-rmse:0.676596+0.038319 
## [25] train-rmse:0.544951+0.005318    test-rmse:0.676740+0.038015 
## [26] train-rmse:0.541005+0.005489    test-rmse:0.676410+0.041827 
## [27] train-rmse:0.537801+0.005871    test-rmse:0.675964+0.042938 
## [28] train-rmse:0.534650+0.006621    test-rmse:0.674595+0.041317 
## [29] train-rmse:0.529778+0.008363    test-rmse:0.671121+0.039140 
## [30] train-rmse:0.527440+0.008216    test-rmse:0.668426+0.039675 
## [31] train-rmse:0.524959+0.008114    test-rmse:0.667888+0.040037 
## [32] train-rmse:0.521776+0.008049    test-rmse:0.667401+0.041763 
## [33] train-rmse:0.518485+0.008450    test-rmse:0.666524+0.042878 
## [34] train-rmse:0.514430+0.009274    test-rmse:0.665133+0.040903 
## [35] train-rmse:0.510972+0.007546    test-rmse:0.664747+0.041160 
## [36] train-rmse:0.506107+0.007790    test-rmse:0.664255+0.040303 
## [37] train-rmse:0.503138+0.008369    test-rmse:0.662754+0.040361 
## [38] train-rmse:0.500704+0.008130    test-rmse:0.661187+0.038765 
## [39] train-rmse:0.498068+0.007986    test-rmse:0.661143+0.039609 
## [40] train-rmse:0.495094+0.007811    test-rmse:0.659260+0.040827 
## [41] train-rmse:0.492828+0.008135    test-rmse:0.660341+0.042115 
## [42] train-rmse:0.488827+0.004670    test-rmse:0.658179+0.044748 
## [43] train-rmse:0.486929+0.004660    test-rmse:0.656973+0.044337 
## [44] train-rmse:0.483813+0.005189    test-rmse:0.655774+0.044033 
## [45] train-rmse:0.481088+0.005421    test-rmse:0.654119+0.046074 
## [46] train-rmse:0.478824+0.005536    test-rmse:0.652858+0.045234 
## [47] train-rmse:0.474862+0.003672    test-rmse:0.649097+0.042005 
## [48] train-rmse:0.472154+0.003118    test-rmse:0.649306+0.041460 
## [49] train-rmse:0.469976+0.003147    test-rmse:0.649686+0.042343 
## [50] train-rmse:0.467251+0.003975    test-rmse:0.649216+0.042634 
## [51] train-rmse:0.464800+0.003783    test-rmse:0.647135+0.043501 
## [52] train-rmse:0.461757+0.005235    test-rmse:0.645820+0.042336 
## [53] train-rmse:0.459506+0.006077    test-rmse:0.645028+0.041153 
## [54] train-rmse:0.454990+0.009886    test-rmse:0.644172+0.042505 
## [55] train-rmse:0.451328+0.009142    test-rmse:0.643349+0.042096 
## [56] train-rmse:0.447709+0.008134    test-rmse:0.642159+0.042961 
## [57] train-rmse:0.445406+0.008197    test-rmse:0.640182+0.043143 
## [58] train-rmse:0.443829+0.008189    test-rmse:0.639615+0.043421 
## [59] train-rmse:0.441630+0.008646    test-rmse:0.640041+0.042969 
## [60] train-rmse:0.440146+0.008887    test-rmse:0.640718+0.043588 
## [61] train-rmse:0.439088+0.009122    test-rmse:0.640008+0.042760 
## [62] train-rmse:0.436423+0.009165    test-rmse:0.639894+0.042359 
## [63] train-rmse:0.434833+0.008814    test-rmse:0.639342+0.043162 
## [64] train-rmse:0.433077+0.009478    test-rmse:0.638030+0.042817 
## [65] train-rmse:0.431290+0.009050    test-rmse:0.637566+0.045202 
## [66] train-rmse:0.429908+0.008952    test-rmse:0.637252+0.045276 
## [67] train-rmse:0.427627+0.007919    test-rmse:0.638848+0.045771 
## [68] train-rmse:0.425619+0.008887    test-rmse:0.638299+0.045942 
## [69] train-rmse:0.422489+0.011430    test-rmse:0.638588+0.045896 
## [70] train-rmse:0.420245+0.011744    test-rmse:0.638201+0.045422 
## [71] train-rmse:0.418905+0.012072    test-rmse:0.637910+0.045341 
## [72] train-rmse:0.417781+0.011836    test-rmse:0.637170+0.045655 
## [73] train-rmse:0.414932+0.011840    test-rmse:0.637559+0.046246 
## [74] train-rmse:0.412262+0.011160    test-rmse:0.635745+0.047093 
## [75] train-rmse:0.410182+0.011218    test-rmse:0.634680+0.046370 
## [76] train-rmse:0.408194+0.011050    test-rmse:0.635160+0.046440 
## [77] train-rmse:0.406233+0.010552    test-rmse:0.633765+0.047315 
## [78] train-rmse:0.402895+0.011501    test-rmse:0.633787+0.045665 
## [79] train-rmse:0.401766+0.012086    test-rmse:0.633619+0.045566 
## [80] train-rmse:0.398793+0.012762    test-rmse:0.633256+0.044620 
## [81] train-rmse:0.396366+0.012240    test-rmse:0.630999+0.044012 
## [82] train-rmse:0.394036+0.012717    test-rmse:0.632171+0.045287 
## [83] train-rmse:0.393100+0.012892    test-rmse:0.632423+0.046142 
## [84] train-rmse:0.391474+0.013385    test-rmse:0.632352+0.046547 
## [85] train-rmse:0.389390+0.012372    test-rmse:0.632170+0.047948 
## [86] train-rmse:0.387264+0.012315    test-rmse:0.632039+0.048360 
## [87] train-rmse:0.384559+0.013920    test-rmse:0.631371+0.049215 
## Stopping. Best iteration:
## [81] train-rmse:0.396366+0.012240    test-rmse:0.630999+0.044012
## 
## 79 
## [1]  train-rmse:1.208547+0.010806    test-rmse:1.221327+0.069510 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.961455+0.007329    test-rmse:0.991333+0.067181 
## [3]  train-rmse:0.816597+0.005870    test-rmse:0.859455+0.066370 
## [4]  train-rmse:0.732017+0.007259    test-rmse:0.785254+0.062107 
## [5]  train-rmse:0.673225+0.011056    test-rmse:0.744053+0.050712 
## [6]  train-rmse:0.637818+0.010147    test-rmse:0.720904+0.040441 
## [7]  train-rmse:0.617135+0.010229    test-rmse:0.716945+0.039095 
## [8]  train-rmse:0.600265+0.008837    test-rmse:0.705971+0.038790 
## [9]  train-rmse:0.585236+0.013115    test-rmse:0.695074+0.037533 
## [10] train-rmse:0.574559+0.014568    test-rmse:0.689172+0.035230 
## [11] train-rmse:0.563738+0.014160    test-rmse:0.687641+0.037934 
## [12] train-rmse:0.552607+0.012886    test-rmse:0.687940+0.040070 
## [13] train-rmse:0.545569+0.011957    test-rmse:0.685973+0.043408 
## [14] train-rmse:0.535803+0.010255    test-rmse:0.682547+0.046407 
## [15] train-rmse:0.525946+0.011593    test-rmse:0.679124+0.052481 
## [16] train-rmse:0.515750+0.013448    test-rmse:0.672273+0.055288 
## [17] train-rmse:0.505009+0.014607    test-rmse:0.661535+0.049249 
## [18] train-rmse:0.498966+0.014026    test-rmse:0.662144+0.049329 
## [19] train-rmse:0.493164+0.014156    test-rmse:0.659736+0.047839 
## [20] train-rmse:0.483788+0.010967    test-rmse:0.651952+0.051187 
## [21] train-rmse:0.476986+0.009741    test-rmse:0.649574+0.050674 
## [22] train-rmse:0.469260+0.014092    test-rmse:0.649428+0.049311 
## [23] train-rmse:0.462818+0.013459    test-rmse:0.647865+0.047906 
## [24] train-rmse:0.456020+0.015156    test-rmse:0.646885+0.046972 
## [25] train-rmse:0.449217+0.016438    test-rmse:0.645341+0.049004 
## [26] train-rmse:0.444001+0.016816    test-rmse:0.642763+0.050894 
## [27] train-rmse:0.437798+0.018494    test-rmse:0.640413+0.053071 
## [28] train-rmse:0.431180+0.015421    test-rmse:0.635449+0.050360 
## [29] train-rmse:0.424159+0.013709    test-rmse:0.635423+0.049364 
## [30] train-rmse:0.417916+0.012251    test-rmse:0.633899+0.049068 
## [31] train-rmse:0.413331+0.012390    test-rmse:0.635589+0.050232 
## [32] train-rmse:0.408650+0.012348    test-rmse:0.631068+0.047844 
## [33] train-rmse:0.405503+0.012605    test-rmse:0.631210+0.049956 
## [34] train-rmse:0.399579+0.014562    test-rmse:0.630575+0.048160 
## [35] train-rmse:0.394918+0.014315    test-rmse:0.631416+0.048266 
## [36] train-rmse:0.390154+0.016749    test-rmse:0.628718+0.044162 
## [37] train-rmse:0.386066+0.017925    test-rmse:0.627107+0.044406 
## [38] train-rmse:0.380592+0.017955    test-rmse:0.626009+0.044276 
## [39] train-rmse:0.375134+0.015224    test-rmse:0.624281+0.045021 
## [40] train-rmse:0.372136+0.014782    test-rmse:0.624234+0.045377 
## [41] train-rmse:0.369148+0.014914    test-rmse:0.625338+0.044820 
## [42] train-rmse:0.366204+0.014831    test-rmse:0.625419+0.045092 
## [43] train-rmse:0.363420+0.015518    test-rmse:0.622442+0.042821 
## [44] train-rmse:0.358691+0.014727    test-rmse:0.621497+0.042469 
## [45] train-rmse:0.355001+0.015157    test-rmse:0.623887+0.044872 
## [46] train-rmse:0.352186+0.016060    test-rmse:0.624482+0.045975 
## [47] train-rmse:0.349567+0.016281    test-rmse:0.624210+0.045836 
## [48] train-rmse:0.346330+0.015140    test-rmse:0.624630+0.046193 
## [49] train-rmse:0.341284+0.013271    test-rmse:0.624946+0.045249 
## [50] train-rmse:0.338711+0.013171    test-rmse:0.627457+0.045481 
## Stopping. Best iteration:
## [44] train-rmse:0.358691+0.014727    test-rmse:0.621497+0.042469
## 
## 80 
## [1]  train-rmse:1.201642+0.010942    test-rmse:1.225484+0.071119 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.944295+0.006957    test-rmse:0.988934+0.074505 
## [3]  train-rmse:0.789173+0.004804    test-rmse:0.854556+0.064366 
## [4]  train-rmse:0.696583+0.004473    test-rmse:0.775889+0.066369 
## [5]  train-rmse:0.629049+0.013666    test-rmse:0.727839+0.061540 
## [6]  train-rmse:0.590773+0.011998    test-rmse:0.706825+0.061919 
## [7]  train-rmse:0.563625+0.010544    test-rmse:0.687562+0.056172 
## [8]  train-rmse:0.543821+0.008952    test-rmse:0.678301+0.057225 
## [9]  train-rmse:0.527582+0.012682    test-rmse:0.677774+0.058416 
## [10] train-rmse:0.512850+0.009881    test-rmse:0.674542+0.056931 
## [11] train-rmse:0.498805+0.013280    test-rmse:0.668585+0.056323 
## [12] train-rmse:0.483120+0.016473    test-rmse:0.661843+0.056725 
## [13] train-rmse:0.471654+0.015751    test-rmse:0.654919+0.053601 
## [14] train-rmse:0.457973+0.016699    test-rmse:0.647934+0.049824 
## [15] train-rmse:0.449176+0.015502    test-rmse:0.651751+0.051952 
## [16] train-rmse:0.439440+0.016566    test-rmse:0.651180+0.049040 
## [17] train-rmse:0.431842+0.016845    test-rmse:0.648671+0.050512 
## [18] train-rmse:0.421317+0.017730    test-rmse:0.640538+0.046473 
## [19] train-rmse:0.414012+0.018205    test-rmse:0.641398+0.046188 
## [20] train-rmse:0.400539+0.016713    test-rmse:0.641122+0.049630 
## [21] train-rmse:0.390942+0.016037    test-rmse:0.638605+0.049047 
## [22] train-rmse:0.383297+0.015918    test-rmse:0.636050+0.047668 
## [23] train-rmse:0.372935+0.013794    test-rmse:0.634423+0.051045 
## [24] train-rmse:0.366979+0.012851    test-rmse:0.632729+0.049314 
## [25] train-rmse:0.360979+0.013274    test-rmse:0.632310+0.048487 
## [26] train-rmse:0.356747+0.013876    test-rmse:0.632661+0.048734 
## [27] train-rmse:0.349334+0.014214    test-rmse:0.629456+0.048328 
## [28] train-rmse:0.342272+0.013653    test-rmse:0.627561+0.050270 
## [29] train-rmse:0.334494+0.015239    test-rmse:0.629312+0.049784 
## [30] train-rmse:0.326183+0.014508    test-rmse:0.629186+0.049868 
## [31] train-rmse:0.321572+0.014501    test-rmse:0.631230+0.051275 
## [32] train-rmse:0.315204+0.014046    test-rmse:0.631867+0.053047 
## [33] train-rmse:0.308892+0.013319    test-rmse:0.633726+0.052734 
## [34] train-rmse:0.303474+0.014566    test-rmse:0.633340+0.052908 
## Stopping. Best iteration:
## [28] train-rmse:0.342272+0.013653    test-rmse:0.627561+0.050270
## 
## 81 
## [1]  train-rmse:1.196912+0.011224    test-rmse:1.219242+0.075301 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.931711+0.006585    test-rmse:0.978321+0.077461 
## [3]  train-rmse:0.765170+0.004949    test-rmse:0.834745+0.075001 
## [4]  train-rmse:0.665482+0.005393    test-rmse:0.758767+0.069172 
## [5]  train-rmse:0.604071+0.008493    test-rmse:0.716749+0.063887 
## [6]  train-rmse:0.554395+0.007420    test-rmse:0.691515+0.056297 
## [7]  train-rmse:0.518259+0.004857    test-rmse:0.670767+0.053453 
## [8]  train-rmse:0.492660+0.010677    test-rmse:0.661940+0.049007 
## [9]  train-rmse:0.472698+0.011062    test-rmse:0.656513+0.053052 
## [10] train-rmse:0.456896+0.013632    test-rmse:0.655319+0.051439 
## [11] train-rmse:0.443219+0.014622    test-rmse:0.657574+0.055707 
## [12] train-rmse:0.428151+0.011511    test-rmse:0.650540+0.054396 
## [13] train-rmse:0.410237+0.013794    test-rmse:0.641570+0.056302 
## [14] train-rmse:0.394427+0.013312    test-rmse:0.646061+0.057077 
## [15] train-rmse:0.381540+0.011427    test-rmse:0.642830+0.058876 
## [16] train-rmse:0.371536+0.010540    test-rmse:0.642346+0.062670 
## [17] train-rmse:0.361437+0.011318    test-rmse:0.640248+0.059589 
## [18] train-rmse:0.352592+0.013068    test-rmse:0.638226+0.060552 
## [19] train-rmse:0.344361+0.014501    test-rmse:0.637639+0.062367 
## [20] train-rmse:0.333354+0.011734    test-rmse:0.637329+0.064342 
## [21] train-rmse:0.324178+0.010868    test-rmse:0.633968+0.065491 
## [22] train-rmse:0.315329+0.011910    test-rmse:0.631778+0.067766 
## [23] train-rmse:0.305001+0.012002    test-rmse:0.630537+0.062923 
## [24] train-rmse:0.295487+0.012364    test-rmse:0.635065+0.061479 
## [25] train-rmse:0.288543+0.012304    test-rmse:0.633969+0.061004 
## [26] train-rmse:0.277854+0.012069    test-rmse:0.632067+0.061005 
## [27] train-rmse:0.272463+0.012624    test-rmse:0.632477+0.060489 
## [28] train-rmse:0.266303+0.012941    test-rmse:0.636473+0.060381 
## [29] train-rmse:0.262228+0.013752    test-rmse:0.635050+0.059949 
## Stopping. Best iteration:
## [23] train-rmse:0.305001+0.012002    test-rmse:0.630537+0.062923
## 
## 82 
## [1]  train-rmse:1.192084+0.010443    test-rmse:1.221855+0.077398 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.921271+0.004606    test-rmse:0.976819+0.078668 
## [3]  train-rmse:0.745324+0.006481    test-rmse:0.828613+0.074070 
## [4]  train-rmse:0.636278+0.005944    test-rmse:0.743867+0.065121 
## [5]  train-rmse:0.563163+0.012661    test-rmse:0.704597+0.066435 
## [6]  train-rmse:0.514961+0.008555    test-rmse:0.683807+0.060195 
## [7]  train-rmse:0.475061+0.013880    test-rmse:0.667141+0.054949 
## [8]  train-rmse:0.448053+0.014817    test-rmse:0.656833+0.052916 
## [9]  train-rmse:0.428029+0.013643    test-rmse:0.655392+0.054451 
## [10] train-rmse:0.409720+0.016760    test-rmse:0.648807+0.056295 
## [11] train-rmse:0.396751+0.018275    test-rmse:0.649153+0.056024 
## [12] train-rmse:0.378855+0.016646    test-rmse:0.641005+0.053026 
## [13] train-rmse:0.367356+0.014945    test-rmse:0.638040+0.052169 
## [14] train-rmse:0.354400+0.020897    test-rmse:0.635087+0.052633 
## [15] train-rmse:0.345891+0.021535    test-rmse:0.637922+0.054469 
## [16] train-rmse:0.326453+0.014340    test-rmse:0.631768+0.053521 
## [17] train-rmse:0.316862+0.015932    test-rmse:0.631500+0.057397 
## [18] train-rmse:0.301826+0.013082    test-rmse:0.629663+0.056118 
## [19] train-rmse:0.293836+0.013731    test-rmse:0.628038+0.052160 
## [20] train-rmse:0.284868+0.013319    test-rmse:0.630469+0.052210 
## [21] train-rmse:0.272086+0.011846    test-rmse:0.627654+0.050879 
## [22] train-rmse:0.256893+0.007396    test-rmse:0.624728+0.048389 
## [23] train-rmse:0.249274+0.008096    test-rmse:0.624907+0.046594 
## [24] train-rmse:0.242059+0.007883    test-rmse:0.621964+0.045033 
## [25] train-rmse:0.235913+0.007351    test-rmse:0.622387+0.045595 
## [26] train-rmse:0.225161+0.008746    test-rmse:0.622991+0.048057 
## [27] train-rmse:0.218384+0.008424    test-rmse:0.623126+0.048203 
## [28] train-rmse:0.212353+0.008042    test-rmse:0.623395+0.045801 
## [29] train-rmse:0.205606+0.010164    test-rmse:0.623278+0.046681 
## [30] train-rmse:0.197720+0.007984    test-rmse:0.621613+0.046781 
## [31] train-rmse:0.189026+0.007137    test-rmse:0.623085+0.046507 
## [32] train-rmse:0.182856+0.006004    test-rmse:0.623282+0.047546 
## [33] train-rmse:0.176624+0.006990    test-rmse:0.625107+0.046580 
## [34] train-rmse:0.170909+0.006160    test-rmse:0.628123+0.046974 
## [35] train-rmse:0.164542+0.005858    test-rmse:0.629927+0.046372 
## [36] train-rmse:0.158718+0.006866    test-rmse:0.632297+0.046747 
## Stopping. Best iteration:
## [30] train-rmse:0.197720+0.007984    test-rmse:0.621613+0.046781
## 
## 83 
## [1]  train-rmse:1.188546+0.009797    test-rmse:1.220116+0.077660 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.911726+0.005586    test-rmse:0.971695+0.075686 
## [3]  train-rmse:0.729763+0.006303    test-rmse:0.835916+0.081433 
## [4]  train-rmse:0.610206+0.007351    test-rmse:0.750202+0.076030 
## [5]  train-rmse:0.539705+0.010728    test-rmse:0.708628+0.074859 
## [6]  train-rmse:0.490652+0.009634    test-rmse:0.688398+0.071730 
## [7]  train-rmse:0.439815+0.012295    test-rmse:0.665511+0.058797 
## [8]  train-rmse:0.407406+0.015117    test-rmse:0.656910+0.059521 
## [9]  train-rmse:0.379619+0.007462    test-rmse:0.650206+0.058292 
## [10] train-rmse:0.360736+0.010625    test-rmse:0.645516+0.058349 
## [11] train-rmse:0.345279+0.011395    test-rmse:0.643584+0.056875 
## [12] train-rmse:0.327153+0.009638    test-rmse:0.644045+0.058901 
## [13] train-rmse:0.315923+0.010787    test-rmse:0.642929+0.058435 
## [14] train-rmse:0.301537+0.014125    test-rmse:0.635651+0.058555 
## [15] train-rmse:0.287191+0.012526    test-rmse:0.631322+0.058440 
## [16] train-rmse:0.277931+0.008391    test-rmse:0.629971+0.058307 
## [17] train-rmse:0.264501+0.011482    test-rmse:0.632173+0.057382 
## [18] train-rmse:0.250262+0.010412    test-rmse:0.631159+0.059893 
## [19] train-rmse:0.240232+0.013070    test-rmse:0.631546+0.060569 
## [20] train-rmse:0.229336+0.015507    test-rmse:0.632814+0.060357 
## [21] train-rmse:0.217075+0.013198    test-rmse:0.630399+0.062435 
## [22] train-rmse:0.209454+0.012820    test-rmse:0.630515+0.065374 
## Stopping. Best iteration:
## [16] train-rmse:0.277931+0.008391    test-rmse:0.629971+0.058307
## 
## 84 
## [1]  train-rmse:1.202750+0.011254    test-rmse:1.205826+0.071093 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.974587+0.008035    test-rmse:0.981001+0.065426 
## [3]  train-rmse:0.851867+0.007455    test-rmse:0.869654+0.058540 
## [4]  train-rmse:0.787618+0.006743    test-rmse:0.807863+0.048492 
## [5]  train-rmse:0.753090+0.005933    test-rmse:0.782915+0.042199 
## [6]  train-rmse:0.733409+0.005311    test-rmse:0.769899+0.038279 
## [7]  train-rmse:0.720607+0.004804    test-rmse:0.756835+0.037349 
## [8]  train-rmse:0.711282+0.004384    test-rmse:0.748537+0.032320 
## [9]  train-rmse:0.703934+0.004521    test-rmse:0.743386+0.027278 
## [10] train-rmse:0.697935+0.004579    test-rmse:0.742265+0.026408 
## [11] train-rmse:0.692802+0.004578    test-rmse:0.740595+0.027079 
## [12] train-rmse:0.688210+0.004659    test-rmse:0.737922+0.027100 
## [13] train-rmse:0.683698+0.004834    test-rmse:0.735920+0.027790 
## [14] train-rmse:0.679564+0.004949    test-rmse:0.734904+0.027198 
## [15] train-rmse:0.675744+0.004902    test-rmse:0.736167+0.027565 
## [16] train-rmse:0.672209+0.004935    test-rmse:0.732778+0.029322 
## [17] train-rmse:0.668768+0.004946    test-rmse:0.729501+0.031323 
## [18] train-rmse:0.665440+0.004932    test-rmse:0.727348+0.031288 
## [19] train-rmse:0.662245+0.004896    test-rmse:0.726277+0.032629 
## [20] train-rmse:0.659183+0.004787    test-rmse:0.723568+0.031753 
## [21] train-rmse:0.656360+0.004846    test-rmse:0.722475+0.030292 
## [22] train-rmse:0.653623+0.005040    test-rmse:0.723369+0.032574 
## [23] train-rmse:0.650991+0.005319    test-rmse:0.724158+0.032305 
## [24] train-rmse:0.648487+0.005453    test-rmse:0.722790+0.032724 
## [25] train-rmse:0.646133+0.005540    test-rmse:0.723771+0.032737 
## [26] train-rmse:0.643899+0.005600    test-rmse:0.720973+0.031014 
## [27] train-rmse:0.641798+0.005594    test-rmse:0.719058+0.031663 
## [28] train-rmse:0.639755+0.005580    test-rmse:0.717636+0.031542 
## [29] train-rmse:0.637805+0.005564    test-rmse:0.717693+0.032232 
## [30] train-rmse:0.635857+0.005530    test-rmse:0.715667+0.031410 
## [31] train-rmse:0.633930+0.005546    test-rmse:0.714396+0.033202 
## [32] train-rmse:0.632079+0.005657    test-rmse:0.711490+0.034395 
## [33] train-rmse:0.630293+0.005712    test-rmse:0.709657+0.032838 
## [34] train-rmse:0.628501+0.005667    test-rmse:0.709293+0.033273 
## [35] train-rmse:0.626756+0.005658    test-rmse:0.708668+0.033930 
## [36] train-rmse:0.625052+0.005667    test-rmse:0.706253+0.032916 
## [37] train-rmse:0.623364+0.005734    test-rmse:0.706589+0.033557 
## [38] train-rmse:0.621746+0.005820    test-rmse:0.705800+0.034434 
## [39] train-rmse:0.620170+0.005917    test-rmse:0.708540+0.034514 
## [40] train-rmse:0.618614+0.006010    test-rmse:0.707725+0.034486 
## [41] train-rmse:0.617058+0.005995    test-rmse:0.707657+0.033405 
## [42] train-rmse:0.615638+0.006047    test-rmse:0.708283+0.033224 
## [43] train-rmse:0.614230+0.006068    test-rmse:0.706169+0.035239 
## [44] train-rmse:0.612826+0.006119    test-rmse:0.705010+0.035386 
## [45] train-rmse:0.611468+0.006122    test-rmse:0.704672+0.036477 
## [46] train-rmse:0.610166+0.006137    test-rmse:0.703787+0.035426 
## [47] train-rmse:0.608851+0.006152    test-rmse:0.703817+0.034477 
## [48] train-rmse:0.607589+0.006151    test-rmse:0.702408+0.033641 
## [49] train-rmse:0.606352+0.006153    test-rmse:0.699882+0.033782 
## [50] train-rmse:0.605163+0.006194    test-rmse:0.700003+0.032993 
## [51] train-rmse:0.603984+0.006213    test-rmse:0.697004+0.034223 
## [52] train-rmse:0.602858+0.006273    test-rmse:0.697194+0.034893 
## [53] train-rmse:0.601724+0.006256    test-rmse:0.696230+0.035766 
## [54] train-rmse:0.600627+0.006323    test-rmse:0.696228+0.036302 
## [55] train-rmse:0.599537+0.006342    test-rmse:0.694556+0.036124 
## [56] train-rmse:0.598511+0.006346    test-rmse:0.694381+0.035028 
## [57] train-rmse:0.597514+0.006389    test-rmse:0.694763+0.034697 
## [58] train-rmse:0.596509+0.006397    test-rmse:0.694324+0.035575 
## [59] train-rmse:0.595508+0.006425    test-rmse:0.692730+0.034994 
## [60] train-rmse:0.594506+0.006455    test-rmse:0.692320+0.034102 
## [61] train-rmse:0.593530+0.006508    test-rmse:0.692694+0.034238 
## [62] train-rmse:0.592605+0.006530    test-rmse:0.692470+0.034229 
## [63] train-rmse:0.591683+0.006543    test-rmse:0.693817+0.035154 
## [64] train-rmse:0.590785+0.006576    test-rmse:0.694076+0.035611 
## [65] train-rmse:0.589882+0.006615    test-rmse:0.693417+0.035956 
## [66] train-rmse:0.589003+0.006657    test-rmse:0.691163+0.035848 
## [67] train-rmse:0.588119+0.006703    test-rmse:0.690574+0.035707 
## [68] train-rmse:0.587275+0.006726    test-rmse:0.690923+0.036023 
## [69] train-rmse:0.586446+0.006738    test-rmse:0.689611+0.036405 
## [70] train-rmse:0.585621+0.006750    test-rmse:0.689525+0.035556 
## [71] train-rmse:0.584791+0.006756    test-rmse:0.688923+0.035069 
## [72] train-rmse:0.584017+0.006778    test-rmse:0.688588+0.033544 
## [73] train-rmse:0.583206+0.006773    test-rmse:0.689133+0.034356 
## [74] train-rmse:0.582405+0.006781    test-rmse:0.688098+0.034608 
## [75] train-rmse:0.581627+0.006780    test-rmse:0.687110+0.034578 
## [76] train-rmse:0.580844+0.006806    test-rmse:0.686508+0.035271 
## [77] train-rmse:0.580075+0.006843    test-rmse:0.685534+0.035804 
## [78] train-rmse:0.579323+0.006860    test-rmse:0.685477+0.036296 
## [79] train-rmse:0.578602+0.006884    test-rmse:0.684675+0.036327 
## [80] train-rmse:0.577871+0.006925    test-rmse:0.685669+0.036036 
## [81] train-rmse:0.577152+0.006918    test-rmse:0.685454+0.036127 
## [82] train-rmse:0.576459+0.006918    test-rmse:0.685758+0.035396 
## [83] train-rmse:0.575758+0.006961    test-rmse:0.684794+0.036731 
## [84] train-rmse:0.575095+0.007006    test-rmse:0.685164+0.036417 
## [85] train-rmse:0.574436+0.007023    test-rmse:0.685765+0.037530 
## Stopping. Best iteration:
## [79] train-rmse:0.578602+0.006884    test-rmse:0.684675+0.036327
## 
## 85 
## [1]  train-rmse:1.192331+0.011018    test-rmse:1.202697+0.071420 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.952776+0.007114    test-rmse:0.976813+0.069206 
## [3]  train-rmse:0.821237+0.004940    test-rmse:0.855858+0.061255 
## [4]  train-rmse:0.741383+0.005345    test-rmse:0.786829+0.053473 
## [5]  train-rmse:0.700498+0.005463    test-rmse:0.757934+0.046089 
## [6]  train-rmse:0.674058+0.005777    test-rmse:0.734604+0.048854 
## [7]  train-rmse:0.657088+0.004323    test-rmse:0.725640+0.046804 
## [8]  train-rmse:0.644330+0.004194    test-rmse:0.720109+0.045336 
## [9]  train-rmse:0.631492+0.004321    test-rmse:0.713272+0.042617 
## [10] train-rmse:0.623778+0.004964    test-rmse:0.708803+0.042819 
## [11] train-rmse:0.616722+0.005577    test-rmse:0.703949+0.044649 
## [12] train-rmse:0.607900+0.005498    test-rmse:0.700720+0.044642 
## [13] train-rmse:0.598213+0.003709    test-rmse:0.696688+0.046676 
## [14] train-rmse:0.590993+0.002383    test-rmse:0.695017+0.044731 
## [15] train-rmse:0.583451+0.004305    test-rmse:0.694783+0.043866 
## [16] train-rmse:0.577457+0.005808    test-rmse:0.691717+0.039388 
## [17] train-rmse:0.572520+0.006821    test-rmse:0.689072+0.042640 
## [18] train-rmse:0.568779+0.006876    test-rmse:0.687347+0.040501 
## [19] train-rmse:0.562219+0.006400    test-rmse:0.679831+0.040599 
## [20] train-rmse:0.557476+0.006711    test-rmse:0.679024+0.043189 
## [21] train-rmse:0.550177+0.007024    test-rmse:0.674486+0.043216 
## [22] train-rmse:0.542800+0.004861    test-rmse:0.668495+0.043650 
## [23] train-rmse:0.538751+0.004697    test-rmse:0.664713+0.047746 
## [24] train-rmse:0.533610+0.004547    test-rmse:0.661798+0.043747 
## [25] train-rmse:0.529514+0.004973    test-rmse:0.658723+0.043487 
## [26] train-rmse:0.525709+0.005761    test-rmse:0.656422+0.042253 
## [27] train-rmse:0.521084+0.006392    test-rmse:0.655891+0.042535 
## [28] train-rmse:0.517727+0.006872    test-rmse:0.653868+0.041190 
## [29] train-rmse:0.514432+0.007201    test-rmse:0.652583+0.043674 
## [30] train-rmse:0.511535+0.007340    test-rmse:0.654226+0.044727 
## [31] train-rmse:0.507978+0.008027    test-rmse:0.654572+0.043003 
## [32] train-rmse:0.504200+0.007275    test-rmse:0.655521+0.043195 
## [33] train-rmse:0.501094+0.007896    test-rmse:0.655078+0.044321 
## [34] train-rmse:0.496606+0.009817    test-rmse:0.654320+0.043674 
## [35] train-rmse:0.492333+0.009221    test-rmse:0.652338+0.042424 
## [36] train-rmse:0.488503+0.008446    test-rmse:0.650786+0.040669 
## [37] train-rmse:0.485828+0.007629    test-rmse:0.649319+0.041743 
## [38] train-rmse:0.483000+0.008026    test-rmse:0.648079+0.041236 
## [39] train-rmse:0.480693+0.008079    test-rmse:0.644940+0.043901 
## [40] train-rmse:0.477417+0.007945    test-rmse:0.644062+0.045893 
## [41] train-rmse:0.474783+0.008088    test-rmse:0.640107+0.044902 
## [42] train-rmse:0.471727+0.008341    test-rmse:0.641299+0.045766 
## [43] train-rmse:0.469439+0.008545    test-rmse:0.640945+0.045547 
## [44] train-rmse:0.465442+0.007929    test-rmse:0.640274+0.044867 
## [45] train-rmse:0.463486+0.008185    test-rmse:0.640811+0.045688 
## [46] train-rmse:0.461291+0.008275    test-rmse:0.641414+0.047537 
## [47] train-rmse:0.459662+0.008576    test-rmse:0.640343+0.046933 
## Stopping. Best iteration:
## [41] train-rmse:0.474783+0.008088    test-rmse:0.640107+0.044902
## 
## 86 
## [1]  train-rmse:1.185176+0.010424    test-rmse:1.199050+0.069456 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.933863+0.006922    test-rmse:0.966147+0.066270 
## [3]  train-rmse:0.793275+0.005662    test-rmse:0.839433+0.064754 
## [4]  train-rmse:0.715440+0.007843    test-rmse:0.775695+0.053634 
## [5]  train-rmse:0.656518+0.005772    test-rmse:0.733687+0.051771 
## [6]  train-rmse:0.625757+0.007523    test-rmse:0.715374+0.043838 
## [7]  train-rmse:0.605005+0.006626    test-rmse:0.707840+0.046260 
## [8]  train-rmse:0.590442+0.006898    test-rmse:0.699305+0.046369 
## [9]  train-rmse:0.572864+0.007955    test-rmse:0.687823+0.051441 
## [10] train-rmse:0.554614+0.004313    test-rmse:0.681451+0.052093 
## [11] train-rmse:0.543107+0.004524    test-rmse:0.680546+0.054967 
## [12] train-rmse:0.535294+0.004851    test-rmse:0.679135+0.053981 
## [13] train-rmse:0.526550+0.002145    test-rmse:0.678173+0.058107 
## [14] train-rmse:0.519811+0.003290    test-rmse:0.676167+0.059472 
## [15] train-rmse:0.511227+0.003038    test-rmse:0.673772+0.058650 
## [16] train-rmse:0.504901+0.003239    test-rmse:0.672919+0.062091 
## [17] train-rmse:0.496613+0.004317    test-rmse:0.669044+0.061252 
## [18] train-rmse:0.490192+0.004652    test-rmse:0.666731+0.061583 
## [19] train-rmse:0.485003+0.003936    test-rmse:0.665847+0.060205 
## [20] train-rmse:0.479441+0.006040    test-rmse:0.663760+0.060631 
## [21] train-rmse:0.470167+0.007593    test-rmse:0.659945+0.061142 
## [22] train-rmse:0.464876+0.006987    test-rmse:0.658976+0.060941 
## [23] train-rmse:0.458007+0.009244    test-rmse:0.656990+0.060969 
## [24] train-rmse:0.449868+0.006318    test-rmse:0.654235+0.059985 
## [25] train-rmse:0.444367+0.006178    test-rmse:0.654493+0.058582 
## [26] train-rmse:0.439184+0.005210    test-rmse:0.653507+0.062875 
## [27] train-rmse:0.433660+0.005325    test-rmse:0.651821+0.063748 
## [28] train-rmse:0.428813+0.006227    test-rmse:0.649486+0.060464 
## [29] train-rmse:0.421658+0.006039    test-rmse:0.644383+0.060483 
## [30] train-rmse:0.416661+0.006531    test-rmse:0.642189+0.059993 
## [31] train-rmse:0.411851+0.008615    test-rmse:0.642719+0.060703 
## [32] train-rmse:0.406088+0.008761    test-rmse:0.640226+0.059806 
## [33] train-rmse:0.399280+0.005351    test-rmse:0.640074+0.063447 
## [34] train-rmse:0.395319+0.005090    test-rmse:0.638028+0.063900 
## [35] train-rmse:0.392013+0.005278    test-rmse:0.638079+0.064086 
## [36] train-rmse:0.387236+0.007662    test-rmse:0.635458+0.061172 
## [37] train-rmse:0.382194+0.007442    test-rmse:0.634617+0.060396 
## [38] train-rmse:0.378528+0.008000    test-rmse:0.631659+0.059500 
## [39] train-rmse:0.374637+0.008672    test-rmse:0.632741+0.058424 
## [40] train-rmse:0.369990+0.007895    test-rmse:0.630798+0.059904 
## [41] train-rmse:0.365835+0.007055    test-rmse:0.629675+0.059338 
## [42] train-rmse:0.362390+0.006143    test-rmse:0.629278+0.059176 
## [43] train-rmse:0.356633+0.008358    test-rmse:0.630663+0.061846 
## [44] train-rmse:0.353215+0.008752    test-rmse:0.631089+0.062794 
## [45] train-rmse:0.349052+0.009295    test-rmse:0.629135+0.063082 
## [46] train-rmse:0.346019+0.009941    test-rmse:0.629642+0.064223 
## [47] train-rmse:0.341502+0.013211    test-rmse:0.627936+0.060944 
## [48] train-rmse:0.337000+0.013197    test-rmse:0.626998+0.061893 
## [49] train-rmse:0.333025+0.013754    test-rmse:0.627225+0.061454 
## [50] train-rmse:0.329260+0.013727    test-rmse:0.625729+0.061768 
## [51] train-rmse:0.325566+0.013699    test-rmse:0.624490+0.063147 
## [52] train-rmse:0.321971+0.014028    test-rmse:0.625223+0.063665 
## [53] train-rmse:0.317704+0.012065    test-rmse:0.625333+0.062986 
## [54] train-rmse:0.313278+0.013912    test-rmse:0.625215+0.064877 
## [55] train-rmse:0.309580+0.012153    test-rmse:0.625281+0.065902 
## [56] train-rmse:0.305678+0.012660    test-rmse:0.626135+0.063223 
## [57] train-rmse:0.302858+0.014808    test-rmse:0.627982+0.065382 
## Stopping. Best iteration:
## [51] train-rmse:0.325566+0.013699    test-rmse:0.624490+0.063147
## 
## 87 
## [1]  train-rmse:1.177738+0.010581    test-rmse:1.203501+0.071294 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.915124+0.006672    test-rmse:0.963648+0.076145 
## [3]  train-rmse:0.763627+0.004932    test-rmse:0.835232+0.066032 
## [4]  train-rmse:0.677385+0.004579    test-rmse:0.762333+0.067373 
## [5]  train-rmse:0.611640+0.011787    test-rmse:0.719537+0.063542 
## [6]  train-rmse:0.574763+0.010322    test-rmse:0.701343+0.059990 
## [7]  train-rmse:0.551986+0.012077    test-rmse:0.690241+0.058416 
## [8]  train-rmse:0.533811+0.011241    test-rmse:0.682977+0.062125 
## [9]  train-rmse:0.516557+0.015188    test-rmse:0.675434+0.054971 
## [10] train-rmse:0.496354+0.015798    test-rmse:0.668911+0.057930 
## [11] train-rmse:0.479120+0.014950    test-rmse:0.660481+0.053392 
## [12] train-rmse:0.469131+0.014972    test-rmse:0.663170+0.052038 
## [13] train-rmse:0.459411+0.015612    test-rmse:0.662614+0.051193 
## [14] train-rmse:0.450780+0.016843    test-rmse:0.663739+0.054264 
## [15] train-rmse:0.437913+0.015504    test-rmse:0.658728+0.050834 
## [16] train-rmse:0.426506+0.016576    test-rmse:0.659139+0.048254 
## [17] train-rmse:0.417438+0.018769    test-rmse:0.657029+0.051054 
## [18] train-rmse:0.407860+0.017536    test-rmse:0.653648+0.051917 
## [19] train-rmse:0.399757+0.014933    test-rmse:0.648582+0.050671 
## [20] train-rmse:0.389290+0.013821    test-rmse:0.645755+0.049711 
## [21] train-rmse:0.380635+0.016005    test-rmse:0.643080+0.047684 
## [22] train-rmse:0.374434+0.015495    test-rmse:0.642714+0.044385 
## [23] train-rmse:0.364321+0.012329    test-rmse:0.640468+0.045359 
## [24] train-rmse:0.356035+0.009926    test-rmse:0.637793+0.048686 
## [25] train-rmse:0.347043+0.012957    test-rmse:0.639011+0.049482 
## [26] train-rmse:0.342326+0.012244    test-rmse:0.640254+0.049694 
## [27] train-rmse:0.336585+0.011557    test-rmse:0.641664+0.048541 
## [28] train-rmse:0.330085+0.014441    test-rmse:0.641392+0.047116 
## [29] train-rmse:0.325522+0.014516    test-rmse:0.642705+0.048416 
## [30] train-rmse:0.317211+0.014462    test-rmse:0.640538+0.049140 
## Stopping. Best iteration:
## [24] train-rmse:0.356035+0.009926    test-rmse:0.637793+0.048686
## 
## 88 
## [1]  train-rmse:1.172636+0.010879    test-rmse:1.196848+0.075821 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.901654+0.006329    test-rmse:0.951344+0.077339 
## [3]  train-rmse:0.737262+0.006817    test-rmse:0.811099+0.070566 
## [4]  train-rmse:0.642810+0.008498    test-rmse:0.744389+0.065857 
## [5]  train-rmse:0.585823+0.009710    test-rmse:0.710546+0.061771 
## [6]  train-rmse:0.541013+0.009239    test-rmse:0.692912+0.052184 
## [7]  train-rmse:0.509878+0.009678    test-rmse:0.680029+0.053159 
## [8]  train-rmse:0.489658+0.011385    test-rmse:0.670719+0.053790 
## [9]  train-rmse:0.472371+0.013263    test-rmse:0.667263+0.049062 
## [10] train-rmse:0.451671+0.008437    test-rmse:0.655773+0.050145 
## [11] train-rmse:0.435804+0.010876    test-rmse:0.655989+0.054004 
## [12] train-rmse:0.419371+0.010199    test-rmse:0.650758+0.052239 
## [13] train-rmse:0.406458+0.010397    test-rmse:0.648828+0.057147 
## [14] train-rmse:0.392820+0.010589    test-rmse:0.651007+0.058407 
## [15] train-rmse:0.381357+0.014027    test-rmse:0.652865+0.059078 
## [16] train-rmse:0.369135+0.015106    test-rmse:0.649504+0.060019 
## [17] train-rmse:0.358492+0.013857    test-rmse:0.646842+0.062262 
## [18] train-rmse:0.347592+0.011339    test-rmse:0.642112+0.058833 
## [19] train-rmse:0.337222+0.013009    test-rmse:0.641251+0.059039 
## [20] train-rmse:0.324477+0.012142    test-rmse:0.640265+0.060580 
## [21] train-rmse:0.316294+0.013192    test-rmse:0.639396+0.060987 
## [22] train-rmse:0.307731+0.013264    test-rmse:0.635755+0.061553 
## [23] train-rmse:0.296391+0.011066    test-rmse:0.635095+0.058984 
## [24] train-rmse:0.287513+0.010888    test-rmse:0.634484+0.056835 
## [25] train-rmse:0.279869+0.014075    test-rmse:0.632220+0.055029 
## [26] train-rmse:0.272347+0.012062    test-rmse:0.630446+0.056566 
## [27] train-rmse:0.264737+0.011527    test-rmse:0.631149+0.054951 
## [28] train-rmse:0.259560+0.009988    test-rmse:0.632067+0.055069 
## [29] train-rmse:0.251049+0.009932    test-rmse:0.630784+0.055272 
## [30] train-rmse:0.243077+0.008889    test-rmse:0.628596+0.053572 
## [31] train-rmse:0.236833+0.010168    test-rmse:0.629863+0.050853 
## [32] train-rmse:0.229777+0.011505    test-rmse:0.631717+0.052903 
## [33] train-rmse:0.220602+0.012496    test-rmse:0.631716+0.052712 
## [34] train-rmse:0.216049+0.014082    test-rmse:0.631568+0.052801 
## [35] train-rmse:0.207935+0.012348    test-rmse:0.632555+0.053195 
## [36] train-rmse:0.202708+0.011062    test-rmse:0.630647+0.052311 
## Stopping. Best iteration:
## [30] train-rmse:0.243077+0.008889    test-rmse:0.628596+0.053572
## 
## 89 
## [1]  train-rmse:1.167420+0.010038    test-rmse:1.199629+0.077957 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.889239+0.003961    test-rmse:0.953308+0.076666 
## [3]  train-rmse:0.716779+0.007119    test-rmse:0.812701+0.064692 
## [4]  train-rmse:0.612190+0.006634    test-rmse:0.738068+0.062703 
## [5]  train-rmse:0.547035+0.013413    test-rmse:0.700565+0.054466 
## [6]  train-rmse:0.497876+0.009786    test-rmse:0.673802+0.046805 
## [7]  train-rmse:0.469850+0.008153    test-rmse:0.665121+0.040741 
## [8]  train-rmse:0.441578+0.010821    test-rmse:0.664137+0.042068 
## [9]  train-rmse:0.416863+0.014853    test-rmse:0.661175+0.039919 
## [10] train-rmse:0.397186+0.020184    test-rmse:0.664085+0.041776 
## [11] train-rmse:0.381848+0.020638    test-rmse:0.664011+0.044445 
## [12] train-rmse:0.368983+0.021155    test-rmse:0.660721+0.045074 
## [13] train-rmse:0.347694+0.015765    test-rmse:0.647232+0.041347 
## [14] train-rmse:0.334060+0.018190    test-rmse:0.648036+0.043474 
## [15] train-rmse:0.322077+0.016277    test-rmse:0.649212+0.043172 
## [16] train-rmse:0.308782+0.017600    test-rmse:0.641723+0.040485 
## [17] train-rmse:0.297252+0.015199    test-rmse:0.643052+0.040700 
## [18] train-rmse:0.287240+0.019249    test-rmse:0.642286+0.038748 
## [19] train-rmse:0.273986+0.017845    test-rmse:0.638331+0.043274 
## [20] train-rmse:0.264897+0.019521    test-rmse:0.640349+0.043352 
## [21] train-rmse:0.256073+0.020276    test-rmse:0.642200+0.042562 
## [22] train-rmse:0.244839+0.020411    test-rmse:0.643961+0.043283 
## [23] train-rmse:0.236975+0.019681    test-rmse:0.640566+0.043079 
## [24] train-rmse:0.226021+0.021566    test-rmse:0.641513+0.042552 
## [25] train-rmse:0.218075+0.019765    test-rmse:0.642612+0.044156 
## Stopping. Best iteration:
## [19] train-rmse:0.273986+0.017845    test-rmse:0.638331+0.043274
## 
## 90 
## [1]  train-rmse:1.163601+0.009347    test-rmse:1.197778+0.078238 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.880491+0.005279    test-rmse:0.943386+0.080964 
## [3]  train-rmse:0.698230+0.009141    test-rmse:0.806197+0.083512 
## [4]  train-rmse:0.587622+0.008720    test-rmse:0.728642+0.083227 
## [5]  train-rmse:0.516640+0.017894    test-rmse:0.691629+0.079465 
## [6]  train-rmse:0.470873+0.011251    test-rmse:0.677468+0.071789 
## [7]  train-rmse:0.432158+0.012619    test-rmse:0.666507+0.071894 
## [8]  train-rmse:0.399261+0.015801    test-rmse:0.658199+0.069667 
## [9]  train-rmse:0.373745+0.015403    test-rmse:0.655883+0.070705 
## [10] train-rmse:0.351943+0.016104    test-rmse:0.652874+0.068117 
## [11] train-rmse:0.340171+0.016777    test-rmse:0.650856+0.069377 
## [12] train-rmse:0.315672+0.018968    test-rmse:0.643457+0.060955 
## [13] train-rmse:0.297897+0.015664    test-rmse:0.640857+0.060127 
## [14] train-rmse:0.284574+0.016591    test-rmse:0.643686+0.059280 
## [15] train-rmse:0.271342+0.018049    test-rmse:0.647377+0.055670 
## [16] train-rmse:0.261467+0.014993    test-rmse:0.648910+0.054821 
## [17] train-rmse:0.249029+0.010079    test-rmse:0.647453+0.055216 
## [18] train-rmse:0.236416+0.007440    test-rmse:0.646986+0.048907 
## [19] train-rmse:0.225624+0.007847    test-rmse:0.651205+0.052564 
## Stopping. Best iteration:
## [13] train-rmse:0.297897+0.015664    test-rmse:0.640857+0.060127
## 
## 91 
## [1]  train-rmse:1.180850+0.010934    test-rmse:1.184243+0.071123 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.951481+0.007734    test-rmse:0.958497+0.064479 
## [3]  train-rmse:0.833773+0.007396    test-rmse:0.854391+0.058424 
## [4]  train-rmse:0.775941+0.006745    test-rmse:0.802822+0.051610 
## [5]  train-rmse:0.745101+0.005838    test-rmse:0.780436+0.044376 
## [6]  train-rmse:0.727554+0.005225    test-rmse:0.768436+0.038465 
## [7]  train-rmse:0.715509+0.004688    test-rmse:0.755007+0.037848 
## [8]  train-rmse:0.706507+0.004216    test-rmse:0.747236+0.034837 
## [9]  train-rmse:0.699686+0.004461    test-rmse:0.744104+0.031281 
## [10] train-rmse:0.693860+0.004664    test-rmse:0.741002+0.028606 
## [11] train-rmse:0.688974+0.004791    test-rmse:0.739450+0.029057 
## [12] train-rmse:0.684344+0.004917    test-rmse:0.737557+0.029501 
## [13] train-rmse:0.680006+0.005054    test-rmse:0.735910+0.028995 
## [14] train-rmse:0.675912+0.005120    test-rmse:0.731292+0.026508 
## [15] train-rmse:0.672026+0.005222    test-rmse:0.731198+0.028217 
## [16] train-rmse:0.668294+0.005351    test-rmse:0.728713+0.029431 
## [17] train-rmse:0.664774+0.005252    test-rmse:0.726050+0.029545 
## [18] train-rmse:0.661418+0.005271    test-rmse:0.722739+0.030715 
## [19] train-rmse:0.658123+0.005211    test-rmse:0.722319+0.034294 
## [20] train-rmse:0.655076+0.005165    test-rmse:0.721506+0.035274 
## [21] train-rmse:0.652262+0.005267    test-rmse:0.722212+0.037962 
## [22] train-rmse:0.649435+0.005462    test-rmse:0.721562+0.034169 
## [23] train-rmse:0.646665+0.005666    test-rmse:0.721316+0.034452 
## [24] train-rmse:0.644151+0.005742    test-rmse:0.721507+0.035556 
## [25] train-rmse:0.641802+0.005824    test-rmse:0.719951+0.034504 
## [26] train-rmse:0.639604+0.005816    test-rmse:0.716686+0.033297 
## [27] train-rmse:0.637542+0.005776    test-rmse:0.717742+0.035464 
## [28] train-rmse:0.635463+0.005763    test-rmse:0.716294+0.033423 
## [29] train-rmse:0.633484+0.005717    test-rmse:0.716050+0.034482 
## [30] train-rmse:0.631579+0.005691    test-rmse:0.713385+0.033810 
## [31] train-rmse:0.629664+0.005747    test-rmse:0.710555+0.033001 
## [32] train-rmse:0.627839+0.005787    test-rmse:0.709101+0.034770 
## [33] train-rmse:0.626002+0.005750    test-rmse:0.707629+0.033247 
## [34] train-rmse:0.624168+0.005737    test-rmse:0.705807+0.034373 
## [35] train-rmse:0.622389+0.005744    test-rmse:0.706684+0.034917 
## [36] train-rmse:0.620667+0.005752    test-rmse:0.704405+0.034166 
## [37] train-rmse:0.619001+0.005766    test-rmse:0.702789+0.033777 
## [38] train-rmse:0.617391+0.005768    test-rmse:0.705231+0.033403 
## [39] train-rmse:0.615849+0.005810    test-rmse:0.704067+0.033124 
## [40] train-rmse:0.614329+0.005881    test-rmse:0.702445+0.032752 
## [41] train-rmse:0.612862+0.005969    test-rmse:0.699736+0.031816 
## [42] train-rmse:0.611396+0.005996    test-rmse:0.698976+0.032169 
## [43] train-rmse:0.609992+0.006002    test-rmse:0.698270+0.031900 
## [44] train-rmse:0.608626+0.006036    test-rmse:0.698805+0.031550 
## [45] train-rmse:0.607315+0.006046    test-rmse:0.699717+0.031936 
## [46] train-rmse:0.606027+0.006004    test-rmse:0.699601+0.033263 
## [47] train-rmse:0.604781+0.005987    test-rmse:0.698376+0.034161 
## [48] train-rmse:0.603528+0.005993    test-rmse:0.697174+0.033654 
## [49] train-rmse:0.602310+0.005994    test-rmse:0.696716+0.035247 
## [50] train-rmse:0.601048+0.006053    test-rmse:0.697263+0.034702 
## [51] train-rmse:0.599822+0.006097    test-rmse:0.695621+0.035528 
## [52] train-rmse:0.598631+0.006128    test-rmse:0.695970+0.036250 
## [53] train-rmse:0.597493+0.006171    test-rmse:0.694828+0.034688 
## [54] train-rmse:0.596372+0.006215    test-rmse:0.694717+0.033894 
## [55] train-rmse:0.595246+0.006219    test-rmse:0.692229+0.033797 
## [56] train-rmse:0.594142+0.006299    test-rmse:0.692720+0.034682 
## [57] train-rmse:0.593149+0.006295    test-rmse:0.692089+0.034933 
## [58] train-rmse:0.592153+0.006293    test-rmse:0.692523+0.033764 
## [59] train-rmse:0.591173+0.006299    test-rmse:0.691768+0.033960 
## [60] train-rmse:0.590217+0.006344    test-rmse:0.690469+0.034212 
## [61] train-rmse:0.589237+0.006386    test-rmse:0.690723+0.034522 
## [62] train-rmse:0.588321+0.006389    test-rmse:0.689598+0.034892 
## [63] train-rmse:0.587388+0.006397    test-rmse:0.688929+0.035121 
## [64] train-rmse:0.586494+0.006420    test-rmse:0.689662+0.035873 
## [65] train-rmse:0.585614+0.006470    test-rmse:0.689866+0.034869 
## [66] train-rmse:0.584766+0.006494    test-rmse:0.687236+0.033223 
## [67] train-rmse:0.583922+0.006537    test-rmse:0.685652+0.034058 
## [68] train-rmse:0.583083+0.006572    test-rmse:0.686702+0.034090 
## [69] train-rmse:0.582250+0.006584    test-rmse:0.686098+0.033781 
## [70] train-rmse:0.581434+0.006576    test-rmse:0.685931+0.035502 
## [71] train-rmse:0.580632+0.006589    test-rmse:0.684575+0.033992 
## [72] train-rmse:0.579825+0.006617    test-rmse:0.684397+0.033102 
## [73] train-rmse:0.579004+0.006654    test-rmse:0.683873+0.033321 
## [74] train-rmse:0.578232+0.006663    test-rmse:0.683259+0.034215 
## [75] train-rmse:0.577474+0.006692    test-rmse:0.682142+0.033970 
## [76] train-rmse:0.576740+0.006716    test-rmse:0.682882+0.034476 
## [77] train-rmse:0.575995+0.006755    test-rmse:0.681924+0.034627 
## [78] train-rmse:0.575254+0.006771    test-rmse:0.681583+0.033828 
## [79] train-rmse:0.574532+0.006800    test-rmse:0.681164+0.033697 
## [80] train-rmse:0.573816+0.006803    test-rmse:0.682191+0.033558 
## [81] train-rmse:0.573075+0.006828    test-rmse:0.683411+0.033494 
## [82] train-rmse:0.572380+0.006827    test-rmse:0.682473+0.033931 
## [83] train-rmse:0.571701+0.006831    test-rmse:0.681492+0.034464 
## [84] train-rmse:0.571021+0.006829    test-rmse:0.679235+0.035582 
## [85] train-rmse:0.570360+0.006821    test-rmse:0.679954+0.036665 
## [86] train-rmse:0.569725+0.006828    test-rmse:0.680238+0.036609 
## [87] train-rmse:0.569114+0.006870    test-rmse:0.680484+0.036445 
## [88] train-rmse:0.568513+0.006899    test-rmse:0.680950+0.035655 
## [89] train-rmse:0.567896+0.006970    test-rmse:0.681398+0.034557 
## [90] train-rmse:0.567313+0.006997    test-rmse:0.681209+0.034576 
## Stopping. Best iteration:
## [84] train-rmse:0.571021+0.006829    test-rmse:0.679235+0.035582
## 
## 92 
## [1]  train-rmse:1.169705+0.010677    test-rmse:1.180925+0.071460 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.928064+0.006750    test-rmse:0.953963+0.068377 
## [3]  train-rmse:0.800367+0.002813    test-rmse:0.837189+0.061785 
## [4]  train-rmse:0.727359+0.005467    test-rmse:0.775684+0.050923 
## [5]  train-rmse:0.690589+0.004383    test-rmse:0.748916+0.045187 
## [6]  train-rmse:0.667754+0.004114    test-rmse:0.736096+0.041228 
## [7]  train-rmse:0.651797+0.006575    test-rmse:0.725988+0.037430 
## [8]  train-rmse:0.640163+0.006874    test-rmse:0.720426+0.039318 
## [9]  train-rmse:0.627389+0.007578    test-rmse:0.714468+0.039301 
## [10] train-rmse:0.619247+0.008206    test-rmse:0.710385+0.039276 
## [11] train-rmse:0.611635+0.008238    test-rmse:0.705830+0.039022 
## [12] train-rmse:0.603688+0.009672    test-rmse:0.696626+0.039294 
## [13] train-rmse:0.594334+0.008800    test-rmse:0.690659+0.039367 
## [14] train-rmse:0.586634+0.010749    test-rmse:0.690014+0.042294 
## [15] train-rmse:0.579380+0.008773    test-rmse:0.693762+0.043321 
## [16] train-rmse:0.573513+0.010179    test-rmse:0.689030+0.042745 
## [17] train-rmse:0.568090+0.010011    test-rmse:0.688449+0.044331 
## [18] train-rmse:0.563160+0.010141    test-rmse:0.687690+0.042202 
## [19] train-rmse:0.558776+0.010076    test-rmse:0.682763+0.040021 
## [20] train-rmse:0.554162+0.010069    test-rmse:0.680081+0.039652 
## [21] train-rmse:0.547143+0.010838    test-rmse:0.679268+0.048217 
## [22] train-rmse:0.542577+0.011532    test-rmse:0.677984+0.046568 
## [23] train-rmse:0.537553+0.011988    test-rmse:0.674427+0.049638 
## [24] train-rmse:0.533151+0.011463    test-rmse:0.673236+0.052504 
## [25] train-rmse:0.527952+0.010561    test-rmse:0.672509+0.049075 
## [26] train-rmse:0.522889+0.011179    test-rmse:0.666682+0.050529 
## [27] train-rmse:0.518990+0.010728    test-rmse:0.665939+0.049780 
## [28] train-rmse:0.515580+0.010020    test-rmse:0.663718+0.049293 
## [29] train-rmse:0.510735+0.009580    test-rmse:0.663231+0.046358 
## [30] train-rmse:0.507312+0.009148    test-rmse:0.662971+0.047288 
## [31] train-rmse:0.504708+0.009161    test-rmse:0.665465+0.048041 
## [32] train-rmse:0.498770+0.009869    test-rmse:0.664604+0.047726 
## [33] train-rmse:0.494994+0.009708    test-rmse:0.664516+0.049725 
## [34] train-rmse:0.490101+0.009248    test-rmse:0.664980+0.050398 
## [35] train-rmse:0.486723+0.007942    test-rmse:0.664140+0.051812 
## [36] train-rmse:0.483206+0.007358    test-rmse:0.663092+0.049258 
## Stopping. Best iteration:
## [30] train-rmse:0.507312+0.009148    test-rmse:0.662971+0.047288
## 
## 93 
## [1]  train-rmse:1.162040+0.010042    test-rmse:1.177042+0.069366 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.908451+0.005908    test-rmse:0.945794+0.070830 
## [3]  train-rmse:0.772634+0.004819    test-rmse:0.823205+0.065791 
## [4]  train-rmse:0.696816+0.010182    test-rmse:0.760061+0.049218 
## [5]  train-rmse:0.644202+0.004842    test-rmse:0.724354+0.057725 
## [6]  train-rmse:0.616311+0.007927    test-rmse:0.705118+0.051829 
## [7]  train-rmse:0.597064+0.007492    test-rmse:0.702435+0.050080 
## [8]  train-rmse:0.580183+0.010528    test-rmse:0.696576+0.053563 
## [9]  train-rmse:0.568590+0.011871    test-rmse:0.686495+0.050738 
## [10] train-rmse:0.554662+0.012235    test-rmse:0.676163+0.054644 
## [11] train-rmse:0.540746+0.011310    test-rmse:0.672420+0.055893 
## [12] train-rmse:0.526969+0.005749    test-rmse:0.669290+0.051892 
## [13] train-rmse:0.517797+0.006208    test-rmse:0.669274+0.053594 
## [14] train-rmse:0.511610+0.007225    test-rmse:0.668702+0.056709 
## [15] train-rmse:0.504011+0.006827    test-rmse:0.665307+0.060191 
## [16] train-rmse:0.497167+0.008540    test-rmse:0.659307+0.056438 
## [17] train-rmse:0.486366+0.009273    test-rmse:0.654771+0.053848 
## [18] train-rmse:0.474590+0.009527    test-rmse:0.651965+0.054210 
## [19] train-rmse:0.467855+0.008930    test-rmse:0.652160+0.054133 
## [20] train-rmse:0.460293+0.008483    test-rmse:0.649565+0.055339 
## [21] train-rmse:0.451011+0.004997    test-rmse:0.644635+0.058644 
## [22] train-rmse:0.444004+0.003862    test-rmse:0.639103+0.056451 
## [23] train-rmse:0.438965+0.004144    test-rmse:0.637412+0.058263 
## [24] train-rmse:0.430005+0.004538    test-rmse:0.635044+0.059415 
## [25] train-rmse:0.424241+0.006084    test-rmse:0.630936+0.058360 
## [26] train-rmse:0.418794+0.005334    test-rmse:0.630849+0.059586 
## [27] train-rmse:0.410195+0.007362    test-rmse:0.630711+0.058823 
## [28] train-rmse:0.406003+0.007343    test-rmse:0.632005+0.058311 
## [29] train-rmse:0.400902+0.006684    test-rmse:0.628423+0.059750 
## [30] train-rmse:0.395436+0.007052    test-rmse:0.629140+0.062741 
## [31] train-rmse:0.388546+0.010396    test-rmse:0.626675+0.061241 
## [32] train-rmse:0.383343+0.011976    test-rmse:0.624211+0.058659 
## [33] train-rmse:0.379100+0.011988    test-rmse:0.623415+0.057921 
## [34] train-rmse:0.374912+0.012478    test-rmse:0.621603+0.060264 
## [35] train-rmse:0.370359+0.011663    test-rmse:0.621956+0.060894 
## [36] train-rmse:0.368293+0.011691    test-rmse:0.623422+0.060885 
## [37] train-rmse:0.363635+0.009490    test-rmse:0.621694+0.061270 
## [38] train-rmse:0.358098+0.008664    test-rmse:0.620390+0.063440 
## [39] train-rmse:0.355012+0.008807    test-rmse:0.620426+0.064697 
## [40] train-rmse:0.352038+0.009557    test-rmse:0.618218+0.063996 
## [41] train-rmse:0.347370+0.007368    test-rmse:0.617982+0.062916 
## [42] train-rmse:0.344052+0.007347    test-rmse:0.618631+0.062331 
## [43] train-rmse:0.338970+0.009241    test-rmse:0.620158+0.065030 
## [44] train-rmse:0.335405+0.008578    test-rmse:0.619368+0.067006 
## [45] train-rmse:0.331379+0.008343    test-rmse:0.619394+0.066735 
## [46] train-rmse:0.326899+0.009964    test-rmse:0.618555+0.066033 
## [47] train-rmse:0.323509+0.009827    test-rmse:0.617872+0.066424 
## [48] train-rmse:0.319666+0.009652    test-rmse:0.616925+0.066085 
## [49] train-rmse:0.316903+0.010077    test-rmse:0.617572+0.065098 
## [50] train-rmse:0.314070+0.011815    test-rmse:0.617706+0.064601 
## [51] train-rmse:0.308978+0.009823    test-rmse:0.616890+0.065995 
## [52] train-rmse:0.306112+0.009002    test-rmse:0.617824+0.065791 
## [53] train-rmse:0.300747+0.008695    test-rmse:0.614749+0.065311 
## [54] train-rmse:0.297259+0.007813    test-rmse:0.613994+0.062615 
## [55] train-rmse:0.293262+0.010517    test-rmse:0.613719+0.064027 
## [56] train-rmse:0.290862+0.010120    test-rmse:0.612233+0.066280 
## [57] train-rmse:0.288437+0.010839    test-rmse:0.612794+0.066940 
## [58] train-rmse:0.286023+0.011509    test-rmse:0.613799+0.067907 
## [59] train-rmse:0.283928+0.011225    test-rmse:0.614924+0.067099 
## [60] train-rmse:0.280892+0.012130    test-rmse:0.616406+0.067205 
## [61] train-rmse:0.277261+0.012309    test-rmse:0.615957+0.066556 
## [62] train-rmse:0.275440+0.012049    test-rmse:0.616528+0.067367 
## Stopping. Best iteration:
## [56] train-rmse:0.290862+0.010120    test-rmse:0.612233+0.066280
## 
## 94 
## [1]  train-rmse:1.154054+0.010226    test-rmse:1.181791+0.071452 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.887382+0.006659    test-rmse:0.937732+0.074292 
## [3]  train-rmse:0.740624+0.005997    test-rmse:0.812792+0.063658 
## [4]  train-rmse:0.659004+0.002085    test-rmse:0.744461+0.068915 
## [5]  train-rmse:0.605174+0.008844    test-rmse:0.710057+0.070006 
## [6]  train-rmse:0.574714+0.009230    test-rmse:0.691848+0.059151 
## [7]  train-rmse:0.549386+0.006944    test-rmse:0.683010+0.062103 
## [8]  train-rmse:0.522727+0.011336    test-rmse:0.672708+0.064981 
## [9]  train-rmse:0.504382+0.007954    test-rmse:0.664235+0.057494 
## [10] train-rmse:0.487332+0.008763    test-rmse:0.661023+0.059671 
## [11] train-rmse:0.473757+0.007840    test-rmse:0.652813+0.058610 
## [12] train-rmse:0.463335+0.008314    test-rmse:0.649916+0.060575 
## [13] train-rmse:0.451273+0.011916    test-rmse:0.650268+0.059394 
## [14] train-rmse:0.440135+0.012371    test-rmse:0.646265+0.054692 
## [15] train-rmse:0.428296+0.011515    test-rmse:0.641814+0.056743 
## [16] train-rmse:0.420273+0.011578    test-rmse:0.637230+0.057032 
## [17] train-rmse:0.410704+0.009669    test-rmse:0.634693+0.058996 
## [18] train-rmse:0.400512+0.013248    test-rmse:0.632016+0.058157 
## [19] train-rmse:0.394112+0.012779    test-rmse:0.629366+0.056337 
## [20] train-rmse:0.385533+0.012036    test-rmse:0.627499+0.059631 
## [21] train-rmse:0.378905+0.012628    test-rmse:0.626466+0.061452 
## [22] train-rmse:0.370033+0.014421    test-rmse:0.621795+0.066473 
## [23] train-rmse:0.360843+0.011269    test-rmse:0.614017+0.062897 
## [24] train-rmse:0.346941+0.009060    test-rmse:0.610525+0.060226 
## [25] train-rmse:0.338885+0.008193    test-rmse:0.611010+0.059786 
## [26] train-rmse:0.332448+0.009482    test-rmse:0.607684+0.058018 
## [27] train-rmse:0.323865+0.006684    test-rmse:0.607582+0.060297 
## [28] train-rmse:0.317732+0.006781    test-rmse:0.605617+0.059856 
## [29] train-rmse:0.310470+0.005932    test-rmse:0.604549+0.058008 
## [30] train-rmse:0.303650+0.005519    test-rmse:0.606800+0.058740 
## [31] train-rmse:0.300181+0.005451    test-rmse:0.604529+0.059818 
## [32] train-rmse:0.292800+0.008180    test-rmse:0.604804+0.060433 
## [33] train-rmse:0.285267+0.008914    test-rmse:0.603685+0.060727 
## [34] train-rmse:0.279393+0.009120    test-rmse:0.604978+0.061530 
## [35] train-rmse:0.275048+0.010397    test-rmse:0.605371+0.061015 
## [36] train-rmse:0.269698+0.006890    test-rmse:0.605165+0.061167 
## [37] train-rmse:0.264648+0.006898    test-rmse:0.607265+0.062233 
## [38] train-rmse:0.258210+0.009008    test-rmse:0.608184+0.061919 
## [39] train-rmse:0.254844+0.008592    test-rmse:0.607658+0.062251 
## Stopping. Best iteration:
## [33] train-rmse:0.285267+0.008914    test-rmse:0.603685+0.060727
## 
## 95 
## [1]  train-rmse:1.148567+0.010538    test-rmse:1.174728+0.076333 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.872923+0.006138    test-rmse:0.926502+0.077592 
## [3]  train-rmse:0.712600+0.007517    test-rmse:0.795198+0.065471 
## [4]  train-rmse:0.624293+0.008709    test-rmse:0.730263+0.063340 
## [5]  train-rmse:0.569914+0.011830    test-rmse:0.707678+0.061927 
## [6]  train-rmse:0.526906+0.016128    test-rmse:0.686876+0.067656 
## [7]  train-rmse:0.496230+0.011555    test-rmse:0.670315+0.067246 
## [8]  train-rmse:0.478124+0.010322    test-rmse:0.670362+0.068206 
## [9]  train-rmse:0.453435+0.017264    test-rmse:0.662543+0.072224 
## [10] train-rmse:0.430736+0.012976    test-rmse:0.654826+0.067628 
## [11] train-rmse:0.418772+0.013744    test-rmse:0.654646+0.068358 
## [12] train-rmse:0.399510+0.009259    test-rmse:0.648870+0.060370 
## [13] train-rmse:0.388639+0.009712    test-rmse:0.642465+0.062598 
## [14] train-rmse:0.369920+0.010312    test-rmse:0.645960+0.062401 
## [15] train-rmse:0.357109+0.011105    test-rmse:0.642149+0.067677 
## [16] train-rmse:0.344574+0.011026    test-rmse:0.640066+0.071375 
## [17] train-rmse:0.335917+0.009916    test-rmse:0.636709+0.071548 
## [18] train-rmse:0.325530+0.010631    test-rmse:0.631832+0.070702 
## [19] train-rmse:0.313539+0.009000    test-rmse:0.630575+0.072122 
## [20] train-rmse:0.305465+0.012397    test-rmse:0.630490+0.073233 
## [21] train-rmse:0.297513+0.013699    test-rmse:0.631805+0.073427 
## [22] train-rmse:0.289916+0.012839    test-rmse:0.629969+0.072360 
## [23] train-rmse:0.282854+0.012134    test-rmse:0.629437+0.072560 
## [24] train-rmse:0.273445+0.009297    test-rmse:0.630101+0.073936 
## [25] train-rmse:0.263119+0.009235    test-rmse:0.629071+0.069356 
## [26] train-rmse:0.255302+0.013241    test-rmse:0.630295+0.071394 
## [27] train-rmse:0.246362+0.013755    test-rmse:0.629399+0.071313 
## [28] train-rmse:0.241136+0.013761    test-rmse:0.630004+0.071510 
## [29] train-rmse:0.233200+0.014084    test-rmse:0.631153+0.072483 
## [30] train-rmse:0.228259+0.014309    test-rmse:0.631651+0.071589 
## [31] train-rmse:0.220998+0.011988    test-rmse:0.632265+0.073825 
## Stopping. Best iteration:
## [25] train-rmse:0.263119+0.009235    test-rmse:0.629071+0.069356
## 
## 96 
## [1]  train-rmse:1.142951+0.009636    test-rmse:1.177677+0.078490 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.859946+0.005297    test-rmse:0.930324+0.074857 
## [3]  train-rmse:0.689654+0.007636    test-rmse:0.796079+0.064208 
## [4]  train-rmse:0.589351+0.007289    test-rmse:0.721769+0.066753 
## [5]  train-rmse:0.523897+0.015303    test-rmse:0.683813+0.054784 
## [6]  train-rmse:0.476430+0.015477    test-rmse:0.662670+0.047013 
## [7]  train-rmse:0.443852+0.015401    test-rmse:0.658825+0.049731 
## [8]  train-rmse:0.423827+0.017498    test-rmse:0.651742+0.047531 
## [9]  train-rmse:0.403327+0.015195    test-rmse:0.647087+0.048963 
## [10] train-rmse:0.377834+0.016646    test-rmse:0.638002+0.046580 
## [11] train-rmse:0.366651+0.018739    test-rmse:0.634815+0.045461 
## [12] train-rmse:0.348749+0.015707    test-rmse:0.636033+0.049738 
## [13] train-rmse:0.327077+0.020291    test-rmse:0.634327+0.047227 
## [14] train-rmse:0.314002+0.021052    test-rmse:0.632302+0.054608 
## [15] train-rmse:0.298949+0.017075    test-rmse:0.630513+0.054193 
## [16] train-rmse:0.286289+0.016370    test-rmse:0.631039+0.057014 
## [17] train-rmse:0.274143+0.016743    test-rmse:0.630475+0.057473 
## [18] train-rmse:0.265850+0.018293    test-rmse:0.632211+0.055583 
## [19] train-rmse:0.251996+0.015407    test-rmse:0.632743+0.054433 
## [20] train-rmse:0.238683+0.014875    test-rmse:0.632868+0.052802 
## [21] train-rmse:0.227785+0.015876    test-rmse:0.634241+0.053976 
## [22] train-rmse:0.219239+0.014215    test-rmse:0.636158+0.053383 
## [23] train-rmse:0.208829+0.011861    test-rmse:0.635371+0.054524 
## Stopping. Best iteration:
## [17] train-rmse:0.274143+0.016743    test-rmse:0.630475+0.057473
## 
## 97 
## [1]  train-rmse:1.138841+0.008902    test-rmse:1.175713+0.078792 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.848966+0.004322    test-rmse:0.919333+0.079747 
## [3]  train-rmse:0.671054+0.008911    test-rmse:0.787631+0.076845 
## [4]  train-rmse:0.567541+0.009715    test-rmse:0.719698+0.074194 
## [5]  train-rmse:0.496115+0.008483    test-rmse:0.686713+0.067192 
## [6]  train-rmse:0.445812+0.007164    test-rmse:0.666244+0.065977 
## [7]  train-rmse:0.410817+0.006289    test-rmse:0.652382+0.058402 
## [8]  train-rmse:0.382273+0.011729    test-rmse:0.652607+0.054009 
## [9]  train-rmse:0.358643+0.009663    test-rmse:0.647890+0.054780 
## [10] train-rmse:0.342443+0.011291    test-rmse:0.643806+0.054730 
## [11] train-rmse:0.316431+0.011331    test-rmse:0.638300+0.055204 
## [12] train-rmse:0.302707+0.013820    test-rmse:0.639416+0.053234 
## [13] train-rmse:0.289240+0.012501    test-rmse:0.639485+0.050900 
## [14] train-rmse:0.274033+0.012320    test-rmse:0.634531+0.051847 
## [15] train-rmse:0.257338+0.014579    test-rmse:0.638362+0.057250 
## [16] train-rmse:0.243009+0.014498    test-rmse:0.636674+0.061256 
## [17] train-rmse:0.226176+0.011246    test-rmse:0.633667+0.057954 
## [18] train-rmse:0.212198+0.011222    test-rmse:0.633015+0.062292 
## [19] train-rmse:0.200220+0.010453    test-rmse:0.633508+0.062951 
## [20] train-rmse:0.189696+0.010337    test-rmse:0.635293+0.065212 
## [21] train-rmse:0.180697+0.011551    test-rmse:0.636368+0.067131 
## [22] train-rmse:0.173923+0.012089    test-rmse:0.637791+0.066110 
## [23] train-rmse:0.164067+0.014186    test-rmse:0.635612+0.064781 
## [24] train-rmse:0.157210+0.012074    test-rmse:0.635587+0.064223 
## Stopping. Best iteration:
## [18] train-rmse:0.212198+0.011222    test-rmse:0.633015+0.062292
## 
## 98 
## [1]  train-rmse:1.159224+0.010618    test-rmse:1.162948+0.071119 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.929923+0.007465    test-rmse:0.937554+0.063435 
## [3]  train-rmse:0.817639+0.007335    test-rmse:0.830631+0.054426 
## [4]  train-rmse:0.765605+0.006356    test-rmse:0.788185+0.046016 
## [5]  train-rmse:0.738815+0.005807    test-rmse:0.765819+0.039468 
## [6]  train-rmse:0.723271+0.005715    test-rmse:0.757156+0.036899 
## [7]  train-rmse:0.712117+0.005445    test-rmse:0.745265+0.037015 
## [8]  train-rmse:0.703439+0.004943    test-rmse:0.737591+0.034197 
## [9]  train-rmse:0.696497+0.004860    test-rmse:0.736672+0.031667 
## [10] train-rmse:0.690795+0.005047    test-rmse:0.734433+0.031633 
## [11] train-rmse:0.685854+0.005179    test-rmse:0.731899+0.029986 
## [12] train-rmse:0.681177+0.005329    test-rmse:0.730232+0.029115 
## [13] train-rmse:0.676829+0.005380    test-rmse:0.728949+0.027722 
## [14] train-rmse:0.672720+0.005410    test-rmse:0.726937+0.028524 
## [15] train-rmse:0.668802+0.005388    test-rmse:0.726672+0.030426 
## [16] train-rmse:0.665189+0.005341    test-rmse:0.722357+0.029122 
## [17] train-rmse:0.661780+0.005367    test-rmse:0.723282+0.031004 
## [18] train-rmse:0.658363+0.005355    test-rmse:0.720724+0.032207 
## [19] train-rmse:0.655069+0.005304    test-rmse:0.718096+0.032763 
## [20] train-rmse:0.651974+0.005376    test-rmse:0.717928+0.037679 
## [21] train-rmse:0.648963+0.005512    test-rmse:0.718162+0.037391 
## [22] train-rmse:0.646035+0.005545    test-rmse:0.713917+0.036956 
## [23] train-rmse:0.643290+0.005718    test-rmse:0.716427+0.036265 
## [24] train-rmse:0.640674+0.005814    test-rmse:0.715680+0.036086 
## [25] train-rmse:0.638218+0.005813    test-rmse:0.714719+0.034835 
## [26] train-rmse:0.635965+0.005883    test-rmse:0.713719+0.035556 
## [27] train-rmse:0.633843+0.005816    test-rmse:0.710424+0.033301 
## [28] train-rmse:0.631754+0.005766    test-rmse:0.711726+0.035408 
## [29] train-rmse:0.629834+0.005714    test-rmse:0.709460+0.033759 
## [30] train-rmse:0.627854+0.005750    test-rmse:0.707332+0.032833 
## [31] train-rmse:0.625916+0.005824    test-rmse:0.707031+0.033173 
## [32] train-rmse:0.624051+0.005849    test-rmse:0.704477+0.034558 
## [33] train-rmse:0.622205+0.005791    test-rmse:0.706663+0.037323 
## [34] train-rmse:0.620345+0.005755    test-rmse:0.704193+0.037654 
## [35] train-rmse:0.618578+0.005690    test-rmse:0.702817+0.036260 
## [36] train-rmse:0.616910+0.005695    test-rmse:0.701322+0.036073 
## [37] train-rmse:0.615294+0.005673    test-rmse:0.699914+0.033532 
## [38] train-rmse:0.613659+0.005688    test-rmse:0.701271+0.034715 
## [39] train-rmse:0.612137+0.005735    test-rmse:0.700709+0.033595 
## [40] train-rmse:0.610612+0.005826    test-rmse:0.699456+0.033640 
## [41] train-rmse:0.609135+0.005851    test-rmse:0.697724+0.034237 
## [42] train-rmse:0.607704+0.005844    test-rmse:0.697302+0.034555 
## [43] train-rmse:0.606271+0.005884    test-rmse:0.696988+0.034864 
## [44] train-rmse:0.604873+0.005907    test-rmse:0.697504+0.034000 
## [45] train-rmse:0.603544+0.005929    test-rmse:0.695619+0.035009 
## [46] train-rmse:0.602255+0.005958    test-rmse:0.695414+0.034193 
## [47] train-rmse:0.600943+0.005983    test-rmse:0.692693+0.033302 
## [48] train-rmse:0.599668+0.006008    test-rmse:0.693212+0.033728 
## [49] train-rmse:0.598433+0.006011    test-rmse:0.691931+0.034169 
## [50] train-rmse:0.597222+0.006046    test-rmse:0.691116+0.035349 
## [51] train-rmse:0.595986+0.006088    test-rmse:0.691738+0.035873 
## [52] train-rmse:0.594800+0.006154    test-rmse:0.691273+0.036075 
## [53] train-rmse:0.593697+0.006202    test-rmse:0.690001+0.036685 
## [54] train-rmse:0.592576+0.006196    test-rmse:0.688346+0.035468 
## [55] train-rmse:0.591507+0.006223    test-rmse:0.689080+0.035465 
## [56] train-rmse:0.590493+0.006209    test-rmse:0.690051+0.036262 
## [57] train-rmse:0.589462+0.006215    test-rmse:0.689543+0.035681 
## [58] train-rmse:0.588485+0.006246    test-rmse:0.688882+0.036400 
## [59] train-rmse:0.587496+0.006274    test-rmse:0.687146+0.034376 
## [60] train-rmse:0.586525+0.006352    test-rmse:0.686682+0.035070 
## [61] train-rmse:0.585573+0.006392    test-rmse:0.686550+0.033903 
## [62] train-rmse:0.584600+0.006389    test-rmse:0.685875+0.035192 
## [63] train-rmse:0.583675+0.006451    test-rmse:0.686553+0.035434 
## [64] train-rmse:0.582791+0.006490    test-rmse:0.686202+0.035727 
## [65] train-rmse:0.581911+0.006499    test-rmse:0.686900+0.036396 
## [66] train-rmse:0.581069+0.006520    test-rmse:0.686482+0.035882 
## [67] train-rmse:0.580200+0.006551    test-rmse:0.686295+0.035023 
## [68] train-rmse:0.579394+0.006542    test-rmse:0.685738+0.034687 
## [69] train-rmse:0.578574+0.006571    test-rmse:0.683694+0.035562 
## [70] train-rmse:0.577766+0.006583    test-rmse:0.682237+0.035325 
## [71] train-rmse:0.576979+0.006597    test-rmse:0.682072+0.034502 
## [72] train-rmse:0.576190+0.006595    test-rmse:0.679360+0.034572 
## [73] train-rmse:0.575420+0.006596    test-rmse:0.679062+0.034521 
## [74] train-rmse:0.574655+0.006608    test-rmse:0.679625+0.035273 
## [75] train-rmse:0.573913+0.006642    test-rmse:0.680697+0.035269 
## [76] train-rmse:0.573168+0.006664    test-rmse:0.680025+0.036040 
## [77] train-rmse:0.572404+0.006734    test-rmse:0.680240+0.035622 
## [78] train-rmse:0.571692+0.006758    test-rmse:0.678822+0.035108 
## [79] train-rmse:0.570990+0.006773    test-rmse:0.678773+0.034419 
## [80] train-rmse:0.570298+0.006796    test-rmse:0.678365+0.034325 
## [81] train-rmse:0.569654+0.006801    test-rmse:0.679026+0.034592 
## [82] train-rmse:0.568985+0.006781    test-rmse:0.678975+0.035771 
## [83] train-rmse:0.568348+0.006803    test-rmse:0.678780+0.037113 
## [84] train-rmse:0.567717+0.006812    test-rmse:0.677611+0.037682 
## [85] train-rmse:0.567067+0.006828    test-rmse:0.676532+0.038078 
## [86] train-rmse:0.566437+0.006875    test-rmse:0.677643+0.038170 
## [87] train-rmse:0.565842+0.006902    test-rmse:0.677883+0.037401 
## [88] train-rmse:0.565254+0.006928    test-rmse:0.678775+0.037324 
## [89] train-rmse:0.564659+0.006927    test-rmse:0.677767+0.037057 
## [90] train-rmse:0.564049+0.006954    test-rmse:0.678446+0.036572 
## [91] train-rmse:0.563446+0.006965    test-rmse:0.679209+0.036831 
## Stopping. Best iteration:
## [85] train-rmse:0.567067+0.006828    test-rmse:0.676532+0.038078
## 
## 99 
## [1]  train-rmse:1.147340+0.010339    test-rmse:1.159439+0.071465 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.904889+0.006413    test-rmse:0.932673+0.067430 
## [3]  train-rmse:0.783195+0.002625    test-rmse:0.822104+0.060404 
## [4]  train-rmse:0.715445+0.005616    test-rmse:0.766805+0.049821 
## [5]  train-rmse:0.681550+0.005660    test-rmse:0.747099+0.043488 
## [6]  train-rmse:0.660973+0.004572    test-rmse:0.733297+0.038191 
## [7]  train-rmse:0.645977+0.006969    test-rmse:0.722703+0.036200 
## [8]  train-rmse:0.631467+0.008783    test-rmse:0.710495+0.041354 
## [9]  train-rmse:0.622342+0.008942    test-rmse:0.713228+0.049334 
## [10] train-rmse:0.614548+0.009414    test-rmse:0.709177+0.046094 
## [11] train-rmse:0.600323+0.007938    test-rmse:0.702145+0.045354 
## [12] train-rmse:0.592726+0.007167    test-rmse:0.700988+0.045623 
## [13] train-rmse:0.585653+0.008277    test-rmse:0.696135+0.048862 
## [14] train-rmse:0.578479+0.009253    test-rmse:0.695242+0.050293 
## [15] train-rmse:0.572158+0.008316    test-rmse:0.695677+0.053735 
## [16] train-rmse:0.564170+0.013040    test-rmse:0.692587+0.052502 
## [17] train-rmse:0.559215+0.013232    test-rmse:0.693209+0.052316 
## [18] train-rmse:0.554835+0.013081    test-rmse:0.692128+0.052940 
## [19] train-rmse:0.545843+0.011579    test-rmse:0.684227+0.051323 
## [20] train-rmse:0.540274+0.010965    test-rmse:0.681386+0.048663 
## [21] train-rmse:0.536192+0.010577    test-rmse:0.678037+0.049279 
## [22] train-rmse:0.532561+0.011254    test-rmse:0.676756+0.048693 
## [23] train-rmse:0.526784+0.009685    test-rmse:0.672711+0.054146 
## [24] train-rmse:0.521817+0.009238    test-rmse:0.672631+0.054356 
## [25] train-rmse:0.517935+0.009420    test-rmse:0.669525+0.055082 
## [26] train-rmse:0.512785+0.010633    test-rmse:0.665903+0.052139 
## [27] train-rmse:0.508989+0.011321    test-rmse:0.662999+0.053732 
## [28] train-rmse:0.505720+0.011047    test-rmse:0.662428+0.052663 
## [29] train-rmse:0.502079+0.011340    test-rmse:0.662862+0.052592 
## [30] train-rmse:0.497312+0.010103    test-rmse:0.659679+0.050931 
## [31] train-rmse:0.493330+0.009447    test-rmse:0.658603+0.050690 
## [32] train-rmse:0.489490+0.009161    test-rmse:0.656383+0.054369 
## [33] train-rmse:0.487054+0.009016    test-rmse:0.656483+0.053880 
## [34] train-rmse:0.483350+0.010437    test-rmse:0.657195+0.055079 
## [35] train-rmse:0.476523+0.013133    test-rmse:0.655446+0.055121 
## [36] train-rmse:0.472517+0.012878    test-rmse:0.655924+0.054099 
## [37] train-rmse:0.469260+0.012653    test-rmse:0.653082+0.055907 
## [38] train-rmse:0.465394+0.013183    test-rmse:0.651581+0.054114 
## [39] train-rmse:0.462538+0.012424    test-rmse:0.651535+0.053237 
## [40] train-rmse:0.458431+0.015482    test-rmse:0.647887+0.053255 
## [41] train-rmse:0.455432+0.014791    test-rmse:0.647005+0.053014 
## [42] train-rmse:0.451748+0.013675    test-rmse:0.643798+0.050950 
## [43] train-rmse:0.449465+0.013346    test-rmse:0.642433+0.050547 
## [44] train-rmse:0.447658+0.013475    test-rmse:0.641844+0.050349 
## [45] train-rmse:0.444525+0.014261    test-rmse:0.643688+0.050215 
## [46] train-rmse:0.442230+0.014215    test-rmse:0.645760+0.050395 
## [47] train-rmse:0.438414+0.014161    test-rmse:0.646059+0.050759 
## [48] train-rmse:0.435179+0.016701    test-rmse:0.647405+0.050972 
## [49] train-rmse:0.433449+0.016687    test-rmse:0.648241+0.050988 
## [50] train-rmse:0.431147+0.015897    test-rmse:0.647926+0.052009 
## Stopping. Best iteration:
## [44] train-rmse:0.447658+0.013475    test-rmse:0.641844+0.050349
## 
## 100 
## [1]  train-rmse:1.139152+0.009663    test-rmse:1.155321+0.069237 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.883940+0.005666    test-rmse:0.924018+0.070036 
## [3]  train-rmse:0.753953+0.004868    test-rmse:0.807407+0.063585 
## [4]  train-rmse:0.683460+0.010345    test-rmse:0.751470+0.044580 
## [5]  train-rmse:0.638911+0.009762    test-rmse:0.723436+0.046575 
## [6]  train-rmse:0.610272+0.006809    test-rmse:0.706459+0.041451 
## [7]  train-rmse:0.591928+0.005428    test-rmse:0.701855+0.041355 
## [8]  train-rmse:0.577970+0.008167    test-rmse:0.693914+0.038931 
## [9]  train-rmse:0.565544+0.007226    test-rmse:0.691262+0.043737 
## [10] train-rmse:0.555229+0.007434    test-rmse:0.691917+0.045790 
## [11] train-rmse:0.540303+0.010019    test-rmse:0.684606+0.046797 
## [12] train-rmse:0.529792+0.007554    test-rmse:0.681433+0.048186 
## [13] train-rmse:0.523236+0.006650    test-rmse:0.686754+0.049752 
## [14] train-rmse:0.510031+0.011683    test-rmse:0.678097+0.043676 
## [15] train-rmse:0.501084+0.014535    test-rmse:0.674925+0.043300 
## [16] train-rmse:0.491066+0.010458    test-rmse:0.662749+0.048007 
## [17] train-rmse:0.484290+0.010943    test-rmse:0.658870+0.050752 
## [18] train-rmse:0.476566+0.010282    test-rmse:0.655791+0.051566 
## [19] train-rmse:0.468126+0.010406    test-rmse:0.648682+0.046484 
## [20] train-rmse:0.457312+0.008704    test-rmse:0.642686+0.045236 
## [21] train-rmse:0.449539+0.011116    test-rmse:0.645032+0.046206 
## [22] train-rmse:0.444047+0.011480    test-rmse:0.647801+0.049048 
## [23] train-rmse:0.438486+0.010642    test-rmse:0.645883+0.048413 
## [24] train-rmse:0.430846+0.010288    test-rmse:0.643804+0.045350 
## [25] train-rmse:0.426844+0.011038    test-rmse:0.641554+0.044699 
## [26] train-rmse:0.421209+0.010875    test-rmse:0.642250+0.045507 
## [27] train-rmse:0.415585+0.012458    test-rmse:0.643017+0.046679 
## [28] train-rmse:0.407641+0.012772    test-rmse:0.640252+0.047900 
## [29] train-rmse:0.402354+0.014253    test-rmse:0.638444+0.046595 
## [30] train-rmse:0.396657+0.013959    test-rmse:0.637164+0.050682 
## [31] train-rmse:0.390964+0.015616    test-rmse:0.638961+0.051412 
## [32] train-rmse:0.384873+0.014308    test-rmse:0.639345+0.050109 
## [33] train-rmse:0.379057+0.014435    test-rmse:0.635585+0.050770 
## [34] train-rmse:0.373845+0.014687    test-rmse:0.639244+0.047387 
## [35] train-rmse:0.367208+0.012065    test-rmse:0.634856+0.048049 
## [36] train-rmse:0.362848+0.012096    test-rmse:0.635916+0.050197 
## [37] train-rmse:0.359149+0.011163    test-rmse:0.635898+0.048987 
## [38] train-rmse:0.355568+0.011821    test-rmse:0.635455+0.048501 
## [39] train-rmse:0.350734+0.011462    test-rmse:0.632311+0.048301 
## [40] train-rmse:0.347023+0.011496    test-rmse:0.631974+0.050317 
## [41] train-rmse:0.343477+0.012583    test-rmse:0.631218+0.050974 
## [42] train-rmse:0.339230+0.013228    test-rmse:0.632230+0.050600 
## [43] train-rmse:0.334297+0.013691    test-rmse:0.630671+0.051931 
## [44] train-rmse:0.328770+0.012423    test-rmse:0.629238+0.051165 
## [45] train-rmse:0.324700+0.013361    test-rmse:0.628148+0.049710 
## [46] train-rmse:0.320512+0.014422    test-rmse:0.629653+0.051443 
## [47] train-rmse:0.317126+0.013633    test-rmse:0.629572+0.051799 
## [48] train-rmse:0.313639+0.012396    test-rmse:0.628790+0.051111 
## [49] train-rmse:0.309529+0.012394    test-rmse:0.625916+0.049037 
## [50] train-rmse:0.306614+0.011127    test-rmse:0.625129+0.049023 
## [51] train-rmse:0.303777+0.009652    test-rmse:0.625131+0.049745 
## [52] train-rmse:0.300200+0.008437    test-rmse:0.625093+0.049054 
## [53] train-rmse:0.296610+0.009665    test-rmse:0.627599+0.050998 
## [54] train-rmse:0.293022+0.010209    test-rmse:0.628442+0.052588 
## [55] train-rmse:0.289271+0.010844    test-rmse:0.628401+0.051846 
## [56] train-rmse:0.286280+0.010926    test-rmse:0.626718+0.050994 
## [57] train-rmse:0.283046+0.010814    test-rmse:0.625134+0.052269 
## [58] train-rmse:0.280907+0.010511    test-rmse:0.626333+0.052236 
## Stopping. Best iteration:
## [52] train-rmse:0.300200+0.008437    test-rmse:0.625093+0.049054
## 
## 101 
## [1]  train-rmse:1.130604+0.009879    test-rmse:1.160370+0.071592 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.863881+0.004001    test-rmse:0.921574+0.080451 
## [3]  train-rmse:0.720167+0.006467    test-rmse:0.794825+0.064997 
## [4]  train-rmse:0.636515+0.014832    test-rmse:0.735490+0.059986 
## [5]  train-rmse:0.586154+0.012206    test-rmse:0.708981+0.065223 
## [6]  train-rmse:0.557285+0.011467    test-rmse:0.695229+0.057910 
## [7]  train-rmse:0.532848+0.010162    test-rmse:0.689244+0.068722 
## [8]  train-rmse:0.509049+0.008959    test-rmse:0.677568+0.056783 
## [9]  train-rmse:0.488585+0.013554    test-rmse:0.667868+0.055834 
## [10] train-rmse:0.474541+0.012971    test-rmse:0.662321+0.052789 
## [11] train-rmse:0.465212+0.013390    test-rmse:0.663417+0.052322 
## [12] train-rmse:0.446442+0.009359    test-rmse:0.656421+0.056572 
## [13] train-rmse:0.434500+0.010632    test-rmse:0.651664+0.056797 
## [14] train-rmse:0.423119+0.011794    test-rmse:0.647771+0.057175 
## [15] train-rmse:0.411577+0.011122    test-rmse:0.644224+0.058200 
## [16] train-rmse:0.404231+0.010943    test-rmse:0.646549+0.059966 
## [17] train-rmse:0.391368+0.011988    test-rmse:0.641293+0.056350 
## [18] train-rmse:0.378854+0.012055    test-rmse:0.638488+0.057117 
## [19] train-rmse:0.370807+0.014694    test-rmse:0.638185+0.057562 
## [20] train-rmse:0.363152+0.013558    test-rmse:0.636705+0.057950 
## [21] train-rmse:0.354768+0.011765    test-rmse:0.634932+0.057777 
## [22] train-rmse:0.347586+0.012844    test-rmse:0.634520+0.058952 
## [23] train-rmse:0.339192+0.013641    test-rmse:0.632384+0.060187 
## [24] train-rmse:0.330461+0.010980    test-rmse:0.630624+0.059582 
## [25] train-rmse:0.323119+0.009498    test-rmse:0.627269+0.057819 
## [26] train-rmse:0.315952+0.011871    test-rmse:0.627596+0.058509 
## [27] train-rmse:0.310003+0.010982    test-rmse:0.629586+0.059953 
## [28] train-rmse:0.304795+0.012954    test-rmse:0.630830+0.062108 
## [29] train-rmse:0.299398+0.011796    test-rmse:0.634410+0.060795 
## [30] train-rmse:0.292207+0.013303    test-rmse:0.637007+0.059986 
## [31] train-rmse:0.285828+0.012772    test-rmse:0.635896+0.059036 
## Stopping. Best iteration:
## [25] train-rmse:0.323119+0.009498    test-rmse:0.627269+0.057819
## 
## 102 
## [1]  train-rmse:1.124719+0.010204    test-rmse:1.152895+0.076835 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.845856+0.006188    test-rmse:0.904480+0.076691 
## [3]  train-rmse:0.691793+0.009325    test-rmse:0.782846+0.059127 
## [4]  train-rmse:0.608295+0.008312    test-rmse:0.719482+0.055110 
## [5]  train-rmse:0.553512+0.006450    test-rmse:0.697102+0.050127 
## [6]  train-rmse:0.519410+0.009653    test-rmse:0.687762+0.051045 
## [7]  train-rmse:0.490330+0.013679    test-rmse:0.683216+0.052764 
## [8]  train-rmse:0.466598+0.013005    test-rmse:0.680976+0.055874 
## [9]  train-rmse:0.453490+0.013300    test-rmse:0.681007+0.051919 
## [10] train-rmse:0.426482+0.016455    test-rmse:0.660673+0.053986 
## [11] train-rmse:0.408757+0.022756    test-rmse:0.662568+0.053844 
## [12] train-rmse:0.397323+0.020599    test-rmse:0.663246+0.056399 
## [13] train-rmse:0.383756+0.022350    test-rmse:0.659335+0.057251 
## [14] train-rmse:0.363744+0.016650    test-rmse:0.647961+0.056728 
## [15] train-rmse:0.351482+0.017857    test-rmse:0.648459+0.054089 
## [16] train-rmse:0.339898+0.014785    test-rmse:0.649836+0.053311 
## [17] train-rmse:0.329044+0.012582    test-rmse:0.646306+0.055073 
## [18] train-rmse:0.319500+0.012841    test-rmse:0.644232+0.054183 
## [19] train-rmse:0.307125+0.009673    test-rmse:0.638847+0.051942 
## [20] train-rmse:0.295888+0.012190    test-rmse:0.638358+0.048550 
## [21] train-rmse:0.284468+0.011536    test-rmse:0.634823+0.049876 
## [22] train-rmse:0.272525+0.010293    test-rmse:0.632859+0.054104 
## [23] train-rmse:0.264676+0.008536    test-rmse:0.635226+0.054235 
## [24] train-rmse:0.258268+0.008079    test-rmse:0.636390+0.053393 
## [25] train-rmse:0.248057+0.009507    test-rmse:0.639175+0.054036 
## [26] train-rmse:0.240618+0.007634    test-rmse:0.635821+0.053218 
## [27] train-rmse:0.233289+0.009956    test-rmse:0.635922+0.054472 
## [28] train-rmse:0.228210+0.010283    test-rmse:0.636532+0.057128 
## Stopping. Best iteration:
## [22] train-rmse:0.272525+0.010293    test-rmse:0.632859+0.054104
## 
## 103 
## [1]  train-rmse:1.118691+0.009240    test-rmse:1.156013+0.078996 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.831471+0.005272    test-rmse:0.907313+0.074154 
## [3]  train-rmse:0.665469+0.008149    test-rmse:0.772461+0.061542 
## [4]  train-rmse:0.574299+0.010038    test-rmse:0.706097+0.066375 
## [5]  train-rmse:0.506552+0.017148    test-rmse:0.672225+0.050186 
## [6]  train-rmse:0.467238+0.019790    test-rmse:0.659664+0.048454 
## [7]  train-rmse:0.436542+0.018336    test-rmse:0.648358+0.047896 
## [8]  train-rmse:0.413379+0.013172    test-rmse:0.639189+0.042840 
## [9]  train-rmse:0.395121+0.011999    test-rmse:0.637081+0.039260 
## [10] train-rmse:0.377825+0.014368    test-rmse:0.635064+0.041726 
## [11] train-rmse:0.353871+0.018978    test-rmse:0.635458+0.038568 
## [12] train-rmse:0.339833+0.021106    test-rmse:0.631641+0.039332 
## [13] train-rmse:0.322278+0.021321    test-rmse:0.629950+0.040386 
## [14] train-rmse:0.305591+0.018842    test-rmse:0.621748+0.037514 
## [15] train-rmse:0.293184+0.020120    test-rmse:0.621926+0.034581 
## [16] train-rmse:0.279480+0.023740    test-rmse:0.625939+0.034600 
## [17] train-rmse:0.267438+0.019029    test-rmse:0.624638+0.034850 
## [18] train-rmse:0.255754+0.018496    test-rmse:0.627378+0.035574 
## [19] train-rmse:0.242758+0.020735    test-rmse:0.624147+0.039557 
## [20] train-rmse:0.233738+0.021741    test-rmse:0.625708+0.041879 
## Stopping. Best iteration:
## [14] train-rmse:0.305591+0.018842    test-rmse:0.621748+0.037514
## 
## 104 
## [1]  train-rmse:1.114279+0.008461    test-rmse:1.153937+0.079318 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.819521+0.004283    test-rmse:0.896927+0.079361 
## [3]  train-rmse:0.645414+0.011754    test-rmse:0.774283+0.078419 
## [4]  train-rmse:0.546569+0.011368    test-rmse:0.709228+0.073107 
## [5]  train-rmse:0.476384+0.014302    test-rmse:0.680108+0.065330 
## [6]  train-rmse:0.431078+0.015423    test-rmse:0.665788+0.067483 
## [7]  train-rmse:0.397273+0.010146    test-rmse:0.662054+0.068278 
## [8]  train-rmse:0.372269+0.013870    test-rmse:0.660888+0.063599 
## [9]  train-rmse:0.346527+0.015257    test-rmse:0.648971+0.059960 
## [10] train-rmse:0.328330+0.017225    test-rmse:0.649755+0.058117 
## [11] train-rmse:0.312274+0.014294    test-rmse:0.648170+0.063229 
## [12] train-rmse:0.293591+0.018863    test-rmse:0.647898+0.063144 
## [13] train-rmse:0.275663+0.021295    test-rmse:0.652905+0.065469 
## [14] train-rmse:0.264973+0.023397    test-rmse:0.650995+0.066699 
## [15] train-rmse:0.248952+0.022305    test-rmse:0.649718+0.067219 
## [16] train-rmse:0.235888+0.021856    test-rmse:0.648648+0.067210 
## [17] train-rmse:0.220782+0.017729    test-rmse:0.647639+0.066719 
## [18] train-rmse:0.208683+0.016252    test-rmse:0.647656+0.068475 
## [19] train-rmse:0.194509+0.018062    test-rmse:0.641633+0.064465 
## [20] train-rmse:0.186326+0.015413    test-rmse:0.644042+0.063400 
## [21] train-rmse:0.177238+0.016183    test-rmse:0.644535+0.062972 
## [22] train-rmse:0.168093+0.014699    test-rmse:0.645397+0.065040 
## [23] train-rmse:0.157820+0.013280    test-rmse:0.646591+0.063855 
## [24] train-rmse:0.148554+0.013249    test-rmse:0.645832+0.062311 
## [25] train-rmse:0.140028+0.013145    test-rmse:0.643234+0.058210 
## Stopping. Best iteration:
## [19] train-rmse:0.194509+0.018062    test-rmse:0.641633+0.064465
## 
## 105 
## [1]  train-rmse:1.137889+0.010305    test-rmse:1.141956+0.071077 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.909891+0.007227    test-rmse:0.918147+0.062296 
## [3]  train-rmse:0.803389+0.007312    test-rmse:0.817333+0.052701 
## [4]  train-rmse:0.756969+0.006375    test-rmse:0.781706+0.045874 
## [5]  train-rmse:0.733560+0.005674    test-rmse:0.763888+0.041392 
## [6]  train-rmse:0.719463+0.005615    test-rmse:0.754543+0.036102 
## [7]  train-rmse:0.708825+0.005360    test-rmse:0.743155+0.036667 
## [8]  train-rmse:0.700204+0.004832    test-rmse:0.736009+0.034514 
## [9]  train-rmse:0.693328+0.004636    test-rmse:0.735418+0.031612 
## [10] train-rmse:0.687662+0.005010    test-rmse:0.730869+0.029066 
## [11] train-rmse:0.682550+0.005130    test-rmse:0.729710+0.029028 
## [12] train-rmse:0.677775+0.005257    test-rmse:0.730578+0.029889 
## [13] train-rmse:0.673495+0.005265    test-rmse:0.728413+0.027947 
## [14] train-rmse:0.669329+0.005222    test-rmse:0.725170+0.028965 
## [15] train-rmse:0.665444+0.005182    test-rmse:0.724523+0.030842 
## [16] train-rmse:0.661825+0.005249    test-rmse:0.722944+0.032191 
## [17] train-rmse:0.658420+0.005226    test-rmse:0.721850+0.035113 
## [18] train-rmse:0.655102+0.005225    test-rmse:0.719965+0.034695 
## [19] train-rmse:0.651844+0.005293    test-rmse:0.718267+0.033889 
## [20] train-rmse:0.648687+0.005396    test-rmse:0.718209+0.034692 
## [21] train-rmse:0.645688+0.005480    test-rmse:0.715872+0.034937 
## [22] train-rmse:0.642731+0.005543    test-rmse:0.714221+0.036246 
## [23] train-rmse:0.639946+0.005660    test-rmse:0.714760+0.036783 
## [24] train-rmse:0.637300+0.005692    test-rmse:0.713040+0.035918 
## [25] train-rmse:0.634853+0.005754    test-rmse:0.712451+0.034592 
## [26] train-rmse:0.632535+0.005845    test-rmse:0.711525+0.034452 
## [27] train-rmse:0.630344+0.005820    test-rmse:0.707246+0.031917 
## [28] train-rmse:0.628220+0.005827    test-rmse:0.704693+0.034003 
## [29] train-rmse:0.626139+0.005838    test-rmse:0.705017+0.035017 
## [30] train-rmse:0.624189+0.005835    test-rmse:0.704638+0.036155 
## [31] train-rmse:0.622242+0.005843    test-rmse:0.701978+0.035221 
## [32] train-rmse:0.620334+0.005766    test-rmse:0.702101+0.034678 
## [33] train-rmse:0.618435+0.005744    test-rmse:0.703120+0.038380 
## [34] train-rmse:0.616637+0.005679    test-rmse:0.700859+0.036722 
## [35] train-rmse:0.614917+0.005717    test-rmse:0.700045+0.035877 
## [36] train-rmse:0.613255+0.005730    test-rmse:0.699135+0.033331 
## [37] train-rmse:0.611614+0.005764    test-rmse:0.697835+0.032960 
## [38] train-rmse:0.610023+0.005802    test-rmse:0.699804+0.034501 
## [39] train-rmse:0.608470+0.005854    test-rmse:0.698950+0.033552 
## [40] train-rmse:0.607002+0.005875    test-rmse:0.696807+0.034119 
## [41] train-rmse:0.605477+0.005865    test-rmse:0.696689+0.034081 
## [42] train-rmse:0.604030+0.005882    test-rmse:0.696640+0.034917 
## [43] train-rmse:0.602707+0.005900    test-rmse:0.696007+0.035193 
## [44] train-rmse:0.601385+0.005914    test-rmse:0.695332+0.035034 
## [45] train-rmse:0.600076+0.005954    test-rmse:0.694770+0.036642 
## [46] train-rmse:0.598779+0.005998    test-rmse:0.691054+0.036287 
## [47] train-rmse:0.597450+0.006080    test-rmse:0.689202+0.035176 
## [48] train-rmse:0.596161+0.006149    test-rmse:0.690875+0.036085 
## [49] train-rmse:0.594909+0.006198    test-rmse:0.691091+0.035397 
## [50] train-rmse:0.593674+0.006234    test-rmse:0.690814+0.035237 
## [51] train-rmse:0.592474+0.006257    test-rmse:0.690805+0.035279 
## [52] train-rmse:0.591286+0.006341    test-rmse:0.689992+0.035845 
## [53] train-rmse:0.590175+0.006327    test-rmse:0.689003+0.037933 
## [54] train-rmse:0.589108+0.006350    test-rmse:0.688588+0.036903 
## [55] train-rmse:0.588063+0.006363    test-rmse:0.686115+0.036062 
## [56] train-rmse:0.587058+0.006361    test-rmse:0.686801+0.035692 
## [57] train-rmse:0.586048+0.006362    test-rmse:0.685393+0.034831 
## [58] train-rmse:0.585024+0.006390    test-rmse:0.686235+0.035161 
## [59] train-rmse:0.584055+0.006389    test-rmse:0.686139+0.034133 
## [60] train-rmse:0.583093+0.006415    test-rmse:0.685064+0.035026 
## [61] train-rmse:0.582130+0.006472    test-rmse:0.685638+0.035534 
## [62] train-rmse:0.581234+0.006498    test-rmse:0.684195+0.035217 
## [63] train-rmse:0.580362+0.006556    test-rmse:0.683697+0.033886 
## [64] train-rmse:0.579470+0.006603    test-rmse:0.684260+0.033653 
## [65] train-rmse:0.578597+0.006621    test-rmse:0.684083+0.033339 
## [66] train-rmse:0.577755+0.006664    test-rmse:0.681636+0.033779 
## [67] train-rmse:0.576949+0.006722    test-rmse:0.680994+0.034691 
## [68] train-rmse:0.576139+0.006747    test-rmse:0.679667+0.034441 
## [69] train-rmse:0.575353+0.006785    test-rmse:0.678659+0.034972 
## [70] train-rmse:0.574541+0.006780    test-rmse:0.679879+0.034710 
## [71] train-rmse:0.573762+0.006771    test-rmse:0.678200+0.035743 
## [72] train-rmse:0.572960+0.006777    test-rmse:0.677483+0.035836 
## [73] train-rmse:0.572206+0.006816    test-rmse:0.678304+0.034811 
## [74] train-rmse:0.571464+0.006838    test-rmse:0.677266+0.035239 
## [75] train-rmse:0.570743+0.006861    test-rmse:0.676590+0.035031 
## [76] train-rmse:0.570038+0.006888    test-rmse:0.677464+0.035460 
## [77] train-rmse:0.569318+0.006892    test-rmse:0.677400+0.035434 
## [78] train-rmse:0.568639+0.006923    test-rmse:0.677311+0.036001 
## [79] train-rmse:0.567942+0.006976    test-rmse:0.677254+0.035460 
## [80] train-rmse:0.567238+0.006983    test-rmse:0.676925+0.036006 
## [81] train-rmse:0.566553+0.007033    test-rmse:0.676974+0.036763 
## Stopping. Best iteration:
## [75] train-rmse:0.570743+0.006861    test-rmse:0.676590+0.035031
## 
## 106 
## [1]  train-rmse:1.125250+0.010004    test-rmse:1.138258+0.071432 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.883240+0.006103    test-rmse:0.912922+0.066369 
## [3]  train-rmse:0.765622+0.006626    test-rmse:0.805164+0.056228 
## [4]  train-rmse:0.703367+0.006457    test-rmse:0.753912+0.044165 
## [5]  train-rmse:0.673194+0.007244    test-rmse:0.735502+0.037436 
## [6]  train-rmse:0.653694+0.006358    test-rmse:0.720581+0.030648 
## [7]  train-rmse:0.639779+0.008421    test-rmse:0.711312+0.029127 
## [8]  train-rmse:0.628359+0.010009    test-rmse:0.710725+0.030296 
## [9]  train-rmse:0.618772+0.009709    test-rmse:0.706006+0.026298 
## [10] train-rmse:0.609721+0.009722    test-rmse:0.699671+0.029875 
## [11] train-rmse:0.598975+0.008975    test-rmse:0.690874+0.029723 
## [12] train-rmse:0.592331+0.008611    test-rmse:0.688887+0.031265 
## [13] train-rmse:0.582357+0.006624    test-rmse:0.683253+0.029679 
## [14] train-rmse:0.576595+0.006912    test-rmse:0.683505+0.035525 
## [15] train-rmse:0.568604+0.007158    test-rmse:0.682689+0.034838 
## [16] train-rmse:0.562631+0.006756    test-rmse:0.681206+0.035263 
## [17] train-rmse:0.556096+0.009308    test-rmse:0.680962+0.031114 
## [18] train-rmse:0.551365+0.008537    test-rmse:0.676615+0.033562 
## [19] train-rmse:0.546048+0.008354    test-rmse:0.672741+0.033722 
## [20] train-rmse:0.539870+0.009805    test-rmse:0.672131+0.036980 
## [21] train-rmse:0.533911+0.011863    test-rmse:0.670404+0.034344 
## [22] train-rmse:0.530330+0.011764    test-rmse:0.669389+0.034894 
## [23] train-rmse:0.524413+0.012353    test-rmse:0.666225+0.031738 
## [24] train-rmse:0.520461+0.013332    test-rmse:0.664493+0.030374 
## [25] train-rmse:0.516148+0.012281    test-rmse:0.662757+0.033915 
## [26] train-rmse:0.509875+0.007248    test-rmse:0.658829+0.036921 
## [27] train-rmse:0.504962+0.008149    test-rmse:0.654372+0.038076 
## [28] train-rmse:0.501608+0.008154    test-rmse:0.653616+0.036610 
## [29] train-rmse:0.495299+0.008434    test-rmse:0.651787+0.035786 
## [30] train-rmse:0.492128+0.008717    test-rmse:0.651614+0.036826 
## [31] train-rmse:0.487134+0.007600    test-rmse:0.652155+0.035949 
## [32] train-rmse:0.483632+0.007273    test-rmse:0.653114+0.037715 
## [33] train-rmse:0.477846+0.009897    test-rmse:0.649808+0.035993 
## [34] train-rmse:0.475173+0.010135    test-rmse:0.647323+0.037121 
## [35] train-rmse:0.469132+0.010382    test-rmse:0.645344+0.037484 
## [36] train-rmse:0.465827+0.009843    test-rmse:0.646021+0.037354 
## [37] train-rmse:0.462341+0.008837    test-rmse:0.642233+0.040325 
## [38] train-rmse:0.458203+0.010972    test-rmse:0.640428+0.038847 
## [39] train-rmse:0.455008+0.011664    test-rmse:0.639403+0.038101 
## [40] train-rmse:0.450853+0.012720    test-rmse:0.639701+0.037698 
## [41] train-rmse:0.445627+0.013976    test-rmse:0.639740+0.039194 
## [42] train-rmse:0.442203+0.013401    test-rmse:0.641810+0.040109 
## [43] train-rmse:0.439139+0.013468    test-rmse:0.643422+0.040834 
## [44] train-rmse:0.437255+0.013869    test-rmse:0.642383+0.040514 
## [45] train-rmse:0.435253+0.013655    test-rmse:0.643373+0.041933 
## Stopping. Best iteration:
## [39] train-rmse:0.455008+0.011664    test-rmse:0.639403+0.038101
## 
## 107 
## [1]  train-rmse:1.116528+0.009287    test-rmse:1.133900+0.069066 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.860870+0.005420    test-rmse:0.902886+0.070068 
## [3]  train-rmse:0.736170+0.007497    test-rmse:0.794610+0.067950 
## [4]  train-rmse:0.660055+0.004531    test-rmse:0.738090+0.052632 
## [5]  train-rmse:0.623339+0.005443    test-rmse:0.715385+0.045960 
## [6]  train-rmse:0.600942+0.004098    test-rmse:0.706948+0.043408 
## [7]  train-rmse:0.584355+0.006108    test-rmse:0.695202+0.041051 
## [8]  train-rmse:0.568997+0.007232    test-rmse:0.689193+0.046742 
## [9]  train-rmse:0.557661+0.007508    test-rmse:0.686965+0.045799 
## [10] train-rmse:0.546101+0.009928    test-rmse:0.682391+0.043199 
## [11] train-rmse:0.530813+0.010764    test-rmse:0.680755+0.052988 
## [12] train-rmse:0.515261+0.015509    test-rmse:0.674891+0.056492 
## [13] train-rmse:0.504581+0.015762    test-rmse:0.675340+0.053717 
## [14] train-rmse:0.491702+0.017030    test-rmse:0.664770+0.050032 
## [15] train-rmse:0.478857+0.014239    test-rmse:0.662669+0.054075 
## [16] train-rmse:0.469484+0.010675    test-rmse:0.657223+0.056600 
## [17] train-rmse:0.461220+0.011943    test-rmse:0.654808+0.054619 
## [18] train-rmse:0.453804+0.011047    test-rmse:0.647980+0.056649 
## [19] train-rmse:0.445281+0.012002    test-rmse:0.644722+0.052866 
## [20] train-rmse:0.435558+0.013505    test-rmse:0.643854+0.049227 
## [21] train-rmse:0.428253+0.014269    test-rmse:0.642903+0.048696 
## [22] train-rmse:0.422924+0.015054    test-rmse:0.643705+0.052316 
## [23] train-rmse:0.416221+0.010987    test-rmse:0.644600+0.054276 
## [24] train-rmse:0.410647+0.012158    test-rmse:0.645877+0.052765 
## [25] train-rmse:0.403488+0.015412    test-rmse:0.642764+0.052069 
## [26] train-rmse:0.395127+0.014457    test-rmse:0.642539+0.049039 
## [27] train-rmse:0.388754+0.015928    test-rmse:0.643284+0.049112 
## [28] train-rmse:0.384551+0.016131    test-rmse:0.643060+0.048047 
## [29] train-rmse:0.379425+0.016481    test-rmse:0.642365+0.048982 
## [30] train-rmse:0.373115+0.017776    test-rmse:0.638287+0.048937 
## [31] train-rmse:0.369676+0.017786    test-rmse:0.637381+0.049985 
## [32] train-rmse:0.365094+0.019671    test-rmse:0.635294+0.049334 
## [33] train-rmse:0.358660+0.019665    test-rmse:0.635311+0.045657 
## [34] train-rmse:0.354831+0.020335    test-rmse:0.634045+0.045162 
## [35] train-rmse:0.350092+0.020685    test-rmse:0.631623+0.045644 
## [36] train-rmse:0.344845+0.019170    test-rmse:0.630249+0.044634 
## [37] train-rmse:0.337327+0.016458    test-rmse:0.626409+0.043513 
## [38] train-rmse:0.334142+0.016042    test-rmse:0.626363+0.043035 
## [39] train-rmse:0.329689+0.015658    test-rmse:0.626299+0.041524 
## [40] train-rmse:0.324957+0.016575    test-rmse:0.624609+0.042001 
## [41] train-rmse:0.320992+0.015853    test-rmse:0.625008+0.042578 
## [42] train-rmse:0.315202+0.014504    test-rmse:0.626090+0.042185 
## [43] train-rmse:0.310262+0.016530    test-rmse:0.626218+0.042220 
## [44] train-rmse:0.307059+0.016022    test-rmse:0.625455+0.042098 
## [45] train-rmse:0.304035+0.015454    test-rmse:0.626096+0.043051 
## [46] train-rmse:0.300869+0.014978    test-rmse:0.626321+0.042133 
## Stopping. Best iteration:
## [40] train-rmse:0.324957+0.016575    test-rmse:0.624609+0.042001
## 
## 108 
## [1]  train-rmse:1.107402+0.009540    test-rmse:1.139256+0.071710 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.841793+0.002259    test-rmse:0.902552+0.080630 
## [3]  train-rmse:0.701316+0.006871    test-rmse:0.779535+0.067386 
## [4]  train-rmse:0.617313+0.010353    test-rmse:0.719611+0.056776 
## [5]  train-rmse:0.568080+0.009571    test-rmse:0.701654+0.055935 
## [6]  train-rmse:0.540026+0.011749    test-rmse:0.692892+0.058982 
## [7]  train-rmse:0.521386+0.011956    test-rmse:0.679744+0.058545 
## [8]  train-rmse:0.507819+0.012423    test-rmse:0.675706+0.060363 
## [9]  train-rmse:0.492778+0.015299    test-rmse:0.673445+0.062540 
## [10] train-rmse:0.477001+0.017311    test-rmse:0.671027+0.065993 
## [11] train-rmse:0.455615+0.014307    test-rmse:0.663010+0.063438 
## [12] train-rmse:0.440597+0.012633    test-rmse:0.667025+0.060962 
## [13] train-rmse:0.426416+0.011121    test-rmse:0.663631+0.068033 
## [14] train-rmse:0.411563+0.015292    test-rmse:0.652863+0.069745 
## [15] train-rmse:0.399738+0.013429    test-rmse:0.654070+0.071401 
## [16] train-rmse:0.389094+0.013736    test-rmse:0.651396+0.070779 
## [17] train-rmse:0.378779+0.017916    test-rmse:0.646520+0.069983 
## [18] train-rmse:0.370555+0.019525    test-rmse:0.647011+0.069575 
## [19] train-rmse:0.363000+0.019174    test-rmse:0.643544+0.070223 
## [20] train-rmse:0.353758+0.018833    test-rmse:0.643759+0.071034 
## [21] train-rmse:0.346943+0.019108    test-rmse:0.643950+0.069048 
## [22] train-rmse:0.339014+0.021289    test-rmse:0.643929+0.068014 
## [23] train-rmse:0.332428+0.024338    test-rmse:0.645459+0.067456 
## [24] train-rmse:0.324171+0.025564    test-rmse:0.647049+0.067466 
## [25] train-rmse:0.315888+0.025252    test-rmse:0.642112+0.069029 
## [26] train-rmse:0.308984+0.022416    test-rmse:0.643418+0.069550 
## [27] train-rmse:0.300180+0.020820    test-rmse:0.641003+0.065240 
## [28] train-rmse:0.292703+0.018470    test-rmse:0.637397+0.063650 
## [29] train-rmse:0.283465+0.020199    test-rmse:0.640374+0.062588 
## [30] train-rmse:0.278042+0.020810    test-rmse:0.638602+0.061229 
## [31] train-rmse:0.271259+0.021271    test-rmse:0.637177+0.062847 
## [32] train-rmse:0.265107+0.019124    test-rmse:0.636164+0.062353 
## [33] train-rmse:0.258771+0.017835    test-rmse:0.637813+0.060988 
## [34] train-rmse:0.252637+0.019174    test-rmse:0.639465+0.062695 
## [35] train-rmse:0.248400+0.018355    test-rmse:0.641109+0.064203 
## [36] train-rmse:0.243987+0.018275    test-rmse:0.640794+0.062051 
## [37] train-rmse:0.239046+0.017229    test-rmse:0.640690+0.061344 
## [38] train-rmse:0.233673+0.016964    test-rmse:0.640274+0.061842 
## Stopping. Best iteration:
## [32] train-rmse:0.265107+0.019124    test-rmse:0.636164+0.062353
## 
## 109 
## [1]  train-rmse:1.101108+0.009877    test-rmse:1.131368+0.077323 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.822123+0.003784    test-rmse:0.889789+0.083825 
## [3]  train-rmse:0.675481+0.009744    test-rmse:0.777048+0.067673 
## [4]  train-rmse:0.596342+0.007671    test-rmse:0.714403+0.057210 
## [5]  train-rmse:0.541939+0.007653    test-rmse:0.691896+0.052506 
## [6]  train-rmse:0.510407+0.006879    test-rmse:0.682071+0.049497 
## [7]  train-rmse:0.479089+0.008284    test-rmse:0.668994+0.039412 
## [8]  train-rmse:0.462904+0.012321    test-rmse:0.665892+0.042472 
## [9]  train-rmse:0.444021+0.012692    test-rmse:0.663770+0.044382 
## [10] train-rmse:0.426620+0.014162    test-rmse:0.659442+0.049646 
## [11] train-rmse:0.409630+0.015040    test-rmse:0.658292+0.058579 
## [12] train-rmse:0.394748+0.010059    test-rmse:0.655882+0.055540 
## [13] train-rmse:0.384158+0.011055    test-rmse:0.654891+0.054599 
## [14] train-rmse:0.371339+0.014638    test-rmse:0.656578+0.057894 
## [15] train-rmse:0.358378+0.013453    test-rmse:0.658064+0.062644 
## [16] train-rmse:0.337802+0.005542    test-rmse:0.646813+0.062525 
## [17] train-rmse:0.326693+0.009551    test-rmse:0.646639+0.065628 
## [18] train-rmse:0.312980+0.010052    test-rmse:0.641954+0.062571 
## [19] train-rmse:0.300223+0.010258    test-rmse:0.643004+0.066170 
## [20] train-rmse:0.287713+0.014157    test-rmse:0.646452+0.070057 
## [21] train-rmse:0.275310+0.011072    test-rmse:0.645159+0.066622 
## [22] train-rmse:0.267700+0.012316    test-rmse:0.644634+0.068153 
## [23] train-rmse:0.259493+0.014712    test-rmse:0.646929+0.068312 
## [24] train-rmse:0.251913+0.016504    test-rmse:0.645235+0.070609 
## Stopping. Best iteration:
## [18] train-rmse:0.312980+0.010052    test-rmse:0.641954+0.062571
## 
## 110 
## [1]  train-rmse:1.094652+0.008852    test-rmse:1.134654+0.079470 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.804496+0.005276    test-rmse:0.886164+0.073556 
## [3]  train-rmse:0.644203+0.002630    test-rmse:0.760510+0.063040 
## [4]  train-rmse:0.561336+0.009665    test-rmse:0.709227+0.057457 
## [5]  train-rmse:0.491504+0.016855    test-rmse:0.677647+0.050417 
## [6]  train-rmse:0.451712+0.016586    test-rmse:0.662462+0.053653 
## [7]  train-rmse:0.425016+0.018294    test-rmse:0.658844+0.055853 
## [8]  train-rmse:0.398434+0.018877    test-rmse:0.652424+0.049818 
## [9]  train-rmse:0.371773+0.011175    test-rmse:0.646001+0.051372 
## [10] train-rmse:0.351797+0.009637    test-rmse:0.642373+0.052019 
## [11] train-rmse:0.336826+0.012365    test-rmse:0.635174+0.053028 
## [12] train-rmse:0.320827+0.010079    test-rmse:0.635800+0.050646 
## [13] train-rmse:0.306068+0.008242    test-rmse:0.634445+0.053032 
## [14] train-rmse:0.292260+0.006719    test-rmse:0.629111+0.058381 
## [15] train-rmse:0.278815+0.005926    test-rmse:0.631151+0.059977 
## [16] train-rmse:0.265408+0.006057    test-rmse:0.633875+0.062215 
## [17] train-rmse:0.253513+0.005742    test-rmse:0.633090+0.065646 
## [18] train-rmse:0.242803+0.009596    test-rmse:0.633944+0.064358 
## [19] train-rmse:0.229519+0.009076    test-rmse:0.626845+0.058633 
## [20] train-rmse:0.217799+0.006955    test-rmse:0.630115+0.058906 
## [21] train-rmse:0.206416+0.008288    test-rmse:0.631103+0.060162 
## [22] train-rmse:0.197638+0.005909    test-rmse:0.629865+0.061898 
## [23] train-rmse:0.189048+0.006602    test-rmse:0.630128+0.061472 
## [24] train-rmse:0.180712+0.006183    test-rmse:0.630762+0.061856 
## [25] train-rmse:0.171941+0.008154    test-rmse:0.630747+0.061490 
## Stopping. Best iteration:
## [19] train-rmse:0.229519+0.009076    test-rmse:0.626845+0.058633
## 
## 111 
## [1]  train-rmse:1.089930+0.008029    test-rmse:1.132467+0.079812 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.791356+0.003859    test-rmse:0.877350+0.077553 
## [3]  train-rmse:0.620705+0.010970    test-rmse:0.756413+0.067745 
## [4]  train-rmse:0.527305+0.008367    test-rmse:0.697432+0.064205 
## [5]  train-rmse:0.454802+0.013130    test-rmse:0.673726+0.061110 
## [6]  train-rmse:0.409249+0.006082    test-rmse:0.658893+0.060048 
## [7]  train-rmse:0.380328+0.010308    test-rmse:0.649553+0.058158 
## [8]  train-rmse:0.357052+0.007869    test-rmse:0.647155+0.059929 
## [9]  train-rmse:0.331043+0.016141    test-rmse:0.647631+0.061141 
## [10] train-rmse:0.307030+0.013239    test-rmse:0.641875+0.060480 
## [11] train-rmse:0.292692+0.013698    test-rmse:0.643807+0.063412 
## [12] train-rmse:0.275619+0.017352    test-rmse:0.636685+0.056419 
## [13] train-rmse:0.263014+0.021565    test-rmse:0.639270+0.055559 
## [14] train-rmse:0.251282+0.022806    test-rmse:0.641956+0.054414 
## [15] train-rmse:0.236014+0.019625    test-rmse:0.640598+0.055303 
## [16] train-rmse:0.221415+0.019372    test-rmse:0.640139+0.055551 
## [17] train-rmse:0.208907+0.018275    test-rmse:0.638767+0.057710 
## [18] train-rmse:0.197404+0.016557    test-rmse:0.640901+0.055710 
## Stopping. Best iteration:
## [12] train-rmse:0.275619+0.017352    test-rmse:0.636685+0.056419
## 
## 112
##    max_depth  eta
## 95         4 0.36
## [1]  train-rmse:1.501674+0.027852    test-rmse:1.497786+0.114244 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.381749+0.025744    test-rmse:1.377894+0.111510 
## [3]  train-rmse:1.276150+0.023952    test-rmse:1.272327+0.109023 
## [4]  train-rmse:1.183530+0.022457    test-rmse:1.179745+0.106728 
## [5]  train-rmse:1.102645+0.021236    test-rmse:1.098908+0.104584 
## [6]  train-rmse:1.032343+0.020263    test-rmse:1.028666+0.102563 
## [7]  train-rmse:0.971547+0.019511    test-rmse:0.967945+0.100650 
## [8]  train-rmse:0.919246+0.018950    test-rmse:0.915733+0.098841 
## [9]  train-rmse:0.874499+0.018549    test-rmse:0.871087+0.097138 
## [10] train-rmse:0.836418+0.018278    test-rmse:0.833117+0.095544 
## [11] train-rmse:0.804184+0.018107    test-rmse:0.801001+0.094067 
## [12] train-rmse:0.777035+0.018011    test-rmse:0.773976+0.092713 
## [13] train-rmse:0.754282+0.017968    test-rmse:0.751344+0.091480 
## [14] train-rmse:0.735295+0.017961    test-rmse:0.732477+0.090370 
## [15] train-rmse:0.719515+0.017978    test-rmse:0.716812+0.089377 
## [16] train-rmse:0.706255+0.017832    test-rmse:0.706275+0.089838 
## [17] train-rmse:0.694626+0.017617    test-rmse:0.693863+0.088839 
## [18] train-rmse:0.684511+0.017342    test-rmse:0.684643+0.088817 
## [19] train-rmse:0.675249+0.017212    test-rmse:0.674817+0.088021 
## [20] train-rmse:0.667191+0.017017    test-rmse:0.667513+0.087701 
## [21] train-rmse:0.659732+0.016920    test-rmse:0.659791+0.087041 
## [22] train-rmse:0.653036+0.016569    test-rmse:0.654694+0.085777 
## [23] train-rmse:0.646911+0.016295    test-rmse:0.648503+0.085372 
## [24] train-rmse:0.641229+0.015866    test-rmse:0.643040+0.084774 
## [25] train-rmse:0.635839+0.015413    test-rmse:0.638526+0.082516 
## [26] train-rmse:0.630818+0.015128    test-rmse:0.632551+0.082432 
## [27] train-rmse:0.626081+0.014745    test-rmse:0.628082+0.079971 
## [28] train-rmse:0.621672+0.014521    test-rmse:0.624243+0.080229 
## [29] train-rmse:0.617528+0.014205    test-rmse:0.620302+0.079542 
## [30] train-rmse:0.613571+0.013857    test-rmse:0.617389+0.078024 
## [31] train-rmse:0.609873+0.013630    test-rmse:0.613235+0.077484 
## [32] train-rmse:0.606292+0.013463    test-rmse:0.609775+0.075632 
## [33] train-rmse:0.603013+0.013283    test-rmse:0.607743+0.075041 
## [34] train-rmse:0.599869+0.013091    test-rmse:0.604385+0.074442 
## [35] train-rmse:0.596787+0.012950    test-rmse:0.601342+0.074457 
## [36] train-rmse:0.593878+0.012748    test-rmse:0.599248+0.072901 
## [37] train-rmse:0.591204+0.012588    test-rmse:0.596015+0.072406 
## [38] train-rmse:0.588584+0.012479    test-rmse:0.594354+0.071091 
## [39] train-rmse:0.586052+0.012347    test-rmse:0.591865+0.070428 
## [40] train-rmse:0.583612+0.012189    test-rmse:0.591140+0.071049 
## [41] train-rmse:0.581337+0.012096    test-rmse:0.588361+0.070395 
## [42] train-rmse:0.579078+0.012039    test-rmse:0.587168+0.069159 
## [43] train-rmse:0.576923+0.011926    test-rmse:0.584905+0.068118 
## [44] train-rmse:0.574857+0.011827    test-rmse:0.584288+0.067946 
## [45] train-rmse:0.572840+0.011741    test-rmse:0.582638+0.067333 
## [46] train-rmse:0.570895+0.011713    test-rmse:0.580303+0.067006 
## [47] train-rmse:0.569065+0.011616    test-rmse:0.579441+0.066334 
## [48] train-rmse:0.567294+0.011485    test-rmse:0.577758+0.065956 
## [49] train-rmse:0.565551+0.011396    test-rmse:0.577155+0.065325 
## [50] train-rmse:0.563825+0.011345    test-rmse:0.575271+0.065777 
## [51] train-rmse:0.562211+0.011256    test-rmse:0.573861+0.065407 
## [52] train-rmse:0.560629+0.011183    test-rmse:0.572172+0.065273 
## [53] train-rmse:0.559110+0.011109    test-rmse:0.570921+0.064813 
## [54] train-rmse:0.557616+0.011076    test-rmse:0.570297+0.063863 
## [55] train-rmse:0.556192+0.011026    test-rmse:0.569023+0.063662 
## [56] train-rmse:0.554816+0.010957    test-rmse:0.567993+0.062471 
## [57] train-rmse:0.553457+0.010886    test-rmse:0.566673+0.062185 
## [58] train-rmse:0.552160+0.010843    test-rmse:0.565863+0.062725 
## [59] train-rmse:0.550870+0.010791    test-rmse:0.565109+0.063204 
## [60] train-rmse:0.549616+0.010734    test-rmse:0.563775+0.061883 
## [61] train-rmse:0.548411+0.010706    test-rmse:0.563351+0.061461 
## [62] train-rmse:0.547252+0.010641    test-rmse:0.562518+0.061388 
## [63] train-rmse:0.546114+0.010596    test-rmse:0.562271+0.061834 
## [64] train-rmse:0.545005+0.010529    test-rmse:0.561586+0.062113 
## [65] train-rmse:0.543917+0.010490    test-rmse:0.560558+0.061002 
## [66] train-rmse:0.542851+0.010475    test-rmse:0.559514+0.060779 
## [67] train-rmse:0.541807+0.010441    test-rmse:0.558870+0.060549 
## [68] train-rmse:0.540794+0.010409    test-rmse:0.558284+0.059904 
## [69] train-rmse:0.539810+0.010357    test-rmse:0.557946+0.060075 
## [70] train-rmse:0.538831+0.010303    test-rmse:0.556547+0.059687 
## [71] train-rmse:0.537851+0.010257    test-rmse:0.556497+0.059748 
## [72] train-rmse:0.536896+0.010215    test-rmse:0.555785+0.060067 
## [73] train-rmse:0.535962+0.010181    test-rmse:0.555414+0.059255 
## [74] train-rmse:0.535065+0.010137    test-rmse:0.554980+0.058811 
## [75] train-rmse:0.534181+0.010098    test-rmse:0.554625+0.058944 
## [76] train-rmse:0.533307+0.010051    test-rmse:0.553541+0.058385 
## [77] train-rmse:0.532431+0.010006    test-rmse:0.553070+0.058637 
## [78] train-rmse:0.531591+0.009973    test-rmse:0.552734+0.058744 
## [79] train-rmse:0.530757+0.009943    test-rmse:0.552435+0.059283 
## [80] train-rmse:0.529924+0.009908    test-rmse:0.551406+0.058901 
## [81] train-rmse:0.529096+0.009869    test-rmse:0.550848+0.058825 
## [82] train-rmse:0.528284+0.009798    test-rmse:0.550588+0.058149 
## [83] train-rmse:0.527498+0.009761    test-rmse:0.549555+0.058133 
## [84] train-rmse:0.526726+0.009720    test-rmse:0.549344+0.058142 
## [85] train-rmse:0.525973+0.009672    test-rmse:0.549373+0.057951 
## [86] train-rmse:0.525209+0.009625    test-rmse:0.548452+0.057761 
## [87] train-rmse:0.524458+0.009583    test-rmse:0.547987+0.057658 
## [88] train-rmse:0.523707+0.009540    test-rmse:0.547738+0.057354 
## [89] train-rmse:0.522975+0.009501    test-rmse:0.547842+0.057729 
## [90] train-rmse:0.522276+0.009459    test-rmse:0.547347+0.057874 
## [91] train-rmse:0.521576+0.009413    test-rmse:0.545997+0.057699 
## [92] train-rmse:0.520876+0.009384    test-rmse:0.545359+0.057063 
## [93] train-rmse:0.520203+0.009347    test-rmse:0.545032+0.057017 
## [94] train-rmse:0.519530+0.009316    test-rmse:0.544523+0.056961 
## [95] train-rmse:0.518846+0.009271    test-rmse:0.543361+0.056509 
## [96] train-rmse:0.518170+0.009226    test-rmse:0.543282+0.056313 
## [97] train-rmse:0.517524+0.009209    test-rmse:0.543359+0.056426 
## [98] train-rmse:0.516882+0.009178    test-rmse:0.543228+0.056519 
## [99] train-rmse:0.516238+0.009143    test-rmse:0.542524+0.056321 
## [100]    train-rmse:0.515614+0.009119    test-rmse:0.542351+0.055560 
## [101]    train-rmse:0.514991+0.009093    test-rmse:0.541402+0.055698 
## [102]    train-rmse:0.514364+0.009061    test-rmse:0.540767+0.055925 
## [103]    train-rmse:0.513753+0.009032    test-rmse:0.540330+0.056176 
## [104]    train-rmse:0.513149+0.009006    test-rmse:0.540051+0.056116 
## [105]    train-rmse:0.512550+0.008982    test-rmse:0.540017+0.055939 
## [106]    train-rmse:0.511956+0.008966    test-rmse:0.538989+0.055485 
## [107]    train-rmse:0.511356+0.008945    test-rmse:0.538356+0.055551 
## [108]    train-rmse:0.510771+0.008923    test-rmse:0.538096+0.055602 
## [109]    train-rmse:0.510191+0.008907    test-rmse:0.537858+0.055592 
## [110]    train-rmse:0.509624+0.008880    test-rmse:0.537366+0.055930 
## [111]    train-rmse:0.509050+0.008850    test-rmse:0.536686+0.055546 
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## [114]    train-rmse:0.507374+0.008783    test-rmse:0.535651+0.055332 
## [115]    train-rmse:0.506828+0.008759    test-rmse:0.535175+0.055277 
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## [118]    train-rmse:0.505216+0.008697    test-rmse:0.534424+0.054668 
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## [126]    train-rmse:0.501116+0.008546    test-rmse:0.531343+0.054474 
## [127]    train-rmse:0.500628+0.008538    test-rmse:0.530734+0.054336 
## [128]    train-rmse:0.500145+0.008526    test-rmse:0.530624+0.054272 
## [129]    train-rmse:0.499665+0.008511    test-rmse:0.530308+0.054298 
## [130]    train-rmse:0.499187+0.008497    test-rmse:0.529790+0.053962 
## [131]    train-rmse:0.498702+0.008490    test-rmse:0.529665+0.054164 
## [132]    train-rmse:0.498222+0.008480    test-rmse:0.529596+0.053819 
## [133]    train-rmse:0.497758+0.008455    test-rmse:0.529064+0.053880 
## [134]    train-rmse:0.497294+0.008430    test-rmse:0.528365+0.053817 
## [135]    train-rmse:0.496836+0.008412    test-rmse:0.528174+0.053348 
## [136]    train-rmse:0.496388+0.008405    test-rmse:0.528233+0.053528 
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## [138]    train-rmse:0.495485+0.008385    test-rmse:0.527799+0.053357 
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## [140]    train-rmse:0.494607+0.008349    test-rmse:0.526925+0.053541 
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## [292]    train-rmse:0.453402+0.007465    test-rmse:0.503185+0.044079 
## [293]    train-rmse:0.453231+0.007463    test-rmse:0.502713+0.043581 
## [294]    train-rmse:0.453064+0.007460    test-rmse:0.502842+0.043536 
## [295]    train-rmse:0.452898+0.007458    test-rmse:0.502864+0.043435 
## [296]    train-rmse:0.452729+0.007457    test-rmse:0.502741+0.043598 
## [297]    train-rmse:0.452558+0.007454    test-rmse:0.502857+0.043681 
## [298]    train-rmse:0.452391+0.007452    test-rmse:0.502471+0.043558 
## [299]    train-rmse:0.452227+0.007451    test-rmse:0.502612+0.043381 
## [300]    train-rmse:0.452064+0.007451    test-rmse:0.502603+0.043491 
## [301]    train-rmse:0.451901+0.007451    test-rmse:0.502515+0.043369 
## [302]    train-rmse:0.451736+0.007452    test-rmse:0.502544+0.043227 
## [303]    train-rmse:0.451572+0.007453    test-rmse:0.502235+0.043241 
## [304]    train-rmse:0.451412+0.007453    test-rmse:0.502407+0.043290 
## [305]    train-rmse:0.451252+0.007453    test-rmse:0.502138+0.043090 
## [306]    train-rmse:0.451093+0.007455    test-rmse:0.502215+0.043268 
## [307]    train-rmse:0.450937+0.007456    test-rmse:0.502264+0.043040 
## [308]    train-rmse:0.450779+0.007456    test-rmse:0.502293+0.042997 
## [309]    train-rmse:0.450621+0.007454    test-rmse:0.502145+0.043024 
## [310]    train-rmse:0.450463+0.007450    test-rmse:0.502127+0.042960 
## [311]    train-rmse:0.450303+0.007450    test-rmse:0.501931+0.043189 
## [312]    train-rmse:0.450147+0.007453    test-rmse:0.501640+0.043037 
## [313]    train-rmse:0.449995+0.007454    test-rmse:0.501579+0.042986 
## [314]    train-rmse:0.449841+0.007451    test-rmse:0.501683+0.043058 
## [315]    train-rmse:0.449690+0.007449    test-rmse:0.501569+0.042979 
## [316]    train-rmse:0.449537+0.007448    test-rmse:0.501854+0.042965 
## [317]    train-rmse:0.449382+0.007447    test-rmse:0.501599+0.042709 
## [318]    train-rmse:0.449233+0.007446    test-rmse:0.501330+0.042842 
## [319]    train-rmse:0.449079+0.007446    test-rmse:0.501469+0.042802 
## [320]    train-rmse:0.448931+0.007444    test-rmse:0.501297+0.042955 
## [321]    train-rmse:0.448784+0.007443    test-rmse:0.501262+0.042776 
## [322]    train-rmse:0.448639+0.007442    test-rmse:0.501270+0.042751 
## [323]    train-rmse:0.448492+0.007441    test-rmse:0.501206+0.042718 
## [324]    train-rmse:0.448345+0.007441    test-rmse:0.500968+0.042626 
## [325]    train-rmse:0.448199+0.007440    test-rmse:0.501072+0.042427 
## [326]    train-rmse:0.448051+0.007442    test-rmse:0.501214+0.042453 
## [327]    train-rmse:0.447905+0.007441    test-rmse:0.500901+0.042658 
## [328]    train-rmse:0.447759+0.007440    test-rmse:0.500903+0.042693 
## [329]    train-rmse:0.447616+0.007441    test-rmse:0.500820+0.042598 
## [330]    train-rmse:0.447474+0.007443    test-rmse:0.500581+0.042601 
## [331]    train-rmse:0.447335+0.007441    test-rmse:0.500668+0.042737 
## [332]    train-rmse:0.447190+0.007442    test-rmse:0.500658+0.042612 
## [333]    train-rmse:0.447049+0.007439    test-rmse:0.500820+0.042562 
## [334]    train-rmse:0.446906+0.007439    test-rmse:0.500310+0.042101 
## [335]    train-rmse:0.446768+0.007439    test-rmse:0.500414+0.042263 
## [336]    train-rmse:0.446630+0.007439    test-rmse:0.500468+0.042361 
## [337]    train-rmse:0.446491+0.007440    test-rmse:0.500350+0.042245 
## [338]    train-rmse:0.446354+0.007443    test-rmse:0.500451+0.042259 
## [339]    train-rmse:0.446217+0.007443    test-rmse:0.500035+0.042143 
## [340]    train-rmse:0.446079+0.007443    test-rmse:0.499963+0.042062 
## [341]    train-rmse:0.445944+0.007442    test-rmse:0.500075+0.041877 
## [342]    train-rmse:0.445810+0.007442    test-rmse:0.499897+0.042048 
## [343]    train-rmse:0.445675+0.007442    test-rmse:0.499770+0.042017 
## [344]    train-rmse:0.445541+0.007442    test-rmse:0.499651+0.041820 
## [345]    train-rmse:0.445404+0.007442    test-rmse:0.499703+0.041915 
## [346]    train-rmse:0.445270+0.007443    test-rmse:0.499728+0.041826 
## [347]    train-rmse:0.445138+0.007443    test-rmse:0.499675+0.041917 
## [348]    train-rmse:0.445004+0.007443    test-rmse:0.499781+0.041756 
## [349]    train-rmse:0.444870+0.007443    test-rmse:0.499682+0.041959 
## [350]    train-rmse:0.444737+0.007442    test-rmse:0.499666+0.041996 
## Stopping. Best iteration:
## [344]    train-rmse:0.445541+0.007442    test-rmse:0.499651+0.041820
## 
## 1 
## [1]  train-rmse:1.495649+0.027857    test-rmse:1.492989+0.115930 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.369327+0.025766    test-rmse:1.369565+0.113754 
## [3]  train-rmse:1.256752+0.023766    test-rmse:1.258817+0.114193 
## [4]  train-rmse:1.156618+0.022182    test-rmse:1.159723+0.109250 
## [5]  train-rmse:1.067682+0.020722    test-rmse:1.070428+0.109585 
## [6]  train-rmse:0.989023+0.019443    test-rmse:0.992355+0.106310 
## [7]  train-rmse:0.919542+0.018394    test-rmse:0.923034+0.106429 
## [8]  train-rmse:0.858236+0.017341    test-rmse:0.861613+0.103341 
## [9]  train-rmse:0.804365+0.016509    test-rmse:0.808984+0.104038 
## [10] train-rmse:0.757203+0.015733    test-rmse:0.761758+0.102224 
## [11] train-rmse:0.715963+0.015118    test-rmse:0.720239+0.100630 
## [12] train-rmse:0.679826+0.014683    test-rmse:0.685548+0.098336 
## [13] train-rmse:0.648510+0.014289    test-rmse:0.655632+0.098085 
## [14] train-rmse:0.621231+0.013840    test-rmse:0.629861+0.096104 
## [15] train-rmse:0.597557+0.013530    test-rmse:0.607509+0.093841 
## [16] train-rmse:0.576888+0.013254    test-rmse:0.586417+0.092024 
## [17] train-rmse:0.558962+0.012988    test-rmse:0.571776+0.089292 
## [18] train-rmse:0.543317+0.012764    test-rmse:0.560583+0.088610 
## [19] train-rmse:0.529599+0.012474    test-rmse:0.547762+0.085080 
## [20] train-rmse:0.517434+0.012208    test-rmse:0.536972+0.083001 
## [21] train-rmse:0.506831+0.011955    test-rmse:0.528328+0.081198 
## [22] train-rmse:0.497461+0.011683    test-rmse:0.519582+0.077797 
## [23] train-rmse:0.489117+0.011402    test-rmse:0.512895+0.077052 
## [24] train-rmse:0.481707+0.011141    test-rmse:0.505391+0.075178 
## [25] train-rmse:0.474577+0.010919    test-rmse:0.500512+0.072286 
## [26] train-rmse:0.467454+0.010394    test-rmse:0.494790+0.071335 
## [27] train-rmse:0.460855+0.009830    test-rmse:0.488990+0.070131 
## [28] train-rmse:0.454921+0.009161    test-rmse:0.484312+0.068977 
## [29] train-rmse:0.449636+0.009016    test-rmse:0.480537+0.067432 
## [30] train-rmse:0.444787+0.008506    test-rmse:0.477200+0.067124 
## [31] train-rmse:0.440251+0.008172    test-rmse:0.475414+0.066647 
## [32] train-rmse:0.436136+0.007919    test-rmse:0.473096+0.066403 
## [33] train-rmse:0.431807+0.007738    test-rmse:0.470405+0.065993 
## [34] train-rmse:0.428328+0.007512    test-rmse:0.469714+0.064371 
## [35] train-rmse:0.425066+0.007183    test-rmse:0.467389+0.063950 
## [36] train-rmse:0.421454+0.007032    test-rmse:0.465488+0.064086 
## [37] train-rmse:0.418363+0.006873    test-rmse:0.464194+0.063309 
## [38] train-rmse:0.415348+0.006870    test-rmse:0.463177+0.062267 
## [39] train-rmse:0.412455+0.006542    test-rmse:0.461690+0.061278 
## [40] train-rmse:0.409735+0.006315    test-rmse:0.460059+0.059504 
## [41] train-rmse:0.407289+0.006228    test-rmse:0.459108+0.059182 
## [42] train-rmse:0.404885+0.006180    test-rmse:0.457478+0.057713 
## [43] train-rmse:0.402515+0.005866    test-rmse:0.456440+0.057698 
## [44] train-rmse:0.399601+0.005319    test-rmse:0.456371+0.057966 
## [45] train-rmse:0.397525+0.005342    test-rmse:0.455320+0.057881 
## [46] train-rmse:0.395319+0.005364    test-rmse:0.454714+0.058062 
## [47] train-rmse:0.393309+0.005330    test-rmse:0.453500+0.058014 
## [48] train-rmse:0.391483+0.005202    test-rmse:0.453536+0.057286 
## [49] train-rmse:0.389533+0.005087    test-rmse:0.453177+0.057222 
## [50] train-rmse:0.387759+0.004974    test-rmse:0.452624+0.057156 
## [51] train-rmse:0.385630+0.004832    test-rmse:0.451149+0.056738 
## [52] train-rmse:0.383748+0.004524    test-rmse:0.450527+0.058022 
## [53] train-rmse:0.382028+0.004446    test-rmse:0.449867+0.057314 
## [54] train-rmse:0.380414+0.004309    test-rmse:0.449927+0.056966 
## [55] train-rmse:0.378706+0.004393    test-rmse:0.449043+0.056025 
## [56] train-rmse:0.376960+0.004159    test-rmse:0.448081+0.056103 
## [57] train-rmse:0.375406+0.004098    test-rmse:0.447461+0.055946 
## [58] train-rmse:0.373932+0.004058    test-rmse:0.447458+0.055583 
## [59] train-rmse:0.372454+0.004166    test-rmse:0.447408+0.055637 
## [60] train-rmse:0.371001+0.004010    test-rmse:0.446940+0.055574 
## [61] train-rmse:0.369546+0.003964    test-rmse:0.446929+0.055079 
## [62] train-rmse:0.368065+0.003669    test-rmse:0.446468+0.054846 
## [63] train-rmse:0.366734+0.003761    test-rmse:0.446018+0.054787 
## [64] train-rmse:0.365370+0.003693    test-rmse:0.445515+0.054656 
## [65] train-rmse:0.363793+0.003730    test-rmse:0.445613+0.054218 
## [66] train-rmse:0.362450+0.003801    test-rmse:0.445343+0.053906 
## [67] train-rmse:0.361368+0.003732    test-rmse:0.445197+0.053840 
## [68] train-rmse:0.359946+0.003675    test-rmse:0.445080+0.053514 
## [69] train-rmse:0.358788+0.003730    test-rmse:0.444448+0.053109 
## [70] train-rmse:0.357638+0.003831    test-rmse:0.444037+0.052398 
## [71] train-rmse:0.356390+0.004000    test-rmse:0.443997+0.052356 
## [72] train-rmse:0.355216+0.004080    test-rmse:0.444200+0.052167 
## [73] train-rmse:0.353891+0.003914    test-rmse:0.443533+0.051674 
## [74] train-rmse:0.352729+0.003892    test-rmse:0.443091+0.051702 
## [75] train-rmse:0.351784+0.003810    test-rmse:0.442435+0.052347 
## [76] train-rmse:0.350781+0.003826    test-rmse:0.441920+0.052166 
## [77] train-rmse:0.349633+0.004065    test-rmse:0.441971+0.052056 
## [78] train-rmse:0.348295+0.003910    test-rmse:0.441875+0.052320 
## [79] train-rmse:0.347200+0.003994    test-rmse:0.440846+0.052588 
## [80] train-rmse:0.345983+0.004057    test-rmse:0.440489+0.052432 
## [81] train-rmse:0.344778+0.004020    test-rmse:0.440372+0.051681 
## [82] train-rmse:0.343664+0.003966    test-rmse:0.440900+0.051750 
## [83] train-rmse:0.342362+0.004120    test-rmse:0.441244+0.051685 
## [84] train-rmse:0.341285+0.004025    test-rmse:0.440948+0.052337 
## [85] train-rmse:0.339874+0.003852    test-rmse:0.440594+0.052153 
## [86] train-rmse:0.338597+0.003853    test-rmse:0.440382+0.052530 
## [87] train-rmse:0.337700+0.003883    test-rmse:0.440357+0.052919 
## [88] train-rmse:0.336823+0.003918    test-rmse:0.440790+0.052486 
## [89] train-rmse:0.335682+0.003885    test-rmse:0.440543+0.052411 
## [90] train-rmse:0.334522+0.003888    test-rmse:0.440440+0.052851 
## [91] train-rmse:0.333714+0.003962    test-rmse:0.439662+0.052735 
## [92] train-rmse:0.332690+0.003944    test-rmse:0.439446+0.052356 
## [93] train-rmse:0.331668+0.003940    test-rmse:0.439494+0.052430 
## [94] train-rmse:0.330765+0.004063    test-rmse:0.439978+0.051943 
## [95] train-rmse:0.329952+0.004041    test-rmse:0.439824+0.052380 
## [96] train-rmse:0.329129+0.003965    test-rmse:0.439752+0.052740 
## [97] train-rmse:0.328016+0.003800    test-rmse:0.439105+0.052713 
## [98] train-rmse:0.327209+0.003841    test-rmse:0.439270+0.053147 
## [99] train-rmse:0.326334+0.004040    test-rmse:0.439444+0.053124 
## [100]    train-rmse:0.325582+0.004058    test-rmse:0.439197+0.053386 
## [101]    train-rmse:0.324782+0.004123    test-rmse:0.439416+0.053419 
## [102]    train-rmse:0.323808+0.004022    test-rmse:0.438483+0.053379 
## [103]    train-rmse:0.322891+0.003878    test-rmse:0.438184+0.053584 
## [104]    train-rmse:0.322025+0.004197    test-rmse:0.437994+0.052865 
## [105]    train-rmse:0.321256+0.004218    test-rmse:0.437375+0.053167 
## [106]    train-rmse:0.320563+0.004084    test-rmse:0.437456+0.052989 
## [107]    train-rmse:0.319730+0.004261    test-rmse:0.437383+0.053232 
## [108]    train-rmse:0.319030+0.004509    test-rmse:0.437250+0.053275 
## [109]    train-rmse:0.317839+0.004489    test-rmse:0.436809+0.053147 
## [110]    train-rmse:0.317303+0.004607    test-rmse:0.436448+0.053195 
## [111]    train-rmse:0.316489+0.004518    test-rmse:0.436068+0.053558 
## [112]    train-rmse:0.315718+0.004575    test-rmse:0.436029+0.053799 
## [113]    train-rmse:0.315068+0.004523    test-rmse:0.435812+0.054137 
## [114]    train-rmse:0.314414+0.004657    test-rmse:0.435653+0.054071 
## [115]    train-rmse:0.313686+0.004469    test-rmse:0.435663+0.054354 
## [116]    train-rmse:0.312979+0.004459    test-rmse:0.435736+0.054004 
## [117]    train-rmse:0.312195+0.004352    test-rmse:0.434876+0.054459 
## [118]    train-rmse:0.311426+0.004458    test-rmse:0.434831+0.055261 
## [119]    train-rmse:0.310775+0.004445    test-rmse:0.434232+0.055155 
## [120]    train-rmse:0.310220+0.004479    test-rmse:0.434288+0.054672 
## [121]    train-rmse:0.309743+0.004507    test-rmse:0.434428+0.054511 
## [122]    train-rmse:0.309246+0.004581    test-rmse:0.434523+0.054955 
## [123]    train-rmse:0.308405+0.004637    test-rmse:0.434355+0.055129 
## [124]    train-rmse:0.307636+0.004722    test-rmse:0.434428+0.055036 
## [125]    train-rmse:0.307104+0.004773    test-rmse:0.434414+0.054765 
## Stopping. Best iteration:
## [119]    train-rmse:0.310775+0.004445    test-rmse:0.434232+0.055155
## 
## 2 
## [1]  train-rmse:1.492531+0.027516    test-rmse:1.491496+0.117086 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.362988+0.025200    test-rmse:1.363089+0.113933 
## [3]  train-rmse:1.246662+0.023116    test-rmse:1.250322+0.113222 
## [4]  train-rmse:1.142918+0.021177    test-rmse:1.145982+0.112986 
## [5]  train-rmse:1.049960+0.019459    test-rmse:1.054743+0.113132 
## [6]  train-rmse:0.967309+0.017959    test-rmse:0.971429+0.114033 
## [7]  train-rmse:0.892797+0.017352    test-rmse:0.898165+0.110112 
## [8]  train-rmse:0.827046+0.015993    test-rmse:0.835777+0.108774 
## [9]  train-rmse:0.768151+0.015444    test-rmse:0.779961+0.104217 
## [10] train-rmse:0.716096+0.014408    test-rmse:0.728352+0.102250 
## [11] train-rmse:0.670075+0.013490    test-rmse:0.687412+0.100827 
## [12] train-rmse:0.629247+0.012651    test-rmse:0.650819+0.097903 
## [13] train-rmse:0.593375+0.011833    test-rmse:0.619651+0.096878 
## [14] train-rmse:0.561608+0.011782    test-rmse:0.589265+0.095234 
## [15] train-rmse:0.533638+0.011340    test-rmse:0.566721+0.093243 
## [16] train-rmse:0.509176+0.010829    test-rmse:0.545329+0.090165 
## [17] train-rmse:0.487196+0.010541    test-rmse:0.528427+0.086228 
## [18] train-rmse:0.468531+0.010204    test-rmse:0.513523+0.085538 
## [19] train-rmse:0.451760+0.010390    test-rmse:0.502846+0.082485 
## [20] train-rmse:0.436527+0.010057    test-rmse:0.489956+0.080374 
## [21] train-rmse:0.423718+0.009607    test-rmse:0.480650+0.079689 
## [22] train-rmse:0.412156+0.008835    test-rmse:0.473032+0.078220 
## [23] train-rmse:0.402243+0.008938    test-rmse:0.466519+0.075304 
## [24] train-rmse:0.393449+0.008792    test-rmse:0.461229+0.074587 
## [25] train-rmse:0.385289+0.008760    test-rmse:0.456333+0.073386 
## [26] train-rmse:0.377952+0.009085    test-rmse:0.452481+0.071309 
## [27] train-rmse:0.371725+0.009009    test-rmse:0.449739+0.069793 
## [28] train-rmse:0.365984+0.009108    test-rmse:0.447366+0.068558 
## [29] train-rmse:0.360969+0.008920    test-rmse:0.445374+0.067827 
## [30] train-rmse:0.356391+0.009005    test-rmse:0.443646+0.065392 
## [31] train-rmse:0.352363+0.009065    test-rmse:0.442885+0.064241 
## [32] train-rmse:0.348387+0.008671    test-rmse:0.442164+0.063120 
## [33] train-rmse:0.344086+0.008283    test-rmse:0.439405+0.062899 
## [34] train-rmse:0.339957+0.008472    test-rmse:0.437760+0.062399 
## [35] train-rmse:0.336345+0.008649    test-rmse:0.436643+0.062513 
## [36] train-rmse:0.332878+0.008357    test-rmse:0.435728+0.061794 
## [37] train-rmse:0.329864+0.008380    test-rmse:0.434333+0.060952 
## [38] train-rmse:0.327113+0.008369    test-rmse:0.434142+0.060707 
## [39] train-rmse:0.324533+0.008082    test-rmse:0.433857+0.060002 
## [40] train-rmse:0.321732+0.008114    test-rmse:0.433207+0.060432 
## [41] train-rmse:0.319251+0.007679    test-rmse:0.433423+0.059786 
## [42] train-rmse:0.316571+0.007920    test-rmse:0.433546+0.059600 
## [43] train-rmse:0.314157+0.007909    test-rmse:0.432981+0.059457 
## [44] train-rmse:0.311912+0.007647    test-rmse:0.433217+0.059327 
## [45] train-rmse:0.310007+0.007616    test-rmse:0.432881+0.058557 
## [46] train-rmse:0.307703+0.007570    test-rmse:0.430558+0.058108 
## [47] train-rmse:0.305879+0.007105    test-rmse:0.430855+0.057758 
## [48] train-rmse:0.303999+0.007156    test-rmse:0.430210+0.058093 
## [49] train-rmse:0.301929+0.007143    test-rmse:0.430123+0.058049 
## [50] train-rmse:0.299681+0.006930    test-rmse:0.429654+0.058253 
## [51] train-rmse:0.297703+0.006245    test-rmse:0.429404+0.058904 
## [52] train-rmse:0.295642+0.005804    test-rmse:0.428482+0.059321 
## [53] train-rmse:0.293860+0.006051    test-rmse:0.428561+0.059413 
## [54] train-rmse:0.292033+0.005499    test-rmse:0.427846+0.059122 
## [55] train-rmse:0.290443+0.005504    test-rmse:0.427166+0.059062 
## [56] train-rmse:0.288755+0.005593    test-rmse:0.427503+0.059158 
## [57] train-rmse:0.286880+0.005214    test-rmse:0.427069+0.059076 
## [58] train-rmse:0.284867+0.004586    test-rmse:0.426866+0.059626 
## [59] train-rmse:0.283286+0.004493    test-rmse:0.427231+0.059499 
## [60] train-rmse:0.281846+0.004552    test-rmse:0.426826+0.059528 
## [61] train-rmse:0.280021+0.004242    test-rmse:0.426455+0.059751 
## [62] train-rmse:0.278085+0.003850    test-rmse:0.426743+0.059873 
## [63] train-rmse:0.276563+0.003481    test-rmse:0.426150+0.060206 
## [64] train-rmse:0.275335+0.003584    test-rmse:0.425758+0.060464 
## [65] train-rmse:0.273638+0.003513    test-rmse:0.425086+0.060434 
## [66] train-rmse:0.272293+0.003411    test-rmse:0.425080+0.060742 
## [67] train-rmse:0.270849+0.003202    test-rmse:0.424232+0.061456 
## [68] train-rmse:0.269448+0.003024    test-rmse:0.424340+0.061686 
## [69] train-rmse:0.268130+0.003074    test-rmse:0.424200+0.061102 
## [70] train-rmse:0.266447+0.002842    test-rmse:0.424064+0.061684 
## [71] train-rmse:0.265171+0.002586    test-rmse:0.423217+0.061512 
## [72] train-rmse:0.263802+0.002674    test-rmse:0.423099+0.061434 
## [73] train-rmse:0.262288+0.002940    test-rmse:0.422694+0.061633 
## [74] train-rmse:0.260789+0.002674    test-rmse:0.422089+0.061308 
## [75] train-rmse:0.259472+0.002642    test-rmse:0.421689+0.061407 
## [76] train-rmse:0.258291+0.002423    test-rmse:0.421755+0.061111 
## [77] train-rmse:0.256989+0.002345    test-rmse:0.421253+0.061042 
## [78] train-rmse:0.256030+0.002391    test-rmse:0.420589+0.060879 
## [79] train-rmse:0.254941+0.002341    test-rmse:0.420499+0.060865 
## [80] train-rmse:0.253892+0.002439    test-rmse:0.420391+0.061464 
## [81] train-rmse:0.252803+0.002476    test-rmse:0.420409+0.061634 
## [82] train-rmse:0.251721+0.002340    test-rmse:0.419759+0.061640 
## [83] train-rmse:0.250476+0.001988    test-rmse:0.419371+0.062204 
## [84] train-rmse:0.249509+0.001949    test-rmse:0.419563+0.062572 
## [85] train-rmse:0.248386+0.001770    test-rmse:0.419966+0.062678 
## [86] train-rmse:0.247568+0.001794    test-rmse:0.419922+0.062444 
## [87] train-rmse:0.246723+0.001760    test-rmse:0.419774+0.062373 
## [88] train-rmse:0.245755+0.001681    test-rmse:0.419627+0.062041 
## [89] train-rmse:0.244639+0.001615    test-rmse:0.419608+0.062770 
## Stopping. Best iteration:
## [83] train-rmse:0.250476+0.001988    test-rmse:0.419371+0.062204
## 
## 3 
## [1]  train-rmse:1.491164+0.027305    test-rmse:1.489894+0.117157 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.359962+0.024670    test-rmse:1.361481+0.117481 
## [3]  train-rmse:1.242160+0.022622    test-rmse:1.243389+0.114675 
## [4]  train-rmse:1.136243+0.020446    test-rmse:1.137909+0.114265 
## [5]  train-rmse:1.041663+0.019037    test-rmse:1.044401+0.111680 
## [6]  train-rmse:0.956310+0.017657    test-rmse:0.961740+0.108485 
## [7]  train-rmse:0.880540+0.016130    test-rmse:0.889853+0.107264 
## [8]  train-rmse:0.812188+0.014868    test-rmse:0.825019+0.104876 
## [9]  train-rmse:0.751073+0.013167    test-rmse:0.767189+0.104177 
## [10] train-rmse:0.696415+0.012250    test-rmse:0.716444+0.100555 
## [11] train-rmse:0.647968+0.011087    test-rmse:0.671142+0.100071 
## [12] train-rmse:0.604884+0.010136    test-rmse:0.633938+0.096994 
## [13] train-rmse:0.566374+0.009385    test-rmse:0.599638+0.092762 
## [14] train-rmse:0.532762+0.008584    test-rmse:0.573116+0.091365 
## [15] train-rmse:0.502480+0.008155    test-rmse:0.549782+0.087618 
## [16] train-rmse:0.475869+0.007856    test-rmse:0.527970+0.083927 
## [17] train-rmse:0.452470+0.007455    test-rmse:0.510767+0.083364 
## [18] train-rmse:0.431850+0.007077    test-rmse:0.496139+0.079762 
## [19] train-rmse:0.413913+0.007138    test-rmse:0.483547+0.076319 
## [20] train-rmse:0.396765+0.006840    test-rmse:0.472554+0.076178 
## [21] train-rmse:0.382312+0.006519    test-rmse:0.462962+0.073162 
## [22] train-rmse:0.368968+0.005407    test-rmse:0.456371+0.071068 
## [23] train-rmse:0.357836+0.004895    test-rmse:0.451192+0.069949 
## [24] train-rmse:0.348265+0.004867    test-rmse:0.445944+0.067739 
## [25] train-rmse:0.338626+0.004599    test-rmse:0.440078+0.067126 
## [26] train-rmse:0.331067+0.004385    test-rmse:0.436919+0.066508 
## [27] train-rmse:0.322955+0.003888    test-rmse:0.434186+0.065028 
## [28] train-rmse:0.316536+0.003301    test-rmse:0.431798+0.065143 
## [29] train-rmse:0.310540+0.003448    test-rmse:0.429981+0.063051 
## [30] train-rmse:0.305793+0.003520    test-rmse:0.428098+0.060869 
## [31] train-rmse:0.299895+0.004382    test-rmse:0.426597+0.059852 
## [32] train-rmse:0.294803+0.003564    test-rmse:0.425263+0.058507 
## [33] train-rmse:0.291039+0.003402    test-rmse:0.424333+0.057439 
## [34] train-rmse:0.287427+0.003243    test-rmse:0.423094+0.057208 
## [35] train-rmse:0.284202+0.003126    test-rmse:0.421665+0.057752 
## [36] train-rmse:0.280832+0.002720    test-rmse:0.420423+0.056637 
## [37] train-rmse:0.277881+0.002751    test-rmse:0.418728+0.055919 
## [38] train-rmse:0.274368+0.003336    test-rmse:0.418775+0.055486 
## [39] train-rmse:0.271524+0.003613    test-rmse:0.418811+0.055418 
## [40] train-rmse:0.268160+0.003219    test-rmse:0.418428+0.055466 
## [41] train-rmse:0.265475+0.002864    test-rmse:0.416999+0.056103 
## [42] train-rmse:0.262544+0.003266    test-rmse:0.416654+0.056254 
## [43] train-rmse:0.259631+0.003357    test-rmse:0.415592+0.055265 
## [44] train-rmse:0.256990+0.003625    test-rmse:0.414655+0.055675 
## [45] train-rmse:0.254467+0.003127    test-rmse:0.413033+0.056071 
## [46] train-rmse:0.252170+0.002954    test-rmse:0.413682+0.056113 
## [47] train-rmse:0.249778+0.003006    test-rmse:0.412731+0.055990 
## [48] train-rmse:0.247531+0.003008    test-rmse:0.411961+0.056804 
## [49] train-rmse:0.245484+0.002903    test-rmse:0.411299+0.057071 
## [50] train-rmse:0.243703+0.002669    test-rmse:0.411186+0.057600 
## [51] train-rmse:0.241363+0.002436    test-rmse:0.411618+0.057515 
## [52] train-rmse:0.239231+0.002434    test-rmse:0.410385+0.057729 
## [53] train-rmse:0.237035+0.002203    test-rmse:0.410146+0.057708 
## [54] train-rmse:0.234716+0.001649    test-rmse:0.409335+0.056948 
## [55] train-rmse:0.232500+0.001326    test-rmse:0.408658+0.056864 
## [56] train-rmse:0.230699+0.001448    test-rmse:0.407909+0.058187 
## [57] train-rmse:0.228723+0.001332    test-rmse:0.407634+0.057751 
## [58] train-rmse:0.226971+0.001466    test-rmse:0.407126+0.057405 
## [59] train-rmse:0.225147+0.001478    test-rmse:0.407115+0.057150 
## [60] train-rmse:0.223260+0.001655    test-rmse:0.406544+0.057709 
## [61] train-rmse:0.221761+0.001595    test-rmse:0.406468+0.057798 
## [62] train-rmse:0.219909+0.001350    test-rmse:0.405818+0.057940 
## [63] train-rmse:0.218535+0.001354    test-rmse:0.405547+0.057788 
## [64] train-rmse:0.216952+0.001507    test-rmse:0.404922+0.057798 
## [65] train-rmse:0.215741+0.001475    test-rmse:0.404677+0.057850 
## [66] train-rmse:0.214064+0.001473    test-rmse:0.403817+0.058034 
## [67] train-rmse:0.212456+0.001900    test-rmse:0.402847+0.058849 
## [68] train-rmse:0.211003+0.001998    test-rmse:0.402996+0.058587 
## [69] train-rmse:0.209268+0.001952    test-rmse:0.402676+0.058219 
## [70] train-rmse:0.207488+0.002177    test-rmse:0.402476+0.058310 
## [71] train-rmse:0.206012+0.002164    test-rmse:0.402449+0.058295 
## [72] train-rmse:0.204339+0.001612    test-rmse:0.402285+0.058360 
## [73] train-rmse:0.202886+0.002160    test-rmse:0.402125+0.058260 
## [74] train-rmse:0.200916+0.002205    test-rmse:0.401820+0.058601 
## [75] train-rmse:0.199376+0.002401    test-rmse:0.400543+0.059318 
## [76] train-rmse:0.198183+0.002698    test-rmse:0.400055+0.059438 
## [77] train-rmse:0.197059+0.002758    test-rmse:0.399831+0.059587 
## [78] train-rmse:0.195246+0.003513    test-rmse:0.399455+0.059804 
## [79] train-rmse:0.194094+0.003662    test-rmse:0.398996+0.060443 
## [80] train-rmse:0.192892+0.003738    test-rmse:0.398360+0.060252 
## [81] train-rmse:0.191369+0.003516    test-rmse:0.398289+0.060586 
## [82] train-rmse:0.190129+0.003698    test-rmse:0.398298+0.060304 
## [83] train-rmse:0.188871+0.003642    test-rmse:0.398079+0.060648 
## [84] train-rmse:0.187763+0.003681    test-rmse:0.397718+0.060451 
## [85] train-rmse:0.186620+0.003725    test-rmse:0.397150+0.061013 
## [86] train-rmse:0.184940+0.003714    test-rmse:0.396703+0.061490 
## [87] train-rmse:0.183913+0.003800    test-rmse:0.396516+0.061240 
## [88] train-rmse:0.182682+0.003972    test-rmse:0.395743+0.060817 
## [89] train-rmse:0.181562+0.004000    test-rmse:0.395411+0.061370 
## [90] train-rmse:0.180400+0.004067    test-rmse:0.395096+0.061099 
## [91] train-rmse:0.179094+0.004099    test-rmse:0.395111+0.061027 
## [92] train-rmse:0.178148+0.003927    test-rmse:0.395125+0.060983 
## [93] train-rmse:0.177327+0.003783    test-rmse:0.395156+0.061138 
## [94] train-rmse:0.175738+0.003982    test-rmse:0.394811+0.061588 
## [95] train-rmse:0.174525+0.003735    test-rmse:0.394690+0.061350 
## [96] train-rmse:0.173436+0.003425    test-rmse:0.394980+0.061231 
## [97] train-rmse:0.172398+0.003673    test-rmse:0.394720+0.061374 
## [98] train-rmse:0.171352+0.003530    test-rmse:0.394738+0.061505 
## [99] train-rmse:0.170286+0.003491    test-rmse:0.394685+0.061885 
## [100]    train-rmse:0.169122+0.003045    test-rmse:0.394477+0.062160 
## [101]    train-rmse:0.167831+0.003282    test-rmse:0.394549+0.062326 
## [102]    train-rmse:0.166504+0.003363    test-rmse:0.394248+0.062405 
## [103]    train-rmse:0.165616+0.003205    test-rmse:0.394076+0.062530 
## [104]    train-rmse:0.164731+0.003280    test-rmse:0.394070+0.062680 
## [105]    train-rmse:0.163694+0.003385    test-rmse:0.393965+0.062040 
## [106]    train-rmse:0.162691+0.003709    test-rmse:0.393887+0.062313 
## [107]    train-rmse:0.161678+0.003767    test-rmse:0.393661+0.062368 
## [108]    train-rmse:0.160557+0.003696    test-rmse:0.393875+0.062156 
## [109]    train-rmse:0.160011+0.003633    test-rmse:0.393907+0.062013 
## [110]    train-rmse:0.159319+0.003612    test-rmse:0.393821+0.061928 
## [111]    train-rmse:0.158619+0.003495    test-rmse:0.393922+0.061833 
## [112]    train-rmse:0.157619+0.003629    test-rmse:0.393979+0.061643 
## [113]    train-rmse:0.156601+0.003722    test-rmse:0.394074+0.061360 
## Stopping. Best iteration:
## [107]    train-rmse:0.161678+0.003767    test-rmse:0.393661+0.062368
## 
## 4 
## [1]  train-rmse:1.490338+0.027452    test-rmse:1.488948+0.115796 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.358379+0.024906    test-rmse:1.359169+0.114591 
## [3]  train-rmse:1.239907+0.022400    test-rmse:1.241768+0.112558 
## [4]  train-rmse:1.133531+0.020967    test-rmse:1.135300+0.108876 
## [5]  train-rmse:1.037633+0.019024    test-rmse:1.040672+0.107692 
## [6]  train-rmse:0.951627+0.017406    test-rmse:0.958024+0.105887 
## [7]  train-rmse:0.874483+0.015730    test-rmse:0.883383+0.103906 
## [8]  train-rmse:0.804386+0.014539    test-rmse:0.817301+0.101173 
## [9]  train-rmse:0.742346+0.013063    test-rmse:0.759231+0.100363 
## [10] train-rmse:0.686361+0.012282    test-rmse:0.707235+0.098265 
## [11] train-rmse:0.636381+0.011862    test-rmse:0.661891+0.094230 
## [12] train-rmse:0.591384+0.011127    test-rmse:0.621884+0.091242 
## [13] train-rmse:0.551379+0.010127    test-rmse:0.588125+0.088211 
## [14] train-rmse:0.515760+0.009427    test-rmse:0.559932+0.087093 
## [15] train-rmse:0.483687+0.008425    test-rmse:0.534564+0.084976 
## [16] train-rmse:0.454679+0.007733    test-rmse:0.513516+0.084379 
## [17] train-rmse:0.428885+0.007388    test-rmse:0.495330+0.081492 
## [18] train-rmse:0.406079+0.006935    test-rmse:0.478974+0.079442 
## [19] train-rmse:0.384702+0.006449    test-rmse:0.467736+0.078788 
## [20] train-rmse:0.366458+0.006491    test-rmse:0.456530+0.076299 
## [21] train-rmse:0.349579+0.006586    test-rmse:0.448485+0.075939 
## [22] train-rmse:0.335088+0.006471    test-rmse:0.442397+0.074726 
## [23] train-rmse:0.321927+0.006775    test-rmse:0.436419+0.072217 
## [24] train-rmse:0.310518+0.007034    test-rmse:0.430463+0.072128 
## [25] train-rmse:0.300349+0.007258    test-rmse:0.426372+0.070011 
## [26] train-rmse:0.291292+0.007780    test-rmse:0.423222+0.069774 
## [27] train-rmse:0.283412+0.008331    test-rmse:0.420009+0.067919 
## [28] train-rmse:0.273704+0.006919    test-rmse:0.417022+0.066057 
## [29] train-rmse:0.266316+0.006899    test-rmse:0.414865+0.064684 
## [30] train-rmse:0.260769+0.007108    test-rmse:0.413170+0.064235 
## [31] train-rmse:0.253243+0.006542    test-rmse:0.411203+0.063264 
## [32] train-rmse:0.248238+0.006860    test-rmse:0.410397+0.062351 
## [33] train-rmse:0.244023+0.007312    test-rmse:0.409517+0.061263 
## [34] train-rmse:0.238823+0.007633    test-rmse:0.408846+0.060115 
## [35] train-rmse:0.235402+0.007646    test-rmse:0.407939+0.060370 
## [36] train-rmse:0.231714+0.008014    test-rmse:0.406814+0.059177 
## [37] train-rmse:0.227528+0.007252    test-rmse:0.406547+0.059559 
## [38] train-rmse:0.224018+0.007414    test-rmse:0.405504+0.058721 
## [39] train-rmse:0.221096+0.007133    test-rmse:0.404521+0.057687 
## [40] train-rmse:0.218155+0.007496    test-rmse:0.404806+0.057269 
## [41] train-rmse:0.215203+0.006515    test-rmse:0.404413+0.057198 
## [42] train-rmse:0.212181+0.006951    test-rmse:0.404107+0.056883 
## [43] train-rmse:0.209592+0.006787    test-rmse:0.404021+0.056659 
## [44] train-rmse:0.207404+0.006579    test-rmse:0.402230+0.056172 
## [45] train-rmse:0.204866+0.006783    test-rmse:0.402805+0.055418 
## [46] train-rmse:0.201947+0.006793    test-rmse:0.402342+0.055598 
## [47] train-rmse:0.199003+0.006929    test-rmse:0.402619+0.055441 
## [48] train-rmse:0.196293+0.007166    test-rmse:0.403128+0.055034 
## [49] train-rmse:0.192767+0.006551    test-rmse:0.403264+0.055019 
## [50] train-rmse:0.189698+0.006290    test-rmse:0.403697+0.054577 
## Stopping. Best iteration:
## [44] train-rmse:0.207404+0.006579    test-rmse:0.402230+0.056172
## 
## 5 
## [1]  train-rmse:1.490099+0.027479    test-rmse:1.488300+0.115261 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.357755+0.024981    test-rmse:1.357604+0.112461 
## [3]  train-rmse:1.238789+0.022621    test-rmse:1.239209+0.110106 
## [4]  train-rmse:1.131739+0.021195    test-rmse:1.132655+0.105295 
## [5]  train-rmse:1.035617+0.019462    test-rmse:1.038766+0.103016 
## [6]  train-rmse:0.948562+0.018370    test-rmse:0.954051+0.099406 
## [7]  train-rmse:0.870432+0.016949    test-rmse:0.878252+0.098304 
## [8]  train-rmse:0.800264+0.015698    test-rmse:0.812681+0.096608 
## [9]  train-rmse:0.737157+0.014432    test-rmse:0.754358+0.095524 
## [10] train-rmse:0.680086+0.013385    test-rmse:0.701486+0.094080 
## [11] train-rmse:0.628939+0.013032    test-rmse:0.656886+0.094124 
## [12] train-rmse:0.582958+0.012445    test-rmse:0.615370+0.091056 
## [13] train-rmse:0.541719+0.011732    test-rmse:0.583416+0.090316 
## [14] train-rmse:0.504737+0.010846    test-rmse:0.555282+0.088350 
## [15] train-rmse:0.470631+0.010467    test-rmse:0.529651+0.086248 
## [16] train-rmse:0.440268+0.009816    test-rmse:0.509077+0.084135 
## [17] train-rmse:0.412910+0.009184    test-rmse:0.491107+0.083086 
## [18] train-rmse:0.388317+0.009324    test-rmse:0.476403+0.082271 
## [19] train-rmse:0.366320+0.009205    test-rmse:0.464394+0.079896 
## [20] train-rmse:0.346803+0.008995    test-rmse:0.454652+0.078535 
## [21] train-rmse:0.329349+0.008510    test-rmse:0.446905+0.077319 
## [22] train-rmse:0.313264+0.008556    test-rmse:0.438402+0.075333 
## [23] train-rmse:0.299072+0.008844    test-rmse:0.433033+0.072338 
## [24] train-rmse:0.286681+0.008698    test-rmse:0.428923+0.072047 
## [25] train-rmse:0.275413+0.009059    test-rmse:0.425057+0.069842 
## [26] train-rmse:0.263344+0.007930    test-rmse:0.420757+0.068856 
## [27] train-rmse:0.253422+0.007739    test-rmse:0.419007+0.067906 
## [28] train-rmse:0.244671+0.007419    test-rmse:0.416616+0.067858 
## [29] train-rmse:0.236791+0.007567    test-rmse:0.415860+0.066411 
## [30] train-rmse:0.228690+0.006532    test-rmse:0.412651+0.065524 
## [31] train-rmse:0.222770+0.006569    test-rmse:0.411818+0.065316 
## [32] train-rmse:0.215783+0.005686    test-rmse:0.409456+0.064421 
## [33] train-rmse:0.210197+0.005391    test-rmse:0.409375+0.063694 
## [34] train-rmse:0.205504+0.005658    test-rmse:0.407463+0.063522 
## [35] train-rmse:0.201151+0.005879    test-rmse:0.406989+0.062758 
## [36] train-rmse:0.196391+0.006792    test-rmse:0.405216+0.063242 
## [37] train-rmse:0.192525+0.006108    test-rmse:0.404721+0.062678 
## [38] train-rmse:0.188160+0.005560    test-rmse:0.404098+0.062868 
## [39] train-rmse:0.184725+0.005738    test-rmse:0.403443+0.063412 
## [40] train-rmse:0.181584+0.006357    test-rmse:0.402667+0.062398 
## [41] train-rmse:0.178723+0.006668    test-rmse:0.402438+0.062758 
## [42] train-rmse:0.176134+0.006793    test-rmse:0.401725+0.062095 
## [43] train-rmse:0.172704+0.004843    test-rmse:0.400981+0.061691 
## [44] train-rmse:0.170356+0.004442    test-rmse:0.400652+0.061107 
## [45] train-rmse:0.166886+0.003981    test-rmse:0.399829+0.060660 
## [46] train-rmse:0.164717+0.004597    test-rmse:0.399408+0.060687 
## [47] train-rmse:0.161978+0.004372    test-rmse:0.399160+0.061929 
## [48] train-rmse:0.159200+0.003756    test-rmse:0.399742+0.061982 
## [49] train-rmse:0.156680+0.003954    test-rmse:0.399377+0.062018 
## [50] train-rmse:0.154188+0.003924    test-rmse:0.399184+0.061800 
## [51] train-rmse:0.152095+0.004058    test-rmse:0.399098+0.061726 
## [52] train-rmse:0.149347+0.004690    test-rmse:0.399447+0.061106 
## [53] train-rmse:0.146487+0.004557    test-rmse:0.400270+0.060681 
## [54] train-rmse:0.143089+0.005175    test-rmse:0.399795+0.060488 
## [55] train-rmse:0.140436+0.005642    test-rmse:0.399259+0.060281 
## [56] train-rmse:0.137687+0.005916    test-rmse:0.399312+0.059205 
## [57] train-rmse:0.135405+0.006306    test-rmse:0.398538+0.059328 
## [58] train-rmse:0.132973+0.006195    test-rmse:0.398494+0.059479 
## [59] train-rmse:0.130928+0.005824    test-rmse:0.398239+0.059986 
## [60] train-rmse:0.128928+0.006025    test-rmse:0.398186+0.059753 
## [61] train-rmse:0.127218+0.005942    test-rmse:0.397559+0.060863 
## [62] train-rmse:0.125121+0.005465    test-rmse:0.397873+0.060850 
## [63] train-rmse:0.123564+0.005227    test-rmse:0.397348+0.060499 
## [64] train-rmse:0.121821+0.005426    test-rmse:0.397263+0.060186 
## [65] train-rmse:0.119546+0.005795    test-rmse:0.396913+0.060543 
## [66] train-rmse:0.117940+0.005482    test-rmse:0.397254+0.060535 
## [67] train-rmse:0.116369+0.006107    test-rmse:0.397148+0.060690 
## [68] train-rmse:0.114707+0.006371    test-rmse:0.397485+0.060967 
## [69] train-rmse:0.113004+0.006141    test-rmse:0.397023+0.061427 
## [70] train-rmse:0.111848+0.006078    test-rmse:0.397035+0.061639 
## [71] train-rmse:0.110295+0.005871    test-rmse:0.396756+0.061380 
## [72] train-rmse:0.108852+0.005755    test-rmse:0.396415+0.061780 
## [73] train-rmse:0.107647+0.005802    test-rmse:0.396418+0.061521 
## [74] train-rmse:0.106145+0.005918    test-rmse:0.396375+0.061752 
## [75] train-rmse:0.105063+0.006008    test-rmse:0.396163+0.061776 
## [76] train-rmse:0.103145+0.006201    test-rmse:0.395881+0.061842 
## [77] train-rmse:0.101926+0.005955    test-rmse:0.395698+0.061786 
## [78] train-rmse:0.100405+0.005455    test-rmse:0.395500+0.061671 
## [79] train-rmse:0.099328+0.005197    test-rmse:0.395626+0.061672 
## [80] train-rmse:0.097941+0.004949    test-rmse:0.395474+0.061879 
## [81] train-rmse:0.096782+0.004633    test-rmse:0.395298+0.061998 
## [82] train-rmse:0.095616+0.004432    test-rmse:0.394976+0.062348 
## [83] train-rmse:0.094371+0.004259    test-rmse:0.394617+0.062360 
## [84] train-rmse:0.093154+0.004158    test-rmse:0.394522+0.062035 
## [85] train-rmse:0.092326+0.004081    test-rmse:0.394406+0.061806 
## [86] train-rmse:0.090909+0.004166    test-rmse:0.394307+0.061745 
## [87] train-rmse:0.089764+0.004278    test-rmse:0.394335+0.061742 
## [88] train-rmse:0.088578+0.004143    test-rmse:0.393941+0.061842 
## [89] train-rmse:0.087642+0.003825    test-rmse:0.393840+0.062001 
## [90] train-rmse:0.086813+0.003679    test-rmse:0.394026+0.061877 
## [91] train-rmse:0.085785+0.003398    test-rmse:0.394144+0.062036 
## [92] train-rmse:0.084898+0.003382    test-rmse:0.394071+0.062076 
## [93] train-rmse:0.083759+0.003359    test-rmse:0.393946+0.062343 
## [94] train-rmse:0.082867+0.003352    test-rmse:0.393911+0.062648 
## [95] train-rmse:0.081811+0.003169    test-rmse:0.394025+0.062575 
## Stopping. Best iteration:
## [89] train-rmse:0.087642+0.003825    test-rmse:0.393840+0.062001
## 
## 6 
## [1]  train-rmse:1.489976+0.027481    test-rmse:1.488741+0.114911 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.357402+0.024997    test-rmse:1.356362+0.112124 
## [3]  train-rmse:1.238071+0.022738    test-rmse:1.236223+0.110244 
## [4]  train-rmse:1.130634+0.021313    test-rmse:1.128950+0.106722 
## [5]  train-rmse:1.034130+0.019661    test-rmse:1.034525+0.104369 
## [6]  train-rmse:0.946655+0.018597    test-rmse:0.953410+0.102742 
## [7]  train-rmse:0.868202+0.017277    test-rmse:0.877942+0.101276 
## [8]  train-rmse:0.797435+0.016023    test-rmse:0.812660+0.099496 
## [9]  train-rmse:0.733894+0.014844    test-rmse:0.754855+0.097937 
## [10] train-rmse:0.676610+0.014106    test-rmse:0.703855+0.097880 
## [11] train-rmse:0.624134+0.013379    test-rmse:0.658081+0.095616 
## [12] train-rmse:0.577391+0.012428    test-rmse:0.619274+0.093966 
## [13] train-rmse:0.535232+0.012191    test-rmse:0.586960+0.092016 
## [14] train-rmse:0.497059+0.011273    test-rmse:0.556280+0.090614 
## [15] train-rmse:0.462581+0.010233    test-rmse:0.530197+0.089476 
## [16] train-rmse:0.430801+0.009987    test-rmse:0.508856+0.086972 
## [17] train-rmse:0.403049+0.009838    test-rmse:0.491939+0.086135 
## [18] train-rmse:0.377898+0.009593    test-rmse:0.478086+0.084898 
## [19] train-rmse:0.354601+0.010098    test-rmse:0.466244+0.083149 
## [20] train-rmse:0.333054+0.009868    test-rmse:0.456150+0.080164 
## [21] train-rmse:0.314019+0.009719    test-rmse:0.448303+0.077898 
## [22] train-rmse:0.297110+0.009497    test-rmse:0.442883+0.076347 
## [23] train-rmse:0.281234+0.009308    test-rmse:0.437801+0.076342 
## [24] train-rmse:0.267019+0.009576    test-rmse:0.432078+0.073793 
## [25] train-rmse:0.254184+0.009201    test-rmse:0.429381+0.073501 
## [26] train-rmse:0.242351+0.009067    test-rmse:0.427199+0.073758 
## [27] train-rmse:0.231118+0.009142    test-rmse:0.423401+0.071987 
## [28] train-rmse:0.221763+0.009175    test-rmse:0.421490+0.070826 
## [29] train-rmse:0.212148+0.009232    test-rmse:0.419629+0.069943 
## [30] train-rmse:0.204305+0.008740    test-rmse:0.418509+0.069144 
## [31] train-rmse:0.195924+0.008654    test-rmse:0.416854+0.069053 
## [32] train-rmse:0.187945+0.009138    test-rmse:0.415260+0.068543 
## [33] train-rmse:0.180468+0.007359    test-rmse:0.413407+0.068202 
## [34] train-rmse:0.174781+0.007452    test-rmse:0.412495+0.067280 
## [35] train-rmse:0.168710+0.006198    test-rmse:0.411360+0.067171 
## [36] train-rmse:0.163356+0.005921    test-rmse:0.410902+0.066295 
## [37] train-rmse:0.158726+0.006005    test-rmse:0.410112+0.066192 
## [38] train-rmse:0.153628+0.006333    test-rmse:0.410058+0.065037 
## [39] train-rmse:0.148851+0.006801    test-rmse:0.409014+0.064462 
## [40] train-rmse:0.145435+0.006750    test-rmse:0.408978+0.063014 
## [41] train-rmse:0.142015+0.006336    test-rmse:0.408272+0.062457 
## [42] train-rmse:0.139001+0.006656    test-rmse:0.407376+0.061223 
## [43] train-rmse:0.136033+0.006741    test-rmse:0.407446+0.060723 
## [44] train-rmse:0.133158+0.006662    test-rmse:0.406911+0.059920 
## [45] train-rmse:0.129600+0.005338    test-rmse:0.406373+0.059168 
## [46] train-rmse:0.127455+0.005255    test-rmse:0.405297+0.058845 
## [47] train-rmse:0.125585+0.005001    test-rmse:0.405496+0.057827 
## [48] train-rmse:0.123016+0.004867    test-rmse:0.405157+0.057088 
## [49] train-rmse:0.120759+0.004403    test-rmse:0.405040+0.056917 
## [50] train-rmse:0.117668+0.004647    test-rmse:0.405010+0.056918 
## [51] train-rmse:0.115397+0.004690    test-rmse:0.404688+0.057184 
## [52] train-rmse:0.112931+0.004438    test-rmse:0.404635+0.056337 
## [53] train-rmse:0.110562+0.004035    test-rmse:0.403999+0.056688 
## [54] train-rmse:0.109131+0.004129    test-rmse:0.403620+0.056314 
## [55] train-rmse:0.106236+0.003746    test-rmse:0.403649+0.056348 
## [56] train-rmse:0.104064+0.003742    test-rmse:0.404048+0.055592 
## [57] train-rmse:0.102437+0.003830    test-rmse:0.404130+0.054764 
## [58] train-rmse:0.100342+0.004208    test-rmse:0.404553+0.054336 
## [59] train-rmse:0.097538+0.004103    test-rmse:0.404784+0.054810 
## [60] train-rmse:0.095476+0.003781    test-rmse:0.404651+0.054793 
## Stopping. Best iteration:
## [54] train-rmse:0.109131+0.004129    test-rmse:0.403620+0.056314
## 
## 7 
## [1]  train-rmse:1.474838+0.027375    test-rmse:1.470957+0.113637 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.335059+0.024942    test-rmse:1.331218+0.110424 
## [3]  train-rmse:1.215506+0.022964    test-rmse:1.211706+0.107537 
## [4]  train-rmse:1.113873+0.021399    test-rmse:1.110129+0.104893 
## [5]  train-rmse:1.028063+0.020206    test-rmse:1.024390+0.102435 
## [6]  train-rmse:0.956145+0.019336    test-rmse:0.952566+0.100137 
## [7]  train-rmse:0.896338+0.018734    test-rmse:0.892874+0.097990 
## [8]  train-rmse:0.846999+0.018345    test-rmse:0.843664+0.096003 
## [9]  train-rmse:0.806613+0.018117    test-rmse:0.803421+0.094185 
## [10] train-rmse:0.773805+0.018002    test-rmse:0.770761+0.092544 
## [11] train-rmse:0.747340+0.017962    test-rmse:0.744445+0.091085 
## [12] train-rmse:0.726129+0.017968    test-rmse:0.723376+0.089803 
## [13] train-rmse:0.709223+0.017999    test-rmse:0.706605+0.088688 
## [14] train-rmse:0.694854+0.017582    test-rmse:0.694461+0.089036 
## [15] train-rmse:0.682760+0.017395    test-rmse:0.680425+0.087952 
## [16] train-rmse:0.671828+0.017114    test-rmse:0.671823+0.087933 
## [17] train-rmse:0.662625+0.017010    test-rmse:0.661428+0.086848 
## [18] train-rmse:0.654194+0.016787    test-rmse:0.654777+0.086643 
## [19] train-rmse:0.646682+0.016136    test-rmse:0.647718+0.083288 
## [20] train-rmse:0.639872+0.015736    test-rmse:0.642342+0.084858 
## [21] train-rmse:0.633426+0.015269    test-rmse:0.636094+0.082274 
## [22] train-rmse:0.627674+0.014928    test-rmse:0.630649+0.080208 
## [23] train-rmse:0.622168+0.014534    test-rmse:0.624002+0.080165 
## [24] train-rmse:0.617076+0.014077    test-rmse:0.620370+0.078895 
## [25] train-rmse:0.612417+0.013829    test-rmse:0.615769+0.078402 
## [26] train-rmse:0.607944+0.013534    test-rmse:0.611554+0.076109 
## [27] train-rmse:0.603857+0.013336    test-rmse:0.607827+0.074714 
## [28] train-rmse:0.600011+0.013143    test-rmse:0.603331+0.073993 
## [29] train-rmse:0.596358+0.012909    test-rmse:0.601337+0.072954 
## [30] train-rmse:0.592825+0.012675    test-rmse:0.598518+0.072719 
## [31] train-rmse:0.589662+0.012525    test-rmse:0.595021+0.072721 
## [32] train-rmse:0.586575+0.012353    test-rmse:0.591543+0.070391 
## [33] train-rmse:0.583646+0.012194    test-rmse:0.589726+0.069176 
## [34] train-rmse:0.580789+0.012082    test-rmse:0.588923+0.069882 
## [35] train-rmse:0.578110+0.011997    test-rmse:0.586281+0.067982 
## [36] train-rmse:0.575583+0.011882    test-rmse:0.583537+0.068372 
## [37] train-rmse:0.573138+0.011742    test-rmse:0.581927+0.067030 
## [38] train-rmse:0.570782+0.011678    test-rmse:0.580859+0.066597 
## [39] train-rmse:0.568536+0.011623    test-rmse:0.578863+0.065921 
## [40] train-rmse:0.566378+0.011551    test-rmse:0.577142+0.065770 
## [41] train-rmse:0.564345+0.011420    test-rmse:0.575096+0.065635 
## [42] train-rmse:0.562362+0.011288    test-rmse:0.573246+0.065026 
## [43] train-rmse:0.560411+0.011173    test-rmse:0.571870+0.063625 
## [44] train-rmse:0.558589+0.011122    test-rmse:0.570958+0.064481 
## [45] train-rmse:0.556797+0.011040    test-rmse:0.568759+0.064324 
## [46] train-rmse:0.555077+0.010943    test-rmse:0.568502+0.063628 
## [47] train-rmse:0.553478+0.010876    test-rmse:0.566218+0.063008 
## [48] train-rmse:0.551893+0.010829    test-rmse:0.565187+0.062875 
## [49] train-rmse:0.550340+0.010784    test-rmse:0.563987+0.062615 
## [50] train-rmse:0.548862+0.010723    test-rmse:0.564276+0.062462 
## [51] train-rmse:0.547454+0.010661    test-rmse:0.563527+0.061804 
## [52] train-rmse:0.546094+0.010591    test-rmse:0.561425+0.061217 
## [53] train-rmse:0.544739+0.010559    test-rmse:0.560482+0.061516 
## [54] train-rmse:0.543434+0.010489    test-rmse:0.560279+0.061197 
## [55] train-rmse:0.542145+0.010458    test-rmse:0.559245+0.060231 
## [56] train-rmse:0.540906+0.010409    test-rmse:0.558347+0.060674 
## [57] train-rmse:0.539697+0.010367    test-rmse:0.556723+0.059579 
## [58] train-rmse:0.538532+0.010300    test-rmse:0.556523+0.059456 
## [59] train-rmse:0.537375+0.010235    test-rmse:0.555776+0.059849 
## [60] train-rmse:0.536220+0.010166    test-rmse:0.555285+0.059030 
## [61] train-rmse:0.535098+0.010133    test-rmse:0.555247+0.058655 
## [62] train-rmse:0.534025+0.010101    test-rmse:0.553851+0.058857 
## [63] train-rmse:0.532965+0.010048    test-rmse:0.553931+0.058954 
## [64] train-rmse:0.531936+0.010003    test-rmse:0.553301+0.059232 
## [65] train-rmse:0.530919+0.009946    test-rmse:0.552328+0.059294 
## [66] train-rmse:0.529916+0.009894    test-rmse:0.551494+0.058726 
## [67] train-rmse:0.528926+0.009824    test-rmse:0.551085+0.059036 
## [68] train-rmse:0.527931+0.009763    test-rmse:0.550428+0.058343 
## [69] train-rmse:0.526982+0.009741    test-rmse:0.549852+0.057706 
## [70] train-rmse:0.526067+0.009715    test-rmse:0.548677+0.057962 
## [71] train-rmse:0.525160+0.009645    test-rmse:0.548706+0.057738 
## [72] train-rmse:0.524268+0.009583    test-rmse:0.547492+0.057182 
## [73] train-rmse:0.523390+0.009551    test-rmse:0.547155+0.057143 
## [74] train-rmse:0.522528+0.009505    test-rmse:0.546772+0.057092 
## [75] train-rmse:0.521674+0.009448    test-rmse:0.546354+0.057306 
## [76] train-rmse:0.520821+0.009394    test-rmse:0.545788+0.056480 
## [77] train-rmse:0.519980+0.009360    test-rmse:0.545181+0.056050 
## [78] train-rmse:0.519156+0.009314    test-rmse:0.544849+0.055875 
## [79] train-rmse:0.518356+0.009277    test-rmse:0.544229+0.055710 
## [80] train-rmse:0.517552+0.009241    test-rmse:0.543490+0.055960 
## [81] train-rmse:0.516756+0.009176    test-rmse:0.543111+0.056074 
## [82] train-rmse:0.515977+0.009131    test-rmse:0.542025+0.055656 
## [83] train-rmse:0.515235+0.009114    test-rmse:0.541283+0.056118 
## [84] train-rmse:0.514494+0.009082    test-rmse:0.541001+0.056017 
## [85] train-rmse:0.513739+0.009058    test-rmse:0.540333+0.056012 
## [86] train-rmse:0.513005+0.009029    test-rmse:0.540388+0.055794 
## [87] train-rmse:0.512274+0.008980    test-rmse:0.539590+0.055114 
## [88] train-rmse:0.511556+0.008962    test-rmse:0.538815+0.054747 
## [89] train-rmse:0.510838+0.008945    test-rmse:0.538031+0.055054 
## [90] train-rmse:0.510138+0.008920    test-rmse:0.537916+0.055309 
## [91] train-rmse:0.509453+0.008889    test-rmse:0.537826+0.055491 
## [92] train-rmse:0.508777+0.008843    test-rmse:0.536625+0.055232 
## [93] train-rmse:0.508102+0.008813    test-rmse:0.536517+0.054559 
## [94] train-rmse:0.507416+0.008793    test-rmse:0.535639+0.054518 
## [95] train-rmse:0.506747+0.008779    test-rmse:0.535524+0.054687 
## [96] train-rmse:0.506099+0.008740    test-rmse:0.535193+0.054997 
## [97] train-rmse:0.505448+0.008710    test-rmse:0.534629+0.054340 
## [98] train-rmse:0.504804+0.008689    test-rmse:0.533839+0.054564 
## [99] train-rmse:0.504177+0.008669    test-rmse:0.533255+0.054218 
## [100]    train-rmse:0.503561+0.008646    test-rmse:0.532770+0.054599 
## [101]    train-rmse:0.502945+0.008633    test-rmse:0.532262+0.054401 
## [102]    train-rmse:0.502329+0.008622    test-rmse:0.531319+0.053839 
## [103]    train-rmse:0.501701+0.008584    test-rmse:0.531220+0.053495 
## [104]    train-rmse:0.501090+0.008561    test-rmse:0.531357+0.054193 
## [105]    train-rmse:0.500498+0.008547    test-rmse:0.530602+0.054074 
## [106]    train-rmse:0.499919+0.008531    test-rmse:0.529951+0.054056 
## [107]    train-rmse:0.499339+0.008506    test-rmse:0.529878+0.053430 
## [108]    train-rmse:0.498764+0.008485    test-rmse:0.530025+0.053835 
## [109]    train-rmse:0.498192+0.008481    test-rmse:0.529377+0.053273 
## [110]    train-rmse:0.497621+0.008471    test-rmse:0.529547+0.053568 
## [111]    train-rmse:0.497060+0.008457    test-rmse:0.529291+0.053886 
## [112]    train-rmse:0.496502+0.008430    test-rmse:0.528340+0.053658 
## [113]    train-rmse:0.495956+0.008411    test-rmse:0.528471+0.053782 
## [114]    train-rmse:0.495403+0.008393    test-rmse:0.527907+0.053415 
## [115]    train-rmse:0.494872+0.008370    test-rmse:0.527353+0.052824 
## [116]    train-rmse:0.494345+0.008353    test-rmse:0.526841+0.052254 
## [117]    train-rmse:0.493816+0.008343    test-rmse:0.526481+0.052834 
## [118]    train-rmse:0.493292+0.008335    test-rmse:0.525962+0.052586 
## [119]    train-rmse:0.492777+0.008321    test-rmse:0.525464+0.052468 
## [120]    train-rmse:0.492275+0.008306    test-rmse:0.525193+0.052698 
## [121]    train-rmse:0.491776+0.008292    test-rmse:0.524835+0.052568 
## [122]    train-rmse:0.491278+0.008277    test-rmse:0.524343+0.052146 
## [123]    train-rmse:0.490775+0.008267    test-rmse:0.524221+0.052023 
## [124]    train-rmse:0.490278+0.008254    test-rmse:0.523835+0.052281 
## [125]    train-rmse:0.489780+0.008240    test-rmse:0.523707+0.052079 
## [126]    train-rmse:0.489298+0.008220    test-rmse:0.524105+0.051589 
## [127]    train-rmse:0.488818+0.008206    test-rmse:0.523781+0.051682 
## [128]    train-rmse:0.488343+0.008198    test-rmse:0.523278+0.051443 
## [129]    train-rmse:0.487882+0.008186    test-rmse:0.523011+0.051832 
## [130]    train-rmse:0.487421+0.008176    test-rmse:0.522374+0.052358 
## [131]    train-rmse:0.486955+0.008154    test-rmse:0.521770+0.052090 
## [132]    train-rmse:0.486503+0.008141    test-rmse:0.521430+0.052010 
## [133]    train-rmse:0.486051+0.008124    test-rmse:0.521865+0.051929 
## [134]    train-rmse:0.485601+0.008112    test-rmse:0.521865+0.051602 
## [135]    train-rmse:0.485162+0.008104    test-rmse:0.521151+0.051843 
## [136]    train-rmse:0.484719+0.008098    test-rmse:0.520662+0.051654 
## [137]    train-rmse:0.484281+0.008090    test-rmse:0.520763+0.051822 
## [138]    train-rmse:0.483858+0.008082    test-rmse:0.520140+0.051800 
## [139]    train-rmse:0.483437+0.008072    test-rmse:0.519595+0.051586 
## [140]    train-rmse:0.483009+0.008065    test-rmse:0.519231+0.051200 
## [141]    train-rmse:0.482587+0.008058    test-rmse:0.519008+0.050576 
## [142]    train-rmse:0.482176+0.008050    test-rmse:0.519328+0.050482 
## [143]    train-rmse:0.481769+0.008041    test-rmse:0.519559+0.050466 
## [144]    train-rmse:0.481369+0.008033    test-rmse:0.519007+0.050392 
## [145]    train-rmse:0.480963+0.008023    test-rmse:0.519440+0.050674 
## [146]    train-rmse:0.480568+0.008007    test-rmse:0.519541+0.050458 
## [147]    train-rmse:0.480173+0.008007    test-rmse:0.519214+0.050422 
## [148]    train-rmse:0.479769+0.007987    test-rmse:0.518934+0.050326 
## [149]    train-rmse:0.479381+0.007978    test-rmse:0.518703+0.050362 
## [150]    train-rmse:0.478995+0.007966    test-rmse:0.518524+0.050263 
## [151]    train-rmse:0.478607+0.007963    test-rmse:0.518562+0.050280 
## [152]    train-rmse:0.478237+0.007958    test-rmse:0.517229+0.049301 
## [153]    train-rmse:0.477865+0.007945    test-rmse:0.516773+0.049216 
## [154]    train-rmse:0.477495+0.007941    test-rmse:0.516593+0.049017 
## [155]    train-rmse:0.477121+0.007942    test-rmse:0.516512+0.048568 
## [156]    train-rmse:0.476752+0.007943    test-rmse:0.515972+0.048135 
## [157]    train-rmse:0.476394+0.007945    test-rmse:0.515941+0.048267 
## [158]    train-rmse:0.476033+0.007936    test-rmse:0.515738+0.048565 
## [159]    train-rmse:0.475667+0.007916    test-rmse:0.515471+0.048793 
## [160]    train-rmse:0.475317+0.007909    test-rmse:0.515350+0.048198 
## [161]    train-rmse:0.474963+0.007909    test-rmse:0.514937+0.048110 
## [162]    train-rmse:0.474618+0.007905    test-rmse:0.514802+0.048156 
## [163]    train-rmse:0.474277+0.007898    test-rmse:0.514858+0.048585 
## [164]    train-rmse:0.473938+0.007895    test-rmse:0.514556+0.048556 
## [165]    train-rmse:0.473595+0.007884    test-rmse:0.514152+0.048263 
## [166]    train-rmse:0.473256+0.007877    test-rmse:0.514115+0.048063 
## [167]    train-rmse:0.472916+0.007872    test-rmse:0.514030+0.048347 
## [168]    train-rmse:0.472582+0.007865    test-rmse:0.513716+0.048601 
## [169]    train-rmse:0.472247+0.007860    test-rmse:0.513067+0.048945 
## [170]    train-rmse:0.471916+0.007857    test-rmse:0.513126+0.048600 
## [171]    train-rmse:0.471589+0.007851    test-rmse:0.513000+0.048526 
## [172]    train-rmse:0.471268+0.007847    test-rmse:0.512454+0.048239 
## [173]    train-rmse:0.470945+0.007840    test-rmse:0.512519+0.048113 
## [174]    train-rmse:0.470622+0.007828    test-rmse:0.512450+0.048142 
## [175]    train-rmse:0.470301+0.007818    test-rmse:0.512254+0.048102 
## [176]    train-rmse:0.469985+0.007807    test-rmse:0.512080+0.048221 
## [177]    train-rmse:0.469671+0.007805    test-rmse:0.511087+0.047302 
## [178]    train-rmse:0.469365+0.007797    test-rmse:0.511442+0.047323 
## [179]    train-rmse:0.469059+0.007787    test-rmse:0.511250+0.047224 
## [180]    train-rmse:0.468751+0.007785    test-rmse:0.511145+0.047003 
## [181]    train-rmse:0.468438+0.007773    test-rmse:0.510877+0.046457 
## [182]    train-rmse:0.468135+0.007775    test-rmse:0.510647+0.046243 
## [183]    train-rmse:0.467834+0.007766    test-rmse:0.510539+0.046666 
## [184]    train-rmse:0.467541+0.007765    test-rmse:0.510290+0.046451 
## [185]    train-rmse:0.467248+0.007760    test-rmse:0.510050+0.046612 
## [186]    train-rmse:0.466952+0.007754    test-rmse:0.510425+0.046569 
## [187]    train-rmse:0.466667+0.007749    test-rmse:0.510347+0.046337 
## [188]    train-rmse:0.466378+0.007741    test-rmse:0.509867+0.046349 
## [189]    train-rmse:0.466088+0.007737    test-rmse:0.509501+0.046455 
## [190]    train-rmse:0.465796+0.007732    test-rmse:0.509274+0.046413 
## [191]    train-rmse:0.465517+0.007726    test-rmse:0.509058+0.046408 
## [192]    train-rmse:0.465243+0.007722    test-rmse:0.509076+0.046529 
## [193]    train-rmse:0.464956+0.007708    test-rmse:0.509222+0.046990 
## [194]    train-rmse:0.464677+0.007695    test-rmse:0.508837+0.046799 
## [195]    train-rmse:0.464404+0.007690    test-rmse:0.508416+0.046490 
## [196]    train-rmse:0.464134+0.007689    test-rmse:0.508596+0.046301 
## [197]    train-rmse:0.463865+0.007684    test-rmse:0.508169+0.046023 
## [198]    train-rmse:0.463594+0.007680    test-rmse:0.508166+0.045919 
## [199]    train-rmse:0.463327+0.007670    test-rmse:0.508123+0.045705 
## [200]    train-rmse:0.463064+0.007663    test-rmse:0.508149+0.045650 
## [201]    train-rmse:0.462803+0.007656    test-rmse:0.507514+0.045555 
## [202]    train-rmse:0.462539+0.007650    test-rmse:0.507406+0.045693 
## [203]    train-rmse:0.462275+0.007644    test-rmse:0.507421+0.045336 
## [204]    train-rmse:0.462012+0.007639    test-rmse:0.507571+0.045501 
## [205]    train-rmse:0.461757+0.007636    test-rmse:0.507577+0.045492 
## [206]    train-rmse:0.461494+0.007635    test-rmse:0.507554+0.045591 
## [207]    train-rmse:0.461241+0.007632    test-rmse:0.506736+0.044639 
## [208]    train-rmse:0.460990+0.007627    test-rmse:0.507391+0.044535 
## [209]    train-rmse:0.460738+0.007620    test-rmse:0.507110+0.044492 
## [210]    train-rmse:0.460486+0.007614    test-rmse:0.506813+0.044525 
## [211]    train-rmse:0.460227+0.007609    test-rmse:0.506691+0.044420 
## [212]    train-rmse:0.459978+0.007607    test-rmse:0.506208+0.044389 
## [213]    train-rmse:0.459731+0.007605    test-rmse:0.506082+0.044664 
## [214]    train-rmse:0.459487+0.007601    test-rmse:0.505765+0.044893 
## [215]    train-rmse:0.459245+0.007597    test-rmse:0.505964+0.045005 
## [216]    train-rmse:0.459000+0.007596    test-rmse:0.505922+0.045239 
## [217]    train-rmse:0.458763+0.007594    test-rmse:0.506203+0.045132 
## [218]    train-rmse:0.458521+0.007582    test-rmse:0.505957+0.045109 
## [219]    train-rmse:0.458284+0.007577    test-rmse:0.506129+0.045069 
## [220]    train-rmse:0.458052+0.007569    test-rmse:0.506017+0.045230 
## Stopping. Best iteration:
## [214]    train-rmse:0.459487+0.007601    test-rmse:0.505765+0.044893
## 
## 8 
## [1]  train-rmse:1.467532+0.027385    test-rmse:1.465154+0.115699 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.319817+0.024919    test-rmse:1.320852+0.113315 
## [3]  train-rmse:1.191424+0.022662    test-rmse:1.192273+0.109494 
## [4]  train-rmse:1.080227+0.020920    test-rmse:1.083222+0.108658 
## [5]  train-rmse:0.984331+0.019393    test-rmse:0.988383+0.108640 
## [6]  train-rmse:0.901629+0.018060    test-rmse:0.905521+0.105267 
## [7]  train-rmse:0.831016+0.016969    test-rmse:0.835631+0.105466 
## [8]  train-rmse:0.770671+0.015957    test-rmse:0.773420+0.102256 
## [9]  train-rmse:0.719158+0.015112    test-rmse:0.725085+0.100547 
## [10] train-rmse:0.675595+0.014647    test-rmse:0.680981+0.098810 
## [11] train-rmse:0.638829+0.014190    test-rmse:0.646603+0.098745 
## [12] train-rmse:0.607726+0.013662    test-rmse:0.616204+0.095482 
## [13] train-rmse:0.581533+0.013376    test-rmse:0.591842+0.093579 
## [14] train-rmse:0.559420+0.013067    test-rmse:0.571270+0.090513 
## [15] train-rmse:0.540608+0.012750    test-rmse:0.553744+0.087964 
## [16] train-rmse:0.524582+0.012409    test-rmse:0.543302+0.085675 
## [17] train-rmse:0.510871+0.012141    test-rmse:0.529675+0.082941 
## [18] train-rmse:0.499002+0.011716    test-rmse:0.520044+0.080688 
## [19] train-rmse:0.488769+0.011378    test-rmse:0.512254+0.076748 
## [20] train-rmse:0.479959+0.011153    test-rmse:0.503840+0.075114 
## [21] train-rmse:0.471227+0.010027    test-rmse:0.496888+0.075011 
## [22] train-rmse:0.463443+0.009794    test-rmse:0.491912+0.073057 
## [23] train-rmse:0.455913+0.009737    test-rmse:0.485196+0.070684 
## [24] train-rmse:0.449586+0.009213    test-rmse:0.479223+0.069538 
## [25] train-rmse:0.443995+0.009078    test-rmse:0.476622+0.068992 
## [26] train-rmse:0.438494+0.008299    test-rmse:0.473246+0.068890 
## [27] train-rmse:0.433628+0.007891    test-rmse:0.471481+0.068513 
## [28] train-rmse:0.428964+0.007718    test-rmse:0.470128+0.067823 
## [29] train-rmse:0.424658+0.007300    test-rmse:0.467167+0.066179 
## [30] train-rmse:0.421012+0.007048    test-rmse:0.464413+0.065269 
## [31] train-rmse:0.417242+0.006799    test-rmse:0.462433+0.064696 
## [32] train-rmse:0.413672+0.006272    test-rmse:0.461441+0.063556 
## [33] train-rmse:0.410425+0.005973    test-rmse:0.459137+0.061449 
## [34] train-rmse:0.407318+0.005904    test-rmse:0.457544+0.060755 
## [35] train-rmse:0.404439+0.005626    test-rmse:0.456659+0.059515 
## [36] train-rmse:0.401776+0.005466    test-rmse:0.456183+0.058860 
## [37] train-rmse:0.399218+0.005321    test-rmse:0.454771+0.059047 
## [38] train-rmse:0.396324+0.004778    test-rmse:0.452781+0.059193 
## [39] train-rmse:0.393913+0.004514    test-rmse:0.452261+0.059229 
## [40] train-rmse:0.391476+0.004321    test-rmse:0.452340+0.059176 
## [41] train-rmse:0.389488+0.004249    test-rmse:0.450520+0.058805 
## [42] train-rmse:0.386702+0.004080    test-rmse:0.448571+0.058807 
## [43] train-rmse:0.384673+0.003819    test-rmse:0.447877+0.058948 
## [44] train-rmse:0.382790+0.003761    test-rmse:0.448232+0.059059 
## [45] train-rmse:0.380399+0.003711    test-rmse:0.448039+0.058141 
## [46] train-rmse:0.378370+0.003479    test-rmse:0.448217+0.057655 
## [47] train-rmse:0.376500+0.003537    test-rmse:0.448060+0.057159 
## [48] train-rmse:0.374671+0.003704    test-rmse:0.447545+0.057201 
## [49] train-rmse:0.372730+0.003447    test-rmse:0.446246+0.057302 
## [50] train-rmse:0.370786+0.003477    test-rmse:0.445644+0.057702 
## [51] train-rmse:0.369148+0.003433    test-rmse:0.445877+0.057698 
## [52] train-rmse:0.367432+0.003263    test-rmse:0.445393+0.057996 
## [53] train-rmse:0.365742+0.003401    test-rmse:0.444844+0.057216 
## [54] train-rmse:0.363802+0.003495    test-rmse:0.444776+0.056926 
## [55] train-rmse:0.362267+0.003418    test-rmse:0.444331+0.056704 
## [56] train-rmse:0.360594+0.003359    test-rmse:0.444098+0.056428 
## [57] train-rmse:0.358941+0.003270    test-rmse:0.444290+0.056200 
## [58] train-rmse:0.357289+0.003197    test-rmse:0.444416+0.056600 
## [59] train-rmse:0.355901+0.003280    test-rmse:0.443280+0.056144 
## [60] train-rmse:0.354518+0.003337    test-rmse:0.442982+0.056104 
## [61] train-rmse:0.353365+0.003275    test-rmse:0.443127+0.055650 
## [62] train-rmse:0.351657+0.003146    test-rmse:0.442710+0.055746 
## [63] train-rmse:0.350472+0.003179    test-rmse:0.442296+0.056431 
## [64] train-rmse:0.349145+0.003179    test-rmse:0.442343+0.056811 
## [65] train-rmse:0.347501+0.002862    test-rmse:0.440925+0.056854 
## [66] train-rmse:0.346171+0.002862    test-rmse:0.440608+0.057153 
## [67] train-rmse:0.345156+0.002956    test-rmse:0.440665+0.057592 
## [68] train-rmse:0.344071+0.002980    test-rmse:0.440687+0.056833 
## [69] train-rmse:0.342985+0.002904    test-rmse:0.440397+0.056515 
## [70] train-rmse:0.340987+0.002730    test-rmse:0.440404+0.056754 
## [71] train-rmse:0.339440+0.002444    test-rmse:0.440030+0.056580 
## [72] train-rmse:0.338151+0.002387    test-rmse:0.439849+0.056193 
## [73] train-rmse:0.337060+0.002327    test-rmse:0.440490+0.056249 
## [74] train-rmse:0.335858+0.002326    test-rmse:0.440526+0.055812 
## [75] train-rmse:0.334001+0.002546    test-rmse:0.439943+0.055463 
## [76] train-rmse:0.332989+0.002457    test-rmse:0.439691+0.055870 
## [77] train-rmse:0.331561+0.002808    test-rmse:0.439835+0.055646 
## [78] train-rmse:0.330161+0.002504    test-rmse:0.439457+0.055775 
## [79] train-rmse:0.328755+0.002386    test-rmse:0.439306+0.055661 
## [80] train-rmse:0.327844+0.002384    test-rmse:0.439451+0.056130 
## [81] train-rmse:0.327047+0.002389    test-rmse:0.439011+0.055779 
## [82] train-rmse:0.326185+0.002362    test-rmse:0.438837+0.056175 
## [83] train-rmse:0.324908+0.002706    test-rmse:0.438032+0.056132 
## [84] train-rmse:0.324070+0.002613    test-rmse:0.438323+0.055898 
## [85] train-rmse:0.323033+0.002669    test-rmse:0.437405+0.055872 
## [86] train-rmse:0.321810+0.002845    test-rmse:0.437589+0.055565 
## [87] train-rmse:0.320706+0.002878    test-rmse:0.437074+0.055760 
## [88] train-rmse:0.319672+0.003123    test-rmse:0.436390+0.056242 
## [89] train-rmse:0.318873+0.003086    test-rmse:0.436285+0.056597 
## [90] train-rmse:0.318092+0.003092    test-rmse:0.436006+0.057557 
## [91] train-rmse:0.317046+0.003124    test-rmse:0.435609+0.057838 
## [92] train-rmse:0.315666+0.002944    test-rmse:0.435672+0.058304 
## [93] train-rmse:0.314915+0.002902    test-rmse:0.435560+0.058335 
## [94] train-rmse:0.314096+0.002802    test-rmse:0.434644+0.057970 
## [95] train-rmse:0.312995+0.002835    test-rmse:0.434159+0.058734 
## [96] train-rmse:0.311823+0.002776    test-rmse:0.433534+0.059362 
## [97] train-rmse:0.311069+0.002798    test-rmse:0.433462+0.059401 
## [98] train-rmse:0.310111+0.002759    test-rmse:0.433690+0.059376 
## [99] train-rmse:0.309186+0.002721    test-rmse:0.433151+0.059281 
## [100]    train-rmse:0.308318+0.002492    test-rmse:0.433107+0.059444 
## [101]    train-rmse:0.307540+0.002394    test-rmse:0.432926+0.059377 
## [102]    train-rmse:0.306421+0.002877    test-rmse:0.432652+0.059526 
## [103]    train-rmse:0.305792+0.002963    test-rmse:0.432344+0.059973 
## [104]    train-rmse:0.304952+0.003065    test-rmse:0.432436+0.060016 
## [105]    train-rmse:0.304145+0.003123    test-rmse:0.432342+0.059916 
## [106]    train-rmse:0.303264+0.003136    test-rmse:0.431827+0.059607 
## [107]    train-rmse:0.302292+0.003089    test-rmse:0.431573+0.059483 
## [108]    train-rmse:0.301258+0.003117    test-rmse:0.431404+0.059518 
## [109]    train-rmse:0.300474+0.003359    test-rmse:0.431685+0.059467 
## [110]    train-rmse:0.299893+0.003382    test-rmse:0.431612+0.059894 
## [111]    train-rmse:0.299172+0.003394    test-rmse:0.431842+0.059565 
## [112]    train-rmse:0.298440+0.003510    test-rmse:0.431947+0.059664 
## [113]    train-rmse:0.297698+0.003587    test-rmse:0.431283+0.059615 
## [114]    train-rmse:0.297007+0.003865    test-rmse:0.430925+0.059372 
## [115]    train-rmse:0.296417+0.003943    test-rmse:0.431036+0.059523 
## [116]    train-rmse:0.295975+0.003930    test-rmse:0.430638+0.059796 
## [117]    train-rmse:0.295257+0.004077    test-rmse:0.430009+0.060455 
## [118]    train-rmse:0.294645+0.004083    test-rmse:0.430126+0.060451 
## [119]    train-rmse:0.293963+0.004006    test-rmse:0.429795+0.060142 
## [120]    train-rmse:0.293505+0.004086    test-rmse:0.429601+0.060051 
## [121]    train-rmse:0.292567+0.003948    test-rmse:0.429427+0.059772 
## [122]    train-rmse:0.291914+0.004094    test-rmse:0.428899+0.059774 
## [123]    train-rmse:0.291278+0.004040    test-rmse:0.428898+0.059590 
## [124]    train-rmse:0.290491+0.004118    test-rmse:0.428759+0.059739 
## [125]    train-rmse:0.289876+0.004035    test-rmse:0.428503+0.059958 
## [126]    train-rmse:0.289212+0.003759    test-rmse:0.428201+0.060345 
## [127]    train-rmse:0.288666+0.003754    test-rmse:0.428107+0.060517 
## [128]    train-rmse:0.288193+0.003678    test-rmse:0.427611+0.060941 
## [129]    train-rmse:0.287405+0.003837    test-rmse:0.427612+0.060462 
## [130]    train-rmse:0.286737+0.004081    test-rmse:0.427599+0.060569 
## [131]    train-rmse:0.285861+0.004130    test-rmse:0.427123+0.060597 
## [132]    train-rmse:0.285075+0.004190    test-rmse:0.426768+0.060284 
## [133]    train-rmse:0.284229+0.004170    test-rmse:0.426608+0.060225 
## [134]    train-rmse:0.283773+0.004136    test-rmse:0.426148+0.060195 
## [135]    train-rmse:0.283319+0.004225    test-rmse:0.426056+0.060388 
## [136]    train-rmse:0.282845+0.004215    test-rmse:0.425832+0.060400 
## [137]    train-rmse:0.282314+0.004218    test-rmse:0.425325+0.060952 
## [138]    train-rmse:0.281341+0.004652    test-rmse:0.425178+0.060708 
## [139]    train-rmse:0.280826+0.004656    test-rmse:0.425782+0.060411 
## [140]    train-rmse:0.280311+0.004591    test-rmse:0.425698+0.060368 
## [141]    train-rmse:0.279378+0.005316    test-rmse:0.425476+0.060202 
## [142]    train-rmse:0.279013+0.005303    test-rmse:0.425361+0.060169 
## [143]    train-rmse:0.278537+0.005226    test-rmse:0.425207+0.060307 
## [144]    train-rmse:0.277982+0.005176    test-rmse:0.425260+0.060652 
## Stopping. Best iteration:
## [138]    train-rmse:0.281341+0.004652    test-rmse:0.425178+0.060708
## 
## 9 
## [1]  train-rmse:1.463740+0.026972    test-rmse:1.463298+0.117089 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.312091+0.024319    test-rmse:1.312985+0.113461 
## [3]  train-rmse:1.179107+0.021922    test-rmse:1.184224+0.112879 
## [4]  train-rmse:1.063315+0.019719    test-rmse:1.066695+0.112701 
## [5]  train-rmse:0.962292+0.017875    test-rmse:0.969047+0.112863 
## [6]  train-rmse:0.874545+0.016071    test-rmse:0.881695+0.113416 
## [7]  train-rmse:0.797845+0.015725    test-rmse:0.808982+0.107020 
## [8]  train-rmse:0.731494+0.014426    test-rmse:0.744284+0.105982 
## [9]  train-rmse:0.674202+0.014156    test-rmse:0.689802+0.102297 
## [10] train-rmse:0.624970+0.013147    test-rmse:0.646152+0.100588 
## [11] train-rmse:0.582510+0.012323    test-rmse:0.607072+0.098524 
## [12] train-rmse:0.546034+0.011267    test-rmse:0.578618+0.096334 
## [13] train-rmse:0.515328+0.010799    test-rmse:0.551403+0.093337 
## [14] train-rmse:0.488204+0.010569    test-rmse:0.528550+0.088921 
## [15] train-rmse:0.465495+0.009967    test-rmse:0.511140+0.086969 
## [16] train-rmse:0.446244+0.009868    test-rmse:0.498462+0.084023 
## [17] train-rmse:0.429566+0.009109    test-rmse:0.485672+0.083208 
## [18] train-rmse:0.415187+0.009103    test-rmse:0.474786+0.079322 
## [19] train-rmse:0.402732+0.009260    test-rmse:0.467159+0.078151 
## [20] train-rmse:0.391947+0.008965    test-rmse:0.459854+0.075393 
## [21] train-rmse:0.382728+0.008780    test-rmse:0.455426+0.073470 
## [22] train-rmse:0.374653+0.008909    test-rmse:0.451630+0.070899 
## [23] train-rmse:0.367794+0.009053    test-rmse:0.447529+0.068040 
## [24] train-rmse:0.361379+0.008737    test-rmse:0.443350+0.066704 
## [25] train-rmse:0.355865+0.008763    test-rmse:0.441484+0.065309 
## [26] train-rmse:0.351140+0.008760    test-rmse:0.440738+0.064487 
## [27] train-rmse:0.346530+0.008366    test-rmse:0.438830+0.063183 
## [28] train-rmse:0.342032+0.008404    test-rmse:0.437402+0.062091 
## [29] train-rmse:0.337524+0.008614    test-rmse:0.435707+0.061021 
## [30] train-rmse:0.333458+0.008926    test-rmse:0.434772+0.060845 
## [31] train-rmse:0.329927+0.009053    test-rmse:0.434716+0.061009 
## [32] train-rmse:0.326606+0.008949    test-rmse:0.434172+0.059959 
## [33] train-rmse:0.323100+0.009163    test-rmse:0.434783+0.060089 
## [34] train-rmse:0.319569+0.008396    test-rmse:0.432670+0.060519 
## [35] train-rmse:0.316547+0.008616    test-rmse:0.432022+0.060136 
## [36] train-rmse:0.313970+0.008422    test-rmse:0.432193+0.059797 
## [37] train-rmse:0.311340+0.008594    test-rmse:0.431588+0.059717 
## [38] train-rmse:0.308625+0.008555    test-rmse:0.430245+0.059986 
## [39] train-rmse:0.306220+0.008419    test-rmse:0.430511+0.059977 
## [40] train-rmse:0.303911+0.008548    test-rmse:0.430810+0.060115 
## [41] train-rmse:0.301145+0.008103    test-rmse:0.430411+0.060967 
## [42] train-rmse:0.298890+0.007993    test-rmse:0.429756+0.061025 
## [43] train-rmse:0.296738+0.007887    test-rmse:0.430345+0.060922 
## [44] train-rmse:0.294526+0.008055    test-rmse:0.429880+0.060497 
## [45] train-rmse:0.291950+0.007718    test-rmse:0.429588+0.060121 
## [46] train-rmse:0.289989+0.007470    test-rmse:0.429000+0.060055 
## [47] train-rmse:0.288061+0.007400    test-rmse:0.428317+0.060620 
## [48] train-rmse:0.286243+0.007177    test-rmse:0.428557+0.060400 
## [49] train-rmse:0.284097+0.007044    test-rmse:0.428463+0.060419 
## [50] train-rmse:0.282603+0.006979    test-rmse:0.428412+0.060545 
## [51] train-rmse:0.280508+0.006705    test-rmse:0.428206+0.060979 
## [52] train-rmse:0.278321+0.006143    test-rmse:0.427516+0.060791 
## [53] train-rmse:0.276771+0.006033    test-rmse:0.427497+0.060791 
## [54] train-rmse:0.274690+0.005650    test-rmse:0.427694+0.060430 
## [55] train-rmse:0.272730+0.005533    test-rmse:0.427759+0.061354 
## [56] train-rmse:0.270962+0.005322    test-rmse:0.427285+0.062094 
## [57] train-rmse:0.269345+0.005063    test-rmse:0.426412+0.061846 
## [58] train-rmse:0.267813+0.005008    test-rmse:0.426184+0.062099 
## [59] train-rmse:0.266259+0.005158    test-rmse:0.426199+0.061569 
## [60] train-rmse:0.264591+0.005092    test-rmse:0.425614+0.061605 
## [61] train-rmse:0.263196+0.005055    test-rmse:0.424197+0.062727 
## [62] train-rmse:0.261778+0.005027    test-rmse:0.424089+0.062886 
## [63] train-rmse:0.260372+0.004952    test-rmse:0.423953+0.062654 
## [64] train-rmse:0.259289+0.005142    test-rmse:0.423936+0.062814 
## [65] train-rmse:0.257462+0.004678    test-rmse:0.422781+0.063145 
## [66] train-rmse:0.256391+0.004578    test-rmse:0.422823+0.063207 
## [67] train-rmse:0.255007+0.004534    test-rmse:0.422727+0.063858 
## [68] train-rmse:0.253453+0.004222    test-rmse:0.422489+0.064583 
## [69] train-rmse:0.251836+0.004621    test-rmse:0.422141+0.063772 
## [70] train-rmse:0.250654+0.004614    test-rmse:0.422268+0.063637 
## [71] train-rmse:0.249387+0.004517    test-rmse:0.422092+0.063587 
## [72] train-rmse:0.247878+0.004659    test-rmse:0.422748+0.063346 
## [73] train-rmse:0.246437+0.004117    test-rmse:0.422170+0.063378 
## [74] train-rmse:0.245396+0.004124    test-rmse:0.421949+0.063496 
## [75] train-rmse:0.244031+0.004096    test-rmse:0.421839+0.063038 
## [76] train-rmse:0.242474+0.003864    test-rmse:0.421821+0.063320 
## [77] train-rmse:0.241477+0.003754    test-rmse:0.421431+0.064043 
## [78] train-rmse:0.240001+0.003348    test-rmse:0.420956+0.063303 
## [79] train-rmse:0.238933+0.003068    test-rmse:0.421071+0.063427 
## [80] train-rmse:0.237554+0.003413    test-rmse:0.419737+0.064110 
## [81] train-rmse:0.236345+0.003827    test-rmse:0.419385+0.064058 
## [82] train-rmse:0.235287+0.004009    test-rmse:0.419149+0.064090 
## [83] train-rmse:0.234278+0.003882    test-rmse:0.419190+0.063889 
## [84] train-rmse:0.233282+0.003688    test-rmse:0.418879+0.063734 
## [85] train-rmse:0.232094+0.003520    test-rmse:0.418457+0.064122 
## [86] train-rmse:0.230856+0.003697    test-rmse:0.417824+0.064113 
## [87] train-rmse:0.229776+0.003712    test-rmse:0.417422+0.064689 
## [88] train-rmse:0.228419+0.004003    test-rmse:0.416806+0.064924 
## [89] train-rmse:0.227500+0.003941    test-rmse:0.416332+0.064944 
## [90] train-rmse:0.226431+0.003909    test-rmse:0.416310+0.064945 
## [91] train-rmse:0.225648+0.003835    test-rmse:0.416613+0.064903 
## [92] train-rmse:0.224473+0.003717    test-rmse:0.416003+0.065176 
## [93] train-rmse:0.223251+0.003993    test-rmse:0.415705+0.065985 
## [94] train-rmse:0.222410+0.004209    test-rmse:0.415144+0.066022 
## [95] train-rmse:0.221480+0.004152    test-rmse:0.415266+0.065646 
## [96] train-rmse:0.220737+0.004132    test-rmse:0.415324+0.065765 
## [97] train-rmse:0.219626+0.004303    test-rmse:0.415575+0.065625 
## [98] train-rmse:0.218804+0.004240    test-rmse:0.415226+0.065351 
## [99] train-rmse:0.217817+0.004008    test-rmse:0.414977+0.066028 
## [100]    train-rmse:0.216822+0.004353    test-rmse:0.415016+0.066171 
## [101]    train-rmse:0.215958+0.004543    test-rmse:0.415347+0.065931 
## [102]    train-rmse:0.214410+0.004709    test-rmse:0.415222+0.066143 
## [103]    train-rmse:0.213261+0.005193    test-rmse:0.415341+0.066208 
## [104]    train-rmse:0.212501+0.005063    test-rmse:0.415436+0.066350 
## [105]    train-rmse:0.211565+0.005016    test-rmse:0.415420+0.066354 
## Stopping. Best iteration:
## [99] train-rmse:0.217817+0.004008    test-rmse:0.414977+0.066028
## 
## 10 
## [1]  train-rmse:1.462069+0.026714    test-rmse:1.461352+0.117207 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.308726+0.023659    test-rmse:1.308665+0.116812 
## [3]  train-rmse:1.174263+0.021557    test-rmse:1.174839+0.112924 
## [4]  train-rmse:1.056279+0.019281    test-rmse:1.058996+0.112829 
## [5]  train-rmse:0.952896+0.017836    test-rmse:0.960407+0.108622 
## [6]  train-rmse:0.862361+0.016157    test-rmse:0.871645+0.106904 
## [7]  train-rmse:0.783418+0.014375    test-rmse:0.796428+0.105631 
## [8]  train-rmse:0.712920+0.013175    test-rmse:0.730375+0.102184 
## [9]  train-rmse:0.653156+0.011714    test-rmse:0.675637+0.099702 
## [10] train-rmse:0.601021+0.010555    test-rmse:0.628105+0.096170 
## [11] train-rmse:0.555378+0.009554    test-rmse:0.590307+0.093224 
## [12] train-rmse:0.516542+0.008924    test-rmse:0.557248+0.089422 
## [13] train-rmse:0.482956+0.008391    test-rmse:0.532759+0.087515 
## [14] train-rmse:0.452604+0.007927    test-rmse:0.508519+0.086305 
## [15] train-rmse:0.428171+0.007947    test-rmse:0.491363+0.082190 
## [16] train-rmse:0.406120+0.006757    test-rmse:0.475954+0.079855 
## [17] train-rmse:0.387724+0.006537    test-rmse:0.464457+0.077912 
## [18] train-rmse:0.371857+0.005773    test-rmse:0.454659+0.078373 
## [19] train-rmse:0.357801+0.005005    test-rmse:0.448020+0.075457 
## [20] train-rmse:0.345883+0.004605    test-rmse:0.442566+0.073817 
## [21] train-rmse:0.334486+0.005173    test-rmse:0.436773+0.070567 
## [22] train-rmse:0.324978+0.004744    test-rmse:0.432109+0.069229 
## [23] train-rmse:0.317640+0.004429    test-rmse:0.429693+0.067901 
## [24] train-rmse:0.310267+0.004032    test-rmse:0.427509+0.067066 
## [25] train-rmse:0.303839+0.002932    test-rmse:0.426359+0.066719 
## [26] train-rmse:0.298337+0.002976    test-rmse:0.424636+0.065996 
## [27] train-rmse:0.292950+0.002777    test-rmse:0.422359+0.064218 
## [28] train-rmse:0.288267+0.002659    test-rmse:0.421380+0.062467 
## [29] train-rmse:0.284157+0.002773    test-rmse:0.421146+0.062586 
## [30] train-rmse:0.280431+0.002726    test-rmse:0.420529+0.062209 
## [31] train-rmse:0.277237+0.002748    test-rmse:0.419136+0.060699 
## [32] train-rmse:0.273075+0.002374    test-rmse:0.417914+0.060360 
## [33] train-rmse:0.269215+0.002334    test-rmse:0.416966+0.060285 
## [34] train-rmse:0.264973+0.002455    test-rmse:0.416559+0.060862 
## [35] train-rmse:0.261259+0.002447    test-rmse:0.415987+0.060329 
## [36] train-rmse:0.257767+0.002652    test-rmse:0.415368+0.060651 
## [37] train-rmse:0.254761+0.002715    test-rmse:0.414674+0.060949 
## [38] train-rmse:0.251575+0.003314    test-rmse:0.413783+0.062488 
## [39] train-rmse:0.249200+0.002896    test-rmse:0.414006+0.062448 
## [40] train-rmse:0.246727+0.003194    test-rmse:0.413019+0.062999 
## [41] train-rmse:0.242917+0.003020    test-rmse:0.412039+0.063930 
## [42] train-rmse:0.240193+0.002863    test-rmse:0.411636+0.063374 
## [43] train-rmse:0.237771+0.002857    test-rmse:0.411198+0.062928 
## [44] train-rmse:0.235652+0.002686    test-rmse:0.411265+0.062895 
## [45] train-rmse:0.232398+0.002216    test-rmse:0.410620+0.063086 
## [46] train-rmse:0.229409+0.002236    test-rmse:0.410799+0.062669 
## [47] train-rmse:0.226905+0.001969    test-rmse:0.410334+0.063242 
## [48] train-rmse:0.224055+0.002216    test-rmse:0.410150+0.063232 
## [49] train-rmse:0.222153+0.002431    test-rmse:0.409627+0.063646 
## [50] train-rmse:0.219775+0.002577    test-rmse:0.409373+0.063585 
## [51] train-rmse:0.217502+0.003094    test-rmse:0.409083+0.063590 
## [52] train-rmse:0.215234+0.003390    test-rmse:0.408940+0.063547 
## [53] train-rmse:0.213132+0.003764    test-rmse:0.408857+0.063908 
## [54] train-rmse:0.211494+0.003883    test-rmse:0.407901+0.063674 
## [55] train-rmse:0.209290+0.004118    test-rmse:0.407418+0.063423 
## [56] train-rmse:0.207618+0.004315    test-rmse:0.406613+0.063850 
## [57] train-rmse:0.205862+0.004173    test-rmse:0.406714+0.063704 
## [58] train-rmse:0.204304+0.003960    test-rmse:0.406207+0.063785 
## [59] train-rmse:0.202800+0.004140    test-rmse:0.406302+0.063741 
## [60] train-rmse:0.201318+0.004092    test-rmse:0.405979+0.064013 
## [61] train-rmse:0.199496+0.004477    test-rmse:0.404943+0.063438 
## [62] train-rmse:0.197526+0.004677    test-rmse:0.404934+0.063485 
## [63] train-rmse:0.196179+0.004976    test-rmse:0.404882+0.063507 
## [64] train-rmse:0.194150+0.003759    test-rmse:0.404499+0.063406 
## [65] train-rmse:0.192658+0.003863    test-rmse:0.404470+0.063620 
## [66] train-rmse:0.190980+0.004274    test-rmse:0.404184+0.064041 
## [67] train-rmse:0.189506+0.004182    test-rmse:0.404334+0.064039 
## [68] train-rmse:0.188056+0.003806    test-rmse:0.404412+0.063846 
## [69] train-rmse:0.186764+0.004052    test-rmse:0.403564+0.063860 
## [70] train-rmse:0.185308+0.004245    test-rmse:0.403667+0.064007 
## [71] train-rmse:0.183961+0.003681    test-rmse:0.403401+0.064598 
## [72] train-rmse:0.182455+0.004109    test-rmse:0.403369+0.064562 
## [73] train-rmse:0.181313+0.004055    test-rmse:0.402954+0.064710 
## [74] train-rmse:0.180184+0.003904    test-rmse:0.402636+0.064852 
## [75] train-rmse:0.178725+0.003896    test-rmse:0.402753+0.064230 
## [76] train-rmse:0.177360+0.003467    test-rmse:0.402248+0.063455 
## [77] train-rmse:0.176230+0.002958    test-rmse:0.401854+0.064055 
## [78] train-rmse:0.174824+0.003034    test-rmse:0.401894+0.063951 
## [79] train-rmse:0.173714+0.003119    test-rmse:0.402074+0.063985 
## [80] train-rmse:0.172323+0.002743    test-rmse:0.401854+0.064223 
## [81] train-rmse:0.171005+0.003072    test-rmse:0.401613+0.064224 
## [82] train-rmse:0.169568+0.003384    test-rmse:0.401492+0.063999 
## [83] train-rmse:0.168180+0.003255    test-rmse:0.401587+0.064169 
## [84] train-rmse:0.166826+0.002997    test-rmse:0.400918+0.064356 
## [85] train-rmse:0.165725+0.002911    test-rmse:0.400935+0.064875 
## [86] train-rmse:0.164665+0.002941    test-rmse:0.401168+0.064764 
## [87] train-rmse:0.163736+0.002634    test-rmse:0.400466+0.065562 
## [88] train-rmse:0.162824+0.002610    test-rmse:0.400417+0.066031 
## [89] train-rmse:0.161733+0.002975    test-rmse:0.399920+0.065633 
## [90] train-rmse:0.160716+0.002813    test-rmse:0.399666+0.065898 
## [91] train-rmse:0.159641+0.002872    test-rmse:0.399432+0.065928 
## [92] train-rmse:0.158598+0.003147    test-rmse:0.399252+0.066138 
## [93] train-rmse:0.157699+0.002917    test-rmse:0.399532+0.065874 
## [94] train-rmse:0.156853+0.002766    test-rmse:0.399362+0.065766 
## [95] train-rmse:0.155967+0.002779    test-rmse:0.399186+0.065671 
## [96] train-rmse:0.154615+0.003246    test-rmse:0.399451+0.065475 
## [97] train-rmse:0.153574+0.003216    test-rmse:0.399287+0.065681 
## [98] train-rmse:0.152829+0.003243    test-rmse:0.398841+0.065546 
## [99] train-rmse:0.151600+0.003564    test-rmse:0.398824+0.065490 
## [100]    train-rmse:0.150827+0.003611    test-rmse:0.398721+0.065348 
## [101]    train-rmse:0.149594+0.003500    test-rmse:0.398564+0.065636 
## [102]    train-rmse:0.148754+0.003821    test-rmse:0.398835+0.065341 
## [103]    train-rmse:0.147819+0.003919    test-rmse:0.398664+0.065384 
## [104]    train-rmse:0.146952+0.003659    test-rmse:0.398818+0.065131 
## [105]    train-rmse:0.146259+0.003509    test-rmse:0.398659+0.065404 
## [106]    train-rmse:0.144899+0.003979    test-rmse:0.398068+0.065863 
## [107]    train-rmse:0.144158+0.004101    test-rmse:0.398142+0.065836 
## [108]    train-rmse:0.142960+0.004484    test-rmse:0.397999+0.065695 
## [109]    train-rmse:0.142259+0.004583    test-rmse:0.398076+0.065774 
## [110]    train-rmse:0.141149+0.004844    test-rmse:0.397817+0.065463 
## [111]    train-rmse:0.140471+0.005174    test-rmse:0.397841+0.065435 
## [112]    train-rmse:0.139648+0.005141    test-rmse:0.398033+0.065086 
## [113]    train-rmse:0.138524+0.004904    test-rmse:0.397754+0.065521 
## [114]    train-rmse:0.137554+0.005064    test-rmse:0.397876+0.065536 
## [115]    train-rmse:0.136866+0.005071    test-rmse:0.398087+0.065343 
## [116]    train-rmse:0.135976+0.005467    test-rmse:0.397877+0.064992 
## [117]    train-rmse:0.135258+0.005679    test-rmse:0.397615+0.065122 
## [118]    train-rmse:0.134717+0.005649    test-rmse:0.397790+0.064948 
## [119]    train-rmse:0.133708+0.005930    test-rmse:0.397643+0.064958 
## [120]    train-rmse:0.132723+0.005667    test-rmse:0.397316+0.065191 
## [121]    train-rmse:0.132099+0.005888    test-rmse:0.397298+0.065212 
## [122]    train-rmse:0.131623+0.005907    test-rmse:0.397352+0.065414 
## [123]    train-rmse:0.130869+0.005927    test-rmse:0.397296+0.065395 
## [124]    train-rmse:0.129969+0.006000    test-rmse:0.397374+0.065290 
## [125]    train-rmse:0.129237+0.005962    test-rmse:0.397346+0.065678 
## [126]    train-rmse:0.128437+0.006039    test-rmse:0.397175+0.065622 
## [127]    train-rmse:0.127513+0.005613    test-rmse:0.397317+0.065718 
## [128]    train-rmse:0.126860+0.005813    test-rmse:0.397382+0.065516 
## [129]    train-rmse:0.126039+0.005520    test-rmse:0.397368+0.065236 
## [130]    train-rmse:0.125009+0.005459    test-rmse:0.397212+0.065718 
## [131]    train-rmse:0.124431+0.005498    test-rmse:0.397166+0.065797 
## [132]    train-rmse:0.123701+0.005296    test-rmse:0.396959+0.066130 
## [133]    train-rmse:0.122759+0.005148    test-rmse:0.397090+0.066101 
## [134]    train-rmse:0.122111+0.005030    test-rmse:0.397084+0.066110 
## [135]    train-rmse:0.121367+0.005022    test-rmse:0.396670+0.065702 
## [136]    train-rmse:0.120329+0.005048    test-rmse:0.396546+0.066185 
## [137]    train-rmse:0.119826+0.005137    test-rmse:0.396609+0.066110 
## [138]    train-rmse:0.118973+0.005230    test-rmse:0.395956+0.066081 
## [139]    train-rmse:0.118269+0.005177    test-rmse:0.395963+0.066217 
## [140]    train-rmse:0.117702+0.005201    test-rmse:0.395464+0.066729 
## [141]    train-rmse:0.117168+0.005305    test-rmse:0.395657+0.066592 
## [142]    train-rmse:0.116621+0.005355    test-rmse:0.395287+0.066819 
## [143]    train-rmse:0.115767+0.005409    test-rmse:0.395092+0.066632 
## [144]    train-rmse:0.114964+0.005392    test-rmse:0.394891+0.067255 
## [145]    train-rmse:0.114076+0.005276    test-rmse:0.395246+0.066701 
## [146]    train-rmse:0.113575+0.005395    test-rmse:0.395205+0.066749 
## [147]    train-rmse:0.112867+0.005508    test-rmse:0.395056+0.067115 
## [148]    train-rmse:0.112316+0.005461    test-rmse:0.395772+0.066595 
## [149]    train-rmse:0.111938+0.005502    test-rmse:0.396047+0.066495 
## [150]    train-rmse:0.111445+0.005660    test-rmse:0.395898+0.066659 
## Stopping. Best iteration:
## [144]    train-rmse:0.114964+0.005392    test-rmse:0.394891+0.067255
## 
## 11 
## [1]  train-rmse:1.461063+0.026892    test-rmse:1.460223+0.115551 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.306537+0.023872    test-rmse:1.306286+0.113780 
## [3]  train-rmse:1.171174+0.021135    test-rmse:1.171847+0.111596 
## [4]  train-rmse:1.051438+0.019368    test-rmse:1.053673+0.107405 
## [5]  train-rmse:0.946547+0.017198    test-rmse:0.952784+0.105951 
## [6]  train-rmse:0.854580+0.015376    test-rmse:0.863730+0.103483 
## [7]  train-rmse:0.773752+0.014387    test-rmse:0.786386+0.100443 
## [8]  train-rmse:0.703471+0.012843    test-rmse:0.721083+0.099450 
## [9]  train-rmse:0.640368+0.011822    test-rmse:0.665820+0.094998 
## [10] train-rmse:0.586240+0.011489    test-rmse:0.618029+0.090873 
## [11] train-rmse:0.538564+0.010273    test-rmse:0.579628+0.089451 
## [12] train-rmse:0.496399+0.009822    test-rmse:0.546305+0.087063 
## [13] train-rmse:0.460067+0.008965    test-rmse:0.518823+0.086057 
## [14] train-rmse:0.428191+0.008106    test-rmse:0.495687+0.084145 
## [15] train-rmse:0.400938+0.007266    test-rmse:0.478037+0.081080 
## [16] train-rmse:0.376756+0.007097    test-rmse:0.463762+0.080691 
## [17] train-rmse:0.355904+0.007180    test-rmse:0.452955+0.078940 
## [18] train-rmse:0.336597+0.005774    test-rmse:0.443588+0.078256 
## [19] train-rmse:0.320775+0.005989    test-rmse:0.435969+0.075525 
## [20] train-rmse:0.305929+0.006219    test-rmse:0.429895+0.075773 
## [21] train-rmse:0.293715+0.006590    test-rmse:0.426122+0.075225 
## [22] train-rmse:0.282900+0.007531    test-rmse:0.422136+0.073193 
## [23] train-rmse:0.273714+0.007746    test-rmse:0.419853+0.071665 
## [24] train-rmse:0.264787+0.006001    test-rmse:0.416446+0.069614 
## [25] train-rmse:0.257789+0.006729    test-rmse:0.414274+0.067701 
## [26] train-rmse:0.249899+0.005703    test-rmse:0.412287+0.067750 
## [27] train-rmse:0.243778+0.006502    test-rmse:0.410453+0.065856 
## [28] train-rmse:0.238828+0.007156    test-rmse:0.408283+0.065962 
## [29] train-rmse:0.234383+0.007576    test-rmse:0.408450+0.065545 
## [30] train-rmse:0.228456+0.005812    test-rmse:0.408331+0.065186 
## [31] train-rmse:0.224716+0.005955    test-rmse:0.407769+0.064016 
## [32] train-rmse:0.221127+0.006308    test-rmse:0.407808+0.063814 
## [33] train-rmse:0.217853+0.006194    test-rmse:0.407481+0.063533 
## [34] train-rmse:0.213857+0.006015    test-rmse:0.404934+0.062812 
## [35] train-rmse:0.209801+0.006109    test-rmse:0.403403+0.062906 
## [36] train-rmse:0.206955+0.006123    test-rmse:0.402930+0.061850 
## [37] train-rmse:0.203685+0.006048    test-rmse:0.403022+0.061088 
## [38] train-rmse:0.199586+0.005436    test-rmse:0.402798+0.060862 
## [39] train-rmse:0.196879+0.005213    test-rmse:0.402383+0.061233 
## [40] train-rmse:0.193557+0.005965    test-rmse:0.402169+0.060299 
## [41] train-rmse:0.190560+0.006548    test-rmse:0.402012+0.060195 
## [42] train-rmse:0.188057+0.006903    test-rmse:0.401681+0.060961 
## [43] train-rmse:0.185535+0.007340    test-rmse:0.401358+0.060752 
## [44] train-rmse:0.182709+0.007621    test-rmse:0.401325+0.060790 
## [45] train-rmse:0.180038+0.008138    test-rmse:0.401002+0.061291 
## [46] train-rmse:0.177271+0.007621    test-rmse:0.401267+0.060783 
## [47] train-rmse:0.175116+0.007685    test-rmse:0.401055+0.060368 
## [48] train-rmse:0.172964+0.007339    test-rmse:0.400211+0.060235 
## [49] train-rmse:0.170386+0.006924    test-rmse:0.400101+0.059977 
## [50] train-rmse:0.168817+0.007184    test-rmse:0.399853+0.059902 
## [51] train-rmse:0.166930+0.006984    test-rmse:0.399793+0.059675 
## [52] train-rmse:0.165207+0.006289    test-rmse:0.399579+0.060069 
## [53] train-rmse:0.163123+0.005953    test-rmse:0.399617+0.060093 
## [54] train-rmse:0.161497+0.005406    test-rmse:0.399763+0.059741 
## [55] train-rmse:0.159796+0.005057    test-rmse:0.400152+0.059531 
## [56] train-rmse:0.157628+0.004719    test-rmse:0.400301+0.059121 
## [57] train-rmse:0.155443+0.004831    test-rmse:0.399281+0.059799 
## [58] train-rmse:0.153460+0.005058    test-rmse:0.399242+0.060342 
## [59] train-rmse:0.151212+0.004803    test-rmse:0.399611+0.059704 
## [60] train-rmse:0.149241+0.004684    test-rmse:0.398348+0.059194 
## [61] train-rmse:0.147276+0.004426    test-rmse:0.397776+0.059216 
## [62] train-rmse:0.145776+0.003882    test-rmse:0.397725+0.059158 
## [63] train-rmse:0.144127+0.003960    test-rmse:0.397334+0.058823 
## [64] train-rmse:0.142992+0.004006    test-rmse:0.396888+0.058650 
## [65] train-rmse:0.141546+0.004237    test-rmse:0.396477+0.058307 
## [66] train-rmse:0.140172+0.003977    test-rmse:0.396381+0.058045 
## [67] train-rmse:0.138421+0.004083    test-rmse:0.396188+0.057669 
## [68] train-rmse:0.136422+0.004060    test-rmse:0.396479+0.057664 
## [69] train-rmse:0.135249+0.004149    test-rmse:0.396213+0.057893 
## [70] train-rmse:0.134478+0.004255    test-rmse:0.396250+0.058021 
## [71] train-rmse:0.132928+0.004653    test-rmse:0.395767+0.057633 
## [72] train-rmse:0.131492+0.004665    test-rmse:0.395609+0.057774 
## [73] train-rmse:0.130277+0.005001    test-rmse:0.395170+0.058355 
## [74] train-rmse:0.128945+0.004603    test-rmse:0.394773+0.058294 
## [75] train-rmse:0.127253+0.004438    test-rmse:0.394237+0.058454 
## [76] train-rmse:0.125967+0.004484    test-rmse:0.393780+0.058786 
## [77] train-rmse:0.124813+0.004598    test-rmse:0.393660+0.058558 
## [78] train-rmse:0.123704+0.004381    test-rmse:0.393291+0.058275 
## [79] train-rmse:0.122838+0.004481    test-rmse:0.393152+0.057883 
## [80] train-rmse:0.121703+0.004333    test-rmse:0.393341+0.057566 
## [81] train-rmse:0.120103+0.003849    test-rmse:0.393453+0.057581 
## [82] train-rmse:0.118730+0.004266    test-rmse:0.393436+0.057895 
## [83] train-rmse:0.117433+0.004285    test-rmse:0.393376+0.057890 
## [84] train-rmse:0.116223+0.004293    test-rmse:0.393761+0.057834 
## [85] train-rmse:0.115067+0.004341    test-rmse:0.393327+0.057942 
## Stopping. Best iteration:
## [79] train-rmse:0.122838+0.004481    test-rmse:0.393152+0.057883
## 
## 12 
## [1]  train-rmse:1.460769+0.026923    test-rmse:1.459447+0.114903 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.305657+0.023981    test-rmse:1.303380+0.111464 
## [3]  train-rmse:1.169410+0.021266    test-rmse:1.167843+0.108728 
## [4]  train-rmse:1.049210+0.019766    test-rmse:1.050096+0.103219 
## [5]  train-rmse:0.943842+0.017840    test-rmse:0.949257+0.101299 
## [6]  train-rmse:0.850420+0.016640    test-rmse:0.860167+0.096954 
## [7]  train-rmse:0.768583+0.015113    test-rmse:0.783093+0.095870 
## [8]  train-rmse:0.696733+0.013517    test-rmse:0.717914+0.094908 
## [9]  train-rmse:0.633534+0.012830    test-rmse:0.661997+0.092729 
## [10] train-rmse:0.577568+0.012068    test-rmse:0.615147+0.089848 
## [11] train-rmse:0.528275+0.011342    test-rmse:0.573327+0.086587 
## [12] train-rmse:0.485383+0.010793    test-rmse:0.541400+0.085185 
## [13] train-rmse:0.447927+0.009989    test-rmse:0.513034+0.082855 
## [14] train-rmse:0.414495+0.009400    test-rmse:0.492744+0.079502 
## [15] train-rmse:0.385266+0.009129    test-rmse:0.473641+0.077232 
## [16] train-rmse:0.359785+0.008660    test-rmse:0.461169+0.074856 
## [17] train-rmse:0.337247+0.008573    test-rmse:0.450243+0.073829 
## [18] train-rmse:0.317020+0.008755    test-rmse:0.442047+0.072329 
## [19] train-rmse:0.299361+0.008368    test-rmse:0.435473+0.071828 
## [20] train-rmse:0.284006+0.008361    test-rmse:0.429309+0.069281 
## [21] train-rmse:0.270393+0.008979    test-rmse:0.425009+0.068368 
## [22] train-rmse:0.257627+0.009614    test-rmse:0.421074+0.067222 
## [23] train-rmse:0.247121+0.009370    test-rmse:0.417061+0.065282 
## [24] train-rmse:0.237095+0.008948    test-rmse:0.412911+0.065789 
## [25] train-rmse:0.228357+0.009722    test-rmse:0.412087+0.063593 
## [26] train-rmse:0.220543+0.010053    test-rmse:0.409907+0.061244 
## [27] train-rmse:0.212114+0.009156    test-rmse:0.408810+0.061740 
## [28] train-rmse:0.205766+0.009224    test-rmse:0.406289+0.060916 
## [29] train-rmse:0.200651+0.009130    test-rmse:0.405505+0.060333 
## [30] train-rmse:0.195851+0.009347    test-rmse:0.404104+0.060145 
## [31] train-rmse:0.190874+0.009722    test-rmse:0.403853+0.059285 
## [32] train-rmse:0.186861+0.010338    test-rmse:0.404100+0.058651 
## [33] train-rmse:0.182450+0.009904    test-rmse:0.403596+0.058073 
## [34] train-rmse:0.179088+0.009889    test-rmse:0.402587+0.056755 
## [35] train-rmse:0.175186+0.008688    test-rmse:0.402799+0.055907 
## [36] train-rmse:0.171080+0.009476    test-rmse:0.402127+0.054880 
## [37] train-rmse:0.168451+0.009551    test-rmse:0.401443+0.053810 
## [38] train-rmse:0.164791+0.008629    test-rmse:0.400404+0.051998 
## [39] train-rmse:0.160753+0.008895    test-rmse:0.400016+0.051317 
## [40] train-rmse:0.158178+0.009513    test-rmse:0.399009+0.051615 
## [41] train-rmse:0.155193+0.010273    test-rmse:0.398596+0.051083 
## [42] train-rmse:0.151782+0.010275    test-rmse:0.398103+0.051579 
## [43] train-rmse:0.149297+0.010546    test-rmse:0.397663+0.050823 
## [44] train-rmse:0.145488+0.010334    test-rmse:0.397447+0.050684 
## [45] train-rmse:0.142241+0.009796    test-rmse:0.397182+0.050165 
## [46] train-rmse:0.138875+0.009540    test-rmse:0.396854+0.050693 
## [47] train-rmse:0.136072+0.009411    test-rmse:0.397378+0.050060 
## [48] train-rmse:0.133745+0.009194    test-rmse:0.397490+0.050004 
## [49] train-rmse:0.131251+0.008897    test-rmse:0.397654+0.050180 
## [50] train-rmse:0.128565+0.009217    test-rmse:0.397995+0.050344 
## [51] train-rmse:0.126134+0.008760    test-rmse:0.397842+0.050303 
## [52] train-rmse:0.123489+0.008815    test-rmse:0.398290+0.050518 
## Stopping. Best iteration:
## [46] train-rmse:0.138875+0.009540    test-rmse:0.396854+0.050693
## 
## 13 
## [1]  train-rmse:1.460620+0.026925    test-rmse:1.459981+0.114504 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.305176+0.024016    test-rmse:1.302791+0.110760 
## [3]  train-rmse:1.168546+0.021455    test-rmse:1.166150+0.108320 
## [4]  train-rmse:1.047938+0.019981    test-rmse:1.048352+0.103918 
## [5]  train-rmse:0.941849+0.018084    test-rmse:0.945950+0.102741 
## [6]  train-rmse:0.847786+0.016975    test-rmse:0.858587+0.100672 
## [7]  train-rmse:0.765325+0.015451    test-rmse:0.781969+0.099330 
## [8]  train-rmse:0.692796+0.014417    test-rmse:0.716043+0.099909 
## [9]  train-rmse:0.628715+0.013789    test-rmse:0.659697+0.096814 
## [10] train-rmse:0.572222+0.013119    test-rmse:0.613255+0.094727 
## [11] train-rmse:0.522052+0.012167    test-rmse:0.573136+0.093952 
## [12] train-rmse:0.477503+0.010811    test-rmse:0.538800+0.093235 
## [13] train-rmse:0.438108+0.010405    test-rmse:0.513512+0.090419 
## [14] train-rmse:0.403494+0.009805    test-rmse:0.492028+0.089526 
## [15] train-rmse:0.372847+0.010246    test-rmse:0.473821+0.087683 
## [16] train-rmse:0.345874+0.010292    test-rmse:0.459503+0.087290 
## [17] train-rmse:0.321869+0.010197    test-rmse:0.449939+0.084914 
## [18] train-rmse:0.300212+0.009739    test-rmse:0.441538+0.084523 
## [19] train-rmse:0.280628+0.010173    test-rmse:0.435053+0.082467 
## [20] train-rmse:0.263633+0.009988    test-rmse:0.429524+0.080286 
## [21] train-rmse:0.248588+0.010129    test-rmse:0.426034+0.079599 
## [22] train-rmse:0.235467+0.009833    test-rmse:0.422548+0.078155 
## [23] train-rmse:0.223301+0.009316    test-rmse:0.420599+0.076625 
## [24] train-rmse:0.211698+0.009014    test-rmse:0.417889+0.074864 
## [25] train-rmse:0.201566+0.008806    test-rmse:0.416042+0.072470 
## [26] train-rmse:0.192639+0.008766    test-rmse:0.413945+0.072314 
## [27] train-rmse:0.185707+0.009154    test-rmse:0.412922+0.071176 
## [28] train-rmse:0.178481+0.009468    test-rmse:0.412185+0.070731 
## [29] train-rmse:0.170898+0.010124    test-rmse:0.411077+0.070379 
## [30] train-rmse:0.164551+0.010876    test-rmse:0.411001+0.069015 
## [31] train-rmse:0.159132+0.011118    test-rmse:0.410860+0.068412 
## [32] train-rmse:0.153617+0.011262    test-rmse:0.411538+0.067003 
## [33] train-rmse:0.148929+0.011061    test-rmse:0.410536+0.066366 
## [34] train-rmse:0.143801+0.011726    test-rmse:0.410814+0.066181 
## [35] train-rmse:0.139725+0.010841    test-rmse:0.409832+0.065985 
## [36] train-rmse:0.134111+0.010107    test-rmse:0.408793+0.064962 
## [37] train-rmse:0.130679+0.010223    test-rmse:0.408567+0.063594 
## [38] train-rmse:0.127511+0.010357    test-rmse:0.408280+0.064106 
## [39] train-rmse:0.124686+0.010300    test-rmse:0.408112+0.063469 
## [40] train-rmse:0.121800+0.010136    test-rmse:0.408724+0.063249 
## [41] train-rmse:0.119097+0.009974    test-rmse:0.408089+0.063016 
## [42] train-rmse:0.116237+0.008635    test-rmse:0.407809+0.062479 
## [43] train-rmse:0.113711+0.007982    test-rmse:0.407742+0.062563 
## [44] train-rmse:0.111024+0.008477    test-rmse:0.407608+0.062174 
## [45] train-rmse:0.108388+0.007565    test-rmse:0.406833+0.062009 
## [46] train-rmse:0.106524+0.007209    test-rmse:0.407224+0.061454 
## [47] train-rmse:0.104176+0.007261    test-rmse:0.407196+0.061602 
## [48] train-rmse:0.102518+0.007240    test-rmse:0.406782+0.061841 
## [49] train-rmse:0.099928+0.007431    test-rmse:0.406508+0.061633 
## [50] train-rmse:0.096979+0.006809    test-rmse:0.406203+0.061560 
## [51] train-rmse:0.094555+0.006435    test-rmse:0.406227+0.061454 
## [52] train-rmse:0.092749+0.005929    test-rmse:0.406261+0.061687 
## [53] train-rmse:0.090525+0.005885    test-rmse:0.406114+0.061615 
## [54] train-rmse:0.087806+0.005694    test-rmse:0.405992+0.060951 
## [55] train-rmse:0.085451+0.005469    test-rmse:0.406214+0.060671 
## [56] train-rmse:0.083178+0.005724    test-rmse:0.406223+0.060611 
## [57] train-rmse:0.080844+0.005629    test-rmse:0.406469+0.060758 
## [58] train-rmse:0.079224+0.005228    test-rmse:0.406321+0.060605 
## [59] train-rmse:0.077199+0.005188    test-rmse:0.406511+0.060727 
## [60] train-rmse:0.075800+0.005037    test-rmse:0.406179+0.060587 
## Stopping. Best iteration:
## [54] train-rmse:0.087806+0.005694    test-rmse:0.405992+0.060951
## 
## 14 
## [1]  train-rmse:1.448119+0.026902    test-rmse:1.444245+0.113031 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.289888+0.024180    test-rmse:1.286061+0.109354 
## [3]  train-rmse:1.158626+0.022071    test-rmse:1.154855+0.106087 
## [4]  train-rmse:1.050708+0.020506    test-rmse:1.047013+0.103110 
## [5]  train-rmse:0.962863+0.019410    test-rmse:0.959274+0.100363 
## [6]  train-rmse:0.892122+0.018696    test-rmse:0.888668+0.097830 
## [7]  train-rmse:0.835784+0.018272    test-rmse:0.832485+0.095518 
## [8]  train-rmse:0.791399+0.018054    test-rmse:0.788271+0.093445 
## [9]  train-rmse:0.756791+0.017970    test-rmse:0.753839+0.091623 
## [10] train-rmse:0.730057+0.017963    test-rmse:0.727276+0.090050 
## [11] train-rmse:0.709574+0.017998    test-rmse:0.706953+0.088713 
## [12] train-rmse:0.692913+0.017523    test-rmse:0.692939+0.089075 
## [13] train-rmse:0.678983+0.017311    test-rmse:0.676718+0.087812 
## [14] train-rmse:0.666927+0.017015    test-rmse:0.667430+0.087682 
## [15] train-rmse:0.656718+0.016882    test-rmse:0.655852+0.086450 
## [16] train-rmse:0.647643+0.016334    test-rmse:0.649579+0.085140 
## [17] train-rmse:0.639615+0.015685    test-rmse:0.640566+0.082148 
## [18] train-rmse:0.632114+0.015302    test-rmse:0.633837+0.082698 
## [19] train-rmse:0.625348+0.014646    test-rmse:0.628955+0.081127 
## [20] train-rmse:0.619183+0.014368    test-rmse:0.620915+0.080018 
## [21] train-rmse:0.613446+0.013924    test-rmse:0.616654+0.077865 
## [22] train-rmse:0.608238+0.013528    test-rmse:0.611492+0.075920 
## [23] train-rmse:0.603471+0.013302    test-rmse:0.607143+0.076756 
## [24] train-rmse:0.598860+0.012987    test-rmse:0.603035+0.073773 
## [25] train-rmse:0.594624+0.012842    test-rmse:0.600980+0.073670 
## [26] train-rmse:0.590733+0.012604    test-rmse:0.596117+0.071263 
## [27] train-rmse:0.587117+0.012428    test-rmse:0.592575+0.071351 
## [28] train-rmse:0.583613+0.012147    test-rmse:0.590615+0.069573 
## [29] train-rmse:0.580311+0.012075    test-rmse:0.587290+0.069577 
## [30] train-rmse:0.577202+0.011983    test-rmse:0.586510+0.069196 
## [31] train-rmse:0.574229+0.011858    test-rmse:0.582789+0.068559 
## [32] train-rmse:0.571426+0.011701    test-rmse:0.581117+0.066935 
## [33] train-rmse:0.568747+0.011580    test-rmse:0.579724+0.066329 
## [34] train-rmse:0.566203+0.011503    test-rmse:0.576558+0.065532 
## [35] train-rmse:0.563784+0.011413    test-rmse:0.575686+0.065635 
## [36] train-rmse:0.561520+0.011222    test-rmse:0.574536+0.065145 
## [37] train-rmse:0.559306+0.011109    test-rmse:0.571638+0.064049 
## [38] train-rmse:0.557168+0.011024    test-rmse:0.569510+0.064404 
## [39] train-rmse:0.555168+0.010988    test-rmse:0.567728+0.063670 
## [40] train-rmse:0.553219+0.010914    test-rmse:0.566665+0.063763 
## [41] train-rmse:0.551380+0.010780    test-rmse:0.566551+0.062965 
## [42] train-rmse:0.549673+0.010721    test-rmse:0.565033+0.063520 
## [43] train-rmse:0.547967+0.010655    test-rmse:0.562659+0.062143 
## [44] train-rmse:0.546276+0.010618    test-rmse:0.562242+0.062057 
## [45] train-rmse:0.544679+0.010567    test-rmse:0.561061+0.062060 
## [46] train-rmse:0.543119+0.010456    test-rmse:0.561155+0.061664 
## [47] train-rmse:0.541656+0.010406    test-rmse:0.560193+0.060113 
## [48] train-rmse:0.540251+0.010363    test-rmse:0.558118+0.059702 
## [49] train-rmse:0.538836+0.010301    test-rmse:0.557061+0.060184 
## [50] train-rmse:0.537469+0.010249    test-rmse:0.556862+0.060590 
## [51] train-rmse:0.536127+0.010194    test-rmse:0.555343+0.059997 
## [52] train-rmse:0.534833+0.010127    test-rmse:0.554027+0.058487 
## [53] train-rmse:0.533529+0.010074    test-rmse:0.553533+0.058941 
## [54] train-rmse:0.532297+0.009993    test-rmse:0.554073+0.058608 
## [55] train-rmse:0.531084+0.009931    test-rmse:0.553150+0.058303 
## [56] train-rmse:0.529909+0.009872    test-rmse:0.552668+0.058679 
## [57] train-rmse:0.528744+0.009809    test-rmse:0.551376+0.058610 
## [58] train-rmse:0.527618+0.009779    test-rmse:0.550510+0.058173 
## [59] train-rmse:0.526523+0.009737    test-rmse:0.550125+0.057799 
## [60] train-rmse:0.525416+0.009636    test-rmse:0.548938+0.058174 
## [61] train-rmse:0.524342+0.009551    test-rmse:0.548584+0.058267 
## [62] train-rmse:0.523284+0.009500    test-rmse:0.548041+0.058302 
## [63] train-rmse:0.522261+0.009465    test-rmse:0.546670+0.057747 
## [64] train-rmse:0.521266+0.009392    test-rmse:0.545775+0.057495 
## [65] train-rmse:0.520297+0.009332    test-rmse:0.545077+0.057463 
## [66] train-rmse:0.519346+0.009266    test-rmse:0.544461+0.055998 
## [67] train-rmse:0.518399+0.009230    test-rmse:0.544379+0.056309 
## [68] train-rmse:0.517476+0.009194    test-rmse:0.543617+0.055954 
## [69] train-rmse:0.516548+0.009155    test-rmse:0.543154+0.055720 
## [70] train-rmse:0.515613+0.009081    test-rmse:0.542715+0.056634 
## [71] train-rmse:0.514719+0.009046    test-rmse:0.542168+0.056341 
## [72] train-rmse:0.513851+0.009010    test-rmse:0.541207+0.056381 
## [73] train-rmse:0.512991+0.008976    test-rmse:0.540441+0.056694 
## [74] train-rmse:0.512149+0.008939    test-rmse:0.539937+0.056286 
## [75] train-rmse:0.511304+0.008888    test-rmse:0.539315+0.055721 
## [76] train-rmse:0.510440+0.008858    test-rmse:0.538888+0.055676 
## [77] train-rmse:0.509610+0.008837    test-rmse:0.536690+0.055624 
## [78] train-rmse:0.508815+0.008818    test-rmse:0.536412+0.055588 
## [79] train-rmse:0.508035+0.008797    test-rmse:0.536158+0.055626 
## [80] train-rmse:0.507271+0.008774    test-rmse:0.535345+0.054607 
## [81] train-rmse:0.506480+0.008754    test-rmse:0.535103+0.054457 
## [82] train-rmse:0.505692+0.008726    test-rmse:0.535279+0.054462 
## [83] train-rmse:0.504943+0.008690    test-rmse:0.534719+0.054662 
## [84] train-rmse:0.504196+0.008645    test-rmse:0.533817+0.054435 
## [85] train-rmse:0.503461+0.008628    test-rmse:0.532992+0.053919 
## [86] train-rmse:0.502698+0.008604    test-rmse:0.532664+0.054491 
## [87] train-rmse:0.501986+0.008577    test-rmse:0.531779+0.054426 
## [88] train-rmse:0.501280+0.008550    test-rmse:0.532150+0.054363 
## [89] train-rmse:0.500585+0.008515    test-rmse:0.530983+0.054507 
## [90] train-rmse:0.499908+0.008480    test-rmse:0.530582+0.054647 
## [91] train-rmse:0.499230+0.008461    test-rmse:0.529919+0.054731 
## [92] train-rmse:0.498534+0.008453    test-rmse:0.529380+0.054164 
## [93] train-rmse:0.497864+0.008435    test-rmse:0.528780+0.054315 
## [94] train-rmse:0.497199+0.008427    test-rmse:0.528563+0.053757 
## [95] train-rmse:0.496539+0.008405    test-rmse:0.528234+0.053905 
## [96] train-rmse:0.495889+0.008382    test-rmse:0.528197+0.053824 
## [97] train-rmse:0.495239+0.008362    test-rmse:0.527216+0.053301 
## [98] train-rmse:0.494609+0.008340    test-rmse:0.527041+0.053159 
## [99] train-rmse:0.493991+0.008336    test-rmse:0.527162+0.053181 
## [100]    train-rmse:0.493383+0.008336    test-rmse:0.526457+0.053590 
## [101]    train-rmse:0.492776+0.008310    test-rmse:0.526051+0.053714 
## [102]    train-rmse:0.492165+0.008285    test-rmse:0.525484+0.053477 
## [103]    train-rmse:0.491555+0.008262    test-rmse:0.525190+0.052601 
## [104]    train-rmse:0.490970+0.008254    test-rmse:0.525345+0.051990 
## [105]    train-rmse:0.490403+0.008242    test-rmse:0.525131+0.052218 
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## [108]    train-rmse:0.488699+0.008196    test-rmse:0.523818+0.052075 
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## [126]    train-rmse:0.479598+0.007962    test-rmse:0.518416+0.050618 
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## [129]    train-rmse:0.478231+0.007929    test-rmse:0.517472+0.050589 
## [130]    train-rmse:0.477787+0.007919    test-rmse:0.517161+0.050627 
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## [133]    train-rmse:0.476484+0.007894    test-rmse:0.515667+0.048837 
## [134]    train-rmse:0.476052+0.007879    test-rmse:0.515478+0.048859 
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## [290]    train-rmse:0.437194+0.007442    test-rmse:0.496423+0.040892 
## [291]    train-rmse:0.437040+0.007444    test-rmse:0.496149+0.040577 
## [292]    train-rmse:0.436884+0.007445    test-rmse:0.496497+0.040740 
## [293]    train-rmse:0.436731+0.007445    test-rmse:0.496366+0.040693 
## [294]    train-rmse:0.436582+0.007447    test-rmse:0.496803+0.040646 
## [295]    train-rmse:0.436433+0.007450    test-rmse:0.496756+0.040624 
## [296]    train-rmse:0.436285+0.007454    test-rmse:0.496666+0.040667 
## [297]    train-rmse:0.436140+0.007452    test-rmse:0.496943+0.040946 
## Stopping. Best iteration:
## [291]    train-rmse:0.437040+0.007444    test-rmse:0.496149+0.040577
## 
## 15 
## [1]  train-rmse:1.439504+0.026920    test-rmse:1.437417+0.115482 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.271640+0.024051    test-rmse:1.271576+0.113815 
## [3]  train-rmse:1.129551+0.021700    test-rmse:1.131199+0.108565 
## [4]  train-rmse:1.009940+0.019778    test-rmse:1.013998+0.107805 
## [5]  train-rmse:0.909803+0.018165    test-rmse:0.909524+0.103966 
## [6]  train-rmse:0.825924+0.016901    test-rmse:0.831223+0.103737 
## [7]  train-rmse:0.756725+0.015635    test-rmse:0.762474+0.104311 
## [8]  train-rmse:0.699484+0.014805    test-rmse:0.706836+0.101950 
## [9]  train-rmse:0.652415+0.014209    test-rmse:0.661603+0.099653 
## [10] train-rmse:0.613842+0.013916    test-rmse:0.623590+0.096835 
## [11] train-rmse:0.582283+0.013417    test-rmse:0.593004+0.093128 
## [12] train-rmse:0.556504+0.012982    test-rmse:0.567724+0.091049 
## [13] train-rmse:0.535108+0.012576    test-rmse:0.549754+0.088943 
## [14] train-rmse:0.517411+0.012293    test-rmse:0.536188+0.084368 
## [15] train-rmse:0.502689+0.011864    test-rmse:0.523860+0.082740 
## [16] train-rmse:0.490213+0.011504    test-rmse:0.509897+0.079111 
## [17] train-rmse:0.479794+0.011122    test-rmse:0.504376+0.075335 
## [18] train-rmse:0.469257+0.009670    test-rmse:0.494674+0.074633 
## [19] train-rmse:0.459964+0.009540    test-rmse:0.487562+0.072507 
## [20] train-rmse:0.451956+0.009333    test-rmse:0.480188+0.070559 
## [21] train-rmse:0.445183+0.008926    test-rmse:0.475955+0.069890 
## [22] train-rmse:0.438824+0.008113    test-rmse:0.472266+0.069622 
## [23] train-rmse:0.433525+0.007889    test-rmse:0.469613+0.067390 
## [24] train-rmse:0.428284+0.007553    test-rmse:0.466888+0.067734 
## [25] train-rmse:0.423215+0.007184    test-rmse:0.465645+0.066090 
## [26] train-rmse:0.418730+0.007040    test-rmse:0.464123+0.062884 
## [27] train-rmse:0.414762+0.006833    test-rmse:0.461947+0.063026 
## [28] train-rmse:0.410621+0.006769    test-rmse:0.458097+0.061821 
## [29] train-rmse:0.407030+0.006276    test-rmse:0.456084+0.062191 
## [30] train-rmse:0.403513+0.005498    test-rmse:0.455528+0.062253 
## [31] train-rmse:0.399981+0.005283    test-rmse:0.454703+0.061213 
## [32] train-rmse:0.397168+0.005191    test-rmse:0.454315+0.060476 
## [33] train-rmse:0.394136+0.004777    test-rmse:0.453358+0.059761 
## [34] train-rmse:0.391468+0.004537    test-rmse:0.450537+0.059653 
## [35] train-rmse:0.388518+0.004447    test-rmse:0.448984+0.057344 
## [36] train-rmse:0.385969+0.004512    test-rmse:0.447729+0.056615 
## [37] train-rmse:0.383550+0.004439    test-rmse:0.446790+0.056474 
## [38] train-rmse:0.380761+0.004158    test-rmse:0.447461+0.056994 
## [39] train-rmse:0.378275+0.004180    test-rmse:0.446774+0.056466 
## [40] train-rmse:0.375997+0.004237    test-rmse:0.447009+0.055452 
## [41] train-rmse:0.373951+0.004205    test-rmse:0.446461+0.054823 
## [42] train-rmse:0.371725+0.004126    test-rmse:0.446491+0.054400 
## [43] train-rmse:0.369522+0.003957    test-rmse:0.446210+0.055227 
## [44] train-rmse:0.367659+0.004064    test-rmse:0.445295+0.055560 
## [45] train-rmse:0.365612+0.003765    test-rmse:0.444550+0.055474 
## [46] train-rmse:0.362680+0.003658    test-rmse:0.443681+0.055073 
## [47] train-rmse:0.360578+0.003629    test-rmse:0.441979+0.053893 
## [48] train-rmse:0.358790+0.003524    test-rmse:0.442192+0.054167 
## [49] train-rmse:0.357114+0.003423    test-rmse:0.442074+0.054525 
## [50] train-rmse:0.354960+0.003291    test-rmse:0.441637+0.054020 
## [51] train-rmse:0.353278+0.003298    test-rmse:0.441994+0.053844 
## [52] train-rmse:0.351772+0.003306    test-rmse:0.442505+0.054297 
## [53] train-rmse:0.349721+0.003574    test-rmse:0.442538+0.054585 
## [54] train-rmse:0.348159+0.003319    test-rmse:0.441102+0.055583 
## [55] train-rmse:0.346719+0.003117    test-rmse:0.441001+0.055422 
## [56] train-rmse:0.344983+0.003089    test-rmse:0.440853+0.055103 
## [57] train-rmse:0.343286+0.002976    test-rmse:0.439721+0.055031 
## [58] train-rmse:0.342048+0.002980    test-rmse:0.439308+0.055519 
## [59] train-rmse:0.340643+0.003038    test-rmse:0.439036+0.055238 
## [60] train-rmse:0.339421+0.002910    test-rmse:0.438464+0.055957 
## [61] train-rmse:0.338021+0.003327    test-rmse:0.437616+0.055820 
## [62] train-rmse:0.336659+0.003272    test-rmse:0.438674+0.055231 
## [63] train-rmse:0.335329+0.003485    test-rmse:0.438884+0.054864 
## [64] train-rmse:0.334076+0.003386    test-rmse:0.438978+0.055088 
## [65] train-rmse:0.332766+0.003309    test-rmse:0.438668+0.055074 
## [66] train-rmse:0.331155+0.003315    test-rmse:0.438835+0.054801 
## [67] train-rmse:0.329953+0.003479    test-rmse:0.438015+0.055124 
## Stopping. Best iteration:
## [61] train-rmse:0.338021+0.003327    test-rmse:0.437616+0.055820
## 
## 16 
## [1]  train-rmse:1.435017+0.026435    test-rmse:1.435173+0.117104 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.262508+0.023445    test-rmse:1.264194+0.113085 
## [3]  train-rmse:1.114929+0.020758    test-rmse:1.121497+0.112691 
## [4]  train-rmse:0.989723+0.018394    test-rmse:0.994484+0.112424 
## [5]  train-rmse:0.883168+0.016351    test-rmse:0.891676+0.112650 
## [6]  train-rmse:0.792264+0.015862    test-rmse:0.800073+0.107866 
## [7]  train-rmse:0.716142+0.014332    test-rmse:0.729609+0.103769 
## [8]  train-rmse:0.652776+0.013370    test-rmse:0.671523+0.103923 
## [9]  train-rmse:0.598742+0.012978    test-rmse:0.622368+0.098067 
## [10] train-rmse:0.554277+0.012241    test-rmse:0.584493+0.094313 
## [11] train-rmse:0.516693+0.011389    test-rmse:0.553307+0.094242 
## [12] train-rmse:0.485540+0.011554    test-rmse:0.528157+0.088092 
## [13] train-rmse:0.459521+0.010906    test-rmse:0.508161+0.085229 
## [14] train-rmse:0.437904+0.010400    test-rmse:0.491598+0.082890 
## [15] train-rmse:0.419972+0.009925    test-rmse:0.477207+0.080949 
## [16] train-rmse:0.405182+0.010321    test-rmse:0.468258+0.076904 
## [17] train-rmse:0.391723+0.009679    test-rmse:0.461740+0.075359 
## [18] train-rmse:0.381323+0.009934    test-rmse:0.454414+0.072252 
## [19] train-rmse:0.372217+0.009811    test-rmse:0.449299+0.070434 
## [20] train-rmse:0.364720+0.009952    test-rmse:0.446644+0.069491 
## [21] train-rmse:0.357855+0.010014    test-rmse:0.444515+0.066482 
## [22] train-rmse:0.351951+0.009691    test-rmse:0.441016+0.066934 
## [23] train-rmse:0.346425+0.009813    test-rmse:0.439723+0.065771 
## [24] train-rmse:0.340624+0.009998    test-rmse:0.437671+0.065560 
## [25] train-rmse:0.335759+0.010022    test-rmse:0.435984+0.063762 
## [26] train-rmse:0.331384+0.010247    test-rmse:0.434210+0.063011 
## [27] train-rmse:0.327432+0.010188    test-rmse:0.434088+0.062441 
## [28] train-rmse:0.323291+0.009776    test-rmse:0.434054+0.062030 
## [29] train-rmse:0.319291+0.009856    test-rmse:0.432435+0.061734 
## [30] train-rmse:0.316144+0.009437    test-rmse:0.431930+0.060690 
## [31] train-rmse:0.313313+0.009307    test-rmse:0.432296+0.060827 
## [32] train-rmse:0.310020+0.009312    test-rmse:0.430845+0.061976 
## [33] train-rmse:0.306900+0.008662    test-rmse:0.429859+0.062063 
## [34] train-rmse:0.303712+0.008335    test-rmse:0.429667+0.061385 
## [35] train-rmse:0.301103+0.008023    test-rmse:0.430233+0.060911 
## [36] train-rmse:0.298126+0.008126    test-rmse:0.430284+0.061314 
## [37] train-rmse:0.295855+0.007815    test-rmse:0.429534+0.061856 
## [38] train-rmse:0.293779+0.007682    test-rmse:0.428671+0.061358 
## [39] train-rmse:0.290642+0.007364    test-rmse:0.427962+0.061471 
## [40] train-rmse:0.288413+0.007217    test-rmse:0.427479+0.061770 
## [41] train-rmse:0.286161+0.006777    test-rmse:0.427489+0.062764 
## [42] train-rmse:0.283459+0.007219    test-rmse:0.427039+0.062478 
## [43] train-rmse:0.281385+0.006784    test-rmse:0.426468+0.062294 
## [44] train-rmse:0.278772+0.006674    test-rmse:0.425451+0.062773 
## [45] train-rmse:0.276495+0.006662    test-rmse:0.426177+0.062834 
## [46] train-rmse:0.274568+0.006528    test-rmse:0.425738+0.065126 
## [47] train-rmse:0.272756+0.006320    test-rmse:0.424803+0.065512 
## [48] train-rmse:0.270473+0.006645    test-rmse:0.424322+0.065966 
## [49] train-rmse:0.268492+0.006411    test-rmse:0.423851+0.066036 
## [50] train-rmse:0.266805+0.006318    test-rmse:0.424329+0.066909 
## [51] train-rmse:0.265064+0.006346    test-rmse:0.424128+0.066554 
## [52] train-rmse:0.263140+0.006170    test-rmse:0.423117+0.066918 
## [53] train-rmse:0.260837+0.006173    test-rmse:0.422554+0.067235 
## [54] train-rmse:0.258867+0.006371    test-rmse:0.422149+0.066913 
## [55] train-rmse:0.256985+0.005749    test-rmse:0.421635+0.067483 
## [56] train-rmse:0.254961+0.005818    test-rmse:0.421504+0.066788 
## [57] train-rmse:0.253283+0.005948    test-rmse:0.421023+0.067109 
## [58] train-rmse:0.251644+0.005948    test-rmse:0.420931+0.067912 
## [59] train-rmse:0.249983+0.005792    test-rmse:0.420906+0.067446 
## [60] train-rmse:0.248118+0.005795    test-rmse:0.419824+0.067904 
## [61] train-rmse:0.246519+0.005502    test-rmse:0.419829+0.068299 
## [62] train-rmse:0.245316+0.005540    test-rmse:0.419672+0.068369 
## [63] train-rmse:0.243921+0.005391    test-rmse:0.419892+0.067991 
## [64] train-rmse:0.242143+0.005103    test-rmse:0.418731+0.068689 
## [65] train-rmse:0.240666+0.004668    test-rmse:0.418573+0.068424 
## [66] train-rmse:0.239334+0.004646    test-rmse:0.418904+0.068353 
## [67] train-rmse:0.237778+0.004448    test-rmse:0.419064+0.068254 
## [68] train-rmse:0.236163+0.004112    test-rmse:0.418254+0.068608 
## [69] train-rmse:0.234806+0.003849    test-rmse:0.417782+0.069674 
## [70] train-rmse:0.233796+0.003602    test-rmse:0.417307+0.069834 
## [71] train-rmse:0.232446+0.003613    test-rmse:0.416503+0.069528 
## [72] train-rmse:0.231394+0.003564    test-rmse:0.416135+0.069221 
## [73] train-rmse:0.229852+0.003868    test-rmse:0.416025+0.068605 
## [74] train-rmse:0.228752+0.003523    test-rmse:0.415607+0.068444 
## [75] train-rmse:0.227377+0.003629    test-rmse:0.414984+0.069417 
## [76] train-rmse:0.225865+0.003807    test-rmse:0.414999+0.069240 
## [77] train-rmse:0.224507+0.004406    test-rmse:0.414832+0.069507 
## [78] train-rmse:0.223135+0.004233    test-rmse:0.415008+0.069305 
## [79] train-rmse:0.221836+0.004180    test-rmse:0.415547+0.069805 
## [80] train-rmse:0.220751+0.003927    test-rmse:0.415643+0.069941 
## [81] train-rmse:0.219466+0.003808    test-rmse:0.415355+0.070278 
## [82] train-rmse:0.218327+0.003985    test-rmse:0.415569+0.069989 
## [83] train-rmse:0.217007+0.003772    test-rmse:0.415246+0.070028 
## Stopping. Best iteration:
## [77] train-rmse:0.224507+0.004406    test-rmse:0.414832+0.069507
## 
## 17 
## [1]  train-rmse:1.433031+0.026127    test-rmse:1.432875+0.117281 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.258632+0.022814    test-rmse:1.259251+0.116819 
## [3]  train-rmse:1.109265+0.020637    test-rmse:1.111780+0.112531 
## [4]  train-rmse:0.980701+0.018546    test-rmse:0.986232+0.108329 
## [5]  train-rmse:0.871193+0.016173    test-rmse:0.879937+0.108903 
## [6]  train-rmse:0.777637+0.014242    test-rmse:0.789814+0.105442 
## [7]  train-rmse:0.697122+0.012521    test-rmse:0.714606+0.101087 
## [8]  train-rmse:0.629909+0.011000    test-rmse:0.655902+0.098942 
## [9]  train-rmse:0.572223+0.009497    test-rmse:0.605440+0.094905 
## [10] train-rmse:0.524520+0.008283    test-rmse:0.565833+0.092060 
## [11] train-rmse:0.484122+0.007980    test-rmse:0.532301+0.087884 
## [12] train-rmse:0.450038+0.007074    test-rmse:0.506790+0.083252 
## [13] train-rmse:0.419571+0.005775    test-rmse:0.486037+0.082571 
## [14] train-rmse:0.395710+0.006084    test-rmse:0.470341+0.078820 
## [15] train-rmse:0.376085+0.006096    test-rmse:0.457925+0.075955 
## [16] train-rmse:0.359752+0.006136    test-rmse:0.448376+0.072554 
## [17] train-rmse:0.345471+0.004986    test-rmse:0.440975+0.074470 
## [18] train-rmse:0.332107+0.006910    test-rmse:0.434878+0.070902 
## [19] train-rmse:0.321461+0.005970    test-rmse:0.430792+0.068541 
## [20] train-rmse:0.313190+0.005996    test-rmse:0.427460+0.065533 
## [21] train-rmse:0.306096+0.005806    test-rmse:0.425085+0.064591 
## [22] train-rmse:0.297782+0.006050    test-rmse:0.423192+0.062830 
## [23] train-rmse:0.291679+0.005768    test-rmse:0.422093+0.062320 
## [24] train-rmse:0.286051+0.006127    test-rmse:0.421099+0.062793 
## [25] train-rmse:0.281620+0.006024    test-rmse:0.419843+0.061218 
## [26] train-rmse:0.276744+0.005432    test-rmse:0.417126+0.062147 
## [27] train-rmse:0.272951+0.005314    test-rmse:0.415704+0.061687 
## [28] train-rmse:0.268523+0.003879    test-rmse:0.414153+0.060024 
## [29] train-rmse:0.264141+0.003533    test-rmse:0.413954+0.060031 
## [30] train-rmse:0.260609+0.003132    test-rmse:0.413524+0.060168 
## [31] train-rmse:0.256097+0.003098    test-rmse:0.412115+0.061741 
## [32] train-rmse:0.252440+0.002786    test-rmse:0.411357+0.060218 
## [33] train-rmse:0.249051+0.001874    test-rmse:0.409554+0.060941 
## [34] train-rmse:0.245328+0.002388    test-rmse:0.409281+0.060468 
## [35] train-rmse:0.242775+0.002309    test-rmse:0.409093+0.060226 
## [36] train-rmse:0.239956+0.002215    test-rmse:0.408366+0.060041 
## [37] train-rmse:0.237328+0.002127    test-rmse:0.408083+0.059486 
## [38] train-rmse:0.234569+0.001519    test-rmse:0.407199+0.058965 
## [39] train-rmse:0.231918+0.001146    test-rmse:0.407447+0.059177 
## [40] train-rmse:0.228699+0.001220    test-rmse:0.407051+0.058696 
## [41] train-rmse:0.226677+0.001150    test-rmse:0.405547+0.059730 
## [42] train-rmse:0.223924+0.001699    test-rmse:0.405221+0.059177 
## [43] train-rmse:0.221650+0.001799    test-rmse:0.404779+0.059168 
## [44] train-rmse:0.219576+0.001704    test-rmse:0.403614+0.058996 
## [45] train-rmse:0.216674+0.002190    test-rmse:0.403691+0.060119 
## [46] train-rmse:0.214500+0.002368    test-rmse:0.403333+0.059671 
## [47] train-rmse:0.212069+0.002279    test-rmse:0.402523+0.059657 
## [48] train-rmse:0.209593+0.002660    test-rmse:0.401418+0.060187 
## [49] train-rmse:0.207912+0.002518    test-rmse:0.401321+0.059964 
## [50] train-rmse:0.205267+0.002263    test-rmse:0.401213+0.060222 
## [51] train-rmse:0.203466+0.002309    test-rmse:0.401182+0.059458 
## [52] train-rmse:0.201206+0.002549    test-rmse:0.401186+0.059101 
## [53] train-rmse:0.199117+0.002975    test-rmse:0.400480+0.059250 
## [54] train-rmse:0.197469+0.003136    test-rmse:0.400209+0.059462 
## [55] train-rmse:0.195116+0.003646    test-rmse:0.399746+0.059945 
## [56] train-rmse:0.193196+0.003644    test-rmse:0.399785+0.060212 
## [57] train-rmse:0.191249+0.003339    test-rmse:0.399341+0.059955 
## [58] train-rmse:0.189433+0.003147    test-rmse:0.398978+0.059383 
## [59] train-rmse:0.187465+0.003663    test-rmse:0.398438+0.059911 
## [60] train-rmse:0.185468+0.003753    test-rmse:0.398196+0.059668 
## [61] train-rmse:0.184298+0.003612    test-rmse:0.398221+0.059656 
## [62] train-rmse:0.182616+0.003272    test-rmse:0.397767+0.059621 
## [63] train-rmse:0.180539+0.003384    test-rmse:0.397548+0.059376 
## [64] train-rmse:0.179435+0.003477    test-rmse:0.396837+0.060370 
## [65] train-rmse:0.178135+0.003549    test-rmse:0.396606+0.060682 
## [66] train-rmse:0.176277+0.003399    test-rmse:0.396287+0.060824 
## [67] train-rmse:0.175204+0.003286    test-rmse:0.396226+0.060658 
## [68] train-rmse:0.173870+0.003240    test-rmse:0.396105+0.060451 
## [69] train-rmse:0.171991+0.003160    test-rmse:0.395941+0.060422 
## [70] train-rmse:0.170657+0.003121    test-rmse:0.395780+0.060204 
## [71] train-rmse:0.169564+0.003228    test-rmse:0.396068+0.060566 
## [72] train-rmse:0.167908+0.003820    test-rmse:0.396207+0.061035 
## [73] train-rmse:0.166432+0.003510    test-rmse:0.395637+0.061436 
## [74] train-rmse:0.165154+0.003586    test-rmse:0.395042+0.060931 
## [75] train-rmse:0.164035+0.003393    test-rmse:0.394872+0.061271 
## [76] train-rmse:0.162794+0.003590    test-rmse:0.394792+0.061238 
## [77] train-rmse:0.161227+0.004077    test-rmse:0.394874+0.061418 
## [78] train-rmse:0.159797+0.004506    test-rmse:0.395045+0.061433 
## [79] train-rmse:0.158686+0.004608    test-rmse:0.395013+0.061587 
## [80] train-rmse:0.157483+0.004774    test-rmse:0.394883+0.061559 
## [81] train-rmse:0.156170+0.004821    test-rmse:0.394870+0.061263 
## [82] train-rmse:0.154772+0.005003    test-rmse:0.394674+0.061009 
## [83] train-rmse:0.153680+0.005124    test-rmse:0.394365+0.061525 
## [84] train-rmse:0.152332+0.004954    test-rmse:0.394439+0.061487 
## [85] train-rmse:0.151114+0.005188    test-rmse:0.394194+0.061420 
## [86] train-rmse:0.150111+0.004892    test-rmse:0.393931+0.061706 
## [87] train-rmse:0.149216+0.005015    test-rmse:0.393744+0.062124 
## [88] train-rmse:0.148248+0.005228    test-rmse:0.393945+0.062186 
## [89] train-rmse:0.147161+0.005378    test-rmse:0.393718+0.062481 
## [90] train-rmse:0.145999+0.005682    test-rmse:0.393745+0.062434 
## [91] train-rmse:0.144863+0.005955    test-rmse:0.393298+0.062100 
## [92] train-rmse:0.144073+0.006232    test-rmse:0.393100+0.062225 
## [93] train-rmse:0.142942+0.006343    test-rmse:0.393107+0.062079 
## [94] train-rmse:0.141886+0.006409    test-rmse:0.393260+0.061968 
## [95] train-rmse:0.141245+0.006431    test-rmse:0.393478+0.062306 
## [96] train-rmse:0.140057+0.006317    test-rmse:0.393362+0.062543 
## [97] train-rmse:0.138723+0.006232    test-rmse:0.392671+0.062869 
## [98] train-rmse:0.137510+0.005925    test-rmse:0.392528+0.062604 
## [99] train-rmse:0.136670+0.006033    test-rmse:0.392323+0.062883 
## [100]    train-rmse:0.135279+0.006126    test-rmse:0.391917+0.063306 
## [101]    train-rmse:0.134253+0.006176    test-rmse:0.391295+0.063944 
## [102]    train-rmse:0.133188+0.006038    test-rmse:0.391065+0.064389 
## [103]    train-rmse:0.132437+0.006238    test-rmse:0.390967+0.064170 
## [104]    train-rmse:0.131154+0.006454    test-rmse:0.391050+0.064427 
## [105]    train-rmse:0.130115+0.006561    test-rmse:0.390916+0.064288 
## [106]    train-rmse:0.129139+0.006781    test-rmse:0.390867+0.064488 
## [107]    train-rmse:0.127930+0.006508    test-rmse:0.390934+0.064384 
## [108]    train-rmse:0.127013+0.006747    test-rmse:0.390833+0.064431 
## [109]    train-rmse:0.126058+0.007097    test-rmse:0.390803+0.064262 
## [110]    train-rmse:0.124900+0.006957    test-rmse:0.390465+0.064635 
## [111]    train-rmse:0.123899+0.006786    test-rmse:0.390566+0.064735 
## [112]    train-rmse:0.122957+0.006721    test-rmse:0.390600+0.064797 
## [113]    train-rmse:0.122309+0.006916    test-rmse:0.390824+0.064880 
## [114]    train-rmse:0.121515+0.006957    test-rmse:0.390866+0.065043 
## [115]    train-rmse:0.120609+0.006995    test-rmse:0.390769+0.065083 
## [116]    train-rmse:0.119799+0.007257    test-rmse:0.390187+0.065583 
## [117]    train-rmse:0.118744+0.007324    test-rmse:0.390428+0.065343 
## [118]    train-rmse:0.118128+0.007343    test-rmse:0.390094+0.064963 
## [119]    train-rmse:0.117369+0.007428    test-rmse:0.390237+0.065110 
## [120]    train-rmse:0.116431+0.007466    test-rmse:0.390102+0.065665 
## [121]    train-rmse:0.115494+0.007661    test-rmse:0.390400+0.065811 
## [122]    train-rmse:0.114638+0.007973    test-rmse:0.390411+0.065820 
## [123]    train-rmse:0.113953+0.008004    test-rmse:0.390327+0.065573 
## [124]    train-rmse:0.113047+0.007938    test-rmse:0.389961+0.066012 
## [125]    train-rmse:0.112055+0.008027    test-rmse:0.389844+0.065479 
## [126]    train-rmse:0.111223+0.008122    test-rmse:0.389825+0.065499 
## [127]    train-rmse:0.110605+0.008124    test-rmse:0.390072+0.065578 
## [128]    train-rmse:0.110036+0.008070    test-rmse:0.389784+0.065598 
## [129]    train-rmse:0.109543+0.008178    test-rmse:0.389974+0.065587 
## [130]    train-rmse:0.108838+0.008082    test-rmse:0.390118+0.065647 
## [131]    train-rmse:0.108314+0.008343    test-rmse:0.390535+0.065888 
## [132]    train-rmse:0.107778+0.008421    test-rmse:0.390473+0.065772 
## [133]    train-rmse:0.107182+0.008263    test-rmse:0.390310+0.065882 
## [134]    train-rmse:0.106127+0.008178    test-rmse:0.390416+0.066330 
## Stopping. Best iteration:
## [128]    train-rmse:0.110036+0.008070    test-rmse:0.389784+0.065598
## 
## 18 
## [1]  train-rmse:1.431841+0.026335    test-rmse:1.431565+0.115322 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.256000+0.022820    test-rmse:1.256197+0.112955 
## [3]  train-rmse:1.104969+0.020731    test-rmse:1.105254+0.107922 
## [4]  train-rmse:0.975356+0.018250    test-rmse:0.980318+0.106521 
## [5]  train-rmse:0.863908+0.016557    test-rmse:0.873814+0.101772 
## [6]  train-rmse:0.768417+0.014541    test-rmse:0.782908+0.100377 
## [7]  train-rmse:0.686769+0.013301    test-rmse:0.705551+0.097365 
## [8]  train-rmse:0.616162+0.011832    test-rmse:0.645118+0.092946 
## [9]  train-rmse:0.556529+0.010817    test-rmse:0.593099+0.089019 
## [10] train-rmse:0.505201+0.009543    test-rmse:0.552357+0.088171 
## [11] train-rmse:0.461781+0.008111    test-rmse:0.519523+0.083997 
## [12] train-rmse:0.425022+0.007987    test-rmse:0.495195+0.081138 
## [13] train-rmse:0.393305+0.007254    test-rmse:0.473179+0.082626 
## [14] train-rmse:0.366467+0.006971    test-rmse:0.458235+0.080939 
## [15] train-rmse:0.344173+0.006524    test-rmse:0.445897+0.078494 
## [16] train-rmse:0.323167+0.004970    test-rmse:0.435779+0.075526 
## [17] train-rmse:0.305598+0.005299    test-rmse:0.426462+0.074160 
## [18] train-rmse:0.292397+0.005609    test-rmse:0.421585+0.073827 
## [19] train-rmse:0.280278+0.006169    test-rmse:0.418622+0.071139 
## [20] train-rmse:0.267706+0.005174    test-rmse:0.413549+0.071308 
## [21] train-rmse:0.258198+0.004937    test-rmse:0.409778+0.068553 
## [22] train-rmse:0.250758+0.004931    test-rmse:0.408997+0.067543 
## [23] train-rmse:0.242930+0.005218    test-rmse:0.408250+0.066942 
## [24] train-rmse:0.236084+0.005213    test-rmse:0.405936+0.063692 
## [25] train-rmse:0.230515+0.005881    test-rmse:0.405341+0.062489 
## [26] train-rmse:0.225288+0.005392    test-rmse:0.406251+0.061609 
## [27] train-rmse:0.219975+0.006281    test-rmse:0.405490+0.063093 
## [28] train-rmse:0.215915+0.006290    test-rmse:0.404571+0.061278 
## [29] train-rmse:0.211784+0.006341    test-rmse:0.403194+0.060241 
## [30] train-rmse:0.208137+0.006026    test-rmse:0.403013+0.060006 
## [31] train-rmse:0.203119+0.004965    test-rmse:0.403375+0.059436 
## [32] train-rmse:0.200076+0.005040    test-rmse:0.403181+0.059439 
## [33] train-rmse:0.196054+0.004495    test-rmse:0.401996+0.057952 
## [34] train-rmse:0.192850+0.004241    test-rmse:0.402205+0.057622 
## [35] train-rmse:0.189819+0.004259    test-rmse:0.401807+0.057116 
## [36] train-rmse:0.186159+0.004802    test-rmse:0.401650+0.057960 
## [37] train-rmse:0.183486+0.004920    test-rmse:0.401224+0.058260 
## [38] train-rmse:0.180865+0.004868    test-rmse:0.400876+0.058023 
## [39] train-rmse:0.178680+0.004649    test-rmse:0.400800+0.057828 
## [40] train-rmse:0.175392+0.004226    test-rmse:0.400669+0.057022 
## [41] train-rmse:0.171986+0.004065    test-rmse:0.399709+0.057167 
## [42] train-rmse:0.169439+0.003864    test-rmse:0.399348+0.056752 
## [43] train-rmse:0.167063+0.003715    test-rmse:0.399188+0.056477 
## [44] train-rmse:0.164635+0.003602    test-rmse:0.399385+0.056483 
## [45] train-rmse:0.161063+0.004395    test-rmse:0.398798+0.056882 
## [46] train-rmse:0.158513+0.004282    test-rmse:0.398092+0.056433 
## [47] train-rmse:0.155878+0.003831    test-rmse:0.397370+0.056382 
## [48] train-rmse:0.153933+0.003978    test-rmse:0.397201+0.056869 
## [49] train-rmse:0.151468+0.003281    test-rmse:0.396354+0.057680 
## [50] train-rmse:0.149280+0.002367    test-rmse:0.395736+0.057137 
## [51] train-rmse:0.147373+0.002599    test-rmse:0.395007+0.056679 
## [52] train-rmse:0.145805+0.002285    test-rmse:0.394819+0.055857 
## [53] train-rmse:0.143745+0.002670    test-rmse:0.394414+0.055947 
## [54] train-rmse:0.142174+0.003601    test-rmse:0.394123+0.056579 
## [55] train-rmse:0.140473+0.003232    test-rmse:0.394398+0.056713 
## [56] train-rmse:0.139055+0.003251    test-rmse:0.394707+0.056401 
## [57] train-rmse:0.137237+0.003490    test-rmse:0.394154+0.056714 
## [58] train-rmse:0.136105+0.003442    test-rmse:0.394435+0.056804 
## [59] train-rmse:0.134428+0.003894    test-rmse:0.394577+0.056135 
## [60] train-rmse:0.132229+0.003893    test-rmse:0.394232+0.056192 
## Stopping. Best iteration:
## [54] train-rmse:0.142174+0.003601    test-rmse:0.394123+0.056579
## 
## 19 
## [1]  train-rmse:1.431489+0.026371    test-rmse:1.430662+0.114558 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.254864+0.022878    test-rmse:1.252409+0.112188 
## [3]  train-rmse:1.102886+0.020879    test-rmse:1.100524+0.105676 
## [4]  train-rmse:0.972221+0.018510    test-rmse:0.974227+0.102307 
## [5]  train-rmse:0.860098+0.016640    test-rmse:0.868964+0.100259 
## [6]  train-rmse:0.763325+0.015378    test-rmse:0.777345+0.095527 
## [7]  train-rmse:0.680491+0.013431    test-rmse:0.700934+0.094443 
## [8]  train-rmse:0.609193+0.012489    test-rmse:0.640677+0.095452 
## [9]  train-rmse:0.547843+0.011927    test-rmse:0.589762+0.091596 
## [10] train-rmse:0.495334+0.011106    test-rmse:0.550788+0.090041 
## [11] train-rmse:0.449956+0.010820    test-rmse:0.518103+0.088037 
## [12] train-rmse:0.411459+0.010588    test-rmse:0.495475+0.084804 
## [13] train-rmse:0.377795+0.010184    test-rmse:0.474059+0.082894 
## [14] train-rmse:0.348873+0.009547    test-rmse:0.459235+0.082115 
## [15] train-rmse:0.324215+0.009386    test-rmse:0.447942+0.080936 
## [16] train-rmse:0.302536+0.008514    test-rmse:0.440047+0.079516 
## [17] train-rmse:0.284063+0.008693    test-rmse:0.433590+0.076414 
## [18] train-rmse:0.267258+0.008685    test-rmse:0.427049+0.074045 
## [19] train-rmse:0.254348+0.008716    test-rmse:0.422177+0.071926 
## [20] train-rmse:0.242267+0.008748    test-rmse:0.420126+0.071495 
## [21] train-rmse:0.232250+0.007809    test-rmse:0.417634+0.069996 
## [22] train-rmse:0.222514+0.008954    test-rmse:0.415121+0.068074 
## [23] train-rmse:0.214827+0.008490    test-rmse:0.413344+0.068345 
## [24] train-rmse:0.208177+0.008115    test-rmse:0.411940+0.068389 
## [25] train-rmse:0.201794+0.009504    test-rmse:0.410464+0.067087 
## [26] train-rmse:0.193924+0.008153    test-rmse:0.409913+0.066106 
## [27] train-rmse:0.188835+0.008055    test-rmse:0.408544+0.065247 
## [28] train-rmse:0.184481+0.007774    test-rmse:0.407248+0.064042 
## [29] train-rmse:0.178732+0.008630    test-rmse:0.406831+0.065137 
## [30] train-rmse:0.174221+0.008112    test-rmse:0.406265+0.064680 
## [31] train-rmse:0.169889+0.007554    test-rmse:0.406450+0.064132 
## [32] train-rmse:0.166531+0.006703    test-rmse:0.406229+0.062635 
## [33] train-rmse:0.163655+0.006644    test-rmse:0.405874+0.062154 
## [34] train-rmse:0.160574+0.006836    test-rmse:0.404683+0.062564 
## [35] train-rmse:0.156838+0.007236    test-rmse:0.405152+0.062426 
## [36] train-rmse:0.152902+0.007176    test-rmse:0.404798+0.062595 
## [37] train-rmse:0.149678+0.007228    test-rmse:0.404974+0.062281 
## [38] train-rmse:0.146675+0.007168    test-rmse:0.405186+0.061080 
## [39] train-rmse:0.141089+0.006825    test-rmse:0.404426+0.061727 
## [40] train-rmse:0.137800+0.006839    test-rmse:0.403897+0.061801 
## [41] train-rmse:0.133860+0.007241    test-rmse:0.404301+0.059825 
## [42] train-rmse:0.130648+0.007620    test-rmse:0.403596+0.059644 
## [43] train-rmse:0.127564+0.008042    test-rmse:0.403335+0.060404 
## [44] train-rmse:0.125059+0.008464    test-rmse:0.403523+0.060280 
## [45] train-rmse:0.122919+0.008576    test-rmse:0.403178+0.059850 
## [46] train-rmse:0.119954+0.007915    test-rmse:0.402245+0.060094 
## [47] train-rmse:0.117568+0.008072    test-rmse:0.401897+0.059831 
## [48] train-rmse:0.115324+0.007925    test-rmse:0.401954+0.060056 
## [49] train-rmse:0.112894+0.007868    test-rmse:0.402284+0.059839 
## [50] train-rmse:0.110818+0.007713    test-rmse:0.401964+0.060113 
## [51] train-rmse:0.109003+0.007785    test-rmse:0.402082+0.059509 
## [52] train-rmse:0.106476+0.006612    test-rmse:0.401822+0.059649 
## [53] train-rmse:0.104448+0.006429    test-rmse:0.401458+0.060196 
## [54] train-rmse:0.102337+0.006853    test-rmse:0.401299+0.059840 
## [55] train-rmse:0.099638+0.006664    test-rmse:0.400870+0.059121 
## [56] train-rmse:0.098188+0.007018    test-rmse:0.400485+0.059208 
## [57] train-rmse:0.096364+0.007095    test-rmse:0.399776+0.059813 
## [58] train-rmse:0.094091+0.006705    test-rmse:0.399760+0.059531 
## [59] train-rmse:0.092821+0.006684    test-rmse:0.399930+0.059583 
## [60] train-rmse:0.091594+0.006742    test-rmse:0.399940+0.060161 
## [61] train-rmse:0.090193+0.006655    test-rmse:0.399707+0.060314 
## [62] train-rmse:0.089010+0.006522    test-rmse:0.399967+0.060293 
## [63] train-rmse:0.087721+0.006380    test-rmse:0.400195+0.060166 
## [64] train-rmse:0.086768+0.006548    test-rmse:0.399971+0.060575 
## [65] train-rmse:0.085110+0.006218    test-rmse:0.400160+0.060524 
## [66] train-rmse:0.083992+0.006225    test-rmse:0.400208+0.060552 
## [67] train-rmse:0.082837+0.006249    test-rmse:0.400133+0.060532 
## Stopping. Best iteration:
## [61] train-rmse:0.090193+0.006655    test-rmse:0.399707+0.060314
## 
## 20 
## [1]  train-rmse:1.431311+0.026373    test-rmse:1.431290+0.114119 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.254247+0.022994    test-rmse:1.253084+0.111212 
## [3]  train-rmse:1.101840+0.021041    test-rmse:1.099563+0.104565 
## [4]  train-rmse:0.970610+0.018621    test-rmse:0.973054+0.101683 
## [5]  train-rmse:0.857314+0.017254    test-rmse:0.868374+0.102051 
## [6]  train-rmse:0.760002+0.015432    test-rmse:0.779161+0.099524 
## [7]  train-rmse:0.675295+0.013758    test-rmse:0.703200+0.096560 
## [8]  train-rmse:0.602618+0.012779    test-rmse:0.640433+0.095330 
## [9]  train-rmse:0.539930+0.011843    test-rmse:0.591064+0.093534 
## [10] train-rmse:0.486067+0.011829    test-rmse:0.550802+0.090252 
## [11] train-rmse:0.439396+0.011002    test-rmse:0.518690+0.089152 
## [12] train-rmse:0.398487+0.010565    test-rmse:0.490283+0.087083 
## [13] train-rmse:0.363605+0.009689    test-rmse:0.469764+0.084158 
## [14] train-rmse:0.333556+0.008925    test-rmse:0.455831+0.083634 
## [15] train-rmse:0.307676+0.008429    test-rmse:0.446245+0.083119 
## [16] train-rmse:0.283976+0.009958    test-rmse:0.435997+0.080999 
## [17] train-rmse:0.263034+0.011097    test-rmse:0.430197+0.078781 
## [18] train-rmse:0.244514+0.011349    test-rmse:0.424942+0.075993 
## [19] train-rmse:0.229248+0.011475    test-rmse:0.420971+0.073177 
## [20] train-rmse:0.215853+0.011898    test-rmse:0.417582+0.071782 
## [21] train-rmse:0.204128+0.011536    test-rmse:0.417158+0.071330 
## [22] train-rmse:0.192884+0.011462    test-rmse:0.414751+0.069277 
## [23] train-rmse:0.183663+0.012424    test-rmse:0.412747+0.068785 
## [24] train-rmse:0.175140+0.012697    test-rmse:0.411745+0.066784 
## [25] train-rmse:0.168435+0.013125    test-rmse:0.411014+0.066067 
## [26] train-rmse:0.161211+0.012029    test-rmse:0.410914+0.064612 
## [27] train-rmse:0.156039+0.011758    test-rmse:0.410695+0.062523 
## [28] train-rmse:0.150499+0.010912    test-rmse:0.410382+0.062046 
## [29] train-rmse:0.145779+0.011028    test-rmse:0.409482+0.061135 
## [30] train-rmse:0.141601+0.011890    test-rmse:0.408959+0.060014 
## [31] train-rmse:0.137313+0.011712    test-rmse:0.408420+0.059059 
## [32] train-rmse:0.132871+0.011039    test-rmse:0.407237+0.058651 
## [33] train-rmse:0.129407+0.010921    test-rmse:0.406192+0.058002 
## [34] train-rmse:0.126611+0.010647    test-rmse:0.405170+0.057176 
## [35] train-rmse:0.122443+0.008999    test-rmse:0.404393+0.056788 
## [36] train-rmse:0.119703+0.008218    test-rmse:0.403272+0.056571 
## [37] train-rmse:0.116427+0.009080    test-rmse:0.402711+0.056619 
## [38] train-rmse:0.112660+0.008871    test-rmse:0.402404+0.055849 
## [39] train-rmse:0.110695+0.008990    test-rmse:0.401791+0.055447 
## [40] train-rmse:0.107521+0.008307    test-rmse:0.401204+0.055775 
## [41] train-rmse:0.104205+0.008578    test-rmse:0.400706+0.054390 
## [42] train-rmse:0.100827+0.009062    test-rmse:0.400980+0.053866 
## [43] train-rmse:0.098095+0.009759    test-rmse:0.400566+0.053588 
## [44] train-rmse:0.094997+0.009653    test-rmse:0.400612+0.053436 
## [45] train-rmse:0.091303+0.009229    test-rmse:0.400192+0.053287 
## [46] train-rmse:0.089194+0.009762    test-rmse:0.399361+0.052802 
## [47] train-rmse:0.087078+0.009507    test-rmse:0.399277+0.052090 
## [48] train-rmse:0.084594+0.009300    test-rmse:0.399561+0.051603 
## [49] train-rmse:0.082696+0.009004    test-rmse:0.399519+0.051379 
## [50] train-rmse:0.080039+0.009191    test-rmse:0.399742+0.051306 
## [51] train-rmse:0.077887+0.008979    test-rmse:0.399885+0.051507 
## [52] train-rmse:0.075839+0.009322    test-rmse:0.399579+0.051349 
## [53] train-rmse:0.073765+0.009063    test-rmse:0.399404+0.051148 
## Stopping. Best iteration:
## [47] train-rmse:0.087078+0.009507    test-rmse:0.399277+0.052090
## 
## 21 
## [1]  train-rmse:1.421522+0.026434    test-rmse:1.417655+0.112425 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.246253+0.023459    test-rmse:1.242440+0.108299 
## [3]  train-rmse:1.105462+0.021274    test-rmse:1.101724+0.104665 
## [4]  train-rmse:0.993775+0.019772    test-rmse:0.990143+0.101374 
## [5]  train-rmse:0.906402+0.018825    test-rmse:0.902915+0.098372 
## [6]  train-rmse:0.839045+0.018292    test-rmse:0.835736+0.095662 
## [7]  train-rmse:0.787872+0.018041    test-rmse:0.784760+0.093270 
## [8]  train-rmse:0.749523+0.017962    test-rmse:0.746614+0.091212 
## [9]  train-rmse:0.721135+0.017974    test-rmse:0.718420+0.089485 
## [10] train-rmse:0.700168+0.017803    test-rmse:0.701925+0.090005 
## [11] train-rmse:0.682801+0.017308    test-rmse:0.682587+0.088698 
## [12] train-rmse:0.668579+0.017098    test-rmse:0.670424+0.087985 
## [13] train-rmse:0.656445+0.016804    test-rmse:0.657788+0.086971 
## [14] train-rmse:0.646201+0.016130    test-rmse:0.650020+0.084986 
## [15] train-rmse:0.637042+0.015557    test-rmse:0.637290+0.083881 
## [16] train-rmse:0.628824+0.015104    test-rmse:0.631945+0.081023 
## [17] train-rmse:0.621342+0.014461    test-rmse:0.623602+0.077779 
## [18] train-rmse:0.614638+0.013908    test-rmse:0.616229+0.077988 
## [19] train-rmse:0.608516+0.013516    test-rmse:0.613477+0.076654 
## [20] train-rmse:0.602947+0.013234    test-rmse:0.605901+0.076392 
## [21] train-rmse:0.597829+0.013064    test-rmse:0.602040+0.072923 
## [22] train-rmse:0.593053+0.012806    test-rmse:0.598575+0.071168 
## [23] train-rmse:0.588582+0.012427    test-rmse:0.594824+0.070268 
## [24] train-rmse:0.584586+0.012192    test-rmse:0.589927+0.070942 
## [25] train-rmse:0.580743+0.012033    test-rmse:0.586878+0.068777 
## [26] train-rmse:0.577181+0.011942    test-rmse:0.585547+0.068349 
## [27] train-rmse:0.573714+0.011872    test-rmse:0.582818+0.066379 
## [28] train-rmse:0.570454+0.011691    test-rmse:0.580706+0.065641 
## [29] train-rmse:0.567519+0.011515    test-rmse:0.576771+0.065461 
## [30] train-rmse:0.564655+0.011410    test-rmse:0.574292+0.066246 
## [31] train-rmse:0.561908+0.011249    test-rmse:0.572339+0.063938 
## [32] train-rmse:0.559359+0.011110    test-rmse:0.571258+0.065007 
## [33] train-rmse:0.556933+0.011018    test-rmse:0.568846+0.063839 
## [34] train-rmse:0.554589+0.010970    test-rmse:0.569023+0.063231 
## [35] train-rmse:0.552385+0.010859    test-rmse:0.565853+0.062470 
## [36] train-rmse:0.550315+0.010766    test-rmse:0.563638+0.062785 
## [37] train-rmse:0.548383+0.010722    test-rmse:0.562058+0.062259 
## [38] train-rmse:0.546485+0.010624    test-rmse:0.562040+0.061159 
## [39] train-rmse:0.544645+0.010555    test-rmse:0.561527+0.061309 
## [40] train-rmse:0.542827+0.010482    test-rmse:0.560495+0.060700 
## [41] train-rmse:0.541113+0.010411    test-rmse:0.559286+0.059364 
## [42] train-rmse:0.539460+0.010373    test-rmse:0.558161+0.059944 
## [43] train-rmse:0.537869+0.010308    test-rmse:0.555956+0.059542 
## [44] train-rmse:0.536347+0.010182    test-rmse:0.553727+0.058647 
## [45] train-rmse:0.534889+0.010111    test-rmse:0.553843+0.058867 
## [46] train-rmse:0.533392+0.010035    test-rmse:0.553929+0.058387 
## [47] train-rmse:0.531955+0.009963    test-rmse:0.553160+0.059037 
## [48] train-rmse:0.530569+0.009893    test-rmse:0.552380+0.058684 
## [49] train-rmse:0.529215+0.009837    test-rmse:0.551527+0.059111 
## [50] train-rmse:0.527920+0.009793    test-rmse:0.549997+0.058440 
## [51] train-rmse:0.526618+0.009762    test-rmse:0.549412+0.058318 
## [52] train-rmse:0.525362+0.009655    test-rmse:0.548759+0.057734 
## [53] train-rmse:0.524132+0.009581    test-rmse:0.547555+0.057741 
## [54] train-rmse:0.522941+0.009500    test-rmse:0.546858+0.057213 
## [55] train-rmse:0.521780+0.009419    test-rmse:0.546308+0.057510 
## [56] train-rmse:0.520670+0.009342    test-rmse:0.544675+0.057468 
## [57] train-rmse:0.519516+0.009287    test-rmse:0.544436+0.056928 
## [58] train-rmse:0.518402+0.009225    test-rmse:0.543858+0.057330 
## [59] train-rmse:0.517318+0.009175    test-rmse:0.543780+0.056885 
## [60] train-rmse:0.516272+0.009139    test-rmse:0.543462+0.056155 
## [61] train-rmse:0.515246+0.009100    test-rmse:0.542140+0.055941 
## [62] train-rmse:0.514212+0.009057    test-rmse:0.541128+0.056214 
## [63] train-rmse:0.513208+0.008994    test-rmse:0.540361+0.055956 
## [64] train-rmse:0.512218+0.008948    test-rmse:0.539710+0.056339 
## [65] train-rmse:0.511233+0.008907    test-rmse:0.539531+0.056383 
## [66] train-rmse:0.510262+0.008873    test-rmse:0.539070+0.055917 
## [67] train-rmse:0.509303+0.008854    test-rmse:0.536709+0.055188 
## [68] train-rmse:0.508375+0.008813    test-rmse:0.536405+0.055069 
## [69] train-rmse:0.507462+0.008765    test-rmse:0.535353+0.054413 
## [70] train-rmse:0.506565+0.008728    test-rmse:0.534961+0.055166 
## [71] train-rmse:0.505637+0.008668    test-rmse:0.534284+0.054909 
## [72] train-rmse:0.504780+0.008657    test-rmse:0.533698+0.054837 
## [73] train-rmse:0.503937+0.008635    test-rmse:0.532340+0.054629 
## [74] train-rmse:0.503095+0.008612    test-rmse:0.532244+0.054368 
## [75] train-rmse:0.502272+0.008589    test-rmse:0.530818+0.054445 
## [76] train-rmse:0.501429+0.008552    test-rmse:0.531156+0.054770 
## [77] train-rmse:0.500620+0.008526    test-rmse:0.531204+0.054740 
## [78] train-rmse:0.499806+0.008498    test-rmse:0.531041+0.054023 
## [79] train-rmse:0.499017+0.008483    test-rmse:0.529706+0.053569 
## [80] train-rmse:0.498239+0.008461    test-rmse:0.529346+0.053878 
## [81] train-rmse:0.497475+0.008458    test-rmse:0.528906+0.054090 
## [82] train-rmse:0.496734+0.008443    test-rmse:0.529023+0.053941 
## [83] train-rmse:0.495996+0.008420    test-rmse:0.528739+0.053681 
## [84] train-rmse:0.495257+0.008385    test-rmse:0.527798+0.053258 
## [85] train-rmse:0.494520+0.008364    test-rmse:0.527034+0.052889 
## [86] train-rmse:0.493763+0.008335    test-rmse:0.525757+0.053189 
## [87] train-rmse:0.493086+0.008325    test-rmse:0.525782+0.053388 
## [88] train-rmse:0.492395+0.008303    test-rmse:0.525879+0.053577 
## [89] train-rmse:0.491711+0.008288    test-rmse:0.525199+0.053230 
## [90] train-rmse:0.491032+0.008269    test-rmse:0.524546+0.053157 
## [91] train-rmse:0.490374+0.008265    test-rmse:0.524052+0.052838 
## [92] train-rmse:0.489708+0.008260    test-rmse:0.523899+0.052930 
## [93] train-rmse:0.489069+0.008227    test-rmse:0.523041+0.051984 
## [94] train-rmse:0.488433+0.008204    test-rmse:0.522829+0.051727 
## [95] train-rmse:0.487790+0.008194    test-rmse:0.522568+0.050827 
## [96] train-rmse:0.487138+0.008191    test-rmse:0.522835+0.051208 
## [97] train-rmse:0.486523+0.008181    test-rmse:0.523079+0.051443 
## [98] train-rmse:0.485911+0.008166    test-rmse:0.522874+0.051459 
## [99] train-rmse:0.485320+0.008146    test-rmse:0.521869+0.051480 
## [100]    train-rmse:0.484735+0.008121    test-rmse:0.521466+0.051179 
## [101]    train-rmse:0.484140+0.008106    test-rmse:0.520826+0.051333 
## [102]    train-rmse:0.483544+0.008085    test-rmse:0.520248+0.051680 
## [103]    train-rmse:0.482958+0.008088    test-rmse:0.519859+0.050781 
## [104]    train-rmse:0.482396+0.008086    test-rmse:0.519698+0.051378 
## [105]    train-rmse:0.481836+0.008048    test-rmse:0.518866+0.051722 
## [106]    train-rmse:0.481286+0.008018    test-rmse:0.518762+0.051435 
## [107]    train-rmse:0.480752+0.008009    test-rmse:0.518621+0.051489 
## [108]    train-rmse:0.480218+0.008001    test-rmse:0.518355+0.050878 
## [109]    train-rmse:0.479679+0.008002    test-rmse:0.517901+0.050141 
## [110]    train-rmse:0.479146+0.007992    test-rmse:0.517606+0.050288 
## [111]    train-rmse:0.478633+0.007983    test-rmse:0.517769+0.050516 
## [112]    train-rmse:0.478128+0.007975    test-rmse:0.517929+0.050317 
## [113]    train-rmse:0.477615+0.007960    test-rmse:0.517572+0.050213 
## [114]    train-rmse:0.477119+0.007940    test-rmse:0.517493+0.049712 
## [115]    train-rmse:0.476624+0.007927    test-rmse:0.516595+0.049435 
## [116]    train-rmse:0.476121+0.007914    test-rmse:0.516352+0.049553 
## [117]    train-rmse:0.475632+0.007916    test-rmse:0.516523+0.049631 
## [118]    train-rmse:0.475151+0.007915    test-rmse:0.515089+0.048469 
## [119]    train-rmse:0.474661+0.007896    test-rmse:0.514612+0.048274 
## [120]    train-rmse:0.474183+0.007888    test-rmse:0.515501+0.048052 
## [121]    train-rmse:0.473725+0.007880    test-rmse:0.514953+0.047847 
## [122]    train-rmse:0.473266+0.007879    test-rmse:0.514253+0.047690 
## [123]    train-rmse:0.472806+0.007873    test-rmse:0.513809+0.047923 
## [124]    train-rmse:0.472349+0.007867    test-rmse:0.513681+0.047983 
## [125]    train-rmse:0.471904+0.007862    test-rmse:0.513276+0.048433 
## [126]    train-rmse:0.471471+0.007859    test-rmse:0.512375+0.048244 
## [127]    train-rmse:0.471056+0.007848    test-rmse:0.511866+0.048106 
## [128]    train-rmse:0.470616+0.007831    test-rmse:0.511614+0.047937 
## [129]    train-rmse:0.470161+0.007832    test-rmse:0.512392+0.047807 
## [130]    train-rmse:0.469740+0.007822    test-rmse:0.512018+0.047743 
## [131]    train-rmse:0.469319+0.007807    test-rmse:0.511769+0.047706 
## [132]    train-rmse:0.468906+0.007791    test-rmse:0.510971+0.047475 
## [133]    train-rmse:0.468494+0.007775    test-rmse:0.510763+0.047237 
## [134]    train-rmse:0.468085+0.007757    test-rmse:0.510373+0.047348 
## [135]    train-rmse:0.467692+0.007749    test-rmse:0.510153+0.046860 
## [136]    train-rmse:0.467288+0.007746    test-rmse:0.510136+0.047078 
## [137]    train-rmse:0.466886+0.007740    test-rmse:0.509822+0.047372 
## [138]    train-rmse:0.466490+0.007743    test-rmse:0.509502+0.047265 
## [139]    train-rmse:0.466095+0.007731    test-rmse:0.509379+0.047653 
## [140]    train-rmse:0.465712+0.007720    test-rmse:0.509289+0.047282 
## [141]    train-rmse:0.465329+0.007706    test-rmse:0.508494+0.045856 
## [142]    train-rmse:0.464947+0.007700    test-rmse:0.508533+0.046211 
## [143]    train-rmse:0.464569+0.007696    test-rmse:0.507964+0.046515 
## [144]    train-rmse:0.464197+0.007693    test-rmse:0.507420+0.046201 
## [145]    train-rmse:0.463821+0.007692    test-rmse:0.507358+0.046201 
## [146]    train-rmse:0.463460+0.007684    test-rmse:0.507297+0.046388 
## [147]    train-rmse:0.463102+0.007677    test-rmse:0.507141+0.045910 
## [148]    train-rmse:0.462743+0.007674    test-rmse:0.507489+0.046039 
## [149]    train-rmse:0.462390+0.007663    test-rmse:0.507550+0.046520 
## [150]    train-rmse:0.462037+0.007654    test-rmse:0.507320+0.046399 
## [151]    train-rmse:0.461679+0.007643    test-rmse:0.507117+0.045703 
## [152]    train-rmse:0.461323+0.007633    test-rmse:0.506633+0.045636 
## [153]    train-rmse:0.460980+0.007625    test-rmse:0.506925+0.045340 
## [154]    train-rmse:0.460640+0.007620    test-rmse:0.507081+0.045073 
## [155]    train-rmse:0.460307+0.007616    test-rmse:0.506672+0.045256 
## [156]    train-rmse:0.459970+0.007608    test-rmse:0.506500+0.045194 
## [157]    train-rmse:0.459631+0.007600    test-rmse:0.506138+0.045417 
## [158]    train-rmse:0.459297+0.007597    test-rmse:0.506370+0.045077 
## [159]    train-rmse:0.458965+0.007583    test-rmse:0.506090+0.044986 
## [160]    train-rmse:0.458643+0.007572    test-rmse:0.505865+0.044568 
## [161]    train-rmse:0.458314+0.007569    test-rmse:0.505865+0.044584 
## [162]    train-rmse:0.458000+0.007561    test-rmse:0.505520+0.044468 
## [163]    train-rmse:0.457686+0.007560    test-rmse:0.505207+0.044461 
## [164]    train-rmse:0.457374+0.007555    test-rmse:0.505123+0.044342 
## [165]    train-rmse:0.457058+0.007556    test-rmse:0.505179+0.044878 
## [166]    train-rmse:0.456753+0.007554    test-rmse:0.504783+0.044743 
## [167]    train-rmse:0.456437+0.007552    test-rmse:0.505032+0.044832 
## [168]    train-rmse:0.456129+0.007549    test-rmse:0.504729+0.044778 
## [169]    train-rmse:0.455824+0.007547    test-rmse:0.504086+0.043600 
## [170]    train-rmse:0.455530+0.007541    test-rmse:0.504295+0.043498 
## [171]    train-rmse:0.455236+0.007538    test-rmse:0.504496+0.043509 
## [172]    train-rmse:0.454934+0.007533    test-rmse:0.503928+0.043605 
## [173]    train-rmse:0.454650+0.007527    test-rmse:0.503667+0.043919 
## [174]    train-rmse:0.454366+0.007523    test-rmse:0.503695+0.043754 
## [175]    train-rmse:0.454084+0.007519    test-rmse:0.503415+0.043802 
## [176]    train-rmse:0.453804+0.007512    test-rmse:0.503678+0.043498 
## [177]    train-rmse:0.453520+0.007509    test-rmse:0.503310+0.043150 
## [178]    train-rmse:0.453242+0.007508    test-rmse:0.503539+0.042597 
## [179]    train-rmse:0.452967+0.007506    test-rmse:0.503537+0.042951 
## [180]    train-rmse:0.452681+0.007509    test-rmse:0.503114+0.042965 
## [181]    train-rmse:0.452413+0.007505    test-rmse:0.503283+0.043200 
## [182]    train-rmse:0.452138+0.007502    test-rmse:0.503260+0.043290 
## [183]    train-rmse:0.451871+0.007503    test-rmse:0.502497+0.043036 
## [184]    train-rmse:0.451605+0.007507    test-rmse:0.502819+0.043304 
## [185]    train-rmse:0.451341+0.007506    test-rmse:0.502143+0.043132 
## [186]    train-rmse:0.451079+0.007507    test-rmse:0.502156+0.043198 
## [187]    train-rmse:0.450817+0.007508    test-rmse:0.502323+0.042815 
## [188]    train-rmse:0.450556+0.007508    test-rmse:0.502191+0.043165 
## [189]    train-rmse:0.450298+0.007510    test-rmse:0.502463+0.043107 
## [190]    train-rmse:0.450049+0.007514    test-rmse:0.502162+0.042992 
## [191]    train-rmse:0.449795+0.007518    test-rmse:0.502038+0.043020 
## [192]    train-rmse:0.449543+0.007519    test-rmse:0.501871+0.042884 
## [193]    train-rmse:0.449299+0.007520    test-rmse:0.501977+0.042679 
## [194]    train-rmse:0.449052+0.007521    test-rmse:0.501781+0.042494 
## [195]    train-rmse:0.448805+0.007519    test-rmse:0.501946+0.042734 
## [196]    train-rmse:0.448558+0.007516    test-rmse:0.501621+0.042776 
## [197]    train-rmse:0.448319+0.007517    test-rmse:0.501597+0.042511 
## [198]    train-rmse:0.448081+0.007518    test-rmse:0.501671+0.042723 
## [199]    train-rmse:0.447836+0.007520    test-rmse:0.501559+0.042624 
## [200]    train-rmse:0.447603+0.007515    test-rmse:0.501185+0.042591 
## [201]    train-rmse:0.447368+0.007517    test-rmse:0.501221+0.042215 
## [202]    train-rmse:0.447135+0.007520    test-rmse:0.500935+0.042085 
## [203]    train-rmse:0.446906+0.007517    test-rmse:0.500745+0.042093 
## [204]    train-rmse:0.446684+0.007518    test-rmse:0.500575+0.041847 
## [205]    train-rmse:0.446453+0.007519    test-rmse:0.500557+0.041923 
## [206]    train-rmse:0.446229+0.007517    test-rmse:0.500596+0.042026 
## [207]    train-rmse:0.446001+0.007516    test-rmse:0.500681+0.042363 
## [208]    train-rmse:0.445779+0.007515    test-rmse:0.499801+0.041721 
## [209]    train-rmse:0.445558+0.007512    test-rmse:0.499984+0.041494 
## [210]    train-rmse:0.445342+0.007511    test-rmse:0.500141+0.041309 
## [211]    train-rmse:0.445125+0.007511    test-rmse:0.499820+0.041384 
## [212]    train-rmse:0.444904+0.007509    test-rmse:0.499910+0.041460 
## [213]    train-rmse:0.444690+0.007509    test-rmse:0.499440+0.041304 
## [214]    train-rmse:0.444471+0.007515    test-rmse:0.499563+0.040807 
## [215]    train-rmse:0.444257+0.007513    test-rmse:0.499984+0.040789 
## [216]    train-rmse:0.444045+0.007511    test-rmse:0.500019+0.041293 
## [217]    train-rmse:0.443827+0.007512    test-rmse:0.499543+0.040906 
## [218]    train-rmse:0.443619+0.007516    test-rmse:0.499592+0.040930 
## [219]    train-rmse:0.443412+0.007521    test-rmse:0.499485+0.041221 
## Stopping. Best iteration:
## [213]    train-rmse:0.444690+0.007509    test-rmse:0.499440+0.041304
## 
## 22 
## [1]  train-rmse:1.411569+0.026462    test-rmse:1.409784+0.115276 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.224842+0.023183    test-rmse:1.225312+0.113611 
## [3]  train-rmse:1.071115+0.020821    test-rmse:1.073652+0.107674 
## [4]  train-rmse:0.945519+0.018734    test-rmse:0.950754+0.106992 
## [5]  train-rmse:0.843417+0.017133    test-rmse:0.844741+0.102446 
## [6]  train-rmse:0.760933+0.015962    test-rmse:0.768008+0.102190 
## [7]  train-rmse:0.694914+0.014703    test-rmse:0.699622+0.100256 
## [8]  train-rmse:0.642271+0.014225    test-rmse:0.650545+0.097856 
## [9]  train-rmse:0.600369+0.013670    test-rmse:0.607730+0.093223 
## [10] train-rmse:0.567384+0.013042    test-rmse:0.578122+0.088307 
## [11] train-rmse:0.540970+0.012834    test-rmse:0.553166+0.088412 
## [12] train-rmse:0.519762+0.012378    test-rmse:0.536757+0.083691 
## [13] train-rmse:0.502575+0.011808    test-rmse:0.522718+0.079479 
## [14] train-rmse:0.488455+0.011454    test-rmse:0.507337+0.077299 
## [15] train-rmse:0.476809+0.011028    test-rmse:0.499629+0.074176 
## [16] train-rmse:0.465645+0.009632    test-rmse:0.492685+0.073183 
## [17] train-rmse:0.454936+0.009308    test-rmse:0.484031+0.071390 
## [18] train-rmse:0.446587+0.008598    test-rmse:0.476900+0.068773 
## [19] train-rmse:0.438712+0.007525    test-rmse:0.473142+0.068491 
## [20] train-rmse:0.432225+0.007181    test-rmse:0.471047+0.067254 
## [21] train-rmse:0.426337+0.006618    test-rmse:0.469397+0.066655 
## [22] train-rmse:0.420559+0.006235    test-rmse:0.465604+0.065855 
## [23] train-rmse:0.415387+0.006106    test-rmse:0.462461+0.064016 
## [24] train-rmse:0.411026+0.005723    test-rmse:0.458810+0.061598 
## [25] train-rmse:0.406793+0.005572    test-rmse:0.456263+0.060968 
## [26] train-rmse:0.402827+0.005307    test-rmse:0.454869+0.060735 
## [27] train-rmse:0.399357+0.005210    test-rmse:0.453352+0.060266 
## [28] train-rmse:0.395988+0.005197    test-rmse:0.452172+0.059613 
## [29] train-rmse:0.392874+0.005048    test-rmse:0.450866+0.058498 
## [30] train-rmse:0.389097+0.004369    test-rmse:0.450087+0.059778 
## [31] train-rmse:0.385736+0.004255    test-rmse:0.447947+0.059428 
## [32] train-rmse:0.382963+0.004094    test-rmse:0.446381+0.059316 
## [33] train-rmse:0.380018+0.004020    test-rmse:0.446735+0.058524 
## [34] train-rmse:0.377198+0.004151    test-rmse:0.445740+0.057091 
## [35] train-rmse:0.374899+0.004162    test-rmse:0.444422+0.056766 
## [36] train-rmse:0.371837+0.003672    test-rmse:0.444236+0.057896 
## [37] train-rmse:0.369645+0.003599    test-rmse:0.444873+0.056776 
## [38] train-rmse:0.367321+0.003650    test-rmse:0.443744+0.056866 
## [39] train-rmse:0.365323+0.003757    test-rmse:0.443785+0.055936 
## [40] train-rmse:0.362456+0.003585    test-rmse:0.443844+0.055626 
## [41] train-rmse:0.360678+0.003615    test-rmse:0.443649+0.055262 
## [42] train-rmse:0.358653+0.003454    test-rmse:0.443288+0.055225 
## [43] train-rmse:0.356604+0.003833    test-rmse:0.443493+0.054556 
## [44] train-rmse:0.354545+0.003857    test-rmse:0.444057+0.055454 
## [45] train-rmse:0.352795+0.004112    test-rmse:0.443244+0.054620 
## [46] train-rmse:0.350924+0.004067    test-rmse:0.442233+0.054820 
## [47] train-rmse:0.348843+0.004187    test-rmse:0.441415+0.054329 
## [48] train-rmse:0.347094+0.004188    test-rmse:0.441712+0.054700 
## [49] train-rmse:0.345103+0.003693    test-rmse:0.441684+0.055013 
## [50] train-rmse:0.343567+0.003552    test-rmse:0.441081+0.054734 
## [51] train-rmse:0.342041+0.003505    test-rmse:0.441475+0.054966 
## [52] train-rmse:0.340653+0.003766    test-rmse:0.440689+0.054805 
## [53] train-rmse:0.339366+0.003969    test-rmse:0.440117+0.054862 
## [54] train-rmse:0.337420+0.004099    test-rmse:0.440204+0.053808 
## [55] train-rmse:0.335931+0.004124    test-rmse:0.440278+0.053552 
## [56] train-rmse:0.334307+0.004037    test-rmse:0.439953+0.054293 
## [57] train-rmse:0.332317+0.003504    test-rmse:0.439167+0.054663 
## [58] train-rmse:0.331104+0.003353    test-rmse:0.438640+0.054379 
## [59] train-rmse:0.329530+0.003709    test-rmse:0.438390+0.054503 
## [60] train-rmse:0.327997+0.004029    test-rmse:0.438120+0.053574 
## [61] train-rmse:0.326790+0.004042    test-rmse:0.437966+0.054385 
## [62] train-rmse:0.325589+0.004176    test-rmse:0.437866+0.055203 
## [63] train-rmse:0.323619+0.004046    test-rmse:0.437399+0.054797 
## [64] train-rmse:0.322256+0.004050    test-rmse:0.436898+0.054762 
## [65] train-rmse:0.320818+0.003814    test-rmse:0.436008+0.054870 
## [66] train-rmse:0.319614+0.004184    test-rmse:0.436890+0.054626 
## [67] train-rmse:0.318555+0.004129    test-rmse:0.435757+0.056248 
## [68] train-rmse:0.317083+0.004481    test-rmse:0.434785+0.054440 
## [69] train-rmse:0.315958+0.004607    test-rmse:0.434572+0.054807 
## [70] train-rmse:0.314863+0.004907    test-rmse:0.433474+0.054966 
## [71] train-rmse:0.313789+0.005261    test-rmse:0.432906+0.054840 
## [72] train-rmse:0.312684+0.005239    test-rmse:0.432774+0.054484 
## [73] train-rmse:0.311560+0.005564    test-rmse:0.432514+0.054271 
## [74] train-rmse:0.310237+0.005232    test-rmse:0.432668+0.054606 
## [75] train-rmse:0.309335+0.005329    test-rmse:0.432524+0.054781 
## [76] train-rmse:0.307605+0.005405    test-rmse:0.432229+0.055261 
## [77] train-rmse:0.306629+0.005599    test-rmse:0.432379+0.054784 
## [78] train-rmse:0.304628+0.005182    test-rmse:0.431624+0.054810 
## [79] train-rmse:0.303808+0.005144    test-rmse:0.430853+0.055365 
## [80] train-rmse:0.302778+0.005159    test-rmse:0.430612+0.055664 
## [81] train-rmse:0.301612+0.005010    test-rmse:0.430794+0.055802 
## [82] train-rmse:0.300384+0.005145    test-rmse:0.430389+0.055557 
## [83] train-rmse:0.299566+0.004923    test-rmse:0.430169+0.055605 
## [84] train-rmse:0.298403+0.004801    test-rmse:0.429619+0.056269 
## [85] train-rmse:0.297277+0.005422    test-rmse:0.429848+0.056587 
## [86] train-rmse:0.296473+0.005733    test-rmse:0.429290+0.056464 
## [87] train-rmse:0.295823+0.005787    test-rmse:0.429143+0.056742 
## [88] train-rmse:0.295001+0.005702    test-rmse:0.429046+0.057070 
## [89] train-rmse:0.293968+0.006436    test-rmse:0.429195+0.057288 
## [90] train-rmse:0.292847+0.006415    test-rmse:0.428609+0.057481 
## [91] train-rmse:0.292139+0.006738    test-rmse:0.428170+0.057461 
## [92] train-rmse:0.291372+0.006628    test-rmse:0.427976+0.057127 
## [93] train-rmse:0.290772+0.006541    test-rmse:0.427782+0.057249 
## [94] train-rmse:0.289090+0.005872    test-rmse:0.427881+0.056937 
## [95] train-rmse:0.287844+0.005681    test-rmse:0.427158+0.057061 
## [96] train-rmse:0.286843+0.005905    test-rmse:0.427253+0.057106 
## [97] train-rmse:0.286208+0.005931    test-rmse:0.426474+0.056937 
## [98] train-rmse:0.285406+0.006092    test-rmse:0.426425+0.056904 
## [99] train-rmse:0.284804+0.006021    test-rmse:0.425713+0.057764 
## [100]    train-rmse:0.284035+0.006114    test-rmse:0.425551+0.057746 
## [101]    train-rmse:0.283349+0.006136    test-rmse:0.425709+0.057690 
## [102]    train-rmse:0.282259+0.006453    test-rmse:0.425789+0.057972 
## [103]    train-rmse:0.281135+0.006306    test-rmse:0.425683+0.057973 
## [104]    train-rmse:0.280413+0.006592    test-rmse:0.425898+0.057675 
## [105]    train-rmse:0.279892+0.006632    test-rmse:0.425931+0.057703 
## [106]    train-rmse:0.279280+0.006566    test-rmse:0.425243+0.058113 
## [107]    train-rmse:0.278624+0.006512    test-rmse:0.425401+0.058448 
## [108]    train-rmse:0.278070+0.006591    test-rmse:0.424936+0.058483 
## [109]    train-rmse:0.277612+0.006618    test-rmse:0.424082+0.058402 
## [110]    train-rmse:0.276589+0.006490    test-rmse:0.423572+0.058104 
## [111]    train-rmse:0.275751+0.006296    test-rmse:0.423582+0.057948 
## [112]    train-rmse:0.274894+0.006352    test-rmse:0.423440+0.057961 
## [113]    train-rmse:0.274100+0.006378    test-rmse:0.423292+0.058149 
## [114]    train-rmse:0.272638+0.007469    test-rmse:0.422943+0.058046 
## [115]    train-rmse:0.272058+0.007345    test-rmse:0.422829+0.058360 
## [116]    train-rmse:0.271371+0.007335    test-rmse:0.422941+0.058228 
## [117]    train-rmse:0.270637+0.007437    test-rmse:0.422684+0.057884 
## [118]    train-rmse:0.268994+0.007291    test-rmse:0.422852+0.058127 
## [119]    train-rmse:0.268103+0.007390    test-rmse:0.422287+0.058223 
## [120]    train-rmse:0.267305+0.007314    test-rmse:0.422031+0.058928 
## [121]    train-rmse:0.266491+0.006962    test-rmse:0.421591+0.058921 
## [122]    train-rmse:0.265597+0.007453    test-rmse:0.421135+0.058416 
## [123]    train-rmse:0.264644+0.007154    test-rmse:0.420607+0.058780 
## [124]    train-rmse:0.264118+0.007323    test-rmse:0.420912+0.058529 
## [125]    train-rmse:0.263408+0.007228    test-rmse:0.420563+0.059180 
## [126]    train-rmse:0.262874+0.007105    test-rmse:0.420586+0.059014 
## [127]    train-rmse:0.262108+0.007081    test-rmse:0.420325+0.059367 
## [128]    train-rmse:0.261080+0.007459    test-rmse:0.419884+0.059054 
## [129]    train-rmse:0.260362+0.007362    test-rmse:0.418999+0.059467 
## [130]    train-rmse:0.259569+0.007314    test-rmse:0.419100+0.059638 
## [131]    train-rmse:0.258599+0.008154    test-rmse:0.419124+0.059965 
## [132]    train-rmse:0.258063+0.008449    test-rmse:0.419129+0.059872 
## [133]    train-rmse:0.257205+0.008279    test-rmse:0.419311+0.059821 
## [134]    train-rmse:0.256752+0.008219    test-rmse:0.419269+0.059668 
## [135]    train-rmse:0.256115+0.008151    test-rmse:0.419551+0.059602 
## Stopping. Best iteration:
## [129]    train-rmse:0.260362+0.007362    test-rmse:0.418999+0.059467
## 
## 23 
## [1]  train-rmse:1.406367+0.025903    test-rmse:1.407126+0.117133 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.214237+0.022612    test-rmse:1.218105+0.112915 
## [3]  train-rmse:1.053782+0.019637    test-rmse:1.060026+0.113289 
## [4]  train-rmse:0.921221+0.017093    test-rmse:0.926332+0.112516 
## [5]  train-rmse:0.811697+0.014771    test-rmse:0.821568+0.112665 
## [6]  train-rmse:0.720812+0.014516    test-rmse:0.732544+0.107288 
## [7]  train-rmse:0.646157+0.012868    test-rmse:0.664830+0.102356 
## [8]  train-rmse:0.586628+0.011605    test-rmse:0.613767+0.099424 
## [9]  train-rmse:0.537642+0.010277    test-rmse:0.569479+0.095038 
## [10] train-rmse:0.497205+0.010840    test-rmse:0.537109+0.089081 
## [11] train-rmse:0.465308+0.010184    test-rmse:0.511094+0.085936 
## [12] train-rmse:0.439044+0.010209    test-rmse:0.490042+0.080312 
## [13] train-rmse:0.418375+0.009452    test-rmse:0.474058+0.079261 
## [14] train-rmse:0.401163+0.008867    test-rmse:0.465752+0.076684 
## [15] train-rmse:0.386606+0.008642    test-rmse:0.458778+0.074868 
## [16] train-rmse:0.375367+0.009033    test-rmse:0.452822+0.070538 
## [17] train-rmse:0.365959+0.009382    test-rmse:0.447828+0.068326 
## [18] train-rmse:0.357585+0.009554    test-rmse:0.444542+0.067320 
## [19] train-rmse:0.350635+0.009391    test-rmse:0.441807+0.064276 
## [20] train-rmse:0.344868+0.009375    test-rmse:0.438760+0.064341 
## [21] train-rmse:0.338300+0.009383    test-rmse:0.436427+0.063985 
## [22] train-rmse:0.332959+0.009535    test-rmse:0.436253+0.063450 
## [23] train-rmse:0.327985+0.009730    test-rmse:0.435236+0.061987 
## [24] train-rmse:0.323718+0.009563    test-rmse:0.433784+0.063224 
## [25] train-rmse:0.319507+0.008469    test-rmse:0.433366+0.061327 
## [26] train-rmse:0.315435+0.008673    test-rmse:0.432936+0.061036 
## [27] train-rmse:0.311664+0.008882    test-rmse:0.433028+0.060547 
## [28] train-rmse:0.308353+0.008145    test-rmse:0.432474+0.060519 
## [29] train-rmse:0.303960+0.007287    test-rmse:0.431448+0.060339 
## [30] train-rmse:0.300710+0.007074    test-rmse:0.431273+0.060004 
## [31] train-rmse:0.297504+0.006877    test-rmse:0.430791+0.060605 
## [32] train-rmse:0.294458+0.006335    test-rmse:0.430144+0.060950 
## [33] train-rmse:0.291623+0.006482    test-rmse:0.429826+0.059857 
## [34] train-rmse:0.289445+0.006409    test-rmse:0.430048+0.059883 
## [35] train-rmse:0.285762+0.006713    test-rmse:0.429591+0.059440 
## [36] train-rmse:0.283398+0.006636    test-rmse:0.428732+0.058668 
## [37] train-rmse:0.280879+0.006191    test-rmse:0.428823+0.058748 
## [38] train-rmse:0.277981+0.005983    test-rmse:0.428705+0.057783 
## [39] train-rmse:0.275709+0.005683    test-rmse:0.428066+0.058584 
## [40] train-rmse:0.273392+0.005692    test-rmse:0.427989+0.059283 
## [41] train-rmse:0.270475+0.005786    test-rmse:0.427835+0.059432 
## [42] train-rmse:0.268100+0.005729    test-rmse:0.427330+0.059934 
## [43] train-rmse:0.265990+0.005439    test-rmse:0.426434+0.059739 
## [44] train-rmse:0.264184+0.005256    test-rmse:0.425669+0.060084 
## [45] train-rmse:0.262541+0.005237    test-rmse:0.425073+0.060287 
## [46] train-rmse:0.260636+0.005247    test-rmse:0.423475+0.060747 
## [47] train-rmse:0.258727+0.004975    test-rmse:0.423078+0.061670 
## [48] train-rmse:0.256405+0.005091    test-rmse:0.424002+0.061839 
## [49] train-rmse:0.254142+0.004913    test-rmse:0.423346+0.062753 
## [50] train-rmse:0.252414+0.004857    test-rmse:0.422135+0.063040 
## [51] train-rmse:0.250902+0.004755    test-rmse:0.422372+0.063203 
## [52] train-rmse:0.249109+0.004673    test-rmse:0.421859+0.063914 
## [53] train-rmse:0.246947+0.003950    test-rmse:0.421716+0.064636 
## [54] train-rmse:0.244720+0.004480    test-rmse:0.422074+0.064467 
## [55] train-rmse:0.242763+0.004760    test-rmse:0.421887+0.064950 
## [56] train-rmse:0.241412+0.004704    test-rmse:0.422248+0.064711 
## [57] train-rmse:0.239919+0.004606    test-rmse:0.422297+0.065063 
## [58] train-rmse:0.237549+0.005245    test-rmse:0.421035+0.064521 
## [59] train-rmse:0.235628+0.005748    test-rmse:0.421070+0.064491 
## [60] train-rmse:0.234105+0.005657    test-rmse:0.419550+0.066001 
## [61] train-rmse:0.232620+0.005457    test-rmse:0.419058+0.065545 
## [62] train-rmse:0.231408+0.005320    test-rmse:0.418545+0.065678 
## [63] train-rmse:0.229917+0.005559    test-rmse:0.417923+0.066136 
## [64] train-rmse:0.228758+0.005272    test-rmse:0.418411+0.066273 
## [65] train-rmse:0.227698+0.005165    test-rmse:0.417517+0.066693 
## [66] train-rmse:0.225673+0.005029    test-rmse:0.416841+0.066504 
## [67] train-rmse:0.224487+0.004925    test-rmse:0.416497+0.066178 
## [68] train-rmse:0.223111+0.004632    test-rmse:0.416633+0.066564 
## [69] train-rmse:0.221435+0.005003    test-rmse:0.416110+0.065890 
## [70] train-rmse:0.219933+0.005092    test-rmse:0.415933+0.065599 
## [71] train-rmse:0.218969+0.005211    test-rmse:0.415776+0.065603 
## [72] train-rmse:0.217472+0.005825    test-rmse:0.414561+0.065685 
## [73] train-rmse:0.215927+0.005250    test-rmse:0.414379+0.065679 
## [74] train-rmse:0.214289+0.005500    test-rmse:0.414448+0.065735 
## [75] train-rmse:0.212972+0.005215    test-rmse:0.414193+0.066074 
## [76] train-rmse:0.211568+0.005494    test-rmse:0.414297+0.066100 
## [77] train-rmse:0.210429+0.005476    test-rmse:0.413962+0.066370 
## [78] train-rmse:0.209084+0.005477    test-rmse:0.414113+0.065961 
## [79] train-rmse:0.207784+0.005217    test-rmse:0.414463+0.065746 
## [80] train-rmse:0.206601+0.005551    test-rmse:0.414448+0.065634 
## [81] train-rmse:0.205280+0.005691    test-rmse:0.414152+0.065583 
## [82] train-rmse:0.203761+0.005831    test-rmse:0.414674+0.065388 
## [83] train-rmse:0.202753+0.005744    test-rmse:0.414520+0.065876 
## Stopping. Best iteration:
## [77] train-rmse:0.210429+0.005476    test-rmse:0.413962+0.066370
## 
## 24 
## [1]  train-rmse:1.404054+0.025543    test-rmse:1.404469+0.117377 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.209775+0.021932    test-rmse:1.212667+0.115287 
## [3]  train-rmse:1.046088+0.019451    test-rmse:1.049437+0.111208 
## [4]  train-rmse:0.910284+0.017328    test-rmse:0.918566+0.106605 
## [5]  train-rmse:0.796397+0.014331    test-rmse:0.810566+0.105889 
## [6]  train-rmse:0.702308+0.012185    test-rmse:0.721744+0.102439 
## [7]  train-rmse:0.624798+0.010688    test-rmse:0.648464+0.099965 
## [8]  train-rmse:0.561554+0.009054    test-rmse:0.593924+0.094905 
## [9]  train-rmse:0.508909+0.008494    test-rmse:0.553120+0.091156 
## [10] train-rmse:0.464716+0.007271    test-rmse:0.518925+0.086692 
## [11] train-rmse:0.429170+0.006408    test-rmse:0.493479+0.082292 
## [12] train-rmse:0.400586+0.007377    test-rmse:0.474513+0.075354 
## [13] train-rmse:0.377584+0.007511    test-rmse:0.463450+0.072034 
## [14] train-rmse:0.357013+0.008005    test-rmse:0.450009+0.072676 
## [15] train-rmse:0.342144+0.007798    test-rmse:0.443061+0.070397 
## [16] train-rmse:0.329022+0.006609    test-rmse:0.438513+0.068793 
## [17] train-rmse:0.316757+0.006454    test-rmse:0.433378+0.065080 
## [18] train-rmse:0.307562+0.005769    test-rmse:0.430073+0.063508 
## [19] train-rmse:0.299028+0.006861    test-rmse:0.428664+0.063124 
## [20] train-rmse:0.291806+0.005990    test-rmse:0.425638+0.061258 
## [21] train-rmse:0.285648+0.006483    test-rmse:0.424585+0.059855 
## [22] train-rmse:0.279834+0.007065    test-rmse:0.423585+0.058716 
## [23] train-rmse:0.274777+0.006631    test-rmse:0.420610+0.060208 
## [24] train-rmse:0.269881+0.005988    test-rmse:0.418580+0.059170 
## [25] train-rmse:0.265380+0.005997    test-rmse:0.417035+0.059039 
## [26] train-rmse:0.260719+0.005726    test-rmse:0.416400+0.059481 
## [27] train-rmse:0.257016+0.006091    test-rmse:0.415726+0.059999 
## [28] train-rmse:0.252383+0.006547    test-rmse:0.415406+0.060364 
## [29] train-rmse:0.248312+0.004676    test-rmse:0.414105+0.059662 
## [30] train-rmse:0.244843+0.005453    test-rmse:0.412081+0.060693 
## [31] train-rmse:0.240288+0.004395    test-rmse:0.411476+0.061720 
## [32] train-rmse:0.237285+0.004313    test-rmse:0.411145+0.061294 
## [33] train-rmse:0.233303+0.003479    test-rmse:0.410272+0.061470 
## [34] train-rmse:0.229550+0.002861    test-rmse:0.410389+0.061703 
## [35] train-rmse:0.227144+0.002482    test-rmse:0.409356+0.061566 
## [36] train-rmse:0.224098+0.002014    test-rmse:0.408127+0.062390 
## [37] train-rmse:0.220873+0.002121    test-rmse:0.406724+0.061060 
## [38] train-rmse:0.218424+0.002287    test-rmse:0.406826+0.060805 
## [39] train-rmse:0.215894+0.002401    test-rmse:0.406180+0.060751 
## [40] train-rmse:0.213546+0.002409    test-rmse:0.405966+0.060847 
## [41] train-rmse:0.211363+0.002139    test-rmse:0.404493+0.061967 
## [42] train-rmse:0.209488+0.002290    test-rmse:0.404084+0.062472 
## [43] train-rmse:0.207015+0.001859    test-rmse:0.404393+0.062008 
## [44] train-rmse:0.204978+0.002009    test-rmse:0.403957+0.061568 
## [45] train-rmse:0.201769+0.002078    test-rmse:0.404322+0.060688 
## [46] train-rmse:0.199513+0.002138    test-rmse:0.404344+0.061929 
## [47] train-rmse:0.196647+0.002399    test-rmse:0.404041+0.061073 
## [48] train-rmse:0.194594+0.002320    test-rmse:0.403875+0.061101 
## [49] train-rmse:0.192807+0.002521    test-rmse:0.403248+0.061216 
## [50] train-rmse:0.190691+0.001453    test-rmse:0.402755+0.061410 
## [51] train-rmse:0.187805+0.001917    test-rmse:0.401736+0.061479 
## [52] train-rmse:0.186187+0.001942    test-rmse:0.401364+0.061157 
## [53] train-rmse:0.184308+0.001776    test-rmse:0.400401+0.060852 
## [54] train-rmse:0.182423+0.002177    test-rmse:0.400797+0.060905 
## [55] train-rmse:0.181118+0.002146    test-rmse:0.400031+0.061598 
## [56] train-rmse:0.179487+0.002283    test-rmse:0.400056+0.061624 
## [57] train-rmse:0.177962+0.002526    test-rmse:0.400293+0.061137 
## [58] train-rmse:0.175283+0.002441    test-rmse:0.400222+0.060625 
## [59] train-rmse:0.172812+0.002410    test-rmse:0.398767+0.062005 
## [60] train-rmse:0.171152+0.002687    test-rmse:0.398750+0.061203 
## [61] train-rmse:0.169903+0.002506    test-rmse:0.398046+0.062146 
## [62] train-rmse:0.167624+0.002876    test-rmse:0.398078+0.061235 
## [63] train-rmse:0.165491+0.002540    test-rmse:0.397764+0.061875 
## [64] train-rmse:0.164093+0.002331    test-rmse:0.397783+0.061856 
## [65] train-rmse:0.162878+0.002303    test-rmse:0.397647+0.062301 
## [66] train-rmse:0.161008+0.002070    test-rmse:0.397639+0.062429 
## [67] train-rmse:0.159681+0.001947    test-rmse:0.397663+0.062737 
## [68] train-rmse:0.158368+0.001602    test-rmse:0.397234+0.063078 
## [69] train-rmse:0.156823+0.002040    test-rmse:0.397416+0.063166 
## [70] train-rmse:0.155416+0.001561    test-rmse:0.397476+0.062750 
## [71] train-rmse:0.153735+0.002096    test-rmse:0.396758+0.063116 
## [72] train-rmse:0.151870+0.001938    test-rmse:0.396634+0.063169 
## [73] train-rmse:0.150763+0.001747    test-rmse:0.395829+0.063752 
## [74] train-rmse:0.149739+0.001849    test-rmse:0.395894+0.063447 
## [75] train-rmse:0.148426+0.001565    test-rmse:0.395705+0.063677 
## [76] train-rmse:0.147052+0.001620    test-rmse:0.395809+0.063881 
## [77] train-rmse:0.145788+0.002141    test-rmse:0.395895+0.063696 
## [78] train-rmse:0.144370+0.002533    test-rmse:0.396532+0.063798 
## [79] train-rmse:0.143134+0.002862    test-rmse:0.396259+0.064346 
## [80] train-rmse:0.141967+0.003307    test-rmse:0.395847+0.064538 
## [81] train-rmse:0.140262+0.002484    test-rmse:0.395736+0.064464 
## Stopping. Best iteration:
## [75] train-rmse:0.148426+0.001565    test-rmse:0.395705+0.063677
## 
## 25 
## [1]  train-rmse:1.402672+0.025782    test-rmse:1.402980+0.115106 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.206753+0.021964    test-rmse:1.207992+0.112095 
## [3]  train-rmse:1.042323+0.019763    test-rmse:1.044087+0.107376 
## [4]  train-rmse:0.904286+0.017025    test-rmse:0.911831+0.105617 
## [5]  train-rmse:0.788651+0.015410    test-rmse:0.802552+0.100231 
## [6]  train-rmse:0.691650+0.013154    test-rmse:0.714497+0.097961 
## [7]  train-rmse:0.610257+0.012107    test-rmse:0.641045+0.092677 
## [8]  train-rmse:0.542138+0.010632    test-rmse:0.586636+0.087433 
## [9]  train-rmse:0.485171+0.008771    test-rmse:0.542019+0.086179 
## [10] train-rmse:0.439037+0.007548    test-rmse:0.506959+0.081701 
## [11] train-rmse:0.400189+0.006387    test-rmse:0.480541+0.081092 
## [12] train-rmse:0.367059+0.004962    test-rmse:0.459271+0.078060 
## [13] train-rmse:0.340444+0.004517    test-rmse:0.445007+0.076683 
## [14] train-rmse:0.318393+0.004476    test-rmse:0.434698+0.073910 
## [15] train-rmse:0.301064+0.004875    test-rmse:0.428268+0.073222 
## [16] train-rmse:0.282372+0.004326    test-rmse:0.422449+0.070492 
## [17] train-rmse:0.269376+0.004590    test-rmse:0.418349+0.066252 
## [18] train-rmse:0.259469+0.004241    test-rmse:0.416534+0.064778 
## [19] train-rmse:0.250334+0.005252    test-rmse:0.413461+0.065259 
## [20] train-rmse:0.239739+0.003813    test-rmse:0.411861+0.063208 
## [21] train-rmse:0.233953+0.004039    test-rmse:0.410620+0.062384 
## [22] train-rmse:0.227790+0.004752    test-rmse:0.408824+0.063569 
## [23] train-rmse:0.221380+0.005128    test-rmse:0.407716+0.062237 
## [24] train-rmse:0.215313+0.003732    test-rmse:0.406224+0.061037 
## [25] train-rmse:0.209431+0.003713    test-rmse:0.406523+0.060642 
## [26] train-rmse:0.205644+0.003261    test-rmse:0.405008+0.061960 
## [27] train-rmse:0.201559+0.002826    test-rmse:0.404518+0.061284 
## [28] train-rmse:0.197358+0.001650    test-rmse:0.404568+0.060243 
## [29] train-rmse:0.192835+0.002170    test-rmse:0.404721+0.059851 
## [30] train-rmse:0.188702+0.003499    test-rmse:0.405105+0.059521 
## [31] train-rmse:0.184328+0.004516    test-rmse:0.404889+0.059379 
## [32] train-rmse:0.180468+0.004990    test-rmse:0.403861+0.058055 
## [33] train-rmse:0.177527+0.005053    test-rmse:0.403602+0.057320 
## [34] train-rmse:0.175144+0.005447    test-rmse:0.404439+0.057337 
## [35] train-rmse:0.171941+0.005720    test-rmse:0.402463+0.057930 
## [36] train-rmse:0.168310+0.005120    test-rmse:0.402877+0.057865 
## [37] train-rmse:0.165880+0.005455    test-rmse:0.403097+0.057459 
## [38] train-rmse:0.163717+0.004983    test-rmse:0.402720+0.057010 
## [39] train-rmse:0.160889+0.005046    test-rmse:0.403252+0.056899 
## [40] train-rmse:0.158293+0.004920    test-rmse:0.402068+0.057106 
## [41] train-rmse:0.155895+0.003959    test-rmse:0.401397+0.057890 
## [42] train-rmse:0.153903+0.003666    test-rmse:0.401652+0.057072 
## [43] train-rmse:0.151680+0.003244    test-rmse:0.401367+0.056960 
## [44] train-rmse:0.149242+0.003120    test-rmse:0.400753+0.057557 
## [45] train-rmse:0.146833+0.002454    test-rmse:0.400743+0.057976 
## [46] train-rmse:0.144594+0.002042    test-rmse:0.400914+0.057067 
## [47] train-rmse:0.141947+0.001528    test-rmse:0.400139+0.057186 
## [48] train-rmse:0.139655+0.001197    test-rmse:0.399434+0.057240 
## [49] train-rmse:0.138202+0.000947    test-rmse:0.399341+0.057011 
## [50] train-rmse:0.136492+0.001017    test-rmse:0.398675+0.057484 
## [51] train-rmse:0.134647+0.001130    test-rmse:0.398590+0.057531 
## [52] train-rmse:0.132583+0.000549    test-rmse:0.397856+0.057546 
## [53] train-rmse:0.130855+0.000805    test-rmse:0.398260+0.057449 
## [54] train-rmse:0.129186+0.001359    test-rmse:0.397667+0.057339 
## [55] train-rmse:0.127631+0.001336    test-rmse:0.396903+0.057506 
## [56] train-rmse:0.125799+0.001710    test-rmse:0.396722+0.057448 
## [57] train-rmse:0.124419+0.001447    test-rmse:0.396212+0.057539 
## [58] train-rmse:0.121788+0.001234    test-rmse:0.395954+0.057788 
## [59] train-rmse:0.119995+0.001299    test-rmse:0.395823+0.056978 
## [60] train-rmse:0.118499+0.001810    test-rmse:0.395977+0.056948 
## [61] train-rmse:0.116709+0.001623    test-rmse:0.395495+0.057221 
## [62] train-rmse:0.115098+0.001930    test-rmse:0.394962+0.057745 
## [63] train-rmse:0.113756+0.001839    test-rmse:0.394875+0.057931 
## [64] train-rmse:0.112136+0.001973    test-rmse:0.394819+0.058136 
## [65] train-rmse:0.110726+0.001938    test-rmse:0.394835+0.058530 
## [66] train-rmse:0.108313+0.001624    test-rmse:0.394703+0.058016 
## [67] train-rmse:0.106825+0.001537    test-rmse:0.394207+0.058223 
## [68] train-rmse:0.105658+0.001690    test-rmse:0.393727+0.057689 
## [69] train-rmse:0.104580+0.001403    test-rmse:0.393827+0.057510 
## [70] train-rmse:0.103028+0.001678    test-rmse:0.393432+0.057798 
## [71] train-rmse:0.100921+0.001989    test-rmse:0.393556+0.057619 
## [72] train-rmse:0.099882+0.001895    test-rmse:0.393497+0.056959 
## [73] train-rmse:0.098418+0.002180    test-rmse:0.393484+0.056950 
## [74] train-rmse:0.097276+0.001872    test-rmse:0.392998+0.056830 
## [75] train-rmse:0.096308+0.001649    test-rmse:0.392939+0.056897 
## [76] train-rmse:0.095041+0.001600    test-rmse:0.392788+0.056964 
## [77] train-rmse:0.094122+0.001504    test-rmse:0.393454+0.056584 
## [78] train-rmse:0.093375+0.001462    test-rmse:0.393548+0.056398 
## [79] train-rmse:0.092220+0.001602    test-rmse:0.393649+0.056519 
## [80] train-rmse:0.090936+0.001606    test-rmse:0.393545+0.056347 
## [81] train-rmse:0.089565+0.002422    test-rmse:0.393353+0.056076 
## [82] train-rmse:0.088455+0.002521    test-rmse:0.393388+0.056019 
## Stopping. Best iteration:
## [76] train-rmse:0.095041+0.001600    test-rmse:0.392788+0.056964
## 
## 26 
## [1]  train-rmse:1.402260+0.025821    test-rmse:1.401950+0.114226 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.205393+0.022039    test-rmse:1.204374+0.110422 
## [3]  train-rmse:1.039785+0.019952    test-rmse:1.038220+0.104466 
## [4]  train-rmse:0.901022+0.017651    test-rmse:0.906020+0.101935 
## [5]  train-rmse:0.784102+0.016257    test-rmse:0.797099+0.097961 
## [6]  train-rmse:0.686663+0.014144    test-rmse:0.709787+0.096337 
## [7]  train-rmse:0.604194+0.013209    test-rmse:0.637828+0.092675 
## [8]  train-rmse:0.535055+0.012227    test-rmse:0.580784+0.088573 
## [9]  train-rmse:0.476676+0.011019    test-rmse:0.537404+0.087232 
## [10] train-rmse:0.428736+0.010446    test-rmse:0.505558+0.085565 
## [11] train-rmse:0.388289+0.009990    test-rmse:0.480127+0.079577 
## [12] train-rmse:0.354199+0.009921    test-rmse:0.462116+0.077918 
## [13] train-rmse:0.325523+0.009149    test-rmse:0.451506+0.078077 
## [14] train-rmse:0.301873+0.008318    test-rmse:0.441639+0.076052 
## [15] train-rmse:0.280032+0.007782    test-rmse:0.432692+0.072307 
## [16] train-rmse:0.260856+0.008137    test-rmse:0.426398+0.071119 
## [17] train-rmse:0.246750+0.007869    test-rmse:0.421504+0.071089 
## [18] train-rmse:0.231052+0.004827    test-rmse:0.417397+0.069036 
## [19] train-rmse:0.219256+0.002617    test-rmse:0.415000+0.069355 
## [20] train-rmse:0.209757+0.003222    test-rmse:0.413710+0.067448 
## [21] train-rmse:0.201432+0.003102    test-rmse:0.411496+0.066479 
## [22] train-rmse:0.195554+0.003287    test-rmse:0.411244+0.065925 
## [23] train-rmse:0.189895+0.003526    test-rmse:0.410546+0.066418 
## [24] train-rmse:0.182637+0.002697    test-rmse:0.410311+0.066357 
## [25] train-rmse:0.176821+0.002606    test-rmse:0.408743+0.066965 
## [26] train-rmse:0.172573+0.001972    test-rmse:0.408376+0.065681 
## [27] train-rmse:0.167435+0.002964    test-rmse:0.407979+0.064206 
## [28] train-rmse:0.163016+0.003383    test-rmse:0.407545+0.062181 
## [29] train-rmse:0.158865+0.003589    test-rmse:0.406635+0.059495 
## [30] train-rmse:0.153573+0.004153    test-rmse:0.406794+0.058924 
## [31] train-rmse:0.149657+0.004832    test-rmse:0.406820+0.059463 
## [32] train-rmse:0.146139+0.005224    test-rmse:0.407657+0.058888 
## [33] train-rmse:0.140944+0.004946    test-rmse:0.407574+0.059185 
## [34] train-rmse:0.136025+0.004647    test-rmse:0.407747+0.058685 
## [35] train-rmse:0.132257+0.005318    test-rmse:0.407707+0.057551 
## Stopping. Best iteration:
## [29] train-rmse:0.158865+0.003589    test-rmse:0.406635+0.059495
## 
## 27 
## [1]  train-rmse:1.402053+0.025823    test-rmse:1.402675+0.113754 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.204707+0.022250    test-rmse:1.204279+0.110463 
## [3]  train-rmse:1.038515+0.020159    test-rmse:1.038237+0.105220 
## [4]  train-rmse:0.898749+0.017654    test-rmse:0.904783+0.102439 
## [5]  train-rmse:0.780893+0.016345    test-rmse:0.796854+0.100132 
## [6]  train-rmse:0.682196+0.014240    test-rmse:0.708027+0.098142 
## [7]  train-rmse:0.598272+0.013440    test-rmse:0.635367+0.093812 
## [8]  train-rmse:0.527666+0.011996    test-rmse:0.578629+0.092060 
## [9]  train-rmse:0.467888+0.011660    test-rmse:0.533151+0.086500 
## [10] train-rmse:0.417453+0.010462    test-rmse:0.498880+0.082707 
## [11] train-rmse:0.374869+0.009936    test-rmse:0.474808+0.082143 
## [12] train-rmse:0.338784+0.010548    test-rmse:0.457659+0.078899 
## [13] train-rmse:0.307604+0.010369    test-rmse:0.444487+0.077359 
## [14] train-rmse:0.281057+0.010267    test-rmse:0.434598+0.075832 
## [15] train-rmse:0.259078+0.009515    test-rmse:0.426394+0.074095 
## [16] train-rmse:0.239476+0.010491    test-rmse:0.422020+0.071633 
## [17] train-rmse:0.222210+0.010525    test-rmse:0.418614+0.071252 
## [18] train-rmse:0.208632+0.009854    test-rmse:0.414863+0.069426 
## [19] train-rmse:0.196015+0.008619    test-rmse:0.413381+0.068475 
## [20] train-rmse:0.184751+0.009949    test-rmse:0.412120+0.066396 
## [21] train-rmse:0.176670+0.010332    test-rmse:0.410466+0.065439 
## [22] train-rmse:0.167688+0.010280    test-rmse:0.410056+0.063638 
## [23] train-rmse:0.160698+0.009354    test-rmse:0.409009+0.062531 
## [24] train-rmse:0.154552+0.010759    test-rmse:0.407267+0.061652 
## [25] train-rmse:0.148298+0.010401    test-rmse:0.406972+0.060754 
## [26] train-rmse:0.141207+0.007727    test-rmse:0.405286+0.061507 
## [27] train-rmse:0.136225+0.007305    test-rmse:0.404065+0.061610 
## [28] train-rmse:0.130704+0.005219    test-rmse:0.404091+0.060485 
## [29] train-rmse:0.126897+0.005367    test-rmse:0.402958+0.060807 
## [30] train-rmse:0.123672+0.005179    test-rmse:0.402432+0.060028 
## [31] train-rmse:0.118517+0.005325    test-rmse:0.401744+0.060291 
## [32] train-rmse:0.115184+0.004935    test-rmse:0.401372+0.060892 
## [33] train-rmse:0.112235+0.004318    test-rmse:0.400511+0.060675 
## [34] train-rmse:0.107891+0.004870    test-rmse:0.400067+0.061232 
## [35] train-rmse:0.103866+0.004964    test-rmse:0.399340+0.060883 
## [36] train-rmse:0.100513+0.004801    test-rmse:0.399232+0.060793 
## [37] train-rmse:0.097607+0.005424    test-rmse:0.399476+0.061210 
## [38] train-rmse:0.092941+0.005425    test-rmse:0.398706+0.061518 
## [39] train-rmse:0.090043+0.005205    test-rmse:0.399147+0.061314 
## [40] train-rmse:0.086672+0.005680    test-rmse:0.399557+0.061149 
## [41] train-rmse:0.083194+0.005934    test-rmse:0.399108+0.061361 
## [42] train-rmse:0.079869+0.005796    test-rmse:0.399029+0.060802 
## [43] train-rmse:0.077513+0.005530    test-rmse:0.399114+0.060739 
## [44] train-rmse:0.074867+0.005115    test-rmse:0.399755+0.059991 
## Stopping. Best iteration:
## [38] train-rmse:0.092941+0.005425    test-rmse:0.398706+0.061518
## 
## 28 
## [1]  train-rmse:1.395054+0.025971    test-rmse:1.391195+0.111819 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.204171+0.022780    test-rmse:1.200376+0.107255 
## [3]  train-rmse:1.055957+0.020575    test-rmse:1.052257+0.103266 
## [4]  train-rmse:0.942802+0.019188    test-rmse:0.939245+0.099682 
## [5]  train-rmse:0.858003+0.018420    test-rmse:0.854636+0.096470 
## [6]  train-rmse:0.795654+0.018069    test-rmse:0.792508+0.093658 
## [7]  train-rmse:0.750633+0.017963    test-rmse:0.747718+0.091277 
## [8]  train-rmse:0.718643+0.017977    test-rmse:0.715947+0.089323 
## [9]  train-rmse:0.695784+0.017691    test-rmse:0.698104+0.089873 
## [10] train-rmse:0.677126+0.017126    test-rmse:0.675006+0.087924 
## [11] train-rmse:0.662240+0.016977    test-rmse:0.664572+0.087502 
## [12] train-rmse:0.649654+0.016630    test-rmse:0.649537+0.085864 
## [13] train-rmse:0.639151+0.015443    test-rmse:0.643703+0.084454 
## [14] train-rmse:0.629543+0.015009    test-rmse:0.630103+0.083118 
## [15] train-rmse:0.620864+0.014452    test-rmse:0.624688+0.079914 
## [16] train-rmse:0.613496+0.013830    test-rmse:0.616635+0.076403 
## [17] train-rmse:0.606744+0.013517    test-rmse:0.609183+0.076278 
## [18] train-rmse:0.600450+0.013017    test-rmse:0.603918+0.073385 
## [19] train-rmse:0.594935+0.012882    test-rmse:0.601525+0.074417 
## [20] train-rmse:0.589788+0.012596    test-rmse:0.595020+0.073226 
## [21] train-rmse:0.585091+0.012347    test-rmse:0.590423+0.070441 
## [22] train-rmse:0.580671+0.012004    test-rmse:0.588268+0.069790 
## [23] train-rmse:0.576677+0.011907    test-rmse:0.585910+0.067302 
## [24] train-rmse:0.572784+0.011828    test-rmse:0.582702+0.067200 
## [25] train-rmse:0.569220+0.011626    test-rmse:0.579928+0.065156 
## [26] train-rmse:0.565950+0.011468    test-rmse:0.577382+0.065767 
## [27] train-rmse:0.562805+0.011273    test-rmse:0.575169+0.066525 
## [28] train-rmse:0.559848+0.011142    test-rmse:0.569926+0.065516 
## [29] train-rmse:0.557057+0.011059    test-rmse:0.568654+0.063469 
## [30] train-rmse:0.554376+0.010971    test-rmse:0.567529+0.062925 
## [31] train-rmse:0.551879+0.010798    test-rmse:0.566936+0.063036 
## [32] train-rmse:0.549547+0.010734    test-rmse:0.564662+0.062230 
## [33] train-rmse:0.547327+0.010670    test-rmse:0.562715+0.061858 
## [34] train-rmse:0.545290+0.010544    test-rmse:0.561591+0.061440 
## [35] train-rmse:0.543243+0.010429    test-rmse:0.560397+0.060401 
## [36] train-rmse:0.541264+0.010360    test-rmse:0.559337+0.060898 
## [37] train-rmse:0.539419+0.010335    test-rmse:0.558312+0.059529 
## [38] train-rmse:0.537614+0.010290    test-rmse:0.556288+0.059156 
## [39] train-rmse:0.535867+0.010202    test-rmse:0.554568+0.059703 
## [40] train-rmse:0.534194+0.010081    test-rmse:0.552616+0.059901 
## [41] train-rmse:0.532587+0.010002    test-rmse:0.552204+0.057977 
## [42] train-rmse:0.530990+0.009921    test-rmse:0.552554+0.057953 
## [43] train-rmse:0.529421+0.009837    test-rmse:0.551897+0.058185 
## [44] train-rmse:0.527915+0.009720    test-rmse:0.550249+0.059256 
## [45] train-rmse:0.526461+0.009659    test-rmse:0.549020+0.058732 
## [46] train-rmse:0.525042+0.009597    test-rmse:0.547644+0.058617 
## [47] train-rmse:0.523670+0.009551    test-rmse:0.546844+0.058083 
## [48] train-rmse:0.522263+0.009485    test-rmse:0.547280+0.057710 
## [49] train-rmse:0.520986+0.009378    test-rmse:0.545866+0.058369 
## [50] train-rmse:0.519721+0.009283    test-rmse:0.544314+0.057569 
## [51] train-rmse:0.518471+0.009216    test-rmse:0.543616+0.056441 
## [52] train-rmse:0.517256+0.009188    test-rmse:0.542879+0.055973 
## [53] train-rmse:0.516062+0.009116    test-rmse:0.542404+0.056358 
## [54] train-rmse:0.514880+0.009052    test-rmse:0.540557+0.056207 
## [55] train-rmse:0.513707+0.009013    test-rmse:0.539565+0.056606 
## [56] train-rmse:0.512534+0.008973    test-rmse:0.539656+0.056286 
## [57] train-rmse:0.511443+0.008905    test-rmse:0.539276+0.056210 
## [58] train-rmse:0.510356+0.008844    test-rmse:0.538000+0.056036 
## [59] train-rmse:0.509288+0.008797    test-rmse:0.536968+0.055641 
## [60] train-rmse:0.508244+0.008775    test-rmse:0.536191+0.055403 
## [61] train-rmse:0.507192+0.008759    test-rmse:0.535513+0.055283 
## [62] train-rmse:0.506182+0.008734    test-rmse:0.535835+0.054832 
## [63] train-rmse:0.505190+0.008694    test-rmse:0.534157+0.055439 
## [64] train-rmse:0.504186+0.008673    test-rmse:0.533036+0.054735 
## [65] train-rmse:0.503221+0.008630    test-rmse:0.532292+0.054497 
## [66] train-rmse:0.502253+0.008564    test-rmse:0.532318+0.053823 
## [67] train-rmse:0.501320+0.008527    test-rmse:0.533023+0.054198 
## [68] train-rmse:0.500418+0.008504    test-rmse:0.532221+0.053859 
## [69] train-rmse:0.499514+0.008451    test-rmse:0.530424+0.054152 
## [70] train-rmse:0.498609+0.008446    test-rmse:0.529788+0.054635 
## [71] train-rmse:0.497701+0.008452    test-rmse:0.529084+0.054126 
## [72] train-rmse:0.496855+0.008413    test-rmse:0.529271+0.053686 
## [73] train-rmse:0.496007+0.008375    test-rmse:0.529023+0.053555 
## [74] train-rmse:0.495190+0.008345    test-rmse:0.527959+0.053113 
## [75] train-rmse:0.494372+0.008343    test-rmse:0.526708+0.052282 
## [76] train-rmse:0.493538+0.008333    test-rmse:0.526950+0.052436 
## [77] train-rmse:0.492712+0.008282    test-rmse:0.525914+0.053116 
## [78] train-rmse:0.491920+0.008261    test-rmse:0.525280+0.053082 
## [79] train-rmse:0.491149+0.008251    test-rmse:0.525357+0.052450 
## [80] train-rmse:0.490387+0.008230    test-rmse:0.524388+0.052342 
## [81] train-rmse:0.489646+0.008227    test-rmse:0.524033+0.051725 
## [82] train-rmse:0.488917+0.008217    test-rmse:0.523458+0.052044 
## [83] train-rmse:0.488187+0.008187    test-rmse:0.523189+0.052156 
## [84] train-rmse:0.487467+0.008181    test-rmse:0.522907+0.051796 
## [85] train-rmse:0.486751+0.008158    test-rmse:0.521649+0.052180 
## [86] train-rmse:0.486055+0.008125    test-rmse:0.522021+0.052516 
## [87] train-rmse:0.485376+0.008107    test-rmse:0.521074+0.051967 
## [88] train-rmse:0.484687+0.008103    test-rmse:0.521301+0.051057 
## [89] train-rmse:0.484031+0.008084    test-rmse:0.520429+0.050641 
## [90] train-rmse:0.483373+0.008073    test-rmse:0.520375+0.050624 
## [91] train-rmse:0.482720+0.008060    test-rmse:0.520636+0.051051 
## [92] train-rmse:0.482086+0.008040    test-rmse:0.520047+0.050321 
## [93] train-rmse:0.481459+0.008019    test-rmse:0.519715+0.051178 
## [94] train-rmse:0.480835+0.007997    test-rmse:0.519166+0.051064 
## [95] train-rmse:0.480206+0.007962    test-rmse:0.518574+0.050933 
## [96] train-rmse:0.479609+0.007934    test-rmse:0.518280+0.050897 
## [97] train-rmse:0.479022+0.007917    test-rmse:0.518641+0.050195 
## [98] train-rmse:0.478422+0.007892    test-rmse:0.518431+0.050471 
## [99] train-rmse:0.477842+0.007909    test-rmse:0.518148+0.050795 
## [100]    train-rmse:0.477268+0.007914    test-rmse:0.517328+0.049751 
## [101]    train-rmse:0.476712+0.007899    test-rmse:0.517072+0.050053 
## [102]    train-rmse:0.476139+0.007882    test-rmse:0.516684+0.050140 
## [103]    train-rmse:0.475586+0.007864    test-rmse:0.515305+0.048653 
## [104]    train-rmse:0.475036+0.007854    test-rmse:0.514985+0.048640 
## [105]    train-rmse:0.474499+0.007851    test-rmse:0.514890+0.048578 
## [106]    train-rmse:0.473976+0.007846    test-rmse:0.514295+0.048576 
## [107]    train-rmse:0.473464+0.007836    test-rmse:0.513773+0.048214 
## [108]    train-rmse:0.472932+0.007829    test-rmse:0.514032+0.047383 
## [109]    train-rmse:0.472422+0.007824    test-rmse:0.513891+0.047476 
## [110]    train-rmse:0.471887+0.007846    test-rmse:0.513633+0.047532 
## [111]    train-rmse:0.471387+0.007839    test-rmse:0.513276+0.047562 
## [112]    train-rmse:0.470886+0.007832    test-rmse:0.512623+0.048024 
## [113]    train-rmse:0.470397+0.007810    test-rmse:0.512143+0.048491 
## [114]    train-rmse:0.469911+0.007792    test-rmse:0.512012+0.048424 
## [115]    train-rmse:0.469436+0.007794    test-rmse:0.511581+0.048332 
## [116]    train-rmse:0.468960+0.007808    test-rmse:0.511178+0.047701 
## [117]    train-rmse:0.468494+0.007796    test-rmse:0.511282+0.047470 
## [118]    train-rmse:0.468029+0.007773    test-rmse:0.510698+0.047691 
## [119]    train-rmse:0.467560+0.007747    test-rmse:0.511015+0.046967 
## [120]    train-rmse:0.467099+0.007728    test-rmse:0.510435+0.046845 
## [121]    train-rmse:0.466648+0.007723    test-rmse:0.510581+0.046352 
## [122]    train-rmse:0.466212+0.007711    test-rmse:0.509996+0.046483 
## [123]    train-rmse:0.465781+0.007705    test-rmse:0.509777+0.046590 
## [124]    train-rmse:0.465347+0.007688    test-rmse:0.508988+0.046793 
## [125]    train-rmse:0.464913+0.007677    test-rmse:0.508852+0.046965 
## [126]    train-rmse:0.464482+0.007666    test-rmse:0.508125+0.045235 
## [127]    train-rmse:0.464059+0.007651    test-rmse:0.508688+0.045282 
## [128]    train-rmse:0.463637+0.007637    test-rmse:0.508016+0.044636 
## [129]    train-rmse:0.463225+0.007628    test-rmse:0.508337+0.045016 
## [130]    train-rmse:0.462811+0.007619    test-rmse:0.507296+0.045112 
## [131]    train-rmse:0.462414+0.007623    test-rmse:0.507479+0.045105 
## [132]    train-rmse:0.462018+0.007612    test-rmse:0.507048+0.044974 
## [133]    train-rmse:0.461621+0.007598    test-rmse:0.507332+0.045200 
## [134]    train-rmse:0.461233+0.007591    test-rmse:0.507593+0.045461 
## [135]    train-rmse:0.460848+0.007586    test-rmse:0.506954+0.045745 
## [136]    train-rmse:0.460460+0.007579    test-rmse:0.507158+0.046105 
## [137]    train-rmse:0.460080+0.007569    test-rmse:0.506382+0.046437 
## [138]    train-rmse:0.459704+0.007557    test-rmse:0.506318+0.046033 
## [139]    train-rmse:0.459324+0.007545    test-rmse:0.506161+0.045498 
## [140]    train-rmse:0.458940+0.007535    test-rmse:0.505962+0.045305 
## [141]    train-rmse:0.458568+0.007536    test-rmse:0.506231+0.045457 
## [142]    train-rmse:0.458196+0.007534    test-rmse:0.505899+0.045426 
## [143]    train-rmse:0.457834+0.007535    test-rmse:0.505855+0.045036 
## [144]    train-rmse:0.457473+0.007532    test-rmse:0.505723+0.044937 
## [145]    train-rmse:0.457112+0.007532    test-rmse:0.505337+0.044480 
## [146]    train-rmse:0.456770+0.007530    test-rmse:0.505020+0.044391 
## [147]    train-rmse:0.456419+0.007525    test-rmse:0.505005+0.044414 
## [148]    train-rmse:0.456079+0.007520    test-rmse:0.504643+0.044147 
## [149]    train-rmse:0.455727+0.007505    test-rmse:0.504586+0.044200 
## [150]    train-rmse:0.455394+0.007506    test-rmse:0.504630+0.044557 
## [151]    train-rmse:0.455064+0.007507    test-rmse:0.504582+0.044426 
## [152]    train-rmse:0.454725+0.007508    test-rmse:0.504592+0.044581 
## [153]    train-rmse:0.454402+0.007506    test-rmse:0.504113+0.044401 
## [154]    train-rmse:0.454079+0.007503    test-rmse:0.504268+0.044232 
## [155]    train-rmse:0.453757+0.007500    test-rmse:0.504287+0.044010 
## [156]    train-rmse:0.453428+0.007495    test-rmse:0.504313+0.043610 
## [157]    train-rmse:0.453110+0.007488    test-rmse:0.504269+0.043730 
## [158]    train-rmse:0.452802+0.007480    test-rmse:0.503974+0.044087 
## [159]    train-rmse:0.452483+0.007476    test-rmse:0.504070+0.044050 
## [160]    train-rmse:0.452184+0.007479    test-rmse:0.503015+0.043555 
## [161]    train-rmse:0.451883+0.007478    test-rmse:0.503395+0.043855 
## [162]    train-rmse:0.451578+0.007475    test-rmse:0.503034+0.043876 
## [163]    train-rmse:0.451280+0.007473    test-rmse:0.503350+0.043596 
## [164]    train-rmse:0.450980+0.007480    test-rmse:0.502839+0.043626 
## [165]    train-rmse:0.450686+0.007489    test-rmse:0.502434+0.043202 
## [166]    train-rmse:0.450393+0.007489    test-rmse:0.502248+0.043456 
## [167]    train-rmse:0.450094+0.007487    test-rmse:0.501792+0.042322 
## [168]    train-rmse:0.449797+0.007484    test-rmse:0.501792+0.042069 
## [169]    train-rmse:0.449516+0.007476    test-rmse:0.501657+0.041758 
## [170]    train-rmse:0.449239+0.007472    test-rmse:0.501610+0.042111 
## [171]    train-rmse:0.448956+0.007471    test-rmse:0.501636+0.041830 
## [172]    train-rmse:0.448678+0.007474    test-rmse:0.501211+0.041870 
## [173]    train-rmse:0.448405+0.007480    test-rmse:0.500897+0.042061 
## [174]    train-rmse:0.448133+0.007481    test-rmse:0.501239+0.042073 
## [175]    train-rmse:0.447869+0.007483    test-rmse:0.501306+0.042246 
## [176]    train-rmse:0.447600+0.007485    test-rmse:0.501645+0.042235 
## [177]    train-rmse:0.447332+0.007481    test-rmse:0.501606+0.042086 
## [178]    train-rmse:0.447072+0.007479    test-rmse:0.501640+0.042306 
## [179]    train-rmse:0.446814+0.007476    test-rmse:0.501013+0.042157 
## Stopping. Best iteration:
## [173]    train-rmse:0.448405+0.007480    test-rmse:0.500897+0.042061
## 
## 29 
## [1]  train-rmse:1.383731+0.026011    test-rmse:1.382262+0.115083 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.179447+0.022319    test-rmse:1.180448+0.113474 
## [3]  train-rmse:1.016068+0.020026    test-rmse:1.019675+0.106774 
## [4]  train-rmse:0.886748+0.017786    test-rmse:0.893265+0.106532 
## [5]  train-rmse:0.784760+0.016174    test-rmse:0.786519+0.102247 
## [6]  train-rmse:0.705441+0.014921    test-rmse:0.712635+0.099314 
## [7]  train-rmse:0.644070+0.014109    test-rmse:0.652852+0.096291 
## [8]  train-rmse:0.596799+0.013572    test-rmse:0.608930+0.094962 
## [9]  train-rmse:0.560680+0.013163    test-rmse:0.572666+0.089691 
## [10] train-rmse:0.532580+0.012732    test-rmse:0.547033+0.084092 
## [11] train-rmse:0.510544+0.012183    test-rmse:0.529087+0.084628 
## [12] train-rmse:0.492914+0.011438    test-rmse:0.516327+0.078717 
## [13] train-rmse:0.478934+0.010998    test-rmse:0.502063+0.076483 
## [14] train-rmse:0.466325+0.010463    test-rmse:0.494126+0.073409 
## [15] train-rmse:0.454292+0.009168    test-rmse:0.484118+0.072793 
## [16] train-rmse:0.445266+0.009011    test-rmse:0.478436+0.069438 
## [17] train-rmse:0.437200+0.008430    test-rmse:0.473575+0.067324 
## [18] train-rmse:0.429715+0.008052    test-rmse:0.468232+0.063666 
## [19] train-rmse:0.423783+0.007636    test-rmse:0.467045+0.063725 
## [20] train-rmse:0.418036+0.007224    test-rmse:0.464144+0.062130 
## [21] train-rmse:0.413026+0.006937    test-rmse:0.460422+0.058494 
## [22] train-rmse:0.407687+0.006493    test-rmse:0.460340+0.056971 
## [23] train-rmse:0.403163+0.005932    test-rmse:0.458738+0.056787 
## [24] train-rmse:0.398543+0.005145    test-rmse:0.456929+0.057054 
## [25] train-rmse:0.394674+0.005151    test-rmse:0.457379+0.056700 
## [26] train-rmse:0.390923+0.005072    test-rmse:0.454383+0.056706 
## [27] train-rmse:0.387231+0.004729    test-rmse:0.452418+0.057458 
## [28] train-rmse:0.383984+0.004634    test-rmse:0.452399+0.057256 
## [29] train-rmse:0.380826+0.004435    test-rmse:0.449738+0.057016 
## [30] train-rmse:0.377658+0.004466    test-rmse:0.446841+0.055450 
## [31] train-rmse:0.375033+0.004499    test-rmse:0.446361+0.055611 
## [32] train-rmse:0.371879+0.004347    test-rmse:0.446275+0.055761 
## [33] train-rmse:0.369020+0.004258    test-rmse:0.445663+0.054572 
## [34] train-rmse:0.366548+0.004277    test-rmse:0.446847+0.055375 
## [35] train-rmse:0.363332+0.004375    test-rmse:0.445474+0.055589 
## [36] train-rmse:0.361084+0.004670    test-rmse:0.444916+0.054881 
## [37] train-rmse:0.358776+0.004555    test-rmse:0.444463+0.054745 
## [38] train-rmse:0.356492+0.004690    test-rmse:0.444027+0.054643 
## [39] train-rmse:0.353696+0.005258    test-rmse:0.442659+0.052359 
## [40] train-rmse:0.351657+0.005260    test-rmse:0.442226+0.052745 
## [41] train-rmse:0.349590+0.005358    test-rmse:0.441972+0.052264 
## [42] train-rmse:0.347562+0.005423    test-rmse:0.440539+0.053003 
## [43] train-rmse:0.344395+0.005022    test-rmse:0.440441+0.053754 
## [44] train-rmse:0.342508+0.004895    test-rmse:0.440734+0.053085 
## [45] train-rmse:0.340137+0.004718    test-rmse:0.440599+0.052839 
## [46] train-rmse:0.338351+0.004918    test-rmse:0.440328+0.052878 
## [47] train-rmse:0.336544+0.005020    test-rmse:0.439943+0.053577 
## [48] train-rmse:0.335027+0.004483    test-rmse:0.438356+0.054341 
## [49] train-rmse:0.332638+0.004284    test-rmse:0.438553+0.054621 
## [50] train-rmse:0.331261+0.004341    test-rmse:0.438680+0.054560 
## [51] train-rmse:0.329636+0.004331    test-rmse:0.438374+0.053580 
## [52] train-rmse:0.327839+0.004617    test-rmse:0.439295+0.053952 
## [53] train-rmse:0.326069+0.004861    test-rmse:0.439176+0.053880 
## [54] train-rmse:0.324617+0.004728    test-rmse:0.439595+0.053806 
## Stopping. Best iteration:
## [48] train-rmse:0.335027+0.004483    test-rmse:0.438356+0.054341
## 
## 30 
## [1]  train-rmse:1.377794+0.025377    test-rmse:1.379164+0.117173 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.167253+0.021730    test-rmse:1.172147+0.112609 
## [3]  train-rmse:0.996020+0.018508    test-rmse:1.003855+0.112873 
## [4]  train-rmse:0.857574+0.017365    test-rmse:0.863914+0.106327 
## [5]  train-rmse:0.746991+0.015245    test-rmse:0.759508+0.107231 
## [6]  train-rmse:0.658554+0.013342    test-rmse:0.675100+0.100152 
## [7]  train-rmse:0.588395+0.011875    test-rmse:0.612523+0.099611 
## [8]  train-rmse:0.532864+0.010691    test-rmse:0.566065+0.094791 
## [9]  train-rmse:0.489901+0.011176    test-rmse:0.532272+0.087320 
## [10] train-rmse:0.456051+0.010386    test-rmse:0.506732+0.081147 
## [11] train-rmse:0.429233+0.009512    test-rmse:0.485024+0.080914 
## [12] train-rmse:0.408642+0.008778    test-rmse:0.471583+0.079803 
## [13] train-rmse:0.391183+0.008017    test-rmse:0.460528+0.075792 
## [14] train-rmse:0.377906+0.007536    test-rmse:0.452589+0.072123 
## [15] train-rmse:0.366853+0.007790    test-rmse:0.450556+0.070552 
## [16] train-rmse:0.358022+0.007630    test-rmse:0.447886+0.068298 
## [17] train-rmse:0.350077+0.007757    test-rmse:0.443192+0.065184 
## [18] train-rmse:0.343563+0.007421    test-rmse:0.441598+0.064021 
## [19] train-rmse:0.336381+0.007591    test-rmse:0.439423+0.063480 
## [20] train-rmse:0.330743+0.007324    test-rmse:0.438345+0.062619 
## [21] train-rmse:0.325413+0.007227    test-rmse:0.436203+0.061334 
## [22] train-rmse:0.321356+0.007014    test-rmse:0.434846+0.061245 
## [23] train-rmse:0.316865+0.007294    test-rmse:0.434764+0.060954 
## [24] train-rmse:0.312633+0.007148    test-rmse:0.434133+0.060145 
## [25] train-rmse:0.309083+0.006740    test-rmse:0.433483+0.059805 
## [26] train-rmse:0.305137+0.006016    test-rmse:0.432670+0.058703 
## [27] train-rmse:0.300911+0.005869    test-rmse:0.432256+0.057682 
## [28] train-rmse:0.297688+0.005307    test-rmse:0.430750+0.057849 
## [29] train-rmse:0.293854+0.005998    test-rmse:0.430181+0.058832 
## [30] train-rmse:0.290811+0.005729    test-rmse:0.429767+0.059302 
## [31] train-rmse:0.287532+0.006069    test-rmse:0.429120+0.060046 
## [32] train-rmse:0.283867+0.006198    test-rmse:0.428522+0.060634 
## [33] train-rmse:0.281530+0.006027    test-rmse:0.427996+0.060438 
## [34] train-rmse:0.277947+0.005667    test-rmse:0.427368+0.059416 
## [35] train-rmse:0.275313+0.005758    test-rmse:0.426627+0.059703 
## [36] train-rmse:0.272806+0.005617    test-rmse:0.426494+0.061000 
## [37] train-rmse:0.270008+0.005991    test-rmse:0.425704+0.060938 
## [38] train-rmse:0.267537+0.005988    test-rmse:0.425082+0.060973 
## [39] train-rmse:0.264975+0.006062    test-rmse:0.425822+0.061590 
## [40] train-rmse:0.262594+0.005566    test-rmse:0.425185+0.062216 
## [41] train-rmse:0.260157+0.005427    test-rmse:0.424934+0.061867 
## [42] train-rmse:0.258093+0.005478    test-rmse:0.424868+0.061048 
## [43] train-rmse:0.256144+0.005416    test-rmse:0.424225+0.062178 
## [44] train-rmse:0.254063+0.005163    test-rmse:0.424008+0.062552 
## [45] train-rmse:0.251626+0.005083    test-rmse:0.423631+0.062453 
## [46] train-rmse:0.249116+0.004293    test-rmse:0.422851+0.062980 
## [47] train-rmse:0.246250+0.003022    test-rmse:0.422441+0.064048 
## [48] train-rmse:0.244822+0.003056    test-rmse:0.422140+0.064135 
## [49] train-rmse:0.242873+0.003385    test-rmse:0.421674+0.063815 
## [50] train-rmse:0.240771+0.002814    test-rmse:0.420416+0.064000 
## [51] train-rmse:0.238678+0.002705    test-rmse:0.420068+0.064754 
## [52] train-rmse:0.236919+0.002919    test-rmse:0.419160+0.064702 
## [53] train-rmse:0.235335+0.003083    test-rmse:0.419658+0.065633 
## [54] train-rmse:0.232690+0.003623    test-rmse:0.418966+0.066847 
## [55] train-rmse:0.231066+0.003719    test-rmse:0.419320+0.066829 
## [56] train-rmse:0.229047+0.003904    test-rmse:0.418486+0.066763 
## [57] train-rmse:0.227049+0.004158    test-rmse:0.417449+0.066968 
## [58] train-rmse:0.225439+0.003801    test-rmse:0.416882+0.066878 
## [59] train-rmse:0.223892+0.003325    test-rmse:0.416880+0.067402 
## [60] train-rmse:0.222311+0.004273    test-rmse:0.416945+0.067778 
## [61] train-rmse:0.220721+0.004230    test-rmse:0.416693+0.067531 
## [62] train-rmse:0.218782+0.004552    test-rmse:0.416595+0.067324 
## [63] train-rmse:0.217420+0.004118    test-rmse:0.416034+0.068225 
## [64] train-rmse:0.216128+0.003751    test-rmse:0.415722+0.068252 
## [65] train-rmse:0.214505+0.003902    test-rmse:0.415590+0.067688 
## [66] train-rmse:0.213300+0.003878    test-rmse:0.415240+0.067533 
## [67] train-rmse:0.212002+0.003734    test-rmse:0.415202+0.067515 
## [68] train-rmse:0.210032+0.003404    test-rmse:0.415166+0.066793 
## [69] train-rmse:0.209159+0.003408    test-rmse:0.415121+0.066627 
## [70] train-rmse:0.207304+0.004078    test-rmse:0.415195+0.066330 
## [71] train-rmse:0.205095+0.003832    test-rmse:0.415186+0.066239 
## [72] train-rmse:0.203429+0.003482    test-rmse:0.415186+0.066250 
## [73] train-rmse:0.202043+0.003674    test-rmse:0.415085+0.066570 
## [74] train-rmse:0.201057+0.003594    test-rmse:0.414600+0.067157 
## [75] train-rmse:0.199511+0.004131    test-rmse:0.414570+0.067005 
## [76] train-rmse:0.198237+0.004398    test-rmse:0.414313+0.067340 
## [77] train-rmse:0.197373+0.004604    test-rmse:0.414042+0.066958 
## [78] train-rmse:0.196175+0.004635    test-rmse:0.413965+0.066723 
## [79] train-rmse:0.194075+0.005093    test-rmse:0.414092+0.067100 
## [80] train-rmse:0.193058+0.004956    test-rmse:0.414400+0.067154 
## [81] train-rmse:0.191546+0.004325    test-rmse:0.413187+0.068430 
## [82] train-rmse:0.190565+0.004685    test-rmse:0.413381+0.068471 
## [83] train-rmse:0.189618+0.004982    test-rmse:0.413304+0.067998 
## [84] train-rmse:0.188661+0.005037    test-rmse:0.413157+0.068089 
## [85] train-rmse:0.187285+0.004775    test-rmse:0.413119+0.068320 
## [86] train-rmse:0.186181+0.004866    test-rmse:0.413078+0.068281 
## [87] train-rmse:0.185414+0.004728    test-rmse:0.412937+0.068061 
## [88] train-rmse:0.184190+0.004622    test-rmse:0.412971+0.068614 
## [89] train-rmse:0.182706+0.005279    test-rmse:0.413093+0.068460 
## [90] train-rmse:0.181865+0.005311    test-rmse:0.412976+0.068557 
## [91] train-rmse:0.180959+0.005278    test-rmse:0.413317+0.068364 
## [92] train-rmse:0.179762+0.005491    test-rmse:0.413179+0.068463 
## [93] train-rmse:0.178054+0.005788    test-rmse:0.413080+0.068720 
## Stopping. Best iteration:
## [87] train-rmse:0.185414+0.004728    test-rmse:0.412937+0.068061
## 
## 31 
## [1]  train-rmse:1.375140+0.024963    test-rmse:1.376136+0.117493 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.162181+0.021006    test-rmse:1.165998+0.115345 
## [3]  train-rmse:0.986989+0.018336    test-rmse:0.992931+0.109406 
## [4]  train-rmse:0.844750+0.015139    test-rmse:0.855736+0.106117 
## [5]  train-rmse:0.730475+0.012841    test-rmse:0.749326+0.104940 
## [6]  train-rmse:0.637431+0.011054    test-rmse:0.662131+0.100811 
## [7]  train-rmse:0.563016+0.008809    test-rmse:0.597543+0.098090 
## [8]  train-rmse:0.503234+0.007121    test-rmse:0.549201+0.091719 
## [9]  train-rmse:0.454863+0.005177    test-rmse:0.513802+0.085446 
## [10] train-rmse:0.417321+0.005545    test-rmse:0.486265+0.080529 
## [11] train-rmse:0.386643+0.002953    test-rmse:0.465888+0.080914 
## [12] train-rmse:0.362741+0.002783    test-rmse:0.455002+0.078453 
## [13] train-rmse:0.343753+0.002858    test-rmse:0.443720+0.073189 
## [14] train-rmse:0.327739+0.002487    test-rmse:0.436776+0.068180 
## [15] train-rmse:0.314634+0.002235    test-rmse:0.432591+0.066965 
## [16] train-rmse:0.305104+0.002319    test-rmse:0.429171+0.063664 
## [17] train-rmse:0.296553+0.002804    test-rmse:0.426608+0.063111 
## [18] train-rmse:0.287498+0.002430    test-rmse:0.424214+0.060710 
## [19] train-rmse:0.280954+0.003298    test-rmse:0.421740+0.061349 
## [20] train-rmse:0.275398+0.003887    test-rmse:0.420368+0.060749 
## [21] train-rmse:0.268622+0.003668    test-rmse:0.419496+0.060748 
## [22] train-rmse:0.262237+0.002298    test-rmse:0.419782+0.061443 
## [23] train-rmse:0.257097+0.003159    test-rmse:0.418826+0.061353 
## [24] train-rmse:0.251670+0.003544    test-rmse:0.416578+0.059512 
## [25] train-rmse:0.247009+0.004644    test-rmse:0.414971+0.061534 
## [26] train-rmse:0.242330+0.005717    test-rmse:0.413392+0.061747 
## [27] train-rmse:0.239297+0.005640    test-rmse:0.413331+0.060787 
## [28] train-rmse:0.235284+0.005905    test-rmse:0.413386+0.060135 
## [29] train-rmse:0.230657+0.005605    test-rmse:0.413112+0.059598 
## [30] train-rmse:0.227206+0.005519    test-rmse:0.411795+0.059358 
## [31] train-rmse:0.223294+0.005247    test-rmse:0.411264+0.057983 
## [32] train-rmse:0.220301+0.005248    test-rmse:0.410118+0.057965 
## [33] train-rmse:0.217044+0.004705    test-rmse:0.409182+0.058080 
## [34] train-rmse:0.213925+0.004287    test-rmse:0.408091+0.058689 
## [35] train-rmse:0.211104+0.004747    test-rmse:0.407309+0.057931 
## [36] train-rmse:0.208289+0.005312    test-rmse:0.407121+0.057822 
## [37] train-rmse:0.204987+0.005151    test-rmse:0.407348+0.057930 
## [38] train-rmse:0.202761+0.005220    test-rmse:0.405761+0.058416 
## [39] train-rmse:0.200587+0.004994    test-rmse:0.405486+0.057777 
## [40] train-rmse:0.198653+0.004957    test-rmse:0.405136+0.057734 
## [41] train-rmse:0.196287+0.004673    test-rmse:0.404808+0.057932 
## [42] train-rmse:0.193810+0.005718    test-rmse:0.404636+0.058285 
## [43] train-rmse:0.191886+0.005519    test-rmse:0.403608+0.057141 
## [44] train-rmse:0.189752+0.005396    test-rmse:0.403589+0.057743 
## [45] train-rmse:0.187300+0.005840    test-rmse:0.403766+0.058013 
## [46] train-rmse:0.184603+0.005589    test-rmse:0.402795+0.057511 
## [47] train-rmse:0.182478+0.005059    test-rmse:0.401736+0.057854 
## [48] train-rmse:0.180727+0.005082    test-rmse:0.401013+0.057537 
## [49] train-rmse:0.178852+0.004815    test-rmse:0.400812+0.057240 
## [50] train-rmse:0.176384+0.005073    test-rmse:0.400141+0.056917 
## [51] train-rmse:0.174514+0.005135    test-rmse:0.399316+0.057303 
## [52] train-rmse:0.172512+0.004890    test-rmse:0.398886+0.056588 
## [53] train-rmse:0.170497+0.005119    test-rmse:0.398309+0.056609 
## [54] train-rmse:0.167781+0.005432    test-rmse:0.396553+0.057445 
## [55] train-rmse:0.166187+0.005539    test-rmse:0.397026+0.057293 
## [56] train-rmse:0.164854+0.005594    test-rmse:0.396971+0.057398 
## [57] train-rmse:0.163351+0.005601    test-rmse:0.395975+0.057126 
## [58] train-rmse:0.161408+0.005733    test-rmse:0.396059+0.058183 
## [59] train-rmse:0.159472+0.005208    test-rmse:0.395996+0.058114 
## [60] train-rmse:0.157750+0.005498    test-rmse:0.395913+0.057835 
## [61] train-rmse:0.156165+0.006020    test-rmse:0.395994+0.058045 
## [62] train-rmse:0.154670+0.006061    test-rmse:0.396062+0.058381 
## [63] train-rmse:0.153111+0.006032    test-rmse:0.395937+0.058159 
## [64] train-rmse:0.151745+0.006347    test-rmse:0.395542+0.058466 
## [65] train-rmse:0.149288+0.005806    test-rmse:0.395230+0.058475 
## [66] train-rmse:0.147675+0.005775    test-rmse:0.394464+0.059042 
## [67] train-rmse:0.146380+0.005606    test-rmse:0.394593+0.058644 
## [68] train-rmse:0.144646+0.005619    test-rmse:0.394121+0.059067 
## [69] train-rmse:0.142953+0.005305    test-rmse:0.393985+0.058257 
## [70] train-rmse:0.141888+0.005199    test-rmse:0.394311+0.058275 
## [71] train-rmse:0.140504+0.004857    test-rmse:0.393400+0.058520 
## [72] train-rmse:0.138628+0.005353    test-rmse:0.393124+0.059070 
## [73] train-rmse:0.137375+0.005579    test-rmse:0.393229+0.058785 
## [74] train-rmse:0.136006+0.005698    test-rmse:0.393156+0.058432 
## [75] train-rmse:0.135056+0.005738    test-rmse:0.393126+0.058346 
## [76] train-rmse:0.133892+0.006236    test-rmse:0.392753+0.057565 
## [77] train-rmse:0.132752+0.006126    test-rmse:0.392774+0.057808 
## [78] train-rmse:0.131883+0.005875    test-rmse:0.392681+0.057724 
## [79] train-rmse:0.130617+0.006077    test-rmse:0.392623+0.057546 
## [80] train-rmse:0.129366+0.005881    test-rmse:0.393062+0.057806 
## [81] train-rmse:0.127494+0.005614    test-rmse:0.393244+0.058246 
## [82] train-rmse:0.126293+0.005586    test-rmse:0.392780+0.057695 
## [83] train-rmse:0.125389+0.005547    test-rmse:0.392159+0.056979 
## [84] train-rmse:0.124653+0.005302    test-rmse:0.392163+0.057072 
## [85] train-rmse:0.123630+0.005064    test-rmse:0.392234+0.056941 
## [86] train-rmse:0.122399+0.005502    test-rmse:0.392355+0.056949 
## [87] train-rmse:0.121164+0.005210    test-rmse:0.392459+0.056538 
## [88] train-rmse:0.119848+0.005005    test-rmse:0.392609+0.056556 
## [89] train-rmse:0.118787+0.005264    test-rmse:0.392375+0.056234 
## Stopping. Best iteration:
## [83] train-rmse:0.125389+0.005547    test-rmse:0.392159+0.056979
## 
## 32 
## [1]  train-rmse:1.373562+0.025233    test-rmse:1.374472+0.114904 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.158914+0.021138    test-rmse:1.161735+0.111411 
## [3]  train-rmse:0.982863+0.018933    test-rmse:0.987564+0.106221 
## [4]  train-rmse:0.838839+0.015785    test-rmse:0.850672+0.103965 
## [5]  train-rmse:0.721004+0.014289    test-rmse:0.740002+0.099165 
## [6]  train-rmse:0.624825+0.012895    test-rmse:0.649874+0.093291 
## [7]  train-rmse:0.547287+0.011143    test-rmse:0.583438+0.091649 
## [8]  train-rmse:0.484202+0.009226    test-rmse:0.533573+0.087025 
## [9]  train-rmse:0.432752+0.008022    test-rmse:0.495544+0.086106 
## [10] train-rmse:0.390280+0.006024    test-rmse:0.469593+0.083441 
## [11] train-rmse:0.358004+0.005953    test-rmse:0.450785+0.081686 
## [12] train-rmse:0.330385+0.005919    test-rmse:0.438521+0.079720 
## [13] train-rmse:0.307866+0.005197    test-rmse:0.425968+0.078378 
## [14] train-rmse:0.290828+0.004685    test-rmse:0.418639+0.076754 
## [15] train-rmse:0.274930+0.006030    test-rmse:0.416366+0.076380 
## [16] train-rmse:0.261584+0.005541    test-rmse:0.413831+0.074957 
## [17] train-rmse:0.252457+0.005219    test-rmse:0.411397+0.074003 
## [18] train-rmse:0.243253+0.004814    test-rmse:0.408313+0.072525 
## [19] train-rmse:0.235879+0.005672    test-rmse:0.406928+0.070768 
## [20] train-rmse:0.228360+0.003960    test-rmse:0.404580+0.069500 
## [21] train-rmse:0.221825+0.003672    test-rmse:0.401842+0.066493 
## [22] train-rmse:0.215928+0.002496    test-rmse:0.400746+0.066285 
## [23] train-rmse:0.210857+0.003039    test-rmse:0.401216+0.065888 
## [24] train-rmse:0.206319+0.002987    test-rmse:0.399251+0.067461 
## [25] train-rmse:0.199810+0.001787    test-rmse:0.399304+0.066981 
## [26] train-rmse:0.194997+0.001454    test-rmse:0.399177+0.066545 
## [27] train-rmse:0.190172+0.000791    test-rmse:0.399033+0.066851 
## [28] train-rmse:0.184836+0.002129    test-rmse:0.399652+0.066814 
## [29] train-rmse:0.179997+0.001973    test-rmse:0.399387+0.066461 
## [30] train-rmse:0.177099+0.002380    test-rmse:0.399280+0.066105 
## [31] train-rmse:0.172887+0.003086    test-rmse:0.399166+0.066773 
## [32] train-rmse:0.169835+0.002315    test-rmse:0.399215+0.067186 
## [33] train-rmse:0.167298+0.001966    test-rmse:0.398843+0.067632 
## [34] train-rmse:0.164569+0.002378    test-rmse:0.398541+0.067567 
## [35] train-rmse:0.161195+0.002604    test-rmse:0.398644+0.067729 
## [36] train-rmse:0.158051+0.002276    test-rmse:0.397725+0.067993 
## [37] train-rmse:0.155461+0.002674    test-rmse:0.396405+0.068083 
## [38] train-rmse:0.152558+0.002464    test-rmse:0.396385+0.066913 
## [39] train-rmse:0.149090+0.002272    test-rmse:0.396615+0.066823 
## [40] train-rmse:0.146747+0.001897    test-rmse:0.396257+0.066922 
## [41] train-rmse:0.143986+0.002025    test-rmse:0.395774+0.067370 
## [42] train-rmse:0.141759+0.002552    test-rmse:0.395577+0.067250 
## [43] train-rmse:0.140305+0.002662    test-rmse:0.394739+0.067257 
## [44] train-rmse:0.138301+0.002493    test-rmse:0.394189+0.067276 
## [45] train-rmse:0.136256+0.002811    test-rmse:0.393990+0.067167 
## [46] train-rmse:0.134018+0.003332    test-rmse:0.394117+0.067149 
## [47] train-rmse:0.131969+0.003044    test-rmse:0.393394+0.067141 
## [48] train-rmse:0.130835+0.003106    test-rmse:0.393124+0.067767 
## [49] train-rmse:0.128929+0.003524    test-rmse:0.392760+0.068312 
## [50] train-rmse:0.126894+0.003431    test-rmse:0.392659+0.067844 
## [51] train-rmse:0.125284+0.003358    test-rmse:0.392584+0.067832 
## [52] train-rmse:0.122494+0.003351    test-rmse:0.392521+0.068107 
## [53] train-rmse:0.120075+0.003456    test-rmse:0.392938+0.067696 
## [54] train-rmse:0.117596+0.003467    test-rmse:0.392874+0.068885 
## [55] train-rmse:0.115828+0.003306    test-rmse:0.392565+0.068620 
## [56] train-rmse:0.113928+0.003509    test-rmse:0.392741+0.068847 
## [57] train-rmse:0.111950+0.003194    test-rmse:0.392871+0.068656 
## [58] train-rmse:0.110706+0.003122    test-rmse:0.393683+0.067546 
## Stopping. Best iteration:
## [52] train-rmse:0.122494+0.003351    test-rmse:0.392521+0.068107
## 
## 33 
## [1]  train-rmse:1.373086+0.025275    test-rmse:1.373315+0.113906 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.157387+0.021173    test-rmse:1.159048+0.109143 
## [3]  train-rmse:0.980061+0.019160    test-rmse:0.984567+0.102363 
## [4]  train-rmse:0.834343+0.016440    test-rmse:0.846670+0.099616 
## [5]  train-rmse:0.715165+0.014867    test-rmse:0.735832+0.096121 
## [6]  train-rmse:0.617799+0.012574    test-rmse:0.648289+0.092263 
## [7]  train-rmse:0.536966+0.012043    test-rmse:0.582072+0.089990 
## [8]  train-rmse:0.470290+0.010662    test-rmse:0.528698+0.086986 
## [9]  train-rmse:0.416347+0.010015    test-rmse:0.491305+0.086323 
## [10] train-rmse:0.371539+0.009465    test-rmse:0.461749+0.085843 
## [11] train-rmse:0.335810+0.009439    test-rmse:0.443107+0.083266 
## [12] train-rmse:0.305457+0.008852    test-rmse:0.429420+0.082246 
## [13] train-rmse:0.281188+0.007791    test-rmse:0.419426+0.079866 
## [14] train-rmse:0.260687+0.007164    test-rmse:0.413091+0.079096 
## [15] train-rmse:0.242676+0.006225    test-rmse:0.408496+0.078384 
## [16] train-rmse:0.228502+0.006623    test-rmse:0.403873+0.075478 
## [17] train-rmse:0.214982+0.004499    test-rmse:0.401844+0.076330 
## [18] train-rmse:0.205808+0.004184    test-rmse:0.399048+0.074080 
## [19] train-rmse:0.196446+0.005400    test-rmse:0.396961+0.075553 
## [20] train-rmse:0.188853+0.005432    test-rmse:0.396806+0.074207 
## [21] train-rmse:0.182680+0.004767    test-rmse:0.396822+0.072414 
## [22] train-rmse:0.176624+0.003351    test-rmse:0.396834+0.073506 
## [23] train-rmse:0.171946+0.003778    test-rmse:0.395537+0.072451 
## [24] train-rmse:0.166422+0.002955    test-rmse:0.394710+0.071530 
## [25] train-rmse:0.161858+0.004178    test-rmse:0.393783+0.071411 
## [26] train-rmse:0.157410+0.004909    test-rmse:0.393921+0.070536 
## [27] train-rmse:0.153769+0.005501    test-rmse:0.393596+0.069914 
## [28] train-rmse:0.148851+0.006571    test-rmse:0.392964+0.069603 
## [29] train-rmse:0.144922+0.007530    test-rmse:0.392354+0.068176 
## [30] train-rmse:0.140206+0.006741    test-rmse:0.393712+0.066810 
## [31] train-rmse:0.135660+0.006632    test-rmse:0.393799+0.066752 
## [32] train-rmse:0.132077+0.006842    test-rmse:0.393105+0.065978 
## [33] train-rmse:0.128565+0.007268    test-rmse:0.392246+0.066220 
## [34] train-rmse:0.124401+0.006683    test-rmse:0.390947+0.065784 
## [35] train-rmse:0.121773+0.006763    test-rmse:0.390681+0.065263 
## [36] train-rmse:0.119521+0.006692    test-rmse:0.390601+0.064609 
## [37] train-rmse:0.116001+0.006487    test-rmse:0.389881+0.064624 
## [38] train-rmse:0.113375+0.006653    test-rmse:0.389672+0.064094 
## [39] train-rmse:0.111596+0.006504    test-rmse:0.389283+0.063318 
## [40] train-rmse:0.109126+0.006095    test-rmse:0.389232+0.062928 
## [41] train-rmse:0.107404+0.006754    test-rmse:0.388777+0.063190 
## [42] train-rmse:0.104941+0.007208    test-rmse:0.388305+0.063278 
## [43] train-rmse:0.102633+0.007319    test-rmse:0.388394+0.062763 
## [44] train-rmse:0.100178+0.007276    test-rmse:0.388340+0.062689 
## [45] train-rmse:0.097652+0.006914    test-rmse:0.387541+0.063255 
## [46] train-rmse:0.095141+0.006830    test-rmse:0.387348+0.064144 
## [47] train-rmse:0.092364+0.006448    test-rmse:0.387576+0.064523 
## [48] train-rmse:0.090387+0.006482    test-rmse:0.387261+0.064407 
## [49] train-rmse:0.088674+0.006834    test-rmse:0.386825+0.063954 
## [50] train-rmse:0.086854+0.006751    test-rmse:0.386837+0.063634 
## [51] train-rmse:0.084880+0.006436    test-rmse:0.386973+0.063707 
## [52] train-rmse:0.083231+0.006163    test-rmse:0.386668+0.064030 
## [53] train-rmse:0.081554+0.006263    test-rmse:0.386540+0.063797 
## [54] train-rmse:0.079454+0.006139    test-rmse:0.386484+0.063400 
## [55] train-rmse:0.077892+0.006012    test-rmse:0.386188+0.063667 
## [56] train-rmse:0.075734+0.006600    test-rmse:0.386146+0.063229 
## [57] train-rmse:0.074668+0.006595    test-rmse:0.385926+0.063664 
## [58] train-rmse:0.073189+0.006689    test-rmse:0.386014+0.063760 
## [59] train-rmse:0.071653+0.005950    test-rmse:0.386312+0.064115 
## [60] train-rmse:0.070092+0.005875    test-rmse:0.386354+0.064260 
## [61] train-rmse:0.068790+0.005730    test-rmse:0.386214+0.064345 
## [62] train-rmse:0.067146+0.005992    test-rmse:0.386143+0.064377 
## [63] train-rmse:0.066111+0.005888    test-rmse:0.386301+0.064127 
## Stopping. Best iteration:
## [57] train-rmse:0.074668+0.006595    test-rmse:0.385926+0.063664
## 
## 34 
## [1]  train-rmse:1.372849+0.025278    test-rmse:1.374139+0.113409 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.156291+0.021373    test-rmse:1.156623+0.109983 
## [3]  train-rmse:0.978177+0.019337    test-rmse:0.980156+0.103992 
## [4]  train-rmse:0.831664+0.016621    test-rmse:0.845254+0.100139 
## [5]  train-rmse:0.711287+0.015181    test-rmse:0.734611+0.097219 
## [6]  train-rmse:0.612423+0.013114    test-rmse:0.648331+0.093816 
## [7]  train-rmse:0.531158+0.013162    test-rmse:0.580125+0.092127 
## [8]  train-rmse:0.463325+0.012223    test-rmse:0.530940+0.089251 
## [9]  train-rmse:0.406696+0.010752    test-rmse:0.493700+0.086413 
## [10] train-rmse:0.359625+0.011177    test-rmse:0.468152+0.083729 
## [11] train-rmse:0.321057+0.011007    test-rmse:0.447857+0.082646 
## [12] train-rmse:0.289259+0.010357    test-rmse:0.436741+0.083683 
## [13] train-rmse:0.261665+0.010420    test-rmse:0.428965+0.080729 
## [14] train-rmse:0.239174+0.010745    test-rmse:0.422553+0.079421 
## [15] train-rmse:0.221171+0.011272    test-rmse:0.418152+0.077684 
## [16] train-rmse:0.204662+0.010312    test-rmse:0.412701+0.074640 
## [17] train-rmse:0.191721+0.011250    test-rmse:0.410540+0.073515 
## [18] train-rmse:0.180539+0.012131    test-rmse:0.408520+0.070987 
## [19] train-rmse:0.167936+0.008601    test-rmse:0.407881+0.068085 
## [20] train-rmse:0.159259+0.010777    test-rmse:0.407537+0.066621 
## [21] train-rmse:0.150228+0.008830    test-rmse:0.409164+0.065316 
## [22] train-rmse:0.142606+0.009472    test-rmse:0.407977+0.062708 
## [23] train-rmse:0.135273+0.007902    test-rmse:0.405930+0.061354 
## [24] train-rmse:0.129264+0.008132    test-rmse:0.405892+0.058413 
## [25] train-rmse:0.124278+0.008385    test-rmse:0.405095+0.056370 
## [26] train-rmse:0.119595+0.008723    test-rmse:0.403389+0.055491 
## [27] train-rmse:0.114897+0.007703    test-rmse:0.402410+0.055372 
## [28] train-rmse:0.111693+0.007270    test-rmse:0.402847+0.055086 
## [29] train-rmse:0.107534+0.005904    test-rmse:0.402559+0.055351 
## [30] train-rmse:0.104474+0.005013    test-rmse:0.402072+0.054632 
## [31] train-rmse:0.101326+0.004995    test-rmse:0.402286+0.053003 
## [32] train-rmse:0.097996+0.005791    test-rmse:0.401594+0.053732 
## [33] train-rmse:0.094312+0.006376    test-rmse:0.401353+0.053540 
## [34] train-rmse:0.091190+0.006858    test-rmse:0.400979+0.052833 
## [35] train-rmse:0.088721+0.006724    test-rmse:0.400757+0.052754 
## [36] train-rmse:0.085477+0.006267    test-rmse:0.400790+0.052296 
## [37] train-rmse:0.082552+0.006054    test-rmse:0.400793+0.052084 
## [38] train-rmse:0.079434+0.006239    test-rmse:0.401124+0.051939 
## [39] train-rmse:0.076677+0.005557    test-rmse:0.400570+0.051978 
## [40] train-rmse:0.074184+0.004820    test-rmse:0.400505+0.052304 
## [41] train-rmse:0.071595+0.004739    test-rmse:0.400700+0.052008 
## [42] train-rmse:0.069166+0.004604    test-rmse:0.400613+0.051757 
## [43] train-rmse:0.066950+0.004651    test-rmse:0.400499+0.051909 
## [44] train-rmse:0.065110+0.004609    test-rmse:0.400334+0.051912 
## [45] train-rmse:0.063369+0.004381    test-rmse:0.400145+0.051809 
## [46] train-rmse:0.061579+0.004042    test-rmse:0.400088+0.051715 
## [47] train-rmse:0.059850+0.004234    test-rmse:0.399638+0.051253 
## [48] train-rmse:0.058557+0.004024    test-rmse:0.399757+0.051281 
## [49] train-rmse:0.057185+0.003789    test-rmse:0.399714+0.051457 
## [50] train-rmse:0.055754+0.003759    test-rmse:0.399672+0.051212 
## [51] train-rmse:0.054321+0.003903    test-rmse:0.399815+0.050587 
## [52] train-rmse:0.052814+0.004450    test-rmse:0.399667+0.051138 
## [53] train-rmse:0.051678+0.004603    test-rmse:0.399483+0.051221 
## [54] train-rmse:0.050435+0.004397    test-rmse:0.399518+0.050770 
## [55] train-rmse:0.048899+0.003998    test-rmse:0.399389+0.050475 
## [56] train-rmse:0.047441+0.004100    test-rmse:0.399530+0.049995 
## [57] train-rmse:0.046177+0.004225    test-rmse:0.399436+0.050083 
## [58] train-rmse:0.044488+0.004195    test-rmse:0.399493+0.049814 
## [59] train-rmse:0.043457+0.003806    test-rmse:0.399734+0.049290 
## [60] train-rmse:0.042531+0.003665    test-rmse:0.399597+0.049191 
## [61] train-rmse:0.041490+0.003697    test-rmse:0.399688+0.049264 
## Stopping. Best iteration:
## [55] train-rmse:0.048899+0.003998    test-rmse:0.399389+0.050475
## 
## 35 
## [1]  train-rmse:1.368724+0.025516    test-rmse:1.364872+0.111211 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.163659+0.022145    test-rmse:1.159885+0.106220 
## [3]  train-rmse:1.010046+0.019972    test-rmse:1.006394+0.101887 
## [4]  train-rmse:0.897489+0.018741    test-rmse:0.894022+0.098039 
## [5]  train-rmse:0.816963+0.018164    test-rmse:0.813730+0.094674 
## [6]  train-rmse:0.760692+0.017973    test-rmse:0.757718+0.091842 
## [7]  train-rmse:0.722197+0.017971    test-rmse:0.719473+0.089555 
## [8]  train-rmse:0.696203+0.017822    test-rmse:0.695537+0.084837 
## [9]  train-rmse:0.675307+0.017042    test-rmse:0.675674+0.088440 
## [10] train-rmse:0.659240+0.017013    test-rmse:0.662135+0.087059 
## [11] train-rmse:0.645726+0.016529    test-rmse:0.645992+0.085359 
## [12] train-rmse:0.634599+0.014962    test-rmse:0.639640+0.083891 
## [13] train-rmse:0.624400+0.014486    test-rmse:0.626695+0.082360 
## [14] train-rmse:0.615374+0.013930    test-rmse:0.619981+0.078919 
## [15] train-rmse:0.607736+0.013536    test-rmse:0.612320+0.075855 
## [16] train-rmse:0.600929+0.013056    test-rmse:0.603965+0.074715 
## [17] train-rmse:0.594308+0.012821    test-rmse:0.598514+0.071449 
## [18] train-rmse:0.588686+0.012421    test-rmse:0.596225+0.072750 
## [19] train-rmse:0.583584+0.012126    test-rmse:0.589264+0.071337 
## [20] train-rmse:0.578659+0.011974    test-rmse:0.585822+0.068625 
## [21] train-rmse:0.574314+0.011894    test-rmse:0.585073+0.067792 
## [22] train-rmse:0.570222+0.011629    test-rmse:0.581038+0.065842 
## [23] train-rmse:0.566497+0.011442    test-rmse:0.578066+0.064887 
## [24] train-rmse:0.562938+0.011405    test-rmse:0.573506+0.064382 
## [25] train-rmse:0.559662+0.011180    test-rmse:0.571084+0.065536 
## [26] train-rmse:0.556491+0.010940    test-rmse:0.569455+0.064970 
## [27] train-rmse:0.553608+0.010866    test-rmse:0.565577+0.063838 
## [28] train-rmse:0.550817+0.010847    test-rmse:0.565397+0.062648 
## [29] train-rmse:0.548296+0.010742    test-rmse:0.563631+0.062900 
## [30] train-rmse:0.545922+0.010544    test-rmse:0.562932+0.062267 
## [31] train-rmse:0.543650+0.010410    test-rmse:0.559156+0.060571 
## [32] train-rmse:0.541435+0.010351    test-rmse:0.558022+0.061252 
## [33] train-rmse:0.539308+0.010336    test-rmse:0.557778+0.059561 
## [34] train-rmse:0.537274+0.010300    test-rmse:0.556640+0.060179 
## [35] train-rmse:0.535362+0.010253    test-rmse:0.555107+0.059798 
## [36] train-rmse:0.533502+0.010112    test-rmse:0.553702+0.059183 
## [37] train-rmse:0.531700+0.010012    test-rmse:0.552935+0.058783 
## [38] train-rmse:0.529924+0.009917    test-rmse:0.551222+0.058244 
## [39] train-rmse:0.528230+0.009809    test-rmse:0.551783+0.057762 
## [40] train-rmse:0.526599+0.009678    test-rmse:0.550773+0.057616 
## [41] train-rmse:0.524928+0.009567    test-rmse:0.548578+0.057847 
## [42] train-rmse:0.523337+0.009508    test-rmse:0.546510+0.057164 
## [43] train-rmse:0.521875+0.009487    test-rmse:0.545628+0.057712 
## [44] train-rmse:0.520434+0.009331    test-rmse:0.544932+0.056251 
## [45] train-rmse:0.519009+0.009241    test-rmse:0.544414+0.056785 
## [46] train-rmse:0.517605+0.009178    test-rmse:0.543902+0.057157 
## [47] train-rmse:0.516247+0.009170    test-rmse:0.541937+0.055922 
## [48] train-rmse:0.514944+0.009111    test-rmse:0.542168+0.056030 
## [49] train-rmse:0.513656+0.009062    test-rmse:0.541141+0.055826 
## [50] train-rmse:0.512407+0.008985    test-rmse:0.540426+0.055485 
## [51] train-rmse:0.511176+0.008927    test-rmse:0.539859+0.055153 
## [52] train-rmse:0.509944+0.008872    test-rmse:0.538908+0.053758 
## [53] train-rmse:0.508698+0.008832    test-rmse:0.537870+0.054293 
## [54] train-rmse:0.507482+0.008764    test-rmse:0.536104+0.054082 
## [55] train-rmse:0.506348+0.008716    test-rmse:0.535692+0.054959 
## [56] train-rmse:0.505244+0.008702    test-rmse:0.535249+0.055196 
## [57] train-rmse:0.504170+0.008676    test-rmse:0.534674+0.054441 
## [58] train-rmse:0.503096+0.008662    test-rmse:0.532866+0.055374 
## [59] train-rmse:0.502035+0.008659    test-rmse:0.531639+0.054734 
## [60] train-rmse:0.500997+0.008614    test-rmse:0.531036+0.054650 
## [61] train-rmse:0.499939+0.008539    test-rmse:0.530551+0.054022 
## [62] train-rmse:0.498906+0.008506    test-rmse:0.530040+0.053101 
## [63] train-rmse:0.497925+0.008486    test-rmse:0.528904+0.053306 
## [64] train-rmse:0.496971+0.008463    test-rmse:0.528512+0.053435 
## [65] train-rmse:0.496020+0.008462    test-rmse:0.527881+0.053155 
## [66] train-rmse:0.495076+0.008431    test-rmse:0.526863+0.054248 
## [67] train-rmse:0.494137+0.008370    test-rmse:0.525961+0.053848 
## [68] train-rmse:0.493209+0.008324    test-rmse:0.525612+0.052651 
## [69] train-rmse:0.492306+0.008328    test-rmse:0.525509+0.052180 
## [70] train-rmse:0.491454+0.008311    test-rmse:0.524645+0.052822 
## [71] train-rmse:0.490601+0.008282    test-rmse:0.525096+0.052742 
## [72] train-rmse:0.489748+0.008244    test-rmse:0.524043+0.052640 
## [73] train-rmse:0.488931+0.008210    test-rmse:0.523611+0.052256 
## [74] train-rmse:0.488115+0.008195    test-rmse:0.522178+0.051487 
## [75] train-rmse:0.487309+0.008185    test-rmse:0.521624+0.050770 
## [76] train-rmse:0.486503+0.008181    test-rmse:0.521491+0.051034 
## [77] train-rmse:0.485692+0.008174    test-rmse:0.521371+0.050599 
## [78] train-rmse:0.484922+0.008123    test-rmse:0.521613+0.050982 
## [79] train-rmse:0.484187+0.008101    test-rmse:0.521153+0.051457 
## [80] train-rmse:0.483467+0.008080    test-rmse:0.521036+0.051866 
## [81] train-rmse:0.482750+0.008060    test-rmse:0.520930+0.051946 
## [82] train-rmse:0.482021+0.008053    test-rmse:0.519885+0.051272 
## [83] train-rmse:0.481306+0.008061    test-rmse:0.519094+0.051442 
## [84] train-rmse:0.480621+0.008036    test-rmse:0.518300+0.050679 
## [85] train-rmse:0.479910+0.008027    test-rmse:0.517983+0.050892 
## [86] train-rmse:0.479227+0.008008    test-rmse:0.516845+0.050202 
## [87] train-rmse:0.478548+0.007968    test-rmse:0.516199+0.049960 
## [88] train-rmse:0.477912+0.007958    test-rmse:0.516117+0.050382 
## [89] train-rmse:0.477270+0.007929    test-rmse:0.516294+0.050564 
## [90] train-rmse:0.476644+0.007915    test-rmse:0.516284+0.050419 
## [91] train-rmse:0.476029+0.007905    test-rmse:0.515560+0.047927 
## [92] train-rmse:0.475415+0.007886    test-rmse:0.514936+0.047929 
## [93] train-rmse:0.474794+0.007875    test-rmse:0.514720+0.046879 
## [94] train-rmse:0.474193+0.007877    test-rmse:0.513997+0.047458 
## [95] train-rmse:0.473607+0.007875    test-rmse:0.513689+0.047344 
## [96] train-rmse:0.473029+0.007874    test-rmse:0.513520+0.047197 
## [97] train-rmse:0.472443+0.007862    test-rmse:0.514204+0.047058 
## [98] train-rmse:0.471876+0.007861    test-rmse:0.513717+0.046583 
## [99] train-rmse:0.471311+0.007843    test-rmse:0.513790+0.047021 
## [100]    train-rmse:0.470741+0.007829    test-rmse:0.513005+0.047098 
## [101]    train-rmse:0.470196+0.007825    test-rmse:0.511836+0.046953 
## [102]    train-rmse:0.469653+0.007821    test-rmse:0.511112+0.047455 
## [103]    train-rmse:0.469110+0.007810    test-rmse:0.511428+0.047143 
## [104]    train-rmse:0.468579+0.007795    test-rmse:0.510964+0.046992 
## [105]    train-rmse:0.468048+0.007808    test-rmse:0.510013+0.047444 
## [106]    train-rmse:0.467529+0.007780    test-rmse:0.509707+0.047720 
## [107]    train-rmse:0.467017+0.007772    test-rmse:0.509627+0.047696 
## [108]    train-rmse:0.466484+0.007745    test-rmse:0.508958+0.047174 
## [109]    train-rmse:0.465983+0.007735    test-rmse:0.509293+0.047184 
## [110]    train-rmse:0.465497+0.007736    test-rmse:0.509280+0.046660 
## [111]    train-rmse:0.465005+0.007732    test-rmse:0.509353+0.046847 
## [112]    train-rmse:0.464533+0.007716    test-rmse:0.508781+0.046966 
## [113]    train-rmse:0.464066+0.007696    test-rmse:0.508091+0.047006 
## [114]    train-rmse:0.463586+0.007683    test-rmse:0.508399+0.046097 
## [115]    train-rmse:0.463122+0.007676    test-rmse:0.507758+0.045666 
## [116]    train-rmse:0.462663+0.007667    test-rmse:0.507335+0.045767 
## [117]    train-rmse:0.462200+0.007668    test-rmse:0.507384+0.046063 
## [118]    train-rmse:0.461751+0.007664    test-rmse:0.505945+0.044732 
## [119]    train-rmse:0.461312+0.007650    test-rmse:0.506611+0.044849 
## [120]    train-rmse:0.460887+0.007630    test-rmse:0.505952+0.045506 
## [121]    train-rmse:0.460447+0.007607    test-rmse:0.505648+0.045209 
## [122]    train-rmse:0.460004+0.007601    test-rmse:0.506085+0.045225 
## [123]    train-rmse:0.459589+0.007595    test-rmse:0.505899+0.045461 
## [124]    train-rmse:0.459160+0.007583    test-rmse:0.505251+0.045143 
## [125]    train-rmse:0.458750+0.007575    test-rmse:0.504959+0.044641 
## [126]    train-rmse:0.458340+0.007569    test-rmse:0.505572+0.044471 
## [127]    train-rmse:0.457939+0.007561    test-rmse:0.505356+0.044900 
## [128]    train-rmse:0.457534+0.007555    test-rmse:0.505586+0.045103 
## [129]    train-rmse:0.457118+0.007540    test-rmse:0.505459+0.044265 
## [130]    train-rmse:0.456732+0.007537    test-rmse:0.505046+0.044353 
## [131]    train-rmse:0.456326+0.007536    test-rmse:0.504910+0.044378 
## [132]    train-rmse:0.455943+0.007533    test-rmse:0.504647+0.044356 
## [133]    train-rmse:0.455561+0.007526    test-rmse:0.504119+0.044142 
## [134]    train-rmse:0.455178+0.007536    test-rmse:0.503749+0.043727 
## [135]    train-rmse:0.454799+0.007553    test-rmse:0.504072+0.043849 
## [136]    train-rmse:0.454428+0.007547    test-rmse:0.503786+0.044076 
## [137]    train-rmse:0.454054+0.007532    test-rmse:0.504230+0.044164 
## [138]    train-rmse:0.453683+0.007519    test-rmse:0.503611+0.044389 
## [139]    train-rmse:0.453323+0.007511    test-rmse:0.503577+0.043858 
## [140]    train-rmse:0.452973+0.007508    test-rmse:0.503147+0.043658 
## [141]    train-rmse:0.452625+0.007515    test-rmse:0.502960+0.043350 
## [142]    train-rmse:0.452276+0.007512    test-rmse:0.502954+0.043750 
## [143]    train-rmse:0.451926+0.007516    test-rmse:0.502752+0.043243 
## [144]    train-rmse:0.451581+0.007503    test-rmse:0.503049+0.043517 
## [145]    train-rmse:0.451226+0.007503    test-rmse:0.503264+0.043409 
## [146]    train-rmse:0.450898+0.007502    test-rmse:0.503237+0.043580 
## [147]    train-rmse:0.450575+0.007501    test-rmse:0.503228+0.043771 
## [148]    train-rmse:0.450262+0.007503    test-rmse:0.503084+0.043858 
## [149]    train-rmse:0.449928+0.007512    test-rmse:0.502981+0.043488 
## Stopping. Best iteration:
## [143]    train-rmse:0.451926+0.007516    test-rmse:0.502752+0.043243
## 
## 36 
## [1]  train-rmse:1.356000+0.025568    test-rmse:1.354857+0.114901 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.135532+0.021344    test-rmse:1.132845+0.105841 
## [3]  train-rmse:0.964354+0.019318    test-rmse:0.968752+0.105626 
## [4]  train-rmse:0.833096+0.016963    test-rmse:0.836522+0.106068 
## [5]  train-rmse:0.733261+0.015229    test-rmse:0.735726+0.101027 
## [6]  train-rmse:0.658213+0.014378    test-rmse:0.663534+0.098042 
## [7]  train-rmse:0.602461+0.013733    test-rmse:0.612945+0.094358 
## [8]  train-rmse:0.561234+0.013097    test-rmse:0.573353+0.087577 
## [9]  train-rmse:0.530369+0.012435    test-rmse:0.544514+0.087645 
## [10] train-rmse:0.506262+0.012099    test-rmse:0.527554+0.082007 
## [11] train-rmse:0.487867+0.011399    test-rmse:0.512850+0.077326 
## [12] train-rmse:0.472402+0.009659    test-rmse:0.499857+0.074359 
## [13] train-rmse:0.458717+0.010164    test-rmse:0.487112+0.069529 
## [14] train-rmse:0.447313+0.008957    test-rmse:0.477378+0.067104 
## [15] train-rmse:0.437994+0.007384    test-rmse:0.471752+0.067385 
## [16] train-rmse:0.429789+0.007540    test-rmse:0.468066+0.065324 
## [17] train-rmse:0.422635+0.007664    test-rmse:0.465868+0.065628 
## [18] train-rmse:0.416638+0.007481    test-rmse:0.463823+0.061346 
## [19] train-rmse:0.410975+0.007402    test-rmse:0.460871+0.060963 
## [20] train-rmse:0.405480+0.007242    test-rmse:0.459816+0.058949 
## [21] train-rmse:0.400481+0.006391    test-rmse:0.455615+0.056624 
## [22] train-rmse:0.396086+0.005913    test-rmse:0.452582+0.058808 
## [23] train-rmse:0.391266+0.005517    test-rmse:0.451540+0.058690 
## [24] train-rmse:0.386252+0.005328    test-rmse:0.451465+0.058192 
## [25] train-rmse:0.382730+0.004764    test-rmse:0.448636+0.058493 
## [26] train-rmse:0.378746+0.004400    test-rmse:0.448439+0.057724 
## [27] train-rmse:0.375641+0.004646    test-rmse:0.448729+0.057823 
## [28] train-rmse:0.372600+0.004401    test-rmse:0.447158+0.057200 
## [29] train-rmse:0.369296+0.003971    test-rmse:0.446100+0.057127 
## [30] train-rmse:0.366366+0.003700    test-rmse:0.446104+0.056074 
## [31] train-rmse:0.363320+0.003468    test-rmse:0.444771+0.056470 
## [32] train-rmse:0.360531+0.003483    test-rmse:0.445036+0.055360 
## [33] train-rmse:0.357897+0.003329    test-rmse:0.444078+0.055378 
## [34] train-rmse:0.355402+0.002991    test-rmse:0.443707+0.055304 
## [35] train-rmse:0.353212+0.003507    test-rmse:0.443930+0.054294 
## [36] train-rmse:0.350930+0.003490    test-rmse:0.443824+0.053876 
## [37] train-rmse:0.348506+0.003192    test-rmse:0.443816+0.054519 
## [38] train-rmse:0.346258+0.003295    test-rmse:0.443647+0.054539 
## [39] train-rmse:0.343941+0.002996    test-rmse:0.440703+0.055863 
## [40] train-rmse:0.341048+0.003729    test-rmse:0.439798+0.056332 
## [41] train-rmse:0.339037+0.004015    test-rmse:0.439291+0.055812 
## [42] train-rmse:0.336949+0.003736    test-rmse:0.439355+0.054859 
## [43] train-rmse:0.335282+0.003806    test-rmse:0.439443+0.055190 
## [44] train-rmse:0.333103+0.003640    test-rmse:0.437515+0.055009 
## [45] train-rmse:0.331104+0.003761    test-rmse:0.436667+0.055308 
## [46] train-rmse:0.329349+0.003922    test-rmse:0.437288+0.056032 
## [47] train-rmse:0.327792+0.003840    test-rmse:0.436976+0.055668 
## [48] train-rmse:0.326227+0.003653    test-rmse:0.436329+0.055262 
## [49] train-rmse:0.323942+0.003874    test-rmse:0.436786+0.055430 
## [50] train-rmse:0.322196+0.003704    test-rmse:0.437242+0.055152 
## [51] train-rmse:0.320349+0.003279    test-rmse:0.437160+0.055318 
## [52] train-rmse:0.318628+0.003309    test-rmse:0.437603+0.055554 
## [53] train-rmse:0.317042+0.003126    test-rmse:0.435783+0.056620 
## [54] train-rmse:0.315598+0.003306    test-rmse:0.434799+0.056970 
## [55] train-rmse:0.314234+0.003335    test-rmse:0.435140+0.057825 
## [56] train-rmse:0.312926+0.003254    test-rmse:0.435076+0.058016 
## [57] train-rmse:0.311960+0.003365    test-rmse:0.434486+0.058304 
## [58] train-rmse:0.310034+0.003252    test-rmse:0.433294+0.058900 
## [59] train-rmse:0.308192+0.003710    test-rmse:0.434026+0.058889 
## [60] train-rmse:0.306298+0.004046    test-rmse:0.433677+0.059034 
## [61] train-rmse:0.305046+0.004387    test-rmse:0.433437+0.058230 
## [62] train-rmse:0.303883+0.004835    test-rmse:0.433070+0.058284 
## [63] train-rmse:0.301869+0.004019    test-rmse:0.432695+0.058330 
## [64] train-rmse:0.300291+0.004205    test-rmse:0.432402+0.058955 
## [65] train-rmse:0.299219+0.004301    test-rmse:0.432170+0.059207 
## [66] train-rmse:0.297503+0.004478    test-rmse:0.431077+0.059162 
## [67] train-rmse:0.295818+0.004743    test-rmse:0.430754+0.058378 
## [68] train-rmse:0.293976+0.005110    test-rmse:0.430809+0.057721 
## [69] train-rmse:0.292713+0.004995    test-rmse:0.430201+0.057868 
## [70] train-rmse:0.291342+0.004910    test-rmse:0.430558+0.058413 
## [71] train-rmse:0.289735+0.005352    test-rmse:0.430583+0.057152 
## [72] train-rmse:0.288464+0.004975    test-rmse:0.430203+0.057558 
## [73] train-rmse:0.287321+0.004784    test-rmse:0.429923+0.058254 
## [74] train-rmse:0.285519+0.006082    test-rmse:0.430230+0.058716 
## [75] train-rmse:0.284347+0.005846    test-rmse:0.430085+0.058471 
## [76] train-rmse:0.283162+0.005668    test-rmse:0.430184+0.058244 
## [77] train-rmse:0.282177+0.005485    test-rmse:0.430082+0.058842 
## [78] train-rmse:0.281215+0.005377    test-rmse:0.429695+0.058793 
## [79] train-rmse:0.280269+0.005614    test-rmse:0.429437+0.058816 
## [80] train-rmse:0.279054+0.006083    test-rmse:0.429170+0.058861 
## [81] train-rmse:0.278102+0.005876    test-rmse:0.428184+0.059672 
## [82] train-rmse:0.277008+0.005515    test-rmse:0.427669+0.060254 
## [83] train-rmse:0.276321+0.005397    test-rmse:0.427465+0.059903 
## [84] train-rmse:0.275529+0.005487    test-rmse:0.426654+0.060230 
## [85] train-rmse:0.274375+0.006079    test-rmse:0.425780+0.060199 
## [86] train-rmse:0.273524+0.006166    test-rmse:0.425698+0.059653 
## [87] train-rmse:0.272268+0.005724    test-rmse:0.425795+0.059519 
## [88] train-rmse:0.271261+0.005682    test-rmse:0.425641+0.059029 
## [89] train-rmse:0.270289+0.005319    test-rmse:0.425225+0.059552 
## [90] train-rmse:0.269102+0.004979    test-rmse:0.425784+0.059798 
## [91] train-rmse:0.267872+0.005225    test-rmse:0.425167+0.059804 
## [92] train-rmse:0.267316+0.005154    test-rmse:0.424926+0.060069 
## [93] train-rmse:0.266441+0.005371    test-rmse:0.425266+0.059761 
## [94] train-rmse:0.265510+0.005936    test-rmse:0.424218+0.060198 
## [95] train-rmse:0.264318+0.006302    test-rmse:0.424926+0.059939 
## [96] train-rmse:0.263668+0.006368    test-rmse:0.424971+0.060125 
## [97] train-rmse:0.263078+0.006429    test-rmse:0.424584+0.059841 
## [98] train-rmse:0.262404+0.006429    test-rmse:0.423533+0.059851 
## [99] train-rmse:0.261680+0.006369    test-rmse:0.422977+0.059671 
## [100]    train-rmse:0.260988+0.006298    test-rmse:0.422483+0.060020 
## [101]    train-rmse:0.260438+0.006220    test-rmse:0.422689+0.059966 
## [102]    train-rmse:0.259437+0.005943    test-rmse:0.422467+0.060898 
## [103]    train-rmse:0.258506+0.006359    test-rmse:0.421966+0.060662 
## [104]    train-rmse:0.257953+0.006285    test-rmse:0.421495+0.061377 
## [105]    train-rmse:0.256429+0.006824    test-rmse:0.421599+0.061700 
## [106]    train-rmse:0.255988+0.006822    test-rmse:0.421379+0.061877 
## [107]    train-rmse:0.255477+0.006824    test-rmse:0.421494+0.062055 
## [108]    train-rmse:0.254597+0.007489    test-rmse:0.422068+0.062268 
## [109]    train-rmse:0.254099+0.007468    test-rmse:0.421655+0.062508 
## [110]    train-rmse:0.253582+0.007459    test-rmse:0.421676+0.062320 
## [111]    train-rmse:0.252270+0.007931    test-rmse:0.421130+0.061650 
## [112]    train-rmse:0.251291+0.008252    test-rmse:0.421049+0.062284 
## [113]    train-rmse:0.250521+0.008222    test-rmse:0.420896+0.062319 
## [114]    train-rmse:0.249796+0.008456    test-rmse:0.421126+0.062608 
## [115]    train-rmse:0.248529+0.008259    test-rmse:0.419833+0.062628 
## [116]    train-rmse:0.248008+0.008127    test-rmse:0.419684+0.062610 
## [117]    train-rmse:0.247348+0.008247    test-rmse:0.419572+0.062558 
## [118]    train-rmse:0.246305+0.007929    test-rmse:0.419821+0.062296 
## [119]    train-rmse:0.245506+0.007793    test-rmse:0.419215+0.062047 
## [120]    train-rmse:0.244815+0.007579    test-rmse:0.418384+0.061992 
## [121]    train-rmse:0.244176+0.007421    test-rmse:0.418600+0.062043 
## [122]    train-rmse:0.243485+0.007641    test-rmse:0.418030+0.062642 
## [123]    train-rmse:0.242589+0.008157    test-rmse:0.417536+0.062313 
## [124]    train-rmse:0.242121+0.008168    test-rmse:0.417253+0.062334 
## [125]    train-rmse:0.241356+0.007882    test-rmse:0.417183+0.062548 
## [126]    train-rmse:0.240182+0.007878    test-rmse:0.416656+0.062263 
## [127]    train-rmse:0.239397+0.007822    test-rmse:0.416501+0.062237 
## [128]    train-rmse:0.238261+0.007934    test-rmse:0.416650+0.061850 
## [129]    train-rmse:0.237176+0.008195    test-rmse:0.416326+0.062106 
## [130]    train-rmse:0.236488+0.008298    test-rmse:0.415985+0.061985 
## [131]    train-rmse:0.235955+0.008356    test-rmse:0.415663+0.061914 
## [132]    train-rmse:0.235313+0.008125    test-rmse:0.415909+0.062089 
## [133]    train-rmse:0.234614+0.007969    test-rmse:0.416446+0.062251 
## [134]    train-rmse:0.233937+0.007916    test-rmse:0.416866+0.062386 
## [135]    train-rmse:0.233131+0.008147    test-rmse:0.416895+0.061456 
## [136]    train-rmse:0.232457+0.007977    test-rmse:0.416548+0.061721 
## [137]    train-rmse:0.231770+0.007747    test-rmse:0.415532+0.062494 
## [138]    train-rmse:0.231238+0.008082    test-rmse:0.415689+0.062811 
## [139]    train-rmse:0.230598+0.008191    test-rmse:0.415536+0.062832 
## [140]    train-rmse:0.229743+0.009021    test-rmse:0.415604+0.063053 
## [141]    train-rmse:0.229049+0.008859    test-rmse:0.415179+0.063232 
## [142]    train-rmse:0.228391+0.009056    test-rmse:0.415014+0.062992 
## [143]    train-rmse:0.227772+0.009467    test-rmse:0.414501+0.062692 
## [144]    train-rmse:0.227316+0.009449    test-rmse:0.414548+0.063001 
## [145]    train-rmse:0.226495+0.010279    test-rmse:0.414743+0.063128 
## [146]    train-rmse:0.226018+0.010228    test-rmse:0.414360+0.063255 
## [147]    train-rmse:0.225521+0.010364    test-rmse:0.413991+0.063333 
## [148]    train-rmse:0.224941+0.010385    test-rmse:0.413936+0.063360 
## [149]    train-rmse:0.224209+0.010137    test-rmse:0.413642+0.063295 
## [150]    train-rmse:0.223821+0.010079    test-rmse:0.414068+0.063108 
## [151]    train-rmse:0.222990+0.009844    test-rmse:0.414104+0.063134 
## [152]    train-rmse:0.222583+0.009813    test-rmse:0.414302+0.063109 
## [153]    train-rmse:0.221955+0.009912    test-rmse:0.413606+0.063232 
## [154]    train-rmse:0.221179+0.010121    test-rmse:0.413092+0.063449 
## [155]    train-rmse:0.220701+0.010020    test-rmse:0.413428+0.063473 
## [156]    train-rmse:0.220070+0.009991    test-rmse:0.414095+0.063563 
## [157]    train-rmse:0.219419+0.010235    test-rmse:0.413655+0.063735 
## [158]    train-rmse:0.218811+0.010108    test-rmse:0.413768+0.063907 
## [159]    train-rmse:0.217990+0.010083    test-rmse:0.413698+0.063537 
## [160]    train-rmse:0.217324+0.009891    test-rmse:0.413988+0.063589 
## Stopping. Best iteration:
## [154]    train-rmse:0.221179+0.010121    test-rmse:0.413092+0.063449
## 
## 37 
## [1]  train-rmse:1.349303+0.024857    test-rmse:1.351289+0.117226 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.121657+0.020905    test-rmse:1.127461+0.112458 
## [3]  train-rmse:0.941300+0.017351    test-rmse:0.950938+0.112709 
## [4]  train-rmse:0.800317+0.016706    test-rmse:0.812759+0.105865 
## [5]  train-rmse:0.690022+0.014409    test-rmse:0.707483+0.106109 
## [6]  train-rmse:0.605854+0.013099    test-rmse:0.627896+0.097719 
## [7]  train-rmse:0.541281+0.011384    test-rmse:0.571297+0.092159 
## [8]  train-rmse:0.491772+0.010713    test-rmse:0.535229+0.087691 
## [9]  train-rmse:0.453469+0.011221    test-rmse:0.505050+0.081337 
## [10] train-rmse:0.424598+0.009889    test-rmse:0.483788+0.082016 
## [11] train-rmse:0.402697+0.010171    test-rmse:0.467061+0.076344 
## [12] train-rmse:0.384706+0.008891    test-rmse:0.456700+0.071813 
## [13] train-rmse:0.371918+0.008831    test-rmse:0.450639+0.069038 
## [14] train-rmse:0.361525+0.009152    test-rmse:0.445855+0.068255 
## [15] train-rmse:0.353044+0.008998    test-rmse:0.442980+0.065916 
## [16] train-rmse:0.345089+0.008645    test-rmse:0.441523+0.064183 
## [17] train-rmse:0.337210+0.008827    test-rmse:0.437576+0.061720 
## [18] train-rmse:0.331152+0.009084    test-rmse:0.435385+0.060888 
## [19] train-rmse:0.325500+0.008713    test-rmse:0.435197+0.059993 
## [20] train-rmse:0.320465+0.008487    test-rmse:0.433064+0.059274 
## [21] train-rmse:0.315757+0.008542    test-rmse:0.431541+0.061200 
## [22] train-rmse:0.311115+0.008619    test-rmse:0.431509+0.062331 
## [23] train-rmse:0.307267+0.008187    test-rmse:0.431470+0.061410 
## [24] train-rmse:0.302170+0.006502    test-rmse:0.427855+0.063720 
## [25] train-rmse:0.298849+0.006408    test-rmse:0.426493+0.062223 
## [26] train-rmse:0.294871+0.006997    test-rmse:0.425822+0.061721 
## [27] train-rmse:0.291384+0.006366    test-rmse:0.426326+0.062291 
## [28] train-rmse:0.287830+0.005464    test-rmse:0.423974+0.061699 
## [29] train-rmse:0.283986+0.005646    test-rmse:0.422350+0.061240 
## [30] train-rmse:0.280424+0.004636    test-rmse:0.422242+0.062215 
## [31] train-rmse:0.277061+0.004470    test-rmse:0.423039+0.062032 
## [32] train-rmse:0.274146+0.004186    test-rmse:0.423830+0.061768 
## [33] train-rmse:0.270915+0.005486    test-rmse:0.423626+0.062011 
## [34] train-rmse:0.267974+0.004710    test-rmse:0.421430+0.062191 
## [35] train-rmse:0.265327+0.004564    test-rmse:0.419298+0.064246 
## [36] train-rmse:0.262200+0.004455    test-rmse:0.418102+0.065172 
## [37] train-rmse:0.259813+0.004246    test-rmse:0.418396+0.065187 
## [38] train-rmse:0.256907+0.004178    test-rmse:0.418275+0.065821 
## [39] train-rmse:0.255090+0.004119    test-rmse:0.417332+0.066863 
## [40] train-rmse:0.251868+0.003034    test-rmse:0.417495+0.067821 
## [41] train-rmse:0.249766+0.002912    test-rmse:0.416468+0.067872 
## [42] train-rmse:0.246620+0.004252    test-rmse:0.415726+0.066549 
## [43] train-rmse:0.244244+0.003927    test-rmse:0.415849+0.066503 
## [44] train-rmse:0.241214+0.004210    test-rmse:0.414987+0.066808 
## [45] train-rmse:0.239208+0.003986    test-rmse:0.415400+0.065798 
## [46] train-rmse:0.237171+0.004827    test-rmse:0.414673+0.066045 
## [47] train-rmse:0.235329+0.004644    test-rmse:0.413951+0.066986 
## [48] train-rmse:0.233328+0.004390    test-rmse:0.412915+0.066598 
## [49] train-rmse:0.231465+0.004600    test-rmse:0.413136+0.066663 
## [50] train-rmse:0.229180+0.004288    test-rmse:0.411807+0.067266 
## [51] train-rmse:0.227391+0.004449    test-rmse:0.411704+0.067281 
## [52] train-rmse:0.224842+0.004866    test-rmse:0.412504+0.066837 
## [53] train-rmse:0.222456+0.004886    test-rmse:0.410955+0.066122 
## [54] train-rmse:0.220768+0.004884    test-rmse:0.410070+0.066037 
## [55] train-rmse:0.218823+0.004006    test-rmse:0.409915+0.066169 
## [56] train-rmse:0.217183+0.004105    test-rmse:0.410288+0.065724 
## [57] train-rmse:0.215326+0.004324    test-rmse:0.409995+0.065948 
## [58] train-rmse:0.213573+0.004270    test-rmse:0.410256+0.066258 
## [59] train-rmse:0.211729+0.004095    test-rmse:0.409192+0.066868 
## [60] train-rmse:0.210171+0.004367    test-rmse:0.409170+0.066732 
## [61] train-rmse:0.208427+0.004482    test-rmse:0.408766+0.067140 
## [62] train-rmse:0.206644+0.004440    test-rmse:0.409047+0.066956 
## [63] train-rmse:0.205399+0.004535    test-rmse:0.409104+0.066503 
## [64] train-rmse:0.203630+0.005458    test-rmse:0.409353+0.066956 
## [65] train-rmse:0.201948+0.005492    test-rmse:0.408586+0.067708 
## [66] train-rmse:0.199884+0.005671    test-rmse:0.408993+0.067050 
## [67] train-rmse:0.198552+0.005950    test-rmse:0.409365+0.067031 
## [68] train-rmse:0.197451+0.006057    test-rmse:0.409124+0.067441 
## [69] train-rmse:0.195788+0.006361    test-rmse:0.408918+0.067796 
## [70] train-rmse:0.194545+0.005955    test-rmse:0.408792+0.067527 
## [71] train-rmse:0.193074+0.006219    test-rmse:0.408820+0.067495 
## Stopping. Best iteration:
## [65] train-rmse:0.201948+0.005492    test-rmse:0.408586+0.067708
## 
## 38 
## [1]  train-rmse:1.346296+0.024386    test-rmse:1.347883+0.117628 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.115896+0.019858    test-rmse:1.122419+0.116495 
## [3]  train-rmse:0.930781+0.017171    test-rmse:0.939329+0.108755 
## [4]  train-rmse:0.785414+0.014380    test-rmse:0.799529+0.103257 
## [5]  train-rmse:0.670632+0.011802    test-rmse:0.688824+0.099921 
## [6]  train-rmse:0.580823+0.010046    test-rmse:0.610487+0.095448 
## [7]  train-rmse:0.510805+0.008127    test-rmse:0.552787+0.088891 
## [8]  train-rmse:0.456568+0.006463    test-rmse:0.507878+0.087140 
## [9]  train-rmse:0.414699+0.008084    test-rmse:0.480753+0.081907 
## [10] train-rmse:0.381537+0.007815    test-rmse:0.459118+0.079186 
## [11] train-rmse:0.356083+0.008266    test-rmse:0.443884+0.072457 
## [12] train-rmse:0.335742+0.006322    test-rmse:0.434823+0.072169 
## [13] train-rmse:0.320668+0.005835    test-rmse:0.430174+0.068238 
## [14] train-rmse:0.307121+0.005060    test-rmse:0.423221+0.065265 
## [15] train-rmse:0.298041+0.004723    test-rmse:0.421651+0.063705 
## [16] train-rmse:0.288290+0.005266    test-rmse:0.418161+0.061399 
## [17] train-rmse:0.281372+0.004509    test-rmse:0.416962+0.062470 
## [18] train-rmse:0.275206+0.003608    test-rmse:0.413405+0.060778 
## [19] train-rmse:0.268391+0.003666    test-rmse:0.411979+0.059123 
## [20] train-rmse:0.262318+0.003527    test-rmse:0.411722+0.059125 
## [21] train-rmse:0.257170+0.003618    test-rmse:0.411103+0.058903 
## [22] train-rmse:0.252197+0.004400    test-rmse:0.409697+0.060729 
## [23] train-rmse:0.247153+0.004225    test-rmse:0.408193+0.059733 
## [24] train-rmse:0.243584+0.004082    test-rmse:0.407605+0.058763 
## [25] train-rmse:0.238194+0.002770    test-rmse:0.406475+0.059567 
## [26] train-rmse:0.233928+0.002667    test-rmse:0.406905+0.058793 
## [27] train-rmse:0.229956+0.001681    test-rmse:0.407007+0.058535 
## [28] train-rmse:0.226043+0.002157    test-rmse:0.406344+0.057431 
## [29] train-rmse:0.221842+0.002587    test-rmse:0.406329+0.057389 
## [30] train-rmse:0.218368+0.002788    test-rmse:0.405587+0.057595 
## [31] train-rmse:0.214477+0.003438    test-rmse:0.403835+0.057258 
## [32] train-rmse:0.211289+0.003583    test-rmse:0.403387+0.056620 
## [33] train-rmse:0.208545+0.003792    test-rmse:0.402800+0.057350 
## [34] train-rmse:0.204846+0.004050    test-rmse:0.402978+0.056703 
## [35] train-rmse:0.200755+0.004032    test-rmse:0.402092+0.057436 
## [36] train-rmse:0.197874+0.003224    test-rmse:0.402101+0.057487 
## [37] train-rmse:0.195293+0.003034    test-rmse:0.401914+0.057495 
## [38] train-rmse:0.192544+0.003339    test-rmse:0.400798+0.057011 
## [39] train-rmse:0.190079+0.003709    test-rmse:0.399346+0.057035 
## [40] train-rmse:0.187363+0.003767    test-rmse:0.398307+0.056693 
## [41] train-rmse:0.184799+0.003334    test-rmse:0.397642+0.056493 
## [42] train-rmse:0.182419+0.002796    test-rmse:0.397417+0.057093 
## [43] train-rmse:0.179542+0.002669    test-rmse:0.397030+0.057169 
## [44] train-rmse:0.177943+0.002483    test-rmse:0.396296+0.057375 
## [45] train-rmse:0.175633+0.002035    test-rmse:0.394992+0.057683 
## [46] train-rmse:0.173728+0.002044    test-rmse:0.394662+0.058131 
## [47] train-rmse:0.171513+0.002122    test-rmse:0.395043+0.057675 
## [48] train-rmse:0.169483+0.002495    test-rmse:0.394796+0.058370 
## [49] train-rmse:0.167051+0.002595    test-rmse:0.394274+0.058830 
## [50] train-rmse:0.164384+0.003303    test-rmse:0.393527+0.057862 
## [51] train-rmse:0.162644+0.003447    test-rmse:0.393726+0.057902 
## [52] train-rmse:0.160659+0.003967    test-rmse:0.394357+0.058104 
## [53] train-rmse:0.159099+0.003972    test-rmse:0.392669+0.059422 
## [54] train-rmse:0.157044+0.004039    test-rmse:0.392656+0.059430 
## [55] train-rmse:0.155597+0.003879    test-rmse:0.392535+0.059331 
## [56] train-rmse:0.154189+0.003935    test-rmse:0.392288+0.059581 
## [57] train-rmse:0.152498+0.004753    test-rmse:0.392959+0.059114 
## [58] train-rmse:0.150893+0.004938    test-rmse:0.392045+0.060451 
## [59] train-rmse:0.148566+0.005489    test-rmse:0.392133+0.060673 
## [60] train-rmse:0.146808+0.005720    test-rmse:0.393154+0.059977 
## [61] train-rmse:0.144859+0.005567    test-rmse:0.393094+0.059780 
## [62] train-rmse:0.143260+0.006276    test-rmse:0.392446+0.059186 
## [63] train-rmse:0.140509+0.006569    test-rmse:0.392149+0.059306 
## [64] train-rmse:0.138949+0.005976    test-rmse:0.392337+0.059187 
## Stopping. Best iteration:
## [58] train-rmse:0.150893+0.004938    test-rmse:0.392045+0.060451
## 
## 39 
## [1]  train-rmse:1.344515+0.024688    test-rmse:1.346046+0.114712 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.112177+0.020266    test-rmse:1.114815+0.111016 
## [3]  train-rmse:0.925776+0.017946    test-rmse:0.932016+0.103441 
## [4]  train-rmse:0.777994+0.014803    test-rmse:0.792661+0.100473 
## [5]  train-rmse:0.659102+0.012898    test-rmse:0.686104+0.095731 
## [6]  train-rmse:0.565399+0.011627    test-rmse:0.604316+0.089491 
## [7]  train-rmse:0.491853+0.009219    test-rmse:0.545027+0.084397 
## [8]  train-rmse:0.434305+0.008372    test-rmse:0.502127+0.083218 
## [9]  train-rmse:0.387417+0.006879    test-rmse:0.472571+0.084328 
## [10] train-rmse:0.352353+0.006284    test-rmse:0.454168+0.084210 
## [11] train-rmse:0.325218+0.006906    test-rmse:0.438600+0.079984 
## [12] train-rmse:0.301178+0.009324    test-rmse:0.428576+0.077461 
## [13] train-rmse:0.284684+0.010332    test-rmse:0.423862+0.076604 
## [14] train-rmse:0.270132+0.010160    test-rmse:0.418588+0.074838 
## [15] train-rmse:0.257523+0.011168    test-rmse:0.416467+0.071729 
## [16] train-rmse:0.246373+0.011032    test-rmse:0.413987+0.070623 
## [17] train-rmse:0.238177+0.010684    test-rmse:0.410631+0.071353 
## [18] train-rmse:0.230625+0.011403    test-rmse:0.410433+0.070357 
## [19] train-rmse:0.222949+0.010438    test-rmse:0.409811+0.069718 
## [20] train-rmse:0.215367+0.007100    test-rmse:0.407038+0.071059 
## [21] train-rmse:0.209406+0.007649    test-rmse:0.407824+0.069303 
## [22] train-rmse:0.204324+0.007861    test-rmse:0.407194+0.067605 
## [23] train-rmse:0.199150+0.005708    test-rmse:0.405973+0.067067 
## [24] train-rmse:0.194140+0.005560    test-rmse:0.404878+0.066863 
## [25] train-rmse:0.188010+0.006309    test-rmse:0.404023+0.064896 
## [26] train-rmse:0.182818+0.007608    test-rmse:0.403282+0.065938 
## [27] train-rmse:0.179535+0.007561    test-rmse:0.403208+0.065911 
## [28] train-rmse:0.174580+0.007472    test-rmse:0.403734+0.065218 
## [29] train-rmse:0.169950+0.006527    test-rmse:0.403097+0.065939 
## [30] train-rmse:0.163693+0.004870    test-rmse:0.403033+0.066932 
## [31] train-rmse:0.159947+0.004775    test-rmse:0.402904+0.066265 
## [32] train-rmse:0.155705+0.005278    test-rmse:0.402841+0.066299 
## [33] train-rmse:0.152230+0.005694    test-rmse:0.402384+0.066378 
## [34] train-rmse:0.149233+0.005732    test-rmse:0.401638+0.065967 
## [35] train-rmse:0.145790+0.005317    test-rmse:0.400096+0.066382 
## [36] train-rmse:0.143204+0.004990    test-rmse:0.401162+0.066071 
## [37] train-rmse:0.140646+0.004279    test-rmse:0.400987+0.066548 
## [38] train-rmse:0.138216+0.004114    test-rmse:0.401102+0.067330 
## [39] train-rmse:0.136079+0.003952    test-rmse:0.401033+0.067131 
## [40] train-rmse:0.134019+0.003636    test-rmse:0.401194+0.066922 
## [41] train-rmse:0.131712+0.003589    test-rmse:0.400663+0.066290 
## Stopping. Best iteration:
## [35] train-rmse:0.145790+0.005317    test-rmse:0.400096+0.066382
## 
## 40 
## [1]  train-rmse:1.343971+0.024734    test-rmse:1.344762+0.113596 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.110474+0.020298    test-rmse:1.112438+0.108652 
## [3]  train-rmse:0.922551+0.018112    test-rmse:0.927979+0.099771 
## [4]  train-rmse:0.772836+0.015385    test-rmse:0.786611+0.097423 
## [5]  train-rmse:0.653415+0.014169    test-rmse:0.679351+0.093389 
## [6]  train-rmse:0.556691+0.012902    test-rmse:0.595899+0.087546 
## [7]  train-rmse:0.480613+0.011482    test-rmse:0.535298+0.085734 
## [8]  train-rmse:0.419830+0.010483    test-rmse:0.492504+0.084637 
## [9]  train-rmse:0.371526+0.010358    test-rmse:0.464180+0.080985 
## [10] train-rmse:0.332169+0.009621    test-rmse:0.445482+0.080389 
## [11] train-rmse:0.301774+0.008908    test-rmse:0.432914+0.078741 
## [12] train-rmse:0.276131+0.009873    test-rmse:0.422634+0.075863 
## [13] train-rmse:0.254472+0.011051    test-rmse:0.415067+0.073499 
## [14] train-rmse:0.238178+0.010553    test-rmse:0.411488+0.073177 
## [15] train-rmse:0.222073+0.005569    test-rmse:0.408166+0.072754 
## [16] train-rmse:0.207857+0.004203    test-rmse:0.404842+0.071192 
## [17] train-rmse:0.196257+0.003993    test-rmse:0.401165+0.071455 
## [18] train-rmse:0.187287+0.003450    test-rmse:0.398720+0.070905 
## [19] train-rmse:0.179081+0.005460    test-rmse:0.398025+0.068381 
## [20] train-rmse:0.172226+0.006439    test-rmse:0.396895+0.065845 
## [21] train-rmse:0.165491+0.004819    test-rmse:0.397144+0.067003 
## [22] train-rmse:0.160313+0.004943    test-rmse:0.397689+0.066648 
## [23] train-rmse:0.155126+0.004550    test-rmse:0.397847+0.063529 
## [24] train-rmse:0.150053+0.004933    test-rmse:0.397974+0.063673 
## [25] train-rmse:0.145658+0.005660    test-rmse:0.397539+0.062982 
## [26] train-rmse:0.141308+0.006019    test-rmse:0.398688+0.061492 
## Stopping. Best iteration:
## [20] train-rmse:0.172226+0.006439    test-rmse:0.396895+0.065845
## 
## 41 
## [1]  train-rmse:1.343703+0.024735    test-rmse:1.345687+0.113081 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.109460+0.020579    test-rmse:1.111858+0.108635 
## [3]  train-rmse:0.920717+0.018522    test-rmse:0.931229+0.100422 
## [4]  train-rmse:0.769940+0.015777    test-rmse:0.787991+0.098432 
## [5]  train-rmse:0.648383+0.014548    test-rmse:0.679991+0.096047 
## [6]  train-rmse:0.549788+0.013289    test-rmse:0.598333+0.093432 
## [7]  train-rmse:0.470716+0.012210    test-rmse:0.537912+0.085931 
## [8]  train-rmse:0.406679+0.011396    test-rmse:0.499322+0.084391 
## [9]  train-rmse:0.355068+0.010799    test-rmse:0.468069+0.080129 
## [10] train-rmse:0.313191+0.011127    test-rmse:0.447609+0.078018 
## [11] train-rmse:0.279207+0.010346    test-rmse:0.436061+0.076079 
## [12] train-rmse:0.251884+0.010595    test-rmse:0.429494+0.075570 
## [13] train-rmse:0.227941+0.011902    test-rmse:0.423405+0.073190 
## [14] train-rmse:0.209864+0.011793    test-rmse:0.419065+0.071037 
## [15] train-rmse:0.192658+0.012090    test-rmse:0.415951+0.070185 
## [16] train-rmse:0.178971+0.011459    test-rmse:0.415139+0.070003 
## [17] train-rmse:0.164224+0.009076    test-rmse:0.412455+0.067240 
## [18] train-rmse:0.154691+0.008793    test-rmse:0.410552+0.066094 
## [19] train-rmse:0.147044+0.008754    test-rmse:0.410225+0.065927 
## [20] train-rmse:0.140122+0.009565    test-rmse:0.409739+0.065000 
## [21] train-rmse:0.133177+0.007586    test-rmse:0.409537+0.064614 
## [22] train-rmse:0.128144+0.008518    test-rmse:0.409303+0.064711 
## [23] train-rmse:0.122874+0.008029    test-rmse:0.409049+0.065370 
## [24] train-rmse:0.116853+0.007912    test-rmse:0.408540+0.064144 
## [25] train-rmse:0.111363+0.008558    test-rmse:0.408969+0.063905 
## [26] train-rmse:0.106922+0.008532    test-rmse:0.407171+0.062773 
## [27] train-rmse:0.103541+0.009048    test-rmse:0.406822+0.062907 
## [28] train-rmse:0.100694+0.008369    test-rmse:0.405778+0.061704 
## [29] train-rmse:0.097053+0.008043    test-rmse:0.406562+0.060251 
## [30] train-rmse:0.092843+0.007331    test-rmse:0.405838+0.059411 
## [31] train-rmse:0.089438+0.006907    test-rmse:0.405875+0.059248 
## [32] train-rmse:0.085485+0.006240    test-rmse:0.406732+0.058424 
## [33] train-rmse:0.081826+0.005880    test-rmse:0.406476+0.058054 
## [34] train-rmse:0.078815+0.005293    test-rmse:0.406433+0.057751 
## Stopping. Best iteration:
## [28] train-rmse:0.100694+0.008369    test-rmse:0.405778+0.061704
## 
## 42 
## [1]  train-rmse:1.342539+0.025066    test-rmse:1.338695+0.110602 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.124737+0.021556    test-rmse:1.120984+0.105192 
## [3]  train-rmse:0.967659+0.019461    test-rmse:0.964062+0.100529 
## [4]  train-rmse:0.857519+0.018415    test-rmse:0.854154+0.096452 
## [5]  train-rmse:0.782551+0.018023    test-rmse:0.779464+0.093003 
## [6]  train-rmse:0.732927+0.017959    test-rmse:0.730126+0.090231 
## [7]  train-rmse:0.700846+0.018022    test-rmse:0.698303+0.088100 
## [8]  train-rmse:0.677317+0.017109    test-rmse:0.679031+0.088552 
## [9]  train-rmse:0.658842+0.017079    test-rmse:0.657004+0.086601 
## [10] train-rmse:0.644274+0.016363    test-rmse:0.648260+0.084177 
## [11] train-rmse:0.632104+0.014873    test-rmse:0.634133+0.079797 
## [12] train-rmse:0.620850+0.014538    test-rmse:0.622347+0.079810 
## [13] train-rmse:0.611782+0.013606    test-rmse:0.617346+0.078157 
## [14] train-rmse:0.603683+0.013254    test-rmse:0.607143+0.077619 
## [15] train-rmse:0.596657+0.012803    test-rmse:0.601489+0.072846 
## [16] train-rmse:0.589793+0.012683    test-rmse:0.595188+0.069832 
## [17] train-rmse:0.584001+0.012096    test-rmse:0.592222+0.071404 
## [18] train-rmse:0.578804+0.011780    test-rmse:0.584148+0.069954 
## [19] train-rmse:0.573794+0.011741    test-rmse:0.581605+0.066979 
## [20] train-rmse:0.569288+0.011631    test-rmse:0.580816+0.066394 
## [21] train-rmse:0.565217+0.011428    test-rmse:0.577092+0.064092 
## [22] train-rmse:0.561453+0.011232    test-rmse:0.575037+0.063210 
## [23] train-rmse:0.557921+0.011069    test-rmse:0.570511+0.062983 
## [24] train-rmse:0.554549+0.010870    test-rmse:0.566509+0.063889 
## [25] train-rmse:0.551367+0.010823    test-rmse:0.565354+0.063201 
## [26] train-rmse:0.548502+0.010693    test-rmse:0.563592+0.061192 
## [27] train-rmse:0.545796+0.010687    test-rmse:0.561020+0.060527 
## [28] train-rmse:0.543291+0.010482    test-rmse:0.560155+0.061453 
## [29] train-rmse:0.540867+0.010314    test-rmse:0.558291+0.061459 
## [30] train-rmse:0.538499+0.010250    test-rmse:0.557352+0.059119 
## [31] train-rmse:0.536351+0.010200    test-rmse:0.556493+0.059956 
## [32] train-rmse:0.534156+0.010107    test-rmse:0.554246+0.059507 
## [33] train-rmse:0.532212+0.009982    test-rmse:0.552835+0.059555 
## [34] train-rmse:0.530285+0.009877    test-rmse:0.551544+0.059990 
## [35] train-rmse:0.528465+0.009784    test-rmse:0.549434+0.059155 
## [36] train-rmse:0.526653+0.009665    test-rmse:0.549310+0.058411 
## [37] train-rmse:0.524833+0.009501    test-rmse:0.547349+0.057719 
## [38] train-rmse:0.523105+0.009435    test-rmse:0.547113+0.057126 
## [39] train-rmse:0.521465+0.009362    test-rmse:0.546824+0.057847 
## [40] train-rmse:0.519853+0.009244    test-rmse:0.545608+0.058326 
## [41] train-rmse:0.518263+0.009171    test-rmse:0.545090+0.056778 
## [42] train-rmse:0.516785+0.009165    test-rmse:0.541885+0.056011 
## [43] train-rmse:0.515334+0.009111    test-rmse:0.541277+0.056007 
## [44] train-rmse:0.513799+0.009049    test-rmse:0.540887+0.056508 
## [45] train-rmse:0.512423+0.008969    test-rmse:0.541345+0.055838 
## [46] train-rmse:0.511046+0.008883    test-rmse:0.538626+0.056137 
## [47] train-rmse:0.509684+0.008855    test-rmse:0.537299+0.054812 
## [48] train-rmse:0.508335+0.008839    test-rmse:0.536119+0.054296 
## [49] train-rmse:0.507039+0.008798    test-rmse:0.534438+0.054367 
## [50] train-rmse:0.505764+0.008729    test-rmse:0.534456+0.054587 
## [51] train-rmse:0.504540+0.008639    test-rmse:0.533100+0.055003 
## [52] train-rmse:0.503326+0.008602    test-rmse:0.532136+0.054427 
## [53] train-rmse:0.502124+0.008567    test-rmse:0.531123+0.053325 
## [54] train-rmse:0.500943+0.008537    test-rmse:0.531636+0.052764 
## [55] train-rmse:0.499837+0.008506    test-rmse:0.531360+0.053794 
## [56] train-rmse:0.498735+0.008464    test-rmse:0.531176+0.053741 
## [57] train-rmse:0.497635+0.008427    test-rmse:0.529598+0.054719 
## [58] train-rmse:0.496566+0.008403    test-rmse:0.528884+0.053945 
## [59] train-rmse:0.495514+0.008406    test-rmse:0.528359+0.053952 
## [60] train-rmse:0.494479+0.008387    test-rmse:0.527763+0.053574 
## [61] train-rmse:0.493451+0.008351    test-rmse:0.528454+0.053365 
## [62] train-rmse:0.492447+0.008292    test-rmse:0.527046+0.052526 
## [63] train-rmse:0.491456+0.008247    test-rmse:0.525232+0.052247 
## [64] train-rmse:0.490489+0.008203    test-rmse:0.523719+0.052171 
## [65] train-rmse:0.489550+0.008174    test-rmse:0.523041+0.052064 
## [66] train-rmse:0.488625+0.008190    test-rmse:0.522752+0.052972 
## [67] train-rmse:0.487719+0.008167    test-rmse:0.522009+0.052221 
## [68] train-rmse:0.486824+0.008149    test-rmse:0.522609+0.051583 
## [69] train-rmse:0.485973+0.008129    test-rmse:0.522814+0.051005 
## [70] train-rmse:0.485135+0.008113    test-rmse:0.521465+0.050518 
## [71] train-rmse:0.484314+0.008108    test-rmse:0.521568+0.050675 
## [72] train-rmse:0.483466+0.008081    test-rmse:0.520959+0.050468 
## [73] train-rmse:0.482626+0.008070    test-rmse:0.520556+0.051199 
## [74] train-rmse:0.481825+0.008064    test-rmse:0.519939+0.051225 
## [75] train-rmse:0.481049+0.008035    test-rmse:0.519839+0.051117 
## [76] train-rmse:0.480283+0.007994    test-rmse:0.519676+0.050248 
## [77] train-rmse:0.479532+0.007997    test-rmse:0.518364+0.050277 
## [78] train-rmse:0.478792+0.008002    test-rmse:0.517889+0.050061 
## [79] train-rmse:0.478083+0.008005    test-rmse:0.517394+0.050700 
## [80] train-rmse:0.477391+0.007978    test-rmse:0.517750+0.050514 
## [81] train-rmse:0.476702+0.007947    test-rmse:0.516453+0.050098 
## [82] train-rmse:0.476007+0.007928    test-rmse:0.516072+0.049541 
## [83] train-rmse:0.475317+0.007916    test-rmse:0.515721+0.049548 
## [84] train-rmse:0.474627+0.007897    test-rmse:0.515407+0.049419 
## [85] train-rmse:0.473963+0.007885    test-rmse:0.512541+0.047839 
## [86] train-rmse:0.473322+0.007863    test-rmse:0.513201+0.048335 
## [87] train-rmse:0.472684+0.007844    test-rmse:0.513348+0.047630 
## [88] train-rmse:0.472034+0.007830    test-rmse:0.512986+0.047961 
## [89] train-rmse:0.471379+0.007802    test-rmse:0.513458+0.047635 
## [90] train-rmse:0.470779+0.007797    test-rmse:0.512859+0.047062 
## [91] train-rmse:0.470175+0.007801    test-rmse:0.512180+0.047534 
## [92] train-rmse:0.469561+0.007786    test-rmse:0.512353+0.047584 
## [93] train-rmse:0.468978+0.007792    test-rmse:0.512203+0.047908 
## [94] train-rmse:0.468397+0.007781    test-rmse:0.511822+0.047714 
## [95] train-rmse:0.467816+0.007770    test-rmse:0.511580+0.047028 
## [96] train-rmse:0.467255+0.007758    test-rmse:0.511766+0.046857 
## [97] train-rmse:0.466700+0.007751    test-rmse:0.510048+0.046575 
## [98] train-rmse:0.466156+0.007722    test-rmse:0.510259+0.046553 
## [99] train-rmse:0.465600+0.007702    test-rmse:0.510232+0.046621 
## [100]    train-rmse:0.465061+0.007692    test-rmse:0.510013+0.046936 
## [101]    train-rmse:0.464503+0.007710    test-rmse:0.509632+0.046714 
## [102]    train-rmse:0.463973+0.007668    test-rmse:0.508726+0.046708 
## [103]    train-rmse:0.463469+0.007658    test-rmse:0.508540+0.046070 
## [104]    train-rmse:0.462957+0.007647    test-rmse:0.508425+0.046454 
## [105]    train-rmse:0.462453+0.007658    test-rmse:0.507620+0.046593 
## [106]    train-rmse:0.461956+0.007644    test-rmse:0.508130+0.046393 
## [107]    train-rmse:0.461462+0.007629    test-rmse:0.508073+0.046391 
## [108]    train-rmse:0.460978+0.007621    test-rmse:0.507844+0.045853 
## [109]    train-rmse:0.460496+0.007603    test-rmse:0.506542+0.044173 
## [110]    train-rmse:0.460020+0.007581    test-rmse:0.506855+0.044488 
## [111]    train-rmse:0.459544+0.007575    test-rmse:0.506762+0.044574 
## [112]    train-rmse:0.459066+0.007569    test-rmse:0.505951+0.043926 
## [113]    train-rmse:0.458609+0.007565    test-rmse:0.505275+0.044056 
## [114]    train-rmse:0.458163+0.007559    test-rmse:0.505374+0.043566 
## [115]    train-rmse:0.457709+0.007554    test-rmse:0.505112+0.043404 
## [116]    train-rmse:0.457261+0.007544    test-rmse:0.504991+0.043412 
## [117]    train-rmse:0.456822+0.007537    test-rmse:0.505568+0.043296 
## [118]    train-rmse:0.456387+0.007538    test-rmse:0.505311+0.043424 
## [119]    train-rmse:0.455947+0.007513    test-rmse:0.505087+0.043954 
## [120]    train-rmse:0.455504+0.007522    test-rmse:0.504874+0.044240 
## [121]    train-rmse:0.455064+0.007516    test-rmse:0.504973+0.043957 
## [122]    train-rmse:0.454658+0.007510    test-rmse:0.504519+0.044324 
## [123]    train-rmse:0.454245+0.007492    test-rmse:0.504253+0.044205 
## [124]    train-rmse:0.453838+0.007498    test-rmse:0.503780+0.043869 
## [125]    train-rmse:0.453445+0.007503    test-rmse:0.504793+0.043596 
## [126]    train-rmse:0.453065+0.007497    test-rmse:0.504471+0.043336 
## [127]    train-rmse:0.452690+0.007492    test-rmse:0.504263+0.043134 
## [128]    train-rmse:0.452307+0.007492    test-rmse:0.503877+0.043325 
## [129]    train-rmse:0.451919+0.007500    test-rmse:0.503604+0.043077 
## [130]    train-rmse:0.451536+0.007522    test-rmse:0.503419+0.042874 
## [131]    train-rmse:0.451177+0.007522    test-rmse:0.502987+0.042796 
## [132]    train-rmse:0.450813+0.007513    test-rmse:0.502703+0.043047 
## [133]    train-rmse:0.450445+0.007513    test-rmse:0.503093+0.043222 
## [134]    train-rmse:0.450083+0.007513    test-rmse:0.503854+0.043296 
## [135]    train-rmse:0.449733+0.007510    test-rmse:0.503084+0.043433 
## [136]    train-rmse:0.449386+0.007508    test-rmse:0.503182+0.043555 
## [137]    train-rmse:0.449040+0.007500    test-rmse:0.502787+0.043577 
## [138]    train-rmse:0.448706+0.007494    test-rmse:0.502102+0.043218 
## [139]    train-rmse:0.448378+0.007490    test-rmse:0.501925+0.043277 
## [140]    train-rmse:0.448047+0.007493    test-rmse:0.501722+0.043429 
## [141]    train-rmse:0.447712+0.007498    test-rmse:0.501846+0.042977 
## [142]    train-rmse:0.447372+0.007513    test-rmse:0.501265+0.042436 
## [143]    train-rmse:0.447045+0.007514    test-rmse:0.501776+0.042356 
## [144]    train-rmse:0.446709+0.007497    test-rmse:0.501620+0.042835 
## [145]    train-rmse:0.446389+0.007490    test-rmse:0.501143+0.042970 
## [146]    train-rmse:0.446069+0.007486    test-rmse:0.500939+0.043081 
## [147]    train-rmse:0.445755+0.007483    test-rmse:0.501170+0.043578 
## [148]    train-rmse:0.445433+0.007486    test-rmse:0.501090+0.043595 
## [149]    train-rmse:0.445119+0.007487    test-rmse:0.501131+0.042999 
## [150]    train-rmse:0.444810+0.007492    test-rmse:0.501070+0.043040 
## [151]    train-rmse:0.444515+0.007495    test-rmse:0.500576+0.042959 
## [152]    train-rmse:0.444216+0.007499    test-rmse:0.500622+0.042582 
## [153]    train-rmse:0.443914+0.007502    test-rmse:0.500020+0.042522 
## [154]    train-rmse:0.443625+0.007509    test-rmse:0.499782+0.042428 
## [155]    train-rmse:0.443327+0.007513    test-rmse:0.499558+0.042200 
## [156]    train-rmse:0.443014+0.007510    test-rmse:0.499797+0.042527 
## [157]    train-rmse:0.442712+0.007514    test-rmse:0.498989+0.041289 
## [158]    train-rmse:0.442428+0.007514    test-rmse:0.499423+0.041302 
## [159]    train-rmse:0.442136+0.007521    test-rmse:0.499098+0.040891 
## [160]    train-rmse:0.441854+0.007531    test-rmse:0.499138+0.041348 
## [161]    train-rmse:0.441565+0.007523    test-rmse:0.498347+0.040872 
## [162]    train-rmse:0.441291+0.007529    test-rmse:0.497714+0.040644 
## [163]    train-rmse:0.441005+0.007535    test-rmse:0.498022+0.040736 
## [164]    train-rmse:0.440739+0.007540    test-rmse:0.497770+0.040928 
## [165]    train-rmse:0.440461+0.007548    test-rmse:0.498007+0.040616 
## [166]    train-rmse:0.440189+0.007546    test-rmse:0.498067+0.040605 
## [167]    train-rmse:0.439928+0.007548    test-rmse:0.497710+0.040278 
## [168]    train-rmse:0.439668+0.007552    test-rmse:0.498173+0.040512 
## [169]    train-rmse:0.439410+0.007559    test-rmse:0.497875+0.040690 
## [170]    train-rmse:0.439155+0.007561    test-rmse:0.498331+0.040837 
## [171]    train-rmse:0.438886+0.007570    test-rmse:0.498469+0.040513 
## [172]    train-rmse:0.438632+0.007566    test-rmse:0.498685+0.040732 
## [173]    train-rmse:0.438382+0.007569    test-rmse:0.498958+0.040865 
## Stopping. Best iteration:
## [167]    train-rmse:0.439928+0.007548    test-rmse:0.497710+0.040278
## 
## 43 
## [1]  train-rmse:1.328379+0.025135    test-rmse:1.327577+0.114729 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.092918+0.020613    test-rmse:1.090374+0.104998 
## [3]  train-rmse:0.915933+0.018686    test-rmse:0.921362+0.104727 
## [4]  train-rmse:0.784651+0.016176    test-rmse:0.789144+0.105113 
## [5]  train-rmse:0.688453+0.014358    test-rmse:0.691891+0.099912 
## [6]  train-rmse:0.618682+0.013951    test-rmse:0.625895+0.096326 
## [7]  train-rmse:0.568581+0.013195    test-rmse:0.579755+0.091701 
## [8]  train-rmse:0.532768+0.012721    test-rmse:0.544563+0.085015 
## [9]  train-rmse:0.506112+0.012313    test-rmse:0.527841+0.080795 
## [10] train-rmse:0.486341+0.011591    test-rmse:0.511903+0.078718 
## [11] train-rmse:0.470281+0.009741    test-rmse:0.494022+0.075445 
## [12] train-rmse:0.454977+0.009930    test-rmse:0.481701+0.071829 
## [13] train-rmse:0.443309+0.008664    test-rmse:0.472256+0.066458 
## [14] train-rmse:0.434201+0.008272    test-rmse:0.469379+0.066572 
## [15] train-rmse:0.425152+0.007191    test-rmse:0.465936+0.064340 
## [16] train-rmse:0.418329+0.007799    test-rmse:0.462944+0.061804 
## [17] train-rmse:0.411320+0.006439    test-rmse:0.458212+0.059978 
## [18] train-rmse:0.405710+0.006108    test-rmse:0.455991+0.057484 
## [19] train-rmse:0.400139+0.005466    test-rmse:0.455554+0.057429 
## [20] train-rmse:0.395571+0.005421    test-rmse:0.452989+0.057006 
## [21] train-rmse:0.390797+0.004626    test-rmse:0.449523+0.057139 
## [22] train-rmse:0.385933+0.004427    test-rmse:0.448087+0.055959 
## [23] train-rmse:0.382238+0.004234    test-rmse:0.447747+0.056366 
## [24] train-rmse:0.378402+0.003868    test-rmse:0.445477+0.057253 
## [25] train-rmse:0.374811+0.003816    test-rmse:0.446714+0.056037 
## [26] train-rmse:0.370200+0.003917    test-rmse:0.444521+0.055121 
## [27] train-rmse:0.366511+0.003728    test-rmse:0.443856+0.055387 
## [28] train-rmse:0.363724+0.003848    test-rmse:0.443080+0.054583 
## [29] train-rmse:0.360553+0.003795    test-rmse:0.442604+0.052871 
## [30] train-rmse:0.357269+0.003691    test-rmse:0.443826+0.053168 
## [31] train-rmse:0.354336+0.003339    test-rmse:0.443348+0.053014 
## [32] train-rmse:0.351570+0.003323    test-rmse:0.441944+0.054209 
## [33] train-rmse:0.348632+0.003090    test-rmse:0.441605+0.052645 
## [34] train-rmse:0.346179+0.003383    test-rmse:0.441170+0.052471 
## [35] train-rmse:0.343814+0.003154    test-rmse:0.440288+0.049192 
## [36] train-rmse:0.340680+0.003497    test-rmse:0.439476+0.048457 
## [37] train-rmse:0.338145+0.003367    test-rmse:0.438705+0.048419 
## [38] train-rmse:0.335825+0.003373    test-rmse:0.438339+0.048231 
## [39] train-rmse:0.333554+0.003980    test-rmse:0.437641+0.048396 
## [40] train-rmse:0.331495+0.004080    test-rmse:0.437688+0.049405 
## [41] train-rmse:0.329010+0.004176    test-rmse:0.436569+0.050809 
## [42] train-rmse:0.326957+0.004480    test-rmse:0.436577+0.049438 
## [43] train-rmse:0.325328+0.004161    test-rmse:0.435706+0.049286 
## [44] train-rmse:0.323558+0.004109    test-rmse:0.435650+0.049575 
## [45] train-rmse:0.321724+0.004169    test-rmse:0.435811+0.050053 
## [46] train-rmse:0.320041+0.003669    test-rmse:0.434254+0.052384 
## [47] train-rmse:0.317978+0.003735    test-rmse:0.434543+0.053103 
## [48] train-rmse:0.316188+0.004072    test-rmse:0.434323+0.053012 
## [49] train-rmse:0.314188+0.004487    test-rmse:0.433762+0.052474 
## [50] train-rmse:0.312267+0.004900    test-rmse:0.434485+0.053064 
## [51] train-rmse:0.310703+0.004972    test-rmse:0.433821+0.053694 
## [52] train-rmse:0.308572+0.004833    test-rmse:0.432610+0.054492 
## [53] train-rmse:0.307169+0.005049    test-rmse:0.432388+0.054471 
## [54] train-rmse:0.306097+0.005322    test-rmse:0.432317+0.055016 
## [55] train-rmse:0.304512+0.005458    test-rmse:0.431238+0.055499 
## [56] train-rmse:0.303059+0.005281    test-rmse:0.432071+0.054866 
## [57] train-rmse:0.301756+0.005171    test-rmse:0.431406+0.054959 
## [58] train-rmse:0.300020+0.004918    test-rmse:0.430988+0.054891 
## [59] train-rmse:0.298572+0.004115    test-rmse:0.430715+0.055352 
## [60] train-rmse:0.297477+0.004055    test-rmse:0.430643+0.054749 
## [61] train-rmse:0.296168+0.003812    test-rmse:0.429577+0.055038 
## [62] train-rmse:0.294913+0.003751    test-rmse:0.429471+0.054887 
## [63] train-rmse:0.293777+0.003890    test-rmse:0.429386+0.055455 
## [64] train-rmse:0.292346+0.003902    test-rmse:0.429264+0.055774 
## [65] train-rmse:0.291438+0.003819    test-rmse:0.428065+0.057982 
## [66] train-rmse:0.290025+0.004597    test-rmse:0.427292+0.057537 
## [67] train-rmse:0.288952+0.004575    test-rmse:0.426672+0.057018 
## [68] train-rmse:0.287030+0.004066    test-rmse:0.426491+0.057314 
## [69] train-rmse:0.285935+0.004103    test-rmse:0.426860+0.057202 
## [70] train-rmse:0.284762+0.004723    test-rmse:0.427463+0.057403 
## [71] train-rmse:0.283741+0.004538    test-rmse:0.426683+0.057150 
## [72] train-rmse:0.282408+0.004390    test-rmse:0.426658+0.057402 
## [73] train-rmse:0.280356+0.005902    test-rmse:0.426596+0.058157 
## [74] train-rmse:0.279355+0.005851    test-rmse:0.426393+0.058338 
## [75] train-rmse:0.278561+0.005882    test-rmse:0.426182+0.058168 
## [76] train-rmse:0.277641+0.005758    test-rmse:0.425507+0.058535 
## [77] train-rmse:0.276608+0.005469    test-rmse:0.425307+0.058547 
## [78] train-rmse:0.275750+0.005490    test-rmse:0.426395+0.058284 
## [79] train-rmse:0.274388+0.006827    test-rmse:0.425536+0.057884 
## [80] train-rmse:0.273002+0.007364    test-rmse:0.424969+0.057802 
## [81] train-rmse:0.271646+0.007715    test-rmse:0.424459+0.057771 
## [82] train-rmse:0.270299+0.007280    test-rmse:0.424330+0.057825 
## [83] train-rmse:0.269385+0.007299    test-rmse:0.423944+0.058116 
## [84] train-rmse:0.268551+0.007051    test-rmse:0.423413+0.058358 
## [85] train-rmse:0.267822+0.007129    test-rmse:0.423649+0.058664 
## [86] train-rmse:0.266685+0.008156    test-rmse:0.423954+0.059003 
## [87] train-rmse:0.265555+0.008391    test-rmse:0.423494+0.058880 
## [88] train-rmse:0.264751+0.008231    test-rmse:0.423346+0.058888 
## [89] train-rmse:0.263746+0.008057    test-rmse:0.423454+0.058855 
## [90] train-rmse:0.262796+0.008325    test-rmse:0.423349+0.058956 
## [91] train-rmse:0.261965+0.008244    test-rmse:0.422619+0.059844 
## [92] train-rmse:0.261117+0.008680    test-rmse:0.422236+0.059588 
## [93] train-rmse:0.260350+0.008624    test-rmse:0.421398+0.060391 
## [94] train-rmse:0.259101+0.008829    test-rmse:0.421847+0.060627 
## [95] train-rmse:0.258555+0.008782    test-rmse:0.421470+0.060459 
## [96] train-rmse:0.257714+0.008856    test-rmse:0.421881+0.059900 
## [97] train-rmse:0.256773+0.008745    test-rmse:0.421678+0.059949 
## [98] train-rmse:0.256009+0.008780    test-rmse:0.421477+0.059890 
## [99] train-rmse:0.255216+0.008659    test-rmse:0.420432+0.060241 
## [100]    train-rmse:0.254213+0.009077    test-rmse:0.419751+0.059733 
## [101]    train-rmse:0.253604+0.009010    test-rmse:0.420013+0.060013 
## [102]    train-rmse:0.252443+0.008627    test-rmse:0.419433+0.060075 
## [103]    train-rmse:0.251373+0.008331    test-rmse:0.419809+0.059788 
## [104]    train-rmse:0.249450+0.008037    test-rmse:0.418769+0.059136 
## [105]    train-rmse:0.248627+0.008116    test-rmse:0.418159+0.059728 
## [106]    train-rmse:0.247404+0.008515    test-rmse:0.418404+0.059518 
## [107]    train-rmse:0.246584+0.008443    test-rmse:0.417950+0.059731 
## [108]    train-rmse:0.245522+0.008863    test-rmse:0.417624+0.059614 
## [109]    train-rmse:0.244694+0.009250    test-rmse:0.417460+0.059423 
## [110]    train-rmse:0.244217+0.009311    test-rmse:0.417736+0.059590 
## [111]    train-rmse:0.243306+0.009089    test-rmse:0.416660+0.059706 
## [112]    train-rmse:0.242706+0.009024    test-rmse:0.416723+0.059570 
## [113]    train-rmse:0.241983+0.009208    test-rmse:0.416653+0.059452 
## [114]    train-rmse:0.241277+0.008953    test-rmse:0.416733+0.059521 
## [115]    train-rmse:0.240810+0.008859    test-rmse:0.416194+0.059811 
## [116]    train-rmse:0.240247+0.008771    test-rmse:0.415941+0.059602 
## [117]    train-rmse:0.239454+0.008567    test-rmse:0.415885+0.059578 
## [118]    train-rmse:0.238742+0.008797    test-rmse:0.415857+0.059255 
## [119]    train-rmse:0.238057+0.008767    test-rmse:0.416320+0.059245 
## [120]    train-rmse:0.237303+0.009165    test-rmse:0.416703+0.059084 
## [121]    train-rmse:0.236694+0.009271    test-rmse:0.417135+0.058915 
## [122]    train-rmse:0.236109+0.009083    test-rmse:0.417379+0.058885 
## [123]    train-rmse:0.235319+0.009397    test-rmse:0.417522+0.058647 
## [124]    train-rmse:0.234593+0.009261    test-rmse:0.416726+0.059278 
## Stopping. Best iteration:
## [118]    train-rmse:0.238742+0.008797    test-rmse:0.415857+0.059255
## 
## 44 
## [1]  train-rmse:1.320899+0.024346    test-rmse:1.323509+0.117289 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.077437+0.020107    test-rmse:1.084134+0.112356 
## [3]  train-rmse:0.889951+0.016348    test-rmse:0.901027+0.112584 
## [4]  train-rmse:0.747757+0.015876    test-rmse:0.762349+0.105342 
## [5]  train-rmse:0.639643+0.013475    test-rmse:0.660416+0.098787 
## [6]  train-rmse:0.560453+0.012291    test-rmse:0.590950+0.094274 
## [7]  train-rmse:0.500843+0.011272    test-rmse:0.540840+0.086747 
## [8]  train-rmse:0.456236+0.010682    test-rmse:0.503117+0.079930 
## [9]  train-rmse:0.424626+0.010630    test-rmse:0.481733+0.074732 
## [10] train-rmse:0.401204+0.010661    test-rmse:0.466660+0.073222 
## [11] train-rmse:0.382617+0.009833    test-rmse:0.453549+0.074316 
## [12] train-rmse:0.368158+0.009162    test-rmse:0.445633+0.071637 
## [13] train-rmse:0.357741+0.009149    test-rmse:0.442246+0.068514 
## [14] train-rmse:0.348064+0.010017    test-rmse:0.440804+0.062446 
## [15] train-rmse:0.339271+0.009661    test-rmse:0.436063+0.059471 
## [16] train-rmse:0.332224+0.009950    test-rmse:0.435358+0.059863 
## [17] train-rmse:0.326509+0.009498    test-rmse:0.434560+0.058773 
## [18] train-rmse:0.319978+0.009485    test-rmse:0.432882+0.057281 
## [19] train-rmse:0.313979+0.009750    test-rmse:0.433541+0.057321 
## [20] train-rmse:0.308850+0.009245    test-rmse:0.430854+0.056438 
## [21] train-rmse:0.303702+0.008108    test-rmse:0.429292+0.056624 
## [22] train-rmse:0.298912+0.008023    test-rmse:0.429234+0.057125 
## [23] train-rmse:0.294624+0.007180    test-rmse:0.429224+0.056609 
## [24] train-rmse:0.290107+0.007468    test-rmse:0.428741+0.055339 
## [25] train-rmse:0.285349+0.006107    test-rmse:0.428450+0.055639 
## [26] train-rmse:0.281071+0.005271    test-rmse:0.426568+0.056258 
## [27] train-rmse:0.277014+0.005698    test-rmse:0.424915+0.057557 
## [28] train-rmse:0.273762+0.005244    test-rmse:0.425297+0.058180 
## [29] train-rmse:0.270482+0.005259    test-rmse:0.424905+0.060782 
## [30] train-rmse:0.267392+0.005227    test-rmse:0.424975+0.061187 
## [31] train-rmse:0.263726+0.005557    test-rmse:0.425925+0.061031 
## [32] train-rmse:0.259799+0.005716    test-rmse:0.426982+0.061629 
## [33] train-rmse:0.257210+0.004853    test-rmse:0.425615+0.061817 
## [34] train-rmse:0.254490+0.004697    test-rmse:0.425281+0.062339 
## [35] train-rmse:0.250908+0.004964    test-rmse:0.424943+0.062333 
## Stopping. Best iteration:
## [29] train-rmse:0.270482+0.005259    test-rmse:0.424905+0.060782
## 
## 45 
## [1]  train-rmse:1.317524+0.023815    test-rmse:1.319714+0.117780 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.070903+0.018929    test-rmse:1.078618+0.116649 
## [3]  train-rmse:0.879097+0.016416    test-rmse:0.888212+0.108196 
## [4]  train-rmse:0.730560+0.013156    test-rmse:0.746191+0.102703 
## [5]  train-rmse:0.618240+0.010074    test-rmse:0.640492+0.100524 
## [6]  train-rmse:0.532338+0.008694    test-rmse:0.565385+0.095522 
## [7]  train-rmse:0.467953+0.006930    test-rmse:0.514551+0.086510 
## [8]  train-rmse:0.417860+0.007674    test-rmse:0.483322+0.083203 
## [9]  train-rmse:0.382214+0.008483    test-rmse:0.460212+0.076598 
## [10] train-rmse:0.355950+0.008108    test-rmse:0.444678+0.074141 
## [11] train-rmse:0.335299+0.009120    test-rmse:0.434581+0.066842 
## [12] train-rmse:0.318734+0.008348    test-rmse:0.427419+0.069660 
## [13] train-rmse:0.304987+0.006491    test-rmse:0.424325+0.064992 
## [14] train-rmse:0.294357+0.007753    test-rmse:0.422628+0.064634 
## [15] train-rmse:0.284488+0.006600    test-rmse:0.418736+0.066087 
## [16] train-rmse:0.278004+0.005927    test-rmse:0.416571+0.064694 
## [17] train-rmse:0.271379+0.006074    test-rmse:0.414667+0.062754 
## [18] train-rmse:0.265303+0.005030    test-rmse:0.413472+0.061485 
## [19] train-rmse:0.258962+0.005264    test-rmse:0.411957+0.063140 
## [20] train-rmse:0.253300+0.004298    test-rmse:0.412507+0.063248 
## [21] train-rmse:0.249555+0.003995    test-rmse:0.409986+0.065613 
## [22] train-rmse:0.241440+0.004389    test-rmse:0.409759+0.065433 
## [23] train-rmse:0.237135+0.004355    test-rmse:0.407809+0.064722 
## [24] train-rmse:0.231434+0.004361    test-rmse:0.406412+0.065022 
## [25] train-rmse:0.226874+0.004348    test-rmse:0.407617+0.062980 
## [26] train-rmse:0.222771+0.004683    test-rmse:0.406489+0.062602 
## [27] train-rmse:0.218288+0.005338    test-rmse:0.404741+0.062436 
## [28] train-rmse:0.215081+0.004483    test-rmse:0.403503+0.063415 
## [29] train-rmse:0.211818+0.004362    test-rmse:0.402598+0.062925 
## [30] train-rmse:0.208793+0.003715    test-rmse:0.402660+0.062909 
## [31] train-rmse:0.205104+0.004605    test-rmse:0.400245+0.062230 
## [32] train-rmse:0.202499+0.004873    test-rmse:0.400180+0.062392 
## [33] train-rmse:0.199222+0.004883    test-rmse:0.400455+0.062445 
## [34] train-rmse:0.195964+0.004347    test-rmse:0.400291+0.062747 
## [35] train-rmse:0.193370+0.004719    test-rmse:0.399249+0.062063 
## [36] train-rmse:0.190370+0.005238    test-rmse:0.398861+0.060734 
## [37] train-rmse:0.187576+0.006165    test-rmse:0.398356+0.060439 
## [38] train-rmse:0.184708+0.005602    test-rmse:0.398681+0.059837 
## [39] train-rmse:0.181858+0.005858    test-rmse:0.397988+0.060611 
## [40] train-rmse:0.178897+0.006348    test-rmse:0.398055+0.060017 
## [41] train-rmse:0.176911+0.006577    test-rmse:0.397269+0.059788 
## [42] train-rmse:0.174699+0.006207    test-rmse:0.396794+0.059341 
## [43] train-rmse:0.171700+0.006170    test-rmse:0.396347+0.059938 
## [44] train-rmse:0.168917+0.006098    test-rmse:0.394684+0.060073 
## [45] train-rmse:0.166647+0.005831    test-rmse:0.394784+0.060141 
## [46] train-rmse:0.163699+0.005102    test-rmse:0.394666+0.059637 
## [47] train-rmse:0.161966+0.004804    test-rmse:0.394550+0.059446 
## [48] train-rmse:0.159393+0.005461    test-rmse:0.394903+0.059611 
## [49] train-rmse:0.157408+0.004962    test-rmse:0.395035+0.060656 
## [50] train-rmse:0.155876+0.005209    test-rmse:0.394893+0.060874 
## [51] train-rmse:0.153750+0.005134    test-rmse:0.395826+0.060195 
## [52] train-rmse:0.151489+0.004918    test-rmse:0.395670+0.060670 
## [53] train-rmse:0.149538+0.005389    test-rmse:0.395986+0.060889 
## Stopping. Best iteration:
## [47] train-rmse:0.161966+0.004804    test-rmse:0.394550+0.059446
## 
## 46 
## [1]  train-rmse:1.315533+0.024148    test-rmse:1.317707+0.114530 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.066540+0.019212    test-rmse:1.071005+0.110531 
## [3]  train-rmse:0.871859+0.017002    test-rmse:0.880571+0.102330 
## [4]  train-rmse:0.720163+0.013318    test-rmse:0.743540+0.098575 
## [5]  train-rmse:0.602931+0.010847    test-rmse:0.638073+0.094883 
## [6]  train-rmse:0.513033+0.009990    test-rmse:0.560712+0.086068 
## [7]  train-rmse:0.443792+0.007591    test-rmse:0.506912+0.084953 
## [8]  train-rmse:0.390857+0.006529    test-rmse:0.470808+0.081311 
## [9]  train-rmse:0.351715+0.005550    test-rmse:0.449237+0.081829 
## [10] train-rmse:0.321661+0.005263    test-rmse:0.432681+0.077751 
## [11] train-rmse:0.298905+0.005718    test-rmse:0.422496+0.072992 
## [12] train-rmse:0.277461+0.007740    test-rmse:0.415364+0.069102 
## [13] train-rmse:0.262600+0.007090    test-rmse:0.410215+0.068470 
## [14] train-rmse:0.250838+0.007709    test-rmse:0.403570+0.068368 
## [15] train-rmse:0.242127+0.007423    test-rmse:0.403301+0.067638 
## [16] train-rmse:0.231699+0.008203    test-rmse:0.402264+0.066349 
## [17] train-rmse:0.223953+0.005908    test-rmse:0.403372+0.064547 
## [18] train-rmse:0.216750+0.004235    test-rmse:0.403428+0.064742 
## [19] train-rmse:0.209200+0.004738    test-rmse:0.402315+0.064202 
## [20] train-rmse:0.203230+0.004789    test-rmse:0.400679+0.063425 
## [21] train-rmse:0.196385+0.006789    test-rmse:0.401746+0.063438 
## [22] train-rmse:0.190922+0.007070    test-rmse:0.399900+0.064044 
## [23] train-rmse:0.186401+0.007150    test-rmse:0.399735+0.063356 
## [24] train-rmse:0.181389+0.007108    test-rmse:0.398146+0.063421 
## [25] train-rmse:0.175324+0.005759    test-rmse:0.398089+0.063884 
## [26] train-rmse:0.171759+0.005609    test-rmse:0.397642+0.064914 
## [27] train-rmse:0.167435+0.004671    test-rmse:0.396681+0.064573 
## [28] train-rmse:0.164192+0.005009    test-rmse:0.397110+0.064905 
## [29] train-rmse:0.160506+0.003849    test-rmse:0.397081+0.062941 
## [30] train-rmse:0.157274+0.004028    test-rmse:0.398251+0.063101 
## [31] train-rmse:0.153811+0.005527    test-rmse:0.397998+0.062384 
## [32] train-rmse:0.150665+0.006348    test-rmse:0.397934+0.062306 
## [33] train-rmse:0.146354+0.006141    test-rmse:0.397801+0.062948 
## Stopping. Best iteration:
## [27] train-rmse:0.167435+0.004671    test-rmse:0.396681+0.064573
## 
## 47 
## [1]  train-rmse:1.314918+0.024196    test-rmse:1.316298+0.113294 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.064597+0.019290    test-rmse:1.068215+0.108025 
## [3]  train-rmse:0.868114+0.017240    test-rmse:0.879135+0.097433 
## [4]  train-rmse:0.715357+0.014398    test-rmse:0.736785+0.094985 
## [5]  train-rmse:0.596167+0.011874    test-rmse:0.631436+0.098548 
## [6]  train-rmse:0.503602+0.011491    test-rmse:0.553214+0.090915 
## [7]  train-rmse:0.431463+0.009547    test-rmse:0.503133+0.085168 
## [8]  train-rmse:0.374891+0.009833    test-rmse:0.464454+0.080323 
## [9]  train-rmse:0.331114+0.009904    test-rmse:0.442892+0.075550 
## [10] train-rmse:0.297450+0.009574    test-rmse:0.428691+0.074164 
## [11] train-rmse:0.271677+0.009318    test-rmse:0.418479+0.070175 
## [12] train-rmse:0.248990+0.009096    test-rmse:0.413037+0.069608 
## [13] train-rmse:0.232778+0.009789    test-rmse:0.408533+0.066071 
## [14] train-rmse:0.216507+0.010781    test-rmse:0.405061+0.064819 
## [15] train-rmse:0.203515+0.007404    test-rmse:0.402939+0.065928 
## [16] train-rmse:0.194302+0.007924    test-rmse:0.402280+0.062716 
## [17] train-rmse:0.185895+0.008816    test-rmse:0.401411+0.061149 
## [18] train-rmse:0.176973+0.006791    test-rmse:0.398793+0.058594 
## [19] train-rmse:0.169923+0.006335    test-rmse:0.397733+0.059000 
## [20] train-rmse:0.164229+0.005969    test-rmse:0.397236+0.057013 
## [21] train-rmse:0.159209+0.006678    test-rmse:0.398514+0.055968 
## [22] train-rmse:0.152304+0.007204    test-rmse:0.400076+0.053948 
## [23] train-rmse:0.146740+0.007472    test-rmse:0.400711+0.051524 
## [24] train-rmse:0.140365+0.007598    test-rmse:0.400329+0.051603 
## [25] train-rmse:0.137057+0.007855    test-rmse:0.400067+0.051839 
## [26] train-rmse:0.132201+0.006962    test-rmse:0.400883+0.050738 
## Stopping. Best iteration:
## [20] train-rmse:0.164229+0.005969    test-rmse:0.397236+0.057013
## 
## 48 
## [1]  train-rmse:1.314618+0.024198    test-rmse:1.317326+0.112770 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.063432+0.019587    test-rmse:1.067330+0.108139 
## [3]  train-rmse:0.865746+0.017639    test-rmse:0.875114+0.100183 
## [4]  train-rmse:0.710773+0.014388    test-rmse:0.737736+0.096495 
## [5]  train-rmse:0.588776+0.011811    test-rmse:0.631430+0.096425 
## [6]  train-rmse:0.493258+0.011408    test-rmse:0.556922+0.091543 
## [7]  train-rmse:0.418354+0.012032    test-rmse:0.506310+0.085206 
## [8]  train-rmse:0.359617+0.012067    test-rmse:0.475568+0.079441 
## [9]  train-rmse:0.313938+0.012730    test-rmse:0.457102+0.074919 
## [10] train-rmse:0.276312+0.012090    test-rmse:0.444040+0.074232 
## [11] train-rmse:0.246513+0.011739    test-rmse:0.435038+0.070545 
## [12] train-rmse:0.223332+0.012547    test-rmse:0.428385+0.066719 
## [13] train-rmse:0.203728+0.013644    test-rmse:0.427555+0.064166 
## [14] train-rmse:0.189262+0.014593    test-rmse:0.427489+0.062224 
## [15] train-rmse:0.172312+0.012474    test-rmse:0.425193+0.060723 
## [16] train-rmse:0.160765+0.013020    test-rmse:0.425544+0.059243 
## [17] train-rmse:0.149927+0.013613    test-rmse:0.423151+0.059341 
## [18] train-rmse:0.141135+0.010296    test-rmse:0.423012+0.057901 
## [19] train-rmse:0.133523+0.009057    test-rmse:0.421764+0.053909 
## [20] train-rmse:0.127955+0.009475    test-rmse:0.420009+0.053011 
## [21] train-rmse:0.123367+0.009317    test-rmse:0.419516+0.051880 
## [22] train-rmse:0.118261+0.008876    test-rmse:0.418709+0.049913 
## [23] train-rmse:0.114345+0.008408    test-rmse:0.418355+0.049673 
## [24] train-rmse:0.109903+0.008870    test-rmse:0.418008+0.049249 
## [25] train-rmse:0.104084+0.008458    test-rmse:0.417563+0.049522 
## [26] train-rmse:0.100418+0.008022    test-rmse:0.417909+0.050104 
## [27] train-rmse:0.095487+0.008122    test-rmse:0.417213+0.050017 
## [28] train-rmse:0.090812+0.007944    test-rmse:0.416564+0.048323 
## [29] train-rmse:0.085591+0.007722    test-rmse:0.415913+0.048027 
## [30] train-rmse:0.082635+0.007080    test-rmse:0.415790+0.047905 
## [31] train-rmse:0.079460+0.007103    test-rmse:0.415140+0.047369 
## [32] train-rmse:0.077061+0.007111    test-rmse:0.414318+0.047494 
## [33] train-rmse:0.073278+0.005109    test-rmse:0.414096+0.047913 
## [34] train-rmse:0.070857+0.004713    test-rmse:0.414070+0.047860 
## [35] train-rmse:0.067905+0.003401    test-rmse:0.413965+0.047776 
## [36] train-rmse:0.064820+0.003816    test-rmse:0.413680+0.048648 
## [37] train-rmse:0.063035+0.004072    test-rmse:0.413166+0.048977 
## [38] train-rmse:0.060309+0.003808    test-rmse:0.413419+0.049062 
## [39] train-rmse:0.059117+0.003689    test-rmse:0.413386+0.048994 
## [40] train-rmse:0.056850+0.003962    test-rmse:0.412668+0.048791 
## [41] train-rmse:0.054766+0.003600    test-rmse:0.412439+0.048510 
## [42] train-rmse:0.052779+0.003145    test-rmse:0.412020+0.048580 
## [43] train-rmse:0.051189+0.002783    test-rmse:0.412149+0.048509 
## [44] train-rmse:0.049092+0.002672    test-rmse:0.412303+0.047973 
## [45] train-rmse:0.047605+0.002737    test-rmse:0.412145+0.047911 
## [46] train-rmse:0.045995+0.002409    test-rmse:0.412052+0.048418 
## [47] train-rmse:0.044659+0.002549    test-rmse:0.411828+0.048578 
## [48] train-rmse:0.043076+0.002036    test-rmse:0.411745+0.048358 
## [49] train-rmse:0.042116+0.002118    test-rmse:0.411683+0.048590 
## [50] train-rmse:0.040387+0.001953    test-rmse:0.411305+0.048615 
## [51] train-rmse:0.039229+0.001910    test-rmse:0.411175+0.048556 
## [52] train-rmse:0.038225+0.002145    test-rmse:0.411034+0.048421 
## [53] train-rmse:0.036782+0.002190    test-rmse:0.410930+0.048441 
## [54] train-rmse:0.035558+0.001995    test-rmse:0.410629+0.048243 
## [55] train-rmse:0.034325+0.001959    test-rmse:0.410451+0.048356 
## [56] train-rmse:0.033048+0.001938    test-rmse:0.410462+0.048485 
## [57] train-rmse:0.032008+0.001814    test-rmse:0.410512+0.048708 
## [58] train-rmse:0.031051+0.001459    test-rmse:0.410414+0.048518 
## [59] train-rmse:0.030173+0.001375    test-rmse:0.410200+0.048395 
## [60] train-rmse:0.029101+0.001467    test-rmse:0.409903+0.048240 
## [61] train-rmse:0.028283+0.001733    test-rmse:0.409805+0.048254 
## [62] train-rmse:0.027557+0.001678    test-rmse:0.409772+0.048157 
## [63] train-rmse:0.026681+0.001817    test-rmse:0.409795+0.048082 
## [64] train-rmse:0.025866+0.001886    test-rmse:0.409817+0.048100 
## [65] train-rmse:0.025289+0.001922    test-rmse:0.409967+0.048134 
## [66] train-rmse:0.024853+0.002029    test-rmse:0.410024+0.048176 
## [67] train-rmse:0.024112+0.001994    test-rmse:0.410023+0.048153 
## [68] train-rmse:0.023378+0.001562    test-rmse:0.409941+0.048196 
## Stopping. Best iteration:
## [62] train-rmse:0.027557+0.001678    test-rmse:0.409772+0.048157
## 
## 49 
## [1]  train-rmse:1.316507+0.024624    test-rmse:1.312671+0.109991 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.087418+0.021011    test-rmse:1.083693+0.104168 
## [3]  train-rmse:0.928710+0.019039    test-rmse:0.925179+0.099190 
## [4]  train-rmse:0.822549+0.018192    test-rmse:0.819297+0.094932 
## [5]  train-rmse:0.754031+0.017964    test-rmse:0.751095+0.091473 
## [6]  train-rmse:0.711192+0.017992    test-rmse:0.708557+0.088827 
## [7]  train-rmse:0.683585+0.017269    test-rmse:0.687554+0.089446 
## [8]  train-rmse:0.661277+0.016973    test-rmse:0.659536+0.086896 
## [9]  train-rmse:0.645585+0.016479    test-rmse:0.650253+0.084161 
## [10] train-rmse:0.631648+0.015315    test-rmse:0.632435+0.082585 
## [11] train-rmse:0.619583+0.014324    test-rmse:0.621443+0.079489 
## [12] train-rmse:0.609838+0.013561    test-rmse:0.614426+0.074299 
## [13] train-rmse:0.600931+0.012903    test-rmse:0.602054+0.074179 
## [14] train-rmse:0.593566+0.012712    test-rmse:0.598888+0.071896 
## [15] train-rmse:0.586713+0.012711    test-rmse:0.592494+0.068385 
## [16] train-rmse:0.580513+0.012043    test-rmse:0.588108+0.067707 
## [17] train-rmse:0.574992+0.011725    test-rmse:0.581609+0.069060 
## [18] train-rmse:0.569733+0.011689    test-rmse:0.578184+0.066385 
## [19] train-rmse:0.565298+0.011472    test-rmse:0.576074+0.062824 
## [20] train-rmse:0.561168+0.011149    test-rmse:0.573337+0.064520 
## [21] train-rmse:0.557164+0.010921    test-rmse:0.568709+0.064432 
## [22] train-rmse:0.553695+0.010883    test-rmse:0.567732+0.065348 
## [23] train-rmse:0.550433+0.010721    test-rmse:0.565220+0.063958 
## [24] train-rmse:0.547244+0.010779    test-rmse:0.563762+0.061410 
## [25] train-rmse:0.544386+0.010642    test-rmse:0.561493+0.059489 
## [26] train-rmse:0.541718+0.010497    test-rmse:0.560510+0.059448 
## [27] train-rmse:0.539177+0.010365    test-rmse:0.558600+0.059273 
## [28] train-rmse:0.536676+0.010176    test-rmse:0.556516+0.060316 
## [29] train-rmse:0.534265+0.010099    test-rmse:0.553274+0.058968 
## [30] train-rmse:0.531972+0.010037    test-rmse:0.552875+0.058368 
## [31] train-rmse:0.529789+0.009880    test-rmse:0.551799+0.057857 
## [32] train-rmse:0.527744+0.009770    test-rmse:0.550902+0.058445 
## [33] train-rmse:0.525795+0.009605    test-rmse:0.548829+0.058187 
## [34] train-rmse:0.523883+0.009514    test-rmse:0.546267+0.056432 
## [35] train-rmse:0.522012+0.009487    test-rmse:0.546130+0.056181 
## [36] train-rmse:0.520239+0.009444    test-rmse:0.545409+0.057015 
## [37] train-rmse:0.518564+0.009294    test-rmse:0.545005+0.057892 
## [38] train-rmse:0.516919+0.009163    test-rmse:0.542498+0.057161 
## [39] train-rmse:0.515242+0.009016    test-rmse:0.541012+0.056046 
## [40] train-rmse:0.513670+0.008946    test-rmse:0.539858+0.056163 
## [41] train-rmse:0.512145+0.008970    test-rmse:0.538432+0.055027 
## [42] train-rmse:0.510625+0.008904    test-rmse:0.538376+0.055361 
## [43] train-rmse:0.509083+0.008799    test-rmse:0.537018+0.055804 
## [44] train-rmse:0.507653+0.008743    test-rmse:0.536850+0.055133 
## [45] train-rmse:0.506244+0.008680    test-rmse:0.535191+0.053833 
## [46] train-rmse:0.504851+0.008652    test-rmse:0.532956+0.053891 
## [47] train-rmse:0.503530+0.008609    test-rmse:0.533517+0.054016 
## [48] train-rmse:0.502274+0.008574    test-rmse:0.532915+0.053613 
## [49] train-rmse:0.500912+0.008501    test-rmse:0.530264+0.053833 
## [50] train-rmse:0.499660+0.008466    test-rmse:0.529222+0.054635 
## [51] train-rmse:0.498424+0.008460    test-rmse:0.528545+0.053385 
## [52] train-rmse:0.497261+0.008470    test-rmse:0.527749+0.052978 
## [53] train-rmse:0.496099+0.008414    test-rmse:0.527440+0.051612 
## [54] train-rmse:0.494962+0.008392    test-rmse:0.527699+0.052211 
## [55] train-rmse:0.493819+0.008354    test-rmse:0.526878+0.052424 
## [56] train-rmse:0.492701+0.008262    test-rmse:0.526818+0.053577 
## [57] train-rmse:0.491657+0.008232    test-rmse:0.526304+0.053486 
## [58] train-rmse:0.490598+0.008212    test-rmse:0.524402+0.052464 
## [59] train-rmse:0.489558+0.008186    test-rmse:0.523127+0.053239 
## [60] train-rmse:0.488524+0.008130    test-rmse:0.522788+0.052730 
## [61] train-rmse:0.487518+0.008102    test-rmse:0.523770+0.052487 
## [62] train-rmse:0.486538+0.008057    test-rmse:0.523716+0.051853 
## [63] train-rmse:0.485565+0.008012    test-rmse:0.521884+0.051768 
## [64] train-rmse:0.484658+0.007990    test-rmse:0.520927+0.051585 
## [65] train-rmse:0.483725+0.007951    test-rmse:0.520353+0.051536 
## [66] train-rmse:0.482809+0.007967    test-rmse:0.519425+0.050938 
## [67] train-rmse:0.481938+0.007981    test-rmse:0.519449+0.051359 
## [68] train-rmse:0.481071+0.007978    test-rmse:0.519320+0.052192 
## [69] train-rmse:0.480225+0.007974    test-rmse:0.518930+0.051307 
## [70] train-rmse:0.479417+0.007954    test-rmse:0.518203+0.051461 
## [71] train-rmse:0.478609+0.007924    test-rmse:0.517883+0.051002 
## [72] train-rmse:0.477792+0.007888    test-rmse:0.517650+0.050475 
## [73] train-rmse:0.476988+0.007897    test-rmse:0.518048+0.050837 
## [74] train-rmse:0.476224+0.007874    test-rmse:0.517889+0.050426 
## [75] train-rmse:0.475490+0.007863    test-rmse:0.516302+0.050841 
## [76] train-rmse:0.474772+0.007865    test-rmse:0.516001+0.050534 
## [77] train-rmse:0.474024+0.007845    test-rmse:0.513916+0.047561 
## [78] train-rmse:0.473319+0.007826    test-rmse:0.513274+0.047069 
## [79] train-rmse:0.472615+0.007826    test-rmse:0.511567+0.047003 
## [80] train-rmse:0.471902+0.007813    test-rmse:0.511548+0.046767 
## [81] train-rmse:0.471171+0.007789    test-rmse:0.512115+0.046436 
## [82] train-rmse:0.470483+0.007746    test-rmse:0.512211+0.047038 
## [83] train-rmse:0.469820+0.007722    test-rmse:0.511524+0.047449 
## [84] train-rmse:0.469164+0.007702    test-rmse:0.511281+0.047643 
## [85] train-rmse:0.468525+0.007700    test-rmse:0.510045+0.047120 
## [86] train-rmse:0.467892+0.007682    test-rmse:0.509959+0.047587 
## [87] train-rmse:0.467258+0.007650    test-rmse:0.508715+0.047218 
## [88] train-rmse:0.466620+0.007632    test-rmse:0.509193+0.046545 
## [89] train-rmse:0.466013+0.007613    test-rmse:0.509010+0.046769 
## [90] train-rmse:0.465413+0.007614    test-rmse:0.509393+0.047429 
## [91] train-rmse:0.464825+0.007590    test-rmse:0.509614+0.047470 
## [92] train-rmse:0.464255+0.007563    test-rmse:0.509472+0.047626 
## [93] train-rmse:0.463692+0.007546    test-rmse:0.509421+0.047225 
## Stopping. Best iteration:
## [87] train-rmse:0.467258+0.007650    test-rmse:0.508715+0.047218
## 
## 50 
## [1]  train-rmse:1.300877+0.024709    test-rmse:1.300430+0.114566 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.051738+0.019914    test-rmse:1.049344+0.104196 
## [3]  train-rmse:0.870650+0.018004    test-rmse:0.877749+0.103053 
## [4]  train-rmse:0.741120+0.015489    test-rmse:0.746921+0.104029 
## [5]  train-rmse:0.649718+0.013618    test-rmse:0.658148+0.098840 
## [6]  train-rmse:0.585676+0.013285    test-rmse:0.595185+0.094872 
## [7]  train-rmse:0.541416+0.012828    test-rmse:0.555299+0.089148 
## [8]  train-rmse:0.510155+0.012439    test-rmse:0.530818+0.080902 
## [9]  train-rmse:0.487456+0.011829    test-rmse:0.511381+0.080839 
## [10] train-rmse:0.470696+0.010897    test-rmse:0.494019+0.075470 
## [11] train-rmse:0.454045+0.009156    test-rmse:0.477144+0.071403 
## [12] train-rmse:0.441402+0.008536    test-rmse:0.470337+0.067351 
## [13] train-rmse:0.430645+0.007610    test-rmse:0.466199+0.065643 
## [14] train-rmse:0.422278+0.006981    test-rmse:0.462180+0.062909 
## [15] train-rmse:0.415189+0.006980    test-rmse:0.460196+0.059758 
## [16] train-rmse:0.407995+0.005761    test-rmse:0.455668+0.057778 
## [17] train-rmse:0.402348+0.005624    test-rmse:0.455058+0.058065 
## [18] train-rmse:0.397235+0.005357    test-rmse:0.453097+0.056254 
## [19] train-rmse:0.391918+0.005968    test-rmse:0.449978+0.056447 
## [20] train-rmse:0.386248+0.005023    test-rmse:0.444183+0.056377 
## [21] train-rmse:0.381419+0.004643    test-rmse:0.442935+0.058625 
## [22] train-rmse:0.377673+0.004248    test-rmse:0.442794+0.058535 
## [23] train-rmse:0.373829+0.004173    test-rmse:0.442503+0.056872 
## [24] train-rmse:0.370054+0.003777    test-rmse:0.442318+0.056727 
## [25] train-rmse:0.365881+0.003921    test-rmse:0.443379+0.057302 
## [26] train-rmse:0.362822+0.004038    test-rmse:0.443355+0.057885 
## [27] train-rmse:0.359588+0.003801    test-rmse:0.442192+0.056502 
## [28] train-rmse:0.356458+0.003938    test-rmse:0.440477+0.057117 
## [29] train-rmse:0.353633+0.003771    test-rmse:0.437468+0.054637 
## [30] train-rmse:0.350088+0.002931    test-rmse:0.438194+0.053014 
## [31] train-rmse:0.346741+0.002433    test-rmse:0.439199+0.054265 
## [32] train-rmse:0.344124+0.002617    test-rmse:0.438625+0.053909 
## [33] train-rmse:0.340129+0.003588    test-rmse:0.438450+0.055491 
## [34] train-rmse:0.337360+0.003587    test-rmse:0.437594+0.054464 
## [35] train-rmse:0.334725+0.002965    test-rmse:0.436278+0.054770 
## [36] train-rmse:0.332846+0.002606    test-rmse:0.435841+0.055242 
## [37] train-rmse:0.330869+0.002713    test-rmse:0.436761+0.056479 
## [38] train-rmse:0.328733+0.003355    test-rmse:0.436535+0.055401 
## [39] train-rmse:0.326843+0.003083    test-rmse:0.436812+0.057091 
## [40] train-rmse:0.323856+0.003197    test-rmse:0.436696+0.057271 
## [41] train-rmse:0.321815+0.003351    test-rmse:0.436331+0.057704 
## [42] train-rmse:0.319866+0.003612    test-rmse:0.435120+0.057418 
## [43] train-rmse:0.318360+0.003571    test-rmse:0.435870+0.058061 
## [44] train-rmse:0.316051+0.003224    test-rmse:0.435440+0.055784 
## [45] train-rmse:0.313945+0.003626    test-rmse:0.435153+0.056266 
## [46] train-rmse:0.312491+0.003494    test-rmse:0.434374+0.057143 
## [47] train-rmse:0.311089+0.003441    test-rmse:0.434115+0.057982 
## [48] train-rmse:0.309626+0.003406    test-rmse:0.435355+0.057952 
## [49] train-rmse:0.307147+0.004149    test-rmse:0.434814+0.057080 
## [50] train-rmse:0.304243+0.005845    test-rmse:0.435209+0.058178 
## [51] train-rmse:0.302814+0.005590    test-rmse:0.434334+0.057660 
## [52] train-rmse:0.300975+0.005311    test-rmse:0.433896+0.056887 
## [53] train-rmse:0.298901+0.004268    test-rmse:0.433335+0.057281 
## [54] train-rmse:0.297075+0.004322    test-rmse:0.432555+0.056834 
## [55] train-rmse:0.296099+0.004396    test-rmse:0.432471+0.057116 
## [56] train-rmse:0.294470+0.004868    test-rmse:0.431899+0.056650 
## [57] train-rmse:0.293051+0.004975    test-rmse:0.431802+0.056982 
## [58] train-rmse:0.291169+0.004929    test-rmse:0.430568+0.057583 
## [59] train-rmse:0.288950+0.005163    test-rmse:0.430840+0.057233 
## [60] train-rmse:0.287662+0.004773    test-rmse:0.430662+0.056979 
## [61] train-rmse:0.286421+0.004850    test-rmse:0.428585+0.058165 
## [62] train-rmse:0.284804+0.005394    test-rmse:0.428504+0.057543 
## [63] train-rmse:0.283351+0.005540    test-rmse:0.428371+0.057662 
## [64] train-rmse:0.281998+0.005734    test-rmse:0.427612+0.058284 
## [65] train-rmse:0.281192+0.005727    test-rmse:0.427524+0.058367 
## [66] train-rmse:0.279461+0.006737    test-rmse:0.427326+0.057844 
## [67] train-rmse:0.277816+0.006351    test-rmse:0.427094+0.057895 
## [68] train-rmse:0.276662+0.006268    test-rmse:0.426715+0.058368 
## [69] train-rmse:0.275475+0.005881    test-rmse:0.426962+0.058354 
## [70] train-rmse:0.274414+0.006085    test-rmse:0.426543+0.058054 
## [71] train-rmse:0.272711+0.006229    test-rmse:0.426372+0.058068 
## [72] train-rmse:0.271673+0.006206    test-rmse:0.426257+0.057741 
## [73] train-rmse:0.270338+0.005925    test-rmse:0.424688+0.057873 
## [74] train-rmse:0.268916+0.005536    test-rmse:0.424884+0.058094 
## [75] train-rmse:0.267794+0.005386    test-rmse:0.424260+0.058566 
## [76] train-rmse:0.266577+0.005065    test-rmse:0.423299+0.059744 
## [77] train-rmse:0.265238+0.004669    test-rmse:0.424269+0.059334 
## [78] train-rmse:0.263470+0.005373    test-rmse:0.423138+0.058872 
## [79] train-rmse:0.262673+0.005416    test-rmse:0.423380+0.058852 
## [80] train-rmse:0.261318+0.004878    test-rmse:0.423488+0.059047 
## [81] train-rmse:0.260711+0.004999    test-rmse:0.423238+0.058687 
## [82] train-rmse:0.259869+0.005220    test-rmse:0.422286+0.058964 
## [83] train-rmse:0.259077+0.005142    test-rmse:0.422174+0.059228 
## [84] train-rmse:0.257333+0.004681    test-rmse:0.422291+0.059817 
## [85] train-rmse:0.256574+0.004722    test-rmse:0.422951+0.059856 
## [86] train-rmse:0.255705+0.004498    test-rmse:0.423328+0.059451 
## [87] train-rmse:0.254645+0.004376    test-rmse:0.422544+0.058864 
## [88] train-rmse:0.253867+0.004267    test-rmse:0.422719+0.058149 
## [89] train-rmse:0.253018+0.004785    test-rmse:0.422107+0.058165 
## [90] train-rmse:0.252087+0.005248    test-rmse:0.422196+0.058418 
## [91] train-rmse:0.250856+0.005782    test-rmse:0.421685+0.057695 
## [92] train-rmse:0.249435+0.005300    test-rmse:0.421435+0.058172 
## [93] train-rmse:0.248473+0.005156    test-rmse:0.421525+0.058387 
## [94] train-rmse:0.247192+0.004801    test-rmse:0.419858+0.058270 
## [95] train-rmse:0.246351+0.004922    test-rmse:0.419315+0.058398 
## [96] train-rmse:0.245640+0.004862    test-rmse:0.419892+0.058152 
## [97] train-rmse:0.245020+0.004875    test-rmse:0.419256+0.058333 
## [98] train-rmse:0.244380+0.004740    test-rmse:0.419441+0.058386 
## [99] train-rmse:0.243628+0.005000    test-rmse:0.419399+0.057762 
## [100]    train-rmse:0.242282+0.005008    test-rmse:0.418637+0.057648 
## [101]    train-rmse:0.241301+0.004821    test-rmse:0.418145+0.057137 
## [102]    train-rmse:0.240112+0.005715    test-rmse:0.417757+0.056910 
## [103]    train-rmse:0.239407+0.005627    test-rmse:0.417588+0.057532 
## [104]    train-rmse:0.238403+0.005340    test-rmse:0.417365+0.057487 
## [105]    train-rmse:0.237839+0.005390    test-rmse:0.417189+0.057095 
## [106]    train-rmse:0.236946+0.005241    test-rmse:0.416501+0.057915 
## [107]    train-rmse:0.236099+0.005063    test-rmse:0.416201+0.058338 
## [108]    train-rmse:0.235649+0.005040    test-rmse:0.416133+0.058353 
## [109]    train-rmse:0.234685+0.005379    test-rmse:0.416122+0.058622 
## [110]    train-rmse:0.234336+0.005347    test-rmse:0.416187+0.058733 
## [111]    train-rmse:0.233562+0.005220    test-rmse:0.415780+0.058617 
## [112]    train-rmse:0.232836+0.005402    test-rmse:0.415457+0.058852 
## [113]    train-rmse:0.232122+0.005197    test-rmse:0.415113+0.059228 
## [114]    train-rmse:0.231141+0.005896    test-rmse:0.414786+0.059451 
## [115]    train-rmse:0.230305+0.005444    test-rmse:0.415052+0.059560 
## [116]    train-rmse:0.229578+0.005796    test-rmse:0.414403+0.059539 
## [117]    train-rmse:0.229026+0.006132    test-rmse:0.414353+0.059482 
## [118]    train-rmse:0.228399+0.006223    test-rmse:0.414222+0.058805 
## [119]    train-rmse:0.227905+0.006214    test-rmse:0.414224+0.058712 
## [120]    train-rmse:0.227301+0.006062    test-rmse:0.413554+0.058887 
## [121]    train-rmse:0.226286+0.006342    test-rmse:0.413159+0.058443 
## [122]    train-rmse:0.225291+0.006231    test-rmse:0.413277+0.058033 
## [123]    train-rmse:0.224053+0.006180    test-rmse:0.413857+0.057621 
## [124]    train-rmse:0.223230+0.005929    test-rmse:0.413530+0.057501 
## [125]    train-rmse:0.222576+0.005836    test-rmse:0.413375+0.057835 
## [126]    train-rmse:0.221744+0.005568    test-rmse:0.413104+0.058268 
## [127]    train-rmse:0.221329+0.005538    test-rmse:0.413445+0.057919 
## [128]    train-rmse:0.220810+0.005421    test-rmse:0.413799+0.058594 
## [129]    train-rmse:0.219938+0.004963    test-rmse:0.413979+0.058969 
## [130]    train-rmse:0.219132+0.005040    test-rmse:0.413592+0.058983 
## [131]    train-rmse:0.218642+0.005301    test-rmse:0.413832+0.059233 
## [132]    train-rmse:0.218030+0.005176    test-rmse:0.413787+0.059026 
## Stopping. Best iteration:
## [126]    train-rmse:0.221744+0.005568    test-rmse:0.413104+0.058268
## 
## 51 
## [1]  train-rmse:1.292589+0.023841    test-rmse:1.295830+0.117360 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.034609+0.019337    test-rmse:1.042184+0.112293 
## [3]  train-rmse:0.842459+0.015523    test-rmse:0.854187+0.113320 
## [4]  train-rmse:0.699229+0.014278    test-rmse:0.717760+0.102850 
## [5]  train-rmse:0.594498+0.011744    test-rmse:0.618848+0.098401 
## [6]  train-rmse:0.520839+0.009982    test-rmse:0.555639+0.094137 
## [7]  train-rmse:0.467417+0.009743    test-rmse:0.515586+0.084309 
## [8]  train-rmse:0.429037+0.009653    test-rmse:0.488649+0.079074 
## [9]  train-rmse:0.402155+0.009203    test-rmse:0.471443+0.078454 
## [10] train-rmse:0.381294+0.009219    test-rmse:0.457434+0.070942 
## [11] train-rmse:0.365912+0.007744    test-rmse:0.447000+0.072641 
## [12] train-rmse:0.354380+0.007899    test-rmse:0.441632+0.070270 
## [13] train-rmse:0.344497+0.007923    test-rmse:0.437587+0.068334 
## [14] train-rmse:0.335686+0.007250    test-rmse:0.434160+0.064800 
## [15] train-rmse:0.328581+0.006733    test-rmse:0.433094+0.064219 
## [16] train-rmse:0.321685+0.005869    test-rmse:0.429987+0.065005 
## [17] train-rmse:0.314945+0.005322    test-rmse:0.430215+0.064839 
## [18] train-rmse:0.308179+0.005738    test-rmse:0.429323+0.065330 
## [19] train-rmse:0.303442+0.004814    test-rmse:0.429000+0.064649 
## [20] train-rmse:0.297345+0.004640    test-rmse:0.426219+0.062261 
## [21] train-rmse:0.293269+0.004541    test-rmse:0.425866+0.063930 
## [22] train-rmse:0.289494+0.004455    test-rmse:0.424251+0.066180 
## [23] train-rmse:0.285073+0.004530    test-rmse:0.423470+0.064016 
## [24] train-rmse:0.279702+0.005543    test-rmse:0.423103+0.064883 
## [25] train-rmse:0.275982+0.005550    test-rmse:0.421792+0.064947 
## [26] train-rmse:0.272881+0.005817    test-rmse:0.421761+0.065466 
## [27] train-rmse:0.269558+0.005873    test-rmse:0.421963+0.065479 
## [28] train-rmse:0.266408+0.005853    test-rmse:0.419570+0.067800 
## [29] train-rmse:0.262915+0.006008    test-rmse:0.419850+0.067742 
## [30] train-rmse:0.258788+0.005823    test-rmse:0.419525+0.067243 
## [31] train-rmse:0.256328+0.006038    test-rmse:0.418893+0.068946 
## [32] train-rmse:0.253638+0.005990    test-rmse:0.417135+0.067580 
## [33] train-rmse:0.250106+0.006341    test-rmse:0.416478+0.067649 
## [34] train-rmse:0.247108+0.006760    test-rmse:0.415302+0.068825 
## [35] train-rmse:0.244346+0.006482    test-rmse:0.415916+0.068709 
## [36] train-rmse:0.240655+0.006428    test-rmse:0.415853+0.069508 
## [37] train-rmse:0.237458+0.005652    test-rmse:0.414512+0.071273 
## [38] train-rmse:0.234656+0.005056    test-rmse:0.414872+0.070843 
## [39] train-rmse:0.232719+0.005339    test-rmse:0.414663+0.070880 
## [40] train-rmse:0.230200+0.005985    test-rmse:0.412505+0.072501 
## [41] train-rmse:0.227789+0.006051    test-rmse:0.412390+0.072126 
## [42] train-rmse:0.226094+0.006177    test-rmse:0.411079+0.072123 
## [43] train-rmse:0.223624+0.005528    test-rmse:0.412607+0.071134 
## [44] train-rmse:0.221588+0.005508    test-rmse:0.412937+0.070513 
## [45] train-rmse:0.219304+0.005569    test-rmse:0.412485+0.071273 
## [46] train-rmse:0.217366+0.005620    test-rmse:0.411964+0.071660 
## [47] train-rmse:0.214318+0.006492    test-rmse:0.411419+0.071699 
## [48] train-rmse:0.212601+0.006216    test-rmse:0.411987+0.070828 
## Stopping. Best iteration:
## [42] train-rmse:0.226094+0.006177    test-rmse:0.411079+0.072123
## 
## 52 
## [1]  train-rmse:1.288830+0.023247    test-rmse:1.291635+0.117945 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.027226+0.018045    test-rmse:1.035652+0.116298 
## [3]  train-rmse:0.829072+0.015515    test-rmse:0.840007+0.107077 
## [4]  train-rmse:0.680214+0.011533    test-rmse:0.698388+0.104663 
## [5]  train-rmse:0.571833+0.009135    test-rmse:0.600736+0.097602 
## [6]  train-rmse:0.490651+0.007002    test-rmse:0.532613+0.092741 
## [7]  train-rmse:0.432211+0.005813    test-rmse:0.489379+0.085514 
## [8]  train-rmse:0.389954+0.005016    test-rmse:0.456397+0.083784 
## [9]  train-rmse:0.357759+0.007031    test-rmse:0.439841+0.081030 
## [10] train-rmse:0.334002+0.005946    test-rmse:0.430508+0.076920 
## [11] train-rmse:0.316162+0.004882    test-rmse:0.425064+0.071127 
## [12] train-rmse:0.302167+0.004328    test-rmse:0.417369+0.067029 
## [13] train-rmse:0.291226+0.006377    test-rmse:0.416864+0.064465 
## [14] train-rmse:0.282122+0.006580    test-rmse:0.413494+0.062604 
## [15] train-rmse:0.272883+0.004957    test-rmse:0.412794+0.062270 
## [16] train-rmse:0.265069+0.003236    test-rmse:0.411645+0.061683 
## [17] train-rmse:0.258734+0.003633    test-rmse:0.409688+0.058677 
## [18] train-rmse:0.252455+0.003771    test-rmse:0.408937+0.058462 
## [19] train-rmse:0.246682+0.003920    test-rmse:0.406564+0.060378 
## [20] train-rmse:0.239333+0.004737    test-rmse:0.406230+0.059160 
## [21] train-rmse:0.234336+0.005322    test-rmse:0.405102+0.057920 
## [22] train-rmse:0.230540+0.004714    test-rmse:0.402991+0.059809 
## [23] train-rmse:0.226019+0.003510    test-rmse:0.403682+0.058776 
## [24] train-rmse:0.221843+0.003512    test-rmse:0.400986+0.058157 
## [25] train-rmse:0.217165+0.003727    test-rmse:0.400812+0.057843 
## [26] train-rmse:0.212561+0.004301    test-rmse:0.400732+0.057517 
## [27] train-rmse:0.208104+0.005678    test-rmse:0.401309+0.056532 
## [28] train-rmse:0.204442+0.005216    test-rmse:0.401428+0.056579 
## [29] train-rmse:0.201229+0.005595    test-rmse:0.400911+0.056146 
## [30] train-rmse:0.197657+0.004602    test-rmse:0.399319+0.057039 
## [31] train-rmse:0.194187+0.005362    test-rmse:0.397288+0.057688 
## [32] train-rmse:0.189774+0.006290    test-rmse:0.396699+0.058087 
## [33] train-rmse:0.187190+0.006208    test-rmse:0.396060+0.057748 
## [34] train-rmse:0.183768+0.006501    test-rmse:0.395740+0.058690 
## [35] train-rmse:0.181201+0.006676    test-rmse:0.395756+0.058425 
## [36] train-rmse:0.178538+0.006658    test-rmse:0.395318+0.058074 
## [37] train-rmse:0.174913+0.005990    test-rmse:0.394785+0.058223 
## [38] train-rmse:0.172002+0.006104    test-rmse:0.395346+0.057878 
## [39] train-rmse:0.169062+0.006986    test-rmse:0.395203+0.057681 
## [40] train-rmse:0.165457+0.007303    test-rmse:0.396177+0.058032 
## [41] train-rmse:0.163129+0.006922    test-rmse:0.395907+0.057460 
## [42] train-rmse:0.160547+0.007186    test-rmse:0.395044+0.058009 
## [43] train-rmse:0.158679+0.006933    test-rmse:0.394762+0.058453 
## [44] train-rmse:0.156657+0.006678    test-rmse:0.393089+0.058823 
## [45] train-rmse:0.154385+0.007133    test-rmse:0.392090+0.059878 
## [46] train-rmse:0.152529+0.006916    test-rmse:0.392695+0.059461 
## [47] train-rmse:0.150315+0.006350    test-rmse:0.392669+0.059191 
## [48] train-rmse:0.148479+0.006776    test-rmse:0.392170+0.059322 
## [49] train-rmse:0.146814+0.006682    test-rmse:0.392443+0.059846 
## [50] train-rmse:0.144913+0.007260    test-rmse:0.391219+0.059714 
## [51] train-rmse:0.141961+0.007238    test-rmse:0.389677+0.059455 
## [52] train-rmse:0.140295+0.007378    test-rmse:0.388855+0.059872 
## [53] train-rmse:0.138280+0.007695    test-rmse:0.387786+0.059502 
## [54] train-rmse:0.135799+0.007994    test-rmse:0.387792+0.059919 
## [55] train-rmse:0.134504+0.007930    test-rmse:0.387630+0.060336 
## [56] train-rmse:0.132482+0.008291    test-rmse:0.387051+0.060204 
## [57] train-rmse:0.130450+0.008764    test-rmse:0.387192+0.060497 
## [58] train-rmse:0.128503+0.008774    test-rmse:0.387315+0.060387 
## [59] train-rmse:0.127334+0.009076    test-rmse:0.387206+0.060765 
## [60] train-rmse:0.125949+0.009055    test-rmse:0.387357+0.061156 
## [61] train-rmse:0.124837+0.009036    test-rmse:0.387256+0.061172 
## [62] train-rmse:0.123403+0.009321    test-rmse:0.387157+0.061217 
## Stopping. Best iteration:
## [56] train-rmse:0.132482+0.008291    test-rmse:0.387051+0.060204
## 
## 53 
## [1]  train-rmse:1.286622+0.023611    test-rmse:1.289462+0.114354 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.022372+0.018406    test-rmse:1.027396+0.109698 
## [3]  train-rmse:0.821209+0.016247    test-rmse:0.831386+0.100246 
## [4]  train-rmse:0.667878+0.011701    test-rmse:0.692837+0.099029 
## [5]  train-rmse:0.553790+0.011000    test-rmse:0.590354+0.089515 
## [6]  train-rmse:0.468294+0.008186    test-rmse:0.523898+0.085140 
## [7]  train-rmse:0.405049+0.007499    test-rmse:0.478093+0.081768 
## [8]  train-rmse:0.357603+0.008361    test-rmse:0.448051+0.079458 
## [9]  train-rmse:0.321451+0.009203    test-rmse:0.429292+0.075184 
## [10] train-rmse:0.294553+0.010613    test-rmse:0.416381+0.075123 
## [11] train-rmse:0.275534+0.011555    test-rmse:0.412037+0.074346 
## [12] train-rmse:0.256752+0.008722    test-rmse:0.406128+0.071576 
## [13] train-rmse:0.245377+0.010088    test-rmse:0.402436+0.069106 
## [14] train-rmse:0.233318+0.011631    test-rmse:0.401256+0.068430 
## [15] train-rmse:0.224007+0.009952    test-rmse:0.401239+0.067762 
## [16] train-rmse:0.215555+0.007791    test-rmse:0.399739+0.067150 
## [17] train-rmse:0.208544+0.007686    test-rmse:0.397623+0.063978 
## [18] train-rmse:0.202333+0.007647    test-rmse:0.397325+0.063757 
## [19] train-rmse:0.195494+0.006774    test-rmse:0.398770+0.062273 
## [20] train-rmse:0.188961+0.007521    test-rmse:0.398452+0.061891 
## [21] train-rmse:0.182554+0.008154    test-rmse:0.398650+0.060797 
## [22] train-rmse:0.176848+0.009709    test-rmse:0.398118+0.061422 
## [23] train-rmse:0.171347+0.010709    test-rmse:0.397037+0.061242 
## [24] train-rmse:0.166779+0.009823    test-rmse:0.395898+0.059700 
## [25] train-rmse:0.162566+0.010059    test-rmse:0.395093+0.061158 
## [26] train-rmse:0.158780+0.010061    test-rmse:0.395275+0.060978 
## [27] train-rmse:0.155701+0.010110    test-rmse:0.395065+0.059611 
## [28] train-rmse:0.152176+0.008705    test-rmse:0.394814+0.060116 
## [29] train-rmse:0.148884+0.008122    test-rmse:0.395204+0.059683 
## [30] train-rmse:0.144777+0.007193    test-rmse:0.395025+0.059532 
## [31] train-rmse:0.141331+0.007688    test-rmse:0.394330+0.059859 
## [32] train-rmse:0.137798+0.006222    test-rmse:0.395058+0.059349 
## [33] train-rmse:0.134448+0.005984    test-rmse:0.395245+0.060163 
## [34] train-rmse:0.130742+0.005150    test-rmse:0.393738+0.060463 
## [35] train-rmse:0.128619+0.005466    test-rmse:0.393289+0.060762 
## [36] train-rmse:0.126113+0.005571    test-rmse:0.393569+0.061039 
## [37] train-rmse:0.124248+0.005146    test-rmse:0.392954+0.061086 
## [38] train-rmse:0.121000+0.005707    test-rmse:0.392565+0.060581 
## [39] train-rmse:0.117884+0.005593    test-rmse:0.392496+0.060734 
## [40] train-rmse:0.115971+0.004980    test-rmse:0.392027+0.061191 
## [41] train-rmse:0.113127+0.004694    test-rmse:0.392681+0.060548 
## [42] train-rmse:0.110564+0.005244    test-rmse:0.392200+0.060993 
## [43] train-rmse:0.107780+0.004786    test-rmse:0.391609+0.061900 
## [44] train-rmse:0.105516+0.005428    test-rmse:0.391508+0.062540 
## [45] train-rmse:0.103310+0.004736    test-rmse:0.391651+0.061400 
## [46] train-rmse:0.100881+0.004780    test-rmse:0.391892+0.061284 
## [47] train-rmse:0.098936+0.004700    test-rmse:0.392233+0.061581 
## [48] train-rmse:0.097348+0.004752    test-rmse:0.391318+0.060612 
## [49] train-rmse:0.095684+0.005112    test-rmse:0.391107+0.060441 
## [50] train-rmse:0.093426+0.005451    test-rmse:0.391604+0.059489 
## [51] train-rmse:0.091022+0.005186    test-rmse:0.391286+0.059213 
## [52] train-rmse:0.089242+0.004812    test-rmse:0.391048+0.059363 
## [53] train-rmse:0.087008+0.004972    test-rmse:0.391412+0.059090 
## [54] train-rmse:0.085116+0.005561    test-rmse:0.390780+0.058713 
## [55] train-rmse:0.083845+0.005513    test-rmse:0.390946+0.058754 
## [56] train-rmse:0.081903+0.005984    test-rmse:0.391151+0.058432 
## [57] train-rmse:0.080046+0.005748    test-rmse:0.391078+0.058509 
## [58] train-rmse:0.078901+0.005193    test-rmse:0.390979+0.058550 
## [59] train-rmse:0.076786+0.004626    test-rmse:0.391290+0.058333 
## [60] train-rmse:0.075364+0.004221    test-rmse:0.391545+0.058023 
## Stopping. Best iteration:
## [54] train-rmse:0.085116+0.005561    test-rmse:0.390780+0.058713
## 
## 54 
## [1]  train-rmse:1.285934+0.023663    test-rmse:1.287928+0.112999 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.020171+0.018479    test-rmse:1.024554+0.107012 
## [3]  train-rmse:0.816953+0.016566    test-rmse:0.830495+0.095986 
## [4]  train-rmse:0.662689+0.013708    test-rmse:0.689222+0.093283 
## [5]  train-rmse:0.544284+0.012361    test-rmse:0.585814+0.095405 
## [6]  train-rmse:0.456878+0.009943    test-rmse:0.519246+0.092295 
## [7]  train-rmse:0.389520+0.008469    test-rmse:0.474051+0.089846 
## [8]  train-rmse:0.338902+0.008849    test-rmse:0.445280+0.084624 
## [9]  train-rmse:0.300443+0.008412    test-rmse:0.430296+0.083998 
## [10] train-rmse:0.270478+0.009490    test-rmse:0.419840+0.079496 
## [11] train-rmse:0.245401+0.008332    test-rmse:0.411750+0.077772 
## [12] train-rmse:0.223623+0.010734    test-rmse:0.405503+0.074110 
## [13] train-rmse:0.210208+0.011776    test-rmse:0.405270+0.073237 
## [14] train-rmse:0.195407+0.011995    test-rmse:0.402621+0.072893 
## [15] train-rmse:0.184381+0.014012    test-rmse:0.401943+0.071037 
## [16] train-rmse:0.175337+0.015375    test-rmse:0.400425+0.069262 
## [17] train-rmse:0.167672+0.013926    test-rmse:0.400239+0.069134 
## [18] train-rmse:0.161334+0.014592    test-rmse:0.400205+0.069681 
## [19] train-rmse:0.154817+0.013794    test-rmse:0.399134+0.068649 
## [20] train-rmse:0.147938+0.012999    test-rmse:0.399970+0.065770 
## [21] train-rmse:0.141362+0.013178    test-rmse:0.399143+0.065013 
## [22] train-rmse:0.135407+0.010549    test-rmse:0.398939+0.065837 
## [23] train-rmse:0.131143+0.010631    test-rmse:0.398602+0.064927 
## [24] train-rmse:0.126934+0.011060    test-rmse:0.398585+0.064753 
## [25] train-rmse:0.121157+0.009609    test-rmse:0.397222+0.063363 
## [26] train-rmse:0.117170+0.008965    test-rmse:0.396998+0.063788 
## [27] train-rmse:0.113019+0.008148    test-rmse:0.397199+0.063696 
## [28] train-rmse:0.108602+0.008588    test-rmse:0.396931+0.062982 
## [29] train-rmse:0.104653+0.008398    test-rmse:0.397395+0.062287 
## [30] train-rmse:0.101699+0.007764    test-rmse:0.397380+0.062570 
## [31] train-rmse:0.099226+0.007614    test-rmse:0.397628+0.062762 
## [32] train-rmse:0.096642+0.007536    test-rmse:0.397665+0.063060 
## [33] train-rmse:0.093732+0.006657    test-rmse:0.397737+0.063487 
## [34] train-rmse:0.090705+0.005805    test-rmse:0.398250+0.063515 
## Stopping. Best iteration:
## [28] train-rmse:0.108602+0.008588    test-rmse:0.396931+0.062982
## 
## 55 
## [1]  train-rmse:1.285599+0.023664    test-rmse:1.289060+0.112473 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.018851+0.018807    test-rmse:1.023601+0.107182 
## [3]  train-rmse:0.814028+0.016864    test-rmse:0.825915+0.098888 
## [4]  train-rmse:0.658081+0.013729    test-rmse:0.683573+0.096978 
## [5]  train-rmse:0.537629+0.012760    test-rmse:0.584650+0.091762 
## [6]  train-rmse:0.446620+0.011313    test-rmse:0.520214+0.088276 
## [7]  train-rmse:0.375400+0.011693    test-rmse:0.479516+0.081608 
## [8]  train-rmse:0.321129+0.010734    test-rmse:0.454688+0.080536 
## [9]  train-rmse:0.278579+0.010584    test-rmse:0.440147+0.076049 
## [10] train-rmse:0.245728+0.010141    test-rmse:0.430012+0.073936 
## [11] train-rmse:0.221008+0.010099    test-rmse:0.422073+0.069819 
## [12] train-rmse:0.199580+0.012543    test-rmse:0.420631+0.066306 
## [13] train-rmse:0.182060+0.012337    test-rmse:0.417099+0.062107 
## [14] train-rmse:0.166081+0.009604    test-rmse:0.416156+0.059904 
## [15] train-rmse:0.155099+0.010178    test-rmse:0.414706+0.056783 
## [16] train-rmse:0.144734+0.008431    test-rmse:0.414312+0.056628 
## [17] train-rmse:0.137498+0.008294    test-rmse:0.413743+0.054276 
## [18] train-rmse:0.129866+0.007483    test-rmse:0.413597+0.052631 
## [19] train-rmse:0.122461+0.006514    test-rmse:0.410924+0.051782 
## [20] train-rmse:0.116699+0.006023    test-rmse:0.409669+0.050058 
## [21] train-rmse:0.110179+0.007259    test-rmse:0.408593+0.048381 
## [22] train-rmse:0.105616+0.007167    test-rmse:0.406599+0.047771 
## [23] train-rmse:0.101179+0.007006    test-rmse:0.407382+0.045896 
## [24] train-rmse:0.096130+0.007620    test-rmse:0.407524+0.044836 
## [25] train-rmse:0.091135+0.008742    test-rmse:0.407657+0.044962 
## [26] train-rmse:0.086359+0.009965    test-rmse:0.407173+0.044424 
## [27] train-rmse:0.083028+0.009735    test-rmse:0.407386+0.043717 
## [28] train-rmse:0.079900+0.010012    test-rmse:0.407142+0.043866 
## Stopping. Best iteration:
## [22] train-rmse:0.105616+0.007167    test-rmse:0.406599+0.047771
## 
## 56 
## [1]  train-rmse:1.290638+0.024188    test-rmse:1.286811+0.109377 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.051722+0.020515    test-rmse:1.048027+0.103147 
## [3]  train-rmse:0.893110+0.018699    test-rmse:0.889652+0.097875 
## [4]  train-rmse:0.792216+0.018053    test-rmse:0.789085+0.093493 
## [5]  train-rmse:0.730668+0.017960    test-rmse:0.727882+0.090093 
## [6]  train-rmse:0.694411+0.018045    test-rmse:0.691932+0.087626 
## [7]  train-rmse:0.668028+0.016841    test-rmse:0.666056+0.087481 
## [8]  train-rmse:0.649221+0.017005    test-rmse:0.647512+0.085819 
## [9]  train-rmse:0.633231+0.015227    test-rmse:0.634353+0.082201 
## [10] train-rmse:0.620268+0.014245    test-rmse:0.624549+0.079351 
## [11] train-rmse:0.609194+0.014047    test-rmse:0.611745+0.077383 
## [12] train-rmse:0.599822+0.012687    test-rmse:0.602427+0.073105 
## [13] train-rmse:0.591966+0.012570    test-rmse:0.595296+0.072149 
## [14] train-rmse:0.584549+0.012447    test-rmse:0.593535+0.070682 
## [15] train-rmse:0.578206+0.012238    test-rmse:0.586414+0.069455 
## [16] train-rmse:0.572202+0.011745    test-rmse:0.581319+0.067593 
## [17] train-rmse:0.566892+0.011529    test-rmse:0.576687+0.064725 
## [18] train-rmse:0.562275+0.011495    test-rmse:0.572271+0.063374 
## [19] train-rmse:0.558019+0.011093    test-rmse:0.570345+0.065245 
## [20] train-rmse:0.553965+0.010673    test-rmse:0.566477+0.062503 
## [21] train-rmse:0.550303+0.010744    test-rmse:0.564877+0.063617 
## [22] train-rmse:0.547035+0.010762    test-rmse:0.561711+0.061960 
## [23] train-rmse:0.543936+0.010722    test-rmse:0.559454+0.060599 
## [24] train-rmse:0.541039+0.010535    test-rmse:0.559976+0.060137 
## [25] train-rmse:0.538212+0.010439    test-rmse:0.558962+0.059235 
## [26] train-rmse:0.535555+0.010139    test-rmse:0.556742+0.059170 
## [27] train-rmse:0.533090+0.010087    test-rmse:0.554612+0.059842 
## [28] train-rmse:0.530655+0.009899    test-rmse:0.551870+0.060461 
## [29] train-rmse:0.528248+0.009817    test-rmse:0.549412+0.058013 
## [30] train-rmse:0.526048+0.009704    test-rmse:0.548795+0.058199 
## [31] train-rmse:0.523974+0.009630    test-rmse:0.547898+0.058356 
## [32] train-rmse:0.522001+0.009543    test-rmse:0.547490+0.057092 
## [33] train-rmse:0.520109+0.009390    test-rmse:0.545859+0.056550 
## [34] train-rmse:0.518203+0.009199    test-rmse:0.544635+0.055961 
## [35] train-rmse:0.516355+0.009118    test-rmse:0.542031+0.054921 
## [36] train-rmse:0.514578+0.009021    test-rmse:0.540855+0.054519 
## [37] train-rmse:0.512851+0.009017    test-rmse:0.539558+0.055004 
## [38] train-rmse:0.511218+0.008948    test-rmse:0.539231+0.055620 
## [39] train-rmse:0.509623+0.008887    test-rmse:0.538538+0.056178 
## [40] train-rmse:0.508025+0.008804    test-rmse:0.536542+0.056125 
## [41] train-rmse:0.506464+0.008746    test-rmse:0.536357+0.054038 
## [42] train-rmse:0.504948+0.008620    test-rmse:0.534434+0.053682 
## [43] train-rmse:0.503475+0.008600    test-rmse:0.533253+0.053525 
## [44] train-rmse:0.502055+0.008572    test-rmse:0.532915+0.054198 
## [45] train-rmse:0.500654+0.008541    test-rmse:0.533571+0.054223 
## [46] train-rmse:0.499305+0.008477    test-rmse:0.530818+0.053624 
## [47] train-rmse:0.497940+0.008480    test-rmse:0.529579+0.053343 
## [48] train-rmse:0.496548+0.008427    test-rmse:0.529097+0.051911 
## [49] train-rmse:0.495289+0.008411    test-rmse:0.527460+0.052849 
## [50] train-rmse:0.494058+0.008358    test-rmse:0.527931+0.053625 
## [51] train-rmse:0.492847+0.008324    test-rmse:0.527183+0.053191 
## [52] train-rmse:0.491665+0.008325    test-rmse:0.525687+0.052913 
## [53] train-rmse:0.490527+0.008280    test-rmse:0.524510+0.052240 
## [54] train-rmse:0.489418+0.008245    test-rmse:0.523550+0.052909 
## [55] train-rmse:0.488331+0.008229    test-rmse:0.523031+0.052288 
## [56] train-rmse:0.487271+0.008210    test-rmse:0.522622+0.051331 
## [57] train-rmse:0.486192+0.008149    test-rmse:0.522943+0.050813 
## [58] train-rmse:0.485130+0.008114    test-rmse:0.522729+0.051073 
## [59] train-rmse:0.484088+0.008073    test-rmse:0.521942+0.050415 
## [60] train-rmse:0.483076+0.008014    test-rmse:0.520875+0.049584 
## [61] train-rmse:0.482142+0.008017    test-rmse:0.518360+0.050189 
## [62] train-rmse:0.481189+0.007984    test-rmse:0.518393+0.050514 
## [63] train-rmse:0.480269+0.007946    test-rmse:0.517942+0.049879 
## [64] train-rmse:0.479391+0.007931    test-rmse:0.517394+0.048395 
## [65] train-rmse:0.478474+0.007937    test-rmse:0.518010+0.048942 
## [66] train-rmse:0.477601+0.007946    test-rmse:0.516587+0.049635 
## [67] train-rmse:0.476731+0.007947    test-rmse:0.517216+0.049765 
## [68] train-rmse:0.475885+0.007916    test-rmse:0.516614+0.049580 
## [69] train-rmse:0.475085+0.007897    test-rmse:0.516710+0.050556 
## [70] train-rmse:0.474282+0.007877    test-rmse:0.516302+0.050012 
## [71] train-rmse:0.473467+0.007882    test-rmse:0.516189+0.049717 
## [72] train-rmse:0.472699+0.007849    test-rmse:0.515143+0.049811 
## [73] train-rmse:0.471941+0.007834    test-rmse:0.514681+0.049739 
## [74] train-rmse:0.471184+0.007821    test-rmse:0.512478+0.046563 
## [75] train-rmse:0.470459+0.007811    test-rmse:0.512683+0.046345 
## [76] train-rmse:0.469741+0.007801    test-rmse:0.513023+0.046019 
## [77] train-rmse:0.469040+0.007788    test-rmse:0.512287+0.046026 
## [78] train-rmse:0.468318+0.007762    test-rmse:0.511187+0.045112 
## [79] train-rmse:0.467620+0.007751    test-rmse:0.510701+0.045814 
## [80] train-rmse:0.466922+0.007756    test-rmse:0.509240+0.045717 
## [81] train-rmse:0.466251+0.007760    test-rmse:0.508918+0.046449 
## [82] train-rmse:0.465599+0.007751    test-rmse:0.507728+0.046955 
## [83] train-rmse:0.464934+0.007719    test-rmse:0.507760+0.047124 
## [84] train-rmse:0.464325+0.007696    test-rmse:0.508060+0.046548 
## [85] train-rmse:0.463688+0.007652    test-rmse:0.507778+0.045866 
## [86] train-rmse:0.463040+0.007634    test-rmse:0.507754+0.046348 
## [87] train-rmse:0.462421+0.007617    test-rmse:0.507204+0.046077 
## [88] train-rmse:0.461812+0.007593    test-rmse:0.507385+0.045852 
## [89] train-rmse:0.461219+0.007584    test-rmse:0.506947+0.046685 
## [90] train-rmse:0.460646+0.007564    test-rmse:0.506707+0.046920 
## [91] train-rmse:0.460081+0.007546    test-rmse:0.507051+0.047111 
## [92] train-rmse:0.459534+0.007536    test-rmse:0.506508+0.046716 
## [93] train-rmse:0.458963+0.007519    test-rmse:0.507045+0.046479 
## [94] train-rmse:0.458405+0.007508    test-rmse:0.506382+0.046066 
## [95] train-rmse:0.457872+0.007503    test-rmse:0.506241+0.046527 
## [96] train-rmse:0.457356+0.007499    test-rmse:0.505779+0.046215 
## [97] train-rmse:0.456815+0.007495    test-rmse:0.506017+0.045745 
## [98] train-rmse:0.456311+0.007491    test-rmse:0.506179+0.045717 
## [99] train-rmse:0.455802+0.007486    test-rmse:0.505614+0.044722 
## [100]    train-rmse:0.455298+0.007479    test-rmse:0.504732+0.044941 
## [101]    train-rmse:0.454792+0.007468    test-rmse:0.505383+0.044762 
## [102]    train-rmse:0.454297+0.007465    test-rmse:0.504598+0.044845 
## [103]    train-rmse:0.453818+0.007464    test-rmse:0.504474+0.043838 
## [104]    train-rmse:0.453328+0.007464    test-rmse:0.504170+0.043390 
## [105]    train-rmse:0.452847+0.007459    test-rmse:0.504717+0.044297 
## [106]    train-rmse:0.452376+0.007469    test-rmse:0.504317+0.044313 
## [107]    train-rmse:0.451916+0.007481    test-rmse:0.503205+0.042994 
## [108]    train-rmse:0.451429+0.007467    test-rmse:0.502727+0.042999 
## [109]    train-rmse:0.450988+0.007463    test-rmse:0.502556+0.042772 
## [110]    train-rmse:0.450555+0.007466    test-rmse:0.502172+0.042880 
## [111]    train-rmse:0.450105+0.007465    test-rmse:0.502058+0.043262 
## [112]    train-rmse:0.449690+0.007469    test-rmse:0.502093+0.042596 
## [113]    train-rmse:0.449268+0.007495    test-rmse:0.501724+0.042524 
## [114]    train-rmse:0.448846+0.007505    test-rmse:0.501039+0.042596 
## [115]    train-rmse:0.448449+0.007510    test-rmse:0.500928+0.042796 
## [116]    train-rmse:0.448035+0.007519    test-rmse:0.500484+0.042861 
## [117]    train-rmse:0.447607+0.007504    test-rmse:0.500477+0.043021 
## [118]    train-rmse:0.447216+0.007501    test-rmse:0.500505+0.042684 
## [119]    train-rmse:0.446837+0.007505    test-rmse:0.500699+0.042698 
## [120]    train-rmse:0.446453+0.007502    test-rmse:0.500542+0.042824 
## [121]    train-rmse:0.446071+0.007498    test-rmse:0.500447+0.042880 
## [122]    train-rmse:0.445689+0.007515    test-rmse:0.500228+0.043174 
## [123]    train-rmse:0.445315+0.007508    test-rmse:0.500144+0.043093 
## [124]    train-rmse:0.444948+0.007510    test-rmse:0.500039+0.043033 
## [125]    train-rmse:0.444585+0.007509    test-rmse:0.499808+0.042829 
## [126]    train-rmse:0.444233+0.007510    test-rmse:0.499817+0.043035 
## [127]    train-rmse:0.443881+0.007502    test-rmse:0.499472+0.042277 
## [128]    train-rmse:0.443502+0.007523    test-rmse:0.499970+0.043000 
## [129]    train-rmse:0.443138+0.007496    test-rmse:0.499674+0.042503 
## [130]    train-rmse:0.442792+0.007486    test-rmse:0.499324+0.041579 
## [131]    train-rmse:0.442449+0.007493    test-rmse:0.499507+0.041693 
## [132]    train-rmse:0.442109+0.007494    test-rmse:0.499500+0.041133 
## [133]    train-rmse:0.441762+0.007502    test-rmse:0.498831+0.041153 
## [134]    train-rmse:0.441420+0.007517    test-rmse:0.498239+0.041727 
## [135]    train-rmse:0.441084+0.007519    test-rmse:0.498553+0.041500 
## [136]    train-rmse:0.440756+0.007524    test-rmse:0.498603+0.041858 
## [137]    train-rmse:0.440435+0.007527    test-rmse:0.498608+0.041891 
## [138]    train-rmse:0.440118+0.007534    test-rmse:0.498504+0.041893 
## [139]    train-rmse:0.439790+0.007537    test-rmse:0.498333+0.041772 
## [140]    train-rmse:0.439479+0.007534    test-rmse:0.498379+0.041947 
## Stopping. Best iteration:
## [134]    train-rmse:0.441420+0.007517    test-rmse:0.498239+0.041727
## 
## 57 
## [1]  train-rmse:1.273501+0.024293    test-rmse:1.273425+0.114412 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.012006+0.019249    test-rmse:1.009773+0.103431 
## [3]  train-rmse:0.828289+0.017327    test-rmse:0.829905+0.104039 
## [4]  train-rmse:0.702251+0.014896    test-rmse:0.709157+0.102856 
## [5]  train-rmse:0.616418+0.013190    test-rmse:0.627819+0.098881 
## [6]  train-rmse:0.558415+0.012664    test-rmse:0.569612+0.092360 
## [7]  train-rmse:0.519820+0.012130    test-rmse:0.535611+0.087870 
## [8]  train-rmse:0.492168+0.011859    test-rmse:0.514735+0.077455 
## [9]  train-rmse:0.472499+0.010893    test-rmse:0.496989+0.074742 
## [10] train-rmse:0.454999+0.009539    test-rmse:0.479012+0.070401 
## [11] train-rmse:0.441197+0.008294    test-rmse:0.469701+0.066854 
## [12] train-rmse:0.431361+0.008211    test-rmse:0.464123+0.060711 
## [13] train-rmse:0.421776+0.008230    test-rmse:0.460403+0.058889 
## [14] train-rmse:0.413516+0.007895    test-rmse:0.456755+0.057838 
## [15] train-rmse:0.406712+0.007402    test-rmse:0.455752+0.058488 
## [16] train-rmse:0.400640+0.006862    test-rmse:0.454594+0.057619 
## [17] train-rmse:0.393989+0.005973    test-rmse:0.451188+0.056217 
## [18] train-rmse:0.388426+0.005362    test-rmse:0.448520+0.055779 
## [19] train-rmse:0.383384+0.005230    test-rmse:0.446305+0.055253 
## [20] train-rmse:0.378574+0.005175    test-rmse:0.446569+0.052762 
## [21] train-rmse:0.374478+0.005385    test-rmse:0.443845+0.051871 
## [22] train-rmse:0.370326+0.005768    test-rmse:0.444964+0.052659 
## [23] train-rmse:0.366358+0.005366    test-rmse:0.443842+0.053161 
## [24] train-rmse:0.362307+0.005695    test-rmse:0.441460+0.052281 
## [25] train-rmse:0.358823+0.005821    test-rmse:0.441916+0.051820 
## [26] train-rmse:0.355808+0.005716    test-rmse:0.442262+0.051458 
## [27] train-rmse:0.351440+0.006404    test-rmse:0.440346+0.051259 
## [28] train-rmse:0.346731+0.006052    test-rmse:0.439187+0.052060 
## [29] train-rmse:0.343437+0.005913    test-rmse:0.438779+0.053481 
## [30] train-rmse:0.340088+0.005552    test-rmse:0.436566+0.052323 
## [31] train-rmse:0.337715+0.005592    test-rmse:0.438373+0.052826 
## [32] train-rmse:0.335456+0.005087    test-rmse:0.438704+0.053328 
## [33] train-rmse:0.332302+0.005891    test-rmse:0.439381+0.053506 
## [34] train-rmse:0.329282+0.005939    test-rmse:0.437810+0.053239 
## [35] train-rmse:0.326251+0.006398    test-rmse:0.437957+0.053752 
## [36] train-rmse:0.324196+0.006556    test-rmse:0.437554+0.052928 
## Stopping. Best iteration:
## [30] train-rmse:0.340088+0.005552    test-rmse:0.436566+0.052323
## 
## 58 
## [1]  train-rmse:1.264377+0.023346    test-rmse:1.268258+0.117439 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.993186+0.018597    test-rmse:1.001626+0.112258 
## [3]  train-rmse:0.797211+0.014502    test-rmse:0.811850+0.111347 
## [4]  train-rmse:0.655881+0.013266    test-rmse:0.678732+0.099810 
## [5]  train-rmse:0.555936+0.010861    test-rmse:0.586359+0.093908 
## [6]  train-rmse:0.488614+0.009369    test-rmse:0.529851+0.087761 
## [7]  train-rmse:0.440447+0.010091    test-rmse:0.487711+0.083192 
## [8]  train-rmse:0.407935+0.009511    test-rmse:0.468720+0.077088 
## [9]  train-rmse:0.384566+0.008780    test-rmse:0.455917+0.080624 
## [10] train-rmse:0.366433+0.007434    test-rmse:0.448856+0.077330 
## [11] train-rmse:0.352796+0.007753    test-rmse:0.439959+0.071585 
## [12] train-rmse:0.342433+0.006976    test-rmse:0.439582+0.070700 
## [13] train-rmse:0.332323+0.007268    test-rmse:0.435446+0.068281 
## [14] train-rmse:0.324301+0.007751    test-rmse:0.435002+0.066583 
## [15] train-rmse:0.316884+0.006897    test-rmse:0.432057+0.068074 
## [16] train-rmse:0.310338+0.005774    test-rmse:0.430887+0.068561 
## [17] train-rmse:0.304826+0.005642    test-rmse:0.430429+0.068400 
## [18] train-rmse:0.299847+0.005431    test-rmse:0.428855+0.065145 
## [19] train-rmse:0.294847+0.005188    test-rmse:0.428950+0.064821 
## [20] train-rmse:0.290207+0.004509    test-rmse:0.427925+0.065547 
## [21] train-rmse:0.285284+0.004249    test-rmse:0.426919+0.065038 
## [22] train-rmse:0.280327+0.003154    test-rmse:0.427404+0.064512 
## [23] train-rmse:0.276089+0.002845    test-rmse:0.427222+0.065337 
## [24] train-rmse:0.270569+0.003064    test-rmse:0.426535+0.065603 
## [25] train-rmse:0.266893+0.002804    test-rmse:0.423499+0.067365 
## [26] train-rmse:0.262964+0.003120    test-rmse:0.423648+0.069821 
## [27] train-rmse:0.259834+0.003414    test-rmse:0.421938+0.068703 
## [28] train-rmse:0.256501+0.002766    test-rmse:0.421022+0.068538 
## [29] train-rmse:0.253664+0.002759    test-rmse:0.420006+0.068606 
## [30] train-rmse:0.250547+0.002797    test-rmse:0.419156+0.067798 
## [31] train-rmse:0.247721+0.002791    test-rmse:0.417954+0.069878 
## [32] train-rmse:0.244267+0.003308    test-rmse:0.417465+0.069256 
## [33] train-rmse:0.240648+0.003873    test-rmse:0.418558+0.069491 
## [34] train-rmse:0.238311+0.004005    test-rmse:0.418558+0.069990 
## [35] train-rmse:0.235505+0.003374    test-rmse:0.416899+0.069796 
## [36] train-rmse:0.233204+0.003909    test-rmse:0.415360+0.070024 
## [37] train-rmse:0.229471+0.004529    test-rmse:0.413363+0.069642 
## [38] train-rmse:0.227349+0.004791    test-rmse:0.413359+0.069934 
## [39] train-rmse:0.224181+0.004820    test-rmse:0.413363+0.069821 
## [40] train-rmse:0.221068+0.005648    test-rmse:0.412218+0.070462 
## [41] train-rmse:0.218792+0.005594    test-rmse:0.411337+0.071263 
## [42] train-rmse:0.216278+0.005442    test-rmse:0.412244+0.070865 
## [43] train-rmse:0.213955+0.005514    test-rmse:0.412143+0.070811 
## [44] train-rmse:0.211270+0.005555    test-rmse:0.410765+0.070892 
## [45] train-rmse:0.209372+0.005846    test-rmse:0.409573+0.071037 
## [46] train-rmse:0.206992+0.006141    test-rmse:0.408032+0.072077 
## [47] train-rmse:0.205420+0.006463    test-rmse:0.407773+0.071786 
## [48] train-rmse:0.203083+0.005825    test-rmse:0.407928+0.071296 
## [49] train-rmse:0.201172+0.005955    test-rmse:0.407916+0.071823 
## [50] train-rmse:0.198987+0.005675    test-rmse:0.407137+0.072131 
## [51] train-rmse:0.196453+0.004593    test-rmse:0.407861+0.071882 
## [52] train-rmse:0.194301+0.005133    test-rmse:0.408434+0.071396 
## [53] train-rmse:0.192673+0.005265    test-rmse:0.407749+0.071214 
## [54] train-rmse:0.190533+0.004905    test-rmse:0.407107+0.072003 
## [55] train-rmse:0.189252+0.005312    test-rmse:0.407573+0.072034 
## [56] train-rmse:0.187121+0.004888    test-rmse:0.406554+0.071612 
## [57] train-rmse:0.185049+0.004734    test-rmse:0.406126+0.072124 
## [58] train-rmse:0.183481+0.004410    test-rmse:0.406378+0.071758 
## [59] train-rmse:0.182132+0.004642    test-rmse:0.406428+0.072475 
## [60] train-rmse:0.180481+0.005105    test-rmse:0.406671+0.072600 
## [61] train-rmse:0.178057+0.005062    test-rmse:0.407033+0.071662 
## [62] train-rmse:0.175764+0.005781    test-rmse:0.406708+0.072335 
## [63] train-rmse:0.174364+0.006037    test-rmse:0.406103+0.073386 
## [64] train-rmse:0.173178+0.005923    test-rmse:0.406174+0.073199 
## [65] train-rmse:0.171752+0.005977    test-rmse:0.406345+0.073013 
## [66] train-rmse:0.170494+0.005720    test-rmse:0.406335+0.073033 
## [67] train-rmse:0.169301+0.005533    test-rmse:0.406273+0.073035 
## [68] train-rmse:0.168400+0.005255    test-rmse:0.406679+0.072932 
## [69] train-rmse:0.166590+0.005622    test-rmse:0.407124+0.073103 
## Stopping. Best iteration:
## [63] train-rmse:0.174364+0.006037    test-rmse:0.406103+0.073386
## 
## 59 
## [1]  train-rmse:1.260220+0.022684    test-rmse:1.263652+0.118122 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.984897+0.017185    test-rmse:0.994215+0.116345 
## [3]  train-rmse:0.781704+0.015504    test-rmse:0.792637+0.102030 
## [4]  train-rmse:0.634056+0.011533    test-rmse:0.655226+0.101511 
## [5]  train-rmse:0.529731+0.008961    test-rmse:0.563160+0.091716 
## [6]  train-rmse:0.454173+0.008333    test-rmse:0.507594+0.086070 
## [7]  train-rmse:0.400245+0.007216    test-rmse:0.468063+0.080619 
## [8]  train-rmse:0.362617+0.006959    test-rmse:0.447400+0.074558 
## [9]  train-rmse:0.336869+0.006324    test-rmse:0.439349+0.072317 
## [10] train-rmse:0.317935+0.005032    test-rmse:0.429076+0.072872 
## [11] train-rmse:0.302429+0.003538    test-rmse:0.423694+0.068549 
## [12] train-rmse:0.291345+0.003413    test-rmse:0.420271+0.062883 
## [13] train-rmse:0.282525+0.003262    test-rmse:0.419292+0.062437 
## [14] train-rmse:0.274121+0.003810    test-rmse:0.417973+0.061397 
## [15] train-rmse:0.265611+0.003717    test-rmse:0.416799+0.061199 
## [16] train-rmse:0.258176+0.004590    test-rmse:0.416427+0.058739 
## [17] train-rmse:0.252214+0.003921    test-rmse:0.414597+0.061183 
## [18] train-rmse:0.244819+0.002716    test-rmse:0.415385+0.061449 
## [19] train-rmse:0.240035+0.002485    test-rmse:0.413573+0.062521 
## [20] train-rmse:0.233899+0.003219    test-rmse:0.415208+0.061490 
## [21] train-rmse:0.229197+0.002547    test-rmse:0.415866+0.061058 
## [22] train-rmse:0.225758+0.002469    test-rmse:0.415127+0.061157 
## [23] train-rmse:0.220703+0.004275    test-rmse:0.414074+0.061358 
## [24] train-rmse:0.214588+0.003798    test-rmse:0.414773+0.061402 
## [25] train-rmse:0.210933+0.003734    test-rmse:0.415875+0.061656 
## Stopping. Best iteration:
## [19] train-rmse:0.240035+0.002485    test-rmse:0.413573+0.062521
## 
## 60 
## [1]  train-rmse:1.257788+0.023081    test-rmse:1.261317+0.114183 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.979532+0.017641    test-rmse:0.985460+0.109339 
## [3]  train-rmse:0.773310+0.015477    test-rmse:0.785541+0.099700 
## [4]  train-rmse:0.621601+0.011259    test-rmse:0.647040+0.098720 
## [5]  train-rmse:0.511223+0.010874    test-rmse:0.554128+0.087673 
## [6]  train-rmse:0.430537+0.009195    test-rmse:0.492367+0.083844 
## [7]  train-rmse:0.373943+0.007790    test-rmse:0.455960+0.083266 
## [8]  train-rmse:0.332778+0.006707    test-rmse:0.432778+0.079553 
## [9]  train-rmse:0.300029+0.005722    test-rmse:0.417910+0.075475 
## [10] train-rmse:0.275896+0.004235    test-rmse:0.414126+0.073181 
## [11] train-rmse:0.259698+0.005154    test-rmse:0.410991+0.071772 
## [12] train-rmse:0.245978+0.002762    test-rmse:0.408437+0.067502 
## [13] train-rmse:0.235139+0.004508    test-rmse:0.406389+0.070065 
## [14] train-rmse:0.225160+0.003076    test-rmse:0.405147+0.069657 
## [15] train-rmse:0.216296+0.002818    test-rmse:0.404579+0.068821 
## [16] train-rmse:0.208393+0.004475    test-rmse:0.404572+0.067920 
## [17] train-rmse:0.201733+0.003809    test-rmse:0.406544+0.064891 
## [18] train-rmse:0.194001+0.002783    test-rmse:0.406274+0.064838 
## [19] train-rmse:0.185596+0.001513    test-rmse:0.405046+0.066390 
## [20] train-rmse:0.179841+0.003047    test-rmse:0.405080+0.065675 
## [21] train-rmse:0.173988+0.003910    test-rmse:0.405298+0.066587 
## [22] train-rmse:0.168952+0.003474    test-rmse:0.402883+0.065374 
## [23] train-rmse:0.163172+0.005072    test-rmse:0.399330+0.066011 
## [24] train-rmse:0.157601+0.005112    test-rmse:0.399587+0.064998 
## [25] train-rmse:0.153475+0.006910    test-rmse:0.398661+0.064412 
## [26] train-rmse:0.147920+0.006196    test-rmse:0.398798+0.063982 
## [27] train-rmse:0.145050+0.006094    test-rmse:0.398376+0.065217 
## [28] train-rmse:0.141714+0.006950    test-rmse:0.397269+0.065736 
## [29] train-rmse:0.136567+0.007673    test-rmse:0.397160+0.066063 
## [30] train-rmse:0.133138+0.008372    test-rmse:0.396894+0.064499 
## [31] train-rmse:0.130646+0.008191    test-rmse:0.396443+0.063640 
## [32] train-rmse:0.127537+0.007310    test-rmse:0.394890+0.065800 
## [33] train-rmse:0.124549+0.006822    test-rmse:0.394594+0.066166 
## [34] train-rmse:0.121029+0.007104    test-rmse:0.394318+0.065619 
## [35] train-rmse:0.118524+0.006881    test-rmse:0.394726+0.064537 
## [36] train-rmse:0.115306+0.007638    test-rmse:0.394975+0.064782 
## [37] train-rmse:0.112678+0.007351    test-rmse:0.394729+0.065232 
## [38] train-rmse:0.110541+0.007449    test-rmse:0.395181+0.065159 
## [39] train-rmse:0.108032+0.007300    test-rmse:0.394784+0.065544 
## [40] train-rmse:0.104929+0.007274    test-rmse:0.393662+0.065117 
## [41] train-rmse:0.102402+0.007882    test-rmse:0.394104+0.065043 
## [42] train-rmse:0.100063+0.007350    test-rmse:0.394038+0.063382 
## [43] train-rmse:0.098488+0.007240    test-rmse:0.393956+0.063298 
## [44] train-rmse:0.096197+0.007478    test-rmse:0.392889+0.063578 
## [45] train-rmse:0.093764+0.007811    test-rmse:0.392782+0.062929 
## [46] train-rmse:0.092042+0.007170    test-rmse:0.392184+0.063508 
## [47] train-rmse:0.089777+0.007300    test-rmse:0.392297+0.063638 
## [48] train-rmse:0.088005+0.007497    test-rmse:0.392529+0.063540 
## [49] train-rmse:0.086203+0.006893    test-rmse:0.392184+0.064064 
## [50] train-rmse:0.084414+0.006855    test-rmse:0.391951+0.064551 
## [51] train-rmse:0.082711+0.007037    test-rmse:0.391667+0.064725 
## [52] train-rmse:0.081238+0.006463    test-rmse:0.391629+0.064627 
## [53] train-rmse:0.079246+0.006818    test-rmse:0.391486+0.064032 
## [54] train-rmse:0.078059+0.006994    test-rmse:0.391337+0.064375 
## [55] train-rmse:0.076778+0.006939    test-rmse:0.391128+0.064736 
## [56] train-rmse:0.075110+0.006480    test-rmse:0.391422+0.064748 
## [57] train-rmse:0.072942+0.005773    test-rmse:0.390644+0.064646 
## [58] train-rmse:0.071458+0.005501    test-rmse:0.389959+0.064650 
## [59] train-rmse:0.070288+0.005543    test-rmse:0.389861+0.064456 
## [60] train-rmse:0.068805+0.005202    test-rmse:0.390305+0.064096 
## [61] train-rmse:0.068043+0.005183    test-rmse:0.390424+0.064381 
## [62] train-rmse:0.066758+0.005444    test-rmse:0.390416+0.064396 
## [63] train-rmse:0.065600+0.005335    test-rmse:0.390424+0.064462 
## [64] train-rmse:0.064554+0.005449    test-rmse:0.390398+0.064211 
## [65] train-rmse:0.063033+0.005469    test-rmse:0.390671+0.064353 
## Stopping. Best iteration:
## [59] train-rmse:0.070288+0.005543    test-rmse:0.389861+0.064456
## 
## 61 
## [1]  train-rmse:1.257020+0.023134    test-rmse:1.259659+0.112708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.977027+0.017719    test-rmse:0.981526+0.107675 
## [3]  train-rmse:0.768594+0.015963    test-rmse:0.784991+0.095287 
## [4]  train-rmse:0.614746+0.012363    test-rmse:0.644509+0.092556 
## [5]  train-rmse:0.500935+0.012254    test-rmse:0.551436+0.091175 
## [6]  train-rmse:0.416733+0.010110    test-rmse:0.494366+0.088594 
## [7]  train-rmse:0.353498+0.007871    test-rmse:0.453728+0.082528 
## [8]  train-rmse:0.306199+0.007950    test-rmse:0.428516+0.080416 
## [9]  train-rmse:0.271369+0.008351    test-rmse:0.417207+0.077456 
## [10] train-rmse:0.244185+0.009302    test-rmse:0.410750+0.075530 
## [11] train-rmse:0.223879+0.009983    test-rmse:0.407094+0.070765 
## [12] train-rmse:0.209152+0.010089    test-rmse:0.403523+0.068350 
## [13] train-rmse:0.193572+0.006978    test-rmse:0.400565+0.069434 
## [14] train-rmse:0.183991+0.005660    test-rmse:0.400668+0.068643 
## [15] train-rmse:0.176860+0.005386    test-rmse:0.398155+0.069601 
## [16] train-rmse:0.169486+0.005924    test-rmse:0.396256+0.067160 
## [17] train-rmse:0.161642+0.008043    test-rmse:0.393946+0.065899 
## [18] train-rmse:0.154494+0.006885    test-rmse:0.393498+0.065774 
## [19] train-rmse:0.146934+0.007447    test-rmse:0.393917+0.063630 
## [20] train-rmse:0.141228+0.006309    test-rmse:0.392745+0.063384 
## [21] train-rmse:0.134641+0.005068    test-rmse:0.393780+0.063053 
## [22] train-rmse:0.129202+0.004126    test-rmse:0.393105+0.064082 
## [23] train-rmse:0.122416+0.004284    test-rmse:0.395150+0.062781 
## [24] train-rmse:0.118213+0.005388    test-rmse:0.394441+0.063514 
## [25] train-rmse:0.113340+0.005306    test-rmse:0.393404+0.062286 
## [26] train-rmse:0.109414+0.004832    test-rmse:0.393201+0.061851 
## Stopping. Best iteration:
## [20] train-rmse:0.141228+0.006309    test-rmse:0.392745+0.063384
## 
## 62 
## [1]  train-rmse:1.256650+0.023135    test-rmse:1.260899+0.112189 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.975548+0.018089    test-rmse:0.981359+0.106755 
## [3]  train-rmse:0.765160+0.016049    test-rmse:0.778702+0.095825 
## [4]  train-rmse:0.608990+0.012370    test-rmse:0.641018+0.091673 
## [5]  train-rmse:0.491147+0.011755    test-rmse:0.551490+0.088761 
## [6]  train-rmse:0.404792+0.010359    test-rmse:0.496259+0.083248 
## [7]  train-rmse:0.339492+0.011209    test-rmse:0.458593+0.076939 
## [8]  train-rmse:0.290617+0.011379    test-rmse:0.439604+0.075439 
## [9]  train-rmse:0.252692+0.012038    test-rmse:0.427886+0.069240 
## [10] train-rmse:0.223882+0.011406    test-rmse:0.420864+0.066394 
## [11] train-rmse:0.202918+0.012809    test-rmse:0.416845+0.062784 
## [12] train-rmse:0.185627+0.013583    test-rmse:0.416903+0.059481 
## [13] train-rmse:0.167241+0.008873    test-rmse:0.415070+0.059313 
## [14] train-rmse:0.153866+0.007663    test-rmse:0.414485+0.058764 
## [15] train-rmse:0.143015+0.008584    test-rmse:0.413432+0.056973 
## [16] train-rmse:0.134329+0.009556    test-rmse:0.413088+0.055495 
## [17] train-rmse:0.127024+0.008782    test-rmse:0.412721+0.053539 
## [18] train-rmse:0.121250+0.009190    test-rmse:0.412223+0.050701 
## [19] train-rmse:0.114494+0.009817    test-rmse:0.409776+0.049580 
## [20] train-rmse:0.108687+0.009608    test-rmse:0.408611+0.049868 
## [21] train-rmse:0.103829+0.009172    test-rmse:0.409077+0.048148 
## [22] train-rmse:0.097376+0.009155    test-rmse:0.410267+0.046996 
## [23] train-rmse:0.090402+0.010892    test-rmse:0.411313+0.046500 
## [24] train-rmse:0.084176+0.010017    test-rmse:0.410730+0.046231 
## [25] train-rmse:0.079455+0.008376    test-rmse:0.411512+0.045923 
## [26] train-rmse:0.076038+0.008020    test-rmse:0.410906+0.046228 
## Stopping. Best iteration:
## [20] train-rmse:0.108687+0.009608    test-rmse:0.408611+0.049868
## 
## 63 
## [1]  train-rmse:1.264943+0.023761    test-rmse:1.261124+0.108760 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:1.017660+0.020065    test-rmse:1.014000+0.102128 
## [3]  train-rmse:0.860747+0.018436    test-rmse:0.857372+0.096590 
## [4]  train-rmse:0.766138+0.017980    test-rmse:0.763134+0.092144 
## [5]  train-rmse:0.711752+0.017989    test-rmse:0.709112+0.088867 
## [6]  train-rmse:0.679995+0.017193    test-rmse:0.685174+0.089330 
## [7]  train-rmse:0.654763+0.016870    test-rmse:0.653287+0.086413 
## [8]  train-rmse:0.637909+0.015517    test-rmse:0.643960+0.083722 
## [9]  train-rmse:0.623161+0.014295    test-rmse:0.624642+0.081895 
## [10] train-rmse:0.610150+0.013875    test-rmse:0.612981+0.077496 
## [11] train-rmse:0.600186+0.012888    test-rmse:0.607397+0.073293 
## [12] train-rmse:0.591471+0.012250    test-rmse:0.594214+0.071652 
## [13] train-rmse:0.583435+0.012178    test-rmse:0.586848+0.070338 
## [14] train-rmse:0.576582+0.012029    test-rmse:0.585238+0.068499 
## [15] train-rmse:0.570538+0.011728    test-rmse:0.583416+0.067503 
## [16] train-rmse:0.565019+0.011405    test-rmse:0.577830+0.063457 
## [17] train-rmse:0.559888+0.011326    test-rmse:0.570332+0.062704 
## [18] train-rmse:0.555564+0.010897    test-rmse:0.568633+0.064648 
## [19] train-rmse:0.551398+0.010623    test-rmse:0.567552+0.064022 
## [20] train-rmse:0.547602+0.010614    test-rmse:0.562137+0.062313 
## [21] train-rmse:0.544210+0.010651    test-rmse:0.560562+0.061500 
## [22] train-rmse:0.541146+0.010472    test-rmse:0.557960+0.060060 
## [23] train-rmse:0.538150+0.010362    test-rmse:0.557198+0.059469 
## [24] train-rmse:0.535375+0.010182    test-rmse:0.554654+0.058504 
## [25] train-rmse:0.532671+0.009972    test-rmse:0.553991+0.057767 
## [26] train-rmse:0.530087+0.009840    test-rmse:0.553795+0.059037 
## [27] train-rmse:0.527433+0.009667    test-rmse:0.550336+0.058355 
## [28] train-rmse:0.525037+0.009476    test-rmse:0.547287+0.059159 
## [29] train-rmse:0.522806+0.009477    test-rmse:0.545849+0.056692 
## [30] train-rmse:0.520722+0.009426    test-rmse:0.543962+0.057497 
## [31] train-rmse:0.518686+0.009354    test-rmse:0.544413+0.056389 
## [32] train-rmse:0.516616+0.009130    test-rmse:0.543203+0.056566 
## [33] train-rmse:0.514703+0.008961    test-rmse:0.540708+0.056339 
## [34] train-rmse:0.512887+0.008805    test-rmse:0.539828+0.056765 
## [35] train-rmse:0.511064+0.008801    test-rmse:0.540053+0.056904 
## [36] train-rmse:0.509363+0.008801    test-rmse:0.538688+0.056064 
## [37] train-rmse:0.507625+0.008831    test-rmse:0.536613+0.055480 
## [38] train-rmse:0.505938+0.008661    test-rmse:0.535671+0.055839 
## [39] train-rmse:0.504276+0.008539    test-rmse:0.534593+0.055544 
## [40] train-rmse:0.502689+0.008466    test-rmse:0.533331+0.054512 
## [41] train-rmse:0.501223+0.008438    test-rmse:0.532722+0.055082 
## [42] train-rmse:0.499760+0.008385    test-rmse:0.530685+0.055447 
## [43] train-rmse:0.498323+0.008272    test-rmse:0.529785+0.056030 
## [44] train-rmse:0.496899+0.008183    test-rmse:0.529308+0.054831 
## [45] train-rmse:0.495559+0.008154    test-rmse:0.528455+0.055101 
## [46] train-rmse:0.494181+0.008205    test-rmse:0.527860+0.055421 
## [47] train-rmse:0.492839+0.008269    test-rmse:0.526383+0.053856 
## [48] train-rmse:0.491503+0.008156    test-rmse:0.525468+0.053524 
## [49] train-rmse:0.490161+0.008119    test-rmse:0.523800+0.053292 
## [50] train-rmse:0.488914+0.008105    test-rmse:0.523920+0.053815 
## [51] train-rmse:0.487770+0.008106    test-rmse:0.524155+0.052546 
## [52] train-rmse:0.486692+0.008092    test-rmse:0.523169+0.052579 
## [53] train-rmse:0.485578+0.008053    test-rmse:0.523438+0.052497 
## [54] train-rmse:0.484481+0.007990    test-rmse:0.521340+0.051802 
## [55] train-rmse:0.483325+0.007948    test-rmse:0.521521+0.051325 
## [56] train-rmse:0.482257+0.007919    test-rmse:0.521177+0.050910 
## [57] train-rmse:0.481241+0.007907    test-rmse:0.520369+0.050438 
## [58] train-rmse:0.480244+0.007876    test-rmse:0.519102+0.051268 
## [59] train-rmse:0.479273+0.007838    test-rmse:0.518599+0.050785 
## [60] train-rmse:0.478327+0.007815    test-rmse:0.517346+0.050863 
## [61] train-rmse:0.477343+0.007815    test-rmse:0.515839+0.049999 
## [62] train-rmse:0.476392+0.007806    test-rmse:0.515208+0.050104 
## [63] train-rmse:0.475475+0.007775    test-rmse:0.514403+0.050479 
## [64] train-rmse:0.474624+0.007751    test-rmse:0.514862+0.050287 
## [65] train-rmse:0.473731+0.007715    test-rmse:0.514533+0.050212 
## [66] train-rmse:0.472902+0.007693    test-rmse:0.513484+0.050439 
## [67] train-rmse:0.472079+0.007728    test-rmse:0.513806+0.050534 
## [68] train-rmse:0.471246+0.007720    test-rmse:0.513324+0.050084 
## [69] train-rmse:0.470459+0.007743    test-rmse:0.512677+0.049077 
## [70] train-rmse:0.469682+0.007724    test-rmse:0.512154+0.046850 
## [71] train-rmse:0.468893+0.007696    test-rmse:0.511511+0.046692 
## [72] train-rmse:0.468138+0.007678    test-rmse:0.511846+0.046242 
## [73] train-rmse:0.467399+0.007663    test-rmse:0.511706+0.046927 
## [74] train-rmse:0.466696+0.007638    test-rmse:0.511087+0.046837 
## [75] train-rmse:0.465965+0.007585    test-rmse:0.510595+0.046811 
## [76] train-rmse:0.465211+0.007576    test-rmse:0.509926+0.046425 
## [77] train-rmse:0.464484+0.007577    test-rmse:0.509852+0.045259 
## [78] train-rmse:0.463790+0.007561    test-rmse:0.509087+0.045620 
## [79] train-rmse:0.463120+0.007549    test-rmse:0.508670+0.046065 
## [80] train-rmse:0.462450+0.007513    test-rmse:0.507894+0.046099 
## [81] train-rmse:0.461783+0.007479    test-rmse:0.508329+0.045973 
## [82] train-rmse:0.461150+0.007473    test-rmse:0.507271+0.046658 
## [83] train-rmse:0.460529+0.007469    test-rmse:0.506236+0.046198 
## [84] train-rmse:0.459921+0.007470    test-rmse:0.505865+0.045902 
## [85] train-rmse:0.459312+0.007472    test-rmse:0.505880+0.046020 
## [86] train-rmse:0.458710+0.007428    test-rmse:0.505983+0.045664 
## [87] train-rmse:0.458106+0.007400    test-rmse:0.505653+0.045946 
## [88] train-rmse:0.457533+0.007382    test-rmse:0.505514+0.045744 
## [89] train-rmse:0.456961+0.007380    test-rmse:0.505274+0.046115 
## [90] train-rmse:0.456413+0.007370    test-rmse:0.505780+0.046106 
## [91] train-rmse:0.455848+0.007364    test-rmse:0.505866+0.046034 
## [92] train-rmse:0.455284+0.007355    test-rmse:0.505083+0.046546 
## [93] train-rmse:0.454736+0.007352    test-rmse:0.504652+0.045719 
## [94] train-rmse:0.454198+0.007352    test-rmse:0.505807+0.045344 
## [95] train-rmse:0.453675+0.007343    test-rmse:0.505076+0.045257 
## [96] train-rmse:0.453153+0.007335    test-rmse:0.504646+0.045937 
## [97] train-rmse:0.452638+0.007320    test-rmse:0.504142+0.045844 
## [98] train-rmse:0.452144+0.007326    test-rmse:0.502825+0.043866 
## [99] train-rmse:0.451630+0.007316    test-rmse:0.503343+0.043542 
## [100]    train-rmse:0.451154+0.007309    test-rmse:0.503387+0.043410 
## [101]    train-rmse:0.450683+0.007296    test-rmse:0.502629+0.042756 
## [102]    train-rmse:0.450213+0.007292    test-rmse:0.502463+0.042096 
## [103]    train-rmse:0.449758+0.007303    test-rmse:0.502447+0.041333 
## [104]    train-rmse:0.449287+0.007326    test-rmse:0.502463+0.041733 
## [105]    train-rmse:0.448842+0.007328    test-rmse:0.501673+0.042029 
## [106]    train-rmse:0.448373+0.007340    test-rmse:0.501395+0.042842 
## [107]    train-rmse:0.447934+0.007350    test-rmse:0.500945+0.043162 
## [108]    train-rmse:0.447512+0.007365    test-rmse:0.501058+0.043262 
## [109]    train-rmse:0.447070+0.007361    test-rmse:0.501263+0.043096 
## [110]    train-rmse:0.446649+0.007352    test-rmse:0.501179+0.042603 
## [111]    train-rmse:0.446225+0.007348    test-rmse:0.501492+0.042985 
## [112]    train-rmse:0.445825+0.007343    test-rmse:0.501467+0.042935 
## [113]    train-rmse:0.445410+0.007336    test-rmse:0.500917+0.042758 
## [114]    train-rmse:0.445006+0.007349    test-rmse:0.500936+0.042512 
## [115]    train-rmse:0.444606+0.007347    test-rmse:0.501106+0.042412 
## [116]    train-rmse:0.444222+0.007351    test-rmse:0.500270+0.042982 
## [117]    train-rmse:0.443843+0.007348    test-rmse:0.499522+0.043397 
## [118]    train-rmse:0.443469+0.007342    test-rmse:0.499402+0.043550 
## [119]    train-rmse:0.443083+0.007330    test-rmse:0.499464+0.042898 
## [120]    train-rmse:0.442693+0.007350    test-rmse:0.499385+0.043152 
## [121]    train-rmse:0.442332+0.007350    test-rmse:0.499386+0.042543 
## [122]    train-rmse:0.441938+0.007364    test-rmse:0.499348+0.043074 
## [123]    train-rmse:0.441574+0.007369    test-rmse:0.499001+0.043257 
## [124]    train-rmse:0.441212+0.007378    test-rmse:0.499302+0.042622 
## [125]    train-rmse:0.440857+0.007387    test-rmse:0.499193+0.042448 
## [126]    train-rmse:0.440513+0.007394    test-rmse:0.498849+0.042155 
## [127]    train-rmse:0.440176+0.007388    test-rmse:0.498879+0.041935 
## [128]    train-rmse:0.439839+0.007393    test-rmse:0.499033+0.041389 
## [129]    train-rmse:0.439501+0.007390    test-rmse:0.499207+0.041471 
## [130]    train-rmse:0.439147+0.007379    test-rmse:0.498390+0.040934 
## [131]    train-rmse:0.438805+0.007405    test-rmse:0.498841+0.041041 
## [132]    train-rmse:0.438452+0.007394    test-rmse:0.498418+0.041107 
## [133]    train-rmse:0.438117+0.007407    test-rmse:0.498806+0.041483 
## [134]    train-rmse:0.437793+0.007392    test-rmse:0.498520+0.041422 
## [135]    train-rmse:0.437464+0.007396    test-rmse:0.498108+0.041038 
## [136]    train-rmse:0.437127+0.007407    test-rmse:0.498111+0.041353 
## [137]    train-rmse:0.436802+0.007426    test-rmse:0.498057+0.041837 
## [138]    train-rmse:0.436486+0.007411    test-rmse:0.497645+0.042023 
## [139]    train-rmse:0.436173+0.007405    test-rmse:0.497168+0.042513 
## [140]    train-rmse:0.435873+0.007408    test-rmse:0.497205+0.042691 
## [141]    train-rmse:0.435563+0.007419    test-rmse:0.496506+0.042289 
## [142]    train-rmse:0.435250+0.007410    test-rmse:0.496773+0.042438 
## [143]    train-rmse:0.434922+0.007431    test-rmse:0.496405+0.042054 
## [144]    train-rmse:0.434626+0.007440    test-rmse:0.496369+0.042212 
## [145]    train-rmse:0.434322+0.007437    test-rmse:0.496500+0.042222 
## [146]    train-rmse:0.434033+0.007431    test-rmse:0.496543+0.042208 
## [147]    train-rmse:0.433736+0.007433    test-rmse:0.495843+0.040564 
## [148]    train-rmse:0.433446+0.007433    test-rmse:0.495532+0.040349 
## [149]    train-rmse:0.433156+0.007429    test-rmse:0.495318+0.039929 
## [150]    train-rmse:0.432847+0.007427    test-rmse:0.495092+0.040308 
## [151]    train-rmse:0.432553+0.007423    test-rmse:0.495383+0.040151 
## [152]    train-rmse:0.432261+0.007433    test-rmse:0.495471+0.039859 
## [153]    train-rmse:0.431991+0.007432    test-rmse:0.495243+0.039571 
## [154]    train-rmse:0.431724+0.007439    test-rmse:0.494757+0.039558 
## [155]    train-rmse:0.431445+0.007442    test-rmse:0.494607+0.039751 
## [156]    train-rmse:0.431166+0.007449    test-rmse:0.494622+0.039648 
## [157]    train-rmse:0.430898+0.007451    test-rmse:0.494601+0.039962 
## [158]    train-rmse:0.430635+0.007451    test-rmse:0.494654+0.039918 
## [159]    train-rmse:0.430377+0.007447    test-rmse:0.494879+0.040251 
## [160]    train-rmse:0.430105+0.007461    test-rmse:0.494571+0.039900 
## [161]    train-rmse:0.429839+0.007458    test-rmse:0.495186+0.040456 
## [162]    train-rmse:0.429571+0.007465    test-rmse:0.495001+0.040166 
## [163]    train-rmse:0.429303+0.007468    test-rmse:0.494816+0.040577 
## [164]    train-rmse:0.429044+0.007464    test-rmse:0.494915+0.040438 
## [165]    train-rmse:0.428792+0.007459    test-rmse:0.494866+0.040724 
## [166]    train-rmse:0.428543+0.007463    test-rmse:0.494807+0.040305 
## Stopping. Best iteration:
## [160]    train-rmse:0.430105+0.007461    test-rmse:0.494571+0.039900
## 
## 64 
## [1]  train-rmse:1.246260+0.023887    test-rmse:1.246569+0.114264 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.973742+0.018616    test-rmse:0.971677+0.102694 
## [3]  train-rmse:0.788997+0.016740    test-rmse:0.792712+0.102762 
## [4]  train-rmse:0.667599+0.014639    test-rmse:0.673044+0.096215 
## [5]  train-rmse:0.587785+0.013113    test-rmse:0.599007+0.093459 
## [6]  train-rmse:0.535634+0.012640    test-rmse:0.546208+0.084977 
## [7]  train-rmse:0.501860+0.011787    test-rmse:0.518048+0.084475 
## [8]  train-rmse:0.477532+0.011601    test-rmse:0.503098+0.075265 
## [9]  train-rmse:0.456183+0.010370    test-rmse:0.483848+0.072733 
## [10] train-rmse:0.441919+0.008766    test-rmse:0.472812+0.069383 
## [11] train-rmse:0.429344+0.007699    test-rmse:0.465378+0.068088 
## [12] train-rmse:0.419406+0.007853    test-rmse:0.465519+0.066080 
## [13] train-rmse:0.411062+0.007395    test-rmse:0.461032+0.061603 
## [14] train-rmse:0.403427+0.006429    test-rmse:0.456272+0.061394 
## [15] train-rmse:0.397359+0.006300    test-rmse:0.453066+0.060187 
## [16] train-rmse:0.390552+0.006351    test-rmse:0.449726+0.058518 
## [17] train-rmse:0.384742+0.005939    test-rmse:0.450087+0.057942 
## [18] train-rmse:0.379748+0.005811    test-rmse:0.449504+0.058777 
## [19] train-rmse:0.374177+0.005198    test-rmse:0.448119+0.056335 
## [20] train-rmse:0.369026+0.005298    test-rmse:0.445458+0.054214 
## [21] train-rmse:0.364431+0.004480    test-rmse:0.444410+0.054451 
## [22] train-rmse:0.360221+0.004884    test-rmse:0.444768+0.052933 
## [23] train-rmse:0.355452+0.004795    test-rmse:0.443681+0.052768 
## [24] train-rmse:0.352065+0.004634    test-rmse:0.443187+0.053270 
## [25] train-rmse:0.348724+0.004533    test-rmse:0.443193+0.052693 
## [26] train-rmse:0.344311+0.005130    test-rmse:0.441347+0.054058 
## [27] train-rmse:0.340698+0.005646    test-rmse:0.439140+0.054587 
## [28] train-rmse:0.338085+0.005729    test-rmse:0.439520+0.053957 
## [29] train-rmse:0.335014+0.005878    test-rmse:0.438667+0.051693 
## [30] train-rmse:0.331954+0.005313    test-rmse:0.438977+0.052848 
## [31] train-rmse:0.329718+0.004936    test-rmse:0.438113+0.052651 
## [32] train-rmse:0.327216+0.004971    test-rmse:0.436789+0.052260 
## [33] train-rmse:0.324808+0.005404    test-rmse:0.437216+0.052826 
## [34] train-rmse:0.322809+0.005386    test-rmse:0.436151+0.052234 
## [35] train-rmse:0.320187+0.005272    test-rmse:0.434849+0.052010 
## [36] train-rmse:0.317745+0.005155    test-rmse:0.434746+0.053291 
## [37] train-rmse:0.315674+0.005320    test-rmse:0.434036+0.054643 
## [38] train-rmse:0.312360+0.005536    test-rmse:0.432538+0.054380 
## [39] train-rmse:0.309851+0.006750    test-rmse:0.432259+0.055381 
## [40] train-rmse:0.307685+0.006525    test-rmse:0.431889+0.055300 
## [41] train-rmse:0.305515+0.006066    test-rmse:0.432586+0.056126 
## [42] train-rmse:0.302861+0.005625    test-rmse:0.430996+0.057711 
## [43] train-rmse:0.301142+0.005841    test-rmse:0.431708+0.058491 
## [44] train-rmse:0.298620+0.004900    test-rmse:0.429839+0.059711 
## [45] train-rmse:0.296762+0.004899    test-rmse:0.429232+0.060184 
## [46] train-rmse:0.295114+0.005312    test-rmse:0.429918+0.059411 
## [47] train-rmse:0.293001+0.005210    test-rmse:0.429612+0.059483 
## [48] train-rmse:0.291784+0.005010    test-rmse:0.429585+0.058258 
## [49] train-rmse:0.290143+0.004975    test-rmse:0.429272+0.059060 
## [50] train-rmse:0.288667+0.004613    test-rmse:0.428616+0.059708 
## [51] train-rmse:0.286949+0.004578    test-rmse:0.428410+0.058945 
## [52] train-rmse:0.283617+0.005449    test-rmse:0.428177+0.058875 
## [53] train-rmse:0.281544+0.005417    test-rmse:0.426913+0.059575 
## [54] train-rmse:0.279656+0.005033    test-rmse:0.426319+0.060040 
## [55] train-rmse:0.278229+0.005137    test-rmse:0.426680+0.060155 
## [56] train-rmse:0.276880+0.005143    test-rmse:0.427207+0.059232 
## [57] train-rmse:0.275911+0.005222    test-rmse:0.426851+0.059021 
## [58] train-rmse:0.273755+0.004711    test-rmse:0.427058+0.059332 
## [59] train-rmse:0.272061+0.005574    test-rmse:0.427356+0.059784 
## [60] train-rmse:0.270362+0.006038    test-rmse:0.426991+0.059682 
## Stopping. Best iteration:
## [54] train-rmse:0.279656+0.005033    test-rmse:0.426319+0.060040
## 
## 65 
## [1]  train-rmse:1.236273+0.022859    test-rmse:1.240800+0.117526 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.952329+0.019536    test-rmse:0.957256+0.103409 
## [3]  train-rmse:0.753782+0.016474    test-rmse:0.768019+0.104185 
## [4]  train-rmse:0.617317+0.014939    test-rmse:0.636342+0.098284 
## [5]  train-rmse:0.523398+0.013947    test-rmse:0.554329+0.092689 
## [6]  train-rmse:0.461931+0.011622    test-rmse:0.509854+0.088119 
## [7]  train-rmse:0.419378+0.011743    test-rmse:0.478382+0.081601 
## [8]  train-rmse:0.389587+0.011336    test-rmse:0.460583+0.072357 
## [9]  train-rmse:0.368892+0.009463    test-rmse:0.446461+0.074620 
## [10] train-rmse:0.354657+0.009349    test-rmse:0.439425+0.069085 
## [11] train-rmse:0.342692+0.008351    test-rmse:0.433804+0.066463 
## [12] train-rmse:0.332239+0.008745    test-rmse:0.430405+0.065678 
## [13] train-rmse:0.323413+0.007884    test-rmse:0.429909+0.064114 
## [14] train-rmse:0.315417+0.008073    test-rmse:0.429495+0.063798 
## [15] train-rmse:0.308899+0.007984    test-rmse:0.427648+0.064413 
## [16] train-rmse:0.302566+0.007783    test-rmse:0.427145+0.064217 
## [17] train-rmse:0.296558+0.007583    test-rmse:0.427517+0.063496 
## [18] train-rmse:0.289973+0.008106    test-rmse:0.427149+0.065364 
## [19] train-rmse:0.284874+0.007214    test-rmse:0.426826+0.064793 
## [20] train-rmse:0.279370+0.006922    test-rmse:0.425225+0.065650 
## [21] train-rmse:0.275107+0.006204    test-rmse:0.425735+0.063747 
## [22] train-rmse:0.271740+0.005639    test-rmse:0.425245+0.065128 
## [23] train-rmse:0.268326+0.005224    test-rmse:0.424365+0.063844 
## [24] train-rmse:0.265287+0.005163    test-rmse:0.423093+0.063805 
## [25] train-rmse:0.260910+0.005056    test-rmse:0.421890+0.064629 
## [26] train-rmse:0.257336+0.004904    test-rmse:0.420876+0.065909 
## [27] train-rmse:0.253624+0.005488    test-rmse:0.420121+0.065530 
## [28] train-rmse:0.250671+0.005648    test-rmse:0.418992+0.065874 
## [29] train-rmse:0.248195+0.005203    test-rmse:0.418345+0.066312 
## [30] train-rmse:0.244659+0.005037    test-rmse:0.418364+0.066200 
## [31] train-rmse:0.241632+0.005129    test-rmse:0.419180+0.067448 
## [32] train-rmse:0.237953+0.005923    test-rmse:0.416872+0.067446 
## [33] train-rmse:0.235046+0.004648    test-rmse:0.415499+0.068236 
## [34] train-rmse:0.231878+0.004996    test-rmse:0.415282+0.068245 
## [35] train-rmse:0.227640+0.006664    test-rmse:0.414124+0.068100 
## [36] train-rmse:0.225182+0.005903    test-rmse:0.414025+0.066727 
## [37] train-rmse:0.222638+0.006169    test-rmse:0.412654+0.068304 
## [38] train-rmse:0.219711+0.006186    test-rmse:0.413115+0.067454 
## [39] train-rmse:0.217018+0.006145    test-rmse:0.413028+0.066593 
## [40] train-rmse:0.214861+0.005878    test-rmse:0.412144+0.067559 
## [41] train-rmse:0.212333+0.006013    test-rmse:0.411192+0.066646 
## [42] train-rmse:0.209081+0.005347    test-rmse:0.411239+0.065836 
## [43] train-rmse:0.206267+0.005870    test-rmse:0.408668+0.064930 
## [44] train-rmse:0.204141+0.005529    test-rmse:0.407811+0.064925 
## [45] train-rmse:0.202200+0.005454    test-rmse:0.407854+0.064370 
## [46] train-rmse:0.199836+0.004646    test-rmse:0.407732+0.065726 
## [47] train-rmse:0.198079+0.004784    test-rmse:0.408007+0.066120 
## [48] train-rmse:0.195829+0.005252    test-rmse:0.406866+0.065336 
## [49] train-rmse:0.193256+0.004082    test-rmse:0.407441+0.065391 
## [50] train-rmse:0.190692+0.003513    test-rmse:0.407436+0.065355 
## [51] train-rmse:0.189393+0.003351    test-rmse:0.407514+0.064875 
## [52] train-rmse:0.186788+0.004450    test-rmse:0.407806+0.065142 
## [53] train-rmse:0.185479+0.004522    test-rmse:0.407262+0.064990 
## [54] train-rmse:0.183571+0.005028    test-rmse:0.407419+0.065359 
## Stopping. Best iteration:
## [48] train-rmse:0.195829+0.005252    test-rmse:0.406866+0.065336
## 
## 66 
## [1]  train-rmse:1.231699+0.022126    test-rmse:1.235772+0.118308 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.943951+0.016348    test-rmse:0.954554+0.116417 
## [3]  train-rmse:0.737746+0.014656    test-rmse:0.750125+0.101262 
## [4]  train-rmse:0.592662+0.010483    test-rmse:0.621517+0.098830 
## [5]  train-rmse:0.493180+0.009166    test-rmse:0.536641+0.087336 
## [6]  train-rmse:0.423079+0.008328    test-rmse:0.483964+0.079755 
## [7]  train-rmse:0.377009+0.008111    test-rmse:0.455759+0.075186 
## [8]  train-rmse:0.341474+0.006013    test-rmse:0.435409+0.075798 
## [9]  train-rmse:0.320539+0.004860    test-rmse:0.429893+0.074068 
## [10] train-rmse:0.303379+0.005679    test-rmse:0.426026+0.070760 
## [11] train-rmse:0.289285+0.003975    test-rmse:0.420647+0.067784 
## [12] train-rmse:0.279069+0.004248    test-rmse:0.417297+0.064231 
## [13] train-rmse:0.270260+0.004741    test-rmse:0.415597+0.063377 
## [14] train-rmse:0.261124+0.004637    test-rmse:0.414439+0.063739 
## [15] train-rmse:0.252144+0.003857    test-rmse:0.414194+0.063192 
## [16] train-rmse:0.246343+0.003029    test-rmse:0.412671+0.063558 
## [17] train-rmse:0.240176+0.003586    test-rmse:0.412203+0.063835 
## [18] train-rmse:0.233613+0.003110    test-rmse:0.409922+0.065839 
## [19] train-rmse:0.226643+0.002348    test-rmse:0.410019+0.065777 
## [20] train-rmse:0.221824+0.002635    test-rmse:0.409949+0.065241 
## [21] train-rmse:0.214899+0.004673    test-rmse:0.411311+0.064507 
## [22] train-rmse:0.209927+0.005080    test-rmse:0.410303+0.065301 
## [23] train-rmse:0.205646+0.005157    test-rmse:0.408830+0.064380 
## [24] train-rmse:0.201004+0.004963    test-rmse:0.405513+0.064691 
## [25] train-rmse:0.197700+0.004487    test-rmse:0.406073+0.063961 
## [26] train-rmse:0.192627+0.005095    test-rmse:0.404309+0.063795 
## [27] train-rmse:0.188807+0.005768    test-rmse:0.403945+0.064119 
## [28] train-rmse:0.185970+0.005932    test-rmse:0.402712+0.063481 
## [29] train-rmse:0.182129+0.005201    test-rmse:0.401397+0.064212 
## [30] train-rmse:0.178731+0.005041    test-rmse:0.399014+0.063891 
## [31] train-rmse:0.173616+0.004891    test-rmse:0.398660+0.064487 
## [32] train-rmse:0.171089+0.005222    test-rmse:0.398502+0.064409 
## [33] train-rmse:0.168479+0.005298    test-rmse:0.398820+0.063735 
## [34] train-rmse:0.165072+0.005616    test-rmse:0.398531+0.064441 
## [35] train-rmse:0.162708+0.006136    test-rmse:0.397830+0.063806 
## [36] train-rmse:0.158940+0.006066    test-rmse:0.394922+0.063087 
## [37] train-rmse:0.156082+0.006672    test-rmse:0.394346+0.062731 
## [38] train-rmse:0.153407+0.005632    test-rmse:0.394351+0.062421 
## [39] train-rmse:0.151431+0.005753    test-rmse:0.394027+0.062668 
## [40] train-rmse:0.148389+0.006219    test-rmse:0.394101+0.062766 
## [41] train-rmse:0.145858+0.005427    test-rmse:0.393667+0.063226 
## [42] train-rmse:0.143114+0.004882    test-rmse:0.392187+0.063700 
## [43] train-rmse:0.140862+0.004676    test-rmse:0.392448+0.062883 
## [44] train-rmse:0.138470+0.005221    test-rmse:0.391984+0.063344 
## [45] train-rmse:0.136265+0.005389    test-rmse:0.391516+0.062901 
## [46] train-rmse:0.133735+0.005944    test-rmse:0.391052+0.063182 
## [47] train-rmse:0.131056+0.005510    test-rmse:0.391057+0.063022 
## [48] train-rmse:0.129440+0.005587    test-rmse:0.390513+0.063434 
## [49] train-rmse:0.127426+0.005254    test-rmse:0.390579+0.063178 
## [50] train-rmse:0.125899+0.005669    test-rmse:0.390446+0.063284 
## [51] train-rmse:0.124198+0.006125    test-rmse:0.390398+0.063366 
## [52] train-rmse:0.122290+0.006445    test-rmse:0.390148+0.064028 
## [53] train-rmse:0.119725+0.006096    test-rmse:0.390792+0.063346 
## [54] train-rmse:0.117952+0.005942    test-rmse:0.389246+0.062769 
## [55] train-rmse:0.115379+0.005027    test-rmse:0.388893+0.063344 
## [56] train-rmse:0.113579+0.005535    test-rmse:0.388708+0.063416 
## [57] train-rmse:0.112081+0.005109    test-rmse:0.388790+0.063783 
## [58] train-rmse:0.110897+0.004910    test-rmse:0.388853+0.063103 
## [59] train-rmse:0.109203+0.004386    test-rmse:0.388886+0.062773 
## [60] train-rmse:0.107339+0.003829    test-rmse:0.388638+0.062626 
## [61] train-rmse:0.105890+0.004417    test-rmse:0.388151+0.062029 
## [62] train-rmse:0.104464+0.004526    test-rmse:0.387940+0.062141 
## [63] train-rmse:0.102980+0.004561    test-rmse:0.387880+0.062087 
## [64] train-rmse:0.101763+0.004836    test-rmse:0.387316+0.061915 
## [65] train-rmse:0.101213+0.004812    test-rmse:0.387192+0.061858 
## [66] train-rmse:0.099845+0.004750    test-rmse:0.387155+0.062088 
## [67] train-rmse:0.098758+0.004467    test-rmse:0.387099+0.062399 
## [68] train-rmse:0.097309+0.004978    test-rmse:0.386482+0.062886 
## [69] train-rmse:0.095776+0.004387    test-rmse:0.386215+0.062355 
## [70] train-rmse:0.094466+0.004498    test-rmse:0.386143+0.062431 
## [71] train-rmse:0.093278+0.004716    test-rmse:0.386212+0.062566 
## [72] train-rmse:0.092262+0.004517    test-rmse:0.386256+0.061715 
## [73] train-rmse:0.090689+0.004833    test-rmse:0.386551+0.061306 
## [74] train-rmse:0.089247+0.004030    test-rmse:0.386361+0.061575 
## [75] train-rmse:0.088297+0.003822    test-rmse:0.386528+0.061697 
## [76] train-rmse:0.086712+0.003731    test-rmse:0.386902+0.061494 
## Stopping. Best iteration:
## [70] train-rmse:0.094466+0.004498    test-rmse:0.386143+0.062431
## 
## 67 
## [1]  train-rmse:1.229034+0.022556    test-rmse:1.233278+0.114014 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.938066+0.016916    test-rmse:0.945978+0.108766 
## [3]  train-rmse:0.727873+0.014754    test-rmse:0.743333+0.098749 
## [4]  train-rmse:0.577500+0.010023    test-rmse:0.610492+0.092855 
## [5]  train-rmse:0.473428+0.007816    test-rmse:0.529726+0.090318 
## [6]  train-rmse:0.398583+0.006980    test-rmse:0.472724+0.083294 
## [7]  train-rmse:0.343312+0.004089    test-rmse:0.447790+0.082474 
## [8]  train-rmse:0.306881+0.004545    test-rmse:0.427046+0.074863 
## [9]  train-rmse:0.281059+0.004760    test-rmse:0.416877+0.069478 
## [10] train-rmse:0.260429+0.005281    test-rmse:0.412484+0.069651 
## [11] train-rmse:0.245479+0.006573    test-rmse:0.411799+0.064240 
## [12] train-rmse:0.232738+0.007137    test-rmse:0.407892+0.065174 
## [13] train-rmse:0.222311+0.004993    test-rmse:0.406691+0.063992 
## [14] train-rmse:0.213536+0.004746    test-rmse:0.405622+0.062513 
## [15] train-rmse:0.206077+0.004330    test-rmse:0.404048+0.060618 
## [16] train-rmse:0.198514+0.004888    test-rmse:0.403441+0.058898 
## [17] train-rmse:0.190450+0.004050    test-rmse:0.401811+0.057205 
## [18] train-rmse:0.181020+0.004972    test-rmse:0.401107+0.060973 
## [19] train-rmse:0.174050+0.005829    test-rmse:0.399111+0.058756 
## [20] train-rmse:0.169416+0.006915    test-rmse:0.399887+0.058740 
## [21] train-rmse:0.164630+0.006710    test-rmse:0.398818+0.060195 
## [22] train-rmse:0.158728+0.005915    test-rmse:0.398307+0.060003 
## [23] train-rmse:0.154874+0.006345    test-rmse:0.397724+0.058968 
## [24] train-rmse:0.149838+0.005811    test-rmse:0.394412+0.059828 
## [25] train-rmse:0.145755+0.004806    test-rmse:0.393773+0.060040 
## [26] train-rmse:0.142036+0.005371    test-rmse:0.394951+0.059224 
## [27] train-rmse:0.138722+0.004659    test-rmse:0.394643+0.058497 
## [28] train-rmse:0.134960+0.004306    test-rmse:0.394500+0.057606 
## [29] train-rmse:0.131548+0.004413    test-rmse:0.393615+0.057252 
## [30] train-rmse:0.127203+0.004072    test-rmse:0.394139+0.056638 
## [31] train-rmse:0.124396+0.004488    test-rmse:0.394561+0.056321 
## [32] train-rmse:0.120499+0.003868    test-rmse:0.395085+0.056520 
## [33] train-rmse:0.118133+0.003969    test-rmse:0.394387+0.055918 
## [34] train-rmse:0.114970+0.004613    test-rmse:0.394842+0.056675 
## [35] train-rmse:0.111430+0.005017    test-rmse:0.394785+0.056450 
## Stopping. Best iteration:
## [29] train-rmse:0.131548+0.004413    test-rmse:0.393615+0.057252
## 
## 68 
## [1]  train-rmse:1.228184+0.022611    test-rmse:1.231497+0.112420 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.934927+0.016603    test-rmse:0.945662+0.104553 
## [3]  train-rmse:0.722602+0.014887    test-rmse:0.744247+0.093420 
## [4]  train-rmse:0.569989+0.011745    test-rmse:0.609390+0.090564 
## [5]  train-rmse:0.459943+0.011799    test-rmse:0.526059+0.090926 
## [6]  train-rmse:0.381640+0.010756    test-rmse:0.473012+0.084535 
## [7]  train-rmse:0.324675+0.011639    test-rmse:0.442186+0.077105 
## [8]  train-rmse:0.282968+0.010433    test-rmse:0.424297+0.073545 
## [9]  train-rmse:0.253219+0.012159    test-rmse:0.417470+0.074136 
## [10] train-rmse:0.231071+0.013111    test-rmse:0.412308+0.070700 
## [11] train-rmse:0.215043+0.014847    test-rmse:0.406305+0.070479 
## [12] train-rmse:0.200668+0.012783    test-rmse:0.402863+0.066360 
## [13] train-rmse:0.186930+0.011835    test-rmse:0.398770+0.064363 
## [14] train-rmse:0.179375+0.012519    test-rmse:0.399493+0.065399 
## [15] train-rmse:0.171570+0.010722    test-rmse:0.399026+0.065467 
## [16] train-rmse:0.162481+0.007108    test-rmse:0.396536+0.061667 
## [17] train-rmse:0.154469+0.006006    test-rmse:0.398803+0.058734 
## [18] train-rmse:0.145859+0.005822    test-rmse:0.399191+0.058739 
## [19] train-rmse:0.139620+0.006479    test-rmse:0.398328+0.057031 
## [20] train-rmse:0.134303+0.004920    test-rmse:0.397831+0.056859 
## [21] train-rmse:0.129321+0.004524    test-rmse:0.397413+0.056638 
## [22] train-rmse:0.123708+0.003779    test-rmse:0.397304+0.055809 
## Stopping. Best iteration:
## [16] train-rmse:0.162481+0.007108    test-rmse:0.396536+0.061667
## 
## 69 
## [1]  train-rmse:1.227777+0.022611    test-rmse:1.232847+0.111915 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.933307+0.017052    test-rmse:0.943470+0.105127 
## [3]  train-rmse:0.718920+0.015089    test-rmse:0.738021+0.093511 
## [4]  train-rmse:0.565371+0.011860    test-rmse:0.605769+0.090246 
## [5]  train-rmse:0.450331+0.011688    test-rmse:0.523451+0.085906 
## [6]  train-rmse:0.368869+0.011870    test-rmse:0.471364+0.081245 
## [7]  train-rmse:0.307300+0.013830    test-rmse:0.441143+0.075086 
## [8]  train-rmse:0.262675+0.014587    test-rmse:0.425175+0.070467 
## [9]  train-rmse:0.225478+0.010793    test-rmse:0.417094+0.070879 
## [10] train-rmse:0.201396+0.011902    test-rmse:0.413057+0.068401 
## [11] train-rmse:0.178268+0.009935    test-rmse:0.409561+0.065197 
## [12] train-rmse:0.163126+0.011867    test-rmse:0.407318+0.063793 
## [13] train-rmse:0.152017+0.012295    test-rmse:0.405992+0.064622 
## [14] train-rmse:0.142116+0.012199    test-rmse:0.404600+0.061305 
## [15] train-rmse:0.133684+0.011429    test-rmse:0.405171+0.061004 
## [16] train-rmse:0.122561+0.011402    test-rmse:0.405744+0.057829 
## [17] train-rmse:0.114250+0.008182    test-rmse:0.403034+0.057683 
## [18] train-rmse:0.108738+0.008495    test-rmse:0.402580+0.056406 
## [19] train-rmse:0.102707+0.009130    test-rmse:0.402126+0.056051 
## [20] train-rmse:0.097398+0.007694    test-rmse:0.403052+0.055670 
## [21] train-rmse:0.090312+0.008917    test-rmse:0.402038+0.055068 
## [22] train-rmse:0.085684+0.008052    test-rmse:0.402205+0.052324 
## [23] train-rmse:0.081175+0.008136    test-rmse:0.401962+0.051619 
## [24] train-rmse:0.077068+0.007563    test-rmse:0.401742+0.052473 
## [25] train-rmse:0.073989+0.007784    test-rmse:0.400749+0.052019 
## [26] train-rmse:0.070743+0.007366    test-rmse:0.399964+0.052113 
## [27] train-rmse:0.067410+0.007654    test-rmse:0.399654+0.052105 
## [28] train-rmse:0.064649+0.006963    test-rmse:0.399159+0.052293 
## [29] train-rmse:0.062483+0.006667    test-rmse:0.398923+0.052113 
## [30] train-rmse:0.059790+0.006037    test-rmse:0.398713+0.052500 
## [31] train-rmse:0.057276+0.005839    test-rmse:0.398555+0.052129 
## [32] train-rmse:0.054214+0.006286    test-rmse:0.398600+0.051853 
## [33] train-rmse:0.052508+0.005939    test-rmse:0.398675+0.051341 
## [34] train-rmse:0.050123+0.006275    test-rmse:0.398599+0.051428 
## [35] train-rmse:0.047675+0.005677    test-rmse:0.398806+0.051136 
## [36] train-rmse:0.045483+0.005709    test-rmse:0.398401+0.050940 
## [37] train-rmse:0.043357+0.005601    test-rmse:0.398541+0.050505 
## [38] train-rmse:0.041776+0.005184    test-rmse:0.398605+0.050479 
## [39] train-rmse:0.039879+0.005165    test-rmse:0.398538+0.050331 
## [40] train-rmse:0.038036+0.005250    test-rmse:0.398575+0.051145 
## [41] train-rmse:0.036722+0.005463    test-rmse:0.398672+0.051000 
## [42] train-rmse:0.034719+0.004722    test-rmse:0.398631+0.050807 
## Stopping. Best iteration:
## [36] train-rmse:0.045483+0.005709    test-rmse:0.398401+0.050940
## 
## 70 
## [1]  train-rmse:1.239431+0.023343    test-rmse:1.235622+0.108139 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.985247+0.019663    test-rmse:0.981626+0.101110 
## [3]  train-rmse:0.831502+0.018240    test-rmse:0.828218+0.095339 
## [4]  train-rmse:0.743924+0.017954    test-rmse:0.741050+0.090895 
## [5]  train-rmse:0.696613+0.018036    test-rmse:0.694111+0.087792 
## [6]  train-rmse:0.666539+0.016706    test-rmse:0.668566+0.088529 
## [7]  train-rmse:0.644645+0.016901    test-rmse:0.643261+0.085475 
## [8]  train-rmse:0.626972+0.014462    test-rmse:0.628719+0.081551 
## [9]  train-rmse:0.613387+0.013910    test-rmse:0.619262+0.077668 
## [10] train-rmse:0.601682+0.013634    test-rmse:0.605331+0.075403 
## [11] train-rmse:0.591806+0.012409    test-rmse:0.597561+0.070808 
## [12] train-rmse:0.583486+0.012087    test-rmse:0.586775+0.069298 
## [13] train-rmse:0.576219+0.011967    test-rmse:0.584869+0.071083 
## [14] train-rmse:0.569638+0.011573    test-rmse:0.579777+0.066141 
## [15] train-rmse:0.563547+0.011497    test-rmse:0.576147+0.064959 
## [16] train-rmse:0.558492+0.011184    test-rmse:0.571895+0.061852 
## [17] train-rmse:0.553866+0.010836    test-rmse:0.565359+0.060379 
## [18] train-rmse:0.549581+0.010677    test-rmse:0.562992+0.062708 
## [19] train-rmse:0.545570+0.010546    test-rmse:0.561651+0.061396 
## [20] train-rmse:0.542079+0.010597    test-rmse:0.558755+0.060458 
## [21] train-rmse:0.538854+0.010368    test-rmse:0.557873+0.059137 
## [22] train-rmse:0.535815+0.010278    test-rmse:0.556795+0.058641 
## [23] train-rmse:0.532712+0.010240    test-rmse:0.553156+0.056904 
## [24] train-rmse:0.529843+0.010015    test-rmse:0.551357+0.058018 
## [25] train-rmse:0.527233+0.009658    test-rmse:0.548387+0.058349 
## [26] train-rmse:0.524711+0.009515    test-rmse:0.548216+0.059918 
## [27] train-rmse:0.522319+0.009386    test-rmse:0.545882+0.057594 
## [28] train-rmse:0.519959+0.009321    test-rmse:0.544012+0.056357 
## [29] train-rmse:0.517705+0.009216    test-rmse:0.544761+0.056313 
## [30] train-rmse:0.515572+0.009120    test-rmse:0.543790+0.056495 
## [31] train-rmse:0.513540+0.009033    test-rmse:0.541202+0.056310 
## [32] train-rmse:0.511620+0.008941    test-rmse:0.539819+0.057767 
## [33] train-rmse:0.509651+0.008865    test-rmse:0.537770+0.054782 
## [34] train-rmse:0.507650+0.008790    test-rmse:0.535913+0.054425 
## [35] train-rmse:0.505855+0.008715    test-rmse:0.534100+0.054978 
## [36] train-rmse:0.504122+0.008587    test-rmse:0.532717+0.054641 
## [37] train-rmse:0.502485+0.008532    test-rmse:0.531707+0.054260 
## [38] train-rmse:0.500916+0.008515    test-rmse:0.532553+0.054842 
## [39] train-rmse:0.499229+0.008492    test-rmse:0.530350+0.054480 
## [40] train-rmse:0.497673+0.008427    test-rmse:0.529388+0.052820 
## [41] train-rmse:0.496149+0.008394    test-rmse:0.526910+0.053402 
## [42] train-rmse:0.494661+0.008379    test-rmse:0.526735+0.052649 
## [43] train-rmse:0.493204+0.008357    test-rmse:0.526336+0.052520 
## [44] train-rmse:0.491822+0.008322    test-rmse:0.525255+0.051495 
## [45] train-rmse:0.490428+0.008218    test-rmse:0.524438+0.052761 
## [46] train-rmse:0.489031+0.008162    test-rmse:0.523485+0.052306 
## [47] train-rmse:0.487691+0.008153    test-rmse:0.523694+0.053743 
## [48] train-rmse:0.486434+0.008121    test-rmse:0.523842+0.052219 
## [49] train-rmse:0.485230+0.008099    test-rmse:0.522198+0.051490 
## [50] train-rmse:0.484070+0.008066    test-rmse:0.521659+0.050463 
## [51] train-rmse:0.482887+0.008030    test-rmse:0.520468+0.050079 
## [52] train-rmse:0.481758+0.008007    test-rmse:0.520101+0.050963 
## [53] train-rmse:0.480642+0.007987    test-rmse:0.520273+0.051146 
## [54] train-rmse:0.479592+0.007983    test-rmse:0.519854+0.051023 
## [55] train-rmse:0.478517+0.007954    test-rmse:0.518736+0.051391 
## [56] train-rmse:0.477517+0.007960    test-rmse:0.518201+0.050484 
## [57] train-rmse:0.476537+0.008000    test-rmse:0.516217+0.049480 
## [58] train-rmse:0.475561+0.007977    test-rmse:0.517049+0.049045 
## [59] train-rmse:0.474596+0.007954    test-rmse:0.514778+0.049646 
## [60] train-rmse:0.473662+0.007911    test-rmse:0.515298+0.049850 
## [61] train-rmse:0.472754+0.007894    test-rmse:0.515077+0.049870 
## [62] train-rmse:0.471826+0.007886    test-rmse:0.512611+0.046686 
## [63] train-rmse:0.470978+0.007861    test-rmse:0.511823+0.046735 
## [64] train-rmse:0.470126+0.007890    test-rmse:0.510906+0.047102 
## [65] train-rmse:0.469229+0.007857    test-rmse:0.509899+0.046784 
## [66] train-rmse:0.468385+0.007883    test-rmse:0.508938+0.046347 
## [67] train-rmse:0.467553+0.007872    test-rmse:0.509768+0.046120 
## [68] train-rmse:0.466766+0.007852    test-rmse:0.509724+0.046828 
## [69] train-rmse:0.465991+0.007812    test-rmse:0.509752+0.046574 
## [70] train-rmse:0.465193+0.007777    test-rmse:0.510210+0.047528 
## [71] train-rmse:0.464472+0.007764    test-rmse:0.510435+0.047186 
## [72] train-rmse:0.463725+0.007730    test-rmse:0.509404+0.046484 
## Stopping. Best iteration:
## [66] train-rmse:0.468385+0.007883    test-rmse:0.508938+0.046347
## 
## 71 
## [1]  train-rmse:1.219162+0.023494    test-rmse:1.219875+0.114121 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.936960+0.018018    test-rmse:0.935077+0.101980 
## [3]  train-rmse:0.752643+0.016210    test-rmse:0.757184+0.101802 
## [4]  train-rmse:0.636928+0.014213    test-rmse:0.650326+0.099191 
## [5]  train-rmse:0.563542+0.013073    test-rmse:0.576805+0.089942 
## [6]  train-rmse:0.517087+0.012503    test-rmse:0.530263+0.082530 
## [7]  train-rmse:0.487080+0.011744    test-rmse:0.503427+0.077751 
## [8]  train-rmse:0.463150+0.009877    test-rmse:0.486402+0.074773 
## [9]  train-rmse:0.444228+0.009098    test-rmse:0.473753+0.069760 
## [10] train-rmse:0.430758+0.007288    test-rmse:0.468703+0.068026 
## [11] train-rmse:0.419215+0.006669    test-rmse:0.464024+0.067353 
## [12] train-rmse:0.410609+0.006466    test-rmse:0.461127+0.063683 
## [13] train-rmse:0.402923+0.006013    test-rmse:0.459832+0.062544 
## [14] train-rmse:0.394961+0.004738    test-rmse:0.454611+0.062165 
## [15] train-rmse:0.388289+0.005137    test-rmse:0.451610+0.059148 
## [16] train-rmse:0.382663+0.004140    test-rmse:0.447975+0.058726 
## [17] train-rmse:0.377108+0.003907    test-rmse:0.446116+0.054651 
## [18] train-rmse:0.372410+0.003679    test-rmse:0.446083+0.055406 
## [19] train-rmse:0.367751+0.003260    test-rmse:0.443896+0.051511 
## [20] train-rmse:0.363259+0.002640    test-rmse:0.443089+0.053184 
## [21] train-rmse:0.358375+0.002618    test-rmse:0.441249+0.050920 
## [22] train-rmse:0.354632+0.002651    test-rmse:0.439339+0.049807 
## [23] train-rmse:0.350672+0.003123    test-rmse:0.438675+0.048944 
## [24] train-rmse:0.347656+0.003402    test-rmse:0.436715+0.050412 
## [25] train-rmse:0.343606+0.002890    test-rmse:0.435911+0.050969 
## [26] train-rmse:0.340324+0.002560    test-rmse:0.435246+0.051977 
## [27] train-rmse:0.336332+0.002627    test-rmse:0.437002+0.052101 
## [28] train-rmse:0.333224+0.003095    test-rmse:0.436037+0.050725 
## [29] train-rmse:0.330174+0.003264    test-rmse:0.434883+0.050936 
## [30] train-rmse:0.326951+0.003368    test-rmse:0.433202+0.048608 
## [31] train-rmse:0.323549+0.003125    test-rmse:0.432294+0.050528 
## [32] train-rmse:0.321193+0.003594    test-rmse:0.430418+0.050740 
## [33] train-rmse:0.317583+0.004562    test-rmse:0.430251+0.050590 
## [34] train-rmse:0.315063+0.004575    test-rmse:0.429185+0.051459 
## [35] train-rmse:0.312482+0.004221    test-rmse:0.428979+0.052033 
## [36] train-rmse:0.310022+0.004275    test-rmse:0.427127+0.052936 
## [37] train-rmse:0.307600+0.003994    test-rmse:0.427024+0.051876 
## [38] train-rmse:0.305314+0.003703    test-rmse:0.426688+0.052798 
## [39] train-rmse:0.302020+0.004796    test-rmse:0.426393+0.052531 
## [40] train-rmse:0.299545+0.005228    test-rmse:0.426341+0.052647 
## [41] train-rmse:0.297728+0.005594    test-rmse:0.427646+0.052719 
## [42] train-rmse:0.294749+0.005381    test-rmse:0.426915+0.052343 
## [43] train-rmse:0.293329+0.005373    test-rmse:0.425569+0.053261 
## [44] train-rmse:0.291682+0.004894    test-rmse:0.425689+0.053128 
## [45] train-rmse:0.289914+0.005072    test-rmse:0.423839+0.053915 
## [46] train-rmse:0.287961+0.004410    test-rmse:0.423323+0.054472 
## [47] train-rmse:0.286017+0.003767    test-rmse:0.422516+0.055087 
## [48] train-rmse:0.283996+0.004022    test-rmse:0.422467+0.055895 
## [49] train-rmse:0.282651+0.004295    test-rmse:0.421993+0.055278 
## [50] train-rmse:0.280646+0.004430    test-rmse:0.422041+0.055520 
## [51] train-rmse:0.279252+0.004202    test-rmse:0.422155+0.055864 
## [52] train-rmse:0.278198+0.004160    test-rmse:0.421311+0.055999 
## [53] train-rmse:0.276396+0.004256    test-rmse:0.419791+0.055445 
## [54] train-rmse:0.275032+0.003931    test-rmse:0.420098+0.056132 
## [55] train-rmse:0.273129+0.004683    test-rmse:0.419395+0.056824 
## [56] train-rmse:0.271526+0.004555    test-rmse:0.418300+0.057003 
## [57] train-rmse:0.269635+0.005392    test-rmse:0.418812+0.057772 
## [58] train-rmse:0.268203+0.005781    test-rmse:0.419566+0.056882 
## [59] train-rmse:0.266449+0.005619    test-rmse:0.419850+0.057072 
## [60] train-rmse:0.265090+0.005415    test-rmse:0.419859+0.057032 
## [61] train-rmse:0.264191+0.005326    test-rmse:0.419866+0.056706 
## [62] train-rmse:0.262864+0.006011    test-rmse:0.419372+0.056217 
## Stopping. Best iteration:
## [56] train-rmse:0.271526+0.004555    test-rmse:0.418300+0.057003
## 
## 72 
## [1]  train-rmse:1.208282+0.022383    test-rmse:1.213465+0.117616 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.913621+0.019087    test-rmse:0.918747+0.103167 
## [3]  train-rmse:0.713867+0.015744    test-rmse:0.732430+0.099741 
## [4]  train-rmse:0.580728+0.013317    test-rmse:0.608790+0.089104 
## [5]  train-rmse:0.492919+0.011766    test-rmse:0.537222+0.086886 
## [6]  train-rmse:0.436747+0.009890    test-rmse:0.493730+0.078484 
## [7]  train-rmse:0.400768+0.009708    test-rmse:0.465489+0.073577 
## [8]  train-rmse:0.376253+0.010222    test-rmse:0.448883+0.069499 
## [9]  train-rmse:0.357706+0.009580    test-rmse:0.443950+0.064960 
## [10] train-rmse:0.345344+0.009719    test-rmse:0.441571+0.061688 
## [11] train-rmse:0.333265+0.009019    test-rmse:0.436999+0.058967 
## [12] train-rmse:0.323335+0.009670    test-rmse:0.430564+0.057069 
## [13] train-rmse:0.314291+0.007605    test-rmse:0.430759+0.057655 
## [14] train-rmse:0.307344+0.006937    test-rmse:0.429969+0.058369 
## [15] train-rmse:0.300206+0.007083    test-rmse:0.430568+0.058687 
## [16] train-rmse:0.293470+0.005674    test-rmse:0.428496+0.061250 
## [17] train-rmse:0.287367+0.005919    test-rmse:0.427362+0.060118 
## [18] train-rmse:0.282296+0.005798    test-rmse:0.427473+0.060594 
## [19] train-rmse:0.276999+0.005701    test-rmse:0.425248+0.061442 
## [20] train-rmse:0.272751+0.006096    test-rmse:0.424723+0.062454 
## [21] train-rmse:0.268111+0.005586    test-rmse:0.425698+0.062502 
## [22] train-rmse:0.264099+0.005262    test-rmse:0.423729+0.064614 
## [23] train-rmse:0.259872+0.004579    test-rmse:0.421730+0.064198 
## [24] train-rmse:0.256318+0.004790    test-rmse:0.419639+0.063699 
## [25] train-rmse:0.250803+0.005786    test-rmse:0.417757+0.064269 
## [26] train-rmse:0.246997+0.005033    test-rmse:0.416878+0.063161 
## [27] train-rmse:0.242575+0.005598    test-rmse:0.418140+0.062719 
## [28] train-rmse:0.238527+0.004775    test-rmse:0.417120+0.064403 
## [29] train-rmse:0.235632+0.004495    test-rmse:0.414695+0.064723 
## [30] train-rmse:0.232283+0.005076    test-rmse:0.412524+0.064366 
## [31] train-rmse:0.229525+0.004739    test-rmse:0.414909+0.064835 
## [32] train-rmse:0.226114+0.004730    test-rmse:0.415546+0.064146 
## [33] train-rmse:0.222891+0.005066    test-rmse:0.416397+0.064395 
## [34] train-rmse:0.219888+0.005820    test-rmse:0.415446+0.063840 
## [35] train-rmse:0.217419+0.005795    test-rmse:0.414312+0.063904 
## [36] train-rmse:0.214639+0.006185    test-rmse:0.414012+0.062972 
## Stopping. Best iteration:
## [30] train-rmse:0.232283+0.005076    test-rmse:0.412524+0.064366
## 
## 73 
## [1]  train-rmse:1.203273+0.021575    test-rmse:1.208003+0.118500 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.904180+0.015727    test-rmse:0.914552+0.115624 
## [3]  train-rmse:0.696818+0.015497    test-rmse:0.707217+0.105257 
## [4]  train-rmse:0.554900+0.010239    test-rmse:0.584858+0.102180 
## [5]  train-rmse:0.460539+0.010536    test-rmse:0.511941+0.090519 
## [6]  train-rmse:0.396476+0.010094    test-rmse:0.465741+0.082940 
## [7]  train-rmse:0.355601+0.009004    test-rmse:0.442006+0.075509 
## [8]  train-rmse:0.327221+0.008376    test-rmse:0.432205+0.069434 
## [9]  train-rmse:0.306852+0.007231    test-rmse:0.424894+0.066285 
## [10] train-rmse:0.292704+0.005954    test-rmse:0.419971+0.063495 
## [11] train-rmse:0.282685+0.006528    test-rmse:0.418220+0.064338 
## [12] train-rmse:0.270636+0.006927    test-rmse:0.417892+0.062084 
## [13] train-rmse:0.259986+0.006950    test-rmse:0.418079+0.061962 
## [14] train-rmse:0.250809+0.007405    test-rmse:0.417453+0.061840 
## [15] train-rmse:0.242887+0.006397    test-rmse:0.416029+0.065105 
## [16] train-rmse:0.235318+0.007357    test-rmse:0.415258+0.065449 
## [17] train-rmse:0.228110+0.006847    test-rmse:0.416167+0.064463 
## [18] train-rmse:0.222814+0.007320    test-rmse:0.415449+0.064938 
## [19] train-rmse:0.216528+0.008609    test-rmse:0.413718+0.064440 
## [20] train-rmse:0.211738+0.008414    test-rmse:0.411347+0.066166 
## [21] train-rmse:0.206132+0.007675    test-rmse:0.409578+0.064383 
## [22] train-rmse:0.201883+0.007639    test-rmse:0.408064+0.065491 
## [23] train-rmse:0.197993+0.009157    test-rmse:0.406922+0.064593 
## [24] train-rmse:0.192117+0.009089    test-rmse:0.403908+0.067600 
## [25] train-rmse:0.188092+0.008893    test-rmse:0.402799+0.067388 
## [26] train-rmse:0.184043+0.008867    test-rmse:0.402084+0.068923 
## [27] train-rmse:0.179398+0.010211    test-rmse:0.403309+0.067516 
## [28] train-rmse:0.175436+0.010015    test-rmse:0.402826+0.066996 
## [29] train-rmse:0.172395+0.010050    test-rmse:0.402947+0.067961 
## [30] train-rmse:0.169149+0.010076    test-rmse:0.402107+0.068008 
## [31] train-rmse:0.165454+0.010087    test-rmse:0.402676+0.067021 
## [32] train-rmse:0.162443+0.009596    test-rmse:0.402233+0.068214 
## Stopping. Best iteration:
## [26] train-rmse:0.184043+0.008867    test-rmse:0.402084+0.068923
## 
## 74 
## [1]  train-rmse:1.200366+0.022037    test-rmse:1.205354+0.113843 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.897596+0.016301    test-rmse:0.907203+0.109367 
## [3]  train-rmse:0.685256+0.014107    test-rmse:0.703721+0.098505 
## [4]  train-rmse:0.538206+0.009217    test-rmse:0.577530+0.091311 
## [5]  train-rmse:0.438657+0.006189    test-rmse:0.500574+0.091659 
## [6]  train-rmse:0.368971+0.005243    test-rmse:0.450650+0.081162 
## [7]  train-rmse:0.323114+0.003265    test-rmse:0.432309+0.077536 
## [8]  train-rmse:0.288892+0.003899    test-rmse:0.419496+0.073425 
## [9]  train-rmse:0.264849+0.001095    test-rmse:0.411973+0.068008 
## [10] train-rmse:0.249068+0.003178    test-rmse:0.407526+0.064484 
## [11] train-rmse:0.234233+0.005349    test-rmse:0.406386+0.063532 
## [12] train-rmse:0.223955+0.004354    test-rmse:0.404090+0.062879 
## [13] train-rmse:0.213300+0.005002    test-rmse:0.399205+0.063079 
## [14] train-rmse:0.205624+0.003387    test-rmse:0.399142+0.062111 
## [15] train-rmse:0.196678+0.006113    test-rmse:0.398220+0.061704 
## [16] train-rmse:0.189273+0.006808    test-rmse:0.397390+0.063947 
## [17] train-rmse:0.181666+0.008668    test-rmse:0.395938+0.064764 
## [18] train-rmse:0.175615+0.008918    test-rmse:0.395878+0.064989 
## [19] train-rmse:0.169557+0.009248    test-rmse:0.394710+0.062846 
## [20] train-rmse:0.164866+0.009681    test-rmse:0.394190+0.063164 
## [21] train-rmse:0.159529+0.007637    test-rmse:0.393311+0.063148 
## [22] train-rmse:0.153809+0.006956    test-rmse:0.392747+0.063806 
## [23] train-rmse:0.149100+0.005998    test-rmse:0.391307+0.062383 
## [24] train-rmse:0.142999+0.006806    test-rmse:0.389564+0.064250 
## [25] train-rmse:0.138555+0.006250    test-rmse:0.390180+0.060834 
## [26] train-rmse:0.135605+0.005685    test-rmse:0.389651+0.060342 
## [27] train-rmse:0.131744+0.006431    test-rmse:0.389851+0.060376 
## [28] train-rmse:0.128222+0.005726    test-rmse:0.389127+0.060260 
## [29] train-rmse:0.124655+0.004334    test-rmse:0.388043+0.059032 
## [30] train-rmse:0.121063+0.003132    test-rmse:0.386076+0.059332 
## [31] train-rmse:0.118269+0.002744    test-rmse:0.385699+0.060063 
## [32] train-rmse:0.115180+0.003056    test-rmse:0.386138+0.059485 
## [33] train-rmse:0.112308+0.002620    test-rmse:0.386394+0.058502 
## [34] train-rmse:0.108455+0.003662    test-rmse:0.386873+0.059153 
## [35] train-rmse:0.106661+0.003000    test-rmse:0.387127+0.060103 
## [36] train-rmse:0.104434+0.003023    test-rmse:0.387280+0.060584 
## [37] train-rmse:0.101496+0.003198    test-rmse:0.386557+0.060487 
## Stopping. Best iteration:
## [31] train-rmse:0.118269+0.002744    test-rmse:0.385699+0.060063
## 
## 75 
## [1]  train-rmse:1.199429+0.022093    test-rmse:1.203451+0.112131 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.894044+0.015975    test-rmse:0.904495+0.103786 
## [3]  train-rmse:0.679296+0.014207    test-rmse:0.703302+0.092884 
## [4]  train-rmse:0.528789+0.011873    test-rmse:0.580091+0.089136 
## [5]  train-rmse:0.423692+0.009932    test-rmse:0.499992+0.084993 
## [6]  train-rmse:0.352906+0.009336    test-rmse:0.456372+0.076634 
## [7]  train-rmse:0.302582+0.010089    test-rmse:0.433265+0.065449 
## [8]  train-rmse:0.266881+0.008572    test-rmse:0.421718+0.065253 
## [9]  train-rmse:0.240654+0.008370    test-rmse:0.415231+0.062691 
## [10] train-rmse:0.223964+0.008910    test-rmse:0.411699+0.059094 
## [11] train-rmse:0.204337+0.009680    test-rmse:0.408299+0.055060 
## [12] train-rmse:0.189936+0.012125    test-rmse:0.404768+0.055682 
## [13] train-rmse:0.179419+0.013662    test-rmse:0.405718+0.053558 
## [14] train-rmse:0.170250+0.013103    test-rmse:0.403240+0.052426 
## [15] train-rmse:0.157100+0.011012    test-rmse:0.404297+0.049663 
## [16] train-rmse:0.150662+0.010849    test-rmse:0.404592+0.049831 
## [17] train-rmse:0.140075+0.009877    test-rmse:0.405079+0.049044 
## [18] train-rmse:0.132952+0.010026    test-rmse:0.401992+0.048178 
## [19] train-rmse:0.126931+0.010511    test-rmse:0.402411+0.047799 
## [20] train-rmse:0.121255+0.009112    test-rmse:0.403345+0.047970 
## [21] train-rmse:0.116386+0.008755    test-rmse:0.401520+0.046953 
## [22] train-rmse:0.112941+0.008139    test-rmse:0.401070+0.046213 
## [23] train-rmse:0.108529+0.008488    test-rmse:0.402451+0.045721 
## [24] train-rmse:0.102484+0.008869    test-rmse:0.402880+0.046336 
## [25] train-rmse:0.098621+0.009312    test-rmse:0.402898+0.046863 
## [26] train-rmse:0.095293+0.009445    test-rmse:0.403232+0.046271 
## [27] train-rmse:0.090855+0.007466    test-rmse:0.402868+0.046674 
## [28] train-rmse:0.087815+0.006903    test-rmse:0.401200+0.047986 
## Stopping. Best iteration:
## [22] train-rmse:0.112941+0.008139    test-rmse:0.401070+0.046213
## 
## 76 
## [1]  train-rmse:1.198984+0.022093    test-rmse:1.204914+0.111648 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.892190+0.016458    test-rmse:0.903795+0.103902 
## [3]  train-rmse:0.674604+0.014236    test-rmse:0.698218+0.092698 
## [4]  train-rmse:0.521118+0.012999    test-rmse:0.573919+0.091363 
## [5]  train-rmse:0.412520+0.011986    test-rmse:0.492586+0.088673 
## [6]  train-rmse:0.335983+0.012843    test-rmse:0.451714+0.080714 
## [7]  train-rmse:0.280907+0.013575    test-rmse:0.433980+0.072789 
## [8]  train-rmse:0.241235+0.013749    test-rmse:0.423587+0.069865 
## [9]  train-rmse:0.209376+0.012638    test-rmse:0.415875+0.066404 
## [10] train-rmse:0.186106+0.010745    test-rmse:0.412158+0.066175 
## [11] train-rmse:0.168515+0.012741    test-rmse:0.409227+0.061960 
## [12] train-rmse:0.154002+0.009217    test-rmse:0.408803+0.058922 
## [13] train-rmse:0.145324+0.009714    test-rmse:0.407351+0.055398 
## [14] train-rmse:0.135716+0.009863    test-rmse:0.408462+0.052064 
## [15] train-rmse:0.126039+0.009862    test-rmse:0.407509+0.051403 
## [16] train-rmse:0.118461+0.010963    test-rmse:0.407775+0.049933 
## [17] train-rmse:0.111465+0.010054    test-rmse:0.409225+0.048140 
## [18] train-rmse:0.105523+0.009396    test-rmse:0.408480+0.049066 
## [19] train-rmse:0.099155+0.010411    test-rmse:0.407009+0.048497 
## [20] train-rmse:0.094252+0.009476    test-rmse:0.406553+0.047546 
## [21] train-rmse:0.086234+0.009404    test-rmse:0.406888+0.046297 
## [22] train-rmse:0.081447+0.008336    test-rmse:0.406681+0.045110 
## [23] train-rmse:0.075620+0.006843    test-rmse:0.406304+0.045123 
## [24] train-rmse:0.072865+0.006135    test-rmse:0.406327+0.045224 
## [25] train-rmse:0.068707+0.004936    test-rmse:0.406112+0.046084 
## [26] train-rmse:0.065097+0.005762    test-rmse:0.406158+0.046093 
## [27] train-rmse:0.061643+0.004925    test-rmse:0.405969+0.045510 
## [28] train-rmse:0.058722+0.004687    test-rmse:0.406068+0.045425 
## [29] train-rmse:0.057123+0.004386    test-rmse:0.405858+0.045454 
## [30] train-rmse:0.054246+0.004081    test-rmse:0.405778+0.045863 
## [31] train-rmse:0.052063+0.004441    test-rmse:0.405097+0.046387 
## [32] train-rmse:0.049523+0.003660    test-rmse:0.405512+0.045641 
## [33] train-rmse:0.047079+0.003910    test-rmse:0.405802+0.045555 
## [34] train-rmse:0.044817+0.003587    test-rmse:0.405124+0.045816 
## [35] train-rmse:0.042835+0.003074    test-rmse:0.405288+0.045924 
## [36] train-rmse:0.040578+0.003247    test-rmse:0.404820+0.046409 
## [37] train-rmse:0.038916+0.003166    test-rmse:0.404605+0.046545 
## [38] train-rmse:0.037001+0.003203    test-rmse:0.404694+0.046173 
## [39] train-rmse:0.035323+0.003474    test-rmse:0.404793+0.046521 
## [40] train-rmse:0.034243+0.003705    test-rmse:0.405023+0.046470 
## [41] train-rmse:0.032915+0.003384    test-rmse:0.404910+0.046597 
## [42] train-rmse:0.031845+0.003563    test-rmse:0.404935+0.046680 
## [43] train-rmse:0.030221+0.003355    test-rmse:0.404592+0.047230 
## [44] train-rmse:0.028831+0.003558    test-rmse:0.404617+0.047001 
## [45] train-rmse:0.027675+0.003095    test-rmse:0.404601+0.046867 
## [46] train-rmse:0.026409+0.002950    test-rmse:0.404457+0.046944 
## [47] train-rmse:0.024958+0.002801    test-rmse:0.404396+0.047205 
## [48] train-rmse:0.023837+0.002524    test-rmse:0.404469+0.047186 
## [49] train-rmse:0.022794+0.002534    test-rmse:0.404305+0.047298 
## [50] train-rmse:0.021845+0.002479    test-rmse:0.404387+0.047366 
## [51] train-rmse:0.020912+0.002455    test-rmse:0.404459+0.047339 
## [52] train-rmse:0.020004+0.002097    test-rmse:0.404546+0.047205 
## [53] train-rmse:0.018914+0.002289    test-rmse:0.404482+0.047061 
## [54] train-rmse:0.018059+0.001917    test-rmse:0.404584+0.047066 
## [55] train-rmse:0.017418+0.001947    test-rmse:0.404458+0.047132 
## Stopping. Best iteration:
## [49] train-rmse:0.022794+0.002534    test-rmse:0.404305+0.047298
## 
## 77 
## [1]  train-rmse:1.214115+0.022932    test-rmse:1.210316+0.107513 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.954492+0.019308    test-rmse:0.950917+0.100093 
## [3]  train-rmse:0.805239+0.018103    test-rmse:0.802054+0.094131 
## [4]  train-rmse:0.725179+0.017964    test-rmse:0.722433+0.089752 
## [5]  train-rmse:0.684635+0.018087    test-rmse:0.682260+0.086860 
## [6]  train-rmse:0.654201+0.016732    test-rmse:0.652560+0.086378 
## [7]  train-rmse:0.635469+0.015249    test-rmse:0.642909+0.083383 
## [8]  train-rmse:0.617731+0.013669    test-rmse:0.619357+0.081322 
## [9]  train-rmse:0.604812+0.013482    test-rmse:0.611128+0.076734 
## [10] train-rmse:0.593949+0.012787    test-rmse:0.602306+0.071110 
## [11] train-rmse:0.584765+0.012084    test-rmse:0.590711+0.070046 
## [12] train-rmse:0.576372+0.011739    test-rmse:0.582207+0.069068 
## [13] train-rmse:0.569250+0.011804    test-rmse:0.579561+0.066642 
## [14] train-rmse:0.563094+0.011465    test-rmse:0.576435+0.062713 
## [15] train-rmse:0.557627+0.011240    test-rmse:0.569417+0.062100 
## [16] train-rmse:0.552656+0.010671    test-rmse:0.562728+0.063063 
## [17] train-rmse:0.548173+0.010605    test-rmse:0.561789+0.062027 
## [18] train-rmse:0.544170+0.010523    test-rmse:0.562160+0.061462 
## [19] train-rmse:0.540548+0.010479    test-rmse:0.558898+0.058622 
## [20] train-rmse:0.537170+0.010327    test-rmse:0.556555+0.057719 
## [21] train-rmse:0.533879+0.010345    test-rmse:0.556566+0.057632 
## [22] train-rmse:0.530852+0.010175    test-rmse:0.552457+0.056858 
## [23] train-rmse:0.527840+0.009801    test-rmse:0.550037+0.057976 
## [24] train-rmse:0.525126+0.009512    test-rmse:0.548760+0.056635 
## [25] train-rmse:0.522499+0.009417    test-rmse:0.545192+0.057056 
## [26] train-rmse:0.520065+0.009389    test-rmse:0.544907+0.056794 
## [27] train-rmse:0.517492+0.009125    test-rmse:0.543628+0.057286 
## [28] train-rmse:0.515163+0.008987    test-rmse:0.541807+0.056639 
## [29] train-rmse:0.513061+0.008852    test-rmse:0.541082+0.056051 
## [30] train-rmse:0.511069+0.008903    test-rmse:0.537787+0.054182 
## [31] train-rmse:0.508954+0.008844    test-rmse:0.536059+0.054892 
## [32] train-rmse:0.506910+0.008751    test-rmse:0.536170+0.055732 
## [33] train-rmse:0.504945+0.008594    test-rmse:0.534873+0.054257 
## [34] train-rmse:0.503060+0.008569    test-rmse:0.533530+0.055240 
## [35] train-rmse:0.501358+0.008477    test-rmse:0.532456+0.054524 
## [36] train-rmse:0.499530+0.008483    test-rmse:0.529206+0.053427 
## [37] train-rmse:0.497845+0.008495    test-rmse:0.527579+0.053860 
## [38] train-rmse:0.496199+0.008451    test-rmse:0.528228+0.053856 
## [39] train-rmse:0.494638+0.008349    test-rmse:0.527422+0.054956 
## [40] train-rmse:0.493051+0.008323    test-rmse:0.527044+0.053542 
## [41] train-rmse:0.491541+0.008291    test-rmse:0.523748+0.052196 
## [42] train-rmse:0.490112+0.008308    test-rmse:0.522381+0.052849 
## [43] train-rmse:0.488631+0.008257    test-rmse:0.522438+0.051919 
## [44] train-rmse:0.487195+0.008179    test-rmse:0.522420+0.052156 
## [45] train-rmse:0.485773+0.008089    test-rmse:0.520935+0.051746 
## [46] train-rmse:0.484436+0.007994    test-rmse:0.519895+0.052493 
## [47] train-rmse:0.483213+0.007949    test-rmse:0.521381+0.052140 
## [48] train-rmse:0.482032+0.008010    test-rmse:0.520820+0.050178 
## [49] train-rmse:0.480805+0.007995    test-rmse:0.519372+0.049682 
## [50] train-rmse:0.479663+0.007962    test-rmse:0.518383+0.049254 
## [51] train-rmse:0.478532+0.007909    test-rmse:0.519356+0.049675 
## [52] train-rmse:0.477457+0.007899    test-rmse:0.518330+0.050193 
## [53] train-rmse:0.476405+0.007886    test-rmse:0.516541+0.049409 
## [54] train-rmse:0.475328+0.007902    test-rmse:0.515361+0.049691 
## [55] train-rmse:0.474306+0.007909    test-rmse:0.515157+0.049911 
## [56] train-rmse:0.473357+0.007909    test-rmse:0.515530+0.049832 
## [57] train-rmse:0.472345+0.007857    test-rmse:0.515385+0.050775 
## [58] train-rmse:0.471377+0.007811    test-rmse:0.514325+0.049164 
## [59] train-rmse:0.470417+0.007794    test-rmse:0.513081+0.048118 
## [60] train-rmse:0.469478+0.007772    test-rmse:0.511795+0.047831 
## [61] train-rmse:0.468556+0.007767    test-rmse:0.511428+0.048544 
## [62] train-rmse:0.467644+0.007776    test-rmse:0.507920+0.046034 
## [63] train-rmse:0.466800+0.007760    test-rmse:0.507566+0.046225 
## [64] train-rmse:0.465899+0.007780    test-rmse:0.507385+0.046753 
## [65] train-rmse:0.465075+0.007763    test-rmse:0.506704+0.046884 
## [66] train-rmse:0.464313+0.007731    test-rmse:0.507900+0.046446 
## [67] train-rmse:0.463542+0.007688    test-rmse:0.506684+0.046615 
## [68] train-rmse:0.462752+0.007634    test-rmse:0.506504+0.045852 
## [69] train-rmse:0.461972+0.007619    test-rmse:0.506141+0.045293 
## [70] train-rmse:0.461214+0.007610    test-rmse:0.506818+0.045036 
## [71] train-rmse:0.460448+0.007631    test-rmse:0.506801+0.045026 
## [72] train-rmse:0.459712+0.007583    test-rmse:0.505592+0.045100 
## [73] train-rmse:0.458967+0.007518    test-rmse:0.506241+0.045139 
## [74] train-rmse:0.458253+0.007493    test-rmse:0.505479+0.045421 
## [75] train-rmse:0.457575+0.007484    test-rmse:0.506049+0.045474 
## [76] train-rmse:0.456929+0.007492    test-rmse:0.505956+0.045488 
## [77] train-rmse:0.456284+0.007510    test-rmse:0.505770+0.045994 
## [78] train-rmse:0.455632+0.007492    test-rmse:0.505692+0.044597 
## [79] train-rmse:0.454964+0.007499    test-rmse:0.505085+0.044123 
## [80] train-rmse:0.454308+0.007488    test-rmse:0.504694+0.044074 
## [81] train-rmse:0.453633+0.007495    test-rmse:0.503646+0.043245 
## [82] train-rmse:0.453011+0.007480    test-rmse:0.503423+0.042779 
## [83] train-rmse:0.452419+0.007478    test-rmse:0.503161+0.043480 
## [84] train-rmse:0.451833+0.007464    test-rmse:0.502370+0.043607 
## [85] train-rmse:0.451269+0.007454    test-rmse:0.501736+0.043435 
## [86] train-rmse:0.450724+0.007464    test-rmse:0.501825+0.043269 
## [87] train-rmse:0.450133+0.007501    test-rmse:0.501838+0.042696 
## [88] train-rmse:0.449595+0.007479    test-rmse:0.499186+0.041063 
## [89] train-rmse:0.449083+0.007448    test-rmse:0.499126+0.040546 
## [90] train-rmse:0.448555+0.007431    test-rmse:0.499471+0.041110 
## [91] train-rmse:0.448048+0.007426    test-rmse:0.500074+0.040838 
## [92] train-rmse:0.447539+0.007418    test-rmse:0.499539+0.041239 
## [93] train-rmse:0.447025+0.007416    test-rmse:0.499988+0.041160 
## [94] train-rmse:0.446548+0.007413    test-rmse:0.499376+0.041495 
## [95] train-rmse:0.446067+0.007400    test-rmse:0.499520+0.041396 
## Stopping. Best iteration:
## [89] train-rmse:0.449083+0.007448    test-rmse:0.499126+0.040546
## 
## 78 
## [1]  train-rmse:1.192217+0.023110    test-rmse:1.193352+0.113982 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.901677+0.017455    test-rmse:0.900281+0.101204 
## [3]  train-rmse:0.719190+0.015745    test-rmse:0.724640+0.100761 
## [4]  train-rmse:0.609587+0.013907    test-rmse:0.624302+0.098286 
## [5]  train-rmse:0.542581+0.012576    test-rmse:0.557149+0.091555 
## [6]  train-rmse:0.501569+0.012295    test-rmse:0.520804+0.080609 
## [7]  train-rmse:0.474594+0.011753    test-rmse:0.500193+0.076296 
## [8]  train-rmse:0.450451+0.010674    test-rmse:0.480762+0.070328 
## [9]  train-rmse:0.435785+0.009189    test-rmse:0.477351+0.070350 
## [10] train-rmse:0.423578+0.008457    test-rmse:0.468165+0.065944 
## [11] train-rmse:0.413460+0.008176    test-rmse:0.465986+0.061531 
## [12] train-rmse:0.405887+0.007897    test-rmse:0.461941+0.060670 
## [13] train-rmse:0.398814+0.007985    test-rmse:0.461050+0.056556 
## [14] train-rmse:0.391651+0.007657    test-rmse:0.452451+0.057519 
## [15] train-rmse:0.386231+0.008082    test-rmse:0.450658+0.053687 
## [16] train-rmse:0.381099+0.008097    test-rmse:0.450943+0.051702 
## [17] train-rmse:0.375838+0.007234    test-rmse:0.451540+0.051813 
## [18] train-rmse:0.369316+0.006484    test-rmse:0.445967+0.049195 
## [19] train-rmse:0.363920+0.007327    test-rmse:0.444412+0.049516 
## [20] train-rmse:0.358550+0.008139    test-rmse:0.445226+0.050387 
## [21] train-rmse:0.354786+0.007928    test-rmse:0.442456+0.049070 
## [22] train-rmse:0.349413+0.008159    test-rmse:0.440759+0.047637 
## [23] train-rmse:0.345986+0.008629    test-rmse:0.440169+0.046989 
## [24] train-rmse:0.342219+0.007319    test-rmse:0.438217+0.048678 
## [25] train-rmse:0.338933+0.007566    test-rmse:0.436562+0.049847 
## [26] train-rmse:0.335528+0.007385    test-rmse:0.436668+0.052304 
## [27] train-rmse:0.331902+0.007231    test-rmse:0.436802+0.052744 
## [28] train-rmse:0.327562+0.008368    test-rmse:0.436825+0.052244 
## [29] train-rmse:0.325062+0.008637    test-rmse:0.435359+0.052005 
## [30] train-rmse:0.322321+0.008633    test-rmse:0.436449+0.051881 
## [31] train-rmse:0.319541+0.008741    test-rmse:0.437896+0.053040 
## [32] train-rmse:0.316831+0.009269    test-rmse:0.437678+0.052335 
## [33] train-rmse:0.314039+0.009954    test-rmse:0.435969+0.051837 
## [34] train-rmse:0.311035+0.008803    test-rmse:0.435503+0.050527 
## [35] train-rmse:0.308070+0.006924    test-rmse:0.435316+0.051822 
## [36] train-rmse:0.306079+0.006369    test-rmse:0.434313+0.050789 
## [37] train-rmse:0.304430+0.006287    test-rmse:0.432659+0.050891 
## [38] train-rmse:0.301655+0.006270    test-rmse:0.432799+0.051323 
## [39] train-rmse:0.299201+0.006988    test-rmse:0.432594+0.051843 
## [40] train-rmse:0.296947+0.006637    test-rmse:0.433062+0.051644 
## [41] train-rmse:0.295225+0.007035    test-rmse:0.433096+0.050580 
## [42] train-rmse:0.292984+0.006616    test-rmse:0.432016+0.051396 
## [43] train-rmse:0.291404+0.007032    test-rmse:0.431382+0.050995 
## [44] train-rmse:0.289230+0.005929    test-rmse:0.431817+0.050543 
## [45] train-rmse:0.287362+0.005565    test-rmse:0.431971+0.050748 
## [46] train-rmse:0.285196+0.005943    test-rmse:0.430340+0.051186 
## [47] train-rmse:0.283438+0.005953    test-rmse:0.430727+0.051531 
## [48] train-rmse:0.281115+0.005541    test-rmse:0.430966+0.051487 
## [49] train-rmse:0.279115+0.005825    test-rmse:0.431297+0.052281 
## [50] train-rmse:0.277317+0.006961    test-rmse:0.430559+0.050771 
## [51] train-rmse:0.275138+0.006186    test-rmse:0.428000+0.050490 
## [52] train-rmse:0.273642+0.006255    test-rmse:0.427268+0.050552 
## [53] train-rmse:0.272290+0.006362    test-rmse:0.425346+0.050111 
## [54] train-rmse:0.270272+0.005992    test-rmse:0.424632+0.050645 
## [55] train-rmse:0.269082+0.005531    test-rmse:0.423563+0.050679 
## [56] train-rmse:0.267015+0.005867    test-rmse:0.422320+0.050808 
## [57] train-rmse:0.264797+0.007159    test-rmse:0.421781+0.052682 
## [58] train-rmse:0.263697+0.007492    test-rmse:0.421172+0.052342 
## [59] train-rmse:0.262105+0.007929    test-rmse:0.422358+0.050644 
## [60] train-rmse:0.260699+0.008499    test-rmse:0.421575+0.050756 
## [61] train-rmse:0.258591+0.008332    test-rmse:0.421153+0.051032 
## [62] train-rmse:0.257135+0.008020    test-rmse:0.418479+0.052968 
## [63] train-rmse:0.255955+0.007876    test-rmse:0.416894+0.052952 
## [64] train-rmse:0.254472+0.007687    test-rmse:0.416627+0.052760 
## [65] train-rmse:0.253392+0.007989    test-rmse:0.416833+0.052801 
## [66] train-rmse:0.252407+0.008161    test-rmse:0.416761+0.052854 
## [67] train-rmse:0.251356+0.007824    test-rmse:0.416796+0.052997 
## [68] train-rmse:0.250229+0.007398    test-rmse:0.417336+0.052805 
## [69] train-rmse:0.249336+0.007285    test-rmse:0.416780+0.054021 
## [70] train-rmse:0.247969+0.007058    test-rmse:0.414385+0.053999 
## [71] train-rmse:0.245697+0.007036    test-rmse:0.414705+0.054082 
## [72] train-rmse:0.244122+0.007029    test-rmse:0.414261+0.054183 
## [73] train-rmse:0.243127+0.007090    test-rmse:0.414870+0.053705 
## [74] train-rmse:0.241329+0.007418    test-rmse:0.415692+0.054523 
## [75] train-rmse:0.240111+0.008151    test-rmse:0.416231+0.054893 
## [76] train-rmse:0.239221+0.007784    test-rmse:0.416528+0.054638 
## [77] train-rmse:0.237586+0.007680    test-rmse:0.415440+0.053963 
## [78] train-rmse:0.236900+0.007645    test-rmse:0.415103+0.054165 
## Stopping. Best iteration:
## [72] train-rmse:0.244122+0.007029    test-rmse:0.414261+0.054183
## 
## 79 
## [1]  train-rmse:1.180412+0.021917    test-rmse:1.186260+0.117710 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.876460+0.018698    test-rmse:0.878288+0.102681 
## [3]  train-rmse:0.676054+0.014844    test-rmse:0.693752+0.099057 
## [4]  train-rmse:0.549507+0.012403    test-rmse:0.581667+0.085586 
## [5]  train-rmse:0.469458+0.010853    test-rmse:0.510392+0.086523 
## [6]  train-rmse:0.420305+0.009965    test-rmse:0.477113+0.077910 
## [7]  train-rmse:0.388756+0.008589    test-rmse:0.457748+0.074979 
## [8]  train-rmse:0.366646+0.006787    test-rmse:0.447488+0.069080 
## [9]  train-rmse:0.350322+0.006905    test-rmse:0.443662+0.064781 
## [10] train-rmse:0.337924+0.005660    test-rmse:0.441681+0.063426 
## [11] train-rmse:0.326842+0.005710    test-rmse:0.435885+0.060767 
## [12] train-rmse:0.316304+0.006295    test-rmse:0.433846+0.060689 
## [13] train-rmse:0.307751+0.006368    test-rmse:0.433403+0.060842 
## [14] train-rmse:0.300082+0.005511    test-rmse:0.433620+0.057743 
## [15] train-rmse:0.293981+0.004715    test-rmse:0.433358+0.059573 
## [16] train-rmse:0.288068+0.005242    test-rmse:0.432560+0.059508 
## [17] train-rmse:0.282730+0.005005    test-rmse:0.430731+0.059269 
## [18] train-rmse:0.276086+0.006135    test-rmse:0.430514+0.059777 
## [19] train-rmse:0.269102+0.005848    test-rmse:0.429242+0.061312 
## [20] train-rmse:0.264598+0.005709    test-rmse:0.428767+0.061830 
## [21] train-rmse:0.259471+0.005221    test-rmse:0.427301+0.063756 
## [22] train-rmse:0.254773+0.005091    test-rmse:0.426818+0.063404 
## [23] train-rmse:0.248635+0.004640    test-rmse:0.423139+0.065629 
## [24] train-rmse:0.245040+0.004455    test-rmse:0.423028+0.065575 
## [25] train-rmse:0.242213+0.004252    test-rmse:0.421521+0.065765 
## [26] train-rmse:0.238679+0.004487    test-rmse:0.419919+0.066331 
## [27] train-rmse:0.234332+0.003725    test-rmse:0.418542+0.067033 
## [28] train-rmse:0.230904+0.002863    test-rmse:0.418177+0.069021 
## [29] train-rmse:0.227064+0.003822    test-rmse:0.416422+0.071245 
## [30] train-rmse:0.223367+0.003273    test-rmse:0.416206+0.070846 
## [31] train-rmse:0.220687+0.002968    test-rmse:0.416101+0.070123 
## [32] train-rmse:0.217845+0.003141    test-rmse:0.415491+0.070523 
## [33] train-rmse:0.215004+0.002890    test-rmse:0.414646+0.070658 
## [34] train-rmse:0.211129+0.003237    test-rmse:0.415386+0.070424 
## [35] train-rmse:0.207513+0.004386    test-rmse:0.412094+0.068973 
## [36] train-rmse:0.203524+0.004470    test-rmse:0.412077+0.070604 
## [37] train-rmse:0.201474+0.004446    test-rmse:0.412172+0.070109 
## [38] train-rmse:0.198179+0.004769    test-rmse:0.412126+0.068956 
## [39] train-rmse:0.195766+0.004456    test-rmse:0.411300+0.069057 
## [40] train-rmse:0.193929+0.004529    test-rmse:0.412010+0.068591 
## [41] train-rmse:0.191484+0.004369    test-rmse:0.412445+0.068790 
## [42] train-rmse:0.189121+0.002999    test-rmse:0.412460+0.067796 
## [43] train-rmse:0.187154+0.002837    test-rmse:0.412799+0.067805 
## [44] train-rmse:0.185038+0.002801    test-rmse:0.412087+0.067240 
## [45] train-rmse:0.182832+0.002645    test-rmse:0.412032+0.066324 
## Stopping. Best iteration:
## [39] train-rmse:0.195766+0.004456    test-rmse:0.411300+0.069057
## 
## 80 
## [1]  train-rmse:1.174950+0.021028    test-rmse:1.180353+0.118694 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.865770+0.014877    test-rmse:0.876716+0.117022 
## [3]  train-rmse:0.657485+0.013988    test-rmse:0.675423+0.099529 
## [4]  train-rmse:0.522374+0.009912    test-rmse:0.562238+0.096608 
## [5]  train-rmse:0.436012+0.009300    test-rmse:0.493311+0.082543 
## [6]  train-rmse:0.377091+0.009595    test-rmse:0.452279+0.082309 
## [7]  train-rmse:0.337667+0.007565    test-rmse:0.430619+0.078165 
## [8]  train-rmse:0.313409+0.006597    test-rmse:0.423469+0.072805 
## [9]  train-rmse:0.296211+0.006248    test-rmse:0.418206+0.071124 
## [10] train-rmse:0.283799+0.005149    test-rmse:0.415498+0.070289 
## [11] train-rmse:0.273521+0.005226    test-rmse:0.411580+0.070331 
## [12] train-rmse:0.263378+0.005914    test-rmse:0.412116+0.070573 
## [13] train-rmse:0.253523+0.005772    test-rmse:0.409949+0.074081 
## [14] train-rmse:0.245942+0.003682    test-rmse:0.409622+0.072467 
## [15] train-rmse:0.237256+0.003042    test-rmse:0.410259+0.073804 
## [16] train-rmse:0.229381+0.003315    test-rmse:0.409293+0.071210 
## [17] train-rmse:0.222866+0.002530    test-rmse:0.407727+0.072200 
## [18] train-rmse:0.216460+0.004988    test-rmse:0.406674+0.072280 
## [19] train-rmse:0.211329+0.005242    test-rmse:0.403260+0.071856 
## [20] train-rmse:0.207244+0.004821    test-rmse:0.402125+0.072243 
## [21] train-rmse:0.202667+0.005290    test-rmse:0.402293+0.071470 
## [22] train-rmse:0.199666+0.004984    test-rmse:0.402425+0.071974 
## [23] train-rmse:0.193877+0.004339    test-rmse:0.398693+0.073381 
## [24] train-rmse:0.187793+0.003594    test-rmse:0.397096+0.072476 
## [25] train-rmse:0.183222+0.003154    test-rmse:0.397575+0.070837 
## [26] train-rmse:0.179964+0.003628    test-rmse:0.397692+0.070145 
## [27] train-rmse:0.175730+0.002740    test-rmse:0.398513+0.071431 
## [28] train-rmse:0.171051+0.002544    test-rmse:0.397015+0.070009 
## [29] train-rmse:0.167814+0.003123    test-rmse:0.397270+0.071062 
## [30] train-rmse:0.163862+0.003540    test-rmse:0.398007+0.070686 
## [31] train-rmse:0.161054+0.004124    test-rmse:0.397307+0.071466 
## [32] train-rmse:0.157632+0.003901    test-rmse:0.395716+0.072582 
## [33] train-rmse:0.153493+0.005608    test-rmse:0.396014+0.072362 
## [34] train-rmse:0.151324+0.006223    test-rmse:0.395065+0.072776 
## [35] train-rmse:0.148938+0.006320    test-rmse:0.394355+0.072792 
## [36] train-rmse:0.146684+0.006437    test-rmse:0.394880+0.072385 
## [37] train-rmse:0.144056+0.006301    test-rmse:0.394374+0.072778 
## [38] train-rmse:0.140789+0.005671    test-rmse:0.392842+0.072994 
## [39] train-rmse:0.137769+0.006471    test-rmse:0.392767+0.072913 
## [40] train-rmse:0.135173+0.006952    test-rmse:0.392084+0.072221 
## [41] train-rmse:0.132134+0.007197    test-rmse:0.391317+0.073141 
## [42] train-rmse:0.129675+0.007237    test-rmse:0.391223+0.072368 
## [43] train-rmse:0.127320+0.006456    test-rmse:0.390770+0.072009 
## [44] train-rmse:0.125172+0.006309    test-rmse:0.389998+0.071730 
## [45] train-rmse:0.122268+0.005581    test-rmse:0.390238+0.072007 
## [46] train-rmse:0.120214+0.005118    test-rmse:0.390493+0.072188 
## [47] train-rmse:0.118289+0.005402    test-rmse:0.390416+0.072484 
## [48] train-rmse:0.115756+0.006245    test-rmse:0.389511+0.072002 
## [49] train-rmse:0.114283+0.005945    test-rmse:0.389502+0.072001 
## [50] train-rmse:0.112145+0.006198    test-rmse:0.389561+0.072049 
## [51] train-rmse:0.110214+0.005699    test-rmse:0.389739+0.072725 
## [52] train-rmse:0.108491+0.005434    test-rmse:0.389914+0.071983 
## [53] train-rmse:0.106518+0.005905    test-rmse:0.389986+0.072412 
## [54] train-rmse:0.104851+0.005968    test-rmse:0.389496+0.071492 
## [55] train-rmse:0.102847+0.005886    test-rmse:0.389349+0.071583 
## [56] train-rmse:0.101417+0.005994    test-rmse:0.389659+0.071527 
## [57] train-rmse:0.099827+0.005661    test-rmse:0.389591+0.071479 
## [58] train-rmse:0.097921+0.005524    test-rmse:0.389556+0.071866 
## [59] train-rmse:0.096505+0.005362    test-rmse:0.389805+0.072442 
## [60] train-rmse:0.094312+0.005013    test-rmse:0.389916+0.071934 
## [61] train-rmse:0.092670+0.004638    test-rmse:0.390037+0.071758 
## Stopping. Best iteration:
## [55] train-rmse:0.102847+0.005886    test-rmse:0.389349+0.071583
## 
## 81 
## [1]  train-rmse:1.171793+0.021524    test-rmse:1.177553+0.113668 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.858079+0.015238    test-rmse:0.872004+0.105915 
## [3]  train-rmse:0.645670+0.013156    test-rmse:0.669238+0.096044 
## [4]  train-rmse:0.503122+0.008359    test-rmse:0.554238+0.086606 
## [5]  train-rmse:0.408060+0.006218    test-rmse:0.484078+0.079919 
## [6]  train-rmse:0.346613+0.007223    test-rmse:0.448330+0.072310 
## [7]  train-rmse:0.302345+0.004770    test-rmse:0.428328+0.074052 
## [8]  train-rmse:0.273912+0.006154    test-rmse:0.423796+0.071504 
## [9]  train-rmse:0.253467+0.005427    test-rmse:0.415501+0.068393 
## [10] train-rmse:0.240136+0.005799    test-rmse:0.411930+0.064355 
## [11] train-rmse:0.227101+0.007307    test-rmse:0.410483+0.064401 
## [12] train-rmse:0.217444+0.006801    test-rmse:0.402598+0.059336 
## [13] train-rmse:0.207308+0.005103    test-rmse:0.402785+0.059069 
## [14] train-rmse:0.197492+0.005275    test-rmse:0.400872+0.058820 
## [15] train-rmse:0.190604+0.004855    test-rmse:0.400262+0.057449 
## [16] train-rmse:0.182736+0.006048    test-rmse:0.397917+0.058144 
## [17] train-rmse:0.174884+0.005564    test-rmse:0.398213+0.059822 
## [18] train-rmse:0.169583+0.006990    test-rmse:0.398404+0.058792 
## [19] train-rmse:0.164465+0.006806    test-rmse:0.397352+0.057662 
## [20] train-rmse:0.158470+0.007149    test-rmse:0.398342+0.058229 
## [21] train-rmse:0.153857+0.007095    test-rmse:0.397146+0.057486 
## [22] train-rmse:0.149995+0.007220    test-rmse:0.397746+0.056272 
## [23] train-rmse:0.143297+0.008257    test-rmse:0.397703+0.055543 
## [24] train-rmse:0.139880+0.008665    test-rmse:0.398078+0.055823 
## [25] train-rmse:0.136146+0.008880    test-rmse:0.398050+0.056288 
## [26] train-rmse:0.131252+0.008615    test-rmse:0.398497+0.055476 
## [27] train-rmse:0.126065+0.010298    test-rmse:0.398721+0.054553 
## Stopping. Best iteration:
## [21] train-rmse:0.153857+0.007095    test-rmse:0.397146+0.057486
## 
## 82 
## [1]  train-rmse:1.170764+0.021580    test-rmse:1.175529+0.111837 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.854586+0.015283    test-rmse:0.868034+0.104741 
## [3]  train-rmse:0.638807+0.013748    test-rmse:0.668059+0.092240 
## [4]  train-rmse:0.489387+0.010319    test-rmse:0.543656+0.089578 
## [5]  train-rmse:0.388966+0.008062    test-rmse:0.469824+0.081810 
## [6]  train-rmse:0.319578+0.008136    test-rmse:0.435181+0.073548 
## [7]  train-rmse:0.273457+0.008673    test-rmse:0.415852+0.067212 
## [8]  train-rmse:0.240546+0.007314    test-rmse:0.407086+0.060226 
## [9]  train-rmse:0.216664+0.008908    test-rmse:0.403454+0.057919 
## [10] train-rmse:0.198752+0.009023    test-rmse:0.402789+0.056557 
## [11] train-rmse:0.182800+0.006996    test-rmse:0.398754+0.056964 
## [12] train-rmse:0.172396+0.006572    test-rmse:0.398073+0.054474 
## [13] train-rmse:0.163247+0.006967    test-rmse:0.398208+0.054604 
## [14] train-rmse:0.153602+0.003298    test-rmse:0.397096+0.054346 
## [15] train-rmse:0.144373+0.002576    test-rmse:0.397013+0.050069 
## [16] train-rmse:0.137171+0.002340    test-rmse:0.396980+0.049667 
## [17] train-rmse:0.128844+0.003310    test-rmse:0.397544+0.048232 
## [18] train-rmse:0.122734+0.001653    test-rmse:0.396714+0.048600 
## [19] train-rmse:0.116516+0.002934    test-rmse:0.398168+0.048868 
## [20] train-rmse:0.111523+0.003257    test-rmse:0.397404+0.048420 
## [21] train-rmse:0.107039+0.003535    test-rmse:0.396831+0.049085 
## [22] train-rmse:0.101441+0.003968    test-rmse:0.394851+0.050579 
## [23] train-rmse:0.097018+0.004017    test-rmse:0.394395+0.050588 
## [24] train-rmse:0.092495+0.004483    test-rmse:0.393753+0.049762 
## [25] train-rmse:0.089228+0.004857    test-rmse:0.392618+0.050680 
## [26] train-rmse:0.084995+0.004864    test-rmse:0.391983+0.050496 
## [27] train-rmse:0.080179+0.005418    test-rmse:0.392138+0.051134 
## [28] train-rmse:0.077172+0.004855    test-rmse:0.392914+0.050661 
## [29] train-rmse:0.074472+0.004816    test-rmse:0.392696+0.050680 
## [30] train-rmse:0.070901+0.004579    test-rmse:0.393279+0.050792 
## [31] train-rmse:0.067513+0.004312    test-rmse:0.393523+0.050490 
## [32] train-rmse:0.064796+0.004363    test-rmse:0.393480+0.050606 
## Stopping. Best iteration:
## [26] train-rmse:0.084995+0.004864    test-rmse:0.391983+0.050496
## 
## 83 
## [1]  train-rmse:1.170278+0.021579    test-rmse:1.177108+0.111386 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.852531+0.015798    test-rmse:0.866838+0.103685 
## [3]  train-rmse:0.633664+0.013618    test-rmse:0.661978+0.092300 
## [4]  train-rmse:0.482781+0.012448    test-rmse:0.544638+0.090343 
## [5]  train-rmse:0.379943+0.011630    test-rmse:0.472929+0.084553 
## [6]  train-rmse:0.307612+0.010998    test-rmse:0.440034+0.076443 
## [7]  train-rmse:0.259585+0.010474    test-rmse:0.427690+0.073424 
## [8]  train-rmse:0.225221+0.011379    test-rmse:0.418507+0.068620 
## [9]  train-rmse:0.194422+0.008599    test-rmse:0.414616+0.064140 
## [10] train-rmse:0.171548+0.006697    test-rmse:0.414770+0.066153 
## [11] train-rmse:0.155746+0.008897    test-rmse:0.415126+0.062233 
## [12] train-rmse:0.144318+0.008526    test-rmse:0.412679+0.061602 
## [13] train-rmse:0.133225+0.010756    test-rmse:0.411707+0.060380 
## [14] train-rmse:0.125258+0.009482    test-rmse:0.412055+0.056530 
## [15] train-rmse:0.117916+0.009200    test-rmse:0.411411+0.057100 
## [16] train-rmse:0.109492+0.006938    test-rmse:0.410470+0.053261 
## [17] train-rmse:0.101090+0.005808    test-rmse:0.409754+0.053231 
## [18] train-rmse:0.094600+0.006242    test-rmse:0.410686+0.052488 
## [19] train-rmse:0.088537+0.006578    test-rmse:0.410704+0.050999 
## [20] train-rmse:0.081344+0.004813    test-rmse:0.410089+0.051616 
## [21] train-rmse:0.076675+0.005307    test-rmse:0.409670+0.052365 
## [22] train-rmse:0.072980+0.005600    test-rmse:0.409338+0.052907 
## [23] train-rmse:0.069640+0.005449    test-rmse:0.410293+0.053535 
## [24] train-rmse:0.065591+0.005488    test-rmse:0.409539+0.052893 
## [25] train-rmse:0.062875+0.006184    test-rmse:0.408880+0.052224 
## [26] train-rmse:0.058897+0.005848    test-rmse:0.409173+0.052502 
## [27] train-rmse:0.055955+0.005201    test-rmse:0.408706+0.051899 
## [28] train-rmse:0.052730+0.004265    test-rmse:0.407833+0.051148 
## [29] train-rmse:0.049694+0.004497    test-rmse:0.407825+0.050946 
## [30] train-rmse:0.046251+0.003633    test-rmse:0.407279+0.051233 
## [31] train-rmse:0.044174+0.003541    test-rmse:0.407006+0.050960 
## [32] train-rmse:0.042744+0.003154    test-rmse:0.406548+0.051367 
## [33] train-rmse:0.040935+0.003148    test-rmse:0.406669+0.051029 
## [34] train-rmse:0.038575+0.003871    test-rmse:0.406635+0.050937 
## [35] train-rmse:0.037139+0.004295    test-rmse:0.406707+0.051518 
## [36] train-rmse:0.035912+0.004269    test-rmse:0.406704+0.051417 
## [37] train-rmse:0.034600+0.004366    test-rmse:0.406473+0.051305 
## [38] train-rmse:0.032844+0.004080    test-rmse:0.406194+0.051165 
## [39] train-rmse:0.030936+0.003529    test-rmse:0.406098+0.051601 
## [40] train-rmse:0.029404+0.003906    test-rmse:0.406041+0.051252 
## [41] train-rmse:0.027777+0.003549    test-rmse:0.406008+0.051437 
## [42] train-rmse:0.026652+0.003850    test-rmse:0.405895+0.051365 
## [43] train-rmse:0.025525+0.003876    test-rmse:0.405813+0.051409 
## [44] train-rmse:0.024824+0.003924    test-rmse:0.405690+0.051399 
## [45] train-rmse:0.023842+0.003680    test-rmse:0.405769+0.051540 
## [46] train-rmse:0.022156+0.003505    test-rmse:0.405632+0.051278 
## [47] train-rmse:0.021308+0.003602    test-rmse:0.405650+0.050875 
## [48] train-rmse:0.020241+0.003432    test-rmse:0.405624+0.050833 
## [49] train-rmse:0.019408+0.003302    test-rmse:0.405580+0.050758 
## [50] train-rmse:0.018564+0.002858    test-rmse:0.405665+0.050583 
## [51] train-rmse:0.017848+0.002790    test-rmse:0.405675+0.050738 
## [52] train-rmse:0.017018+0.002606    test-rmse:0.405806+0.050775 
## [53] train-rmse:0.016423+0.002493    test-rmse:0.405792+0.051036 
## [54] train-rmse:0.015658+0.002333    test-rmse:0.405750+0.051156 
## [55] train-rmse:0.015143+0.002159    test-rmse:0.405799+0.051210 
## Stopping. Best iteration:
## [49] train-rmse:0.019408+0.003302    test-rmse:0.405580+0.050758
## 
## 84 
## [1]  train-rmse:1.189006+0.022533    test-rmse:1.185219+0.106881 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.925403+0.018999    test-rmse:0.921878+0.099079 
## [3]  train-rmse:0.781810+0.018015    test-rmse:0.778727+0.092973 
## [4]  train-rmse:0.709510+0.017993    test-rmse:0.706889+0.088717 
## [5]  train-rmse:0.672778+0.016967    test-rmse:0.677757+0.088485 
## [6]  train-rmse:0.644437+0.016761    test-rmse:0.643387+0.085433 
## [7]  train-rmse:0.625770+0.013895    test-rmse:0.628164+0.081377 
## [8]  train-rmse:0.609760+0.013257    test-rmse:0.613994+0.082100 
## [9]  train-rmse:0.597319+0.013157    test-rmse:0.602789+0.072809 
## [10] train-rmse:0.586794+0.012457    test-rmse:0.593613+0.068735 
## [11] train-rmse:0.577946+0.011808    test-rmse:0.585512+0.068501 
## [12] train-rmse:0.570132+0.011527    test-rmse:0.576527+0.066379 
## [13] train-rmse:0.562934+0.011598    test-rmse:0.574379+0.065275 
## [14] train-rmse:0.557233+0.011197    test-rmse:0.569769+0.061132 
## [15] train-rmse:0.551975+0.010651    test-rmse:0.562141+0.059850 
## [16] train-rmse:0.547252+0.010462    test-rmse:0.559952+0.061772 
## [17] train-rmse:0.543089+0.010502    test-rmse:0.562203+0.061159 
## [18] train-rmse:0.539338+0.010445    test-rmse:0.558769+0.059387 
## [19] train-rmse:0.535793+0.010230    test-rmse:0.559110+0.057667 
## [20] train-rmse:0.532513+0.010201    test-rmse:0.557775+0.058649 
## [21] train-rmse:0.529283+0.010195    test-rmse:0.552572+0.056582 
## [22] train-rmse:0.526291+0.009920    test-rmse:0.549004+0.055808 
## [23] train-rmse:0.523203+0.009626    test-rmse:0.546523+0.055392 
## [24] train-rmse:0.520339+0.009420    test-rmse:0.543441+0.057588 
## [25] train-rmse:0.517798+0.009080    test-rmse:0.542522+0.057932 
## [26] train-rmse:0.515394+0.008997    test-rmse:0.541376+0.056378 
## [27] train-rmse:0.513139+0.008869    test-rmse:0.541749+0.057447 
## [28] train-rmse:0.510970+0.008837    test-rmse:0.540068+0.056156 
## [29] train-rmse:0.508740+0.008725    test-rmse:0.538594+0.053544 
## [30] train-rmse:0.506536+0.008767    test-rmse:0.535367+0.054047 
## [31] train-rmse:0.504444+0.008554    test-rmse:0.534685+0.054065 
## [32] train-rmse:0.502552+0.008528    test-rmse:0.534770+0.054475 
## [33] train-rmse:0.500484+0.008426    test-rmse:0.533272+0.055943 
## [34] train-rmse:0.498636+0.008401    test-rmse:0.531283+0.053749 
## [35] train-rmse:0.496931+0.008457    test-rmse:0.529597+0.053227 
## [36] train-rmse:0.495124+0.008453    test-rmse:0.526436+0.053384 
## [37] train-rmse:0.493421+0.008379    test-rmse:0.525837+0.054045 
## [38] train-rmse:0.491857+0.008289    test-rmse:0.525157+0.054848 
## [39] train-rmse:0.490314+0.008240    test-rmse:0.525481+0.053073 
## [40] train-rmse:0.488773+0.008272    test-rmse:0.525100+0.052406 
## [41] train-rmse:0.487235+0.008229    test-rmse:0.524097+0.052775 
## [42] train-rmse:0.485757+0.008127    test-rmse:0.523124+0.052256 
## [43] train-rmse:0.484304+0.008047    test-rmse:0.521881+0.051059 
## [44] train-rmse:0.482986+0.008058    test-rmse:0.521027+0.049627 
## [45] train-rmse:0.481597+0.008054    test-rmse:0.519714+0.048258 
## [46] train-rmse:0.480336+0.008036    test-rmse:0.519714+0.048864 
## [47] train-rmse:0.479028+0.007896    test-rmse:0.518694+0.050382 
## [48] train-rmse:0.477846+0.007897    test-rmse:0.518497+0.050256 
## [49] train-rmse:0.476702+0.007868    test-rmse:0.517431+0.051553 
## [50] train-rmse:0.475616+0.007882    test-rmse:0.516278+0.050557 
## [51] train-rmse:0.474572+0.007921    test-rmse:0.514898+0.049437 
## [52] train-rmse:0.473530+0.007958    test-rmse:0.514677+0.049062 
## [53] train-rmse:0.472477+0.007899    test-rmse:0.513766+0.048529 
## [54] train-rmse:0.471472+0.007860    test-rmse:0.514038+0.048911 
## [55] train-rmse:0.470466+0.007798    test-rmse:0.514513+0.049052 
## [56] train-rmse:0.469414+0.007746    test-rmse:0.511425+0.046501 
## [57] train-rmse:0.468435+0.007747    test-rmse:0.510702+0.047248 
## [58] train-rmse:0.467511+0.007757    test-rmse:0.508725+0.045823 
## [59] train-rmse:0.466606+0.007767    test-rmse:0.508045+0.046000 
## [60] train-rmse:0.465683+0.007747    test-rmse:0.507878+0.045429 
## [61] train-rmse:0.464794+0.007737    test-rmse:0.507250+0.045382 
## [62] train-rmse:0.463820+0.007713    test-rmse:0.506615+0.044776 
## [63] train-rmse:0.462990+0.007732    test-rmse:0.506159+0.045390 
## [64] train-rmse:0.462149+0.007694    test-rmse:0.506176+0.046139 
## [65] train-rmse:0.461357+0.007673    test-rmse:0.504383+0.046090 
## [66] train-rmse:0.460562+0.007641    test-rmse:0.505756+0.046035 
## [67] train-rmse:0.459815+0.007611    test-rmse:0.505944+0.044819 
## [68] train-rmse:0.459015+0.007574    test-rmse:0.505599+0.044601 
## [69] train-rmse:0.458273+0.007556    test-rmse:0.506148+0.044656 
## [70] train-rmse:0.457564+0.007527    test-rmse:0.505165+0.045290 
## [71] train-rmse:0.456863+0.007521    test-rmse:0.505701+0.044891 
## Stopping. Best iteration:
## [65] train-rmse:0.461357+0.007673    test-rmse:0.504383+0.046090
## 
## 85 
## [1]  train-rmse:1.165436+0.022741    test-rmse:1.167013+0.113843 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.867899+0.016927    test-rmse:0.866738+0.100504 
## [3]  train-rmse:0.688368+0.015071    test-rmse:0.693104+0.102464 
## [4]  train-rmse:0.585486+0.013657    test-rmse:0.598380+0.097938 
## [5]  train-rmse:0.525170+0.012217    test-rmse:0.540923+0.090036 
## [6]  train-rmse:0.488813+0.012168    test-rmse:0.513840+0.077962 
## [7]  train-rmse:0.462193+0.013769    test-rmse:0.490644+0.074406 
## [8]  train-rmse:0.441760+0.010054    test-rmse:0.471395+0.069256 
## [9]  train-rmse:0.427944+0.009394    test-rmse:0.466407+0.065107 
## [10] train-rmse:0.416079+0.009095    test-rmse:0.462713+0.061309 
## [11] train-rmse:0.407579+0.008353    test-rmse:0.460308+0.058619 
## [12] train-rmse:0.399519+0.007554    test-rmse:0.458106+0.057443 
## [13] train-rmse:0.390937+0.007119    test-rmse:0.453215+0.059347 
## [14] train-rmse:0.384370+0.007056    test-rmse:0.452473+0.058941 
## [15] train-rmse:0.378316+0.005651    test-rmse:0.447994+0.058453 
## [16] train-rmse:0.372606+0.005508    test-rmse:0.447556+0.053726 
## [17] train-rmse:0.367423+0.006247    test-rmse:0.446214+0.055290 
## [18] train-rmse:0.361396+0.006217    test-rmse:0.442162+0.053652 
## [19] train-rmse:0.355950+0.006423    test-rmse:0.442627+0.053637 
## [20] train-rmse:0.350719+0.006935    test-rmse:0.441555+0.049261 
## [21] train-rmse:0.346202+0.007292    test-rmse:0.438184+0.049170 
## [22] train-rmse:0.341409+0.007744    test-rmse:0.435622+0.050668 
## [23] train-rmse:0.336646+0.008329    test-rmse:0.435947+0.050447 
## [24] train-rmse:0.332643+0.007900    test-rmse:0.435427+0.051988 
## [25] train-rmse:0.330081+0.007969    test-rmse:0.434445+0.052171 
## [26] train-rmse:0.327260+0.008425    test-rmse:0.435147+0.052320 
## [27] train-rmse:0.324687+0.008139    test-rmse:0.433186+0.055000 
## [28] train-rmse:0.321980+0.008571    test-rmse:0.432544+0.055253 
## [29] train-rmse:0.318491+0.008749    test-rmse:0.432147+0.056431 
## [30] train-rmse:0.314770+0.009508    test-rmse:0.431952+0.054478 
## [31] train-rmse:0.312388+0.009347    test-rmse:0.431699+0.053157 
## [32] train-rmse:0.309569+0.008737    test-rmse:0.430127+0.054613 
## [33] train-rmse:0.306339+0.006905    test-rmse:0.430191+0.054691 
## [34] train-rmse:0.303992+0.006844    test-rmse:0.427397+0.052387 
## [35] train-rmse:0.301919+0.007170    test-rmse:0.427688+0.052246 
## [36] train-rmse:0.298133+0.008294    test-rmse:0.427626+0.053740 
## [37] train-rmse:0.296135+0.007660    test-rmse:0.425902+0.055436 
## [38] train-rmse:0.293994+0.007265    test-rmse:0.426001+0.053849 
## [39] train-rmse:0.291967+0.007526    test-rmse:0.424064+0.054155 
## [40] train-rmse:0.290043+0.007629    test-rmse:0.424209+0.054471 
## [41] train-rmse:0.286734+0.007155    test-rmse:0.423518+0.055102 
## [42] train-rmse:0.284949+0.007070    test-rmse:0.422073+0.056831 
## [43] train-rmse:0.282932+0.007491    test-rmse:0.421314+0.056952 
## [44] train-rmse:0.280433+0.006875    test-rmse:0.420617+0.056802 
## [45] train-rmse:0.279046+0.006610    test-rmse:0.421227+0.055181 
## [46] train-rmse:0.277431+0.006493    test-rmse:0.420870+0.055726 
## [47] train-rmse:0.275222+0.005881    test-rmse:0.419567+0.055417 
## [48] train-rmse:0.273804+0.005899    test-rmse:0.418658+0.054490 
## [49] train-rmse:0.271988+0.005823    test-rmse:0.418356+0.054630 
## [50] train-rmse:0.270599+0.005772    test-rmse:0.418023+0.054072 
## [51] train-rmse:0.268368+0.005411    test-rmse:0.416734+0.054992 
## [52] train-rmse:0.266472+0.005015    test-rmse:0.417843+0.054885 
## [53] train-rmse:0.264579+0.005522    test-rmse:0.416871+0.055771 
## [54] train-rmse:0.262589+0.005723    test-rmse:0.416774+0.056904 
## [55] train-rmse:0.260759+0.005500    test-rmse:0.416194+0.056875 
## [56] train-rmse:0.259187+0.005228    test-rmse:0.416109+0.057845 
## [57] train-rmse:0.257561+0.005399    test-rmse:0.416205+0.058248 
## [58] train-rmse:0.256089+0.006242    test-rmse:0.416435+0.059191 
## [59] train-rmse:0.255043+0.005990    test-rmse:0.416850+0.058604 
## [60] train-rmse:0.253963+0.006185    test-rmse:0.417224+0.058139 
## [61] train-rmse:0.252417+0.006159    test-rmse:0.417637+0.058552 
## [62] train-rmse:0.251311+0.006209    test-rmse:0.417871+0.058176 
## Stopping. Best iteration:
## [56] train-rmse:0.259187+0.005228    test-rmse:0.416109+0.057845
## 
## 86 
## [1]  train-rmse:1.152674+0.021464    test-rmse:1.159197+0.117803 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.840502+0.018259    test-rmse:0.842779+0.102820 
## [3]  train-rmse:0.641521+0.013917    test-rmse:0.661618+0.098683 
## [4]  train-rmse:0.520688+0.011383    test-rmse:0.557184+0.083899 
## [5]  train-rmse:0.448515+0.010066    test-rmse:0.494629+0.085020 
## [6]  train-rmse:0.403815+0.008819    test-rmse:0.466398+0.081151 
## [7]  train-rmse:0.374624+0.007733    test-rmse:0.449416+0.073048 
## [8]  train-rmse:0.354977+0.007213    test-rmse:0.438601+0.064910 
## [9]  train-rmse:0.339765+0.008046    test-rmse:0.434341+0.060783 
## [10] train-rmse:0.328070+0.008196    test-rmse:0.431534+0.059785 
## [11] train-rmse:0.316989+0.007375    test-rmse:0.429401+0.057373 
## [12] train-rmse:0.307730+0.007308    test-rmse:0.430546+0.057944 
## [13] train-rmse:0.300516+0.005286    test-rmse:0.427668+0.059013 
## [14] train-rmse:0.293937+0.005288    test-rmse:0.426728+0.059346 
## [15] train-rmse:0.286753+0.005865    test-rmse:0.428854+0.059152 
## [16] train-rmse:0.280192+0.005904    test-rmse:0.429294+0.058304 
## [17] train-rmse:0.275125+0.005861    test-rmse:0.431264+0.057796 
## [18] train-rmse:0.270802+0.005802    test-rmse:0.431021+0.057320 
## [19] train-rmse:0.265327+0.004770    test-rmse:0.427457+0.059705 
## [20] train-rmse:0.261510+0.004877    test-rmse:0.428133+0.060483 
## Stopping. Best iteration:
## [14] train-rmse:0.293937+0.005288    test-rmse:0.426728+0.059346
## 
## 87 
## [1]  train-rmse:1.146736+0.020489    test-rmse:1.152830+0.118885 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.827991+0.013938    test-rmse:0.840792+0.114570 
## [3]  train-rmse:0.620212+0.013304    test-rmse:0.641182+0.104460 
## [4]  train-rmse:0.489496+0.010528    test-rmse:0.532169+0.093901 
## [5]  train-rmse:0.408787+0.008957    test-rmse:0.475242+0.087668 
## [6]  train-rmse:0.356448+0.010057    test-rmse:0.445216+0.076930 
## [7]  train-rmse:0.325945+0.008339    test-rmse:0.432115+0.075046 
## [8]  train-rmse:0.303812+0.011055    test-rmse:0.423580+0.065436 
## [9]  train-rmse:0.287401+0.007950    test-rmse:0.417341+0.070020 
## [10] train-rmse:0.275704+0.009435    test-rmse:0.413369+0.068219 
## [11] train-rmse:0.265140+0.007456    test-rmse:0.411198+0.066814 
## [12] train-rmse:0.255338+0.006343    test-rmse:0.406204+0.068553 
## [13] train-rmse:0.246290+0.006202    test-rmse:0.406044+0.068376 
## [14] train-rmse:0.239864+0.006073    test-rmse:0.404395+0.067467 
## [15] train-rmse:0.231258+0.005867    test-rmse:0.402418+0.067260 
## [16] train-rmse:0.224498+0.005938    test-rmse:0.397565+0.066317 
## [17] train-rmse:0.216534+0.005123    test-rmse:0.395176+0.067143 
## [18] train-rmse:0.210371+0.005315    test-rmse:0.394463+0.066606 
## [19] train-rmse:0.205612+0.005417    test-rmse:0.395786+0.064655 
## [20] train-rmse:0.198443+0.005130    test-rmse:0.394772+0.061831 
## [21] train-rmse:0.193041+0.004103    test-rmse:0.393789+0.062661 
## [22] train-rmse:0.189127+0.004260    test-rmse:0.393750+0.062877 
## [23] train-rmse:0.184097+0.004542    test-rmse:0.392773+0.062444 
## [24] train-rmse:0.180502+0.003904    test-rmse:0.391656+0.063827 
## [25] train-rmse:0.174874+0.003296    test-rmse:0.393021+0.064329 
## [26] train-rmse:0.171366+0.002916    test-rmse:0.389529+0.065455 
## [27] train-rmse:0.166046+0.003523    test-rmse:0.387910+0.063199 
## [28] train-rmse:0.162973+0.003465    test-rmse:0.387910+0.063563 
## [29] train-rmse:0.159596+0.004280    test-rmse:0.387823+0.064062 
## [30] train-rmse:0.155866+0.003655    test-rmse:0.388123+0.064661 
## [31] train-rmse:0.150219+0.002526    test-rmse:0.387449+0.063256 
## [32] train-rmse:0.147929+0.002331    test-rmse:0.388200+0.062423 
## [33] train-rmse:0.145016+0.003028    test-rmse:0.386733+0.062508 
## [34] train-rmse:0.141974+0.003281    test-rmse:0.387127+0.062542 
## [35] train-rmse:0.139394+0.003644    test-rmse:0.386537+0.061551 
## [36] train-rmse:0.136544+0.003331    test-rmse:0.386469+0.060978 
## [37] train-rmse:0.132488+0.003888    test-rmse:0.386888+0.060756 
## [38] train-rmse:0.129171+0.004035    test-rmse:0.386104+0.062740 
## [39] train-rmse:0.126610+0.004894    test-rmse:0.386287+0.061902 
## [40] train-rmse:0.124356+0.005436    test-rmse:0.385828+0.061974 
## [41] train-rmse:0.121600+0.005083    test-rmse:0.384939+0.062167 
## [42] train-rmse:0.119703+0.005317    test-rmse:0.386292+0.062586 
## [43] train-rmse:0.117400+0.005735    test-rmse:0.385589+0.062896 
## [44] train-rmse:0.115368+0.005619    test-rmse:0.386184+0.062715 
## [45] train-rmse:0.113032+0.005732    test-rmse:0.385779+0.062367 
## [46] train-rmse:0.110781+0.005970    test-rmse:0.385989+0.062230 
## [47] train-rmse:0.108822+0.006409    test-rmse:0.385674+0.061441 
## Stopping. Best iteration:
## [41] train-rmse:0.121600+0.005083    test-rmse:0.384939+0.062167
## 
## 88 
## [1]  train-rmse:1.143318+0.021018    test-rmse:1.149886+0.113484 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.820222+0.014525    test-rmse:0.834828+0.105377 
## [3]  train-rmse:0.608786+0.012568    test-rmse:0.637830+0.095555 
## [4]  train-rmse:0.469679+0.006836    test-rmse:0.530343+0.089179 
## [5]  train-rmse:0.381391+0.006280    test-rmse:0.472264+0.082840 
## [6]  train-rmse:0.323318+0.007667    test-rmse:0.440573+0.076373 
## [7]  train-rmse:0.284622+0.008514    test-rmse:0.426456+0.070749 
## [8]  train-rmse:0.260477+0.010102    test-rmse:0.420110+0.069997 
## [9]  train-rmse:0.245091+0.010343    test-rmse:0.417963+0.066676 
## [10] train-rmse:0.232345+0.010448    test-rmse:0.413673+0.068882 
## [11] train-rmse:0.216841+0.005258    test-rmse:0.410460+0.072623 
## [12] train-rmse:0.206086+0.006439    test-rmse:0.410135+0.069973 
## [13] train-rmse:0.194431+0.005636    test-rmse:0.412018+0.070202 
## [14] train-rmse:0.186163+0.004431    test-rmse:0.411815+0.069325 
## [15] train-rmse:0.178914+0.004358    test-rmse:0.409879+0.070922 
## [16] train-rmse:0.173221+0.004494    test-rmse:0.409176+0.071860 
## [17] train-rmse:0.167549+0.004852    test-rmse:0.409494+0.072780 
## [18] train-rmse:0.162483+0.004964    test-rmse:0.409056+0.072793 
## [19] train-rmse:0.158239+0.004306    test-rmse:0.408018+0.070797 
## [20] train-rmse:0.153245+0.004757    test-rmse:0.406523+0.070830 
## [21] train-rmse:0.146815+0.006072    test-rmse:0.405876+0.072581 
## [22] train-rmse:0.141344+0.007153    test-rmse:0.405872+0.073840 
## [23] train-rmse:0.135540+0.006270    test-rmse:0.406625+0.072879 
## [24] train-rmse:0.129351+0.008267    test-rmse:0.406552+0.071098 
## [25] train-rmse:0.125738+0.008616    test-rmse:0.406589+0.071641 
## [26] train-rmse:0.122347+0.008911    test-rmse:0.407118+0.071230 
## [27] train-rmse:0.118217+0.009624    test-rmse:0.406822+0.069950 
## [28] train-rmse:0.113670+0.009972    test-rmse:0.406657+0.070282 
## Stopping. Best iteration:
## [22] train-rmse:0.141344+0.007153    test-rmse:0.405872+0.073840
## 
## 89 
## [1]  train-rmse:1.142192+0.021075    test-rmse:1.147741+0.111537 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.816428+0.014663    test-rmse:0.831961+0.104140 
## [3]  train-rmse:0.600774+0.013163    test-rmse:0.633311+0.091961 
## [4]  train-rmse:0.456976+0.010782    test-rmse:0.514786+0.089895 
## [5]  train-rmse:0.363254+0.009524    test-rmse:0.456077+0.085055 
## [6]  train-rmse:0.304019+0.011776    test-rmse:0.425943+0.070704 
## [7]  train-rmse:0.263930+0.011019    test-rmse:0.408336+0.064188 
## [8]  train-rmse:0.234317+0.014361    test-rmse:0.403357+0.064258 
## [9]  train-rmse:0.214139+0.013342    test-rmse:0.399960+0.060237 
## [10] train-rmse:0.198621+0.012356    test-rmse:0.398558+0.058756 
## [11] train-rmse:0.180578+0.008793    test-rmse:0.394350+0.057018 
## [12] train-rmse:0.169102+0.009210    test-rmse:0.394065+0.052148 
## [13] train-rmse:0.159291+0.008109    test-rmse:0.392981+0.050430 
## [14] train-rmse:0.147404+0.006948    test-rmse:0.392898+0.051038 
## [15] train-rmse:0.139546+0.009293    test-rmse:0.391509+0.052432 
## [16] train-rmse:0.131822+0.010041    test-rmse:0.392548+0.049193 
## [17] train-rmse:0.125019+0.009792    test-rmse:0.390190+0.049868 
## [18] train-rmse:0.116712+0.010285    test-rmse:0.389397+0.050165 
## [19] train-rmse:0.111215+0.010153    test-rmse:0.389020+0.049748 
## [20] train-rmse:0.106791+0.010490    test-rmse:0.388688+0.050297 
## [21] train-rmse:0.102489+0.010654    test-rmse:0.388331+0.050334 
## [22] train-rmse:0.098025+0.010672    test-rmse:0.387554+0.050125 
## [23] train-rmse:0.093361+0.009624    test-rmse:0.388332+0.049260 
## [24] train-rmse:0.089260+0.009410    test-rmse:0.386818+0.049044 
## [25] train-rmse:0.084582+0.007768    test-rmse:0.387284+0.049750 
## [26] train-rmse:0.079739+0.006456    test-rmse:0.387353+0.049493 
## [27] train-rmse:0.076189+0.006282    test-rmse:0.387772+0.048757 
## [28] train-rmse:0.072860+0.005246    test-rmse:0.387766+0.049183 
## [29] train-rmse:0.069951+0.003971    test-rmse:0.388908+0.049893 
## [30] train-rmse:0.066594+0.003984    test-rmse:0.389065+0.049230 
## Stopping. Best iteration:
## [24] train-rmse:0.089260+0.009410    test-rmse:0.386818+0.049044
## 
## 90 
## [1]  train-rmse:1.141665+0.021072    test-rmse:1.149439+0.111123 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.814171+0.015196    test-rmse:0.830822+0.103121 
## [3]  train-rmse:0.595372+0.013113    test-rmse:0.627192+0.092592 
## [4]  train-rmse:0.449203+0.012462    test-rmse:0.515959+0.091198 
## [5]  train-rmse:0.349190+0.009331    test-rmse:0.455008+0.086091 
## [6]  train-rmse:0.282531+0.007210    test-rmse:0.432805+0.080843 
## [7]  train-rmse:0.234336+0.007950    test-rmse:0.419401+0.071512 
## [8]  train-rmse:0.200949+0.008667    test-rmse:0.411296+0.068495 
## [9]  train-rmse:0.177771+0.010805    test-rmse:0.404666+0.064063 
## [10] train-rmse:0.159978+0.008291    test-rmse:0.401319+0.062705 
## [11] train-rmse:0.147124+0.009840    test-rmse:0.401538+0.059459 
## [12] train-rmse:0.135628+0.007961    test-rmse:0.401265+0.056839 
## [13] train-rmse:0.125943+0.008434    test-rmse:0.399211+0.055312 
## [14] train-rmse:0.117414+0.008448    test-rmse:0.399647+0.054492 
## [15] train-rmse:0.108278+0.006965    test-rmse:0.401026+0.054700 
## [16] train-rmse:0.099847+0.005374    test-rmse:0.401465+0.055870 
## [17] train-rmse:0.091192+0.003863    test-rmse:0.401269+0.054845 
## [18] train-rmse:0.083133+0.004517    test-rmse:0.400196+0.055123 
## [19] train-rmse:0.079036+0.004795    test-rmse:0.399809+0.054949 
## Stopping. Best iteration:
## [13] train-rmse:0.125943+0.008434    test-rmse:0.399211+0.055312
## 
## 91 
## [1]  train-rmse:1.164118+0.022144    test-rmse:1.160344+0.106242 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.897983+0.018737    test-rmse:0.894514+0.098069 
## [3]  train-rmse:0.761054+0.017968    test-rmse:0.758077+0.091872 
## [4]  train-rmse:0.696538+0.018034    test-rmse:0.694036+0.087789 
## [5]  train-rmse:0.661569+0.016538    test-rmse:0.664732+0.088353 
## [6]  train-rmse:0.636768+0.016790    test-rmse:0.635814+0.084627 
## [7]  train-rmse:0.616249+0.013578    test-rmse:0.616834+0.076918 
## [8]  train-rmse:0.602691+0.013488    test-rmse:0.609905+0.074515 
## [9]  train-rmse:0.591151+0.013127    test-rmse:0.596252+0.071958 
## [10] train-rmse:0.580638+0.012213    test-rmse:0.587902+0.066303 
## [11] train-rmse:0.571544+0.011498    test-rmse:0.579113+0.066132 
## [12] train-rmse:0.563962+0.011548    test-rmse:0.575916+0.064607 
## [13] train-rmse:0.557642+0.011408    test-rmse:0.569780+0.062673 
## [14] train-rmse:0.551932+0.010963    test-rmse:0.563144+0.062057 
## [15] train-rmse:0.546673+0.010432    test-rmse:0.560252+0.060870 
## [16] train-rmse:0.542532+0.010388    test-rmse:0.559446+0.062537 
## [17] train-rmse:0.538496+0.010359    test-rmse:0.557884+0.059536 
## [18] train-rmse:0.534826+0.010285    test-rmse:0.558076+0.058111 
## [19] train-rmse:0.531259+0.010109    test-rmse:0.556313+0.058793 
## [20] train-rmse:0.527945+0.010010    test-rmse:0.552853+0.057858 
## [21] train-rmse:0.524878+0.009772    test-rmse:0.549549+0.058030 
## [22] train-rmse:0.521751+0.009273    test-rmse:0.545350+0.058719 
## [23] train-rmse:0.518899+0.009004    test-rmse:0.546427+0.058396 
## [24] train-rmse:0.516159+0.008917    test-rmse:0.542886+0.058037 
## [25] train-rmse:0.513546+0.008846    test-rmse:0.540840+0.054594 
## [26] train-rmse:0.511202+0.008789    test-rmse:0.538324+0.054780 
## [27] train-rmse:0.508939+0.008683    test-rmse:0.538547+0.055201 
## [28] train-rmse:0.506638+0.008748    test-rmse:0.537885+0.056163 
## [29] train-rmse:0.504431+0.008659    test-rmse:0.535752+0.055384 
## [30] train-rmse:0.502266+0.008643    test-rmse:0.535116+0.052496 
## [31] train-rmse:0.500109+0.008443    test-rmse:0.532762+0.052332 
## [32] train-rmse:0.498207+0.008374    test-rmse:0.529716+0.053727 
## [33] train-rmse:0.496427+0.008415    test-rmse:0.529746+0.054099 
## [34] train-rmse:0.494584+0.008571    test-rmse:0.527954+0.053957 
## [35] train-rmse:0.492848+0.008511    test-rmse:0.527565+0.054910 
## [36] train-rmse:0.491085+0.008415    test-rmse:0.526405+0.054552 
## [37] train-rmse:0.489416+0.008465    test-rmse:0.523733+0.054099 
## [38] train-rmse:0.487754+0.008326    test-rmse:0.522592+0.053499 
## [39] train-rmse:0.486209+0.008236    test-rmse:0.523165+0.052809 
## [40] train-rmse:0.484714+0.008166    test-rmse:0.521086+0.052686 
## [41] train-rmse:0.483224+0.008126    test-rmse:0.521455+0.051832 
## [42] train-rmse:0.481790+0.008142    test-rmse:0.519512+0.050337 
## [43] train-rmse:0.480426+0.008085    test-rmse:0.520227+0.049389 
## [44] train-rmse:0.479044+0.008029    test-rmse:0.518455+0.049550 
## [45] train-rmse:0.477703+0.007923    test-rmse:0.518981+0.049890 
## [46] train-rmse:0.476508+0.007868    test-rmse:0.518836+0.049378 
## [47] train-rmse:0.475252+0.007872    test-rmse:0.516885+0.049672 
## [48] train-rmse:0.474109+0.007849    test-rmse:0.517214+0.048396 
## [49] train-rmse:0.472990+0.007830    test-rmse:0.515559+0.049035 
## [50] train-rmse:0.471885+0.007809    test-rmse:0.514974+0.048386 
## [51] train-rmse:0.470847+0.007774    test-rmse:0.515537+0.048861 
## [52] train-rmse:0.469829+0.007747    test-rmse:0.515360+0.050044 
## [53] train-rmse:0.468791+0.007736    test-rmse:0.514226+0.050287 
## [54] train-rmse:0.467793+0.007715    test-rmse:0.514093+0.050396 
## [55] train-rmse:0.466821+0.007696    test-rmse:0.513233+0.050188 
## [56] train-rmse:0.465809+0.007626    test-rmse:0.510642+0.045840 
## [57] train-rmse:0.464865+0.007602    test-rmse:0.510057+0.045881 
## [58] train-rmse:0.463911+0.007597    test-rmse:0.508888+0.045538 
## [59] train-rmse:0.463001+0.007609    test-rmse:0.507974+0.045726 
## [60] train-rmse:0.462075+0.007575    test-rmse:0.506011+0.045958 
## [61] train-rmse:0.461218+0.007616    test-rmse:0.505693+0.045731 
## [62] train-rmse:0.460397+0.007603    test-rmse:0.506136+0.044672 
## [63] train-rmse:0.459625+0.007605    test-rmse:0.505285+0.044073 
## [64] train-rmse:0.458832+0.007641    test-rmse:0.505371+0.044504 
## [65] train-rmse:0.458044+0.007613    test-rmse:0.504884+0.044476 
## [66] train-rmse:0.457219+0.007529    test-rmse:0.503558+0.045824 
## [67] train-rmse:0.456435+0.007456    test-rmse:0.504061+0.045265 
## [68] train-rmse:0.455732+0.007453    test-rmse:0.505034+0.045550 
## [69] train-rmse:0.455030+0.007494    test-rmse:0.505048+0.045666 
## [70] train-rmse:0.454301+0.007500    test-rmse:0.504572+0.045591 
## [71] train-rmse:0.453563+0.007479    test-rmse:0.505126+0.045440 
## [72] train-rmse:0.452854+0.007445    test-rmse:0.504319+0.044487 
## Stopping. Best iteration:
## [66] train-rmse:0.457219+0.007529    test-rmse:0.503558+0.045824
## 
## 92 
## [1]  train-rmse:1.138830+0.022385    test-rmse:1.140870+0.113704 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.835653+0.016434    test-rmse:0.834746+0.099799 
## [3]  train-rmse:0.660263+0.014450    test-rmse:0.665793+0.101798 
## [4]  train-rmse:0.564595+0.013460    test-rmse:0.579043+0.096733 
## [5]  train-rmse:0.510172+0.011834    test-rmse:0.525142+0.088834 
## [6]  train-rmse:0.477026+0.011621    test-rmse:0.502091+0.076444 
## [7]  train-rmse:0.450879+0.010567    test-rmse:0.477891+0.074339 
## [8]  train-rmse:0.433075+0.010240    test-rmse:0.467331+0.065583 
## [9]  train-rmse:0.418984+0.008977    test-rmse:0.460763+0.064404 
## [10] train-rmse:0.407555+0.007605    test-rmse:0.458863+0.063483 
## [11] train-rmse:0.398467+0.006429    test-rmse:0.456384+0.062714 
## [12] train-rmse:0.391011+0.005913    test-rmse:0.452625+0.063080 
## [13] train-rmse:0.383819+0.005556    test-rmse:0.452785+0.062886 
## [14] train-rmse:0.377290+0.005086    test-rmse:0.445748+0.066546 
## [15] train-rmse:0.371713+0.004896    test-rmse:0.445632+0.062161 
## [16] train-rmse:0.365759+0.004734    test-rmse:0.444222+0.063769 
## [17] train-rmse:0.360756+0.004525    test-rmse:0.444311+0.061135 
## [18] train-rmse:0.356160+0.004555    test-rmse:0.441995+0.061285 
## [19] train-rmse:0.352058+0.004385    test-rmse:0.440253+0.059539 
## [20] train-rmse:0.346174+0.004035    test-rmse:0.441303+0.061128 
## [21] train-rmse:0.341710+0.005412    test-rmse:0.441383+0.058924 
## [22] train-rmse:0.338116+0.005480    test-rmse:0.441844+0.059772 
## [23] train-rmse:0.335121+0.005394    test-rmse:0.438895+0.060830 
## [24] train-rmse:0.331489+0.005137    test-rmse:0.439844+0.061153 
## [25] train-rmse:0.328683+0.004675    test-rmse:0.440004+0.061935 
## [26] train-rmse:0.325423+0.004712    test-rmse:0.439356+0.060790 
## [27] train-rmse:0.321674+0.004740    test-rmse:0.439305+0.060343 
## [28] train-rmse:0.317934+0.005016    test-rmse:0.438480+0.060066 
## [29] train-rmse:0.313775+0.005229    test-rmse:0.436516+0.061117 
## [30] train-rmse:0.310708+0.004887    test-rmse:0.432851+0.059983 
## [31] train-rmse:0.308529+0.004730    test-rmse:0.433175+0.059386 
## [32] train-rmse:0.306149+0.004610    test-rmse:0.433146+0.060098 
## [33] train-rmse:0.303114+0.004538    test-rmse:0.432485+0.060604 
## [34] train-rmse:0.301179+0.005183    test-rmse:0.432303+0.060947 
## [35] train-rmse:0.298376+0.005488    test-rmse:0.431564+0.060850 
## [36] train-rmse:0.295761+0.005892    test-rmse:0.429833+0.060843 
## [37] train-rmse:0.294112+0.006279    test-rmse:0.430020+0.061361 
## [38] train-rmse:0.292675+0.006101    test-rmse:0.427466+0.063955 
## [39] train-rmse:0.290168+0.005059    test-rmse:0.426624+0.064652 
## [40] train-rmse:0.287649+0.005691    test-rmse:0.426225+0.065494 
## [41] train-rmse:0.285152+0.005648    test-rmse:0.426388+0.065888 
## [42] train-rmse:0.283487+0.005862    test-rmse:0.426218+0.065360 
## [43] train-rmse:0.281740+0.005956    test-rmse:0.426133+0.065512 
## [44] train-rmse:0.278587+0.006834    test-rmse:0.426117+0.065580 
## [45] train-rmse:0.276671+0.006940    test-rmse:0.425514+0.065153 
## [46] train-rmse:0.275586+0.006855    test-rmse:0.426225+0.064298 
## [47] train-rmse:0.273279+0.006752    test-rmse:0.425192+0.063999 
## [48] train-rmse:0.271799+0.006400    test-rmse:0.424753+0.064406 
## [49] train-rmse:0.270456+0.006611    test-rmse:0.424214+0.064531 
## [50] train-rmse:0.268312+0.007371    test-rmse:0.421746+0.064376 
## [51] train-rmse:0.266826+0.007614    test-rmse:0.422323+0.064298 
## [52] train-rmse:0.265630+0.007507    test-rmse:0.421272+0.064308 
## [53] train-rmse:0.262874+0.007925    test-rmse:0.420274+0.064540 
## [54] train-rmse:0.261461+0.007729    test-rmse:0.419908+0.065193 
## [55] train-rmse:0.259693+0.007167    test-rmse:0.419866+0.065533 
## [56] train-rmse:0.257344+0.007672    test-rmse:0.420557+0.065683 
## [57] train-rmse:0.256022+0.007454    test-rmse:0.421089+0.065203 
## [58] train-rmse:0.254160+0.007944    test-rmse:0.420967+0.064367 
## [59] train-rmse:0.251750+0.007694    test-rmse:0.420915+0.064723 
## [60] train-rmse:0.250700+0.007323    test-rmse:0.420719+0.064849 
## [61] train-rmse:0.249721+0.007672    test-rmse:0.419816+0.063053 
## [62] train-rmse:0.248390+0.007452    test-rmse:0.419271+0.062957 
## [63] train-rmse:0.246942+0.006649    test-rmse:0.419812+0.062667 
## [64] train-rmse:0.245197+0.006116    test-rmse:0.420181+0.062890 
## [65] train-rmse:0.244050+0.006820    test-rmse:0.419240+0.061878 
## [66] train-rmse:0.242980+0.006771    test-rmse:0.418190+0.062181 
## [67] train-rmse:0.241508+0.007105    test-rmse:0.418226+0.061543 
## [68] train-rmse:0.240209+0.006569    test-rmse:0.417862+0.062023 
## [69] train-rmse:0.238713+0.007216    test-rmse:0.418436+0.061998 
## [70] train-rmse:0.237466+0.007204    test-rmse:0.417445+0.061688 
## [71] train-rmse:0.236032+0.007059    test-rmse:0.416930+0.062154 
## [72] train-rmse:0.234597+0.007039    test-rmse:0.416367+0.061926 
## [73] train-rmse:0.233356+0.007431    test-rmse:0.416592+0.061818 
## [74] train-rmse:0.231888+0.006908    test-rmse:0.416407+0.061794 
## [75] train-rmse:0.231010+0.006831    test-rmse:0.415889+0.061731 
## [76] train-rmse:0.229831+0.006978    test-rmse:0.416430+0.061929 
## [77] train-rmse:0.228801+0.006600    test-rmse:0.416653+0.062255 
## [78] train-rmse:0.227480+0.007347    test-rmse:0.415252+0.062971 
## [79] train-rmse:0.225930+0.007516    test-rmse:0.415898+0.062354 
## [80] train-rmse:0.224280+0.007114    test-rmse:0.415298+0.063425 
## [81] train-rmse:0.223527+0.007028    test-rmse:0.415068+0.063749 
## [82] train-rmse:0.222696+0.007005    test-rmse:0.415925+0.063512 
## [83] train-rmse:0.221621+0.007115    test-rmse:0.415720+0.063107 
## [84] train-rmse:0.220781+0.006924    test-rmse:0.415554+0.062982 
## [85] train-rmse:0.219354+0.007390    test-rmse:0.413770+0.063543 
## [86] train-rmse:0.218126+0.007120    test-rmse:0.414462+0.063765 
## [87] train-rmse:0.217175+0.006929    test-rmse:0.413708+0.064074 
## [88] train-rmse:0.216669+0.006936    test-rmse:0.413741+0.063695 
## [89] train-rmse:0.215118+0.006710    test-rmse:0.414944+0.062728 
## [90] train-rmse:0.213608+0.006432    test-rmse:0.414215+0.062518 
## [91] train-rmse:0.212500+0.006311    test-rmse:0.414247+0.062533 
## [92] train-rmse:0.211272+0.006097    test-rmse:0.415140+0.062532 
## [93] train-rmse:0.209738+0.005954    test-rmse:0.415513+0.062016 
## Stopping. Best iteration:
## [87] train-rmse:0.217175+0.006929    test-rmse:0.413708+0.064074
## 
## 93 
## [1]  train-rmse:1.125075+0.021024    test-rmse:1.132284+0.117895 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.806048+0.017895    test-rmse:0.809299+0.102263 
## [3]  train-rmse:0.611059+0.014241    test-rmse:0.628801+0.103500 
## [4]  train-rmse:0.495985+0.011070    test-rmse:0.532747+0.089062 
## [5]  train-rmse:0.431186+0.009968    test-rmse:0.481897+0.085821 
## [6]  train-rmse:0.392889+0.008685    test-rmse:0.456307+0.079818 
## [7]  train-rmse:0.368838+0.008076    test-rmse:0.442248+0.080822 
## [8]  train-rmse:0.350129+0.006461    test-rmse:0.434508+0.077453 
## [9]  train-rmse:0.334812+0.005703    test-rmse:0.430081+0.073520 
## [10] train-rmse:0.322066+0.005031    test-rmse:0.429677+0.072215 
## [11] train-rmse:0.312467+0.004424    test-rmse:0.430385+0.070878 
## [12] train-rmse:0.303174+0.005169    test-rmse:0.429120+0.070965 
## [13] train-rmse:0.296247+0.005403    test-rmse:0.427362+0.068772 
## [14] train-rmse:0.286795+0.006053    test-rmse:0.426081+0.070372 
## [15] train-rmse:0.280874+0.005929    test-rmse:0.426742+0.069431 
## [16] train-rmse:0.275102+0.005684    test-rmse:0.424684+0.071282 
## [17] train-rmse:0.270644+0.005793    test-rmse:0.422273+0.073651 
## [18] train-rmse:0.264311+0.005948    test-rmse:0.422857+0.072671 
## [19] train-rmse:0.257973+0.004787    test-rmse:0.418555+0.071236 
## [20] train-rmse:0.253047+0.004345    test-rmse:0.418204+0.070645 
## [21] train-rmse:0.248881+0.004431    test-rmse:0.416642+0.070901 
## [22] train-rmse:0.243308+0.004660    test-rmse:0.414029+0.072631 
## [23] train-rmse:0.238862+0.004960    test-rmse:0.414504+0.072107 
## [24] train-rmse:0.235512+0.005076    test-rmse:0.413698+0.073459 
## [25] train-rmse:0.231263+0.005077    test-rmse:0.413172+0.074342 
## [26] train-rmse:0.227709+0.004829    test-rmse:0.412914+0.074773 
## [27] train-rmse:0.222265+0.005470    test-rmse:0.412657+0.076094 
## [28] train-rmse:0.218428+0.006585    test-rmse:0.412446+0.077850 
## [29] train-rmse:0.215814+0.005766    test-rmse:0.413626+0.078262 
## [30] train-rmse:0.212656+0.005729    test-rmse:0.411889+0.077354 
## [31] train-rmse:0.210166+0.006222    test-rmse:0.410447+0.078705 
## [32] train-rmse:0.207502+0.006356    test-rmse:0.409496+0.078472 
## [33] train-rmse:0.205135+0.006098    test-rmse:0.409441+0.078207 
## [34] train-rmse:0.201556+0.004749    test-rmse:0.409172+0.077921 
## [35] train-rmse:0.198572+0.003618    test-rmse:0.408379+0.078596 
## [36] train-rmse:0.196045+0.002699    test-rmse:0.408763+0.078901 
## [37] train-rmse:0.193136+0.002172    test-rmse:0.408406+0.078444 
## [38] train-rmse:0.191524+0.002699    test-rmse:0.409076+0.078281 
## [39] train-rmse:0.187134+0.004969    test-rmse:0.409822+0.077253 
## [40] train-rmse:0.184293+0.006081    test-rmse:0.409281+0.077010 
## [41] train-rmse:0.181005+0.006193    test-rmse:0.409542+0.075875 
## Stopping. Best iteration:
## [35] train-rmse:0.198572+0.003618    test-rmse:0.408379+0.078596
## 
## 94 
## [1]  train-rmse:1.118641+0.019956    test-rmse:1.125444+0.119070 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.792291+0.013286    test-rmse:0.804649+0.110809 
## [3]  train-rmse:0.587799+0.011686    test-rmse:0.614330+0.095292 
## [4]  train-rmse:0.461926+0.007996    test-rmse:0.510770+0.084042 
## [5]  train-rmse:0.389042+0.009804    test-rmse:0.467546+0.079944 
## [6]  train-rmse:0.340995+0.006364    test-rmse:0.442930+0.071987 
## [7]  train-rmse:0.312874+0.005131    test-rmse:0.427747+0.078167 
## [8]  train-rmse:0.295122+0.006150    test-rmse:0.422868+0.073620 
## [9]  train-rmse:0.282240+0.006913    test-rmse:0.417019+0.071482 
## [10] train-rmse:0.267771+0.003054    test-rmse:0.417231+0.066023 
## [11] train-rmse:0.253300+0.005113    test-rmse:0.416216+0.066551 
## [12] train-rmse:0.242777+0.005620    test-rmse:0.410249+0.067022 
## [13] train-rmse:0.235864+0.005452    test-rmse:0.409166+0.067358 
## [14] train-rmse:0.227253+0.004445    test-rmse:0.409309+0.067724 
## [15] train-rmse:0.218550+0.002866    test-rmse:0.410244+0.067649 
## [16] train-rmse:0.212958+0.002337    test-rmse:0.408865+0.067563 
## [17] train-rmse:0.205536+0.003727    test-rmse:0.406653+0.063611 
## [18] train-rmse:0.200405+0.003588    test-rmse:0.408073+0.062928 
## [19] train-rmse:0.195182+0.003338    test-rmse:0.406176+0.063095 
## [20] train-rmse:0.188381+0.001470    test-rmse:0.402820+0.062996 
## [21] train-rmse:0.182324+0.003308    test-rmse:0.401943+0.065747 
## [22] train-rmse:0.177162+0.003672    test-rmse:0.401856+0.066633 
## [23] train-rmse:0.172955+0.004819    test-rmse:0.401046+0.067004 
## [24] train-rmse:0.168882+0.005314    test-rmse:0.402683+0.067221 
## [25] train-rmse:0.162791+0.004305    test-rmse:0.404193+0.066550 
## [26] train-rmse:0.159233+0.004256    test-rmse:0.403301+0.067741 
## [27] train-rmse:0.155512+0.004819    test-rmse:0.402981+0.067741 
## [28] train-rmse:0.152451+0.005438    test-rmse:0.401906+0.068725 
## [29] train-rmse:0.148472+0.005054    test-rmse:0.399605+0.068342 
## [30] train-rmse:0.145960+0.005027    test-rmse:0.398797+0.068489 
## [31] train-rmse:0.143663+0.005145    test-rmse:0.399123+0.069032 
## [32] train-rmse:0.139465+0.005145    test-rmse:0.399940+0.069114 
## [33] train-rmse:0.135850+0.005989    test-rmse:0.398910+0.070085 
## [34] train-rmse:0.132572+0.006969    test-rmse:0.399349+0.069291 
## [35] train-rmse:0.129078+0.007955    test-rmse:0.398319+0.069877 
## [36] train-rmse:0.126571+0.007437    test-rmse:0.398671+0.070150 
## [37] train-rmse:0.124157+0.007387    test-rmse:0.399073+0.070196 
## [38] train-rmse:0.122031+0.006598    test-rmse:0.398591+0.069523 
## [39] train-rmse:0.118735+0.006366    test-rmse:0.399096+0.070082 
## [40] train-rmse:0.116683+0.007050    test-rmse:0.399113+0.069998 
## [41] train-rmse:0.114333+0.007559    test-rmse:0.398837+0.069710 
## Stopping. Best iteration:
## [35] train-rmse:0.129078+0.007955    test-rmse:0.398319+0.069877
## 
## 95 
## [1]  train-rmse:1.114953+0.020520    test-rmse:1.122361+0.113287 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.783700+0.013829    test-rmse:0.802021+0.104113 
## [3]  train-rmse:0.573991+0.011722    test-rmse:0.610362+0.093443 
## [4]  train-rmse:0.439958+0.006625    test-rmse:0.505720+0.087350 
## [5]  train-rmse:0.357442+0.007685    test-rmse:0.447773+0.079896 
## [6]  train-rmse:0.308135+0.008045    test-rmse:0.428855+0.077217 
## [7]  train-rmse:0.273966+0.005343    test-rmse:0.419472+0.075607 
## [8]  train-rmse:0.252802+0.005805    test-rmse:0.411678+0.075517 
## [9]  train-rmse:0.237742+0.009206    test-rmse:0.409757+0.077192 
## [10] train-rmse:0.225795+0.008234    test-rmse:0.409443+0.073384 
## [11] train-rmse:0.212374+0.004973    test-rmse:0.406936+0.072342 
## [12] train-rmse:0.202637+0.003983    test-rmse:0.406385+0.070515 
## [13] train-rmse:0.190635+0.006230    test-rmse:0.407967+0.071335 
## [14] train-rmse:0.181750+0.006354    test-rmse:0.405672+0.071816 
## [15] train-rmse:0.172711+0.006257    test-rmse:0.406063+0.068618 
## [16] train-rmse:0.166024+0.005948    test-rmse:0.407088+0.068425 
## [17] train-rmse:0.159003+0.005766    test-rmse:0.402447+0.068501 
## [18] train-rmse:0.151418+0.005299    test-rmse:0.403026+0.069690 
## [19] train-rmse:0.147050+0.005590    test-rmse:0.402589+0.069474 
## [20] train-rmse:0.142265+0.004849    test-rmse:0.403818+0.069665 
## [21] train-rmse:0.136212+0.006344    test-rmse:0.401919+0.068891 
## [22] train-rmse:0.131856+0.006399    test-rmse:0.402283+0.069099 
## [23] train-rmse:0.127766+0.006673    test-rmse:0.402209+0.068588 
## [24] train-rmse:0.123266+0.007931    test-rmse:0.401697+0.069518 
## [25] train-rmse:0.119346+0.009938    test-rmse:0.402105+0.069743 
## [26] train-rmse:0.114712+0.010948    test-rmse:0.401828+0.071375 
## [27] train-rmse:0.111095+0.011265    test-rmse:0.403582+0.071361 
## [28] train-rmse:0.107544+0.010569    test-rmse:0.404590+0.070670 
## [29] train-rmse:0.103260+0.010475    test-rmse:0.404593+0.071957 
## [30] train-rmse:0.100138+0.010655    test-rmse:0.404841+0.071798 
## Stopping. Best iteration:
## [24] train-rmse:0.123266+0.007931    test-rmse:0.401697+0.069518
## 
## 96 
## [1]  train-rmse:1.113724+0.020575    test-rmse:1.120096+0.111226 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.779504+0.014067    test-rmse:0.798096+0.103144 
## [3]  train-rmse:0.566189+0.012725    test-rmse:0.601694+0.092635 
## [4]  train-rmse:0.428202+0.010060    test-rmse:0.498309+0.089722 
## [5]  train-rmse:0.341115+0.011363    test-rmse:0.449378+0.082528 
## [6]  train-rmse:0.285353+0.011502    test-rmse:0.428535+0.078384 
## [7]  train-rmse:0.251145+0.012774    test-rmse:0.418036+0.073839 
## [8]  train-rmse:0.223337+0.015774    test-rmse:0.409574+0.068341 
## [9]  train-rmse:0.200536+0.014418    test-rmse:0.406969+0.062542 
## [10] train-rmse:0.183566+0.017419    test-rmse:0.402748+0.057436 
## [11] train-rmse:0.173647+0.015429    test-rmse:0.402133+0.057438 
## [12] train-rmse:0.160726+0.016401    test-rmse:0.399761+0.057704 
## [13] train-rmse:0.151090+0.017602    test-rmse:0.399445+0.060345 
## [14] train-rmse:0.140852+0.015222    test-rmse:0.398966+0.057806 
## [15] train-rmse:0.132658+0.014915    test-rmse:0.397131+0.057710 
## [16] train-rmse:0.125809+0.014183    test-rmse:0.399099+0.054088 
## [17] train-rmse:0.120152+0.012045    test-rmse:0.398560+0.055712 
## [18] train-rmse:0.114045+0.011246    test-rmse:0.398841+0.054776 
## [19] train-rmse:0.105848+0.012089    test-rmse:0.396927+0.055982 
## [20] train-rmse:0.099018+0.010174    test-rmse:0.397116+0.055555 
## [21] train-rmse:0.094191+0.008345    test-rmse:0.396883+0.055545 
## [22] train-rmse:0.090574+0.009354    test-rmse:0.395564+0.055631 
## [23] train-rmse:0.086073+0.007761    test-rmse:0.396142+0.055521 
## [24] train-rmse:0.081565+0.008466    test-rmse:0.396245+0.054662 
## [25] train-rmse:0.077734+0.008605    test-rmse:0.395964+0.054820 
## [26] train-rmse:0.073942+0.008070    test-rmse:0.396583+0.055378 
## [27] train-rmse:0.071141+0.008197    test-rmse:0.397621+0.056151 
## [28] train-rmse:0.068281+0.007705    test-rmse:0.397714+0.056233 
## Stopping. Best iteration:
## [22] train-rmse:0.090574+0.009354    test-rmse:0.395564+0.055631
## 
## 97 
## [1]  train-rmse:1.113152+0.020573    test-rmse:1.121916+0.110858 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.776726+0.014542    test-rmse:0.797210+0.102040 
## [3]  train-rmse:0.559565+0.012472    test-rmse:0.596585+0.092984 
## [4]  train-rmse:0.415741+0.011880    test-rmse:0.500117+0.090903 
## [5]  train-rmse:0.322919+0.011216    test-rmse:0.448697+0.084185 
## [6]  train-rmse:0.263059+0.010844    test-rmse:0.429038+0.081054 
## [7]  train-rmse:0.220132+0.009485    test-rmse:0.418527+0.074767 
## [8]  train-rmse:0.188517+0.009649    test-rmse:0.411108+0.073817 
## [9]  train-rmse:0.170534+0.011444    test-rmse:0.408848+0.070283 
## [10] train-rmse:0.154781+0.009342    test-rmse:0.406768+0.068613 
## [11] train-rmse:0.138426+0.012535    test-rmse:0.404778+0.066620 
## [12] train-rmse:0.126554+0.014239    test-rmse:0.403744+0.066400 
## [13] train-rmse:0.116877+0.012889    test-rmse:0.402030+0.066056 
## [14] train-rmse:0.109531+0.013980    test-rmse:0.401466+0.065190 
## [15] train-rmse:0.100930+0.012177    test-rmse:0.402141+0.065591 
## [16] train-rmse:0.094899+0.011327    test-rmse:0.400801+0.065479 
## [17] train-rmse:0.088614+0.011436    test-rmse:0.400418+0.064620 
## [18] train-rmse:0.082952+0.012020    test-rmse:0.401063+0.064322 
## [19] train-rmse:0.078483+0.012017    test-rmse:0.401329+0.064767 
## [20] train-rmse:0.071996+0.010700    test-rmse:0.401409+0.064449 
## [21] train-rmse:0.067185+0.009244    test-rmse:0.400282+0.064599 
## [22] train-rmse:0.062367+0.007843    test-rmse:0.400243+0.064649 
## [23] train-rmse:0.058791+0.008060    test-rmse:0.400975+0.064011 
## [24] train-rmse:0.054564+0.008940    test-rmse:0.401084+0.063023 
## [25] train-rmse:0.051668+0.008793    test-rmse:0.399952+0.063375 
## [26] train-rmse:0.048698+0.009543    test-rmse:0.399640+0.062977 
## [27] train-rmse:0.045582+0.008945    test-rmse:0.399203+0.063210 
## [28] train-rmse:0.042898+0.007728    test-rmse:0.399097+0.063397 
## [29] train-rmse:0.040417+0.007385    test-rmse:0.399253+0.062780 
## [30] train-rmse:0.037925+0.006654    test-rmse:0.399007+0.062482 
## [31] train-rmse:0.036076+0.006037    test-rmse:0.398881+0.062457 
## [32] train-rmse:0.033941+0.005387    test-rmse:0.399250+0.061981 
## [33] train-rmse:0.031864+0.005001    test-rmse:0.399498+0.061695 
## [34] train-rmse:0.029563+0.005200    test-rmse:0.399616+0.061820 
## [35] train-rmse:0.028077+0.005491    test-rmse:0.399652+0.061708 
## [36] train-rmse:0.026548+0.005206    test-rmse:0.399758+0.061349 
## [37] train-rmse:0.024738+0.004877    test-rmse:0.399969+0.061239 
## Stopping. Best iteration:
## [31] train-rmse:0.036076+0.006037    test-rmse:0.398881+0.062457
## 
## 98 
## [1]  train-rmse:1.139467+0.021767    test-rmse:1.135707+0.105597 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.872231+0.018517    test-rmse:0.868825+0.097066 
## [3]  train-rmse:0.742796+0.017951    test-rmse:0.739929+0.090834 
## [4]  train-rmse:0.685902+0.018080    test-rmse:0.683512+0.086966 
## [5]  train-rmse:0.650716+0.016571    test-rmse:0.649363+0.085964 
## [6]  train-rmse:0.629412+0.015138    test-rmse:0.634459+0.076045 
## [7]  train-rmse:0.608285+0.013316    test-rmse:0.608349+0.076219 
## [8]  train-rmse:0.596032+0.013064    test-rmse:0.604351+0.072569 
## [9]  train-rmse:0.584457+0.012798    test-rmse:0.590536+0.071400 
## [10] train-rmse:0.575055+0.012018    test-rmse:0.584383+0.066563 
## [11] train-rmse:0.566373+0.011178    test-rmse:0.575591+0.065809 
## [12] train-rmse:0.558288+0.011297    test-rmse:0.570286+0.063953 
## [13] train-rmse:0.552508+0.010907    test-rmse:0.565311+0.062340 
## [14] train-rmse:0.547135+0.010434    test-rmse:0.560709+0.060945 
## [15] train-rmse:0.542082+0.010273    test-rmse:0.555826+0.059863 
## [16] train-rmse:0.538028+0.010214    test-rmse:0.557280+0.060270 
## [17] train-rmse:0.534359+0.010157    test-rmse:0.554961+0.058552 
## [18] train-rmse:0.530689+0.010234    test-rmse:0.554654+0.057280 
## [19] train-rmse:0.527106+0.009983    test-rmse:0.551241+0.056727 
## [20] train-rmse:0.523743+0.009708    test-rmse:0.550015+0.055949 
## [21] train-rmse:0.520733+0.009568    test-rmse:0.549621+0.053043 
## [22] train-rmse:0.517832+0.009416    test-rmse:0.546356+0.054430 
## [23] train-rmse:0.515039+0.009179    test-rmse:0.543630+0.053793 
## [24] train-rmse:0.512187+0.008738    test-rmse:0.541375+0.055163 
## [25] train-rmse:0.509821+0.008647    test-rmse:0.537779+0.053005 
## [26] train-rmse:0.507366+0.008678    test-rmse:0.536666+0.053042 
## [27] train-rmse:0.505026+0.008628    test-rmse:0.534728+0.053765 
## [28] train-rmse:0.502683+0.008722    test-rmse:0.533765+0.054521 
## [29] train-rmse:0.500484+0.008548    test-rmse:0.532341+0.055041 
## [30] train-rmse:0.498312+0.008291    test-rmse:0.533073+0.054328 
## [31] train-rmse:0.496322+0.008232    test-rmse:0.529095+0.054426 
## [32] train-rmse:0.494368+0.008357    test-rmse:0.527899+0.052553 
## [33] train-rmse:0.492446+0.008324    test-rmse:0.526691+0.052073 
## [34] train-rmse:0.490583+0.008370    test-rmse:0.525063+0.053411 
## [35] train-rmse:0.488813+0.008289    test-rmse:0.525087+0.054131 
## [36] train-rmse:0.487165+0.008171    test-rmse:0.524410+0.052320 
## [37] train-rmse:0.485561+0.008217    test-rmse:0.523136+0.051080 
## [38] train-rmse:0.483941+0.008239    test-rmse:0.520048+0.051222 
## [39] train-rmse:0.482351+0.008149    test-rmse:0.519940+0.052013 
## [40] train-rmse:0.480954+0.008116    test-rmse:0.521108+0.050524 
## [41] train-rmse:0.479569+0.008067    test-rmse:0.518646+0.050935 
## [42] train-rmse:0.478191+0.008038    test-rmse:0.519679+0.050974 
## [43] train-rmse:0.476760+0.008000    test-rmse:0.517518+0.049732 
## [44] train-rmse:0.475451+0.007932    test-rmse:0.517195+0.048551 
## [45] train-rmse:0.474271+0.007893    test-rmse:0.516560+0.048304 
## [46] train-rmse:0.473060+0.007802    test-rmse:0.517344+0.047980 
## [47] train-rmse:0.471824+0.007747    test-rmse:0.516797+0.048085 
## [48] train-rmse:0.470719+0.007708    test-rmse:0.514815+0.048413 
## [49] train-rmse:0.469622+0.007656    test-rmse:0.513562+0.047598 
## [50] train-rmse:0.468529+0.007621    test-rmse:0.512910+0.049138 
## [51] train-rmse:0.467490+0.007703    test-rmse:0.512066+0.048675 
## [52] train-rmse:0.466496+0.007729    test-rmse:0.512556+0.048923 
## [53] train-rmse:0.465435+0.007724    test-rmse:0.512854+0.047175 
## [54] train-rmse:0.464408+0.007728    test-rmse:0.509633+0.044099 
## [55] train-rmse:0.463418+0.007688    test-rmse:0.508278+0.044000 
## [56] train-rmse:0.462460+0.007645    test-rmse:0.508046+0.044096 
## [57] train-rmse:0.461573+0.007595    test-rmse:0.505995+0.043933 
## [58] train-rmse:0.460689+0.007601    test-rmse:0.506007+0.044544 
## [59] train-rmse:0.459802+0.007567    test-rmse:0.504672+0.044708 
## [60] train-rmse:0.458923+0.007486    test-rmse:0.505300+0.045041 
## [61] train-rmse:0.458035+0.007548    test-rmse:0.504863+0.044189 
## [62] train-rmse:0.457203+0.007535    test-rmse:0.504175+0.044867 
## [63] train-rmse:0.456420+0.007495    test-rmse:0.504647+0.044350 
## [64] train-rmse:0.455655+0.007466    test-rmse:0.502974+0.044130 
## [65] train-rmse:0.454868+0.007408    test-rmse:0.502021+0.044123 
## [66] train-rmse:0.454079+0.007383    test-rmse:0.502113+0.043143 
## [67] train-rmse:0.453346+0.007364    test-rmse:0.503066+0.043785 
## [68] train-rmse:0.452615+0.007389    test-rmse:0.503051+0.042925 
## [69] train-rmse:0.451913+0.007389    test-rmse:0.503398+0.042942 
## [70] train-rmse:0.451224+0.007402    test-rmse:0.503009+0.043178 
## [71] train-rmse:0.450541+0.007414    test-rmse:0.502471+0.043485 
## Stopping. Best iteration:
## [65] train-rmse:0.454868+0.007408    test-rmse:0.502021+0.044123
## 
## 99 
## [1]  train-rmse:1.112413+0.022043    test-rmse:1.114938+0.113561 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.804953+0.015979    test-rmse:0.804321+0.099076 
## [3]  train-rmse:0.634750+0.013856    test-rmse:0.641117+0.101062 
## [4]  train-rmse:0.546626+0.013298    test-rmse:0.559159+0.094880 
## [5]  train-rmse:0.497399+0.011476    test-rmse:0.508906+0.085734 
## [6]  train-rmse:0.466211+0.012751    test-rmse:0.494299+0.075113 
## [7]  train-rmse:0.442503+0.010725    test-rmse:0.468695+0.066973 
## [8]  train-rmse:0.425886+0.009748    test-rmse:0.463309+0.065317 
## [9]  train-rmse:0.413124+0.009717    test-rmse:0.460070+0.060231 
## [10] train-rmse:0.402649+0.007794    test-rmse:0.457887+0.060243 
## [11] train-rmse:0.393659+0.006694    test-rmse:0.454980+0.056510 
## [12] train-rmse:0.385568+0.006381    test-rmse:0.449474+0.060050 
## [13] train-rmse:0.377987+0.006496    test-rmse:0.448500+0.060137 
## [14] train-rmse:0.371626+0.006634    test-rmse:0.444162+0.060154 
## [15] train-rmse:0.365926+0.006460    test-rmse:0.443582+0.060781 
## [16] train-rmse:0.360983+0.006274    test-rmse:0.444765+0.060395 
## [17] train-rmse:0.355267+0.006569    test-rmse:0.443755+0.058907 
## [18] train-rmse:0.348856+0.006839    test-rmse:0.441489+0.058114 
## [19] train-rmse:0.343775+0.005583    test-rmse:0.442007+0.057563 
## [20] train-rmse:0.340371+0.005011    test-rmse:0.441089+0.059071 
## [21] train-rmse:0.336334+0.004121    test-rmse:0.440766+0.057771 
## [22] train-rmse:0.333321+0.004136    test-rmse:0.441421+0.058578 
## [23] train-rmse:0.329304+0.003715    test-rmse:0.438489+0.057839 
## [24] train-rmse:0.325745+0.003972    test-rmse:0.436507+0.058844 
## [25] train-rmse:0.321188+0.004137    test-rmse:0.434460+0.060098 
## [26] train-rmse:0.318451+0.004338    test-rmse:0.431309+0.058262 
## [27] train-rmse:0.315359+0.004664    test-rmse:0.430567+0.058539 
## [28] train-rmse:0.312066+0.004940    test-rmse:0.430670+0.058593 
## [29] train-rmse:0.308589+0.005874    test-rmse:0.430675+0.058444 
## [30] train-rmse:0.305669+0.005276    test-rmse:0.429259+0.058562 
## [31] train-rmse:0.302349+0.005842    test-rmse:0.427912+0.058338 
## [32] train-rmse:0.299047+0.004908    test-rmse:0.425260+0.060164 
## [33] train-rmse:0.295958+0.004427    test-rmse:0.424830+0.061380 
## [34] train-rmse:0.292358+0.003077    test-rmse:0.425460+0.060659 
## [35] train-rmse:0.289317+0.003402    test-rmse:0.425862+0.058534 
## [36] train-rmse:0.286787+0.004067    test-rmse:0.422180+0.057843 
## [37] train-rmse:0.283563+0.003967    test-rmse:0.420483+0.059751 
## [38] train-rmse:0.281566+0.003996    test-rmse:0.420357+0.059944 
## [39] train-rmse:0.279341+0.004066    test-rmse:0.420218+0.059348 
## [40] train-rmse:0.277757+0.004655    test-rmse:0.419466+0.059866 
## [41] train-rmse:0.275018+0.005925    test-rmse:0.419998+0.059254 
## [42] train-rmse:0.272833+0.006536    test-rmse:0.419879+0.058165 
## [43] train-rmse:0.270491+0.007153    test-rmse:0.418181+0.058484 
## [44] train-rmse:0.268314+0.006439    test-rmse:0.418406+0.058442 
## [45] train-rmse:0.266924+0.006430    test-rmse:0.418236+0.058665 
## [46] train-rmse:0.265317+0.007080    test-rmse:0.418760+0.059197 
## [47] train-rmse:0.263666+0.006787    test-rmse:0.417885+0.059533 
## [48] train-rmse:0.262455+0.006874    test-rmse:0.417555+0.059048 
## [49] train-rmse:0.260548+0.008543    test-rmse:0.417733+0.059280 
## [50] train-rmse:0.258445+0.008640    test-rmse:0.418726+0.058930 
## [51] train-rmse:0.256835+0.008147    test-rmse:0.417513+0.058900 
## [52] train-rmse:0.255491+0.007709    test-rmse:0.418435+0.058595 
## [53] train-rmse:0.254244+0.007477    test-rmse:0.417923+0.059231 
## [54] train-rmse:0.252683+0.007503    test-rmse:0.418739+0.059411 
## [55] train-rmse:0.250397+0.007999    test-rmse:0.417309+0.058236 
## [56] train-rmse:0.249459+0.007931    test-rmse:0.416502+0.059200 
## [57] train-rmse:0.248055+0.007738    test-rmse:0.417164+0.059332 
## [58] train-rmse:0.246191+0.007250    test-rmse:0.416654+0.059806 
## [59] train-rmse:0.243955+0.007387    test-rmse:0.415106+0.058853 
## [60] train-rmse:0.242469+0.007709    test-rmse:0.415268+0.058894 
## [61] train-rmse:0.239999+0.008583    test-rmse:0.414885+0.059473 
## [62] train-rmse:0.237697+0.007661    test-rmse:0.414234+0.060072 
## [63] train-rmse:0.235762+0.008494    test-rmse:0.415316+0.059749 
## [64] train-rmse:0.234046+0.008383    test-rmse:0.415289+0.060090 
## [65] train-rmse:0.232835+0.008187    test-rmse:0.414886+0.060353 
## [66] train-rmse:0.231371+0.008012    test-rmse:0.415088+0.059849 
## [67] train-rmse:0.229719+0.009138    test-rmse:0.414891+0.060068 
## [68] train-rmse:0.228137+0.008649    test-rmse:0.412891+0.059286 
## [69] train-rmse:0.226614+0.008642    test-rmse:0.411443+0.060585 
## [70] train-rmse:0.224802+0.008724    test-rmse:0.412032+0.060423 
## [71] train-rmse:0.222953+0.010324    test-rmse:0.410515+0.059761 
## [72] train-rmse:0.222169+0.010116    test-rmse:0.410156+0.059660 
## [73] train-rmse:0.220856+0.009610    test-rmse:0.409586+0.059688 
## [74] train-rmse:0.219341+0.010160    test-rmse:0.409607+0.059943 
## [75] train-rmse:0.218391+0.009939    test-rmse:0.409656+0.059925 
## [76] train-rmse:0.217220+0.010080    test-rmse:0.408674+0.059362 
## [77] train-rmse:0.215785+0.009802    test-rmse:0.408282+0.059723 
## [78] train-rmse:0.214283+0.009703    test-rmse:0.407625+0.060124 
## [79] train-rmse:0.212815+0.010179    test-rmse:0.407803+0.059985 
## [80] train-rmse:0.211846+0.010528    test-rmse:0.407680+0.059717 
## [81] train-rmse:0.210997+0.010313    test-rmse:0.407284+0.060227 
## [82] train-rmse:0.210047+0.010251    test-rmse:0.406228+0.061113 
## [83] train-rmse:0.208971+0.010084    test-rmse:0.406623+0.061504 
## [84] train-rmse:0.207503+0.010466    test-rmse:0.406227+0.061037 
## [85] train-rmse:0.206685+0.010851    test-rmse:0.405255+0.059212 
## [86] train-rmse:0.205653+0.010515    test-rmse:0.405286+0.058785 
## [87] train-rmse:0.204252+0.010766    test-rmse:0.404546+0.058264 
## [88] train-rmse:0.202514+0.011041    test-rmse:0.404563+0.058334 
## [89] train-rmse:0.201716+0.010828    test-rmse:0.404269+0.058135 
## [90] train-rmse:0.200782+0.010933    test-rmse:0.403974+0.058425 
## [91] train-rmse:0.199950+0.011109    test-rmse:0.404976+0.057850 
## [92] train-rmse:0.198627+0.011515    test-rmse:0.404711+0.057663 
## [93] train-rmse:0.197640+0.011047    test-rmse:0.404391+0.057153 
## [94] train-rmse:0.196579+0.010847    test-rmse:0.403411+0.057489 
## [95] train-rmse:0.195299+0.010403    test-rmse:0.403297+0.057164 
## [96] train-rmse:0.194068+0.010380    test-rmse:0.403614+0.057028 
## [97] train-rmse:0.193288+0.010631    test-rmse:0.402689+0.057896 
## [98] train-rmse:0.192318+0.010386    test-rmse:0.402268+0.057385 
## [99] train-rmse:0.191806+0.010394    test-rmse:0.402191+0.056964 
## [100]    train-rmse:0.190638+0.010704    test-rmse:0.402022+0.056598 
## [101]    train-rmse:0.189814+0.010759    test-rmse:0.402124+0.056080 
## [102]    train-rmse:0.189142+0.010643    test-rmse:0.401954+0.056960 
## [103]    train-rmse:0.188473+0.010614    test-rmse:0.402318+0.056219 
## [104]    train-rmse:0.187869+0.010411    test-rmse:0.402386+0.056485 
## [105]    train-rmse:0.186631+0.010373    test-rmse:0.402253+0.056649 
## [106]    train-rmse:0.185415+0.009776    test-rmse:0.401963+0.056751 
## [107]    train-rmse:0.184731+0.009855    test-rmse:0.402981+0.056722 
## [108]    train-rmse:0.183729+0.009226    test-rmse:0.403488+0.056140 
## Stopping. Best iteration:
## [102]    train-rmse:0.189142+0.010643    test-rmse:0.401954+0.056960
## 
## 100 
## [1]  train-rmse:1.097627+0.020598    test-rmse:1.105533+0.117982 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.773008+0.017553    test-rmse:0.776553+0.101621 
## [3]  train-rmse:0.582240+0.013827    test-rmse:0.602088+0.102045 
## [4]  train-rmse:0.473891+0.010332    test-rmse:0.514621+0.086494 
## [5]  train-rmse:0.415399+0.009139    test-rmse:0.467399+0.078035 
## [6]  train-rmse:0.381307+0.008324    test-rmse:0.445561+0.071258 
## [7]  train-rmse:0.360001+0.007420    test-rmse:0.432498+0.079096 
## [8]  train-rmse:0.341179+0.005181    test-rmse:0.424724+0.074020 
## [9]  train-rmse:0.325967+0.005901    test-rmse:0.427751+0.068824 
## [10] train-rmse:0.312827+0.005224    test-rmse:0.426009+0.066545 
## [11] train-rmse:0.303091+0.005081    test-rmse:0.426119+0.067925 
## [12] train-rmse:0.295094+0.005799    test-rmse:0.423485+0.069259 
## [13] train-rmse:0.288217+0.005954    test-rmse:0.420332+0.066290 
## [14] train-rmse:0.281794+0.006281    test-rmse:0.418356+0.069509 
## [15] train-rmse:0.275371+0.006083    test-rmse:0.418260+0.070340 
## [16] train-rmse:0.270129+0.005884    test-rmse:0.415800+0.071148 
## [17] train-rmse:0.264700+0.005781    test-rmse:0.413921+0.073794 
## [18] train-rmse:0.259490+0.006537    test-rmse:0.414017+0.075106 
## [19] train-rmse:0.255015+0.006841    test-rmse:0.411677+0.074245 
## [20] train-rmse:0.249318+0.006015    test-rmse:0.411768+0.076316 
## [21] train-rmse:0.242504+0.004490    test-rmse:0.410649+0.076259 
## [22] train-rmse:0.236368+0.006350    test-rmse:0.409128+0.075614 
## [23] train-rmse:0.231824+0.007363    test-rmse:0.408137+0.074134 
## [24] train-rmse:0.226896+0.006664    test-rmse:0.407503+0.076620 
## [25] train-rmse:0.222588+0.006222    test-rmse:0.405329+0.077083 
## [26] train-rmse:0.219389+0.005530    test-rmse:0.406087+0.076551 
## [27] train-rmse:0.216273+0.005933    test-rmse:0.405262+0.076719 
## [28] train-rmse:0.213580+0.006026    test-rmse:0.404887+0.077283 
## [29] train-rmse:0.209996+0.005880    test-rmse:0.407162+0.076620 
## [30] train-rmse:0.206618+0.005955    test-rmse:0.405214+0.076009 
## [31] train-rmse:0.203445+0.006021    test-rmse:0.405446+0.076941 
## [32] train-rmse:0.199426+0.005712    test-rmse:0.406375+0.077135 
## [33] train-rmse:0.196971+0.005207    test-rmse:0.405806+0.077804 
## [34] train-rmse:0.193767+0.004391    test-rmse:0.404392+0.078082 
## [35] train-rmse:0.190289+0.004810    test-rmse:0.403908+0.078757 
## [36] train-rmse:0.187876+0.004965    test-rmse:0.403972+0.078187 
## [37] train-rmse:0.185515+0.005470    test-rmse:0.404518+0.077737 
## [38] train-rmse:0.183136+0.004988    test-rmse:0.404307+0.077761 
## [39] train-rmse:0.179879+0.005525    test-rmse:0.405008+0.077392 
## [40] train-rmse:0.177781+0.005915    test-rmse:0.404888+0.078075 
## [41] train-rmse:0.175524+0.005939    test-rmse:0.405979+0.078052 
## Stopping. Best iteration:
## [35] train-rmse:0.190289+0.004810    test-rmse:0.403908+0.078757
## 
## 101 
## [1]  train-rmse:1.090673+0.019432    test-rmse:1.098207+0.119244 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.758039+0.012608    test-rmse:0.771045+0.110931 
## [3]  train-rmse:0.555033+0.009199    test-rmse:0.586087+0.093111 
## [4]  train-rmse:0.438262+0.005360    test-rmse:0.494647+0.079004 
## [5]  train-rmse:0.367898+0.003585    test-rmse:0.453872+0.077755 
## [6]  train-rmse:0.328363+0.004301    test-rmse:0.436902+0.071606 
## [7]  train-rmse:0.302753+0.004233    test-rmse:0.429250+0.061304 
## [8]  train-rmse:0.284162+0.002147    test-rmse:0.422548+0.057220 
## [9]  train-rmse:0.271477+0.002645    test-rmse:0.421019+0.054508 
## [10] train-rmse:0.257599+0.001980    test-rmse:0.420226+0.054265 
## [11] train-rmse:0.247447+0.002418    test-rmse:0.416920+0.049351 
## [12] train-rmse:0.240140+0.003523    test-rmse:0.418129+0.050104 
## [13] train-rmse:0.231535+0.005185    test-rmse:0.418344+0.049873 
## [14] train-rmse:0.225081+0.005124    test-rmse:0.417073+0.047330 
## [15] train-rmse:0.216091+0.006157    test-rmse:0.415750+0.048380 
## [16] train-rmse:0.208924+0.004753    test-rmse:0.414507+0.049346 
## [17] train-rmse:0.202209+0.005021    test-rmse:0.413140+0.049682 
## [18] train-rmse:0.196615+0.003638    test-rmse:0.412605+0.050293 
## [19] train-rmse:0.190341+0.006255    test-rmse:0.411942+0.050954 
## [20] train-rmse:0.185532+0.005711    test-rmse:0.410695+0.053526 
## [21] train-rmse:0.180504+0.005761    test-rmse:0.410474+0.054749 
## [22] train-rmse:0.176836+0.004994    test-rmse:0.410429+0.054982 
## [23] train-rmse:0.172045+0.005335    test-rmse:0.409629+0.055608 
## [24] train-rmse:0.166756+0.006276    test-rmse:0.406161+0.055740 
## [25] train-rmse:0.161360+0.006774    test-rmse:0.405281+0.057270 
## [26] train-rmse:0.157554+0.006326    test-rmse:0.405158+0.055747 
## [27] train-rmse:0.154291+0.006643    test-rmse:0.405157+0.054969 
## [28] train-rmse:0.151251+0.005886    test-rmse:0.403349+0.054241 
## [29] train-rmse:0.147347+0.005963    test-rmse:0.403324+0.055145 
## [30] train-rmse:0.144830+0.006696    test-rmse:0.403121+0.055726 
## [31] train-rmse:0.141897+0.006525    test-rmse:0.402520+0.054698 
## [32] train-rmse:0.137769+0.006876    test-rmse:0.402491+0.056029 
## [33] train-rmse:0.135705+0.007722    test-rmse:0.401517+0.056021 
## [34] train-rmse:0.131303+0.007774    test-rmse:0.401436+0.056750 
## [35] train-rmse:0.128217+0.008564    test-rmse:0.399367+0.058076 
## [36] train-rmse:0.125843+0.009010    test-rmse:0.399374+0.057181 
## [37] train-rmse:0.122949+0.008520    test-rmse:0.399995+0.057670 
## [38] train-rmse:0.120524+0.007689    test-rmse:0.399534+0.058037 
## [39] train-rmse:0.117655+0.008144    test-rmse:0.400583+0.059077 
## [40] train-rmse:0.115116+0.009334    test-rmse:0.400170+0.060230 
## [41] train-rmse:0.111385+0.007899    test-rmse:0.399955+0.059719 
## Stopping. Best iteration:
## [35] train-rmse:0.128217+0.008564    test-rmse:0.399367+0.058076
## 
## 102 
## [1]  train-rmse:1.086703+0.020030    test-rmse:1.094990+0.113071 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.748617+0.013195    test-rmse:0.769160+0.103151 
## [3]  train-rmse:0.542108+0.011149    test-rmse:0.589771+0.090952 
## [4]  train-rmse:0.412221+0.006846    test-rmse:0.491158+0.082439 
## [5]  train-rmse:0.335977+0.007217    test-rmse:0.438890+0.080460 
## [6]  train-rmse:0.292596+0.007682    test-rmse:0.418618+0.073981 
## [7]  train-rmse:0.261272+0.003792    test-rmse:0.411155+0.072078 
## [8]  train-rmse:0.240045+0.006342    test-rmse:0.409822+0.068773 
## [9]  train-rmse:0.225620+0.008560    test-rmse:0.409209+0.068640 
## [10] train-rmse:0.211462+0.008481    test-rmse:0.407379+0.071104 
## [11] train-rmse:0.198647+0.010177    test-rmse:0.407175+0.068503 
## [12] train-rmse:0.190487+0.009130    test-rmse:0.403107+0.064061 
## [13] train-rmse:0.179604+0.008627    test-rmse:0.402331+0.063188 
## [14] train-rmse:0.171415+0.009067    test-rmse:0.401767+0.062470 
## [15] train-rmse:0.162848+0.008467    test-rmse:0.403066+0.061387 
## [16] train-rmse:0.156133+0.009987    test-rmse:0.401438+0.061192 
## [17] train-rmse:0.150368+0.009808    test-rmse:0.401678+0.059997 
## [18] train-rmse:0.146128+0.009413    test-rmse:0.399967+0.062189 
## [19] train-rmse:0.140261+0.008451    test-rmse:0.399163+0.062637 
## [20] train-rmse:0.135047+0.009266    test-rmse:0.399074+0.063015 
## [21] train-rmse:0.129725+0.008977    test-rmse:0.400142+0.063143 
## [22] train-rmse:0.124793+0.009299    test-rmse:0.399545+0.062087 
## [23] train-rmse:0.120562+0.008879    test-rmse:0.399416+0.062551 
## [24] train-rmse:0.114867+0.008294    test-rmse:0.399430+0.063435 
## [25] train-rmse:0.111047+0.007439    test-rmse:0.398503+0.063489 
## [26] train-rmse:0.107478+0.006707    test-rmse:0.399341+0.062902 
## [27] train-rmse:0.103636+0.006072    test-rmse:0.396781+0.063517 
## [28] train-rmse:0.100619+0.005959    test-rmse:0.396566+0.063923 
## [29] train-rmse:0.097116+0.005173    test-rmse:0.396268+0.064955 
## [30] train-rmse:0.093217+0.004855    test-rmse:0.395181+0.064117 
## [31] train-rmse:0.089884+0.005544    test-rmse:0.394737+0.063315 
## [32] train-rmse:0.086524+0.005751    test-rmse:0.394248+0.064243 
## [33] train-rmse:0.083441+0.005043    test-rmse:0.393600+0.065526 
## [34] train-rmse:0.080481+0.004153    test-rmse:0.394051+0.065852 
## [35] train-rmse:0.077262+0.003378    test-rmse:0.394349+0.066594 
## [36] train-rmse:0.074469+0.003236    test-rmse:0.394544+0.065713 
## [37] train-rmse:0.072859+0.002955    test-rmse:0.394380+0.066126 
## [38] train-rmse:0.070260+0.002454    test-rmse:0.393976+0.066397 
## [39] train-rmse:0.068537+0.002612    test-rmse:0.393777+0.066344 
## Stopping. Best iteration:
## [33] train-rmse:0.083441+0.005043    test-rmse:0.393600+0.065526
## 
## 103 
## [1]  train-rmse:1.085366+0.020084    test-rmse:1.092607+0.110899 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.744117+0.013547    test-rmse:0.764524+0.102467 
## [3]  train-rmse:0.532548+0.011943    test-rmse:0.574790+0.092386 
## [4]  train-rmse:0.400058+0.008995    test-rmse:0.480969+0.078008 
## [5]  train-rmse:0.320634+0.005960    test-rmse:0.439133+0.076217 
## [6]  train-rmse:0.270475+0.008590    test-rmse:0.422205+0.068197 
## [7]  train-rmse:0.236506+0.007288    test-rmse:0.413921+0.063226 
## [8]  train-rmse:0.215632+0.009619    test-rmse:0.409985+0.061830 
## [9]  train-rmse:0.197509+0.006813    test-rmse:0.407187+0.061832 
## [10] train-rmse:0.184660+0.009407    test-rmse:0.404855+0.058229 
## [11] train-rmse:0.169735+0.012112    test-rmse:0.406387+0.060342 
## [12] train-rmse:0.155966+0.009798    test-rmse:0.406100+0.059754 
## [13] train-rmse:0.144843+0.006300    test-rmse:0.404420+0.059848 
## [14] train-rmse:0.135787+0.007133    test-rmse:0.402683+0.057933 
## [15] train-rmse:0.125154+0.009162    test-rmse:0.404574+0.056159 
## [16] train-rmse:0.118622+0.009319    test-rmse:0.405380+0.056380 
## [17] train-rmse:0.112091+0.010851    test-rmse:0.405356+0.056375 
## [18] train-rmse:0.105679+0.009576    test-rmse:0.405627+0.056397 
## [19] train-rmse:0.101265+0.008908    test-rmse:0.406265+0.056487 
## [20] train-rmse:0.096951+0.009244    test-rmse:0.405510+0.056070 
## Stopping. Best iteration:
## [14] train-rmse:0.135787+0.007133    test-rmse:0.402683+0.057933
## 
## 104 
## [1]  train-rmse:1.084748+0.020080    test-rmse:1.094552+0.110585 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.740992+0.013996    test-rmse:0.764200+0.101273 
## [3]  train-rmse:0.525094+0.011480    test-rmse:0.571343+0.091152 
## [4]  train-rmse:0.386716+0.010616    test-rmse:0.485479+0.080138 
## [5]  train-rmse:0.299974+0.009324    test-rmse:0.438194+0.073776 
## [6]  train-rmse:0.245953+0.010766    test-rmse:0.421561+0.069390 
## [7]  train-rmse:0.208708+0.012089    test-rmse:0.413471+0.061622 
## [8]  train-rmse:0.184904+0.013608    test-rmse:0.412521+0.058611 
## [9]  train-rmse:0.165552+0.015630    test-rmse:0.408058+0.058292 
## [10] train-rmse:0.146760+0.007769    test-rmse:0.407563+0.053820 
## [11] train-rmse:0.131795+0.008450    test-rmse:0.410340+0.050457 
## [12] train-rmse:0.119623+0.008781    test-rmse:0.409383+0.047844 
## [13] train-rmse:0.108832+0.008672    test-rmse:0.411381+0.044735 
## [14] train-rmse:0.099199+0.009074    test-rmse:0.412588+0.045061 
## [15] train-rmse:0.090232+0.009127    test-rmse:0.412962+0.045760 
## [16] train-rmse:0.081061+0.007856    test-rmse:0.412221+0.046434 
## Stopping. Best iteration:
## [10] train-rmse:0.146760+0.007769    test-rmse:0.407563+0.053820
## 
## 105 
## [1]  train-rmse:1.115068+0.021402    test-rmse:1.111322+0.104943 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.848141+0.018340    test-rmse:0.844804+0.096071 
## [3]  train-rmse:0.726857+0.017959    test-rmse:0.724099+0.089863 
## [4]  train-rmse:0.677266+0.018126    test-rmse:0.674980+0.086240 
## [5]  train-rmse:0.641855+0.016606    test-rmse:0.640680+0.085078 
## [6]  train-rmse:0.621238+0.013231    test-rmse:0.628053+0.078922 
## [7]  train-rmse:0.601573+0.013059    test-rmse:0.601852+0.074406 
## [8]  train-rmse:0.589549+0.012446    test-rmse:0.597127+0.070446 
## [9]  train-rmse:0.578602+0.012443    test-rmse:0.586940+0.064536 
## [10] train-rmse:0.569579+0.011429    test-rmse:0.582247+0.065318 
## [11] train-rmse:0.561303+0.011288    test-rmse:0.571566+0.066673 
## [12] train-rmse:0.553480+0.011129    test-rmse:0.566070+0.061137 
## [13] train-rmse:0.547777+0.010629    test-rmse:0.564103+0.063632 
## [14] train-rmse:0.542524+0.010321    test-rmse:0.556638+0.059386 
## [15] train-rmse:0.538206+0.010085    test-rmse:0.556184+0.061315 
## [16] train-rmse:0.534007+0.009971    test-rmse:0.552689+0.057822 
## [17] train-rmse:0.529780+0.010028    test-rmse:0.551304+0.059105 
## [18] train-rmse:0.526329+0.009847    test-rmse:0.548310+0.059308 
## [19] train-rmse:0.523043+0.009485    test-rmse:0.547735+0.057918 
## [20] train-rmse:0.519798+0.009445    test-rmse:0.543704+0.056691 
## [21] train-rmse:0.516447+0.008998    test-rmse:0.542017+0.054483 
## [22] train-rmse:0.513439+0.008870    test-rmse:0.540894+0.054723 
## [23] train-rmse:0.510774+0.008841    test-rmse:0.540761+0.056617 
## [24] train-rmse:0.508211+0.008833    test-rmse:0.538166+0.056659 
## [25] train-rmse:0.505759+0.008632    test-rmse:0.534348+0.054496 
## [26] train-rmse:0.503365+0.008603    test-rmse:0.534317+0.052009 
## [27] train-rmse:0.501002+0.008490    test-rmse:0.532681+0.051645 
## [28] train-rmse:0.498682+0.008424    test-rmse:0.529970+0.051545 
## [29] train-rmse:0.496529+0.008272    test-rmse:0.528764+0.053076 
## [30] train-rmse:0.494472+0.008172    test-rmse:0.526991+0.054372 
## [31] train-rmse:0.492475+0.008297    test-rmse:0.526124+0.053752 
## [32] train-rmse:0.490445+0.008358    test-rmse:0.522830+0.052905 
## [33] train-rmse:0.488540+0.008182    test-rmse:0.522933+0.052091 
## [34] train-rmse:0.486758+0.008106    test-rmse:0.522283+0.052158 
## [35] train-rmse:0.485020+0.008012    test-rmse:0.520598+0.053105 
## [36] train-rmse:0.483410+0.008028    test-rmse:0.518791+0.052928 
## [37] train-rmse:0.481761+0.008013    test-rmse:0.517207+0.050941 
## [38] train-rmse:0.480226+0.007981    test-rmse:0.518201+0.050459 
## [39] train-rmse:0.478607+0.007981    test-rmse:0.517967+0.048499 
## [40] train-rmse:0.477152+0.008033    test-rmse:0.516982+0.050392 
## [41] train-rmse:0.475768+0.007993    test-rmse:0.517642+0.050630 
## [42] train-rmse:0.474267+0.007950    test-rmse:0.517598+0.049876 
## [43] train-rmse:0.473038+0.007936    test-rmse:0.517342+0.048366 
## [44] train-rmse:0.471787+0.007958    test-rmse:0.515329+0.049305 
## [45] train-rmse:0.470578+0.007935    test-rmse:0.514697+0.049091 
## [46] train-rmse:0.469367+0.007932    test-rmse:0.512081+0.045479 
## [47] train-rmse:0.468245+0.007880    test-rmse:0.512144+0.044379 
## [48] train-rmse:0.467109+0.007819    test-rmse:0.511317+0.045384 
## [49] train-rmse:0.465992+0.007785    test-rmse:0.509938+0.045417 
## [50] train-rmse:0.464884+0.007727    test-rmse:0.508894+0.045361 
## [51] train-rmse:0.463857+0.007656    test-rmse:0.508873+0.046486 
## [52] train-rmse:0.462816+0.007629    test-rmse:0.507020+0.045195 
## [53] train-rmse:0.461824+0.007704    test-rmse:0.505193+0.044856 
## [54] train-rmse:0.460820+0.007664    test-rmse:0.503286+0.044185 
## [55] train-rmse:0.459809+0.007649    test-rmse:0.503263+0.044402 
## [56] train-rmse:0.458908+0.007635    test-rmse:0.503527+0.043218 
## [57] train-rmse:0.457990+0.007628    test-rmse:0.503313+0.043630 
## [58] train-rmse:0.457105+0.007559    test-rmse:0.502577+0.044427 
## [59] train-rmse:0.456259+0.007524    test-rmse:0.502485+0.044359 
## [60] train-rmse:0.455420+0.007492    test-rmse:0.501305+0.044033 
## [61] train-rmse:0.454544+0.007474    test-rmse:0.502257+0.043333 
## [62] train-rmse:0.453707+0.007444    test-rmse:0.502630+0.043813 
## [63] train-rmse:0.452869+0.007382    test-rmse:0.501376+0.043349 
## [64] train-rmse:0.452054+0.007432    test-rmse:0.501252+0.043688 
## [65] train-rmse:0.451283+0.007453    test-rmse:0.501134+0.042603 
## [66] train-rmse:0.450554+0.007465    test-rmse:0.501558+0.041422 
## [67] train-rmse:0.449836+0.007485    test-rmse:0.501719+0.040580 
## [68] train-rmse:0.449099+0.007479    test-rmse:0.500857+0.041280 
## [69] train-rmse:0.448380+0.007480    test-rmse:0.500812+0.041102 
## [70] train-rmse:0.447689+0.007488    test-rmse:0.501545+0.041659 
## [71] train-rmse:0.447024+0.007529    test-rmse:0.500872+0.041237 
## [72] train-rmse:0.446334+0.007592    test-rmse:0.500706+0.041874 
## [73] train-rmse:0.445700+0.007610    test-rmse:0.501713+0.042628 
## [74] train-rmse:0.445083+0.007604    test-rmse:0.501114+0.042568 
## [75] train-rmse:0.444470+0.007580    test-rmse:0.500234+0.042688 
## [76] train-rmse:0.443864+0.007547    test-rmse:0.500869+0.042333 
## [77] train-rmse:0.443266+0.007548    test-rmse:0.500879+0.040935 
## [78] train-rmse:0.442716+0.007531    test-rmse:0.500047+0.041551 
## [79] train-rmse:0.442156+0.007510    test-rmse:0.500192+0.041551 
## [80] train-rmse:0.441594+0.007511    test-rmse:0.500820+0.041140 
## [81] train-rmse:0.441046+0.007506    test-rmse:0.501040+0.042189 
## [82] train-rmse:0.440473+0.007524    test-rmse:0.499511+0.041593 
## [83] train-rmse:0.439955+0.007539    test-rmse:0.498576+0.041736 
## [84] train-rmse:0.439405+0.007559    test-rmse:0.498028+0.041764 
## [85] train-rmse:0.438846+0.007548    test-rmse:0.498587+0.042081 
## [86] train-rmse:0.438340+0.007565    test-rmse:0.496964+0.040617 
## [87] train-rmse:0.437801+0.007573    test-rmse:0.496737+0.040385 
## [88] train-rmse:0.437288+0.007577    test-rmse:0.495927+0.039792 
## [89] train-rmse:0.436756+0.007599    test-rmse:0.496516+0.040860 
## [90] train-rmse:0.436221+0.007600    test-rmse:0.496340+0.040840 
## [91] train-rmse:0.435733+0.007599    test-rmse:0.496337+0.041390 
## [92] train-rmse:0.435248+0.007574    test-rmse:0.496279+0.041474 
## [93] train-rmse:0.434792+0.007571    test-rmse:0.496745+0.042038 
## [94] train-rmse:0.434335+0.007607    test-rmse:0.495648+0.041356 
## [95] train-rmse:0.433875+0.007598    test-rmse:0.495030+0.040842 
## [96] train-rmse:0.433420+0.007600    test-rmse:0.495272+0.039458 
## [97] train-rmse:0.432957+0.007592    test-rmse:0.494704+0.039451 
## [98] train-rmse:0.432527+0.007599    test-rmse:0.494505+0.039278 
## [99] train-rmse:0.432096+0.007618    test-rmse:0.494226+0.039104 
## [100]    train-rmse:0.431668+0.007615    test-rmse:0.494784+0.039713 
## [101]    train-rmse:0.431249+0.007621    test-rmse:0.495710+0.039771 
## [102]    train-rmse:0.430846+0.007617    test-rmse:0.495739+0.039851 
## [103]    train-rmse:0.430420+0.007633    test-rmse:0.495564+0.039418 
## [104]    train-rmse:0.430013+0.007622    test-rmse:0.495183+0.039102 
## [105]    train-rmse:0.429584+0.007652    test-rmse:0.495041+0.039732 
## Stopping. Best iteration:
## [99] train-rmse:0.432096+0.007618    test-rmse:0.494226+0.039104
## 
## 106 
## [1]  train-rmse:1.086197+0.021718    test-rmse:1.089233+0.113411 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.775802+0.015559    test-rmse:0.775615+0.098324 
## [3]  train-rmse:0.611712+0.013294    test-rmse:0.618969+0.100239 
## [4]  train-rmse:0.530554+0.012657    test-rmse:0.545278+0.092545 
## [5]  train-rmse:0.486362+0.011113    test-rmse:0.509678+0.084928 
## [6]  train-rmse:0.454873+0.011251    test-rmse:0.486502+0.072268 
## [7]  train-rmse:0.434491+0.009539    test-rmse:0.474802+0.067879 
## [8]  train-rmse:0.419738+0.009304    test-rmse:0.466007+0.058931 
## [9]  train-rmse:0.406655+0.008005    test-rmse:0.457605+0.060118 
## [10] train-rmse:0.396054+0.006996    test-rmse:0.454793+0.055361 
## [11] train-rmse:0.388443+0.007227    test-rmse:0.453647+0.053639 
## [12] train-rmse:0.381514+0.007917    test-rmse:0.452504+0.056594 
## [13] train-rmse:0.375015+0.008446    test-rmse:0.447634+0.053308 
## [14] train-rmse:0.368962+0.008253    test-rmse:0.445039+0.053348 
## [15] train-rmse:0.364219+0.008015    test-rmse:0.447271+0.053780 
## [16] train-rmse:0.357207+0.007570    test-rmse:0.444960+0.055605 
## [17] train-rmse:0.351164+0.007264    test-rmse:0.445957+0.057208 
## [18] train-rmse:0.346687+0.007066    test-rmse:0.445319+0.054962 
## [19] train-rmse:0.342302+0.006667    test-rmse:0.442830+0.051239 
## [20] train-rmse:0.338322+0.006586    test-rmse:0.442678+0.052124 
## [21] train-rmse:0.334244+0.006935    test-rmse:0.441224+0.049447 
## [22] train-rmse:0.329799+0.006979    test-rmse:0.439298+0.050836 
## [23] train-rmse:0.324833+0.007002    test-rmse:0.438993+0.050221 
## [24] train-rmse:0.321167+0.006823    test-rmse:0.437730+0.050798 
## [25] train-rmse:0.317348+0.006952    test-rmse:0.433123+0.047257 
## [26] train-rmse:0.313923+0.007205    test-rmse:0.432884+0.048902 
## [27] train-rmse:0.308620+0.008966    test-rmse:0.432417+0.050494 
## [28] train-rmse:0.305729+0.008897    test-rmse:0.430410+0.050960 
## [29] train-rmse:0.302290+0.008878    test-rmse:0.430013+0.051764 
## [30] train-rmse:0.299946+0.009309    test-rmse:0.431239+0.051775 
## [31] train-rmse:0.297457+0.009294    test-rmse:0.430862+0.052029 
## [32] train-rmse:0.295300+0.008964    test-rmse:0.430532+0.051790 
## [33] train-rmse:0.293005+0.009018    test-rmse:0.430062+0.051562 
## [34] train-rmse:0.289872+0.007849    test-rmse:0.429133+0.051496 
## [35] train-rmse:0.286685+0.008107    test-rmse:0.428500+0.050809 
## [36] train-rmse:0.284013+0.008028    test-rmse:0.426893+0.052711 
## [37] train-rmse:0.281498+0.007857    test-rmse:0.426649+0.053320 
## [38] train-rmse:0.279123+0.008107    test-rmse:0.424525+0.055169 
## [39] train-rmse:0.276218+0.008063    test-rmse:0.424239+0.056281 
## [40] train-rmse:0.274517+0.008268    test-rmse:0.423767+0.055980 
## [41] train-rmse:0.272549+0.008446    test-rmse:0.423831+0.057180 
## [42] train-rmse:0.270567+0.007363    test-rmse:0.423373+0.057667 
## [43] train-rmse:0.269290+0.007712    test-rmse:0.423031+0.057903 
## [44] train-rmse:0.267884+0.007682    test-rmse:0.423278+0.058023 
## [45] train-rmse:0.266421+0.007662    test-rmse:0.422723+0.059487 
## [46] train-rmse:0.265072+0.007645    test-rmse:0.422434+0.059584 
## [47] train-rmse:0.263696+0.007512    test-rmse:0.421744+0.059966 
## [48] train-rmse:0.260363+0.008075    test-rmse:0.421017+0.060246 
## [49] train-rmse:0.258721+0.008169    test-rmse:0.421753+0.060470 
## [50] train-rmse:0.256465+0.009232    test-rmse:0.421181+0.059298 
## [51] train-rmse:0.255219+0.008887    test-rmse:0.422459+0.058948 
## [52] train-rmse:0.252979+0.008875    test-rmse:0.422470+0.058108 
## [53] train-rmse:0.250580+0.008565    test-rmse:0.421585+0.058544 
## [54] train-rmse:0.247577+0.009716    test-rmse:0.420452+0.058951 
## [55] train-rmse:0.245879+0.009577    test-rmse:0.419055+0.060444 
## [56] train-rmse:0.244291+0.009491    test-rmse:0.418815+0.060714 
## [57] train-rmse:0.242086+0.010457    test-rmse:0.418751+0.061791 
## [58] train-rmse:0.240349+0.010241    test-rmse:0.418572+0.061140 
## [59] train-rmse:0.238838+0.010063    test-rmse:0.417999+0.060607 
## [60] train-rmse:0.237312+0.010407    test-rmse:0.417103+0.060763 
## [61] train-rmse:0.234311+0.009295    test-rmse:0.418020+0.060344 
## [62] train-rmse:0.232379+0.008967    test-rmse:0.417441+0.060848 
## [63] train-rmse:0.230468+0.009113    test-rmse:0.416821+0.061122 
## [64] train-rmse:0.228460+0.008253    test-rmse:0.417424+0.061671 
## [65] train-rmse:0.227631+0.008216    test-rmse:0.417734+0.061756 
## [66] train-rmse:0.225845+0.007552    test-rmse:0.417420+0.062007 
## [67] train-rmse:0.224285+0.007002    test-rmse:0.417354+0.062211 
## [68] train-rmse:0.221872+0.006452    test-rmse:0.416846+0.061848 
## [69] train-rmse:0.220247+0.007403    test-rmse:0.416364+0.062139 
## [70] train-rmse:0.218021+0.007534    test-rmse:0.416677+0.061745 
## [71] train-rmse:0.217416+0.007555    test-rmse:0.416532+0.062135 
## [72] train-rmse:0.216330+0.008064    test-rmse:0.416108+0.062245 
## [73] train-rmse:0.215207+0.008093    test-rmse:0.415561+0.061802 
## [74] train-rmse:0.213360+0.008666    test-rmse:0.415652+0.062174 
## [75] train-rmse:0.212052+0.008441    test-rmse:0.414416+0.062733 
## [76] train-rmse:0.211148+0.008523    test-rmse:0.415386+0.062933 
## [77] train-rmse:0.209978+0.008310    test-rmse:0.416246+0.063083 
## [78] train-rmse:0.208692+0.008334    test-rmse:0.415682+0.062586 
## [79] train-rmse:0.208124+0.008291    test-rmse:0.416619+0.062769 
## [80] train-rmse:0.206995+0.007926    test-rmse:0.416349+0.063068 
## [81] train-rmse:0.205943+0.008434    test-rmse:0.416966+0.063358 
## Stopping. Best iteration:
## [75] train-rmse:0.212052+0.008441    test-rmse:0.414416+0.062733
## 
## 107 
## [1]  train-rmse:1.070342+0.020187    test-rmse:1.078957+0.118060 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.741515+0.017267    test-rmse:0.746221+0.101011 
## [3]  train-rmse:0.556247+0.013510    test-rmse:0.578741+0.100714 
## [4]  train-rmse:0.455397+0.009972    test-rmse:0.500139+0.084029 
## [5]  train-rmse:0.402796+0.008011    test-rmse:0.460626+0.080655 
## [6]  train-rmse:0.373354+0.008525    test-rmse:0.449378+0.075390 
## [7]  train-rmse:0.353882+0.006877    test-rmse:0.432832+0.078974 
## [8]  train-rmse:0.337301+0.005900    test-rmse:0.426409+0.075362 
## [9]  train-rmse:0.322972+0.007354    test-rmse:0.426029+0.073952 
## [10] train-rmse:0.309687+0.005575    test-rmse:0.426633+0.073435 
## [11] train-rmse:0.300194+0.006304    test-rmse:0.423398+0.072168 
## [12] train-rmse:0.293629+0.006176    test-rmse:0.423028+0.071411 
## [13] train-rmse:0.282882+0.006994    test-rmse:0.421170+0.072089 
## [14] train-rmse:0.275883+0.006515    test-rmse:0.419546+0.072062 
## [15] train-rmse:0.269722+0.007214    test-rmse:0.417227+0.072300 
## [16] train-rmse:0.263703+0.007974    test-rmse:0.416620+0.071540 
## [17] train-rmse:0.257256+0.009513    test-rmse:0.415791+0.074832 
## [18] train-rmse:0.252608+0.009120    test-rmse:0.416389+0.074205 
## [19] train-rmse:0.247761+0.009561    test-rmse:0.415723+0.075841 
## [20] train-rmse:0.241899+0.008837    test-rmse:0.416016+0.073786 
## [21] train-rmse:0.234401+0.008445    test-rmse:0.413812+0.075321 
## [22] train-rmse:0.228491+0.006213    test-rmse:0.412956+0.074810 
## [23] train-rmse:0.224857+0.005222    test-rmse:0.413773+0.074806 
## [24] train-rmse:0.220853+0.005680    test-rmse:0.413213+0.074317 
## [25] train-rmse:0.216725+0.005661    test-rmse:0.414673+0.074966 
## [26] train-rmse:0.212803+0.005590    test-rmse:0.413389+0.074235 
## [27] train-rmse:0.209468+0.006004    test-rmse:0.412654+0.074009 
## [28] train-rmse:0.205573+0.006382    test-rmse:0.411253+0.075001 
## [29] train-rmse:0.202121+0.007344    test-rmse:0.410659+0.075543 
## [30] train-rmse:0.199082+0.006415    test-rmse:0.410162+0.078108 
## [31] train-rmse:0.196071+0.007489    test-rmse:0.410046+0.078883 
## [32] train-rmse:0.193671+0.007663    test-rmse:0.410422+0.078945 
## [33] train-rmse:0.190300+0.007898    test-rmse:0.409130+0.077932 
## [34] train-rmse:0.187131+0.008124    test-rmse:0.409114+0.077961 
## [35] train-rmse:0.183808+0.006136    test-rmse:0.410192+0.077005 
## [36] train-rmse:0.181407+0.006367    test-rmse:0.410492+0.076968 
## [37] train-rmse:0.178832+0.007163    test-rmse:0.410453+0.075584 
## [38] train-rmse:0.177089+0.007433    test-rmse:0.410398+0.075497 
## [39] train-rmse:0.173736+0.007538    test-rmse:0.411411+0.074523 
## [40] train-rmse:0.170794+0.006891    test-rmse:0.410399+0.074940 
## Stopping. Best iteration:
## [34] train-rmse:0.187131+0.008124    test-rmse:0.409114+0.077961
## 
## 108 
## [1]  train-rmse:1.062843+0.018914    test-rmse:1.071130+0.119400 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.725266+0.011769    test-rmse:0.740164+0.110433 
## [3]  train-rmse:0.527359+0.008854    test-rmse:0.564044+0.089993 
## [4]  train-rmse:0.417857+0.005408    test-rmse:0.480431+0.074166 
## [5]  train-rmse:0.354359+0.005251    test-rmse:0.439585+0.078038 
## [6]  train-rmse:0.319172+0.005625    test-rmse:0.425551+0.072725 
## [7]  train-rmse:0.296834+0.004102    test-rmse:0.418632+0.071180 
## [8]  train-rmse:0.281228+0.004630    test-rmse:0.414980+0.066453 
## [9]  train-rmse:0.266711+0.005231    test-rmse:0.414920+0.065887 
## [10] train-rmse:0.254590+0.008965    test-rmse:0.404176+0.068142 
## [11] train-rmse:0.243510+0.012126    test-rmse:0.404438+0.065607 
## [12] train-rmse:0.233724+0.012279    test-rmse:0.402509+0.065925 
## [13] train-rmse:0.223075+0.013009    test-rmse:0.398545+0.067159 
## [14] train-rmse:0.215722+0.010712    test-rmse:0.397117+0.066141 
## [15] train-rmse:0.208985+0.011431    test-rmse:0.396124+0.067478 
## [16] train-rmse:0.201618+0.009491    test-rmse:0.398160+0.068377 
## [17] train-rmse:0.195884+0.009305    test-rmse:0.397406+0.069161 
## [18] train-rmse:0.188557+0.008431    test-rmse:0.394330+0.067795 
## [19] train-rmse:0.183885+0.009402    test-rmse:0.394000+0.068626 
## [20] train-rmse:0.179659+0.009854    test-rmse:0.394457+0.066450 
## [21] train-rmse:0.175109+0.008437    test-rmse:0.393947+0.065616 
## [22] train-rmse:0.171029+0.009633    test-rmse:0.392511+0.064525 
## [23] train-rmse:0.164192+0.009512    test-rmse:0.392147+0.064615 
## [24] train-rmse:0.159434+0.008673    test-rmse:0.391567+0.063048 
## [25] train-rmse:0.155648+0.008602    test-rmse:0.391161+0.064202 
## [26] train-rmse:0.152490+0.008357    test-rmse:0.391850+0.063511 
## [27] train-rmse:0.148245+0.008297    test-rmse:0.390782+0.063115 
## [28] train-rmse:0.144514+0.007501    test-rmse:0.389687+0.061416 
## [29] train-rmse:0.140690+0.009104    test-rmse:0.389163+0.061801 
## [30] train-rmse:0.136979+0.009944    test-rmse:0.387856+0.061510 
## [31] train-rmse:0.133069+0.008292    test-rmse:0.387551+0.061093 
## [32] train-rmse:0.130144+0.007662    test-rmse:0.388370+0.061377 
## [33] train-rmse:0.126178+0.007659    test-rmse:0.389847+0.061813 
## [34] train-rmse:0.123267+0.007612    test-rmse:0.388287+0.061825 
## [35] train-rmse:0.119832+0.007029    test-rmse:0.388136+0.062152 
## [36] train-rmse:0.116815+0.007709    test-rmse:0.389041+0.062608 
## [37] train-rmse:0.114465+0.006666    test-rmse:0.389340+0.062994 
## Stopping. Best iteration:
## [31] train-rmse:0.133069+0.008292    test-rmse:0.387551+0.061093
## 
## 109 
## [1]  train-rmse:1.058579+0.019548    test-rmse:1.067785+0.112832 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.715473+0.013053    test-rmse:0.735913+0.101725 
## [3]  train-rmse:0.509467+0.009982    test-rmse:0.553814+0.086850 
## [4]  train-rmse:0.389390+0.005493    test-rmse:0.467725+0.082341 
## [5]  train-rmse:0.320172+0.005869    test-rmse:0.426575+0.076004 
## [6]  train-rmse:0.282349+0.007561    test-rmse:0.410647+0.071099 
## [7]  train-rmse:0.257710+0.008118    test-rmse:0.405411+0.069559 
## [8]  train-rmse:0.239565+0.008920    test-rmse:0.401631+0.068095 
## [9]  train-rmse:0.227844+0.007908    test-rmse:0.399327+0.070633 
## [10] train-rmse:0.214681+0.010817    test-rmse:0.398972+0.068046 
## [11] train-rmse:0.201179+0.009291    test-rmse:0.398086+0.067622 
## [12] train-rmse:0.187995+0.004800    test-rmse:0.395386+0.068587 
## [13] train-rmse:0.175763+0.004378    test-rmse:0.395013+0.064898 
## [14] train-rmse:0.168655+0.002699    test-rmse:0.393059+0.063942 
## [15] train-rmse:0.162168+0.002878    test-rmse:0.393338+0.065121 
## [16] train-rmse:0.156030+0.003136    test-rmse:0.393521+0.066832 
## [17] train-rmse:0.149970+0.001882    test-rmse:0.392387+0.068215 
## [18] train-rmse:0.142577+0.002912    test-rmse:0.390642+0.069078 
## [19] train-rmse:0.138101+0.004210    test-rmse:0.389491+0.071263 
## [20] train-rmse:0.130240+0.005673    test-rmse:0.388866+0.071107 
## [21] train-rmse:0.124327+0.005397    test-rmse:0.390498+0.069915 
## [22] train-rmse:0.117273+0.006044    test-rmse:0.391518+0.072049 
## [23] train-rmse:0.112704+0.006625    test-rmse:0.390702+0.070811 
## [24] train-rmse:0.108400+0.007894    test-rmse:0.392082+0.069111 
## [25] train-rmse:0.105051+0.008162    test-rmse:0.392862+0.068759 
## [26] train-rmse:0.100445+0.007968    test-rmse:0.393294+0.069087 
## Stopping. Best iteration:
## [20] train-rmse:0.130240+0.005673    test-rmse:0.388866+0.071107
## 
## 110 
## [1]  train-rmse:1.057127+0.019600    test-rmse:1.065285+0.110552 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.710004+0.013043    test-rmse:0.733101+0.101644 
## [3]  train-rmse:0.500453+0.012132    test-rmse:0.544470+0.084558 
## [4]  train-rmse:0.375373+0.009881    test-rmse:0.464034+0.077242 
## [5]  train-rmse:0.299261+0.008480    test-rmse:0.425951+0.073163 
## [6]  train-rmse:0.254815+0.007264    test-rmse:0.414461+0.071745 
## [7]  train-rmse:0.223680+0.008626    test-rmse:0.402506+0.066819 
## [8]  train-rmse:0.200636+0.012267    test-rmse:0.398057+0.059127 
## [9]  train-rmse:0.181212+0.006620    test-rmse:0.395553+0.059668 
## [10] train-rmse:0.170920+0.006891    test-rmse:0.396262+0.057235 
## [11] train-rmse:0.157048+0.004678    test-rmse:0.400091+0.053171 
## [12] train-rmse:0.146553+0.001863    test-rmse:0.398454+0.051924 
## [13] train-rmse:0.137420+0.005044    test-rmse:0.398886+0.051373 
## [14] train-rmse:0.125672+0.004795    test-rmse:0.398158+0.052993 
## [15] train-rmse:0.118475+0.003781    test-rmse:0.397109+0.054929 
## Stopping. Best iteration:
## [9]  train-rmse:0.181212+0.006620    test-rmse:0.395553+0.059668
## 
## 111 
## [1]  train-rmse:1.056461+0.019595    test-rmse:1.067360+0.110299 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:0.706778+0.013587    test-rmse:0.733248+0.100276 
## [3]  train-rmse:0.493695+0.012709    test-rmse:0.549352+0.082384 
## [4]  train-rmse:0.362441+0.012037    test-rmse:0.465365+0.073881 
## [5]  train-rmse:0.281333+0.013469    test-rmse:0.422677+0.069231 
## [6]  train-rmse:0.230052+0.014199    test-rmse:0.408418+0.062697 
## [7]  train-rmse:0.199616+0.013957    test-rmse:0.404749+0.057401 
## [8]  train-rmse:0.174315+0.012524    test-rmse:0.404333+0.056640 
## [9]  train-rmse:0.156863+0.012423    test-rmse:0.403803+0.053178 
## [10] train-rmse:0.136455+0.006254    test-rmse:0.402040+0.049421 
## [11] train-rmse:0.125165+0.006882    test-rmse:0.401563+0.047451 
## [12] train-rmse:0.113026+0.008085    test-rmse:0.402449+0.043666 
## [13] train-rmse:0.105160+0.007852    test-rmse:0.403136+0.043630 
## [14] train-rmse:0.097666+0.007516    test-rmse:0.400886+0.044206 
## [15] train-rmse:0.091116+0.009313    test-rmse:0.401317+0.042666 
## [16] train-rmse:0.084066+0.008681    test-rmse:0.402610+0.042224 
## [17] train-rmse:0.076500+0.009760    test-rmse:0.402478+0.042456 
## [18] train-rmse:0.071516+0.008747    test-rmse:0.402851+0.042653 
## [19] train-rmse:0.065477+0.007691    test-rmse:0.403201+0.041633 
## [20] train-rmse:0.061847+0.007456    test-rmse:0.403595+0.041923 
## Stopping. Best iteration:
## [14] train-rmse:0.097666+0.007516    test-rmse:0.400886+0.044206
## 
## 112
##    max_depth  eta
## 88         4 0.34
## [1] 1.22
## [1] 0.125
## [1] 32
## [1]  train-rmse:8.801410+0.063773    test-rmse:8.797373+0.276559 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.996855+0.056656    test-rmse:7.989608+0.274703 
## [3]  train-rmse:7.278528+0.049931    test-rmse:7.272552+0.269181 
## [4]  train-rmse:6.638549+0.043868    test-rmse:6.633544+0.268302 
## [5]  train-rmse:6.069083+0.038534    test-rmse:6.063704+0.261787 
## [6]  train-rmse:5.563678+0.033672    test-rmse:5.559228+0.258198 
## [7]  train-rmse:5.116204+0.029358    test-rmse:5.115140+0.248005 
## [8]  train-rmse:4.721193+0.025636    test-rmse:4.723271+0.241130 
## [9]  train-rmse:4.373668+0.022102    test-rmse:4.375931+0.232943 
## [10] train-rmse:4.069049+0.019086    test-rmse:4.076793+0.221844 
## [11] train-rmse:3.803135+0.016653    test-rmse:3.815533+0.211051 
## [12] train-rmse:3.571802+0.014668    test-rmse:3.589126+0.194586 
## [13] train-rmse:3.371021+0.013296    test-rmse:3.389444+0.183583 
## [14] train-rmse:3.197823+0.012415    test-rmse:3.214830+0.169173 
## [15] train-rmse:3.048821+0.011963    test-rmse:3.068779+0.157202 
## [16] train-rmse:2.920928+0.011896    test-rmse:2.942512+0.139706 
## [17] train-rmse:2.811491+0.012036    test-rmse:2.838470+0.126802 
## [18] train-rmse:2.718158+0.012204    test-rmse:2.745614+0.114746 
## [19] train-rmse:2.638732+0.012649    test-rmse:2.669374+0.103178 
## [20] train-rmse:2.571121+0.013307    test-rmse:2.603682+0.087958 
## [21] train-rmse:2.513557+0.013840    test-rmse:2.546997+0.079999 
## [22] train-rmse:2.464540+0.014197    test-rmse:2.499386+0.070405 
## [23] train-rmse:2.422579+0.014552    test-rmse:2.459368+0.064334 
## [24] train-rmse:2.387059+0.014931    test-rmse:2.426760+0.058062 
## [25] train-rmse:2.356525+0.015394    test-rmse:2.398854+0.051602 
## [26] train-rmse:2.330121+0.015750    test-rmse:2.373342+0.049059 
## [27] train-rmse:2.307214+0.016097    test-rmse:2.351414+0.047126 
## [28] train-rmse:2.287261+0.016553    test-rmse:2.331835+0.048614 
## [29] train-rmse:2.269726+0.016960    test-rmse:2.318372+0.049404 
## [30] train-rmse:2.254295+0.017330    test-rmse:2.299190+0.051618 
## [31] train-rmse:2.240519+0.017613    test-rmse:2.282832+0.054081 
## [32] train-rmse:2.228073+0.017954    test-rmse:2.273255+0.058416 
## [33] train-rmse:2.216764+0.018350    test-rmse:2.261358+0.058326 
## [34] train-rmse:2.206411+0.018821    test-rmse:2.253755+0.061330 
## [35] train-rmse:2.196807+0.019102    test-rmse:2.247709+0.061817 
## [36] train-rmse:2.188017+0.019272    test-rmse:2.238772+0.062839 
## [37] train-rmse:2.179795+0.019658    test-rmse:2.228281+0.064032 
## [38] train-rmse:2.172055+0.019983    test-rmse:2.219967+0.069714 
## [39] train-rmse:2.164723+0.020325    test-rmse:2.215204+0.069977 
## [40] train-rmse:2.157729+0.020651    test-rmse:2.210421+0.069968 
## [41] train-rmse:2.150941+0.021003    test-rmse:2.204343+0.073018 
## [42] train-rmse:2.144343+0.021289    test-rmse:2.201669+0.074435 
## [43] train-rmse:2.137950+0.021482    test-rmse:2.190643+0.075139 
## [44] train-rmse:2.131818+0.021644    test-rmse:2.187295+0.075681 
## [45] train-rmse:2.125911+0.021816    test-rmse:2.182742+0.076662 
## [46] train-rmse:2.120160+0.022003    test-rmse:2.176844+0.080089 
## [47] train-rmse:2.114567+0.022220    test-rmse:2.172236+0.080136 
## [48] train-rmse:2.108995+0.022466    test-rmse:2.165274+0.081577 
## [49] train-rmse:2.103602+0.022712    test-rmse:2.161130+0.080989 
## [50] train-rmse:2.098310+0.022958    test-rmse:2.157396+0.082878 
## [51] train-rmse:2.093086+0.023206    test-rmse:2.154564+0.083290 
## [52] train-rmse:2.087891+0.023469    test-rmse:2.150352+0.083454 
## [53] train-rmse:2.082753+0.023664    test-rmse:2.143669+0.085480 
## [54] train-rmse:2.077750+0.023913    test-rmse:2.142922+0.084870 
## [55] train-rmse:2.072812+0.024162    test-rmse:2.136422+0.087012 
## [56] train-rmse:2.067922+0.024420    test-rmse:2.128346+0.087267 
## [57] train-rmse:2.063124+0.024664    test-rmse:2.125789+0.088538 
## [58] train-rmse:2.058320+0.024926    test-rmse:2.121199+0.086628 
## [59] train-rmse:2.053624+0.025146    test-rmse:2.117615+0.087937 
## [60] train-rmse:2.048973+0.025313    test-rmse:2.114638+0.088425 
## [61] train-rmse:2.044454+0.025488    test-rmse:2.109907+0.090288 
## [62] train-rmse:2.039949+0.025730    test-rmse:2.105870+0.087282 
## [63] train-rmse:2.035496+0.026013    test-rmse:2.101151+0.087077 
## [64] train-rmse:2.031092+0.026257    test-rmse:2.099773+0.089878 
## [65] train-rmse:2.026756+0.026520    test-rmse:2.093617+0.089762 
## [66] train-rmse:2.022457+0.026725    test-rmse:2.090098+0.090139 
## [67] train-rmse:2.018176+0.026900    test-rmse:2.087204+0.090027 
## [68] train-rmse:2.013953+0.027145    test-rmse:2.083629+0.090077 
## [69] train-rmse:2.009750+0.027351    test-rmse:2.077344+0.088818 
## [70] train-rmse:2.005613+0.027562    test-rmse:2.073614+0.088181 
## [71] train-rmse:2.001512+0.027726    test-rmse:2.071082+0.088317 
## [72] train-rmse:1.997443+0.027867    test-rmse:2.069893+0.088980 
## [73] train-rmse:1.993435+0.028054    test-rmse:2.064396+0.091087 
## [74] train-rmse:1.989492+0.028247    test-rmse:2.062507+0.092278 
## [75] train-rmse:1.985613+0.028451    test-rmse:2.060399+0.094583 
## [76] train-rmse:1.981753+0.028631    test-rmse:2.056383+0.093867 
## [77] train-rmse:1.977923+0.028808    test-rmse:2.052069+0.093253 
## [78] train-rmse:1.974084+0.028971    test-rmse:2.049236+0.093656 
## [79] train-rmse:1.970274+0.029088    test-rmse:2.046252+0.093230 
## [80] train-rmse:1.966517+0.029248    test-rmse:2.040168+0.093957 
## [81] train-rmse:1.962767+0.029419    test-rmse:2.037784+0.093786 
## [82] train-rmse:1.959120+0.029630    test-rmse:2.034057+0.091003 
## [83] train-rmse:1.955484+0.029829    test-rmse:2.029274+0.089237 
## [84] train-rmse:1.951893+0.030013    test-rmse:2.028684+0.087961 
## [85] train-rmse:1.948308+0.030258    test-rmse:2.024581+0.085697 
## [86] train-rmse:1.944800+0.030473    test-rmse:2.021054+0.086572 
## [87] train-rmse:1.941316+0.030681    test-rmse:2.016672+0.086987 
## [88] train-rmse:1.937816+0.030850    test-rmse:2.014531+0.089025 
## [89] train-rmse:1.934350+0.030997    test-rmse:2.011894+0.088445 
## [90] train-rmse:1.930949+0.031163    test-rmse:2.010199+0.088552 
## [91] train-rmse:1.927554+0.031331    test-rmse:2.007725+0.088822 
## [92] train-rmse:1.924176+0.031529    test-rmse:2.003015+0.088466 
## [93] train-rmse:1.920829+0.031705    test-rmse:2.000966+0.089898 
## [94] train-rmse:1.917523+0.031881    test-rmse:1.997571+0.092148 
## [95] train-rmse:1.914226+0.032082    test-rmse:1.994675+0.092251 
## [96] train-rmse:1.910965+0.032287    test-rmse:1.991232+0.090533 
## [97] train-rmse:1.907715+0.032445    test-rmse:1.986547+0.088253 
## [98] train-rmse:1.904520+0.032619    test-rmse:1.983774+0.090132 
## [99] train-rmse:1.901362+0.032807    test-rmse:1.980518+0.089991 
## [100]    train-rmse:1.898215+0.033009    test-rmse:1.976046+0.090049 
## [101]    train-rmse:1.895080+0.033205    test-rmse:1.974794+0.087696 
## [102]    train-rmse:1.891955+0.033418    test-rmse:1.972175+0.087066 
## [103]    train-rmse:1.888873+0.033629    test-rmse:1.969172+0.087689 
## [104]    train-rmse:1.885832+0.033804    test-rmse:1.966943+0.087176 
## [105]    train-rmse:1.882807+0.034000    test-rmse:1.964977+0.087646 
## [106]    train-rmse:1.879810+0.034199    test-rmse:1.963345+0.088764 
## [107]    train-rmse:1.876833+0.034361    test-rmse:1.960922+0.088566 
## [108]    train-rmse:1.873870+0.034549    test-rmse:1.960909+0.088715 
## [109]    train-rmse:1.870941+0.034728    test-rmse:1.958506+0.086750 
## [110]    train-rmse:1.868025+0.034898    test-rmse:1.958311+0.088988 
## [111]    train-rmse:1.865137+0.035067    test-rmse:1.956107+0.089134 
## [112]    train-rmse:1.862290+0.035230    test-rmse:1.953913+0.089508 
## [113]    train-rmse:1.859436+0.035405    test-rmse:1.949821+0.089122 
## [114]    train-rmse:1.856600+0.035556    test-rmse:1.947286+0.089338 
## [115]    train-rmse:1.853769+0.035711    test-rmse:1.944039+0.091244 
## [116]    train-rmse:1.850958+0.035888    test-rmse:1.940574+0.092552 
## [117]    train-rmse:1.848157+0.036048    test-rmse:1.938081+0.092482 
## [118]    train-rmse:1.845377+0.036231    test-rmse:1.934308+0.091089 
## [119]    train-rmse:1.842627+0.036405    test-rmse:1.932912+0.091233 
## [120]    train-rmse:1.839881+0.036587    test-rmse:1.932233+0.091012 
## [121]    train-rmse:1.837138+0.036760    test-rmse:1.927705+0.090583 
## [122]    train-rmse:1.834430+0.036938    test-rmse:1.925406+0.091190 
## [123]    train-rmse:1.831744+0.037118    test-rmse:1.922611+0.091266 
## [124]    train-rmse:1.829109+0.037282    test-rmse:1.920747+0.090437 
## [125]    train-rmse:1.826483+0.037413    test-rmse:1.920648+0.091913 
## [126]    train-rmse:1.823841+0.037559    test-rmse:1.917835+0.090869 
## [127]    train-rmse:1.821269+0.037695    test-rmse:1.915892+0.090410 
## [128]    train-rmse:1.818708+0.037854    test-rmse:1.913579+0.090550 
## [129]    train-rmse:1.816170+0.037994    test-rmse:1.911426+0.089903 
## [130]    train-rmse:1.813624+0.038134    test-rmse:1.910208+0.090360 
## [131]    train-rmse:1.811127+0.038284    test-rmse:1.907703+0.091489 
## [132]    train-rmse:1.808618+0.038426    test-rmse:1.905098+0.092253 
## [133]    train-rmse:1.806124+0.038563    test-rmse:1.903520+0.091326 
## [134]    train-rmse:1.803641+0.038687    test-rmse:1.901314+0.091954 
## [135]    train-rmse:1.801195+0.038832    test-rmse:1.900485+0.093203 
## [136]    train-rmse:1.798738+0.039004    test-rmse:1.897657+0.092196 
## [137]    train-rmse:1.796286+0.039146    test-rmse:1.896751+0.094725 
## [138]    train-rmse:1.793836+0.039299    test-rmse:1.895267+0.094344 
## [139]    train-rmse:1.791407+0.039447    test-rmse:1.891239+0.096639 
## [140]    train-rmse:1.789009+0.039575    test-rmse:1.888162+0.094676 
## [141]    train-rmse:1.786623+0.039710    test-rmse:1.887738+0.094134 
## [142]    train-rmse:1.784267+0.039853    test-rmse:1.885569+0.093800 
## [143]    train-rmse:1.781926+0.039980    test-rmse:1.883001+0.092138 
## [144]    train-rmse:1.779604+0.040129    test-rmse:1.879752+0.093841 
## [145]    train-rmse:1.777301+0.040271    test-rmse:1.877325+0.095165 
## [146]    train-rmse:1.775001+0.040381    test-rmse:1.874680+0.094611 
## [147]    train-rmse:1.772697+0.040516    test-rmse:1.873305+0.093467 
## [148]    train-rmse:1.770386+0.040675    test-rmse:1.870893+0.094286 
## [149]    train-rmse:1.768089+0.040842    test-rmse:1.868068+0.094901 
## [150]    train-rmse:1.765858+0.040995    test-rmse:1.866268+0.094320 
## [151]    train-rmse:1.763651+0.041118    test-rmse:1.864738+0.094599 
## [152]    train-rmse:1.761457+0.041238    test-rmse:1.864303+0.094020 
## [153]    train-rmse:1.759266+0.041360    test-rmse:1.861790+0.094407 
## [154]    train-rmse:1.757075+0.041481    test-rmse:1.858935+0.094161 
## [155]    train-rmse:1.754911+0.041589    test-rmse:1.857130+0.093483 
## [156]    train-rmse:1.752747+0.041703    test-rmse:1.855888+0.094635 
## [157]    train-rmse:1.750625+0.041825    test-rmse:1.855588+0.095235 
## [158]    train-rmse:1.748468+0.041945    test-rmse:1.854581+0.095784 
## [159]    train-rmse:1.746360+0.042081    test-rmse:1.852966+0.095383 
## [160]    train-rmse:1.744264+0.042211    test-rmse:1.851403+0.095347 
## [161]    train-rmse:1.742178+0.042334    test-rmse:1.849066+0.095376 
## [162]    train-rmse:1.740096+0.042448    test-rmse:1.848030+0.097960 
## [163]    train-rmse:1.738027+0.042573    test-rmse:1.845435+0.098492 
## [164]    train-rmse:1.735972+0.042718    test-rmse:1.843582+0.099315 
## [165]    train-rmse:1.733916+0.042856    test-rmse:1.840373+0.098740 
## [166]    train-rmse:1.731872+0.042990    test-rmse:1.837670+0.099395 
## [167]    train-rmse:1.729837+0.043105    test-rmse:1.835301+0.098508 
## [168]    train-rmse:1.727804+0.043194    test-rmse:1.832205+0.097973 
## [169]    train-rmse:1.725797+0.043264    test-rmse:1.829824+0.098273 
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## [589]    train-rmse:1.378594+0.053386    test-rmse:1.524179+0.178810 
## [590]    train-rmse:1.378314+0.053384    test-rmse:1.523407+0.178769 
## [591]    train-rmse:1.378034+0.053378    test-rmse:1.523288+0.179030 
## [592]    train-rmse:1.377753+0.053378    test-rmse:1.522595+0.178769 
## [593]    train-rmse:1.377475+0.053376    test-rmse:1.522226+0.178780 
## [594]    train-rmse:1.377200+0.053374    test-rmse:1.521794+0.178956 
## [595]    train-rmse:1.376924+0.053373    test-rmse:1.521575+0.178501 
## [596]    train-rmse:1.376651+0.053372    test-rmse:1.521772+0.179386 
## [597]    train-rmse:1.376378+0.053370    test-rmse:1.521134+0.179998 
## [598]    train-rmse:1.376107+0.053369    test-rmse:1.521073+0.180628 
## [599]    train-rmse:1.375837+0.053367    test-rmse:1.521351+0.181124 
## [600]    train-rmse:1.375570+0.053366    test-rmse:1.521681+0.180885 
## [601]    train-rmse:1.375301+0.053364    test-rmse:1.521557+0.180760 
## [602]    train-rmse:1.375032+0.053362    test-rmse:1.522010+0.180743 
## [603]    train-rmse:1.374764+0.053360    test-rmse:1.522048+0.180410 
## [604]    train-rmse:1.374498+0.053359    test-rmse:1.521771+0.180501 
## Stopping. Best iteration:
## [598]    train-rmse:1.376107+0.053369    test-rmse:1.521073+0.180628
## 
## 1 
## [1]  train-rmse:8.789440+0.063950    test-rmse:8.785614+0.274244 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.973733+0.058414    test-rmse:7.969443+0.266366 
## [3]  train-rmse:7.245054+0.053821    test-rmse:7.238426+0.258899 
## [4]  train-rmse:6.593414+0.047359    test-rmse:6.587706+0.251795 
## [5]  train-rmse:6.011371+0.041493    test-rmse:6.005166+0.247941 
## [6]  train-rmse:5.493581+0.036832    test-rmse:5.493338+0.239819 
## [7]  train-rmse:5.033976+0.033897    test-rmse:5.036446+0.231822 
## [8]  train-rmse:4.626179+0.030294    test-rmse:4.632743+0.221405 
## [9]  train-rmse:4.265695+0.028100    test-rmse:4.275876+0.207858 
## [10] train-rmse:3.945516+0.026091    test-rmse:3.961258+0.194472 
## [11] train-rmse:3.659155+0.019991    test-rmse:3.681070+0.187429 
## [12] train-rmse:3.412688+0.019013    test-rmse:3.438998+0.172137 
## [13] train-rmse:3.194715+0.017853    test-rmse:3.232044+0.156321 
## [14] train-rmse:3.004899+0.017085    test-rmse:3.048188+0.143495 
## [15] train-rmse:2.840541+0.015698    test-rmse:2.891989+0.131423 
## [16] train-rmse:2.697824+0.015652    test-rmse:2.753195+0.115386 
## [17] train-rmse:2.571741+0.013227    test-rmse:2.631053+0.105848 
## [18] train-rmse:2.466009+0.012430    test-rmse:2.533699+0.092956 
## [19] train-rmse:2.372024+0.012673    test-rmse:2.444969+0.085975 
## [20] train-rmse:2.292583+0.012831    test-rmse:2.370657+0.072239 
## [21] train-rmse:2.223290+0.013660    test-rmse:2.310521+0.067247 
## [22] train-rmse:2.164191+0.012647    test-rmse:2.256828+0.061961 
## [23] train-rmse:2.110912+0.015075    test-rmse:2.207143+0.055104 
## [24] train-rmse:2.067049+0.014750    test-rmse:2.169404+0.050468 
## [25] train-rmse:2.027909+0.015005    test-rmse:2.138279+0.050620 
## [26] train-rmse:1.992941+0.014680    test-rmse:2.108110+0.056745 
## [27] train-rmse:1.963419+0.014969    test-rmse:2.081645+0.057166 
## [28] train-rmse:1.937721+0.015419    test-rmse:2.057528+0.060502 
## [29] train-rmse:1.914648+0.016066    test-rmse:2.035725+0.062348 
## [30] train-rmse:1.894045+0.016011    test-rmse:2.018113+0.063086 
## [31] train-rmse:1.874803+0.016099    test-rmse:2.008082+0.070723 
## [32] train-rmse:1.857602+0.015710    test-rmse:1.991403+0.073072 
## [33] train-rmse:1.842550+0.016328    test-rmse:1.977142+0.076635 
## [34] train-rmse:1.828589+0.016741    test-rmse:1.963488+0.077517 
## [35] train-rmse:1.814223+0.019934    test-rmse:1.950846+0.081300 
## [36] train-rmse:1.801308+0.020961    test-rmse:1.940979+0.080977 
## [37] train-rmse:1.789437+0.021225    test-rmse:1.932796+0.085100 
## [38] train-rmse:1.778466+0.021586    test-rmse:1.928760+0.089244 
## [39] train-rmse:1.767346+0.021853    test-rmse:1.916452+0.091471 
## [40] train-rmse:1.757277+0.022165    test-rmse:1.908181+0.089533 
## [41] train-rmse:1.747603+0.022769    test-rmse:1.899221+0.091723 
## [42] train-rmse:1.738553+0.022858    test-rmse:1.890477+0.092348 
## [43] train-rmse:1.729682+0.023765    test-rmse:1.883310+0.094436 
## [44] train-rmse:1.721260+0.024052    test-rmse:1.875505+0.095056 
## [45] train-rmse:1.712836+0.024185    test-rmse:1.866528+0.094957 
## [46] train-rmse:1.703535+0.026760    test-rmse:1.861743+0.101486 
## [47] train-rmse:1.695044+0.026973    test-rmse:1.854693+0.100780 
## [48] train-rmse:1.686351+0.027058    test-rmse:1.848715+0.101528 
## [49] train-rmse:1.678502+0.027796    test-rmse:1.842067+0.099097 
## [50] train-rmse:1.670313+0.027484    test-rmse:1.838141+0.100431 
## [51] train-rmse:1.660521+0.027423    test-rmse:1.830973+0.100647 
## [52] train-rmse:1.652818+0.028849    test-rmse:1.826236+0.100443 
## [53] train-rmse:1.645339+0.029047    test-rmse:1.821675+0.103131 
## [54] train-rmse:1.638368+0.029462    test-rmse:1.816624+0.108231 
## [55] train-rmse:1.629949+0.030266    test-rmse:1.808059+0.108299 
## [56] train-rmse:1.622572+0.030204    test-rmse:1.802954+0.108415 
## [57] train-rmse:1.616274+0.030267    test-rmse:1.796715+0.107617 
## [58] train-rmse:1.609399+0.030371    test-rmse:1.792574+0.105187 
## [59] train-rmse:1.602857+0.030987    test-rmse:1.789024+0.104568 
## [60] train-rmse:1.596244+0.030558    test-rmse:1.782346+0.103990 
## [61] train-rmse:1.589562+0.030618    test-rmse:1.777576+0.104477 
## [62] train-rmse:1.581436+0.032196    test-rmse:1.770020+0.106327 
## [63] train-rmse:1.575024+0.033393    test-rmse:1.767470+0.107170 
## [64] train-rmse:1.568186+0.033608    test-rmse:1.763823+0.110115 
## [65] train-rmse:1.562332+0.034057    test-rmse:1.760777+0.110164 
## [66] train-rmse:1.556136+0.033755    test-rmse:1.757946+0.110292 
## [67] train-rmse:1.550234+0.033611    test-rmse:1.753279+0.112770 
## [68] train-rmse:1.544501+0.033859    test-rmse:1.748956+0.112649 
## [69] train-rmse:1.538632+0.033290    test-rmse:1.743388+0.114226 
## [70] train-rmse:1.533603+0.033476    test-rmse:1.736859+0.113210 
## [71] train-rmse:1.527971+0.033849    test-rmse:1.731062+0.112349 
## [72] train-rmse:1.522824+0.033869    test-rmse:1.727647+0.112839 
## [73] train-rmse:1.517553+0.034289    test-rmse:1.725868+0.110601 
## [74] train-rmse:1.512016+0.034825    test-rmse:1.720627+0.112096 
## [75] train-rmse:1.506744+0.034960    test-rmse:1.717496+0.115259 
## [76] train-rmse:1.498740+0.035275    test-rmse:1.709360+0.118247 
## [77] train-rmse:1.492910+0.035481    test-rmse:1.707086+0.117422 
## [78] train-rmse:1.488189+0.035861    test-rmse:1.703898+0.117114 
## [79] train-rmse:1.483249+0.036619    test-rmse:1.698317+0.118521 
## [80] train-rmse:1.478358+0.037151    test-rmse:1.694394+0.117640 
## [81] train-rmse:1.473265+0.037566    test-rmse:1.689151+0.118764 
## [82] train-rmse:1.468197+0.037370    test-rmse:1.684193+0.117742 
## [83] train-rmse:1.463669+0.037612    test-rmse:1.681392+0.118698 
## [84] train-rmse:1.458865+0.037559    test-rmse:1.679087+0.119367 
## [85] train-rmse:1.454120+0.038014    test-rmse:1.674491+0.119980 
## [86] train-rmse:1.449036+0.038565    test-rmse:1.673564+0.121178 
## [87] train-rmse:1.444497+0.038750    test-rmse:1.671056+0.122433 
## [88] train-rmse:1.439427+0.038626    test-rmse:1.667833+0.121719 
## [89] train-rmse:1.431727+0.039066    test-rmse:1.658806+0.107909 
## [90] train-rmse:1.427514+0.039388    test-rmse:1.655350+0.107768 
## [91] train-rmse:1.423216+0.040095    test-rmse:1.652229+0.108358 
## [92] train-rmse:1.418818+0.040306    test-rmse:1.646241+0.108443 
## [93] train-rmse:1.414439+0.040692    test-rmse:1.641878+0.107781 
## [94] train-rmse:1.409848+0.041142    test-rmse:1.640209+0.107059 
## [95] train-rmse:1.402485+0.045425    test-rmse:1.638776+0.107139 
## [96] train-rmse:1.398065+0.046196    test-rmse:1.635755+0.106666 
## [97] train-rmse:1.394063+0.046690    test-rmse:1.633087+0.107125 
## [98] train-rmse:1.387456+0.047308    test-rmse:1.627927+0.108311 
## [99] train-rmse:1.381226+0.048084    test-rmse:1.621107+0.111809 
## [100]    train-rmse:1.376836+0.047672    test-rmse:1.620209+0.111379 
## [101]    train-rmse:1.373219+0.047853    test-rmse:1.617311+0.111684 
## [102]    train-rmse:1.369698+0.048087    test-rmse:1.614153+0.110650 
## [103]    train-rmse:1.365451+0.048125    test-rmse:1.612356+0.111094 
## [104]    train-rmse:1.359727+0.045184    test-rmse:1.608760+0.113094 
## [105]    train-rmse:1.352520+0.048438    test-rmse:1.607190+0.115950 
## [106]    train-rmse:1.347776+0.048741    test-rmse:1.600513+0.118691 
## [107]    train-rmse:1.344183+0.048990    test-rmse:1.597780+0.119991 
## [108]    train-rmse:1.340447+0.049619    test-rmse:1.594161+0.119478 
## [109]    train-rmse:1.336490+0.050009    test-rmse:1.593021+0.121450 
## [110]    train-rmse:1.333228+0.050159    test-rmse:1.590226+0.120983 
## [111]    train-rmse:1.327625+0.049724    test-rmse:1.581239+0.123902 
## [112]    train-rmse:1.324265+0.050001    test-rmse:1.579054+0.124606 
## [113]    train-rmse:1.319448+0.050937    test-rmse:1.575261+0.125509 
## [114]    train-rmse:1.315887+0.050705    test-rmse:1.572707+0.125682 
## [115]    train-rmse:1.310030+0.054502    test-rmse:1.571362+0.126065 
## [116]    train-rmse:1.306500+0.054555    test-rmse:1.569383+0.128729 
## [117]    train-rmse:1.302487+0.054327    test-rmse:1.566693+0.130740 
## [118]    train-rmse:1.298858+0.054500    test-rmse:1.565166+0.132538 
## [119]    train-rmse:1.295669+0.054622    test-rmse:1.562973+0.133476 
## [120]    train-rmse:1.292314+0.054848    test-rmse:1.561787+0.133712 
## [121]    train-rmse:1.289344+0.055025    test-rmse:1.559358+0.133527 
## [122]    train-rmse:1.285601+0.054831    test-rmse:1.555607+0.133949 
## [123]    train-rmse:1.282116+0.056009    test-rmse:1.553852+0.132841 
## [124]    train-rmse:1.279063+0.056104    test-rmse:1.550571+0.133103 
## [125]    train-rmse:1.273437+0.056749    test-rmse:1.546701+0.134229 
## [126]    train-rmse:1.269929+0.056389    test-rmse:1.544898+0.133971 
## [127]    train-rmse:1.266929+0.056512    test-rmse:1.543026+0.133924 
## [128]    train-rmse:1.263787+0.056255    test-rmse:1.539932+0.136153 
## [129]    train-rmse:1.260927+0.056290    test-rmse:1.538467+0.136137 
## [130]    train-rmse:1.258159+0.056479    test-rmse:1.540147+0.137413 
## [131]    train-rmse:1.255076+0.056434    test-rmse:1.538058+0.138781 
## [132]    train-rmse:1.251758+0.056781    test-rmse:1.535539+0.138911 
## [133]    train-rmse:1.248569+0.057184    test-rmse:1.532569+0.138306 
## [134]    train-rmse:1.246019+0.057335    test-rmse:1.531297+0.138195 
## [135]    train-rmse:1.243417+0.057521    test-rmse:1.529761+0.138927 
## [136]    train-rmse:1.240607+0.057893    test-rmse:1.528059+0.138736 
## [137]    train-rmse:1.237981+0.058148    test-rmse:1.526300+0.139757 
## [138]    train-rmse:1.234350+0.059525    test-rmse:1.523776+0.138390 
## [139]    train-rmse:1.231087+0.058908    test-rmse:1.519414+0.137593 
## [140]    train-rmse:1.228141+0.058573    test-rmse:1.517540+0.137015 
## [141]    train-rmse:1.225370+0.058901    test-rmse:1.516886+0.136910 
## [142]    train-rmse:1.222290+0.058915    test-rmse:1.515159+0.136699 
## [143]    train-rmse:1.219650+0.058706    test-rmse:1.511562+0.138391 
## [144]    train-rmse:1.216778+0.058522    test-rmse:1.511773+0.139425 
## [145]    train-rmse:1.213949+0.058772    test-rmse:1.509254+0.140299 
## [146]    train-rmse:1.207515+0.053308    test-rmse:1.504968+0.145020 
## [147]    train-rmse:1.204737+0.053692    test-rmse:1.504207+0.144047 
## [148]    train-rmse:1.201813+0.054592    test-rmse:1.502914+0.144301 
## [149]    train-rmse:1.199299+0.054572    test-rmse:1.499515+0.146664 
## [150]    train-rmse:1.196872+0.054673    test-rmse:1.496978+0.146593 
## [151]    train-rmse:1.194337+0.054480    test-rmse:1.495676+0.147923 
## [152]    train-rmse:1.191902+0.054426    test-rmse:1.494104+0.147172 
## [153]    train-rmse:1.189385+0.054691    test-rmse:1.492317+0.147102 
## [154]    train-rmse:1.186505+0.055010    test-rmse:1.488928+0.145397 
## [155]    train-rmse:1.183702+0.054709    test-rmse:1.487193+0.145500 
## [156]    train-rmse:1.178194+0.050587    test-rmse:1.483802+0.148644 
## [157]    train-rmse:1.175339+0.050612    test-rmse:1.482436+0.147176 
## [158]    train-rmse:1.172895+0.050465    test-rmse:1.481087+0.149283 
## [159]    train-rmse:1.170478+0.050290    test-rmse:1.479695+0.149797 
## [160]    train-rmse:1.166919+0.050643    test-rmse:1.477845+0.152002 
## [161]    train-rmse:1.164576+0.051011    test-rmse:1.477307+0.152697 
## [162]    train-rmse:1.162393+0.051194    test-rmse:1.476339+0.153160 
## [163]    train-rmse:1.160134+0.051349    test-rmse:1.474602+0.153221 
## [164]    train-rmse:1.157195+0.050948    test-rmse:1.473572+0.152355 
## [165]    train-rmse:1.152227+0.049285    test-rmse:1.466644+0.141734 
## [166]    train-rmse:1.148000+0.048468    test-rmse:1.463797+0.138498 
## [167]    train-rmse:1.143616+0.047546    test-rmse:1.457474+0.140646 
## [168]    train-rmse:1.141238+0.047305    test-rmse:1.455636+0.140320 
## [169]    train-rmse:1.139052+0.047348    test-rmse:1.455181+0.141195 
## [170]    train-rmse:1.136608+0.047381    test-rmse:1.454068+0.141655 
## [171]    train-rmse:1.134559+0.047588    test-rmse:1.452136+0.141862 
## [172]    train-rmse:1.132577+0.047868    test-rmse:1.449546+0.141906 
## [173]    train-rmse:1.130674+0.048146    test-rmse:1.447298+0.143309 
## [174]    train-rmse:1.128059+0.048073    test-rmse:1.444476+0.141981 
## [175]    train-rmse:1.125202+0.048605    test-rmse:1.443314+0.142414 
## [176]    train-rmse:1.123180+0.048681    test-rmse:1.443547+0.143329 
## [177]    train-rmse:1.121332+0.048950    test-rmse:1.443023+0.142655 
## [178]    train-rmse:1.119361+0.048983    test-rmse:1.441667+0.142779 
## [179]    train-rmse:1.114789+0.048164    test-rmse:1.434935+0.134803 
## [180]    train-rmse:1.111412+0.048175    test-rmse:1.432889+0.132073 
## [181]    train-rmse:1.109316+0.047807    test-rmse:1.430846+0.132868 
## [182]    train-rmse:1.107053+0.047445    test-rmse:1.429411+0.133427 
## [183]    train-rmse:1.105071+0.047434    test-rmse:1.427390+0.133385 
## [184]    train-rmse:1.102833+0.047660    test-rmse:1.426650+0.134531 
## [185]    train-rmse:1.101003+0.047609    test-rmse:1.425750+0.134530 
## [186]    train-rmse:1.096036+0.043064    test-rmse:1.423430+0.137535 
## [187]    train-rmse:1.094009+0.043008    test-rmse:1.423552+0.138362 
## [188]    train-rmse:1.092212+0.043184    test-rmse:1.421849+0.138070 
## [189]    train-rmse:1.090214+0.043230    test-rmse:1.420400+0.139209 
## [190]    train-rmse:1.086369+0.044207    test-rmse:1.417780+0.141022 
## [191]    train-rmse:1.084221+0.043960    test-rmse:1.417531+0.141740 
## [192]    train-rmse:1.079837+0.040464    test-rmse:1.415398+0.143707 
## [193]    train-rmse:1.077361+0.039974    test-rmse:1.414271+0.143740 
## [194]    train-rmse:1.075660+0.039911    test-rmse:1.413695+0.144459 
## [195]    train-rmse:1.073935+0.039953    test-rmse:1.413205+0.145312 
## [196]    train-rmse:1.071732+0.039687    test-rmse:1.412008+0.144824 
## [197]    train-rmse:1.070031+0.039869    test-rmse:1.410743+0.145452 
## [198]    train-rmse:1.068554+0.039917    test-rmse:1.409476+0.145922 
## [199]    train-rmse:1.066720+0.039748    test-rmse:1.408481+0.146861 
## [200]    train-rmse:1.064990+0.039729    test-rmse:1.410180+0.148283 
## [201]    train-rmse:1.062946+0.039388    test-rmse:1.410753+0.147798 
## [202]    train-rmse:1.061262+0.039485    test-rmse:1.409282+0.146011 
## [203]    train-rmse:1.059788+0.039518    test-rmse:1.409427+0.145283 
## [204]    train-rmse:1.057672+0.040560    test-rmse:1.408345+0.145870 
## [205]    train-rmse:1.054090+0.039626    test-rmse:1.401850+0.148325 
## [206]    train-rmse:1.049740+0.039957    test-rmse:1.396236+0.142279 
## [207]    train-rmse:1.048036+0.039909    test-rmse:1.394142+0.143143 
## [208]    train-rmse:1.045115+0.040017    test-rmse:1.391527+0.142141 
## [209]    train-rmse:1.043256+0.039989    test-rmse:1.390098+0.142003 
## [210]    train-rmse:1.041569+0.039790    test-rmse:1.388476+0.143589 
## [211]    train-rmse:1.039883+0.039709    test-rmse:1.388130+0.143553 
## [212]    train-rmse:1.038290+0.039536    test-rmse:1.387576+0.142946 
## [213]    train-rmse:1.034432+0.036247    test-rmse:1.386317+0.144470 
## [214]    train-rmse:1.030085+0.034911    test-rmse:1.383399+0.146961 
## [215]    train-rmse:1.027982+0.035761    test-rmse:1.381813+0.148492 
## [216]    train-rmse:1.025469+0.034680    test-rmse:1.379915+0.150501 
## [217]    train-rmse:1.024123+0.034778    test-rmse:1.379552+0.150420 
## [218]    train-rmse:1.022646+0.034744    test-rmse:1.379407+0.148657 
## [219]    train-rmse:1.021146+0.034721    test-rmse:1.378964+0.150069 
## [220]    train-rmse:1.019487+0.034412    test-rmse:1.378448+0.152108 
## [221]    train-rmse:1.018054+0.034606    test-rmse:1.377593+0.153045 
## [222]    train-rmse:1.016698+0.034635    test-rmse:1.377867+0.153137 
## [223]    train-rmse:1.015078+0.034363    test-rmse:1.377272+0.153181 
## [224]    train-rmse:1.013366+0.033941    test-rmse:1.376582+0.153014 
## [225]    train-rmse:1.012026+0.034034    test-rmse:1.375726+0.153451 
## [226]    train-rmse:1.009688+0.033977    test-rmse:1.374706+0.153618 
## [227]    train-rmse:1.008381+0.034074    test-rmse:1.373837+0.153014 
## [228]    train-rmse:1.007097+0.034126    test-rmse:1.374821+0.153708 
## [229]    train-rmse:1.005160+0.034886    test-rmse:1.373710+0.153612 
## [230]    train-rmse:1.003865+0.034851    test-rmse:1.372574+0.153731 
## [231]    train-rmse:1.002327+0.034863    test-rmse:1.371723+0.152618 
## [232]    train-rmse:1.000986+0.035111    test-rmse:1.370320+0.151935 
## [233]    train-rmse:0.999686+0.035087    test-rmse:1.370173+0.152806 
## [234]    train-rmse:0.998084+0.035321    test-rmse:1.369909+0.153295 
## [235]    train-rmse:0.996716+0.035308    test-rmse:1.369737+0.153587 
## [236]    train-rmse:0.994982+0.034875    test-rmse:1.368066+0.153705 
## [237]    train-rmse:0.993683+0.034788    test-rmse:1.367588+0.153797 
## [238]    train-rmse:0.991229+0.034117    test-rmse:1.364581+0.153920 
## [239]    train-rmse:0.990041+0.034200    test-rmse:1.364785+0.155287 
## [240]    train-rmse:0.987941+0.034172    test-rmse:1.363270+0.154649 
## [241]    train-rmse:0.986836+0.034212    test-rmse:1.363068+0.154533 
## [242]    train-rmse:0.985746+0.034101    test-rmse:1.361672+0.154984 
## [243]    train-rmse:0.984619+0.034185    test-rmse:1.361232+0.155248 
## [244]    train-rmse:0.983310+0.034329    test-rmse:1.360023+0.155310 
## [245]    train-rmse:0.982025+0.034387    test-rmse:1.360776+0.154887 
## [246]    train-rmse:0.980706+0.034664    test-rmse:1.360075+0.153405 
## [247]    train-rmse:0.977018+0.031707    test-rmse:1.358258+0.155378 
## [248]    train-rmse:0.975059+0.031712    test-rmse:1.357156+0.155238 
## [249]    train-rmse:0.973611+0.031550    test-rmse:1.356437+0.154977 
## [250]    train-rmse:0.972483+0.031611    test-rmse:1.357200+0.157695 
## [251]    train-rmse:0.971149+0.032255    test-rmse:1.357666+0.157590 
## [252]    train-rmse:0.970147+0.032264    test-rmse:1.358307+0.157079 
## [253]    train-rmse:0.969073+0.032303    test-rmse:1.357852+0.156828 
## [254]    train-rmse:0.966556+0.032472    test-rmse:1.355627+0.157135 
## [255]    train-rmse:0.965457+0.032498    test-rmse:1.355187+0.157280 
## [256]    train-rmse:0.964381+0.032549    test-rmse:1.353894+0.156857 
## [257]    train-rmse:0.963330+0.032732    test-rmse:1.354057+0.157150 
## [258]    train-rmse:0.962256+0.032804    test-rmse:1.353111+0.157043 
## [259]    train-rmse:0.960941+0.032838    test-rmse:1.353773+0.156803 
## [260]    train-rmse:0.957872+0.036699    test-rmse:1.353494+0.156493 
## [261]    train-rmse:0.956888+0.036666    test-rmse:1.352620+0.156994 
## [262]    train-rmse:0.955605+0.036833    test-rmse:1.352041+0.157845 
## [263]    train-rmse:0.952674+0.034791    test-rmse:1.351036+0.159926 
## [264]    train-rmse:0.951563+0.034776    test-rmse:1.349868+0.159654 
## [265]    train-rmse:0.950508+0.034758    test-rmse:1.349375+0.159271 
## [266]    train-rmse:0.949356+0.034556    test-rmse:1.348536+0.160246 
## [267]    train-rmse:0.948402+0.034540    test-rmse:1.347587+0.160123 
## [268]    train-rmse:0.947439+0.034503    test-rmse:1.347119+0.160596 
## [269]    train-rmse:0.945354+0.034650    test-rmse:1.347070+0.160576 
## [270]    train-rmse:0.944304+0.034828    test-rmse:1.346117+0.160606 
## [271]    train-rmse:0.939998+0.033248    test-rmse:1.343503+0.162203 
## [272]    train-rmse:0.939079+0.033238    test-rmse:1.344049+0.163583 
## [273]    train-rmse:0.938145+0.033328    test-rmse:1.342977+0.163877 
## [274]    train-rmse:0.937148+0.033425    test-rmse:1.342633+0.163899 
## [275]    train-rmse:0.936080+0.033591    test-rmse:1.343143+0.164078 
## [276]    train-rmse:0.935136+0.033474    test-rmse:1.342957+0.163864 
## [277]    train-rmse:0.933912+0.033473    test-rmse:1.342805+0.164404 
## [278]    train-rmse:0.932842+0.033347    test-rmse:1.342030+0.163469 
## [279]    train-rmse:0.930638+0.033614    test-rmse:1.339977+0.164197 
## [280]    train-rmse:0.929760+0.033587    test-rmse:1.340014+0.164449 
## [281]    train-rmse:0.928773+0.033650    test-rmse:1.339712+0.164310 
## [282]    train-rmse:0.927685+0.034106    test-rmse:1.339068+0.164593 
## [283]    train-rmse:0.926620+0.033996    test-rmse:1.339640+0.164727 
## [284]    train-rmse:0.924520+0.033075    test-rmse:1.337982+0.164991 
## [285]    train-rmse:0.923728+0.033030    test-rmse:1.338094+0.165620 
## [286]    train-rmse:0.922961+0.033025    test-rmse:1.338142+0.165698 
## [287]    train-rmse:0.922229+0.033016    test-rmse:1.337874+0.165453 
## [288]    train-rmse:0.921233+0.032936    test-rmse:1.337294+0.165914 
## [289]    train-rmse:0.920435+0.032972    test-rmse:1.338140+0.166427 
## [290]    train-rmse:0.919388+0.033235    test-rmse:1.338086+0.167120 
## [291]    train-rmse:0.918359+0.033532    test-rmse:1.337506+0.167182 
## [292]    train-rmse:0.917573+0.033629    test-rmse:1.336934+0.166222 
## [293]    train-rmse:0.916720+0.033638    test-rmse:1.336753+0.166608 
## [294]    train-rmse:0.915441+0.033568    test-rmse:1.336025+0.166214 
## [295]    train-rmse:0.912202+0.036576    test-rmse:1.335324+0.166621 
## [296]    train-rmse:0.911368+0.036579    test-rmse:1.335523+0.166401 
## [297]    train-rmse:0.910614+0.036626    test-rmse:1.334959+0.166487 
## [298]    train-rmse:0.909869+0.036676    test-rmse:1.335133+0.166588 
## [299]    train-rmse:0.908787+0.036856    test-rmse:1.334588+0.167945 
## [300]    train-rmse:0.908041+0.036997    test-rmse:1.333965+0.168088 
## [301]    train-rmse:0.907290+0.036967    test-rmse:1.333366+0.168405 
## [302]    train-rmse:0.906462+0.036874    test-rmse:1.333187+0.168297 
## [303]    train-rmse:0.905589+0.036761    test-rmse:1.333341+0.168930 
## [304]    train-rmse:0.904336+0.036459    test-rmse:1.332749+0.168648 
## [305]    train-rmse:0.903368+0.036605    test-rmse:1.332565+0.168620 
## [306]    train-rmse:0.902629+0.036573    test-rmse:1.332120+0.168252 
## [307]    train-rmse:0.900298+0.035380    test-rmse:1.330959+0.168757 
## [308]    train-rmse:0.899444+0.035528    test-rmse:1.331091+0.168741 
## [309]    train-rmse:0.898730+0.035534    test-rmse:1.331189+0.169021 
## [310]    train-rmse:0.898048+0.035543    test-rmse:1.331599+0.168642 
## [311]    train-rmse:0.897121+0.035495    test-rmse:1.331758+0.169211 
## [312]    train-rmse:0.895707+0.036690    test-rmse:1.331189+0.169009 
## [313]    train-rmse:0.895098+0.036713    test-rmse:1.331087+0.169018 
## Stopping. Best iteration:
## [307]    train-rmse:0.900298+0.035380    test-rmse:1.330959+0.168757
## 
## 2 
## [1]  train-rmse:8.784801+0.064460    test-rmse:8.781121+0.271381 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.962677+0.057657    test-rmse:7.956898+0.264117 
## [3]  train-rmse:7.226935+0.051559    test-rmse:7.220718+0.257179 
## [4]  train-rmse:6.568486+0.045933    test-rmse:6.564711+0.251924 
## [5]  train-rmse:5.980547+0.041228    test-rmse:5.978368+0.243856 
## [6]  train-rmse:5.456164+0.036954    test-rmse:5.456378+0.237199 
## [7]  train-rmse:4.988096+0.032861    test-rmse:4.996288+0.227499 
## [8]  train-rmse:4.570705+0.030001    test-rmse:4.587146+0.209648 
## [9]  train-rmse:4.200109+0.027517    test-rmse:4.218981+0.197555 
## [10] train-rmse:3.870079+0.025852    test-rmse:3.899702+0.190762 
## [11] train-rmse:3.577693+0.024741    test-rmse:3.611828+0.176786 
## [12] train-rmse:3.318644+0.022165    test-rmse:3.358112+0.166289 
## [13] train-rmse:3.091135+0.021382    test-rmse:3.139308+0.154590 
## [14] train-rmse:2.889369+0.018685    test-rmse:2.947466+0.146213 
## [15] train-rmse:2.713538+0.018548    test-rmse:2.776654+0.132555 
## [16] train-rmse:2.559030+0.017414    test-rmse:2.631263+0.121422 
## [17] train-rmse:2.423600+0.015584    test-rmse:2.506089+0.116927 
## [18] train-rmse:2.304550+0.014589    test-rmse:2.399764+0.108358 
## [19] train-rmse:2.201847+0.014770    test-rmse:2.309068+0.098250 
## [20] train-rmse:2.113020+0.014640    test-rmse:2.231966+0.095529 
## [21] train-rmse:2.034095+0.013383    test-rmse:2.162986+0.091503 
## [22] train-rmse:1.966365+0.013685    test-rmse:2.106573+0.088609 
## [23] train-rmse:1.905046+0.012498    test-rmse:2.054669+0.083624 
## [24] train-rmse:1.852915+0.013439    test-rmse:2.010761+0.076744 
## [25] train-rmse:1.805612+0.012695    test-rmse:1.963946+0.074000 
## [26] train-rmse:1.764273+0.012549    test-rmse:1.935866+0.074512 
## [27] train-rmse:1.726453+0.014500    test-rmse:1.909210+0.075833 
## [28] train-rmse:1.694534+0.014963    test-rmse:1.879751+0.071318 
## [29] train-rmse:1.665436+0.015869    test-rmse:1.858082+0.070711 
## [30] train-rmse:1.639985+0.016421    test-rmse:1.836434+0.068805 
## [31] train-rmse:1.617384+0.017179    test-rmse:1.817008+0.068962 
## [32] train-rmse:1.595337+0.016761    test-rmse:1.802905+0.068976 
## [33] train-rmse:1.575571+0.016863    test-rmse:1.790758+0.066570 
## [34] train-rmse:1.556048+0.018497    test-rmse:1.773774+0.066485 
## [35] train-rmse:1.538201+0.018490    test-rmse:1.761849+0.065064 
## [36] train-rmse:1.522899+0.018744    test-rmse:1.748968+0.066248 
## [37] train-rmse:1.508633+0.018833    test-rmse:1.739041+0.064685 
## [38] train-rmse:1.494375+0.019405    test-rmse:1.731302+0.063170 
## [39] train-rmse:1.480408+0.019662    test-rmse:1.720444+0.064177 
## [40] train-rmse:1.467464+0.019762    test-rmse:1.712953+0.064105 
## [41] train-rmse:1.454362+0.020354    test-rmse:1.704951+0.067786 
## [42] train-rmse:1.441964+0.019190    test-rmse:1.696095+0.069982 
## [43] train-rmse:1.429148+0.019134    test-rmse:1.686065+0.072167 
## [44] train-rmse:1.418592+0.019548    test-rmse:1.678544+0.072095 
## [45] train-rmse:1.406359+0.021835    test-rmse:1.669278+0.071742 
## [46] train-rmse:1.395302+0.021093    test-rmse:1.661811+0.075251 
## [47] train-rmse:1.383826+0.021344    test-rmse:1.656612+0.077590 
## [48] train-rmse:1.373966+0.021664    test-rmse:1.649923+0.080077 
## [49] train-rmse:1.365076+0.021790    test-rmse:1.645395+0.080809 
## [50] train-rmse:1.355124+0.021861    test-rmse:1.639554+0.081479 
## [51] train-rmse:1.346033+0.022088    test-rmse:1.632524+0.082959 
## [52] train-rmse:1.337228+0.022010    test-rmse:1.624491+0.082213 
## [53] train-rmse:1.328546+0.022086    test-rmse:1.615893+0.086210 
## [54] train-rmse:1.319179+0.022725    test-rmse:1.610082+0.086002 
## [55] train-rmse:1.309287+0.023323    test-rmse:1.604476+0.087836 
## [56] train-rmse:1.300825+0.021952    test-rmse:1.598662+0.089762 
## [57] train-rmse:1.292369+0.021560    test-rmse:1.591497+0.089857 
## [58] train-rmse:1.284758+0.021715    test-rmse:1.586667+0.090921 
## [59] train-rmse:1.276569+0.022709    test-rmse:1.583775+0.093506 
## [60] train-rmse:1.267906+0.023245    test-rmse:1.577669+0.094868 
## [61] train-rmse:1.258531+0.024875    test-rmse:1.571389+0.097986 
## [62] train-rmse:1.251005+0.024171    test-rmse:1.564689+0.097463 
## [63] train-rmse:1.243201+0.024676    test-rmse:1.560482+0.097438 
## [64] train-rmse:1.235057+0.025798    test-rmse:1.552309+0.096414 
## [65] train-rmse:1.226997+0.026653    test-rmse:1.545246+0.099951 
## [66] train-rmse:1.219530+0.027459    test-rmse:1.542418+0.101922 
## [67] train-rmse:1.212364+0.027009    test-rmse:1.539413+0.104783 
## [68] train-rmse:1.204741+0.025570    test-rmse:1.534683+0.105789 
## [69] train-rmse:1.198691+0.025113    test-rmse:1.530774+0.108501 
## [70] train-rmse:1.191461+0.025380    test-rmse:1.527809+0.106697 
## [71] train-rmse:1.182754+0.025780    test-rmse:1.522663+0.106870 
## [72] train-rmse:1.176839+0.025716    test-rmse:1.519217+0.107572 
## [73] train-rmse:1.168352+0.025676    test-rmse:1.509055+0.103684 
## [74] train-rmse:1.160777+0.026686    test-rmse:1.506030+0.105411 
## [75] train-rmse:1.154272+0.027375    test-rmse:1.503049+0.106708 
## [76] train-rmse:1.147737+0.028302    test-rmse:1.499952+0.107227 
## [77] train-rmse:1.141740+0.027616    test-rmse:1.493818+0.110962 
## [78] train-rmse:1.134664+0.028420    test-rmse:1.490047+0.111325 
## [79] train-rmse:1.129202+0.028475    test-rmse:1.487715+0.110816 
## [80] train-rmse:1.122667+0.029315    test-rmse:1.481985+0.107118 
## [81] train-rmse:1.116568+0.030111    test-rmse:1.480140+0.108491 
## [82] train-rmse:1.111173+0.030262    test-rmse:1.478764+0.110673 
## [83] train-rmse:1.105982+0.030557    test-rmse:1.475923+0.111378 
## [84] train-rmse:1.099528+0.030242    test-rmse:1.468782+0.112648 
## [85] train-rmse:1.094797+0.029921    test-rmse:1.463776+0.114228 
## [86] train-rmse:1.086442+0.027551    test-rmse:1.456322+0.113799 
## [87] train-rmse:1.081164+0.027689    test-rmse:1.455581+0.114502 
## [88] train-rmse:1.075620+0.028507    test-rmse:1.453662+0.115067 
## [89] train-rmse:1.069863+0.027610    test-rmse:1.451141+0.115332 
## [90] train-rmse:1.064254+0.027070    test-rmse:1.447624+0.117224 
## [91] train-rmse:1.057492+0.026432    test-rmse:1.443611+0.118751 
## [92] train-rmse:1.052507+0.026230    test-rmse:1.444172+0.120662 
## [93] train-rmse:1.046942+0.025709    test-rmse:1.441974+0.123573 
## [94] train-rmse:1.042051+0.026179    test-rmse:1.438141+0.126892 
## [95] train-rmse:1.037164+0.026429    test-rmse:1.436064+0.126028 
## [96] train-rmse:1.031537+0.026413    test-rmse:1.431670+0.126905 
## [97] train-rmse:1.027651+0.026435    test-rmse:1.428819+0.127702 
## [98] train-rmse:1.023171+0.026834    test-rmse:1.423452+0.128521 
## [99] train-rmse:1.017367+0.026438    test-rmse:1.419511+0.130399 
## [100]    train-rmse:1.011999+0.026962    test-rmse:1.413740+0.130531 
## [101]    train-rmse:1.008210+0.026838    test-rmse:1.411741+0.131511 
## [102]    train-rmse:1.002891+0.026763    test-rmse:1.408339+0.131478 
## [103]    train-rmse:0.997833+0.026017    test-rmse:1.407477+0.132597 
## [104]    train-rmse:0.992619+0.026285    test-rmse:1.402514+0.131991 
## [105]    train-rmse:0.988469+0.026235    test-rmse:1.402022+0.133929 
## [106]    train-rmse:0.984503+0.025954    test-rmse:1.400435+0.137156 
## [107]    train-rmse:0.979535+0.024988    test-rmse:1.398479+0.139810 
## [108]    train-rmse:0.974466+0.025303    test-rmse:1.394282+0.138413 
## [109]    train-rmse:0.971249+0.025379    test-rmse:1.392818+0.138688 
## [110]    train-rmse:0.967021+0.024683    test-rmse:1.391504+0.138677 
## [111]    train-rmse:0.963400+0.024645    test-rmse:1.388929+0.138908 
## [112]    train-rmse:0.959909+0.025228    test-rmse:1.388319+0.140471 
## [113]    train-rmse:0.955405+0.024633    test-rmse:1.385393+0.140052 
## [114]    train-rmse:0.950791+0.024829    test-rmse:1.383438+0.141245 
## [115]    train-rmse:0.943853+0.023359    test-rmse:1.381117+0.142756 
## [116]    train-rmse:0.939900+0.023733    test-rmse:1.380493+0.143990 
## [117]    train-rmse:0.936394+0.023747    test-rmse:1.379227+0.142601 
## [118]    train-rmse:0.932347+0.025126    test-rmse:1.375701+0.142094 
## [119]    train-rmse:0.928280+0.025425    test-rmse:1.373592+0.143205 
## [120]    train-rmse:0.925257+0.025278    test-rmse:1.372114+0.144705 
## [121]    train-rmse:0.921407+0.024419    test-rmse:1.370271+0.144877 
## [122]    train-rmse:0.918036+0.024378    test-rmse:1.367851+0.146088 
## [123]    train-rmse:0.913022+0.023751    test-rmse:1.366979+0.148252 
## [124]    train-rmse:0.908876+0.024896    test-rmse:1.366148+0.148548 
## [125]    train-rmse:0.905725+0.024905    test-rmse:1.364945+0.149022 
## [126]    train-rmse:0.900384+0.022797    test-rmse:1.362784+0.149986 
## [127]    train-rmse:0.897509+0.022609    test-rmse:1.361207+0.151383 
## [128]    train-rmse:0.893478+0.023554    test-rmse:1.360086+0.152150 
## [129]    train-rmse:0.890021+0.022754    test-rmse:1.357926+0.153657 
## [130]    train-rmse:0.886311+0.023322    test-rmse:1.355732+0.155923 
## [131]    train-rmse:0.882314+0.022531    test-rmse:1.354148+0.155607 
## [132]    train-rmse:0.879345+0.023148    test-rmse:1.353003+0.155406 
## [133]    train-rmse:0.876175+0.023701    test-rmse:1.352708+0.156261 
## [134]    train-rmse:0.872008+0.024546    test-rmse:1.352187+0.156907 
## [135]    train-rmse:0.868331+0.025862    test-rmse:1.349192+0.156752 
## [136]    train-rmse:0.866002+0.025590    test-rmse:1.348284+0.157290 
## [137]    train-rmse:0.862887+0.024846    test-rmse:1.347012+0.157786 
## [138]    train-rmse:0.860302+0.024733    test-rmse:1.345177+0.157675 
## [139]    train-rmse:0.857140+0.025147    test-rmse:1.343398+0.158249 
## [140]    train-rmse:0.854311+0.025135    test-rmse:1.342036+0.157071 
## [141]    train-rmse:0.850743+0.024672    test-rmse:1.339527+0.157879 
## [142]    train-rmse:0.848274+0.024719    test-rmse:1.338723+0.158537 
## [143]    train-rmse:0.843852+0.024753    test-rmse:1.335502+0.157689 
## [144]    train-rmse:0.841364+0.024849    test-rmse:1.335908+0.157573 
## [145]    train-rmse:0.838419+0.024926    test-rmse:1.334270+0.157488 
## [146]    train-rmse:0.835881+0.024724    test-rmse:1.334997+0.157917 
## [147]    train-rmse:0.833057+0.024349    test-rmse:1.333517+0.159192 
## [148]    train-rmse:0.830781+0.024549    test-rmse:1.331602+0.159201 
## [149]    train-rmse:0.828066+0.025081    test-rmse:1.331482+0.159420 
## [150]    train-rmse:0.824859+0.024700    test-rmse:1.330710+0.159851 
## [151]    train-rmse:0.821867+0.024224    test-rmse:1.330202+0.160638 
## [152]    train-rmse:0.818670+0.022773    test-rmse:1.329539+0.160941 
## [153]    train-rmse:0.816017+0.022972    test-rmse:1.328940+0.162206 
## [154]    train-rmse:0.813393+0.023631    test-rmse:1.327305+0.162750 
## [155]    train-rmse:0.810501+0.023065    test-rmse:1.327550+0.164622 
## [156]    train-rmse:0.807810+0.022755    test-rmse:1.326265+0.164661 
## [157]    train-rmse:0.805386+0.023127    test-rmse:1.324309+0.165056 
## [158]    train-rmse:0.803264+0.023193    test-rmse:1.324594+0.164950 
## [159]    train-rmse:0.801515+0.023289    test-rmse:1.325051+0.166070 
## [160]    train-rmse:0.798451+0.022030    test-rmse:1.323702+0.166989 
## [161]    train-rmse:0.796225+0.022132    test-rmse:1.323762+0.167991 
## [162]    train-rmse:0.792951+0.022921    test-rmse:1.322373+0.168440 
## [163]    train-rmse:0.789462+0.023491    test-rmse:1.321320+0.169035 
## [164]    train-rmse:0.786699+0.023641    test-rmse:1.318065+0.169442 
## [165]    train-rmse:0.784189+0.023296    test-rmse:1.316402+0.169819 
## [166]    train-rmse:0.782001+0.024126    test-rmse:1.315661+0.169163 
## [167]    train-rmse:0.779647+0.024264    test-rmse:1.314599+0.169049 
## [168]    train-rmse:0.776670+0.024769    test-rmse:1.312867+0.168153 
## [169]    train-rmse:0.773960+0.024905    test-rmse:1.311306+0.168196 
## [170]    train-rmse:0.771960+0.024703    test-rmse:1.310683+0.167647 
## [171]    train-rmse:0.769798+0.024276    test-rmse:1.309430+0.167520 
## [172]    train-rmse:0.767660+0.024022    test-rmse:1.309744+0.169093 
## [173]    train-rmse:0.764825+0.025297    test-rmse:1.307865+0.169300 
## [174]    train-rmse:0.763036+0.025065    test-rmse:1.308099+0.169953 
## [175]    train-rmse:0.760242+0.025738    test-rmse:1.307470+0.169725 
## [176]    train-rmse:0.757519+0.025343    test-rmse:1.305768+0.169408 
## [177]    train-rmse:0.755746+0.025340    test-rmse:1.306106+0.169495 
## [178]    train-rmse:0.753933+0.025355    test-rmse:1.306066+0.169303 
## [179]    train-rmse:0.752316+0.025374    test-rmse:1.304927+0.170108 
## [180]    train-rmse:0.749771+0.025499    test-rmse:1.304277+0.170329 
## [181]    train-rmse:0.748021+0.025526    test-rmse:1.304734+0.170152 
## [182]    train-rmse:0.745797+0.026260    test-rmse:1.304239+0.171452 
## [183]    train-rmse:0.743767+0.025656    test-rmse:1.303158+0.172030 
## [184]    train-rmse:0.741926+0.025484    test-rmse:1.304036+0.171849 
## [185]    train-rmse:0.740035+0.025748    test-rmse:1.302790+0.171697 
## [186]    train-rmse:0.737599+0.025332    test-rmse:1.303240+0.170085 
## [187]    train-rmse:0.735502+0.026151    test-rmse:1.302752+0.170255 
## [188]    train-rmse:0.733848+0.026206    test-rmse:1.301377+0.170364 
## [189]    train-rmse:0.731388+0.025614    test-rmse:1.301443+0.171644 
## [190]    train-rmse:0.729928+0.025570    test-rmse:1.300919+0.171631 
## [191]    train-rmse:0.728039+0.025587    test-rmse:1.301202+0.170943 
## [192]    train-rmse:0.726418+0.025527    test-rmse:1.300086+0.171342 
## [193]    train-rmse:0.725189+0.025680    test-rmse:1.301011+0.171101 
## [194]    train-rmse:0.722745+0.026046    test-rmse:1.300190+0.170665 
## [195]    train-rmse:0.720767+0.025485    test-rmse:1.299578+0.171069 
## [196]    train-rmse:0.719331+0.025849    test-rmse:1.299057+0.171145 
## [197]    train-rmse:0.717077+0.024797    test-rmse:1.298728+0.172507 
## [198]    train-rmse:0.715515+0.024898    test-rmse:1.298926+0.172284 
## [199]    train-rmse:0.713458+0.024698    test-rmse:1.296748+0.172175 
## [200]    train-rmse:0.711756+0.025143    test-rmse:1.296696+0.173204 
## [201]    train-rmse:0.709819+0.025631    test-rmse:1.295470+0.173984 
## [202]    train-rmse:0.708558+0.025731    test-rmse:1.294880+0.174153 
## [203]    train-rmse:0.707063+0.025728    test-rmse:1.295021+0.174977 
## [204]    train-rmse:0.705284+0.025581    test-rmse:1.295015+0.175231 
## [205]    train-rmse:0.703899+0.025685    test-rmse:1.295748+0.174307 
## [206]    train-rmse:0.702333+0.025969    test-rmse:1.295007+0.174281 
## [207]    train-rmse:0.699819+0.026289    test-rmse:1.293572+0.173571 
## [208]    train-rmse:0.698239+0.026463    test-rmse:1.292249+0.174080 
## [209]    train-rmse:0.696450+0.026993    test-rmse:1.291978+0.174589 
## [210]    train-rmse:0.694206+0.027476    test-rmse:1.292500+0.174783 
## [211]    train-rmse:0.692479+0.028172    test-rmse:1.291688+0.174640 
## [212]    train-rmse:0.691138+0.028320    test-rmse:1.291621+0.173981 
## [213]    train-rmse:0.689631+0.028210    test-rmse:1.291581+0.174246 
## [214]    train-rmse:0.687838+0.028315    test-rmse:1.291226+0.174731 
## [215]    train-rmse:0.686493+0.028741    test-rmse:1.290929+0.175047 
## [216]    train-rmse:0.685180+0.028673    test-rmse:1.291749+0.175051 
## [217]    train-rmse:0.683730+0.028848    test-rmse:1.290914+0.175045 
## [218]    train-rmse:0.682138+0.028785    test-rmse:1.289047+0.175098 
## [219]    train-rmse:0.681019+0.029202    test-rmse:1.288547+0.175579 
## [220]    train-rmse:0.679788+0.029136    test-rmse:1.287660+0.177273 
## [221]    train-rmse:0.678617+0.029058    test-rmse:1.286905+0.177045 
## [222]    train-rmse:0.676625+0.029174    test-rmse:1.287648+0.176344 
## [223]    train-rmse:0.675033+0.029035    test-rmse:1.287653+0.176017 
## [224]    train-rmse:0.674061+0.029245    test-rmse:1.287320+0.176445 
## [225]    train-rmse:0.672594+0.029660    test-rmse:1.287015+0.176492 
## [226]    train-rmse:0.671137+0.030457    test-rmse:1.285992+0.175790 
## [227]    train-rmse:0.669217+0.030068    test-rmse:1.285439+0.176009 
## [228]    train-rmse:0.667576+0.030477    test-rmse:1.285177+0.176591 
## [229]    train-rmse:0.665675+0.030024    test-rmse:1.284692+0.176267 
## [230]    train-rmse:0.664550+0.030135    test-rmse:1.284402+0.176742 
## [231]    train-rmse:0.663272+0.030152    test-rmse:1.284643+0.176395 
## [232]    train-rmse:0.662184+0.030118    test-rmse:1.285320+0.175818 
## [233]    train-rmse:0.661205+0.030136    test-rmse:1.285512+0.175539 
## [234]    train-rmse:0.660045+0.030067    test-rmse:1.285619+0.175303 
## [235]    train-rmse:0.657795+0.029999    test-rmse:1.283706+0.174293 
## [236]    train-rmse:0.656526+0.030214    test-rmse:1.283731+0.174752 
## [237]    train-rmse:0.654924+0.030674    test-rmse:1.282454+0.174838 
## [238]    train-rmse:0.653601+0.031240    test-rmse:1.282084+0.174330 
## [239]    train-rmse:0.651112+0.030399    test-rmse:1.281481+0.174608 
## [240]    train-rmse:0.650137+0.030704    test-rmse:1.280803+0.174841 
## [241]    train-rmse:0.648869+0.031143    test-rmse:1.281248+0.174907 
## [242]    train-rmse:0.646781+0.031831    test-rmse:1.279402+0.173771 
## [243]    train-rmse:0.645566+0.032052    test-rmse:1.278445+0.173451 
## [244]    train-rmse:0.643667+0.031894    test-rmse:1.276390+0.173462 
## [245]    train-rmse:0.642791+0.032080    test-rmse:1.276084+0.173131 
## [246]    train-rmse:0.640711+0.031443    test-rmse:1.276010+0.172964 
## [247]    train-rmse:0.639697+0.031531    test-rmse:1.275723+0.173291 
## [248]    train-rmse:0.638407+0.031931    test-rmse:1.276258+0.172756 
## [249]    train-rmse:0.636975+0.031717    test-rmse:1.277123+0.172752 
## [250]    train-rmse:0.636128+0.031655    test-rmse:1.276896+0.172708 
## [251]    train-rmse:0.634694+0.032021    test-rmse:1.275115+0.172291 
## [252]    train-rmse:0.633564+0.031878    test-rmse:1.275471+0.172030 
## [253]    train-rmse:0.632321+0.031848    test-rmse:1.275168+0.172549 
## [254]    train-rmse:0.631228+0.031841    test-rmse:1.275438+0.172842 
## [255]    train-rmse:0.629739+0.031577    test-rmse:1.274520+0.172853 
## [256]    train-rmse:0.628533+0.031943    test-rmse:1.274763+0.172316 
## [257]    train-rmse:0.627748+0.032232    test-rmse:1.274467+0.172382 
## [258]    train-rmse:0.626757+0.032193    test-rmse:1.274300+0.172488 
## [259]    train-rmse:0.625700+0.032246    test-rmse:1.275056+0.173099 
## [260]    train-rmse:0.623610+0.032005    test-rmse:1.274778+0.173315 
## [261]    train-rmse:0.622486+0.031874    test-rmse:1.274843+0.173204 
## [262]    train-rmse:0.621533+0.031926    test-rmse:1.275031+0.173611 
## [263]    train-rmse:0.620689+0.031905    test-rmse:1.275189+0.173253 
## [264]    train-rmse:0.619512+0.031823    test-rmse:1.275044+0.173541 
## Stopping. Best iteration:
## [258]    train-rmse:0.626757+0.032193    test-rmse:1.274300+0.172488
## 
## 3 
## [1]  train-rmse:8.782948+0.064675    test-rmse:8.779056+0.272155 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.959312+0.058467    test-rmse:7.956778+0.263932 
## [3]  train-rmse:7.221282+0.052739    test-rmse:7.222496+0.255251 
## [4]  train-rmse:6.560292+0.047560    test-rmse:6.566499+0.248318 
## [5]  train-rmse:5.968593+0.042588    test-rmse:5.975348+0.244310 
## [6]  train-rmse:5.439521+0.037597    test-rmse:5.448422+0.232807 
## [7]  train-rmse:4.968710+0.033677    test-rmse:4.982271+0.220672 
## [8]  train-rmse:4.546313+0.030751    test-rmse:4.562564+0.210810 
## [9]  train-rmse:4.168490+0.027293    test-rmse:4.191473+0.201352 
## [10] train-rmse:3.832638+0.024767    test-rmse:3.862432+0.183189 
## [11] train-rmse:3.533442+0.022334    test-rmse:3.570166+0.177272 
## [12] train-rmse:3.266563+0.019023    test-rmse:3.310650+0.164082 
## [13] train-rmse:3.031070+0.015962    test-rmse:3.083351+0.153975 
## [14] train-rmse:2.821377+0.013250    test-rmse:2.880742+0.144580 
## [15] train-rmse:2.636625+0.012331    test-rmse:2.713098+0.137006 
## [16] train-rmse:2.473081+0.011870    test-rmse:2.563221+0.123092 
## [17] train-rmse:2.329521+0.010273    test-rmse:2.431183+0.113047 
## [18] train-rmse:2.202832+0.010554    test-rmse:2.320206+0.109369 
## [19] train-rmse:2.093414+0.009577    test-rmse:2.224594+0.102637 
## [20] train-rmse:1.993275+0.008515    test-rmse:2.132109+0.097120 
## [21] train-rmse:1.906262+0.010239    test-rmse:2.056611+0.093923 
## [22] train-rmse:1.829491+0.009113    test-rmse:1.994075+0.090036 
## [23] train-rmse:1.761652+0.009467    test-rmse:1.936723+0.080085 
## [24] train-rmse:1.701732+0.010702    test-rmse:1.891571+0.079998 
## [25] train-rmse:1.648942+0.012204    test-rmse:1.850042+0.071210 
## [26] train-rmse:1.601715+0.013213    test-rmse:1.813823+0.071059 
## [27] train-rmse:1.561124+0.013887    test-rmse:1.778690+0.067348 
## [28] train-rmse:1.519646+0.014462    test-rmse:1.750862+0.065750 
## [29] train-rmse:1.487867+0.013968    test-rmse:1.727296+0.064335 
## [30] train-rmse:1.457132+0.013803    test-rmse:1.703157+0.066787 
## [31] train-rmse:1.426999+0.016407    test-rmse:1.681531+0.066938 
## [32] train-rmse:1.399246+0.017818    test-rmse:1.666096+0.063837 
## [33] train-rmse:1.375824+0.019249    test-rmse:1.648348+0.061530 
## [34] train-rmse:1.354428+0.020057    test-rmse:1.633871+0.065025 
## [35] train-rmse:1.333889+0.021321    test-rmse:1.618910+0.064368 
## [36] train-rmse:1.315716+0.021375    test-rmse:1.603557+0.064244 
## [37] train-rmse:1.296270+0.021323    test-rmse:1.592916+0.066059 
## [38] train-rmse:1.279482+0.021753    test-rmse:1.584826+0.069621 
## [39] train-rmse:1.262751+0.021053    test-rmse:1.573477+0.076198 
## [40] train-rmse:1.248542+0.020197    test-rmse:1.559713+0.076494 
## [41] train-rmse:1.233741+0.019848    test-rmse:1.552195+0.079773 
## [42] train-rmse:1.219032+0.020129    test-rmse:1.545548+0.079342 
## [43] train-rmse:1.206888+0.020482    test-rmse:1.538599+0.080028 
## [44] train-rmse:1.194613+0.020532    test-rmse:1.531124+0.082063 
## [45] train-rmse:1.182169+0.020527    test-rmse:1.522198+0.084721 
## [46] train-rmse:1.170673+0.020183    test-rmse:1.516292+0.084289 
## [47] train-rmse:1.157946+0.019360    test-rmse:1.509202+0.088085 
## [48] train-rmse:1.146629+0.019473    test-rmse:1.500727+0.091748 
## [49] train-rmse:1.134974+0.020211    test-rmse:1.496099+0.092347 
## [50] train-rmse:1.125306+0.020013    test-rmse:1.488223+0.094140 
## [51] train-rmse:1.114619+0.020945    test-rmse:1.485087+0.096930 
## [52] train-rmse:1.102910+0.021644    test-rmse:1.476466+0.093993 
## [53] train-rmse:1.092599+0.020712    test-rmse:1.471426+0.096264 
## [54] train-rmse:1.083556+0.020443    test-rmse:1.465713+0.096992 
## [55] train-rmse:1.074441+0.018242    test-rmse:1.457016+0.102096 
## [56] train-rmse:1.064889+0.017811    test-rmse:1.451267+0.106582 
## [57] train-rmse:1.056747+0.017863    test-rmse:1.448101+0.109744 
## [58] train-rmse:1.047080+0.018856    test-rmse:1.442758+0.111671 
## [59] train-rmse:1.038045+0.019978    test-rmse:1.437136+0.112386 
## [60] train-rmse:1.029510+0.018011    test-rmse:1.430727+0.112985 
## [61] train-rmse:1.021446+0.018236    test-rmse:1.426695+0.111107 
## [62] train-rmse:1.012837+0.016933    test-rmse:1.422885+0.111505 
## [63] train-rmse:1.005278+0.017560    test-rmse:1.420126+0.116867 
## [64] train-rmse:0.997024+0.017128    test-rmse:1.415179+0.119202 
## [65] train-rmse:0.989510+0.017384    test-rmse:1.412777+0.120793 
## [66] train-rmse:0.982228+0.015826    test-rmse:1.408260+0.122153 
## [67] train-rmse:0.974862+0.014731    test-rmse:1.403135+0.123504 
## [68] train-rmse:0.966773+0.014933    test-rmse:1.399939+0.126362 
## [69] train-rmse:0.958709+0.014904    test-rmse:1.397998+0.129339 
## [70] train-rmse:0.952537+0.014192    test-rmse:1.398338+0.130031 
## [71] train-rmse:0.944711+0.015928    test-rmse:1.393523+0.129939 
## [72] train-rmse:0.937513+0.016545    test-rmse:1.389859+0.130687 
## [73] train-rmse:0.931247+0.016229    test-rmse:1.385961+0.130595 
## [74] train-rmse:0.924930+0.015168    test-rmse:1.382831+0.133485 
## [75] train-rmse:0.916848+0.016198    test-rmse:1.375761+0.133493 
## [76] train-rmse:0.910349+0.016503    test-rmse:1.375218+0.134968 
## [77] train-rmse:0.902561+0.017776    test-rmse:1.370138+0.135525 
## [78] train-rmse:0.896956+0.017871    test-rmse:1.369508+0.136268 
## [79] train-rmse:0.891464+0.017154    test-rmse:1.367060+0.136999 
## [80] train-rmse:0.886048+0.017211    test-rmse:1.364213+0.137634 
## [81] train-rmse:0.879156+0.017315    test-rmse:1.359014+0.141859 
## [82] train-rmse:0.873663+0.017219    test-rmse:1.355538+0.142838 
## [83] train-rmse:0.867811+0.017549    test-rmse:1.353520+0.143831 
## [84] train-rmse:0.862508+0.017169    test-rmse:1.351539+0.145436 
## [85] train-rmse:0.857167+0.017621    test-rmse:1.348936+0.147677 
## [86] train-rmse:0.850082+0.017105    test-rmse:1.345270+0.147324 
## [87] train-rmse:0.844448+0.017578    test-rmse:1.342722+0.146500 
## [88] train-rmse:0.838631+0.018205    test-rmse:1.339008+0.146784 
## [89] train-rmse:0.832909+0.017076    test-rmse:1.336064+0.146756 
## [90] train-rmse:0.827630+0.016544    test-rmse:1.335247+0.149048 
## [91] train-rmse:0.821832+0.016381    test-rmse:1.333206+0.148713 
## [92] train-rmse:0.817525+0.016586    test-rmse:1.331176+0.149731 
## [93] train-rmse:0.813024+0.016901    test-rmse:1.329392+0.149319 
## [94] train-rmse:0.807769+0.017440    test-rmse:1.327897+0.149475 
## [95] train-rmse:0.803149+0.017453    test-rmse:1.326279+0.150602 
## [96] train-rmse:0.798682+0.016783    test-rmse:1.323877+0.150897 
## [97] train-rmse:0.793942+0.017054    test-rmse:1.321789+0.151629 
## [98] train-rmse:0.788760+0.017751    test-rmse:1.320404+0.153034 
## [99] train-rmse:0.783517+0.016763    test-rmse:1.319784+0.155453 
## [100]    train-rmse:0.778792+0.016003    test-rmse:1.319646+0.156825 
## [101]    train-rmse:0.773717+0.016480    test-rmse:1.316534+0.155461 
## [102]    train-rmse:0.769227+0.017329    test-rmse:1.313941+0.154802 
## [103]    train-rmse:0.764527+0.016446    test-rmse:1.310530+0.155875 
## [104]    train-rmse:0.760851+0.016726    test-rmse:1.309797+0.155891 
## [105]    train-rmse:0.757130+0.016557    test-rmse:1.308460+0.156532 
## [106]    train-rmse:0.753640+0.017111    test-rmse:1.306815+0.157040 
## [107]    train-rmse:0.748800+0.018123    test-rmse:1.306545+0.157767 
## [108]    train-rmse:0.744907+0.018646    test-rmse:1.304714+0.157528 
## [109]    train-rmse:0.740948+0.018554    test-rmse:1.303175+0.158677 
## [110]    train-rmse:0.736376+0.019799    test-rmse:1.303029+0.158821 
## [111]    train-rmse:0.732181+0.018910    test-rmse:1.300158+0.158124 
## [112]    train-rmse:0.728166+0.018695    test-rmse:1.299002+0.159096 
## [113]    train-rmse:0.724234+0.018645    test-rmse:1.298839+0.160116 
## [114]    train-rmse:0.721324+0.018584    test-rmse:1.297443+0.160860 
## [115]    train-rmse:0.718019+0.018072    test-rmse:1.297639+0.160922 
## [116]    train-rmse:0.715055+0.017770    test-rmse:1.296704+0.160897 
## [117]    train-rmse:0.711020+0.018568    test-rmse:1.294224+0.159452 
## [118]    train-rmse:0.707247+0.018693    test-rmse:1.291651+0.158925 
## [119]    train-rmse:0.702921+0.018851    test-rmse:1.290070+0.159511 
## [120]    train-rmse:0.698373+0.018699    test-rmse:1.288048+0.160228 
## [121]    train-rmse:0.694855+0.018684    test-rmse:1.288471+0.160354 
## [122]    train-rmse:0.690959+0.018310    test-rmse:1.287147+0.162610 
## [123]    train-rmse:0.687483+0.018624    test-rmse:1.284804+0.162945 
## [124]    train-rmse:0.684201+0.019059    test-rmse:1.285208+0.161869 
## [125]    train-rmse:0.680889+0.017652    test-rmse:1.284506+0.162370 
## [126]    train-rmse:0.677579+0.016753    test-rmse:1.284904+0.163034 
## [127]    train-rmse:0.674088+0.018143    test-rmse:1.282564+0.162369 
## [128]    train-rmse:0.670503+0.018870    test-rmse:1.282139+0.162083 
## [129]    train-rmse:0.668117+0.018420    test-rmse:1.281514+0.162665 
## [130]    train-rmse:0.665142+0.017884    test-rmse:1.280870+0.162558 
## [131]    train-rmse:0.661982+0.017967    test-rmse:1.279539+0.162882 
## [132]    train-rmse:0.658742+0.016933    test-rmse:1.277987+0.164648 
## [133]    train-rmse:0.656186+0.017179    test-rmse:1.276989+0.164436 
## [134]    train-rmse:0.653295+0.017877    test-rmse:1.276055+0.164667 
## [135]    train-rmse:0.649321+0.017536    test-rmse:1.276214+0.164270 
## [136]    train-rmse:0.646785+0.017551    test-rmse:1.275650+0.165098 
## [137]    train-rmse:0.644766+0.017296    test-rmse:1.276151+0.165993 
## [138]    train-rmse:0.641695+0.017511    test-rmse:1.274610+0.166668 
## [139]    train-rmse:0.638297+0.017206    test-rmse:1.272197+0.167494 
## [140]    train-rmse:0.635933+0.016940    test-rmse:1.271956+0.166824 
## [141]    train-rmse:0.633630+0.017229    test-rmse:1.271609+0.167489 
## [142]    train-rmse:0.630734+0.017534    test-rmse:1.272062+0.167557 
## [143]    train-rmse:0.628514+0.017302    test-rmse:1.273705+0.168475 
## [144]    train-rmse:0.625744+0.017632    test-rmse:1.272895+0.167954 
## [145]    train-rmse:0.623406+0.017118    test-rmse:1.273477+0.167715 
## [146]    train-rmse:0.620881+0.017028    test-rmse:1.273510+0.168164 
## [147]    train-rmse:0.618176+0.016684    test-rmse:1.272488+0.167416 
## Stopping. Best iteration:
## [141]    train-rmse:0.633630+0.017229    test-rmse:1.271609+0.167489
## 
## 4 
## [1]  train-rmse:8.781437+0.064833    test-rmse:8.779204+0.272339 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.955775+0.058724    test-rmse:7.956083+0.264611 
## [3]  train-rmse:7.215436+0.053317    test-rmse:7.220302+0.254016 
## [4]  train-rmse:6.551793+0.047957    test-rmse:6.560112+0.244548 
## [5]  train-rmse:5.957146+0.043440    test-rmse:5.968863+0.237982 
## [6]  train-rmse:5.424753+0.039108    test-rmse:5.439298+0.223282 
## [7]  train-rmse:4.948888+0.035043    test-rmse:4.965037+0.211486 
## [8]  train-rmse:4.522894+0.032563    test-rmse:4.544291+0.197525 
## [9]  train-rmse:4.141407+0.029840    test-rmse:4.170567+0.186225 
## [10] train-rmse:3.800104+0.027069    test-rmse:3.835774+0.171860 
## [11] train-rmse:3.496263+0.024697    test-rmse:3.537075+0.162836 
## [12] train-rmse:3.224674+0.021953    test-rmse:3.278072+0.155369 
## [13] train-rmse:2.982878+0.021621    test-rmse:3.042374+0.145489 
## [14] train-rmse:2.767962+0.019401    test-rmse:2.843447+0.142189 
## [15] train-rmse:2.576908+0.019139    test-rmse:2.662956+0.128711 
## [16] train-rmse:2.407958+0.019172    test-rmse:2.508312+0.126365 
## [17] train-rmse:2.258498+0.017966    test-rmse:2.379107+0.122434 
## [18] train-rmse:2.125302+0.015900    test-rmse:2.262296+0.116106 
## [19] train-rmse:2.004834+0.018863    test-rmse:2.157174+0.103868 
## [20] train-rmse:1.899500+0.017048    test-rmse:2.072201+0.106341 
## [21] train-rmse:1.806534+0.018657    test-rmse:1.993217+0.089402 
## [22] train-rmse:1.721830+0.016572    test-rmse:1.926842+0.092293 
## [23] train-rmse:1.649412+0.017518    test-rmse:1.869215+0.090030 
## [24] train-rmse:1.582194+0.017890    test-rmse:1.812510+0.086397 
## [25] train-rmse:1.523179+0.016278    test-rmse:1.770948+0.086138 
## [26] train-rmse:1.472191+0.016424    test-rmse:1.732272+0.083832 
## [27] train-rmse:1.425524+0.016995    test-rmse:1.695624+0.081610 
## [28] train-rmse:1.382386+0.016586    test-rmse:1.663660+0.082822 
## [29] train-rmse:1.346024+0.017153    test-rmse:1.641384+0.081174 
## [30] train-rmse:1.312515+0.016006    test-rmse:1.617753+0.082498 
## [31] train-rmse:1.280340+0.017748    test-rmse:1.593553+0.079507 
## [32] train-rmse:1.250712+0.018318    test-rmse:1.574211+0.081542 
## [33] train-rmse:1.224855+0.018304    test-rmse:1.560779+0.081178 
## [34] train-rmse:1.200266+0.019791    test-rmse:1.544223+0.080009 
## [35] train-rmse:1.177194+0.020957    test-rmse:1.530916+0.082153 
## [36] train-rmse:1.157745+0.022066    test-rmse:1.520445+0.083672 
## [37] train-rmse:1.137442+0.022412    test-rmse:1.506477+0.086220 
## [38] train-rmse:1.118516+0.022319    test-rmse:1.495572+0.089081 
## [39] train-rmse:1.101799+0.022198    test-rmse:1.486226+0.088702 
## [40] train-rmse:1.083872+0.022210    test-rmse:1.477993+0.088865 
## [41] train-rmse:1.066962+0.023876    test-rmse:1.468089+0.090825 
## [42] train-rmse:1.052740+0.024007    test-rmse:1.460385+0.091559 
## [43] train-rmse:1.037239+0.022225    test-rmse:1.456184+0.097356 
## [44] train-rmse:1.022614+0.023287    test-rmse:1.446433+0.101574 
## [45] train-rmse:1.008958+0.022594    test-rmse:1.439999+0.102940 
## [46] train-rmse:0.994591+0.024091    test-rmse:1.433911+0.104291 
## [47] train-rmse:0.978443+0.023789    test-rmse:1.427447+0.103526 
## [48] train-rmse:0.966076+0.023106    test-rmse:1.419783+0.107053 
## [49] train-rmse:0.956184+0.023058    test-rmse:1.411292+0.108498 
## [50] train-rmse:0.945198+0.023803    test-rmse:1.407161+0.111449 
## [51] train-rmse:0.933912+0.024051    test-rmse:1.404806+0.112996 
## [52] train-rmse:0.922860+0.022641    test-rmse:1.401502+0.115743 
## [53] train-rmse:0.912574+0.022573    test-rmse:1.397764+0.119119 
## [54] train-rmse:0.902502+0.023282    test-rmse:1.391595+0.121801 
## [55] train-rmse:0.893672+0.024451    test-rmse:1.386073+0.121949 
## [56] train-rmse:0.885305+0.024644    test-rmse:1.381967+0.123052 
## [57] train-rmse:0.875410+0.023620    test-rmse:1.377818+0.125343 
## [58] train-rmse:0.865404+0.022225    test-rmse:1.374338+0.128245 
## [59] train-rmse:0.856125+0.022567    test-rmse:1.369550+0.127412 
## [60] train-rmse:0.848226+0.022116    test-rmse:1.367713+0.129662 
## [61] train-rmse:0.840792+0.021723    test-rmse:1.364028+0.130699 
## [62] train-rmse:0.831708+0.022444    test-rmse:1.360879+0.130646 
## [63] train-rmse:0.823909+0.021984    test-rmse:1.356193+0.132519 
## [64] train-rmse:0.816920+0.021891    test-rmse:1.353147+0.133168 
## [65] train-rmse:0.810137+0.022094    test-rmse:1.350295+0.134840 
## [66] train-rmse:0.801765+0.022922    test-rmse:1.348211+0.137081 
## [67] train-rmse:0.793987+0.022072    test-rmse:1.346317+0.139343 
## [68] train-rmse:0.785933+0.021641    test-rmse:1.344723+0.138366 
## [69] train-rmse:0.779713+0.021081    test-rmse:1.340894+0.142786 
## [70] train-rmse:0.772634+0.021571    test-rmse:1.339173+0.142855 
## [71] train-rmse:0.765943+0.021418    test-rmse:1.336411+0.143175 
## [72] train-rmse:0.758395+0.021277    test-rmse:1.333855+0.143707 
## [73] train-rmse:0.752656+0.021857    test-rmse:1.332537+0.146598 
## [74] train-rmse:0.745636+0.021192    test-rmse:1.326973+0.145564 
## [75] train-rmse:0.739478+0.021038    test-rmse:1.326202+0.145147 
## [76] train-rmse:0.733492+0.022064    test-rmse:1.324593+0.144125 
## [77] train-rmse:0.726297+0.021229    test-rmse:1.321219+0.146446 
## [78] train-rmse:0.721283+0.021626    test-rmse:1.318605+0.147566 
## [79] train-rmse:0.715033+0.020957    test-rmse:1.315125+0.149010 
## [80] train-rmse:0.709788+0.020642    test-rmse:1.313878+0.149381 
## [81] train-rmse:0.703102+0.020984    test-rmse:1.310687+0.149392 
## [82] train-rmse:0.698232+0.020240    test-rmse:1.307928+0.149349 
## [83] train-rmse:0.692773+0.019917    test-rmse:1.308765+0.150598 
## [84] train-rmse:0.688286+0.020999    test-rmse:1.306199+0.150764 
## [85] train-rmse:0.683367+0.020516    test-rmse:1.304360+0.153546 
## [86] train-rmse:0.677724+0.020382    test-rmse:1.302617+0.153465 
## [87] train-rmse:0.672083+0.020358    test-rmse:1.300675+0.154096 
## [88] train-rmse:0.666744+0.019980    test-rmse:1.297690+0.156228 
## [89] train-rmse:0.661244+0.018752    test-rmse:1.297108+0.158480 
## [90] train-rmse:0.657490+0.019095    test-rmse:1.295495+0.159796 
## [91] train-rmse:0.652647+0.019063    test-rmse:1.293783+0.161499 
## [92] train-rmse:0.648481+0.019574    test-rmse:1.295320+0.162234 
## [93] train-rmse:0.644330+0.020139    test-rmse:1.293345+0.161247 
## [94] train-rmse:0.640218+0.019675    test-rmse:1.291422+0.162215 
## [95] train-rmse:0.636442+0.019636    test-rmse:1.291248+0.161612 
## [96] train-rmse:0.631796+0.019743    test-rmse:1.290175+0.162255 
## [97] train-rmse:0.625866+0.019058    test-rmse:1.289581+0.162562 
## [98] train-rmse:0.621746+0.019090    test-rmse:1.289047+0.163289 
## [99] train-rmse:0.617737+0.019240    test-rmse:1.286401+0.165010 
## [100]    train-rmse:0.613113+0.019563    test-rmse:1.285298+0.165382 
## [101]    train-rmse:0.608940+0.019931    test-rmse:1.286296+0.165029 
## [102]    train-rmse:0.605215+0.019529    test-rmse:1.285349+0.165597 
## [103]    train-rmse:0.600936+0.019006    test-rmse:1.285394+0.165841 
## [104]    train-rmse:0.597329+0.019073    test-rmse:1.285464+0.166177 
## [105]    train-rmse:0.593729+0.019653    test-rmse:1.284185+0.165391 
## [106]    train-rmse:0.590663+0.019960    test-rmse:1.283268+0.166471 
## [107]    train-rmse:0.587108+0.020242    test-rmse:1.282489+0.166987 
## [108]    train-rmse:0.583913+0.019934    test-rmse:1.281411+0.167631 
## [109]    train-rmse:0.579799+0.019748    test-rmse:1.280913+0.165296 
## [110]    train-rmse:0.576888+0.020483    test-rmse:1.280835+0.164910 
## [111]    train-rmse:0.572932+0.021633    test-rmse:1.279847+0.165510 
## [112]    train-rmse:0.568427+0.019917    test-rmse:1.279001+0.167036 
## [113]    train-rmse:0.564730+0.020119    test-rmse:1.278187+0.167249 
## [114]    train-rmse:0.561543+0.020223    test-rmse:1.278356+0.166726 
## [115]    train-rmse:0.558271+0.020528    test-rmse:1.276950+0.166230 
## [116]    train-rmse:0.555390+0.021063    test-rmse:1.276733+0.166575 
## [117]    train-rmse:0.552836+0.021208    test-rmse:1.276425+0.168278 
## [118]    train-rmse:0.547816+0.020146    test-rmse:1.275030+0.170866 
## [119]    train-rmse:0.545203+0.019833    test-rmse:1.275371+0.171815 
## [120]    train-rmse:0.541725+0.019790    test-rmse:1.274748+0.171817 
## [121]    train-rmse:0.538264+0.019287    test-rmse:1.272540+0.172125 
## [122]    train-rmse:0.534955+0.019499    test-rmse:1.272448+0.171960 
## [123]    train-rmse:0.531974+0.020189    test-rmse:1.271205+0.171393 
## [124]    train-rmse:0.529191+0.019493    test-rmse:1.270401+0.172140 
## [125]    train-rmse:0.526790+0.019531    test-rmse:1.269630+0.172811 
## [126]    train-rmse:0.524480+0.019901    test-rmse:1.269158+0.172418 
## [127]    train-rmse:0.522111+0.020837    test-rmse:1.267776+0.172663 
## [128]    train-rmse:0.517389+0.020038    test-rmse:1.266850+0.173009 
## [129]    train-rmse:0.514672+0.019957    test-rmse:1.268077+0.173000 
## [130]    train-rmse:0.511700+0.020121    test-rmse:1.265894+0.172946 
## [131]    train-rmse:0.509670+0.020381    test-rmse:1.265301+0.172431 
## [132]    train-rmse:0.507049+0.020371    test-rmse:1.263906+0.171096 
## [133]    train-rmse:0.504449+0.019721    test-rmse:1.263395+0.171920 
## [134]    train-rmse:0.501481+0.019428    test-rmse:1.262503+0.172844 
## [135]    train-rmse:0.499224+0.020102    test-rmse:1.262100+0.172885 
## [136]    train-rmse:0.497225+0.020224    test-rmse:1.262055+0.173360 
## [137]    train-rmse:0.494330+0.020388    test-rmse:1.261270+0.174853 
## [138]    train-rmse:0.492224+0.021103    test-rmse:1.260978+0.174836 
## [139]    train-rmse:0.489469+0.021524    test-rmse:1.260619+0.174789 
## [140]    train-rmse:0.487197+0.021889    test-rmse:1.260826+0.174123 
## [141]    train-rmse:0.484149+0.021729    test-rmse:1.259133+0.173977 
## [142]    train-rmse:0.481428+0.021356    test-rmse:1.259151+0.172802 
## [143]    train-rmse:0.479084+0.021500    test-rmse:1.258430+0.173006 
## [144]    train-rmse:0.476439+0.020727    test-rmse:1.258479+0.172797 
## [145]    train-rmse:0.474426+0.020834    test-rmse:1.258441+0.173156 
## [146]    train-rmse:0.472441+0.021597    test-rmse:1.258074+0.173195 
## [147]    train-rmse:0.469609+0.021661    test-rmse:1.258202+0.172777 
## [148]    train-rmse:0.467937+0.021660    test-rmse:1.258724+0.171987 
## [149]    train-rmse:0.465826+0.021651    test-rmse:1.257404+0.172401 
## [150]    train-rmse:0.463498+0.021866    test-rmse:1.257077+0.172821 
## [151]    train-rmse:0.461594+0.022310    test-rmse:1.257515+0.173526 
## [152]    train-rmse:0.460135+0.022441    test-rmse:1.258267+0.174144 
## [153]    train-rmse:0.457879+0.021961    test-rmse:1.258498+0.173709 
## [154]    train-rmse:0.455763+0.022165    test-rmse:1.257865+0.173322 
## [155]    train-rmse:0.453437+0.022211    test-rmse:1.257156+0.172996 
## [156]    train-rmse:0.451544+0.022211    test-rmse:1.257612+0.173039 
## Stopping. Best iteration:
## [150]    train-rmse:0.463498+0.021866    test-rmse:1.257077+0.172821
## 
## 5 
## [1]  train-rmse:8.781147+0.064908    test-rmse:8.779598+0.269073 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.954948+0.058862    test-rmse:7.959590+0.259149 
## [3]  train-rmse:7.213000+0.053363    test-rmse:7.222035+0.247410 
## [4]  train-rmse:6.547675+0.048468    test-rmse:6.561636+0.239535 
## [5]  train-rmse:5.950918+0.043756    test-rmse:5.968801+0.225135 
## [6]  train-rmse:5.415611+0.039766    test-rmse:5.438809+0.211575 
## [7]  train-rmse:4.936452+0.035680    test-rmse:4.963846+0.198924 
## [8]  train-rmse:4.507307+0.033458    test-rmse:4.536331+0.186452 
## [9]  train-rmse:4.122302+0.031176    test-rmse:4.156437+0.171012 
## [10] train-rmse:3.776337+0.028419    test-rmse:3.817662+0.164566 
## [11] train-rmse:3.466959+0.025565    test-rmse:3.515659+0.150774 
## [12] train-rmse:3.191727+0.023390    test-rmse:3.249179+0.137632 
## [13] train-rmse:2.945365+0.021344    test-rmse:3.015992+0.135334 
## [14] train-rmse:2.726227+0.019670    test-rmse:2.810164+0.125819 
## [15] train-rmse:2.531188+0.016974    test-rmse:2.630820+0.126586 
## [16] train-rmse:2.357657+0.016451    test-rmse:2.478692+0.123263 
## [17] train-rmse:2.200434+0.014429    test-rmse:2.335784+0.117451 
## [18] train-rmse:2.060202+0.013227    test-rmse:2.212831+0.113346 
## [19] train-rmse:1.936649+0.012836    test-rmse:2.110905+0.110809 
## [20] train-rmse:1.826503+0.015414    test-rmse:2.012550+0.101894 
## [21] train-rmse:1.728572+0.014159    test-rmse:1.926954+0.100373 
## [22] train-rmse:1.638422+0.015670    test-rmse:1.854747+0.095603 
## [23] train-rmse:1.559423+0.013483    test-rmse:1.792426+0.094306 
## [24] train-rmse:1.489762+0.014470    test-rmse:1.741234+0.090130 
## [25] train-rmse:1.428092+0.015486    test-rmse:1.697805+0.087485 
## [26] train-rmse:1.373205+0.015234    test-rmse:1.653623+0.090300 
## [27] train-rmse:1.322016+0.015442    test-rmse:1.619037+0.089276 
## [28] train-rmse:1.275875+0.018027    test-rmse:1.584045+0.088789 
## [29] train-rmse:1.234283+0.016563    test-rmse:1.557922+0.088346 
## [30] train-rmse:1.196819+0.018189    test-rmse:1.528751+0.089080 
## [31] train-rmse:1.162968+0.018890    test-rmse:1.505674+0.088275 
## [32] train-rmse:1.133722+0.018672    test-rmse:1.490532+0.091072 
## [33] train-rmse:1.104885+0.019126    test-rmse:1.471961+0.093699 
## [34] train-rmse:1.076453+0.018800    test-rmse:1.456310+0.095703 
## [35] train-rmse:1.052211+0.019693    test-rmse:1.445157+0.098115 
## [36] train-rmse:1.027628+0.019020    test-rmse:1.433209+0.099462 
## [37] train-rmse:1.006428+0.018627    test-rmse:1.424161+0.099531 
## [38] train-rmse:0.985086+0.018828    test-rmse:1.412391+0.104662 
## [39] train-rmse:0.966206+0.020122    test-rmse:1.402028+0.106927 
## [40] train-rmse:0.946487+0.018759    test-rmse:1.393222+0.108510 
## [41] train-rmse:0.931110+0.018196    test-rmse:1.386520+0.110872 
## [42] train-rmse:0.914350+0.018448    test-rmse:1.383537+0.113542 
## [43] train-rmse:0.899034+0.018977    test-rmse:1.375932+0.114139 
## [44] train-rmse:0.883465+0.018131    test-rmse:1.371797+0.115333 
## [45] train-rmse:0.870797+0.018628    test-rmse:1.364702+0.118016 
## [46] train-rmse:0.855685+0.020531    test-rmse:1.358864+0.118990 
## [47] train-rmse:0.843265+0.020069    test-rmse:1.352268+0.123794 
## [48] train-rmse:0.828805+0.019671    test-rmse:1.346536+0.124187 
## [49] train-rmse:0.818070+0.020742    test-rmse:1.343719+0.125451 
## [50] train-rmse:0.805893+0.020900    test-rmse:1.342571+0.125529 
## [51] train-rmse:0.795681+0.022305    test-rmse:1.338921+0.127317 
## [52] train-rmse:0.783976+0.022520    test-rmse:1.332331+0.129076 
## [53] train-rmse:0.774225+0.022241    test-rmse:1.331016+0.132029 
## [54] train-rmse:0.763345+0.021444    test-rmse:1.325435+0.134330 
## [55] train-rmse:0.754203+0.021258    test-rmse:1.320020+0.134401 
## [56] train-rmse:0.744578+0.020033    test-rmse:1.317599+0.136231 
## [57] train-rmse:0.735311+0.021686    test-rmse:1.314925+0.135693 
## [58] train-rmse:0.726485+0.022135    test-rmse:1.311339+0.133599 
## [59] train-rmse:0.717109+0.022659    test-rmse:1.307003+0.134572 
## [60] train-rmse:0.709883+0.022489    test-rmse:1.301355+0.136883 
## [61] train-rmse:0.701740+0.021038    test-rmse:1.299646+0.139098 
## [62] train-rmse:0.694175+0.021912    test-rmse:1.297459+0.139183 
## [63] train-rmse:0.684951+0.020876    test-rmse:1.293213+0.143128 
## [64] train-rmse:0.677021+0.020532    test-rmse:1.288422+0.142164 
## [65] train-rmse:0.669873+0.019849    test-rmse:1.285271+0.142828 
## [66] train-rmse:0.663460+0.020478    test-rmse:1.282739+0.143089 
## [67] train-rmse:0.655529+0.020322    test-rmse:1.281314+0.142840 
## [68] train-rmse:0.648707+0.020379    test-rmse:1.280626+0.143742 
## [69] train-rmse:0.641093+0.019501    test-rmse:1.277998+0.145536 
## [70] train-rmse:0.634576+0.019648    test-rmse:1.276320+0.145991 
## [71] train-rmse:0.628586+0.019004    test-rmse:1.274135+0.146482 
## [72] train-rmse:0.622654+0.019158    test-rmse:1.273071+0.148283 
## [73] train-rmse:0.616214+0.018744    test-rmse:1.271384+0.148265 
## [74] train-rmse:0.610495+0.018982    test-rmse:1.268388+0.147122 
## [75] train-rmse:0.603647+0.018599    test-rmse:1.267872+0.146801 
## [76] train-rmse:0.597561+0.017960    test-rmse:1.265904+0.149045 
## [77] train-rmse:0.591667+0.016672    test-rmse:1.265662+0.149419 
## [78] train-rmse:0.585537+0.015565    test-rmse:1.263081+0.151160 
## [79] train-rmse:0.578945+0.015686    test-rmse:1.262943+0.150805 
## [80] train-rmse:0.573938+0.015656    test-rmse:1.260701+0.152069 
## [81] train-rmse:0.568912+0.016523    test-rmse:1.258804+0.152215 
## [82] train-rmse:0.563945+0.016230    test-rmse:1.255637+0.151989 
## [83] train-rmse:0.557910+0.016335    test-rmse:1.256086+0.151663 
## [84] train-rmse:0.553795+0.016986    test-rmse:1.256698+0.152315 
## [85] train-rmse:0.549519+0.016346    test-rmse:1.256001+0.152474 
## [86] train-rmse:0.544464+0.016224    test-rmse:1.255244+0.152115 
## [87] train-rmse:0.539880+0.015630    test-rmse:1.256463+0.153077 
## [88] train-rmse:0.535128+0.015859    test-rmse:1.254280+0.152154 
## [89] train-rmse:0.530194+0.014800    test-rmse:1.254310+0.152916 
## [90] train-rmse:0.525844+0.015644    test-rmse:1.252559+0.153132 
## [91] train-rmse:0.521106+0.015326    test-rmse:1.253415+0.153620 
## [92] train-rmse:0.516398+0.015039    test-rmse:1.252549+0.153694 
## [93] train-rmse:0.510804+0.014990    test-rmse:1.252519+0.152644 
## [94] train-rmse:0.506700+0.015433    test-rmse:1.252348+0.152154 
## [95] train-rmse:0.501482+0.014647    test-rmse:1.252384+0.154307 
## [96] train-rmse:0.497463+0.014513    test-rmse:1.251327+0.155239 
## [97] train-rmse:0.493821+0.014551    test-rmse:1.252140+0.155902 
## [98] train-rmse:0.489550+0.013823    test-rmse:1.252280+0.156251 
## [99] train-rmse:0.485827+0.014754    test-rmse:1.250221+0.156830 
## [100]    train-rmse:0.481591+0.014279    test-rmse:1.248984+0.157554 
## [101]    train-rmse:0.478317+0.013711    test-rmse:1.247803+0.159351 
## [102]    train-rmse:0.475172+0.012947    test-rmse:1.247062+0.161171 
## [103]    train-rmse:0.471179+0.013311    test-rmse:1.246014+0.161348 
## [104]    train-rmse:0.466735+0.013618    test-rmse:1.244991+0.162276 
## [105]    train-rmse:0.463587+0.013201    test-rmse:1.244169+0.163780 
## [106]    train-rmse:0.460202+0.013570    test-rmse:1.243687+0.163389 
## [107]    train-rmse:0.456305+0.012927    test-rmse:1.243744+0.163453 
## [108]    train-rmse:0.452838+0.013801    test-rmse:1.242567+0.163871 
## [109]    train-rmse:0.449943+0.014165    test-rmse:1.241721+0.164042 
## [110]    train-rmse:0.446318+0.014309    test-rmse:1.241251+0.164627 
## [111]    train-rmse:0.442586+0.014672    test-rmse:1.240038+0.165890 
## [112]    train-rmse:0.439185+0.013967    test-rmse:1.239920+0.166092 
## [113]    train-rmse:0.436628+0.014434    test-rmse:1.239831+0.167562 
## [114]    train-rmse:0.434344+0.014428    test-rmse:1.239043+0.167802 
## [115]    train-rmse:0.431534+0.013869    test-rmse:1.238891+0.168704 
## [116]    train-rmse:0.429196+0.013966    test-rmse:1.238170+0.168424 
## [117]    train-rmse:0.425551+0.014629    test-rmse:1.237166+0.169122 
## [118]    train-rmse:0.422053+0.014277    test-rmse:1.236582+0.169133 
## [119]    train-rmse:0.418796+0.014106    test-rmse:1.236420+0.169909 
## [120]    train-rmse:0.415643+0.014023    test-rmse:1.235787+0.169357 
## [121]    train-rmse:0.413286+0.013878    test-rmse:1.235268+0.169174 
## [122]    train-rmse:0.410782+0.013707    test-rmse:1.235771+0.170072 
## [123]    train-rmse:0.407847+0.014089    test-rmse:1.234884+0.170637 
## [124]    train-rmse:0.405421+0.014366    test-rmse:1.234458+0.170968 
## [125]    train-rmse:0.402994+0.014617    test-rmse:1.235227+0.172061 
## [126]    train-rmse:0.400909+0.014507    test-rmse:1.235214+0.172713 
## [127]    train-rmse:0.398289+0.015395    test-rmse:1.234892+0.172646 
## [128]    train-rmse:0.396353+0.015318    test-rmse:1.234787+0.173613 
## [129]    train-rmse:0.393925+0.015103    test-rmse:1.234304+0.173558 
## [130]    train-rmse:0.390904+0.015844    test-rmse:1.234168+0.173658 
## [131]    train-rmse:0.388153+0.015872    test-rmse:1.234707+0.173866 
## [132]    train-rmse:0.385297+0.015276    test-rmse:1.234580+0.173480 
## [133]    train-rmse:0.382460+0.015342    test-rmse:1.233175+0.174037 
## [134]    train-rmse:0.379242+0.014722    test-rmse:1.232777+0.174240 
## [135]    train-rmse:0.376850+0.014422    test-rmse:1.233130+0.173674 
## [136]    train-rmse:0.374378+0.014463    test-rmse:1.233845+0.173174 
## [137]    train-rmse:0.372202+0.013772    test-rmse:1.233382+0.173600 
## [138]    train-rmse:0.369426+0.013422    test-rmse:1.233177+0.172634 
## [139]    train-rmse:0.367076+0.013644    test-rmse:1.233059+0.172966 
## [140]    train-rmse:0.365428+0.013471    test-rmse:1.233405+0.173815 
## Stopping. Best iteration:
## [134]    train-rmse:0.379242+0.014722    test-rmse:1.232777+0.174240
## 
## 6 
## [1]  train-rmse:8.781147+0.064908    test-rmse:8.779598+0.269073 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.954747+0.058941    test-rmse:7.957469+0.257884 
## [3]  train-rmse:7.212286+0.053602    test-rmse:7.219896+0.243580 
## [4]  train-rmse:6.545748+0.048620    test-rmse:6.556030+0.235052 
## [5]  train-rmse:5.947111+0.043858    test-rmse:5.961826+0.219783 
## [6]  train-rmse:5.409957+0.039574    test-rmse:5.421333+0.207070 
## [7]  train-rmse:4.927937+0.036003    test-rmse:4.945133+0.194158 
## [8]  train-rmse:4.495938+0.032398    test-rmse:4.518853+0.179084 
## [9]  train-rmse:4.108970+0.030627    test-rmse:4.137652+0.166274 
## [10] train-rmse:3.760750+0.028316    test-rmse:3.797024+0.156411 
## [11] train-rmse:3.447695+0.025538    test-rmse:3.489306+0.145241 
## [12] train-rmse:3.168867+0.024070    test-rmse:3.218906+0.135496 
## [13] train-rmse:2.918985+0.022545    test-rmse:2.984145+0.128550 
## [14] train-rmse:2.695654+0.020622    test-rmse:2.771615+0.130469 
## [15] train-rmse:2.494766+0.016815    test-rmse:2.585344+0.132204 
## [16] train-rmse:2.315890+0.016183    test-rmse:2.429323+0.120208 
## [17] train-rmse:2.155639+0.016379    test-rmse:2.287847+0.113461 
## [18] train-rmse:2.007957+0.016466    test-rmse:2.158687+0.107883 
## [19] train-rmse:1.878739+0.016138    test-rmse:2.050769+0.100474 
## [20] train-rmse:1.761938+0.017261    test-rmse:1.948485+0.093990 
## [21] train-rmse:1.659425+0.018129    test-rmse:1.866008+0.089219 
## [22] train-rmse:1.563532+0.018607    test-rmse:1.792708+0.086973 
## [23] train-rmse:1.480064+0.018558    test-rmse:1.727693+0.084001 
## [24] train-rmse:1.404389+0.017819    test-rmse:1.672911+0.082166 
## [25] train-rmse:1.337176+0.019605    test-rmse:1.621478+0.084116 
## [26] train-rmse:1.273831+0.018969    test-rmse:1.581596+0.080620 
## [27] train-rmse:1.219560+0.020465    test-rmse:1.546328+0.084237 
## [28] train-rmse:1.170204+0.019904    test-rmse:1.516046+0.085767 
## [29] train-rmse:1.125639+0.020308    test-rmse:1.487811+0.087501 
## [30] train-rmse:1.086345+0.020733    test-rmse:1.462924+0.089508 
## [31] train-rmse:1.051758+0.021284    test-rmse:1.444011+0.090416 
## [32] train-rmse:1.016954+0.020077    test-rmse:1.426010+0.097095 
## [33] train-rmse:0.986883+0.019850    test-rmse:1.411146+0.096582 
## [34] train-rmse:0.957155+0.018668    test-rmse:1.394745+0.100589 
## [35] train-rmse:0.930766+0.018333    test-rmse:1.383581+0.101234 
## [36] train-rmse:0.906364+0.018670    test-rmse:1.371389+0.101560 
## [37] train-rmse:0.882561+0.015991    test-rmse:1.361019+0.105672 
## [38] train-rmse:0.861268+0.016760    test-rmse:1.349316+0.106029 
## [39] train-rmse:0.841862+0.016847    test-rmse:1.341899+0.110760 
## [40] train-rmse:0.822637+0.015985    test-rmse:1.333602+0.114366 
## [41] train-rmse:0.804477+0.016104    test-rmse:1.327791+0.117122 
## [42] train-rmse:0.787566+0.016687    test-rmse:1.318468+0.118012 
## [43] train-rmse:0.771400+0.016335    test-rmse:1.312746+0.119878 
## [44] train-rmse:0.759033+0.015784    test-rmse:1.308771+0.118745 
## [45] train-rmse:0.744713+0.014626    test-rmse:1.303725+0.120872 
## [46] train-rmse:0.731211+0.014119    test-rmse:1.299963+0.122197 
## [47] train-rmse:0.717480+0.015507    test-rmse:1.292763+0.121992 
## [48] train-rmse:0.706037+0.014613    test-rmse:1.290015+0.123977 
## [49] train-rmse:0.692231+0.014755    test-rmse:1.282414+0.124304 
## [50] train-rmse:0.681561+0.013952    test-rmse:1.276993+0.128723 
## [51] train-rmse:0.669866+0.014552    test-rmse:1.274070+0.126608 
## [52] train-rmse:0.661247+0.013943    test-rmse:1.273871+0.128566 
## [53] train-rmse:0.651896+0.013860    test-rmse:1.269568+0.129460 
## [54] train-rmse:0.642377+0.014460    test-rmse:1.265600+0.131264 
## [55] train-rmse:0.632444+0.013882    test-rmse:1.264069+0.130146 
## [56] train-rmse:0.624179+0.014048    test-rmse:1.264860+0.132125 
## [57] train-rmse:0.615484+0.014246    test-rmse:1.263152+0.132052 
## [58] train-rmse:0.605938+0.014790    test-rmse:1.261437+0.132096 
## [59] train-rmse:0.597521+0.014803    test-rmse:1.257300+0.134040 
## [60] train-rmse:0.588871+0.014978    test-rmse:1.255338+0.136198 
## [61] train-rmse:0.581567+0.015590    test-rmse:1.252784+0.136719 
## [62] train-rmse:0.575678+0.014963    test-rmse:1.252263+0.137658 
## [63] train-rmse:0.569266+0.014778    test-rmse:1.249845+0.140089 
## [64] train-rmse:0.561504+0.014748    test-rmse:1.245953+0.141461 
## [65] train-rmse:0.555078+0.014434    test-rmse:1.245489+0.142724 
## [66] train-rmse:0.549259+0.014428    test-rmse:1.244172+0.144619 
## [67] train-rmse:0.542238+0.015083    test-rmse:1.240748+0.142450 
## [68] train-rmse:0.534545+0.013817    test-rmse:1.238107+0.143588 
## [69] train-rmse:0.527970+0.012956    test-rmse:1.237882+0.143180 
## [70] train-rmse:0.521904+0.013374    test-rmse:1.237194+0.143921 
## [71] train-rmse:0.515759+0.012798    test-rmse:1.236502+0.143406 
## [72] train-rmse:0.509441+0.013818    test-rmse:1.234623+0.143864 
## [73] train-rmse:0.503372+0.014755    test-rmse:1.233518+0.144153 
## [74] train-rmse:0.498261+0.015167    test-rmse:1.232819+0.145789 
## [75] train-rmse:0.494199+0.015314    test-rmse:1.230894+0.146556 
## [76] train-rmse:0.489350+0.015040    test-rmse:1.230766+0.147325 
## [77] train-rmse:0.483631+0.015464    test-rmse:1.229086+0.149961 
## [78] train-rmse:0.477648+0.013984    test-rmse:1.228265+0.151072 
## [79] train-rmse:0.472767+0.013271    test-rmse:1.227470+0.151288 
## [80] train-rmse:0.467651+0.013213    test-rmse:1.227179+0.152280 
## [81] train-rmse:0.463080+0.013952    test-rmse:1.224173+0.152735 
## [82] train-rmse:0.458189+0.012665    test-rmse:1.223560+0.153459 
## [83] train-rmse:0.453918+0.013216    test-rmse:1.222702+0.153648 
## [84] train-rmse:0.449976+0.012868    test-rmse:1.221962+0.154386 
## [85] train-rmse:0.444361+0.013525    test-rmse:1.221809+0.155385 
## [86] train-rmse:0.440198+0.013735    test-rmse:1.221443+0.154511 
## [87] train-rmse:0.434762+0.011328    test-rmse:1.221494+0.154984 
## [88] train-rmse:0.431318+0.011954    test-rmse:1.220681+0.155388 
## [89] train-rmse:0.426739+0.012212    test-rmse:1.221341+0.156622 
## [90] train-rmse:0.422482+0.012246    test-rmse:1.220726+0.156348 
## [91] train-rmse:0.419001+0.013014    test-rmse:1.220926+0.156490 
## [92] train-rmse:0.415502+0.014103    test-rmse:1.220674+0.156658 
## [93] train-rmse:0.410603+0.014386    test-rmse:1.219156+0.156446 
## [94] train-rmse:0.406982+0.014170    test-rmse:1.218287+0.157470 
## [95] train-rmse:0.403920+0.014901    test-rmse:1.217566+0.157511 
## [96] train-rmse:0.400406+0.014790    test-rmse:1.216395+0.158107 
## [97] train-rmse:0.397778+0.014745    test-rmse:1.216229+0.158751 
## [98] train-rmse:0.393871+0.014964    test-rmse:1.215168+0.159120 
## [99] train-rmse:0.390255+0.015227    test-rmse:1.214456+0.159087 
## [100]    train-rmse:0.387087+0.016143    test-rmse:1.214297+0.159634 
## [101]    train-rmse:0.382787+0.013796    test-rmse:1.215182+0.160653 
## [102]    train-rmse:0.379338+0.014254    test-rmse:1.215052+0.160976 
## [103]    train-rmse:0.376011+0.015462    test-rmse:1.214227+0.161253 
## [104]    train-rmse:0.371705+0.014775    test-rmse:1.214687+0.160716 
## [105]    train-rmse:0.368620+0.015604    test-rmse:1.214253+0.160501 
## [106]    train-rmse:0.365039+0.015355    test-rmse:1.213701+0.160495 
## [107]    train-rmse:0.361461+0.014939    test-rmse:1.214065+0.160008 
## [108]    train-rmse:0.358953+0.014911    test-rmse:1.214617+0.159796 
## [109]    train-rmse:0.356301+0.015542    test-rmse:1.214368+0.160474 
## [110]    train-rmse:0.354416+0.015769    test-rmse:1.214252+0.160511 
## [111]    train-rmse:0.351752+0.016006    test-rmse:1.213926+0.160064 
## [112]    train-rmse:0.349402+0.015015    test-rmse:1.213230+0.161084 
## [113]    train-rmse:0.345906+0.014950    test-rmse:1.213077+0.161166 
## [114]    train-rmse:0.342871+0.014592    test-rmse:1.213060+0.161528 
## [115]    train-rmse:0.339690+0.014722    test-rmse:1.212369+0.162635 
## [116]    train-rmse:0.336905+0.014570    test-rmse:1.213104+0.163177 
## [117]    train-rmse:0.333231+0.013458    test-rmse:1.212167+0.163510 
## [118]    train-rmse:0.329227+0.012382    test-rmse:1.211693+0.163222 
## [119]    train-rmse:0.326856+0.012806    test-rmse:1.211232+0.164270 
## [120]    train-rmse:0.323766+0.012280    test-rmse:1.211299+0.164860 
## [121]    train-rmse:0.321657+0.012817    test-rmse:1.210869+0.165473 
## [122]    train-rmse:0.319137+0.012209    test-rmse:1.210752+0.165381 
## [123]    train-rmse:0.317321+0.012564    test-rmse:1.210584+0.166085 
## [124]    train-rmse:0.314276+0.012312    test-rmse:1.210521+0.166189 
## [125]    train-rmse:0.312514+0.012483    test-rmse:1.210231+0.166156 
## [126]    train-rmse:0.309879+0.012150    test-rmse:1.210020+0.166049 
## [127]    train-rmse:0.307801+0.013010    test-rmse:1.210191+0.165764 
## [128]    train-rmse:0.304594+0.012091    test-rmse:1.210820+0.165876 
## [129]    train-rmse:0.302752+0.012100    test-rmse:1.210550+0.167501 
## [130]    train-rmse:0.300180+0.013312    test-rmse:1.210744+0.167367 
## [131]    train-rmse:0.298128+0.013206    test-rmse:1.210819+0.167813 
## [132]    train-rmse:0.296086+0.012495    test-rmse:1.211311+0.168268 
## Stopping. Best iteration:
## [126]    train-rmse:0.309879+0.012150    test-rmse:1.210020+0.166049
## 
## 7 
## [1]  train-rmse:8.622191+0.062206    test-rmse:8.618123+0.275463 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.680580+0.053765    test-rmse:7.672911+0.273038 
## [3]  train-rmse:6.860691+0.045917    test-rmse:6.854712+0.266215 
## [4]  train-rmse:6.148834+0.039280    test-rmse:6.142053+0.264393 
## [5]  train-rmse:5.532528+0.033311    test-rmse:5.528606+0.255469 
## [6]  train-rmse:5.000972+0.028270    test-rmse:4.999558+0.245476 
## [7]  train-rmse:4.544460+0.023828    test-rmse:4.547617+0.237755 
## [8]  train-rmse:4.154864+0.020301    test-rmse:4.161322+0.226229 
## [9]  train-rmse:3.823647+0.016975    test-rmse:3.830972+0.215107 
## [10] train-rmse:3.543768+0.014101    test-rmse:3.560156+0.198097 
## [11] train-rmse:3.308699+0.012701    test-rmse:3.331081+0.176971 
## [12] train-rmse:3.112170+0.011862    test-rmse:3.135893+0.163541 
## [13] train-rmse:2.948649+0.011766    test-rmse:2.973790+0.144853 
## [14] train-rmse:2.813402+0.012261    test-rmse:2.838214+0.125632 
## [15] train-rmse:2.701751+0.012706    test-rmse:2.730942+0.109554 
## [16] train-rmse:2.609745+0.013021    test-rmse:2.638827+0.096109 
## [17] train-rmse:2.534149+0.013647    test-rmse:2.566783+0.084159 
## [18] train-rmse:2.472306+0.014137    test-rmse:2.506587+0.069961 
## [19] train-rmse:2.421181+0.014634    test-rmse:2.460810+0.060812 
## [20] train-rmse:2.378858+0.015090    test-rmse:2.415976+0.057163 
## [21] train-rmse:2.343420+0.015434    test-rmse:2.385544+0.053285 
## [22] train-rmse:2.314114+0.016093    test-rmse:2.360776+0.049444 
## [23] train-rmse:2.289416+0.016549    test-rmse:2.335205+0.047592 
## [24] train-rmse:2.268250+0.016979    test-rmse:2.315313+0.049302 
## [25] train-rmse:2.249857+0.017454    test-rmse:2.296416+0.049316 
## [26] train-rmse:2.233760+0.017852    test-rmse:2.279193+0.053782 
## [27] train-rmse:2.219737+0.018331    test-rmse:2.266317+0.058626 
## [28] train-rmse:2.207102+0.018797    test-rmse:2.257577+0.062296 
## [29] train-rmse:2.195662+0.019097    test-rmse:2.247601+0.063779 
## [30] train-rmse:2.185103+0.019420    test-rmse:2.236349+0.065554 
## [31] train-rmse:2.175138+0.019842    test-rmse:2.223420+0.065551 
## [32] train-rmse:2.166034+0.020247    test-rmse:2.215501+0.069825 
## [33] train-rmse:2.157401+0.020625    test-rmse:2.208560+0.073281 
## [34] train-rmse:2.149352+0.020983    test-rmse:2.204358+0.073075 
## [35] train-rmse:2.141686+0.021280    test-rmse:2.197805+0.077301 
## [36] train-rmse:2.134211+0.021461    test-rmse:2.189442+0.075641 
## [37] train-rmse:2.126963+0.021800    test-rmse:2.185513+0.075824 
## [38] train-rmse:2.119969+0.022115    test-rmse:2.179768+0.078284 
## [39] train-rmse:2.113207+0.022440    test-rmse:2.176037+0.078623 
## [40] train-rmse:2.106597+0.022676    test-rmse:2.169063+0.081680 
## [41] train-rmse:2.100074+0.022911    test-rmse:2.163180+0.080821 
## [42] train-rmse:2.093634+0.023231    test-rmse:2.151774+0.084152 
## [43] train-rmse:2.087301+0.023508    test-rmse:2.145290+0.084421 
## [44] train-rmse:2.081097+0.023791    test-rmse:2.141227+0.085373 
## [45] train-rmse:2.075086+0.024080    test-rmse:2.134619+0.084634 
## [46] train-rmse:2.069240+0.024348    test-rmse:2.132387+0.085728 
## [47] train-rmse:2.063442+0.024610    test-rmse:2.127683+0.088101 
## [48] train-rmse:2.057604+0.024810    test-rmse:2.119239+0.087262 
## [49] train-rmse:2.051963+0.025102    test-rmse:2.114610+0.084761 
## [50] train-rmse:2.046328+0.025410    test-rmse:2.108785+0.086434 
## [51] train-rmse:2.040773+0.025756    test-rmse:2.107007+0.086893 
## [52] train-rmse:2.035375+0.026116    test-rmse:2.100433+0.090810 
## [53] train-rmse:2.030056+0.026413    test-rmse:2.097298+0.092651 
## [54] train-rmse:2.024885+0.026674    test-rmse:2.093624+0.090845 
## [55] train-rmse:2.019717+0.026881    test-rmse:2.089012+0.090180 
## [56] train-rmse:2.014568+0.027059    test-rmse:2.085436+0.087890 
## [57] train-rmse:2.009569+0.027321    test-rmse:2.079082+0.090303 
## [58] train-rmse:2.004577+0.027545    test-rmse:2.074610+0.088623 
## [59] train-rmse:1.999632+0.027756    test-rmse:2.069781+0.089035 
## [60] train-rmse:1.994751+0.027972    test-rmse:2.066259+0.091531 
## [61] train-rmse:1.989926+0.028187    test-rmse:2.059884+0.089437 
## [62] train-rmse:1.985152+0.028464    test-rmse:2.056736+0.090154 
## [63] train-rmse:1.980441+0.028710    test-rmse:2.053814+0.090301 
## [64] train-rmse:1.975804+0.028917    test-rmse:2.050227+0.092257 
## [65] train-rmse:1.971247+0.029126    test-rmse:2.048022+0.093674 
## [66] train-rmse:1.966721+0.029320    test-rmse:2.044138+0.090789 
## [67] train-rmse:1.962222+0.029462    test-rmse:2.036831+0.091421 
## [68] train-rmse:1.957760+0.029668    test-rmse:2.034281+0.093309 
## [69] train-rmse:1.953364+0.029903    test-rmse:2.029301+0.093320 
## [70] train-rmse:1.948985+0.030154    test-rmse:2.024067+0.088795 
## [71] train-rmse:1.944651+0.030384    test-rmse:2.020708+0.088902 
## [72] train-rmse:1.940330+0.030673    test-rmse:2.015397+0.086509 
## [73] train-rmse:1.936085+0.030942    test-rmse:2.010028+0.087751 
## [74] train-rmse:1.931926+0.031126    test-rmse:2.006394+0.087441 
## [75] train-rmse:1.927860+0.031358    test-rmse:2.004426+0.088522 
## [76] train-rmse:1.923826+0.031547    test-rmse:2.003795+0.090455 
## [77] train-rmse:1.919795+0.031758    test-rmse:1.999624+0.090240 
## [78] train-rmse:1.915850+0.031974    test-rmse:1.997332+0.087528 
## [79] train-rmse:1.911901+0.032178    test-rmse:1.992807+0.089565 
## [80] train-rmse:1.908009+0.032394    test-rmse:1.990718+0.090398 
## [81] train-rmse:1.904143+0.032631    test-rmse:1.985245+0.093041 
## [82] train-rmse:1.900320+0.032854    test-rmse:1.982951+0.092371 
## [83] train-rmse:1.896541+0.033107    test-rmse:1.979249+0.090538 
## [84] train-rmse:1.892764+0.033374    test-rmse:1.977084+0.090307 
## [85] train-rmse:1.889018+0.033561    test-rmse:1.971437+0.088480 
## [86] train-rmse:1.885300+0.033783    test-rmse:1.969672+0.088602 
## [87] train-rmse:1.881620+0.034022    test-rmse:1.965735+0.089148 
## [88] train-rmse:1.877954+0.034306    test-rmse:1.960952+0.088525 
## [89] train-rmse:1.874324+0.034596    test-rmse:1.955950+0.087119 
## [90] train-rmse:1.870715+0.034824    test-rmse:1.954842+0.088046 
## [91] train-rmse:1.867169+0.035002    test-rmse:1.951871+0.087731 
## [92] train-rmse:1.863647+0.035176    test-rmse:1.950385+0.087867 
## [93] train-rmse:1.860222+0.035383    test-rmse:1.948436+0.091593 
## [94] train-rmse:1.856811+0.035597    test-rmse:1.947697+0.093132 
## [95] train-rmse:1.853393+0.035809    test-rmse:1.944151+0.093108 
## [96] train-rmse:1.849997+0.036003    test-rmse:1.940834+0.091495 
## [97] train-rmse:1.846619+0.036212    test-rmse:1.937995+0.090876 
## [98] train-rmse:1.843286+0.036366    test-rmse:1.935306+0.088340 
## [99] train-rmse:1.840003+0.036535    test-rmse:1.933170+0.089389 
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## [518]    train-rmse:1.368478+0.053342    test-rmse:1.516136+0.182988 
## [519]    train-rmse:1.368188+0.053340    test-rmse:1.515993+0.183159 
## [520]    train-rmse:1.367900+0.053341    test-rmse:1.515848+0.183427 
## [521]    train-rmse:1.367610+0.053342    test-rmse:1.514677+0.183108 
## [522]    train-rmse:1.367320+0.053343    test-rmse:1.514322+0.183620 
## [523]    train-rmse:1.367036+0.053342    test-rmse:1.514767+0.184033 
## [524]    train-rmse:1.366752+0.053342    test-rmse:1.514470+0.183906 
## [525]    train-rmse:1.366471+0.053340    test-rmse:1.513985+0.183680 
## [526]    train-rmse:1.366192+0.053339    test-rmse:1.513054+0.184565 
## [527]    train-rmse:1.365915+0.053335    test-rmse:1.512685+0.184062 
## [528]    train-rmse:1.365639+0.053333    test-rmse:1.513068+0.184046 
## [529]    train-rmse:1.365365+0.053331    test-rmse:1.512891+0.183949 
## [530]    train-rmse:1.365092+0.053330    test-rmse:1.512029+0.183515 
## [531]    train-rmse:1.364820+0.053327    test-rmse:1.511890+0.183589 
## [532]    train-rmse:1.364548+0.053324    test-rmse:1.511913+0.183780 
## [533]    train-rmse:1.364273+0.053324    test-rmse:1.511586+0.184742 
## [534]    train-rmse:1.364001+0.053325    test-rmse:1.510967+0.184076 
## [535]    train-rmse:1.363733+0.053322    test-rmse:1.511235+0.183760 
## [536]    train-rmse:1.363468+0.053320    test-rmse:1.511498+0.184264 
## [537]    train-rmse:1.363202+0.053319    test-rmse:1.511506+0.184445 
## [538]    train-rmse:1.362937+0.053319    test-rmse:1.511444+0.184583 
## [539]    train-rmse:1.362676+0.053317    test-rmse:1.511247+0.184536 
## [540]    train-rmse:1.362412+0.053312    test-rmse:1.510583+0.184054 
## [541]    train-rmse:1.362151+0.053306    test-rmse:1.510243+0.184688 
## [542]    train-rmse:1.361892+0.053303    test-rmse:1.510182+0.184903 
## [543]    train-rmse:1.361635+0.053298    test-rmse:1.509717+0.184989 
## [544]    train-rmse:1.361379+0.053296    test-rmse:1.509367+0.185490 
## [545]    train-rmse:1.361127+0.053294    test-rmse:1.509404+0.185931 
## [546]    train-rmse:1.360875+0.053291    test-rmse:1.509124+0.185832 
## [547]    train-rmse:1.360625+0.053289    test-rmse:1.508223+0.185383 
## [548]    train-rmse:1.360374+0.053285    test-rmse:1.508237+0.185741 
## [549]    train-rmse:1.360122+0.053282    test-rmse:1.507640+0.186455 
## [550]    train-rmse:1.359871+0.053280    test-rmse:1.507456+0.186441 
## [551]    train-rmse:1.359622+0.053279    test-rmse:1.506998+0.186521 
## [552]    train-rmse:1.359376+0.053277    test-rmse:1.506687+0.186233 
## [553]    train-rmse:1.359133+0.053276    test-rmse:1.506798+0.185937 
## [554]    train-rmse:1.358887+0.053275    test-rmse:1.506354+0.186533 
## [555]    train-rmse:1.358643+0.053274    test-rmse:1.506270+0.186685 
## [556]    train-rmse:1.358401+0.053273    test-rmse:1.506194+0.186765 
## [557]    train-rmse:1.358163+0.053272    test-rmse:1.506510+0.187117 
## [558]    train-rmse:1.357925+0.053273    test-rmse:1.506744+0.187241 
## [559]    train-rmse:1.357687+0.053273    test-rmse:1.506551+0.187436 
## [560]    train-rmse:1.357448+0.053273    test-rmse:1.505970+0.187035 
## [561]    train-rmse:1.357210+0.053268    test-rmse:1.505782+0.187122 
## [562]    train-rmse:1.356977+0.053265    test-rmse:1.505900+0.188083 
## [563]    train-rmse:1.356743+0.053261    test-rmse:1.505781+0.187923 
## [564]    train-rmse:1.356512+0.053260    test-rmse:1.505819+0.187921 
## [565]    train-rmse:1.356281+0.053257    test-rmse:1.505645+0.187870 
## [566]    train-rmse:1.356051+0.053253    test-rmse:1.504961+0.188362 
## [567]    train-rmse:1.355821+0.053249    test-rmse:1.504768+0.188098 
## [568]    train-rmse:1.355593+0.053246    test-rmse:1.505264+0.188369 
## [569]    train-rmse:1.355367+0.053242    test-rmse:1.505210+0.188141 
## [570]    train-rmse:1.355142+0.053242    test-rmse:1.505085+0.187824 
## [571]    train-rmse:1.354916+0.053240    test-rmse:1.504583+0.187989 
## [572]    train-rmse:1.354694+0.053240    test-rmse:1.504364+0.188011 
## [573]    train-rmse:1.354472+0.053239    test-rmse:1.503968+0.187995 
## [574]    train-rmse:1.354249+0.053241    test-rmse:1.504189+0.188216 
## [575]    train-rmse:1.354029+0.053240    test-rmse:1.503787+0.188250 
## [576]    train-rmse:1.353812+0.053236    test-rmse:1.503788+0.188663 
## [577]    train-rmse:1.353593+0.053234    test-rmse:1.503929+0.187979 
## [578]    train-rmse:1.353376+0.053229    test-rmse:1.503747+0.187945 
## [579]    train-rmse:1.353159+0.053226    test-rmse:1.502918+0.188618 
## [580]    train-rmse:1.352944+0.053223    test-rmse:1.502705+0.188635 
## [581]    train-rmse:1.352727+0.053218    test-rmse:1.502360+0.188969 
## [582]    train-rmse:1.352514+0.053213    test-rmse:1.502111+0.189137 
## [583]    train-rmse:1.352301+0.053208    test-rmse:1.501036+0.188664 
## [584]    train-rmse:1.352089+0.053204    test-rmse:1.501476+0.188665 
## [585]    train-rmse:1.351880+0.053199    test-rmse:1.501733+0.189407 
## [586]    train-rmse:1.351672+0.053197    test-rmse:1.501580+0.189546 
## [587]    train-rmse:1.351464+0.053193    test-rmse:1.501790+0.189227 
## [588]    train-rmse:1.351261+0.053190    test-rmse:1.502422+0.189275 
## [589]    train-rmse:1.351057+0.053187    test-rmse:1.502471+0.190266 
## Stopping. Best iteration:
## [583]    train-rmse:1.352301+0.053208    test-rmse:1.501036+0.188664
## 
## 8 
## [1]  train-rmse:8.607669+0.062417    test-rmse:8.603851+0.272725 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.653456+0.056176    test-rmse:7.646251+0.263750 
## [3]  train-rmse:6.819654+0.050579    test-rmse:6.813798+0.254260 
## [4]  train-rmse:6.095678+0.046127    test-rmse:6.092175+0.240744 
## [5]  train-rmse:5.462803+0.037736    test-rmse:5.462488+0.236042 
## [6]  train-rmse:4.915407+0.032147    test-rmse:4.923890+0.229098 
## [7]  train-rmse:4.441358+0.025397    test-rmse:4.453839+0.215999 
## [8]  train-rmse:4.032191+0.021548    test-rmse:4.048703+0.200958 
## [9]  train-rmse:3.681272+0.019229    test-rmse:3.704442+0.185357 
## [10] train-rmse:3.381431+0.016596    test-rmse:3.408252+0.172759 
## [11] train-rmse:3.126108+0.015864    test-rmse:3.163614+0.154985 
## [12] train-rmse:2.910810+0.014495    test-rmse:2.953770+0.141556 
## [13] train-rmse:2.727200+0.011264    test-rmse:2.780382+0.127195 
## [14] train-rmse:2.574499+0.010981    test-rmse:2.636684+0.106031 
## [15] train-rmse:2.446396+0.010757    test-rmse:2.519173+0.093810 
## [16] train-rmse:2.336410+0.012934    test-rmse:2.415900+0.084777 
## [17] train-rmse:2.247028+0.012301    test-rmse:2.337683+0.067278 
## [18] train-rmse:2.172614+0.012389    test-rmse:2.267142+0.059631 
## [19] train-rmse:2.110398+0.013219    test-rmse:2.209251+0.054013 
## [20] train-rmse:2.058116+0.012813    test-rmse:2.166790+0.049097 
## [21] train-rmse:2.013301+0.011759    test-rmse:2.130560+0.054067 
## [22] train-rmse:1.974598+0.012743    test-rmse:2.098561+0.055750 
## [23] train-rmse:1.939721+0.015430    test-rmse:2.066327+0.059478 
## [24] train-rmse:1.912584+0.014900    test-rmse:2.043560+0.059213 
## [25] train-rmse:1.888210+0.014955    test-rmse:2.022143+0.062963 
## [26] train-rmse:1.866879+0.015564    test-rmse:2.008803+0.072205 
## [27] train-rmse:1.845306+0.019505    test-rmse:1.988794+0.075050 
## [28] train-rmse:1.825520+0.021388    test-rmse:1.967573+0.077662 
## [29] train-rmse:1.809191+0.022226    test-rmse:1.954555+0.080769 
## [30] train-rmse:1.794751+0.022230    test-rmse:1.937892+0.080178 
## [31] train-rmse:1.780445+0.022254    test-rmse:1.927903+0.083376 
## [32] train-rmse:1.767876+0.022638    test-rmse:1.918840+0.087673 
## [33] train-rmse:1.754573+0.023253    test-rmse:1.909171+0.092842 
## [34] train-rmse:1.740696+0.023288    test-rmse:1.897272+0.097059 
## [35] train-rmse:1.729365+0.022002    test-rmse:1.885245+0.097089 
## [36] train-rmse:1.718386+0.022446    test-rmse:1.879526+0.096896 
## [37] train-rmse:1.706679+0.024554    test-rmse:1.870105+0.097792 
## [38] train-rmse:1.697136+0.024874    test-rmse:1.858882+0.098525 
## [39] train-rmse:1.685337+0.020817    test-rmse:1.849024+0.097884 
## [40] train-rmse:1.676004+0.021202    test-rmse:1.845621+0.099205 
## [41] train-rmse:1.666737+0.021320    test-rmse:1.837869+0.103475 
## [42] train-rmse:1.654757+0.023496    test-rmse:1.827213+0.107855 
## [43] train-rmse:1.645586+0.024377    test-rmse:1.821177+0.107677 
## [44] train-rmse:1.636689+0.023901    test-rmse:1.813818+0.108373 
## [45] train-rmse:1.627906+0.024246    test-rmse:1.810067+0.112260 
## [46] train-rmse:1.618088+0.025952    test-rmse:1.796216+0.113201 
## [47] train-rmse:1.608219+0.028366    test-rmse:1.790285+0.112648 
## [48] train-rmse:1.600110+0.028323    test-rmse:1.783206+0.112820 
## [49] train-rmse:1.592304+0.028371    test-rmse:1.778743+0.112196 
## [50] train-rmse:1.583848+0.029289    test-rmse:1.774908+0.114579 
## [51] train-rmse:1.572833+0.028194    test-rmse:1.761417+0.099733 
## [52] train-rmse:1.560853+0.029652    test-rmse:1.757310+0.101768 
## [53] train-rmse:1.553022+0.029186    test-rmse:1.749312+0.104102 
## [54] train-rmse:1.546144+0.029336    test-rmse:1.744516+0.103496 
## [55] train-rmse:1.539095+0.028970    test-rmse:1.738452+0.102557 
## [56] train-rmse:1.531946+0.029665    test-rmse:1.734991+0.102242 
## [57] train-rmse:1.523457+0.029857    test-rmse:1.728678+0.107349 
## [58] train-rmse:1.516645+0.029958    test-rmse:1.725491+0.106341 
## [59] train-rmse:1.509712+0.030295    test-rmse:1.721335+0.103370 
## [60] train-rmse:1.502160+0.029605    test-rmse:1.716350+0.103764 
## [61] train-rmse:1.495453+0.029412    test-rmse:1.710434+0.104561 
## [62] train-rmse:1.488072+0.030511    test-rmse:1.705980+0.107898 
## [63] train-rmse:1.479812+0.027336    test-rmse:1.700095+0.111327 
## [64] train-rmse:1.473869+0.027534    test-rmse:1.695360+0.112745 
## [65] train-rmse:1.467402+0.027389    test-rmse:1.690988+0.113306 
## [66] train-rmse:1.461470+0.027958    test-rmse:1.686098+0.112662 
## [67] train-rmse:1.455311+0.027621    test-rmse:1.683026+0.111357 
## [68] train-rmse:1.449837+0.027695    test-rmse:1.678295+0.109864 
## [69] train-rmse:1.444059+0.027868    test-rmse:1.675446+0.107461 
## [70] train-rmse:1.438165+0.028289    test-rmse:1.669200+0.107904 
## [71] train-rmse:1.432146+0.028900    test-rmse:1.661660+0.109323 
## [72] train-rmse:1.426563+0.029671    test-rmse:1.658643+0.111313 
## [73] train-rmse:1.418342+0.032125    test-rmse:1.653498+0.113952 
## [74] train-rmse:1.412152+0.031314    test-rmse:1.648520+0.112968 
## [75] train-rmse:1.406992+0.031691    test-rmse:1.646302+0.111446 
## [76] train-rmse:1.401177+0.031064    test-rmse:1.642540+0.115806 
## [77] train-rmse:1.394653+0.028605    test-rmse:1.639586+0.117728 
## [78] train-rmse:1.388375+0.029010    test-rmse:1.637155+0.117535 
## [79] train-rmse:1.383356+0.029589    test-rmse:1.633037+0.118082 
## [80] train-rmse:1.378355+0.029747    test-rmse:1.625463+0.119077 
## [81] train-rmse:1.373670+0.029905    test-rmse:1.621785+0.118345 
## [82] train-rmse:1.368956+0.029868    test-rmse:1.618307+0.120525 
## [83] train-rmse:1.359550+0.033484    test-rmse:1.608601+0.128349 
## [84] train-rmse:1.354271+0.033878    test-rmse:1.605486+0.129402 
## [85] train-rmse:1.349312+0.033857    test-rmse:1.601156+0.129947 
## [86] train-rmse:1.344594+0.033529    test-rmse:1.595314+0.131551 
## [87] train-rmse:1.338703+0.032196    test-rmse:1.590718+0.131633 
## [88] train-rmse:1.333386+0.031814    test-rmse:1.589545+0.133241 
## [89] train-rmse:1.329173+0.032200    test-rmse:1.589053+0.132008 
## [90] train-rmse:1.324060+0.031772    test-rmse:1.585581+0.132428 
## [91] train-rmse:1.318274+0.032841    test-rmse:1.581087+0.130658 
## [92] train-rmse:1.313844+0.032940    test-rmse:1.578396+0.131094 
## [93] train-rmse:1.309436+0.033004    test-rmse:1.576200+0.132536 
## [94] train-rmse:1.304849+0.033218    test-rmse:1.572827+0.132877 
## [95] train-rmse:1.300602+0.033613    test-rmse:1.571535+0.132456 
## [96] train-rmse:1.296857+0.033855    test-rmse:1.567494+0.134284 
## [97] train-rmse:1.292489+0.034482    test-rmse:1.561948+0.138142 
## [98] train-rmse:1.285076+0.037386    test-rmse:1.559406+0.138349 
## [99] train-rmse:1.275223+0.030869    test-rmse:1.551296+0.140637 
## [100]    train-rmse:1.271048+0.030793    test-rmse:1.546297+0.140586 
## [101]    train-rmse:1.266916+0.030678    test-rmse:1.545080+0.142129 
## [102]    train-rmse:1.262761+0.030191    test-rmse:1.540524+0.144449 
## [103]    train-rmse:1.258859+0.030760    test-rmse:1.538884+0.143692 
## [104]    train-rmse:1.254855+0.030594    test-rmse:1.536476+0.143919 
## [105]    train-rmse:1.251276+0.030801    test-rmse:1.532592+0.143388 
## [106]    train-rmse:1.245613+0.032257    test-rmse:1.525061+0.148634 
## [107]    train-rmse:1.241981+0.032662    test-rmse:1.521306+0.151529 
## [108]    train-rmse:1.237099+0.033782    test-rmse:1.520709+0.153485 
## [109]    train-rmse:1.232390+0.034788    test-rmse:1.517193+0.154077 
## [110]    train-rmse:1.228257+0.034299    test-rmse:1.514142+0.153062 
## [111]    train-rmse:1.224627+0.034138    test-rmse:1.510921+0.154838 
## [112]    train-rmse:1.221521+0.034263    test-rmse:1.510027+0.154364 
## [113]    train-rmse:1.218373+0.034338    test-rmse:1.507987+0.153400 
## [114]    train-rmse:1.214491+0.034447    test-rmse:1.505857+0.155312 
## [115]    train-rmse:1.211014+0.034779    test-rmse:1.503106+0.154925 
## [116]    train-rmse:1.206934+0.035408    test-rmse:1.499882+0.152805 
## [117]    train-rmse:1.203324+0.035516    test-rmse:1.497486+0.152981 
## [118]    train-rmse:1.196492+0.033126    test-rmse:1.486051+0.142105 
## [119]    train-rmse:1.190701+0.032117    test-rmse:1.482672+0.138843 
## [120]    train-rmse:1.187404+0.032174    test-rmse:1.481074+0.139088 
## [121]    train-rmse:1.184107+0.032515    test-rmse:1.479407+0.139220 
## [122]    train-rmse:1.180660+0.032890    test-rmse:1.476137+0.140358 
## [123]    train-rmse:1.177843+0.033194    test-rmse:1.474902+0.141028 
## [124]    train-rmse:1.174567+0.032725    test-rmse:1.473194+0.141137 
## [125]    train-rmse:1.167281+0.025693    test-rmse:1.468216+0.143561 
## [126]    train-rmse:1.164148+0.025463    test-rmse:1.465584+0.143388 
## [127]    train-rmse:1.161073+0.025557    test-rmse:1.462244+0.145671 
## [128]    train-rmse:1.158307+0.025785    test-rmse:1.462507+0.146075 
## [129]    train-rmse:1.154836+0.025439    test-rmse:1.460279+0.148424 
## [130]    train-rmse:1.152119+0.025441    test-rmse:1.457805+0.149020 
## [131]    train-rmse:1.148751+0.026353    test-rmse:1.455784+0.148879 
## [132]    train-rmse:1.143747+0.026620    test-rmse:1.452760+0.150827 
## [133]    train-rmse:1.140052+0.027477    test-rmse:1.449575+0.149272 
## [134]    train-rmse:1.137388+0.027809    test-rmse:1.449423+0.149776 
## [135]    train-rmse:1.133910+0.027847    test-rmse:1.446626+0.151636 
## [136]    train-rmse:1.131434+0.028046    test-rmse:1.446750+0.152939 
## [137]    train-rmse:1.126660+0.026989    test-rmse:1.442308+0.149736 
## [138]    train-rmse:1.123948+0.027383    test-rmse:1.440677+0.149638 
## [139]    train-rmse:1.121054+0.027490    test-rmse:1.436262+0.148838 
## [140]    train-rmse:1.118295+0.027196    test-rmse:1.435465+0.148281 
## [141]    train-rmse:1.116011+0.027318    test-rmse:1.432580+0.148802 
## [142]    train-rmse:1.113184+0.027578    test-rmse:1.431739+0.149372 
## [143]    train-rmse:1.110926+0.027577    test-rmse:1.430482+0.150362 
## [144]    train-rmse:1.107531+0.028415    test-rmse:1.426679+0.151922 
## [145]    train-rmse:1.104861+0.028351    test-rmse:1.425309+0.152020 
## [146]    train-rmse:1.102747+0.028668    test-rmse:1.423373+0.151751 
## [147]    train-rmse:1.099469+0.029103    test-rmse:1.422037+0.152235 
## [148]    train-rmse:1.096908+0.028997    test-rmse:1.421084+0.152448 
## [149]    train-rmse:1.094450+0.028667    test-rmse:1.419451+0.153970 
## [150]    train-rmse:1.089968+0.029080    test-rmse:1.417323+0.155000 
## [151]    train-rmse:1.087197+0.028939    test-rmse:1.416245+0.155160 
## [152]    train-rmse:1.085025+0.029104    test-rmse:1.414350+0.155273 
## [153]    train-rmse:1.082370+0.029077    test-rmse:1.413464+0.155147 
## [154]    train-rmse:1.079722+0.029274    test-rmse:1.412879+0.155925 
## [155]    train-rmse:1.077627+0.029188    test-rmse:1.410295+0.154967 
## [156]    train-rmse:1.075406+0.029428    test-rmse:1.409751+0.156441 
## [157]    train-rmse:1.073206+0.029583    test-rmse:1.410152+0.158061 
## [158]    train-rmse:1.071234+0.029845    test-rmse:1.409681+0.159494 
## [159]    train-rmse:1.069061+0.029648    test-rmse:1.407443+0.159222 
## [160]    train-rmse:1.067135+0.029898    test-rmse:1.405617+0.158568 
## [161]    train-rmse:1.064689+0.029733    test-rmse:1.403288+0.160471 
## [162]    train-rmse:1.062160+0.029478    test-rmse:1.402401+0.160943 
## [163]    train-rmse:1.056092+0.022921    test-rmse:1.399854+0.163100 
## [164]    train-rmse:1.054076+0.022733    test-rmse:1.397923+0.164491 
## [165]    train-rmse:1.051429+0.022466    test-rmse:1.395752+0.165336 
## [166]    train-rmse:1.049646+0.022479    test-rmse:1.394057+0.165259 
## [167]    train-rmse:1.046614+0.023358    test-rmse:1.392871+0.165584 
## [168]    train-rmse:1.043223+0.024313    test-rmse:1.389608+0.165402 
## [169]    train-rmse:1.041013+0.023971    test-rmse:1.388659+0.165361 
## [170]    train-rmse:1.038853+0.023856    test-rmse:1.388755+0.167089 
## [171]    train-rmse:1.036982+0.024004    test-rmse:1.388075+0.167274 
## [172]    train-rmse:1.035278+0.024291    test-rmse:1.385809+0.168323 
## [173]    train-rmse:1.033116+0.024629    test-rmse:1.384150+0.169210 
## [174]    train-rmse:1.031345+0.024682    test-rmse:1.382292+0.169485 
## [175]    train-rmse:1.029096+0.024807    test-rmse:1.381982+0.169193 
## [176]    train-rmse:1.027289+0.024522    test-rmse:1.380245+0.169527 
## [177]    train-rmse:1.025662+0.024536    test-rmse:1.378201+0.169198 
## [178]    train-rmse:1.023749+0.024398    test-rmse:1.378611+0.169261 
## [179]    train-rmse:1.021723+0.024027    test-rmse:1.378575+0.169318 
## [180]    train-rmse:1.020220+0.024202    test-rmse:1.377442+0.168963 
## [181]    train-rmse:1.018434+0.024275    test-rmse:1.376431+0.169127 
## [182]    train-rmse:1.016655+0.024222    test-rmse:1.375535+0.167878 
## [183]    train-rmse:1.015073+0.024327    test-rmse:1.375392+0.169097 
## [184]    train-rmse:1.013462+0.024546    test-rmse:1.374413+0.169452 
## [185]    train-rmse:1.011617+0.024653    test-rmse:1.372566+0.170054 
## [186]    train-rmse:1.010072+0.024760    test-rmse:1.370103+0.170492 
## [187]    train-rmse:1.007725+0.025314    test-rmse:1.369101+0.170965 
## [188]    train-rmse:1.006115+0.025537    test-rmse:1.367095+0.171340 
## [189]    train-rmse:1.003486+0.026493    test-rmse:1.366794+0.172012 
## [190]    train-rmse:1.001963+0.026521    test-rmse:1.366143+0.171355 
## [191]    train-rmse:1.000474+0.026784    test-rmse:1.366359+0.171361 
## [192]    train-rmse:0.995792+0.025416    test-rmse:1.358846+0.163948 
## [193]    train-rmse:0.992370+0.024685    test-rmse:1.358322+0.164274 
## [194]    train-rmse:0.990397+0.024510    test-rmse:1.357831+0.164953 
## [195]    train-rmse:0.987696+0.024134    test-rmse:1.355916+0.164916 
## [196]    train-rmse:0.986235+0.024216    test-rmse:1.354807+0.164001 
## [197]    train-rmse:0.984886+0.024369    test-rmse:1.355131+0.162340 
## [198]    train-rmse:0.983534+0.024392    test-rmse:1.355914+0.163393 
## [199]    train-rmse:0.978870+0.018361    test-rmse:1.355466+0.165552 
## [200]    train-rmse:0.977275+0.018173    test-rmse:1.353992+0.166301 
## [201]    train-rmse:0.975778+0.018276    test-rmse:1.353842+0.165621 
## [202]    train-rmse:0.974248+0.018165    test-rmse:1.351147+0.166265 
## [203]    train-rmse:0.972594+0.018053    test-rmse:1.351043+0.166853 
## [204]    train-rmse:0.971292+0.018257    test-rmse:1.351319+0.166739 
## [205]    train-rmse:0.967678+0.020156    test-rmse:1.348790+0.167571 
## [206]    train-rmse:0.966041+0.020127    test-rmse:1.347605+0.167822 
## [207]    train-rmse:0.961928+0.015705    test-rmse:1.344834+0.169413 
## [208]    train-rmse:0.959954+0.015967    test-rmse:1.344837+0.170448 
## [209]    train-rmse:0.958565+0.016323    test-rmse:1.343718+0.170554 
## [210]    train-rmse:0.957171+0.016449    test-rmse:1.342239+0.171164 
## [211]    train-rmse:0.955659+0.016705    test-rmse:1.342511+0.171188 
## [212]    train-rmse:0.954203+0.016617    test-rmse:1.342859+0.172071 
## [213]    train-rmse:0.952980+0.016659    test-rmse:1.342415+0.171899 
## [214]    train-rmse:0.950504+0.017716    test-rmse:1.340950+0.172107 
## [215]    train-rmse:0.949132+0.017616    test-rmse:1.339155+0.173051 
## [216]    train-rmse:0.947602+0.017425    test-rmse:1.339135+0.174578 
## [217]    train-rmse:0.946425+0.017533    test-rmse:1.338511+0.174735 
## [218]    train-rmse:0.945000+0.017380    test-rmse:1.337501+0.173970 
## [219]    train-rmse:0.943548+0.017251    test-rmse:1.337692+0.174154 
## [220]    train-rmse:0.940227+0.014341    test-rmse:1.336218+0.177149 
## [221]    train-rmse:0.938897+0.014370    test-rmse:1.335159+0.176770 
## [222]    train-rmse:0.937689+0.014488    test-rmse:1.335572+0.176748 
## [223]    train-rmse:0.935696+0.015366    test-rmse:1.335678+0.176608 
## [224]    train-rmse:0.934370+0.015156    test-rmse:1.335554+0.176811 
## [225]    train-rmse:0.933257+0.015094    test-rmse:1.335034+0.177110 
## [226]    train-rmse:0.931907+0.015097    test-rmse:1.334488+0.177509 
## [227]    train-rmse:0.929068+0.013561    test-rmse:1.332013+0.179590 
## [228]    train-rmse:0.927196+0.013998    test-rmse:1.332533+0.180940 
## [229]    train-rmse:0.926136+0.014047    test-rmse:1.330740+0.180257 
## [230]    train-rmse:0.924882+0.013956    test-rmse:1.330372+0.180471 
## [231]    train-rmse:0.921218+0.014982    test-rmse:1.324827+0.182638 
## [232]    train-rmse:0.920046+0.014947    test-rmse:1.324432+0.183445 
## [233]    train-rmse:0.919096+0.015062    test-rmse:1.325989+0.184038 
## [234]    train-rmse:0.917711+0.015046    test-rmse:1.325267+0.184227 
## [235]    train-rmse:0.916563+0.015039    test-rmse:1.325127+0.183975 
## [236]    train-rmse:0.915314+0.015200    test-rmse:1.323661+0.183431 
## [237]    train-rmse:0.913975+0.015646    test-rmse:1.323055+0.183955 
## [238]    train-rmse:0.912773+0.015583    test-rmse:1.323255+0.183960 
## [239]    train-rmse:0.910868+0.015659    test-rmse:1.322320+0.184684 
## [240]    train-rmse:0.909683+0.015546    test-rmse:1.322126+0.184719 
## [241]    train-rmse:0.908405+0.015674    test-rmse:1.321636+0.185017 
## [242]    train-rmse:0.907488+0.015691    test-rmse:1.320828+0.186154 
## [243]    train-rmse:0.906566+0.015657    test-rmse:1.320809+0.186263 
## [244]    train-rmse:0.905555+0.015779    test-rmse:1.321750+0.185761 
## [245]    train-rmse:0.904658+0.015814    test-rmse:1.321894+0.184560 
## [246]    train-rmse:0.900802+0.013658    test-rmse:1.316752+0.179187 
## [247]    train-rmse:0.899653+0.013612    test-rmse:1.316134+0.179259 
## [248]    train-rmse:0.895865+0.011489    test-rmse:1.315438+0.180128 
## [249]    train-rmse:0.894922+0.011519    test-rmse:1.315043+0.181424 
## [250]    train-rmse:0.893808+0.011525    test-rmse:1.314641+0.181785 
## [251]    train-rmse:0.892995+0.011553    test-rmse:1.313854+0.182152 
## [252]    train-rmse:0.891605+0.012487    test-rmse:1.313726+0.182410 
## [253]    train-rmse:0.889040+0.013216    test-rmse:1.308718+0.184852 
## [254]    train-rmse:0.887886+0.013106    test-rmse:1.308813+0.184973 
## [255]    train-rmse:0.886644+0.012974    test-rmse:1.308596+0.184947 
## [256]    train-rmse:0.885839+0.012969    test-rmse:1.308939+0.184711 
## [257]    train-rmse:0.884826+0.012897    test-rmse:1.308845+0.184771 
## [258]    train-rmse:0.883716+0.013185    test-rmse:1.309344+0.184576 
## [259]    train-rmse:0.882254+0.013890    test-rmse:1.309594+0.184659 
## [260]    train-rmse:0.881201+0.013946    test-rmse:1.308682+0.184630 
## [261]    train-rmse:0.880349+0.013905    test-rmse:1.308588+0.184659 
## [262]    train-rmse:0.879462+0.014009    test-rmse:1.307891+0.184105 
## [263]    train-rmse:0.878575+0.014143    test-rmse:1.306943+0.184022 
## [264]    train-rmse:0.877784+0.014080    test-rmse:1.307174+0.183807 
## [265]    train-rmse:0.876744+0.014023    test-rmse:1.307408+0.184430 
## [266]    train-rmse:0.875697+0.014217    test-rmse:1.306926+0.184338 
## [267]    train-rmse:0.871548+0.019005    test-rmse:1.305108+0.182575 
## [268]    train-rmse:0.870487+0.019381    test-rmse:1.305843+0.183889 
## [269]    train-rmse:0.869591+0.019477    test-rmse:1.305277+0.184046 
## [270]    train-rmse:0.868724+0.019469    test-rmse:1.305754+0.184542 
## [271]    train-rmse:0.867187+0.018799    test-rmse:1.304769+0.183700 
## [272]    train-rmse:0.866339+0.018765    test-rmse:1.305548+0.183171 
## [273]    train-rmse:0.865565+0.018848    test-rmse:1.304751+0.183445 
## [274]    train-rmse:0.864919+0.018937    test-rmse:1.304414+0.182623 
## [275]    train-rmse:0.864145+0.018899    test-rmse:1.304466+0.182500 
## [276]    train-rmse:0.863217+0.018590    test-rmse:1.303424+0.182480 
## [277]    train-rmse:0.862337+0.018980    test-rmse:1.304476+0.182891 
## [278]    train-rmse:0.860122+0.017961    test-rmse:1.304136+0.183264 
## [279]    train-rmse:0.858467+0.018605    test-rmse:1.302950+0.184286 
## [280]    train-rmse:0.856331+0.019163    test-rmse:1.298574+0.185879 
## [281]    train-rmse:0.855201+0.019311    test-rmse:1.298940+0.186216 
## [282]    train-rmse:0.854369+0.019450    test-rmse:1.298946+0.186368 
## [283]    train-rmse:0.853644+0.019462    test-rmse:1.298016+0.186587 
## [284]    train-rmse:0.853079+0.019532    test-rmse:1.297791+0.186319 
## [285]    train-rmse:0.851684+0.020375    test-rmse:1.297953+0.186664 
## [286]    train-rmse:0.851020+0.020344    test-rmse:1.299181+0.186470 
## [287]    train-rmse:0.849988+0.020947    test-rmse:1.298810+0.185742 
## [288]    train-rmse:0.849353+0.020933    test-rmse:1.298315+0.185315 
## [289]    train-rmse:0.848556+0.020800    test-rmse:1.297764+0.185857 
## [290]    train-rmse:0.847582+0.020645    test-rmse:1.298477+0.186320 
## [291]    train-rmse:0.846827+0.020529    test-rmse:1.298807+0.186373 
## [292]    train-rmse:0.845805+0.020664    test-rmse:1.299346+0.186311 
## [293]    train-rmse:0.844870+0.020693    test-rmse:1.299748+0.187095 
## [294]    train-rmse:0.844012+0.020804    test-rmse:1.299491+0.187147 
## [295]    train-rmse:0.842094+0.020130    test-rmse:1.298681+0.187115 
## Stopping. Best iteration:
## [289]    train-rmse:0.848556+0.020800    test-rmse:1.297764+0.185857
## 
## 9 
## [1]  train-rmse:8.602038+0.063038    test-rmse:8.598392+0.269333 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.638928+0.054970    test-rmse:7.632945+0.260851 
## [3]  train-rmse:6.797607+0.047864    test-rmse:6.791304+0.252814 
## [4]  train-rmse:6.063359+0.042153    test-rmse:6.061784+0.243236 
## [5]  train-rmse:5.424092+0.037057    test-rmse:5.424835+0.233342 
## [6]  train-rmse:4.866962+0.033502    test-rmse:4.877109+0.222512 
## [7]  train-rmse:4.383603+0.029610    test-rmse:4.401899+0.201847 
## [8]  train-rmse:3.964169+0.026793    test-rmse:3.985774+0.189885 
## [9]  train-rmse:3.600896+0.024690    test-rmse:3.640523+0.180605 
## [10] train-rmse:3.287172+0.021365    test-rmse:3.328875+0.163171 
## [11] train-rmse:3.018218+0.020240    test-rmse:3.075841+0.150619 
## [12] train-rmse:2.788148+0.018258    test-rmse:2.855221+0.132929 
## [13] train-rmse:2.592052+0.016771    test-rmse:2.667986+0.119925 
## [14] train-rmse:2.426551+0.016418    test-rmse:2.521083+0.114009 
## [15] train-rmse:2.284662+0.015648    test-rmse:2.394612+0.102504 
## [16] train-rmse:2.164839+0.013545    test-rmse:2.277553+0.096830 
## [17] train-rmse:2.063256+0.014581    test-rmse:2.196047+0.088665 
## [18] train-rmse:1.976327+0.013204    test-rmse:2.117674+0.081778 
## [19] train-rmse:1.903386+0.012227    test-rmse:2.048476+0.075582 
## [20] train-rmse:1.842334+0.012522    test-rmse:1.998090+0.074710 
## [21] train-rmse:1.787912+0.012901    test-rmse:1.956050+0.072151 
## [22] train-rmse:1.742584+0.013486    test-rmse:1.915275+0.071214 
## [23] train-rmse:1.699969+0.010853    test-rmse:1.888341+0.069871 
## [24] train-rmse:1.664604+0.011308    test-rmse:1.854534+0.069742 
## [25] train-rmse:1.633540+0.012091    test-rmse:1.837158+0.067486 
## [26] train-rmse:1.606159+0.012393    test-rmse:1.816219+0.064074 
## [27] train-rmse:1.581172+0.013785    test-rmse:1.797731+0.066196 
## [28] train-rmse:1.559484+0.015088    test-rmse:1.779746+0.067474 
## [29] train-rmse:1.538488+0.017221    test-rmse:1.763355+0.067759 
## [30] train-rmse:1.519858+0.018026    test-rmse:1.752915+0.067910 
## [31] train-rmse:1.501629+0.017052    test-rmse:1.743224+0.070320 
## [32] train-rmse:1.484191+0.017453    test-rmse:1.732403+0.075145 
## [33] train-rmse:1.465937+0.018646    test-rmse:1.720823+0.072823 
## [34] train-rmse:1.449253+0.021350    test-rmse:1.714049+0.074693 
## [35] train-rmse:1.434387+0.021997    test-rmse:1.703640+0.075380 
## [36] train-rmse:1.418793+0.020425    test-rmse:1.692535+0.076407 
## [37] train-rmse:1.404701+0.021340    test-rmse:1.678602+0.072940 
## [38] train-rmse:1.390935+0.022273    test-rmse:1.660960+0.067564 
## [39] train-rmse:1.378173+0.022511    test-rmse:1.654562+0.070373 
## [40] train-rmse:1.366515+0.023374    test-rmse:1.645102+0.069610 
## [41] train-rmse:1.354185+0.024649    test-rmse:1.636400+0.071916 
## [42] train-rmse:1.341953+0.025015    test-rmse:1.628040+0.072752 
## [43] train-rmse:1.330823+0.025133    test-rmse:1.619594+0.073024 
## [44] train-rmse:1.319187+0.025727    test-rmse:1.613517+0.073925 
## [45] train-rmse:1.307765+0.026415    test-rmse:1.605895+0.072003 
## [46] train-rmse:1.297472+0.026776    test-rmse:1.599565+0.074123 
## [47] train-rmse:1.286099+0.027389    test-rmse:1.590260+0.078961 
## [48] train-rmse:1.277050+0.027332    test-rmse:1.582729+0.077668 
## [49] train-rmse:1.267573+0.027626    test-rmse:1.577371+0.080164 
## [50] train-rmse:1.256048+0.026668    test-rmse:1.569875+0.080584 
## [51] train-rmse:1.245291+0.027961    test-rmse:1.565962+0.082015 
## [52] train-rmse:1.235575+0.028992    test-rmse:1.555600+0.081981 
## [53] train-rmse:1.226796+0.029750    test-rmse:1.551246+0.083803 
## [54] train-rmse:1.218887+0.029832    test-rmse:1.544650+0.085655 
## [55] train-rmse:1.210095+0.029922    test-rmse:1.539323+0.083750 
## [56] train-rmse:1.200965+0.031228    test-rmse:1.533123+0.085062 
## [57] train-rmse:1.192397+0.031662    test-rmse:1.529262+0.087346 
## [58] train-rmse:1.182504+0.032459    test-rmse:1.520670+0.086755 
## [59] train-rmse:1.173941+0.033590    test-rmse:1.515272+0.088009 
## [60] train-rmse:1.166606+0.033849    test-rmse:1.510555+0.086674 
## [61] train-rmse:1.158798+0.034892    test-rmse:1.503448+0.084175 
## [62] train-rmse:1.151245+0.035191    test-rmse:1.496603+0.085164 
## [63] train-rmse:1.144361+0.035847    test-rmse:1.492973+0.084911 
## [64] train-rmse:1.136485+0.034728    test-rmse:1.486881+0.088917 
## [65] train-rmse:1.128747+0.034665    test-rmse:1.484321+0.091222 
## [66] train-rmse:1.121407+0.035092    test-rmse:1.480818+0.090974 
## [67] train-rmse:1.114502+0.034465    test-rmse:1.475870+0.090490 
## [68] train-rmse:1.107097+0.035613    test-rmse:1.473067+0.090427 
## [69] train-rmse:1.099475+0.034393    test-rmse:1.468430+0.095966 
## [70] train-rmse:1.091718+0.034791    test-rmse:1.462360+0.099440 
## [71] train-rmse:1.086167+0.034868    test-rmse:1.460504+0.099947 
## [72] train-rmse:1.079154+0.034674    test-rmse:1.453590+0.098618 
## [73] train-rmse:1.073672+0.034889    test-rmse:1.449694+0.099867 
## [74] train-rmse:1.067009+0.034165    test-rmse:1.445441+0.105102 
## [75] train-rmse:1.060367+0.035274    test-rmse:1.441440+0.106556 
## [76] train-rmse:1.052969+0.035854    test-rmse:1.437609+0.107684 
## [77] train-rmse:1.047023+0.036117    test-rmse:1.436550+0.109026 
## [78] train-rmse:1.041050+0.036539    test-rmse:1.431491+0.109054 
## [79] train-rmse:1.035029+0.034436    test-rmse:1.429139+0.112233 
## [80] train-rmse:1.028805+0.033885    test-rmse:1.423501+0.115572 
## [81] train-rmse:1.022846+0.033968    test-rmse:1.420754+0.115652 
## [82] train-rmse:1.017152+0.033767    test-rmse:1.414899+0.117050 
## [83] train-rmse:1.011887+0.033019    test-rmse:1.411901+0.116981 
## [84] train-rmse:1.006000+0.034653    test-rmse:1.407694+0.118446 
## [85] train-rmse:1.000946+0.034355    test-rmse:1.405132+0.119058 
## [86] train-rmse:0.995939+0.033771    test-rmse:1.401547+0.120328 
## [87] train-rmse:0.990628+0.033507    test-rmse:1.398695+0.121473 
## [88] train-rmse:0.984538+0.031565    test-rmse:1.394899+0.122826 
## [89] train-rmse:0.979547+0.031816    test-rmse:1.392326+0.123877 
## [90] train-rmse:0.974206+0.032805    test-rmse:1.389018+0.123704 
## [91] train-rmse:0.967045+0.031463    test-rmse:1.385890+0.124651 
## [92] train-rmse:0.962294+0.031282    test-rmse:1.384798+0.125656 
## [93] train-rmse:0.957226+0.031275    test-rmse:1.381209+0.127977 
## [94] train-rmse:0.952617+0.029772    test-rmse:1.378235+0.129661 
## [95] train-rmse:0.947292+0.030956    test-rmse:1.377005+0.129764 
## [96] train-rmse:0.943726+0.030828    test-rmse:1.376412+0.130706 
## [97] train-rmse:0.939658+0.031014    test-rmse:1.374050+0.130768 
## [98] train-rmse:0.935439+0.031065    test-rmse:1.372859+0.130318 
## [99] train-rmse:0.930171+0.030416    test-rmse:1.370681+0.130675 
## [100]    train-rmse:0.925103+0.031037    test-rmse:1.367349+0.132241 
## [101]    train-rmse:0.919592+0.031302    test-rmse:1.364128+0.136204 
## [102]    train-rmse:0.915089+0.030797    test-rmse:1.363978+0.136935 
## [103]    train-rmse:0.910754+0.030995    test-rmse:1.362764+0.138249 
## [104]    train-rmse:0.904418+0.027693    test-rmse:1.359878+0.140290 
## [105]    train-rmse:0.899371+0.028835    test-rmse:1.355737+0.140535 
## [106]    train-rmse:0.894691+0.029703    test-rmse:1.351545+0.139664 
## [107]    train-rmse:0.890175+0.030303    test-rmse:1.350592+0.139024 
## [108]    train-rmse:0.886409+0.031008    test-rmse:1.348863+0.139256 
## [109]    train-rmse:0.882373+0.031454    test-rmse:1.347098+0.139755 
## [110]    train-rmse:0.878890+0.031210    test-rmse:1.345296+0.142017 
## [111]    train-rmse:0.874944+0.031355    test-rmse:1.343628+0.141667 
## [112]    train-rmse:0.871720+0.031806    test-rmse:1.343337+0.140520 
## [113]    train-rmse:0.867030+0.030476    test-rmse:1.340435+0.141492 
## [114]    train-rmse:0.862172+0.028909    test-rmse:1.337087+0.141795 
## [115]    train-rmse:0.858337+0.029636    test-rmse:1.334957+0.142927 
## [116]    train-rmse:0.854921+0.029786    test-rmse:1.334404+0.144271 
## [117]    train-rmse:0.851520+0.028992    test-rmse:1.332533+0.144363 
## [118]    train-rmse:0.848735+0.029127    test-rmse:1.330838+0.144860 
## [119]    train-rmse:0.846011+0.028852    test-rmse:1.329725+0.145614 
## [120]    train-rmse:0.842245+0.028941    test-rmse:1.328814+0.146526 
## [121]    train-rmse:0.837611+0.030027    test-rmse:1.325457+0.145292 
## [122]    train-rmse:0.834528+0.029052    test-rmse:1.324554+0.146207 
## [123]    train-rmse:0.830955+0.028515    test-rmse:1.324577+0.145692 
## [124]    train-rmse:0.828271+0.028775    test-rmse:1.323375+0.144966 
## [125]    train-rmse:0.825384+0.028329    test-rmse:1.322062+0.144304 
## [126]    train-rmse:0.822203+0.028684    test-rmse:1.320459+0.142691 
## [127]    train-rmse:0.817028+0.027417    test-rmse:1.318352+0.143339 
## [128]    train-rmse:0.813849+0.027697    test-rmse:1.318293+0.145469 
## [129]    train-rmse:0.811043+0.028014    test-rmse:1.317619+0.145455 
## [130]    train-rmse:0.807658+0.028179    test-rmse:1.315819+0.144749 
## [131]    train-rmse:0.803602+0.027658    test-rmse:1.310866+0.147146 
## [132]    train-rmse:0.801038+0.027806    test-rmse:1.309310+0.146000 
## [133]    train-rmse:0.798651+0.028302    test-rmse:1.307739+0.146591 
## [134]    train-rmse:0.795823+0.028120    test-rmse:1.307199+0.147729 
## [135]    train-rmse:0.793480+0.028194    test-rmse:1.308347+0.147303 
## [136]    train-rmse:0.790929+0.028136    test-rmse:1.307632+0.147709 
## [137]    train-rmse:0.787667+0.027640    test-rmse:1.307193+0.146990 
## [138]    train-rmse:0.783258+0.024474    test-rmse:1.305975+0.148449 
## [139]    train-rmse:0.780488+0.025242    test-rmse:1.304661+0.149122 
## [140]    train-rmse:0.777818+0.025182    test-rmse:1.303410+0.149103 
## [141]    train-rmse:0.775445+0.025024    test-rmse:1.300608+0.148987 
## [142]    train-rmse:0.771360+0.025665    test-rmse:1.299322+0.150029 
## [143]    train-rmse:0.768995+0.025331    test-rmse:1.299701+0.150737 
## [144]    train-rmse:0.766043+0.025460    test-rmse:1.299025+0.149956 
## [145]    train-rmse:0.762320+0.026703    test-rmse:1.295380+0.148270 
## [146]    train-rmse:0.760485+0.026609    test-rmse:1.294615+0.148239 
## [147]    train-rmse:0.756598+0.028027    test-rmse:1.294271+0.148590 
## [148]    train-rmse:0.753573+0.026946    test-rmse:1.293375+0.149495 
## [149]    train-rmse:0.751277+0.026766    test-rmse:1.293533+0.149211 
## [150]    train-rmse:0.749158+0.026578    test-rmse:1.293292+0.148941 
## [151]    train-rmse:0.747339+0.026899    test-rmse:1.292358+0.149709 
## [152]    train-rmse:0.744610+0.027139    test-rmse:1.291144+0.150255 
## [153]    train-rmse:0.742394+0.027457    test-rmse:1.291327+0.149758 
## [154]    train-rmse:0.740351+0.027334    test-rmse:1.290834+0.150603 
## [155]    train-rmse:0.737519+0.028115    test-rmse:1.288049+0.149298 
## [156]    train-rmse:0.735548+0.027805    test-rmse:1.287212+0.150023 
## [157]    train-rmse:0.733694+0.027323    test-rmse:1.288299+0.152147 
## [158]    train-rmse:0.731941+0.027542    test-rmse:1.287884+0.151987 
## [159]    train-rmse:0.729893+0.027610    test-rmse:1.288282+0.150192 
## [160]    train-rmse:0.726532+0.027523    test-rmse:1.286593+0.149611 
## [161]    train-rmse:0.724488+0.027922    test-rmse:1.285683+0.150155 
## [162]    train-rmse:0.722753+0.027782    test-rmse:1.285083+0.150161 
## [163]    train-rmse:0.719187+0.027155    test-rmse:1.283078+0.149940 
## [164]    train-rmse:0.717665+0.027160    test-rmse:1.282171+0.150057 
## [165]    train-rmse:0.716100+0.027235    test-rmse:1.282188+0.150946 
## [166]    train-rmse:0.714636+0.027025    test-rmse:1.282049+0.150923 
## [167]    train-rmse:0.712542+0.027222    test-rmse:1.283138+0.151613 
## [168]    train-rmse:0.710878+0.027348    test-rmse:1.283991+0.151551 
## [169]    train-rmse:0.708741+0.026290    test-rmse:1.283525+0.150810 
## [170]    train-rmse:0.706502+0.026219    test-rmse:1.283015+0.151077 
## [171]    train-rmse:0.704947+0.026466    test-rmse:1.281681+0.150655 
## [172]    train-rmse:0.703246+0.026576    test-rmse:1.282202+0.150109 
## [173]    train-rmse:0.701477+0.026383    test-rmse:1.283630+0.150072 
## [174]    train-rmse:0.699476+0.026132    test-rmse:1.282649+0.150749 
## [175]    train-rmse:0.697777+0.026163    test-rmse:1.282845+0.150789 
## [176]    train-rmse:0.695458+0.026522    test-rmse:1.281632+0.149815 
## [177]    train-rmse:0.694129+0.026457    test-rmse:1.282313+0.150664 
## [178]    train-rmse:0.692704+0.026237    test-rmse:1.282017+0.150626 
## [179]    train-rmse:0.690474+0.027575    test-rmse:1.281669+0.150267 
## [180]    train-rmse:0.689254+0.027627    test-rmse:1.281259+0.149105 
## [181]    train-rmse:0.687744+0.027651    test-rmse:1.281186+0.149337 
## [182]    train-rmse:0.685022+0.026601    test-rmse:1.280965+0.149143 
## [183]    train-rmse:0.683672+0.026804    test-rmse:1.281045+0.150565 
## [184]    train-rmse:0.681846+0.026577    test-rmse:1.281447+0.149630 
## [185]    train-rmse:0.679478+0.026694    test-rmse:1.279412+0.149919 
## [186]    train-rmse:0.676700+0.024425    test-rmse:1.279036+0.150438 
## [187]    train-rmse:0.675190+0.024365    test-rmse:1.279034+0.151180 
## [188]    train-rmse:0.673818+0.024779    test-rmse:1.278782+0.151912 
## [189]    train-rmse:0.672482+0.024776    test-rmse:1.278892+0.152162 
## [190]    train-rmse:0.669944+0.024992    test-rmse:1.277366+0.151848 
## [191]    train-rmse:0.667997+0.025323    test-rmse:1.277108+0.151586 
## [192]    train-rmse:0.666347+0.024855    test-rmse:1.276937+0.151724 
## [193]    train-rmse:0.665171+0.024667    test-rmse:1.276871+0.152079 
## [194]    train-rmse:0.663290+0.024421    test-rmse:1.276310+0.152437 
## [195]    train-rmse:0.661145+0.024661    test-rmse:1.274527+0.151239 
## [196]    train-rmse:0.659970+0.024704    test-rmse:1.273897+0.151153 
## [197]    train-rmse:0.659010+0.024613    test-rmse:1.273374+0.151447 
## [198]    train-rmse:0.656672+0.023939    test-rmse:1.271891+0.150940 
## [199]    train-rmse:0.654224+0.025589    test-rmse:1.272521+0.150300 
## [200]    train-rmse:0.652831+0.025503    test-rmse:1.272252+0.150070 
## [201]    train-rmse:0.651200+0.025745    test-rmse:1.273822+0.150670 
## [202]    train-rmse:0.650003+0.025776    test-rmse:1.274141+0.150820 
## [203]    train-rmse:0.648660+0.025971    test-rmse:1.274220+0.151083 
## [204]    train-rmse:0.647157+0.026345    test-rmse:1.273363+0.150042 
## Stopping. Best iteration:
## [198]    train-rmse:0.656672+0.023939    test-rmse:1.271891+0.150940
## 
## 10 
## [1]  train-rmse:8.599779+0.063296    test-rmse:8.595917+0.270248 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.634780+0.056010    test-rmse:7.632757+0.260612 
## [3]  train-rmse:6.790282+0.049380    test-rmse:6.794255+0.250304 
## [4]  train-rmse:6.051739+0.043207    test-rmse:6.056684+0.243030 
## [5]  train-rmse:5.407077+0.037773    test-rmse:5.412808+0.229712 
## [6]  train-rmse:4.844985+0.033884    test-rmse:4.851709+0.214091 
## [7]  train-rmse:4.353955+0.029553    test-rmse:4.366780+0.198254 
## [8]  train-rmse:3.926916+0.025198    test-rmse:3.943761+0.184198 
## [9]  train-rmse:3.557206+0.021737    test-rmse:3.581915+0.178976 
## [10] train-rmse:3.234822+0.016710    test-rmse:3.277683+0.170330 
## [11] train-rmse:2.957219+0.015395    test-rmse:3.004378+0.150801 
## [12] train-rmse:2.716783+0.013267    test-rmse:2.779840+0.139679 
## [13] train-rmse:2.511181+0.012144    test-rmse:2.592742+0.127930 
## [14] train-rmse:2.331681+0.011262    test-rmse:2.419291+0.113733 
## [15] train-rmse:2.182248+0.011274    test-rmse:2.296311+0.108459 
## [16] train-rmse:2.049637+0.009764    test-rmse:2.175168+0.098132 
## [17] train-rmse:1.937822+0.011320    test-rmse:2.081710+0.091378 
## [18] train-rmse:1.843288+0.011266    test-rmse:2.009150+0.087920 
## [19] train-rmse:1.760281+0.013285    test-rmse:1.941480+0.078440 
## [20] train-rmse:1.687311+0.015126    test-rmse:1.884844+0.078158 
## [21] train-rmse:1.623367+0.017249    test-rmse:1.838693+0.072546 
## [22] train-rmse:1.571348+0.017570    test-rmse:1.793063+0.073784 
## [23] train-rmse:1.524880+0.017536    test-rmse:1.755986+0.072016 
## [24] train-rmse:1.484512+0.016991    test-rmse:1.729674+0.069982 
## [25] train-rmse:1.447568+0.018705    test-rmse:1.704223+0.067854 
## [26] train-rmse:1.414924+0.016918    test-rmse:1.688964+0.070289 
## [27] train-rmse:1.385078+0.017782    test-rmse:1.667487+0.071009 
## [28] train-rmse:1.357993+0.019030    test-rmse:1.642010+0.075684 
## [29] train-rmse:1.332832+0.020545    test-rmse:1.630203+0.075408 
## [30] train-rmse:1.310916+0.019897    test-rmse:1.615856+0.083245 
## [31] train-rmse:1.290154+0.019070    test-rmse:1.601561+0.082595 
## [32] train-rmse:1.270377+0.019823    test-rmse:1.586734+0.085004 
## [33] train-rmse:1.249520+0.020755    test-rmse:1.575241+0.090223 
## [34] train-rmse:1.233618+0.019754    test-rmse:1.563585+0.088987 
## [35] train-rmse:1.217844+0.019462    test-rmse:1.551669+0.091491 
## [36] train-rmse:1.201071+0.020960    test-rmse:1.546821+0.097752 
## [37] train-rmse:1.186570+0.020777    test-rmse:1.539623+0.097766 
## [38] train-rmse:1.172131+0.020030    test-rmse:1.527964+0.104177 
## [39] train-rmse:1.158978+0.020721    test-rmse:1.519934+0.107804 
## [40] train-rmse:1.144815+0.020842    test-rmse:1.510955+0.110470 
## [41] train-rmse:1.130779+0.020979    test-rmse:1.505450+0.108591 
## [42] train-rmse:1.117904+0.021654    test-rmse:1.493792+0.108635 
## [43] train-rmse:1.105216+0.022937    test-rmse:1.487385+0.113029 
## [44] train-rmse:1.093327+0.023734    test-rmse:1.481253+0.116169 
## [45] train-rmse:1.080766+0.024695    test-rmse:1.474643+0.119509 
## [46] train-rmse:1.069289+0.022773    test-rmse:1.465272+0.121692 
## [47] train-rmse:1.057697+0.023277    test-rmse:1.461505+0.121124 
## [48] train-rmse:1.046387+0.025002    test-rmse:1.455310+0.123913 
## [49] train-rmse:1.036394+0.024858    test-rmse:1.453585+0.125725 
## [50] train-rmse:1.025369+0.022645    test-rmse:1.447213+0.126903 
## [51] train-rmse:1.013990+0.023158    test-rmse:1.439926+0.132082 
## [52] train-rmse:1.004504+0.022986    test-rmse:1.435100+0.134591 
## [53] train-rmse:0.994861+0.022315    test-rmse:1.429977+0.133975 
## [54] train-rmse:0.984242+0.022334    test-rmse:1.424890+0.137667 
## [55] train-rmse:0.976329+0.021865    test-rmse:1.419268+0.141685 
## [56] train-rmse:0.967569+0.021314    test-rmse:1.413723+0.139956 
## [57] train-rmse:0.959317+0.021276    test-rmse:1.410952+0.140813 
## [58] train-rmse:0.949979+0.022181    test-rmse:1.406253+0.141000 
## [59] train-rmse:0.941962+0.022541    test-rmse:1.402803+0.143517 
## [60] train-rmse:0.934150+0.021399    test-rmse:1.399659+0.144643 
## [61] train-rmse:0.925079+0.020841    test-rmse:1.393792+0.148117 
## [62] train-rmse:0.917297+0.020649    test-rmse:1.387282+0.153205 
## [63] train-rmse:0.909453+0.020457    test-rmse:1.383006+0.152814 
## [64] train-rmse:0.900887+0.021290    test-rmse:1.379487+0.153399 
## [65] train-rmse:0.892196+0.018529    test-rmse:1.376356+0.154646 
## [66] train-rmse:0.884146+0.019051    test-rmse:1.372422+0.155535 
## [67] train-rmse:0.876863+0.019472    test-rmse:1.368584+0.158119 
## [68] train-rmse:0.869395+0.018970    test-rmse:1.364951+0.158396 
## [69] train-rmse:0.861145+0.019945    test-rmse:1.362111+0.159813 
## [70] train-rmse:0.853960+0.018473    test-rmse:1.357710+0.160398 
## [71] train-rmse:0.846678+0.018413    test-rmse:1.358395+0.162952 
## [72] train-rmse:0.841466+0.018427    test-rmse:1.354786+0.162666 
## [73] train-rmse:0.835211+0.018936    test-rmse:1.351272+0.162582 
## [74] train-rmse:0.827716+0.018978    test-rmse:1.349488+0.162784 
## [75] train-rmse:0.822531+0.018539    test-rmse:1.346769+0.163953 
## [76] train-rmse:0.815491+0.019656    test-rmse:1.342723+0.165683 
## [77] train-rmse:0.809914+0.019675    test-rmse:1.339943+0.164299 
## [78] train-rmse:0.804387+0.019598    test-rmse:1.338968+0.166209 
## [79] train-rmse:0.797575+0.020012    test-rmse:1.337598+0.166619 
## [80] train-rmse:0.791666+0.019167    test-rmse:1.336688+0.170990 
## [81] train-rmse:0.787149+0.019065    test-rmse:1.333245+0.171702 
## [82] train-rmse:0.781527+0.019591    test-rmse:1.329855+0.171848 
## [83] train-rmse:0.776546+0.019854    test-rmse:1.327887+0.173096 
## [84] train-rmse:0.770131+0.020385    test-rmse:1.325202+0.174477 
## [85] train-rmse:0.764723+0.021577    test-rmse:1.322983+0.173292 
## [86] train-rmse:0.759439+0.021924    test-rmse:1.322506+0.174239 
## [87] train-rmse:0.754714+0.021871    test-rmse:1.321889+0.175159 
## [88] train-rmse:0.749871+0.021216    test-rmse:1.319741+0.173491 
## [89] train-rmse:0.744701+0.021732    test-rmse:1.316645+0.173345 
## [90] train-rmse:0.740511+0.021810    test-rmse:1.316040+0.174208 
## [91] train-rmse:0.735343+0.022302    test-rmse:1.313182+0.174002 
## [92] train-rmse:0.730793+0.020784    test-rmse:1.311803+0.175082 
## [93] train-rmse:0.726336+0.021011    test-rmse:1.311318+0.175146 
## [94] train-rmse:0.720410+0.021323    test-rmse:1.309979+0.174490 
## [95] train-rmse:0.714425+0.021318    test-rmse:1.307557+0.175716 
## [96] train-rmse:0.710733+0.020835    test-rmse:1.306766+0.176052 
## [97] train-rmse:0.705133+0.020599    test-rmse:1.304973+0.176929 
## [98] train-rmse:0.701159+0.020052    test-rmse:1.304891+0.177746 
## [99] train-rmse:0.696164+0.020505    test-rmse:1.303278+0.177528 
## [100]    train-rmse:0.691963+0.019913    test-rmse:1.299632+0.177754 
## [101]    train-rmse:0.688519+0.020260    test-rmse:1.300368+0.178226 
## [102]    train-rmse:0.684823+0.020268    test-rmse:1.298370+0.177997 
## [103]    train-rmse:0.680698+0.019996    test-rmse:1.296502+0.177913 
## [104]    train-rmse:0.676888+0.020045    test-rmse:1.296612+0.178974 
## [105]    train-rmse:0.672653+0.020827    test-rmse:1.294605+0.180257 
## [106]    train-rmse:0.668530+0.021430    test-rmse:1.292869+0.179473 
## [107]    train-rmse:0.665099+0.020708    test-rmse:1.289947+0.181057 
## [108]    train-rmse:0.661784+0.020968    test-rmse:1.289866+0.181698 
## [109]    train-rmse:0.657826+0.021912    test-rmse:1.288462+0.180151 
## [110]    train-rmse:0.653447+0.021365    test-rmse:1.287473+0.180295 
## [111]    train-rmse:0.650397+0.021481    test-rmse:1.287416+0.181099 
## [112]    train-rmse:0.646535+0.020804    test-rmse:1.286474+0.181905 
## [113]    train-rmse:0.643080+0.021583    test-rmse:1.284999+0.181770 
## [114]    train-rmse:0.639087+0.021563    test-rmse:1.283614+0.181912 
## [115]    train-rmse:0.636429+0.021005    test-rmse:1.282986+0.182609 
## [116]    train-rmse:0.633403+0.020533    test-rmse:1.282308+0.183244 
## [117]    train-rmse:0.630332+0.021352    test-rmse:1.283087+0.184526 
## [118]    train-rmse:0.626541+0.021966    test-rmse:1.281900+0.183647 
## [119]    train-rmse:0.622956+0.022385    test-rmse:1.282654+0.183876 
## [120]    train-rmse:0.619720+0.022047    test-rmse:1.283228+0.184478 
## [121]    train-rmse:0.617196+0.022071    test-rmse:1.283817+0.184436 
## [122]    train-rmse:0.614354+0.021802    test-rmse:1.281317+0.185753 
## [123]    train-rmse:0.611352+0.022436    test-rmse:1.279711+0.185352 
## [124]    train-rmse:0.608342+0.021691    test-rmse:1.278312+0.186960 
## [125]    train-rmse:0.604709+0.021543    test-rmse:1.274677+0.187013 
## [126]    train-rmse:0.602149+0.021263    test-rmse:1.274694+0.186993 
## [127]    train-rmse:0.598848+0.020481    test-rmse:1.274889+0.186692 
## [128]    train-rmse:0.596446+0.020208    test-rmse:1.274481+0.188097 
## [129]    train-rmse:0.592827+0.020263    test-rmse:1.272656+0.187985 
## [130]    train-rmse:0.589915+0.020262    test-rmse:1.274839+0.188131 
## [131]    train-rmse:0.586492+0.019912    test-rmse:1.274197+0.188945 
## [132]    train-rmse:0.584370+0.019723    test-rmse:1.272823+0.189175 
## [133]    train-rmse:0.582287+0.020168    test-rmse:1.272967+0.189260 
## [134]    train-rmse:0.580109+0.020130    test-rmse:1.273097+0.188970 
## [135]    train-rmse:0.577781+0.020442    test-rmse:1.271392+0.188547 
## [136]    train-rmse:0.575136+0.021198    test-rmse:1.268909+0.186390 
## [137]    train-rmse:0.571814+0.021607    test-rmse:1.268051+0.185518 
## [138]    train-rmse:0.569882+0.021335    test-rmse:1.268762+0.186271 
## [139]    train-rmse:0.567992+0.021188    test-rmse:1.269297+0.188362 
## [140]    train-rmse:0.565286+0.021396    test-rmse:1.267871+0.188267 
## [141]    train-rmse:0.562258+0.021283    test-rmse:1.267181+0.188161 
## [142]    train-rmse:0.560045+0.021362    test-rmse:1.267155+0.188819 
## [143]    train-rmse:0.557053+0.021406    test-rmse:1.267639+0.190176 
## [144]    train-rmse:0.554372+0.021257    test-rmse:1.267711+0.189615 
## [145]    train-rmse:0.552349+0.022044    test-rmse:1.267664+0.189663 
## [146]    train-rmse:0.549859+0.022166    test-rmse:1.265358+0.189657 
## [147]    train-rmse:0.547734+0.022240    test-rmse:1.264633+0.189162 
## [148]    train-rmse:0.545853+0.022132    test-rmse:1.265938+0.190796 
## [149]    train-rmse:0.544091+0.022168    test-rmse:1.265489+0.190632 
## [150]    train-rmse:0.542249+0.022687    test-rmse:1.265918+0.190684 
## [151]    train-rmse:0.538710+0.022543    test-rmse:1.263143+0.188675 
## [152]    train-rmse:0.536725+0.022040    test-rmse:1.262780+0.189171 
## [153]    train-rmse:0.534447+0.022673    test-rmse:1.262198+0.188672 
## [154]    train-rmse:0.531514+0.022520    test-rmse:1.263106+0.186295 
## [155]    train-rmse:0.529698+0.022116    test-rmse:1.264127+0.187144 
## [156]    train-rmse:0.527036+0.021598    test-rmse:1.264211+0.187418 
## [157]    train-rmse:0.524861+0.021494    test-rmse:1.263567+0.187226 
## [158]    train-rmse:0.523334+0.021488    test-rmse:1.263680+0.186858 
## [159]    train-rmse:0.521341+0.021817    test-rmse:1.263249+0.185352 
## Stopping. Best iteration:
## [153]    train-rmse:0.534447+0.022673    test-rmse:1.262198+0.188672
## 
## 11 
## [1]  train-rmse:8.597928+0.063487    test-rmse:8.596086+0.270479 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.630484+0.056269    test-rmse:7.634652+0.255911 
## [3]  train-rmse:6.783073+0.049842    test-rmse:6.790280+0.249988 
## [4]  train-rmse:6.041616+0.044428    test-rmse:6.051096+0.238762 
## [5]  train-rmse:5.393189+0.039341    test-rmse:5.408186+0.220414 
## [6]  train-rmse:4.828324+0.035163    test-rmse:4.843369+0.205496 
## [7]  train-rmse:4.331585+0.031334    test-rmse:4.352436+0.191724 
## [8]  train-rmse:3.898417+0.028031    test-rmse:3.924614+0.170213 
## [9]  train-rmse:3.521510+0.024843    test-rmse:3.561430+0.160333 
## [10] train-rmse:3.192155+0.021502    test-rmse:3.244208+0.141725 
## [11] train-rmse:2.906508+0.018013    test-rmse:2.967277+0.136053 
## [12] train-rmse:2.658924+0.017814    test-rmse:2.735506+0.118731 
## [13] train-rmse:2.445923+0.015565    test-rmse:2.550449+0.117140 
## [14] train-rmse:2.260320+0.015020    test-rmse:2.378616+0.114734 
## [15] train-rmse:2.101847+0.016864    test-rmse:2.235878+0.096956 
## [16] train-rmse:1.961547+0.013783    test-rmse:2.111174+0.094648 
## [17] train-rmse:1.839642+0.014036    test-rmse:2.013723+0.090603 
## [18] train-rmse:1.738296+0.014948    test-rmse:1.930241+0.086958 
## [19] train-rmse:1.647412+0.014404    test-rmse:1.858539+0.085062 
## [20] train-rmse:1.571449+0.013499    test-rmse:1.800997+0.079213 
## [21] train-rmse:1.502253+0.015845    test-rmse:1.751944+0.073366 
## [22] train-rmse:1.444658+0.016228    test-rmse:1.707123+0.069245 
## [23] train-rmse:1.388751+0.020495    test-rmse:1.658491+0.071634 
## [24] train-rmse:1.343752+0.020940    test-rmse:1.627157+0.072696 
## [25] train-rmse:1.301431+0.021042    test-rmse:1.604395+0.066465 
## [26] train-rmse:1.265239+0.021970    test-rmse:1.580557+0.077842 
## [27] train-rmse:1.231468+0.021852    test-rmse:1.561871+0.079832 
## [28] train-rmse:1.200839+0.021658    test-rmse:1.543597+0.080690 
## [29] train-rmse:1.171483+0.021482    test-rmse:1.521509+0.081260 
## [30] train-rmse:1.144831+0.023370    test-rmse:1.504227+0.084714 
## [31] train-rmse:1.120095+0.024222    test-rmse:1.497167+0.082442 
## [32] train-rmse:1.097461+0.024809    test-rmse:1.482050+0.089009 
## [33] train-rmse:1.077682+0.023340    test-rmse:1.471011+0.093645 
## [34] train-rmse:1.056171+0.022803    test-rmse:1.455813+0.099114 
## [35] train-rmse:1.038940+0.022371    test-rmse:1.444525+0.098269 
## [36] train-rmse:1.022225+0.021874    test-rmse:1.437153+0.099017 
## [37] train-rmse:1.004451+0.021610    test-rmse:1.431231+0.101996 
## [38] train-rmse:0.989636+0.021523    test-rmse:1.421940+0.104611 
## [39] train-rmse:0.974530+0.021626    test-rmse:1.413521+0.104990 
## [40] train-rmse:0.961434+0.022050    test-rmse:1.404791+0.106325 
## [41] train-rmse:0.947349+0.022435    test-rmse:1.399933+0.108848 
## [42] train-rmse:0.933441+0.021721    test-rmse:1.398314+0.112156 
## [43] train-rmse:0.920750+0.022578    test-rmse:1.392278+0.113858 
## [44] train-rmse:0.909593+0.021970    test-rmse:1.385985+0.118479 
## [45] train-rmse:0.898884+0.021545    test-rmse:1.382001+0.116692 
## [46] train-rmse:0.885554+0.021567    test-rmse:1.374576+0.116249 
## [47] train-rmse:0.873574+0.020897    test-rmse:1.369933+0.119365 
## [48] train-rmse:0.862144+0.021298    test-rmse:1.365174+0.120564 
## [49] train-rmse:0.849929+0.022207    test-rmse:1.361871+0.126968 
## [50] train-rmse:0.839650+0.020578    test-rmse:1.356081+0.128551 
## [51] train-rmse:0.830982+0.020262    test-rmse:1.353116+0.129334 
## [52] train-rmse:0.820646+0.021157    test-rmse:1.349626+0.128475 
## [53] train-rmse:0.810484+0.021671    test-rmse:1.348051+0.128367 
## [54] train-rmse:0.799846+0.021323    test-rmse:1.346618+0.131345 
## [55] train-rmse:0.791045+0.021132    test-rmse:1.343601+0.132562 
## [56] train-rmse:0.781906+0.020855    test-rmse:1.340835+0.134361 
## [57] train-rmse:0.771496+0.019754    test-rmse:1.333062+0.135718 
## [58] train-rmse:0.764196+0.018573    test-rmse:1.326842+0.140139 
## [59] train-rmse:0.756667+0.018683    test-rmse:1.324863+0.142775 
## [60] train-rmse:0.749289+0.019043    test-rmse:1.321937+0.144229 
## [61] train-rmse:0.741863+0.018508    test-rmse:1.317597+0.146414 
## [62] train-rmse:0.732884+0.018616    test-rmse:1.312337+0.146144 
## [63] train-rmse:0.724718+0.018449    test-rmse:1.311165+0.149694 
## [64] train-rmse:0.717715+0.017921    test-rmse:1.307300+0.150962 
## [65] train-rmse:0.710428+0.019300    test-rmse:1.300349+0.148393 
## [66] train-rmse:0.703065+0.019496    test-rmse:1.299211+0.151208 
## [67] train-rmse:0.696937+0.019814    test-rmse:1.300763+0.150680 
## [68] train-rmse:0.690842+0.019969    test-rmse:1.298538+0.152814 
## [69] train-rmse:0.685172+0.020040    test-rmse:1.296456+0.153111 
## [70] train-rmse:0.679129+0.019761    test-rmse:1.292321+0.152952 
## [71] train-rmse:0.672346+0.019230    test-rmse:1.289207+0.153075 
## [72] train-rmse:0.666154+0.019478    test-rmse:1.288955+0.152478 
## [73] train-rmse:0.658685+0.019722    test-rmse:1.289012+0.153512 
## [74] train-rmse:0.653924+0.018973    test-rmse:1.285977+0.154816 
## [75] train-rmse:0.648359+0.020173    test-rmse:1.284303+0.154400 
## [76] train-rmse:0.642798+0.020431    test-rmse:1.282060+0.155593 
## [77] train-rmse:0.636881+0.020084    test-rmse:1.278838+0.156676 
## [78] train-rmse:0.631165+0.019256    test-rmse:1.279965+0.156984 
## [79] train-rmse:0.625256+0.019774    test-rmse:1.276483+0.157988 
## [80] train-rmse:0.620340+0.019424    test-rmse:1.276920+0.158360 
## [81] train-rmse:0.615555+0.018973    test-rmse:1.275369+0.159863 
## [82] train-rmse:0.611351+0.019259    test-rmse:1.273998+0.159837 
## [83] train-rmse:0.606428+0.019212    test-rmse:1.272021+0.159584 
## [84] train-rmse:0.601478+0.019222    test-rmse:1.273147+0.159476 
## [85] train-rmse:0.596249+0.019684    test-rmse:1.272600+0.160061 
## [86] train-rmse:0.592144+0.019506    test-rmse:1.272312+0.159006 
## [87] train-rmse:0.587592+0.019020    test-rmse:1.271062+0.160077 
## [88] train-rmse:0.583340+0.018360    test-rmse:1.268661+0.161686 
## [89] train-rmse:0.578199+0.018683    test-rmse:1.266004+0.163204 
## [90] train-rmse:0.573976+0.019214    test-rmse:1.263885+0.165044 
## [91] train-rmse:0.569473+0.018119    test-rmse:1.262912+0.166036 
## [92] train-rmse:0.565584+0.017909    test-rmse:1.265118+0.166903 
## [93] train-rmse:0.562418+0.017653    test-rmse:1.264855+0.167697 
## [94] train-rmse:0.558424+0.018355    test-rmse:1.261651+0.168728 
## [95] train-rmse:0.554241+0.018716    test-rmse:1.260812+0.168092 
## [96] train-rmse:0.550118+0.018693    test-rmse:1.260075+0.168633 
## [97] train-rmse:0.547153+0.018519    test-rmse:1.259183+0.169092 
## [98] train-rmse:0.543038+0.018441    test-rmse:1.257640+0.169457 
## [99] train-rmse:0.538676+0.018979    test-rmse:1.256447+0.168964 
## [100]    train-rmse:0.534708+0.018221    test-rmse:1.256282+0.169338 
## [101]    train-rmse:0.531033+0.017874    test-rmse:1.257299+0.168053 
## [102]    train-rmse:0.527637+0.018286    test-rmse:1.256659+0.168121 
## [103]    train-rmse:0.524631+0.018347    test-rmse:1.257667+0.170125 
## [104]    train-rmse:0.521745+0.017685    test-rmse:1.256684+0.170654 
## [105]    train-rmse:0.518452+0.018482    test-rmse:1.256129+0.170119 
## [106]    train-rmse:0.515304+0.018841    test-rmse:1.254387+0.170076 
## [107]    train-rmse:0.511210+0.019153    test-rmse:1.253863+0.169907 
## [108]    train-rmse:0.508014+0.019254    test-rmse:1.252861+0.169554 
## [109]    train-rmse:0.503774+0.018381    test-rmse:1.253104+0.171140 
## [110]    train-rmse:0.500438+0.018691    test-rmse:1.252360+0.172998 
## [111]    train-rmse:0.498070+0.018341    test-rmse:1.253161+0.172278 
## [112]    train-rmse:0.495079+0.018196    test-rmse:1.252622+0.172509 
## [113]    train-rmse:0.491388+0.018887    test-rmse:1.252569+0.173077 
## [114]    train-rmse:0.488397+0.018500    test-rmse:1.252293+0.174877 
## [115]    train-rmse:0.484270+0.018567    test-rmse:1.251475+0.174815 
## [116]    train-rmse:0.481559+0.019126    test-rmse:1.251890+0.174735 
## [117]    train-rmse:0.478150+0.018606    test-rmse:1.250772+0.175286 
## [118]    train-rmse:0.475358+0.018107    test-rmse:1.251216+0.175468 
## [119]    train-rmse:0.473050+0.018121    test-rmse:1.252135+0.176998 
## [120]    train-rmse:0.470806+0.017620    test-rmse:1.252136+0.177729 
## [121]    train-rmse:0.467668+0.017413    test-rmse:1.252421+0.177075 
## [122]    train-rmse:0.465246+0.017248    test-rmse:1.251304+0.176844 
## [123]    train-rmse:0.463299+0.017431    test-rmse:1.250721+0.177154 
## [124]    train-rmse:0.459872+0.016962    test-rmse:1.249427+0.177944 
## [125]    train-rmse:0.456532+0.016808    test-rmse:1.247848+0.176920 
## [126]    train-rmse:0.452374+0.016365    test-rmse:1.246167+0.176586 
## [127]    train-rmse:0.450121+0.015364    test-rmse:1.246641+0.176211 
## [128]    train-rmse:0.447024+0.014522    test-rmse:1.246855+0.175651 
## [129]    train-rmse:0.445384+0.015076    test-rmse:1.245940+0.175461 
## [130]    train-rmse:0.442567+0.014217    test-rmse:1.247004+0.175021 
## [131]    train-rmse:0.439519+0.013523    test-rmse:1.247565+0.174640 
## [132]    train-rmse:0.436973+0.012939    test-rmse:1.246794+0.173909 
## [133]    train-rmse:0.433606+0.012755    test-rmse:1.246469+0.173425 
## [134]    train-rmse:0.430682+0.012040    test-rmse:1.246384+0.173604 
## [135]    train-rmse:0.428731+0.011484    test-rmse:1.246334+0.175068 
## Stopping. Best iteration:
## [129]    train-rmse:0.445384+0.015076    test-rmse:1.245940+0.175461
## 
## 12 
## [1]  train-rmse:8.597563+0.063580    test-rmse:8.596548+0.266574 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.629038+0.056336    test-rmse:7.634638+0.256595 
## [3]  train-rmse:6.779605+0.050272    test-rmse:6.793583+0.243870 
## [4]  train-rmse:6.035081+0.044259    test-rmse:6.046747+0.230798 
## [5]  train-rmse:5.382682+0.039354    test-rmse:5.399235+0.214991 
## [6]  train-rmse:4.812152+0.034632    test-rmse:4.835240+0.194444 
## [7]  train-rmse:4.312985+0.030448    test-rmse:4.337753+0.184257 
## [8]  train-rmse:3.876792+0.027630    test-rmse:3.911385+0.170607 
## [9]  train-rmse:3.492692+0.024660    test-rmse:3.535052+0.154773 
## [10] train-rmse:3.158581+0.021955    test-rmse:3.207781+0.143279 
## [11] train-rmse:2.867908+0.018784    test-rmse:2.939927+0.138909 
## [12] train-rmse:2.615902+0.015168    test-rmse:2.713372+0.134971 
## [13] train-rmse:2.397146+0.014557    test-rmse:2.510076+0.127629 
## [14] train-rmse:2.207406+0.014271    test-rmse:2.340386+0.122811 
## [15] train-rmse:2.038252+0.013560    test-rmse:2.193979+0.114881 
## [16] train-rmse:1.891882+0.011787    test-rmse:2.069323+0.106639 
## [17] train-rmse:1.762297+0.014110    test-rmse:1.962547+0.096321 
## [18] train-rmse:1.654061+0.012540    test-rmse:1.874255+0.092439 
## [19] train-rmse:1.555523+0.014218    test-rmse:1.798393+0.083880 
## [20] train-rmse:1.470764+0.014922    test-rmse:1.735292+0.080408 
## [21] train-rmse:1.399382+0.015384    test-rmse:1.679112+0.084830 
## [22] train-rmse:1.335576+0.016326    test-rmse:1.634074+0.078346 
## [23] train-rmse:1.277527+0.016704    test-rmse:1.592956+0.081787 
## [24] train-rmse:1.228059+0.018685    test-rmse:1.561278+0.079234 
## [25] train-rmse:1.184356+0.020642    test-rmse:1.533181+0.081314 
## [26] train-rmse:1.142828+0.020003    test-rmse:1.503904+0.077928 
## [27] train-rmse:1.109900+0.020068    test-rmse:1.480043+0.085872 
## [28] train-rmse:1.076726+0.020599    test-rmse:1.461120+0.086127 
## [29] train-rmse:1.047814+0.020682    test-rmse:1.443730+0.091237 
## [30] train-rmse:1.020055+0.016923    test-rmse:1.433091+0.096320 
## [31] train-rmse:0.992986+0.019313    test-rmse:1.416245+0.097043 
## [32] train-rmse:0.970530+0.018002    test-rmse:1.403074+0.099518 
## [33] train-rmse:0.946180+0.021416    test-rmse:1.391938+0.102432 
## [34] train-rmse:0.925859+0.019257    test-rmse:1.385358+0.106552 
## [35] train-rmse:0.906707+0.018605    test-rmse:1.376856+0.106526 
## [36] train-rmse:0.887217+0.019189    test-rmse:1.366545+0.110871 
## [37] train-rmse:0.869434+0.020135    test-rmse:1.357369+0.113496 
## [38] train-rmse:0.853439+0.018962    test-rmse:1.349885+0.116152 
## [39] train-rmse:0.838189+0.019403    test-rmse:1.345880+0.119183 
## [40] train-rmse:0.823249+0.019013    test-rmse:1.337773+0.117755 
## [41] train-rmse:0.807705+0.016780    test-rmse:1.330408+0.123439 
## [42] train-rmse:0.793829+0.019310    test-rmse:1.323680+0.124111 
## [43] train-rmse:0.782384+0.019404    test-rmse:1.316383+0.124731 
## [44] train-rmse:0.769129+0.018913    test-rmse:1.310070+0.128415 
## [45] train-rmse:0.756209+0.019260    test-rmse:1.302433+0.126419 
## [46] train-rmse:0.746055+0.019277    test-rmse:1.298905+0.128684 
## [47] train-rmse:0.734790+0.020121    test-rmse:1.296326+0.130038 
## [48] train-rmse:0.723986+0.017928    test-rmse:1.291737+0.131453 
## [49] train-rmse:0.714965+0.017808    test-rmse:1.291626+0.132910 
## [50] train-rmse:0.702312+0.018869    test-rmse:1.285911+0.133630 
## [51] train-rmse:0.692613+0.018677    test-rmse:1.283260+0.134568 
## [52] train-rmse:0.682387+0.019259    test-rmse:1.280422+0.137243 
## [53] train-rmse:0.672981+0.021119    test-rmse:1.276597+0.137077 
## [54] train-rmse:0.663412+0.021159    test-rmse:1.273223+0.139669 
## [55] train-rmse:0.654104+0.019863    test-rmse:1.267090+0.141046 
## [56] train-rmse:0.645259+0.019229    test-rmse:1.266388+0.140588 
## [57] train-rmse:0.636316+0.019685    test-rmse:1.266850+0.143094 
## [58] train-rmse:0.629292+0.020421    test-rmse:1.265773+0.142827 
## [59] train-rmse:0.621834+0.020405    test-rmse:1.264118+0.144348 
## [60] train-rmse:0.614520+0.019889    test-rmse:1.263346+0.145093 
## [61] train-rmse:0.606122+0.019047    test-rmse:1.259258+0.145341 
## [62] train-rmse:0.598401+0.018259    test-rmse:1.257856+0.147195 
## [63] train-rmse:0.591525+0.017232    test-rmse:1.255167+0.145424 
## [64] train-rmse:0.584242+0.018313    test-rmse:1.254029+0.146499 
## [65] train-rmse:0.576440+0.017135    test-rmse:1.251371+0.147783 
## [66] train-rmse:0.569379+0.018363    test-rmse:1.249429+0.148853 
## [67] train-rmse:0.563198+0.017905    test-rmse:1.249017+0.149607 
## [68] train-rmse:0.557063+0.017711    test-rmse:1.248239+0.150502 
## [69] train-rmse:0.550910+0.017056    test-rmse:1.248269+0.151831 
## [70] train-rmse:0.544182+0.016922    test-rmse:1.246766+0.153907 
## [71] train-rmse:0.539090+0.016430    test-rmse:1.246597+0.154783 
## [72] train-rmse:0.533776+0.016923    test-rmse:1.247064+0.156058 
## [73] train-rmse:0.529147+0.016171    test-rmse:1.246587+0.157176 
## [74] train-rmse:0.523855+0.016296    test-rmse:1.245897+0.158017 
## [75] train-rmse:0.517239+0.017320    test-rmse:1.243937+0.156575 
## [76] train-rmse:0.511743+0.018143    test-rmse:1.244405+0.156132 
## [77] train-rmse:0.505814+0.018228    test-rmse:1.242744+0.156366 
## [78] train-rmse:0.501038+0.017873    test-rmse:1.241266+0.156263 
## [79] train-rmse:0.494308+0.016344    test-rmse:1.241031+0.157919 
## [80] train-rmse:0.490607+0.015973    test-rmse:1.240104+0.157133 
## [81] train-rmse:0.486254+0.015129    test-rmse:1.241456+0.158192 
## [82] train-rmse:0.481520+0.014883    test-rmse:1.241148+0.159079 
## [83] train-rmse:0.477430+0.014618    test-rmse:1.240998+0.160368 
## [84] train-rmse:0.472131+0.013949    test-rmse:1.240500+0.160312 
## [85] train-rmse:0.467778+0.013925    test-rmse:1.242175+0.159172 
## [86] train-rmse:0.463707+0.013033    test-rmse:1.241619+0.160611 
## Stopping. Best iteration:
## [80] train-rmse:0.490607+0.015973    test-rmse:1.240104+0.157133
## 
## 13 
## [1]  train-rmse:8.597563+0.063580    test-rmse:8.596548+0.266574 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.628717+0.056478    test-rmse:7.635774+0.249370 
## [3]  train-rmse:6.778183+0.050495    test-rmse:6.791447+0.235434 
## [4]  train-rmse:6.032119+0.044869    test-rmse:6.041971+0.220321 
## [5]  train-rmse:5.377560+0.039929    test-rmse:5.395916+0.203687 
## [6]  train-rmse:4.804059+0.035711    test-rmse:4.825172+0.189196 
## [7]  train-rmse:4.302120+0.032945    test-rmse:4.328937+0.171354 
## [8]  train-rmse:3.861901+0.031035    test-rmse:3.898374+0.162019 
## [9]  train-rmse:3.473919+0.027170    test-rmse:3.517812+0.146915 
## [10] train-rmse:3.137834+0.025039    test-rmse:3.194822+0.136385 
## [11] train-rmse:2.842460+0.022931    test-rmse:2.911778+0.137199 
## [12] train-rmse:2.584506+0.021677    test-rmse:2.671828+0.128889 
## [13] train-rmse:2.359836+0.021057    test-rmse:2.475039+0.122723 
## [14] train-rmse:2.161302+0.018495    test-rmse:2.293902+0.117755 
## [15] train-rmse:1.986451+0.020700    test-rmse:2.138600+0.106371 
## [16] train-rmse:1.835670+0.019105    test-rmse:2.018050+0.103680 
## [17] train-rmse:1.702261+0.020293    test-rmse:1.905465+0.094652 
## [18] train-rmse:1.585549+0.019426    test-rmse:1.808003+0.093648 
## [19] train-rmse:1.482277+0.019197    test-rmse:1.730486+0.093518 
## [20] train-rmse:1.393727+0.020243    test-rmse:1.665277+0.093010 
## [21] train-rmse:1.314213+0.023989    test-rmse:1.607631+0.087748 
## [22] train-rmse:1.246432+0.024304    test-rmse:1.564967+0.088136 
## [23] train-rmse:1.186041+0.023749    test-rmse:1.525865+0.091742 
## [24] train-rmse:1.132056+0.024587    test-rmse:1.491073+0.089291 
## [25] train-rmse:1.083662+0.022811    test-rmse:1.464491+0.094008 
## [26] train-rmse:1.040740+0.023039    test-rmse:1.437969+0.100479 
## [27] train-rmse:1.003460+0.021928    test-rmse:1.418219+0.102794 
## [28] train-rmse:0.966295+0.023065    test-rmse:1.396428+0.105612 
## [29] train-rmse:0.936162+0.024024    test-rmse:1.380013+0.111082 
## [30] train-rmse:0.905452+0.022148    test-rmse:1.364935+0.111726 
## [31] train-rmse:0.879214+0.022014    test-rmse:1.353010+0.111476 
## [32] train-rmse:0.853449+0.022084    test-rmse:1.342673+0.116062 
## [33] train-rmse:0.830554+0.020353    test-rmse:1.332493+0.118834 
## [34] train-rmse:0.810007+0.019896    test-rmse:1.324758+0.123033 
## [35] train-rmse:0.792431+0.020190    test-rmse:1.317770+0.125377 
## [36] train-rmse:0.772498+0.019282    test-rmse:1.311548+0.132635 
## [37] train-rmse:0.754056+0.018790    test-rmse:1.302301+0.131216 
## [38] train-rmse:0.735762+0.015188    test-rmse:1.295922+0.137038 
## [39] train-rmse:0.720666+0.015867    test-rmse:1.289711+0.136149 
## [40] train-rmse:0.704742+0.018469    test-rmse:1.285788+0.137614 
## [41] train-rmse:0.692143+0.017126    test-rmse:1.283286+0.138538 
## [42] train-rmse:0.680694+0.016559    test-rmse:1.278953+0.142665 
## [43] train-rmse:0.666941+0.017203    test-rmse:1.272201+0.140868 
## [44] train-rmse:0.655439+0.014570    test-rmse:1.268262+0.144408 
## [45] train-rmse:0.643515+0.015513    test-rmse:1.263535+0.139543 
## [46] train-rmse:0.628945+0.017509    test-rmse:1.261490+0.140328 
## [47] train-rmse:0.619304+0.016493    test-rmse:1.260094+0.140421 
## [48] train-rmse:0.607857+0.014392    test-rmse:1.257780+0.141874 
## [49] train-rmse:0.597730+0.015005    test-rmse:1.255487+0.141430 
## [50] train-rmse:0.587059+0.014763    test-rmse:1.253016+0.142049 
## [51] train-rmse:0.576606+0.015414    test-rmse:1.251055+0.140476 
## [52] train-rmse:0.568549+0.014919    test-rmse:1.249903+0.143571 
## [53] train-rmse:0.559951+0.015076    test-rmse:1.248709+0.143479 
## [54] train-rmse:0.550491+0.014688    test-rmse:1.247155+0.143840 
## [55] train-rmse:0.544232+0.013880    test-rmse:1.245404+0.142931 
## [56] train-rmse:0.536022+0.015040    test-rmse:1.242329+0.142897 
## [57] train-rmse:0.526620+0.014702    test-rmse:1.240366+0.143250 
## [58] train-rmse:0.519004+0.013858    test-rmse:1.238807+0.142734 
## [59] train-rmse:0.511187+0.013957    test-rmse:1.236938+0.141485 
## [60] train-rmse:0.504145+0.014249    test-rmse:1.236101+0.143675 
## [61] train-rmse:0.497891+0.014042    test-rmse:1.236174+0.143278 
## [62] train-rmse:0.489963+0.013020    test-rmse:1.235241+0.144679 
## [63] train-rmse:0.483798+0.012071    test-rmse:1.234754+0.142570 
## [64] train-rmse:0.476819+0.012492    test-rmse:1.234776+0.142442 
## [65] train-rmse:0.471294+0.012113    test-rmse:1.235223+0.143091 
## [66] train-rmse:0.465911+0.012636    test-rmse:1.233338+0.143099 
## [67] train-rmse:0.460466+0.012042    test-rmse:1.232508+0.142468 
## [68] train-rmse:0.454162+0.014302    test-rmse:1.229937+0.143261 
## [69] train-rmse:0.448454+0.012591    test-rmse:1.228861+0.142633 
## [70] train-rmse:0.443107+0.012596    test-rmse:1.227058+0.142444 
## [71] train-rmse:0.436804+0.012853    test-rmse:1.225726+0.143936 
## [72] train-rmse:0.430302+0.012875    test-rmse:1.226425+0.144304 
## [73] train-rmse:0.426025+0.013182    test-rmse:1.226352+0.143742 
## [74] train-rmse:0.419820+0.013088    test-rmse:1.225402+0.143338 
## [75] train-rmse:0.415359+0.013483    test-rmse:1.224675+0.144318 
## [76] train-rmse:0.411000+0.012770    test-rmse:1.223409+0.145609 
## [77] train-rmse:0.407249+0.013282    test-rmse:1.223424+0.146557 
## [78] train-rmse:0.401157+0.012441    test-rmse:1.221218+0.147525 
## [79] train-rmse:0.396615+0.012125    test-rmse:1.220443+0.150017 
## [80] train-rmse:0.391985+0.011599    test-rmse:1.219105+0.149727 
## [81] train-rmse:0.387866+0.011991    test-rmse:1.218949+0.149914 
## [82] train-rmse:0.382967+0.010816    test-rmse:1.217126+0.149709 
## [83] train-rmse:0.378417+0.009691    test-rmse:1.217186+0.150703 
## [84] train-rmse:0.375017+0.010159    test-rmse:1.216888+0.151724 
## [85] train-rmse:0.371500+0.010174    test-rmse:1.217165+0.151381 
## [86] train-rmse:0.365770+0.009532    test-rmse:1.216935+0.150784 
## [87] train-rmse:0.361977+0.010160    test-rmse:1.217476+0.150539 
## [88] train-rmse:0.357527+0.008989    test-rmse:1.216706+0.150362 
## [89] train-rmse:0.354059+0.009792    test-rmse:1.216451+0.149796 
## [90] train-rmse:0.349832+0.009297    test-rmse:1.216227+0.150981 
## [91] train-rmse:0.346294+0.009766    test-rmse:1.216115+0.150355 
## [92] train-rmse:0.343913+0.009771    test-rmse:1.216183+0.151526 
## [93] train-rmse:0.340270+0.010615    test-rmse:1.216126+0.151564 
## [94] train-rmse:0.337056+0.011399    test-rmse:1.215448+0.150653 
## [95] train-rmse:0.333168+0.011113    test-rmse:1.215745+0.150959 
## [96] train-rmse:0.330602+0.011412    test-rmse:1.216201+0.151660 
## [97] train-rmse:0.327957+0.010819    test-rmse:1.215932+0.152072 
## [98] train-rmse:0.325070+0.010995    test-rmse:1.215783+0.152600 
## [99] train-rmse:0.321310+0.010997    test-rmse:1.215471+0.152576 
## [100]    train-rmse:0.317303+0.009246    test-rmse:1.215217+0.151820 
## [101]    train-rmse:0.314931+0.009096    test-rmse:1.215115+0.151031 
## [102]    train-rmse:0.312564+0.009537    test-rmse:1.215783+0.150901 
## [103]    train-rmse:0.310109+0.009998    test-rmse:1.215870+0.151253 
## [104]    train-rmse:0.307256+0.010354    test-rmse:1.216828+0.150874 
## [105]    train-rmse:0.304236+0.010128    test-rmse:1.216699+0.151316 
## [106]    train-rmse:0.299733+0.008775    test-rmse:1.217465+0.150909 
## [107]    train-rmse:0.297718+0.008862    test-rmse:1.217640+0.150879 
## Stopping. Best iteration:
## [101]    train-rmse:0.314931+0.009096    test-rmse:1.215115+0.151031
## 
## 14 
## [1]  train-rmse:8.443288+0.060639    test-rmse:8.439194+0.274341 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.372684+0.050903    test-rmse:7.364692+0.271260 
## [3]  train-rmse:6.464429+0.042016    test-rmse:6.461018+0.267420 
## [4]  train-rmse:5.696782+0.034954    test-rmse:5.691126+0.259328 
## [5]  train-rmse:5.051332+0.028809    test-rmse:5.052022+0.247100 
## [6]  train-rmse:4.511761+0.023437    test-rmse:4.515635+0.233996 
## [7]  train-rmse:4.063932+0.019376    test-rmse:4.070827+0.223138 
## [8]  train-rmse:3.695430+0.015977    test-rmse:3.712601+0.202397 
## [9]  train-rmse:3.394037+0.013570    test-rmse:3.410422+0.183754 
## [10] train-rmse:3.149194+0.012193    test-rmse:3.168328+0.163636 
## [11] train-rmse:2.952112+0.011796    test-rmse:2.974429+0.141996 
## [12] train-rmse:2.794768+0.012050    test-rmse:2.823919+0.122094 
## [13] train-rmse:2.669218+0.012465    test-rmse:2.701736+0.109245 
## [14] train-rmse:2.569327+0.012990    test-rmse:2.599641+0.093567 
## [15] train-rmse:2.490093+0.013930    test-rmse:2.525011+0.075100 
## [16] train-rmse:2.427408+0.014780    test-rmse:2.464465+0.065245 
## [17] train-rmse:2.377352+0.015205    test-rmse:2.419437+0.053463 
## [18] train-rmse:2.336738+0.015825    test-rmse:2.376679+0.052098 
## [19] train-rmse:2.303793+0.016267    test-rmse:2.347152+0.048280 
## [20] train-rmse:2.276567+0.016574    test-rmse:2.319084+0.050025 
## [21] train-rmse:2.254241+0.017108    test-rmse:2.302153+0.049529 
## [22] train-rmse:2.235158+0.017527    test-rmse:2.282358+0.055546 
## [23] train-rmse:2.218471+0.017868    test-rmse:2.264809+0.059256 
## [24] train-rmse:2.203793+0.018777    test-rmse:2.251461+0.064263 
## [25] train-rmse:2.190690+0.019392    test-rmse:2.242351+0.066367 
## [26] train-rmse:2.178821+0.019675    test-rmse:2.233175+0.069687 
## [27] train-rmse:2.167952+0.020046    test-rmse:2.219595+0.068629 
## [28] train-rmse:2.157858+0.020524    test-rmse:2.209878+0.070352 
## [29] train-rmse:2.148263+0.020930    test-rmse:2.197292+0.073576 
## [30] train-rmse:2.139292+0.021337    test-rmse:2.191646+0.074951 
## [31] train-rmse:2.130665+0.021717    test-rmse:2.184220+0.081722 
## [32] train-rmse:2.122394+0.021983    test-rmse:2.179163+0.085036 
## [33] train-rmse:2.114513+0.022267    test-rmse:2.172116+0.085854 
## [34] train-rmse:2.106540+0.022650    test-rmse:2.165186+0.081060 
## [35] train-rmse:2.098703+0.023027    test-rmse:2.158733+0.082738 
## [36] train-rmse:2.091172+0.023358    test-rmse:2.151164+0.083289 
## [37] train-rmse:2.083820+0.023710    test-rmse:2.147877+0.083445 
## [38] train-rmse:2.076602+0.024021    test-rmse:2.141284+0.087459 
## [39] train-rmse:2.069447+0.024344    test-rmse:2.135507+0.085480 
## [40] train-rmse:2.062580+0.024602    test-rmse:2.125214+0.084515 
## [41] train-rmse:2.055834+0.024913    test-rmse:2.120856+0.087674 
## [42] train-rmse:2.049313+0.025329    test-rmse:2.113840+0.088392 
## [43] train-rmse:2.042917+0.025609    test-rmse:2.106444+0.089110 
## [44] train-rmse:2.036557+0.025911    test-rmse:2.101484+0.088525 
## [45] train-rmse:2.030311+0.026291    test-rmse:2.094989+0.085030 
## [46] train-rmse:2.023961+0.026643    test-rmse:2.091078+0.086487 
## [47] train-rmse:2.017846+0.026925    test-rmse:2.083320+0.089284 
## [48] train-rmse:2.011890+0.027229    test-rmse:2.079522+0.090188 
## [49] train-rmse:2.006008+0.027471    test-rmse:2.073692+0.093444 
## [50] train-rmse:2.000163+0.027767    test-rmse:2.071433+0.092788 
## [51] train-rmse:1.994397+0.028111    test-rmse:2.066952+0.092502 
## [52] train-rmse:1.988792+0.028281    test-rmse:2.061604+0.095244 
## [53] train-rmse:1.983228+0.028458    test-rmse:2.055282+0.091014 
## [54] train-rmse:1.977705+0.028746    test-rmse:2.051653+0.090085 
## [55] train-rmse:1.972256+0.029062    test-rmse:2.047799+0.091008 
## [56] train-rmse:1.966850+0.029340    test-rmse:2.045379+0.092825 
## [57] train-rmse:1.961554+0.029527    test-rmse:2.038619+0.091397 
## [58] train-rmse:1.956305+0.029650    test-rmse:2.035787+0.093374 
## [59] train-rmse:1.951200+0.029969    test-rmse:2.031773+0.091031 
## [60] train-rmse:1.946122+0.030250    test-rmse:2.023492+0.089562 
## [61] train-rmse:1.941061+0.030572    test-rmse:2.015479+0.087021 
## [62] train-rmse:1.936049+0.030867    test-rmse:2.010870+0.089310 
## [63] train-rmse:1.931198+0.031180    test-rmse:2.006415+0.087774 
## [64] train-rmse:1.926392+0.031490    test-rmse:2.002371+0.086860 
## [65] train-rmse:1.921663+0.031769    test-rmse:1.999777+0.088380 
## [66] train-rmse:1.916957+0.032007    test-rmse:1.994413+0.088408 
## [67] train-rmse:1.912250+0.032250    test-rmse:1.989060+0.089215 
## [68] train-rmse:1.907686+0.032431    test-rmse:1.985595+0.090640 
## [69] train-rmse:1.903085+0.032658    test-rmse:1.980588+0.088244 
## [70] train-rmse:1.898589+0.032873    test-rmse:1.977189+0.087874 
## [71] train-rmse:1.894098+0.033055    test-rmse:1.974745+0.090607 
## [72] train-rmse:1.889627+0.033354    test-rmse:1.971476+0.090941 
## [73] train-rmse:1.885222+0.033648    test-rmse:1.968692+0.094055 
## [74] train-rmse:1.880946+0.033940    test-rmse:1.966745+0.092364 
## [75] train-rmse:1.876762+0.034263    test-rmse:1.963335+0.093946 
## [76] train-rmse:1.872573+0.034618    test-rmse:1.958403+0.090993 
## [77] train-rmse:1.868448+0.034903    test-rmse:1.955405+0.089531 
## [78] train-rmse:1.864340+0.035167    test-rmse:1.951998+0.090143 
## [79] train-rmse:1.860243+0.035409    test-rmse:1.949969+0.091061 
## [80] train-rmse:1.856240+0.035639    test-rmse:1.946708+0.090845 
## [81] train-rmse:1.852219+0.035926    test-rmse:1.942768+0.088085 
## [82] train-rmse:1.848159+0.036221    test-rmse:1.939029+0.087089 
## [83] train-rmse:1.844250+0.036419    test-rmse:1.936721+0.089717 
## [84] train-rmse:1.840276+0.036693    test-rmse:1.936046+0.090611 
## [85] train-rmse:1.836350+0.036936    test-rmse:1.930593+0.094304 
## [86] train-rmse:1.832495+0.037187    test-rmse:1.925171+0.096257 
## [87] train-rmse:1.828746+0.037364    test-rmse:1.920774+0.095915 
## [88] train-rmse:1.825041+0.037512    test-rmse:1.916381+0.093881 
## [89] train-rmse:1.821259+0.037673    test-rmse:1.913441+0.095034 
## [90] train-rmse:1.817538+0.037904    test-rmse:1.908636+0.096221 
## [91] train-rmse:1.813875+0.038085    test-rmse:1.905527+0.093943 
## [92] train-rmse:1.810266+0.038283    test-rmse:1.901668+0.093752 
## [93] train-rmse:1.806672+0.038481    test-rmse:1.899761+0.093003 
## [94] train-rmse:1.803093+0.038642    test-rmse:1.898543+0.092377 
## [95] train-rmse:1.799524+0.038886    test-rmse:1.894438+0.090723 
## [96] train-rmse:1.796075+0.039103    test-rmse:1.893329+0.093039 
## [97] train-rmse:1.792613+0.039298    test-rmse:1.889956+0.092708 
## [98] train-rmse:1.789247+0.039508    test-rmse:1.887977+0.094657 
## [99] train-rmse:1.785895+0.039695    test-rmse:1.883690+0.093058 
## [100]    train-rmse:1.782553+0.039897    test-rmse:1.880913+0.091603 
## [101]    train-rmse:1.779281+0.040126    test-rmse:1.878440+0.091096 
## [102]    train-rmse:1.776014+0.040364    test-rmse:1.877433+0.092082 
## [103]    train-rmse:1.772774+0.040566    test-rmse:1.875010+0.093066 
## [104]    train-rmse:1.769543+0.040780    test-rmse:1.871539+0.091768 
## [105]    train-rmse:1.766312+0.040989    test-rmse:1.868089+0.092683 
## [106]    train-rmse:1.763056+0.041232    test-rmse:1.866557+0.090064 
## [107]    train-rmse:1.759858+0.041393    test-rmse:1.861816+0.090712 
## [108]    train-rmse:1.756754+0.041565    test-rmse:1.858752+0.091028 
## [109]    train-rmse:1.753637+0.041713    test-rmse:1.857338+0.093263 
## [110]    train-rmse:1.750560+0.041833    test-rmse:1.852794+0.094633 
## [111]    train-rmse:1.747507+0.041963    test-rmse:1.849186+0.096075 
## [112]    train-rmse:1.744486+0.042089    test-rmse:1.846126+0.096733 
## [113]    train-rmse:1.741512+0.042243    test-rmse:1.845229+0.098801 
## [114]    train-rmse:1.738571+0.042414    test-rmse:1.842878+0.098352 
## [115]    train-rmse:1.735618+0.042592    test-rmse:1.841856+0.096542 
## [116]    train-rmse:1.732674+0.042791    test-rmse:1.841248+0.096673 
## [117]    train-rmse:1.729755+0.043004    test-rmse:1.837756+0.098997 
## [118]    train-rmse:1.726887+0.043225    test-rmse:1.836015+0.098913 
## [119]    train-rmse:1.724056+0.043401    test-rmse:1.833349+0.098552 
## [120]    train-rmse:1.721241+0.043547    test-rmse:1.832165+0.100932 
## [121]    train-rmse:1.718427+0.043677    test-rmse:1.827745+0.100662 
## [122]    train-rmse:1.715675+0.043827    test-rmse:1.825557+0.101074 
## [123]    train-rmse:1.712968+0.044008    test-rmse:1.823190+0.100182 
## [124]    train-rmse:1.710262+0.044170    test-rmse:1.821317+0.099845 
## [125]    train-rmse:1.707563+0.044320    test-rmse:1.818812+0.101059 
## [126]    train-rmse:1.704880+0.044460    test-rmse:1.816503+0.101094 
## [127]    train-rmse:1.702228+0.044607    test-rmse:1.813959+0.101700 
## [128]    train-rmse:1.699588+0.044732    test-rmse:1.809839+0.101386 
## [129]    train-rmse:1.696971+0.044879    test-rmse:1.807097+0.099212 
## [130]    train-rmse:1.694331+0.044997    test-rmse:1.804697+0.100962 
## [131]    train-rmse:1.691769+0.045143    test-rmse:1.802134+0.102938 
## [132]    train-rmse:1.689274+0.045259    test-rmse:1.801110+0.101271 
## [133]    train-rmse:1.686756+0.045394    test-rmse:1.799860+0.104127 
## [134]    train-rmse:1.684259+0.045509    test-rmse:1.797965+0.104386 
## [135]    train-rmse:1.681764+0.045588    test-rmse:1.795028+0.106323 
## [136]    train-rmse:1.679327+0.045714    test-rmse:1.793907+0.106079 
## [137]    train-rmse:1.676890+0.045841    test-rmse:1.792631+0.107412 
## [138]    train-rmse:1.674497+0.045945    test-rmse:1.791392+0.106729 
## [139]    train-rmse:1.672105+0.046060    test-rmse:1.788761+0.107706 
## [140]    train-rmse:1.669738+0.046155    test-rmse:1.786909+0.106633 
## [141]    train-rmse:1.667354+0.046257    test-rmse:1.783999+0.106614 
## [142]    train-rmse:1.664983+0.046350    test-rmse:1.782054+0.105634 
## [143]    train-rmse:1.662659+0.046458    test-rmse:1.780096+0.106692 
## [144]    train-rmse:1.660372+0.046561    test-rmse:1.779639+0.106618 
## [145]    train-rmse:1.658095+0.046666    test-rmse:1.779355+0.108549 
## [146]    train-rmse:1.655836+0.046786    test-rmse:1.776027+0.108044 
## [147]    train-rmse:1.653586+0.046885    test-rmse:1.772703+0.112129 
## [148]    train-rmse:1.651364+0.046988    test-rmse:1.769399+0.112269 
## [149]    train-rmse:1.649161+0.047079    test-rmse:1.768203+0.111374 
## [150]    train-rmse:1.646949+0.047218    test-rmse:1.766883+0.111257 
## [151]    train-rmse:1.644731+0.047321    test-rmse:1.765639+0.112050 
## [152]    train-rmse:1.642557+0.047401    test-rmse:1.762129+0.111724 
## [153]    train-rmse:1.640406+0.047479    test-rmse:1.759955+0.112787 
## [154]    train-rmse:1.638255+0.047579    test-rmse:1.758477+0.114282 
## [155]    train-rmse:1.636145+0.047692    test-rmse:1.754957+0.114890 
## [156]    train-rmse:1.634033+0.047786    test-rmse:1.753273+0.115203 
## [157]    train-rmse:1.631949+0.047875    test-rmse:1.752396+0.115149 
## [158]    train-rmse:1.629880+0.047961    test-rmse:1.750034+0.113405 
## [159]    train-rmse:1.627851+0.048048    test-rmse:1.748156+0.112190 
## [160]    train-rmse:1.625813+0.048156    test-rmse:1.746549+0.113342 
## [161]    train-rmse:1.623765+0.048227    test-rmse:1.745475+0.113208 
## [162]    train-rmse:1.621708+0.048293    test-rmse:1.743413+0.113493 
## [163]    train-rmse:1.619678+0.048389    test-rmse:1.744473+0.113316 
## [164]    train-rmse:1.617652+0.048508    test-rmse:1.741428+0.114183 
## [165]    train-rmse:1.615682+0.048599    test-rmse:1.739862+0.115299 
## [166]    train-rmse:1.613757+0.048693    test-rmse:1.739667+0.114609 
## [167]    train-rmse:1.611849+0.048757    test-rmse:1.737976+0.116083 
## [168]    train-rmse:1.609950+0.048831    test-rmse:1.736000+0.115865 
## [169]    train-rmse:1.608009+0.048880    test-rmse:1.734515+0.117068 
## [170]    train-rmse:1.606105+0.048930    test-rmse:1.730789+0.119531 
## [171]    train-rmse:1.604225+0.048986    test-rmse:1.729781+0.119693 
## [172]    train-rmse:1.602357+0.049032    test-rmse:1.729030+0.120420 
## [173]    train-rmse:1.600495+0.049063    test-rmse:1.727384+0.120930 
## [174]    train-rmse:1.598648+0.049138    test-rmse:1.726338+0.121323 
## [175]    train-rmse:1.596809+0.049210    test-rmse:1.723139+0.122042 
## [176]    train-rmse:1.594997+0.049298    test-rmse:1.720190+0.120488 
## [177]    train-rmse:1.593169+0.049407    test-rmse:1.718268+0.119709 
## [178]    train-rmse:1.591369+0.049500    test-rmse:1.716068+0.120651 
## [179]    train-rmse:1.589580+0.049555    test-rmse:1.715116+0.120247 
## [180]    train-rmse:1.587808+0.049619    test-rmse:1.713748+0.122451 
## [181]    train-rmse:1.586057+0.049694    test-rmse:1.711568+0.124074 
## [182]    train-rmse:1.584335+0.049760    test-rmse:1.711312+0.124132 
## [183]    train-rmse:1.582628+0.049816    test-rmse:1.710107+0.124912 
## [184]    train-rmse:1.580921+0.049869    test-rmse:1.707870+0.124742 
## [185]    train-rmse:1.579207+0.049931    test-rmse:1.705326+0.125991 
## [186]    train-rmse:1.577515+0.049968    test-rmse:1.705185+0.127210 
## [187]    train-rmse:1.575809+0.050007    test-rmse:1.704227+0.128910 
## [188]    train-rmse:1.574104+0.050037    test-rmse:1.703858+0.128004 
## [189]    train-rmse:1.572441+0.050094    test-rmse:1.700994+0.129713 
## [190]    train-rmse:1.570785+0.050158    test-rmse:1.700191+0.128491 
## [191]    train-rmse:1.569148+0.050201    test-rmse:1.699495+0.127768 
## [192]    train-rmse:1.567499+0.050223    test-rmse:1.698566+0.127500 
## [193]    train-rmse:1.565876+0.050268    test-rmse:1.697437+0.127487 
## [194]    train-rmse:1.564278+0.050320    test-rmse:1.695870+0.128303 
## [195]    train-rmse:1.562679+0.050354    test-rmse:1.694084+0.128483 
## [196]    train-rmse:1.561073+0.050402    test-rmse:1.693090+0.129069 
## [197]    train-rmse:1.559479+0.050422    test-rmse:1.689502+0.132118 
## [198]    train-rmse:1.557934+0.050456    test-rmse:1.689083+0.132637 
## [199]    train-rmse:1.556403+0.050510    test-rmse:1.687104+0.132147 
## [200]    train-rmse:1.554897+0.050556    test-rmse:1.686004+0.132940 
## [201]    train-rmse:1.553376+0.050613    test-rmse:1.683651+0.132653 
## [202]    train-rmse:1.551868+0.050650    test-rmse:1.681187+0.131920 
## [203]    train-rmse:1.550369+0.050699    test-rmse:1.680458+0.132950 
## [204]    train-rmse:1.548880+0.050749    test-rmse:1.679464+0.131706 
## [205]    train-rmse:1.547414+0.050797    test-rmse:1.677917+0.131950 
## [206]    train-rmse:1.545945+0.050849    test-rmse:1.676039+0.134387 
## [207]    train-rmse:1.544454+0.050876    test-rmse:1.673313+0.134331 
## [208]    train-rmse:1.542962+0.050930    test-rmse:1.671949+0.134728 
## [209]    train-rmse:1.541514+0.050985    test-rmse:1.670260+0.134687 
## [210]    train-rmse:1.540080+0.051038    test-rmse:1.668861+0.136261 
## [211]    train-rmse:1.538654+0.051083    test-rmse:1.667530+0.136579 
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## [419]    train-rmse:1.375974+0.053387    test-rmse:1.521130+0.179181 
## [420]    train-rmse:1.375586+0.053382    test-rmse:1.520030+0.178562 
## [421]    train-rmse:1.375206+0.053380    test-rmse:1.519719+0.178461 
## [422]    train-rmse:1.374825+0.053377    test-rmse:1.519099+0.178272 
## [423]    train-rmse:1.374445+0.053374    test-rmse:1.519319+0.178867 
## [424]    train-rmse:1.374068+0.053369    test-rmse:1.518528+0.178397 
## [425]    train-rmse:1.373691+0.053368    test-rmse:1.519363+0.179267 
## [426]    train-rmse:1.373320+0.053365    test-rmse:1.519668+0.179659 
## [427]    train-rmse:1.372950+0.053363    test-rmse:1.518511+0.180928 
## [428]    train-rmse:1.372583+0.053362    test-rmse:1.518940+0.181281 
## [429]    train-rmse:1.372219+0.053358    test-rmse:1.519595+0.181171 
## [430]    train-rmse:1.371855+0.053361    test-rmse:1.518492+0.180743 
## [431]    train-rmse:1.371490+0.053365    test-rmse:1.517562+0.180996 
## [432]    train-rmse:1.371132+0.053366    test-rmse:1.516580+0.181424 
## [433]    train-rmse:1.370774+0.053364    test-rmse:1.516024+0.181913 
## [434]    train-rmse:1.370418+0.053366    test-rmse:1.516530+0.182497 
## [435]    train-rmse:1.370056+0.053360    test-rmse:1.516120+0.182609 
## [436]    train-rmse:1.369704+0.053352    test-rmse:1.515910+0.182685 
## [437]    train-rmse:1.369353+0.053344    test-rmse:1.516227+0.182559 
## [438]    train-rmse:1.369007+0.053341    test-rmse:1.515710+0.182901 
## [439]    train-rmse:1.368664+0.053336    test-rmse:1.516247+0.182883 
## [440]    train-rmse:1.368322+0.053334    test-rmse:1.516034+0.183143 
## [441]    train-rmse:1.367984+0.053329    test-rmse:1.516180+0.183592 
## [442]    train-rmse:1.367651+0.053327    test-rmse:1.516188+0.183614 
## [443]    train-rmse:1.367315+0.053323    test-rmse:1.515638+0.183754 
## [444]    train-rmse:1.366983+0.053322    test-rmse:1.515308+0.183454 
## [445]    train-rmse:1.366650+0.053321    test-rmse:1.514728+0.183487 
## [446]    train-rmse:1.366319+0.053320    test-rmse:1.514408+0.183944 
## [447]    train-rmse:1.365987+0.053322    test-rmse:1.513890+0.184136 
## [448]    train-rmse:1.365656+0.053323    test-rmse:1.513933+0.184238 
## [449]    train-rmse:1.365333+0.053323    test-rmse:1.512881+0.183700 
## [450]    train-rmse:1.365012+0.053321    test-rmse:1.511893+0.184761 
## [451]    train-rmse:1.364692+0.053319    test-rmse:1.511203+0.185099 
## [452]    train-rmse:1.364375+0.053317    test-rmse:1.511475+0.186087 
## [453]    train-rmse:1.364059+0.053317    test-rmse:1.510367+0.185761 
## [454]    train-rmse:1.363741+0.053317    test-rmse:1.510162+0.184632 
## [455]    train-rmse:1.363425+0.053317    test-rmse:1.509981+0.184825 
## [456]    train-rmse:1.363108+0.053314    test-rmse:1.510536+0.185827 
## [457]    train-rmse:1.362795+0.053313    test-rmse:1.510405+0.185775 
## [458]    train-rmse:1.362483+0.053310    test-rmse:1.510376+0.185986 
## [459]    train-rmse:1.362178+0.053309    test-rmse:1.509692+0.185679 
## [460]    train-rmse:1.361870+0.053309    test-rmse:1.509824+0.186228 
## [461]    train-rmse:1.361563+0.053308    test-rmse:1.508903+0.186363 
## [462]    train-rmse:1.361256+0.053306    test-rmse:1.508914+0.186559 
## [463]    train-rmse:1.360952+0.053306    test-rmse:1.508890+0.186574 
## [464]    train-rmse:1.360653+0.053301    test-rmse:1.508982+0.186130 
## [465]    train-rmse:1.360359+0.053300    test-rmse:1.509154+0.186043 
## [466]    train-rmse:1.360070+0.053300    test-rmse:1.509430+0.185995 
## [467]    train-rmse:1.359776+0.053297    test-rmse:1.508717+0.185746 
## [468]    train-rmse:1.359484+0.053293    test-rmse:1.508650+0.185610 
## [469]    train-rmse:1.359196+0.053288    test-rmse:1.508422+0.185554 
## [470]    train-rmse:1.358909+0.053284    test-rmse:1.507675+0.186176 
## [471]    train-rmse:1.358624+0.053280    test-rmse:1.507263+0.186311 
## [472]    train-rmse:1.358337+0.053276    test-rmse:1.506731+0.187600 
## [473]    train-rmse:1.358052+0.053273    test-rmse:1.506388+0.187896 
## [474]    train-rmse:1.357772+0.053271    test-rmse:1.506345+0.187438 
## [475]    train-rmse:1.357495+0.053270    test-rmse:1.506205+0.187021 
## [476]    train-rmse:1.357220+0.053268    test-rmse:1.505590+0.187263 
## [477]    train-rmse:1.356946+0.053267    test-rmse:1.505028+0.187311 
## [478]    train-rmse:1.356672+0.053265    test-rmse:1.505024+0.187467 
## [479]    train-rmse:1.356400+0.053263    test-rmse:1.504810+0.187906 
## [480]    train-rmse:1.356129+0.053260    test-rmse:1.505204+0.188537 
## [481]    train-rmse:1.355860+0.053256    test-rmse:1.504644+0.188607 
## [482]    train-rmse:1.355592+0.053254    test-rmse:1.504401+0.188270 
## [483]    train-rmse:1.355326+0.053253    test-rmse:1.503989+0.187923 
## [484]    train-rmse:1.355062+0.053251    test-rmse:1.504197+0.188480 
## [485]    train-rmse:1.354803+0.053247    test-rmse:1.503910+0.188757 
## [486]    train-rmse:1.354542+0.053242    test-rmse:1.504001+0.188436 
## [487]    train-rmse:1.354277+0.053238    test-rmse:1.503352+0.188317 
## [488]    train-rmse:1.354018+0.053237    test-rmse:1.503217+0.188420 
## [489]    train-rmse:1.353761+0.053234    test-rmse:1.503144+0.188251 
## [490]    train-rmse:1.353504+0.053229    test-rmse:1.503816+0.188847 
## [491]    train-rmse:1.353245+0.053221    test-rmse:1.502928+0.189050 
## [492]    train-rmse:1.352991+0.053213    test-rmse:1.502770+0.189344 
## [493]    train-rmse:1.352740+0.053208    test-rmse:1.502411+0.189059 
## [494]    train-rmse:1.352490+0.053203    test-rmse:1.502040+0.188727 
## [495]    train-rmse:1.352242+0.053197    test-rmse:1.502908+0.188983 
## [496]    train-rmse:1.351997+0.053194    test-rmse:1.503328+0.189155 
## [497]    train-rmse:1.351753+0.053192    test-rmse:1.502707+0.188967 
## [498]    train-rmse:1.351507+0.053188    test-rmse:1.502146+0.189063 
## [499]    train-rmse:1.351264+0.053187    test-rmse:1.501286+0.189091 
## [500]    train-rmse:1.351018+0.053184    test-rmse:1.500769+0.190577 
## [501]    train-rmse:1.350772+0.053181    test-rmse:1.500815+0.190566 
## [502]    train-rmse:1.350531+0.053178    test-rmse:1.500383+0.190508 
## [503]    train-rmse:1.350290+0.053175    test-rmse:1.500342+0.190811 
## [504]    train-rmse:1.350052+0.053171    test-rmse:1.500854+0.190708 
## [505]    train-rmse:1.349815+0.053166    test-rmse:1.500788+0.190128 
## [506]    train-rmse:1.349579+0.053167    test-rmse:1.500421+0.190312 
## [507]    train-rmse:1.349345+0.053166    test-rmse:1.499918+0.190426 
## [508]    train-rmse:1.349113+0.053164    test-rmse:1.499632+0.190639 
## [509]    train-rmse:1.348885+0.053162    test-rmse:1.500057+0.190481 
## [510]    train-rmse:1.348658+0.053158    test-rmse:1.499727+0.190109 
## [511]    train-rmse:1.348432+0.053154    test-rmse:1.499633+0.190519 
## [512]    train-rmse:1.348205+0.053149    test-rmse:1.499074+0.190583 
## [513]    train-rmse:1.347983+0.053143    test-rmse:1.499038+0.190759 
## [514]    train-rmse:1.347761+0.053139    test-rmse:1.498410+0.190826 
## [515]    train-rmse:1.347542+0.053136    test-rmse:1.498295+0.190769 
## [516]    train-rmse:1.347320+0.053136    test-rmse:1.497831+0.190772 
## [517]    train-rmse:1.347100+0.053131    test-rmse:1.497617+0.190723 
## [518]    train-rmse:1.346882+0.053125    test-rmse:1.497129+0.190935 
## [519]    train-rmse:1.346664+0.053121    test-rmse:1.496344+0.190955 
## [520]    train-rmse:1.346450+0.053119    test-rmse:1.496422+0.191095 
## [521]    train-rmse:1.346235+0.053114    test-rmse:1.496538+0.190800 
## [522]    train-rmse:1.346020+0.053108    test-rmse:1.497119+0.191557 
## [523]    train-rmse:1.345804+0.053102    test-rmse:1.496634+0.191721 
## [524]    train-rmse:1.345589+0.053096    test-rmse:1.497066+0.191661 
## [525]    train-rmse:1.345377+0.053092    test-rmse:1.497307+0.192106 
## Stopping. Best iteration:
## [519]    train-rmse:1.346664+0.053121    test-rmse:1.496344+0.190955
## 
## 15 
## [1]  train-rmse:8.426154+0.060886    test-rmse:8.422350+0.271196 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.340416+0.053681    test-rmse:7.332899+0.260787 
## [3]  train-rmse:6.417331+0.047955    test-rmse:6.408774+0.249889 
## [4]  train-rmse:5.632658+0.042763    test-rmse:5.631033+0.236644 
## [5]  train-rmse:4.967715+0.033523    test-rmse:4.973947+0.229546 
## [6]  train-rmse:4.409512+0.029325    test-rmse:4.421727+0.215721 
## [7]  train-rmse:3.940521+0.026409    test-rmse:3.955519+0.196409 
## [8]  train-rmse:3.546830+0.024623    test-rmse:3.572406+0.177733 
## [9]  train-rmse:3.219021+0.017544    test-rmse:3.251741+0.165657 
## [10] train-rmse:2.952903+0.016825    test-rmse:3.001071+0.140641 
## [11] train-rmse:2.732952+0.015660    test-rmse:2.789129+0.126051 
## [12] train-rmse:2.551957+0.018589    test-rmse:2.619256+0.107490 
## [13] train-rmse:2.407324+0.019597    test-rmse:2.484748+0.084593 
## [14] train-rmse:2.287754+0.021607    test-rmse:2.374636+0.064561 
## [15] train-rmse:2.194326+0.020749    test-rmse:2.293487+0.050880 
## [16] train-rmse:2.117694+0.021987    test-rmse:2.221231+0.051110 
## [17] train-rmse:2.056944+0.023107    test-rmse:2.164427+0.047480 
## [18] train-rmse:2.001974+0.025703    test-rmse:2.118392+0.044590 
## [19] train-rmse:1.956925+0.026556    test-rmse:2.080728+0.049080 
## [20] train-rmse:1.921323+0.027638    test-rmse:2.053427+0.056768 
## [21] train-rmse:1.890609+0.029065    test-rmse:2.027732+0.063355 
## [22] train-rmse:1.860987+0.030920    test-rmse:2.005190+0.057501 
## [23] train-rmse:1.838742+0.030825    test-rmse:1.987221+0.064220 
## [24] train-rmse:1.818909+0.031305    test-rmse:1.971921+0.064096 
## [25] train-rmse:1.797932+0.032678    test-rmse:1.954299+0.070696 
## [26] train-rmse:1.781115+0.032803    test-rmse:1.939667+0.072427 
## [27] train-rmse:1.765955+0.033175    test-rmse:1.926029+0.075535 
## [28] train-rmse:1.747551+0.035630    test-rmse:1.908701+0.079646 
## [29] train-rmse:1.733936+0.036370    test-rmse:1.900837+0.085870 
## [30] train-rmse:1.720155+0.037383    test-rmse:1.888804+0.086175 
## [31] train-rmse:1.704788+0.039562    test-rmse:1.869263+0.070918 
## [32] train-rmse:1.692656+0.040060    test-rmse:1.853388+0.074577 
## [33] train-rmse:1.681486+0.040536    test-rmse:1.843121+0.074360 
## [34] train-rmse:1.667008+0.040403    test-rmse:1.833079+0.079619 
## [35] train-rmse:1.656152+0.040927    test-rmse:1.824977+0.080206 
## [36] train-rmse:1.644398+0.042114    test-rmse:1.818749+0.082491 
## [37] train-rmse:1.633452+0.042329    test-rmse:1.812465+0.081182 
## [38] train-rmse:1.622873+0.042806    test-rmse:1.800764+0.079789 
## [39] train-rmse:1.612818+0.043676    test-rmse:1.795635+0.077308 
## [40] train-rmse:1.603713+0.044277    test-rmse:1.790541+0.083117 
## [41] train-rmse:1.593883+0.045000    test-rmse:1.783823+0.082922 
## [42] train-rmse:1.584912+0.045902    test-rmse:1.776881+0.084354 
## [43] train-rmse:1.576312+0.046256    test-rmse:1.768604+0.085340 
## [44] train-rmse:1.564668+0.047433    test-rmse:1.754712+0.087176 
## [45] train-rmse:1.555560+0.046861    test-rmse:1.747108+0.087643 
## [46] train-rmse:1.545901+0.046281    test-rmse:1.741894+0.091151 
## [47] train-rmse:1.538023+0.046580    test-rmse:1.736290+0.092095 
## [48] train-rmse:1.529482+0.047543    test-rmse:1.732134+0.095943 
## [49] train-rmse:1.521737+0.048114    test-rmse:1.725148+0.097813 
## [50] train-rmse:1.511303+0.051115    test-rmse:1.713467+0.082219 
## [51] train-rmse:1.500433+0.051951    test-rmse:1.707828+0.082923 
## [52] train-rmse:1.492511+0.052303    test-rmse:1.703271+0.084172 
## [53] train-rmse:1.485371+0.052483    test-rmse:1.696555+0.083762 
## [54] train-rmse:1.477947+0.052065    test-rmse:1.689733+0.083346 
## [55] train-rmse:1.468295+0.054569    test-rmse:1.682877+0.077437 
## [56] train-rmse:1.461255+0.054344    test-rmse:1.678492+0.076657 
## [57] train-rmse:1.453615+0.054823    test-rmse:1.671302+0.076078 
## [58] train-rmse:1.445978+0.055128    test-rmse:1.666693+0.079287 
## [59] train-rmse:1.438805+0.054710    test-rmse:1.659363+0.082083 
## [60] train-rmse:1.431663+0.055660    test-rmse:1.652158+0.084351 
## [61] train-rmse:1.425058+0.055847    test-rmse:1.648320+0.086359 
## [62] train-rmse:1.418648+0.056086    test-rmse:1.645337+0.086160 
## [63] train-rmse:1.411821+0.055781    test-rmse:1.639063+0.085930 
## [64] train-rmse:1.405884+0.056036    test-rmse:1.638056+0.084450 
## [65] train-rmse:1.397000+0.056943    test-rmse:1.631258+0.081649 
## [66] train-rmse:1.390017+0.056761    test-rmse:1.626487+0.081129 
## [67] train-rmse:1.383029+0.057441    test-rmse:1.621167+0.084036 
## [68] train-rmse:1.377622+0.057534    test-rmse:1.614926+0.085161 
## [69] train-rmse:1.370092+0.057340    test-rmse:1.608135+0.089010 
## [70] train-rmse:1.364104+0.056891    test-rmse:1.604880+0.089934 
## [71] train-rmse:1.356164+0.058133    test-rmse:1.600876+0.090384 
## [72] train-rmse:1.342990+0.051545    test-rmse:1.587752+0.080829 
## [73] train-rmse:1.336945+0.051184    test-rmse:1.582539+0.082580 
## [74] train-rmse:1.331212+0.051061    test-rmse:1.577437+0.084031 
## [75] train-rmse:1.325144+0.050886    test-rmse:1.572165+0.091174 
## [76] train-rmse:1.320257+0.050987    test-rmse:1.570112+0.090516 
## [77] train-rmse:1.314954+0.050712    test-rmse:1.565586+0.090757 
## [78] train-rmse:1.305172+0.052906    test-rmse:1.550640+0.088893 
## [79] train-rmse:1.299784+0.053392    test-rmse:1.547593+0.088941 
## [80] train-rmse:1.294554+0.053864    test-rmse:1.540483+0.088077 
## [81] train-rmse:1.289350+0.054009    test-rmse:1.538623+0.089174 
## [82] train-rmse:1.284539+0.054252    test-rmse:1.534282+0.089245 
## [83] train-rmse:1.273277+0.056550    test-rmse:1.525489+0.097042 
## [84] train-rmse:1.267580+0.057132    test-rmse:1.523497+0.097197 
## [85] train-rmse:1.262717+0.057791    test-rmse:1.520371+0.097538 
## [86] train-rmse:1.258294+0.057902    test-rmse:1.517801+0.098518 
## [87] train-rmse:1.254177+0.057954    test-rmse:1.513390+0.100803 
## [88] train-rmse:1.250065+0.058248    test-rmse:1.510796+0.100776 
## [89] train-rmse:1.244916+0.058272    test-rmse:1.505471+0.105845 
## [90] train-rmse:1.238910+0.060274    test-rmse:1.501452+0.102561 
## [91] train-rmse:1.234660+0.060367    test-rmse:1.498242+0.101931 
## [92] train-rmse:1.230239+0.060396    test-rmse:1.494090+0.102698 
## [93] train-rmse:1.226435+0.060497    test-rmse:1.491645+0.101946 
## [94] train-rmse:1.222130+0.060458    test-rmse:1.492462+0.101832 
## [95] train-rmse:1.215801+0.061424    test-rmse:1.485755+0.093275 
## [96] train-rmse:1.210049+0.061967    test-rmse:1.479520+0.095327 
## [97] train-rmse:1.206270+0.062215    test-rmse:1.477013+0.093725 
## [98] train-rmse:1.202173+0.062750    test-rmse:1.475585+0.095765 
## [99] train-rmse:1.195319+0.065249    test-rmse:1.471580+0.096026 
## [100]    train-rmse:1.191591+0.065599    test-rmse:1.468402+0.096152 
## [101]    train-rmse:1.187650+0.065180    test-rmse:1.464388+0.097568 
## [102]    train-rmse:1.178892+0.057115    test-rmse:1.458720+0.098550 
## [103]    train-rmse:1.169631+0.051817    test-rmse:1.452137+0.100968 
## [104]    train-rmse:1.163650+0.051227    test-rmse:1.444940+0.105919 
## [105]    train-rmse:1.157352+0.046923    test-rmse:1.439404+0.105712 
## [106]    train-rmse:1.152905+0.047682    test-rmse:1.439026+0.106040 
## [107]    train-rmse:1.149531+0.047781    test-rmse:1.434886+0.108677 
## [108]    train-rmse:1.146086+0.047895    test-rmse:1.432842+0.108663 
## [109]    train-rmse:1.142359+0.047327    test-rmse:1.429245+0.111161 
## [110]    train-rmse:1.138922+0.046994    test-rmse:1.426629+0.113384 
## [111]    train-rmse:1.135638+0.046874    test-rmse:1.424900+0.113382 
## [112]    train-rmse:1.132164+0.047341    test-rmse:1.422876+0.113145 
## [113]    train-rmse:1.128349+0.046173    test-rmse:1.420200+0.114769 
## [114]    train-rmse:1.125091+0.046508    test-rmse:1.418935+0.114111 
## [115]    train-rmse:1.121783+0.046888    test-rmse:1.418961+0.114328 
## [116]    train-rmse:1.118643+0.046670    test-rmse:1.414499+0.113669 
## [117]    train-rmse:1.115396+0.046479    test-rmse:1.413763+0.115227 
## [118]    train-rmse:1.112081+0.046055    test-rmse:1.413006+0.115307 
## [119]    train-rmse:1.108706+0.046451    test-rmse:1.409959+0.115194 
## [120]    train-rmse:1.105178+0.046000    test-rmse:1.408501+0.114846 
## [121]    train-rmse:1.101891+0.046035    test-rmse:1.407799+0.115930 
## [122]    train-rmse:1.098494+0.046759    test-rmse:1.404892+0.117826 
## [123]    train-rmse:1.094465+0.048340    test-rmse:1.399697+0.117022 
## [124]    train-rmse:1.091292+0.048232    test-rmse:1.398382+0.118151 
## [125]    train-rmse:1.088336+0.048227    test-rmse:1.395823+0.118901 
## [126]    train-rmse:1.085575+0.048180    test-rmse:1.392680+0.118960 
## [127]    train-rmse:1.082042+0.047734    test-rmse:1.393293+0.120334 
## [128]    train-rmse:1.074047+0.042648    test-rmse:1.385647+0.117234 
## [129]    train-rmse:1.071212+0.042615    test-rmse:1.384089+0.119804 
## [130]    train-rmse:1.066992+0.044396    test-rmse:1.382315+0.119430 
## [131]    train-rmse:1.064174+0.044136    test-rmse:1.380383+0.118888 
## [132]    train-rmse:1.061282+0.044255    test-rmse:1.378678+0.118785 
## [133]    train-rmse:1.058854+0.044457    test-rmse:1.376638+0.118967 
## [134]    train-rmse:1.056315+0.044673    test-rmse:1.376824+0.117560 
## [135]    train-rmse:1.053192+0.044707    test-rmse:1.375104+0.119927 
## [136]    train-rmse:1.050664+0.044712    test-rmse:1.372023+0.120321 
## [137]    train-rmse:1.047524+0.044640    test-rmse:1.371521+0.121771 
## [138]    train-rmse:1.044796+0.045048    test-rmse:1.370389+0.122913 
## [139]    train-rmse:1.042334+0.045077    test-rmse:1.368896+0.123766 
## [140]    train-rmse:1.036920+0.039897    test-rmse:1.368385+0.124898 
## [141]    train-rmse:1.032675+0.036935    test-rmse:1.364329+0.126461 
## [142]    train-rmse:1.030269+0.036735    test-rmse:1.364299+0.126772 
## [143]    train-rmse:1.028260+0.036618    test-rmse:1.361793+0.128850 
## [144]    train-rmse:1.026220+0.036687    test-rmse:1.358682+0.129559 
## [145]    train-rmse:1.022870+0.035966    test-rmse:1.357924+0.130305 
## [146]    train-rmse:1.020864+0.036021    test-rmse:1.358293+0.130505 
## [147]    train-rmse:1.017845+0.035479    test-rmse:1.358636+0.129995 
## [148]    train-rmse:1.015655+0.035311    test-rmse:1.356691+0.130497 
## [149]    train-rmse:1.013434+0.035502    test-rmse:1.356807+0.129927 
## [150]    train-rmse:1.011330+0.035831    test-rmse:1.355517+0.130719 
## [151]    train-rmse:1.008858+0.036037    test-rmse:1.352918+0.129920 
## [152]    train-rmse:1.004521+0.035372    test-rmse:1.349784+0.131568 
## [153]    train-rmse:1.002223+0.035038    test-rmse:1.347603+0.133895 
## [154]    train-rmse:1.000117+0.034777    test-rmse:1.344920+0.133382 
## [155]    train-rmse:0.997813+0.034594    test-rmse:1.344503+0.133535 
## [156]    train-rmse:0.995685+0.034561    test-rmse:1.345165+0.135041 
## [157]    train-rmse:0.993576+0.034717    test-rmse:1.345215+0.136083 
## [158]    train-rmse:0.991730+0.034742    test-rmse:1.345256+0.135557 
## [159]    train-rmse:0.987196+0.030487    test-rmse:1.343629+0.137038 
## [160]    train-rmse:0.983726+0.032404    test-rmse:1.338363+0.133402 
## [161]    train-rmse:0.981765+0.032311    test-rmse:1.337308+0.134728 
## [162]    train-rmse:0.980074+0.032221    test-rmse:1.334775+0.135460 
## [163]    train-rmse:0.977066+0.033380    test-rmse:1.335038+0.135156 
## [164]    train-rmse:0.975446+0.033327    test-rmse:1.334717+0.134486 
## [165]    train-rmse:0.971131+0.032156    test-rmse:1.327887+0.139301 
## [166]    train-rmse:0.968727+0.031716    test-rmse:1.329717+0.140530 
## [167]    train-rmse:0.966881+0.031978    test-rmse:1.327516+0.140132 
## [168]    train-rmse:0.963800+0.033524    test-rmse:1.323407+0.138261 
## [169]    train-rmse:0.961524+0.033467    test-rmse:1.321932+0.139218 
## [170]    train-rmse:0.958875+0.035153    test-rmse:1.319310+0.139048 
## [171]    train-rmse:0.956784+0.034470    test-rmse:1.318590+0.140367 
## [172]    train-rmse:0.953806+0.035086    test-rmse:1.318390+0.139761 
## [173]    train-rmse:0.952105+0.034784    test-rmse:1.316803+0.140304 
## [174]    train-rmse:0.950462+0.034733    test-rmse:1.315057+0.140872 
## [175]    train-rmse:0.948305+0.035080    test-rmse:1.314121+0.140558 
## [176]    train-rmse:0.946613+0.034946    test-rmse:1.313626+0.140282 
## [177]    train-rmse:0.945065+0.034809    test-rmse:1.312789+0.140693 
## [178]    train-rmse:0.942919+0.034974    test-rmse:1.313152+0.141590 
## [179]    train-rmse:0.940841+0.034976    test-rmse:1.311535+0.142139 
## [180]    train-rmse:0.938409+0.035794    test-rmse:1.311180+0.141320 
## [181]    train-rmse:0.935784+0.037011    test-rmse:1.310064+0.142038 
## [182]    train-rmse:0.934212+0.036716    test-rmse:1.309498+0.141907 
## [183]    train-rmse:0.932788+0.036496    test-rmse:1.309876+0.143123 
## [184]    train-rmse:0.931351+0.036517    test-rmse:1.308590+0.143234 
## [185]    train-rmse:0.929831+0.036786    test-rmse:1.308238+0.143937 
## [186]    train-rmse:0.928098+0.036720    test-rmse:1.307142+0.144131 
## [187]    train-rmse:0.926685+0.036407    test-rmse:1.306863+0.143969 
## [188]    train-rmse:0.924917+0.036004    test-rmse:1.306594+0.144996 
## [189]    train-rmse:0.921014+0.032632    test-rmse:1.304389+0.146708 
## [190]    train-rmse:0.919694+0.032659    test-rmse:1.301992+0.147749 
## [191]    train-rmse:0.917172+0.032981    test-rmse:1.301010+0.147874 
## [192]    train-rmse:0.915217+0.033410    test-rmse:1.301624+0.149259 
## [193]    train-rmse:0.913900+0.033447    test-rmse:1.300997+0.149724 
## [194]    train-rmse:0.912542+0.033406    test-rmse:1.301227+0.149531 
## [195]    train-rmse:0.908348+0.036261    test-rmse:1.301687+0.149261 
## [196]    train-rmse:0.906820+0.036490    test-rmse:1.300057+0.149270 
## [197]    train-rmse:0.905553+0.036377    test-rmse:1.300898+0.148904 
## [198]    train-rmse:0.904286+0.036480    test-rmse:1.300626+0.148391 
## [199]    train-rmse:0.903120+0.036663    test-rmse:1.298880+0.148925 
## [200]    train-rmse:0.901721+0.036442    test-rmse:1.298393+0.149055 
## [201]    train-rmse:0.899657+0.037369    test-rmse:1.296683+0.149251 
## [202]    train-rmse:0.898346+0.037299    test-rmse:1.295045+0.149429 
## [203]    train-rmse:0.896696+0.037251    test-rmse:1.295501+0.149179 
## [204]    train-rmse:0.893639+0.037045    test-rmse:1.291048+0.152988 
## [205]    train-rmse:0.892310+0.037220    test-rmse:1.290150+0.153475 
## [206]    train-rmse:0.891275+0.037298    test-rmse:1.290127+0.152790 
## [207]    train-rmse:0.890164+0.037277    test-rmse:1.290262+0.152675 
## [208]    train-rmse:0.888861+0.037304    test-rmse:1.289530+0.152092 
## [209]    train-rmse:0.887635+0.037234    test-rmse:1.288951+0.152148 
## [210]    train-rmse:0.885875+0.036746    test-rmse:1.287732+0.152940 
## [211]    train-rmse:0.884672+0.036915    test-rmse:1.288383+0.153174 
## [212]    train-rmse:0.883379+0.036755    test-rmse:1.288247+0.154050 
## [213]    train-rmse:0.879833+0.036198    test-rmse:1.286865+0.154340 
## [214]    train-rmse:0.877923+0.036718    test-rmse:1.285793+0.153850 
## [215]    train-rmse:0.876530+0.036729    test-rmse:1.285529+0.154259 
## [216]    train-rmse:0.875240+0.036590    test-rmse:1.284467+0.154501 
## [217]    train-rmse:0.874218+0.036690    test-rmse:1.281868+0.154576 
## [218]    train-rmse:0.873228+0.036663    test-rmse:1.281750+0.153652 
## [219]    train-rmse:0.872248+0.036573    test-rmse:1.282169+0.153402 
## [220]    train-rmse:0.871320+0.036642    test-rmse:1.282115+0.152824 
## [221]    train-rmse:0.870171+0.036770    test-rmse:1.281307+0.153047 
## [222]    train-rmse:0.868883+0.036839    test-rmse:1.282395+0.153392 
## [223]    train-rmse:0.867750+0.037071    test-rmse:1.282609+0.155134 
## [224]    train-rmse:0.866358+0.037119    test-rmse:1.282390+0.155642 
## [225]    train-rmse:0.862927+0.040260    test-rmse:1.282066+0.156215 
## [226]    train-rmse:0.861647+0.040660    test-rmse:1.281680+0.157110 
## [227]    train-rmse:0.859690+0.041771    test-rmse:1.280279+0.154860 
## [228]    train-rmse:0.856552+0.042113    test-rmse:1.275810+0.153308 
## [229]    train-rmse:0.855664+0.042289    test-rmse:1.275328+0.152763 
## [230]    train-rmse:0.852736+0.042003    test-rmse:1.271129+0.154935 
## [231]    train-rmse:0.851689+0.041967    test-rmse:1.270667+0.155172 
## [232]    train-rmse:0.850669+0.041915    test-rmse:1.269878+0.155511 
## [233]    train-rmse:0.847256+0.038739    test-rmse:1.269607+0.155593 
## [234]    train-rmse:0.844942+0.037096    test-rmse:1.268290+0.157493 
## [235]    train-rmse:0.843885+0.037178    test-rmse:1.267647+0.157081 
## [236]    train-rmse:0.843008+0.037261    test-rmse:1.266710+0.157490 
## [237]    train-rmse:0.841923+0.037170    test-rmse:1.266062+0.157907 
## [238]    train-rmse:0.840665+0.037058    test-rmse:1.265819+0.158077 
## [239]    train-rmse:0.839576+0.036974    test-rmse:1.265668+0.158015 
## [240]    train-rmse:0.838704+0.036980    test-rmse:1.265523+0.158401 
## [241]    train-rmse:0.837634+0.037007    test-rmse:1.265075+0.158612 
## [242]    train-rmse:0.836671+0.037367    test-rmse:1.264838+0.159549 
## [243]    train-rmse:0.835764+0.037502    test-rmse:1.265905+0.161357 
## [244]    train-rmse:0.834901+0.037479    test-rmse:1.265805+0.161627 
## [245]    train-rmse:0.834132+0.037425    test-rmse:1.265588+0.161633 
## [246]    train-rmse:0.833350+0.037387    test-rmse:1.265061+0.161969 
## [247]    train-rmse:0.832641+0.037320    test-rmse:1.264931+0.162019 
## [248]    train-rmse:0.831795+0.037353    test-rmse:1.265499+0.161594 
## Stopping. Best iteration:
## [242]    train-rmse:0.836671+0.037367    test-rmse:1.264838+0.159549
## 
## 16 
## [1]  train-rmse:8.419507+0.061618    test-rmse:8.415899+0.267291 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.323309+0.052313    test-rmse:7.317192+0.257656 
## [3]  train-rmse:6.389040+0.044369    test-rmse:6.384067+0.251838 
## [4]  train-rmse:5.594691+0.038073    test-rmse:5.594532+0.238608 
## [5]  train-rmse:4.922178+0.032879    test-rmse:4.927424+0.222727 
## [6]  train-rmse:4.349643+0.029661    test-rmse:4.360761+0.205170 
## [7]  train-rmse:3.866741+0.025534    test-rmse:3.892117+0.190174 
## [8]  train-rmse:3.459728+0.023375    test-rmse:3.490501+0.167287 
## [9]  train-rmse:3.117987+0.021451    test-rmse:3.163814+0.149571 
## [10] train-rmse:2.833312+0.020950    test-rmse:2.889908+0.132570 
## [11] train-rmse:2.597596+0.017161    test-rmse:2.668512+0.127576 
## [12] train-rmse:2.402193+0.014086    test-rmse:2.476099+0.115050 
## [13] train-rmse:2.242039+0.013311    test-rmse:2.335210+0.099923 
## [14] train-rmse:2.109415+0.012728    test-rmse:2.218513+0.089162 
## [15] train-rmse:2.000228+0.011630    test-rmse:2.128857+0.086752 
## [16] train-rmse:1.912136+0.011069    test-rmse:2.049637+0.079734 
## [17] train-rmse:1.837423+0.010862    test-rmse:1.990361+0.076711 
## [18] train-rmse:1.774628+0.010052    test-rmse:1.936604+0.079675 
## [19] train-rmse:1.721425+0.011877    test-rmse:1.898623+0.080111 
## [20] train-rmse:1.675910+0.014143    test-rmse:1.864497+0.077892 
## [21] train-rmse:1.637247+0.013215    test-rmse:1.836863+0.069633 
## [22] train-rmse:1.604533+0.012800    test-rmse:1.807922+0.073423 
## [23] train-rmse:1.576344+0.013137    test-rmse:1.789617+0.077168 
## [24] train-rmse:1.550734+0.013090    test-rmse:1.765610+0.076781 
## [25] train-rmse:1.526337+0.013492    test-rmse:1.752549+0.078442 
## [26] train-rmse:1.505947+0.013512    test-rmse:1.737207+0.071204 
## [27] train-rmse:1.485237+0.014183    test-rmse:1.718563+0.069529 
## [28] train-rmse:1.465946+0.015009    test-rmse:1.712505+0.074042 
## [29] train-rmse:1.448483+0.017178    test-rmse:1.698494+0.071903 
## [30] train-rmse:1.431782+0.018223    test-rmse:1.684744+0.075590 
## [31] train-rmse:1.414760+0.016719    test-rmse:1.666907+0.078852 
## [32] train-rmse:1.399411+0.016307    test-rmse:1.657282+0.076444 
## [33] train-rmse:1.380848+0.015451    test-rmse:1.648099+0.082778 
## [34] train-rmse:1.366769+0.015311    test-rmse:1.644479+0.085370 
## [35] train-rmse:1.353086+0.015480    test-rmse:1.632775+0.092368 
## [36] train-rmse:1.338857+0.016565    test-rmse:1.618626+0.083139 
## [37] train-rmse:1.324962+0.015454    test-rmse:1.612952+0.087023 
## [38] train-rmse:1.312225+0.014958    test-rmse:1.604134+0.089878 
## [39] train-rmse:1.299572+0.015418    test-rmse:1.595395+0.091283 
## [40] train-rmse:1.287479+0.016726    test-rmse:1.580116+0.086986 
## [41] train-rmse:1.274247+0.016481    test-rmse:1.569439+0.091159 
## [42] train-rmse:1.261800+0.016447    test-rmse:1.561920+0.088868 
## [43] train-rmse:1.251926+0.016291    test-rmse:1.554961+0.085614 
## [44] train-rmse:1.239408+0.017852    test-rmse:1.542270+0.085332 
## [45] train-rmse:1.229658+0.018426    test-rmse:1.538899+0.089117 
## [46] train-rmse:1.218642+0.018942    test-rmse:1.534722+0.087847 
## [47] train-rmse:1.208281+0.018913    test-rmse:1.530400+0.086951 
## [48] train-rmse:1.198635+0.018787    test-rmse:1.527480+0.088763 
## [49] train-rmse:1.188736+0.019502    test-rmse:1.519351+0.094578 
## [50] train-rmse:1.178391+0.021458    test-rmse:1.511968+0.095473 
## [51] train-rmse:1.167159+0.022755    test-rmse:1.505804+0.095570 
## [52] train-rmse:1.155991+0.017979    test-rmse:1.496594+0.098362 
## [53] train-rmse:1.146601+0.018533    test-rmse:1.489944+0.099281 
## [54] train-rmse:1.138810+0.018244    test-rmse:1.485705+0.099669 
## [55] train-rmse:1.129879+0.018534    test-rmse:1.482151+0.104372 
## [56] train-rmse:1.119592+0.020596    test-rmse:1.473939+0.104547 
## [57] train-rmse:1.110408+0.019364    test-rmse:1.470257+0.106495 
## [58] train-rmse:1.101890+0.018196    test-rmse:1.466114+0.108926 
## [59] train-rmse:1.093377+0.017826    test-rmse:1.460351+0.109612 
## [60] train-rmse:1.085109+0.018744    test-rmse:1.454365+0.107418 
## [61] train-rmse:1.076041+0.019049    test-rmse:1.448788+0.106612 
## [62] train-rmse:1.067875+0.021031    test-rmse:1.441965+0.108498 
## [63] train-rmse:1.059136+0.018771    test-rmse:1.433671+0.110369 
## [64] train-rmse:1.050322+0.020794    test-rmse:1.426904+0.110371 
## [65] train-rmse:1.042756+0.020700    test-rmse:1.424120+0.113717 
## [66] train-rmse:1.036146+0.019810    test-rmse:1.420384+0.115042 
## [67] train-rmse:1.029485+0.019346    test-rmse:1.416917+0.113353 
## [68] train-rmse:1.022578+0.019844    test-rmse:1.414682+0.116589 
## [69] train-rmse:1.016813+0.020001    test-rmse:1.410267+0.114549 
## [70] train-rmse:1.009213+0.021127    test-rmse:1.402462+0.112153 
## [71] train-rmse:1.001066+0.023587    test-rmse:1.398752+0.113100 
## [72] train-rmse:0.994477+0.023087    test-rmse:1.393065+0.115743 
## [73] train-rmse:0.987064+0.022093    test-rmse:1.390704+0.117231 
## [74] train-rmse:0.980922+0.021049    test-rmse:1.389905+0.119847 
## [75] train-rmse:0.975769+0.020895    test-rmse:1.387510+0.119992 
## [76] train-rmse:0.969457+0.019957    test-rmse:1.383614+0.120436 
## [77] train-rmse:0.963555+0.020288    test-rmse:1.380688+0.120782 
## [78] train-rmse:0.958432+0.019940    test-rmse:1.379214+0.123510 
## [79] train-rmse:0.952588+0.019796    test-rmse:1.372398+0.124454 
## [80] train-rmse:0.945341+0.020410    test-rmse:1.367866+0.126739 
## [81] train-rmse:0.939179+0.019877    test-rmse:1.365280+0.129122 
## [82] train-rmse:0.933207+0.021718    test-rmse:1.363345+0.129025 
## [83] train-rmse:0.928875+0.021388    test-rmse:1.361944+0.129134 
## [84] train-rmse:0.922759+0.021377    test-rmse:1.358449+0.127828 
## [85] train-rmse:0.916680+0.020297    test-rmse:1.355448+0.128272 
## [86] train-rmse:0.911747+0.019795    test-rmse:1.353067+0.130701 
## [87] train-rmse:0.906975+0.019833    test-rmse:1.351182+0.132762 
## [88] train-rmse:0.901574+0.019979    test-rmse:1.348645+0.131639 
## [89] train-rmse:0.896360+0.019595    test-rmse:1.346215+0.132025 
## [90] train-rmse:0.890734+0.019522    test-rmse:1.345142+0.131257 
## [91] train-rmse:0.887005+0.019212    test-rmse:1.343514+0.130932 
## [92] train-rmse:0.881429+0.018688    test-rmse:1.341294+0.131035 
## [93] train-rmse:0.877434+0.019041    test-rmse:1.338374+0.131964 
## [94] train-rmse:0.873484+0.019204    test-rmse:1.336434+0.131134 
## [95] train-rmse:0.868568+0.019659    test-rmse:1.332152+0.129597 
## [96] train-rmse:0.864622+0.019506    test-rmse:1.333607+0.131994 
## [97] train-rmse:0.859341+0.018934    test-rmse:1.330240+0.131574 
## [98] train-rmse:0.855504+0.019469    test-rmse:1.330729+0.131131 
## [99] train-rmse:0.851425+0.018713    test-rmse:1.328797+0.131095 
## [100]    train-rmse:0.846760+0.017427    test-rmse:1.327311+0.132012 
## [101]    train-rmse:0.843023+0.017335    test-rmse:1.325087+0.131398 
## [102]    train-rmse:0.838756+0.017943    test-rmse:1.323006+0.132935 
## [103]    train-rmse:0.835034+0.017945    test-rmse:1.321669+0.134632 
## [104]    train-rmse:0.831142+0.018328    test-rmse:1.321696+0.133884 
## [105]    train-rmse:0.827434+0.018802    test-rmse:1.320124+0.134490 
## [106]    train-rmse:0.822083+0.021047    test-rmse:1.316766+0.134745 
## [107]    train-rmse:0.818957+0.020671    test-rmse:1.316133+0.134675 
## [108]    train-rmse:0.814135+0.022940    test-rmse:1.315756+0.136685 
## [109]    train-rmse:0.809866+0.021525    test-rmse:1.315205+0.137343 
## [110]    train-rmse:0.805368+0.020827    test-rmse:1.313797+0.137546 
## [111]    train-rmse:0.802350+0.020877    test-rmse:1.313030+0.137717 
## [112]    train-rmse:0.798805+0.021457    test-rmse:1.312486+0.137714 
## [113]    train-rmse:0.795689+0.021388    test-rmse:1.312315+0.137177 
## [114]    train-rmse:0.791951+0.022556    test-rmse:1.310402+0.137509 
## [115]    train-rmse:0.788914+0.022006    test-rmse:1.310447+0.139112 
## [116]    train-rmse:0.785411+0.022190    test-rmse:1.309884+0.138543 
## [117]    train-rmse:0.782362+0.021982    test-rmse:1.308413+0.139194 
## [118]    train-rmse:0.779458+0.022461    test-rmse:1.308717+0.141765 
## [119]    train-rmse:0.776518+0.022361    test-rmse:1.306681+0.141738 
## [120]    train-rmse:0.772444+0.021547    test-rmse:1.304509+0.143898 
## [121]    train-rmse:0.770033+0.021680    test-rmse:1.302470+0.144653 
## [122]    train-rmse:0.766081+0.022219    test-rmse:1.302278+0.145100 
## [123]    train-rmse:0.763704+0.022532    test-rmse:1.302327+0.144385 
## [124]    train-rmse:0.760460+0.023384    test-rmse:1.301457+0.144375 
## [125]    train-rmse:0.758183+0.023236    test-rmse:1.301269+0.145170 
## [126]    train-rmse:0.756133+0.023480    test-rmse:1.301367+0.145144 
## [127]    train-rmse:0.752545+0.023599    test-rmse:1.299502+0.143847 
## [128]    train-rmse:0.747787+0.023679    test-rmse:1.296023+0.141533 
## [129]    train-rmse:0.744471+0.022422    test-rmse:1.294718+0.142240 
## [130]    train-rmse:0.741976+0.022305    test-rmse:1.293738+0.142187 
## [131]    train-rmse:0.738835+0.021602    test-rmse:1.294754+0.144917 
## [132]    train-rmse:0.736842+0.021607    test-rmse:1.294198+0.144712 
## [133]    train-rmse:0.734223+0.021792    test-rmse:1.293616+0.143482 
## [134]    train-rmse:0.731249+0.021206    test-rmse:1.292680+0.143600 
## [135]    train-rmse:0.728340+0.020319    test-rmse:1.291481+0.144487 
## [136]    train-rmse:0.726108+0.020960    test-rmse:1.290517+0.144828 
## [137]    train-rmse:0.723500+0.021386    test-rmse:1.291531+0.144459 
## [138]    train-rmse:0.720463+0.021242    test-rmse:1.290057+0.143506 
## [139]    train-rmse:0.718126+0.021533    test-rmse:1.289882+0.144151 
## [140]    train-rmse:0.715331+0.021500    test-rmse:1.290453+0.144923 
## [141]    train-rmse:0.712652+0.021252    test-rmse:1.289445+0.145036 
## [142]    train-rmse:0.710948+0.020959    test-rmse:1.289663+0.146371 
## [143]    train-rmse:0.708499+0.020316    test-rmse:1.289365+0.147336 
## [144]    train-rmse:0.706043+0.020728    test-rmse:1.288852+0.145322 
## [145]    train-rmse:0.703236+0.021785    test-rmse:1.285628+0.144794 
## [146]    train-rmse:0.700707+0.021997    test-rmse:1.285276+0.145397 
## [147]    train-rmse:0.698931+0.022020    test-rmse:1.285317+0.146611 
## [148]    train-rmse:0.695473+0.020855    test-rmse:1.282514+0.145011 
## [149]    train-rmse:0.693690+0.020553    test-rmse:1.282085+0.145417 
## [150]    train-rmse:0.691407+0.020095    test-rmse:1.280974+0.145511 
## [151]    train-rmse:0.688997+0.019049    test-rmse:1.280843+0.146242 
## [152]    train-rmse:0.686922+0.019688    test-rmse:1.279613+0.145334 
## [153]    train-rmse:0.685142+0.020055    test-rmse:1.279394+0.146286 
## [154]    train-rmse:0.682271+0.020932    test-rmse:1.278085+0.145921 
## [155]    train-rmse:0.680330+0.020739    test-rmse:1.277634+0.145791 
## [156]    train-rmse:0.678725+0.020560    test-rmse:1.277754+0.144715 
## [157]    train-rmse:0.676876+0.020468    test-rmse:1.278210+0.145538 
## [158]    train-rmse:0.675244+0.020290    test-rmse:1.278679+0.144329 
## [159]    train-rmse:0.673783+0.020732    test-rmse:1.279040+0.145960 
## [160]    train-rmse:0.672169+0.020838    test-rmse:1.278517+0.145659 
## [161]    train-rmse:0.670540+0.021139    test-rmse:1.277054+0.143728 
## [162]    train-rmse:0.668594+0.021312    test-rmse:1.277392+0.144238 
## [163]    train-rmse:0.666512+0.021479    test-rmse:1.277035+0.142741 
## [164]    train-rmse:0.664491+0.020580    test-rmse:1.275490+0.142854 
## [165]    train-rmse:0.662945+0.020960    test-rmse:1.275621+0.143832 
## [166]    train-rmse:0.660933+0.021450    test-rmse:1.272530+0.144463 
## [167]    train-rmse:0.658802+0.021418    test-rmse:1.272999+0.143969 
## [168]    train-rmse:0.655716+0.019928    test-rmse:1.272782+0.144378 
## [169]    train-rmse:0.653803+0.020371    test-rmse:1.275463+0.145188 
## [170]    train-rmse:0.651921+0.019620    test-rmse:1.273416+0.146116 
## [171]    train-rmse:0.650708+0.019726    test-rmse:1.273116+0.146484 
## [172]    train-rmse:0.649150+0.019759    test-rmse:1.270910+0.147645 
## [173]    train-rmse:0.647990+0.019799    test-rmse:1.270776+0.147610 
## [174]    train-rmse:0.646520+0.019755    test-rmse:1.271309+0.148662 
## [175]    train-rmse:0.644586+0.019211    test-rmse:1.270926+0.149220 
## [176]    train-rmse:0.641760+0.017810    test-rmse:1.271800+0.149424 
## [177]    train-rmse:0.640249+0.017683    test-rmse:1.271650+0.150214 
## [178]    train-rmse:0.638654+0.017527    test-rmse:1.272157+0.150642 
## [179]    train-rmse:0.637007+0.016919    test-rmse:1.272700+0.149878 
## Stopping. Best iteration:
## [173]    train-rmse:0.647990+0.019799    test-rmse:1.270776+0.147610
## 
## 17 
## [1]  train-rmse:8.416828+0.061919    test-rmse:8.413012+0.268342 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.318333+0.053608    test-rmse:7.316918+0.257360 
## [3]  train-rmse:6.380129+0.046126    test-rmse:6.383760+0.247489 
## [4]  train-rmse:5.580331+0.039350    test-rmse:5.588299+0.240614 
## [5]  train-rmse:4.901447+0.034111    test-rmse:4.912928+0.226484 
## [6]  train-rmse:4.322437+0.029544    test-rmse:4.345181+0.205264 
## [7]  train-rmse:3.828304+0.025730    test-rmse:3.861348+0.180067 
## [8]  train-rmse:3.412123+0.021696    test-rmse:3.456998+0.171092 
## [9]  train-rmse:3.059439+0.016547    test-rmse:3.110062+0.154282 
## [10] train-rmse:2.765167+0.015229    test-rmse:2.839674+0.140314 
## [11] train-rmse:2.517787+0.013031    test-rmse:2.600276+0.128245 
## [12] train-rmse:2.309920+0.012179    test-rmse:2.413734+0.108831 
## [13] train-rmse:2.138373+0.011958    test-rmse:2.264182+0.100889 
## [14] train-rmse:1.995462+0.013678    test-rmse:2.137424+0.087112 
## [15] train-rmse:1.874818+0.012697    test-rmse:2.034254+0.081013 
## [16] train-rmse:1.773403+0.012220    test-rmse:1.947897+0.081212 
## [17] train-rmse:1.690898+0.014686    test-rmse:1.890429+0.069252 
## [18] train-rmse:1.620602+0.015206    test-rmse:1.835329+0.065340 
## [19] train-rmse:1.560693+0.018876    test-rmse:1.790249+0.066389 
## [20] train-rmse:1.508119+0.020612    test-rmse:1.755047+0.063812 
## [21] train-rmse:1.461310+0.019084    test-rmse:1.723517+0.061786 
## [22] train-rmse:1.424926+0.019744    test-rmse:1.701036+0.064037 
## [23] train-rmse:1.393953+0.019846    test-rmse:1.674384+0.064418 
## [24] train-rmse:1.359915+0.020221    test-rmse:1.647625+0.071859 
## [25] train-rmse:1.332550+0.020036    test-rmse:1.632281+0.078843 
## [26] train-rmse:1.306947+0.020541    test-rmse:1.610842+0.084322 
## [27] train-rmse:1.282041+0.021657    test-rmse:1.590676+0.080723 
## [28] train-rmse:1.258338+0.020001    test-rmse:1.577930+0.082371 
## [29] train-rmse:1.236215+0.021741    test-rmse:1.562357+0.078040 
## [30] train-rmse:1.217590+0.021561    test-rmse:1.554705+0.079944 
## [31] train-rmse:1.199108+0.022200    test-rmse:1.544549+0.087056 
## [32] train-rmse:1.180722+0.020394    test-rmse:1.536055+0.092598 
## [33] train-rmse:1.164506+0.018985    test-rmse:1.526236+0.097907 
## [34] train-rmse:1.147022+0.021502    test-rmse:1.513469+0.097021 
## [35] train-rmse:1.129561+0.020263    test-rmse:1.501029+0.101504 
## [36] train-rmse:1.114825+0.021452    test-rmse:1.492466+0.106905 
## [37] train-rmse:1.100560+0.021149    test-rmse:1.484638+0.107825 
## [38] train-rmse:1.086757+0.021065    test-rmse:1.477748+0.106480 
## [39] train-rmse:1.074242+0.021320    test-rmse:1.471389+0.107612 
## [40] train-rmse:1.061975+0.020722    test-rmse:1.463865+0.109709 
## [41] train-rmse:1.047400+0.021366    test-rmse:1.456155+0.114202 
## [42] train-rmse:1.036897+0.020894    test-rmse:1.448488+0.117881 
## [43] train-rmse:1.024401+0.021807    test-rmse:1.439636+0.119849 
## [44] train-rmse:1.012012+0.022753    test-rmse:1.428683+0.122241 
## [45] train-rmse:0.999447+0.022058    test-rmse:1.425143+0.123910 
## [46] train-rmse:0.988330+0.022168    test-rmse:1.420657+0.124474 
## [47] train-rmse:0.978139+0.023521    test-rmse:1.412627+0.122898 
## [48] train-rmse:0.968455+0.022462    test-rmse:1.406658+0.124935 
## [49] train-rmse:0.958416+0.022466    test-rmse:1.405107+0.123930 
## [50] train-rmse:0.948785+0.022390    test-rmse:1.401688+0.127874 
## [51] train-rmse:0.938171+0.023598    test-rmse:1.395166+0.127779 
## [52] train-rmse:0.927260+0.023179    test-rmse:1.391835+0.130477 
## [53] train-rmse:0.919077+0.022672    test-rmse:1.385415+0.132986 
## [54] train-rmse:0.909109+0.022160    test-rmse:1.379149+0.130650 
## [55] train-rmse:0.899743+0.022241    test-rmse:1.376421+0.130612 
## [56] train-rmse:0.891326+0.024088    test-rmse:1.375426+0.132373 
## [57] train-rmse:0.882967+0.024501    test-rmse:1.372212+0.133943 
## [58] train-rmse:0.874538+0.024347    test-rmse:1.369716+0.133859 
## [59] train-rmse:0.866500+0.024092    test-rmse:1.367234+0.137642 
## [60] train-rmse:0.859623+0.023986    test-rmse:1.363558+0.137685 
## [61] train-rmse:0.852113+0.024626    test-rmse:1.360684+0.139556 
## [62] train-rmse:0.845015+0.023527    test-rmse:1.357445+0.140927 
## [63] train-rmse:0.837568+0.023764    test-rmse:1.354858+0.142278 
## [64] train-rmse:0.829559+0.023264    test-rmse:1.352611+0.143195 
## [65] train-rmse:0.822217+0.022429    test-rmse:1.346156+0.145259 
## [66] train-rmse:0.815772+0.023301    test-rmse:1.343782+0.143805 
## [67] train-rmse:0.808518+0.021933    test-rmse:1.341820+0.144984 
## [68] train-rmse:0.802385+0.021105    test-rmse:1.338162+0.146046 
## [69] train-rmse:0.797085+0.021970    test-rmse:1.334569+0.145106 
## [70] train-rmse:0.789678+0.022733    test-rmse:1.329309+0.146054 
## [71] train-rmse:0.784253+0.021830    test-rmse:1.330012+0.147822 
## [72] train-rmse:0.778911+0.021264    test-rmse:1.330472+0.150278 
## [73] train-rmse:0.773555+0.020383    test-rmse:1.328124+0.150430 
## [74] train-rmse:0.766471+0.020502    test-rmse:1.326376+0.151578 
## [75] train-rmse:0.760256+0.021335    test-rmse:1.322408+0.149543 
## [76] train-rmse:0.754961+0.021502    test-rmse:1.318967+0.150646 
## [77] train-rmse:0.749871+0.021101    test-rmse:1.317584+0.151376 
## [78] train-rmse:0.743436+0.019853    test-rmse:1.317032+0.152448 
## [79] train-rmse:0.738321+0.019863    test-rmse:1.315247+0.151839 
## [80] train-rmse:0.732889+0.018580    test-rmse:1.315368+0.154182 
## [81] train-rmse:0.727124+0.018599    test-rmse:1.315382+0.155110 
## [82] train-rmse:0.721997+0.017538    test-rmse:1.312612+0.155308 
## [83] train-rmse:0.716878+0.018298    test-rmse:1.311238+0.157342 
## [84] train-rmse:0.710726+0.017086    test-rmse:1.308448+0.158478 
## [85] train-rmse:0.705193+0.019134    test-rmse:1.306187+0.158261 
## [86] train-rmse:0.699791+0.018855    test-rmse:1.305980+0.158259 
## [87] train-rmse:0.692421+0.018193    test-rmse:1.304685+0.157587 
## [88] train-rmse:0.688109+0.018873    test-rmse:1.303972+0.158508 
## [89] train-rmse:0.683643+0.018552    test-rmse:1.302874+0.160312 
## [90] train-rmse:0.679886+0.017645    test-rmse:1.301482+0.160789 
## [91] train-rmse:0.674846+0.018561    test-rmse:1.302505+0.162350 
## [92] train-rmse:0.669884+0.018280    test-rmse:1.302696+0.163500 
## [93] train-rmse:0.665553+0.017683    test-rmse:1.300783+0.163068 
## [94] train-rmse:0.661275+0.017951    test-rmse:1.299757+0.164120 
## [95] train-rmse:0.656877+0.018862    test-rmse:1.298605+0.164294 
## [96] train-rmse:0.652492+0.018482    test-rmse:1.297167+0.163366 
## [97] train-rmse:0.649013+0.019145    test-rmse:1.296022+0.164049 
## [98] train-rmse:0.644898+0.018185    test-rmse:1.295142+0.165250 
## [99] train-rmse:0.641863+0.017454    test-rmse:1.293260+0.166552 
## [100]    train-rmse:0.636859+0.017090    test-rmse:1.291931+0.166767 
## [101]    train-rmse:0.633038+0.018353    test-rmse:1.291963+0.166706 
## [102]    train-rmse:0.628334+0.016965    test-rmse:1.293320+0.168630 
## [103]    train-rmse:0.624964+0.016540    test-rmse:1.291545+0.168053 
## [104]    train-rmse:0.621833+0.016160    test-rmse:1.291053+0.168432 
## [105]    train-rmse:0.618748+0.016824    test-rmse:1.291832+0.168430 
## [106]    train-rmse:0.616108+0.016383    test-rmse:1.292908+0.168163 
## [107]    train-rmse:0.612543+0.015449    test-rmse:1.294024+0.168941 
## [108]    train-rmse:0.609650+0.016019    test-rmse:1.294213+0.170361 
## [109]    train-rmse:0.605979+0.017179    test-rmse:1.292810+0.169301 
## [110]    train-rmse:0.602667+0.018066    test-rmse:1.292528+0.169364 
## Stopping. Best iteration:
## [104]    train-rmse:0.621833+0.016160    test-rmse:1.291053+0.168432
## 
## 18 
## [1]  train-rmse:8.414624+0.062147    test-rmse:8.413201+0.268625 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.312891+0.053867    test-rmse:7.317948+0.252161 
## [3]  train-rmse:6.371178+0.046648    test-rmse:6.385164+0.242531 
## [4]  train-rmse:5.567149+0.040337    test-rmse:5.579473+0.229377 
## [5]  train-rmse:4.880200+0.034274    test-rmse:4.900421+0.209184 
## [6]  train-rmse:4.294722+0.030302    test-rmse:4.321350+0.183302 
## [7]  train-rmse:3.796572+0.026260    test-rmse:3.831689+0.169194 
## [8]  train-rmse:3.371763+0.023754    test-rmse:3.418629+0.149520 
## [9]  train-rmse:3.013335+0.021513    test-rmse:3.078430+0.142601 
## [10] train-rmse:2.709109+0.017869    test-rmse:2.794117+0.130515 
## [11] train-rmse:2.451383+0.016747    test-rmse:2.545826+0.124288 
## [12] train-rmse:2.238483+0.014606    test-rmse:2.358674+0.114788 
## [13] train-rmse:2.057362+0.014147    test-rmse:2.201004+0.110816 
## [14] train-rmse:1.900003+0.016081    test-rmse:2.067742+0.095564 
## [15] train-rmse:1.772854+0.016209    test-rmse:1.953323+0.095891 
## [16] train-rmse:1.661489+0.019014    test-rmse:1.868981+0.081988 
## [17] train-rmse:1.569428+0.019523    test-rmse:1.800112+0.077452 
## [18] train-rmse:1.490798+0.019250    test-rmse:1.741685+0.076168 
## [19] train-rmse:1.423295+0.022212    test-rmse:1.689650+0.072578 
## [20] train-rmse:1.365154+0.018286    test-rmse:1.649220+0.076409 
## [21] train-rmse:1.314326+0.020317    test-rmse:1.609086+0.085600 
## [22] train-rmse:1.271581+0.019642    test-rmse:1.577484+0.083840 
## [23] train-rmse:1.231813+0.023956    test-rmse:1.555582+0.082246 
## [24] train-rmse:1.196538+0.023110    test-rmse:1.536045+0.083293 
## [25] train-rmse:1.165923+0.024796    test-rmse:1.523978+0.090014 
## [26] train-rmse:1.137126+0.026524    test-rmse:1.505001+0.093423 
## [27] train-rmse:1.110239+0.025021    test-rmse:1.488917+0.095775 
## [28] train-rmse:1.082508+0.026163    test-rmse:1.475355+0.101036 
## [29] train-rmse:1.060546+0.027003    test-rmse:1.462243+0.104432 
## [30] train-rmse:1.041830+0.026225    test-rmse:1.456059+0.109052 
## [31] train-rmse:1.023188+0.027554    test-rmse:1.441863+0.108757 
## [32] train-rmse:1.003068+0.030271    test-rmse:1.429790+0.110139 
## [33] train-rmse:0.987344+0.030021    test-rmse:1.421909+0.112556 
## [34] train-rmse:0.969714+0.027034    test-rmse:1.412256+0.114098 
## [35] train-rmse:0.953889+0.027402    test-rmse:1.397695+0.116568 
## [36] train-rmse:0.938330+0.028070    test-rmse:1.386909+0.118929 
## [37] train-rmse:0.922209+0.024715    test-rmse:1.376590+0.126007 
## [38] train-rmse:0.907320+0.025327    test-rmse:1.370794+0.131283 
## [39] train-rmse:0.893355+0.023025    test-rmse:1.365074+0.130859 
## [40] train-rmse:0.878329+0.023511    test-rmse:1.357448+0.132546 
## [41] train-rmse:0.864158+0.024785    test-rmse:1.347791+0.135919 
## [42] train-rmse:0.851770+0.024368    test-rmse:1.344452+0.135346 
## [43] train-rmse:0.839052+0.021563    test-rmse:1.338446+0.137941 
## [44] train-rmse:0.827425+0.020918    test-rmse:1.334008+0.136420 
## [45] train-rmse:0.816292+0.019609    test-rmse:1.328255+0.139137 
## [46] train-rmse:0.804167+0.019314    test-rmse:1.325693+0.138950 
## [47] train-rmse:0.792972+0.020436    test-rmse:1.317654+0.138400 
## [48] train-rmse:0.782867+0.018721    test-rmse:1.315198+0.138603 
## [49] train-rmse:0.772227+0.017537    test-rmse:1.310198+0.139360 
## [50] train-rmse:0.762210+0.018798    test-rmse:1.303721+0.140570 
## [51] train-rmse:0.752283+0.019185    test-rmse:1.303832+0.141395 
## [52] train-rmse:0.743911+0.018722    test-rmse:1.301252+0.143460 
## [53] train-rmse:0.734748+0.019818    test-rmse:1.297769+0.143246 
## [54] train-rmse:0.727016+0.018433    test-rmse:1.295352+0.145921 
## [55] train-rmse:0.717325+0.020063    test-rmse:1.291481+0.146653 
## [56] train-rmse:0.708149+0.020407    test-rmse:1.287226+0.147825 
## [57] train-rmse:0.699636+0.021462    test-rmse:1.282561+0.148027 
## [58] train-rmse:0.692180+0.020296    test-rmse:1.280539+0.149132 
## [59] train-rmse:0.684501+0.020577    test-rmse:1.276164+0.150521 
## [60] train-rmse:0.677525+0.022383    test-rmse:1.273473+0.150130 
## [61] train-rmse:0.669800+0.022012    test-rmse:1.271754+0.150047 
## [62] train-rmse:0.661912+0.020763    test-rmse:1.267418+0.152081 
## [63] train-rmse:0.654509+0.022024    test-rmse:1.264510+0.154086 
## [64] train-rmse:0.648362+0.020671    test-rmse:1.265409+0.153726 
## [65] train-rmse:0.642200+0.021567    test-rmse:1.264188+0.153164 
## [66] train-rmse:0.635775+0.021754    test-rmse:1.262595+0.154154 
## [67] train-rmse:0.629232+0.020640    test-rmse:1.259623+0.154466 
## [68] train-rmse:0.623318+0.021369    test-rmse:1.257707+0.153164 
## [69] train-rmse:0.617825+0.020546    test-rmse:1.257477+0.154724 
## [70] train-rmse:0.610889+0.019486    test-rmse:1.256126+0.156510 
## [71] train-rmse:0.605443+0.019737    test-rmse:1.256445+0.155934 
## [72] train-rmse:0.600476+0.020493    test-rmse:1.257087+0.156708 
## [73] train-rmse:0.595484+0.019509    test-rmse:1.256122+0.157506 
## [74] train-rmse:0.589560+0.019403    test-rmse:1.255030+0.159323 
## [75] train-rmse:0.585704+0.019510    test-rmse:1.253169+0.160525 
## [76] train-rmse:0.580777+0.018941    test-rmse:1.253923+0.159874 
## [77] train-rmse:0.574683+0.017873    test-rmse:1.251893+0.161409 
## [78] train-rmse:0.570227+0.018102    test-rmse:1.251857+0.161435 
## [79] train-rmse:0.565009+0.017375    test-rmse:1.251190+0.161344 
## [80] train-rmse:0.558388+0.017833    test-rmse:1.248906+0.160567 
## [81] train-rmse:0.554996+0.017689    test-rmse:1.249772+0.161782 
## [82] train-rmse:0.550982+0.016848    test-rmse:1.249030+0.162938 
## [83] train-rmse:0.546226+0.018144    test-rmse:1.246708+0.162571 
## [84] train-rmse:0.541772+0.017690    test-rmse:1.247864+0.162592 
## [85] train-rmse:0.537409+0.017455    test-rmse:1.247829+0.163017 
## [86] train-rmse:0.533447+0.017666    test-rmse:1.248915+0.164468 
## [87] train-rmse:0.529648+0.017830    test-rmse:1.250041+0.164951 
## [88] train-rmse:0.524600+0.018295    test-rmse:1.247853+0.162819 
## [89] train-rmse:0.520001+0.018204    test-rmse:1.246236+0.164910 
## [90] train-rmse:0.516189+0.018992    test-rmse:1.244499+0.165214 
## [91] train-rmse:0.512814+0.018469    test-rmse:1.242846+0.166554 
## [92] train-rmse:0.509619+0.018265    test-rmse:1.242814+0.165895 
## [93] train-rmse:0.506042+0.019497    test-rmse:1.243277+0.165567 
## [94] train-rmse:0.503270+0.019004    test-rmse:1.242717+0.165740 
## [95] train-rmse:0.499994+0.019407    test-rmse:1.242565+0.166940 
## [96] train-rmse:0.496811+0.019437    test-rmse:1.242106+0.167804 
## [97] train-rmse:0.494142+0.019327    test-rmse:1.242442+0.168623 
## [98] train-rmse:0.489796+0.019365    test-rmse:1.241015+0.166832 
## [99] train-rmse:0.485038+0.019573    test-rmse:1.239092+0.166142 
## [100]    train-rmse:0.479881+0.018824    test-rmse:1.239578+0.167712 
## [101]    train-rmse:0.476040+0.018827    test-rmse:1.238387+0.167396 
## [102]    train-rmse:0.473241+0.018271    test-rmse:1.237445+0.166725 
## [103]    train-rmse:0.468369+0.019173    test-rmse:1.237242+0.166793 
## [104]    train-rmse:0.466329+0.019106    test-rmse:1.236478+0.167162 
## [105]    train-rmse:0.462312+0.018928    test-rmse:1.236280+0.166567 
## [106]    train-rmse:0.459704+0.018997    test-rmse:1.236242+0.166912 
## [107]    train-rmse:0.456081+0.017349    test-rmse:1.235351+0.166659 
## [108]    train-rmse:0.454321+0.018052    test-rmse:1.236350+0.166510 
## [109]    train-rmse:0.451535+0.017693    test-rmse:1.235596+0.166612 
## [110]    train-rmse:0.447781+0.017479    test-rmse:1.233345+0.166988 
## [111]    train-rmse:0.444630+0.017531    test-rmse:1.231977+0.166406 
## [112]    train-rmse:0.441661+0.017312    test-rmse:1.232375+0.166098 
## [113]    train-rmse:0.439984+0.016992    test-rmse:1.232126+0.166096 
## [114]    train-rmse:0.436820+0.017611    test-rmse:1.232053+0.165847 
## [115]    train-rmse:0.433188+0.017363    test-rmse:1.230910+0.164731 
## [116]    train-rmse:0.431451+0.017265    test-rmse:1.230932+0.165244 
## [117]    train-rmse:0.428886+0.017482    test-rmse:1.230697+0.165563 
## [118]    train-rmse:0.425915+0.018097    test-rmse:1.230761+0.165714 
## [119]    train-rmse:0.423545+0.018580    test-rmse:1.230018+0.166093 
## [120]    train-rmse:0.420498+0.019179    test-rmse:1.229320+0.165806 
## [121]    train-rmse:0.417715+0.019941    test-rmse:1.227194+0.165347 
## [122]    train-rmse:0.415660+0.019864    test-rmse:1.227919+0.164674 
## [123]    train-rmse:0.412787+0.018725    test-rmse:1.228084+0.165716 
## [124]    train-rmse:0.410798+0.018546    test-rmse:1.228379+0.165376 
## [125]    train-rmse:0.408098+0.018089    test-rmse:1.228340+0.166150 
## [126]    train-rmse:0.405968+0.018446    test-rmse:1.227850+0.165961 
## [127]    train-rmse:0.402719+0.018543    test-rmse:1.226088+0.165343 
## [128]    train-rmse:0.400201+0.019200    test-rmse:1.225509+0.165399 
## [129]    train-rmse:0.398225+0.019680    test-rmse:1.226362+0.165032 
## [130]    train-rmse:0.394172+0.019119    test-rmse:1.224614+0.163178 
## [131]    train-rmse:0.392355+0.019315    test-rmse:1.224450+0.162819 
## [132]    train-rmse:0.389419+0.018172    test-rmse:1.224961+0.163560 
## [133]    train-rmse:0.387449+0.018187    test-rmse:1.224904+0.164032 
## [134]    train-rmse:0.385142+0.017796    test-rmse:1.225952+0.164596 
## [135]    train-rmse:0.381288+0.018252    test-rmse:1.225589+0.164197 
## [136]    train-rmse:0.379229+0.018299    test-rmse:1.226303+0.164803 
## [137]    train-rmse:0.377496+0.018323    test-rmse:1.225817+0.164381 
## Stopping. Best iteration:
## [131]    train-rmse:0.392355+0.019315    test-rmse:1.224450+0.162819
## 
## 19 
## [1]  train-rmse:8.414179+0.062258    test-rmse:8.413727+0.264087 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.311039+0.053910    test-rmse:7.317775+0.253045 
## [3]  train-rmse:6.366366+0.047261    test-rmse:6.380102+0.239374 
## [4]  train-rmse:5.557974+0.040698    test-rmse:5.577010+0.221682 
## [5]  train-rmse:4.866925+0.035157    test-rmse:4.892786+0.203985 
## [6]  train-rmse:4.277521+0.032288    test-rmse:4.302446+0.185683 
## [7]  train-rmse:3.771630+0.028563    test-rmse:3.803053+0.166036 
## [8]  train-rmse:3.340733+0.025083    test-rmse:3.378763+0.155826 
## [9]  train-rmse:2.972933+0.021480    test-rmse:3.030739+0.145295 
## [10] train-rmse:2.661926+0.020537    test-rmse:2.743451+0.133460 
## [11] train-rmse:2.400121+0.017454    test-rmse:2.507819+0.134047 
## [12] train-rmse:2.177309+0.016694    test-rmse:2.312062+0.121412 
## [13] train-rmse:1.984105+0.020667    test-rmse:2.135511+0.108814 
## [14] train-rmse:1.823970+0.018382    test-rmse:2.006811+0.101919 
## [15] train-rmse:1.684839+0.020101    test-rmse:1.896297+0.091000 
## [16] train-rmse:1.568612+0.022016    test-rmse:1.807902+0.085174 
## [17] train-rmse:1.465174+0.022450    test-rmse:1.716051+0.090570 
## [18] train-rmse:1.380137+0.023206    test-rmse:1.663865+0.087279 
## [19] train-rmse:1.307878+0.022994    test-rmse:1.605508+0.084902 
## [20] train-rmse:1.244362+0.025078    test-rmse:1.562654+0.086776 
## [21] train-rmse:1.189738+0.025064    test-rmse:1.523393+0.095693 
## [22] train-rmse:1.141550+0.022049    test-rmse:1.494726+0.097736 
## [23] train-rmse:1.100841+0.020360    test-rmse:1.469120+0.101798 
## [24] train-rmse:1.061746+0.020614    test-rmse:1.441236+0.102476 
## [25] train-rmse:1.026191+0.020321    test-rmse:1.423386+0.108321 
## [26] train-rmse:0.993858+0.021077    test-rmse:1.409273+0.109656 
## [27] train-rmse:0.966348+0.022273    test-rmse:1.392891+0.108385 
## [28] train-rmse:0.940357+0.022071    test-rmse:1.380293+0.115457 
## [29] train-rmse:0.915322+0.025170    test-rmse:1.362902+0.116032 
## [30] train-rmse:0.893017+0.026641    test-rmse:1.356433+0.118880 
## [31] train-rmse:0.872469+0.027948    test-rmse:1.344851+0.118646 
## [32] train-rmse:0.853398+0.024940    test-rmse:1.336648+0.124969 
## [33] train-rmse:0.833792+0.024107    test-rmse:1.329833+0.127852 
## [34] train-rmse:0.818120+0.024525    test-rmse:1.324705+0.130308 
## [35] train-rmse:0.799941+0.023262    test-rmse:1.317740+0.132055 
## [36] train-rmse:0.784924+0.023757    test-rmse:1.312324+0.134410 
## [37] train-rmse:0.770751+0.021135    test-rmse:1.306425+0.136899 
## [38] train-rmse:0.754451+0.022244    test-rmse:1.305694+0.136235 
## [39] train-rmse:0.741377+0.021531    test-rmse:1.300569+0.139316 
## [40] train-rmse:0.727835+0.019959    test-rmse:1.292134+0.139750 
## [41] train-rmse:0.716396+0.021659    test-rmse:1.286243+0.138455 
## [42] train-rmse:0.703855+0.020257    test-rmse:1.283171+0.142960 
## [43] train-rmse:0.691647+0.020202    test-rmse:1.279404+0.144768 
## [44] train-rmse:0.680789+0.020816    test-rmse:1.276532+0.144346 
## [45] train-rmse:0.668289+0.019440    test-rmse:1.269009+0.147158 
## [46] train-rmse:0.659408+0.019547    test-rmse:1.268501+0.152908 
## [47] train-rmse:0.648594+0.019551    test-rmse:1.263309+0.151490 
## [48] train-rmse:0.637445+0.019622    test-rmse:1.260308+0.152584 
## [49] train-rmse:0.626714+0.019546    test-rmse:1.259647+0.150448 
## [50] train-rmse:0.618443+0.018349    test-rmse:1.257110+0.152713 
## [51] train-rmse:0.610051+0.018053    test-rmse:1.256355+0.151588 
## [52] train-rmse:0.600949+0.018672    test-rmse:1.252469+0.150060 
## [53] train-rmse:0.592615+0.018873    test-rmse:1.247980+0.152188 
## [54] train-rmse:0.584498+0.019361    test-rmse:1.243617+0.152549 
## [55] train-rmse:0.576963+0.018096    test-rmse:1.240295+0.153840 
## [56] train-rmse:0.569254+0.019926    test-rmse:1.241236+0.152958 
## [57] train-rmse:0.560835+0.018595    test-rmse:1.237912+0.156153 
## [58] train-rmse:0.553796+0.019021    test-rmse:1.237270+0.157742 
## [59] train-rmse:0.547441+0.019851    test-rmse:1.236506+0.158232 
## [60] train-rmse:0.539505+0.019143    test-rmse:1.235861+0.156644 
## [61] train-rmse:0.533814+0.018833    test-rmse:1.234412+0.158072 
## [62] train-rmse:0.528000+0.018918    test-rmse:1.234238+0.159946 
## [63] train-rmse:0.520939+0.017977    test-rmse:1.234206+0.160535 
## [64] train-rmse:0.513465+0.017425    test-rmse:1.231272+0.161638 
## [65] train-rmse:0.507863+0.017235    test-rmse:1.231625+0.162621 
## [66] train-rmse:0.502428+0.016971    test-rmse:1.230136+0.165856 
## [67] train-rmse:0.498091+0.017080    test-rmse:1.230252+0.165287 
## [68] train-rmse:0.492232+0.016430    test-rmse:1.230867+0.165981 
## [69] train-rmse:0.483460+0.018189    test-rmse:1.226859+0.164949 
## [70] train-rmse:0.478477+0.018099    test-rmse:1.226602+0.166093 
## [71] train-rmse:0.473300+0.018248    test-rmse:1.226973+0.163576 
## [72] train-rmse:0.467966+0.017317    test-rmse:1.224742+0.164344 
## [73] train-rmse:0.462794+0.017645    test-rmse:1.224163+0.165288 
## [74] train-rmse:0.457448+0.017149    test-rmse:1.224731+0.164709 
## [75] train-rmse:0.453087+0.017268    test-rmse:1.223376+0.164433 
## [76] train-rmse:0.449168+0.017974    test-rmse:1.221622+0.163487 
## [77] train-rmse:0.444888+0.018332    test-rmse:1.220378+0.164468 
## [78] train-rmse:0.439679+0.018174    test-rmse:1.218810+0.165159 
## [79] train-rmse:0.433913+0.016556    test-rmse:1.219326+0.167528 
## [80] train-rmse:0.430521+0.016772    test-rmse:1.218812+0.167599 
## [81] train-rmse:0.426986+0.016778    test-rmse:1.218159+0.167615 
## [82] train-rmse:0.421453+0.014511    test-rmse:1.217332+0.166968 
## [83] train-rmse:0.417333+0.014603    test-rmse:1.216910+0.166205 
## [84] train-rmse:0.413404+0.015138    test-rmse:1.217507+0.167406 
## [85] train-rmse:0.409182+0.016983    test-rmse:1.216721+0.167343 
## [86] train-rmse:0.403115+0.015975    test-rmse:1.215134+0.167870 
## [87] train-rmse:0.400011+0.015483    test-rmse:1.214757+0.169264 
## [88] train-rmse:0.397100+0.015918    test-rmse:1.214066+0.169942 
## [89] train-rmse:0.393883+0.015651    test-rmse:1.212891+0.170834 
## [90] train-rmse:0.389906+0.016535    test-rmse:1.212799+0.171312 
## [91] train-rmse:0.387584+0.016486    test-rmse:1.213238+0.170749 
## [92] train-rmse:0.384569+0.015979    test-rmse:1.212357+0.171268 
## [93] train-rmse:0.382219+0.016714    test-rmse:1.211219+0.170860 
## [94] train-rmse:0.378953+0.017211    test-rmse:1.211619+0.171834 
## [95] train-rmse:0.374581+0.018467    test-rmse:1.211043+0.171387 
## [96] train-rmse:0.371974+0.018901    test-rmse:1.211253+0.170945 
## [97] train-rmse:0.367068+0.015954    test-rmse:1.211269+0.170460 
## [98] train-rmse:0.363591+0.017196    test-rmse:1.210391+0.170580 
## [99] train-rmse:0.360618+0.018005    test-rmse:1.210949+0.170882 
## [100]    train-rmse:0.357496+0.019115    test-rmse:1.210754+0.170550 
## [101]    train-rmse:0.354919+0.018927    test-rmse:1.210939+0.170397 
## [102]    train-rmse:0.351576+0.018649    test-rmse:1.210633+0.170322 
## [103]    train-rmse:0.347630+0.018262    test-rmse:1.211858+0.169600 
## [104]    train-rmse:0.344469+0.019406    test-rmse:1.210888+0.169387 
## Stopping. Best iteration:
## [98] train-rmse:0.363591+0.017196    test-rmse:1.210391+0.170580
## 
## 20 
## [1]  train-rmse:8.414179+0.062258    test-rmse:8.413727+0.264087 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.310448+0.054060    test-rmse:7.318706+0.244898 
## [3]  train-rmse:6.364224+0.047489    test-rmse:6.378444+0.229779 
## [4]  train-rmse:5.553561+0.040829    test-rmse:5.576745+0.206176 
## [5]  train-rmse:4.859259+0.035335    test-rmse:4.884487+0.181970 
## [6]  train-rmse:4.266186+0.032149    test-rmse:4.297749+0.168526 
## [7]  train-rmse:3.756373+0.028485    test-rmse:3.791426+0.154260 
## [8]  train-rmse:3.320130+0.025043    test-rmse:3.365415+0.137886 
## [9]  train-rmse:2.948978+0.021485    test-rmse:3.015403+0.133706 
## [10] train-rmse:2.632114+0.019620    test-rmse:2.716818+0.124584 
## [11] train-rmse:2.362404+0.017878    test-rmse:2.468000+0.125506 
## [12] train-rmse:2.129447+0.017855    test-rmse:2.262105+0.111506 
## [13] train-rmse:1.932257+0.013908    test-rmse:2.089486+0.102481 
## [14] train-rmse:1.764641+0.014945    test-rmse:1.961517+0.094123 
## [15] train-rmse:1.618898+0.012549    test-rmse:1.847736+0.094925 
## [16] train-rmse:1.494984+0.011345    test-rmse:1.745650+0.097021 
## [17] train-rmse:1.390483+0.009728    test-rmse:1.668308+0.096533 
## [18] train-rmse:1.298247+0.011791    test-rmse:1.601517+0.093919 
## [19] train-rmse:1.219993+0.010326    test-rmse:1.550861+0.099146 
## [20] train-rmse:1.150732+0.013796    test-rmse:1.507418+0.099382 
## [21] train-rmse:1.092776+0.014865    test-rmse:1.473864+0.100823 
## [22] train-rmse:1.039909+0.013203    test-rmse:1.439047+0.103995 
## [23] train-rmse:0.995620+0.013194    test-rmse:1.419598+0.110780 
## [24] train-rmse:0.953185+0.012394    test-rmse:1.396197+0.118549 
## [25] train-rmse:0.919630+0.011657    test-rmse:1.378795+0.121368 
## [26] train-rmse:0.887917+0.012548    test-rmse:1.362907+0.126992 
## [27] train-rmse:0.857377+0.014645    test-rmse:1.345683+0.128475 
## [28] train-rmse:0.833363+0.013379    test-rmse:1.337742+0.135024 
## [29] train-rmse:0.811003+0.015274    test-rmse:1.328627+0.133162 
## [30] train-rmse:0.787503+0.012880    test-rmse:1.315849+0.142159 
## [31] train-rmse:0.767306+0.011875    test-rmse:1.309229+0.145822 
## [32] train-rmse:0.746065+0.011953    test-rmse:1.299689+0.147819 
## [33] train-rmse:0.728846+0.011140    test-rmse:1.293610+0.150138 
## [34] train-rmse:0.711847+0.014825    test-rmse:1.285563+0.146613 
## [35] train-rmse:0.695543+0.015943    test-rmse:1.281210+0.147931 
## [36] train-rmse:0.677764+0.013958    test-rmse:1.280532+0.150357 
## [37] train-rmse:0.663430+0.013163    test-rmse:1.277573+0.152396 
## [38] train-rmse:0.648010+0.011891    test-rmse:1.269640+0.154868 
## [39] train-rmse:0.634794+0.010882    test-rmse:1.268570+0.153811 
## [40] train-rmse:0.623167+0.009069    test-rmse:1.267542+0.155162 
## [41] train-rmse:0.611400+0.009617    test-rmse:1.267957+0.160374 
## [42] train-rmse:0.596585+0.010469    test-rmse:1.262590+0.160751 
## [43] train-rmse:0.585683+0.010007    test-rmse:1.262184+0.162119 
## [44] train-rmse:0.575189+0.010075    test-rmse:1.257694+0.161788 
## [45] train-rmse:0.564348+0.008112    test-rmse:1.257065+0.164403 
## [46] train-rmse:0.554545+0.008109    test-rmse:1.256026+0.164397 
## [47] train-rmse:0.545586+0.008260    test-rmse:1.252613+0.166058 
## [48] train-rmse:0.536439+0.007486    test-rmse:1.247115+0.165360 
## [49] train-rmse:0.526525+0.008390    test-rmse:1.245377+0.163692 
## [50] train-rmse:0.518150+0.008652    test-rmse:1.244155+0.165463 
## [51] train-rmse:0.509584+0.009236    test-rmse:1.240024+0.164711 
## [52] train-rmse:0.501455+0.010175    test-rmse:1.241762+0.164627 
## [53] train-rmse:0.494802+0.010036    test-rmse:1.240426+0.165772 
## [54] train-rmse:0.487609+0.009416    test-rmse:1.240988+0.167518 
## [55] train-rmse:0.480372+0.009344    test-rmse:1.241107+0.167082 
## [56] train-rmse:0.472295+0.009087    test-rmse:1.240355+0.166997 
## [57] train-rmse:0.465350+0.009652    test-rmse:1.237666+0.167500 
## [58] train-rmse:0.458543+0.010001    test-rmse:1.236220+0.169282 
## [59] train-rmse:0.453765+0.011220    test-rmse:1.234326+0.169952 
## [60] train-rmse:0.445930+0.009088    test-rmse:1.231038+0.170528 
## [61] train-rmse:0.440213+0.009268    test-rmse:1.230305+0.172146 
## [62] train-rmse:0.433562+0.009963    test-rmse:1.227487+0.172470 
## [63] train-rmse:0.428354+0.009402    test-rmse:1.227016+0.172594 
## [64] train-rmse:0.419193+0.007776    test-rmse:1.228034+0.171754 
## [65] train-rmse:0.414466+0.007231    test-rmse:1.228592+0.172572 
## [66] train-rmse:0.410959+0.007495    test-rmse:1.228467+0.173638 
## [67] train-rmse:0.405520+0.008442    test-rmse:1.228304+0.172042 
## [68] train-rmse:0.401661+0.009378    test-rmse:1.228071+0.173468 
## [69] train-rmse:0.397746+0.010251    test-rmse:1.227397+0.173552 
## Stopping. Best iteration:
## [63] train-rmse:0.428354+0.009402    test-rmse:1.227016+0.172594
## 
## 21 
## [1]  train-rmse:8.264722+0.059069    test-rmse:8.260608+0.273191 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.073244+0.048071    test-rmse:7.065033+0.269355 
## [3]  train-rmse:6.089241+0.038642    test-rmse:6.086336+0.263827 
## [4]  train-rmse:5.281084+0.030897    test-rmse:5.276163+0.253816 
## [5]  train-rmse:4.622433+0.024635    test-rmse:4.625573+0.237674 
## [6]  train-rmse:4.090524+0.019187    test-rmse:4.097756+0.221690 
## [7]  train-rmse:3.665491+0.015760    test-rmse:3.677271+0.207129 
## [8]  train-rmse:3.328571+0.013045    test-rmse:3.348549+0.177905 
## [9]  train-rmse:3.064749+0.012045    test-rmse:3.086331+0.153614 
## [10] train-rmse:2.860173+0.012327    test-rmse:2.881776+0.135343 
## [11] train-rmse:2.702459+0.012789    test-rmse:2.724357+0.115209 
## [12] train-rmse:2.581458+0.013442    test-rmse:2.614429+0.087619 
## [13] train-rmse:2.488857+0.013831    test-rmse:2.523697+0.075512 
## [14] train-rmse:2.417808+0.014537    test-rmse:2.450856+0.061864 
## [15] train-rmse:2.362590+0.015222    test-rmse:2.398023+0.053872 
## [16] train-rmse:2.319879+0.016052    test-rmse:2.360780+0.049052 
## [17] train-rmse:2.285959+0.016506    test-rmse:2.330815+0.049435 
## [18] train-rmse:2.258737+0.017193    test-rmse:2.304313+0.051389 
## [19] train-rmse:2.236241+0.017807    test-rmse:2.283692+0.054849 
## [20] train-rmse:2.217015+0.018401    test-rmse:2.260762+0.059002 
## [21] train-rmse:2.200428+0.019060    test-rmse:2.247334+0.063171 
## [22] train-rmse:2.186011+0.019781    test-rmse:2.235395+0.066213 
## [23] train-rmse:2.173058+0.020157    test-rmse:2.223333+0.063974 
## [24] train-rmse:2.160904+0.020298    test-rmse:2.218050+0.070247 
## [25] train-rmse:2.149581+0.020710    test-rmse:2.206257+0.072115 
## [26] train-rmse:2.138919+0.021323    test-rmse:2.195226+0.075771 
## [27] train-rmse:2.128977+0.021749    test-rmse:2.184594+0.080796 
## [28] train-rmse:2.119537+0.022119    test-rmse:2.174098+0.078879 
## [29] train-rmse:2.110436+0.022677    test-rmse:2.168091+0.080038 
## [30] train-rmse:2.101678+0.022974    test-rmse:2.158993+0.082488 
## [31] train-rmse:2.092921+0.023281    test-rmse:2.151039+0.080619 
## [32] train-rmse:2.084494+0.023570    test-rmse:2.144335+0.086325 
## [33] train-rmse:2.076189+0.023948    test-rmse:2.138327+0.087443 
## [34] train-rmse:2.067982+0.024346    test-rmse:2.132627+0.087681 
## [35] train-rmse:2.060092+0.024690    test-rmse:2.129590+0.088139 
## [36] train-rmse:2.052474+0.025078    test-rmse:2.115748+0.087040 
## [37] train-rmse:2.044850+0.025429    test-rmse:2.112136+0.086950 
## [38] train-rmse:2.037422+0.025915    test-rmse:2.102116+0.091175 
## [39] train-rmse:2.030179+0.026268    test-rmse:2.093215+0.091172 
## [40] train-rmse:2.023009+0.026581    test-rmse:2.087288+0.089762 
## [41] train-rmse:2.016003+0.026984    test-rmse:2.079663+0.085365 
## [42] train-rmse:2.009112+0.027399    test-rmse:2.077659+0.087542 
## [43] train-rmse:2.002338+0.027866    test-rmse:2.073677+0.089709 
## [44] train-rmse:1.995810+0.028093    test-rmse:2.069704+0.087023 
## [45] train-rmse:1.989383+0.028392    test-rmse:2.064668+0.089512 
## [46] train-rmse:1.982907+0.028713    test-rmse:2.058481+0.090973 
## [47] train-rmse:1.976580+0.028914    test-rmse:2.052641+0.091776 
## [48] train-rmse:1.970269+0.029202    test-rmse:2.046775+0.090574 
## [49] train-rmse:1.964110+0.029397    test-rmse:2.043572+0.095602 
## [50] train-rmse:1.958012+0.029700    test-rmse:2.036933+0.093751 
## [51] train-rmse:1.951946+0.029992    test-rmse:2.032458+0.093219 
## [52] train-rmse:1.945968+0.030231    test-rmse:2.027351+0.093095 
## [53] train-rmse:1.940118+0.030545    test-rmse:2.017442+0.094538 
## [54] train-rmse:1.934389+0.030834    test-rmse:2.009981+0.090070 
## [55] train-rmse:1.928812+0.031092    test-rmse:2.006818+0.088871 
## [56] train-rmse:1.923319+0.031415    test-rmse:2.001433+0.088290 
## [57] train-rmse:1.917961+0.031670    test-rmse:1.995854+0.085489 
## [58] train-rmse:1.912599+0.032022    test-rmse:1.990740+0.084580 
## [59] train-rmse:1.907341+0.032358    test-rmse:1.987162+0.084432 
## [60] train-rmse:1.902148+0.032724    test-rmse:1.987528+0.084393 
## [61] train-rmse:1.896949+0.033063    test-rmse:1.983179+0.083870 
## [62] train-rmse:1.891784+0.033355    test-rmse:1.979562+0.083793 
## [63] train-rmse:1.886679+0.033709    test-rmse:1.975408+0.085908 
## [64] train-rmse:1.881625+0.034030    test-rmse:1.969264+0.085632 
## [65] train-rmse:1.876624+0.034371    test-rmse:1.960637+0.090361 
## [66] train-rmse:1.871800+0.034701    test-rmse:1.953299+0.089043 
## [67] train-rmse:1.866957+0.035018    test-rmse:1.946616+0.089891 
## [68] train-rmse:1.862198+0.035320    test-rmse:1.946501+0.092710 
## [69] train-rmse:1.857512+0.035598    test-rmse:1.943232+0.092972 
## [70] train-rmse:1.852875+0.035930    test-rmse:1.936031+0.090991 
## [71] train-rmse:1.848315+0.036208    test-rmse:1.933142+0.089643 
## [72] train-rmse:1.843831+0.036470    test-rmse:1.930841+0.089743 
## [73] train-rmse:1.839335+0.036756    test-rmse:1.929619+0.090595 
## [74] train-rmse:1.834947+0.037005    test-rmse:1.926084+0.090933 
## [75] train-rmse:1.830583+0.037291    test-rmse:1.922068+0.088743 
## [76] train-rmse:1.826205+0.037439    test-rmse:1.919749+0.089955 
## [77] train-rmse:1.821833+0.037594    test-rmse:1.917304+0.092330 
## [78] train-rmse:1.817508+0.037814    test-rmse:1.915797+0.090691 
## [79] train-rmse:1.813256+0.038033    test-rmse:1.911429+0.090759 
## [80] train-rmse:1.809020+0.038212    test-rmse:1.906931+0.091295 
## [81] train-rmse:1.804906+0.038462    test-rmse:1.902069+0.092549 
## [82] train-rmse:1.800864+0.038761    test-rmse:1.898151+0.091817 
## [83] train-rmse:1.796863+0.039018    test-rmse:1.892679+0.091244 
## [84] train-rmse:1.792904+0.039314    test-rmse:1.889353+0.090750 
## [85] train-rmse:1.788975+0.039555    test-rmse:1.886968+0.092844 
## [86] train-rmse:1.785127+0.039779    test-rmse:1.883282+0.091907 
## [87] train-rmse:1.781289+0.040010    test-rmse:1.881928+0.093500 
## [88] train-rmse:1.777480+0.040271    test-rmse:1.880356+0.095518 
## [89] train-rmse:1.773711+0.040526    test-rmse:1.876281+0.096241 
## [90] train-rmse:1.769966+0.040768    test-rmse:1.874539+0.096554 
## [91] train-rmse:1.766282+0.040960    test-rmse:1.872409+0.099748 
## [92] train-rmse:1.762580+0.041195    test-rmse:1.866753+0.099879 
## [93] train-rmse:1.758906+0.041436    test-rmse:1.861405+0.098459 
## [94] train-rmse:1.755299+0.041655    test-rmse:1.859276+0.099091 
## [95] train-rmse:1.751714+0.041869    test-rmse:1.856872+0.099147 
## [96] train-rmse:1.748142+0.042117    test-rmse:1.853598+0.097988 
## [97] train-rmse:1.744594+0.042288    test-rmse:1.849428+0.099403 
## [98] train-rmse:1.741100+0.042451    test-rmse:1.845982+0.099791 
## [99] train-rmse:1.737654+0.042640    test-rmse:1.841178+0.097094 
## [100]    train-rmse:1.734265+0.042783    test-rmse:1.837716+0.096936 
## [101]    train-rmse:1.730922+0.042976    test-rmse:1.834829+0.097762 
## [102]    train-rmse:1.727625+0.043103    test-rmse:1.832243+0.099777 
## [103]    train-rmse:1.724305+0.043245    test-rmse:1.829156+0.096531 
## [104]    train-rmse:1.721010+0.043434    test-rmse:1.827193+0.097498 
## [105]    train-rmse:1.717784+0.043623    test-rmse:1.825417+0.098193 
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## [314]    train-rmse:1.402041+0.053412    test-rmse:1.543038+0.171323 
## [315]    train-rmse:1.401418+0.053410    test-rmse:1.543210+0.171745 
## [316]    train-rmse:1.400800+0.053409    test-rmse:1.542501+0.171849 
## [317]    train-rmse:1.400179+0.053411    test-rmse:1.541933+0.171801 
## [318]    train-rmse:1.399567+0.053411    test-rmse:1.542251+0.171348 
## [319]    train-rmse:1.398965+0.053413    test-rmse:1.541524+0.170443 
## [320]    train-rmse:1.398358+0.053421    test-rmse:1.541291+0.171489 
## [321]    train-rmse:1.397757+0.053418    test-rmse:1.540853+0.171751 
## [322]    train-rmse:1.397164+0.053424    test-rmse:1.540169+0.171675 
## [323]    train-rmse:1.396573+0.053429    test-rmse:1.539497+0.171924 
## [324]    train-rmse:1.395979+0.053435    test-rmse:1.539029+0.172422 
## [325]    train-rmse:1.395380+0.053430    test-rmse:1.538067+0.172256 
## [326]    train-rmse:1.394789+0.053429    test-rmse:1.537118+0.173222 
## [327]    train-rmse:1.394215+0.053431    test-rmse:1.536981+0.173078 
## [328]    train-rmse:1.393647+0.053427    test-rmse:1.535669+0.173040 
## [329]    train-rmse:1.393076+0.053427    test-rmse:1.536162+0.173295 
## [330]    train-rmse:1.392503+0.053420    test-rmse:1.535554+0.173451 
## [331]    train-rmse:1.391934+0.053419    test-rmse:1.535199+0.173575 
## [332]    train-rmse:1.391372+0.053417    test-rmse:1.535201+0.174421 
## [333]    train-rmse:1.390817+0.053409    test-rmse:1.534508+0.174262 
## [334]    train-rmse:1.390276+0.053411    test-rmse:1.534593+0.174488 
## [335]    train-rmse:1.389733+0.053410    test-rmse:1.532700+0.175313 
## [336]    train-rmse:1.389197+0.053409    test-rmse:1.532443+0.174021 
## [337]    train-rmse:1.388667+0.053404    test-rmse:1.531705+0.173948 
## [338]    train-rmse:1.388141+0.053398    test-rmse:1.530566+0.174051 
## [339]    train-rmse:1.387614+0.053385    test-rmse:1.530593+0.174227 
## [340]    train-rmse:1.387097+0.053383    test-rmse:1.530149+0.174713 
## [341]    train-rmse:1.386580+0.053382    test-rmse:1.528152+0.174609 
## [342]    train-rmse:1.386070+0.053382    test-rmse:1.529942+0.175770 
## [343]    train-rmse:1.385564+0.053388    test-rmse:1.529897+0.175730 
## [344]    train-rmse:1.385043+0.053389    test-rmse:1.528808+0.175366 
## [345]    train-rmse:1.384531+0.053388    test-rmse:1.528483+0.175320 
## [346]    train-rmse:1.384024+0.053386    test-rmse:1.527453+0.175455 
## [347]    train-rmse:1.383524+0.053389    test-rmse:1.526994+0.175549 
## [348]    train-rmse:1.383021+0.053392    test-rmse:1.526663+0.175467 
## [349]    train-rmse:1.382526+0.053392    test-rmse:1.525971+0.176012 
## [350]    train-rmse:1.382040+0.053397    test-rmse:1.524978+0.175593 
## [351]    train-rmse:1.381552+0.053402    test-rmse:1.524508+0.175488 
## [352]    train-rmse:1.381072+0.053406    test-rmse:1.524636+0.175620 
## [353]    train-rmse:1.380596+0.053405    test-rmse:1.523886+0.177818 
## [354]    train-rmse:1.380112+0.053418    test-rmse:1.524190+0.178008 
## [355]    train-rmse:1.379633+0.053418    test-rmse:1.523504+0.178229 
## [356]    train-rmse:1.379159+0.053416    test-rmse:1.522855+0.178480 
## [357]    train-rmse:1.378693+0.053411    test-rmse:1.523107+0.179356 
## [358]    train-rmse:1.378229+0.053407    test-rmse:1.522536+0.178859 
## [359]    train-rmse:1.377757+0.053390    test-rmse:1.521801+0.178995 
## [360]    train-rmse:1.377292+0.053380    test-rmse:1.522068+0.179286 
## [361]    train-rmse:1.376840+0.053376    test-rmse:1.521809+0.179288 
## [362]    train-rmse:1.376392+0.053374    test-rmse:1.521743+0.180862 
## [363]    train-rmse:1.375949+0.053369    test-rmse:1.521056+0.181034 
## [364]    train-rmse:1.375506+0.053368    test-rmse:1.520632+0.181288 
## [365]    train-rmse:1.375061+0.053367    test-rmse:1.520661+0.180839 
## [366]    train-rmse:1.374618+0.053368    test-rmse:1.519727+0.180741 
## [367]    train-rmse:1.374173+0.053369    test-rmse:1.520196+0.180565 
## [368]    train-rmse:1.373735+0.053363    test-rmse:1.520100+0.180474 
## [369]    train-rmse:1.373307+0.053360    test-rmse:1.519660+0.180127 
## [370]    train-rmse:1.372882+0.053357    test-rmse:1.520012+0.180408 
## [371]    train-rmse:1.372453+0.053354    test-rmse:1.519434+0.181115 
## [372]    train-rmse:1.372028+0.053348    test-rmse:1.519769+0.180887 
## [373]    train-rmse:1.371606+0.053345    test-rmse:1.518854+0.180670 
## [374]    train-rmse:1.371191+0.053342    test-rmse:1.519198+0.181083 
## [375]    train-rmse:1.370780+0.053341    test-rmse:1.518888+0.181256 
## [376]    train-rmse:1.370372+0.053339    test-rmse:1.518677+0.181150 
## [377]    train-rmse:1.369969+0.053337    test-rmse:1.517771+0.180497 
## [378]    train-rmse:1.369565+0.053337    test-rmse:1.517623+0.180699 
## [379]    train-rmse:1.369162+0.053334    test-rmse:1.517324+0.181254 
## [380]    train-rmse:1.368764+0.053330    test-rmse:1.515887+0.181076 
## [381]    train-rmse:1.368367+0.053325    test-rmse:1.515421+0.181023 
## [382]    train-rmse:1.367969+0.053319    test-rmse:1.515759+0.180826 
## [383]    train-rmse:1.367581+0.053318    test-rmse:1.515232+0.180990 
## [384]    train-rmse:1.367191+0.053320    test-rmse:1.515104+0.180438 
## [385]    train-rmse:1.366805+0.053321    test-rmse:1.513516+0.181735 
## [386]    train-rmse:1.366420+0.053326    test-rmse:1.513119+0.182721 
## [387]    train-rmse:1.366036+0.053333    test-rmse:1.513386+0.182436 
## [388]    train-rmse:1.365656+0.053337    test-rmse:1.512345+0.182600 
## [389]    train-rmse:1.365286+0.053338    test-rmse:1.512778+0.182806 
## [390]    train-rmse:1.364914+0.053336    test-rmse:1.512649+0.183566 
## [391]    train-rmse:1.364543+0.053328    test-rmse:1.511485+0.183822 
## [392]    train-rmse:1.364179+0.053324    test-rmse:1.510443+0.183518 
## [393]    train-rmse:1.363815+0.053323    test-rmse:1.511953+0.184339 
## [394]    train-rmse:1.363453+0.053321    test-rmse:1.511356+0.184586 
## [395]    train-rmse:1.363096+0.053318    test-rmse:1.511352+0.184277 
## [396]    train-rmse:1.362735+0.053314    test-rmse:1.510914+0.184402 
## [397]    train-rmse:1.362374+0.053311    test-rmse:1.510574+0.184374 
## [398]    train-rmse:1.362017+0.053309    test-rmse:1.511159+0.185584 
## Stopping. Best iteration:
## [392]    train-rmse:1.364179+0.053324    test-rmse:1.510443+0.183518
## 
## 22 
## [1]  train-rmse:8.244913+0.059351    test-rmse:8.241126+0.269653 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.035620+0.051202    test-rmse:7.027877+0.257810 
## [3]  train-rmse:6.035524+0.044837    test-rmse:6.029809+0.242337 
## [4]  train-rmse:5.203720+0.035496    test-rmse:5.206150+0.236013 
## [5]  train-rmse:4.524140+0.030788    test-rmse:4.536401+0.217767 
## [6]  train-rmse:3.967544+0.028415    test-rmse:3.987373+0.195860 
## [7]  train-rmse:3.516766+0.026908    test-rmse:3.542907+0.173206 
## [8]  train-rmse:3.151321+0.026916    test-rmse:3.190709+0.148479 
## [9]  train-rmse:2.862866+0.026947    test-rmse:2.909006+0.128938 
## [10] train-rmse:2.626852+0.024349    test-rmse:2.687727+0.103315 
## [11] train-rmse:2.446084+0.023842    test-rmse:2.520274+0.085670 
## [12] train-rmse:2.305069+0.023005    test-rmse:2.387446+0.058306 
## [13] train-rmse:2.189657+0.025504    test-rmse:2.284608+0.038529 
## [14] train-rmse:2.101989+0.025849    test-rmse:2.204603+0.041235 
## [15] train-rmse:2.033881+0.026303    test-rmse:2.144431+0.036720 
## [16] train-rmse:1.979362+0.024889    test-rmse:2.104565+0.046184 
## [17] train-rmse:1.935504+0.024742    test-rmse:2.063782+0.054559 
## [18] train-rmse:1.897989+0.025119    test-rmse:2.030834+0.063647 
## [19] train-rmse:1.866939+0.025561    test-rmse:2.010768+0.071269 
## [20] train-rmse:1.840646+0.024967    test-rmse:1.988763+0.080368 
## [21] train-rmse:1.817213+0.025655    test-rmse:1.963680+0.085561 
## [22] train-rmse:1.797164+0.025965    test-rmse:1.950771+0.087138 
## [23] train-rmse:1.778781+0.026275    test-rmse:1.933408+0.087786 
## [24] train-rmse:1.761267+0.026149    test-rmse:1.921777+0.092046 
## [25] train-rmse:1.745465+0.026466    test-rmse:1.913347+0.095318 
## [26] train-rmse:1.726892+0.029797    test-rmse:1.891117+0.101540 
## [27] train-rmse:1.711932+0.031733    test-rmse:1.879219+0.112709 
## [28] train-rmse:1.697667+0.032378    test-rmse:1.869772+0.109784 
## [29] train-rmse:1.684519+0.031716    test-rmse:1.857961+0.108340 
## [30] train-rmse:1.672027+0.032603    test-rmse:1.848446+0.106907 
## [31] train-rmse:1.659395+0.032070    test-rmse:1.837046+0.103699 
## [32] train-rmse:1.647190+0.032555    test-rmse:1.833760+0.105083 
## [33] train-rmse:1.636231+0.032968    test-rmse:1.823906+0.104789 
## [34] train-rmse:1.623823+0.033963    test-rmse:1.813132+0.111665 
## [35] train-rmse:1.613050+0.035076    test-rmse:1.807043+0.114256 
## [36] train-rmse:1.602904+0.035136    test-rmse:1.798872+0.112015 
## [37] train-rmse:1.587056+0.029379    test-rmse:1.788132+0.118683 
## [38] train-rmse:1.572101+0.027338    test-rmse:1.772804+0.113339 
## [39] train-rmse:1.561483+0.029040    test-rmse:1.766643+0.112371 
## [40] train-rmse:1.547745+0.027862    test-rmse:1.750718+0.111786 
## [41] train-rmse:1.536426+0.027195    test-rmse:1.741032+0.114139 
## [42] train-rmse:1.527705+0.027444    test-rmse:1.734368+0.113591 
## [43] train-rmse:1.519003+0.027548    test-rmse:1.726312+0.114488 
## [44] train-rmse:1.509774+0.027957    test-rmse:1.719074+0.111210 
## [45] train-rmse:1.498560+0.029590    test-rmse:1.710231+0.117297 
## [46] train-rmse:1.488781+0.029670    test-rmse:1.709934+0.119249 
## [47] train-rmse:1.479790+0.029993    test-rmse:1.707410+0.118559 
## [48] train-rmse:1.471765+0.030446    test-rmse:1.700035+0.120671 
## [49] train-rmse:1.463384+0.031014    test-rmse:1.691578+0.123395 
## [50] train-rmse:1.455783+0.031564    test-rmse:1.685287+0.121706 
## [51] train-rmse:1.447579+0.030747    test-rmse:1.675477+0.124033 
## [52] train-rmse:1.440231+0.030828    test-rmse:1.667994+0.125263 
## [53] train-rmse:1.431913+0.031192    test-rmse:1.664982+0.123121 
## [54] train-rmse:1.423948+0.030521    test-rmse:1.661303+0.123177 
## [55] train-rmse:1.416435+0.030467    test-rmse:1.654117+0.122841 
## [56] train-rmse:1.408075+0.030379    test-rmse:1.647459+0.126807 
## [57] train-rmse:1.400898+0.030204    test-rmse:1.645434+0.130241 
## [58] train-rmse:1.391078+0.033719    test-rmse:1.639945+0.130566 
## [59] train-rmse:1.384003+0.034521    test-rmse:1.633149+0.130376 
## [60] train-rmse:1.377184+0.035038    test-rmse:1.630291+0.128870 
## [61] train-rmse:1.369854+0.034767    test-rmse:1.625167+0.130247 
## [62] train-rmse:1.363057+0.035096    test-rmse:1.619193+0.130873 
## [63] train-rmse:1.355958+0.035120    test-rmse:1.614199+0.131728 
## [64] train-rmse:1.344856+0.040862    test-rmse:1.608526+0.131980 
## [65] train-rmse:1.339074+0.041275    test-rmse:1.603278+0.130496 
## [66] train-rmse:1.332701+0.041223    test-rmse:1.601036+0.131060 
## [67] train-rmse:1.327149+0.041367    test-rmse:1.594023+0.131437 
## [68] train-rmse:1.319846+0.043308    test-rmse:1.589037+0.132854 
## [69] train-rmse:1.313484+0.043008    test-rmse:1.582383+0.134173 
## [70] train-rmse:1.307203+0.043027    test-rmse:1.579309+0.133892 
## [71] train-rmse:1.301281+0.043393    test-rmse:1.575482+0.133857 
## [72] train-rmse:1.295794+0.043818    test-rmse:1.573343+0.133319 
## [73] train-rmse:1.290841+0.043947    test-rmse:1.572512+0.135342 
## [74] train-rmse:1.285473+0.043743    test-rmse:1.569506+0.135705 
## [75] train-rmse:1.279964+0.043554    test-rmse:1.566753+0.138216 
## [76] train-rmse:1.270602+0.037483    test-rmse:1.559092+0.142037 
## [77] train-rmse:1.265331+0.037307    test-rmse:1.553254+0.143833 
## [78] train-rmse:1.260201+0.036979    test-rmse:1.550105+0.144287 
## [79] train-rmse:1.250373+0.033837    test-rmse:1.537623+0.124773 
## [80] train-rmse:1.243934+0.034168    test-rmse:1.535447+0.127775 
## [81] train-rmse:1.238845+0.034004    test-rmse:1.533640+0.126072 
## [82] train-rmse:1.234317+0.034430    test-rmse:1.530334+0.127276 
## [83] train-rmse:1.226144+0.033941    test-rmse:1.520021+0.112376 
## [84] train-rmse:1.221677+0.034305    test-rmse:1.512378+0.113865 
## [85] train-rmse:1.216679+0.034928    test-rmse:1.508636+0.113967 
## [86] train-rmse:1.208487+0.036258    test-rmse:1.501804+0.109098 
## [87] train-rmse:1.200696+0.040725    test-rmse:1.498975+0.108924 
## [88] train-rmse:1.195494+0.040257    test-rmse:1.494727+0.108737 
## [89] train-rmse:1.191055+0.039908    test-rmse:1.491436+0.109595 
## [90] train-rmse:1.186923+0.040006    test-rmse:1.489478+0.112096 
## [91] train-rmse:1.182588+0.041000    test-rmse:1.487049+0.110812 
## [92] train-rmse:1.178105+0.040413    test-rmse:1.483043+0.112539 
## [93] train-rmse:1.171136+0.035220    test-rmse:1.477220+0.117373 
## [94] train-rmse:1.166875+0.035061    test-rmse:1.474670+0.118748 
## [95] train-rmse:1.163069+0.035154    test-rmse:1.470227+0.115411 
## [96] train-rmse:1.158899+0.035628    test-rmse:1.465589+0.115621 
## [97] train-rmse:1.154866+0.035692    test-rmse:1.464247+0.117417 
## [98] train-rmse:1.148524+0.036778    test-rmse:1.460752+0.114722 
## [99] train-rmse:1.144043+0.037202    test-rmse:1.459496+0.114815 
## [100]    train-rmse:1.139990+0.037986    test-rmse:1.459271+0.115967 
## [101]    train-rmse:1.136115+0.038431    test-rmse:1.455716+0.118215 
## [102]    train-rmse:1.132651+0.037893    test-rmse:1.450506+0.120501 
## [103]    train-rmse:1.129302+0.037611    test-rmse:1.448009+0.121692 
## [104]    train-rmse:1.126036+0.037360    test-rmse:1.448270+0.120855 
## [105]    train-rmse:1.121663+0.038159    test-rmse:1.446244+0.121394 
## [106]    train-rmse:1.116372+0.040192    test-rmse:1.440040+0.117472 
## [107]    train-rmse:1.108476+0.041507    test-rmse:1.431546+0.107076 
## [108]    train-rmse:1.104983+0.041702    test-rmse:1.428035+0.106256 
## [109]    train-rmse:1.099755+0.043077    test-rmse:1.423239+0.103950 
## [110]    train-rmse:1.096410+0.043346    test-rmse:1.422236+0.103385 
## [111]    train-rmse:1.093597+0.043284    test-rmse:1.422121+0.102578 
## [112]    train-rmse:1.090592+0.043544    test-rmse:1.419867+0.102364 
## [113]    train-rmse:1.083517+0.037422    test-rmse:1.418266+0.106817 
## [114]    train-rmse:1.080542+0.037017    test-rmse:1.416057+0.108880 
## [115]    train-rmse:1.075547+0.034109    test-rmse:1.413710+0.112587 
## [116]    train-rmse:1.072432+0.034359    test-rmse:1.411339+0.113401 
## [117]    train-rmse:1.068020+0.035634    test-rmse:1.408856+0.111757 
## [118]    train-rmse:1.065268+0.035938    test-rmse:1.403921+0.114944 
## [119]    train-rmse:1.062430+0.035964    test-rmse:1.401960+0.114476 
## [120]    train-rmse:1.059569+0.035410    test-rmse:1.399783+0.114119 
## [121]    train-rmse:1.056248+0.035319    test-rmse:1.396888+0.115573 
## [122]    train-rmse:1.053597+0.034954    test-rmse:1.395224+0.118600 
## [123]    train-rmse:1.051141+0.035050    test-rmse:1.397165+0.118693 
## [124]    train-rmse:1.047028+0.033030    test-rmse:1.392815+0.120007 
## [125]    train-rmse:1.044347+0.032725    test-rmse:1.390890+0.120850 
## [126]    train-rmse:1.039562+0.031098    test-rmse:1.388484+0.121588 
## [127]    train-rmse:1.037091+0.031266    test-rmse:1.389385+0.123235 
## [128]    train-rmse:1.034543+0.031012    test-rmse:1.388375+0.126981 
## [129]    train-rmse:1.031931+0.031338    test-rmse:1.385366+0.129105 
## [130]    train-rmse:1.026411+0.032587    test-rmse:1.382476+0.129142 
## [131]    train-rmse:1.022935+0.033465    test-rmse:1.379408+0.127833 
## [132]    train-rmse:1.020376+0.033847    test-rmse:1.378378+0.128822 
## [133]    train-rmse:1.018243+0.033723    test-rmse:1.376703+0.129674 
## [134]    train-rmse:1.016228+0.033788    test-rmse:1.374526+0.129414 
## [135]    train-rmse:1.013727+0.034234    test-rmse:1.375353+0.129341 
## [136]    train-rmse:1.011763+0.034033    test-rmse:1.374630+0.130245 
## [137]    train-rmse:1.009361+0.033719    test-rmse:1.375007+0.130906 
## [138]    train-rmse:1.003850+0.037332    test-rmse:1.370361+0.129085 
## [139]    train-rmse:1.001684+0.037589    test-rmse:1.367710+0.129347 
## [140]    train-rmse:0.999495+0.037840    test-rmse:1.365562+0.127929 
## [141]    train-rmse:0.997095+0.037983    test-rmse:1.364655+0.128998 
## [142]    train-rmse:0.994947+0.038000    test-rmse:1.363590+0.128934 
## [143]    train-rmse:0.993027+0.037865    test-rmse:1.360403+0.130840 
## [144]    train-rmse:0.988414+0.039763    test-rmse:1.353732+0.126473 
## [145]    train-rmse:0.985788+0.040031    test-rmse:1.354420+0.129492 
## [146]    train-rmse:0.981232+0.041903    test-rmse:1.352994+0.129965 
## [147]    train-rmse:0.975699+0.037679    test-rmse:1.350571+0.133162 
## [148]    train-rmse:0.973817+0.037323    test-rmse:1.350560+0.135197 
## [149]    train-rmse:0.971685+0.037402    test-rmse:1.350320+0.135304 
## [150]    train-rmse:0.969651+0.037111    test-rmse:1.348568+0.134668 
## [151]    train-rmse:0.967179+0.037462    test-rmse:1.345973+0.135976 
## [152]    train-rmse:0.965437+0.037351    test-rmse:1.345781+0.135862 
## [153]    train-rmse:0.963864+0.037178    test-rmse:1.345607+0.135155 
## [154]    train-rmse:0.959675+0.037879    test-rmse:1.343740+0.135728 
## [155]    train-rmse:0.956879+0.039341    test-rmse:1.340489+0.133884 
## [156]    train-rmse:0.954635+0.039027    test-rmse:1.339822+0.134378 
## [157]    train-rmse:0.952482+0.038191    test-rmse:1.338417+0.134728 
## [158]    train-rmse:0.950070+0.037959    test-rmse:1.338536+0.134546 
## [159]    train-rmse:0.948289+0.037944    test-rmse:1.337839+0.135847 
## [160]    train-rmse:0.946279+0.038020    test-rmse:1.337035+0.134876 
## [161]    train-rmse:0.944166+0.038390    test-rmse:1.338617+0.136221 
## [162]    train-rmse:0.942402+0.038280    test-rmse:1.338247+0.135882 
## [163]    train-rmse:0.940728+0.038213    test-rmse:1.337157+0.136426 
## [164]    train-rmse:0.939000+0.038628    test-rmse:1.336167+0.137704 
## [165]    train-rmse:0.937118+0.038526    test-rmse:1.336304+0.139299 
## [166]    train-rmse:0.935526+0.038252    test-rmse:1.336058+0.138336 
## [167]    train-rmse:0.933892+0.038358    test-rmse:1.334220+0.138747 
## [168]    train-rmse:0.932239+0.038045    test-rmse:1.332767+0.139595 
## [169]    train-rmse:0.930580+0.038232    test-rmse:1.331871+0.140352 
## [170]    train-rmse:0.929337+0.038126    test-rmse:1.332024+0.140224 
## [171]    train-rmse:0.927984+0.038230    test-rmse:1.332332+0.141556 
## [172]    train-rmse:0.926359+0.038394    test-rmse:1.332580+0.141741 
## [173]    train-rmse:0.924169+0.037649    test-rmse:1.331555+0.140514 
## [174]    train-rmse:0.922599+0.037440    test-rmse:1.329902+0.140779 
## [175]    train-rmse:0.921319+0.037449    test-rmse:1.331148+0.141387 
## [176]    train-rmse:0.920108+0.037366    test-rmse:1.331126+0.141071 
## [177]    train-rmse:0.914776+0.034869    test-rmse:1.328290+0.142221 
## [178]    train-rmse:0.913303+0.034839    test-rmse:1.327070+0.143866 
## [179]    train-rmse:0.911652+0.034736    test-rmse:1.325345+0.144948 
## [180]    train-rmse:0.910294+0.034759    test-rmse:1.326059+0.146777 
## [181]    train-rmse:0.908817+0.034728    test-rmse:1.326974+0.147979 
## [182]    train-rmse:0.907500+0.034679    test-rmse:1.325691+0.147186 
## [183]    train-rmse:0.905698+0.034922    test-rmse:1.324555+0.147244 
## [184]    train-rmse:0.902048+0.032674    test-rmse:1.322765+0.147707 
## [185]    train-rmse:0.900814+0.032587    test-rmse:1.322221+0.148488 
## [186]    train-rmse:0.899312+0.032490    test-rmse:1.321103+0.149930 
## [187]    train-rmse:0.898255+0.032406    test-rmse:1.320406+0.150304 
## [188]    train-rmse:0.896407+0.033381    test-rmse:1.320170+0.150298 
## [189]    train-rmse:0.891206+0.028182    test-rmse:1.310443+0.150496 
## [190]    train-rmse:0.889476+0.027796    test-rmse:1.309880+0.150105 
## [191]    train-rmse:0.887698+0.027566    test-rmse:1.309544+0.150217 
## [192]    train-rmse:0.885982+0.028027    test-rmse:1.309053+0.149659 
## [193]    train-rmse:0.884555+0.027991    test-rmse:1.309528+0.150998 
## [194]    train-rmse:0.883251+0.027941    test-rmse:1.311512+0.152621 
## [195]    train-rmse:0.882003+0.028133    test-rmse:1.313144+0.152287 
## [196]    train-rmse:0.880892+0.027871    test-rmse:1.313765+0.153600 
## [197]    train-rmse:0.879642+0.027955    test-rmse:1.314194+0.155141 
## [198]    train-rmse:0.878477+0.027788    test-rmse:1.313122+0.156062 
## Stopping. Best iteration:
## [192]    train-rmse:0.885982+0.028027    test-rmse:1.309053+0.149659
## 
## 23 
## [1]  train-rmse:8.237223+0.060198    test-rmse:8.233657+0.265253 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.015877+0.049685    test-rmse:7.009700+0.254524 
## [3]  train-rmse:6.002563+0.042775    test-rmse:6.000282+0.241801 
## [4]  train-rmse:5.162975+0.035612    test-rmse:5.169400+0.224466 
## [5]  train-rmse:4.468253+0.031624    test-rmse:4.487491+0.200933 
## [6]  train-rmse:3.895492+0.026479    test-rmse:3.926548+0.192199 
## [7]  train-rmse:3.424670+0.022201    test-rmse:3.462898+0.171764 
## [8]  train-rmse:3.043152+0.019490    test-rmse:3.098740+0.144362 
## [9]  train-rmse:2.734006+0.019089    test-rmse:2.800809+0.126570 
## [10] train-rmse:2.486555+0.015045    test-rmse:2.571553+0.117360 
## [11] train-rmse:2.286755+0.014639    test-rmse:2.395572+0.101031 
## [12] train-rmse:2.129410+0.015159    test-rmse:2.257554+0.095458 
## [13] train-rmse:1.999719+0.011459    test-rmse:2.146611+0.094227 
## [14] train-rmse:1.897595+0.012755    test-rmse:2.050355+0.081606 
## [15] train-rmse:1.814720+0.012574    test-rmse:1.984999+0.079124 
## [16] train-rmse:1.750224+0.013609    test-rmse:1.931688+0.075458 
## [17] train-rmse:1.696036+0.013906    test-rmse:1.880255+0.076738 
## [18] train-rmse:1.651916+0.015005    test-rmse:1.846397+0.072444 
## [19] train-rmse:1.611415+0.016486    test-rmse:1.821654+0.068479 
## [20] train-rmse:1.577610+0.015977    test-rmse:1.797700+0.068962 
## [21] train-rmse:1.548058+0.016568    test-rmse:1.780707+0.069496 
## [22] train-rmse:1.520287+0.019144    test-rmse:1.759792+0.074763 
## [23] train-rmse:1.497046+0.021702    test-rmse:1.739408+0.074413 
## [24] train-rmse:1.475385+0.022709    test-rmse:1.726617+0.067708 
## [25] train-rmse:1.451601+0.021571    test-rmse:1.713803+0.072255 
## [26] train-rmse:1.432005+0.021118    test-rmse:1.696622+0.070120 
## [27] train-rmse:1.414408+0.022212    test-rmse:1.686549+0.075435 
## [28] train-rmse:1.394716+0.023599    test-rmse:1.671433+0.072548 
## [29] train-rmse:1.379155+0.023290    test-rmse:1.660173+0.080257 
## [30] train-rmse:1.363050+0.024373    test-rmse:1.649585+0.085871 
## [31] train-rmse:1.347248+0.022431    test-rmse:1.640591+0.084765 
## [32] train-rmse:1.331456+0.021292    test-rmse:1.625235+0.084638 
## [33] train-rmse:1.317407+0.021015    test-rmse:1.620210+0.083630 
## [34] train-rmse:1.301519+0.024727    test-rmse:1.609565+0.083448 
## [35] train-rmse:1.287816+0.024461    test-rmse:1.601067+0.084562 
## [36] train-rmse:1.273277+0.026812    test-rmse:1.588077+0.076918 
## [37] train-rmse:1.262000+0.026285    test-rmse:1.578553+0.080619 
## [38] train-rmse:1.249475+0.025516    test-rmse:1.574318+0.082828 
## [39] train-rmse:1.232807+0.022698    test-rmse:1.565823+0.089446 
## [40] train-rmse:1.221008+0.022615    test-rmse:1.557725+0.089899 
## [41] train-rmse:1.210180+0.021570    test-rmse:1.551132+0.092368 
## [42] train-rmse:1.198839+0.021638    test-rmse:1.542736+0.092844 
## [43] train-rmse:1.187253+0.020261    test-rmse:1.530517+0.094409 
## [44] train-rmse:1.174444+0.022292    test-rmse:1.521955+0.098291 
## [45] train-rmse:1.164617+0.023083    test-rmse:1.521017+0.098831 
## [46] train-rmse:1.153407+0.021349    test-rmse:1.513447+0.101783 
## [47] train-rmse:1.142339+0.019939    test-rmse:1.502157+0.102818 
## [48] train-rmse:1.133477+0.019740    test-rmse:1.497159+0.102831 
## [49] train-rmse:1.123336+0.019497    test-rmse:1.486754+0.108869 
## [50] train-rmse:1.114514+0.019650    test-rmse:1.480960+0.107639 
## [51] train-rmse:1.105196+0.020808    test-rmse:1.478508+0.110172 
## [52] train-rmse:1.094813+0.020668    test-rmse:1.474861+0.113922 
## [53] train-rmse:1.086762+0.021464    test-rmse:1.471107+0.111966 
## [54] train-rmse:1.077770+0.021680    test-rmse:1.467388+0.114872 
## [55] train-rmse:1.068063+0.023306    test-rmse:1.461111+0.113316 
## [56] train-rmse:1.058692+0.021981    test-rmse:1.457833+0.113924 
## [57] train-rmse:1.051150+0.021958    test-rmse:1.455177+0.114590 
## [58] train-rmse:1.041999+0.021303    test-rmse:1.448540+0.116204 
## [59] train-rmse:1.034133+0.021009    test-rmse:1.442551+0.118229 
## [60] train-rmse:1.025291+0.021517    test-rmse:1.430582+0.117237 
## [61] train-rmse:1.017813+0.021651    test-rmse:1.425746+0.115526 
## [62] train-rmse:1.009847+0.022650    test-rmse:1.425233+0.120061 
## [63] train-rmse:1.001782+0.023558    test-rmse:1.420133+0.117723 
## [64] train-rmse:0.993215+0.023900    test-rmse:1.420139+0.118654 
## [65] train-rmse:0.982641+0.027315    test-rmse:1.413475+0.120529 
## [66] train-rmse:0.974186+0.026187    test-rmse:1.408681+0.119491 
## [67] train-rmse:0.966396+0.027029    test-rmse:1.401887+0.120971 
## [68] train-rmse:0.959966+0.027076    test-rmse:1.400041+0.123324 
## [69] train-rmse:0.952662+0.027123    test-rmse:1.396057+0.127112 
## [70] train-rmse:0.945152+0.027216    test-rmse:1.394386+0.131058 
## [71] train-rmse:0.938570+0.026183    test-rmse:1.389538+0.132791 
## [72] train-rmse:0.932154+0.024918    test-rmse:1.386184+0.132663 
## [73] train-rmse:0.926205+0.024883    test-rmse:1.379625+0.136988 
## [74] train-rmse:0.921026+0.024116    test-rmse:1.378005+0.136241 
## [75] train-rmse:0.914865+0.023208    test-rmse:1.376110+0.139116 
## [76] train-rmse:0.909918+0.022391    test-rmse:1.374263+0.140359 
## [77] train-rmse:0.904843+0.022196    test-rmse:1.370173+0.143213 
## [78] train-rmse:0.898499+0.023019    test-rmse:1.364871+0.143641 
## [79] train-rmse:0.893785+0.023434    test-rmse:1.361860+0.144752 
## [80] train-rmse:0.887251+0.024023    test-rmse:1.359649+0.146208 
## [81] train-rmse:0.881533+0.023281    test-rmse:1.358342+0.145887 
## [82] train-rmse:0.874427+0.024318    test-rmse:1.352537+0.145039 
## [83] train-rmse:0.869206+0.025437    test-rmse:1.351831+0.145407 
## [84] train-rmse:0.863393+0.025704    test-rmse:1.346927+0.149328 
## [85] train-rmse:0.858876+0.025432    test-rmse:1.345301+0.150220 
## [86] train-rmse:0.854279+0.025679    test-rmse:1.345249+0.151582 
## [87] train-rmse:0.850097+0.025637    test-rmse:1.341128+0.152994 
## [88] train-rmse:0.846194+0.025909    test-rmse:1.340377+0.151712 
## [89] train-rmse:0.842293+0.025389    test-rmse:1.338176+0.151776 
## [90] train-rmse:0.838460+0.025080    test-rmse:1.337043+0.152498 
## [91] train-rmse:0.833596+0.025621    test-rmse:1.335825+0.151995 
## [92] train-rmse:0.829786+0.025720    test-rmse:1.335608+0.152306 
## [93] train-rmse:0.825702+0.025015    test-rmse:1.334968+0.154409 
## [94] train-rmse:0.820284+0.027979    test-rmse:1.333946+0.153905 
## [95] train-rmse:0.815524+0.027604    test-rmse:1.333239+0.155639 
## [96] train-rmse:0.811965+0.027066    test-rmse:1.334388+0.155324 
## [97] train-rmse:0.805357+0.027798    test-rmse:1.327034+0.155244 
## [98] train-rmse:0.801215+0.027526    test-rmse:1.324860+0.156236 
## [99] train-rmse:0.798046+0.027502    test-rmse:1.323724+0.156999 
## [100]    train-rmse:0.795209+0.027905    test-rmse:1.322707+0.156727 
## [101]    train-rmse:0.790090+0.027141    test-rmse:1.321112+0.157900 
## [102]    train-rmse:0.785271+0.027493    test-rmse:1.318889+0.158228 
## [103]    train-rmse:0.780030+0.029343    test-rmse:1.317074+0.158009 
## [104]    train-rmse:0.776300+0.029440    test-rmse:1.317143+0.159331 
## [105]    train-rmse:0.773253+0.029305    test-rmse:1.318162+0.160234 
## [106]    train-rmse:0.770197+0.029010    test-rmse:1.318026+0.160137 
## [107]    train-rmse:0.768015+0.029083    test-rmse:1.317102+0.160232 
## [108]    train-rmse:0.764352+0.028836    test-rmse:1.316137+0.159743 
## [109]    train-rmse:0.761459+0.028579    test-rmse:1.317451+0.159028 
## [110]    train-rmse:0.758171+0.028621    test-rmse:1.316051+0.158806 
## [111]    train-rmse:0.754747+0.030419    test-rmse:1.316318+0.160159 
## [112]    train-rmse:0.752012+0.030225    test-rmse:1.315037+0.159207 
## [113]    train-rmse:0.748506+0.029953    test-rmse:1.314146+0.161586 
## [114]    train-rmse:0.745552+0.030716    test-rmse:1.313912+0.161223 
## [115]    train-rmse:0.739963+0.028965    test-rmse:1.311526+0.163862 
## [116]    train-rmse:0.736359+0.027917    test-rmse:1.309351+0.165014 
## [117]    train-rmse:0.733206+0.027737    test-rmse:1.310054+0.164403 
## [118]    train-rmse:0.728992+0.027310    test-rmse:1.305024+0.161700 
## [119]    train-rmse:0.724250+0.026604    test-rmse:1.303409+0.161432 
## [120]    train-rmse:0.721753+0.026823    test-rmse:1.303167+0.161829 
## [121]    train-rmse:0.719001+0.026865    test-rmse:1.304854+0.162211 
## [122]    train-rmse:0.716507+0.026954    test-rmse:1.304886+0.163879 
## [123]    train-rmse:0.713701+0.028356    test-rmse:1.304843+0.163423 
## [124]    train-rmse:0.711138+0.027695    test-rmse:1.303969+0.163080 
## [125]    train-rmse:0.707760+0.028349    test-rmse:1.303812+0.163750 
## [126]    train-rmse:0.705005+0.028769    test-rmse:1.301778+0.163209 
## [127]    train-rmse:0.702872+0.028720    test-rmse:1.301083+0.163610 
## [128]    train-rmse:0.700388+0.028431    test-rmse:1.301858+0.165436 
## [129]    train-rmse:0.698558+0.028491    test-rmse:1.302187+0.164648 
## [130]    train-rmse:0.695088+0.028679    test-rmse:1.297159+0.162223 
## [131]    train-rmse:0.693021+0.028781    test-rmse:1.296969+0.162404 
## [132]    train-rmse:0.690372+0.028488    test-rmse:1.296593+0.163301 
## [133]    train-rmse:0.688607+0.028341    test-rmse:1.296540+0.163883 
## [134]    train-rmse:0.685574+0.028136    test-rmse:1.292828+0.164853 
## [135]    train-rmse:0.683470+0.028396    test-rmse:1.292841+0.163995 
## [136]    train-rmse:0.679547+0.029367    test-rmse:1.291839+0.162448 
## [137]    train-rmse:0.677873+0.029558    test-rmse:1.291567+0.162039 
## [138]    train-rmse:0.676422+0.029920    test-rmse:1.291476+0.162067 
## [139]    train-rmse:0.673943+0.031449    test-rmse:1.291603+0.162841 
## [140]    train-rmse:0.672182+0.031452    test-rmse:1.291892+0.164682 
## [141]    train-rmse:0.670274+0.031420    test-rmse:1.290617+0.164577 
## [142]    train-rmse:0.668242+0.031428    test-rmse:1.290520+0.164809 
## [143]    train-rmse:0.665559+0.031484    test-rmse:1.289378+0.164364 
## [144]    train-rmse:0.662934+0.031467    test-rmse:1.289366+0.164683 
## [145]    train-rmse:0.661377+0.031393    test-rmse:1.289241+0.163498 
## [146]    train-rmse:0.657556+0.033038    test-rmse:1.286196+0.161864 
## [147]    train-rmse:0.655022+0.034457    test-rmse:1.286117+0.162278 
## [148]    train-rmse:0.652766+0.034829    test-rmse:1.285594+0.162069 
## [149]    train-rmse:0.650262+0.034641    test-rmse:1.287155+0.164378 
## [150]    train-rmse:0.648843+0.034411    test-rmse:1.288123+0.163223 
## [151]    train-rmse:0.646915+0.034554    test-rmse:1.288388+0.163414 
## [152]    train-rmse:0.645182+0.034396    test-rmse:1.288387+0.163572 
## [153]    train-rmse:0.643536+0.034473    test-rmse:1.288824+0.165394 
## [154]    train-rmse:0.641143+0.034316    test-rmse:1.289091+0.166560 
## Stopping. Best iteration:
## [148]    train-rmse:0.652766+0.034829    test-rmse:1.285594+0.162069
## 
## 24 
## [1]  train-rmse:8.234109+0.060544    test-rmse:8.230359+0.266434 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.010027+0.051262    test-rmse:7.009323+0.254171 
## [3]  train-rmse:5.990892+0.043398    test-rmse:5.997430+0.241636 
## [4]  train-rmse:5.145758+0.036528    test-rmse:5.153261+0.224212 
## [5]  train-rmse:4.442205+0.030874    test-rmse:4.466286+0.196596 
## [6]  train-rmse:3.858761+0.025808    test-rmse:3.884766+0.176223 
## [7]  train-rmse:3.378160+0.021666    test-rmse:3.417301+0.166546 
## [8]  train-rmse:2.984764+0.017639    test-rmse:3.038289+0.158509 
## [9]  train-rmse:2.660158+0.015363    test-rmse:2.737354+0.134220 
## [10] train-rmse:2.400778+0.013013    test-rmse:2.503119+0.126598 
## [11] train-rmse:2.188191+0.011592    test-rmse:2.310908+0.127579 
## [12] train-rmse:2.013807+0.014149    test-rmse:2.156808+0.109427 
## [13] train-rmse:1.875196+0.015573    test-rmse:2.030558+0.097970 
## [14] train-rmse:1.761799+0.014873    test-rmse:1.940458+0.097564 
## [15] train-rmse:1.668052+0.017593    test-rmse:1.863679+0.086881 
## [16] train-rmse:1.593250+0.019288    test-rmse:1.806442+0.086347 
## [17] train-rmse:1.532785+0.018774    test-rmse:1.756729+0.080519 
## [18] train-rmse:1.476970+0.020212    test-rmse:1.721154+0.077878 
## [19] train-rmse:1.429179+0.020460    test-rmse:1.695705+0.073771 
## [20] train-rmse:1.387120+0.021222    test-rmse:1.665439+0.076036 
## [21] train-rmse:1.352137+0.023302    test-rmse:1.639721+0.082687 
## [22] train-rmse:1.322887+0.023602    test-rmse:1.618292+0.085638 
## [23] train-rmse:1.293614+0.024177    test-rmse:1.596098+0.086262 
## [24] train-rmse:1.268876+0.024191    test-rmse:1.584332+0.090930 
## [25] train-rmse:1.245902+0.027369    test-rmse:1.567141+0.091218 
## [26] train-rmse:1.224432+0.027596    test-rmse:1.552565+0.094020 
## [27] train-rmse:1.203484+0.028075    test-rmse:1.535409+0.099215 
## [28] train-rmse:1.181894+0.023321    test-rmse:1.520665+0.103012 
## [29] train-rmse:1.162933+0.023963    test-rmse:1.511679+0.110462 
## [30] train-rmse:1.144113+0.023415    test-rmse:1.504061+0.113192 
## [31] train-rmse:1.127790+0.022558    test-rmse:1.497226+0.117054 
## [32] train-rmse:1.112502+0.022868    test-rmse:1.483087+0.116677 
## [33] train-rmse:1.094259+0.023896    test-rmse:1.475533+0.117860 
## [34] train-rmse:1.077084+0.022647    test-rmse:1.469844+0.125197 
## [35] train-rmse:1.061654+0.022490    test-rmse:1.459315+0.129014 
## [36] train-rmse:1.047600+0.021252    test-rmse:1.453585+0.127058 
## [37] train-rmse:1.033283+0.022618    test-rmse:1.438769+0.124979 
## [38] train-rmse:1.020031+0.023464    test-rmse:1.430739+0.126483 
## [39] train-rmse:1.005924+0.024162    test-rmse:1.419859+0.128684 
## [40] train-rmse:0.992677+0.023416    test-rmse:1.413856+0.130923 
## [41] train-rmse:0.982571+0.022130    test-rmse:1.404994+0.128910 
## [42] train-rmse:0.970706+0.022449    test-rmse:1.398016+0.134177 
## [43] train-rmse:0.958705+0.023937    test-rmse:1.397406+0.134784 
## [44] train-rmse:0.946780+0.026161    test-rmse:1.388340+0.135209 
## [45] train-rmse:0.934964+0.025179    test-rmse:1.383757+0.138118 
## [46] train-rmse:0.924580+0.026869    test-rmse:1.380886+0.138446 
## [47] train-rmse:0.912961+0.026335    test-rmse:1.374557+0.142751 
## [48] train-rmse:0.901451+0.026733    test-rmse:1.373100+0.145002 
## [49] train-rmse:0.891928+0.025535    test-rmse:1.369268+0.143513 
## [50] train-rmse:0.882041+0.024470    test-rmse:1.366982+0.150362 
## [51] train-rmse:0.872925+0.022785    test-rmse:1.361130+0.150592 
## [52] train-rmse:0.864460+0.024704    test-rmse:1.357715+0.151049 
## [53] train-rmse:0.854811+0.025974    test-rmse:1.354952+0.151639 
## [54] train-rmse:0.847726+0.025658    test-rmse:1.349861+0.154547 
## [55] train-rmse:0.837130+0.024594    test-rmse:1.345017+0.150622 
## [56] train-rmse:0.829539+0.024022    test-rmse:1.339450+0.148609 
## [57] train-rmse:0.821223+0.023150    test-rmse:1.338002+0.149537 
## [58] train-rmse:0.812509+0.023377    test-rmse:1.331319+0.149526 
## [59] train-rmse:0.805165+0.023698    test-rmse:1.328133+0.150214 
## [60] train-rmse:0.797871+0.024303    test-rmse:1.324812+0.151497 
## [61] train-rmse:0.790990+0.024608    test-rmse:1.320649+0.152133 
## [62] train-rmse:0.782645+0.024672    test-rmse:1.318381+0.154870 
## [63] train-rmse:0.775051+0.022625    test-rmse:1.316900+0.155468 
## [64] train-rmse:0.766744+0.024276    test-rmse:1.313535+0.155591 
## [65] train-rmse:0.759802+0.024120    test-rmse:1.313458+0.153968 
## [66] train-rmse:0.752960+0.023032    test-rmse:1.309150+0.155456 
## [67] train-rmse:0.746598+0.023693    test-rmse:1.307771+0.156198 
## [68] train-rmse:0.738928+0.024934    test-rmse:1.303859+0.158181 
## [69] train-rmse:0.732349+0.025862    test-rmse:1.301513+0.156063 
## [70] train-rmse:0.725857+0.025118    test-rmse:1.298601+0.155928 
## [71] train-rmse:0.721078+0.025451    test-rmse:1.296002+0.156427 
## [72] train-rmse:0.715398+0.026543    test-rmse:1.295456+0.157696 
## [73] train-rmse:0.710497+0.026121    test-rmse:1.294765+0.160268 
## [74] train-rmse:0.705438+0.025328    test-rmse:1.292153+0.160058 
## [75] train-rmse:0.700261+0.024866    test-rmse:1.291044+0.160832 
## [76] train-rmse:0.695145+0.024102    test-rmse:1.288735+0.161012 
## [77] train-rmse:0.686942+0.023183    test-rmse:1.286813+0.160983 
## [78] train-rmse:0.680565+0.024052    test-rmse:1.285132+0.162195 
## [79] train-rmse:0.673480+0.023279    test-rmse:1.283329+0.161044 
## [80] train-rmse:0.668739+0.022354    test-rmse:1.284470+0.163445 
## [81] train-rmse:0.663216+0.022130    test-rmse:1.285220+0.164513 
## [82] train-rmse:0.658695+0.021711    test-rmse:1.283676+0.163480 
## [83] train-rmse:0.655113+0.021660    test-rmse:1.283646+0.164297 
## [84] train-rmse:0.650498+0.022139    test-rmse:1.283326+0.166435 
## [85] train-rmse:0.646518+0.022483    test-rmse:1.283596+0.165357 
## [86] train-rmse:0.641973+0.024015    test-rmse:1.284977+0.165145 
## [87] train-rmse:0.638008+0.023757    test-rmse:1.283301+0.164121 
## [88] train-rmse:0.634283+0.023518    test-rmse:1.284227+0.165409 
## [89] train-rmse:0.628901+0.022963    test-rmse:1.282704+0.165102 
## [90] train-rmse:0.624000+0.024408    test-rmse:1.280156+0.165854 
## [91] train-rmse:0.618842+0.022285    test-rmse:1.278767+0.166102 
## [92] train-rmse:0.614770+0.022519    test-rmse:1.280232+0.164615 
## [93] train-rmse:0.611209+0.021967    test-rmse:1.280740+0.165635 
## [94] train-rmse:0.607988+0.022078    test-rmse:1.279608+0.165254 
## [95] train-rmse:0.603918+0.021887    test-rmse:1.280369+0.165269 
## [96] train-rmse:0.600580+0.021696    test-rmse:1.280202+0.165039 
## [97] train-rmse:0.595023+0.023154    test-rmse:1.280059+0.164677 
## Stopping. Best iteration:
## [91] train-rmse:0.618842+0.022285    test-rmse:1.278767+0.166102
## 
## 25 
## [1]  train-rmse:8.231537+0.060807    test-rmse:8.230565+0.266774 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.003653+0.051618    test-rmse:7.010138+0.248406 
## [3]  train-rmse:5.979616+0.043543    test-rmse:5.993730+0.231142 
## [4]  train-rmse:5.125632+0.036160    test-rmse:5.143746+0.208866 
## [5]  train-rmse:4.416896+0.031925    test-rmse:4.437808+0.183755 
## [6]  train-rmse:3.828164+0.028870    test-rmse:3.856725+0.169541 
## [7]  train-rmse:3.337892+0.024262    test-rmse:3.389434+0.156901 
## [8]  train-rmse:2.933526+0.023179    test-rmse:2.998724+0.140470 
## [9]  train-rmse:2.600211+0.020245    test-rmse:2.693919+0.126673 
## [10] train-rmse:2.325259+0.018766    test-rmse:2.440370+0.120575 
## [11] train-rmse:2.103418+0.016775    test-rmse:2.249832+0.115117 
## [12] train-rmse:1.923222+0.017360    test-rmse:2.102292+0.108405 
## [13] train-rmse:1.770320+0.020059    test-rmse:1.970658+0.094011 
## [14] train-rmse:1.644259+0.020994    test-rmse:1.865858+0.089410 
## [15] train-rmse:1.542744+0.019289    test-rmse:1.790901+0.089061 
## [16] train-rmse:1.457769+0.021553    test-rmse:1.730284+0.075638 
## [17] train-rmse:1.385734+0.022356    test-rmse:1.668968+0.080400 
## [18] train-rmse:1.326639+0.022948    test-rmse:1.630677+0.084850 
## [19] train-rmse:1.277860+0.025514    test-rmse:1.597005+0.081214 
## [20] train-rmse:1.232849+0.023816    test-rmse:1.560254+0.089655 
## [21] train-rmse:1.194270+0.025532    test-rmse:1.533063+0.091092 
## [22] train-rmse:1.156956+0.025924    test-rmse:1.505424+0.090450 
## [23] train-rmse:1.124575+0.027087    test-rmse:1.489053+0.091190 
## [24] train-rmse:1.095549+0.025600    test-rmse:1.472166+0.090059 
## [25] train-rmse:1.067385+0.027760    test-rmse:1.462545+0.094438 
## [26] train-rmse:1.044655+0.028013    test-rmse:1.454602+0.100929 
## [27] train-rmse:1.017391+0.029719    test-rmse:1.444806+0.107982 
## [28] train-rmse:0.997732+0.030150    test-rmse:1.429051+0.104729 
## [29] train-rmse:0.975409+0.027840    test-rmse:1.416382+0.106841 
## [30] train-rmse:0.953336+0.027401    test-rmse:1.401853+0.116664 
## [31] train-rmse:0.938536+0.026701    test-rmse:1.395586+0.125542 
## [32] train-rmse:0.922677+0.024498    test-rmse:1.389227+0.125699 
## [33] train-rmse:0.904545+0.022090    test-rmse:1.377038+0.132850 
## [34] train-rmse:0.890990+0.021610    test-rmse:1.371186+0.134460 
## [35] train-rmse:0.875467+0.022423    test-rmse:1.364304+0.139340 
## [36] train-rmse:0.860161+0.022482    test-rmse:1.359256+0.140555 
## [37] train-rmse:0.847245+0.022322    test-rmse:1.353481+0.144151 
## [38] train-rmse:0.834644+0.022499    test-rmse:1.350736+0.148671 
## [39] train-rmse:0.819845+0.024117    test-rmse:1.342051+0.144236 
## [40] train-rmse:0.804977+0.022744    test-rmse:1.331766+0.148496 
## [41] train-rmse:0.792071+0.023609    test-rmse:1.327369+0.149958 
## [42] train-rmse:0.782028+0.023540    test-rmse:1.319575+0.152476 
## [43] train-rmse:0.773219+0.023373    test-rmse:1.316082+0.156067 
## [44] train-rmse:0.762161+0.023056    test-rmse:1.313555+0.157574 
## [45] train-rmse:0.751341+0.022732    test-rmse:1.307613+0.159578 
## [46] train-rmse:0.741710+0.023569    test-rmse:1.303868+0.158512 
## [47] train-rmse:0.732671+0.023379    test-rmse:1.298469+0.158191 
## [48] train-rmse:0.723752+0.023993    test-rmse:1.295996+0.158889 
## [49] train-rmse:0.712721+0.020857    test-rmse:1.293104+0.163804 
## [50] train-rmse:0.705961+0.020404    test-rmse:1.290717+0.163240 
## [51] train-rmse:0.695513+0.020647    test-rmse:1.286072+0.164892 
## [52] train-rmse:0.687519+0.019353    test-rmse:1.283307+0.163749 
## [53] train-rmse:0.679596+0.019024    test-rmse:1.280580+0.165390 
## [54] train-rmse:0.671906+0.018868    test-rmse:1.277492+0.166212 
## [55] train-rmse:0.661913+0.018407    test-rmse:1.273817+0.165476 
## [56] train-rmse:0.653844+0.016737    test-rmse:1.271188+0.168287 
## [57] train-rmse:0.647562+0.016441    test-rmse:1.268718+0.169550 
## [58] train-rmse:0.640415+0.016890    test-rmse:1.266402+0.167026 
## [59] train-rmse:0.632208+0.017730    test-rmse:1.266977+0.167401 
## [60] train-rmse:0.625452+0.017094    test-rmse:1.266591+0.170800 
## [61] train-rmse:0.618242+0.017517    test-rmse:1.266427+0.172142 
## [62] train-rmse:0.611064+0.017997    test-rmse:1.263588+0.170796 
## [63] train-rmse:0.604493+0.015822    test-rmse:1.264827+0.173427 
## [64] train-rmse:0.598027+0.014880    test-rmse:1.264018+0.175928 
## [65] train-rmse:0.593049+0.015689    test-rmse:1.261812+0.175145 
## [66] train-rmse:0.586740+0.016981    test-rmse:1.258125+0.175462 
## [67] train-rmse:0.580725+0.015996    test-rmse:1.258036+0.175859 
## [68] train-rmse:0.575994+0.015943    test-rmse:1.256403+0.178368 
## [69] train-rmse:0.570794+0.016579    test-rmse:1.254892+0.178779 
## [70] train-rmse:0.564947+0.017409    test-rmse:1.254664+0.176826 
## [71] train-rmse:0.558153+0.016000    test-rmse:1.252250+0.176923 
## [72] train-rmse:0.552616+0.017461    test-rmse:1.249919+0.175258 
## [73] train-rmse:0.546179+0.016775    test-rmse:1.249452+0.174744 
## [74] train-rmse:0.541407+0.017571    test-rmse:1.248817+0.175787 
## [75] train-rmse:0.535761+0.015599    test-rmse:1.248319+0.175689 
## [76] train-rmse:0.531778+0.015416    test-rmse:1.247742+0.176299 
## [77] train-rmse:0.527244+0.016108    test-rmse:1.247624+0.176889 
## [78] train-rmse:0.523079+0.015897    test-rmse:1.246683+0.179538 
## [79] train-rmse:0.516942+0.015393    test-rmse:1.246841+0.179476 
## [80] train-rmse:0.512299+0.016227    test-rmse:1.247844+0.180224 
## [81] train-rmse:0.508096+0.014835    test-rmse:1.247049+0.179903 
## [82] train-rmse:0.504458+0.013970    test-rmse:1.247016+0.179925 
## [83] train-rmse:0.500958+0.014565    test-rmse:1.245888+0.179580 
## [84] train-rmse:0.495509+0.016351    test-rmse:1.244862+0.179462 
## [85] train-rmse:0.490637+0.017362    test-rmse:1.244057+0.179593 
## [86] train-rmse:0.487140+0.017454    test-rmse:1.244060+0.180247 
## [87] train-rmse:0.483685+0.017915    test-rmse:1.245640+0.179099 
## [88] train-rmse:0.479268+0.017911    test-rmse:1.246201+0.178891 
## [89] train-rmse:0.475542+0.018524    test-rmse:1.245223+0.177634 
## [90] train-rmse:0.470441+0.018052    test-rmse:1.242385+0.179161 
## [91] train-rmse:0.467363+0.017290    test-rmse:1.243019+0.179507 
## [92] train-rmse:0.464681+0.017342    test-rmse:1.242070+0.179101 
## [93] train-rmse:0.461623+0.018472    test-rmse:1.241275+0.177755 
## [94] train-rmse:0.458534+0.018092    test-rmse:1.242455+0.175496 
## [95] train-rmse:0.455104+0.017200    test-rmse:1.242297+0.175138 
## [96] train-rmse:0.451590+0.016546    test-rmse:1.241840+0.175244 
## [97] train-rmse:0.448685+0.016024    test-rmse:1.241365+0.174702 
## [98] train-rmse:0.445513+0.016423    test-rmse:1.241053+0.173508 
## [99] train-rmse:0.442104+0.018107    test-rmse:1.240280+0.173532 
## [100]    train-rmse:0.439546+0.017841    test-rmse:1.240958+0.175080 
## [101]    train-rmse:0.435784+0.017369    test-rmse:1.240940+0.174234 
## [102]    train-rmse:0.433020+0.018444    test-rmse:1.240862+0.174325 
## [103]    train-rmse:0.429072+0.018689    test-rmse:1.239387+0.175740 
## [104]    train-rmse:0.426209+0.017811    test-rmse:1.238531+0.175145 
## [105]    train-rmse:0.424588+0.017830    test-rmse:1.238268+0.175582 
## [106]    train-rmse:0.421054+0.017461    test-rmse:1.237929+0.176521 
## [107]    train-rmse:0.418228+0.018607    test-rmse:1.239099+0.175455 
## [108]    train-rmse:0.416042+0.018038    test-rmse:1.239786+0.175091 
## [109]    train-rmse:0.413757+0.018983    test-rmse:1.239026+0.175544 
## [110]    train-rmse:0.411074+0.017946    test-rmse:1.237823+0.177542 
## [111]    train-rmse:0.409248+0.017723    test-rmse:1.238825+0.178444 
## [112]    train-rmse:0.406235+0.017546    test-rmse:1.239575+0.177589 
## [113]    train-rmse:0.403034+0.017656    test-rmse:1.238434+0.178169 
## [114]    train-rmse:0.398735+0.017295    test-rmse:1.238129+0.178013 
## [115]    train-rmse:0.395357+0.019014    test-rmse:1.237874+0.177555 
## [116]    train-rmse:0.392176+0.020298    test-rmse:1.237711+0.177480 
## [117]    train-rmse:0.390172+0.020183    test-rmse:1.237555+0.178750 
## [118]    train-rmse:0.388081+0.020691    test-rmse:1.237344+0.177691 
## [119]    train-rmse:0.384192+0.020914    test-rmse:1.236741+0.177000 
## [120]    train-rmse:0.382152+0.020902    test-rmse:1.237696+0.177987 
## [121]    train-rmse:0.379076+0.020075    test-rmse:1.238085+0.177263 
## [122]    train-rmse:0.377247+0.020882    test-rmse:1.238301+0.177480 
## [123]    train-rmse:0.374765+0.022211    test-rmse:1.238456+0.176569 
## [124]    train-rmse:0.372828+0.022061    test-rmse:1.238678+0.176153 
## [125]    train-rmse:0.370368+0.022501    test-rmse:1.239499+0.174654 
## Stopping. Best iteration:
## [119]    train-rmse:0.384192+0.020914    test-rmse:1.236741+0.177000
## 
## 26 
## [1]  train-rmse:8.231005+0.060939    test-rmse:8.231149+0.261610 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:7.000695+0.051762    test-rmse:7.003591+0.246565 
## [3]  train-rmse:5.972831+0.044148    test-rmse:5.986918+0.232337 
## [4]  train-rmse:5.115268+0.037435    test-rmse:5.136525+0.202723 
## [5]  train-rmse:4.401219+0.031307    test-rmse:4.433035+0.183241 
## [6]  train-rmse:3.807012+0.029391    test-rmse:3.842613+0.162788 
## [7]  train-rmse:3.308208+0.024073    test-rmse:3.358036+0.142263 
## [8]  train-rmse:2.895613+0.021015    test-rmse:2.969897+0.135730 
## [9]  train-rmse:2.555317+0.019742    test-rmse:2.649359+0.119839 
## [10] train-rmse:2.276611+0.018461    test-rmse:2.398003+0.112067 
## [11] train-rmse:2.040174+0.015729    test-rmse:2.190715+0.109826 
## [12] train-rmse:1.849169+0.017427    test-rmse:2.033729+0.103007 
## [13] train-rmse:1.688907+0.017642    test-rmse:1.904561+0.102020 
## [14] train-rmse:1.555578+0.017730    test-rmse:1.792721+0.086633 
## [15] train-rmse:1.445778+0.019703    test-rmse:1.711229+0.088090 
## [16] train-rmse:1.352115+0.017931    test-rmse:1.648718+0.087348 
## [17] train-rmse:1.275051+0.021702    test-rmse:1.593062+0.082424 
## [18] train-rmse:1.209729+0.022477    test-rmse:1.542320+0.077727 
## [19] train-rmse:1.154728+0.018323    test-rmse:1.516001+0.077403 
## [20] train-rmse:1.105423+0.020335    test-rmse:1.484817+0.078753 
## [21] train-rmse:1.059765+0.020956    test-rmse:1.458036+0.081498 
## [22] train-rmse:1.020979+0.020746    test-rmse:1.440783+0.084531 
## [23] train-rmse:0.989505+0.018321    test-rmse:1.421650+0.092120 
## [24] train-rmse:0.956988+0.019973    test-rmse:1.409923+0.091014 
## [25] train-rmse:0.929803+0.017259    test-rmse:1.393240+0.100913 
## [26] train-rmse:0.903505+0.017832    test-rmse:1.380071+0.104369 
## [27] train-rmse:0.880457+0.019840    test-rmse:1.368222+0.103350 
## [28] train-rmse:0.856826+0.018205    test-rmse:1.360839+0.111776 
## [29] train-rmse:0.836818+0.017863    test-rmse:1.351088+0.113595 
## [30] train-rmse:0.816358+0.016825    test-rmse:1.338154+0.118674 
## [31] train-rmse:0.797354+0.015970    test-rmse:1.327610+0.121119 
## [32] train-rmse:0.782260+0.015423    test-rmse:1.322698+0.122850 
## [33] train-rmse:0.766704+0.016825    test-rmse:1.314558+0.125069 
## [34] train-rmse:0.750544+0.017120    test-rmse:1.310333+0.131218 
## [35] train-rmse:0.735505+0.017936    test-rmse:1.303957+0.130864 
## [36] train-rmse:0.721191+0.018634    test-rmse:1.297047+0.134948 
## [37] train-rmse:0.707234+0.018484    test-rmse:1.290682+0.137362 
## [38] train-rmse:0.693485+0.020110    test-rmse:1.290443+0.135496 
## [39] train-rmse:0.678766+0.020891    test-rmse:1.286261+0.137655 
## [40] train-rmse:0.667414+0.017932    test-rmse:1.281809+0.139873 
## [41] train-rmse:0.655488+0.016443    test-rmse:1.280552+0.143138 
## [42] train-rmse:0.645158+0.017316    test-rmse:1.276368+0.144617 
## [43] train-rmse:0.633099+0.016496    test-rmse:1.270741+0.145421 
## [44] train-rmse:0.624656+0.015194    test-rmse:1.270282+0.147958 
## [45] train-rmse:0.615151+0.015881    test-rmse:1.266500+0.146788 
## [46] train-rmse:0.603797+0.014046    test-rmse:1.260943+0.149696 
## [47] train-rmse:0.591840+0.012950    test-rmse:1.257658+0.150460 
## [48] train-rmse:0.584908+0.012587    test-rmse:1.256165+0.152448 
## [49] train-rmse:0.575615+0.011908    test-rmse:1.250775+0.153263 
## [50] train-rmse:0.567607+0.013090    test-rmse:1.249454+0.153040 
## [51] train-rmse:0.558839+0.014531    test-rmse:1.247484+0.153313 
## [52] train-rmse:0.549939+0.014244    test-rmse:1.245268+0.157348 
## [53] train-rmse:0.541873+0.013049    test-rmse:1.244203+0.158573 
## [54] train-rmse:0.534264+0.013168    test-rmse:1.241720+0.161163 
## [55] train-rmse:0.527399+0.013999    test-rmse:1.239893+0.159704 
## [56] train-rmse:0.520926+0.014519    test-rmse:1.240084+0.160643 
## [57] train-rmse:0.513459+0.014964    test-rmse:1.239187+0.161408 
## [58] train-rmse:0.506332+0.013865    test-rmse:1.237082+0.163309 
## [59] train-rmse:0.500942+0.013872    test-rmse:1.234939+0.162957 
## [60] train-rmse:0.493782+0.014451    test-rmse:1.234853+0.162520 
## [61] train-rmse:0.489492+0.014510    test-rmse:1.234519+0.163620 
## [62] train-rmse:0.483852+0.014577    test-rmse:1.235108+0.163140 
## [63] train-rmse:0.477737+0.016766    test-rmse:1.236220+0.163135 
## [64] train-rmse:0.472136+0.016203    test-rmse:1.235969+0.164486 
## [65] train-rmse:0.465007+0.013862    test-rmse:1.236375+0.164955 
## [66] train-rmse:0.460693+0.014767    test-rmse:1.234880+0.165655 
## [67] train-rmse:0.455657+0.013547    test-rmse:1.236174+0.166931 
## Stopping. Best iteration:
## [61] train-rmse:0.489492+0.014510    test-rmse:1.234519+0.163620
## 
## 27 
## [1]  train-rmse:8.231005+0.060939    test-rmse:8.231149+0.261610 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.999801+0.051870    test-rmse:7.007982+0.240368 
## [3]  train-rmse:5.969970+0.044430    test-rmse:5.985404+0.225188 
## [4]  train-rmse:5.109511+0.037562    test-rmse:5.130418+0.193858 
## [5]  train-rmse:4.391281+0.031631    test-rmse:4.418208+0.171087 
## [6]  train-rmse:3.793175+0.030053    test-rmse:3.820819+0.152880 
## [7]  train-rmse:3.288494+0.024205    test-rmse:3.329165+0.137237 
## [8]  train-rmse:2.870816+0.022088    test-rmse:2.934286+0.127474 
## [9]  train-rmse:2.523999+0.020599    test-rmse:2.620834+0.117849 
## [10] train-rmse:2.237465+0.017824    test-rmse:2.369893+0.110878 
## [11] train-rmse:1.997417+0.020703    test-rmse:2.144259+0.096124 
## [12] train-rmse:1.794873+0.019730    test-rmse:1.981540+0.092909 
## [13] train-rmse:1.627733+0.022853    test-rmse:1.852886+0.091455 
## [14] train-rmse:1.483734+0.021094    test-rmse:1.737903+0.082535 
## [15] train-rmse:1.365705+0.018435    test-rmse:1.657876+0.085940 
## [16] train-rmse:1.267661+0.020817    test-rmse:1.578165+0.083384 
## [17] train-rmse:1.180209+0.019583    test-rmse:1.525937+0.089854 
## [18] train-rmse:1.109294+0.021424    test-rmse:1.470143+0.093833 
## [19] train-rmse:1.050319+0.022189    test-rmse:1.440908+0.102769 
## [20] train-rmse:0.994942+0.025264    test-rmse:1.406506+0.101484 
## [21] train-rmse:0.951110+0.023312    test-rmse:1.386963+0.100215 
## [22] train-rmse:0.911109+0.023435    test-rmse:1.377899+0.100861 
## [23] train-rmse:0.873802+0.023376    test-rmse:1.356521+0.111546 
## [24] train-rmse:0.841555+0.024989    test-rmse:1.342724+0.112104 
## [25] train-rmse:0.812134+0.025554    test-rmse:1.330445+0.119381 
## [26] train-rmse:0.786007+0.025663    test-rmse:1.316480+0.122056 
## [27] train-rmse:0.761693+0.025500    test-rmse:1.299650+0.122842 
## [28] train-rmse:0.737113+0.022847    test-rmse:1.291524+0.127671 
## [29] train-rmse:0.717077+0.019864    test-rmse:1.283871+0.134050 
## [30] train-rmse:0.698532+0.022245    test-rmse:1.277153+0.137438 
## [31] train-rmse:0.679227+0.023707    test-rmse:1.269321+0.138169 
## [32] train-rmse:0.661152+0.021916    test-rmse:1.264764+0.138542 
## [33] train-rmse:0.646076+0.021762    test-rmse:1.263056+0.143307 
## [34] train-rmse:0.631060+0.022176    test-rmse:1.257986+0.145519 
## [35] train-rmse:0.616952+0.022752    test-rmse:1.255614+0.147151 
## [36] train-rmse:0.601458+0.022292    test-rmse:1.254768+0.147748 
## [37] train-rmse:0.589481+0.019654    test-rmse:1.252199+0.149467 
## [38] train-rmse:0.576348+0.018745    test-rmse:1.249532+0.153660 
## [39] train-rmse:0.564351+0.019705    test-rmse:1.245230+0.153013 
## [40] train-rmse:0.553797+0.019745    test-rmse:1.242749+0.154231 
## [41] train-rmse:0.540491+0.018362    test-rmse:1.240811+0.155051 
## [42] train-rmse:0.528475+0.019875    test-rmse:1.236822+0.156314 
## [43] train-rmse:0.520292+0.018867    test-rmse:1.236202+0.159129 
## [44] train-rmse:0.510401+0.020574    test-rmse:1.233865+0.159269 
## [45] train-rmse:0.501721+0.020122    test-rmse:1.233940+0.158203 
## [46] train-rmse:0.491969+0.020806    test-rmse:1.234836+0.159625 
## [47] train-rmse:0.482728+0.020641    test-rmse:1.232552+0.160445 
## [48] train-rmse:0.475634+0.020222    test-rmse:1.233399+0.161174 
## [49] train-rmse:0.466477+0.019836    test-rmse:1.231715+0.161115 
## [50] train-rmse:0.457797+0.018418    test-rmse:1.230107+0.160396 
## [51] train-rmse:0.450171+0.018885    test-rmse:1.227904+0.159999 
## [52] train-rmse:0.444102+0.018535    test-rmse:1.226551+0.160660 
## [53] train-rmse:0.436974+0.018837    test-rmse:1.226340+0.160542 
## [54] train-rmse:0.431001+0.019186    test-rmse:1.223900+0.161091 
## [55] train-rmse:0.424438+0.019601    test-rmse:1.222358+0.160419 
## [56] train-rmse:0.415643+0.019539    test-rmse:1.220239+0.160889 
## [57] train-rmse:0.409516+0.017334    test-rmse:1.220092+0.160997 
## [58] train-rmse:0.401897+0.017537    test-rmse:1.219907+0.158522 
## [59] train-rmse:0.396526+0.018333    test-rmse:1.219788+0.159724 
## [60] train-rmse:0.387829+0.018876    test-rmse:1.219809+0.159312 
## [61] train-rmse:0.382998+0.019052    test-rmse:1.219700+0.159437 
## [62] train-rmse:0.378703+0.018816    test-rmse:1.220392+0.158820 
## [63] train-rmse:0.373962+0.018372    test-rmse:1.219470+0.158831 
## [64] train-rmse:0.368201+0.018681    test-rmse:1.217084+0.160593 
## [65] train-rmse:0.364282+0.018825    test-rmse:1.217540+0.161506 
## [66] train-rmse:0.358322+0.019176    test-rmse:1.217658+0.161694 
## [67] train-rmse:0.354187+0.018873    test-rmse:1.218611+0.162021 
## [68] train-rmse:0.349779+0.018522    test-rmse:1.218378+0.161245 
## [69] train-rmse:0.345322+0.018578    test-rmse:1.218523+0.159955 
## [70] train-rmse:0.341391+0.018571    test-rmse:1.218224+0.160667 
## Stopping. Best iteration:
## [64] train-rmse:0.368201+0.018681    test-rmse:1.217084+0.160593
## 
## 28 
## [1]  train-rmse:8.086516+0.057499    test-rmse:8.082386+0.272011 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.782281+0.045261    test-rmse:6.775442+0.267096 
## [3]  train-rmse:5.734850+0.035447    test-rmse:5.729225+0.260300 
## [4]  train-rmse:4.900583+0.027143    test-rmse:4.900642+0.246714 
## [5]  train-rmse:4.243027+0.020943    test-rmse:4.247597+0.226863 
## [6]  train-rmse:3.731424+0.015924    test-rmse:3.743665+0.209512 
## [7]  train-rmse:3.339081+0.012866    test-rmse:3.363427+0.181001 
## [8]  train-rmse:3.041262+0.011578    test-rmse:3.068478+0.155138 
## [9]  train-rmse:2.817789+0.011816    test-rmse:2.842160+0.127514 
## [10] train-rmse:2.652577+0.012941    test-rmse:2.685164+0.102631 
## [11] train-rmse:2.530764+0.013812    test-rmse:2.566154+0.086979 
## [12] train-rmse:2.440349+0.014649    test-rmse:2.478745+0.065004 
## [13] train-rmse:2.372938+0.015203    test-rmse:2.409546+0.054385 
## [14] train-rmse:2.322455+0.015582    test-rmse:2.365133+0.047526 
## [15] train-rmse:2.283918+0.016524    test-rmse:2.325681+0.054725 
## [16] train-rmse:2.253765+0.016794    test-rmse:2.298030+0.051726 
## [17] train-rmse:2.229320+0.017635    test-rmse:2.277243+0.056589 
## [18] train-rmse:2.208945+0.018445    test-rmse:2.253268+0.065027 
## [19] train-rmse:2.191646+0.019070    test-rmse:2.241209+0.067588 
## [20] train-rmse:2.176366+0.019744    test-rmse:2.226802+0.067194 
## [21] train-rmse:2.162145+0.020373    test-rmse:2.214168+0.070701 
## [22] train-rmse:2.149013+0.020970    test-rmse:2.202673+0.072144 
## [23] train-rmse:2.137307+0.021467    test-rmse:2.195626+0.074522 
## [24] train-rmse:2.126561+0.021705    test-rmse:2.186961+0.073843 
## [25] train-rmse:2.116067+0.021988    test-rmse:2.170145+0.074287 
## [26] train-rmse:2.105964+0.022489    test-rmse:2.160117+0.081612 
## [27] train-rmse:2.095966+0.022954    test-rmse:2.150444+0.084296 
## [28] train-rmse:2.086153+0.023393    test-rmse:2.147575+0.086872 
## [29] train-rmse:2.076704+0.023897    test-rmse:2.142536+0.085862 
## [30] train-rmse:2.067312+0.024468    test-rmse:2.134294+0.089536 
## [31] train-rmse:2.058339+0.024918    test-rmse:2.126950+0.086511 
## [32] train-rmse:2.049555+0.025384    test-rmse:2.119110+0.086322 
## [33] train-rmse:2.041015+0.025824    test-rmse:2.106161+0.083217 
## [34] train-rmse:2.032560+0.026195    test-rmse:2.097641+0.089363 
## [35] train-rmse:2.024400+0.026377    test-rmse:2.091875+0.090788 
## [36] train-rmse:2.016464+0.026743    test-rmse:2.084319+0.088764 
## [37] train-rmse:2.008521+0.027119    test-rmse:2.078700+0.087793 
## [38] train-rmse:2.000837+0.027646    test-rmse:2.067320+0.086739 
## [39] train-rmse:1.993499+0.028161    test-rmse:2.065263+0.090020 
## [40] train-rmse:1.986193+0.028695    test-rmse:2.059764+0.091661 
## [41] train-rmse:1.979116+0.028951    test-rmse:2.052382+0.090940 
## [42] train-rmse:1.972140+0.029175    test-rmse:2.047821+0.094085 
## [43] train-rmse:1.965026+0.029412    test-rmse:2.038632+0.093798 
## [44] train-rmse:1.958187+0.029641    test-rmse:2.031414+0.096001 
## [45] train-rmse:1.951474+0.029915    test-rmse:2.025615+0.093532 
## [46] train-rmse:1.944840+0.030164    test-rmse:2.018669+0.089582 
## [47] train-rmse:1.938222+0.030378    test-rmse:2.015581+0.090670 
## [48] train-rmse:1.931708+0.030665    test-rmse:2.008903+0.088009 
## [49] train-rmse:1.925359+0.031106    test-rmse:2.007681+0.086403 
## [50] train-rmse:1.919081+0.031526    test-rmse:2.003287+0.085575 
## [51] train-rmse:1.912848+0.032000    test-rmse:1.996254+0.091380 
## [52] train-rmse:1.906671+0.032465    test-rmse:1.991506+0.087897 
## [53] train-rmse:1.900531+0.032764    test-rmse:1.984589+0.090013 
## [54] train-rmse:1.894673+0.033041    test-rmse:1.974611+0.090116 
## [55] train-rmse:1.888953+0.033465    test-rmse:1.972006+0.093112 
## [56] train-rmse:1.883362+0.033903    test-rmse:1.968146+0.092650 
## [57] train-rmse:1.877789+0.034349    test-rmse:1.960550+0.093131 
## [58] train-rmse:1.872322+0.034668    test-rmse:1.954309+0.093048 
## [59] train-rmse:1.866815+0.035024    test-rmse:1.950698+0.095722 
## [60] train-rmse:1.861519+0.035421    test-rmse:1.945270+0.094543 
## [61] train-rmse:1.856173+0.035761    test-rmse:1.942203+0.095300 
## [62] train-rmse:1.850971+0.036096    test-rmse:1.937539+0.091321 
## [63] train-rmse:1.845801+0.036421    test-rmse:1.933097+0.089527 
## [64] train-rmse:1.840702+0.036685    test-rmse:1.928963+0.091684 
## [65] train-rmse:1.835608+0.036935    test-rmse:1.925000+0.090655 
## [66] train-rmse:1.830569+0.037150    test-rmse:1.923129+0.091253 
## [67] train-rmse:1.825621+0.037347    test-rmse:1.918712+0.087912 
## [68] train-rmse:1.820691+0.037541    test-rmse:1.915435+0.086942 
## [69] train-rmse:1.815792+0.037809    test-rmse:1.912685+0.088087 
## [70] train-rmse:1.810892+0.038180    test-rmse:1.909520+0.090444 
## [71] train-rmse:1.806242+0.038426    test-rmse:1.905150+0.091311 
## [72] train-rmse:1.801598+0.038684    test-rmse:1.905062+0.092066 
## [73] train-rmse:1.797068+0.038962    test-rmse:1.898535+0.091630 
## [74] train-rmse:1.792474+0.039204    test-rmse:1.890896+0.094637 
## [75] train-rmse:1.787971+0.039466    test-rmse:1.884991+0.097331 
## [76] train-rmse:1.783494+0.039823    test-rmse:1.883212+0.095943 
## [77] train-rmse:1.779146+0.040024    test-rmse:1.880537+0.096215 
## [78] train-rmse:1.774842+0.040314    test-rmse:1.875118+0.096782 
## [79] train-rmse:1.770573+0.040600    test-rmse:1.872543+0.096744 
## [80] train-rmse:1.766366+0.040925    test-rmse:1.868534+0.095552 
## [81] train-rmse:1.762199+0.041232    test-rmse:1.861580+0.094943 
## [82] train-rmse:1.758142+0.041510    test-rmse:1.857102+0.096675 
## [83] train-rmse:1.754084+0.041725    test-rmse:1.855125+0.100951 
## [84] train-rmse:1.750092+0.041919    test-rmse:1.853563+0.098902 
## [85] train-rmse:1.746192+0.042127    test-rmse:1.851991+0.101569 
## [86] train-rmse:1.742291+0.042360    test-rmse:1.848537+0.100144 
## [87] train-rmse:1.738440+0.042570    test-rmse:1.845087+0.101068 
## [88] train-rmse:1.734524+0.042817    test-rmse:1.842146+0.101345 
## [89] train-rmse:1.730668+0.043037    test-rmse:1.839210+0.101765 
## [90] train-rmse:1.726919+0.043180    test-rmse:1.835695+0.101466 
## [91] train-rmse:1.723134+0.043374    test-rmse:1.832043+0.099414 
## [92] train-rmse:1.719425+0.043631    test-rmse:1.830999+0.098500 
## [93] train-rmse:1.715769+0.043836    test-rmse:1.827765+0.099483 
## [94] train-rmse:1.712116+0.044063    test-rmse:1.825608+0.099054 
## [95] train-rmse:1.708466+0.044182    test-rmse:1.822506+0.101286 
## [96] train-rmse:1.704882+0.044368    test-rmse:1.818078+0.099158 
## [97] train-rmse:1.701335+0.044521    test-rmse:1.815076+0.099533 
## [98] train-rmse:1.697949+0.044651    test-rmse:1.810184+0.099768 
## [99] train-rmse:1.694586+0.044775    test-rmse:1.807851+0.098963 
## [100]    train-rmse:1.691239+0.044887    test-rmse:1.803988+0.100821 
## [101]    train-rmse:1.687998+0.045075    test-rmse:1.799035+0.102569 
## [102]    train-rmse:1.684745+0.045276    test-rmse:1.794492+0.103340 
## [103]    train-rmse:1.681526+0.045431    test-rmse:1.794198+0.104823 
## [104]    train-rmse:1.678276+0.045719    test-rmse:1.794650+0.105407 
## [105]    train-rmse:1.675111+0.045965    test-rmse:1.791263+0.105984 
## [106]    train-rmse:1.671942+0.046149    test-rmse:1.787233+0.102040 
## [107]    train-rmse:1.668812+0.046301    test-rmse:1.784890+0.105061 
## [108]    train-rmse:1.665754+0.046411    test-rmse:1.783652+0.106402 
## [109]    train-rmse:1.662735+0.046528    test-rmse:1.783327+0.105787 
## [110]    train-rmse:1.659695+0.046709    test-rmse:1.780836+0.105262 
## [111]    train-rmse:1.656740+0.046911    test-rmse:1.777781+0.105524 
## [112]    train-rmse:1.653829+0.047095    test-rmse:1.776040+0.106196 
## [113]    train-rmse:1.650864+0.047291    test-rmse:1.773300+0.107558 
## [114]    train-rmse:1.647970+0.047391    test-rmse:1.767927+0.107011 
## [115]    train-rmse:1.645104+0.047504    test-rmse:1.766500+0.110156 
## [116]    train-rmse:1.642267+0.047583    test-rmse:1.760807+0.114948 
## [117]    train-rmse:1.639408+0.047679    test-rmse:1.760892+0.116041 
## [118]    train-rmse:1.636569+0.047760    test-rmse:1.759247+0.115764 
## [119]    train-rmse:1.633798+0.047799    test-rmse:1.756769+0.116076 
## [120]    train-rmse:1.631090+0.047845    test-rmse:1.754189+0.116532 
## [121]    train-rmse:1.628390+0.047924    test-rmse:1.749731+0.117174 
## [122]    train-rmse:1.625724+0.048026    test-rmse:1.749198+0.117235 
## [123]    train-rmse:1.623061+0.048115    test-rmse:1.745132+0.116342 
## [124]    train-rmse:1.620426+0.048232    test-rmse:1.743654+0.115696 
## [125]    train-rmse:1.617809+0.048341    test-rmse:1.740746+0.118210 
## [126]    train-rmse:1.615187+0.048415    test-rmse:1.738463+0.117432 
## [127]    train-rmse:1.612621+0.048523    test-rmse:1.735198+0.115278 
## [128]    train-rmse:1.610087+0.048658    test-rmse:1.733700+0.116061 
## [129]    train-rmse:1.607574+0.048815    test-rmse:1.732488+0.116621 
## [130]    train-rmse:1.605102+0.048930    test-rmse:1.731266+0.117672 
## [131]    train-rmse:1.602664+0.049036    test-rmse:1.730999+0.116528 
## [132]    train-rmse:1.600209+0.049123    test-rmse:1.726892+0.117100 
## [133]    train-rmse:1.597798+0.049216    test-rmse:1.724357+0.115759 
## [134]    train-rmse:1.595416+0.049291    test-rmse:1.722538+0.116295 
## [135]    train-rmse:1.593049+0.049388    test-rmse:1.721758+0.114817 
## [136]    train-rmse:1.590719+0.049476    test-rmse:1.718922+0.115317 
## [137]    train-rmse:1.588395+0.049551    test-rmse:1.719045+0.117187 
## [138]    train-rmse:1.586083+0.049613    test-rmse:1.716007+0.119374 
## [139]    train-rmse:1.583772+0.049708    test-rmse:1.716709+0.121478 
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## [471]    train-rmse:1.330166+0.052704    test-rmse:1.485050+0.198028 
## [472]    train-rmse:1.330004+0.052697    test-rmse:1.484792+0.197813 
## [473]    train-rmse:1.329844+0.052690    test-rmse:1.484975+0.197499 
## Stopping. Best iteration:
## [467]    train-rmse:1.330833+0.052732    test-rmse:1.484284+0.197782
## 
## 29 
## [1]  train-rmse:8.063964+0.057815    test-rmse:8.060198+0.268096 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.739139+0.048735    test-rmse:6.731255+0.254806 
## [3]  train-rmse:5.673326+0.041966    test-rmse:5.672520+0.235858 
## [4]  train-rmse:4.814800+0.034749    test-rmse:4.826408+0.226860 
## [5]  train-rmse:4.134450+0.030936    test-rmse:4.156872+0.203363 
## [6]  train-rmse:3.591443+0.028773    test-rmse:3.621099+0.178670 
## [7]  train-rmse:3.167096+0.026393    test-rmse:3.202528+0.155171 
## [8]  train-rmse:2.839798+0.025919    test-rmse:2.897377+0.126661 
## [9]  train-rmse:2.590214+0.025021    test-rmse:2.651126+0.112862 
## [10] train-rmse:2.397884+0.026045    test-rmse:2.473041+0.088013 
## [11] train-rmse:2.247410+0.014202    test-rmse:2.331845+0.075781 
## [12] train-rmse:2.138160+0.013016    test-rmse:2.236067+0.063180 
## [13] train-rmse:2.054915+0.015172    test-rmse:2.165431+0.056045 
## [14] train-rmse:1.991430+0.015181    test-rmse:2.112904+0.057279 
## [15] train-rmse:1.936457+0.017806    test-rmse:2.071226+0.072547 
## [16] train-rmse:1.887353+0.015781    test-rmse:2.030867+0.084158 
## [17] train-rmse:1.853557+0.014656    test-rmse:2.001971+0.088084 
## [18] train-rmse:1.820923+0.013400    test-rmse:1.977793+0.074258 
## [19] train-rmse:1.797172+0.013893    test-rmse:1.955236+0.073473 
## [20] train-rmse:1.776136+0.014950    test-rmse:1.931434+0.074739 
## [21] train-rmse:1.756693+0.014962    test-rmse:1.915310+0.080931 
## [22] train-rmse:1.733042+0.018348    test-rmse:1.899714+0.086923 
## [23] train-rmse:1.715886+0.019815    test-rmse:1.886629+0.092787 
## [24] train-rmse:1.700902+0.020260    test-rmse:1.876104+0.096740 
## [25] train-rmse:1.686778+0.020774    test-rmse:1.860290+0.093738 
## [26] train-rmse:1.672271+0.021487    test-rmse:1.846753+0.093200 
## [27] train-rmse:1.655301+0.023452    test-rmse:1.830757+0.091312 
## [28] train-rmse:1.638969+0.024791    test-rmse:1.817324+0.096689 
## [29] train-rmse:1.626242+0.025468    test-rmse:1.811292+0.097020 
## [30] train-rmse:1.612704+0.024461    test-rmse:1.803071+0.100233 
## [31] train-rmse:1.600183+0.025194    test-rmse:1.793160+0.105468 
## [32] train-rmse:1.587323+0.025540    test-rmse:1.785402+0.104813 
## [33] train-rmse:1.567702+0.032065    test-rmse:1.763460+0.084317 
## [34] train-rmse:1.555257+0.033291    test-rmse:1.759957+0.087892 
## [35] train-rmse:1.544746+0.033880    test-rmse:1.752649+0.085953 
## [36] train-rmse:1.532972+0.034260    test-rmse:1.744660+0.084819 
## [37] train-rmse:1.521876+0.035207    test-rmse:1.735866+0.085508 
## [38] train-rmse:1.498727+0.027419    test-rmse:1.708661+0.074592 
## [39] train-rmse:1.487571+0.026885    test-rmse:1.703154+0.077674 
## [40] train-rmse:1.477417+0.027035    test-rmse:1.696561+0.073338 
## [41] train-rmse:1.467219+0.027406    test-rmse:1.689523+0.077097 
## [42] train-rmse:1.456663+0.029491    test-rmse:1.689278+0.079025 
## [43] train-rmse:1.447437+0.029593    test-rmse:1.678551+0.079689 
## [44] train-rmse:1.437468+0.030518    test-rmse:1.675657+0.082867 
## [45] train-rmse:1.425469+0.035346    test-rmse:1.666746+0.077471 
## [46] train-rmse:1.416463+0.035485    test-rmse:1.658490+0.074328 
## [47] train-rmse:1.407376+0.034812    test-rmse:1.650324+0.075334 
## [48] train-rmse:1.398031+0.035187    test-rmse:1.645493+0.077031 
## [49] train-rmse:1.388787+0.034678    test-rmse:1.634391+0.079613 
## [50] train-rmse:1.380857+0.035190    test-rmse:1.627229+0.081555 
## [51] train-rmse:1.373115+0.034389    test-rmse:1.619361+0.080372 
## [52] train-rmse:1.364540+0.034239    test-rmse:1.614142+0.081303 
## [53] train-rmse:1.356117+0.034528    test-rmse:1.606651+0.082050 
## [54] train-rmse:1.343868+0.035425    test-rmse:1.600593+0.086332 
## [55] train-rmse:1.335083+0.036386    test-rmse:1.599124+0.090193 
## [56] train-rmse:1.328516+0.036322    test-rmse:1.594915+0.088965 
## [57] train-rmse:1.321735+0.035796    test-rmse:1.589543+0.091365 
## [58] train-rmse:1.314518+0.035493    test-rmse:1.584737+0.093038 
## [59] train-rmse:1.307851+0.035544    test-rmse:1.576414+0.097809 
## [60] train-rmse:1.301330+0.035992    test-rmse:1.573168+0.098349 
## [61] train-rmse:1.294147+0.035146    test-rmse:1.569091+0.100324 
## [62] train-rmse:1.284859+0.038742    test-rmse:1.556683+0.090478 
## [63] train-rmse:1.278739+0.038819    test-rmse:1.548935+0.094390 
## [64] train-rmse:1.270913+0.040891    test-rmse:1.546693+0.095910 
## [65] train-rmse:1.263871+0.041143    test-rmse:1.542406+0.095748 
## [66] train-rmse:1.252405+0.043620    test-rmse:1.536850+0.095916 
## [67] train-rmse:1.243893+0.047168    test-rmse:1.531587+0.091221 
## [68] train-rmse:1.237338+0.047252    test-rmse:1.526099+0.089951 
## [69] train-rmse:1.232075+0.047241    test-rmse:1.525695+0.088993 
## [70] train-rmse:1.226355+0.046814    test-rmse:1.523616+0.089302 
## [71] train-rmse:1.219736+0.046649    test-rmse:1.518310+0.093046 
## [72] train-rmse:1.214309+0.046506    test-rmse:1.516473+0.094984 
## [73] train-rmse:1.203487+0.039438    test-rmse:1.508516+0.099259 
## [74] train-rmse:1.197712+0.039359    test-rmse:1.503948+0.098660 
## [75] train-rmse:1.192482+0.039785    test-rmse:1.501251+0.100643 
## [76] train-rmse:1.186330+0.039505    test-rmse:1.496036+0.102246 
## [77] train-rmse:1.180760+0.038913    test-rmse:1.490656+0.105725 
## [78] train-rmse:1.174628+0.038713    test-rmse:1.482758+0.110560 
## [79] train-rmse:1.169009+0.039588    test-rmse:1.480898+0.108590 
## [80] train-rmse:1.160689+0.035564    test-rmse:1.475927+0.114987 
## [81] train-rmse:1.153465+0.038638    test-rmse:1.471841+0.114696 
## [82] train-rmse:1.148914+0.039204    test-rmse:1.469026+0.116156 
## [83] train-rmse:1.144220+0.038632    test-rmse:1.464986+0.117792 
## [84] train-rmse:1.139265+0.039213    test-rmse:1.459839+0.118190 
## [85] train-rmse:1.131409+0.039225    test-rmse:1.454888+0.122180 
## [86] train-rmse:1.126519+0.039648    test-rmse:1.448495+0.118759 
## [87] train-rmse:1.122395+0.039939    test-rmse:1.449326+0.119330 
## [88] train-rmse:1.118118+0.040066    test-rmse:1.445686+0.117404 
## [89] train-rmse:1.113624+0.039625    test-rmse:1.443188+0.121117 
## [90] train-rmse:1.108642+0.039671    test-rmse:1.438379+0.125980 
## [91] train-rmse:1.103487+0.041139    test-rmse:1.436500+0.123793 
## [92] train-rmse:1.099558+0.041491    test-rmse:1.436136+0.126798 
## [93] train-rmse:1.095111+0.042645    test-rmse:1.434677+0.125638 
## [94] train-rmse:1.091323+0.042735    test-rmse:1.431857+0.123997 
## [95] train-rmse:1.087605+0.042543    test-rmse:1.431638+0.124003 
## [96] train-rmse:1.084020+0.042566    test-rmse:1.430538+0.125776 
## [97] train-rmse:1.080394+0.042709    test-rmse:1.427742+0.127013 
## [98] train-rmse:1.076897+0.042912    test-rmse:1.424520+0.128227 
## [99] train-rmse:1.073475+0.043019    test-rmse:1.420007+0.126792 
## [100]    train-rmse:1.070097+0.043343    test-rmse:1.416528+0.127657 
## [101]    train-rmse:1.066948+0.043219    test-rmse:1.416638+0.127482 
## [102]    train-rmse:1.063685+0.043350    test-rmse:1.414511+0.126344 
## [103]    train-rmse:1.057223+0.043774    test-rmse:1.409214+0.132316 
## [104]    train-rmse:1.053470+0.043909    test-rmse:1.405849+0.134133 
## [105]    train-rmse:1.048773+0.045162    test-rmse:1.404584+0.133362 
## [106]    train-rmse:1.044991+0.045802    test-rmse:1.403749+0.133243 
## [107]    train-rmse:1.041622+0.046388    test-rmse:1.402388+0.134566 
## [108]    train-rmse:1.036843+0.044688    test-rmse:1.398351+0.137354 
## [109]    train-rmse:1.033922+0.044824    test-rmse:1.397578+0.138773 
## [110]    train-rmse:1.031211+0.044679    test-rmse:1.397059+0.139175 
## [111]    train-rmse:1.028312+0.044737    test-rmse:1.395624+0.139123 
## [112]    train-rmse:1.025369+0.045051    test-rmse:1.395330+0.139949 
## [113]    train-rmse:1.022951+0.045011    test-rmse:1.394755+0.141125 
## [114]    train-rmse:1.020150+0.044835    test-rmse:1.393753+0.142610 
## [115]    train-rmse:1.016985+0.045590    test-rmse:1.390409+0.144839 
## [116]    train-rmse:1.014377+0.045770    test-rmse:1.386805+0.146208 
## [117]    train-rmse:1.011522+0.045296    test-rmse:1.384233+0.145569 
## [118]    train-rmse:1.003159+0.038213    test-rmse:1.369314+0.147825 
## [119]    train-rmse:1.000785+0.038239    test-rmse:1.368320+0.148431 
## [120]    train-rmse:0.997954+0.038909    test-rmse:1.367569+0.147833 
## [121]    train-rmse:0.995488+0.039061    test-rmse:1.366245+0.146122 
## [122]    train-rmse:0.993233+0.039315    test-rmse:1.365198+0.146239 
## [123]    train-rmse:0.989909+0.038522    test-rmse:1.364345+0.146549 
## [124]    train-rmse:0.987534+0.038786    test-rmse:1.362551+0.147096 
## [125]    train-rmse:0.981752+0.038580    test-rmse:1.356743+0.148587 
## [126]    train-rmse:0.979637+0.038293    test-rmse:1.356186+0.151185 
## [127]    train-rmse:0.974889+0.041400    test-rmse:1.351787+0.147159 
## [128]    train-rmse:0.972657+0.041591    test-rmse:1.350481+0.147724 
## [129]    train-rmse:0.970408+0.041816    test-rmse:1.348722+0.148100 
## [130]    train-rmse:0.967763+0.041957    test-rmse:1.348396+0.147045 
## [131]    train-rmse:0.965464+0.041668    test-rmse:1.345354+0.148408 
## [132]    train-rmse:0.963518+0.041711    test-rmse:1.345909+0.147837 
## [133]    train-rmse:0.961061+0.042465    test-rmse:1.346533+0.148009 
## [134]    train-rmse:0.958570+0.042653    test-rmse:1.345966+0.150083 
## [135]    train-rmse:0.956093+0.043165    test-rmse:1.346415+0.150184 
## [136]    train-rmse:0.954039+0.043151    test-rmse:1.346070+0.150596 
## [137]    train-rmse:0.952146+0.043464    test-rmse:1.347459+0.150732 
## Stopping. Best iteration:
## [131]    train-rmse:0.965464+0.041668    test-rmse:1.345354+0.148408
## 
## 30 
## [1]  train-rmse:8.055202+0.058780    test-rmse:8.051680+0.263219 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.716698+0.047086    test-rmse:6.710539+0.251448 
## [3]  train-rmse:5.634388+0.038559    test-rmse:5.632943+0.238753 
## [4]  train-rmse:4.763912+0.031910    test-rmse:4.780465+0.221536 
## [5]  train-rmse:4.060938+0.025719    test-rmse:4.071797+0.204217 
## [6]  train-rmse:3.501352+0.023238    test-rmse:3.529014+0.180714 
## [7]  train-rmse:3.057267+0.018617    test-rmse:3.096828+0.165721 
## [8]  train-rmse:2.706157+0.013839    test-rmse:2.762432+0.141163 
## [9]  train-rmse:2.432698+0.008433    test-rmse:2.505938+0.130309 
## [10] train-rmse:2.223498+0.008139    test-rmse:2.317193+0.115002 
## [11] train-rmse:2.057852+0.007372    test-rmse:2.182255+0.107725 
## [12] train-rmse:1.931677+0.008992    test-rmse:2.072202+0.091830 
## [13] train-rmse:1.833002+0.011030    test-rmse:1.991188+0.082920 
## [14] train-rmse:1.753803+0.015677    test-rmse:1.932685+0.079621 
## [15] train-rmse:1.692052+0.015872    test-rmse:1.874607+0.073046 
## [16] train-rmse:1.643188+0.017092    test-rmse:1.836745+0.063812 
## [17] train-rmse:1.598543+0.016981    test-rmse:1.807567+0.064911 
## [18] train-rmse:1.559154+0.017025    test-rmse:1.782556+0.060016 
## [19] train-rmse:1.529615+0.017257    test-rmse:1.755135+0.058230 
## [20] train-rmse:1.502301+0.017708    test-rmse:1.738562+0.070015 
## [21] train-rmse:1.476299+0.019207    test-rmse:1.717223+0.066656 
## [22] train-rmse:1.451413+0.020252    test-rmse:1.701829+0.065008 
## [23] train-rmse:1.426216+0.019711    test-rmse:1.688326+0.072779 
## [24] train-rmse:1.404624+0.018350    test-rmse:1.671849+0.075120 
## [25] train-rmse:1.382876+0.017987    test-rmse:1.657803+0.079134 
## [26] train-rmse:1.364772+0.017718    test-rmse:1.647052+0.077330 
## [27] train-rmse:1.345895+0.020526    test-rmse:1.629022+0.081275 
## [28] train-rmse:1.328809+0.021570    test-rmse:1.612755+0.081802 
## [29] train-rmse:1.310351+0.020666    test-rmse:1.598413+0.081820 
## [30] train-rmse:1.291978+0.019654    test-rmse:1.589864+0.082679 
## [31] train-rmse:1.277894+0.019246    test-rmse:1.579224+0.082172 
## [32] train-rmse:1.261015+0.020414    test-rmse:1.569713+0.078728 
## [33] train-rmse:1.246435+0.022066    test-rmse:1.561460+0.083465 
## [34] train-rmse:1.228822+0.023493    test-rmse:1.551412+0.085474 
## [35] train-rmse:1.214760+0.024014    test-rmse:1.542447+0.089347 
## [36] train-rmse:1.201888+0.024449    test-rmse:1.532270+0.095513 
## [37] train-rmse:1.189542+0.024000    test-rmse:1.524258+0.094232 
## [38] train-rmse:1.176022+0.021751    test-rmse:1.514745+0.097761 
## [39] train-rmse:1.163403+0.022120    test-rmse:1.505487+0.098566 
## [40] train-rmse:1.151790+0.021874    test-rmse:1.500834+0.099304 
## [41] train-rmse:1.139980+0.022546    test-rmse:1.496681+0.099502 
## [42] train-rmse:1.126696+0.024291    test-rmse:1.491490+0.102703 
## [43] train-rmse:1.116634+0.024467    test-rmse:1.481625+0.108664 
## [44] train-rmse:1.105757+0.024512    test-rmse:1.476213+0.110361 
## [45] train-rmse:1.094771+0.025093    test-rmse:1.468923+0.113959 
## [46] train-rmse:1.084698+0.024573    test-rmse:1.462526+0.112514 
## [47] train-rmse:1.075902+0.024885    test-rmse:1.456712+0.113371 
## [48] train-rmse:1.066518+0.025473    test-rmse:1.449836+0.111933 
## [49] train-rmse:1.057761+0.025850    test-rmse:1.444264+0.112892 
## [50] train-rmse:1.047707+0.025521    test-rmse:1.437042+0.117314 
## [51] train-rmse:1.038276+0.025642    test-rmse:1.433840+0.118068 
## [52] train-rmse:1.029536+0.025322    test-rmse:1.427522+0.119196 
## [53] train-rmse:1.019226+0.025016    test-rmse:1.421658+0.124073 
## [54] train-rmse:1.010375+0.025439    test-rmse:1.416204+0.124663 
## [55] train-rmse:1.002477+0.026295    test-rmse:1.412296+0.125420 
## [56] train-rmse:0.993450+0.024180    test-rmse:1.410872+0.129139 
## [57] train-rmse:0.985324+0.023726    test-rmse:1.402333+0.132567 
## [58] train-rmse:0.978928+0.023423    test-rmse:1.399319+0.133370 
## [59] train-rmse:0.970649+0.023808    test-rmse:1.396850+0.135576 
## [60] train-rmse:0.963304+0.024462    test-rmse:1.393496+0.133210 
## [61] train-rmse:0.956282+0.025663    test-rmse:1.387678+0.134667 
## [62] train-rmse:0.946766+0.026054    test-rmse:1.383252+0.137779 
## [63] train-rmse:0.935934+0.030094    test-rmse:1.374335+0.139422 
## [64] train-rmse:0.927304+0.030633    test-rmse:1.365206+0.133518 
## [65] train-rmse:0.920691+0.029884    test-rmse:1.361928+0.139313 
## [66] train-rmse:0.913999+0.030063    test-rmse:1.359916+0.138169 
## [67] train-rmse:0.907833+0.029709    test-rmse:1.354442+0.141159 
## [68] train-rmse:0.901251+0.028965    test-rmse:1.347892+0.140636 
## [69] train-rmse:0.895149+0.029861    test-rmse:1.346940+0.142185 
## [70] train-rmse:0.889295+0.028927    test-rmse:1.343718+0.146683 
## [71] train-rmse:0.882114+0.029242    test-rmse:1.338765+0.146298 
## [72] train-rmse:0.877362+0.029207    test-rmse:1.335681+0.147290 
## [73] train-rmse:0.870444+0.028080    test-rmse:1.336788+0.150576 
## [74] train-rmse:0.860747+0.031300    test-rmse:1.330286+0.147841 
## [75] train-rmse:0.854911+0.032577    test-rmse:1.328086+0.147434 
## [76] train-rmse:0.850508+0.033190    test-rmse:1.322714+0.146910 
## [77] train-rmse:0.842805+0.028280    test-rmse:1.320663+0.148534 
## [78] train-rmse:0.835855+0.029146    test-rmse:1.315062+0.145446 
## [79] train-rmse:0.829078+0.025853    test-rmse:1.311022+0.149375 
## [80] train-rmse:0.821086+0.027675    test-rmse:1.305812+0.148882 
## [81] train-rmse:0.816601+0.027222    test-rmse:1.305547+0.149902 
## [82] train-rmse:0.810708+0.027066    test-rmse:1.303893+0.147893 
## [83] train-rmse:0.805555+0.027912    test-rmse:1.302637+0.148763 
## [84] train-rmse:0.800594+0.028864    test-rmse:1.301854+0.149825 
## [85] train-rmse:0.795765+0.030041    test-rmse:1.299822+0.149325 
## [86] train-rmse:0.790475+0.028305    test-rmse:1.298739+0.151013 
## [87] train-rmse:0.785613+0.028051    test-rmse:1.297472+0.151532 
## [88] train-rmse:0.781229+0.028766    test-rmse:1.297162+0.151372 
## [89] train-rmse:0.777587+0.028203    test-rmse:1.296462+0.151769 
## [90] train-rmse:0.774013+0.027903    test-rmse:1.296430+0.152011 
## [91] train-rmse:0.768147+0.027880    test-rmse:1.288414+0.151489 
## [92] train-rmse:0.763469+0.027673    test-rmse:1.285843+0.152970 
## [93] train-rmse:0.759981+0.027561    test-rmse:1.285337+0.153801 
## [94] train-rmse:0.756460+0.028322    test-rmse:1.283379+0.153048 
## [95] train-rmse:0.752217+0.028011    test-rmse:1.283438+0.153302 
## [96] train-rmse:0.747722+0.028939    test-rmse:1.283379+0.155217 
## [97] train-rmse:0.743574+0.028511    test-rmse:1.281515+0.154906 
## [98] train-rmse:0.739115+0.028201    test-rmse:1.281356+0.158440 
## [99] train-rmse:0.735632+0.029249    test-rmse:1.284441+0.158627 
## [100]    train-rmse:0.732410+0.030332    test-rmse:1.281288+0.157329 
## [101]    train-rmse:0.728603+0.031301    test-rmse:1.277176+0.155386 
## [102]    train-rmse:0.723546+0.031785    test-rmse:1.271145+0.154047 
## [103]    train-rmse:0.720594+0.031728    test-rmse:1.270474+0.154015 
## [104]    train-rmse:0.716606+0.032040    test-rmse:1.270012+0.153138 
## [105]    train-rmse:0.713603+0.031832    test-rmse:1.266373+0.155289 
## [106]    train-rmse:0.708103+0.028830    test-rmse:1.266701+0.157375 
## [107]    train-rmse:0.704829+0.028643    test-rmse:1.264777+0.157866 
## [108]    train-rmse:0.702636+0.028471    test-rmse:1.264440+0.157681 
## [109]    train-rmse:0.699606+0.028480    test-rmse:1.264284+0.158008 
## [110]    train-rmse:0.696698+0.028881    test-rmse:1.264303+0.158717 
## [111]    train-rmse:0.694029+0.029099    test-rmse:1.264692+0.159676 
## [112]    train-rmse:0.691181+0.029386    test-rmse:1.263285+0.159870 
## [113]    train-rmse:0.688852+0.029223    test-rmse:1.264619+0.161884 
## [114]    train-rmse:0.686009+0.028795    test-rmse:1.262987+0.162984 
## [115]    train-rmse:0.683868+0.028867    test-rmse:1.261119+0.163208 
## [116]    train-rmse:0.681703+0.028950    test-rmse:1.261707+0.162676 
## [117]    train-rmse:0.677756+0.027010    test-rmse:1.260702+0.163296 
## [118]    train-rmse:0.674127+0.028237    test-rmse:1.261189+0.162826 
## [119]    train-rmse:0.670046+0.028109    test-rmse:1.257030+0.163000 
## [120]    train-rmse:0.667177+0.029035    test-rmse:1.257579+0.165295 
## [121]    train-rmse:0.664996+0.028907    test-rmse:1.256205+0.166319 
## [122]    train-rmse:0.662438+0.028567    test-rmse:1.255714+0.166519 
## [123]    train-rmse:0.660122+0.027896    test-rmse:1.255843+0.167103 
## [124]    train-rmse:0.657970+0.028004    test-rmse:1.255693+0.166114 
## [125]    train-rmse:0.655141+0.028981    test-rmse:1.256309+0.164864 
## [126]    train-rmse:0.653533+0.029164    test-rmse:1.255218+0.164822 
## [127]    train-rmse:0.650837+0.029817    test-rmse:1.253169+0.163879 
## [128]    train-rmse:0.646537+0.029621    test-rmse:1.252638+0.164978 
## [129]    train-rmse:0.643516+0.029439    test-rmse:1.252431+0.165348 
## [130]    train-rmse:0.640892+0.029545    test-rmse:1.252385+0.165760 
## [131]    train-rmse:0.638847+0.029905    test-rmse:1.251287+0.165585 
## [132]    train-rmse:0.637236+0.029800    test-rmse:1.251613+0.166393 
## [133]    train-rmse:0.635151+0.029368    test-rmse:1.250916+0.167302 
## [134]    train-rmse:0.633323+0.029347    test-rmse:1.254617+0.166274 
## [135]    train-rmse:0.631198+0.029911    test-rmse:1.255457+0.166654 
## [136]    train-rmse:0.629528+0.029939    test-rmse:1.255541+0.166882 
## [137]    train-rmse:0.626643+0.028586    test-rmse:1.256061+0.167305 
## [138]    train-rmse:0.624877+0.029525    test-rmse:1.255819+0.168092 
## [139]    train-rmse:0.623111+0.029804    test-rmse:1.255681+0.168005 
## Stopping. Best iteration:
## [133]    train-rmse:0.635151+0.029368    test-rmse:1.250916+0.167302
## 
## 31 
## [1]  train-rmse:8.051639+0.059171    test-rmse:8.047974+0.264527 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.709657+0.048883    test-rmse:6.713727+0.250042 
## [3]  train-rmse:5.620473+0.040109    test-rmse:5.628791+0.237195 
## [4]  train-rmse:4.742311+0.032259    test-rmse:4.752261+0.217757 
## [5]  train-rmse:4.031851+0.027926    test-rmse:4.046914+0.196448 
## [6]  train-rmse:3.455110+0.021131    test-rmse:3.487097+0.164906 
## [7]  train-rmse:2.998732+0.020287    test-rmse:3.045015+0.145351 
## [8]  train-rmse:2.633209+0.015403    test-rmse:2.702101+0.133137 
## [9]  train-rmse:2.347488+0.012946    test-rmse:2.438699+0.125320 
## [10] train-rmse:2.116730+0.017157    test-rmse:2.223952+0.101473 
## [11] train-rmse:1.941189+0.018004    test-rmse:2.083587+0.097187 
## [12] train-rmse:1.800603+0.019644    test-rmse:1.968230+0.087943 
## [13] train-rmse:1.688005+0.020786    test-rmse:1.867522+0.077941 
## [14] train-rmse:1.599409+0.020591    test-rmse:1.806271+0.075225 
## [15] train-rmse:1.525658+0.025435    test-rmse:1.754457+0.071071 
## [16] train-rmse:1.463410+0.026138    test-rmse:1.712461+0.068862 
## [17] train-rmse:1.414598+0.027209    test-rmse:1.672631+0.070638 
## [18] train-rmse:1.367259+0.021924    test-rmse:1.644887+0.072646 
## [19] train-rmse:1.326212+0.023970    test-rmse:1.616509+0.076446 
## [20] train-rmse:1.294224+0.023674    test-rmse:1.600475+0.082283 
## [21] train-rmse:1.263280+0.024277    test-rmse:1.577651+0.091711 
## [22] train-rmse:1.237750+0.025508    test-rmse:1.557091+0.088425 
## [23] train-rmse:1.212482+0.025506    test-rmse:1.539823+0.094591 
## [24] train-rmse:1.188931+0.027298    test-rmse:1.533209+0.098685 
## [25] train-rmse:1.164855+0.026372    test-rmse:1.518625+0.101803 
## [26] train-rmse:1.144693+0.026654    test-rmse:1.508090+0.102378 
## [27] train-rmse:1.121953+0.028316    test-rmse:1.488218+0.106899 
## [28] train-rmse:1.103744+0.029070    test-rmse:1.479934+0.114979 
## [29] train-rmse:1.085257+0.030927    test-rmse:1.468030+0.116189 
## [30] train-rmse:1.067954+0.031013    test-rmse:1.460532+0.120077 
## [31] train-rmse:1.051353+0.033046    test-rmse:1.448549+0.122082 
## [32] train-rmse:1.034952+0.031535    test-rmse:1.444938+0.125373 
## [33] train-rmse:1.016801+0.032663    test-rmse:1.434418+0.133204 
## [34] train-rmse:1.002195+0.034116    test-rmse:1.422709+0.127410 
## [35] train-rmse:0.988340+0.034990    test-rmse:1.411763+0.127022 
## [36] train-rmse:0.974358+0.034280    test-rmse:1.398568+0.128680 
## [37] train-rmse:0.960794+0.034307    test-rmse:1.386560+0.129553 
## [38] train-rmse:0.946116+0.035489    test-rmse:1.384018+0.126643 
## [39] train-rmse:0.932994+0.034504    test-rmse:1.373266+0.137071 
## [40] train-rmse:0.920907+0.034603    test-rmse:1.366745+0.142643 
## [41] train-rmse:0.909753+0.034925    test-rmse:1.366104+0.143914 
## [42] train-rmse:0.897346+0.036748    test-rmse:1.360698+0.141643 
## [43] train-rmse:0.885216+0.038570    test-rmse:1.354190+0.145255 
## [44] train-rmse:0.873740+0.039325    test-rmse:1.351478+0.146763 
## [45] train-rmse:0.864252+0.039696    test-rmse:1.348417+0.149278 
## [46] train-rmse:0.854609+0.040800    test-rmse:1.341919+0.149877 
## [47] train-rmse:0.844990+0.041306    test-rmse:1.338252+0.154010 
## [48] train-rmse:0.834860+0.041455    test-rmse:1.332317+0.154104 
## [49] train-rmse:0.824187+0.039638    test-rmse:1.327596+0.154956 
## [50] train-rmse:0.813314+0.036870    test-rmse:1.327276+0.157061 
## [51] train-rmse:0.806486+0.037373    test-rmse:1.326625+0.154256 
## [52] train-rmse:0.798464+0.037492    test-rmse:1.323726+0.155104 
## [53] train-rmse:0.789345+0.038106    test-rmse:1.325096+0.156050 
## [54] train-rmse:0.781241+0.037149    test-rmse:1.322293+0.156852 
## [55] train-rmse:0.772880+0.037508    test-rmse:1.317367+0.157618 
## [56] train-rmse:0.762802+0.035692    test-rmse:1.314735+0.161517 
## [57] train-rmse:0.754377+0.035859    test-rmse:1.310656+0.158969 
## [58] train-rmse:0.747165+0.036157    test-rmse:1.303473+0.160893 
## [59] train-rmse:0.739431+0.036284    test-rmse:1.299890+0.163189 
## [60] train-rmse:0.732911+0.037236    test-rmse:1.297248+0.164065 
## [61] train-rmse:0.726570+0.038276    test-rmse:1.295125+0.161349 
## [62] train-rmse:0.718867+0.035283    test-rmse:1.296380+0.162201 
## [63] train-rmse:0.713729+0.035776    test-rmse:1.294823+0.161911 
## [64] train-rmse:0.706692+0.034837    test-rmse:1.294239+0.164470 
## [65] train-rmse:0.699561+0.035560    test-rmse:1.291071+0.164801 
## [66] train-rmse:0.693317+0.032382    test-rmse:1.289742+0.166173 
## [67] train-rmse:0.686488+0.033585    test-rmse:1.291169+0.165259 
## [68] train-rmse:0.681098+0.033866    test-rmse:1.288960+0.166039 
## [69] train-rmse:0.675207+0.034093    test-rmse:1.289840+0.166866 
## [70] train-rmse:0.668500+0.034105    test-rmse:1.287200+0.165573 
## [71] train-rmse:0.662800+0.034546    test-rmse:1.284957+0.166030 
## [72] train-rmse:0.657608+0.034120    test-rmse:1.284448+0.164579 
## [73] train-rmse:0.652074+0.034784    test-rmse:1.281231+0.164133 
## [74] train-rmse:0.647343+0.035037    test-rmse:1.280251+0.164089 
## [75] train-rmse:0.642058+0.035617    test-rmse:1.281652+0.163729 
## [76] train-rmse:0.636064+0.036886    test-rmse:1.281403+0.163856 
## [77] train-rmse:0.632048+0.036703    test-rmse:1.280267+0.164044 
## [78] train-rmse:0.628208+0.037251    test-rmse:1.278495+0.165192 
## [79] train-rmse:0.622350+0.035995    test-rmse:1.274204+0.164876 
## [80] train-rmse:0.618511+0.035985    test-rmse:1.274400+0.167149 
## [81] train-rmse:0.611753+0.035301    test-rmse:1.271783+0.168889 
## [82] train-rmse:0.607642+0.035601    test-rmse:1.270893+0.168066 
## [83] train-rmse:0.603773+0.035102    test-rmse:1.271642+0.166869 
## [84] train-rmse:0.598448+0.034902    test-rmse:1.273074+0.167104 
## [85] train-rmse:0.594828+0.035211    test-rmse:1.271012+0.166917 
## [86] train-rmse:0.590983+0.033962    test-rmse:1.269807+0.167713 
## [87] train-rmse:0.587292+0.034350    test-rmse:1.268535+0.169021 
## [88] train-rmse:0.582965+0.031767    test-rmse:1.268153+0.168581 
## [89] train-rmse:0.577429+0.032445    test-rmse:1.264686+0.167194 
## [90] train-rmse:0.572215+0.030777    test-rmse:1.264438+0.166711 
## [91] train-rmse:0.566883+0.029871    test-rmse:1.263281+0.166408 
## [92] train-rmse:0.564222+0.030259    test-rmse:1.263434+0.166138 
## [93] train-rmse:0.560490+0.029927    test-rmse:1.263316+0.166490 
## [94] train-rmse:0.557317+0.029738    test-rmse:1.266681+0.168448 
## [95] train-rmse:0.554861+0.030591    test-rmse:1.266155+0.168291 
## [96] train-rmse:0.551828+0.031234    test-rmse:1.266005+0.168036 
## [97] train-rmse:0.546166+0.031342    test-rmse:1.263322+0.166269 
## Stopping. Best iteration:
## [91] train-rmse:0.566883+0.029871    test-rmse:1.263281+0.166408
## 
## 32 
## [1]  train-rmse:8.048684+0.059471    test-rmse:8.048192+0.264928 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.701828+0.048963    test-rmse:6.710370+0.244974 
## [3]  train-rmse:5.607077+0.040321    test-rmse:5.623191+0.226189 
## [4]  train-rmse:4.717904+0.032310    test-rmse:4.739982+0.201719 
## [5]  train-rmse:4.000863+0.029206    test-rmse:4.030558+0.171346 
## [6]  train-rmse:3.417561+0.024777    test-rmse:3.460864+0.156806 
## [7]  train-rmse:2.947424+0.021225    test-rmse:3.013527+0.146671 
## [8]  train-rmse:2.569899+0.021631    test-rmse:2.652586+0.120636 
## [9]  train-rmse:2.272408+0.018630    test-rmse:2.394267+0.121980 
## [10] train-rmse:2.036626+0.019397    test-rmse:2.186568+0.110765 
## [11] train-rmse:1.839504+0.022559    test-rmse:2.012298+0.086628 
## [12] train-rmse:1.687291+0.020093    test-rmse:1.903142+0.084341 
## [13] train-rmse:1.566431+0.021400    test-rmse:1.805189+0.079498 
## [14] train-rmse:1.468962+0.022098    test-rmse:1.720096+0.071333 
## [15] train-rmse:1.392148+0.020935    test-rmse:1.668989+0.072436 
## [16] train-rmse:1.319635+0.023283    test-rmse:1.621040+0.070941 
## [17] train-rmse:1.264118+0.020240    test-rmse:1.582644+0.076130 
## [18] train-rmse:1.216497+0.019730    test-rmse:1.549974+0.082661 
## [19] train-rmse:1.174090+0.016633    test-rmse:1.524408+0.083967 
## [20] train-rmse:1.137955+0.019605    test-rmse:1.508328+0.086363 
## [21] train-rmse:1.097020+0.015527    test-rmse:1.488799+0.091007 
## [22] train-rmse:1.067394+0.019012    test-rmse:1.467980+0.092846 
## [23] train-rmse:1.040219+0.019580    test-rmse:1.454862+0.097211 
## [24] train-rmse:1.016322+0.018823    test-rmse:1.442629+0.102755 
## [25] train-rmse:0.992692+0.017571    test-rmse:1.430953+0.107217 
## [26] train-rmse:0.970758+0.018403    test-rmse:1.419923+0.113531 
## [27] train-rmse:0.947722+0.019844    test-rmse:1.406891+0.113680 
## [28] train-rmse:0.928984+0.020304    test-rmse:1.402396+0.115562 
## [29] train-rmse:0.911065+0.018002    test-rmse:1.390292+0.119489 
## [30] train-rmse:0.892703+0.016628    test-rmse:1.389932+0.122514 
## [31] train-rmse:0.878842+0.017114    test-rmse:1.384042+0.127247 
## [32] train-rmse:0.861020+0.019974    test-rmse:1.373728+0.128693 
## [33] train-rmse:0.845933+0.017926    test-rmse:1.366427+0.131431 
## [34] train-rmse:0.829924+0.018583    test-rmse:1.357066+0.138250 
## [35] train-rmse:0.814636+0.019543    test-rmse:1.348987+0.138781 
## [36] train-rmse:0.800153+0.017331    test-rmse:1.343314+0.141566 
## [37] train-rmse:0.787800+0.016658    test-rmse:1.339150+0.143117 
## [38] train-rmse:0.774529+0.016572    test-rmse:1.335140+0.146423 
## [39] train-rmse:0.764287+0.014629    test-rmse:1.334303+0.146899 
## [40] train-rmse:0.753514+0.015004    test-rmse:1.329695+0.143855 
## [41] train-rmse:0.740518+0.015418    test-rmse:1.322074+0.146594 
## [42] train-rmse:0.729449+0.015045    test-rmse:1.317343+0.151019 
## [43] train-rmse:0.716787+0.014899    test-rmse:1.311539+0.152808 
## [44] train-rmse:0.706481+0.017645    test-rmse:1.307810+0.152770 
## [45] train-rmse:0.697665+0.017058    test-rmse:1.303644+0.154082 
## [46] train-rmse:0.689061+0.017459    test-rmse:1.299362+0.155470 
## [47] train-rmse:0.680896+0.016576    test-rmse:1.297948+0.157718 
## [48] train-rmse:0.669721+0.014989    test-rmse:1.292856+0.157214 
## [49] train-rmse:0.661430+0.014409    test-rmse:1.293845+0.159094 
## [50] train-rmse:0.651862+0.014976    test-rmse:1.291555+0.160141 
## [51] train-rmse:0.643804+0.015391    test-rmse:1.290724+0.162698 
## [52] train-rmse:0.634317+0.012741    test-rmse:1.287243+0.162881 
## [53] train-rmse:0.624508+0.014669    test-rmse:1.282067+0.161392 
## [54] train-rmse:0.617815+0.014791    test-rmse:1.281306+0.159866 
## [55] train-rmse:0.608852+0.015682    test-rmse:1.274849+0.159647 
## [56] train-rmse:0.600922+0.013784    test-rmse:1.270823+0.159609 
## [57] train-rmse:0.593648+0.013577    test-rmse:1.270491+0.160687 
## [58] train-rmse:0.587902+0.013907    test-rmse:1.268385+0.161786 
## [59] train-rmse:0.582783+0.014239    test-rmse:1.268119+0.161659 
## [60] train-rmse:0.576567+0.014892    test-rmse:1.268109+0.161149 
## [61] train-rmse:0.569877+0.014527    test-rmse:1.267657+0.163703 
## [62] train-rmse:0.564054+0.013645    test-rmse:1.265733+0.164140 
## [63] train-rmse:0.557957+0.012949    test-rmse:1.262946+0.165859 
## [64] train-rmse:0.551057+0.014691    test-rmse:1.262085+0.168669 
## [65] train-rmse:0.546084+0.013746    test-rmse:1.261124+0.169101 
## [66] train-rmse:0.541292+0.013143    test-rmse:1.261842+0.168895 
## [67] train-rmse:0.535869+0.013856    test-rmse:1.258568+0.169075 
## [68] train-rmse:0.531458+0.013830    test-rmse:1.257394+0.170430 
## [69] train-rmse:0.525918+0.014552    test-rmse:1.254270+0.169294 
## [70] train-rmse:0.521374+0.015161    test-rmse:1.254101+0.167479 
## [71] train-rmse:0.516192+0.014712    test-rmse:1.254304+0.167561 
## [72] train-rmse:0.511441+0.014522    test-rmse:1.255208+0.169170 
## [73] train-rmse:0.505836+0.013961    test-rmse:1.254341+0.169776 
## [74] train-rmse:0.500925+0.012994    test-rmse:1.253551+0.171561 
## [75] train-rmse:0.497102+0.012315    test-rmse:1.254159+0.170176 
## [76] train-rmse:0.489789+0.014176    test-rmse:1.253524+0.170206 
## [77] train-rmse:0.485751+0.013618    test-rmse:1.252004+0.170892 
## [78] train-rmse:0.480131+0.012248    test-rmse:1.254251+0.169680 
## [79] train-rmse:0.476829+0.011514    test-rmse:1.254662+0.170031 
## [80] train-rmse:0.471807+0.010562    test-rmse:1.254767+0.169967 
## [81] train-rmse:0.468584+0.010718    test-rmse:1.254847+0.169838 
## [82] train-rmse:0.465127+0.010454    test-rmse:1.254435+0.168701 
## [83] train-rmse:0.459991+0.012341    test-rmse:1.253268+0.167181 
## Stopping. Best iteration:
## [77] train-rmse:0.485751+0.013618    test-rmse:1.252004+0.170892
## 
## 33 
## [1]  train-rmse:8.048058+0.059624    test-rmse:8.048831+0.259144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.698121+0.049205    test-rmse:6.702773+0.242866 
## [3]  train-rmse:5.599459+0.040379    test-rmse:5.608384+0.217884 
## [4]  train-rmse:4.705984+0.033831    test-rmse:4.731683+0.191969 
## [5]  train-rmse:3.980292+0.031045    test-rmse:4.009253+0.168640 
## [6]  train-rmse:3.388506+0.024443    test-rmse:3.424835+0.151770 
## [7]  train-rmse:2.908644+0.020875    test-rmse:2.957974+0.132744 
## [8]  train-rmse:2.522658+0.018842    test-rmse:2.608712+0.124723 
## [9]  train-rmse:2.213458+0.017914    test-rmse:2.336957+0.111889 
## [10] train-rmse:1.961833+0.021504    test-rmse:2.099274+0.099366 
## [11] train-rmse:1.757826+0.019705    test-rmse:1.941364+0.094725 
## [12] train-rmse:1.598899+0.020517    test-rmse:1.810774+0.092887 
## [13] train-rmse:1.465618+0.021870    test-rmse:1.703053+0.082725 
## [14] train-rmse:1.357483+0.020849    test-rmse:1.627396+0.084094 
## [15] train-rmse:1.267852+0.025613    test-rmse:1.564430+0.080489 
## [16] train-rmse:1.193185+0.027180    test-rmse:1.511918+0.082142 
## [17] train-rmse:1.131547+0.027334    test-rmse:1.474721+0.082840 
## [18] train-rmse:1.080284+0.027973    test-rmse:1.445450+0.094105 
## [19] train-rmse:1.035452+0.028166    test-rmse:1.415144+0.098256 
## [20] train-rmse:0.995844+0.029710    test-rmse:1.394824+0.099662 
## [21] train-rmse:0.960088+0.032038    test-rmse:1.380783+0.103977 
## [22] train-rmse:0.927584+0.025566    test-rmse:1.360846+0.118953 
## [23] train-rmse:0.897757+0.029396    test-rmse:1.353399+0.113093 
## [24] train-rmse:0.871516+0.028200    test-rmse:1.337372+0.125417 
## [25] train-rmse:0.846770+0.027243    test-rmse:1.331709+0.129313 
## [26] train-rmse:0.824262+0.029031    test-rmse:1.321064+0.127782 
## [27] train-rmse:0.805130+0.029578    test-rmse:1.312761+0.130346 
## [28] train-rmse:0.784412+0.028923    test-rmse:1.302518+0.132002 
## [29] train-rmse:0.764619+0.028493    test-rmse:1.292487+0.135849 
## [30] train-rmse:0.746001+0.029044    test-rmse:1.282101+0.133242 
## [31] train-rmse:0.730584+0.028938    test-rmse:1.275170+0.138075 
## [32] train-rmse:0.711935+0.028661    test-rmse:1.269548+0.138436 
## [33] train-rmse:0.695201+0.025229    test-rmse:1.260030+0.146548 
## [34] train-rmse:0.682560+0.025649    test-rmse:1.257051+0.146320 
## [35] train-rmse:0.669803+0.026970    test-rmse:1.251503+0.148333 
## [36] train-rmse:0.655740+0.026036    test-rmse:1.247678+0.145715 
## [37] train-rmse:0.643260+0.024133    test-rmse:1.245929+0.150002 
## [38] train-rmse:0.630352+0.022120    test-rmse:1.242902+0.150430 
## [39] train-rmse:0.617335+0.021668    test-rmse:1.241701+0.150734 
## [40] train-rmse:0.606894+0.019784    test-rmse:1.235717+0.152280 
## [41] train-rmse:0.597037+0.018933    test-rmse:1.236151+0.151788 
## [42] train-rmse:0.586076+0.019701    test-rmse:1.234908+0.151318 
## [43] train-rmse:0.577886+0.020185    test-rmse:1.231481+0.153597 
## [44] train-rmse:0.568354+0.021965    test-rmse:1.230698+0.154726 
## [45] train-rmse:0.559804+0.019878    test-rmse:1.227064+0.154046 
## [46] train-rmse:0.549475+0.020977    test-rmse:1.225743+0.156425 
## [47] train-rmse:0.540287+0.020832    test-rmse:1.225226+0.159672 
## [48] train-rmse:0.531520+0.021031    test-rmse:1.223907+0.160844 
## [49] train-rmse:0.523831+0.021065    test-rmse:1.221397+0.161209 
## [50] train-rmse:0.514375+0.019266    test-rmse:1.221600+0.162002 
## [51] train-rmse:0.506420+0.019183    test-rmse:1.220423+0.160933 
## [52] train-rmse:0.498254+0.017101    test-rmse:1.221379+0.160201 
## [53] train-rmse:0.490415+0.016622    test-rmse:1.221023+0.161314 
## [54] train-rmse:0.482433+0.016873    test-rmse:1.219217+0.161004 
## [55] train-rmse:0.475398+0.016952    test-rmse:1.218345+0.162578 
## [56] train-rmse:0.469829+0.017067    test-rmse:1.216242+0.162411 
## [57] train-rmse:0.463879+0.017537    test-rmse:1.215877+0.162117 
## [58] train-rmse:0.455404+0.016945    test-rmse:1.215363+0.161275 
## [59] train-rmse:0.448377+0.018266    test-rmse:1.214879+0.159185 
## [60] train-rmse:0.443214+0.017745    test-rmse:1.213595+0.159198 
## [61] train-rmse:0.437899+0.017412    test-rmse:1.212775+0.157478 
## [62] train-rmse:0.432770+0.016878    test-rmse:1.212362+0.158087 
## [63] train-rmse:0.425045+0.017243    test-rmse:1.208783+0.158175 
## [64] train-rmse:0.419957+0.018051    test-rmse:1.208320+0.159756 
## [65] train-rmse:0.415283+0.017056    test-rmse:1.209096+0.159363 
## [66] train-rmse:0.410153+0.017019    test-rmse:1.209107+0.159069 
## [67] train-rmse:0.405833+0.017034    test-rmse:1.210317+0.159126 
## [68] train-rmse:0.400257+0.017471    test-rmse:1.211525+0.157680 
## [69] train-rmse:0.394367+0.018456    test-rmse:1.210247+0.158316 
## [70] train-rmse:0.388656+0.017824    test-rmse:1.210349+0.158936 
## Stopping. Best iteration:
## [64] train-rmse:0.419957+0.018051    test-rmse:1.208320+0.159756
## 
## 34 
## [1]  train-rmse:8.048058+0.059624    test-rmse:8.048831+0.259144 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.696936+0.049378    test-rmse:6.707031+0.235249 
## [3]  train-rmse:5.594928+0.041310    test-rmse:5.609382+0.212385 
## [4]  train-rmse:4.697124+0.034570    test-rmse:4.725340+0.188819 
## [5]  train-rmse:3.966086+0.032032    test-rmse:3.994053+0.167230 
## [6]  train-rmse:3.369063+0.026354    test-rmse:3.403156+0.146011 
## [7]  train-rmse:2.881973+0.021380    test-rmse:2.933326+0.131634 
## [8]  train-rmse:2.489678+0.017722    test-rmse:2.576904+0.123083 
## [9]  train-rmse:2.172998+0.014233    test-rmse:2.311825+0.120416 
## [10] train-rmse:1.911575+0.013043    test-rmse:2.079269+0.116722 
## [11] train-rmse:1.703359+0.012902    test-rmse:1.905113+0.115181 
## [12] train-rmse:1.527475+0.015239    test-rmse:1.766558+0.101122 
## [13] train-rmse:1.385922+0.014925    test-rmse:1.652194+0.100808 
## [14] train-rmse:1.271266+0.011684    test-rmse:1.584714+0.098494 
## [15] train-rmse:1.173409+0.015776    test-rmse:1.515788+0.099587 
## [16] train-rmse:1.095086+0.013003    test-rmse:1.475110+0.100514 
## [17] train-rmse:1.025670+0.016957    test-rmse:1.434524+0.108255 
## [18] train-rmse:0.971611+0.014213    test-rmse:1.407723+0.108054 
## [19] train-rmse:0.922011+0.014762    test-rmse:1.376751+0.118849 
## [20] train-rmse:0.880617+0.013956    test-rmse:1.356433+0.124780 
## [21] train-rmse:0.841518+0.017966    test-rmse:1.338949+0.135148 
## [22] train-rmse:0.806544+0.016639    test-rmse:1.317264+0.139545 
## [23] train-rmse:0.777648+0.016905    test-rmse:1.311609+0.141530 
## [24] train-rmse:0.751735+0.016553    test-rmse:1.299031+0.148133 
## [25] train-rmse:0.727488+0.015969    test-rmse:1.289104+0.151199 
## [26] train-rmse:0.705210+0.017048    test-rmse:1.276417+0.154234 
## [27] train-rmse:0.683446+0.014110    test-rmse:1.271205+0.160207 
## [28] train-rmse:0.664587+0.009101    test-rmse:1.262650+0.165574 
## [29] train-rmse:0.646865+0.010997    test-rmse:1.257023+0.164945 
## [30] train-rmse:0.629019+0.010134    test-rmse:1.253079+0.167220 
## [31] train-rmse:0.611591+0.011864    test-rmse:1.251659+0.165956 
## [32] train-rmse:0.598894+0.010367    test-rmse:1.248266+0.165873 
## [33] train-rmse:0.581355+0.008162    test-rmse:1.247038+0.170884 
## [34] train-rmse:0.566383+0.008637    test-rmse:1.242752+0.168195 
## [35] train-rmse:0.554174+0.009177    test-rmse:1.238101+0.169587 
## [36] train-rmse:0.537589+0.009203    test-rmse:1.230161+0.167615 
## [37] train-rmse:0.525411+0.008686    test-rmse:1.225773+0.168156 
## [38] train-rmse:0.514490+0.008194    test-rmse:1.225185+0.169773 
## [39] train-rmse:0.504374+0.008320    test-rmse:1.223980+0.170855 
## [40] train-rmse:0.492919+0.007791    test-rmse:1.223137+0.171140 
## [41] train-rmse:0.482837+0.007784    test-rmse:1.221463+0.167547 
## [42] train-rmse:0.473957+0.008023    test-rmse:1.220910+0.170320 
## [43] train-rmse:0.464175+0.008154    test-rmse:1.218963+0.171683 
## [44] train-rmse:0.455165+0.006924    test-rmse:1.218850+0.171145 
## [45] train-rmse:0.444995+0.008365    test-rmse:1.218914+0.171862 
## [46] train-rmse:0.434755+0.007898    test-rmse:1.219658+0.173080 
## [47] train-rmse:0.426396+0.008068    test-rmse:1.220308+0.172450 
## [48] train-rmse:0.419205+0.008113    test-rmse:1.218234+0.172448 
## [49] train-rmse:0.412313+0.008535    test-rmse:1.217899+0.173163 
## [50] train-rmse:0.404718+0.009361    test-rmse:1.218142+0.172402 
## [51] train-rmse:0.397931+0.007967    test-rmse:1.218417+0.173353 
## [52] train-rmse:0.390664+0.007632    test-rmse:1.215940+0.173978 
## [53] train-rmse:0.384368+0.006663    test-rmse:1.215450+0.174359 
## [54] train-rmse:0.378045+0.009239    test-rmse:1.216336+0.174454 
## [55] train-rmse:0.371557+0.008444    test-rmse:1.215686+0.173436 
## [56] train-rmse:0.364659+0.009453    test-rmse:1.216011+0.174479 
## [57] train-rmse:0.359423+0.009834    test-rmse:1.216239+0.174257 
## [58] train-rmse:0.354192+0.010626    test-rmse:1.213480+0.174923 
## [59] train-rmse:0.349075+0.009867    test-rmse:1.212521+0.175716 
## [60] train-rmse:0.342785+0.010970    test-rmse:1.213467+0.175065 
## [61] train-rmse:0.338156+0.011595    test-rmse:1.213983+0.175962 
## [62] train-rmse:0.331750+0.012223    test-rmse:1.213972+0.174739 
## [63] train-rmse:0.327683+0.011753    test-rmse:1.212798+0.174420 
## [64] train-rmse:0.321247+0.010459    test-rmse:1.212388+0.174881 
## [65] train-rmse:0.317110+0.010196    test-rmse:1.214045+0.174453 
## [66] train-rmse:0.312814+0.009783    test-rmse:1.214254+0.175737 
## [67] train-rmse:0.308515+0.009226    test-rmse:1.214270+0.175624 
## [68] train-rmse:0.304343+0.009290    test-rmse:1.215024+0.175649 
## [69] train-rmse:0.300650+0.009285    test-rmse:1.215975+0.175031 
## [70] train-rmse:0.295409+0.010225    test-rmse:1.214701+0.174439 
## Stopping. Best iteration:
## [64] train-rmse:0.321247+0.010459    test-rmse:1.212388+0.174881
## 
## 35 
## [1]  train-rmse:7.908694+0.055927    test-rmse:7.904554+0.270795 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.499872+0.042468    test-rmse:6.493187+0.264877 
## [3]  train-rmse:5.401059+0.032435    test-rmse:5.393868+0.258347 
## [4]  train-rmse:4.553801+0.023800    test-rmse:4.556607+0.241748 
## [5]  train-rmse:3.909760+0.017683    test-rmse:3.917387+0.215083 
## [6]  train-rmse:3.429148+0.013615    test-rmse:3.445671+0.193907 
## [7]  train-rmse:3.075590+0.011617    test-rmse:3.103803+0.155243 
## [8]  train-rmse:2.819733+0.011351    test-rmse:2.847101+0.133034 
## [9]  train-rmse:2.636003+0.012090    test-rmse:2.667122+0.102228 
## [10] train-rmse:2.505960+0.013803    test-rmse:2.548212+0.077211 
## [11] train-rmse:2.413852+0.014895    test-rmse:2.451644+0.061787 
## [12] train-rmse:2.347449+0.015603    test-rmse:2.389828+0.052558 
## [13] train-rmse:2.297732+0.016535    test-rmse:2.339413+0.050086 
## [14] train-rmse:2.260791+0.017248    test-rmse:2.302412+0.052104 
## [15] train-rmse:2.232923+0.018189    test-rmse:2.274973+0.049947 
## [16] train-rmse:2.210149+0.018670    test-rmse:2.251243+0.060015 
## [17] train-rmse:2.190625+0.019232    test-rmse:2.240160+0.063967 
## [18] train-rmse:2.173529+0.019827    test-rmse:2.222867+0.068958 
## [19] train-rmse:2.158041+0.020454    test-rmse:2.213869+0.072131 
## [20] train-rmse:2.143982+0.021131    test-rmse:2.199867+0.079433 
## [21] train-rmse:2.131239+0.021744    test-rmse:2.185245+0.079158 
## [22] train-rmse:2.118928+0.022364    test-rmse:2.175479+0.078438 
## [23] train-rmse:2.107237+0.023079    test-rmse:2.167625+0.080254 
## [24] train-rmse:2.096118+0.023250    test-rmse:2.162558+0.081300 
## [25] train-rmse:2.085434+0.023685    test-rmse:2.146629+0.089313 
## [26] train-rmse:2.075182+0.023992    test-rmse:2.135087+0.090148 
## [27] train-rmse:2.065051+0.024423    test-rmse:2.131286+0.090865 
## [28] train-rmse:2.055027+0.024957    test-rmse:2.119543+0.084767 
## [29] train-rmse:2.045475+0.025723    test-rmse:2.110724+0.084074 
## [30] train-rmse:2.035977+0.026236    test-rmse:2.100217+0.083610 
## [31] train-rmse:2.026489+0.026635    test-rmse:2.095214+0.087592 
## [32] train-rmse:2.017221+0.027181    test-rmse:2.087445+0.085090 
## [33] train-rmse:2.008419+0.027383    test-rmse:2.075809+0.083737 
## [34] train-rmse:1.999866+0.028060    test-rmse:2.066728+0.086748 
## [35] train-rmse:1.991441+0.028577    test-rmse:2.058616+0.090320 
## [36] train-rmse:1.983256+0.029077    test-rmse:2.054627+0.092437 
## [37] train-rmse:1.975283+0.029322    test-rmse:2.050732+0.090120 
## [38] train-rmse:1.967526+0.029652    test-rmse:2.043598+0.092729 
## [39] train-rmse:1.959774+0.029837    test-rmse:2.035053+0.093409 
## [40] train-rmse:1.952251+0.030148    test-rmse:2.025928+0.092793 
## [41] train-rmse:1.944778+0.030446    test-rmse:2.017866+0.090247 
## [42] train-rmse:1.937359+0.030693    test-rmse:2.015134+0.094255 
## [43] train-rmse:1.930079+0.031012    test-rmse:2.007810+0.088793 
## [44] train-rmse:1.922960+0.031364    test-rmse:1.999202+0.085149 
## [45] train-rmse:1.915998+0.031761    test-rmse:1.991083+0.087355 
## [46] train-rmse:1.909153+0.032228    test-rmse:1.988143+0.084382 
## [47] train-rmse:1.902357+0.032583    test-rmse:1.986069+0.089321 
## [48] train-rmse:1.895766+0.033056    test-rmse:1.979416+0.089842 
## [49] train-rmse:1.889215+0.033369    test-rmse:1.975444+0.088869 
## [50] train-rmse:1.882939+0.033714    test-rmse:1.967805+0.087463 
## [51] train-rmse:1.876644+0.034203    test-rmse:1.959363+0.093553 
## [52] train-rmse:1.870379+0.034758    test-rmse:1.953962+0.093753 
## [53] train-rmse:1.864247+0.035289    test-rmse:1.948815+0.091811 
## [54] train-rmse:1.858238+0.035771    test-rmse:1.947221+0.094254 
## [55] train-rmse:1.852346+0.036261    test-rmse:1.942703+0.091980 
## [56] train-rmse:1.846599+0.036662    test-rmse:1.936581+0.090103 
## [57] train-rmse:1.840954+0.037056    test-rmse:1.929292+0.087030 
## [58] train-rmse:1.835346+0.037335    test-rmse:1.926896+0.088473 
## [59] train-rmse:1.829743+0.037634    test-rmse:1.922463+0.090870 
## [60] train-rmse:1.824314+0.037799    test-rmse:1.912388+0.090894 
## [61] train-rmse:1.818789+0.038047    test-rmse:1.907505+0.091087 
## [62] train-rmse:1.813315+0.038285    test-rmse:1.903224+0.090096 
## [63] train-rmse:1.807840+0.038582    test-rmse:1.897959+0.090427 
## [64] train-rmse:1.802566+0.038859    test-rmse:1.897363+0.089144 
## [65] train-rmse:1.797339+0.039087    test-rmse:1.894985+0.090927 
## [66] train-rmse:1.792177+0.039406    test-rmse:1.891239+0.088926 
## [67] train-rmse:1.786981+0.039678    test-rmse:1.887119+0.088469 
## [68] train-rmse:1.782011+0.039984    test-rmse:1.884054+0.092744 
## [69] train-rmse:1.777132+0.040259    test-rmse:1.881102+0.092461 
## [70] train-rmse:1.772355+0.040544    test-rmse:1.875704+0.092713 
## [71] train-rmse:1.767532+0.040739    test-rmse:1.870451+0.094246 
## [72] train-rmse:1.762833+0.041019    test-rmse:1.867975+0.095245 
## [73] train-rmse:1.758231+0.041304    test-rmse:1.859815+0.095841 
## [74] train-rmse:1.753752+0.041543    test-rmse:1.857089+0.098282 
## [75] train-rmse:1.749319+0.041823    test-rmse:1.851253+0.097501 
## [76] train-rmse:1.744906+0.042132    test-rmse:1.845395+0.098931 
## [77] train-rmse:1.740665+0.042436    test-rmse:1.843470+0.098322 
## [78] train-rmse:1.736373+0.042728    test-rmse:1.839694+0.096712 
## [79] train-rmse:1.732093+0.042964    test-rmse:1.833524+0.099121 
## [80] train-rmse:1.727857+0.043194    test-rmse:1.831116+0.100284 
## [81] train-rmse:1.723631+0.043417    test-rmse:1.826554+0.099373 
## [82] train-rmse:1.719477+0.043671    test-rmse:1.825015+0.098268 
## [83] train-rmse:1.715373+0.044012    test-rmse:1.822614+0.101019 
## [84] train-rmse:1.711319+0.044291    test-rmse:1.820413+0.102608 
## [85] train-rmse:1.707348+0.044551    test-rmse:1.814247+0.102054 
## [86] train-rmse:1.703356+0.044864    test-rmse:1.811225+0.104025 
## [87] train-rmse:1.699422+0.045130    test-rmse:1.808761+0.105123 
## [88] train-rmse:1.695564+0.045320    test-rmse:1.806911+0.107262 
## [89] train-rmse:1.691750+0.045457    test-rmse:1.807584+0.108589 
## [90] train-rmse:1.688034+0.045510    test-rmse:1.802568+0.107979 
## [91] train-rmse:1.684387+0.045586    test-rmse:1.799253+0.106549 
## [92] train-rmse:1.680727+0.045626    test-rmse:1.796012+0.106768 
## [93] train-rmse:1.677184+0.045760    test-rmse:1.793537+0.106909 
## [94] train-rmse:1.673679+0.045841    test-rmse:1.791397+0.106542 
## [95] train-rmse:1.670205+0.045986    test-rmse:1.790134+0.102368 
## [96] train-rmse:1.666736+0.046156    test-rmse:1.788894+0.102664 
## [97] train-rmse:1.663307+0.046312    test-rmse:1.786374+0.104703 
## [98] train-rmse:1.659870+0.046520    test-rmse:1.781521+0.104080 
## [99] train-rmse:1.656514+0.046732    test-rmse:1.781155+0.106050 
## [100]    train-rmse:1.653167+0.046920    test-rmse:1.778737+0.107248 
## [101]    train-rmse:1.649820+0.047135    test-rmse:1.774607+0.108491 
## [102]    train-rmse:1.646554+0.047259    test-rmse:1.772090+0.107095 
## [103]    train-rmse:1.643281+0.047444    test-rmse:1.765374+0.111131 
## [104]    train-rmse:1.640100+0.047559    test-rmse:1.757665+0.110390 
## [105]    train-rmse:1.636929+0.047708    test-rmse:1.753575+0.110719 
## [106]    train-rmse:1.633818+0.047845    test-rmse:1.752455+0.111849 
## [107]    train-rmse:1.630779+0.047990    test-rmse:1.748382+0.113555 
## [108]    train-rmse:1.627726+0.048089    test-rmse:1.745656+0.114230 
## [109]    train-rmse:1.624714+0.048250    test-rmse:1.745389+0.115707 
## [110]    train-rmse:1.621785+0.048387    test-rmse:1.743628+0.117744 
## [111]    train-rmse:1.618816+0.048517    test-rmse:1.741493+0.119497 
## [112]    train-rmse:1.615858+0.048577    test-rmse:1.742230+0.121328 
## [113]    train-rmse:1.612974+0.048665    test-rmse:1.740251+0.120773 
## [114]    train-rmse:1.610131+0.048814    test-rmse:1.738712+0.120873 
## [115]    train-rmse:1.607341+0.048958    test-rmse:1.734055+0.119830 
## [116]    train-rmse:1.604625+0.049054    test-rmse:1.730686+0.119551 
## [117]    train-rmse:1.601889+0.049162    test-rmse:1.728422+0.120353 
## [118]    train-rmse:1.599197+0.049252    test-rmse:1.726087+0.119625 
## [119]    train-rmse:1.596515+0.049358    test-rmse:1.723181+0.120778 
## [120]    train-rmse:1.593877+0.049450    test-rmse:1.719334+0.120957 
## [121]    train-rmse:1.591231+0.049582    test-rmse:1.716415+0.122880 
## [122]    train-rmse:1.588610+0.049728    test-rmse:1.712400+0.123579 
## [123]    train-rmse:1.586015+0.049824    test-rmse:1.709975+0.125068 
## [124]    train-rmse:1.583404+0.049919    test-rmse:1.707345+0.124230 
## [125]    train-rmse:1.580827+0.050000    test-rmse:1.706202+0.125392 
## [126]    train-rmse:1.578261+0.050048    test-rmse:1.707422+0.121748 
## [127]    train-rmse:1.575713+0.050106    test-rmse:1.707104+0.127469 
## [128]    train-rmse:1.573217+0.050169    test-rmse:1.701934+0.131214 
## [129]    train-rmse:1.570801+0.050269    test-rmse:1.697637+0.128740 
## [130]    train-rmse:1.568403+0.050345    test-rmse:1.695845+0.127917 
## [131]    train-rmse:1.566008+0.050415    test-rmse:1.693038+0.128099 
## [132]    train-rmse:1.563643+0.050469    test-rmse:1.691061+0.129261 
## [133]    train-rmse:1.561287+0.050486    test-rmse:1.690000+0.129582 
## [134]    train-rmse:1.558969+0.050527    test-rmse:1.687671+0.129556 
## [135]    train-rmse:1.556695+0.050573    test-rmse:1.684989+0.130426 
## [136]    train-rmse:1.554405+0.050620    test-rmse:1.682612+0.132014 
## [137]    train-rmse:1.552156+0.050656    test-rmse:1.682695+0.130826 
## [138]    train-rmse:1.549960+0.050694    test-rmse:1.679255+0.131728 
## [139]    train-rmse:1.547780+0.050753    test-rmse:1.679026+0.133492 
## [140]    train-rmse:1.545609+0.050831    test-rmse:1.677250+0.133219 
## [141]    train-rmse:1.543493+0.050895    test-rmse:1.673410+0.132937 
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## [245]    train-rmse:1.402796+0.053374    test-rmse:1.544636+0.171293 
## [246]    train-rmse:1.401990+0.053382    test-rmse:1.545221+0.171370 
## [247]    train-rmse:1.401197+0.053403    test-rmse:1.544409+0.170696 
## [248]    train-rmse:1.400421+0.053425    test-rmse:1.543308+0.170252 
## [249]    train-rmse:1.399654+0.053443    test-rmse:1.543635+0.170873 
## Stopping. Best iteration:
## [243]    train-rmse:1.404465+0.053372    test-rmse:1.543021+0.169365
## 
## 36 
## [1]  train-rmse:7.883326+0.056278    test-rmse:7.879588+0.266523 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.451047+0.046276    test-rmse:6.443117+0.251766 
## [3]  train-rmse:5.330540+0.039397    test-rmse:5.329892+0.230513 
## [4]  train-rmse:4.454277+0.032458    test-rmse:4.468604+0.215713 
## [5]  train-rmse:3.778179+0.031574    test-rmse:3.797720+0.187139 
## [6]  train-rmse:3.268291+0.029637    test-rmse:3.303118+0.155267 
## [7]  train-rmse:2.879840+0.031544    test-rmse:2.928340+0.122437 
## [8]  train-rmse:2.591149+0.031057    test-rmse:2.650773+0.098799 
## [9]  train-rmse:2.376743+0.028077    test-rmse:2.453404+0.081184 
## [10] train-rmse:2.217132+0.030884    test-rmse:2.309200+0.059946 
## [11] train-rmse:2.103930+0.030493    test-rmse:2.207655+0.056962 
## [12] train-rmse:2.016484+0.021992    test-rmse:2.124534+0.051926 
## [13] train-rmse:1.954148+0.023996    test-rmse:2.074005+0.063185 
## [14] train-rmse:1.904324+0.024793    test-rmse:2.027444+0.068874 
## [15] train-rmse:1.862565+0.023684    test-rmse:2.000318+0.075099 
## [16] train-rmse:1.829702+0.025872    test-rmse:1.970879+0.081300 
## [17] train-rmse:1.802832+0.026726    test-rmse:1.948092+0.082674 
## [18] train-rmse:1.775315+0.032053    test-rmse:1.918669+0.085925 
## [19] train-rmse:1.754568+0.032884    test-rmse:1.904042+0.089119 
## [20] train-rmse:1.734938+0.033472    test-rmse:1.888059+0.104008 
## [21] train-rmse:1.717727+0.033119    test-rmse:1.871893+0.107462 
## [22] train-rmse:1.697822+0.032389    test-rmse:1.860110+0.106692 
## [23] train-rmse:1.676182+0.039169    test-rmse:1.848898+0.111884 
## [24] train-rmse:1.656328+0.039227    test-rmse:1.819272+0.091775 
## [25] train-rmse:1.641173+0.040689    test-rmse:1.806062+0.093834 
## [26] train-rmse:1.626772+0.041965    test-rmse:1.795921+0.089255 
## [27] train-rmse:1.611241+0.042891    test-rmse:1.783930+0.085488 
## [28] train-rmse:1.595273+0.041832    test-rmse:1.775773+0.085111 
## [29] train-rmse:1.582226+0.043375    test-rmse:1.765178+0.081686 
## [30] train-rmse:1.565692+0.046580    test-rmse:1.754889+0.091879 
## [31] train-rmse:1.552916+0.047743    test-rmse:1.745748+0.091053 
## [32] train-rmse:1.539908+0.048236    test-rmse:1.736575+0.090945 
## [33] train-rmse:1.527080+0.047318    test-rmse:1.724363+0.089879 
## [34] train-rmse:1.514736+0.048182    test-rmse:1.716526+0.094046 
## [35] train-rmse:1.503416+0.048543    test-rmse:1.705170+0.090685 
## [36] train-rmse:1.488588+0.054488    test-rmse:1.699062+0.090116 
## [37] train-rmse:1.478142+0.054864    test-rmse:1.693368+0.091108 
## [38] train-rmse:1.466605+0.054586    test-rmse:1.687209+0.096012 
## [39] train-rmse:1.452328+0.057769    test-rmse:1.674354+0.093179 
## [40] train-rmse:1.442359+0.057853    test-rmse:1.668239+0.092770 
## [41] train-rmse:1.432862+0.058890    test-rmse:1.663276+0.100092 
## [42] train-rmse:1.422708+0.060073    test-rmse:1.658539+0.099489 
## [43] train-rmse:1.411229+0.058964    test-rmse:1.647759+0.101950 
## [44] train-rmse:1.401487+0.059838    test-rmse:1.641373+0.101432 
## [45] train-rmse:1.392481+0.060058    test-rmse:1.635563+0.098363 
## [46] train-rmse:1.379997+0.058618    test-rmse:1.625377+0.098760 
## [47] train-rmse:1.370891+0.059926    test-rmse:1.618819+0.101039 
## [48] train-rmse:1.362914+0.060455    test-rmse:1.613266+0.100492 
## [49] train-rmse:1.354239+0.060020    test-rmse:1.607117+0.099630 
## [50] train-rmse:1.346678+0.060796    test-rmse:1.601316+0.098616 
## [51] train-rmse:1.337494+0.061575    test-rmse:1.590206+0.104317 
## [52] train-rmse:1.329312+0.062285    test-rmse:1.582754+0.107004 
## [53] train-rmse:1.321423+0.062391    test-rmse:1.581909+0.112749 
## [54] train-rmse:1.310038+0.060224    test-rmse:1.572781+0.114875 
## [55] train-rmse:1.301747+0.059066    test-rmse:1.567663+0.115308 
## [56] train-rmse:1.294896+0.059437    test-rmse:1.560306+0.113641 
## [57] train-rmse:1.288296+0.059841    test-rmse:1.551447+0.114476 
## [58] train-rmse:1.281220+0.060714    test-rmse:1.549417+0.114677 
## [59] train-rmse:1.275268+0.061324    test-rmse:1.546884+0.115463 
## [60] train-rmse:1.268465+0.061608    test-rmse:1.543479+0.118437 
## [61] train-rmse:1.262105+0.062290    test-rmse:1.540481+0.119390 
## [62] train-rmse:1.256199+0.063024    test-rmse:1.540996+0.118464 
## [63] train-rmse:1.248764+0.062849    test-rmse:1.539225+0.120672 
## [64] train-rmse:1.242431+0.062807    test-rmse:1.534634+0.122542 
## [65] train-rmse:1.236404+0.062825    test-rmse:1.529628+0.128136 
## [66] train-rmse:1.222850+0.054677    test-rmse:1.517336+0.134181 
## [67] train-rmse:1.211741+0.053246    test-rmse:1.501816+0.114877 
## [68] train-rmse:1.200702+0.053576    test-rmse:1.489804+0.113064 
## [69] train-rmse:1.194086+0.054336    test-rmse:1.483851+0.114949 
## [70] train-rmse:1.189222+0.054734    test-rmse:1.478178+0.116567 
## [71] train-rmse:1.184178+0.054994    test-rmse:1.472989+0.116698 
## [72] train-rmse:1.179186+0.055404    test-rmse:1.473615+0.116918 
## [73] train-rmse:1.174074+0.055513    test-rmse:1.470947+0.116446 
## [74] train-rmse:1.168945+0.056074    test-rmse:1.470763+0.116525 
## [75] train-rmse:1.162674+0.056347    test-rmse:1.467334+0.118447 
## [76] train-rmse:1.157311+0.056258    test-rmse:1.464351+0.118724 
## [77] train-rmse:1.152057+0.057505    test-rmse:1.466149+0.117001 
## [78] train-rmse:1.147607+0.057601    test-rmse:1.459890+0.117712 
## [79] train-rmse:1.143260+0.057663    test-rmse:1.453394+0.116915 
## [80] train-rmse:1.138802+0.057314    test-rmse:1.449767+0.118196 
## [81] train-rmse:1.132653+0.056303    test-rmse:1.446985+0.118417 
## [82] train-rmse:1.127700+0.056207    test-rmse:1.441326+0.120408 
## [83] train-rmse:1.117825+0.050475    test-rmse:1.437550+0.124369 
## [84] train-rmse:1.112374+0.050885    test-rmse:1.436446+0.125799 
## [85] train-rmse:1.108408+0.050768    test-rmse:1.435446+0.125387 
## [86] train-rmse:1.103375+0.052982    test-rmse:1.433503+0.129247 
## [87] train-rmse:1.099087+0.052730    test-rmse:1.432530+0.130862 
## [88] train-rmse:1.095880+0.053013    test-rmse:1.431949+0.130500 
## [89] train-rmse:1.085890+0.047871    test-rmse:1.417051+0.130852 
## [90] train-rmse:1.077528+0.047858    test-rmse:1.404517+0.118004 
## [91] train-rmse:1.071065+0.049242    test-rmse:1.399685+0.114313 
## [92] train-rmse:1.065837+0.047446    test-rmse:1.399226+0.114481 
## [93] train-rmse:1.062380+0.047737    test-rmse:1.397063+0.115462 
## [94] train-rmse:1.058721+0.047606    test-rmse:1.398025+0.115171 
## [95] train-rmse:1.055215+0.047612    test-rmse:1.396657+0.115764 
## [96] train-rmse:1.051444+0.048178    test-rmse:1.392062+0.117344 
## [97] train-rmse:1.047136+0.047957    test-rmse:1.389152+0.119089 
## [98] train-rmse:1.043999+0.048159    test-rmse:1.387458+0.118376 
## [99] train-rmse:1.038762+0.047297    test-rmse:1.384518+0.121466 
## [100]    train-rmse:1.035084+0.046339    test-rmse:1.382294+0.119519 
## [101]    train-rmse:1.031866+0.046117    test-rmse:1.380201+0.123851 
## [102]    train-rmse:1.028748+0.046183    test-rmse:1.378638+0.124022 
## [103]    train-rmse:1.025154+0.046695    test-rmse:1.376181+0.124107 
## [104]    train-rmse:1.022549+0.046968    test-rmse:1.376453+0.126410 
## [105]    train-rmse:1.019821+0.047172    test-rmse:1.376232+0.125600 
## [106]    train-rmse:1.017283+0.047447    test-rmse:1.373640+0.123691 
## [107]    train-rmse:1.014269+0.047702    test-rmse:1.374456+0.123685 
## [108]    train-rmse:1.011453+0.047990    test-rmse:1.375284+0.123467 
## [109]    train-rmse:1.008043+0.048404    test-rmse:1.372941+0.125157 
## [110]    train-rmse:0.997710+0.045260    test-rmse:1.360066+0.122775 
## [111]    train-rmse:0.994700+0.044828    test-rmse:1.358346+0.123833 
## [112]    train-rmse:0.990322+0.046438    test-rmse:1.356471+0.120966 
## [113]    train-rmse:0.987325+0.046729    test-rmse:1.354959+0.121860 
## [114]    train-rmse:0.985056+0.046934    test-rmse:1.355566+0.122114 
## [115]    train-rmse:0.982594+0.046962    test-rmse:1.355891+0.121921 
## [116]    train-rmse:0.977960+0.047908    test-rmse:1.351941+0.122454 
## [117]    train-rmse:0.975702+0.048016    test-rmse:1.351628+0.123833 
## [118]    train-rmse:0.968779+0.044483    test-rmse:1.337004+0.125465 
## [119]    train-rmse:0.965869+0.043889    test-rmse:1.335167+0.126975 
## [120]    train-rmse:0.961702+0.042675    test-rmse:1.331518+0.128713 
## [121]    train-rmse:0.959112+0.042745    test-rmse:1.329846+0.129750 
## [122]    train-rmse:0.956443+0.042815    test-rmse:1.329044+0.129652 
## [123]    train-rmse:0.953908+0.043627    test-rmse:1.329587+0.128358 
## [124]    train-rmse:0.947101+0.041575    test-rmse:1.326020+0.131621 
## [125]    train-rmse:0.944425+0.041546    test-rmse:1.324067+0.132659 
## [126]    train-rmse:0.942239+0.041365    test-rmse:1.323389+0.133020 
## [127]    train-rmse:0.939989+0.041501    test-rmse:1.322163+0.133839 
## [128]    train-rmse:0.938116+0.041285    test-rmse:1.321587+0.135348 
## [129]    train-rmse:0.935336+0.041124    test-rmse:1.318798+0.132585 
## [130]    train-rmse:0.933411+0.041417    test-rmse:1.319193+0.133380 
## [131]    train-rmse:0.928417+0.046581    test-rmse:1.318189+0.134019 
## [132]    train-rmse:0.925679+0.048153    test-rmse:1.317032+0.132830 
## [133]    train-rmse:0.923497+0.048051    test-rmse:1.316946+0.133024 
## [134]    train-rmse:0.921453+0.048023    test-rmse:1.314490+0.132614 
## [135]    train-rmse:0.918589+0.048725    test-rmse:1.314381+0.131409 
## [136]    train-rmse:0.916057+0.048648    test-rmse:1.312812+0.131708 
## [137]    train-rmse:0.913319+0.049212    test-rmse:1.314204+0.131894 
## [138]    train-rmse:0.911035+0.049483    test-rmse:1.312840+0.134560 
## [139]    train-rmse:0.907268+0.049264    test-rmse:1.314433+0.133743 
## [140]    train-rmse:0.901871+0.044896    test-rmse:1.311962+0.135686 
## [141]    train-rmse:0.899779+0.044488    test-rmse:1.311524+0.136214 
## [142]    train-rmse:0.897765+0.044351    test-rmse:1.310480+0.134982 
## [143]    train-rmse:0.896081+0.044280    test-rmse:1.310433+0.135433 
## [144]    train-rmse:0.894349+0.044105    test-rmse:1.308777+0.135782 
## [145]    train-rmse:0.891775+0.043659    test-rmse:1.306717+0.136054 
## [146]    train-rmse:0.889211+0.043692    test-rmse:1.305458+0.136183 
## [147]    train-rmse:0.887591+0.043684    test-rmse:1.304532+0.135936 
## [148]    train-rmse:0.886217+0.043867    test-rmse:1.304044+0.134866 
## [149]    train-rmse:0.884619+0.043945    test-rmse:1.304966+0.136858 
## [150]    train-rmse:0.882688+0.043792    test-rmse:1.304532+0.135539 
## [151]    train-rmse:0.881123+0.043782    test-rmse:1.303990+0.136761 
## [152]    train-rmse:0.879735+0.043901    test-rmse:1.303752+0.137830 
## [153]    train-rmse:0.878298+0.043660    test-rmse:1.302487+0.137912 
## [154]    train-rmse:0.876988+0.043594    test-rmse:1.302620+0.137531 
## [155]    train-rmse:0.875715+0.043650    test-rmse:1.301061+0.137514 
## [156]    train-rmse:0.868411+0.040046    test-rmse:1.295275+0.140774 
## [157]    train-rmse:0.867145+0.040070    test-rmse:1.294354+0.139106 
## [158]    train-rmse:0.865551+0.040281    test-rmse:1.292820+0.137925 
## [159]    train-rmse:0.863645+0.040152    test-rmse:1.293273+0.137684 
## [160]    train-rmse:0.861897+0.040444    test-rmse:1.292980+0.138976 
## [161]    train-rmse:0.860111+0.040633    test-rmse:1.292621+0.140087 
## [162]    train-rmse:0.858056+0.039710    test-rmse:1.292664+0.142073 
## [163]    train-rmse:0.856592+0.039395    test-rmse:1.292602+0.142459 
## [164]    train-rmse:0.855132+0.039061    test-rmse:1.292573+0.141836 
## [165]    train-rmse:0.853715+0.039174    test-rmse:1.292770+0.141280 
## [166]    train-rmse:0.852465+0.039196    test-rmse:1.292658+0.141911 
## [167]    train-rmse:0.851051+0.039200    test-rmse:1.293245+0.141691 
## [168]    train-rmse:0.849996+0.039436    test-rmse:1.293130+0.142587 
## [169]    train-rmse:0.847868+0.040273    test-rmse:1.291988+0.142046 
## [170]    train-rmse:0.846792+0.040307    test-rmse:1.291758+0.142165 
## [171]    train-rmse:0.845690+0.040372    test-rmse:1.290342+0.142241 
## [172]    train-rmse:0.844560+0.040425    test-rmse:1.289282+0.141138 
## [173]    train-rmse:0.840373+0.040315    test-rmse:1.286222+0.142587 
## [174]    train-rmse:0.838875+0.040151    test-rmse:1.285838+0.144319 
## [175]    train-rmse:0.837149+0.040350    test-rmse:1.285375+0.143872 
## [176]    train-rmse:0.835433+0.039603    test-rmse:1.284427+0.143617 
## [177]    train-rmse:0.834386+0.039776    test-rmse:1.285222+0.143744 
## [178]    train-rmse:0.833395+0.039528    test-rmse:1.285120+0.144113 
## [179]    train-rmse:0.832176+0.039393    test-rmse:1.284940+0.143890 
## [180]    train-rmse:0.830807+0.039545    test-rmse:1.286401+0.144033 
## [181]    train-rmse:0.829379+0.039284    test-rmse:1.286498+0.143057 
## [182]    train-rmse:0.826004+0.039297    test-rmse:1.284213+0.143410 
## [183]    train-rmse:0.823076+0.037522    test-rmse:1.283583+0.143124 
## [184]    train-rmse:0.821990+0.037394    test-rmse:1.284165+0.142714 
## [185]    train-rmse:0.821196+0.037235    test-rmse:1.283656+0.143075 
## [186]    train-rmse:0.819985+0.037950    test-rmse:1.281742+0.144760 
## [187]    train-rmse:0.818945+0.037954    test-rmse:1.281527+0.144821 
## [188]    train-rmse:0.817749+0.037807    test-rmse:1.281763+0.144839 
## [189]    train-rmse:0.816583+0.037778    test-rmse:1.281424+0.144219 
## [190]    train-rmse:0.815692+0.037910    test-rmse:1.281064+0.144199 
## [191]    train-rmse:0.814834+0.037873    test-rmse:1.280277+0.144548 
## [192]    train-rmse:0.813804+0.037674    test-rmse:1.280346+0.144847 
## [193]    train-rmse:0.812808+0.037591    test-rmse:1.280874+0.144325 
## [194]    train-rmse:0.811878+0.038111    test-rmse:1.280725+0.144556 
## [195]    train-rmse:0.810564+0.038183    test-rmse:1.280319+0.144889 
## [196]    train-rmse:0.809501+0.038145    test-rmse:1.280872+0.145569 
## [197]    train-rmse:0.808797+0.038142    test-rmse:1.281617+0.144936 
## Stopping. Best iteration:
## [191]    train-rmse:0.814834+0.037873    test-rmse:1.280277+0.144548
## 
## 37 
## [1]  train-rmse:7.873465+0.057363    test-rmse:7.869991+0.261188 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.425839+0.044514    test-rmse:6.419784+0.248417 
## [3]  train-rmse:5.286801+0.035511    test-rmse:5.287199+0.235940 
## [4]  train-rmse:4.393763+0.029067    test-rmse:4.412989+0.208787 
## [5]  train-rmse:3.699592+0.021289    test-rmse:3.723711+0.186281 
## [6]  train-rmse:3.159953+0.016613    test-rmse:3.191618+0.158472 
## [7]  train-rmse:2.747214+0.012700    test-rmse:2.805559+0.138654 
## [8]  train-rmse:2.434140+0.009403    test-rmse:2.516879+0.119720 
## [9]  train-rmse:2.202183+0.010112    test-rmse:2.298130+0.101323 
## [10] train-rmse:2.023656+0.009767    test-rmse:2.154928+0.090014 
## [11] train-rmse:1.894163+0.011016    test-rmse:2.042802+0.088379 
## [12] train-rmse:1.796791+0.012670    test-rmse:1.958164+0.078034 
## [13] train-rmse:1.719642+0.014921    test-rmse:1.895159+0.070310 
## [14] train-rmse:1.656983+0.013916    test-rmse:1.850015+0.070297 
## [15] train-rmse:1.607059+0.014370    test-rmse:1.810169+0.065429 
## [16] train-rmse:1.562062+0.015631    test-rmse:1.782149+0.060896 
## [17] train-rmse:1.528293+0.016211    test-rmse:1.763412+0.067753 
## [18] train-rmse:1.497425+0.016869    test-rmse:1.747069+0.064148 
## [19] train-rmse:1.470645+0.017909    test-rmse:1.720718+0.068203 
## [20] train-rmse:1.443410+0.020935    test-rmse:1.701799+0.073062 
## [21] train-rmse:1.415169+0.018384    test-rmse:1.679631+0.082002 
## [22] train-rmse:1.390184+0.020230    test-rmse:1.660948+0.083473 
## [23] train-rmse:1.368715+0.019567    test-rmse:1.652373+0.081319 
## [24] train-rmse:1.348162+0.020127    test-rmse:1.636396+0.086182 
## [25] train-rmse:1.329902+0.021226    test-rmse:1.624108+0.089160 
## [26] train-rmse:1.312290+0.021491    test-rmse:1.608211+0.089344 
## [27] train-rmse:1.291828+0.023867    test-rmse:1.592255+0.089849 
## [28] train-rmse:1.275707+0.025349    test-rmse:1.583514+0.090361 
## [29] train-rmse:1.258851+0.025977    test-rmse:1.568078+0.091998 
## [30] train-rmse:1.241581+0.027508    test-rmse:1.556102+0.100468 
## [31] train-rmse:1.224740+0.027207    test-rmse:1.546635+0.101451 
## [32] train-rmse:1.209258+0.029584    test-rmse:1.533595+0.101548 
## [33] train-rmse:1.193343+0.028232    test-rmse:1.523963+0.104245 
## [34] train-rmse:1.181572+0.028483    test-rmse:1.517182+0.102361 
## [35] train-rmse:1.167639+0.025957    test-rmse:1.509152+0.105393 
## [36] train-rmse:1.151873+0.025686    test-rmse:1.495636+0.113308 
## [37] train-rmse:1.138681+0.026120    test-rmse:1.489077+0.114260 
## [38] train-rmse:1.126732+0.026966    test-rmse:1.477593+0.117520 
## [39] train-rmse:1.112019+0.025822    test-rmse:1.470841+0.120423 
## [40] train-rmse:1.100823+0.026195    test-rmse:1.465437+0.119645 
## [41] train-rmse:1.090113+0.026339    test-rmse:1.454107+0.123039 
## [42] train-rmse:1.072938+0.022054    test-rmse:1.442168+0.132177 
## [43] train-rmse:1.060100+0.024730    test-rmse:1.435945+0.136158 
## [44] train-rmse:1.046496+0.022477    test-rmse:1.422158+0.127858 
## [45] train-rmse:1.035404+0.022316    test-rmse:1.415270+0.126107 
## [46] train-rmse:1.024755+0.021282    test-rmse:1.409508+0.128372 
## [47] train-rmse:1.013993+0.019849    test-rmse:1.409599+0.132753 
## [48] train-rmse:1.003244+0.022355    test-rmse:1.398759+0.135190 
## [49] train-rmse:0.994114+0.022528    test-rmse:1.392634+0.134626 
## [50] train-rmse:0.985427+0.023321    test-rmse:1.387848+0.135318 
## [51] train-rmse:0.976550+0.023526    test-rmse:1.384961+0.135538 
## [52] train-rmse:0.967479+0.022211    test-rmse:1.379131+0.135852 
## [53] train-rmse:0.957753+0.022065    test-rmse:1.375218+0.138257 
## [54] train-rmse:0.949481+0.023235    test-rmse:1.371940+0.139846 
## [55] train-rmse:0.941783+0.022475    test-rmse:1.368375+0.142783 
## [56] train-rmse:0.933782+0.024628    test-rmse:1.365576+0.144493 
## [57] train-rmse:0.926021+0.024985    test-rmse:1.358812+0.145442 
## [58] train-rmse:0.918225+0.026281    test-rmse:1.353274+0.144086 
## [59] train-rmse:0.912006+0.026540    test-rmse:1.351071+0.146961 
## [60] train-rmse:0.903087+0.024418    test-rmse:1.347302+0.149526 
## [61] train-rmse:0.896227+0.023926    test-rmse:1.346481+0.148946 
## [62] train-rmse:0.889633+0.024121    test-rmse:1.344397+0.151024 
## [63] train-rmse:0.879891+0.021793    test-rmse:1.333003+0.140471 
## [64] train-rmse:0.873901+0.022096    test-rmse:1.329009+0.145122 
## [65] train-rmse:0.865045+0.019959    test-rmse:1.323107+0.146947 
## [66] train-rmse:0.857462+0.021990    test-rmse:1.320722+0.146479 
## [67] train-rmse:0.850250+0.020395    test-rmse:1.318099+0.149100 
## [68] train-rmse:0.843288+0.020781    test-rmse:1.315626+0.151912 
## [69] train-rmse:0.837340+0.020046    test-rmse:1.310845+0.151492 
## [70] train-rmse:0.831636+0.020063    test-rmse:1.304714+0.153745 
## [71] train-rmse:0.827146+0.020375    test-rmse:1.303134+0.154531 
## [72] train-rmse:0.821487+0.019132    test-rmse:1.303281+0.153582 
## [73] train-rmse:0.814488+0.018275    test-rmse:1.300932+0.155568 
## [74] train-rmse:0.809209+0.019089    test-rmse:1.298580+0.155806 
## [75] train-rmse:0.803778+0.018857    test-rmse:1.299294+0.159166 
## [76] train-rmse:0.798291+0.018836    test-rmse:1.297952+0.160941 
## [77] train-rmse:0.793862+0.018597    test-rmse:1.297152+0.161608 
## [78] train-rmse:0.789369+0.018424    test-rmse:1.291341+0.162187 
## [79] train-rmse:0.781841+0.020791    test-rmse:1.289176+0.159549 
## [80] train-rmse:0.777103+0.021751    test-rmse:1.288593+0.159523 
## [81] train-rmse:0.772580+0.022093    test-rmse:1.288319+0.160257 
## [82] train-rmse:0.766930+0.023397    test-rmse:1.286819+0.161372 
## [83] train-rmse:0.762826+0.023920    test-rmse:1.290118+0.161263 
## [84] train-rmse:0.758454+0.022997    test-rmse:1.288810+0.161682 
## [85] train-rmse:0.754978+0.023697    test-rmse:1.288710+0.162410 
## [86] train-rmse:0.749172+0.020870    test-rmse:1.286375+0.159749 
## [87] train-rmse:0.745293+0.021471    test-rmse:1.282585+0.161803 
## [88] train-rmse:0.740807+0.024136    test-rmse:1.280831+0.162326 
## [89] train-rmse:0.735257+0.023891    test-rmse:1.277239+0.162924 
## [90] train-rmse:0.731580+0.023965    test-rmse:1.278837+0.161751 
## [91] train-rmse:0.727482+0.023834    test-rmse:1.279107+0.162361 
## [92] train-rmse:0.724318+0.023827    test-rmse:1.278913+0.163181 
## [93] train-rmse:0.720109+0.022730    test-rmse:1.277048+0.162858 
## [94] train-rmse:0.713434+0.023055    test-rmse:1.271566+0.157756 
## [95] train-rmse:0.710425+0.023270    test-rmse:1.270702+0.157871 
## [96] train-rmse:0.706025+0.022383    test-rmse:1.271041+0.158958 
## [97] train-rmse:0.702925+0.023949    test-rmse:1.269842+0.159501 
## [98] train-rmse:0.700311+0.023819    test-rmse:1.267080+0.160927 
## [99] train-rmse:0.696372+0.023571    test-rmse:1.265438+0.160195 
## [100]    train-rmse:0.693024+0.024068    test-rmse:1.265860+0.162074 
## [101]    train-rmse:0.688380+0.024248    test-rmse:1.265803+0.163070 
## [102]    train-rmse:0.685940+0.024395    test-rmse:1.264922+0.162586 
## [103]    train-rmse:0.682266+0.024751    test-rmse:1.263352+0.163173 
## [104]    train-rmse:0.679381+0.025010    test-rmse:1.263657+0.160992 
## [105]    train-rmse:0.676981+0.025137    test-rmse:1.262314+0.160162 
## [106]    train-rmse:0.671909+0.024067    test-rmse:1.257196+0.158849 
## [107]    train-rmse:0.669195+0.024993    test-rmse:1.256037+0.157320 
## [108]    train-rmse:0.666241+0.025052    test-rmse:1.256264+0.158195 
## [109]    train-rmse:0.663231+0.025876    test-rmse:1.255868+0.158271 
## [110]    train-rmse:0.661066+0.026141    test-rmse:1.258346+0.157524 
## [111]    train-rmse:0.658530+0.025927    test-rmse:1.257851+0.157463 
## [112]    train-rmse:0.656561+0.025859    test-rmse:1.258253+0.158124 
## [113]    train-rmse:0.652381+0.024496    test-rmse:1.256332+0.158825 
## [114]    train-rmse:0.650737+0.024497    test-rmse:1.256341+0.158831 
## [115]    train-rmse:0.648362+0.024675    test-rmse:1.256278+0.160365 
## Stopping. Best iteration:
## [109]    train-rmse:0.663231+0.025876    test-rmse:1.255868+0.158271
## 
## 38 
## [1]  train-rmse:7.869436+0.057797    test-rmse:7.865875+0.262617 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.416680+0.045604    test-rmse:6.422274+0.248946 
## [3]  train-rmse:5.269623+0.035819    test-rmse:5.280480+0.236620 
## [4]  train-rmse:4.367523+0.029752    test-rmse:4.385335+0.198018 
## [5]  train-rmse:3.658163+0.024741    test-rmse:3.694937+0.181263 
## [6]  train-rmse:3.099882+0.020468    test-rmse:3.154237+0.153087 
## [7]  train-rmse:2.672176+0.016558    test-rmse:2.738559+0.136974 
## [8]  train-rmse:2.344504+0.016816    test-rmse:2.443586+0.118354 
## [9]  train-rmse:2.092104+0.015359    test-rmse:2.213811+0.097263 
## [10] train-rmse:1.900306+0.016672    test-rmse:2.050636+0.096064 
## [11] train-rmse:1.752877+0.017601    test-rmse:1.933752+0.081781 
## [12] train-rmse:1.640969+0.017274    test-rmse:1.845037+0.074634 
## [13] train-rmse:1.549068+0.021528    test-rmse:1.778657+0.068722 
## [14] train-rmse:1.478628+0.023312    test-rmse:1.735563+0.067270 
## [15] train-rmse:1.419456+0.023983    test-rmse:1.697618+0.071639 
## [16] train-rmse:1.369841+0.025294    test-rmse:1.653473+0.070547 
## [17] train-rmse:1.327419+0.024585    test-rmse:1.626986+0.080345 
## [18] train-rmse:1.288439+0.020007    test-rmse:1.600936+0.087244 
## [19] train-rmse:1.257727+0.021454    test-rmse:1.577151+0.080357 
## [20] train-rmse:1.230691+0.021206    test-rmse:1.558713+0.078549 
## [21] train-rmse:1.203615+0.019380    test-rmse:1.540173+0.090829 
## [22] train-rmse:1.172649+0.019377    test-rmse:1.523607+0.083068 
## [23] train-rmse:1.149208+0.020171    test-rmse:1.509821+0.086594 
## [24] train-rmse:1.123851+0.017322    test-rmse:1.493305+0.090245 
## [25] train-rmse:1.101182+0.016202    test-rmse:1.487922+0.096513 
## [26] train-rmse:1.081733+0.018816    test-rmse:1.469693+0.097010 
## [27] train-rmse:1.063223+0.019616    test-rmse:1.458515+0.103263 
## [28] train-rmse:1.044803+0.019325    test-rmse:1.451006+0.104708 
## [29] train-rmse:1.025120+0.021371    test-rmse:1.439330+0.108537 
## [30] train-rmse:1.008054+0.023029    test-rmse:1.427182+0.110306 
## [31] train-rmse:0.993515+0.022750    test-rmse:1.420817+0.114638 
## [32] train-rmse:0.979837+0.022868    test-rmse:1.411981+0.120188 
## [33] train-rmse:0.964403+0.024907    test-rmse:1.410282+0.122526 
## [34] train-rmse:0.949305+0.025955    test-rmse:1.400945+0.120818 
## [35] train-rmse:0.930351+0.025253    test-rmse:1.384571+0.121029 
## [36] train-rmse:0.914729+0.023605    test-rmse:1.372894+0.121757 
## [37] train-rmse:0.901426+0.021185    test-rmse:1.371179+0.119036 
## [38] train-rmse:0.889675+0.023663    test-rmse:1.368267+0.119691 
## [39] train-rmse:0.877001+0.023342    test-rmse:1.359360+0.123823 
## [40] train-rmse:0.864672+0.022652    test-rmse:1.350141+0.133175 
## [41] train-rmse:0.853428+0.021961    test-rmse:1.349241+0.134546 
## [42] train-rmse:0.840539+0.021041    test-rmse:1.343157+0.136946 
## [43] train-rmse:0.827983+0.020896    test-rmse:1.338525+0.137779 
## [44] train-rmse:0.819480+0.021193    test-rmse:1.331370+0.139791 
## [45] train-rmse:0.807448+0.022204    test-rmse:1.324937+0.142280 
## [46] train-rmse:0.799026+0.021235    test-rmse:1.321734+0.141560 
## [47] train-rmse:0.789596+0.022031    test-rmse:1.314453+0.140653 
## [48] train-rmse:0.780628+0.021791    test-rmse:1.310893+0.144567 
## [49] train-rmse:0.771828+0.022069    test-rmse:1.307331+0.147988 
## [50] train-rmse:0.761640+0.023780    test-rmse:1.306277+0.146580 
## [51] train-rmse:0.751743+0.022932    test-rmse:1.304010+0.147071 
## [52] train-rmse:0.743591+0.022036    test-rmse:1.303697+0.149045 
## [53] train-rmse:0.735964+0.021114    test-rmse:1.303850+0.147890 
## [54] train-rmse:0.728106+0.019931    test-rmse:1.304209+0.149099 
## [55] train-rmse:0.720896+0.020884    test-rmse:1.303705+0.152869 
## [56] train-rmse:0.713861+0.020021    test-rmse:1.298396+0.155597 
## [57] train-rmse:0.706036+0.021255    test-rmse:1.294804+0.157728 
## [58] train-rmse:0.699213+0.020234    test-rmse:1.292726+0.158915 
## [59] train-rmse:0.692866+0.021547    test-rmse:1.291833+0.157376 
## [60] train-rmse:0.685363+0.020476    test-rmse:1.291473+0.159927 
## [61] train-rmse:0.678457+0.019775    test-rmse:1.290500+0.159649 
## [62] train-rmse:0.672563+0.021770    test-rmse:1.290660+0.162467 
## [63] train-rmse:0.664325+0.022780    test-rmse:1.287037+0.160961 
## [64] train-rmse:0.656631+0.022144    test-rmse:1.286032+0.162245 
## [65] train-rmse:0.650546+0.020474    test-rmse:1.286232+0.161880 
## [66] train-rmse:0.645894+0.020484    test-rmse:1.283717+0.162473 
## [67] train-rmse:0.639344+0.021617    test-rmse:1.283563+0.163243 
## [68] train-rmse:0.631417+0.021373    test-rmse:1.278670+0.160191 
## [69] train-rmse:0.626682+0.021296    test-rmse:1.278403+0.158359 
## [70] train-rmse:0.621447+0.022545    test-rmse:1.278375+0.158491 
## [71] train-rmse:0.616341+0.022451    test-rmse:1.277744+0.159003 
## [72] train-rmse:0.611341+0.021586    test-rmse:1.275648+0.160690 
## [73] train-rmse:0.605852+0.022574    test-rmse:1.270895+0.160817 
## [74] train-rmse:0.600871+0.024599    test-rmse:1.268262+0.159536 
## [75] train-rmse:0.594773+0.022851    test-rmse:1.267128+0.159904 
## [76] train-rmse:0.591667+0.023200    test-rmse:1.267924+0.160518 
## [77] train-rmse:0.585883+0.023388    test-rmse:1.268944+0.158806 
## [78] train-rmse:0.580041+0.021771    test-rmse:1.266339+0.161038 
## [79] train-rmse:0.575262+0.020180    test-rmse:1.267202+0.161487 
## [80] train-rmse:0.571112+0.019982    test-rmse:1.267497+0.162565 
## [81] train-rmse:0.567723+0.019972    test-rmse:1.267593+0.162778 
## [82] train-rmse:0.563179+0.020026    test-rmse:1.266034+0.163512 
## [83] train-rmse:0.559009+0.020543    test-rmse:1.264346+0.165386 
## [84] train-rmse:0.553637+0.020617    test-rmse:1.264956+0.165444 
## [85] train-rmse:0.551134+0.020651    test-rmse:1.264882+0.165673 
## [86] train-rmse:0.547682+0.020226    test-rmse:1.262515+0.165162 
## [87] train-rmse:0.543757+0.020675    test-rmse:1.261129+0.164161 
## [88] train-rmse:0.540589+0.020979    test-rmse:1.259741+0.164733 
## [89] train-rmse:0.536817+0.019801    test-rmse:1.259184+0.166782 
## [90] train-rmse:0.534433+0.019275    test-rmse:1.258566+0.167719 
## [91] train-rmse:0.530576+0.018549    test-rmse:1.261010+0.166393 
## [92] train-rmse:0.528285+0.019067    test-rmse:1.263504+0.167815 
## [93] train-rmse:0.524493+0.018551    test-rmse:1.263326+0.169138 
## [94] train-rmse:0.521833+0.018406    test-rmse:1.264148+0.168791 
## [95] train-rmse:0.519357+0.018908    test-rmse:1.263653+0.168265 
## [96] train-rmse:0.516387+0.019193    test-rmse:1.262103+0.167465 
## Stopping. Best iteration:
## [90] train-rmse:0.534433+0.019275    test-rmse:1.258566+0.167719
## 
## 39 
## [1]  train-rmse:7.866079+0.058136    test-rmse:7.866103+0.263086 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.408394+0.046670    test-rmse:6.419045+0.241432 
## [3]  train-rmse:5.254581+0.037942    test-rmse:5.271280+0.226953 
## [4]  train-rmse:4.343115+0.033961    test-rmse:4.351040+0.199979 
## [5]  train-rmse:3.624815+0.027362    test-rmse:3.648356+0.167888 
## [6]  train-rmse:3.056779+0.020287    test-rmse:3.105241+0.149587 
## [7]  train-rmse:2.614993+0.020013    test-rmse:2.683284+0.120398 
## [8]  train-rmse:2.271914+0.015833    test-rmse:2.379263+0.109672 
## [9]  train-rmse:2.007939+0.015616    test-rmse:2.155759+0.103213 
## [10] train-rmse:1.797039+0.018044    test-rmse:1.973490+0.082299 
## [11] train-rmse:1.640072+0.019729    test-rmse:1.842152+0.083930 
## [12] train-rmse:1.514959+0.023887    test-rmse:1.755314+0.082550 
## [13] train-rmse:1.407391+0.022544    test-rmse:1.674200+0.074040 
## [14] train-rmse:1.329159+0.022984    test-rmse:1.628751+0.077064 
## [15] train-rmse:1.265866+0.021937    test-rmse:1.586460+0.087267 
## [16] train-rmse:1.208618+0.025132    test-rmse:1.551175+0.092730 
## [17] train-rmse:1.161259+0.026329    test-rmse:1.513731+0.094131 
## [18] train-rmse:1.123137+0.024511    test-rmse:1.495245+0.100593 
## [19] train-rmse:1.084648+0.024754    test-rmse:1.471002+0.104075 
## [20] train-rmse:1.048386+0.022960    test-rmse:1.453681+0.110957 
## [21] train-rmse:1.020232+0.025345    test-rmse:1.440768+0.115813 
## [22] train-rmse:0.992556+0.027552    test-rmse:1.426038+0.116793 
## [23] train-rmse:0.966473+0.025733    test-rmse:1.410055+0.118986 
## [24] train-rmse:0.943569+0.025914    test-rmse:1.397980+0.129112 
## [25] train-rmse:0.920384+0.027308    test-rmse:1.384527+0.132830 
## [26] train-rmse:0.898351+0.025912    test-rmse:1.376256+0.140889 
## [27] train-rmse:0.876015+0.023765    test-rmse:1.365282+0.142974 
## [28] train-rmse:0.857429+0.025761    test-rmse:1.357403+0.141342 
## [29] train-rmse:0.838128+0.020605    test-rmse:1.349723+0.152409 
## [30] train-rmse:0.821076+0.018779    test-rmse:1.340087+0.152150 
## [31] train-rmse:0.803726+0.018756    test-rmse:1.331190+0.154989 
## [32] train-rmse:0.788096+0.021380    test-rmse:1.322388+0.154889 
## [33] train-rmse:0.771390+0.021932    test-rmse:1.316704+0.158156 
## [34] train-rmse:0.758304+0.022232    test-rmse:1.312812+0.158559 
## [35] train-rmse:0.743197+0.021984    test-rmse:1.311423+0.161827 
## [36] train-rmse:0.731307+0.021373    test-rmse:1.306263+0.163130 
## [37] train-rmse:0.718531+0.021385    test-rmse:1.302573+0.167370 
## [38] train-rmse:0.705476+0.020794    test-rmse:1.295581+0.166791 
## [39] train-rmse:0.694805+0.020031    test-rmse:1.291793+0.166977 
## [40] train-rmse:0.682216+0.019706    test-rmse:1.287026+0.171382 
## [41] train-rmse:0.672549+0.019031    test-rmse:1.284133+0.174303 
## [42] train-rmse:0.662463+0.018643    test-rmse:1.285651+0.174609 
## [43] train-rmse:0.653146+0.018667    test-rmse:1.282835+0.176682 
## [44] train-rmse:0.643682+0.018800    test-rmse:1.279225+0.175565 
## [45] train-rmse:0.632899+0.020318    test-rmse:1.274905+0.172819 
## [46] train-rmse:0.624410+0.019660    test-rmse:1.273861+0.175518 
## [47] train-rmse:0.615924+0.019510    test-rmse:1.271981+0.177819 
## [48] train-rmse:0.607050+0.017707    test-rmse:1.267997+0.178143 
## [49] train-rmse:0.599591+0.018431    test-rmse:1.268774+0.180748 
## [50] train-rmse:0.593023+0.018114    test-rmse:1.268441+0.180663 
## [51] train-rmse:0.584749+0.018171    test-rmse:1.264699+0.184212 
## [52] train-rmse:0.575433+0.016169    test-rmse:1.263259+0.186639 
## [53] train-rmse:0.569338+0.015405    test-rmse:1.263340+0.186465 
## [54] train-rmse:0.562745+0.015006    test-rmse:1.262424+0.187698 
## [55] train-rmse:0.558071+0.015017    test-rmse:1.263667+0.189613 
## [56] train-rmse:0.548926+0.016634    test-rmse:1.258059+0.190889 
## [57] train-rmse:0.542364+0.018174    test-rmse:1.256271+0.190452 
## [58] train-rmse:0.536999+0.017189    test-rmse:1.257781+0.192430 
## [59] train-rmse:0.528824+0.016433    test-rmse:1.254473+0.194467 
## [60] train-rmse:0.523460+0.017527    test-rmse:1.254046+0.195182 
## [61] train-rmse:0.518591+0.017629    test-rmse:1.253403+0.194645 
## [62] train-rmse:0.513441+0.017465    test-rmse:1.251023+0.195071 
## [63] train-rmse:0.506601+0.015859    test-rmse:1.250861+0.195609 
## [64] train-rmse:0.501821+0.016361    test-rmse:1.250328+0.195327 
## [65] train-rmse:0.497026+0.017633    test-rmse:1.252831+0.195287 
## [66] train-rmse:0.491694+0.017578    test-rmse:1.250698+0.195711 
## [67] train-rmse:0.488187+0.017347    test-rmse:1.249528+0.195853 
## [68] train-rmse:0.483746+0.017542    test-rmse:1.248552+0.196159 
## [69] train-rmse:0.476559+0.017711    test-rmse:1.247504+0.196614 
## [70] train-rmse:0.472764+0.017525    test-rmse:1.246325+0.196076 
## [71] train-rmse:0.467925+0.016542    test-rmse:1.246614+0.195963 
## [72] train-rmse:0.463328+0.015342    test-rmse:1.247247+0.198005 
## [73] train-rmse:0.458226+0.016620    test-rmse:1.246819+0.195582 
## [74] train-rmse:0.454118+0.015909    test-rmse:1.246816+0.195298 
## [75] train-rmse:0.449156+0.015758    test-rmse:1.244814+0.194156 
## [76] train-rmse:0.446164+0.016379    test-rmse:1.243979+0.193749 
## [77] train-rmse:0.441956+0.016205    test-rmse:1.243549+0.194900 
## [78] train-rmse:0.437027+0.017089    test-rmse:1.243433+0.194574 
## [79] train-rmse:0.430838+0.016676    test-rmse:1.241802+0.194111 
## [80] train-rmse:0.426767+0.016394    test-rmse:1.243454+0.194727 
## [81] train-rmse:0.424228+0.016994    test-rmse:1.242585+0.195081 
## [82] train-rmse:0.421025+0.016175    test-rmse:1.242319+0.195615 
## [83] train-rmse:0.418190+0.016321    test-rmse:1.240277+0.196006 
## [84] train-rmse:0.413644+0.015987    test-rmse:1.241159+0.197383 
## [85] train-rmse:0.410999+0.016797    test-rmse:1.242525+0.198500 
## [86] train-rmse:0.406898+0.017279    test-rmse:1.242109+0.198278 
## [87] train-rmse:0.403024+0.018380    test-rmse:1.241539+0.196597 
## [88] train-rmse:0.399961+0.018493    test-rmse:1.241469+0.196175 
## [89] train-rmse:0.397347+0.019153    test-rmse:1.241942+0.195465 
## Stopping. Best iteration:
## [83] train-rmse:0.418190+0.016321    test-rmse:1.240277+0.196006
## 
## 40 
## [1]  train-rmse:7.865351+0.058314    test-rmse:7.866791+0.256691 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.404043+0.047049    test-rmse:6.415631+0.238084 
## [3]  train-rmse:5.245533+0.038382    test-rmse:5.263406+0.211601 
## [4]  train-rmse:4.327564+0.033936    test-rmse:4.359995+0.186368 
## [5]  train-rmse:3.600377+0.031230    test-rmse:3.636383+0.164022 
## [6]  train-rmse:3.025014+0.023672    test-rmse:3.090946+0.151250 
## [7]  train-rmse:2.570874+0.022275    test-rmse:2.664360+0.121498 
## [8]  train-rmse:2.219717+0.020119    test-rmse:2.346648+0.121247 
## [9]  train-rmse:1.945001+0.019649    test-rmse:2.110638+0.104075 
## [10] train-rmse:1.722830+0.020138    test-rmse:1.923815+0.097614 
## [11] train-rmse:1.555568+0.026089    test-rmse:1.793579+0.095781 
## [12] train-rmse:1.416003+0.029867    test-rmse:1.677639+0.091357 
## [13] train-rmse:1.303697+0.024517    test-rmse:1.609619+0.097638 
## [14] train-rmse:1.217846+0.024190    test-rmse:1.543336+0.103856 
## [15] train-rmse:1.147162+0.024229    test-rmse:1.497481+0.097484 
## [16] train-rmse:1.089425+0.023189    test-rmse:1.461611+0.102845 
## [17] train-rmse:1.036443+0.019260    test-rmse:1.429359+0.107693 
## [18] train-rmse:0.994143+0.019702    test-rmse:1.412642+0.111336 
## [19] train-rmse:0.954884+0.022071    test-rmse:1.391903+0.113334 
## [20] train-rmse:0.917723+0.022716    test-rmse:1.378386+0.121123 
## [21] train-rmse:0.889594+0.022200    test-rmse:1.359258+0.126557 
## [22] train-rmse:0.862027+0.022157    test-rmse:1.352030+0.134981 
## [23] train-rmse:0.837730+0.022108    test-rmse:1.348666+0.138236 
## [24] train-rmse:0.813259+0.023085    test-rmse:1.331056+0.138652 
## [25] train-rmse:0.786706+0.019575    test-rmse:1.321282+0.145080 
## [26] train-rmse:0.767984+0.020372    test-rmse:1.316868+0.148079 
## [27] train-rmse:0.748979+0.020441    test-rmse:1.312393+0.148589 
## [28] train-rmse:0.728766+0.017616    test-rmse:1.312321+0.154165 
## [29] train-rmse:0.710521+0.015842    test-rmse:1.299340+0.156956 
## [30] train-rmse:0.688600+0.014747    test-rmse:1.285530+0.158424 
## [31] train-rmse:0.674684+0.013146    test-rmse:1.280336+0.164416 
## [32] train-rmse:0.658788+0.013540    test-rmse:1.271996+0.170067 
## [33] train-rmse:0.643018+0.015443    test-rmse:1.268383+0.165019 
## [34] train-rmse:0.630369+0.014166    test-rmse:1.262978+0.169591 
## [35] train-rmse:0.618430+0.013765    test-rmse:1.256598+0.168042 
## [36] train-rmse:0.603663+0.013794    test-rmse:1.253479+0.167210 
## [37] train-rmse:0.592445+0.015145    test-rmse:1.252444+0.170645 
## [38] train-rmse:0.580095+0.015420    test-rmse:1.248240+0.174694 
## [39] train-rmse:0.569987+0.014655    test-rmse:1.245631+0.176469 
## [40] train-rmse:0.560383+0.013646    test-rmse:1.243087+0.176097 
## [41] train-rmse:0.550640+0.013082    test-rmse:1.241316+0.178699 
## [42] train-rmse:0.539290+0.013240    test-rmse:1.240427+0.178244 
## [43] train-rmse:0.531230+0.013225    test-rmse:1.239674+0.178774 
## [44] train-rmse:0.522875+0.014770    test-rmse:1.238725+0.178592 
## [45] train-rmse:0.516179+0.014190    test-rmse:1.236754+0.179339 
## [46] train-rmse:0.506993+0.012128    test-rmse:1.235852+0.179111 
## [47] train-rmse:0.499832+0.011858    test-rmse:1.236876+0.179064 
## [48] train-rmse:0.490733+0.011538    test-rmse:1.236390+0.178786 
## [49] train-rmse:0.483503+0.011282    test-rmse:1.233049+0.177937 
## [50] train-rmse:0.472808+0.008872    test-rmse:1.233763+0.177961 
## [51] train-rmse:0.464512+0.010861    test-rmse:1.230205+0.176824 
## [52] train-rmse:0.457873+0.009978    test-rmse:1.228713+0.178786 
## [53] train-rmse:0.451217+0.009997    test-rmse:1.228353+0.177698 
## [54] train-rmse:0.444592+0.011230    test-rmse:1.227438+0.178397 
## [55] train-rmse:0.435292+0.011636    test-rmse:1.226473+0.176696 
## [56] train-rmse:0.429345+0.012538    test-rmse:1.225197+0.178556 
## [57] train-rmse:0.421803+0.009731    test-rmse:1.225711+0.180453 
## [58] train-rmse:0.415742+0.009898    test-rmse:1.227557+0.179814 
## [59] train-rmse:0.408722+0.011092    test-rmse:1.224275+0.180749 
## [60] train-rmse:0.404682+0.011417    test-rmse:1.224481+0.179669 
## [61] train-rmse:0.400418+0.010976    test-rmse:1.223041+0.180093 
## [62] train-rmse:0.396198+0.010667    test-rmse:1.224455+0.180877 
## [63] train-rmse:0.391208+0.010363    test-rmse:1.224427+0.181354 
## [64] train-rmse:0.385373+0.011731    test-rmse:1.224972+0.179017 
## [65] train-rmse:0.380588+0.010270    test-rmse:1.224801+0.177713 
## [66] train-rmse:0.375682+0.011925    test-rmse:1.224653+0.178029 
## [67] train-rmse:0.371141+0.011366    test-rmse:1.224842+0.178923 
## Stopping. Best iteration:
## [61] train-rmse:0.400418+0.010976    test-rmse:1.223041+0.180093
## 
## 41 
## [1]  train-rmse:7.865351+0.058314    test-rmse:7.866791+0.256691 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.402262+0.047257    test-rmse:6.418812+0.229866 
## [3]  train-rmse:5.239467+0.038119    test-rmse:5.256364+0.197088 
## [4]  train-rmse:4.316681+0.031402    test-rmse:4.351797+0.172700 
## [5]  train-rmse:3.582371+0.027554    test-rmse:3.630455+0.147249 
## [6]  train-rmse:3.002419+0.023576    test-rmse:3.055434+0.128827 
## [7]  train-rmse:2.540638+0.017429    test-rmse:2.633321+0.130114 
## [8]  train-rmse:2.178462+0.017355    test-rmse:2.319204+0.117738 
## [9]  train-rmse:1.888482+0.016521    test-rmse:2.067030+0.104777 
## [10] train-rmse:1.658069+0.013334    test-rmse:1.876897+0.095930 
## [11] train-rmse:1.478924+0.019502    test-rmse:1.743829+0.083880 
## [12] train-rmse:1.331492+0.020174    test-rmse:1.634301+0.086899 
## [13] train-rmse:1.212519+0.015620    test-rmse:1.545614+0.091811 
## [14] train-rmse:1.122272+0.020452    test-rmse:1.494239+0.088993 
## [15] train-rmse:1.043006+0.022556    test-rmse:1.455579+0.097138 
## [16] train-rmse:0.978034+0.019497    test-rmse:1.427200+0.105053 
## [17] train-rmse:0.922171+0.021293    test-rmse:1.401687+0.117181 
## [18] train-rmse:0.872842+0.020159    test-rmse:1.369899+0.116121 
## [19] train-rmse:0.832902+0.018863    test-rmse:1.352164+0.118979 
## [20] train-rmse:0.797528+0.018990    test-rmse:1.339241+0.123942 
## [21] train-rmse:0.765872+0.019180    test-rmse:1.326626+0.128639 
## [22] train-rmse:0.740233+0.017122    test-rmse:1.315595+0.131476 
## [23] train-rmse:0.710292+0.015129    test-rmse:1.303142+0.143036 
## [24] train-rmse:0.685813+0.014753    test-rmse:1.296019+0.144265 
## [25] train-rmse:0.663195+0.012751    test-rmse:1.283146+0.151851 
## [26] train-rmse:0.643857+0.009284    test-rmse:1.276854+0.157359 
## [27] train-rmse:0.625895+0.007357    test-rmse:1.272723+0.157256 
## [28] train-rmse:0.606334+0.008883    test-rmse:1.267890+0.155244 
## [29] train-rmse:0.590331+0.007083    test-rmse:1.268333+0.157610 
## [30] train-rmse:0.573890+0.009519    test-rmse:1.262614+0.159102 
## [31] train-rmse:0.559708+0.008940    test-rmse:1.259852+0.162809 
## [32] train-rmse:0.546201+0.009624    test-rmse:1.258794+0.164864 
## [33] train-rmse:0.532701+0.008409    test-rmse:1.254972+0.168084 
## [34] train-rmse:0.521320+0.007714    test-rmse:1.253749+0.168946 
## [35] train-rmse:0.509391+0.009729    test-rmse:1.251029+0.169032 
## [36] train-rmse:0.496769+0.009980    test-rmse:1.248874+0.169013 
## [37] train-rmse:0.488149+0.009847    test-rmse:1.250134+0.168015 
## [38] train-rmse:0.477886+0.010387    test-rmse:1.247662+0.167881 
## [39] train-rmse:0.468638+0.009500    test-rmse:1.243379+0.167384 
## [40] train-rmse:0.459232+0.008844    test-rmse:1.242575+0.168975 
## [41] train-rmse:0.448678+0.009301    test-rmse:1.241836+0.167695 
## [42] train-rmse:0.441289+0.008701    test-rmse:1.239075+0.166367 
## [43] train-rmse:0.430954+0.012597    test-rmse:1.237653+0.164575 
## [44] train-rmse:0.423765+0.013980    test-rmse:1.236958+0.165771 
## [45] train-rmse:0.415181+0.011497    test-rmse:1.236497+0.167167 
## [46] train-rmse:0.406554+0.012843    test-rmse:1.235997+0.169232 
## [47] train-rmse:0.397938+0.012545    test-rmse:1.235523+0.169849 
## [48] train-rmse:0.390012+0.010170    test-rmse:1.233048+0.168913 
## [49] train-rmse:0.383961+0.009725    test-rmse:1.232536+0.169486 
## [50] train-rmse:0.376793+0.012244    test-rmse:1.232017+0.170217 
## [51] train-rmse:0.368688+0.010974    test-rmse:1.230290+0.169176 
## [52] train-rmse:0.363212+0.011417    test-rmse:1.230977+0.169215 
## [53] train-rmse:0.358025+0.012416    test-rmse:1.232823+0.170425 
## [54] train-rmse:0.350656+0.011977    test-rmse:1.232034+0.171240 
## [55] train-rmse:0.344103+0.009918    test-rmse:1.231433+0.169433 
## [56] train-rmse:0.336377+0.009967    test-rmse:1.229625+0.168072 
## [57] train-rmse:0.329900+0.010825    test-rmse:1.228545+0.167991 
## [58] train-rmse:0.324366+0.009050    test-rmse:1.228081+0.168362 
## [59] train-rmse:0.319715+0.009483    test-rmse:1.227826+0.169010 
## [60] train-rmse:0.313802+0.008064    test-rmse:1.225976+0.168103 
## [61] train-rmse:0.306982+0.008446    test-rmse:1.226261+0.167080 
## [62] train-rmse:0.303716+0.008183    test-rmse:1.227272+0.167865 
## [63] train-rmse:0.297516+0.006131    test-rmse:1.226335+0.168282 
## [64] train-rmse:0.291555+0.006074    test-rmse:1.226264+0.166330 
## [65] train-rmse:0.287607+0.005720    test-rmse:1.226947+0.165805 
## [66] train-rmse:0.283361+0.004694    test-rmse:1.227842+0.164600 
## Stopping. Best iteration:
## [60] train-rmse:0.313802+0.008064    test-rmse:1.225976+0.168103
## 
## 42 
## [1]  train-rmse:7.731281+0.054356    test-rmse:7.727139+0.269544 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.226154+0.039700    test-rmse:6.219747+0.262488 
## [3]  train-rmse:5.087537+0.029467    test-rmse:5.081068+0.253417 
## [4]  train-rmse:4.239465+0.021167    test-rmse:4.243872+0.233231 
## [5]  train-rmse:3.619914+0.015045    test-rmse:3.630991+0.202211 
## [6]  train-rmse:3.177763+0.012237    test-rmse:3.199426+0.169698 
## [7]  train-rmse:2.866652+0.011526    test-rmse:2.900409+0.133846 
## [8]  train-rmse:2.651440+0.012051    test-rmse:2.679247+0.104475 
## [9]  train-rmse:2.503948+0.013084    test-rmse:2.540381+0.083313 
## [10] train-rmse:2.403840+0.014622    test-rmse:2.441061+0.059369 
## [11] train-rmse:2.334536+0.015409    test-rmse:2.375684+0.051684 
## [12] train-rmse:2.284308+0.016273    test-rmse:2.322693+0.050936 
## [13] train-rmse:2.247313+0.017281    test-rmse:2.289681+0.056451 
## [14] train-rmse:2.218642+0.018484    test-rmse:2.265193+0.056601 
## [15] train-rmse:2.196237+0.019377    test-rmse:2.244285+0.061578 
## [16] train-rmse:2.177446+0.020193    test-rmse:2.225437+0.062057 
## [17] train-rmse:2.160535+0.021046    test-rmse:2.212566+0.068816 
## [18] train-rmse:2.144936+0.021120    test-rmse:2.201895+0.073788 
## [19] train-rmse:2.130203+0.021354    test-rmse:2.189201+0.079707 
## [20] train-rmse:2.116496+0.022225    test-rmse:2.180550+0.085728 
## [21] train-rmse:2.103748+0.022724    test-rmse:2.166541+0.085539 
## [22] train-rmse:2.091310+0.023433    test-rmse:2.148302+0.082948 
## [23] train-rmse:2.079679+0.024172    test-rmse:2.134210+0.094308 
## [24] train-rmse:2.068473+0.024896    test-rmse:2.125662+0.095084 
## [25] train-rmse:2.057530+0.025451    test-rmse:2.117968+0.094658 
## [26] train-rmse:2.046563+0.025986    test-rmse:2.110161+0.092734 
## [27] train-rmse:2.035821+0.026151    test-rmse:2.103101+0.093421 
## [28] train-rmse:2.024950+0.026386    test-rmse:2.097231+0.092901 
## [29] train-rmse:2.015185+0.026986    test-rmse:2.083320+0.087398 
## [30] train-rmse:2.005461+0.027972    test-rmse:2.077561+0.083783 
## [31] train-rmse:1.996217+0.028361    test-rmse:2.068903+0.085108 
## [32] train-rmse:1.987316+0.028782    test-rmse:2.060990+0.090766 
## [33] train-rmse:1.978251+0.029122    test-rmse:2.055403+0.091279 
## [34] train-rmse:1.969663+0.029324    test-rmse:2.045495+0.086390 
## [35] train-rmse:1.961067+0.029737    test-rmse:2.036417+0.090090 
## [36] train-rmse:1.952683+0.030109    test-rmse:2.027068+0.095758 
## [37] train-rmse:1.944432+0.030515    test-rmse:2.017417+0.092707 
## [38] train-rmse:1.936299+0.030854    test-rmse:2.013130+0.093158 
## [39] train-rmse:1.928120+0.031241    test-rmse:2.009760+0.091356 
## [40] train-rmse:1.920309+0.031654    test-rmse:2.001632+0.096130 
## [41] train-rmse:1.912501+0.032231    test-rmse:1.992438+0.093856 
## [42] train-rmse:1.904724+0.032700    test-rmse:1.983368+0.087156 
## [43] train-rmse:1.897198+0.033044    test-rmse:1.978930+0.087975 
## [44] train-rmse:1.890057+0.033499    test-rmse:1.972596+0.091891 
## [45] train-rmse:1.882859+0.033880    test-rmse:1.965350+0.086923 
## [46] train-rmse:1.875708+0.034377    test-rmse:1.957820+0.088191 
## [47] train-rmse:1.868772+0.034888    test-rmse:1.954775+0.088789 
## [48] train-rmse:1.861837+0.035380    test-rmse:1.948744+0.086855 
## [49] train-rmse:1.855318+0.035731    test-rmse:1.945706+0.092863 
## [50] train-rmse:1.848890+0.036201    test-rmse:1.940611+0.093780 
## [51] train-rmse:1.842544+0.036661    test-rmse:1.935087+0.094628 
## [52] train-rmse:1.836274+0.037055    test-rmse:1.931652+0.097292 
## [53] train-rmse:1.830162+0.037448    test-rmse:1.925674+0.100708 
## [54] train-rmse:1.824014+0.037959    test-rmse:1.920797+0.095723 
## [55] train-rmse:1.818020+0.038220    test-rmse:1.915334+0.095377 
## [56] train-rmse:1.812194+0.038493    test-rmse:1.909449+0.093786 
## [57] train-rmse:1.806337+0.038715    test-rmse:1.903937+0.094580 
## [58] train-rmse:1.800575+0.038970    test-rmse:1.897084+0.093606 
## [59] train-rmse:1.794958+0.039267    test-rmse:1.893658+0.095199 
## [60] train-rmse:1.789252+0.039639    test-rmse:1.887594+0.092729 
## [61] train-rmse:1.783624+0.039984    test-rmse:1.884355+0.094107 
## [62] train-rmse:1.778197+0.040238    test-rmse:1.882329+0.093461 
## [63] train-rmse:1.772733+0.040617    test-rmse:1.875425+0.091971 
## [64] train-rmse:1.767292+0.041063    test-rmse:1.868722+0.094535 
## [65] train-rmse:1.762032+0.041345    test-rmse:1.866069+0.094784 
## [66] train-rmse:1.756720+0.041443    test-rmse:1.859669+0.094920 
## [67] train-rmse:1.751667+0.041781    test-rmse:1.853399+0.094813 
## [68] train-rmse:1.746633+0.042082    test-rmse:1.849111+0.094801 
## [69] train-rmse:1.741747+0.042311    test-rmse:1.842889+0.094344 
## [70] train-rmse:1.736909+0.042725    test-rmse:1.841203+0.093763 
## [71] train-rmse:1.732234+0.043102    test-rmse:1.838068+0.095626 
## [72] train-rmse:1.727664+0.043458    test-rmse:1.833200+0.093947 
## [73] train-rmse:1.723107+0.043646    test-rmse:1.828931+0.095060 
## [74] train-rmse:1.718577+0.043893    test-rmse:1.829291+0.095175 
## [75] train-rmse:1.714003+0.044093    test-rmse:1.824169+0.097398 
## [76] train-rmse:1.709495+0.044364    test-rmse:1.817864+0.096816 
## [77] train-rmse:1.705130+0.044593    test-rmse:1.816920+0.100134 
## [78] train-rmse:1.700814+0.044870    test-rmse:1.814116+0.101303 
## [79] train-rmse:1.696624+0.045103    test-rmse:1.811701+0.100935 
## [80] train-rmse:1.692500+0.045301    test-rmse:1.809251+0.097737 
## [81] train-rmse:1.688332+0.045566    test-rmse:1.805995+0.099609 
## [82] train-rmse:1.684142+0.045878    test-rmse:1.802314+0.100649 
## [83] train-rmse:1.679971+0.046130    test-rmse:1.799209+0.102003 
## [84] train-rmse:1.675983+0.046286    test-rmse:1.794460+0.104227 
## [85] train-rmse:1.672040+0.046477    test-rmse:1.793750+0.109340 
## [86] train-rmse:1.668123+0.046565    test-rmse:1.792078+0.110528 
## [87] train-rmse:1.664286+0.046713    test-rmse:1.788037+0.109888 
## [88] train-rmse:1.660528+0.046805    test-rmse:1.782736+0.113550 
## [89] train-rmse:1.656725+0.046916    test-rmse:1.776008+0.111052 
## [90] train-rmse:1.653081+0.047001    test-rmse:1.773098+0.109702 
## [91] train-rmse:1.649284+0.047232    test-rmse:1.773246+0.107170 
## [92] train-rmse:1.645650+0.047357    test-rmse:1.763998+0.107361 
## [93] train-rmse:1.642060+0.047447    test-rmse:1.757034+0.113966 
## [94] train-rmse:1.638570+0.047600    test-rmse:1.754838+0.113765 
## [95] train-rmse:1.635142+0.047778    test-rmse:1.751877+0.113828 
## [96] train-rmse:1.631699+0.047905    test-rmse:1.748655+0.114946 
## [97] train-rmse:1.628265+0.048030    test-rmse:1.747051+0.114459 
## [98] train-rmse:1.624904+0.048157    test-rmse:1.745454+0.114286 
## [99] train-rmse:1.621629+0.048262    test-rmse:1.744257+0.115213 
## [100]    train-rmse:1.618366+0.048395    test-rmse:1.740075+0.115464 
## [101]    train-rmse:1.615112+0.048516    test-rmse:1.737228+0.116093 
## [102]    train-rmse:1.611968+0.048680    test-rmse:1.735550+0.117215 
## [103]    train-rmse:1.608903+0.048863    test-rmse:1.731359+0.118642 
## [104]    train-rmse:1.605763+0.048979    test-rmse:1.730656+0.121168 
## [105]    train-rmse:1.602724+0.049096    test-rmse:1.727088+0.121781 
## [106]    train-rmse:1.599690+0.049183    test-rmse:1.728573+0.121475 
## [107]    train-rmse:1.596721+0.049348    test-rmse:1.727087+0.121128 
## [108]    train-rmse:1.593772+0.049482    test-rmse:1.722632+0.118752 
## [109]    train-rmse:1.590878+0.049584    test-rmse:1.722839+0.118168 
## [110]    train-rmse:1.588010+0.049723    test-rmse:1.723587+0.120785 
## [111]    train-rmse:1.585142+0.049820    test-rmse:1.719054+0.120301 
## [112]    train-rmse:1.582243+0.049907    test-rmse:1.714559+0.122329 
## [113]    train-rmse:1.579413+0.050023    test-rmse:1.711049+0.123835 
## [114]    train-rmse:1.576595+0.050164    test-rmse:1.709227+0.125851 
## [115]    train-rmse:1.573884+0.050208    test-rmse:1.706233+0.127347 
## [116]    train-rmse:1.571204+0.050275    test-rmse:1.704414+0.128265 
## [117]    train-rmse:1.568551+0.050297    test-rmse:1.703642+0.129168 
## [118]    train-rmse:1.565954+0.050339    test-rmse:1.698904+0.129989 
## [119]    train-rmse:1.563317+0.050475    test-rmse:1.699901+0.131242 
## [120]    train-rmse:1.560686+0.050515    test-rmse:1.693061+0.133457 
## [121]    train-rmse:1.558037+0.050590    test-rmse:1.691080+0.134241 
## [122]    train-rmse:1.555394+0.050618    test-rmse:1.689316+0.135905 
## [123]    train-rmse:1.552903+0.050733    test-rmse:1.688240+0.137807 
## [124]    train-rmse:1.550455+0.050832    test-rmse:1.684347+0.136153 
## [125]    train-rmse:1.547994+0.050936    test-rmse:1.680386+0.136023 
## [126]    train-rmse:1.545514+0.050992    test-rmse:1.675475+0.137904 
## [127]    train-rmse:1.543049+0.051079    test-rmse:1.672876+0.138627 
## [128]    train-rmse:1.540674+0.051094    test-rmse:1.669155+0.137379 
## [129]    train-rmse:1.538378+0.051110    test-rmse:1.667884+0.138164 
## [130]    train-rmse:1.536083+0.051145    test-rmse:1.666961+0.135412 
## [131]    train-rmse:1.533841+0.051167    test-rmse:1.664436+0.135391 
## [132]    train-rmse:1.531602+0.051254    test-rmse:1.663545+0.136442 
## [133]    train-rmse:1.529394+0.051307    test-rmse:1.660975+0.134962 
## [134]    train-rmse:1.527205+0.051368    test-rmse:1.657249+0.136922 
## [135]    train-rmse:1.525022+0.051387    test-rmse:1.657875+0.137951 
## [136]    train-rmse:1.522853+0.051417    test-rmse:1.655351+0.137770 
## [137]    train-rmse:1.520688+0.051443    test-rmse:1.653524+0.142352 
## [138]    train-rmse:1.518615+0.051466    test-rmse:1.653978+0.142607 
## [139]    train-rmse:1.516554+0.051498    test-rmse:1.650430+0.141231 
## [140]    train-rmse:1.514518+0.051539    test-rmse:1.648518+0.141734 
## [141]    train-rmse:1.512494+0.051607    test-rmse:1.645369+0.139412 
## [142]    train-rmse:1.510489+0.051639    test-rmse:1.640120+0.142685 
## [143]    train-rmse:1.508502+0.051691    test-rmse:1.638048+0.142113 
## [144]    train-rmse:1.506548+0.051752    test-rmse:1.635384+0.141278 
## [145]    train-rmse:1.504601+0.051788    test-rmse:1.636525+0.143721 
## [146]    train-rmse:1.502727+0.051821    test-rmse:1.635279+0.143905 
## [147]    train-rmse:1.500844+0.051850    test-rmse:1.635059+0.144506 
## [148]    train-rmse:1.498940+0.051909    test-rmse:1.632368+0.144959 
## [149]    train-rmse:1.497007+0.051954    test-rmse:1.629833+0.145444 
## [150]    train-rmse:1.495127+0.051984    test-rmse:1.628889+0.145187 
## [151]    train-rmse:1.493257+0.052019    test-rmse:1.627538+0.146906 
## [152]    train-rmse:1.491441+0.052090    test-rmse:1.627720+0.148104 
## [153]    train-rmse:1.489646+0.052160    test-rmse:1.626985+0.148007 
## [154]    train-rmse:1.487858+0.052202    test-rmse:1.625987+0.148050 
## [155]    train-rmse:1.486096+0.052248    test-rmse:1.624454+0.149375 
## [156]    train-rmse:1.484363+0.052320    test-rmse:1.623914+0.149877 
## [157]    train-rmse:1.482667+0.052380    test-rmse:1.622564+0.148511 
## [158]    train-rmse:1.480926+0.052473    test-rmse:1.619713+0.149191 
## [159]    train-rmse:1.479169+0.052540    test-rmse:1.617651+0.149300 
## [160]    train-rmse:1.477451+0.052593    test-rmse:1.615796+0.149970 
## [161]    train-rmse:1.475799+0.052634    test-rmse:1.614274+0.149387 
## [162]    train-rmse:1.474162+0.052673    test-rmse:1.610869+0.150276 
## [163]    train-rmse:1.472556+0.052718    test-rmse:1.609053+0.150807 
## [164]    train-rmse:1.470969+0.052735    test-rmse:1.607453+0.151819 
## [165]    train-rmse:1.469420+0.052748    test-rmse:1.606789+0.153747 
## [166]    train-rmse:1.467876+0.052763    test-rmse:1.606058+0.153541 
## [167]    train-rmse:1.466330+0.052817    test-rmse:1.603844+0.152597 
## [168]    train-rmse:1.464799+0.052865    test-rmse:1.602982+0.151395 
## [169]    train-rmse:1.463330+0.052894    test-rmse:1.601333+0.151813 
## [170]    train-rmse:1.461854+0.052930    test-rmse:1.602180+0.154305 
## [171]    train-rmse:1.460385+0.052952    test-rmse:1.600057+0.154112 
## [172]    train-rmse:1.458925+0.052969    test-rmse:1.594971+0.157882 
## [173]    train-rmse:1.457475+0.052963    test-rmse:1.593706+0.156491 
## [174]    train-rmse:1.456043+0.052985    test-rmse:1.590478+0.157525 
## [175]    train-rmse:1.454610+0.053031    test-rmse:1.589042+0.156841 
## [176]    train-rmse:1.453192+0.053068    test-rmse:1.590823+0.157309 
## [177]    train-rmse:1.451800+0.053076    test-rmse:1.590228+0.158228 
## [178]    train-rmse:1.450426+0.053105    test-rmse:1.587654+0.156293 
## [179]    train-rmse:1.449055+0.053097    test-rmse:1.585909+0.156191 
## [180]    train-rmse:1.447681+0.053077    test-rmse:1.585660+0.154656 
## [181]    train-rmse:1.446319+0.053035    test-rmse:1.582782+0.153944 
## [182]    train-rmse:1.445011+0.053044    test-rmse:1.582036+0.153849 
## [183]    train-rmse:1.443720+0.053044    test-rmse:1.581936+0.153849 
## [184]    train-rmse:1.442430+0.053061    test-rmse:1.579991+0.154636 
## [185]    train-rmse:1.441147+0.053064    test-rmse:1.577859+0.155382 
## [186]    train-rmse:1.439873+0.053085    test-rmse:1.575380+0.156783 
## [187]    train-rmse:1.438600+0.053108    test-rmse:1.575373+0.158040 
## [188]    train-rmse:1.437335+0.053140    test-rmse:1.574843+0.158624 
## [189]    train-rmse:1.436093+0.053171    test-rmse:1.574301+0.160576 
## [190]    train-rmse:1.434864+0.053199    test-rmse:1.573716+0.161511 
## [191]    train-rmse:1.433657+0.053218    test-rmse:1.572276+0.161830 
## [192]    train-rmse:1.432432+0.053253    test-rmse:1.571029+0.161670 
## [193]    train-rmse:1.431222+0.053276    test-rmse:1.571745+0.163061 
## [194]    train-rmse:1.430026+0.053298    test-rmse:1.568861+0.162491 
## [195]    train-rmse:1.428831+0.053273    test-rmse:1.567003+0.163082 
## [196]    train-rmse:1.427625+0.053280    test-rmse:1.565961+0.163310 
## [197]    train-rmse:1.426481+0.053307    test-rmse:1.565507+0.164473 
## [198]    train-rmse:1.425364+0.053298    test-rmse:1.566435+0.164201 
## [199]    train-rmse:1.424245+0.053300    test-rmse:1.566620+0.163556 
## [200]    train-rmse:1.423131+0.053296    test-rmse:1.566009+0.164010 
## [201]    train-rmse:1.422018+0.053290    test-rmse:1.565310+0.168377 
## [202]    train-rmse:1.420934+0.053296    test-rmse:1.563652+0.167631 
## [203]    train-rmse:1.419875+0.053311    test-rmse:1.560428+0.166423 
## [204]    train-rmse:1.418811+0.053306    test-rmse:1.559765+0.166537 
## [205]    train-rmse:1.417766+0.053313    test-rmse:1.558822+0.165716 
## [206]    train-rmse:1.416726+0.053328    test-rmse:1.557391+0.165603 
## [207]    train-rmse:1.415675+0.053351    test-rmse:1.555207+0.165207 
## [208]    train-rmse:1.414666+0.053363    test-rmse:1.554580+0.165426 
## [209]    train-rmse:1.413629+0.053378    test-rmse:1.554222+0.165933 
## [210]    train-rmse:1.412605+0.053392    test-rmse:1.554214+0.164824 
## [211]    train-rmse:1.411618+0.053397    test-rmse:1.553888+0.164474 
## [212]    train-rmse:1.410651+0.053397    test-rmse:1.553353+0.164172 
## [213]    train-rmse:1.409667+0.053408    test-rmse:1.553754+0.166282 
## [214]    train-rmse:1.408718+0.053404    test-rmse:1.551328+0.166604 
## [215]    train-rmse:1.407771+0.053414    test-rmse:1.550961+0.167413 
## [216]    train-rmse:1.406843+0.053421    test-rmse:1.549503+0.168365 
## [217]    train-rmse:1.405932+0.053405    test-rmse:1.548177+0.168117 
## [218]    train-rmse:1.405016+0.053410    test-rmse:1.546506+0.168384 
## [219]    train-rmse:1.404111+0.053410    test-rmse:1.545660+0.168758 
## [220]    train-rmse:1.403210+0.053416    test-rmse:1.544745+0.168662 
## [221]    train-rmse:1.402316+0.053406    test-rmse:1.541308+0.170837 
## [222]    train-rmse:1.401414+0.053373    test-rmse:1.541097+0.170738 
## [223]    train-rmse:1.400514+0.053337    test-rmse:1.539670+0.170785 
## [224]    train-rmse:1.399642+0.053306    test-rmse:1.539111+0.171371 
## [225]    train-rmse:1.398793+0.053306    test-rmse:1.538460+0.171267 
## [226]    train-rmse:1.397926+0.053305    test-rmse:1.538788+0.172159 
## [227]    train-rmse:1.397094+0.053319    test-rmse:1.539328+0.171825 
## [228]    train-rmse:1.396268+0.053337    test-rmse:1.538887+0.171462 
## [229]    train-rmse:1.395450+0.053369    test-rmse:1.538963+0.170990 
## [230]    train-rmse:1.394644+0.053383    test-rmse:1.539492+0.172731 
## [231]    train-rmse:1.393837+0.053403    test-rmse:1.539464+0.172563 
## Stopping. Best iteration:
## [225]    train-rmse:1.398793+0.053306    test-rmse:1.538460+0.171267
## 
## 43 
## [1]  train-rmse:7.703024+0.054739    test-rmse:7.699317+0.264931 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.171347+0.043823    test-rmse:6.165040+0.248365 
## [3]  train-rmse:5.008519+0.035339    test-rmse:5.012505+0.228441 
## [4]  train-rmse:4.128750+0.028987    test-rmse:4.143591+0.203237 
## [5]  train-rmse:3.474198+0.024079    test-rmse:3.499772+0.168047 
## [6]  train-rmse:2.998160+0.021413    test-rmse:3.031620+0.145838 
## [7]  train-rmse:2.651412+0.022097    test-rmse:2.707955+0.113921 
## [8]  train-rmse:2.406729+0.019387    test-rmse:2.470054+0.078161 
## [9]  train-rmse:2.235851+0.017990    test-rmse:2.303046+0.066208 
## [10] train-rmse:2.112971+0.019865    test-rmse:2.204879+0.064479 
## [11] train-rmse:2.018624+0.026561    test-rmse:2.128414+0.061176 
## [12] train-rmse:1.952312+0.023018    test-rmse:2.068860+0.068328 
## [13] train-rmse:1.902318+0.022521    test-rmse:2.023933+0.077100 
## [14] train-rmse:1.862185+0.021748    test-rmse:2.007046+0.089816 
## [15] train-rmse:1.826348+0.027839    test-rmse:1.968621+0.091891 
## [16] train-rmse:1.799382+0.027666    test-rmse:1.945418+0.099348 
## [17] train-rmse:1.776001+0.027922    test-rmse:1.925342+0.103655 
## [18] train-rmse:1.754658+0.029015    test-rmse:1.902883+0.101715 
## [19] train-rmse:1.732981+0.029638    test-rmse:1.883194+0.105932 
## [20] train-rmse:1.708165+0.033049    test-rmse:1.861597+0.120248 
## [21] train-rmse:1.689679+0.033205    test-rmse:1.847015+0.115225 
## [22] train-rmse:1.666130+0.038192    test-rmse:1.834188+0.119601 
## [23] train-rmse:1.648567+0.037845    test-rmse:1.821788+0.118228 
## [24] train-rmse:1.631953+0.038529    test-rmse:1.818361+0.123939 
## [25] train-rmse:1.608082+0.036742    test-rmse:1.801782+0.108362 
## [26] train-rmse:1.593660+0.037921    test-rmse:1.786699+0.105891 
## [27] train-rmse:1.577451+0.038107    test-rmse:1.772968+0.105492 
## [28] train-rmse:1.563847+0.038875    test-rmse:1.765526+0.105268 
## [29] train-rmse:1.549011+0.040079    test-rmse:1.750110+0.108443 
## [30] train-rmse:1.534751+0.041388    test-rmse:1.742546+0.119166 
## [31] train-rmse:1.522334+0.042265    test-rmse:1.736626+0.119832 
## [32] train-rmse:1.504449+0.045088    test-rmse:1.712088+0.095203 
## [33] train-rmse:1.490611+0.044293    test-rmse:1.703099+0.098319 
## [34] train-rmse:1.478821+0.045303    test-rmse:1.694909+0.098317 
## [35] train-rmse:1.466648+0.043782    test-rmse:1.685041+0.097763 
## [36] train-rmse:1.450840+0.048237    test-rmse:1.676049+0.088005 
## [37] train-rmse:1.437783+0.048279    test-rmse:1.670364+0.095190 
## [38] train-rmse:1.426161+0.048968    test-rmse:1.657469+0.093256 
## [39] train-rmse:1.411483+0.049934    test-rmse:1.647682+0.097846 
## [40] train-rmse:1.395114+0.052168    test-rmse:1.626116+0.108266 
## [41] train-rmse:1.384066+0.051242    test-rmse:1.614175+0.112469 
## [42] train-rmse:1.370845+0.054896    test-rmse:1.599147+0.096838 
## [43] train-rmse:1.359461+0.054563    test-rmse:1.590931+0.104521 
## [44] train-rmse:1.350215+0.055254    test-rmse:1.586535+0.100449 
## [45] train-rmse:1.341343+0.056040    test-rmse:1.577715+0.102207 
## [46] train-rmse:1.332974+0.056588    test-rmse:1.570405+0.105331 
## [47] train-rmse:1.324399+0.057386    test-rmse:1.565831+0.106399 
## [48] train-rmse:1.316128+0.056902    test-rmse:1.558150+0.103842 
## [49] train-rmse:1.308387+0.057271    test-rmse:1.555403+0.108887 
## [50] train-rmse:1.295160+0.061824    test-rmse:1.547845+0.107297 
## [51] train-rmse:1.286413+0.062748    test-rmse:1.543639+0.108545 
## [52] train-rmse:1.277447+0.063231    test-rmse:1.536813+0.107726 
## [53] train-rmse:1.269146+0.062322    test-rmse:1.532451+0.111368 
## [54] train-rmse:1.261002+0.061938    test-rmse:1.525818+0.114827 
## [55] train-rmse:1.253810+0.062841    test-rmse:1.523319+0.117072 
## [56] train-rmse:1.247354+0.063204    test-rmse:1.518811+0.118995 
## [57] train-rmse:1.237244+0.064222    test-rmse:1.510573+0.120078 
## [58] train-rmse:1.230599+0.064975    test-rmse:1.509387+0.120745 
## [59] train-rmse:1.223285+0.065770    test-rmse:1.505422+0.124482 
## [60] train-rmse:1.208919+0.050901    test-rmse:1.493993+0.127278 
## [61] train-rmse:1.192248+0.046588    test-rmse:1.478500+0.139590 
## [62] train-rmse:1.181478+0.050978    test-rmse:1.472519+0.137204 
## [63] train-rmse:1.175492+0.051266    test-rmse:1.465443+0.135904 
## [64] train-rmse:1.168901+0.051063    test-rmse:1.461039+0.138043 
## [65] train-rmse:1.162949+0.051650    test-rmse:1.457911+0.134231 
## [66] train-rmse:1.155499+0.050738    test-rmse:1.453961+0.135149 
## [67] train-rmse:1.149865+0.050648    test-rmse:1.449052+0.135352 
## [68] train-rmse:1.140408+0.051538    test-rmse:1.441096+0.140248 
## [69] train-rmse:1.134863+0.052109    test-rmse:1.438175+0.138306 
## [70] train-rmse:1.127464+0.052074    test-rmse:1.432698+0.139096 
## [71] train-rmse:1.121722+0.051345    test-rmse:1.425748+0.136170 
## [72] train-rmse:1.115732+0.052348    test-rmse:1.418664+0.139856 
## [73] train-rmse:1.111121+0.052436    test-rmse:1.415121+0.139650 
## [74] train-rmse:1.105874+0.052666    test-rmse:1.414079+0.137778 
## [75] train-rmse:1.101266+0.053043    test-rmse:1.413689+0.141552 
## [76] train-rmse:1.096322+0.053221    test-rmse:1.413300+0.142867 
## [77] train-rmse:1.092326+0.053302    test-rmse:1.410481+0.142721 
## [78] train-rmse:1.081020+0.042907    test-rmse:1.405635+0.147843 
## [79] train-rmse:1.076517+0.042434    test-rmse:1.405102+0.147100 
## [80] train-rmse:1.072171+0.042144    test-rmse:1.403677+0.151349 
## [81] train-rmse:1.063344+0.041090    test-rmse:1.391641+0.140980 
## [82] train-rmse:1.055296+0.041193    test-rmse:1.385493+0.137556 
## [83] train-rmse:1.050891+0.041105    test-rmse:1.382149+0.136043 
## [84] train-rmse:1.047245+0.041554    test-rmse:1.379406+0.137855 
## [85] train-rmse:1.042702+0.040939    test-rmse:1.377381+0.141098 
## [86] train-rmse:1.033350+0.034589    test-rmse:1.371193+0.143633 
## [87] train-rmse:1.029019+0.034554    test-rmse:1.366965+0.146714 
## [88] train-rmse:1.024612+0.034293    test-rmse:1.365870+0.144992 
## [89] train-rmse:1.019496+0.033507    test-rmse:1.363681+0.146043 
## [90] train-rmse:1.015856+0.033611    test-rmse:1.359452+0.144576 
## [91] train-rmse:1.012507+0.033655    test-rmse:1.358525+0.142754 
## [92] train-rmse:1.008339+0.033619    test-rmse:1.357396+0.147079 
## [93] train-rmse:1.004667+0.033164    test-rmse:1.354552+0.148390 
## [94] train-rmse:0.997567+0.037305    test-rmse:1.352288+0.145247 
## [95] train-rmse:0.993684+0.037544    test-rmse:1.350843+0.144419 
## [96] train-rmse:0.987695+0.039017    test-rmse:1.341837+0.138554 
## [97] train-rmse:0.984876+0.039168    test-rmse:1.338212+0.140433 
## [98] train-rmse:0.981574+0.038795    test-rmse:1.336704+0.141325 
## [99] train-rmse:0.975464+0.039425    test-rmse:1.332700+0.141517 
## [100]    train-rmse:0.969492+0.039135    test-rmse:1.327120+0.145485 
## [101]    train-rmse:0.964368+0.042433    test-rmse:1.325496+0.145658 
## [102]    train-rmse:0.959193+0.041247    test-rmse:1.314438+0.150056 
## [103]    train-rmse:0.955348+0.041054    test-rmse:1.315463+0.149248 
## [104]    train-rmse:0.952566+0.041093    test-rmse:1.315620+0.149341 
## [105]    train-rmse:0.949600+0.040586    test-rmse:1.313118+0.150935 
## [106]    train-rmse:0.946719+0.040081    test-rmse:1.313729+0.150633 
## [107]    train-rmse:0.943972+0.039969    test-rmse:1.312621+0.148993 
## [108]    train-rmse:0.941150+0.039438    test-rmse:1.312200+0.148634 
## [109]    train-rmse:0.938671+0.039598    test-rmse:1.311071+0.147857 
## [110]    train-rmse:0.936290+0.039843    test-rmse:1.309381+0.148325 
## [111]    train-rmse:0.933740+0.039975    test-rmse:1.309004+0.150840 
## [112]    train-rmse:0.930771+0.040184    test-rmse:1.307778+0.151942 
## [113]    train-rmse:0.928718+0.040139    test-rmse:1.306606+0.152397 
## [114]    train-rmse:0.926336+0.039628    test-rmse:1.307763+0.152616 
## [115]    train-rmse:0.920149+0.033974    test-rmse:1.306133+0.154464 
## [116]    train-rmse:0.914856+0.031777    test-rmse:1.304264+0.156639 
## [117]    train-rmse:0.912465+0.031915    test-rmse:1.304029+0.158889 
## [118]    train-rmse:0.910198+0.032377    test-rmse:1.301288+0.158933 
## [119]    train-rmse:0.907523+0.032105    test-rmse:1.301793+0.159388 
## [120]    train-rmse:0.902601+0.033194    test-rmse:1.294685+0.154833 
## [121]    train-rmse:0.899545+0.032394    test-rmse:1.292524+0.156559 
## [122]    train-rmse:0.897235+0.032430    test-rmse:1.289956+0.156528 
## [123]    train-rmse:0.893768+0.034629    test-rmse:1.289440+0.155622 
## [124]    train-rmse:0.892014+0.034683    test-rmse:1.287809+0.156350 
## [125]    train-rmse:0.890046+0.034312    test-rmse:1.286334+0.158006 
## [126]    train-rmse:0.887879+0.034688    test-rmse:1.287535+0.158599 
## [127]    train-rmse:0.884999+0.034746    test-rmse:1.286944+0.157498 
## [128]    train-rmse:0.881617+0.037338    test-rmse:1.286138+0.156948 
## [129]    train-rmse:0.880018+0.037348    test-rmse:1.286299+0.157127 
## [130]    train-rmse:0.876065+0.041078    test-rmse:1.281779+0.150808 
## [131]    train-rmse:0.874154+0.040984    test-rmse:1.281076+0.152586 
## [132]    train-rmse:0.870545+0.039449    test-rmse:1.279407+0.155066 
## [133]    train-rmse:0.868729+0.039698    test-rmse:1.279045+0.155428 
## [134]    train-rmse:0.864320+0.038051    test-rmse:1.276639+0.156672 
## [135]    train-rmse:0.862476+0.038082    test-rmse:1.275520+0.157432 
## [136]    train-rmse:0.858880+0.038111    test-rmse:1.273103+0.157074 
## [137]    train-rmse:0.855946+0.037406    test-rmse:1.273764+0.156281 
## [138]    train-rmse:0.854213+0.037575    test-rmse:1.273215+0.157105 
## [139]    train-rmse:0.852777+0.037546    test-rmse:1.274257+0.156132 
## [140]    train-rmse:0.851420+0.037576    test-rmse:1.273406+0.155812 
## [141]    train-rmse:0.849579+0.037714    test-rmse:1.274373+0.157148 
## [142]    train-rmse:0.848066+0.037677    test-rmse:1.273489+0.158273 
## Stopping. Best iteration:
## [136]    train-rmse:0.858880+0.038111    test-rmse:1.273103+0.157074
## 
## 44 
## [1]  train-rmse:7.692030+0.055948    test-rmse:7.688607+0.259158 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.143300+0.041962    test-rmse:6.139013+0.245130 
## [3]  train-rmse:4.958204+0.033934    test-rmse:4.961825+0.229426 
## [4]  train-rmse:4.055561+0.028603    test-rmse:4.064797+0.199284 
## [5]  train-rmse:3.379000+0.026274    test-rmse:3.414383+0.181632 
## [6]  train-rmse:2.870117+0.020099    test-rmse:2.913240+0.152835 
## [7]  train-rmse:2.493488+0.015763    test-rmse:2.566059+0.118866 
## [8]  train-rmse:2.222850+0.015093    test-rmse:2.318615+0.096409 
## [9]  train-rmse:2.023179+0.013593    test-rmse:2.151534+0.089392 
## [10] train-rmse:1.881256+0.015625    test-rmse:2.028942+0.093390 
## [11] train-rmse:1.774667+0.017457    test-rmse:1.938239+0.091133 
## [12] train-rmse:1.695079+0.018401    test-rmse:1.880289+0.083098 
## [13] train-rmse:1.634157+0.017765    test-rmse:1.839751+0.082886 
## [14] train-rmse:1.584708+0.020196    test-rmse:1.799173+0.074788 
## [15] train-rmse:1.540129+0.020844    test-rmse:1.776246+0.070880 
## [16] train-rmse:1.505098+0.023358    test-rmse:1.745131+0.074428 
## [17] train-rmse:1.473128+0.024615    test-rmse:1.722887+0.079555 
## [18] train-rmse:1.445472+0.025370    test-rmse:1.704534+0.079766 
## [19] train-rmse:1.420218+0.027353    test-rmse:1.690063+0.077661 
## [20] train-rmse:1.394634+0.026282    test-rmse:1.664939+0.077473 
## [21] train-rmse:1.371069+0.028546    test-rmse:1.652158+0.071944 
## [22] train-rmse:1.349121+0.027691    test-rmse:1.634980+0.081828 
## [23] train-rmse:1.328317+0.026572    test-rmse:1.626410+0.085379 
## [24] train-rmse:1.308833+0.026355    test-rmse:1.612435+0.094387 
## [25] train-rmse:1.291656+0.027470    test-rmse:1.599768+0.095777 
## [26] train-rmse:1.273709+0.026220    test-rmse:1.589589+0.094965 
## [27] train-rmse:1.256029+0.026774    test-rmse:1.579442+0.096504 
## [28] train-rmse:1.237160+0.029074    test-rmse:1.559739+0.099306 
## [29] train-rmse:1.219046+0.029821    test-rmse:1.540902+0.084039 
## [30] train-rmse:1.200916+0.027802    test-rmse:1.535139+0.090372 
## [31] train-rmse:1.184587+0.026470    test-rmse:1.522659+0.099611 
## [32] train-rmse:1.168094+0.026565    test-rmse:1.513946+0.099286 
## [33] train-rmse:1.151271+0.024535    test-rmse:1.506172+0.100635 
## [34] train-rmse:1.135221+0.019743    test-rmse:1.495455+0.102784 
## [35] train-rmse:1.122976+0.020439    test-rmse:1.484945+0.106651 
## [36] train-rmse:1.109123+0.020674    test-rmse:1.471607+0.107357 
## [37] train-rmse:1.095945+0.019917    test-rmse:1.465030+0.108558 
## [38] train-rmse:1.078820+0.020051    test-rmse:1.454108+0.112559 
## [39] train-rmse:1.064705+0.023666    test-rmse:1.448565+0.118896 
## [40] train-rmse:1.054084+0.025431    test-rmse:1.441064+0.120128 
## [41] train-rmse:1.041830+0.023495    test-rmse:1.437406+0.121823 
## [42] train-rmse:1.029745+0.022840    test-rmse:1.432676+0.123811 
## [43] train-rmse:1.018544+0.022114    test-rmse:1.428467+0.121204 
## [44] train-rmse:1.008446+0.022855    test-rmse:1.421818+0.123673 
## [45] train-rmse:0.998727+0.022991    test-rmse:1.416381+0.126315 
## [46] train-rmse:0.988007+0.022071    test-rmse:1.412279+0.128813 
## [47] train-rmse:0.977583+0.022447    test-rmse:1.405294+0.133648 
## [48] train-rmse:0.967128+0.022882    test-rmse:1.399677+0.136785 
## [49] train-rmse:0.960120+0.022900    test-rmse:1.397744+0.136378 
## [50] train-rmse:0.950929+0.023226    test-rmse:1.388564+0.139666 
## [51] train-rmse:0.941283+0.022201    test-rmse:1.383424+0.137874 
## [52] train-rmse:0.933206+0.021630    test-rmse:1.381107+0.138824 
## [53] train-rmse:0.924919+0.021396    test-rmse:1.372570+0.139077 
## [54] train-rmse:0.914909+0.020259    test-rmse:1.373894+0.139265 
## [55] train-rmse:0.908997+0.020376    test-rmse:1.372420+0.142422 
## [56] train-rmse:0.898456+0.025730    test-rmse:1.365840+0.142898 
## [57] train-rmse:0.890923+0.028222    test-rmse:1.361198+0.143230 
## [58] train-rmse:0.884080+0.028347    test-rmse:1.354797+0.144918 
## [59] train-rmse:0.876793+0.029254    test-rmse:1.353147+0.147734 
## [60] train-rmse:0.869645+0.028343    test-rmse:1.354688+0.146729 
## [61] train-rmse:0.859685+0.026515    test-rmse:1.348343+0.146392 
## [62] train-rmse:0.853558+0.026228    test-rmse:1.347572+0.146800 
## [63] train-rmse:0.846506+0.025099    test-rmse:1.346548+0.150448 
## [64] train-rmse:0.838361+0.024644    test-rmse:1.344746+0.149290 
## [65] train-rmse:0.832384+0.024885    test-rmse:1.342827+0.151402 
## [66] train-rmse:0.826187+0.025051    test-rmse:1.340452+0.152784 
## [67] train-rmse:0.821123+0.025086    test-rmse:1.338326+0.152318 
## [68] train-rmse:0.814593+0.022977    test-rmse:1.336505+0.154949 
## [69] train-rmse:0.804980+0.025330    test-rmse:1.334753+0.155704 
## [70] train-rmse:0.797952+0.027528    test-rmse:1.329108+0.151921 
## [71] train-rmse:0.792553+0.027981    test-rmse:1.327564+0.152046 
## [72] train-rmse:0.787496+0.027216    test-rmse:1.325853+0.151646 
## [73] train-rmse:0.782977+0.027876    test-rmse:1.323391+0.151784 
## [74] train-rmse:0.779371+0.027792    test-rmse:1.322843+0.152676 
## [75] train-rmse:0.774457+0.027674    test-rmse:1.320533+0.153561 
## [76] train-rmse:0.768464+0.028596    test-rmse:1.320174+0.154796 
## [77] train-rmse:0.763416+0.028725    test-rmse:1.321828+0.157922 
## [78] train-rmse:0.758239+0.030995    test-rmse:1.319799+0.156776 
## [79] train-rmse:0.753414+0.031191    test-rmse:1.319840+0.155406 
## [80] train-rmse:0.750235+0.031519    test-rmse:1.319125+0.156293 
## [81] train-rmse:0.746532+0.030912    test-rmse:1.318570+0.157850 
## [82] train-rmse:0.741374+0.035268    test-rmse:1.316463+0.158482 
## [83] train-rmse:0.737597+0.034929    test-rmse:1.317344+0.159664 
## [84] train-rmse:0.734132+0.035015    test-rmse:1.317117+0.160618 
## [85] train-rmse:0.727466+0.032554    test-rmse:1.310365+0.154935 
## [86] train-rmse:0.722888+0.031658    test-rmse:1.310904+0.154127 
## [87] train-rmse:0.719137+0.032190    test-rmse:1.307205+0.153648 
## [88] train-rmse:0.715192+0.032629    test-rmse:1.306422+0.154108 
## [89] train-rmse:0.708191+0.031834    test-rmse:1.306035+0.153935 
## [90] train-rmse:0.705334+0.032397    test-rmse:1.307942+0.154877 
## [91] train-rmse:0.702204+0.032342    test-rmse:1.308506+0.155020 
## [92] train-rmse:0.698376+0.032092    test-rmse:1.308700+0.155564 
## [93] train-rmse:0.695256+0.033301    test-rmse:1.307766+0.155213 
## [94] train-rmse:0.692014+0.033314    test-rmse:1.308353+0.154720 
## [95] train-rmse:0.688639+0.035016    test-rmse:1.307413+0.154449 
## Stopping. Best iteration:
## [89] train-rmse:0.708191+0.031834    test-rmse:1.306035+0.153935
## 
## 45 
## [1]  train-rmse:7.687516+0.056427    test-rmse:7.684084+0.260706 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.132698+0.043409    test-rmse:6.141381+0.245607 
## [3]  train-rmse:4.938933+0.032580    test-rmse:4.946127+0.222311 
## [4]  train-rmse:4.027196+0.027880    test-rmse:4.056770+0.194184 
## [5]  train-rmse:3.328452+0.019849    test-rmse:3.380152+0.172477 
## [6]  train-rmse:2.805357+0.016531    test-rmse:2.877283+0.150335 
## [7]  train-rmse:2.413657+0.019260    test-rmse:2.507635+0.123033 
## [8]  train-rmse:2.118629+0.015574    test-rmse:2.237319+0.114479 
## [9]  train-rmse:1.905025+0.012696    test-rmse:2.059239+0.098461 
## [10] train-rmse:1.746832+0.013933    test-rmse:1.922743+0.095107 
## [11] train-rmse:1.625115+0.014779    test-rmse:1.829396+0.090194 
## [12] train-rmse:1.532288+0.019077    test-rmse:1.769274+0.079815 
## [13] train-rmse:1.459724+0.023076    test-rmse:1.727130+0.075369 
## [14] train-rmse:1.404335+0.025822    test-rmse:1.692266+0.071078 
## [15] train-rmse:1.354087+0.026766    test-rmse:1.654612+0.079749 
## [16] train-rmse:1.309221+0.030797    test-rmse:1.620144+0.082499 
## [17] train-rmse:1.274007+0.031895    test-rmse:1.596008+0.083512 
## [18] train-rmse:1.240788+0.032966    test-rmse:1.574254+0.098709 
## [19] train-rmse:1.208260+0.028287    test-rmse:1.549089+0.104512 
## [20] train-rmse:1.178163+0.030634    test-rmse:1.533650+0.108621 
## [21] train-rmse:1.150222+0.030854    test-rmse:1.515889+0.111190 
## [22] train-rmse:1.127846+0.028415    test-rmse:1.497100+0.116467 
## [23] train-rmse:1.102317+0.028485    test-rmse:1.486899+0.127721 
## [24] train-rmse:1.078366+0.026175    test-rmse:1.471140+0.126318 
## [25] train-rmse:1.055042+0.027127    test-rmse:1.456622+0.124693 
## [26] train-rmse:1.035031+0.026508    test-rmse:1.447845+0.131531 
## [27] train-rmse:1.017005+0.027292    test-rmse:1.434391+0.134772 
## [28] train-rmse:0.996509+0.030659    test-rmse:1.424342+0.134595 
## [29] train-rmse:0.979604+0.032525    test-rmse:1.412862+0.137675 
## [30] train-rmse:0.962387+0.029104    test-rmse:1.408998+0.141296 
## [31] train-rmse:0.945152+0.029888    test-rmse:1.402947+0.146611 
## [32] train-rmse:0.928619+0.027276    test-rmse:1.397829+0.152012 
## [33] train-rmse:0.915637+0.026384    test-rmse:1.388780+0.157522 
## [34] train-rmse:0.898764+0.025426    test-rmse:1.378566+0.160955 
## [35] train-rmse:0.885154+0.026622    test-rmse:1.371819+0.167398 
## [36] train-rmse:0.869296+0.024665    test-rmse:1.358975+0.161478 
## [37] train-rmse:0.857500+0.024260    test-rmse:1.354535+0.166957 
## [38] train-rmse:0.846904+0.024365    test-rmse:1.344779+0.170436 
## [39] train-rmse:0.835943+0.024960    test-rmse:1.349132+0.174675 
## [40] train-rmse:0.826381+0.025081    test-rmse:1.346824+0.173558 
## [41] train-rmse:0.815871+0.026532    test-rmse:1.343515+0.176440 
## [42] train-rmse:0.803851+0.024970    test-rmse:1.337550+0.179274 
## [43] train-rmse:0.791263+0.023272    test-rmse:1.328975+0.183000 
## [44] train-rmse:0.781405+0.024475    test-rmse:1.323099+0.182205 
## [45] train-rmse:0.772409+0.023381    test-rmse:1.320596+0.185120 
## [46] train-rmse:0.759778+0.021389    test-rmse:1.318560+0.187469 
## [47] train-rmse:0.750138+0.021748    test-rmse:1.314014+0.185587 
## [48] train-rmse:0.739631+0.022650    test-rmse:1.311914+0.182404 
## [49] train-rmse:0.730907+0.021336    test-rmse:1.307676+0.183277 
## [50] train-rmse:0.722534+0.019563    test-rmse:1.304008+0.183411 
## [51] train-rmse:0.713452+0.018370    test-rmse:1.303587+0.182307 
## [52] train-rmse:0.704462+0.018811    test-rmse:1.304244+0.181942 
## [53] train-rmse:0.697311+0.016428    test-rmse:1.303024+0.181743 
## [54] train-rmse:0.688458+0.017997    test-rmse:1.304331+0.183252 
## [55] train-rmse:0.680909+0.018287    test-rmse:1.299389+0.188025 
## [56] train-rmse:0.674341+0.017891    test-rmse:1.298374+0.189749 
## [57] train-rmse:0.668169+0.019101    test-rmse:1.297828+0.190764 
## [58] train-rmse:0.661189+0.019535    test-rmse:1.296919+0.193447 
## [59] train-rmse:0.654986+0.018646    test-rmse:1.294289+0.196536 
## [60] train-rmse:0.647496+0.020076    test-rmse:1.292535+0.198078 
## [61] train-rmse:0.642082+0.020126    test-rmse:1.293818+0.197729 
## [62] train-rmse:0.634971+0.021223    test-rmse:1.290405+0.199241 
## [63] train-rmse:0.629496+0.021889    test-rmse:1.290338+0.196154 
## [64] train-rmse:0.624189+0.021519    test-rmse:1.291174+0.197509 
## [65] train-rmse:0.618731+0.022645    test-rmse:1.292297+0.195919 
## [66] train-rmse:0.614120+0.021673    test-rmse:1.287403+0.195338 
## [67] train-rmse:0.607774+0.020772    test-rmse:1.283513+0.198328 
## [68] train-rmse:0.601880+0.023613    test-rmse:1.284079+0.196236 
## [69] train-rmse:0.597687+0.023298    test-rmse:1.283755+0.196519 
## [70] train-rmse:0.590885+0.020800    test-rmse:1.281130+0.193370 
## [71] train-rmse:0.585095+0.023008    test-rmse:1.281126+0.193535 
## [72] train-rmse:0.580345+0.022173    test-rmse:1.280637+0.192267 
## [73] train-rmse:0.576830+0.023366    test-rmse:1.281902+0.193656 
## [74] train-rmse:0.572938+0.023290    test-rmse:1.281562+0.193789 
## [75] train-rmse:0.568455+0.024261    test-rmse:1.280652+0.195334 
## [76] train-rmse:0.562862+0.023813    test-rmse:1.278593+0.193150 
## [77] train-rmse:0.556613+0.024339    test-rmse:1.272420+0.192318 
## [78] train-rmse:0.552431+0.023956    test-rmse:1.271516+0.191624 
## [79] train-rmse:0.546817+0.025197    test-rmse:1.268521+0.191113 
## [80] train-rmse:0.543147+0.025504    test-rmse:1.270011+0.190513 
## [81] train-rmse:0.538832+0.025241    test-rmse:1.271529+0.190061 
## [82] train-rmse:0.535267+0.025669    test-rmse:1.271494+0.187938 
## [83] train-rmse:0.532528+0.025653    test-rmse:1.271399+0.188297 
## [84] train-rmse:0.527957+0.027697    test-rmse:1.270602+0.188760 
## [85] train-rmse:0.523616+0.028190    test-rmse:1.268878+0.187513 
## Stopping. Best iteration:
## [79] train-rmse:0.546817+0.025197    test-rmse:1.268521+0.191113
## 
## 46 
## [1]  train-rmse:7.683741+0.056806    test-rmse:7.684318+0.261247 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.123049+0.044409    test-rmse:6.137570+0.237632 
## [3]  train-rmse:4.921186+0.034497    test-rmse:4.940935+0.213195 
## [4]  train-rmse:3.999049+0.028976    test-rmse:4.039040+0.175680 
## [5]  train-rmse:3.291077+0.027009    test-rmse:3.335051+0.149643 
## [6]  train-rmse:2.744199+0.018622    test-rmse:2.809741+0.136629 
## [7]  train-rmse:2.336310+0.015428    test-rmse:2.440036+0.123379 
## [8]  train-rmse:2.028182+0.014498    test-rmse:2.179662+0.102030 
## [9]  train-rmse:1.798593+0.014614    test-rmse:1.980756+0.107286 
## [10] train-rmse:1.624269+0.015874    test-rmse:1.840292+0.090520 
## [11] train-rmse:1.495209+0.020475    test-rmse:1.737619+0.085997 
## [12] train-rmse:1.393985+0.019521    test-rmse:1.684419+0.071335 
## [13] train-rmse:1.311168+0.024528    test-rmse:1.619724+0.075001 
## [14] train-rmse:1.246151+0.026066    test-rmse:1.569822+0.067849 
## [15] train-rmse:1.189597+0.020934    test-rmse:1.541094+0.081192 
## [16] train-rmse:1.140481+0.023190    test-rmse:1.504171+0.088204 
## [17] train-rmse:1.100841+0.024947    test-rmse:1.480592+0.080215 
## [18] train-rmse:1.067816+0.024172    test-rmse:1.463039+0.087893 
## [19] train-rmse:1.032700+0.025464    test-rmse:1.439979+0.095750 
## [20] train-rmse:1.004372+0.026420    test-rmse:1.421944+0.096166 
## [21] train-rmse:0.976366+0.023654    test-rmse:1.413497+0.105827 
## [22] train-rmse:0.949906+0.024967    test-rmse:1.401173+0.112587 
## [23] train-rmse:0.922983+0.023167    test-rmse:1.391765+0.119575 
## [24] train-rmse:0.898892+0.026152    test-rmse:1.381453+0.117908 
## [25] train-rmse:0.876807+0.023058    test-rmse:1.366877+0.123121 
## [26] train-rmse:0.857374+0.024236    test-rmse:1.361216+0.123849 
## [27] train-rmse:0.837584+0.022092    test-rmse:1.346830+0.130466 
## [28] train-rmse:0.819093+0.019577    test-rmse:1.344101+0.130582 
## [29] train-rmse:0.797924+0.020733    test-rmse:1.335730+0.136845 
## [30] train-rmse:0.782442+0.021568    test-rmse:1.326490+0.137543 
## [31] train-rmse:0.766970+0.021221    test-rmse:1.322619+0.136922 
## [32] train-rmse:0.750832+0.018825    test-rmse:1.317333+0.140936 
## [33] train-rmse:0.738895+0.018214    test-rmse:1.312829+0.140869 
## [34] train-rmse:0.723829+0.017194    test-rmse:1.306440+0.141756 
## [35] train-rmse:0.708411+0.019610    test-rmse:1.297099+0.143395 
## [36] train-rmse:0.696048+0.019614    test-rmse:1.292647+0.147639 
## [37] train-rmse:0.683796+0.019154    test-rmse:1.287603+0.154361 
## [38] train-rmse:0.669724+0.019137    test-rmse:1.280416+0.155819 
## [39] train-rmse:0.656393+0.019444    test-rmse:1.273332+0.151484 
## [40] train-rmse:0.645906+0.018110    test-rmse:1.272505+0.153168 
## [41] train-rmse:0.635382+0.017179    test-rmse:1.269427+0.158786 
## [42] train-rmse:0.626967+0.018665    test-rmse:1.267630+0.157701 
## [43] train-rmse:0.618428+0.018871    test-rmse:1.264583+0.158256 
## [44] train-rmse:0.608991+0.018912    test-rmse:1.259403+0.159271 
## [45] train-rmse:0.598279+0.019131    test-rmse:1.257275+0.159020 
## [46] train-rmse:0.590506+0.019108    test-rmse:1.255629+0.159484 
## [47] train-rmse:0.581217+0.021013    test-rmse:1.254956+0.160712 
## [48] train-rmse:0.573860+0.021524    test-rmse:1.257701+0.158388 
## [49] train-rmse:0.564169+0.022465    test-rmse:1.254156+0.161588 
## [50] train-rmse:0.554826+0.022478    test-rmse:1.252123+0.165692 
## [51] train-rmse:0.547643+0.021524    test-rmse:1.253650+0.166856 
## [52] train-rmse:0.540772+0.023216    test-rmse:1.249745+0.168264 
## [53] train-rmse:0.532892+0.022725    test-rmse:1.248214+0.168757 
## [54] train-rmse:0.526402+0.021379    test-rmse:1.244763+0.171458 
## [55] train-rmse:0.517163+0.020610    test-rmse:1.246405+0.169353 
## [56] train-rmse:0.511957+0.021409    test-rmse:1.247726+0.169035 
## [57] train-rmse:0.504485+0.019572    test-rmse:1.248644+0.169555 
## [58] train-rmse:0.498298+0.018686    test-rmse:1.245596+0.170771 
## [59] train-rmse:0.493682+0.019208    test-rmse:1.244627+0.171350 
## [60] train-rmse:0.488402+0.018905    test-rmse:1.244556+0.174257 
## [61] train-rmse:0.482179+0.019057    test-rmse:1.243824+0.174569 
## [62] train-rmse:0.475708+0.018351    test-rmse:1.243934+0.172098 
## [63] train-rmse:0.469747+0.017294    test-rmse:1.245930+0.172576 
## [64] train-rmse:0.463930+0.018978    test-rmse:1.243794+0.173301 
## [65] train-rmse:0.458365+0.017890    test-rmse:1.242412+0.172314 
## [66] train-rmse:0.453955+0.017113    test-rmse:1.241520+0.171238 
## [67] train-rmse:0.447048+0.015016    test-rmse:1.239486+0.172712 
## [68] train-rmse:0.442420+0.014466    test-rmse:1.239626+0.171133 
## [69] train-rmse:0.439013+0.014380    test-rmse:1.239917+0.171912 
## [70] train-rmse:0.433706+0.014675    test-rmse:1.239613+0.172467 
## [71] train-rmse:0.427587+0.015886    test-rmse:1.238516+0.173025 
## [72] train-rmse:0.424827+0.016163    test-rmse:1.238627+0.172049 
## [73] train-rmse:0.419601+0.016125    test-rmse:1.238589+0.171864 
## [74] train-rmse:0.414681+0.015534    test-rmse:1.238981+0.173196 
## [75] train-rmse:0.410232+0.016847    test-rmse:1.238318+0.173495 
## [76] train-rmse:0.406372+0.016257    test-rmse:1.239679+0.175160 
## [77] train-rmse:0.402556+0.015687    test-rmse:1.239175+0.174749 
## [78] train-rmse:0.399048+0.017146    test-rmse:1.238737+0.172421 
## [79] train-rmse:0.393840+0.016318    test-rmse:1.235702+0.171355 
## [80] train-rmse:0.390910+0.016317    test-rmse:1.235485+0.171001 
## [81] train-rmse:0.385288+0.015360    test-rmse:1.235539+0.171207 
## [82] train-rmse:0.380250+0.015417    test-rmse:1.235680+0.172469 
## [83] train-rmse:0.376526+0.016559    test-rmse:1.235529+0.171740 
## [84] train-rmse:0.372437+0.016398    test-rmse:1.236132+0.171496 
## [85] train-rmse:0.369195+0.016186    test-rmse:1.235999+0.170243 
## [86] train-rmse:0.366713+0.016894    test-rmse:1.235718+0.169461 
## Stopping. Best iteration:
## [80] train-rmse:0.390910+0.016317    test-rmse:1.235485+0.171001
## 
## 47 
## [1]  train-rmse:7.682904+0.057008    test-rmse:7.685049+0.254248 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.117735+0.044927    test-rmse:6.133432+0.233998 
## [3]  train-rmse:4.909099+0.035664    test-rmse:4.930035+0.191705 
## [4]  train-rmse:3.981423+0.028736    test-rmse:4.021556+0.169466 
## [5]  train-rmse:3.260318+0.024109    test-rmse:3.305261+0.151856 
## [6]  train-rmse:2.705827+0.015735    test-rmse:2.777445+0.147689 
## [7]  train-rmse:2.283029+0.017601    test-rmse:2.394513+0.115966 
## [8]  train-rmse:1.965492+0.014779    test-rmse:2.125365+0.107224 
## [9]  train-rmse:1.718708+0.016769    test-rmse:1.909055+0.105872 
## [10] train-rmse:1.527333+0.023822    test-rmse:1.749064+0.093374 
## [11] train-rmse:1.384785+0.022270    test-rmse:1.640983+0.083748 
## [12] train-rmse:1.271087+0.023864    test-rmse:1.560045+0.077635 
## [13] train-rmse:1.181841+0.018811    test-rmse:1.499723+0.090713 
## [14] train-rmse:1.109848+0.020247    test-rmse:1.462590+0.089518 
## [15] train-rmse:1.050077+0.022357    test-rmse:1.427534+0.092897 
## [16] train-rmse:1.003081+0.021917    test-rmse:1.404281+0.106486 
## [17] train-rmse:0.956956+0.024536    test-rmse:1.379620+0.107533 
## [18] train-rmse:0.918960+0.023852    test-rmse:1.354788+0.116121 
## [19] train-rmse:0.883841+0.027029    test-rmse:1.335987+0.117794 
## [20] train-rmse:0.852732+0.023720    test-rmse:1.328131+0.118647 
## [21] train-rmse:0.827165+0.024619    test-rmse:1.318677+0.125257 
## [22] train-rmse:0.799338+0.024996    test-rmse:1.308221+0.127316 
## [23] train-rmse:0.777713+0.024995    test-rmse:1.296024+0.133796 
## [24] train-rmse:0.756595+0.026098    test-rmse:1.285111+0.136125 
## [25] train-rmse:0.731962+0.024763    test-rmse:1.274442+0.133724 
## [26] train-rmse:0.713125+0.024063    test-rmse:1.271920+0.138173 
## [27] train-rmse:0.693962+0.020139    test-rmse:1.270282+0.147478 
## [28] train-rmse:0.675258+0.019339    test-rmse:1.264551+0.145312 
## [29] train-rmse:0.658410+0.020930    test-rmse:1.254169+0.145022 
## [30] train-rmse:0.642354+0.020436    test-rmse:1.245785+0.146262 
## [31] train-rmse:0.630706+0.019658    test-rmse:1.249473+0.145682 
## [32] train-rmse:0.613616+0.017848    test-rmse:1.245947+0.146612 
## [33] train-rmse:0.599348+0.018859    test-rmse:1.243592+0.143598 
## [34] train-rmse:0.584677+0.019550    test-rmse:1.237813+0.145805 
## [35] train-rmse:0.574232+0.020790    test-rmse:1.234378+0.149450 
## [36] train-rmse:0.563758+0.017551    test-rmse:1.233326+0.149090 
## [37] train-rmse:0.552254+0.018478    test-rmse:1.227009+0.150994 
## [38] train-rmse:0.541396+0.019612    test-rmse:1.225039+0.153922 
## [39] train-rmse:0.532269+0.021050    test-rmse:1.224603+0.153895 
## [40] train-rmse:0.520591+0.020648    test-rmse:1.223043+0.153652 
## [41] train-rmse:0.511188+0.017941    test-rmse:1.221451+0.157500 
## [42] train-rmse:0.501407+0.019836    test-rmse:1.220071+0.156303 
## [43] train-rmse:0.493454+0.020099    test-rmse:1.217135+0.156892 
## [44] train-rmse:0.483489+0.019172    test-rmse:1.214359+0.158560 
## [45] train-rmse:0.474136+0.016880    test-rmse:1.211311+0.158835 
## [46] train-rmse:0.464390+0.016348    test-rmse:1.211861+0.158429 
## [47] train-rmse:0.455061+0.016626    test-rmse:1.211155+0.159185 
## [48] train-rmse:0.448994+0.016212    test-rmse:1.211460+0.162386 
## [49] train-rmse:0.442588+0.016263    test-rmse:1.207681+0.160379 
## [50] train-rmse:0.436885+0.016191    test-rmse:1.208078+0.162173 
## [51] train-rmse:0.429533+0.017825    test-rmse:1.205187+0.162039 
## [52] train-rmse:0.421527+0.015034    test-rmse:1.204715+0.163453 
## [53] train-rmse:0.415900+0.016442    test-rmse:1.204079+0.161818 
## [54] train-rmse:0.410680+0.016252    test-rmse:1.202907+0.160987 
## [55] train-rmse:0.404237+0.015997    test-rmse:1.201325+0.159816 
## [56] train-rmse:0.398991+0.017547    test-rmse:1.201816+0.161125 
## [57] train-rmse:0.393616+0.018201    test-rmse:1.201894+0.159642 
## [58] train-rmse:0.387720+0.018418    test-rmse:1.203116+0.158470 
## [59] train-rmse:0.383533+0.018136    test-rmse:1.202230+0.158378 
## [60] train-rmse:0.379716+0.018603    test-rmse:1.202743+0.159397 
## [61] train-rmse:0.372921+0.017584    test-rmse:1.201657+0.159949 
## Stopping. Best iteration:
## [55] train-rmse:0.404237+0.015997    test-rmse:1.201325+0.159816
## 
## 48 
## [1]  train-rmse:7.682904+0.057008    test-rmse:7.685049+0.254248 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:6.115531+0.045206    test-rmse:6.136740+0.225061 
## [3]  train-rmse:4.902681+0.036116    test-rmse:4.928370+0.183833 
## [4]  train-rmse:3.963773+0.028263    test-rmse:4.004956+0.168973 
## [5]  train-rmse:3.237120+0.025891    test-rmse:3.289500+0.142193 
## [6]  train-rmse:2.680009+0.021937    test-rmse:2.768703+0.127255 
## [7]  train-rmse:2.247840+0.018881    test-rmse:2.376366+0.117090 
## [8]  train-rmse:1.920674+0.018563    test-rmse:2.087498+0.107437 
## [9]  train-rmse:1.661398+0.021451    test-rmse:1.865397+0.080248 
## [10] train-rmse:1.463352+0.017866    test-rmse:1.723682+0.086188 
## [11] train-rmse:1.307895+0.021031    test-rmse:1.612448+0.081761 
## [12] train-rmse:1.185086+0.022369    test-rmse:1.527452+0.092719 
## [13] train-rmse:1.087735+0.020376    test-rmse:1.474137+0.092990 
## [14] train-rmse:1.014872+0.017792    test-rmse:1.425301+0.102070 
## [15] train-rmse:0.949945+0.015683    test-rmse:1.401331+0.116787 
## [16] train-rmse:0.891209+0.020331    test-rmse:1.367549+0.113139 
## [17] train-rmse:0.847273+0.019443    test-rmse:1.352776+0.116726 
## [18] train-rmse:0.807352+0.019444    test-rmse:1.337888+0.119390 
## [19] train-rmse:0.768977+0.021109    test-rmse:1.322545+0.127258 
## [20] train-rmse:0.737204+0.013327    test-rmse:1.312870+0.134545 
## [21] train-rmse:0.709944+0.011189    test-rmse:1.302529+0.140467 
## [22] train-rmse:0.682847+0.013379    test-rmse:1.292522+0.143370 
## [23] train-rmse:0.656574+0.012560    test-rmse:1.280291+0.142719 
## [24] train-rmse:0.635273+0.013546    test-rmse:1.277245+0.142611 
## [25] train-rmse:0.614527+0.010825    test-rmse:1.270238+0.144828 
## [26] train-rmse:0.595864+0.012528    test-rmse:1.261235+0.147830 
## [27] train-rmse:0.577831+0.011765    test-rmse:1.255715+0.148032 
## [28] train-rmse:0.561913+0.011345    test-rmse:1.255813+0.150560 
## [29] train-rmse:0.548221+0.010785    test-rmse:1.253154+0.154826 
## [30] train-rmse:0.534191+0.010219    test-rmse:1.248368+0.155591 
## [31] train-rmse:0.518697+0.012177    test-rmse:1.243872+0.155243 
## [32] train-rmse:0.507358+0.012542    test-rmse:1.243061+0.157485 
## [33] train-rmse:0.494164+0.011963    test-rmse:1.240090+0.158473 
## [34] train-rmse:0.482769+0.011359    test-rmse:1.238271+0.162310 
## [35] train-rmse:0.471337+0.012420    test-rmse:1.239318+0.157945 
## [36] train-rmse:0.460892+0.012499    test-rmse:1.236444+0.157778 
## [37] train-rmse:0.449268+0.014720    test-rmse:1.234451+0.157053 
## [38] train-rmse:0.437389+0.012064    test-rmse:1.232718+0.161978 
## [39] train-rmse:0.429228+0.012289    test-rmse:1.232364+0.158602 
## [40] train-rmse:0.418409+0.010422    test-rmse:1.229594+0.157274 
## [41] train-rmse:0.411375+0.011127    test-rmse:1.226507+0.158878 
## [42] train-rmse:0.402905+0.011245    test-rmse:1.226287+0.158277 
## [43] train-rmse:0.394709+0.012475    test-rmse:1.228179+0.157436 
## [44] train-rmse:0.387921+0.012732    test-rmse:1.227829+0.157664 
## [45] train-rmse:0.378941+0.013088    test-rmse:1.225003+0.157094 
## [46] train-rmse:0.370386+0.013185    test-rmse:1.226954+0.155671 
## [47] train-rmse:0.362505+0.010108    test-rmse:1.228187+0.154432 
## [48] train-rmse:0.354815+0.010753    test-rmse:1.230927+0.153863 
## [49] train-rmse:0.349321+0.011132    test-rmse:1.230955+0.153976 
## [50] train-rmse:0.344040+0.012037    test-rmse:1.230766+0.152920 
## [51] train-rmse:0.335990+0.014116    test-rmse:1.230798+0.153567 
## Stopping. Best iteration:
## [45] train-rmse:0.378941+0.013088    test-rmse:1.225003+0.157094
## 
## 49 
## [1]  train-rmse:7.554308+0.052782    test-rmse:7.550169+0.268254 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.961228+0.036958    test-rmse:5.955231+0.259913 
## [3]  train-rmse:4.794106+0.026666    test-rmse:4.788623+0.247893 
## [4]  train-rmse:3.956415+0.018986    test-rmse:3.962652+0.223826 
## [5]  train-rmse:3.370281+0.013139    test-rmse:3.387853+0.187395 
## [6]  train-rmse:2.970501+0.012412    test-rmse:2.993186+0.143852 
## [7]  train-rmse:2.702790+0.012850    test-rmse:2.734175+0.105170 
## [8]  train-rmse:2.525371+0.012988    test-rmse:2.560751+0.081494 
## [9]  train-rmse:2.409178+0.014233    test-rmse:2.450052+0.069609 
## [10] train-rmse:2.332148+0.015368    test-rmse:2.372331+0.052773 
## [11] train-rmse:2.278822+0.015928    test-rmse:2.314711+0.052288 
## [12] train-rmse:2.240335+0.016978    test-rmse:2.280749+0.055145 
## [13] train-rmse:2.211329+0.018260    test-rmse:2.255706+0.061833 
## [14] train-rmse:2.187501+0.019548    test-rmse:2.230003+0.067027 
## [15] train-rmse:2.167690+0.020611    test-rmse:2.219057+0.069803 
## [16] train-rmse:2.150142+0.021158    test-rmse:2.209589+0.073818 
## [17] train-rmse:2.134006+0.021611    test-rmse:2.190956+0.070251 
## [18] train-rmse:2.119480+0.022062    test-rmse:2.180253+0.075520 
## [19] train-rmse:2.105274+0.022776    test-rmse:2.167505+0.083114 
## [20] train-rmse:2.091992+0.023033    test-rmse:2.163643+0.082860 
## [21] train-rmse:2.078988+0.023644    test-rmse:2.150830+0.089280 
## [22] train-rmse:2.066105+0.024345    test-rmse:2.139964+0.089088 
## [23] train-rmse:2.053816+0.025055    test-rmse:2.120284+0.097856 
## [24] train-rmse:2.041916+0.025707    test-rmse:2.103381+0.091615 
## [25] train-rmse:2.030294+0.026524    test-rmse:2.089251+0.089009 
## [26] train-rmse:2.019114+0.027231    test-rmse:2.084326+0.091291 
## [27] train-rmse:2.008082+0.027670    test-rmse:2.077392+0.092641 
## [28] train-rmse:1.997179+0.027725    test-rmse:2.068954+0.094914 
## [29] train-rmse:1.987251+0.028052    test-rmse:2.061389+0.092398 
## [30] train-rmse:1.977224+0.028552    test-rmse:2.054625+0.083321 
## [31] train-rmse:1.967394+0.029238    test-rmse:2.045817+0.084945 
## [32] train-rmse:1.958340+0.029555    test-rmse:2.032350+0.088339 
## [33] train-rmse:1.949386+0.029872    test-rmse:2.024716+0.086946 
## [34] train-rmse:1.940614+0.030456    test-rmse:2.015937+0.081377 
## [35] train-rmse:1.931898+0.030784    test-rmse:2.008816+0.078704 
## [36] train-rmse:1.923338+0.031201    test-rmse:1.996969+0.079608 
## [37] train-rmse:1.914860+0.031484    test-rmse:1.991870+0.082690 
## [38] train-rmse:1.906533+0.031907    test-rmse:1.984709+0.085571 
## [39] train-rmse:1.898162+0.032322    test-rmse:1.975247+0.087362 
## [40] train-rmse:1.890429+0.032835    test-rmse:1.974917+0.088976 
## [41] train-rmse:1.882610+0.033335    test-rmse:1.961644+0.087990 
## [42] train-rmse:1.874659+0.034002    test-rmse:1.954412+0.093140 
## [43] train-rmse:1.866876+0.034772    test-rmse:1.949332+0.093202 
## [44] train-rmse:1.859336+0.035496    test-rmse:1.947086+0.096669 
## [45] train-rmse:1.851903+0.036155    test-rmse:1.939934+0.095389 
## [46] train-rmse:1.844792+0.036637    test-rmse:1.936237+0.095143 
## [47] train-rmse:1.837703+0.037164    test-rmse:1.928658+0.095832 
## [48] train-rmse:1.830814+0.037574    test-rmse:1.922637+0.092603 
## [49] train-rmse:1.823974+0.037970    test-rmse:1.914993+0.093238 
## [50] train-rmse:1.817282+0.038272    test-rmse:1.912381+0.093253 
## [51] train-rmse:1.810730+0.038533    test-rmse:1.909076+0.094559 
## [52] train-rmse:1.804292+0.038803    test-rmse:1.905150+0.093295 
## [53] train-rmse:1.798045+0.039032    test-rmse:1.902745+0.092266 
## [54] train-rmse:1.791740+0.039290    test-rmse:1.897501+0.090957 
## [55] train-rmse:1.785644+0.039560    test-rmse:1.891147+0.088086 
## [56] train-rmse:1.779649+0.039920    test-rmse:1.884324+0.089590 
## [57] train-rmse:1.773761+0.040347    test-rmse:1.880271+0.091204 
## [58] train-rmse:1.767899+0.040691    test-rmse:1.875577+0.090282 
## [59] train-rmse:1.762247+0.041000    test-rmse:1.866588+0.095602 
## [60] train-rmse:1.756635+0.041288    test-rmse:1.865279+0.098556 
## [61] train-rmse:1.751005+0.041570    test-rmse:1.860533+0.101685 
## [62] train-rmse:1.745457+0.041867    test-rmse:1.855802+0.097884 
## [63] train-rmse:1.739957+0.042200    test-rmse:1.846361+0.099184 
## [64] train-rmse:1.734640+0.042552    test-rmse:1.836260+0.098435 
## [65] train-rmse:1.729365+0.042911    test-rmse:1.829551+0.099372 
## [66] train-rmse:1.724182+0.043181    test-rmse:1.826574+0.100454 
## [67] train-rmse:1.719047+0.043432    test-rmse:1.822168+0.098709 
## [68] train-rmse:1.713963+0.043663    test-rmse:1.817995+0.102053 
## [69] train-rmse:1.708965+0.043970    test-rmse:1.816951+0.100431 
## [70] train-rmse:1.704035+0.044246    test-rmse:1.812012+0.101273 
## [71] train-rmse:1.699283+0.044560    test-rmse:1.807151+0.100024 
## [72] train-rmse:1.694691+0.044908    test-rmse:1.803826+0.103069 
## [73] train-rmse:1.690261+0.045164    test-rmse:1.802906+0.105256 
## [74] train-rmse:1.685815+0.045465    test-rmse:1.799640+0.104065 
## [75] train-rmse:1.681399+0.045830    test-rmse:1.796705+0.104444 
## [76] train-rmse:1.677010+0.046212    test-rmse:1.792494+0.106749 
## [77] train-rmse:1.672692+0.046581    test-rmse:1.792632+0.107758 
## [78] train-rmse:1.668359+0.046797    test-rmse:1.789889+0.106358 
## [79] train-rmse:1.664149+0.046960    test-rmse:1.787109+0.106399 
## [80] train-rmse:1.660070+0.047093    test-rmse:1.781123+0.104404 
## [81] train-rmse:1.656070+0.047223    test-rmse:1.779295+0.105457 
## [82] train-rmse:1.652132+0.047326    test-rmse:1.772195+0.111528 
## [83] train-rmse:1.648213+0.047394    test-rmse:1.766427+0.110047 
## [84] train-rmse:1.644214+0.047600    test-rmse:1.763225+0.111451 
## [85] train-rmse:1.640336+0.047782    test-rmse:1.757111+0.112509 
## [86] train-rmse:1.636479+0.047935    test-rmse:1.757342+0.109200 
## [87] train-rmse:1.632610+0.048069    test-rmse:1.750056+0.107003 
## [88] train-rmse:1.628790+0.048156    test-rmse:1.747344+0.109703 
## [89] train-rmse:1.625022+0.048244    test-rmse:1.745914+0.112974 
## [90] train-rmse:1.621290+0.048353    test-rmse:1.740760+0.112218 
## [91] train-rmse:1.617617+0.048443    test-rmse:1.739201+0.114895 
## [92] train-rmse:1.614024+0.048555    test-rmse:1.736998+0.118952 
## [93] train-rmse:1.610404+0.048677    test-rmse:1.732744+0.118156 
## [94] train-rmse:1.606936+0.048771    test-rmse:1.728987+0.120787 
## [95] train-rmse:1.603497+0.048910    test-rmse:1.724005+0.122208 
## [96] train-rmse:1.600085+0.049044    test-rmse:1.721133+0.123529 
## [97] train-rmse:1.596806+0.049098    test-rmse:1.721246+0.123906 
## [98] train-rmse:1.593526+0.049244    test-rmse:1.717076+0.123756 
## [99] train-rmse:1.590290+0.049383    test-rmse:1.714874+0.124633 
## [100]    train-rmse:1.587095+0.049477    test-rmse:1.712476+0.127816 
## [101]    train-rmse:1.583946+0.049604    test-rmse:1.713414+0.123572 
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## [104]    train-rmse:1.574851+0.050044    test-rmse:1.703347+0.125995 
## [105]    train-rmse:1.571875+0.050174    test-rmse:1.699671+0.126322 
## [106]    train-rmse:1.568942+0.050301    test-rmse:1.698134+0.127867 
## [107]    train-rmse:1.566006+0.050438    test-rmse:1.695564+0.125319 
## [108]    train-rmse:1.563109+0.050534    test-rmse:1.690740+0.130249 
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## [299]    train-rmse:1.342321+0.053026    test-rmse:1.496724+0.192696 
## [300]    train-rmse:1.341977+0.053022    test-rmse:1.495500+0.192620 
## [301]    train-rmse:1.341636+0.053017    test-rmse:1.495197+0.193265 
## Stopping. Best iteration:
## [295]    train-rmse:1.343738+0.053072    test-rmse:1.494995+0.194353
## 
## 50 
## [1]  train-rmse:7.523080+0.053198    test-rmse:7.519408+0.263317 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.900232+0.041375    test-rmse:5.894119+0.245192 
## [3]  train-rmse:4.705416+0.032721    test-rmse:4.714387+0.221928 
## [4]  train-rmse:3.828286+0.026431    test-rmse:3.835366+0.189068 
## [5]  train-rmse:3.193784+0.026583    test-rmse:3.233101+0.150043 
## [6]  train-rmse:2.743786+0.011802    test-rmse:2.787922+0.130510 
## [7]  train-rmse:2.434268+0.008722    test-rmse:2.496815+0.102312 
## [8]  train-rmse:2.226536+0.005896    test-rmse:2.312603+0.078005 
## [9]  train-rmse:2.086618+0.005209    test-rmse:2.177169+0.066370 
## [10] train-rmse:1.988646+0.007183    test-rmse:2.108809+0.067851 
## [11] train-rmse:1.915149+0.007108    test-rmse:2.052083+0.077731 
## [12] train-rmse:1.862759+0.008609    test-rmse:2.002692+0.089311 
## [13] train-rmse:1.822287+0.008607    test-rmse:1.971016+0.087004 
## [14] train-rmse:1.789654+0.010075    test-rmse:1.935106+0.090387 
## [15] train-rmse:1.763215+0.011691    test-rmse:1.908545+0.092378 
## [16] train-rmse:1.733788+0.014760    test-rmse:1.890754+0.098838 
## [17] train-rmse:1.708471+0.018623    test-rmse:1.874541+0.111978 
## [18] train-rmse:1.686265+0.019013    test-rmse:1.856780+0.105273 
## [19] train-rmse:1.659718+0.012283    test-rmse:1.827531+0.104321 
## [20] train-rmse:1.640713+0.013218    test-rmse:1.815863+0.103359 
## [21] train-rmse:1.621219+0.014047    test-rmse:1.795140+0.102760 
## [22] train-rmse:1.603049+0.015440    test-rmse:1.792090+0.103561 
## [23] train-rmse:1.586645+0.016866    test-rmse:1.780601+0.100888 
## [24] train-rmse:1.568313+0.017028    test-rmse:1.772652+0.110080 
## [25] train-rmse:1.551854+0.016689    test-rmse:1.757738+0.109005 
## [26] train-rmse:1.535082+0.016570    test-rmse:1.742742+0.117275 
## [27] train-rmse:1.510836+0.013803    test-rmse:1.722508+0.113208 
## [28] train-rmse:1.497483+0.013431    test-rmse:1.715775+0.110647 
## [29] train-rmse:1.482764+0.015444    test-rmse:1.707364+0.114271 
## [30] train-rmse:1.467144+0.016816    test-rmse:1.694897+0.112216 
## [31] train-rmse:1.454696+0.016578    test-rmse:1.681395+0.110578 
## [32] train-rmse:1.440950+0.017653    test-rmse:1.672053+0.110044 
## [33] train-rmse:1.427605+0.017640    test-rmse:1.664795+0.109366 
## [34] train-rmse:1.416030+0.018361    test-rmse:1.659621+0.108485 
## [35] train-rmse:1.403301+0.018955    test-rmse:1.651662+0.112958 
## [36] train-rmse:1.392052+0.019591    test-rmse:1.640163+0.122647 
## [37] train-rmse:1.378860+0.019340    test-rmse:1.630334+0.127412 
## [38] train-rmse:1.366875+0.018697    test-rmse:1.623828+0.126850 
## [39] train-rmse:1.350888+0.019570    test-rmse:1.609976+0.131995 
## [40] train-rmse:1.339094+0.020317    test-rmse:1.603984+0.135154 
## [41] train-rmse:1.328560+0.021178    test-rmse:1.594749+0.137156 
## [42] train-rmse:1.317540+0.022222    test-rmse:1.584553+0.139770 
## [43] train-rmse:1.308457+0.022415    test-rmse:1.578181+0.140409 
## [44] train-rmse:1.299712+0.022821    test-rmse:1.571802+0.141530 
## [45] train-rmse:1.290658+0.022761    test-rmse:1.565541+0.139868 
## [46] train-rmse:1.282371+0.023685    test-rmse:1.563124+0.139066 
## [47] train-rmse:1.273803+0.023236    test-rmse:1.555632+0.139631 
## [48] train-rmse:1.265029+0.021102    test-rmse:1.553981+0.139158 
## [49] train-rmse:1.257087+0.021651    test-rmse:1.547751+0.140283 
## [50] train-rmse:1.247797+0.022717    test-rmse:1.542279+0.142959 
## [51] train-rmse:1.239373+0.023296    test-rmse:1.537733+0.144021 
## [52] train-rmse:1.232066+0.024011    test-rmse:1.536652+0.148490 
## [53] train-rmse:1.223757+0.024812    test-rmse:1.527087+0.148920 
## [54] train-rmse:1.212602+0.029345    test-rmse:1.522207+0.152673 
## [55] train-rmse:1.204852+0.029297    test-rmse:1.511699+0.160440 
## [56] train-rmse:1.189965+0.023656    test-rmse:1.493171+0.139930 
## [57] train-rmse:1.182528+0.024153    test-rmse:1.490105+0.137903 
## [58] train-rmse:1.175416+0.024785    test-rmse:1.486398+0.139765 
## [59] train-rmse:1.168882+0.025125    test-rmse:1.480979+0.133967 
## [60] train-rmse:1.157848+0.029992    test-rmse:1.476444+0.133719 
## [61] train-rmse:1.150255+0.030279    test-rmse:1.472476+0.135457 
## [62] train-rmse:1.138778+0.032898    test-rmse:1.464846+0.138933 
## [63] train-rmse:1.132535+0.033019    test-rmse:1.460006+0.139742 
## [64] train-rmse:1.121874+0.026099    test-rmse:1.447473+0.141430 
## [65] train-rmse:1.115630+0.025692    test-rmse:1.443629+0.143219 
## [66] train-rmse:1.109373+0.025668    test-rmse:1.438451+0.142960 
## [67] train-rmse:1.103753+0.026045    test-rmse:1.439346+0.144264 
## [68] train-rmse:1.092750+0.023960    test-rmse:1.422523+0.133391 
## [69] train-rmse:1.084237+0.025519    test-rmse:1.414439+0.138048 
## [70] train-rmse:1.078284+0.026916    test-rmse:1.415111+0.138800 
## [71] train-rmse:1.071967+0.027161    test-rmse:1.415327+0.139351 
## [72] train-rmse:1.065834+0.026533    test-rmse:1.411607+0.141883 
## [73] train-rmse:1.058586+0.026313    test-rmse:1.409765+0.142101 
## [74] train-rmse:1.053644+0.026465    test-rmse:1.405499+0.143789 
## [75] train-rmse:1.049436+0.026792    test-rmse:1.400625+0.147166 
## [76] train-rmse:1.044451+0.026475    test-rmse:1.399802+0.146136 
## [77] train-rmse:1.040017+0.026682    test-rmse:1.399062+0.143595 
## [78] train-rmse:1.035105+0.026859    test-rmse:1.394411+0.143146 
## [79] train-rmse:1.031392+0.027103    test-rmse:1.392796+0.143656 
## [80] train-rmse:1.027381+0.027244    test-rmse:1.390356+0.144756 
## [81] train-rmse:1.023308+0.027544    test-rmse:1.387151+0.144444 
## [82] train-rmse:1.019464+0.027761    test-rmse:1.382318+0.146044 
## [83] train-rmse:1.014484+0.028932    test-rmse:1.377713+0.146970 
## [84] train-rmse:1.007176+0.030445    test-rmse:1.377766+0.149125 
## [85] train-rmse:1.002471+0.031810    test-rmse:1.375816+0.150503 
## [86] train-rmse:0.998516+0.031626    test-rmse:1.375390+0.152055 
## [87] train-rmse:0.995196+0.031645    test-rmse:1.374266+0.152884 
## [88] train-rmse:0.988511+0.029712    test-rmse:1.369484+0.153293 
## [89] train-rmse:0.984604+0.029486    test-rmse:1.368057+0.156111 
## [90] train-rmse:0.981524+0.029387    test-rmse:1.366802+0.156232 
## [91] train-rmse:0.976213+0.030018    test-rmse:1.366143+0.154934 
## [92] train-rmse:0.973067+0.030129    test-rmse:1.364117+0.154617 
## [93] train-rmse:0.967253+0.025477    test-rmse:1.357773+0.154810 
## [94] train-rmse:0.963802+0.025716    test-rmse:1.357729+0.154944 
## [95] train-rmse:0.960696+0.025909    test-rmse:1.357855+0.155775 
## [96] train-rmse:0.957916+0.026164    test-rmse:1.357537+0.157257 
## [97] train-rmse:0.955223+0.026124    test-rmse:1.355059+0.157689 
## [98] train-rmse:0.952646+0.026087    test-rmse:1.352061+0.159113 
## [99] train-rmse:0.949326+0.026596    test-rmse:1.351504+0.161652 
## [100]    train-rmse:0.945582+0.027069    test-rmse:1.348308+0.162953 
## [101]    train-rmse:0.941407+0.027914    test-rmse:1.346666+0.165706 
## [102]    train-rmse:0.930568+0.031919    test-rmse:1.335692+0.160064 
## [103]    train-rmse:0.927541+0.032332    test-rmse:1.334150+0.159664 
## [104]    train-rmse:0.925068+0.032398    test-rmse:1.334397+0.158901 
## [105]    train-rmse:0.916584+0.024431    test-rmse:1.321035+0.160354 
## [106]    train-rmse:0.913564+0.024669    test-rmse:1.319940+0.160440 
## [107]    train-rmse:0.909780+0.025294    test-rmse:1.320063+0.160105 
## [108]    train-rmse:0.907649+0.025366    test-rmse:1.319712+0.159515 
## [109]    train-rmse:0.905299+0.025590    test-rmse:1.315501+0.162621 
## [110]    train-rmse:0.901009+0.025949    test-rmse:1.312262+0.161634 
## [111]    train-rmse:0.897003+0.025281    test-rmse:1.309637+0.163121 
## [112]    train-rmse:0.894715+0.025129    test-rmse:1.309156+0.165539 
## [113]    train-rmse:0.892490+0.025343    test-rmse:1.307987+0.165637 
## [114]    train-rmse:0.889880+0.024883    test-rmse:1.306838+0.165052 
## [115]    train-rmse:0.887659+0.025284    test-rmse:1.304771+0.165925 
## [116]    train-rmse:0.885276+0.025838    test-rmse:1.305670+0.167419 
## [117]    train-rmse:0.880378+0.024977    test-rmse:1.298018+0.162912 
## [118]    train-rmse:0.878001+0.024759    test-rmse:1.297869+0.163337 
## [119]    train-rmse:0.875402+0.024851    test-rmse:1.297374+0.162187 
## [120]    train-rmse:0.872596+0.024206    test-rmse:1.295686+0.162468 
## [121]    train-rmse:0.870686+0.024313    test-rmse:1.298761+0.163701 
## [122]    train-rmse:0.862461+0.018760    test-rmse:1.288252+0.165454 
## [123]    train-rmse:0.857688+0.023242    test-rmse:1.283050+0.160008 
## [124]    train-rmse:0.853661+0.023244    test-rmse:1.281083+0.160169 
## [125]    train-rmse:0.851500+0.023448    test-rmse:1.279110+0.159872 
## [126]    train-rmse:0.848777+0.023153    test-rmse:1.276987+0.161120 
## [127]    train-rmse:0.847032+0.023025    test-rmse:1.277733+0.162218 
## [128]    train-rmse:0.845292+0.023042    test-rmse:1.277392+0.162937 
## [129]    train-rmse:0.843134+0.022486    test-rmse:1.277584+0.162683 
## [130]    train-rmse:0.841555+0.022564    test-rmse:1.277337+0.162661 
## [131]    train-rmse:0.839673+0.022726    test-rmse:1.278971+0.165385 
## [132]    train-rmse:0.838245+0.022390    test-rmse:1.276333+0.165297 
## [133]    train-rmse:0.836592+0.022441    test-rmse:1.276220+0.165927 
## [134]    train-rmse:0.834875+0.022845    test-rmse:1.276097+0.168196 
## [135]    train-rmse:0.833144+0.023367    test-rmse:1.277692+0.167859 
## [136]    train-rmse:0.831387+0.023327    test-rmse:1.275509+0.168620 
## [137]    train-rmse:0.830157+0.023518    test-rmse:1.275531+0.168704 
## [138]    train-rmse:0.825540+0.020755    test-rmse:1.268293+0.170258 
## [139]    train-rmse:0.824153+0.020898    test-rmse:1.267757+0.169480 
## [140]    train-rmse:0.822490+0.020549    test-rmse:1.268457+0.171033 
## [141]    train-rmse:0.821057+0.020731    test-rmse:1.269180+0.171609 
## [142]    train-rmse:0.819850+0.020692    test-rmse:1.268062+0.171377 
## [143]    train-rmse:0.817948+0.020459    test-rmse:1.269500+0.173910 
## [144]    train-rmse:0.816138+0.020454    test-rmse:1.269352+0.173478 
## [145]    train-rmse:0.814622+0.021342    test-rmse:1.268680+0.172901 
## Stopping. Best iteration:
## [139]    train-rmse:0.824153+0.020898    test-rmse:1.267757+0.169480
## 
## 51 
## [1]  train-rmse:7.510920+0.054535    test-rmse:7.507551+0.257130 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.869266+0.039440    test-rmse:5.865302+0.242149 
## [3]  train-rmse:4.652252+0.031774    test-rmse:4.661731+0.223446 
## [4]  train-rmse:3.750789+0.026281    test-rmse:3.755353+0.190111 
## [5]  train-rmse:3.093019+0.019034    test-rmse:3.141244+0.162072 
## [6]  train-rmse:2.622744+0.015775    test-rmse:2.691289+0.124128 
## [7]  train-rmse:2.290294+0.013397    test-rmse:2.370094+0.111111 
## [8]  train-rmse:2.053472+0.009285    test-rmse:2.159831+0.089083 
## [9]  train-rmse:1.889465+0.010243    test-rmse:2.031347+0.069616 
## [10] train-rmse:1.771555+0.012047    test-rmse:1.933087+0.074807 
## [11] train-rmse:1.685804+0.013922    test-rmse:1.863389+0.060699 
## [12] train-rmse:1.622483+0.015501    test-rmse:1.815401+0.057909 
## [13] train-rmse:1.567574+0.015893    test-rmse:1.773536+0.058313 
## [14] train-rmse:1.523127+0.017495    test-rmse:1.755487+0.058350 
## [15] train-rmse:1.482315+0.017864    test-rmse:1.721987+0.053984 
## [16] train-rmse:1.450196+0.019259    test-rmse:1.695286+0.058087 
## [17] train-rmse:1.418628+0.020756    test-rmse:1.677361+0.058635 
## [18] train-rmse:1.391246+0.024714    test-rmse:1.665101+0.067211 
## [19] train-rmse:1.361332+0.022086    test-rmse:1.637333+0.071709 
## [20] train-rmse:1.332700+0.022742    test-rmse:1.612596+0.060321 
## [21] train-rmse:1.308377+0.023837    test-rmse:1.592023+0.057057 
## [22] train-rmse:1.283521+0.026034    test-rmse:1.579371+0.065173 
## [23] train-rmse:1.262381+0.028084    test-rmse:1.566751+0.065894 
## [24] train-rmse:1.241657+0.029544    test-rmse:1.552839+0.074272 
## [25] train-rmse:1.220830+0.028757    test-rmse:1.539687+0.077896 
## [26] train-rmse:1.203690+0.029485    test-rmse:1.521834+0.075023 
## [27] train-rmse:1.186788+0.028744    test-rmse:1.510135+0.072558 
## [28] train-rmse:1.170492+0.029970    test-rmse:1.496875+0.075901 
## [29] train-rmse:1.153709+0.029008    test-rmse:1.487847+0.086940 
## [30] train-rmse:1.137408+0.030670    test-rmse:1.478308+0.084595 
## [31] train-rmse:1.122703+0.030569    test-rmse:1.474315+0.088789 
## [32] train-rmse:1.106771+0.030082    test-rmse:1.466591+0.090942 
## [33] train-rmse:1.089881+0.028680    test-rmse:1.449500+0.090845 
## [34] train-rmse:1.076941+0.027590    test-rmse:1.438022+0.098288 
## [35] train-rmse:1.062045+0.026247    test-rmse:1.426597+0.099150 
## [36] train-rmse:1.048599+0.023692    test-rmse:1.417866+0.105439 
## [37] train-rmse:1.033864+0.024687    test-rmse:1.402029+0.099936 
## [38] train-rmse:1.020919+0.025786    test-rmse:1.396572+0.104349 
## [39] train-rmse:1.010778+0.025919    test-rmse:1.395588+0.105946 
## [40] train-rmse:0.998665+0.027118    test-rmse:1.388041+0.100889 
## [41] train-rmse:0.988734+0.026967    test-rmse:1.381625+0.104442 
## [42] train-rmse:0.978965+0.027228    test-rmse:1.375684+0.107204 
## [43] train-rmse:0.969452+0.027348    test-rmse:1.371012+0.108043 
## [44] train-rmse:0.958662+0.026520    test-rmse:1.365516+0.111374 
## [45] train-rmse:0.948514+0.024850    test-rmse:1.365141+0.117615 
## [46] train-rmse:0.937854+0.024097    test-rmse:1.358401+0.118269 
## [47] train-rmse:0.928413+0.025054    test-rmse:1.353787+0.120218 
## [48] train-rmse:0.919131+0.026352    test-rmse:1.350015+0.124544 
## [49] train-rmse:0.908872+0.026703    test-rmse:1.347315+0.127087 
## [50] train-rmse:0.899679+0.026391    test-rmse:1.341242+0.129665 
## [51] train-rmse:0.891310+0.028289    test-rmse:1.336089+0.133010 
## [52] train-rmse:0.884743+0.027833    test-rmse:1.330905+0.134947 
## [53] train-rmse:0.877126+0.027616    test-rmse:1.327576+0.135647 
## [54] train-rmse:0.869578+0.026105    test-rmse:1.328097+0.138295 
## [55] train-rmse:0.860592+0.024605    test-rmse:1.327811+0.138029 
## [56] train-rmse:0.853369+0.024349    test-rmse:1.325722+0.138341 
## [57] train-rmse:0.844534+0.028106    test-rmse:1.315788+0.135356 
## [58] train-rmse:0.835808+0.030100    test-rmse:1.312679+0.137572 
## [59] train-rmse:0.826950+0.031701    test-rmse:1.310289+0.135950 
## [60] train-rmse:0.819726+0.032237    test-rmse:1.305689+0.134891 
## [61] train-rmse:0.812586+0.033241    test-rmse:1.300817+0.133464 
## [62] train-rmse:0.805150+0.033015    test-rmse:1.296374+0.135129 
## [63] train-rmse:0.797132+0.033194    test-rmse:1.296442+0.133988 
## [64] train-rmse:0.792432+0.033311    test-rmse:1.294910+0.134112 
## [65] train-rmse:0.786387+0.033981    test-rmse:1.292740+0.135702 
## [66] train-rmse:0.779788+0.031033    test-rmse:1.293332+0.137661 
## [67] train-rmse:0.774145+0.030360    test-rmse:1.292016+0.136708 
## [68] train-rmse:0.769428+0.029931    test-rmse:1.289672+0.137549 
## [69] train-rmse:0.763807+0.029153    test-rmse:1.287997+0.140100 
## [70] train-rmse:0.758847+0.028523    test-rmse:1.285076+0.143531 
## [71] train-rmse:0.753594+0.028828    test-rmse:1.281098+0.142550 
## [72] train-rmse:0.749197+0.028309    test-rmse:1.281869+0.142515 
## [73] train-rmse:0.745003+0.028341    test-rmse:1.280007+0.144897 
## [74] train-rmse:0.740023+0.028848    test-rmse:1.283999+0.143015 
## [75] train-rmse:0.736024+0.028058    test-rmse:1.284401+0.146434 
## [76] train-rmse:0.728888+0.028741    test-rmse:1.282374+0.146774 
## [77] train-rmse:0.725427+0.028753    test-rmse:1.281480+0.146935 
## [78] train-rmse:0.720858+0.030459    test-rmse:1.278967+0.146603 
## [79] train-rmse:0.715832+0.030736    test-rmse:1.279592+0.147661 
## [80] train-rmse:0.710115+0.031505    test-rmse:1.273824+0.145696 
## [81] train-rmse:0.705468+0.032568    test-rmse:1.273397+0.144662 
## [82] train-rmse:0.702951+0.032369    test-rmse:1.272782+0.146441 
## [83] train-rmse:0.698551+0.031371    test-rmse:1.273469+0.147140 
## [84] train-rmse:0.692436+0.032927    test-rmse:1.274320+0.147173 
## [85] train-rmse:0.686239+0.031522    test-rmse:1.265467+0.147942 
## [86] train-rmse:0.682193+0.031640    test-rmse:1.263383+0.150173 
## [87] train-rmse:0.679037+0.031106    test-rmse:1.262345+0.150445 
## [88] train-rmse:0.674966+0.032246    test-rmse:1.259704+0.148601 
## [89] train-rmse:0.671611+0.032302    test-rmse:1.260808+0.147818 
## [90] train-rmse:0.667597+0.031974    test-rmse:1.260651+0.146769 
## [91] train-rmse:0.664440+0.030481    test-rmse:1.258207+0.147664 
## [92] train-rmse:0.660095+0.031204    test-rmse:1.253737+0.143698 
## [93] train-rmse:0.657987+0.031390    test-rmse:1.252731+0.145981 
## [94] train-rmse:0.654752+0.030822    test-rmse:1.251969+0.145601 
## [95] train-rmse:0.651190+0.031178    test-rmse:1.253908+0.149137 
## [96] train-rmse:0.647327+0.033610    test-rmse:1.253182+0.147773 
## [97] train-rmse:0.644150+0.033345    test-rmse:1.252749+0.147705 
## [98] train-rmse:0.641962+0.032890    test-rmse:1.252717+0.148729 
## [99] train-rmse:0.636217+0.032961    test-rmse:1.250031+0.148121 
## [100]    train-rmse:0.634231+0.033511    test-rmse:1.249194+0.148473 
## [101]    train-rmse:0.631097+0.033385    test-rmse:1.249701+0.148305 
## [102]    train-rmse:0.628211+0.032322    test-rmse:1.248672+0.148623 
## [103]    train-rmse:0.626567+0.032116    test-rmse:1.249781+0.146140 
## [104]    train-rmse:0.624340+0.032110    test-rmse:1.249494+0.147470 
## [105]    train-rmse:0.621730+0.031743    test-rmse:1.249901+0.147571 
## [106]    train-rmse:0.618935+0.030915    test-rmse:1.250175+0.147379 
## [107]    train-rmse:0.615754+0.031274    test-rmse:1.250821+0.148466 
## [108]    train-rmse:0.613770+0.031490    test-rmse:1.249103+0.148142 
## Stopping. Best iteration:
## [102]    train-rmse:0.628211+0.032322    test-rmse:1.248672+0.148623
## 
## 52 
## [1]  train-rmse:7.505902+0.055057    test-rmse:7.502623+0.258791 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.857099+0.041298    test-rmse:5.867470+0.242511 
## [3]  train-rmse:4.629260+0.030341    test-rmse:4.647738+0.215169 
## [4]  train-rmse:3.715686+0.024982    test-rmse:3.739438+0.183881 
## [5]  train-rmse:3.038351+0.014826    test-rmse:3.093326+0.165819 
## [6]  train-rmse:2.544839+0.011020    test-rmse:2.623694+0.132198 
## [7]  train-rmse:2.189829+0.007520    test-rmse:2.297764+0.111053 
## [8]  train-rmse:1.934335+0.013154    test-rmse:2.069305+0.076833 
## [9]  train-rmse:1.752171+0.014771    test-rmse:1.928689+0.078218 
## [10] train-rmse:1.618407+0.016263    test-rmse:1.821230+0.074914 
## [11] train-rmse:1.517497+0.018771    test-rmse:1.746538+0.072332 
## [12] train-rmse:1.442690+0.015476    test-rmse:1.689534+0.059058 
## [13] train-rmse:1.377362+0.018308    test-rmse:1.641460+0.069335 
## [14] train-rmse:1.329540+0.018112    test-rmse:1.611120+0.074769 
## [15] train-rmse:1.288791+0.019571    test-rmse:1.584227+0.077954 
## [16] train-rmse:1.252045+0.018298    test-rmse:1.560127+0.077435 
## [17] train-rmse:1.213634+0.022077    test-rmse:1.529489+0.089063 
## [18] train-rmse:1.180151+0.025908    test-rmse:1.518442+0.088764 
## [19] train-rmse:1.148256+0.025712    test-rmse:1.492181+0.098468 
## [20] train-rmse:1.123535+0.025328    test-rmse:1.478787+0.102045 
## [21] train-rmse:1.097848+0.027396    test-rmse:1.467399+0.104106 
## [22] train-rmse:1.074001+0.028091    test-rmse:1.444736+0.097103 
## [23] train-rmse:1.045235+0.020579    test-rmse:1.432973+0.108909 
## [24] train-rmse:1.023304+0.022011    test-rmse:1.422378+0.116273 
## [25] train-rmse:1.003690+0.021686    test-rmse:1.409551+0.118417 
## [26] train-rmse:0.984726+0.021186    test-rmse:1.406035+0.118835 
## [27] train-rmse:0.966265+0.022506    test-rmse:1.396851+0.116848 
## [28] train-rmse:0.946214+0.023580    test-rmse:1.387661+0.122559 
## [29] train-rmse:0.931716+0.023516    test-rmse:1.379168+0.124971 
## [30] train-rmse:0.916382+0.020853    test-rmse:1.373160+0.130151 
## [31] train-rmse:0.899852+0.017097    test-rmse:1.364473+0.138392 
## [32] train-rmse:0.886679+0.017921    test-rmse:1.361492+0.133349 
## [33] train-rmse:0.868732+0.017962    test-rmse:1.351739+0.132894 
## [34] train-rmse:0.855124+0.016871    test-rmse:1.348599+0.138821 
## [35] train-rmse:0.842563+0.018543    test-rmse:1.339372+0.144343 
## [36] train-rmse:0.829254+0.017354    test-rmse:1.336599+0.143580 
## [37] train-rmse:0.818323+0.017294    test-rmse:1.327918+0.147072 
## [38] train-rmse:0.806218+0.016648    test-rmse:1.319952+0.145552 
## [39] train-rmse:0.792268+0.014147    test-rmse:1.313468+0.149597 
## [40] train-rmse:0.780405+0.013611    test-rmse:1.313284+0.150996 
## [41] train-rmse:0.770607+0.013021    test-rmse:1.309582+0.153224 
## [42] train-rmse:0.762148+0.013370    test-rmse:1.305975+0.156538 
## [43] train-rmse:0.753840+0.013413    test-rmse:1.301472+0.155671 
## [44] train-rmse:0.744894+0.012520    test-rmse:1.299806+0.156110 
## [45] train-rmse:0.731165+0.014639    test-rmse:1.289084+0.153990 
## [46] train-rmse:0.722567+0.015396    test-rmse:1.288974+0.156008 
## [47] train-rmse:0.715257+0.016316    test-rmse:1.283228+0.155072 
## [48] train-rmse:0.708031+0.016670    test-rmse:1.279938+0.153778 
## [49] train-rmse:0.698026+0.014064    test-rmse:1.279213+0.156413 
## [50] train-rmse:0.689322+0.013351    test-rmse:1.278403+0.158356 
## [51] train-rmse:0.682009+0.012904    test-rmse:1.279206+0.161018 
## [52] train-rmse:0.673018+0.010406    test-rmse:1.277769+0.162858 
## [53] train-rmse:0.665664+0.012042    test-rmse:1.277224+0.164805 
## [54] train-rmse:0.657142+0.011034    test-rmse:1.274288+0.163061 
## [55] train-rmse:0.649933+0.008607    test-rmse:1.272078+0.161954 
## [56] train-rmse:0.645209+0.008525    test-rmse:1.272323+0.163854 
## [57] train-rmse:0.639284+0.010273    test-rmse:1.272166+0.163594 
## [58] train-rmse:0.633723+0.009887    test-rmse:1.271869+0.164113 
## [59] train-rmse:0.627569+0.011274    test-rmse:1.270629+0.161954 
## [60] train-rmse:0.620760+0.010594    test-rmse:1.268022+0.163108 
## [61] train-rmse:0.615569+0.010237    test-rmse:1.267796+0.162313 
## [62] train-rmse:0.607634+0.009082    test-rmse:1.267311+0.164375 
## [63] train-rmse:0.600258+0.009303    test-rmse:1.264169+0.162514 
## [64] train-rmse:0.596286+0.009404    test-rmse:1.262830+0.160988 
## [65] train-rmse:0.591092+0.009188    test-rmse:1.261316+0.160804 
## [66] train-rmse:0.586736+0.010377    test-rmse:1.261506+0.161430 
## [67] train-rmse:0.582935+0.009966    test-rmse:1.262258+0.159798 
## [68] train-rmse:0.577173+0.011258    test-rmse:1.260478+0.160296 
## [69] train-rmse:0.572257+0.011950    test-rmse:1.258691+0.160010 
## [70] train-rmse:0.565893+0.010255    test-rmse:1.256566+0.161623 
## [71] train-rmse:0.561890+0.009939    test-rmse:1.256224+0.159826 
## [72] train-rmse:0.557309+0.008405    test-rmse:1.255903+0.159351 
## [73] train-rmse:0.551670+0.008508    test-rmse:1.255789+0.159703 
## [74] train-rmse:0.548090+0.007364    test-rmse:1.254286+0.159806 
## [75] train-rmse:0.544468+0.007561    test-rmse:1.254059+0.160008 
## [76] train-rmse:0.541724+0.008051    test-rmse:1.253776+0.159939 
## [77] train-rmse:0.537591+0.008362    test-rmse:1.253606+0.158741 
## [78] train-rmse:0.533308+0.007901    test-rmse:1.252444+0.159949 
## [79] train-rmse:0.530249+0.007398    test-rmse:1.251921+0.159753 
## [80] train-rmse:0.524663+0.007077    test-rmse:1.250520+0.159207 
## [81] train-rmse:0.521744+0.007438    test-rmse:1.251317+0.158999 
## [82] train-rmse:0.519356+0.007765    test-rmse:1.249299+0.158902 
## [83] train-rmse:0.515998+0.007801    test-rmse:1.249576+0.158748 
## [84] train-rmse:0.514056+0.007925    test-rmse:1.249518+0.159503 
## [85] train-rmse:0.511596+0.008052    test-rmse:1.249158+0.158825 
## [86] train-rmse:0.508276+0.008343    test-rmse:1.249313+0.159160 
## [87] train-rmse:0.503567+0.008597    test-rmse:1.246311+0.160227 
## [88] train-rmse:0.500518+0.008351    test-rmse:1.245830+0.158431 
## [89] train-rmse:0.495027+0.005311    test-rmse:1.243940+0.158486 
## [90] train-rmse:0.492395+0.004810    test-rmse:1.242705+0.159841 
## [91] train-rmse:0.490543+0.004832    test-rmse:1.241999+0.160554 
## [92] train-rmse:0.487119+0.004424    test-rmse:1.244207+0.161292 
## [93] train-rmse:0.481418+0.010366    test-rmse:1.244967+0.161312 
## [94] train-rmse:0.474925+0.014658    test-rmse:1.244533+0.161522 
## [95] train-rmse:0.472443+0.014473    test-rmse:1.244671+0.161632 
## [96] train-rmse:0.470614+0.014208    test-rmse:1.244814+0.161926 
## [97] train-rmse:0.467977+0.013692    test-rmse:1.245111+0.161404 
## Stopping. Best iteration:
## [91] train-rmse:0.490543+0.004832    test-rmse:1.241999+0.160554
## 
## 53 
## [1]  train-rmse:7.501690+0.055479    test-rmse:7.502858+0.259411 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.845968+0.042192    test-rmse:5.863011+0.234254 
## [3]  train-rmse:4.606404+0.031748    test-rmse:4.630660+0.200274 
## [4]  train-rmse:3.681485+0.026386    test-rmse:3.735739+0.160666 
## [5]  train-rmse:2.992910+0.020637    test-rmse:3.068286+0.146786 
## [6]  train-rmse:2.478891+0.021060    test-rmse:2.580977+0.119053 
## [7]  train-rmse:2.108350+0.018690    test-rmse:2.257293+0.103322 
## [8]  train-rmse:1.834223+0.014635    test-rmse:2.011133+0.092405 
## [9]  train-rmse:1.637885+0.017267    test-rmse:1.851329+0.075079 
## [10] train-rmse:1.491678+0.019676    test-rmse:1.745319+0.072075 
## [11] train-rmse:1.384466+0.018946    test-rmse:1.660820+0.072017 
## [12] train-rmse:1.301531+0.020318    test-rmse:1.622126+0.065284 
## [13] train-rmse:1.232456+0.024338    test-rmse:1.569222+0.080519 
## [14] train-rmse:1.167374+0.022435    test-rmse:1.533464+0.082652 
## [15] train-rmse:1.121521+0.020344    test-rmse:1.500349+0.099085 
## [16] train-rmse:1.078928+0.023252    test-rmse:1.484661+0.108686 
## [17] train-rmse:1.045080+0.023097    test-rmse:1.465353+0.107091 
## [18] train-rmse:1.007427+0.023696    test-rmse:1.449543+0.104583 
## [19] train-rmse:0.978238+0.021809    test-rmse:1.435217+0.113967 
## [20] train-rmse:0.947294+0.023292    test-rmse:1.422534+0.124131 
## [21] train-rmse:0.922686+0.023197    test-rmse:1.408344+0.129622 
## [22] train-rmse:0.893967+0.017418    test-rmse:1.400039+0.130408 
## [23] train-rmse:0.870506+0.017590    test-rmse:1.380588+0.135617 
## [24] train-rmse:0.849769+0.019063    test-rmse:1.368080+0.144867 
## [25] train-rmse:0.826790+0.020381    test-rmse:1.358103+0.150251 
## [26] train-rmse:0.804156+0.021182    test-rmse:1.350748+0.148300 
## [27] train-rmse:0.785791+0.023502    test-rmse:1.343590+0.156256 
## [28] train-rmse:0.768019+0.023429    test-rmse:1.336256+0.155330 
## [29] train-rmse:0.754228+0.024352    test-rmse:1.331410+0.159292 
## [30] train-rmse:0.735762+0.025276    test-rmse:1.325987+0.162336 
## [31] train-rmse:0.720261+0.023960    test-rmse:1.326046+0.164747 
## [32] train-rmse:0.707434+0.021564    test-rmse:1.317900+0.168198 
## [33] train-rmse:0.693342+0.022705    test-rmse:1.308618+0.169598 
## [34] train-rmse:0.680150+0.021230    test-rmse:1.301955+0.165696 
## [35] train-rmse:0.668961+0.021607    test-rmse:1.294952+0.166849 
## [36] train-rmse:0.657407+0.020832    test-rmse:1.289075+0.167004 
## [37] train-rmse:0.642367+0.020541    test-rmse:1.283832+0.165562 
## [38] train-rmse:0.631365+0.022219    test-rmse:1.281747+0.168530 
## [39] train-rmse:0.619413+0.020606    test-rmse:1.275267+0.167942 
## [40] train-rmse:0.608687+0.021248    test-rmse:1.273266+0.171314 
## [41] train-rmse:0.598611+0.021105    test-rmse:1.273239+0.171243 
## [42] train-rmse:0.590560+0.020673    test-rmse:1.269906+0.171148 
## [43] train-rmse:0.581258+0.022127    test-rmse:1.268473+0.171113 
## [44] train-rmse:0.573426+0.021389    test-rmse:1.269289+0.172919 
## [45] train-rmse:0.564590+0.020885    test-rmse:1.269170+0.175757 
## [46] train-rmse:0.552400+0.017582    test-rmse:1.267002+0.179341 
## [47] train-rmse:0.542748+0.016156    test-rmse:1.266313+0.179788 
## [48] train-rmse:0.536904+0.016486    test-rmse:1.267088+0.183535 
## [49] train-rmse:0.529935+0.016870    test-rmse:1.265406+0.182951 
## [50] train-rmse:0.523555+0.018720    test-rmse:1.262557+0.183014 
## [51] train-rmse:0.514852+0.017695    test-rmse:1.263459+0.183774 
## [52] train-rmse:0.507254+0.017778    test-rmse:1.260649+0.181588 
## [53] train-rmse:0.502325+0.017826    test-rmse:1.259505+0.180837 
## [54] train-rmse:0.496401+0.018291    test-rmse:1.260232+0.181803 
## [55] train-rmse:0.488290+0.015257    test-rmse:1.260505+0.185450 
## [56] train-rmse:0.483031+0.015271    test-rmse:1.255925+0.183550 
## [57] train-rmse:0.477319+0.013901    test-rmse:1.254540+0.184385 
## [58] train-rmse:0.470959+0.011922    test-rmse:1.255220+0.185627 
## [59] train-rmse:0.464420+0.012071    test-rmse:1.254845+0.186854 
## [60] train-rmse:0.461535+0.012096    test-rmse:1.254266+0.188466 
## [61] train-rmse:0.454812+0.009623    test-rmse:1.249444+0.191793 
## [62] train-rmse:0.448992+0.011441    test-rmse:1.249022+0.191019 
## [63] train-rmse:0.442629+0.013624    test-rmse:1.250306+0.188997 
## [64] train-rmse:0.438304+0.015425    test-rmse:1.249828+0.189172 
## [65] train-rmse:0.433898+0.016487    test-rmse:1.250019+0.187442 
## [66] train-rmse:0.429931+0.017926    test-rmse:1.249099+0.185708 
## [67] train-rmse:0.424534+0.017476    test-rmse:1.247373+0.185207 
## [68] train-rmse:0.421099+0.017849    test-rmse:1.247412+0.183515 
## [69] train-rmse:0.417594+0.017928    test-rmse:1.246878+0.184625 
## [70] train-rmse:0.411979+0.015999    test-rmse:1.246764+0.183610 
## [71] train-rmse:0.407441+0.015749    test-rmse:1.247542+0.183569 
## [72] train-rmse:0.403939+0.016891    test-rmse:1.247894+0.184118 
## [73] train-rmse:0.398962+0.016545    test-rmse:1.245512+0.182866 
## [74] train-rmse:0.394188+0.014326    test-rmse:1.244830+0.182652 
## [75] train-rmse:0.388555+0.013377    test-rmse:1.243824+0.183266 
## [76] train-rmse:0.384704+0.015858    test-rmse:1.244038+0.183589 
## [77] train-rmse:0.380709+0.014230    test-rmse:1.245520+0.185270 
## [78] train-rmse:0.378331+0.013809    test-rmse:1.245901+0.185360 
## [79] train-rmse:0.374361+0.013528    test-rmse:1.246513+0.185446 
## [80] train-rmse:0.370441+0.012969    test-rmse:1.248286+0.185610 
## [81] train-rmse:0.364822+0.011331    test-rmse:1.247144+0.184425 
## Stopping. Best iteration:
## [75] train-rmse:0.388555+0.013377    test-rmse:1.243824+0.183266
## 
## 54 
## [1]  train-rmse:7.500734+0.055707    test-rmse:7.503625+0.251818 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.839574+0.042874    test-rmse:5.858078+0.230176 
## [3]  train-rmse:4.591629+0.033312    test-rmse:4.613396+0.185723 
## [4]  train-rmse:3.663419+0.029790    test-rmse:3.707306+0.160021 
## [5]  train-rmse:2.953073+0.023333    test-rmse:3.014316+0.133938 
## [6]  train-rmse:2.430078+0.022687    test-rmse:2.533704+0.119140 
## [7]  train-rmse:2.045863+0.023812    test-rmse:2.185575+0.097649 
## [8]  train-rmse:1.760803+0.025165    test-rmse:1.957899+0.092690 
## [9]  train-rmse:1.541892+0.021587    test-rmse:1.774024+0.093809 
## [10] train-rmse:1.378775+0.029822    test-rmse:1.648127+0.083685 
## [11] train-rmse:1.256893+0.029932    test-rmse:1.567290+0.078243 
## [12] train-rmse:1.160873+0.036019    test-rmse:1.493814+0.071646 
## [13] train-rmse:1.088662+0.033773    test-rmse:1.449082+0.087560 
## [14] train-rmse:1.029321+0.034029    test-rmse:1.412040+0.087798 
## [15] train-rmse:0.973365+0.035618    test-rmse:1.376697+0.094084 
## [16] train-rmse:0.923482+0.032381    test-rmse:1.363205+0.098603 
## [17] train-rmse:0.888884+0.033711    test-rmse:1.348570+0.107244 
## [18] train-rmse:0.853488+0.031266    test-rmse:1.325403+0.118167 
## [19] train-rmse:0.820398+0.030780    test-rmse:1.316443+0.126301 
## [20] train-rmse:0.796352+0.029320    test-rmse:1.308214+0.126020 
## [21] train-rmse:0.770784+0.028575    test-rmse:1.301108+0.131675 
## [22] train-rmse:0.744659+0.027333    test-rmse:1.283295+0.139453 
## [23] train-rmse:0.722623+0.029098    test-rmse:1.269990+0.141120 
## [24] train-rmse:0.701840+0.031774    test-rmse:1.256972+0.133821 
## [25] train-rmse:0.678345+0.030590    test-rmse:1.249758+0.139798 
## [26] train-rmse:0.658859+0.026643    test-rmse:1.247255+0.146773 
## [27] train-rmse:0.639253+0.024930    test-rmse:1.241910+0.145429 
## [28] train-rmse:0.624292+0.023502    test-rmse:1.242980+0.150744 
## [29] train-rmse:0.609555+0.024202    test-rmse:1.235894+0.152345 
## [30] train-rmse:0.596815+0.025186    test-rmse:1.234151+0.152629 
## [31] train-rmse:0.582142+0.024561    test-rmse:1.231745+0.153292 
## [32] train-rmse:0.565798+0.027867    test-rmse:1.227746+0.157054 
## [33] train-rmse:0.554895+0.026427    test-rmse:1.225881+0.155183 
## [34] train-rmse:0.542318+0.025449    test-rmse:1.221962+0.155407 
## [35] train-rmse:0.532164+0.026971    test-rmse:1.218503+0.159485 
## [36] train-rmse:0.519305+0.025642    test-rmse:1.215445+0.157592 
## [37] train-rmse:0.510347+0.026873    test-rmse:1.212579+0.160254 
## [38] train-rmse:0.501261+0.026959    test-rmse:1.210489+0.159141 
## [39] train-rmse:0.489976+0.028320    test-rmse:1.208100+0.162155 
## [40] train-rmse:0.480335+0.027154    test-rmse:1.210566+0.164070 
## [41] train-rmse:0.468728+0.027446    test-rmse:1.210720+0.163338 
## [42] train-rmse:0.459383+0.024670    test-rmse:1.210011+0.165227 
## [43] train-rmse:0.451579+0.026493    test-rmse:1.205842+0.166604 
## [44] train-rmse:0.443157+0.025995    test-rmse:1.206466+0.164264 
## [45] train-rmse:0.436349+0.026794    test-rmse:1.206926+0.166907 
## [46] train-rmse:0.425784+0.022332    test-rmse:1.203557+0.169758 
## [47] train-rmse:0.418717+0.019268    test-rmse:1.203124+0.167749 
## [48] train-rmse:0.411972+0.019596    test-rmse:1.201779+0.168983 
## [49] train-rmse:0.405081+0.020842    test-rmse:1.203291+0.171346 
## [50] train-rmse:0.399336+0.020295    test-rmse:1.202156+0.171803 
## [51] train-rmse:0.394314+0.020861    test-rmse:1.202404+0.172308 
## [52] train-rmse:0.386014+0.020070    test-rmse:1.202762+0.171755 
## [53] train-rmse:0.379935+0.021095    test-rmse:1.203047+0.172324 
## [54] train-rmse:0.375164+0.022106    test-rmse:1.202080+0.174469 
## Stopping. Best iteration:
## [48] train-rmse:0.411972+0.019596    test-rmse:1.201779+0.168983
## 
## 55 
## [1]  train-rmse:7.500734+0.055707    test-rmse:7.503625+0.251818 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.836894+0.043236    test-rmse:5.861467+0.220654 
## [3]  train-rmse:4.584451+0.034142    test-rmse:4.618150+0.176143 
## [4]  train-rmse:3.641613+0.029645    test-rmse:3.685269+0.148300 
## [5]  train-rmse:2.926980+0.020913    test-rmse:3.000448+0.128588 
## [6]  train-rmse:2.394976+0.012898    test-rmse:2.507286+0.131455 
## [7]  train-rmse:1.998874+0.017113    test-rmse:2.155835+0.097879 
## [8]  train-rmse:1.701588+0.020172    test-rmse:1.901240+0.092804 
## [9]  train-rmse:1.479061+0.025498    test-rmse:1.734150+0.073458 
## [10] train-rmse:1.310473+0.022058    test-rmse:1.597041+0.073648 
## [11] train-rmse:1.172162+0.021860    test-rmse:1.506851+0.074694 
## [12] train-rmse:1.067904+0.021591    test-rmse:1.451694+0.088673 
## [13] train-rmse:0.991897+0.021382    test-rmse:1.411836+0.097463 
## [14] train-rmse:0.920635+0.017973    test-rmse:1.365816+0.111712 
## [15] train-rmse:0.868692+0.017336    test-rmse:1.339619+0.131632 
## [16] train-rmse:0.819945+0.023162    test-rmse:1.322786+0.137239 
## [17] train-rmse:0.781241+0.020654    test-rmse:1.303709+0.141859 
## [18] train-rmse:0.743209+0.018004    test-rmse:1.282437+0.146787 
## [19] train-rmse:0.712669+0.019997    test-rmse:1.274037+0.143561 
## [20] train-rmse:0.689166+0.020248    test-rmse:1.268188+0.150615 
## [21] train-rmse:0.662770+0.015050    test-rmse:1.260130+0.153307 
## [22] train-rmse:0.639286+0.019273    test-rmse:1.248177+0.148837 
## [23] train-rmse:0.617931+0.017331    test-rmse:1.246853+0.155639 
## [24] train-rmse:0.597628+0.019216    test-rmse:1.240335+0.151135 
## [25] train-rmse:0.577828+0.015502    test-rmse:1.236614+0.154218 
## [26] train-rmse:0.559482+0.011738    test-rmse:1.232884+0.155741 
## [27] train-rmse:0.545464+0.012727    test-rmse:1.228331+0.160213 
## [28] train-rmse:0.528435+0.011965    test-rmse:1.224325+0.162436 
## [29] train-rmse:0.515060+0.011433    test-rmse:1.226446+0.161817 
## [30] train-rmse:0.502804+0.010027    test-rmse:1.226741+0.166296 
## [31] train-rmse:0.488893+0.011213    test-rmse:1.225637+0.166644 
## [32] train-rmse:0.472934+0.013648    test-rmse:1.221963+0.162548 
## [33] train-rmse:0.464394+0.013627    test-rmse:1.220091+0.163739 
## [34] train-rmse:0.451298+0.016660    test-rmse:1.218448+0.164101 
## [35] train-rmse:0.442397+0.015521    test-rmse:1.219198+0.166072 
## [36] train-rmse:0.429862+0.015526    test-rmse:1.217976+0.164890 
## [37] train-rmse:0.416166+0.013065    test-rmse:1.213626+0.165857 
## [38] train-rmse:0.408751+0.013227    test-rmse:1.214165+0.165391 
## [39] train-rmse:0.398155+0.011136    test-rmse:1.214885+0.165792 
## [40] train-rmse:0.386036+0.013006    test-rmse:1.212696+0.166001 
## [41] train-rmse:0.380237+0.012685    test-rmse:1.213619+0.165905 
## [42] train-rmse:0.372852+0.010343    test-rmse:1.213479+0.167133 
## [43] train-rmse:0.362640+0.007950    test-rmse:1.214517+0.168503 
## [44] train-rmse:0.353681+0.009077    test-rmse:1.210619+0.167537 
## [45] train-rmse:0.344780+0.008982    test-rmse:1.210879+0.168438 
## [46] train-rmse:0.338822+0.008657    test-rmse:1.213347+0.169404 
## [47] train-rmse:0.330602+0.011009    test-rmse:1.212874+0.169629 
## [48] train-rmse:0.323899+0.007087    test-rmse:1.213474+0.169101 
## [49] train-rmse:0.318347+0.008347    test-rmse:1.213609+0.168045 
## [50] train-rmse:0.312812+0.008511    test-rmse:1.213975+0.170195 
## Stopping. Best iteration:
## [44] train-rmse:0.353681+0.009077    test-rmse:1.210619+0.167537
## 
## 56 
## [1]  train-rmse:7.377807+0.051207    test-rmse:7.373679+0.266919 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.705260+0.034140    test-rmse:5.703851+0.264563 
## [3]  train-rmse:4.520452+0.024100    test-rmse:4.516231+0.241671 
## [4]  train-rmse:3.702929+0.017071    test-rmse:3.719042+0.203624 
## [5]  train-rmse:3.156887+0.011811    test-rmse:3.187219+0.170894 
## [6]  train-rmse:2.801925+0.012242    test-rmse:2.835341+0.121967 
## [7]  train-rmse:2.575166+0.013762    test-rmse:2.604944+0.082933 
## [8]  train-rmse:2.431911+0.014261    test-rmse:2.466770+0.064291 
## [9]  train-rmse:2.341250+0.015580    test-rmse:2.389252+0.057428 
## [10] train-rmse:2.281361+0.016691    test-rmse:2.322012+0.055304 
## [11] train-rmse:2.239101+0.016948    test-rmse:2.283129+0.056475 
## [12] train-rmse:2.207995+0.017590    test-rmse:2.249940+0.066725 
## [13] train-rmse:2.183585+0.019342    test-rmse:2.220335+0.067438 
## [14] train-rmse:2.162816+0.020909    test-rmse:2.202501+0.072274 
## [15] train-rmse:2.143753+0.022192    test-rmse:2.193531+0.075630 
## [16] train-rmse:2.125825+0.022560    test-rmse:2.187521+0.076682 
## [17] train-rmse:2.110205+0.022408    test-rmse:2.178459+0.080627 
## [18] train-rmse:2.095841+0.023012    test-rmse:2.159811+0.077230 
## [19] train-rmse:2.081802+0.023867    test-rmse:2.144923+0.080201 
## [20] train-rmse:2.068400+0.024718    test-rmse:2.135975+0.082677 
## [21] train-rmse:2.055448+0.025580    test-rmse:2.123329+0.083243 
## [22] train-rmse:2.042336+0.026398    test-rmse:2.107297+0.089818 
## [23] train-rmse:2.029409+0.027191    test-rmse:2.093135+0.092017 
## [24] train-rmse:2.016778+0.027488    test-rmse:2.081582+0.097346 
## [25] train-rmse:2.004582+0.028190    test-rmse:2.069704+0.088032 
## [26] train-rmse:1.993075+0.028765    test-rmse:2.062717+0.092120 
## [27] train-rmse:1.981780+0.029133    test-rmse:2.052375+0.095216 
## [28] train-rmse:1.971010+0.029606    test-rmse:2.046468+0.097256 
## [29] train-rmse:1.960823+0.029873    test-rmse:2.042256+0.094850 
## [30] train-rmse:1.950694+0.030103    test-rmse:2.036838+0.092443 
## [31] train-rmse:1.940840+0.030419    test-rmse:2.022736+0.088881 
## [32] train-rmse:1.931242+0.030844    test-rmse:2.012814+0.086445 
## [33] train-rmse:1.921861+0.031383    test-rmse:1.994128+0.084190 
## [34] train-rmse:1.912814+0.032003    test-rmse:1.991090+0.078125 
## [35] train-rmse:1.903751+0.032743    test-rmse:1.984841+0.078180 
## [36] train-rmse:1.894841+0.033370    test-rmse:1.973726+0.082626 
## [37] train-rmse:1.886219+0.034036    test-rmse:1.965829+0.081540 
## [38] train-rmse:1.877848+0.034463    test-rmse:1.960581+0.081912 
## [39] train-rmse:1.869632+0.034767    test-rmse:1.955092+0.088649 
## [40] train-rmse:1.861655+0.035181    test-rmse:1.949095+0.088889 
## [41] train-rmse:1.853719+0.035655    test-rmse:1.941684+0.090442 
## [42] train-rmse:1.845804+0.036319    test-rmse:1.940864+0.095070 
## [43] train-rmse:1.837940+0.036746    test-rmse:1.928140+0.094995 
## [44] train-rmse:1.830331+0.037351    test-rmse:1.920867+0.095403 
## [45] train-rmse:1.822731+0.038083    test-rmse:1.914723+0.096679 
## [46] train-rmse:1.815364+0.038739    test-rmse:1.908481+0.096870 
## [47] train-rmse:1.808205+0.039272    test-rmse:1.903497+0.100570 
## [48] train-rmse:1.801100+0.039715    test-rmse:1.899322+0.100598 
## [49] train-rmse:1.794380+0.040115    test-rmse:1.891608+0.104622 
## [50] train-rmse:1.787629+0.040403    test-rmse:1.886721+0.102320 
## [51] train-rmse:1.780955+0.040577    test-rmse:1.881913+0.100375 
## [52] train-rmse:1.774319+0.040781    test-rmse:1.878183+0.099015 
## [53] train-rmse:1.767750+0.040830    test-rmse:1.873013+0.095867 
## [54] train-rmse:1.761290+0.041075    test-rmse:1.869670+0.098780 
## [55] train-rmse:1.755013+0.041454    test-rmse:1.867272+0.094998 
## [56] train-rmse:1.748958+0.041854    test-rmse:1.863337+0.094859 
## [57] train-rmse:1.742958+0.042316    test-rmse:1.854112+0.092090 
## [58] train-rmse:1.737191+0.042762    test-rmse:1.846279+0.094667 
## [59] train-rmse:1.731636+0.043158    test-rmse:1.842794+0.092663 
## [60] train-rmse:1.726162+0.043520    test-rmse:1.835202+0.092006 
## [61] train-rmse:1.720583+0.043973    test-rmse:1.831159+0.095960 
## [62] train-rmse:1.715145+0.044290    test-rmse:1.821870+0.093843 
## [63] train-rmse:1.709714+0.044614    test-rmse:1.816125+0.100879 
## [64] train-rmse:1.704356+0.044696    test-rmse:1.807289+0.102867 
## [65] train-rmse:1.699105+0.044834    test-rmse:1.803444+0.102748 
## [66] train-rmse:1.693979+0.045036    test-rmse:1.797654+0.108127 
## [67] train-rmse:1.688840+0.045170    test-rmse:1.793303+0.107187 
## [68] train-rmse:1.683794+0.045393    test-rmse:1.792772+0.107841 
## [69] train-rmse:1.678951+0.045537    test-rmse:1.791373+0.108658 
## [70] train-rmse:1.674106+0.045797    test-rmse:1.790913+0.103222 
## [71] train-rmse:1.669300+0.046233    test-rmse:1.788552+0.106261 
## [72] train-rmse:1.664640+0.046609    test-rmse:1.786894+0.107599 
## [73] train-rmse:1.660076+0.046908    test-rmse:1.779108+0.106987 
## [74] train-rmse:1.655723+0.047054    test-rmse:1.777811+0.106414 
## [75] train-rmse:1.651316+0.047202    test-rmse:1.772867+0.105675 
## [76] train-rmse:1.646957+0.047418    test-rmse:1.770379+0.104038 
## [77] train-rmse:1.642566+0.047656    test-rmse:1.764154+0.109480 
## [78] train-rmse:1.638203+0.047815    test-rmse:1.758817+0.114731 
## [79] train-rmse:1.634023+0.048052    test-rmse:1.754945+0.114590 
## [80] train-rmse:1.629914+0.048211    test-rmse:1.749192+0.110253 
## [81] train-rmse:1.625968+0.048395    test-rmse:1.746719+0.111790 
## [82] train-rmse:1.622053+0.048501    test-rmse:1.740570+0.112736 
## [83] train-rmse:1.618148+0.048611    test-rmse:1.736555+0.112894 
## [84] train-rmse:1.614318+0.048762    test-rmse:1.734338+0.115092 
## [85] train-rmse:1.610546+0.048927    test-rmse:1.729804+0.116633 
## [86] train-rmse:1.606781+0.049079    test-rmse:1.730611+0.119859 
## [87] train-rmse:1.603148+0.049239    test-rmse:1.729259+0.120151 
## [88] train-rmse:1.599494+0.049393    test-rmse:1.725419+0.118263 
## [89] train-rmse:1.595869+0.049500    test-rmse:1.721476+0.116778 
## [90] train-rmse:1.592265+0.049576    test-rmse:1.718390+0.117516 
## [91] train-rmse:1.588692+0.049637    test-rmse:1.714420+0.118897 
## [92] train-rmse:1.585189+0.049726    test-rmse:1.709802+0.123491 
## [93] train-rmse:1.581733+0.049860    test-rmse:1.706216+0.123021 
## [94] train-rmse:1.578324+0.050028    test-rmse:1.702747+0.126153 
## [95] train-rmse:1.574990+0.050150    test-rmse:1.702624+0.129829 
## [96] train-rmse:1.571609+0.050291    test-rmse:1.702195+0.128718 
## [97] train-rmse:1.568335+0.050360    test-rmse:1.700595+0.126829 
## [98] train-rmse:1.565116+0.050477    test-rmse:1.694138+0.127513 
## [99] train-rmse:1.561904+0.050571    test-rmse:1.695048+0.127262 
## [100]    train-rmse:1.558803+0.050725    test-rmse:1.693320+0.130547 
## [101]    train-rmse:1.555700+0.050854    test-rmse:1.691106+0.131398 
## [102]    train-rmse:1.552686+0.050974    test-rmse:1.689818+0.132088 
## [103]    train-rmse:1.549681+0.051014    test-rmse:1.687584+0.133553 
## [104]    train-rmse:1.546671+0.050943    test-rmse:1.684261+0.134133 
## [105]    train-rmse:1.543815+0.051003    test-rmse:1.679293+0.132626 
## [106]    train-rmse:1.540996+0.051052    test-rmse:1.673062+0.136284 
## [107]    train-rmse:1.538241+0.051146    test-rmse:1.672565+0.136872 
## [108]    train-rmse:1.535423+0.051245    test-rmse:1.666857+0.134963 
## [109]    train-rmse:1.532702+0.051405    test-rmse:1.663670+0.134609 
## [110]    train-rmse:1.530027+0.051475    test-rmse:1.660336+0.132898 
## [111]    train-rmse:1.527436+0.051556    test-rmse:1.658526+0.137675 
## [112]    train-rmse:1.524806+0.051565    test-rmse:1.654998+0.137685 
## [113]    train-rmse:1.522196+0.051582    test-rmse:1.652469+0.138717 
## [114]    train-rmse:1.519617+0.051617    test-rmse:1.650605+0.140848 
## [115]    train-rmse:1.517079+0.051622    test-rmse:1.648427+0.140315 
## [116]    train-rmse:1.514570+0.051631    test-rmse:1.650044+0.143208 
## [117]    train-rmse:1.512129+0.051631    test-rmse:1.647370+0.143447 
## [118]    train-rmse:1.509714+0.051683    test-rmse:1.647538+0.145416 
## [119]    train-rmse:1.507275+0.051781    test-rmse:1.645338+0.142594 
## [120]    train-rmse:1.504847+0.051941    test-rmse:1.641539+0.142954 
## [121]    train-rmse:1.502486+0.052017    test-rmse:1.639826+0.143348 
## [122]    train-rmse:1.500186+0.052066    test-rmse:1.633124+0.143598 
## [123]    train-rmse:1.497906+0.052121    test-rmse:1.632852+0.143054 
## [124]    train-rmse:1.495672+0.052168    test-rmse:1.630854+0.144194 
## [125]    train-rmse:1.493463+0.052187    test-rmse:1.628240+0.146167 
## [126]    train-rmse:1.491299+0.052201    test-rmse:1.625412+0.147811 
## [127]    train-rmse:1.489129+0.052178    test-rmse:1.623653+0.148459 
## [128]    train-rmse:1.487003+0.052246    test-rmse:1.618926+0.151228 
## [129]    train-rmse:1.484924+0.052287    test-rmse:1.620463+0.151261 
## [130]    train-rmse:1.482818+0.052394    test-rmse:1.620452+0.152679 
## [131]    train-rmse:1.480750+0.052434    test-rmse:1.616899+0.151575 
## [132]    train-rmse:1.478693+0.052480    test-rmse:1.616726+0.150791 
## [133]    train-rmse:1.476697+0.052520    test-rmse:1.615269+0.151701 
## [134]    train-rmse:1.474772+0.052577    test-rmse:1.612277+0.153730 
## [135]    train-rmse:1.472840+0.052611    test-rmse:1.612134+0.153557 
## [136]    train-rmse:1.470921+0.052628    test-rmse:1.607325+0.152116 
## [137]    train-rmse:1.469081+0.052638    test-rmse:1.606058+0.153670 
## [138]    train-rmse:1.467233+0.052666    test-rmse:1.602904+0.152089 
## [139]    train-rmse:1.465343+0.052698    test-rmse:1.602602+0.151885 
## [140]    train-rmse:1.463483+0.052725    test-rmse:1.600734+0.151990 
## [141]    train-rmse:1.461628+0.052836    test-rmse:1.601405+0.153539 
## [142]    train-rmse:1.459822+0.052941    test-rmse:1.599566+0.154385 
## [143]    train-rmse:1.458083+0.053021    test-rmse:1.596182+0.153503 
## [144]    train-rmse:1.456357+0.053091    test-rmse:1.593872+0.152649 
## [145]    train-rmse:1.454638+0.053143    test-rmse:1.591574+0.154193 
## [146]    train-rmse:1.452913+0.053201    test-rmse:1.590331+0.153769 
## [147]    train-rmse:1.451182+0.053253    test-rmse:1.590937+0.156266 
## [148]    train-rmse:1.449478+0.053258    test-rmse:1.591783+0.156849 
## [149]    train-rmse:1.447803+0.053256    test-rmse:1.586635+0.154431 
## [150]    train-rmse:1.446200+0.053248    test-rmse:1.586134+0.154149 
## [151]    train-rmse:1.444626+0.053254    test-rmse:1.583772+0.154764 
## [152]    train-rmse:1.443047+0.053247    test-rmse:1.581209+0.157588 
## [153]    train-rmse:1.441520+0.053230    test-rmse:1.579413+0.158264 
## [154]    train-rmse:1.439997+0.053221    test-rmse:1.578416+0.157401 
## [155]    train-rmse:1.438516+0.053175    test-rmse:1.578007+0.158600 
## [156]    train-rmse:1.437046+0.053176    test-rmse:1.578988+0.159057 
## [157]    train-rmse:1.435577+0.053169    test-rmse:1.576815+0.160556 
## [158]    train-rmse:1.434074+0.053198    test-rmse:1.574429+0.160676 
## [159]    train-rmse:1.432567+0.053203    test-rmse:1.573703+0.160683 
## [160]    train-rmse:1.431101+0.053185    test-rmse:1.570477+0.160268 
## [161]    train-rmse:1.429668+0.053182    test-rmse:1.565110+0.162124 
## [162]    train-rmse:1.428278+0.053161    test-rmse:1.563932+0.163285 
## [163]    train-rmse:1.426901+0.053154    test-rmse:1.564084+0.164534 
## [164]    train-rmse:1.425541+0.053138    test-rmse:1.564028+0.164448 
## [165]    train-rmse:1.424220+0.053143    test-rmse:1.564553+0.165082 
## [166]    train-rmse:1.422863+0.053152    test-rmse:1.563722+0.165796 
## [167]    train-rmse:1.421523+0.053199    test-rmse:1.562311+0.165329 
## [168]    train-rmse:1.420182+0.053217    test-rmse:1.559599+0.165902 
## [169]    train-rmse:1.418856+0.053282    test-rmse:1.557757+0.166082 
## [170]    train-rmse:1.417541+0.053371    test-rmse:1.557031+0.167424 
## [171]    train-rmse:1.416275+0.053369    test-rmse:1.555907+0.168542 
## [172]    train-rmse:1.415044+0.053348    test-rmse:1.553866+0.167871 
## [173]    train-rmse:1.413851+0.053365    test-rmse:1.552353+0.168626 
## [174]    train-rmse:1.412673+0.053368    test-rmse:1.551157+0.168900 
## [175]    train-rmse:1.411493+0.053380    test-rmse:1.550171+0.168883 
## [176]    train-rmse:1.410318+0.053398    test-rmse:1.550224+0.170309 
## [177]    train-rmse:1.409142+0.053398    test-rmse:1.550289+0.172467 
## [178]    train-rmse:1.407991+0.053385    test-rmse:1.548628+0.172505 
## [179]    train-rmse:1.406842+0.053388    test-rmse:1.548218+0.171629 
## [180]    train-rmse:1.405710+0.053422    test-rmse:1.548745+0.170923 
## [181]    train-rmse:1.404601+0.053430    test-rmse:1.548776+0.170115 
## [182]    train-rmse:1.403492+0.053421    test-rmse:1.546731+0.170248 
## [183]    train-rmse:1.402380+0.053432    test-rmse:1.543666+0.171123 
## [184]    train-rmse:1.401306+0.053437    test-rmse:1.543267+0.172796 
## [185]    train-rmse:1.400239+0.053442    test-rmse:1.540115+0.172834 
## [186]    train-rmse:1.399186+0.053450    test-rmse:1.540657+0.173155 
## [187]    train-rmse:1.398158+0.053453    test-rmse:1.540370+0.173390 
## [188]    train-rmse:1.397142+0.053433    test-rmse:1.540324+0.173872 
## [189]    train-rmse:1.396127+0.053422    test-rmse:1.539913+0.173062 
## [190]    train-rmse:1.395122+0.053424    test-rmse:1.540210+0.174952 
## [191]    train-rmse:1.394123+0.053457    test-rmse:1.538872+0.174503 
## [192]    train-rmse:1.393136+0.053462    test-rmse:1.537361+0.173642 
## [193]    train-rmse:1.392178+0.053462    test-rmse:1.537229+0.174582 
## [194]    train-rmse:1.391221+0.053441    test-rmse:1.536240+0.175202 
## [195]    train-rmse:1.390248+0.053407    test-rmse:1.535074+0.175856 
## [196]    train-rmse:1.389321+0.053414    test-rmse:1.533342+0.175812 
## [197]    train-rmse:1.388410+0.053414    test-rmse:1.532863+0.174882 
## [198]    train-rmse:1.387520+0.053412    test-rmse:1.533150+0.175678 
## [199]    train-rmse:1.386625+0.053417    test-rmse:1.532996+0.176550 
## [200]    train-rmse:1.385746+0.053415    test-rmse:1.532025+0.176899 
## [201]    train-rmse:1.384884+0.053429    test-rmse:1.531135+0.176259 
## [202]    train-rmse:1.384025+0.053426    test-rmse:1.530192+0.176232 
## [203]    train-rmse:1.383175+0.053407    test-rmse:1.529520+0.175003 
## [204]    train-rmse:1.382331+0.053395    test-rmse:1.528079+0.175187 
## [205]    train-rmse:1.381487+0.053393    test-rmse:1.525944+0.175148 
## [206]    train-rmse:1.380671+0.053389    test-rmse:1.523853+0.175729 
## [207]    train-rmse:1.379851+0.053386    test-rmse:1.524422+0.178417 
## [208]    train-rmse:1.379044+0.053391    test-rmse:1.523345+0.179157 
## [209]    train-rmse:1.378257+0.053411    test-rmse:1.524640+0.180254 
## [210]    train-rmse:1.377462+0.053439    test-rmse:1.522282+0.181410 
## [211]    train-rmse:1.376687+0.053445    test-rmse:1.521273+0.181828 
## [212]    train-rmse:1.375916+0.053440    test-rmse:1.521723+0.181703 
## [213]    train-rmse:1.375133+0.053426    test-rmse:1.521500+0.182625 
## [214]    train-rmse:1.374367+0.053409    test-rmse:1.521369+0.183482 
## [215]    train-rmse:1.373617+0.053399    test-rmse:1.519464+0.182838 
## [216]    train-rmse:1.372864+0.053394    test-rmse:1.517834+0.182625 
## [217]    train-rmse:1.372133+0.053378    test-rmse:1.517640+0.182522 
## [218]    train-rmse:1.371432+0.053368    test-rmse:1.517480+0.182263 
## [219]    train-rmse:1.370741+0.053358    test-rmse:1.516068+0.181817 
## [220]    train-rmse:1.370051+0.053343    test-rmse:1.516557+0.180891 
## [221]    train-rmse:1.369365+0.053335    test-rmse:1.515254+0.181854 
## [222]    train-rmse:1.368697+0.053325    test-rmse:1.514798+0.181423 
## [223]    train-rmse:1.368027+0.053323    test-rmse:1.514369+0.182989 
## [224]    train-rmse:1.367353+0.053336    test-rmse:1.512639+0.182116 
## [225]    train-rmse:1.366673+0.053326    test-rmse:1.511433+0.183226 
## [226]    train-rmse:1.366011+0.053324    test-rmse:1.510894+0.184039 
## [227]    train-rmse:1.365357+0.053326    test-rmse:1.510335+0.183837 
## [228]    train-rmse:1.364709+0.053319    test-rmse:1.510783+0.184710 
## [229]    train-rmse:1.364074+0.053310    test-rmse:1.510908+0.184217 
## [230]    train-rmse:1.363434+0.053308    test-rmse:1.509696+0.183679 
## [231]    train-rmse:1.362806+0.053303    test-rmse:1.509234+0.186068 
## [232]    train-rmse:1.362190+0.053297    test-rmse:1.509975+0.185411 
## [233]    train-rmse:1.361598+0.053308    test-rmse:1.509527+0.185862 
## [234]    train-rmse:1.361007+0.053310    test-rmse:1.508602+0.185966 
## [235]    train-rmse:1.360412+0.053314    test-rmse:1.507707+0.185831 
## [236]    train-rmse:1.359833+0.053319    test-rmse:1.507025+0.184711 
## [237]    train-rmse:1.359247+0.053312    test-rmse:1.505435+0.186654 
## [238]    train-rmse:1.358662+0.053304    test-rmse:1.506004+0.187460 
## [239]    train-rmse:1.358099+0.053299    test-rmse:1.504810+0.187248 
## [240]    train-rmse:1.357548+0.053295    test-rmse:1.503241+0.186384 
## [241]    train-rmse:1.357005+0.053297    test-rmse:1.504676+0.188401 
## [242]    train-rmse:1.356464+0.053297    test-rmse:1.503957+0.188637 
## [243]    train-rmse:1.355928+0.053303    test-rmse:1.504075+0.190225 
## [244]    train-rmse:1.355396+0.053299    test-rmse:1.503079+0.190118 
## [245]    train-rmse:1.354866+0.053294    test-rmse:1.504146+0.189933 
## [246]    train-rmse:1.354337+0.053272    test-rmse:1.504107+0.189556 
## [247]    train-rmse:1.353813+0.053259    test-rmse:1.503716+0.189910 
## [248]    train-rmse:1.353295+0.053248    test-rmse:1.503034+0.188523 
## [249]    train-rmse:1.352784+0.053243    test-rmse:1.502477+0.187803 
## [250]    train-rmse:1.352299+0.053236    test-rmse:1.501809+0.187160 
## [251]    train-rmse:1.351817+0.053237    test-rmse:1.501245+0.188046 
## [252]    train-rmse:1.351339+0.053234    test-rmse:1.501407+0.189183 
## [253]    train-rmse:1.350872+0.053234    test-rmse:1.499409+0.189237 
## [254]    train-rmse:1.350414+0.053222    test-rmse:1.498702+0.189604 
## [255]    train-rmse:1.349961+0.053211    test-rmse:1.500109+0.189877 
## [256]    train-rmse:1.349496+0.053199    test-rmse:1.499762+0.190694 
## [257]    train-rmse:1.349024+0.053184    test-rmse:1.499091+0.191455 
## [258]    train-rmse:1.348566+0.053175    test-rmse:1.498126+0.191932 
## [259]    train-rmse:1.348115+0.053165    test-rmse:1.498184+0.191257 
## [260]    train-rmse:1.347675+0.053154    test-rmse:1.496896+0.192278 
## [261]    train-rmse:1.347247+0.053143    test-rmse:1.497236+0.192189 
## [262]    train-rmse:1.346821+0.053135    test-rmse:1.495791+0.193772 
## [263]    train-rmse:1.346395+0.053131    test-rmse:1.495894+0.193251 
## [264]    train-rmse:1.345979+0.053125    test-rmse:1.497121+0.192304 
## [265]    train-rmse:1.345571+0.053118    test-rmse:1.496854+0.192721 
## [266]    train-rmse:1.345156+0.053115    test-rmse:1.496213+0.192590 
## [267]    train-rmse:1.344738+0.053115    test-rmse:1.495754+0.192970 
## [268]    train-rmse:1.344333+0.053107    test-rmse:1.495278+0.192877 
## [269]    train-rmse:1.343929+0.053097    test-rmse:1.493876+0.192215 
## [270]    train-rmse:1.343526+0.053081    test-rmse:1.493849+0.192051 
## [271]    train-rmse:1.343133+0.053073    test-rmse:1.494069+0.191647 
## [272]    train-rmse:1.342740+0.053065    test-rmse:1.493469+0.191430 
## [273]    train-rmse:1.342357+0.053060    test-rmse:1.492815+0.191148 
## [274]    train-rmse:1.341978+0.053053    test-rmse:1.493346+0.190878 
## [275]    train-rmse:1.341593+0.053034    test-rmse:1.492517+0.192052 
## [276]    train-rmse:1.341212+0.053013    test-rmse:1.493678+0.193090 
## [277]    train-rmse:1.340837+0.052993    test-rmse:1.494631+0.192872 
## [278]    train-rmse:1.340473+0.052994    test-rmse:1.494449+0.193305 
## [279]    train-rmse:1.340119+0.052988    test-rmse:1.493554+0.192912 
## [280]    train-rmse:1.339764+0.052980    test-rmse:1.492959+0.193705 
## [281]    train-rmse:1.339419+0.052979    test-rmse:1.492693+0.193204 
## Stopping. Best iteration:
## [275]    train-rmse:1.341593+0.053034    test-rmse:1.492517+0.192052
## 
## 57 
## [1]  train-rmse:7.343519+0.051656    test-rmse:7.339890+0.261680 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.637765+0.038928    test-rmse:5.631937+0.241943 
## [3]  train-rmse:4.421253+0.030096    test-rmse:4.433045+0.215797 
## [4]  train-rmse:3.561427+0.026165    test-rmse:3.580057+0.175215 
## [5]  train-rmse:2.968157+0.025025    test-rmse:3.014354+0.131861 
## [6]  train-rmse:2.564466+0.024433    test-rmse:2.631174+0.093846 
## [7]  train-rmse:2.293082+0.012272    test-rmse:2.378722+0.074283 
## [8]  train-rmse:2.118910+0.013850    test-rmse:2.220546+0.055001 
## [9]  train-rmse:2.005548+0.015912    test-rmse:2.117749+0.047033 
## [10] train-rmse:1.926791+0.019007    test-rmse:2.056001+0.056100 
## [11] train-rmse:1.868991+0.020609    test-rmse:2.016819+0.080439 
## [12] train-rmse:1.824871+0.024272    test-rmse:1.983205+0.075845 
## [13] train-rmse:1.783406+0.015062    test-rmse:1.938519+0.086879 
## [14] train-rmse:1.748020+0.012380    test-rmse:1.904695+0.092398 
## [15] train-rmse:1.722346+0.014451    test-rmse:1.883510+0.101285 
## [16] train-rmse:1.697699+0.015973    test-rmse:1.861467+0.097308 
## [17] train-rmse:1.674629+0.017773    test-rmse:1.840153+0.098376 
## [18] train-rmse:1.653281+0.019480    test-rmse:1.826919+0.106660 
## [19] train-rmse:1.630920+0.020320    test-rmse:1.817617+0.114139 
## [20] train-rmse:1.611530+0.022076    test-rmse:1.805119+0.104546 
## [21] train-rmse:1.591227+0.022828    test-rmse:1.788761+0.114089 
## [22] train-rmse:1.565987+0.027614    test-rmse:1.766345+0.081203 
## [23] train-rmse:1.544055+0.034688    test-rmse:1.742914+0.075317 
## [24] train-rmse:1.527861+0.035276    test-rmse:1.735488+0.076351 
## [25] train-rmse:1.512199+0.036852    test-rmse:1.721105+0.078665 
## [26] train-rmse:1.492008+0.044820    test-rmse:1.707633+0.078903 
## [27] train-rmse:1.477177+0.045931    test-rmse:1.692137+0.076462 
## [28] train-rmse:1.462143+0.045556    test-rmse:1.678155+0.071783 
## [29] train-rmse:1.446709+0.045369    test-rmse:1.671322+0.082174 
## [30] train-rmse:1.431188+0.045828    test-rmse:1.664267+0.083014 
## [31] train-rmse:1.418602+0.045809    test-rmse:1.646606+0.088294 
## [32] train-rmse:1.405696+0.047079    test-rmse:1.638281+0.091024 
## [33] train-rmse:1.387423+0.046542    test-rmse:1.625803+0.094767 
## [34] train-rmse:1.375736+0.047310    test-rmse:1.615635+0.095298 
## [35] train-rmse:1.363599+0.048706    test-rmse:1.608707+0.100362 
## [36] train-rmse:1.346257+0.054596    test-rmse:1.591009+0.082099 
## [37] train-rmse:1.334427+0.056406    test-rmse:1.582018+0.086326 
## [38] train-rmse:1.324139+0.056297    test-rmse:1.573029+0.084969 
## [39] train-rmse:1.309723+0.061430    test-rmse:1.560900+0.085423 
## [40] train-rmse:1.298550+0.062077    test-rmse:1.555671+0.084838 
## [41] train-rmse:1.288888+0.062523    test-rmse:1.546126+0.086503 
## [42] train-rmse:1.276258+0.066427    test-rmse:1.531420+0.072305 
## [43] train-rmse:1.266675+0.066407    test-rmse:1.520251+0.081594 
## [44] train-rmse:1.256544+0.065714    test-rmse:1.511418+0.082543 
## [45] train-rmse:1.247746+0.065691    test-rmse:1.506749+0.088341 
## [46] train-rmse:1.239271+0.065636    test-rmse:1.498520+0.091727 
## [47] train-rmse:1.231431+0.066182    test-rmse:1.495958+0.094101 
## [48] train-rmse:1.222267+0.065839    test-rmse:1.489895+0.093980 
## [49] train-rmse:1.214309+0.067062    test-rmse:1.482176+0.094063 
## [50] train-rmse:1.204815+0.065520    test-rmse:1.478705+0.096149 
## [51] train-rmse:1.193464+0.071533    test-rmse:1.470480+0.096134 
## [52] train-rmse:1.185921+0.071706    test-rmse:1.465042+0.095858 
## [53] train-rmse:1.179262+0.071684    test-rmse:1.467066+0.095613 
## [54] train-rmse:1.167350+0.071507    test-rmse:1.455488+0.097481 
## [55] train-rmse:1.157902+0.069589    test-rmse:1.453141+0.098116 
## [56] train-rmse:1.150381+0.069844    test-rmse:1.451478+0.096853 
## [57] train-rmse:1.140969+0.072181    test-rmse:1.436748+0.094139 
## [58] train-rmse:1.132142+0.076654    test-rmse:1.427651+0.092832 
## [59] train-rmse:1.125291+0.077141    test-rmse:1.421837+0.094566 
## [60] train-rmse:1.119173+0.076621    test-rmse:1.416377+0.099053 
## [61] train-rmse:1.106011+0.068870    test-rmse:1.396401+0.094534 
## [62] train-rmse:1.100110+0.069643    test-rmse:1.395759+0.092609 
## [63] train-rmse:1.091360+0.070329    test-rmse:1.389441+0.090519 
## [64] train-rmse:1.085810+0.070312    test-rmse:1.387450+0.092232 
## [65] train-rmse:1.080070+0.069801    test-rmse:1.382229+0.097291 
## [66] train-rmse:1.073183+0.071632    test-rmse:1.378142+0.099398 
## [67] train-rmse:1.067875+0.070954    test-rmse:1.373246+0.100762 
## [68] train-rmse:1.059840+0.068163    test-rmse:1.368553+0.104756 
## [69] train-rmse:1.043146+0.059781    test-rmse:1.360817+0.111713 
## [70] train-rmse:1.038144+0.059347    test-rmse:1.354134+0.117774 
## [71] train-rmse:1.033556+0.059853    test-rmse:1.349393+0.118858 
## [72] train-rmse:1.028952+0.059979    test-rmse:1.349151+0.121831 
## [73] train-rmse:1.022748+0.059300    test-rmse:1.344790+0.119550 
## [74] train-rmse:1.018008+0.060211    test-rmse:1.343838+0.121108 
## [75] train-rmse:1.013164+0.061947    test-rmse:1.342941+0.120079 
## [76] train-rmse:1.008909+0.061808    test-rmse:1.338999+0.122633 
## [77] train-rmse:1.004402+0.061343    test-rmse:1.337915+0.123716 
## [78] train-rmse:0.993170+0.058228    test-rmse:1.324469+0.124491 
## [79] train-rmse:0.988269+0.057702    test-rmse:1.324640+0.128764 
## [80] train-rmse:0.981511+0.057549    test-rmse:1.317705+0.131122 
## [81] train-rmse:0.976616+0.058655    test-rmse:1.317250+0.130322 
## [82] train-rmse:0.972975+0.059059    test-rmse:1.314427+0.130345 
## [83] train-rmse:0.969743+0.059055    test-rmse:1.313682+0.130150 
## [84] train-rmse:0.966377+0.059272    test-rmse:1.310841+0.130654 
## [85] train-rmse:0.960384+0.056228    test-rmse:1.308495+0.130513 
## [86] train-rmse:0.956190+0.057436    test-rmse:1.305285+0.133037 
## [87] train-rmse:0.952456+0.057439    test-rmse:1.304045+0.133874 
## [88] train-rmse:0.945828+0.057349    test-rmse:1.299504+0.132911 
## [89] train-rmse:0.939976+0.060279    test-rmse:1.297298+0.132758 
## [90] train-rmse:0.931984+0.055340    test-rmse:1.294052+0.135957 
## [91] train-rmse:0.927309+0.054163    test-rmse:1.292732+0.138948 
## [92] train-rmse:0.924052+0.054242    test-rmse:1.292555+0.138578 
## [93] train-rmse:0.921279+0.054313    test-rmse:1.290522+0.138466 
## [94] train-rmse:0.918263+0.054377    test-rmse:1.287678+0.140553 
## [95] train-rmse:0.915400+0.054008    test-rmse:1.285175+0.142528 
## [96] train-rmse:0.912735+0.054450    test-rmse:1.285127+0.144930 
## [97] train-rmse:0.909747+0.054580    test-rmse:1.287509+0.144414 
## [98] train-rmse:0.899231+0.047871    test-rmse:1.268868+0.144169 
## [99] train-rmse:0.894532+0.047203    test-rmse:1.267077+0.145276 
## [100]    train-rmse:0.889988+0.048976    test-rmse:1.264831+0.144786 
## [101]    train-rmse:0.887240+0.048761    test-rmse:1.264227+0.146123 
## [102]    train-rmse:0.885061+0.048785    test-rmse:1.262150+0.146603 
## [103]    train-rmse:0.882797+0.048951    test-rmse:1.258721+0.147652 
## [104]    train-rmse:0.880708+0.049132    test-rmse:1.256693+0.146716 
## [105]    train-rmse:0.876574+0.051508    test-rmse:1.257832+0.144644 
## [106]    train-rmse:0.873266+0.051634    test-rmse:1.255884+0.144343 
## [107]    train-rmse:0.870763+0.051781    test-rmse:1.255068+0.146062 
## [108]    train-rmse:0.868403+0.051405    test-rmse:1.255374+0.147747 
## [109]    train-rmse:0.862818+0.047987    test-rmse:1.244921+0.150038 
## [110]    train-rmse:0.861108+0.048053    test-rmse:1.245071+0.148867 
## [111]    train-rmse:0.858875+0.048138    test-rmse:1.242932+0.150136 
## [112]    train-rmse:0.856704+0.048222    test-rmse:1.242060+0.150542 
## [113]    train-rmse:0.854488+0.047802    test-rmse:1.241912+0.150644 
## [114]    train-rmse:0.851002+0.047198    test-rmse:1.241015+0.150470 
## [115]    train-rmse:0.849198+0.046983    test-rmse:1.239867+0.151338 
## [116]    train-rmse:0.846028+0.046037    test-rmse:1.240464+0.156321 
## [117]    train-rmse:0.843617+0.046567    test-rmse:1.241474+0.156529 
## [118]    train-rmse:0.842143+0.046665    test-rmse:1.240196+0.156309 
## [119]    train-rmse:0.840178+0.046544    test-rmse:1.238059+0.156369 
## [120]    train-rmse:0.835913+0.049234    test-rmse:1.235399+0.153107 
## [121]    train-rmse:0.833208+0.050362    test-rmse:1.235673+0.152833 
## [122]    train-rmse:0.831669+0.050350    test-rmse:1.235295+0.152797 
## [123]    train-rmse:0.829869+0.050288    test-rmse:1.234968+0.153378 
## [124]    train-rmse:0.828451+0.050078    test-rmse:1.234088+0.153455 
## [125]    train-rmse:0.826757+0.050099    test-rmse:1.234372+0.153113 
## [126]    train-rmse:0.823900+0.049106    test-rmse:1.235204+0.154747 
## [127]    train-rmse:0.822506+0.049178    test-rmse:1.235645+0.154907 
## [128]    train-rmse:0.820995+0.049160    test-rmse:1.234844+0.154376 
## [129]    train-rmse:0.818901+0.049937    test-rmse:1.234334+0.155446 
## [130]    train-rmse:0.817427+0.050066    test-rmse:1.233262+0.155653 
## [131]    train-rmse:0.815752+0.049753    test-rmse:1.232725+0.154238 
## [132]    train-rmse:0.814092+0.049426    test-rmse:1.232287+0.155459 
## [133]    train-rmse:0.812509+0.049596    test-rmse:1.233680+0.156042 
## [134]    train-rmse:0.807409+0.046155    test-rmse:1.234213+0.157125 
## [135]    train-rmse:0.805730+0.046767    test-rmse:1.232674+0.158144 
## [136]    train-rmse:0.804409+0.047223    test-rmse:1.231371+0.158664 
## [137]    train-rmse:0.802875+0.047245    test-rmse:1.231082+0.159590 
## [138]    train-rmse:0.800515+0.046638    test-rmse:1.230229+0.159148 
## [139]    train-rmse:0.798651+0.046242    test-rmse:1.229452+0.159365 
## [140]    train-rmse:0.792573+0.047062    test-rmse:1.225710+0.159108 
## [141]    train-rmse:0.791236+0.046930    test-rmse:1.225473+0.159681 
## [142]    train-rmse:0.789999+0.046753    test-rmse:1.224723+0.160473 
## [143]    train-rmse:0.789063+0.046715    test-rmse:1.224167+0.160344 
## [144]    train-rmse:0.786036+0.048732    test-rmse:1.220705+0.159137 
## [145]    train-rmse:0.784847+0.049330    test-rmse:1.220673+0.158612 
## [146]    train-rmse:0.783818+0.049204    test-rmse:1.220824+0.158262 
## [147]    train-rmse:0.782577+0.049155    test-rmse:1.220818+0.158089 
## [148]    train-rmse:0.781507+0.049020    test-rmse:1.220211+0.158098 
## [149]    train-rmse:0.780164+0.048983    test-rmse:1.219229+0.159298 
## [150]    train-rmse:0.778979+0.049145    test-rmse:1.219178+0.157659 
## [151]    train-rmse:0.777624+0.049905    test-rmse:1.218060+0.156719 
## [152]    train-rmse:0.775780+0.049537    test-rmse:1.217669+0.157099 
## [153]    train-rmse:0.773802+0.049420    test-rmse:1.217863+0.157012 
## [154]    train-rmse:0.771781+0.048562    test-rmse:1.219242+0.156500 
## [155]    train-rmse:0.770792+0.048659    test-rmse:1.219748+0.157505 
## [156]    train-rmse:0.769387+0.048542    test-rmse:1.221294+0.159756 
## [157]    train-rmse:0.767308+0.048149    test-rmse:1.224377+0.161312 
## [158]    train-rmse:0.765948+0.048436    test-rmse:1.223767+0.161400 
## Stopping. Best iteration:
## [152]    train-rmse:0.775780+0.049537    test-rmse:1.217669+0.157099
## 
## 58 
## [1]  train-rmse:7.330158+0.053125    test-rmse:7.326849+0.255100 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.602812+0.037075    test-rmse:5.601525+0.245347 
## [3]  train-rmse:4.361028+0.026937    test-rmse:4.371819+0.221021 
## [4]  train-rmse:3.474439+0.022906    test-rmse:3.501569+0.169901 
## [5]  train-rmse:2.852313+0.021270    test-rmse:2.899095+0.140589 
## [6]  train-rmse:2.420799+0.015361    test-rmse:2.496526+0.126303 
## [7]  train-rmse:2.125725+0.014109    test-rmse:2.215932+0.081434 
## [8]  train-rmse:1.927079+0.015436    test-rmse:2.058916+0.073592 
## [9]  train-rmse:1.792655+0.019301    test-rmse:1.941982+0.070481 
## [10] train-rmse:1.699984+0.023348    test-rmse:1.862827+0.057162 
## [11] train-rmse:1.622053+0.022087    test-rmse:1.810834+0.064436 
## [12] train-rmse:1.567079+0.024604    test-rmse:1.775986+0.077747 
## [13] train-rmse:1.520490+0.022873    test-rmse:1.744990+0.070568 
## [14] train-rmse:1.477998+0.024998    test-rmse:1.705897+0.067928 
## [15] train-rmse:1.441539+0.022058    test-rmse:1.673070+0.069365 
## [16] train-rmse:1.410372+0.023788    test-rmse:1.652193+0.067194 
## [17] train-rmse:1.379688+0.025762    test-rmse:1.638276+0.080910 
## [18] train-rmse:1.351978+0.024902    test-rmse:1.618216+0.087291 
## [19] train-rmse:1.327389+0.023369    test-rmse:1.597089+0.091773 
## [20] train-rmse:1.303511+0.024888    test-rmse:1.581334+0.088423 
## [21] train-rmse:1.282370+0.027434    test-rmse:1.567811+0.085678 
## [22] train-rmse:1.258066+0.028069    test-rmse:1.546336+0.091399 
## [23] train-rmse:1.235707+0.026019    test-rmse:1.536748+0.092827 
## [24] train-rmse:1.214330+0.026486    test-rmse:1.523800+0.102924 
## [25] train-rmse:1.193797+0.022097    test-rmse:1.510836+0.113236 
## [26] train-rmse:1.171501+0.023643    test-rmse:1.502925+0.110341 
## [27] train-rmse:1.154868+0.024289    test-rmse:1.491119+0.119584 
## [28] train-rmse:1.134331+0.025218    test-rmse:1.475459+0.118299 
## [29] train-rmse:1.114776+0.028581    test-rmse:1.460994+0.121062 
## [30] train-rmse:1.097584+0.032299    test-rmse:1.454022+0.120224 
## [31] train-rmse:1.081878+0.032279    test-rmse:1.443895+0.120744 
## [32] train-rmse:1.067811+0.034016    test-rmse:1.438405+0.120402 
## [33] train-rmse:1.055793+0.033555    test-rmse:1.431516+0.119625 
## [34] train-rmse:1.041720+0.031601    test-rmse:1.424775+0.126412 
## [35] train-rmse:1.026835+0.033623    test-rmse:1.414404+0.125671 
## [36] train-rmse:1.012709+0.034297    test-rmse:1.397491+0.128728 
## [37] train-rmse:0.998067+0.031613    test-rmse:1.388699+0.134429 
## [38] train-rmse:0.984931+0.029222    test-rmse:1.382306+0.138196 
## [39] train-rmse:0.972749+0.030634    test-rmse:1.375452+0.136927 
## [40] train-rmse:0.957787+0.028860    test-rmse:1.358786+0.126997 
## [41] train-rmse:0.947626+0.029183    test-rmse:1.354474+0.126061 
## [42] train-rmse:0.936325+0.030464    test-rmse:1.347869+0.127567 
## [43] train-rmse:0.923803+0.031796    test-rmse:1.343915+0.121988 
## [44] train-rmse:0.912045+0.032611    test-rmse:1.339600+0.125505 
## [45] train-rmse:0.902768+0.032125    test-rmse:1.338957+0.135083 
## [46] train-rmse:0.890359+0.027526    test-rmse:1.336760+0.137737 
## [47] train-rmse:0.881199+0.028783    test-rmse:1.331536+0.140616 
## [48] train-rmse:0.872715+0.027858    test-rmse:1.327744+0.137382 
## [49] train-rmse:0.862968+0.028688    test-rmse:1.321688+0.143398 
## [50] train-rmse:0.851281+0.025844    test-rmse:1.314811+0.148412 
## [51] train-rmse:0.842571+0.028397    test-rmse:1.314123+0.144540 
## [52] train-rmse:0.833717+0.026783    test-rmse:1.310351+0.145358 
## [53] train-rmse:0.827064+0.026147    test-rmse:1.307995+0.147443 
## [54] train-rmse:0.821267+0.026488    test-rmse:1.307204+0.146755 
## [55] train-rmse:0.812858+0.029093    test-rmse:1.303400+0.146405 
## [56] train-rmse:0.806378+0.029188    test-rmse:1.300946+0.147588 
## [57] train-rmse:0.799630+0.028543    test-rmse:1.301599+0.149321 
## [58] train-rmse:0.791728+0.028388    test-rmse:1.297004+0.148987 
## [59] train-rmse:0.782610+0.026110    test-rmse:1.286094+0.145232 
## [60] train-rmse:0.774079+0.027072    test-rmse:1.281949+0.144661 
## [61] train-rmse:0.769414+0.026921    test-rmse:1.281939+0.143956 
## [62] train-rmse:0.764330+0.026984    test-rmse:1.280156+0.144445 
## [63] train-rmse:0.758379+0.027386    test-rmse:1.280780+0.146494 
## [64] train-rmse:0.748992+0.027824    test-rmse:1.274800+0.147713 
## [65] train-rmse:0.742563+0.028193    test-rmse:1.266949+0.145598 
## [66] train-rmse:0.736591+0.027976    test-rmse:1.265417+0.147032 
## [67] train-rmse:0.731583+0.028464    test-rmse:1.262355+0.153011 
## [68] train-rmse:0.726589+0.027893    test-rmse:1.261077+0.153585 
## [69] train-rmse:0.721300+0.027195    test-rmse:1.263937+0.151243 
## [70] train-rmse:0.717682+0.026893    test-rmse:1.263157+0.151428 
## [71] train-rmse:0.713002+0.027720    test-rmse:1.261480+0.151532 
## [72] train-rmse:0.708573+0.027450    test-rmse:1.261093+0.151533 
## [73] train-rmse:0.703660+0.029683    test-rmse:1.261418+0.151450 
## [74] train-rmse:0.696922+0.028605    test-rmse:1.257587+0.157418 
## [75] train-rmse:0.692382+0.029296    test-rmse:1.260931+0.156665 
## [76] train-rmse:0.686734+0.029271    test-rmse:1.257534+0.157579 
## [77] train-rmse:0.677899+0.029124    test-rmse:1.248959+0.152762 
## [78] train-rmse:0.673866+0.029074    test-rmse:1.249521+0.153111 
## [79] train-rmse:0.668706+0.029441    test-rmse:1.245365+0.152651 
## [80] train-rmse:0.663688+0.027953    test-rmse:1.245211+0.151734 
## [81] train-rmse:0.659651+0.026404    test-rmse:1.245335+0.152761 
## [82] train-rmse:0.656672+0.026392    test-rmse:1.243194+0.151985 
## [83] train-rmse:0.653246+0.026471    test-rmse:1.242849+0.153103 
## [84] train-rmse:0.649184+0.027934    test-rmse:1.241716+0.152409 
## [85] train-rmse:0.646417+0.027390    test-rmse:1.242098+0.152221 
## [86] train-rmse:0.643894+0.027928    test-rmse:1.241533+0.151104 
## [87] train-rmse:0.640803+0.027640    test-rmse:1.240807+0.150480 
## [88] train-rmse:0.637772+0.026921    test-rmse:1.239990+0.149337 
## [89] train-rmse:0.634886+0.027281    test-rmse:1.234931+0.148364 
## [90] train-rmse:0.630531+0.028682    test-rmse:1.232830+0.147084 
## [91] train-rmse:0.625982+0.029596    test-rmse:1.234330+0.146264 
## [92] train-rmse:0.623190+0.029831    test-rmse:1.234937+0.145576 
## [93] train-rmse:0.620724+0.029459    test-rmse:1.234225+0.145785 
## [94] train-rmse:0.616743+0.028940    test-rmse:1.230491+0.145391 
## [95] train-rmse:0.614324+0.029672    test-rmse:1.228567+0.146445 
## [96] train-rmse:0.611910+0.030192    test-rmse:1.229887+0.144460 
## [97] train-rmse:0.609222+0.031478    test-rmse:1.230757+0.145659 
## [98] train-rmse:0.607444+0.032001    test-rmse:1.229831+0.145034 
## [99] train-rmse:0.604594+0.031899    test-rmse:1.230215+0.146666 
## [100]    train-rmse:0.602385+0.032232    test-rmse:1.230765+0.146207 
## [101]    train-rmse:0.600598+0.032088    test-rmse:1.231186+0.145839 
## Stopping. Best iteration:
## [95] train-rmse:0.614324+0.029672    test-rmse:1.228567+0.146445
## 
## 59 
## [1]  train-rmse:7.324617+0.053692    test-rmse:7.321514+0.256872 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.589787+0.039233    test-rmse:5.599891+0.239852 
## [3]  train-rmse:4.338941+0.028741    test-rmse:4.363274+0.208873 
## [4]  train-rmse:3.431830+0.020827    test-rmse:3.466378+0.173174 
## [5]  train-rmse:2.781433+0.013788    test-rmse:2.844909+0.153111 
## [6]  train-rmse:2.329618+0.014403    test-rmse:2.414963+0.125221 
## [7]  train-rmse:2.012359+0.013220    test-rmse:2.142601+0.107005 
## [8]  train-rmse:1.797700+0.014446    test-rmse:1.955004+0.091625 
## [9]  train-rmse:1.642936+0.010251    test-rmse:1.824592+0.064634 
## [10] train-rmse:1.529342+0.011344    test-rmse:1.748191+0.071077 
## [11] train-rmse:1.447509+0.012965    test-rmse:1.687977+0.070954 
## [12] train-rmse:1.380635+0.012639    test-rmse:1.634285+0.067986 
## [13] train-rmse:1.330104+0.012447    test-rmse:1.601290+0.066787 
## [14] train-rmse:1.286259+0.011165    test-rmse:1.560284+0.066491 
## [15] train-rmse:1.244717+0.014584    test-rmse:1.530236+0.081320 
## [16] train-rmse:1.209941+0.014896    test-rmse:1.516323+0.082352 
## [17] train-rmse:1.176373+0.012707    test-rmse:1.495040+0.083913 
## [18] train-rmse:1.145030+0.013011    test-rmse:1.467314+0.104667 
## [19] train-rmse:1.116214+0.014485    test-rmse:1.447901+0.105348 
## [20] train-rmse:1.089775+0.013669    test-rmse:1.433016+0.101605 
## [21] train-rmse:1.063495+0.011749    test-rmse:1.423714+0.108180 
## [22] train-rmse:1.040649+0.014702    test-rmse:1.410601+0.112549 
## [23] train-rmse:1.016955+0.010322    test-rmse:1.393258+0.115750 
## [24] train-rmse:0.992076+0.009615    test-rmse:1.385308+0.114803 
## [25] train-rmse:0.972810+0.007996    test-rmse:1.372125+0.122661 
## [26] train-rmse:0.950232+0.009504    test-rmse:1.362769+0.129797 
## [27] train-rmse:0.932140+0.011188    test-rmse:1.356356+0.128914 
## [28] train-rmse:0.914810+0.011309    test-rmse:1.351319+0.130657 
## [29] train-rmse:0.899196+0.011515    test-rmse:1.337450+0.137771 
## [30] train-rmse:0.882036+0.009136    test-rmse:1.331380+0.137503 
## [31] train-rmse:0.864520+0.011463    test-rmse:1.326656+0.135961 
## [32] train-rmse:0.849276+0.012551    test-rmse:1.319131+0.143079 
## [33] train-rmse:0.835166+0.014390    test-rmse:1.312679+0.146279 
## [34] train-rmse:0.821198+0.011525    test-rmse:1.306248+0.149458 
## [35] train-rmse:0.804551+0.009828    test-rmse:1.302567+0.153401 
## [36] train-rmse:0.791313+0.009672    test-rmse:1.299540+0.154719 
## [37] train-rmse:0.779514+0.011046    test-rmse:1.292036+0.153649 
## [38] train-rmse:0.767371+0.011872    test-rmse:1.285319+0.152086 
## [39] train-rmse:0.755603+0.011397    test-rmse:1.285994+0.159672 
## [40] train-rmse:0.745798+0.010503    test-rmse:1.282244+0.161873 
## [41] train-rmse:0.734723+0.011476    test-rmse:1.278662+0.163370 
## [42] train-rmse:0.724492+0.011781    test-rmse:1.276200+0.168869 
## [43] train-rmse:0.715549+0.012231    test-rmse:1.273098+0.173433 
## [44] train-rmse:0.708188+0.012916    test-rmse:1.273494+0.171187 
## [45] train-rmse:0.699486+0.014854    test-rmse:1.274539+0.168444 
## [46] train-rmse:0.689832+0.012309    test-rmse:1.271238+0.168453 
## [47] train-rmse:0.680530+0.013379    test-rmse:1.268773+0.167751 
## [48] train-rmse:0.670535+0.014906    test-rmse:1.263273+0.169295 
## [49] train-rmse:0.663811+0.014974    test-rmse:1.263413+0.170584 
## [50] train-rmse:0.655987+0.016028    test-rmse:1.259898+0.169012 
## [51] train-rmse:0.645836+0.017075    test-rmse:1.260317+0.169650 
## [52] train-rmse:0.639740+0.017107    test-rmse:1.257165+0.169572 
## [53] train-rmse:0.630812+0.018749    test-rmse:1.255645+0.169213 
## [54] train-rmse:0.622068+0.019820    test-rmse:1.255143+0.168435 
## [55] train-rmse:0.614231+0.019665    test-rmse:1.246906+0.163752 
## [56] train-rmse:0.608577+0.019229    test-rmse:1.250061+0.161482 
## [57] train-rmse:0.602162+0.017176    test-rmse:1.248742+0.163925 
## [58] train-rmse:0.596691+0.016919    test-rmse:1.246525+0.162157 
## [59] train-rmse:0.591057+0.016924    test-rmse:1.245079+0.163103 
## [60] train-rmse:0.586314+0.017135    test-rmse:1.244266+0.163063 
## [61] train-rmse:0.580950+0.018829    test-rmse:1.243378+0.164530 
## [62] train-rmse:0.575456+0.016865    test-rmse:1.244397+0.163183 
## [63] train-rmse:0.571423+0.017136    test-rmse:1.242805+0.162092 
## [64] train-rmse:0.565229+0.017342    test-rmse:1.242353+0.163089 
## [65] train-rmse:0.560436+0.019191    test-rmse:1.241045+0.163081 
## [66] train-rmse:0.555961+0.019976    test-rmse:1.240279+0.162280 
## [67] train-rmse:0.551623+0.019392    test-rmse:1.236867+0.161645 
## [68] train-rmse:0.543386+0.016875    test-rmse:1.235917+0.159840 
## [69] train-rmse:0.538917+0.015666    test-rmse:1.236826+0.162283 
## [70] train-rmse:0.533751+0.016141    test-rmse:1.236881+0.161278 
## [71] train-rmse:0.529140+0.016070    test-rmse:1.236447+0.161540 
## [72] train-rmse:0.523659+0.017175    test-rmse:1.234994+0.160617 
## [73] train-rmse:0.520375+0.017119    test-rmse:1.234926+0.164697 
## [74] train-rmse:0.516600+0.017925    test-rmse:1.234170+0.163201 
## [75] train-rmse:0.512405+0.017732    test-rmse:1.236102+0.162486 
## [76] train-rmse:0.505889+0.016595    test-rmse:1.236893+0.162256 
## [77] train-rmse:0.500873+0.018333    test-rmse:1.235601+0.162440 
## [78] train-rmse:0.497066+0.019456    test-rmse:1.235146+0.162529 
## [79] train-rmse:0.493207+0.020930    test-rmse:1.235246+0.162312 
## [80] train-rmse:0.489134+0.020965    test-rmse:1.234439+0.162115 
## Stopping. Best iteration:
## [74] train-rmse:0.516600+0.017925    test-rmse:1.234170+0.163201
## 
## 60 
## [1]  train-rmse:7.319946+0.054155    test-rmse:7.321748+0.257577 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.577043+0.039963    test-rmse:5.594723+0.231228 
## [3]  train-rmse:4.311103+0.030027    test-rmse:4.333684+0.202307 
## [4]  train-rmse:3.389877+0.023484    test-rmse:3.423992+0.165746 
## [5]  train-rmse:2.727118+0.017586    test-rmse:2.805640+0.139817 
## [6]  train-rmse:2.255862+0.021126    test-rmse:2.345459+0.108338 
## [7]  train-rmse:1.924645+0.021032    test-rmse:2.068236+0.106430 
## [8]  train-rmse:1.693622+0.026590    test-rmse:1.877859+0.086707 
## [9]  train-rmse:1.518497+0.025929    test-rmse:1.761107+0.079893 
## [10] train-rmse:1.400870+0.026177    test-rmse:1.665831+0.080059 
## [11] train-rmse:1.306046+0.024431    test-rmse:1.583353+0.080835 
## [12] train-rmse:1.233886+0.025884    test-rmse:1.548941+0.083822 
## [13] train-rmse:1.172457+0.024942    test-rmse:1.515242+0.085768 
## [14] train-rmse:1.121191+0.018461    test-rmse:1.485193+0.086880 
## [15] train-rmse:1.075455+0.020396    test-rmse:1.463857+0.103597 
## [16] train-rmse:1.035630+0.022154    test-rmse:1.440015+0.104823 
## [17] train-rmse:1.000252+0.023627    test-rmse:1.421476+0.110039 
## [18] train-rmse:0.965833+0.021953    test-rmse:1.403689+0.118068 
## [19] train-rmse:0.934603+0.018601    test-rmse:1.387845+0.122860 
## [20] train-rmse:0.908385+0.020764    test-rmse:1.370916+0.125823 
## [21] train-rmse:0.885543+0.020645    test-rmse:1.363561+0.123998 
## [22] train-rmse:0.859739+0.023121    test-rmse:1.357274+0.133270 
## [23] train-rmse:0.830361+0.022830    test-rmse:1.332952+0.131342 
## [24] train-rmse:0.810413+0.024008    test-rmse:1.320607+0.138305 
## [25] train-rmse:0.786275+0.025999    test-rmse:1.304189+0.135790 
## [26] train-rmse:0.765164+0.020700    test-rmse:1.298448+0.141320 
## [27] train-rmse:0.747970+0.019323    test-rmse:1.293059+0.139455 
## [28] train-rmse:0.730488+0.019149    test-rmse:1.281792+0.141309 
## [29] train-rmse:0.714608+0.018383    test-rmse:1.280215+0.148682 
## [30] train-rmse:0.699007+0.018420    test-rmse:1.279239+0.150294 
## [31] train-rmse:0.682838+0.019131    test-rmse:1.276920+0.150793 
## [32] train-rmse:0.669810+0.016944    test-rmse:1.265066+0.154651 
## [33] train-rmse:0.658182+0.018652    test-rmse:1.258512+0.154241 
## [34] train-rmse:0.642854+0.018520    test-rmse:1.249450+0.153455 
## [35] train-rmse:0.629972+0.021001    test-rmse:1.250390+0.152085 
## [36] train-rmse:0.618867+0.021653    test-rmse:1.248979+0.154863 
## [37] train-rmse:0.607577+0.020721    test-rmse:1.250187+0.154235 
## [38] train-rmse:0.596254+0.018874    test-rmse:1.248873+0.156996 
## [39] train-rmse:0.584360+0.022087    test-rmse:1.244024+0.156296 
## [40] train-rmse:0.575895+0.022099    test-rmse:1.239763+0.155591 
## [41] train-rmse:0.569450+0.022575    test-rmse:1.239021+0.155184 
## [42] train-rmse:0.561999+0.023803    test-rmse:1.241323+0.157437 
## [43] train-rmse:0.551239+0.022733    test-rmse:1.239721+0.154316 
## [44] train-rmse:0.543961+0.021537    test-rmse:1.240262+0.153370 
## [45] train-rmse:0.533995+0.021981    test-rmse:1.235565+0.156796 
## [46] train-rmse:0.527973+0.022388    test-rmse:1.234875+0.158370 
## [47] train-rmse:0.520147+0.022865    test-rmse:1.233373+0.158162 
## [48] train-rmse:0.510821+0.020888    test-rmse:1.235697+0.156012 
## [49] train-rmse:0.503575+0.019848    test-rmse:1.233510+0.155489 
## [50] train-rmse:0.496927+0.021349    test-rmse:1.233820+0.155568 
## [51] train-rmse:0.491398+0.021368    test-rmse:1.236062+0.156115 
## [52] train-rmse:0.484292+0.022344    test-rmse:1.237427+0.158111 
## [53] train-rmse:0.475905+0.020078    test-rmse:1.234863+0.158237 
## Stopping. Best iteration:
## [47] train-rmse:0.520147+0.022865    test-rmse:1.233373+0.158162
## 
## 61 
## [1]  train-rmse:7.318862+0.054412    test-rmse:7.322546+0.249401 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.569446+0.040835    test-rmse:5.588905+0.226898 
## [3]  train-rmse:4.292926+0.031028    test-rmse:4.324159+0.188218 
## [4]  train-rmse:3.364945+0.028692    test-rmse:3.423808+0.154206 
## [5]  train-rmse:2.681449+0.022174    test-rmse:2.774670+0.140472 
## [6]  train-rmse:2.193948+0.020711    test-rmse:2.325187+0.117880 
## [7]  train-rmse:1.846631+0.018365    test-rmse:2.037956+0.101734 
## [8]  train-rmse:1.599951+0.019203    test-rmse:1.842753+0.098967 
## [9]  train-rmse:1.412613+0.025751    test-rmse:1.692873+0.082229 
## [10] train-rmse:1.273415+0.018060    test-rmse:1.588347+0.083689 
## [11] train-rmse:1.164089+0.023641    test-rmse:1.514345+0.085173 
## [12] train-rmse:1.087066+0.022857    test-rmse:1.460752+0.099505 
## [13] train-rmse:1.026152+0.025103    test-rmse:1.424622+0.100559 
## [14] train-rmse:0.964955+0.029213    test-rmse:1.388556+0.106463 
## [15] train-rmse:0.924444+0.031991    test-rmse:1.373151+0.111541 
## [16] train-rmse:0.883378+0.027985    test-rmse:1.345622+0.118319 
## [17] train-rmse:0.844192+0.028142    test-rmse:1.319595+0.122514 
## [18] train-rmse:0.809014+0.023748    test-rmse:1.312827+0.128869 
## [19] train-rmse:0.778821+0.020001    test-rmse:1.287831+0.140315 
## [20] train-rmse:0.749423+0.023127    test-rmse:1.280886+0.135856 
## [21] train-rmse:0.726257+0.022722    test-rmse:1.268133+0.131898 
## [22] train-rmse:0.704187+0.024522    test-rmse:1.262981+0.136363 
## [23] train-rmse:0.679177+0.021553    test-rmse:1.258634+0.141037 
## [24] train-rmse:0.658866+0.022989    test-rmse:1.250554+0.143641 
## [25] train-rmse:0.642288+0.021895    test-rmse:1.249258+0.143468 
## [26] train-rmse:0.622362+0.022681    test-rmse:1.242255+0.145006 
## [27] train-rmse:0.606134+0.022209    test-rmse:1.237665+0.144221 
## [28] train-rmse:0.591890+0.023065    test-rmse:1.233046+0.142665 
## [29] train-rmse:0.577815+0.021832    test-rmse:1.230750+0.147219 
## [30] train-rmse:0.561946+0.018908    test-rmse:1.228352+0.155986 
## [31] train-rmse:0.548668+0.019793    test-rmse:1.223425+0.156265 
## [32] train-rmse:0.534171+0.023457    test-rmse:1.220832+0.157173 
## [33] train-rmse:0.522538+0.021331    test-rmse:1.217306+0.158479 
## [34] train-rmse:0.511565+0.023238    test-rmse:1.215289+0.158673 
## [35] train-rmse:0.502591+0.022874    test-rmse:1.213388+0.161780 
## [36] train-rmse:0.492052+0.021672    test-rmse:1.211538+0.160616 
## [37] train-rmse:0.480879+0.023730    test-rmse:1.207750+0.161248 
## [38] train-rmse:0.471050+0.023184    test-rmse:1.206635+0.157917 
## [39] train-rmse:0.461225+0.022938    test-rmse:1.206707+0.159559 
## [40] train-rmse:0.453450+0.022679    test-rmse:1.205906+0.159947 
## [41] train-rmse:0.444272+0.021042    test-rmse:1.206120+0.161059 
## [42] train-rmse:0.436657+0.020637    test-rmse:1.204789+0.163171 
## [43] train-rmse:0.429804+0.021306    test-rmse:1.203229+0.160560 
## [44] train-rmse:0.421922+0.021412    test-rmse:1.201338+0.159404 
## [45] train-rmse:0.414029+0.019369    test-rmse:1.202929+0.160793 
## [46] train-rmse:0.405056+0.021869    test-rmse:1.201794+0.160505 
## [47] train-rmse:0.399050+0.023045    test-rmse:1.200376+0.162448 
## [48] train-rmse:0.394716+0.022910    test-rmse:1.200167+0.162021 
## [49] train-rmse:0.389737+0.022713    test-rmse:1.199359+0.163366 
## [50] train-rmse:0.381690+0.021228    test-rmse:1.199040+0.163202 
## [51] train-rmse:0.378241+0.020598    test-rmse:1.197807+0.162782 
## [52] train-rmse:0.369928+0.019965    test-rmse:1.198671+0.162593 
## [53] train-rmse:0.363511+0.021303    test-rmse:1.198906+0.161970 
## [54] train-rmse:0.355183+0.018844    test-rmse:1.197149+0.161738 
## [55] train-rmse:0.349085+0.015932    test-rmse:1.197302+0.163026 
## [56] train-rmse:0.342605+0.013598    test-rmse:1.196871+0.162000 
## [57] train-rmse:0.335913+0.016078    test-rmse:1.196673+0.160923 
## [58] train-rmse:0.331728+0.016440    test-rmse:1.197169+0.159622 
## [59] train-rmse:0.327161+0.016970    test-rmse:1.196809+0.160159 
## [60] train-rmse:0.322300+0.016030    test-rmse:1.196493+0.160167 
## [61] train-rmse:0.317830+0.016074    test-rmse:1.197248+0.159790 
## [62] train-rmse:0.314419+0.016845    test-rmse:1.198472+0.160515 
## [63] train-rmse:0.311425+0.015351    test-rmse:1.199150+0.160908 
## [64] train-rmse:0.307498+0.014130    test-rmse:1.198593+0.160209 
## [65] train-rmse:0.303499+0.013397    test-rmse:1.199356+0.159727 
## [66] train-rmse:0.298282+0.011729    test-rmse:1.197860+0.158578 
## Stopping. Best iteration:
## [60] train-rmse:0.322300+0.016030    test-rmse:1.196493+0.160167
## 
## 62 
## [1]  train-rmse:7.318862+0.054412    test-rmse:7.322546+0.249401 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.566230+0.041302    test-rmse:5.592322+0.216858 
## [3]  train-rmse:4.283992+0.031409    test-rmse:4.323845+0.169368 
## [4]  train-rmse:3.348786+0.030234    test-rmse:3.398454+0.140729 
## [5]  train-rmse:2.651709+0.018546    test-rmse:2.732812+0.142072 
## [6]  train-rmse:2.153559+0.017874    test-rmse:2.279077+0.122683 
## [7]  train-rmse:1.791248+0.022713    test-rmse:1.982844+0.088370 
## [8]  train-rmse:1.525179+0.027460    test-rmse:1.765394+0.076413 
## [9]  train-rmse:1.326920+0.027601    test-rmse:1.601397+0.085982 
## [10] train-rmse:1.175504+0.030066    test-rmse:1.507634+0.080624 
## [11] train-rmse:1.062130+0.024296    test-rmse:1.433796+0.089848 
## [12] train-rmse:0.976327+0.026946    test-rmse:1.382941+0.096560 
## [13] train-rmse:0.903941+0.020498    test-rmse:1.357208+0.112984 
## [14] train-rmse:0.846169+0.020578    test-rmse:1.335198+0.112428 
## [15] train-rmse:0.799342+0.023675    test-rmse:1.315480+0.117276 
## [16] train-rmse:0.756426+0.023126    test-rmse:1.296012+0.123140 
## [17] train-rmse:0.724712+0.020732    test-rmse:1.285940+0.126627 
## [18] train-rmse:0.694216+0.020342    test-rmse:1.272822+0.133010 
## [19] train-rmse:0.668210+0.016040    test-rmse:1.266953+0.140856 
## [20] train-rmse:0.643181+0.016973    test-rmse:1.260935+0.141738 
## [21] train-rmse:0.619843+0.017347    test-rmse:1.253225+0.145054 
## [22] train-rmse:0.592485+0.022107    test-rmse:1.247144+0.146907 
## [23] train-rmse:0.571195+0.020168    test-rmse:1.245091+0.152678 
## [24] train-rmse:0.550633+0.019749    test-rmse:1.244471+0.158068 
## [25] train-rmse:0.530729+0.021947    test-rmse:1.239389+0.156812 
## [26] train-rmse:0.515211+0.021876    test-rmse:1.233060+0.155742 
## [27] train-rmse:0.501452+0.019956    test-rmse:1.228388+0.156987 
## [28] train-rmse:0.487126+0.022928    test-rmse:1.227285+0.156223 
## [29] train-rmse:0.472732+0.019613    test-rmse:1.223171+0.161395 
## [30] train-rmse:0.461316+0.020707    test-rmse:1.222125+0.163495 
## [31] train-rmse:0.447965+0.024468    test-rmse:1.219590+0.163725 
## [32] train-rmse:0.436484+0.024506    test-rmse:1.219955+0.164419 
## [33] train-rmse:0.423527+0.022110    test-rmse:1.218577+0.164879 
## [34] train-rmse:0.408090+0.018885    test-rmse:1.213735+0.164830 
## [35] train-rmse:0.397291+0.016373    test-rmse:1.212832+0.163762 
## [36] train-rmse:0.386731+0.018351    test-rmse:1.211406+0.163386 
## [37] train-rmse:0.377501+0.017653    test-rmse:1.211143+0.163211 
## [38] train-rmse:0.370297+0.016765    test-rmse:1.210180+0.165782 
## [39] train-rmse:0.359426+0.016446    test-rmse:1.209542+0.164830 
## [40] train-rmse:0.350387+0.012621    test-rmse:1.209976+0.168757 
## [41] train-rmse:0.343288+0.013370    test-rmse:1.209686+0.169169 
## [42] train-rmse:0.335007+0.015388    test-rmse:1.208583+0.167442 
## [43] train-rmse:0.329026+0.016520    test-rmse:1.208130+0.168117 
## [44] train-rmse:0.316835+0.012942    test-rmse:1.207959+0.168906 
## [45] train-rmse:0.311552+0.014191    test-rmse:1.207669+0.170116 
## [46] train-rmse:0.306210+0.014028    test-rmse:1.208517+0.169414 
## [47] train-rmse:0.298889+0.012691    test-rmse:1.209086+0.169725 
## [48] train-rmse:0.292980+0.012663    test-rmse:1.208938+0.170109 
## [49] train-rmse:0.288155+0.014052    test-rmse:1.209251+0.170219 
## [50] train-rmse:0.282783+0.013593    test-rmse:1.210768+0.169262 
## [51] train-rmse:0.275904+0.011491    test-rmse:1.209616+0.168000 
## Stopping. Best iteration:
## [45] train-rmse:0.311552+0.014191    test-rmse:1.207669+0.170116
## 
## 63 
## [1]  train-rmse:7.201810+0.049632    test-rmse:7.197702+0.265536 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.457991+0.031908    test-rmse:5.457369+0.261221 
## [3]  train-rmse:4.266353+0.021652    test-rmse:4.265121+0.234746 
## [4]  train-rmse:3.477821+0.015423    test-rmse:3.497645+0.191068 
## [5]  train-rmse:2.975860+0.012073    test-rmse:3.003872+0.147272 
## [6]  train-rmse:2.667016+0.012964    test-rmse:2.705606+0.103338 
## [7]  train-rmse:2.477382+0.015022    test-rmse:2.511159+0.067988 
## [8]  train-rmse:2.362439+0.015532    test-rmse:2.400549+0.054486 
## [9]  train-rmse:2.291153+0.016826    test-rmse:2.340440+0.053023 
## [10] train-rmse:2.243564+0.017806    test-rmse:2.292684+0.052599 
## [11] train-rmse:2.209489+0.017975    test-rmse:2.254616+0.061749 
## [12] train-rmse:2.182202+0.018687    test-rmse:2.219310+0.077914 
## [13] train-rmse:2.159601+0.020162    test-rmse:2.202402+0.076736 
## [14] train-rmse:2.139847+0.021975    test-rmse:2.182495+0.073743 
## [15] train-rmse:2.121449+0.023577    test-rmse:2.173391+0.082822 
## [16] train-rmse:2.104415+0.023710    test-rmse:2.166708+0.079823 
## [17] train-rmse:2.088129+0.023471    test-rmse:2.158338+0.085502 
## [18] train-rmse:2.073114+0.024055    test-rmse:2.145985+0.089749 
## [19] train-rmse:2.059054+0.024567    test-rmse:2.134264+0.094278 
## [20] train-rmse:2.045059+0.025377    test-rmse:2.113529+0.093512 
## [21] train-rmse:2.031371+0.026485    test-rmse:2.094364+0.090883 
## [22] train-rmse:2.018111+0.027693    test-rmse:2.074784+0.092328 
## [23] train-rmse:2.005365+0.028734    test-rmse:2.064675+0.088766 
## [24] train-rmse:1.993068+0.029102    test-rmse:2.056348+0.095766 
## [25] train-rmse:1.980758+0.029188    test-rmse:2.051714+0.096874 
## [26] train-rmse:1.968843+0.029434    test-rmse:2.041760+0.092855 
## [27] train-rmse:1.957365+0.029828    test-rmse:2.037709+0.094115 
## [28] train-rmse:1.946374+0.030346    test-rmse:2.030917+0.095285 
## [29] train-rmse:1.935627+0.030647    test-rmse:2.020805+0.096192 
## [30] train-rmse:1.925248+0.030950    test-rmse:2.011733+0.097129 
## [31] train-rmse:1.915286+0.031601    test-rmse:2.005879+0.095319 
## [32] train-rmse:1.905566+0.032342    test-rmse:1.996085+0.085204 
## [33] train-rmse:1.895883+0.033093    test-rmse:1.987623+0.083398 
## [34] train-rmse:1.886756+0.033858    test-rmse:1.978705+0.075986 
## [35] train-rmse:1.877632+0.034384    test-rmse:1.968658+0.076457 
## [36] train-rmse:1.868616+0.034738    test-rmse:1.952108+0.080339 
## [37] train-rmse:1.859748+0.035450    test-rmse:1.949446+0.082076 
## [38] train-rmse:1.851011+0.036183    test-rmse:1.936917+0.082708 
## [39] train-rmse:1.842467+0.036780    test-rmse:1.932619+0.085237 
## [40] train-rmse:1.834187+0.037365    test-rmse:1.922370+0.085767 
## [41] train-rmse:1.826036+0.037984    test-rmse:1.916373+0.088648 
## [42] train-rmse:1.818045+0.038560    test-rmse:1.908216+0.091770 
## [43] train-rmse:1.810120+0.039159    test-rmse:1.901459+0.090044 
## [44] train-rmse:1.802358+0.039759    test-rmse:1.895202+0.090250 
## [45] train-rmse:1.794826+0.039948    test-rmse:1.892880+0.096644 
## [46] train-rmse:1.787431+0.040251    test-rmse:1.885911+0.097224 
## [47] train-rmse:1.780380+0.040561    test-rmse:1.883652+0.099962 
## [48] train-rmse:1.773073+0.041064    test-rmse:1.877818+0.098449 
## [49] train-rmse:1.766214+0.041300    test-rmse:1.874341+0.097366 
## [50] train-rmse:1.759473+0.041580    test-rmse:1.868340+0.092157 
## [51] train-rmse:1.752798+0.041836    test-rmse:1.863519+0.092875 
## [52] train-rmse:1.746207+0.042168    test-rmse:1.860920+0.094216 
## [53] train-rmse:1.739651+0.042560    test-rmse:1.854958+0.097028 
## [54] train-rmse:1.733380+0.042889    test-rmse:1.847013+0.098461 
## [55] train-rmse:1.726999+0.043271    test-rmse:1.842459+0.095282 
## [56] train-rmse:1.720854+0.043592    test-rmse:1.836947+0.098467 
## [57] train-rmse:1.714858+0.043908    test-rmse:1.827165+0.096196 
## [58] train-rmse:1.708972+0.044317    test-rmse:1.820172+0.097751 
## [59] train-rmse:1.703320+0.044690    test-rmse:1.812655+0.097429 
## [60] train-rmse:1.697783+0.045022    test-rmse:1.809491+0.095617 
## [61] train-rmse:1.692265+0.045300    test-rmse:1.807441+0.096802 
## [62] train-rmse:1.686826+0.045571    test-rmse:1.802114+0.099288 
## [63] train-rmse:1.681477+0.045853    test-rmse:1.798773+0.099169 
## [64] train-rmse:1.676028+0.046130    test-rmse:1.793340+0.101601 
## [65] train-rmse:1.670733+0.046505    test-rmse:1.787201+0.105687 
## [66] train-rmse:1.665685+0.046677    test-rmse:1.781012+0.108420 
## [67] train-rmse:1.660621+0.046791    test-rmse:1.778869+0.104827 
## [68] train-rmse:1.655621+0.046983    test-rmse:1.775645+0.105880 
## [69] train-rmse:1.650847+0.047152    test-rmse:1.771001+0.104524 
## [70] train-rmse:1.646177+0.047323    test-rmse:1.765899+0.107337 
## [71] train-rmse:1.641559+0.047578    test-rmse:1.765810+0.110475 
## [72] train-rmse:1.636886+0.047925    test-rmse:1.759522+0.109166 
## [73] train-rmse:1.632372+0.048161    test-rmse:1.757513+0.108619 
## [74] train-rmse:1.627956+0.048238    test-rmse:1.753504+0.111658 
## [75] train-rmse:1.623599+0.048407    test-rmse:1.750900+0.112313 
## [76] train-rmse:1.619249+0.048504    test-rmse:1.744252+0.115663 
## [77] train-rmse:1.614889+0.048532    test-rmse:1.734243+0.119497 
## [78] train-rmse:1.610633+0.048684    test-rmse:1.731615+0.120227 
## [79] train-rmse:1.606578+0.048884    test-rmse:1.728761+0.120324 
## [80] train-rmse:1.602569+0.049106    test-rmse:1.724688+0.121279 
## [81] train-rmse:1.598659+0.049296    test-rmse:1.720965+0.120517 
## [82] train-rmse:1.594678+0.049560    test-rmse:1.721243+0.124553 
## [83] train-rmse:1.590904+0.049617    test-rmse:1.717229+0.124209 
## [84] train-rmse:1.587184+0.049712    test-rmse:1.712506+0.125184 
## [85] train-rmse:1.583494+0.049846    test-rmse:1.712996+0.127890 
## [86] train-rmse:1.579866+0.050018    test-rmse:1.707208+0.128748 
## [87] train-rmse:1.576293+0.050138    test-rmse:1.705040+0.129953 
## [88] train-rmse:1.572684+0.050198    test-rmse:1.702074+0.129616 
## [89] train-rmse:1.569029+0.050296    test-rmse:1.699336+0.130230 
## [90] train-rmse:1.565489+0.050348    test-rmse:1.700635+0.129032 
## [91] train-rmse:1.561957+0.050428    test-rmse:1.704472+0.126988 
## [92] train-rmse:1.558564+0.050556    test-rmse:1.698511+0.126970 
## [93] train-rmse:1.555274+0.050677    test-rmse:1.695152+0.127497 
## [94] train-rmse:1.551980+0.050779    test-rmse:1.687366+0.124975 
## [95] train-rmse:1.548763+0.050881    test-rmse:1.687424+0.124433 
## [96] train-rmse:1.545536+0.051009    test-rmse:1.685846+0.129437 
## [97] train-rmse:1.542308+0.051091    test-rmse:1.684874+0.131491 
## [98] train-rmse:1.539177+0.051152    test-rmse:1.679663+0.135671 
## [99] train-rmse:1.536141+0.051239    test-rmse:1.672297+0.140541 
## [100]    train-rmse:1.533143+0.051320    test-rmse:1.668874+0.142777 
## [101]    train-rmse:1.530194+0.051386    test-rmse:1.664966+0.141162 
## [102]    train-rmse:1.527233+0.051476    test-rmse:1.660263+0.139989 
## [103]    train-rmse:1.524328+0.051527    test-rmse:1.655277+0.141670 
## [104]    train-rmse:1.521467+0.051516    test-rmse:1.652020+0.141763 
## [105]    train-rmse:1.518608+0.051516    test-rmse:1.649667+0.141832 
## [106]    train-rmse:1.515882+0.051559    test-rmse:1.647633+0.139676 
## [107]    train-rmse:1.513244+0.051601    test-rmse:1.645792+0.140635 
## [108]    train-rmse:1.510688+0.051645    test-rmse:1.641047+0.138223 
## [109]    train-rmse:1.508128+0.051672    test-rmse:1.638535+0.137611 
## [110]    train-rmse:1.505593+0.051746    test-rmse:1.637232+0.135790 
## [111]    train-rmse:1.503060+0.051823    test-rmse:1.635803+0.137654 
## [112]    train-rmse:1.500567+0.051882    test-rmse:1.634594+0.139601 
## [113]    train-rmse:1.498139+0.051978    test-rmse:1.633896+0.142260 
## [114]    train-rmse:1.495734+0.052041    test-rmse:1.631737+0.147912 
## [115]    train-rmse:1.493345+0.052160    test-rmse:1.628060+0.146666 
## [116]    train-rmse:1.490952+0.052309    test-rmse:1.623503+0.148480 
## [117]    train-rmse:1.488575+0.052344    test-rmse:1.620690+0.148124 
## [118]    train-rmse:1.486307+0.052392    test-rmse:1.619280+0.148304 
## [119]    train-rmse:1.484034+0.052499    test-rmse:1.617113+0.150041 
## [120]    train-rmse:1.481756+0.052579    test-rmse:1.616856+0.149532 
## [121]    train-rmse:1.479486+0.052659    test-rmse:1.616542+0.150225 
## [122]    train-rmse:1.477226+0.052701    test-rmse:1.615522+0.150034 
## [123]    train-rmse:1.474972+0.052706    test-rmse:1.616280+0.151129 
## [124]    train-rmse:1.472808+0.052705    test-rmse:1.613145+0.151987 
## [125]    train-rmse:1.470639+0.052721    test-rmse:1.615929+0.151917 
## [126]    train-rmse:1.468571+0.052705    test-rmse:1.611297+0.156471 
## [127]    train-rmse:1.466533+0.052697    test-rmse:1.610405+0.159656 
## [128]    train-rmse:1.464540+0.052725    test-rmse:1.606779+0.161348 
## [129]    train-rmse:1.462564+0.052730    test-rmse:1.604200+0.162355 
## [130]    train-rmse:1.460577+0.052777    test-rmse:1.601789+0.161269 
## [131]    train-rmse:1.458609+0.052819    test-rmse:1.598657+0.161252 
## [132]    train-rmse:1.456659+0.052849    test-rmse:1.597626+0.159764 
## [133]    train-rmse:1.454744+0.052889    test-rmse:1.592214+0.157871 
## [134]    train-rmse:1.452899+0.052936    test-rmse:1.589579+0.157543 
## [135]    train-rmse:1.451078+0.052948    test-rmse:1.589201+0.154111 
## [136]    train-rmse:1.449316+0.053001    test-rmse:1.587280+0.157030 
## [137]    train-rmse:1.447605+0.053044    test-rmse:1.585034+0.157978 
## [138]    train-rmse:1.445900+0.053087    test-rmse:1.585678+0.158148 
## [139]    train-rmse:1.444158+0.053131    test-rmse:1.582606+0.158547 
## [140]    train-rmse:1.442458+0.053164    test-rmse:1.579907+0.160191 
## [141]    train-rmse:1.440751+0.053217    test-rmse:1.578176+0.158450 
## [142]    train-rmse:1.439098+0.053237    test-rmse:1.578202+0.157234 
## [143]    train-rmse:1.437454+0.053258    test-rmse:1.576216+0.157003 
## [144]    train-rmse:1.435865+0.053277    test-rmse:1.572462+0.161856 
## [145]    train-rmse:1.434288+0.053302    test-rmse:1.571294+0.160053 
## [146]    train-rmse:1.432732+0.053330    test-rmse:1.572994+0.163428 
## [147]    train-rmse:1.431186+0.053321    test-rmse:1.570656+0.163788 
## [148]    train-rmse:1.429629+0.053315    test-rmse:1.570788+0.162231 
## [149]    train-rmse:1.428076+0.053324    test-rmse:1.569572+0.162514 
## [150]    train-rmse:1.426546+0.053335    test-rmse:1.569470+0.162241 
## [151]    train-rmse:1.425035+0.053365    test-rmse:1.568624+0.161542 
## [152]    train-rmse:1.423598+0.053390    test-rmse:1.568611+0.162366 
## [153]    train-rmse:1.422163+0.053392    test-rmse:1.566586+0.162665 
## [154]    train-rmse:1.420688+0.053337    test-rmse:1.565203+0.162805 
## [155]    train-rmse:1.419229+0.053273    test-rmse:1.563254+0.162725 
## [156]    train-rmse:1.417847+0.053269    test-rmse:1.559263+0.162785 
## [157]    train-rmse:1.416495+0.053275    test-rmse:1.556874+0.162559 
## [158]    train-rmse:1.415142+0.053280    test-rmse:1.555830+0.164058 
## [159]    train-rmse:1.413798+0.053349    test-rmse:1.554291+0.167135 
## [160]    train-rmse:1.412476+0.053390    test-rmse:1.554844+0.166782 
## [161]    train-rmse:1.411180+0.053417    test-rmse:1.553599+0.167913 
## [162]    train-rmse:1.409845+0.053394    test-rmse:1.551068+0.167444 
## [163]    train-rmse:1.408597+0.053384    test-rmse:1.549564+0.167620 
## [164]    train-rmse:1.407354+0.053376    test-rmse:1.548301+0.166849 
## [165]    train-rmse:1.406120+0.053350    test-rmse:1.547123+0.167968 
## [166]    train-rmse:1.404878+0.053316    test-rmse:1.545881+0.167782 
## [167]    train-rmse:1.403674+0.053309    test-rmse:1.544867+0.168242 
## [168]    train-rmse:1.402502+0.053313    test-rmse:1.545200+0.170168 
## [169]    train-rmse:1.401330+0.053334    test-rmse:1.541584+0.169889 
## [170]    train-rmse:1.400156+0.053337    test-rmse:1.540247+0.171505 
## [171]    train-rmse:1.399029+0.053376    test-rmse:1.539303+0.170583 
## [172]    train-rmse:1.397893+0.053402    test-rmse:1.537806+0.171494 
## [173]    train-rmse:1.396796+0.053426    test-rmse:1.536307+0.172061 
## [174]    train-rmse:1.395694+0.053441    test-rmse:1.535476+0.172562 
## [175]    train-rmse:1.394611+0.053472    test-rmse:1.533590+0.171350 
## [176]    train-rmse:1.393522+0.053504    test-rmse:1.534906+0.173353 
## [177]    train-rmse:1.392481+0.053519    test-rmse:1.533963+0.173769 
## [178]    train-rmse:1.391454+0.053529    test-rmse:1.533474+0.174472 
## [179]    train-rmse:1.390417+0.053519    test-rmse:1.535929+0.175121 
## [180]    train-rmse:1.389407+0.053512    test-rmse:1.533745+0.174221 
## [181]    train-rmse:1.388402+0.053488    test-rmse:1.531009+0.176968 
## [182]    train-rmse:1.387431+0.053488    test-rmse:1.530874+0.176241 
## [183]    train-rmse:1.386497+0.053491    test-rmse:1.529936+0.176234 
## [184]    train-rmse:1.385530+0.053497    test-rmse:1.528640+0.176049 
## [185]    train-rmse:1.384569+0.053477    test-rmse:1.528246+0.176656 
## [186]    train-rmse:1.383636+0.053460    test-rmse:1.527199+0.177913 
## [187]    train-rmse:1.382717+0.053456    test-rmse:1.526588+0.176717 
## [188]    train-rmse:1.381804+0.053464    test-rmse:1.526398+0.177211 
## [189]    train-rmse:1.380853+0.053466    test-rmse:1.526630+0.176816 
## [190]    train-rmse:1.379953+0.053443    test-rmse:1.524888+0.176803 
## [191]    train-rmse:1.379040+0.053418    test-rmse:1.524696+0.176732 
## [192]    train-rmse:1.378148+0.053390    test-rmse:1.522727+0.177044 
## [193]    train-rmse:1.377288+0.053364    test-rmse:1.522293+0.177150 
## [194]    train-rmse:1.376437+0.053331    test-rmse:1.521152+0.178226 
## [195]    train-rmse:1.375606+0.053306    test-rmse:1.522673+0.179230 
## [196]    train-rmse:1.374787+0.053302    test-rmse:1.522337+0.180945 
## [197]    train-rmse:1.373968+0.053299    test-rmse:1.521275+0.179070 
## [198]    train-rmse:1.373171+0.053304    test-rmse:1.520528+0.178819 
## [199]    train-rmse:1.372371+0.053309    test-rmse:1.519299+0.177834 
## [200]    train-rmse:1.371582+0.053318    test-rmse:1.518068+0.178452 
## [201]    train-rmse:1.370813+0.053329    test-rmse:1.516337+0.181118 
## [202]    train-rmse:1.370020+0.053344    test-rmse:1.515231+0.181134 
## [203]    train-rmse:1.369255+0.053343    test-rmse:1.512514+0.178780 
## [204]    train-rmse:1.368514+0.053349    test-rmse:1.512672+0.179788 
## [205]    train-rmse:1.367782+0.053343    test-rmse:1.514219+0.179698 
## [206]    train-rmse:1.367058+0.053330    test-rmse:1.514281+0.181978 
## [207]    train-rmse:1.366340+0.053322    test-rmse:1.513640+0.183068 
## [208]    train-rmse:1.365631+0.053310    test-rmse:1.513059+0.183087 
## [209]    train-rmse:1.364919+0.053312    test-rmse:1.514479+0.184906 
## Stopping. Best iteration:
## [203]    train-rmse:1.369255+0.053343    test-rmse:1.512514+0.178780
## 
## 64 
## [1]  train-rmse:7.164372+0.050111    test-rmse:7.160793+0.260016 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.386446+0.036893    test-rmse:5.381956+0.237926 
## [3]  train-rmse:4.159824+0.028925    test-rmse:4.172955+0.211835 
## [4]  train-rmse:3.322261+0.027308    test-rmse:3.346224+0.156828 
## [5]  train-rmse:2.779536+0.024143    test-rmse:2.817318+0.117753 
## [6]  train-rmse:2.419282+0.033409    test-rmse:2.487176+0.082664 
## [7]  train-rmse:2.195386+0.036986    test-rmse:2.283900+0.047779 
## [8]  train-rmse:2.053011+0.033653    test-rmse:2.157263+0.050189 
## [9]  train-rmse:1.960040+0.033742    test-rmse:2.065704+0.059271 
## [10] train-rmse:1.891741+0.036388    test-rmse:2.012463+0.072909 
## [11] train-rmse:1.844461+0.038357    test-rmse:1.981567+0.077884 
## [12] train-rmse:1.803238+0.037067    test-rmse:1.957442+0.098743 
## [13] train-rmse:1.758493+0.033476    test-rmse:1.915132+0.121579 
## [14] train-rmse:1.729669+0.034936    test-rmse:1.886396+0.116357 
## [15] train-rmse:1.698739+0.031794    test-rmse:1.857200+0.102188 
## [16] train-rmse:1.673942+0.034427    test-rmse:1.839659+0.099932 
## [17] train-rmse:1.651366+0.035663    test-rmse:1.822050+0.099817 
## [18] train-rmse:1.630153+0.035817    test-rmse:1.803453+0.098268 
## [19] train-rmse:1.609061+0.035391    test-rmse:1.797679+0.107768 
## [20] train-rmse:1.588133+0.035097    test-rmse:1.788782+0.108094 
## [21] train-rmse:1.567923+0.037074    test-rmse:1.768622+0.105863 
## [22] train-rmse:1.544535+0.040988    test-rmse:1.756634+0.112533 
## [23] train-rmse:1.528251+0.043256    test-rmse:1.738480+0.111881 
## [24] train-rmse:1.496993+0.035541    test-rmse:1.698352+0.085651 
## [25] train-rmse:1.482115+0.036004    test-rmse:1.682538+0.087379 
## [26] train-rmse:1.467603+0.036470    test-rmse:1.672214+0.089657 
## [27] train-rmse:1.452556+0.036277    test-rmse:1.665771+0.084567 
## [28] train-rmse:1.437842+0.037710    test-rmse:1.652552+0.080723 
## [29] train-rmse:1.422280+0.039427    test-rmse:1.638339+0.082922 
## [30] train-rmse:1.403375+0.049284    test-rmse:1.633521+0.078549 
## [31] train-rmse:1.383945+0.048498    test-rmse:1.618411+0.088970 
## [32] train-rmse:1.369341+0.047890    test-rmse:1.612278+0.086873 
## [33] train-rmse:1.355750+0.046976    test-rmse:1.604398+0.091739 
## [34] train-rmse:1.341915+0.049010    test-rmse:1.592432+0.101676 
## [35] train-rmse:1.329517+0.049571    test-rmse:1.575389+0.108828 
## [36] train-rmse:1.312434+0.049483    test-rmse:1.562693+0.111725 
## [37] train-rmse:1.302111+0.049911    test-rmse:1.559358+0.109883 
## [38] train-rmse:1.290997+0.050848    test-rmse:1.547409+0.111411 
## [39] train-rmse:1.280847+0.051809    test-rmse:1.534487+0.111961 
## [40] train-rmse:1.269376+0.051941    test-rmse:1.530177+0.113292 
## [41] train-rmse:1.259474+0.051986    test-rmse:1.522240+0.115023 
## [42] train-rmse:1.250930+0.052668    test-rmse:1.517874+0.113415 
## [43] train-rmse:1.241636+0.054545    test-rmse:1.511972+0.114603 
## [44] train-rmse:1.232328+0.054363    test-rmse:1.505669+0.111900 
## [45] train-rmse:1.223589+0.054712    test-rmse:1.506441+0.116419 
## [46] train-rmse:1.215306+0.054737    test-rmse:1.499723+0.116808 
## [47] train-rmse:1.208041+0.055260    test-rmse:1.492999+0.117415 
## [48] train-rmse:1.200200+0.055578    test-rmse:1.485653+0.121588 
## [49] train-rmse:1.192505+0.055307    test-rmse:1.487313+0.120128 
## [50] train-rmse:1.177631+0.055934    test-rmse:1.479262+0.123369 
## [51] train-rmse:1.168818+0.057952    test-rmse:1.473757+0.123309 
## [52] train-rmse:1.161963+0.058532    test-rmse:1.465104+0.119014 
## [53] train-rmse:1.148366+0.058893    test-rmse:1.443894+0.104848 
## [54] train-rmse:1.141877+0.059063    test-rmse:1.439459+0.107934 
## [55] train-rmse:1.134700+0.059827    test-rmse:1.433145+0.104505 
## [56] train-rmse:1.128953+0.059942    test-rmse:1.429492+0.101373 
## [57] train-rmse:1.113586+0.060527    test-rmse:1.418387+0.106366 
## [58] train-rmse:1.105703+0.062302    test-rmse:1.414167+0.116919 
## [59] train-rmse:1.098514+0.060355    test-rmse:1.407569+0.122997 
## [60] train-rmse:1.092366+0.061984    test-rmse:1.405380+0.122322 
## [61] train-rmse:1.086538+0.061682    test-rmse:1.400322+0.120498 
## [62] train-rmse:1.078496+0.056591    test-rmse:1.396318+0.121725 
## [63] train-rmse:1.068838+0.050708    test-rmse:1.387763+0.122856 
## [64] train-rmse:1.063250+0.051328    test-rmse:1.389405+0.127285 
## [65] train-rmse:1.057674+0.050658    test-rmse:1.384955+0.127781 
## [66] train-rmse:1.050430+0.051129    test-rmse:1.378269+0.126222 
## [67] train-rmse:1.041165+0.050380    test-rmse:1.375416+0.124670 
## [68] train-rmse:1.036586+0.050622    test-rmse:1.373934+0.122889 
## [69] train-rmse:1.030136+0.050619    test-rmse:1.371015+0.121826 
## [70] train-rmse:1.026012+0.050946    test-rmse:1.368907+0.118791 
## [71] train-rmse:1.021524+0.051117    test-rmse:1.365633+0.121462 
## [72] train-rmse:1.014017+0.051078    test-rmse:1.358960+0.121364 
## [73] train-rmse:1.009442+0.050984    test-rmse:1.358357+0.124947 
## [74] train-rmse:1.004829+0.051390    test-rmse:1.357709+0.124278 
## [75] train-rmse:0.996704+0.051369    test-rmse:1.355826+0.127511 
## [76] train-rmse:0.986785+0.050660    test-rmse:1.348482+0.127038 
## [77] train-rmse:0.979018+0.049922    test-rmse:1.340418+0.121345 
## [78] train-rmse:0.974098+0.050939    test-rmse:1.336497+0.126226 
## [79] train-rmse:0.970347+0.051529    test-rmse:1.335701+0.126300 
## [80] train-rmse:0.967094+0.051567    test-rmse:1.331526+0.125875 
## [81] train-rmse:0.963127+0.051070    test-rmse:1.330436+0.127842 
## [82] train-rmse:0.950674+0.037110    test-rmse:1.307307+0.126697 
## [83] train-rmse:0.946925+0.036580    test-rmse:1.308355+0.130819 
## [84] train-rmse:0.942934+0.036351    test-rmse:1.307404+0.129233 
## [85] train-rmse:0.939278+0.037335    test-rmse:1.303192+0.127041 
## [86] train-rmse:0.936080+0.037707    test-rmse:1.299947+0.126992 
## [87] train-rmse:0.932969+0.038676    test-rmse:1.298390+0.125913 
## [88] train-rmse:0.925806+0.045876    test-rmse:1.297180+0.127125 
## [89] train-rmse:0.916576+0.039565    test-rmse:1.281505+0.132895 
## [90] train-rmse:0.913768+0.039916    test-rmse:1.280786+0.133707 
## [91] train-rmse:0.910531+0.039810    test-rmse:1.279249+0.133535 
## [92] train-rmse:0.907515+0.040122    test-rmse:1.281095+0.133030 
## [93] train-rmse:0.903848+0.040366    test-rmse:1.277496+0.130768 
## [94] train-rmse:0.893996+0.037501    test-rmse:1.265926+0.127259 
## [95] train-rmse:0.889159+0.036751    test-rmse:1.263561+0.127298 
## [96] train-rmse:0.885490+0.036699    test-rmse:1.261522+0.127083 
## [97] train-rmse:0.882876+0.036663    test-rmse:1.262239+0.126972 
## [98] train-rmse:0.879580+0.036378    test-rmse:1.260867+0.125372 
## [99] train-rmse:0.876430+0.035808    test-rmse:1.259130+0.125651 
## [100]    train-rmse:0.873892+0.035984    test-rmse:1.261071+0.128044 
## [101]    train-rmse:0.870668+0.036643    test-rmse:1.263367+0.130041 
## [102]    train-rmse:0.868158+0.036868    test-rmse:1.262263+0.130731 
## [103]    train-rmse:0.865733+0.037946    test-rmse:1.260377+0.129290 
## [104]    train-rmse:0.863396+0.038096    test-rmse:1.259215+0.128552 
## [105]    train-rmse:0.860010+0.039763    test-rmse:1.256792+0.124816 
## [106]    train-rmse:0.857486+0.039944    test-rmse:1.256271+0.125076 
## [107]    train-rmse:0.855510+0.040196    test-rmse:1.254526+0.126227 
## [108]    train-rmse:0.852925+0.039614    test-rmse:1.253949+0.126284 
## [109]    train-rmse:0.850398+0.039617    test-rmse:1.253535+0.127280 
## [110]    train-rmse:0.848613+0.039604    test-rmse:1.255296+0.127183 
## [111]    train-rmse:0.840913+0.035270    test-rmse:1.244623+0.131338 
## [112]    train-rmse:0.836609+0.034733    test-rmse:1.241931+0.134034 
## [113]    train-rmse:0.834442+0.034400    test-rmse:1.241375+0.133890 
## [114]    train-rmse:0.830320+0.034283    test-rmse:1.242232+0.136425 
## [115]    train-rmse:0.826134+0.032551    test-rmse:1.238729+0.140099 
## [116]    train-rmse:0.824694+0.032643    test-rmse:1.238704+0.143849 
## [117]    train-rmse:0.822937+0.032699    test-rmse:1.238468+0.143090 
## [118]    train-rmse:0.821104+0.033408    test-rmse:1.237986+0.144396 
## [119]    train-rmse:0.819068+0.033460    test-rmse:1.237335+0.143369 
## [120]    train-rmse:0.817377+0.033962    test-rmse:1.238318+0.144296 
## [121]    train-rmse:0.815098+0.034468    test-rmse:1.237272+0.143696 
## [122]    train-rmse:0.809368+0.038787    test-rmse:1.238663+0.142619 
## [123]    train-rmse:0.807686+0.038918    test-rmse:1.237497+0.143327 
## [124]    train-rmse:0.806177+0.039440    test-rmse:1.236103+0.143254 
## [125]    train-rmse:0.802472+0.038828    test-rmse:1.235143+0.142821 
## [126]    train-rmse:0.800701+0.038931    test-rmse:1.236440+0.142841 
## [127]    train-rmse:0.799265+0.038889    test-rmse:1.236427+0.141513 
## [128]    train-rmse:0.797034+0.039052    test-rmse:1.236510+0.142226 
## [129]    train-rmse:0.795871+0.039110    test-rmse:1.236340+0.142990 
## [130]    train-rmse:0.794689+0.039178    test-rmse:1.236847+0.140204 
## [131]    train-rmse:0.793154+0.039266    test-rmse:1.238164+0.142804 
## Stopping. Best iteration:
## [125]    train-rmse:0.802472+0.038828    test-rmse:1.235143+0.142821
## 
## 65 
## [1]  train-rmse:7.149771+0.051716    test-rmse:7.146526+0.253068 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.345518+0.034890    test-rmse:5.345011+0.242600 
## [3]  train-rmse:4.089439+0.026351    test-rmse:4.109422+0.208327 
## [4]  train-rmse:3.222557+0.019219    test-rmse:3.269696+0.165815 
## [5]  train-rmse:2.638080+0.017891    test-rmse:2.695916+0.132097 
## [6]  train-rmse:2.249044+0.017383    test-rmse:2.357835+0.113241 
## [7]  train-rmse:1.993320+0.015394    test-rmse:2.132806+0.078512 
## [8]  train-rmse:1.827198+0.016391    test-rmse:1.984869+0.070874 
## [9]  train-rmse:1.716386+0.019601    test-rmse:1.901554+0.064665 
## [10] train-rmse:1.633417+0.023027    test-rmse:1.826047+0.053184 
## [11] train-rmse:1.574583+0.023078    test-rmse:1.768293+0.047064 
## [12] train-rmse:1.520766+0.022010    test-rmse:1.734277+0.057463 
## [13] train-rmse:1.477446+0.020901    test-rmse:1.712290+0.064753 
## [14] train-rmse:1.439616+0.022865    test-rmse:1.682816+0.058483 
## [15] train-rmse:1.403896+0.027848    test-rmse:1.660666+0.065720 
## [16] train-rmse:1.374344+0.029491    test-rmse:1.630015+0.069077 
## [17] train-rmse:1.342349+0.023600    test-rmse:1.607817+0.081245 
## [18] train-rmse:1.317083+0.023726    test-rmse:1.590595+0.081453 
## [19] train-rmse:1.284502+0.025361    test-rmse:1.558428+0.090750 
## [20] train-rmse:1.259792+0.026085    test-rmse:1.543465+0.084943 
## [21] train-rmse:1.236130+0.027143    test-rmse:1.541845+0.081531 
## [22] train-rmse:1.215049+0.027965    test-rmse:1.525238+0.083350 
## [23] train-rmse:1.189993+0.030199    test-rmse:1.509767+0.094142 
## [24] train-rmse:1.170200+0.031091    test-rmse:1.496293+0.096086 
## [25] train-rmse:1.148396+0.032359    test-rmse:1.469285+0.089908 
## [26] train-rmse:1.130163+0.030493    test-rmse:1.461462+0.093241 
## [27] train-rmse:1.112320+0.032547    test-rmse:1.455942+0.097223 
## [28] train-rmse:1.097991+0.033221    test-rmse:1.444654+0.102119 
## [29] train-rmse:1.077278+0.036101    test-rmse:1.428110+0.102987 
## [30] train-rmse:1.062671+0.035471    test-rmse:1.418799+0.104992 
## [31] train-rmse:1.046117+0.038495    test-rmse:1.407947+0.101413 
## [32] train-rmse:1.032133+0.040242    test-rmse:1.396261+0.101616 
## [33] train-rmse:1.010311+0.032460    test-rmse:1.387346+0.104983 
## [34] train-rmse:0.996105+0.031543    test-rmse:1.377811+0.112495 
## [35] train-rmse:0.983829+0.031437    test-rmse:1.374038+0.118467 
## [36] train-rmse:0.968925+0.031486    test-rmse:1.366897+0.118760 
## [37] train-rmse:0.956210+0.031915    test-rmse:1.362348+0.120101 
## [38] train-rmse:0.943969+0.034263    test-rmse:1.353019+0.128061 
## [39] train-rmse:0.930470+0.036396    test-rmse:1.350505+0.127628 
## [40] train-rmse:0.917953+0.038199    test-rmse:1.345770+0.130549 
## [41] train-rmse:0.907518+0.038319    test-rmse:1.339173+0.127267 
## [42] train-rmse:0.897374+0.037501    test-rmse:1.331153+0.126947 
## [43] train-rmse:0.887684+0.037371    test-rmse:1.321597+0.137686 
## [44] train-rmse:0.878395+0.037004    test-rmse:1.318707+0.142703 
## [45] train-rmse:0.867929+0.037483    test-rmse:1.314483+0.144666 
## [46] train-rmse:0.858070+0.040074    test-rmse:1.305780+0.144528 
## [47] train-rmse:0.849600+0.039729    test-rmse:1.305924+0.145775 
## [48] train-rmse:0.837581+0.034318    test-rmse:1.302525+0.148526 
## [49] train-rmse:0.827768+0.032507    test-rmse:1.296273+0.152061 
## [50] train-rmse:0.820033+0.032288    test-rmse:1.289336+0.151797 
## [51] train-rmse:0.811961+0.032166    test-rmse:1.287728+0.151728 
## [52] train-rmse:0.803648+0.031982    test-rmse:1.287357+0.155895 
## [53] train-rmse:0.797078+0.032942    test-rmse:1.283294+0.158066 
## [54] train-rmse:0.787768+0.036429    test-rmse:1.279280+0.158480 
## [55] train-rmse:0.778831+0.035434    test-rmse:1.279798+0.160722 
## [56] train-rmse:0.769792+0.037434    test-rmse:1.269945+0.152962 
## [57] train-rmse:0.764486+0.037018    test-rmse:1.271766+0.154926 
## [58] train-rmse:0.757862+0.036319    test-rmse:1.271249+0.154330 
## [59] train-rmse:0.751731+0.036381    test-rmse:1.269572+0.154641 
## [60] train-rmse:0.744410+0.035639    test-rmse:1.265454+0.152816 
## [61] train-rmse:0.737621+0.035501    test-rmse:1.261997+0.156450 
## [62] train-rmse:0.731065+0.036811    test-rmse:1.257168+0.152422 
## [63] train-rmse:0.725582+0.037167    test-rmse:1.256134+0.152684 
## [64] train-rmse:0.716579+0.034224    test-rmse:1.257811+0.153246 
## [65] train-rmse:0.712438+0.035116    test-rmse:1.257697+0.155218 
## [66] train-rmse:0.706839+0.035024    test-rmse:1.256808+0.155406 
## [67] train-rmse:0.701861+0.034722    test-rmse:1.256587+0.156432 
## [68] train-rmse:0.695093+0.032289    test-rmse:1.252331+0.157723 
## [69] train-rmse:0.691029+0.032151    test-rmse:1.253329+0.157022 
## [70] train-rmse:0.686805+0.031879    test-rmse:1.255677+0.156092 
## [71] train-rmse:0.683012+0.032404    test-rmse:1.254289+0.156425 
## [72] train-rmse:0.678604+0.033733    test-rmse:1.253713+0.156971 
## [73] train-rmse:0.674597+0.032820    test-rmse:1.249819+0.158158 
## [74] train-rmse:0.670585+0.032756    test-rmse:1.248967+0.159260 
## [75] train-rmse:0.665621+0.032240    test-rmse:1.248363+0.158274 
## [76] train-rmse:0.662415+0.032440    test-rmse:1.250104+0.155932 
## [77] train-rmse:0.657296+0.032934    test-rmse:1.246760+0.156016 
## [78] train-rmse:0.653950+0.032427    test-rmse:1.246783+0.156957 
## [79] train-rmse:0.650791+0.032725    test-rmse:1.248814+0.156403 
## [80] train-rmse:0.646746+0.032102    test-rmse:1.246765+0.157634 
## [81] train-rmse:0.641717+0.032118    test-rmse:1.245447+0.157847 
## [82] train-rmse:0.636109+0.033086    test-rmse:1.243869+0.158114 
## [83] train-rmse:0.633262+0.034210    test-rmse:1.244055+0.159186 
## [84] train-rmse:0.630201+0.034719    test-rmse:1.242228+0.157112 
## [85] train-rmse:0.627026+0.034563    test-rmse:1.241011+0.156562 
## [86] train-rmse:0.623597+0.034005    test-rmse:1.241847+0.157486 
## [87] train-rmse:0.618958+0.031232    test-rmse:1.237221+0.157499 
## [88] train-rmse:0.614248+0.032030    test-rmse:1.237188+0.157012 
## [89] train-rmse:0.611079+0.030563    test-rmse:1.238135+0.156712 
## [90] train-rmse:0.609056+0.030592    test-rmse:1.238839+0.155942 
## [91] train-rmse:0.606027+0.030404    test-rmse:1.237648+0.156893 
## [92] train-rmse:0.603947+0.030562    test-rmse:1.237856+0.156468 
## [93] train-rmse:0.600975+0.031346    test-rmse:1.237930+0.156426 
## [94] train-rmse:0.597511+0.031602    test-rmse:1.234221+0.153628 
## [95] train-rmse:0.594903+0.032306    test-rmse:1.232933+0.150024 
## [96] train-rmse:0.591672+0.032223    test-rmse:1.232341+0.150093 
## [97] train-rmse:0.589795+0.032076    test-rmse:1.232512+0.149783 
## [98] train-rmse:0.587077+0.031470    test-rmse:1.233137+0.149364 
## [99] train-rmse:0.583544+0.031449    test-rmse:1.232940+0.150029 
## [100]    train-rmse:0.581092+0.031240    test-rmse:1.234652+0.150198 
## [101]    train-rmse:0.579294+0.031188    test-rmse:1.234157+0.151156 
## [102]    train-rmse:0.578048+0.031414    test-rmse:1.235271+0.152164 
## Stopping. Best iteration:
## [96] train-rmse:0.591672+0.032223    test-rmse:1.232341+0.150093
## 
## 66 
## [1]  train-rmse:7.143685+0.052327    test-rmse:7.140787+0.254948 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.330526+0.036618    test-rmse:5.345684+0.233180 
## [3]  train-rmse:4.061875+0.029686    test-rmse:4.084839+0.192156 
## [4]  train-rmse:3.168735+0.018896    test-rmse:3.220727+0.161896 
## [5]  train-rmse:2.552306+0.016409    test-rmse:2.621257+0.113075 
## [6]  train-rmse:2.145518+0.016389    test-rmse:2.283615+0.100079 
## [7]  train-rmse:1.867513+0.022624    test-rmse:2.020173+0.081579 
## [8]  train-rmse:1.681915+0.019505    test-rmse:1.861418+0.077115 
## [9]  train-rmse:1.557320+0.017730    test-rmse:1.778775+0.063299 
## [10] train-rmse:1.465385+0.020250    test-rmse:1.716141+0.072909 
## [11] train-rmse:1.384938+0.026914    test-rmse:1.668747+0.057332 
## [12] train-rmse:1.328351+0.026471    test-rmse:1.609827+0.075715 
## [13] train-rmse:1.278123+0.034066    test-rmse:1.578184+0.076297 
## [14] train-rmse:1.230981+0.038479    test-rmse:1.549629+0.083451 
## [15] train-rmse:1.195216+0.032965    test-rmse:1.535013+0.091622 
## [16] train-rmse:1.161401+0.034070    test-rmse:1.519427+0.095997 
## [17] train-rmse:1.129006+0.031055    test-rmse:1.489072+0.096187 
## [18] train-rmse:1.094768+0.028888    test-rmse:1.463336+0.114190 
## [19] train-rmse:1.066882+0.026013    test-rmse:1.443531+0.117640 
## [20] train-rmse:1.042144+0.030386    test-rmse:1.427025+0.119415 
## [21] train-rmse:1.020474+0.031034    test-rmse:1.416671+0.126163 
## [22] train-rmse:0.994856+0.027634    test-rmse:1.405015+0.130334 
## [23] train-rmse:0.974929+0.024685    test-rmse:1.394258+0.133582 
## [24] train-rmse:0.954019+0.025890    test-rmse:1.379633+0.133584 
## [25] train-rmse:0.931873+0.029078    test-rmse:1.371757+0.141226 
## [26] train-rmse:0.914284+0.028988    test-rmse:1.354625+0.147750 
## [27] train-rmse:0.892647+0.021875    test-rmse:1.351766+0.154515 
## [28] train-rmse:0.878710+0.020632    test-rmse:1.346074+0.155017 
## [29] train-rmse:0.862126+0.018573    test-rmse:1.340600+0.155412 
## [30] train-rmse:0.845850+0.023471    test-rmse:1.334216+0.157185 
## [31] train-rmse:0.831955+0.022094    test-rmse:1.319622+0.152829 
## [32] train-rmse:0.817895+0.021188    test-rmse:1.310041+0.150380 
## [33] train-rmse:0.801131+0.019382    test-rmse:1.303322+0.153360 
## [34] train-rmse:0.789401+0.018029    test-rmse:1.298707+0.158818 
## [35] train-rmse:0.777684+0.018308    test-rmse:1.301923+0.161786 
## [36] train-rmse:0.765814+0.015713    test-rmse:1.297463+0.167579 
## [37] train-rmse:0.751197+0.018120    test-rmse:1.289264+0.164911 
## [38] train-rmse:0.740954+0.018758    test-rmse:1.286881+0.167114 
## [39] train-rmse:0.730063+0.019051    test-rmse:1.282123+0.167914 
## [40] train-rmse:0.720187+0.016259    test-rmse:1.281275+0.172343 
## [41] train-rmse:0.712543+0.015165    test-rmse:1.281892+0.171718 
## [42] train-rmse:0.702710+0.014200    test-rmse:1.281486+0.173903 
## [43] train-rmse:0.692065+0.015401    test-rmse:1.279789+0.174824 
## [44] train-rmse:0.681232+0.021073    test-rmse:1.269618+0.169938 
## [45] train-rmse:0.674356+0.019961    test-rmse:1.268523+0.170648 
## [46] train-rmse:0.660505+0.019322    test-rmse:1.266451+0.168208 
## [47] train-rmse:0.652558+0.017622    test-rmse:1.266737+0.171304 
## [48] train-rmse:0.644674+0.017353    test-rmse:1.263749+0.169093 
## [49] train-rmse:0.637916+0.017457    test-rmse:1.264772+0.169075 
## [50] train-rmse:0.630631+0.017318    test-rmse:1.261624+0.170639 
## [51] train-rmse:0.624249+0.015669    test-rmse:1.260667+0.171408 
## [52] train-rmse:0.618097+0.014473    test-rmse:1.259907+0.175107 
## [53] train-rmse:0.611643+0.014807    test-rmse:1.259059+0.173208 
## [54] train-rmse:0.600466+0.014963    test-rmse:1.257477+0.172707 
## [55] train-rmse:0.595731+0.015284    test-rmse:1.254192+0.171455 
## [56] train-rmse:0.591608+0.017005    test-rmse:1.252077+0.168445 
## [57] train-rmse:0.587499+0.016729    test-rmse:1.247614+0.169922 
## [58] train-rmse:0.580973+0.015284    test-rmse:1.245698+0.171664 
## [59] train-rmse:0.574236+0.017384    test-rmse:1.243869+0.171679 
## [60] train-rmse:0.569511+0.017747    test-rmse:1.242092+0.171083 
## [61] train-rmse:0.565207+0.016919    test-rmse:1.242535+0.169306 
## [62] train-rmse:0.561078+0.017645    test-rmse:1.239465+0.171537 
## [63] train-rmse:0.556848+0.017386    test-rmse:1.242039+0.170613 
## [64] train-rmse:0.552629+0.017730    test-rmse:1.242256+0.167897 
## [65] train-rmse:0.546992+0.016461    test-rmse:1.241717+0.165624 
## [66] train-rmse:0.544034+0.016552    test-rmse:1.241635+0.165953 
## [67] train-rmse:0.539231+0.017341    test-rmse:1.242548+0.166910 
## [68] train-rmse:0.533271+0.016179    test-rmse:1.242155+0.167231 
## Stopping. Best iteration:
## [62] train-rmse:0.561078+0.017645    test-rmse:1.239465+0.171537
## 
## 67 
## [1]  train-rmse:7.138533+0.052833    test-rmse:7.141013+0.255745 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.316646+0.037501    test-rmse:5.337499+0.227562 
## [3]  train-rmse:4.027993+0.028738    test-rmse:4.053287+0.184844 
## [4]  train-rmse:3.124071+0.021394    test-rmse:3.170466+0.156616 
## [5]  train-rmse:2.495503+0.017975    test-rmse:2.587355+0.135151 
## [6]  train-rmse:2.060026+0.022415    test-rmse:2.181743+0.110100 
## [7]  train-rmse:1.769010+0.026897    test-rmse:1.946507+0.100514 
## [8]  train-rmse:1.566056+0.034388    test-rmse:1.792344+0.067117 
## [9]  train-rmse:1.414719+0.034457    test-rmse:1.687838+0.063598 
## [10] train-rmse:1.305033+0.041847    test-rmse:1.614552+0.055478 
## [11] train-rmse:1.222235+0.040351    test-rmse:1.557772+0.052912 
## [12] train-rmse:1.162138+0.039989    test-rmse:1.514115+0.056076 
## [13] train-rmse:1.108923+0.038034    test-rmse:1.484280+0.065025 
## [14] train-rmse:1.063688+0.035914    test-rmse:1.461429+0.076442 
## [15] train-rmse:1.024736+0.035723    test-rmse:1.442165+0.087252 
## [16] train-rmse:0.982638+0.033041    test-rmse:1.416831+0.091421 
## [17] train-rmse:0.945934+0.035999    test-rmse:1.394012+0.105286 
## [18] train-rmse:0.913027+0.036741    test-rmse:1.376556+0.102994 
## [19] train-rmse:0.886277+0.036279    test-rmse:1.367146+0.110345 
## [20] train-rmse:0.856793+0.032585    test-rmse:1.354614+0.124820 
## [21] train-rmse:0.830138+0.032433    test-rmse:1.344862+0.132643 
## [22] train-rmse:0.810039+0.029928    test-rmse:1.340629+0.128699 
## [23] train-rmse:0.783496+0.031656    test-rmse:1.330340+0.126025 
## [24] train-rmse:0.758369+0.028498    test-rmse:1.318437+0.136433 
## [25] train-rmse:0.737706+0.029351    test-rmse:1.307417+0.139404 
## [26] train-rmse:0.719345+0.030132    test-rmse:1.309304+0.140400 
## [27] train-rmse:0.703499+0.029088    test-rmse:1.303087+0.147034 
## [28] train-rmse:0.687215+0.023749    test-rmse:1.290697+0.153911 
## [29] train-rmse:0.672156+0.023977    test-rmse:1.283948+0.157995 
## [30] train-rmse:0.657652+0.025527    test-rmse:1.278982+0.164121 
## [31] train-rmse:0.642780+0.027555    test-rmse:1.272264+0.168840 
## [32] train-rmse:0.630066+0.028611    test-rmse:1.265710+0.166273 
## [33] train-rmse:0.617185+0.025763    test-rmse:1.260891+0.172484 
## [34] train-rmse:0.605855+0.025749    test-rmse:1.258151+0.170389 
## [35] train-rmse:0.595377+0.023796    test-rmse:1.259228+0.175345 
## [36] train-rmse:0.582285+0.020328    test-rmse:1.258719+0.175874 
## [37] train-rmse:0.570789+0.021463    test-rmse:1.251036+0.178049 
## [38] train-rmse:0.560123+0.022804    test-rmse:1.247915+0.175172 
## [39] train-rmse:0.547178+0.018684    test-rmse:1.246746+0.177825 
## [40] train-rmse:0.537415+0.019664    test-rmse:1.248976+0.175499 
## [41] train-rmse:0.529327+0.018650    test-rmse:1.243491+0.177169 
## [42] train-rmse:0.518930+0.019537    test-rmse:1.243660+0.176886 
## [43] train-rmse:0.509746+0.020053    test-rmse:1.238720+0.175006 
## [44] train-rmse:0.499497+0.020700    test-rmse:1.237550+0.173663 
## [45] train-rmse:0.492529+0.021947    test-rmse:1.234225+0.174420 
## [46] train-rmse:0.487137+0.020800    test-rmse:1.234115+0.173510 
## [47] train-rmse:0.479239+0.021964    test-rmse:1.234053+0.172455 
## [48] train-rmse:0.471424+0.021100    test-rmse:1.231756+0.174157 
## [49] train-rmse:0.465365+0.021262    test-rmse:1.228426+0.173027 
## [50] train-rmse:0.459435+0.021935    test-rmse:1.230482+0.172452 
## [51] train-rmse:0.453618+0.022423    test-rmse:1.230139+0.171772 
## [52] train-rmse:0.444810+0.022334    test-rmse:1.228824+0.169104 
## [53] train-rmse:0.439471+0.021506    test-rmse:1.226029+0.168670 
## [54] train-rmse:0.434232+0.021112    test-rmse:1.225581+0.171160 
## [55] train-rmse:0.429620+0.018471    test-rmse:1.226428+0.175004 
## [56] train-rmse:0.424188+0.017018    test-rmse:1.229568+0.174051 
## [57] train-rmse:0.416357+0.019470    test-rmse:1.227734+0.173913 
## [58] train-rmse:0.412200+0.019722    test-rmse:1.227426+0.174260 
## [59] train-rmse:0.407413+0.018580    test-rmse:1.227150+0.173441 
## [60] train-rmse:0.403836+0.019309    test-rmse:1.227092+0.174136 
## Stopping. Best iteration:
## [54] train-rmse:0.434232+0.021112    test-rmse:1.225581+0.171160
## 
## 68 
## [1]  train-rmse:7.137312+0.053122    test-rmse:7.141834+0.246996 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.308328+0.038787    test-rmse:5.330348+0.224466 
## [3]  train-rmse:4.011516+0.032622    test-rmse:4.040434+0.175957 
## [4]  train-rmse:3.097256+0.028325    test-rmse:3.137383+0.147241 
## [5]  train-rmse:2.449108+0.020215    test-rmse:2.550125+0.146296 
## [6]  train-rmse:2.003611+0.013497    test-rmse:2.157959+0.122012 
## [7]  train-rmse:1.690623+0.024651    test-rmse:1.895520+0.094919 
## [8]  train-rmse:1.465232+0.021417    test-rmse:1.751660+0.088059 
## [9]  train-rmse:1.306637+0.024883    test-rmse:1.630815+0.096021 
## [10] train-rmse:1.185560+0.025846    test-rmse:1.558888+0.096360 
## [11] train-rmse:1.101661+0.019412    test-rmse:1.489744+0.118215 
## [12] train-rmse:1.031021+0.024204    test-rmse:1.448250+0.124593 
## [13] train-rmse:0.970676+0.027054    test-rmse:1.422760+0.120414 
## [14] train-rmse:0.914574+0.023521    test-rmse:1.396580+0.126566 
## [15] train-rmse:0.875295+0.017392    test-rmse:1.375892+0.133722 
## [16] train-rmse:0.831369+0.018377    test-rmse:1.361363+0.135742 
## [17] train-rmse:0.800947+0.016994    test-rmse:1.351340+0.142522 
## [18] train-rmse:0.771555+0.018022    test-rmse:1.342029+0.141079 
## [19] train-rmse:0.744061+0.018390    test-rmse:1.325745+0.146381 
## [20] train-rmse:0.717473+0.018324    test-rmse:1.316752+0.149816 
## [21] train-rmse:0.688859+0.018176    test-rmse:1.298610+0.158899 
## [22] train-rmse:0.663987+0.019473    test-rmse:1.286179+0.162326 
## [23] train-rmse:0.645026+0.016494    test-rmse:1.286357+0.161337 
## [24] train-rmse:0.627272+0.017923    test-rmse:1.280982+0.165277 
## [25] train-rmse:0.607044+0.015452    test-rmse:1.277296+0.164448 
## [26] train-rmse:0.591509+0.016165    test-rmse:1.275125+0.163562 
## [27] train-rmse:0.576213+0.019135    test-rmse:1.265619+0.168324 
## [28] train-rmse:0.563699+0.018508    test-rmse:1.266056+0.165831 
## [29] train-rmse:0.549497+0.019027    test-rmse:1.264655+0.169794 
## [30] train-rmse:0.532082+0.017837    test-rmse:1.264261+0.171858 
## [31] train-rmse:0.522017+0.019709    test-rmse:1.262024+0.172222 
## [32] train-rmse:0.509500+0.020168    test-rmse:1.263943+0.173743 
## [33] train-rmse:0.500626+0.018461    test-rmse:1.262317+0.175955 
## [34] train-rmse:0.489864+0.017119    test-rmse:1.261731+0.177251 
## [35] train-rmse:0.478424+0.014536    test-rmse:1.264915+0.176849 
## [36] train-rmse:0.467006+0.014086    test-rmse:1.266795+0.177631 
## [37] train-rmse:0.457567+0.012817    test-rmse:1.266074+0.178836 
## [38] train-rmse:0.449718+0.010384    test-rmse:1.262582+0.178965 
## [39] train-rmse:0.438137+0.008415    test-rmse:1.261646+0.180286 
## [40] train-rmse:0.432036+0.009696    test-rmse:1.262667+0.182172 
## [41] train-rmse:0.421645+0.010074    test-rmse:1.258725+0.181633 
## [42] train-rmse:0.416816+0.009717    test-rmse:1.259161+0.181368 
## [43] train-rmse:0.406834+0.013025    test-rmse:1.259589+0.177671 
## [44] train-rmse:0.399935+0.016863    test-rmse:1.256560+0.179215 
## [45] train-rmse:0.392252+0.017060    test-rmse:1.260093+0.178755 
## [46] train-rmse:0.386443+0.017149    test-rmse:1.258495+0.182104 
## [47] train-rmse:0.379409+0.017310    test-rmse:1.257224+0.181680 
## [48] train-rmse:0.372344+0.017581    test-rmse:1.257752+0.181299 
## [49] train-rmse:0.366549+0.018353    test-rmse:1.259659+0.180620 
## [50] train-rmse:0.362419+0.017952    test-rmse:1.259993+0.181627 
## Stopping. Best iteration:
## [44] train-rmse:0.399935+0.016863    test-rmse:1.256560+0.179215
## 
## 69 
## [1]  train-rmse:7.137312+0.053122    test-rmse:7.141834+0.246996 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.303677+0.039464    test-rmse:5.332754+0.212247 
## [3]  train-rmse:3.998404+0.033382    test-rmse:4.043921+0.159172 
## [4]  train-rmse:3.073334+0.031412    test-rmse:3.140075+0.130601 
## [5]  train-rmse:2.411857+0.021555    test-rmse:2.537402+0.128057 
## [6]  train-rmse:1.954379+0.017033    test-rmse:2.126676+0.117820 
## [7]  train-rmse:1.625180+0.027272    test-rmse:1.854390+0.092246 
## [8]  train-rmse:1.388633+0.030583    test-rmse:1.667133+0.092485 
## [9]  train-rmse:1.213536+0.030827    test-rmse:1.554487+0.094346 
## [10] train-rmse:1.082491+0.031365    test-rmse:1.470629+0.101003 
## [11] train-rmse:0.991626+0.030834    test-rmse:1.423943+0.114081 
## [12] train-rmse:0.920196+0.031954    test-rmse:1.384019+0.114300 
## [13] train-rmse:0.857832+0.029576    test-rmse:1.361189+0.126412 
## [14] train-rmse:0.800582+0.029761    test-rmse:1.344254+0.129892 
## [15] train-rmse:0.762020+0.025682    test-rmse:1.331704+0.136571 
## [16] train-rmse:0.720853+0.021430    test-rmse:1.316242+0.147710 
## [17] train-rmse:0.685886+0.021870    test-rmse:1.305829+0.151131 
## [18] train-rmse:0.652955+0.025610    test-rmse:1.294034+0.148858 
## [19] train-rmse:0.621364+0.021659    test-rmse:1.285706+0.154395 
## [20] train-rmse:0.597055+0.021662    test-rmse:1.278336+0.161477 
## [21] train-rmse:0.578588+0.021439    test-rmse:1.273253+0.166374 
## [22] train-rmse:0.557432+0.019067    test-rmse:1.270316+0.165458 
## [23] train-rmse:0.533355+0.018655    test-rmse:1.265650+0.170829 
## [24] train-rmse:0.512388+0.017160    test-rmse:1.265739+0.172372 
## [25] train-rmse:0.496678+0.016950    test-rmse:1.267653+0.169577 
## [26] train-rmse:0.477301+0.016981    test-rmse:1.261760+0.171738 
## [27] train-rmse:0.461160+0.016155    test-rmse:1.255955+0.173119 
## [28] train-rmse:0.446892+0.017879    test-rmse:1.256616+0.175789 
## [29] train-rmse:0.431807+0.017948    test-rmse:1.256087+0.175184 
## [30] train-rmse:0.421683+0.017843    test-rmse:1.254589+0.173970 
## [31] train-rmse:0.411595+0.016860    test-rmse:1.254382+0.170951 
## [32] train-rmse:0.400652+0.020893    test-rmse:1.254523+0.171764 
## [33] train-rmse:0.388191+0.019769    test-rmse:1.251448+0.173096 
## [34] train-rmse:0.379727+0.019738    test-rmse:1.251763+0.172283 
## [35] train-rmse:0.371433+0.021260    test-rmse:1.253342+0.172625 
## [36] train-rmse:0.362180+0.020741    test-rmse:1.255325+0.172804 
## [37] train-rmse:0.350041+0.019498    test-rmse:1.250011+0.172014 
## [38] train-rmse:0.342426+0.020927    test-rmse:1.251371+0.172791 
## [39] train-rmse:0.334253+0.019393    test-rmse:1.250689+0.171411 
## [40] train-rmse:0.326589+0.020858    test-rmse:1.249383+0.171922 
## [41] train-rmse:0.318178+0.023198    test-rmse:1.250452+0.173059 
## [42] train-rmse:0.310505+0.021832    test-rmse:1.252776+0.170649 
## [43] train-rmse:0.305173+0.022384    test-rmse:1.250221+0.171312 
## [44] train-rmse:0.296835+0.022818    test-rmse:1.250078+0.172377 
## [45] train-rmse:0.291397+0.022193    test-rmse:1.249571+0.173076 
## [46] train-rmse:0.278523+0.019859    test-rmse:1.251135+0.173258 
## Stopping. Best iteration:
## [40] train-rmse:0.326589+0.020858    test-rmse:1.249383+0.171922
## 
## 70 
## [1]  train-rmse:7.026356+0.048057    test-rmse:7.022277+0.264102 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.219930+0.029815    test-rmse:5.214355+0.258267 
## [3]  train-rmse:4.031154+0.019484    test-rmse:4.032050+0.227361 
## [4]  train-rmse:3.279577+0.014247    test-rmse:3.303640+0.177838 
## [5]  train-rmse:2.824524+0.012750    test-rmse:2.856531+0.130172 
## [6]  train-rmse:2.558604+0.014223    test-rmse:2.591059+0.083244 
## [7]  train-rmse:2.402601+0.016150    test-rmse:2.440235+0.058345 
## [8]  train-rmse:2.310487+0.016266    test-rmse:2.351722+0.050573 
## [9]  train-rmse:2.253226+0.017690    test-rmse:2.294818+0.059452 
## [10] train-rmse:2.214395+0.018615    test-rmse:2.261316+0.059770 
## [11] train-rmse:2.185155+0.019320    test-rmse:2.232598+0.065655 
## [12] train-rmse:2.160250+0.019765    test-rmse:2.205579+0.078555 
## [13] train-rmse:2.138285+0.021013    test-rmse:2.183969+0.079202 
## [14] train-rmse:2.118764+0.022547    test-rmse:2.163909+0.075722 
## [15] train-rmse:2.100142+0.023667    test-rmse:2.160997+0.080370 
## [16] train-rmse:2.083636+0.024356    test-rmse:2.148629+0.089605 
## [17] train-rmse:2.067355+0.024885    test-rmse:2.140592+0.093683 
## [18] train-rmse:2.051812+0.025048    test-rmse:2.130326+0.094574 
## [19] train-rmse:2.036692+0.025922    test-rmse:2.117145+0.094621 
## [20] train-rmse:2.022023+0.026579    test-rmse:2.097508+0.095666 
## [21] train-rmse:2.007787+0.027284    test-rmse:2.071855+0.091806 
## [22] train-rmse:1.993974+0.028814    test-rmse:2.063329+0.088838 
## [23] train-rmse:1.981039+0.029649    test-rmse:2.045030+0.091038 
## [24] train-rmse:1.969262+0.030159    test-rmse:2.037900+0.095233 
## [25] train-rmse:1.957460+0.030263    test-rmse:2.031789+0.099574 
## [26] train-rmse:1.945760+0.030233    test-rmse:2.021362+0.096119 
## [27] train-rmse:1.934184+0.030461    test-rmse:2.009817+0.084792 
## [28] train-rmse:1.922935+0.030463    test-rmse:2.001161+0.077842 
## [29] train-rmse:1.911923+0.030988    test-rmse:1.993243+0.082192 
## [30] train-rmse:1.900934+0.031814    test-rmse:1.984810+0.084091 
## [31] train-rmse:1.890469+0.032522    test-rmse:1.980676+0.090744 
## [32] train-rmse:1.880588+0.033664    test-rmse:1.972287+0.087739 
## [33] train-rmse:1.870890+0.034556    test-rmse:1.965387+0.085598 
## [34] train-rmse:1.861481+0.035418    test-rmse:1.960122+0.083089 
## [35] train-rmse:1.852225+0.036204    test-rmse:1.949404+0.084731 
## [36] train-rmse:1.843025+0.036801    test-rmse:1.934348+0.089513 
## [37] train-rmse:1.833966+0.037322    test-rmse:1.918266+0.085734 
## [38] train-rmse:1.825090+0.038059    test-rmse:1.910699+0.089466 
## [39] train-rmse:1.816199+0.038322    test-rmse:1.903667+0.090833 
## [40] train-rmse:1.807627+0.038943    test-rmse:1.898791+0.093026 
## [41] train-rmse:1.799361+0.039611    test-rmse:1.890679+0.094201 
## [42] train-rmse:1.791078+0.040259    test-rmse:1.888503+0.091936 
## [43] train-rmse:1.782947+0.041000    test-rmse:1.880700+0.096168 
## [44] train-rmse:1.775087+0.041516    test-rmse:1.873494+0.093660 
## [45] train-rmse:1.767766+0.041529    test-rmse:1.866050+0.096951 
## [46] train-rmse:1.760607+0.041676    test-rmse:1.861913+0.101491 
## [47] train-rmse:1.753351+0.041855    test-rmse:1.862206+0.099649 
## [48] train-rmse:1.746311+0.042109    test-rmse:1.854623+0.098864 
## [49] train-rmse:1.739169+0.042353    test-rmse:1.848658+0.096234 
## [50] train-rmse:1.732297+0.042664    test-rmse:1.843338+0.097126 
## [51] train-rmse:1.725641+0.043054    test-rmse:1.835294+0.093820 
## [52] train-rmse:1.719073+0.043372    test-rmse:1.832284+0.100190 
## [53] train-rmse:1.712558+0.043881    test-rmse:1.825372+0.100771 
## [54] train-rmse:1.706239+0.043987    test-rmse:1.817409+0.101378 
## [55] train-rmse:1.700090+0.044238    test-rmse:1.814973+0.104269 
## [56] train-rmse:1.694151+0.044595    test-rmse:1.806876+0.106586 
## [57] train-rmse:1.688263+0.045007    test-rmse:1.800304+0.101193 
## [58] train-rmse:1.682453+0.045383    test-rmse:1.797541+0.101973 
## [59] train-rmse:1.676909+0.045740    test-rmse:1.794720+0.103507 
## [60] train-rmse:1.671328+0.046052    test-rmse:1.789259+0.102899 
## [61] train-rmse:1.665677+0.046330    test-rmse:1.784853+0.108488 
## [62] train-rmse:1.660239+0.046596    test-rmse:1.780753+0.107769 
## [63] train-rmse:1.654902+0.046857    test-rmse:1.779703+0.103627 
## [64] train-rmse:1.649535+0.047065    test-rmse:1.771266+0.103125 
## [65] train-rmse:1.644337+0.047478    test-rmse:1.767829+0.111099 
## [66] train-rmse:1.639184+0.047835    test-rmse:1.761587+0.114216 
## [67] train-rmse:1.634217+0.048198    test-rmse:1.759282+0.114774 
## [68] train-rmse:1.629366+0.048503    test-rmse:1.751907+0.116912 
## [69] train-rmse:1.624676+0.048649    test-rmse:1.749036+0.116053 
## [70] train-rmse:1.620076+0.048810    test-rmse:1.745524+0.111792 
## [71] train-rmse:1.615490+0.048927    test-rmse:1.739969+0.109708 
## [72] train-rmse:1.610877+0.049158    test-rmse:1.735444+0.111999 
## [73] train-rmse:1.606408+0.049387    test-rmse:1.727944+0.122265 
## [74] train-rmse:1.602039+0.049507    test-rmse:1.722085+0.119933 
## [75] train-rmse:1.597663+0.049507    test-rmse:1.717722+0.122581 
## [76] train-rmse:1.593405+0.049564    test-rmse:1.716070+0.122613 
## [77] train-rmse:1.589251+0.049710    test-rmse:1.712261+0.125345 
## [78] train-rmse:1.585160+0.049837    test-rmse:1.705511+0.125359 
## [79] train-rmse:1.581193+0.049894    test-rmse:1.706317+0.131657 
## [80] train-rmse:1.577291+0.049967    test-rmse:1.704908+0.129086 
## [81] train-rmse:1.573426+0.050049    test-rmse:1.700195+0.132542 
## [82] train-rmse:1.569564+0.050088    test-rmse:1.695695+0.131480 
## [83] train-rmse:1.565779+0.050170    test-rmse:1.692632+0.133908 
## [84] train-rmse:1.562029+0.050209    test-rmse:1.691601+0.134093 
## [85] train-rmse:1.558299+0.050301    test-rmse:1.691431+0.131658 
## [86] train-rmse:1.554670+0.050432    test-rmse:1.690399+0.130407 
## [87] train-rmse:1.551157+0.050592    test-rmse:1.687457+0.131720 
## [88] train-rmse:1.547677+0.050692    test-rmse:1.681598+0.135079 
## [89] train-rmse:1.544313+0.050795    test-rmse:1.677784+0.130228 
## [90] train-rmse:1.541038+0.050896    test-rmse:1.674310+0.131522 
## [91] train-rmse:1.537710+0.051027    test-rmse:1.671994+0.132496 
## [92] train-rmse:1.534439+0.051011    test-rmse:1.668853+0.132743 
## [93] train-rmse:1.531193+0.051161    test-rmse:1.663071+0.138800 
## [94] train-rmse:1.528009+0.051314    test-rmse:1.661371+0.140833 
## [95] train-rmse:1.524870+0.051346    test-rmse:1.660075+0.141500 
## [96] train-rmse:1.521691+0.051363    test-rmse:1.656762+0.141730 
## [97] train-rmse:1.518662+0.051429    test-rmse:1.652773+0.139188 
## [98] train-rmse:1.515663+0.051541    test-rmse:1.646736+0.138851 
## [99] train-rmse:1.512692+0.051701    test-rmse:1.645194+0.140214 
## [100]    train-rmse:1.509798+0.051815    test-rmse:1.644008+0.141248 
## [101]    train-rmse:1.506883+0.051843    test-rmse:1.640497+0.143347 
## [102]    train-rmse:1.504043+0.051836    test-rmse:1.637736+0.146741 
## [103]    train-rmse:1.501362+0.051859    test-rmse:1.637868+0.147009 
## [104]    train-rmse:1.498655+0.051954    test-rmse:1.633225+0.147062 
## [105]    train-rmse:1.496015+0.052035    test-rmse:1.628443+0.146026 
## [106]    train-rmse:1.493432+0.052124    test-rmse:1.629602+0.146494 
## [107]    train-rmse:1.490876+0.052204    test-rmse:1.628436+0.150837 
## [108]    train-rmse:1.488370+0.052390    test-rmse:1.622981+0.147661 
## [109]    train-rmse:1.485896+0.052529    test-rmse:1.620509+0.148508 
## [110]    train-rmse:1.483426+0.052610    test-rmse:1.618911+0.148091 
## [111]    train-rmse:1.480995+0.052623    test-rmse:1.616947+0.148369 
## [112]    train-rmse:1.478562+0.052672    test-rmse:1.613953+0.149041 
## [113]    train-rmse:1.476253+0.052754    test-rmse:1.610785+0.147115 
## [114]    train-rmse:1.474011+0.052814    test-rmse:1.607462+0.149290 
## [115]    train-rmse:1.471797+0.052884    test-rmse:1.608445+0.150473 
## [116]    train-rmse:1.469628+0.052889    test-rmse:1.609163+0.151584 
## [117]    train-rmse:1.467492+0.052947    test-rmse:1.606255+0.155083 
## [118]    train-rmse:1.465372+0.053012    test-rmse:1.607423+0.157998 
## [119]    train-rmse:1.463275+0.053043    test-rmse:1.605922+0.156147 
## [120]    train-rmse:1.461181+0.053041    test-rmse:1.604611+0.154574 
## [121]    train-rmse:1.459085+0.053044    test-rmse:1.604658+0.158652 
## [122]    train-rmse:1.456961+0.053102    test-rmse:1.602559+0.159561 
## [123]    train-rmse:1.454849+0.053124    test-rmse:1.598684+0.160965 
## [124]    train-rmse:1.452805+0.053198    test-rmse:1.597841+0.162445 
## [125]    train-rmse:1.450745+0.053200    test-rmse:1.595711+0.162457 
## [126]    train-rmse:1.448691+0.053192    test-rmse:1.591413+0.163169 
## [127]    train-rmse:1.446680+0.053191    test-rmse:1.587202+0.160493 
## [128]    train-rmse:1.444765+0.053219    test-rmse:1.587062+0.159860 
## [129]    train-rmse:1.442869+0.053249    test-rmse:1.583703+0.160361 
## [130]    train-rmse:1.441031+0.053245    test-rmse:1.579478+0.160881 
## [131]    train-rmse:1.439215+0.053224    test-rmse:1.579518+0.161490 
## [132]    train-rmse:1.437428+0.053240    test-rmse:1.577519+0.163097 
## [133]    train-rmse:1.435619+0.053238    test-rmse:1.575552+0.164650 
## [134]    train-rmse:1.433846+0.053230    test-rmse:1.570582+0.163113 
## [135]    train-rmse:1.432139+0.053198    test-rmse:1.572061+0.161053 
## [136]    train-rmse:1.430477+0.053182    test-rmse:1.568198+0.161337 
## [137]    train-rmse:1.428837+0.053154    test-rmse:1.568406+0.159696 
## [138]    train-rmse:1.427260+0.053147    test-rmse:1.567118+0.160524 
## [139]    train-rmse:1.425651+0.053158    test-rmse:1.563022+0.161588 
## [140]    train-rmse:1.424076+0.053180    test-rmse:1.561863+0.160174 
## [141]    train-rmse:1.422527+0.053186    test-rmse:1.557366+0.163760 
## [142]    train-rmse:1.420952+0.053183    test-rmse:1.555720+0.163680 
## [143]    train-rmse:1.419415+0.053242    test-rmse:1.554957+0.164925 
## [144]    train-rmse:1.417886+0.053278    test-rmse:1.553998+0.165931 
## [145]    train-rmse:1.416389+0.053367    test-rmse:1.552635+0.166404 
## [146]    train-rmse:1.414872+0.053407    test-rmse:1.550822+0.166291 
## [147]    train-rmse:1.413378+0.053454    test-rmse:1.552664+0.168397 
## [148]    train-rmse:1.411940+0.053486    test-rmse:1.553114+0.168590 
## [149]    train-rmse:1.410543+0.053503    test-rmse:1.551247+0.168190 
## [150]    train-rmse:1.409156+0.053477    test-rmse:1.550434+0.167278 
## [151]    train-rmse:1.407796+0.053454    test-rmse:1.550342+0.166480 
## [152]    train-rmse:1.406469+0.053468    test-rmse:1.551044+0.168076 
## [153]    train-rmse:1.405161+0.053472    test-rmse:1.552477+0.168693 
## [154]    train-rmse:1.403886+0.053505    test-rmse:1.550527+0.167551 
## [155]    train-rmse:1.402609+0.053521    test-rmse:1.548532+0.167250 
## [156]    train-rmse:1.401325+0.053513    test-rmse:1.547212+0.168899 
## [157]    train-rmse:1.400065+0.053516    test-rmse:1.546503+0.168539 
## [158]    train-rmse:1.398803+0.053505    test-rmse:1.547123+0.169499 
## [159]    train-rmse:1.397567+0.053465    test-rmse:1.546127+0.168901 
## [160]    train-rmse:1.396348+0.053403    test-rmse:1.543881+0.169640 
## [161]    train-rmse:1.395186+0.053397    test-rmse:1.541891+0.171665 
## [162]    train-rmse:1.394056+0.053397    test-rmse:1.538685+0.172212 
## [163]    train-rmse:1.392901+0.053409    test-rmse:1.537234+0.173577 
## [164]    train-rmse:1.391769+0.053440    test-rmse:1.535103+0.174193 
## [165]    train-rmse:1.390619+0.053474    test-rmse:1.529992+0.172362 
## [166]    train-rmse:1.389562+0.053463    test-rmse:1.530358+0.173875 
## [167]    train-rmse:1.388442+0.053432    test-rmse:1.529663+0.174459 
## [168]    train-rmse:1.387357+0.053417    test-rmse:1.527421+0.174799 
## [169]    train-rmse:1.386296+0.053415    test-rmse:1.527566+0.175601 
## [170]    train-rmse:1.385266+0.053413    test-rmse:1.527553+0.176891 
## [171]    train-rmse:1.384229+0.053421    test-rmse:1.527260+0.176560 
## [172]    train-rmse:1.383211+0.053428    test-rmse:1.526614+0.177380 
## [173]    train-rmse:1.382209+0.053411    test-rmse:1.525639+0.177799 
## [174]    train-rmse:1.381212+0.053416    test-rmse:1.525079+0.178995 
## [175]    train-rmse:1.380242+0.053430    test-rmse:1.522623+0.181771 
## [176]    train-rmse:1.379296+0.053435    test-rmse:1.521218+0.181810 
## [177]    train-rmse:1.378361+0.053434    test-rmse:1.520991+0.181968 
## [178]    train-rmse:1.377461+0.053439    test-rmse:1.521681+0.182068 
## [179]    train-rmse:1.376561+0.053429    test-rmse:1.520355+0.182280 
## [180]    train-rmse:1.375663+0.053428    test-rmse:1.519933+0.184600 
## [181]    train-rmse:1.374765+0.053401    test-rmse:1.518445+0.184367 
## [182]    train-rmse:1.373886+0.053397    test-rmse:1.517940+0.182042 
## [183]    train-rmse:1.373042+0.053401    test-rmse:1.517654+0.182445 
## [184]    train-rmse:1.372199+0.053376    test-rmse:1.516763+0.182590 
## [185]    train-rmse:1.371363+0.053351    test-rmse:1.515667+0.182254 
## [186]    train-rmse:1.370547+0.053330    test-rmse:1.513557+0.181507 
## [187]    train-rmse:1.369732+0.053310    test-rmse:1.513943+0.181911 
## [188]    train-rmse:1.368923+0.053301    test-rmse:1.513888+0.181706 
## [189]    train-rmse:1.368120+0.053282    test-rmse:1.513915+0.182093 
## [190]    train-rmse:1.367312+0.053267    test-rmse:1.513350+0.182402 
## [191]    train-rmse:1.366516+0.053273    test-rmse:1.513024+0.183045 
## [192]    train-rmse:1.365730+0.053281    test-rmse:1.515116+0.183364 
## [193]    train-rmse:1.364956+0.053275    test-rmse:1.514381+0.183375 
## [194]    train-rmse:1.364189+0.053263    test-rmse:1.512913+0.183403 
## [195]    train-rmse:1.363440+0.053253    test-rmse:1.513438+0.184447 
## [196]    train-rmse:1.362692+0.053234    test-rmse:1.513354+0.183320 
## [197]    train-rmse:1.361954+0.053224    test-rmse:1.512451+0.183388 
## [198]    train-rmse:1.361221+0.053214    test-rmse:1.509295+0.184933 
## [199]    train-rmse:1.360506+0.053218    test-rmse:1.509063+0.185068 
## [200]    train-rmse:1.359813+0.053199    test-rmse:1.508833+0.186973 
## [201]    train-rmse:1.359124+0.053199    test-rmse:1.508543+0.186721 
## [202]    train-rmse:1.358465+0.053194    test-rmse:1.507498+0.187842 
## [203]    train-rmse:1.357826+0.053197    test-rmse:1.506561+0.187497 
## [204]    train-rmse:1.357186+0.053199    test-rmse:1.506313+0.187740 
## [205]    train-rmse:1.356556+0.053190    test-rmse:1.506757+0.187456 
## [206]    train-rmse:1.355923+0.053198    test-rmse:1.505952+0.187003 
## [207]    train-rmse:1.355321+0.053223    test-rmse:1.504604+0.186470 
## [208]    train-rmse:1.354725+0.053243    test-rmse:1.503050+0.188221 
## [209]    train-rmse:1.354127+0.053256    test-rmse:1.503471+0.188569 
## [210]    train-rmse:1.353543+0.053255    test-rmse:1.503027+0.188754 
## [211]    train-rmse:1.352962+0.053244    test-rmse:1.502997+0.190660 
## [212]    train-rmse:1.352386+0.053235    test-rmse:1.502982+0.192083 
## [213]    train-rmse:1.351807+0.053231    test-rmse:1.501879+0.192343 
## [214]    train-rmse:1.351242+0.053224    test-rmse:1.499701+0.191734 
## [215]    train-rmse:1.350675+0.053226    test-rmse:1.500987+0.191842 
## [216]    train-rmse:1.350122+0.053223    test-rmse:1.500275+0.192146 
## [217]    train-rmse:1.349580+0.053230    test-rmse:1.499674+0.192183 
## [218]    train-rmse:1.349049+0.053239    test-rmse:1.499724+0.193556 
## [219]    train-rmse:1.348530+0.053231    test-rmse:1.499283+0.193252 
## [220]    train-rmse:1.347994+0.053217    test-rmse:1.498220+0.194207 
## [221]    train-rmse:1.347459+0.053207    test-rmse:1.498262+0.194362 
## [222]    train-rmse:1.346943+0.053191    test-rmse:1.498947+0.193734 
## [223]    train-rmse:1.346436+0.053173    test-rmse:1.498144+0.193547 
## [224]    train-rmse:1.345944+0.053161    test-rmse:1.498765+0.192985 
## [225]    train-rmse:1.345457+0.053143    test-rmse:1.497203+0.194458 
## [226]    train-rmse:1.344966+0.053135    test-rmse:1.497554+0.193212 
## [227]    train-rmse:1.344485+0.053127    test-rmse:1.496750+0.193942 
## [228]    train-rmse:1.343999+0.053115    test-rmse:1.495669+0.193572 
## [229]    train-rmse:1.343511+0.053105    test-rmse:1.495241+0.193229 
## [230]    train-rmse:1.343023+0.053092    test-rmse:1.494171+0.192695 
## [231]    train-rmse:1.342565+0.053076    test-rmse:1.494156+0.192822 
## [232]    train-rmse:1.342110+0.053062    test-rmse:1.493114+0.191817 
## [233]    train-rmse:1.341656+0.053051    test-rmse:1.493818+0.191070 
## [234]    train-rmse:1.341217+0.053045    test-rmse:1.493826+0.191046 
## [235]    train-rmse:1.340789+0.053038    test-rmse:1.494370+0.193105 
## [236]    train-rmse:1.340358+0.053026    test-rmse:1.493751+0.193091 
## [237]    train-rmse:1.339940+0.053003    test-rmse:1.493219+0.193270 
## [238]    train-rmse:1.339523+0.052991    test-rmse:1.492377+0.195171 
## [239]    train-rmse:1.339115+0.052976    test-rmse:1.491681+0.195258 
## [240]    train-rmse:1.338717+0.052962    test-rmse:1.491620+0.195196 
## [241]    train-rmse:1.338325+0.052952    test-rmse:1.489838+0.195122 
## [242]    train-rmse:1.337928+0.052958    test-rmse:1.490527+0.196453 
## [243]    train-rmse:1.337533+0.052964    test-rmse:1.489394+0.195177 
## [244]    train-rmse:1.337148+0.052961    test-rmse:1.488729+0.194924 
## [245]    train-rmse:1.336760+0.052954    test-rmse:1.490524+0.196065 
## [246]    train-rmse:1.336384+0.052937    test-rmse:1.489947+0.195515 
## [247]    train-rmse:1.336013+0.052926    test-rmse:1.488949+0.195772 
## [248]    train-rmse:1.335646+0.052913    test-rmse:1.488004+0.197069 
## [249]    train-rmse:1.335287+0.052900    test-rmse:1.487354+0.196658 
## [250]    train-rmse:1.334944+0.052883    test-rmse:1.486706+0.196159 
## [251]    train-rmse:1.334603+0.052867    test-rmse:1.487078+0.196030 
## [252]    train-rmse:1.334262+0.052853    test-rmse:1.486359+0.196706 
## [253]    train-rmse:1.333914+0.052856    test-rmse:1.487838+0.197773 
## [254]    train-rmse:1.333569+0.052836    test-rmse:1.487266+0.197863 
## [255]    train-rmse:1.333217+0.052813    test-rmse:1.485380+0.198939 
## [256]    train-rmse:1.332880+0.052800    test-rmse:1.485067+0.197411 
## [257]    train-rmse:1.332566+0.052801    test-rmse:1.485036+0.198059 
## [258]    train-rmse:1.332253+0.052795    test-rmse:1.486363+0.197231 
## [259]    train-rmse:1.331952+0.052786    test-rmse:1.485795+0.196806 
## [260]    train-rmse:1.331649+0.052777    test-rmse:1.484946+0.197186 
## [261]    train-rmse:1.331348+0.052765    test-rmse:1.484921+0.197204 
## [262]    train-rmse:1.331052+0.052753    test-rmse:1.485103+0.197218 
## [263]    train-rmse:1.330754+0.052741    test-rmse:1.485047+0.196949 
## [264]    train-rmse:1.330460+0.052724    test-rmse:1.484653+0.196638 
## [265]    train-rmse:1.330166+0.052712    test-rmse:1.484146+0.196332 
## [266]    train-rmse:1.329872+0.052706    test-rmse:1.485922+0.195744 
## [267]    train-rmse:1.329583+0.052699    test-rmse:1.485442+0.195317 
## [268]    train-rmse:1.329294+0.052693    test-rmse:1.485167+0.195736 
## [269]    train-rmse:1.329013+0.052680    test-rmse:1.485810+0.196672 
## [270]    train-rmse:1.328741+0.052665    test-rmse:1.484339+0.197483 
## [271]    train-rmse:1.328471+0.052654    test-rmse:1.483815+0.197833 
## [272]    train-rmse:1.328201+0.052642    test-rmse:1.483136+0.198276 
## [273]    train-rmse:1.327935+0.052628    test-rmse:1.482454+0.198368 
## [274]    train-rmse:1.327678+0.052619    test-rmse:1.482178+0.198812 
## [275]    train-rmse:1.327422+0.052607    test-rmse:1.481034+0.199724 
## [276]    train-rmse:1.327169+0.052594    test-rmse:1.480906+0.199526 
## [277]    train-rmse:1.326925+0.052582    test-rmse:1.480979+0.199615 
## [278]    train-rmse:1.326679+0.052573    test-rmse:1.480329+0.199194 
## [279]    train-rmse:1.326433+0.052559    test-rmse:1.480758+0.199154 
## [280]    train-rmse:1.326193+0.052549    test-rmse:1.481715+0.199701 
## [281]    train-rmse:1.325947+0.052540    test-rmse:1.480850+0.199521 
## [282]    train-rmse:1.325712+0.052535    test-rmse:1.481272+0.201070 
## [283]    train-rmse:1.325473+0.052527    test-rmse:1.481504+0.201376 
## [284]    train-rmse:1.325242+0.052523    test-rmse:1.480999+0.201553 
## Stopping. Best iteration:
## [278]    train-rmse:1.326679+0.052573    test-rmse:1.480329+0.199194
## 
## 71 
## [1]  train-rmse:6.985669+0.048566    test-rmse:6.982148+0.258323 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.141150+0.033978    test-rmse:5.142435+0.228104 
## [3]  train-rmse:3.912043+0.021054    test-rmse:3.926359+0.205699 
## [4]  train-rmse:3.103670+0.022382    test-rmse:3.141471+0.160501 
## [5]  train-rmse:2.590827+0.024975    test-rmse:2.647628+0.114209 
## [6]  train-rmse:2.280305+0.032239    test-rmse:2.363743+0.059172 
## [7]  train-rmse:2.091784+0.031062    test-rmse:2.188790+0.035186 
## [8]  train-rmse:1.976154+0.034144    test-rmse:2.092748+0.036346 
## [9]  train-rmse:1.893010+0.037165    test-rmse:2.023839+0.053784 
## [10] train-rmse:1.834800+0.040755    test-rmse:1.996150+0.075765 
## [11] train-rmse:1.789472+0.043682    test-rmse:1.949912+0.077076 
## [12] train-rmse:1.753429+0.043228    test-rmse:1.918510+0.078299 
## [13] train-rmse:1.723003+0.044375    test-rmse:1.881879+0.074928 
## [14] train-rmse:1.685035+0.030230    test-rmse:1.840207+0.086008 
## [15] train-rmse:1.659958+0.031952    test-rmse:1.824314+0.093539 
## [16] train-rmse:1.631602+0.035649    test-rmse:1.808190+0.099073 
## [17] train-rmse:1.603898+0.040605    test-rmse:1.788767+0.100194 
## [18] train-rmse:1.578794+0.042646    test-rmse:1.763551+0.100141 
## [19] train-rmse:1.556436+0.043429    test-rmse:1.749999+0.096155 
## [20] train-rmse:1.536206+0.046479    test-rmse:1.743926+0.112738 
## [21] train-rmse:1.511187+0.046738    test-rmse:1.710406+0.079465 
## [22] train-rmse:1.487109+0.048823    test-rmse:1.689009+0.071847 
## [23] train-rmse:1.468885+0.050391    test-rmse:1.672063+0.067332 
## [24] train-rmse:1.449142+0.049311    test-rmse:1.655658+0.072342 
## [25] train-rmse:1.433412+0.049729    test-rmse:1.639019+0.076778 
## [26] train-rmse:1.418178+0.049440    test-rmse:1.621552+0.076677 
## [27] train-rmse:1.402357+0.049271    test-rmse:1.610969+0.072154 
## [28] train-rmse:1.386001+0.050472    test-rmse:1.600819+0.071262 
## [29] train-rmse:1.371082+0.050427    test-rmse:1.592723+0.065790 
## [30] train-rmse:1.356922+0.050492    test-rmse:1.587718+0.065390 
## [31] train-rmse:1.343412+0.049862    test-rmse:1.576528+0.072091 
## [32] train-rmse:1.330809+0.049122    test-rmse:1.563852+0.076104 
## [33] train-rmse:1.309290+0.033642    test-rmse:1.554890+0.093958 
## [34] train-rmse:1.296066+0.033365    test-rmse:1.547894+0.098006 
## [35] train-rmse:1.284543+0.033875    test-rmse:1.539322+0.094865 
## [36] train-rmse:1.268416+0.045255    test-rmse:1.527964+0.098847 
## [37] train-rmse:1.256350+0.045744    test-rmse:1.517722+0.097176 
## [38] train-rmse:1.245531+0.046651    test-rmse:1.506566+0.097015 
## [39] train-rmse:1.235578+0.046383    test-rmse:1.499997+0.096078 
## [40] train-rmse:1.225828+0.046459    test-rmse:1.494097+0.096051 
## [41] train-rmse:1.216619+0.046890    test-rmse:1.486521+0.100844 
## [42] train-rmse:1.207087+0.047007    test-rmse:1.481740+0.103063 
## [43] train-rmse:1.198200+0.047914    test-rmse:1.475664+0.098939 
## [44] train-rmse:1.189425+0.047683    test-rmse:1.470149+0.099337 
## [45] train-rmse:1.181723+0.048413    test-rmse:1.462543+0.099547 
## [46] train-rmse:1.174099+0.049073    test-rmse:1.456972+0.097729 
## [47] train-rmse:1.166178+0.049053    test-rmse:1.456756+0.100101 
## [48] train-rmse:1.150849+0.042689    test-rmse:1.451528+0.104720 
## [49] train-rmse:1.141887+0.042636    test-rmse:1.444111+0.107256 
## [50] train-rmse:1.134627+0.043393    test-rmse:1.434252+0.111034 
## [51] train-rmse:1.123246+0.050442    test-rmse:1.427228+0.117872 
## [52] train-rmse:1.115133+0.052345    test-rmse:1.423657+0.116190 
## [53] train-rmse:1.107846+0.052427    test-rmse:1.419282+0.116428 
## [54] train-rmse:1.101606+0.052628    test-rmse:1.415985+0.115974 
## [55] train-rmse:1.090688+0.056787    test-rmse:1.410404+0.117966 
## [56] train-rmse:1.083999+0.057617    test-rmse:1.404668+0.119248 
## [57] train-rmse:1.077937+0.059054    test-rmse:1.400710+0.120958 
## [58] train-rmse:1.070897+0.058330    test-rmse:1.397416+0.120299 
## [59] train-rmse:1.061352+0.056287    test-rmse:1.394646+0.125109 
## [60] train-rmse:1.054104+0.055569    test-rmse:1.390679+0.128113 
## [61] train-rmse:1.048563+0.056013    test-rmse:1.382790+0.136304 
## [62] train-rmse:1.041874+0.055933    test-rmse:1.380143+0.129588 
## [63] train-rmse:1.034191+0.059974    test-rmse:1.380218+0.132882 
## [64] train-rmse:1.029119+0.059980    test-rmse:1.381658+0.131466 
## [65] train-rmse:1.015685+0.055863    test-rmse:1.374996+0.130867 
## [66] train-rmse:1.009236+0.055700    test-rmse:1.367552+0.132395 
## [67] train-rmse:1.004721+0.055858    test-rmse:1.361585+0.130972 
## [68] train-rmse:0.998065+0.054655    test-rmse:1.358027+0.133501 
## [69] train-rmse:0.987547+0.052562    test-rmse:1.340312+0.119628 
## [70] train-rmse:0.982918+0.052344    test-rmse:1.336183+0.123200 
## [71] train-rmse:0.977657+0.054171    test-rmse:1.334808+0.124042 
## [72] train-rmse:0.973254+0.053949    test-rmse:1.332158+0.131255 
## [73] train-rmse:0.968130+0.053540    test-rmse:1.331421+0.132562 
## [74] train-rmse:0.963717+0.053314    test-rmse:1.327080+0.133357 
## [75] train-rmse:0.958992+0.053732    test-rmse:1.325215+0.134385 
## [76] train-rmse:0.949216+0.051752    test-rmse:1.321140+0.141189 
## [77] train-rmse:0.943977+0.054981    test-rmse:1.318939+0.140342 
## [78] train-rmse:0.940725+0.055054    test-rmse:1.319952+0.139299 
## [79] train-rmse:0.936975+0.055268    test-rmse:1.319356+0.137266 
## [80] train-rmse:0.933420+0.054988    test-rmse:1.317236+0.138594 
## [81] train-rmse:0.930151+0.055157    test-rmse:1.311758+0.138810 
## [82] train-rmse:0.922098+0.057105    test-rmse:1.298608+0.133477 
## [83] train-rmse:0.918717+0.057192    test-rmse:1.298152+0.132673 
## [84] train-rmse:0.911826+0.056583    test-rmse:1.297968+0.134297 
## [85] train-rmse:0.907401+0.056822    test-rmse:1.301940+0.136865 
## [86] train-rmse:0.904520+0.056967    test-rmse:1.298853+0.137454 
## [87] train-rmse:0.901439+0.056756    test-rmse:1.298438+0.137618 
## [88] train-rmse:0.898699+0.056590    test-rmse:1.295830+0.137294 
## [89] train-rmse:0.895100+0.056870    test-rmse:1.294997+0.136516 
## [90] train-rmse:0.891829+0.058054    test-rmse:1.296030+0.137357 
## [91] train-rmse:0.888892+0.057764    test-rmse:1.293667+0.139376 
## [92] train-rmse:0.883804+0.063330    test-rmse:1.292305+0.139700 
## [93] train-rmse:0.878376+0.063606    test-rmse:1.284945+0.137557 
## [94] train-rmse:0.875964+0.063695    test-rmse:1.285608+0.137273 
## [95] train-rmse:0.873884+0.063886    test-rmse:1.282539+0.135164 
## [96] train-rmse:0.870770+0.063858    test-rmse:1.281353+0.136068 
## [97] train-rmse:0.864783+0.060717    test-rmse:1.276077+0.135886 
## [98] train-rmse:0.861850+0.060211    test-rmse:1.275202+0.137479 
## [99] train-rmse:0.859122+0.061080    test-rmse:1.276357+0.137415 
## [100]    train-rmse:0.856271+0.062548    test-rmse:1.274013+0.134990 
## [101]    train-rmse:0.853861+0.062596    test-rmse:1.274204+0.136764 
## [102]    train-rmse:0.851820+0.062747    test-rmse:1.274156+0.137485 
## [103]    train-rmse:0.849560+0.063155    test-rmse:1.275266+0.138824 
## [104]    train-rmse:0.847013+0.062827    test-rmse:1.273657+0.139418 
## [105]    train-rmse:0.844687+0.062470    test-rmse:1.272547+0.139620 
## [106]    train-rmse:0.839306+0.064008    test-rmse:1.265571+0.139449 
## [107]    train-rmse:0.836913+0.063717    test-rmse:1.263890+0.140054 
## [108]    train-rmse:0.835010+0.064316    test-rmse:1.265382+0.140279 
## [109]    train-rmse:0.831050+0.063661    test-rmse:1.264671+0.139251 
## [110]    train-rmse:0.829013+0.064322    test-rmse:1.264904+0.140650 
## [111]    train-rmse:0.826404+0.065185    test-rmse:1.264669+0.139329 
## [112]    train-rmse:0.824574+0.065075    test-rmse:1.266009+0.138748 
## [113]    train-rmse:0.821889+0.064424    test-rmse:1.264177+0.139904 
## Stopping. Best iteration:
## [107]    train-rmse:0.836913+0.063717    test-rmse:1.263890+0.140054
## 
## 72 
## [1]  train-rmse:6.969788+0.050311    test-rmse:6.966611+0.251033 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.097812+0.038057    test-rmse:5.102324+0.225835 
## [3]  train-rmse:3.835733+0.033230    test-rmse:3.856261+0.185271 
## [4]  train-rmse:3.001683+0.026402    test-rmse:3.059129+0.145395 
## [5]  train-rmse:2.450853+0.018130    test-rmse:2.529153+0.126932 
## [6]  train-rmse:2.108497+0.014784    test-rmse:2.205720+0.080455 
## [7]  train-rmse:1.893731+0.016932    test-rmse:2.036905+0.074259 
## [8]  train-rmse:1.756886+0.016832    test-rmse:1.916802+0.069855 
## [9]  train-rmse:1.651539+0.022625    test-rmse:1.832023+0.062244 
## [10] train-rmse:1.581282+0.024605    test-rmse:1.779617+0.067071 
## [11] train-rmse:1.528926+0.026360    test-rmse:1.736316+0.063583 
## [12] train-rmse:1.476577+0.018798    test-rmse:1.711848+0.071074 
## [13] train-rmse:1.438885+0.019072    test-rmse:1.682006+0.072839 
## [14] train-rmse:1.399636+0.026886    test-rmse:1.651128+0.073816 
## [15] train-rmse:1.364280+0.030672    test-rmse:1.626966+0.080291 
## [16] train-rmse:1.333770+0.033591    test-rmse:1.605641+0.076219 
## [17] train-rmse:1.305269+0.034468    test-rmse:1.579499+0.072577 
## [18] train-rmse:1.277058+0.034525    test-rmse:1.560084+0.083257 
## [19] train-rmse:1.251983+0.035655    test-rmse:1.542170+0.089002 
## [20] train-rmse:1.227204+0.036153    test-rmse:1.529331+0.092069 
## [21] train-rmse:1.197656+0.029908    test-rmse:1.518955+0.106762 
## [22] train-rmse:1.176184+0.029019    test-rmse:1.511272+0.107357 
## [23] train-rmse:1.154545+0.031232    test-rmse:1.485479+0.115386 
## [24] train-rmse:1.134308+0.031820    test-rmse:1.474369+0.121851 
## [25] train-rmse:1.113824+0.029195    test-rmse:1.453506+0.110601 
## [26] train-rmse:1.094942+0.029771    test-rmse:1.445938+0.108567 
## [27] train-rmse:1.070910+0.022762    test-rmse:1.432365+0.112613 
## [28] train-rmse:1.055156+0.024552    test-rmse:1.430800+0.115857 
## [29] train-rmse:1.035949+0.024060    test-rmse:1.419880+0.121877 
## [30] train-rmse:1.022435+0.023956    test-rmse:1.412990+0.126762 
## [31] train-rmse:1.007179+0.023585    test-rmse:1.403951+0.127760 
## [32] train-rmse:0.989666+0.022728    test-rmse:1.389022+0.134373 
## [33] train-rmse:0.975187+0.021297    test-rmse:1.377536+0.137461 
## [34] train-rmse:0.957718+0.022191    test-rmse:1.357975+0.133269 
## [35] train-rmse:0.943734+0.023012    test-rmse:1.349511+0.135306 
## [36] train-rmse:0.932085+0.023653    test-rmse:1.347402+0.143922 
## [37] train-rmse:0.918917+0.023415    test-rmse:1.343263+0.144657 
## [38] train-rmse:0.909332+0.023560    test-rmse:1.338943+0.144081 
## [39] train-rmse:0.898695+0.023392    test-rmse:1.338835+0.146536 
## [40] train-rmse:0.888187+0.023715    test-rmse:1.337634+0.146011 
## [41] train-rmse:0.873116+0.027931    test-rmse:1.333065+0.145529 
## [42] train-rmse:0.862497+0.025598    test-rmse:1.328723+0.146085 
## [43] train-rmse:0.853188+0.026476    test-rmse:1.327965+0.143139 
## [44] train-rmse:0.840221+0.028691    test-rmse:1.316415+0.149217 
## [45] train-rmse:0.829991+0.026986    test-rmse:1.313054+0.151594 
## [46] train-rmse:0.819903+0.027956    test-rmse:1.304890+0.153015 
## [47] train-rmse:0.812230+0.027884    test-rmse:1.302612+0.155642 
## [48] train-rmse:0.802891+0.026801    test-rmse:1.298544+0.154031 
## [49] train-rmse:0.796830+0.026885    test-rmse:1.294939+0.153753 
## [50] train-rmse:0.791132+0.027431    test-rmse:1.296069+0.157300 
## [51] train-rmse:0.781070+0.031601    test-rmse:1.295549+0.158304 
## [52] train-rmse:0.775210+0.031989    test-rmse:1.296419+0.160065 
## [53] train-rmse:0.765245+0.030250    test-rmse:1.292093+0.161184 
## [54] train-rmse:0.759133+0.031005    test-rmse:1.290991+0.161474 
## [55] train-rmse:0.753823+0.031234    test-rmse:1.288547+0.163808 
## [56] train-rmse:0.747124+0.031612    test-rmse:1.284263+0.161783 
## [57] train-rmse:0.742556+0.032501    test-rmse:1.283938+0.159453 
## [58] train-rmse:0.736158+0.031080    test-rmse:1.280061+0.160929 
## [59] train-rmse:0.727609+0.029093    test-rmse:1.277787+0.160709 
## [60] train-rmse:0.722012+0.028278    test-rmse:1.278887+0.165816 
## [61] train-rmse:0.715748+0.028068    test-rmse:1.276835+0.165783 
## [62] train-rmse:0.711104+0.028088    test-rmse:1.276823+0.165549 
## [63] train-rmse:0.705682+0.027580    test-rmse:1.276001+0.165780 
## [64] train-rmse:0.698527+0.028503    test-rmse:1.277598+0.165578 
## [65] train-rmse:0.692730+0.028494    test-rmse:1.277529+0.166423 
## [66] train-rmse:0.687134+0.028683    test-rmse:1.274276+0.164658 
## [67] train-rmse:0.682612+0.028742    test-rmse:1.275664+0.164614 
## [68] train-rmse:0.679006+0.029206    test-rmse:1.271026+0.165310 
## [69] train-rmse:0.674228+0.030183    test-rmse:1.267431+0.165399 
## [70] train-rmse:0.669377+0.030747    test-rmse:1.267622+0.164298 
## [71] train-rmse:0.664221+0.030132    test-rmse:1.263256+0.163182 
## [72] train-rmse:0.660075+0.031049    test-rmse:1.261595+0.164397 
## [73] train-rmse:0.652961+0.029714    test-rmse:1.259763+0.164489 
## [74] train-rmse:0.648933+0.029447    test-rmse:1.260128+0.165442 
## [75] train-rmse:0.641989+0.026723    test-rmse:1.254509+0.162623 
## [76] train-rmse:0.638432+0.027602    test-rmse:1.254705+0.164327 
## [77] train-rmse:0.635972+0.027469    test-rmse:1.253556+0.166280 
## [78] train-rmse:0.632996+0.027447    test-rmse:1.252216+0.165988 
## [79] train-rmse:0.629973+0.028282    test-rmse:1.253022+0.167353 
## [80] train-rmse:0.626283+0.026620    test-rmse:1.252745+0.167067 
## [81] train-rmse:0.620457+0.028691    test-rmse:1.252481+0.166641 
## [82] train-rmse:0.615475+0.028330    test-rmse:1.249076+0.165305 
## [83] train-rmse:0.612638+0.027675    test-rmse:1.249070+0.165020 
## [84] train-rmse:0.608389+0.028071    test-rmse:1.247507+0.163861 
## [85] train-rmse:0.605720+0.028036    test-rmse:1.246612+0.164729 
## [86] train-rmse:0.601828+0.028697    test-rmse:1.246438+0.165336 
## [87] train-rmse:0.598006+0.031941    test-rmse:1.246791+0.165725 
## [88] train-rmse:0.596169+0.032147    test-rmse:1.246751+0.166480 
## [89] train-rmse:0.591163+0.030454    test-rmse:1.246308+0.166255 
## [90] train-rmse:0.586908+0.027902    test-rmse:1.246436+0.166132 
## [91] train-rmse:0.584983+0.027812    test-rmse:1.248069+0.169899 
## [92] train-rmse:0.581338+0.028562    test-rmse:1.247508+0.170101 
## [93] train-rmse:0.578261+0.028659    test-rmse:1.246580+0.169510 
## [94] train-rmse:0.575799+0.028097    test-rmse:1.246689+0.169329 
## [95] train-rmse:0.573037+0.027637    test-rmse:1.246276+0.170128 
## [96] train-rmse:0.571012+0.027660    test-rmse:1.245316+0.168865 
## [97] train-rmse:0.568286+0.027600    test-rmse:1.241970+0.168723 
## [98] train-rmse:0.566081+0.027586    test-rmse:1.241319+0.169198 
## [99] train-rmse:0.564336+0.027481    test-rmse:1.239970+0.167495 
## [100]    train-rmse:0.562524+0.027328    test-rmse:1.240562+0.168408 
## [101]    train-rmse:0.559796+0.026756    test-rmse:1.241494+0.170613 
## [102]    train-rmse:0.557593+0.026548    test-rmse:1.241815+0.170854 
## [103]    train-rmse:0.553992+0.027681    test-rmse:1.243522+0.171646 
## [104]    train-rmse:0.551479+0.029463    test-rmse:1.243314+0.172069 
## [105]    train-rmse:0.550215+0.029401    test-rmse:1.245135+0.174532 
## Stopping. Best iteration:
## [99] train-rmse:0.564336+0.027481    test-rmse:1.239970+0.167495
## 
## 73 
## [1]  train-rmse:6.963134+0.050966    test-rmse:6.960471+0.253016 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.081203+0.035964    test-rmse:5.096390+0.227794 
## [3]  train-rmse:3.801087+0.028714    test-rmse:3.816571+0.188826 
## [4]  train-rmse:2.935717+0.018145    test-rmse:2.983368+0.150369 
## [5]  train-rmse:2.359766+0.013530    test-rmse:2.446213+0.098834 
## [6]  train-rmse:1.995816+0.013512    test-rmse:2.130526+0.086110 
## [7]  train-rmse:1.750353+0.017749    test-rmse:1.915340+0.061707 
## [8]  train-rmse:1.593503+0.014643    test-rmse:1.799107+0.078185 
## [9]  train-rmse:1.479830+0.018252    test-rmse:1.720087+0.072665 
## [10] train-rmse:1.395931+0.023109    test-rmse:1.665359+0.070904 
## [11] train-rmse:1.330765+0.022663    test-rmse:1.620733+0.082367 
## [12] train-rmse:1.280601+0.023381    test-rmse:1.576393+0.097905 
## [13] train-rmse:1.233297+0.030122    test-rmse:1.536900+0.106087 
## [14] train-rmse:1.187275+0.027349    test-rmse:1.509927+0.119479 
## [15] train-rmse:1.150766+0.023464    test-rmse:1.489549+0.123890 
## [16] train-rmse:1.117500+0.025177    test-rmse:1.467812+0.135894 
## [17] train-rmse:1.086163+0.024309    test-rmse:1.449498+0.140498 
## [18] train-rmse:1.054939+0.023378    test-rmse:1.434602+0.138364 
## [19] train-rmse:1.025627+0.025276    test-rmse:1.424108+0.144000 
## [20] train-rmse:1.001011+0.027693    test-rmse:1.415727+0.151618 
## [21] train-rmse:0.973879+0.028230    test-rmse:1.386825+0.139926 
## [22] train-rmse:0.952095+0.029280    test-rmse:1.381445+0.148554 
## [23] train-rmse:0.928369+0.028813    test-rmse:1.366710+0.153353 
## [24] train-rmse:0.904088+0.026004    test-rmse:1.358343+0.157914 
## [25] train-rmse:0.884732+0.025332    test-rmse:1.353576+0.160390 
## [26] train-rmse:0.865982+0.024844    test-rmse:1.348165+0.161131 
## [27] train-rmse:0.847971+0.024123    test-rmse:1.332368+0.167056 
## [28] train-rmse:0.831434+0.021000    test-rmse:1.325628+0.177565 
## [29] train-rmse:0.814950+0.021506    test-rmse:1.319239+0.178597 
## [30] train-rmse:0.799812+0.021270    test-rmse:1.316468+0.177797 
## [31] train-rmse:0.784395+0.020877    test-rmse:1.311838+0.174436 
## [32] train-rmse:0.772678+0.018749    test-rmse:1.307425+0.179099 
## [33] train-rmse:0.756417+0.017350    test-rmse:1.300404+0.179030 
## [34] train-rmse:0.740192+0.017189    test-rmse:1.294471+0.184905 
## [35] train-rmse:0.724325+0.020365    test-rmse:1.290441+0.187923 
## [36] train-rmse:0.713970+0.019874    test-rmse:1.289374+0.191514 
## [37] train-rmse:0.702415+0.022447    test-rmse:1.283382+0.193252 
## [38] train-rmse:0.692460+0.022297    test-rmse:1.282054+0.191021 
## [39] train-rmse:0.683320+0.022479    test-rmse:1.282419+0.193784 
## [40] train-rmse:0.673863+0.021669    test-rmse:1.282411+0.189800 
## [41] train-rmse:0.666310+0.021771    test-rmse:1.279759+0.190831 
## [42] train-rmse:0.656412+0.021923    test-rmse:1.273913+0.192641 
## [43] train-rmse:0.645751+0.022920    test-rmse:1.277277+0.192136 
## [44] train-rmse:0.636568+0.023144    test-rmse:1.274497+0.197069 
## [45] train-rmse:0.630673+0.022734    test-rmse:1.273024+0.197577 
## [46] train-rmse:0.623581+0.022451    test-rmse:1.271019+0.200306 
## [47] train-rmse:0.617438+0.023367    test-rmse:1.268149+0.197268 
## [48] train-rmse:0.609639+0.023216    test-rmse:1.269483+0.196781 
## [49] train-rmse:0.602689+0.022902    test-rmse:1.263422+0.193882 
## [50] train-rmse:0.593417+0.020877    test-rmse:1.261944+0.194602 
## [51] train-rmse:0.584496+0.020974    test-rmse:1.261099+0.192740 
## [52] train-rmse:0.577913+0.020563    test-rmse:1.259687+0.191965 
## [53] train-rmse:0.569245+0.018780    test-rmse:1.259880+0.191558 
## [54] train-rmse:0.561065+0.019254    test-rmse:1.260413+0.193728 
## [55] train-rmse:0.556073+0.019616    test-rmse:1.257602+0.194027 
## [56] train-rmse:0.549898+0.021442    test-rmse:1.256088+0.193448 
## [57] train-rmse:0.544296+0.019621    test-rmse:1.257160+0.192454 
## [58] train-rmse:0.538999+0.019461    test-rmse:1.251789+0.190438 
## [59] train-rmse:0.533434+0.018097    test-rmse:1.251407+0.190904 
## [60] train-rmse:0.527890+0.019101    test-rmse:1.248537+0.190208 
## [61] train-rmse:0.523374+0.018603    test-rmse:1.247889+0.190177 
## [62] train-rmse:0.518160+0.018642    test-rmse:1.244724+0.190593 
## [63] train-rmse:0.508506+0.017189    test-rmse:1.241954+0.191461 
## [64] train-rmse:0.504410+0.016138    test-rmse:1.242755+0.191083 
## [65] train-rmse:0.500459+0.015289    test-rmse:1.242390+0.189635 
## [66] train-rmse:0.496072+0.015487    test-rmse:1.244425+0.187807 
## [67] train-rmse:0.491925+0.015572    test-rmse:1.243903+0.186820 
## [68] train-rmse:0.488000+0.015293    test-rmse:1.244367+0.187416 
## [69] train-rmse:0.484895+0.015681    test-rmse:1.245667+0.188938 
## Stopping. Best iteration:
## [63] train-rmse:0.508506+0.017189    test-rmse:1.241954+0.191461
## 
## 74 
## [1]  train-rmse:6.957476+0.051519    test-rmse:6.960685+0.253914 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.064951+0.036454    test-rmse:5.084575+0.225193 
## [3]  train-rmse:3.771214+0.031876    test-rmse:3.807530+0.173757 
## [4]  train-rmse:2.886489+0.020975    test-rmse:2.945040+0.152141 
## [5]  train-rmse:2.284456+0.022540    test-rmse:2.393352+0.105654 
## [6]  train-rmse:1.892346+0.022109    test-rmse:2.061231+0.099727 
## [7]  train-rmse:1.636293+0.021683    test-rmse:1.866190+0.085519 
## [8]  train-rmse:1.456368+0.025159    test-rmse:1.724098+0.072327 
## [9]  train-rmse:1.327642+0.018899    test-rmse:1.647202+0.077034 
## [10] train-rmse:1.233761+0.026549    test-rmse:1.576306+0.085616 
## [11] train-rmse:1.163670+0.028394    test-rmse:1.525641+0.090559 
## [12] train-rmse:1.101621+0.029027    test-rmse:1.487537+0.094965 
## [13] train-rmse:1.047729+0.025581    test-rmse:1.469800+0.106406 
## [14] train-rmse:1.001086+0.027234    test-rmse:1.439632+0.115630 
## [15] train-rmse:0.960435+0.024148    test-rmse:1.420220+0.125516 
## [16] train-rmse:0.925016+0.024305    test-rmse:1.407455+0.133537 
## [17] train-rmse:0.888852+0.027270    test-rmse:1.395033+0.127722 
## [18] train-rmse:0.862594+0.025204    test-rmse:1.383917+0.134679 
## [19] train-rmse:0.831319+0.022690    test-rmse:1.370676+0.142291 
## [20] train-rmse:0.809403+0.023274    test-rmse:1.365354+0.150881 
## [21] train-rmse:0.784474+0.022501    test-rmse:1.352769+0.155129 
## [22] train-rmse:0.763492+0.022966    test-rmse:1.346985+0.153630 
## [23] train-rmse:0.738762+0.018480    test-rmse:1.338614+0.159707 
## [24] train-rmse:0.721167+0.017008    test-rmse:1.335508+0.160127 
## [25] train-rmse:0.701914+0.015247    test-rmse:1.328210+0.163657 
## [26] train-rmse:0.681357+0.015057    test-rmse:1.319847+0.166506 
## [27] train-rmse:0.667183+0.015029    test-rmse:1.309189+0.170453 
## [28] train-rmse:0.650326+0.017827    test-rmse:1.302056+0.177210 
## [29] train-rmse:0.635911+0.017061    test-rmse:1.298969+0.175755 
## [30] train-rmse:0.622535+0.016352    test-rmse:1.296188+0.172353 
## [31] train-rmse:0.610045+0.014894    test-rmse:1.294015+0.173299 
## [32] train-rmse:0.596850+0.015009    test-rmse:1.288548+0.173084 
## [33] train-rmse:0.584882+0.016012    test-rmse:1.290381+0.171500 
## [34] train-rmse:0.573242+0.014834    test-rmse:1.284100+0.175793 
## [35] train-rmse:0.562783+0.014745    test-rmse:1.281533+0.174343 
## [36] train-rmse:0.555005+0.014369    test-rmse:1.279095+0.175694 
## [37] train-rmse:0.545194+0.014429    test-rmse:1.279123+0.176652 
## [38] train-rmse:0.536970+0.016489    test-rmse:1.283067+0.175144 
## [39] train-rmse:0.527474+0.019238    test-rmse:1.282654+0.175649 
## [40] train-rmse:0.515769+0.017506    test-rmse:1.278333+0.176344 
## [41] train-rmse:0.508096+0.017752    test-rmse:1.274457+0.179814 
## [42] train-rmse:0.497988+0.015741    test-rmse:1.274206+0.182756 
## [43] train-rmse:0.491212+0.015288    test-rmse:1.273846+0.184916 
## [44] train-rmse:0.482427+0.013446    test-rmse:1.268564+0.185573 
## [45] train-rmse:0.472005+0.013512    test-rmse:1.265561+0.184273 
## [46] train-rmse:0.466910+0.013208    test-rmse:1.262272+0.184533 
## [47] train-rmse:0.458780+0.011890    test-rmse:1.263700+0.184072 
## [48] train-rmse:0.450859+0.009922    test-rmse:1.263782+0.183404 
## [49] train-rmse:0.445730+0.009433    test-rmse:1.265273+0.186093 
## [50] train-rmse:0.438847+0.009777    test-rmse:1.265457+0.187111 
## [51] train-rmse:0.433287+0.010672    test-rmse:1.267688+0.187168 
## [52] train-rmse:0.423765+0.012099    test-rmse:1.264956+0.183772 
## Stopping. Best iteration:
## [46] train-rmse:0.466910+0.013208    test-rmse:1.262272+0.184533
## 
## 75 
## [1]  train-rmse:6.956108+0.051839    test-rmse:6.961522+0.244604 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.054462+0.036840    test-rmse:5.076050+0.223101 
## [3]  train-rmse:3.746282+0.030914    test-rmse:3.785072+0.168559 
## [4]  train-rmse:2.846803+0.025426    test-rmse:2.920417+0.129121 
## [5]  train-rmse:2.246829+0.021828    test-rmse:2.379318+0.120412 
## [6]  train-rmse:1.839171+0.031675    test-rmse:2.036993+0.106466 
## [7]  train-rmse:1.549160+0.033794    test-rmse:1.799751+0.079407 
## [8]  train-rmse:1.352810+0.030775    test-rmse:1.663677+0.080935 
## [9]  train-rmse:1.207378+0.022201    test-rmse:1.572177+0.090232 
## [10] train-rmse:1.115393+0.018543    test-rmse:1.522454+0.108742 
## [11] train-rmse:1.038430+0.022054    test-rmse:1.497174+0.123508 
## [12] train-rmse:0.971808+0.022622    test-rmse:1.470290+0.138164 
## [13] train-rmse:0.922514+0.020185    test-rmse:1.440326+0.154858 
## [14] train-rmse:0.871694+0.029068    test-rmse:1.415605+0.149860 
## [15] train-rmse:0.828060+0.020925    test-rmse:1.380614+0.168754 
## [16] train-rmse:0.797754+0.021377    test-rmse:1.369333+0.176585 
## [17] train-rmse:0.764788+0.024144    test-rmse:1.360442+0.186575 
## [18] train-rmse:0.730406+0.019112    test-rmse:1.346868+0.188292 
## [19] train-rmse:0.701094+0.015505    test-rmse:1.322397+0.184389 
## [20] train-rmse:0.678917+0.013517    test-rmse:1.313589+0.190722 
## [21] train-rmse:0.655745+0.013904    test-rmse:1.304740+0.193333 
## [22] train-rmse:0.634463+0.012746    test-rmse:1.303000+0.196482 
## [23] train-rmse:0.614085+0.012873    test-rmse:1.300859+0.199358 
## [24] train-rmse:0.594882+0.009830    test-rmse:1.293688+0.197003 
## [25] train-rmse:0.577248+0.007549    test-rmse:1.289853+0.195822 
## [26] train-rmse:0.560652+0.006196    test-rmse:1.281425+0.195767 
## [27] train-rmse:0.545897+0.007735    test-rmse:1.282722+0.193984 
## [28] train-rmse:0.530380+0.007321    test-rmse:1.282217+0.198121 
## [29] train-rmse:0.517476+0.006680    test-rmse:1.281887+0.196099 
## [30] train-rmse:0.505665+0.010934    test-rmse:1.277860+0.195780 
## [31] train-rmse:0.492270+0.007793    test-rmse:1.271327+0.192709 
## [32] train-rmse:0.477915+0.009453    test-rmse:1.268032+0.193349 
## [33] train-rmse:0.467317+0.008071    test-rmse:1.268226+0.195533 
## [34] train-rmse:0.453008+0.003376    test-rmse:1.262606+0.193554 
## [35] train-rmse:0.445563+0.005214    test-rmse:1.266177+0.190294 
## [36] train-rmse:0.434632+0.004790    test-rmse:1.263342+0.188565 
## [37] train-rmse:0.425736+0.004583    test-rmse:1.259607+0.191213 
## [38] train-rmse:0.418550+0.005004    test-rmse:1.261492+0.193482 
## [39] train-rmse:0.408349+0.005773    test-rmse:1.257071+0.191778 
## [40] train-rmse:0.400473+0.007733    test-rmse:1.254544+0.190685 
## [41] train-rmse:0.390919+0.009934    test-rmse:1.254325+0.190926 
## [42] train-rmse:0.384723+0.009355    test-rmse:1.252985+0.192196 
## [43] train-rmse:0.378665+0.007782    test-rmse:1.254203+0.193370 
## [44] train-rmse:0.370615+0.006512    test-rmse:1.254166+0.193184 
## [45] train-rmse:0.365747+0.006330    test-rmse:1.255518+0.194895 
## [46] train-rmse:0.358150+0.005703    test-rmse:1.255876+0.195414 
## [47] train-rmse:0.351694+0.005735    test-rmse:1.261118+0.192708 
## [48] train-rmse:0.344824+0.004771    test-rmse:1.261759+0.192454 
## Stopping. Best iteration:
## [42] train-rmse:0.384723+0.009355    test-rmse:1.252985+0.192196
## 
## 76 
## [1]  train-rmse:6.956108+0.051839    test-rmse:6.961522+0.244604 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:5.049765+0.037977    test-rmse:5.076842+0.210563 
## [3]  train-rmse:3.730874+0.032998    test-rmse:3.782795+0.156028 
## [4]  train-rmse:2.825443+0.030110    test-rmse:2.918628+0.126710 
## [5]  train-rmse:2.203614+0.028109    test-rmse:2.346965+0.103274 
## [6]  train-rmse:1.776315+0.028609    test-rmse:1.984866+0.084976 
## [7]  train-rmse:1.483040+0.034485    test-rmse:1.774504+0.080641 
## [8]  train-rmse:1.272266+0.033933    test-rmse:1.636192+0.087709 
## [9]  train-rmse:1.119770+0.027854    test-rmse:1.540157+0.095107 
## [10] train-rmse:1.014519+0.028226    test-rmse:1.485351+0.092562 
## [11] train-rmse:0.934641+0.024741    test-rmse:1.441669+0.101424 
## [12] train-rmse:0.873987+0.023933    test-rmse:1.418842+0.125688 
## [13] train-rmse:0.817091+0.026388    test-rmse:1.389932+0.131146 
## [14] train-rmse:0.770884+0.027159    test-rmse:1.377017+0.144559 
## [15] train-rmse:0.728004+0.019690    test-rmse:1.364180+0.159270 
## [16] train-rmse:0.691620+0.020171    test-rmse:1.355867+0.162691 
## [17] train-rmse:0.661624+0.021292    test-rmse:1.341917+0.164514 
## [18] train-rmse:0.630876+0.022611    test-rmse:1.328997+0.171700 
## [19] train-rmse:0.605335+0.018659    test-rmse:1.322453+0.175789 
## [20] train-rmse:0.583856+0.021143    test-rmse:1.318685+0.182054 
## [21] train-rmse:0.561790+0.017602    test-rmse:1.315538+0.183057 
## [22] train-rmse:0.538397+0.013986    test-rmse:1.304086+0.182943 
## [23] train-rmse:0.520856+0.010778    test-rmse:1.301368+0.182554 
## [24] train-rmse:0.501697+0.010426    test-rmse:1.296311+0.176669 
## [25] train-rmse:0.484856+0.011129    test-rmse:1.295086+0.180892 
## [26] train-rmse:0.469408+0.010211    test-rmse:1.291170+0.180283 
## [27] train-rmse:0.458194+0.011261    test-rmse:1.287949+0.183571 
## [28] train-rmse:0.443567+0.010438    test-rmse:1.287372+0.180290 
## [29] train-rmse:0.431295+0.009654    test-rmse:1.286595+0.182664 
## [30] train-rmse:0.416082+0.012345    test-rmse:1.283335+0.184858 
## [31] train-rmse:0.405520+0.014381    test-rmse:1.284428+0.185480 
## [32] train-rmse:0.393214+0.016615    test-rmse:1.285912+0.187926 
## [33] train-rmse:0.380334+0.015686    test-rmse:1.285983+0.182533 
## [34] train-rmse:0.372779+0.014986    test-rmse:1.284630+0.183209 
## [35] train-rmse:0.364200+0.015166    test-rmse:1.286130+0.183153 
## [36] train-rmse:0.352325+0.014422    test-rmse:1.286588+0.184321 
## Stopping. Best iteration:
## [30] train-rmse:0.416082+0.012345    test-rmse:1.283335+0.184858
## 
## 77 
## [1]  train-rmse:6.851487+0.046483    test-rmse:6.847448+0.262610 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.990714+0.027764    test-rmse:4.985845+0.254116 
## [3]  train-rmse:3.814792+0.017594    test-rmse:3.817925+0.219340 
## [4]  train-rmse:3.106002+0.013457    test-rmse:3.126478+0.164517 
## [5]  train-rmse:2.699505+0.013790    test-rmse:2.735479+0.113631 
## [6]  train-rmse:2.472553+0.015458    test-rmse:2.502477+0.069684 
## [7]  train-rmse:2.344862+0.016634    test-rmse:2.390355+0.053050 
## [8]  train-rmse:2.271348+0.017608    test-rmse:2.322438+0.052451 
## [9]  train-rmse:2.224043+0.018702    test-rmse:2.262377+0.055691 
## [10] train-rmse:2.191138+0.020063    test-rmse:2.234235+0.055336 
## [11] train-rmse:2.164846+0.021077    test-rmse:2.209676+0.077110 
## [12] train-rmse:2.141263+0.021289    test-rmse:2.189842+0.077671 
## [13] train-rmse:2.119387+0.022269    test-rmse:2.170167+0.078974 
## [14] train-rmse:2.099929+0.023253    test-rmse:2.150137+0.073041 
## [15] train-rmse:2.080804+0.024030    test-rmse:2.138265+0.078466 
## [16] train-rmse:2.063761+0.024821    test-rmse:2.125065+0.083219 
## [17] train-rmse:2.047102+0.026196    test-rmse:2.117929+0.087410 
## [18] train-rmse:2.031492+0.026735    test-rmse:2.107605+0.090061 
## [19] train-rmse:2.016050+0.027460    test-rmse:2.090915+0.095846 
## [20] train-rmse:2.000747+0.028072    test-rmse:2.077945+0.095233 
## [21] train-rmse:1.985849+0.028639    test-rmse:2.064429+0.095416 
## [22] train-rmse:1.971468+0.029897    test-rmse:2.043203+0.089551 
## [23] train-rmse:1.958562+0.030789    test-rmse:2.024807+0.092392 
## [24] train-rmse:1.946517+0.031012    test-rmse:2.019592+0.092930 
## [25] train-rmse:1.934554+0.031278    test-rmse:2.010890+0.091981 
## [26] train-rmse:1.922991+0.031816    test-rmse:1.997140+0.085441 
## [27] train-rmse:1.911421+0.031880    test-rmse:1.990705+0.086621 
## [28] train-rmse:1.900273+0.032186    test-rmse:1.988537+0.088319 
## [29] train-rmse:1.888948+0.032542    test-rmse:1.977284+0.091570 
## [30] train-rmse:1.878079+0.033287    test-rmse:1.966508+0.085757 
## [31] train-rmse:1.867319+0.034064    test-rmse:1.953570+0.082621 
## [32] train-rmse:1.857409+0.034967    test-rmse:1.947108+0.084551 
## [33] train-rmse:1.847398+0.036207    test-rmse:1.939439+0.081369 
## [34] train-rmse:1.837499+0.037121    test-rmse:1.928138+0.079471 
## [35] train-rmse:1.827808+0.037713    test-rmse:1.917650+0.086741 
## [36] train-rmse:1.818156+0.038454    test-rmse:1.908917+0.088323 
## [37] train-rmse:1.808861+0.039087    test-rmse:1.904296+0.092145 
## [38] train-rmse:1.799975+0.039720    test-rmse:1.893112+0.095728 
## [39] train-rmse:1.791366+0.040130    test-rmse:1.884879+0.106465 
## [40] train-rmse:1.782769+0.040424    test-rmse:1.880015+0.106288 
## [41] train-rmse:1.774114+0.041252    test-rmse:1.874315+0.103168 
## [42] train-rmse:1.765856+0.041574    test-rmse:1.866505+0.103543 
## [43] train-rmse:1.757700+0.041971    test-rmse:1.864639+0.100747 
## [44] train-rmse:1.749874+0.042478    test-rmse:1.855099+0.107549 
## [45] train-rmse:1.742356+0.042703    test-rmse:1.855794+0.105471 
## [46] train-rmse:1.735109+0.042990    test-rmse:1.848055+0.106203 
## [47] train-rmse:1.728034+0.043243    test-rmse:1.839391+0.101617 
## [48] train-rmse:1.720950+0.043564    test-rmse:1.830151+0.099190 
## [49] train-rmse:1.714004+0.043996    test-rmse:1.825967+0.098649 
## [50] train-rmse:1.707128+0.044359    test-rmse:1.815854+0.097441 
## [51] train-rmse:1.700547+0.044588    test-rmse:1.806424+0.098048 
## [52] train-rmse:1.694085+0.044740    test-rmse:1.803878+0.099749 
## [53] train-rmse:1.687627+0.044948    test-rmse:1.798568+0.096282 
## [54] train-rmse:1.681314+0.045154    test-rmse:1.791725+0.099192 
## [55] train-rmse:1.675152+0.045459    test-rmse:1.786515+0.099190 
## [56] train-rmse:1.669098+0.045748    test-rmse:1.783671+0.105721 
## [57] train-rmse:1.663366+0.046077    test-rmse:1.780154+0.103999 
## [58] train-rmse:1.657776+0.046324    test-rmse:1.778578+0.103779 
## [59] train-rmse:1.652063+0.046599    test-rmse:1.777533+0.108902 
## [60] train-rmse:1.646500+0.046891    test-rmse:1.776520+0.104119 
## [61] train-rmse:1.640950+0.047319    test-rmse:1.772969+0.105735 
## [62] train-rmse:1.635454+0.047449    test-rmse:1.766248+0.106563 
## [63] train-rmse:1.630163+0.047682    test-rmse:1.762254+0.111616 
## [64] train-rmse:1.624940+0.048066    test-rmse:1.754410+0.112536 
## [65] train-rmse:1.619845+0.048270    test-rmse:1.753026+0.117386 
## [66] train-rmse:1.614876+0.048733    test-rmse:1.749853+0.115666 
## [67] train-rmse:1.609928+0.049074    test-rmse:1.736443+0.119589 
## [68] train-rmse:1.604966+0.049409    test-rmse:1.733364+0.122146 
## [69] train-rmse:1.600228+0.049595    test-rmse:1.728875+0.119680 
## [70] train-rmse:1.595579+0.049851    test-rmse:1.725334+0.121297 
## [71] train-rmse:1.591092+0.050139    test-rmse:1.721689+0.123867 
## [72] train-rmse:1.586577+0.050336    test-rmse:1.716267+0.121802 
## [73] train-rmse:1.582063+0.050395    test-rmse:1.712479+0.120194 
## [74] train-rmse:1.577846+0.050458    test-rmse:1.707582+0.124157 
## [75] train-rmse:1.573660+0.050457    test-rmse:1.702084+0.127474 
## [76] train-rmse:1.569654+0.050448    test-rmse:1.694731+0.130047 
## [77] train-rmse:1.565699+0.050491    test-rmse:1.690730+0.128458 
## [78] train-rmse:1.561701+0.050516    test-rmse:1.684897+0.128476 
## [79] train-rmse:1.557789+0.050739    test-rmse:1.682260+0.133483 
## [80] train-rmse:1.553823+0.050808    test-rmse:1.678778+0.133900 
## [81] train-rmse:1.549987+0.050854    test-rmse:1.678070+0.134622 
## [82] train-rmse:1.546121+0.050960    test-rmse:1.677193+0.132881 
## [83] train-rmse:1.542384+0.051043    test-rmse:1.672491+0.135070 
## [84] train-rmse:1.538871+0.051063    test-rmse:1.670070+0.132668 
## [85] train-rmse:1.535302+0.051153    test-rmse:1.666294+0.132230 
## [86] train-rmse:1.531811+0.051167    test-rmse:1.664188+0.135442 
## [87] train-rmse:1.528428+0.051133    test-rmse:1.658065+0.139594 
## [88] train-rmse:1.525140+0.051141    test-rmse:1.657056+0.137945 
## [89] train-rmse:1.521933+0.051196    test-rmse:1.656432+0.141947 
## [90] train-rmse:1.518712+0.051193    test-rmse:1.649580+0.139982 
## [91] train-rmse:1.515569+0.051183    test-rmse:1.644473+0.141251 
## [92] train-rmse:1.512452+0.051191    test-rmse:1.643003+0.141049 
## [93] train-rmse:1.509322+0.051261    test-rmse:1.639425+0.143218 
## [94] train-rmse:1.506273+0.051379    test-rmse:1.637136+0.144037 
## [95] train-rmse:1.503254+0.051438    test-rmse:1.639333+0.146411 
## [96] train-rmse:1.500191+0.051542    test-rmse:1.634579+0.145098 
## [97] train-rmse:1.497199+0.051677    test-rmse:1.635228+0.148694 
## [98] train-rmse:1.494266+0.051836    test-rmse:1.633046+0.149669 
## [99] train-rmse:1.491468+0.052039    test-rmse:1.630511+0.150362 
## [100]    train-rmse:1.488757+0.052139    test-rmse:1.627585+0.151190 
## [101]    train-rmse:1.486095+0.052265    test-rmse:1.621521+0.151045 
## [102]    train-rmse:1.483485+0.052359    test-rmse:1.618896+0.151687 
## [103]    train-rmse:1.480894+0.052460    test-rmse:1.616067+0.149386 
## [104]    train-rmse:1.478361+0.052560    test-rmse:1.609877+0.146813 
## [105]    train-rmse:1.475798+0.052681    test-rmse:1.606426+0.146569 
## [106]    train-rmse:1.473336+0.052783    test-rmse:1.606286+0.146347 
## [107]    train-rmse:1.470922+0.052928    test-rmse:1.604890+0.151136 
## [108]    train-rmse:1.468536+0.053032    test-rmse:1.603320+0.150434 
## [109]    train-rmse:1.466060+0.053166    test-rmse:1.601785+0.150170 
## [110]    train-rmse:1.463707+0.053226    test-rmse:1.600121+0.151423 
## [111]    train-rmse:1.461429+0.053260    test-rmse:1.598704+0.153041 
## [112]    train-rmse:1.459171+0.053289    test-rmse:1.595884+0.153311 
## [113]    train-rmse:1.456891+0.053269    test-rmse:1.593819+0.153226 
## [114]    train-rmse:1.454662+0.053296    test-rmse:1.589611+0.153261 
## [115]    train-rmse:1.452469+0.053281    test-rmse:1.587225+0.153067 
## [116]    train-rmse:1.450343+0.053316    test-rmse:1.585380+0.158386 
## [117]    train-rmse:1.448258+0.053370    test-rmse:1.584081+0.157411 
## [118]    train-rmse:1.446181+0.053387    test-rmse:1.586667+0.160176 
## [119]    train-rmse:1.444114+0.053358    test-rmse:1.587050+0.164463 
## [120]    train-rmse:1.442115+0.053351    test-rmse:1.585549+0.162110 
## [121]    train-rmse:1.440161+0.053318    test-rmse:1.585396+0.160319 
## [122]    train-rmse:1.438239+0.053281    test-rmse:1.581048+0.162808 
## [123]    train-rmse:1.436381+0.053250    test-rmse:1.577991+0.162825 
## [124]    train-rmse:1.434473+0.053200    test-rmse:1.575391+0.163594 
## [125]    train-rmse:1.432563+0.053126    test-rmse:1.573831+0.164455 
## [126]    train-rmse:1.430770+0.053126    test-rmse:1.571102+0.164383 
## [127]    train-rmse:1.428961+0.053145    test-rmse:1.566957+0.165635 
## [128]    train-rmse:1.427183+0.053162    test-rmse:1.565202+0.165652 
## [129]    train-rmse:1.425461+0.053196    test-rmse:1.559906+0.163374 
## [130]    train-rmse:1.423734+0.053204    test-rmse:1.561271+0.164282 
## [131]    train-rmse:1.422078+0.053249    test-rmse:1.559372+0.164822 
## [132]    train-rmse:1.420441+0.053297    test-rmse:1.558291+0.167857 
## [133]    train-rmse:1.418798+0.053305    test-rmse:1.556713+0.167573 
## [134]    train-rmse:1.417197+0.053324    test-rmse:1.556143+0.168320 
## [135]    train-rmse:1.415612+0.053340    test-rmse:1.553917+0.168224 
## [136]    train-rmse:1.414008+0.053314    test-rmse:1.552166+0.169612 
## [137]    train-rmse:1.412466+0.053277    test-rmse:1.548632+0.169508 
## [138]    train-rmse:1.410910+0.053221    test-rmse:1.544935+0.171153 
## [139]    train-rmse:1.409421+0.053201    test-rmse:1.544411+0.170971 
## [140]    train-rmse:1.407953+0.053227    test-rmse:1.542398+0.170213 
## [141]    train-rmse:1.406523+0.053268    test-rmse:1.542686+0.168268 
## [142]    train-rmse:1.405081+0.053307    test-rmse:1.542917+0.167106 
## [143]    train-rmse:1.403608+0.053410    test-rmse:1.545771+0.169286 
## [144]    train-rmse:1.402234+0.053426    test-rmse:1.544569+0.172931 
## [145]    train-rmse:1.400892+0.053443    test-rmse:1.544281+0.171450 
## [146]    train-rmse:1.399540+0.053460    test-rmse:1.545155+0.171479 
## Stopping. Best iteration:
## [140]    train-rmse:1.407953+0.053227    test-rmse:1.542398+0.170213
## 
## 78 
## [1]  train-rmse:6.807448+0.047018    test-rmse:6.803991+0.256594 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.906122+0.028943    test-rmse:4.913806+0.234815 
## [3]  train-rmse:3.682603+0.017113    test-rmse:3.706251+0.208314 
## [4]  train-rmse:2.916032+0.012017    test-rmse:2.955440+0.153475 
## [5]  train-rmse:2.463461+0.017567    test-rmse:2.523779+0.119446 
## [6]  train-rmse:2.197402+0.023518    test-rmse:2.297415+0.072819 
## [7]  train-rmse:2.034017+0.024755    test-rmse:2.157662+0.049095 
## [8]  train-rmse:1.935739+0.027858    test-rmse:2.072712+0.063196 
## [9]  train-rmse:1.865576+0.038389    test-rmse:1.994016+0.048918 
## [10] train-rmse:1.816771+0.041139    test-rmse:1.955717+0.059990 
## [11] train-rmse:1.777732+0.043632    test-rmse:1.933448+0.058321 
## [12] train-rmse:1.744651+0.044060    test-rmse:1.895937+0.054731 
## [13] train-rmse:1.711520+0.050154    test-rmse:1.866305+0.054802 
## [14] train-rmse:1.675956+0.054860    test-rmse:1.839825+0.059366 
## [15] train-rmse:1.649960+0.057284    test-rmse:1.822026+0.059110 
## [16] train-rmse:1.624569+0.059394    test-rmse:1.798114+0.055387 
## [17] train-rmse:1.600471+0.062361    test-rmse:1.788229+0.055628 
## [18] train-rmse:1.570958+0.059265    test-rmse:1.767870+0.047214 
## [19] train-rmse:1.548693+0.060211    test-rmse:1.754007+0.042073 
## [20] train-rmse:1.526571+0.064231    test-rmse:1.742391+0.044491 
## [21] train-rmse:1.506048+0.065714    test-rmse:1.732253+0.055197 
## [22] train-rmse:1.486750+0.065165    test-rmse:1.716822+0.054780 
## [23] train-rmse:1.468828+0.067513    test-rmse:1.702010+0.054851 
## [24] train-rmse:1.452186+0.067629    test-rmse:1.686951+0.057454 
## [25] train-rmse:1.429601+0.068293    test-rmse:1.666930+0.068077 
## [26] train-rmse:1.412867+0.068533    test-rmse:1.665337+0.070383 
## [27] train-rmse:1.396871+0.069228    test-rmse:1.653549+0.068049 
## [28] train-rmse:1.380821+0.071026    test-rmse:1.637591+0.067518 
## [29] train-rmse:1.362458+0.074260    test-rmse:1.622466+0.077162 
## [30] train-rmse:1.346926+0.073521    test-rmse:1.599316+0.086080 
## [31] train-rmse:1.322451+0.075647    test-rmse:1.590106+0.085502 
## [32] train-rmse:1.303835+0.079369    test-rmse:1.574571+0.078618 
## [33] train-rmse:1.291205+0.081191    test-rmse:1.568441+0.080835 
## [34] train-rmse:1.275936+0.083075    test-rmse:1.562156+0.081846 
## [35] train-rmse:1.264888+0.083491    test-rmse:1.557024+0.083266 
## [36] train-rmse:1.252506+0.081819    test-rmse:1.550033+0.085047 
## [37] train-rmse:1.241250+0.082209    test-rmse:1.542890+0.083780 
## [38] train-rmse:1.211957+0.059936    test-rmse:1.509860+0.095190 
## [39] train-rmse:1.201447+0.059734    test-rmse:1.501221+0.099818 
## [40] train-rmse:1.187630+0.062666    test-rmse:1.495133+0.105689 
## [41] train-rmse:1.177973+0.062565    test-rmse:1.486329+0.107526 
## [42] train-rmse:1.167156+0.059338    test-rmse:1.475604+0.109158 
## [43] train-rmse:1.157996+0.060036    test-rmse:1.469753+0.110842 
## [44] train-rmse:1.148796+0.061313    test-rmse:1.461953+0.113869 
## [45] train-rmse:1.135640+0.063489    test-rmse:1.445154+0.110428 
## [46] train-rmse:1.126962+0.064346    test-rmse:1.439946+0.112521 
## [47] train-rmse:1.111957+0.052754    test-rmse:1.433946+0.119304 
## [48] train-rmse:1.098835+0.057416    test-rmse:1.423554+0.120032 
## [49] train-rmse:1.091230+0.058983    test-rmse:1.420994+0.119147 
## [50] train-rmse:1.083797+0.058426    test-rmse:1.420789+0.124262 
## [51] train-rmse:1.076889+0.058680    test-rmse:1.412557+0.123217 
## [52] train-rmse:1.065786+0.061060    test-rmse:1.407510+0.128069 
## [53] train-rmse:1.059380+0.061133    test-rmse:1.407637+0.123920 
## [54] train-rmse:1.053059+0.061079    test-rmse:1.405041+0.129103 
## [55] train-rmse:1.047318+0.060961    test-rmse:1.399832+0.129792 
## [56] train-rmse:1.040686+0.062133    test-rmse:1.399656+0.131827 
## [57] train-rmse:1.033880+0.062511    test-rmse:1.395942+0.131026 
## [58] train-rmse:1.027251+0.064371    test-rmse:1.392866+0.131738 
## [59] train-rmse:1.019520+0.064314    test-rmse:1.385799+0.130540 
## [60] train-rmse:1.012628+0.066405    test-rmse:1.378537+0.133054 
## [61] train-rmse:1.006240+0.065653    test-rmse:1.378214+0.133501 
## [62] train-rmse:0.992683+0.053076    test-rmse:1.371415+0.143466 
## [63] train-rmse:0.988203+0.053436    test-rmse:1.366741+0.145333 
## [64] train-rmse:0.982994+0.054207    test-rmse:1.365115+0.147488 
## [65] train-rmse:0.978393+0.054247    test-rmse:1.360720+0.151541 
## [66] train-rmse:0.974154+0.054265    test-rmse:1.356759+0.153022 
## [67] train-rmse:0.970290+0.054307    test-rmse:1.355483+0.153074 
## [68] train-rmse:0.966300+0.054312    test-rmse:1.352824+0.153596 
## [69] train-rmse:0.962148+0.054827    test-rmse:1.352161+0.154679 
## [70] train-rmse:0.957270+0.053681    test-rmse:1.350814+0.154856 
## [71] train-rmse:0.947322+0.046472    test-rmse:1.343933+0.159583 
## [72] train-rmse:0.936101+0.037161    test-rmse:1.333901+0.165006 
## [73] train-rmse:0.926894+0.032943    test-rmse:1.317938+0.169251 
## [74] train-rmse:0.913898+0.042375    test-rmse:1.298919+0.155209 
## [75] train-rmse:0.910110+0.042730    test-rmse:1.298869+0.156793 
## [76] train-rmse:0.906253+0.042867    test-rmse:1.296456+0.157821 
## [77] train-rmse:0.900179+0.041385    test-rmse:1.295440+0.161991 
## [78] train-rmse:0.896406+0.041774    test-rmse:1.291783+0.160941 
## [79] train-rmse:0.892286+0.041753    test-rmse:1.293419+0.161071 
## [80] train-rmse:0.889055+0.041835    test-rmse:1.292631+0.161793 
## [81] train-rmse:0.885668+0.041366    test-rmse:1.291202+0.160355 
## [82] train-rmse:0.882973+0.041447    test-rmse:1.291797+0.158254 
## [83] train-rmse:0.877127+0.039384    test-rmse:1.283141+0.162945 
## [84] train-rmse:0.871544+0.039368    test-rmse:1.276959+0.167526 
## [85] train-rmse:0.868873+0.040020    test-rmse:1.272556+0.167320 
## [86] train-rmse:0.866418+0.040260    test-rmse:1.273478+0.172448 
## [87] train-rmse:0.859887+0.039099    test-rmse:1.270690+0.173632 
## [88] train-rmse:0.856425+0.039033    test-rmse:1.269426+0.174279 
## [89] train-rmse:0.854199+0.039099    test-rmse:1.269997+0.173655 
## [90] train-rmse:0.852210+0.039113    test-rmse:1.268865+0.173718 
## [91] train-rmse:0.845893+0.039389    test-rmse:1.254291+0.175829 
## [92] train-rmse:0.841669+0.041065    test-rmse:1.253049+0.175362 
## [93] train-rmse:0.838882+0.041058    test-rmse:1.254144+0.175662 
## [94] train-rmse:0.835982+0.040853    test-rmse:1.254114+0.175409 
## [95] train-rmse:0.833996+0.040797    test-rmse:1.254997+0.175684 
## [96] train-rmse:0.831560+0.040709    test-rmse:1.254285+0.176783 
## [97] train-rmse:0.828961+0.040667    test-rmse:1.256822+0.174225 
## [98] train-rmse:0.827084+0.040586    test-rmse:1.256752+0.175542 
## Stopping. Best iteration:
## [92] train-rmse:0.841669+0.041065    test-rmse:1.253049+0.175362
## 
## 79 
## [1]  train-rmse:6.790241+0.048909    test-rmse:6.787139+0.248991 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.857591+0.035862    test-rmse:4.863660+0.222247 
## [3]  train-rmse:3.598458+0.032193    test-rmse:3.618012+0.177607 
## [4]  train-rmse:2.793557+0.019725    test-rmse:2.847092+0.152938 
## [5]  train-rmse:2.293338+0.014301    test-rmse:2.381242+0.110237 
## [6]  train-rmse:1.988679+0.013873    test-rmse:2.111408+0.077349 
## [7]  train-rmse:1.803424+0.015920    test-rmse:1.975290+0.061205 
## [8]  train-rmse:1.685902+0.021971    test-rmse:1.872671+0.069426 
## [9]  train-rmse:1.594810+0.018065    test-rmse:1.788346+0.065186 
## [10] train-rmse:1.532182+0.021622    test-rmse:1.746769+0.058977 
## [11] train-rmse:1.483967+0.024032    test-rmse:1.706672+0.053525 
## [12] train-rmse:1.437338+0.023704    test-rmse:1.678895+0.054054 
## [13] train-rmse:1.397689+0.023609    test-rmse:1.651790+0.054399 
## [14] train-rmse:1.352839+0.029899    test-rmse:1.621277+0.059135 
## [15] train-rmse:1.317725+0.027162    test-rmse:1.593806+0.071322 
## [16] train-rmse:1.284755+0.031471    test-rmse:1.575405+0.076650 
## [17] train-rmse:1.253962+0.034134    test-rmse:1.560594+0.075643 
## [18] train-rmse:1.226745+0.030585    test-rmse:1.533498+0.079545 
## [19] train-rmse:1.196906+0.033098    test-rmse:1.497395+0.083023 
## [20] train-rmse:1.171996+0.033993    test-rmse:1.487438+0.096305 
## [21] train-rmse:1.146641+0.032258    test-rmse:1.472746+0.096208 
## [22] train-rmse:1.121347+0.030100    test-rmse:1.458977+0.099749 
## [23] train-rmse:1.100688+0.031286    test-rmse:1.446380+0.105027 
## [24] train-rmse:1.081470+0.030276    test-rmse:1.429507+0.107903 
## [25] train-rmse:1.056591+0.028685    test-rmse:1.417524+0.113310 
## [26] train-rmse:1.039802+0.027755    test-rmse:1.405062+0.119172 
## [27] train-rmse:1.023156+0.028711    test-rmse:1.396393+0.123083 
## [28] train-rmse:1.006032+0.031798    test-rmse:1.384818+0.120433 
## [29] train-rmse:0.989258+0.032584    test-rmse:1.376644+0.124947 
## [30] train-rmse:0.974136+0.032451    test-rmse:1.372476+0.127147 
## [31] train-rmse:0.958342+0.034165    test-rmse:1.366828+0.135175 
## [32] train-rmse:0.942858+0.033270    test-rmse:1.356043+0.136088 
## [33] train-rmse:0.926567+0.028675    test-rmse:1.348711+0.141588 
## [34] train-rmse:0.915123+0.028323    test-rmse:1.345825+0.139805 
## [35] train-rmse:0.900269+0.028461    test-rmse:1.335015+0.150846 
## [36] train-rmse:0.881266+0.035289    test-rmse:1.316841+0.148800 
## [37] train-rmse:0.869105+0.034154    test-rmse:1.310278+0.149006 
## [38] train-rmse:0.857027+0.032710    test-rmse:1.302438+0.150324 
## [39] train-rmse:0.846471+0.032067    test-rmse:1.301475+0.148938 
## [40] train-rmse:0.836389+0.031481    test-rmse:1.296520+0.149291 
## [41] train-rmse:0.826516+0.032423    test-rmse:1.290488+0.148893 
## [42] train-rmse:0.815540+0.032536    test-rmse:1.283520+0.154892 
## [43] train-rmse:0.804239+0.030307    test-rmse:1.284108+0.161876 
## [44] train-rmse:0.794013+0.030333    test-rmse:1.280359+0.162131 
## [45] train-rmse:0.785365+0.031601    test-rmse:1.276007+0.164752 
## [46] train-rmse:0.773460+0.037338    test-rmse:1.270956+0.161976 
## [47] train-rmse:0.765067+0.037510    test-rmse:1.263517+0.160366 
## [48] train-rmse:0.758113+0.036454    test-rmse:1.257592+0.162397 
## [49] train-rmse:0.751161+0.037610    test-rmse:1.256950+0.163546 
## [50] train-rmse:0.744538+0.036849    test-rmse:1.256246+0.163844 
## [51] train-rmse:0.736493+0.036731    test-rmse:1.252805+0.159438 
## [52] train-rmse:0.729081+0.038589    test-rmse:1.248236+0.158646 
## [53] train-rmse:0.722427+0.037544    test-rmse:1.250160+0.161688 
## [54] train-rmse:0.716161+0.037582    test-rmse:1.247387+0.162392 
## [55] train-rmse:0.710333+0.037563    test-rmse:1.245593+0.162769 
## [56] train-rmse:0.705080+0.037211    test-rmse:1.245880+0.161921 
## [57] train-rmse:0.699752+0.037355    test-rmse:1.245511+0.164423 
## [58] train-rmse:0.693162+0.038308    test-rmse:1.244447+0.165569 
## [59] train-rmse:0.686435+0.038149    test-rmse:1.242289+0.161517 
## [60] train-rmse:0.681973+0.039012    test-rmse:1.242236+0.161350 
## [61] train-rmse:0.674141+0.036001    test-rmse:1.245149+0.160336 
## [62] train-rmse:0.670414+0.035682    test-rmse:1.246447+0.161887 
## [63] train-rmse:0.666372+0.036560    test-rmse:1.247258+0.162004 
## [64] train-rmse:0.660111+0.036652    test-rmse:1.247044+0.161914 
## [65] train-rmse:0.654995+0.036135    test-rmse:1.243997+0.163643 
## [66] train-rmse:0.650744+0.037649    test-rmse:1.244875+0.164519 
## Stopping. Best iteration:
## [60] train-rmse:0.681973+0.039012    test-rmse:1.242236+0.161350
## 
## 80 
## [1]  train-rmse:6.782995+0.049607    test-rmse:6.780599+0.251076 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.839909+0.033925    test-rmse:4.852426+0.224804 
## [3]  train-rmse:3.557779+0.027313    test-rmse:3.582748+0.177602 
## [4]  train-rmse:2.727720+0.017660    test-rmse:2.806758+0.141015 
## [5]  train-rmse:2.199410+0.014427    test-rmse:2.329875+0.095357 
## [6]  train-rmse:1.868915+0.024551    test-rmse:2.013555+0.066328 
## [7]  train-rmse:1.656158+0.023795    test-rmse:1.832571+0.059439 
## [8]  train-rmse:1.521580+0.024016    test-rmse:1.760561+0.055005 
## [9]  train-rmse:1.423789+0.026800    test-rmse:1.691013+0.054265 
## [10] train-rmse:1.352813+0.030109    test-rmse:1.651437+0.074549 
## [11] train-rmse:1.291672+0.025432    test-rmse:1.612536+0.085940 
## [12] train-rmse:1.244998+0.027297    test-rmse:1.580052+0.093954 
## [13] train-rmse:1.200326+0.023911    test-rmse:1.538991+0.100245 
## [14] train-rmse:1.159405+0.023111    test-rmse:1.509645+0.114093 
## [15] train-rmse:1.120760+0.020254    test-rmse:1.503335+0.111173 
## [16] train-rmse:1.079739+0.019285    test-rmse:1.476526+0.118169 
## [17] train-rmse:1.045247+0.017663    test-rmse:1.464253+0.119942 
## [18] train-rmse:1.017142+0.016914    test-rmse:1.449461+0.120774 
## [19] train-rmse:0.990378+0.017628    test-rmse:1.437170+0.129668 
## [20] train-rmse:0.968624+0.018562    test-rmse:1.421733+0.134141 
## [21] train-rmse:0.946045+0.016017    test-rmse:1.401974+0.135208 
## [22] train-rmse:0.923588+0.014068    test-rmse:1.394518+0.138562 
## [23] train-rmse:0.898347+0.011753    test-rmse:1.377396+0.129374 
## [24] train-rmse:0.881969+0.011488    test-rmse:1.367433+0.131868 
## [25] train-rmse:0.862612+0.012233    test-rmse:1.362111+0.135424 
## [26] train-rmse:0.843801+0.014021    test-rmse:1.344737+0.142599 
## [27] train-rmse:0.828294+0.014948    test-rmse:1.333896+0.142725 
## [28] train-rmse:0.805134+0.016871    test-rmse:1.330028+0.142627 
## [29] train-rmse:0.788934+0.014382    test-rmse:1.323164+0.142381 
## [30] train-rmse:0.774441+0.012415    test-rmse:1.318851+0.144098 
## [31] train-rmse:0.760092+0.010678    test-rmse:1.311595+0.146786 
## [32] train-rmse:0.747194+0.011568    test-rmse:1.313824+0.149252 
## [33] train-rmse:0.735547+0.012673    test-rmse:1.311148+0.149557 
## [34] train-rmse:0.725058+0.013303    test-rmse:1.306959+0.150952 
## [35] train-rmse:0.712402+0.008706    test-rmse:1.301996+0.153847 
## [36] train-rmse:0.700904+0.011815    test-rmse:1.298595+0.160079 
## [37] train-rmse:0.688672+0.012131    test-rmse:1.297632+0.159407 
## [38] train-rmse:0.677902+0.011161    test-rmse:1.293086+0.156967 
## [39] train-rmse:0.668677+0.010293    test-rmse:1.291952+0.157580 
## [40] train-rmse:0.659231+0.009956    test-rmse:1.284246+0.155041 
## [41] train-rmse:0.649859+0.009666    test-rmse:1.281814+0.157514 
## [42] train-rmse:0.643261+0.009982    test-rmse:1.281451+0.162059 
## [43] train-rmse:0.632856+0.011203    test-rmse:1.280551+0.162575 
## [44] train-rmse:0.624615+0.011855    test-rmse:1.279602+0.161760 
## [45] train-rmse:0.616996+0.011221    test-rmse:1.280183+0.162438 
## [46] train-rmse:0.609228+0.011624    test-rmse:1.280145+0.163228 
## [47] train-rmse:0.600170+0.012799    test-rmse:1.275902+0.160180 
## [48] train-rmse:0.592982+0.014234    test-rmse:1.272675+0.157693 
## [49] train-rmse:0.582369+0.005754    test-rmse:1.270773+0.156880 
## [50] train-rmse:0.575842+0.006990    test-rmse:1.266770+0.153940 
## [51] train-rmse:0.568842+0.007189    test-rmse:1.266409+0.156697 
## [52] train-rmse:0.560553+0.006887    test-rmse:1.265227+0.155983 
## [53] train-rmse:0.554047+0.004735    test-rmse:1.265280+0.153655 
## [54] train-rmse:0.547961+0.005890    test-rmse:1.264663+0.155499 
## [55] train-rmse:0.542542+0.005364    test-rmse:1.267366+0.154330 
## [56] train-rmse:0.538062+0.007557    test-rmse:1.268300+0.151586 
## [57] train-rmse:0.534508+0.007823    test-rmse:1.268721+0.151045 
## [58] train-rmse:0.528511+0.007717    test-rmse:1.269833+0.149726 
## [59] train-rmse:0.522425+0.007424    test-rmse:1.271333+0.152261 
## [60] train-rmse:0.518822+0.007210    test-rmse:1.270765+0.152588 
## Stopping. Best iteration:
## [54] train-rmse:0.547961+0.005890    test-rmse:1.264663+0.155499
## 
## 81 
## [1]  train-rmse:6.776807+0.050207    test-rmse:6.780794+0.252084 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.820900+0.034425    test-rmse:4.843898+0.221789 
## [3]  train-rmse:3.523312+0.028768    test-rmse:3.559032+0.166501 
## [4]  train-rmse:2.671398+0.020612    test-rmse:2.762615+0.139911 
## [5]  train-rmse:2.109425+0.025354    test-rmse:2.241607+0.091031 
## [6]  train-rmse:1.754859+0.023239    test-rmse:1.926348+0.088178 
## [7]  train-rmse:1.530029+0.024028    test-rmse:1.757812+0.075075 
## [8]  train-rmse:1.370969+0.023343    test-rmse:1.641048+0.062761 
## [9]  train-rmse:1.266449+0.025025    test-rmse:1.587511+0.083056 
## [10] train-rmse:1.186868+0.021656    test-rmse:1.538432+0.086553 
## [11] train-rmse:1.119232+0.019748    test-rmse:1.499113+0.107280 
## [12] train-rmse:1.059406+0.014949    test-rmse:1.470140+0.117372 
## [13] train-rmse:1.006164+0.011904    test-rmse:1.443915+0.127241 
## [14] train-rmse:0.968497+0.010944    test-rmse:1.424056+0.141765 
## [15] train-rmse:0.934664+0.011186    test-rmse:1.400216+0.152358 
## [16] train-rmse:0.895825+0.013866    test-rmse:1.374956+0.161557 
## [17] train-rmse:0.866257+0.014689    test-rmse:1.364135+0.171799 
## [18] train-rmse:0.834095+0.010405    test-rmse:1.351657+0.180334 
## [19] train-rmse:0.808255+0.010032    test-rmse:1.338837+0.185829 
## [20] train-rmse:0.784886+0.012405    test-rmse:1.334774+0.187151 
## [21] train-rmse:0.764202+0.012102    test-rmse:1.324330+0.189681 
## [22] train-rmse:0.742671+0.007672    test-rmse:1.321953+0.187057 
## [23] train-rmse:0.721502+0.005539    test-rmse:1.304780+0.180999 
## [24] train-rmse:0.697777+0.007013    test-rmse:1.294595+0.188973 
## [25] train-rmse:0.677122+0.008972    test-rmse:1.290983+0.187841 
## [26] train-rmse:0.661888+0.007045    test-rmse:1.286594+0.193333 
## [27] train-rmse:0.649084+0.006624    test-rmse:1.282676+0.188468 
## [28] train-rmse:0.634128+0.006538    test-rmse:1.285433+0.190266 
## [29] train-rmse:0.620499+0.008674    test-rmse:1.283129+0.192478 
## [30] train-rmse:0.604586+0.004117    test-rmse:1.282546+0.191751 
## [31] train-rmse:0.591056+0.005386    test-rmse:1.277810+0.197861 
## [32] train-rmse:0.579317+0.007071    test-rmse:1.276823+0.200483 
## [33] train-rmse:0.567415+0.005870    test-rmse:1.276835+0.202263 
## [34] train-rmse:0.556664+0.005468    test-rmse:1.275476+0.203388 
## [35] train-rmse:0.545796+0.008827    test-rmse:1.275043+0.203584 
## [36] train-rmse:0.535735+0.008226    test-rmse:1.268762+0.206500 
## [37] train-rmse:0.524464+0.009193    test-rmse:1.265291+0.204666 
## [38] train-rmse:0.515621+0.009896    test-rmse:1.265671+0.204531 
## [39] train-rmse:0.506764+0.008761    test-rmse:1.262446+0.204602 
## [40] train-rmse:0.498972+0.008310    test-rmse:1.266083+0.202611 
## [41] train-rmse:0.491274+0.009489    test-rmse:1.267280+0.204413 
## [42] train-rmse:0.479602+0.008666    test-rmse:1.269856+0.203523 
## [43] train-rmse:0.473964+0.008920    test-rmse:1.268583+0.204189 
## [44] train-rmse:0.465962+0.008348    test-rmse:1.263467+0.199414 
## [45] train-rmse:0.457853+0.006070    test-rmse:1.262869+0.200576 
## Stopping. Best iteration:
## [39] train-rmse:0.506764+0.008761    test-rmse:1.262446+0.204602
## 
## 82 
## [1]  train-rmse:6.775278+0.050563    test-rmse:6.781639+0.242227 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.809715+0.036016    test-rmse:4.839656+0.203004 
## [3]  train-rmse:3.496515+0.030778    test-rmse:3.535058+0.147933 
## [4]  train-rmse:2.631149+0.026115    test-rmse:2.718371+0.135957 
## [5]  train-rmse:2.051157+0.030005    test-rmse:2.192708+0.108865 
## [6]  train-rmse:1.677909+0.028403    test-rmse:1.875903+0.095060 
## [7]  train-rmse:1.428669+0.028556    test-rmse:1.712835+0.088285 
## [8]  train-rmse:1.267955+0.038253    test-rmse:1.584837+0.086674 
## [9]  train-rmse:1.145649+0.037341    test-rmse:1.511669+0.089766 
## [10] train-rmse:1.052206+0.034866    test-rmse:1.459356+0.093878 
## [11] train-rmse:0.986879+0.038260    test-rmse:1.431210+0.114033 
## [12] train-rmse:0.928719+0.033868    test-rmse:1.393911+0.141103 
## [13] train-rmse:0.877496+0.032139    test-rmse:1.358765+0.143966 
## [14] train-rmse:0.833135+0.029045    test-rmse:1.346319+0.150798 
## [15] train-rmse:0.795682+0.024807    test-rmse:1.337881+0.158437 
## [16] train-rmse:0.762151+0.027335    test-rmse:1.321717+0.163103 
## [17] train-rmse:0.731720+0.020105    test-rmse:1.314440+0.166887 
## [18] train-rmse:0.702864+0.018732    test-rmse:1.310076+0.171522 
## [19] train-rmse:0.677585+0.013958    test-rmse:1.302089+0.181149 
## [20] train-rmse:0.654277+0.013395    test-rmse:1.297148+0.183776 
## [21] train-rmse:0.628592+0.010707    test-rmse:1.287641+0.187072 
## [22] train-rmse:0.610218+0.009883    test-rmse:1.280881+0.187375 
## [23] train-rmse:0.588623+0.015193    test-rmse:1.271714+0.189400 
## [24] train-rmse:0.569216+0.012646    test-rmse:1.261990+0.186866 
## [25] train-rmse:0.551473+0.012479    test-rmse:1.254100+0.181587 
## [26] train-rmse:0.538714+0.015017    test-rmse:1.251260+0.183848 
## [27] train-rmse:0.522716+0.015215    test-rmse:1.247895+0.185184 
## [28] train-rmse:0.511312+0.013775    test-rmse:1.245325+0.186919 
## [29] train-rmse:0.497812+0.010730    test-rmse:1.248767+0.187851 
## [30] train-rmse:0.485846+0.012531    test-rmse:1.243895+0.188544 
## [31] train-rmse:0.471484+0.013148    test-rmse:1.244950+0.183839 
## [32] train-rmse:0.456180+0.014584    test-rmse:1.239783+0.183219 
## [33] train-rmse:0.444449+0.013353    test-rmse:1.238537+0.188979 
## [34] train-rmse:0.433334+0.008585    test-rmse:1.238903+0.186985 
## [35] train-rmse:0.425645+0.007350    test-rmse:1.241803+0.186420 
## [36] train-rmse:0.414783+0.007004    test-rmse:1.243067+0.189958 
## [37] train-rmse:0.405891+0.007952    test-rmse:1.245455+0.187903 
## [38] train-rmse:0.397570+0.008558    test-rmse:1.243903+0.189582 
## [39] train-rmse:0.392680+0.007411    test-rmse:1.243238+0.189766 
## Stopping. Best iteration:
## [33] train-rmse:0.444449+0.013353    test-rmse:1.238537+0.188979
## 
## 83 
## [1]  train-rmse:6.775278+0.050563    test-rmse:6.781639+0.242227 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.805389+0.036085    test-rmse:4.835142+0.210778 
## [3]  train-rmse:3.481521+0.030734    test-rmse:3.540392+0.154831 
## [4]  train-rmse:2.599061+0.024211    test-rmse:2.703255+0.115296 
## [5]  train-rmse:2.007231+0.019210    test-rmse:2.171386+0.114522 
## [6]  train-rmse:1.613603+0.021921    test-rmse:1.854058+0.099340 
## [7]  train-rmse:1.341875+0.040087    test-rmse:1.646312+0.083080 
## [8]  train-rmse:1.162668+0.037582    test-rmse:1.554130+0.094866 
## [9]  train-rmse:1.032732+0.027280    test-rmse:1.455903+0.104081 
## [10] train-rmse:0.931804+0.024132    test-rmse:1.402146+0.109364 
## [11] train-rmse:0.861146+0.020512    test-rmse:1.366826+0.138434 
## [12] train-rmse:0.798929+0.015960    test-rmse:1.340666+0.150789 
## [13] train-rmse:0.753614+0.019971    test-rmse:1.317096+0.154575 
## [14] train-rmse:0.713814+0.016958    test-rmse:1.300564+0.164562 
## [15] train-rmse:0.672311+0.019868    test-rmse:1.293111+0.168582 
## [16] train-rmse:0.647420+0.019220    test-rmse:1.286598+0.173913 
## [17] train-rmse:0.617537+0.022255    test-rmse:1.281151+0.173605 
## [18] train-rmse:0.585686+0.020519    test-rmse:1.267299+0.178530 
## [19] train-rmse:0.563662+0.017842    test-rmse:1.264891+0.177375 
## [20] train-rmse:0.539314+0.019832    test-rmse:1.255354+0.168911 
## [21] train-rmse:0.518040+0.016954    test-rmse:1.251880+0.167582 
## [22] train-rmse:0.496287+0.012773    test-rmse:1.251449+0.171530 
## [23] train-rmse:0.476660+0.017727    test-rmse:1.246467+0.170728 
## [24] train-rmse:0.458739+0.018923    test-rmse:1.247120+0.174228 
## [25] train-rmse:0.442517+0.016796    test-rmse:1.241296+0.176168 
## [26] train-rmse:0.429413+0.012990    test-rmse:1.240839+0.174134 
## [27] train-rmse:0.418218+0.013199    test-rmse:1.239410+0.175048 
## [28] train-rmse:0.410064+0.011711    test-rmse:1.242719+0.173920 
## [29] train-rmse:0.398911+0.012826    test-rmse:1.239854+0.175008 
## [30] train-rmse:0.388991+0.015494    test-rmse:1.240159+0.173726 
## [31] train-rmse:0.375760+0.012945    test-rmse:1.237002+0.174651 
## [32] train-rmse:0.363197+0.015772    test-rmse:1.238294+0.174847 
## [33] train-rmse:0.356473+0.015834    test-rmse:1.236029+0.172785 
## [34] train-rmse:0.343468+0.015117    test-rmse:1.235357+0.168912 
## [35] train-rmse:0.335474+0.013043    test-rmse:1.234484+0.170417 
## [36] train-rmse:0.325314+0.014580    test-rmse:1.235887+0.170650 
## [37] train-rmse:0.314933+0.016520    test-rmse:1.236458+0.169174 
## [38] train-rmse:0.306939+0.019825    test-rmse:1.239145+0.168992 
## [39] train-rmse:0.296422+0.015268    test-rmse:1.238094+0.173420 
## [40] train-rmse:0.289873+0.012320    test-rmse:1.237938+0.172027 
## [41] train-rmse:0.280586+0.011801    test-rmse:1.238354+0.171253 
## Stopping. Best iteration:
## [35] train-rmse:0.335474+0.013043    test-rmse:1.234484+0.170417
## 
## 84 
## [1]  train-rmse:6.677250+0.044909    test-rmse:6.673260+0.261055 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.770800+0.025835    test-rmse:4.766795+0.249507 
## [3]  train-rmse:3.616810+0.016031    test-rmse:3.622476+0.210681 
## [4]  train-rmse:2.955699+0.013153    test-rmse:2.980157+0.150516 
## [5]  train-rmse:2.596877+0.014738    test-rmse:2.625328+0.099845 
## [6]  train-rmse:2.404950+0.016837    test-rmse:2.437980+0.059551 
## [7]  train-rmse:2.300967+0.017557    test-rmse:2.349963+0.052999 
## [8]  train-rmse:2.240431+0.018441    test-rmse:2.287313+0.058777 
## [9]  train-rmse:2.200269+0.019723    test-rmse:2.240625+0.059488 
## [10] train-rmse:2.170867+0.020934    test-rmse:2.210629+0.065495 
## [11] train-rmse:2.146165+0.022377    test-rmse:2.185374+0.074503 
## [12] train-rmse:2.123947+0.022864    test-rmse:2.174265+0.078905 
## [13] train-rmse:2.102034+0.023160    test-rmse:2.162026+0.084543 
## [14] train-rmse:2.081616+0.023985    test-rmse:2.148059+0.085072 
## [15] train-rmse:2.062469+0.024300    test-rmse:2.124667+0.082869 
## [16] train-rmse:2.044677+0.025311    test-rmse:2.114956+0.089919 
## [17] train-rmse:2.027384+0.026963    test-rmse:2.092930+0.090825 
## [18] train-rmse:2.011001+0.027740    test-rmse:2.080033+0.083123 
## [19] train-rmse:1.995258+0.028686    test-rmse:2.067788+0.087071 
## [20] train-rmse:1.979454+0.029827    test-rmse:2.060081+0.096874 
## [21] train-rmse:1.965028+0.029884    test-rmse:2.042910+0.100486 
## [22] train-rmse:1.950435+0.030622    test-rmse:2.023497+0.093484 
## [23] train-rmse:1.937099+0.031443    test-rmse:2.012730+0.093099 
## [24] train-rmse:1.924621+0.031750    test-rmse:2.004170+0.090340 
## [25] train-rmse:1.912438+0.032079    test-rmse:1.995081+0.096595 
## [26] train-rmse:1.900433+0.032737    test-rmse:1.980198+0.088497 
## [27] train-rmse:1.888781+0.033111    test-rmse:1.971439+0.085534 
## [28] train-rmse:1.877329+0.033781    test-rmse:1.966918+0.087550 
## [29] train-rmse:1.865956+0.034328    test-rmse:1.952097+0.082637 
## [30] train-rmse:1.855533+0.035076    test-rmse:1.938930+0.088040 
## [31] train-rmse:1.845196+0.035917    test-rmse:1.929769+0.086371 
## [32] train-rmse:1.834871+0.036895    test-rmse:1.921364+0.084164 
## [33] train-rmse:1.824750+0.037889    test-rmse:1.913289+0.086183 
## [34] train-rmse:1.814623+0.038932    test-rmse:1.909101+0.088266 
## [35] train-rmse:1.804680+0.039652    test-rmse:1.897364+0.086542 
## [36] train-rmse:1.794863+0.040135    test-rmse:1.885590+0.092485 
## [37] train-rmse:1.785364+0.040996    test-rmse:1.883159+0.098162 
## [38] train-rmse:1.776128+0.041497    test-rmse:1.884480+0.100688 
## [39] train-rmse:1.767212+0.041899    test-rmse:1.877019+0.097555 
## [40] train-rmse:1.758669+0.042154    test-rmse:1.866504+0.093293 
## [41] train-rmse:1.749912+0.042812    test-rmse:1.859873+0.095192 
## [42] train-rmse:1.741588+0.043063    test-rmse:1.852007+0.096927 
## [43] train-rmse:1.733170+0.043330    test-rmse:1.847407+0.100673 
## [44] train-rmse:1.725386+0.043686    test-rmse:1.838601+0.096528 
## [45] train-rmse:1.717713+0.044242    test-rmse:1.827903+0.103381 
## [46] train-rmse:1.710492+0.044657    test-rmse:1.822008+0.101850 
## [47] train-rmse:1.703452+0.045064    test-rmse:1.815811+0.101306 
## [48] train-rmse:1.696409+0.045507    test-rmse:1.804413+0.101746 
## [49] train-rmse:1.689498+0.045950    test-rmse:1.796447+0.098351 
## [50] train-rmse:1.683074+0.046249    test-rmse:1.798494+0.100142 
## [51] train-rmse:1.676702+0.046478    test-rmse:1.794033+0.103406 
## [52] train-rmse:1.670043+0.046569    test-rmse:1.789500+0.104920 
## [53] train-rmse:1.663834+0.046697    test-rmse:1.780088+0.101038 
## [54] train-rmse:1.657725+0.046752    test-rmse:1.771630+0.106755 
## [55] train-rmse:1.651858+0.046847    test-rmse:1.767218+0.106301 
## [56] train-rmse:1.645919+0.046892    test-rmse:1.758921+0.106518 
## [57] train-rmse:1.640124+0.047007    test-rmse:1.754722+0.108127 
## [58] train-rmse:1.634524+0.047016    test-rmse:1.758110+0.101277 
## [59] train-rmse:1.628828+0.047509    test-rmse:1.751576+0.097103 
## [60] train-rmse:1.623045+0.048035    test-rmse:1.747616+0.099880 
## [61] train-rmse:1.617649+0.048434    test-rmse:1.748019+0.102419 
## [62] train-rmse:1.612253+0.048738    test-rmse:1.741398+0.103242 
## [63] train-rmse:1.606848+0.049088    test-rmse:1.735591+0.112334 
## [64] train-rmse:1.601635+0.049295    test-rmse:1.734962+0.112590 
## [65] train-rmse:1.596559+0.049410    test-rmse:1.726669+0.124742 
## [66] train-rmse:1.591808+0.049530    test-rmse:1.721737+0.126593 
## [67] train-rmse:1.586929+0.049906    test-rmse:1.717082+0.125798 
## [68] train-rmse:1.582242+0.050098    test-rmse:1.712806+0.128238 
## [69] train-rmse:1.577486+0.050262    test-rmse:1.705142+0.130986 
## [70] train-rmse:1.572807+0.050436    test-rmse:1.700732+0.128974 
## [71] train-rmse:1.568304+0.050646    test-rmse:1.698824+0.128536 
## [72] train-rmse:1.563929+0.050806    test-rmse:1.694851+0.130547 
## [73] train-rmse:1.559579+0.050891    test-rmse:1.690301+0.130170 
## [74] train-rmse:1.555271+0.051078    test-rmse:1.684082+0.129467 
## [75] train-rmse:1.551299+0.051135    test-rmse:1.683010+0.127781 
## [76] train-rmse:1.547389+0.051153    test-rmse:1.681076+0.127884 
## [77] train-rmse:1.543428+0.051135    test-rmse:1.678043+0.133609 
## [78] train-rmse:1.539670+0.051122    test-rmse:1.677661+0.135953 
## [79] train-rmse:1.535957+0.051202    test-rmse:1.670946+0.133240 
## [80] train-rmse:1.532132+0.051427    test-rmse:1.670757+0.135757 
## [81] train-rmse:1.528502+0.051532    test-rmse:1.663317+0.138618 
## [82] train-rmse:1.524908+0.051635    test-rmse:1.663596+0.136962 
## [83] train-rmse:1.521362+0.051651    test-rmse:1.659465+0.133963 
## [84] train-rmse:1.517924+0.051667    test-rmse:1.656919+0.136929 
## [85] train-rmse:1.514498+0.051713    test-rmse:1.648797+0.138285 
## [86] train-rmse:1.511086+0.051712    test-rmse:1.645162+0.140356 
## [87] train-rmse:1.507733+0.051650    test-rmse:1.636991+0.145521 
## [88] train-rmse:1.504528+0.051642    test-rmse:1.636812+0.145776 
## [89] train-rmse:1.501272+0.051679    test-rmse:1.633309+0.146636 
## [90] train-rmse:1.498123+0.051708    test-rmse:1.632337+0.146225 
## [91] train-rmse:1.495099+0.051777    test-rmse:1.628900+0.147751 
## [92] train-rmse:1.492155+0.051809    test-rmse:1.625387+0.144555 
## [93] train-rmse:1.489209+0.051878    test-rmse:1.620857+0.141440 
## [94] train-rmse:1.486302+0.051979    test-rmse:1.621159+0.146046 
## [95] train-rmse:1.483457+0.051978    test-rmse:1.616210+0.145625 
## [96] train-rmse:1.480654+0.052036    test-rmse:1.612667+0.147987 
## [97] train-rmse:1.477882+0.052120    test-rmse:1.610778+0.148943 
## [98] train-rmse:1.475113+0.052257    test-rmse:1.611536+0.150040 
## [99] train-rmse:1.472308+0.052334    test-rmse:1.607108+0.148035 
## [100]    train-rmse:1.469728+0.052479    test-rmse:1.604524+0.147529 
## [101]    train-rmse:1.467145+0.052611    test-rmse:1.604371+0.145012 
## [102]    train-rmse:1.464541+0.052702    test-rmse:1.601960+0.147384 
## [103]    train-rmse:1.461992+0.052825    test-rmse:1.599557+0.147551 
## [104]    train-rmse:1.459657+0.052902    test-rmse:1.598159+0.148492 
## [105]    train-rmse:1.457295+0.052944    test-rmse:1.597576+0.150018 
## [106]    train-rmse:1.454921+0.053035    test-rmse:1.594847+0.154179 
## [107]    train-rmse:1.452564+0.053204    test-rmse:1.593851+0.156494 
## [108]    train-rmse:1.450265+0.053294    test-rmse:1.590910+0.155610 
## [109]    train-rmse:1.447953+0.053408    test-rmse:1.591862+0.155439 
## [110]    train-rmse:1.445649+0.053439    test-rmse:1.587288+0.160402 
## [111]    train-rmse:1.443419+0.053465    test-rmse:1.584730+0.162187 
## [112]    train-rmse:1.441274+0.053527    test-rmse:1.581740+0.164893 
## [113]    train-rmse:1.439182+0.053504    test-rmse:1.580802+0.163774 
## [114]    train-rmse:1.437104+0.053485    test-rmse:1.580060+0.162732 
## [115]    train-rmse:1.435108+0.053458    test-rmse:1.576089+0.161189 
## [116]    train-rmse:1.433166+0.053423    test-rmse:1.572526+0.160148 
## [117]    train-rmse:1.431208+0.053384    test-rmse:1.567301+0.155916 
## [118]    train-rmse:1.429323+0.053358    test-rmse:1.568279+0.160813 
## [119]    train-rmse:1.427436+0.053280    test-rmse:1.568360+0.158576 
## [120]    train-rmse:1.425527+0.053243    test-rmse:1.568175+0.158113 
## [121]    train-rmse:1.423619+0.053304    test-rmse:1.567240+0.161315 
## [122]    train-rmse:1.421759+0.053316    test-rmse:1.568270+0.163515 
## [123]    train-rmse:1.419932+0.053318    test-rmse:1.566746+0.165028 
## [124]    train-rmse:1.418097+0.053332    test-rmse:1.562040+0.165568 
## [125]    train-rmse:1.416326+0.053301    test-rmse:1.558402+0.167025 
## [126]    train-rmse:1.414610+0.053305    test-rmse:1.554758+0.166550 
## [127]    train-rmse:1.412944+0.053261    test-rmse:1.553147+0.165884 
## [128]    train-rmse:1.411314+0.053219    test-rmse:1.550766+0.166947 
## [129]    train-rmse:1.409736+0.053203    test-rmse:1.547412+0.168035 
## [130]    train-rmse:1.408166+0.053209    test-rmse:1.544548+0.170670 
## [131]    train-rmse:1.406658+0.053178    test-rmse:1.542619+0.171118 
## [132]    train-rmse:1.405156+0.053179    test-rmse:1.540908+0.171580 
## [133]    train-rmse:1.403668+0.053226    test-rmse:1.541385+0.174124 
## [134]    train-rmse:1.402243+0.053228    test-rmse:1.540859+0.173978 
## [135]    train-rmse:1.400821+0.053215    test-rmse:1.540316+0.173953 
## [136]    train-rmse:1.399385+0.053217    test-rmse:1.539295+0.174211 
## [137]    train-rmse:1.397994+0.053232    test-rmse:1.538156+0.175430 
## [138]    train-rmse:1.396622+0.053236    test-rmse:1.537453+0.175023 
## [139]    train-rmse:1.395248+0.053272    test-rmse:1.536795+0.175212 
## [140]    train-rmse:1.393888+0.053301    test-rmse:1.534715+0.174693 
## [141]    train-rmse:1.392538+0.053322    test-rmse:1.534133+0.172035 
## [142]    train-rmse:1.391220+0.053345    test-rmse:1.533750+0.170780 
## [143]    train-rmse:1.389944+0.053411    test-rmse:1.532325+0.171568 
## [144]    train-rmse:1.388639+0.053481    test-rmse:1.528755+0.175590 
## [145]    train-rmse:1.387399+0.053485    test-rmse:1.529660+0.173807 
## [146]    train-rmse:1.386168+0.053499    test-rmse:1.527053+0.173658 
## [147]    train-rmse:1.384998+0.053542    test-rmse:1.526433+0.173702 
## [148]    train-rmse:1.383841+0.053597    test-rmse:1.525596+0.172180 
## [149]    train-rmse:1.382710+0.053619    test-rmse:1.524834+0.173080 
## [150]    train-rmse:1.381582+0.053628    test-rmse:1.524105+0.177214 
## [151]    train-rmse:1.380463+0.053600    test-rmse:1.524977+0.176574 
## [152]    train-rmse:1.379325+0.053543    test-rmse:1.523964+0.175909 
## [153]    train-rmse:1.378217+0.053518    test-rmse:1.525150+0.176509 
## [154]    train-rmse:1.377134+0.053484    test-rmse:1.523088+0.175325 
## [155]    train-rmse:1.376054+0.053449    test-rmse:1.523278+0.176398 
## [156]    train-rmse:1.374988+0.053411    test-rmse:1.521584+0.176234 
## [157]    train-rmse:1.373922+0.053403    test-rmse:1.521264+0.176835 
## [158]    train-rmse:1.372890+0.053431    test-rmse:1.521853+0.178271 
## [159]    train-rmse:1.371900+0.053437    test-rmse:1.520391+0.179094 
## [160]    train-rmse:1.370941+0.053430    test-rmse:1.518173+0.178273 
## [161]    train-rmse:1.369975+0.053423    test-rmse:1.517719+0.178503 
## [162]    train-rmse:1.369007+0.053423    test-rmse:1.515977+0.178018 
## [163]    train-rmse:1.368013+0.053383    test-rmse:1.516494+0.178871 
## [164]    train-rmse:1.367094+0.053388    test-rmse:1.515984+0.180127 
## [165]    train-rmse:1.366188+0.053352    test-rmse:1.512102+0.182750 
## [166]    train-rmse:1.365298+0.053327    test-rmse:1.510834+0.182931 
## [167]    train-rmse:1.364421+0.053301    test-rmse:1.509343+0.184501 
## [168]    train-rmse:1.363548+0.053298    test-rmse:1.509295+0.184894 
## [169]    train-rmse:1.362717+0.053322    test-rmse:1.509558+0.187668 
## [170]    train-rmse:1.361881+0.053348    test-rmse:1.509892+0.187273 
## [171]    train-rmse:1.361077+0.053370    test-rmse:1.509304+0.187117 
## [172]    train-rmse:1.360287+0.053376    test-rmse:1.508709+0.186587 
## [173]    train-rmse:1.359516+0.053355    test-rmse:1.507783+0.187625 
## [174]    train-rmse:1.358757+0.053338    test-rmse:1.505126+0.187949 
## [175]    train-rmse:1.358005+0.053327    test-rmse:1.506428+0.187685 
## [176]    train-rmse:1.357265+0.053299    test-rmse:1.506395+0.188203 
## [177]    train-rmse:1.356528+0.053288    test-rmse:1.505115+0.187166 
## [178]    train-rmse:1.355791+0.053273    test-rmse:1.504901+0.188245 
## [179]    train-rmse:1.355058+0.053265    test-rmse:1.507003+0.188758 
## [180]    train-rmse:1.354344+0.053261    test-rmse:1.505820+0.188297 
## [181]    train-rmse:1.353622+0.053258    test-rmse:1.505031+0.188958 
## [182]    train-rmse:1.352897+0.053275    test-rmse:1.503467+0.190352 
## [183]    train-rmse:1.352207+0.053276    test-rmse:1.502301+0.189503 
## [184]    train-rmse:1.351522+0.053259    test-rmse:1.501760+0.189692 
## [185]    train-rmse:1.350863+0.053265    test-rmse:1.503671+0.188995 
## [186]    train-rmse:1.350229+0.053263    test-rmse:1.502644+0.188141 
## [187]    train-rmse:1.349590+0.053257    test-rmse:1.501905+0.188543 
## [188]    train-rmse:1.348943+0.053243    test-rmse:1.502899+0.188963 
## [189]    train-rmse:1.348331+0.053229    test-rmse:1.503157+0.189492 
## [190]    train-rmse:1.347727+0.053203    test-rmse:1.502934+0.191232 
## Stopping. Best iteration:
## [184]    train-rmse:1.351522+0.053259    test-rmse:1.501760+0.189692
## 
## 85 
## [1]  train-rmse:6.629745+0.045469    test-rmse:6.626362+0.254828 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.678645+0.026442    test-rmse:4.689304+0.230338 
## [3]  train-rmse:3.469374+0.016383    test-rmse:3.492947+0.200084 
## [4]  train-rmse:2.742691+0.016938    test-rmse:2.794714+0.138899 
## [5]  train-rmse:2.336640+0.024310    test-rmse:2.418361+0.101445 
## [6]  train-rmse:2.105661+0.030253    test-rmse:2.219528+0.058404 
## [7]  train-rmse:1.963548+0.032853    test-rmse:2.086319+0.047547 
## [8]  train-rmse:1.882283+0.030734    test-rmse:2.008880+0.061338 
## [9]  train-rmse:1.824013+0.033452    test-rmse:1.963659+0.065242 
## [10] train-rmse:1.778467+0.037468    test-rmse:1.926514+0.084023 
## [11] train-rmse:1.742222+0.039959    test-rmse:1.896305+0.075137 
## [12] train-rmse:1.703005+0.026577    test-rmse:1.865905+0.082721 
## [13] train-rmse:1.670704+0.024859    test-rmse:1.843766+0.097752 
## [14] train-rmse:1.642724+0.026530    test-rmse:1.820323+0.089186 
## [15] train-rmse:1.617293+0.027695    test-rmse:1.806373+0.085962 
## [16] train-rmse:1.582598+0.031637    test-rmse:1.785686+0.086263 
## [17] train-rmse:1.557472+0.032763    test-rmse:1.769729+0.094680 
## [18] train-rmse:1.534330+0.032103    test-rmse:1.747822+0.091072 
## [19] train-rmse:1.510783+0.032448    test-rmse:1.736695+0.097399 
## [20] train-rmse:1.486240+0.031892    test-rmse:1.725155+0.101904 
## [21] train-rmse:1.466699+0.033609    test-rmse:1.711989+0.097663 
## [22] train-rmse:1.446645+0.033558    test-rmse:1.692361+0.103043 
## [23] train-rmse:1.429016+0.034042    test-rmse:1.679110+0.095724 
## [24] train-rmse:1.404066+0.033753    test-rmse:1.644295+0.116606 
## [25] train-rmse:1.388326+0.034120    test-rmse:1.631701+0.115169 
## [26] train-rmse:1.372236+0.035587    test-rmse:1.622790+0.129427 
## [27] train-rmse:1.356268+0.034445    test-rmse:1.614333+0.127764 
## [28] train-rmse:1.341361+0.034289    test-rmse:1.597318+0.132717 
## [29] train-rmse:1.324551+0.033040    test-rmse:1.582975+0.134339 
## [30] train-rmse:1.302693+0.019787    test-rmse:1.575202+0.143432 
## [31] train-rmse:1.275933+0.025890    test-rmse:1.536362+0.120536 
## [32] train-rmse:1.255850+0.031092    test-rmse:1.519133+0.115301 
## [33] train-rmse:1.241193+0.031351    test-rmse:1.507234+0.109259 
## [34] train-rmse:1.230527+0.031018    test-rmse:1.498397+0.109044 
## [35] train-rmse:1.220384+0.031174    test-rmse:1.492490+0.112496 
## [36] train-rmse:1.203431+0.035817    test-rmse:1.468099+0.094759 
## [37] train-rmse:1.193501+0.036130    test-rmse:1.458563+0.098919 
## [38] train-rmse:1.183702+0.035886    test-rmse:1.452356+0.103594 
## [39] train-rmse:1.174895+0.035975    test-rmse:1.440502+0.103319 
## [40] train-rmse:1.165018+0.037736    test-rmse:1.435484+0.106956 
## [41] train-rmse:1.148897+0.045537    test-rmse:1.429347+0.106841 
## [42] train-rmse:1.136591+0.049457    test-rmse:1.419449+0.104835 
## [43] train-rmse:1.125277+0.048259    test-rmse:1.407192+0.109292 
## [44] train-rmse:1.116208+0.048096    test-rmse:1.404233+0.112268 
## [45] train-rmse:1.107497+0.047735    test-rmse:1.401985+0.111142 
## [46] train-rmse:1.098366+0.047367    test-rmse:1.397189+0.109219 
## [47] train-rmse:1.083600+0.037886    test-rmse:1.390221+0.120051 
## [48] train-rmse:1.074835+0.040037    test-rmse:1.377293+0.128436 
## [49] train-rmse:1.067717+0.039226    test-rmse:1.377797+0.126571 
## [50] train-rmse:1.060734+0.039024    test-rmse:1.375423+0.125906 
## [51] train-rmse:1.054134+0.038748    test-rmse:1.375401+0.124980 
## [52] train-rmse:1.045560+0.037377    test-rmse:1.368927+0.127745 
## [53] train-rmse:1.039461+0.037768    test-rmse:1.365670+0.131223 
## [54] train-rmse:1.030683+0.042857    test-rmse:1.358637+0.123102 
## [55] train-rmse:1.014816+0.037157    test-rmse:1.349313+0.130559 
## [56] train-rmse:1.007585+0.035882    test-rmse:1.344370+0.134416 
## [57] train-rmse:0.999587+0.034127    test-rmse:1.336303+0.139189 
## [58] train-rmse:0.994184+0.034009    test-rmse:1.335921+0.143442 
## [59] train-rmse:0.988408+0.033621    test-rmse:1.331634+0.146335 
## [60] train-rmse:0.983805+0.033417    test-rmse:1.330054+0.147083 
## [61] train-rmse:0.978486+0.034560    test-rmse:1.325128+0.147481 
## [62] train-rmse:0.972288+0.037763    test-rmse:1.327830+0.146734 
## [63] train-rmse:0.968080+0.037740    test-rmse:1.326656+0.145703 
## [64] train-rmse:0.963198+0.037300    test-rmse:1.326388+0.143399 
## [65] train-rmse:0.952764+0.032001    test-rmse:1.321796+0.148426 
## [66] train-rmse:0.947579+0.031040    test-rmse:1.319701+0.148168 
## [67] train-rmse:0.943997+0.031320    test-rmse:1.315247+0.151259 
## [68] train-rmse:0.939645+0.031722    test-rmse:1.312091+0.149220 
## [69] train-rmse:0.934333+0.031834    test-rmse:1.312703+0.150074 
## [70] train-rmse:0.925419+0.039856    test-rmse:1.310434+0.149661 
## [71] train-rmse:0.920929+0.040052    test-rmse:1.310761+0.152038 
## [72] train-rmse:0.916680+0.039584    test-rmse:1.308296+0.153857 
## [73] train-rmse:0.912895+0.039149    test-rmse:1.308254+0.154940 
## [74] train-rmse:0.909370+0.038948    test-rmse:1.307652+0.157315 
## [75] train-rmse:0.903984+0.042925    test-rmse:1.303422+0.155887 
## [76] train-rmse:0.899975+0.043130    test-rmse:1.302370+0.156979 
## [77] train-rmse:0.896288+0.043036    test-rmse:1.298538+0.155774 
## [78] train-rmse:0.893428+0.043736    test-rmse:1.298330+0.154890 
## [79] train-rmse:0.890439+0.044053    test-rmse:1.299891+0.156605 
## [80] train-rmse:0.887229+0.043681    test-rmse:1.295726+0.156202 
## [81] train-rmse:0.881187+0.042593    test-rmse:1.293745+0.158311 
## [82] train-rmse:0.877730+0.043030    test-rmse:1.291561+0.160085 
## [83] train-rmse:0.864136+0.039260    test-rmse:1.265460+0.162044 
## [84] train-rmse:0.861406+0.039743    test-rmse:1.265307+0.161285 
## [85] train-rmse:0.857031+0.039083    test-rmse:1.265055+0.161676 
## [86] train-rmse:0.849147+0.039485    test-rmse:1.262900+0.161798 
## [87] train-rmse:0.845850+0.039139    test-rmse:1.261734+0.162570 
## [88] train-rmse:0.839347+0.038428    test-rmse:1.254464+0.155216 
## [89] train-rmse:0.835678+0.039862    test-rmse:1.253547+0.155708 
## [90] train-rmse:0.832814+0.040212    test-rmse:1.255724+0.157697 
## [91] train-rmse:0.830469+0.040881    test-rmse:1.253116+0.158505 
## [92] train-rmse:0.824049+0.038948    test-rmse:1.239628+0.165086 
## [93] train-rmse:0.820424+0.037899    test-rmse:1.241144+0.163587 
## [94] train-rmse:0.817474+0.038860    test-rmse:1.237421+0.160632 
## [95] train-rmse:0.815502+0.039173    test-rmse:1.237822+0.160893 
## [96] train-rmse:0.813711+0.038956    test-rmse:1.237190+0.162499 
## [97] train-rmse:0.810613+0.038695    test-rmse:1.236245+0.162101 
## [98] train-rmse:0.808915+0.039107    test-rmse:1.238353+0.160344 
## [99] train-rmse:0.806551+0.039432    test-rmse:1.235515+0.159210 
## [100]    train-rmse:0.803452+0.038953    test-rmse:1.233496+0.158801 
## [101]    train-rmse:0.801895+0.039078    test-rmse:1.232239+0.159142 
## [102]    train-rmse:0.799077+0.040632    test-rmse:1.232283+0.158546 
## [103]    train-rmse:0.797395+0.040748    test-rmse:1.232993+0.158789 
## [104]    train-rmse:0.793249+0.039546    test-rmse:1.230916+0.157775 
## [105]    train-rmse:0.791459+0.039389    test-rmse:1.230299+0.158716 
## [106]    train-rmse:0.789360+0.039434    test-rmse:1.230481+0.160974 
## [107]    train-rmse:0.787416+0.039488    test-rmse:1.229254+0.161635 
## [108]    train-rmse:0.785892+0.039456    test-rmse:1.228931+0.162191 
## [109]    train-rmse:0.783894+0.039045    test-rmse:1.229978+0.161921 
## [110]    train-rmse:0.778709+0.036741    test-rmse:1.227398+0.162283 
## [111]    train-rmse:0.776492+0.036674    test-rmse:1.229660+0.161620 
## [112]    train-rmse:0.774689+0.036281    test-rmse:1.229683+0.161421 
## [113]    train-rmse:0.773019+0.036907    test-rmse:1.228566+0.162113 
## [114]    train-rmse:0.771314+0.037449    test-rmse:1.229960+0.163114 
## [115]    train-rmse:0.770033+0.037419    test-rmse:1.229084+0.162704 
## [116]    train-rmse:0.767211+0.038199    test-rmse:1.229575+0.163489 
## Stopping. Best iteration:
## [110]    train-rmse:0.778709+0.036741    test-rmse:1.227398+0.162283
## 
## 86 
## [1]  train-rmse:6.611167+0.047510    test-rmse:6.608144+0.246941 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.626128+0.033628    test-rmse:4.634334+0.218560 
## [3]  train-rmse:3.376781+0.030667    test-rmse:3.398873+0.169968 
## [4]  train-rmse:2.612706+0.021174    test-rmse:2.663267+0.141696 
## [5]  train-rmse:2.161250+0.020814    test-rmse:2.256490+0.092689 
## [6]  train-rmse:1.894782+0.014893    test-rmse:2.035846+0.067671 
## [7]  train-rmse:1.738002+0.016371    test-rmse:1.918480+0.070555 
## [8]  train-rmse:1.638425+0.020469    test-rmse:1.831278+0.059221 
## [9]  train-rmse:1.559213+0.018258    test-rmse:1.764218+0.064073 
## [10] train-rmse:1.504068+0.020834    test-rmse:1.733438+0.059172 
## [11] train-rmse:1.456110+0.020717    test-rmse:1.687750+0.054479 
## [12] train-rmse:1.408783+0.020955    test-rmse:1.673300+0.060413 
## [13] train-rmse:1.371496+0.024018    test-rmse:1.652489+0.054271 
## [14] train-rmse:1.332981+0.024588    test-rmse:1.613432+0.067632 
## [15] train-rmse:1.292570+0.020978    test-rmse:1.591009+0.077705 
## [16] train-rmse:1.261010+0.021301    test-rmse:1.566561+0.088289 
## [17] train-rmse:1.230153+0.026974    test-rmse:1.536762+0.077807 
## [18] train-rmse:1.204534+0.026888    test-rmse:1.515449+0.083969 
## [19] train-rmse:1.178351+0.025569    test-rmse:1.496202+0.089798 
## [20] train-rmse:1.146167+0.021942    test-rmse:1.484115+0.085825 
## [21] train-rmse:1.123032+0.020003    test-rmse:1.466756+0.101199 
## [22] train-rmse:1.095852+0.022472    test-rmse:1.447106+0.102168 
## [23] train-rmse:1.073970+0.022988    test-rmse:1.430778+0.105165 
## [24] train-rmse:1.055254+0.023491    test-rmse:1.416206+0.107996 
## [25] train-rmse:1.037381+0.022863    test-rmse:1.403814+0.111631 
## [26] train-rmse:1.017557+0.028317    test-rmse:1.399682+0.113588 
## [27] train-rmse:0.998710+0.024908    test-rmse:1.388632+0.118311 
## [28] train-rmse:0.979339+0.022678    test-rmse:1.375763+0.119147 
## [29] train-rmse:0.959917+0.025722    test-rmse:1.360437+0.110005 
## [30] train-rmse:0.944416+0.025799    test-rmse:1.341669+0.116597 
## [31] train-rmse:0.928706+0.025956    test-rmse:1.331662+0.117738 
## [32] train-rmse:0.914539+0.025992    test-rmse:1.326452+0.121673 
## [33] train-rmse:0.901074+0.025801    test-rmse:1.320762+0.125638 
## [34] train-rmse:0.889088+0.023160    test-rmse:1.313237+0.128984 
## [35] train-rmse:0.872398+0.027527    test-rmse:1.307782+0.131495 
## [36] train-rmse:0.861957+0.027744    test-rmse:1.303145+0.134216 
## [37] train-rmse:0.851617+0.027822    test-rmse:1.293938+0.140786 
## [38] train-rmse:0.839119+0.026722    test-rmse:1.284774+0.138580 
## [39] train-rmse:0.826974+0.027068    test-rmse:1.279931+0.140700 
## [40] train-rmse:0.815715+0.024819    test-rmse:1.273530+0.142153 
## [41] train-rmse:0.807067+0.024985    test-rmse:1.280441+0.142674 
## [42] train-rmse:0.799053+0.025392    test-rmse:1.277931+0.140344 
## [43] train-rmse:0.786570+0.024719    test-rmse:1.277842+0.141080 
## [44] train-rmse:0.779007+0.025482    test-rmse:1.272250+0.146784 
## [45] train-rmse:0.770030+0.026756    test-rmse:1.269863+0.154077 
## [46] train-rmse:0.762986+0.027994    test-rmse:1.268580+0.155041 
## [47] train-rmse:0.752335+0.024692    test-rmse:1.263764+0.158729 
## [48] train-rmse:0.744573+0.023360    test-rmse:1.260918+0.161559 
## [49] train-rmse:0.737901+0.024158    test-rmse:1.259846+0.160162 
## [50] train-rmse:0.730376+0.023812    test-rmse:1.255567+0.160510 
## [51] train-rmse:0.725257+0.023496    test-rmse:1.252255+0.162135 
## [52] train-rmse:0.714674+0.029865    test-rmse:1.248870+0.155545 
## [53] train-rmse:0.707880+0.030156    test-rmse:1.250119+0.156773 
## [54] train-rmse:0.702007+0.030029    test-rmse:1.253769+0.157953 
## [55] train-rmse:0.693555+0.029287    test-rmse:1.254191+0.158684 
## [56] train-rmse:0.687437+0.030256    test-rmse:1.254829+0.158933 
## [57] train-rmse:0.681862+0.029901    test-rmse:1.253645+0.160294 
## [58] train-rmse:0.676921+0.030223    test-rmse:1.253829+0.160897 
## Stopping. Best iteration:
## [52] train-rmse:0.714674+0.029865    test-rmse:1.248870+0.155545
## 
## 87 
## [1]  train-rmse:6.603301+0.048251    test-rmse:6.601206+0.249125 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.607070+0.031658    test-rmse:4.623000+0.220141 
## [3]  train-rmse:3.337076+0.027732    test-rmse:3.368353+0.159975 
## [4]  train-rmse:2.538310+0.015192    test-rmse:2.615381+0.127898 
## [5]  train-rmse:2.051457+0.013217    test-rmse:2.179444+0.080302 
## [6]  train-rmse:1.761922+0.021139    test-rmse:1.935975+0.079587 
## [7]  train-rmse:1.590645+0.026706    test-rmse:1.808359+0.062158 
## [8]  train-rmse:1.470917+0.027704    test-rmse:1.723992+0.046452 
## [9]  train-rmse:1.384980+0.029942    test-rmse:1.660668+0.023883 
## [10] train-rmse:1.313573+0.035836    test-rmse:1.601939+0.045778 
## [11] train-rmse:1.248108+0.028152    test-rmse:1.563386+0.060443 
## [12] train-rmse:1.199665+0.023901    test-rmse:1.531271+0.064426 
## [13] train-rmse:1.158953+0.023484    test-rmse:1.504848+0.053805 
## [14] train-rmse:1.118832+0.022803    test-rmse:1.476679+0.073319 
## [15] train-rmse:1.080400+0.026046    test-rmse:1.454853+0.071125 
## [16] train-rmse:1.043722+0.027034    test-rmse:1.444375+0.073780 
## [17] train-rmse:1.015321+0.025772    test-rmse:1.417310+0.089134 
## [18] train-rmse:0.985072+0.022114    test-rmse:1.395829+0.091533 
## [19] train-rmse:0.959981+0.024363    test-rmse:1.388079+0.101596 
## [20] train-rmse:0.932694+0.024135    test-rmse:1.372637+0.106926 
## [21] train-rmse:0.912197+0.021337    test-rmse:1.362121+0.109933 
## [22] train-rmse:0.888309+0.024205    test-rmse:1.349586+0.113669 
## [23] train-rmse:0.872386+0.024414    test-rmse:1.338769+0.113094 
## [24] train-rmse:0.852130+0.023660    test-rmse:1.329371+0.119023 
## [25] train-rmse:0.831357+0.025235    test-rmse:1.325016+0.125582 
## [26] train-rmse:0.815512+0.025471    test-rmse:1.319581+0.126801 
## [27] train-rmse:0.800908+0.023680    test-rmse:1.313423+0.132862 
## [28] train-rmse:0.780880+0.024607    test-rmse:1.301285+0.123514 
## [29] train-rmse:0.764851+0.025463    test-rmse:1.300375+0.121402 
## [30] train-rmse:0.752126+0.024928    test-rmse:1.293156+0.124023 
## [31] train-rmse:0.734871+0.025436    test-rmse:1.284305+0.120783 
## [32] train-rmse:0.718277+0.025418    test-rmse:1.286331+0.125268 
## [33] train-rmse:0.702605+0.025096    test-rmse:1.282098+0.128689 
## [34] train-rmse:0.689073+0.020708    test-rmse:1.280396+0.130236 
## [35] train-rmse:0.679058+0.020211    test-rmse:1.275105+0.133683 
## [36] train-rmse:0.666817+0.023882    test-rmse:1.272107+0.134283 
## [37] train-rmse:0.656231+0.024883    test-rmse:1.261706+0.128359 
## [38] train-rmse:0.644982+0.026211    test-rmse:1.259057+0.128203 
## [39] train-rmse:0.634409+0.024267    test-rmse:1.262200+0.135000 
## [40] train-rmse:0.627044+0.024650    test-rmse:1.261883+0.133159 
## [41] train-rmse:0.618732+0.026577    test-rmse:1.257959+0.133248 
## [42] train-rmse:0.607730+0.026739    test-rmse:1.253652+0.131200 
## [43] train-rmse:0.600810+0.025985    test-rmse:1.253189+0.133420 
## [44] train-rmse:0.592031+0.027650    test-rmse:1.245008+0.132947 
## [45] train-rmse:0.580266+0.025864    test-rmse:1.243826+0.133424 
## [46] train-rmse:0.572987+0.024381    test-rmse:1.243054+0.135766 
## [47] train-rmse:0.565057+0.024199    test-rmse:1.242466+0.134321 
## [48] train-rmse:0.556579+0.022894    test-rmse:1.242139+0.137586 
## [49] train-rmse:0.551289+0.024132    test-rmse:1.244630+0.141112 
## [50] train-rmse:0.545355+0.022667    test-rmse:1.245026+0.140083 
## [51] train-rmse:0.539211+0.021878    test-rmse:1.242864+0.140786 
## [52] train-rmse:0.534304+0.020842    test-rmse:1.247182+0.139808 
## [53] train-rmse:0.528785+0.020329    test-rmse:1.244920+0.141338 
## [54] train-rmse:0.523144+0.020426    test-rmse:1.247443+0.143298 
## Stopping. Best iteration:
## [48] train-rmse:0.556579+0.022894    test-rmse:1.242139+0.137586
## 
## 88 
## [1]  train-rmse:6.596553+0.048902    test-rmse:6.601378+0.250251 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.584417+0.031867    test-rmse:4.610929+0.218927 
## [3]  train-rmse:3.293325+0.026664    test-rmse:3.335495+0.163138 
## [4]  train-rmse:2.471998+0.017812    test-rmse:2.576673+0.140065 
## [5]  train-rmse:1.953065+0.020236    test-rmse:2.115321+0.084300 
## [6]  train-rmse:1.648405+0.027105    test-rmse:1.859156+0.076409 
## [7]  train-rmse:1.444231+0.039616    test-rmse:1.735355+0.055317 
## [8]  train-rmse:1.307042+0.035341    test-rmse:1.623787+0.070634 
## [9]  train-rmse:1.199547+0.030240    test-rmse:1.572329+0.086573 
## [10] train-rmse:1.128356+0.026631    test-rmse:1.515438+0.112670 
## [11] train-rmse:1.070907+0.027391    test-rmse:1.491729+0.119537 
## [12] train-rmse:1.024862+0.028363    test-rmse:1.465227+0.125859 
## [13] train-rmse:0.974624+0.018131    test-rmse:1.441081+0.130128 
## [14] train-rmse:0.934220+0.018536    test-rmse:1.421670+0.142521 
## [15] train-rmse:0.894129+0.017597    test-rmse:1.399833+0.142493 
## [16] train-rmse:0.863960+0.019532    test-rmse:1.375584+0.149287 
## [17] train-rmse:0.830149+0.027052    test-rmse:1.355349+0.147864 
## [18] train-rmse:0.798783+0.024070    test-rmse:1.342654+0.157742 
## [19] train-rmse:0.768790+0.020005    test-rmse:1.324647+0.160870 
## [20] train-rmse:0.747135+0.018300    test-rmse:1.323054+0.163595 
## [21] train-rmse:0.725805+0.018039    test-rmse:1.313809+0.166737 
## [22] train-rmse:0.704750+0.015023    test-rmse:1.306708+0.169112 
## [23] train-rmse:0.686441+0.017903    test-rmse:1.299314+0.169278 
## [24] train-rmse:0.669453+0.015828    test-rmse:1.293354+0.167800 
## [25] train-rmse:0.656222+0.015114    test-rmse:1.293728+0.173278 
## [26] train-rmse:0.639952+0.011532    test-rmse:1.287174+0.176774 
## [27] train-rmse:0.623240+0.012278    test-rmse:1.286002+0.178087 
## [28] train-rmse:0.609611+0.010848    test-rmse:1.284803+0.184688 
## [29] train-rmse:0.597488+0.009937    test-rmse:1.286644+0.188853 
## [30] train-rmse:0.583404+0.011145    test-rmse:1.281792+0.185367 
## [31] train-rmse:0.569976+0.012063    test-rmse:1.277730+0.184695 
## [32] train-rmse:0.557892+0.011755    test-rmse:1.277165+0.184370 
## [33] train-rmse:0.544966+0.009468    test-rmse:1.278169+0.186776 
## [34] train-rmse:0.532928+0.011740    test-rmse:1.273448+0.185782 
## [35] train-rmse:0.523940+0.011458    test-rmse:1.271635+0.183563 
## [36] train-rmse:0.515311+0.011605    test-rmse:1.269233+0.183375 
## [37] train-rmse:0.504424+0.014056    test-rmse:1.265990+0.183610 
## [38] train-rmse:0.494465+0.010832    test-rmse:1.262042+0.182778 
## [39] train-rmse:0.485518+0.010929    test-rmse:1.263210+0.182834 
## [40] train-rmse:0.476886+0.013729    test-rmse:1.263897+0.184221 
## [41] train-rmse:0.468854+0.012727    test-rmse:1.264782+0.188715 
## [42] train-rmse:0.461310+0.011769    test-rmse:1.270399+0.187259 
## [43] train-rmse:0.456086+0.011264    test-rmse:1.272106+0.186981 
## [44] train-rmse:0.446470+0.013476    test-rmse:1.269032+0.186914 
## Stopping. Best iteration:
## [38] train-rmse:0.494465+0.010832    test-rmse:1.262042+0.182778
## 
## 89 
## [1]  train-rmse:6.594853+0.049294    test-rmse:6.602220+0.239862 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.572588+0.034929    test-rmse:4.606884+0.197797 
## [3]  train-rmse:3.272615+0.035818    test-rmse:3.321764+0.147887 
## [4]  train-rmse:2.427828+0.023373    test-rmse:2.541243+0.126028 
## [5]  train-rmse:1.900187+0.029684    test-rmse:2.076207+0.100898 
## [6]  train-rmse:1.559687+0.034936    test-rmse:1.803018+0.096806 
## [7]  train-rmse:1.341932+0.030325    test-rmse:1.648544+0.103900 
## [8]  train-rmse:1.183288+0.031765    test-rmse:1.540596+0.099103 
## [9]  train-rmse:1.073120+0.034074    test-rmse:1.472319+0.108054 
## [10] train-rmse:0.994855+0.033528    test-rmse:1.426596+0.124299 
## [11] train-rmse:0.930551+0.034564    test-rmse:1.407600+0.138214 
## [12] train-rmse:0.869338+0.031086    test-rmse:1.366876+0.138008 
## [13] train-rmse:0.822710+0.030098    test-rmse:1.349461+0.149954 
## [14] train-rmse:0.785625+0.031288    test-rmse:1.335552+0.142497 
## [15] train-rmse:0.748290+0.020558    test-rmse:1.320725+0.150202 
## [16] train-rmse:0.720324+0.022224    test-rmse:1.321032+0.163578 
## [17] train-rmse:0.689381+0.022660    test-rmse:1.309338+0.177086 
## [18] train-rmse:0.661963+0.021127    test-rmse:1.302467+0.178357 
## [19] train-rmse:0.642019+0.019853    test-rmse:1.299104+0.177785 
## [20] train-rmse:0.620382+0.017089    test-rmse:1.294617+0.188696 
## [21] train-rmse:0.595918+0.020267    test-rmse:1.281965+0.188100 
## [22] train-rmse:0.579245+0.018920    test-rmse:1.281149+0.190554 
## [23] train-rmse:0.561924+0.013865    test-rmse:1.279705+0.196014 
## [24] train-rmse:0.545955+0.013129    test-rmse:1.276058+0.198635 
## [25] train-rmse:0.524478+0.009601    test-rmse:1.268723+0.193943 
## [26] train-rmse:0.507591+0.009406    test-rmse:1.263453+0.197307 
## [27] train-rmse:0.496066+0.010320    test-rmse:1.258131+0.197020 
## [28] train-rmse:0.480465+0.013385    test-rmse:1.256086+0.198065 
## [29] train-rmse:0.471832+0.014131    test-rmse:1.254353+0.198997 
## [30] train-rmse:0.460276+0.013748    test-rmse:1.250905+0.200059 
## [31] train-rmse:0.453068+0.014253    test-rmse:1.251506+0.200669 
## [32] train-rmse:0.442519+0.017680    test-rmse:1.248577+0.198208 
## [33] train-rmse:0.431956+0.018512    test-rmse:1.249978+0.197146 
## [34] train-rmse:0.415506+0.018262    test-rmse:1.250266+0.193048 
## [35] train-rmse:0.405873+0.016446    test-rmse:1.251042+0.191215 
## [36] train-rmse:0.396255+0.011509    test-rmse:1.250089+0.191216 
## [37] train-rmse:0.389153+0.010631    test-rmse:1.253029+0.192024 
## [38] train-rmse:0.380761+0.013674    test-rmse:1.253282+0.190995 
## Stopping. Best iteration:
## [32] train-rmse:0.442519+0.017680    test-rmse:1.248577+0.198208
## 
## 90 
## [1]  train-rmse:6.594853+0.049294    test-rmse:6.602220+0.239862 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.567412+0.034724    test-rmse:4.608260+0.203106 
## [3]  train-rmse:3.251728+0.032962    test-rmse:3.309955+0.150762 
## [4]  train-rmse:2.394771+0.020272    test-rmse:2.524303+0.129904 
## [5]  train-rmse:1.848043+0.023084    test-rmse:2.027660+0.127556 
## [6]  train-rmse:1.494208+0.020096    test-rmse:1.739469+0.120480 
## [7]  train-rmse:1.240727+0.022486    test-rmse:1.573099+0.104438 
## [8]  train-rmse:1.088855+0.022362    test-rmse:1.483047+0.131735 
## [9]  train-rmse:0.973134+0.018501    test-rmse:1.416157+0.146592 
## [10] train-rmse:0.887320+0.019366    test-rmse:1.365482+0.156909 
## [11] train-rmse:0.827349+0.016767    test-rmse:1.344418+0.163426 
## [12] train-rmse:0.765973+0.022930    test-rmse:1.323038+0.171969 
## [13] train-rmse:0.717854+0.019307    test-rmse:1.301539+0.182800 
## [14] train-rmse:0.679131+0.017437    test-rmse:1.294375+0.191812 
## [15] train-rmse:0.644853+0.013081    test-rmse:1.287657+0.192809 
## [16] train-rmse:0.616007+0.011194    test-rmse:1.281356+0.194437 
## [17] train-rmse:0.591867+0.011839    test-rmse:1.274764+0.195784 
## [18] train-rmse:0.565243+0.006127    test-rmse:1.267383+0.203937 
## [19] train-rmse:0.541092+0.004606    test-rmse:1.260239+0.202508 
## [20] train-rmse:0.517299+0.006019    test-rmse:1.264314+0.201439 
## [21] train-rmse:0.499057+0.005286    test-rmse:1.259871+0.198835 
## [22] train-rmse:0.477324+0.011375    test-rmse:1.252321+0.199483 
## [23] train-rmse:0.460147+0.011749    test-rmse:1.251276+0.196544 
## [24] train-rmse:0.441959+0.010021    test-rmse:1.252200+0.196513 
## [25] train-rmse:0.427905+0.011950    test-rmse:1.249582+0.194242 
## [26] train-rmse:0.412050+0.011948    test-rmse:1.246122+0.195223 
## [27] train-rmse:0.402598+0.010887    test-rmse:1.248743+0.195966 
## [28] train-rmse:0.391870+0.011452    test-rmse:1.248398+0.195534 
## [29] train-rmse:0.378466+0.009810    test-rmse:1.246871+0.198197 
## [30] train-rmse:0.368335+0.012027    test-rmse:1.244039+0.199600 
## [31] train-rmse:0.355313+0.012805    test-rmse:1.239751+0.197496 
## [32] train-rmse:0.343839+0.008803    test-rmse:1.236178+0.197720 
## [33] train-rmse:0.333463+0.009502    test-rmse:1.238582+0.199232 
## [34] train-rmse:0.320140+0.006487    test-rmse:1.237498+0.197469 
## [35] train-rmse:0.312806+0.008482    test-rmse:1.235365+0.195412 
## [36] train-rmse:0.301096+0.009416    test-rmse:1.237141+0.194733 
## [37] train-rmse:0.292620+0.010977    test-rmse:1.237962+0.195285 
## [38] train-rmse:0.284360+0.008271    test-rmse:1.238587+0.193258 
## [39] train-rmse:0.277407+0.008938    test-rmse:1.240923+0.193622 
## [40] train-rmse:0.266952+0.006722    test-rmse:1.240304+0.190144 
## [41] train-rmse:0.257998+0.007222    test-rmse:1.241072+0.189302 
## Stopping. Best iteration:
## [35] train-rmse:0.312806+0.008482    test-rmse:1.235365+0.195412
## 
## 91 
## [1]  train-rmse:6.503695+0.043337    test-rmse:6.499766+0.259430 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.560319+0.024040    test-rmse:4.557352+0.244413 
## [3]  train-rmse:3.436740+0.014849    test-rmse:3.445222+0.201416 
## [4]  train-rmse:2.826395+0.012734    test-rmse:2.865402+0.131174 
## [5]  train-rmse:2.513061+0.015155    test-rmse:2.544458+0.086163 
## [6]  train-rmse:2.351592+0.018065    test-rmse:2.386604+0.053326 
## [7]  train-rmse:2.267319+0.019104    test-rmse:2.313365+0.053527 
## [8]  train-rmse:2.215795+0.019990    test-rmse:2.264432+0.062857 
## [9]  train-rmse:2.180938+0.021220    test-rmse:2.219395+0.065263 
## [10] train-rmse:2.153388+0.022224    test-rmse:2.197531+0.071689 
## [11] train-rmse:2.129235+0.023745    test-rmse:2.171968+0.077858 
## [12] train-rmse:2.107525+0.024872    test-rmse:2.157151+0.086041 
## [13] train-rmse:2.085668+0.024845    test-rmse:2.144869+0.080133 
## [14] train-rmse:2.064964+0.025408    test-rmse:2.134195+0.085702 
## [15] train-rmse:2.045361+0.025779    test-rmse:2.117245+0.096228 
## [16] train-rmse:2.026963+0.026281    test-rmse:2.102618+0.100127 
## [17] train-rmse:2.009241+0.027515    test-rmse:2.080112+0.089869 
## [18] train-rmse:1.991752+0.028710    test-rmse:2.063876+0.085659 
## [19] train-rmse:1.975034+0.030362    test-rmse:2.045281+0.093446 
## [20] train-rmse:1.959326+0.031591    test-rmse:2.040191+0.100607 
## [21] train-rmse:1.944845+0.031723    test-rmse:2.019640+0.106106 
## [22] train-rmse:1.930604+0.032022    test-rmse:2.004427+0.097179 
## [23] train-rmse:1.917175+0.032634    test-rmse:1.998477+0.092451 
## [24] train-rmse:1.903977+0.032982    test-rmse:1.982027+0.085130 
## [25] train-rmse:1.891258+0.033412    test-rmse:1.972634+0.087439 
## [26] train-rmse:1.878756+0.034268    test-rmse:1.957799+0.084650 
## [27] train-rmse:1.866723+0.035003    test-rmse:1.953603+0.085880 
## [28] train-rmse:1.855143+0.035658    test-rmse:1.944829+0.087180 
## [29] train-rmse:1.844123+0.036727    test-rmse:1.941226+0.084278 
## [30] train-rmse:1.833484+0.037708    test-rmse:1.926449+0.084734 
## [31] train-rmse:1.822924+0.038684    test-rmse:1.920260+0.085715 
## [32] train-rmse:1.812493+0.039354    test-rmse:1.907153+0.082903 
## [33] train-rmse:1.802379+0.040044    test-rmse:1.899399+0.086550 
## [34] train-rmse:1.792342+0.040600    test-rmse:1.896546+0.086789 
## [35] train-rmse:1.782371+0.041077    test-rmse:1.887828+0.088447 
## [36] train-rmse:1.772757+0.041551    test-rmse:1.872226+0.092109 
## [37] train-rmse:1.763060+0.042133    test-rmse:1.864744+0.098200 
## [38] train-rmse:1.753630+0.042649    test-rmse:1.863824+0.099832 
## [39] train-rmse:1.744519+0.043036    test-rmse:1.856064+0.098153 
## [40] train-rmse:1.735482+0.043432    test-rmse:1.854622+0.102020 
## [41] train-rmse:1.726661+0.043962    test-rmse:1.840136+0.101362 
## [42] train-rmse:1.718677+0.044437    test-rmse:1.831872+0.102053 
## [43] train-rmse:1.710632+0.044713    test-rmse:1.822763+0.103491 
## [44] train-rmse:1.703051+0.045114    test-rmse:1.813704+0.104713 
## [45] train-rmse:1.695265+0.045547    test-rmse:1.811958+0.106860 
## [46] train-rmse:1.687831+0.046069    test-rmse:1.804144+0.106876 
## [47] train-rmse:1.680766+0.046478    test-rmse:1.793022+0.110548 
## [48] train-rmse:1.673746+0.046877    test-rmse:1.789966+0.106054 
## [49] train-rmse:1.666909+0.047292    test-rmse:1.785499+0.105516 
## [50] train-rmse:1.660237+0.047580    test-rmse:1.779684+0.103308 
## [51] train-rmse:1.653661+0.047845    test-rmse:1.772649+0.105694 
## [52] train-rmse:1.647540+0.048107    test-rmse:1.769993+0.097609 
## [53] train-rmse:1.641386+0.048156    test-rmse:1.764222+0.099045 
## [54] train-rmse:1.635236+0.048062    test-rmse:1.757403+0.101737 
## [55] train-rmse:1.629140+0.048225    test-rmse:1.753268+0.104772 
## [56] train-rmse:1.623167+0.048362    test-rmse:1.746724+0.104883 
## [57] train-rmse:1.617355+0.048464    test-rmse:1.738289+0.108888 
## [58] train-rmse:1.611662+0.048756    test-rmse:1.722443+0.112896 
## [59] train-rmse:1.606320+0.049028    test-rmse:1.722860+0.116326 
## [60] train-rmse:1.600984+0.049364    test-rmse:1.719875+0.116264 
## [61] train-rmse:1.595668+0.049686    test-rmse:1.720434+0.116205 
## [62] train-rmse:1.590392+0.049933    test-rmse:1.719163+0.121808 
## [63] train-rmse:1.585273+0.050251    test-rmse:1.716219+0.123839 
## [64] train-rmse:1.580257+0.050511    test-rmse:1.710347+0.124407 
## [65] train-rmse:1.575384+0.050696    test-rmse:1.706143+0.128296 
## [66] train-rmse:1.570390+0.050730    test-rmse:1.700618+0.130143 
## [67] train-rmse:1.565611+0.050798    test-rmse:1.696911+0.130907 
## [68] train-rmse:1.561024+0.050943    test-rmse:1.689777+0.131177 
## [69] train-rmse:1.556354+0.050956    test-rmse:1.689143+0.136205 
## [70] train-rmse:1.551652+0.051038    test-rmse:1.682105+0.137519 
## [71] train-rmse:1.547198+0.051077    test-rmse:1.677335+0.136560 
## [72] train-rmse:1.542798+0.051237    test-rmse:1.673490+0.134870 
## [73] train-rmse:1.538627+0.051297    test-rmse:1.671791+0.140080 
## [74] train-rmse:1.534504+0.051442    test-rmse:1.671865+0.139480 
## [75] train-rmse:1.530613+0.051569    test-rmse:1.666002+0.141092 
## [76] train-rmse:1.526806+0.051622    test-rmse:1.661182+0.142274 
## [77] train-rmse:1.523033+0.051632    test-rmse:1.659039+0.141848 
## [78] train-rmse:1.519393+0.051750    test-rmse:1.649889+0.143686 
## [79] train-rmse:1.515829+0.051797    test-rmse:1.648956+0.143391 
## [80] train-rmse:1.512258+0.051865    test-rmse:1.649032+0.140382 
## [81] train-rmse:1.508765+0.051957    test-rmse:1.644925+0.148847 
## [82] train-rmse:1.505304+0.052049    test-rmse:1.642081+0.148893 
## [83] train-rmse:1.501941+0.052084    test-rmse:1.637170+0.149860 
## [84] train-rmse:1.498599+0.052095    test-rmse:1.632493+0.150323 
## [85] train-rmse:1.495226+0.052061    test-rmse:1.627674+0.149182 
## [86] train-rmse:1.491915+0.052090    test-rmse:1.624107+0.152511 
## [87] train-rmse:1.488739+0.052162    test-rmse:1.615628+0.150161 
## [88] train-rmse:1.485667+0.052319    test-rmse:1.611292+0.150007 
## [89] train-rmse:1.482618+0.052446    test-rmse:1.608116+0.150172 
## [90] train-rmse:1.479666+0.052498    test-rmse:1.605068+0.145979 
## [91] train-rmse:1.476727+0.052690    test-rmse:1.601950+0.143131 
## [92] train-rmse:1.473861+0.052831    test-rmse:1.600856+0.146545 
## [93] train-rmse:1.471002+0.052886    test-rmse:1.599389+0.147190 
## [94] train-rmse:1.468094+0.052948    test-rmse:1.600568+0.149082 
## [95] train-rmse:1.465325+0.053070    test-rmse:1.603304+0.153951 
## [96] train-rmse:1.462631+0.053127    test-rmse:1.598555+0.154721 
## [97] train-rmse:1.459988+0.053083    test-rmse:1.595735+0.155517 
## [98] train-rmse:1.457405+0.053107    test-rmse:1.593129+0.154729 
## [99] train-rmse:1.454909+0.053077    test-rmse:1.590700+0.155443 
## [100]    train-rmse:1.452430+0.053019    test-rmse:1.588108+0.157290 
## [101]    train-rmse:1.450025+0.052933    test-rmse:1.588100+0.155667 
## [102]    train-rmse:1.447570+0.052942    test-rmse:1.588942+0.152338 
## [103]    train-rmse:1.445114+0.053048    test-rmse:1.586606+0.152928 
## [104]    train-rmse:1.442647+0.053242    test-rmse:1.587395+0.157741 
## [105]    train-rmse:1.440359+0.053221    test-rmse:1.583066+0.154601 
## [106]    train-rmse:1.438104+0.053205    test-rmse:1.582082+0.156664 
## [107]    train-rmse:1.435912+0.053159    test-rmse:1.582500+0.161179 
## [108]    train-rmse:1.433730+0.053124    test-rmse:1.579082+0.162899 
## [109]    train-rmse:1.431610+0.053165    test-rmse:1.576603+0.163400 
## [110]    train-rmse:1.429483+0.053264    test-rmse:1.574916+0.163275 
## [111]    train-rmse:1.427469+0.053267    test-rmse:1.572793+0.167453 
## [112]    train-rmse:1.425514+0.053321    test-rmse:1.572105+0.166541 
## [113]    train-rmse:1.423550+0.053391    test-rmse:1.566968+0.167102 
## [114]    train-rmse:1.421616+0.053439    test-rmse:1.563483+0.165838 
## [115]    train-rmse:1.419724+0.053518    test-rmse:1.560918+0.164161 
## [116]    train-rmse:1.417897+0.053520    test-rmse:1.556123+0.161397 
## [117]    train-rmse:1.416056+0.053527    test-rmse:1.556903+0.163942 
## [118]    train-rmse:1.414274+0.053502    test-rmse:1.553946+0.166390 
## [119]    train-rmse:1.412475+0.053469    test-rmse:1.554085+0.168801 
## [120]    train-rmse:1.410729+0.053430    test-rmse:1.555219+0.168156 
## [121]    train-rmse:1.409019+0.053394    test-rmse:1.555156+0.169698 
## [122]    train-rmse:1.407321+0.053376    test-rmse:1.555711+0.170399 
## [123]    train-rmse:1.405609+0.053408    test-rmse:1.551990+0.170992 
## [124]    train-rmse:1.403953+0.053429    test-rmse:1.549399+0.171325 
## [125]    train-rmse:1.402337+0.053460    test-rmse:1.547015+0.172079 
## [126]    train-rmse:1.400743+0.053441    test-rmse:1.544967+0.173971 
## [127]    train-rmse:1.399183+0.053423    test-rmse:1.541211+0.174293 
## [128]    train-rmse:1.397650+0.053399    test-rmse:1.539651+0.175403 
## [129]    train-rmse:1.396119+0.053398    test-rmse:1.536939+0.177087 
## [130]    train-rmse:1.394649+0.053400    test-rmse:1.535009+0.175782 
## [131]    train-rmse:1.393182+0.053414    test-rmse:1.532778+0.176112 
## [132]    train-rmse:1.391783+0.053409    test-rmse:1.532883+0.176524 
## [133]    train-rmse:1.390416+0.053444    test-rmse:1.531438+0.177296 
## [134]    train-rmse:1.389090+0.053437    test-rmse:1.530616+0.175383 
## [135]    train-rmse:1.387773+0.053400    test-rmse:1.530181+0.178196 
## [136]    train-rmse:1.386430+0.053426    test-rmse:1.529371+0.177063 
## [137]    train-rmse:1.385186+0.053451    test-rmse:1.528863+0.176126 
## [138]    train-rmse:1.383962+0.053450    test-rmse:1.526622+0.180859 
## [139]    train-rmse:1.382731+0.053449    test-rmse:1.524891+0.181225 
## [140]    train-rmse:1.381522+0.053454    test-rmse:1.524051+0.179594 
## [141]    train-rmse:1.380331+0.053464    test-rmse:1.523169+0.181880 
## [142]    train-rmse:1.379145+0.053486    test-rmse:1.522217+0.182955 
## [143]    train-rmse:1.377956+0.053465    test-rmse:1.521766+0.182355 
## [144]    train-rmse:1.376821+0.053467    test-rmse:1.522685+0.181401 
## [145]    train-rmse:1.375698+0.053469    test-rmse:1.519473+0.181453 
## [146]    train-rmse:1.374592+0.053441    test-rmse:1.518307+0.181004 
## [147]    train-rmse:1.373499+0.053441    test-rmse:1.517641+0.178195 
## [148]    train-rmse:1.372434+0.053416    test-rmse:1.517328+0.178077 
## [149]    train-rmse:1.371395+0.053417    test-rmse:1.518743+0.178594 
## [150]    train-rmse:1.370354+0.053417    test-rmse:1.516953+0.179680 
## [151]    train-rmse:1.369313+0.053408    test-rmse:1.516197+0.178652 
## [152]    train-rmse:1.368301+0.053409    test-rmse:1.512507+0.178336 
## [153]    train-rmse:1.367296+0.053399    test-rmse:1.511485+0.178062 
## [154]    train-rmse:1.366326+0.053401    test-rmse:1.510097+0.178926 
## [155]    train-rmse:1.365361+0.053373    test-rmse:1.510181+0.178556 
## [156]    train-rmse:1.364354+0.053305    test-rmse:1.509320+0.179154 
## [157]    train-rmse:1.363426+0.053316    test-rmse:1.511090+0.183173 
## [158]    train-rmse:1.362530+0.053313    test-rmse:1.512382+0.185883 
## [159]    train-rmse:1.361623+0.053307    test-rmse:1.512792+0.185718 
## [160]    train-rmse:1.360724+0.053314    test-rmse:1.511229+0.184920 
## [161]    train-rmse:1.359901+0.053322    test-rmse:1.509169+0.188612 
## [162]    train-rmse:1.359081+0.053354    test-rmse:1.509581+0.189828 
## [163]    train-rmse:1.358267+0.053360    test-rmse:1.508578+0.189754 
## [164]    train-rmse:1.357432+0.053369    test-rmse:1.507492+0.190966 
## [165]    train-rmse:1.356650+0.053371    test-rmse:1.507065+0.190927 
## [166]    train-rmse:1.355861+0.053360    test-rmse:1.505156+0.189528 
## [167]    train-rmse:1.355082+0.053334    test-rmse:1.502983+0.191489 
## [168]    train-rmse:1.354308+0.053322    test-rmse:1.501553+0.191421 
## [169]    train-rmse:1.353561+0.053297    test-rmse:1.503315+0.191647 
## [170]    train-rmse:1.352836+0.053285    test-rmse:1.500589+0.192452 
## [171]    train-rmse:1.352105+0.053281    test-rmse:1.499255+0.191482 
## [172]    train-rmse:1.351396+0.053255    test-rmse:1.499269+0.191842 
## [173]    train-rmse:1.350709+0.053254    test-rmse:1.498712+0.191085 
## [174]    train-rmse:1.350033+0.053248    test-rmse:1.498319+0.191474 
## [175]    train-rmse:1.349368+0.053244    test-rmse:1.496816+0.190771 
## [176]    train-rmse:1.348733+0.053224    test-rmse:1.496401+0.191099 
## [177]    train-rmse:1.348093+0.053211    test-rmse:1.496264+0.191183 
## [178]    train-rmse:1.347437+0.053204    test-rmse:1.495178+0.192815 
## [179]    train-rmse:1.346775+0.053192    test-rmse:1.495981+0.191763 
## [180]    train-rmse:1.346126+0.053181    test-rmse:1.496032+0.192205 
## [181]    train-rmse:1.345492+0.053159    test-rmse:1.495404+0.190808 
## [182]    train-rmse:1.344864+0.053127    test-rmse:1.493475+0.191393 
## [183]    train-rmse:1.344257+0.053128    test-rmse:1.492790+0.191685 
## [184]    train-rmse:1.343653+0.053116    test-rmse:1.494651+0.193803 
## [185]    train-rmse:1.343081+0.053100    test-rmse:1.495876+0.194838 
## [186]    train-rmse:1.342497+0.053044    test-rmse:1.498856+0.196402 
## [187]    train-rmse:1.341946+0.053047    test-rmse:1.497569+0.193597 
## [188]    train-rmse:1.341388+0.053068    test-rmse:1.498164+0.192946 
## [189]    train-rmse:1.340814+0.053080    test-rmse:1.496715+0.194660 
## Stopping. Best iteration:
## [183]    train-rmse:1.344257+0.053128    test-rmse:1.492790+0.191685
## 
## 92 
## [1]  train-rmse:6.452603+0.043919    test-rmse:6.449304+0.253018 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.459874+0.023986    test-rmse:4.472986+0.225781 
## [3]  train-rmse:3.274163+0.017361    test-rmse:3.295099+0.176981 
## [4]  train-rmse:2.598537+0.017003    test-rmse:2.638764+0.105529 
## [5]  train-rmse:2.238507+0.020014    test-rmse:2.319695+0.057794 
## [6]  train-rmse:2.026607+0.019797    test-rmse:2.115649+0.045407 
## [7]  train-rmse:1.910583+0.018110    test-rmse:2.034953+0.065862 
## [8]  train-rmse:1.833281+0.018211    test-rmse:1.968381+0.060171 
## [9]  train-rmse:1.783126+0.020318    test-rmse:1.918245+0.083561 
## [10] train-rmse:1.741660+0.022560    test-rmse:1.887922+0.099690 
## [11] train-rmse:1.700376+0.028192    test-rmse:1.846569+0.083458 
## [12] train-rmse:1.667681+0.031305    test-rmse:1.806838+0.087330 
## [13] train-rmse:1.634174+0.026200    test-rmse:1.786137+0.091852 
## [14] train-rmse:1.605739+0.027378    test-rmse:1.768720+0.098179 
## [15] train-rmse:1.570795+0.032063    test-rmse:1.727546+0.055877 
## [16] train-rmse:1.543445+0.036717    test-rmse:1.705589+0.062417 
## [17] train-rmse:1.515795+0.039804    test-rmse:1.685719+0.069137 
## [18] train-rmse:1.488581+0.035178    test-rmse:1.674207+0.066282 
## [19] train-rmse:1.450861+0.038100    test-rmse:1.644059+0.042162 
## [20] train-rmse:1.425800+0.042976    test-rmse:1.614999+0.057355 
## [21] train-rmse:1.403937+0.045611    test-rmse:1.601185+0.063385 
## [22] train-rmse:1.383131+0.045941    test-rmse:1.570816+0.073760 
## [23] train-rmse:1.363805+0.046217    test-rmse:1.555714+0.071638 
## [24] train-rmse:1.347194+0.047177    test-rmse:1.540066+0.069553 
## [25] train-rmse:1.328357+0.050407    test-rmse:1.521038+0.068028 
## [26] train-rmse:1.297999+0.027207    test-rmse:1.515610+0.079899 
## [27] train-rmse:1.279920+0.027087    test-rmse:1.504833+0.083386 
## [28] train-rmse:1.261992+0.028592    test-rmse:1.493683+0.084025 
## [29] train-rmse:1.246595+0.029909    test-rmse:1.473592+0.087186 
## [30] train-rmse:1.231651+0.030714    test-rmse:1.457627+0.089341 
## [31] train-rmse:1.215909+0.030665    test-rmse:1.451231+0.092364 
## [32] train-rmse:1.200829+0.033902    test-rmse:1.434856+0.080362 
## [33] train-rmse:1.186732+0.033713    test-rmse:1.423789+0.079524 
## [34] train-rmse:1.167225+0.024012    test-rmse:1.410494+0.088848 
## [35] train-rmse:1.155510+0.023085    test-rmse:1.404104+0.090070 
## [36] train-rmse:1.144871+0.023463    test-rmse:1.392574+0.086493 
## [37] train-rmse:1.133354+0.023193    test-rmse:1.373693+0.094840 
## [38] train-rmse:1.122253+0.022671    test-rmse:1.367362+0.092893 
## [39] train-rmse:1.109650+0.024859    test-rmse:1.358628+0.087578 
## [40] train-rmse:1.099567+0.024118    test-rmse:1.353137+0.090609 
## [41] train-rmse:1.090553+0.024342    test-rmse:1.345513+0.096429 
## [42] train-rmse:1.079450+0.026550    test-rmse:1.337938+0.104478 
## [43] train-rmse:1.070971+0.026868    test-rmse:1.335803+0.105652 
## [44] train-rmse:1.061346+0.027035    test-rmse:1.338031+0.113973 
## [45] train-rmse:1.053145+0.027359    test-rmse:1.328345+0.118033 
## [46] train-rmse:1.042005+0.029417    test-rmse:1.316844+0.110994 
## [47] train-rmse:1.032500+0.026706    test-rmse:1.307170+0.113227 
## [48] train-rmse:1.024300+0.028621    test-rmse:1.305447+0.117920 
## [49] train-rmse:1.016999+0.028954    test-rmse:1.299368+0.114984 
## [50] train-rmse:1.004503+0.025144    test-rmse:1.292791+0.119781 
## [51] train-rmse:0.997857+0.025096    test-rmse:1.284529+0.124805 
## [52] train-rmse:0.989123+0.024535    test-rmse:1.278622+0.122371 
## [53] train-rmse:0.973665+0.032710    test-rmse:1.269833+0.124943 
## [54] train-rmse:0.967608+0.033261    test-rmse:1.264746+0.125328 
## [55] train-rmse:0.962472+0.033838    test-rmse:1.264216+0.124537 
## [56] train-rmse:0.956570+0.034503    test-rmse:1.267387+0.128512 
## [57] train-rmse:0.950107+0.035558    test-rmse:1.265645+0.129127 
## [58] train-rmse:0.944698+0.036502    test-rmse:1.264393+0.131516 
## [59] train-rmse:0.939339+0.037158    test-rmse:1.258980+0.134839 
## [60] train-rmse:0.933401+0.037682    test-rmse:1.250301+0.132059 
## [61] train-rmse:0.927690+0.037380    test-rmse:1.247297+0.136009 
## [62] train-rmse:0.923649+0.037629    test-rmse:1.247550+0.140562 
## [63] train-rmse:0.918228+0.035834    test-rmse:1.247182+0.141280 
## [64] train-rmse:0.913045+0.035440    test-rmse:1.243650+0.141461 
## [65] train-rmse:0.906257+0.039084    test-rmse:1.241311+0.141517 
## [66] train-rmse:0.902279+0.039010    test-rmse:1.239395+0.143628 
## [67] train-rmse:0.898367+0.039423    test-rmse:1.235121+0.146448 
## [68] train-rmse:0.894716+0.039919    test-rmse:1.232902+0.147348 
## [69] train-rmse:0.889826+0.039791    test-rmse:1.231763+0.149998 
## [70] train-rmse:0.883108+0.038679    test-rmse:1.228581+0.150173 
## [71] train-rmse:0.879446+0.038839    test-rmse:1.226875+0.149121 
## [72] train-rmse:0.875580+0.038931    test-rmse:1.226128+0.152048 
## [73] train-rmse:0.866944+0.046579    test-rmse:1.226414+0.153822 
## [74] train-rmse:0.863374+0.046539    test-rmse:1.224308+0.152663 
## [75] train-rmse:0.858570+0.046220    test-rmse:1.223144+0.153784 
## [76] train-rmse:0.853755+0.046203    test-rmse:1.220847+0.152762 
## [77] train-rmse:0.850826+0.046644    test-rmse:1.220051+0.154627 
## [78] train-rmse:0.846746+0.046762    test-rmse:1.218725+0.156327 
## [79] train-rmse:0.843687+0.046452    test-rmse:1.222657+0.153664 
## [80] train-rmse:0.840888+0.046197    test-rmse:1.221595+0.154636 
## [81] train-rmse:0.837782+0.046640    test-rmse:1.217349+0.151344 
## [82] train-rmse:0.834609+0.046827    test-rmse:1.219128+0.149355 
## [83] train-rmse:0.831509+0.046823    test-rmse:1.218363+0.149472 
## [84] train-rmse:0.828768+0.046639    test-rmse:1.217324+0.150306 
## [85] train-rmse:0.824120+0.043298    test-rmse:1.216890+0.150908 
## [86] train-rmse:0.821824+0.043384    test-rmse:1.216571+0.149694 
## [87] train-rmse:0.819990+0.043485    test-rmse:1.216033+0.149743 
## [88] train-rmse:0.814621+0.046708    test-rmse:1.218940+0.147033 
## [89] train-rmse:0.806165+0.038014    test-rmse:1.212175+0.148192 
## [90] train-rmse:0.801979+0.035694    test-rmse:1.214388+0.153329 
## [91] train-rmse:0.798079+0.034975    test-rmse:1.209261+0.149339 
## [92] train-rmse:0.795491+0.035490    test-rmse:1.208559+0.149370 
## [93] train-rmse:0.792953+0.036429    test-rmse:1.210878+0.149703 
## [94] train-rmse:0.790290+0.036102    test-rmse:1.209988+0.149430 
## [95] train-rmse:0.787378+0.035735    test-rmse:1.211135+0.146843 
## [96] train-rmse:0.785891+0.035911    test-rmse:1.209415+0.146243 
## [97] train-rmse:0.784005+0.036439    test-rmse:1.209278+0.146257 
## [98] train-rmse:0.782318+0.036596    test-rmse:1.209331+0.145556 
## Stopping. Best iteration:
## [92] train-rmse:0.795491+0.035490    test-rmse:1.208559+0.149370
## 
## 93 
## [1]  train-rmse:6.432603+0.046117    test-rmse:6.429668+0.244878 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.403065+0.031741    test-rmse:4.413530+0.217669 
## [3]  train-rmse:3.171056+0.024783    test-rmse:3.212409+0.156750 
## [4]  train-rmse:2.450898+0.018031    test-rmse:2.520937+0.120846 
## [5]  train-rmse:2.044775+0.019631    test-rmse:2.154789+0.083705 
## [6]  train-rmse:1.811173+0.015980    test-rmse:1.957766+0.062533 
## [7]  train-rmse:1.676093+0.015663    test-rmse:1.852728+0.067194 
## [8]  train-rmse:1.588053+0.019348    test-rmse:1.793512+0.057219 
## [9]  train-rmse:1.521738+0.023089    test-rmse:1.731683+0.060669 
## [10] train-rmse:1.464687+0.016757    test-rmse:1.694969+0.067597 
## [11] train-rmse:1.419536+0.014516    test-rmse:1.673471+0.070536 
## [12] train-rmse:1.370055+0.014758    test-rmse:1.625056+0.040828 
## [13] train-rmse:1.323599+0.025459    test-rmse:1.604123+0.046691 
## [14] train-rmse:1.288647+0.021815    test-rmse:1.578795+0.059284 
## [15] train-rmse:1.255911+0.019178    test-rmse:1.551637+0.071509 
## [16] train-rmse:1.222130+0.023777    test-rmse:1.523815+0.071287 
## [17] train-rmse:1.195144+0.022917    test-rmse:1.489418+0.071405 
## [18] train-rmse:1.167269+0.022852    test-rmse:1.477825+0.079407 
## [19] train-rmse:1.135993+0.020235    test-rmse:1.460087+0.105048 
## [20] train-rmse:1.109147+0.021718    test-rmse:1.443076+0.093911 
## [21] train-rmse:1.086344+0.023978    test-rmse:1.424390+0.092348 
## [22] train-rmse:1.062501+0.029148    test-rmse:1.405266+0.102558 
## [23] train-rmse:1.041039+0.031088    test-rmse:1.389276+0.102074 
## [24] train-rmse:1.020349+0.031124    test-rmse:1.379043+0.108257 
## [25] train-rmse:0.999965+0.030501    test-rmse:1.369789+0.109070 
## [26] train-rmse:0.985357+0.030131    test-rmse:1.359172+0.115348 
## [27] train-rmse:0.965515+0.032461    test-rmse:1.348598+0.117423 
## [28] train-rmse:0.946840+0.033433    test-rmse:1.330642+0.117225 
## [29] train-rmse:0.930644+0.033195    test-rmse:1.309405+0.117070 
## [30] train-rmse:0.915906+0.034645    test-rmse:1.298511+0.117431 
## [31] train-rmse:0.903448+0.035929    test-rmse:1.294098+0.122206 
## [32] train-rmse:0.890223+0.036181    test-rmse:1.290129+0.124468 
## [33] train-rmse:0.867898+0.034837    test-rmse:1.275403+0.124920 
## [34] train-rmse:0.854431+0.034684    test-rmse:1.265820+0.132281 
## [35] train-rmse:0.842932+0.037143    test-rmse:1.260762+0.133688 
## [36] train-rmse:0.832969+0.037222    test-rmse:1.260193+0.129552 
## [37] train-rmse:0.817577+0.038333    test-rmse:1.245675+0.125694 
## [38] train-rmse:0.806318+0.038475    test-rmse:1.244589+0.129269 
## [39] train-rmse:0.798077+0.037564    test-rmse:1.242463+0.130715 
## [40] train-rmse:0.786862+0.037050    test-rmse:1.236708+0.135078 
## [41] train-rmse:0.777683+0.038571    test-rmse:1.229797+0.133884 
## [42] train-rmse:0.769695+0.037603    test-rmse:1.230776+0.135035 
## [43] train-rmse:0.758623+0.041774    test-rmse:1.225780+0.132131 
## [44] train-rmse:0.750343+0.043120    test-rmse:1.219713+0.129388 
## [45] train-rmse:0.741219+0.043279    test-rmse:1.212052+0.126513 
## [46] train-rmse:0.733913+0.044588    test-rmse:1.210539+0.126124 
## [47] train-rmse:0.727372+0.045616    test-rmse:1.207162+0.125597 
## [48] train-rmse:0.720465+0.044343    test-rmse:1.202915+0.129522 
## [49] train-rmse:0.712253+0.041541    test-rmse:1.199630+0.124618 
## [50] train-rmse:0.703201+0.043878    test-rmse:1.199301+0.131955 
## [51] train-rmse:0.694867+0.042964    test-rmse:1.198230+0.131983 
## [52] train-rmse:0.689182+0.043073    test-rmse:1.196414+0.132830 
## [53] train-rmse:0.681657+0.046088    test-rmse:1.195983+0.132959 
## [54] train-rmse:0.677054+0.046120    test-rmse:1.192340+0.131769 
## [55] train-rmse:0.671796+0.046046    test-rmse:1.192490+0.131696 
## [56] train-rmse:0.666254+0.047039    test-rmse:1.188561+0.129948 
## [57] train-rmse:0.661464+0.047054    test-rmse:1.192168+0.131252 
## [58] train-rmse:0.656873+0.046021    test-rmse:1.193681+0.135797 
## [59] train-rmse:0.647784+0.038712    test-rmse:1.192998+0.136611 
## [60] train-rmse:0.642473+0.037855    test-rmse:1.197383+0.134036 
## [61] train-rmse:0.639060+0.037588    test-rmse:1.196786+0.134554 
## [62] train-rmse:0.631958+0.036411    test-rmse:1.197220+0.135487 
## Stopping. Best iteration:
## [56] train-rmse:0.666254+0.047039    test-rmse:1.188561+0.129948
## 
## 94 
## [1]  train-rmse:6.424089+0.046899    test-rmse:6.422334+0.247160 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.381831+0.029600    test-rmse:4.403653+0.219221 
## [3]  train-rmse:3.131718+0.021484    test-rmse:3.170830+0.154298 
## [4]  train-rmse:2.372594+0.012817    test-rmse:2.462866+0.125278 
## [5]  train-rmse:1.936754+0.019946    test-rmse:2.065504+0.073959 
## [6]  train-rmse:1.684651+0.022198    test-rmse:1.851090+0.080148 
## [7]  train-rmse:1.536010+0.025656    test-rmse:1.732116+0.073604 
## [8]  train-rmse:1.428082+0.025015    test-rmse:1.657171+0.062975 
## [9]  train-rmse:1.352167+0.024616    test-rmse:1.607786+0.084937 
## [10] train-rmse:1.288610+0.027935    test-rmse:1.566379+0.089174 
## [11] train-rmse:1.237713+0.033601    test-rmse:1.527688+0.082168 
## [12] train-rmse:1.182418+0.024389    test-rmse:1.479684+0.092038 
## [13] train-rmse:1.139115+0.023855    test-rmse:1.460928+0.103417 
## [14] train-rmse:1.103890+0.026485    test-rmse:1.442676+0.115555 
## [15] train-rmse:1.069376+0.021386    test-rmse:1.425865+0.123250 
## [16] train-rmse:1.018470+0.020368    test-rmse:1.398530+0.118301 
## [17] train-rmse:0.990263+0.023286    test-rmse:1.388881+0.114417 
## [18] train-rmse:0.962882+0.023737    test-rmse:1.371663+0.118508 
## [19] train-rmse:0.936373+0.024071    test-rmse:1.356575+0.132958 
## [20] train-rmse:0.914578+0.027430    test-rmse:1.344857+0.132591 
## [21] train-rmse:0.892082+0.026773    test-rmse:1.335124+0.137395 
## [22] train-rmse:0.869055+0.028983    test-rmse:1.321288+0.130043 
## [23] train-rmse:0.847407+0.028698    test-rmse:1.316984+0.140367 
## [24] train-rmse:0.828133+0.027604    test-rmse:1.310644+0.153930 
## [25] train-rmse:0.809182+0.025661    test-rmse:1.306433+0.154497 
## [26] train-rmse:0.792577+0.028616    test-rmse:1.300162+0.151448 
## [27] train-rmse:0.772908+0.026634    test-rmse:1.284401+0.145067 
## [28] train-rmse:0.757319+0.027155    test-rmse:1.269234+0.141112 
## [29] train-rmse:0.740218+0.025702    test-rmse:1.270554+0.140895 
## [30] train-rmse:0.720394+0.028223    test-rmse:1.269978+0.138903 
## [31] train-rmse:0.704498+0.022943    test-rmse:1.267446+0.138397 
## [32] train-rmse:0.689469+0.022277    test-rmse:1.265059+0.141997 
## [33] train-rmse:0.677168+0.023530    test-rmse:1.255489+0.141294 
## [34] train-rmse:0.666797+0.022337    test-rmse:1.249747+0.140906 
## [35] train-rmse:0.655807+0.024931    test-rmse:1.247855+0.145921 
## [36] train-rmse:0.647365+0.023449    test-rmse:1.243280+0.148874 
## [37] train-rmse:0.633999+0.017029    test-rmse:1.237652+0.145823 
## [38] train-rmse:0.620643+0.015431    test-rmse:1.242379+0.146507 
## [39] train-rmse:0.610444+0.017615    test-rmse:1.242158+0.147262 
## [40] train-rmse:0.602587+0.016312    test-rmse:1.237630+0.147750 
## [41] train-rmse:0.593402+0.017699    test-rmse:1.236490+0.148600 
## [42] train-rmse:0.587272+0.017104    test-rmse:1.231577+0.149124 
## [43] train-rmse:0.581708+0.019073    test-rmse:1.227436+0.146532 
## [44] train-rmse:0.573424+0.019192    test-rmse:1.230135+0.143334 
## [45] train-rmse:0.563845+0.018938    test-rmse:1.231399+0.144224 
## [46] train-rmse:0.557575+0.020950    test-rmse:1.230321+0.139972 
## [47] train-rmse:0.552332+0.022454    test-rmse:1.233036+0.142073 
## [48] train-rmse:0.542271+0.023687    test-rmse:1.232400+0.146492 
## [49] train-rmse:0.537445+0.023916    test-rmse:1.232966+0.146166 
## Stopping. Best iteration:
## [43] train-rmse:0.581708+0.019073    test-rmse:1.227436+0.146532
## 
## 95 
## [1]  train-rmse:6.416753+0.047601    test-rmse:6.422475+0.248417 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.356933+0.030383    test-rmse:4.393720+0.216575 
## [3]  train-rmse:3.082903+0.021967    test-rmse:3.166234+0.163385 
## [4]  train-rmse:2.293571+0.022165    test-rmse:2.412036+0.118219 
## [5]  train-rmse:1.832109+0.027485    test-rmse:2.040251+0.086813 
## [6]  train-rmse:1.552316+0.029424    test-rmse:1.811847+0.073453 
## [7]  train-rmse:1.378449+0.021259    test-rmse:1.697172+0.058305 
## [8]  train-rmse:1.244716+0.024975    test-rmse:1.595147+0.072231 
## [9]  train-rmse:1.155160+0.019890    test-rmse:1.533309+0.076934 
## [10] train-rmse:1.091288+0.020013    test-rmse:1.505006+0.078592 
## [11] train-rmse:1.028302+0.026662    test-rmse:1.466534+0.085274 
## [12] train-rmse:0.980168+0.026076    test-rmse:1.443000+0.097739 
## [13] train-rmse:0.932225+0.014453    test-rmse:1.397938+0.115395 
## [14] train-rmse:0.895486+0.012023    test-rmse:1.386803+0.126557 
## [15] train-rmse:0.867911+0.012149    test-rmse:1.368548+0.137932 
## [16] train-rmse:0.835312+0.013194    test-rmse:1.355520+0.126164 
## [17] train-rmse:0.803524+0.013846    test-rmse:1.343171+0.126294 
## [18] train-rmse:0.772554+0.013131    test-rmse:1.337879+0.129521 
## [19] train-rmse:0.741415+0.009367    test-rmse:1.322262+0.143489 
## [20] train-rmse:0.719125+0.010359    test-rmse:1.312421+0.148695 
## [21] train-rmse:0.698390+0.011438    test-rmse:1.299741+0.147386 
## [22] train-rmse:0.677410+0.012625    test-rmse:1.292948+0.149926 
## [23] train-rmse:0.657353+0.007241    test-rmse:1.287000+0.154689 
## [24] train-rmse:0.640033+0.010675    test-rmse:1.281703+0.156088 
## [25] train-rmse:0.626948+0.011989    test-rmse:1.283947+0.161295 
## [26] train-rmse:0.605811+0.012610    test-rmse:1.277526+0.155009 
## [27] train-rmse:0.590117+0.011519    test-rmse:1.274669+0.153442 
## [28] train-rmse:0.573028+0.012854    test-rmse:1.267369+0.158254 
## [29] train-rmse:0.558919+0.012869    test-rmse:1.269555+0.165116 
## [30] train-rmse:0.546856+0.010987    test-rmse:1.267760+0.165794 
## [31] train-rmse:0.537488+0.009296    test-rmse:1.269419+0.166131 
## [32] train-rmse:0.525674+0.012954    test-rmse:1.262289+0.163933 
## [33] train-rmse:0.515304+0.015239    test-rmse:1.262333+0.165965 
## [34] train-rmse:0.504278+0.016352    test-rmse:1.264775+0.164630 
## [35] train-rmse:0.493449+0.015699    test-rmse:1.263287+0.163094 
## [36] train-rmse:0.483264+0.016275    test-rmse:1.265232+0.165294 
## [37] train-rmse:0.475082+0.019939    test-rmse:1.264781+0.165720 
## [38] train-rmse:0.468522+0.020844    test-rmse:1.262704+0.165133 
## Stopping. Best iteration:
## [32] train-rmse:0.525674+0.012954    test-rmse:1.262289+0.163933
## 
## 96 
## [1]  train-rmse:6.414868+0.048035    test-rmse:6.423305+0.237511 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.344137+0.031117    test-rmse:4.378870+0.180860 
## [3]  train-rmse:3.050740+0.027325    test-rmse:3.105413+0.137490 
## [4]  train-rmse:2.248678+0.021644    test-rmse:2.366016+0.120972 
## [5]  train-rmse:1.762645+0.023851    test-rmse:1.949884+0.114553 
## [6]  train-rmse:1.458133+0.036514    test-rmse:1.727042+0.094999 
## [7]  train-rmse:1.279336+0.033547    test-rmse:1.596814+0.106648 
## [8]  train-rmse:1.139996+0.037583    test-rmse:1.510264+0.105162 
## [9]  train-rmse:1.045145+0.033660    test-rmse:1.457050+0.098281 
## [10] train-rmse:0.975263+0.036545    test-rmse:1.417109+0.117558 
## [11] train-rmse:0.902948+0.037565    test-rmse:1.404794+0.111856 
## [12] train-rmse:0.850820+0.036468    test-rmse:1.377751+0.130502 
## [13] train-rmse:0.800647+0.028851    test-rmse:1.350714+0.138315 
## [14] train-rmse:0.762494+0.021166    test-rmse:1.341424+0.142704 
## [15] train-rmse:0.727051+0.016298    test-rmse:1.329044+0.145152 
## [16] train-rmse:0.694061+0.018514    test-rmse:1.327655+0.155911 
## [17] train-rmse:0.664413+0.016814    test-rmse:1.319150+0.158455 
## [18] train-rmse:0.639406+0.017809    test-rmse:1.314528+0.159563 
## [19] train-rmse:0.607836+0.017653    test-rmse:1.295273+0.145897 
## [20] train-rmse:0.583973+0.014682    test-rmse:1.285633+0.158353 
## [21] train-rmse:0.566113+0.015997    test-rmse:1.285646+0.165873 
## [22] train-rmse:0.551582+0.015840    test-rmse:1.281950+0.163457 
## [23] train-rmse:0.532452+0.018844    test-rmse:1.282031+0.166391 
## [24] train-rmse:0.516139+0.015946    test-rmse:1.278274+0.172463 
## [25] train-rmse:0.504129+0.015877    test-rmse:1.284931+0.173688 
## [26] train-rmse:0.490981+0.017138    test-rmse:1.277442+0.173491 
## [27] train-rmse:0.475265+0.017240    test-rmse:1.275360+0.171806 
## [28] train-rmse:0.458932+0.013682    test-rmse:1.268635+0.167243 
## [29] train-rmse:0.440476+0.015635    test-rmse:1.265193+0.168954 
## [30] train-rmse:0.429983+0.014635    test-rmse:1.264423+0.169064 
## [31] train-rmse:0.418837+0.011483    test-rmse:1.265768+0.173234 
## [32] train-rmse:0.407810+0.011331    test-rmse:1.263711+0.172315 
## [33] train-rmse:0.396112+0.012667    test-rmse:1.264396+0.173451 
## [34] train-rmse:0.387678+0.012161    test-rmse:1.265671+0.172504 
## [35] train-rmse:0.380543+0.012795    test-rmse:1.265050+0.171690 
## [36] train-rmse:0.371390+0.015385    test-rmse:1.262710+0.171839 
## [37] train-rmse:0.362030+0.016853    test-rmse:1.261501+0.168337 
## [38] train-rmse:0.355331+0.016705    test-rmse:1.264214+0.168432 
## [39] train-rmse:0.350227+0.017471    test-rmse:1.264585+0.171510 
## [40] train-rmse:0.338456+0.015987    test-rmse:1.264666+0.171001 
## [41] train-rmse:0.329024+0.014330    test-rmse:1.263264+0.171617 
## [42] train-rmse:0.321583+0.015531    test-rmse:1.266153+0.170466 
## [43] train-rmse:0.314382+0.017931    test-rmse:1.263703+0.171157 
## Stopping. Best iteration:
## [37] train-rmse:0.362030+0.016853    test-rmse:1.261501+0.168337
## 
## 97 
## [1]  train-rmse:6.414868+0.048035    test-rmse:6.423305+0.237511 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.338762+0.031247    test-rmse:4.379525+0.190165 
## [3]  train-rmse:3.029448+0.024649    test-rmse:3.087889+0.157817 
## [4]  train-rmse:2.207980+0.021990    test-rmse:2.345707+0.126658 
## [5]  train-rmse:1.709214+0.024857    test-rmse:1.906011+0.114451 
## [6]  train-rmse:1.386979+0.024268    test-rmse:1.676182+0.109694 
## [7]  train-rmse:1.168068+0.030566    test-rmse:1.504934+0.102949 
## [8]  train-rmse:1.037299+0.035503    test-rmse:1.423821+0.124140 
## [9]  train-rmse:0.925614+0.038156    test-rmse:1.367489+0.129133 
## [10] train-rmse:0.844968+0.034398    test-rmse:1.323751+0.143605 
## [11] train-rmse:0.781044+0.033273    test-rmse:1.318545+0.151023 
## [12] train-rmse:0.730986+0.031284    test-rmse:1.305333+0.161446 
## [13] train-rmse:0.688280+0.036522    test-rmse:1.298135+0.163611 
## [14] train-rmse:0.646131+0.029268    test-rmse:1.272760+0.175685 
## [15] train-rmse:0.612820+0.027794    test-rmse:1.257965+0.171232 
## [16] train-rmse:0.582503+0.026760    test-rmse:1.251132+0.178147 
## [17] train-rmse:0.555220+0.027109    test-rmse:1.246053+0.178945 
## [18] train-rmse:0.526757+0.025058    test-rmse:1.245904+0.175114 
## [19] train-rmse:0.504596+0.023443    test-rmse:1.243500+0.176710 
## [20] train-rmse:0.485738+0.023037    test-rmse:1.247314+0.169924 
## [21] train-rmse:0.466479+0.018644    test-rmse:1.242593+0.172990 
## [22] train-rmse:0.447542+0.016682    test-rmse:1.235909+0.174529 
## [23] train-rmse:0.433948+0.016404    test-rmse:1.231118+0.173653 
## [24] train-rmse:0.414465+0.018600    test-rmse:1.233000+0.169711 
## [25] train-rmse:0.400305+0.018672    test-rmse:1.231791+0.167503 
## [26] train-rmse:0.388296+0.020406    test-rmse:1.231614+0.162860 
## [27] train-rmse:0.378803+0.020303    test-rmse:1.230583+0.162428 
## [28] train-rmse:0.365986+0.021394    test-rmse:1.231837+0.162535 
## [29] train-rmse:0.346358+0.019532    test-rmse:1.229149+0.164907 
## [30] train-rmse:0.335689+0.020020    test-rmse:1.234610+0.164779 
## [31] train-rmse:0.325011+0.019809    test-rmse:1.234600+0.167640 
## [32] train-rmse:0.314527+0.017749    test-rmse:1.233339+0.168308 
## [33] train-rmse:0.301070+0.016603    test-rmse:1.233548+0.168847 
## [34] train-rmse:0.290826+0.014908    test-rmse:1.232754+0.163670 
## [35] train-rmse:0.284187+0.015335    test-rmse:1.231936+0.164240 
## Stopping. Best iteration:
## [29] train-rmse:0.346358+0.019532    test-rmse:1.229149+0.164907
## 
## 98 
## [1]  train-rmse:6.330877+0.041766    test-rmse:6.327024+0.257730 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.359408+0.022387    test-rmse:4.357663+0.238808 
## [3]  train-rmse:3.274038+0.014094    test-rmse:3.285592+0.191603 
## [4]  train-rmse:2.716604+0.012806    test-rmse:2.760620+0.116498 
## [5]  train-rmse:2.445408+0.015465    test-rmse:2.479918+0.074379 
## [6]  train-rmse:2.309065+0.018761    test-rmse:2.346120+0.051488 
## [7]  train-rmse:2.239462+0.019651    test-rmse:2.292285+0.049752 
## [8]  train-rmse:2.194949+0.020843    test-rmse:2.244640+0.067768 
## [9]  train-rmse:2.163363+0.021998    test-rmse:2.202155+0.069380 
## [10] train-rmse:2.136868+0.023085    test-rmse:2.185617+0.076105 
## [11] train-rmse:2.113088+0.024358    test-rmse:2.161036+0.075930 
## [12] train-rmse:2.090673+0.025514    test-rmse:2.144703+0.085451 
## [13] train-rmse:2.069518+0.026170    test-rmse:2.131825+0.086544 
## [14] train-rmse:2.048664+0.026528    test-rmse:2.118028+0.086080 
## [15] train-rmse:2.028399+0.026788    test-rmse:2.108109+0.094564 
## [16] train-rmse:2.009183+0.027588    test-rmse:2.080041+0.097194 
## [17] train-rmse:1.990738+0.028571    test-rmse:2.061726+0.095269 
## [18] train-rmse:1.972631+0.029648    test-rmse:2.045993+0.087857 
## [19] train-rmse:1.955399+0.031404    test-rmse:2.031839+0.094386 
## [20] train-rmse:1.939524+0.032361    test-rmse:2.021687+0.103342 
## [21] train-rmse:1.925337+0.032941    test-rmse:2.004457+0.106058 
## [22] train-rmse:1.911481+0.032963    test-rmse:1.987843+0.106075 
## [23] train-rmse:1.898021+0.033542    test-rmse:1.980248+0.097283 
## [24] train-rmse:1.884270+0.033811    test-rmse:1.968984+0.096052 
## [25] train-rmse:1.870876+0.034324    test-rmse:1.955166+0.095238 
## [26] train-rmse:1.857891+0.035750    test-rmse:1.942843+0.084169 
## [27] train-rmse:1.845735+0.036507    test-rmse:1.937111+0.083010 
## [28] train-rmse:1.834038+0.037396    test-rmse:1.928498+0.087871 
## [29] train-rmse:1.822496+0.038512    test-rmse:1.917949+0.093095 
## [30] train-rmse:1.811511+0.039903    test-rmse:1.909018+0.088818 
## [31] train-rmse:1.800983+0.040606    test-rmse:1.901191+0.091543 
## [32] train-rmse:1.790519+0.041302    test-rmse:1.888686+0.093388 
## [33] train-rmse:1.780534+0.041651    test-rmse:1.884368+0.093137 
## [34] train-rmse:1.770454+0.041756    test-rmse:1.875545+0.090343 
## [35] train-rmse:1.760323+0.042344    test-rmse:1.865466+0.090808 
## [36] train-rmse:1.750583+0.042656    test-rmse:1.862770+0.090469 
## [37] train-rmse:1.740759+0.043020    test-rmse:1.850715+0.091472 
## [38] train-rmse:1.731194+0.043622    test-rmse:1.843192+0.099876 
## [39] train-rmse:1.722217+0.043907    test-rmse:1.833403+0.105590 
## [40] train-rmse:1.713196+0.044343    test-rmse:1.821577+0.103444 
## [41] train-rmse:1.704674+0.044689    test-rmse:1.818422+0.095553 
## [42] train-rmse:1.696745+0.045120    test-rmse:1.818266+0.106960 
## [43] train-rmse:1.688820+0.045650    test-rmse:1.806219+0.107554 
## [44] train-rmse:1.681176+0.046283    test-rmse:1.800789+0.105359 
## [45] train-rmse:1.673807+0.046666    test-rmse:1.794409+0.109161 
## [46] train-rmse:1.666510+0.047001    test-rmse:1.788160+0.112497 
## [47] train-rmse:1.659374+0.047750    test-rmse:1.783341+0.114081 
## [48] train-rmse:1.652669+0.048111    test-rmse:1.775339+0.115815 
## [49] train-rmse:1.645961+0.048523    test-rmse:1.769521+0.115323 
## [50] train-rmse:1.639227+0.048651    test-rmse:1.761829+0.110152 
## [51] train-rmse:1.632541+0.048839    test-rmse:1.753511+0.114134 
## [52] train-rmse:1.626200+0.048935    test-rmse:1.752735+0.103647 
## [53] train-rmse:1.620074+0.049125    test-rmse:1.748229+0.100542 
## [54] train-rmse:1.613921+0.049134    test-rmse:1.740601+0.103335 
## [55] train-rmse:1.608084+0.049276    test-rmse:1.733902+0.106389 
## [56] train-rmse:1.602191+0.049542    test-rmse:1.731468+0.107463 
## [57] train-rmse:1.596318+0.049651    test-rmse:1.714228+0.116601 
## [58] train-rmse:1.590671+0.049741    test-rmse:1.708600+0.120901 
## [59] train-rmse:1.585228+0.050072    test-rmse:1.710407+0.123422 
## [60] train-rmse:1.579915+0.050272    test-rmse:1.706491+0.123211 
## [61] train-rmse:1.574616+0.050368    test-rmse:1.706049+0.126642 
## [62] train-rmse:1.569580+0.050404    test-rmse:1.701800+0.131001 
## [63] train-rmse:1.564583+0.050575    test-rmse:1.694604+0.130998 
## [64] train-rmse:1.559657+0.050892    test-rmse:1.691214+0.132084 
## [65] train-rmse:1.554966+0.051127    test-rmse:1.685125+0.131008 
## [66] train-rmse:1.550351+0.051251    test-rmse:1.678827+0.129682 
## [67] train-rmse:1.545802+0.051371    test-rmse:1.674847+0.130665 
## [68] train-rmse:1.541028+0.051404    test-rmse:1.677937+0.131363 
## [69] train-rmse:1.536335+0.051366    test-rmse:1.671203+0.135327 
## [70] train-rmse:1.531838+0.051589    test-rmse:1.666603+0.137695 
## [71] train-rmse:1.527540+0.051459    test-rmse:1.662754+0.135758 
## [72] train-rmse:1.523377+0.051580    test-rmse:1.660200+0.137905 
## [73] train-rmse:1.519376+0.051653    test-rmse:1.658983+0.142430 
## [74] train-rmse:1.515542+0.051836    test-rmse:1.652408+0.141636 
## [75] train-rmse:1.511707+0.052049    test-rmse:1.649596+0.141426 
## [76] train-rmse:1.507897+0.052211    test-rmse:1.648144+0.140684 
## [77] train-rmse:1.504197+0.052176    test-rmse:1.639264+0.141599 
## [78] train-rmse:1.500616+0.052150    test-rmse:1.636449+0.142142 
## [79] train-rmse:1.497085+0.052090    test-rmse:1.630390+0.149994 
## [80] train-rmse:1.493713+0.052094    test-rmse:1.627203+0.144938 
## [81] train-rmse:1.490360+0.052165    test-rmse:1.622447+0.145223 
## [82] train-rmse:1.486980+0.052241    test-rmse:1.618020+0.148024 
## [83] train-rmse:1.483729+0.052259    test-rmse:1.616628+0.150948 
## [84] train-rmse:1.480514+0.052271    test-rmse:1.614049+0.151544 
## [85] train-rmse:1.477398+0.052351    test-rmse:1.610751+0.153181 
## [86] train-rmse:1.474278+0.052398    test-rmse:1.608306+0.152694 
## [87] train-rmse:1.471230+0.052446    test-rmse:1.602804+0.151221 
## [88] train-rmse:1.468432+0.052578    test-rmse:1.603036+0.151279 
## [89] train-rmse:1.465658+0.052616    test-rmse:1.598148+0.150968 
## [90] train-rmse:1.462885+0.052696    test-rmse:1.595031+0.153081 
## [91] train-rmse:1.459949+0.053007    test-rmse:1.595125+0.158848 
## [92] train-rmse:1.456983+0.053259    test-rmse:1.587680+0.159144 
## [93] train-rmse:1.454153+0.053382    test-rmse:1.586858+0.162133 
## [94] train-rmse:1.451495+0.053420    test-rmse:1.582146+0.163151 
## [95] train-rmse:1.448882+0.053498    test-rmse:1.580855+0.162711 
## [96] train-rmse:1.446334+0.053491    test-rmse:1.575244+0.163374 
## [97] train-rmse:1.443838+0.053443    test-rmse:1.575073+0.160146 
## [98] train-rmse:1.441407+0.053462    test-rmse:1.574360+0.161642 
## [99] train-rmse:1.439061+0.053471    test-rmse:1.576353+0.161273 
## [100]    train-rmse:1.436730+0.053507    test-rmse:1.578945+0.159857 
## [101]    train-rmse:1.434454+0.053463    test-rmse:1.578632+0.159471 
## [102]    train-rmse:1.432212+0.053501    test-rmse:1.579969+0.162417 
## [103]    train-rmse:1.429873+0.053661    test-rmse:1.581030+0.162826 
## [104]    train-rmse:1.427618+0.053610    test-rmse:1.576862+0.160295 
## Stopping. Best iteration:
## [98] train-rmse:1.441407+0.053462    test-rmse:1.574360+0.161642
## 
## 99 
## [1]  train-rmse:6.276071+0.042367    test-rmse:6.272866+0.251159 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.250284+0.021582    test-rmse:4.266104+0.220915 
## [3]  train-rmse:3.095537+0.018704    test-rmse:3.117638+0.165033 
## [4]  train-rmse:2.472643+0.016620    test-rmse:2.521349+0.116253 
## [5]  train-rmse:2.140337+0.026063    test-rmse:2.231038+0.060121 
## [6]  train-rmse:1.965796+0.029484    test-rmse:2.081055+0.057990 
## [7]  train-rmse:1.865996+0.031854    test-rmse:2.010128+0.065442 
## [8]  train-rmse:1.800074+0.034569    test-rmse:1.964814+0.096221 
## [9]  train-rmse:1.744374+0.028696    test-rmse:1.916747+0.088044 
## [10] train-rmse:1.700237+0.032617    test-rmse:1.893623+0.093586 
## [11] train-rmse:1.661082+0.039244    test-rmse:1.850327+0.096514 
## [12] train-rmse:1.629175+0.043818    test-rmse:1.815343+0.097670 
## [13] train-rmse:1.597474+0.047060    test-rmse:1.788289+0.092071 
## [14] train-rmse:1.565771+0.047755    test-rmse:1.760174+0.092682 
## [15] train-rmse:1.529316+0.051069    test-rmse:1.725031+0.049384 
## [16] train-rmse:1.497966+0.051645    test-rmse:1.704250+0.053100 
## [17] train-rmse:1.460572+0.041532    test-rmse:1.670309+0.060213 
## [18] train-rmse:1.433253+0.047234    test-rmse:1.660683+0.069720 
## [19] train-rmse:1.404326+0.051488    test-rmse:1.629235+0.052818 
## [20] train-rmse:1.382004+0.052898    test-rmse:1.617416+0.056227 
## [21] train-rmse:1.362332+0.054482    test-rmse:1.597423+0.050159 
## [22] train-rmse:1.339264+0.059866    test-rmse:1.586039+0.050081 
## [23] train-rmse:1.315301+0.057476    test-rmse:1.563470+0.058836 
## [24] train-rmse:1.296321+0.056430    test-rmse:1.542792+0.068761 
## [25] train-rmse:1.279177+0.057851    test-rmse:1.533189+0.069604 
## [26] train-rmse:1.256181+0.061721    test-rmse:1.521814+0.073359 
## [27] train-rmse:1.240147+0.062503    test-rmse:1.508520+0.069586 
## [28] train-rmse:1.222546+0.063707    test-rmse:1.491439+0.060091 
## [29] train-rmse:1.207661+0.064259    test-rmse:1.474579+0.070119 
## [30] train-rmse:1.192102+0.065099    test-rmse:1.461363+0.071284 
## [31] train-rmse:1.178748+0.066252    test-rmse:1.453905+0.071553 
## [32] train-rmse:1.165992+0.066666    test-rmse:1.447340+0.071193 
## [33] train-rmse:1.150471+0.064749    test-rmse:1.447558+0.077833 
## [34] train-rmse:1.138131+0.064112    test-rmse:1.433861+0.088758 
## [35] train-rmse:1.126607+0.064706    test-rmse:1.433592+0.091555 
## [36] train-rmse:1.116169+0.065368    test-rmse:1.427023+0.085648 
## [37] train-rmse:1.104891+0.065318    test-rmse:1.423422+0.085861 
## [38] train-rmse:1.094516+0.063320    test-rmse:1.415124+0.087750 
## [39] train-rmse:1.085406+0.063875    test-rmse:1.403911+0.090039 
## [40] train-rmse:1.077247+0.064211    test-rmse:1.391919+0.089787 
## [41] train-rmse:1.067871+0.063958    test-rmse:1.388878+0.088587 
## [42] train-rmse:1.059794+0.065552    test-rmse:1.381326+0.089031 
## [43] train-rmse:1.047162+0.067870    test-rmse:1.365864+0.093251 
## [44] train-rmse:1.039883+0.068264    test-rmse:1.364069+0.094289 
## [45] train-rmse:1.030842+0.069126    test-rmse:1.353893+0.102864 
## [46] train-rmse:1.022658+0.070163    test-rmse:1.350529+0.104140 
## [47] train-rmse:1.015874+0.070307    test-rmse:1.346580+0.109506 
## [48] train-rmse:1.006600+0.067537    test-rmse:1.338350+0.118164 
## [49] train-rmse:0.998439+0.068182    test-rmse:1.337979+0.122726 
## [50] train-rmse:0.987781+0.069194    test-rmse:1.328354+0.126135 
## [51] train-rmse:0.969560+0.059696    test-rmse:1.306041+0.116397 
## [52] train-rmse:0.956796+0.054236    test-rmse:1.301078+0.118714 
## [53] train-rmse:0.947602+0.051730    test-rmse:1.294819+0.119841 
## [54] train-rmse:0.941573+0.051232    test-rmse:1.290058+0.123238 
## [55] train-rmse:0.936821+0.051589    test-rmse:1.283643+0.119559 
## [56] train-rmse:0.930179+0.051151    test-rmse:1.280485+0.121334 
## [57] train-rmse:0.923642+0.052199    test-rmse:1.279792+0.121302 
## [58] train-rmse:0.917723+0.050185    test-rmse:1.277363+0.123751 
## [59] train-rmse:0.912700+0.049019    test-rmse:1.275759+0.122039 
## [60] train-rmse:0.906331+0.049327    test-rmse:1.271116+0.124628 
## [61] train-rmse:0.901036+0.048717    test-rmse:1.272140+0.123581 
## [62] train-rmse:0.894779+0.050654    test-rmse:1.268426+0.125805 
## [63] train-rmse:0.890587+0.050487    test-rmse:1.267965+0.125824 
## [64] train-rmse:0.885288+0.049580    test-rmse:1.264525+0.127099 
## [65] train-rmse:0.881056+0.049230    test-rmse:1.262017+0.131320 
## [66] train-rmse:0.876936+0.050039    test-rmse:1.259945+0.132150 
## [67] train-rmse:0.871781+0.050586    test-rmse:1.256542+0.131742 
## [68] train-rmse:0.868767+0.050409    test-rmse:1.255809+0.133530 
## [69] train-rmse:0.865194+0.050334    test-rmse:1.252823+0.135130 
## [70] train-rmse:0.860736+0.051681    test-rmse:1.252670+0.137861 
## [71] train-rmse:0.857114+0.051945    test-rmse:1.251977+0.135909 
## [72] train-rmse:0.854110+0.052001    test-rmse:1.252474+0.137000 
## [73] train-rmse:0.849809+0.051729    test-rmse:1.251473+0.138295 
## [74] train-rmse:0.841342+0.045510    test-rmse:1.235269+0.135732 
## [75] train-rmse:0.837763+0.044943    test-rmse:1.237290+0.137075 
## [76] train-rmse:0.835169+0.044924    test-rmse:1.233973+0.136460 
## [77] train-rmse:0.831884+0.045867    test-rmse:1.233648+0.136647 
## [78] train-rmse:0.827094+0.050160    test-rmse:1.231924+0.137891 
## [79] train-rmse:0.824944+0.050203    test-rmse:1.231806+0.136587 
## [80] train-rmse:0.821758+0.049796    test-rmse:1.229654+0.136157 
## [81] train-rmse:0.816093+0.054040    test-rmse:1.229094+0.136456 
## [82] train-rmse:0.812871+0.053407    test-rmse:1.231669+0.136250 
## [83] train-rmse:0.809693+0.053422    test-rmse:1.232012+0.134817 
## [84] train-rmse:0.807171+0.054296    test-rmse:1.236119+0.141852 
## [85] train-rmse:0.805010+0.054231    test-rmse:1.235813+0.141407 
## [86] train-rmse:0.800760+0.051971    test-rmse:1.235537+0.141989 
## [87] train-rmse:0.798103+0.051788    test-rmse:1.235620+0.141972 
## Stopping. Best iteration:
## [81] train-rmse:0.816093+0.054040    test-rmse:1.229094+0.136456
## 
## 100 
## [1]  train-rmse:6.254594+0.044725    test-rmse:6.251754+0.242802 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.189278+0.029594    test-rmse:4.203469+0.213763 
## [3]  train-rmse:2.986030+0.022481    test-rmse:3.033444+0.151150 
## [4]  train-rmse:2.314234+0.015259    test-rmse:2.392299+0.106791 
## [5]  train-rmse:1.952184+0.016956    test-rmse:2.067876+0.068782 
## [6]  train-rmse:1.755157+0.012607    test-rmse:1.916046+0.052306 
## [7]  train-rmse:1.638985+0.014083    test-rmse:1.813965+0.068405 
## [8]  train-rmse:1.558923+0.015400    test-rmse:1.767849+0.055380 
## [9]  train-rmse:1.496276+0.015265    test-rmse:1.726654+0.042581 
## [10] train-rmse:1.443779+0.019843    test-rmse:1.692825+0.041258 
## [11] train-rmse:1.399052+0.026091    test-rmse:1.659075+0.044873 
## [12] train-rmse:1.352620+0.027597    test-rmse:1.631348+0.060257 
## [13] train-rmse:1.315243+0.029810    test-rmse:1.606638+0.056245 
## [14] train-rmse:1.279252+0.031256    test-rmse:1.573980+0.061781 
## [15] train-rmse:1.245455+0.032815    test-rmse:1.545983+0.072136 
## [16] train-rmse:1.208392+0.035452    test-rmse:1.516615+0.075992 
## [17] train-rmse:1.178677+0.038459    test-rmse:1.495373+0.080891 
## [18] train-rmse:1.138561+0.034774    test-rmse:1.477516+0.083380 
## [19] train-rmse:1.111518+0.031961    test-rmse:1.463402+0.098660 
## [20] train-rmse:1.086315+0.033636    test-rmse:1.435110+0.089161 
## [21] train-rmse:1.063115+0.031847    test-rmse:1.424243+0.095165 
## [22] train-rmse:1.044470+0.032747    test-rmse:1.415902+0.094357 
## [23] train-rmse:1.022719+0.031219    test-rmse:1.394279+0.095742 
## [24] train-rmse:1.004615+0.029047    test-rmse:1.376036+0.092903 
## [25] train-rmse:0.988288+0.029219    test-rmse:1.367184+0.106417 
## [26] train-rmse:0.956701+0.023351    test-rmse:1.354674+0.111869 
## [27] train-rmse:0.940895+0.022017    test-rmse:1.343939+0.115426 
## [28] train-rmse:0.926194+0.020657    test-rmse:1.330908+0.121295 
## [29] train-rmse:0.905755+0.023801    test-rmse:1.325793+0.124628 
## [30] train-rmse:0.889913+0.028093    test-rmse:1.322052+0.133033 
## [31] train-rmse:0.878199+0.027632    test-rmse:1.315688+0.128651 
## [32] train-rmse:0.863573+0.028271    test-rmse:1.309803+0.136359 
## [33] train-rmse:0.851598+0.028430    test-rmse:1.304092+0.134830 
## [34] train-rmse:0.839881+0.025307    test-rmse:1.302833+0.139793 
## [35] train-rmse:0.828561+0.023556    test-rmse:1.288108+0.142848 
## [36] train-rmse:0.819277+0.023073    test-rmse:1.285515+0.144447 
## [37] train-rmse:0.807910+0.021839    test-rmse:1.283456+0.145115 
## [38] train-rmse:0.794061+0.025701    test-rmse:1.280745+0.143772 
## [39] train-rmse:0.786645+0.026115    test-rmse:1.276308+0.145106 
## [40] train-rmse:0.775780+0.021674    test-rmse:1.275925+0.145737 
## [41] train-rmse:0.767654+0.020218    test-rmse:1.272970+0.147374 
## [42] train-rmse:0.753581+0.022208    test-rmse:1.271604+0.148203 
## [43] train-rmse:0.742909+0.024449    test-rmse:1.270355+0.150540 
## [44] train-rmse:0.731776+0.026165    test-rmse:1.261406+0.141739 
## [45] train-rmse:0.721456+0.024175    test-rmse:1.260897+0.143575 
## [46] train-rmse:0.715637+0.023712    test-rmse:1.254118+0.147904 
## [47] train-rmse:0.710043+0.024156    test-rmse:1.252045+0.147507 
## [48] train-rmse:0.703688+0.023143    test-rmse:1.248488+0.148974 
## [49] train-rmse:0.698084+0.024820    test-rmse:1.247112+0.146968 
## [50] train-rmse:0.689605+0.022790    test-rmse:1.246283+0.147699 
## [51] train-rmse:0.683911+0.022283    test-rmse:1.244875+0.150105 
## [52] train-rmse:0.675498+0.023354    test-rmse:1.241282+0.149367 
## [53] train-rmse:0.668627+0.027372    test-rmse:1.238421+0.147462 
## [54] train-rmse:0.662418+0.027462    test-rmse:1.234303+0.147149 
## [55] train-rmse:0.655940+0.027545    test-rmse:1.234653+0.144756 
## [56] train-rmse:0.650256+0.027056    test-rmse:1.230477+0.141043 
## [57] train-rmse:0.644913+0.026354    test-rmse:1.229435+0.142643 
## [58] train-rmse:0.640972+0.025750    test-rmse:1.229166+0.142553 
## [59] train-rmse:0.634113+0.025073    test-rmse:1.228184+0.145220 
## [60] train-rmse:0.630351+0.024631    test-rmse:1.228007+0.145114 
## [61] train-rmse:0.625254+0.025425    test-rmse:1.228053+0.153411 
## [62] train-rmse:0.621083+0.024946    test-rmse:1.226540+0.151405 
## [63] train-rmse:0.616598+0.025397    test-rmse:1.225821+0.151503 
## [64] train-rmse:0.613089+0.024647    test-rmse:1.225644+0.153205 
## [65] train-rmse:0.606824+0.025747    test-rmse:1.221804+0.154192 
## [66] train-rmse:0.601944+0.024466    test-rmse:1.222545+0.155189 
## [67] train-rmse:0.598802+0.024004    test-rmse:1.221928+0.156349 
## [68] train-rmse:0.593756+0.023843    test-rmse:1.220428+0.155864 
## [69] train-rmse:0.589675+0.023881    test-rmse:1.219732+0.155679 
## [70] train-rmse:0.587308+0.023388    test-rmse:1.218093+0.154702 
## [71] train-rmse:0.582408+0.024147    test-rmse:1.219247+0.153536 
## [72] train-rmse:0.576566+0.024772    test-rmse:1.221894+0.154836 
## [73] train-rmse:0.572243+0.025094    test-rmse:1.222362+0.155808 
## [74] train-rmse:0.569333+0.025977    test-rmse:1.220684+0.154493 
## [75] train-rmse:0.565437+0.027257    test-rmse:1.218220+0.152311 
## [76] train-rmse:0.563376+0.026965    test-rmse:1.218042+0.150677 
## [77] train-rmse:0.560653+0.026456    test-rmse:1.216864+0.150929 
## [78] train-rmse:0.557707+0.025882    test-rmse:1.217688+0.149633 
## [79] train-rmse:0.554807+0.025686    test-rmse:1.219346+0.151449 
## [80] train-rmse:0.553052+0.025721    test-rmse:1.220333+0.150486 
## [81] train-rmse:0.550416+0.025272    test-rmse:1.220906+0.149650 
## [82] train-rmse:0.545985+0.025270    test-rmse:1.221487+0.148480 
## [83] train-rmse:0.543713+0.025449    test-rmse:1.220949+0.150168 
## Stopping. Best iteration:
## [77] train-rmse:0.560653+0.026456    test-rmse:1.216864+0.150929
## 
## 101 
## [1]  train-rmse:6.245400+0.045549    test-rmse:6.244026+0.245181 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.165232+0.027180    test-rmse:4.202703+0.206435 
## [3]  train-rmse:2.940302+0.018664    test-rmse:2.982529+0.148950 
## [4]  train-rmse:2.230944+0.011088    test-rmse:2.327113+0.099550 
## [5]  train-rmse:1.839933+0.014509    test-rmse:1.997955+0.082703 
## [6]  train-rmse:1.612614+0.023991    test-rmse:1.806045+0.052894 
## [7]  train-rmse:1.477429+0.030919    test-rmse:1.711406+0.053652 
## [8]  train-rmse:1.379970+0.026247    test-rmse:1.647775+0.051668 
## [9]  train-rmse:1.297225+0.027293    test-rmse:1.586217+0.067269 
## [10] train-rmse:1.244422+0.029365    test-rmse:1.554705+0.060527 
## [11] train-rmse:1.194832+0.025430    test-rmse:1.509301+0.081236 
## [12] train-rmse:1.140956+0.019359    test-rmse:1.490767+0.102242 
## [13] train-rmse:1.092540+0.030520    test-rmse:1.455245+0.087253 
## [14] train-rmse:1.041789+0.039718    test-rmse:1.427539+0.084463 
## [15] train-rmse:1.007252+0.032234    test-rmse:1.406844+0.103710 
## [16] train-rmse:0.975437+0.030490    test-rmse:1.391473+0.107836 
## [17] train-rmse:0.946949+0.028679    test-rmse:1.376867+0.115054 
## [18] train-rmse:0.918058+0.029939    test-rmse:1.363009+0.113715 
## [19] train-rmse:0.889262+0.029204    test-rmse:1.351696+0.115057 
## [20] train-rmse:0.866251+0.031833    test-rmse:1.336903+0.129833 
## [21] train-rmse:0.843815+0.030336    test-rmse:1.329493+0.133362 
## [22] train-rmse:0.820057+0.026848    test-rmse:1.323566+0.134621 
## [23] train-rmse:0.798159+0.028075    test-rmse:1.311058+0.137005 
## [24] train-rmse:0.777175+0.027596    test-rmse:1.307119+0.138768 
## [25] train-rmse:0.758360+0.026279    test-rmse:1.297781+0.142772 
## [26] train-rmse:0.743144+0.024995    test-rmse:1.290573+0.153216 
## [27] train-rmse:0.724540+0.022282    test-rmse:1.278027+0.155468 
## [28] train-rmse:0.706758+0.019508    test-rmse:1.261557+0.147943 
## [29] train-rmse:0.692812+0.019566    test-rmse:1.265241+0.153937 
## [30] train-rmse:0.679941+0.018879    test-rmse:1.261681+0.151742 
## [31] train-rmse:0.663101+0.021795    test-rmse:1.253642+0.150655 
## [32] train-rmse:0.649074+0.021043    test-rmse:1.252811+0.150587 
## [33] train-rmse:0.638506+0.021289    test-rmse:1.252293+0.150723 
## [34] train-rmse:0.625707+0.017038    test-rmse:1.244834+0.153455 
## [35] train-rmse:0.615098+0.015920    test-rmse:1.240755+0.158167 
## [36] train-rmse:0.604439+0.017543    test-rmse:1.241814+0.164496 
## [37] train-rmse:0.594870+0.019014    test-rmse:1.240393+0.163558 
## [38] train-rmse:0.586618+0.016820    test-rmse:1.233217+0.165820 
## [39] train-rmse:0.576147+0.016704    test-rmse:1.231088+0.160816 
## [40] train-rmse:0.565756+0.014872    test-rmse:1.235731+0.164780 
## [41] train-rmse:0.559702+0.015488    test-rmse:1.236699+0.163744 
## [42] train-rmse:0.550638+0.018233    test-rmse:1.236851+0.162783 
## [43] train-rmse:0.541076+0.018460    test-rmse:1.230582+0.165044 
## [44] train-rmse:0.532423+0.015759    test-rmse:1.229004+0.165346 
## [45] train-rmse:0.523484+0.014924    test-rmse:1.231886+0.164404 
## [46] train-rmse:0.516518+0.017040    test-rmse:1.232491+0.163032 
## [47] train-rmse:0.510777+0.016072    test-rmse:1.231435+0.163893 
## [48] train-rmse:0.505418+0.015659    test-rmse:1.232373+0.167259 
## [49] train-rmse:0.499785+0.015959    test-rmse:1.234635+0.170322 
## [50] train-rmse:0.495609+0.016633    test-rmse:1.234528+0.170942 
## Stopping. Best iteration:
## [44] train-rmse:0.532423+0.015759    test-rmse:1.229004+0.165346
## 
## 102 
## [1]  train-rmse:6.237445+0.046305    test-rmse:6.244130+0.246578 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.138109+0.028326    test-rmse:4.173827+0.205866 
## [3]  train-rmse:2.887988+0.021037    test-rmse:2.968399+0.159713 
## [4]  train-rmse:2.141865+0.024751    test-rmse:2.287038+0.097713 
## [5]  train-rmse:1.725532+0.033067    test-rmse:1.919095+0.081702 
## [6]  train-rmse:1.476462+0.031004    test-rmse:1.727829+0.069890 
## [7]  train-rmse:1.323456+0.031322    test-rmse:1.633261+0.053368 
## [8]  train-rmse:1.222208+0.031911    test-rmse:1.575885+0.041202 
## [9]  train-rmse:1.136187+0.021931    test-rmse:1.502840+0.070622 
## [10] train-rmse:1.068118+0.021562    test-rmse:1.476307+0.077280 
## [11] train-rmse:1.002624+0.025865    test-rmse:1.448244+0.098845 
## [12] train-rmse:0.955785+0.016786    test-rmse:1.418851+0.114776 
## [13] train-rmse:0.912943+0.012143    test-rmse:1.383110+0.121747 
## [14] train-rmse:0.875319+0.012146    test-rmse:1.363066+0.129887 
## [15] train-rmse:0.839418+0.007814    test-rmse:1.356327+0.130582 
## [16] train-rmse:0.808989+0.007986    test-rmse:1.349380+0.128089 
## [17] train-rmse:0.781587+0.007788    test-rmse:1.349416+0.133965 
## [18] train-rmse:0.750057+0.006155    test-rmse:1.342121+0.134784 
## [19] train-rmse:0.725458+0.006368    test-rmse:1.337414+0.139638 
## [20] train-rmse:0.700198+0.006200    test-rmse:1.322958+0.150391 
## [21] train-rmse:0.673534+0.009149    test-rmse:1.304626+0.148273 
## [22] train-rmse:0.650815+0.009741    test-rmse:1.299451+0.148465 
## [23] train-rmse:0.631041+0.006913    test-rmse:1.299440+0.153202 
## [24] train-rmse:0.613966+0.008412    test-rmse:1.297276+0.156639 
## [25] train-rmse:0.598670+0.009213    test-rmse:1.297628+0.158165 
## [26] train-rmse:0.585233+0.009012    test-rmse:1.294932+0.158311 
## [27] train-rmse:0.569523+0.010319    test-rmse:1.287220+0.153288 
## [28] train-rmse:0.558243+0.010296    test-rmse:1.286458+0.155320 
## [29] train-rmse:0.546664+0.010817    test-rmse:1.280773+0.153163 
## [30] train-rmse:0.530824+0.011851    test-rmse:1.284938+0.155062 
## [31] train-rmse:0.520131+0.012358    test-rmse:1.283581+0.157916 
## [32] train-rmse:0.509725+0.012876    test-rmse:1.282920+0.160755 
## [33] train-rmse:0.501213+0.014166    test-rmse:1.279348+0.159484 
## [34] train-rmse:0.492414+0.014648    test-rmse:1.281742+0.158110 
## [35] train-rmse:0.483189+0.013093    test-rmse:1.284302+0.159134 
## [36] train-rmse:0.473718+0.016377    test-rmse:1.282727+0.161823 
## [37] train-rmse:0.458327+0.016878    test-rmse:1.280679+0.165081 
## [38] train-rmse:0.449659+0.013766    test-rmse:1.281906+0.170063 
## [39] train-rmse:0.440889+0.010784    test-rmse:1.277126+0.169061 
## [40] train-rmse:0.435009+0.011607    test-rmse:1.277292+0.170765 
## [41] train-rmse:0.428150+0.012270    test-rmse:1.279522+0.170773 
## [42] train-rmse:0.421661+0.014695    test-rmse:1.277832+0.174401 
## [43] train-rmse:0.414319+0.011857    test-rmse:1.279936+0.174122 
## [44] train-rmse:0.407806+0.013583    test-rmse:1.276480+0.173451 
## [45] train-rmse:0.399846+0.017002    test-rmse:1.272067+0.176811 
## [46] train-rmse:0.391961+0.018434    test-rmse:1.268868+0.174714 
## [47] train-rmse:0.385918+0.015927    test-rmse:1.273056+0.171996 
## [48] train-rmse:0.379154+0.016257    test-rmse:1.272931+0.172315 
## [49] train-rmse:0.374155+0.015501    test-rmse:1.272344+0.174210 
## [50] train-rmse:0.369140+0.014807    test-rmse:1.273003+0.175038 
## [51] train-rmse:0.363827+0.014706    test-rmse:1.270636+0.173161 
## [52] train-rmse:0.360387+0.016291    test-rmse:1.269948+0.173492 
## Stopping. Best iteration:
## [46] train-rmse:0.391961+0.018434    test-rmse:1.268868+0.174714
## 
## 103 
## [1]  train-rmse:6.235359+0.046784    test-rmse:6.244938+0.235175 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.123226+0.029101    test-rmse:4.166322+0.169124 
## [3]  train-rmse:2.851707+0.030892    test-rmse:2.904482+0.121992 
## [4]  train-rmse:2.088213+0.020728    test-rmse:2.222424+0.124241 
## [5]  train-rmse:1.645565+0.026323    test-rmse:1.870605+0.094857 
## [6]  train-rmse:1.368562+0.050405    test-rmse:1.661236+0.100839 
## [7]  train-rmse:1.201710+0.049809    test-rmse:1.542663+0.102074 
## [8]  train-rmse:1.081362+0.053257    test-rmse:1.481630+0.101109 
## [9]  train-rmse:1.001452+0.043850    test-rmse:1.423358+0.109831 
## [10] train-rmse:0.934188+0.038958    test-rmse:1.409961+0.116253 
## [11] train-rmse:0.873156+0.039013    test-rmse:1.377120+0.137568 
## [12] train-rmse:0.831153+0.039586    test-rmse:1.370611+0.137687 
## [13] train-rmse:0.787759+0.040897    test-rmse:1.353172+0.141868 
## [14] train-rmse:0.743817+0.036862    test-rmse:1.335799+0.155205 
## [15] train-rmse:0.708406+0.032420    test-rmse:1.327290+0.156667 
## [16] train-rmse:0.672244+0.025986    test-rmse:1.317059+0.163400 
## [17] train-rmse:0.648674+0.022532    test-rmse:1.306540+0.171596 
## [18] train-rmse:0.623165+0.022402    test-rmse:1.297503+0.172473 
## [19] train-rmse:0.596337+0.021500    test-rmse:1.282506+0.175281 
## [20] train-rmse:0.574362+0.023910    test-rmse:1.276192+0.173561 
## [21] train-rmse:0.559626+0.021920    test-rmse:1.274558+0.174857 
## [22] train-rmse:0.539939+0.020009    test-rmse:1.274265+0.178372 
## [23] train-rmse:0.525106+0.019942    test-rmse:1.272590+0.179424 
## [24] train-rmse:0.501803+0.023855    test-rmse:1.270043+0.180038 
## [25] train-rmse:0.488445+0.023520    test-rmse:1.266854+0.181419 
## [26] train-rmse:0.473222+0.022080    test-rmse:1.260882+0.182998 
## [27] train-rmse:0.456397+0.021983    test-rmse:1.259368+0.184346 
## [28] train-rmse:0.445176+0.019656    test-rmse:1.261996+0.187207 
## [29] train-rmse:0.429939+0.018313    test-rmse:1.259502+0.184242 
## [30] train-rmse:0.414666+0.020590    test-rmse:1.257488+0.181533 
## [31] train-rmse:0.402707+0.018937    test-rmse:1.255123+0.182927 
## [32] train-rmse:0.389636+0.017999    test-rmse:1.255896+0.182743 
## [33] train-rmse:0.381930+0.020135    test-rmse:1.252715+0.184816 
## [34] train-rmse:0.368799+0.022347    test-rmse:1.253025+0.185177 
## [35] train-rmse:0.359795+0.020134    test-rmse:1.253557+0.185717 
## [36] train-rmse:0.351661+0.019163    test-rmse:1.253791+0.187206 
## [37] train-rmse:0.342109+0.019802    test-rmse:1.251440+0.186029 
## [38] train-rmse:0.332687+0.021586    test-rmse:1.251748+0.186596 
## [39] train-rmse:0.319674+0.023358    test-rmse:1.252269+0.185178 
## [40] train-rmse:0.312459+0.023648    test-rmse:1.250070+0.185737 
## [41] train-rmse:0.305738+0.023308    test-rmse:1.251046+0.186446 
## [42] train-rmse:0.299090+0.024048    test-rmse:1.254712+0.191739 
## [43] train-rmse:0.291069+0.021313    test-rmse:1.254748+0.192352 
## [44] train-rmse:0.285891+0.021859    test-rmse:1.254770+0.191364 
## [45] train-rmse:0.278613+0.023647    test-rmse:1.254545+0.191431 
## [46] train-rmse:0.273439+0.025340    test-rmse:1.255502+0.191242 
## Stopping. Best iteration:
## [40] train-rmse:0.312459+0.023648    test-rmse:1.250070+0.185737
## 
## 104 
## [1]  train-rmse:6.235359+0.046784    test-rmse:6.244938+0.235175 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.116959+0.029453    test-rmse:4.169600+0.178266 
## [3]  train-rmse:2.830137+0.029311    test-rmse:2.892826+0.134992 
## [4]  train-rmse:2.050791+0.016648    test-rmse:2.196506+0.113346 
## [5]  train-rmse:1.583995+0.022822    test-rmse:1.814232+0.095177 
## [6]  train-rmse:1.274856+0.040582    test-rmse:1.590838+0.090471 
## [7]  train-rmse:1.093339+0.030326    test-rmse:1.483766+0.109015 
## [8]  train-rmse:0.972954+0.038641    test-rmse:1.411844+0.117315 
## [9]  train-rmse:0.886778+0.040774    test-rmse:1.379578+0.134563 
## [10] train-rmse:0.809347+0.029255    test-rmse:1.350338+0.151241 
## [11] train-rmse:0.747659+0.028766    test-rmse:1.316736+0.154367 
## [12] train-rmse:0.695636+0.029887    test-rmse:1.302183+0.165402 
## [13] train-rmse:0.650889+0.022727    test-rmse:1.293255+0.179231 
## [14] train-rmse:0.617566+0.022424    test-rmse:1.278844+0.179639 
## [15] train-rmse:0.586778+0.025997    test-rmse:1.274716+0.183688 
## [16] train-rmse:0.557824+0.027031    test-rmse:1.266768+0.195737 
## [17] train-rmse:0.529107+0.024908    test-rmse:1.263903+0.197164 
## [18] train-rmse:0.504506+0.021464    test-rmse:1.264412+0.195208 
## [19] train-rmse:0.482894+0.017647    test-rmse:1.256060+0.195925 
## [20] train-rmse:0.464240+0.018862    test-rmse:1.256491+0.192190 
## [21] train-rmse:0.443966+0.015088    test-rmse:1.252654+0.190153 
## [22] train-rmse:0.424711+0.015057    test-rmse:1.247282+0.189446 
## [23] train-rmse:0.409549+0.017349    test-rmse:1.242799+0.188315 
## [24] train-rmse:0.391039+0.015923    test-rmse:1.243864+0.186803 
## [25] train-rmse:0.376385+0.018077    test-rmse:1.245498+0.186129 
## [26] train-rmse:0.362947+0.017006    test-rmse:1.247787+0.187221 
## [27] train-rmse:0.350891+0.017437    test-rmse:1.245135+0.186714 
## [28] train-rmse:0.340528+0.018741    test-rmse:1.246491+0.186889 
## [29] train-rmse:0.330790+0.017857    test-rmse:1.246985+0.189932 
## Stopping. Best iteration:
## [23] train-rmse:0.409549+0.017349    test-rmse:1.242799+0.188315
## 
## 105 
## [1]  train-rmse:6.158859+0.040200    test-rmse:6.155096+0.255945 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.168201+0.020889    test-rmse:4.167875+0.232667 
## [3]  train-rmse:3.128158+0.013738    test-rmse:3.142591+0.180757 
## [4]  train-rmse:2.624027+0.013133    test-rmse:2.673245+0.102762 
## [5]  train-rmse:2.390836+0.015718    test-rmse:2.428532+0.065179 
## [6]  train-rmse:2.274800+0.019556    test-rmse:2.313955+0.052568 
## [7]  train-rmse:2.216269+0.020510    test-rmse:2.271618+0.053983 
## [8]  train-rmse:2.176536+0.021804    test-rmse:2.217928+0.079562 
## [9]  train-rmse:2.146792+0.022834    test-rmse:2.192276+0.074866 
## [10] train-rmse:2.120187+0.024076    test-rmse:2.173389+0.076443 
## [11] train-rmse:2.096294+0.025360    test-rmse:2.155379+0.077077 
## [12] train-rmse:2.073747+0.026697    test-rmse:2.135454+0.089983 
## [13] train-rmse:2.052892+0.027201    test-rmse:2.120411+0.094986 
## [14] train-rmse:2.032259+0.028312    test-rmse:2.109695+0.094022 
## [15] train-rmse:2.012173+0.029179    test-rmse:2.083563+0.096844 
## [16] train-rmse:1.992077+0.029934    test-rmse:2.068811+0.088203 
## [17] train-rmse:1.972994+0.030070    test-rmse:2.042754+0.080370 
## [18] train-rmse:1.953923+0.031099    test-rmse:2.029691+0.088826 
## [19] train-rmse:1.936680+0.032576    test-rmse:2.014537+0.097154 
## [20] train-rmse:1.920693+0.033211    test-rmse:2.005284+0.106707 
## [21] train-rmse:1.906445+0.033639    test-rmse:1.996157+0.104312 
## [22] train-rmse:1.892315+0.033970    test-rmse:1.980946+0.106001 
## [23] train-rmse:1.878299+0.034674    test-rmse:1.961558+0.097834 
## [24] train-rmse:1.864693+0.034955    test-rmse:1.951211+0.091505 
## [25] train-rmse:1.851127+0.035657    test-rmse:1.939161+0.093727 
## [26] train-rmse:1.838353+0.036722    test-rmse:1.926289+0.084792 
## [27] train-rmse:1.825927+0.037787    test-rmse:1.920812+0.085027 
## [28] train-rmse:1.813794+0.038821    test-rmse:1.912635+0.091099 
## [29] train-rmse:1.801926+0.040023    test-rmse:1.902258+0.090049 
## [30] train-rmse:1.790474+0.041331    test-rmse:1.890452+0.096492 
## [31] train-rmse:1.779867+0.042225    test-rmse:1.879993+0.095934 
## [32] train-rmse:1.769302+0.042913    test-rmse:1.870246+0.097502 
## [33] train-rmse:1.759245+0.043309    test-rmse:1.858835+0.097232 
## [34] train-rmse:1.749218+0.043636    test-rmse:1.847732+0.096247 
## [35] train-rmse:1.739026+0.044051    test-rmse:1.853407+0.094753 
## [36] train-rmse:1.729015+0.044189    test-rmse:1.841667+0.101882 
## [37] train-rmse:1.719483+0.044402    test-rmse:1.831986+0.103060 
## [38] train-rmse:1.709869+0.044670    test-rmse:1.823104+0.106223 
## [39] train-rmse:1.700657+0.045020    test-rmse:1.817980+0.107083 
## [40] train-rmse:1.692082+0.045434    test-rmse:1.809879+0.101400 
## [41] train-rmse:1.683689+0.045891    test-rmse:1.804679+0.102945 
## [42] train-rmse:1.675686+0.046376    test-rmse:1.800841+0.103344 
## [43] train-rmse:1.667871+0.046913    test-rmse:1.786979+0.100869 
## [44] train-rmse:1.660524+0.047493    test-rmse:1.780459+0.103867 
## [45] train-rmse:1.653333+0.048023    test-rmse:1.776085+0.107751 
## [46] train-rmse:1.646130+0.048268    test-rmse:1.774495+0.108123 
## [47] train-rmse:1.639046+0.048650    test-rmse:1.769875+0.118765 
## [48] train-rmse:1.632485+0.048907    test-rmse:1.765236+0.116707 
## [49] train-rmse:1.625677+0.049335    test-rmse:1.762207+0.115742 
## [50] train-rmse:1.619160+0.049523    test-rmse:1.752121+0.115197 
## [51] train-rmse:1.612486+0.049516    test-rmse:1.741137+0.116328 
## [52] train-rmse:1.606132+0.049675    test-rmse:1.732434+0.116303 
## [53] train-rmse:1.599773+0.049800    test-rmse:1.711816+0.120162 
## [54] train-rmse:1.593777+0.049965    test-rmse:1.711838+0.119577 
## [55] train-rmse:1.587862+0.050038    test-rmse:1.712981+0.121825 
## [56] train-rmse:1.582045+0.050092    test-rmse:1.705749+0.121375 
## [57] train-rmse:1.576364+0.050247    test-rmse:1.704093+0.120938 
## [58] train-rmse:1.570901+0.050389    test-rmse:1.696375+0.123855 
## [59] train-rmse:1.565479+0.050654    test-rmse:1.690497+0.131577 
## [60] train-rmse:1.560282+0.051017    test-rmse:1.690368+0.128669 
## [61] train-rmse:1.555257+0.051247    test-rmse:1.691022+0.130648 
## [62] train-rmse:1.550283+0.051365    test-rmse:1.685351+0.129362 
## [63] train-rmse:1.545348+0.051512    test-rmse:1.680145+0.127923 
## [64] train-rmse:1.540304+0.051628    test-rmse:1.674873+0.135075 
## [65] train-rmse:1.535599+0.051738    test-rmse:1.672419+0.135499 
## [66] train-rmse:1.530932+0.051801    test-rmse:1.669389+0.137057 
## [67] train-rmse:1.526086+0.051818    test-rmse:1.663932+0.140840 
## [68] train-rmse:1.521429+0.051959    test-rmse:1.657116+0.142762 
## [69] train-rmse:1.517177+0.051880    test-rmse:1.653048+0.140548 
## [70] train-rmse:1.512883+0.051821    test-rmse:1.650276+0.142923 
## [71] train-rmse:1.508929+0.051768    test-rmse:1.643868+0.142674 
## [72] train-rmse:1.505121+0.051770    test-rmse:1.639637+0.148079 
## [73] train-rmse:1.501379+0.051974    test-rmse:1.638980+0.149552 
## [74] train-rmse:1.497717+0.052110    test-rmse:1.632473+0.149465 
## [75] train-rmse:1.494053+0.052303    test-rmse:1.634695+0.147643 
## [76] train-rmse:1.490377+0.052421    test-rmse:1.630995+0.148828 
## [77] train-rmse:1.486697+0.052348    test-rmse:1.627949+0.151309 
## [78] train-rmse:1.483164+0.052411    test-rmse:1.613740+0.148841 
## [79] train-rmse:1.479722+0.052482    test-rmse:1.610131+0.149181 
## [80] train-rmse:1.476445+0.052540    test-rmse:1.602870+0.157457 
## [81] train-rmse:1.473177+0.052569    test-rmse:1.601507+0.154598 
## [82] train-rmse:1.469856+0.052692    test-rmse:1.602021+0.152943 
## [83] train-rmse:1.466768+0.052718    test-rmse:1.601419+0.157319 
## [84] train-rmse:1.463759+0.052681    test-rmse:1.599342+0.155990 
## [85] train-rmse:1.460841+0.052717    test-rmse:1.598791+0.155259 
## [86] train-rmse:1.457899+0.052759    test-rmse:1.595659+0.154900 
## [87] train-rmse:1.455074+0.052894    test-rmse:1.592274+0.154414 
## [88] train-rmse:1.452357+0.053039    test-rmse:1.590527+0.152688 
## [89] train-rmse:1.449698+0.053138    test-rmse:1.585157+0.152423 
## [90] train-rmse:1.446883+0.053237    test-rmse:1.584579+0.155061 
## [91] train-rmse:1.443957+0.053508    test-rmse:1.580020+0.156822 
## [92] train-rmse:1.441366+0.053645    test-rmse:1.577833+0.159943 
## [93] train-rmse:1.438850+0.053694    test-rmse:1.574180+0.157830 
## [94] train-rmse:1.436330+0.053783    test-rmse:1.571215+0.159034 
## [95] train-rmse:1.433800+0.053891    test-rmse:1.572229+0.165382 
## [96] train-rmse:1.431473+0.053903    test-rmse:1.572276+0.161544 
## [97] train-rmse:1.429213+0.053909    test-rmse:1.572117+0.160643 
## [98] train-rmse:1.426970+0.053916    test-rmse:1.568321+0.162575 
## [99] train-rmse:1.424759+0.053822    test-rmse:1.565867+0.164548 
## [100]    train-rmse:1.422561+0.053769    test-rmse:1.569041+0.159698 
## [101]    train-rmse:1.420410+0.053677    test-rmse:1.565490+0.161507 
## [102]    train-rmse:1.418307+0.053671    test-rmse:1.563662+0.163582 
## [103]    train-rmse:1.416284+0.053624    test-rmse:1.561975+0.164013 
## [104]    train-rmse:1.414184+0.053534    test-rmse:1.563529+0.166815 
## [105]    train-rmse:1.412123+0.053432    test-rmse:1.559176+0.166172 
## [106]    train-rmse:1.410090+0.053329    test-rmse:1.558789+0.167750 
## [107]    train-rmse:1.408082+0.053327    test-rmse:1.554819+0.169043 
## [108]    train-rmse:1.406119+0.053304    test-rmse:1.550444+0.173842 
## [109]    train-rmse:1.404225+0.053322    test-rmse:1.544761+0.169577 
## [110]    train-rmse:1.402368+0.053355    test-rmse:1.543259+0.169300 
## [111]    train-rmse:1.400495+0.053376    test-rmse:1.540416+0.170089 
## [112]    train-rmse:1.398722+0.053364    test-rmse:1.538477+0.170051 
## [113]    train-rmse:1.397055+0.053361    test-rmse:1.537354+0.169440 
## [114]    train-rmse:1.395390+0.053344    test-rmse:1.536044+0.169150 
## [115]    train-rmse:1.393761+0.053405    test-rmse:1.536560+0.172338 
## [116]    train-rmse:1.392167+0.053464    test-rmse:1.534479+0.169932 
## [117]    train-rmse:1.390546+0.053531    test-rmse:1.534689+0.170075 
## [118]    train-rmse:1.388996+0.053551    test-rmse:1.533837+0.168873 
## [119]    train-rmse:1.387472+0.053576    test-rmse:1.532845+0.169697 
## [120]    train-rmse:1.385985+0.053557    test-rmse:1.530692+0.170244 
## [121]    train-rmse:1.384504+0.053562    test-rmse:1.530003+0.171278 
## [122]    train-rmse:1.383020+0.053564    test-rmse:1.531294+0.172660 
## [123]    train-rmse:1.381562+0.053631    test-rmse:1.529621+0.173919 
## [124]    train-rmse:1.380204+0.053674    test-rmse:1.528343+0.181660 
## [125]    train-rmse:1.378870+0.053652    test-rmse:1.528021+0.180578 
## [126]    train-rmse:1.377533+0.053631    test-rmse:1.524258+0.181898 
## [127]    train-rmse:1.376182+0.053579    test-rmse:1.523557+0.182781 
## [128]    train-rmse:1.374878+0.053561    test-rmse:1.520601+0.184045 
## [129]    train-rmse:1.373597+0.053568    test-rmse:1.519996+0.184188 
## [130]    train-rmse:1.372396+0.053542    test-rmse:1.518943+0.185216 
## [131]    train-rmse:1.371244+0.053547    test-rmse:1.517682+0.183942 
## [132]    train-rmse:1.370124+0.053533    test-rmse:1.516704+0.183986 
## [133]    train-rmse:1.369039+0.053530    test-rmse:1.515156+0.185967 
## [134]    train-rmse:1.367949+0.053501    test-rmse:1.514959+0.184202 
## [135]    train-rmse:1.366784+0.053460    test-rmse:1.513318+0.184575 
## [136]    train-rmse:1.365644+0.053400    test-rmse:1.511858+0.183536 
## [137]    train-rmse:1.364557+0.053320    test-rmse:1.510948+0.181727 
## [138]    train-rmse:1.363476+0.053253    test-rmse:1.511428+0.184785 
## [139]    train-rmse:1.362490+0.053213    test-rmse:1.512024+0.184846 
## [140]    train-rmse:1.361521+0.053162    test-rmse:1.513500+0.184453 
## [141]    train-rmse:1.360550+0.053152    test-rmse:1.512818+0.184387 
## [142]    train-rmse:1.359609+0.053154    test-rmse:1.509859+0.183707 
## [143]    train-rmse:1.358682+0.053141    test-rmse:1.507429+0.182290 
## [144]    train-rmse:1.357795+0.053126    test-rmse:1.506617+0.183157 
## [145]    train-rmse:1.356898+0.053113    test-rmse:1.508032+0.185802 
## [146]    train-rmse:1.355977+0.053098    test-rmse:1.505539+0.187855 
## [147]    train-rmse:1.355064+0.053106    test-rmse:1.503785+0.186264 
## [148]    train-rmse:1.354158+0.053118    test-rmse:1.503578+0.185717 
## [149]    train-rmse:1.353282+0.053122    test-rmse:1.501901+0.188005 
## [150]    train-rmse:1.352436+0.053131    test-rmse:1.500229+0.187054 
## [151]    train-rmse:1.351608+0.053151    test-rmse:1.500968+0.186246 
## [152]    train-rmse:1.350799+0.053152    test-rmse:1.500265+0.184535 
## [153]    train-rmse:1.349975+0.053140    test-rmse:1.500090+0.187731 
## [154]    train-rmse:1.349167+0.053134    test-rmse:1.501511+0.190796 
## [155]    train-rmse:1.348395+0.053115    test-rmse:1.500244+0.191354 
## [156]    train-rmse:1.347639+0.053107    test-rmse:1.498488+0.190945 
## [157]    train-rmse:1.346909+0.053076    test-rmse:1.498495+0.190892 
## [158]    train-rmse:1.346196+0.053045    test-rmse:1.498537+0.191992 
## [159]    train-rmse:1.345502+0.053028    test-rmse:1.496441+0.192775 
## [160]    train-rmse:1.344801+0.053024    test-rmse:1.495559+0.194188 
## [161]    train-rmse:1.344109+0.053027    test-rmse:1.496222+0.194000 
## [162]    train-rmse:1.343456+0.053058    test-rmse:1.495738+0.194885 
## [163]    train-rmse:1.342793+0.053057    test-rmse:1.496830+0.194340 
## [164]    train-rmse:1.342150+0.053050    test-rmse:1.494693+0.194941 
## [165]    train-rmse:1.341502+0.053042    test-rmse:1.494275+0.194934 
## [166]    train-rmse:1.340863+0.053085    test-rmse:1.494572+0.196613 
## [167]    train-rmse:1.340254+0.053085    test-rmse:1.494006+0.195615 
## [168]    train-rmse:1.339651+0.053074    test-rmse:1.492990+0.197106 
## [169]    train-rmse:1.339057+0.053066    test-rmse:1.490688+0.197649 
## [170]    train-rmse:1.338476+0.053054    test-rmse:1.490132+0.196360 
## [171]    train-rmse:1.337887+0.053041    test-rmse:1.491144+0.195421 
## [172]    train-rmse:1.337315+0.053038    test-rmse:1.489407+0.194488 
## [173]    train-rmse:1.336765+0.053029    test-rmse:1.489511+0.193051 
## [174]    train-rmse:1.336240+0.053013    test-rmse:1.488431+0.193763 
## [175]    train-rmse:1.335726+0.052990    test-rmse:1.487292+0.194419 
## [176]    train-rmse:1.335231+0.052968    test-rmse:1.488071+0.191970 
## [177]    train-rmse:1.334741+0.052953    test-rmse:1.489799+0.191772 
## [178]    train-rmse:1.334245+0.052942    test-rmse:1.488453+0.193532 
## [179]    train-rmse:1.333727+0.052940    test-rmse:1.487434+0.193969 
## [180]    train-rmse:1.333241+0.052917    test-rmse:1.486938+0.194532 
## [181]    train-rmse:1.332766+0.052896    test-rmse:1.485772+0.198769 
## [182]    train-rmse:1.332312+0.052882    test-rmse:1.486279+0.198748 
## [183]    train-rmse:1.331855+0.052859    test-rmse:1.485833+0.198119 
## [184]    train-rmse:1.331424+0.052845    test-rmse:1.485161+0.198886 
## [185]    train-rmse:1.330995+0.052829    test-rmse:1.484809+0.198181 
## [186]    train-rmse:1.330578+0.052801    test-rmse:1.482923+0.197232 
## [187]    train-rmse:1.330181+0.052779    test-rmse:1.483813+0.196931 
## [188]    train-rmse:1.329796+0.052762    test-rmse:1.483753+0.196973 
## [189]    train-rmse:1.329397+0.052746    test-rmse:1.484258+0.198899 
## [190]    train-rmse:1.329000+0.052730    test-rmse:1.485757+0.200394 
## [191]    train-rmse:1.328626+0.052724    test-rmse:1.485857+0.200486 
## [192]    train-rmse:1.328246+0.052702    test-rmse:1.485845+0.200752 
## Stopping. Best iteration:
## [186]    train-rmse:1.330578+0.052801    test-rmse:1.482923+0.197232
## 
## 106 
## [1]  train-rmse:6.100201+0.040815    test-rmse:6.097103+0.249245 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:4.050014+0.019261    test-rmse:4.068808+0.215712 
## [3]  train-rmse:2.933563+0.020367    test-rmse:2.960622+0.152387 
## [4]  train-rmse:2.370209+0.010615    test-rmse:2.421023+0.095957 
## [5]  train-rmse:2.088718+0.022289    test-rmse:2.186990+0.059989 
## [6]  train-rmse:1.932136+0.032725    test-rmse:2.062075+0.034195 
## [7]  train-rmse:1.847593+0.037934    test-rmse:1.974398+0.057800 
## [8]  train-rmse:1.783557+0.043516    test-rmse:1.942800+0.087270 
## [9]  train-rmse:1.717192+0.029170    test-rmse:1.877702+0.091357 
## [10] train-rmse:1.666259+0.019394    test-rmse:1.823454+0.115630 
## [11] train-rmse:1.628583+0.019731    test-rmse:1.796703+0.112875 
## [12] train-rmse:1.590297+0.029115    test-rmse:1.766967+0.112864 
## [13] train-rmse:1.556003+0.030491    test-rmse:1.742265+0.115706 
## [14] train-rmse:1.523739+0.031085    test-rmse:1.717954+0.110895 
## [15] train-rmse:1.482540+0.030821    test-rmse:1.673907+0.061708 
## [16] train-rmse:1.454374+0.032014    test-rmse:1.652582+0.061087 
## [17] train-rmse:1.429343+0.033299    test-rmse:1.633425+0.072467 
## [18] train-rmse:1.404177+0.035875    test-rmse:1.623712+0.077597 
## [19] train-rmse:1.383568+0.036218    test-rmse:1.614725+0.081657 
## [20] train-rmse:1.364070+0.036709    test-rmse:1.590292+0.087228 
## [21] train-rmse:1.338130+0.038912    test-rmse:1.568039+0.064544 
## [22] train-rmse:1.317379+0.039694    test-rmse:1.559745+0.070294 
## [23] train-rmse:1.300562+0.040461    test-rmse:1.531243+0.077366 
## [24] train-rmse:1.281267+0.042349    test-rmse:1.524564+0.080688 
## [25] train-rmse:1.264912+0.043307    test-rmse:1.513222+0.080923 
## [26] train-rmse:1.244144+0.045337    test-rmse:1.506540+0.084745 
## [27] train-rmse:1.220040+0.040865    test-rmse:1.488699+0.083898 
## [28] train-rmse:1.201978+0.040951    test-rmse:1.476036+0.096465 
## [29] train-rmse:1.186155+0.041981    test-rmse:1.461968+0.095799 
## [30] train-rmse:1.169598+0.043789    test-rmse:1.446892+0.093224 
## [31] train-rmse:1.156393+0.044015    test-rmse:1.431918+0.099064 
## [32] train-rmse:1.142648+0.048674    test-rmse:1.423414+0.101992 
## [33] train-rmse:1.127689+0.047410    test-rmse:1.411537+0.102720 
## [34] train-rmse:1.116204+0.048685    test-rmse:1.410249+0.100000 
## [35] train-rmse:1.103582+0.048652    test-rmse:1.396383+0.097760 
## [36] train-rmse:1.091135+0.048515    test-rmse:1.384073+0.095700 
## [37] train-rmse:1.079972+0.048618    test-rmse:1.380293+0.094563 
## [38] train-rmse:1.068798+0.047616    test-rmse:1.382310+0.105117 
## [39] train-rmse:1.060598+0.048076    test-rmse:1.377197+0.106288 
## [40] train-rmse:1.051714+0.048125    test-rmse:1.371329+0.109927 
## [41] train-rmse:1.041580+0.048037    test-rmse:1.368083+0.111707 
## [42] train-rmse:1.033951+0.048324    test-rmse:1.354874+0.117266 
## [43] train-rmse:1.023958+0.047602    test-rmse:1.350412+0.119811 
## [44] train-rmse:1.011528+0.047390    test-rmse:1.324584+0.136162 
## [45] train-rmse:1.003330+0.047499    test-rmse:1.322539+0.136485 
## [46] train-rmse:0.995220+0.046615    test-rmse:1.322799+0.133630 
## [47] train-rmse:0.987305+0.047132    test-rmse:1.321187+0.133320 
## [48] train-rmse:0.980738+0.047645    test-rmse:1.315067+0.133813 
## [49] train-rmse:0.972248+0.046468    test-rmse:1.310994+0.135708 
## [50] train-rmse:0.965768+0.046507    test-rmse:1.303931+0.137135 
## [51] train-rmse:0.951871+0.045941    test-rmse:1.286684+0.155433 
## [52] train-rmse:0.939074+0.036898    test-rmse:1.284120+0.161789 
## [53] train-rmse:0.933185+0.036937    test-rmse:1.281907+0.158218 
## [54] train-rmse:0.925650+0.033297    test-rmse:1.273484+0.162314 
## [55] train-rmse:0.915893+0.030562    test-rmse:1.260739+0.157009 
## [56] train-rmse:0.910505+0.030612    test-rmse:1.260864+0.156226 
## [57] train-rmse:0.905663+0.031228    test-rmse:1.260510+0.156383 
## [58] train-rmse:0.899964+0.030139    test-rmse:1.258562+0.157157 
## [59] train-rmse:0.895046+0.030189    test-rmse:1.258905+0.156201 
## [60] train-rmse:0.888324+0.032883    test-rmse:1.257214+0.151253 
## [61] train-rmse:0.883633+0.033180    test-rmse:1.248251+0.154688 
## [62] train-rmse:0.880105+0.033375    test-rmse:1.240563+0.156902 
## [63] train-rmse:0.875994+0.033160    test-rmse:1.245033+0.152595 
## [64] train-rmse:0.872173+0.033599    test-rmse:1.244220+0.155271 
## [65] train-rmse:0.866950+0.033980    test-rmse:1.242310+0.155432 
## [66] train-rmse:0.863196+0.034391    test-rmse:1.241039+0.153310 
## [67] train-rmse:0.856852+0.038832    test-rmse:1.241291+0.151852 
## [68] train-rmse:0.853563+0.038961    test-rmse:1.241261+0.152039 
## Stopping. Best iteration:
## [62] train-rmse:0.880105+0.033375    test-rmse:1.240563+0.156902
## 
## 107 
## [1]  train-rmse:6.077189+0.043342    test-rmse:6.074451+0.240708 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.984703+0.027427    test-rmse:4.001316+0.209838 
## [3]  train-rmse:2.815095+0.022978    test-rmse:2.857825+0.138027 
## [4]  train-rmse:2.198848+0.015406    test-rmse:2.297962+0.091267 
## [5]  train-rmse:1.877698+0.020415    test-rmse:2.009468+0.051985 
## [6]  train-rmse:1.699431+0.020148    test-rmse:1.863456+0.059877 
## [7]  train-rmse:1.588991+0.020510    test-rmse:1.780808+0.073832 
## [8]  train-rmse:1.511408+0.023792    test-rmse:1.719648+0.057464 
## [9]  train-rmse:1.450569+0.022087    test-rmse:1.683555+0.046097 
## [10] train-rmse:1.403697+0.022502    test-rmse:1.653053+0.062839 
## [11] train-rmse:1.363359+0.023819    test-rmse:1.629691+0.067541 
## [12] train-rmse:1.325918+0.026725    test-rmse:1.597613+0.071301 
## [13] train-rmse:1.282968+0.029958    test-rmse:1.561358+0.065473 
## [14] train-rmse:1.244600+0.024320    test-rmse:1.531260+0.077430 
## [15] train-rmse:1.204756+0.028006    test-rmse:1.503847+0.086203 
## [16] train-rmse:1.168486+0.023274    test-rmse:1.479435+0.087324 
## [17] train-rmse:1.139172+0.026550    test-rmse:1.463507+0.109959 
## [18] train-rmse:1.108453+0.027304    test-rmse:1.448486+0.116075 
## [19] train-rmse:1.082268+0.025983    test-rmse:1.431669+0.119199 
## [20] train-rmse:1.056921+0.030080    test-rmse:1.422212+0.118610 
## [21] train-rmse:1.031467+0.027971    test-rmse:1.409035+0.124700 
## [22] train-rmse:1.012686+0.029765    test-rmse:1.397326+0.115667 
## [23] train-rmse:0.994862+0.029822    test-rmse:1.380951+0.117489 
## [24] train-rmse:0.974407+0.030268    test-rmse:1.370151+0.121944 
## [25] train-rmse:0.953981+0.029094    test-rmse:1.359909+0.118399 
## [26] train-rmse:0.934674+0.029254    test-rmse:1.345839+0.123406 
## [27] train-rmse:0.918774+0.031645    test-rmse:1.334698+0.118709 
## [28] train-rmse:0.900347+0.030759    test-rmse:1.315678+0.127687 
## [29] train-rmse:0.888205+0.030972    test-rmse:1.311928+0.125698 
## [30] train-rmse:0.876945+0.030653    test-rmse:1.308557+0.129795 
## [31] train-rmse:0.862159+0.028005    test-rmse:1.293918+0.122526 
## [32] train-rmse:0.848115+0.029522    test-rmse:1.286537+0.129247 
## [33] train-rmse:0.833215+0.028961    test-rmse:1.275016+0.132787 
## [34] train-rmse:0.821356+0.028643    test-rmse:1.278094+0.135597 
## [35] train-rmse:0.810718+0.031099    test-rmse:1.269706+0.135842 
## [36] train-rmse:0.797312+0.028089    test-rmse:1.265303+0.138822 
## [37] train-rmse:0.776514+0.028286    test-rmse:1.254403+0.136724 
## [38] train-rmse:0.767026+0.027060    test-rmse:1.249601+0.140103 
## [39] train-rmse:0.757048+0.026408    test-rmse:1.241686+0.141447 
## [40] train-rmse:0.747346+0.027349    test-rmse:1.237207+0.140080 
## [41] train-rmse:0.738320+0.026972    test-rmse:1.234402+0.139957 
## [42] train-rmse:0.728547+0.026248    test-rmse:1.229715+0.144596 
## [43] train-rmse:0.718806+0.028617    test-rmse:1.225143+0.143332 
## [44] train-rmse:0.710077+0.029247    test-rmse:1.222274+0.142150 
## [45] train-rmse:0.704430+0.029508    test-rmse:1.220949+0.140536 
## [46] train-rmse:0.695720+0.029983    test-rmse:1.221302+0.142163 
## [47] train-rmse:0.689414+0.030824    test-rmse:1.221065+0.143990 
## [48] train-rmse:0.683904+0.031673    test-rmse:1.220964+0.140837 
## [49] train-rmse:0.673879+0.033244    test-rmse:1.220537+0.146293 
## [50] train-rmse:0.664504+0.029964    test-rmse:1.218467+0.148445 
## [51] train-rmse:0.658483+0.029537    test-rmse:1.215954+0.148748 
## [52] train-rmse:0.649544+0.027721    test-rmse:1.211469+0.144015 
## [53] train-rmse:0.642061+0.028104    test-rmse:1.209072+0.144977 
## [54] train-rmse:0.638221+0.029046    test-rmse:1.207875+0.145871 
## [55] train-rmse:0.633942+0.029130    test-rmse:1.209256+0.145760 
## [56] train-rmse:0.629311+0.029006    test-rmse:1.214579+0.142647 
## [57] train-rmse:0.624274+0.028388    test-rmse:1.214961+0.141036 
## [58] train-rmse:0.616232+0.027478    test-rmse:1.207490+0.140499 
## [59] train-rmse:0.611595+0.028150    test-rmse:1.205274+0.138554 
## [60] train-rmse:0.606842+0.027592    test-rmse:1.206490+0.134507 
## [61] train-rmse:0.603002+0.027479    test-rmse:1.207966+0.133731 
## [62] train-rmse:0.598026+0.026373    test-rmse:1.208249+0.132766 
## [63] train-rmse:0.593386+0.027595    test-rmse:1.208996+0.139303 
## [64] train-rmse:0.590377+0.027157    test-rmse:1.207920+0.139535 
## [65] train-rmse:0.586489+0.028162    test-rmse:1.207789+0.139971 
## Stopping. Best iteration:
## [59] train-rmse:0.611595+0.028150    test-rmse:1.205274+0.138554
## 
## 108 
## [1]  train-rmse:6.067282+0.044204    test-rmse:6.066333+0.243182 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.957109+0.024898    test-rmse:3.978158+0.197400 
## [3]  train-rmse:2.771499+0.017711    test-rmse:2.823178+0.148435 
## [4]  train-rmse:2.095014+0.013152    test-rmse:2.207670+0.105618 
## [5]  train-rmse:1.742595+0.015877    test-rmse:1.900681+0.096663 
## [6]  train-rmse:1.549591+0.021132    test-rmse:1.752421+0.084316 
## [7]  train-rmse:1.424827+0.023313    test-rmse:1.665453+0.074178 
## [8]  train-rmse:1.333706+0.029110    test-rmse:1.604992+0.063016 
## [9]  train-rmse:1.269990+0.027073    test-rmse:1.563187+0.067436 
## [10] train-rmse:1.212325+0.024945    test-rmse:1.521276+0.085551 
## [11] train-rmse:1.154469+0.031202    test-rmse:1.483213+0.081395 
## [12] train-rmse:1.103241+0.029487    test-rmse:1.457808+0.098373 
## [13] train-rmse:1.051793+0.029258    test-rmse:1.428490+0.102762 
## [14] train-rmse:1.010095+0.029280    test-rmse:1.404899+0.120672 
## [15] train-rmse:0.977906+0.026965    test-rmse:1.388631+0.121935 
## [16] train-rmse:0.951674+0.026911    test-rmse:1.372050+0.121085 
## [17] train-rmse:0.922634+0.020379    test-rmse:1.362085+0.129365 
## [18] train-rmse:0.897309+0.015659    test-rmse:1.343160+0.140001 
## [19] train-rmse:0.872201+0.016409    test-rmse:1.325152+0.144396 
## [20] train-rmse:0.842980+0.014163    test-rmse:1.318527+0.139764 
## [21] train-rmse:0.818116+0.017000    test-rmse:1.307177+0.144300 
## [22] train-rmse:0.796530+0.017999    test-rmse:1.300209+0.141528 
## [23] train-rmse:0.779814+0.019075    test-rmse:1.289591+0.148632 
## [24] train-rmse:0.761962+0.018053    test-rmse:1.278343+0.150862 
## [25] train-rmse:0.744308+0.022817    test-rmse:1.280940+0.150836 
## [26] train-rmse:0.726569+0.020718    test-rmse:1.283304+0.156406 
## [27] train-rmse:0.706499+0.020903    test-rmse:1.277524+0.154772 
## [28] train-rmse:0.683755+0.018948    test-rmse:1.264469+0.159505 
## [29] train-rmse:0.668137+0.016423    test-rmse:1.253333+0.149636 
## [30] train-rmse:0.658530+0.015573    test-rmse:1.250358+0.148905 
## [31] train-rmse:0.646042+0.013474    test-rmse:1.244114+0.152838 
## [32] train-rmse:0.629742+0.011070    test-rmse:1.235711+0.156494 
## [33] train-rmse:0.619105+0.009483    test-rmse:1.236171+0.156141 
## [34] train-rmse:0.609311+0.010849    test-rmse:1.240367+0.159892 
## [35] train-rmse:0.598375+0.009714    test-rmse:1.245721+0.160044 
## [36] train-rmse:0.586008+0.011314    test-rmse:1.243050+0.157070 
## [37] train-rmse:0.577185+0.011151    test-rmse:1.238881+0.156666 
## [38] train-rmse:0.566993+0.011551    test-rmse:1.230241+0.153553 
## [39] train-rmse:0.557626+0.011349    test-rmse:1.228453+0.150988 
## [40] train-rmse:0.550687+0.011278    test-rmse:1.227861+0.148468 
## [41] train-rmse:0.545251+0.011017    test-rmse:1.229100+0.149657 
## [42] train-rmse:0.536903+0.013853    test-rmse:1.229865+0.152156 
## [43] train-rmse:0.528948+0.009785    test-rmse:1.231493+0.152400 
## [44] train-rmse:0.522435+0.010113    test-rmse:1.230240+0.152111 
## [45] train-rmse:0.517086+0.009471    test-rmse:1.229181+0.150805 
## [46] train-rmse:0.511860+0.008654    test-rmse:1.231118+0.149256 
## Stopping. Best iteration:
## [40] train-rmse:0.550687+0.011278    test-rmse:1.227861+0.148468
## 
## 109 
## [1]  train-rmse:6.058671+0.045017    test-rmse:6.066392+0.244734 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.927992+0.026351    test-rmse:3.963612+0.192928 
## [3]  train-rmse:2.702508+0.025274    test-rmse:2.756823+0.130478 
## [4]  train-rmse:2.007874+0.021115    test-rmse:2.142097+0.106607 
## [5]  train-rmse:1.631526+0.018025    test-rmse:1.836310+0.068460 
## [6]  train-rmse:1.415632+0.022468    test-rmse:1.686639+0.068171 
## [7]  train-rmse:1.274708+0.018213    test-rmse:1.608723+0.062239 
## [8]  train-rmse:1.178592+0.023983    test-rmse:1.527489+0.060190 
## [9]  train-rmse:1.087846+0.021299    test-rmse:1.463670+0.078855 
## [10] train-rmse:1.027423+0.020988    test-rmse:1.438825+0.085928 
## [11] train-rmse:0.961853+0.023630    test-rmse:1.415088+0.105673 
## [12] train-rmse:0.917857+0.023111    test-rmse:1.375163+0.109753 
## [13] train-rmse:0.878160+0.021905    test-rmse:1.358335+0.110493 
## [14] train-rmse:0.844523+0.020946    test-rmse:1.344580+0.105953 
## [15] train-rmse:0.807811+0.020076    test-rmse:1.340967+0.112249 
## [16] train-rmse:0.779288+0.021643    test-rmse:1.320453+0.123102 
## [17] train-rmse:0.751491+0.021515    test-rmse:1.303589+0.131704 
## [18] train-rmse:0.725693+0.026047    test-rmse:1.297817+0.136212 
## [19] train-rmse:0.702360+0.027104    test-rmse:1.295998+0.142118 
## [20] train-rmse:0.677428+0.027850    test-rmse:1.287511+0.150247 
## [21] train-rmse:0.655670+0.024706    test-rmse:1.285644+0.149781 
## [22] train-rmse:0.636955+0.026880    test-rmse:1.280207+0.144382 
## [23] train-rmse:0.619037+0.030337    test-rmse:1.268041+0.141189 
## [24] train-rmse:0.604006+0.028603    test-rmse:1.261066+0.143463 
## [25] train-rmse:0.588425+0.027022    test-rmse:1.253167+0.145135 
## [26] train-rmse:0.574702+0.025442    test-rmse:1.255162+0.146150 
## [27] train-rmse:0.554942+0.025808    test-rmse:1.249343+0.154401 
## [28] train-rmse:0.542211+0.027941    test-rmse:1.247169+0.153884 
## [29] train-rmse:0.533939+0.029521    test-rmse:1.247702+0.152025 
## [30] train-rmse:0.520285+0.029426    test-rmse:1.246755+0.154837 
## [31] train-rmse:0.508376+0.030127    test-rmse:1.241315+0.151912 
## [32] train-rmse:0.497005+0.030514    test-rmse:1.240203+0.156065 
## [33] train-rmse:0.488543+0.031805    test-rmse:1.243058+0.153922 
## [34] train-rmse:0.479903+0.029277    test-rmse:1.243132+0.154513 
## [35] train-rmse:0.463748+0.027670    test-rmse:1.237068+0.149082 
## [36] train-rmse:0.456555+0.028133    test-rmse:1.236962+0.149436 
## [37] train-rmse:0.442729+0.024672    test-rmse:1.236283+0.150176 
## [38] train-rmse:0.434100+0.023210    test-rmse:1.233255+0.153593 
## [39] train-rmse:0.424802+0.023219    test-rmse:1.234697+0.152856 
## [40] train-rmse:0.418894+0.024269    test-rmse:1.232292+0.151964 
## [41] train-rmse:0.410363+0.023329    test-rmse:1.230068+0.154575 
## [42] train-rmse:0.403727+0.024174    test-rmse:1.227785+0.155091 
## [43] train-rmse:0.393848+0.019679    test-rmse:1.227045+0.154656 
## [44] train-rmse:0.386979+0.019926    test-rmse:1.225794+0.156056 
## [45] train-rmse:0.375276+0.018172    test-rmse:1.225315+0.155252 
## [46] train-rmse:0.369739+0.020344    test-rmse:1.224360+0.154651 
## [47] train-rmse:0.361650+0.022036    test-rmse:1.224703+0.154045 
## [48] train-rmse:0.356679+0.021580    test-rmse:1.226327+0.151757 
## [49] train-rmse:0.351311+0.021855    test-rmse:1.227292+0.151378 
## [50] train-rmse:0.344733+0.021612    test-rmse:1.223602+0.150110 
## [51] train-rmse:0.338014+0.021188    test-rmse:1.224818+0.150249 
## [52] train-rmse:0.332649+0.019674    test-rmse:1.226933+0.147014 
## [53] train-rmse:0.329097+0.019458    test-rmse:1.227336+0.145832 
## [54] train-rmse:0.320426+0.017282    test-rmse:1.229748+0.144930 
## [55] train-rmse:0.314777+0.014624    test-rmse:1.231225+0.144248 
## [56] train-rmse:0.309171+0.013770    test-rmse:1.231485+0.144256 
## Stopping. Best iteration:
## [50] train-rmse:0.344733+0.021612    test-rmse:1.223602+0.150110
## 
## 110 
## [1]  train-rmse:6.056370+0.045544    test-rmse:6.067166+0.232851 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.911367+0.027353    test-rmse:3.959382+0.164647 
## [3]  train-rmse:2.666770+0.032396    test-rmse:2.727670+0.113725 
## [4]  train-rmse:1.943007+0.023351    test-rmse:2.098148+0.109931 
## [5]  train-rmse:1.539552+0.029554    test-rmse:1.793190+0.087939 
## [6]  train-rmse:1.298579+0.043881    test-rmse:1.635477+0.073381 
## [7]  train-rmse:1.150037+0.040692    test-rmse:1.520684+0.077550 
## [8]  train-rmse:1.042296+0.034050    test-rmse:1.469553+0.093100 
## [9]  train-rmse:0.946578+0.026102    test-rmse:1.428067+0.097975 
## [10] train-rmse:0.879737+0.029363    test-rmse:1.403466+0.100010 
## [11] train-rmse:0.825533+0.019725    test-rmse:1.373994+0.114269 
## [12] train-rmse:0.774336+0.023022    test-rmse:1.346296+0.106932 
## [13] train-rmse:0.738635+0.024288    test-rmse:1.340467+0.108983 
## [14] train-rmse:0.699587+0.027741    test-rmse:1.316967+0.115318 
## [15] train-rmse:0.663705+0.024711    test-rmse:1.308266+0.119558 
## [16] train-rmse:0.635496+0.017621    test-rmse:1.299345+0.135752 
## [17] train-rmse:0.612763+0.018795    test-rmse:1.295944+0.135634 
## [18] train-rmse:0.589023+0.019085    test-rmse:1.289166+0.137310 
## [19] train-rmse:0.565110+0.012797    test-rmse:1.285349+0.137137 
## [20] train-rmse:0.540727+0.011948    test-rmse:1.277273+0.137833 
## [21] train-rmse:0.522898+0.009414    test-rmse:1.273954+0.140418 
## [22] train-rmse:0.506204+0.010533    test-rmse:1.270256+0.144453 
## [23] train-rmse:0.487010+0.012165    test-rmse:1.263157+0.144402 
## [24] train-rmse:0.469945+0.016674    test-rmse:1.264641+0.143600 
## [25] train-rmse:0.458934+0.017203    test-rmse:1.260555+0.138966 
## [26] train-rmse:0.443115+0.018330    test-rmse:1.259249+0.140235 
## [27] train-rmse:0.428432+0.018117    test-rmse:1.261449+0.139325 
## [28] train-rmse:0.415829+0.019363    test-rmse:1.260552+0.140551 
## [29] train-rmse:0.402849+0.018875    test-rmse:1.258015+0.141026 
## [30] train-rmse:0.389619+0.020190    test-rmse:1.255654+0.138544 
## [31] train-rmse:0.377161+0.017672    test-rmse:1.255029+0.135929 
## [32] train-rmse:0.368772+0.016597    test-rmse:1.254553+0.133974 
## [33] train-rmse:0.357726+0.014682    test-rmse:1.257355+0.137166 
## [34] train-rmse:0.344734+0.016354    test-rmse:1.255470+0.136618 
## [35] train-rmse:0.336175+0.014814    test-rmse:1.254333+0.136138 
## [36] train-rmse:0.327158+0.014274    test-rmse:1.254180+0.133922 
## [37] train-rmse:0.319865+0.014276    test-rmse:1.254822+0.135444 
## [38] train-rmse:0.306215+0.015324    test-rmse:1.251721+0.132923 
## [39] train-rmse:0.296488+0.015878    test-rmse:1.253647+0.131738 
## [40] train-rmse:0.289606+0.015961    test-rmse:1.254594+0.130857 
## [41] train-rmse:0.281642+0.014973    test-rmse:1.256012+0.130265 
## [42] train-rmse:0.276497+0.015330    test-rmse:1.255571+0.130588 
## [43] train-rmse:0.269510+0.012995    test-rmse:1.256111+0.131343 
## [44] train-rmse:0.262615+0.013256    test-rmse:1.254318+0.128686 
## Stopping. Best iteration:
## [38] train-rmse:0.306215+0.015324    test-rmse:1.251721+0.132923
## 
## 111 
## [1]  train-rmse:6.056370+0.045544    test-rmse:6.067166+0.232851 
## Multiple eval metrics are present. Will use test_rmse for early stopping.
## Will train until test_rmse hasn't improved in 6 rounds.
## 
## [2]  train-rmse:3.904299+0.027968    test-rmse:3.964018+0.172356 
## [3]  train-rmse:2.641570+0.030332    test-rmse:2.707926+0.129332 
## [4]  train-rmse:1.903251+0.019348    test-rmse:2.056928+0.110278 
## [5]  train-rmse:1.475092+0.032196    test-rmse:1.718929+0.078203 
## [6]  train-rmse:1.203363+0.047375    test-rmse:1.537997+0.077387 
## [7]  train-rmse:1.039518+0.045240    test-rmse:1.434962+0.099673 
## [8]  train-rmse:0.926311+0.043043    test-rmse:1.382576+0.118284 
## [9]  train-rmse:0.851449+0.040060    test-rmse:1.352891+0.126814 
## [10] train-rmse:0.782557+0.024745    test-rmse:1.333512+0.130596 
## [11] train-rmse:0.721918+0.027265    test-rmse:1.292630+0.145303 
## [12] train-rmse:0.678850+0.023024    test-rmse:1.289246+0.145461 
## [13] train-rmse:0.636856+0.020943    test-rmse:1.284034+0.142572 
## [14] train-rmse:0.605162+0.022863    test-rmse:1.279650+0.150068 
## [15] train-rmse:0.568477+0.018351    test-rmse:1.268070+0.162779 
## [16] train-rmse:0.536751+0.017649    test-rmse:1.258463+0.155899 
## [17] train-rmse:0.510305+0.015432    test-rmse:1.255277+0.156696 
## [18] train-rmse:0.487938+0.017106    test-rmse:1.254953+0.153808 
## [19] train-rmse:0.467291+0.013493    test-rmse:1.259732+0.150319 
## [20] train-rmse:0.447581+0.015560    test-rmse:1.253116+0.148658 
## [21] train-rmse:0.430468+0.017511    test-rmse:1.253448+0.146047 
## [22] train-rmse:0.408885+0.018553    test-rmse:1.248670+0.144612 
## [23] train-rmse:0.391066+0.016915    test-rmse:1.253156+0.141918 
## [24] train-rmse:0.377500+0.016659    test-rmse:1.250863+0.141002 
## [25] train-rmse:0.363700+0.019734    test-rmse:1.248364+0.139914 
## [26] train-rmse:0.352041+0.022001    test-rmse:1.250083+0.140442 
## [27] train-rmse:0.333527+0.012596    test-rmse:1.248891+0.141249 
## [28] train-rmse:0.320676+0.017530    test-rmse:1.248430+0.142748 
## [29] train-rmse:0.311437+0.014623    test-rmse:1.252370+0.140800 
## [30] train-rmse:0.302115+0.013271    test-rmse:1.251629+0.144310 
## [31] train-rmse:0.285485+0.017311    test-rmse:1.253276+0.141961 
## Stopping. Best iteration:
## [25] train-rmse:0.363700+0.019734    test-rmse:1.248364+0.139914
## 
## 112
##    max_depth  eta
## 94         3 0.36